diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 399156fd7c1507507153c172f44a32fb8ead2ccb..838b6e95696c803bfb58b3f0591c0eb7f81dac9a 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -5,10 +5,10 @@ pages:
   stage: deploy
   script:
   - mkdir public
-  - tar czf public/pandas-tutorial-tasks.tar.gz --transform 's,^,pandas-tutorial-tasks/,' Introduction-to-Pandas--tasks.ipynb Introduction-to-Pandas--solution.ipynb lost.json nest-data.csv
+  - tar czf public/pandas-tutorial-tasks.tar.gz --transform 's,^,pandas-tutorial-tasks/,' Introduction-to-Pandas--tasks.ipynb Introduction-to-Pandas--solution.ipynb data-lost.json data-nest.csv
   - cp Introduction-to-Pandas--slides.ipynb public/
   - cp Introduction-to-Pandas--slides.pdf public/
-  - tar czf public/pandas-tutorial-slides.tar.gz  --transform 's,^,pandas-tutorial-slides/,' Introduction-to-Pandas--slides.html custom.css fzj.js img/ reveal.js/
+  # - tar czf public/pandas-tutorial-slides.tar.gz  --transform 's,^,pandas-tutorial-slides/,' Introduction-to-Pandas--slides.html img/ fzj-reveal.js/
   - git checkout -f pages
   - git reset --hard origin/pages
   - cp index.html public/
diff --git a/Introduction-to-Pandas--JURECA--solution.ipynb b/Introduction-to-Pandas--JURECA--solution.ipynb
deleted file mode 100644
index 8cb2e4da8374edaa9d65d0d3ed3e84a5480517b6..0000000000000000000000000000000000000000
--- a/Introduction-to-Pandas--JURECA--solution.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells": [{"cell_type": "markdown", "metadata": {"exercise": "task"}, "source": ["# *Introduction to* Data Analysis and Plotting with Pandas\n", "## JSC Tutorial\n", "\n", "Andreas Herten, Forschungszentrum J\u00fclich, 26 February 2019"]}, {"cell_type": "markdown", "metadata": {"exercise": "onlysolution", "slideshow": {"slide_type": "skip"}}, "source": ["**Version: Solutions**"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "source": ["## Task Outline\n", "\n", "* [Task 1](#task1)\n", "* [Task 2](#task2)\n", "* [Task 3](#task3)\n", "* [Task 4](#task4)\n", "* [Task 5](#task5)\n", "* [Task 6](#task6)\n", "* [Task 7](#task7)\n", "* [Bonus Task](#taskb)"]}, {"cell_type": "code", "execution_count": 2, "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["import pandas as pd"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 1\n", "<a name=\"task1\"></a>\n", "\n", "* Create data frame with\n", "    - 10 names of dinosaurs, \n", "    - their favourite prime number, \n", "    - and their favourite color\n", "* Play around with the frame\n", "* Tell me on poll when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "markdown", "metadata": {"exercise": "nopresentation", "slideshow": {"slide_type": "skip"}}, "source": ["Jupyter Notebook 101:\n", "\n", "* Execute cell: `shift+enter`\n", "* New cell in front of current cell: `a`\n", "* New cell after current cell: `b`"]}, {"cell_type": "code", "execution_count": 21, "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["happy_dinos = {\n", "    \"Dinosaur Name\": [],\n", "    \"Favourite Prime\": [],\n", "    \"Favourite Color\": []\n", "}\n", "#df_dinos = "]}, {"cell_type": "code", "execution_count": 22, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th>Dinosaur Name</th>\n", "      <th>Aegyptosaurus</th>\n", "      <th>Tyrannosaurus</th>\n", "      <th>Panoplosaurus</th>\n", "      <th>Isisaurus</th>\n", "      <th>Triceratops</th>\n", "      <th>Velociraptor</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Favourite Prime</th>\n", "      <td>4</td>\n", "      <td>8</td>\n", "      <td>15</td>\n", "      <td>16</td>\n", "      <td>23</td>\n", "      <td>42</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Favourite Color</th>\n", "      <td>blue</td>\n", "      <td>white</td>\n", "      <td>blue</td>\n", "      <td>purple</td>\n", "      <td>violet</td>\n", "      <td>gray</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["Dinosaur Name   Aegyptosaurus Tyrannosaurus Panoplosaurus Isisaurus  \\\n", "Favourite Prime             4             8            15        16   \n", "Favourite Color          blue         white          blue    purple   \n", "\n", "Dinosaur Name   Triceratops Velociraptor  \n", "Favourite Prime          23           42  \n", "Favourite Color      violet         gray  "]}, "execution_count": 22, "metadata": {}, "output_type": "execute_result"}], "source": ["happy_dinos = {\n", "    \"Dinosaur Name\": [\"Aegyptosaurus\", \"Tyrannosaurus\", \"Panoplosaurus\", \"Isisaurus\", \"Triceratops\", \"Velociraptor\"],\n", "    \"Favourite Prime\": [\"4\", \"8\", \"15\", \"16\", \"23\", \"42\"],\n", "    \"Favourite Color\": [\"blue\", \"white\", \"blue\", \"purple\", \"violet\", \"gray\"]\n", "}\n", "df_dinos = pd.DataFrame(happy_dinos).set_index(\"Dinosaur Name\")\n", "df_dinos.T"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 2\n", "<a name=\"task2\"></a>\n", "\n", "* Read in `nest-data.csv` to `DataFrame`; call it `df`  \n", " *Data was produced with [JUBE](http://www.fz-juelich.de/ias/jsc/EN/Expertise/Support/Software/JUBE/_node.html), Pandas works **very** well together with JUBE*\n", "* Get to know it and play a bit with it\n", "* Tell me when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 30, "metadata": {"exercise": "task"}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["id,Nodes,Tasks/Node,Threads/Task,Runtime Program / s,Scale,Plastic,Avg. Neuron Build Time / s,Min. Edge Build Time / s,Max. Edge Build Time / s,Min. Init. Time / s,Max. Init. Time / s,Presim. Time / s,Sim. Time / s,Virt. Memory (Sum) / kB,Local Spike Counter (Sum),Average Rate (Sum),Number of Neurons,Number of Connections,Min. Delay,Max. Delay\n", "5,1,2,4,420.42,10,true,0.29,88.12,88.18,1.14,1.20,17.26,311.52,46560664.00,825499,7.48,112500,1265738500,1.5,1.5\n", "5,1,4,4,200.84,10,true,0.15,46.03,46.34,0.70,1.01,7.87,142.97,46903088.00,802865,7.03,112500,1265738500,1.5,1.5\n"]}], "source": ["!cat nest-data.csv | head -3"]}, {"cell_type": "code", "execution_count": 31, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>id</th>\n", "      <th>Nodes</th>\n", "      <th>Tasks/Node</th>\n", "      <th>Threads/Task</th>\n", "      <th>Runtime Program / s</th>\n", "      <th>Scale</th>\n", "      <th>Plastic</th>\n", "      <th>Avg. Neuron Build Time / s</th>\n", "      <th>Min. Edge Build Time / s</th>\n", "      <th>Max. Edge Build Time / s</th>\n", "      <th>...</th>\n", "      <th>Max. Init. Time / s</th>\n", "      <th>Presim. Time / s</th>\n", "      <th>Sim. Time / s</th>\n", "      <th>Virt. Memory (Sum) / kB</th>\n", "      <th>Local Spike Counter (Sum)</th>\n", "      <th>Average Rate (Sum)</th>\n", "      <th>Number of Neurons</th>\n", "      <th>Number of Connections</th>\n", "      <th>Min. Delay</th>\n", "      <th>Max. Delay</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>2</td>\n", "      <td>4</td>\n", "      <td>420.42</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.29</td>\n", "      <td>88.12</td>\n", "      <td>88.18</td>\n", "      <td>...</td>\n", "      <td>1.20</td>\n", "      <td>17.26</td>\n", "      <td>311.52</td>\n", "      <td>46560664.0</td>\n", "      <td>825499</td>\n", "      <td>7.48</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>4</td>\n", "      <td>4</td>\n", "      <td>200.84</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.15</td>\n", "      <td>46.03</td>\n", "      <td>46.34</td>\n", "      <td>...</td>\n", "      <td>1.01</td>\n", "      <td>7.87</td>\n", "      <td>142.97</td>\n", "      <td>46903088.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>2</td>\n", "      <td>8</td>\n", "      <td>202.15</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.28</td>\n", "      <td>47.98</td>\n", "      <td>48.48</td>\n", "      <td>...</td>\n", "      <td>1.20</td>\n", "      <td>7.95</td>\n", "      <td>142.81</td>\n", "      <td>47699384.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>4</td>\n", "      <td>8</td>\n", "      <td>89.57</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.15</td>\n", "      <td>20.41</td>\n", "      <td>23.21</td>\n", "      <td>...</td>\n", "      <td>3.04</td>\n", "      <td>3.19</td>\n", "      <td>60.31</td>\n", "      <td>46813040.0</td>\n", "      <td>821491</td>\n", "      <td>7.23</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>5</td>\n", "      <td>2</td>\n", "      <td>2</td>\n", "      <td>4</td>\n", "      <td>164.16</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.20</td>\n", "      <td>40.03</td>\n", "      <td>41.09</td>\n", "      <td>...</td>\n", "      <td>1.58</td>\n", "      <td>6.08</td>\n", "      <td>114.88</td>\n", "      <td>46937216.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>5 rows \u00d7 21 columns</p>\n", "</div>"], "text/plain": ["   id  Nodes  Tasks/Node  Threads/Task  Runtime Program / s  Scale  Plastic  \\\n", "0   5      1           2             4               420.42     10     True   \n", "1   5      1           4             4               200.84     10     True   \n", "2   5      1           2             8               202.15     10     True   \n", "3   5      1           4             8                89.57     10     True   \n", "4   5      2           2             4               164.16     10     True   \n", "\n", "   Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n", "0                        0.29                     88.12   \n", "1                        0.15                     46.03   \n", "2                        0.28                     47.98   \n", "3                        0.15                     20.41   \n", "4                        0.20                     40.03   \n", "\n", "   Max. Edge Build Time / s  ...  Max. Init. Time / s  Presim. Time / s  \\\n", "0                     88.18  ...                 1.20             17.26   \n", "1                     46.34  ...                 1.01              7.87   \n", "2                     48.48  ...                 1.20              7.95   \n", "3                     23.21  ...                 3.04              3.19   \n", "4                     41.09  ...                 1.58              6.08   \n", "\n", "   Sim. Time / s  Virt. Memory (Sum) / kB  Local Spike Counter (Sum)  \\\n", "0         311.52               46560664.0                     825499   \n", "1         142.97               46903088.0                     802865   \n", "2         142.81               47699384.0                     802865   \n", "3          60.31               46813040.0                     821491   \n", "4         114.88               46937216.0                     802865   \n", "\n", "   Average Rate (Sum)  Number of Neurons  Number of Connections  Min. Delay  \\\n", "0                7.48             112500             1265738500         1.5   \n", "1                7.03             112500             1265738500         1.5   \n", "2                7.03             112500             1265738500         1.5   \n", "3                7.23             112500             1265738500         1.5   \n", "4                7.03             112500             1265738500         1.5   \n", "\n", "   Max. Delay  \n", "0         1.5  \n", "1         1.5  \n", "2         1.5  \n", "3         1.5  \n", "4         1.5  \n", "\n", "[5 rows x 21 columns]"]}, "execution_count": 31, "metadata": {}, "output_type": "execute_result"}], "source": ["df = pd.read_csv(\"nest-data.csv\")\n", "df.head()"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "subslide"}}, "source": ["## Task 3\n", "<a name=\"task3\"></a>\n", "\n", "* Add a column to the Nest data frame called `Virtual Processes` which is the total number of threads across all nodes (i.e. the product of threads per task and tasks per node and nodes)\n", "* Remember to tell me when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 54, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>id</th>\n", "      <th>Nodes</th>\n", "      <th>Tasks/Node</th>\n", "      <th>Threads/Task</th>\n", "      <th>Runtime Program / s</th>\n", "      <th>Scale</th>\n", "      <th>Plastic</th>\n", "      <th>Avg. Neuron Build Time / s</th>\n", "      <th>Min. Edge Build Time / s</th>\n", "      <th>Max. Edge Build Time / s</th>\n", "      <th>...</th>\n", "      <th>Presim. Time / s</th>\n", "      <th>Sim. Time / s</th>\n", "      <th>Virt. Memory (Sum) / kB</th>\n", "      <th>Local Spike Counter (Sum)</th>\n", "      <th>Average Rate (Sum)</th>\n", "      <th>Number of Neurons</th>\n", "      <th>Number of Connections</th>\n", "      <th>Min. Delay</th>\n", "      <th>Max. Delay</th>\n", "      <th>Virtual Processes</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>2</td>\n", "      <td>4</td>\n", "      <td>420.42</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.29</td>\n", "      <td>88.12</td>\n", "      <td>88.18</td>\n", "      <td>...</td>\n", "      <td>17.26</td>\n", "      <td>311.52</td>\n", "      <td>46560664.0</td>\n", "      <td>825499</td>\n", "      <td>7.48</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>8</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>4</td>\n", "      <td>4</td>\n", "      <td>200.84</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.15</td>\n", "      <td>46.03</td>\n", "      <td>46.34</td>\n", "      <td>...</td>\n", "      <td>7.87</td>\n", "      <td>142.97</td>\n", "      <td>46903088.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>16</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>2</td>\n", "      <td>8</td>\n", "      <td>202.15</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.28</td>\n", "      <td>47.98</td>\n", "      <td>48.48</td>\n", "      <td>...</td>\n", "      <td>7.95</td>\n", "      <td>142.81</td>\n", "      <td>47699384.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>16</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>4</td>\n", "      <td>8</td>\n", "      <td>89.57</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.15</td>\n", "      <td>20.41</td>\n", "      <td>23.21</td>\n", "      <td>...</td>\n", "      <td>3.19</td>\n", "      <td>60.31</td>\n", "      <td>46813040.0</td>\n", "      <td>821491</td>\n", "      <td>7.23</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>32</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>5</td>\n", "      <td>2</td>\n", "      <td>2</td>\n", "      <td>4</td>\n", "      <td>164.16</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.20</td>\n", "      <td>40.03</td>\n", "      <td>41.09</td>\n", "      <td>...</td>\n", "      <td>6.08</td>\n", "      <td>114.88</td>\n", "      <td>46937216.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>16</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>5 rows \u00d7 22 columns</p>\n", "</div>"], "text/plain": ["   id  Nodes  Tasks/Node  Threads/Task  Runtime Program / s  Scale  Plastic  \\\n", "0   5      1           2             4               420.42     10     True   \n", "1   5      1           4             4               200.84     10     True   \n", "2   5      1           2             8               202.15     10     True   \n", "3   5      1           4             8                89.57     10     True   \n", "4   5      2           2             4               164.16     10     True   \n", "\n", "   Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n", "0                        0.29                     88.12   \n", "1                        0.15                     46.03   \n", "2                        0.28                     47.98   \n", "3                        0.15                     20.41   \n", "4                        0.20                     40.03   \n", "\n", "   Max. Edge Build Time / s  ...  Presim. Time / s  Sim. Time / s  \\\n", "0                     88.18  ...             17.26         311.52   \n", "1                     46.34  ...              7.87         142.97   \n", "2                     48.48  ...              7.95         142.81   \n", "3                     23.21  ...              3.19          60.31   \n", "4                     41.09  ...              6.08         114.88   \n", "\n", "   Virt. Memory (Sum) / kB  Local Spike Counter (Sum)  Average Rate (Sum)  \\\n", "0               46560664.0                     825499                7.48   \n", "1               46903088.0                     802865                7.03   \n", "2               47699384.0                     802865                7.03   \n", "3               46813040.0                     821491                7.23   \n", "4               46937216.0                     802865                7.03   \n", "\n", "   Number of Neurons  Number of Connections  Min. Delay  Max. Delay  \\\n", "0             112500             1265738500         1.5         1.5   \n", "1             112500             1265738500         1.5         1.5   \n", "2             112500             1265738500         1.5         1.5   \n", "3             112500             1265738500         1.5         1.5   \n", "4             112500             1265738500         1.5         1.5   \n", "\n", "   Virtual Processes  \n", "0                  8  \n", "1                 16  \n", "2                 16  \n", "3                 32  \n", "4                 16  \n", "\n", "[5 rows x 22 columns]"]}, "execution_count": 54, "metadata": {}, "output_type": "execute_result"}], "source": ["df[\"Virtual Processes\"] = df[\"Nodes\"] * df[\"Tasks/Node\"] * df[\"Threads/Task\"]\n", "df.head()"]}, {"cell_type": "code", "execution_count": 55, "metadata": {"exercise": "solution"}, "outputs": [{"data": {"text/plain": ["Index(['id', 'Nodes', 'Tasks/Node', 'Threads/Task', 'Runtime Program / s',\n", "       'Scale', 'Plastic', 'Avg. Neuron Build Time / s',\n", "       'Min. Edge Build Time / s', 'Max. Edge Build Time / s',\n", "       'Min. Init. Time / s', 'Max. Init. Time / s', 'Presim. Time / s',\n", "       'Sim. Time / s', 'Virt. Memory (Sum) / kB', 'Local Spike Counter (Sum)',\n", "       'Average Rate (Sum)', 'Number of Neurons', 'Number of Connections',\n", "       'Min. Delay', 'Max. Delay', 'Virtual Processes'],\n", "      dtype='object')"]}, "execution_count": 55, "metadata": {}, "output_type": "execute_result"}], "source": ["df.columns"]}, {"cell_type": "code", "execution_count": 56, "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["import matplotlib.pyplot as plt\n", "%matplotlib inline"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 4\n", "<a name=\"task4\"></a>\n", "\n", "* Sort the data frame by the virtual proccesses\n", "* Plot `\"Presim. Time / s\"` and `\"Sim. Time / s\"` of our data frame `df` as a function of the virtual processes\n", "* Use a dashed, red line for `\"Presim. Time / s\"`, a blue line for `\"Sim. Time / s\"` (see [API description](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot))\n", "* Don't forget to label your axes and to add a legend\n", "* Submit when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 61, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["df.sort_values([\"Virtual Processes\", \"Nodes\", \"Tasks/Node\", \"Threads/Task\"], inplace=True)"]}, {"cell_type": "code", "execution_count": 62, "metadata": {"exercise": "solution"}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots()\n", "ax.plot(df[\"Virtual Processes\"], df[\"Presim. Time / s\"], linestyle=\"dashed\", color=\"red\")\n", "ax.plot(df[\"Virtual Processes\"], df[\"Sim. Time / s\"], \"-b\")\n", "ax.set_xlabel(\"Virtual Process\")\n", "ax.set_ylabel(\"Time / s\")\n", "ax.legend();"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 5\n", "<a name=\"task5\"></a>\n", "\n", "Use the NEST data frame `df` to:\n", "\n", "1. Make the virtual processes the index of the data frame (`.set_index()`)\n", "2. Plot `\"Presim. Program / s\"` and `\"Sim. Time / s`\" individually\n", "3. Plot them onto one common canvas!\n", "4. Make them have the same line colors and styles as before\n", "5. Add a legend, add missing labels\n", "\n", "* Done? Tell me! [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 68, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "subslide"}}, "outputs": [], "source": ["df.set_index(\"Virtual Processes\", inplace=True)"]}, {"cell_type": "code", "execution_count": 69, "metadata": {"exercise": "solution"}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x216 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df[\"Presim. Time / s\"].plot(figsize=(10, 3));"]}, {"cell_type": "code", "execution_count": 70, "metadata": {"exercise": "solution"}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x216 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df[\"Sim. Time / s\"].plot(figsize=(10, 3));"]}, {"cell_type": "code", "execution_count": 71, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "subslide"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df[\"Presim. Time / s\"].plot();\n", "df[\"Sim. Time / s\"].plot();"]}, {"cell_type": "code", "execution_count": 72, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["ax = df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot();\n", "ax.set_ylabel(\"Time / s\");"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 6\n", "<a name=\"task6\"></a>\n", "\n", "* To your `df` NEST data frame, add a column with the unaccounted time (`Unaccounted Time / s`), which is the difference of program runtime, average neuron build time, minimal edge build time, minimal initialization time, presimulation time, and simulation time.  \n", "(*I know this is technically not super correct, but it will do for our example.*)\n", "* Plot a stacked bar plot of all these columns (except for program runtime) over the virtual processes\n", "* Remember: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 92, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["cols = [\n", "    'Avg. Neuron Build Time / s', \n", "    'Min. Edge Build Time / s', \n", "    'Min. Init. Time / s', \n", "    'Presim. Time / s', \n", "    'Sim. Time / s'\n", "]\n", "df[\"Unaccounted Time / s\"] = df['Runtime Program / s']\n", "for entry in cols:\n", "    df[\"Unaccounted Time / s\"] = df[\"Unaccounted Time / s\"] - df[entry]"]}, {"cell_type": "code", "execution_count": 93, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "subslide"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Runtime Program / s</th>\n", "      <th>Unaccounted Time / s</th>\n", "      <th>Avg. Neuron Build Time / s</th>\n", "      <th>Min. Edge Build Time / s</th>\n", "      <th>Min. Init. Time / s</th>\n", "      <th>Presim. Time / s</th>\n", "      <th>Sim. Time / s</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Virtual Processes</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>8</th>\n", "      <td>420.42</td>\n", "      <td>2.09</td>\n", "      <td>0.29</td>\n", "      <td>88.12</td>\n", "      <td>1.14</td>\n", "      <td>17.26</td>\n", "      <td>311.52</td>\n", "    </tr>\n", "    <tr>\n", "      <th>16</th>\n", "      <td>202.15</td>\n", "      <td>2.43</td>\n", "      <td>0.28</td>\n", "      <td>47.98</td>\n", "      <td>0.70</td>\n", "      <td>7.95</td>\n", "      <td>142.81</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                   Runtime Program / s  Unaccounted Time / s  \\\n", "Virtual Processes                                              \n", "8                               420.42                  2.09   \n", "16                              202.15                  2.43   \n", "\n", "                   Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n", "Virtual Processes                                                         \n", "8                                        0.29                     88.12   \n", "16                                       0.28                     47.98   \n", "\n", "                   Min. Init. Time / s  Presim. Time / s  Sim. Time / s  \n", "Virtual Processes                                                        \n", "8                                 1.14             17.26         311.52  \n", "16                                0.70              7.95         142.81  "]}, "execution_count": 93, "metadata": {}, "output_type": "execute_result"}], "source": ["df[[\"Runtime Program / s\", \"Unaccounted Time / s\", *cols]].head(2)"]}, {"cell_type": "code", "execution_count": 94, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "-"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 864x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df[[\"Unaccounted Time / s\", *cols]].plot(kind=\"bar\", stacked=True, figsize=(12, 4));"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 7\n", "<a name=\"task7\"></a>\n", "\n", "* Create a pivot table based on the NEST `df` data frame\n", "* Let the `x` axis show the number of nodes; display the values of the simulation time `\"Sim. Time / s\"` for the tasks per node and threas per task configurations\n", "* Please plot a bar plot\n", "* Done? [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 101, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 864x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df.pivot_table(\n", "    index=[\"Nodes\"],\n", "    columns=[\"Tasks/Node\", \"Threads/Task\"],\n", "    values=\"Sim. Time / s\",\n", ").plot(kind=\"bar\", figsize=(12, 4));"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "source": ["<a name=\"taskb\"></a>\n", "\n", "* Bonus task\n", "    - Same pivot table as before (that is, `x` with nodes, and columns for Tasks/Node and Threads/Task)\n", "    - But now, use `Sim. Time / s` and `Presim. Time / s` as values to show\n", "    - Show them as a stack of those two values inside the pivot table"]}, {"cell_type": "markdown", "metadata": {"exercise": "task"}, "source": ["<span class=\"feedback\">Tell me what you think about this tutorial! <a href=\"mailto:a.herten@fz-juelich.de\">a.herten@fz-juelich.de</a></span>\n", "\n", "Next slide: Further reading"]}], "metadata": {"kernelspec": {"display_name": "JSC Pandas Tutorial", "language": "python", "name": "pandas"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.2"}}, "nbformat": 4, "nbformat_minor": 2}
diff --git a/Introduction-to-Pandas--JURECA--tasks.ipynb b/Introduction-to-Pandas--JURECA--tasks.ipynb
deleted file mode 100644
index 22d2afeb88c50ee014170e3c70a00f64b2edff2e..0000000000000000000000000000000000000000
--- a/Introduction-to-Pandas--JURECA--tasks.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells": [{"cell_type": "markdown", "metadata": {"exercise": "task"}, "source": ["# *Introduction to* Data Analysis and Plotting with Pandas\n", "## JSC Tutorial\n", "\n", "Andreas Herten, Forschungszentrum J\u00fclich, 26 February 2019"]}, {"cell_type": "markdown", "metadata": {"exercise": "onlytask", "slideshow": {"slide_type": "skip"}}, "source": ["**Version: Tasks**"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "source": ["## Task Outline\n", "\n", "* [Task 1](#task1)\n", "* [Task 2](#task2)\n", "* [Task 3](#task3)\n", "* [Task 4](#task4)\n", "* [Task 5](#task5)\n", "* [Task 6](#task6)\n", "* [Task 7](#task7)\n", "* [Bonus Task](#taskb)"]}, {"cell_type": "code", "execution_count": 2, "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["import pandas as pd"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 1\n", "<a name=\"task1\"></a>\n", "\n", "* Create data frame with\n", "    - 10 names of dinosaurs, \n", "    - their favourite prime number, \n", "    - and their favourite color\n", "* Play around with the frame\n", "* Tell me on poll when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "markdown", "metadata": {"exercise": "nopresentation", "slideshow": {"slide_type": "skip"}}, "source": ["Jupyter Notebook 101:\n", "\n", "* Execute cell: `shift+enter`\n", "* New cell in front of current cell: `a`\n", "* New cell after current cell: `b`"]}, {"cell_type": "code", "execution_count": 21, "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["happy_dinos = {\n", "    \"Dinosaur Name\": [],\n", "    \"Favourite Prime\": [],\n", "    \"Favourite Color\": []\n", "}\n", "#df_dinos = "]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 2\n", "<a name=\"task2\"></a>\n", "\n", "* Read in `nest-data.csv` to `DataFrame`; call it `df`  \n", " *Data was produced with [JUBE](http://www.fz-juelich.de/ias/jsc/EN/Expertise/Support/Software/JUBE/_node.html), Pandas works **very** well together with JUBE*\n", "* Get to know it and play a bit with it\n", "* Tell me when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 30, "metadata": {"exercise": "task"}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["id,Nodes,Tasks/Node,Threads/Task,Runtime Program / s,Scale,Plastic,Avg. Neuron Build Time / s,Min. Edge Build Time / s,Max. Edge Build Time / s,Min. Init. Time / s,Max. Init. Time / s,Presim. Time / s,Sim. Time / s,Virt. Memory (Sum) / kB,Local Spike Counter (Sum),Average Rate (Sum),Number of Neurons,Number of Connections,Min. Delay,Max. Delay\n", "5,1,2,4,420.42,10,true,0.29,88.12,88.18,1.14,1.20,17.26,311.52,46560664.00,825499,7.48,112500,1265738500,1.5,1.5\n", "5,1,4,4,200.84,10,true,0.15,46.03,46.34,0.70,1.01,7.87,142.97,46903088.00,802865,7.03,112500,1265738500,1.5,1.5\n"]}], "source": ["!cat nest-data.csv | head -3"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "subslide"}}, "source": ["## Task 3\n", "<a name=\"task3\"></a>\n", "\n", "* Add a column to the Nest data frame called `Virtual Processes` which is the total number of threads across all nodes (i.e. the product of threads per task and tasks per node and nodes)\n", "* Remember to tell me when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 56, "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["import matplotlib.pyplot as plt\n", "%matplotlib inline"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 4\n", "<a name=\"task4\"></a>\n", "\n", "* Sort the data frame by the virtual proccesses\n", "* Plot `\"Presim. Time / s\"` and `\"Sim. Time / s\"` of our data frame `df` as a function of the virtual processes\n", "* Use a dashed, red line for `\"Presim. Time / s\"`, a blue line for `\"Sim. Time / s\"` (see [API description](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot))\n", "* Don't forget to label your axes and to add a legend\n", "* Submit when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 5\n", "<a name=\"task5\"></a>\n", "\n", "Use the NEST data frame `df` to:\n", "\n", "1. Make the virtual processes the index of the data frame (`.set_index()`)\n", "2. Plot `\"Presim. Program / s\"` and `\"Sim. Time / s`\" individually\n", "3. Plot them onto one common canvas!\n", "4. Make them have the same line colors and styles as before\n", "5. Add a legend, add missing labels\n", "\n", "* Done? Tell me! [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 6\n", "<a name=\"task6\"></a>\n", "\n", "* To your `df` NEST data frame, add a column with the unaccounted time (`Unaccounted Time / s`), which is the difference of program runtime, average neuron build time, minimal edge build time, minimal initialization time, presimulation time, and simulation time.  \n", "(*I know this is technically not super correct, but it will do for our example.*)\n", "* Plot a stacked bar plot of all these columns (except for program runtime) over the virtual processes\n", "* Remember: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 7\n", "<a name=\"task7\"></a>\n", "\n", "* Create a pivot table based on the NEST `df` data frame\n", "* Let the `x` axis show the number of nodes; display the values of the simulation time `\"Sim. Time / s\"` for the tasks per node and threas per task configurations\n", "* Please plot a bar plot\n", "* Done? [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "source": ["<a name=\"taskb\"></a>\n", "\n", "* Bonus task\n", "    - Same pivot table as before (that is, `x` with nodes, and columns for Tasks/Node and Threads/Task)\n", "    - But now, use `Sim. Time / s` and `Presim. Time / s` as values to show\n", "    - Show them as a stack of those two values inside the pivot table"]}, {"cell_type": "markdown", "metadata": {"exercise": "task"}, "source": ["<span class=\"feedback\">Tell me what you think about this tutorial! <a href=\"mailto:a.herten@fz-juelich.de\">a.herten@fz-juelich.de</a></span>\n", "\n", "Next slide: Further reading"]}], "metadata": {"kernelspec": {"display_name": "JSC Pandas Tutorial", "language": "python", "name": "pandas"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.2"}}, "nbformat": 4, "nbformat_minor": 2}
diff --git a/Introduction-to-Pandas--master.ipynb b/Introduction-to-Pandas--master.ipynb
index 2db61521fa38cc0700a5995c4f5645daba7bbc04..c4772f0eb03a5aaefbea6892b61d2340987f7880 100644
--- a/Introduction-to-Pandas--master.ipynb
+++ b/Introduction-to-Pandas--master.ipynb
@@ -3,13 +3,15 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "exercise": "task"
+    "exercise": "task",
+    "tags": [
+     "task"
+    ]
    },
    "source": [
-    "# *Introduction to* Data Analysis and Plotting with Pandas\n",
-    "## JSC Tutorial\n",
+    "# Data Analysis and Plotting in Python with Pandas\n",
     "\n",
-    "Andreas Herten, Forschungszentrum Jülich, 26 February 2019"
+    "_Andreas Herten, Jülich Supercomputing Centre, Forschungszentrum Jülich, 27 May 2021_"
    ]
   },
   {
@@ -62,6 +64,7 @@
     "* I like plotting data\n",
     "* I like sharing\n",
     "* I think Pandas is awesome and you should use it too\n",
+    "* …_but I'm no Python expert!_\n",
     "\n",
     "<span style=\"color: #023d6b\"><em>Motto: <strong>»Pandas as early as possible!«</strong></em></span>"
    ]
@@ -97,11 +100,9 @@
    "source": [
     "## Tutorial Setup\n",
     "\n",
-    "* 60 minutes (we might do this again for some advanced stuff if you want to)\n",
-    "    - *Well, as it turns out, 60 minutes weren't nearly enought*\n",
-    "    - *We ended up spending nearly 2 hours on it, and we needed to rush quickly through the material*\n",
+    "* 3 hours, including break around 10:30\n",
     "* Alternating between lecture and hands-on\n",
-    "* Please give status of hands-ons via **[pollev.com/aherten538](https://pollev.com/aherten538)**"
+    "* Please give status of hands-ons via 👍 as BigBlueButton status"
    ]
   },
   {
@@ -112,11 +113,8 @@
     }
    },
    "source": [
-    "* Please open Jupyter Notebook of this session\n",
-    "    - … either on your **local machine** (`pip install --user pandas seaborn`)\n",
-    "    - … or on the **JSC Jupyter service** at https://jupyter-jsc.fz-juelich.de/  \n",
-    "    *Pandas and seaborn should already be there!*\n",
-    "* Tell me when you're done on **[pollev.com/aherten538](https://pollev.com/aherten538)**"
+    "* Please now open Jupyter Notebook of this session: https://go.fzj.de/jsc-pd21\n",
+    "* Give thumbs up! 👍"
    ]
   },
   {
@@ -131,13 +129,14 @@
     "\n",
     "<img style=\"float: right; max-width: 200px;\" width=\"200px\" src=\"img/adorable-animal-animal-photography-1661535.jpg\" />\n",
     "\n",
-    "* Python package (Python 2, Python 3)\n",
-    "* For data analysis\n",
+    "* Python package (~~Python 2,~~ Python 3)\n",
+    "* For data analysis and manipulation\n",
     "* With data structures (multi-dimensional table; time series), operations\n",
     "* Name from »**Pan**el **Da**ta« (multi-dimensional time series in economics)\n",
     "* Since 2008\n",
     "* https://pandas.pydata.org/\n",
-    "* Install [via PyPI](https://pypi.org/project/pandas/): `pip install pandas`"
+    "* Install [via PyPI](https://pypi.org/project/pandas/): `pip install pandas`\n",
+    "* *Cheatsheet: https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf*"
    ]
   },
   {
@@ -153,8 +152,11 @@
     "* Pandas works great together with other established Python tools\n",
     "    * [Jupyter Notebooks](https://jupyter.org/)\n",
     "    * Plotting with [`matplotlib`](https://matplotlib.org/)\n",
+    "    * Numerical analysis with [`numpy`](https://numpy.org/)\n",
     "    * Modelling with [`statsmodels`](https://www.statsmodels.org/stable/index.html), [`scikit-learn`](https://scikit-learn.org/)\n",
-    "    * Nicer plots with [`seaborn`](https://seaborn.pydata.org/), [`altair`](https://altair-viz.github.io/), [`plotly`](https://plot.ly/)"
+    "    * Nicer plots with [`seaborn`](https://seaborn.pydata.org/), [`altair`](https://altair-viz.github.io/), [`plotly`](https://plot.ly/)\n",
+    "    * Performance enhancement with [Cython](https://cython.org/), [Numba](numba.pydata.org/), …\n",
+    "* Tools building up on Pandas: [cuDF](https://github.com/rapidsai/cudf) (GPU-accelerated DataFrames in [Rapids](https://rapids.ai/)), …"
    ]
   },
   {
@@ -207,7 +209,7 @@
     {
      "data": {
       "text/plain": [
-       "'0.24.1'"
+       "'1.2.4'"
       ]
      },
      "execution_count": 3,
@@ -268,8 +270,7 @@
        "        Excel files, databases, and saving/loading data from the ultrafast HDF5\n",
        "        format.\n",
        "      - Time series-specific functionality: date range generation and frequency\n",
-       "        conversion, moving window statistics, moving window linear regressions,\n",
-       "        date shifting and lagging, etc."
+       "        conversion, moving window statistics, date shifting and lagging."
       ]
      },
      "metadata": {},
@@ -291,11 +292,13 @@
     "## DataFrames\n",
     "### It's all about DataFrames\n",
     "\n",
-    "* Main data containers of Pandas\n",
+    "<img style=\"float: right; max-width: 200px;\" width=\"200px\" src=\"img/buzz-dataframes.jpg\" />\n",
+    "\n",
+    "* Data containers of Pandas:\n",
     "    - Linear: `Series`\n",
     "    - Multi Dimension: `DataFrame`\n",
-    "* `Series` is *only* special case of `DataFrame`\n",
-    "* → Talk about `DataFrame`s as the more general case"
+    "* `Series` is *only* special (1D) case of `DataFrame`\n",
+    "* → We use `DataFrame`s as the more general case here"
    ]
   },
   {
@@ -309,7 +312,7 @@
     "## DataFrames\n",
     "### Construction\n",
     "\n",
-    "* To show features of `DataFrame`, let's construct one!\n",
+    "* To show features of `DataFrame`, let's construct one and show by example!\n",
     "* Many construction possibilities\n",
     "    - From lists, dictionaries, `numpy` objects\n",
     "    - From CSV, HDF5, JSON, Excel, HTML, fixed-width files\n",
@@ -536,14 +539,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'Names': ['Liu', 'Rowland', 'Rivers', 'Waters', 'Rice', 'Fields', 'Kerr', 'Romero', 'Davis', 'Hall'], 'Ages': [41, 56, 56, 57, 39, 59, 43, 56, 38, 60]}\n"
+      "{'Name': ['Liu', 'Rowland', 'Rivers', 'Waters', 'Rice', 'Fields', 'Kerr', 'Romero', 'Davis', 'Hall'], 'Age': [41, 56, 56, 57, 39, 59, 43, 56, 38, 60]}\n"
      ]
     }
    ],
    "source": [
     "data = {\n",
-    "    \"Names\": [\"Liu\", \"Rowland\", \"Rivers\", \"Waters\", \"Rice\", \"Fields\", \"Kerr\", \"Romero\", \"Davis\", \"Hall\"],\n",
-    "    \"Ages\": ages\n",
+    "    \"Name\": [\"Liu\", \"Rowland\", \"Rivers\", \"Waters\", \"Rice\", \"Fields\", \"Kerr\", \"Romero\", \"Davis\", \"Hall\"],\n",
+    "    \"Age\": ages\n",
     "}\n",
     "print(data)"
    ]
@@ -578,8 +581,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Names</th>\n",
-       "      <th>Ages</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Age</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -608,11 +611,11 @@
        "</div>"
       ],
       "text/plain": [
-       "     Names  Ages\n",
-       "0      Liu    41\n",
-       "1  Rowland    56\n",
-       "2   Rivers    56\n",
-       "3   Waters    57"
+       "      Name  Age\n",
+       "0      Liu   41\n",
+       "1  Rowland   56\n",
+       "2   Rivers   56\n",
+       "3   Waters   57"
       ]
      },
      "execution_count": 9,
@@ -633,6 +636,7 @@
     }
    },
    "source": [
+    "* Automatically creates columns from dictionary\n",
     "* Two columns now; one for names, one for ages"
    ]
   },
@@ -644,7 +648,7 @@
     {
      "data": {
       "text/plain": [
-       "Index(['Names', 'Ages'], dtype='object')"
+       "Index(['Name', 'Age'], dtype='object')"
       ]
      },
      "execution_count": 10,
@@ -664,6 +668,7 @@
     }
    },
    "source": [
+    "* First column is _index_\n",
     "* `DataFrame` always have indexes; auto-generated or custom"
    ]
   },
@@ -695,7 +700,7 @@
     }
    },
    "source": [
-    "* Make `Names` be index with `.set_index()`\n",
+    "* Make `Name` be index with `.set_index()`\n",
     "* `inplace=True` will modifiy the parent frame (*I don't like it*)"
    ]
   },
@@ -729,10 +734,10 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Ages</th>\n",
+       "      <th>Age</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Names</th>\n",
+       "      <th>Name</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -782,18 +787,18 @@
        "</div>"
       ],
       "text/plain": [
-       "         Ages\n",
-       "Names        \n",
-       "Liu        41\n",
-       "Rowland    56\n",
-       "Rivers     56\n",
-       "Waters     57\n",
-       "Rice       39\n",
-       "Fields     59\n",
-       "Kerr       43\n",
-       "Romero     56\n",
-       "Davis      38\n",
-       "Hall       60"
+       "         Age\n",
+       "Name        \n",
+       "Liu       41\n",
+       "Rowland   56\n",
+       "Rivers    56\n",
+       "Waters    57\n",
+       "Rice      39\n",
+       "Fields    59\n",
+       "Kerr      43\n",
+       "Romero    56\n",
+       "Davis     38\n",
+       "Hall      60"
       ]
      },
      "execution_count": 12,
@@ -802,7 +807,7 @@
     }
    ],
    "source": [
-    "df_sample.set_index(\"Names\", inplace=True)\n",
+    "df_sample.set_index(\"Name\", inplace=True)\n",
     "df_sample"
    ]
   },
@@ -847,7 +852,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Ages</th>\n",
+       "      <th>Age</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -888,7 +893,7 @@
        "</div>"
       ],
       "text/plain": [
-       "            Ages\n",
+       "             Age\n",
        "count  10.000000\n",
        "mean   50.500000\n",
        "std     9.009255\n",
@@ -937,7 +942,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>Names</th>\n",
+       "      <th>Name</th>\n",
        "      <th>Liu</th>\n",
        "      <th>Rowland</th>\n",
        "      <th>Rivers</th>\n",
@@ -952,7 +957,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>Ages</th>\n",
+       "      <th>Age</th>\n",
        "      <td>41</td>\n",
        "      <td>56</td>\n",
        "      <td>56</td>\n",
@@ -969,8 +974,8 @@
        "</div>"
       ],
       "text/plain": [
-       "Names  Liu  Rowland  Rivers  Waters  Rice  Fields  Kerr  Romero  Davis  Hall\n",
-       "Ages    41       56      56      57    39      59    43      56     38    60"
+       "Name  Liu  Rowland  Rivers  Waters  Rice  Fields  Kerr  Romero  Davis  Hall\n",
+       "Age    41       56      56      57    39      59    43      56     38    60"
       ]
      },
      "execution_count": 14,
@@ -996,7 +1001,7 @@
       "text/plain": [
        "Index(['Liu', 'Rowland', 'Rivers', 'Waters', 'Rice', 'Fields', 'Kerr',\n",
        "       'Romero', 'Davis', 'Hall'],\n",
-       "      dtype='object', name='Names')"
+       "      dtype='object', name='Name')"
       ]
      },
      "execution_count": 15,
@@ -1049,10 +1054,10 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Ages</th>\n",
+       "      <th>Age</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Names</th>\n",
+       "      <th>Name</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -1074,11 +1079,11 @@
        "</div>"
       ],
       "text/plain": [
-       "         Ages\n",
-       "Names        \n",
-       "Liu        82\n",
-       "Rowland   112\n",
-       "Rivers    112"
+       "         Age\n",
+       "Name        \n",
+       "Liu       82\n",
+       "Rowland  112\n",
+       "Rivers   112"
       ]
      },
      "execution_count": 16,
@@ -1120,8 +1125,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Names</th>\n",
-       "      <th>Ages</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Age</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1145,10 +1150,10 @@
        "</div>"
       ],
       "text/plain": [
-       "            Names  Ages\n",
-       "0          LiuLiu    82\n",
-       "1  RowlandRowland   112\n",
-       "2    RiversRivers   112"
+       "             Name  Age\n",
+       "0          LiuLiu   82\n",
+       "1  RowlandRowland  112\n",
+       "2    RiversRivers  112"
       ]
      },
      "execution_count": 17,
@@ -1190,10 +1195,10 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Ages</th>\n",
+       "      <th>Age</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Names</th>\n",
+       "      <th>Name</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -1215,8 +1220,8 @@
        "</div>"
       ],
       "text/plain": [
-       "         Ages\n",
-       "Names        \n",
+       "          Age\n",
+       "Name         \n",
        "Liu      20.5\n",
        "Rowland  28.0\n",
        "Rivers   28.0"
@@ -1261,10 +1266,10 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Ages</th>\n",
+       "      <th>Age</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Names</th>\n",
+       "      <th>Name</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -1286,8 +1291,8 @@
        "</div>"
       ],
       "text/plain": [
-       "         Ages\n",
-       "Names        \n",
+       "          Age\n",
+       "Name         \n",
        "Liu      1681\n",
        "Rowland  3136\n",
        "Rivers   3136"
@@ -1343,10 +1348,10 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Ages</th>\n",
+       "      <th>Age</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Names</th>\n",
+       "      <th>Name</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -1396,8 +1401,8 @@
        "</div>"
       ],
       "text/plain": [
-       "          Ages\n",
-       "Names         \n",
+       "           Age\n",
+       "Name          \n",
        "Liu       True\n",
        "Rowland   True\n",
        "Rivers    True\n",
@@ -1430,13 +1435,14 @@
    "source": [
     "## Task 1\n",
     "<a name=\"task1\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
     "\n",
     "* Create data frame with\n",
-    "    - 10 names of dinosaurs, \n",
+    "    - 6 names of dinosaurs, \n",
     "    - their favourite prime number, \n",
-    "    - and their favourite color\n",
+    "    - and their favorite color.\n",
     "* Play around with the frame\n",
-    "* Tell me on poll when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"
+    "* Tell me when you're done with status icon in BigBlueButton: 👍"
    ]
   },
   {
@@ -1873,10 +1879,9 @@
     {
      "data": {
       "text/plain": [
-       "A                              6\n",
-       "C                          -2.03\n",
-       "D    Thiscolumnhasentriesentries\n",
-       "E           SameSameSameSameSame\n",
+       "A                     6.0\n",
+       "C                   -2.03\n",
+       "E    SameSameSameSameSame\n",
        "dtype: object"
       ]
      },
@@ -1950,7 +1955,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 117,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2033,13 +2038,13 @@
        "Walt       Malcolm David Kelley      False"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pd.read_json(\"lost.json\").set_index(\"Character\").sort_index()"
+    "pd.read_json(\"data-lost.json\").set_index(\"Character\").sort_index()"
    ]
   },
   {
@@ -2053,11 +2058,12 @@
    "source": [
     "## Task 2\n",
     "<a name=\"task2\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
     "\n",
-    "* Read in `nest-data.csv` to `DataFrame`; call it `df`  \n",
-    " *Data was produced with [JUBE](http://www.fz-juelich.de/ias/jsc/EN/Expertise/Support/Software/JUBE/_node.html), Pandas works **very** well together with JUBE*\n",
+    "* Read in `data-nest.csv` to `DataFrame`; call it `df`  \n",
+    "  *(Data was produced with [JUBE](http://www.fz-juelich.de/ias/jsc/EN/Expertise/Support/Software/JUBE/_node.html))*\n",
     "* Get to know it and play a bit with it\n",
-    "* Tell me when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"
+    "* Tell me when you're done with status icon in BigBlueButton: 👍"
    ]
   },
   {
@@ -2083,7 +2089,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 118,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -2307,13 +2313,13 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df = pd.read_csv(\"nest-data.csv\")\n",
+    "df = pd.read_csv(\"data-nest.csv\")\n",
     "df.head()"
    ]
   },
@@ -2340,7 +2346,7 @@
     "    - `decimal`: Decimal point divider – for German data…\n",
     "    \n",
     "```python\n",
-    "pandas.read_csv(filepath_or_buffer, sep=', ', delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, iterator=False, chunksize=None, compression='infer', thousands=None, decimal=b'.', lineterminator=None, quotechar='\"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=None, error_bad_lines=True, warn_bad_lines=True, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None)\n",
+    "pandas.read_csv(filepath_or_buffer, sep=<object object>, delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='\"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, dialect=None, error_bad_lines=True, warn_bad_lines=True, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None, storage_options=None)\n",
     "```"
    ]
   },
@@ -2354,11 +2360,13 @@
    "source": [
     "## Slicing of Data Frames\n",
     "\n",
-    "### Slicing Columns\n",
+    "* Pandas documentation: [Detailed documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html), [short documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html#selection)\n",
     "\n",
-    "* Use square-bracket operators to slice data frame: `[]`\n",
+    "### Quick Slices\n",
+    "\n",
+    "* Use square-bracket operators to slice data frame quickly: `[]`\n",
     "    * Use column name to select column\n",
-    "    * Also: Slice horizontally\n",
+    "    * Use numerical value to select row\n",
     "* Example: Select only columnn `C` from `df_demo`"
    ]
   },
@@ -2462,7 +2470,37 @@
     }
    ],
    "source": [
-    "df_demo[\"C\"]"
+    "df_demo['C']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0   -2.718282\n",
+       "1    1.718282\n",
+       "2   -1.304068\n",
+       "3    0.986231\n",
+       "4   -0.718282\n",
+       "Name: C, dtype: float64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo.C"
    ]
   },
   {
@@ -2473,14 +2511,13 @@
     }
    },
    "source": [
-    "* Select more than one column by providing list `[]` to slice operator `[]`\n",
-    "* *You usually end up forgetting one of the brackets…*\n",
-    "* Example: Select list of columns `A` and `C`, `[\"A\", \"C\"]` from `df_demo`"
+    "* Select more than one column by providing `list` to slice operator `[]`\n",
+    "* Example: Select list of columns `A` and `C`, `['A', 'C']` from `df_demo`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2551,13 +2588,14 @@
        "4  1.2 -0.718282"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_demo[[\"A\", \"C\"]]"
+    "my_slice = ['A', 'C']\n",
+    "df_demo[my_slice]"
    ]
   },
   {
@@ -2568,17 +2606,13 @@
     }
    },
    "source": [
-    "## Slicing of Data Frames\n",
-    "\n",
-    "### Slicing rows\n",
-    "\n",
-    "* Use numberical values to slice into rows\n",
+    "* Use numerical values in brackets to slice along rows\n",
     "* Use ranges just like with Python lists"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -2636,7 +2670,7 @@
        "2  1.2 2018-02-26 -1.304068     has  Same"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2646,19 +2680,93 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>1.718282</td>\n",
+       "      <td>column</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>0.986231</td>\n",
+       "      <td>entries</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A          B         C        D     E\n",
+       "1  1.2 2018-02-26  1.718282   column  Same\n",
+       "3  1.2 2018-02-26  0.986231  entries  Same"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo[1:6:2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
     }
    },
    "source": [
-    "* Get a certain range as **per the current sort structure**"
+    "* Attention: location might change after re-sorting!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -2716,18 +2824,18 @@
        "2  1.2 2018-02-26 -1.304068     has  Same"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_demo.iloc[1:3]"
+    "df_demo[1:3]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -2760,18 +2868,18 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
+       "      <th>2</th>\n",
        "      <td>1.2</td>\n",
        "      <td>2018-02-26</td>\n",
-       "      <td>1.718282</td>\n",
-       "      <td>column</td>\n",
+       "      <td>-1.304068</td>\n",
+       "      <td>has</td>\n",
        "      <td>Same</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>4</th>\n",
        "      <td>1.2</td>\n",
        "      <td>2018-02-26</td>\n",
-       "      <td>0.986231</td>\n",
+       "      <td>-0.718282</td>\n",
        "      <td>entries</td>\n",
        "      <td>Same</td>\n",
        "    </tr>\n",
@@ -2781,17 +2889,17 @@
       ],
       "text/plain": [
        "     A          B         C        D     E\n",
-       "1  1.2 2018-02-26  1.718282   column  Same\n",
-       "3  1.2 2018-02-26  0.986231  entries  Same"
+       "2  1.2 2018-02-26 -1.304068      has  Same\n",
+       "4  1.2 2018-02-26 -0.718282  entries  Same"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_demo.iloc[1:6:2]"
+    "df_demo.sort_values(\"C\")[1:3]"
    ]
   },
   {
@@ -2799,19 +2907,22 @@
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": []
    },
    "source": [
-    "* Attention: `.iloc[]` location might change after re-sorting!"
+    "## Slicing of Data Frames\n",
+    "\n",
+    "### Better Slicing\n",
+    "\n",
+    "* `.iloc[]` and `.loc[]`: Faster slicing interfaces with more options"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 40,
    "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
+    "tags": []
    },
    "outputs": [
     {
@@ -2844,6 +2955,14 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>1.718282</td>\n",
+       "      <td>column</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>1.2</td>\n",
        "      <td>2018-02-26</td>\n",
@@ -2851,31 +2970,95 @@
        "      <td>has</td>\n",
        "      <td>Same</td>\n",
        "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A          B         C       D     E\n",
+       "1  1.2 2018-02-26  1.718282  column  Same\n",
+       "2  1.2 2018-02-26 -1.304068     has  Same"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo.iloc[1:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
+   "source": [
+    "* Also slice rows (second argument)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
+       "      <th>1</th>\n",
        "      <td>1.2</td>\n",
-       "      <td>2018-02-26</td>\n",
-       "      <td>-0.718282</td>\n",
-       "      <td>entries</td>\n",
-       "      <td>Same</td>\n",
+       "      <td>1.718282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>-1.304068</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "     A          B         C        D     E\n",
-       "2  1.2 2018-02-26 -1.304068      has  Same\n",
-       "4  1.2 2018-02-26 -0.718282  entries  Same"
+       "     A         C\n",
+       "1  1.2  1.718282\n",
+       "2  1.2 -1.304068"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_demo.sort_values(\"C\").iloc[1:3]"
+    "df_demo.iloc[1:3, [0, 2]]"
    ]
   },
   {
@@ -2886,13 +3069,14 @@
     }
    },
    "source": [
-    "* One more row-slicing option: `.loc[]`\n",
-    "* See the difference with a *proper* index (and not the auto-generated default index from before)"
+    "* `.iloc[]`: Slice by **position** (_numerical/integer_)\n",
+    "* `.loc[]`: Slice by **label** (_named_)\n",
+    "* See difference with a *proper* index (and not the auto-generated default index from before)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2983,7 +3167,7 @@
        "entries  1.2 2018-02-26 -0.718282  Same"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2995,7 +3179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -3058,7 +3242,7 @@
        "entries  1.2 2018-02-26 -0.718282  Same"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3067,6 +3251,83 @@
     "df_demo_indexed.loc[\"entries\"]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>has</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>-1.304068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>entries</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>0.986231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>entries</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>-0.718282</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           A         C\n",
+       "D                     \n",
+       "has      1.2 -1.304068\n",
+       "entries  1.2  0.986231\n",
+       "entries  1.2 -0.718282"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo_indexed.loc[[\"has\", \"entries\"], [\"A\", \"C\"]]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -3075,13 +3336,20 @@
     }
    },
    "source": [
-    "### Advanced Slicing: Logical Slicing\n",
-    "\n"
+    "## Slicing of Data Frames\n",
+    "### Advanced Slicing: Logical Slicing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Slice can also be array of booleans"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -3134,23 +3402,48 @@
        "</div>"
       ],
       "text/plain": [
-       "     A          B         C        D     E\n",
-       "1  1.2 2018-02-26  1.718282   column  Same\n",
-       "3  1.2 2018-02-26  0.986231  entries  Same"
+       "     A          B         C        D     E\n",
+       "1  1.2 2018-02-26  1.718282   column  Same\n",
+       "3  1.2 2018-02-26  0.986231  entries  Same"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo[df_demo[\"C\"] > 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    False\n",
+       "1     True\n",
+       "2    False\n",
+       "3     True\n",
+       "4    False\n",
+       "Name: C, dtype: bool"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_demo[df_demo[\"C\"] > 0]"
+    "df_demo[\"C\"] > 0"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3203,7 +3496,7 @@
        "4  1.2 2018-02-26 -0.718282  entries  Same"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3233,7 +3526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3304,7 +3597,7 @@
        "2  1.2 2018-02-26 -1.304068     has  Same"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3315,7 +3608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -3390,7 +3683,7 @@
        "2  1.2 2018-02-26 -1.304068     has  Same -2.504068"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3402,16 +3695,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_demo.insert(df_demo.shape[1], \"G\", df_demo[\"C\"] ** 2)"
+    "df_demo.insert(df_demo.shape[1] - 1, \"E2\", df_demo[\"C\"] ** 2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -3444,8 +3737,8 @@
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "      <th>E</th>\n",
+       "      <th>E2</th>\n",
        "      <th>F</th>\n",
-       "      <th>G</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -3456,8 +3749,8 @@
        "      <td>-1.304068</td>\n",
        "      <td>has</td>\n",
        "      <td>Same</td>\n",
-       "      <td>-2.504068</td>\n",
        "      <td>1.700594</td>\n",
+       "      <td>-2.504068</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -3466,8 +3759,8 @@
        "      <td>0.986231</td>\n",
        "      <td>entries</td>\n",
        "      <td>Same</td>\n",
-       "      <td>-0.213769</td>\n",
        "      <td>0.972652</td>\n",
+       "      <td>-0.213769</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -3476,21 +3769,21 @@
        "      <td>-0.718282</td>\n",
        "      <td>entries</td>\n",
        "      <td>Same</td>\n",
-       "      <td>-1.918282</td>\n",
        "      <td>0.515929</td>\n",
+       "      <td>-1.918282</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "     A          B         C        D     E         F         G\n",
-       "2  1.2 2018-02-26 -1.304068      has  Same -2.504068  1.700594\n",
-       "3  1.2 2018-02-26  0.986231  entries  Same -0.213769  0.972652\n",
-       "4  1.2 2018-02-26 -0.718282  entries  Same -1.918282  0.515929"
+       "     A          B         C        D     E        E2         F\n",
+       "2  1.2 2018-02-26 -1.304068      has  Same  1.700594 -2.504068\n",
+       "3  1.2 2018-02-26  0.986231  entries  Same  0.972652 -0.213769\n",
+       "4  1.2 2018-02-26 -0.718282  entries  Same  0.515929 -1.918282"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3501,7 +3794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3534,8 +3827,8 @@
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "      <th>E</th>\n",
+       "      <th>E2</th>\n",
        "      <th>F</th>\n",
-       "      <th>G</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -3546,8 +3839,8 @@
        "      <td>-2.718282</td>\n",
        "      <td>This</td>\n",
        "      <td>Same</td>\n",
-       "      <td>-3.918282</td>\n",
        "      <td>7.389056</td>\n",
+       "      <td>-3.918282</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -3556,8 +3849,8 @@
        "      <td>1.718282</td>\n",
        "      <td>column</td>\n",
        "      <td>Same</td>\n",
-       "      <td>0.518282</td>\n",
        "      <td>2.952492</td>\n",
+       "      <td>0.518282</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -3566,8 +3859,8 @@
        "      <td>-1.304068</td>\n",
        "      <td>has</td>\n",
        "      <td>Same</td>\n",
-       "      <td>-2.504068</td>\n",
        "      <td>1.700594</td>\n",
+       "      <td>-2.504068</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -3576,8 +3869,8 @@
        "      <td>0.986231</td>\n",
        "      <td>entries</td>\n",
        "      <td>Same</td>\n",
-       "      <td>-0.213769</td>\n",
        "      <td>0.972652</td>\n",
+       "      <td>-0.213769</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -3586,8 +3879,8 @@
        "      <td>-0.718282</td>\n",
        "      <td>entries</td>\n",
        "      <td>Same</td>\n",
-       "      <td>-1.918282</td>\n",
        "      <td>0.515929</td>\n",
+       "      <td>-1.918282</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -3596,24 +3889,24 @@
        "      <td>-0.777000</td>\n",
        "      <td>has it?</td>\n",
        "      <td>Same</td>\n",
-       "      <td>23.000000</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>23.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "     A          B         C        D     E          F         G\n",
-       "0  1.2 2018-02-26 -2.718282     This  Same  -3.918282  7.389056\n",
-       "1  1.2 2018-02-26  1.718282   column  Same   0.518282  2.952492\n",
-       "2  1.2 2018-02-26 -1.304068      has  Same  -2.504068  1.700594\n",
-       "3  1.2 2018-02-26  0.986231  entries  Same  -0.213769  0.972652\n",
-       "4  1.2 2018-02-26 -0.718282  entries  Same  -1.918282  0.515929\n",
-       "5  1.3 2018-02-27 -0.777000  has it?  Same  23.000000       NaN"
+       "     A          B         C        D     E        E2          F\n",
+       "0  1.2 2018-02-26 -2.718282     This  Same  7.389056  -3.918282\n",
+       "1  1.2 2018-02-26  1.718282   column  Same  2.952492   0.518282\n",
+       "2  1.2 2018-02-26 -1.304068      has  Same  1.700594  -2.504068\n",
+       "3  1.2 2018-02-26  0.986231  entries  Same  0.972652  -0.213769\n",
+       "4  1.2 2018-02-26 -0.718282  entries  Same  0.515929  -1.918282\n",
+       "5  1.3 2018-02-27 -0.777000  has it?  Same       NaN  23.000000"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3640,7 +3933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3693,7 +3986,7 @@
        "1  Second      1"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3705,7 +3998,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -3754,7 +4047,7 @@
        "1  Second      2"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3777,7 +4070,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -3838,7 +4131,7 @@
        "1  Second      2"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3860,7 +4153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -3921,7 +4214,7 @@
        "3  Second      2"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3943,7 +4236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -3998,7 +4291,7 @@
        "1  Second      1  Second      2"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4020,7 +4313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -4072,7 +4365,7 @@
        "1  Second        1        2"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4092,14 +4385,15 @@
    "source": [
     "## Task 3\n",
     "<a name=\"task3\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
     "\n",
-    "* Add a column to the Nest data frame called `Virtual Processes` which is the total number of threads across all nodes (i.e. the product of threads per task and tasks per node and nodes)\n",
-    "* Remember to tell me when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"
+    "* Add a column to the Nest data frame form Task 2 called `Threads` which is the total number of threads across all nodes (i.e. the product of threads per task and tasks per node and nodes)\n",
+    "* Tell me when you're done with status icon in BigBlueButton: 👍"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 59,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -4148,7 +4442,7 @@
        "      <th>Number of Connections</th>\n",
        "      <th>Min. Delay</th>\n",
        "      <th>Max. Delay</th>\n",
-       "      <th>Virtual Processes</th>\n",
+       "      <th>Threads</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -4306,36 +4600,29 @@
        "3               46813040.0                     821491                7.23   \n",
        "4               46937216.0                     802865                7.03   \n",
        "\n",
-       "   Number of Neurons  Number of Connections  Min. Delay  Max. Delay  \\\n",
-       "0             112500             1265738500         1.5         1.5   \n",
-       "1             112500             1265738500         1.5         1.5   \n",
-       "2             112500             1265738500         1.5         1.5   \n",
-       "3             112500             1265738500         1.5         1.5   \n",
-       "4             112500             1265738500         1.5         1.5   \n",
-       "\n",
-       "   Virtual Processes  \n",
-       "0                  8  \n",
-       "1                 16  \n",
-       "2                 16  \n",
-       "3                 32  \n",
-       "4                 16  \n",
+       "   Number of Neurons  Number of Connections  Min. Delay  Max. Delay  Threads  \n",
+       "0             112500             1265738500         1.5         1.5        8  \n",
+       "1             112500             1265738500         1.5         1.5       16  \n",
+       "2             112500             1265738500         1.5         1.5       16  \n",
+       "3             112500             1265738500         1.5         1.5       32  \n",
+       "4             112500             1265738500         1.5         1.5       16  \n",
        "\n",
        "[5 rows x 22 columns]"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df[\"Virtual Processes\"] = df[\"Nodes\"] * df[\"Tasks/Node\"] * df[\"Threads/Task\"]\n",
+    "df[\"Threads\"] = df[\"Nodes\"] * df[\"Tasks/Node\"] * df[\"Threads/Task\"]\n",
     "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 60,
    "metadata": {
     "exercise": "solution"
    },
@@ -4349,11 +4636,11 @@
        "       'Min. Init. Time / s', 'Max. Init. Time / s', 'Presim. Time / s',\n",
        "       'Sim. Time / s', 'Virt. Memory (Sum) / kB', 'Local Spike Counter (Sum)',\n",
        "       'Average Rate (Sum)', 'Number of Neurons', 'Number of Connections',\n",
-       "       'Min. Delay', 'Max. Delay', 'Virtual Processes'],\n",
+       "       'Min. Delay', 'Max. Delay', 'Threads'],\n",
        "      dtype='object')"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4384,7 +4671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 61,
    "metadata": {
     "exercise": "task",
     "slideshow": {
@@ -4399,7 +4686,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -4413,7 +4700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4422,12 +4709,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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LBRGxrLZ7jyQjmJd8rYVC1dX6CqCEZUORK9fw5SeP42cu3yLd3uHGSyYAaDbnG4FX9EG+PX2EZedIAguFqmcLq7pxqkMtQyAedzsdNqdHBWvnLUQdcCbrbMQyn68gQDhjj46wcnEiYlkqaH8n2msfoqYjc6neQr56RlTkBkpYNhD3/WAK+UodvyypttLO5oE4Lts6gIc3iLAcnMljJBk5Iz/eCSHgx+b9FbWcWtHaejuxdcgjYdEjpLW+XKt1DmfbtufzVQwnIwi2NWMMJ51PhbVHLOJ6MjvDsuUaBuLh/i+UiBKWDUKzyfjiE8dw5fZBXLG989yFXd50yQSePrHsyP+EfuPQXL5lO9KL1uS4S15XRmg0GadXymfVMgTi8ZMOzm90Yi5XQThIZ90EY+EgUtGQ43+vFvKVszqnwsEAhpMRhyIWXVjaognxu6+U5EW42VINGSUsCid47NA8Ds8X8IGf3OnYNX7qgjEwA987NO/YNfwAM+PQbN7QYOkOfcblmEdzIZ2Yz1dQbzI2dxGWVDSEdDTkeOppLXO5CsZS0Y7Ovk50S62lWy1ixIHZEkCrsaRjIYTb3K+FsGQlCUuzychV6sjE3DWyV8KyQfjC40cxmorgrZdtcuwal24dQCYWwuOH3FvM5AXz+SpWSjWcP9ZfWOKRIDZlYjjqowK+SHFt7ZIKA7R01JzLkedcvtLVnn40FcW8w80E3bqnBhNhLDvgOLxUrLZSXwLZEUu+WgczVMSikM/xhSK+eWAW7712B6Ih53rZgwHCT5w3gu8emj+n246FA++eCWNWODtHE67Zvhthell0X3WOWACtiOx2V9h8rruwjKQi0ttw17KQr57RESYYiEewLDE1JVgsVM9oFNCupQmArOuJyGfDCgsR3UREB4joEBF9tMPznyKip/U/Pyai5bbnGm3PPeDuyf3PF588iiAR3nvdpOPXet35ozi5XPJli60sRKvxeQYiFgDYOZLEMR8Ji0hxbeoxxzSWdj5CWEvfiMXBVFi90USuUsdg4uwb8FAi7IjfW7ZcP6ueJK4vK2LJlrTuskzMXWHxxQZJIgoC+FsAbwIwBWAfET3AzC+K1zDzb7a9/tcBXNX2I0rMfKVb511PlKoN3LPvBN5y6SZsGpAzENmL154/CgD43ivz2Dnq3sY6Nzm+UEA0FOh5Y25nciSJ+XwVuXINaZf/B+/EXF4rkne6iQrGUlF8x0VhaTYZC/nKGcOJ7YykolgqVh3byCmm+jt1TzmVCsuVa9i2ps4VDwcRDpI0YVlpRSwbs8ZyLYBDzHyYmasA7gZwc4/XvwfAV1w52TrnweemkS3X8T4XohVA64LaPBDD46+cu3WWE4slbB9OdPUIW8sufXOfX6K42Wz3IrlgLB1FrlJHqdpw5UzZcg1NPrND6ozzpCJgXm3RlX59/QbcWVgiKNUaKNfk/rvIletIrymqE2ldcdIiFn1+yu2IxS/CshXAibbvp/THzoKIJgHsAvDNtodjRLSfiJ4kord3ed9t+mv2z83NyTq377ln3wnsHEngNbuHXbkeEeGancPYf3TxnK2zHF8strq9jLBdf63sBU5W6ZVyEjg5GNiJTlPo7QzrbcBiqFA2Kz2FRX4LMADkOwgLoNVD5KXCuv9eTuIXYTHDLQDuY+b2jw+TzLwXwHsBfJqIzlv7Jma+g5n3MvPesTHz2xLXI6/M5fHU0UW8+5odPT+dymbvziHMZCuO7rHwCmbGCZPCItxyT7m0U6Qfcz2K5ALx/KxL6TCx531tMVvQKmo7FLGs9ChyD8Yj+rXlCUut0USp1uiYGh2Ih6W1G4sU30aNWE4C2N72/Tb9sU7cgjVpMGY+qf/zMIBv48z6y4bl3n0nEAoQ3vHqjsGfY+yd1KKj/ccWXb2uGywXa8hV6q0oxAiDiTBi4QBOeeAY3AlDwtLyyHJHWFpOv12ERUQNTnRnAb0jliH92ksSRS2v3/A7RSxSU2H6z0lt0DmWfQD2ENEuIopAE4+zuruI6CIAQwCeaHtsiIii+tejAF4L4MW1791oVOtN/NMPp/DGi8fP2PfgBhduSiMdDWH/0SVXr+sGx/V0lpmIhYiwZTDuuImiEeqNJhYKlZZwdEP4dbk1yyIilqEuDQVOTKS300tYBoSoSYxYci1h6RyxyKyxpKOhM2xq3MAXXWHMXCeijwB4CEAQwJ3M/AIR3Q5gPzMLkbkFwN18ZvL+YgD/QERNaEL5yfZuso3KN1+ewXy+iluu2eH6tYMBwlWTQ+e0sGwf7j4D0oktA/HWIikvWSxUwQyM9eloG05GQORBxNLFe61V53CgOwvoXeQW6TmZaThxvU4RSyoaakU0dilU6khG3b/N+0JYAICZHwTw4JrH/njN9x/v8L7HAVzm6OHWIV956gQ2ZWK4/gJv6knXTA7hfzzyY6wUa61PfOcCLWEZMh6xAJr1+3cOet80Imom/SKWUDCAkWTENWFZKlYRChDSXW6CKf1T93LJuRpLJBhALHx2EkfYoeQkLhprRSwdft9UNIS8pI2V+Urd9TQY4J9UmEIiJ5dL+M7BObxr7zbXQ2DB1ZNDYAaemVru/+J1xNRSEaOpiOlPgVsG45jNVVCtNx06mTFEaqtfjQXQhhLdE5YaBhPhrk0mRITBuDPzJMCqUWOn6ycjIRBpcyeyEMLRKRWWioZQqTdRa9j/u5KvNDyJWJSwnIN8db/Wuf0Le7f3eaVzXLp1AADw7DkmLMcXi6YK94ItgzEwO79TpB9z2c47Tzrhpl/YcvFse5O1DCTCjhbvB7oMEQYChFQkJHU1cq5HKkwIQUFC1FKo1JGKureSWKCE5Ryj0WR8df8UXnf+qKUboCwG4mHsHk3imakVz87gBGZnWATCl2va45ZjMxGLm7YuS4Va18K9YDAedq7GUqr39NNKx+Slp4D24n3nVBgAKdfLl+tIRlTEorDJdw/N4+RyCe++xrtoRXD5toFzKmJpNBmnlsvYNmSucA+s7jjxujNsLldBOhZCLNz/U+yYbkTpxqDrkoGIZTARcbTG0muIMB0LS02FrUYsZ19zNWKxP+mvaiwKKdyz7ziGEmG8SV8V7CWXbxvETLbiefpHFnO5ChpN7ukK3A2xrdHrzjAjMyyCsVQU1UZTagqoG0vFqqGIxakaSz9hSTkQsURDAURCZ9+Ck3rqSsb1CtV6KwJyEyUs5xAL+QoefnEGP3/1Nkft8Y1yxXatzvLMiXMjajmlRxvdVvr2IhEJYSAe9nxIUizTMoITbbadYGYsFWtdhyMFMq1O1tI/YglJ7QrLlutdDUllpcKY2bN2YyUs5xD/+vQp1BqMd3lYtG/nks0DCAYIz54jdRYje0x64Ychydlc2XDEsjpx7szNXFCqNVCtNw2kwsLIleuoS+iWaqfZZOTKtZ62J6moXGHRnK67tFbH5BTvtc4yVhGLwjrMjHv3ncAV2wdx4aa018cBoG1P3DOewnMnzxFhERGLVWEZiHmeClvIV7ta069FDCuK3exO0W/qXjAo1vZKTs3lq3U0ubdRo1ZjkXfdfKWzASWAVrHdbsQihCkZUV1hCos8M7WCAzM5vNsn0Yrgki0ZvDSd9foYUji1XEYiErS822I8E3XN1LET1bq2zGrtOtxuiNSUTI+sTgjhMlK8B+Sn5kSnWS9hycRCkov33YUlJandWBT/Ux7sAFLCco5w7/4TiIUD+NkrNnt9lDO4ZHMGs7mKa/brTjK9UsLmgZhlp+ixdAwLhYr0VI5RhEAYFxZ3UmGiIN/vXAMOGVH2cjYWiKFFWQOuuXIN6Wjn68maYxERj5pjUViiVG3ga0+fwn+6bLPr9tj9uHhzBgDOiajl1Eq51TZshfF0FMzAgsOppW6InSdGhSUTCyNAbqTChLOxsVSY7FmWlk9Yj0hURBeyOsN6RSyRUACRYAA5ScKiivcKSzz43DRylbpvivbtnEvCMr1cMryOuBNi2n026030tmRSWAIBwmAi4nwqrGgyFSZ5lsXIMizRwSUrHZbr0RUGaAV8+6kwJSwKG9y7X9sSed0ud7ZEmmE4GcGmTAwvTee8PootqvUm5vIVbLYTseiiNJvzpoC/YFJYAOf2vbcjtkIOGoxYZJ+nl2W+ICXRiLLR5L6Di8lo0PaAZKEqUmFKWBQmOTpfwPePLOIX9m53dUukGS7enF73EctsrgxmrbPLKuMub2Vcy1KfZVqdGE5EWik0p8iWa0hGgggHe9+OMh4KS1qisIgbfqaXsETsD2QWq5owxQ24LMhGCcs65979JxAg4B1Xb/P6KF25eHMGh2bzqNTtW1R4hfD4shOxiDZfr1Jhrb3yJtYYuJEKE87C/QgGCJlYSPqQZK5cBxF6emqJQruMVFgvnzBBKmo/FVYSwqLajRVmqDe0LZFvuHAcm2x8knaaizdnUG8yDs7kvT6KZcTEvJ2IJRIKYDgZ8SwVtlioYiAeRqhPZNDOkAupsF6F7LUMJMLS243zFc2oMdBjxYTM4n0vnzBBUsJOllJNE5bERhYWIrqJiA4Q0SEi+miH5z9ARHNE9LT+55fbnruViA7qf2519+Te8Z2Dc5jJVnxZtG/nXCjgy4hYAC0d5lUqbLFQxYiJ+gqg1WMWi1VHjSizfabe28lIHlQExJbF3jdfmakwoxGLrFRYzAN7J19skCSiIIC/BfAmAFMA9hHRAx1WDN/DzB9Z895hAH8CYC8ABvAD/b3n3l7cNdy7bwojyQhuuGjc66P0ZNdoErFwYF0X8KeXS0jHQrYLoWMeCstSsdp19W83BhMRVOtNlGoNJByyX8+WaxhPG4sEZXt2AdogYb/OqdXivf3ozUgbsJxUWB2xcKBnJOYUfolYrgVwiJkPM3MVwN0Abjb43rcAeJiZF3UxeRjATQ6d0zfM5yt45KUZ/PzVWzs6pPqJYIBw4ab1PYF/aqVs2cqlnfF0DHMeuT0v5KumCvcAMJx0fkjSTCosHQu35k5kka/0dwCOhoKIhOzPlgCrtY9eNZ1kNGS7K6xYde7DQD/8ckfaCuBE2/dT+mNreQcRPUtE9xGRyP8Yei8R3UZE+4lo/9yc97vH7fIvPzyJetM/hpP9uGRzGi9OZ13Z7eEE0yslKXWs8Yy2ldGLfw9LRfOpMDE74uSQZLZkPBWWlmwGCeipMAM3YFnXFpFIr9pHKhpEvlJHs2n970mp1vCkIwzwj7AY4WsAdjLz5dCikrvMvJmZ72Dmvcy8d2xszJEDugUz4+59x3HVjkHsmfCH4WQ/LphIY6VUc23VrWyml8uW7PLXMp6OotZgx21S1sLM2pZGk8LitF8YM5uMWOR6dgF68d5AilNWGs5IUV2cp1izHrWUqg1PCveAf4TlJID2j97b9MdaMPMCM4u70mcBvNroe881vn9kEa/MFfDea3d4fRTD7BnXBPDQOuwMK9caWChULdvltyNqCW53huUrdVQbTQvFe2dTYaVaA/UmG2o3BrRUWL5Slxrxacuw+t+A07Ew8hJETaS4eomZDL+wYrXhSasx4B9h2QdgDxHtIqIIgFsAPND+AiJqd1d8G4CX9K8fAvBmIhoioiEAb9YfO2f58vePIxML4Wcv3+L1UQyzZyIFADg4u/6E5bToCJOUCgPcn2UR0+1Wivfa+52JWIx0SLWTjoXQ5NWOJxkYKd4D8nayFKva3Ey0R21URnuzl6kwX3SFMXOdiD4CTRCCAO5k5heI6HYA+5n5AQD/lYjeBqAOYBHAB/T3LhLRn0ITJwC4nZkXXf8lXGI+X8HXn5/G+14z6dmnESuMp6NIx0I4OLv+OsNarcYSIhaxvXHO5c6wRT2VZbrGEhcRizPCIny6DNdYWp5d8jYjGinea9cO4dhC0fb1itUGkpFQT6cMUfOxE7GUqg2Mpsz995aFL4QFAJj5QQAPrnnsj9u+/hiAj3V5750A7nT0gD7hq/unUGswfvG69ZMGAwAiwp7x1LockhRpq00DxhZk9WJE/x/daZuUtSwWNCEzG7GEggGkYyHHhiSzJiOW9rZfGc0UtYZmhW8oYpG0975YrfetfYjz5G1ESMVqHfGI/Q9DVvBLKkxhgGaT8Y9PHcN1u4Zx/vj6KNq3c8FEGofWYSpMpK3GDM5a9CIVDSHH2k6wAAAgAElEQVQSCri+n2ZRT4UNm2w3BjQPLad2za9a1huNWEL6++R0hplxAM5IanUuVPoX1WXsvS/XmoiHN3a7scIAjx2ax4nFEt73mkmvj2KJ88dTWChUsbDOOsNmc2XEwoGepoFGISKMpaKYz7sbsQgblMGk+X09TgqLqFkYn7yXuxfFzDKsZDSIYrVhu3HAyHyJcAIQhpXWrtM/MnIKJSzriC89eQwjyQje8qpNXh/FEqI1er1FLTPZCsbT1jdHrmUkFcFCwV1xXS7WEAwQ0hbqEgPxcKsWIpvVGovxAUlA3l4UIx1agkQkhEaTUbG5RdLIDX81YrHepKC6whR9mV4p4dGXZvCua7b7ftK+G3vG12dn2GyujImM/fqKYCQZwYLLEctSsYrBeNiSOGZi/kmFiRuurCFJM1sWk/pN2m5HWrHaQKLP9YQglCxGLE1dANWApKInX3nqBBjAe65ZX0X7djYPxJCMBNddxDKrRyyyGElFXU8HLpdqrZ3xZnE6FRYOUs/W23bSEj27gNUai5GusISE2RJAi1iSfSIJkSqzKmJeOhsDSljWBZV6A//4/WN4wwVj2DGS8Po4liEinD+RXnctx7O5Smv+RAYjqQjmC846Bq9lpVhrtQ6bZSAh359LIOxcjEZSWpuuvW6pdlrFewOWLkmbN/vVa/ZPUQUDhEgo0PIVM0vRw10sgBKWdcHXnpnGfL6KD71ut9dHsc16azkuVOrIV+pSI5bRZBTVelNaAdoIyyXzBpSCgXgY5VrTkUVtZuxcACAQIKSiIWldYXkTEYsoqMvYk2JEyBKRoGURK9e82x4JKGHxPcyMO797BBdMpPDa80e8Po5t9oynMJurYMVlryyrCIt7qTUWfZbFzTrLctF6KkwU1p1Ih2XLxrZHtiPTiHK13dhIV5iIWOxdu1Ax1q2VCFsXFvG+je5urOjC948s4sXpLD742l2+3WlvBmHtcmhufaTDZnSLe9k1FgCudoZpqTBrEYu48WdL8iMssxELoHWGSauxVM10hektwDY6tURXmZEbfjwSbEUeZhHip2osio7c+d0jGEqE8farOm0RWH+cN6YJyytzBY9PYgxHIhZ9+t2tWZZao4lcpY5BG8V7wKGIxYRlvkDmsq98pY5QwFjzwGqNxd5sCWAsQkpEQpavJYr3MZUKU6zl+EIRD780g1+8btKzvyCy2ToYRzhIODK/ToTFgYhlVEQsLgmLmBWxKiyrEYt8YbEWscixVgHEWuLevl2CRGto0d5sCWCsqB63UWMpVVVXmKILd37vCIJE+H9+Yn1O2nciFAxgx3ACR9ZRxBINBZCJy8tVDydFjcWdVNiyLggDVrvChLA40BlmZt+9ICUxFWbUgBJYLfDbtbIHjHWhJSLBVuRh9TpKWBRnsJCv4J59J/C2K7dgIiPv07If2DWaWjcRy0y2jPFMVGp9KxLS7GEWXDKiFAaSgza6wgD5qbB6o4litWG+eC8xFaZFLMZuvrFQEERA0YawCFEyErHY6QpTqTBFRz7/+FGU6w18+A3neX0U6eweS+LIQsHW2lW3mM1WMCExDSYYTUVdM6JcKek+YRYjFhFRyO7kM7uLRSBXWIztYgG0VudEOGgrFSZu+EYilng4ZHmORaXCFGeRK9fw+ceP4qZXbVqXLsb92DWaRLXexKmVktdH6ctMrix1OFIwmoq6VmNZjVisCUskFEA8HJSeCmvZuZhMhWViYVQbTcsdU+2YSYUB2vS9neJ9a9+9oeJ90PK1VLuxDhHdREQHiOgQEX20w/O/RUQvEtGzRPQoEU22Pdcgoqf1Pw+sfe9644tPHkOuXMeH33C+10dxhF2jSQDA4XVQZ5mTbOciGElFXItYWsJisd0YcMbWxWrEIsNSXlCo1A1FD4JkJGir3dhM7cNW8V4XXaNWObLxhbAQURDA3wJ4K4BLALyHiC5Z87IfAdjLzJcDuA/AX7Q9V2LmK/U/b3Pl0A5RqjbwuceO4PoLxnDZtgGvj+MIu8c0YfF7naVYrSNXqTsSsWgOxy5FLKUaiMzfwNvJxEPShaXlbGyhxgLIMaIUXWFGSdqMWMwU7+PhICr1JhoWUsalah3xcBCBgDezb74QFgDXAjjEzIeZuQrgbgA3t7+Amb/FzGIv6JMAtrl8Rle4d/8JLBSq+MhPn5vRCqCt501FQ74XFrHgy4kay0gyiqViFfWGPQt2I6wUqxiIh23dZDTrfLkDkma3RwpkWudrqTDjdYhkxF6rsxAlo8V7AJY6w7SdL96NKPhFWLYCONH2/ZT+WDc+BOD/tn0fI6L9RPQkEb290xuI6Db9Nfvn5ubsn9gByrUG/u7bh3DNziFcu2vY6+M4BhFh12gSh30uLK2pe0dqLBEwA0suWNssl6wbUAqcSIVZrbHISoUxMwpV48V7QKuN2DGhbO1/MdhuDFgbyCxVG57OvvlFWAxDRO8DsBfAX7Y9PMnMewG8F8CnieisVipmvoOZ9zLz3rGxMZdOa44vPnEMM9kKfufNF3p9FMfZNZrEkXl/m1GuTt07UWNxz9ZF8wmzXl8BnNnJYnZ7pEBEOHYdjkWayVQqLBKyNcdSqtZBBMTC/W+9cV18rHSGlWoqYgGAkwC2t32/TX/sDIjoRgB/COBtzNz6P5KZT+r/PAzg2wCucvKwTpAr1/B33z6E6y8Yw3W717/ZZD92jSYxtVRyxDFXFqs+YQ7UWJLuGVHKiFgycfnW+aLGkvKoeG/G2VhgZ7YE0Kb2E+GgsUl/G4vFVCpMYx+APUS0i4giAG4BcEZ3FxFdBeAfoInKbNvjQ0QU1b8eBfBaAC+6dnJJfO67R7BUrOF3N0C0AmgFfGbNtsavzOUqiIQClifWeyEiFjc6w5aLVcutxoKBeBi5ct1SIbkbuXId6WgIQZO1n5Skvferzsbmivd2J+/7bY8UxG0Ii0qFAWDmOoCPAHgIwEsA7mXmF4jodiISXV5/CSAF4Ktr2oovBrCfiJ4B8C0An2TmdSUss7kyPvvYEbz10k3nbCfYWkTLsZ/NKGeyZYyn5U7dC0ZdtM5ftrHkSyA6t2RZqQBajcVKp5qs9cSrEYuJ4r1eY7G6pM3IvntBIizWE6+/VJg30zMdYOYHATy45rE/bvv6xi7vexzAZc6ezln+6qEDqNQb+L2bLvL6KK4hhMXPnWGzuYpjdjqZWBihADleY2k0Gdmy/RrLQJt1vlVrmLXkLOxiAbTZjHCQJEQsxi3zBYlICHXd+t5KRFCoNAwPLSZsuClrAubdtllfRCwbmeemVvDVH0zhl167q3Wz3QikY2GMpaO+LuCLiMUJAgHCcDKC+ZyzEUuuXAOzdTsXgRN+YdmSeWdjQOsqTEVDtov3ZvbdC5I20lMAUKr133cviNtoNy5V+68/dhIlLB7CzPhvX3sBw4kIPnLDuTu30g2tM2xjRiyA5nLs9JCkXTsXgRNbJLVUmLVzpSRY51sq3tt0ODay7751LTs1llrDs7XEgBIWT/nXp09i/7El/M5bLjTdcnkusNvHwlKqNpAr1zHmUMQCaNP3iw6nwpZt7mIRiLXGMjvD8hVrEQsApKJh2zUWK8X7VGs9sXVzSKMWMqorTGGahXwFt3/tRVy9YxDv2ru9/xvOQXaNJjGfrzqymdAuszmt1djJiGUkGcWi4xGL9vMHbPiEae+XnwqzsuRLkI6GkK/YO0vegrC01hNbtHUpmCjet1JhJq8l1h+rVNgG5PZ/exH5Sh2ffMflptstzxX8XMCf0e1cnKqxAO6kwlYkRSwt63xJwsLMyHmcCludgjfTFWYvFaa1Gxu7XiQYQDBApiMW4fqsUmEbjG++PIP7nz6FX/vp83HBxLlni28UYUZ51IfC4k7EEkGuXEe17pxf2KqzsT1hSUSCCAVI2nriSr2JWoNtpMIkFO+rdcTCAYSCxm+DrYjFosNxsWrcTZlI2/9iVli83h4JKGFxnblcBb9337O4cCKN//ccXOJlhm1DCRABx3w4JOlKxKLPsjiZDhPCYnfIk4ik+oW1LPNNpKHakVW8N1O4B1Y9vqy0ADeajHLNXIoqHgmanmMRr497tIsFUMLiKs0m47fufRq5ch1//d6rEA1594nCD8TCQWzKxHBswZ8RSyQYsJ1C6kXL1sXBAv5yqYp0NGTqU3k3MlKFRfs5VlNh6aj9LZJmLfOBtlSYpYK6XtMxccO3sve+pFJhG4vPPHYYjx2cxx//50s2dAqsncmRBI4t+i9imc1WMObQ1L1A2Lo4GbGsFGsYTMoRR80vTI51vtUlX4JUNIRKvWkrjWh2yRegTd4D1vber0YSZiKWkIVUmL6lUqXCzn2+fWAWf/71l/HWSzfhvdfu8Po4vmFyOOnLVNhsrowJB+zy2xlOupAKK9VsbY5sx5FUmI3iPWC9iA5YS4XFQkEQWYtYxHuSJixktIjF3O9oRcBko4TFBQ7O5PDr//gjXLgpg7/6hSsc/RS83pgcTWA+X5GyZlYmMw6tJG7HDYdjGQaUgkwsJK14L1qFzd7YBTIcjguVhqmbPKA5JiTCQUuCthpJOOumrFJhG4CZbBkfvGsfYpEgPnfrXtM53XOdyWGtM8xvLsezWecjlkwsjKDDfmHLpZo0d2Zti6QcYbG6PVIgYz2xlRoLoE3fW/PvMt+tFQ+bL96vi64wIvLvwgyfc3qljFvueBKL+So++/692DIY9/pIvmNyRDPK81MBv1xrIFuuY9zBVmNA+/Q7lIg4X2ORFbHoO1msOvu2Y3XJlyAV1d5nJ2KxkgrTrh2y1G4sohzHI5Z1kgpTeRsLnFwu4ZY7nsBcroIvfOhaXLF90Osj+ZIdQlh8VMCfdaHVWDCSjDiWCmNmqTWWTCyMWkNrmbWL6Aozm4oSrO5ksR5BWY5YIkHL64LF+41ipXgvUmFmBEw2Rq4sb7PPBuFHx5fwX77wA1RqDdz1wWvx6skhr4/kWzKxMIaTEV8V8GdyYte9sxELIPzCnBGWfEVbzCUvYtFuF9lyzfan4XxZszax2gZtdydLs2l+371AW09so3hv4oavpcLM/Y5CiNZFjYWI/iepqnNPmk3GHd95Be/6hyeQiATxzx/+SSUqBpgcSfgqFSYiFqdrLIDWGeaUsMgajhSItJWMOosdnzCgbe+9VWsV/VO9mSVfgkTUasRSb73f8LUiQRRr5haLlap1EAGxsHcldDNXzgF4gIiSAEBEbyGi78k6CBHdREQHiOgQEX20w/NRIrpHf/77RLSz7bmP6Y8fIKK3yDqTGX5wbBE/93ffw39/8GXccNE47v+112KPmlUxxORwwlcRi7BzcborDNBTYQ4Li6zFXGIplwyH41zFuk8Y0NYVZjFiseJsLEhGrE39FyylwoJg1ixwjCIs872MAwz/W2XmPyKi9wL4NhFVAeQBnCUAViCiIIC/BfAmAFMA9hHRA2tWDH8IwBIzn09EtwD4cwDvJqJLANwC4FUAtgB4hIguYGbHmw5WijU8+vIM7t53Ak8dWcREJopPv/tK3HzlFtVSbILJkSTuf+YUKvWGL9wIZrIVhIOEIQen7gXDyShWSjXUGk2EJUzHt7Nc0gRLViqsfYukXXJla4VzQSKizZNYjVis7GIRiPXEZilW9EjCxN/xdut8oxsri1Vvd7EAJoSFiN4I4L8AKADYDOCDzHxA0jmuBXCImQ/r17obwM0A2oXlZgAf17++D8Df6Km5mwHczcwVAEeI6JD+856QdLYW8/kK/uT+F5At13ByqYQjCwUwA1sH4/ijn7kY77l2h2ontsDkSALMwNRSCeeNpbw+DmZzZYynY658OBB+YUuFqvSajiwDSoFY9iUlYrGZChNbJK3WWFoRi4UCdyISsjjHot3wAybczFeFpd4aqO2H19sjAXM77/8QwP/HzN8lossA3ENEv8XM35Rwjq0ATrR9PwXgum6vYeY6Ea0AGNEff3LNe7euvQAR3QbgNgDYscPa5HuACC+fzmIgHsaeiRRuvnIrrr9gFFdsGzT1l0VxJqLl+PhC0R/Ckq1g3IX6CtDuF+aAsOi1kAGJ7caAHOv8XLmGrTbb77WdLPYiFkupMD1iYWZTHz4KVeP77gXCSNLMLIvXS74Ac6mwG9q+fo6I3grgnwD8pBMHkw0z3wHgDgDYu3evpU634WQEj/72G2QeSwEtFQYAR31SwJ/NlVu7YpxmxEFbl5XWki85wiIiDD8U7wHd4dhyxCKK99YilnqTUW00TaVui9W66fbqRNj8Fkmv1xIDNuZYmHkawBslneMkgPY1itv0xzq+hohCAAYALBh8r8LHjCQjSEaCvingz2Sd3XXfzkhqNWKRzXKxhkQkKK1uFQ0FEQsHpBhRWh1ObCdlI2JZLd6b/3cjFoMVTbYcW6l9WFlP7IdUmBFh+RIR/QERTa59gplLks6xD8AeItpFRBFoxfgH1rzmAQC36l+/E8A3WevBewDALXrX2C4AewA8JelcChcgIkyOJH3RclyuNbBSqrkmLMNJLeW2kJdv66INR8ptQMjE7Nu61BtNFKsNW11hAJCKhZHzpHhvrdVZi1jMpsL09cQmjCiLtbqnw5GAgVQYM7+fiDZB68DaAeA5AF9lZml3Ab1m8hEADwEIAriTmV8gotsB7GfmBwB8DsAX9eL8IjTxgf66e6EV+usAfs2NjjCFXCZHEjgwk/P6GK5O3QNaYT1AzqTClos1DEhqNRYIWxc7iBuy3VRYOhrCySVrUa6tduOoWPZlPmIxK2SJiPlrldZLVxgznwbwPwGAiC4C8BtEFAfwbWZ+RMZBmPlBAA+ueeyP274uA/iFLu/9MwB/JuMcCm/YMZLAIy/NoNFkBD1shJhxYSVxO8IvzIlU2EqpKj1i0Ywo7aXC7O5iEdhNhRFZM2oU7zEdsVQapj+wnMupsLWcAjAD4PUA/oSI1kXxXuFvdo4kUWswpldkZVetMZN1V1gA3dbFAb+wZYkGlIJMLGQ7YpEmLDaK9/lKA8lIyFJL+WrEYu7aBRP77gWtVJiZrrCa911hpoSFiN4M4LsANgH4ADO/npkfd+Rkig3F5PBqy7GXuJ0KA5yzdVkuOSAsEpZ92V1LLEhFQyhUG2g0zTd5agaU1m6+QhzM+oVZiSQ2SsSyH5qgfIKZjzhxIMXGZHJUtBx7KywzLuy6X8tIMip9JwszY6VYw4AkZ2OBjOK9rIhFvL9gwbcrb6GQLmitJ7YSsVjYWAnAsBFlo8mo1Jvro8YiYOZFaIVzhUIqmzIxRIIBHFv0tjNMDEe6ackz7IBfWKnWQLXRdCBiCSFbrpseDmzHTkdWO+1+YWb3uhRstDsLcTAzfd9oausGzN7wAwFCPGzcQmbVMn99RSwKhSMEA4Ttw3Ecm/c4YsmWXa2vAJqwLBdrqDfs7zkRyLZzEWRiYTSabMkrSyAtFWbD4bhQMV/vELRSYSaHFgFrczPa3nuDwtJa8uVtu7ESFoVvmBxJer7wa8aFlcRrEUOSS0U5a3+Bdmdj+TUWwJ5fmN21xAI7O1nyFWu7WADNjj5A5iKWooXtkYJ4xPh64pIPdrEASlgUPmLHcALHFwpSVt9aZTZbccUuv50RfUhSZgF/uWXnIr/GAthzOM6V64gEA4bderthZyeLlgqzdn0iMr3sy84eejPriYs1IWBKWBQKAMDOkQQK1QbmHVrV249itY5cpe6aAaVguGVEKa+AL6KfoaT8ORbAXsSSr9RaaSw7tPbeW4hYrK4lFiSiQVMRi2gwsBaxhFqLyfrhh333gBIWhY8QZpTHPSrgtzZHuh2xCL8wiYIqdrEMSZ+8t29EKcOAErC3996uV1kyGjLVjWYrYgkHW6m0fqhUmEKxBmGff9SjAr4Xw5HAasQiNxUmdy2xQKTC7MyySBMWizWWeqOJSr1pK2JJmtzJIoTFavHecCrMhoDJRAmLwjdsG0ogQPCsgD+Tc2/XfTtDiQiI5DocLxWqiIeDtusYa2kV720JSw3pqH3BS1k0gxS1EVvCEg2a6gqzU7xPREPGu8JUu7FCcSaRUACbB+I47pHL8awescheuNWPYIAwGA9jUXKNxYnVyq2dLDas83PlupQaSzBASESCpmssOT11ZrV4D1iPWCynwgym3UQqTPYHCrMoYVH4ip2jCc+m72eyZcTCgdYKXjcZSUWld4UNSq6vAEA4GEAiEvRFjQWwZkQpJ2IJmZrlKdoq3gcN736xcx2ZKGFR+Aov97LM6K3Gbk7dC4aTEcnF+5r0jjBBJmbPOj9XrpmelO9GKhYyvZNFxuR/0nRXmM1241rDUBt+qda0fB2ZKGFR+IrJ4QSWijUpe9XNMptzfzhSMCLZ1mWpWMWg5BkWQSYesjzHwszIV+RFLOmoeYfjggxhsZgKs9KtlYyG0NBXIfejVNXWAURD3t7albAofEWr5diDdJjmE+ZufUUg2+HYCct8wYCNZV/FagNNtu8TJkjFrKTCrC/5EiSi2mxJ06CzcrFSRyISRMDCriEhRkam78X6Yy+i7nY8FxYiGiaih4nooP7PoQ6vuZKIniCiF4joWSJ6d9tznyeiI0T0tP7nSnd/A4VMdo7qLccepMNmsmXXZ1gEI8kIlopVSxbwa2k2GcvFqvQZFkEmZt06f9XZWFIqzELEIiUVFgmCGYa7tezsSDFjnV/ywS4WwAfCAuCjAB5l5j0AHtW/X0sRwPuZ+VUAbgLwaSIabHv+d5n5Sv3P084fWeEUO/S9LG7XWfKVOgrVhnepsFQUzKtWLHbIVeposnyfMIGd9cSrBpSyivdh0xGLnBqLOct+LWKxdr14xLhNvx92sQD+EJabAdylf30XgLevfQEz/5iZD+pfnwIwC2DMtRMqXCMRCWEiE8Uxl1NhXg1HCmQOSQpxcqIrDNC3SFqssYhCu4x2Y0ATqJxJkZORChODjkb9wgpV65FE0sTee5EK8xo/CMsEM0/rX58GMNHrxUR0LYAIgFfaHv4zPUX2KSLq+JGTiG4jov1EtH9ubk7KwRXOoHWGeSMsbvuECUZafmH2haXlE+ZgxJIr1wzXF9oRqTBZLd2i3diMcWmuUkckFEDERoF7dYuk8fkSt1JhXlvmAy4JCxE9QkTPd/hzc/vrWPvb0fVvCBFtBvBFAL/EzKJF4mMALgJwDYBhAL/f6b3MfAcz72XmvWNjKtjxM5PDCddrLKsriT2KWCT6hS05HrGE0WRrmxtl7WIRpGIhNE3UOgDNtDJts3nA7LIvK9sjBWb23peqDSQ2SsTCzDcy86Ud/twPYEYXDCEcs51+BhFlAPw7gD9k5ifbfvY0a1QA/B8A1zr/GymcZOdoErO5iunVr3aYzYlUmDcRy2oqzP70vUiFORexWJ++l7WWWNC+RdIodp2NgVVhMbzZ0UaKKhExXs8p1uqqxqLzAIBb9a9vBXD/2hcQUQTAvwD4AjPft+Y5IUoErT7zvKOnVTiOMKN0Mx02k60gEQlKa4M1i+jgkpEKW13y5UzEMmDDL0wIgKx/z0KgzAxJ2nU2BrSuMPGzjGAnYjGTCiuq4n2LTwJ4ExEdBHCj/j2IaC8RfVZ/zbsAXA/gAx3air9MRM8BeA7AKIBPuHt8hWx26rMsbnaGiZXEXvX/h4MBDCbCUor3S8UaiOQ7GwtWl32ZF5ZcWTub1bXAa7ESseQr9r3KViMWo11h9mssRlJhZZ+kwjyv8jDzAoA3dnh8P4Bf1r/+EoAvdXn/DY4eUOE6OzyIWLTNkd6kwQTDkqbvl4tVZGJhBC0M4xlBOBxbmWXJlrVowcqgYCesOBznK3XbtbTV4r1xO3vrwmKiK6ymIhaFoiOZWBgjyYirZpQzubJnU/eCkWQEC3n7NRannI0FrYjFYo1Flk8YsNq2bGYnS75sPxWWaLUb979uo8n64KK1a8bCARBpdi39UKkwhaIHO0YSrqXCmBnTK2VsHvBWWGTZuiwXqxhwqL4C2Nsima/UpNaxxF4XcxFLw3bxPhzU2pWN7GSxuyOFiJAI99//0mgyqvUmEmHPE1FKWBT+ZKeLsyyLhSqq9aYPhEWOdf6ywxGLEAYr0/cyLfOBtvXEJs6Sr9SknCEZMeZw3FryZUPM4pH+Nv1+WfIFKGFR+JTJkQROrZRQNjGfYJXpFa3VePNA3PFr9ULzC7M2eNjOkoM+YQAQCgaQilqbvpctLGIC3mjEUm80Ua41pTQPGN17v2ohY/2Gn4gE+6bCRCNBTAmLQtGZnSNJMANTS85HLaeWSwDgecQykoqg0WTbKwOcdDYWZGIhixFLDSmJNZZoKIhIKGC43VgU22VYyhi1zm8tFrMhZkb23per+i4WH3SFKWFR+BIxy3J03nlhOa3buWwe9DoVZn+WpVpvIl+pOxqxALoRpcWusIG43BqAmZ0s+ar96EGQjPa/2QNyTC+NCIuInpISfje7KGFR+JLWLMuiGxFLGeEgYTTpbbvxiH59O51hItpxPGKx4HDMrEVjsudrzOxkWR3QtH+GpMG1yFL2v0RCfWdm/LKWGFDCovApg4kw0rGQK51h0yslbBqISZutsIoMh2OnnY0F2k4Wkwu2qg00miy13Rgwt5MlX9HEUFYqzMgu+tVIwk7xvn/EkhcpN4/cI9pRwqLwJUSEnSNJV2ZZppfL2JzxtnAPaDUWwF4qzGlnY4G2nthcxCKiKekRS9T43ntx85WRCktEg6aK93aaFhKRYF+jzWJFpcIUir5MujTLMp0teV5fAVb9wuxELEstA0pnI5aBuPktklmHhCUdMxGxyEyFGS7ey0qF9aux2G8SkIUSFoVv2TmSxNRSCbVGs/+LLdJsMk6vlD1vNQaASCiATCxkS1hWis7cvNcyGI8gX6mb+m/jZMRi2AxS4qd6rd3YSPFeny+x0a2ViARbEUk3VmssKmJRKLoyOZJAo8k4uVRy7BrzhQpqDcYWH0QsgLaieN5G8b4VsSSdjVhEc4CZdJgQloyHxQtJ18QAAB9uSURBVHuRMktLiViCqNabfcW1UKkjGQnaquElIkEUa42eC83yEiIjWShhUfiWnaPOd4ad1ocjN3nsEyawa+uyVKwhHKSWrbtTCGERNR0jOBexhE2nwmRFLAD6FvBl7H+JR4JgBir17iJWrDQQDBCiNjZjysL7EygUXZgcFi7HztVZTi1rwrJl0PtUGGBfWBYLFQwnI47b/4uus5WS8bNmHYpY0rEQqo0mKnVjHVqxcAChoP1bX2vqv08BX87+l/4Ox4VqHYlI0LPVD+0oYVH4lrF0FPFw0NEhyekVf0zdC0ZsWucv5KuteRgnGdTFYdlExJItabtY7K4FXku65RfWP2rJSXA2FqxGLL2vKytiET+rG8VKwxeFe8AHwkJEw0T0MBEd1P851OV1jbYlXw+0Pb6LiL5PRIeI6B5926TiHICIMDmSwFEHI5bplTIioUBrhsRrRlIRLBWqPXPpvVgoVFtty04iUmFmhGWlVEMmFpY+L2RmJ0tBQvQgEDfxftctVBq2U2+tZV89Wo7z1XrLzt9rPBcWAB8F8Cgz7wHwqP59J0rMfKX+521tj/85gE8x8/kAlgB8yNnjKtxk91gSh+fyjv18YZfvh/QBoDkc15tsyeARABYKFYy4IJKDce0ayyaL9xnJdi7AqrAY2ckiY3ukwOjee5mpsN4RS11FLG3cDOAu/eu7oO2tN4S+5/4GAPdZeb/C/+weTeHEUgnVHkVLO0wvl3yTBgPQEoX5grXOsMV8FcMupMLSsRACtDrpbwQn7FyANut8AxFLvizv5pswuPfezr57gXh/r42Vhar9yEgWfhCWCWae1r8+DWCiy+tiRLSfiJ4kIiEeIwCWmVn8l50CsLXTm4noNv39++fm5qQdXuEsu8eSaDQZxx3qDJteKWOLD2ZYBHZsXcq1BgrVhiupsECAMBAPm6uxlOuOCEtr2ZfRiEVSKizVilicr7EYSfcVq/6JWFw5BRE9AmBTh6f+sP0bZmYi6pZcnmTmk0S0G8A3ieg5ACtGz8DMdwC4AwD27t1rb+GFwjV2j6UAAIfn8jh/PCX1ZzeajJls2RdT94KWw3HevLCIor8bqTBA6wwzmwqbyMj9bwiYi1iy5Rou3JSWct3V9cTOp8LSBn7HQqWBxOgGEhZmvrHbc0Q0Q0SbmXmaiDYDmO3yM07q/zxMRN8GcBWAfwIwSEQhPWrZBuCk9F9A4Rm7x7RZlsPz8gv48/kK6k3GJh9FLCLasBKxCFfkkZQ7Ls1axOKDVJiosRgRFolnMFL3kLVYTEQ8vTZlikFMP+CHVNgDAG7Vv74VwP1rX0BEQ0QU1b8eBfBaAC+y1jrzLQDv7PV+xfolEwtjNBV1pIA/pU/0b/VhxLJoocYiIha3OtwGE+b8wkRXmGyMths3m4xcpY6MpOK9NjPSW1haS75s1j7E+3tZyBSrDV9Y5gP+EJZPAngTER0EcKP+PYhoLxF9Vn/NxQD2E9Ez0ITkk8z8ov7c7wP4LSI6BK3m8jlXT69wHK0zTH7EIrZTbh9KSP/ZVomGgkhHQ5i3kApb1N8z6kKNBdBmWZYMRizlWgPVelP6cCQAREMBhALUssTvRq5SB7O8AU0i0lY09xC01cVi9m74rU2ZXa7FzChU61Jcm2Xgubwx8wKAN3Z4fD+AX9a/fhzAZV3efxjAtU6eUeEt540l8dALM9J/rohYtvlIWABgOGVt+n5Bj3Lci1gihov3TjkbA/oN3oDDsROT/5lYuGebswxnY0E6GuoqnuVaE8xAwgc+YYA/IhaFoie7R1NYLFRN5fONcGKxiNFUpDXV7Be06XtrqbBIKCCt66kfgwntplo34HDslE+YwMhOlpYJpsR0XDoW6rlJU8ZaYkGyx0KzlgGlT/4uK2FR+J5duhnlK5LTYVNLJd9FKwAwno5hNmtBWPJVjLjgEyYQti69UkECp5yNBUa2SAoBkClumXi4p8OzzIhFWw/Qucbip7XEgBIWxTqg1RkmuYB/YqmI7cP+E5aJTBQz2bLp9y26ZOciEEaURuosTkcs6Vio7+T9aipM3s03Ewv3FFaZ+1+09QCdRUxWk4AslLAofM/24QRCAZLactxoMk4tl7BtyD+txoLxTAzZch3lPqto17KQr7gydS8YMOEX5rSwaDf43ucQNjlSI5ZYCLmeqTCxCllWjaWziImIxQ+7WAAlLIp1QDgYwI6RhNSIZSZbRq3BvuoIE4ynNXEwmw5bKFQx6qKZ5pAJ63yxt2Uo4YywDCT6uwA4kY5zMxVmpMaiUmEKhQl2j6ZwRGLEckK3iNk+7L+IZUJfOjaTM5cOW8hXXXVpNmOdv1SoIhggR+ZYAE3k+jV3ZMuabX9K4s03E9OaBprNzmYeMov3WiqsW41FpcIUCtOcN57E0fmioQ4kI5zwaasx0CYsJuospWoDpVoDw67WWDSRMNIavVCoYigh3zK/dZZ4GIVqo6dZadYB2/50LAzm7su+CpU6QpK2OvZqN25FRipiUSiMc8F4GtVGU9qa4qmlIojgm1337UxktFTYjIlU2Kwe3Yyn3ft9MrEwQgEyJCxis6VTDCaFjX/3szhh2y9+XrfGAWFAKaNTLxkNoVxrotbhw5WIWBKq3VihMI4wDvzx6ZyUn3disYRNmRiiIX/8j9jOQDyMSCjQEgsjCBESouQGgQBhOBkxZJi5VKi1ajJOINJyKz3Sck64K4vUXrc6S84BN+VOFjJ5ibUcGShhUawLzh9PgQg4MCNHWI4vFnzZagxok+Tj6aip4r1Im4k0mluMpKKGhjkXChVHW6GHWq3P3YXFCa8y0QjQTViypbq0ZoFeLs7Fah1BSSk3GfjjFApFH2LhIHaOJPFjScJyZL6A3frgpR+ZyMRM1Vhmc3rE4mIqDNB8yeYMRCyLBWcbC1ZXJXc/i0xnY4EwwOw2y5It16SZXqZ77GQpVBq6KaY/NqEqYVGsGy6YSOFlCamwbLmG+XwVO30sLONpc0OSs9kyoqGAI6t/ezGairbs+rvRaDKWSzUMO5kKMzBTo93knUmFdZtlyZZq0iKWVev8zhGLXwr3gBIWxTriwok0js4XTA8OruWo3ra8y8fCMpGJtaIQI8xkyxjPRF3/xDpioMayXKyC2VlzTOEC4H7xvk+NpVyXJmYiFdbJE61QabQWj/kBJSyKdcMFm9JoMvCKzUHJI+tAWMYzUeTK9b5rbwUz2YrraTBAq7GUao2e5xSWL8MOLiBLRoIIB6lrjaVSb6Bca3qTCpMkZukexXsnojE7KGFRrBsunNA7w2zWWY7MF0AE7PBp8R5YrZUYLeDP5MquF+6B1Y2XvaIW8ZyTqTAiwkC8u42/sHORbYIZDgYQDwc7psKaTUa+Ij9i6ZQKy5brLZHzA0pYFOuGnaNJhIOEA6ftRyxbBuKIhf2TOliLEInTBussc9kKxl1sNRaM6VHIXI86y6JLmy2HEt1XJTvhbCzIxEMt4WpH9mKxZI/ifa4sr5YjA8+FhYiGiehhIjqo/3Oow2t+moiebvtTJqK36899noiOtD13pfu/hcINwsEAzhtL2Y5Yjs4XWo7JfmWzPrh5arnU97WFSh25St3V4UiBkYhlUb/ZO+28PNjDLyzrwC4WQbqLAaa4pqxIQhTnOw1jZkvyIiMZeC4sAD4K4FFm3gPgUf37M2DmbzHzlcx8JYAbABQBfKPtJb8rnmfmp105tcITLphI44CNzjBmxuH5AnaO+FtYtg5qHmYnl/oLS6vV2IOIZUSPWHp1homVyYMOGVAKBhMRLHcpoju5DybTZdmXeEzWDT8YICQjwR41FpUKa+dmAHfpX98F4O19Xv9OAP+XmeV4eyjWFRduSuPkcqmnVXkvFgtV5Mp1XxfuAW1uZzQVba1P7oVXw5GA1hUGaF5g3VgoVJGOhhx3ORiMd0+Frdr2y7/5ZuKd1xOv1nXkXVMzojzzWuWa5pGmUmFnMsHM0/rXpwFM9Hn9LQC+suaxPyOiZ4noU0TU8WMbEd1GRPuJaP/c3JzNIyu8YrWAb63OIrZQ7vJ5KgwAtg7FcdJAKszLiCUWDiIVDWGuR2v0UrHqijlmr1TYki58TtjKpGOdrfNlRyyAVmdZ224sRG3DRSxE9AgRPd/hz83tr2NmBtDZf1r7OZsBXAbgobaHPwbgIgDXABgG8Pud3svMdzDzXmbeOzY2ZvdXUnjExVsyAIAXT61Yev+B01kAqwLlZ7YNGhQWPWIZ9yBiAbTp+14Ry2Kh6qhPmGAwEUGp1ug457RYqIJodd5FJloq7OyIxYnlZukOO1mEgKV9VGNxReKY+cZuzxHRDBFtZuZpXThme/yodwH4F2ZufTxoi3YqRPR/APyOlEMrfMmWgRhGkhE8d9KasLx8Ood0LITNA/5zNV7LtqE4Hn5pBs0m97R6n8mWEQsHWnMObjPSZ/p+sVDFJhdET9RwVkq1szr+FnRxCzpg2y+WfTHzGQOqrShJYjdcJh5uCZagFbG47LrQCz+kwh4AcKv+9a0A7u/x2vdgTRpMFyOQ9l/07QCed+CMCp9ARLh06wCenbIaseRw0aa0bzyVerF1KI5qvYn5PiaPp5bL2DIQ9+x36jd9v1ioSr25dmPViPLsszjpVZaJhVFvcsu6XrBUrCESDCAp0cp+MBE5S1ic7Hizih+E5ZMA3kREBwHcqH8PItpLRJ8VLyKinQC2A/iPNe//MhE9B+A5AKMAPuHCmRUectnWARyczZu2dmFmHJjJtSz4/Y7oDOtXwJ9aLmHrkHebMEdSUcx3iViaTcZcrtJat+wkvTZaLjgoLMPJzgvPlgpVDCbCUgV/MB4+SzhFxOKnVJjnwsLMC8z8Rmbew8w3MvOi/vh+Zv7lttcdZeatzNxc8/4bmPkyZr6Umd/HzPIWoyt8yWXbBtBoMl6czpp636mVMnLlOi7clHHoZHIR2y37tRyfXCq2RMgLxtJRLBarHRdQLRarqDfZlY614R4zNYuFaquDTfp1k9HWNdpZKsoXs6GElgprX4XcahJQqTCFwjqXbR0AADxvss4iCvcXrZeIRY9CehXwy7UG5vNVbPMwYtk8EAMzOppmnl4RrdDORywtF4AOC9KcTIWJn9tJWGQ3LQwmImDGGXMzIkJzwlXAKkpYFOuOzQMxjKYipusswnL/gnXQEQZoGwMH4uGeEYtIk3mZCtukN0KcXjn7nK2VyS5ELEOJCEIBOsteptFkLBWdi1i6zfJotSW5N3vRoNButrlYqCAWDiChbPMVCusQES7bOoBnTiybet+B0zlsGYj56pNdP7YOxjG11H0WWEQzWwe9M9QUHXbTK2dHCmJlshtdYYEAYTQVPWumxmnbfpGCW1ojLMtF+euYxc9rHwRdLDi768YKSlgU65K9O4dxcDZ/VvqhF89NreCSLeujviLYMZzAscXuwnJ8QRv43D7sZSpMu/bpDsIiHhtzoXgvrrM2JSe+H3PISy0dDSEcpDMilqYeJckWs04LzdwaQDWDEhbFuuTaXcMAgH1HFw29fqlQxeH5Aq6ePMvj1NfsHkvi+EKxY2EcAI7MFxELBzzZxSLIxEJIRIIdI5bZXBmjqQjCQXduNWPpsyMWYXmzacAZcSMiDCcjWGxrC8+V62iy/IHMTgvNFlwaQDWDEhbFuuTybQOIhALYd8SYsPzoxBIA4Ood601YUqg3GSe6RC1HFzRDzV4DlE5DRNg0EMN0hxrLqWV398SMdUiFCWFx0v15OBk9I3oWs0ey6zpDosZSaItYHGxMsIoSFsW6JBoK4srtg3jKYMTyw2PLCAYIl28bcPhkchFmmYd1j7O1HJ0v+MJQc+tgvGOTwYmloqsL1cYz2kxNo60d9/SK8FJzTlhGkhHMt7U5z6w4YwyaiYURoDM70JzseLOKEhbFuuW6XcN4/uRKx8VHa/nh8SVcvDntq84ZI5ynm2Uenj97PKveaOL4YhE7fSAsnWpBzSZjaqmE7a4KSwxNxhlRy+lsGSPJCCIh525345loy7MN0DZ6AvLbrAMB0utI2s+v1BvIV+qqeK9QyOLaXcNocv86S73RxNMnlvHqdZYGA7Sc+mgq2tHN+fhiEfUm+yJimRxJYLlYw0pbUXkuX0G1/v+3d//BUdd3HsefryTEhJAQE2gIv8FGBHQUQSri0drqHbWdoj2d6lnr9Dw9ncOr13Puete5uzrT6VzH02tnzrmbnnq1o61n0Q5a689qtdc7BbTyM4CAP8hPIhBDICFA3vfH97PJgkvIht397sL7McNk95vvfvf9hWHf+/n1/vQzJYdToRPreZJn0rV39WZ9uvPEseW07xtsKWWzlVRXVTYw2y7RJZaLkjnp8MTiCtaCaTWUjSri5cah6pbClvZ9HOg7UnAD9wmz6ytpTFFlILEuZ3YeVBKYWhMlt/f3DHbZJcaFctlimRKqFSSXwWnv6mVClhdo1leXcaTfBloS7V29VJ5RMrCdcCZ9orJsYNxod5bGck6WJxZXsMpLi1nSMJ6XGtuJdlxI7XfbPgQGZ5IVmjn1VbzT3v2xmWGbW7soEjTUjYkpskHTaqMP9Pd3D7YUPoghsaRqsTR39lCf5ZI3E8OU65bOwcRSl6UK2nVVg1OqE+83Ic+qdXticQXtijl1tH7Uy4bm49cNe3VrB7PqKgfWWxSa2fVV9B3pZ3vH0d1hjW37mDl+zMdKxMchkVje+3CwxfLuh/spLlJOy80cu/Pm3v19dB44xMwsdxdOGFgkGr1vW1dv1srY1FWVsWd/HwcPH6E5JNA4Ky+k4onFFbTPza6jSPDipraUv9/Xe4jV7+7l07MKd3O3cydFXV3rdh5dwmZTS1fe1D0bXVrClJryge45gMbWfcwcV5H1LYmPNfnM8oHEsiMkumyPQ008ZpFo20e9WVtblEhYu7oO0tzZQ2lJEeMqcr976FA8sbiCVlNRyoJpNTy7oS1ld9gLG9vpO9LP0nMnxBBdZpw1fgxnjh511NTqpr0HaO7sYX4ejRvNrR97VMXpzW1dnFOf+/GfGeMq2LYrat0lWlDZnjlXVV5CRWkxTXt76D54mNaPepmZpe2vExMR2rt6ae7sYVJ1eazrmFLxxOIK3lXzJvHOrm7e+uDjtcNWrm1hUnU586ZUxxBZZkhiwfQaViUtBk3MhMuncaM5E6t4b/d+ug8epqv3EE17e2JpUc2pr6Ktq5fd3QcHuuMSg/rZIomGuko2t3UNJLWGLBU7nVEbJaztHd007+2JdcuE4/HE4gresgsmMuaMEh763btHHd/e0c1rWzu4Zv7kgtgxcigXz6zlgz0HBr6B/9/23VSWlXBOHswIS5g7sQqzaDuD9aHy9JwYWixzQz24jS1dNLZ2Ma12dFbXsCTMmVjFppYutrZH3YENn8jOpIqpNaMZXVpMY+s+mjyxpCbpWkkbJfVLWjDEeUslbZG0TdK3ko7PkPRGOP7fkvJr3p3LuoozSvjaomk8s66VjS2D4xD3v7yN0uIivnrxtBijy4xEV97Ta1voPXSEZze0cfnsuqzs4T5SF82oYVSxeGXzLl7evIvS4qJYWlRzJ0bVFd7e2ckb7+5h0czanLzv7PoqunoP8+rWDkpLirJWcaCoSMyaUMlzG9rYvb8vLwurxp5YiPao/zLw2vFOkFQM3A98HpgDXC9pTvj194F/NbNPAnuBm7MbrstHty6ZSW1FKX/9+Fo6D/Tx3IY2nvx9M7csmZGzyrrZNKm6nIXTa3hs9U5+tuoD9vUe5up5k+IO6yhVZaO4eGYtv1zXynMb2rj4rNqsrOM4kbGjR3HOhErue3Er3QcPs/iT43LyvomW0jPrWjlv0lhKslh4c3bo7gO4tCE395eO2BOLmTWa2ZYTnLYQ2GZmO8ysD3gMWKaof+OzwIpw3sPAVdmL1uWr6tGl3PeVC9jRsZ9Pfe/X3PbIm1wwpZrllzXEHVrG3PVHs2ju7OHupzdx0fQzc/aBmY7rF06lubOH5s4e/mThlNji+Pri6QOPF5+Vm7+n8ydXDyxUvOmS6UOffJKuPLd+4HG2p1KPRKEUTpoE7Ex63gR8CqgFOs3scNLxlF/jJN0K3AowderU7EXqYvPps8ez4vZF/HxNE3VVZ3DTJdMpL41/jUemLJxRw49unM+Gli5uvHhaXnWDJVx5Xj3/cu35HDrSz9KkD79cu3reZHbv72Pp3AmMHZ2bjd2Ki8SK2y/h6bUtXJnlWYiXNozjidsvAcjL8UMNtWI5Y28ivQSk+pv+tpmtDOf8BrjLzNakeP01wFIz+7Pw/EaixPId4PXQDYakKcCzZnbuUPEsWLDA1qz52Ns455wbgqQ3zey4Y+EJOWmxmNnlJ3mJZiC5XT05HNsNVEsqCa2WxHHnnHMxiX2MZZhWAw1hBlgpcB3wlEXNrVeAa8J5NwErY4rROecceZBYJF0tqQlYBDwj6flwfKKkXwGE1shy4HmgEXjczDaGS/wt8E1J24jGXB7M9T0455wblJMxlnzjYyzOOZe+4Y6xxN5icc45d2rxxOKccy6jPLE455zLKE8szjnnMuq0HLyX1AG8fxKXGAd8mKFw4lDo8UPh34PHH79Cv4c44p9mZifcNe+0TCwnS9Ka4cyMyFeFHj8U/j14/PEr9HvI5/i9K8w551xGeWJxzjmXUZ5YRuZHcQdwkgo9fij8e/D441fo95C38fsYi3POuYzyFotzzrmM8sTinHMuozyxpEHSUklbJG2T9K2440mXpIck7ZK0Ie5YRkLSFEmvSNokaaOkb8QdU7oklUlaJWltuIe7445pJCQVS/q9pF/GHUu6JL0nab2ktyUVZDVaSdWSVkjaLKlR0qK4Y0rmYyzDJKkY2ApcQbQF8mrgejPbFGtgaZC0BOgGfnKiXTbzkaR6oN7M3pJUCbwJXFVg/wYCKsysW9Io4H+Ab5jZ6zGHlhZJ3wQWAFVm9sW440mHpPeABWZWsIsjJT0M/NbMHgh7VI02s86440rwFsvwLQS2mdkOM+sDHgOWxRxTWszsNWBP3HGMlJm1mtlb4fE+or15JsUbVXos0h2ejgp/CurbnaTJwBeAB+KO5XQkaSywhLD3lJn15VNSAU8s6ZgE7Ex63kSBfaidSiRNB+YBb8QbSfpCN9LbwC7gRTMrtHv4AfA3QH/cgYyQAS9IelPSrXEHMwIzgA7gv0J35AOSKuIOKpknFldwJI0BngDuNLOuuONJl5kdMbMLgMnAQkkF0y0p6YvALjN7M+5YTsKlZnYh8HngL0IXcSEpAS4E/t3M5gH7gbwa8/XEMnzNwJSk55PDMZdDYVziCeBRM3sy7nhORui+eAVYGncsaVgMfCmMUzwGfFbSI/GGlB4zaw4/dwG/IOrmLiRNQFNSS3cFUaLJG55Yhm810CBpRhgsuw54KuaYTith4PtBoNHM7os7npGQNF5SdXhcTjQZZHO8UQ2fmf2dmU02s+lE/wdeNrOvxhzWsEmqCBM/CN1HfwgU1CxJM2sDdkqaFQ59DsirCSwlcQdQKMzssKTlwPNAMfCQmW2MOay0SPoZ8BlgnKQm4J/M7MF4o0rLYuBGYH0YowD4ezP7VYwxpaseeDjMMiwCHjezgpuyW8DqgF9E31EoAX5qZs/FG9KI3AE8Gr7k7gC+HnM8R/Hpxs455zLKu8Kcc85llCcW55xzGeWJxTnnXEZ5YnHOOZdRnlicc85llCcWd1qTZJLuTXp+l6TvZOjaP5Z0TSaulQuS/jfuGNypwROLO90dBL4saVzcgSSTlPM1ZmZ2Sa7f052aPLG4091hor3D/+rYXxzb4pDUHX5+RtKrklZK2iHpnyXdEPZZWS/prKTLXC5pjaStoc5WogjlPZJWS1on6c+TrvtbSU8Bm8Iq8WfC3i0bJH0lRYy3hOuslfSEpNHh+FmSXg/xfDcp9jGSfi3prfC7ZUnXSr6/3yTt9/FoqHrg3LB4YnEO7gduCOXIh+t84DZgNlE1gLPNbCFRKfk7ks6bTlSL6gvAf0gqA24GPjKzi4CLgFskzQjnX0i0P8vZRDXEWszs/LB/TqoV4k+a2UVmdj7RNgI3h+M/BH5oZucR1ZZK6AWuDkUYLwPuPU7SmAfcCcwBZhJVPXBuWDyxuNNeqJD8E+Av03jZ6rA/zEFgO/BCOL6eKJkkPG5m/Wb2DlHpjXOI6lN9LZSleQOoBRrC+avM7N2ka10h6fuS/sDMPkoRx7mhlbMeuAGYG44vAn4eHv806XwB35O0DniJaOuHuhTXXWVmTWbWD7x9zD05NyRPLM5FfkD0bT95X4vDhP8jkoqA0qTfHUx63J/0vJ+ja/AdWzPJiD7c7zCzC8KfGWaWSEz7B04020rUglkPfFfSP6aI+8fA8tAyuRsoO8F93gCMB+aH0v3tx3lN8v0dwesKujR4YnEOMLM9wOMMdiUBvAfMD4+/RLTbY7qulVQUxl1mAluICpneHrYAQNLZqTZqkjQROGBmjwD3kLo0eiXQGq51Q9Lx14E/Do+vSzo+lmg/lUOSLgOmjeCenBuSfwtxbtC9wPKk5/8JrJS0lmh8Y3/KVw3tA2AVUAXcZma9kh4g6lp6K4xvdABXpXjtecA9kvqBQ8DtKc75B6LutI7wszIcvxN4RNK3Q+yJbrRHgadD19kaCqhkvyscXt3YuVNQmB3WY2Ym6TrgejNbdqLXOZcJ3mJx7tQ0H/i30CLqBP405njcacRbLM455zLKB++dc85llCcW55xzGeWJxTnnXEZ5YnHOOZdRnlicc85l1P8DAeaXvjs3NJ4AAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -4435,7 +4724,7 @@
     "fig, ax = plt.subplots()\n",
     "ax.plot(x, y)\n",
     "ax.set_title('Use like this')\n",
-    "ax.set_xlabel(\"Numbers again\");\n",
+    "ax.set_xlabel(\"Numbers\");\n",
     "ax.set_ylabel(\"$\\sqrt{x}$\");"
    ]
   },
@@ -4453,7 +4742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4466,17 +4755,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -4488,6 +4779,44 @@
     "ax.set_title(\"This plot makes no sense\");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "* Matplotlib can also plot DataFrame data\n",
+    "* Because DataFrame data is _only_ array-like data with stuff on top"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(df_demo.index, df_demo[\"C\"], label=\"C\")\n",
+    "ax.legend()\n",
+    "ax.set_title(\"Nope, no sense at all\");"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -4499,17 +4828,19 @@
    "source": [
     "## Task 4\n",
     "<a name=\"task4\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
     "\n",
-    "* Sort the data frame by the virtual proccesses\n",
-    "* Plot `\"Presim. Time / s\"` and `\"Sim. Time / s\"` of our data frame `df` as a function of the virtual processes\n",
+    "\n",
+    "* Sort the data frame by threads\n",
+    "* Plot `\"Presim. Time / s\"` and `\"Sim. Time / s\"` of our data frame `df` as a function of threads\n",
     "* Use a dashed, red line for `\"Presim. Time / s\"`, a blue line for `\"Sim. Time / s\"` (see [API description](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot))\n",
-    "* Don't forget to label your axes and to add a legend\n",
-    "* Submit when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"
+    "* Don't forget to label your axes and to add a legend _(1st rule of plotting)_\n",
+    "* Tell me when you're done with status icon in BigBlueButton: 👍"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 67,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -4518,34 +4849,36 @@
    },
    "outputs": [],
    "source": [
-    "df.sort_values([\"Virtual Processes\", \"Nodes\", \"Tasks/Node\", \"Threads/Task\"], inplace=True)"
+    "df.sort_values([\"Threads\", \"Nodes\", \"Tasks/Node\", \"Threads/Task\"], inplace=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 68,
    "metadata": {
     "exercise": "solution"
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "fig, ax = plt.subplots()\n",
-    "ax.plot(df[\"Virtual Processes\"], df[\"Presim. Time / s\"], linestyle=\"dashed\", color=\"red\")\n",
-    "ax.plot(df[\"Virtual Processes\"], df[\"Sim. Time / s\"], \"-b\")\n",
+    "ax.plot(df[\"Threads\"], df[\"Presim. Time / s\"], linestyle=\"dashed\", color=\"red\", label=\"Presim. Time / s\")\n",
+    "ax.plot(df[\"Threads\"], df[\"Sim. Time / s\"], \"-b\", label=\"Sim. Time / s\")\n",
     "ax.set_xlabel(\"Virtual Process\")\n",
     "ax.set_ylabel(\"Time / s\")\n",
-    "ax.legend();"
+    "ax.legend(loc='best');"
    ]
   },
   {
@@ -4592,7 +4925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4601,12 +4934,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x144 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -4627,7 +4962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4636,12 +4971,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x144 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -4658,7 +4995,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -4667,12 +5004,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -4690,7 +5029,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4699,12 +5038,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -4714,7 +5055,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -4723,12 +5064,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 864x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -4747,21 +5090,21 @@
    "source": [
     "## Task 5\n",
     "<a name=\"task5\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
     "\n",
     "Use the NEST data frame `df` to:\n",
     "\n",
-    "1. Make the virtual processes the index of the data frame (`.set_index()`)\n",
+    "1. Make the threads the index of the data frame (`.set_index()`)\n",
     "2. Plot `\"Presim. Program / s\"` and `\"Sim. Time / s`\" individually\n",
     "3. Plot them onto one common canvas!\n",
     "4. Make them have the same line colors and styles as before\n",
-    "5. Add a legend, add missing labels\n",
-    "\n",
-    "* Done? Tell me! [pollev.com/aherten538](https://pollev.com/aherten538)"
+    "5. Add a legend, add missing axes labels\n",
+    "6. Tell me when you're done with status icon in BigBlueButton: 👍"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 74,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -4770,24 +5113,26 @@
    },
    "outputs": [],
    "source": [
-    "df.set_index(\"Virtual Processes\", inplace=True)"
+    "df.set_index(\"Threads\", inplace=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 75,
    "metadata": {
     "exercise": "solution"
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x216 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -4797,19 +5142,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 76,
    "metadata": {
     "exercise": "solution"
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x216 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -4819,7 +5166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 77,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -4829,12 +5176,14 @@
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -4845,7 +5194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 78,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -4855,12 +5204,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -4878,22 +5229,24 @@
    },
    "source": [
     "## More Plotting with Pandas\n",
-    "### Our first proper Pandas plot\n"
+    "### Recap: Our first proper Pandas plot\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -4906,7 +5259,7 @@
    "metadata": {},
    "source": [
     "* **That's why I think Pandas is great!**\n",
-    "* It has great defaults to quickly plot data\n",
+    "* It has great defaults to quickly plot data; basically publication-grade already\n",
     "* Plotting functionality is very versatile\n",
     "* Before plotting, data can be *massaged* within data frames, if needed"
    ]
@@ -4925,17 +5278,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD4CAYAAAD4k815AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAADSNJREFUeJzt3W+MXHW9x/HPh1JcjI3kbisIS5010CC4gHZFE5AryNV6uREbntQ/uGpig1GhuSaCNkZ9YFIk0T64JmZjMd5Et0G0LYlcFWwlVCN227QdoIj/tnaJxWUxcBvbQsvXBztlS912lzln58x+5/1KCOw5s+d8Mxne/fXMP0eEAAB5nFb1AACAchF2AEiGsANAMoQdAJIh7ACQDGEHgGQIOwAkQ9gBIBnCDgDJnF7FSRcuXBi1Wq2KUwPAnLV9+/anI2LRdLerJOy1Wk3Dw8NVnBoA5izbe2dyOy7FAEAyhB0AkiHsAJBMJdfYAaAKL7zwgkZHR3Xo0KGqRzmlrq4u9fT0aP78+U39PmEH0DFGR0e1YMEC1Wo12a56nClFhMbHxzU6Oqre3t6mjsGlGAAd49ChQ+ru7m7bqEuSbXV3dxf6WwVhB9BR2jnqxxSdkbADQDJcY0+g73t9VY+g+kC96hGAV6x2+09KPd7ImutndLuNGzdq+fLl2rNnjy666KJSZ5BYsQNAyw0NDemqq67S0NDQrByfsANACx04cEBbt27VunXrtH79+lk5B2EHgBbatGmTli1bpiVLlqi7u1vbt28v/RyEHQBaaGhoSCtWrJAkrVixYlYux/DkKQC0yDPPPKPNmzerXq/Lto4ePSrbuvPOO0t9GSYrdgBokXvuuUc33XST9u7dq5GREe3bt0+9vb166KGHSj0PK3YAHWumL08sy9DQkG677baXbbvxxhs1NDSkq6++urTzEHYAaJEtW7b8y7Zbbrml9PNwKQYAkmHFnkD9z3+pegQAbYQVOwAkQ9gBIBnCDgDJEHYASIYnTwF0rq+8tuTjPTvtTfbv369Vq1Zp27ZtOuuss3T22Wdr7dq1WrJkSWljEHYAaJGI0PLlyzUwMPDSJzvu2rVLTz31FGEHgLloy5Ytmj9/vm6++eaXtl122WWln4dr7ADQIo888oiWLl066+ch7ACQDGEHgBa55JJLZuWLNU5E2AGgRa699lodPnxYg4ODL23bvXs3H9sLAKWZwcsTy2RbGzZs0KpVq3THHXeoq6tLtVpNa9euLfU8hB0AWujcc8/V3XffPavnIOxAVmW/+aapGVq7IsaEwtfYbZ9ve4vtx2w/avvWMgYDADSnjBX7EUmfi4gdthdI2m77/oh4rIRjAwBeocIr9oj4a0TsaPz3/0vaI+m8oscFADSn1Jc72q5Jeoukh6fYt9L2sO3hsbGxMk8LADhOaWG3/RpJP5K0KiKeO3F/RAxGRH9E9C9atKis0wIATlDKq2Jsz9dE1L8fET8u45gAMNv6vtdX6vHqA/VpbzNv3jz19U2ed+PGjarVaqXOUTjsti1pnaQ9EfGN4iMBQF5nnnmmdu7cOavnKONSzJWSbpJ0re2djX/+s4TjAgCaUHjFHhFbJbmEWQAgvYMHD+ryyy+XJPX29mrDhg2ln4N3ngJAC82VSzEAgDZC2AEgGS7FAOhYM3l54lzEih0AWujAgQOzfg7CDgDJEHYASIawA+goEVH1CNMqOiNhB9Axurq6ND4+3tZxjwiNj4+rq6ur6WPwqhgAHaOnp0ejo6Nq948O7+rqUk9PT9O/P2fDXrv9J1WPoJE111c9giSpdugHVY+gkaoHaOBxMYnHxaROe1xwKQYAkiHsAJAMYQeAZAg7ACRD2AEgGcIOAMkQdgBIhrADQDKEHQCSIewAkAxhB4BkCDsAJEPYASAZwg4AyRB2AEiGsANAMoQdAJIh7ACQDGEHgGRKCbvtZbZ/Z/sPtm8v45gAgOYUDrvteZK+Jel9ki6W9EHbFxc9LgCgOWWs2K+Q9IeI+FNEPC9pvaQbSjguAKAJp5dwjPMk7Tvu51FJbz/xRrZXSlopSYsXLy580pGuDxU+RnHPVj2AJGlkzfVVj9A2eFxMWvCmdrgq2h6PzU57XLTsydOIGIyI/ojoX7RoUatOCwAdp4ywPynp/ON+7mlsAwBUoIywb5N0oe1e22dIWiHp3hKOCwBoQuFr7BFxxPZnJP1M0jxJd0XEo4UnAwA0pYwnTxUR90m6r4xjAShHfaBe9QioCO88BYBkCDsAJEPYASAZwg4AyRB2AEiGsANAMoQdAJIh7ACQTClvUAKAdtbXW/wTZYtq5dvFWLEDQDKEHQCSIewAkAxhB4BkCDsAJEPYASAZwg4AyRB2AEiGsANAMoQdAJIh7ACQDJ8Vg1Q67TNBgKmwYgeAZAg7ACRD2AEgGcIOAMkQdgBIhrADQDKEHQCSIewAkAxhB4BkCoXd9p22H7e92/YG22eVNRgAoDlFV+z3S3pzRFwq6QlJXyg+EgCgiEJhj4ifR8SRxo+/kdRTfCQAQBFlXmP/hKT/K/F4AIAmTPvpjrYfkHTOFLtWR8Smxm1WSzoi6funOM5KSSslafHi6j+BDwCymjbsEXHdqfbb/pik/5L07oiIUxxnUNKgJPX395/0dgCAYgp9HrvtZZI+L+nfI+If5YwEACii6DX2/5G0QNL9tnfa/nYJMwEACii0Yo+IC8oaBABQDt55CgDJEHYASIawA0AyhB0AkiHsAJAMYQeAZAg7ACRT6HXsADAX1AfqVY/QUqzYASCZObti7+ut/hMiO2sNAGCuYMUOAMkQdgBIhrADQDKEHQCSIewAkAxhB4BkCDsAJEPYASAZwg4AyRB2AEiGsANAMoQdAJIh7ACQDGEHgGQIOwAkQ9gBIBnCDgDJEHYASIawA0AyhB0AkpmzX2YNTKU+wFeMA6Ws2G1/znbYXljG8QAAzSscdtvnS3qPpL8UHwcAUFQZK/ZvSvq8pCjhWACAggqF3fYNkp6MiF0zuO1K28O2h8fGxoqcFgBwCtM+eWr7AUnnTLFrtaQvauIyzLQiYlDSoCT19/ezugeAWTJt2CPiuqm22+6T1Ctpl21J6pG0w/YVEbG/1CkBADPW9MsdI6Iu6XXHfrY9Iqk/Ip4uYS4AQJN4gxIAJFPaG5QiolbWsQAAzWPFDgDJEHYASIawA0AyhB0AkiHsAJAMYQeAZAg7ACRD2AEgGcIOAMkQdgBIhrADQDKEHQCSIewAkAxhB4BkCDsAJEPYASAZwg4AyRB2AEiGsANAMoQdAJIh7ACQDGEHgGQIOwAkQ9gBIBnCDgDJEHYASIawA0AyhB0AkiHsAJAMYQeAZAqH3fZnbT9u+1HbXy9jKABA804v8su2r5F0g6TLIuKw7deVMxYAoFlFV+yfkrQmIg5LUkT8rfhIAIAiioZ9iaR32n7Y9oO233ayG9peaXvY9vDY2FjB0wIATmbaSzG2H5B0zhS7Vjd+/98kvUPS2yTdbfuNEREn3jgiBiUNSlJ/f/+/7AcAlGPasEfEdSfbZ/tTkn7cCPlvbb8oaaEkluQAUJGil2I2SrpGkmwvkXSGpKeLDgUAaF6hV8VIukvSXbYfkfS8pIGpLsMAAFqnUNgj4nlJHylpFgBACXjnKQAkQ9gBIBnCDgDJEHYASIawA0AyhB0AkiHsAJBM0TcoVaY+UK96BABoS6zYASAZwg4AyRB2AEiGsANAMoQdAJIh7ACQDGEHgGQIOwAk4yq+8Mj2mKS9LT/xyy0UX+N3DPfFJO6LSdwXk9rlvnhDRCya7kaVhL0d2B6OiP6q52gH3BeTuC8mcV9Mmmv3BZdiACAZwg4AyXRy2AerHqCNcF9M4r6YxH0xaU7dFx17jR0AsurkFTsApETYASAZwg4AyczZb1B6pWxfJOkGSec1Nj0p6d6I2FPdVED7sH2FpIiIbbYvlrRM0uMRcV/Fo1XO9v9GxEernmOmOuLJU9u3SfqgpPWSRhubeyStkLQ+ItZUNRuq1fgD/zxJD0fEgeO2L4uIn1Y3WWvZ/rKk92lisXe/pLdL2iLpPyT9LCK+VuF4LWX73hM3SbpG0mZJioj3t3yoV6hTwv6EpEsi4oUTtp8h6dGIuLCaydqP7Y9HxHernqMVbN8i6dOS9ki6XNKtEbGpsW9HRLy1yvlayXZdE/fBqyTtl9QTEc/ZPlMTf+hdWumALWR7h6THJH1HUmgi7EOaWAgqIh6sbrqZ6ZRr7C9KOneK7a9v7MOkr1Y9QAt9UtLSiPiApHdJ+pLtWxv7XNlU1TgSEUcj4h+S/hgRz0lSRBxU5/0/0i9pu6TVkp6NiF9KOhgRD86FqEudc419laRf2P69pH2NbYslXSDpM5VNVRHbu0+2S9LZrZylYqcdu/wSESO23yXpHttvUOeF/Xnbr26EfemxjbZfqw4Le0S8KOmbtn/Y+PdTmmOt7IhLMZJk+zRJV+jlT55ui4ij1U1VjcYD9b2S/n7iLkm/joip/naTju3Nkv47InYet+10SXdJ+nBEzKtsuBaz/aqIODzF9oWSXh8R9QrGagu2r5d0ZUR8sepZZqpjwo5JttdJ+m5EbJ1i3w8i4kMVjNVytns0cQli/xT7royIX1UwFlAYYQeAZDrlyVMA6BiEHQCSIewAkAxhB4Bk/gkPuxYtgwxTmAAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -4945,7 +5300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -4954,12 +5309,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -4969,7 +5326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4978,23 +5335,25 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x288 with 3 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "df_demo[df_demo[\"F\"] < 0][[\"A\", \"C\", \"F\"]]\\\n",
-    "    .plot(kind=\"barh\", subplots=True, sharex=True, title=\"Subplots\", figsize=(12, 4));"
+    "    .plot(kind=\"barh\", subplots=True, sharex=True, title=\"Subplots Demo\", figsize=(12, 4));"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -5003,17 +5362,19 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df_demo[df_demo[\"F\"] < 0][[\"A\", \"F\"]]\\\n",
+    "df_demo.loc[df_demo[\"F\"] < 0, [\"A\", \"F\"]]\\\n",
     "    .plot(\n",
     "        style=[\"-*r\", \"--ob\"], \n",
     "        secondary_y=\"A\", \n",
@@ -5024,7 +5385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 119,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -5033,7 +5394,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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r5wBzQrEvEREREYkeHTt2pE+fPuTk5PxkreuTDR48mAMHDpCZmcl9991XomIN7gTKP/3pT8HELXNhO6FRRERERGJT/iX1CrNw4cKgjhPJ00GO0eXPRURERERCROVaRERERCREVK5FREREREJE5VpEREREJER0QqOIiIiI/ER8fDxnnXXW8dtvvPEGKSkp/gJFCZVrERERkVjwcGs4shsq14O71wa9u+TkZL744osQBCtfNC1ERERESm/rJ7B5iSt24teR3Se+Fy80ci0iIiKll5vl3qvQlZ13xsPOrwu+f+snga+DAax7P7E6xCVAk/NO/Zz6Z8HFDxV62KNHj9KhQwcAmjdvzuuvv16q+OWNyrWIiIiU3LEpCMfEJ7pCF6IpCVICx37BwZ74/vj20tG0kNJRuRYREZGSO3mkOifz1NsleEWMMB//RSe+IuRk5L2vXA9unB2ejHKcyrWIiIiUjLWQUAmy0vJtDExJSEiG7EyokOgrXflz7C8FE6u79zkZMPGgvzzlnE5oFBERkeKzFub+3hXrTjfmvwO6jIGso/BMf9i33lvEcqtyvRPfixcq1yIiIlI81sL798Ky/4PON8El/3QnzYErdIMehmFT4cdNMKkXfD3Da9xy5+61bsQ6RHPeU1NTQ7Kf8kblWkRERIpmLbw7AT563I1QX/IPiItzq1E0655X6E4fDOOWwGlnwGs3waxfQuYRv9lFwkjlWkRERApnLbxzD3zyXzjvVrj4b2BMwY+v0QRumA097oLPp8KTfWDXt+HLK+KRyrWIiIgULDcX5twFn06CC26DgQ8WXqyPia8A/e6D69+A9APwVF/47BlX1EVimMq1iIiInFpuLsy+Ez57Grr+Gi56oHjFOr8Wvd00kWbd3L5eHQVHD5RF2phj9YtIiUTK66VyLSIiIj+Vmwtv3w4rnoPud0D/P5a8WB9TpR5cO8PtY9VseKIHbP0stHljTFJSEvv27YuYwhjprLXs27ePpKQk31G0zrWIiIicJDcX3voVfP6imzfd997SF+tj4uKg2+3QtCu8NhqeGwh973Mj4nEa6ztZ48aN2bZtG3v27PEdJWokJSXRuHFj3zFUrkVERCSf3ByYdRt8+RL0ugd6Twi+WOfX5FwYuxje+jXMux82LoIrJ7nRbTkuISGB5s2b+44hpaBfFUVERMTJzYE3bnXFuvcE6PPb0BbrY5JrwNDJMPgR2LwM/tsN1i8I/XFEPFC5FhEREcjJhplj4Kvp0Ode6D2+bI9nDHQeDbd8AMk1YcqVMO8PkJNVtscVKWMq1yIiIuVdTjbMvAW+mQH9fg+97g7fsU87A8YsgHNGwpJ/wvOXwIEt4Tu+SIgZH2ehJtVtatP36B+OiIiIdzlZ7kqK382CC/8A3X9T9HMWPAiLHir4/l7joc+Ekmf5ega89Rt3guNlj0P7y0q+D5GTGGPSrLWVw3Y8lWsREZFyKjvTrdzx/VtuDeuuv/KdCPZvgBmjYfvncO7NcNGfIcH/8moSvcJdrjUtREREpDzKzoRXb3DFesBfIqNYA9RqAaPfd1eD/OxpeLof7FnjO5VIsalci4iIlDfZGfDK9bB6Ngz8K1zwS9+JTlQhEQb8GUa8Cod3wJO94POpunS6RAWVaxERkfIkKx2mXwdr3oFBf4fzx/lOVLA2F7lLpzfqBLN+Aa+PhYzDvlOJFErlWkREpLzISofp18La9+CSf0KXW3wnKlq1hnD9LOjzO/j6VZjUE7Z/4TuVSIFUrkVERMqDrKPw8jWwbh4M/hece5PvRMUXFw+9/hdGve1+QXimP3z8hKaJSERSuRYREYl1mWkwbbi7CuJlj0HnG30nKp2UbnDrUmjZD969B6ZdA2n7facSOYHKtYiISCzLPALThsGGRXD5v6Hj9b4TBadSLbhmGgx8yI3CP9HdXUJdJEKoXIuIiMSqzCPw0jDYuBiu+C+cc63vRKFhDJx/K9w8FypUdFd1XPQ3yM3xnUxE5VpERCQmZaTCi1fD5qUw5EnocI3vRKHX8BwY+yGceTUs+DO8cDkc2uE7lZRzKtciIiKxJuMwvHgVbP0YhjwFP/u570Rlp2JV98vD5f+BH1bAE91g7VzfqaQcU7kWERGJJemHYMoQ2PYZXPUMnHW170Rlzxg35WXMIqjaAKZeDe/9zl2FUiTMVK5FRERiRfpBeHEIbF8JVz8LZw7xnSi86raBm+fBuTfDR4/DswNg/0bfqaScUbkWERGJBUcPwJQrYfvnMPR5OOMK34n8SEiGS/4BP58C+9e7i85885rvVFKOqFyLiIhEu6M/wpQrYMdX8PMX4PRLfSfyr/1lMHYx1G0HM0bDm792632LlDGVaxERkWiWtt+tkrHrWxg2Bdpd4jtR5KjZDG6cA93vhJUvwFN9YNd3vlNJjFO5FhERiVZp++GFy2D39zBsKrS92HeiyBOfABfeD9fNhLR9rmCveF6XTpcyo3ItIiISjY7shcmXwp41MHwatLnId6LI1rIvjFsKTS+At26HGTe6E0ClXDPGJBljPjXGfGmM+dYY84dg96lyLSIiEm1S97hivW+duxR46wt9J4oOVU+DkTOh3/3w3ZvwRA/YtsJ3KvErA+hrrT0b6AAMNMacH8wOVa5FRESiSepumDzYLTF3zcvQqp/vRNElLg563Amj33VTQ569CJb+H+Tm+k4mHlgnNXAzIfAW1JwhlWsREZFocXgXPD8YftwMI6ZDyz6+E0WvJl1g3Idunvrc++Cloe4vAlLuGGPijTFfALuBudbaT4LZn8q1iIhINDi0A56/BA5uhZEzoEUv34miX3JNtx72Jf+AjYvhie6wYZHvVBJ6FYwxy/O9jcl/p7U2x1rbAWgMdDHGnBnMwYz1cLZsUt2mNn3PlrAfV0REJCod2u5GrA/vdMW6WVffiWLPzm/cSY5710LPu6DXeIiv4DuVhIAxJs1aW7mYj/09kGat/Xtpj6eRaxERkUh28Ac3Yp26yy0np2JdNuqfCWMWQodr4cOH3bz2A1t9p5IyZoypa4ypEfg4GegPrApmnyrXIiIikerAVnh+kJsLfN3r0DSoRQykKImV4Yp/w5CnYOfXbprI92/7TiVlqwGwwBjzFfAZbs51UF90TQsRERGJRAe2uKkgR390xbpxZ9+Jypd96900kR1fQpex0P+PkJDkO5WUQkmmhYSCRq5FREQizY+b4LlLIP0AXP+GirUPtVvCTXPh/F/Ap5PgmQth7zrfqSQKqFyLiIhEkv0b3Yh1xiG4fhY06uQ7UflVoSIMfBCume7mvk/qCV++7DuVRDiVaxERkUixb707eTEzFUa9CQ3P8Z1IANoOhHFLoGEHeH0svD4OMlKLfp6USyrXIiIikWDfejdinXUURr0FDc72nUjyq97IfV16jYevpsOTvWDHV75TSQRSuRYREfFt71p4bhDkZLgCV/8s34nkVOLioc8EuP5NyDwCT/eDT550l1EXCVC5FhER8WnPajcVJDcbRr3t1luWyNa8B4xbCi36wDt3w/SRkLbfdyqJECrXIiIivuxe5aaCWAs3zIbT2vtOJMVVuTaMmA4D/gJr3oMnesCWj32nkgigci0iIuLDru/ciLUxrljXa+c7kZSUMXDBL+Gm9yE+wU3t+fBhyM3xnUw8UrkWEREJt53fuMtrx1VwxbpuG9+JJBiNOsLYD+GMK+CDB2DKlXB4p+9U4onKtYiISDjt+AomXwrxFeHGOVCnte9EEgpJ1eCqZ+Cyx2Hrp/DfbrB2nu9U4oHKtYiISLhs/wJeuAwSkuGGt91VACV2GAMdr4MxC6FKPZh6Fcz9PeRk+U4mYaRyLSIiEg7bP3fFOrGKmwqiYh276rWDWz6AzqNh6aPw7EB3SXspF4Iq18aYocaYb40xucaYzqEKJSIiElN+WAGTL4eK1V2xrtXcdyIpawnJMPgRGPq8W8f8iR7w7eu+U0kYBDty/Q0wBPgwBFlERERiz7bl8MIVkFwDbpwNNZv5TiThdMaVMO5DqNMGXr0B3vqNuwqnxKygyrW19ntr7epQhREREYkpWz91xbpSLTdiXaOp70TiQ80UGP0udPsNrHgOnurr1jiXmKQ51yIiImVhy8duSbYqdeGGOVCjie9E4lN8AvT/A4x8DVJ3w5O9YeULunR6DCqyXBtj5hljvjnF2+UlOZAxZowxZrkxZrnVN5KIiMSyzctgyhCoWt+NWFdv5DuRRIpWF8KtS6FJF3jzV/DaTZB+yHcqCSETiqJrjFkI3GWtXV6cxyfVbWrT92wJ+rgiIiIRZ9MSmPpzqNbQLbdXtb7vRBKJcnNgySOw4C/urxpXP+cuRiMhZ4xJs9ZWDtfxNC1EREQkVDZ+CFOHQvXGbsRaxVoKEhcPPe9yFxLKyYZnLoJlj0Nuru9kEqRgl+K70hizDbgAmG2MeS80sURERKLMhoVuxLpGs8CI9Wm+E0k0aHo+jFsMbQbA+7+DacPgyF7fqSQIIZkWUlKaFiIiIjFl3Xx4eQTUagnXz3InMYqUhLXw2dPw3m+hUm0Y8hQ07+E7VUzQtBAREZFosnYeTLsGareCUW+pWEvpGANdboGb50NiZZh8qZuPnZPtO5mUkEauRUQi1CNz1/Do/LUF3n97v9bc0b9NGBPJT6x5H6ZfC3XbwvVvuvWsRYKVkQpz7oYvX4Jm3dwotlacKbVwj1yrXIuIRIFhkz4CYPrYCzwnkeNWvwuvXAf1Tofr3lCxltD78mV4+06okAhX/BfaXuw7UVTStBAREZFIt2o2TB8Jp53h5lirWEtZOHs4jP0QqjeBacPhnfGQneE7lRRB5VpERKQkvn8LXrkeGvzMjVgn1/SdSGJZnVZw8zw4bxx88l94pj/sW+87lRRC5VpERKS4vpsFr94ADc+B616H5Bq+E0l5UKEiXPxXGD4NDmyBST3hq1d8p5ICqFyLiIgUxzcz4dUboVEnGDkTkqr7TiTlTbtBMG4J1D8LZt4Cb/wCMo/4TiUnUbkWEREpytcz4LWboUkXGPkaJFXznUjKq+qNYdTb0PN/4YuXYFIv2Pm171SSj8q1iIhIYb56xY0SNj0frp0BFav6TiTlXXwF6Ps7GPUmZByGp/rBp0+5C9GIdyrXIiIiBfnyZXh9rFtr+NpXoWIV34lE8jTv6aaJNO8Jc+5yS0Me/dF3qnJP5VpERORUPp8Kr4+DlO4w4hV31TyRSFOlrvv+vOgBWP0OPNEDtnziO1W5pnItIiJyspVTYNYvoUUvuGY6JFbynUikYHFx0PVXMPp9MHHw3MWw+B+Qm+s7Wbmkci0iIpLfiufhzdugZR+45mUVa4kejTvBuMXQ/jKY/0d48Uo4vMt3qnJH5VpEROSYz56Bt26HVv3dmsIJyb4TiZRMUnW4+jm49P/c9JAnusG6+b5TlSsq1yIiIuBWW5h9J7QeAMOnQkKS70QipWMMdBoFYxZApTrw4hCYNxFysnwnKxdUrkVERD6Z5FZbaHMxDJvirognEu3qnQ63fACdboAlj7i52D9u9p0q5qlci4hI+fbRf+Cd/4V2g+HnL6hYS2xJrASXPgpXPwt7VsOkHvDdLN+pYprKtYiIlF/LHoP3JsDpl8LQ56FCou9EImXjzKtg7IdQqyW8cj28fSdkHfWdKiZV8B1ARETEiyX/gnn3Q/sr4KqnIT7Bd6Ko8cjcNTw6f22B99/erzV39G8TxkRSLLWaw+j34IM/ul8st37iRrTrtvWdLKYY6+FSmUl1m9r0PVvCflwRkWg1bNJHAEwfe4HnJDFi8T/cUmVnDIEhT7nLSUup6HszSq2d664+mnUUBj0MHa51J0LGIGNMmrU2bFeB0rQQEREpXxY97Ir1WUNVrKX8at0fxi2FRp3cBZNm3gIZh32nigkq1yIiUn4sfAgWPAA/GwZXTlKxlvKtWgO4fhb0uRe+eQ0m9YTtn/tOFfVUrkVEJPZZCwv+AgsfhLNHwBX/hbh436lE/IuLh153ww2zITsDnu7vVtDxMG04Vqhci4hIbLMWPngAFv0VOoyEyx9XsRY5WbOuMG6Jmy7y3gSYNhyO7POdKiqpXIuISOyyFub/ARb/HTpeD5c9pmItUpBKtWD4S3Dx32D9B/BEd9i01HeqqKPutKpJAAAgAElEQVRyLSIisclamPt7d2W6TjfC4EchTv/tiRTKGDhvLNw0FxKSYfJgWPhXyM3xnaxMGGOaGGMWGGO+M8Z8a4y5Pdh96qeMiIjEHmvh/Xth2f9B55vgkn+qWIuURMMOMHaRW1Vn4V9g8mVwaLvvVGUhG/gfa2174Hzgl8aY9sHsUD9pREQktlgL706Ajx6HLmPgkn+oWIuURsWqMORJdwLw9pVumsia93ynCilr7Q5r7crAx4eB74FGwexTP21ERCR2WAvv3AOf/BfOu9XNHY3RC2OIhE2HEe7S6VUbwks/h/d+B9mZvlOVRAVjzPJ8b2NO9SBjTApwDvBJUAcL5skiIiIRIzcX5twFy5+BC26Dix5QsRYJlTqt4eZ5MPc+91ehzUvdpdNrtfCdrDiyrbWdC3uAMaYK8BrwG2vtoWAOppFrERGJfrm5MPsOV6y7/lrFWqQsJCS5S6UPmwr7N8ITPeHrGb5TBc0Yk4Ar1lOttTOD3Z/KtYiIRLfcXHj7dljxPHS/A/r/UcVapCydPtitiX3aGfDaTTDrNsg84jtVqRhjDPAM8L219p+h2KfKtYiIRK/cHHjzV7DyBehxF/S7X8VaJBxqNHFXdexxF3z+IjzZB3Z96ztVaXQDrgP6GmO+CLwNCmaHmnMtIiLRKTcHZv0SvpwGvcZD7/Eq1iLhFF8B+t0HzXvAzDHwVF8Y8BfoPNrrv8VH5q7h0flrj99OrN+qUkGPtdYuAUIaViPXIiISfXJz4I1bXbHu/VvoM0HFWsSXFr3dNJFm3WD2nfDqKDh6wFucO/q3YdNDl7DpoUs4r3ktMneuSwvn8VWuRUQkuuRku1Gyr6ZD33uh9z2+E4lIlXpw7Qx3zsOq2fBED9j6me9UXqhci4hI9MjJhpm3wDcz3Pzqnnf7TiQix8TFQbfb4cZ33USL5wbCkn+5k47LEZVrERGJDjlZ8Npo+HamGx3rcafvRCJyKk3OhbGLod0lMO9+mHoVpO72nSpsVK5FRCTyZWfCjBvhu1lw0Z/d6JiIRK7kGjB0Mgx+BDYvc5dOX7/Ad6qwULkWEZHIlp0Jr94A378FAx6Errf5TiQixWGMWznklg8gqQZMuRLm/9FN74phKtciIhK5sjPgleth9Wy4+G9wwS98JxKRkjrtDBizAM4ZCYv/Ac8PggNbfacqMyrXIiISmbLSYfp1sOYdGPR3OG+s70QiUlqJleHyx+GqZ2DXd/BEN/fXqBikci0iIpEnKx2mXwtr33NzNrvc4juRiITCWVfDuA+hVguYPhJm3+X+vccQlWsREYksWUfh5Wtg3Ty49FE3Z1NEYketFjD6fbjgNvjsKXj6Qti7tujnRQmVaxERiRyZaTBtuFtV4LLHodMNvhOJSFmokAgD/gwjXoFDP8CkXvDFS75ThYTKtYiIRIbMIzBtGGxYBFf8Bzpe5zuRiJS1NgPg1qXQ8Bx441aYORYyDvtOFRSVaxER8S/zCLw0DDYtgSufgA4jfCcSkXCp1hBGvQm9fwtfv+JGsXd86TtVqalci4iIXxmp8OLVsHkpXPkknD3cdyIRCbe4eOh9D4x6y5138fSF8PETYG3J97XgQZhY3b1tWkKlBCqFPnDBVK5FRMSfjMPw4lWw9RMY8hT8bKjvRCLiU0p3GLcEWvaFd++Bl0dA2v6S7aPPBJh40L0lVScti7SyCXtqKtciIuJH+iGYMgS2fQZXP+OW6BIRqVwbrnkZBj4Ea+e6S6dvXuY7VbGpXIuISPilH4QXh8D2lTD0OTjjSt+JRCSSGAPn3wo3z4X4RHj+Elj0MOTm+E5WJJVrEREJr6MHYMqVsP1zGPo8tL/cdyIRiVQNz4GxH8KZV8GCB+CFy+HQDt+pCqVyLSIi4XP0R5hyBez4Cn4+BU6/1HciEYl0SdXcORmX/wd+WOGmiayd6ztVgVSuRUQkPNL2u1GnXd/CsBeh3SDfiUQkWhgD51wLYxZB1fow9Wp4/17IzvSd7CdUrkVEpOyl7YcXLoPdq2DYVGg70HciEYlGddvAzfPg3Jth2WPw3EDYv9F3qhOoXIuISNk6shcmXwp71sA1L0Gbi3wnEpFolpAMl/zDTS3btw4m9YRvZvpOdZzKtYiIlJ3UPa5Y71sHI16GVhf6TiQisaL9ZTB2MdRtBzNuhDd/DZlhXdL6lFSuRUSkbKTuhsmD3Z9sR0x3F4UQEQmlms3gxjnQ/U5Y+QI81Rd2f+81ksq1iIiE3uFd8PxgOLAFrn0FWvT2nUhEYlV8Alx4P1w3E9L2wpN9YMXz7tLpGYd0+XMREYlyh3a4Cz4c3AbXvgrNe/pOJCLlQcu+MG4pND0f3rrdTRWxNuwxVK5FRCR0Dm13xfrwDhg5A1K6+04kIuVJ1dNg5ExIrAzfvu4lQgUvRxURkdhz8Ac3xzp1D4x8zY0eiYiEW1wcZB7xd3hvRxYRkdhxYCs8P8gV6+tmqliLiF+V63k7tMq1iIgE58AWNxUkbT9c/wY06eI7kYiUd3evhYkHvRw6qHJtjHnYGLPKGPOVMeZ1Y0yNUAUTEZEo8OMmeO4SSD/ginXjzr4TiYjkMSbshwx25HoucKa19mfAGmBC8JFERCQq7N/oltvLOATXz4JGnXwnEhE5UcVqpGUR1ivLBFWurbXvW2uzAzc/BhoHH0lERCLevvVuKkhmKox6Exqe4zuRiEhECOWc69HAOwXdaYwZY4xZboxZbj2sOSgiIiGyb70bsc46CqPeggZn+04kIhIxilyKzxgzD6h/irt+Z62dFXjM74BsYGpB+7HWPgk8CZBUt6natYhINNq71hXr3CxXrOuf6TuRiEhEKXLk2lp7obX2zFO8HSvWNwCDgWttMYekbYWKdH5gblDBRUTKk5Wbf+STjfv9/uzcs9pNBcnNhlFvq1gLECHfmyIFWHm0Pon1W4X18udBXUTGGDMQ+F+gl7W2RJPF96ZmBnNoEZFyJSvXjV14+9m5exVMvtR9fMNsqNfOTw6JON6/N0UKkUV82I9pgpn/bIxZB1QE9gU2fWytHVfU8yo2aG0bjvoXFkiIM3RsVrPUGUREYtnKzT8eLy8ABsL/szMrDXZ+7Y5e/yxISA7PcSWiRcT3pkgB8r4/LTsm30HGjrVhW5MvqJFra22rUj838D7/P0wRETnRyT8jw/6zM/MI7PoGFWs5mffvTZFC5H0fhn+d66DKdTASK8SRmZ1LnSqJTB97ga8YIiIRrfMDc9mbmnl8VDCsPzt3fAUvjICaSXDD21C7ZdkeT6KK1+9NkSLkfX+G/5e9oKaFlFbFBq1tg1H/YtNDl4T92CIi0Shl/OzjH4flZ+f2L2DKFZBQya0KomItBQj796ZICaSMn82Oyb8J67SQUK5zXSJ1qiT6OrSISNRJiHP/L4TlZ+f2z+GFyyCxijt5UcVaChHW702R4ljwIEysDhOrk0B20Y8PMS8j10l1m9r0PVvCflwRkWg1bNJHAGX/5/YfVsALV0JSdTcVpGazsj2eRL2wfW+KlMKwSR/xyriuadbayuE6prc51yIiEmG2LYcpV0JyTVesazT1nUhEJOp4mxYiIiIRZOun8MIVUKmWmwqiYi0iUioq1yIi5d2Wj92IdZW6cMMcqNHEdyIRkbAxxjxrjNltjPkmFPtTuRYRKc82L4MpQ6BqfTdiXb2R70QiIuH2PDAwVDtTuRYRKa82LYEXr4ZqDV2xrtbQdyIRkbCz1n4I7A/V/lSuRUTKo40fwtShUL2xK9ZV6/tOJCISE7RaiIhIebNhIbw0HGqmwKg3oUo934lERMpSBWPM8ny3n7TWPllmByurHYuISARaNx9eHgG1WsL1s9xJjCIisS3bWts5XAdTuRYRKS/WznPFuk5ruP5NqFzbdyIRkZijOdciIuXBmvfh5WugbhsY9ZaKtYhIgDFmGvAR0NYYs80Yc1Mw+9PItYhIrFv9LrxyHdQ7Ha57w10oRkREALDWXhPK/WnkWkQklq2aDdNHwmlnujnWKtYiImVKI9ciIpFqwYOw6CH3cca97v3EfNc56DUe+kwo+PnfvwWv3gANOsDI1yC5RplFFRERR+VaRCRS9ZmQV57/EFg16v6DxXvud7NgxmhoeI4r1knVyyajiIicQNNCRERizTcz4dUboVEnGDlTxVpEJIxUrkVEYsnXM+C1m6FJl8CIdTXfiUREyhWVaxGRWPHVKzDzFmh6Plw7AypW9Z1IRKTcUbkWEYkFX74Mr4+FZt3g2lehYhXfiUREyiWVaxGRaPf5VHh9HKT0gBGvQGJl34lERMotlWsRkWi2cgrM+iW06A0jpkNiJd+JRETKNZVrEZFoteJ5ePM2aNkXrpkGCcm+E4mIlHsq1yIi0eizZ+Ct26H1RTD8JRVrEZEIoXItIhJtPn0KZt8JbQbCsBchIcl3IhERCdAVGkVEokHGIbAW/tIIMlOh7SAY+jxUqOg7mYiI5KORaxGRaGCte5+ZCu0Gw9DJKtYiIhFI5VpEJJI93Bom5rt8uYmDVW/DI2f4yyQiIgXStBARkUj04yZY/Q4c2X3idpvr3p+8XUREIoLKtYhIJMjNhe2fw+o5rlTv/tZtN/Fgc/IeF18RcjKgcj0/OUVEpFAq1yIivmQdhQ2LXKFe8y6k7nJlullXGPAXtxpI7ZbusRMedu9zMmDiQX+ZRUSkUCrXIiLhlLrHFenV78D6DyD7KCRWgVYXuhVAWveHSrV++jxj3EmNGrEWEYloKtciImXJWti7Jm+6x9ZPAQvVGsM5I6HtxZDSveiVPypWc+/vXlvmkUVEpPRUrkVEQi0nG7Z+7Mr06jmwf4Pb3uBs6D3eFer6P3Oj0SIiElNUrkVEQiHjMKyb7wr12vfg6I8QnwjNe8IFv3Tzp6s39p1SRETKmMq1iEhpHdwWGJ1+BzYthpxMSK4JrQdAu0HQsi9UrOo7pYiIhJHKtYhIcVkLO79yZXrVbPcxQK0W0GWMOyGxyXkQrx+tIiK+PDJ3DY/Ozzs/JbF+q0rhPL7+BxARKUx2hhuVPjZCfegHwLgSfeEfXKGu01rzp0VEIsQd/dtwR/82x2+bvw5OC+fxVa5FRE6Wth/Wvu9ORlw3HzJTIaGSm+bR57du2keVur5TiohIBFK5FhEB2Lc+b3R6y0fuqohV6sNZV7vR6eY9ISHZd0oREYlwKtciUj7l5sC25XnrT+9d7bafdib0uNMtl9fgHIiL85tTRESiisq1iJQfmUdg/QJXpte8C2l7Ia4CNOsGnUdD24FQM8V3ShERiWIq1yIS2w7vzLvc+IaFkJ0OFau7y4y3vdhddjy5hu+UIiISI1SuRSS2WAu7v8ub7vHDCre9RlPodKMr1M26QnyC35zFseBBWPSQ+zjjXvd+YvW8+3uNhz4Twp9LREQKpHItItEvJws2LwsU6jlwYIvb3qgT9L3XnZBYr330LZfXZ0JeeZ70kXs/9qC/PCIiUiSVaxGJTkcPwLp5gcuNz4WMg1AhCVr0hh7/4y43XrW+75QiIlLOqFyLSPT4cXNgubw5sHkp5GZDpTpw+qXucuMtekNiZd8pRUSkHFO5FpHIlZsLOz4PXG58Duz+1m2v0xYuuM1N92jcGeLi/eYUEREJULkWkciSdRQ2fhiYP/0upO4EEwdNL4CL/uxOSKzd0ndKERGRU1K5FhH/UvfA2vfcCPX6DyArDRKrQKt+bnS69UVQqZbvlCIiIkVSuRaR8LMW9q7NWy5v6yeAhWqNoMMINzqd0gMqVPSdVEREpERUrkUkPHKyXYk+Vqj3r3fb6/8Met0TuNz42dG3XJ5IOfTI3DU8On/tCdtSxs8+/vHt/VpzR/824Y4lEhFUrkWk7GQcdtM8Vr8Da96Do/shLgGa94Tzb3WFunpj3ylFpITu6N9G5VmkACrXIhJaB3+ANe+4Qr3xQ8jJhOSa0HqAK9Mt+0JSNd8pRUREyoTKtYgEx1rY+XXe1RF3fOm212wOXca4Qt3kfIjXjxsREYl9+t9OREouOwM2LQlc0OUdOLQNMNCkC1w40a3wUaeN5k+LiEi5o3ItIsWTtt9dZnz1HFg3HzIPQ4VkN82j93h3ufEqdX2nFBER8UrlWkQKtn9D3tURt3wENgeqnAZnDnGj0y16QUKy75QiIiIRQ+VaRPLk5sAPK/KWy9uzym2vdwZ0v8MV6obnQFyc35wiIiIRSuVapLzLTIMNC1yhXvMeHNkDJh5SukGnG9x0j1rNfacUEREpE8aYgcCjQDzwtLX2oWD2p3ItUh4d3gVr3nWj0xsWQHY6VKwGrfu70elW/dzyeSIiIjHMGBMP/BvoD2wDPjPGvGmt/a60+wyqXBtj/gRcDuQCu4EbrLXbg9mniJQBa2H393nTPX5Y7rZXb+pGp9teDE27QoVErzFFRETCrAuwzlq7AcAY8zKu2/op18DD1tr7AmF+DfweGBfkPkUkFHKyYPOywHJ5c+DAZre9YUfoc68r1KedoeXyREQk1lUwxizPd/tJa+2TgY8bAVvz3bcNOC+ogwXzZGvtoXw3KwM2mP2JSJDSD8K6ea5Qr33f3Y6vCC16uxMS2wyEag18pxQREQmnbGtt53AdLOg518aYPwPXAweBPoU8bgwwBiCxduNgDysix/y4OTB/eo67sEtuNlSqDe0G511uPLGy75QiIiKR6AegSb7bjQPbSs1YW/hgszFmHlD/FHf9zlo7K9/jJgBJ1tr7izpoUt2mNn3PlpJmFRGA3FzY8UXe/Old37jtddq4Mt12EDQ+F+Li/eaUkBo26SMApo+9wHMSEZHoYoxJs9aecpTJGFMBWAP0w5Xqz4AR1tpvS3u8IkeurbUXFnNfU4E5QJHlWkRKKCsdNn4YWC7vXTi8A0wcNDkf+v8pcLnxVr5TioiIRBVrbbYx5jbgPdxSfM8GU6wh+NVCWltr1wZuXg6sCmZ/IpLPkb1u3enVc2D9Asg6AgmV3TJ5bQdB64ugcm3fKUVERKKatXYOboA4JIqcFlLok415DWiLW4pvMzDOWlvkPBVNCxEpwN61rkyvmgNbPwEsVG2YN90jpTskJPlOKWHyyNw1PDp/bYH3396vNXf0bxPGRCIi0aewaSFlcrxgynVpqVyLBOTmuBJ9bP70vnVue/2zXJluezE06KDl8kREREop3OVaV2gUCbeMVFj/gSvTa96Fo/shLgGa94Dzxrnl8mo0KXo/IiIiEnFUrkXC4dD2wMVc3oGNiyAnE5JqQJsBgeXy+kFSNd8pRUREJEgq1yJlwVrY+XXe1RF3fOG210yBc28JXG78fIhP8BpTREREQkvlWiRUsjNh85K8EeqDWwHj1pzud7+bQ123reZPi4iIxDCVa5FgpO0PXG58DqydB5mHoUIytOwDve5x0z6q1POdUkRERMJE5VqkpPZvyBud3rwMbA5UrgdnXulGp5v3gsRKvlOKiIiIByrXIkXJzYUfVuQtl7fne7e9Xnvo/htXqBt2hLg4vzlFRETEO5VrkVPJTIMNCwOXG38PjuwGEw/NukLHB90JibWa+04pIiIiEUblWuSY1N1u3elVc2DDAshOh4rVoNWFgcuNXwjJNX2nFBERkQimci3ll7WwZ1XedI9tywEL1ZtAx1FudLpZN6iQ6DupiIiIRAmVaylfcrJgy0d560//uMltb3gO9PmtK9Snnanl8kRERKRUVK4l9qUfhHXzXaFe+z6kH4D4itCiF3S73V1uvFpD3ylFREQkBqhcS2w6sAVWv+tGpzctgdwsSK7l5k63vRha9oWKVXynFBERkRijci2xwVrY/nne+tO7vnbba7eG8291pbpJF4iL95tTREREYprKtUSvrHTYtDjvhMTDO8DEQZPzoP+f3Ah1nda+U4qIiEg5onIt0eXIPlj7nivU6z6ArCOQUBla9Q0sl3cRVK7jO6WIiIiUUyrXEvn2rssbnd76MdhcqNoAzh7mCnVKD0hI8p1SREREROVaIlBuDmz9NK9Q71vrtp92FvS4C9oNggYdtFyeiIiIRByVa4kMGanuqoir5rhpH2n7IC4BUrpDlzHQdiDUaOo7pYiIiEihVK7Fn0M7YE1gdY8NiyAnA5KqQ+sB7mTEVv3cbREREZEooXIt4WMt7Pom7+qI2z9322s0g3NvcoW66QUQn+A3p4iIiEgpqVxL2crOhM1L89afPrgFMNC4M/T7vTshsW47zZ8WERGRmKByLaF39EdYOy+wXN48yDgEFZKhZR/odbeb9lH1NN8pRUREREJO5VpCY//GvOkem5eBzYHKdaH95W50ukVvSKzkO6WIiIhImVK5ltLJzYXtK/OWy9v9ndte93Todrsr1I06QVyc35wiIiIiYaRyLcWXmQYbFwUK9btwZDeYeGjWFQY86JbLq9XCd0oRERERb1SupXCpu2HNu250ev0CyD4KiVWh9YVudLrVhVCplu+UIiIiIhFB5VpOZC3sWZ033WPbZ4CFao2h43Vuubxm3aFCou+kIiIiIhFH5VogJxu2fJR3QuKPG932Bh2g9wRXqOufpeXyRERERIqgcl1epR+C9fNdoV7zHqQfgPhEaN4Luv4K2gyE6o18pxQRERGJKirX5cmBrYH503Ng42LIzYLkWm5kuu3F0LIvVKzqO6WIiIhI1FK5jmXWwo4v8qZ77Pzaba/VEs4f505IbNwF4vVtICIiIhIKalWxJjvDjUofOyHx8HbAQNPzof8fXaGu09p3ShEREZGYpHIdC47sg7Xvu0K9/gPITIWESm6aR9t7oc0AqFzHd0oRERGRmKdyHa32rc8bnd7yEdhcqFIfzhrqRqeb94SEJN8pRURERMoVletokZvj1pw+Vqj3rnHbTzsTetzlTkhs0EGXGxcRERHxSOU6kmUecVdFXD3HrfKRtg/iKkBKdzj3ZrdcXs1mvlOKiIiISIDKdaQ5tCPvcuMbFkJOBiRVh9YXudHpVhe62yIiIiIScVSufbMWdn2bt1ze9pVue41m0Hk0tBsETS+A+AS/OUVERESkSCrXPuRkwealeYX6wBa3vVFn6HufOyGx3um63LiIiIhIlFG5DpejB2DdPFem186DjINQIQla9HEnJLYZAFXr+04pIiIiIkFQuS5LP27KG53evAxys6FSHWh/qRudbtEHEiv5TikiIiIiIaJyHUq5ubD987zl8nZ/67bXbQddf+UKdaNOEBfvN6eIiIiIlAmV62BlHYUNi/KWy0vdBSYOmnaFi/7sVvio3dJ3ShEREREpIWPMUGAicDrQxVq7vKjnqFyXRuqevOXy1n8A2UchsYpbJq/tIGjdHyrV8p1SRERERILzDTAEmFTcJ6hcF4e17oqIx6Z7bP0UsFCtMZwz0o1Op3SHChV9JxURERGRELHWfg9gSrCCm59ybXNhYr4LofQaD30meIlSoJxs2Ppx3gmJ+ze47Q3Oht7jXaGu/zMtlyciIiIix/kbuZ540NuhC5RxGNbND8yffg/SD0B8IjTvCRf80l1uvHpj3ylFREREpPgqGGPyz5V+0lr75LEbxph5wKnWQ/6dtXZWiQ9WioCx5eC2wOj0O7BpMeRkQnJNV6TbDYKWfaFiVd8pRURERKR0sq21nQu601p7YSgPVv7KtbWw8ytYNceNUO/8ym2v1QK6jHEnJDY5D+LL30sjIiIiIsEx1tqwHzSpTmObvndb+A6YneFGpY+NUB/6ATCuRLe92BXqOq01f1pEREQkxhhj0qy1lUv53CuBx4C6wAHgC2vtgEKfE7PlOm0/rH3fjU6vmw+ZqZBQyU3zaHsxtB4AVeqWbQYRERER8SqYcl0asTX3Yd/6vNHpLR+BzYEq9eGsq93odPOekJDsO6WIiIiIxKjoLte5ObBted7603tXu+2nnQk97nQj1A3Ogbg4vzlFREREpFyIvnKdeQTWL3Bles27kLYX4ipAs27QeTS0HQg1U3ynFBEREZFyKDrK9eGdeZcb37AQstOhYnV3mfG2F7vLjifX8J1SRERERMq5yCzX1sLu7/Kme/ywwm2v0RQ63egKdbOuEJ/gN6eIiIiISD6RU65zsmDzskChngMHtrjtjTpB33vdCYn12mu5PBERERGJWP7K9cOt4bbPYN08Nzq9di5kHIQKSf/f3r3F2lWVYRh+P9oCtaIYi5FQtIBcGM9KqoaYNHgIKrYXktgLD3hMTIjVoAQx8XRnNGo8REOQAGoUA8RUhBgMJOoFSKlUBDw0hmgJWgUsUkhN6e/FmtRms3f33GWsNbuW75Ps7MMc7fzzZXSPv2uNOSecuh5ed8HoKYnHzfc0SkmSJOnIM1xzvWcXfPE02L8PnrYaXvi20ePGT10PR0/sVoSSJElSMwNuC8mosV75LPj4H+GoZcOVIkmSJDUw4A2guydDPvaQjbUkSZJmwnDN9bJjRp9XPWewEiRJkqSWmmwLSXIB8CXghKr6Z68/9Phe+OzuFqeXJEmSjghP+ZXrJCcDbwL+sqQ/6CvWkiRJmjEttoV8BbiQA5uoe/rEnxqcWpIkSTpyPKXmOslG4L6q2t5j7IeSbE2ydWlduCRJkjQdFt1zneTnwHxPcvkUcDGjLSGLqqpLgEsAjl29xv5akiRJM2fR5rqq3jDfz5O8BDgF2J7RI8nXANuSrKuqvzWtUpIkSZoCh323kKq6EzhwVWKSe4Ezet8tRJIkSZoxAz5ERpIkSZotzR5/XlVrW/1dkiRJ0jTylWtJkiSpEZtrSZIkqRGba0mSJKmRZnuul6JWrGTtRT898P3m0/7Oxz74viFKkSRJkppJ1eSf57Jq1aras2fPxM8rSZKk/y9JHq2qVZM6n9tCJEmSpEZsriVJkqRGbK4lSZKkRmyuJUmSpEZsriVJkqRGbK4lSZKkRmyuJUmSpEZsriVJkqRGbK4lSZKkRmyuJUmSpEZsriVJkqRGbK4lSZKkRmyuJUmSpEZSVZM/abIfeGziJ55dy4F9QxcxI56T8tsAAAStSURBVMyyLfNsyzzbMcu2zLMt82xrZVVN7AXl5ZM60RzbquqMgc49c5JsNc82zLIt82zLPNsxy7bMsy3zbCvJ1kmez20hkiRJUiM215IkSVIjQzXXlwx03lllnu2YZVvm2ZZ5tmOWbZlnW+bZ1kTzHOSCRkmSJGkWuS1EkiRJamSszXWSs5P8IcmOJBfNc/yYJFd1x29Nsnac9UyzHlmel+QfSe7oPj4wRJ3TIsllSXYl+d0Cx5Pka13ev03yyknXOC16ZLk+ye6D5uanJ13jNElycpKbk9yd5K4km+cZ4/zsoWeWzs+ekhyb5NdJtnd5fm6eMa7rPfXM07V9CZIsS/KbJNfNc2xic3Nst+JLsgz4JvBGYCdwW5ItVXX3QcPeDzxUVS9Isgn4AvCOcdU0rXpmCXBVVZ0/8QKn0+XAN4ArFzj+ZuD07uPVwLe6z3qyyzl0lgC/rKpzJlPO1NsHXFBV25IcB9ye5MY5/96dn/30yRKcn33tBc6qqkeSrAB+leSGqrrloDGu6/31yRNc25diM3AP8Ix5jk1sbo7zlet1wI6q+nNV/Qf4IbBxzpiNwBXd11cDr0+SMdY0rfpkqSWoql8ADx5iyEbgyhq5BTg+yYmTqW669MhSS1BV91fVtu7rfzNaKE6aM8z52UPPLNVTN98e6b5d0X3MvXDLdb2nnnmqpyRrgLcCly4wZGJzc5zN9UnAXw/6fidP/qV2YExV7QN2A88eY03Tqk+WAG/v3iK+OsnJkyltZvXNXP28tnvr84YkLxq6mGnRvW35CuDWOYecn0t0iCzB+dlb97b7HcAu4MaqWnBuuq4vrkee4Nre11eBC4H9Cxyf2Nz0gsbZ8RNgbVW9FLiR//3vTBraNuD5VfUy4OvAjweuZyokeTpwDfDRqnp46Hqm2SJZOj+XoKoer6qXA2uAdUlePHRN06xHnq7tPSQ5B9hVVbcPXQuMt7m+Dzj4f1hrup/NOybJcuCZwANjrGlaLZplVT1QVXu7by8FXjWh2mZVn/mrHqrq4Sfe+qyq64EVSVYPXNYRrdt/eQ3w/aq6dp4hzs+eFsvS+Xl4qupfwM3A2XMOua4fhoXydG3v7UxgQ5J7GW2dPSvJ9+aMmdjcHGdzfRtwepJTkhwNbAK2zBmzBXhP9/W5wE3ljbfns2iWc/ZbbmC0t1CHbwvw7u6uDK8BdlfV/UMXNY2SPPeJfW1J1jH6veNiu4Auq+8A91TVlxcY5vzsoU+Wzs/+kpyQ5Pju65WMLrL//Zxhrus99cnTtb2fqvpkVa2pqrWMeqSbquqdc4ZNbG6O7W4hVbUvyfnAz4BlwGVVdVeSzwNbq2oLo196302yg9EFUZvGVc8065nlR5JsYHR1/IPAeYMVPAWS/ABYD6xOshP4DKOLSaiqbwPXA28BdgCPAu8dptIjX48szwU+nGQf8BiwycX2kM4E3gXc2e3FBLgYeB44P5eoT5bOz/5OBK7o7mB1FPCjqrrOdf2w9cnTtf0pGGpu+oRGSZIkqREvaJQkSZIasbmWJEmSGrG5liRJkhqxuZYkSZIasbmWJEmSGrG5liRJkhqxuZYkSZIasbmWJEmSGvkvPOs8Pzwt1xsAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 2 Axes>"
       ]
@@ -5043,7 +5404,7 @@
     }
    ],
    "source": [
-    "df_demo[df_demo[\"F\"] < 0][[\"A\", \"F\"]]\\\n",
+    "df_demo.loc[df_demo[\"F\"] < 0, [\"A\", \"F\"]]\\\n",
     "    .plot(\n",
     "        style=[\"-*r\", \"--ob\"], \n",
     "        secondary_y=\"A\", \n",
@@ -5091,25 +5452,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "ax = df_demo[\"C\"].plot(figsize=(10, 4))\n",
-    "ax.set_title(\"Hello there!\");\n",
+    "ax.set_title(\"Hello There!\");\n",
     "fig = ax.get_figure()\n",
-    "fig.suptitle(\"This title is super!\");"
+    "fig.suptitle(\"This title is super (literally)!\");"
    ]
   },
   {
@@ -5125,25 +5488,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "fig, ax = plt.subplots(figsize=(10, 4))\n",
     "df_demo[\"C\"].plot(ax=ax)\n",
-    "ax.set_title(\"Hello there!\");\n",
-    "fig.suptitle(\"This title is super!\");"
+    "ax.set_title(\"Hello There!\");\n",
+    "fig.suptitle(\"This title is super (still, literally)!\");"
    ]
   },
   {
@@ -5159,7 +5524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5168,12 +5533,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -5203,7 +5570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5212,17 +5579,17 @@
    "outputs": [],
    "source": [
     "import seaborn as sns\n",
-    "sns.set()"
+    "sns.set()  # set defaults"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5250,12 +5617,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 720x72 with 1 Axes>"
       ]
@@ -5270,12 +5637,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 720x72 with 1 Axes>"
       ]
@@ -5290,12 +5657,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1440x72 with 1 Axes>"
       ]
@@ -5310,7 +5677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -5319,7 +5686,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 720x72 with 1 Axes>"
       ]
@@ -5334,12 +5701,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 576x72 with 1 Axes>"
       ]
@@ -5354,12 +5721,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 720x72 with 1 Axes>"
       ]
@@ -5387,21 +5754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "warnings.simplefilter(action='ignore', category=FutureWarning)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5410,7 +5763,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5422,7 +5775,7 @@
    "source": [
     "with sns.color_palette(\"hls\", 2):\n",
     "    sns.regplot(x=\"C\", y=\"F\", data=df_demo);\n",
-    "    sns.regplot(x=\"C\", y=\"G\", data=df_demo);"
+    "    sns.regplot(x=\"C\", y=\"E2\", data=df_demo);"
    ]
   },
   {
@@ -5439,7 +5792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5448,12 +5801,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x432 with 3 Axes>"
       ]
@@ -5477,16 +5830,17 @@
    "source": [
     "## Task 6\n",
     "<a name=\"task6\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
     "\n",
     "* To your `df` NEST data frame, add a column with the unaccounted time (`Unaccounted Time / s`), which is the difference of program runtime, average neuron build time, minimal edge build time, minimal initialization time, presimulation time, and simulation time.  \n",
     "(*I know this is technically not super correct, but it will do for our example.*)\n",
-    "* Plot a stacked bar plot of all these columns (except for program runtime) over the virtual processes\n",
-    "* Remember: [pollev.com/aherten538](https://pollev.com/aherten538)"
+    "* Plot a stacked bar plot of all these columns (except for program runtime) over the threads\n",
+    "* Tell me when you're done with status icon in BigBlueButton: 👍"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 102,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -5509,7 +5863,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 103,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -5547,7 +5901,7 @@
        "      <th>Sim. Time / s</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Virtual Processes</th>\n",
+       "      <th>Threads</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
@@ -5583,23 +5937,23 @@
        "</div>"
       ],
       "text/plain": [
-       "                   Runtime Program / s  Unaccounted Time / s  \\\n",
-       "Virtual Processes                                              \n",
-       "8                               420.42                  2.09   \n",
-       "16                              202.15                  2.43   \n",
+       "         Runtime Program / s  Unaccounted Time / s  \\\n",
+       "Threads                                              \n",
+       "8                     420.42                  2.09   \n",
+       "16                    202.15                  2.43   \n",
        "\n",
-       "                   Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n",
-       "Virtual Processes                                                         \n",
-       "8                                        0.29                     88.12   \n",
-       "16                                       0.28                     47.98   \n",
+       "         Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n",
+       "Threads                                                         \n",
+       "8                              0.29                     88.12   \n",
+       "16                             0.28                     47.98   \n",
        "\n",
-       "                   Min. Init. Time / s  Presim. Time / s  Sim. Time / s  \n",
-       "Virtual Processes                                                        \n",
-       "8                                 1.14             17.26         311.52  \n",
-       "16                                0.70              7.95         142.81  "
+       "         Min. Init. Time / s  Presim. Time / s  Sim. Time / s  \n",
+       "Threads                                                        \n",
+       "8                       1.14             17.26         311.52  \n",
+       "16                      0.70              7.95         142.81  "
       ]
      },
-     "execution_count": 93,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5610,7 +5964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 104,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -5620,7 +5974,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x288 with 1 Axes>"
       ]
@@ -5642,13 +5996,13 @@
    },
    "source": [
     "* Make it relative to the total program run time\n",
-    "* **Slight complication**: Our virtual processes as indexes are not unique; we need to find new unique indexes\n",
-    "* Let's use a multi index!"
+    "* **Slight complication**: Our threads as indexes are not unique; we need to find new unique indexes\n",
+    "* Could be anythig, but we use a **multi index**!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5919,7 +6273,7 @@
        "1     2          12                   1.5         1.5                  2.28  "
       ]
      },
-     "execution_count": 95,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5931,7 +6285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -5940,7 +6294,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1008x432 with 1 Axes>"
       ]
@@ -5963,17 +6317,17 @@
     }
    },
    "source": [
-    "## Next Level: Hierarchical Data\n",
+    "## Next _Level_: Hierarchical Data\n",
     "\n",
     "* `MultiIndex` only a first level\n",
     "* More powerful:\n",
-    "    - Grouping: `.groupby()` ([API](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html))\n",
-    "    - Pivoting: `.pivot_table()` ([API](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot_table.html)); also `.pivot()` ([API](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot.html))"
+    "    - Grouping: `.groupby()` (\"Split-apply-combine\", [API](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html), [User Guide](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html))\n",
+    "    - Pivoting: `.pivot_table()` ([API](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot_table.html), [User Guide](https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html)); also `.pivot()` (specialized version of `.pivot_table()`, [API](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot.html))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
@@ -6252,7 +6606,7 @@
        "[6 rows x 21 columns]"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6273,10 +6627,10 @@
     "\n",
     "* Combine categorically-similar columns\n",
     "* Creates hierarchical index\n",
-    "* Respected during plotting!\n",
-    "* A pivot table has three *layers*; if confused, think about these questions\n",
+    "* Respected during plotting with Pandas!\n",
+    "* A pivot table has three *layers*; if confused, think about the related questions\n",
     "    - `index`: »What's on the `x` axis?«\n",
-    "    - `values`: »What value do I want to plot?«\n",
+    "    - `values`: »What value do I want to plot [on the `y` axis]?«\n",
     "    - `columns`: »What categories do I want [to be in the legend]?«\n",
     "* All can be populated from base data frame\n",
     "* Might be aggregated, if needed"
@@ -6284,7 +6638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 109,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -6297,7 +6651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 110,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6374,7 +6728,7 @@
        " 0.518282  2.952492       NaN"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6382,7 +6736,7 @@
    "source": [
     "df_pivot = df_demo.pivot_table(\n",
     "    index=\"F\",\n",
-    "    values=\"G\",\n",
+    "    values=\"E2\",\n",
     "    columns=\"H\"\n",
     ")\n",
     "df_pivot"
@@ -6390,7 +6744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 111,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6399,7 +6753,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -6423,16 +6777,17 @@
    "source": [
     "## Task 7\n",
     "<a name=\"task7\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
     "\n",
     "* Create a pivot table based on the NEST `df` data frame\n",
-    "* Let the `x` axis show the number of nodes; display the values of the simulation time `\"Sim. Time / s\"` for the tasks per node and threas per task configurations\n",
+    "* Let the `x` axis show the number of nodes; display the values of the simulation time `\"Sim. Time / s\"` for the tasks per node and threads per task configurations\n",
     "* Please plot a bar plot\n",
-    "* Done? [pollev.com/aherten538](https://pollev.com/aherten538)"
+    "* Tell me when you're done with status icon in BigBlueButton: 👍"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 116,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -6442,7 +6797,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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       "text/plain": [
        "<Figure size 864x288 with 1 Axes>"
       ]
@@ -6453,7 +6808,7 @@
    ],
    "source": [
     "df.pivot_table(\n",
-    "    index=[\"Nodes\"],\n",
+    "    index=\"Nodes\",\n",
     "    columns=[\"Tasks/Node\", \"Threads/Task\"],\n",
     "    values=\"Sim. Time / s\",\n",
     ").plot(kind=\"bar\", figsize=(12, 4));"
@@ -6484,13 +6839,13 @@
     }
    },
    "source": [
-    "## The End\n",
+    "## Conclusion\n",
     "\n",
-    "* Pandas works on data frames\n",
-    "* Slice frames to your likings\n",
-    "* Plot frames\n",
+    "* Pandas works with and on **data frames**, which are central\n",
+    "* **Slice** frames to your likings\n",
+    "* **Plot** frames\n",
     "    - Together with Matplotlib, Seaborn, others\n",
-    "* Pivot tables are next level greatness\n",
+    "* **Pivot** tables are next level greatness\n",
     "* Remember: ***Pandas as early as possible!***\n",
     "* Thanks for being here! 😍"
    ]
@@ -6501,7 +6856,7 @@
     "exercise": "task"
    },
    "source": [
-    "<span class=\"feedback\">Tell me what you think about this tutorial! <a href=\"mailto:a.herten@fz-juelich.de\">a.herten@fz-juelich.de</a></span>\n",
+    "<span class=\"feedback\">Feedback to <a href=\"mailto:a.herten@fz-juelich.de\">a.herten@fz-juelich.de</a></span>\n",
     "\n",
     "Next slide: Further reading"
    ]
@@ -6525,19 +6880,6 @@
     "    * [An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning](https://towardsdatascience.com/an-introduction-to-scikit-learn-the-gold-standard-of-python-machine-learning-e2b9238a98ab)\n",
     "    * [Mapping with Matplotlib, Pandas, Geopandas and Basemap in Python](https://towardsdatascience.com/mapping-with-matplotlib-pandas-geopandas-and-basemap-in-python-d11b57ab5dac)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "## Poll Results\n",
-    "\n",
-    "![Poll Results](img/poll-results.png)"
-   ]
   }
  ],
  "metadata": {
@@ -6556,9 +6898,13 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
-  }
+   "version": "3.9.5"
+  },
+  "toc-autonumbering": false,
+  "toc-showcode": true,
+  "toc-showmarkdowntxt": false,
+  "toc-showtags": true
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
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+<link rel="stylesheet" href="fzj-reveal.js/reveal.js/dist/reveal.css">
+<link rel="stylesheet" href="fzj-reveal.js/reveal.js/dist/theme/simple.css" id="theme">
+<link rel="stylesheet" href="fzj-reveal.js/custom.css" id="custom">
 
 <!-- If the query includes 'print-pdf', include the PDF print sheet -->
 <script>
@@ -25,13294 +21,14256 @@ if( window.location.search.match( /print-pdf/gi ) ) {
         var link = document.createElement( 'link' );
         link.rel = 'stylesheet';
         link.type = 'text/css';
-        link.href = 'reveal.js/css/print/pdf.css';
         document.getElementsByTagName( 'head' )[0].appendChild( link );
 }
-
 </script>
 
-<!--[if lt IE 9]>
-<script src="reveal.js/lib/js/html5shiv.js"></script>
-<![endif]-->
-
 <!-- Loading the mathjax macro -->
 <!-- Load mathjax -->
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS_HTML"></script>
+    
+<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-MML-AM_CHTML-full,Safe"> </script>
     <!-- MathJax configuration -->
     <script type="text/x-mathjax-config">
-    MathJax.Hub.Config({
-        tex2jax: {
-            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
-            processEscapes: true,
-            processEnvironments: true
-        },
-        // Center justify equations in code and markdown cells. Elsewhere
-        // we use CSS to left justify single line equations in code cells.
-        displayAlign: 'center',
-        "HTML-CSS": {
-            styles: {'.MathJax_Display': {"margin": 0}},
-            linebreaks: { automatic: true }
+    init_mathjax = function() {
+        if (window.MathJax) {
+        // MathJax loaded
+            MathJax.Hub.Config({
+                TeX: {
+                    equationNumbers: {
+                    autoNumber: "AMS",
+                    useLabelIds: true
+                    }
+                },
+                tex2jax: {
+                    inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+                    displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+                    processEscapes: true,
+                    processEnvironments: true
+                },
+                displayAlign: 'center',
+                CommonHTML: {
+                    linebreaks: { 
+                    automatic: true 
+                    }
+                },
+                "HTML-CSS": {
+                    linebreaks: { 
+                    automatic: true 
+                    }
+                }
+            });
+        
+            MathJax.Hub.Queue(["Typeset", MathJax.Hub]);
         }
-    });
+    }
+    init_mathjax();
     </script>
     <!-- End of mathjax configuration -->
 
-<!-- Get Font-awesome from cdn -->
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.css">
+<style type="text/css">
+    pre { line-height: 125%; margin: 0; }
+td.linenos pre { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; }
+td.linenos pre.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: var(--jp-cell-editor-active-background) }
+.highlight { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
+.highlight .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
+.highlight .err { color: var(--jp-mirror-editor-error-color) } /* Error */
+.highlight .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
+.highlight .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
+.highlight .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
+.highlight .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
+.highlight .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
+.highlight .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
+.highlight .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
+.highlight .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
+.highlight .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
+.highlight .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
+.highlight .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
+.highlight .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
+.highlight .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
+.highlight .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
+.highlight .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
+.highlight .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
+.highlight .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
+.highlight .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
+.highlight .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
+.highlight .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
+.highlight .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
+.highlight .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
+.highlight .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
+.highlight .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
+.highlight .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
+.highlight .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
+.highlight .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
+.highlight .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
+.highlight .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
+.highlight .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
+.highlight .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
+  </style>
+
+
 
 <style type="text/css">
-    /*!
-*
-* Twitter Bootstrap
-*
-*/
-/*!
- * Bootstrap v3.3.7 (http://getbootstrap.com)
- * Copyright 2011-2016 Twitter, Inc.
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*
+ * Mozilla scrollbar styling
  */
-/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
-html {
-  font-family: sans-serif;
-  -ms-text-size-adjust: 100%;
-  -webkit-text-size-adjust: 100%;
+
+/* use standard opaque scrollbars for most nodes */
+[data-jp-theme-scrollbars='true'] {
+  scrollbar-color: rgb(var(--jp-scrollbar-thumb-color))
+    var(--jp-scrollbar-background-color);
 }
-body {
-  margin: 0;
+
+/* for code nodes, use a transparent style of scrollbar. These selectors
+ * will match lower in the tree, and so will override the above */
+[data-jp-theme-scrollbars='true'] .CodeMirror-hscrollbar,
+[data-jp-theme-scrollbars='true'] .CodeMirror-vscrollbar {
+  scrollbar-color: rgba(var(--jp-scrollbar-thumb-color), 0.5) transparent;
 }
-article,
-aside,
-details,
-figcaption,
-figure,
-footer,
-header,
-hgroup,
-main,
-menu,
-nav,
-section,
-summary {
-  display: block;
+
+/*
+ * Webkit scrollbar styling
+ */
+
+/* use standard opaque scrollbars for most nodes */
+
+[data-jp-theme-scrollbars='true'] ::-webkit-scrollbar,
+[data-jp-theme-scrollbars='true'] ::-webkit-scrollbar-corner {
+  background: var(--jp-scrollbar-background-color);
 }
-audio,
-canvas,
-progress,
-video {
-  display: inline-block;
-  vertical-align: baseline;
+
+[data-jp-theme-scrollbars='true'] ::-webkit-scrollbar-thumb {
+  background: rgb(var(--jp-scrollbar-thumb-color));
+  border: var(--jp-scrollbar-thumb-margin) solid transparent;
+  background-clip: content-box;
+  border-radius: var(--jp-scrollbar-thumb-radius);
 }
-audio:not([controls]) {
-  display: none;
-  height: 0;
+
+[data-jp-theme-scrollbars='true'] ::-webkit-scrollbar-track:horizontal {
+  border-left: var(--jp-scrollbar-endpad) solid
+    var(--jp-scrollbar-background-color);
+  border-right: var(--jp-scrollbar-endpad) solid
+    var(--jp-scrollbar-background-color);
 }
-[hidden],
-template {
-  display: none;
+
+[data-jp-theme-scrollbars='true'] ::-webkit-scrollbar-track:vertical {
+  border-top: var(--jp-scrollbar-endpad) solid
+    var(--jp-scrollbar-background-color);
+  border-bottom: var(--jp-scrollbar-endpad) solid
+    var(--jp-scrollbar-background-color);
 }
-a {
+
+/* for code nodes, use a transparent style of scrollbar */
+
+[data-jp-theme-scrollbars='true'] .CodeMirror-hscrollbar::-webkit-scrollbar,
+[data-jp-theme-scrollbars='true'] .CodeMirror-vscrollbar::-webkit-scrollbar,
+[data-jp-theme-scrollbars='true']
+  .CodeMirror-hscrollbar::-webkit-scrollbar-corner,
+[data-jp-theme-scrollbars='true']
+  .CodeMirror-vscrollbar::-webkit-scrollbar-corner {
   background-color: transparent;
 }
-a:active,
-a:hover {
-  outline: 0;
-}
-abbr[title] {
-  border-bottom: 1px dotted;
-}
-b,
-strong {
-  font-weight: bold;
-}
-dfn {
-  font-style: italic;
-}
-h1 {
-  font-size: 2em;
-  margin: 0.67em 0;
+
+[data-jp-theme-scrollbars='true']
+  .CodeMirror-hscrollbar::-webkit-scrollbar-thumb,
+[data-jp-theme-scrollbars='true']
+  .CodeMirror-vscrollbar::-webkit-scrollbar-thumb {
+  background: rgba(var(--jp-scrollbar-thumb-color), 0.5);
+  border: var(--jp-scrollbar-thumb-margin) solid transparent;
+  background-clip: content-box;
+  border-radius: var(--jp-scrollbar-thumb-radius);
 }
-mark {
-  background: #ff0;
-  color: #000;
+
+[data-jp-theme-scrollbars='true']
+  .CodeMirror-hscrollbar::-webkit-scrollbar-track:horizontal {
+  border-left: var(--jp-scrollbar-endpad) solid transparent;
+  border-right: var(--jp-scrollbar-endpad) solid transparent;
 }
-small {
-  font-size: 80%;
+
+[data-jp-theme-scrollbars='true']
+  .CodeMirror-vscrollbar::-webkit-scrollbar-track:vertical {
+  border-top: var(--jp-scrollbar-endpad) solid transparent;
+  border-bottom: var(--jp-scrollbar-endpad) solid transparent;
 }
-sub,
-sup {
-  font-size: 75%;
-  line-height: 0;
-  position: relative;
-  vertical-align: baseline;
+
+/*
+ * Phosphor
+ */
+
+.lm-ScrollBar[data-orientation='horizontal'] {
+  min-height: 16px;
+  max-height: 16px;
+  min-width: 45px;
+  border-top: 1px solid #a0a0a0;
 }
-sup {
-  top: -0.5em;
+
+.lm-ScrollBar[data-orientation='vertical'] {
+  min-width: 16px;
+  max-width: 16px;
+  min-height: 45px;
+  border-left: 1px solid #a0a0a0;
 }
-sub {
-  bottom: -0.25em;
+
+.lm-ScrollBar-button {
+  background-color: #f0f0f0;
+  background-position: center center;
+  min-height: 15px;
+  max-height: 15px;
+  min-width: 15px;
+  max-width: 15px;
 }
-img {
-  border: 0;
+
+.lm-ScrollBar-button:hover {
+  background-color: #dadada;
 }
-svg:not(:root) {
-  overflow: hidden;
+
+.lm-ScrollBar-button.lm-mod-active {
+  background-color: #cdcdcd;
 }
-figure {
-  margin: 1em 40px;
+
+.lm-ScrollBar-track {
+  background: #f0f0f0;
 }
-hr {
-  box-sizing: content-box;
-  height: 0;
+
+.lm-ScrollBar-thumb {
+  background: #cdcdcd;
 }
-pre {
-  overflow: auto;
+
+.lm-ScrollBar-thumb:hover {
+  background: #bababa;
 }
-code,
-kbd,
-pre,
-samp {
-  font-family: monospace, monospace;
-  font-size: 1em;
-}
-button,
-input,
-optgroup,
-select,
-textarea {
-  color: inherit;
-  font: inherit;
-  margin: 0;
+
+.lm-ScrollBar-thumb.lm-mod-active {
+  background: #a0a0a0;
 }
-button {
-  overflow: visible;
+
+.lm-ScrollBar[data-orientation='horizontal'] .lm-ScrollBar-thumb {
+  height: 100%;
+  min-width: 15px;
+  border-left: 1px solid #a0a0a0;
+  border-right: 1px solid #a0a0a0;
 }
-button,
-select {
-  text-transform: none;
+
+.lm-ScrollBar[data-orientation='vertical'] .lm-ScrollBar-thumb {
+  width: 100%;
+  min-height: 15px;
+  border-top: 1px solid #a0a0a0;
+  border-bottom: 1px solid #a0a0a0;
 }
-button,
-html input[type="button"],
-input[type="reset"],
-input[type="submit"] {
-  -webkit-appearance: button;
-  cursor: pointer;
+
+.lm-ScrollBar[data-orientation='horizontal']
+  .lm-ScrollBar-button[data-action='decrement'] {
+  background-image: var(--jp-icon-caret-left);
+  background-size: 17px;
 }
-button[disabled],
-html input[disabled] {
-  cursor: default;
+
+.lm-ScrollBar[data-orientation='horizontal']
+  .lm-ScrollBar-button[data-action='increment'] {
+  background-image: var(--jp-icon-caret-right);
+  background-size: 17px;
 }
-button::-moz-focus-inner,
-input::-moz-focus-inner {
-  border: 0;
-  padding: 0;
+
+.lm-ScrollBar[data-orientation='vertical']
+  .lm-ScrollBar-button[data-action='decrement'] {
+  background-image: var(--jp-icon-caret-up);
+  background-size: 17px;
 }
-input {
-  line-height: normal;
+
+.lm-ScrollBar[data-orientation='vertical']
+  .lm-ScrollBar-button[data-action='increment'] {
+  background-image: var(--jp-icon-caret-down);
+  background-size: 17px;
 }
-input[type="checkbox"],
-input[type="radio"] {
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-Widget, /* </DEPRECATED> */
+.lm-Widget {
   box-sizing: border-box;
-  padding: 0;
-}
-input[type="number"]::-webkit-inner-spin-button,
-input[type="number"]::-webkit-outer-spin-button {
-  height: auto;
+  position: relative;
+  overflow: hidden;
+  cursor: default;
 }
-input[type="search"] {
-  -webkit-appearance: textfield;
-  box-sizing: content-box;
+
+
+/* <DEPRECATED> */ .p-Widget.p-mod-hidden, /* </DEPRECATED> */
+.lm-Widget.lm-mod-hidden {
+  display: none !important;
 }
-input[type="search"]::-webkit-search-cancel-button,
-input[type="search"]::-webkit-search-decoration {
-  -webkit-appearance: none;
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-CommandPalette, /* </DEPRECATED> */
+.lm-CommandPalette {
+  display: flex;
+  flex-direction: column;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
 }
-fieldset {
-  border: 1px solid #c0c0c0;
-  margin: 0 2px;
-  padding: 0.35em 0.625em 0.75em;
+
+
+/* <DEPRECATED> */ .p-CommandPalette-search, /* </DEPRECATED> */
+.lm-CommandPalette-search {
+  flex: 0 0 auto;
 }
-legend {
-  border: 0;
+
+
+/* <DEPRECATED> */ .p-CommandPalette-content, /* </DEPRECATED> */
+.lm-CommandPalette-content {
+  flex: 1 1 auto;
+  margin: 0;
   padding: 0;
-}
-textarea {
+  min-height: 0;
   overflow: auto;
+  list-style-type: none;
 }
-optgroup {
-  font-weight: bold;
+
+
+/* <DEPRECATED> */ .p-CommandPalette-header, /* </DEPRECATED> */
+.lm-CommandPalette-header {
+  overflow: hidden;
+  white-space: nowrap;
+  text-overflow: ellipsis;
 }
-table {
-  border-collapse: collapse;
-  border-spacing: 0;
+
+
+/* <DEPRECATED> */ .p-CommandPalette-item, /* </DEPRECATED> */
+.lm-CommandPalette-item {
+  display: flex;
+  flex-direction: row;
 }
-td,
-th {
-  padding: 0;
+
+
+/* <DEPRECATED> */ .p-CommandPalette-itemIcon, /* </DEPRECATED> */
+.lm-CommandPalette-itemIcon {
+  flex: 0 0 auto;
 }
-/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
-@media print {
-  *,
-  *:before,
-  *:after {
-    background: transparent !important;
-    box-shadow: none !important;
-    text-shadow: none !important;
-  }
-  a,
-  a:visited {
-    text-decoration: underline;
-  }
-  a[href]:after {
-    content: " (" attr(href) ")";
-  }
-  abbr[title]:after {
-    content: " (" attr(title) ")";
-  }
-  a[href^="#"]:after,
-  a[href^="javascript:"]:after {
-    content: "";
-  }
-  pre,
-  blockquote {
-    border: 1px solid #999;
-    page-break-inside: avoid;
-  }
-  thead {
-    display: table-header-group;
-  }
-  tr,
-  img {
-    page-break-inside: avoid;
-  }
-  img {
-    max-width: 100% !important;
-  }
-  p,
-  h2,
-  h3 {
-    orphans: 3;
-    widows: 3;
-  }
-  h2,
-  h3 {
-    page-break-after: avoid;
-  }
-  .navbar {
-    display: none;
-  }
-  .btn > .caret,
-  .dropup > .btn > .caret {
-    border-top-color: #000 !important;
-  }
-  .label {
-    border: 1px solid #000;
-  }
-  .table {
-    border-collapse: collapse !important;
-  }
-  .table td,
-  .table th {
-    background-color: #fff !important;
-  }
-  .table-bordered th,
-  .table-bordered td {
-    border: 1px solid #ddd !important;
-  }
+
+
+/* <DEPRECATED> */ .p-CommandPalette-itemContent, /* </DEPRECATED> */
+.lm-CommandPalette-itemContent {
+  flex: 1 1 auto;
+  overflow: hidden;
 }
-@font-face {
-  font-family: 'Glyphicons Halflings';
-  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
-  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
+
+
+/* <DEPRECATED> */ .p-CommandPalette-itemShortcut, /* </DEPRECATED> */
+.lm-CommandPalette-itemShortcut {
+  flex: 0 0 auto;
 }
-.glyphicon {
-  position: relative;
-  top: 1px;
-  display: inline-block;
-  font-family: 'Glyphicons Halflings';
-  font-style: normal;
-  font-weight: normal;
-  line-height: 1;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
+
+
+/* <DEPRECATED> */ .p-CommandPalette-itemLabel, /* </DEPRECATED> */
+.lm-CommandPalette-itemLabel {
+  overflow: hidden;
+  white-space: nowrap;
+  text-overflow: ellipsis;
 }
-.glyphicon-asterisk:before {
-  content: "\002a";
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-DockPanel, /* </DEPRECATED> */
+.lm-DockPanel {
+  z-index: 0;
 }
-.glyphicon-plus:before {
-  content: "\002b";
+
+
+/* <DEPRECATED> */ .p-DockPanel-widget, /* </DEPRECATED> */
+.lm-DockPanel-widget {
+  z-index: 0;
 }
-.glyphicon-euro:before,
-.glyphicon-eur:before {
-  content: "\20ac";
+
+
+/* <DEPRECATED> */ .p-DockPanel-tabBar, /* </DEPRECATED> */
+.lm-DockPanel-tabBar {
+  z-index: 1;
 }
-.glyphicon-minus:before {
-  content: "\2212";
+
+
+/* <DEPRECATED> */ .p-DockPanel-handle, /* </DEPRECATED> */
+.lm-DockPanel-handle {
+  z-index: 2;
 }
-.glyphicon-cloud:before {
-  content: "\2601";
+
+
+/* <DEPRECATED> */ .p-DockPanel-handle.p-mod-hidden, /* </DEPRECATED> */
+.lm-DockPanel-handle.lm-mod-hidden {
+  display: none !important;
 }
-.glyphicon-envelope:before {
-  content: "\2709";
+
+
+/* <DEPRECATED> */ .p-DockPanel-handle:after, /* </DEPRECATED> */
+.lm-DockPanel-handle:after {
+  position: absolute;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  content: '';
 }
-.glyphicon-pencil:before {
-  content: "\270f";
+
+
+/* <DEPRECATED> */
+.p-DockPanel-handle[data-orientation='horizontal'],
+/* </DEPRECATED> */
+.lm-DockPanel-handle[data-orientation='horizontal'] {
+  cursor: ew-resize;
 }
-.glyphicon-glass:before {
-  content: "\e001";
+
+
+/* <DEPRECATED> */
+.p-DockPanel-handle[data-orientation='vertical'],
+/* </DEPRECATED> */
+.lm-DockPanel-handle[data-orientation='vertical'] {
+  cursor: ns-resize;
 }
-.glyphicon-music:before {
-  content: "\e002";
+
+
+/* <DEPRECATED> */
+.p-DockPanel-handle[data-orientation='horizontal']:after,
+/* </DEPRECATED> */
+.lm-DockPanel-handle[data-orientation='horizontal']:after {
+  left: 50%;
+  min-width: 8px;
+  transform: translateX(-50%);
 }
-.glyphicon-search:before {
-  content: "\e003";
+
+
+/* <DEPRECATED> */
+.p-DockPanel-handle[data-orientation='vertical']:after,
+/* </DEPRECATED> */
+.lm-DockPanel-handle[data-orientation='vertical']:after {
+  top: 50%;
+  min-height: 8px;
+  transform: translateY(-50%);
 }
-.glyphicon-heart:before {
-  content: "\e005";
+
+
+/* <DEPRECATED> */ .p-DockPanel-overlay, /* </DEPRECATED> */
+.lm-DockPanel-overlay {
+  z-index: 3;
+  box-sizing: border-box;
+  pointer-events: none;
 }
-.glyphicon-star:before {
-  content: "\e006";
+
+
+/* <DEPRECATED> */ .p-DockPanel-overlay.p-mod-hidden, /* </DEPRECATED> */
+.lm-DockPanel-overlay.lm-mod-hidden {
+  display: none !important;
 }
-.glyphicon-star-empty:before {
-  content: "\e007";
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-Menu, /* </DEPRECATED> */
+.lm-Menu {
+  z-index: 10000;
+  position: absolute;
+  white-space: nowrap;
+  overflow-x: hidden;
+  overflow-y: auto;
+  outline: none;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
 }
-.glyphicon-user:before {
-  content: "\e008";
+
+
+/* <DEPRECATED> */ .p-Menu-content, /* </DEPRECATED> */
+.lm-Menu-content {
+  margin: 0;
+  padding: 0;
+  display: table;
+  list-style-type: none;
 }
-.glyphicon-film:before {
-  content: "\e009";
+
+
+/* <DEPRECATED> */ .p-Menu-item, /* </DEPRECATED> */
+.lm-Menu-item {
+  display: table-row;
 }
-.glyphicon-th-large:before {
-  content: "\e010";
+
+
+/* <DEPRECATED> */
+.p-Menu-item.p-mod-hidden,
+.p-Menu-item.p-mod-collapsed,
+/* </DEPRECATED> */
+.lm-Menu-item.lm-mod-hidden,
+.lm-Menu-item.lm-mod-collapsed {
+  display: none !important;
 }
-.glyphicon-th:before {
-  content: "\e011";
+
+
+/* <DEPRECATED> */
+.p-Menu-itemIcon,
+.p-Menu-itemSubmenuIcon,
+/* </DEPRECATED> */
+.lm-Menu-itemIcon,
+.lm-Menu-itemSubmenuIcon {
+  display: table-cell;
+  text-align: center;
 }
-.glyphicon-th-list:before {
-  content: "\e012";
+
+
+/* <DEPRECATED> */ .p-Menu-itemLabel, /* </DEPRECATED> */
+.lm-Menu-itemLabel {
+  display: table-cell;
+  text-align: left;
 }
-.glyphicon-ok:before {
-  content: "\e013";
+
+
+/* <DEPRECATED> */ .p-Menu-itemShortcut, /* </DEPRECATED> */
+.lm-Menu-itemShortcut {
+  display: table-cell;
+  text-align: right;
 }
-.glyphicon-remove:before {
-  content: "\e014";
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-MenuBar, /* </DEPRECATED> */
+.lm-MenuBar {
+  outline: none;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
 }
-.glyphicon-zoom-in:before {
-  content: "\e015";
+
+
+/* <DEPRECATED> */ .p-MenuBar-content, /* </DEPRECATED> */
+.lm-MenuBar-content {
+  margin: 0;
+  padding: 0;
+  display: flex;
+  flex-direction: row;
+  list-style-type: none;
 }
-.glyphicon-zoom-out:before {
-  content: "\e016";
-}
-.glyphicon-off:before {
-  content: "\e017";
-}
-.glyphicon-signal:before {
-  content: "\e018";
-}
-.glyphicon-cog:before {
-  content: "\e019";
-}
-.glyphicon-trash:before {
-  content: "\e020";
-}
-.glyphicon-home:before {
-  content: "\e021";
-}
-.glyphicon-file:before {
-  content: "\e022";
-}
-.glyphicon-time:before {
-  content: "\e023";
-}
-.glyphicon-road:before {
-  content: "\e024";
-}
-.glyphicon-download-alt:before {
-  content: "\e025";
-}
-.glyphicon-download:before {
-  content: "\e026";
-}
-.glyphicon-upload:before {
-  content: "\e027";
-}
-.glyphicon-inbox:before {
-  content: "\e028";
-}
-.glyphicon-play-circle:before {
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-}
-.glyphicon-repeat:before {
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-}
-.glyphicon-refresh:before {
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-}
-.glyphicon-list-alt:before {
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-}
-.glyphicon-lock:before {
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-}
-.glyphicon-flag:before {
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-}
-.glyphicon-headphones:before {
-  content: "\e035";
-}
-.glyphicon-volume-off:before {
-  content: "\e036";
-}
-.glyphicon-volume-down:before {
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-}
-.glyphicon-volume-up:before {
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-}
-.glyphicon-qrcode:before {
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-.glyphicon-barcode:before {
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-}
-.glyphicon-tag:before {
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-.glyphicon-tags:before {
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-.glyphicon-book:before {
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-.glyphicon-bookmark:before {
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-}
-.glyphicon-print:before {
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-.glyphicon-camera:before {
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-}
-.glyphicon-font:before {
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-}
-.glyphicon-bold:before {
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-.glyphicon-italic:before {
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-.glyphicon-text-height:before {
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-.glyphicon-text-width:before {
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-.glyphicon-align-left:before {
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-}
-.glyphicon-list:before {
-  content: "\e056";
-}
-.glyphicon-indent-left:before {
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-}
-.glyphicon-indent-right:before {
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-.glyphicon-facetime-video:before {
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-}
-.glyphicon-picture:before {
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-.glyphicon-map-marker:before {
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-.glyphicon-step-backward:before {
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-.glyphicon-play:before {
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-.glyphicon-forward:before {
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-.glyphicon-eject:before {
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-.glyphicon-chevron-left:before {
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-.glyphicon-chevron-right:before {
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-.glyphicon-plus-sign:before {
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-.glyphicon-minus-sign:before {
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-.glyphicon-remove-sign:before {
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-.glyphicon-ok-sign:before {
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-.glyphicon-question-sign:before {
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-.glyphicon-info-sign:before {
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-.glyphicon-screenshot:before {
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-.glyphicon-remove-circle:before {
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-.glyphicon-ban-circle:before {
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-.glyphicon-arrow-right:before {
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-.glyphicon-arrow-down:before {
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-.glyphicon-share-alt:before {
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-.glyphicon-resize-full:before {
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-.glyphicon-eye-open:before {
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-.glyphicon-chevron-up:before {
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-.glyphicon-chevron-down:before {
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-.glyphicon-retweet:before {
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-.glyphicon-shopping-cart:before {
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-.glyphicon-folder-close:before {
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-.glyphicon-folder-open:before {
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-}
-.glyphicon-resize-vertical:before {
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-.glyphicon-resize-horizontal:before {
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-.glyphicon-hdd:before {
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-}
-.glyphicon-bullhorn:before {
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-.glyphicon-bell:before {
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-.glyphicon-certificate:before {
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-}
-.glyphicon-thumbs-up:before {
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-.glyphicon-thumbs-down:before {
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-.glyphicon-hand-right:before {
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-.glyphicon-hand-left:before {
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-.glyphicon-hand-up:before {
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-.glyphicon-hand-down:before {
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-}
-.glyphicon-circle-arrow-right:before {
-  content: "\e131";
-}
-.glyphicon-circle-arrow-left:before {
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-}
-.glyphicon-circle-arrow-up:before {
-  content: "\e133";
-}
-.glyphicon-circle-arrow-down:before {
-  content: "\e134";
-}
-.glyphicon-globe:before {
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-}
-.glyphicon-wrench:before {
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-}
-.glyphicon-tasks:before {
-  content: "\e137";
-}
-.glyphicon-filter:before {
-  content: "\e138";
-}
-.glyphicon-briefcase:before {
-  content: "\e139";
-}
-.glyphicon-fullscreen:before {
-  content: "\e140";
-}
-.glyphicon-dashboard:before {
-  content: "\e141";
-}
-.glyphicon-paperclip:before {
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-}
-.glyphicon-heart-empty:before {
-  content: "\e143";
-}
-.glyphicon-link:before {
-  content: "\e144";
-}
-.glyphicon-phone:before {
-  content: "\e145";
-}
-.glyphicon-pushpin:before {
-  content: "\e146";
-}
-.glyphicon-usd:before {
-  content: "\e148";
-}
-.glyphicon-gbp:before {
-  content: "\e149";
-}
-.glyphicon-sort:before {
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-}
-.glyphicon-sort-by-alphabet:before {
-  content: "\e151";
-}
-.glyphicon-sort-by-alphabet-alt:before {
-  content: "\e152";
-}
-.glyphicon-sort-by-order:before {
-  content: "\e153";
-}
-.glyphicon-sort-by-order-alt:before {
-  content: "\e154";
-}
-.glyphicon-sort-by-attributes:before {
-  content: "\e155";
-}
-.glyphicon-sort-by-attributes-alt:before {
-  content: "\e156";
-}
-.glyphicon-unchecked:before {
-  content: "\e157";
-}
-.glyphicon-expand:before {
-  content: "\e158";
-}
-.glyphicon-collapse-down:before {
-  content: "\e159";
-}
-.glyphicon-collapse-up:before {
-  content: "\e160";
+
+
+/* <DEPRECATED> */ .p--MenuBar-item, /* </DEPRECATED> */
+.lm-MenuBar-item {
+  box-sizing: border-box;
 }
-.glyphicon-log-in:before {
-  content: "\e161";
+
+
+/* <DEPRECATED> */
+.p-MenuBar-itemIcon,
+.p-MenuBar-itemLabel,
+/* </DEPRECATED> */
+.lm-MenuBar-itemIcon,
+.lm-MenuBar-itemLabel {
+  display: inline-block;
 }
-.glyphicon-flash:before {
-  content: "\e162";
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-ScrollBar, /* </DEPRECATED> */
+.lm-ScrollBar {
+  display: flex;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
 }
-.glyphicon-log-out:before {
-  content: "\e163";
+
+
+/* <DEPRECATED> */
+.p-ScrollBar[data-orientation='horizontal'],
+/* </DEPRECATED> */
+.lm-ScrollBar[data-orientation='horizontal'] {
+  flex-direction: row;
 }
-.glyphicon-new-window:before {
-  content: "\e164";
+
+
+/* <DEPRECATED> */
+.p-ScrollBar[data-orientation='vertical'],
+/* </DEPRECATED> */
+.lm-ScrollBar[data-orientation='vertical'] {
+  flex-direction: column;
 }
-.glyphicon-record:before {
-  content: "\e165";
+
+
+/* <DEPRECATED> */ .p-ScrollBar-button, /* </DEPRECATED> */
+.lm-ScrollBar-button {
+  box-sizing: border-box;
+  flex: 0 0 auto;
 }
-.glyphicon-save:before {
-  content: "\e166";
+
+
+/* <DEPRECATED> */ .p-ScrollBar-track, /* </DEPRECATED> */
+.lm-ScrollBar-track {
+  box-sizing: border-box;
+  position: relative;
+  overflow: hidden;
+  flex: 1 1 auto;
 }
-.glyphicon-open:before {
-  content: "\e167";
+
+
+/* <DEPRECATED> */ .p-ScrollBar-thumb, /* </DEPRECATED> */
+.lm-ScrollBar-thumb {
+  box-sizing: border-box;
+  position: absolute;
 }
-.glyphicon-saved:before {
-  content: "\e168";
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-SplitPanel-child, /* </DEPRECATED> */
+.lm-SplitPanel-child {
+  z-index: 0;
 }
-.glyphicon-import:before {
-  content: "\e169";
+
+
+/* <DEPRECATED> */ .p-SplitPanel-handle, /* </DEPRECATED> */
+.lm-SplitPanel-handle {
+  z-index: 1;
 }
-.glyphicon-export:before {
-  content: "\e170";
+
+
+/* <DEPRECATED> */ .p-SplitPanel-handle.p-mod-hidden, /* </DEPRECATED> */
+.lm-SplitPanel-handle.lm-mod-hidden {
+  display: none !important;
 }
-.glyphicon-send:before {
-  content: "\e171";
+
+
+/* <DEPRECATED> */ .p-SplitPanel-handle:after, /* </DEPRECATED> */
+.lm-SplitPanel-handle:after {
+  position: absolute;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  content: '';
 }
-.glyphicon-floppy-disk:before {
-  content: "\e172";
+
+
+/* <DEPRECATED> */
+.p-SplitPanel[data-orientation='horizontal'] > .p-SplitPanel-handle,
+/* </DEPRECATED> */
+.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle {
+  cursor: ew-resize;
 }
-.glyphicon-floppy-saved:before {
-  content: "\e173";
+
+
+/* <DEPRECATED> */
+.p-SplitPanel[data-orientation='vertical'] > .p-SplitPanel-handle,
+/* </DEPRECATED> */
+.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle {
+  cursor: ns-resize;
 }
-.glyphicon-floppy-remove:before {
-  content: "\e174";
+
+
+/* <DEPRECATED> */
+.p-SplitPanel[data-orientation='horizontal'] > .p-SplitPanel-handle:after,
+/* </DEPRECATED> */
+.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle:after {
+  left: 50%;
+  min-width: 8px;
+  transform: translateX(-50%);
 }
-.glyphicon-floppy-save:before {
-  content: "\e175";
+
+
+/* <DEPRECATED> */
+.p-SplitPanel[data-orientation='vertical'] > .p-SplitPanel-handle:after,
+/* </DEPRECATED> */
+.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle:after {
+  top: 50%;
+  min-height: 8px;
+  transform: translateY(-50%);
 }
-.glyphicon-floppy-open:before {
-  content: "\e176";
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-TabBar, /* </DEPRECATED> */
+.lm-TabBar {
+  display: flex;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
 }
-.glyphicon-credit-card:before {
-  content: "\e177";
+
+
+/* <DEPRECATED> */ .p-TabBar[data-orientation='horizontal'], /* </DEPRECATED> */
+.lm-TabBar[data-orientation='horizontal'] {
+  flex-direction: row;
 }
-.glyphicon-transfer:before {
-  content: "\e178";
+
+
+/* <DEPRECATED> */ .p-TabBar[data-orientation='vertical'], /* </DEPRECATED> */
+.lm-TabBar[data-orientation='vertical'] {
+  flex-direction: column;
 }
-.glyphicon-cutlery:before {
-  content: "\e179";
+
+
+/* <DEPRECATED> */ .p-TabBar-content, /* </DEPRECATED> */
+.lm-TabBar-content {
+  margin: 0;
+  padding: 0;
+  display: flex;
+  flex: 1 1 auto;
+  list-style-type: none;
 }
-.glyphicon-header:before {
-  content: "\e180";
+
+
+/* <DEPRECATED> */
+.p-TabBar[data-orientation='horizontal'] > .p-TabBar-content,
+/* </DEPRECATED> */
+.lm-TabBar[data-orientation='horizontal'] > .lm-TabBar-content {
+  flex-direction: row;
 }
-.glyphicon-compressed:before {
-  content: "\e181";
+
+
+/* <DEPRECATED> */
+.p-TabBar[data-orientation='vertical'] > .p-TabBar-content,
+/* </DEPRECATED> */
+.lm-TabBar[data-orientation='vertical'] > .lm-TabBar-content {
+  flex-direction: column;
 }
-.glyphicon-earphone:before {
-  content: "\e182";
+
+
+/* <DEPRECATED> */ .p-TabBar-tab, /* </DEPRECATED> */
+.lm-TabBar-tab {
+  display: flex;
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+/*-----------------------------------------------------------------------------
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-/*!
-*
-* Font Awesome
-*
-*/
-/*!
- *  Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
- *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
- */
-/* FONT PATH
- * -------------------------- */
-@font-face {
-  font-family: 'FontAwesome';
-  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0');
-  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');
-  font-weight: normal;
-  font-style: normal;
-}
-.fa {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
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-  line-height: 0.75em;
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-.fa-2x {
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-.fa-3x {
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-.fa-4x {
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-.fa-5x {
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-.fa-fw {
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-  text-align: center;
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-.fa-ul {
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-.fa-ul > li {
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-.fa-li {
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-  text-align: center;
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-.fa-li.fa-lg {
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-}
-.fa-border {
-  padding: .2em .25em .15em;
-  border: solid 0.08em #eee;
-  border-radius: .1em;
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-.fa-pull-left {
-  float: left;
-}
-.fa-pull-right {
-  float: right;
-}
-.fa.fa-pull-left {
-  margin-right: .3em;
-}
-.fa.fa-pull-right {
-  margin-left: .3em;
-}
-/* Deprecated as of 4.4.0 */
-.pull-right {
-  float: right;
-}
-.pull-left {
-  float: left;
-}
-.fa.pull-left {
-  margin-right: .3em;
-}
-.fa.pull-right {
-  margin-left: .3em;
-}
-.fa-spin {
-  -webkit-animation: fa-spin 2s infinite linear;
-  animation: fa-spin 2s infinite linear;
-}
-.fa-pulse {
-  -webkit-animation: fa-spin 1s infinite steps(8);
-  animation: fa-spin 1s infinite steps(8);
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-@-webkit-keyframes fa-spin {
-  0% {
-    -webkit-transform: rotate(0deg);
-    transform: rotate(0deg);
-  }
-  100% {
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-    transform: rotate(359deg);
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-}
-@keyframes fa-spin {
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-    transform: rotate(0deg);
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-    transform: rotate(359deg);
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-.fa-rotate-90 {
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-  transform: rotate(90deg);
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-.fa-rotate-180 {
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-  transform: rotate(180deg);
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-.fa-rotate-270 {
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-  transform: rotate(270deg);
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-.fa-flip-horizontal {
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-  transform: scale(-1, 1);
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-.fa-flip-vertical {
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-  -webkit-transform: scale(1, -1);
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-  transform: scale(1, -1);
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-:root .fa-rotate-90,
-:root .fa-rotate-180,
-:root .fa-rotate-270,
-:root .fa-flip-horizontal,
-:root .fa-flip-vertical {
-  filter: none;
-}
-.fa-stack {
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-  display: inline-block;
-  width: 2em;
-  height: 2em;
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-.fa-stack-1x,
-.fa-stack-2x {
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-.fa-stack-1x {
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-.fa-stack-2x {
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-.fa-inverse {
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-/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
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-.fa-music:before {
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-.fa-search:before {
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-.fa-envelope-o:before {
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-.fa-heart:before {
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-.fa-star:before {
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-.fa-star-o:before {
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-.fa-user:before {
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-.fa-film:before {
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-.fa-th-large:before {
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-.fa-th:before {
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-.fa-th-list:before {
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-.fa-check:before {
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-.fa-remove:before,
-.fa-close:before,
-.fa-times:before {
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-.fa-search-plus:before {
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-.fa-search-minus:before {
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-.fa-power-off:before {
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-.fa-signal:before {
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-.fa-gear:before,
-.fa-cog:before {
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-.fa-trash-o:before {
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-.fa-home:before {
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-.fa-file-o:before {
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-.fa-road:before {
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-.fa-download:before {
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-.fa-arrow-circle-o-down:before {
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-.fa-arrow-circle-o-up:before {
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-.fa-inbox:before {
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-.fa-play-circle-o:before {
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-.fa-rotate-right:before,
-.fa-repeat:before {
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-.fa-refresh:before {
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-.fa-list-alt:before {
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-.fa-lock:before {
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-.fa-flag:before {
-  content: "\f024";
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-.fa-headphones:before {
-  content: "\f025";
-}
-.fa-volume-off:before {
-  content: "\f026";
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-.fa-volume-down:before {
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-}
-.fa-volume-up:before {
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-}
-.fa-qrcode:before {
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-}
-.fa-barcode:before {
-  content: "\f02a";
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-.fa-tag:before {
-  content: "\f02b";
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-.fa-tags:before {
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-}
-.fa-book:before {
-  content: "\f02d";
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-.fa-bookmark:before {
-  content: "\f02e";
-}
-.fa-print:before {
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-}
-.fa-camera:before {
-  content: "\f030";
-}
-.fa-font:before {
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-}
-.fa-bold:before {
-  content: "\f032";
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-.fa-italic:before {
-  content: "\f033";
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-.fa-text-height:before {
-  content: "\f034";
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-.fa-text-width:before {
-  content: "\f035";
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-.fa-align-left:before {
-  content: "\f036";
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-.fa-align-center:before {
-  content: "\f037";
-}
-.fa-align-right:before {
-  content: "\f038";
-}
-.fa-align-justify:before {
-  content: "\f039";
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-.fa-list:before {
-  content: "\f03a";
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-.fa-dedent:before,
-.fa-outdent:before {
-  content: "\f03b";
-}
-.fa-indent:before {
-  content: "\f03c";
-}
-.fa-video-camera:before {
-  content: "\f03d";
-}
-.fa-photo:before,
-.fa-image:before,
-.fa-picture-o:before {
-  content: "\f03e";
-}
-.fa-pencil:before {
-  content: "\f040";
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-.fa-map-marker:before {
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-.fa-adjust:before {
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-.fa-tint:before {
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-.fa-edit:before,
-.fa-pencil-square-o:before {
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-.fa-share-square-o:before {
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-.fa-check-square-o:before {
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-.fa-arrows:before {
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-.fa-step-backward:before {
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-.fa-fast-backward:before {
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-.fa-backward:before {
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-}
-.fa-play:before {
-  content: "\f04b";
-}
-.fa-pause:before {
-  content: "\f04c";
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-.fa-stop:before {
-  content: "\f04d";
-}
-.fa-forward:before {
-  content: "\f04e";
-}
-.fa-fast-forward:before {
-  content: "\f050";
-}
-.fa-step-forward:before {
-  content: "\f051";
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-.fa-eject:before {
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-.fa-chevron-left:before {
-  content: "\f053";
-}
-.fa-chevron-right:before {
-  content: "\f054";
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-.fa-plus-circle:before {
-  content: "\f055";
-}
-.fa-minus-circle:before {
-  content: "\f056";
-}
-.fa-times-circle:before {
-  content: "\f057";
-}
-.fa-check-circle:before {
-  content: "\f058";
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-.fa-question-circle:before {
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-.fa-info-circle:before {
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-.fa-crosshairs:before {
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-.fa-times-circle-o:before {
-  content: "\f05c";
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-.fa-check-circle-o:before {
-  content: "\f05d";
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-.fa-ban:before {
-  content: "\f05e";
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-.fa-arrow-left:before {
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-}
-.fa-arrow-right:before {
-  content: "\f061";
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-.fa-arrow-up:before {
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-}
-.fa-arrow-down:before {
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-}
-.fa-mail-forward:before,
-.fa-share:before {
-  content: "\f064";
-}
-.fa-expand:before {
-  content: "\f065";
-}
-.fa-compress:before {
-  content: "\f066";
-}
-.fa-plus:before {
-  content: "\f067";
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-.fa-minus:before {
-  content: "\f068";
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-.fa-asterisk:before {
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-.fa-exclamation-circle:before {
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-.fa-gift:before {
-  content: "\f06b";
-}
-.fa-leaf:before {
-  content: "\f06c";
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-.fa-fire:before {
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-.fa-eye:before {
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-.fa-eye-slash:before {
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-.fa-warning:before,
-.fa-exclamation-triangle:before {
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-.fa-plane:before {
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-.fa-calendar:before {
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-.fa-random:before {
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-.fa-comment:before {
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-.fa-magnet:before {
-  content: "\f076";
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-.fa-chevron-up:before {
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-.fa-chevron-down:before {
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-.fa-retweet:before {
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-.fa-shopping-cart:before {
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-.fa-folder:before {
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-.fa-folder-open:before {
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-.fa-arrows-v:before {
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-.fa-arrows-h:before {
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-.fa-twitter-square:before {
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-.fa-gears:before,
-.fa-cogs:before {
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-.fa-heart-o:before {
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-.fa-sign-out:before {
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-.fa-linkedin-square:before {
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-.fa-thumb-tack:before {
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-.fa-external-link:before {
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-.fa-sign-in:before {
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-.fa-trophy:before {
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-.fa-github-square:before {
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-.fa-upload:before {
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-.fa-lemon-o:before {
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-.fa-phone:before {
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-.fa-square-o:before {
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-.fa-bookmark-o:before {
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-.fa-phone-square:before {
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-.fa-twitter:before {
-  content: "\f099";
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-.fa-facebook-f:before,
-.fa-facebook:before {
-  content: "\f09a";
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-.fa-github:before {
-  content: "\f09b";
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-.fa-unlock:before {
-  content: "\f09c";
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-.fa-credit-card:before {
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-.fa-feed:before,
-.fa-rss:before {
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-.fa-hdd-o:before {
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-.fa-bullhorn:before {
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-.fa-bell:before {
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-.fa-certificate:before {
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-.fa-hand-o-up:before {
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-.fa-arrow-circle-left:before {
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-.fa-arrow-circle-right:before {
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-.fa-arrow-circle-up:before {
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-.fa-arrow-circle-down:before {
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-.fa-globe:before {
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-}
-.fa-wrench:before {
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-}
-.fa-tasks:before {
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-.fa-filter:before {
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-.fa-briefcase:before {
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-.fa-arrows-alt:before {
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-.fa-group:before,
-.fa-users:before {
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-.fa-chain:before,
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-.fa-cloud:before {
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-.fa-flask:before {
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-.fa-cut:before,
-.fa-scissors:before {
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-.fa-copy:before,
-.fa-files-o:before {
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-.fa-paperclip:before {
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-.fa-save:before,
-.fa-floppy-o:before {
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-.fa-square:before {
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-.fa-navicon:before,
-.fa-reorder:before,
-.fa-bars:before {
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-.fa-list-ul:before {
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-.fa-list-ol:before {
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-.fa-strikethrough:before {
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-.fa-underline:before {
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-.fa-table:before {
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-.fa-magic:before {
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-.fa-truck:before {
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-.fa-pinterest:before {
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-.fa-pinterest-square:before {
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-.fa-google-plus-square:before {
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-.fa-google-plus:before {
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-.fa-money:before {
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-.fa-caret-down:before {
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-.fa-caret-up:before {
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-.fa-caret-left:before {
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-.fa-unsorted:before,
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-.fa-sort-down:before,
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-.fa-sort-up:before,
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-.fa-envelope:before {
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-.fa-rotate-left:before,
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-.fa-legal:before,
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-.fa-dashboard:before,
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-.fa-comment-o:before {
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-.fa-paste:before,
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-.fa-cloud-upload:before {
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-.fa-user-md:before {
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-.fa-stethoscope:before {
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-.fa-suitcase:before {
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-.fa-bell-o:before {
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-.fa-coffee:before {
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-.fa-cutlery:before {
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-.fa-file-text-o:before {
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-.fa-building-o:before {
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-.fa-hospital-o:before {
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-.fa-ambulance:before {
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-.fa-medkit:before {
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-.fa-fighter-jet:before {
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-.fa-beer:before {
-  content: "\f0fc";
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-.fa-h-square:before {
-  content: "\f0fd";
-}
-.fa-plus-square:before {
-  content: "\f0fe";
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-.fa-angle-double-left:before {
-  content: "\f100";
-}
-.fa-angle-double-right:before {
-  content: "\f101";
-}
-.fa-angle-double-up:before {
-  content: "\f102";
-}
-.fa-angle-double-down:before {
-  content: "\f103";
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-.fa-angle-left:before {
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-.fa-angle-right:before {
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+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+@charset "UTF-8";
+/*!
+
+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
+*/
+html{
+  -webkit-box-sizing:border-box;
+          box-sizing:border-box; }
+
+*,
+*::before,
+*::after{
+  -webkit-box-sizing:inherit;
+          box-sizing:inherit; }
+
+body{
+  text-transform:none;
+  line-height:1.28581;
+  letter-spacing:0;
+  font-size:14px;
+  font-weight:400;
+  color:#182026;
+  font-family:-apple-system, "BlinkMacSystemFont", "Segoe UI", "Roboto", "Oxygen", "Ubuntu", "Cantarell", "Open Sans", "Helvetica Neue", "Icons16", sans-serif; }
+
+p{
+  margin-top:0;
+  margin-bottom:10px; }
+
+small{
+  font-size:12px; }
+
+strong{
+  font-weight:600; }
+
+::-moz-selection{
+  background:rgba(125, 188, 255, 0.6); }
+
+::selection{
+  background:rgba(125, 188, 255, 0.6); }
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+  color:#182026;
+  font-weight:600;
+  margin:0 0 10px;
+  padding:0; }
+  .bp3-dark .bp3-heading{
+    color:#f5f8fa; }
+
+h1.bp3-heading, .bp3-running-text h1{
+  line-height:40px;
+  font-size:36px; }
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+  line-height:32px;
+  font-size:28px; }
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+h3.bp3-heading, .bp3-running-text h3{
+  line-height:25px;
+  font-size:22px; }
+
+h4.bp3-heading, .bp3-running-text h4{
+  line-height:21px;
+  font-size:18px; }
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+  font-size:16px; }
+
+h6.bp3-heading, .bp3-running-text h6{
+  line-height:16px;
+  font-size:14px; }
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+  text-transform:none;
+  line-height:1.28581;
+  letter-spacing:0;
+  font-size:14px;
+  font-weight:400; }
+
+.bp3-monospace-text{
+  text-transform:none;
+  font-family:monospace; }
+
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+  color:#5c7080; }
+  .bp3-dark .bp3-text-muted{
+    color:#a7b6c2; }
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+  color:rgba(92, 112, 128, 0.6); }
+  .bp3-dark .bp3-text-disabled{
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+  white-space:nowrap;
+  word-wrap:normal; }
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+  font-size:14px; }
+  .bp3-running-text h1{
+    color:#182026;
+    font-weight:600;
+    margin-top:40px;
+    margin-bottom:20px; }
+    .bp3-dark .bp3-running-text h1{
+      color:#f5f8fa; }
+  .bp3-running-text h2{
+    color:#182026;
+    font-weight:600;
+    margin-top:40px;
+    margin-bottom:20px; }
+    .bp3-dark .bp3-running-text h2{
+      color:#f5f8fa; }
+  .bp3-running-text h3{
+    color:#182026;
+    font-weight:600;
+    margin-top:40px;
+    margin-bottom:20px; }
+    .bp3-dark .bp3-running-text h3{
+      color:#f5f8fa; }
+  .bp3-running-text h4{
+    color:#182026;
+    font-weight:600;
+    margin-top:40px;
+    margin-bottom:20px; }
+    .bp3-dark .bp3-running-text h4{
+      color:#f5f8fa; }
+  .bp3-running-text h5{
+    color:#182026;
+    font-weight:600;
+    margin-top:40px;
+    margin-bottom:20px; }
+    .bp3-dark .bp3-running-text h5{
+      color:#f5f8fa; }
+  .bp3-running-text h6{
+    color:#182026;
+    font-weight:600;
+    margin-top:40px;
+    margin-bottom:20px; }
+    .bp3-dark .bp3-running-text h6{
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+    margin:20px 0;
+    border:none;
+    border-bottom:1px solid rgba(16, 22, 26, 0.15); }
+    .bp3-dark .bp3-running-text hr{
+      border-color:rgba(255, 255, 255, 0.15); }
+  .bp3-running-text p{
+    margin:0 0 10px;
+    padding:0; }
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+  font-size:16px; }
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+  font-size:12px; }
+a{
+  text-decoration:none;
+  color:#106ba3; }
+  a:hover{
+    cursor:pointer;
+    text-decoration:underline;
+    color:#106ba3; }
+  a .bp3-icon, a .bp3-icon-standard, a .bp3-icon-large{
+    color:inherit; }
+  a code,
+  .bp3-dark a code{
+    color:inherit; }
+  .bp3-dark a,
+  .bp3-dark a:hover{
+    color:#48aff0; }
+    .bp3-dark a .bp3-icon, .bp3-dark a .bp3-icon-standard, .bp3-dark a .bp3-icon-large,
+    .bp3-dark a:hover .bp3-icon,
+    .bp3-dark a:hover .bp3-icon-standard,
+    .bp3-dark a:hover .bp3-icon-large{
+      color:inherit; }
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+  text-transform:none;
+  font-family:monospace;
+  border-radius:3px;
+  -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2);
+          box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2);
+  background:rgba(255, 255, 255, 0.7);
+  padding:2px 5px;
+  color:#5c7080;
+  font-size:smaller; }
+  .bp3-dark .bp3-running-text code, .bp3-running-text .bp3-dark code, .bp3-dark .bp3-code{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+    background:rgba(16, 22, 26, 0.3);
+    color:#a7b6c2; }
+  .bp3-running-text a > code, a > .bp3-code{
+    color:#137cbd; }
+    .bp3-dark .bp3-running-text a > code, .bp3-running-text .bp3-dark a > code, .bp3-dark a > .bp3-code{
+      color:inherit; }
+
+.bp3-running-text pre, .bp3-code-block{
+  text-transform:none;
+  font-family:monospace;
+  display:block;
+  margin:10px 0;
+  border-radius:3px;
+  -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);
+          box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);
+  background:rgba(255, 255, 255, 0.7);
+  padding:13px 15px 12px;
+  line-height:1.4;
+  color:#182026;
+  font-size:13px;
+  word-break:break-all;
+  word-wrap:break-word; }
+  .bp3-dark .bp3-running-text pre, .bp3-running-text .bp3-dark pre, .bp3-dark .bp3-code-block{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+    background:rgba(16, 22, 26, 0.3);
+    color:#f5f8fa; }
+  .bp3-running-text pre > code, .bp3-code-block > code{
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    background:none;
+    padding:0;
+    color:inherit;
+    font-size:inherit; }
+
+.bp3-running-text kbd, .bp3-key{
+  display:-webkit-inline-box;
+  display:-ms-inline-flexbox;
+  display:inline-flex;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+  background:#ffffff;
+  min-width:24px;
+  height:24px;
+  padding:3px 6px;
+  vertical-align:middle;
+  line-height:24px;
+  color:#5c7080;
+  font-family:inherit;
+  font-size:12px; }
+  .bp3-running-text kbd .bp3-icon, .bp3-key .bp3-icon, .bp3-running-text kbd .bp3-icon-standard, .bp3-key .bp3-icon-standard, .bp3-running-text kbd .bp3-icon-large, .bp3-key .bp3-icon-large{
+    margin-right:5px; }
+  .bp3-dark .bp3-running-text kbd, .bp3-running-text .bp3-dark kbd, .bp3-dark .bp3-key{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
+    background:#394b59;
+    color:#a7b6c2; }
+.bp3-running-text blockquote, .bp3-blockquote{
+  margin:0 0 10px;
+  border-left:solid 4px rgba(167, 182, 194, 0.5);
+  padding:0 20px; }
+  .bp3-dark .bp3-running-text blockquote, .bp3-running-text .bp3-dark blockquote, .bp3-dark .bp3-blockquote{
+    border-color:rgba(115, 134, 148, 0.5); }
+.bp3-running-text ul,
+.bp3-running-text ol, .bp3-list{
+  margin:10px 0;
+  padding-left:30px; }
+  .bp3-running-text ul li:not(:last-child), .bp3-running-text ol li:not(:last-child), .bp3-list li:not(:last-child){
+    margin-bottom:5px; }
+  .bp3-running-text ul ol, .bp3-running-text ol ol, .bp3-list ol,
+  .bp3-running-text ul ul,
+  .bp3-running-text ol ul,
+  .bp3-list ul{
+    margin-top:5px; }
+
+.bp3-list-unstyled{
+  margin:0;
+  padding:0;
+  list-style:none; }
+  .bp3-list-unstyled li{
+    padding:0; }
+.bp3-rtl{
+  text-align:right; }
+
+.bp3-dark{
+  color:#f5f8fa; }
+
+:focus{
+  outline:rgba(19, 124, 189, 0.6) auto 2px;
+  outline-offset:2px;
+  -moz-outline-radius:6px; }
+
+.bp3-focus-disabled :focus{
+  outline:none !important; }
+  .bp3-focus-disabled :focus ~ .bp3-control-indicator{
+    outline:none !important; }
+
+.bp3-alert{
+  max-width:400px;
+  padding:20px; }
+
+.bp3-alert-body{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex; }
+  .bp3-alert-body .bp3-icon{
+    margin-top:0;
+    margin-right:20px;
+    font-size:40px; }
+
+.bp3-alert-footer{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:reverse;
+      -ms-flex-direction:row-reverse;
+          flex-direction:row-reverse;
+  margin-top:10px; }
+  .bp3-alert-footer .bp3-button{
+    margin-left:10px; }
+.bp3-breadcrumbs{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -ms-flex-wrap:wrap;
+      flex-wrap:wrap;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  margin:0;
+  cursor:default;
+  height:30px;
+  padding:0;
+  list-style:none; }
+  .bp3-breadcrumbs > li{
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    -webkit-box-align:center;
+        -ms-flex-align:center;
+            align-items:center; }
+    .bp3-breadcrumbs > li::after{
+      display:block;
+      margin:0 5px;
+      background:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill-rule='evenodd' clip-rule='evenodd' d='M10.71 7.29l-4-4a1.003 1.003 0 0 0-1.42 1.42L8.59 8 5.3 11.29c-.19.18-.3.43-.3.71a1.003 1.003 0 0 0 1.71.71l4-4c.18-.18.29-.43.29-.71 0-.28-.11-.53-.29-.71z' fill='%235C7080'/%3e%3c/svg%3e");
+      width:16px;
+      height:16px;
+      content:""; }
+    .bp3-breadcrumbs > li:last-of-type::after{
+      display:none; }
+
+.bp3-breadcrumb,
+.bp3-breadcrumb-current,
+.bp3-breadcrumbs-collapsed{
+  display:-webkit-inline-box;
+  display:-ms-inline-flexbox;
+  display:inline-flex;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  font-size:16px; }
+
+.bp3-breadcrumb,
+.bp3-breadcrumbs-collapsed{
+  color:#5c7080; }
+
+.bp3-breadcrumb:hover{
+  text-decoration:none; }
+
+.bp3-breadcrumb.bp3-disabled{
+  cursor:not-allowed;
+  color:rgba(92, 112, 128, 0.6); }
+
+.bp3-breadcrumb .bp3-icon{
+  margin-right:5px; }
+
+.bp3-breadcrumb-current{
+  color:inherit;
+  font-weight:600; }
+  .bp3-breadcrumb-current .bp3-input{
+    vertical-align:baseline;
+    font-size:inherit;
+    font-weight:inherit; }
+
+.bp3-breadcrumbs-collapsed{
+  margin-right:2px;
+  border:none;
+  border-radius:3px;
+  background:#ced9e0;
+  cursor:pointer;
+  padding:1px 5px;
+  vertical-align:text-bottom; }
+  .bp3-breadcrumbs-collapsed::before{
+    display:block;
+    background:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cg fill='%235C7080'%3e%3ccircle cx='2' cy='8.03' r='2'/%3e%3ccircle cx='14' cy='8.03' r='2'/%3e%3ccircle cx='8' cy='8.03' r='2'/%3e%3c/g%3e%3c/svg%3e") center no-repeat;
+    width:16px;
+    height:16px;
+    content:""; }
+  .bp3-breadcrumbs-collapsed:hover{
+    background:#bfccd6;
+    text-decoration:none;
+    color:#182026; }
+
+.bp3-dark .bp3-breadcrumb,
+.bp3-dark .bp3-breadcrumbs-collapsed{
+  color:#a7b6c2; }
+
+.bp3-dark .bp3-breadcrumbs > li::after{
+  color:#a7b6c2; }
+
+.bp3-dark .bp3-breadcrumb.bp3-disabled{
+  color:rgba(167, 182, 194, 0.6); }
+
+.bp3-dark .bp3-breadcrumb-current{
+  color:#f5f8fa; }
+
+.bp3-dark .bp3-breadcrumbs-collapsed{
+  background:rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-breadcrumbs-collapsed:hover{
+    background:rgba(16, 22, 26, 0.6);
+    color:#f5f8fa; }
+.bp3-button{
+  display:-webkit-inline-box;
+  display:-ms-inline-flexbox;
+  display:inline-flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  border:none;
+  border-radius:3px;
+  cursor:pointer;
+  padding:5px 10px;
+  vertical-align:middle;
+  text-align:left;
+  font-size:14px;
+  min-width:30px;
+  min-height:30px; }
+  .bp3-button > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-button > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
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+  .bp3-button > *{
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+  .bp3-button:empty::before,
+  .bp3-button > :last-child{
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+    cursor:not-allowed; }
+  .bp3-button.bp3-fill{
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    width:100%; }
+  .bp3-button.bp3-align-right,
+  .bp3-align-right .bp3-button{
+    text-align:right; }
+  .bp3-button.bp3-align-left,
+  .bp3-align-left .bp3-button{
+    text-align:left; }
+  .bp3-button:not([class*="bp3-intent-"]){
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+    background-color:#f5f8fa;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
+    color:#182026; }
+    .bp3-button:not([class*="bp3-intent-"]):hover{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+      background-clip:padding-box;
+      background-color:#ebf1f5; }
+    .bp3-button:not([class*="bp3-intent-"]):active, .bp3-button:not([class*="bp3-intent-"]).bp3-active{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+      background-color:#d8e1e8;
+      background-image:none; }
+    .bp3-button:not([class*="bp3-intent-"]):disabled, .bp3-button:not([class*="bp3-intent-"]).bp3-disabled{
+      outline:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background-color:rgba(206, 217, 224, 0.5);
+      background-image:none;
+      cursor:not-allowed;
+      color:rgba(92, 112, 128, 0.6); }
+      .bp3-button:not([class*="bp3-intent-"]):disabled.bp3-active, .bp3-button:not([class*="bp3-intent-"]):disabled.bp3-active:hover, .bp3-button:not([class*="bp3-intent-"]).bp3-disabled.bp3-active, .bp3-button:not([class*="bp3-intent-"]).bp3-disabled.bp3-active:hover{
+        background:rgba(206, 217, 224, 0.7); }
+  .bp3-button.bp3-intent-primary{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    background-color:#137cbd;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
+    color:#ffffff; }
+    .bp3-button.bp3-intent-primary:hover, .bp3-button.bp3-intent-primary:active, .bp3-button.bp3-intent-primary.bp3-active{
+      color:#ffffff; }
+    .bp3-button.bp3-intent-primary:hover{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+      background-color:#106ba3; }
+    .bp3-button.bp3-intent-primary:active, .bp3-button.bp3-intent-primary.bp3-active{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+      background-color:#0e5a8a;
+      background-image:none; }
+    .bp3-button.bp3-intent-primary:disabled, .bp3-button.bp3-intent-primary.bp3-disabled{
+      border-color:transparent;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background-color:rgba(19, 124, 189, 0.5);
+      background-image:none;
+      color:rgba(255, 255, 255, 0.6); }
+  .bp3-button.bp3-intent-success{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    background-color:#0f9960;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
+    color:#ffffff; }
+    .bp3-button.bp3-intent-success:hover, .bp3-button.bp3-intent-success:active, .bp3-button.bp3-intent-success.bp3-active{
+      color:#ffffff; }
+    .bp3-button.bp3-intent-success:hover{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+      background-color:#0d8050; }
+    .bp3-button.bp3-intent-success:active, .bp3-button.bp3-intent-success.bp3-active{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+      background-color:#0a6640;
+      background-image:none; }
+    .bp3-button.bp3-intent-success:disabled, .bp3-button.bp3-intent-success.bp3-disabled{
+      border-color:transparent;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background-color:rgba(15, 153, 96, 0.5);
+      background-image:none;
+      color:rgba(255, 255, 255, 0.6); }
+  .bp3-button.bp3-intent-warning{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    background-color:#d9822b;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
+    color:#ffffff; }
+    .bp3-button.bp3-intent-warning:hover, .bp3-button.bp3-intent-warning:active, .bp3-button.bp3-intent-warning.bp3-active{
+      color:#ffffff; }
+    .bp3-button.bp3-intent-warning:hover{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+      background-color:#bf7326; }
+    .bp3-button.bp3-intent-warning:active, .bp3-button.bp3-intent-warning.bp3-active{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+      background-color:#a66321;
+      background-image:none; }
+    .bp3-button.bp3-intent-warning:disabled, .bp3-button.bp3-intent-warning.bp3-disabled{
+      border-color:transparent;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background-color:rgba(217, 130, 43, 0.5);
+      background-image:none;
+      color:rgba(255, 255, 255, 0.6); }
+  .bp3-button.bp3-intent-danger{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    background-color:#db3737;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
+    color:#ffffff; }
+    .bp3-button.bp3-intent-danger:hover, .bp3-button.bp3-intent-danger:active, .bp3-button.bp3-intent-danger.bp3-active{
+      color:#ffffff; }
+    .bp3-button.bp3-intent-danger:hover{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+      background-color:#c23030; }
+    .bp3-button.bp3-intent-danger:active, .bp3-button.bp3-intent-danger.bp3-active{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+      background-color:#a82a2a;
+      background-image:none; }
+    .bp3-button.bp3-intent-danger:disabled, .bp3-button.bp3-intent-danger.bp3-disabled{
+      border-color:transparent;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background-color:rgba(219, 55, 55, 0.5);
+      background-image:none;
+      color:rgba(255, 255, 255, 0.6); }
+  .bp3-button[class*="bp3-intent-"] .bp3-button-spinner .bp3-spinner-head{
+    stroke:#ffffff; }
+  .bp3-button.bp3-large,
+  .bp3-large .bp3-button{
+    min-width:40px;
+    min-height:40px;
+    padding:5px 15px;
+    font-size:16px; }
+    .bp3-button.bp3-large::before,
+    .bp3-button.bp3-large > *,
+    .bp3-large .bp3-button::before,
+    .bp3-large .bp3-button > *{
+      margin-right:10px; }
+    .bp3-button.bp3-large:empty::before,
+    .bp3-button.bp3-large > :last-child,
+    .bp3-large .bp3-button:empty::before,
+    .bp3-large .bp3-button > :last-child{
+      margin-right:0; }
+  .bp3-button.bp3-small,
+  .bp3-small .bp3-button{
+    min-width:24px;
+    min-height:24px;
+    padding:0 7px; }
+  .bp3-button.bp3-loading{
+    position:relative; }
+    .bp3-button.bp3-loading[class*="bp3-icon-"]::before{
+      visibility:hidden; }
+    .bp3-button.bp3-loading .bp3-button-spinner{
+      position:absolute;
+      margin:0; }
+    .bp3-button.bp3-loading > :not(.bp3-button-spinner){
+      visibility:hidden; }
+  .bp3-button[class*="bp3-icon-"]::before{
+    line-height:1;
+    font-family:"Icons16", sans-serif;
+    font-size:16px;
+    font-weight:400;
+    font-style:normal;
+    -moz-osx-font-smoothing:grayscale;
+    -webkit-font-smoothing:antialiased;
+    color:#5c7080; }
+  .bp3-button .bp3-icon, .bp3-button .bp3-icon-standard, .bp3-button .bp3-icon-large{
+    color:#5c7080; }
+    .bp3-button .bp3-icon.bp3-align-right, .bp3-button .bp3-icon-standard.bp3-align-right, .bp3-button .bp3-icon-large.bp3-align-right{
+      margin-left:7px; }
+  .bp3-button .bp3-icon:first-child:last-child,
+  .bp3-button .bp3-spinner + .bp3-icon:last-child{
+    margin:0 -7px; }
+  .bp3-dark .bp3-button:not([class*="bp3-intent-"]){
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+    background-color:#394b59;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
+    color:#f5f8fa; }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"]):hover, .bp3-dark .bp3-button:not([class*="bp3-intent-"]):active, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-active{
+      color:#f5f8fa; }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"]):hover{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+      background-color:#30404d; }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"]):active, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-active{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+      background-color:#202b33;
+      background-image:none; }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"]):disabled, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background-color:rgba(57, 75, 89, 0.5);
+      background-image:none;
+      color:rgba(167, 182, 194, 0.6); }
+      .bp3-dark .bp3-button:not([class*="bp3-intent-"]):disabled.bp3-active, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-disabled.bp3-active{
+        background:rgba(57, 75, 89, 0.7); }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-button-spinner .bp3-spinner-head{
+      background:rgba(16, 22, 26, 0.5);
+      stroke:#8a9ba8; }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"])[class*="bp3-icon-"]::before{
+      color:#a7b6c2; }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-icon, .bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-icon-standard, .bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-icon-large{
+      color:#a7b6c2; }
+  .bp3-dark .bp3-button[class*="bp3-intent-"]{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-button[class*="bp3-intent-"]:hover{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-button[class*="bp3-intent-"]:active, .bp3-dark .bp3-button[class*="bp3-intent-"].bp3-active{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-dark .bp3-button[class*="bp3-intent-"]:disabled, .bp3-dark .bp3-button[class*="bp3-intent-"].bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background-image:none;
+      color:rgba(255, 255, 255, 0.3); }
+    .bp3-dark .bp3-button[class*="bp3-intent-"] .bp3-button-spinner .bp3-spinner-head{
+      stroke:#8a9ba8; }
+  .bp3-button:disabled::before,
+  .bp3-button:disabled .bp3-icon, .bp3-button:disabled .bp3-icon-standard, .bp3-button:disabled .bp3-icon-large, .bp3-button.bp3-disabled::before,
+  .bp3-button.bp3-disabled .bp3-icon, .bp3-button.bp3-disabled .bp3-icon-standard, .bp3-button.bp3-disabled .bp3-icon-large, .bp3-button[class*="bp3-intent-"]::before,
+  .bp3-button[class*="bp3-intent-"] .bp3-icon, .bp3-button[class*="bp3-intent-"] .bp3-icon-standard, .bp3-button[class*="bp3-intent-"] .bp3-icon-large{
+    color:inherit !important; }
+  .bp3-button.bp3-minimal{
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    background:none; }
+    .bp3-button.bp3-minimal:hover{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background:rgba(167, 182, 194, 0.3);
+      text-decoration:none;
+      color:#182026; }
+    .bp3-button.bp3-minimal:active, .bp3-button.bp3-minimal.bp3-active{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background:rgba(115, 134, 148, 0.3);
+      color:#182026; }
+    .bp3-button.bp3-minimal:disabled, .bp3-button.bp3-minimal:disabled:hover, .bp3-button.bp3-minimal.bp3-disabled, .bp3-button.bp3-minimal.bp3-disabled:hover{
+      background:none;
+      cursor:not-allowed;
+      color:rgba(92, 112, 128, 0.6); }
+      .bp3-button.bp3-minimal:disabled.bp3-active, .bp3-button.bp3-minimal:disabled:hover.bp3-active, .bp3-button.bp3-minimal.bp3-disabled.bp3-active, .bp3-button.bp3-minimal.bp3-disabled:hover.bp3-active{
+        background:rgba(115, 134, 148, 0.3); }
+    .bp3-dark .bp3-button.bp3-minimal{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background:none;
+      color:inherit; }
+      .bp3-dark .bp3-button.bp3-minimal:hover, .bp3-dark .bp3-button.bp3-minimal:active, .bp3-dark .bp3-button.bp3-minimal.bp3-active{
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        background:none; }
+      .bp3-dark .bp3-button.bp3-minimal:hover{
+        background:rgba(138, 155, 168, 0.15); }
+      .bp3-dark .bp3-button.bp3-minimal:active, .bp3-dark .bp3-button.bp3-minimal.bp3-active{
+        background:rgba(138, 155, 168, 0.3);
+        color:#f5f8fa; }
+      .bp3-dark .bp3-button.bp3-minimal:disabled, .bp3-dark .bp3-button.bp3-minimal:disabled:hover, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled:hover{
+        background:none;
+        cursor:not-allowed;
+        color:rgba(167, 182, 194, 0.6); }
+        .bp3-dark .bp3-button.bp3-minimal:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal:disabled:hover.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled:hover.bp3-active{
+          background:rgba(138, 155, 168, 0.3); }
+    .bp3-button.bp3-minimal.bp3-intent-primary{
+      color:#106ba3; }
+      .bp3-button.bp3-minimal.bp3-intent-primary:hover, .bp3-button.bp3-minimal.bp3-intent-primary:active, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-active{
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        background:none;
+        color:#106ba3; }
+      .bp3-button.bp3-minimal.bp3-intent-primary:hover{
+        background:rgba(19, 124, 189, 0.15);
+        color:#106ba3; }
+      .bp3-button.bp3-minimal.bp3-intent-primary:active, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-active{
+        background:rgba(19, 124, 189, 0.3);
+        color:#106ba3; }
+      .bp3-button.bp3-minimal.bp3-intent-primary:disabled, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled{
+        background:none;
+        color:rgba(16, 107, 163, 0.5); }
+        .bp3-button.bp3-minimal.bp3-intent-primary:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled.bp3-active{
+          background:rgba(19, 124, 189, 0.3); }
+      .bp3-button.bp3-minimal.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{
+        stroke:#106ba3; }
+      .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary{
+        color:#48aff0; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:hover{
+          background:rgba(19, 124, 189, 0.2);
+          color:#48aff0; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary.bp3-active{
+          background:rgba(19, 124, 189, 0.3);
+          color:#48aff0; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled{
+          background:none;
+          color:rgba(72, 175, 240, 0.5); }
+          .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled.bp3-active{
+            background:rgba(19, 124, 189, 0.3); }
+    .bp3-button.bp3-minimal.bp3-intent-success{
+      color:#0d8050; }
+      .bp3-button.bp3-minimal.bp3-intent-success:hover, .bp3-button.bp3-minimal.bp3-intent-success:active, .bp3-button.bp3-minimal.bp3-intent-success.bp3-active{
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        background:none;
+        color:#0d8050; }
+      .bp3-button.bp3-minimal.bp3-intent-success:hover{
+        background:rgba(15, 153, 96, 0.15);
+        color:#0d8050; }
+      .bp3-button.bp3-minimal.bp3-intent-success:active, .bp3-button.bp3-minimal.bp3-intent-success.bp3-active{
+        background:rgba(15, 153, 96, 0.3);
+        color:#0d8050; }
+      .bp3-button.bp3-minimal.bp3-intent-success:disabled, .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled{
+        background:none;
+        color:rgba(13, 128, 80, 0.5); }
+        .bp3-button.bp3-minimal.bp3-intent-success:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled.bp3-active{
+          background:rgba(15, 153, 96, 0.3); }
+      .bp3-button.bp3-minimal.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{
+        stroke:#0d8050; }
+      .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success{
+        color:#3dcc91; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:hover{
+          background:rgba(15, 153, 96, 0.2);
+          color:#3dcc91; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success.bp3-active{
+          background:rgba(15, 153, 96, 0.3);
+          color:#3dcc91; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled{
+          background:none;
+          color:rgba(61, 204, 145, 0.5); }
+          .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled.bp3-active{
+            background:rgba(15, 153, 96, 0.3); }
+    .bp3-button.bp3-minimal.bp3-intent-warning{
+      color:#bf7326; }
+      .bp3-button.bp3-minimal.bp3-intent-warning:hover, .bp3-button.bp3-minimal.bp3-intent-warning:active, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-active{
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        background:none;
+        color:#bf7326; }
+      .bp3-button.bp3-minimal.bp3-intent-warning:hover{
+        background:rgba(217, 130, 43, 0.15);
+        color:#bf7326; }
+      .bp3-button.bp3-minimal.bp3-intent-warning:active, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-active{
+        background:rgba(217, 130, 43, 0.3);
+        color:#bf7326; }
+      .bp3-button.bp3-minimal.bp3-intent-warning:disabled, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled{
+        background:none;
+        color:rgba(191, 115, 38, 0.5); }
+        .bp3-button.bp3-minimal.bp3-intent-warning:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled.bp3-active{
+          background:rgba(217, 130, 43, 0.3); }
+      .bp3-button.bp3-minimal.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{
+        stroke:#bf7326; }
+      .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning{
+        color:#ffb366; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:hover{
+          background:rgba(217, 130, 43, 0.2);
+          color:#ffb366; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning.bp3-active{
+          background:rgba(217, 130, 43, 0.3);
+          color:#ffb366; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled{
+          background:none;
+          color:rgba(255, 179, 102, 0.5); }
+          .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled.bp3-active{
+            background:rgba(217, 130, 43, 0.3); }
+    .bp3-button.bp3-minimal.bp3-intent-danger{
+      color:#c23030; }
+      .bp3-button.bp3-minimal.bp3-intent-danger:hover, .bp3-button.bp3-minimal.bp3-intent-danger:active, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-active{
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        background:none;
+        color:#c23030; }
+      .bp3-button.bp3-minimal.bp3-intent-danger:hover{
+        background:rgba(219, 55, 55, 0.15);
+        color:#c23030; }
+      .bp3-button.bp3-minimal.bp3-intent-danger:active, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-active{
+        background:rgba(219, 55, 55, 0.3);
+        color:#c23030; }
+      .bp3-button.bp3-minimal.bp3-intent-danger:disabled, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled{
+        background:none;
+        color:rgba(194, 48, 48, 0.5); }
+        .bp3-button.bp3-minimal.bp3-intent-danger:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled.bp3-active{
+          background:rgba(219, 55, 55, 0.3); }
+      .bp3-button.bp3-minimal.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{
+        stroke:#c23030; }
+      .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger{
+        color:#ff7373; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:hover{
+          background:rgba(219, 55, 55, 0.2);
+          color:#ff7373; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger.bp3-active{
+          background:rgba(219, 55, 55, 0.3);
+          color:#ff7373; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled{
+          background:none;
+          color:rgba(255, 115, 115, 0.5); }
+          .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled.bp3-active{
+            background:rgba(219, 55, 55, 0.3); }
+
+a.bp3-button{
+  text-align:center;
+  text-decoration:none;
+  -webkit-transition:none;
+  transition:none; }
+  a.bp3-button, a.bp3-button:hover, a.bp3-button:active{
+    color:#182026; }
+  a.bp3-button.bp3-disabled{
+    color:rgba(92, 112, 128, 0.6); }
+
+.bp3-button-text{
+  -webkit-box-flex:0;
+      -ms-flex:0 1 auto;
+          flex:0 1 auto; }
+
+.bp3-button.bp3-align-left .bp3-button-text, .bp3-button.bp3-align-right .bp3-button-text,
+.bp3-button-group.bp3-align-left .bp3-button-text,
+.bp3-button-group.bp3-align-right .bp3-button-text{
+  -webkit-box-flex:1;
+      -ms-flex:1 1 auto;
+          flex:1 1 auto; }
+.bp3-button-group{
+  display:-webkit-inline-box;
+  display:-ms-inline-flexbox;
+  display:inline-flex; }
+  .bp3-button-group .bp3-button{
+    -webkit-box-flex:0;
+        -ms-flex:0 0 auto;
+            flex:0 0 auto;
+    position:relative;
+    z-index:4; }
+    .bp3-button-group .bp3-button:focus{
+      z-index:5; }
+    .bp3-button-group .bp3-button:hover{
+      z-index:6; }
+    .bp3-button-group .bp3-button:active, .bp3-button-group .bp3-button.bp3-active{
+      z-index:7; }
+    .bp3-button-group .bp3-button:disabled, .bp3-button-group .bp3-button.bp3-disabled{
+      z-index:3; }
+    .bp3-button-group .bp3-button[class*="bp3-intent-"]{
+      z-index:9; }
+      .bp3-button-group .bp3-button[class*="bp3-intent-"]:focus{
+        z-index:10; }
+      .bp3-button-group .bp3-button[class*="bp3-intent-"]:hover{
+        z-index:11; }
+      .bp3-button-group .bp3-button[class*="bp3-intent-"]:active, .bp3-button-group .bp3-button[class*="bp3-intent-"].bp3-active{
+        z-index:12; }
+      .bp3-button-group .bp3-button[class*="bp3-intent-"]:disabled, .bp3-button-group .bp3-button[class*="bp3-intent-"].bp3-disabled{
+        z-index:8; }
+  .bp3-button-group:not(.bp3-minimal) > .bp3-popover-wrapper:not(:first-child) .bp3-button,
+  .bp3-button-group:not(.bp3-minimal) > .bp3-button:not(:first-child){
+    border-top-left-radius:0;
+    border-bottom-left-radius:0; }
+  .bp3-button-group:not(.bp3-minimal) > .bp3-popover-wrapper:not(:last-child) .bp3-button,
+  .bp3-button-group:not(.bp3-minimal) > .bp3-button:not(:last-child){
+    margin-right:-1px;
+    border-top-right-radius:0;
+    border-bottom-right-radius:0; }
+  .bp3-button-group.bp3-minimal .bp3-button{
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    background:none; }
+    .bp3-button-group.bp3-minimal .bp3-button:hover{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background:rgba(167, 182, 194, 0.3);
+      text-decoration:none;
+      color:#182026; }
+    .bp3-button-group.bp3-minimal .bp3-button:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-active{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background:rgba(115, 134, 148, 0.3);
+      color:#182026; }
+    .bp3-button-group.bp3-minimal .bp3-button:disabled, .bp3-button-group.bp3-minimal .bp3-button:disabled:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover{
+      background:none;
+      cursor:not-allowed;
+      color:rgba(92, 112, 128, 0.6); }
+      .bp3-button-group.bp3-minimal .bp3-button:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button:disabled:hover.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover.bp3-active{
+        background:rgba(115, 134, 148, 0.3); }
+    .bp3-dark .bp3-button-group.bp3-minimal .bp3-button{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background:none;
+      color:inherit; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:hover, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-active{
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        background:none; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:hover{
+        background:rgba(138, 155, 168, 0.15); }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-active{
+        background:rgba(138, 155, 168, 0.3);
+        color:#f5f8fa; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled:hover, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover{
+        background:none;
+        cursor:not-allowed;
+        color:rgba(167, 182, 194, 0.6); }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled:hover.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover.bp3-active{
+          background:rgba(138, 155, 168, 0.3); }
+    .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary{
+      color:#106ba3; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-active{
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        background:none;
+        color:#106ba3; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:hover{
+        background:rgba(19, 124, 189, 0.15);
+        color:#106ba3; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-active{
+        background:rgba(19, 124, 189, 0.3);
+        color:#106ba3; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled{
+        background:none;
+        color:rgba(16, 107, 163, 0.5); }
+        .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled.bp3-active{
+          background:rgba(19, 124, 189, 0.3); }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{
+        stroke:#106ba3; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary{
+        color:#48aff0; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:hover{
+          background:rgba(19, 124, 189, 0.2);
+          color:#48aff0; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-active{
+          background:rgba(19, 124, 189, 0.3);
+          color:#48aff0; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled{
+          background:none;
+          color:rgba(72, 175, 240, 0.5); }
+          .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled.bp3-active{
+            background:rgba(19, 124, 189, 0.3); }
+    .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success{
+      color:#0d8050; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-active{
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        background:none;
+        color:#0d8050; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:hover{
+        background:rgba(15, 153, 96, 0.15);
+        color:#0d8050; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-active{
+        background:rgba(15, 153, 96, 0.3);
+        color:#0d8050; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled{
+        background:none;
+        color:rgba(13, 128, 80, 0.5); }
+        .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled.bp3-active{
+          background:rgba(15, 153, 96, 0.3); }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{
+        stroke:#0d8050; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success{
+        color:#3dcc91; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:hover{
+          background:rgba(15, 153, 96, 0.2);
+          color:#3dcc91; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-active{
+          background:rgba(15, 153, 96, 0.3);
+          color:#3dcc91; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled{
+          background:none;
+          color:rgba(61, 204, 145, 0.5); }
+          .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled.bp3-active{
+            background:rgba(15, 153, 96, 0.3); }
+    .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning{
+      color:#bf7326; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-active{
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        background:none;
+        color:#bf7326; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:hover{
+        background:rgba(217, 130, 43, 0.15);
+        color:#bf7326; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-active{
+        background:rgba(217, 130, 43, 0.3);
+        color:#bf7326; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled{
+        background:none;
+        color:rgba(191, 115, 38, 0.5); }
+        .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled.bp3-active{
+          background:rgba(217, 130, 43, 0.3); }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{
+        stroke:#bf7326; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning{
+        color:#ffb366; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:hover{
+          background:rgba(217, 130, 43, 0.2);
+          color:#ffb366; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-active{
+          background:rgba(217, 130, 43, 0.3);
+          color:#ffb366; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled{
+          background:none;
+          color:rgba(255, 179, 102, 0.5); }
+          .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled.bp3-active{
+            background:rgba(217, 130, 43, 0.3); }
+    .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger{
+      color:#c23030; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-active{
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        background:none;
+        color:#c23030; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:hover{
+        background:rgba(219, 55, 55, 0.15);
+        color:#c23030; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-active{
+        background:rgba(219, 55, 55, 0.3);
+        color:#c23030; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled{
+        background:none;
+        color:rgba(194, 48, 48, 0.5); }
+        .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled.bp3-active{
+          background:rgba(219, 55, 55, 0.3); }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{
+        stroke:#c23030; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger{
+        color:#ff7373; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:hover{
+          background:rgba(219, 55, 55, 0.2);
+          color:#ff7373; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-active{
+          background:rgba(219, 55, 55, 0.3);
+          color:#ff7373; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled{
+          background:none;
+          color:rgba(255, 115, 115, 0.5); }
+          .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled.bp3-active{
+            background:rgba(219, 55, 55, 0.3); }
+  .bp3-button-group .bp3-popover-wrapper,
+  .bp3-button-group .bp3-popover-target{
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto; }
+  .bp3-button-group.bp3-fill{
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    width:100%; }
+  .bp3-button-group .bp3-button.bp3-fill,
+  .bp3-button-group.bp3-fill .bp3-button:not(.bp3-fixed){
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto; }
+  .bp3-button-group.bp3-vertical{
+    -webkit-box-orient:vertical;
+    -webkit-box-direction:normal;
+        -ms-flex-direction:column;
+            flex-direction:column;
+    -webkit-box-align:stretch;
+        -ms-flex-align:stretch;
+            align-items:stretch;
+    vertical-align:top; }
+    .bp3-button-group.bp3-vertical.bp3-fill{
+      width:unset;
+      height:100%; }
+    .bp3-button-group.bp3-vertical .bp3-button{
+      margin-right:0 !important;
+      width:100%; }
+    .bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-popover-wrapper:first-child .bp3-button,
+    .bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-button:first-child{
+      border-radius:3px 3px 0 0; }
+    .bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-popover-wrapper:last-child .bp3-button,
+    .bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-button:last-child{
+      border-radius:0 0 3px 3px; }
+    .bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-popover-wrapper:not(:last-child) .bp3-button,
+    .bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-button:not(:last-child){
+      margin-bottom:-1px; }
+  .bp3-button-group.bp3-align-left .bp3-button{
+    text-align:left; }
+  .bp3-dark .bp3-button-group:not(.bp3-minimal) > .bp3-popover-wrapper:not(:last-child) .bp3-button,
+  .bp3-dark .bp3-button-group:not(.bp3-minimal) > .bp3-button:not(:last-child){
+    margin-right:1px; }
+  .bp3-dark .bp3-button-group.bp3-vertical > .bp3-popover-wrapper:not(:last-child) .bp3-button,
+  .bp3-dark .bp3-button-group.bp3-vertical > .bp3-button:not(:last-child){
+    margin-bottom:1px; }
+.bp3-callout{
+  line-height:1.5;
+  font-size:14px;
+  position:relative;
+  border-radius:3px;
+  background-color:rgba(138, 155, 168, 0.15);
+  width:100%;
+  padding:10px 12px 9px; }
+  .bp3-callout[class*="bp3-icon-"]{
+    padding-left:40px; }
+    .bp3-callout[class*="bp3-icon-"]::before{
+      line-height:1;
+      font-family:"Icons20", sans-serif;
+      font-size:20px;
+      font-weight:400;
+      font-style:normal;
+      -moz-osx-font-smoothing:grayscale;
+      -webkit-font-smoothing:antialiased;
+      position:absolute;
+      top:10px;
+      left:10px;
+      color:#5c7080; }
+  .bp3-callout.bp3-callout-icon{
+    padding-left:40px; }
+    .bp3-callout.bp3-callout-icon > .bp3-icon:first-child{
+      position:absolute;
+      top:10px;
+      left:10px;
+      color:#5c7080; }
+  .bp3-callout .bp3-heading{
+    margin-top:0;
+    margin-bottom:5px;
+    line-height:20px; }
+    .bp3-callout .bp3-heading:last-child{
+      margin-bottom:0; }
+  .bp3-dark .bp3-callout{
+    background-color:rgba(138, 155, 168, 0.2); }
+    .bp3-dark .bp3-callout[class*="bp3-icon-"]::before{
+      color:#a7b6c2; }
+  .bp3-callout.bp3-intent-primary{
+    background-color:rgba(19, 124, 189, 0.15); }
+    .bp3-callout.bp3-intent-primary[class*="bp3-icon-"]::before,
+    .bp3-callout.bp3-intent-primary > .bp3-icon:first-child,
+    .bp3-callout.bp3-intent-primary .bp3-heading{
+      color:#106ba3; }
+    .bp3-dark .bp3-callout.bp3-intent-primary{
+      background-color:rgba(19, 124, 189, 0.25); }
+      .bp3-dark .bp3-callout.bp3-intent-primary[class*="bp3-icon-"]::before,
+      .bp3-dark .bp3-callout.bp3-intent-primary > .bp3-icon:first-child,
+      .bp3-dark .bp3-callout.bp3-intent-primary .bp3-heading{
+        color:#48aff0; }
+  .bp3-callout.bp3-intent-success{
+    background-color:rgba(15, 153, 96, 0.15); }
+    .bp3-callout.bp3-intent-success[class*="bp3-icon-"]::before,
+    .bp3-callout.bp3-intent-success > .bp3-icon:first-child,
+    .bp3-callout.bp3-intent-success .bp3-heading{
+      color:#0d8050; }
+    .bp3-dark .bp3-callout.bp3-intent-success{
+      background-color:rgba(15, 153, 96, 0.25); }
+      .bp3-dark .bp3-callout.bp3-intent-success[class*="bp3-icon-"]::before,
+      .bp3-dark .bp3-callout.bp3-intent-success > .bp3-icon:first-child,
+      .bp3-dark .bp3-callout.bp3-intent-success .bp3-heading{
+        color:#3dcc91; }
+  .bp3-callout.bp3-intent-warning{
+    background-color:rgba(217, 130, 43, 0.15); }
+    .bp3-callout.bp3-intent-warning[class*="bp3-icon-"]::before,
+    .bp3-callout.bp3-intent-warning > .bp3-icon:first-child,
+    .bp3-callout.bp3-intent-warning .bp3-heading{
+      color:#bf7326; }
+    .bp3-dark .bp3-callout.bp3-intent-warning{
+      background-color:rgba(217, 130, 43, 0.25); }
+      .bp3-dark .bp3-callout.bp3-intent-warning[class*="bp3-icon-"]::before,
+      .bp3-dark .bp3-callout.bp3-intent-warning > .bp3-icon:first-child,
+      .bp3-dark .bp3-callout.bp3-intent-warning .bp3-heading{
+        color:#ffb366; }
+  .bp3-callout.bp3-intent-danger{
+    background-color:rgba(219, 55, 55, 0.15); }
+    .bp3-callout.bp3-intent-danger[class*="bp3-icon-"]::before,
+    .bp3-callout.bp3-intent-danger > .bp3-icon:first-child,
+    .bp3-callout.bp3-intent-danger .bp3-heading{
+      color:#c23030; }
+    .bp3-dark .bp3-callout.bp3-intent-danger{
+      background-color:rgba(219, 55, 55, 0.25); }
+      .bp3-dark .bp3-callout.bp3-intent-danger[class*="bp3-icon-"]::before,
+      .bp3-dark .bp3-callout.bp3-intent-danger > .bp3-icon:first-child,
+      .bp3-dark .bp3-callout.bp3-intent-danger .bp3-heading{
+        color:#ff7373; }
+  .bp3-running-text .bp3-callout{
+    margin:20px 0; }
+.bp3-card{
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
+  background-color:#ffffff;
+  padding:20px;
+  -webkit-transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-card.bp3-dark,
+  .bp3-dark .bp3-card{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
+    background-color:#30404d; }
+
+.bp3-elevation-0{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); }
+  .bp3-elevation-0.bp3-dark,
+  .bp3-dark .bp3-elevation-0{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); }
+
+.bp3-elevation-1{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-elevation-1.bp3-dark,
+  .bp3-dark .bp3-elevation-1{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }
+
+.bp3-elevation-2{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 1px 1px rgba(16, 22, 26, 0.2), 0 2px 6px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 1px 1px rgba(16, 22, 26, 0.2), 0 2px 6px rgba(16, 22, 26, 0.2); }
+  .bp3-elevation-2.bp3-dark,
+  .bp3-dark .bp3-elevation-2{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.4), 0 2px 6px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.4), 0 2px 6px rgba(16, 22, 26, 0.4); }
+
+.bp3-elevation-3{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2); }
+  .bp3-elevation-3.bp3-dark,
+  .bp3-dark .bp3-elevation-3{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
+
+.bp3-elevation-4{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2); }
+  .bp3-elevation-4.bp3-dark,
+  .bp3-dark .bp3-elevation-4{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4); }
+
+.bp3-card.bp3-interactive:hover{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+  cursor:pointer; }
+  .bp3-card.bp3-interactive:hover.bp3-dark,
+  .bp3-dark .bp3-card.bp3-interactive:hover{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
+
+.bp3-card.bp3-interactive:active{
+  opacity:0.9;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+  -webkit-transition-duration:0;
+          transition-duration:0; }
+  .bp3-card.bp3-interactive:active.bp3-dark,
+  .bp3-dark .bp3-card.bp3-interactive:active{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }
+
+.bp3-collapse{
+  height:0;
+  overflow-y:hidden;
+  -webkit-transition:height 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:height 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-collapse .bp3-collapse-body{
+    -webkit-transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+    .bp3-collapse .bp3-collapse-body[aria-hidden="true"]{
+      display:none; }
+
+.bp3-context-menu .bp3-popover-target{
+  display:block; }
+
+.bp3-context-menu-popover-target{
+  position:fixed; }
+
+.bp3-divider{
+  margin:5px;
+  border-right:1px solid rgba(16, 22, 26, 0.15);
+  border-bottom:1px solid rgba(16, 22, 26, 0.15); }
+  .bp3-dark .bp3-divider{
+    border-color:rgba(16, 22, 26, 0.4); }
+.bp3-dialog-container{
+  opacity:1;
+  -webkit-transform:scale(1);
+          transform:scale(1);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  width:100%;
+  min-height:100%;
+  pointer-events:none;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-dialog-container.bp3-overlay-enter > .bp3-dialog, .bp3-dialog-container.bp3-overlay-appear > .bp3-dialog{
+    opacity:0;
+    -webkit-transform:scale(0.5);
+            transform:scale(0.5); }
+  .bp3-dialog-container.bp3-overlay-enter-active > .bp3-dialog, .bp3-dialog-container.bp3-overlay-appear-active > .bp3-dialog{
+    opacity:1;
+    -webkit-transform:scale(1);
+            transform:scale(1);
+    -webkit-transition-property:opacity, -webkit-transform;
+    transition-property:opacity, -webkit-transform;
+    transition-property:opacity, transform;
+    transition-property:opacity, transform, -webkit-transform;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+            transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+    -webkit-transition-delay:0;
+            transition-delay:0; }
+  .bp3-dialog-container.bp3-overlay-exit > .bp3-dialog{
+    opacity:1;
+    -webkit-transform:scale(1);
+            transform:scale(1); }
+  .bp3-dialog-container.bp3-overlay-exit-active > .bp3-dialog{
+    opacity:0;
+    -webkit-transform:scale(0.5);
+            transform:scale(0.5);
+    -webkit-transition-property:opacity, -webkit-transform;
+    transition-property:opacity, -webkit-transform;
+    transition-property:opacity, transform;
+    transition-property:opacity, transform, -webkit-transform;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+            transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+    -webkit-transition-delay:0;
+            transition-delay:0; }
+
+.bp3-dialog{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  margin:30px 0;
+  border-radius:6px;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+  background:#ebf1f5;
+  width:500px;
+  padding-bottom:20px;
+  pointer-events:all;
+  -webkit-user-select:text;
+     -moz-user-select:text;
+      -ms-user-select:text;
+          user-select:text; }
+  .bp3-dialog:focus{
+    outline:0; }
+  .bp3-dialog.bp3-dark,
+  .bp3-dark .bp3-dialog{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+    background:#293742;
+    color:#f5f8fa; }
+
+.bp3-dialog-header{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  border-radius:6px 6px 0 0;
+  -webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);
+          box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);
+  background:#ffffff;
+  min-height:40px;
+  padding-right:5px;
+  padding-left:20px; }
+  .bp3-dialog-header .bp3-icon-large,
+  .bp3-dialog-header .bp3-icon{
+    -webkit-box-flex:0;
+        -ms-flex:0 0 auto;
+            flex:0 0 auto;
+    margin-right:10px;
+    color:#5c7080; }
+  .bp3-dialog-header .bp3-heading{
+    overflow:hidden;
+    text-overflow:ellipsis;
+    white-space:nowrap;
+    word-wrap:normal;
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto;
+    margin:0;
+    line-height:inherit; }
+    .bp3-dialog-header .bp3-heading:last-child{
+      margin-right:20px; }
+  .bp3-dark .bp3-dialog-header{
+    -webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.4);
+            box-shadow:0 1px 0 rgba(16, 22, 26, 0.4);
+    background:#30404d; }
+    .bp3-dark .bp3-dialog-header .bp3-icon-large,
+    .bp3-dark .bp3-dialog-header .bp3-icon{
+      color:#a7b6c2; }
+
+.bp3-dialog-body{
+  -webkit-box-flex:1;
+      -ms-flex:1 1 auto;
+          flex:1 1 auto;
+  margin:20px;
+  line-height:18px; }
+
+.bp3-dialog-footer{
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  margin:0 20px; }
+
+.bp3-dialog-footer-actions{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-pack:end;
+      -ms-flex-pack:end;
+          justify-content:flex-end; }
+  .bp3-dialog-footer-actions .bp3-button{
+    margin-left:10px; }
+.bp3-drawer{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  margin:0;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+  background:#ffffff;
+  padding:0; }
+  .bp3-drawer:focus{
+    outline:0; }
+  .bp3-drawer.bp3-position-top{
+    top:0;
+    right:0;
+    left:0;
+    height:50%; }
+    .bp3-drawer.bp3-position-top.bp3-overlay-enter, .bp3-drawer.bp3-position-top.bp3-overlay-appear{
+      -webkit-transform:translateY(-100%);
+              transform:translateY(-100%); }
+    .bp3-drawer.bp3-position-top.bp3-overlay-enter-active, .bp3-drawer.bp3-position-top.bp3-overlay-appear-active{
+      -webkit-transform:translateY(0);
+              transform:translateY(0);
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-duration:200ms;
+              transition-duration:200ms;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+      -webkit-transition-delay:0;
+              transition-delay:0; }
+    .bp3-drawer.bp3-position-top.bp3-overlay-exit{
+      -webkit-transform:translateY(0);
+              transform:translateY(0); }
+    .bp3-drawer.bp3-position-top.bp3-overlay-exit-active{
+      -webkit-transform:translateY(-100%);
+              transform:translateY(-100%);
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-duration:100ms;
+              transition-duration:100ms;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+      -webkit-transition-delay:0;
+              transition-delay:0; }
+  .bp3-drawer.bp3-position-bottom{
+    right:0;
+    bottom:0;
+    left:0;
+    height:50%; }
+    .bp3-drawer.bp3-position-bottom.bp3-overlay-enter, .bp3-drawer.bp3-position-bottom.bp3-overlay-appear{
+      -webkit-transform:translateY(100%);
+              transform:translateY(100%); }
+    .bp3-drawer.bp3-position-bottom.bp3-overlay-enter-active, .bp3-drawer.bp3-position-bottom.bp3-overlay-appear-active{
+      -webkit-transform:translateY(0);
+              transform:translateY(0);
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-duration:200ms;
+              transition-duration:200ms;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+      -webkit-transition-delay:0;
+              transition-delay:0; }
+    .bp3-drawer.bp3-position-bottom.bp3-overlay-exit{
+      -webkit-transform:translateY(0);
+              transform:translateY(0); }
+    .bp3-drawer.bp3-position-bottom.bp3-overlay-exit-active{
+      -webkit-transform:translateY(100%);
+              transform:translateY(100%);
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-duration:100ms;
+              transition-duration:100ms;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+      -webkit-transition-delay:0;
+              transition-delay:0; }
+  .bp3-drawer.bp3-position-left{
+    top:0;
+    bottom:0;
+    left:0;
+    width:50%; }
+    .bp3-drawer.bp3-position-left.bp3-overlay-enter, .bp3-drawer.bp3-position-left.bp3-overlay-appear{
+      -webkit-transform:translateX(-100%);
+              transform:translateX(-100%); }
+    .bp3-drawer.bp3-position-left.bp3-overlay-enter-active, .bp3-drawer.bp3-position-left.bp3-overlay-appear-active{
+      -webkit-transform:translateX(0);
+              transform:translateX(0);
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-duration:200ms;
+              transition-duration:200ms;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+      -webkit-transition-delay:0;
+              transition-delay:0; }
+    .bp3-drawer.bp3-position-left.bp3-overlay-exit{
+      -webkit-transform:translateX(0);
+              transform:translateX(0); }
+    .bp3-drawer.bp3-position-left.bp3-overlay-exit-active{
+      -webkit-transform:translateX(-100%);
+              transform:translateX(-100%);
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-duration:100ms;
+              transition-duration:100ms;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+      -webkit-transition-delay:0;
+              transition-delay:0; }
+  .bp3-drawer.bp3-position-right{
+    top:0;
+    right:0;
+    bottom:0;
+    width:50%; }
+    .bp3-drawer.bp3-position-right.bp3-overlay-enter, .bp3-drawer.bp3-position-right.bp3-overlay-appear{
+      -webkit-transform:translateX(100%);
+              transform:translateX(100%); }
+    .bp3-drawer.bp3-position-right.bp3-overlay-enter-active, .bp3-drawer.bp3-position-right.bp3-overlay-appear-active{
+      -webkit-transform:translateX(0);
+              transform:translateX(0);
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-duration:200ms;
+              transition-duration:200ms;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+      -webkit-transition-delay:0;
+              transition-delay:0; }
+    .bp3-drawer.bp3-position-right.bp3-overlay-exit{
+      -webkit-transform:translateX(0);
+              transform:translateX(0); }
+    .bp3-drawer.bp3-position-right.bp3-overlay-exit-active{
+      -webkit-transform:translateX(100%);
+              transform:translateX(100%);
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-duration:100ms;
+              transition-duration:100ms;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+      -webkit-transition-delay:0;
+              transition-delay:0; }
+  .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+  .bp3-position-right):not(.bp3-vertical){
+    top:0;
+    right:0;
+    bottom:0;
+    width:50%; }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right):not(.bp3-vertical).bp3-overlay-enter, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right):not(.bp3-vertical).bp3-overlay-appear{
+      -webkit-transform:translateX(100%);
+              transform:translateX(100%); }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right):not(.bp3-vertical).bp3-overlay-enter-active, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right):not(.bp3-vertical).bp3-overlay-appear-active{
+      -webkit-transform:translateX(0);
+              transform:translateX(0);
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-duration:200ms;
+              transition-duration:200ms;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+      -webkit-transition-delay:0;
+              transition-delay:0; }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right):not(.bp3-vertical).bp3-overlay-exit{
+      -webkit-transform:translateX(0);
+              transform:translateX(0); }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right):not(.bp3-vertical).bp3-overlay-exit-active{
+      -webkit-transform:translateX(100%);
+              transform:translateX(100%);
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-duration:100ms;
+              transition-duration:100ms;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+      -webkit-transition-delay:0;
+              transition-delay:0; }
+  .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+  .bp3-position-right).bp3-vertical{
+    right:0;
+    bottom:0;
+    left:0;
+    height:50%; }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right).bp3-vertical.bp3-overlay-enter, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right).bp3-vertical.bp3-overlay-appear{
+      -webkit-transform:translateY(100%);
+              transform:translateY(100%); }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right).bp3-vertical.bp3-overlay-enter-active, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right).bp3-vertical.bp3-overlay-appear-active{
+      -webkit-transform:translateY(0);
+              transform:translateY(0);
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-duration:200ms;
+              transition-duration:200ms;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+      -webkit-transition-delay:0;
+              transition-delay:0; }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right).bp3-vertical.bp3-overlay-exit{
+      -webkit-transform:translateY(0);
+              transform:translateY(0); }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right).bp3-vertical.bp3-overlay-exit-active{
+      -webkit-transform:translateY(100%);
+              transform:translateY(100%);
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-duration:100ms;
+              transition-duration:100ms;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+      -webkit-transition-delay:0;
+              transition-delay:0; }
+  .bp3-drawer.bp3-dark,
+  .bp3-dark .bp3-drawer{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+    background:#30404d;
+    color:#f5f8fa; }
+
+.bp3-drawer-header{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  position:relative;
+  border-radius:0;
+  -webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);
+          box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);
+  min-height:40px;
+  padding:5px;
+  padding-left:20px; }
+  .bp3-drawer-header .bp3-icon-large,
+  .bp3-drawer-header .bp3-icon{
+    -webkit-box-flex:0;
+        -ms-flex:0 0 auto;
+            flex:0 0 auto;
+    margin-right:10px;
+    color:#5c7080; }
+  .bp3-drawer-header .bp3-heading{
+    overflow:hidden;
+    text-overflow:ellipsis;
+    white-space:nowrap;
+    word-wrap:normal;
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto;
+    margin:0;
+    line-height:inherit; }
+    .bp3-drawer-header .bp3-heading:last-child{
+      margin-right:20px; }
+  .bp3-dark .bp3-drawer-header{
+    -webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.4);
+            box-shadow:0 1px 0 rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-drawer-header .bp3-icon-large,
+    .bp3-dark .bp3-drawer-header .bp3-icon{
+      color:#a7b6c2; }
+
+.bp3-drawer-body{
+  -webkit-box-flex:1;
+      -ms-flex:1 1 auto;
+          flex:1 1 auto;
+  overflow:auto;
+  line-height:18px; }
+
+.bp3-drawer-footer{
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  position:relative;
+  -webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);
+          box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);
+  padding:10px 20px; }
+  .bp3-dark .bp3-drawer-footer{
+    -webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.4);
+            box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.4); }
+.bp3-editable-text{
+  display:inline-block;
+  position:relative;
+  cursor:text;
+  max-width:100%;
+  vertical-align:top;
+  white-space:nowrap; }
+  .bp3-editable-text::before{
+    position:absolute;
+    top:-3px;
+    right:-3px;
+    bottom:-3px;
+    left:-3px;
+    border-radius:3px;
+    content:"";
+    -webkit-transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-editable-text:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15); }
+  .bp3-editable-text.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+    background-color:#ffffff; }
+  .bp3-editable-text.bp3-disabled::before{
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+  .bp3-editable-text.bp3-intent-primary .bp3-editable-text-input,
+  .bp3-editable-text.bp3-intent-primary .bp3-editable-text-content{
+    color:#137cbd; }
+  .bp3-editable-text.bp3-intent-primary:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(19, 124, 189, 0.4);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(19, 124, 189, 0.4); }
+  .bp3-editable-text.bp3-intent-primary.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-editable-text.bp3-intent-success .bp3-editable-text-input,
+  .bp3-editable-text.bp3-intent-success .bp3-editable-text-content{
+    color:#0f9960; }
+  .bp3-editable-text.bp3-intent-success:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px rgba(15, 153, 96, 0.4);
+            box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px rgba(15, 153, 96, 0.4); }
+  .bp3-editable-text.bp3-intent-success.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-editable-text.bp3-intent-warning .bp3-editable-text-input,
+  .bp3-editable-text.bp3-intent-warning .bp3-editable-text-content{
+    color:#d9822b; }
+  .bp3-editable-text.bp3-intent-warning:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px rgba(217, 130, 43, 0.4);
+            box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px rgba(217, 130, 43, 0.4); }
+  .bp3-editable-text.bp3-intent-warning.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-editable-text.bp3-intent-danger .bp3-editable-text-input,
+  .bp3-editable-text.bp3-intent-danger .bp3-editable-text-content{
+    color:#db3737; }
+  .bp3-editable-text.bp3-intent-danger:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px rgba(219, 55, 55, 0.4);
+            box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px rgba(219, 55, 55, 0.4); }
+  .bp3-editable-text.bp3-intent-danger.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-dark .bp3-editable-text:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(255, 255, 255, 0.15);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(255, 255, 255, 0.15); }
+  .bp3-dark .bp3-editable-text.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+    background-color:rgba(16, 22, 26, 0.3); }
+  .bp3-dark .bp3-editable-text.bp3-disabled::before{
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+  .bp3-dark .bp3-editable-text.bp3-intent-primary .bp3-editable-text-content{
+    color:#48aff0; }
+  .bp3-dark .bp3-editable-text.bp3-intent-primary:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(72, 175, 240, 0), 0 0 0 0 rgba(72, 175, 240, 0), inset 0 0 0 1px rgba(72, 175, 240, 0.4);
+            box-shadow:0 0 0 0 rgba(72, 175, 240, 0), 0 0 0 0 rgba(72, 175, 240, 0), inset 0 0 0 1px rgba(72, 175, 240, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-primary.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #48aff0, 0 0 0 3px rgba(72, 175, 240, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px #48aff0, 0 0 0 3px rgba(72, 175, 240, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-success .bp3-editable-text-content{
+    color:#3dcc91; }
+  .bp3-dark .bp3-editable-text.bp3-intent-success:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(61, 204, 145, 0), 0 0 0 0 rgba(61, 204, 145, 0), inset 0 0 0 1px rgba(61, 204, 145, 0.4);
+            box-shadow:0 0 0 0 rgba(61, 204, 145, 0), 0 0 0 0 rgba(61, 204, 145, 0), inset 0 0 0 1px rgba(61, 204, 145, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-success.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #3dcc91, 0 0 0 3px rgba(61, 204, 145, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px #3dcc91, 0 0 0 3px rgba(61, 204, 145, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-warning .bp3-editable-text-content{
+    color:#ffb366; }
+  .bp3-dark .bp3-editable-text.bp3-intent-warning:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(255, 179, 102, 0), 0 0 0 0 rgba(255, 179, 102, 0), inset 0 0 0 1px rgba(255, 179, 102, 0.4);
+            box-shadow:0 0 0 0 rgba(255, 179, 102, 0), 0 0 0 0 rgba(255, 179, 102, 0), inset 0 0 0 1px rgba(255, 179, 102, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-warning.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #ffb366, 0 0 0 3px rgba(255, 179, 102, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px #ffb366, 0 0 0 3px rgba(255, 179, 102, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-danger .bp3-editable-text-content{
+    color:#ff7373; }
+  .bp3-dark .bp3-editable-text.bp3-intent-danger:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(255, 115, 115, 0), 0 0 0 0 rgba(255, 115, 115, 0), inset 0 0 0 1px rgba(255, 115, 115, 0.4);
+            box-shadow:0 0 0 0 rgba(255, 115, 115, 0), 0 0 0 0 rgba(255, 115, 115, 0), inset 0 0 0 1px rgba(255, 115, 115, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-danger.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #ff7373, 0 0 0 3px rgba(255, 115, 115, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px #ff7373, 0 0 0 3px rgba(255, 115, 115, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+
+.bp3-editable-text-input,
+.bp3-editable-text-content{
+  display:inherit;
+  position:relative;
+  min-width:inherit;
+  max-width:inherit;
+  vertical-align:top;
+  text-transform:inherit;
+  letter-spacing:inherit;
+  color:inherit;
+  font:inherit;
+  resize:none; }
+
+.bp3-editable-text-input{
+  border:none;
+  -webkit-box-shadow:none;
+          box-shadow:none;
+  background:none;
+  width:100%;
+  padding:0;
+  white-space:pre-wrap; }
+  .bp3-editable-text-input::-webkit-input-placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-editable-text-input::-moz-placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-editable-text-input:-ms-input-placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-editable-text-input::-ms-input-placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-editable-text-input::placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-editable-text-input:focus{
+    outline:none; }
+  .bp3-editable-text-input::-ms-clear{
+    display:none; }
+
+.bp3-editable-text-content{
+  overflow:hidden;
+  padding-right:2px;
+  text-overflow:ellipsis;
+  white-space:pre; }
+  .bp3-editable-text-editing > .bp3-editable-text-content{
+    position:absolute;
+    left:0;
+    visibility:hidden; }
+  .bp3-editable-text-placeholder > .bp3-editable-text-content{
+    color:rgba(92, 112, 128, 0.6); }
+    .bp3-dark .bp3-editable-text-placeholder > .bp3-editable-text-content{
+      color:rgba(167, 182, 194, 0.6); }
+
+.bp3-editable-text.bp3-multiline{
+  display:block; }
+  .bp3-editable-text.bp3-multiline .bp3-editable-text-content{
+    overflow:auto;
+    white-space:pre-wrap;
+    word-wrap:break-word; }
+.bp3-control-group{
+  -webkit-transform:translateZ(0);
+          transform:translateZ(0);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:stretch;
+      -ms-flex-align:stretch;
+          align-items:stretch; }
+  .bp3-control-group > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-control-group > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-control-group .bp3-button,
+  .bp3-control-group .bp3-html-select,
+  .bp3-control-group .bp3-input,
+  .bp3-control-group .bp3-select{
+    position:relative; }
+  .bp3-control-group .bp3-input{
+    z-index:2;
+    border-radius:inherit; }
+    .bp3-control-group .bp3-input:focus{
+      z-index:14;
+      border-radius:3px; }
+    .bp3-control-group .bp3-input[class*="bp3-intent"]{
+      z-index:13; }
+      .bp3-control-group .bp3-input[class*="bp3-intent"]:focus{
+        z-index:15; }
+    .bp3-control-group .bp3-input[readonly], .bp3-control-group .bp3-input:disabled, .bp3-control-group .bp3-input.bp3-disabled{
+      z-index:1; }
+  .bp3-control-group .bp3-input-group[class*="bp3-intent"] .bp3-input{
+    z-index:13; }
+    .bp3-control-group .bp3-input-group[class*="bp3-intent"] .bp3-input:focus{
+      z-index:15; }
+  .bp3-control-group .bp3-button,
+  .bp3-control-group .bp3-html-select select,
+  .bp3-control-group .bp3-select select{
+    -webkit-transform:translateZ(0);
+            transform:translateZ(0);
+    z-index:4;
+    border-radius:inherit; }
+    .bp3-control-group .bp3-button:focus,
+    .bp3-control-group .bp3-html-select select:focus,
+    .bp3-control-group .bp3-select select:focus{
+      z-index:5; }
+    .bp3-control-group .bp3-button:hover,
+    .bp3-control-group .bp3-html-select select:hover,
+    .bp3-control-group .bp3-select select:hover{
+      z-index:6; }
+    .bp3-control-group .bp3-button:active,
+    .bp3-control-group .bp3-html-select select:active,
+    .bp3-control-group .bp3-select select:active{
+      z-index:7; }
+    .bp3-control-group .bp3-button[readonly], .bp3-control-group .bp3-button:disabled, .bp3-control-group .bp3-button.bp3-disabled,
+    .bp3-control-group .bp3-html-select select[readonly],
+    .bp3-control-group .bp3-html-select select:disabled,
+    .bp3-control-group .bp3-html-select select.bp3-disabled,
+    .bp3-control-group .bp3-select select[readonly],
+    .bp3-control-group .bp3-select select:disabled,
+    .bp3-control-group .bp3-select select.bp3-disabled{
+      z-index:3; }
+    .bp3-control-group .bp3-button[class*="bp3-intent"],
+    .bp3-control-group .bp3-html-select select[class*="bp3-intent"],
+    .bp3-control-group .bp3-select select[class*="bp3-intent"]{
+      z-index:9; }
+      .bp3-control-group .bp3-button[class*="bp3-intent"]:focus,
+      .bp3-control-group .bp3-html-select select[class*="bp3-intent"]:focus,
+      .bp3-control-group .bp3-select select[class*="bp3-intent"]:focus{
+        z-index:10; }
+      .bp3-control-group .bp3-button[class*="bp3-intent"]:hover,
+      .bp3-control-group .bp3-html-select select[class*="bp3-intent"]:hover,
+      .bp3-control-group .bp3-select select[class*="bp3-intent"]:hover{
+        z-index:11; }
+      .bp3-control-group .bp3-button[class*="bp3-intent"]:active,
+      .bp3-control-group .bp3-html-select select[class*="bp3-intent"]:active,
+      .bp3-control-group .bp3-select select[class*="bp3-intent"]:active{
+        z-index:12; }
+      .bp3-control-group .bp3-button[class*="bp3-intent"][readonly], .bp3-control-group .bp3-button[class*="bp3-intent"]:disabled, .bp3-control-group .bp3-button[class*="bp3-intent"].bp3-disabled,
+      .bp3-control-group .bp3-html-select select[class*="bp3-intent"][readonly],
+      .bp3-control-group .bp3-html-select select[class*="bp3-intent"]:disabled,
+      .bp3-control-group .bp3-html-select select[class*="bp3-intent"].bp3-disabled,
+      .bp3-control-group .bp3-select select[class*="bp3-intent"][readonly],
+      .bp3-control-group .bp3-select select[class*="bp3-intent"]:disabled,
+      .bp3-control-group .bp3-select select[class*="bp3-intent"].bp3-disabled{
+        z-index:8; }
+  .bp3-control-group .bp3-input-group > .bp3-icon,
+  .bp3-control-group .bp3-input-group > .bp3-button,
+  .bp3-control-group .bp3-input-group > .bp3-input-action{
+    z-index:16; }
+  .bp3-control-group .bp3-select::after,
+  .bp3-control-group .bp3-html-select::after,
+  .bp3-control-group .bp3-select > .bp3-icon,
+  .bp3-control-group .bp3-html-select > .bp3-icon{
+    z-index:17; }
+  .bp3-control-group:not(.bp3-vertical) > *{
+    margin-right:-1px; }
+  .bp3-dark .bp3-control-group:not(.bp3-vertical) > *{
+    margin-right:0; }
+  .bp3-dark .bp3-control-group:not(.bp3-vertical) > .bp3-button + .bp3-button{
+    margin-left:1px; }
+  .bp3-control-group .bp3-popover-wrapper,
+  .bp3-control-group .bp3-popover-target{
+    border-radius:inherit; }
+  .bp3-control-group > :first-child{
+    border-radius:3px 0 0 3px; }
+  .bp3-control-group > :last-child{
+    margin-right:0;
+    border-radius:0 3px 3px 0; }
+  .bp3-control-group > :only-child{
+    margin-right:0;
+    border-radius:3px; }
+  .bp3-control-group .bp3-input-group .bp3-button{
+    border-radius:3px; }
+  .bp3-control-group > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto; }
+  .bp3-control-group.bp3-fill > *:not(.bp3-fixed){
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto; }
+  .bp3-control-group.bp3-vertical{
+    -webkit-box-orient:vertical;
+    -webkit-box-direction:normal;
+        -ms-flex-direction:column;
+            flex-direction:column; }
+    .bp3-control-group.bp3-vertical > *{
+      margin-top:-1px; }
+    .bp3-control-group.bp3-vertical > :first-child{
+      margin-top:0;
+      border-radius:3px 3px 0 0; }
+    .bp3-control-group.bp3-vertical > :last-child{
+      border-radius:0 0 3px 3px; }
+.bp3-control{
+  display:block;
+  position:relative;
+  margin-bottom:10px;
+  cursor:pointer;
+  text-transform:none; }
+  .bp3-control input:checked ~ .bp3-control-indicator{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    background-color:#137cbd;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
+    color:#ffffff; }
+  .bp3-control:hover input:checked ~ .bp3-control-indicator{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    background-color:#106ba3; }
+  .bp3-control input:not(:disabled):active:checked ~ .bp3-control-indicator{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+    background:#0e5a8a; }
+  .bp3-control input:disabled:checked ~ .bp3-control-indicator{
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    background:rgba(19, 124, 189, 0.5); }
+  .bp3-dark .bp3-control input:checked ~ .bp3-control-indicator{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-control:hover input:checked ~ .bp3-control-indicator{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+    background-color:#106ba3; }
+  .bp3-dark .bp3-control input:not(:disabled):active:checked ~ .bp3-control-indicator{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+    background-color:#0e5a8a; }
+  .bp3-dark .bp3-control input:disabled:checked ~ .bp3-control-indicator{
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    background:rgba(14, 90, 138, 0.5); }
+  .bp3-control:not(.bp3-align-right){
+    padding-left:26px; }
+    .bp3-control:not(.bp3-align-right) .bp3-control-indicator{
+      margin-left:-26px; }
+  .bp3-control.bp3-align-right{
+    padding-right:26px; }
+    .bp3-control.bp3-align-right .bp3-control-indicator{
+      margin-right:-26px; }
+  .bp3-control.bp3-disabled{
+    cursor:not-allowed;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-control.bp3-inline{
+    display:inline-block;
+    margin-right:20px; }
+  .bp3-control input{
+    position:absolute;
+    top:0;
+    left:0;
+    opacity:0;
+    z-index:-1; }
+  .bp3-control .bp3-control-indicator{
+    display:inline-block;
+    position:relative;
+    margin-top:-3px;
+    margin-right:10px;
+    border:none;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+    background-clip:padding-box;
+    background-color:#f5f8fa;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
+    cursor:pointer;
+    width:1em;
+    height:1em;
+    vertical-align:middle;
+    font-size:16px;
+    -webkit-user-select:none;
+       -moz-user-select:none;
+        -ms-user-select:none;
+            user-select:none; }
+    .bp3-control .bp3-control-indicator::before{
+      display:block;
+      width:1em;
+      height:1em;
+      content:""; }
+  .bp3-control:hover .bp3-control-indicator{
+    background-color:#ebf1f5; }
+  .bp3-control input:not(:disabled):active ~ .bp3-control-indicator{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+    background:#d8e1e8; }
+  .bp3-control input:disabled ~ .bp3-control-indicator{
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    background:rgba(206, 217, 224, 0.5);
+    cursor:not-allowed; }
+  .bp3-control input:focus ~ .bp3-control-indicator{
+    outline:rgba(19, 124, 189, 0.6) auto 2px;
+    outline-offset:2px;
+    -moz-outline-radius:6px; }
+  .bp3-control.bp3-align-right .bp3-control-indicator{
+    float:right;
+    margin-top:1px;
+    margin-left:10px; }
+  .bp3-control.bp3-large{
+    font-size:16px; }
+    .bp3-control.bp3-large:not(.bp3-align-right){
+      padding-left:30px; }
+      .bp3-control.bp3-large:not(.bp3-align-right) .bp3-control-indicator{
+        margin-left:-30px; }
+    .bp3-control.bp3-large.bp3-align-right{
+      padding-right:30px; }
+      .bp3-control.bp3-large.bp3-align-right .bp3-control-indicator{
+        margin-right:-30px; }
+    .bp3-control.bp3-large .bp3-control-indicator{
+      font-size:20px; }
+    .bp3-control.bp3-large.bp3-align-right .bp3-control-indicator{
+      margin-top:0; }
+  .bp3-control.bp3-checkbox input:indeterminate ~ .bp3-control-indicator{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    background-color:#137cbd;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
+    color:#ffffff; }
+  .bp3-control.bp3-checkbox:hover input:indeterminate ~ .bp3-control-indicator{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    background-color:#106ba3; }
+  .bp3-control.bp3-checkbox input:not(:disabled):active:indeterminate ~ .bp3-control-indicator{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+    background:#0e5a8a; }
+  .bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    background:rgba(19, 124, 189, 0.5); }
+  .bp3-dark .bp3-control.bp3-checkbox input:indeterminate ~ .bp3-control-indicator{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-control.bp3-checkbox:hover input:indeterminate ~ .bp3-control-indicator{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+    background-color:#106ba3; }
+  .bp3-dark .bp3-control.bp3-checkbox input:not(:disabled):active:indeterminate ~ .bp3-control-indicator{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+    background-color:#0e5a8a; }
+  .bp3-dark .bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    background:rgba(14, 90, 138, 0.5); }
+  .bp3-control.bp3-checkbox .bp3-control-indicator{
+    border-radius:3px; }
+  .bp3-control.bp3-checkbox input:checked ~ .bp3-control-indicator::before{
+    background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill-rule='evenodd' clip-rule='evenodd' d='M12 5c-.28 0-.53.11-.71.29L7 9.59l-2.29-2.3a1.003 1.003 0 0 0-1.42 1.42l3 3c.18.18.43.29.71.29s.53-.11.71-.29l5-5A1.003 1.003 0 0 0 12 5z' fill='white'/%3e%3c/svg%3e"); }
+  .bp3-control.bp3-checkbox input:indeterminate ~ .bp3-control-indicator::before{
+    background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 7H5c-.55 0-1 .45-1 1s.45 1 1 1h6c.55 0 1-.45 1-1s-.45-1-1-1z' fill='white'/%3e%3c/svg%3e"); }
+  .bp3-control.bp3-radio .bp3-control-indicator{
+    border-radius:50%; }
+  .bp3-control.bp3-radio input:checked ~ .bp3-control-indicator::before{
+    background-image:radial-gradient(#ffffff, #ffffff 28%, transparent 32%); }
+  .bp3-control.bp3-radio input:checked:disabled ~ .bp3-control-indicator::before{
+    opacity:0.5; }
+  .bp3-control.bp3-radio input:focus ~ .bp3-control-indicator{
+    -moz-outline-radius:16px; }
+  .bp3-control.bp3-switch input ~ .bp3-control-indicator{
+    background:rgba(167, 182, 194, 0.5); }
+  .bp3-control.bp3-switch:hover input ~ .bp3-control-indicator{
+    background:rgba(115, 134, 148, 0.5); }
+  .bp3-control.bp3-switch input:not(:disabled):active ~ .bp3-control-indicator{
+    background:rgba(92, 112, 128, 0.5); }
+  .bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator{
+    background:rgba(206, 217, 224, 0.5); }
+    .bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator::before{
+      background:rgba(255, 255, 255, 0.8); }
+  .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator{
+    background:#137cbd; }
+  .bp3-control.bp3-switch:hover input:checked ~ .bp3-control-indicator{
+    background:#106ba3; }
+  .bp3-control.bp3-switch input:checked:not(:disabled):active ~ .bp3-control-indicator{
+    background:#0e5a8a; }
+  .bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator{
+    background:rgba(19, 124, 189, 0.5); }
+    .bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator::before{
+      background:rgba(255, 255, 255, 0.8); }
+  .bp3-control.bp3-switch:not(.bp3-align-right){
+    padding-left:38px; }
+    .bp3-control.bp3-switch:not(.bp3-align-right) .bp3-control-indicator{
+      margin-left:-38px; }
+  .bp3-control.bp3-switch.bp3-align-right{
+    padding-right:38px; }
+    .bp3-control.bp3-switch.bp3-align-right .bp3-control-indicator{
+      margin-right:-38px; }
+  .bp3-control.bp3-switch .bp3-control-indicator{
+    border:none;
+    border-radius:1.75em;
+    -webkit-box-shadow:none !important;
+            box-shadow:none !important;
+    width:auto;
+    min-width:1.75em;
+    -webkit-transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+    .bp3-control.bp3-switch .bp3-control-indicator::before{
+      position:absolute;
+      left:0;
+      margin:2px;
+      border-radius:50%;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
+      background:#ffffff;
+      width:calc(1em - 4px);
+      height:calc(1em - 4px);
+      -webkit-transition:left 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+      transition:left 100ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator::before{
+    left:calc(100% - 1em); }
+  .bp3-control.bp3-switch.bp3-large:not(.bp3-align-right){
+    padding-left:45px; }
+    .bp3-control.bp3-switch.bp3-large:not(.bp3-align-right) .bp3-control-indicator{
+      margin-left:-45px; }
+  .bp3-control.bp3-switch.bp3-large.bp3-align-right{
+    padding-right:45px; }
+    .bp3-control.bp3-switch.bp3-large.bp3-align-right .bp3-control-indicator{
+      margin-right:-45px; }
+  .bp3-dark .bp3-control.bp3-switch input ~ .bp3-control-indicator{
+    background:rgba(16, 22, 26, 0.5); }
+  .bp3-dark .bp3-control.bp3-switch:hover input ~ .bp3-control-indicator{
+    background:rgba(16, 22, 26, 0.7); }
+  .bp3-dark .bp3-control.bp3-switch input:not(:disabled):active ~ .bp3-control-indicator{
+    background:rgba(16, 22, 26, 0.9); }
+  .bp3-dark .bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator{
+    background:rgba(57, 75, 89, 0.5); }
+    .bp3-dark .bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator::before{
+      background:rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator{
+    background:#137cbd; }
+  .bp3-dark .bp3-control.bp3-switch:hover input:checked ~ .bp3-control-indicator{
+    background:#106ba3; }
+  .bp3-dark .bp3-control.bp3-switch input:checked:not(:disabled):active ~ .bp3-control-indicator{
+    background:#0e5a8a; }
+  .bp3-dark .bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator{
+    background:rgba(14, 90, 138, 0.5); }
+    .bp3-dark .bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator::before{
+      background:rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-control.bp3-switch .bp3-control-indicator::before{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+    background:#394b59; }
+  .bp3-dark .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator::before{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }
+  .bp3-control.bp3-switch .bp3-switch-inner-text{
+    text-align:center;
+    font-size:0.7em; }
+  .bp3-control.bp3-switch .bp3-control-indicator-child:first-child{
+    visibility:hidden;
+    margin-right:1.2em;
+    margin-left:0.5em;
+    line-height:0; }
+  .bp3-control.bp3-switch .bp3-control-indicator-child:last-child{
+    visibility:visible;
+    margin-right:0.5em;
+    margin-left:1.2em;
+    line-height:1em; }
+  .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator .bp3-control-indicator-child:first-child{
+    visibility:visible;
+    line-height:1em; }
+  .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator .bp3-control-indicator-child:last-child{
+    visibility:hidden;
+    line-height:0; }
+  .bp3-dark .bp3-control{
+    color:#f5f8fa; }
+    .bp3-dark .bp3-control.bp3-disabled{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-control .bp3-control-indicator{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+      background-color:#394b59;
+      background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
+      background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0)); }
+    .bp3-dark .bp3-control:hover .bp3-control-indicator{
+      background-color:#30404d; }
+    .bp3-dark .bp3-control input:not(:disabled):active ~ .bp3-control-indicator{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+      background:#202b33; }
+    .bp3-dark .bp3-control input:disabled ~ .bp3-control-indicator{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background:rgba(57, 75, 89, 0.5);
+      cursor:not-allowed; }
+    .bp3-dark .bp3-control.bp3-checkbox input:disabled:checked ~ .bp3-control-indicator, .bp3-dark .bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{
+      color:rgba(167, 182, 194, 0.6); }
+.bp3-file-input{
+  display:inline-block;
+  position:relative;
+  cursor:pointer;
+  height:30px; }
+  .bp3-file-input input{
+    opacity:0;
+    margin:0;
+    min-width:200px; }
+    .bp3-file-input input:disabled + .bp3-file-upload-input,
+    .bp3-file-input input.bp3-disabled + .bp3-file-upload-input{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background:rgba(206, 217, 224, 0.5);
+      cursor:not-allowed;
+      color:rgba(92, 112, 128, 0.6);
+      resize:none; }
+      .bp3-file-input input:disabled + .bp3-file-upload-input::after,
+      .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after{
+        outline:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        background-color:rgba(206, 217, 224, 0.5);
+        background-image:none;
+        cursor:not-allowed;
+        color:rgba(92, 112, 128, 0.6); }
+        .bp3-file-input input:disabled + .bp3-file-upload-input::after.bp3-active, .bp3-file-input input:disabled + .bp3-file-upload-input::after.bp3-active:hover,
+        .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after.bp3-active,
+        .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after.bp3-active:hover{
+          background:rgba(206, 217, 224, 0.7); }
+      .bp3-dark .bp3-file-input input:disabled + .bp3-file-upload-input, .bp3-dark
+      .bp3-file-input input.bp3-disabled + .bp3-file-upload-input{
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        background:rgba(57, 75, 89, 0.5);
+        color:rgba(167, 182, 194, 0.6); }
+        .bp3-dark .bp3-file-input input:disabled + .bp3-file-upload-input::after, .bp3-dark
+        .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after{
+          -webkit-box-shadow:none;
+                  box-shadow:none;
+          background-color:rgba(57, 75, 89, 0.5);
+          background-image:none;
+          color:rgba(167, 182, 194, 0.6); }
+          .bp3-dark .bp3-file-input input:disabled + .bp3-file-upload-input::after.bp3-active, .bp3-dark
+          .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after.bp3-active{
+            background:rgba(57, 75, 89, 0.7); }
+  .bp3-file-input.bp3-file-input-has-selection .bp3-file-upload-input{
+    color:#182026; }
+  .bp3-dark .bp3-file-input.bp3-file-input-has-selection .bp3-file-upload-input{
+    color:#f5f8fa; }
+  .bp3-file-input.bp3-fill{
+    width:100%; }
+  .bp3-file-input.bp3-large,
+  .bp3-large .bp3-file-input{
+    height:40px; }
+  .bp3-file-input .bp3-file-upload-input-custom-text::after{
+    content:attr(bp3-button-text); }
+
+.bp3-file-upload-input{
+  outline:none;
+  border:none;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+  background:#ffffff;
+  height:30px;
+  padding:0 10px;
+  vertical-align:middle;
+  line-height:30px;
+  color:#182026;
+  font-size:14px;
+  font-weight:400;
+  -webkit-transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  -webkit-appearance:none;
+     -moz-appearance:none;
+          appearance:none;
+  overflow:hidden;
+  text-overflow:ellipsis;
+  white-space:nowrap;
+  word-wrap:normal;
+  position:absolute;
+  top:0;
+  right:0;
+  left:0;
+  padding-right:80px;
+  color:rgba(92, 112, 128, 0.6);
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-file-upload-input::-webkit-input-placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-file-upload-input::-moz-placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-file-upload-input:-ms-input-placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-file-upload-input::-ms-input-placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-file-upload-input::placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-file-upload-input:focus, .bp3-file-upload-input.bp3-active{
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-file-upload-input[type="search"], .bp3-file-upload-input.bp3-round{
+    border-radius:30px;
+    -webkit-box-sizing:border-box;
+            box-sizing:border-box;
+    padding-left:10px; }
+  .bp3-file-upload-input[readonly]{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15); }
+  .bp3-file-upload-input:disabled, .bp3-file-upload-input.bp3-disabled{
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    background:rgba(206, 217, 224, 0.5);
+    cursor:not-allowed;
+    color:rgba(92, 112, 128, 0.6);
+    resize:none; }
+  .bp3-file-upload-input::after{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+    background-color:#f5f8fa;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
+    color:#182026;
+    min-width:24px;
+    min-height:24px;
+    overflow:hidden;
+    text-overflow:ellipsis;
+    white-space:nowrap;
+    word-wrap:normal;
+    position:absolute;
+    top:0;
+    right:0;
+    margin:3px;
+    border-radius:3px;
+    width:70px;
+    text-align:center;
+    line-height:24px;
+    content:"Browse"; }
+    .bp3-file-upload-input::after:hover{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+      background-clip:padding-box;
+      background-color:#ebf1f5; }
+    .bp3-file-upload-input::after:active, .bp3-file-upload-input::after.bp3-active{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+      background-color:#d8e1e8;
+      background-image:none; }
+    .bp3-file-upload-input::after:disabled, .bp3-file-upload-input::after.bp3-disabled{
+      outline:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background-color:rgba(206, 217, 224, 0.5);
+      background-image:none;
+      cursor:not-allowed;
+      color:rgba(92, 112, 128, 0.6); }
+      .bp3-file-upload-input::after:disabled.bp3-active, .bp3-file-upload-input::after:disabled.bp3-active:hover, .bp3-file-upload-input::after.bp3-disabled.bp3-active, .bp3-file-upload-input::after.bp3-disabled.bp3-active:hover{
+        background:rgba(206, 217, 224, 0.7); }
+  .bp3-file-upload-input:hover::after{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+    background-clip:padding-box;
+    background-color:#ebf1f5; }
+  .bp3-file-upload-input:active::after{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+    background-color:#d8e1e8;
+    background-image:none; }
+  .bp3-large .bp3-file-upload-input{
+    height:40px;
+    line-height:40px;
+    font-size:16px;
+    padding-right:95px; }
+    .bp3-large .bp3-file-upload-input[type="search"], .bp3-large .bp3-file-upload-input.bp3-round{
+      padding:0 15px; }
+    .bp3-large .bp3-file-upload-input::after{
+      min-width:30px;
+      min-height:30px;
+      margin:5px;
+      width:85px;
+      line-height:30px; }
+  .bp3-dark .bp3-file-upload-input{
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+    background:rgba(16, 22, 26, 0.3);
+    color:#f5f8fa;
+    color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input::-webkit-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input::-moz-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input:-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input::-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input::placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input:focus{
+      -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-file-upload-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-file-upload-input:disabled, .bp3-dark .bp3-file-upload-input.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background:rgba(57, 75, 89, 0.5);
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input::after{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+      background-color:#394b59;
+      background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
+      background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
+      color:#f5f8fa; }
+      .bp3-dark .bp3-file-upload-input::after:hover, .bp3-dark .bp3-file-upload-input::after:active, .bp3-dark .bp3-file-upload-input::after.bp3-active{
+        color:#f5f8fa; }
+      .bp3-dark .bp3-file-upload-input::after:hover{
+        -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+                box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+        background-color:#30404d; }
+      .bp3-dark .bp3-file-upload-input::after:active, .bp3-dark .bp3-file-upload-input::after.bp3-active{
+        -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+                box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+        background-color:#202b33;
+        background-image:none; }
+      .bp3-dark .bp3-file-upload-input::after:disabled, .bp3-dark .bp3-file-upload-input::after.bp3-disabled{
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        background-color:rgba(57, 75, 89, 0.5);
+        background-image:none;
+        color:rgba(167, 182, 194, 0.6); }
+        .bp3-dark .bp3-file-upload-input::after:disabled.bp3-active, .bp3-dark .bp3-file-upload-input::after.bp3-disabled.bp3-active{
+          background:rgba(57, 75, 89, 0.7); }
+      .bp3-dark .bp3-file-upload-input::after .bp3-button-spinner .bp3-spinner-head{
+        background:rgba(16, 22, 26, 0.5);
+        stroke:#8a9ba8; }
+    .bp3-dark .bp3-file-upload-input:hover::after{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+      background-color:#30404d; }
+    .bp3-dark .bp3-file-upload-input:active::after{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+      background-color:#202b33;
+      background-image:none; }
+
+.bp3-file-upload-input::after{
+  -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+          box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
+.bp3-form-group{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  margin:0 0 15px; }
+  .bp3-form-group label.bp3-label{
+    margin-bottom:5px; }
+  .bp3-form-group .bp3-control{
+    margin-top:7px; }
+  .bp3-form-group .bp3-form-helper-text{
+    margin-top:5px;
+    color:#5c7080;
+    font-size:12px; }
+  .bp3-form-group.bp3-intent-primary .bp3-form-helper-text{
+    color:#106ba3; }
+  .bp3-form-group.bp3-intent-success .bp3-form-helper-text{
+    color:#0d8050; }
+  .bp3-form-group.bp3-intent-warning .bp3-form-helper-text{
+    color:#bf7326; }
+  .bp3-form-group.bp3-intent-danger .bp3-form-helper-text{
+    color:#c23030; }
+  .bp3-form-group.bp3-inline{
+    -webkit-box-orient:horizontal;
+    -webkit-box-direction:normal;
+        -ms-flex-direction:row;
+            flex-direction:row;
+    -webkit-box-align:start;
+        -ms-flex-align:start;
+            align-items:flex-start; }
+    .bp3-form-group.bp3-inline.bp3-large label.bp3-label{
+      margin:0 10px 0 0;
+      line-height:40px; }
+    .bp3-form-group.bp3-inline label.bp3-label{
+      margin:0 10px 0 0;
+      line-height:30px; }
+  .bp3-form-group.bp3-disabled .bp3-label,
+  .bp3-form-group.bp3-disabled .bp3-text-muted,
+  .bp3-form-group.bp3-disabled .bp3-form-helper-text{
+    color:rgba(92, 112, 128, 0.6) !important; }
+  .bp3-dark .bp3-form-group.bp3-intent-primary .bp3-form-helper-text{
+    color:#48aff0; }
+  .bp3-dark .bp3-form-group.bp3-intent-success .bp3-form-helper-text{
+    color:#3dcc91; }
+  .bp3-dark .bp3-form-group.bp3-intent-warning .bp3-form-helper-text{
+    color:#ffb366; }
+  .bp3-dark .bp3-form-group.bp3-intent-danger .bp3-form-helper-text{
+    color:#ff7373; }
+  .bp3-dark .bp3-form-group .bp3-form-helper-text{
+    color:#a7b6c2; }
+  .bp3-dark .bp3-form-group.bp3-disabled .bp3-label,
+  .bp3-dark .bp3-form-group.bp3-disabled .bp3-text-muted,
+  .bp3-dark .bp3-form-group.bp3-disabled .bp3-form-helper-text{
+    color:rgba(167, 182, 194, 0.6) !important; }
+.bp3-input-group{
+  display:block;
+  position:relative; }
+  .bp3-input-group .bp3-input{
+    position:relative;
+    width:100%; }
+    .bp3-input-group .bp3-input:not(:first-child){
+      padding-left:30px; }
+    .bp3-input-group .bp3-input:not(:last-child){
+      padding-right:30px; }
+  .bp3-input-group .bp3-input-action,
+  .bp3-input-group > .bp3-button,
+  .bp3-input-group > .bp3-icon{
+    position:absolute;
+    top:0; }
+    .bp3-input-group .bp3-input-action:first-child,
+    .bp3-input-group > .bp3-button:first-child,
+    .bp3-input-group > .bp3-icon:first-child{
+      left:0; }
+    .bp3-input-group .bp3-input-action:last-child,
+    .bp3-input-group > .bp3-button:last-child,
+    .bp3-input-group > .bp3-icon:last-child{
+      right:0; }
+  .bp3-input-group .bp3-button{
+    min-width:24px;
+    min-height:24px;
+    margin:3px;
+    padding:0 7px; }
+    .bp3-input-group .bp3-button:empty{
+      padding:0; }
+  .bp3-input-group > .bp3-icon{
+    z-index:1;
+    color:#5c7080; }
+    .bp3-input-group > .bp3-icon:empty{
+      line-height:1;
+      font-family:"Icons16", sans-serif;
+      font-size:16px;
+      font-weight:400;
+      font-style:normal;
+      -moz-osx-font-smoothing:grayscale;
+      -webkit-font-smoothing:antialiased; }
+  .bp3-input-group > .bp3-icon,
+  .bp3-input-group .bp3-input-action > .bp3-spinner{
+    margin:7px; }
+  .bp3-input-group .bp3-tag{
+    margin:5px; }
+  .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus),
+  .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus){
+    color:#5c7080; }
+    .bp3-dark .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus), .bp3-dark
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus){
+      color:#a7b6c2; }
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-standard, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-large,
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon,
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-standard,
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-large{
+      color:#5c7080; }
+  .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled,
+  .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled{
+    color:rgba(92, 112, 128, 0.6) !important; }
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled .bp3-icon, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled .bp3-icon-standard, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled .bp3-icon-large,
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled .bp3-icon,
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled .bp3-icon-standard,
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled .bp3-icon-large{
+      color:rgba(92, 112, 128, 0.6) !important; }
+  .bp3-input-group.bp3-disabled{
+    cursor:not-allowed; }
+    .bp3-input-group.bp3-disabled .bp3-icon{
+      color:rgba(92, 112, 128, 0.6); }
+  .bp3-input-group.bp3-large .bp3-button{
+    min-width:30px;
+    min-height:30px;
+    margin:5px; }
+  .bp3-input-group.bp3-large > .bp3-icon,
+  .bp3-input-group.bp3-large .bp3-input-action > .bp3-spinner{
+    margin:12px; }
+  .bp3-input-group.bp3-large .bp3-input{
+    height:40px;
+    line-height:40px;
+    font-size:16px; }
+    .bp3-input-group.bp3-large .bp3-input[type="search"], .bp3-input-group.bp3-large .bp3-input.bp3-round{
+      padding:0 15px; }
+    .bp3-input-group.bp3-large .bp3-input:not(:first-child){
+      padding-left:40px; }
+    .bp3-input-group.bp3-large .bp3-input:not(:last-child){
+      padding-right:40px; }
+  .bp3-input-group.bp3-small .bp3-button{
+    min-width:20px;
+    min-height:20px;
+    margin:2px; }
+  .bp3-input-group.bp3-small .bp3-tag{
+    min-width:20px;
+    min-height:20px;
+    margin:2px; }
+  .bp3-input-group.bp3-small > .bp3-icon,
+  .bp3-input-group.bp3-small .bp3-input-action > .bp3-spinner{
+    margin:4px; }
+  .bp3-input-group.bp3-small .bp3-input{
+    height:24px;
+    padding-right:8px;
+    padding-left:8px;
+    line-height:24px;
+    font-size:12px; }
+    .bp3-input-group.bp3-small .bp3-input[type="search"], .bp3-input-group.bp3-small .bp3-input.bp3-round{
+      padding:0 12px; }
+    .bp3-input-group.bp3-small .bp3-input:not(:first-child){
+      padding-left:24px; }
+    .bp3-input-group.bp3-small .bp3-input:not(:last-child){
+      padding-right:24px; }
+  .bp3-input-group.bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto;
+    width:100%; }
+  .bp3-input-group.bp3-round .bp3-button,
+  .bp3-input-group.bp3-round .bp3-input,
+  .bp3-input-group.bp3-round .bp3-tag{
+    border-radius:30px; }
+  .bp3-dark .bp3-input-group .bp3-icon{
+    color:#a7b6c2; }
+  .bp3-dark .bp3-input-group.bp3-disabled .bp3-icon{
+    color:rgba(167, 182, 194, 0.6); }
+  .bp3-input-group.bp3-intent-primary .bp3-input{
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-primary .bp3-input:focus{
+      -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-primary .bp3-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #137cbd;
+              box-shadow:inset 0 0 0 1px #137cbd; }
+    .bp3-input-group.bp3-intent-primary .bp3-input:disabled, .bp3-input-group.bp3-intent-primary .bp3-input.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-input-group.bp3-intent-primary > .bp3-icon{
+    color:#106ba3; }
+    .bp3-dark .bp3-input-group.bp3-intent-primary > .bp3-icon{
+      color:#48aff0; }
+  .bp3-input-group.bp3-intent-success .bp3-input{
+    -webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-success .bp3-input:focus{
+      -webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-success .bp3-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #0f9960;
+              box-shadow:inset 0 0 0 1px #0f9960; }
+    .bp3-input-group.bp3-intent-success .bp3-input:disabled, .bp3-input-group.bp3-intent-success .bp3-input.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-input-group.bp3-intent-success > .bp3-icon{
+    color:#0d8050; }
+    .bp3-dark .bp3-input-group.bp3-intent-success > .bp3-icon{
+      color:#3dcc91; }
+  .bp3-input-group.bp3-intent-warning .bp3-input{
+    -webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-warning .bp3-input:focus{
+      -webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-warning .bp3-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #d9822b;
+              box-shadow:inset 0 0 0 1px #d9822b; }
+    .bp3-input-group.bp3-intent-warning .bp3-input:disabled, .bp3-input-group.bp3-intent-warning .bp3-input.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-input-group.bp3-intent-warning > .bp3-icon{
+    color:#bf7326; }
+    .bp3-dark .bp3-input-group.bp3-intent-warning > .bp3-icon{
+      color:#ffb366; }
+  .bp3-input-group.bp3-intent-danger .bp3-input{
+    -webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-danger .bp3-input:focus{
+      -webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-danger .bp3-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #db3737;
+              box-shadow:inset 0 0 0 1px #db3737; }
+    .bp3-input-group.bp3-intent-danger .bp3-input:disabled, .bp3-input-group.bp3-intent-danger .bp3-input.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-input-group.bp3-intent-danger > .bp3-icon{
+    color:#c23030; }
+    .bp3-dark .bp3-input-group.bp3-intent-danger > .bp3-icon{
+      color:#ff7373; }
+.bp3-input{
+  outline:none;
+  border:none;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+  background:#ffffff;
+  height:30px;
+  padding:0 10px;
+  vertical-align:middle;
+  line-height:30px;
+  color:#182026;
+  font-size:14px;
+  font-weight:400;
+  -webkit-transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  -webkit-appearance:none;
+     -moz-appearance:none;
+          appearance:none; }
+  .bp3-input::-webkit-input-placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-input::-moz-placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-input:-ms-input-placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-input::-ms-input-placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-input::placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-input:focus, .bp3-input.bp3-active{
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-input[type="search"], .bp3-input.bp3-round{
+    border-radius:30px;
+    -webkit-box-sizing:border-box;
+            box-sizing:border-box;
+    padding-left:10px; }
+  .bp3-input[readonly]{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15); }
+  .bp3-input:disabled, .bp3-input.bp3-disabled{
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    background:rgba(206, 217, 224, 0.5);
+    cursor:not-allowed;
+    color:rgba(92, 112, 128, 0.6);
+    resize:none; }
+  .bp3-input.bp3-large{
+    height:40px;
+    line-height:40px;
+    font-size:16px; }
+    .bp3-input.bp3-large[type="search"], .bp3-input.bp3-large.bp3-round{
+      padding:0 15px; }
+  .bp3-input.bp3-small{
+    height:24px;
+    padding-right:8px;
+    padding-left:8px;
+    line-height:24px;
+    font-size:12px; }
+    .bp3-input.bp3-small[type="search"], .bp3-input.bp3-small.bp3-round{
+      padding:0 12px; }
+  .bp3-input.bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto;
+    width:100%; }
+  .bp3-dark .bp3-input{
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+    background:rgba(16, 22, 26, 0.3);
+    color:#f5f8fa; }
+    .bp3-dark .bp3-input::-webkit-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-input::-moz-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-input:-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-input::-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-input::placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-input:focus{
+      -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-input:disabled, .bp3-dark .bp3-input.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background:rgba(57, 75, 89, 0.5);
+      color:rgba(167, 182, 194, 0.6); }
+  .bp3-input.bp3-intent-primary{
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-primary:focus{
+      -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-primary[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #137cbd;
+              box-shadow:inset 0 0 0 1px #137cbd; }
+    .bp3-input.bp3-intent-primary:disabled, .bp3-input.bp3-intent-primary.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+    .bp3-dark .bp3-input.bp3-intent-primary{
+      -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-primary:focus{
+        -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+                box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-primary[readonly]{
+        -webkit-box-shadow:inset 0 0 0 1px #137cbd;
+                box-shadow:inset 0 0 0 1px #137cbd; }
+      .bp3-dark .bp3-input.bp3-intent-primary:disabled, .bp3-dark .bp3-input.bp3-intent-primary.bp3-disabled{
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+  .bp3-input.bp3-intent-success{
+    -webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-success:focus{
+      -webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-success[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #0f9960;
+              box-shadow:inset 0 0 0 1px #0f9960; }
+    .bp3-input.bp3-intent-success:disabled, .bp3-input.bp3-intent-success.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+    .bp3-dark .bp3-input.bp3-intent-success{
+      -webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-success:focus{
+        -webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+                box-shadow:0 0 0 1px #0f9960, 0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-success[readonly]{
+        -webkit-box-shadow:inset 0 0 0 1px #0f9960;
+                box-shadow:inset 0 0 0 1px #0f9960; }
+      .bp3-dark .bp3-input.bp3-intent-success:disabled, .bp3-dark .bp3-input.bp3-intent-success.bp3-disabled{
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+  .bp3-input.bp3-intent-warning{
+    -webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-warning:focus{
+      -webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-warning[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #d9822b;
+              box-shadow:inset 0 0 0 1px #d9822b; }
+    .bp3-input.bp3-intent-warning:disabled, .bp3-input.bp3-intent-warning.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+    .bp3-dark .bp3-input.bp3-intent-warning{
+      -webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-warning:focus{
+        -webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+                box-shadow:0 0 0 1px #d9822b, 0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-warning[readonly]{
+        -webkit-box-shadow:inset 0 0 0 1px #d9822b;
+                box-shadow:inset 0 0 0 1px #d9822b; }
+      .bp3-dark .bp3-input.bp3-intent-warning:disabled, .bp3-dark .bp3-input.bp3-intent-warning.bp3-disabled{
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+  .bp3-input.bp3-intent-danger{
+    -webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-danger:focus{
+      -webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-danger[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #db3737;
+              box-shadow:inset 0 0 0 1px #db3737; }
+    .bp3-input.bp3-intent-danger:disabled, .bp3-input.bp3-intent-danger.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+    .bp3-dark .bp3-input.bp3-intent-danger{
+      -webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-danger:focus{
+        -webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+                box-shadow:0 0 0 1px #db3737, 0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-danger[readonly]{
+        -webkit-box-shadow:inset 0 0 0 1px #db3737;
+                box-shadow:inset 0 0 0 1px #db3737; }
+      .bp3-dark .bp3-input.bp3-intent-danger:disabled, .bp3-dark .bp3-input.bp3-intent-danger.bp3-disabled{
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+  .bp3-input::-ms-clear{
+    display:none; }
+textarea.bp3-input{
+  max-width:100%;
+  padding:10px; }
+  textarea.bp3-input, textarea.bp3-input.bp3-large, textarea.bp3-input.bp3-small{
+    height:auto;
+    line-height:inherit; }
+  textarea.bp3-input.bp3-small{
+    padding:8px; }
+  .bp3-dark textarea.bp3-input{
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+    background:rgba(16, 22, 26, 0.3);
+    color:#f5f8fa; }
+    .bp3-dark textarea.bp3-input::-webkit-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark textarea.bp3-input::-moz-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark textarea.bp3-input:-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark textarea.bp3-input::-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark textarea.bp3-input::placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark textarea.bp3-input:focus{
+      -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark textarea.bp3-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark textarea.bp3-input:disabled, .bp3-dark textarea.bp3-input.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background:rgba(57, 75, 89, 0.5);
+      color:rgba(167, 182, 194, 0.6); }
+label.bp3-label{
+  display:block;
+  margin-top:0;
+  margin-bottom:15px; }
+  label.bp3-label .bp3-html-select,
+  label.bp3-label .bp3-input,
+  label.bp3-label .bp3-select,
+  label.bp3-label .bp3-slider,
+  label.bp3-label .bp3-popover-wrapper{
+    display:block;
+    margin-top:5px;
+    text-transform:none; }
+  label.bp3-label .bp3-button-group{
+    margin-top:5px; }
+  label.bp3-label .bp3-select select,
+  label.bp3-label .bp3-html-select select{
+    width:100%;
+    vertical-align:top;
+    font-weight:400; }
+  label.bp3-label.bp3-disabled,
+  label.bp3-label.bp3-disabled .bp3-text-muted{
+    color:rgba(92, 112, 128, 0.6); }
+  label.bp3-label.bp3-inline{
+    line-height:30px; }
+    label.bp3-label.bp3-inline .bp3-html-select,
+    label.bp3-label.bp3-inline .bp3-input,
+    label.bp3-label.bp3-inline .bp3-input-group,
+    label.bp3-label.bp3-inline .bp3-select,
+    label.bp3-label.bp3-inline .bp3-popover-wrapper{
+      display:inline-block;
+      margin:0 0 0 5px;
+      vertical-align:top; }
+    label.bp3-label.bp3-inline .bp3-button-group{
+      margin:0 0 0 5px; }
+    label.bp3-label.bp3-inline .bp3-input-group .bp3-input{
+      margin-left:0; }
+    label.bp3-label.bp3-inline.bp3-large{
+      line-height:40px; }
+  label.bp3-label:not(.bp3-inline) .bp3-popover-target{
+    display:block; }
+  .bp3-dark label.bp3-label{
+    color:#f5f8fa; }
+    .bp3-dark label.bp3-label.bp3-disabled,
+    .bp3-dark label.bp3-label.bp3-disabled .bp3-text-muted{
+      color:rgba(167, 182, 194, 0.6); }
+.bp3-numeric-input .bp3-button-group.bp3-vertical > .bp3-button{
+  -webkit-box-flex:1;
+      -ms-flex:1 1 14px;
+          flex:1 1 14px;
+  width:30px;
+  min-height:0;
+  padding:0; }
+  .bp3-numeric-input .bp3-button-group.bp3-vertical > .bp3-button:first-child{
+    border-radius:0 3px 0 0; }
+  .bp3-numeric-input .bp3-button-group.bp3-vertical > .bp3-button:last-child{
+    border-radius:0 0 3px 0; }
+
+.bp3-numeric-input .bp3-button-group.bp3-vertical:first-child > .bp3-button:first-child{
+  border-radius:3px 0 0 0; }
+
+.bp3-numeric-input .bp3-button-group.bp3-vertical:first-child > .bp3-button:last-child{
+  border-radius:0 0 0 3px; }
+
+.bp3-numeric-input.bp3-large .bp3-button-group.bp3-vertical > .bp3-button{
+  width:40px; }
+
+form{
+  display:block; }
+.bp3-html-select select,
+.bp3-select select{
+  display:-webkit-inline-box;
+  display:-ms-inline-flexbox;
+  display:inline-flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  border:none;
+  border-radius:3px;
+  cursor:pointer;
+  padding:5px 10px;
+  vertical-align:middle;
+  text-align:left;
+  font-size:14px;
+  -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+          box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+  background-color:#f5f8fa;
+  background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
+  background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
+  color:#182026;
+  border-radius:3px;
+  width:100%;
+  height:30px;
+  padding:0 25px 0 10px;
+  -moz-appearance:none;
+  -webkit-appearance:none; }
+  .bp3-html-select select > *, .bp3-select select > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-html-select select > .bp3-fill, .bp3-select select > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-html-select select::before,
+  .bp3-select select::before, .bp3-html-select select > *, .bp3-select select > *{
+    margin-right:7px; }
+  .bp3-html-select select:empty::before,
+  .bp3-select select:empty::before,
+  .bp3-html-select select > :last-child,
+  .bp3-select select > :last-child{
+    margin-right:0; }
+  .bp3-html-select select:hover,
+  .bp3-select select:hover{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+    background-clip:padding-box;
+    background-color:#ebf1f5; }
+  .bp3-html-select select:active,
+  .bp3-select select:active, .bp3-html-select select.bp3-active,
+  .bp3-select select.bp3-active{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+    background-color:#d8e1e8;
+    background-image:none; }
+  .bp3-html-select select:disabled,
+  .bp3-select select:disabled, .bp3-html-select select.bp3-disabled,
+  .bp3-select select.bp3-disabled{
+    outline:none;
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    background-color:rgba(206, 217, 224, 0.5);
+    background-image:none;
+    cursor:not-allowed;
+    color:rgba(92, 112, 128, 0.6); }
+    .bp3-html-select select:disabled.bp3-active,
+    .bp3-select select:disabled.bp3-active, .bp3-html-select select:disabled.bp3-active:hover,
+    .bp3-select select:disabled.bp3-active:hover, .bp3-html-select select.bp3-disabled.bp3-active,
+    .bp3-select select.bp3-disabled.bp3-active, .bp3-html-select select.bp3-disabled.bp3-active:hover,
+    .bp3-select select.bp3-disabled.bp3-active:hover{
+      background:rgba(206, 217, 224, 0.7); }
+
+.bp3-html-select.bp3-minimal select,
+.bp3-select.bp3-minimal select{
+  -webkit-box-shadow:none;
+          box-shadow:none;
+  background:none; }
+  .bp3-html-select.bp3-minimal select:hover,
+  .bp3-select.bp3-minimal select:hover{
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    background:rgba(167, 182, 194, 0.3);
+    text-decoration:none;
+    color:#182026; }
+  .bp3-html-select.bp3-minimal select:active,
+  .bp3-select.bp3-minimal select:active, .bp3-html-select.bp3-minimal select.bp3-active,
+  .bp3-select.bp3-minimal select.bp3-active{
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    background:rgba(115, 134, 148, 0.3);
+    color:#182026; }
+  .bp3-html-select.bp3-minimal select:disabled,
+  .bp3-select.bp3-minimal select:disabled, .bp3-html-select.bp3-minimal select:disabled:hover,
+  .bp3-select.bp3-minimal select:disabled:hover, .bp3-html-select.bp3-minimal select.bp3-disabled,
+  .bp3-select.bp3-minimal select.bp3-disabled, .bp3-html-select.bp3-minimal select.bp3-disabled:hover,
+  .bp3-select.bp3-minimal select.bp3-disabled:hover{
+    background:none;
+    cursor:not-allowed;
+    color:rgba(92, 112, 128, 0.6); }
+    .bp3-html-select.bp3-minimal select:disabled.bp3-active,
+    .bp3-select.bp3-minimal select:disabled.bp3-active, .bp3-html-select.bp3-minimal select:disabled:hover.bp3-active,
+    .bp3-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-html-select.bp3-minimal select.bp3-disabled.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-disabled:hover.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-disabled:hover.bp3-active{
+      background:rgba(115, 134, 148, 0.3); }
+  .bp3-dark .bp3-html-select.bp3-minimal select, .bp3-html-select.bp3-minimal .bp3-dark select,
+  .bp3-dark .bp3-select.bp3-minimal select, .bp3-select.bp3-minimal .bp3-dark select{
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    background:none;
+    color:inherit; }
+    .bp3-dark .bp3-html-select.bp3-minimal select:hover, .bp3-html-select.bp3-minimal .bp3-dark select:hover,
+    .bp3-dark .bp3-select.bp3-minimal select:hover, .bp3-select.bp3-minimal .bp3-dark select:hover, .bp3-dark .bp3-html-select.bp3-minimal select:active, .bp3-html-select.bp3-minimal .bp3-dark select:active,
+    .bp3-dark .bp3-select.bp3-minimal select:active, .bp3-select.bp3-minimal .bp3-dark select:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-active,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-active{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background:none; }
+    .bp3-dark .bp3-html-select.bp3-minimal select:hover, .bp3-html-select.bp3-minimal .bp3-dark select:hover,
+    .bp3-dark .bp3-select.bp3-minimal select:hover, .bp3-select.bp3-minimal .bp3-dark select:hover{
+      background:rgba(138, 155, 168, 0.15); }
+    .bp3-dark .bp3-html-select.bp3-minimal select:active, .bp3-html-select.bp3-minimal .bp3-dark select:active,
+    .bp3-dark .bp3-select.bp3-minimal select:active, .bp3-select.bp3-minimal .bp3-dark select:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-active,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-active{
+      background:rgba(138, 155, 168, 0.3);
+      color:#f5f8fa; }
+    .bp3-dark .bp3-html-select.bp3-minimal select:disabled, .bp3-html-select.bp3-minimal .bp3-dark select:disabled,
+    .bp3-dark .bp3-select.bp3-minimal select:disabled, .bp3-select.bp3-minimal .bp3-dark select:disabled, .bp3-dark .bp3-html-select.bp3-minimal select:disabled:hover, .bp3-html-select.bp3-minimal .bp3-dark select:disabled:hover,
+    .bp3-dark .bp3-select.bp3-minimal select:disabled:hover, .bp3-select.bp3-minimal .bp3-dark select:disabled:hover, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled:hover,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-disabled:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled:hover{
+      background:none;
+      cursor:not-allowed;
+      color:rgba(167, 182, 194, 0.6); }
+      .bp3-dark .bp3-html-select.bp3-minimal select:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select:disabled.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select:disabled:hover.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-select.bp3-minimal .bp3-dark select:disabled:hover.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled:hover.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled:hover.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-disabled:hover.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled:hover.bp3-active{
+        background:rgba(138, 155, 168, 0.3); }
+  .bp3-html-select.bp3-minimal select.bp3-intent-primary,
+  .bp3-select.bp3-minimal select.bp3-intent-primary{
+    color:#106ba3; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-primary:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-html-select.bp3-minimal select.bp3-intent-primary:active,
+    .bp3-select.bp3-minimal select.bp3-intent-primary:active, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-active{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background:none;
+      color:#106ba3; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-primary:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-primary:hover{
+      background:rgba(19, 124, 189, 0.15);
+      color:#106ba3; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-primary:active,
+    .bp3-select.bp3-minimal select.bp3-intent-primary:active, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-active{
+      background:rgba(19, 124, 189, 0.3);
+      color:#106ba3; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-primary:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled{
+      background:none;
+      color:rgba(16, 107, 163, 0.5); }
+      .bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active{
+        background:rgba(19, 124, 189, 0.3); }
+    .bp3-html-select.bp3-minimal select.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{
+      stroke:#106ba3; }
+    .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary{
+      color:#48aff0; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:hover,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:hover{
+        background:rgba(19, 124, 189, 0.2);
+        color:#48aff0; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-active{
+        background:rgba(19, 124, 189, 0.3);
+        color:#48aff0; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled{
+        background:none;
+        color:rgba(72, 175, 240, 0.5); }
+        .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled.bp3-active{
+          background:rgba(19, 124, 189, 0.3); }
+  .bp3-html-select.bp3-minimal select.bp3-intent-success,
+  .bp3-select.bp3-minimal select.bp3-intent-success{
+    color:#0d8050; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-success:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-success:hover, .bp3-html-select.bp3-minimal select.bp3-intent-success:active,
+    .bp3-select.bp3-minimal select.bp3-intent-success:active, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-success.bp3-active{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background:none;
+      color:#0d8050; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-success:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-success:hover{
+      background:rgba(15, 153, 96, 0.15);
+      color:#0d8050; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-success:active,
+    .bp3-select.bp3-minimal select.bp3-intent-success:active, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-success.bp3-active{
+      background:rgba(15, 153, 96, 0.3);
+      color:#0d8050; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-success:disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-success:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled{
+      background:none;
+      color:rgba(13, 128, 80, 0.5); }
+      .bp3-html-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active{
+        background:rgba(15, 153, 96, 0.3); }
+    .bp3-html-select.bp3-minimal select.bp3-intent-success .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{
+      stroke:#0d8050; }
+    .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success{
+      color:#3dcc91; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:hover,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:hover{
+        background:rgba(15, 153, 96, 0.2);
+        color:#3dcc91; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-active{
+        background:rgba(15, 153, 96, 0.3);
+        color:#3dcc91; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled{
+        background:none;
+        color:rgba(61, 204, 145, 0.5); }
+        .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled.bp3-active{
+          background:rgba(15, 153, 96, 0.3); }
+  .bp3-html-select.bp3-minimal select.bp3-intent-warning,
+  .bp3-select.bp3-minimal select.bp3-intent-warning{
+    color:#bf7326; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-warning:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-html-select.bp3-minimal select.bp3-intent-warning:active,
+    .bp3-select.bp3-minimal select.bp3-intent-warning:active, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-active{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background:none;
+      color:#bf7326; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-warning:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-warning:hover{
+      background:rgba(217, 130, 43, 0.15);
+      color:#bf7326; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-warning:active,
+    .bp3-select.bp3-minimal select.bp3-intent-warning:active, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-active{
+      background:rgba(217, 130, 43, 0.3);
+      color:#bf7326; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-warning:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled{
+      background:none;
+      color:rgba(191, 115, 38, 0.5); }
+      .bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active{
+        background:rgba(217, 130, 43, 0.3); }
+    .bp3-html-select.bp3-minimal select.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{
+      stroke:#bf7326; }
+    .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning{
+      color:#ffb366; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:hover,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:hover{
+        background:rgba(217, 130, 43, 0.2);
+        color:#ffb366; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-active{
+        background:rgba(217, 130, 43, 0.3);
+        color:#ffb366; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled{
+        background:none;
+        color:rgba(255, 179, 102, 0.5); }
+        .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled.bp3-active{
+          background:rgba(217, 130, 43, 0.3); }
+  .bp3-html-select.bp3-minimal select.bp3-intent-danger,
+  .bp3-select.bp3-minimal select.bp3-intent-danger{
+    color:#c23030; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-danger:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-html-select.bp3-minimal select.bp3-intent-danger:active,
+    .bp3-select.bp3-minimal select.bp3-intent-danger:active, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-active{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background:none;
+      color:#c23030; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-danger:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-danger:hover{
+      background:rgba(219, 55, 55, 0.15);
+      color:#c23030; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-danger:active,
+    .bp3-select.bp3-minimal select.bp3-intent-danger:active, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-active{
+      background:rgba(219, 55, 55, 0.3);
+      color:#c23030; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-danger:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled{
+      background:none;
+      color:rgba(194, 48, 48, 0.5); }
+      .bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active{
+        background:rgba(219, 55, 55, 0.3); }
+    .bp3-html-select.bp3-minimal select.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{
+      stroke:#c23030; }
+    .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger{
+      color:#ff7373; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:hover,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:hover{
+        background:rgba(219, 55, 55, 0.2);
+        color:#ff7373; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-active{
+        background:rgba(219, 55, 55, 0.3);
+        color:#ff7373; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled{
+        background:none;
+        color:rgba(255, 115, 115, 0.5); }
+        .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled.bp3-active{
+          background:rgba(219, 55, 55, 0.3); }
+
+.bp3-html-select.bp3-large select,
+.bp3-select.bp3-large select{
+  height:40px;
+  padding-right:35px;
+  font-size:16px; }
+
+.bp3-dark .bp3-html-select select, .bp3-dark .bp3-select select{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+  background-color:#394b59;
+  background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
+  background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
+  color:#f5f8fa; }
+  .bp3-dark .bp3-html-select select:hover, .bp3-dark .bp3-select select:hover, .bp3-dark .bp3-html-select select:active, .bp3-dark .bp3-select select:active, .bp3-dark .bp3-html-select select.bp3-active, .bp3-dark .bp3-select select.bp3-active{
+    color:#f5f8fa; }
+  .bp3-dark .bp3-html-select select:hover, .bp3-dark .bp3-select select:hover{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+    background-color:#30404d; }
+  .bp3-dark .bp3-html-select select:active, .bp3-dark .bp3-select select:active, .bp3-dark .bp3-html-select select.bp3-active, .bp3-dark .bp3-select select.bp3-active{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+    background-color:#202b33;
+    background-image:none; }
+  .bp3-dark .bp3-html-select select:disabled, .bp3-dark .bp3-select select:disabled, .bp3-dark .bp3-html-select select.bp3-disabled, .bp3-dark .bp3-select select.bp3-disabled{
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    background-color:rgba(57, 75, 89, 0.5);
+    background-image:none;
+    color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-html-select select:disabled.bp3-active, .bp3-dark .bp3-select select:disabled.bp3-active, .bp3-dark .bp3-html-select select.bp3-disabled.bp3-active, .bp3-dark .bp3-select select.bp3-disabled.bp3-active{
+      background:rgba(57, 75, 89, 0.7); }
+  .bp3-dark .bp3-html-select select .bp3-button-spinner .bp3-spinner-head, .bp3-dark .bp3-select select .bp3-button-spinner .bp3-spinner-head{
+    background:rgba(16, 22, 26, 0.5);
+    stroke:#8a9ba8; }
+
+.bp3-html-select select:disabled,
+.bp3-select select:disabled{
+  -webkit-box-shadow:none;
+          box-shadow:none;
+  background-color:rgba(206, 217, 224, 0.5);
+  cursor:not-allowed;
+  color:rgba(92, 112, 128, 0.6); }
+
+.bp3-html-select .bp3-icon,
+.bp3-select .bp3-icon, .bp3-select::after{
+  position:absolute;
+  top:7px;
+  right:7px;
+  color:#5c7080;
+  pointer-events:none; }
+  .bp3-html-select .bp3-disabled.bp3-icon,
+  .bp3-select .bp3-disabled.bp3-icon, .bp3-disabled.bp3-select::after{
+    color:rgba(92, 112, 128, 0.6); }
+.bp3-html-select,
+.bp3-select{
+  display:inline-block;
+  position:relative;
+  vertical-align:middle;
+  letter-spacing:normal; }
+  .bp3-html-select select::-ms-expand,
+  .bp3-select select::-ms-expand{
+    display:none; }
+  .bp3-html-select .bp3-icon,
+  .bp3-select .bp3-icon{
+    color:#5c7080; }
+    .bp3-html-select .bp3-icon:hover,
+    .bp3-select .bp3-icon:hover{
+      color:#182026; }
+    .bp3-dark .bp3-html-select .bp3-icon, .bp3-dark
+    .bp3-select .bp3-icon{
+      color:#a7b6c2; }
+      .bp3-dark .bp3-html-select .bp3-icon:hover, .bp3-dark
+      .bp3-select .bp3-icon:hover{
+        color:#f5f8fa; }
+  .bp3-html-select.bp3-large::after,
+  .bp3-html-select.bp3-large .bp3-icon,
+  .bp3-select.bp3-large::after,
+  .bp3-select.bp3-large .bp3-icon{
+    top:12px;
+    right:12px; }
+  .bp3-html-select.bp3-fill,
+  .bp3-html-select.bp3-fill select,
+  .bp3-select.bp3-fill,
+  .bp3-select.bp3-fill select{
+    width:100%; }
+  .bp3-dark .bp3-html-select option, .bp3-dark
+  .bp3-select option{
+    background-color:#30404d;
+    color:#f5f8fa; }
+  .bp3-dark .bp3-html-select::after, .bp3-dark
+  .bp3-select::after{
+    color:#a7b6c2; }
+
+.bp3-select::after{
+  line-height:1;
+  font-family:"Icons16", sans-serif;
+  font-size:16px;
+  font-weight:400;
+  font-style:normal;
+  -moz-osx-font-smoothing:grayscale;
+  -webkit-font-smoothing:antialiased;
+  content:""; }
+.bp3-running-text table, table.bp3-html-table{
+  border-spacing:0;
+  font-size:14px; }
+  .bp3-running-text table th, table.bp3-html-table th,
+  .bp3-running-text table td,
+  table.bp3-html-table td{
+    padding:11px;
+    vertical-align:top;
+    text-align:left; }
+  .bp3-running-text table th, table.bp3-html-table th{
+    color:#182026;
+    font-weight:600; }
+  
+  .bp3-running-text table td,
+  table.bp3-html-table td{
+    color:#182026; }
+  .bp3-running-text table tbody tr:first-child th, table.bp3-html-table tbody tr:first-child th,
+  .bp3-running-text table tbody tr:first-child td,
+  table.bp3-html-table tbody tr:first-child td{
+    -webkit-box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15);
+            box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15); }
+  .bp3-dark .bp3-running-text table th, .bp3-running-text .bp3-dark table th, .bp3-dark table.bp3-html-table th{
+    color:#f5f8fa; }
+  .bp3-dark .bp3-running-text table td, .bp3-running-text .bp3-dark table td, .bp3-dark table.bp3-html-table td{
+    color:#f5f8fa; }
+  .bp3-dark .bp3-running-text table tbody tr:first-child th, .bp3-running-text .bp3-dark table tbody tr:first-child th, .bp3-dark table.bp3-html-table tbody tr:first-child th,
+  .bp3-dark .bp3-running-text table tbody tr:first-child td,
+  .bp3-running-text .bp3-dark table tbody tr:first-child td,
+  .bp3-dark table.bp3-html-table tbody tr:first-child td{
+    -webkit-box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15);
+            box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15); }
+
+table.bp3-html-table.bp3-html-table-condensed th,
+table.bp3-html-table.bp3-html-table-condensed td, table.bp3-html-table.bp3-small th,
+table.bp3-html-table.bp3-small td{
+  padding-top:6px;
+  padding-bottom:6px; }
+
+table.bp3-html-table.bp3-html-table-striped tbody tr:nth-child(odd) td{
+  background:rgba(191, 204, 214, 0.15); }
+
+table.bp3-html-table.bp3-html-table-bordered th:not(:first-child){
+  -webkit-box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15);
+          box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15); }
+
+table.bp3-html-table.bp3-html-table-bordered tbody tr td{
+  -webkit-box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15);
+          box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15); }
+  table.bp3-html-table.bp3-html-table-bordered tbody tr td:not(:first-child){
+    -webkit-box-shadow:inset 1px 1px 0 0 rgba(16, 22, 26, 0.15);
+            box-shadow:inset 1px 1px 0 0 rgba(16, 22, 26, 0.15); }
+
+table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td{
+  -webkit-box-shadow:none;
+          box-shadow:none; }
+  table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td:not(:first-child){
+    -webkit-box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15);
+            box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15); }
+
+table.bp3-html-table.bp3-interactive tbody tr:hover td{
+  background-color:rgba(191, 204, 214, 0.3);
+  cursor:pointer; }
+
+table.bp3-html-table.bp3-interactive tbody tr:active td{
+  background-color:rgba(191, 204, 214, 0.4); }
+
+.bp3-dark table.bp3-html-table.bp3-html-table-striped tbody tr:nth-child(odd) td{
+  background:rgba(92, 112, 128, 0.15); }
+
+.bp3-dark table.bp3-html-table.bp3-html-table-bordered th:not(:first-child){
+  -webkit-box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15);
+          box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15); }
+
+.bp3-dark table.bp3-html-table.bp3-html-table-bordered tbody tr td{
+  -webkit-box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15);
+          box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15); }
+  .bp3-dark table.bp3-html-table.bp3-html-table-bordered tbody tr td:not(:first-child){
+    -webkit-box-shadow:inset 1px 1px 0 0 rgba(255, 255, 255, 0.15);
+            box-shadow:inset 1px 1px 0 0 rgba(255, 255, 255, 0.15); }
+
+.bp3-dark table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td{
+  -webkit-box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15);
+          box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15); }
+  .bp3-dark table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td:first-child{
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+
+.bp3-dark table.bp3-html-table.bp3-interactive tbody tr:hover td{
+  background-color:rgba(92, 112, 128, 0.3);
+  cursor:pointer; }
+
+.bp3-dark table.bp3-html-table.bp3-interactive tbody tr:active td{
+  background-color:rgba(92, 112, 128, 0.4); }
+
+.bp3-key-combo{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center; }
+  .bp3-key-combo > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-key-combo > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-key-combo::before,
+  .bp3-key-combo > *{
+    margin-right:5px; }
+  .bp3-key-combo:empty::before,
+  .bp3-key-combo > :last-child{
+    margin-right:0; }
+
+.bp3-hotkey-dialog{
+  top:40px;
+  padding-bottom:0; }
+  .bp3-hotkey-dialog .bp3-dialog-body{
+    margin:0;
+    padding:0; }
+  .bp3-hotkey-dialog .bp3-hotkey-label{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1; }
+
+.bp3-hotkey-column{
+  margin:auto;
+  max-height:80vh;
+  overflow-y:auto;
+  padding:30px; }
+  .bp3-hotkey-column .bp3-heading{
+    margin-bottom:20px; }
+    .bp3-hotkey-column .bp3-heading:not(:first-child){
+      margin-top:40px; }
+
+.bp3-hotkey{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  -webkit-box-pack:justify;
+      -ms-flex-pack:justify;
+          justify-content:space-between;
+  margin-right:0;
+  margin-left:0; }
+  .bp3-hotkey:not(:last-child){
+    margin-bottom:10px; }
+.bp3-icon{
+  display:inline-block;
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  vertical-align:text-bottom; }
+  .bp3-icon:not(:empty)::before{
+    content:"" !important;
+    content:unset !important; }
+  .bp3-icon > svg{
+    display:block; }
+    .bp3-icon > svg:not([fill]){
+      fill:currentColor; }
+
+.bp3-icon.bp3-intent-primary, .bp3-icon-standard.bp3-intent-primary, .bp3-icon-large.bp3-intent-primary{
+  color:#106ba3; }
+  .bp3-dark .bp3-icon.bp3-intent-primary, .bp3-dark .bp3-icon-standard.bp3-intent-primary, .bp3-dark .bp3-icon-large.bp3-intent-primary{
+    color:#48aff0; }
+
+.bp3-icon.bp3-intent-success, .bp3-icon-standard.bp3-intent-success, .bp3-icon-large.bp3-intent-success{
+  color:#0d8050; }
+  .bp3-dark .bp3-icon.bp3-intent-success, .bp3-dark .bp3-icon-standard.bp3-intent-success, .bp3-dark .bp3-icon-large.bp3-intent-success{
+    color:#3dcc91; }
+
+.bp3-icon.bp3-intent-warning, .bp3-icon-standard.bp3-intent-warning, .bp3-icon-large.bp3-intent-warning{
+  color:#bf7326; }
+  .bp3-dark .bp3-icon.bp3-intent-warning, .bp3-dark .bp3-icon-standard.bp3-intent-warning, .bp3-dark .bp3-icon-large.bp3-intent-warning{
+    color:#ffb366; }
+
+.bp3-icon.bp3-intent-danger, .bp3-icon-standard.bp3-intent-danger, .bp3-icon-large.bp3-intent-danger{
+  color:#c23030; }
+  .bp3-dark .bp3-icon.bp3-intent-danger, .bp3-dark .bp3-icon-standard.bp3-intent-danger, .bp3-dark .bp3-icon-large.bp3-intent-danger{
+    color:#ff7373; }
+
+span.bp3-icon-standard{
+  line-height:1;
+  font-family:"Icons16", sans-serif;
+  font-size:16px;
+  font-weight:400;
+  font-style:normal;
+  -moz-osx-font-smoothing:grayscale;
+  -webkit-font-smoothing:antialiased;
+  display:inline-block; }
+
+span.bp3-icon-large{
+  line-height:1;
+  font-family:"Icons20", sans-serif;
+  font-size:20px;
+  font-weight:400;
+  font-style:normal;
+  -moz-osx-font-smoothing:grayscale;
+  -webkit-font-smoothing:antialiased;
+  display:inline-block; }
+
+span.bp3-icon:empty{
+  line-height:1;
+  font-family:"Icons20";
+  font-size:inherit;
+  font-weight:400;
+  font-style:normal; }
+  span.bp3-icon:empty::before{
+    -moz-osx-font-smoothing:grayscale;
+    -webkit-font-smoothing:antialiased; }
+
+.bp3-icon-add::before{
+  content:""; }
+
+.bp3-icon-add-column-left::before{
+  content:""; }
+
+.bp3-icon-add-column-right::before{
+  content:""; }
+
+.bp3-icon-add-row-bottom::before{
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+  content:""; }
+
+.bp3-icon-new-grid-item::before{
+  content:""; }
+
+.bp3-icon-new-layer::before{
+  content:""; }
+
+.bp3-icon-new-layers::before{
+  content:""; }
+
+.bp3-icon-new-link::before{
+  content:""; }
+
+.bp3-icon-new-object::before{
+  content:""; }
+
+.bp3-icon-new-person::before{
+  content:""; }
+
+.bp3-icon-new-prescription::before{
+  content:""; }
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+.bp3-icon-new-text-box::before{
+  content:""; }
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+.bp3-icon-ninja::before{
+  content:""; }
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+.bp3-icon-not-equal-to::before{
+  content:""; }
+
+.bp3-icon-notifications::before{
+  content:""; }
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+.bp3-icon-notifications-updated::before{
+  content:""; }
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+.bp3-icon-numbered-list::before{
+  content:""; }
+
+.bp3-icon-numerical::before{
+  content:""; }
+
+.bp3-icon-office::before{
+  content:""; }
+
+.bp3-icon-offline::before{
+  content:""; }
+
+.bp3-icon-oil-field::before{
+  content:""; }
+
+.bp3-icon-one-column::before{
+  content:""; }
+
+.bp3-icon-outdated::before{
+  content:""; }
+
+.bp3-icon-page-layout::before{
+  content:""; }
+
+.bp3-icon-panel-stats::before{
+  content:""; }
+
+.bp3-icon-panel-table::before{
+  content:""; }
+
+.bp3-icon-paperclip::before{
+  content:""; }
+
+.bp3-icon-paragraph::before{
+  content:""; }
+
+.bp3-icon-path::before{
+  content:""; }
+
+.bp3-icon-path-search::before{
+  content:""; }
+
+.bp3-icon-pause::before{
+  content:""; }
+
+.bp3-icon-people::before{
+  content:""; }
+
+.bp3-icon-percentage::before{
+  content:""; }
+
+.bp3-icon-person::before{
+  content:""; }
+
+.bp3-icon-phone::before{
+  content:"☎"; }
+
+.bp3-icon-pie-chart::before{
+  content:""; }
+
+.bp3-icon-pin::before{
+  content:""; }
+
+.bp3-icon-pivot::before{
+  content:""; }
+
+.bp3-icon-pivot-table::before{
+  content:""; }
+
+.bp3-icon-play::before{
+  content:""; }
+
+.bp3-icon-plus::before{
+  content:"+"; }
+
+.bp3-icon-polygon-filter::before{
+  content:""; }
+
+.bp3-icon-power::before{
+  content:""; }
+
+.bp3-icon-predictive-analysis::before{
+  content:""; }
+
+.bp3-icon-prescription::before{
+  content:""; }
+
+.bp3-icon-presentation::before{
+  content:""; }
+
+.bp3-icon-print::before{
+  content:"⎙"; }
+
+.bp3-icon-projects::before{
+  content:""; }
+
+.bp3-icon-properties::before{
+  content:""; }
+
+.bp3-icon-property::before{
+  content:""; }
+
+.bp3-icon-publish-function::before{
+  content:""; }
+
+.bp3-icon-pulse::before{
+  content:""; }
+
+.bp3-icon-random::before{
+  content:""; }
+
+.bp3-icon-record::before{
+  content:""; }
+
+.bp3-icon-redo::before{
+  content:""; }
+
+.bp3-icon-refresh::before{
+  content:""; }
+
+.bp3-icon-regression-chart::before{
+  content:""; }
+
+.bp3-icon-remove::before{
+  content:""; }
+
+.bp3-icon-remove-column::before{
+  content:""; }
+
+.bp3-icon-remove-column-left::before{
+  content:""; }
+
+.bp3-icon-remove-column-right::before{
+  content:""; }
+
+.bp3-icon-remove-row-bottom::before{
+  content:""; }
+
+.bp3-icon-remove-row-top::before{
+  content:""; }
+
+.bp3-icon-repeat::before{
+  content:""; }
+
+.bp3-icon-reset::before{
+  content:""; }
+
+.bp3-icon-resolve::before{
+  content:""; }
+
+.bp3-icon-rig::before{
+  content:""; }
+
+.bp3-icon-right-join::before{
+  content:""; }
+
+.bp3-icon-ring::before{
+  content:""; }
+
+.bp3-icon-rotate-document::before{
+  content:""; }
+
+.bp3-icon-rotate-page::before{
+  content:""; }
+
+.bp3-icon-satellite::before{
+  content:""; }
+
+.bp3-icon-saved::before{
+  content:""; }
+
+.bp3-icon-scatter-plot::before{
+  content:""; }
+
+.bp3-icon-search::before{
+  content:""; }
+
+.bp3-icon-search-around::before{
+  content:""; }
+
+.bp3-icon-search-template::before{
+  content:""; }
+
+.bp3-icon-search-text::before{
+  content:""; }
+
+.bp3-icon-segmented-control::before{
+  content:""; }
+
+.bp3-icon-select::before{
+  content:""; }
+
+.bp3-icon-selection::before{
+  content:"⦿"; }
+
+.bp3-icon-send-to::before{
+  content:""; }
+
+.bp3-icon-send-to-graph::before{
+  content:""; }
+
+.bp3-icon-send-to-map::before{
+  content:""; }
+
+.bp3-icon-series-add::before{
+  content:""; }
+
+.bp3-icon-series-configuration::before{
+  content:""; }
+
+.bp3-icon-series-derived::before{
+  content:""; }
+
+.bp3-icon-series-filtered::before{
+  content:""; }
+
+.bp3-icon-series-search::before{
+  content:""; }
+
+.bp3-icon-settings::before{
+  content:""; }
+
+.bp3-icon-share::before{
+  content:""; }
+
+.bp3-icon-shield::before{
+  content:""; }
+
+.bp3-icon-shop::before{
+  content:""; }
+
+.bp3-icon-shopping-cart::before{
+  content:""; }
+
+.bp3-icon-signal-search::before{
+  content:""; }
+
+.bp3-icon-sim-card::before{
+  content:""; }
+
+.bp3-icon-slash::before{
+  content:""; }
+
+.bp3-icon-small-cross::before{
+  content:""; }
+
+.bp3-icon-small-minus::before{
+  content:""; }
+
+.bp3-icon-small-plus::before{
+  content:""; }
+
+.bp3-icon-small-tick::before{
+  content:""; }
+
+.bp3-icon-snowflake::before{
+  content:""; }
+
+.bp3-icon-social-media::before{
+  content:""; }
+
+.bp3-icon-sort::before{
+  content:""; }
+
+.bp3-icon-sort-alphabetical::before{
+  content:""; }
+
+.bp3-icon-sort-alphabetical-desc::before{
+  content:""; }
+
+.bp3-icon-sort-asc::before{
+  content:""; }
+
+.bp3-icon-sort-desc::before{
+  content:""; }
+
+.bp3-icon-sort-numerical::before{
+  content:""; }
+
+.bp3-icon-sort-numerical-desc::before{
+  content:""; }
+
+.bp3-icon-split-columns::before{
+  content:""; }
+
+.bp3-icon-square::before{
+  content:""; }
+
+.bp3-icon-stacked-chart::before{
+  content:""; }
+
+.bp3-icon-star::before{
+  content:"★"; }
+
+.bp3-icon-star-empty::before{
+  content:"☆"; }
+
+.bp3-icon-step-backward::before{
+  content:""; }
+
+.bp3-icon-step-chart::before{
+  content:""; }
+
+.bp3-icon-step-forward::before{
+  content:""; }
+
+.bp3-icon-stop::before{
+  content:""; }
+
+.bp3-icon-stopwatch::before{
+  content:""; }
+
+.bp3-icon-strikethrough::before{
+  content:""; }
+
+.bp3-icon-style::before{
+  content:""; }
+
+.bp3-icon-swap-horizontal::before{
+  content:""; }
+
+.bp3-icon-swap-vertical::before{
+  content:""; }
+
+.bp3-icon-symbol-circle::before{
+  content:""; }
+
+.bp3-icon-symbol-cross::before{
+  content:""; }
+
+.bp3-icon-symbol-diamond::before{
+  content:""; }
+
+.bp3-icon-symbol-square::before{
+  content:""; }
+
+.bp3-icon-symbol-triangle-down::before{
+  content:""; }
+
+.bp3-icon-symbol-triangle-up::before{
+  content:""; }
+
+.bp3-icon-tag::before{
+  content:""; }
+
+.bp3-icon-take-action::before{
+  content:""; }
+
+.bp3-icon-taxi::before{
+  content:""; }
+
+.bp3-icon-text-highlight::before{
+  content:""; }
+
+.bp3-icon-th::before{
+  content:""; }
+
+.bp3-icon-th-derived::before{
+  content:""; }
+
+.bp3-icon-th-disconnect::before{
+  content:""; }
+
+.bp3-icon-th-filtered::before{
+  content:""; }
+
+.bp3-icon-th-list::before{
+  content:""; }
+
+.bp3-icon-thumbs-down::before{
+  content:""; }
+
+.bp3-icon-thumbs-up::before{
+  content:""; }
+
+.bp3-icon-tick::before{
+  content:"✓"; }
+
+.bp3-icon-tick-circle::before{
+  content:""; }
+
+.bp3-icon-time::before{
+  content:"⏲"; }
+
+.bp3-icon-timeline-area-chart::before{
+  content:""; }
+
+.bp3-icon-timeline-bar-chart::before{
+  content:""; }
+
+.bp3-icon-timeline-events::before{
+  content:""; }
+
+.bp3-icon-timeline-line-chart::before{
+  content:""; }
+
+.bp3-icon-tint::before{
+  content:""; }
+
+.bp3-icon-torch::before{
+  content:""; }
+
+.bp3-icon-tractor::before{
+  content:""; }
+
+.bp3-icon-train::before{
+  content:""; }
+
+.bp3-icon-translate::before{
+  content:""; }
+
+.bp3-icon-trash::before{
+  content:""; }
+
+.bp3-icon-tree::before{
+  content:""; }
+
+.bp3-icon-trending-down::before{
+  content:""; }
+
+.bp3-icon-trending-up::before{
+  content:""; }
+
+.bp3-icon-truck::before{
+  content:""; }
+
+.bp3-icon-two-columns::before{
+  content:""; }
+
+.bp3-icon-unarchive::before{
+  content:""; }
+
+.bp3-icon-underline::before{
+  content:"⎁"; }
+
+.bp3-icon-undo::before{
+  content:"⎌"; }
+
+.bp3-icon-ungroup-objects::before{
+  content:""; }
+
+.bp3-icon-unknown-vehicle::before{
+  content:""; }
+
+.bp3-icon-unlock::before{
+  content:""; }
+
+.bp3-icon-unpin::before{
+  content:""; }
+
+.bp3-icon-unresolve::before{
+  content:""; }
+
+.bp3-icon-updated::before{
+  content:""; }
+
+.bp3-icon-upload::before{
+  content:""; }
+
+.bp3-icon-user::before{
+  content:""; }
+
+.bp3-icon-variable::before{
+  content:""; }
+
+.bp3-icon-vertical-bar-chart-asc::before{
+  content:""; }
+
+.bp3-icon-vertical-bar-chart-desc::before{
+  content:""; }
+
+.bp3-icon-vertical-distribution::before{
+  content:""; }
+
+.bp3-icon-video::before{
+  content:""; }
+
+.bp3-icon-volume-down::before{
+  content:""; }
+
+.bp3-icon-volume-off::before{
+  content:""; }
+
+.bp3-icon-volume-up::before{
+  content:""; }
+
+.bp3-icon-walk::before{
+  content:""; }
+
+.bp3-icon-warning-sign::before{
+  content:""; }
+
+.bp3-icon-waterfall-chart::before{
+  content:""; }
+
+.bp3-icon-widget::before{
+  content:""; }
+
+.bp3-icon-widget-button::before{
+  content:""; }
+
+.bp3-icon-widget-footer::before{
+  content:""; }
+
+.bp3-icon-widget-header::before{
+  content:""; }
+
+.bp3-icon-wrench::before{
+  content:""; }
+
+.bp3-icon-zoom-in::before{
+  content:""; }
+
+.bp3-icon-zoom-out::before{
+  content:""; }
+
+.bp3-icon-zoom-to-fit::before{
+  content:""; }
+.bp3-submenu > .bp3-popover-wrapper{
+  display:block; }
+
+.bp3-submenu .bp3-popover-target{
+  display:block; }
+
+.bp3-submenu.bp3-popover{
+  -webkit-box-shadow:none;
+          box-shadow:none;
+  padding:0 5px; }
+  .bp3-submenu.bp3-popover > .bp3-popover-content{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2); }
+  .bp3-dark .bp3-submenu.bp3-popover, .bp3-submenu.bp3-popover.bp3-dark{
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+    .bp3-dark .bp3-submenu.bp3-popover > .bp3-popover-content, .bp3-submenu.bp3-popover.bp3-dark > .bp3-popover-content{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
+.bp3-menu{
+  margin:0;
+  border-radius:3px;
+  background:#ffffff;
+  min-width:180px;
+  padding:5px;
+  list-style:none;
+  text-align:left;
+  color:#182026; }
+
+.bp3-menu-divider{
+  display:block;
+  margin:5px;
+  border-top:1px solid rgba(16, 22, 26, 0.15); }
+  .bp3-dark .bp3-menu-divider{
+    border-top-color:rgba(255, 255, 255, 0.15); }
+
+.bp3-menu-item{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:start;
+      -ms-flex-align:start;
+          align-items:flex-start;
+  border-radius:2px;
+  padding:5px 7px;
+  text-decoration:none;
+  line-height:20px;
+  color:inherit;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-menu-item > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-menu-item > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-menu-item::before,
+  .bp3-menu-item > *{
+    margin-right:7px; }
+  .bp3-menu-item:empty::before,
+  .bp3-menu-item > :last-child{
+    margin-right:0; }
+  .bp3-menu-item > .bp3-fill{
+    word-break:break-word; }
+  .bp3-menu-item:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item{
+    background-color:rgba(167, 182, 194, 0.3);
+    cursor:pointer;
+    text-decoration:none; }
+  .bp3-menu-item.bp3-disabled{
+    background-color:inherit;
+    cursor:not-allowed;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-dark .bp3-menu-item{
+    color:inherit; }
+    .bp3-dark .bp3-menu-item:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-menu-item{
+      background-color:rgba(138, 155, 168, 0.15);
+      color:inherit; }
+    .bp3-dark .bp3-menu-item.bp3-disabled{
+      background-color:inherit;
+      color:rgba(167, 182, 194, 0.6); }
+  .bp3-menu-item.bp3-intent-primary{
+    color:#106ba3; }
+    .bp3-menu-item.bp3-intent-primary .bp3-icon{
+      color:inherit; }
+    .bp3-menu-item.bp3-intent-primary::before, .bp3-menu-item.bp3-intent-primary::after,
+    .bp3-menu-item.bp3-intent-primary .bp3-menu-item-label{
+      color:#106ba3; }
+    .bp3-menu-item.bp3-intent-primary:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-menu-item.bp3-intent-primary.bp3-active{
+      background-color:#137cbd; }
+    .bp3-menu-item.bp3-intent-primary:active{
+      background-color:#106ba3; }
+    .bp3-menu-item.bp3-intent-primary:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-menu-item.bp3-intent-primary:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-menu-item.bp3-intent-primary:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after,
+    .bp3-menu-item.bp3-intent-primary:hover .bp3-menu-item-label,
+    .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-primary:active, .bp3-menu-item.bp3-intent-primary:active::before, .bp3-menu-item.bp3-intent-primary:active::after,
+    .bp3-menu-item.bp3-intent-primary:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-primary.bp3-active, .bp3-menu-item.bp3-intent-primary.bp3-active::before, .bp3-menu-item.bp3-intent-primary.bp3-active::after,
+    .bp3-menu-item.bp3-intent-primary.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-menu-item.bp3-intent-success{
+    color:#0d8050; }
+    .bp3-menu-item.bp3-intent-success .bp3-icon{
+      color:inherit; }
+    .bp3-menu-item.bp3-intent-success::before, .bp3-menu-item.bp3-intent-success::after,
+    .bp3-menu-item.bp3-intent-success .bp3-menu-item-label{
+      color:#0d8050; }
+    .bp3-menu-item.bp3-intent-success:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-menu-item.bp3-intent-success.bp3-active{
+      background-color:#0f9960; }
+    .bp3-menu-item.bp3-intent-success:active{
+      background-color:#0d8050; }
+    .bp3-menu-item.bp3-intent-success:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-menu-item.bp3-intent-success:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-menu-item.bp3-intent-success:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after,
+    .bp3-menu-item.bp3-intent-success:hover .bp3-menu-item-label,
+    .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-success:active, .bp3-menu-item.bp3-intent-success:active::before, .bp3-menu-item.bp3-intent-success:active::after,
+    .bp3-menu-item.bp3-intent-success:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-success.bp3-active, .bp3-menu-item.bp3-intent-success.bp3-active::before, .bp3-menu-item.bp3-intent-success.bp3-active::after,
+    .bp3-menu-item.bp3-intent-success.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-menu-item.bp3-intent-warning{
+    color:#bf7326; }
+    .bp3-menu-item.bp3-intent-warning .bp3-icon{
+      color:inherit; }
+    .bp3-menu-item.bp3-intent-warning::before, .bp3-menu-item.bp3-intent-warning::after,
+    .bp3-menu-item.bp3-intent-warning .bp3-menu-item-label{
+      color:#bf7326; }
+    .bp3-menu-item.bp3-intent-warning:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-menu-item.bp3-intent-warning.bp3-active{
+      background-color:#d9822b; }
+    .bp3-menu-item.bp3-intent-warning:active{
+      background-color:#bf7326; }
+    .bp3-menu-item.bp3-intent-warning:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-menu-item.bp3-intent-warning:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-menu-item.bp3-intent-warning:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after,
+    .bp3-menu-item.bp3-intent-warning:hover .bp3-menu-item-label,
+    .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-warning:active, .bp3-menu-item.bp3-intent-warning:active::before, .bp3-menu-item.bp3-intent-warning:active::after,
+    .bp3-menu-item.bp3-intent-warning:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-warning.bp3-active, .bp3-menu-item.bp3-intent-warning.bp3-active::before, .bp3-menu-item.bp3-intent-warning.bp3-active::after,
+    .bp3-menu-item.bp3-intent-warning.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-menu-item.bp3-intent-danger{
+    color:#c23030; }
+    .bp3-menu-item.bp3-intent-danger .bp3-icon{
+      color:inherit; }
+    .bp3-menu-item.bp3-intent-danger::before, .bp3-menu-item.bp3-intent-danger::after,
+    .bp3-menu-item.bp3-intent-danger .bp3-menu-item-label{
+      color:#c23030; }
+    .bp3-menu-item.bp3-intent-danger:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-menu-item.bp3-intent-danger.bp3-active{
+      background-color:#db3737; }
+    .bp3-menu-item.bp3-intent-danger:active{
+      background-color:#c23030; }
+    .bp3-menu-item.bp3-intent-danger:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-menu-item.bp3-intent-danger:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-menu-item.bp3-intent-danger:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after,
+    .bp3-menu-item.bp3-intent-danger:hover .bp3-menu-item-label,
+    .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-danger:active, .bp3-menu-item.bp3-intent-danger:active::before, .bp3-menu-item.bp3-intent-danger:active::after,
+    .bp3-menu-item.bp3-intent-danger:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-danger.bp3-active, .bp3-menu-item.bp3-intent-danger.bp3-active::before, .bp3-menu-item.bp3-intent-danger.bp3-active::after,
+    .bp3-menu-item.bp3-intent-danger.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-menu-item::before{
+    line-height:1;
+    font-family:"Icons16", sans-serif;
+    font-size:16px;
+    font-weight:400;
+    font-style:normal;
+    -moz-osx-font-smoothing:grayscale;
+    -webkit-font-smoothing:antialiased;
+    margin-right:7px; }
+  .bp3-menu-item::before,
+  .bp3-menu-item > .bp3-icon{
+    margin-top:2px;
+    color:#5c7080; }
+  .bp3-menu-item .bp3-menu-item-label{
+    color:#5c7080; }
+  .bp3-menu-item:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item{
+    color:inherit; }
+  .bp3-menu-item.bp3-active, .bp3-menu-item:active{
+    background-color:rgba(115, 134, 148, 0.3); }
+  .bp3-menu-item.bp3-disabled{
+    outline:none !important;
+    background-color:inherit !important;
+    cursor:not-allowed !important;
+    color:rgba(92, 112, 128, 0.6) !important; }
+    .bp3-menu-item.bp3-disabled::before,
+    .bp3-menu-item.bp3-disabled > .bp3-icon,
+    .bp3-menu-item.bp3-disabled .bp3-menu-item-label{
+      color:rgba(92, 112, 128, 0.6) !important; }
+  .bp3-large .bp3-menu-item{
+    padding:9px 7px;
+    line-height:22px;
+    font-size:16px; }
+    .bp3-large .bp3-menu-item .bp3-icon{
+      margin-top:3px; }
+    .bp3-large .bp3-menu-item::before{
+      line-height:1;
+      font-family:"Icons20", sans-serif;
+      font-size:20px;
+      font-weight:400;
+      font-style:normal;
+      -moz-osx-font-smoothing:grayscale;
+      -webkit-font-smoothing:antialiased;
+      margin-top:1px;
+      margin-right:10px; }
+
+button.bp3-menu-item{
+  border:none;
+  background:none;
+  width:100%;
+  text-align:left; }
+.bp3-menu-header{
+  display:block;
+  margin:5px;
+  border-top:1px solid rgba(16, 22, 26, 0.15);
+  cursor:default;
+  padding-left:2px; }
+  .bp3-dark .bp3-menu-header{
+    border-top-color:rgba(255, 255, 255, 0.15); }
+  .bp3-menu-header:first-of-type{
+    border-top:none; }
+  .bp3-menu-header > h6{
+    color:#182026;
+    font-weight:600;
+    overflow:hidden;
+    text-overflow:ellipsis;
+    white-space:nowrap;
+    word-wrap:normal;
+    margin:0;
+    padding:10px 7px 0 1px;
+    line-height:17px; }
+    .bp3-dark .bp3-menu-header > h6{
+      color:#f5f8fa; }
+  .bp3-menu-header:first-of-type > h6{
+    padding-top:0; }
+  .bp3-large .bp3-menu-header > h6{
+    padding-top:15px;
+    padding-bottom:5px;
+    font-size:18px; }
+  .bp3-large .bp3-menu-header:first-of-type > h6{
+    padding-top:0; }
+
+.bp3-dark .bp3-menu{
+  background:#30404d;
+  color:#f5f8fa; }
+
+.bp3-dark .bp3-menu-item.bp3-intent-primary{
+  color:#48aff0; }
+  .bp3-dark .bp3-menu-item.bp3-intent-primary .bp3-icon{
+    color:inherit; }
+  .bp3-dark .bp3-menu-item.bp3-intent-primary::before, .bp3-dark .bp3-menu-item.bp3-intent-primary::after,
+  .bp3-dark .bp3-menu-item.bp3-intent-primary .bp3-menu-item-label{
+    color:#48aff0; }
+  .bp3-dark .bp3-menu-item.bp3-intent-primary:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active{
+    background-color:#137cbd; }
+  .bp3-dark .bp3-menu-item.bp3-intent-primary:active{
+    background-color:#106ba3; }
+  .bp3-dark .bp3-menu-item.bp3-intent-primary:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-primary:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-primary:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after,
+  .bp3-dark .bp3-menu-item.bp3-intent-primary:hover .bp3-menu-item-label,
+  .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label,
+  .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-primary:active, .bp3-dark .bp3-menu-item.bp3-intent-primary:active::before, .bp3-dark .bp3-menu-item.bp3-intent-primary:active::after,
+  .bp3-dark .bp3-menu-item.bp3-intent-primary:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active::after,
+  .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active .bp3-menu-item-label{
+    color:#ffffff; }
+
+.bp3-dark .bp3-menu-item.bp3-intent-success{
+  color:#3dcc91; }
+  .bp3-dark .bp3-menu-item.bp3-intent-success .bp3-icon{
+    color:inherit; }
+  .bp3-dark .bp3-menu-item.bp3-intent-success::before, .bp3-dark .bp3-menu-item.bp3-intent-success::after,
+  .bp3-dark .bp3-menu-item.bp3-intent-success .bp3-menu-item-label{
+    color:#3dcc91; }
+  .bp3-dark .bp3-menu-item.bp3-intent-success:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active{
+    background-color:#0f9960; }
+  .bp3-dark .bp3-menu-item.bp3-intent-success:active{
+    background-color:#0d8050; }
+  .bp3-dark .bp3-menu-item.bp3-intent-success:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-success:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-success:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after,
+  .bp3-dark .bp3-menu-item.bp3-intent-success:hover .bp3-menu-item-label,
+  .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label,
+  .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-success:active, .bp3-dark .bp3-menu-item.bp3-intent-success:active::before, .bp3-dark .bp3-menu-item.bp3-intent-success:active::after,
+  .bp3-dark .bp3-menu-item.bp3-intent-success:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active::after,
+  .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active .bp3-menu-item-label{
+    color:#ffffff; }
+
+.bp3-dark .bp3-menu-item.bp3-intent-warning{
+  color:#ffb366; }
+  .bp3-dark .bp3-menu-item.bp3-intent-warning .bp3-icon{
+    color:inherit; }
+  .bp3-dark .bp3-menu-item.bp3-intent-warning::before, .bp3-dark .bp3-menu-item.bp3-intent-warning::after,
+  .bp3-dark .bp3-menu-item.bp3-intent-warning .bp3-menu-item-label{
+    color:#ffb366; }
+  .bp3-dark .bp3-menu-item.bp3-intent-warning:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active{
+    background-color:#d9822b; }
+  .bp3-dark .bp3-menu-item.bp3-intent-warning:active{
+    background-color:#bf7326; }
+  .bp3-dark .bp3-menu-item.bp3-intent-warning:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-warning:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-warning:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after,
+  .bp3-dark .bp3-menu-item.bp3-intent-warning:hover .bp3-menu-item-label,
+  .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label,
+  .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-warning:active, .bp3-dark .bp3-menu-item.bp3-intent-warning:active::before, .bp3-dark .bp3-menu-item.bp3-intent-warning:active::after,
+  .bp3-dark .bp3-menu-item.bp3-intent-warning:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active::after,
+  .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active .bp3-menu-item-label{
+    color:#ffffff; }
+
+.bp3-dark .bp3-menu-item.bp3-intent-danger{
+  color:#ff7373; }
+  .bp3-dark .bp3-menu-item.bp3-intent-danger .bp3-icon{
+    color:inherit; }
+  .bp3-dark .bp3-menu-item.bp3-intent-danger::before, .bp3-dark .bp3-menu-item.bp3-intent-danger::after,
+  .bp3-dark .bp3-menu-item.bp3-intent-danger .bp3-menu-item-label{
+    color:#ff7373; }
+  .bp3-dark .bp3-menu-item.bp3-intent-danger:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active{
+    background-color:#db3737; }
+  .bp3-dark .bp3-menu-item.bp3-intent-danger:active{
+    background-color:#c23030; }
+  .bp3-dark .bp3-menu-item.bp3-intent-danger:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-danger:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-danger:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after,
+  .bp3-dark .bp3-menu-item.bp3-intent-danger:hover .bp3-menu-item-label,
+  .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label,
+  .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-danger:active, .bp3-dark .bp3-menu-item.bp3-intent-danger:active::before, .bp3-dark .bp3-menu-item.bp3-intent-danger:active::after,
+  .bp3-dark .bp3-menu-item.bp3-intent-danger:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active::after,
+  .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active .bp3-menu-item-label{
+    color:#ffffff; }
+
+.bp3-dark .bp3-menu-item::before,
+.bp3-dark .bp3-menu-item > .bp3-icon{
+  color:#a7b6c2; }
+
+.bp3-dark .bp3-menu-item .bp3-menu-item-label{
+  color:#a7b6c2; }
+
+.bp3-dark .bp3-menu-item.bp3-active, .bp3-dark .bp3-menu-item:active{
+  background-color:rgba(138, 155, 168, 0.3); }
+
+.bp3-dark .bp3-menu-item.bp3-disabled{
+  color:rgba(167, 182, 194, 0.6) !important; }
+  .bp3-dark .bp3-menu-item.bp3-disabled::before,
+  .bp3-dark .bp3-menu-item.bp3-disabled > .bp3-icon,
+  .bp3-dark .bp3-menu-item.bp3-disabled .bp3-menu-item-label{
+    color:rgba(167, 182, 194, 0.6) !important; }
+
+.bp3-dark .bp3-menu-divider,
+.bp3-dark .bp3-menu-header{
+  border-color:rgba(255, 255, 255, 0.15); }
+
+.bp3-dark .bp3-menu-header > h6{
+  color:#f5f8fa; }
+
+.bp3-label .bp3-menu{
+  margin-top:5px; }
+.bp3-navbar{
+  position:relative;
+  z-index:10;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+  background-color:#ffffff;
+  width:100%;
+  height:50px;
+  padding:0 15px; }
+  .bp3-navbar.bp3-dark,
+  .bp3-dark .bp3-navbar{
+    background-color:#394b59; }
+  .bp3-navbar.bp3-dark{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-navbar{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }
+  .bp3-navbar.bp3-fixed-top{
+    position:fixed;
+    top:0;
+    right:0;
+    left:0; }
+
+.bp3-navbar-heading{
+  margin-right:15px;
+  font-size:16px; }
+
+.bp3-navbar-group{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  height:50px; }
+  .bp3-navbar-group.bp3-align-left{
+    float:left; }
+  .bp3-navbar-group.bp3-align-right{
+    float:right; }
+
+.bp3-navbar-divider{
+  margin:0 10px;
+  border-left:1px solid rgba(16, 22, 26, 0.15);
+  height:20px; }
+  .bp3-dark .bp3-navbar-divider{
+    border-left-color:rgba(255, 255, 255, 0.15); }
+.bp3-non-ideal-state{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  width:100%;
+  height:100%;
+  text-align:center; }
+  .bp3-non-ideal-state > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-non-ideal-state > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-non-ideal-state::before,
+  .bp3-non-ideal-state > *{
+    margin-bottom:20px; }
+  .bp3-non-ideal-state:empty::before,
+  .bp3-non-ideal-state > :last-child{
+    margin-bottom:0; }
+  .bp3-non-ideal-state > *{
+    max-width:400px; }
+
+.bp3-non-ideal-state-visual{
+  color:rgba(92, 112, 128, 0.6);
+  font-size:60px; }
+  .bp3-dark .bp3-non-ideal-state-visual{
+    color:rgba(167, 182, 194, 0.6); }
+
+.bp3-overflow-list{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -ms-flex-wrap:nowrap;
+      flex-wrap:nowrap;
+  min-width:0; }
+
+.bp3-overflow-list-spacer{
+  -ms-flex-negative:1;
+      flex-shrink:1;
+  width:1px; }
+
+body.bp3-overlay-open{
+  overflow:hidden; }
+
+.bp3-overlay{
+  position:static;
+  top:0;
+  right:0;
+  bottom:0;
+  left:0;
+  z-index:20; }
+  .bp3-overlay:not(.bp3-overlay-open){
+    pointer-events:none; }
+  .bp3-overlay.bp3-overlay-container{
+    position:fixed;
+    overflow:hidden; }
+    .bp3-overlay.bp3-overlay-container.bp3-overlay-inline{
+      position:absolute; }
+  .bp3-overlay.bp3-overlay-scroll-container{
+    position:fixed;
+    overflow:auto; }
+    .bp3-overlay.bp3-overlay-scroll-container.bp3-overlay-inline{
+      position:absolute; }
+  .bp3-overlay.bp3-overlay-inline{
+    display:inline;
+    overflow:visible; }
+
+.bp3-overlay-content{
+  position:fixed;
+  z-index:20; }
+  .bp3-overlay-inline .bp3-overlay-content,
+  .bp3-overlay-scroll-container .bp3-overlay-content{
+    position:absolute; }
+
+.bp3-overlay-backdrop{
+  position:fixed;
+  top:0;
+  right:0;
+  bottom:0;
+  left:0;
+  opacity:1;
+  z-index:20;
+  background-color:rgba(16, 22, 26, 0.7);
+  overflow:auto;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-overlay-backdrop.bp3-overlay-enter, .bp3-overlay-backdrop.bp3-overlay-appear{
+    opacity:0; }
+  .bp3-overlay-backdrop.bp3-overlay-enter-active, .bp3-overlay-backdrop.bp3-overlay-appear-active{
+    opacity:1;
+    -webkit-transition-property:opacity;
+    transition-property:opacity;
+    -webkit-transition-duration:200ms;
+            transition-duration:200ms;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+    -webkit-transition-delay:0;
+            transition-delay:0; }
+  .bp3-overlay-backdrop.bp3-overlay-exit{
+    opacity:1; }
+  .bp3-overlay-backdrop.bp3-overlay-exit-active{
+    opacity:0;
+    -webkit-transition-property:opacity;
+    transition-property:opacity;
+    -webkit-transition-duration:200ms;
+            transition-duration:200ms;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+    -webkit-transition-delay:0;
+            transition-delay:0; }
+  .bp3-overlay-backdrop:focus{
+    outline:none; }
+  .bp3-overlay-inline .bp3-overlay-backdrop{
+    position:absolute; }
+.bp3-panel-stack{
+  position:relative;
+  overflow:hidden; }
+
+.bp3-panel-stack-header{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -ms-flex-negative:0;
+      flex-shrink:0;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  z-index:1;
+  -webkit-box-shadow:0 1px rgba(16, 22, 26, 0.15);
+          box-shadow:0 1px rgba(16, 22, 26, 0.15);
+  height:30px; }
+  .bp3-dark .bp3-panel-stack-header{
+    -webkit-box-shadow:0 1px rgba(255, 255, 255, 0.15);
+            box-shadow:0 1px rgba(255, 255, 255, 0.15); }
+  .bp3-panel-stack-header > span{
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    -webkit-box-flex:1;
+        -ms-flex:1;
+            flex:1;
+    -webkit-box-align:stretch;
+        -ms-flex-align:stretch;
+            align-items:stretch; }
+  .bp3-panel-stack-header .bp3-heading{
+    margin:0 5px; }
+
+.bp3-button.bp3-panel-stack-header-back{
+  margin-left:5px;
+  padding-left:0;
+  white-space:nowrap; }
+  .bp3-button.bp3-panel-stack-header-back .bp3-icon{
+    margin:0 2px; }
+
+.bp3-panel-stack-view{
+  position:absolute;
+  top:0;
+  right:0;
+  bottom:0;
+  left:0;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  margin-right:-1px;
+  border-right:1px solid rgba(16, 22, 26, 0.15);
+  background-color:#ffffff;
+  overflow-y:auto; }
+  .bp3-dark .bp3-panel-stack-view{
+    background-color:#30404d; }
+
+.bp3-panel-stack-push .bp3-panel-stack-enter, .bp3-panel-stack-push .bp3-panel-stack-appear{
+  -webkit-transform:translateX(100%);
+          transform:translateX(100%);
+  opacity:0; }
+
+.bp3-panel-stack-push .bp3-panel-stack-enter-active, .bp3-panel-stack-push .bp3-panel-stack-appear-active{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease;
+  -webkit-transition-delay:0;
+          transition-delay:0; }
+
+.bp3-panel-stack-push .bp3-panel-stack-exit{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1; }
+
+.bp3-panel-stack-push .bp3-panel-stack-exit-active{
+  -webkit-transform:translateX(-50%);
+          transform:translateX(-50%);
+  opacity:0;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease;
+  -webkit-transition-delay:0;
+          transition-delay:0; }
+
+.bp3-panel-stack-pop .bp3-panel-stack-enter, .bp3-panel-stack-pop .bp3-panel-stack-appear{
+  -webkit-transform:translateX(-50%);
+          transform:translateX(-50%);
+  opacity:0; }
+
+.bp3-panel-stack-pop .bp3-panel-stack-enter-active, .bp3-panel-stack-pop .bp3-panel-stack-appear-active{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease;
+  -webkit-transition-delay:0;
+          transition-delay:0; }
+
+.bp3-panel-stack-pop .bp3-panel-stack-exit{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1; }
+
+.bp3-panel-stack-pop .bp3-panel-stack-exit-active{
+  -webkit-transform:translateX(100%);
+          transform:translateX(100%);
+  opacity:0;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease;
+  -webkit-transition-delay:0;
+          transition-delay:0; }
+.bp3-popover{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+  -webkit-transform:scale(1);
+          transform:scale(1);
+  display:inline-block;
+  z-index:20;
+  border-radius:3px; }
+  .bp3-popover .bp3-popover-arrow{
+    position:absolute;
+    width:30px;
+    height:30px; }
+    .bp3-popover .bp3-popover-arrow::before{
+      margin:5px;
+      width:20px;
+      height:20px; }
+  .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover{
+    margin-top:-17px;
+    margin-bottom:17px; }
+    .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover > .bp3-popover-arrow{
+      bottom:-11px; }
+      .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(-90deg);
+                transform:rotate(-90deg); }
+  .bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-popover{
+    margin-left:17px; }
+    .bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-popover > .bp3-popover-arrow{
+      left:-11px; }
+      .bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-popover > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(0);
+                transform:rotate(0); }
+  .bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-popover{
+    margin-top:17px; }
+    .bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-popover > .bp3-popover-arrow{
+      top:-11px; }
+      .bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-popover > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(90deg);
+                transform:rotate(90deg); }
+  .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-popover{
+    margin-right:17px;
+    margin-left:-17px; }
+    .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-popover > .bp3-popover-arrow{
+      right:-11px; }
+      .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-popover > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(180deg);
+                transform:rotate(180deg); }
+  .bp3-tether-element-attached-middle > .bp3-popover > .bp3-popover-arrow{
+    top:50%;
+    -webkit-transform:translateY(-50%);
+            transform:translateY(-50%); }
+  .bp3-tether-element-attached-center > .bp3-popover > .bp3-popover-arrow{
+    right:50%;
+    -webkit-transform:translateX(50%);
+            transform:translateX(50%); }
+  .bp3-tether-element-attached-top.bp3-tether-target-attached-top > .bp3-popover > .bp3-popover-arrow{
+    top:-0.3934px; }
+  .bp3-tether-element-attached-right.bp3-tether-target-attached-right > .bp3-popover > .bp3-popover-arrow{
+    right:-0.3934px; }
+  .bp3-tether-element-attached-left.bp3-tether-target-attached-left > .bp3-popover > .bp3-popover-arrow{
+    left:-0.3934px; }
+  .bp3-tether-element-attached-bottom.bp3-tether-target-attached-bottom > .bp3-popover > .bp3-popover-arrow{
+    bottom:-0.3934px; }
+  .bp3-tether-element-attached-top.bp3-tether-element-attached-left > .bp3-popover{
+    -webkit-transform-origin:top left;
+            transform-origin:top left; }
+  .bp3-tether-element-attached-top.bp3-tether-element-attached-center > .bp3-popover{
+    -webkit-transform-origin:top center;
+            transform-origin:top center; }
+  .bp3-tether-element-attached-top.bp3-tether-element-attached-right > .bp3-popover{
+    -webkit-transform-origin:top right;
+            transform-origin:top right; }
+  .bp3-tether-element-attached-middle.bp3-tether-element-attached-left > .bp3-popover{
+    -webkit-transform-origin:center left;
+            transform-origin:center left; }
+  .bp3-tether-element-attached-middle.bp3-tether-element-attached-center > .bp3-popover{
+    -webkit-transform-origin:center center;
+            transform-origin:center center; }
+  .bp3-tether-element-attached-middle.bp3-tether-element-attached-right > .bp3-popover{
+    -webkit-transform-origin:center right;
+            transform-origin:center right; }
+  .bp3-tether-element-attached-bottom.bp3-tether-element-attached-left > .bp3-popover{
+    -webkit-transform-origin:bottom left;
+            transform-origin:bottom left; }
+  .bp3-tether-element-attached-bottom.bp3-tether-element-attached-center > .bp3-popover{
+    -webkit-transform-origin:bottom center;
+            transform-origin:bottom center; }
+  .bp3-tether-element-attached-bottom.bp3-tether-element-attached-right > .bp3-popover{
+    -webkit-transform-origin:bottom right;
+            transform-origin:bottom right; }
+  .bp3-popover .bp3-popover-content{
+    background:#ffffff;
+    color:inherit; }
+  .bp3-popover .bp3-popover-arrow::before{
+    -webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2);
+            box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2); }
+  .bp3-popover .bp3-popover-arrow-border{
+    fill:#10161a;
+    fill-opacity:0.1; }
+  .bp3-popover .bp3-popover-arrow-fill{
+    fill:#ffffff; }
+  .bp3-popover-enter > .bp3-popover, .bp3-popover-appear > .bp3-popover{
+    -webkit-transform:scale(0.3);
+            transform:scale(0.3); }
+  .bp3-popover-enter-active > .bp3-popover, .bp3-popover-appear-active > .bp3-popover{
+    -webkit-transform:scale(1);
+            transform:scale(1);
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+            transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+    -webkit-transition-delay:0;
+            transition-delay:0; }
+  .bp3-popover-exit > .bp3-popover{
+    -webkit-transform:scale(1);
+            transform:scale(1); }
+  .bp3-popover-exit-active > .bp3-popover{
+    -webkit-transform:scale(0.3);
+            transform:scale(0.3);
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+            transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+    -webkit-transition-delay:0;
+            transition-delay:0; }
+  .bp3-popover .bp3-popover-content{
+    position:relative;
+    border-radius:3px; }
+  .bp3-popover.bp3-popover-content-sizing .bp3-popover-content{
+    max-width:350px;
+    padding:20px; }
+  .bp3-popover-target + .bp3-overlay .bp3-popover.bp3-popover-content-sizing{
+    width:350px; }
+  .bp3-popover.bp3-minimal{
+    margin:0 !important; }
+    .bp3-popover.bp3-minimal .bp3-popover-arrow{
+      display:none; }
+    .bp3-popover.bp3-minimal.bp3-popover{
+      -webkit-transform:scale(1);
+              transform:scale(1); }
+      .bp3-popover-enter > .bp3-popover.bp3-minimal.bp3-popover, .bp3-popover-appear > .bp3-popover.bp3-minimal.bp3-popover{
+        -webkit-transform:scale(1);
+                transform:scale(1); }
+      .bp3-popover-enter-active > .bp3-popover.bp3-minimal.bp3-popover, .bp3-popover-appear-active > .bp3-popover.bp3-minimal.bp3-popover{
+        -webkit-transform:scale(1);
+                transform:scale(1);
+        -webkit-transition-property:-webkit-transform;
+        transition-property:-webkit-transform;
+        transition-property:transform;
+        transition-property:transform, -webkit-transform;
+        -webkit-transition-duration:100ms;
+                transition-duration:100ms;
+        -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+                transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+        -webkit-transition-delay:0;
+                transition-delay:0; }
+      .bp3-popover-exit > .bp3-popover.bp3-minimal.bp3-popover{
+        -webkit-transform:scale(1);
+                transform:scale(1); }
+      .bp3-popover-exit-active > .bp3-popover.bp3-minimal.bp3-popover{
+        -webkit-transform:scale(1);
+                transform:scale(1);
+        -webkit-transition-property:-webkit-transform;
+        transition-property:-webkit-transform;
+        transition-property:transform;
+        transition-property:transform, -webkit-transform;
+        -webkit-transition-duration:100ms;
+                transition-duration:100ms;
+        -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+                transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+        -webkit-transition-delay:0;
+                transition-delay:0; }
+  .bp3-popover.bp3-dark,
+  .bp3-dark .bp3-popover{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
+    .bp3-popover.bp3-dark .bp3-popover-content,
+    .bp3-dark .bp3-popover .bp3-popover-content{
+      background:#30404d;
+      color:inherit; }
+    .bp3-popover.bp3-dark .bp3-popover-arrow::before,
+    .bp3-dark .bp3-popover .bp3-popover-arrow::before{
+      -webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4);
+              box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4); }
+    .bp3-popover.bp3-dark .bp3-popover-arrow-border,
+    .bp3-dark .bp3-popover .bp3-popover-arrow-border{
+      fill:#10161a;
+      fill-opacity:0.2; }
+    .bp3-popover.bp3-dark .bp3-popover-arrow-fill,
+    .bp3-dark .bp3-popover .bp3-popover-arrow-fill{
+      fill:#30404d; }
+
+.bp3-popover-arrow::before{
+  display:block;
+  position:absolute;
+  -webkit-transform:rotate(45deg);
+          transform:rotate(45deg);
+  border-radius:2px;
+  content:""; }
+
+.bp3-tether-pinned .bp3-popover-arrow{
+  display:none; }
+
+.bp3-popover-backdrop{
+  background:rgba(255, 255, 255, 0); }
+
+.bp3-transition-container{
+  opacity:1;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  z-index:20; }
+  .bp3-transition-container.bp3-popover-enter, .bp3-transition-container.bp3-popover-appear{
+    opacity:0; }
+  .bp3-transition-container.bp3-popover-enter-active, .bp3-transition-container.bp3-popover-appear-active{
+    opacity:1;
+    -webkit-transition-property:opacity;
+    transition-property:opacity;
+    -webkit-transition-duration:100ms;
+            transition-duration:100ms;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+    -webkit-transition-delay:0;
+            transition-delay:0; }
+  .bp3-transition-container.bp3-popover-exit{
+    opacity:1; }
+  .bp3-transition-container.bp3-popover-exit-active{
+    opacity:0;
+    -webkit-transition-property:opacity;
+    transition-property:opacity;
+    -webkit-transition-duration:100ms;
+            transition-duration:100ms;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+    -webkit-transition-delay:0;
+            transition-delay:0; }
+  .bp3-transition-container:focus{
+    outline:none; }
+  .bp3-transition-container.bp3-popover-leave .bp3-popover-content{
+    pointer-events:none; }
+  .bp3-transition-container[data-x-out-of-boundaries]{
+    display:none; }
+
+span.bp3-popover-target{
+  display:inline-block; }
+
+.bp3-popover-wrapper.bp3-fill{
+  width:100%; }
+
+.bp3-portal{
+  position:absolute;
+  top:0;
+  right:0;
+  left:0; }
+@-webkit-keyframes linear-progress-bar-stripes{
+  from{
+    background-position:0 0; }
+  to{
+    background-position:30px 0; } }
+@keyframes linear-progress-bar-stripes{
+  from{
+    background-position:0 0; }
+  to{
+    background-position:30px 0; } }
+
+.bp3-progress-bar{
+  display:block;
+  position:relative;
+  border-radius:40px;
+  background:rgba(92, 112, 128, 0.2);
+  width:100%;
+  height:8px;
+  overflow:hidden; }
+  .bp3-progress-bar .bp3-progress-meter{
+    position:absolute;
+    border-radius:40px;
+    background:linear-gradient(-45deg, rgba(255, 255, 255, 0.2) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.2) 50%, rgba(255, 255, 255, 0.2) 75%, transparent 75%);
+    background-color:rgba(92, 112, 128, 0.8);
+    background-size:30px 30px;
+    width:100%;
+    height:100%;
+    -webkit-transition:width 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:width 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-progress-bar:not(.bp3-no-animation):not(.bp3-no-stripes) .bp3-progress-meter{
+    animation:linear-progress-bar-stripes 300ms linear infinite reverse; }
+  .bp3-progress-bar.bp3-no-stripes .bp3-progress-meter{
+    background-image:none; }
+
+.bp3-dark .bp3-progress-bar{
+  background:rgba(16, 22, 26, 0.5); }
+  .bp3-dark .bp3-progress-bar .bp3-progress-meter{
+    background-color:#8a9ba8; }
+
+.bp3-progress-bar.bp3-intent-primary .bp3-progress-meter{
+  background-color:#137cbd; }
+
+.bp3-progress-bar.bp3-intent-success .bp3-progress-meter{
+  background-color:#0f9960; }
+
+.bp3-progress-bar.bp3-intent-warning .bp3-progress-meter{
+  background-color:#d9822b; }
+
+.bp3-progress-bar.bp3-intent-danger .bp3-progress-meter{
+  background-color:#db3737; }
+@-webkit-keyframes skeleton-glow{
+  from{
+    border-color:rgba(206, 217, 224, 0.2);
+    background:rgba(206, 217, 224, 0.2); }
+  to{
+    border-color:rgba(92, 112, 128, 0.2);
+    background:rgba(92, 112, 128, 0.2); } }
+@keyframes skeleton-glow{
+  from{
+    border-color:rgba(206, 217, 224, 0.2);
+    background:rgba(206, 217, 224, 0.2); }
+  to{
+    border-color:rgba(92, 112, 128, 0.2);
+    background:rgba(92, 112, 128, 0.2); } }
+.bp3-skeleton{
+  border-color:rgba(206, 217, 224, 0.2) !important;
+  border-radius:2px;
+  -webkit-box-shadow:none !important;
+          box-shadow:none !important;
+  background:rgba(206, 217, 224, 0.2);
+  background-clip:padding-box !important;
+  cursor:default;
+  color:transparent !important;
+  -webkit-animation:1000ms linear infinite alternate skeleton-glow;
+          animation:1000ms linear infinite alternate skeleton-glow;
+  pointer-events:none;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-skeleton::before, .bp3-skeleton::after,
+  .bp3-skeleton *{
+    visibility:hidden !important; }
+.bp3-slider{
+  width:100%;
+  min-width:150px;
+  height:40px;
+  position:relative;
+  outline:none;
+  cursor:default;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-slider:hover{
+    cursor:pointer; }
+  .bp3-slider:active{
+    cursor:-webkit-grabbing;
+    cursor:grabbing; }
+  .bp3-slider.bp3-disabled{
+    opacity:0.5;
+    cursor:not-allowed; }
+  .bp3-slider.bp3-slider-unlabeled{
+    height:16px; }
+
+.bp3-slider-track,
+.bp3-slider-progress{
+  top:5px;
+  right:0;
+  left:0;
+  height:6px;
+  position:absolute; }
+
+.bp3-slider-track{
+  border-radius:3px;
+  overflow:hidden; }
+
+.bp3-slider-progress{
+  background:rgba(92, 112, 128, 0.2); }
+  .bp3-dark .bp3-slider-progress{
+    background:rgba(16, 22, 26, 0.5); }
+  .bp3-slider-progress.bp3-intent-primary{
+    background-color:#137cbd; }
+  .bp3-slider-progress.bp3-intent-success{
+    background-color:#0f9960; }
+  .bp3-slider-progress.bp3-intent-warning{
+    background-color:#d9822b; }
+  .bp3-slider-progress.bp3-intent-danger{
+    background-color:#db3737; }
+
+.bp3-slider-handle{
+  -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+          box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+  background-color:#f5f8fa;
+  background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
+  background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
+  color:#182026;
+  position:absolute;
+  top:0;
+  left:0;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
+  cursor:pointer;
+  width:16px;
+  height:16px; }
+  .bp3-slider-handle:hover{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+    background-clip:padding-box;
+    background-color:#ebf1f5; }
+  .bp3-slider-handle:active, .bp3-slider-handle.bp3-active{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+    background-color:#d8e1e8;
+    background-image:none; }
+  .bp3-slider-handle:disabled, .bp3-slider-handle.bp3-disabled{
+    outline:none;
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    background-color:rgba(206, 217, 224, 0.5);
+    background-image:none;
+    cursor:not-allowed;
+    color:rgba(92, 112, 128, 0.6); }
+    .bp3-slider-handle:disabled.bp3-active, .bp3-slider-handle:disabled.bp3-active:hover, .bp3-slider-handle.bp3-disabled.bp3-active, .bp3-slider-handle.bp3-disabled.bp3-active:hover{
+      background:rgba(206, 217, 224, 0.7); }
+  .bp3-slider-handle:focus{
+    z-index:1; }
+  .bp3-slider-handle:hover{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+    background-clip:padding-box;
+    background-color:#ebf1f5;
+    z-index:2;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
+    cursor:-webkit-grab;
+    cursor:grab; }
+  .bp3-slider-handle.bp3-active{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+    background-color:#d8e1e8;
+    background-image:none;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 1px rgba(16, 22, 26, 0.1);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 1px rgba(16, 22, 26, 0.1);
+    cursor:-webkit-grabbing;
+    cursor:grabbing; }
+  .bp3-disabled .bp3-slider-handle{
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    background:#bfccd6;
+    pointer-events:none; }
+  .bp3-dark .bp3-slider-handle{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+    background-color:#394b59;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
+    color:#f5f8fa; }
+    .bp3-dark .bp3-slider-handle:hover, .bp3-dark .bp3-slider-handle:active, .bp3-dark .bp3-slider-handle.bp3-active{
+      color:#f5f8fa; }
+    .bp3-dark .bp3-slider-handle:hover{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+      background-color:#30404d; }
+    .bp3-dark .bp3-slider-handle:active, .bp3-dark .bp3-slider-handle.bp3-active{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+      background-color:#202b33;
+      background-image:none; }
+    .bp3-dark .bp3-slider-handle:disabled, .bp3-dark .bp3-slider-handle.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      background-color:rgba(57, 75, 89, 0.5);
+      background-image:none;
+      color:rgba(167, 182, 194, 0.6); }
+      .bp3-dark .bp3-slider-handle:disabled.bp3-active, .bp3-dark .bp3-slider-handle.bp3-disabled.bp3-active{
+        background:rgba(57, 75, 89, 0.7); }
+    .bp3-dark .bp3-slider-handle .bp3-button-spinner .bp3-spinner-head{
+      background:rgba(16, 22, 26, 0.5);
+      stroke:#8a9ba8; }
+    .bp3-dark .bp3-slider-handle, .bp3-dark .bp3-slider-handle:hover{
+      background-color:#394b59; }
+    .bp3-dark .bp3-slider-handle.bp3-active{
+      background-color:#293742; }
+  .bp3-dark .bp3-disabled .bp3-slider-handle{
+    border-color:#5c7080;
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    background:#5c7080; }
+  .bp3-slider-handle .bp3-slider-label{
+    margin-left:8px;
+    border-radius:3px;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+    background:#394b59;
+    color:#f5f8fa; }
+    .bp3-dark .bp3-slider-handle .bp3-slider-label{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+      background:#e1e8ed;
+      color:#394b59; }
+    .bp3-disabled .bp3-slider-handle .bp3-slider-label{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-slider-handle.bp3-start, .bp3-slider-handle.bp3-end{
+    width:8px; }
+  .bp3-slider-handle.bp3-start{
+    border-top-right-radius:0;
+    border-bottom-right-radius:0; }
+  .bp3-slider-handle.bp3-end{
+    margin-left:8px;
+    border-top-left-radius:0;
+    border-bottom-left-radius:0; }
+    .bp3-slider-handle.bp3-end .bp3-slider-label{
+      margin-left:0; }
+
+.bp3-slider-label{
+  -webkit-transform:translate(-50%, 20px);
+          transform:translate(-50%, 20px);
+  display:inline-block;
+  position:absolute;
+  padding:2px 5px;
+  vertical-align:top;
+  line-height:1;
+  font-size:12px; }
+
+.bp3-slider.bp3-vertical{
+  width:40px;
+  min-width:40px;
+  height:150px; }
+  .bp3-slider.bp3-vertical .bp3-slider-track,
+  .bp3-slider.bp3-vertical .bp3-slider-progress{
+    top:0;
+    bottom:0;
+    left:5px;
+    width:6px;
+    height:auto; }
+  .bp3-slider.bp3-vertical .bp3-slider-progress{
+    top:auto; }
+  .bp3-slider.bp3-vertical .bp3-slider-label{
+    -webkit-transform:translate(20px, 50%);
+            transform:translate(20px, 50%); }
+  .bp3-slider.bp3-vertical .bp3-slider-handle{
+    top:auto; }
+    .bp3-slider.bp3-vertical .bp3-slider-handle .bp3-slider-label{
+      margin-top:-8px;
+      margin-left:0; }
+    .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-end, .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start{
+      margin-left:0;
+      width:16px;
+      height:8px; }
+    .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start{
+      border-top-left-radius:0;
+      border-bottom-right-radius:3px; }
+      .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start .bp3-slider-label{
+        -webkit-transform:translate(20px);
+                transform:translate(20px); }
+    .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-end{
+      margin-bottom:8px;
+      border-top-left-radius:3px;
+      border-bottom-left-radius:0;
+      border-bottom-right-radius:0; }
+
+@-webkit-keyframes pt-spinner-animation{
+  from{
+    -webkit-transform:rotate(0deg);
+            transform:rotate(0deg); }
+  to{
+    -webkit-transform:rotate(360deg);
+            transform:rotate(360deg); } }
+
+@keyframes pt-spinner-animation{
+  from{
+    -webkit-transform:rotate(0deg);
+            transform:rotate(0deg); }
+  to{
+    -webkit-transform:rotate(360deg);
+            transform:rotate(360deg); } }
+
+.bp3-spinner{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  overflow:visible;
+  vertical-align:middle; }
+  .bp3-spinner svg{
+    display:block; }
+  .bp3-spinner path{
+    fill-opacity:0; }
+  .bp3-spinner .bp3-spinner-head{
+    -webkit-transform-origin:center;
+            transform-origin:center;
+    -webkit-transition:stroke-dashoffset 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:stroke-dashoffset 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    stroke:rgba(92, 112, 128, 0.8);
+    stroke-linecap:round; }
+  .bp3-spinner .bp3-spinner-track{
+    stroke:rgba(92, 112, 128, 0.2); }
+
+.bp3-spinner-animation{
+  -webkit-animation:pt-spinner-animation 500ms linear infinite;
+          animation:pt-spinner-animation 500ms linear infinite; }
+  .bp3-no-spin > .bp3-spinner-animation{
+    -webkit-animation:none;
+            animation:none; }
+
+.bp3-dark .bp3-spinner .bp3-spinner-head{
+  stroke:#8a9ba8; }
+
+.bp3-dark .bp3-spinner .bp3-spinner-track{
+  stroke:rgba(16, 22, 26, 0.5); }
+
+.bp3-spinner.bp3-intent-primary .bp3-spinner-head{
+  stroke:#137cbd; }
+
+.bp3-spinner.bp3-intent-success .bp3-spinner-head{
+  stroke:#0f9960; }
+
+.bp3-spinner.bp3-intent-warning .bp3-spinner-head{
+  stroke:#d9822b; }
+
+.bp3-spinner.bp3-intent-danger .bp3-spinner-head{
+  stroke:#db3737; }
+.bp3-tabs.bp3-vertical{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex; }
+  .bp3-tabs.bp3-vertical > .bp3-tab-list{
+    -webkit-box-orient:vertical;
+    -webkit-box-direction:normal;
+        -ms-flex-direction:column;
+            flex-direction:column;
+    -webkit-box-align:start;
+        -ms-flex-align:start;
+            align-items:flex-start; }
+    .bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab{
+      border-radius:3px;
+      width:100%;
+      padding:0 10px; }
+      .bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab[aria-selected="true"]{
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        background-color:rgba(19, 124, 189, 0.2); }
+    .bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab-indicator-wrapper .bp3-tab-indicator{
+      top:0;
+      right:0;
+      bottom:0;
+      left:0;
+      border-radius:3px;
+      background-color:rgba(19, 124, 189, 0.2);
+      height:auto; }
+  .bp3-tabs.bp3-vertical > .bp3-tab-panel{
+    margin-top:0;
+    padding-left:20px; }
+
+.bp3-tab-list{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  -webkit-box-align:end;
+      -ms-flex-align:end;
+          align-items:flex-end;
+  position:relative;
+  margin:0;
+  border:none;
+  padding:0;
+  list-style:none; }
+  .bp3-tab-list > *:not(:last-child){
+    margin-right:20px; }
+
+.bp3-tab{
+  overflow:hidden;
+  text-overflow:ellipsis;
+  white-space:nowrap;
+  word-wrap:normal;
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  position:relative;
+  cursor:pointer;
+  max-width:100%;
+  vertical-align:top;
+  line-height:30px;
+  color:#182026;
+  font-size:14px; }
+  .bp3-tab a{
+    display:block;
+    text-decoration:none;
+    color:inherit; }
+  .bp3-tab-indicator-wrapper ~ .bp3-tab{
+    -webkit-box-shadow:none !important;
+            box-shadow:none !important;
+    background-color:transparent !important; }
+  .bp3-tab[aria-disabled="true"]{
+    cursor:not-allowed;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-tab[aria-selected="true"]{
+    border-radius:0;
+    -webkit-box-shadow:inset 0 -3px 0 #106ba3;
+            box-shadow:inset 0 -3px 0 #106ba3; }
+  .bp3-tab[aria-selected="true"], .bp3-tab:not([aria-disabled="true"]):hover{
+    color:#106ba3; }
+  .bp3-tab:focus{
+    -moz-outline-radius:0; }
+  .bp3-large > .bp3-tab{
+    line-height:40px;
+    font-size:16px; }
+
+.bp3-tab-panel{
+  margin-top:20px; }
+  .bp3-tab-panel[aria-hidden="true"]{
+    display:none; }
+
+.bp3-tab-indicator-wrapper{
+  position:absolute;
+  top:0;
+  left:0;
+  -webkit-transform:translateX(0), translateY(0);
+          transform:translateX(0), translateY(0);
+  -webkit-transition:height, width, -webkit-transform;
+  transition:height, width, -webkit-transform;
+  transition:height, transform, width;
+  transition:height, transform, width, -webkit-transform;
+  -webkit-transition-duration:200ms;
+          transition-duration:200ms;
+  -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+          transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+  pointer-events:none; }
+  .bp3-tab-indicator-wrapper .bp3-tab-indicator{
+    position:absolute;
+    right:0;
+    bottom:0;
+    left:0;
+    background-color:#106ba3;
+    height:3px; }
+  .bp3-tab-indicator-wrapper.bp3-no-animation{
+    -webkit-transition:none;
+    transition:none; }
+
+.bp3-dark .bp3-tab{
+  color:#f5f8fa; }
+  .bp3-dark .bp3-tab[aria-disabled="true"]{
+    color:rgba(167, 182, 194, 0.6); }
+  .bp3-dark .bp3-tab[aria-selected="true"]{
+    -webkit-box-shadow:inset 0 -3px 0 #48aff0;
+            box-shadow:inset 0 -3px 0 #48aff0; }
+  .bp3-dark .bp3-tab[aria-selected="true"], .bp3-dark .bp3-tab:not([aria-disabled="true"]):hover{
+    color:#48aff0; }
+
+.bp3-dark .bp3-tab-indicator{
+  background-color:#48aff0; }
+
+.bp3-flex-expander{
+  -webkit-box-flex:1;
+      -ms-flex:1 1;
+          flex:1 1; }
+.bp3-tag{
+  display:-webkit-inline-box;
+  display:-ms-inline-flexbox;
+  display:inline-flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  position:relative;
+  border:none;
+  border-radius:3px;
+  -webkit-box-shadow:none;
+          box-shadow:none;
+  background-color:#5c7080;
+  min-width:20px;
+  max-width:100%;
+  min-height:20px;
+  padding:2px 6px;
+  line-height:16px;
+  color:#f5f8fa;
+  font-size:12px; }
+  .bp3-tag.bp3-interactive{
+    cursor:pointer; }
+    .bp3-tag.bp3-interactive:hover{
+      background-color:rgba(92, 112, 128, 0.85); }
+    .bp3-tag.bp3-interactive.bp3-active, .bp3-tag.bp3-interactive:active{
+      background-color:rgba(92, 112, 128, 0.7); }
+  .bp3-tag > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-tag > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-tag::before,
+  .bp3-tag > *{
+    margin-right:4px; }
+  .bp3-tag:empty::before,
+  .bp3-tag > :last-child{
+    margin-right:0; }
+  .bp3-tag:focus{
+    outline:rgba(19, 124, 189, 0.6) auto 2px;
+    outline-offset:0;
+    -moz-outline-radius:6px; }
+  .bp3-tag.bp3-round{
+    border-radius:30px;
+    padding-right:8px;
+    padding-left:8px; }
+  .bp3-dark .bp3-tag{
+    background-color:#bfccd6;
+    color:#182026; }
+    .bp3-dark .bp3-tag.bp3-interactive{
+      cursor:pointer; }
+      .bp3-dark .bp3-tag.bp3-interactive:hover{
+        background-color:rgba(191, 204, 214, 0.85); }
+      .bp3-dark .bp3-tag.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-interactive:active{
+        background-color:rgba(191, 204, 214, 0.7); }
+    .bp3-dark .bp3-tag > .bp3-icon, .bp3-dark .bp3-tag .bp3-icon-standard, .bp3-dark .bp3-tag .bp3-icon-large{
+      fill:currentColor; }
+  .bp3-tag > .bp3-icon, .bp3-tag .bp3-icon-standard, .bp3-tag .bp3-icon-large{
+    fill:#ffffff; }
+  .bp3-tag.bp3-large,
+  .bp3-large .bp3-tag{
+    min-width:30px;
+    min-height:30px;
+    padding:0 10px;
+    line-height:20px;
+    font-size:14px; }
+    .bp3-tag.bp3-large::before,
+    .bp3-tag.bp3-large > *,
+    .bp3-large .bp3-tag::before,
+    .bp3-large .bp3-tag > *{
+      margin-right:7px; }
+    .bp3-tag.bp3-large:empty::before,
+    .bp3-tag.bp3-large > :last-child,
+    .bp3-large .bp3-tag:empty::before,
+    .bp3-large .bp3-tag > :last-child{
+      margin-right:0; }
+    .bp3-tag.bp3-large.bp3-round,
+    .bp3-large .bp3-tag.bp3-round{
+      padding-right:12px;
+      padding-left:12px; }
+  .bp3-tag.bp3-intent-primary{
+    background:#137cbd;
+    color:#ffffff; }
+    .bp3-tag.bp3-intent-primary.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-intent-primary.bp3-interactive:hover{
+        background-color:rgba(19, 124, 189, 0.85); }
+      .bp3-tag.bp3-intent-primary.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-primary.bp3-interactive:active{
+        background-color:rgba(19, 124, 189, 0.7); }
+  .bp3-tag.bp3-intent-success{
+    background:#0f9960;
+    color:#ffffff; }
+    .bp3-tag.bp3-intent-success.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-intent-success.bp3-interactive:hover{
+        background-color:rgba(15, 153, 96, 0.85); }
+      .bp3-tag.bp3-intent-success.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-success.bp3-interactive:active{
+        background-color:rgba(15, 153, 96, 0.7); }
+  .bp3-tag.bp3-intent-warning{
+    background:#d9822b;
+    color:#ffffff; }
+    .bp3-tag.bp3-intent-warning.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-intent-warning.bp3-interactive:hover{
+        background-color:rgba(217, 130, 43, 0.85); }
+      .bp3-tag.bp3-intent-warning.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-warning.bp3-interactive:active{
+        background-color:rgba(217, 130, 43, 0.7); }
+  .bp3-tag.bp3-intent-danger{
+    background:#db3737;
+    color:#ffffff; }
+    .bp3-tag.bp3-intent-danger.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-intent-danger.bp3-interactive:hover{
+        background-color:rgba(219, 55, 55, 0.85); }
+      .bp3-tag.bp3-intent-danger.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-danger.bp3-interactive:active{
+        background-color:rgba(219, 55, 55, 0.7); }
+  .bp3-tag.bp3-fill{
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    width:100%; }
+  .bp3-tag.bp3-minimal > .bp3-icon, .bp3-tag.bp3-minimal .bp3-icon-standard, .bp3-tag.bp3-minimal .bp3-icon-large{
+    fill:#5c7080; }
+  .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]){
+    background-color:rgba(138, 155, 168, 0.2);
+    color:#182026; }
+    .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:hover{
+        background-color:rgba(92, 112, 128, 0.3); }
+      .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive.bp3-active, .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:active{
+        background-color:rgba(92, 112, 128, 0.4); }
+    .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]){
+      color:#f5f8fa; }
+      .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive{
+        cursor:pointer; }
+        .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:hover{
+          background-color:rgba(191, 204, 214, 0.3); }
+        .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:active{
+          background-color:rgba(191, 204, 214, 0.4); }
+      .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]) > .bp3-icon, .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]) .bp3-icon-standard, .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]) .bp3-icon-large{
+        fill:#a7b6c2; }
+  .bp3-tag.bp3-minimal.bp3-intent-primary{
+    background-color:rgba(19, 124, 189, 0.15);
+    color:#106ba3; }
+    .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:hover{
+        background-color:rgba(19, 124, 189, 0.25); }
+      .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:active{
+        background-color:rgba(19, 124, 189, 0.35); }
+    .bp3-tag.bp3-minimal.bp3-intent-primary > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-primary .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-primary .bp3-icon-large{
+      fill:#137cbd; }
+    .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary{
+      background-color:rgba(19, 124, 189, 0.25);
+      color:#48aff0; }
+      .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive{
+        cursor:pointer; }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:hover{
+          background-color:rgba(19, 124, 189, 0.35); }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:active{
+          background-color:rgba(19, 124, 189, 0.45); }
+  .bp3-tag.bp3-minimal.bp3-intent-success{
+    background-color:rgba(15, 153, 96, 0.15);
+    color:#0d8050; }
+    .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:hover{
+        background-color:rgba(15, 153, 96, 0.25); }
+      .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:active{
+        background-color:rgba(15, 153, 96, 0.35); }
+    .bp3-tag.bp3-minimal.bp3-intent-success > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-success .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-success .bp3-icon-large{
+      fill:#0f9960; }
+    .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success{
+      background-color:rgba(15, 153, 96, 0.25);
+      color:#3dcc91; }
+      .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive{
+        cursor:pointer; }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:hover{
+          background-color:rgba(15, 153, 96, 0.35); }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:active{
+          background-color:rgba(15, 153, 96, 0.45); }
+  .bp3-tag.bp3-minimal.bp3-intent-warning{
+    background-color:rgba(217, 130, 43, 0.15);
+    color:#bf7326; }
+    .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:hover{
+        background-color:rgba(217, 130, 43, 0.25); }
+      .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:active{
+        background-color:rgba(217, 130, 43, 0.35); }
+    .bp3-tag.bp3-minimal.bp3-intent-warning > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-warning .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-warning .bp3-icon-large{
+      fill:#d9822b; }
+    .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning{
+      background-color:rgba(217, 130, 43, 0.25);
+      color:#ffb366; }
+      .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive{
+        cursor:pointer; }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:hover{
+          background-color:rgba(217, 130, 43, 0.35); }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:active{
+          background-color:rgba(217, 130, 43, 0.45); }
+  .bp3-tag.bp3-minimal.bp3-intent-danger{
+    background-color:rgba(219, 55, 55, 0.15);
+    color:#c23030; }
+    .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:hover{
+        background-color:rgba(219, 55, 55, 0.25); }
+      .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:active{
+        background-color:rgba(219, 55, 55, 0.35); }
+    .bp3-tag.bp3-minimal.bp3-intent-danger > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-danger .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-danger .bp3-icon-large{
+      fill:#db3737; }
+    .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger{
+      background-color:rgba(219, 55, 55, 0.25);
+      color:#ff7373; }
+      .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive{
+        cursor:pointer; }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:hover{
+          background-color:rgba(219, 55, 55, 0.35); }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:active{
+          background-color:rgba(219, 55, 55, 0.45); }
+
+.bp3-tag-remove{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  opacity:0.5;
+  margin-top:-2px;
+  margin-right:-6px !important;
+  margin-bottom:-2px;
+  border:none;
+  background:none;
+  cursor:pointer;
+  padding:2px;
+  padding-left:0;
+  color:inherit; }
+  .bp3-tag-remove:hover{
+    opacity:0.8;
+    background:none;
+    text-decoration:none; }
+  .bp3-tag-remove:active{
+    opacity:1; }
+  .bp3-tag-remove:empty::before{
+    line-height:1;
+    font-family:"Icons16", sans-serif;
+    font-size:16px;
+    font-weight:400;
+    font-style:normal;
+    -moz-osx-font-smoothing:grayscale;
+    -webkit-font-smoothing:antialiased;
+    content:""; }
+  .bp3-large .bp3-tag-remove{
+    margin-right:-10px !important;
+    padding:5px;
+    padding-left:0; }
+    .bp3-large .bp3-tag-remove:empty::before{
+      line-height:1;
+      font-family:"Icons20", sans-serif;
+      font-size:20px;
+      font-weight:400;
+      font-style:normal; }
+.bp3-tag-input{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:start;
+      -ms-flex-align:start;
+          align-items:flex-start;
+  cursor:text;
+  height:auto;
+  min-height:30px;
+  padding-right:0;
+  padding-left:5px;
+  line-height:inherit; }
+  .bp3-tag-input > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-tag-input > .bp3-tag-input-values{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-tag-input .bp3-tag-input-icon{
+    margin-top:7px;
+    margin-right:7px;
+    margin-left:2px;
+    color:#5c7080; }
+  .bp3-tag-input .bp3-tag-input-values{
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    -webkit-box-orient:horizontal;
+    -webkit-box-direction:normal;
+        -ms-flex-direction:row;
+            flex-direction:row;
+    -ms-flex-wrap:wrap;
+        flex-wrap:wrap;
+    -webkit-box-align:center;
+        -ms-flex-align:center;
+            align-items:center;
+    -ms-flex-item-align:stretch;
+        align-self:stretch;
+    margin-top:5px;
+    margin-right:7px;
+    min-width:0; }
+    .bp3-tag-input .bp3-tag-input-values > *{
+      -webkit-box-flex:0;
+          -ms-flex-positive:0;
+              flex-grow:0;
+      -ms-flex-negative:0;
+          flex-shrink:0; }
+    .bp3-tag-input .bp3-tag-input-values > .bp3-fill{
+      -webkit-box-flex:1;
+          -ms-flex-positive:1;
+              flex-grow:1;
+      -ms-flex-negative:1;
+          flex-shrink:1; }
+    .bp3-tag-input .bp3-tag-input-values::before,
+    .bp3-tag-input .bp3-tag-input-values > *{
+      margin-right:5px; }
+    .bp3-tag-input .bp3-tag-input-values:empty::before,
+    .bp3-tag-input .bp3-tag-input-values > :last-child{
+      margin-right:0; }
+    .bp3-tag-input .bp3-tag-input-values:first-child .bp3-input-ghost:first-child{
+      padding-left:5px; }
+    .bp3-tag-input .bp3-tag-input-values > *{
+      margin-bottom:5px; }
+  .bp3-tag-input .bp3-tag{
+    overflow-wrap:break-word; }
+    .bp3-tag-input .bp3-tag.bp3-active{
+      outline:rgba(19, 124, 189, 0.6) auto 2px;
+      outline-offset:0;
+      -moz-outline-radius:6px; }
+  .bp3-tag-input .bp3-input-ghost{
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto;
+    width:80px;
+    line-height:20px; }
+    .bp3-tag-input .bp3-input-ghost:disabled, .bp3-tag-input .bp3-input-ghost.bp3-disabled{
+      cursor:not-allowed; }
+  .bp3-tag-input .bp3-button,
+  .bp3-tag-input .bp3-spinner{
+    margin:3px;
+    margin-left:0; }
+  .bp3-tag-input .bp3-button{
+    min-width:24px;
+    min-height:24px;
+    padding:0 7px; }
+  .bp3-tag-input.bp3-large{
+    height:auto;
+    min-height:40px; }
+    .bp3-tag-input.bp3-large::before,
+    .bp3-tag-input.bp3-large > *{
+      margin-right:10px; }
+    .bp3-tag-input.bp3-large:empty::before,
+    .bp3-tag-input.bp3-large > :last-child{
+      margin-right:0; }
+    .bp3-tag-input.bp3-large .bp3-tag-input-icon{
+      margin-top:10px;
+      margin-left:5px; }
+    .bp3-tag-input.bp3-large .bp3-input-ghost{
+      line-height:30px; }
+    .bp3-tag-input.bp3-large .bp3-button{
+      min-width:30px;
+      min-height:30px;
+      padding:5px 10px;
+      margin:5px;
+      margin-left:0; }
+    .bp3-tag-input.bp3-large .bp3-spinner{
+      margin:8px;
+      margin-left:0; }
+  .bp3-tag-input.bp3-active{
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+    background-color:#ffffff; }
+    .bp3-tag-input.bp3-active.bp3-intent-primary{
+      -webkit-box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-tag-input.bp3-active.bp3-intent-success{
+      -webkit-box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-tag-input.bp3-active.bp3-intent-warning{
+      -webkit-box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-tag-input.bp3-active.bp3-intent-danger{
+      -webkit-box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-dark .bp3-tag-input .bp3-tag-input-icon, .bp3-tag-input.bp3-dark .bp3-tag-input-icon{
+    color:#a7b6c2; }
+  .bp3-dark .bp3-tag-input .bp3-input-ghost, .bp3-tag-input.bp3-dark .bp3-input-ghost{
+    color:#f5f8fa; }
+    .bp3-dark .bp3-tag-input .bp3-input-ghost::-webkit-input-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::-webkit-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-tag-input .bp3-input-ghost::-moz-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::-moz-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-tag-input .bp3-input-ghost:-ms-input-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost:-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-tag-input .bp3-input-ghost::-ms-input-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-tag-input .bp3-input-ghost::placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+  .bp3-dark .bp3-tag-input.bp3-active, .bp3-tag-input.bp3-dark.bp3-active{
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+    background-color:rgba(16, 22, 26, 0.3); }
+    .bp3-dark .bp3-tag-input.bp3-active.bp3-intent-primary, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-primary{
+      -webkit-box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-tag-input.bp3-active.bp3-intent-success, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-success{
+      -webkit-box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-tag-input.bp3-active.bp3-intent-warning, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-warning{
+      -webkit-box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-tag-input.bp3-active.bp3-intent-danger, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-danger{
+      -webkit-box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+
+.bp3-input-ghost{
+  border:none;
+  -webkit-box-shadow:none;
+          box-shadow:none;
+  background:none;
+  padding:0; }
+  .bp3-input-ghost::-webkit-input-placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-input-ghost::-moz-placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-input-ghost:-ms-input-placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-input-ghost::-ms-input-placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-input-ghost::placeholder{
+    opacity:1;
+    color:rgba(92, 112, 128, 0.6); }
+  .bp3-input-ghost:focus{
+    outline:none !important; }
+.bp3-toast{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-align:start;
+      -ms-flex-align:start;
+          align-items:flex-start;
+  position:relative !important;
+  margin:20px 0 0;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+  background-color:#ffffff;
+  min-width:300px;
+  max-width:500px;
+  pointer-events:all; }
+  .bp3-toast.bp3-toast-enter, .bp3-toast.bp3-toast-appear{
+    -webkit-transform:translateY(-40px);
+            transform:translateY(-40px); }
+  .bp3-toast.bp3-toast-enter-active, .bp3-toast.bp3-toast-appear-active{
+    -webkit-transform:translateY(0);
+            transform:translateY(0);
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+            transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+    -webkit-transition-delay:0;
+            transition-delay:0; }
+  .bp3-toast.bp3-toast-enter ~ .bp3-toast, .bp3-toast.bp3-toast-appear ~ .bp3-toast{
+    -webkit-transform:translateY(-40px);
+            transform:translateY(-40px); }
+  .bp3-toast.bp3-toast-enter-active ~ .bp3-toast, .bp3-toast.bp3-toast-appear-active ~ .bp3-toast{
+    -webkit-transform:translateY(0);
+            transform:translateY(0);
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+            transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+    -webkit-transition-delay:0;
+            transition-delay:0; }
+  .bp3-toast.bp3-toast-exit{
+    opacity:1;
+    -webkit-filter:blur(0);
+            filter:blur(0); }
+  .bp3-toast.bp3-toast-exit-active{
+    opacity:0;
+    -webkit-filter:blur(10px);
+            filter:blur(10px);
+    -webkit-transition-property:opacity, -webkit-filter;
+    transition-property:opacity, -webkit-filter;
+    transition-property:opacity, filter;
+    transition-property:opacity, filter, -webkit-filter;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+    -webkit-transition-delay:0;
+            transition-delay:0; }
+  .bp3-toast.bp3-toast-exit ~ .bp3-toast{
+    -webkit-transform:translateY(0);
+            transform:translateY(0); }
+  .bp3-toast.bp3-toast-exit-active ~ .bp3-toast{
+    -webkit-transform:translateY(-40px);
+            transform:translateY(-40px);
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-duration:100ms;
+            transition-duration:100ms;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+    -webkit-transition-delay:50ms;
+            transition-delay:50ms; }
+  .bp3-toast .bp3-button-group{
+    -webkit-box-flex:0;
+        -ms-flex:0 0 auto;
+            flex:0 0 auto;
+    padding:5px;
+    padding-left:0; }
+  .bp3-toast > .bp3-icon{
+    margin:12px;
+    margin-right:0;
+    color:#5c7080; }
+  .bp3-toast.bp3-dark,
+  .bp3-dark .bp3-toast{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+    background-color:#394b59; }
+    .bp3-toast.bp3-dark > .bp3-icon,
+    .bp3-dark .bp3-toast > .bp3-icon{
+      color:#a7b6c2; }
+  .bp3-toast[class*="bp3-intent-"] a{
+    color:rgba(255, 255, 255, 0.7); }
+    .bp3-toast[class*="bp3-intent-"] a:hover{
+      color:#ffffff; }
+  .bp3-toast[class*="bp3-intent-"] > .bp3-icon{
+    color:#ffffff; }
+  .bp3-toast[class*="bp3-intent-"] .bp3-button, .bp3-toast[class*="bp3-intent-"] .bp3-button::before,
+  .bp3-toast[class*="bp3-intent-"] .bp3-button .bp3-icon, .bp3-toast[class*="bp3-intent-"] .bp3-button:active{
+    color:rgba(255, 255, 255, 0.7) !important; }
+  .bp3-toast[class*="bp3-intent-"] .bp3-button:focus{
+    outline-color:rgba(255, 255, 255, 0.5); }
+  .bp3-toast[class*="bp3-intent-"] .bp3-button:hover{
+    background-color:rgba(255, 255, 255, 0.15) !important;
+    color:#ffffff !important; }
+  .bp3-toast[class*="bp3-intent-"] .bp3-button:active{
+    background-color:rgba(255, 255, 255, 0.3) !important;
+    color:#ffffff !important; }
+  .bp3-toast[class*="bp3-intent-"] .bp3-button::after{
+    background:rgba(255, 255, 255, 0.3) !important; }
+  .bp3-toast.bp3-intent-primary{
+    background-color:#137cbd;
+    color:#ffffff; }
+  .bp3-toast.bp3-intent-success{
+    background-color:#0f9960;
+    color:#ffffff; }
+  .bp3-toast.bp3-intent-warning{
+    background-color:#d9822b;
+    color:#ffffff; }
+  .bp3-toast.bp3-intent-danger{
+    background-color:#db3737;
+    color:#ffffff; }
+
+.bp3-toast-message{
+  -webkit-box-flex:1;
+      -ms-flex:1 1 auto;
+          flex:1 1 auto;
+  padding:11px;
+  word-break:break-word; }
+
+.bp3-toast-container{
+  display:-webkit-box !important;
+  display:-ms-flexbox !important;
+  display:flex !important;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  position:fixed;
+  right:0;
+  left:0;
+  z-index:40;
+  overflow:hidden;
+  padding:0 20px 20px;
+  pointer-events:none; }
+  .bp3-toast-container.bp3-toast-container-top{
+    top:0;
+    bottom:auto; }
+  .bp3-toast-container.bp3-toast-container-bottom{
+    -webkit-box-orient:vertical;
+    -webkit-box-direction:reverse;
+        -ms-flex-direction:column-reverse;
+            flex-direction:column-reverse;
+    top:auto;
+    bottom:0; }
+  .bp3-toast-container.bp3-toast-container-left{
+    -webkit-box-align:start;
+        -ms-flex-align:start;
+            align-items:flex-start; }
+  .bp3-toast-container.bp3-toast-container-right{
+    -webkit-box-align:end;
+        -ms-flex-align:end;
+            align-items:flex-end; }
+
+.bp3-toast-container-bottom .bp3-toast.bp3-toast-enter:not(.bp3-toast-enter-active),
+.bp3-toast-container-bottom .bp3-toast.bp3-toast-enter:not(.bp3-toast-enter-active) ~ .bp3-toast, .bp3-toast-container-bottom .bp3-toast.bp3-toast-appear:not(.bp3-toast-appear-active),
+.bp3-toast-container-bottom .bp3-toast.bp3-toast-appear:not(.bp3-toast-appear-active) ~ .bp3-toast,
+.bp3-toast-container-bottom .bp3-toast.bp3-toast-leave-active ~ .bp3-toast{
+  -webkit-transform:translateY(60px);
+          transform:translateY(60px); }
+.bp3-tooltip{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+  -webkit-transform:scale(1);
+          transform:scale(1); }
+  .bp3-tooltip .bp3-popover-arrow{
+    position:absolute;
+    width:22px;
+    height:22px; }
+    .bp3-tooltip .bp3-popover-arrow::before{
+      margin:4px;
+      width:14px;
+      height:14px; }
+  .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip{
+    margin-top:-11px;
+    margin-bottom:11px; }
+    .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip > .bp3-popover-arrow{
+      bottom:-8px; }
+      .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(-90deg);
+                transform:rotate(-90deg); }
+  .bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-tooltip{
+    margin-left:11px; }
+    .bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-tooltip > .bp3-popover-arrow{
+      left:-8px; }
+      .bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-tooltip > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(0);
+                transform:rotate(0); }
+  .bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-tooltip{
+    margin-top:11px; }
+    .bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-tooltip > .bp3-popover-arrow{
+      top:-8px; }
+      .bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-tooltip > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(90deg);
+                transform:rotate(90deg); }
+  .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-tooltip{
+    margin-right:11px;
+    margin-left:-11px; }
+    .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-tooltip > .bp3-popover-arrow{
+      right:-8px; }
+      .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-tooltip > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(180deg);
+                transform:rotate(180deg); }
+  .bp3-tether-element-attached-middle > .bp3-tooltip > .bp3-popover-arrow{
+    top:50%;
+    -webkit-transform:translateY(-50%);
+            transform:translateY(-50%); }
+  .bp3-tether-element-attached-center > .bp3-tooltip > .bp3-popover-arrow{
+    right:50%;
+    -webkit-transform:translateX(50%);
+            transform:translateX(50%); }
+  .bp3-tether-element-attached-top.bp3-tether-target-attached-top > .bp3-tooltip > .bp3-popover-arrow{
+    top:-0.22183px; }
+  .bp3-tether-element-attached-right.bp3-tether-target-attached-right > .bp3-tooltip > .bp3-popover-arrow{
+    right:-0.22183px; }
+  .bp3-tether-element-attached-left.bp3-tether-target-attached-left > .bp3-tooltip > .bp3-popover-arrow{
+    left:-0.22183px; }
+  .bp3-tether-element-attached-bottom.bp3-tether-target-attached-bottom > .bp3-tooltip > .bp3-popover-arrow{
+    bottom:-0.22183px; }
+  .bp3-tether-element-attached-top.bp3-tether-element-attached-left > .bp3-tooltip{
+    -webkit-transform-origin:top left;
+            transform-origin:top left; }
+  .bp3-tether-element-attached-top.bp3-tether-element-attached-center > .bp3-tooltip{
+    -webkit-transform-origin:top center;
+            transform-origin:top center; }
+  .bp3-tether-element-attached-top.bp3-tether-element-attached-right > .bp3-tooltip{
+    -webkit-transform-origin:top right;
+            transform-origin:top right; }
+  .bp3-tether-element-attached-middle.bp3-tether-element-attached-left > .bp3-tooltip{
+    -webkit-transform-origin:center left;
+            transform-origin:center left; }
+  .bp3-tether-element-attached-middle.bp3-tether-element-attached-center > .bp3-tooltip{
+    -webkit-transform-origin:center center;
+            transform-origin:center center; }
+  .bp3-tether-element-attached-middle.bp3-tether-element-attached-right > .bp3-tooltip{
+    -webkit-transform-origin:center right;
+            transform-origin:center right; }
+  .bp3-tether-element-attached-bottom.bp3-tether-element-attached-left > .bp3-tooltip{
+    -webkit-transform-origin:bottom left;
+            transform-origin:bottom left; }
+  .bp3-tether-element-attached-bottom.bp3-tether-element-attached-center > .bp3-tooltip{
+    -webkit-transform-origin:bottom center;
+            transform-origin:bottom center; }
+  .bp3-tether-element-attached-bottom.bp3-tether-element-attached-right > .bp3-tooltip{
+    -webkit-transform-origin:bottom right;
+            transform-origin:bottom right; }
+  .bp3-tooltip .bp3-popover-content{
+    background:#394b59;
+    color:#f5f8fa; }
+  .bp3-tooltip .bp3-popover-arrow::before{
+    -webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2);
+            box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2); }
+  .bp3-tooltip .bp3-popover-arrow-border{
+    fill:#10161a;
+    fill-opacity:0.1; }
+  .bp3-tooltip .bp3-popover-arrow-fill{
+    fill:#394b59; }
+  .bp3-popover-enter > .bp3-tooltip, .bp3-popover-appear > .bp3-tooltip{
+    -webkit-transform:scale(0.8);
+            transform:scale(0.8); }
+  .bp3-popover-enter-active > .bp3-tooltip, .bp3-popover-appear-active > .bp3-tooltip{
+    -webkit-transform:scale(1);
+            transform:scale(1);
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-duration:100ms;
+            transition-duration:100ms;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+    -webkit-transition-delay:0;
+            transition-delay:0; }
+  .bp3-popover-exit > .bp3-tooltip{
+    -webkit-transform:scale(1);
+            transform:scale(1); }
+  .bp3-popover-exit-active > .bp3-tooltip{
+    -webkit-transform:scale(0.8);
+            transform:scale(0.8);
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-duration:100ms;
+            transition-duration:100ms;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+    -webkit-transition-delay:0;
+            transition-delay:0; }
+  .bp3-tooltip .bp3-popover-content{
+    padding:10px 12px; }
+  .bp3-tooltip.bp3-dark,
+  .bp3-dark .bp3-tooltip{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
+    .bp3-tooltip.bp3-dark .bp3-popover-content,
+    .bp3-dark .bp3-tooltip .bp3-popover-content{
+      background:#e1e8ed;
+      color:#394b59; }
+    .bp3-tooltip.bp3-dark .bp3-popover-arrow::before,
+    .bp3-dark .bp3-tooltip .bp3-popover-arrow::before{
+      -webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4);
+              box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4); }
+    .bp3-tooltip.bp3-dark .bp3-popover-arrow-border,
+    .bp3-dark .bp3-tooltip .bp3-popover-arrow-border{
+      fill:#10161a;
+      fill-opacity:0.2; }
+    .bp3-tooltip.bp3-dark .bp3-popover-arrow-fill,
+    .bp3-dark .bp3-tooltip .bp3-popover-arrow-fill{
+      fill:#e1e8ed; }
+  .bp3-tooltip.bp3-intent-primary .bp3-popover-content{
+    background:#137cbd;
+    color:#ffffff; }
+  .bp3-tooltip.bp3-intent-primary .bp3-popover-arrow-fill{
+    fill:#137cbd; }
+  .bp3-tooltip.bp3-intent-success .bp3-popover-content{
+    background:#0f9960;
+    color:#ffffff; }
+  .bp3-tooltip.bp3-intent-success .bp3-popover-arrow-fill{
+    fill:#0f9960; }
+  .bp3-tooltip.bp3-intent-warning .bp3-popover-content{
+    background:#d9822b;
+    color:#ffffff; }
+  .bp3-tooltip.bp3-intent-warning .bp3-popover-arrow-fill{
+    fill:#d9822b; }
+  .bp3-tooltip.bp3-intent-danger .bp3-popover-content{
+    background:#db3737;
+    color:#ffffff; }
+  .bp3-tooltip.bp3-intent-danger .bp3-popover-arrow-fill{
+    fill:#db3737; }
+
+.bp3-tooltip-indicator{
+  border-bottom:dotted 1px;
+  cursor:help; }
+.bp3-tree .bp3-icon, .bp3-tree .bp3-icon-standard, .bp3-tree .bp3-icon-large{
+  color:#5c7080; }
+  .bp3-tree .bp3-icon.bp3-intent-primary, .bp3-tree .bp3-icon-standard.bp3-intent-primary, .bp3-tree .bp3-icon-large.bp3-intent-primary{
+    color:#137cbd; }
+  .bp3-tree .bp3-icon.bp3-intent-success, .bp3-tree .bp3-icon-standard.bp3-intent-success, .bp3-tree .bp3-icon-large.bp3-intent-success{
+    color:#0f9960; }
+  .bp3-tree .bp3-icon.bp3-intent-warning, .bp3-tree .bp3-icon-standard.bp3-intent-warning, .bp3-tree .bp3-icon-large.bp3-intent-warning{
+    color:#d9822b; }
+  .bp3-tree .bp3-icon.bp3-intent-danger, .bp3-tree .bp3-icon-standard.bp3-intent-danger, .bp3-tree .bp3-icon-large.bp3-intent-danger{
+    color:#db3737; }
+
+.bp3-tree-node-list{
+  margin:0;
+  padding-left:0;
+  list-style:none; }
+
+.bp3-tree-root{
+  position:relative;
+  background-color:transparent;
+  cursor:default;
+  padding-left:0; }
+
+.bp3-tree-node-content-0{
+  padding-left:0px; }
+
+.bp3-tree-node-content-1{
+  padding-left:23px; }
+
+.bp3-tree-node-content-2{
+  padding-left:46px; }
+
+.bp3-tree-node-content-3{
+  padding-left:69px; }
+
+.bp3-tree-node-content-4{
+  padding-left:92px; }
+
+.bp3-tree-node-content-5{
+  padding-left:115px; }
+
+.bp3-tree-node-content-6{
+  padding-left:138px; }
+
+.bp3-tree-node-content-7{
+  padding-left:161px; }
+
+.bp3-tree-node-content-8{
+  padding-left:184px; }
+
+.bp3-tree-node-content-9{
+  padding-left:207px; }
+
+.bp3-tree-node-content-10{
+  padding-left:230px; }
+
+.bp3-tree-node-content-11{
+  padding-left:253px; }
+
+.bp3-tree-node-content-12{
+  padding-left:276px; }
+
+.bp3-tree-node-content-13{
+  padding-left:299px; }
+
+.bp3-tree-node-content-14{
+  padding-left:322px; }
+
+.bp3-tree-node-content-15{
+  padding-left:345px; }
+
+.bp3-tree-node-content-16{
+  padding-left:368px; }
+
+.bp3-tree-node-content-17{
+  padding-left:391px; }
+
+.bp3-tree-node-content-18{
+  padding-left:414px; }
+
+.bp3-tree-node-content-19{
+  padding-left:437px; }
+
+.bp3-tree-node-content-20{
+  padding-left:460px; }
+
+.bp3-tree-node-content{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  width:100%;
+  height:30px;
+  padding-right:5px; }
+  .bp3-tree-node-content:hover{
+    background-color:rgba(191, 204, 214, 0.4); }
+
+.bp3-tree-node-caret,
+.bp3-tree-node-caret-none{
+  min-width:30px; }
+
+.bp3-tree-node-caret{
+  color:#5c7080;
+  -webkit-transform:rotate(0deg);
+          transform:rotate(0deg);
+  cursor:pointer;
+  padding:7px;
+  -webkit-transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-tree-node-caret:hover{
+    color:#182026; }
+  .bp3-dark .bp3-tree-node-caret{
+    color:#a7b6c2; }
+    .bp3-dark .bp3-tree-node-caret:hover{
+      color:#f5f8fa; }
+  .bp3-tree-node-caret.bp3-tree-node-caret-open{
+    -webkit-transform:rotate(90deg);
+            transform:rotate(90deg); }
+  .bp3-tree-node-caret.bp3-icon-standard::before{
+    content:""; }
+
+.bp3-tree-node-icon{
+  position:relative;
+  margin-right:7px; }
+
+.bp3-tree-node-label{
+  overflow:hidden;
+  text-overflow:ellipsis;
+  white-space:nowrap;
+  word-wrap:normal;
+  -webkit-box-flex:1;
+      -ms-flex:1 1 auto;
+          flex:1 1 auto;
+  position:relative;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-tree-node-label span{
+    display:inline; }
+
+.bp3-tree-node-secondary-label{
+  padding:0 5px;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-tree-node-secondary-label .bp3-popover-wrapper,
+  .bp3-tree-node-secondary-label .bp3-popover-target{
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    -webkit-box-align:center;
+        -ms-flex-align:center;
+            align-items:center; }
+
+.bp3-tree-node.bp3-disabled .bp3-tree-node-content{
+  background-color:inherit;
+  cursor:not-allowed;
+  color:rgba(92, 112, 128, 0.6); }
+
+.bp3-tree-node.bp3-disabled .bp3-tree-node-caret,
+.bp3-tree-node.bp3-disabled .bp3-tree-node-icon{
+  cursor:not-allowed;
+  color:rgba(92, 112, 128, 0.6); }
+
+.bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content{
+  background-color:#137cbd; }
+  .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content,
+  .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-icon, .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-icon-standard, .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-icon-large{
+    color:#ffffff; }
+  .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-tree-node-caret::before{
+    color:rgba(255, 255, 255, 0.7); }
+  .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-tree-node-caret:hover::before{
+    color:#ffffff; }
+
+.bp3-dark .bp3-tree-node-content:hover{
+  background-color:rgba(92, 112, 128, 0.3); }
+
+.bp3-dark .bp3-tree .bp3-icon, .bp3-dark .bp3-tree .bp3-icon-standard, .bp3-dark .bp3-tree .bp3-icon-large{
+  color:#a7b6c2; }
+  .bp3-dark .bp3-tree .bp3-icon.bp3-intent-primary, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-primary, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-primary{
+    color:#137cbd; }
+  .bp3-dark .bp3-tree .bp3-icon.bp3-intent-success, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-success, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-success{
+    color:#0f9960; }
+  .bp3-dark .bp3-tree .bp3-icon.bp3-intent-warning, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-warning, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-warning{
+    color:#d9822b; }
+  .bp3-dark .bp3-tree .bp3-icon.bp3-intent-danger, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-danger, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-danger{
+    color:#db3737; }
+
+.bp3-dark .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content{
+  background-color:#137cbd; }
+/*!
+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
+*/
+.bp3-omnibar{
+  -webkit-filter:blur(0);
+          filter:blur(0);
+  opacity:1;
+  top:20vh;
+  left:calc(50% - 250px);
+  z-index:21;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+  background-color:#ffffff;
+  width:500px; }
+  .bp3-omnibar.bp3-overlay-enter, .bp3-omnibar.bp3-overlay-appear{
+    -webkit-filter:blur(20px);
+            filter:blur(20px);
+    opacity:0.2; }
+  .bp3-omnibar.bp3-overlay-enter-active, .bp3-omnibar.bp3-overlay-appear-active{
+    -webkit-filter:blur(0);
+            filter:blur(0);
+    opacity:1;
+    -webkit-transition-property:opacity, -webkit-filter;
+    transition-property:opacity, -webkit-filter;
+    transition-property:filter, opacity;
+    transition-property:filter, opacity, -webkit-filter;
+    -webkit-transition-duration:200ms;
+            transition-duration:200ms;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+    -webkit-transition-delay:0;
+            transition-delay:0; }
+  .bp3-omnibar.bp3-overlay-exit{
+    -webkit-filter:blur(0);
+            filter:blur(0);
+    opacity:1; }
+  .bp3-omnibar.bp3-overlay-exit-active{
+    -webkit-filter:blur(20px);
+            filter:blur(20px);
+    opacity:0.2;
+    -webkit-transition-property:opacity, -webkit-filter;
+    transition-property:opacity, -webkit-filter;
+    transition-property:filter, opacity;
+    transition-property:filter, opacity, -webkit-filter;
+    -webkit-transition-duration:200ms;
+            transition-duration:200ms;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+    -webkit-transition-delay:0;
+            transition-delay:0; }
+  .bp3-omnibar .bp3-input{
+    border-radius:0;
+    background-color:transparent; }
+    .bp3-omnibar .bp3-input, .bp3-omnibar .bp3-input:focus{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-omnibar .bp3-menu{
+    border-radius:0;
+    -webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);
+            box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);
+    background-color:transparent;
+    max-height:calc(60vh - 40px);
+    overflow:auto; }
+    .bp3-omnibar .bp3-menu:empty{
+      display:none; }
+  .bp3-dark .bp3-omnibar, .bp3-omnibar.bp3-dark{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+    background-color:#30404d; }
+
+.bp3-omnibar-overlay .bp3-overlay-backdrop{
+  background-color:rgba(16, 22, 26, 0.2); }
+
+.bp3-select-popover .bp3-popover-content{
+  padding:5px; }
+
+.bp3-select-popover .bp3-input-group{
+  margin-bottom:0; }
+
+.bp3-select-popover .bp3-menu{
+  max-width:400px;
+  max-height:300px;
+  overflow:auto;
+  padding:0; }
+  .bp3-select-popover .bp3-menu:not(:first-child){
+    padding-top:5px; }
+
+.bp3-multi-select{
+  min-width:150px; }
+
+.bp3-multi-select-popover .bp3-menu{
+  max-width:400px;
+  max-height:300px;
+  overflow:auto; }
+
+.bp3-select-popover .bp3-popover-content{
+  padding:5px; }
+
+.bp3-select-popover .bp3-input-group{
+  margin-bottom:0; }
+
+.bp3-select-popover .bp3-menu{
+  max-width:400px;
+  max-height:300px;
+  overflow:auto;
+  padding:0; }
+  .bp3-select-popover .bp3-menu:not(:first-child){
+    padding-top:5px; }
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* This file was auto-generated by ensureUiComponents() in @jupyterlab/buildutils */
+
+/**
+ * (DEPRECATED) Support for consuming icons as CSS background images
+ */
+
+/* Icons urls */
+
+:root {
+  --jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-bug: url(data:image/svg+xml;base64,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);
+  --jp-icon-build: url(data:image/svg+xml;base64,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);
+  --jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
+  --jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-caret-right: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik03LjIsNS4yTDEwLjksOWwtMy44LDMuOFY1LjJINy4yeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-caret-up-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iMTUuNCwxMy4zIDkuOSw3LjcgNC40LDEzLjIgMy42LDEyLjUgOS45LDYuMyAxNi4xLDEyLjYgIi8+Cgk8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-caret-up: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik01LjIsMTAuNUw5LDYuOGwzLjgsMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-case-sensitive: url(data:image/svg+xml;base64,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);
+  --jp-icon-check: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTkgMTYuMTdMNC44MyAxMmwtMS40MiAxLjQxTDkgMTkgMjEgN2wtMS40MS0xLjQxeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-circle-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyIDJDNi40NyAyIDIgNi40NyAyIDEyczQuNDcgMTAgMTAgMTAgMTAtNC40NyAxMC0xMFMxNy41MyAyIDEyIDJ6bTAgMThjLTQuNDEgMC04LTMuNTktOC04czMuNTktOCA4LTggOCAzLjU5IDggOC0zLjU5IDgtOCA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-circle: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMTggMTgiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPGNpcmNsZSBjeD0iOSIgY3k9IjkiIHI9IjgiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-clear: url(data:image/svg+xml;base64,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);
+  --jp-icon-close: url(data:image/svg+xml;base64,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);
+  --jp-icon-console: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwMCAyMDAiPgogIDxnIGNsYXNzPSJqcC1pY29uLWJyYW5kMSBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiMwMjg4RDEiPgogICAgPHBhdGggZD0iTTIwIDE5LjhoMTYwdjE1OS45SDIweiIvPgogIDwvZz4KICA8ZyBjbGFzcz0ianAtaWNvbi1zZWxlY3RhYmxlLWludmVyc2UiIGZpbGw9IiNmZmYiPgogICAgPHBhdGggZD0iTTEwNSAxMjcuM2g0MHYxMi44aC00MHpNNTEuMSA3N0w3NCA5OS45bC0yMy4zIDIzLjMgMTAuNSAxMC41IDIzLjMtMjMuM0w5NSA5OS45IDg0LjUgODkuNCA2MS42IDY2LjV6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-copy: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMTggMTgiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTExLjksMUgzLjJDMi40LDEsMS43LDEuNywxLjcsMi41djEwLjJoMS41VjIuNWg4LjdWMXogTTE0LjEsMy45aC04Yy0wLjgsMC0xLjUsMC43LTEuNSwxLjV2MTAuMmMwLDAuOCwwLjcsMS41LDEuNSwxLjVoOCBjMC44LDAsMS41LTAuNywxLjUtMS41VjUuNEMxNS41LDQuNiwxNC45LDMuOSwxNC4xLDMuOXogTTE0LjEsMTUuNWgtOFY1LjRoOFYxNS41eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-cut: url(data:image/svg+xml;base64,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);
+  --jp-icon-download: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDloLTRWM0g5djZINWw3IDcgNy03ek01IDE4djJoMTR2LTJINXoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-edit: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTMgMTcuMjVWMjFoMy43NUwxNy44MSA5Ljk0bC0zLjc1LTMuNzVMMyAxNy4yNXpNMjAuNzEgNy4wNGMuMzktLjM5LjM5LTEuMDIgMC0xLjQxbC0yLjM0LTIuMzRjLS4zOS0uMzktMS4wMi0uMzktMS40MSAwbC0xLjgzIDEuODMgMy43NSAzLjc1IDEuODMtMS44M3oiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-ellipses: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPGNpcmNsZSBjeD0iNSIgY3k9IjEyIiByPSIyIi8+CiAgICA8Y2lyY2xlIGN4PSIxMiIgY3k9IjEyIiByPSIyIi8+CiAgICA8Y2lyY2xlIGN4PSIxOSIgY3k9IjEyIiByPSIyIi8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-extension: url(data:image/svg+xml;base64,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);
+  --jp-icon-fast-forward: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTQgMThsOC41LTZMNCA2djEyem05LTEydjEybDguNS02TDEzIDZ6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-file-upload: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTkgMTZoNnYtNmg0bC03LTctNyA3aDR6bS00IDJoMTR2Mkg1eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-file: url(data:image/svg+xml;base64,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);
+  --jp-icon-filter-list: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEwIDE4aDR2LTJoLTR2MnpNMyA2djJoMThWNkgzem0zIDdoMTJ2LTJINnYyeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-folder: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMTAgNEg0Yy0xLjEgMC0xLjk5LjktMS45OSAyTDIgMThjMCAxLjEuOSAyIDIgMmgxNmMxLjEgMCAyLS45IDItMlY4YzAtMS4xLS45LTItMi0yaC04bC0yLTJ6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-html5: url(data:image/svg+xml;base64,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);
+  --jp-icon-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8cGF0aCBjbGFzcz0ianAtaWNvbi1icmFuZDQganAtaWNvbi1zZWxlY3RhYmxlLWludmVyc2UiIGZpbGw9IiNGRkYiIGQ9Ik0yLjIgMi4yaDE3LjV2MTcuNUgyLjJ6Ii8+CiAgPHBhdGggY2xhc3M9ImpwLWljb24tYnJhbmQwIGpwLWljb24tc2VsZWN0YWJsZSIgZmlsbD0iIzNGNTFCNSIgZD0iTTIuMiAyLjJ2MTcuNWgxNy41bC4xLTE3LjVIMi4yem0xMi4xIDIuMmMxLjIgMCAyLjIgMSAyLjIgMi4ycy0xIDIuMi0yLjIgMi4yLTIuMi0xLTIuMi0yLjIgMS0yLjIgMi4yLTIuMnpNNC40IDE3LjZsMy4zLTguOCAzLjMgNi42IDIuMi0zLjIgNC40IDUuNEg0LjR6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-inspector: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMjAgNEg0Yy0xLjEgMC0xLjk5LjktMS45OSAyTDIgMThjMCAxLjEuOSAyIDIgMmgxNmMxLjEgMCAyLS45IDItMlY2YzAtMS4xLS45LTItMi0yem0tNSAxNEg0di00aDExdjR6bTAtNUg0VjloMTF2NHptNSA1aC00VjloNHY5eiIvPgo8L3N2Zz4K);
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+  --jp-icon-jupyter-favicon: url(data:image/svg+xml;base64,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);
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+  --jp-icon-jupyterlab-wordmark: 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+  --jp-icon-kernel: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGNsYXNzPSJqcC1pY29uMiIgZmlsbD0iIzYxNjE2MSIgZD0iTTE1IDlIOXY2aDZWOXptLTIgNGgtMnYtMmgydjJ6bTgtMlY5aC0yVjdjMC0xLjEtLjktMi0yLTJoLTJWM2gtMnYyaC0yVjNIOXYySDdjLTEuMSAwLTIgLjktMiAydjJIM3YyaDJ2MkgzdjJoMnYyYzAgMS4xLjkgMiAyIDJoMnYyaDJ2LTJoMnYyaDJ2LTJoMmMxLjEgMCAyLS45IDItMnYtMmgydi0yaC0ydi0yaDJ6bS00IDZIN1Y3aDEwdjEweiIvPgo8L3N2Zz4K);
+  --jp-icon-keyboard: url(data:image/svg+xml;base64,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);
+  --jp-icon-launcher: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMTkgMTlINVY1aDdWM0g1YTIgMiAwIDAwLTIgMnYxNGEyIDIgMCAwMDIgMmgxNGMxLjEgMCAyLS45IDItMnYtN2gtMnY3ek0xNCAzdjJoMy41OWwtOS44MyA5LjgzIDEuNDEgMS40MUwxOSA2LjQxVjEwaDJWM2gtN3oiLz4KPC9zdmc+Cg==);
+  --jp-icon-line-form: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGZpbGw9IndoaXRlIiBkPSJNNS44OCA0LjEyTDEzLjc2IDEybC03Ljg4IDcuODhMOCAyMmwxMC0xMEw4IDJ6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-link: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTMuOSAxMmMwLTEuNzEgMS4zOS0zLjEgMy4xLTMuMWg0VjdIN2MtMi43NiAwLTUgMi4yNC01IDVzMi4yNCA1IDUgNWg0di0xLjlIN2MtMS43MSAwLTMuMS0xLjM5LTMuMS0zLjF6TTggMTNoOHYtMkg4djJ6bTktNmgtNHYxLjloNGMxLjcxIDAgMy4xIDEuMzkgMy4xIDMuMXMtMS4zOSAzLjEtMy4xIDMuMWgtNFYxN2g0YzIuNzYgMCA1LTIuMjQgNS01cy0yLjI0LTUtNS01eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-list: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGNsYXNzPSJqcC1pY29uMiBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xOSA1djE0SDVWNWgxNG0xLjEtMkgzLjljLS41IDAtLjkuNC0uOS45djE2LjJjMCAuNC40LjkuOS45aDE2LjJjLjQgMCAuOS0uNS45LS45VjMuOWMwLS41LS41LS45LS45LS45ek0xMSA3aDZ2MmgtNlY3em0wIDRoNnYyaC02di0yem0wIDRoNnYyaC02ek03IDdoMnYySDd6bTAgNGgydjJIN3ptMCA0aDJ2Mkg3eiIvPgo8L3N2Zz4=);
+  --jp-icon-listings-info: url(data:image/svg+xml;base64,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);
+  --jp-icon-markdown: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8cGF0aCBjbGFzcz0ianAtaWNvbi1jb250cmFzdDAganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjN0IxRkEyIiBkPSJNNSAxNC45aDEybC02LjEgNnptOS40LTYuOGMwLTEuMy0uMS0yLjktLjEtNC41LS40IDEuNC0uOSAyLjktMS4zIDQuM2wtMS4zIDQuM2gtMkw4LjUgNy45Yy0uNC0xLjMtLjctMi45LTEtNC4zLS4xIDEuNi0uMSAzLjItLjIgNC42TDcgMTIuNEg0LjhsLjctMTFoMy4zTDEwIDVjLjQgMS4yLjcgMi43IDEgMy45LjMtMS4yLjctMi42IDEtMy45bDEuMi0zLjdoMy4zbC42IDExaC0yLjRsLS4zLTQuMnoiLz4KPC9zdmc+Cg==);
+  --jp-icon-new-folder: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTIwIDZoLThsLTItMkg0Yy0xLjExIDAtMS45OS44OS0xLjk5IDJMMiAxOGMwIDEuMTEuODkgMiAyIDJoMTZjMS4xMSAwIDItLjg5IDItMlY4YzAtMS4xMS0uODktMi0yLTJ6bS0xIDhoLTN2M2gtMnYtM2gtM3YtMmgzVjloMnYzaDN2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-not-trusted: url(data:image/svg+xml;base64,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);
+  --jp-icon-notebook: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbi13YXJuMCBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiNFRjZDMDAiPgogICAgPHBhdGggZD0iTTE4LjcgMy4zdjE1LjRIMy4zVjMuM2gxNS40bTEuNS0xLjVIMS44djE4LjNoMTguM2wuMS0xOC4zeiIvPgogICAgPHBhdGggZD0iTTE2LjUgMTYuNWwtNS40LTQuMy01LjYgNC4zdi0xMWgxMXoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-palette: url(data:image/svg+xml;base64,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);
+  --jp-icon-paste: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE5IDJoLTQuMThDMTQuNC44NCAxMy4zIDAgMTIgMGMtMS4zIDAtMi40Ljg0LTIuODIgMkg1Yy0xLjEgMC0yIC45LTIgMnYxNmMwIDEuMS45IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjRjMC0xLjEtLjktMi0yLTJ6bS03IDBjLjU1IDAgMSAuNDUgMSAxcy0uNDUgMS0xIDEtMS0uNDUtMS0xIC40NS0xIDEtMXptNyAxOEg1VjRoMnYzaDEwVjRoMnYxNnoiLz4KICAgIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-python: url(data:image/svg+xml;base64,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);
+  --jp-icon-r-kernel: url(data:image/svg+xml;base64,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);
+  --jp-icon-react: url(data:image/svg+xml;base64,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);
+  --jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-regex: url(data:image/svg+xml;base64,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);
+  --jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-running: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDUxMiA1MTIiPgogIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICA8cGF0aCBkPSJNMjU2IDhDMTE5IDggOCAxMTkgOCAyNTZzMTExIDI0OCAyNDggMjQ4IDI0OC0xMTEgMjQ4LTI0OFMzOTMgOCAyNTYgOHptOTYgMzI4YzAgOC44LTcuMiAxNi0xNiAxNkgxNzZjLTguOCAwLTE2LTcuMi0xNi0xNlYxNzZjMC04LjggNy4yLTE2IDE2LTE2aDE2MGM4LjggMCAxNiA3LjIgMTYgMTZ2MTYweiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-search: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMTggMTgiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjEsMTAuOWgtMC43bC0wLjItMC4yYzAuOC0wLjksMS4zLTIuMiwxLjMtMy41YzAtMy0yLjQtNS40LTUuNC01LjRTMS44LDQuMiwxLjgsNy4xczIuNCw1LjQsNS40LDUuNCBjMS4zLDAsMi41LTAuNSwzLjUtMS4zbDAuMiwwLjJ2MC43bDQuMSw0LjFsMS4yLTEuMkwxMi4xLDEwLjl6IE03LjEsMTAuOWMtMi4xLDAtMy43LTEuNy0zLjctMy43czEuNy0zLjcsMy43LTMuN3MzLjcsMS43LDMuNywzLjcgUzkuMiwxMC45LDcuMSwxMC45eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-settings: url(data:image/svg+xml;base64,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);
+  --jp-icon-spreadsheet: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8cGF0aCBjbGFzcz0ianAtaWNvbi1jb250cmFzdDEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNENBRjUwIiBkPSJNMi4yIDIuMnYxNy42aDE3LjZWMi4ySDIuMnptMTUuNCA3LjdoLTUuNVY0LjRoNS41djUuNXpNOS45IDQuNHY1LjVINC40VjQuNGg1LjV6bS01LjUgNy43aDUuNXY1LjVINC40di01LjV6bTcuNyA1LjV2LTUuNWg1LjV2NS41aC01LjV6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-stop: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik02IDZoMTJ2MTJINnoiLz4KICAgIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-tab: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTIxIDNIM2MtMS4xIDAtMiAuOS0yIDJ2MTRjMCAxLjEuOSAyIDIgMmgxOGMxLjEgMCAyLS45IDItMlY1YzAtMS4xLS45LTItMi0yem0wIDE2SDNWNWgxMHY0aDh2MTB6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-terminal: url(data:image/svg+xml;base64,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);
+  --jp-icon-text-editor: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMTUgMTVIM3YyaDEydi0yem0wLThIM3YyaDEyVjd6TTMgMTNoMTh2LTJIM3Yyem0wIDhoMTh2LTJIM3Yyek0zIDN2MmgxOFYzSDN6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-trusted: url(data:image/svg+xml;base64,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);
+  --jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-yaml: url(data:image/svg+xml;base64,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);
+}
+
+/* Icon CSS class declarations */
+
+.jp-AddIcon {
+  background-image: var(--jp-icon-add);
+}
+.jp-BugIcon {
+  background-image: var(--jp-icon-bug);
+}
+.jp-BuildIcon {
+  background-image: var(--jp-icon-build);
+}
+.jp-CaretDownEmptyIcon {
+  background-image: var(--jp-icon-caret-down-empty);
+}
+.jp-CaretDownEmptyThinIcon {
+  background-image: var(--jp-icon-caret-down-empty-thin);
+}
+.jp-CaretDownIcon {
+  background-image: var(--jp-icon-caret-down);
+}
+.jp-CaretLeftIcon {
+  background-image: var(--jp-icon-caret-left);
+}
+.jp-CaretRightIcon {
+  background-image: var(--jp-icon-caret-right);
+}
+.jp-CaretUpEmptyThinIcon {
+  background-image: var(--jp-icon-caret-up-empty-thin);
+}
+.jp-CaretUpIcon {
+  background-image: var(--jp-icon-caret-up);
+}
+.jp-CaseSensitiveIcon {
+  background-image: var(--jp-icon-case-sensitive);
+}
+.jp-CheckIcon {
+  background-image: var(--jp-icon-check);
+}
+.jp-CircleEmptyIcon {
+  background-image: var(--jp-icon-circle-empty);
+}
+.jp-CircleIcon {
+  background-image: var(--jp-icon-circle);
 }
-.fa-fonticons:before {
-  content: "\f280";
+.jp-ClearIcon {
+  background-image: var(--jp-icon-clear);
 }
-.fa-reddit-alien:before {
-  content: "\f281";
+.jp-CloseIcon {
+  background-image: var(--jp-icon-close);
 }
-.fa-edge:before {
-  content: "\f282";
+.jp-ConsoleIcon {
+  background-image: var(--jp-icon-console);
 }
-.fa-credit-card-alt:before {
-  content: "\f283";
+.jp-CopyIcon {
+  background-image: var(--jp-icon-copy);
 }
-.fa-codiepie:before {
-  content: "\f284";
+.jp-CutIcon {
+  background-image: var(--jp-icon-cut);
 }
-.fa-modx:before {
-  content: "\f285";
+.jp-DownloadIcon {
+  background-image: var(--jp-icon-download);
 }
-.fa-fort-awesome:before {
-  content: "\f286";
+.jp-EditIcon {
+  background-image: var(--jp-icon-edit);
 }
-.fa-usb:before {
-  content: "\f287";
+.jp-EllipsesIcon {
+  background-image: var(--jp-icon-ellipses);
 }
-.fa-product-hunt:before {
-  content: "\f288";
+.jp-ExtensionIcon {
+  background-image: var(--jp-icon-extension);
 }
-.fa-mixcloud:before {
-  content: "\f289";
+.jp-FastForwardIcon {
+  background-image: var(--jp-icon-fast-forward);
 }
-.fa-scribd:before {
-  content: "\f28a";
+.jp-FileIcon {
+  background-image: var(--jp-icon-file);
 }
-.fa-pause-circle:before {
-  content: "\f28b";
+.jp-FileUploadIcon {
+  background-image: var(--jp-icon-file-upload);
 }
-.fa-pause-circle-o:before {
-  content: "\f28c";
+.jp-FilterListIcon {
+  background-image: var(--jp-icon-filter-list);
 }
-.fa-stop-circle:before {
-  content: "\f28d";
+.jp-FolderIcon {
+  background-image: var(--jp-icon-folder);
 }
-.fa-stop-circle-o:before {
-  content: "\f28e";
+.jp-Html5Icon {
+  background-image: var(--jp-icon-html5);
 }
-.fa-shopping-bag:before {
-  content: "\f290";
+.jp-ImageIcon {
+  background-image: var(--jp-icon-image);
 }
-.fa-shopping-basket:before {
-  content: "\f291";
+.jp-InspectorIcon {
+  background-image: var(--jp-icon-inspector);
 }
-.fa-hashtag:before {
-  content: "\f292";
+.jp-JsonIcon {
+  background-image: var(--jp-icon-json);
 }
-.fa-bluetooth:before {
-  content: "\f293";
+.jp-JupyterFaviconIcon {
+  background-image: var(--jp-icon-jupyter-favicon);
 }
-.fa-bluetooth-b:before {
-  content: "\f294";
+.jp-JupyterIcon {
+  background-image: var(--jp-icon-jupyter);
 }
-.fa-percent:before {
-  content: "\f295";
+.jp-JupyterlabWordmarkIcon {
+  background-image: var(--jp-icon-jupyterlab-wordmark);
 }
-.fa-gitlab:before {
-  content: "\f296";
+.jp-KernelIcon {
+  background-image: var(--jp-icon-kernel);
 }
-.fa-wpbeginner:before {
-  content: "\f297";
+.jp-KeyboardIcon {
+  background-image: var(--jp-icon-keyboard);
 }
-.fa-wpforms:before {
-  content: "\f298";
+.jp-LauncherIcon {
+  background-image: var(--jp-icon-launcher);
 }
-.fa-envira:before {
-  content: "\f299";
+.jp-LineFormIcon {
+  background-image: var(--jp-icon-line-form);
 }
-.fa-universal-access:before {
-  content: "\f29a";
+.jp-LinkIcon {
+  background-image: var(--jp-icon-link);
 }
-.fa-wheelchair-alt:before {
-  content: "\f29b";
+.jp-ListIcon {
+  background-image: var(--jp-icon-list);
 }
-.fa-question-circle-o:before {
-  content: "\f29c";
+.jp-ListingsInfoIcon {
+  background-image: var(--jp-icon-listings-info);
 }
-.fa-blind:before {
-  content: "\f29d";
+.jp-MarkdownIcon {
+  background-image: var(--jp-icon-markdown);
 }
-.fa-audio-description:before {
-  content: "\f29e";
+.jp-NewFolderIcon {
+  background-image: var(--jp-icon-new-folder);
 }
-.fa-volume-control-phone:before {
-  content: "\f2a0";
+.jp-NotTrustedIcon {
+  background-image: var(--jp-icon-not-trusted);
 }
-.fa-braille:before {
-  content: "\f2a1";
+.jp-NotebookIcon {
+  background-image: var(--jp-icon-notebook);
 }
-.fa-assistive-listening-systems:before {
-  content: "\f2a2";
+.jp-PaletteIcon {
+  background-image: var(--jp-icon-palette);
 }
-.fa-asl-interpreting:before,
-.fa-american-sign-language-interpreting:before {
-  content: "\f2a3";
+.jp-PasteIcon {
+  background-image: var(--jp-icon-paste);
 }
-.fa-deafness:before,
-.fa-hard-of-hearing:before,
-.fa-deaf:before {
-  content: "\f2a4";
+.jp-PythonIcon {
+  background-image: var(--jp-icon-python);
 }
-.fa-glide:before {
-  content: "\f2a5";
+.jp-RKernelIcon {
+  background-image: var(--jp-icon-r-kernel);
 }
-.fa-glide-g:before {
-  content: "\f2a6";
+.jp-ReactIcon {
+  background-image: var(--jp-icon-react);
 }
-.fa-signing:before,
-.fa-sign-language:before {
-  content: "\f2a7";
+.jp-RefreshIcon {
+  background-image: var(--jp-icon-refresh);
 }
-.fa-low-vision:before {
-  content: "\f2a8";
+.jp-RegexIcon {
+  background-image: var(--jp-icon-regex);
 }
-.fa-viadeo:before {
-  content: "\f2a9";
+.jp-RunIcon {
+  background-image: var(--jp-icon-run);
 }
-.fa-viadeo-square:before {
-  content: "\f2aa";
+.jp-RunningIcon {
+  background-image: var(--jp-icon-running);
 }
-.fa-snapchat:before {
-  content: "\f2ab";
+.jp-SaveIcon {
+  background-image: var(--jp-icon-save);
 }
-.fa-snapchat-ghost:before {
-  content: "\f2ac";
+.jp-SearchIcon {
+  background-image: var(--jp-icon-search);
 }
-.fa-snapchat-square:before {
-  content: "\f2ad";
+.jp-SettingsIcon {
+  background-image: var(--jp-icon-settings);
 }
-.fa-pied-piper:before {
-  content: "\f2ae";
+.jp-SpreadsheetIcon {
+  background-image: var(--jp-icon-spreadsheet);
 }
-.fa-first-order:before {
-  content: "\f2b0";
+.jp-StopIcon {
+  background-image: var(--jp-icon-stop);
 }
-.fa-yoast:before {
-  content: "\f2b1";
+.jp-TabIcon {
+  background-image: var(--jp-icon-tab);
 }
-.fa-themeisle:before {
-  content: "\f2b2";
+.jp-TerminalIcon {
+  background-image: var(--jp-icon-terminal);
 }
-.fa-google-plus-circle:before,
-.fa-google-plus-official:before {
-  content: "\f2b3";
+.jp-TextEditorIcon {
+  background-image: var(--jp-icon-text-editor);
 }
-.fa-fa:before,
-.fa-font-awesome:before {
-  content: "\f2b4";
+.jp-TrustedIcon {
+  background-image: var(--jp-icon-trusted);
 }
-.fa-handshake-o:before {
-  content: "\f2b5";
+.jp-UndoIcon {
+  background-image: var(--jp-icon-undo);
 }
-.fa-envelope-open:before {
-  content: "\f2b6";
+.jp-VegaIcon {
+  background-image: var(--jp-icon-vega);
 }
-.fa-envelope-open-o:before {
-  content: "\f2b7";
+.jp-YamlIcon {
+  background-image: var(--jp-icon-yaml);
 }
-.fa-linode:before {
-  content: "\f2b8";
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/**
+ * (DEPRECATED) Support for consuming icons as CSS background images
+ */
+
+:root {
+  --jp-icon-search-white: url(data:image/svg+xml;base64,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);
 }
-.fa-address-book:before {
-  content: "\f2b9";
+
+.jp-Icon,
+.jp-MaterialIcon {
+  background-position: center;
+  background-repeat: no-repeat;
+  background-size: 16px;
+  min-width: 16px;
+  min-height: 16px;
 }
-.fa-address-book-o:before {
-  content: "\f2ba";
+
+.jp-Icon-cover {
+  background-position: center;
+  background-repeat: no-repeat;
+  background-size: cover;
 }
-.fa-vcard:before,
-.fa-address-card:before {
-  content: "\f2bb";
+
+/**
+ * (DEPRECATED) Support for specific CSS icon sizes
+ */
+
+.jp-Icon-16 {
+  background-size: 16px;
+  min-width: 16px;
+  min-height: 16px;
 }
-.fa-vcard-o:before,
-.fa-address-card-o:before {
-  content: "\f2bc";
+
+.jp-Icon-18 {
+  background-size: 18px;
+  min-width: 18px;
+  min-height: 18px;
 }
-.fa-user-circle:before {
-  content: "\f2bd";
+
+.jp-Icon-20 {
+  background-size: 20px;
+  min-width: 20px;
+  min-height: 20px;
 }
-.fa-user-circle-o:before {
-  content: "\f2be";
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/**
+ * Support for icons as inline SVG HTMLElements
+ */
+
+/* recolor the primary elements of an icon */
+.jp-icon0[fill] {
+  fill: var(--jp-inverse-layout-color0);
 }
-.fa-user-o:before {
-  content: "\f2c0";
+.jp-icon1[fill] {
+  fill: var(--jp-inverse-layout-color1);
 }
-.fa-id-badge:before {
-  content: "\f2c1";
+.jp-icon2[fill] {
+  fill: var(--jp-inverse-layout-color2);
 }
-.fa-drivers-license:before,
-.fa-id-card:before {
-  content: "\f2c2";
+.jp-icon3[fill] {
+  fill: var(--jp-inverse-layout-color3);
 }
-.fa-drivers-license-o:before,
-.fa-id-card-o:before {
-  content: "\f2c3";
+.jp-icon4[fill] {
+  fill: var(--jp-inverse-layout-color4);
 }
-.fa-quora:before {
-  content: "\f2c4";
+
+.jp-icon0[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
 }
-.fa-free-code-camp:before {
-  content: "\f2c5";
+.jp-icon1[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
 }
-.fa-telegram:before {
-  content: "\f2c6";
+.jp-icon2[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
 }
-.fa-thermometer-4:before,
-.fa-thermometer:before,
-.fa-thermometer-full:before {
-  content: "\f2c7";
+.jp-icon3[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
 }
-.fa-thermometer-3:before,
-.fa-thermometer-three-quarters:before {
-  content: "\f2c8";
+.jp-icon4[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
 }
-.fa-thermometer-2:before,
-.fa-thermometer-half:before {
-  content: "\f2c9";
+/* recolor the accent elements of an icon */
+.jp-icon-accent0[fill] {
+  fill: var(--jp-layout-color0);
 }
-.fa-thermometer-1:before,
-.fa-thermometer-quarter:before {
-  content: "\f2ca";
+.jp-icon-accent1[fill] {
+  fill: var(--jp-layout-color1);
 }
-.fa-thermometer-0:before,
-.fa-thermometer-empty:before {
-  content: "\f2cb";
+.jp-icon-accent2[fill] {
+  fill: var(--jp-layout-color2);
 }
-.fa-shower:before {
-  content: "\f2cc";
+.jp-icon-accent3[fill] {
+  fill: var(--jp-layout-color3);
 }
-.fa-bathtub:before,
-.fa-s15:before,
-.fa-bath:before {
-  content: "\f2cd";
+.jp-icon-accent4[fill] {
+  fill: var(--jp-layout-color4);
 }
-.fa-podcast:before {
-  content: "\f2ce";
+
+.jp-icon-accent0[stroke] {
+  stroke: var(--jp-layout-color0);
 }
-.fa-window-maximize:before {
-  content: "\f2d0";
+.jp-icon-accent1[stroke] {
+  stroke: var(--jp-layout-color1);
 }
-.fa-window-minimize:before {
-  content: "\f2d1";
+.jp-icon-accent2[stroke] {
+  stroke: var(--jp-layout-color2);
 }
-.fa-window-restore:before {
-  content: "\f2d2";
+.jp-icon-accent3[stroke] {
+  stroke: var(--jp-layout-color3);
 }
-.fa-times-rectangle:before,
-.fa-window-close:before {
-  content: "\f2d3";
+.jp-icon-accent4[stroke] {
+  stroke: var(--jp-layout-color4);
 }
-.fa-times-rectangle-o:before,
-.fa-window-close-o:before {
-  content: "\f2d4";
+/* set the color of an icon to transparent */
+.jp-icon-none[fill] {
+  fill: none;
 }
-.fa-bandcamp:before {
-  content: "\f2d5";
+
+.jp-icon-none[stroke] {
+  stroke: none;
 }
-.fa-grav:before {
-  content: "\f2d6";
+/* brand icon colors. Same for light and dark */
+.jp-icon-brand0[fill] {
+  fill: var(--jp-brand-color0);
 }
-.fa-etsy:before {
-  content: "\f2d7";
+.jp-icon-brand1[fill] {
+  fill: var(--jp-brand-color1);
 }
-.fa-imdb:before {
-  content: "\f2d8";
+.jp-icon-brand2[fill] {
+  fill: var(--jp-brand-color2);
 }
-.fa-ravelry:before {
-  content: "\f2d9";
+.jp-icon-brand3[fill] {
+  fill: var(--jp-brand-color3);
 }
-.fa-eercast:before {
-  content: "\f2da";
+.jp-icon-brand4[fill] {
+  fill: var(--jp-brand-color4);
 }
-.fa-microchip:before {
-  content: "\f2db";
+
+.jp-icon-brand0[stroke] {
+  stroke: var(--jp-brand-color0);
 }
-.fa-snowflake-o:before {
-  content: "\f2dc";
+.jp-icon-brand1[stroke] {
+  stroke: var(--jp-brand-color1);
 }
-.fa-superpowers:before {
-  content: "\f2dd";
+.jp-icon-brand2[stroke] {
+  stroke: var(--jp-brand-color2);
 }
-.fa-wpexplorer:before {
-  content: "\f2de";
+.jp-icon-brand3[stroke] {
+  stroke: var(--jp-brand-color3);
 }
-.fa-meetup:before {
-  content: "\f2e0";
+.jp-icon-brand4[stroke] {
+  stroke: var(--jp-brand-color4);
 }
-.sr-only {
-  position: absolute;
-  width: 1px;
-  height: 1px;
-  padding: 0;
-  margin: -1px;
-  overflow: hidden;
-  clip: rect(0, 0, 0, 0);
-  border: 0;
+/* warn icon colors. Same for light and dark */
+.jp-icon-warn0[fill] {
+  fill: var(--jp-warn-color0);
 }
-.sr-only-focusable:active,
-.sr-only-focusable:focus {
-  position: static;
-  width: auto;
-  height: auto;
-  margin: 0;
-  overflow: visible;
-  clip: auto;
+.jp-icon-warn1[fill] {
+  fill: var(--jp-warn-color1);
 }
-.sr-only-focusable:active,
-.sr-only-focusable:focus {
-  position: static;
-  width: auto;
-  height: auto;
-  margin: 0;
-  overflow: visible;
-  clip: auto;
+.jp-icon-warn2[fill] {
+  fill: var(--jp-warn-color2);
 }
-/*!
-*
-* IPython base
-*
-*/
-.modal.fade .modal-dialog {
-  -webkit-transform: translate(0, 0);
-  -ms-transform: translate(0, 0);
-  -o-transform: translate(0, 0);
-  transform: translate(0, 0);
+.jp-icon-warn3[fill] {
+  fill: var(--jp-warn-color3);
 }
-code {
-  color: #000;
+
+.jp-icon-warn0[stroke] {
+  stroke: var(--jp-warn-color0);
 }
-pre {
-  font-size: inherit;
-  line-height: inherit;
+.jp-icon-warn1[stroke] {
+  stroke: var(--jp-warn-color1);
 }
-label {
-  font-weight: normal;
+.jp-icon-warn2[stroke] {
+  stroke: var(--jp-warn-color2);
 }
-/* Make the page background atleast 100% the height of the view port */
-/* Make the page itself atleast 70% the height of the view port */
-.border-box-sizing {
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
+.jp-icon-warn3[stroke] {
+  stroke: var(--jp-warn-color3);
 }
-.corner-all {
-  border-radius: 2px;
+/* icon colors that contrast well with each other and most backgrounds */
+.jp-icon-contrast0[fill] {
+  fill: var(--jp-icon-contrast-color0);
 }
-.no-padding {
-  padding: 0px;
+.jp-icon-contrast1[fill] {
+  fill: var(--jp-icon-contrast-color1);
 }
-/* Flexible box model classes */
-/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
-/* This file is a compatability layer.  It allows the usage of flexible box 
-model layouts accross multiple browsers, including older browsers.  The newest,
-universal implementation of the flexible box model is used when available (see
-`Modern browsers` comments below).  Browsers that are known to implement this 
-new spec completely include:
-
-    Firefox 28.0+
-    Chrome 29.0+
-    Internet Explorer 11+ 
-    Opera 17.0+
-
-Browsers not listed, including Safari, are supported via the styling under the
-`Old browsers` comments below.
-*/
-.hbox {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
+.jp-icon-contrast2[fill] {
+  fill: var(--jp-icon-contrast-color2);
 }
-.hbox > * {
-  /* Old browsers */
-  -webkit-box-flex: 0;
-  -moz-box-flex: 0;
-  box-flex: 0;
-  /* Modern browsers */
-  flex: none;
-}
-.vbox {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
+.jp-icon-contrast3[fill] {
+  fill: var(--jp-icon-contrast-color3);
 }
-.vbox > * {
-  /* Old browsers */
-  -webkit-box-flex: 0;
-  -moz-box-flex: 0;
-  box-flex: 0;
-  /* Modern browsers */
-  flex: none;
-}
-.hbox.reverse,
-.vbox.reverse,
-.reverse {
-  /* Old browsers */
-  -webkit-box-direction: reverse;
-  -moz-box-direction: reverse;
-  box-direction: reverse;
-  /* Modern browsers */
-  flex-direction: row-reverse;
-}
-.hbox.box-flex0,
-.vbox.box-flex0,
-.box-flex0 {
-  /* Old browsers */
-  -webkit-box-flex: 0;
-  -moz-box-flex: 0;
-  box-flex: 0;
-  /* Modern browsers */
-  flex: none;
-  width: auto;
+
+.jp-icon-contrast0[stroke] {
+  stroke: var(--jp-icon-contrast-color0);
 }
-.hbox.box-flex1,
-.vbox.box-flex1,
-.box-flex1 {
-  /* Old browsers */
-  -webkit-box-flex: 1;
-  -moz-box-flex: 1;
-  box-flex: 1;
-  /* Modern browsers */
-  flex: 1;
+.jp-icon-contrast1[stroke] {
+  stroke: var(--jp-icon-contrast-color1);
 }
-.hbox.box-flex,
-.vbox.box-flex,
-.box-flex {
-  /* Old browsers */
-  /* Old browsers */
-  -webkit-box-flex: 1;
-  -moz-box-flex: 1;
-  box-flex: 1;
-  /* Modern browsers */
-  flex: 1;
+.jp-icon-contrast2[stroke] {
+  stroke: var(--jp-icon-contrast-color2);
 }
-.hbox.box-flex2,
-.vbox.box-flex2,
-.box-flex2 {
-  /* Old browsers */
-  -webkit-box-flex: 2;
-  -moz-box-flex: 2;
-  box-flex: 2;
-  /* Modern browsers */
-  flex: 2;
-}
-.box-group1 {
-  /*  Deprecated */
-  -webkit-box-flex-group: 1;
-  -moz-box-flex-group: 1;
-  box-flex-group: 1;
-}
-.box-group2 {
-  /* Deprecated */
-  -webkit-box-flex-group: 2;
-  -moz-box-flex-group: 2;
-  box-flex-group: 2;
-}
-.hbox.start,
-.vbox.start,
-.start {
-  /* Old browsers */
-  -webkit-box-pack: start;
-  -moz-box-pack: start;
-  box-pack: start;
-  /* Modern browsers */
-  justify-content: flex-start;
-}
-.hbox.end,
-.vbox.end,
-.end {
-  /* Old browsers */
-  -webkit-box-pack: end;
-  -moz-box-pack: end;
-  box-pack: end;
-  /* Modern browsers */
-  justify-content: flex-end;
+.jp-icon-contrast3[stroke] {
+  stroke: var(--jp-icon-contrast-color3);
 }
-.hbox.center,
-.vbox.center,
-.center {
-  /* Old browsers */
-  -webkit-box-pack: center;
-  -moz-box-pack: center;
-  box-pack: center;
-  /* Modern browsers */
-  justify-content: center;
+
+/* CSS for icons in selected items in the settings editor */
+#setting-editor .jp-PluginList .jp-mod-selected .jp-icon-selectable[fill] {
+  fill: #fff;
 }
-.hbox.baseline,
-.vbox.baseline,
-.baseline {
-  /* Old browsers */
-  -webkit-box-pack: baseline;
-  -moz-box-pack: baseline;
-  box-pack: baseline;
-  /* Modern browsers */
-  justify-content: baseline;
-}
-.hbox.stretch,
-.vbox.stretch,
-.stretch {
-  /* Old browsers */
-  -webkit-box-pack: stretch;
-  -moz-box-pack: stretch;
-  box-pack: stretch;
-  /* Modern browsers */
-  justify-content: stretch;
-}
-.hbox.align-start,
-.vbox.align-start,
-.align-start {
-  /* Old browsers */
-  -webkit-box-align: start;
-  -moz-box-align: start;
-  box-align: start;
-  /* Modern browsers */
-  align-items: flex-start;
-}
-.hbox.align-end,
-.vbox.align-end,
-.align-end {
-  /* Old browsers */
-  -webkit-box-align: end;
-  -moz-box-align: end;
-  box-align: end;
-  /* Modern browsers */
-  align-items: flex-end;
-}
-.hbox.align-center,
-.vbox.align-center,
-.align-center {
-  /* Old browsers */
-  -webkit-box-align: center;
-  -moz-box-align: center;
-  box-align: center;
-  /* Modern browsers */
-  align-items: center;
+#setting-editor
+  .jp-PluginList
+  .jp-mod-selected
+  .jp-icon-selectable-inverse[fill] {
+  fill: var(--jp-brand-color1);
 }
-.hbox.align-baseline,
-.vbox.align-baseline,
-.align-baseline {
-  /* Old browsers */
-  -webkit-box-align: baseline;
-  -moz-box-align: baseline;
-  box-align: baseline;
-  /* Modern browsers */
-  align-items: baseline;
-}
-.hbox.align-stretch,
-.vbox.align-stretch,
-.align-stretch {
-  /* Old browsers */
-  -webkit-box-align: stretch;
-  -moz-box-align: stretch;
-  box-align: stretch;
-  /* Modern browsers */
-  align-items: stretch;
+
+/* CSS for icons in selected filebrowser listing items */
+.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
+  fill: #fff;
 }
-div.error {
-  margin: 2em;
-  text-align: center;
+.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
+  fill: var(--jp-brand-color1);
 }
-div.error > h1 {
-  font-size: 500%;
-  line-height: normal;
+
+/* CSS for icons in selected tabs in the sidebar tab manager */
+#tab-manager .lm-TabBar-tab.jp-mod-active .jp-icon-selectable[fill] {
+  fill: #fff;
 }
-div.error > p {
-  font-size: 200%;
-  line-height: normal;
+
+#tab-manager .lm-TabBar-tab.jp-mod-active .jp-icon-selectable-inverse[fill] {
+  fill: var(--jp-brand-color1);
 }
-div.traceback-wrapper {
-  text-align: left;
-  max-width: 800px;
-  margin: auto;
+#tab-manager
+  .lm-TabBar-tab.jp-mod-active
+  .jp-icon-hover
+  :hover
+  .jp-icon-selectable[fill] {
+  fill: var(--jp-brand-color1);
 }
-div.traceback-wrapper pre.traceback {
-  max-height: 600px;
-  overflow: auto;
+
+#tab-manager
+  .lm-TabBar-tab.jp-mod-active
+  .jp-icon-hover
+  :hover
+  .jp-icon-selectable-inverse[fill] {
+  fill: #fff;
 }
+
 /**
- * Primary styles
- *
- * Author: Jupyter Development Team
+ * TODO: come up with non css-hack solution for showing the busy icon on top
+ *  of the close icon
+ * CSS for complex behavior of close icon of tabs in the sidebar tab manager
  */
-body {
-  background-color: #fff;
-  /* This makes sure that the body covers the entire window and needs to
-       be in a different element than the display: box in wrapper below */
-  position: absolute;
-  left: 0px;
-  right: 0px;
-  top: 0px;
-  bottom: 0px;
-  overflow: visible;
-}
-body > #header {
-  /* Initially hidden to prevent FLOUC */
-  display: none;
-  background-color: #fff;
-  /* Display over codemirror */
-  position: relative;
-  z-index: 100;
-}
-body > #header #header-container {
-  display: flex;
-  flex-direction: row;
-  justify-content: space-between;
-  padding: 5px;
-  padding-bottom: 5px;
-  padding-top: 5px;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-}
-body > #header .header-bar {
-  width: 100%;
-  height: 1px;
-  background: #e7e7e7;
-  margin-bottom: -1px;
-}
-@media print {
-  body > #header {
-    display: none !important;
-  }
-}
-#header-spacer {
-  width: 100%;
-  visibility: hidden;
-}
-@media print {
-  #header-spacer {
-    display: none;
-  }
-}
-#ipython_notebook {
-  padding-left: 0px;
-  padding-top: 1px;
-  padding-bottom: 1px;
-}
-[dir="rtl"] #ipython_notebook {
-  margin-right: 10px;
-  margin-left: 0;
-}
-[dir="rtl"] #ipython_notebook.pull-left {
-  float: right !important;
-  float: right;
-}
-.flex-spacer {
-  flex: 1;
-}
-#noscript {
-  width: auto;
-  padding-top: 16px;
-  padding-bottom: 16px;
-  text-align: center;
-  font-size: 22px;
-  color: red;
-  font-weight: bold;
-}
-#ipython_notebook img {
-  height: 28px;
-}
-#site {
-  width: 100%;
-  display: none;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-  overflow: auto;
-}
-@media print {
-  #site {
-    height: auto !important;
-  }
-}
-/* Smaller buttons */
-.ui-button .ui-button-text {
-  padding: 0.2em 0.8em;
-  font-size: 77%;
-}
-input.ui-button {
-  padding: 0.3em 0.9em;
-}
-span#kernel_logo_widget {
-  margin: 0 10px;
-}
-span#login_widget {
-  float: right;
-}
-[dir="rtl"] span#login_widget {
-  float: left;
-}
-span#login_widget > .button,
-#logout {
-  color: #333;
-  background-color: #fff;
-  border-color: #ccc;
-}
-span#login_widget > .button:focus,
-#logout:focus,
-span#login_widget > .button.focus,
-#logout.focus {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #8c8c8c;
-}
-span#login_widget > .button:hover,
-#logout:hover {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #adadad;
-}
-span#login_widget > .button:active,
-#logout:active,
-span#login_widget > .button.active,
-#logout.active,
-.open > .dropdown-togglespan#login_widget > .button,
-.open > .dropdown-toggle#logout {
-  color: #333;
-  background-color: #e6e6e6;
-  border-color: #adadad;
-}
-span#login_widget > .button:active:hover,
-#logout:active:hover,
-span#login_widget > .button.active:hover,
-#logout.active:hover,
-.open > .dropdown-togglespan#login_widget > .button:hover,
-.open > .dropdown-toggle#logout:hover,
-span#login_widget > .button:active:focus,
-#logout:active:focus,
-span#login_widget > .button.active:focus,
-#logout.active:focus,
-.open > .dropdown-togglespan#login_widget > .button:focus,
-.open > .dropdown-toggle#logout:focus,
-span#login_widget > .button:active.focus,
-#logout:active.focus,
-span#login_widget > .button.active.focus,
-#logout.active.focus,
-.open > .dropdown-togglespan#login_widget > .button.focus,
-.open > .dropdown-toggle#logout.focus {
-  color: #333;
-  background-color: #d4d4d4;
-  border-color: #8c8c8c;
-}
-span#login_widget > .button:active,
-#logout:active,
-span#login_widget > .button.active,
-#logout.active,
-.open > .dropdown-togglespan#login_widget > .button,
-.open > .dropdown-toggle#logout {
-  background-image: none;
-}
-span#login_widget > .button.disabled:hover,
-#logout.disabled:hover,
-span#login_widget > .button[disabled]:hover,
-#logout[disabled]:hover,
-fieldset[disabled] span#login_widget > .button:hover,
-fieldset[disabled] #logout:hover,
-span#login_widget > .button.disabled:focus,
-#logout.disabled:focus,
-span#login_widget > .button[disabled]:focus,
-#logout[disabled]:focus,
-fieldset[disabled] span#login_widget > .button:focus,
-fieldset[disabled] #logout:focus,
-span#login_widget > .button.disabled.focus,
-#logout.disabled.focus,
-span#login_widget > .button[disabled].focus,
-#logout[disabled].focus,
-fieldset[disabled] span#login_widget > .button.focus,
-fieldset[disabled] #logout.focus {
-  background-color: #fff;
-  border-color: #ccc;
-}
-span#login_widget > .button .badge,
-#logout .badge {
-  color: #fff;
-  background-color: #333;
-}
-.nav-header {
-  text-transform: none;
-}
-#header > span {
-  margin-top: 10px;
-}
-.modal_stretch .modal-dialog {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-  min-height: 80vh;
-}
-.modal_stretch .modal-dialog .modal-body {
-  max-height: calc(100vh - 200px);
-  overflow: auto;
-  flex: 1;
-}
-.modal-header {
-  cursor: move;
-}
-@media (min-width: 768px) {
-  .modal .modal-dialog {
-    width: 700px;
-  }
-}
-@media (min-width: 768px) {
-  select.form-control {
-    margin-left: 12px;
-    margin-right: 12px;
-  }
-}
-/*!
-*
-* IPython auth
-*
-*/
-.center-nav {
-  display: inline-block;
-  margin-bottom: -4px;
+#tab-manager
+  .lm-TabBar-tab.jp-mod-dirty
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon3[fill] {
+  fill: none;
 }
-[dir="rtl"] .center-nav form.pull-left {
-  float: right !important;
-  float: right;
+#tab-manager
+  .lm-TabBar-tab.jp-mod-dirty
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon-busy[fill] {
+  fill: var(--jp-inverse-layout-color3);
 }
-[dir="rtl"] .center-nav .navbar-text {
-  float: right;
-}
-[dir="rtl"] .navbar-inner {
-  text-align: right;
-}
-[dir="rtl"] div.text-left {
-  text-align: right;
-}
-/*!
-*
-* IPython tree view
-*
-*/
-/* We need an invisible input field on top of the sentense*/
-/* "Drag file onto the list ..." */
-.alternate_upload {
-  background-color: none;
-  display: inline;
-}
-.alternate_upload.form {
-  padding: 0;
-  margin: 0;
-}
-.alternate_upload input.fileinput {
-  position: absolute;
-  display: block;
-  width: 100%;
-  height: 100%;
-  overflow: hidden;
-  cursor: pointer;
-  opacity: 0;
-  z-index: 2;
-}
-.alternate_upload .btn-xs > input.fileinput {
-  margin: -1px -5px;
-}
-.alternate_upload .btn-upload {
-  position: relative;
-  height: 22px;
-}
-::-webkit-file-upload-button {
-  cursor: pointer;
+
+#tab-manager
+  .lm-TabBar-tab.jp-mod-dirty.jp-mod-active
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon-busy[fill] {
+  fill: #fff;
 }
+
 /**
- * Primary styles
- *
- * Author: Jupyter Development Team
- */
-ul#tabs {
-  margin-bottom: 4px;
+* TODO: come up with non css-hack solution for showing the busy icon on top
+*  of the close icon
+* CSS for complex behavior of close icon of tabs in the main area tabbar
+*/
+.lm-DockPanel-tabBar
+  .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon3[fill] {
+  fill: none;
 }
-ul#tabs a {
-  padding-top: 6px;
-  padding-bottom: 4px;
+.lm-DockPanel-tabBar
+  .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon-busy[fill] {
+  fill: var(--jp-inverse-layout-color3);
 }
-[dir="rtl"] ul#tabs.nav-tabs > li {
-  float: right;
+
+/* CSS for icons in status bar */
+#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
+  fill: #fff;
 }
-[dir="rtl"] ul#tabs.nav.nav-tabs {
-  padding-right: 0;
+
+#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
+  fill: var(--jp-brand-color1);
 }
-ul.breadcrumb a:focus,
-ul.breadcrumb a:hover {
-  text-decoration: none;
+/* special handling for splash icon CSS. While the theme CSS reloads during
+   splash, the splash icon can loose theming. To prevent that, we set a
+   default for its color variable */
+:root {
+  --jp-warn-color0: var(--md-orange-700);
 }
-ul.breadcrumb i.icon-home {
-  font-size: 16px;
+
+/* not sure what to do with this one, used in filebrowser listing */
+.jp-DragIcon {
   margin-right: 4px;
 }
-ul.breadcrumb span {
-  color: #5e5e5e;
-}
-.list_toolbar {
-  padding: 4px 0 4px 0;
-  vertical-align: middle;
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/**
+ * Support for alt colors for icons as inline SVG HTMLElements
+ */
+
+/* alt recolor the primary elements of an icon */
+.jp-icon-alt .jp-icon0[fill] {
+  fill: var(--jp-layout-color0);
 }
-.list_toolbar .tree-buttons {
-  padding-top: 1px;
+.jp-icon-alt .jp-icon1[fill] {
+  fill: var(--jp-layout-color1);
 }
-[dir="rtl"] .list_toolbar .tree-buttons .pull-right {
-  float: left !important;
-  float: left;
+.jp-icon-alt .jp-icon2[fill] {
+  fill: var(--jp-layout-color2);
 }
-[dir="rtl"] .list_toolbar .col-sm-4,
-[dir="rtl"] .list_toolbar .col-sm-8 {
-  float: right;
+.jp-icon-alt .jp-icon3[fill] {
+  fill: var(--jp-layout-color3);
 }
-.dynamic-buttons {
-  padding-top: 3px;
-  display: inline-block;
+.jp-icon-alt .jp-icon4[fill] {
+  fill: var(--jp-layout-color4);
 }
-.list_toolbar [class*="span"] {
-  min-height: 24px;
+
+.jp-icon-alt .jp-icon0[stroke] {
+  stroke: var(--jp-layout-color0);
 }
-.list_header {
-  font-weight: bold;
-  background-color: #EEE;
+.jp-icon-alt .jp-icon1[stroke] {
+  stroke: var(--jp-layout-color1);
 }
-.list_placeholder {
-  font-weight: bold;
-  padding-top: 4px;
-  padding-bottom: 4px;
-  padding-left: 7px;
-  padding-right: 7px;
+.jp-icon-alt .jp-icon2[stroke] {
+  stroke: var(--jp-layout-color2);
 }
-.list_container {
-  margin-top: 4px;
-  margin-bottom: 20px;
-  border: 1px solid #ddd;
-  border-radius: 2px;
+.jp-icon-alt .jp-icon3[stroke] {
+  stroke: var(--jp-layout-color3);
 }
-.list_container > div {
-  border-bottom: 1px solid #ddd;
+.jp-icon-alt .jp-icon4[stroke] {
+  stroke: var(--jp-layout-color4);
 }
-.list_container > div:hover .list-item {
-  background-color: red;
+
+/* alt recolor the accent elements of an icon */
+.jp-icon-alt .jp-icon-accent0[fill] {
+  fill: var(--jp-inverse-layout-color0);
 }
-.list_container > div:last-child {
-  border: none;
+.jp-icon-alt .jp-icon-accent1[fill] {
+  fill: var(--jp-inverse-layout-color1);
 }
-.list_item:hover .list_item {
-  background-color: #ddd;
+.jp-icon-alt .jp-icon-accent2[fill] {
+  fill: var(--jp-inverse-layout-color2);
 }
-.list_item a {
-  text-decoration: none;
+.jp-icon-alt .jp-icon-accent3[fill] {
+  fill: var(--jp-inverse-layout-color3);
 }
-.list_item:hover {
-  background-color: #fafafa;
+.jp-icon-alt .jp-icon-accent4[fill] {
+  fill: var(--jp-inverse-layout-color4);
 }
-.list_header > div,
-.list_item > div {
-  padding-top: 4px;
-  padding-bottom: 4px;
-  padding-left: 7px;
-  padding-right: 7px;
-  line-height: 22px;
-}
-.list_header > div input,
-.list_item > div input {
-  margin-right: 7px;
-  margin-left: 14px;
-  vertical-align: text-bottom;
-  line-height: 22px;
-  position: relative;
-  top: -1px;
+
+.jp-icon-alt .jp-icon-accent0[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
 }
-.list_header > div .item_link,
-.list_item > div .item_link {
-  margin-left: -1px;
-  vertical-align: baseline;
-  line-height: 22px;
+.jp-icon-alt .jp-icon-accent1[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
 }
-[dir="rtl"] .list_item > div input {
-  margin-right: 0;
+.jp-icon-alt .jp-icon-accent2[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
 }
-.new-file input[type=checkbox] {
-  visibility: hidden;
+.jp-icon-alt .jp-icon-accent3[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
 }
-.item_name {
-  line-height: 22px;
-  height: 24px;
+.jp-icon-alt .jp-icon-accent4[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
 }
-.item_icon {
-  font-size: 14px;
-  color: #5e5e5e;
-  margin-right: 7px;
-  margin-left: 7px;
-  line-height: 22px;
-  vertical-align: baseline;
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-icon-hoverShow:not(:hover) svg {
+  display: none !important;
 }
-.item_modified {
-  margin-right: 7px;
-  margin-left: 7px;
+
+/**
+ * Support for hover colors for icons as inline SVG HTMLElements
+ */
+
+/**
+ * regular colors
+ */
+
+/* recolor the primary elements of an icon */
+.jp-icon-hover :hover .jp-icon0-hover[fill] {
+  fill: var(--jp-inverse-layout-color0);
 }
-[dir="rtl"] .item_modified.pull-right {
-  float: left !important;
-  float: left;
+.jp-icon-hover :hover .jp-icon1-hover[fill] {
+  fill: var(--jp-inverse-layout-color1);
 }
-.item_buttons {
-  line-height: 1em;
-  margin-left: -5px;
+.jp-icon-hover :hover .jp-icon2-hover[fill] {
+  fill: var(--jp-inverse-layout-color2);
 }
-.item_buttons .btn,
-.item_buttons .btn-group,
-.item_buttons .input-group {
-  float: left;
+.jp-icon-hover :hover .jp-icon3-hover[fill] {
+  fill: var(--jp-inverse-layout-color3);
 }
-.item_buttons > .btn,
-.item_buttons > .btn-group,
-.item_buttons > .input-group {
-  margin-left: 5px;
+.jp-icon-hover :hover .jp-icon4-hover[fill] {
+  fill: var(--jp-inverse-layout-color4);
 }
-.item_buttons .btn {
-  min-width: 13ex;
+
+.jp-icon-hover :hover .jp-icon0-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
 }
-.item_buttons .running-indicator {
-  padding-top: 4px;
-  color: #5cb85c;
+.jp-icon-hover :hover .jp-icon1-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
 }
-.item_buttons .kernel-name {
-  padding-top: 4px;
-  color: #5bc0de;
-  margin-right: 7px;
-  float: left;
+.jp-icon-hover :hover .jp-icon2-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
 }
-[dir="rtl"] .item_buttons.pull-right {
-  float: left !important;
-  float: left;
+.jp-icon-hover :hover .jp-icon3-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
 }
-[dir="rtl"] .item_buttons .kernel-name {
-  margin-left: 7px;
-  float: right;
+.jp-icon-hover :hover .jp-icon4-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
 }
-.toolbar_info {
-  height: 24px;
-  line-height: 24px;
+
+/* recolor the accent elements of an icon */
+.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
+  fill: var(--jp-layout-color0);
 }
-.list_item input:not([type=checkbox]) {
-  padding-top: 3px;
-  padding-bottom: 3px;
-  height: 22px;
-  line-height: 14px;
-  margin: 0px;
+.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
+  fill: var(--jp-layout-color1);
 }
-.highlight_text {
-  color: blue;
+.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
+  fill: var(--jp-layout-color2);
 }
-#project_name {
-  display: inline-block;
-  padding-left: 7px;
-  margin-left: -2px;
+.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
+  fill: var(--jp-layout-color3);
 }
-#project_name > .breadcrumb {
-  padding: 0px;
-  margin-bottom: 0px;
-  background-color: transparent;
-  font-weight: bold;
+.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
+  fill: var(--jp-layout-color4);
 }
-.sort_button {
-  display: inline-block;
-  padding-left: 7px;
+
+.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
+  stroke: var(--jp-layout-color0);
 }
-[dir="rtl"] .sort_button.pull-right {
-  float: left !important;
-  float: left;
+.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
+  stroke: var(--jp-layout-color1);
 }
-#tree-selector {
-  padding-right: 0px;
+.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
+  stroke: var(--jp-layout-color2);
 }
-#button-select-all {
-  min-width: 50px;
+.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
+  stroke: var(--jp-layout-color3);
 }
-[dir="rtl"] #button-select-all.btn {
-  float: right ;
+.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
+  stroke: var(--jp-layout-color4);
 }
-#select-all {
-  margin-left: 7px;
-  margin-right: 2px;
-  margin-top: 2px;
-  height: 16px;
+
+/* set the color of an icon to transparent */
+.jp-icon-hover :hover .jp-icon-none-hover[fill] {
+  fill: none;
 }
-[dir="rtl"] #select-all.pull-left {
-  float: right !important;
-  float: right;
+
+.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
+  stroke: none;
 }
-.menu_icon {
-  margin-right: 2px;
+
+/**
+ * inverse colors
+ */
+
+/* inverse recolor the primary elements of an icon */
+.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
+  fill: var(--jp-layout-color0);
 }
-.tab-content .row {
-  margin-left: 0px;
-  margin-right: 0px;
+.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
+  fill: var(--jp-layout-color1);
 }
-.folder_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f114";
+.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
+  fill: var(--jp-layout-color2);
 }
-.folder_icon:before.fa-pull-left {
-  margin-right: .3em;
+.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
+  fill: var(--jp-layout-color3);
 }
-.folder_icon:before.fa-pull-right {
-  margin-left: .3em;
+.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
+  fill: var(--jp-layout-color4);
 }
-.folder_icon:before.pull-left {
-  margin-right: .3em;
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
+  stroke: var(--jp-layout-color0);
 }
-.folder_icon:before.pull-right {
-  margin-left: .3em;
+.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
+  stroke: var(--jp-layout-color1);
 }
-.notebook_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f02d";
-  position: relative;
-  top: -1px;
+.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
+  stroke: var(--jp-layout-color2);
 }
-.notebook_icon:before.fa-pull-left {
-  margin-right: .3em;
+.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
+  stroke: var(--jp-layout-color3);
 }
-.notebook_icon:before.fa-pull-right {
-  margin-left: .3em;
+.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
+  stroke: var(--jp-layout-color4);
 }
-.notebook_icon:before.pull-left {
-  margin-right: .3em;
+
+/* inverse recolor the accent elements of an icon */
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
+  fill: var(--jp-inverse-layout-color0);
 }
-.notebook_icon:before.pull-right {
-  margin-left: .3em;
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
+  fill: var(--jp-inverse-layout-color1);
 }
-.running_notebook_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f02d";
-  position: relative;
-  top: -1px;
-  color: #5cb85c;
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
+  fill: var(--jp-inverse-layout-color2);
 }
-.running_notebook_icon:before.fa-pull-left {
-  margin-right: .3em;
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
+  fill: var(--jp-inverse-layout-color3);
 }
-.running_notebook_icon:before.fa-pull-right {
-  margin-left: .3em;
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
+  fill: var(--jp-inverse-layout-color4);
 }
-.running_notebook_icon:before.pull-left {
-  margin-right: .3em;
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
 }
-.running_notebook_icon:before.pull-right {
-  margin-left: .3em;
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
 }
-.file_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f016";
-  position: relative;
-  top: -2px;
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
 }
-.file_icon:before.fa-pull-left {
-  margin-right: .3em;
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
 }
-.file_icon:before.fa-pull-right {
-  margin-left: .3em;
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
 }
-.file_icon:before.pull-left {
-  margin-right: .3em;
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* Sibling imports */
+
+/* Override Blueprint's _reset.scss styles */
+html {
+  box-sizing: unset;
 }
-.file_icon:before.pull-right {
-  margin-left: .3em;
+
+*,
+*::before,
+*::after {
+  box-sizing: unset;
 }
-#notebook_toolbar .pull-right {
-  padding-top: 0px;
-  margin-right: -1px;
+
+body {
+  color: unset;
+  font-family: var(--jp-ui-font-family);
 }
-ul#new-menu {
-  left: auto;
-  right: 0;
+
+p {
+  margin-top: unset;
+  margin-bottom: unset;
 }
-#new-menu .dropdown-header {
-  font-size: 10px;
-  border-bottom: 1px solid #e5e5e5;
-  padding: 0 0 3px;
-  margin: -3px 20px 0;
+
+small {
+  font-size: unset;
 }
-.kernel-menu-icon {
-  padding-right: 12px;
-  width: 24px;
-  content: "\f096";
+
+strong {
+  font-weight: unset;
 }
-.kernel-menu-icon:before {
-  content: "\f096";
+
+/* Override Blueprint's _typography.scss styles */
+a {
+  text-decoration: unset;
+  color: unset;
 }
-.kernel-menu-icon-current:before {
-  content: "\f00c";
+a:hover {
+  text-decoration: unset;
+  color: unset;
 }
-#tab_content {
-  padding-top: 20px;
+
+/* Override Blueprint's _accessibility.scss styles */
+:focus {
+  outline: unset;
+  outline-offset: unset;
+  -moz-outline-radius: unset;
 }
-#running .panel-group .panel {
-  margin-top: 3px;
-  margin-bottom: 1em;
+
+/* Styles for ui-components */
+.jp-Button {
+  border-radius: var(--jp-border-radius);
+  padding: 0px 12px;
+  font-size: var(--jp-ui-font-size1);
 }
-#running .panel-group .panel .panel-heading {
-  background-color: #EEE;
-  padding-top: 4px;
-  padding-bottom: 4px;
-  padding-left: 7px;
-  padding-right: 7px;
-  line-height: 22px;
+
+/* Use our own theme for hover styles */
+button.jp-Button.bp3-button.bp3-minimal:hover {
+  background-color: var(--jp-layout-color2);
 }
-#running .panel-group .panel .panel-heading a:focus,
-#running .panel-group .panel .panel-heading a:hover {
-  text-decoration: none;
+.jp-Button.minimal {
+  color: unset !important;
 }
-#running .panel-group .panel .panel-body {
-  padding: 0px;
+
+.jp-Button.jp-ToolbarButtonComponent {
+  text-transform: none;
 }
-#running .panel-group .panel .panel-body .list_container {
-  margin-top: 0px;
-  margin-bottom: 0px;
-  border: 0px;
-  border-radius: 0px;
+
+.jp-InputGroup input {
+  box-sizing: border-box;
+  border-radius: 0;
+  background-color: transparent;
+  color: var(--jp-ui-font-color0);
+  box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
 }
-#running .panel-group .panel .panel-body .list_container .list_item {
-  border-bottom: 1px solid #ddd;
+
+.jp-InputGroup input:focus {
+  box-shadow: inset 0 0 0 var(--jp-border-width)
+      var(--jp-input-active-box-shadow-color),
+    inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
 }
-#running .panel-group .panel .panel-body .list_container .list_item:last-child {
-  border-bottom: 0px;
+
+.jp-InputGroup input::placeholder,
+input::placeholder {
+  color: var(--jp-ui-font-color3);
 }
-.delete-button {
-  display: none;
+
+.jp-BPIcon {
+  display: inline-block;
+  vertical-align: middle;
+  margin: auto;
 }
-.duplicate-button {
-  display: none;
+
+/* Stop blueprint futzing with our icon fills */
+.bp3-icon.jp-BPIcon > svg:not([fill]) {
+  fill: var(--jp-inverse-layout-color3);
 }
-.rename-button {
-  display: none;
+
+.jp-InputGroupAction {
+  padding: 6px;
 }
-.move-button {
-  display: none;
+
+.jp-HTMLSelect.jp-DefaultStyle select {
+  background-color: initial;
+  border: none;
+  border-radius: 0;
+  box-shadow: none;
+  color: var(--jp-ui-font-color0);
+  display: block;
+  font-size: var(--jp-ui-font-size1);
+  height: 24px;
+  line-height: 14px;
+  padding: 0 25px 0 10px;
+  text-align: left;
+  -moz-appearance: none;
+  -webkit-appearance: none;
 }
-.download-button {
-  display: none;
+
+/* Use our own theme for hover and option styles */
+.jp-HTMLSelect.jp-DefaultStyle select:hover,
+.jp-HTMLSelect.jp-DefaultStyle select > option {
+  background-color: var(--jp-layout-color2);
+  color: var(--jp-ui-font-color0);
 }
-.shutdown-button {
-  display: none;
+select {
+  box-sizing: border-box;
 }
-.dynamic-instructions {
-  display: inline-block;
-  padding-top: 4px;
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* This file was auto-generated by ensurePackage() in @jupyterlab/buildutils */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Collapse {
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  border-top: 1px solid var(--jp-border-color2);
+  border-bottom: 1px solid var(--jp-border-color2);
 }
-/*!
-*
-* IPython text editor webapp
-*
-*/
-.selected-keymap i.fa {
-  padding: 0px 5px;
+
+.jp-Collapse-header {
+  padding: 1px 12px;
+  color: var(--jp-ui-font-color1);
+  background-color: var(--jp-layout-color1);
+  font-size: var(--jp-ui-font-size2);
 }
-.selected-keymap i.fa:before {
-  content: "\f00c";
+
+.jp-Collapse-header:hover {
+  background-color: var(--jp-layout-color2);
 }
-#mode-menu {
+
+.jp-Collapse-contents {
+  padding: 0px 12px 0px 12px;
+  background-color: var(--jp-layout-color1);
+  color: var(--jp-ui-font-color1);
   overflow: auto;
-  max-height: 20em;
 }
-.edit_app #header {
-  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-commandpalette-search-height: 28px;
+}
+
+/*-----------------------------------------------------------------------------
+| Overall styles
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette {
+  padding-bottom: 0px;
+  color: var(--jp-ui-font-color1);
+  background: var(--jp-layout-color1);
+  /* This is needed so that all font sizing of children done in ems is
+   * relative to this base size */
+  font-size: var(--jp-ui-font-size1);
+}
+
+/*-----------------------------------------------------------------------------
+| Search
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette-search {
+  padding: 4px;
+  background-color: var(--jp-layout-color1);
+  z-index: 2;
 }
-.edit_app #menubar .navbar {
-  /* Use a negative 1 bottom margin, so the border overlaps the border of the
-    header */
-  margin-bottom: -1px;
+
+.lm-CommandPalette-wrapper {
+  overflow: overlay;
+  padding: 0px 9px;
+  background-color: var(--jp-input-active-background);
+  height: 30px;
+  box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
 }
-.dirty-indicator {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  width: 20px;
+
+.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
+  box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
+    inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
 }
-.dirty-indicator.fa-pull-left {
-  margin-right: .3em;
+
+.lm-CommandPalette-wrapper::after {
+  content: ' ';
+  color: white;
+  background-color: var(--jp-brand-color1);
+  position: absolute;
+  top: 4px;
+  right: 4px;
+  height: 30px;
+  width: 10px;
+  padding: 0px 10px;
+  background-image: var(--jp-icon-search-white);
+  background-size: 20px;
+  background-repeat: no-repeat;
+  background-position: center;
 }
-.dirty-indicator.fa-pull-right {
-  margin-left: .3em;
+
+.lm-CommandPalette-input {
+  background: transparent;
+  width: calc(100% - 18px);
+  float: left;
+  border: none;
+  outline: none;
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color0);
+  line-height: var(--jp-private-commandpalette-search-height);
 }
-.dirty-indicator.pull-left {
-  margin-right: .3em;
+
+.lm-CommandPalette-input::-webkit-input-placeholder,
+.lm-CommandPalette-input::-moz-placeholder,
+.lm-CommandPalette-input:-ms-input-placeholder {
+  color: var(--jp-ui-font-color3);
+  font-size: var(--jp-ui-font-size1);
 }
-.dirty-indicator.pull-right {
-  margin-left: .3em;
+
+/*-----------------------------------------------------------------------------
+| Results
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette-header:first-child {
+  margin-top: 0px;
 }
-.dirty-indicator-dirty {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  width: 20px;
+
+.lm-CommandPalette-header {
+  border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
+  color: var(--jp-ui-font-color1);
+  cursor: pointer;
+  display: flex;
+  font-size: var(--jp-ui-font-size0);
+  font-weight: 600;
+  letter-spacing: 1px;
+  margin-top: 8px;
+  padding: 8px 0 8px 12px;
+  text-transform: uppercase;
 }
-.dirty-indicator-dirty.fa-pull-left {
-  margin-right: .3em;
+
+.lm-CommandPalette-header.lm-mod-active {
+  background: var(--jp-layout-color2);
 }
-.dirty-indicator-dirty.fa-pull-right {
-  margin-left: .3em;
+
+.lm-CommandPalette-header > mark {
+  background-color: transparent;
+  font-weight: bold;
+  color: var(--jp-ui-font-color1);
 }
-.dirty-indicator-dirty.pull-left {
-  margin-right: .3em;
+
+.lm-CommandPalette-item {
+  padding: 4px 12px 4px 4px;
+  color: var(--jp-ui-font-color1);
+  font-size: var(--jp-ui-font-size1);
+  font-weight: 400;
+  display: flex;
 }
-.dirty-indicator-dirty.pull-right {
-  margin-left: .3em;
+
+.lm-CommandPalette-item.lm-mod-disabled {
+  color: var(--jp-ui-font-color3);
 }
-.dirty-indicator-clean {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  width: 20px;
+
+.lm-CommandPalette-item.lm-mod-active {
+  background: var(--jp-layout-color3);
 }
-.dirty-indicator-clean.fa-pull-left {
-  margin-right: .3em;
+
+.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
+  background: var(--jp-layout-color4);
 }
-.dirty-indicator-clean.fa-pull-right {
-  margin-left: .3em;
+
+.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
+  background: var(--jp-layout-color2);
 }
-.dirty-indicator-clean.pull-left {
-  margin-right: .3em;
+
+.lm-CommandPalette-itemContent {
+  overflow: hidden;
 }
-.dirty-indicator-clean.pull-right {
-  margin-left: .3em;
+
+.lm-CommandPalette-itemLabel > mark {
+  color: var(--jp-ui-font-color0);
+  background-color: transparent;
+  font-weight: bold;
 }
-.dirty-indicator-clean:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f00c";
+
+.lm-CommandPalette-item.lm-mod-disabled mark {
+  color: var(--jp-ui-font-color3);
 }
-.dirty-indicator-clean:before.fa-pull-left {
-  margin-right: .3em;
+
+.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
+  margin: 0 4px 0 0;
+  position: relative;
+  width: 16px;
+  top: 2px;
+  flex: 0 0 auto;
 }
-.dirty-indicator-clean:before.fa-pull-right {
-  margin-left: .3em;
+
+.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
+  opacity: 0.4;
 }
-.dirty-indicator-clean:before.pull-left {
-  margin-right: .3em;
+
+.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
+  flex: 0 0 auto;
 }
-.dirty-indicator-clean:before.pull-right {
-  margin-left: .3em;
+
+.lm-CommandPalette-itemCaption {
+  display: none;
 }
-#filename {
-  font-size: 16pt;
-  display: table;
-  padding: 0px 5px;
+
+.lm-CommandPalette-content {
+  background-color: var(--jp-layout-color1);
 }
-#current-mode {
-  padding-left: 5px;
-  padding-right: 5px;
+
+.lm-CommandPalette-content:empty:after {
+  content: 'No results';
+  margin: auto;
+  margin-top: 20px;
+  width: 100px;
+  display: block;
+  font-size: var(--jp-ui-font-size2);
+  font-family: var(--jp-ui-font-family);
+  font-weight: lighter;
 }
-#texteditor-backdrop {
-  padding-top: 20px;
-  padding-bottom: 20px;
+
+.lm-CommandPalette-emptyMessage {
+  text-align: center;
+  margin-top: 24px;
+  line-height: 1.32;
+  padding: 0px 8px;
+  color: var(--jp-content-font-color3);
 }
-@media not print {
-  #texteditor-backdrop {
-    background-color: #EEE;
-  }
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Dialog {
+  position: absolute;
+  z-index: 10000;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  top: 0px;
+  left: 0px;
+  margin: 0;
+  padding: 0;
+  width: 100%;
+  height: 100%;
+  background: var(--jp-dialog-background);
 }
-@media print {
-  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
-  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
-    background-color: #fff;
-  }
+
+.jp-Dialog-content {
+  display: flex;
+  flex-direction: column;
+  margin-left: auto;
+  margin-right: auto;
+  background: var(--jp-layout-color1);
+  padding: 24px;
+  padding-bottom: 12px;
+  min-width: 300px;
+  min-height: 150px;
+  max-width: 1000px;
+  max-height: 500px;
+  box-sizing: border-box;
+  box-shadow: var(--jp-elevation-z20);
+  word-wrap: break-word;
+  border-radius: var(--jp-border-radius);
+  /* This is needed so that all font sizing of children done in ems is
+   * relative to this base size */
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color1);
 }
-@media not print {
-  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
-  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
-    background-color: #fff;
-  }
+
+.jp-Dialog-button {
+  overflow: visible;
 }
-@media not print {
-  #texteditor-backdrop #texteditor-container {
-    padding: 0px;
-    background-color: #fff;
-    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-  }
+
+button.jp-Dialog-button:focus {
+  outline: 1px solid var(--jp-brand-color1);
+  outline-offset: 4px;
+  -moz-outline-radius: 0px;
 }
-.CodeMirror-dialog {
-  background-color: #fff;
+
+button.jp-Dialog-button:focus::-moz-focus-inner {
+  border: 0;
 }
-/*!
-*
-* IPython notebook
-*
-*/
-/* CSS font colors for translated ANSI escape sequences */
-/* The color values are a mix of
-   http://www.xcolors.net/dl/baskerville-ivorylight and
-   http://www.xcolors.net/dl/euphrasia */
-.ansi-black-fg {
-  color: #3E424D;
+
+.jp-Dialog-header {
+  flex: 0 0 auto;
+  padding-bottom: 12px;
+  font-size: var(--jp-ui-font-size3);
+  font-weight: 400;
+  color: var(--jp-ui-font-color0);
 }
-.ansi-black-bg {
-  background-color: #3E424D;
+
+.jp-Dialog-body {
+  display: flex;
+  flex-direction: column;
+  flex: 1 1 auto;
+  font-size: var(--jp-ui-font-size1);
+  background: var(--jp-layout-color1);
+  overflow: auto;
 }
-.ansi-black-intense-fg {
-  color: #282C36;
+
+.jp-Dialog-footer {
+  display: flex;
+  flex-direction: row;
+  justify-content: flex-end;
+  flex: 0 0 auto;
+  margin-left: -12px;
+  margin-right: -12px;
+  padding: 12px;
 }
-.ansi-black-intense-bg {
-  background-color: #282C36;
+
+.jp-Dialog-title {
+  overflow: hidden;
+  white-space: nowrap;
+  text-overflow: ellipsis;
 }
-.ansi-red-fg {
-  color: #E75C58;
+
+.jp-Dialog-body > .jp-select-wrapper {
+  width: 100%;
 }
-.ansi-red-bg {
-  background-color: #E75C58;
+
+.jp-Dialog-body > button {
+  padding: 0px 16px;
 }
-.ansi-red-intense-fg {
-  color: #B22B31;
+
+.jp-Dialog-body > label {
+  line-height: 1.4;
+  color: var(--jp-ui-font-color0);
 }
-.ansi-red-intense-bg {
-  background-color: #B22B31;
+
+.jp-Dialog-button.jp-mod-styled:not(:last-child) {
+  margin-right: 12px;
 }
-.ansi-green-fg {
-  color: #00A250;
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-HoverBox {
+  position: fixed;
 }
-.ansi-green-bg {
-  background-color: #00A250;
+
+.jp-HoverBox.jp-mod-outofview {
+  display: none;
 }
-.ansi-green-intense-fg {
-  color: #007427;
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-IFrame {
+  width: 100%;
+  height: 100%;
 }
-.ansi-green-intense-bg {
-  background-color: #007427;
+
+.jp-IFrame > iframe {
+  border: none;
 }
-.ansi-yellow-fg {
-  color: #DDB62B;
+
+/*
+When drag events occur, `p-mod-override-cursor` is added to the body.
+Because iframes steal all cursor events, the following two rules are necessary
+to suppress pointer events while resize drags are occurring. There may be a
+better solution to this problem.
+*/
+body.lm-mod-override-cursor .jp-IFrame {
+  position: relative;
 }
-.ansi-yellow-bg {
-  background-color: #DDB62B;
+
+body.lm-mod-override-cursor .jp-IFrame:before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: transparent;
 }
-.ansi-yellow-intense-fg {
-  color: #B27D12;
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-MainAreaWidget > :focus {
+  outline: none;
 }
-.ansi-yellow-intense-bg {
-  background-color: #B27D12;
+
+/**
+ * google-material-color v1.2.6
+ * https://github.com/danlevan/google-material-color
+ */
+:root {
+  --md-red-50: #ffebee;
+  --md-red-100: #ffcdd2;
+  --md-red-200: #ef9a9a;
+  --md-red-300: #e57373;
+  --md-red-400: #ef5350;
+  --md-red-500: #f44336;
+  --md-red-600: #e53935;
+  --md-red-700: #d32f2f;
+  --md-red-800: #c62828;
+  --md-red-900: #b71c1c;
+  --md-red-A100: #ff8a80;
+  --md-red-A200: #ff5252;
+  --md-red-A400: #ff1744;
+  --md-red-A700: #d50000;
+
+  --md-pink-50: #fce4ec;
+  --md-pink-100: #f8bbd0;
+  --md-pink-200: #f48fb1;
+  --md-pink-300: #f06292;
+  --md-pink-400: #ec407a;
+  --md-pink-500: #e91e63;
+  --md-pink-600: #d81b60;
+  --md-pink-700: #c2185b;
+  --md-pink-800: #ad1457;
+  --md-pink-900: #880e4f;
+  --md-pink-A100: #ff80ab;
+  --md-pink-A200: #ff4081;
+  --md-pink-A400: #f50057;
+  --md-pink-A700: #c51162;
+
+  --md-purple-50: #f3e5f5;
+  --md-purple-100: #e1bee7;
+  --md-purple-200: #ce93d8;
+  --md-purple-300: #ba68c8;
+  --md-purple-400: #ab47bc;
+  --md-purple-500: #9c27b0;
+  --md-purple-600: #8e24aa;
+  --md-purple-700: #7b1fa2;
+  --md-purple-800: #6a1b9a;
+  --md-purple-900: #4a148c;
+  --md-purple-A100: #ea80fc;
+  --md-purple-A200: #e040fb;
+  --md-purple-A400: #d500f9;
+  --md-purple-A700: #aa00ff;
+
+  --md-deep-purple-50: #ede7f6;
+  --md-deep-purple-100: #d1c4e9;
+  --md-deep-purple-200: #b39ddb;
+  --md-deep-purple-300: #9575cd;
+  --md-deep-purple-400: #7e57c2;
+  --md-deep-purple-500: #673ab7;
+  --md-deep-purple-600: #5e35b1;
+  --md-deep-purple-700: #512da8;
+  --md-deep-purple-800: #4527a0;
+  --md-deep-purple-900: #311b92;
+  --md-deep-purple-A100: #b388ff;
+  --md-deep-purple-A200: #7c4dff;
+  --md-deep-purple-A400: #651fff;
+  --md-deep-purple-A700: #6200ea;
+
+  --md-indigo-50: #e8eaf6;
+  --md-indigo-100: #c5cae9;
+  --md-indigo-200: #9fa8da;
+  --md-indigo-300: #7986cb;
+  --md-indigo-400: #5c6bc0;
+  --md-indigo-500: #3f51b5;
+  --md-indigo-600: #3949ab;
+  --md-indigo-700: #303f9f;
+  --md-indigo-800: #283593;
+  --md-indigo-900: #1a237e;
+  --md-indigo-A100: #8c9eff;
+  --md-indigo-A200: #536dfe;
+  --md-indigo-A400: #3d5afe;
+  --md-indigo-A700: #304ffe;
+
+  --md-blue-50: #e3f2fd;
+  --md-blue-100: #bbdefb;
+  --md-blue-200: #90caf9;
+  --md-blue-300: #64b5f6;
+  --md-blue-400: #42a5f5;
+  --md-blue-500: #2196f3;
+  --md-blue-600: #1e88e5;
+  --md-blue-700: #1976d2;
+  --md-blue-800: #1565c0;
+  --md-blue-900: #0d47a1;
+  --md-blue-A100: #82b1ff;
+  --md-blue-A200: #448aff;
+  --md-blue-A400: #2979ff;
+  --md-blue-A700: #2962ff;
+
+  --md-light-blue-50: #e1f5fe;
+  --md-light-blue-100: #b3e5fc;
+  --md-light-blue-200: #81d4fa;
+  --md-light-blue-300: #4fc3f7;
+  --md-light-blue-400: #29b6f6;
+  --md-light-blue-500: #03a9f4;
+  --md-light-blue-600: #039be5;
+  --md-light-blue-700: #0288d1;
+  --md-light-blue-800: #0277bd;
+  --md-light-blue-900: #01579b;
+  --md-light-blue-A100: #80d8ff;
+  --md-light-blue-A200: #40c4ff;
+  --md-light-blue-A400: #00b0ff;
+  --md-light-blue-A700: #0091ea;
+
+  --md-cyan-50: #e0f7fa;
+  --md-cyan-100: #b2ebf2;
+  --md-cyan-200: #80deea;
+  --md-cyan-300: #4dd0e1;
+  --md-cyan-400: #26c6da;
+  --md-cyan-500: #00bcd4;
+  --md-cyan-600: #00acc1;
+  --md-cyan-700: #0097a7;
+  --md-cyan-800: #00838f;
+  --md-cyan-900: #006064;
+  --md-cyan-A100: #84ffff;
+  --md-cyan-A200: #18ffff;
+  --md-cyan-A400: #00e5ff;
+  --md-cyan-A700: #00b8d4;
+
+  --md-teal-50: #e0f2f1;
+  --md-teal-100: #b2dfdb;
+  --md-teal-200: #80cbc4;
+  --md-teal-300: #4db6ac;
+  --md-teal-400: #26a69a;
+  --md-teal-500: #009688;
+  --md-teal-600: #00897b;
+  --md-teal-700: #00796b;
+  --md-teal-800: #00695c;
+  --md-teal-900: #004d40;
+  --md-teal-A100: #a7ffeb;
+  --md-teal-A200: #64ffda;
+  --md-teal-A400: #1de9b6;
+  --md-teal-A700: #00bfa5;
+
+  --md-green-50: #e8f5e9;
+  --md-green-100: #c8e6c9;
+  --md-green-200: #a5d6a7;
+  --md-green-300: #81c784;
+  --md-green-400: #66bb6a;
+  --md-green-500: #4caf50;
+  --md-green-600: #43a047;
+  --md-green-700: #388e3c;
+  --md-green-800: #2e7d32;
+  --md-green-900: #1b5e20;
+  --md-green-A100: #b9f6ca;
+  --md-green-A200: #69f0ae;
+  --md-green-A400: #00e676;
+  --md-green-A700: #00c853;
+
+  --md-light-green-50: #f1f8e9;
+  --md-light-green-100: #dcedc8;
+  --md-light-green-200: #c5e1a5;
+  --md-light-green-300: #aed581;
+  --md-light-green-400: #9ccc65;
+  --md-light-green-500: #8bc34a;
+  --md-light-green-600: #7cb342;
+  --md-light-green-700: #689f38;
+  --md-light-green-800: #558b2f;
+  --md-light-green-900: #33691e;
+  --md-light-green-A100: #ccff90;
+  --md-light-green-A200: #b2ff59;
+  --md-light-green-A400: #76ff03;
+  --md-light-green-A700: #64dd17;
+
+  --md-lime-50: #f9fbe7;
+  --md-lime-100: #f0f4c3;
+  --md-lime-200: #e6ee9c;
+  --md-lime-300: #dce775;
+  --md-lime-400: #d4e157;
+  --md-lime-500: #cddc39;
+  --md-lime-600: #c0ca33;
+  --md-lime-700: #afb42b;
+  --md-lime-800: #9e9d24;
+  --md-lime-900: #827717;
+  --md-lime-A100: #f4ff81;
+  --md-lime-A200: #eeff41;
+  --md-lime-A400: #c6ff00;
+  --md-lime-A700: #aeea00;
+
+  --md-yellow-50: #fffde7;
+  --md-yellow-100: #fff9c4;
+  --md-yellow-200: #fff59d;
+  --md-yellow-300: #fff176;
+  --md-yellow-400: #ffee58;
+  --md-yellow-500: #ffeb3b;
+  --md-yellow-600: #fdd835;
+  --md-yellow-700: #fbc02d;
+  --md-yellow-800: #f9a825;
+  --md-yellow-900: #f57f17;
+  --md-yellow-A100: #ffff8d;
+  --md-yellow-A200: #ffff00;
+  --md-yellow-A400: #ffea00;
+  --md-yellow-A700: #ffd600;
+
+  --md-amber-50: #fff8e1;
+  --md-amber-100: #ffecb3;
+  --md-amber-200: #ffe082;
+  --md-amber-300: #ffd54f;
+  --md-amber-400: #ffca28;
+  --md-amber-500: #ffc107;
+  --md-amber-600: #ffb300;
+  --md-amber-700: #ffa000;
+  --md-amber-800: #ff8f00;
+  --md-amber-900: #ff6f00;
+  --md-amber-A100: #ffe57f;
+  --md-amber-A200: #ffd740;
+  --md-amber-A400: #ffc400;
+  --md-amber-A700: #ffab00;
+
+  --md-orange-50: #fff3e0;
+  --md-orange-100: #ffe0b2;
+  --md-orange-200: #ffcc80;
+  --md-orange-300: #ffb74d;
+  --md-orange-400: #ffa726;
+  --md-orange-500: #ff9800;
+  --md-orange-600: #fb8c00;
+  --md-orange-700: #f57c00;
+  --md-orange-800: #ef6c00;
+  --md-orange-900: #e65100;
+  --md-orange-A100: #ffd180;
+  --md-orange-A200: #ffab40;
+  --md-orange-A400: #ff9100;
+  --md-orange-A700: #ff6d00;
+
+  --md-deep-orange-50: #fbe9e7;
+  --md-deep-orange-100: #ffccbc;
+  --md-deep-orange-200: #ffab91;
+  --md-deep-orange-300: #ff8a65;
+  --md-deep-orange-400: #ff7043;
+  --md-deep-orange-500: #ff5722;
+  --md-deep-orange-600: #f4511e;
+  --md-deep-orange-700: #e64a19;
+  --md-deep-orange-800: #d84315;
+  --md-deep-orange-900: #bf360c;
+  --md-deep-orange-A100: #ff9e80;
+  --md-deep-orange-A200: #ff6e40;
+  --md-deep-orange-A400: #ff3d00;
+  --md-deep-orange-A700: #dd2c00;
+
+  --md-brown-50: #efebe9;
+  --md-brown-100: #d7ccc8;
+  --md-brown-200: #bcaaa4;
+  --md-brown-300: #a1887f;
+  --md-brown-400: #8d6e63;
+  --md-brown-500: #795548;
+  --md-brown-600: #6d4c41;
+  --md-brown-700: #5d4037;
+  --md-brown-800: #4e342e;
+  --md-brown-900: #3e2723;
+
+  --md-grey-50: #fafafa;
+  --md-grey-100: #f5f5f5;
+  --md-grey-200: #eeeeee;
+  --md-grey-300: #e0e0e0;
+  --md-grey-400: #bdbdbd;
+  --md-grey-500: #9e9e9e;
+  --md-grey-600: #757575;
+  --md-grey-700: #616161;
+  --md-grey-800: #424242;
+  --md-grey-900: #212121;
+
+  --md-blue-grey-50: #eceff1;
+  --md-blue-grey-100: #cfd8dc;
+  --md-blue-grey-200: #b0bec5;
+  --md-blue-grey-300: #90a4ae;
+  --md-blue-grey-400: #78909c;
+  --md-blue-grey-500: #607d8b;
+  --md-blue-grey-600: #546e7a;
+  --md-blue-grey-700: #455a64;
+  --md-blue-grey-800: #37474f;
+  --md-blue-grey-900: #263238;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Spinner {
+  position: absolute;
+  display: flex;
+  justify-content: center;
+  align-items: center;
+  z-index: 10;
+  left: 0;
+  top: 0;
+  width: 100%;
+  height: 100%;
+  background: var(--jp-layout-color0);
+  outline: none;
 }
-.ansi-blue-fg {
-  color: #208FFB;
+
+.jp-SpinnerContent {
+  font-size: 10px;
+  margin: 50px auto;
+  text-indent: -9999em;
+  width: 3em;
+  height: 3em;
+  border-radius: 50%;
+  background: var(--jp-brand-color3);
+  background: linear-gradient(
+    to right,
+    #f37626 10%,
+    rgba(255, 255, 255, 0) 42%
+  );
+  position: relative;
+  animation: load3 1s infinite linear, fadeIn 1s;
 }
-.ansi-blue-bg {
-  background-color: #208FFB;
+
+.jp-SpinnerContent:before {
+  width: 50%;
+  height: 50%;
+  background: #f37626;
+  border-radius: 100% 0 0 0;
+  position: absolute;
+  top: 0;
+  left: 0;
+  content: '';
 }
-.ansi-blue-intense-fg {
-  color: #0065CA;
+
+.jp-SpinnerContent:after {
+  background: var(--jp-layout-color0);
+  width: 75%;
+  height: 75%;
+  border-radius: 50%;
+  content: '';
+  margin: auto;
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  right: 0;
 }
-.ansi-blue-intense-bg {
-  background-color: #0065CA;
+
+@keyframes fadeIn {
+  0% {
+    opacity: 0;
+  }
+  100% {
+    opacity: 1;
+  }
 }
-.ansi-magenta-fg {
-  color: #D160C4;
+
+@keyframes load3 {
+  0% {
+    transform: rotate(0deg);
+  }
+  100% {
+    transform: rotate(360deg);
+  }
 }
-.ansi-magenta-bg {
-  background-color: #D160C4;
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+button.jp-mod-styled {
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color0);
+  border: none;
+  box-sizing: border-box;
+  text-align: center;
+  line-height: 32px;
+  height: 32px;
+  padding: 0px 12px;
+  letter-spacing: 0.8px;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
 }
-.ansi-magenta-intense-fg {
-  color: #A03196;
+
+input.jp-mod-styled {
+  background: var(--jp-input-background);
+  height: 28px;
+  box-sizing: border-box;
+  border: var(--jp-border-width) solid var(--jp-border-color1);
+  padding-left: 7px;
+  padding-right: 7px;
+  font-size: var(--jp-ui-font-size2);
+  color: var(--jp-ui-font-color0);
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
 }
-.ansi-magenta-intense-bg {
-  background-color: #A03196;
+
+input.jp-mod-styled:focus {
+  border: var(--jp-border-width) solid var(--md-blue-500);
+  box-shadow: inset 0 0 4px var(--md-blue-300);
 }
-.ansi-cyan-fg {
-  color: #60C6C8;
+
+.jp-select-wrapper {
+  display: flex;
+  position: relative;
+  flex-direction: column;
+  padding: 1px;
+  background-color: var(--jp-layout-color1);
+  height: 28px;
+  box-sizing: border-box;
+  margin-bottom: 12px;
 }
-.ansi-cyan-bg {
-  background-color: #60C6C8;
+
+.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
+  border: var(--jp-border-width) solid var(--jp-input-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
+  background-color: var(--jp-input-active-background);
 }
-.ansi-cyan-intense-fg {
-  color: #258F8F;
+
+select.jp-mod-styled:hover {
+  background-color: var(--jp-layout-color1);
+  cursor: pointer;
+  color: var(--jp-ui-font-color0);
+  background-color: var(--jp-input-hover-background);
+  box-shadow: inset 0 0px 1px rgba(0, 0, 0, 0.5);
 }
-.ansi-cyan-intense-bg {
-  background-color: #258F8F;
+
+select.jp-mod-styled {
+  flex: 1 1 auto;
+  height: 32px;
+  width: 100%;
+  font-size: var(--jp-ui-font-size2);
+  background: var(--jp-input-background);
+  color: var(--jp-ui-font-color0);
+  padding: 0 25px 0 8px;
+  border: var(--jp-border-width) solid var(--jp-input-border-color);
+  border-radius: 0px;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
 }
-.ansi-white-fg {
-  color: #C5C1B4;
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-toolbar-height: calc(
+    28px + var(--jp-border-width)
+  ); /* leave 28px for content */
 }
-.ansi-white-bg {
-  background-color: #C5C1B4;
+
+.jp-Toolbar {
+  color: var(--jp-ui-font-color1);
+  flex: 0 0 auto;
+  display: flex;
+  flex-direction: row;
+  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+  box-shadow: var(--jp-toolbar-box-shadow);
+  background: var(--jp-toolbar-background);
+  min-height: var(--jp-toolbar-micro-height);
+  padding: 2px;
+  z-index: 1;
 }
-.ansi-white-intense-fg {
-  color: #A1A6B2;
+
+/* Toolbar items */
+
+.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
+  flex-grow: 1;
+  flex-shrink: 1;
 }
-.ansi-white-intense-bg {
-  background-color: #A1A6B2;
+
+.jp-Toolbar-item.jp-Toolbar-kernelStatus {
+  display: inline-block;
+  width: 32px;
+  background-repeat: no-repeat;
+  background-position: center;
+  background-size: 16px;
 }
-.ansi-default-inverse-fg {
-  color: #FFFFFF;
+
+.jp-Toolbar > .jp-Toolbar-item {
+  flex: 0 0 auto;
+  display: flex;
+  padding-left: 1px;
+  padding-right: 1px;
+  font-size: var(--jp-ui-font-size1);
+  line-height: var(--jp-private-toolbar-height);
+  height: 100%;
 }
-.ansi-default-inverse-bg {
-  background-color: #000000;
+
+/* Toolbar buttons */
+
+/* This is the div we use to wrap the react component into a Widget */
+div.jp-ToolbarButton {
+  color: transparent;
+  border: none;
+  box-sizing: border-box;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  padding: 0px;
+  margin: 0px;
 }
-.ansi-bold {
-  font-weight: bold;
+
+button.jp-ToolbarButtonComponent {
+  background: var(--jp-layout-color1);
+  border: none;
+  box-sizing: border-box;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  padding: 0px 6px;
+  margin: 0px;
+  height: 24px;
+  border-radius: var(--jp-border-radius);
+  display: flex;
+  align-items: center;
+  text-align: center;
+  font-size: 14px;
+  min-width: unset;
+  min-height: unset;
 }
-.ansi-underline {
-  text-decoration: underline;
+
+button.jp-ToolbarButtonComponent:disabled {
+  opacity: 0.4;
 }
-/* The following styles are deprecated an will be removed in a future version */
-.ansibold {
-  font-weight: bold;
+
+button.jp-ToolbarButtonComponent span {
+  padding: 0px;
+  flex: 0 0 auto;
 }
-.ansi-inverse {
-  outline: 0.5px dotted;
+
+button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
+  font-size: var(--jp-ui-font-size1);
+  line-height: 100%;
+  padding-left: 2px;
+  color: var(--jp-ui-font-color1);
 }
-/* use dark versions for foreground, to improve visibility */
-.ansiblack {
-  color: black;
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* This file was auto-generated by ensurePackage() in @jupyterlab/buildutils */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ body.p-mod-override-cursor *, /* </DEPRECATED> */
+body.lm-mod-override-cursor * {
+  cursor: inherit !important;
 }
-.ansired {
-  color: darkred;
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-JSONEditor {
+  display: flex;
+  flex-direction: column;
+  width: 100%;
 }
-.ansigreen {
-  color: darkgreen;
+
+.jp-JSONEditor-host {
+  flex: 1 1 auto;
+  border: var(--jp-border-width) solid var(--jp-input-border-color);
+  border-radius: 0px;
+  background: var(--jp-layout-color0);
+  min-height: 50px;
+  padding: 1px;
 }
-.ansiyellow {
-  color: #c4a000;
+
+.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
+  border-color: red;
+  outline-color: red;
 }
-.ansiblue {
-  color: darkblue;
+
+.jp-JSONEditor-header {
+  display: flex;
+  flex: 1 0 auto;
+  padding: 0 0 0 12px;
 }
-.ansipurple {
-  color: darkviolet;
+
+.jp-JSONEditor-header label {
+  flex: 0 0 auto;
 }
-.ansicyan {
-  color: steelblue;
+
+.jp-JSONEditor-commitButton {
+  height: 16px;
+  width: 16px;
+  background-size: 18px;
+  background-repeat: no-repeat;
+  background-position: center;
 }
-.ansigray {
-  color: gray;
+
+.jp-JSONEditor-host.jp-mod-focused {
+  background-color: var(--jp-input-active-background);
+  border: 1px solid var(--jp-input-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
 }
-/* and light for background, for the same reason */
-.ansibgblack {
-  background-color: black;
+
+.jp-Editor.jp-mod-dropTarget {
+  border: var(--jp-border-width) solid var(--jp-input-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
 }
-.ansibgred {
-  background-color: red;
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* This file was auto-generated by ensurePackage() in @jupyterlab/buildutils */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* BASICS */
+
+.CodeMirror {
+  /* Set height, width, borders, and global font properties here */
+  font-family: monospace;
+  height: 300px;
+  color: black;
+  direction: ltr;
 }
-.ansibggreen {
-  background-color: green;
+
+/* PADDING */
+
+.CodeMirror-lines {
+  padding: 4px 0; /* Vertical padding around content */
 }
-.ansibgyellow {
-  background-color: yellow;
+.CodeMirror pre.CodeMirror-line,
+.CodeMirror pre.CodeMirror-line-like {
+  padding: 0 4px; /* Horizontal padding of content */
 }
-.ansibgblue {
-  background-color: blue;
+
+.CodeMirror-scrollbar-filler, .CodeMirror-gutter-filler {
+  background-color: white; /* The little square between H and V scrollbars */
 }
-.ansibgpurple {
-  background-color: magenta;
+
+/* GUTTER */
+
+.CodeMirror-gutters {
+  border-right: 1px solid #ddd;
+  background-color: #f7f7f7;
+  white-space: nowrap;
 }
-.ansibgcyan {
-  background-color: cyan;
+.CodeMirror-linenumbers {}
+.CodeMirror-linenumber {
+  padding: 0 3px 0 5px;
+  min-width: 20px;
+  text-align: right;
+  color: #999;
+  white-space: nowrap;
 }
-.ansibggray {
-  background-color: gray;
+
+.CodeMirror-guttermarker { color: black; }
+.CodeMirror-guttermarker-subtle { color: #999; }
+
+/* CURSOR */
+
+.CodeMirror-cursor {
+  border-left: 1px solid black;
+  border-right: none;
+  width: 0;
 }
-div.cell {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-  border-radius: 2px;
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-  border-width: 1px;
-  border-style: solid;
-  border-color: transparent;
-  width: 100%;
-  padding: 5px;
-  /* This acts as a spacer between cells, that is outside the border */
-  margin: 0px;
-  outline: none;
-  position: relative;
-  overflow: visible;
+/* Shown when moving in bi-directional text */
+.CodeMirror div.CodeMirror-secondarycursor {
+  border-left: 1px solid silver;
 }
-div.cell:before {
-  position: absolute;
-  display: block;
-  top: -1px;
-  left: -1px;
-  width: 5px;
-  height: calc(100% +  2px);
-  content: '';
-  background: transparent;
+.cm-fat-cursor .CodeMirror-cursor {
+  width: auto;
+  border: 0 !important;
+  background: #7e7;
 }
-div.cell.jupyter-soft-selected {
-  border-left-color: #E3F2FD;
-  border-left-width: 1px;
-  padding-left: 5px;
-  border-right-color: #E3F2FD;
-  border-right-width: 1px;
-  background: #E3F2FD;
+.cm-fat-cursor div.CodeMirror-cursors {
+  z-index: 1;
 }
-@media print {
-  div.cell.jupyter-soft-selected {
-    border-color: transparent;
-  }
+.cm-fat-cursor-mark {
+  background-color: rgba(20, 255, 20, 0.5);
+  -webkit-animation: blink 1.06s steps(1) infinite;
+  -moz-animation: blink 1.06s steps(1) infinite;
+  animation: blink 1.06s steps(1) infinite;
 }
-div.cell.selected,
-div.cell.selected.jupyter-soft-selected {
-  border-color: #ababab;
+.cm-animate-fat-cursor {
+  width: auto;
+  border: 0;
+  -webkit-animation: blink 1.06s steps(1) infinite;
+  -moz-animation: blink 1.06s steps(1) infinite;
+  animation: blink 1.06s steps(1) infinite;
+  background-color: #7e7;
 }
-div.cell.selected:before,
-div.cell.selected.jupyter-soft-selected:before {
-  position: absolute;
-  display: block;
-  top: -1px;
-  left: -1px;
-  width: 5px;
-  height: calc(100% +  2px);
-  content: '';
-  background: #42A5F5;
+@-moz-keyframes blink {
+  0% {}
+  50% { background-color: transparent; }
+  100% {}
 }
-@media print {
-  div.cell.selected,
-  div.cell.selected.jupyter-soft-selected {
-    border-color: transparent;
-  }
+@-webkit-keyframes blink {
+  0% {}
+  50% { background-color: transparent; }
+  100% {}
 }
-.edit_mode div.cell.selected {
-  border-color: #66BB6A;
+@keyframes blink {
+  0% {}
+  50% { background-color: transparent; }
+  100% {}
 }
-.edit_mode div.cell.selected:before {
+
+/* Can style cursor different in overwrite (non-insert) mode */
+.CodeMirror-overwrite .CodeMirror-cursor {}
+
+.cm-tab { display: inline-block; text-decoration: inherit; }
+
+.CodeMirror-rulers {
   position: absolute;
-  display: block;
-  top: -1px;
-  left: -1px;
-  width: 5px;
-  height: calc(100% +  2px);
-  content: '';
-  background: #66BB6A;
+  left: 0; right: 0; top: -50px; bottom: 0;
+  overflow: hidden;
 }
-@media print {
-  .edit_mode div.cell.selected {
-    border-color: transparent;
-  }
+.CodeMirror-ruler {
+  border-left: 1px solid #ccc;
+  top: 0; bottom: 0;
+  position: absolute;
 }
-.prompt {
-  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
-  min-width: 14ex;
-  /* This padding is tuned to match the padding on the CodeMirror editor. */
-  padding: 0.4em;
-  margin: 0px;
-  font-family: monospace;
-  text-align: right;
-  /* This has to match that of the the CodeMirror class line-height below */
-  line-height: 1.21429em;
-  /* Don't highlight prompt number selection */
-  -webkit-touch-callout: none;
-  -webkit-user-select: none;
-  -khtml-user-select: none;
-  -moz-user-select: none;
-  -ms-user-select: none;
-  user-select: none;
-  /* Use default cursor */
-  cursor: default;
+
+/* DEFAULT THEME */
+
+.cm-s-default .cm-header {color: blue;}
+.cm-s-default .cm-quote {color: #090;}
+.cm-negative {color: #d44;}
+.cm-positive {color: #292;}
+.cm-header, .cm-strong {font-weight: bold;}
+.cm-em {font-style: italic;}
+.cm-link {text-decoration: underline;}
+.cm-strikethrough {text-decoration: line-through;}
+
+.cm-s-default .cm-keyword {color: #708;}
+.cm-s-default .cm-atom {color: #219;}
+.cm-s-default .cm-number {color: #164;}
+.cm-s-default .cm-def {color: #00f;}
+.cm-s-default .cm-variable,
+.cm-s-default .cm-punctuation,
+.cm-s-default .cm-property,
+.cm-s-default .cm-operator {}
+.cm-s-default .cm-variable-2 {color: #05a;}
+.cm-s-default .cm-variable-3, .cm-s-default .cm-type {color: #085;}
+.cm-s-default .cm-comment {color: #a50;}
+.cm-s-default .cm-string {color: #a11;}
+.cm-s-default .cm-string-2 {color: #f50;}
+.cm-s-default .cm-meta {color: #555;}
+.cm-s-default .cm-qualifier {color: #555;}
+.cm-s-default .cm-builtin {color: #30a;}
+.cm-s-default .cm-bracket {color: #997;}
+.cm-s-default .cm-tag {color: #170;}
+.cm-s-default .cm-attribute {color: #00c;}
+.cm-s-default .cm-hr {color: #999;}
+.cm-s-default .cm-link {color: #00c;}
+
+.cm-s-default .cm-error {color: #f00;}
+.cm-invalidchar {color: #f00;}
+
+.CodeMirror-composing { border-bottom: 2px solid; }
+
+/* Default styles for common addons */
+
+div.CodeMirror span.CodeMirror-matchingbracket {color: #0b0;}
+div.CodeMirror span.CodeMirror-nonmatchingbracket {color: #a22;}
+.CodeMirror-matchingtag { background: rgba(255, 150, 0, .3); }
+.CodeMirror-activeline-background {background: #e8f2ff;}
+
+/* STOP */
+
+/* The rest of this file contains styles related to the mechanics of
+   the editor. You probably shouldn't touch them. */
+
+.CodeMirror {
+  position: relative;
+  overflow: hidden;
+  background: white;
 }
-@media (max-width: 540px) {
-  .prompt {
-    text-align: left;
-  }
+
+.CodeMirror-scroll {
+  overflow: scroll !important; /* Things will break if this is overridden */
+  /* 30px is the magic margin used to hide the element's real scrollbars */
+  /* See overflow: hidden in .CodeMirror */
+  margin-bottom: -30px; margin-right: -30px;
+  padding-bottom: 30px;
+  height: 100%;
+  outline: none; /* Prevent dragging from highlighting the element */
+  position: relative;
 }
-div.inner_cell {
-  min-width: 0;
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-  /* Old browsers */
-  -webkit-box-flex: 1;
-  -moz-box-flex: 1;
-  box-flex: 1;
-  /* Modern browsers */
-  flex: 1;
+.CodeMirror-sizer {
+  position: relative;
+  border-right: 30px solid transparent;
 }
-/* input_area and input_prompt must match in top border and margin for alignment */
-div.input_area {
-  border: 1px solid #cfcfcf;
-  border-radius: 2px;
-  background: #f7f7f7;
-  line-height: 1.21429em;
-}
-/* This is needed so that empty prompt areas can collapse to zero height when there
-   is no content in the output_subarea and the prompt. The main purpose of this is
-   to make sure that empty JavaScript output_subareas have no height. */
-div.prompt:empty {
-  padding-top: 0;
-  padding-bottom: 0;
-}
-div.unrecognized_cell {
-  padding: 5px 5px 5px 0px;
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
+
+/* The fake, visible scrollbars. Used to force redraw during scrolling
+   before actual scrolling happens, thus preventing shaking and
+   flickering artifacts. */
+.CodeMirror-vscrollbar, .CodeMirror-hscrollbar, .CodeMirror-scrollbar-filler, .CodeMirror-gutter-filler {
+  position: absolute;
+  z-index: 6;
+  display: none;
 }
-div.unrecognized_cell .inner_cell {
-  border-radius: 2px;
-  padding: 5px;
-  font-weight: bold;
-  color: red;
-  border: 1px solid #cfcfcf;
-  background: #eaeaea;
+.CodeMirror-vscrollbar {
+  right: 0; top: 0;
+  overflow-x: hidden;
+  overflow-y: scroll;
 }
-div.unrecognized_cell .inner_cell a {
-  color: inherit;
-  text-decoration: none;
+.CodeMirror-hscrollbar {
+  bottom: 0; left: 0;
+  overflow-y: hidden;
+  overflow-x: scroll;
 }
-div.unrecognized_cell .inner_cell a:hover {
-  color: inherit;
-  text-decoration: none;
+.CodeMirror-scrollbar-filler {
+  right: 0; bottom: 0;
 }
-@media (max-width: 540px) {
-  div.unrecognized_cell > div.prompt {
-    display: none;
-  }
+.CodeMirror-gutter-filler {
+  left: 0; bottom: 0;
 }
-div.code_cell {
-  /* avoid page breaking on code cells when printing */
+
+.CodeMirror-gutters {
+  position: absolute; left: 0; top: 0;
+  min-height: 100%;
+  z-index: 3;
 }
-@media print {
-  div.code_cell {
-    page-break-inside: avoid;
-  }
+.CodeMirror-gutter {
+  white-space: normal;
+  height: 100%;
+  display: inline-block;
+  vertical-align: top;
+  margin-bottom: -30px;
 }
-/* any special styling for code cells that are currently running goes here */
-div.input {
-  page-break-inside: avoid;
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
+.CodeMirror-gutter-wrapper {
+  position: absolute;
+  z-index: 4;
+  background: none !important;
+  border: none !important;
 }
-@media (max-width: 540px) {
-  div.input {
-    /* Old browsers */
-    display: -webkit-box;
-    -webkit-box-orient: vertical;
-    -webkit-box-align: stretch;
-    display: -moz-box;
-    -moz-box-orient: vertical;
-    -moz-box-align: stretch;
-    display: box;
-    box-orient: vertical;
-    box-align: stretch;
-    /* Modern browsers */
-    display: flex;
-    flex-direction: column;
-    align-items: stretch;
-  }
+.CodeMirror-gutter-background {
+  position: absolute;
+  top: 0; bottom: 0;
+  z-index: 4;
 }
-/* input_area and input_prompt must match in top border and margin for alignment */
-div.input_prompt {
-  color: #303F9F;
-  border-top: 1px solid transparent;
+.CodeMirror-gutter-elt {
+  position: absolute;
+  cursor: default;
+  z-index: 4;
 }
-div.input_area > div.highlight {
-  margin: 0.4em;
-  border: none;
-  padding: 0px;
-  background-color: transparent;
+.CodeMirror-gutter-wrapper ::selection { background-color: transparent }
+.CodeMirror-gutter-wrapper ::-moz-selection { background-color: transparent }
+
+.CodeMirror-lines {
+  cursor: text;
+  min-height: 1px; /* prevents collapsing before first draw */
+}
+.CodeMirror pre.CodeMirror-line,
+.CodeMirror pre.CodeMirror-line-like {
+  /* Reset some styles that the rest of the page might have set */
+  -moz-border-radius: 0; -webkit-border-radius: 0; border-radius: 0;
+  border-width: 0;
+  background: transparent;
+  font-family: inherit;
+  font-size: inherit;
+  margin: 0;
+  white-space: pre;
+  word-wrap: normal;
+  line-height: inherit;
+  color: inherit;
+  z-index: 2;
+  position: relative;
+  overflow: visible;
+  -webkit-tap-highlight-color: transparent;
+  -webkit-font-variant-ligatures: contextual;
+  font-variant-ligatures: contextual;
 }
-div.input_area > div.highlight > pre {
-  margin: 0px;
-  border: none;
-  padding: 0px;
-  background-color: transparent;
+.CodeMirror-wrap pre.CodeMirror-line,
+.CodeMirror-wrap pre.CodeMirror-line-like {
+  word-wrap: break-word;
+  white-space: pre-wrap;
+  word-break: normal;
 }
-/* The following gets added to the <head> if it is detected that the user has a
- * monospace font with inconsistent normal/bold/italic height.  See
- * notebookmain.js.  Such fonts will have keywords vertically offset with
- * respect to the rest of the text.  The user should select a better font.
- * See: https://github.com/ipython/ipython/issues/1503
- *
- * .CodeMirror span {
- *      vertical-align: bottom;
- * }
- */
-.CodeMirror {
-  line-height: 1.21429em;
-  /* Changed from 1em to our global default */
-  font-size: 14px;
-  height: auto;
-  /* Changed to auto to autogrow */
-  background: none;
-  /* Changed from white to allow our bg to show through */
+
+.CodeMirror-linebackground {
+  position: absolute;
+  left: 0; right: 0; top: 0; bottom: 0;
+  z-index: 0;
 }
-.CodeMirror-scroll {
-  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
-  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
-  overflow-y: hidden;
-  overflow-x: auto;
+
+.CodeMirror-linewidget {
+  position: relative;
+  z-index: 2;
+  padding: 0.1px; /* Force widget margins to stay inside of the container */
 }
-.CodeMirror-lines {
-  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
-  /* we have set a different line-height and want this to scale with that. */
-  /* Note that this should set vertical padding only, since CodeMirror assumes
-       that horizontal padding will be set on CodeMirror pre */
-  padding: 0.4em 0;
+
+.CodeMirror-widget {}
+
+.CodeMirror-rtl pre { direction: rtl; }
+
+.CodeMirror-code {
+  outline: none;
 }
+
+/* Force content-box sizing for the elements where we expect it */
+.CodeMirror-scroll,
+.CodeMirror-sizer,
+.CodeMirror-gutter,
+.CodeMirror-gutters,
 .CodeMirror-linenumber {
-  padding: 0 8px 0 4px;
-}
-.CodeMirror-gutters {
-  border-bottom-left-radius: 2px;
-  border-top-left-radius: 2px;
+  -moz-box-sizing: content-box;
+  box-sizing: content-box;
 }
-.CodeMirror pre {
-  /* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only,
-    use .CodeMirror-lines for vertical */
-  padding: 0 0.4em;
-  border: 0;
-  border-radius: 0;
+
+.CodeMirror-measure {
+  position: absolute;
+  width: 100%;
+  height: 0;
+  overflow: hidden;
+  visibility: hidden;
 }
+
 .CodeMirror-cursor {
-  border-left: 1.4px solid black;
+  position: absolute;
+  pointer-events: none;
 }
-@media screen and (min-width: 2138px) and (max-width: 4319px) {
-  .CodeMirror-cursor {
-    border-left: 2px solid black;
-  }
+.CodeMirror-measure pre { position: static; }
+
+div.CodeMirror-cursors {
+  visibility: hidden;
+  position: relative;
+  z-index: 3;
 }
-@media screen and (min-width: 4320px) {
-  .CodeMirror-cursor {
-    border-left: 4px solid black;
-  }
+div.CodeMirror-dragcursors {
+  visibility: visible;
+}
+
+.CodeMirror-focused div.CodeMirror-cursors {
+  visibility: visible;
 }
-/*
 
-Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
-Adapted from GitHub theme
+.CodeMirror-selected { background: #d9d9d9; }
+.CodeMirror-focused .CodeMirror-selected { background: #d7d4f0; }
+.CodeMirror-crosshair { cursor: crosshair; }
+.CodeMirror-line::selection, .CodeMirror-line > span::selection, .CodeMirror-line > span > span::selection { background: #d7d4f0; }
+.CodeMirror-line::-moz-selection, .CodeMirror-line > span::-moz-selection, .CodeMirror-line > span > span::-moz-selection { background: #d7d4f0; }
 
-*/
-.highlight-base {
-  color: #000;
+.cm-searching {
+  background-color: #ffa;
+  background-color: rgba(255, 255, 0, .4);
 }
-.highlight-variable {
-  color: #000;
+
+/* Used to force a border model for a node */
+.cm-force-border { padding-right: .1px; }
+
+@media print {
+  /* Hide the cursor when printing */
+  .CodeMirror div.CodeMirror-cursors {
+    visibility: hidden;
+  }
 }
-.highlight-variable-2 {
-  color: #1a1a1a;
+
+/* See issue #2901 */
+.cm-tab-wrap-hack:after { content: ''; }
+
+/* Help users use markselection to safely style text background */
+span.CodeMirror-selectedtext { background: none; }
+
+.CodeMirror-dialog {
+  position: absolute;
+  left: 0; right: 0;
+  background: inherit;
+  z-index: 15;
+  padding: .1em .8em;
+  overflow: hidden;
+  color: inherit;
 }
-.highlight-variable-3 {
-  color: #333333;
+
+.CodeMirror-dialog-top {
+  border-bottom: 1px solid #eee;
+  top: 0;
 }
-.highlight-string {
-  color: #BA2121;
+
+.CodeMirror-dialog-bottom {
+  border-top: 1px solid #eee;
+  bottom: 0;
 }
-.highlight-comment {
-  color: #408080;
-  font-style: italic;
+
+.CodeMirror-dialog input {
+  border: none;
+  outline: none;
+  background: transparent;
+  width: 20em;
+  color: inherit;
+  font-family: monospace;
 }
-.highlight-number {
-  color: #080;
+
+.CodeMirror-dialog button {
+  font-size: 70%;
 }
-.highlight-atom {
-  color: #88F;
+
+.CodeMirror-foldmarker {
+  color: blue;
+  text-shadow: #b9f 1px 1px 2px, #b9f -1px -1px 2px, #b9f 1px -1px 2px, #b9f -1px 1px 2px;
+  font-family: arial;
+  line-height: .3;
+  cursor: pointer;
 }
-.highlight-keyword {
-  color: #008000;
-  font-weight: bold;
+.CodeMirror-foldgutter {
+  width: .7em;
 }
-.highlight-builtin {
-  color: #008000;
+.CodeMirror-foldgutter-open,
+.CodeMirror-foldgutter-folded {
+  cursor: pointer;
 }
-.highlight-error {
-  color: #f00;
+.CodeMirror-foldgutter-open:after {
+  content: "\25BE";
 }
-.highlight-operator {
-  color: #AA22FF;
-  font-weight: bold;
+.CodeMirror-foldgutter-folded:after {
+  content: "\25B8";
 }
-.highlight-meta {
-  color: #AA22FF;
+
+/*
+  Name:       material
+  Author:     Mattia Astorino (http://github.com/equinusocio)
+  Website:    https://material-theme.site/
+*/
+
+.cm-s-material.CodeMirror {
+  background-color: #263238;
+  color: #EEFFFF;
 }
-/* previously not defined, copying from default codemirror */
-.highlight-def {
-  color: #00f;
+
+.cm-s-material .CodeMirror-gutters {
+  background: #263238;
+  color: #546E7A;
+  border: none;
 }
-.highlight-string-2 {
-  color: #f50;
+
+.cm-s-material .CodeMirror-guttermarker,
+.cm-s-material .CodeMirror-guttermarker-subtle,
+.cm-s-material .CodeMirror-linenumber {
+  color: #546E7A;
 }
-.highlight-qualifier {
-  color: #555;
+
+.cm-s-material .CodeMirror-cursor {
+  border-left: 1px solid #FFCC00;
 }
-.highlight-bracket {
-  color: #997;
+
+.cm-s-material div.CodeMirror-selected {
+  background: rgba(128, 203, 196, 0.2);
 }
-.highlight-tag {
-  color: #170;
+
+.cm-s-material.CodeMirror-focused div.CodeMirror-selected {
+  background: rgba(128, 203, 196, 0.2);
 }
-.highlight-attribute {
-  color: #00c;
+
+.cm-s-material .CodeMirror-line::selection,
+.cm-s-material .CodeMirror-line>span::selection,
+.cm-s-material .CodeMirror-line>span>span::selection {
+  background: rgba(128, 203, 196, 0.2);
 }
-.highlight-header {
-  color: blue;
+
+.cm-s-material .CodeMirror-line::-moz-selection,
+.cm-s-material .CodeMirror-line>span::-moz-selection,
+.cm-s-material .CodeMirror-line>span>span::-moz-selection {
+  background: rgba(128, 203, 196, 0.2);
 }
-.highlight-quote {
-  color: #090;
+
+.cm-s-material .CodeMirror-activeline-background {
+  background: rgba(0, 0, 0, 0.5);
 }
-.highlight-link {
-  color: #00c;
+
+.cm-s-material .cm-keyword {
+  color: #C792EA;
 }
-/* apply the same style to codemirror */
-.cm-s-ipython span.cm-keyword {
-  color: #008000;
-  font-weight: bold;
+
+.cm-s-material .cm-operator {
+  color: #89DDFF;
 }
-.cm-s-ipython span.cm-atom {
-  color: #88F;
+
+.cm-s-material .cm-variable-2 {
+  color: #EEFFFF;
 }
-.cm-s-ipython span.cm-number {
-  color: #080;
+
+.cm-s-material .cm-variable-3,
+.cm-s-material .cm-type {
+  color: #f07178;
 }
-.cm-s-ipython span.cm-def {
-  color: #00f;
+
+.cm-s-material .cm-builtin {
+  color: #FFCB6B;
 }
-.cm-s-ipython span.cm-variable {
-  color: #000;
+
+.cm-s-material .cm-atom {
+  color: #F78C6C;
 }
-.cm-s-ipython span.cm-operator {
-  color: #AA22FF;
-  font-weight: bold;
+
+.cm-s-material .cm-number {
+  color: #FF5370;
 }
-.cm-s-ipython span.cm-variable-2 {
-  color: #1a1a1a;
+
+.cm-s-material .cm-def {
+  color: #82AAFF;
 }
-.cm-s-ipython span.cm-variable-3 {
-  color: #333333;
+
+.cm-s-material .cm-string {
+  color: #C3E88D;
 }
-.cm-s-ipython span.cm-comment {
-  color: #408080;
-  font-style: italic;
+
+.cm-s-material .cm-string-2 {
+  color: #f07178;
 }
-.cm-s-ipython span.cm-string {
-  color: #BA2121;
+
+.cm-s-material .cm-comment {
+  color: #546E7A;
 }
-.cm-s-ipython span.cm-string-2 {
-  color: #f50;
+
+.cm-s-material .cm-variable {
+  color: #f07178;
 }
-.cm-s-ipython span.cm-meta {
-  color: #AA22FF;
+
+.cm-s-material .cm-tag {
+  color: #FF5370;
 }
-.cm-s-ipython span.cm-qualifier {
-  color: #555;
+
+.cm-s-material .cm-meta {
+  color: #FFCB6B;
 }
-.cm-s-ipython span.cm-builtin {
-  color: #008000;
+
+.cm-s-material .cm-attribute {
+  color: #C792EA;
 }
-.cm-s-ipython span.cm-bracket {
-  color: #997;
+
+.cm-s-material .cm-property {
+  color: #C792EA;
 }
-.cm-s-ipython span.cm-tag {
-  color: #170;
+
+.cm-s-material .cm-qualifier {
+  color: #DECB6B;
 }
-.cm-s-ipython span.cm-attribute {
-  color: #00c;
+
+.cm-s-material .cm-variable-3,
+.cm-s-material .cm-type {
+  color: #DECB6B;
 }
-.cm-s-ipython span.cm-header {
-  color: blue;
+
+
+.cm-s-material .cm-error {
+  color: rgba(255, 255, 255, 1.0);
+  background-color: #FF5370;
 }
-.cm-s-ipython span.cm-quote {
-  color: #090;
+
+.cm-s-material .CodeMirror-matchingbracket {
+  text-decoration: underline;
+  color: white !important;
 }
-.cm-s-ipython span.cm-link {
-  color: #00c;
+/**
+ * "
+ *  Using Zenburn color palette from the Emacs Zenburn Theme
+ *  https://github.com/bbatsov/zenburn-emacs/blob/master/zenburn-theme.el
+ *
+ *  Also using parts of https://github.com/xavi/coderay-lighttable-theme
+ * "
+ * From: https://github.com/wisenomad/zenburn-lighttable-theme/blob/master/zenburn.css
+ */
+
+.cm-s-zenburn .CodeMirror-gutters { background: #3f3f3f !important; }
+.cm-s-zenburn .CodeMirror-foldgutter-open, .CodeMirror-foldgutter-folded { color: #999; }
+.cm-s-zenburn .CodeMirror-cursor { border-left: 1px solid white; }
+.cm-s-zenburn { background-color: #3f3f3f; color: #dcdccc; }
+.cm-s-zenburn span.cm-builtin { color: #dcdccc; font-weight: bold; }
+.cm-s-zenburn span.cm-comment { color: #7f9f7f; }
+.cm-s-zenburn span.cm-keyword { color: #f0dfaf; font-weight: bold; }
+.cm-s-zenburn span.cm-atom { color: #bfebbf; }
+.cm-s-zenburn span.cm-def { color: #dcdccc; }
+.cm-s-zenburn span.cm-variable { color: #dfaf8f; }
+.cm-s-zenburn span.cm-variable-2 { color: #dcdccc; }
+.cm-s-zenburn span.cm-string { color: #cc9393; }
+.cm-s-zenburn span.cm-string-2 { color: #cc9393; }
+.cm-s-zenburn span.cm-number { color: #dcdccc; }
+.cm-s-zenburn span.cm-tag { color: #93e0e3; }
+.cm-s-zenburn span.cm-property { color: #dfaf8f; }
+.cm-s-zenburn span.cm-attribute { color: #dfaf8f; }
+.cm-s-zenburn span.cm-qualifier { color: #7cb8bb; }
+.cm-s-zenburn span.cm-meta { color: #f0dfaf; }
+.cm-s-zenburn span.cm-header { color: #f0efd0; }
+.cm-s-zenburn span.cm-operator { color: #f0efd0; }
+.cm-s-zenburn span.CodeMirror-matchingbracket { box-sizing: border-box; background: transparent; border-bottom: 1px solid; }
+.cm-s-zenburn span.CodeMirror-nonmatchingbracket { border-bottom: 1px solid; background: none; }
+.cm-s-zenburn .CodeMirror-activeline { background: #000000; }
+.cm-s-zenburn .CodeMirror-activeline-background { background: #000000; }
+.cm-s-zenburn div.CodeMirror-selected { background: #545454; }
+.cm-s-zenburn .CodeMirror-focused div.CodeMirror-selected { background: #4f4f4f; }
+
+.cm-s-abcdef.CodeMirror { background: #0f0f0f; color: #defdef; }
+.cm-s-abcdef div.CodeMirror-selected { background: #515151; }
+.cm-s-abcdef .CodeMirror-line::selection, .cm-s-abcdef .CodeMirror-line > span::selection, .cm-s-abcdef .CodeMirror-line > span > span::selection { background: rgba(56, 56, 56, 0.99); }
+.cm-s-abcdef .CodeMirror-line::-moz-selection, .cm-s-abcdef .CodeMirror-line > span::-moz-selection, .cm-s-abcdef .CodeMirror-line > span > span::-moz-selection { background: rgba(56, 56, 56, 0.99); }
+.cm-s-abcdef .CodeMirror-gutters { background: #555; border-right: 2px solid #314151; }
+.cm-s-abcdef .CodeMirror-guttermarker { color: #222; }
+.cm-s-abcdef .CodeMirror-guttermarker-subtle { color: azure; }
+.cm-s-abcdef .CodeMirror-linenumber { color: #FFFFFF; }
+.cm-s-abcdef .CodeMirror-cursor { border-left: 1px solid #00FF00; }
+
+.cm-s-abcdef span.cm-keyword { color: darkgoldenrod; font-weight: bold; }
+.cm-s-abcdef span.cm-atom { color: #77F; }
+.cm-s-abcdef span.cm-number { color: violet; }
+.cm-s-abcdef span.cm-def { color: #fffabc; }
+.cm-s-abcdef span.cm-variable { color: #abcdef; }
+.cm-s-abcdef span.cm-variable-2 { color: #cacbcc; }
+.cm-s-abcdef span.cm-variable-3, .cm-s-abcdef span.cm-type { color: #def; }
+.cm-s-abcdef span.cm-property { color: #fedcba; }
+.cm-s-abcdef span.cm-operator { color: #ff0; }
+.cm-s-abcdef span.cm-comment { color: #7a7b7c; font-style: italic;}
+.cm-s-abcdef span.cm-string { color: #2b4; }
+.cm-s-abcdef span.cm-meta { color: #C9F; }
+.cm-s-abcdef span.cm-qualifier { color: #FFF700; }
+.cm-s-abcdef span.cm-builtin { color: #30aabc; }
+.cm-s-abcdef span.cm-bracket { color: #8a8a8a; }
+.cm-s-abcdef span.cm-tag { color: #FFDD44; }
+.cm-s-abcdef span.cm-attribute { color: #DDFF00; }
+.cm-s-abcdef span.cm-error { color: #FF0000; }
+.cm-s-abcdef span.cm-header { color: aquamarine; font-weight: bold; }
+.cm-s-abcdef span.cm-link { color: blueviolet; }
+
+.cm-s-abcdef .CodeMirror-activeline-background { background: #314151; }
+
+/*
+
+    Name:       Base16 Default Light
+    Author:     Chris Kempson (http://chriskempson.com)
+
+    CodeMirror template by Jan T. Sott (https://github.com/idleberg/base16-codemirror)
+    Original Base16 color scheme by Chris Kempson (https://github.com/chriskempson/base16)
+
+*/
+
+.cm-s-base16-light.CodeMirror { background: #f5f5f5; color: #202020; }
+.cm-s-base16-light div.CodeMirror-selected { background: #e0e0e0; }
+.cm-s-base16-light .CodeMirror-line::selection, .cm-s-base16-light .CodeMirror-line > span::selection, .cm-s-base16-light .CodeMirror-line > span > span::selection { background: #e0e0e0; }
+.cm-s-base16-light .CodeMirror-line::-moz-selection, .cm-s-base16-light .CodeMirror-line > span::-moz-selection, .cm-s-base16-light .CodeMirror-line > span > span::-moz-selection { background: #e0e0e0; }
+.cm-s-base16-light .CodeMirror-gutters { background: #f5f5f5; border-right: 0px; }
+.cm-s-base16-light .CodeMirror-guttermarker { color: #ac4142; }
+.cm-s-base16-light .CodeMirror-guttermarker-subtle { color: #b0b0b0; }
+.cm-s-base16-light .CodeMirror-linenumber { color: #b0b0b0; }
+.cm-s-base16-light .CodeMirror-cursor { border-left: 1px solid #505050; }
+
+.cm-s-base16-light span.cm-comment { color: #8f5536; }
+.cm-s-base16-light span.cm-atom { color: #aa759f; }
+.cm-s-base16-light span.cm-number { color: #aa759f; }
+
+.cm-s-base16-light span.cm-property, .cm-s-base16-light span.cm-attribute { color: #90a959; }
+.cm-s-base16-light span.cm-keyword { color: #ac4142; }
+.cm-s-base16-light span.cm-string { color: #f4bf75; }
+
+.cm-s-base16-light span.cm-variable { color: #90a959; }
+.cm-s-base16-light span.cm-variable-2 { color: #6a9fb5; }
+.cm-s-base16-light span.cm-def { color: #d28445; }
+.cm-s-base16-light span.cm-bracket { color: #202020; }
+.cm-s-base16-light span.cm-tag { color: #ac4142; }
+.cm-s-base16-light span.cm-link { color: #aa759f; }
+.cm-s-base16-light span.cm-error { background: #ac4142; color: #505050; }
+
+.cm-s-base16-light .CodeMirror-activeline-background { background: #DDDCDC; }
+.cm-s-base16-light .CodeMirror-matchingbracket { color: #f5f5f5 !important; background-color: #6A9FB5 !important}
+
+/*
+
+    Name:       Base16 Default Dark
+    Author:     Chris Kempson (http://chriskempson.com)
+
+    CodeMirror template by Jan T. Sott (https://github.com/idleberg/base16-codemirror)
+    Original Base16 color scheme by Chris Kempson (https://github.com/chriskempson/base16)
+
+*/
+
+.cm-s-base16-dark.CodeMirror { background: #151515; color: #e0e0e0; }
+.cm-s-base16-dark div.CodeMirror-selected { background: #303030; }
+.cm-s-base16-dark .CodeMirror-line::selection, .cm-s-base16-dark .CodeMirror-line > span::selection, .cm-s-base16-dark .CodeMirror-line > span > span::selection { background: rgba(48, 48, 48, .99); }
+.cm-s-base16-dark .CodeMirror-line::-moz-selection, .cm-s-base16-dark .CodeMirror-line > span::-moz-selection, .cm-s-base16-dark .CodeMirror-line > span > span::-moz-selection { background: rgba(48, 48, 48, .99); }
+.cm-s-base16-dark .CodeMirror-gutters { background: #151515; border-right: 0px; }
+.cm-s-base16-dark .CodeMirror-guttermarker { color: #ac4142; }
+.cm-s-base16-dark .CodeMirror-guttermarker-subtle { color: #505050; }
+.cm-s-base16-dark .CodeMirror-linenumber { color: #505050; }
+.cm-s-base16-dark .CodeMirror-cursor { border-left: 1px solid #b0b0b0; }
+
+.cm-s-base16-dark span.cm-comment { color: #8f5536; }
+.cm-s-base16-dark span.cm-atom { color: #aa759f; }
+.cm-s-base16-dark span.cm-number { color: #aa759f; }
+
+.cm-s-base16-dark span.cm-property, .cm-s-base16-dark span.cm-attribute { color: #90a959; }
+.cm-s-base16-dark span.cm-keyword { color: #ac4142; }
+.cm-s-base16-dark span.cm-string { color: #f4bf75; }
+
+.cm-s-base16-dark span.cm-variable { color: #90a959; }
+.cm-s-base16-dark span.cm-variable-2 { color: #6a9fb5; }
+.cm-s-base16-dark span.cm-def { color: #d28445; }
+.cm-s-base16-dark span.cm-bracket { color: #e0e0e0; }
+.cm-s-base16-dark span.cm-tag { color: #ac4142; }
+.cm-s-base16-dark span.cm-link { color: #aa759f; }
+.cm-s-base16-dark span.cm-error { background: #ac4142; color: #b0b0b0; }
+
+.cm-s-base16-dark .CodeMirror-activeline-background { background: #202020; }
+.cm-s-base16-dark .CodeMirror-matchingbracket { text-decoration: underline; color: white !important; }
+
+/*
+
+    Name:       dracula
+    Author:     Michael Kaminsky (http://github.com/mkaminsky11)
+
+    Original dracula color scheme by Zeno Rocha (https://github.com/zenorocha/dracula-theme)
+
+*/
+
+
+.cm-s-dracula.CodeMirror, .cm-s-dracula .CodeMirror-gutters {
+  background-color: #282a36 !important;
+  color: #f8f8f2 !important;
+  border: none;
 }
-.cm-s-ipython span.cm-error {
-  color: #f00;
+.cm-s-dracula .CodeMirror-gutters { color: #282a36; }
+.cm-s-dracula .CodeMirror-cursor { border-left: solid thin #f8f8f0; }
+.cm-s-dracula .CodeMirror-linenumber { color: #6D8A88; }
+.cm-s-dracula .CodeMirror-selected { background: rgba(255, 255, 255, 0.10); }
+.cm-s-dracula .CodeMirror-line::selection, .cm-s-dracula .CodeMirror-line > span::selection, .cm-s-dracula .CodeMirror-line > span > span::selection { background: rgba(255, 255, 255, 0.10); }
+.cm-s-dracula .CodeMirror-line::-moz-selection, .cm-s-dracula .CodeMirror-line > span::-moz-selection, .cm-s-dracula .CodeMirror-line > span > span::-moz-selection { background: rgba(255, 255, 255, 0.10); }
+.cm-s-dracula span.cm-comment { color: #6272a4; }
+.cm-s-dracula span.cm-string, .cm-s-dracula span.cm-string-2 { color: #f1fa8c; }
+.cm-s-dracula span.cm-number { color: #bd93f9; }
+.cm-s-dracula span.cm-variable { color: #50fa7b; }
+.cm-s-dracula span.cm-variable-2 { color: white; }
+.cm-s-dracula span.cm-def { color: #50fa7b; }
+.cm-s-dracula span.cm-operator { color: #ff79c6; }
+.cm-s-dracula span.cm-keyword { color: #ff79c6; }
+.cm-s-dracula span.cm-atom { color: #bd93f9; }
+.cm-s-dracula span.cm-meta { color: #f8f8f2; }
+.cm-s-dracula span.cm-tag { color: #ff79c6; }
+.cm-s-dracula span.cm-attribute { color: #50fa7b; }
+.cm-s-dracula span.cm-qualifier { color: #50fa7b; }
+.cm-s-dracula span.cm-property { color: #66d9ef; }
+.cm-s-dracula span.cm-builtin { color: #50fa7b; }
+.cm-s-dracula span.cm-variable-3, .cm-s-dracula span.cm-type { color: #ffb86c; }
+
+.cm-s-dracula .CodeMirror-activeline-background { background: rgba(255,255,255,0.1); }
+.cm-s-dracula .CodeMirror-matchingbracket { text-decoration: underline; color: white !important; }
+
+/*
+
+    Name:       Hopscotch
+    Author:     Jan T. Sott
+
+    CodeMirror template by Jan T. Sott (https://github.com/idleberg/base16-codemirror)
+    Original Base16 color scheme by Chris Kempson (https://github.com/chriskempson/base16)
+
+*/
+
+.cm-s-hopscotch.CodeMirror {background: #322931; color: #d5d3d5;}
+.cm-s-hopscotch div.CodeMirror-selected {background: #433b42 !important;}
+.cm-s-hopscotch .CodeMirror-gutters {background: #322931; border-right: 0px;}
+.cm-s-hopscotch .CodeMirror-linenumber {color: #797379;}
+.cm-s-hopscotch .CodeMirror-cursor {border-left: 1px solid #989498 !important;}
+
+.cm-s-hopscotch span.cm-comment {color: #b33508;}
+.cm-s-hopscotch span.cm-atom {color: #c85e7c;}
+.cm-s-hopscotch span.cm-number {color: #c85e7c;}
+
+.cm-s-hopscotch span.cm-property, .cm-s-hopscotch span.cm-attribute {color: #8fc13e;}
+.cm-s-hopscotch span.cm-keyword {color: #dd464c;}
+.cm-s-hopscotch span.cm-string {color: #fdcc59;}
+
+.cm-s-hopscotch span.cm-variable {color: #8fc13e;}
+.cm-s-hopscotch span.cm-variable-2 {color: #1290bf;}
+.cm-s-hopscotch span.cm-def {color: #fd8b19;}
+.cm-s-hopscotch span.cm-error {background: #dd464c; color: #989498;}
+.cm-s-hopscotch span.cm-bracket {color: #d5d3d5;}
+.cm-s-hopscotch span.cm-tag {color: #dd464c;}
+.cm-s-hopscotch span.cm-link {color: #c85e7c;}
+
+.cm-s-hopscotch .CodeMirror-matchingbracket { text-decoration: underline; color: white !important;}
+.cm-s-hopscotch .CodeMirror-activeline-background { background: #302020; }
+
+/****************************************************************/
+/*   Based on mbonaci's Brackets mbo theme                      */
+/*   https://github.com/mbonaci/global/blob/master/Mbo.tmTheme  */
+/*   Create your own: http://tmtheme-editor.herokuapp.com       */
+/****************************************************************/
+
+.cm-s-mbo.CodeMirror { background: #2c2c2c; color: #ffffec; }
+.cm-s-mbo div.CodeMirror-selected { background: #716C62; }
+.cm-s-mbo .CodeMirror-line::selection, .cm-s-mbo .CodeMirror-line > span::selection, .cm-s-mbo .CodeMirror-line > span > span::selection { background: rgba(113, 108, 98, .99); }
+.cm-s-mbo .CodeMirror-line::-moz-selection, .cm-s-mbo .CodeMirror-line > span::-moz-selection, .cm-s-mbo .CodeMirror-line > span > span::-moz-selection { background: rgba(113, 108, 98, .99); }
+.cm-s-mbo .CodeMirror-gutters { background: #4e4e4e; border-right: 0px; }
+.cm-s-mbo .CodeMirror-guttermarker { color: white; }
+.cm-s-mbo .CodeMirror-guttermarker-subtle { color: grey; }
+.cm-s-mbo .CodeMirror-linenumber { color: #dadada; }
+.cm-s-mbo .CodeMirror-cursor { border-left: 1px solid #ffffec; }
+
+.cm-s-mbo span.cm-comment { color: #95958a; }
+.cm-s-mbo span.cm-atom { color: #00a8c6; }
+.cm-s-mbo span.cm-number { color: #00a8c6; }
+
+.cm-s-mbo span.cm-property, .cm-s-mbo span.cm-attribute { color: #9ddfe9; }
+.cm-s-mbo span.cm-keyword { color: #ffb928; }
+.cm-s-mbo span.cm-string { color: #ffcf6c; }
+.cm-s-mbo span.cm-string.cm-property { color: #ffffec; }
+
+.cm-s-mbo span.cm-variable { color: #ffffec; }
+.cm-s-mbo span.cm-variable-2 { color: #00a8c6; }
+.cm-s-mbo span.cm-def { color: #ffffec; }
+.cm-s-mbo span.cm-bracket { color: #fffffc; font-weight: bold; }
+.cm-s-mbo span.cm-tag { color: #9ddfe9; }
+.cm-s-mbo span.cm-link { color: #f54b07; }
+.cm-s-mbo span.cm-error { border-bottom: #636363; color: #ffffec; }
+.cm-s-mbo span.cm-qualifier { color: #ffffec; }
+
+.cm-s-mbo .CodeMirror-activeline-background { background: #494b41; }
+.cm-s-mbo .CodeMirror-matchingbracket { color: #ffb928 !important; }
+.cm-s-mbo .CodeMirror-matchingtag { background: rgba(255, 255, 255, .37); }
+
+/*
+  MDN-LIKE Theme - Mozilla
+  Ported to CodeMirror by Peter Kroon <plakroon@gmail.com>
+  Report bugs/issues here: https://github.com/codemirror/CodeMirror/issues
+  GitHub: @peterkroon
+
+  The mdn-like theme is inspired on the displayed code examples at: https://developer.mozilla.org/en-US/docs/Web/CSS/animation
+
+*/
+.cm-s-mdn-like.CodeMirror { color: #999; background-color: #fff; }
+.cm-s-mdn-like div.CodeMirror-selected { background: #cfc; }
+.cm-s-mdn-like .CodeMirror-line::selection, .cm-s-mdn-like .CodeMirror-line > span::selection, .cm-s-mdn-like .CodeMirror-line > span > span::selection { background: #cfc; }
+.cm-s-mdn-like .CodeMirror-line::-moz-selection, .cm-s-mdn-like .CodeMirror-line > span::-moz-selection, .cm-s-mdn-like .CodeMirror-line > span > span::-moz-selection { background: #cfc; }
+
+.cm-s-mdn-like .CodeMirror-gutters { background: #f8f8f8; border-left: 6px solid rgba(0,83,159,0.65); color: #333; }
+.cm-s-mdn-like .CodeMirror-linenumber { color: #aaa; padding-left: 8px; }
+.cm-s-mdn-like .CodeMirror-cursor { border-left: 2px solid #222; }
+
+.cm-s-mdn-like .cm-keyword { color: #6262FF; }
+.cm-s-mdn-like .cm-atom { color: #F90; }
+.cm-s-mdn-like .cm-number { color:  #ca7841; }
+.cm-s-mdn-like .cm-def { color: #8DA6CE; }
+.cm-s-mdn-like span.cm-variable-2, .cm-s-mdn-like span.cm-tag { color: #690; }
+.cm-s-mdn-like span.cm-variable-3, .cm-s-mdn-like span.cm-def, .cm-s-mdn-like span.cm-type { color: #07a; }
+
+.cm-s-mdn-like .cm-variable { color: #07a; }
+.cm-s-mdn-like .cm-property { color: #905; }
+.cm-s-mdn-like .cm-qualifier { color: #690; }
+
+.cm-s-mdn-like .cm-operator { color: #cda869; }
+.cm-s-mdn-like .cm-comment { color:#777; font-weight:normal; }
+.cm-s-mdn-like .cm-string { color:#07a; font-style:italic; }
+.cm-s-mdn-like .cm-string-2 { color:#bd6b18; } /*?*/
+.cm-s-mdn-like .cm-meta { color: #000; } /*?*/
+.cm-s-mdn-like .cm-builtin { color: #9B7536; } /*?*/
+.cm-s-mdn-like .cm-tag { color: #997643; }
+.cm-s-mdn-like .cm-attribute { color: #d6bb6d; } /*?*/
+.cm-s-mdn-like .cm-header { color: #FF6400; }
+.cm-s-mdn-like .cm-hr { color: #AEAEAE; }
+.cm-s-mdn-like .cm-link { color:#ad9361; font-style:italic; text-decoration:none; }
+.cm-s-mdn-like .cm-error { border-bottom: 1px solid red; }
+
+div.cm-s-mdn-like .CodeMirror-activeline-background { background: #efefff; }
+div.cm-s-mdn-like span.CodeMirror-matchingbracket { outline:1px solid grey; color: inherit; }
+
+.cm-s-mdn-like.CodeMirror { background-image: url(data:image/png;base64,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); }
+
+/*
+
+    Name:       seti
+    Author:     Michael Kaminsky (http://github.com/mkaminsky11)
+
+    Original seti color scheme by Jesse Weed (https://github.com/jesseweed/seti-syntax)
+
+*/
+
+
+.cm-s-seti.CodeMirror {
+  background-color: #151718 !important;
+  color: #CFD2D1 !important;
+  border: none;
 }
-.cm-s-ipython span.cm-tab {
-  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
-  background-position: right;
-  background-repeat: no-repeat;
+.cm-s-seti .CodeMirror-gutters {
+  color: #404b53;
+  background-color: #0E1112;
+  border: none;
 }
-div.output_wrapper {
-  /* this position must be relative to enable descendents to be absolute within it */
-  position: relative;
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
-  z-index: 1;
+.cm-s-seti .CodeMirror-cursor { border-left: solid thin #f8f8f0; }
+.cm-s-seti .CodeMirror-linenumber { color: #6D8A88; }
+.cm-s-seti.CodeMirror-focused div.CodeMirror-selected { background: rgba(255, 255, 255, 0.10); }
+.cm-s-seti .CodeMirror-line::selection, .cm-s-seti .CodeMirror-line > span::selection, .cm-s-seti .CodeMirror-line > span > span::selection { background: rgba(255, 255, 255, 0.10); }
+.cm-s-seti .CodeMirror-line::-moz-selection, .cm-s-seti .CodeMirror-line > span::-moz-selection, .cm-s-seti .CodeMirror-line > span > span::-moz-selection { background: rgba(255, 255, 255, 0.10); }
+.cm-s-seti span.cm-comment { color: #41535b; }
+.cm-s-seti span.cm-string, .cm-s-seti span.cm-string-2 { color: #55b5db; }
+.cm-s-seti span.cm-number { color: #cd3f45; }
+.cm-s-seti span.cm-variable { color: #55b5db; }
+.cm-s-seti span.cm-variable-2 { color: #a074c4; }
+.cm-s-seti span.cm-def { color: #55b5db; }
+.cm-s-seti span.cm-keyword { color: #ff79c6; }
+.cm-s-seti span.cm-operator { color: #9fca56; }
+.cm-s-seti span.cm-keyword { color: #e6cd69; }
+.cm-s-seti span.cm-atom { color: #cd3f45; }
+.cm-s-seti span.cm-meta { color: #55b5db; }
+.cm-s-seti span.cm-tag { color: #55b5db; }
+.cm-s-seti span.cm-attribute { color: #9fca56; }
+.cm-s-seti span.cm-qualifier { color: #9fca56; }
+.cm-s-seti span.cm-property { color: #a074c4; }
+.cm-s-seti span.cm-variable-3, .cm-s-seti span.cm-type { color: #9fca56; }
+.cm-s-seti span.cm-builtin { color: #9fca56; }
+.cm-s-seti .CodeMirror-activeline-background { background: #101213; }
+.cm-s-seti .CodeMirror-matchingbracket { text-decoration: underline; color: white !important; }
+
+/*
+Solarized theme for code-mirror
+http://ethanschoonover.com/solarized
+*/
+
+/*
+Solarized color palette
+http://ethanschoonover.com/solarized/img/solarized-palette.png
+*/
+
+.solarized.base03 { color: #002b36; }
+.solarized.base02 { color: #073642; }
+.solarized.base01 { color: #586e75; }
+.solarized.base00 { color: #657b83; }
+.solarized.base0 { color: #839496; }
+.solarized.base1 { color: #93a1a1; }
+.solarized.base2 { color: #eee8d5; }
+.solarized.base3  { color: #fdf6e3; }
+.solarized.solar-yellow  { color: #b58900; }
+.solarized.solar-orange  { color: #cb4b16; }
+.solarized.solar-red { color: #dc322f; }
+.solarized.solar-magenta { color: #d33682; }
+.solarized.solar-violet  { color: #6c71c4; }
+.solarized.solar-blue { color: #268bd2; }
+.solarized.solar-cyan { color: #2aa198; }
+.solarized.solar-green { color: #859900; }
+
+/* Color scheme for code-mirror */
+
+.cm-s-solarized {
+  line-height: 1.45em;
+  color-profile: sRGB;
+  rendering-intent: auto;
+}
+.cm-s-solarized.cm-s-dark {
+  color: #839496;
+  background-color: #002b36;
+  text-shadow: #002b36 0 1px;
+}
+.cm-s-solarized.cm-s-light {
+  background-color: #fdf6e3;
+  color: #657b83;
+  text-shadow: #eee8d5 0 1px;
+}
+
+.cm-s-solarized .CodeMirror-widget {
+  text-shadow: none;
 }
-/* class for the output area when it should be height-limited */
-div.output_scroll {
-  /* ideally, this would be max-height, but FF barfs all over that */
-  height: 24em;
-  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
-  width: 100%;
-  overflow: auto;
-  border-radius: 2px;
-  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
-  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
+
+.cm-s-solarized .cm-header { color: #586e75; }
+.cm-s-solarized .cm-quote { color: #93a1a1; }
+
+.cm-s-solarized .cm-keyword { color: #cb4b16; }
+.cm-s-solarized .cm-atom { color: #d33682; }
+.cm-s-solarized .cm-number { color: #d33682; }
+.cm-s-solarized .cm-def { color: #2aa198; }
+
+.cm-s-solarized .cm-variable { color: #839496; }
+.cm-s-solarized .cm-variable-2 { color: #b58900; }
+.cm-s-solarized .cm-variable-3, .cm-s-solarized .cm-type { color: #6c71c4; }
+
+.cm-s-solarized .cm-property { color: #2aa198; }
+.cm-s-solarized .cm-operator { color: #6c71c4; }
+
+.cm-s-solarized .cm-comment { color: #586e75; font-style:italic; }
+
+.cm-s-solarized .cm-string { color: #859900; }
+.cm-s-solarized .cm-string-2 { color: #b58900; }
+
+.cm-s-solarized .cm-meta { color: #859900; }
+.cm-s-solarized .cm-qualifier { color: #b58900; }
+.cm-s-solarized .cm-builtin { color: #d33682; }
+.cm-s-solarized .cm-bracket { color: #cb4b16; }
+.cm-s-solarized .CodeMirror-matchingbracket { color: #859900; }
+.cm-s-solarized .CodeMirror-nonmatchingbracket { color: #dc322f; }
+.cm-s-solarized .cm-tag { color: #93a1a1; }
+.cm-s-solarized .cm-attribute { color: #2aa198; }
+.cm-s-solarized .cm-hr {
+  color: transparent;
+  border-top: 1px solid #586e75;
   display: block;
 }
-/* output div while it is collapsed */
-div.output_collapsed {
-  margin: 0px;
-  padding: 0px;
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
+.cm-s-solarized .cm-link { color: #93a1a1; cursor: pointer; }
+.cm-s-solarized .cm-special { color: #6c71c4; }
+.cm-s-solarized .cm-em {
+  color: #999;
+  text-decoration: underline;
+  text-decoration-style: dotted;
 }
-div.out_prompt_overlay {
-  height: 100%;
-  padding: 0px 0.4em;
-  position: absolute;
-  border-radius: 2px;
+.cm-s-solarized .cm-error,
+.cm-s-solarized .cm-invalidchar {
+  color: #586e75;
+  border-bottom: 1px dotted #dc322f;
 }
-div.out_prompt_overlay:hover {
-  /* use inner shadow to get border that is computed the same on WebKit/FF */
-  -webkit-box-shadow: inset 0 0 1px #000;
-  box-shadow: inset 0 0 1px #000;
-  background: rgba(240, 240, 240, 0.5);
+
+.cm-s-solarized.cm-s-dark div.CodeMirror-selected { background: #073642; }
+.cm-s-solarized.cm-s-dark.CodeMirror ::selection { background: rgba(7, 54, 66, 0.99); }
+.cm-s-solarized.cm-s-dark .CodeMirror-line::-moz-selection, .cm-s-dark .CodeMirror-line > span::-moz-selection, .cm-s-dark .CodeMirror-line > span > span::-moz-selection { background: rgba(7, 54, 66, 0.99); }
+
+.cm-s-solarized.cm-s-light div.CodeMirror-selected { background: #eee8d5; }
+.cm-s-solarized.cm-s-light .CodeMirror-line::selection, .cm-s-light .CodeMirror-line > span::selection, .cm-s-light .CodeMirror-line > span > span::selection { background: #eee8d5; }
+.cm-s-solarized.cm-s-light .CodeMirror-line::-moz-selection, .cm-s-ligh .CodeMirror-line > span::-moz-selection, .cm-s-ligh .CodeMirror-line > span > span::-moz-selection { background: #eee8d5; }
+
+/* Editor styling */
+
+
+
+/* Little shadow on the view-port of the buffer view */
+.cm-s-solarized.CodeMirror {
+  -moz-box-shadow: inset 7px 0 12px -6px #000;
+  -webkit-box-shadow: inset 7px 0 12px -6px #000;
+  box-shadow: inset 7px 0 12px -6px #000;
 }
-div.output_prompt {
-  color: #D84315;
+
+/* Remove gutter border */
+.cm-s-solarized .CodeMirror-gutters {
+  border-right: 0;
 }
-/* This class is the outer container of all output sections. */
-div.output_area {
-  padding: 0px;
-  page-break-inside: avoid;
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
+
+/* Gutter colors and line number styling based of color scheme (dark / light) */
+
+/* Dark */
+.cm-s-solarized.cm-s-dark .CodeMirror-gutters {
+  background-color: #073642;
 }
-div.output_area .MathJax_Display {
-  text-align: left !important;
+
+.cm-s-solarized.cm-s-dark .CodeMirror-linenumber {
+  color: #586e75;
+  text-shadow: #021014 0 -1px;
 }
-div.output_area .rendered_html table {
-  margin-left: 0;
-  margin-right: 0;
+
+/* Light */
+.cm-s-solarized.cm-s-light .CodeMirror-gutters {
+  background-color: #eee8d5;
 }
-div.output_area .rendered_html img {
-  margin-left: 0;
-  margin-right: 0;
+
+.cm-s-solarized.cm-s-light .CodeMirror-linenumber {
+  color: #839496;
 }
-div.output_area img,
-div.output_area svg {
-  max-width: 100%;
-  height: auto;
+
+/* Common */
+.cm-s-solarized .CodeMirror-linenumber {
+  padding: 0 5px;
 }
-div.output_area img.unconfined,
-div.output_area svg.unconfined {
-  max-width: none;
+.cm-s-solarized .CodeMirror-guttermarker-subtle { color: #586e75; }
+.cm-s-solarized.cm-s-dark .CodeMirror-guttermarker { color: #ddd; }
+.cm-s-solarized.cm-s-light .CodeMirror-guttermarker { color: #cb4b16; }
+
+.cm-s-solarized .CodeMirror-gutter .CodeMirror-gutter-text {
+  color: #586e75;
+}
+
+/* Cursor */
+.cm-s-solarized .CodeMirror-cursor { border-left: 1px solid #819090; }
+
+/* Fat cursor */
+.cm-s-solarized.cm-s-light.cm-fat-cursor .CodeMirror-cursor { background: #77ee77; }
+.cm-s-solarized.cm-s-light .cm-animate-fat-cursor { background-color: #77ee77; }
+.cm-s-solarized.cm-s-dark.cm-fat-cursor .CodeMirror-cursor { background: #586e75; }
+.cm-s-solarized.cm-s-dark .cm-animate-fat-cursor { background-color: #586e75; }
+
+/* Active line */
+.cm-s-solarized.cm-s-dark .CodeMirror-activeline-background {
+  background: rgba(255, 255, 255, 0.06);
+}
+.cm-s-solarized.cm-s-light .CodeMirror-activeline-background {
+  background: rgba(0, 0, 0, 0.06);
+}
+
+.cm-s-the-matrix.CodeMirror { background: #000000; color: #00FF00; }
+.cm-s-the-matrix div.CodeMirror-selected { background: #2D2D2D; }
+.cm-s-the-matrix .CodeMirror-line::selection, .cm-s-the-matrix .CodeMirror-line > span::selection, .cm-s-the-matrix .CodeMirror-line > span > span::selection { background: rgba(45, 45, 45, 0.99); }
+.cm-s-the-matrix .CodeMirror-line::-moz-selection, .cm-s-the-matrix .CodeMirror-line > span::-moz-selection, .cm-s-the-matrix .CodeMirror-line > span > span::-moz-selection { background: rgba(45, 45, 45, 0.99); }
+.cm-s-the-matrix .CodeMirror-gutters { background: #060; border-right: 2px solid #00FF00; }
+.cm-s-the-matrix .CodeMirror-guttermarker { color: #0f0; }
+.cm-s-the-matrix .CodeMirror-guttermarker-subtle { color: white; }
+.cm-s-the-matrix .CodeMirror-linenumber { color: #FFFFFF; }
+.cm-s-the-matrix .CodeMirror-cursor { border-left: 1px solid #00FF00; }
+
+.cm-s-the-matrix span.cm-keyword { color: #008803; font-weight: bold; }
+.cm-s-the-matrix span.cm-atom { color: #3FF; }
+.cm-s-the-matrix span.cm-number { color: #FFB94F; }
+.cm-s-the-matrix span.cm-def { color: #99C; }
+.cm-s-the-matrix span.cm-variable { color: #F6C; }
+.cm-s-the-matrix span.cm-variable-2 { color: #C6F; }
+.cm-s-the-matrix span.cm-variable-3, .cm-s-the-matrix span.cm-type { color: #96F; }
+.cm-s-the-matrix span.cm-property { color: #62FFA0; }
+.cm-s-the-matrix span.cm-operator { color: #999; }
+.cm-s-the-matrix span.cm-comment { color: #CCCCCC; }
+.cm-s-the-matrix span.cm-string { color: #39C; }
+.cm-s-the-matrix span.cm-meta { color: #C9F; }
+.cm-s-the-matrix span.cm-qualifier { color: #FFF700; }
+.cm-s-the-matrix span.cm-builtin { color: #30a; }
+.cm-s-the-matrix span.cm-bracket { color: #cc7; }
+.cm-s-the-matrix span.cm-tag { color: #FFBD40; }
+.cm-s-the-matrix span.cm-attribute { color: #FFF700; }
+.cm-s-the-matrix span.cm-error { color: #FF0000; }
+
+.cm-s-the-matrix .CodeMirror-activeline-background { background: #040; }
+
+/*
+Copyright (C) 2011 by MarkLogic Corporation
+Author: Mike Brevoort <mike@brevoort.com>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+*/
+.cm-s-xq-light span.cm-keyword { line-height: 1em; font-weight: bold; color: #5A5CAD; }
+.cm-s-xq-light span.cm-atom { color: #6C8CD5; }
+.cm-s-xq-light span.cm-number { color: #164; }
+.cm-s-xq-light span.cm-def { text-decoration:underline; }
+.cm-s-xq-light span.cm-variable { color: black; }
+.cm-s-xq-light span.cm-variable-2 { color:black; }
+.cm-s-xq-light span.cm-variable-3, .cm-s-xq-light span.cm-type { color: black; }
+.cm-s-xq-light span.cm-property {}
+.cm-s-xq-light span.cm-operator {}
+.cm-s-xq-light span.cm-comment { color: #0080FF; font-style: italic; }
+.cm-s-xq-light span.cm-string { color: red; }
+.cm-s-xq-light span.cm-meta { color: yellow; }
+.cm-s-xq-light span.cm-qualifier { color: grey; }
+.cm-s-xq-light span.cm-builtin { color: #7EA656; }
+.cm-s-xq-light span.cm-bracket { color: #cc7; }
+.cm-s-xq-light span.cm-tag { color: #3F7F7F; }
+.cm-s-xq-light span.cm-attribute { color: #7F007F; }
+.cm-s-xq-light span.cm-error { color: #f00; }
+
+.cm-s-xq-light .CodeMirror-activeline-background { background: #e8f2ff; }
+.cm-s-xq-light .CodeMirror-matchingbracket { outline:1px solid grey;color:black !important;background:yellow; }
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.CodeMirror {
+  line-height: var(--jp-code-line-height);
+  font-size: var(--jp-code-font-size);
+  font-family: var(--jp-code-font-family);
+  border: 0;
+  border-radius: 0;
+  height: auto;
+  /* Changed to auto to autogrow */
 }
-div.output_area .mglyph > img {
-  max-width: none;
+
+.CodeMirror pre {
+  padding: 0 var(--jp-code-padding);
 }
-/* This is needed to protect the pre formating from global settings such
-   as that of bootstrap */
-.output {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: vertical;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: vertical;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: vertical;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: column;
-  align-items: stretch;
+
+.jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-dialog {
+  background-color: var(--jp-layout-color0);
+  color: var(--jp-content-font-color1);
 }
-@media (max-width: 540px) {
-  div.output_area {
-    /* Old browsers */
-    display: -webkit-box;
-    -webkit-box-orient: vertical;
-    -webkit-box-align: stretch;
-    display: -moz-box;
-    -moz-box-orient: vertical;
-    -moz-box-align: stretch;
-    display: box;
-    box-orient: vertical;
-    box-align: stretch;
-    /* Modern browsers */
-    display: flex;
-    flex-direction: column;
-    align-items: stretch;
-  }
+
+/* This causes https://github.com/jupyter/jupyterlab/issues/522 */
+/* May not cause it not because we changed it! */
+.CodeMirror-lines {
+  padding: var(--jp-code-padding) 0;
 }
-div.output_area pre {
-  margin: 0;
-  padding: 1px 0 1px 0;
-  border: 0;
-  vertical-align: baseline;
-  color: black;
-  background-color: transparent;
-  border-radius: 0;
+
+.CodeMirror-linenumber {
+  padding: 0 8px;
 }
-/* This class is for the output subarea inside the output_area and after
-   the prompt div. */
-div.output_subarea {
-  overflow-x: auto;
-  padding: 0.4em;
-  /* Old browsers */
-  -webkit-box-flex: 1;
-  -moz-box-flex: 1;
-  box-flex: 1;
-  /* Modern browsers */
-  flex: 1;
-  max-width: calc(100% - 14ex);
+
+.jp-CodeMirrorEditor-static {
+  margin: var(--jp-code-padding);
 }
-div.output_scroll div.output_subarea {
-  overflow-x: visible;
+
+.jp-CodeMirrorEditor,
+.jp-CodeMirrorEditor-static {
+  cursor: text;
 }
-/* The rest of the output_* classes are for special styling of the different
-   output types */
-/* all text output has this class: */
-div.output_text {
-  text-align: left;
-  color: #000;
-  /* This has to match that of the the CodeMirror class line-height below */
-  line-height: 1.21429em;
+
+.jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-cursor {
+  border-left: var(--jp-code-cursor-width0) solid var(--jp-editor-cursor-color);
 }
-/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
-div.output_stderr {
-  background: #fdd;
-  /* very light red background for stderr */
+
+/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
+@media screen and (min-width: 2138px) and (max-width: 4319px) {
+  .jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-cursor {
+    border-left: var(--jp-code-cursor-width1) solid
+      var(--jp-editor-cursor-color);
+  }
 }
-div.output_latex {
-  text-align: left;
+
+/* When zoomed out less than 33% */
+@media screen and (min-width: 4320px) {
+  .jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-cursor {
+    border-left: var(--jp-code-cursor-width2) solid
+      var(--jp-editor-cursor-color);
+  }
 }
-/* Empty output_javascript divs should have no height */
-div.output_javascript:empty {
-  padding: 0;
+
+.CodeMirror.jp-mod-readOnly .CodeMirror-cursor {
+  display: none;
 }
-.js-error {
-  color: darkred;
+
+.CodeMirror-gutters {
+  border-right: 1px solid var(--jp-border-color2);
+  background-color: var(--jp-layout-color0);
 }
-/* raw_input styles */
-div.raw_input_container {
-  line-height: 1.21429em;
-  padding-top: 5px;
+
+.jp-CollaboratorCursor {
+  border-left: 5px solid transparent;
+  border-right: 5px solid transparent;
+  border-top: none;
+  border-bottom: 3px solid;
+  background-clip: content-box;
+  margin-left: -5px;
+  margin-right: -5px;
 }
-pre.raw_input_prompt {
-  /* nothing needed here. */
+
+.CodeMirror-selectedtext.cm-searching {
+  background-color: var(--jp-search-selected-match-background-color) !important;
+  color: var(--jp-search-selected-match-color) !important;
 }
-input.raw_input {
-  font-family: monospace;
-  font-size: inherit;
-  color: inherit;
-  width: auto;
-  /* make sure input baseline aligns with prompt */
-  vertical-align: baseline;
-  /* padding + margin = 0.5em between prompt and cursor */
-  padding: 0em 0.25em;
-  margin: 0em 0.25em;
+
+.cm-searching {
+  background-color: var(
+    --jp-search-unselected-match-background-color
+  ) !important;
+  color: var(--jp-search-unselected-match-color) !important;
 }
-input.raw_input:focus {
-  box-shadow: none;
+
+.CodeMirror-focused .CodeMirror-selected {
+  background-color: var(--jp-editor-selected-focused-background);
 }
-p.p-space {
-  margin-bottom: 10px;
+
+.CodeMirror-selected {
+  background-color: var(--jp-editor-selected-background);
 }
-div.output_unrecognized {
-  padding: 5px;
-  font-weight: bold;
-  color: red;
+
+.jp-CollaboratorCursor-hover {
+  position: absolute;
+  z-index: 1;
+  transform: translateX(-50%);
+  color: white;
+  border-radius: 3px;
+  padding-left: 4px;
+  padding-right: 4px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+  text-align: center;
+  font-size: var(--jp-ui-font-size1);
+  white-space: nowrap;
 }
-div.output_unrecognized a {
-  color: inherit;
-  text-decoration: none;
+
+.jp-CodeMirror-ruler {
+  border-left: 1px dashed var(--jp-border-color2);
 }
-div.output_unrecognized a:hover {
-  color: inherit;
-  text-decoration: none;
+
+/**
+ * Here is our jupyter theme for CodeMirror syntax highlighting
+ * This is used in our marked.js syntax highlighting and CodeMirror itself
+ * The string "jupyter" is set in ../codemirror/widget.DEFAULT_CODEMIRROR_THEME
+ * This came from the classic notebook, which came form highlight.js/GitHub
+ */
+
+/**
+ * CodeMirror themes are handling the background/color in this way. This works
+ * fine for CodeMirror editors outside the notebook, but the notebook styles
+ * these things differently.
+ */
+.CodeMirror.cm-s-jupyter {
+  background: var(--jp-layout-color0);
+  color: var(--jp-content-font-color1);
 }
-.rendered_html {
-  color: #000;
-  /* any extras will just be numbers: */
+
+/* In the notebook, we want this styling to be handled by its container */
+.jp-CodeConsole .CodeMirror.cm-s-jupyter,
+.jp-Notebook .CodeMirror.cm-s-jupyter {
+  background: transparent;
 }
-.rendered_html em {
-  font-style: italic;
+
+.cm-s-jupyter .CodeMirror-cursor {
+  border-left: var(--jp-code-cursor-width0) solid var(--jp-editor-cursor-color);
 }
-.rendered_html strong {
+.cm-s-jupyter span.cm-keyword {
+  color: var(--jp-mirror-editor-keyword-color);
   font-weight: bold;
 }
-.rendered_html u {
-  text-decoration: underline;
+.cm-s-jupyter span.cm-atom {
+  color: var(--jp-mirror-editor-atom-color);
 }
-.rendered_html :link {
-  text-decoration: underline;
+.cm-s-jupyter span.cm-number {
+  color: var(--jp-mirror-editor-number-color);
 }
-.rendered_html :visited {
-  text-decoration: underline;
+.cm-s-jupyter span.cm-def {
+  color: var(--jp-mirror-editor-def-color);
 }
-.rendered_html h1 {
-  font-size: 185.7%;
-  margin: 1.08em 0 0 0;
-  font-weight: bold;
-  line-height: 1.0;
+.cm-s-jupyter span.cm-variable {
+  color: var(--jp-mirror-editor-variable-color);
 }
-.rendered_html h2 {
-  font-size: 157.1%;
-  margin: 1.27em 0 0 0;
-  font-weight: bold;
-  line-height: 1.0;
+.cm-s-jupyter span.cm-variable-2 {
+  color: var(--jp-mirror-editor-variable-2-color);
 }
-.rendered_html h3 {
-  font-size: 128.6%;
-  margin: 1.55em 0 0 0;
-  font-weight: bold;
-  line-height: 1.0;
+.cm-s-jupyter span.cm-variable-3 {
+  color: var(--jp-mirror-editor-variable-3-color);
 }
-.rendered_html h4 {
-  font-size: 100%;
-  margin: 2em 0 0 0;
-  font-weight: bold;
-  line-height: 1.0;
+.cm-s-jupyter span.cm-punctuation {
+  color: var(--jp-mirror-editor-punctuation-color);
 }
-.rendered_html h5 {
-  font-size: 100%;
-  margin: 2em 0 0 0;
-  font-weight: bold;
-  line-height: 1.0;
-  font-style: italic;
+.cm-s-jupyter span.cm-property {
+  color: var(--jp-mirror-editor-property-color);
 }
-.rendered_html h6 {
-  font-size: 100%;
-  margin: 2em 0 0 0;
+.cm-s-jupyter span.cm-operator {
+  color: var(--jp-mirror-editor-operator-color);
   font-weight: bold;
-  line-height: 1.0;
-  font-style: italic;
-}
-.rendered_html h1:first-child {
-  margin-top: 0.538em;
 }
-.rendered_html h2:first-child {
-  margin-top: 0.636em;
+.cm-s-jupyter span.cm-comment {
+  color: var(--jp-mirror-editor-comment-color);
+  font-style: italic;
 }
-.rendered_html h3:first-child {
-  margin-top: 0.777em;
+.cm-s-jupyter span.cm-string {
+  color: var(--jp-mirror-editor-string-color);
 }
-.rendered_html h4:first-child {
-  margin-top: 1em;
+.cm-s-jupyter span.cm-string-2 {
+  color: var(--jp-mirror-editor-string-2-color);
 }
-.rendered_html h5:first-child {
-  margin-top: 1em;
+.cm-s-jupyter span.cm-meta {
+  color: var(--jp-mirror-editor-meta-color);
 }
-.rendered_html h6:first-child {
-  margin-top: 1em;
+.cm-s-jupyter span.cm-qualifier {
+  color: var(--jp-mirror-editor-qualifier-color);
 }
-.rendered_html ul:not(.list-inline),
-.rendered_html ol:not(.list-inline) {
-  padding-left: 2em;
+.cm-s-jupyter span.cm-builtin {
+  color: var(--jp-mirror-editor-builtin-color);
 }
-.rendered_html ul {
-  list-style: disc;
+.cm-s-jupyter span.cm-bracket {
+  color: var(--jp-mirror-editor-bracket-color);
 }
-.rendered_html ul ul {
-  list-style: square;
-  margin-top: 0;
+.cm-s-jupyter span.cm-tag {
+  color: var(--jp-mirror-editor-tag-color);
 }
-.rendered_html ul ul ul {
-  list-style: circle;
+.cm-s-jupyter span.cm-attribute {
+  color: var(--jp-mirror-editor-attribute-color);
 }
-.rendered_html ol {
-  list-style: decimal;
+.cm-s-jupyter span.cm-header {
+  color: var(--jp-mirror-editor-header-color);
 }
-.rendered_html ol ol {
-  list-style: upper-alpha;
-  margin-top: 0;
+.cm-s-jupyter span.cm-quote {
+  color: var(--jp-mirror-editor-quote-color);
 }
-.rendered_html ol ol ol {
-  list-style: lower-alpha;
+.cm-s-jupyter span.cm-link {
+  color: var(--jp-mirror-editor-link-color);
 }
-.rendered_html ol ol ol ol {
-  list-style: lower-roman;
+.cm-s-jupyter span.cm-error {
+  color: var(--jp-mirror-editor-error-color);
 }
-.rendered_html ol ol ol ol ol {
-  list-style: decimal;
+.cm-s-jupyter span.cm-hr {
+  color: #999;
 }
-.rendered_html * + ul {
-  margin-top: 1em;
+
+.cm-s-jupyter span.cm-tab {
+  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+  background-position: right;
+  background-repeat: no-repeat;
 }
-.rendered_html * + ol {
-  margin-top: 1em;
+
+.cm-s-jupyter .CodeMirror-activeline-background,
+.cm-s-jupyter .CodeMirror-gutter {
+  background-color: var(--jp-layout-color2);
 }
-.rendered_html hr {
-  color: black;
-  background-color: black;
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* This file was auto-generated by ensurePackage() in @jupyterlab/buildutils */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| RenderedText
+|----------------------------------------------------------------------------*/
+
+.jp-RenderedText {
+  text-align: left;
+  padding-left: var(--jp-code-padding);
+  line-height: var(--jp-code-line-height);
+  font-family: var(--jp-code-font-family);
 }
-.rendered_html pre {
-  margin: 1em 2em;
+
+.jp-RenderedText pre,
+.jp-RenderedJavaScript pre,
+.jp-RenderedHTMLCommon pre {
+  color: var(--jp-content-font-color1);
+  font-size: var(--jp-code-font-size);
+  border: none;
+  margin: 0px;
   padding: 0px;
-  background-color: #fff;
-}
-.rendered_html code {
-  background-color: #eff0f1;
-}
-.rendered_html p code {
-  padding: 1px 5px;
+  line-height: normal;
 }
-.rendered_html pre code {
-  background-color: #fff;
+
+.jp-RenderedText pre a:link {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
 }
-.rendered_html pre,
-.rendered_html code {
-  border: 0;
-  color: #000;
-  font-size: 100%;
+.jp-RenderedText pre a:hover {
+  text-decoration: underline;
+  color: var(--jp-content-link-color);
 }
-.rendered_html blockquote {
-  margin: 1em 2em;
+.jp-RenderedText pre a:visited {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
 }
-.rendered_html table {
-  margin-left: auto;
-  margin-right: auto;
-  border: none;
-  border-collapse: collapse;
-  border-spacing: 0;
-  color: black;
-  font-size: 12px;
-  table-layout: fixed;
+
+/* console foregrounds and backgrounds */
+.jp-RenderedText pre .ansi-black-fg {
+  color: #3e424d;
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-.rendered_html thead {
-  border-bottom: 1px solid black;
-  vertical-align: bottom;
+.jp-RenderedText pre .ansi-red-fg {
+  color: #e75c58;
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-.rendered_html tr,
-.rendered_html th,
-.rendered_html td {
-  text-align: right;
-  vertical-align: middle;
-  padding: 0.5em 0.5em;
-  line-height: normal;
-  white-space: normal;
-  max-width: none;
-  border: none;
+.jp-RenderedText pre .ansi-green-fg {
+  color: #00a250;
 }
-.rendered_html th {
-  font-weight: bold;
+.jp-RenderedText pre .ansi-yellow-fg {
+  color: #ddb62b;
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-.rendered_html tbody tr:nth-child(odd) {
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+.jp-RenderedText pre .ansi-blue-fg {
+  color: #208ffb;
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-.rendered_html tbody tr:hover {
-  background: rgba(66, 165, 245, 0.2);
+.jp-RenderedText pre .ansi-magenta-fg {
+  color: #d160c4;
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-.rendered_html * + table {
-  margin-top: 1em;
+.jp-RenderedText pre .ansi-cyan-fg {
+  color: #60c6c8;
 }
-.rendered_html p {
-  text-align: left;
+.jp-RenderedText pre .ansi-white-fg {
+  color: #c5c1b4;
 }
-.rendered_html * + p {
-  margin-top: 1em;
+
+.jp-RenderedText pre .ansi-black-bg {
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-.rendered_html img {
-  display: block;
-  margin-left: auto;
-  margin-right: auto;
+.jp-RenderedText pre .ansi-red-bg {
+  background-color: #e75c58;
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-.rendered_html * + img {
-  margin-top: 1em;
+.jp-RenderedText pre .ansi-green-bg {
+  background-color: #00a250;
 }
-.rendered_html img,
-.rendered_html svg {
-  max-width: 100%;
-  height: auto;
+.jp-RenderedText pre .ansi-yellow-bg {
+  background-color: #ddb62b;
 }
-.rendered_html img.unconfined,
-.rendered_html svg.unconfined {
-  max-width: none;
+.jp-RenderedText pre .ansi-blue-bg {
+  background-color: #208ffb;
 }
-.rendered_html .alert {
-  margin-bottom: initial;
+.jp-RenderedText pre .ansi-magenta-bg {
+  background-color: #d160c4;
 }
-.rendered_html * + .alert {
-  margin-top: 1em;
+.jp-RenderedText pre .ansi-cyan-bg {
+  background-color: #60c6c8;
 }
-[dir="rtl"] .rendered_html p {
-  text-align: right;
+.jp-RenderedText pre .ansi-white-bg {
+  background-color: #c5c1b4;
 }
-div.text_cell {
-  /* Old browsers */
-  display: -webkit-box;
-  -webkit-box-orient: horizontal;
-  -webkit-box-align: stretch;
-  display: -moz-box;
-  -moz-box-orient: horizontal;
-  -moz-box-align: stretch;
-  display: box;
-  box-orient: horizontal;
-  box-align: stretch;
-  /* Modern browsers */
-  display: flex;
-  flex-direction: row;
-  align-items: stretch;
+
+.jp-RenderedText pre .ansi-black-intense-fg {
+  color: #282c36;
 }
-@media (max-width: 540px) {
-  div.text_cell > div.prompt {
-    display: none;
-  }
+.jp-RenderedText pre .ansi-red-intense-fg {
+  color: #b22b31;
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-div.text_cell_render {
-  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
-  outline: none;
-  resize: none;
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-  border-style: none;
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-select[multiple].tags-input button {
-  height: auto;
+[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
+  background-color: var(--jp-inverse-layout-color1);
 }
-.cell-tag,
-.tags-input button {
-  padding: 0px 4px;
+/* ...or leave it untouched if they don't */
+[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-dark-background {
 }
-.cell-tag {
-  background-color: #fff;
-  white-space: nowrap;
+[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-light-background {
 }
-.tags-input input[type=text]:focus {
-  outline: none;
-  box-shadow: none;
-  border-color: #ccc;
+
+.jp-RenderedHTMLCommon img,
+.jp-RenderedImage img,
+.jp-RenderedHTMLCommon svg,
+.jp-RenderedSVG svg {
+  max-width: 100%;
+  height: auto;
 }
-.completions {
-  position: absolute;
-  z-index: 110;
-  overflow: hidden;
-  border: 1px solid #ababab;
-  border-radius: 2px;
-  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
-  box-shadow: 0px 6px 10px -1px #adadad;
-  line-height: 1;
+
+.jp-RenderedHTMLCommon img.jp-mod-unconfined,
+.jp-RenderedImage img.jp-mod-unconfined,
+.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
+.jp-RenderedSVG svg.jp-mod-unconfined {
+  max-width: none;
 }
-.completions select {
-  background: white;
-  outline: none;
-  border: none;
-  padding: 0px;
-  margin: 0px;
-  overflow: auto;
-  font-family: monospace;
-  font-size: 110%;
-  color: #000;
-  width: auto;
+
+.jp-RenderedHTMLCommon .alert {
+  padding: var(--jp-notebook-padding);
+  border: var(--jp-border-width) solid transparent;
+  border-radius: var(--jp-border-radius);
+  margin-bottom: 1em;
 }
-.completions select option.context {
-  color: #286090;
+
+.jp-RenderedHTMLCommon .alert-info {
+  color: var(--jp-info-color0);
+  background-color: var(--jp-info-color3);
+  border-color: var(--jp-info-color2);
 }
-#kernel_logo_widget .current_kernel_logo {
-  display: none;
-  margin-top: -1px;
-  margin-bottom: -1px;
-  width: 32px;
-  height: 32px;
+.jp-RenderedHTMLCommon .alert-info hr {
+  border-color: var(--jp-info-color3);
 }
-[dir="rtl"] #kernel_logo_widget {
-  float: left !important;
-  float: left;
+.jp-RenderedHTMLCommon .alert-info > p:last-child,
+.jp-RenderedHTMLCommon .alert-info > ul:last-child {
+  margin-bottom: 0;
 }
-.modal .modal-body .move-path {
-  display: flex;
-  flex-direction: row;
-  justify-content: space;
-  align-items: center;
+
+.jp-RenderedHTMLCommon .alert-warning {
+  color: var(--jp-warn-color0);
+  background-color: var(--jp-warn-color3);
+  border-color: var(--jp-warn-color2);
 }
-.modal .modal-body .move-path .server-root {
-  padding-right: 20px;
+.jp-RenderedHTMLCommon .alert-warning hr {
+  border-color: var(--jp-warn-color3);
 }
-.modal .modal-body .move-path .path-input {
-  flex: 1;
+.jp-RenderedHTMLCommon .alert-warning > p:last-child,
+.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
+  margin-bottom: 0;
 }
-#menubar {
-  box-sizing: border-box;
-  -moz-box-sizing: border-box;
-  -webkit-box-sizing: border-box;
-  margin-top: 1px;
+
+.jp-RenderedHTMLCommon .alert-success {
+  color: var(--jp-success-color0);
+  background-color: var(--jp-success-color3);
+  border-color: var(--jp-success-color2);
 }
-#menubar .navbar {
-  border-top: 1px;
-  border-radius: 0px 0px 2px 2px;
-  margin-bottom: 0px;
+.jp-RenderedHTMLCommon .alert-success hr {
+  border-color: var(--jp-success-color3);
 }
-#menubar .navbar-toggle {
-  float: left;
-  padding-top: 7px;
-  padding-bottom: 7px;
-  border: none;
+.jp-RenderedHTMLCommon .alert-success > p:last-child,
+.jp-RenderedHTMLCommon .alert-success > ul:last-child {
+  margin-bottom: 0;
 }
-#menubar .navbar-collapse {
-  clear: left;
+
+.jp-RenderedHTMLCommon .alert-danger {
+  color: var(--jp-error-color0);
+  background-color: var(--jp-error-color3);
+  border-color: var(--jp-error-color2);
 }
-[dir="rtl"] #menubar .navbar-toggle {
-  float: right;
+.jp-RenderedHTMLCommon .alert-danger hr {
+  border-color: var(--jp-error-color3);
 }
-[dir="rtl"] #menubar .navbar-collapse {
-  clear: right;
+.jp-RenderedHTMLCommon .alert-danger > p:last-child,
+.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
+  margin-bottom: 0;
 }
-[dir="rtl"] #menubar .navbar-nav {
-  float: right;
+
+.jp-RenderedHTMLCommon blockquote {
+  margin: 1em 2em;
+  padding: 0 1em;
+  border-left: 5px solid var(--jp-border-color2);
 }
-[dir="rtl"] #menubar .nav {
-  padding-right: 0px;
+
+a.jp-InternalAnchorLink {
+  visibility: hidden;
+  margin-left: 8px;
+  color: var(--md-blue-800);
 }
-[dir="rtl"] #menubar .navbar-nav > li {
-  float: right;
+
+h1:hover .jp-InternalAnchorLink,
+h2:hover .jp-InternalAnchorLink,
+h3:hover .jp-InternalAnchorLink,
+h4:hover .jp-InternalAnchorLink,
+h5:hover .jp-InternalAnchorLink,
+h6:hover .jp-InternalAnchorLink {
+  visibility: visible;
 }
-[dir="rtl"] #menubar .navbar-right {
-  float: left !important;
+
+.jp-RenderedHTMLCommon kbd {
+  background-color: var(--jp-rendermime-table-row-background);
+  border: 1px solid var(--jp-border-color0);
+  border-bottom-color: var(--jp-border-color2);
+  border-radius: 3px;
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+  display: inline-block;
+  font-size: 0.8em;
+  line-height: 1em;
+  padding: 0.2em 0.5em;
 }
-[dir="rtl"] ul.dropdown-menu {
-  text-align: right;
-  left: auto;
+
+/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
+ * At the bottom of cells this is a bit too much as there is also spacing
+ * between cells. Going all the way to 0 gets too tight between markdown and
+ * code cells.
+ */
+.jp-RenderedHTMLCommon > *:last-child {
+  margin-bottom: 0.5em;
 }
-[dir="rtl"] ul#new-menu.dropdown-menu {
-  right: auto;
-  left: 0;
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* This file was auto-generated by ensurePackage() in @jupyterlab/buildutils */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-MimeDocument {
+  outline: none;
 }
-.nav-wrapper {
-  border-bottom: 1px solid #e7e7e7;
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* This file was auto-generated by ensurePackage() in @jupyterlab/buildutils */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-filebrowser-button-height: 28px;
+  --jp-private-filebrowser-button-width: 48px;
 }
-i.menu-icon {
-  padding-top: 4px;
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-FileBrowser {
+  display: flex;
+  flex-direction: column;
+  color: var(--jp-ui-font-color1);
+  background: var(--jp-layout-color1);
+  /* This is needed so that all font sizing of children done in ems is
+   * relative to this base size */
+  font-size: var(--jp-ui-font-size1);
 }
-[dir="rtl"] i.menu-icon.pull-right {
-  float: left !important;
-  float: left;
+
+.jp-FileBrowser-toolbar.jp-Toolbar {
+  border-bottom: none;
+  height: auto;
+  margin: var(--jp-toolbar-header-margin);
+  box-shadow: none;
 }
-ul#help_menu li a {
-  overflow: hidden;
-  padding-right: 2.2em;
+
+.jp-BreadCrumbs {
+  flex: 0 0 auto;
+  margin: 4px 12px;
 }
-ul#help_menu li a i {
-  margin-right: -1.2em;
+
+.jp-BreadCrumbs-item {
+  margin: 0px 2px;
+  padding: 0px 2px;
+  border-radius: var(--jp-border-radius);
+  cursor: pointer;
 }
-[dir="rtl"] ul#help_menu li a {
-  padding-left: 2.2em;
+
+.jp-BreadCrumbs-item:hover {
+  background-color: var(--jp-layout-color2);
 }
-[dir="rtl"] ul#help_menu li a i {
-  margin-right: 0;
-  margin-left: -1.2em;
+
+.jp-BreadCrumbs-item:first-child {
+  margin-left: 0px;
 }
-[dir="rtl"] ul#help_menu li a i.pull-right {
-  float: left !important;
-  float: left;
+
+.jp-BreadCrumbs-item.jp-mod-dropTarget {
+  background-color: var(--jp-brand-color2);
+  opacity: 0.7;
 }
-.dropdown-submenu {
-  position: relative;
+
+/*-----------------------------------------------------------------------------
+| Buttons
+|----------------------------------------------------------------------------*/
+
+.jp-FileBrowser-toolbar.jp-Toolbar {
+  padding: 0px;
 }
-.dropdown-submenu > .dropdown-menu {
-  top: 0;
-  left: 100%;
-  margin-top: -6px;
-  margin-left: -1px;
+
+.jp-FileBrowser-toolbar.jp-Toolbar {
+  justify-content: space-evenly;
 }
-[dir="rtl"] .dropdown-submenu > .dropdown-menu {
-  right: 100%;
-  margin-right: -1px;
+
+.jp-FileBrowser-toolbar.jp-Toolbar .jp-Toolbar-item {
+  flex: 1;
 }
-.dropdown-submenu:hover > .dropdown-menu {
-  display: block;
+
+.jp-FileBrowser-toolbar.jp-Toolbar .jp-ToolbarButtonComponent {
+  width: 100%;
 }
-.dropdown-submenu > a:after {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  display: block;
-  content: "\f0da";
-  float: right;
-  color: #333333;
-  margin-top: 2px;
-  margin-right: -10px;
+
+/*-----------------------------------------------------------------------------
+| DirListing
+|----------------------------------------------------------------------------*/
+
+.jp-DirListing {
+  flex: 1 1 auto;
+  display: flex;
+  flex-direction: column;
+  outline: 0;
 }
-.dropdown-submenu > a:after.fa-pull-left {
-  margin-right: .3em;
+
+.jp-DirListing-header {
+  flex: 0 0 auto;
+  display: flex;
+  flex-direction: row;
+  overflow: hidden;
+  border-top: var(--jp-border-width) solid var(--jp-border-color2);
+  border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
+  box-shadow: var(--jp-toolbar-box-shadow);
+  z-index: 2;
 }
-.dropdown-submenu > a:after.fa-pull-right {
-  margin-left: .3em;
+
+.jp-DirListing-headerItem {
+  padding: 4px 12px 2px 12px;
+  font-weight: 500;
 }
-.dropdown-submenu > a:after.pull-left {
-  margin-right: .3em;
+
+.jp-DirListing-headerItem:hover {
+  background: var(--jp-layout-color2);
 }
-.dropdown-submenu > a:after.pull-right {
-  margin-left: .3em;
+
+.jp-DirListing-headerItem.jp-id-name {
+  flex: 1 0 84px;
 }
-[dir="rtl"] .dropdown-submenu > a:after {
-  float: left;
-  content: "\f0d9";
-  margin-right: 0;
-  margin-left: -10px;
+
+.jp-DirListing-headerItem.jp-id-modified {
+  flex: 0 0 112px;
+  border-left: var(--jp-border-width) solid var(--jp-border-color2);
+  text-align: right;
 }
-.dropdown-submenu:hover > a:after {
-  color: #262626;
+
+.jp-DirListing-narrow .jp-id-modified,
+.jp-DirListing-narrow .jp-DirListing-itemModified {
+  display: none;
 }
-.dropdown-submenu.pull-left {
-  float: none;
+
+.jp-DirListing-headerItem.jp-mod-selected {
+  font-weight: 600;
 }
-.dropdown-submenu.pull-left > .dropdown-menu {
-  left: -100%;
-  margin-left: 10px;
+
+/* increase specificity to override bundled default */
+.jp-DirListing-content {
+  flex: 1 1 auto;
+  margin: 0;
+  padding: 0;
+  list-style-type: none;
+  overflow: auto;
+  background-color: var(--jp-layout-color1);
 }
-#notification_area {
-  float: right !important;
-  float: right;
-  z-index: 10;
+
+/* Style the directory listing content when a user drops a file to upload */
+.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {
+  outline: 5px dashed rgba(128, 128, 128, 0.5);
+  outline-offset: -10px;
+  cursor: copy;
 }
-[dir="rtl"] #notification_area {
-  float: left !important;
-  float: left;
+
+.jp-DirListing-item {
+  display: flex;
+  flex-direction: row;
+  padding: 4px 12px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
 }
-.indicator_area {
-  float: right !important;
-  float: right;
-  color: #777;
-  margin-left: 5px;
-  margin-right: 5px;
-  width: 11px;
-  z-index: 10;
-  text-align: center;
-  width: auto;
+
+.jp-DirListing-item.jp-mod-selected {
+  color: white;
+  background: var(--jp-brand-color1);
 }
-[dir="rtl"] .indicator_area {
-  float: left !important;
-  float: left;
+
+.jp-DirListing-item.jp-mod-dropTarget {
+  background: var(--jp-brand-color3);
 }
-#kernel_indicator {
-  float: right !important;
-  float: right;
-  color: #777;
-  margin-left: 5px;
-  margin-right: 5px;
-  width: 11px;
-  z-index: 10;
-  text-align: center;
-  width: auto;
-  border-left: 1px solid;
+
+.jp-DirListing-item:hover:not(.jp-mod-selected) {
+  background: var(--jp-layout-color2);
 }
-#kernel_indicator .kernel_indicator_name {
-  padding-left: 5px;
-  padding-right: 5px;
+
+.jp-DirListing-itemIcon {
+  flex: 0 0 20px;
+  margin-right: 4px;
 }
-[dir="rtl"] #kernel_indicator {
-  float: left !important;
-  float: left;
-  border-left: 0;
-  border-right: 1px solid;
-}
-#modal_indicator {
-  float: right !important;
-  float: right;
-  color: #777;
-  margin-left: 5px;
-  margin-right: 5px;
-  width: 11px;
-  z-index: 10;
-  text-align: center;
-  width: auto;
+
+.jp-DirListing-itemText {
+  flex: 1 0 64px;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  user-select: none;
 }
-[dir="rtl"] #modal_indicator {
-  float: left !important;
-  float: left;
+
+.jp-DirListing-itemModified {
+  flex: 0 0 125px;
+  text-align: right;
 }
-#readonly-indicator {
-  float: right !important;
-  float: right;
-  color: #777;
-  margin-left: 5px;
-  margin-right: 5px;
-  width: 11px;
-  z-index: 10;
-  text-align: center;
-  width: auto;
-  margin-top: 2px;
-  margin-bottom: 0px;
-  margin-left: 0px;
-  margin-right: 0px;
-  display: none;
+
+.jp-DirListing-editor {
+  flex: 1 0 64px;
+  outline: none;
+  border: none;
 }
-.modal_indicator:before {
-  width: 1.28571429em;
-  text-align: center;
+
+.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon:before {
+  color: limegreen;
+  content: '\25CF';
+  font-size: 8px;
+  position: absolute;
+  left: -8px;
 }
-.edit_mode .modal_indicator:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f040";
+
+.jp-DirListing-item.lm-mod-drag-image,
+.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
+  font-size: var(--jp-ui-font-size1);
+  padding-left: 4px;
+  margin-left: 4px;
+  width: 160px;
+  background-color: var(--jp-ui-inverse-font-color2);
+  box-shadow: var(--jp-elevation-z2);
+  border-radius: 0px;
+  color: var(--jp-ui-font-color1);
+  transform: translateX(-40%) translateY(-58%);
 }
-.edit_mode .modal_indicator:before.fa-pull-left {
-  margin-right: .3em;
+
+.jp-DirListing-deadSpace {
+  flex: 1 1 auto;
+  margin: 0;
+  padding: 0;
+  list-style-type: none;
+  overflow: auto;
+  background-color: var(--jp-layout-color1);
 }
-.edit_mode .modal_indicator:before.fa-pull-right {
-  margin-left: .3em;
+
+.jp-Document {
+  min-width: 120px;
+  min-height: 120px;
+  outline: none;
 }
-.edit_mode .modal_indicator:before.pull-left {
-  margin-right: .3em;
+
+.jp-FileDialog.jp-mod-conflict input {
+  color: red;
 }
-.edit_mode .modal_indicator:before.pull-right {
-  margin-left: .3em;
+
+.jp-FileDialog .jp-new-name-title {
+  margin-top: 12px;
 }
-.command_mode .modal_indicator:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: ' ';
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* This file was auto-generated by ensurePackage() in @jupyterlab/buildutils */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Private CSS variables
+|----------------------------------------------------------------------------*/
+
+:root {
 }
-.command_mode .modal_indicator:before.fa-pull-left {
-  margin-right: .3em;
+
+/*-----------------------------------------------------------------------------
+| Main OutputArea
+| OutputArea has a list of Outputs
+|----------------------------------------------------------------------------*/
+
+.jp-OutputArea {
+  overflow-y: auto;
 }
-.command_mode .modal_indicator:before.fa-pull-right {
-  margin-left: .3em;
+
+.jp-OutputArea-child {
+  display: flex;
+  flex-direction: row;
 }
-.command_mode .modal_indicator:before.pull-left {
-  margin-right: .3em;
+
+.jp-OutputPrompt {
+  flex: 0 0 var(--jp-cell-prompt-width);
+  color: var(--jp-cell-outprompt-font-color);
+  font-family: var(--jp-cell-prompt-font-family);
+  padding: var(--jp-code-padding);
+  letter-spacing: var(--jp-cell-prompt-letter-spacing);
+  line-height: var(--jp-code-line-height);
+  font-size: var(--jp-code-font-size);
+  border: var(--jp-border-width) solid transparent;
+  opacity: var(--jp-cell-prompt-opacity);
+  /* Right align prompt text, don't wrap to handle large prompt numbers */
+  text-align: right;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  /* Disable text selection */
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
 }
-.command_mode .modal_indicator:before.pull-right {
-  margin-left: .3em;
+
+.jp-OutputArea-output {
+  height: auto;
+  overflow: auto;
+  user-select: text;
+  -moz-user-select: text;
+  -webkit-user-select: text;
+  -ms-user-select: text;
 }
-.kernel_idle_icon:before {
-  display: inline-block;
-  font: normal normal normal 14px/1 FontAwesome;
-  font-size: inherit;
-  text-rendering: auto;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-  content: "\f10c";
+
+.jp-OutputArea-child .jp-OutputArea-output {
+  flex-grow: 1;
+  flex-shrink: 1;
 }
-.kernel_idle_icon:before.fa-pull-left {
-  margin-right: .3em;
+
+/**
+ * Isolated output.
+ */
+.jp-OutputArea-output.jp-mod-isolated {
+  width: 100%;
+  display: block;
 }
-.kernel_idle_icon:before.fa-pull-right {
-  margin-left: .3em;
+
+/*
+When drag events occur, `p-mod-override-cursor` is added to the body.
+Because iframes steal all cursor events, the following two rules are necessary
+to suppress pointer events while resize drags are occurring. There may be a
+better solution to this problem.
+*/
+body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
+  position: relative;
 }
-.kernel_idle_icon:before.pull-left {
-  margin-right: .3em;
+
+body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated:before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: transparent;
 }
-.kernel_idle_icon:before.pull-right {
-  margin-left: .3em;
+
+/* pre */
+
+.jp-OutputArea-output pre {
+  border: none;
+  margin: 0px;
+  padding: 0px;
+  overflow-x: auto;
+  overflow-y: auto;
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+ * in a manner that leaves it in the layout and preserves its width.
+ */
+.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
+  border: none !important;
+  background: transparent !important;
 }
-.toolbar select,
-.toolbar label {
-  width: auto;
-  vertical-align: middle;
-  margin-right: 2px;
-  margin-bottom: 0px;
-  display: inline;
-  font-size: 92%;
-  margin-left: 0.3em;
-  margin-right: 0.3em;
-  padding: 0px;
-  padding-top: 3px;
+
+.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
+  min-height: var(--jp-cell-collapser-min-height);
 }
-.toolbar .btn {
-  padding: 2px 8px;
+
+/*-----------------------------------------------------------------------------
+| Output
+|----------------------------------------------------------------------------*/
+
+/* Put a space between input and output when there IS output */
+.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
+  margin-top: 5px;
 }
-.toolbar .btn-group {
-  margin-top: 0px;
-  margin-left: 5px;
+
+/* Text output with the Out[] prompt needs a top padding to match the
+ * alignment of the Out[] prompt itself.
+ */
+.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {
+  padding-top: var(--jp-code-padding);
 }
-.toolbar-btn-label {
-  margin-left: 6px;
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {
+  overflow-y: auto;
+  max-height: 200px;
+  box-shadow: inset 0 0 6px 2px rgba(0, 0, 0, 0.3);
+  margin-left: var(--jp-private-cell-scrolling-output-offset);
 }
-#maintoolbar {
-  margin-bottom: -3px;
-  margin-top: -8px;
-  border: 0px;
-  min-height: 27px;
-  margin-left: 0px;
-  padding-top: 11px;
-  padding-bottom: 3px;
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
+  flex: 0 0
+    calc(
+      var(--jp-cell-prompt-width) -
+        var(--jp-private-cell-scrolling-output-offset)
+    );
 }
-#maintoolbar .navbar-text {
-  float: none;
-  vertical-align: middle;
-  text-align: right;
-  margin-left: 5px;
-  margin-right: 0px;
-  margin-top: 0px;
+
+/*-----------------------------------------------------------------------------
+| CodeCell
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| MarkdownCell
+|----------------------------------------------------------------------------*/
+
+.jp-MarkdownOutput {
+  flex: 1 1 auto;
+  margin-top: 0;
+  margin-bottom: 0;
+  padding-left: var(--jp-code-padding);
 }
-.select-xs {
-  height: 24px;
+
+.jp-MarkdownOutput.jp-RenderedHTMLCommon {
+  overflow: auto;
 }
-[dir="rtl"] .btn-group > .btn,
-.btn-group-vertical > .btn {
-  float: right;
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* This file was auto-generated by ensurePackage() in @jupyterlab/buildutils */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+
+/*-----------------------------------------------------------------------------
+| Styles
+|----------------------------------------------------------------------------*/
+
+.jp-NotebookPanel-toolbar {
+  padding: 2px;
 }
-.pulse,
-.dropdown-menu > li > a.pulse,
-li.pulse > a.dropdown-toggle,
-li.pulse.open > a.dropdown-toggle {
-  background-color: #F37626;
-  color: white;
+
+.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
+  border: none;
+  box-shadow: none;
 }
-/**
- * Primary styles
- *
- * Author: Jupyter Development Team
- */
-/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
- * of chance of beeing generated from the ../less/[samename].less file, you can
- * try to get back the less file by reverting somme commit in history
- **/
-/*
- * We'll try to get something pretty, so we
- * have some strange css to have the scroll bar on
- * the left with fix button on the top right of the tooltip
- */
-@-moz-keyframes fadeOut {
-  from {
-    opacity: 1;
-  }
-  to {
-    opacity: 0;
-  }
+
+.jp-Notebook-toolbarCellTypeDropdown select {
+  height: 24px;
+  font-size: var(--jp-ui-font-size1);
+  line-height: 14px;
+  border-radius: 0;
+  display: block;
 }
-@-webkit-keyframes fadeOut {
-  from {
-    opacity: 1;
-  }
-  to {
-    opacity: 0;
-  }
+
+.jp-Notebook-toolbarCellTypeDropdown span {
+  top: 5px !important;
 }
-@-moz-keyframes fadeIn {
-  from {
-    opacity: 0;
-  }
-  to {
-    opacity: 1;
-  }
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Private CSS variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-notebook-dragImage-width: 304px;
+  --jp-private-notebook-dragImage-height: 36px;
+  --jp-private-notebook-selected-color: var(--md-blue-400);
+  --jp-private-notebook-active-color: var(--md-green-400);
 }
-@-webkit-keyframes fadeIn {
-  from {
-    opacity: 0;
-  }
-  to {
-    opacity: 1;
-  }
+
+/*-----------------------------------------------------------------------------
+| Imports
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Notebook
+|----------------------------------------------------------------------------*/
+
+.jp-NotebookPanel {
+  display: block;
+  height: 100%;
+}
+
+.jp-NotebookPanel.jp-Document {
+  min-width: 240px;
+  min-height: 120px;
 }
-/*properties of tooltip after "expand"*/
-.bigtooltip {
+
+.jp-Notebook {
+  padding: var(--jp-notebook-padding);
+  outline: none;
   overflow: auto;
-  height: 200px;
-  -webkit-transition-property: height;
-            width: 1280,
-            height: 720,
-            center: false,
-            controls: false,
-  -webkit-transition-duration: 500ms;
-            width: 1280,
-            height: 720,
-            center: false,
-            controls: false,
-  -moz-transition-property: height;
-            width: 1280,
-            height: 720,
-            center: false,
-            controls: false,
-  -moz-transition-duration: 500ms;
-            width: 1280,
-            height: 720,
-            center: false,
-            controls: false,
-  transition-property: height;
-            width: 1280,
-            height: 720,
-            center: false,
-            controls: false,
-  transition-duration: 500ms;
-            width: 1280,
-            height: 720,
-            center: false,
-            controls: false,
-}
-/*properties of tooltip before "expand"*/
-.smalltooltip {
-  -webkit-transition-property: height;
-            width: 1280,
-            height: 720,
-            center: false,
-            controls: false,
-  -webkit-transition-duration: 500ms;
-            width: 1280,
-            height: 720,
-            center: false,
-            controls: false,
-  -moz-transition-property: height;
-            width: 1280,
-            height: 720,
-            center: false,
-            controls: false,
-  -moz-transition-duration: 500ms;
-            width: 1280,
-            height: 720,
-            center: false,
-            controls: false,
-  transition-property: height;
-            width: 1280,
-            height: 720,
-            center: false,
-            controls: false,
-  transition-duration: 500ms;
-            width: 1280,
-            height: 720,
-            center: false,
-            controls: false,
-  text-overflow: ellipsis;
-  overflow: hidden;
-  height: 80px;
+  background: var(--jp-layout-color0);
 }
-.tooltipbuttons {
-  position: absolute;
-  padding-right: 15px;
-  top: 0px;
-  right: 0px;
-}
-.tooltiptext {
-  /*avoid the button to overlap on some docstring*/
-  padding-right: 30px;
-}
-.ipython_tooltip {
-  max-width: 700px;
-  /*fade-in animation when inserted*/
-  -webkit-animation: fadeOut 400ms;
-  -moz-animation: fadeOut 400ms;
-  animation: fadeOut 400ms;
-  -webkit-animation: fadeIn 400ms;
-  -moz-animation: fadeIn 400ms;
-  animation: fadeIn 400ms;
-  vertical-align: middle;
-  background-color: #f7f7f7;
+
+.jp-Notebook.jp-mod-scrollPastEnd::after {
+  display: block;
+  content: '';
+  min-height: var(--jp-notebook-scroll-padding);
+}
+
+.jp-Notebook .jp-Cell {
   overflow: visible;
-  border: #ababab 1px solid;
-  outline: none;
-  padding: 3px;
-  margin: 0px;
-  padding-left: 7px;
-  font-family: monospace;
-  min-height: 50px;
-  -moz-box-shadow: 0px 6px 10px -1px #adadad;
-  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
-  box-shadow: 0px 6px 10px -1px #adadad;
-  border-radius: 2px;
-  position: absolute;
-  z-index: 1000;
 }
-.ipython_tooltip a {
-  float: right;
+
+.jp-Notebook .jp-Cell .jp-InputPrompt {
+  cursor: move;
 }
-.ipython_tooltip .tooltiptext pre {
-  border: 0;
-  border-radius: 0;
-  font-size: 100%;
-  background-color: #f7f7f7;
+
+/*-----------------------------------------------------------------------------
+| Notebook state related styling
+|
+| The notebook and cells each have states, here are the possibilities:
+|
+| - Notebook
+|   - Command
+|   - Edit
+| - Cell
+|   - None
+|   - Active (only one can be active)
+|   - Selected (the cells actions are applied to)
+|   - Multiselected (when multiple selected, the cursor)
+|   - No outputs
+|----------------------------------------------------------------------------*/
+
+/* Command or edit modes */
+
+.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
+  opacity: var(--jp-cell-prompt-not-active-opacity);
+  color: var(--jp-cell-prompt-not-active-font-color);
 }
-.pretooltiparrow {
-  left: 0px;
-  margin: 0px;
-  top: -16px;
-  width: 40px;
-  height: 16px;
-  overflow: hidden;
-  position: absolute;
+
+.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
+  opacity: var(--jp-cell-prompt-not-active-opacity);
+  color: var(--jp-cell-prompt-not-active-font-color);
 }
-.pretooltiparrow:before {
-  background-color: #f7f7f7;
-  border: 1px #ababab solid;
-  z-index: 11;
-  content: "";
-  position: absolute;
-  left: 15px;
-  top: 10px;
-  width: 25px;
-  height: 25px;
-  -webkit-transform: rotate(45deg);
-  -moz-transform: rotate(45deg);
-  -ms-transform: rotate(45deg);
-  -o-transform: rotate(45deg);
-}
-ul.typeahead-list i {
-  margin-left: -10px;
-  width: 18px;
-}
-[dir="rtl"] ul.typeahead-list i {
-  margin-left: 0;
-  margin-right: -10px;
+
+/* cell is active */
+.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
+  background: var(--jp-brand-color1);
 }
-ul.typeahead-list {
-  max-height: 80vh;
-  overflow: auto;
+
+/* collapser is hovered */
+.jp-Notebook .jp-Cell .jp-Collapser:hover {
+  box-shadow: var(--jp-elevation-z2);
+  background: var(--jp-brand-color1);
+  opacity: var(--jp-cell-collapser-not-active-hover-opacity);
 }
-ul.typeahead-list > li > a {
-  /** Firefox bug **/
-  /* see https://github.com/jupyter/notebook/issues/559 */
-  white-space: normal;
+
+/* cell is active and collapser is hovered */
+.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
+  background: var(--jp-brand-color0);
+  opacity: 1;
 }
-ul.typeahead-list  > li > a.pull-right {
-  float: left !important;
-  float: left;
+
+/* Command mode */
+
+.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
+  background: var(--jp-notebook-multiselected-color);
+}
+
+.jp-Notebook.jp-mod-commandMode
+  .jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
+  background: transparent;
+}
+
+/* Edit mode */
+
+.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
+  border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
+  background-color: var(--jp-cell-editor-active-background);
+}
+
+/*-----------------------------------------------------------------------------
+| Notebook drag and drop
+|----------------------------------------------------------------------------*/
+
+.jp-Notebook-cell.jp-mod-dropSource {
+  opacity: 0.5;
 }
-[dir="rtl"] .typeahead-list {
-  text-align: right;
+
+.jp-Notebook-cell.jp-mod-dropTarget,
+.jp-Notebook.jp-mod-commandMode
+  .jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
+  border-top-color: var(--jp-private-notebook-selected-color);
+  border-top-style: solid;
+  border-top-width: 2px;
 }
-.cmd-palette .modal-body {
-  padding: 7px;
+
+.jp-dragImage {
+  display: flex;
+  flex-direction: row;
+  width: var(--jp-private-notebook-dragImage-width);
+  height: var(--jp-private-notebook-dragImage-height);
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  background: var(--jp-cell-editor-background);
+  overflow: visible;
 }
-.cmd-palette form {
-  background: white;
+
+.jp-dragImage-singlePrompt {
+  box-shadow: 2px 2px 4px 0px rgba(0, 0, 0, 0.12);
 }
-.cmd-palette input {
-  outline: none;
+
+.jp-dragImage .jp-dragImage-content {
+  flex: 1 1 auto;
+  z-index: 2;
+  font-size: var(--jp-code-font-size);
+  font-family: var(--jp-code-font-family);
+  line-height: var(--jp-code-line-height);
+  padding: var(--jp-code-padding);
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  background: var(--jp-cell-editor-background-color);
+  color: var(--jp-content-font-color3);
+  text-align: left;
+  margin: 4px 4px 4px 0px;
 }
-.no-shortcut {
-  min-width: 20px;
-  color: transparent;
+
+.jp-dragImage .jp-dragImage-prompt {
+  flex: 0 0 auto;
+  min-width: 36px;
+  color: var(--jp-cell-inprompt-font-color);
+  padding: var(--jp-code-padding);
+  padding-left: 12px;
+  font-family: var(--jp-cell-prompt-font-family);
+  letter-spacing: var(--jp-cell-prompt-letter-spacing);
+  line-height: 1.9;
+  font-size: var(--jp-code-font-size);
+  border: var(--jp-border-width) solid transparent;
 }
-[dir="rtl"] .no-shortcut.pull-right {
-  float: left !important;
-  float: left;
+
+.jp-dragImage-multipleBack {
+  z-index: -1;
+  position: absolute;
+  height: 32px;
+  width: 300px;
+  top: 8px;
+  left: 8px;
+  background: var(--jp-layout-color2);
+  border: var(--jp-border-width) solid var(--jp-input-border-color);
+  box-shadow: 2px 2px 4px 0px rgba(0, 0, 0, 0.12);
 }
-[dir="rtl"] .command-shortcut.pull-right {
-  float: left !important;
-  float: left;
+
+/*-----------------------------------------------------------------------------
+| Cell toolbar
+|----------------------------------------------------------------------------*/
+
+.jp-NotebookTools {
+  display: block;
+  min-width: var(--jp-sidebar-min-width);
+  color: var(--jp-ui-font-color1);
+  background: var(--jp-layout-color1);
+  /* This is needed so that all font sizing of children done in ems is
+    * relative to this base size */
+  font-size: var(--jp-ui-font-size1);
+  overflow: auto;
 }
-.command-shortcut:before {
-  content: "(command mode)";
-  padding-right: 3px;
-  color: #777777;
+
+.jp-NotebookTools-tool {
+  padding: 0px 12px 0 12px;
 }
-.edit-shortcut:before {
-  content: "(edit)";
-  padding-right: 3px;
-  color: #777777;
+
+.jp-ActiveCellTool {
+  padding: 12px;
+  background-color: var(--jp-layout-color1);
+  border-top: none !important;
 }
-[dir="rtl"] .edit-shortcut.pull-right {
-  float: left !important;
-  float: left;
+
+.jp-ActiveCellTool .jp-InputArea-prompt {
+  flex: 0 0 auto;
+  padding-left: 0px;
 }
-#find-and-replace #replace-preview .match,
-#find-and-replace #replace-preview .insert {
-  background-color: #BBDEFB;
-  border-color: #90CAF9;
-  border-style: solid;
-  border-width: 1px;
-  border-radius: 0px;
+
+.jp-ActiveCellTool .jp-InputArea-editor {
+  flex: 1 1 auto;
+  background: var(--jp-cell-editor-background);
+  border-color: var(--jp-cell-editor-border-color);
 }
-[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
-  border-left: none;
+
+.jp-ActiveCellTool .jp-InputArea-editor .CodeMirror {
+  background: transparent;
 }
-[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
-  border-right: none;
+
+.jp-MetadataEditorTool {
+  flex-direction: column;
+  padding: 12px 0px 12px 0px;
 }
-#find-and-replace #replace-preview .replace .match {
-  background-color: #FFCDD2;
-  border-color: #EF9A9A;
-  border-radius: 0px;
+
+.jp-RankedPanel > :not(:first-child) {
+  margin-top: 12px;
 }
-#find-and-replace #replace-preview .replace .insert {
-  background-color: #C8E6C9;
-  border-color: #A5D6A7;
-  border-radius: 0px;
+
+.jp-KeySelector select.jp-mod-styled {
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color0);
+  border: var(--jp-border-width) solid var(--jp-border-color1);
 }
-#find-and-replace #replace-preview {
-  max-height: 60vh;
-  overflow: auto;
+
+.jp-KeySelector label,
+.jp-MetadataEditorTool label {
+  line-height: 1.4;
 }
-#find-and-replace #replace-preview pre {
-  padding: 5px 10px;
+
+/*-----------------------------------------------------------------------------
+| Presentation Mode (.jp-mod-presentationMode)
+|----------------------------------------------------------------------------*/
+
+.jp-mod-presentationMode .jp-Notebook {
+  --jp-content-font-size1: var(--jp-content-presentation-font-size1);
+  --jp-code-font-size: var(--jp-code-presentation-font-size);
 }
-.terminal-app {
-  background: #EEE;
+
+.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
+.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
+  flex: 0 0 110px;
 }
-.terminal-app #header {
-  background: #fff;
-  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
-  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* This file was auto-generated by ensurePackage() in @jupyterlab/buildutils */
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+</style>
+
+    <style type="text/css">
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*
+The following CSS variables define the main, public API for styling JupyterLab.
+These variables should be used by all plugins wherever possible. In other
+words, plugins should not define custom colors, sizes, etc unless absolutely
+necessary. This enables users to change the visual theme of JupyterLab
+by changing these variables.
+
+Many variables appear in an ordered sequence (0,1,2,3). These sequences
+are designed to work well together, so for example, `--jp-border-color1` should
+be used with `--jp-layout-color1`. The numbers have the following meanings:
+
+* 0: super-primary, reserved for special emphasis
+* 1: primary, most important under normal situations
+* 2: secondary, next most important under normal situations
+* 3: tertiary, next most important under normal situations
+
+Throughout JupyterLab, we are mostly following principles from Google's
+Material Design when selecting colors. We are not, however, following
+all of MD as it is not optimized for dense, information rich UIs.
+*/
+
+:root {
+  /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+  --jp-shadow-base-lightness: 0;
+  --jp-shadow-umbra-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.2
+  );
+  --jp-shadow-penumbra-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.14
+  );
+  --jp-shadow-ambient-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.12
+  );
+  --jp-elevation-z0: none;
+  --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+    0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 3px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+    0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 5px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+    0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 10px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+    0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 18px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+    0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+    0px 3px 14px 2px var(--jp-shadow-ambient-color);
+  --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+    0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+    0px 5px 22px 4px var(--jp-shadow-ambient-color);
+  --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+    0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+    0px 6px 30px 5px var(--jp-shadow-ambient-color);
+  --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+    0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+    0px 8px 38px 7px var(--jp-shadow-ambient-color);
+  --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+    0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+    0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+  /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+  --jp-border-width: 1px;
+  --jp-border-color0: var(--md-grey-400);
+  --jp-border-color1: var(--md-grey-400);
+  --jp-border-color2: var(--md-grey-300);
+  --jp-border-color3: var(--md-grey-200);
+  --jp-border-radius: 2px;
+
+  /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+  --jp-ui-font-scale-factor: 1.2;
+  --jp-ui-font-size0: 0.83333em;
+  --jp-ui-font-size1: 13px; /* Base font size */
+  --jp-ui-font-size2: 1.2em;
+  --jp-ui-font-size3: 1.44em;
+
+  --jp-ui-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica,
+    Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';
+
+  /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+  /* Defaults use Material Design specification */
+  --jp-ui-font-color0: rgba(0, 0, 0, 1);
+  --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+  --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+  --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+  /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+  --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+  --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+  --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+  --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+  /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+  --jp-content-line-height: 1.6;
+  --jp-content-font-scale-factor: 1.2;
+  --jp-content-font-size0: 0.83333em;
+  --jp-content-font-size1: 14px; /* Base font size */
+  --jp-content-font-size2: 1.2em;
+  --jp-content-font-size3: 1.44em;
+  --jp-content-font-size4: 1.728em;
+  --jp-content-font-size5: 2.0736em;
+
+  /* This gives a magnification of about 125% in presentation mode over normal. */
+  --jp-content-presentation-font-size1: 17px;
+
+  --jp-content-heading-line-height: 1;
+  --jp-content-heading-margin-top: 1.2em;
+  --jp-content-heading-margin-bottom: 0.8em;
+  --jp-content-heading-font-weight: 500;
+
+  /* Defaults use Material Design specification */
+  --jp-content-font-color0: rgba(0, 0, 0, 1);
+  --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+  --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+  --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+  --jp-content-link-color: var(--md-blue-700);
+
+  --jp-content-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI',
+    Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
+    'Segoe UI Symbol';
+
+  /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+  --jp-code-font-size: 13px;
+  --jp-code-line-height: 1.3077; /* 17px for 13px base */
+  --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+  --jp-code-font-family-default: Menlo, Consolas, 'DejaVu Sans Mono', monospace;
+  --jp-code-font-family: var(--jp-code-font-family-default);
+
+  /* This gives a magnification of about 125% in presentation mode over normal. */
+  --jp-code-presentation-font-size: 16px;
+
+  /* may need to tweak cursor width if you change font size */
+  --jp-code-cursor-width0: 1.4px;
+  --jp-code-cursor-width1: 2px;
+  --jp-code-cursor-width2: 4px;
+
+  /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+  --jp-layout-color0: white;
+  --jp-layout-color1: white;
+  --jp-layout-color2: var(--md-grey-200);
+  --jp-layout-color3: var(--md-grey-400);
+  --jp-layout-color4: var(--md-grey-600);
+
+  /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+  --jp-inverse-layout-color0: #111111;
+  --jp-inverse-layout-color1: var(--md-grey-900);
+  --jp-inverse-layout-color2: var(--md-grey-800);
+  --jp-inverse-layout-color3: var(--md-grey-700);
+  --jp-inverse-layout-color4: var(--md-grey-600);
+
+  /* Brand/accent */
+
+  --jp-brand-color0: var(--md-blue-700);
+  --jp-brand-color1: var(--md-blue-500);
+  --jp-brand-color2: var(--md-blue-300);
+  --jp-brand-color3: var(--md-blue-100);
+  --jp-brand-color4: var(--md-blue-50);
+
+  --jp-accent-color0: var(--md-green-700);
+  --jp-accent-color1: var(--md-green-500);
+  --jp-accent-color2: var(--md-green-300);
+  --jp-accent-color3: var(--md-green-100);
+
+  /* State colors (warn, error, success, info) */
+
+  --jp-warn-color0: var(--md-orange-700);
+  --jp-warn-color1: var(--md-orange-500);
+  --jp-warn-color2: var(--md-orange-300);
+  --jp-warn-color3: var(--md-orange-100);
+
+  --jp-error-color0: var(--md-red-700);
+  --jp-error-color1: var(--md-red-500);
+  --jp-error-color2: var(--md-red-300);
+  --jp-error-color3: var(--md-red-100);
+
+  --jp-success-color0: var(--md-green-700);
+  --jp-success-color1: var(--md-green-500);
+  --jp-success-color2: var(--md-green-300);
+  --jp-success-color3: var(--md-green-100);
+
+  --jp-info-color0: var(--md-cyan-700);
+  --jp-info-color1: var(--md-cyan-500);
+  --jp-info-color2: var(--md-cyan-300);
+  --jp-info-color3: var(--md-cyan-100);
+
+  /* Cell specific styles */
+
+  --jp-cell-padding: 5px;
+
+  --jp-cell-collapser-width: 8px;
+  --jp-cell-collapser-min-height: 20px;
+  --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+  --jp-cell-editor-background: var(--md-grey-100);
+  --jp-cell-editor-border-color: var(--md-grey-300);
+  --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+  --jp-cell-editor-active-background: var(--jp-layout-color0);
+  --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+  --jp-cell-prompt-width: 64px;
+  --jp-cell-prompt-font-family: 'Source Code Pro', monospace;
+  --jp-cell-prompt-letter-spacing: 0px;
+  --jp-cell-prompt-opacity: 1;
+  --jp-cell-prompt-not-active-opacity: 0.5;
+  --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+  /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+  --jp-cell-inprompt-font-color: #307fc1;
+  /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+  --jp-cell-outprompt-font-color: #bf5b3d;
+
+  /* Notebook specific styles */
+
+  --jp-notebook-padding: 10px;
+  --jp-notebook-select-background: var(--jp-layout-color1);
+  --jp-notebook-multiselected-color: var(--md-blue-50);
+
+  /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+  --jp-notebook-scroll-padding: calc(
+    100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+      var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+  );
+
+  /* Rendermime styles */
+
+  --jp-rendermime-error-background: #fdd;
+  --jp-rendermime-table-row-background: var(--md-grey-100);
+  --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+  /* Dialog specific styles */
+
+  --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+  /* Console specific styles */
+
+  --jp-console-padding: 10px;
+
+  /* Toolbar specific styles */
+
+  --jp-toolbar-border-color: var(--jp-border-color1);
+  --jp-toolbar-micro-height: 8px;
+  --jp-toolbar-background: var(--jp-layout-color1);
+  --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+  --jp-toolbar-header-margin: 4px 4px 0px 4px;
+  --jp-toolbar-active-background: var(--md-grey-300);
+
+  /* Input field styles */
+
+  --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+  --jp-input-active-background: var(--jp-layout-color1);
+  --jp-input-hover-background: var(--jp-layout-color1);
+  --jp-input-background: var(--md-grey-100);
+  --jp-input-border-color: var(--jp-border-color1);
+  --jp-input-active-border-color: var(--jp-brand-color1);
+  --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+  /* General editor styles */
+
+  --jp-editor-selected-background: #d9d9d9;
+  --jp-editor-selected-focused-background: #d7d4f0;
+  --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+  /* Code mirror specific styles */
+
+  --jp-mirror-editor-keyword-color: #008000;
+  --jp-mirror-editor-atom-color: #88f;
+  --jp-mirror-editor-number-color: #080;
+  --jp-mirror-editor-def-color: #00f;
+  --jp-mirror-editor-variable-color: var(--md-grey-900);
+  --jp-mirror-editor-variable-2-color: #05a;
+  --jp-mirror-editor-variable-3-color: #085;
+  --jp-mirror-editor-punctuation-color: #05a;
+  --jp-mirror-editor-property-color: #05a;
+  --jp-mirror-editor-operator-color: #aa22ff;
+  --jp-mirror-editor-comment-color: #408080;
+  --jp-mirror-editor-string-color: #ba2121;
+  --jp-mirror-editor-string-2-color: #708;
+  --jp-mirror-editor-meta-color: #aa22ff;
+  --jp-mirror-editor-qualifier-color: #555;
+  --jp-mirror-editor-builtin-color: #008000;
+  --jp-mirror-editor-bracket-color: #997;
+  --jp-mirror-editor-tag-color: #170;
+  --jp-mirror-editor-attribute-color: #00c;
+  --jp-mirror-editor-header-color: blue;
+  --jp-mirror-editor-quote-color: #090;
+  --jp-mirror-editor-link-color: #00c;
+  --jp-mirror-editor-error-color: #f00;
+  --jp-mirror-editor-hr-color: #999;
+
+  /* Vega extension styles */
+
+  --jp-vega-background: white;
+
+  /* Sidebar-related styles */
+
+  --jp-sidebar-min-width: 180px;
+
+  /* Search-related styles */
+
+  --jp-search-toggle-off-opacity: 0.5;
+  --jp-search-toggle-hover-opacity: 0.8;
+  --jp-search-toggle-on-opacity: 1;
+  --jp-search-selected-match-background-color: rgb(245, 200, 0);
+  --jp-search-selected-match-color: black;
+  --jp-search-unselected-match-background-color: var(
+    --jp-inverse-layout-color0
+  );
+  --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+  /* Icon colors that work well with light or dark backgrounds */
+  --jp-icon-contrast-color0: var(--md-purple-600);
+  --jp-icon-contrast-color1: var(--md-green-600);
+  --jp-icon-contrast-color2: var(--md-pink-600);
+  --jp-icon-contrast-color3: var(--md-blue-600);
 }
-.terminal-app .terminal {
-  width: 100%;
-  float: left;
-  font-family: monospace;
-  color: white;
-  background: black;
-  padding: 0.4em;
-  border-radius: 2px;
-  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
-  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
+</style>
+
+<style type="text/css">
+a.anchor-link {
+   display: none;
 }
-.terminal-app .terminal,
-.terminal-app .terminal dummy-screen {
-  line-height: 1em;
-  font-size: 14px;
+.highlight  {
+    margin: 0.4em;
 }
-.terminal-app .terminal .xterm-rows {
-  padding: 10px;
+.jp-Notebook {
+    padding: 0;
 }
-.terminal-app .terminal-cursor {
-  color: black;
-  background: white;
+:root {
+    --jp-ui-font-size1: 20px;       /* instead of 14px */
+    --jp-content-font-size1: 20px;  /* instead of 14px */
+    --jp-code-font-size: 19px;      /* instead of 13px */
+    --jp-cell-prompt-width: 110px;  /* instead of 64px */
 }
-.terminal-app #terminado-container {
-  margin-top: 20px;
+@media print {
+  body {
+    margin: 0;
+  }
 }
-/*# sourceMappingURL=style.min.css.map */
-    </style>
-<style type="text/css">
-    .highlight .hll { background-color: #ffffcc }
-.highlight  { background: #f8f8f8; }
-.highlight .c { color: #408080; font-style: italic } /* Comment */
-.highlight .err { border: 1px solid #FF0000 } /* Error */
-.highlight .k { color: #008000; font-weight: bold } /* Keyword */
-.highlight .o { color: #666666 } /* Operator */
-.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
-.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
-.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
-.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
-.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
-.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
-.highlight .gd { color: #A00000 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gr { color: #FF0000 } /* Generic.Error */
-.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
-.highlight .gi { color: #00A000 } /* Generic.Inserted */
-.highlight .go { color: #888888 } /* Generic.Output */
-.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
-.highlight .gt { color: #0044DD } /* Generic.Traceback */
-.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
-.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
-.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
-.highlight .kp { color: #008000 } /* Keyword.Pseudo */
-.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
-.highlight .kt { color: #B00040 } /* Keyword.Type */
-.highlight .m { color: #666666 } /* Literal.Number */
-.highlight .s { color: #BA2121 } /* Literal.String */
-.highlight .na { color: #7D9029 } /* Name.Attribute */
-.highlight .nb { color: #008000 } /* Name.Builtin */
-.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
-.highlight .no { color: #880000 } /* Name.Constant */
-.highlight .nd { color: #AA22FF } /* Name.Decorator */
-.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
-.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
-.highlight .nf { color: #0000FF } /* Name.Function */
-.highlight .nl { color: #A0A000 } /* Name.Label */
-.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
-.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
-.highlight .nv { color: #19177C } /* Name.Variable */
-.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
-.highlight .w { color: #bbbbbb } /* Text.Whitespace */
-.highlight .mb { color: #666666 } /* Literal.Number.Bin */
-.highlight .mf { color: #666666 } /* Literal.Number.Float */
-.highlight .mh { color: #666666 } /* Literal.Number.Hex */
-.highlight .mi { color: #666666 } /* Literal.Number.Integer */
-.highlight .mo { color: #666666 } /* Literal.Number.Oct */
-.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
-.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
-.highlight .sc { color: #BA2121 } /* Literal.String.Char */
-.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
-.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
-.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
-.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
-.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
-.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
-.highlight .sx { color: #008000 } /* Literal.String.Other */
-.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
-.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
-.highlight .ss { color: #19177C } /* Literal.String.Symbol */
-.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
-.highlight .fm { color: #0000FF } /* Name.Function.Magic */
-.highlight .vc { color: #19177C } /* Name.Variable.Class */
-.highlight .vg { color: #19177C } /* Name.Variable.Global */
-.highlight .vi { color: #19177C } /* Name.Variable.Instance */
-.highlight .vm { color: #19177C } /* Name.Variable.Magic */
-.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
-    </style>
-<style type="text/css">
-    
-/* Temporary definitions which will become obsolete with Notebook release 5.0 */
-.ansi-black-fg { color: #3E424D; }
-.ansi-black-bg { background-color: #3E424D; }
-.ansi-black-intense-fg { color: #282C36; }
-.ansi-black-intense-bg { background-color: #282C36; }
-.ansi-red-fg { color: #E75C58; }
-.ansi-red-bg { background-color: #E75C58; }
-.ansi-red-intense-fg { color: #B22B31; }
-.ansi-red-intense-bg { background-color: #B22B31; }
-.ansi-green-fg { color: #00A250; }
-.ansi-green-bg { background-color: #00A250; }
-.ansi-green-intense-fg { color: #007427; }
-.ansi-green-intense-bg { background-color: #007427; }
-.ansi-yellow-fg { color: #DDB62B; }
-.ansi-yellow-bg { background-color: #DDB62B; }
-.ansi-yellow-intense-fg { color: #B27D12; }
-.ansi-yellow-intense-bg { background-color: #B27D12; }
-.ansi-blue-fg { color: #208FFB; }
-.ansi-blue-bg { background-color: #208FFB; }
-.ansi-blue-intense-fg { color: #0065CA; }
-.ansi-blue-intense-bg { background-color: #0065CA; }
-.ansi-magenta-fg { color: #D160C4; }
-.ansi-magenta-bg { background-color: #D160C4; }
-.ansi-magenta-intense-fg { color: #A03196; }
-.ansi-magenta-intense-bg { background-color: #A03196; }
-.ansi-cyan-fg { color: #60C6C8; }
-.ansi-cyan-bg { background-color: #60C6C8; }
-.ansi-cyan-intense-fg { color: #258F8F; }
-.ansi-cyan-intense-bg { background-color: #258F8F; }
-.ansi-white-fg { color: #C5C1B4; }
-.ansi-white-bg { background-color: #C5C1B4; }
-.ansi-white-intense-fg { color: #A1A6B2; }
-.ansi-white-intense-bg { background-color: #A1A6B2; }
-
-.ansi-bold { font-weight: bold; }
-
-    </style>
-
+</style>
 
 <style type="text/css">
 /* Overrides of notebook CSS for static HTML export */
@@ -13348,6 +14306,27 @@ ul.typeahead-list  > li > a.pull-right {
 .reveal .progress {
   position: static;
 }
+
+div.jp-InputArea-editor {
+  padding: 0.06em;
+}
+
+div.code_cell {
+  background-color: transparent;
+}
+
+div.output_area pre {
+  font-family: monospace, sans-serif;
+  font-size: 80%;
+}
+
+div.jp-OutputPrompt {
+  /* 5px right shift to account for margin in parent container */
+  margin: 5px 5px 0 0;
+}
+
+/* Reveal navigation controls */
+
 .reveal .controls .navigate-left,
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 }
 </style>
 
-<!-- Custom stylesheet, it must be in the same directory as the html file -->
-<link rel="stylesheet" href="custom.css">
-
 </head>
 
-
-<body>
-
+<body class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light">
 
 <div class="reveal">
-<div class="slides">
-<section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h1 id="Introduction-to-Data-Analysis-and-Plotting-with-Pandas"><em>Introduction to</em> Data Analysis and Plotting with Pandas<a class="anchor-link" href="#Introduction-to-Data-Analysis-and-Plotting-with-Pandas">&#182;</a></h1><h2 id="JSC-Tutorial">JSC Tutorial<a class="anchor-link" href="#JSC-Tutorial">&#182;</a></h2><p>Andreas Herten, Forschungszentrum Jülich, 26 February 2019</p>
+<div class="slides"><section><section>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput  celltag_task" data-mime-type="text/markdown">
+<h1 id="Data-Analysis-and-Plotting-in-Python-with-Pandas">Data Analysis and Plotting in Python with Pandas<a class="anchor-link" href="#Data-Analysis-and-Plotting-in-Python-with-Pandas">&#182;</a></h1><p><em>Andreas Herten, Jülich Supercomputing Centre, Forschungszentrum Jülich, 27 May 2021</em></p>
 
-</div>
 </div>
 </div></section></section><section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
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+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <h2 id="My-Motivation">My Motivation<a class="anchor-link" href="#My-Motivation">&#182;</a></h2><ul>
 <li>I like Python</li>
 <li>I like plotting data</li>
 <li>I like sharing</li>
 <li>I think Pandas is awesome and you should use it too</li>
+<li>…<em>but I'm no Python expert!</em></li>
 </ul>
 <p><span style="color: #023d6b"><em>Motto: <strong>»Pandas as early as possible!«</strong></em></span></p>
 
-</div>
 </div>
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <h2 id="Task-Outline">Task Outline<a class="anchor-link" href="#Task-Outline">&#182;</a></h2><ul>
 <li><a href="#task1">Task 1</a></li>
 <li><a href="#task2">Task 2</a></li>
@@ -13479,164 +14418,157 @@ a.anchor-link {
 <li><a href="#taskb">Bonus Task</a></li>
 </ul>
 
-</div>
 </div>
 </div></div></section></section><section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <h2 id="Tutorial-Setup">Tutorial Setup<a class="anchor-link" href="#Tutorial-Setup">&#182;</a></h2><ul>
-<li>60 minutes (we might do this again for some advanced stuff if you want to)<ul>
-<li><em>Well, as it turns out, 60 minutes weren't nearly enought</em></li>
-<li><em>We ended up spending nearly 2 hours on it, and we needed to rush quickly through the material</em></li>
-</ul>
-</li>
+<li>3 hours, including break around 10:30</li>
 <li>Alternating between lecture and hands-on</li>
-<li>Please give status of hands-ons via <strong><a href="https://pollev.com/aherten538">pollev.com/aherten538</a></strong></li>
+<li>Please give status of hands-ons via 👍 as BigBlueButton status</li>
 </ul>
 
-</div>
 </div>
 </div><div class="fragment">
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <ul>
-<li>Please open Jupyter Notebook of this session<ul>
-<li>… either on your <strong>local machine</strong> (<code>pip install --user pandas seaborn</code>)</li>
-<li>… or on the <strong>JSC Jupyter service</strong> at <a href="https://jupyter-jsc.fz-juelich.de/">https://jupyter-jsc.fz-juelich.de/</a><br>
-<em>Pandas and seaborn should already be there!</em></li>
-</ul>
-</li>
-<li>Tell me when you're done on <strong><a href="https://pollev.com/aherten538">pollev.com/aherten538</a></strong></li>
+<li>Please now open Jupyter Notebook of this session: <a href="https://go.fzj.de/jsc-pd21">https://go.fzj.de/jsc-pd21</a></li>
+<li>Give thumbs up! 👍</li>
 </ul>
 
-</div>
 </div>
 </div></div></section></section><section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <h2 id="About-Pandas">About Pandas<a class="anchor-link" href="#About-Pandas">&#182;</a></h2><p><img style="float: right; max-width: 200px;" width="200px" src="img/adorable-animal-animal-photography-1661535.jpg" /></p>
 <ul>
-<li>Python package (Python 2, Python 3)</li>
-<li>For data analysis</li>
+<li>Python package (<del>Python 2,</del> Python 3)</li>
+<li>For data analysis and manipulation</li>
 <li>With data structures (multi-dimensional table; time series), operations</li>
 <li>Name from »<strong>Pan</strong>el <strong>Da</strong>ta« (multi-dimensional time series in economics)</li>
 <li>Since 2008</li>
 <li><a href="https://pandas.pydata.org/">https://pandas.pydata.org/</a></li>
 <li>Install <a href="https://pypi.org/project/pandas/">via PyPI</a>: <code>pip install pandas</code></li>
+<li><em>Cheatsheet: <a href="https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf">https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf</a></em></li>
 </ul>
 
-</div>
 </div>
 </div></section></section><section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
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+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <h2 id="Pandas-Cohabitation">Pandas Cohabitation<a class="anchor-link" href="#Pandas-Cohabitation">&#182;</a></h2><ul>
 <li>Pandas works great together with other established Python tools<ul>
 <li><a href="https://jupyter.org/">Jupyter Notebooks</a></li>
 <li>Plotting with <a href="https://matplotlib.org/"><code>matplotlib</code></a></li>
+<li>Numerical analysis with <a href="https://numpy.org/"><code>numpy</code></a></li>
 <li>Modelling with <a href="https://www.statsmodels.org/stable/index.html"><code>statsmodels</code></a>, <a href="https://scikit-learn.org/"><code>scikit-learn</code></a></li>
 <li>Nicer plots with <a href="https://seaborn.pydata.org/"><code>seaborn</code></a>, <a href="https://altair-viz.github.io/"><code>altair</code></a>, <a href="https://plot.ly/"><code>plotly</code></a></li>
+<li>Performance enhancement with <a href="https://cython.org/">Cython</a>, <a href="numba.pydata.org/">Numba</a>, …</li>
 </ul>
 </li>
+<li>Tools building up on Pandas: <a href="https://github.com/rapidsai/cudf">cuDF</a> (GPU-accelerated DataFrames in <a href="https://rapids.ai/">Rapids</a>), …</li>
 </ul>
 
-</div>
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 </div></section></section><section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
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+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <h2 id="First-Steps">First Steps<a class="anchor-link" href="#First-Steps">&#182;</a></h2>
 </div>
-</div>
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[1]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[1]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-</div></div><div class="fragment">
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-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[2]:</div>
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+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
-
 </div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[3]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[3]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">__version__</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
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+<div class="jp-OutputArea jp-Cell-outputArea">
 
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+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[3]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[3]:</div>
 
 
 
 
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-<pre>&#39;0.24.1&#39;</pre>
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>&#39;1.2.4&#39;</pre>
 </div>
 
 </div>
 
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+
 </div>
 
-</div></div><div class="fragment">
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-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[4]:</div>
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+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">pdoc</span> pd
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
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+<div class="jp-Cell-outputWrapper">
 
 
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+<div class="jp-OutputArea jp-Cell-outputArea">
 
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-<div class="output_text output_subarea ">
+<div class="jp-RenderedText jp-OutputArea-output " data-mime-type="text/plain">
 <pre><span class="ansi-red-fg">Class docstring:</span>
     pandas - a powerful data analysis and manipulation library for Python
     =====================================================================
@@ -13674,37 +14606,35 @@ a.anchor-link {
         Excel files, databases, and saving/loading data from the ultrafast HDF5
         format.
       - Time series-specific functionality: date range generation and frequency
-        conversion, moving window statistics, moving window linear regressions,
-        date shifting and lagging, etc.</pre>
+        conversion, moving window statistics, date shifting and lagging.</pre>
 </div>
 
 </div>
 
 </div>
+
 </div>
 
 </div></div></section></section><section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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-<li>Main data containers of Pandas<ul>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="DataFrames">DataFrames<a class="anchor-link" href="#DataFrames">&#182;</a></h2><h3 id="It's-all-about-DataFrames">It's all about DataFrames<a class="anchor-link" href="#It's-all-about-DataFrames">&#182;</a></h3><p><img style="float: right; max-width: 200px;" width="200px" src="img/buzz-dataframes.jpg" /></p>
+<ul>
+<li>Data containers of Pandas:<ul>
 <li>Linear: <code>Series</code></li>
 <li>Multi Dimension: <code>DataFrame</code></li>
 </ul>
 </li>
-<li><code>Series</code> is <em>only</em> special case of <code>DataFrame</code></li>
-<li>→ Talk about <code>DataFrame</code>s as the more general case</li>
+<li><code>Series</code> is <em>only</em> special (1D) case of <code>DataFrame</code></li>
+<li>→ We use <code>DataFrame</code>s as the more general case here</li>
 </ul>
 
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 </div></section></section><section><section>
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <h2 id="DataFrames">DataFrames<a class="anchor-link" href="#DataFrames">&#182;</a></h2><h3 id="Construction">Construction<a class="anchor-link" href="#Construction">&#182;</a></h3><ul>
-<li>To show features of <code>DataFrame</code>, let's construct one!</li>
+<li>To show features of <code>DataFrame</code>, let's construct one and show by example!</li>
 <li>Many construction possibilities<ul>
 <li>From lists, dictionaries, <code>numpy</code> objects</li>
 <li>From CSV, HDF5, JSON, Excel, HTML, fixed-width files</li>
@@ -13715,52 +14645,53 @@ a.anchor-link {
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 </ul>
 
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-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <h2 id="DataFrames">DataFrames<a class="anchor-link" href="#DataFrames">&#182;</a></h2><h3 id="Examples,-finally">Examples, finally<a class="anchor-link" href="#Examples,-finally">&#182;</a></h3>
 </div>
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-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[5]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">ages</span>  <span class="o">=</span> <span class="p">[</span><span class="mi">41</span><span class="p">,</span> <span class="mi">56</span><span class="p">,</span> <span class="mi">56</span><span class="p">,</span> <span class="mi">57</span><span class="p">,</span> <span class="mi">39</span><span class="p">,</span> <span class="mi">59</span><span class="p">,</span> <span class="mi">43</span><span class="p">,</span> <span class="mi">56</span><span class="p">,</span> <span class="mi">38</span><span class="p">,</span> <span class="mi">60</span><span class="p">]</span>
 </pre></div>
 
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+     </div>
+</div>
 </div>
 </div>
 
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+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ages</span><span class="p">)</span>
 </pre></div>
 
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 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -13831,33 +14762,37 @@ a.anchor-link {
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+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_ages</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ages</span><span class="p">)</span>
 <span class="n">df_ages</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
-<div class="output">
+<div class="jp-Cell-outputWrapper">
 
 
-<div class="output_area">
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt output_prompt">Out[7]:</div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[7]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -13900,78 +14835,84 @@ a.anchor-link {
 </div>
 
 </div>
+
 </div>
 
 </div></div></section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <ul>
 <li>Let's add names to ages; put everything into a <code>dict()</code></li>
 </ul>
 
 </div>
-</div>
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[8]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[8]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s2">&quot;Names&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Liu&quot;</span><span class="p">,</span> <span class="s2">&quot;Rowland&quot;</span><span class="p">,</span> <span class="s2">&quot;Rivers&quot;</span><span class="p">,</span> <span class="s2">&quot;Waters&quot;</span><span class="p">,</span> <span class="s2">&quot;Rice&quot;</span><span class="p">,</span> <span class="s2">&quot;Fields&quot;</span><span class="p">,</span> <span class="s2">&quot;Kerr&quot;</span><span class="p">,</span> <span class="s2">&quot;Romero&quot;</span><span class="p">,</span> <span class="s2">&quot;Davis&quot;</span><span class="p">,</span> <span class="s2">&quot;Hall&quot;</span><span class="p">],</span>
-    <span class="s2">&quot;Ages&quot;</span><span class="p">:</span> <span class="n">ages</span>
+    <span class="s2">&quot;Name&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Liu&quot;</span><span class="p">,</span> <span class="s2">&quot;Rowland&quot;</span><span class="p">,</span> <span class="s2">&quot;Rivers&quot;</span><span class="p">,</span> <span class="s2">&quot;Waters&quot;</span><span class="p">,</span> <span class="s2">&quot;Rice&quot;</span><span class="p">,</span> <span class="s2">&quot;Fields&quot;</span><span class="p">,</span> <span class="s2">&quot;Kerr&quot;</span><span class="p">,</span> <span class="s2">&quot;Romero&quot;</span><span class="p">,</span> <span class="s2">&quot;Davis&quot;</span><span class="p">,</span> <span class="s2">&quot;Hall&quot;</span><span class="p">],</span>
+    <span class="s2">&quot;Age&quot;</span><span class="p">:</span> <span class="n">ages</span>
 <span class="p">}</span>
 <span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
-<div class="output">
+<div class="jp-Cell-outputWrapper">
 
 
-<div class="output_area">
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt"></div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 
 
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>{&#39;Names&#39;: [&#39;Liu&#39;, &#39;Rowland&#39;, &#39;Rivers&#39;, &#39;Waters&#39;, &#39;Rice&#39;, &#39;Fields&#39;, &#39;Kerr&#39;, &#39;Romero&#39;, &#39;Davis&#39;, &#39;Hall&#39;], &#39;Ages&#39;: [41, 56, 56, 57, 39, 59, 43, 56, 38, 60]}
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>{&#39;Name&#39;: [&#39;Liu&#39;, &#39;Rowland&#39;, &#39;Rivers&#39;, &#39;Waters&#39;, &#39;Rice&#39;, &#39;Fields&#39;, &#39;Kerr&#39;, &#39;Romero&#39;, &#39;Davis&#39;, &#39;Hall&#39;], &#39;Age&#39;: [41, 56, 56, 57, 39, 59, 43, 56, 38, 60]}
 </pre>
 </div>
 </div>
 
 </div>
+
 </div>
 
-</div></div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[9]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_sample</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
 <span class="n">df_sample</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[9]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[9]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -13990,8 +14931,8 @@ a.anchor-link {
   <thead>
     <tr style="text-align: right;">
       <th></th>
-      <th>Names</th>
-      <th>Ages</th>
+      <th>Name</th>
+      <th>Age</th>
     </tr>
   </thead>
   <tbody>
@@ -14023,130 +14964,138 @@ a.anchor-link {
 </div>
 
 </div>
+
 </div>
 
 </div></div><div class="fragment">
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <ul>
+<li>Automatically creates columns from dictionary</li>
 <li>Two columns now; one for names, one for ages</li>
 </ul>
 
 </div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[10]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[10]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_sample</span><span class="o">.</span><span class="n">columns</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[10]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[10]:</div>
 
 
 
 
-<div class="output_text output_subarea output_execute_result">
-<pre>Index([&#39;Names&#39;, &#39;Ages&#39;], dtype=&#39;object&#39;)</pre>
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>Index([&#39;Name&#39;, &#39;Age&#39;], dtype=&#39;object&#39;)</pre>
 </div>
 
 </div>
 
 </div>
+
 </div>
 
 </div></div></section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <ul>
+<li>First column is <em>index</em></li>
 <li><code>DataFrame</code> always have indexes; auto-generated or custom</li>
 </ul>
 
 </div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[11]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[11]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_sample</span><span class="o">.</span><span class="n">index</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[11]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[11]:</div>
 
 
 
 
-<div class="output_text output_subarea output_execute_result">
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
 <pre>RangeIndex(start=0, stop=10, step=1)</pre>
 </div>
 
 </div>
 
 </div>
+
 </div>
 
 </div><div class="fragment">
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <ul>
-<li>Make <code>Names</code> be index with <code>.set_index()</code></li>
+<li>Make <code>Name</code> be index with <code>.set_index()</code></li>
 <li><code>inplace=True</code> will modifiy the parent frame (<em>I don't like it</em>)</li>
 </ul>
 
 </div>
-</div>
-</div></div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[12]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_sample</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;Names&quot;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[12]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_sample</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;Name&quot;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="n">df_sample</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[12]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[12]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -14165,10 +15114,10 @@ a.anchor-link {
   <thead>
     <tr style="text-align: right;">
       <th></th>
-      <th>Ages</th>
+      <th>Age</th>
     </tr>
     <tr>
-      <th>Names</th>
+      <th>Name</th>
       <th></th>
     </tr>
   </thead>
@@ -14221,42 +15170,44 @@ a.anchor-link {
 </div>
 
 </div>
+
 </div>
 
 </div></div></section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <ul>
 <li>Some more operations</li>
 </ul>
 
 </div>
-</div>
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[13]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[13]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_sample</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[13]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[13]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -14275,7 +15226,7 @@ a.anchor-link {
   <thead>
     <tr style="text-align: right;">
       <th></th>
-      <th>Ages</th>
+      <th>Age</th>
     </tr>
   </thead>
   <tbody>
@@ -14319,32 +15270,36 @@ a.anchor-link {
 </div>
 
 </div>
+
 </div>
 
-</div></div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[14]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[14]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_sample</span><span class="o">.</span><span class="n">T</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
-<div class="output">
+<div class="jp-Cell-outputWrapper">
 
 
-<div class="output_area">
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt output_prompt">Out[14]:</div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[14]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -14362,7 +15317,7 @@ a.anchor-link {
 <table border="1" class="dataframe">
   <thead>
     <tr style="text-align: right;">
-      <th>Names</th>
+      <th>Name</th>
       <th>Liu</th>
       <th>Rowland</th>
       <th>Rivers</th>
@@ -14377,7 +15332,7 @@ a.anchor-link {
   </thead>
   <tbody>
     <tr>
-      <th>Ages</th>
+      <th>Age</th>
       <td>41</td>
       <td>56</td>
       <td>56</td>
@@ -14397,77 +15352,83 @@ a.anchor-link {
 </div>
 
 </div>
+
 </div>
 
-</div></div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[15]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[15]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_sample</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">columns</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[15]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[15]:</div>
 
 
 
 
-<div class="output_text output_subarea output_execute_result">
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
 <pre>Index([&#39;Liu&#39;, &#39;Rowland&#39;, &#39;Rivers&#39;, &#39;Waters&#39;, &#39;Rice&#39;, &#39;Fields&#39;, &#39;Kerr&#39;,
        &#39;Romero&#39;, &#39;Davis&#39;, &#39;Hall&#39;],
-      dtype=&#39;object&#39;, name=&#39;Names&#39;)</pre>
+      dtype=&#39;object&#39;, name=&#39;Name&#39;)</pre>
 </div>
 
 </div>
 
 </div>
+
 </div>
 
 </div></div></section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <ul>
 <li>Also: Arithmetic operations</li>
 </ul>
 
 </div>
-</div>
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[16]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[16]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_sample</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[16]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[16]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -14486,10 +15447,10 @@ a.anchor-link {
   <thead>
     <tr style="text-align: right;">
       <th></th>
-      <th>Ages</th>
+      <th>Age</th>
     </tr>
     <tr>
-      <th>Names</th>
+      <th>Name</th>
       <th></th>
     </tr>
   </thead>
@@ -14514,32 +15475,36 @@ a.anchor-link {
 </div>
 
 </div>
+
 </div>
 
-</div></div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[17]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[17]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_sample</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
-<div class="output">
+<div class="jp-Cell-outputWrapper">
 
 
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+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt output_prompt">Out[17]:</div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[17]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -14558,8 +15523,8 @@ a.anchor-link {
   <thead>
     <tr style="text-align: right;">
       <th></th>
-      <th>Names</th>
-      <th>Ages</th>
+      <th>Name</th>
+      <th>Age</th>
     </tr>
   </thead>
   <tbody>
@@ -14586,32 +15551,36 @@ a.anchor-link {
 </div>
 
 </div>
+
 </div>
 
-</div></div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[18]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[18]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="p">(</span><span class="n">df_sample</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[18]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[18]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -14630,10 +15599,10 @@ a.anchor-link {
   <thead>
     <tr style="text-align: right;">
       <th></th>
-      <th>Ages</th>
+      <th>Age</th>
     </tr>
     <tr>
-      <th>Names</th>
+      <th>Name</th>
       <th></th>
     </tr>
   </thead>
@@ -14658,32 +15627,36 @@ a.anchor-link {
 </div>
 
 </div>
+
 </div>
 
-</div></div></section><section>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[19]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[19]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="p">(</span><span class="n">df_sample</span> <span class="o">*</span> <span class="n">df_sample</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
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+<div class="jp-Cell-outputWrapper">
 
 
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+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt output_prompt">Out[19]:</div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[19]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -14702,10 +15675,10 @@ a.anchor-link {
   <thead>
     <tr style="text-align: right;">
       <th></th>
-      <th>Ages</th>
+      <th>Age</th>
     </tr>
     <tr>
-      <th>Names</th>
+      <th>Name</th>
       <th></th>
     </tr>
   </thead>
@@ -14730,40 +15703,42 @@ a.anchor-link {
 </div>
 
 </div>
+
 </div>
 
 </div><div class="fragment">
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p>Logical operations allowed as well</p>
 
 </div>
-</div>
-</div></div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[20]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[20]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_sample</span> <span class="o">&gt;</span> <span class="mi">40</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
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+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt output_prompt">Out[20]:</div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[20]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -14782,10 +15757,10 @@ a.anchor-link {
   <thead>
     <tr style="text-align: right;">
       <th></th>
-      <th>Ages</th>
+      <th>Age</th>
     </tr>
     <tr>
-      <th>Names</th>
+      <th>Name</th>
       <th></th>
     </tr>
   </thead>
@@ -14838,32 +15813,32 @@ a.anchor-link {
 </div>
 
 </div>
+
 </div>
 
 </div></div></section></section><section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Task-1">Task 1<a class="anchor-link" href="#Task-1">&#182;</a></h2><p><a name="task1"></a></p>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="Task-1">Task 1<a class="anchor-link" href="#Task-1">&#182;</a></h2><p><a name="task1"></a>
+<span style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</em></span></p>
 <ul>
 <li>Create data frame with<ul>
-<li>10 names of dinosaurs, </li>
+<li>6 names of dinosaurs, </li>
 <li>their favourite prime number, </li>
-<li>and their favourite color</li>
+<li>and their favorite color.</li>
 </ul>
 </li>
 <li>Play around with the frame</li>
-<li>Tell me on poll when you're done: <a href="https://pollev.com/aherten538">pollev.com/aherten538</a></li>
+<li>Tell me when you're done with status icon in BigBlueButton: 👍</li>
 </ul>
 
 </div>
-</div>
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[21]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[21]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">happy_dinos</span> <span class="o">=</span> <span class="p">{</span>
     <span class="s2">&quot;Dinosaur Name&quot;</span><span class="p">:</span> <span class="p">[],</span>
     <span class="s2">&quot;Favourite Prime&quot;</span><span class="p">:</span> <span class="p">[],</span>
@@ -14872,16 +15847,17 @@ a.anchor-link {
 <span class="c1">#df_dinos = </span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-</div></div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[22]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[22]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">happy_dinos</span> <span class="o">=</span> <span class="p">{</span>
     <span class="s2">&quot;Dinosaur Name&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;Aegyptosaurus&quot;</span><span class="p">,</span> <span class="s2">&quot;Tyrannosaurus&quot;</span><span class="p">,</span> <span class="s2">&quot;Panoplosaurus&quot;</span><span class="p">,</span> <span class="s2">&quot;Isisaurus&quot;</span><span class="p">,</span> <span class="s2">&quot;Triceratops&quot;</span><span class="p">,</span> <span class="s2">&quot;Velociraptor&quot;</span><span class="p">],</span>
     <span class="s2">&quot;Favourite Prime&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;4&quot;</span><span class="p">,</span> <span class="s2">&quot;8&quot;</span><span class="p">,</span> <span class="s2">&quot;15&quot;</span><span class="p">,</span> <span class="s2">&quot;16&quot;</span><span class="p">,</span> <span class="s2">&quot;23&quot;</span><span class="p">,</span> <span class="s2">&quot;42&quot;</span><span class="p">],</span>
@@ -14891,21 +15867,24 @@ a.anchor-link {
 <span class="n">df_dinos</span><span class="o">.</span><span class="n">T</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
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+<div class="jp-Cell-outputWrapper">
 
 
-<div class="output_area">
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt output_prompt">Out[22]:</div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[22]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -14959,22 +15938,21 @@ a.anchor-link {
 </div>
 
 </div>
+
 </div>
 
 </div></div></section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p>Some more <code>DataFrame</code> examples</p>
 
 </div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[24]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[24]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
     <span class="s2">&quot;A&quot;</span><span class="p">:</span> <span class="mf">1.2</span><span class="p">,</span>
     <span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s1">&#39;20180226&#39;</span><span class="p">),</span>
@@ -14985,21 +15963,24 @@ a.anchor-link {
 <span class="n">df_demo</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
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+<div class="jp-Cell-outputWrapper">
 
 
-<div class="output_area">
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt output_prompt">Out[24]:</div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[24]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -15074,32 +16055,36 @@ a.anchor-link {
 </div>
 
 </div>
+
 </div>
 
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[25]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[25]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s2">&quot;C&quot;</span><span class="p">)</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[25]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[25]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -15174,32 +16159,36 @@ a.anchor-link {
 </div>
 
 </div>
+
 </div>
 
-</div></div></section><section>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[26]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[26]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
-<div class="output">
+<div class="jp-Cell-outputWrapper">
 
 
-<div class="output_area">
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt output_prompt">Out[26]:</div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[26]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -15250,68 +16239,75 @@ a.anchor-link {
 </div>
 
 </div>
+
 </div>
 
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[27]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[27]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[27]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[27]:</div>
 
 
 
 
-<div class="output_text output_subarea output_execute_result">
-<pre>A                              6
-C                          -2.03
-D    Thiscolumnhasentriesentries
-E           SameSameSameSameSame
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>A                     6.0
+C                   -2.03
+E    SameSameSameSameSame
 dtype: object</pre>
 </div>
 
 </div>
 
 </div>
+
 </div>
 
-</div></div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[28]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[28]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">df_demo</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">to_latex</span><span class="p">())</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
-<div class="output">
+<div class="jp-Cell-outputWrapper">
 
 
-<div class="output_area">
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt"></div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 
 
-<div class="output_subarea output_stream output_stdout output_text">
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>\begin{tabular}{lrlrll}
 \toprule
 {} &amp;    A &amp;          B &amp;     C &amp;        D &amp;     E \\
@@ -15329,12 +16325,12 @@ dtype: object</pre>
 </div>
 
 </div>
+
 </div>
 
 </div></div></section></section><section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <h2 id="Reading-External-Data">Reading External Data<a class="anchor-link" href="#Reading-External-Data">&#182;</a></h2><p>(Links to documentation)</p>
 <ul>
 <li><a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_json.html#pandas.read_json"><code>.read_json()</code></a></li>
@@ -15351,31 +16347,33 @@ dtype: object</pre>
 </pre></div>
 
 </div>
-</div>
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[29]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="s2">&quot;lost.json&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;Character&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span>
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[117]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="s2">&quot;data-lost.json&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;Character&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
-<div class="output">
+<div class="jp-Cell-outputWrapper">
 
 
-<div class="output_area">
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt output_prompt">Out[29]:</div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[117]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -15442,45 +16440,48 @@ dtype: object</pre>
 </div>
 
 </div>
+
 </div>
 
 </div></div></section></section><section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Task-2">Task 2<a class="anchor-link" href="#Task-2">&#182;</a></h2><p><a name="task2"></a></p>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="Task-2">Task 2<a class="anchor-link" href="#Task-2">&#182;</a></h2><p><a name="task2"></a>
+<span style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</em></span></p>
 <ul>
-<li>Read in <code>nest-data.csv</code> to <code>DataFrame</code>; call it <code>df</code><br>
-<em>Data was produced with <a href="http://www.fz-juelich.de/ias/jsc/EN/Expertise/Support/Software/JUBE/_node.html">JUBE</a>, Pandas works <strong>very</strong> well together with JUBE</em></li>
+<li>Read in <code>data-nest.csv</code> to <code>DataFrame</code>; call it <code>df</code><br>
+<em>(Data was produced with <a href="http://www.fz-juelich.de/ias/jsc/EN/Expertise/Support/Software/JUBE/_node.html">JUBE</a>)</em></li>
 <li>Get to know it and play a bit with it</li>
-<li>Tell me when you're done: <a href="https://pollev.com/aherten538">pollev.com/aherten538</a></li>
+<li>Tell me when you're done with status icon in BigBlueButton: 👍</li>
 </ul>
 
 </div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[30]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[30]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>cat nest-data.csv <span class="p">|</span> head -3
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
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+<div class="jp-Cell-outputWrapper">
 
 
-<div class="output_area">
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt"></div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 
 
-<div class="output_subarea output_stream output_stdout output_text">
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>id,Nodes,Tasks/Node,Threads/Task,Runtime Program / s,Scale,Plastic,Avg. Neuron Build Time / s,Min. Edge Build Time / s,Max. Edge Build Time / s,Min. Init. Time / s,Max. Init. Time / s,Presim. Time / s,Sim. Time / s,Virt. Memory (Sum) / kB,Local Spike Counter (Sum),Average Rate (Sum),Number of Neurons,Number of Connections,Min. Delay,Max. Delay
 5,1,2,4,420.42,10,true,0.29,88.12,88.18,1.14,1.20,17.26,311.52,46560664.00,825499,7.48,112500,1265738500,1.5,1.5
 5,1,4,4,200.84,10,true,0.15,46.03,46.34,0.70,1.01,7.87,142.97,46903088.00,802865,7.03,112500,1265738500,1.5,1.5
@@ -15489,33 +16490,37 @@ dtype: object</pre>
 </div>
 
 </div>
+
 </div>
 
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[31]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;nest-data.csv&quot;</span><span class="p">)</span>
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[118]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;data-nest.csv&quot;</span><span class="p">)</span>
 <span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[31]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[118]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -15687,12 +16692,12 @@ dtype: object</pre>
 </div>
 
 </div>
+
 </div>
 
 </div></div></section></section><section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <h2 id="Read-CSV-Options">Read CSV Options<a class="anchor-link" href="#Read-CSV-Options">&#182;</a></h2><ul>
 <li>See also full <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html">API documentation</a></li>
 <li>Important parameters<ul>
@@ -15708,50 +16713,53 @@ dtype: object</pre>
 </ul>
 </li>
 </ul>
-<div class="highlight"><pre><span></span><span class="n">pandas</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">filepath_or_buffer</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">&#39;, &#39;</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="s1">&#39;infer&#39;</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">squeeze</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">mangle_dupe_cols</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">converters</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">true_values</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">false_values</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">skipinitialspace</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">skiprows</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">skipfooter</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">nrows</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">na_values</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">keep_default_na</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">na_filter</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">skip_blank_lines</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">parse_dates</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">infer_datetime_format</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">keep_date_col</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">date_parser</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">dayfirst</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">iterator</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">chunksize</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">compression</span><span class="o">=</span><span class="s1">&#39;infer&#39;</span><span class="p">,</span> <span class="n">thousands</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">decimal</span><span class="o">=</span><span class="sa">b</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">lineterminator</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">quotechar</span><span class="o">=</span><span class="s1">&#39;&quot;&#39;</span><span class="p">,</span> <span class="n">quoting</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">doublequote</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">escapechar</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">comment</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">dialect</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">tupleize_cols</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">error_bad_lines</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">warn_bad_lines</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">delim_whitespace</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">low_memory</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">memory_map</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">float_precision</span><span class="o">=</span><span class="bp">None</span><span class="p">)</span>
+<div class="highlight"><pre><span></span><span class="n">pandas</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">filepath_or_buffer</span><span class="p">,</span> <span class="n">sep</span><span class="o">=&lt;</span><span class="nb">object</span> <span class="nb">object</span><span class="o">&gt;</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="s1">&#39;infer&#39;</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">squeeze</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mangle_dupe_cols</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">converters</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">true_values</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">false_values</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">skipinitialspace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">skiprows</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">skipfooter</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">nrows</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">na_values</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">keep_default_na</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">na_filter</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">skip_blank_lines</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">parse_dates</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">infer_datetime_format</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">keep_date_col</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">date_parser</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dayfirst</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">cache_dates</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">iterator</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">chunksize</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">compression</span><span class="o">=</span><span class="s1">&#39;infer&#39;</span><span class="p">,</span> <span class="n">thousands</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">decimal</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">lineterminator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">quotechar</span><span class="o">=</span><span class="s1">&#39;&quot;&#39;</span><span class="p">,</span> <span class="n">quoting</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">doublequote</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">escapechar</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">comment</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dialect</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">error_bad_lines</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">warn_bad_lines</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">delim_whitespace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">low_memory</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">memory_map</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">float_precision</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">storage_options</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
 </pre></div>
 
-</div>
 </div>
 </div></section></section><section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Slicing-of-Data-Frames">Slicing of Data Frames<a class="anchor-link" href="#Slicing-of-Data-Frames">&#182;</a></h2><h3 id="Slicing-Columns">Slicing Columns<a class="anchor-link" href="#Slicing-Columns">&#182;</a></h3><ul>
-<li>Use square-bracket operators to slice data frame: <code>[]</code><ul>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="Slicing-of-Data-Frames">Slicing of Data Frames<a class="anchor-link" href="#Slicing-of-Data-Frames">&#182;</a></h2><ul>
+<li>Pandas documentation: <a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html">Detailed documentation</a>, <a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html#selection">short documentation</a></li>
+</ul>
+<h3 id="Quick-Slices">Quick Slices<a class="anchor-link" href="#Quick-Slices">&#182;</a></h3><ul>
+<li>Use square-bracket operators to slice data frame quickly: <code>[]</code><ul>
 <li>Use column name to select column</li>
-<li>Also: Slice horizontally</li>
+<li>Use numerical value to select row</li>
 </ul>
 </li>
 <li>Example: Select only columnn <code>C</code> from <code>df_demo</code></li>
 </ul>
 
 </div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[32]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[32]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[32]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[32]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -15810,33 +16818,79 @@ dtype: object</pre>
 </div>
 
 </div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[33]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="s1">&#39;C&#39;</span><span class="p">]</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[33]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>0   -2.718282
+1    1.718282
+2   -1.304068
+3    0.986231
+4   -0.718282
+Name: C, dtype: float64</pre>
+</div>
+
+</div>
+
 </div>
 
 </div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[33]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span>
+
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[34]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">C</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[33]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[34]:</div>
 
 
 
 
-<div class="output_text output_subarea output_execute_result">
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
 <pre>0   -2.718282
 1    1.718282
 2   -1.304068
@@ -15848,44 +16902,46 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
-</div></section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+</div></div></section><section>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <ul>
-<li>Select more than one column by providing list <code>[]</code> to slice operator <code>[]</code></li>
-<li><em>You usually end up forgetting one of the brackets…</em></li>
-<li>Example: Select list of columns <code>A</code> and <code>C</code>, <code>["A", "C"]</code> from <code>df_demo</code></li>
+<li>Select more than one column by providing <code>list</code> to slice operator <code>[]</code></li>
+<li>Example: Select list of columns <code>A</code> and <code>C</code>, <code>['A', 'C']</code> from <code>df_demo</code></li>
 </ul>
 
 </div>
-</div>
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[34]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">]]</span>
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[35]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">my_slice</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">]</span>
+<span class="n">df_demo</span><span class="p">[</span><span class="n">my_slice</span><span class="p">]</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[34]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[35]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -15942,43 +16998,45 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
 </div></div></section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Slicing-of-Data-Frames">Slicing of Data Frames<a class="anchor-link" href="#Slicing-of-Data-Frames">&#182;</a></h2><h3 id="Slicing-rows">Slicing rows<a class="anchor-link" href="#Slicing-rows">&#182;</a></h3><ul>
-<li>Use numberical values to slice into rows</li>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<ul>
+<li>Use numerical values in brackets to slice along rows</li>
 <li>Use ranges just like with Python lists</li>
 </ul>
 
 </div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[35]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[36]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
-<div class="output">
+<div class="jp-Cell-outputWrapper">
 
 
-<div class="output_area">
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt output_prompt">Out[35]:</div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[36]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -16029,42 +17087,124 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
-</div><div class="fragment">
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<ul>
-<li>Get a certain range as <strong>per the current sort structure</strong></li>
-</ul>
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[37]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">6</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
+</pre></div>
 
+     </div>
 </div>
 </div>
 </div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[36]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
+
+<div class="jp-Cell-outputWrapper">
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[37]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+      <th>E</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>1</th>
+      <td>1.2</td>
+      <td>2018-02-26</td>
+      <td>1.718282</td>
+      <td>column</td>
+      <td>Same</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>1.2</td>
+      <td>2018-02-26</td>
+      <td>0.986231</td>
+      <td>entries</td>
+      <td>Same</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div></div></section><section>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<ul>
+<li>Attention: location might change after re-sorting!</li>
+</ul>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[38]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[36]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[38]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -16115,32 +17255,124 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[39]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s2">&quot;C&quot;</span><span class="p">)[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[39]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>B</th>
+      <th>C</th>
+      <th>D</th>
+      <th>E</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2</th>
+      <td>1.2</td>
+      <td>2018-02-26</td>
+      <td>-1.304068</td>
+      <td>has</td>
+      <td>Same</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>1.2</td>
+      <td>2018-02-26</td>
+      <td>-0.718282</td>
+      <td>entries</td>
+      <td>Same</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
 </div>
 
+</div></section><section>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="Slicing-of-Data-Frames">Slicing of Data Frames<a class="anchor-link" href="#Slicing-of-Data-Frames">&#182;</a></h2><h3 id="Better-Slicing">Better Slicing<a class="anchor-link" href="#Better-Slicing">&#182;</a></h3><ul>
+<li><code>.iloc[]</code> and <code>.loc[]</code>: Faster slicing interfaces with more options</li>
+</ul>
+
 </div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[37]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">6</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[40]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
-<div class="output">
+<div class="jp-Cell-outputWrapper">
 
 
-<div class="output_area">
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt output_prompt">Out[37]:</div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[40]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -16176,11 +17408,11 @@ Name: C, dtype: float64</pre>
       <td>Same</td>
     </tr>
     <tr>
-      <th>3</th>
+      <th>2</th>
       <td>1.2</td>
       <td>2018-02-26</td>
-      <td>0.986231</td>
-      <td>entries</td>
+      <td>-1.304068</td>
+      <td>has</td>
       <td>Same</td>
     </tr>
   </tbody>
@@ -16191,42 +17423,44 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
-</div></div></section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+</div><div class="fragment">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <ul>
-<li>Attention: <code>.iloc[]</code> location might change after re-sorting!</li>
+<li>Also slice rows (second argument)</li>
 </ul>
 
 </div>
-</div>
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[38]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s2">&quot;C&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[41]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]]</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
-<div class="output">
+<div class="jp-Cell-outputWrapper">
 
 
-<div class="output_area">
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt output_prompt">Out[38]:</div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[41]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -16246,28 +17480,19 @@ Name: C, dtype: float64</pre>
     <tr style="text-align: right;">
       <th></th>
       <th>A</th>
-      <th>B</th>
       <th>C</th>
-      <th>D</th>
-      <th>E</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <th>2</th>
+      <th>1</th>
       <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-1.304068</td>
-      <td>has</td>
-      <td>Same</td>
+      <td>1.718282</td>
     </tr>
     <tr>
-      <th>4</th>
+      <th>2</th>
       <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-0.718282</td>
-      <td>entries</td>
-      <td>Same</td>
+      <td>-1.304068</td>
     </tr>
   </tbody>
 </table>
@@ -16277,44 +17502,47 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
 </div></div></section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <ul>
-<li>One more row-slicing option: <code>.loc[]</code></li>
-<li>See the difference with a <em>proper</em> index (and not the auto-generated default index from before)</li>
+<li><code>.iloc[]</code>: Slice by <strong>position</strong> (<em>numerical/integer</em>)</li>
+<li><code>.loc[]</code>: Slice by <strong>label</strong> (<em>named</em>)</li>
+<li>See difference with a <em>proper</em> index (and not the auto-generated default index from before)</li>
 </ul>
 
 </div>
-</div>
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[39]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[42]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo_indexed</span> <span class="o">=</span> <span class="n">df_demo</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;D&quot;</span><span class="p">)</span>
 <span class="n">df_demo_indexed</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[39]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[42]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -16389,33 +17617,37 @@ Name: C, dtype: float64</pre>
 
 </div>
 
-</div>
 </div>
 
 </div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[40]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[43]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo_indexed</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;entries&quot;</span><span class="p">]</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
-<div class="output">
+<div class="jp-Cell-outputWrapper">
 
 
-<div class="output_area">
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt output_prompt">Out[40]:</div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[43]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -16470,39 +17702,130 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
+</div>
+
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[44]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo_indexed</span><span class="o">.</span><span class="n">loc</span><span class="p">[[</span><span class="s2">&quot;has&quot;</span><span class="p">,</span> <span class="s2">&quot;entries&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">]]</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[44]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>A</th>
+      <th>C</th>
+    </tr>
+    <tr>
+      <th>D</th>
+      <th></th>
+      <th></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>has</th>
+      <td>1.2</td>
+      <td>-1.304068</td>
+    </tr>
+    <tr>
+      <th>entries</th>
+      <td>1.2</td>
+      <td>0.986231</td>
+    </tr>
+    <tr>
+      <th>entries</th>
+      <td>1.2</td>
+      <td>-0.718282</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+
 </div>
 
 </div></div></section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="Advanced-Slicing:-Logical-Slicing">Advanced Slicing: Logical Slicing<a class="anchor-link" href="#Advanced-Slicing:-Logical-Slicing">&#182;</a></h3>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="Slicing-of-Data-Frames">Slicing of Data Frames<a class="anchor-link" href="#Slicing-of-Data-Frames">&#182;</a></h2><h3 id="Advanced-Slicing:-Logical-Slicing">Advanced Slicing: Logical Slicing<a class="anchor-link" href="#Advanced-Slicing:-Logical-Slicing">&#182;</a></h3>
 </div>
 </div>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<ul>
+<li>Slice can also be array of booleans</li>
+</ul>
+
 </div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[41]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[45]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">]</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[41]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[45]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -16552,33 +17875,79 @@ Name: C, dtype: float64</pre>
 
 </div>
 
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[46]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span>
+</pre></div>
+
+     </div>
+</div>
 </div>
 </div>
 
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[42]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+<div class="jp-Cell-outputWrapper">
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[46]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>0    False
+1     True
+2    False
+3     True
+4    False
+Name: C, dtype: bool</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[47]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[(</span><span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;D&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;entries&quot;</span><span class="p">)]</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[42]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[47]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -16621,12 +17990,12 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
 </div></div></section></section><section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <h2 id="Adding-to-Existing-Data-Frame">Adding to Existing Data Frame<a class="anchor-link" href="#Adding-to-Existing-Data-Frame">&#182;</a></h2><ul>
 <li>Add new columns with <code>frame["new col"] = something</code> or <code>.insert()</code></li>
 <li>Add new rows with <code>frame.append()</code></li>
@@ -16640,31 +18009,33 @@ Name: C, dtype: float64</pre>
 </ul>
 
 </div>
-</div>
-</div><div class="fragment">
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-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[43]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[48]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
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+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[48]:</div>
 
 
 
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+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
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@@ -16723,33 +18094,37 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
-</div></div></section><section>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[44]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[49]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;F&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">]</span>
 <span class="n">df_demo</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
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+<div class="jp-Cell-outputWrapper">
 
 
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+<div class="jp-OutputArea jp-Cell-outputArea">
 
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@@ -16811,46 +18186,51 @@ Name: C, dtype: float64</pre>
 
 </div>
 
-</div>
 </div>
 
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-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[45]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">df_demo</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;G&quot;</span><span class="p">,</span> <span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[50]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">df_demo</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;E2&quot;</span><span class="p">,</span> <span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-</div></section><section>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[46]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[51]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
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+<div class="jp-OutputArea-child">
 
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 <div>
 <style scoped>
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@@ -16874,8 +18254,8 @@ Name: C, dtype: float64</pre>
       <th>C</th>
       <th>D</th>
       <th>E</th>
+      <th>E2</th>
       <th>F</th>
-      <th>G</th>
     </tr>
   </thead>
   <tbody>
@@ -16886,8 +18266,8 @@ Name: C, dtype: float64</pre>
       <td>-1.304068</td>
       <td>has</td>
       <td>Same</td>
-      <td>-2.504068</td>
       <td>1.700594</td>
+      <td>-2.504068</td>
     </tr>
     <tr>
       <th>3</th>
@@ -16896,8 +18276,8 @@ Name: C, dtype: float64</pre>
       <td>0.986231</td>
       <td>entries</td>
       <td>Same</td>
-      <td>-0.213769</td>
       <td>0.972652</td>
+      <td>-0.213769</td>
     </tr>
     <tr>
       <th>4</th>
@@ -16906,8 +18286,8 @@ Name: C, dtype: float64</pre>
       <td>-0.718282</td>
       <td>entries</td>
       <td>Same</td>
-      <td>-1.918282</td>
       <td>0.515929</td>
+      <td>-1.918282</td>
     </tr>
   </tbody>
 </table>
@@ -16917,35 +18297,39 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[47]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[52]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
     <span class="p">{</span><span class="s2">&quot;A&quot;</span><span class="p">:</span> <span class="mf">1.3</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="p">(</span><span class="s2">&quot;2018-02-27&quot;</span><span class="p">),</span> <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">0.777</span><span class="p">,</span> <span class="s2">&quot;D&quot;</span><span class="p">:</span> <span class="s2">&quot;has it?&quot;</span><span class="p">,</span> <span class="s2">&quot;E&quot;</span><span class="p">:</span> <span class="s2">&quot;Same&quot;</span><span class="p">,</span> <span class="s2">&quot;F&quot;</span><span class="p">:</span> <span class="mi">23</span><span class="p">},</span>
     <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span>
 <span class="p">)</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
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+<div class="jp-Cell-outputWrapper">
 
 
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+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt output_prompt">Out[47]:</div>
+<div class="jp-OutputArea-child">
+
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 <div>
 <style scoped>
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@@ -16969,8 +18353,8 @@ Name: C, dtype: float64</pre>
       <th>C</th>
       <th>D</th>
       <th>E</th>
+      <th>E2</th>
       <th>F</th>
-      <th>G</th>
     </tr>
   </thead>
   <tbody>
@@ -16981,8 +18365,8 @@ Name: C, dtype: float64</pre>
       <td>-2.718282</td>
       <td>This</td>
       <td>Same</td>
-      <td>-3.918282</td>
       <td>7.389056</td>
+      <td>-3.918282</td>
     </tr>
     <tr>
       <th>1</th>
@@ -16991,8 +18375,8 @@ Name: C, dtype: float64</pre>
       <td>1.718282</td>
       <td>column</td>
       <td>Same</td>
-      <td>0.518282</td>
       <td>2.952492</td>
+      <td>0.518282</td>
     </tr>
     <tr>
       <th>2</th>
@@ -17001,8 +18385,8 @@ Name: C, dtype: float64</pre>
       <td>-1.304068</td>
       <td>has</td>
       <td>Same</td>
-      <td>-2.504068</td>
       <td>1.700594</td>
+      <td>-2.504068</td>
     </tr>
     <tr>
       <th>3</th>
@@ -17011,8 +18395,8 @@ Name: C, dtype: float64</pre>
       <td>0.986231</td>
       <td>entries</td>
       <td>Same</td>
-      <td>-0.213769</td>
       <td>0.972652</td>
+      <td>-0.213769</td>
     </tr>
     <tr>
       <th>4</th>
@@ -17021,8 +18405,8 @@ Name: C, dtype: float64</pre>
       <td>-0.718282</td>
       <td>entries</td>
       <td>Same</td>
-      <td>-1.918282</td>
       <td>0.515929</td>
+      <td>-1.918282</td>
     </tr>
     <tr>
       <th>5</th>
@@ -17031,8 +18415,8 @@ Name: C, dtype: float64</pre>
       <td>-0.777000</td>
       <td>has it?</td>
       <td>Same</td>
-      <td>23.000000</td>
       <td>NaN</td>
+      <td>23.000000</td>
     </tr>
   </tbody>
 </table>
@@ -17042,43 +18426,45 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
 </div></div></section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <h2 id="Combining-Frames">Combining Frames<a class="anchor-link" href="#Combining-Frames">&#182;</a></h2><ul>
 <li>First, create some simpler data frame to show <code>.concat()</code> and <code>.merge()</code></li>
 </ul>
 
 </div>
-</div>
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[48]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[53]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;Key&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;First&quot;</span><span class="p">,</span> <span class="s2">&quot;Second&quot;</span><span class="p">],</span> <span class="s2">&quot;Value&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]})</span>
 <span class="n">df_1</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
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+<div class="jp-Cell-outputWrapper">
 
 
-<div class="output_area">
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt output_prompt">Out[48]:</div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[53]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -17119,34 +18505,38 @@ Name: C, dtype: float64</pre>
 
 </div>
 
-</div>
 </div>
 
 </div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[49]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[54]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;Key&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;First&quot;</span><span class="p">,</span> <span class="s2">&quot;Second&quot;</span><span class="p">],</span> <span class="s2">&quot;Value&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]})</span>
 <span class="n">df_2</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[49]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[54]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -17188,42 +18578,44 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
 </div></div></section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <ul>
 <li>Concatenate list of data frame vertically (<code>axis=0</code>)</li>
 </ul>
 
 </div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[50]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[55]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df_1</span><span class="p">,</span> <span class="n">df_2</span><span class="p">])</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[50]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[55]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -17275,42 +18667,44 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
 </div><div class="fragment">
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <ul>
 <li>Same, but re-index</li>
 </ul>
 
 </div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[51]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[56]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df_1</span><span class="p">,</span> <span class="n">df_2</span><span class="p">],</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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 <ul>
 <li>Concat, but horizontally</li>
 </ul>
 
 </div>
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-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[52]:</div>
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+<div class="jp-Cell-inputWrapper">
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[57]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df_1</span><span class="p">,</span> <span class="n">df_2</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 </pre></div>
 
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <ul>
 <li>Merge on common column</li>
 </ul>
 
 </div>
-</div>
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-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[53]:</div>
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+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[58]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df_1</span><span class="p">,</span> <span class="n">df_2</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;Key&quot;</span><span class="p">)</span>
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-<h2 id="Task-3">Task 3<a class="anchor-link" href="#Task-3">&#182;</a></h2><p><a name="task3"></a></p>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="Task-3">Task 3<a class="anchor-link" href="#Task-3">&#182;</a></h2><p><a name="task3"></a>
+<span style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</em></span></p>
 <ul>
-<li>Add a column to the Nest data frame called <code>Virtual Processes</code> which is the total number of threads across all nodes (i.e. the product of threads per task and tasks per node and nodes)</li>
-<li>Remember to tell me when you're done: <a href="https://pollev.com/aherten538">pollev.com/aherten538</a></li>
+<li>Add a column to the Nest data frame form Task 2 called <code>Threads</code> which is the total number of threads across all nodes (i.e. the product of threads per task and tasks per node and nodes)</li>
+<li>Tell me when you're done with status icon in BigBlueButton: 👍</li>
 </ul>
 
 </div>
-</div>
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;Virtual Processes&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Nodes&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Tasks/Node&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Threads/Task&quot;</span><span class="p">]</span>
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[59]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;Threads&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Nodes&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Tasks/Node&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Threads/Task&quot;</span><span class="p">]</span>
 <span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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       <th>Number of Connections</th>
       <th>Min. Delay</th>
       <th>Max. Delay</th>
-      <th>Virtual Processes</th>
+      <th>Threads</th>
     </tr>
   </thead>
   <tbody>
@@ -17734,53 +19135,57 @@ Name: C, dtype: float64</pre>
 
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-</div>
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+
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+<div class="jp-Cell-inputWrapper">
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[60]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">columns</span>
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 <pre>Index([&#39;id&#39;, &#39;Nodes&#39;, &#39;Tasks/Node&#39;, &#39;Threads/Task&#39;, &#39;Runtime Program / s&#39;,
        &#39;Scale&#39;, &#39;Plastic&#39;, &#39;Avg. Neuron Build Time / s&#39;,
        &#39;Min. Edge Build Time / s&#39;, &#39;Max. Edge Build Time / s&#39;,
        &#39;Min. Init. Time / s&#39;, &#39;Max. Init. Time / s&#39;, &#39;Presim. Time / s&#39;,
        &#39;Sim. Time / s&#39;, &#39;Virt. Memory (Sum) / kB&#39;, &#39;Local Spike Counter (Sum)&#39;,
        &#39;Average Rate (Sum)&#39;, &#39;Number of Neurons&#39;, &#39;Number of Connections&#39;,
-       &#39;Min. Delay&#39;, &#39;Max. Delay&#39;, &#39;Virtual Processes&#39;],
+       &#39;Min. Delay&#39;, &#39;Max. Delay&#39;, &#39;Threads&#39;],
       dtype=&#39;object&#39;)</pre>
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 <h2 id="Aside:-Plotting-without-Pandas">Aside: Plotting without Pandas<a class="anchor-link" href="#Aside:-Plotting-without-Pandas">&#182;</a></h2><h3 id="Matplotlib-101">Matplotlib 101<a class="anchor-link" href="#Matplotlib-101">&#182;</a></h3><ul>
 <li>Matplotlib: de-facto standard for plotting in Python</li>
 <li>Main interface: <code>pyplot</code>; provides MATLAB-like interface</li>
@@ -17791,65 +19196,69 @@ Name: C, dtype: float64</pre>
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-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[56]:</div>
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-    <div class="input_area">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[61]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="o">%</span><span class="k">matplotlib</span> inline
 </pre></div>
 
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-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[57]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[62]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">400</span><span class="p">)</span>
 <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
 </pre></div>
 
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+     </div>
+</div>
 </div>
 </div>
 
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-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[58]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[63]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Use like this&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Numbers again&quot;</span><span class="p">);</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Numbers&quot;</span><span class="p">);</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;$\sqrt</span><span class="si">{x}</span><span class="s2">$&quot;</span><span class="p">);</span>
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
 "
 >
 </div>
@@ -17857,62 +19266,116 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
 </div></div></section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <ul>
 <li>Plot multiple lines into one canvas</li>
 <li>Call <code>ax.plot()</code> multiple times</li>
 </ul>
 
 </div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[64]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y2</span> <span class="o">=</span> <span class="n">y</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">y</span><span class="o">*</span><span class="mf">1.5</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[65]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;y&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;y2&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;This plot makes no sense&quot;</span><span class="p">);</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
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+<img 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
+"
+>
+</div>
+
 </div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[59]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y2</span> <span class="o">=</span> <span class="n">y</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">y</span><span class="o">*</span><span class="mf">1.5</span><span class="p">)</span>
-</pre></div>
 
-    </div>
 </div>
+
 </div>
 
+</div></section><section>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<ul>
+<li>Matplotlib can also plot DataFrame data</li>
+<li>Because DataFrame data is <em>only</em> array-like data with stuff on top</li>
+</ul>
+
 </div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[60]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[66]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;y&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;y2&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df_demo</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;C&quot;</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;This plot makes no sense&quot;</span><span class="p">);</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Nope, no sense at all&quot;</span><span class="p">);</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
-<div class="output">
+<div class="jp-Cell-outputWrapper">
 
 
-<div class="output_area">
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt"></div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 
 
 
 
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-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Task-4">Task 4<a class="anchor-link" href="#Task-4">&#182;</a></h2><p><a name="task4"></a></p>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="Task-4">Task 4<a class="anchor-link" href="#Task-4">&#182;</a></h2><p><a name="task4"></a>
+<span style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</em></span></p>
 <ul>
-<li>Sort the data frame by the virtual proccesses</li>
-<li>Plot <code>"Presim. Time / s"</code> and <code>"Sim. Time / s"</code> of our data frame <code>df</code> as a function of the virtual processes</li>
+<li>Sort the data frame by threads</li>
+<li>Plot <code>"Presim. Time / s"</code> and <code>"Sim. Time / s"</code> of our data frame <code>df</code> as a function of threads</li>
 <li>Use a dashed, red line for <code>"Presim. Time / s"</code>, a blue line for <code>"Sim. Time / s"</code> (see <a href="https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot">API description</a>)</li>
-<li>Don't forget to label your axes and to add a legend</li>
-<li>Submit when you're done: <a href="https://pollev.com/aherten538">pollev.com/aherten538</a></li>
+<li>Don't forget to label your axes and to add a legend <em>(1st rule of plotting)</em></li>
+<li>Tell me when you're done with status icon in BigBlueButton: 👍</li>
 </ul>
 
 </div>
-</div>
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-<div class="prompt input_prompt">In&nbsp;[61]:</div>
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s2">&quot;Virtual Processes&quot;</span><span class="p">,</span> <span class="s2">&quot;Nodes&quot;</span><span class="p">,</span> <span class="s2">&quot;Tasks/Node&quot;</span><span class="p">,</span> <span class="s2">&quot;Threads/Task&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[67]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s2">&quot;Threads&quot;</span><span class="p">,</span> <span class="s2">&quot;Nodes&quot;</span><span class="p">,</span> <span class="s2">&quot;Tasks/Node&quot;</span><span class="p">,</span> <span class="s2">&quot;Threads/Task&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 
-    </div>
+     </div>
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-
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-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[62]:</div>
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[68]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;Virtual Processes&quot;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Presim. Time / s&quot;</span><span class="p">],</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;dashed&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;red&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;Virtual Processes&quot;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Sim. Time / s&quot;</span><span class="p">],</span> <span class="s2">&quot;-b&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;Threads&quot;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Presim. Time / s&quot;</span><span class="p">],</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;dashed&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;red&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Presim. Time / s&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;Threads&quot;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Sim. Time / s&quot;</span><span class="p">],</span> <span class="s2">&quot;-b&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Sim. Time / s&quot;</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Virtual Process&quot;</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Time / s&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">);</span>
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j0RkeSUMIDc3DAIoVoYIiLJKWFEdPOeiEjNlDAiBQVKGCIiNVHCiGgAQhGRmilhRNQlJSJSMyWMSKyF4Z7pSEREspMSRqSgALZuhfXrMx2JiEh2UsKI6OY9EZGaKWFEYglDJ75FRBJTwoiohSEiUjMljIgShohIzZQwIhqxVkSkZkoYkV12gTZtdA5DRCQZJYw4unlPRCS5tCUMM+tuZtPNbIGZfWBm/x2VdzKzV8xscTTvGJWbmd1pZkvMbJ6ZDUhXbMkoYYiIJJfOFkYZcKm79wYOBc43s97AlcBr7t4LeC16DTAC6BVN44G70xhbQkoYIiLJpS1huPsKd58TLX8HLAS6AqOBB6PVHgROipZHAw958E+gg5l1SVd8iWjEWhGR5BrkHIaZFQH9gZnAbu6+Iqr6CtgtWu4KfBH3tmVRWdVtjTezUjMrXVXP3+4asVZEJLm0JwwzyweeAia4+7fxde7uwE4N9+fu97p7ibuXFMZunqgnhYWwcSNs2lSvmxURaRLSmjDMLJeQLB5x96ej4q9jXU3RfGVUvhzoHvf2blFZg9HNeyIiyaXzKikD7gMWuvvtcVXTgLHR8lhgalz52dHVUocC6+O6rhqEbt4TEUmuZRq3fQRwFvC+mc2Nyv4PcBMwxczOAT4DTovqngdGAkuA74F/T2NsCamFISKSXNoShrvPACxJ9eAE6ztwfrriSYVGrBURSU53esdRC0NEJDkljDgdOkBOjhKGiEgiShhxzHTznohIMkoYVejmPRGRxJQwqtB4UiIiie0wYZjZqWbWLlr+lZk9nYmRZBuKEoaISGKptDB+7e7fmdmRwHGEm/EafCTZhqJzGCIiiaWSMMqj+fHAve7+HNAqfSFlVmEhfPMNbN2a6UhERLJLKgljuZndA5wOPG9mrVN8X6MUuxdj7drMxiEikm1S+eI/DXgJGObu64BOwMR0BpVJunlPRCSxHQ4N4u7fA0/HvV4BNOiggA1JCUNEJLEm27VUWxqxVkQkMSWMKjQAoYhIYkkThpm9ZGYXm9l+DRlQpnXuHOZqYYiIVFZTC2Ms8A0wyczmmNndZjbazHZpoNgyIjcXOnZUwhARqSrpSW93/wp4AHjAzFoAhwAjgMvNbBPwsrvf0iBRNjDdvCciUl1KD1By923AO9H0GzMrAIalM7BM0vAgIiLV1eqkt7uvdvdH6juYbKERa0VEqtNVUgmohSEiUp0SRgKxFoZ7piMREckeqQxvvpuZ3WdmL0Sve5vZOekPLXMKCqCsDNaty3QkIiLZI5UWxgOEsaR2j15/BExIUzxZQTfviYhUl0rCKHD3KcA2AHcvo2LI8yZJ40mJiFSXSsLYaGadAQcws0OB9Tt6k5ndb2YrzWx+XNkkM1tuZnOjaWRc3VVmtsTMFplZRi/ZVcIQEakulfswLgGmAXuZ2T+AQuCUFN73AHAX8FCV8jvc/XfxBWbWGxgD9CF0fb1qZvu4e0ZaMhqAUESkulSGN59jZkcD+wIGLHL3HT6Pzt3fNLOiFOMYDTzm7luAT81sCXAw4UbBBqcWhohIdalcJZUDjAQGA0OBC83skjp85gVmNi/qsuoYlXUFvohbZ1lUliie8WZWamalq9L0jd62bZh00ltEpEIq5zD+BowDOgPt4qbauBvYCygmPITptp3dgLvf6+4l7l5SGGsKpIFu3hMRqSyVcxjd3H3/+vgwd/86tmxm/ws8G71cDnSP/8yoLGOUMEREKkulhfGCmQ2tjw8zsy5xL08GYldQTQPGmFlrM+sB9AJm1cdn1tbcufDii5mMQEQku6TSwvgn8NdoiPOthBPf7u671vQmM3sUGAQUmNky4BpgkJkVEy7RXQqcS9jYB2Y2BVgAlAHnZ+oKqZiyskx+uohI9jHfwYBJZvYp4Sqm931HKzewkpISLy0tTcu2f/5zmDEDvvhix+uKiDQmZjbb3Ut29n2pdEl9AczPtmSRbi1bQqtWmY5CRCR7pNIl9QnwejT44JZYobvfnraoREQk66SSMD6NplbRJCIizVAqd3pf2xCBiIhIdkuaMMzsLne/wMz+RjTwYDx3H5XWyEREJKvU1MI4G7gA+F0N64iISDNRU8L4GMDd32igWEREJIvVlDAKaxpkUFdJiYg0LzUljBwgn3Bnt4iINHM1JYwV7n5dg0UiIiJZraY7vdWyEBGR7WpKGIMbLAoREcl6SROGu69tyEBERCS7pTL4oIiIiBKGiIikRgmjBmVl0LwGdRcRSU4JI4kDDoDPP4cbbsh0JCIi2SGV4c2bpUsugXnz4Ne/ho4d4fzzMx2RiEhmKWEk0aIF3HcfrF8PF1wAHTrAGWdkOioRkcxRl1QNcnPh8cdh0CAYOxaefTbTEYmIZI4Sxg7k5cHUqVBcDKeeCm++memIREQyQwkjBbvuCi+8AEVFcOKJMGdOpiMSEWl4aUsYZna/ma00s/lxZZ3M7BUzWxzNO0blZmZ3mtkSM5tnZgPSFVdtFRbCyy+HcxnDh8OiRZmOSESkYaWzhfEAMLxK2ZXAa+7eC3gteg0wAugVTeOBu9MYV6117w6vvBKWhwwJl92KiDQXaUsY7v4mUHU8qtHAg9Hyg8BJceUPefBPoIOZdUlXbHWxzz7w0kvh6qkhQ2DlykxHJCLSMBr6HMZu7r4iWv4K2C1a7gp8EbfesqgsK/XvH66YWrIE7rwz09GIiDSMjJ30dncHdnrgDTMbb2alZla6atWqNESWmoEDIT8fNm7MWAgiIg2qoRPG17Gupmge69BZDnSPW69bVFaNu9/r7iXuXlJYWJjWYEVEpEJDJ4xpwNhoeSwwNa787OhqqUOB9XFdVyIikgXSNjSImT0KDAIKzGwZcA1wEzDFzM4BPgNOi1Z/HhgJLAG+B/49XXGJiEjtpC1huPvPk1RVe/RrdD5Dw/uJiGQx3ektIiIpUcIQEZGUKGGIiEhKlDBERCQlShgiIpISJQwREUmJEoaIiKRECUNERFKihCEiIilRwhARkZQoYYiISEqUMEREJCVKGCIikhIlDBERSYkShoiIpEQJQ0REUqKEUUfLl8O332Y6ChGR9FPCqIO99oInnoCCAhg8GG67DRYuBPdMRyYiUv+UMOpg5kx44w24+GJYuRIuuwx69w6J5IIL4PnnYdOmTEcpIlI/zBvxv8MlJSVeWlqa6TC2+/xzeOGFkChefRW+/x7y82HOHOjVK9PRiYgEZjbb3Ut29n1qYdSjPfaAc8+FqVNhzRr4wx9gwwb44otMRyYiUndKGGmSlwf775/pKERE6o8ShoiIpKRlJj7UzJYC3wHlQJm7l5hZJ+BxoAhYCpzm7t9kIj4REakuky2MY9y9OO7Ey5XAa+7eC3gtei0iIlkim7qkRgMPRssPAidlLhQREakqUwnDgZfNbLaZjY/KdnP3FdHyV8Buid5oZuPNrNTMSletWtUQsYqICBk6hwEc6e7LzexHwCtm9mF8pbu7mSW8QcTd7wXuhXAfRvpDFRERyFALw92XR/OVwF+Bg4GvzawLQDRfmYnYREQksQZPGGa2i5m1iy0DQ4H5wDRgbLTaWGBqQ8cmIiLJZaJLajfgr2YW+/y/uPuLZvYvYIqZnQN8BpyWgdjqVbt2YX7PPXDoodC2bWbjERGpiwZPGO7+CXBAgvI1wOCGjiediovhuuvgmmvgww/h6afDwIQiIo1RNl1W2+SYwa9/Dc89F8aTKikJAxOKiDRGShgNYMQIKC2FoiI44QS49lrYti3TUYmI7BwljAbSsyf84x9w1lkwaRKMGgXfpDjwyaxZ8OmnaQ1PRGSHMnUfRrPUti088AAccghMmBC6qP761+qj2m7ZAp98AkuWwOLFcOmlobwRP7pERJoAJYwGZgbnnQf9+8Mpp4Srpy65JLQ2Fi8O0+efq8tKRLKPEkaGHHZYeBLf6afDDTdAhw7hqXyHHw5nnx2WY9Ohh4bWiIhIJilhZNBuu8H06fDtt7DrrqH1ISKSrZQwMswM2rfPdBQiIjumq6QaifJynfQWkcxSC6MRaNUKpkyBadOgSxf48Y/DFFuuWvajH0FubqajFpGmRgmjEXjgAXjjDfjqK1ixIsw/+iiUrV1bfX0zKCionlgGDAgn2UVEakMJoxEoKUl+ldSWLfD11yGJxCeUqsllxQr44QcYPRry8ho2fhFpGpQwGrnWrWGPPcJUk1tugSuugOuvh5YtQ6L54Ycwj18uKoKTT4aDD9ZVWyJSmRJGMxFLKDfcEOa5uSHZtGpVMW/VCp56Cm6+Gbp1g5/+FH72MzjiCMjJyVzsIpIdzBvxpTclJSVeWlpauzd/+CHk54dvxmbi++/DF3+rVslbD998A88+GxLHSy/B5s3hJPpJJ4XkccwxOqEu0tiZ2Wx33+nbgZtnwnCHI4+EDz6AO+8MIwKq/6WaDRvCcOxPPRWGaN+4MSSb0aND4mjbFtq0CfP45arzvDz9eEWyiRLGzvr4Yxg3DmbMCN+A99wTbr2WhDZtCknj1FNr9/42bSonklNOgRtvrN8YRSQ1Shi1UV4Ov/89XH116J6aPh369au3+Jqi8vLQ0ti0KXRxxebxyzua339/2NYf/1hx7iT+PEr8lKg8viw3V60XkZ1V24TRvE965+SEscNHjAiXEe27byh317dQEjk5YdyrXXet/Tbat4c77gij9taH3Nyak07LlmGdqlOsvF076NQpTB07VizHv27XTr8SIs27hZHIunUwbFh4EPfIkfW7bQFCPv7mG9i6teKS3vgpUVldysvKwmdVncrKQv1334UbILdsSR5zTk7yhPL227BgQejRzM+HXXap/bxNm3DOp3XrMK/pAgWR2lILo76sXh36TY4/PiSNSZMyHVGTYxa+bLPNpk0hccSmb75J/vqrr0KS+OYbWL8+vP+oo8KFAhs3hvmaNZVfb9xYu+ec5OVVTiLxUyplO/u+LVvgwQcrWm6JWm876iqs6bUu0W68lDCq2nvv8ADugw8OV1B16gQXXRTqS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 <h2 id="Plotting-with-Pandas">Plotting with Pandas<a class="anchor-link" href="#Plotting-with-Pandas">&#182;</a></h2><ul>
 <li>Each data frame hast a <code>.plot()</code> function (see <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html">API</a>)</li>
 <li>Plots with Matplotlib</li>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">));</span>
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
 "
 >
 </div>
@@ -18065,44 +19532,46 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
 </div><div class="fragment">
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <ul>
 <li>… or plot and select</li>
 </ul>
 
 </div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[64]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[70]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">));</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt"></div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 
 
 
 
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@@ -18110,44 +19579,46 @@ Name: C, dtype: float64</pre>
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 <li>I prefer slicing first, as it allows for further operations on the sliced data frame</li>
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-<div class="prompt input_prompt">In&nbsp;[65]:</div>
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[71]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">);</span>
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@@ -18155,45 +19626,47 @@ Name: C, dtype: float64</pre>
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 <ul>
 <li>There are pseudo-sub-functions for each of the plot <code>kind</code>s</li>
 <li>I prefer to just call <code>.plot(kind="smthng")</code></li>
 </ul>
 
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-<div class="prompt input_prompt">In&nbsp;[66]:</div>
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[72]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">();</span>
 </pre></div>
 
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@@ -18201,34 +19674,38 @@ Name: C, dtype: float64</pre>
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-</div></div></section><section>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">ylim</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;This is a C plot&quot;</span><span class="p">);</span>
 </pre></div>
 
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@@ -18236,101 +19713,107 @@ Name: C, dtype: float64</pre>
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+
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 </div></section></section><section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Task-5">Task 5<a class="anchor-link" href="#Task-5">&#182;</a></h2><p><a name="task5"></a></p>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="Task-5">Task 5<a class="anchor-link" href="#Task-5">&#182;</a></h2><p><a name="task5"></a>
+<span style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</em></span></p>
 <p>Use the NEST data frame <code>df</code> to:</p>
 <ol>
-<li>Make the virtual processes the index of the data frame (<code>.set_index()</code>)</li>
+<li>Make the threads the index of the data frame (<code>.set_index()</code>)</li>
 <li>Plot <code>"Presim. Program / s"</code> and <code>"Sim. Time / s</code>" individually</li>
 <li>Plot them onto one common canvas!</li>
 <li>Make them have the same line colors and styles as before</li>
-<li><p>Add a legend, add missing labels</p>
-</li>
-<li><p>Done? Tell me! <a href="https://pollev.com/aherten538">pollev.com/aherten538</a></p>
-</li>
+<li>Add a legend, add missing axes labels</li>
+<li>Tell me when you're done with status icon in BigBlueButton: 👍</li>
 </ol>
 
 </div>
-</div>
-</div></section><section>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[68]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;Virtual Processes&quot;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[74]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;Threads&quot;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
-
 </div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[69]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[75]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;Presim. Time / s&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">));</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
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+<div class="jp-Cell-outputWrapper">
 
 
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+<div class="jp-OutputArea jp-Cell-outputArea">
 
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+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 
 
 
 
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;Sim. Time / s&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">));</span>
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 >
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@@ -18338,35 +19821,39 @@ Name: C, dtype: float64</pre>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;Presim. Time / s&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">();</span>
 <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Sim. Time / s&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">();</span>
 </pre></div>
 
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
 "
 >
 </div>
@@ -18374,35 +19861,39 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[72]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[78]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s2">&quot;Presim. Time / s&quot;</span><span class="p">,</span> <span class="s2">&quot;Sim. Time / s&quot;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">();</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Time / s&quot;</span><span class="p">);</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt"></div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 
 
 
 
-<div class="output_png output_subarea ">
-<img 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hGRDJZdib9M3TaIiLQmuxJ/bEdtIiISV5Ylfh3xi4i0JrsSf5mO+EVEWpNdib/hiF89dIqIJJJdiT+/EEp6qalHRKQF2ZX4IbjAq6YeEZGEsjDx99URv4hIC7Iv8ZdVqI1fRKQF2Zf41VGbiEiLsi/xR7tmdk93JCIiGSn7En9pX6ivhZ1b0h2JiEhGysLEHz7EpReyiIjElX2Jv0yJX0SkJdmX+KNP7+oCr4hIXNmX+BuO+JX4RUTiyb7EryN+EZEWZV/iLyqDgh5q4xcRSSD7Ej/suZdfRESaaTXxm9kgM5ttZsvNbJmZ/TAs72NmL5rZynC4d1huZvYHM1tlZkvM7MjOrkQzenpXRCShthzx1wE/dvdhwHHApWY2DLgGeNndhwIvh98BTgeGhp+pwLSUR92asgpd3BURSaDVxO/u69x9YTj+BfA2MACYCNwdznY3MCkcnwjc44F/AL3NrH+qA29RaV/YpqYeEZF42tXGb2aDgSOAeUA/d18XTvoY6BeODwA+jPlZTVjWdFlTzazazKo3bNjQ3rhbVqo2fhGRRNqc+M2sHHgMuMLdP4+d5u4OtKtXNHef4e6j3H1UZWVle37aurK+ULsNanekdrkiIlmgTYnfzAoJkv597v54WLw+2oQTDj8Jy9cCg2J+PjAs6zqleum6iEgibbmrx4A7gbfd/Xcxk2YBU8LxKcBTMeUXhnf3HAdsiWkS6hoNL11X4hcRaaqgDfOcAFwAvGVmi8KyfwduBh42s4uBD4BvhdOeASYAq4DtwD+nMuA2iXbboAu8IiLNtJr43X0uYAkmnxJnfgcuTTKu5KhrZhGRhLL0yV019YiIJJKdib+kN1i+Lu6KiMSRnYnfLLjAqyN+EZFmsjPxQ9htw+Z0RyEiknGyN/GrozYRkbiyN/GrozYRkbiyN/HriF9EJK4sTvwVsPMziNSmOxIRkYySvYk/+vTujk/TG4eISIbJ3sSvl66LiMSVvYk/esSvC7wiIo1kb+LXEb+ISFxZnPjVUZuISDxZnPj7BEMlfhGRRrI38ecXBp21qalHRKSR7E38oI7aRETiyO7EX1ahI34RkSayO/GXqodOEZGmsjvxl6mpR0SkqexO/KUVwV097umOREQkY2R54u8L9XVBZ20iIgJke+Jv6LZB7fwiIlHZnfijT+/qzh4RkQatJn4zm2lmn5jZ0piyG8xsrZktCj8TYqb91MxWmdkKMxvXWYG3SVnYX48u8IqINGjLEf9dwPg45be5e1X4eQbAzIYBk4Hh4W/+y8zyUxVsu6mjNhGRZlpN/O7+CtDWRvKJwIPuvsvd3wdWAcckEV9yStU1s4hIU8m08V9mZkvCpqC9w7IBwIcx89SEZc2Y2VQzqzaz6g0bNiQRRguKSqGwVBd3RURidDTxTwMOBKqAdcBv27sAd5/h7qPcfVRlZWUHw2iDUnXbICISq0OJ393Xu3vE3euBP7OnOWctMChm1oFhWfro6V0RkUY6lPjNrH/M17OA6B0/s4DJZlZsZkOAocD85EJM0sdvwaqX0hqCiEgmKWhtBjN7ABgDVJhZDXA9MMbMqgAH1gDfA3D3ZWb2MLAcqAMudfdIp0TeVvV1aV29iEimaTXxu/u34xTf2cL8NwE3JRNUSo04G/7vH+mOQkQkY2T3k7sAeQXB27hERATIhcQvIiKNKPGLiOQYJX4RkRyjxC8ikmOU+EVEcowSv4hIjlHiFxHJMUr8IiI5RolfRCTHKPGLiOQYJX4RkRyjxC8ikmOU+EVEcowSv4hIjsmNxF8fAfd0RyEikhGyP/H3GwFbPoRXfpPuSEREMkKrb+Dq9o6/DNYvg9k3Qo/ecMx30x2RiEhaZX/iz8uDibfDrs/hmaugpBcc9q10RyUikjbZ39QDwasXz/kfGDwanvgXWPFcuiMSEUmb3Ej8AIUlMPl+2GckPDIF1vw93RGJiKRF7iR+gJK94PzHoPd+8MBk+GhRuiMSEelyrSZ+M5tpZp+Y2dKYsj5m9qKZrQyHe4flZmZ/MLNVZrbEzI7szOA7pKwCLngiaOu/92zYuDLdEYmIdKm2HPHfBYxvUnYN8LK7DwVeDr8DnA4MDT9TgWmpCTPFeg2EC54Mxu+ZBJ99mM5oRES6VKuJ391fATY3KZ4I3B2O3w1Miim/xwP/AHqbWf8UxZpaFQfBBY8Hd/v8ZRJs3ZDuiEREukRH2/j7ufu6cPxjoF84PgCIPXyuCcsyU//D4Z8egs3vwbzp6Y5GRKRLJH1x190daHd/CGY21cyqzax6w4Y0Hm3v/xUoKofa7emLQUSkC3U08a+PNuGEw0/C8rXAoJj5BoZlzbj7DHcf5e6jKisrOxiGiIi0V0cT/yxgSjg+BXgqpvzC8O6e44AtMU1CIiKSAVrtssHMHgDGABVmVgNcD9wMPGxmFwMfANE+EJ4BJgCrgO3AP3dCzCIikoRWE7+7fzvBpFPizOvApckGJSIinSe3ntwVERElfhGRXKPELyKSY5T4RURyjBK/iEiOUeIXEckxSvwiIjlGiV9EJMco8YuI5BglfhGRHKPELyKSY5T4RURyjBK/iEiOUeIXEckxSvwiIjlGiV9EJMco8Ud9/hHs/DzdUYiIdLpW38CVE/YeDMufhHeehv2Ph6Fjg0/FwWCW7uhERFLKgrclpteoUaO8uro6fQFEauHD+bDyeVj5InyyPCjvvf+encCQ0VDYI30xiog0YWYL3H1Uu3+nxB/HZx/CqheDncB7c6B2OxSVw/degb4Hpjs6ERGg44lfbfzx9B4Eo74D334AfvI+nH4r7N4KW2rSHZmISNKU+FtTWAL9hqc7ChGRlFHiFxHJMUnd1WNma4AvgAhQ5+6jzKwP8BAwGFgDfMvdP00uTBERSZVUHPF/1d2rYi4wXAO87O5DgZfD7yIikiE6o6lnInB3OH43MKkT1iEiIh2UbOJ34AUzW2BmU8Oyfu6+Lhz/GOgX74dmNtXMqs2sesOGDUmGISIibZXsk7snuvtaM/sS8KKZvRM70d3dzOI+KODuM4AZENzHn2QcIiLSRkkd8bv72nD4CfAEcAyw3sz6A4TDT5INUkREUqfDid/MysysZ3QcGAssBWYBU8LZpgBPJRukiIikTjJNPf2AJyzoxKwAuN/dnzOzN4CHzexi4APgW8mHmWbF5cFwwf/AwKOhqDS98YiIJKHDid/d3wMOj1O+CTglmaAyzj6HwVevhdm/hI0r4dy/QJ8D0h2ViEiH6MndtjCDk38C5z0S9NczYwy8+0K6oxIR6RAl/vYYehpMnQO994P7vwVzbob6+nRHJSLSLkr87dVnCHznBTh8Msz5FTwwGXa0sUeKmgXw6ZpODU9EpDV6A1dHFJXCpGkw4Ch47qdB08+598E+IxrPV7crSPSb34NNq+GFa4PyG7Z0dcQiIg2U+DvKDI75LvQ/HB6+EO44FY6/FHZ+FiT5zauD6wGupiARySxK/MkadEzwZq5H/hle/Q2U9II+B8KgY+HwbwfjfQ8M7gK641TY94h0RywiOU6JPxXKvwQX/RV2fQ7Fe+kF7SKS0ZT4U8UsONoXEclwuqunq3kEMuAF9yKSu3TE35Xyi2DZE7DiWSjvF36+BD33ifkeU1ZWCfmF6Y5aRLKMEn9XmvRf8MHfYet6+GJ9MNy0OiiL+yyAQWnfJjuILwV3Eo04u8vDF5HsoMTflQYcGXziqdsFWz8JP+th68d7xmN3Els/hshuOOQMKCzp2vhFJCso8WeKgmLoPSj4tGTu7+Gl6+GVWyGvACK7IFIb7Dhix3vvB1/+RvCQme4yEpEYSvzdTa+BwfDV3wTDvMJgp5FfCPnFwXWE/EJ4exb8/few14BgB/DlM2G/4yAvP22hi0hm6NaJ/8PN27np6be5/sxh9O/VI93hdI2R58AhE4IEnl+U+Gh+x6fw7vOwfBYsuAvmTQ8uFh96RrATGHKSLhyL5CjzDLi1cNSoUV5dXd3u3720fD2XP/AmBfnGz88czllHDMDUrNHcrq2w8oXgLODdF6B2W7DTOGQCDBkNhaVQUBIMC3vs+RT0aPK9RM1GIhnEzBa4+6h2/647J36ADzZt46pHFvPGmk8ZO6wfN501ksqexSmOMIvU7gjOBB6Z0vq88RT0CC4qR3cWwybCqdenNkYRaZOcTfwAkXpn5tz3ufWFFZQXF3DjpBFMGNk/hRFmofoI7N4GdTuhdjvURoc7oG5HMIyWNZ0n+v3Ne4NlnfHb8NpC00/hnvGCBOWxZTqbEGmXnE78USvXf8GVDy/mrbVbmFi1Lz8/czi9S4tSEKHE9dy/wz/+lLrl5RW2sJMoDO5iyivcM55fGHzPyw/Gi8qhx97hp3fM+N5QEn4v7qkdjGQNJf5QbaSeaXNW84eXV9KnrIhbzj6Mrx76pZQsW5pwDy4i19eFt5PuDm4nbRg2Ldsdc+tpnPLo7aiNynaH89cG66mvhUh0WBsM6yPBfLu2BvFEdiWO2fIT7xg+nAcbVkB5ZbATKSyForI9n8LSoLwoLC+MTittPn9BSfgpCoYtXYgX6SAl/iaWrt3Cjx9ezIr1X/DDU4byo9MOTunyJYPV7gh2AA2fzxp/39nk+45PYccW2BW+IOfwb8PurbB7e9AcVrstGMZ+78h7FgpKwltvi/eMNwyLm3wPdxaN5iuK2aEkmidmOXW7YPH9e86k8qO3/saeSRU3aXqLvT04QfNddBm6NTjtOpr4u/XtnC0ZMaAXsy4/gYm3/527XltDj6J8yooLKGsYFlBWHIyXFxfQv1eJ7gjKFtG7kPbat32/i9QFw/xW/lu4B9c5dm8PdhC14Q4h+oleB6nbGSTfRsPd8csju2HXF7BtQ+J5Sf9BWiOWl/i6TsKdR0vXfYpbmV60Z4djecHZW15+zDAv+DQqy4e8vHbOn5/1Z2dZm/gBigvymVg1gF8//w43P/tOi/Nec/qh/MvJB3ZRZJKRWkv4UWZ7di5lfTs3pij3sJks3Ek02nm0sEMpKgv6dXIPm8daam7b3YYmuQTNcImmR3YHsdVuSdykVxcO62u75m/ZVs12BuEOIekdS5P5h02CI87r0qp1WuI3s/HAfwL5wB3ufnNnrasl/zrmQL530gHsqI2wbXcd23ZF2Larjm276ti+O8LWXXVc9chiZs59n5ffXo+ZkW9Gfp5hBvl5xvZdEeav2cxj/3o8w/ftRUmhTnGli5mFTT0dvFnBDPLCZqBMFd25tbgT2h00s9VHgi7OG4b1QXmjskhY1s75G37XdP5InGV1YP663Y3n3721y//UnZL4zSwf+BNwGlADvGFms9x9eWesrzV5eRY07xQXQM/m09/5+HMWf7iFenci9U5dfT27I8FtovXuLKkJ2n7PnvY6AEUFeexVUshePQrCYSF7lRTQozCfSLiM6Keu3qkPh//77gbM4KqxhzRaf+xZpWFxyvbMV/PpDpbUbOGC4/YnLw/yzBo++XlgTcajO7FgnmBHlpdnROqdovw86j2oY11kT7yxsUf/HonmGdqvnKpBvZvUp/FpcryT5qZn0tZkrmTOtM2C5UWXYWFMsX/HjjbrRa+JRS+NeUx5vIaY6Lr3jEdjyO6mhA5JducmbdZZR/zHAKvc/T0AM3sQmAikJfG35upxh7Y4vb7eefmdT1j/+U4+31nL5zvqwmEtn++sY8uOWmo2b2dHbYT8PKMgTK4FYcItyA8SMAQJ49bnVyQd86IPP0t6GRIIdhR7knFDcg+nd/b9D9H1Q5wdFHv2XvHKE/2WJuWx62q07kZxJN5hN99PtXWZTad1LJZG88WZlGj2pgcUCX+fcF1xfh9/RR1e5uSjB3HJ6AMSzN05OivxDwA+jPleAxwbO4OZTQWmAuy3336dFEZq5OUZpw3rl/Ry3J3aSDSpND5ybDxfzDjNjzA/3bY7OCsOj9bdnUg9DUfv9eF4xIOzjeDMhYYzmog7m7fuplePQvLzrGFnlZ8X7qTy8hrOFILvMdPz8sjLg4K8PB6p/pDnln3MGTEPyzWtT7zj4ObztDw90XLiafg7xRyZe6O/nzeURWdoOr3p2UIwEueovcnZWey02HXvGfdm5cScKSSaJ7acRuXe6N9F0zo2/9s0LvRG05rMGzO1pe3VfD0t/C7Ov+v409r+u7g/aKE43l2Mif5lxf93mNwy402oKO/65re0Xdx19xnADAhu50xXHF3JzCgqSP4Uv7w4M67Jf+/kA/meLoiLdDud9c7dtUBsx/IDwzIREUmzzkr8bwBDzWyImRUBk4FZnbQuERFph05pM3D3OjO7DHie4HbOme6+rDPWJSIi7dNpjcXu/gzwTGctX0REOqazmnpERCRDKfGLiOQYJX4RkRyjxC8ikmMyoj9+M9sAfJBgcgWwsQvD6SqqV/eTrXVTvbqfaN32d/fK9v44IxJ/S8ysuiMvGsh0qlf3k611U726n2TrpqYeEZEco8QvIpJjukPin5HuADqJ6tX9ZGvdVK/uJ6m6ZXwbv4iIpFZ3OOIXEZEUUuIXEckxGZv4zWy8ma0ws1Vmdk2640mWma0xs7fMbJGZVYdlfczsRTNbGQ73TnecrTGzmWb2iZktjSmLWw8L/CHchkvM7Mj0Rd6yBPW6wczWhttskZlNiJn207BeK8xsXHqibp2ZDTKz2Wa23MyWmdkPw/Js2GaJ6tatt5uZlZjZfDNbHNbr52H5EDObF8b/UNjlPWZWHH5fFU4f3OpKPHx1XyZ9CLpyXg0cABQBi4Fh6Y4ryTqtASqalP0auCYcvwa4Jd1xtqEeJwFHAktbqwcwAXiW4K2ExwHz0h1/O+t1A3BVnHmHhf8mi4Eh4b/V/HTXIUG9+gNHhuM9gXfD+LNhmyWqW7febuHfvjwcLwTmhdviYWByWD4d+Ndw/PvA9HB8MvBQa+vI1CP+hpe1u/tuIPqy9mwzEbg7HL8bmJS+UNrG3V8BNjcpTlSPicA9HvgH0NvM+pOBEtQrkYnAg+6+y93fB1YR/JvNOO6+zt0XhuNfAG8TvBM7G7ZZorol0i22W/i33xp+LQw/DnwNeDQsb7rNotvyUeAUa+lt9WRuU0+8l7W3tEG7AwdeMLMF4YvmAfq5+7pw/GMg+Te6p0eiemTDdrwsbPKYGdMU1y3rFTYBHEFwBJlV26xJ3aCbbzczyzezRcAnwIsEZyefuXtdOEts7A31CqdvAfq2tPxMTfzZ6ER3PxI4HbjUzE6KnejBeVq3v7c2W+oRmgYcCFQB64DfpjWaJJhZOfAYcIW7fx47rbtvszh16/bbzd0j7l5F8L7yY4BDU7n8TE38WfeydndfGw4/AZ4g2Jjro6fR4fCT9EWYlET16Nbb0d3Xh/8B64E/s6dZoFvVy8wKCRLjfe7+eFicFdssXt2yZbsBuPtnwGzgeIJmt+hbE2Njb6hXOL0XsKml5WZq4s+ql7WbWZmZ9YyOA2OBpQR1mhLONgV4Kj0RJi1RPWYBF4Z3ihwHbIlpXsh4Tdq2zyLYZhDUa3J4N8UQYCgwv6vja4uwrfdO4G13/13MpG6/zRLVrbtvNzOrNLPe4XgP4DSC6xezgXPC2Zpus+i2PAf4W3gWl1i6r2C3cGV7AsFV+tXAtemOJ8m6HEBwN8FiYFm0PgTtcC8DK4GXgD7pjrUNdXmA4PS5lqCd8eJE9SC4O+FP4TZ8CxiV7vjbWa+/hHEvCf9z9Y+Z/9qwXiuA09Mdfwv1OpGgGWcJsCj8TMiSbZaobt16uwGHAW+G8S8FfhaWH0Cwo1oFPAIUh+Ul4fdV4fQDWluHumwQEckxmdrUIyIinUSJX0Qkxyjxi4jkGCV+EZEco8QvIpJjClqfRaT7MLPobYoA+wARYAMwGPjI3Yd1QQxb3b28s9cj0lE64pes4u6b3L3Kg8fdpwO3heNVQH1rv495MlIkaynxSy7JN7M/h32cvxA+FYmZzTGz31vwnoQfmtlRZva/YYd6z8d0bfBdM3sj7Cf9MTMrDcuHmNnrFrxv4cboysysv5m9EvYJv9TMRqel1iJNKPFLLhkK/MndhwOfAWfHTCty91HAH4A/Aue4+1HATOCmcJ7H3f1odz+c4BH6i8Py/wSmuftIgqd/o/4JeD484zic4MlSkbTTaa3kkvfdfVE4voCg3T/qoXB4CDACeDHs0jyfPcl8RHhE3xsoB54Py09gz07kL8At4fgbwMywI7EnY9YtklY64pdcsitmPELjA59t4dCAZdHrBO4+0t3HhtPuAi4Lj+x/TtBHSlSzvk88eLnLSQS9J95lZhemphoiyVHiF2lsBVBpZsdD0O2vmQ0Pp/UE1oVH8OfF/ObvBD3IEltuZvsD6939z8AdBK92FEk7JX6RGB686vMc4BYzW0zQLv+VcPJ/ELzh6e/AOzE/+yHBy3XeovEbncYAi83sTeBcgmsBImmn3jlFRHKMjvhFRHKMEr+ISI5R4hcRyTFK/CIiOUaJX0Qkxyjxi4jkGCV+EZEc8/8BUIND1sMRjA0AAAAASUVORK5CYII=
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@@ -18452,54 +19945,54 @@ Name: C, dtype: float64</pre>
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 <ul>
 <li><strong>That's why I think Pandas is great!</strong></li>
-<li>It has great defaults to quickly plot data</li>
+<li>It has great defaults to quickly plot data; basically publication-grade already</li>
 <li>Plotting functionality is very versatile</li>
 <li>Before plotting, data can be <em>massaged</em> within data frames, if needed</li>
 </ul>
 
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 <h2 id="More-Plotting-with-Pandas">More Plotting with Pandas<a class="anchor-link" href="#More-Plotting-with-Pandas">&#182;</a></h2><h3 id="Some-versatility">Some versatility<a class="anchor-link" href="#Some-versatility">&#182;</a></h3>
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-<div class="prompt input_prompt">In&nbsp;[74]:</div>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="s2">&quot;F&quot;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">,</span> <span class="n">stacked</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
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 "
 >
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@@ -18507,34 +20000,38 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
-</div></section><section>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[75]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[81]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;F&quot;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">][[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="s2">&quot;F&quot;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">,</span> <span class="n">stacked</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
 </pre></div>
 
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+</div>
 </div>
 </div>
 
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-<div class="output">
+<div class="jp-Cell-outputWrapper">
 
 
-<div class="output_area">
+<div class="jp-OutputArea jp-Cell-outputArea">
 
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+<div class="jp-OutputArea-child">
+
+    
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@@ -18542,35 +20039,39 @@ Name: C, dtype: float64</pre>
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-<div class="input">
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+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;F&quot;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">][[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="s2">&quot;F&quot;</span><span class="p">]]</span>\
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+    <span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">&quot;barh&quot;</span><span class="p">,</span> <span class="n">subplots</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Subplots Demo&quot;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">4</span><span class="p">));</span>
 </pre></div>
 
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
 "
 >
 </div>
@@ -18578,15 +20079,16 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
-</div></div></section><section>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[77]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;F&quot;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">][[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;F&quot;</span><span class="p">]]</span>\
+</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[83]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;F&quot;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;F&quot;</span><span class="p">]]</span>\
     <span class="o">.</span><span class="n">plot</span><span class="p">(</span>
         <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;-*r&quot;</span><span class="p">,</span> <span class="s2">&quot;--ob&quot;</span><span class="p">],</span> 
         <span class="n">secondary_y</span><span class="o">=</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> 
@@ -18595,23 +20097,26 @@ Name: C, dtype: float64</pre>
     <span class="p">);</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
-<div class="output">
+<div class="jp-Cell-outputWrapper">
 
 
-<div class="output_area">
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt"></div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 
 
 
 
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
 "
 >
 </div>
@@ -18619,15 +20124,16 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
-</div></section><section>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[78]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;F&quot;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">][[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;F&quot;</span><span class="p">]]</span>\
+</div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[119]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;F&quot;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;F&quot;</span><span class="p">]]</span>\
     <span class="o">.</span><span class="n">plot</span><span class="p">(</span>
         <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;-*r&quot;</span><span class="p">,</span> <span class="s2">&quot;--ob&quot;</span><span class="p">],</span> 
         <span class="n">secondary_y</span><span class="o">=</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> 
@@ -18642,23 +20148,26 @@ Name: C, dtype: float64</pre>
     <span class="p">);</span>  
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt"></div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 
 
 
 
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@@ -18666,12 +20175,12 @@ Name: C, dtype: float64</pre>
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 <h2 id="Combine-Pandas-with-Matplotlib">Combine Pandas with Matplotlib<a class="anchor-link" href="#Combine-Pandas-with-Matplotlib">&#182;</a></h2><ul>
 <li>Pandas shortcuts very handy</li>
 <li>But sometimes, one needs to access underlying Matplotlib functionality</li>
@@ -18687,44 +20196,44 @@ Name: C, dtype: float64</pre>
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 <h3 id="Option-1:-Pandas-Returns-Axis">Option 1: Pandas Returns Axis<a class="anchor-link" href="#Option-1:-Pandas-Returns-Axis">&#182;</a></h3>
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-<div class="prompt input_prompt">In&nbsp;[79]:</div>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Hello there!&quot;</span><span class="p">);</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Hello There!&quot;</span><span class="p">);</span>
 <span class="n">fig</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_figure</span><span class="p">()</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;This title is super!&quot;</span><span class="p">);</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;This title is super (literally)!&quot;</span><span class="p">);</span>
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@@ -18732,44 +20241,46 @@ Name: C, dtype: float64</pre>
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+
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 </div></section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
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+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <h3 id="Option-2:-Draw-on-Matplotlib-Axes">Option 2: Draw on Matplotlib Axes<a class="anchor-link" href="#Option-2:-Draw-on-Matplotlib-Axes">&#182;</a></h3>
 </div>
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-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[80]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[86]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
 <span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Hello there!&quot;</span><span class="p">);</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;This title is super!&quot;</span><span class="p">);</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Hello There!&quot;</span><span class="p">);</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">&quot;This title is super (still, literally)!&quot;</span><span class="p">);</span>
 </pre></div>
 
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+     </div>
+</div>
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+<div class="jp-OutputArea jp-Cell-outputArea">
 
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@@ -18777,46 +20288,48 @@ Name: C, dtype: float64</pre>
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 </div></section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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 <ul>
 <li>We can also get fancy!</li>
 </ul>
 
 </div>
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-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[81]:</div>
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+<div class="jp-Cell-inputWrapper">
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[87]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
 <span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">column</span><span class="p">,</span> <span class="n">color</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="s2">&quot;F&quot;</span><span class="p">],</span> <span class="p">[</span><span class="s2">&quot;blue&quot;</span><span class="p">,</span> <span class="s2">&quot;#b2e123&quot;</span><span class="p">]):</span>
     <span class="n">df_demo</span><span class="p">[</span><span class="n">column</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">)</span>
 </pre></div>
 
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
 "
 >
 </div>
@@ -18824,12 +20337,12 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
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-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
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+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
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 <h2 id="Aside:-Seaborn">Aside: Seaborn<a class="anchor-link" href="#Aside:-Seaborn">&#182;</a></h2><ul>
 <li>Python package on top of Matplotlib</li>
 <li>Powerful API shortcuts for plotting of statistical data</li>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
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kuhlx2HnDHbkucxO8s2NgtSQg+4d74NxuhWxVBgnka4cl6CkNADjjkrY+7n64dCr0PTpt9Bq6viHSmiqMwjTHXmNQ/V4vdUFzMQFAeS5v4DjPKT0IryeceJeowxuL/6C1hTLeKufwiCvQvvSFxcWuglb/0GRkPsFDqVeQQxQ4Na+A3k/mMtN1QrEAnXzIWYPACe/A1gmpd3nKimfvu/0E4fgH3KbZBSB/OOw9XFQteb6prPocdGoVOZR5B+5giYqy4qZ2OEgyCIsE9fAdZYDe/hrbzjRC29/CQ8+esgDxgPZcwNvOOYgpQ2FOkr/h3MVd98l4v1Cz2oMv/kk0+waNEiLFiwAGvXrg1VJstSnbkQ4rpDsvBQLX/JvTMhD5wI78HNNK86AC3nyeO6wzHnJzF3nrw9jj7DEZ/9GJi7ISYKPeAyLysrwx//+Ee8/fbb2LhxI9atW4cTJ+jOhKsxmmqhnTkEZfhMCKLEO46p2KfeBhgavHs+4B0lqjDG4M59HayhCnHXPwjBkcA7kulIqUNiptADLvMdO3Zg2rRpSExMRHx8PBYuXIitW+lX5atRC3cAzICSYf2hWv4Su6dBGTUfqjMPeuVp3nGihnp0G7Tv98I2+RZIvazx7NhwiJVCD3juanl5OVJSUlr+nJqaisOHD3f688nJgR9FpKR0Dfiz4XS1XIwxFBd9A3vfTKQNGx7hVD5m32f6DXfi7IkdMPZtQNqd/8H9dIHZ95fn/Emc2/UO4oaMR6/5t0IQ+F7+Mvv+Qso4uBOfQuk7T8OzZTV6//hpyN1T2v9wJHKFUMBlzhhr9Zo/fwGrqhpgGK3X0ZGUlK6oqKj3+3Ph1l4u7fxxqFUlcMzJ4pI9WvaZMn4p3Dvewvm9eZAHjjdNLrO4mIt5XWj84A8QHF0hzlyJyspGU+Qym1a5bL0Ql/UYmrb8Hmf//u+IX7IKYkIy/1ydJIpCuwfBAf9znpaWhsrKypY/l5eXIzU1NdDVWZrmzAUUR0wN1QqEMnIuxMR0uPPfBdM13nFMiTEGd94bYPUVcFz3AESHOY+IzUpKHYz4RY+Deax3yiXgMp8xYwZ27tyJ6upquFwufP7555g9m265uxLzuqAW7YYyeEpUPECXJ0GUfVMVL5RBPZrDO44pqQVfQyvKh23STZDTM3jHiUqtCr2+suMPRYGgjswfeeQR3HXXXVi2bBkWL16Ma66hW+6upJ3cA2geuvDZSVK/ayD1GQXP/o/B3A2845iKp+wUPDvWQuo7GrZx2bzjRLXLCn3TaksUelBXTZYsWYJNmzbhs88+w7333huqTJbideZCTEyHGKNDtfwlCALs01cA3iZ49m/kHcc0mOpG+Yf/DcEWD8e8+7hf8LSCyws9+o/Q6ScijPTaEhhlJ6BkXMv97oxoIiX1hZI5B+p322DUlvKOw53vPPnfoVafh+P6ByDGdeMdyTKk1MGIz/4VmKcx6gudyjyM1II8QJAgD5vJO0rUsU28CZAVuHet4x2FO82ZB+3ETvSYdSvk3iN4x7EcKWVQc6E3NRd6Be9IAaEyDxNmaNCOb4c8YCzE+O6840QdMb47bOOXQD9zENq5o7zjcKNXn4N7+1uQeo9A4qzlvONYlq/QH28u9NVRWehU5mGinTncPFSLLnwGyjZ6AYSuPeHZ+Q6YEXszz5nqgTvnzxBsDjiuu5/GQITZZUfon0TfETqVeZhozjzfUK1+dIdPoATZBvuU22BUn4VamMc7TsS5t78Fo6YUjnn308jkCJFSBvoK3euKukKnMg8DGqoVOvLgyZDShsG7530wr4t3nIhRC7dDK8yDbfxiyH1H8Y4TU1oKXXVHVaFTmYeBWri9eagWfYkqWBdvVWSuOngPbuYdJyL02hK4v3kTUnoGbBOX8Y4Tk3yF/nhUFTqVeYgxxqA68yD1Gg4xsRfvOJYgpQ6GPHQ6vEe2RvWtY53BNC/cX7wIQbbBcd0D9JsdR1LPKwq9ztyFTmUeYnrZcbAL5+nCZ4jZp9wCQIRn9wbeUcLKs2MtjOpiOObdC7FLD95xYt5lhb7J3IVOZR5iakEeDdUKAzEhGbaxN0IryodeZs2HoKgndkEt+Bq2sYsg04Vz0/AV+q8uKfRy3pHaRGUeQszrgnZyN5QhNFQrHGxjF0GIT4R75zttjmCOZsaF83DnvQExbShsk2/mHYdcQeo54JJCX23KQqcyDyH15O7moVp04TMcBMUB++TlMMqLoBXl844TMkzzwvXFi4Ao+R7/Jgb8mAESRpcV+ifmO0KnMg8h1ZkHMbE3xNQhvKNYljxsJsTk/vDs3gCmeXnHCQnPrndhVJ1B3Nx7uTwsgXReS6FrHtMVOpV5iHgri31DtTJpqFY4CaLou1WxoQreI5/xjhM09eQeqEe3QbnmRsgDxvGOQzrBrIVOZR4i9YdyaKhWhMi9R0AeOAHeg5thNNXyjhMwo64c7q//BjF1cPPdOiRamLHQqcxDgBkaGo58DXnAOBpPGiH2qbcBugrv3g94RwkI01XfeXJBoPPkUUrqOQDxi58wTaFTmYeAduYQ9MYLdG95BInde0EZNR9qQR70qjO84/jNk78eRuUpOOb+BGJXfk+JJ8GRkvsjfvETgOblXuhU5iGgFuRBSugBqd8Y3lFiin3CjwB7vG+qYhTdqqh+vw/qt/8LZfQCKAMn8o5DgiQl90fc4l/9UOgXyrjkoDIPktFUC/3sYXS9Zi599TrCBHsX2Ccug15yDPqZg7zjdIpRXwH313+F2HOg71QRsYTLCn3Tai6FTmUeJLXwG4AZ6Dr2Ot5RYpIych7ExHS4d60D0zXecdrFdA2uL14CGEPc/IcgSArvSCSEfIXefMplU+SP0KnMg3DpUC0lqTfvODFJEGXYp90OduE81KPbeMdpl2fPezAqTsIxZyXEbqm845AwkJL7+Qpd1yJe6FTmQdDPF4JdKIOSSd/45EnqNxZSn1Hw7N8I5m7gHadN2umDUA9vhTLyOig0t8fSpOR+iMv+1SWFfj4i26UyD4LqbB6qNYj+cvLkm3l+B+Btgmf/Rt5xWjEaquD66jWIyf1hn3YH7zgkAnxH6BcLfXVECp3KPEA/DNWaCkGx844T86SkflAy5kD9bhuM2lLecVowQ4Mr5yXA0H3nyWUb70gkQqSkSwr9k/AfoVOZB8g3VMtL95abiG3STYCswJO/nneUFt49H8AoOwHHtfdA7E4PK4k1LYVu6GEvdCrzAKkFuRB70FAtMxHju8M2fjG00wegnTvKOw60M4fhPbQFSuYcKEOn8Y5DOPEV+hMtha7WhueiKJV5APSaczDKi6BkzKahWiZjG30DhIRk3xeJDINbDqOxBu6vXoOY1Bf2GXdyy0HMQUrq6yt0UYKnuDAs26AyD4DqzGseqjWDdxRyBUG2wT71NhjVZ6EW5nHJwAwd7pyXwDQvHHSenDSTkvqiy4o/oMuoWWFZP5W5n5ihQSvcTkO1TEwePAVi2lB497wP5nVFfPvefR9BP18Ix6y7ICXS9w/IDwRBCNtv81TmftJOHwJz10PJpAufZiUIAhzTV4C56uA9uDmi29aKv4P3wCbIw6+FMpzGIZPIoTL3k+rMhRCfCKkvDdUyMyl1COSh0+A9shVGfWVEtmk01cL95SsQe6TDMfPHEdkmIRdRmfvBaKyBfvYwlOGzaKhWFLBPuRWAAM/u98K+LWYYcG97BczrhuP6n9F3D0jEUZn7QS3cDjBG95ZHCTEhGbZrboRWtAt62Ymwbst74GPoJcfgmPWPkJL6hHVbhLSFyryTWoZqpWdA7J7GOw7pJNu4bAhx3eEO48xzreQYvPs2Qh46HfLw8NypQEhHqMw7ST9fCFZXBiWDhmpFE0FxwD55OYzyImhF+SFfv9F0Ae6clyF2T4Pj2rvpeweEm6DLfM2aNfjTn/4Uiiympjpzm4dqTeIdhfhJHj4LYnJ/eHZvANO8IVsvYwbcX74K5m2CY/7PICiOkK2bEH8FXOb19fV48skn8be//S2UeUzJN1RrD5Qh0+jCVhQSRBH26SvAGqrgPfJ5yNbrPbgZ+rnvYJ9xJ6TkfiFbLyGBCLjMc3JyMHDgQPzTP/1TKPOYklqU7xuqRXPLo5bcewTkAePhPbgJRlNt0OvTSp3w7v0A8pCpUDLnBB+QkCAFXObLli3DfffdB0my/i16qjMXYo8+EFMG8Y5CgmCfejugqfDu/SCo9Rjueri3vQyhayoc195D58mJKcgdveHTTz/Fs88+e9lrgwcPxhtvvBHUhpOTEwL+bEpK16C27Q9vxRnUl59E0vx7kJja/tf3I5nLX2bNFtFcKV1ROTkLdbs3I3XWUtjTBvqdizED59etAXPXo889z8LeK7KPf6P/j/6JpVwdlnlWVhaysrJCvuGqqgYYhv+3iqWkdEVFRX3I81yNe+dW36Sz9AntbjfSufxh1mw8crERWcChL3H+078ibtHjbR5Vt5fLc3ALvEUHYJ/5j6iTUoAI5qf/j/6xWi5RFNo9CKZbE9vBdA3a8R2QB4ynoVoWIdi7wD5xGfRzR6GfOeTXZ/Xzx+Hd8x7kQZOgjLwuTAkJCQyVeTu0Mwd9Q7XoG5+WooycB7F7L3h2vQtmaJ36DHM3wJXzEoSEZDjmrKTz5MR0OjzN0pF//ud/DkUOU1ILciF06UFDtSxGEGXYp90B12f/F+rRL2EbvaDd9zPG4PrqL2CuC4hf+msItvgIJSWk8+jI/CqMxhroxUegDJsJQaTdZDVS/7GQ+oyEZ99HYO6Gdt+rHvkc+pmDsE+9HRLd0URMilrqKtTCb2ioloUJggD7tBWAtwme/R9f9X16+Ul4dq+HPHAClA6O4Anhicq8DTRUKzZIyf2gZMyG+l0OjNrWT01nnka4cl6EEJ8Ix5yf0HlyYmpU5m3QS51gdeU0VCsG2CbdBMgKPPnrLnudMQb3138Da6hB3PyHINi7cEpISOdQmbdBdeYBShzkwTRUy+rE+ETYxi2GdvoAtJJjLa+r330B7dQ+2KfeAil1CMeEhHQOlfkVmLfJN1Rr6FQIMg3VigW2MTdASEiGZ+fbYIYBT2kRPLvWQeo/FsqYG3nHI6RTqMyvoBbtBnQvnWKJIYJsg33KrTCqzkL99n9R9sF/Q4jrhri599J5chI1qMyvoBbkQuzRl4ZqxRh5yFSIaUPh2fUOtAsVcFz/IARH4PODCIk0KvNL6NXFMCpOQsm8lo7IYowgCHBMXwFIMpLm3Qm51zDekQjxS9DfALUS1ZkHiBLkYTN4RyEcSKlDkHDXC0jsnWLKAU2EtIeOzJtdNlTLYc6xmST86NFvJFpRmTfTTh9oHqpFFz4JIdGHyryZ6sxrHqo1mncUQgjxG5U5AKOh2jdUa/gsGqpFCIlK1FygoVqEkOgX82XOmNE8VCsTYrfIPs+REEJCJebLXC91gtVXQMmkC5+EkOgV82WuOvMAWxzkQTRUixASvWK6zH1DtfZCGTINgmzjHYcQQgIW02Wunsj3DdWiUyyEkCgX22XuzIWY1Bdiz4G8oxBCSFBitsz16rMwKr6HkjGbhmoRQqJezJa5WnBxqNZ03lEIISRoMVnmLUO1Bk6goVqEEEuIyTLXTh8A8zTQNz4JIZYRk2WuOnMhdEmC1IeGahFCrCHmytxoqIJ+9lsow2fSUC1CiGXEXJuphdsB0FAtQoi1xFSZtwzV6j2ChmoRQiwlpsq8ZagWHZUTQiwmpspcLciloVqEEEuKmTJnnkZo3++FMnQ6DdUihFhOzJS5WpQP6Co9sJkQYkmxU+bOPIhJ/SD2HMA7CiGEhFxMlLle1TxUK5OGahFCrCkmylx15gKiDGUoDdUihFhTwGW+b98+LF++HEuXLsXdd9+Nc+fOhTJXyDBdhdo8VEtwJPCOQwghYRFwmT/++ON45plnsHHjRixZsgS//e1vQ5krZLTTBwBPI91bTgixtIDK3Ov14he/+AUyMzMBABkZGSgtLQ1psFBRCy4O1RrFOwohhISNwBhjwazAMAw8+OCDGDNmDH7+85+HKldIaHWVOPOnB5A4azmS5qzgHYcQQsJG7ugNn376KZ599tnLXhs8eDDeeOMNeL1erFq1Cpqm4f777/drw1VVDTAM//8dSUnpioqK+k6917N/KwAGte/UTn8mUP7kijSzZqNc/qFc/rFaLlEUkJx89et+HZZ5VlYWsrKyWr3e2NiIBx98EImJiXjppZegKIrf4cLJN1TrG0h9RkLslsI7DiGEhFWHZX41jz/+OAYMGICnn37alPdu6yUFvqFak2/mHYUQwoGqqqisLIWmeXlHuUx5uQjDMK66XBQlxMUlICGhu1/dGlCZHz16FDk5ORg6dCiWLVsGAEhNTcVrr70WyOrCQnXmAbZ4yAMn8o5CCOHg7NmzcDji0aVLL1MdcMqyCE1ru8wZY9B1DfX1taipqUBSUudHdQdU5iNHjoTT6QzkoxHRMlQr41oaqkVIjHK53EhJ6WmqIu+IIAiQZQWJickoKyv267OW/AZoy1CtTBqqRUgsi6Yiv5QgiAD8u0HEmmVekAsxuR/EZBqqRQgxl6KiE5g1axK++ionpOu1XJnrVWdgVJ6CkkFDtQgh5rNp08eYO/d6fPTR+yFdr+XKXHXm0VAtQogpaZqGrVu34L77HsLx406cO+ffefH2BHxrohnRUC1CSFu2HynFN4fDM3Jk1jXpmDkmvVPv3bnzG6Snp6N//wG49tq52LjxfTz00C9CksNSR+baqeahWnThkxBiQlu2fIIFCxYCAK6/fgG2bNkEVVVDsm5LHZmrzlwICcmQeo/kHYUQYiIzx3T+6DlcamqqsXPndhQUHMO6de+AMYb6+jp89VUOFiy4Mej1W6bMjYYq6MXfwTbhRxBES/3CQQixgM8+24KJE6dgzZoXWr409Ne/voKNGz8ISZlbpvVU5zcAACVjFuckhBDS2pYtn+Cmm2657LWbb74Vx459h9OnTwW9fkscmTNmQC3M8w3V6kpDtQgh5vPmm+tavdajRxJycraHZP2WODL3DdWqpKcJEUJiliXKXHXmNg/VmsA7CiGEcBH1Zd4yVGvodBqqRQiJWVFf5uqJXYCu0b3lhJCYFv1l7syFmNwfUk8aqkUIiV1RXeZ65WkYlaehZNBROSEktkV1mavOPECSoQydxjsKIYRwFbX3mTPNC/XETsgDJ9JQLUJIVGhsbMCrr/4Z+/fvgyTJ6Nq1K37+80eQkZEZ9Lqjtsy1081DtejeckJIFDAMA4899gtMmjQZr7/+NmRZxv79e/HYYw/jrbfWo3v3xKDWH7VlrhY0D9XqQ0O1CCHmt3//XlRWVuLeex+A0fw85wkTJuHJJ38Dw2j7Ac/+iMoyN+oroZ87CtvEpc3PyiOEkKtTC7f7vlwYBkrGbCjDZ3b4vsJCJ0aMGAlRFC8r7+nTQzNPKiqbUC1sHqo1nIZqEUKigygKYMy/hzT7I+qOzBkzoDovDtXqyTsOISQKKMNnduroOZwyM0fiww/fa1Xor7zyZ0yePBUTJkwKav1Rd2TuOnUErKGKLnwSQqLK2LHj0aNHEv7yl1eg6zoAID9/J7Zs+RgDBw4Kev1Rd2Ref2gbYO9CQ7UIIVFFEAT87nf/gxde+B/cddftkGUZ3bsn4ve/X4OkpOSg1x9VZc48jWgqyIeSOZuGahFCok5iYiL+4z9+2/KkoVCKqtMsxoUyAICSOZdvEEIIMZmoOjKXUgdjwKOvo+qCxjsKIYSYSlQdmQOAaIvjHYEQQkwn6sqcEEI6K5z3dYcTYwYAwa/PUJkTQiwpLs6Bxsa6qCp0xhg0TUVtbSVsNodfn42qc+aEENJZ/fr1w4kT36OhoZZ3lMtc+XX+1sslxMUlICGhu1/rpTInhFiSoijo2TOdd4xWUlK6oqKiPuTrpdMshBBiAVTmhBBiAdxOs4iif1dqQ/XZcDJrLsC82SiXfyiXf6yUq6PPCCyaLvUSQghpE51mIYQQC6AyJ4QQC6AyJ4QQC6AyJ4QQC6AyJ4QQC6AyJ4QQC6AyJ4QQC6AyJ4QQC6AyJ4QQCzBtmX/yySdYtGgRFixYgLVr17ZafuzYMSxfvhwLFy7Ev/3bv0HTIvMouY5yvfDCC5g3bx6WLl2KpUuXtvmecGloaMDixYtRXFzcahmv/dVRLl7764UXXkB2djays7Px3HPPtVrOa391lIvX/lqzZg0WLVqE7OxsvP76662W8/z56igbz7+Tq1evxqpVq1q9XlJSgjvvvBM33ngjHnzwQTQ2Nga/MWZC58+fZ/PmzWM1NTWssbGRLVmyhB0/fvyy92RnZ7MDBw4wxhj713/9V7Z27VpT5Lr//vvZ/v37w57lSgcPHmSLFy9mo0aNYmfPnm21nMf+6kwuHvtr+/bt7Pbbb2cej4d5vV521113sc8///yy9/DYX53JxWN/5efnszvuuIOpqspcLhebN28eKyoquuw9vH6+OpON19/JHTt2sKlTp7Innnii1bL77ruPbdq0iTHG2AsvvMCee+65oLdnyiPzHTt2YNq0aUhMTER8fDwWLlyIrVu3tiw/d+4c3G43xo0bBwC4+eabL1vOKxcAfPvtt3jttdewZMkSPP300/B4PGHPBQDr16/HU089hdTU1FbLeO2vjnIBfPZXSkoKVq1aBZvNBkVRMGTIEJSUlLQs57W/OsoF8NlfU6ZMwZtvvglZllFVVQVd1xEfH9+ynOfPV0fZAD77rLa2Fn/84x/xwAMPtFqmqir27NmDhQsXAgjd/jJlmZeXlyMlJaXlz6mpqSgrK7vq8pSUlMuW88rV2NiIESNG4IknnsCHH36Iuro6vPjii2HPBQDPPPMMJk2a1OYyXvuro1y89tewYcNaiufUqVPYsmUL5syZ07Kc1/7qKBfPny9FUfD8888jOzsb06dPR1paWssynj9fHWXjtc9+85vf4JFHHkG3bt1aLaupqUFCQgJk2Te0NlT7y5RlztoY5CgIQqeXh0tH2+3SpQtee+01DBgwALIsY+XKlfj666/DnqsjvPZXR3jvr+PHj2PlypV44oknMHDgwJbXee+vq+Xivb8efvhh7Ny5E6WlpVi/fn3L67z3F3D1bDz22YYNG5Ceno7p06e3uTxc+8uUZZ6WlobKysqWP5eXl1/2a/qVyysqKq76a3wkc5WUlOC9995r+TNjrOVfX5547a+O8Nxf+/btwz333INf/vKXuOmmmy5bxnN/tZeL1/4qKirCsWPHAABxcXG44YYb4HQ6W5bz3F8dZeOxz7Zs2YLt27dj6dKleP7557Ft2zb813/9V8vypKQkNDQ0QNd1AKHbX6Ys8xkzZmDnzp2orq6Gy+XC559/jtmzZ7cs79OnD+x2O/bt2wcA+Oijjy5bziuXw+HA73//e5w9exaMMaxduxYLFiwIe66O8NpfHeG1v0pLS/Gzn/0Mf/jDH5Cdnd1qOa/91VEuXvuruLgYv/71r+H1euH1epGTk4OJEye2LOf589VRNh777PXXX8emTZuwceNGPPzww7juuuvw5JNPtixXFAWTJk3Cli1bAIRwfwV9CTVMPv74Y5adnc1uuOEG9uqrrzLGGPvpT3/KDh8+zBhj7NixY2z58uXsxhtvZI8++ijzeDymyLV169aW5atWrYpYrovmzZvXcteIGfZXR7l47K///M//ZOPGjWM/+tGPWv57++23ue+vzuTi9fO1Zs0alpWVxRYvXsyef/55xph5fr46ysbz7+T777/fcjfLk08+yb744gvGGGPFxcXsxz/+McvKymIrV65ktbW1QW+LnjRECCEWYMrTLIQQQvxDZU4IIRZAZU4IIRZAZU4IIRZAZU4IIRZAZU4IIRZAZU4IIRZAZU4IIRbw/wGRVjjH6AEUTQAAAABJRU5ErkJggg==
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@@ -18888,114 +20404,124 @@ Name: C, dtype: float64</pre>
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 <h3 id="Seaborn-Color-Palette-Example">Seaborn Color Palette Example<a class="anchor-link" href="#Seaborn-Color-Palette-Example">&#182;</a></h3><ul>
 <li><a href="https://seaborn.pydata.org/tutorial/color_palettes.html">Documentation</a></li>
 </ul>
 
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">palplot</span><span class="p">(</span><span class="n">sns</span><span class="o">.</span><span class="n">color_palette</span><span class="p">())</span>
 </pre></div>
 
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">palplot</span><span class="p">(</span><span class="n">sns</span><span class="o">.</span><span class="n">color_palette</span><span class="p">(</span><span class="s2">&quot;Paired&quot;</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
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-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[129]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[95]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">palplot</span><span class="p">(</span><span class="n">sns</span><span class="o">.</span><span class="n">color_palette</span><span class="p">(</span><span class="s2">&quot;cubehelix&quot;</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
 </pre></div>
 
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-<img src="data:image/png;base64,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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[96]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">palplot</span><span class="p">(</span><span class="n">sns</span><span class="o">.</span><span class="n">color_palette</span><span class="p">(</span><span class="s2">&quot;colorblind&quot;</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
 </pre></div>
 
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@@ -19108,46 +20646,48 @@ Name: C, dtype: float64</pre>
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-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <h3 id="Seaborn-Plot-Examples">Seaborn Plot Examples<a class="anchor-link" href="#Seaborn-Plot-Examples">&#182;</a></h3><ul>
 <li>Most of the time, I use a regression plot from Seaborn</li>
 </ul>
 
 </div>
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-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[89]:</div>
-<div class="inner_cell">
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[99]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="n">sns</span><span class="o">.</span><span class="n">color_palette</span><span class="p">(</span><span class="s2">&quot;hls&quot;</span><span class="p">,</span> <span class="mi">2</span><span class="p">):</span>
     <span class="n">sns</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;F&quot;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">df_demo</span><span class="p">);</span>
-    <span class="n">sns</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;G&quot;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">df_demo</span><span class="p">);</span>
+    <span class="n">sns</span><span class="o">.</span><span class="n">regplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;E2&quot;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">df_demo</span><span class="p">);</span>
 </pre></div>
 
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 <ul>
 <li>A <em>joint plot</em> combines two plots relating to distribution of values into one</li>
 <li>Very handy for showing a fuller picture of two-dimensionally scattered variables</li>
 </ul>
 
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-<div class="prompt input_prompt">In&nbsp;[90]:</div>
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[100]:</div>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="o">-.</span><span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="o">-.</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">]],</span> <span class="n">size</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
 </pre></div>
 
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">jointplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;reg&quot;</span><span class="p">);</span>
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cS2bVIZk1hSJ5bUiWf/jCV1ogk9G2wuBZ1oQp/0Gqoi01DjcRq0G4P5Zm1j9s+GGi9BnyasTwQrEtO0UBS56DNL66Z4rLDMbJoWKd0knTFJZUzSuvNn7t+TGZPRWJq0bpXmDawAyhKETpwfxbZtZFlCliQkSUKWQUJCliUkiezj5J8jS87/Uz0a0URm0uOyTP615oNt29i2U84yTRvdtDBNK/un89+GaWGYNoZhYVgWhmFnH3OeZxgWad0ilTHI6Jd+WZElIrE0ad0kkzFJZX9x40l9UtaSQ5Ig7HdRE3BTH/LQ0RaiJuCmJuimJuA8Hg642dBexzd/1Jn/uUxap6dfp6c/UvR7F+UCwXJFUWQe/9mxeWf1E/9uKIqMX5FnlX9btp3/uy6YmbIEoW89d3TRXlvCCVxSPkiRt4WxbBvLBhsbywIKAk+p0bKpuUtz0vSAT6Pe5XXqyJqCx63i92r4PCohn4bf63Iaql4Nv9f5b0UuLqAGfAtX+Ex8DZ9HQ7Lndje30HUsxvuo5DXM9BlX6vuYy+9FJXyf4Bx2nevvcknWIMzgi0Ky7UXYgQUCgUAgKAJxklAgEAgEZUMEIYFAIBCUDRGEBAKBQFA2RBASCAQCQdkQQUggEAgEZUMEIYFAIBCUDRGEBAKBQFA2RBASCAQCQdkQQUggEAgEZUMEIYFAIBCUjbK5aA8NxbCmMe+cidpaHyMjiUVY0eIh1rw0VNuaq229INY8H4p18M4x372xkpnpM6i6TEhVq28+h1jz0lBta6629YJYs6D0VF0QEggEAsHyQQQhgUAgEJQNEYQEAoFAUDZEEBIIBAJB2RBBSCAQCARlQwQhgUAgEJQNEYQEAoFAUDZEEBIIBAJB2RBBSCAQCARlQwQhgUAgEJSNsnnHCQSdpwbZs7+LwbEUDWEP917fzo6OhnIvSyAQLCELCkJf+cpX+NnPfoYkSTz88MN84QtfKNW6BMuczlODfPfFEyiKjM+jMhrP8N0XTwCIQCRY0dj28jIvnY15l+Pefvtt3nrrLZ555hn+9V//lX/5l3/h9OnTpVybYBmzZ38XiiLj1hQkScKtKSiKzJ79XeVemkBQVk5dGCv3EpaUeQeh6667jn/+539GVVWGhoYwTROfz1fKtQmWMYNjKVzq+F8/lyozOJYq04oEgsrAVhTCNStnL12QMEHTNP72b/+W+++/nxtuuIHm5uZSrUuwzGkIe8gY1rjHMoZFQ9hTphUJBJXBm4cu4NJWzvgJyS5BATKZTPJ7v/d7fOITn+CRRx4pxboEy5wDR/v5+6c7UVWnFJfWTQzD5ncf2sE1W8XNjGDl8tUfvM9/+OUry72MJWPewoRTp06RyWTYunUrXq+Xe+65h+PHjxf98/OdHtjYGOTixeicf66ciDVPZm2Dj8/ctXGSOm5tg2/e1622z7na1gtizfO9/lxIZ0yAqvucZ2Kmz2DeQai7u5u//du/5Xvf+x4Ae/fu5dOf/vR8X06wAtnR0SCUcALBRFaYOm7eQei2227j0KFDfOpTn0JRFO655x7uv//+Uq5NIBAIVhwrKwQt8JzQH/zBH/AHf/AHpVqLQCAQCFZYFBK2PQKBQFBBrLAYJIKQQCAQVBQrrCckgpBAIBBUECsrBIkgJBAIBIIyIly0BSsG4dotqAZWWDVOBCHBykC4dguqh5UVhUQ5TrAiEK7dgqphZcUgEYQEKwPh2i2oFlZYDBJBSLAyEK7dgmphpfWERBASrAjuvb4d07RI6ya2bZPWTUzT4t7r28u9NIFgAisrCglhgmBFkBMfCHWcoNLZvrGRjG6WexlLhghCghWDcO0WVAOxeJqx0US5l7FkiHKcQCAQVBDzGLNW1YggJBAIBBWEbc3+nOWECEICgUBQQcxn4nQ1I4KQQCAQVBDmCtNoiyAkEAgEFYRhrqx6nAhCAoFAUEGkMytHng0iCAkEAkFFkcoY5V7CkiKCkEAgEFQQqRV0UBVEEBIIBIKKQpTjBAKBQFA2RDlOIBAIBGUjlhRBSCAQCARlIqObKyobEkFIIBAIKoyxWKbcS1gyRBASCASCCmM0li73EpaMsgWhtLGyTgULBAJBsQxHVk4QKts8oVjcSTcDPg1NEQmZQCAQAMgS9I+snHlCZQtCNjYZ3SITMfG6VQJeDVmSyrUcgUAgqAhqw14GRpLlXsaSUfYUxLYhkTIYGkuRyBgrbLq6QCAQjKe51ke/CEJLj2nZRGIZhiPJ7Hx1EY4EAsHKo6nOS/9wAnuFjHSomCCUQzdsRmJpRmMZDHNlfAkCgUCQY3VTkETaYCiSKvdSloSKC0LglOhSGZOhSJJoUsdaIXcEAoFA0N4cBOB8f6zMK1kayiZMKAbbhnhSJ5k2CHg1fO6KXq6ggug8Ncie/V0MjqVoCHu49/p2dnQ0lHtZAsGsrG4KIElwrj/KlZsay72cRacqdnXLsonEMyTSOr6AG6dfJJR0gqnpPDXId188gaLI+Dwqo/EM333xBIAIRIKKx+1Saanz0bVCMqGKLMdNh2HYjMYyjEQz6CtsBK6gePbs70JRZNyagiRJuDUFRZHZs7+r3EsTCIqivTnI+YFouZexJFRVEMqR1k2GIykiiQymJfpFgvEMjqVwqeN/tV2qzODYymj0Cqqftc1BhiJpIvHl7yFXlUEICs4XRVLE0yvHcVYwOw1hD5kJtlAZw6Ih7CnTigSCubFxVRiAj7rHyrySxadqg1AOy7KJxjMMRpKkxfkiAXDv9e2YpkVaN7Ftm7RuYpoW917fXu6lCQRFsbYliKbKfNQ9Wu6lLDoLEiZ89atf5fnnnwfgtttu49FHHy3JouaDYdiMRtO4NYWAz4WqCOHCSiUnPhDqOEG1oqky61tDIgjNxBtvvMHrr7/OD3/4QyRJ4t/9u3/Hiy++yN13313K9c0JG0jpJulIUvjRrXB2dDSIoCOoai5bHeb5t7pIZ0zcLqXcy1k05h2EGhsb+eM//mNcLhcAHR0d9PT0lGxhCyHXL0pnTPxeDa9LRcSilYE4HyRYLmxaU8Ozb57jVM8Y29bVlXs5i8a8g9Bll12W//ezZ8/y3HPP8cQTT5RkUaXCzJ4vSqZ1gl4XLk1GnC9avhw42j/l+aCzvRGOdY2KwCSoKjrawsiSxLGuERGEZuKjjz7id3/3d/kv/+W/sG7duqJ/Lhz2zVteXVfnn9fPoSqEfBruMjgvNDYGl/yaC6Xa1vzlpzpxuxQ8Luf7dWkKI9E0z73VRVOdl3DARSyl88Tek4TDPq7Z2lzmFVffZwxizUtBbr2b19Zy7PxY1a1/LixoN3733Xf5gz/4A770pS9x//33z+lnx8YSZPS5Hzitq/MzPByf88/l6JfA61bxezQUeXGyookloUfu2cLaBt+iXGuxaGwMcvFidR2W6x9O4NFk9AJ5diyRIWNYXBxNYRgWqirjdSk8+cKxsn8n1fgZizXP7/pzwTQtlOygz6u3NPGdPccYjabxeVTGRqtz2N1Mn8G8Jdq9vb38+3//7/mbv/mbOQcggJfeu8BYGQ5iTTxfZJdY0p2zjBmNZ/Ilob9/upPOU4MlvY5gMs11vsnng7I3OqZpIUvOn9GkzoXB+d/ICASLyfdfPMY/Pn2If3z6EOd6nHNCX/7uAVza8hQnzDsT+uY3v0k6neav//qv84995jOf4bOf/WxRP//OsQH27O9i12UN3LKzjaYa73yXMi9y54uSKYmAz4WnRP2iQssYALemYFoWe/Z3iT5EAYshIHjo9o18/amDpHEcEnIBSZbIqyQlnF6hKcaECKqAmoALlyozMLp8h9zNOwj96Z/+KX/6p3867wv7PBoDI0nePX6R945fZOu6Wm7btYo1TYF5v+Z8MMxL54v8Xg23JrOQyRGDYyl8nvEfq1tThGVMAYtlMHrN1mY+f/emccEtEkuT1k0s2wlGThvSFufIBFWBJEk01ngZGE1iLVOLsrK5aP/eJy/nzcN9vHaoh+FomiNnRzhydoQNbSFu3dnGZavDSEuoq07rJhnDXHC/qCHsYTSeyWdCudcWljGXmCpbTGcfX2g2NPF80GOPv0f/SJJk2sAwLVRFxut201y7tJm3QDBfmmu9XBiMc6JrhHq/Vu7llJyy2faoisT125r5o0d28Zm7NtJa7zSJT/dE+Pbzx/jq0x9w6OTgkhqUlsKPbirLGMOwhWVMAUtpMHrv9e2oikRN0E1bg5+aoBtVkcT3Iagamut8yBLs66yMc5ilpuzzhBRZYkdHA1dsqOfkhTFeOdjD6Z4IvUMJnvzFSV585zy37Gzjqk2NaOrSxMx8vyitE/DOrV80lWVMNarjFpOpssWJBqOl6hkJCx9BtaOpMo01XvZ19vDA7vYlrRAtBWUPQjkkSeKy1TVctrqG7oEYrxzs4cjZYYajaX78+hl+/m43N21v4d6b1i/ZmgzDZiyaJqkpBHwamlJcEJxYEiq3RLTSuPf6dr774olxAoJCg9FS94yEhY+g2mlt8HPwo0HO9EbZ0BYq93JKSsUEoUJWNwX4/D2buDia5LVDPbz/0SDxpM4L75zn1UM9XLuliZuuaCXkdy36Wmyy/aLIwvtFAofZspPF7BkJBNVIS50XVZF4+2i/CEJLSWONl4du6+Cua9aw74Ne3j7aTypj8lpnL28c7uPKTY3cuqOVhiWQd+f6RamsH52/DK4L5WCxvNhmyk6mUhiKoXSClYxLVbh2WwtvftjHw7d3oBZZlakGquKdhP0uPrF7Lf/lc1fx4K0b8HtUTMvmwLEBvvz9Q3z3xRN0DyzNPPaVNL9oqoO3333xxKIfvBVD6QSCydx9XTvRhE7nqaFyL6WkVEUQyuF1q3zixvU8+rmrePDmddQG3djAh2eG+fqPDvNPPz3CR92j2As56FMkuflFo9EMumktS5fuwrKYJEm4NQVFkdmzv2tRryuG0gkEk7lqcxPhgIvXO3vLvZSSUpU1JU2V2b2thWu3NHP49BCvHuqhdyjB6Z4Ip3sitDX4uXVnG9vX1yEvYv/m0vyiXL9IRZGrKq7PSLnKYkLRJhBMRlFkbtreyp79XYxE09QG3eVeUkmoyiCUQ5Eldm5sYEdHPR91j/HKwQuc6Y3SMxjnib0fUR/ycPOO1kWXd+f7RWkTr8cJRsthmF4xUurFQijaBILJ3LqzleffOsdL71/goVs3lHs5JWFZ3LZLksSmNTX89gOX8/ufupxt62oBGIqk+PHrZ/i/v/c+rxy8QCozvwOoxWLZNvGkzuDY4pijLjWiLCYQVBZNtT52XdbAy+9fIKOb5V5OSVgWQaiQNU1BfvWezfynX97J1ZsaUWSJWFLnZ2+f539893327O8iklhc9+6ceGFoLEUqu4FXIzs6Gvj83Zuo8btIpAxq/C4+f/cmkaEIBGXk7mvWEEvqvPlhX7mXUhKquhw3E021Xj59ewcfu2Y1r3/QyztHB0jrJq8e6mHfB71ctamRW3e2Ub+IpaW8OaoniW5YCzZHLQeiLLZ4HDjaz5MvHJu17yVGlgsK2dxeQ3tTgBcPdHPrzraqd1CQ7DLdph8/fXFJh9olUgZvHenjjcN9JFJOWU6SYPv6Om7dtYpVDfOc1loEdXV+RkbieFwqAW91iBeq0eWhmtbceWqQbz9/jETKwLRsFFnC41L4rfu3jgswhe4Rhe4S5cpIq+kzzlHuNZdqKmrhsLtfHDjPl7/3Hn/yG9dy4462cc/L6GbFDb+b6TNYtpnQRHwelTuvWs3NO1p59/hFXu/sZSSa5oPTw3xwepiNq8LcuquNjrbQotxZ2DYk0wbpjInPq+JzLw/xgmB+PPXSSaIJHUnKjZiwiacMnnrp5LjgItwjVh7f23OEWEKf8TmWbRPwavzd9w9y+KOBcXvWbz+0c7GXWFJWTBDK4VIVbri8heu2NvPB6SFePdhD33CCkxfGOHlhjFUNfm7d1cbl6xZH3m3ZNrGETipl4PdpeFxqCUbpCaqN/pEUskR+85AAC5v+kfHyd+EeIZgKWZLY0l7DgeMXOT8Qo725NNlWOVhxQSiHIkvs2tjAzo56Tpwf5ZWDPZzti3JhMM73fv4R9WEPt+5o5cpNjYtikWFYNmOxDEnNJODRcLuqr19USXSeGmTvU530XoxVSd/E0U5aloVlk//uZWn8L0E5ZfKCyqa13kfY7+L4+VFWNQaq1tOybEFIUWQk3Sq7iFmSJDa317K5vZZzfVFePdTD0XMjDI2l+OFrOffuVq7b1oTHVfqPK6ObjBgmbpdCwKuhVkG/aKGUutGe65u4XUpJXLeXQgjQXOfjwsX4pN9/SZLoPDWYv95sjuOClYskSWxdW8tbR/o511e97tplC0K1ATcpl0oq45iCVsLo2rUtQX6tZTP9IwleO9TDwY+GiCZ09rzdxcsHL3D9tmZu3N5C0Fda927bhlTaJJOxKv6w60I36MUY7Z3rm3hcalaFOP++yWKNHp/Iw7d38NWnP8Awnd97CZBkiZDfNW7dwj1CMBONNR7qQx5OdI/S3hyoSmPTspbjXKqMS3UR8DoHIZNpE90wy16Waq718fDtG/nYNWvY19nL28cGSGVMXjl4Sd59y8426kOlLYnkDrum0k6/yFth/aKZNmgobqNcjEZ7KfsmSyUE2NHRQMDrIpnWMS0bVZEJ+V14XMqkdQuZvGA6JEli67oaXu/s43RPhE1rasq9pDlTET0hWZLwuhzFmG7YJDMG6bSBUebsqCbg5v4b13HHVat488N+3jzcRyJt8PbRAd45NsD29fXctquNthLLu03LJhLLkFQNgl4Nt0spe2CG6Tfop146SdqwisoeFqPRnuubuIrsm8yUzS2lEGBNS5CLI4lx/Z60bop+j2BO1AU9tNR5OXlhjHUt1SdQqKjczbZBVSSCXo2GGi+1QTcel1L20pTPo3HX1at59HNX8ks3rqUm4MK24YPTQ3z16Q/4388d5VTPWMmdEXTDYiSWZiSazpdtysngWArXBA8+lyrTP5Iq2m17McY05OyFUhljVnuh2cZTLOUYiYdu3yhskQQlYUt7LYZpc/LCWLmXMmcqIhOaDrem4HEpGKZN2jBJphbX+202XJrCjdtbuX5bM50nh3jlUA8DI0k+6h7jo+4xVjf6uXXXKratqy1Z4LRtSGVM0noSn0cra79oOqUW2FMGp6myh8VotOeymL3v98yqjput3Daf9c23T3bN1mY+f/cm0e8RLJiQ38WqRj9neqMMjSXLvZw5UdFBCJxNWJElfC4Vn0shGPKQTmbKWq5TZJkrNzWy87IGTnQ58u5z/VG6L8Z5/MUTNIQ93LqzjV2XNZSsUWjbXOoXeTV8ZZjsOt0G3VznI5MVBOSYLntYrEb7jo4G7tq9ftaT8bOV26Za35b2Gvbs7+I7L5yYtN6FChlEv0dQKrasqaFnMM6TPz/BL1eRw3bFB6HxSHjcKkGvRtCrkcmKGdJGedR1siSxZW0tW9bWcrYvwqsHeznWNcLgWIqnXz3Nzw+c56YrWvn4jetLdk3TsonEMyTSOgGvC48mwxLJF6YLIMCcsodybrzFnLspXN9sQaYcjgbCS04wFX6vxtrmIC+8dY7brmihqdZX7iUVRZUFofG4NAW3S8G0bFK6SaqM6rp1LSHW3Ruib9iRdx86OUQkofP8/i5eOdjDdVubuPGKVgJerSTXy012dWsKfq+2ZOaoMwWQatgYp8rmEkkdVZZ49BtvTFr7bEGmMLNKpg0i8Qy6YTI0mhx33qdULJWEXFCdbFoTpmcowY9eP8PvPHB5uZdTFFUdhMApU8mSU67zu51zIk5AMsrSzG+p8/HLdzjy7tc/6OXAsQESaYOXD/bw+ge9XL25iVt2tFJXInl3WjfJGOWf7FqpZaWpsobCPoxHk0GSMCx7yk19tvJdLrOyLJvhSAqQkCTnn8UIDsJLTjATHpfKAzev5+mXTvKJ69eyuilQ7iXNStUHoUIcdZ1MQJHxezR03VFMpfSlL9fVBt08cOM67rxqFQdPDfOLA+dJpg32H+nn7aP9XLHBkXe31i9M3n28a4TXDvUwEk3TWu/j7uva2dFRj1RRJ4xKz1TB5a4JTr3TZQ2fv3sTj37uKgAee/w9DJtpN/XZyne5zGoslsG2QZIcO55wwIVh2vzDM0fwedSSZYfCS04wG5++8zKee+MsT796mj94eEe5lzMrFSXRLiUS4NKcA4ANYQ81QTdubenl3n6PxgO3bOC/fO5K7r9hLWG/I+/uPDXE3/3rB3z7+aOc7onMS959vGuEZ/adIZLU8bhVBsZS/MvPjrH/SD8p3YSymyItDtPJrA8c7R/3vMKsYTrp+HSy89ymPtt02dzgv9zXpygydUE3kiQRiadJ6eaUUvD5spQSckF1EvS5uPf6dg6eHKwKyfayyoSmQ5YkPJqC16VgmBZp3SKZNjBMa8n6Ry5N4aYrWtl9eTOHTg7xalbefeL8GCfOj7GmKcCtO9vYOgd592uHerJzZpy7dJeqkDHg5++cZ11LCLemEPBpuNTlZY46VUlqLG3wN999F69LyWcdxWQNs2U6xaj5dnQ00LEqNO51+ocTgISmyvkAWIqymfCSExTD3desZu+B8zz9yql81l+prIgglMORe8v43DI+t3P+KNc/Mk17SfIGRZa5alMjuy5r4Pi5EV451ENXf4zzAzG+++IJGmsceffOjbPLu0eiaTwTpNqaIjMSTQPZflEk1y/SqtZldyITg0sipRNN6mBDfcidzzo8LmVW6Xgxm3ox/a6Jr6MbJkgSId8lIUopymbCS27589l7ty3o503TYs2qWn7l7s38048P0zeW5oqN438/Kmnw3YoKQuORUBWpbP0jWZLYuq4uK++O8urBHo6fH+XiaIp/feU0Lx7o5uYrWrl2SxNulzLla9QG3USSej4TAtBNi9qgO//ftu1MlU1lTPxeDX8ZzheVmonZSyThBCBXQdktDWDbTimNmQMMLHxTn/g6bk3Fpcn4PJeCUKnKZpUqAhGUhmKG2hWDaTo3YP/Pdw9w4/aWih18V/07UgnI9Y9cmouA7WRHydTSlOskSWJ9a4j1rY68+9WDPXSeGiQSz/DcW+d46f1udm9r4YbtLZPk3bfsbOOZfWfI4GRAuulssrfsbJt0HcuyicYzJMtwvqjUTM46LCRsagqCr0uVSaQMfvWeAiWcS0GV5Oyh0658sCnVpj7V+aK0boqymaAsKIrMpjVhPjg9zOBYisYab7mXNCUiCE0gJ/f2uRR00yaVMUmmjSXJjlrqfPzKnRu5+9rVvNbZy7vHLpJMm7z0/gVe7+zl6i2N3LKjldqgcze9ub2WByGvjnNpCooq88zrZ6gN9nDLzjY2t9eOu0bh+aKAT0OrQuv3iVmHJ3tezO9xJPpwKevIBYb5nK8pVOB5XArYNindGndId6qDuxN/JpEyRNlMUBbamwN81D3G8a5RGsKecdlQpSCC0LRIaIqE5pUJeFUyhkUyaZAxLKxFTo9qgx4evGk9d161mjc/7OOtD/tIpk3e+rCft4/0s6OjgVt2ttJa788P5Msp5RRFxuNWiSR1ntl3hgdhUiCC6u8XTZV1pDIGsiRNmXXM9XxNYdCSJOgdigMStUEXo/EM33r2KEgSPo+aD2rfeu4Y2DY+r4bPo+bX8av3bFr2wUe4OFQmiixz2WonG7o4mqKptvKyIRGEikBCwq0qeEJLq64LeDXuvmYNt+5s452jA+z7oJexeIaDJwc5eHKQzWtquHVXG+taglMr5XCypKmCEEzoF2U302o8X1SMgelcz9cUBq3+aBpJkgGbaNKgpc7FcPbncv03t6Zceix7EHmlHCQtt4uDCIAz094c5OSFMY53jdBYU3nZkAhCc2Ciui5XrltsM1W3pnDzjpy8e5BXD/VycTTJ8fOjHD/vTFQcGksR9I3vGRUq5WbCsmyiCZ1kyhmmtxhjzBeb2QxMc2IG07SIJHQMw0KRJZpqxgsFchvaifOjaKpCyK9hmBayJGHbYGTLfaZlM/EclmlZTOyzTRXoctcYjmWoC7iqftMsp4tDuQNgNaDIEptW13Do1BADI0ma6yrLU676dpuK4VK5Lmemmkhny3WLFJBURebqzU1cuamRY+dGeOVgD+cHYnT1xwBn5EPQp+F1q0iSNEkpNxuGZTOWHaYXCHqQJOaV6S3lnWnhtVobA9x1ZduU17r3+na+9exR4ikj+75sMobNhaEEX/z6PrwuhbGETiptOr0yVcYwLYajaWQg95Wq2YOtTvlyfMCZyjJpoiKucNMMepfHpllOFwdhY1Qca5qc3tCxrtGKK8mVLQj9/Y8Pc/225mXzi5IzU83NPlpMM1VZkti2ro6ta2s50xvl1UM9nDg/imnZjMYyRBI6XpeCpkrcsnPtnF8/Y1iMRNMkYmkCXheqUnz6vpR3phOvNRJJTnutHR0NhPwuUhkTw7LzI0LAZiiSRpGdQqQNRBMZAl6NWFJ3vj9Zws5mOUGvSlo3UWTIGDbdA1FUxZFie9wqZF0VplPETeXiUO2bZjHO5IuFsDEqDlmW2LQmzMGTQ/QPV9a8oQVLo2KxGL/0S79Ed3f3nH4uktRLYmNSSRTOPqoPuakPeQj4NBRpccTQkiSxoS3Eb963hf/46StY3+p4p1mWTTxlkEybnB+IEU/N78xBKmMyFEkSTerZ8tPsFGOVUyomXsvjUme8Vipj0lLvw6XKKIqMIkuYWQccSXIyQUefIZHKmNSFPKiKhGlatNb7aa3zAhKqBJqmEvRpqIpTlo0lde68so3fun8rNX4XiZRBjd/F5+/eNKk3VewAwGphNmujxUTYGBXP6qYAPo/KsfMjZRl9Mx0LyoQOHTrEn/7pn3L27Nk5/6xLvbQ5Vesd4EwUmqnW1/kxddM5DJtZnMOwrfV+fvuByxmOpHi9s5cDxwdI6xa/eO8Crx3q5ZotTdy8o3VO5bnc+4gndZL5YXrKjOKFUt2ZTlfSK3x8LJZ2zgYV3IHPdK3cHXuux5NDKrhJMEwbGxvTBNtnUxvyUON3jbM+KTQ9DWdNitO6ybGuUR68ecOMv8/lzBoWi3K6OAgbo+KRJYnNa2p4/6NB3vigh81toXIvCVhgEPr+97/Pf/2v/5VHH310Xj9f7XeAxSLLEi5VxqW6CHhtMoZFKrU4cu+6kIcHb17PnVev5o3Djrw7lTF588M+9h/pY0dHA7fuaqNljs3J/GHXlETA53KyjymeV4pNdrqS3tneCPsO9+Ufj8QzDEdSSJKEN+sEMdO1chuWLEmYlk0uDkmQ/x5y34YNDEfS+D0qn7lz47jXWUigLdw0VUWac9ZQqUqwcrk4CBujubG60c/J7jG+u+cY/9dvXotcAUczFhSE/uIv/mJBF6/2O8D5sFRmqgGvxj3XruG2nW28fayffZ29RBJ6Xt69pT0n757b3ZBhOoddXZpCwKPhdo03Ry3Fnel0zeYX3ukmFHDlHw8HXAxH0oxG03hcCqmMMetEV4CnXj5Fz2AcRZYJ+VTiKR3TAllySpyWZSPJEoosEfK7Jm1oxQTa6YJF4aY5EstQOwd1nFCCTY2wMSoeSZLY3F7DgeMXeetIHzduby33ksonTDAtC2x45J4tNE6YAzMbc31+JTDTmm3bJp11ZkhlDEpdrftkS4hP3NzB2x/28cL+c/QPJzjWNcqxrlE6Vof5+O51bO+on+TeXVdXxKwjxZExu7Oy7rsag4TDPp5++SQDwwma6nw8dPtGrtnaXPR6h2MZgl513HkGVZEYGEnS6rn0eDjgRpElhiJp0rpV1LXuagxy1+71HDjan19jXdhLV38ECQmXplATdOP3qNi2TSxpTPruHrlnC3//dCem5XhzpXUz/7t8bjDBt5/9kPP9MTRFpjbkJpbSeWLvScJhH9dsbc6vYa7sfaoTt0vJS+hdmhN4977fM6/Xmy9T/S7nPs/+4QTN8/jOF5tq2jN8Xje2tHhOJht9LoZjGX765jnuv3XjrEbJi41kz2eQzQTuvPNO/vmf/5nVq1cX/TN/+a235qWOa2wMTnsWpFKZy5ptbNL64pXrLNvm6NkRXjl4ge6L8fzjTbXerHt3PYosU1fnZ3g4PsMrXUKWJLweZ7JrKeY1Pfb4e5MyjbRuEollxmVCucdzPZuF/G5Md82J/aAcU2U6QH7AnfO9OXq7upAHWZYmvdZc1/voN95wDhQXfMZ21hao0CNvMUtSU625MEMrzH4nijLKRbn3jLkGwFIZmM7Eri3N/Nk39/Pr927m9l2rFvVaMPNnULZM6Hc/ub2iFBqVgsT4cl1Kt0ildadhXoKPS5YkLl9fx7Z1tZzujfDqwR4+6h5jYCTJUy+f4ucHznPzjlbunsOdtWXbxJM6qbRz2NWrqSwkFk1X0rvn2tXsO9y3KE3o/ITUtKMq1E3nMOvurU2zltZyPPb4eyiKjJlV2UkSWLZEJJ6hqda74P7ndGVAj0spa5lOnNWpPq7Z2kzHqhDPvH6G3duay3pAvfrcK1cIOXcGv1ulPuSlNujB79VQS9RIlCSJjrYwX/jEVv7DQ1dwxYZ6JAlGYxl++sY5vvSNfex9t5vEHOTdpmUTiWUYjiadEtU8JzTlppVOlDo/ePOGKR8vlQP2TdtbiCcNDNNEUyQCXo1fvN/Dt549OmmK61RHCwbHUpimhW3b6KaNblqAjWFaJel/TieFxraXTBY/FctRdr7ckSSJR+64jNFYhufeWprfk+koSfj7xS9+UYqXEcyAo65z3BnSutM/KpU7Q1uDn89+7DKGImt4vbOXd48PEE/q7H23m1cP9XBtVt5dEyhO3q0XOHX7S+zUvZhN6GNdo9TXeMZlGr2DTkmyGD84j0vJGp062LYj5FCyZ40WmrFNpwT7zgsnynpgcznKzsvJQofaFYNpWtxw5Wpu/bCPF97u4t/ccRk1QXdZBt0J254qxK0peArcGUo1+6g+5OGTN6/nzqtW8f6pIV5+t5tUxsxKvfvZubGeW3e2FeU9ZQMp3SQ9D6fucqnAppJeF+sHB2Q9jiTkrM+PbTufgypLJc3YJqv1usoaBMRZndKyFD2hHG7FOa7w//1f+/j7L929JNeciCjHVSkT3RnqQqUr1wV9Lj5120Ye/dyV3Ht9O0GfhmXbvP/RIF95qpN/3nOcc31FCi2yTt1DkRTxtFHUzyyl60IhU52+V2R5UvCcboNP6Ra1QVd2/IOEy6XQEHYT9E2WeZeScjoWwPTlU9EPqny8bpWOVSF6hhIcLpN7jQhCywIJTXFKdQ01XmqDbjwuZcFKNY9L5dadbXzxs1fyb27dkN94j3WN8PfPfMg/PPMhx7pGKEZgmTvsOjiWJDVLv6hcPYapNnOPW8XjUora4BvCHlRVoaXOx+qmAC11PlRVWfSMpNxBoFIP0AqKY+OqMD63yteeOpQfCrmUiHJclTLTX/xSl+tURebaLU1cvamRI2eHefVQD90X45zti3J2z3Fa6nzcurONKzrqZy255Q67ujUFv1fDrcmT1lWuHsPkia0yIZ9GJJ4hEsugKBKrGvzTbrLlLEuV68CmOEBb/aiKzI6Oet460s+zb57lU7dsWNrrL+nVBCWhmL/4heU6n0vBMCGlG6RS8599JMsS2zfUc/n6Ok73RHjlYA8nL4zRN5zg+y+d5IV3urhlRxtXb2nMD9ebjrRukjGcflHAoyIXjEGohM288DOuCbrHrWG6zbXUFjKdpwZ56uVT9A8nAInmWg8P37GxojZ3Ic9eHjTVern9qtU8++Y5rt3azKqGIg6qlwgRhKqQuf/Fl1AVCCgafreGbpgkMyZpfbyZ6vGuEV471MNINE1jnZ8btjVNOZVVkiQ6VoXpWBXmwsUYrx7q4fCZYUZjGX7yxln2vtfNDZe3cMPlLZMa/YXkJ7umzXGHXSvBD2y+m2upMpLOU4N867ljxJMZQEKSbHqHk3zr2aP81v1by7bBHzjaz5MvHMt/LxcG45NMcYU8uzr5tw9u550jffy/zx/jj3/1qpIcPC8GEYSqkIUYaEqSY/fi0hSsbI8jlTb58OwQz+w7g6LIeNwqY7EUz+w7w4Mw7XhwgFWNAT77sU0MjaV4rbOH905cJJEy2PtuN68d6uHarU3cdMXM8u7Cw66+rFN3uf3AZvuMS90Hmfh6sUSGVNpAkmRyFU4LZ5JvubKMzlODPLH3JEjkM/BU2iQiZwgXfL+VJM/uPDXI3qc6px37LrhETdDNI3dexreeO8ov3u3mY9esWZLriiBUhZSqZyJLEl6Xis+tcvj0MK2NAUzDJpk2ABvTsnntUM+MQShHfdjDp27ZwF159+5+0rrJvg/6ePNwP7sua+DWnW0zTnU0c07dSSk/ZrxcHr8zfcYL7YNMHO+9pb1mnDv4aDzDwHAC2watQKAh4XxG5coy9uzvQlWl/ARZt6YQ8DnD/zxuteLk2bnvye1SRL+qSG66ooW3j/Xz1Cun2LGxgaaaxZ/CKtRxVUipJbm2Dad7IiQSOpJkUxt001DjJex3MRbPzOm1gj4XH7+unf/y+Su597p2gl5H3v3eiYv8zx8c4l9+dpyu/pnl3bkx4yPRNBnDmpMFUOepQR57/D0e/cYbPPb4e/MemrilvYah0RTdA1H6huJE4pn8Z1xYqktlTEajaYYjKf7hmSOzXi+3MY7GM/nx3s++1YVh2uMk6aoig8Q4M1sbp89XrixjcCw1LigDzjh5l1KR8uzc9+RxqWVxkqhGJEniN+/dgixJfPu5oyX3rpwKkQlVIYvRM8nd+dtAMu0IBpyRwDV4NIX0HEeVe1wqt+5q44btLbz/0UVeO9TLUCTF0XMjHD03wrrWILftbGPTmppxhpyFZHSTEcPE41IJeseLF6biwNH+kii1Ok8Nsu9wH36vmveRiyV17t/tBPlTF8awbBtZzvnESciSREo3J11vqjLbxDNQpmWTSOmE/K78GsIBF4NjKWzbwrQlJMm5WfC4lbJlGQ1hD7GUns+EwMkO2xr8U5q8lhsx+nt+1IU8fOauy/j288d45f0L3HFV8cbU80EEoSql1D2TiYq0WDJDOmNy/+611IbceTPVZFrHnIOZqqbKXLe1mWs2N/Hh2WFePdjDhcE4Z3ujnO3Nyrt3tXHFhqnl3bYNybRBWjfxZcUL0012ffrlk7OKCYrp5eTuoH0ebdzk1APHL7LvcB+S5KzAMCxsQFacLEXLjg3PXW+qst3AcIK6kHtcRqEpMoZpjluDosh5hVJOHddSV1513L3Xt/PE3pMYkllxpbepyN1YuYSd0Jy5ZUcr7xzt5/svn+KKDfU0LGJZTgQhATA5u2ptDHDXlW3s6GgoMFN1DFUzhjXnUeWyLHHFhno0ReKFd84zOJbCMG1H3v2Lk7z4znlu2dHK1ZubxvVBcliWTSyhk0oZ0/aL+ocTeLTpD7kW28uZeAedTBuMxZyZRS5VxutWiKWM/HHbnD9cyKeNu95UCjtVkYnEdfxeF/GUwUgkRUZ3AlAkniHo0/Kb+2fuLN+B0y3tNRzrGp0UrMNh3zh1XCU3+nM3VqmMgSxJFR80KwlJkvjN+7byp9/cz7f3HOP/eGTXtBWLhSKCkCBPYXY10wyWiaPKEykDvYhy3fGuEX7yxlkURaaxxksibZBI6eiGzUg0zTP7zrL33W5u3N7K7sub8yO7C8n1ixKqQdCr4dJkct5uzXU+Lo4kphVsFCu7LhQlJNMGw5FU/r2Zlk08ZRDwqEQTer5nUxd04/M45rK5601VDgoHXAyNpZwDsNl+m6xIuFWZWFLHNJ3y1lJt7rmzSD2DcVRFJuTX6BtOcKJ7lLDfTdCnjQvWd+1ez9qGuY2GLxe5z2/v+z1CHTcP6sMeHrljI//8s+O8eqiH2xZp7pAIQsuMpbRQKRxV7mRHjrv3dNnRa4d6soPPnCDg92hoqoxHU2ip9/P+iYvEUwYvHjjPK4cucN3WZlrqvLx3/CIj0TS1QTe37Gxjc3stumExkh0znnNeeOj2jXz9qYPTHnKdLsMZGE7w2OPv5T+rwtLkWCyd8yVFVXJ3ghJp3SLo0xiLO0aTkbiOblhoqpy/3lQKO0WRaWvwMxxJA6CqjitDLoBNN0RvMchlhmOxjDPW3LYZiWWQcOZaJdMGIb9rXLBeyATXctj77Oho4K7d66tuEGalcNuuNt45NsCTvzjJjo6GSWfCSoFQxy0jCpVXs82+KSW2Td67rrHGQ03QjUtTJqnaRqLpSWMdNEUmntR56NYNfPGzV3LLjlbcmkJGt3i9s5enXj7NhaEEqioTSeo8s+8Mx7tGnOvi9GpGoilGYxm2d9Tzax/fPK1Sq9CgNJfhGKaNpir0jyT52tOH+cO/fY09+7u4aXsLNX4XumGhKjJ1QTe1QXe2BOeMY48lDaRscDJMk3jS4KbtLfl+UCzh9IB6B+MkUnpexfjw7R34PCrrWoO01PnweTRg6ZvmuczQsm0U2RFXSDjlRVkCw7zkI7bQtZXrd1OwMCRJ4jfu3Yxh2vzgpZOLcg2RCS0jKsFCJTcZ1qPJ6KY9LjuqDbqJJPVxlj66aeXvrkJ+F/ftXsvtV67i7aP9/PxAN6blbPgXMyYel4LbpUw6u5QTLwyPpVjbEuL/+MyVU4ocpstwPC6ZaCKDbUNGtxiNZ9h3uI/P370JYFw2k86YRJM6Nk6fKhxw5Q9qpnWTY12jrCvoPdWF3ETiOkNjKdoa/Pk+T0O4a0qlWWHTfLEzh1xmqCpyPvDkPjVrwhmljGHh0WS+9I198yptVcLvpmB+NNX6+MTudp7Zd5Zbdraxde3s5wbngghCy4jKkqRKaIqE5pUJeFXSusXHr1vDv756moxhoikyuumUy27Z2TbuJ71uldt2rWL/h31YQDxpYFpOQEtlTOJJnRPnR7lsdXhcs9QG4kmdZNrA71EdmXnB/y8UXwwMJ9BUhZBfI5rQAQlZcqagFm6QhYHLMEziKQNZcq4lSRKxpI5LU/BmD2sOjqXGbbiOUauLtG4S8Gr5NcymNJvrgdj5BKxcuTDkdzEcSWHZzmelKk5pzutWse1s3y+pO3YbcnJe8vfK+t2sbJZiqN1UmKZFY2Nwyv/36w9sZ//RAX7w8im+8p9vRy5yZExGN2cdlCeC0DKiUidc5rKj67a14PNqvPJ+D71DccJejZuzPZ6pqAt5iCR1/B6NVMYkltDRTQvDtPn288dorXfcu7dPkHdblk00oZNIXbIBysm6c+KLxx5/j/6RJNGETipjOvurBGo2S8ttkIWB69SFCIosURP0MJY9vAoSkXgGb1Y12BD2FLXh5pRm3/xRJ72DCcAeNyxwLpnDfAPWhcE4qbRJwKdRG3QzFstgmDZt9T6u2dI0Th2nyhKGZeNxqeiGNedMplJ/NyuRpRxqNxdWN/h498Qg/+0f32B1Y6Con/nth3bO+hwRhJYRlT7h0rbh8nX1XL6uPu9bl5hhzMQtO9t4Zt8ZwBmdLctOOSzgddE3nKB3KMGTWXn3zTtb+dj168b9fKENkM+r4S0IRlvaazjRPZr/b9sG04ZAVuJduEHmAtej33gDn8c5fW/bNsPRNBI2umGNc63Ys7/4Sadpw6K+xpP/vnLBYy6Zw8SAZZoWY7EMX3v6AzpWhcdlRc+8fppn3+zCsp1el0tzVHlel8KGttC45z5YcI3cey9mPVNR6b+bgtlpa/DzUfcYx7tGaav3F50NzYYIQsuIhTgpTCznPHLPlkWV4uZ86xxlnT3luaPN7bU8CHln79qgm1tucEp3L75znoujSXTTCQbPvH6Wl967wA2Xt3D9tvHybsOyicQzJAqC0YnuMUI+F8mMia2bWLbTD0ll3cUTKQNVcjbf3OdYeDefExOMxRyXiRq/a9xnXcyGO9Ph2rlkDoUBK5HSGY6mwbYxLfioe5RTFyLcf0M761pDPPtWV1aIIGNaYFomQZ+L5lrvjKq8hR78rARndMHCkCSJLe21vH1sgJ7BOKubisuGZkMEoWXGfJwUpirn/P3TnXzmrqU4nS/hUqX8uaOJ2dHm9tpx5brjXSN5t++GGi/JjOE837CIJnReeOc8rxzs4bqse3ehFU5hMLIsm7YGP8m0iWU7tjmOzNp0RqTbNoYNYHO6J8LfPtVJXciTP1jqyrojhAOTvdKK3XAnHq4tlIy31ftIpIz8tWbKHAoDViSh5wOQBNlgY/PsW10013gwLZuc0lyWwLIlEimdwbGZ72pLcfCz3M7oc0VMjJ1Mc50Xv0flTF9UBCFB6Ziq/2Ba1ryVS/P9y1uYHeWUdYdPD/LSexfoHUpgmjYZw0SRJYJ+F5Iq4XNrqIqMz6WweV09r7zfTVo3ea2zlzcO93HlpkZu3dE6znbEsGx0w8S0oSboIZNxpNaKIlOTDVqGZWOa1qVzM5LT+/G4VVTJmYM003srZsMtPFxbeChWUxUnANo2qizNeq3x4gmLnLJayUYbWXJKk/0jKTRFxrSsvAouJ8XOZTTTfXcr7eCnmBg7NZIksa4lyIdnRxiLpceN8JgvIggJpuw/uDVlXsql0vzldZR1x3tH+PmBblRFxu/TSCZ1MoZzjmUsloaAY5SqKTLRhM6n77yM3Vub2H+kn32H+4gndQ4cG+DdYwNsW1/HbTvb8ndvN+9w+k2JlEFNwIXfq4Ftc+/17XznhRP4PCr90TS2ZWPh9Iwsy8aXrT7lBAg5R+a5ljw9mkzGtBkcTaJmz+rYNtjYWJbF4GgSWZII+eGx379xxtctzLyGRlOY2KiKlFcG2jjnsSzLwutWiSYtLNtGwpFiy5JzwHa2724lHfwUkvLpWdMc4Oi5Ec4PxEUQEpSGqfoPhfYzc6GUf3mfe+scyYzJSDSBKkv4PSoBn5u07pw9iiV1R61VcNbI61a5/cpV3HRFK++eGOC1Q72MRNN8eGaYD88Ms6EtxG27HPfuB29az2uHerg4kqQ+e2e/pb2W1Y1++keSZDImhXoJGxiNZRiNZXCpKcIB14xBdlzQcSkMjSUxLSfzyPWggj5H+WeYl65k5a9qc2EwTuepwVk/u8Kx5F97+rAT1LCxs+v2uhUCXg+ReCZ/IBVAluH+G9bmFYNi43UQkvLpcakKDWEv/SMJtlO34NcTjgmCKecTGYY9L+XS4FgKlzq9iWixdJ4a5NSFMS6OJEhnTFIZg9FYhoujcVIZg7Bfoy7oRlUlLGvyWSNNldm9rYX//MguHrlzI631jsjidE+E//3cMb729AekdZPfun8bX/zcVfzW/dtobw4xEk1zw+XNhAMufF5t2vVZtuMAYVn2lDNqJjoE9A7FSWYsTMvK+805YzMMamawQrFteGoOJ9V3dDRw/w3tyLKEmTWeDXode6RrNjdCduyEIjvfS8DrYl1rCCjdd7ccKHTXyCEk5ZdorvMSTzk3ggtFZEKCKRvp81XHleI8SG4Dz41MkMg22iUbkNF1k+GIhaZIbG2s46FbOmirn3qtiiyxc2MDOzrq+ah7jFcO9nCmN0LPUIIn9p6kLnSeW3a0cdWmRjRVxgbWNIe4YZvF4OhZgj4te+ZIHycjzzX1I/EMTbXeSRv1xIzQyu5nuQAk4QQhw7TzRqbT0T8ytyDw4M0bWNcamtTb2bO/C59HzWeNiZQ+TsrtyXoAirM8c5OUr0QBQ2O2xzo0liIww81aMYggJAAmN9JnctGeiVKcB8lt4OGAyzmLkx3oZpg2imwT9LtRFWmSKs3jdxGLpqZ09JYkZ0DfpjU1nB+I8uyb5+jqjzEcSfPj18/ws7e7uG1XG9dva8bjUrlsTQ2KLDE4miLo0wjW+YgmdOIFd365pv5UG/XEck6+wGaTfz+5xws92qZm7tMtpxJG5HpdcEnKLQG2bTMaz1xyRUCc5SlW4bhSBQx+j4oiS0QTc5u8PBUiCAlKSinOg+Q2cCl7Rx5J6GQyjjTa79ForvVO+ZpBn4v6kHtWR+9EyiCayFATdJHOmCTTjh3Qz94+z8vv93D9tiZuvKIVsiKBSDyNlJAJ+V0EvBqxRCZbunQyrak26okZoabK6IaVlU1L+Z6Mpspckg9MTaGTwkKYKOV2ApDzT04IEfRp1PhdCz5nthyygWIUjitVwCBlf1eiohwnWEzmu7HM9zxI7npjsTSReMbpy3jmNuYg5+iteWX8HnVKV4bCkRI+t0bIZzGWcCbJpnWTVw/1su+DPhRZIuBVSeoWum4xHEnh1mT8Xhe1QTejsTQ1ATcP396RFwUUqt8Kz/n4PSpj8Qyy7LgtKIqUDUjZ7Iipw5DXrfDw7R3jPp/5bvQTpdy2bWNlA6ksSZiWM9fp1z6+eU6vO9GFwbTsFZENwMoWMPg9GqOx9IJfRwShFc50G9uBo/1LWmYoLGvUBN0MR1IMR9LYto2qKvMqC010ZUjrJum0wdiEk/+KIlMbcJNI6axvC9N5agjTsjEtx7zT41II13gwTYtYQicSS9EYDvPgzevxuhV+fqCbbz17lGTGxO9RCfldTlO74JxPS52P23e1jfNju2prMy/uP4eiyJimxXA0jZnNkBRZorXOmx/nvdCyT+57TmdMDNPAzkrCFVnK++5JkiNkmMtdfOepwUkuDNFEhqDPteyzAVjZnnjOCJPZSslFvE4J1iKoUmba2Pa+37OkZYZxZQ1NQZIkRqNpxmI6HaumLr8VT86VwVGKXbYmzOBYGt2w0LMKKN10gs25vgi1QWeOUDThiBFy7t2aKuN2KTx06wa2rK3jeNcIT718kpqgh6Bfw7IhmtTRVDlv6xPwajx8ewd79nfx+gd9NIQ9/Oo9Ti/ry091jnvPM2V8Cyn7PPP6aZ59qwvTstEUZzy5qrocubbk9IRyUu6QX5vTXfye/V3zdmFYDqxkTzwlOwJkoYggtIKZaWMbjmXGWcrA4pYZJpY1vG4Vj0shkTLmNGm0mJLV7m0tPP3qafxeDVWRGItnSGUsFEXJl+ncmlNuGImmSWfPC+WC1rNvnnMOho6lkLP9nWTaIOx3Oed+dBMJ5/PqGYxPG+gn2vbA9J/xfMs+nacGs6UyJ1CYlkU0aRHMTqO1bcdNITfhtdA1ohgGx2Z3YVjOrGRPvNwNzEIRQWgFM9PG1toYyFvK5FjMMkMpyhrFlhALN46LkTSrG/18+tYOfvjaKUzT2ZTBab7WBt0kUzqfvGUDrxzq4WxvlKHIpTq4ZdqMRtPIssTF0RQuVXJ6RiEPY/G0kyVME+gLbXtme8/z/Xz27Hd6NbnheY5Lgk0ybTrzjly5wDu/u/iGsAfDtKZ1YVgJVJsnXqkwDAtVXfhRU3FYdQUz04G8h27fOOkA62KWGaY6MDvX6xW6UkuShFtTpjxICs7G8ejnruIvf2c3N+9oZc/+c4xE0llrHicLA6dMVxfysLm9lt954HLa6n3jDnTaOJuuZdnZdVuMxTP0DMawTIvmeh+1Ade44Xq5QD+Xz3i+n8/gWCprC3TpMSn7vtoa/Hz+7k3TjkMvhnuvb0fLljkVWca0QZYl7r9hZWQDKxnDdMq7C0VkQiuYmerZ12xt5vN3b1qyMkMpyhrTlbd6BuM89vh7U75uYV/MpSn0DcfxuFRa633UhNxE42lu3eW4MRzvGmFgJIll26iqBJZjdArjD6GqioTfrXLLzjb2dfaS0i3CQTeZjEEybZLMWiLN5TOey+dTWJJMpAw0VcbMmFi2lDcztW24MBjPT4+d7/e6kstRi0W5JqvOlVNfe51QwD3j4Lqc6/xMiCC0gpltA1nqMsNCrzdVeSua0ElmzLx9zsQS3VSCiKFIilMXInhcMmtbgtSFPJwfiLLn7XPOWU4bbMvOe7KlM+Y4Kx7dsGio8dJQ4+WmHa08s+8M8aROTcCFz6sCFvdlM5i5vOdinjtRbGKYFpFEBp/bmYiaMRypus+tUBt00z+S5GtPH8bjVljV4M9//3ORg6/UctRiUamTVXP89kM7uXgxSvdAlO3r6xZsaCuC0ApnOW0gD92+ka8/dXBcZhdL6Pi96rSqsqn6Ytg2kizRWOtjNK7znZ8dJxRwURf2Eg546B2MkUiZSNgYhkU44OLj17YzGEnxxgd9JNIGbx8d4J1jA2xfX89NV7Ry5MywY5Qa8nD3te1sXFOT7z3NxlwCwkSxSc7lOKNbBH1O2c2lyYQDbpJp59CubTv/Pxegz/ZG2He4b8re2l2Nwbl/MYJlRypjMBbL0FKCg9SiJySoWjpPDfLY4+/x6Dfe4LHH3wOY1OPwuJVxg+1gvKpsYl/M8XGTHCeDbF9JVmTO9UaJJ3Qsy6al3s+qRj9+rwszu4G/fbSfVQ1+Hv3clfzSjeuoCbiwbfjg9BA/fcM5C/TQbR184f6trGsJMRbLMDAcJ5E2sGZQGE00Qs0FhM5Tg1M+fyoT0pDfhapINIQ9pDIGybTzT+695tRsuR7aC+90F91bE6xMuvpjgDPye6GITEhQlcw0DbZQ0v3Y4+/NqCrb0l4z7rR/xrCQZYmQ75Ipo7OpO9NEpazAQDcsZAna6n0EvBqj8QzP7DvDgzet58btLVy/rYnOU0O8erCH/pEkH3WP8VH3GKsb/dy6axXb1tZi2U7QiyUlvG4Vr1tBU+RxvndzPR80lYqusCSpqQqG6bg/WDaosuT8mQ1cLlUmlTFoUMer7laKC4CgOE73RADY0BZe8GstKBP6yU9+wic+8Qnuvvtuvvvd7y54MQJBsRRuzrm7dVWVJt2tz6Qq6zw1yL7Dffi9KqriTHMF8GQPjubIGBbNtZ5xrzM0liKa0MnozhmiljofjbU+9h/pAxzngSsva+QPHt7Br9+7mbUtThmr+2Kcx188wf/8wSH2HepxZgtZNvGkztBYipFoOpuZOWuZ63iFqd7vWMxxnriYFVXkB+hlXSFsyAddxyFCzWeHybRB/3CCCxdjJFIGB472L+h7K5aJWe50mZ+gPJzuGaMh7CE8hzNl0zHvTKi/v58vf/nLPP3007hcLj7zmc9w/fXXs3HjxgUvSiCYDWcTdrzOcucVagOuSZvzTOKL3BA3n0cj7AxcZSyWJp40SOvmOMXgZ+7eNO51bBtqgy68Hi3vqODSZCzA79Xy5qmSJLGlvZYt7bWc64vyysELeeuef3n+KCGfxk1XtHLt1iY8LpVU1r9OUxV8HpW1LQH6R5IoBRLvmc4HTXy/Hk3Oe9LJ2cOFkuSo+JAcObXfo+J1q/kAfc+1q9l3uI9IxiQSTztPlCRcmpzPNhezj7hSnamrBdOyOXpuhJ0bS/NdzDsIvfHGG+zevZuamhoAPv7xj7Nnzx7+w3/4DyVZmEAwEx5Npnc4iSQ5m6tpWlwcTdFa55303OnEF1OJEkJ+F6ZlT+sknfszV+YrJJrQqfG7CHo1fG6VlG6STOp5GffaliC/fu8W+oYTvHaoh0Mnh4gkdJ7f38VL719g97ZmbtjeQtDnIqObZHSTKy9r5PXOXshKq6MJHdO02NJeM63svDAQnboQcdR82fM7l1wNJDa2hdnSXsML73TTPRDD41K559rV+XlE//DMEexsfyzkc4xkTctadE+4lepMXS18dH6EeMrgig31JXm9eQehgYEBGhsb8//d1NREZ2dnSRYlEMxKTiuNXDCfx87PwykGjybTN5SYZFsT9s0+pGs2zzBFds4K+bPBKJ7U8z51LXU+fvmOjTz8sU389LXTHDg2QCpj8vLBHl7/oJerNzdxy45W6kIeNrSF0Q2Lt4/0Y1o2a1uCrG3y8/LBHmR56kyhMJOwsplPTomnyJIzm8lyAtm+w32EAi4aVEegse9wH+taQ+zoaMDnUWmo8eT7YOAEhPn2hopV+a1kZ+pq4N2jA0gSXL5+4aO9YQFBaCrPIGkOG0B9fWC+l6axCmWiK2nNB4728/TLJ+kfTtBc5+Oh2zdyzdbmkq4tY9o01voYi2XQDQtNdYbg6aY9ad1TrQcgnjazPRGbdMbkYnZmkSyBL6FjGBYj0TSnew7z8F2X8dl7tuRf867GIOGwj6dfPsnAcIKmWd6nZdkk0zrxpIFe4Dz8G790OZ++8zJeerebl989TzxlsP9IP/uP9BP0aXzipvXccfUabti1Ov8z//ijD2ip9yMByYyJS7NJZQz2vt/DXbvXs/epTtwuBdO08+MawLHrkWwJVZFpbfBzqi+G26XgcTnbgEtTxr1Oa2OAkUgST4HIIZUxaG0MzPl348DRfp7YexJVlQgHXMRSOk/sPUk47Jv0mZXyujmq6e+fz+vGlipXuHzgaD+b22tZ317mINTc3MyBAwfy/z0wMEBTU1PRPz80FJty4NhszHfiZzlZSWsuvAv3aDIXRxJ8/amDc7aDmY26gIvR7GjtHLGkTjJl8IU/+1n+ThuYcj0537SgrTEaG19Ws2yIJXQUmfycnR/8/COaQu5x72Ftg48/enjHuJ+d7TOTsEG3SaR0fEEPw8NxAG66vJmwV+WHr53OH36NJnSefPEEr79/gft2r2V9axBJkjjdPerMWfJq+L0qhmFhWRa9F2NcvBil92IMsBmZ8L5sG4I+x7R1V0cdz755Dsu20VRHxu51q8iSlH+du65s47svnsAw7Xy2hw13Xdk259+NJ184BtlREc6EXBlDMnnyhWOTxshPdV3TtOZ1XSj/37+5BsBEMk28Qg+rJtMGJ7tH+dTN6+f0mc70Gcw7CN1444383d/9HcPDw3i9Xl544QX++3//7/N9OcEyYanq+RPLYdGETiSeIejTxpWocmdcJq6nfzhBa4Of0ag57UC53MC3nPt04XuY/4A5CZcm4Xa58QfdJONpUhkD24a3PuzD79WoyR4kjSV1DNPmwmCcf/rpEdY0BbhtVxs1ARfRlIGdhERSx+tR8bg1vG6VL3//IGOxNIZlI0tkh8xZWE78IJ0xuS0rPJAkp0dkmDbDkRR1IQ+yLOVFD1OJOh65Z8ukoFEMcymxCSugyqVnMI5tw3UlrGwsKBP6oz/6I379138dXdd5+OGH2bFjx+w/KFjWLHY9f9z0UpcCtk0iZZDOmNQEXQS8jmR0YrCZuJ7cuR/DtLCZerKpnS9jOZt57j2UQr1l2+B1a9QEXOiGRjylE0/paIpzSNbncYJKKmMQieuYls35gRjfeeEEYb8L3TSx3TYuVWE0liaZNvG6FOrDHjpWhzk/ECeR0pFsG0mSURSb2qAb24ZjXaNO7yvgYjiaRsKRbI9G04QDrnGmqBNFHY2NQfa+dWbOAWKuLuDLycljOXFhMM7G1eGSjZyHBR5WfeCBB3jggQdKtRbBMmAxJ01O3PxzZZpfvWeTszkHXOOGbBUGm4nraa71kDasrLu1PWUmBDmzTydMjcXSPPb4e8QSmaKyvWKyJdt2DE9rAi6a6nwMjqYYi6XQTZuA1xFKrG0O8LFr1/DKwR6Od40yllXlpTMWLlWmudbrZGvZEp6myvjcCiGfi2TaGW3u86jIspRX/fk8KlJ2/ZFs/8u2mbVsOt+Juyt5+NtyIZbUGY1leOiOy0r6usIxQVBSFnOzmanU1xD2EEvp+bk5MD7YTFxP7tzPUy+f4kK2xDAVZrasZds2tSEnwA4MJ6gLuccFtonZ3lyzpUMnBznXG8W0bYI+FzZOXwpJ4v7da1nXEmLdvSH6hhO8erCHzlODmJZNMmPSP5rEtmxCWZ84PZvhjcTS+N0qHatCpHWTWELn3uvb2bO/K3+j4PNo4ya6zpZ9FI7LmPgdzPSzcy2xzb/cKVgsLlx0+pc371wFhlGy1xVBSFBSFrOeX1jqy3mf6YbJ0GiS+29Yy1tHBjCkmQ+ZFgoW9uzvIpUxWVXvo2cogWVfGsXg86jEkwaG5ajLcnNT3JqCqshE4jp+76XT4hOzvbn2xvbs78Ln1TBNi5FYBkWSCPk1GsJetq6ry4t4Wup8/MqdG7n72tW83tnHgWMDJNOOqi81ksTnUQl4NbxuDcO0SWcsuvqitNT5+NQt69mytg5Vkfnnnx0jrRd/o5ALCie6x9AUmZBfy7tKFFtuna3ElrtGz2CcZMbE71EJ+V3isGoFYNs2XQNRGsIeGmu9JRV6iCAkKDmLVc/Plfosy2mkg+Q01yWJfYf7uPv6tbx3tH/GA5wwdVlPUWTCXi1vdppI6cSSzt2epkjYts1w1JmoGg64GBpLTXJVKNzE59obKyyR5TZ327bz7yWZNkikjPx5n9qghwduWscdV63izQ/72PdBLxndIpFynufWZFyazCN3bWRze23+OiPRFC31fj5/92ZeO9TDhcE4NX7XrFlJfuaSKqMbVv6z8Hm0kpRbC6+R1k0syyaazJYWPZo4rFpmLo4mSaZNLl9XGll2ISIICaqGXKlvLOaMH5Akp5cTDrhQFJkPTg2NMy+djqmyFL9HJZbQs+drrLw1jyRdUslZtk0koVMbdNPW4Cfg1abN9qbrjXlcSt7poLUxwF1XtrGjo2HS85Npg9FoGtuGv/ne+9y3u53L19eTTBvEU0Y+Mwp4Ne6+Zg237mzj2TfP8v4Jp0yX1i3SusVbH/bj0hTWtTjybmdsg0ljjZeH79g4rXHqdJ9XbcjDxZEEtg2RuI6iyCUptxZew7Ts/Oc9GssQTej5jLfz1OCyD0SVONTuL7/9NuGAiy/91u6iBtXNBRGEBFVDbvP52tOHAVCUS3Yytm0zMJwo6nVmsutRZYmBYScAORU4KZ99yJLTbzFNi8/cOXMDf6reWCJlgG0TTxkkUjojp4c4dmaY+29oH/d8M+tyDRK1Qacc9Z0XTuRFA163SjLjWAJ9eHaY1w71MBJNUxt089mPXUYybfDqoV4ujiY5fn6U4+dHaW8OcOvONrasrc2PGs8ZpyZSutMfcqtomsLEI+eFn5ffo2KGPIzF0uiGOWsWVSyF11AV5yyRbdvOn1nFoCRJK6IsVylD7XLD60ZjafYf7uPj161hdCRe8utU7rFcgWAKdnQ00LEqRGOtl5Y6X750lTEsmoqUjRbOEEqkdPqGE3RfjGOajslaU50Pt8tx51ZkKWt147hPezRlWgVZofPznv1d3LS9Zdxso5BXRVFkookMpuX0nizb5tm3HOfv3Cyk0WgaRZapC7nxe12T5vnIkmMJNDCaZP/RfkwbvG6VSFLn2TfPEvBq/OEv7+BX79nEmibHmaSr35F3f+UHnbx7fACjwLXBtiGVMRmOphkaTRJL6RiWlXdAmjhzyetWqQ152LSmhkc/d1VJAkLhNZySqM2lJY7PeMVco6Xl9c5eLNvm1p1ti/L6IggJqo7pxjPk7HiK/fmxWJrhrAu3hI3bpdAzlMAwTEI+DRvH6kZ2TKSpC3n4nQe3TRuAJg6f23e4j3uvb+ex37+RRz93FSndIpHSyQ2Sy51O0g2Lrz39AXv2d3Hv9e2EA25a6p2A2jecoHsgxkgkzYXB8XehP33jLNGETtCn0VTnozboQVVkXjvUgyxJbFtXx+998nJ++4FtbFpTAzi1/X995TT/zxMH2fdBL+kJpRXDsoklsmMlImkyuskv3bgWsp/zxHEYpaLwO/W4FII+pzcn4WS8dUE3Po8mPOSWGMu2efVQD1vX1pb0bFAhohwnqDqmU+Bds7V5WtXORMnvTdtbeOGdbphgWxNP6kTiev6AayThGI96NHXKDCj3uqcujCFJji+apClTquEawh5GoikUebyLATiuBTkVmEeTicQzRJM6EuQnn1qWPa4nkithRRM6iuwo+toaAwyPJfPrkySJ9a0h1reG6B2K8+qhHjpPDTEWz/Dsm+f4xXvd3HB5Czdsb8FfMEPJtiGlm6R0k6ZaH4/cdRlvHu6jbyRZshJcIRO/0+ZaL363gmGzKGfOBMVx5Owwg2MpHr69Y9GuIYKQoCqZiwJvqjM7+w73oSgSq2oC44x3C5VvXreab7xPF4DGuVXDONXYxLv2e69v59SFCLphTXZnwOkFKYoMkkQ8ZUB2/IKVFWEEfNqkoJYTM+TGPBimRUudl3DARTylYxiXrtRa7+eROy/j7mvW8FpnL+8ed+Tdv3jvAq8d6uWaLU3cvKOV2qB73NoM06a51sdDt3bQ3Bjkg4/6eeX9Czyx9yQ1gdIFpInfae7zFQdcy8crB3sIeDWuvKxx9ifPExGEBMue6c7smKYxyU1BUeRZlW9Tva4zNttGwlHQTSddDk8xeA8cEcRIthekG2bW183OWwaF/G48LmVSUJtqk75lRxtel4pHU0npxqRgVBfy8Mmb13PX1at543Afb33YRypj8uaHfew/0seOjgZu3dVGy4Tyi2XbvHu8n++9cIyA10VjrQfDsvnhq6eRgCtKLBYQHnLlZSSa4uBHg9x19Wo0dfE6NyIICaqeXElsOJahboo78+nO7KiK5PQhmOCmMIvybarXDfldDEccVZ1hWJP6Jrm7elUtnH/koCoSVm7Utu2MpbBtMC3yvRCAtG6OC2qzbdKSxIzBKODVuOfaNdy2s423j/Wzr7OXSELn4MlBDp4cZEt7DbfuamNdSyj/My/s70KSJHTDYmgsjabKKKrMGx/2sW19PaoiTSv1ng/CQ658vHTgPKZlc9uuxREk5BBBSFDVFJbEgt6p7XGmO7PT1uDP29jM5U47F/RGommGxlLO9Guy/Z1sCW1i36Qwa3JrTtZkY2OZji9dbj6XJEE4a78zNJZkLJbB61anLUUVs0lPCkZZd+7jXSPj5N0P3ryORMrk1UM9DI6lONY1yrEuR959265VbG6vYXAsibvgrlg3LDK6zYmxUYbGUrg0eVqpt6B6sG2bF/af47LVYVrr/bP/wAIQQUhQ1RRu7pIkTSkImMnPbq532rmgZ5g2lmXnA0/u5l+WnLM0M2VjuaxJgvx4cgMnI6oLefC6nefVhTyMRtMkUkZJSlG5YOR1qXSeGuLn754nrVt48vLuczx403r+06/s5OjZEV45eIHui3G6+mP8y8+O01TrzX5+Jm7t0tahmxa1QTeWbZPKmKQyJqoi4XGreFwzH4QVVCbDkTQXLsb5t/dvXfRriSAkqGqKsccpZW8hF/SiiTSKIo87byPhZEE+r8ZTL50cdz2PS8n3n7xulbqQh7FYBgmbDW0hZ3aQZU/qT3WsChflAjFXXjzQRcDnol5TiCZ0pIxJBnjtUA+b22u5fH0d29bVcqY3wquHejhxfoyBEUd1J0vg9ZgEvBqmZTt9qIIzJIUZVlOtlzuvWsX2jgbcmpI/KCuobM71R/F5VK7ZUvyg0vkigpCgqpnWHkeT8/Y4uaAz1WY+V7fmXNAzTGvShuocPgXDMBmIpGmyyavxEknHERucICnLErVBN5+5ayM7OhqKUoKV0lm6f9gxOzVNm6DPmdAaiWUYyar7wJF3b2gLs6EtTM+gI+/+4PQQlg3xpEEiaVATcHHPde15f7rjXSM8s++MM8nWrTIUTfPPPztB0HsGWZZobfBz25VtbF9XP691CxYf3TDpGUrw8d1rx/29WizEYVVBVTPVwdVEUieSNMYdHP3uiyfoPDU47menOmA61fMKyZ3szzXnC7EBVXUcttUJJUKfVyPkVcc5KPzuQzvGGazmHBNy/79QFj6ftc5E7n3ohsVYLEMmYxHwu1jbOvUY5rYGP5+56zL+7Hdv5PptzY4AARiJZXj61dP85I2zjMbSvHaoJ2t06rx307RIpg2GIilsnDvsf9lznP1H+kikDSxRp6s4LgwmsCybe65buyTXE5mQoKopLLWNxDLUBlyosjSutDXdGIX5jCK/9/p2vvXcsfxE1kJMyybgVYjEM9RPkGa7VJlEyuDPCrKxxsbguMO1M/Wncms1TYv+rMuDIks89dLJeWVDE/tkkUQG07T49Xs3UxN0E0tkxg0IzK+5xssnb17PnVet4o3Dfew/0u/Iuw/3sf/DflRFyjuRA8RSRtYE1kaSJFyqQsYwefGd86xtCRFLSnhcCh6Xmh1CKCg3PYNxAl6VjtVhBgdji349EYQEVU9u885t6o9+442ixihM10+6MBifVMorzFhCXpVU2pk1hG3n5xDJkkRLnY+AV3P+XwFzPek/sfR2YTCOW5MZiWXyLgqWbdMzlJiXs/R0fbLLs2Uyd9hDMm0ST+p5A9dCgj4XH7+undt2tfHO0QFe/6CXaEInYzjjJ9wuhaBXw8xmi2pBgNEUOV/2syxnPHsybaAqMl6Pikf0jspGKmMwOJZi05rwuEPci4kIQoKqodieSLEjxqd6XiSeIZU2J5W94NLGPZbQ84dJVU0h5NPwulUSKYNHP3fVgk/6T+XwkEqbJNNGPtjBpdHg852zM1PmJSHhy6rbJo6PKMTjUrllZxs3bG/h/Y8G2XvgPJGETjpjks6YeZm23zNZTVeIbTtybz2WISaL7Khc9Aw6TvSrGhZXll2I+IYFVUHnqUG+9dwxTvdEGImmON0T4VvPHZuyJzKdwenEIDDV8+Ipg4BPG9fPKXRu7jw1SCptZoUJ2bEL0TSReCYf5Gbr78zGVLLzgE/LjjdwnmNlteHTOTCUClmS8Hs0GsIeAj6NiTfHx7tG+KeffMiXnzzIoY8u8slb1nP7rjbcmrO12Nl/InFnZERaNyap6SaSy45GoikGI0niaQOzwNVbsHhcGIwT8rvyBrJLgciEBFXBUy+fIp7MIEkyiixnFVoZnnr51KTNvVhJ9lTPSyQdV+pCCkt5e/Z3EfBpRBMZ7Kwbtmk5M4IKg9xCTvpPVSYM+jQi8Ux+2FvOyid3MHaxkSWJgEejpsZLKpEhmTY4enZ4nBIuktT56RtnefCm9dx97RpO90R45WAPJy+MYVrOgDpVkbhqUyPr20KzXtNxn7CJGhnikoRLlfF4VNyajLSMj8KWa6hdJJ7hJ2+c5bN3b+azH99S8uF10yGCkKAq6B9OcGkEgtMTMW0p+/hkig0CE5/32OPvzVjKGxxLEfRpaKrjdG2YFqriZCvFXK+wpFg4WbWQ6cqJrXVe0tlR5LOV+Uop5y5EU51eT1d/hJfev4BuWE726NXwuNRxZ406VoXpWBXmwmCcVw9e4PCZYQzT5u2jAxw+M+y4d1/eMingToVl23lXb3mZl+uWaqhdbmhdjreO9GHbsKElOK0b/WIggpCgSpCQpPE9CSk/k6d0zOSuAJcChDMW2/nrk9bNKbORiYFgS3tN1r3b6fWMRJJTTgqdbipryKuSypiYpoGqSHnboZncvafray2E3OtHEzphv0ZQVogmHKGBW1PGnTUCp7/w2Y9tYmgsxWudPbx34iKJlMHed7t57VAP125p4qYdrdQE3FNdbhKFYgZFkfC6NTyajCqcGRbE4dPDBLwa61qmlukvFsvvNkKwLGmu9WDbzh1xbsqpbTuPl5LZ+jnF9pumOtfz7FtdGKad7/V4XOqUk0InrkGVHcdTw4baoJtQwIXbpczo7m2YNqPRND2DcUajaQzTLtlE0lzPygaGImmi8TR+j4bPo+YP4U5FfdjDp27ZwBc/eyW37WrDrTkuEvsO9/E33zvIUy+fyrsyFEO+XBfPMDSWZiSSnjSkT1Aclm1z+PQQl6+vQ5aXttQpMiFBVfDwHRv51rNHnUzAslFkCY9b4eE7ipumOhdmKuUV22+a6gySadkkUnr+HE08qTMSSTMwnOCxx9+bJAXP/ftjj79X1LmnHD2DceIpHUmSkSUJw7SJxNOYpjXpufMh17MK+TSGo2lSuklaN9FUmTXNAT529ZpJTuGFFMq73z4ywL4Peokmdd47cZH3Tlxk69pabtvVRntz8XfkheU6VZayvSMFTVm+vaNScr4/RiShc8WGuiW/tghCgqpgR0cDv3X/1rLPlim21zKVuEBTZAzTuVNPpHRGomlsGzRVnrFkNtVrGYbJqQsRHv3GG5PWYWSduSf2z6Y6fDofciXJ3IiJ3PRZDYn7rl/Lzo0NpHWLWFKfsbntcancuisn777Ia4d6GYqkOHpuhKPnRljXGuS2nW1sWlMzpzMruRHlcUlHVSRcXjemaaGqolw3HZ2nhwC4fP3S2ymJICSoGnLZQS4QfOeFEzSEu5YsGM2l11IoLkimDSLxTH5DjsQzJFIGNpdGN8yU3UwUKjgBLJMf6T1xHYri2HLnpr3aABLO4yWgsGc11fRZ23Z6WXVBNyndJJbMjJtjNBFNlbluazPXbG7iw7PDvHKwh57BOGd7o5ztPU5LnY9bd7VxxYZ6lDmUipyzRzaxpM5oxJl95PU4YgZxGHY8h08PsbYlSHgJlJYTEUFIUFXMFAjualzchupcbH5yG3UkYxKJpwEJWZFwq7LjmG06jtohvysvcJjK1aHwtXJChbFYBrDxulX6R5KTLHxWNfjpG06QzJgYhpMBeF3KpEmp82UuruQeTcGjeUmkjWndF3LIssQVG+rZvr6OUxcivHLoAqcuROgbTvD9X5zkuTfP4dacgFcX8nDLzra8cepsWNn+XbpQXaepaJq8jMXexZFI6Zy6EOETN5RnbLoIQoKqYqZAcNfu9Yt67WLGRuTIbcj/8MwRbCQ0VSbk0/B5NNK6SSSWoS7sRpEvaYOms/aZuOnbto3fozm+bEy28MkFLY9bnZdjQzHM9RxUzn0hnjJIpmY2LpUkiY2rw2xcHab7YoxXD/Vw+PQwsaROLOm834xp86PXT/OpmzcUHYhy5NV1qZy6TsW9gucefXh2BMu2uWJDeZzNhTpOUFUMjqUmnQ2ZLhCUmpzzdCEzecLt6GjA51FZ3einpc6X76G4VBlFkTAMe1aVXeFrPfq5q3js92+kY1WYVIEljmHZeTeF3OHdiQq/m7a3sGd/F49+4w0ee/y9ebtvLwRZkgh6NerCbjxupSgHhNWNAT73sU2sbvTjdjk3HpYNiZRBJKbzo9dOMxZLz/IqU2Pj9M+iCZ2hsRRDEUdkYU+ypl3efHB6CJ9bZUMRB4gXA5EJCaqKYn3h5stMwoPZzhDNZb2rGvw8cs8Wnnzh2JyFFvde387fPtUJ5Ox7HCTJUcblDE1zr/XM66d59q0uTMvOiiOseZ8bOnC0f15rLkSVZWoDbtIZi1hqZvFCjnhSpy7oxsq6U8RTOrYNY3Gdv3niILsua+CWnW001XjntJYctg0Z3SSTLdd53c4EWk2VlnV2ZNk2naeG2La+blxWvpSIICSoKuYTCIplNuFBbrN96uVT9A7GAWnWc0ozrfearc2MjSXyQS93jme2TX1HRwNtDX4uXIwDTvDJNexlabyhaeepQZ59swvLtlEkMC2LaNIi6NXmbHzaeWqQJ/aeBMm55umeCP/zB524Nac8Ot3h2amwbXBpMrWam7RuTjs6Ikdt0E0kqeNSnT5awKsRSWRIZyX77x6/yHvHL7J1nSPvXtM0//6gZdnEk47XnUtV8C5jq6BzfVEi8Qy7NpZvyKAoxwmqioWag87EVMahUx0mTesm9TVeWht8GDYzDpebab0HjvbPe1Ddw7d3IEmOi7aaDUA2EPJr40qTe/Z3YdmOcEGSJGTJ2UqTaXPOJcw9+7tQVQnLshmJpvOjzdO6RTyl0z+SnPOgPQlHvFAf9hD0u6ZVv92ysw3TtMgYTvnSsCzcmsxn79rIJ29eT13IjQ0cOTvCN370If/4kyOcOD+KvYA0xrad73o0mubiaIpIIkNGnzxHqpo5+NEgklQaJ435IjIhQdWxEHPQmShGeDCfQXjTrffpl0/O+bUKX7Ot3sfAaArTslGzwgdFkcdZCA2OpVAVGdMif25IwhmnMNcS5uBYynHtHk0B0rjeiSTJJNMGNUH3vEZLSEj43Spel0IibZBIOQaprx3qYSSapjbo5upNjVkXdee/C9Vx125p4vCZYV49eIGeoQRneiOc6Y3QWu/jEzetZ11TYE7y7olMJWbwuJSqtwo6dHKQjavCBLza7E9eJEQQEix7SjmHaC4KudnoH07g0eYvsnj4jo358uF0pcmGsAfTsokmMli2lFXSOeW7ucw32rO/i7FYhmhCRzctZ3ptdvOVJCfAGaa1YJFIzq27qy/KGx/2kdTNvEP3uycu8uBN66dUw8myxI6Oeq7YUMfJC2O8crCH0z0ReocSfPOZD7NBq5WrNzWhLcD0tFDMEEvquFUFr1fFrSqz/myl0TcUp2sgxi/f0VHWdYggJFjWzOWAaTH9plIKI5rrfFwcScz7tYo5r5N7T0Gfi0RKz85Bkrl/d3G9m8LPLxzQGIlmsCwbo+D2PzeQTs1+ZguZIJtb/0/fOEvGsGiu9ZHWTaIJnVTGyDt0F3K8a2RcxnTLzjb+3S9to3sgxisHezhydpiRaJpnXj/L3ncvcNP2Fq7f1pw/nzVfbJtxVkHuKhMzvPJeNwDXbWku6zpEEBIsa+ZSPpvLpl4KYcRDt2/k608dXNBrzVaaHP+epDkr2go/P7emoKkKF0eTk0QETobguH1/5s7i/Pyeef10VjRhZUuGNt998QRneyOcujCGZdv0DyVoqfPRUOMlnsgwNDbe4PR418ikmUbP7DvDg8Dm9lo+f88mMjb89NVTvP/RIPGkzgvvnOeVgz1ct7WJm65ozXv5LQTDsjGyYgZNVfC6nc9rISXAxcS2bV56t5vNa2qoL5GydL6IICRY1sy1fDa3Tf3SiIZLNkKzb/K5u//h2CVbn6HRJMWo7eZDqQbsJVJOCSp3l6/IYFk4ajlwBu151aIzrGffyqr2ZKdnFU1k8LgUnn2rKzs+3Sl9dV+MkcqYBP0al62pQZal/Kjx1w71ZMuRzk2GS1XGzTQCaKn389BtHdx1zRre+KCX/Uf7Sesmr3X28sbhPq7c1MitO1ppmKe8u5CJUm+XKhMMeXG7it9ql2Ko3ZEzQ/zkjXP85n1bFv1asyGCkGBZsxjnigo39bnO7il8ftCrMhxJMxZPE/K5CPldZIz5n+FZjEF2uc8vN8Z8nMmNJNFQ484fwrVtp3lfDHv2O+eWcnZ2Tq9KIp40kGSJ+pCb4WjakT/YMBxJYVoWD9ywjvqQh3hKJ5k2GImm8Uwoq2mKPG6m0eFTgzz3+ul8ue6hWzcwHEmz73Af8aTOgWMDvHtsgG3r67htZxurmwIL+sxyWJZNKuOMgi9uUpLDUgy1e/f4Rfweleu3lrcUByWQaH/lK1/h7/7u70qxFoGg5BQ7/2e+FCvrnu75ybQBtmNqOp/ZP52nBnns8ff4T3/7Gl/74WH6hhNzlnvPRO7zG4tlkAALO5+FmKYzsjvHXIL74FjKsckpeEyWnLKepsj4PBp1QTeK4mxRtg2fv3sT27MmpiGfi7qQh3VtwUkWQLpp5WcaHe8a4YkXjhNJ6vly3c/e7qK13sejn72SB29eR23QkXd/eGaYr//oMP/00yN81L0weXclk8oY9AzFueu69rwLRTmZdxCKRqN86Utf4lvf+lYp1yMQlJTFPFcEc7cRmvj8jGFh2Y5irXD2T89gfNZrFw7OS+smlmUTTToZwmzBsFhyn59t25iWjWXayLKEmk1hdMMikdLnHNwbwh68bgWbS4MKTctx/fa6nY3R59FoqfPRWOulY1Vo0nemKTJ3Xrma2qBjAwQ2GcNZxy0724BcuU7CpTpB36U6n8trh3rQVJnd21r4z4/s4pE7N9Ja7xi8nu6J8L+fO8bXnv6AzlND+aC7XDjdE8W24f4bF9drsVjmXY7bu3cv69at4wtf+EIp1yMQlJzZeiILKWNNNbJBN0zcmpq3z5nu+fGkPm6DMy3L6Y8UOfunMKvKDfqzbJtIQsfn0Urmqbejo4GOVWFO90Sc8RPZxyXJKZVdHE3hc6vcc+3qoj+3vGrPq5FMm+imc6D2ui2NnOyJkNbNosQal6+vx7RsXj3UQ0KRURWJqzc35ftBI9E0AZ9G4Ty/ieU6RZbYubGBHR31fNQ9xisHL3CmN0rPUIIn9n5EfcjDzTtauWpT44Lk3ZVARjc50xuhrcFHW2OAixej5V7S/IPQpz71KYB5l+Lq6+dfd21cZMv+xUCseWmY65oPHO3nib0nUVWJcMBFLKXzxN6ThMM+rimiXv7IPVv4+6c7nZk10XTW0kbC61GmfJ1Lz89kRzJcwrTAti1kWULT5Gnfy4Gj/Tz98klOdI/hUmVqg240VcnKryVM00JTZVIZg9bGQEm+x0fu2cJ//9Z+p4cjSZiWE4Dk7PutC7t568gAO7e0FPW53dUYJBz28fTLJxkYTtBU5+Oh2zdyzdbm/Pub+PhMr5VzUDdMi1hCJ5F2BBSNdX7GYqlxPcG0btFY56euzj/pterrA+zeuYozPWP87K1zHDpxkaFIih+/foaX3r/Andes4bYrV+P1LF473ed1Y0uLE+xOnxzEtGx2XtYEVMbf8Vk/yeeff56/+qu/GvfYhg0b+Pa3v72gCw8NxeaV5jY2Bisies8FsealYT5rfvKFY87AN1nGMB2lliGZPPnCMdY2zD5/Z22Dj8/ctTE7ssG5yy4c2TDxdcY9377kYmDj9D1sIOB1ylBTvZdCYYOmSOiGxcBokoBHRTdsLNtRqcWSOomkDjZ84c9+tmChwtoGH211Xi6OpTBMG8u2kSWQZSf7mOlzmy7TXNvg448e3jHuuRcvRqd9fC4olkU0aXDT9mZ+su8sCctAU2R008msbtjWxPDw9CXPsEflV27v4PZdbbx2qIeDHw0SiWf40SuneP6Ns1y/rYkbr2gl5Jtd3l0TmJsEPJFME18EYUIq47hQtNb7yJ2RXqq/4zMFu1mD0H333cd9991X0gUJBJVCKRwQciMbGmo848ZQzzRryOdRaW3wEU3oDEccGxwJGzM77nu68lNhCS4ccDMcSTmHJjMWQZ+LWELHpcmo2YzFsOyiVHvF8PAdG/MGphdHcud1bEJ+97Tvd67qwVIhyzI1ARfXb2uhvtbPj176iKFIapLdz2w01Xj59G0dfOzq1ez7oI+3s/LuVw/1su+DPq7a1MgtO1tpCC9c3r3YHO8axbJttq6d2/ylxUZItAUrmsIeTSKlE0no6IaFR1Om7OkU8zo5ZlKLNYQ9xFI6XrdKXciT7SVZeDR1RuFELmjm1mrZ5FV/61uD/Ma9m9nR0cBjj7+HYTMvX7rp2NHRQDjs48kXjjE0mkKSoCbozjsPTPV+5+O1VypyY8ZvvXIVG1oCxOIZjHmKDMIBN5+4YS23X7mKt4708cbhPhIpg3eODXDg2ACXr6/jtl1trGosjby71ETiGc71x9jQGiyrT9xUVHeXTSBYIJckyGmGo2kMw0LCxu1S5iRxnqsU/N7r2/ND7TwuhZqgm7qQh995cNuMm3ND2AlYw9E0pmmhSI6qTlPkceW2xRr+d83WZh793FX8+4e2Ew64kGVpxvdbziGEOSRJcpy6azwEfBpyMdP0psHnUbnzqtU8+rkreeCmS/Luw2eG+doPD/OtZ49ysnus4uTdH54dRlNlNq2pKfdSJrHgTOg//sf/WIp1CARloXAMN7aNlp1X43WrpHWz6Dv2Yix/Jj4/l1XMRZV37/XtfO2Hh8F2TDst21GpBXzj5wMt9vC/Yt/vYq9jLkg45qhel0IsaZDKGPP2eHOpCjdc3sJ1W5v54PQQrx7soW84wckLY5y8MMaqBj+37mrj8nV1pX0T86BvKMHF0RSXr6vFpZX/XNBERDlOsOKZa09npteZS4npmq3Ns4ofpmrqe12Kk3lYtmOV43fjcSnj1jqTx12pnBWKeb+LOYRwvijZflFaV4kli5vsOv1rSeza2MDOjnpOnB/llUM9nO2NcmEwzvd+7si7v/boHSVc/dwwTIsPTg8R9Gmsby3P+O7ZEEFIIKC0d+yl2uSna+qH/C4My54gOzbHrXW6TAVYUqHAXDPEpSLXL6oNukllDOIJfd79InBKfpvba9ncXktXf5RXDvZw9NwIQ5GlKztOxbGuUZIZk5s3NyJXqJmqCEICAaW7Yy+lGmy6pj627fSfZlnrVJnKY4+/t+RCgcUaQlgKJMDrUp3Dw9mhdRNtgOZKe3OQX/v4ZvpHErx9dKA0C50HY7E0Z3oirG0OUBcqr1P2TIggJBBQujv2UqrBppOPJ1IGv3rPpnmt9cJgnIxuYZhWfhqr160umVBgMUxWS4EsSQS9Gl63Qiypk86YC54J1Fzr44Eb15VkfXPFtm0OnRpC02S2rqssSfZERBASCLKU4o59pnNHEzfgR+7ZMmNPaKYS4XzW2nlqkFTazB80zTljBw2LlrrZD+bO9VrlLgXOB1WWqQ24SWcsoklHNl+NnO2LMhrLcNWmhvyYi0pFSLQFghLSEPaQmbBxZQwLT1byPRrP5Dfgv3+6c0YJeKkdwPfs7yLg05AksJEcqbIN8ZRRUqFAobFqYbB56qWTc3IcLxe2DS5Npi7kIRRwoVZoL2U6UmmDo+dGaKzxsKphsjVRpSEyIcGyZylLQNP1llRJnlSmMy1rxjJdqZv6g2Mpgj4NTZWJxDNOSU5xgkEpP4/pSpL9IylaJ2R+S31maC5IgM+l4plnv+h41wivH+rB7dH4H//hlqJ/bqFD7f7HP7+DJEn8+e/fRFvD9IdnF6IKLCUiCAmWNUttGzNd4PjOCycmlencmjLrBlzKpn6uvOd1q3mXg7RuUrPA8dYTg3zPYJya4Pgxbs6BVZuMYVXEmaG5kOsX+dzFny/KjR1XFZm1c5zYOtehdr/90M68B9zh00O8fqiHT92yHs22q8L/UQQhwbKmHLYxUwWOhnDXpP7ORFn1YrMYZ3amCvLJjImS0AkVBLeMYdFc5yOtmxV1ZqiQ2TLmwvNFiaRO2phevFA4dlxagEPDXMjoJt954QTNdT7uu37tklyzFIiekGBZUwm2MTB1f8cw7CXdgBdjwN9Uk2X9HpVYQp/Uy3r49o5FHTC4EKbrY03s2eXOF9UEXdQGPNM6EIxE02jK0m6vz755joHRJL9+z6aqmnskMiHBsqZSbGOmKtPNpo5brHWUctOfSg0Y8rswLZsav2vKrKISgs5E5p4xS7g0iTrNTUo3iSUzGMaltKg26CaS1HEvkTKtdyjOc2+dY/flzWytAKuguSCCkGBZU0m2MRMDQDXObJrIdEF+VYOfRz93VUmusRTCkoWM9PBoCm7VS1I3SGSdF27Z2cYz+86QgSUxM33yFydxaTKP3HnZol+r1FRPziYQzIPFKEEJLlFqGflEii2TLZTppPXFZsyS5Cjp6sIegj6NrevqePCm9YS8GsmUUdK1TqTz5EU6Tw1x/w3rCC9QZFIORCYkWPZUsm1MtbPY3nBLJSwpVcYsSxJ+j4bHpXLlpka2rK1l/ZrFcyywbZv//ZMPqQu5+djVqxftOouJCEICgWBBLGaQL8Xk22IodTBVZImw34XHpS6qceiFwTgnu8f4d7+0tSLHNBSDCEICgWDJmGt/ZymFJaUOpjklnde9OJNMLcvm2LlRNqwKs/vylkW5xlIgekICgWBJmE9/Z7F7TtVM92CcRNrg8/duWdC02HIjMiGBYJlSaY7V8+nvVOo8onJj2zYnu0cJ+TWu3drM4GCs3EuaNyIICQRl4sDR/jmP9y6WpbYrKob59nfKKSyptECeo284QSxpcPXmxiVzZFgsRDlOICgDnacG+funOxdNejyVk0G5HasXKoNeapZKHj4fzvRG8boV2uqX9rDzYiCCkEBQBvbs70JVpUULEpViV1RItfV3KjGQA0QTGQbHUqxrCVV9FgSiHCcQlIXBsRThgAvDvHSavpRBolLsigpZaH9nqUtjSyUPnyvn+qLIErQ3Tz+moZoQQUggmAOl2ggbwh5iKR1FvpStlDJIVJJdUSHz7e+Uo8dViYHcsmy6B+M01/nGrauaEUFIICiSUm6E917fzhN7T2JI5qIEieWmKivHSI5yBfKZhtodONrPT988x799cDvXb28FKmc43XwRQUggKJLCjTCZNojEM+iGyT88c4TfeXDbnDbDHR0NhMO+RVPH5a5RrUFnIuUojZUrkE8calc4tO7Ft87i96i0N/iq3vw2hwhCAkGR5DbCZNpgOJICJGRJIqWb88qIrtnavOSjHKqVcpXGKimQW5ZN56khruioR13iWUWLyfJ5JwLBIpOTGEfiGZwABDagqXJFqKaWM9WmrFsMTvdGiCV1dlZIUCwVIggJBEWS2wh1w0TCxrJtbCDk0ypCNbWcESM5nJ6kLEls31BdQ+tmQ5TjBIIiyW14//DMEVK6iabKhHwaPo9GWjcr9tDlfJlKCXhXY7Bs66mk0lg56Dw5xMbVYfyexTFELRciExII5sCOjgZ+58Ft1Ifc1AbdeN3qsiwNTecWcOBof7mXtiJJpHTOD8TYtnbxZhOVC5EJCQRzZL6qqYmZxSP3bKlYYcJ0kuinXz7JHz28o7yLW4GcvBDBBjauDpd7KSVHBCGBYB7MtTQ01Rmjv3+6k8/ctbEiS0zTSaIHhhNlWtHK5uSFUWRJYkNbqNxLKTmiHCcQLAFT+ZCpqlSxirrpzEab6iozc1vunOweY01zAI9r+eUNIggJBEvAVIaibk2pWEXddJLoh27fWO6lrTgsy+ZMX5SNbcuvFAciCAkES8JUmUUlK+qmk0Rfs7W53EtbcfQNx0lnTNYsE8PSiSy/3E4gqECm8iHDpqIVdStdEl0pnLkQAZaPa/ZERBASCJaAqRR1layOE1QOZ3rGkCWJVQ3+ci9lURBBSCBYIiZmFo2NwWVjQilYPE73jNFa70NTl8fohomInpBAIBBUMGd6IqxpWp6lOFhAJvTuu+/yl3/5lxiGQU1NDX/5l3/JqlWrSrk2gUAwDUs9ZbRSWGnvO2OYDI4muX1XW7mXsmjMOwh98Ytf5Otf/zpbtmzhqaee4s///M/5xje+Ucq1CQSCKSjHlNFy03lqkKdeOknPUAJVkQkHXMv2fRcOtfvw9BB79p/n8o2NhGt8jI0uv8PC8wpCmUyGP/zDP2TLli0AbN68me985zslXZhAIJiapZoyWikGprmgOxbLIMsSlg0j0TR1IU9+hMZyCkKFQ+3O9jrKuH0Hu5etPH5eQcjlcvHJT34SAMuy+OpXv8rHPvaxOb1Gff38a5yNZXTynS9izUtDta15PusdjmUIelUkSco/pioSI7FMyd7/gaP9PLH3JKoqEQ64iKV0nth7knDYt+Sb4d6nOnG7FCzbRpYkJMkJRNGETluDr6j3XU2/Fz6vG1ty2vVJ3UJVZBpqHRVlNb2PYpk1CD3//PP81V/91bjHNmzYwLe//W0ymQx//Md/jGEY/O7v/u6cLjw0FMOy7LmtlupUFIk1Lw3Vtub5rrcuW4oqnDKa1k1qA66Svf8nXzgGEiiyjGHazp+SydMvn1xyWXnvxRg+j4qiyJimhQxIgG6YxFPGrO+73L8Xcw0ciWSaeDYTGh5LEvRpJBIZgKr6/S5kps9g1iB03333cd999016PB6P8/u///vU1NTwjW98A01bXjMuBIJKZaqDr6UeJVFJBqa50d4hn8ZwNO0ME7RBkaVlN0KjENu2iSZ0WuuX91myeUu0v/jFL7J27Vq+8pWv4HK5SrkmgUAwA0sxZbSSDExzPnaKIlMbcCFLErZt01TrXdbTVdO6ScawCPqW9/46r57QkSNH2Lt3Lxs3buRTn/oUAE1NTfzjP/5jKdcmEAimYbEtdabLtsphYDrRbWJDW6gs0uyllodHsyW5kG95V5nmFYS2bdvG8ePHS70WgUBQIUw3uO+arc1l6UuU28euHLL4SLYPFPSLTEggEKxAyr3xVxJzlcUXZk2tjQH+8vdvmvM1o3EdlyaPE6AsR0QQEggEglmYTqgx1TyoiVnTSCQ5r2tGEhlCy7wfBCIICQRlZaXZ0FQrOYVeYVaSMawp50FNzJo888hkcsq45Tq+oRBhYCoQlIkDR/v57osnGI1nxvUZOk8NlntpgglMN2l2Knn4VFN050oibWBa9orIhEQQEgjKxNMvn8zfMUuShFtT8jY0gspiLrL4qeTtcyWnjAsuc2UciHKcQFA2+ocTeLTx94HT9RkE5adYocZEeXsqY8z5WtGcMk5kQgKBYLForvNNeSB0qj6DoHqYmDXVhrxzfo1IXMfrVtAWWNarBkQmJBCUiYdu38jXnzq4qPY7gvJQmDXNx3Q0ukKUcSAyIYGgbFyztXnR7XcE1YdlQSypr4h+EIhMSCAoK+JAqGAid1zbzhN7P+LB2zZyx9Vr8o9ndLOMq1o8RBASCASCCuLYaUeiH3IrVTu6YS6IcpxAIBBUEP0jCSSJZT/CIYcIQgKBQFBBXBxNUR/yoKnL2zMuhwhCAoFAUEEMjqVoKcPcpnIhgpBAIBBUEIOjSZprRRASCAQCQRlI6ybNdXM/4FqtiCAkEAgEFUazKMcJBAKBoFw01YpMSCAQCARlojbgLvcSlgwRhAQCgaCC8LpUXMt8pHchIggJBAJBBREKrAzj0hwiCAkEAkEFEVohxqU5RBASCASCCmIlDLIrRAQhgUAgqCCCXhGEBAKBQFAm3O6VtS2vrHcrEAgEFY5HW1kTdkQQEggEggrC7VpZ2/LKercCgUBQ4dSEV45bAoggJBAIBBVFQAgTBAKBQFAuVpJbAoggJBAIBBWFLEnlXsKSIoKQQCAQVBArLAaJICQQCASVhLTCopAIQgKBQFBByLIIQgKBQCAoE8oK25VX2NsVCASCykaU4wQCgUBQNlZYDBJBSCAQCCoJIdEWCAQCQdkQwoQiOXDgAA899BAPPPAAv/d7v8fY2Fgp1yUQCAQrEpEJFcmf/Mmf8Nhjj/GTn/yEjRs38s1vfrOU6xIIBIIVyUoTJsx7cMVzzz2Hpmnouk5/fz+bN28u5boEAkEJ6Dw1yJ79XQyOpWgIe7j3+nZ2dDSUe1mCGZBlCbPci1hCJNu27fn+8PHjx/nCF76Aqqo8+eSTtLa2lnJtAoFgARw42s/fP92Jqkq4NYW0bmIYNr/70A6u2dpc7uUJpmE0mqYm6C73MpaMWYPQ888/z1/91V+Ne2zDhg18+9vfzv/3E088wY9+9COeeOKJoi88NBTDsuYe/xobg1y8GJ3zz5UTsealodrWvNjrfezx9xiNZ3AXuDKndZMav4tHP3fVvF6z2j5jKP+aGxuDc3p+NJEhFU8v0mrKw0yfwazluPvuu4/77rtv3GPpdJqf//znfOxjHwPgwQcf5H/8j/+xwGUKBIJSMjiWwucZ/1fcpcoMjqXKtCJBMbg1hZX0Dc1LmKCqKv/tv/03Dh8+DDjZ0lVXze/OSiAQLA4NYQ8Zwxr3WMawaAh7yrQiQTFEI8lyL2FJmZcwQVEUvvzlL/N//p//J6Zp0tzczF/8xV+Uem0CgWAB3Ht9O9998QRpnAwoY1iYpsW917eXe2kCQZ55q+OuueYann766VKuRSAQlJCcCk6o4wSVzLyDkEAgqHx2dDSIoCOoaIRtj0AgEAjKhghCAoFAICgbIggJBAKBoGyIICQQCASCsiGCkEAgEAjKhghCAoFAICgbIggJBAKBoGyIICQQCASCslG2w6oLGWFbjeNvxZqXhmpbc7WtF8SaBaVlQfOEBAKBQCBYCKIcJxAIBIKyIYKQQCAQCMqGCEICgUAgKBsiCAkEAoGgbIggJBAIBIKyIYKQQCAQCMqGCEICgUAgKBsiCAkEAoGgbIggJBAIBIKyUdVB6MiRI2zfvr3cyyiKAwcO8NBDD/HAAw/we7/3e4yNjZV7STPy7rvv8ulPf5pPfvKT/MZv/AYXLlwo95KK5itf+Qp/93d/V+5lzMhPfvITPvGJT3D33Xfz3e9+t9zLKYpYLMYv/dIv0d3dXe6lFMVXv/pV7r//fu6//34ee+yxci9HMA1VG4SSySR/9md/hq7r5V5KUfzJn/wJjz32GD/5yU/YuHEj3/zmN8u9pBn54he/yF/8xV/w4x//mAceeIA///M/L/eSZiUajfKlL32Jb33rW+Veyoz09/fz5S9/mccff5wf//jHPPnkk5w8ebLcy5qRQ4cO8dnPfpazZ8+WeylF8cYbb/D666/zwx/+kB/96Ed8+OGHvPjii+VelmAKqjYI/fVf/zW/+Zu/We5lFM1zzz3Hxo0b0XWd/v5+QqFQuZc0LZlMhj/8wz9ky5YtAGzevJne3t4yr2p29u7dy7p16/jCF75Q7qXMyBtvvMHu3bupqanB5/Px8Y9/nD179pR7WTPy/e9/n//6X/8rTU1N5V5KUTQ2NvLHf/zHuFyu/3979w+STBzHcfwTeU4NLnqjUkqbQ2NDU0MiaEOCEQhNEoJwQRQNgYOkEIQgiETgIghOtxiIrrnUEhFEi5PBDU1F2T8aHjgIz3weHp7ne8rnNd39pvdyfO9+B3dQFAVzc3Po9XrSWWRB7Cvaf6PdbuPl5QUrKyvSKb9NURTc3t5ic3MTDocD29vb0klDOZ1ORKNRAMDn5yeKxSKWl5eFq0ZbXV0FANtvxRmGAbfbbZ57PB5cXV0JFo2WzWalE/5IIBAwj7vdLhqNBmq1mmARDWPrIXR2dobDw8Nva7Ozs3h8fESlUpGJGmFYc6VSwfz8PM7Pz1Gr1aBpmi0uip96X19fsbe3h/f3dySTSaHCQT81jwOrD9dPTfFXA//C3d0dkskkdnd34fP5pHPIgq2HUCgUQigU+rZWr9dRLpexsbFhrkWjUVSrVczMzPzvxAFWzf1+H61Wy3yaiEQiyOfzEnkDrHoB4OnpCVtbW3C5XCiVSlAURaDO2rDmcaGqKi4uLsxzwzDGZptrnFxeXiKdTmN/fx/hcFg6h4YYu3dCsVgMrVYLuq5D13UAgK7rthhAwzgcDmQyGVxfXwP4dSe/sLAgXPWznZ0deL1eFAoFOJ1O6ZyJsri4iE6ng4eHBzw/P6PZbGJpaUk6a6Lc398jlUrh6OiIA8jmbP0kNCmmp6dxfHyMg4MDfHx8QFVVW++x39zcoN1uw+/3m+9ZPB4PTk5OZMMmhKqq0DQNiUQCb29vWFtbQzAYlM6aKKenp+j3+8jlcuZaPB7H+vq6YBVZ4Z9ViYhIzNhtxxER0eTgECIiIjEcQkREJIZDiIiIxHAIERGRGA4hIiISwyFERERiOISIiEjMFx/sYpX1nPQDAAAAAElFTkSuQmCC
 "
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@@ -19214,28 +20757,28 @@ Name: C, dtype: float64</pre>
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+
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 </div></section></section><section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Task-6">Task 6<a class="anchor-link" href="#Task-6">&#182;</a></h2><p><a name="task6"></a></p>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="Task-6">Task 6<a class="anchor-link" href="#Task-6">&#182;</a></h2><p><a name="task6"></a>
+<span style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</em></span></p>
 <ul>
 <li>To your <code>df</code> NEST data frame, add a column with the unaccounted time (<code>Unaccounted Time / s</code>), which is the difference of program runtime, average neuron build time, minimal edge build time, minimal initialization time, presimulation time, and simulation time.<br>
 (<em>I know this is technically not super correct, but it will do for our example.</em>)</li>
-<li>Plot a stacked bar plot of all these columns (except for program runtime) over the virtual processes</li>
-<li>Remember: <a href="https://pollev.com/aherten538">pollev.com/aherten538</a></li>
+<li>Plot a stacked bar plot of all these columns (except for program runtime) over the threads</li>
+<li>Tell me when you're done with status icon in BigBlueButton: 👍</li>
 </ul>
 
 </div>
-</div>
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-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[92]:</div>
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+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[102]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">cols</span> <span class="o">=</span> <span class="p">[</span>
     <span class="s1">&#39;Avg. Neuron Build Time / s&#39;</span><span class="p">,</span> 
     <span class="s1">&#39;Min. Edge Build Time / s&#39;</span><span class="p">,</span> 
@@ -19248,34 +20791,38 @@ Name: C, dtype: float64</pre>
     <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Unaccounted Time / s&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;Unaccounted Time / s&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">df</span><span class="p">[</span><span class="n">entry</span><span class="p">]</span>
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-<div class="cell border-box-sizing code_cell rendered">
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-<div class="prompt input_prompt">In&nbsp;[93]:</div>
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+<div class="jp-Cell-inputWrapper">
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[103]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[[</span><span class="s2">&quot;Runtime Program / s&quot;</span><span class="p">,</span> <span class="s2">&quot;Unaccounted Time / s&quot;</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">]]</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
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+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[103]:</div>
 
 
 
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@@ -19303,7 +20850,7 @@ Name: C, dtype: float64</pre>
       <th>Sim. Time / s</th>
     </tr>
     <tr>
-      <th>Virtual Processes</th>
+      <th>Threads</th>
       <th></th>
       <th></th>
       <th></th>
@@ -19341,35 +20888,39 @@ Name: C, dtype: float64</pre>
 
 </div>
 
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-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[94]:</div>
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+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[104]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[[</span><span class="s2">&quot;Unaccounted Time / s&quot;</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">,</span> <span class="n">stacked</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">4</span><span class="p">));</span>
 </pre></div>
 
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
 "
 >
 </div>
@@ -19377,45 +20928,47 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
 </div></section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <ul>
 <li>Make it relative to the total program run time</li>
-<li><strong>Slight complication</strong>: Our virtual processes as indexes are not unique; we need to find new unique indexes</li>
-<li>Let's use a multi index!</li>
+<li><strong>Slight complication</strong>: Our threads as indexes are not unique; we need to find new unique indexes</li>
+<li>Could be anythig, but we use a <strong>multi index</strong>!</li>
 </ul>
 
 </div>
-</div>
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[95]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[105]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_multind</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">([</span><span class="s2">&quot;Nodes&quot;</span><span class="p">,</span> <span class="s2">&quot;Tasks/Node&quot;</span><span class="p">,</span> <span class="s2">&quot;Threads/Task&quot;</span><span class="p">])</span>
 <span class="n">df_multind</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
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+<div class="jp-Cell-outputWrapper">
 
 
-<div class="output_area">
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-    <div class="prompt output_prompt">Out[95]:</div>
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[105]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
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@@ -19607,36 +21160,40 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
-</div></div></section><section>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[96]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[106]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_multind</span><span class="p">[[</span><span class="s2">&quot;Unaccounted Time / s&quot;</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">]]</span>\
     <span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">df_multind</span><span class="p">[</span><span class="s2">&quot;Runtime Program / s&quot;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="s2">&quot;index&quot;</span><span class="p">)</span>\
     <span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">,</span> <span class="n">stacked</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Relative Time Distribution&quot;</span><span class="p">);</span>
 </pre></div>
 
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@@ -19644,47 +21201,49 @@ Name: C, dtype: float64</pre>
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+
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-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Next-Level:-Hierarchical-Data">Next Level: Hierarchical Data<a class="anchor-link" href="#Next-Level:-Hierarchical-Data">&#182;</a></h2><ul>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="Next-Level:-Hierarchical-Data">Next <em>Level</em>: Hierarchical Data<a class="anchor-link" href="#Next-Level:-Hierarchical-Data">&#182;</a></h2><ul>
 <li><code>MultiIndex</code> only a first level</li>
 <li>More powerful:<ul>
-<li>Grouping: <code>.groupby()</code> (<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html">API</a>)</li>
-<li>Pivoting: <code>.pivot_table()</code> (<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot_table.html">API</a>); also <code>.pivot()</code> (<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot.html">API</a>)</li>
+<li>Grouping: <code>.groupby()</code> ("Split-apply-combine", <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html">API</a>, <a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html">User Guide</a>)</li>
+<li>Pivoting: <code>.pivot_table()</code> (<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot_table.html">API</a>, <a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html">User Guide</a>); also <code>.pivot()</code> (specialized version of <code>.pivot_table()</code>, <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot.html">API</a>)</li>
 </ul>
 </li>
 </ul>
 
 </div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[97]:</div>
-<div class="inner_cell">
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[108]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">&quot;Nodes&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
 </pre></div>
 
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+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[108]:</div>
 
 
 
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@@ -19904,19 +21463,19 @@ Name: C, dtype: float64</pre>
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-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <h3 id="Pivoting">Pivoting<a class="anchor-link" href="#Pivoting">&#182;</a></h3><ul>
 <li>Combine categorically-similar columns</li>
 <li>Creates hierarchical index</li>
-<li>Respected during plotting!</li>
-<li>A pivot table has three <em>layers</em>; if confused, think about these questions<ul>
+<li>Respected during plotting with Pandas!</li>
+<li>A pivot table has three <em>layers</em>; if confused, think about the related questions<ul>
 <li><code>index</code>: »What's on the <code>x</code> axis?«</li>
-<li><code>values</code>: »What value do I want to plot?«</li>
+<li><code>values</code>: »What value do I want to plot [on the <code>y</code> axis]?«</li>
 <li><code>columns</code>: »What categories do I want [to be in the legend]?«</li>
 </ul>
 </li>
@@ -19925,49 +21484,52 @@ Name: C, dtype: float64</pre>
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-<div class="inner_cell">
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+</div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[109]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="s2">&quot;H&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">**</span><span class="n">n</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)]</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[99]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[110]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_pivot</span> <span class="o">=</span> <span class="n">df_demo</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span>
     <span class="n">index</span><span class="o">=</span><span class="s2">&quot;F&quot;</span><span class="p">,</span>
-    <span class="n">values</span><span class="o">=</span><span class="s2">&quot;G&quot;</span><span class="p">,</span>
+    <span class="n">values</span><span class="o">=</span><span class="s2">&quot;E2&quot;</span><span class="p">,</span>
     <span class="n">columns</span><span class="o">=</span><span class="s2">&quot;H&quot;</span>
 <span class="p">)</span>
 <span class="n">df_pivot</span>
 </pre></div>
 
-    </div>
+     </div>
 </div>
 </div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
 
-<div class="output_wrapper">
-<div class="output">
 
+<div class="jp-OutputArea jp-Cell-outputArea">
 
-<div class="output_area">
+<div class="jp-OutputArea-child">
 
-    <div class="prompt output_prompt">Out[99]:</div>
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[110]:</div>
 
 
 
-<div class="output_html rendered_html output_subarea output_execute_result">
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
 <style scoped>
     .dataframe tbody tr th:only-of-type {
@@ -20029,34 +21591,38 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
-</div></div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[100]:</div>
-<div class="inner_cell">
-    <div class="input_area">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[111]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_pivot</span><span class="o">.</span><span class="n">plot</span><span class="p">();</span>
 </pre></div>
 
-    </div>
+     </div>
+</div>
 </div>
 </div>
 
-<div class="output_wrapper">
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+<div class="jp-Cell-outputWrapper">
 
 
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@@ -20064,52 +21630,55 @@ Name: C, dtype: float64</pre>
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-<h2 id="Task-7">Task 7<a class="anchor-link" href="#Task-7">&#182;</a></h2><p><a name="task7"></a></p>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="Task-7">Task 7<a class="anchor-link" href="#Task-7">&#182;</a></h2><p><a name="task7"></a>
+<span style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</em></span></p>
 <ul>
 <li>Create a pivot table based on the NEST <code>df</code> data frame</li>
-<li>Let the <code>x</code> axis show the number of nodes; display the values of the simulation time <code>"Sim. Time / s"</code> for the tasks per node and threas per task configurations</li>
+<li>Let the <code>x</code> axis show the number of nodes; display the values of the simulation time <code>"Sim. Time / s"</code> for the tasks per node and threads per task configurations</li>
 <li>Please plot a bar plot</li>
-<li>Done? <a href="https://pollev.com/aherten538">pollev.com/aherten538</a></li>
+<li>Tell me when you're done with status icon in BigBlueButton: 👍</li>
 </ul>
 
 </div>
-</div>
-</div><div class="fragment">
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[101]:</div>
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+<div class="jp-Cell-inputWrapper">
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[116]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span>
-    <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Nodes&quot;</span><span class="p">],</span>
+    <span class="n">index</span><span class="o">=</span><span class="s2">&quot;Nodes&quot;</span><span class="p">,</span>
     <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Tasks/Node&quot;</span><span class="p">,</span> <span class="s2">&quot;Threads/Task&quot;</span><span class="p">],</span>
     <span class="n">values</span><span class="o">=</span><span class="s2">&quot;Sim. Time / s&quot;</span><span class="p">,</span>
 <span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">4</span><span class="p">));</span>
 </pre></div>
 
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 "
 >
 </div>
@@ -20117,12 +21686,12 @@ Name: C, dtype: float64</pre>
 </div>
 
 </div>
+
 </div>
 
 </div></div><div class="fragment">
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p><a name="taskb"></a></p>
 <ul>
 <li>Bonus task<ul>
@@ -20133,39 +21702,33 @@ Name: C, dtype: float64</pre>
 </li>
 </ul>
 
-</div>
 </div>
 </div></div></section></section><section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="The-End">The End<a class="anchor-link" href="#The-End">&#182;</a></h2><ul>
-<li>Pandas works on data frames</li>
-<li>Slice frames to your likings</li>
-<li>Plot frames<ul>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="Conclusion">Conclusion<a class="anchor-link" href="#Conclusion">&#182;</a></h2><ul>
+<li>Pandas works with and on <strong>data frames</strong>, which are central</li>
+<li><strong>Slice</strong> frames to your likings</li>
+<li><strong>Plot</strong> frames<ul>
 <li>Together with Matplotlib, Seaborn, others</li>
 </ul>
 </li>
-<li>Pivot tables are next level greatness</li>
+<li><strong>Pivot</strong> tables are next level greatness</li>
 <li>Remember: <strong><em>Pandas as early as possible!</em></strong></li>
 <li>Thanks for being here! 😍</li>
 </ul>
 
 </div>
 </div>
-</div>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><span class="feedback">Tell me what you think about this tutorial! <a href="mailto:a.herten@fz-juelich.de">a.herten@fz-juelich.de</a></span></p>
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p><span class="feedback">Feedback to <a href="mailto:a.herten@fz-juelich.de">a.herten@fz-juelich.de</a></span></p>
 <p>Next slide: Further reading</p>
 
-</div>
 </div>
 </div></section></section><section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
+<div class="jp-Cell-inputWrapper"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <h2 id="Further-Reading">Further Reading<a class="anchor-link" href="#Further-Reading">&#182;</a></h2><ul>
 <li><a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/index.html">Pandas User Guide</a></li>
 <li><a href="http://sbillaudelle.de/2015/02/23/seamlessly-embedding-matplotlib-output-into-latex.html">Matplotlib and LaTeX Plots</a></li>
@@ -20179,89 +21742,27 @@ Name: C, dtype: float64</pre>
 </li>
 </ul>
 
-</div>
-</div>
-</div></section><section>
-<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
-</div><div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Poll-Results">Poll Results<a class="anchor-link" href="#Poll-Results">&#182;</a></h2><p><img src="img/poll-results.png" alt="Poll Results"></p>
-
-</div>
 </div>
 </div></section></section>
 </div>
 </div>
+</body>
 
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diff --git a/Introduction-to-Pandas--slides.ipynb b/Introduction-to-Pandas--slides.ipynb
index dc621e3f03ed6b4a20b16c3351c7ccdf5da5de1d..ea4d0ba3941c53f13b43229aff01ad14faa446c3 100644
--- a/Introduction-to-Pandas--slides.ipynb
+++ b/Introduction-to-Pandas--slides.ipynb
@@ -1 +1,6870 @@
-{"cells": [{"cell_type": "markdown", "metadata": {"exercise": "task"}, "source": ["# *Introduction to* Data Analysis and Plotting with Pandas\n", "## JSC Tutorial\n", "\n", "Andreas Herten, Forschungszentrum J\u00fclich, 26 February 2019"]}, {"cell_type": "markdown", "metadata": {"exercise": "onlypresentation", "slideshow": {"slide_type": "skip"}}, "source": ["**Version: Slides**"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## My Motivation\n", "\n", "* I like Python\n", "* I like plotting data\n", "* I like sharing\n", "* I think Pandas is awesome and you should use it too\n", "\n", "<span style=\"color: #023d6b\"><em>Motto: <strong>\u00bbPandas as early as possible!\u00ab</strong></em></span>"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "source": ["## Task Outline\n", "\n", "* [Task 1](#task1)\n", "* [Task 2](#task2)\n", "* [Task 3](#task3)\n", "* [Task 4](#task4)\n", "* [Task 5](#task5)\n", "* [Task 6](#task6)\n", "* [Task 7](#task7)\n", "* [Bonus Task](#taskb)"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## Tutorial Setup\n", "\n", "* 60 minutes (we might do this again for some advanced stuff if you want to)\n", "    - *Well, as it turns out, 60 minutes weren't nearly enought*\n", "    - *We ended up spending nearly 2 hours on it, and we needed to rush quickly through the material*\n", "* Alternating between lecture and hands-on\n", "* Please give status of hands-ons via **[pollev.com/aherten538](https://pollev.com/aherten538)**"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "fragment"}}, "source": ["* Please open Jupyter Notebook of this session\n", "    - \u2026\u00a0either on your **local machine** (`pip install --user pandas seaborn`)\n", "    - \u2026 or on the **JSC Jupyter service** at https://jupyter-jsc.fz-juelich.de/  \n", "    *Pandas and seaborn should already be there!*\n", "* Tell me when you're done on **[pollev.com/aherten538](https://pollev.com/aherten538)**"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## About Pandas\n", "\n", "<img style=\"float: right; max-width: 200px;\" width=\"200px\" src=\"img/adorable-animal-animal-photography-1661535.jpg\" />\n", "\n", "* Python package (Python 2, Python 3)\n", "* For data analysis\n", "* With data structures (multi-dimensional table; time series), operations\n", "* Name from \u00bb**Pan**el **Da**ta\u00ab\u00a0(multi-dimensional time series in economics)\n", "* Since 2008\n", "* https://pandas.pydata.org/\n", "* Install [via PyPI](https://pypi.org/project/pandas/): `pip install pandas`"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## Pandas Cohabitation\n", "\n", "* Pandas works great together with other established Python tools\n", "    * [Jupyter Notebooks](https://jupyter.org/)\n", "    * Plotting with [`matplotlib`](https://matplotlib.org/)\n", "    * Modelling with [`statsmodels`](https://www.statsmodels.org/stable/index.html), [`scikit-learn`](https://scikit-learn.org/)\n", "    * Nicer plots with [`seaborn`](https://seaborn.pydata.org/), [`altair`](https://altair-viz.github.io/), [`plotly`](https://plot.ly/)"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## First Steps"]}, {"cell_type": "code", "execution_count": 1, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["import pandas"]}, {"cell_type": "code", "execution_count": 2, "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["import pandas as pd"]}, {"cell_type": "code", "execution_count": 3, "metadata": {"slideshow": {"slide_type": "-"}}, "outputs": [{"data": {"text/plain": ["'0.24.1'"]}, "execution_count": 3, "metadata": {}, "output_type": "execute_result"}], "source": ["pd.__version__"]}, {"cell_type": "code", "execution_count": 4, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/plain": ["\u001b[0;31mClass docstring:\u001b[0m\n", "    pandas - a powerful data analysis and manipulation library for Python\n", "    =====================================================================\n", "    \n", "    **pandas** is a Python package providing fast, flexible, and expressive data\n", "    structures designed to make working with \"relational\" or \"labeled\" data both\n", "    easy and intuitive. It aims to be the fundamental high-level building block for\n", "    doing practical, **real world** data analysis in Python. Additionally, it has\n", "    the broader goal of becoming **the most powerful and flexible open source data\n", "    analysis / manipulation tool available in any language**. It is already well on\n", "    its way toward this goal.\n", "    \n", "    Main Features\n", "    -------------\n", "    Here are just a few of the things that pandas does well:\n", "    \n", "      - Easy handling of missing data in floating point as well as non-floating\n", "        point data.\n", "      - Size mutability: columns can be inserted and deleted from DataFrame and\n", "        higher dimensional objects\n", "      - Automatic and explicit data alignment: objects can be explicitly aligned\n", "        to a set of labels, or the user can simply ignore the labels and let\n", "        `Series`, `DataFrame`, etc. automatically align the data for you in\n", "        computations.\n", "      - Powerful, flexible group by functionality to perform split-apply-combine\n", "        operations on data sets, for both aggregating and transforming data.\n", "      - Make it easy to convert ragged, differently-indexed data in other Python\n", "        and NumPy data structures into DataFrame objects.\n", "      - Intelligent label-based slicing, fancy indexing, and subsetting of large\n", "        data sets.\n", "      - Intuitive merging and joining data sets.\n", "      - Flexible reshaping and pivoting of data sets.\n", "      - Hierarchical labeling of axes (possible to have multiple labels per tick).\n", "      - Robust IO tools for loading data from flat files (CSV and delimited),\n", "        Excel files, databases, and saving/loading data from the ultrafast HDF5\n", "        format.\n", "      - Time series-specific functionality: date range generation and frequency\n", "        conversion, moving window statistics, moving window linear regressions,\n", "        date shifting and lagging, etc."]}, "metadata": {}, "output_type": "display_data"}], "source": ["%pdoc pd"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## DataFrames\n", "### It's all about DataFrames\n", "\n", "* Main data containers of Pandas\n", "    - Linear: `Series`\n", "    - Multi Dimension: `DataFrame`\n", "* `Series` is *only* special case of `DataFrame`\n", "* \u2192 Talk about `DataFrame`s as the more general case"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## DataFrames\n", "### Construction\n", "\n", "* To show features of `DataFrame`, let's construct one!\n", "* Many construction possibilities\n", "    - From lists, dictionaries, `numpy` objects\n", "    - From CSV, HDF5, JSON, Excel, HTML, fixed-width files\n", "    - From pickled Pandas data\n", "    - From clipboard\n", "    - *From Feather, Parquest, SAS, SQL, Google BigQuery, STATA*"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## DataFrames\n", "\n", "### Examples, finally"]}, {"cell_type": "code", "execution_count": 5, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["ages  = [41, 56, 56, 57, 39, 59, 43, 56, 38, 60]"]}, {"cell_type": "code", "execution_count": 6, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>0</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>41</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>56</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>56</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>57</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>39</td>\n", "    </tr>\n", "    <tr>\n", "      <th>5</th>\n", "      <td>59</td>\n", "    </tr>\n", "    <tr>\n", "      <th>6</th>\n", "      <td>43</td>\n", "    </tr>\n", "    <tr>\n", "      <th>7</th>\n", "      <td>56</td>\n", "    </tr>\n", "    <tr>\n", "      <th>8</th>\n", "      <td>38</td>\n", "    </tr>\n", "    <tr>\n", "      <th>9</th>\n", "      <td>60</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["    0\n", "0  41\n", "1  56\n", "2  56\n", "3  57\n", "4  39\n", "5  59\n", "6  43\n", "7  56\n", "8  38\n", "9  60"]}, "execution_count": 6, "metadata": {}, "output_type": "execute_result"}], "source": ["pd.DataFrame(ages)"]}, {"cell_type": "code", "execution_count": 7, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>0</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>41</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>56</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>56</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["    0\n", "0  41\n", "1  56\n", "2  56"]}, "execution_count": 7, "metadata": {}, "output_type": "execute_result"}], "source": ["df_ages = pd.DataFrame(ages)\n", "df_ages.head(3)"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["* Let's add names to ages; put everything into a `dict()`"]}, {"cell_type": "code", "execution_count": 8, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["{'Names': ['Liu', 'Rowland', 'Rivers', 'Waters', 'Rice', 'Fields', 'Kerr', 'Romero', 'Davis', 'Hall'], 'Ages': [41, 56, 56, 57, 39, 59, 43, 56, 38, 60]}\n"]}], "source": ["data = {\n", "    \"Names\": [\"Liu\", \"Rowland\", \"Rivers\", \"Waters\", \"Rice\", \"Fields\", \"Kerr\", \"Romero\", \"Davis\", \"Hall\"],\n", "    \"Ages\": ages\n", "}\n", "print(data)"]}, {"cell_type": "code", "execution_count": 9, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Names</th>\n", "      <th>Ages</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>Liu</td>\n", "      <td>41</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>Rowland</td>\n", "      <td>56</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>Rivers</td>\n", "      <td>56</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>Waters</td>\n", "      <td>57</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     Names  Ages\n", "0      Liu    41\n", "1  Rowland    56\n", "2   Rivers    56\n", "3   Waters    57"]}, "execution_count": 9, "metadata": {}, "output_type": "execute_result"}], "source": ["df_sample = pd.DataFrame(data)\n", "df_sample.head(4)"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "fragment"}}, "source": ["* Two columns now; one for names, one for ages"]}, {"cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [{"data": {"text/plain": ["Index(['Names', 'Ages'], dtype='object')"]}, "execution_count": 10, "metadata": {}, "output_type": "execute_result"}], "source": ["df_sample.columns"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["* `DataFrame` always have indexes; auto-generated or custom"]}, {"cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [{"data": {"text/plain": ["RangeIndex(start=0, stop=10, step=1)"]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}], "source": ["df_sample.index"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "fragment"}}, "source": ["* Make `Names` be index with `.set_index()`\n", "* `inplace=True` will modifiy the parent frame (*I don't like it*)"]}, {"cell_type": "code", "execution_count": 12, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Ages</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Names</th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Liu</th>\n", "      <td>41</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Rowland</th>\n", "      <td>56</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Rivers</th>\n", "      <td>56</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Waters</th>\n", "      <td>57</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Rice</th>\n", "      <td>39</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Fields</th>\n", "      <td>59</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Kerr</th>\n", "      <td>43</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Romero</th>\n", "      <td>56</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Davis</th>\n", "      <td>38</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Hall</th>\n", "      <td>60</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["         Ages\n", "Names        \n", "Liu        41\n", "Rowland    56\n", "Rivers     56\n", "Waters     57\n", "Rice       39\n", "Fields     59\n", "Kerr       43\n", "Romero     56\n", "Davis      38\n", "Hall       60"]}, "execution_count": 12, "metadata": {}, "output_type": "execute_result"}], "source": ["df_sample.set_index(\"Names\", inplace=True)\n", "df_sample"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["* Some more operations"]}, {"cell_type": "code", "execution_count": 13, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Ages</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>count</th>\n", "      <td>10.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>mean</th>\n", "      <td>50.500000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>std</th>\n", "      <td>9.009255</td>\n", "    </tr>\n", "    <tr>\n", "      <th>min</th>\n", "      <td>38.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>25%</th>\n", "      <td>41.500000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>50%</th>\n", "      <td>56.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>75%</th>\n", "      <td>56.750000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>max</th>\n", "      <td>60.000000</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["            Ages\n", "count  10.000000\n", "mean   50.500000\n", "std     9.009255\n", "min    38.000000\n", "25%    41.500000\n", "50%    56.000000\n", "75%    56.750000\n", "max    60.000000"]}, "execution_count": 13, "metadata": {}, "output_type": "execute_result"}], "source": ["df_sample.describe()"]}, {"cell_type": "code", "execution_count": 14, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th>Names</th>\n", "      <th>Liu</th>\n", "      <th>Rowland</th>\n", "      <th>Rivers</th>\n", "      <th>Waters</th>\n", "      <th>Rice</th>\n", "      <th>Fields</th>\n", "      <th>Kerr</th>\n", "      <th>Romero</th>\n", "      <th>Davis</th>\n", "      <th>Hall</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Ages</th>\n", "      <td>41</td>\n", "      <td>56</td>\n", "      <td>56</td>\n", "      <td>57</td>\n", "      <td>39</td>\n", "      <td>59</td>\n", "      <td>43</td>\n", "      <td>56</td>\n", "      <td>38</td>\n", "      <td>60</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["Names  Liu  Rowland  Rivers  Waters  Rice  Fields  Kerr  Romero  Davis  Hall\n", "Ages    41       56      56      57    39      59    43      56     38    60"]}, "execution_count": 14, "metadata": {}, "output_type": "execute_result"}], "source": ["df_sample.T"]}, {"cell_type": "code", "execution_count": 15, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/plain": ["Index(['Liu', 'Rowland', 'Rivers', 'Waters', 'Rice', 'Fields', 'Kerr',\n", "       'Romero', 'Davis', 'Hall'],\n", "      dtype='object', name='Names')"]}, "execution_count": 15, "metadata": {}, "output_type": "execute_result"}], "source": ["df_sample.T.columns"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["* Also: Arithmetic operations"]}, {"cell_type": "code", "execution_count": 16, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Ages</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Names</th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Liu</th>\n", "      <td>82</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Rowland</th>\n", "      <td>112</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Rivers</th>\n", "      <td>112</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["         Ages\n", "Names        \n", "Liu        82\n", "Rowland   112\n", "Rivers    112"]}, "execution_count": 16, "metadata": {}, "output_type": "execute_result"}], "source": ["df_sample.multiply(2).head(3)"]}, {"cell_type": "code", "execution_count": 17, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Names</th>\n", "      <th>Ages</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>LiuLiu</td>\n", "      <td>82</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>RowlandRowland</td>\n", "      <td>112</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>RiversRivers</td>\n", "      <td>112</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["            Names  Ages\n", "0          LiuLiu    82\n", "1  RowlandRowland   112\n", "2    RiversRivers   112"]}, "execution_count": 17, "metadata": {}, "output_type": "execute_result"}], "source": ["df_sample.reset_index().multiply(2).head(3)"]}, {"cell_type": "code", "execution_count": 18, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Ages</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Names</th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Liu</th>\n", "      <td>20.5</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Rowland</th>\n", "      <td>28.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Rivers</th>\n", "      <td>28.0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["         Ages\n", "Names        \n", "Liu      20.5\n", "Rowland  28.0\n", "Rivers   28.0"]}, "execution_count": 18, "metadata": {}, "output_type": "execute_result"}], "source": ["(df_sample / 2).head(3)"]}, {"cell_type": "code", "execution_count": 19, "metadata": {"slideshow": {"slide_type": "subslide"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Ages</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Names</th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Liu</th>\n", "      <td>1681</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Rowland</th>\n", "      <td>3136</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Rivers</th>\n", "      <td>3136</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["         Ages\n", "Names        \n", "Liu      1681\n", "Rowland  3136\n", "Rivers   3136"]}, "execution_count": 19, "metadata": {}, "output_type": "execute_result"}], "source": ["(df_sample * df_sample).head(3)"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "fragment"}}, "source": ["Logical operations allowed as well"]}, {"cell_type": "code", "execution_count": 20, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Ages</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Names</th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Liu</th>\n", "      <td>True</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Rowland</th>\n", "      <td>True</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Rivers</th>\n", "      <td>True</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Waters</th>\n", "      <td>True</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Rice</th>\n", "      <td>False</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Fields</th>\n", "      <td>True</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Kerr</th>\n", "      <td>True</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Romero</th>\n", "      <td>True</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Davis</th>\n", "      <td>False</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Hall</th>\n", "      <td>True</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["          Ages\n", "Names         \n", "Liu       True\n", "Rowland   True\n", "Rivers    True\n", "Waters    True\n", "Rice     False\n", "Fields    True\n", "Kerr      True\n", "Romero    True\n", "Davis    False\n", "Hall      True"]}, "execution_count": 20, "metadata": {}, "output_type": "execute_result"}], "source": ["df_sample > 40"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 1\n", "<a name=\"task1\"></a>\n", "\n", "* Create data frame with\n", "    - 10 names of dinosaurs, \n", "    - their favourite prime number, \n", "    - and their favourite color\n", "* Play around with the frame\n", "* Tell me on poll when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 21, "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["happy_dinos = {\n", "    \"Dinosaur Name\": [],\n", "    \"Favourite Prime\": [],\n", "    \"Favourite Color\": []\n", "}\n", "#df_dinos = "]}, {"cell_type": "code", "execution_count": 22, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th>Dinosaur Name</th>\n", "      <th>Aegyptosaurus</th>\n", "      <th>Tyrannosaurus</th>\n", "      <th>Panoplosaurus</th>\n", "      <th>Isisaurus</th>\n", "      <th>Triceratops</th>\n", "      <th>Velociraptor</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Favourite Prime</th>\n", "      <td>4</td>\n", "      <td>8</td>\n", "      <td>15</td>\n", "      <td>16</td>\n", "      <td>23</td>\n", "      <td>42</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Favourite Color</th>\n", "      <td>blue</td>\n", "      <td>white</td>\n", "      <td>blue</td>\n", "      <td>purple</td>\n", "      <td>violet</td>\n", "      <td>gray</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["Dinosaur Name   Aegyptosaurus Tyrannosaurus Panoplosaurus Isisaurus  \\\n", "Favourite Prime             4             8            15        16   \n", "Favourite Color          blue         white          blue    purple   \n", "\n", "Dinosaur Name   Triceratops Velociraptor  \n", "Favourite Prime          23           42  \n", "Favourite Color      violet         gray  "]}, "execution_count": 22, "metadata": {}, "output_type": "execute_result"}], "source": ["happy_dinos = {\n", "    \"Dinosaur Name\": [\"Aegyptosaurus\", \"Tyrannosaurus\", \"Panoplosaurus\", \"Isisaurus\", \"Triceratops\", \"Velociraptor\"],\n", "    \"Favourite Prime\": [\"4\", \"8\", \"15\", \"16\", \"23\", \"42\"],\n", "    \"Favourite Color\": [\"blue\", \"white\", \"blue\", \"purple\", \"violet\", \"gray\"]\n", "}\n", "df_dinos = pd.DataFrame(happy_dinos).set_index(\"Dinosaur Name\")\n", "df_dinos.T"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["Some more `DataFrame` examples"]}, {"cell_type": "code", "execution_count": 23, "metadata": {"slideshow": {"slide_type": "skip"}}, "outputs": [], "source": ["import numpy as np"]}, {"cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>A</th>\n", "      <th>B</th>\n", "      <th>C</th>\n", "      <th>D</th>\n", "      <th>E</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-2.718282</td>\n", "      <td>This</td>\n", "      <td>Same</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>1.718282</td>\n", "      <td>column</td>\n", "      <td>Same</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-1.304068</td>\n", "      <td>has</td>\n", "      <td>Same</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>0.986231</td>\n", "      <td>entries</td>\n", "      <td>Same</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-0.718282</td>\n", "      <td>entries</td>\n", "      <td>Same</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     A          B         C        D     E\n", "0  1.2 2018-02-26 -2.718282     This  Same\n", "1  1.2 2018-02-26  1.718282   column  Same\n", "2  1.2 2018-02-26 -1.304068      has  Same\n", "3  1.2 2018-02-26  0.986231  entries  Same\n", "4  1.2 2018-02-26 -0.718282  entries  Same"]}, "execution_count": 24, "metadata": {}, "output_type": "execute_result"}], "source": ["df_demo = pd.DataFrame({\n", "    \"A\": 1.2,\n", "    \"B\": pd.Timestamp('20180226'),\n", "    \"C\": [(-1)**i * np.sqrt(i) + np.e * (-1)**(i-1) for i in range(5)],\n", "    \"D\": pd.Categorical([\"This\", \"column\", \"has\", \"entries\", \"entries\"]),\n", "    \"E\": \"Same\"\n", "})\n", "df_demo"]}, {"cell_type": "code", "execution_count": 25, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>A</th>\n", "      <th>B</th>\n", "      <th>C</th>\n", "      <th>D</th>\n", "      <th>E</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-2.718282</td>\n", "      <td>This</td>\n", "      <td>Same</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-1.304068</td>\n", "      <td>has</td>\n", "      <td>Same</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-0.718282</td>\n", "      <td>entries</td>\n", "      <td>Same</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>0.986231</td>\n", "      <td>entries</td>\n", "      <td>Same</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>1.718282</td>\n", "      <td>column</td>\n", "      <td>Same</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     A          B         C        D     E\n", "0  1.2 2018-02-26 -2.718282     This  Same\n", "2  1.2 2018-02-26 -1.304068      has  Same\n", "4  1.2 2018-02-26 -0.718282  entries  Same\n", "3  1.2 2018-02-26  0.986231  entries  Same\n", "1  1.2 2018-02-26  1.718282   column  Same"]}, "execution_count": 25, "metadata": {}, "output_type": "execute_result"}], "source": ["df_demo.sort_values(\"C\")"]}, {"cell_type": "code", "execution_count": 26, "metadata": {"slideshow": {"slide_type": "subslide"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>A</th>\n", "      <th>B</th>\n", "      <th>C</th>\n", "      <th>D</th>\n", "      <th>E</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>0.99</td>\n", "      <td>entries</td>\n", "      <td>Same</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-0.72</td>\n", "      <td>entries</td>\n", "      <td>Same</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     A          B     C        D     E\n", "3  1.2 2018-02-26  0.99  entries  Same\n", "4  1.2 2018-02-26 -0.72  entries  Same"]}, "execution_count": 26, "metadata": {}, "output_type": "execute_result"}], "source": ["df_demo.round(2).tail(2)"]}, {"cell_type": "code", "execution_count": 27, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/plain": ["A                              6\n", "C                          -2.03\n", "D    Thiscolumnhasentriesentries\n", "E           SameSameSameSameSame\n", "dtype: object"]}, "execution_count": 27, "metadata": {}, "output_type": "execute_result"}], "source": ["df_demo.round(2).sum()"]}, {"cell_type": "code", "execution_count": 28, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["\\begin{tabular}{lrlrll}\n", "\\toprule\n", "{} &    A &          B &     C &        D &     E \\\\\n", "\\midrule\n", "0 &  1.2 & 2018-02-26 & -2.72 &     This &  Same \\\\\n", "1 &  1.2 & 2018-02-26 &  1.72 &   column &  Same \\\\\n", "2 &  1.2 & 2018-02-26 & -1.30 &      has &  Same \\\\\n", "3 &  1.2 & 2018-02-26 &  0.99 &  entries &  Same \\\\\n", "4 &  1.2 & 2018-02-26 & -0.72 &  entries &  Same \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "\n"]}], "source": ["print(df_demo.round(2).to_latex())"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## Reading External Data\n", "\n", "(Links to documentation)\n", "* [`.read_json()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_json.html#pandas.read_json)\n", "* [`.read_csv()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html#pandas.read_csv)\n", "* [`.read_hdf5()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_hdf.html#pandas.read_hdf)\n", "* [`.read_excel()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html#pandas.read_excel)\n", "\n", "Example:\n", "\n", "```json\n", "{\n", "    \"Character\": [\"Sawyer\", \"\u2026\", \"Walt\"],\n", "    \"Actor\": [\"Josh Holloway\", \"\u2026\", \"Malcolm David Kelley\"],\n", "    \"Main Cast\": [true,  \"\u2026\", false]\n", "}\n", "```"]}, {"cell_type": "code", "execution_count": 29, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Actor</th>\n", "      <th>Main Cast</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Character</th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Hurley</th>\n", "      <td>Jorge Garcia</td>\n", "      <td>True</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Jack</th>\n", "      <td>Matthew Fox</td>\n", "      <td>True</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Kate</th>\n", "      <td>Evangeline Lilly</td>\n", "      <td>True</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Locke</th>\n", "      <td>Terry O'Quinn</td>\n", "      <td>True</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Sawyer</th>\n", "      <td>Josh Holloway</td>\n", "      <td>True</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Walt</th>\n", "      <td>Malcolm David Kelley</td>\n", "      <td>False</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                          Actor  Main Cast\n", "Character                                 \n", "Hurley             Jorge Garcia       True\n", "Jack                Matthew Fox       True\n", "Kate           Evangeline Lilly       True\n", "Locke             Terry O'Quinn       True\n", "Sawyer            Josh Holloway       True\n", "Walt       Malcolm David Kelley      False"]}, "execution_count": 29, "metadata": {}, "output_type": "execute_result"}], "source": ["pd.read_json(\"lost.json\").set_index(\"Character\").sort_index()"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 2\n", "<a name=\"task2\"></a>\n", "\n", "* Read in `nest-data.csv` to `DataFrame`; call it `df`  \n", " *Data was produced with [JUBE](http://www.fz-juelich.de/ias/jsc/EN/Expertise/Support/Software/JUBE/_node.html), Pandas works **very** well together with JUBE*\n", "* Get to know it and play a bit with it\n", "* Tell me when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 30, "metadata": {"exercise": "task"}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["id,Nodes,Tasks/Node,Threads/Task,Runtime Program / s,Scale,Plastic,Avg. Neuron Build Time / s,Min. Edge Build Time / s,Max. Edge Build Time / s,Min. Init. Time / s,Max. Init. Time / s,Presim. Time / s,Sim. Time / s,Virt. Memory (Sum) / kB,Local Spike Counter (Sum),Average Rate (Sum),Number of Neurons,Number of Connections,Min. Delay,Max. Delay\n", "5,1,2,4,420.42,10,true,0.29,88.12,88.18,1.14,1.20,17.26,311.52,46560664.00,825499,7.48,112500,1265738500,1.5,1.5\n", "5,1,4,4,200.84,10,true,0.15,46.03,46.34,0.70,1.01,7.87,142.97,46903088.00,802865,7.03,112500,1265738500,1.5,1.5\n"]}], "source": ["!cat nest-data.csv | head -3"]}, {"cell_type": "code", "execution_count": 31, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>id</th>\n", "      <th>Nodes</th>\n", "      <th>Tasks/Node</th>\n", "      <th>Threads/Task</th>\n", "      <th>Runtime Program / s</th>\n", "      <th>Scale</th>\n", "      <th>Plastic</th>\n", "      <th>Avg. Neuron Build Time / s</th>\n", "      <th>Min. Edge Build Time / s</th>\n", "      <th>Max. Edge Build Time / s</th>\n", "      <th>...</th>\n", "      <th>Max. Init. Time / s</th>\n", "      <th>Presim. Time / s</th>\n", "      <th>Sim. Time / s</th>\n", "      <th>Virt. Memory (Sum) / kB</th>\n", "      <th>Local Spike Counter (Sum)</th>\n", "      <th>Average Rate (Sum)</th>\n", "      <th>Number of Neurons</th>\n", "      <th>Number of Connections</th>\n", "      <th>Min. Delay</th>\n", "      <th>Max. Delay</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>2</td>\n", "      <td>4</td>\n", "      <td>420.42</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.29</td>\n", "      <td>88.12</td>\n", "      <td>88.18</td>\n", "      <td>...</td>\n", "      <td>1.20</td>\n", "      <td>17.26</td>\n", "      <td>311.52</td>\n", "      <td>46560664.0</td>\n", "      <td>825499</td>\n", "      <td>7.48</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>4</td>\n", "      <td>4</td>\n", "      <td>200.84</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.15</td>\n", "      <td>46.03</td>\n", "      <td>46.34</td>\n", "      <td>...</td>\n", "      <td>1.01</td>\n", "      <td>7.87</td>\n", "      <td>142.97</td>\n", "      <td>46903088.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>2</td>\n", "      <td>8</td>\n", "      <td>202.15</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.28</td>\n", "      <td>47.98</td>\n", "      <td>48.48</td>\n", "      <td>...</td>\n", "      <td>1.20</td>\n", "      <td>7.95</td>\n", "      <td>142.81</td>\n", "      <td>47699384.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>4</td>\n", "      <td>8</td>\n", "      <td>89.57</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.15</td>\n", "      <td>20.41</td>\n", "      <td>23.21</td>\n", "      <td>...</td>\n", "      <td>3.04</td>\n", "      <td>3.19</td>\n", "      <td>60.31</td>\n", "      <td>46813040.0</td>\n", "      <td>821491</td>\n", "      <td>7.23</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>5</td>\n", "      <td>2</td>\n", "      <td>2</td>\n", "      <td>4</td>\n", "      <td>164.16</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.20</td>\n", "      <td>40.03</td>\n", "      <td>41.09</td>\n", "      <td>...</td>\n", "      <td>1.58</td>\n", "      <td>6.08</td>\n", "      <td>114.88</td>\n", "      <td>46937216.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>5 rows \u00d7 21 columns</p>\n", "</div>"], "text/plain": ["   id  Nodes  Tasks/Node  Threads/Task  Runtime Program / s  Scale  Plastic  \\\n", "0   5      1           2             4               420.42     10     True   \n", "1   5      1           4             4               200.84     10     True   \n", "2   5      1           2             8               202.15     10     True   \n", "3   5      1           4             8                89.57     10     True   \n", "4   5      2           2             4               164.16     10     True   \n", "\n", "   Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n", "0                        0.29                     88.12   \n", "1                        0.15                     46.03   \n", "2                        0.28                     47.98   \n", "3                        0.15                     20.41   \n", "4                        0.20                     40.03   \n", "\n", "   Max. Edge Build Time / s  ...  Max. Init. Time / s  Presim. Time / s  \\\n", "0                     88.18  ...                 1.20             17.26   \n", "1                     46.34  ...                 1.01              7.87   \n", "2                     48.48  ...                 1.20              7.95   \n", "3                     23.21  ...                 3.04              3.19   \n", "4                     41.09  ...                 1.58              6.08   \n", "\n", "   Sim. Time / s  Virt. Memory (Sum) / kB  Local Spike Counter (Sum)  \\\n", "0         311.52               46560664.0                     825499   \n", "1         142.97               46903088.0                     802865   \n", "2         142.81               47699384.0                     802865   \n", "3          60.31               46813040.0                     821491   \n", "4         114.88               46937216.0                     802865   \n", "\n", "   Average Rate (Sum)  Number of Neurons  Number of Connections  Min. Delay  \\\n", "0                7.48             112500             1265738500         1.5   \n", "1                7.03             112500             1265738500         1.5   \n", "2                7.03             112500             1265738500         1.5   \n", "3                7.23             112500             1265738500         1.5   \n", "4                7.03             112500             1265738500         1.5   \n", "\n", "   Max. Delay  \n", "0         1.5  \n", "1         1.5  \n", "2         1.5  \n", "3         1.5  \n", "4         1.5  \n", "\n", "[5 rows x 21 columns]"]}, "execution_count": 31, "metadata": {}, "output_type": "execute_result"}], "source": ["df = pd.read_csv(\"nest-data.csv\")\n", "df.head()"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## Read CSV Options\n", "\n", "* See also full [API documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)\n", "* Important parameters\n", "    - `sep`: Set separator (for example `:` instead of `,`)\n", "    - `header`: Specify info about headers for columns; able to use multi-index for columns!\n", "    - `names`: Alternative to `header` \u2013\u00a0provide your own column titles\n", "    - `usecols`: Don't read whole set of columns, but only these; works with any list (`range(0:20:2)`)\u2026\n", "    - `skiprows`: Don't read in these rows\n", "    - `na_values`: What string(s) to recognize as `N/A` values (which will be ignored during operations on data frame)\n", "    - `parse_dates`: Try to parse dates in CSV; different behaviours as to provided data structure; optionally used together with `date_parser`\n", "    - `compression`: Treat input file as compressed file (\"infer\", \"gzip\", \"zip\", \u2026)\n", "    - `decimal`: Decimal point divider \u2013\u00a0for German data\u2026\n", "    \n", "```python\n", "pandas.read_csv(filepath_or_buffer, sep=', ', delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, iterator=False, chunksize=None, compression='infer', thousands=None, decimal=b'.', lineterminator=None, quotechar='\"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=None, error_bad_lines=True, warn_bad_lines=True, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None)\n", "```"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## Slicing of Data Frames\n", "\n", "### Slicing Columns\n", "\n", "* Use square-bracket operators to slice data frame: `[]`\n", "    * Use column name to select column\n", "    * Also: Slice horizontally\n", "* Example: Select only columnn `C` from `df_demo`"]}, {"cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>A</th>\n", "      <th>B</th>\n", "      <th>C</th>\n", "      <th>D</th>\n", "      <th>E</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-2.718282</td>\n", "      <td>This</td>\n", "      <td>Same</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>1.718282</td>\n", "      <td>column</td>\n", "      <td>Same</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-1.304068</td>\n", "      <td>has</td>\n", "      <td>Same</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     A          B         C       D     E\n", "0  1.2 2018-02-26 -2.718282    This  Same\n", "1  1.2 2018-02-26  1.718282  column  Same\n", "2  1.2 2018-02-26 -1.304068     has  Same"]}, "execution_count": 32, "metadata": {}, "output_type": "execute_result"}], "source": ["df_demo.head(3)"]}, {"cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [{"data": {"text/plain": ["0   -2.718282\n", "1    1.718282\n", "2   -1.304068\n", "3    0.986231\n", "4   -0.718282\n", "Name: C, dtype: float64"]}, "execution_count": 33, "metadata": {}, "output_type": "execute_result"}], "source": ["df_demo[\"C\"]"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["* Select more than one column by providing list `[]` to slice operator `[]`\n", "* *You usually end up forgetting one of the brackets\u2026*\n", "* Example: Select list of columns `A` and `C`, `[\"A\", \"C\"]` from `df_demo`"]}, {"cell_type": "code", "execution_count": 34, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>A</th>\n", "      <th>C</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>1.2</td>\n", "      <td>-2.718282</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>1.2</td>\n", "      <td>1.718282</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>1.2</td>\n", "      <td>-1.304068</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>1.2</td>\n", "      <td>0.986231</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>1.2</td>\n", "      <td>-0.718282</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     A         C\n", "0  1.2 -2.718282\n", "1  1.2  1.718282\n", "2  1.2 -1.304068\n", "3  1.2  0.986231\n", "4  1.2 -0.718282"]}, "execution_count": 34, "metadata": {}, "output_type": "execute_result"}], "source": ["df_demo[[\"A\", \"C\"]]"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["## Slicing of Data Frames\n", "\n", "### Slicing rows\n", "\n", "* Use numberical values to slice into rows\n", "* Use ranges just like with Python lists"]}, {"cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>A</th>\n", "      <th>B</th>\n", "      <th>C</th>\n", "      <th>D</th>\n", "      <th>E</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>1.718282</td>\n", "      <td>column</td>\n", "      <td>Same</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-1.304068</td>\n", "      <td>has</td>\n", "      <td>Same</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     A          B         C       D     E\n", "1  1.2 2018-02-26  1.718282  column  Same\n", "2  1.2 2018-02-26 -1.304068     has  Same"]}, "execution_count": 35, "metadata": {}, "output_type": "execute_result"}], "source": ["df_demo[1:3]"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "fragment"}}, "source": ["* Get a certain range as **per the current sort structure**"]}, {"cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>A</th>\n", "      <th>B</th>\n", "      <th>C</th>\n", "      <th>D</th>\n", "      <th>E</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "     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border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>A</th>\n", "      <th>B</th>\n", "      <th>C</th>\n", "      <th>D</th>\n", "      <th>E</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>1.718282</td>\n", "      <td>column</td>\n", "      <td>Same</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>0.986231</td>\n", "      <td>entries</td>\n", "      <td>Same</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     A          B         C        D     E\n", "1  1.2 2018-02-26  1.718282   column  Same\n", "3  1.2 2018-02-26  0.986231  entries  Same"]}, "execution_count": 37, "metadata": {}, "output_type": "execute_result"}], "source": ["df_demo.iloc[1:6:2]"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["* Attention: `.iloc[]` location might change after re-sorting!"]}, {"cell_type": "code", "execution_count": 38, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>A</th>\n", "      <th>B</th>\n", "      <th>C</th>\n", "      <th>D</th>\n", "      <th>E</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-1.304068</td>\n", "      <td>has</td>\n", "      <td>Same</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-0.718282</td>\n", "      <td>entries</td>\n", "      <td>Same</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     A          B         C        D     E\n", "2  1.2 2018-02-26 -1.304068      has  Same\n", "4  1.2 2018-02-26 -0.718282  entries  Same"]}, "execution_count": 38, "metadata": {}, "output_type": "execute_result"}], "source": ["df_demo.sort_values(\"C\").iloc[1:3]"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["* One more row-slicing option: `.loc[]`\n", "* See the difference with a *proper* index (and not the auto-generated default index from before)"]}, {"cell_type": "code", "execution_count": 39, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type 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<td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-1.304068</td>\n", "      <td>Same</td>\n", "    </tr>\n", "    <tr>\n", "      <th>entries</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>0.986231</td>\n", "      <td>Same</td>\n", "    </tr>\n", "    <tr>\n", "      <th>entries</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-0.718282</td>\n", "      <td>Same</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["           A          B         C     E\n", "D                                      \n", "This     1.2 2018-02-26 -2.718282  Same\n", "column   1.2 2018-02-26  1.718282  Same\n", "has      1.2 2018-02-26 -1.304068  Same\n", "entries  1.2 2018-02-26  0.986231  Same\n", "entries  1.2 2018-02-26 -0.718282  Same"]}, "execution_count": 39, "metadata": {}, "output_type": "execute_result"}], "source": ["df_demo_indexed = df_demo.set_index(\"D\")\n", "df_demo_indexed"]}, {"cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>A</th>\n", "      <th>B</th>\n", "      <th>C</th>\n", "      <th>E</th>\n", "    </tr>\n", "    <tr>\n", "      <th>D</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>entries</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>0.986231</td>\n", "      <td>Same</td>\n", "    </tr>\n", "    <tr>\n", "      <th>entries</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-0.718282</td>\n", "      <td>Same</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["           A          B         C     E\n", "D                                      \n", "entries  1.2 2018-02-26  0.986231  Same\n", "entries  1.2 2018-02-26 -0.718282  Same"]}, "execution_count": 40, "metadata": {}, "output_type": "execute_result"}], "source": ["df_demo_indexed.loc[\"entries\"]"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["### Advanced Slicing: Logical Slicing\n", "\n"]}, {"cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "   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{"cell_type": "code", "execution_count": 42, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>A</th>\n", "      <th>B</th>\n", "      <th>C</th>\n", "      <th>D</th>\n", "      <th>E</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-0.718282</td>\n", "      <td>entries</td>\n", "      <td>Same</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     A          B         C        D     E\n", "4  1.2 2018-02-26 -0.718282  entries  Same"]}, "execution_count": 42, "metadata": {}, "output_type": "execute_result"}], "source": ["df_demo[(df_demo[\"C\"] < 0) & (df_demo[\"D\"] == \"entries\")]"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## Adding to Existing Data Frame\n", "\n", "* Add new columns with `frame[\"new col\"] = something` or `.insert()`\n", "* Add new rows with `frame.append()`\n", "* Combine data frames\n", "    - *Concat*: Combine several data frames along an axis\n", "    - *Merge*: Combine data frames on basis of common columns; database-style\n", "    - (Join)\n", "    - See user guide [on merging](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html)"]}, {"cell_type": "code", "execution_count": 43, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        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<td>has</td>\n", "      <td>Same</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     A          B         C       D     E\n", "0  1.2 2018-02-26 -2.718282    This  Same\n", "1  1.2 2018-02-26  1.718282  column  Same\n", "2  1.2 2018-02-26 -1.304068     has  Same"]}, "execution_count": 43, "metadata": {}, "output_type": "execute_result"}], "source": ["df_demo.head(3)"]}, {"cell_type": "code", "execution_count": 44, "metadata": {"slideshow": {"slide_type": "subslide"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>A</th>\n", "     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column  Same  0.518282\n", "2  1.2 2018-02-26 -1.304068     has  Same -2.504068"]}, "execution_count": 44, "metadata": {}, "output_type": "execute_result"}], "source": ["df_demo[\"F\"] = df_demo[\"C\"] - df_demo[\"A\"]\n", "df_demo.head(3)"]}, {"cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": ["df_demo.insert(df_demo.shape[1], \"G\", df_demo[\"C\"] ** 2)"]}, {"cell_type": "code", "execution_count": 46, "metadata": {"slideshow": {"slide_type": "subslide"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>A</th>\n", "      <th>B</th>\n", "      <th>C</th>\n", "      <th>D</th>\n", "      <th>E</th>\n", "      <th>F</th>\n", "      <th>G</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-1.304068</td>\n", "      <td>has</td>\n", "      <td>Same</td>\n", "      <td>-2.504068</td>\n", "      <td>1.700594</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>0.986231</td>\n", "      <td>entries</td>\n", "      <td>Same</td>\n", "      <td>-0.213769</td>\n", "      <td>0.972652</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-0.718282</td>\n", "      <td>entries</td>\n", "      <td>Same</td>\n", "      <td>-1.918282</td>\n", "      <td>0.515929</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     A          B         C        D     E         F         G\n", "2  1.2 2018-02-26 -1.304068      has  Same -2.504068  1.700594\n", "3  1.2 2018-02-26  0.986231  entries  Same -0.213769  0.972652\n", "4  1.2 2018-02-26 -0.718282  entries  Same -1.918282  0.515929"]}, "execution_count": 46, "metadata": {}, "output_type": "execute_result"}], "source": ["df_demo.tail(3)"]}, {"cell_type": "code", "execution_count": 47, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>A</th>\n", "      <th>B</th>\n", "      <th>C</th>\n", "      <th>D</th>\n", "      <th>E</th>\n", "      <th>F</th>\n", "      <th>G</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-2.718282</td>\n", "      <td>This</td>\n", "      <td>Same</td>\n", "      <td>-3.918282</td>\n", "      <td>7.389056</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>1.718282</td>\n", "      <td>column</td>\n", "      <td>Same</td>\n", "      <td>0.518282</td>\n", "      <td>2.952492</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-1.304068</td>\n", "      <td>has</td>\n", "      <td>Same</td>\n", "      <td>-2.504068</td>\n", "      <td>1.700594</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>0.986231</td>\n", "      <td>entries</td>\n", "      <td>Same</td>\n", "      <td>-0.213769</td>\n", "      <td>0.972652</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>1.2</td>\n", "      <td>2018-02-26</td>\n", "      <td>-0.718282</td>\n", "      <td>entries</td>\n", "      <td>Same</td>\n", "      <td>-1.918282</td>\n", "      <td>0.515929</td>\n", "    </tr>\n", "    <tr>\n", "      <th>5</th>\n", "      <td>1.3</td>\n", "      <td>2018-02-27</td>\n", "      <td>-0.777000</td>\n", "      <td>has it?</td>\n", "      <td>Same</td>\n", "      <td>23.000000</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     A          B         C        D     E          F         G\n", "0  1.2 2018-02-26 -2.718282     This  Same  -3.918282  7.389056\n", "1  1.2 2018-02-26  1.718282   column  Same   0.518282  2.952492\n", "2  1.2 2018-02-26 -1.304068      has  Same  -2.504068  1.700594\n", "3  1.2 2018-02-26  0.986231  entries  Same  -0.213769  0.972652\n", "4  1.2 2018-02-26 -0.718282  entries  Same  -1.918282  0.515929\n", "5  1.3 2018-02-27 -0.777000  has it?  Same  23.000000       NaN"]}, "execution_count": 47, "metadata": {}, "output_type": "execute_result"}], "source": ["df_demo.append(\n", "    {\"A\": 1.3, \"B\": pd.Timestamp(\"2018-02-27\"), \"C\": -0.777, \"D\": \"has it?\", \"E\": \"Same\", \"F\": 23},\n", "    ignore_index=True\n", ")"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["## Combining Frames\n", "\n", "* First, create some simpler data frame to show `.concat()` and `.merge()`"]}, {"cell_type": "code", "execution_count": 48, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Key</th>\n", "      <th>Value</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>First</td>\n", "      <td>1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>Second</td>\n", "      <td>1</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["      Key  Value\n", "0   First      1\n", "1  Second      1"]}, "execution_count": 48, "metadata": {}, "output_type": "execute_result"}], "source": ["df_1 = pd.DataFrame({\"Key\": [\"First\", \"Second\"], \"Value\": [1, 1]})\n", "df_1"]}, {"cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Key</th>\n", "      <th>Value</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>First</td>\n", "      <td>2</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>Second</td>\n", "      <td>2</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["      Key  Value\n", "0   First      2\n", "1  Second      2"]}, "execution_count": 49, "metadata": {}, "output_type": "execute_result"}], "source": ["df_2 = pd.DataFrame({\"Key\": [\"First\", \"Second\"], \"Value\": [2, 2]})\n", "df_2"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["* Concatenate list of data frame vertically (`axis=0`)"]}, {"cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Key</th>\n", "      <th>Value</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>First</td>\n", "      <td>1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>Second</td>\n", "      <td>1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>First</td>\n", "      <td>2</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>Second</td>\n", "      <td>2</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["      Key  Value\n", "0   First      1\n", "1  Second      1\n", "0   First      2\n", "1  Second      2"]}, "execution_count": 50, "metadata": {}, "output_type": "execute_result"}], "source": ["pd.concat([df_1, df_2])"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "fragment"}}, "source": ["* Same, but re-index"]}, {"cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Key</th>\n", "      <th>Value</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>First</td>\n", "      <td>1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>Second</td>\n", "      <td>1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>First</td>\n", "      <td>2</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>Second</td>\n", "      <td>2</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["      Key  Value\n", "0   First      1\n", "1  Second      1\n", "2   First      2\n", "3  Second      2"]}, "execution_count": 51, "metadata": {}, "output_type": "execute_result"}], "source": ["pd.concat([df_1, df_2], ignore_index=True)"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["* Concat, but horizontally"]}, {"cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Key</th>\n", "      <th>Value</th>\n", "      <th>Key</th>\n", "      <th>Value</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>First</td>\n", "      <td>1</td>\n", "      <td>First</td>\n", "      <td>2</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>Second</td>\n", "      <td>1</td>\n", "      <td>Second</td>\n", "      <td>2</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["      Key  Value     Key  Value\n", "0   First      1   First      2\n", "1  Second      1  Second      2"]}, "execution_count": 52, "metadata": {}, "output_type": "execute_result"}], "source": ["pd.concat([df_1, df_2], axis=1)"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "fragment"}}, "source": ["* Merge on common column"]}, {"cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Key</th>\n", "      <th>Value_x</th>\n", "      <th>Value_y</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>First</td>\n", "      <td>1</td>\n", "      <td>2</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>Second</td>\n", "      <td>1</td>\n", "      <td>2</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["      Key  Value_x  Value_y\n", "0   First        1        2\n", "1  Second        1        2"]}, "execution_count": 53, "metadata": {}, "output_type": "execute_result"}], "source": ["pd.merge(df_1, df_2, on=\"Key\")"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "subslide"}}, "source": ["## Task 3\n", "<a name=\"task3\"></a>\n", "\n", "* Add a column to the Nest data frame called `Virtual Processes` which is the total number of threads across all nodes (i.e. the product of threads per task and tasks per node and nodes)\n", "* Remember to tell me when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 54, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>id</th>\n", "      <th>Nodes</th>\n", "      <th>Tasks/Node</th>\n", "      <th>Threads/Task</th>\n", "      <th>Runtime Program / s</th>\n", "      <th>Scale</th>\n", "      <th>Plastic</th>\n", "      <th>Avg. Neuron Build Time / s</th>\n", "      <th>Min. Edge Build Time / s</th>\n", "      <th>Max. Edge Build Time / s</th>\n", "      <th>...</th>\n", "      <th>Presim. Time / s</th>\n", "      <th>Sim. Time / s</th>\n", "      <th>Virt. Memory (Sum) / kB</th>\n", "      <th>Local Spike Counter (Sum)</th>\n", "      <th>Average Rate (Sum)</th>\n", "      <th>Number of Neurons</th>\n", "      <th>Number of Connections</th>\n", "      <th>Min. Delay</th>\n", "      <th>Max. Delay</th>\n", "      <th>Virtual Processes</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>2</td>\n", "      <td>4</td>\n", "      <td>420.42</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.29</td>\n", "      <td>88.12</td>\n", "      <td>88.18</td>\n", "      <td>...</td>\n", "      <td>17.26</td>\n", "      <td>311.52</td>\n", "      <td>46560664.0</td>\n", "      <td>825499</td>\n", "      <td>7.48</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>8</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>4</td>\n", "      <td>4</td>\n", "      <td>200.84</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.15</td>\n", "      <td>46.03</td>\n", "      <td>46.34</td>\n", "      <td>...</td>\n", "      <td>7.87</td>\n", "      <td>142.97</td>\n", "      <td>46903088.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>16</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>2</td>\n", "      <td>8</td>\n", "      <td>202.15</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.28</td>\n", "      <td>47.98</td>\n", "      <td>48.48</td>\n", "      <td>...</td>\n", "      <td>7.95</td>\n", "      <td>142.81</td>\n", "      <td>47699384.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>16</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>4</td>\n", "      <td>8</td>\n", "      <td>89.57</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.15</td>\n", "      <td>20.41</td>\n", "      <td>23.21</td>\n", "      <td>...</td>\n", "      <td>3.19</td>\n", "      <td>60.31</td>\n", "      <td>46813040.0</td>\n", "      <td>821491</td>\n", "      <td>7.23</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>32</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>5</td>\n", "      <td>2</td>\n", "      <td>2</td>\n", "      <td>4</td>\n", "      <td>164.16</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.20</td>\n", "      <td>40.03</td>\n", "      <td>41.09</td>\n", "      <td>...</td>\n", "      <td>6.08</td>\n", "      <td>114.88</td>\n", "      <td>46937216.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>16</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>5 rows \u00d7 22 columns</p>\n", "</div>"], "text/plain": ["   id  Nodes  Tasks/Node  Threads/Task  Runtime Program / s  Scale  Plastic  \\\n", "0   5      1           2             4               420.42     10     True   \n", "1   5      1           4             4               200.84     10     True   \n", "2   5      1           2             8               202.15     10     True   \n", "3   5      1           4             8                89.57     10     True   \n", "4   5      2           2             4               164.16     10     True   \n", "\n", "   Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n", "0                        0.29                     88.12   \n", "1                        0.15                     46.03   \n", "2                        0.28                     47.98   \n", "3                        0.15                     20.41   \n", "4                        0.20                     40.03   \n", "\n", "   Max. Edge Build Time / s  ...  Presim. Time / s  Sim. Time / s  \\\n", "0                     88.18  ...             17.26         311.52   \n", "1                     46.34  ...              7.87         142.97   \n", "2                     48.48  ...              7.95         142.81   \n", "3                     23.21  ...              3.19          60.31   \n", "4                     41.09  ...              6.08         114.88   \n", "\n", "   Virt. Memory (Sum) / kB  Local Spike Counter (Sum)  Average Rate (Sum)  \\\n", "0               46560664.0                     825499                7.48   \n", "1               46903088.0                     802865                7.03   \n", "2               47699384.0                     802865                7.03   \n", "3               46813040.0                     821491                7.23   \n", "4               46937216.0                     802865                7.03   \n", "\n", "   Number of Neurons  Number of Connections  Min. Delay  Max. Delay  \\\n", "0             112500             1265738500         1.5         1.5   \n", "1             112500             1265738500         1.5         1.5   \n", "2             112500             1265738500         1.5         1.5   \n", "3             112500             1265738500         1.5         1.5   \n", "4             112500             1265738500         1.5         1.5   \n", "\n", "   Virtual Processes  \n", "0                  8  \n", "1                 16  \n", "2                 16  \n", "3                 32  \n", "4                 16  \n", "\n", "[5 rows x 22 columns]"]}, "execution_count": 54, "metadata": {}, "output_type": "execute_result"}], "source": ["df[\"Virtual Processes\"] = df[\"Nodes\"] * df[\"Tasks/Node\"] * df[\"Threads/Task\"]\n", "df.head()"]}, {"cell_type": "code", "execution_count": 55, "metadata": {"exercise": "solution"}, "outputs": [{"data": {"text/plain": ["Index(['id', 'Nodes', 'Tasks/Node', 'Threads/Task', 'Runtime Program / s',\n", "       'Scale', 'Plastic', 'Avg. Neuron Build Time / s',\n", "       'Min. Edge Build Time / s', 'Max. Edge Build Time / s',\n", "       'Min. Init. Time / s', 'Max. Init. Time / s', 'Presim. Time / s',\n", "       'Sim. Time / s', 'Virt. Memory (Sum) / kB', 'Local Spike Counter (Sum)',\n", "       'Average Rate (Sum)', 'Number of Neurons', 'Number of Connections',\n", "       'Min. Delay', 'Max. Delay', 'Virtual Processes'],\n", "      dtype='object')"]}, "execution_count": 55, "metadata": {}, "output_type": "execute_result"}], "source": ["df.columns"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## Aside: Plotting without Pandas\n", "\n", "### Matplotlib 101\n", "\n", "* Matplotlib: de-facto standard for plotting in Python\n", "* Main interface: `pyplot`; provides MATLAB-like interface\n", "* Better: Use object-oriented API with `Figure` and `Axis`\n", "* Great integration into Jupyter Notebooks\n", "* Since v. 3: Only support for Python 3\n", "* \u2192 https://matplotlib.org/"]}, {"cell_type": "code", "execution_count": 56, "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["import matplotlib.pyplot as plt\n", "%matplotlib inline"]}, {"cell_type": "code", "execution_count": 57, "metadata": {"slideshow": {"slide_type": "subslide"}}, "outputs": [], "source": ["x = np.linspace(0, 2*np.pi, 400)\n", "y = np.sin(x**2)"]}, {"cell_type": "code", "execution_count": 58, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots()\n", "ax.plot(x, y)\n", "ax.set_title('Use like this')\n", "ax.set_xlabel(\"Numbers again\");\n", "ax.set_ylabel(\"$\\sqrt{x}$\");"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["* Plot multiple lines into one canvas\n", "* Call `ax.plot()` multiple times"]}, {"cell_type": "code", "execution_count": 59, "metadata": {"slideshow": {"slide_type": "-"}}, "outputs": [], "source": ["y2 = y/np.exp(y*1.5)"]}, {"cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots()\n", "ax.plot(x, y, label=\"y\")\n", "ax.plot(x, y2, label=\"y2\")\n", "ax.legend()\n", "ax.set_title(\"This plot makes no sense\");"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 4\n", "<a name=\"task4\"></a>\n", "\n", "* Sort the data frame by the virtual proccesses\n", "* Plot `\"Presim. Time / s\"` and `\"Sim. Time / s\"` of our data frame `df` as a function of the virtual processes\n", "* Use a dashed, red line for `\"Presim. Time / s\"`, a blue line for `\"Sim. Time / s\"` (see [API description](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot))\n", "* Don't forget to label your axes and to add a legend\n", "* Submit when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 61, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["df.sort_values([\"Virtual Processes\", \"Nodes\", \"Tasks/Node\", \"Threads/Task\"], inplace=True)"]}, {"cell_type": "code", "execution_count": 62, "metadata": {"exercise": "solution"}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots()\n", "ax.plot(df[\"Virtual Processes\"], df[\"Presim. Time / s\"], linestyle=\"dashed\", color=\"red\")\n", "ax.plot(df[\"Virtual Processes\"], df[\"Sim. Time / s\"], \"-b\")\n", "ax.set_xlabel(\"Virtual Process\")\n", "ax.set_ylabel(\"Time / s\")\n", "ax.legend();"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## Plotting with Pandas\n", "\n", "* Each data frame hast a `.plot()` function (see [API](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html))\n", "* Plots with Matplotlib\n", "* Important API options:\n", "    - `kind`: `line` (default), `bar[h]`, `hist`, `box`, `kde`, `scatter`, `hexbin`\n", "    - `subplots`: Make a sub-plot for each column (good together with `sharex`, `sharey`)\n", "    - `figsize`\n", "    - `grid`: Add a grid to plot (use Matplotlib options)\n", "    - `style`: Line style per column (accepts list or dict)\n", "    - `logx`, `logy`, `loglog`: Logarithmic plots\n", "    - `xticks`, `yticks`: Use values for ticks\n", "    - `xlim`, `ylim`: Limits of axes\n", "    - `yerr`, `xerr`: Add uncertainty to data points\n", "    - `stacked`: Stack a bar plot\n", "    - `secondary_y`: Use a secondary `y` axis for this plot\n", "    - Labeling\n", "        * `title`: Add title to plot (Use a list of strings if `subplots=True`)\n", "        * `legend`: Add a legend\n", "        * `table`: If `true`, add table of data under plot\n", "    - `**kwds`: Every non-parsed keyword is passed through to Matplotlib's plotting methods"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["* Either slice and plot\u2026"]}, {"cell_type": "code", "execution_count": 63, "metadata": {"slideshow": {"slide_type": "-"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x144 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df_demo[\"C\"].plot(figsize=(10, 2));"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "fragment"}}, "source": ["* \u2026 or plot and select"]}, {"cell_type": "code", "execution_count": 64, "metadata": {"slideshow": {"slide_type": "-"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x144 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df_demo.plot(y=\"C\", figsize=(10, 2));"]}, {"cell_type": "markdown", "metadata": {}, "source": ["* I prefer slicing first, as it allows for further operations on the sliced data frame"]}, {"cell_type": "code", "execution_count": 65, "metadata": {"slideshow": {"slide_type": "subslide"}}, "outputs": [{"data": {"image/png": "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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df_demo[\"C\"].plot(kind=\"bar\");"]}, {"cell_type": "markdown", "metadata": {}, "source": ["* There are pseudo-sub-functions for each of the plot `kind`s\n", "* I prefer to just call `.plot(kind=\"smthng\")`"]}, {"cell_type": "code", "execution_count": 66, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"image/png": "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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df_demo[\"C\"].plot.bar();"]}, {"cell_type": "code", "execution_count": 67, "metadata": {"slideshow": {"slide_type": "subslide"}}, "outputs": [{"data": {"image/png": "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\n", "text/plain": ["<Figure size 864x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df_demo[\"C\"].plot(kind=\"bar\", legend=True, figsize=(12, 4), ylim=(-1, 3), title=\"This is a C plot\");"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 5\n", "<a name=\"task5\"></a>\n", "\n", "Use the NEST data frame `df` to:\n", "\n", "1. Make the virtual processes the index of the data frame (`.set_index()`)\n", "2. Plot `\"Presim. Program / s\"` and `\"Sim. Time / s`\" individually\n", "3. Plot them onto one common canvas!\n", "4. Make them have the same line colors and styles as before\n", "5. Add a legend, add missing labels\n", "\n", "* Done? Tell me! [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 68, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "subslide"}}, "outputs": [], "source": ["df.set_index(\"Virtual Processes\", inplace=True)"]}, {"cell_type": "code", "execution_count": 69, "metadata": {"exercise": "solution"}, "outputs": [{"data": {"image/png": 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qtwO/kt7F+FpgJCK2VLNfMzMzs1ZT7d2Li4E1Su76ywH/FBFfl/Q+gIi4AbgDuAx4BtgD/GqV+zQzMzNrOVWFroh4DjinTPkNJdMBXF3NfszMzMxaXT0eGWFmZmZ23HPoMjMzM6sDhy4zMzOzOnDoMjMzM6sDhy4zMzOzOnDoMjMzM6sDhy4zMzOzOnDoKiN5tJiZmZlZ7VT7RPq2ks0IgHfccD8L53SwYE4ni+Z0smBOB4vmdrJwbicL53SwcE5n8je3Mymf00H3rA4y6fpmZmZmkzl0lfjJ1yziT96+kq0j+9i+Z4wde8bYvnuM51/ew8Mv7OCVPWOM58v3gmUEPbM72LF3nPUfeTNzu3xozczM7CAngxJduSzvfu2rKy6PCHaP5XlldxLGXtmT/G3fPc6OPWN86pvPAPDNJ7bxc+f01avZZmZm1gIcuo6CJOZ15ZjXlWPZojmHLS+Grlf2jNW7aWZmZtbkPJC+hj52+VkAvO7UExvcEjMzM2s2Mw5dkpZJukfSY5I2SPrtMnUulDQi6eH074+qa25z65nd0egmmJmZWZOq5vLiBPC7EbFW0nzgIUl3RcRjk+p9JyLeWsV+zMzMzFrejHu6ImJLRKxNp3cCjwP9tWqYmZmZWTupyZguScuBHwf+o8zin5L0iKSvSTrrCNu4StKgpMHh4eFaNKthRveON7oJZmZm1mSqDl2S5gFfAq6JiNFJi9cCr46Ic4BPAf9SaTsRcWNEDETEQG9vb7XNaoiTF84hmxHv+swDfPhLj/Lc8K5GN8nMzMyaRFWhS1IHSeC6NSK+PHl5RIxGxK50+g6gQ1Lb3tr3E69eyLc+dCGrz1/Gmu8P8cZPfJurb13L+qGRRjfNzMzMGkwzfc+gJAG3ANsj4poKdZYAWyMiJJ0PfJGk5+uIOx0YGIjBwcEZtatZDO/czz/c9wP+8f7n2bl/gjesOJH3X3gKP/WaE0gOnZmZmbUDSQ9FxMCU9aoIXa8HvgOsAwpp8R8ArwKIiBskfQB4P8mdjnuB34mI/zfVttshdBWN7hvn1gd+yGe/+wNe2rWfVcsW8P4LT+FNZyz2uxrNzMzawDEPXcdSO4Wuon3jeb740CY+c++zvLB9L6eeNI/3/fQpXL6qj46sn1FrZmbWqhy6mtREvsBX123h+m89yxMv7qSvZxb/9YLXsPq8VzG7MzutbUQEO/aMs3XnPl4c2cfW0X1sHd3PZ779LLvH8jz7Py8j6140MzOzuphu6PK7F+ssl81w+ap+3nZOH996cpi//dYzfPRfH+NT33yG9/yn5aw+bxl7x/NJmNq5n60j+3hxtBisitP7GZsoVNzHvvE8c7t8as3MzJqJ/2VuEElcdPpJXHT6STy4cTvXf+tZPnHXU3zirqcOqzurI8OS7lks7p7Fjy9byJKeZHpxd9eB8pO6u/iH+zZy7deeIOOB+mZmZk3HoasJnLd8Eee9ZxGPbxnl3qeGWTS3k8Xdsw6Eq+5ZuaO64/GprTuZ25UjmxFZiUwGMhLZjA58ZiWUgeyk8ozw3ZVmZmbHgENXEzljaTdnLO2e8fqzcsmA/Ms/fV9V7ZBIw1oSworTB8KaRDZzaHlG4gcv7QZg47VvqWr/zW7/RHL5d/OOfbw4upfNO/axZWQvL47s522r+njbOX2NbqKZmTUhh6428s7zlrF0wWzG8wXyhaAQQb4AhQgKhSBf/CwEhSBdfrC8EJSsl5RHWlYsP7DNQ9YL8sGB0PXrtzxI9+wOumd10DP74F/37EPne2Z3MKsj01Q9a2MTBbaO7mPLSBKktozsY8uOvWweKQarfby0a+yw9RbM6WDP/jwQDl1mZlaWQ1cbmdOZ481nLWnY/left4zrvvkMm3fs4/EtOxndO87O/RNHXKczm6F7du5AIFswu4NFc7s4YV4ni+Ymfycc+Oxi0bxO5nZmZxTUJvIFtu7cz5Ydew+Eqs07kjtAt4wkweqlXfuZfENv96wcS3tms3TBLM7uX0BfT3Lpt2/BbJam03M6c7zlb75z1G0yM7Pjh0OX1czrTj2R15166Fue8oVg575xRvYe+je6d2LS/Dij+8YZ3rWfJ1/cycu7x9hf4Q7NzlzmQBA7GMqSoNY9K8eiuV0He6lKgtW2nfsoTApU87pyLO2ZxdIFszl9STdLF8yir2d2GqpmsaRnNvN8J6iZmdWA/zWxYyqbEQvmdLJgTudRrRcR7BnLs333GC/vHmP77v28vGuM7bvHSsqSz40v7+blXWPsGcsfso3ZHdkDIeoNK05kadoztTTtpVrSM4vuWR21/LpmZmYVOXRZU5LE3K4cc7tyLFs0Z1rr/PDlPTw7vCvppeqZTffso7vrs1obNo+yYfMo//jA82SU3DFavBu0OJ2R0IGy8nVUYZ3MUdRJpqeuc9h2qe54ZbMilxGd2Yxfc2VmNolDl7WNV50wh1edML2Adiz9j39Z3+gmNIWMoCOboTObIZcVHdlM+idy6XTngenDl3dmM+QyoiOXoSOTLi+ZLq7XmcuQyyT7iAjG88mNHxOFIF8oMFEIJvKHzufT+YlCctPJRLrOeLFOyTYmJs3nC3HgZpXSsnwhyGZELg2eHen3zmWS71E8Bsl0+j3TdndkDh6j4jZKy8qukxXZTHI8ctnJ2ymtl2yzuKzctrMZNdUNLWbtyqHLrEae+bNL2bF3nIjk8mjxDtFCehdooaRsqjox6bNSncO3e+g+yu+7fDvykwe8HaUguat1LJ+ElPF8gfFCgfGJZHqiUGBsIgkx4/lDp8fzwa79EwfWO2Qb6edEOj2Wr/w2hqkUA0bx80DIySjtpTsYQnJpqMmly7s6MswpWd5Rsrz4OJXSkJZ85zS4pd9n18TEocvS71Rc50BZ+lnlKTkqHZMCYi5bPtAdLD8Y7HKZJBAe2puafmZKe1or9/AWg1/FXuKMDj7OZvJ+yqx7oAc3c+R9ZzNT9yxnMlP1PB/8DkdaPrlN5Y5XsRfb2pNDl1mN5LIZTpzX1ehmtL2IYm9TpKHuYDArBqhcaZgq+Wy1f8wKheQ7TuST3rjxkkCXLxRD66HLSkNbMdAVj1fpskrrHOwZLFRY5+D0nokJJkq2HRwa9POF0tBf5j8k0uX5Csub8NXAdVEMYtkKoaw0BKqknkjmVRrgSKYplpVsH0qDXulyTZpPy8rUP2QbHBocS/d1aFlxOMPBtomDQyeK9ZlcVtK2yfXLbiM9mJlJy8ptQxw69KO0naXbUHHoxqT601VV6JJ0CfDXQBb4+4i4dtLyLuBzwE8ALwPvioiN1ezTzI5vUrEnBmYzvZfEt6pMRnRlshyvN9CW9ubmy/XaFg4PdPni8ikC35SBsOQ5hVP1GBf3d6TlceB5icX9TOqVLhy6bn6K5aXtLH6XgAO916UBuDhNaQ83xXaVr1967EvrHzgelPTIl9QPgkKhTFmUzpeum9RnclnaNjj03FTcRgCT9lXPnuLpmvFPWVIW+DTwJmAT8KCk2yPisZJq7wVeiYhTJa0GPg68q5oGm5nZ8eFAjwvyZRmbscnDNYLi0IpJgRCIwuFlBwPrwXUPDs9IypZ/fHptqeZ/x+cDz0TEcwCS/hm4HCgNXZcDH0mnvwhcJ0kRx2unsZmZmdVTcgkWqPLu7FrIVLFuP/BCyfymtKxsnYiYAEaAE8ptTNJVkgYlDQ4PD1fRLDMzM7PmU03oqqmIuDEiBiJioLe3t9HNMTMzM6upakLXELCsZP7ktKxsHUk5oIdkQL2ZmZnZcaWa0PUgsELSj0jqBFYDt0+qcztwZTr9DuCbHs9lZmZmx6MZD6SPiAlJHwC+QfLIiJsiYoOkjwGDEXE78FngHyU9A2wnCWZmZmZmxx01Y8eTpGHg+Ua3o02cCLzU6EbYAT4fzcXno3n4XDQXn4+j8+qImHJAelOGLqsdSYMRMdDodljC56O5+Hw0D5+L5uLzcWw0zd2LZmZmZu3MocvMzMysDhy62t+NjW6AHcLno7n4fDQPn4vm4vNxDHhMl5mZmVkduKfLzMzMrA4cuszMzMzqwKGrzUjaKGmdpIclDaZliyTdJenp9HNho9vZriTdJGmbpPUlZWWPvxJ/I+kZSY9KOrdxLW8/Fc7FRyQNpb+PhyVdVrLs99Nz8aSkNzem1e1L0jJJ90h6TNIGSb+dlvv3UWdHOBf+fRxjDl3t6aKIWFXyjJUPA3dHxArg7nTejo2bgUsmlVU6/pcCK9K/q4Dr69TG48XNHH4uAD6Z/j5WRcQdAJLOJHljxlnpOn8rKVu3lh4fJoDfjYgzgdcCV6fH3b+P+qt0LsC/j2PKoev4cDlwSzp9C/D2BralrUXEvSSvvCpV6fhfDnwuEg8ACyQtrU9L21+Fc1HJ5cA/R8T+iPgB8Axw/jFr3HEoIrZExNp0eifwONCPfx91d4RzUYl/HzXi0NV+ArhT0kOSrkrLFkfElnT6RWBxY5p23Kp0/PuBF0rqbeLI/8dntfGB9HLVTSWX2n0u6kjScuDHgf/Av4+GmnQuwL+PY8qhq/28PiLOJemav1rSBaULI3lGiJ8T0iA+/g13PXAKsArYAvxVY5tz/JE0D/gScE1EjJYu8++jvsqcC/8+jjGHrjYTEUPp5zZgDUkX8NZit3z6ua1xLTwuVTr+Q8Cyknonp2V2jETE1ojIR0QB+DsOXiLxuagDSR0k/8jfGhFfTov9+2iAcufCv49jz6GrjUiaK2l+cRq4GFgP3A5cmVa7EvhKY1p43Kp0/G8HfiW9S+u1wEjJZRY7BiaNCfp5kt8HJOditaQuST9CMnj7e/VuXzuTJOCzwOMR8YmSRf591Fmlc+Hfx7GXa3QDrKYWA2uS3xM54J8i4uuSHgS+IOm9wPPAOxvYxrYm6TbgQuBESZuAPwaupfzxvwO4jGRQ6h7gV+ve4DZW4VxcKGkVySWsjcBvAETEBklfAB4jubPr6ojIN6Ldbex1wLuBdZIeTsv+AP8+GqHSubjCv49jy68BMjMzM6sDX140MzMzqwOHLjMzM7M6cOgyMzMzqwOHLjMzM7M6cOgyMzMzqwOHLjOriqR7JL15Utk1kq6X1CfpixXWWy7pP1e5729JGqhQ/qSkRyTdJ+m0avZjZlYLDl1mVq3bgNWTylYDt0XE5oh4x+QVJOWA5UBVoWsKvxwR55C8RPkvyrQhewz3bWZ2GIcuM6vWF4G3SOqEAy/Q7QO+k/ZmrU/L3yPpdknfBO4meSjmGyQ9LOmD6fLrihuV9G+SLkynr5c0KGmDpI8eZfvuBU5Nt7NR0sclrQV+SdIqSQ+kL/hdU3zBr6RTJf172lO2VtIpafl/k/RgWv+jadlcSV9N666X9K60/FpJj6V1/zIt65X0pXQbD0p6XVr+0+lxeFjS94tvljCz9uIn0ptZVSJiu6Tvkbxk/SskvVxfiIhI345Q6lzgx9J1LgQ+FBFvhSSUHWE3f5iukwXulvRjEfHoNJv4c8C6kvmX05fCI+lR4Dcj4tuSPkby1PprgFuBayNijaRZQEbSxSSvPzkfEHC7khfK9wKbI+It6TZ7JJ1A8hqV09PjsCDd918Dn4yI70p6FfAN4AzgQyRP+b5PyUuI903zu5lZC3FPl5nVQuklxtXpfDl3RcT2GWz/nWnv1PeBs4Azp7HOrekrTl5HEmqKPg9JOAIWRMS30/JbgAvSXqb+iFgDEBH7ImIPybtML07bsBY4nSSErQPelPagvSEiRoARkuD0WUm/QPIaG4CfBa5L23U70J2GrPuAT0j6rbRNE0d7gMys+bmny8xq4SvAJyWdC8yJiIcq1Nt9hG1McOh/CM4CSF+w+yHgvIh4RdLNxWVT+OWIGDzKNhyJgP8VEZ85bEHyvS8D/lTS3RHxMUnnA28E3gF8APgZku/32oiY3JN1raSvptu4T9KbI+KJGbbTzJqUe7rMrGoRsQu4B7iJyr1ck+0ESscubQRWScpIWkZyGQ+gmyQojUhaTHIZsxZtHgFekfSGtOjdwLcjYiewSdLbASR1SZpDcinw19KeKST1SzpJUh+wJyL+D8mA/XPTOj0RcQfwQeCcdB93Ar9ZbEP6cmEknRIR6yLi48CDJL1oZtZm3NNlZrVyG7CGw+9krORRIC/pEeBm4H9AKxUnAAAAsElEQVQDPwAeAx4nuYRHRDwi6fvAE8ALJJfiauVK4IY0VD0H/Gpa/m7gM+k4r3HglyLiTklnAPenY9V2Af+FZJD+X0gqpHXfTxImv5KOBxPwO+l2fwv4dDqWLEcyyP99wDWSLgIKwAbgazX8jmbWJBQRjW6DmZmZWdvz5UUzMzOzOnDoMjMzM6sDhy4zMzOzOnDoMjMzM6sDhy4zMzOzOnDoMjMzM6sDhy4zMzOzOvj/r1L+SfYYynAAAAAASUVORK5CYII=\n", "text/plain": ["<Figure size 720x216 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df[\"Presim. Time / s\"].plot(figsize=(10, 3));"]}, {"cell_type": "code", "execution_count": 70, "metadata": {"exercise": "solution"}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x216 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df[\"Sim. Time / s\"].plot(figsize=(10, 3));"]}, {"cell_type": "code", "execution_count": 71, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "subslide"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df[\"Presim. Time / s\"].plot();\n", "df[\"Sim. Time / s\"].plot();"]}, {"cell_type": "code", "execution_count": 72, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["ax = df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot();\n", "ax.set_ylabel(\"Time / s\");"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## More Plotting with Pandas\n", "### Our first proper Pandas plot\n"]}, {"cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot();"]}, {"cell_type": "markdown", "metadata": {}, "source": ["* **That's why I think Pandas is great!**\n", "* It has great defaults to quickly plot data\n", "* Plotting functionality is very versatile\n", "* Before plotting, data can be *massaged* within data frames, if needed"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## More Plotting with Pandas\n", "### Some versatility"]}, {"cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [{"data": {"image/png": 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"text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df_demo[[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True);"]}, {"cell_type": "code", "execution_count": 75, "metadata": {"slideshow": {"slide_type": "subslide"}}, "outputs": [{"data": {"image/png": 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"text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df_demo[df_demo[\"F\"] < 0][[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True);"]}, {"cell_type": "code", "execution_count": 76, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 864x288 with 3 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df_demo[df_demo[\"F\"] < 0][[\"A\", \"C\", \"F\"]]\\\n", "    .plot(kind=\"barh\", subplots=True, sharex=True, title=\"Subplots\", figsize=(12, 4));"]}, {"cell_type": "code", "execution_count": 77, "metadata": {"slideshow": {"slide_type": "subslide"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 864x432 with 2 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df_demo[df_demo[\"F\"] < 0][[\"A\", \"F\"]]\\\n", "    .plot(\n", "        style=[\"-*r\", \"--ob\"], \n", "        secondary_y=\"A\", \n", "        figsize=(12, 6),\n", "        table=True\n", "    );"]}, {"cell_type": "code", "execution_count": 78, "metadata": {"slideshow": {"slide_type": "subslide"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 864x432 with 2 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df_demo[df_demo[\"F\"] < 0][[\"A\", \"F\"]]\\\n", "    .plot(\n", "        style=[\"-*r\", \"--ob\"], \n", "        secondary_y=\"A\", \n", "        figsize=(12, 6),\n", "        yerr={\n", "            \"A\": df_demo[df_demo[\"F\"] < 0][\"C\"], \n", "            \"F\": 0.2\n", "        }, \n", "        capsize=4,\n", "        title=\"Bug: style is ignored with yerr\",\n", "        marker=\"P\"\n", "    );  "]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## Combine Pandas with Matplotlib\n", "\n", "* Pandas shortcuts very handy\n", "* But sometimes, one needs to access underlying Matplotlib functionality\n", "* No problemo!\n", "* **Option 1**: Pandas always returns axis\n", "    - Use this to manipulate the canvas\n", "    - Get underlying `figure` with `ax.get_figure()` (for `fig.savefig()`)\n", "* **Option 2**: Create figure and axes with Matplotlib, use when drawing\n", "    - `.plot()`: Use `ax` option"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["### Option 1: Pandas Returns Axis"]}, {"cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["ax = df_demo[\"C\"].plot(figsize=(10, 4))\n", "ax.set_title(\"Hello there!\");\n", "fig = ax.get_figure()\n", "fig.suptitle(\"This title is super!\");"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["### Option 2: Draw on Matplotlib Axes"]}, {"cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(figsize=(10, 4))\n", "df_demo[\"C\"].plot(ax=ax)\n", "ax.set_title(\"Hello there!\");\n", "fig.suptitle(\"This title is super!\");"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["* We can also get fancy!"]}, {"cell_type": "code", "execution_count": 81, "metadata": {"slideshow": {"slide_type": "-"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 864x288 with 2 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(12, 4))\n", "for ax, column, color in zip([ax1, ax2], [\"C\", \"F\"], [\"blue\", \"#b2e123\"]):\n", "    df_demo[column].plot(ax=ax, legend=True, color=color)"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## Aside: Seaborn\n", "\n", "* Python package on top of Matplotlib\n", "* Powerful API shortcuts for plotting of statistical data\n", "* Manipulate color palettes\n", "* Works well together with Pandas\n", "* Also: New, well-looking defaults for Matplotlib (IMHO)\n", "* \u2192 https://seaborn.pydata.org/"]}, {"cell_type": "code", "execution_count": 82, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["import seaborn as sns\n", "sns.set()"]}, {"cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df_demo[[\"A\", \"C\"]].plot();"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["### Seaborn Color Palette Example\n", "\n", "* [Documentation](https://seaborn.pydata.org/tutorial/color_palettes.html)"]}, {"cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [{"data": {"image/png": "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\n", "text/plain": ["<Figure size 720x72 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["sns.palplot(sns.color_palette())"]}, {"cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [{"data": {"image/png": "iVBORw0KGgoAAAANSUhEUgAAAkgAAABQCAYAAADiBIpwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAAplJREFUeJzt2TGKE3EYxuFvZRQJihpIZzGd9R5CLDyEF4g3EAvZSrByLmDtCcTKE1jbTWEX2VUUER0Yq1i8nWGHyQ7P06T6s+8HS/hBTsZxHAsAgH+uzT0AAODYCCQAgCCQAACCQAIACAIJACAIJACAIJAAAIJAAgAIAgkAIAgkAIAgkAAAgkACAAjNoQ8/nz2v4eL8MrccjfZVV/277dwzJtM+6upDv8z73lfVWdvVs4Xed9Z2te3fzD1jMl37pLaf+rlnTKZ70Nb2dT/3jMl0T9t6ue3nnjGJh1V12rX1caH3nXZt9du3c8+YRLNe1f0Xj///3aF/cLg4r+HL7tDnR2/4udzbqqp+Dcu8b5/s5wu9r6pqN3yfe8Kkdn+GuSdMavdt2fd93S3zvt/7z4XeV1U17H7MPeGo+IkNACAIJACAIJAAAIJAAgAIAgkAIAgkAIAgkAAAgkACAAgCCQAgCCQAgCCQAACCQAIACAIJACAIJACAIJAAAIJAAgAIAgkAIAgkAIAgkAAAgkACAAgCCQAgCCQAgCCQAACCQAIACAIJACAIJACAIJAAAIJAAgAIAgkAIAgkAIAgkAAAgkACAAgCCQAgCCQAgCCQAACCQAIACAIJACAIJACAIJAAAIJAAgAIAgkAIAgkAIAgkAAAgkACAAgCCQAgCCQAgCCQAACCQAIACAIJACAIJACAIJAAAEJz8MN768vccXSa1WbuCZO62Szzvv1/5Xqh91VVbZrbc0+Y1Ob6wV9LV8LmzrLvu7tZ5n039p8Lva+qqtncmnvCJJr16qB3J+M4jpe8BQDgSvMTGwBAEEgAAEEgAQAEgQQAEAQSAEAQSAAAQSABAASBBAAQBBIAQBBIAABBIAEABIEEABD+AsYQTZBcSEeRAAAAAElFTkSuQmCC\n", "text/plain": ["<Figure size 720x72 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["sns.palplot(sns.color_palette(\"hls\", 10))"]}, {"cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [{"data": {"image/png": "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\n", "text/plain": ["<Figure size 1440x72 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["sns.palplot(sns.color_palette(\"hsv\", 20))"]}, {"cell_type": "code", "execution_count": 87, "metadata": {"slideshow": {"slide_type": "subslide"}}, "outputs": [{"data": {"image/png": "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\n", "text/plain": ["<Figure size 720x72 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["sns.palplot(sns.color_palette(\"Paired\", 10))"]}, {"cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [{"data": {"image/png": "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\n", "text/plain": ["<Figure size 576x72 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["sns.palplot(sns.color_palette(\"cubehelix\", 8))"]}, {"cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [{"data": {"image/png": "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\n", "text/plain": ["<Figure size 720x72 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["sns.palplot(sns.color_palette(\"colorblind\", 10))"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["### Seaborn Plot Examples\n", "\n", "* Most of the time, I use a regression plot from Seaborn"]}, {"cell_type": "code", "execution_count": 88, "metadata": {"slideshow": {"slide_type": "skip"}}, "outputs": [], "source": ["import warnings\n", "warnings.simplefilter(action='ignore', category=FutureWarning)"]}, {"cell_type": "code", "execution_count": 89, "metadata": {"slideshow": {"slide_type": "-"}}, "outputs": [{"data": {"image/png": 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ojFX0syb2tduLfhaS9NAJQ+bQUSTWdEylMLWOHgiV0CkaEiwir+JFP+tMc9R96UU/4+s6nUmVpqda9DM5dKToZ/akhE784GfMeKGTPNKRHWyzhwSLcK3JFv3MVINtOkU/j+1XeCMRKfqZZZMJHZJCx5LQKXgSLKIgGUpRbZpUZxjpxIt+plSZTgqgeOGb3thuts/ipXAGBxOvMWbRT9PEJ6GTNcmh42ii9dRiYnmTMtKJ/4kfCi2WwPnPYJDdg4N02jZ1psk3y8r4cklJvps1JgkWMeskF/08fpJFP+ObC0KxT6mJin6mB06dFP3MuljeoMcIHaWiO9e8xDYRaB07qzO7wuY/g0Fe7O/HIlr7r9dxeDFWIWMmwiX+e5MD2NP8HUqCRRSV8Yp+VteU8Xln/6ida/HwiYdOvOjnwQmKfqZfV6dUQidrEoU8kzYRZLweDiRGN4W6cWD34CAWJKZnvQBas3twcNLBkggLDSgIOQ4RFf071EolNsnYjoOtozs5tdZYhpFxp+dEJFiEiFGxgp8TFf3sTJpiO9Kin4nQkVI4RyzTJQpgpL6aWaDFPDttm7KkfzcMoERBn+OkBIZW0b8DJxYWduxrR2sisb+XxFpXOEJ37OzSeH8N0/0rclWwPPHEE+zcuROAc889l7/7u78bdf9LL71EVVUVAFdccQXXXHNNztspis94RT+11gylVZqedNHPGF966EjRz6xJrq+WXDlaKWA4TL92Yudx3FNjLTkwjvN6GNQ6sbbnEK3lN0cpuhwnJTDibZ6o6XoSjzkSrgmWPXv28Jvf/IaXX34ZpRTf+c53+Ld/+zfOP//8xGMOHDjAww8/zJlnnpnHlgqRSilFmVKUjVH0cyjt6qHJB0WTi3622DYtGULHCynnc+riU2xS9HPa4qObCJoh2xk1ujEUI1uiYyMcFTuPkz6hmfwBrYhWJ5golJKDw1GxkYVS0WrWOrp7zolds+dLHi+vDQ6Ajr52hGgVi6VlZZO6Gm4+uCZY/H4/69evx+v1AnDCCSfQ3Nyc8pgDBw7w1FNPcfDgQb72ta9xxx134PP58tFcISat1DAoNQzmZyiFM5y+ey3p63jRzxBTK/oZDyAp+jl1idFN2rZoSN6lluF5sf+Ln9fxGNFQil9TRxEdaejY1JSto+e0nNjmhNj/Mlro9bJEa34XDHLYtqk2Tc4uKeEkF3/2uSZYTjrppMTXn376KTt27OD5559P3DYwMMBpp53GHXfcwVFHHcX69evZunUr69aty0dzhcgK3ziVpkNJVw9ND58+KfqZcyO71MZ5TFJlgmw6yedzdZCkU1q7a5/ERx99xI033sgtt9zCX/7lX475uPfee48NGzawffv2Kb1+63CI0f8JClFYQo5DZzhCRzhCezhCZyhCRzhMRzjC4Yg94fy5qaDOspjrtZjr8TDXYyX+1Hgsqb8mgOgvJ/U+75Sf55oRC8DevXtZs2YNGzZsYPny5Sn3NTc3s2fPHi677DIgOqS0MvyWN5GenqFJzUvW1JbT3TUw5dcvdMXabyi8vpcARwNHY4LHBE/0N9pI0kaCbnt0penolSmhLRyhLRwBgimvm6noZ/zr2Vb0s9B+5tky2X57TIP6QAEHS0tLCzfddBNbtmzh7LPPHnV/SUkJDzzwAF//+tc5+uijee6551IW9oUQUZMt+hkPnz4D2oIhuo+g6Ge80rQU/RTgomB5+umnGR4e5r777kvcduWVV/KrX/2KNWvWcPrpp7Nx40ZWr15NOBzmq1/9Ktddd10eWyxE4clU9DP+22u86Gf6dul4VYKJin4qokU/x6o0LUU/i4fr1lhm2odtPTIVNo5i7TcUb98n0+/kop/JVaa704p+jqfKMEYFTnyKLV9FP+VnPj6PaXByYM6UX981IxYhpuKj4eGC2n5Z6JKLfi5Muy8eOt1OatHPeAClF/38NEMpnExFP+NfS9HPwiPBIgrOR8PD7BocxCRaJqXfcdgVq0ws4ZJ7yaGTqehnX9oB0eRNBfEVnKkW/YxXJ5Cin+4kwSIKzu+CQUxIzNl7AGIHyCRY3EUpRZVpUmWao4p+xitNZzocOpWin+nTalL0M/8kWETBOWzblKRNj1ix20XhSK40PV7Rz+Qq02MV/fw8w+vHi34mAkeKfuaMBIsoONWmSb/jkPxRFIndLmaHXBX9rB8eosLWUvQzyyRYRME5u6QkuqaiNRYjRfnOdvEV9UT2TLfoZ5dtM5he9LN/cNTz04t+Ji5xIEU/J02CRRSc+DqK7AoTmUym6GeiurTH4NDgsBT9zDIJFlGQCq0on3AHn2HQHw6zN/ZLiR8v34j9UhIv+hkf5SSXw+mdRNFPi+h0rBT9lGARQhSR9K3qvRGbXcPRTc8n+Xw0WBYNGUY64aTda+nTbD2x0IkAHbZNh22PKoVjkho6ySOeObMwdCRYhBBFY9RW9dglfCfaqu5RioBlEcgQOpmKfsa/ThT9JHqJ4c4MoZNc9DN963ShFv2UYBFCFI2Z2Ko+UdHP9Gm1+Gjn8BSKfo46q+Pyop8SLEKIopHrreqmUsw1TeZmeP14penutO3S8dBJL/qZbqyin/GvLTuC6mhDtR9Cl5ajTzx1RvqYiQSLEFkgtcsKQ/pW9ZDWeduqnqnSdJwTqzQdD5yOzg66eg/TXVJKT0UltmGigcOOw2HH4ZO05yutqerrofZwF3XdndQd/j9UVVVTUzuX2hyc95JgEeIISe2ywpG+Vd1vmSz2ue+XACNWlLPGNDnmjx8y/MtXMVCYjo0KDjFgWfQ2HM1hr5dOXyld1bV0VdfSWV1HxONBK0VPVTU9VdV8cmxS2dCeHgCqenqojpXDSZ9my0bRTwkWIY6Q1C4rLMlb1V1XNl9r6O1BtbdgtLag2g9hHHiHsuAQRtI6UCXQ0Hww9anlFdiBBvqOXkDnvKPprPXTWTmHLsuTWNuJr+D02ja9MGHRz+O8Hk5GyuYLkXNSu0xMmeNAVwdG+yFU2yFU+yFUW0v06+HUS0WnT1w5Xh9OaSm214f537+J429A+xugohKAstifY9Kel1z0c7jE4mDf0IRFP1scm2um0T1XBcurr77Kk08+STgc5lvf+hbXXJPapffff5/vf//79Pf3s3jxYu69995pXfdeiGyS2mViTJFwdAG9LRYc8SDpaEVlGC3EaaWgzo8ONBDu7SWiwPaVEDFMME0IhaCigvL/9j8m3ZTkop81c8o5yR75ZShe9DO9DI42pjct5ppP5dbWVrZs2cLPf/5zvF4vV155JV//+tc58cQTE4+5/fbb+Yd/+Ae+8pWvsGHDBl544QWuvvrqPLZaCKldJoDhIKq9dWTU0X4I1dqC6u5AZdjRFactC+bWowMNOIF56EB09KHnBsCK/qoSjq2xKA0YBoRCaDuC72uTD5WJJBf9PCap0rTHnN6lB1wTLHv27OGss86iuroagAsvvJBdu3Zx8803A/DFF18QDAb5yle+AsAll1zCY489JsEi8k5qlxWRgf6UkYcRH4n0dI/7NO31oesbIDAvGiD+ehz/PKidGw2LcXhOOBloIvTW/48+3I2qrsH3tf8Ru92dXBMsbW1t+P0jR4wCgQD79u0b836/309ra2tO2yjEWE72+TjZ50PnuyFiQuE/fpj4kA77/fCVs1M/pOML6G0tGMnTV20tqIH+cV9bl1dERx2x0Yfjj35NVTUcwW4rzwknuzpI0rkmWLQe/Z9kcqXQie6frDlzSpnskmpNbfmUX382KNZ+w+i+p/8bpmO3KaK7wCylMGP/1Gj6bYdwhn9X3a5YfuZDH7zH4L//bwzTQpWX43R0YPzif+E76WTMiI1u+QLnUAsEh8Z9HVVTi2qYjzFvPmrefIyG+Rjzj0LFFtALwWR+5tNdJXRNsNTX1/P2228nvm9rayMQCKTc39HRkfi+vb095f7J6ukZImyPPecZ57ptiDlSTP1WRH+JVCgMBXNryunrGcJQCgONicIAlAZHjYSKEfujgVBahmjAVjAQsbELJGBm/c88aQE9/KsdlA30YYbDmKFhVPxndKh51C+c0QX0ALo+uu7h+KNTWDrQAL4M62choED+Hif7M/eYBvUB75Rf3zXB8o1vfIPHH3+crq4uSktLef311/nhD3+YuP+oo47C5/Oxd+9e/vRP/5Tt27dzzjnn5LHFwu3SgyM+usgUHEpBrdeDTTCaDmljFSNDgIz1niUafJbJoNYM2TZOYeRL4RsOJm3dTdq+29WeCBBPhqdppXB8JahTvpyYutL++pQFdDE1rgmW+vp61q1bx6pVqwiHw1x22WUsWrSI66+/njVr1nD66afz4IMP8v3vf5+BgQG+9KUvsWrVqnw3W7hAcoBYRnRaylIKQ+toiGgwVHTqHMgYHNkeXCgN5ShKLIt+xyFkO7L+ki3JC+itLSNnQSZaQPeV4Hi92F4fTmkZEY8X7fMSDkWgspLyK/8mRx2Y/ZTOtHgxi33Y1iNTYeNwc7/HCxAzFiBKTT8k/P5K2tv7strmKE1IKfptm4gLhy+u/JlrDb2HYzuvWlIPEU64gF4ZW0BvQNfPGzlAWFVN+OOPolt3TQs8HgzHxg6F8C1pKqjF8SM1lamwkwNy8l7MAmMFiJk0AkkESHz0Efu8duevSQqvhlrDZNDQDMr02IiUE+ixAImHSNoJ9HS6uiax+0oH5uH469H+eVBeMeZz0rfuWn4/VvquMHHEJgyWQ4cOMTg4yMKFC9myZQsDAwMYhsGtt96KT/bpiyMwUYDEd6QUToBMrCxpemy4mKbHsnACPRoeEyygT0Ly1l1XjtZmgXGDZd++fdx4443ceeedLFy4kJ07d7Jy5Ur+4z/+g+eee45vf/vbuWqnKGDJAeIxRrboJgfIqPUPPfYCeaEzdPQ6GsOGot92XDk9Nm0pC+hJI5CkBfRMtGWBvz4aHoF50dPnsoBesMYNlkcffZQtW7Zw1llnAVBeXs7NN99Mc3Mza9askWARKRIBolTKGQ8zvgZCcQXIeLQGL4oawyRoRrcnO4U0DIsvoMdGIFNZQNeBBoiXMPFHDxFO5gS6KBzjBsvBgwcToQIjhxTnz5/P8PDwzLZMuNaYAUL0QJUEyOQpoFSD1zIZdByCbpoey8YCen3SCfTYAvqRnEAXhWHcYPF4Uoegzz333Jj3idknHiCGGln/sGIjj8wBMupLMUmmhirDoMRQDNgOoRxOj2nHgY620eVL2ltnZAFdzH7jBkt5eTmHDh2ioaEh8T1AS0sLpaWlM986kRPJAVKiFBWWKQGSB1qDB0V10vRYVk/vpy+gx0YgQx1t+CLhMZ82EwvoYnYbN1guv/xybr31Vh577DHq6uoA6Onp4c477+Sqq67KSQNF9kxmBJJ6+jxKAiT34qf3B2Kn96eUL5M4gZ5JdAG9YaSEuyygi2maMFgOHjzIN7/5TU444QSUUnz88cesWrWKFStW5KqNYorGChATxjiFPupL4QJKQ0Vse/JAptP7/X0p4WHEw2QyC+j186IL6P4GyhcuoLe0BmrqZAFdZMWE51i+973vce211/LOO+8AsGjRomkVfxTZFw2QaB0sT1KV3QkDRBKkYGitMQ53U9bajNV+iFBLM8TXQiZaQK+oTCrhnrT+UTUnZQHdrC0vmOKJojBM6uR9XV0dS5Ysmem2iDFIgMx+2nFwOttx2lqwW1tw2qJ/7LZDkLSAnmk8oatrR0qYBObhBBrQcxtkAV3kjZR0cZHxAsRMr4MlAeIq4ff3M/zmazhdHRi1c/GdeyGe004f9TgdCeO0t2K3teDEAsRubcFpbwV77BPoGAZGnR+zfh5mYB7Uzyc4N0CoLoCWBXThMhIseZAeIJZhJM6ASIAUnvD7+xna/j9RloUqK0f39TD48rP42pdglJWnjkI628f/QVoWpr8Bs2E+hr8Bo34+RqABY24AlbaAXqJgGFxb3FIULwmWGTRegKRMYaVf60M+IwqG099HcNfLEA6hQ8Po8GGIhMG2GX71hbGf6CuJjj5iIxCjfh7KPw+jdi5qkgvo0dP7UtxSuI8ESxbE62CZsYtJWYaBRTRYTAmQgqe1Rvd0j4w6WluiU1ltLegJFtBVRWUiQIzAyB9VVT2tS2uPpQxe7ZkIAAAVJ0lEQVSFz7Lcd3pfFCUJlimQAJndtONgt7dmWEBvgYlKGHk8qJJSDF8JWBbasaGsisqb/i43jWfk9L4vdno/LMMXkScSLBlEA4REJd7oFJbGQEmAzAI6EsZpa02MOuJB0tPRCuOUcB9ZQJ+fGIEofwNOdyfB//0zlGWhPd7otFjEpvQvGnPXqZh4cUtPoRa3FLOCa4Jl7969/OhHPyISiVBdXc2PfvQjjjrqqJTHNDc3s3z5co499lgA5s6dy9NPPz3t90wegXiUwowFiIki4PXgMc3Ua4HEyH+n+TeZXVg6OITdfih199VUF9AT01eZF9ABOGYByuNNaU/JGLvCciVe3DJ+ej8YsWV6TOSMay5N/Bd/8Rds3bqVU089lZ/97Gfs3r2bJ598MuUxr732Gr/97W/ZuHHjtN/nYEcvGpUIkFHnQGJm7jK17lYI/U7ehYXHix4OooeH8Zz+VZRhxNZAmtE9h8d9HVVSihGYh1nfgFk/n+oTj6fPVz2lBfRCEUHTrzWhDJflLuaLXRVr34vi0sShUIi1a9dy6qmnAnDKKafw7LPPjnrc/v37+fDDD7nkkkuoqKjgrrvu4pRTTpnSe5UpA8fRyAiksCQvoA+98lP04EC0Km8kHL28LRDe8+8ZnzuygD5/ZPSRYQG93F/JoMtDdbosFNVKMewx6M92cUsh0rhmxBLnOA6rV6/m9NNP5+abb0657/HHH8fv93PllVfy5ptv8sMf/pAdO3bg9Xrz1FqRbdq2Cbe3EWr+gnDzF4SaPyfU8gWhlmZ0cPwS7lgeyk77Ep75R+GddxTeo47GO+8ozAo5gZ4sojX9EZshR3aPifGZQL1v6p+vOQ+WnTt3snnz5pTbFi5cyLZt2wiFQqxfv56enh7+6Z/+acJrvqxcuZL7778/MdKZjM7O/tiIZXyFMCU0E3LVbx1OPoHenChfMpkT6MrrQ8V2YWFZoBROOIRRVUPFd2+bdpuK7WduK+h3HMrmlBbldBDIVNhECmYqrLGxkcbG0btlBgYGWL16NdXV1Tz55JMZQ+WZZ55hxYoV1NTUANHpEctyxWyeGIMODkUDo7U5tgvr0OQX0AOx8x/18zD88QX0eiIfvc/Q9v8ZK+FsQjgEtoPv3Atz17FZwNQwRynKLJM+Q8npfZE1rvlUvv322znuuOPYuHHjmAfH3nrrLYLBINdffz2///3vcRyHhQsX5rilIp3WGj3Ql3RwcCRIprSAPskT6NHdVle7ahdW4VKUmyY1psmQnN4XWeKKYHnvvffYvXs3J554IhdffDEAgUCAp556ip/+9Ke0tbWxdu1a7rrrLtavX88rr7yCz+fjoYcewphlu3fcTGuNPtyVFB7R3VdO6yH04GROoI+c/4iPQlTVnGmdQPecdroESRYpHT29X2JZ9DsOw3J6XxwB1y3ezzRZYxmf319J26HDOF3tOK2HRq2BEBr/BLpRU5eYvjLj5UsC8zDKynPUg+kr5p95cr+VghDQVwTFLWWNZXwFs8Yi3CPTAvp/dbYSOnRo4hLucwMjayBJhwiV15e7DogZoTV4gBo5vS+mSYKlCEx/Ad2DGWgYFR7G3Pro4UQxq8VP73stU4pbiimRT4dZIvMC+hRPoDfMo3LBAoIVtdEprJq6WXcCXUxdvLhliaHol+KWYhIkWApMYgG9daTybryUux4cf85UVVZFp68akk6gpy2g1xTpOoMYX3R6TFFtmAzHpsfk9L4YiwSLS2nbji2gt6RexnYKC+jJU1iFsoAu3E0BJfHpMa0Zsm0piSRGkWDJs4wn0FtbcDraJreAnrT7ShbQRa4YGiqStidnKm4pipcEywxJL+vuPfvPMWpqoyOQpItIOV0dEy+gJx0eNPyxf9YFZAFd5J2liRW3tKS4pUiQT6Ysii+gh37/W4bffC0aGI6D3dPN0B8/GPe5qqQ0evYjbQQiC+jC/RS+tOkxWd8vbhIs0zDTC+hCFCKloTw2PTYgp/eLmgTLJEQ+/b9EPv5wSgvoeLyokhKMkhIwLbQycIYGqbrrx7lptBB5YmqYYxgMGwb9RXB6X4wmwTIB+/PPGPjHMcIgfQG9fj6Gv4HB/+9/wUBf6iJ6aBizzp+bRguRZ1qDF6g1TAaluGXRkWCZgKqdi3ncCRAJjYRHfA2kzp9xAb3kvMZoWXcAjxfCIXQkQomUdRdFSIpbFh8JlgkYZeVU3Lx+Ss+Rsu5CpDIS02PR0/syPTa7SbDMECnrLkSq6PSYkuKWRUD2sQohcipe3LLWMim1TGQv5OwjwSKEyAtDQyWKasvEa0i8zCaumQrbvn07Dz74IHV1dQD8+Z//OevWrUt5TG9vL7fddhsHDx6ktraWRx55BL9fdloJUciSi1vK6f3ZwTXBsn//ftavX8+KFSvGfMwjjzzC4sWL+clPfsL27dvZtGkTjzzySA5bKYSYKXJ6f/ZwzVTY/v372b59OytXruS2226jp6dn1GPeeOMNmpqaAFixYgW//vWvCYfDuW6qEGKGxE/vV1sWPtOQ9ZcC5ZoRi9/v54YbbmDRokU8/PDDbNy4kYceeijlMW1tbYmpL8uyqKiooKuri/r6+km/T11dxRTaVDnpx84mxdpvKN6+u7HfWmsGbYd+28aewfepqS3Oy0lMpt/mNF8758Gyc+dONm/enHLbwoUL2bZtW+L773znOyxZsmRSr2dMsUBjZ2c/ziTG2P4iveBVsfYbirfvbu+3UhDWM3N6v6a2nO6u8ev7zUaT7bfHNKgPeKf8+jkPlsbGRhobG1Nu6+vrY9u2bXzrW98Cor+pWBlOtAcCATo6OmhoaCASidDf3091dXUumi2EyBOlo6f3fVLcsmC4Yo2lrKyMf/7nf+bdd98F4Nlnn+X8888f9bhzzz2X7du3A7Bjxw4WL16Mx+PJaVuFEPkRL25Z7bGwZHuyq7lijcU0TR555BHuuecegsEgCxYs4P777wfg0UcfJRAIcNVVV7F27VrWr1/P8uXLqays5MEHH8xzy4UQuaQ1eIAaw2RIilu6ltK6uDaNyxrL+Iq131C8fS/kftsKBh2H4DSnx2SNZXwe0+DkwJwpv74rpsKEEGI6TA1VhsEcj4lHpsdcwxVTYUIIMV3x4pYeKW7pGjJiEULMCsnFLcukuGVeSbAIIWYVQ0MFihrLxGvKR1w+yN+6EGJWslBUK8Ucj4WpZPySS7LGIoSYxZQUt8wDGbEIIWa9eHHLGilumRMSLEKIomFqmBObHpPT+zNHgkUIUWQUXg21hkmFYSD5kn0SLEKIolXlsai1LEpkeiyrJFiEEEXNiBW3nOMxZXosSyRYhBBFT2vwakWNYVLpsTBke/IRkWARQoiY+On9GsukVKbHpk2CRQgh0sSLW1Z7TLwyPTZlEixCCJGB1uDRijmGSZWc3p8SCRYhhBiHAkqSi1tKvkzIFSVdOjs7+fa3v534vq+vj+7ubt55552UxzU3N7N8+XKOPfZYAObOncvTTz+d07YKIYqTihW3LLEsBhyHYdvJd5NcyxXBUldXxyuvvAKA4zhce+21rFu3btTj9u/fT1NTExs3bsx1E4UQAgArdnp/2GMxELGJyLVfRnHdVNhLL71EaWkpTU1No+7bv38/H374IZdccgmrVq3igw8+yEMLhRAiWtyyxjIpt0w5vZ/GVcFi2zZPPvkkt956a8b7fT4fF198MT//+c/5m7/5G2666SZCoVCOWymEEFHJxS3l9P4IpXVux3E7d+5k8+bNKbctXLiQbdu28cYbb/DMM89Met1k5cqV3H///Zx66qkz0VQhhJiSQdumP+IQYXZMj5lAvc875eflfI2lsbGRxsbGjPf98pe/ZNmyZWM+95lnnmHFihXU1NQAoLXGsqbWhc7OfpxJXJDB76+kvb1vSq89GxRrv6F4+16s/YaZ6bsCwmgGXXztl5racrq7BiZ8nMc0qA9MPVhcNRX2hz/8gcWLF495/1tvvcXPfvYzAH7/+9/jOA4LFy7MVfOEEGJSyop8eswVu8LiDh48SENDQ8ptP/3pT2lra2Pt2rXcddddrF+/nldeeQWfz8dDDz2EYbgqG4UQAohd+8UwGDYUA7ZD2K3DlxmQ8zWWfJOpsPEVa7+hePterP2G3PVdA0EFAxEbxwUfuVOZCjs5MGfKry+/7gshxAyLF7estUxKLXPWT49JsAghRI4YGipR1FgmXnP2fvzO3p4JIYRLWSiqlcGcWVrc0lWL90IIUUx8GryWyaDWDLl4e/JUyYhFCCHyKPn0vm+WbE+WYBFCCBcwY8Ut53gsrAIvPibBIoQQrqHwaqgxTSoKuLilBIsQQriM0oV9el+CRQghXCp+er+6wKbHJFiEEMLFtAaPhhrDpNJjYRTA9mQJFiGEKADx0/s1lkmpy6fHJFiEEKKAmBqqDINqj4nHpdNjEixCCFFgotNjimrDpMqFp/fl5L0QQhQoBZSknd53QfFkGbEIIUShMzRUxLYnu6G4Zf5bIIQQIissDdWx0/v5nB7LW7A8+uijPP7444nve3t7ueGGG2hsbOSaa66hvb191HO01vz4xz9m6dKlLFu2jL179+ayyUIIUQAUvti1X8rzdHo/58HS19fHhg0b+Jd/+ZeU2x955BEWL17Mzp07ufzyy9m0adOo57722mv88Y9/ZMeOHfzjP/4j69evJxKJ5KrpQghRMJKLW+b69H7Og2X37t0sWLCA6667LuX2N954g6amJgBWrFjBr3/9a8LhcMpj3nzzTZYtW4ZhGBx//PHMnz+fd955J2dtF0KIQmNqqMpxccucB8vFF1/MDTfcgGmaKbe3tbXh9/sBsCyLiooKurq6Rj0mEAgkvvf7/Rw6dGjmGy2EEAUtWtyy1ogWt5zpeJmx7cY7d+5k8+bNKbctXLiQbdu2Tfo1DCM193SGfXTpj5lIXV3FpB/r91dO6bVni2LtNxRv34u131CcfQ85Dr66CoIT7E02x713bDMWLI2NjTQ2Nk768YFAgI6ODhoaGohEIvT391NdXZ3ymPr6+pRF/fb29pQRzGR0dvbjTOIybX5/Je3tfVN67dmgWPsNxdv3Yu03FG/f/f5KnN4BNJp+2yEyxmeixzSoD3in/Pqu2W587rnnsn37dgB27NjB4sWL8Xg8KY8555xzePXVV7Ftm88++4xPP/2U008/PR/NFUKIgqY1eLWakeKWrjl5v3btWtavX8/y5cuprKzkwQcfBKKL/b/61a/YtGkTS5cuZd++faxcuRKATZs2UVJSks9mCyFEQYsXt/RZJgOOQ9B2ONLD+0pnWriYxWQqbHzF2m8o3r4Xa7+hePs+Vr+VghCaAdsh5Gg8psHJgTlTfn3XjFiEEELkl9bgIVrcMmhCaJrjDgkWIYQQo5RoKJ1m3THXLN4LIYRwFzXNhRIJFiGEEFklwSKEECKrJFiEEEJklQSLEEKIrJJgEUIIkVUSLEIIIbJKgkUIIURWFd0BSWMKF7qZymNnk2LtNxRv34u131C8fZ9Mv6f7d1N0tcKEEELMLJkKE0IIkVUSLEIIIbJKgkUIIURWSbAIIYTIKgkWIYQQWSXBIoQQIqskWIQQQmSVBIsQQoiskmARQgiRVRIsY3j77be55JJLaGpq4rvf/S49PT35blLO7N27l0svvZSLLrqIa6+9li+++CLfTcqpRx99lMcffzzfzciJV199lWXLlnH++efz3HPP5bs5OdXf38+KFSv4/PPP892UnHniiSdYvnw5y5cv5/7775+5N9IioyVLluiPPvpIa631Aw88oB966KE8tyh3zjvvPP3+++9rrbV+8cUX9Xe/+908tyg3ent79Z133qkXLVqkH3vssXw3Z8YdOnRIn3feebq7u1sPDAzopqamxL/zs90f/vAHvWLFCv3lL39ZHzx4MN/NyYnf/va3+q/+6q/08PCwDoVCetWqVfr111+fkfeSEcsYduzYwYknnkg4HKa1tZWqqqp8NyknQqEQa9eu5dRTTwXglFNOoaWlJc+tyo3du3ezYMECrrvuunw3JSf27NnDWWedRXV1NWVlZVx44YXs2rUr383KiRdeeIG7776bQCCQ76bkjN/vZ/369Xi9XjweDyeccALNzc0z8l5FV914sjweDx988AHXXXcdlmXxve99L99Nygmv18tFF10EgOM4PPHEEyxZsiTPrcqNiy++GKBopsHa2trw+/2J7wOBAPv27ctji3Jn06ZN+W5Czp100kmJrz/99FN27NjB888/PyPvVfTBsnPnTjZv3pxy28KFC9m2bRunnHIKe/bs4fnnn2fdunUz9kPIl/H6HgqFWL9+PZFIhBtvvDFPLZwZ4/W7mOgMhc2VKs4S8sXko48+4sYbb+SOO+5gwYIFM/IeRR8sjY2NNDY2ptw2PDzML3/5y8Rv6itXruTHP/5xPpo3ozL1HWBgYIDVq1dTXV3Nk08+icfjyUPrZs5Y/S429fX1vP3224nv29raimpqqBjt3buXNWvWsGHDBpYvXz5j7yNrLBlYlsW9997LgQMHgOhvuF/96lfz3Krcuf322znuuON49NFH8Xq9+W6OmCHf+MY3+N3vfkdXVxdDQ0O8/vrrnHPOOflulpghLS0t3HTTTTz44IMzGiogI5aMTNNky5Yt/OAHP8C2berr64tmTva9995j9+7dnHjiiYk1h0AgwFNPPZXnlolsq6+vZ926daxatYpwOMxll13GokWL8t0sMUOefvpphoeHue+++xK3XXnllVx11VVZfy+5gqQQQoiskqkwIYQQWSXBIoQQIqskWIQQQmSVBIsQQoiskmARQgiRVbLdWIg8sW2bf/3Xf+XVV1/Ftm3C4TDnnXcea9eulfNDoqDJdmMh8uTv//7v6enpYdOmTVRWVjI4OMhtt91GeXk5DzzwQL6bJ8S0SbAIkQcHDx6kqamJ3/zmN1RUVCRub29v55133uGCCy7IY+uEODKyxiJEHrz33nuceOKJKaEC0dLmEiqi0EmwCJEHhmHgOE6+myHEjJBgESIPFi1axMcff0x/f3/K7a2trdxwww0Eg8E8tUyIIyfBIkQe1NfX09TUxIYNGxLh0t/fzz333EN1dTUlJSV5bqEQ0yeL90LkSSQSYevWrbz++uuYpkkoFGLJkiXccsstst1YFDQJFiGEEFklU2FCCCGySoJFCCFEVkmwCCGEyCoJFiGEEFklwSKEECKrJFiEEEJklQSLEEKIrJJgEUIIkVX/D4B6G/8JnAi9AAAAAElFTkSuQmCC\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["with sns.color_palette(\"hls\", 2):\n", "    sns.regplot(x=\"C\", y=\"F\", data=df_demo);\n", "    sns.regplot(x=\"C\", y=\"G\", data=df_demo);"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["* A *joint plot* combines two plots relating to distribution of values into one\n", "* Very handy for showing a fuller picture of two-dimensionally scattered variables"]}, {"cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [], "source": ["x, y = np.random.multivariate_normal([0, 0], [[1, -.5], [-.5, 1]], size=300).T"]}, {"cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x432 with 3 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["sns.jointplot(x=x, y=y, kind=\"reg\");"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 6\n", "<a name=\"task6\"></a>\n", "\n", "* To your `df` NEST data frame, add a column with the unaccounted time (`Unaccounted Time / s`), which is the difference of program runtime, average neuron build time, minimal edge build time, minimal initialization time, presimulation time, and simulation time.  \n", "(*I know this is technically not super correct, but it will do for our example.*)\n", "* Plot a stacked bar plot of all these columns (except for program runtime) over the virtual processes\n", "* Remember: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 92, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["cols = [\n", "    'Avg. Neuron Build Time / s', \n", "    'Min. Edge Build Time / s', \n", "    'Min. Init. Time / s', \n", "    'Presim. Time / s', \n", "    'Sim. Time / s'\n", "]\n", "df[\"Unaccounted Time / s\"] = df['Runtime Program / s']\n", "for entry in cols:\n", "    df[\"Unaccounted Time / s\"] = df[\"Unaccounted Time / s\"] - df[entry]"]}, {"cell_type": "code", "execution_count": 93, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "subslide"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Runtime Program / s</th>\n", "      <th>Unaccounted Time / s</th>\n", "      <th>Avg. Neuron Build Time / s</th>\n", "      <th>Min. Edge Build Time / s</th>\n", "      <th>Min. Init. Time / s</th>\n", "      <th>Presim. Time / s</th>\n", "      <th>Sim. Time / s</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Virtual Processes</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>8</th>\n", "      <td>420.42</td>\n", "      <td>2.09</td>\n", "      <td>0.29</td>\n", "      <td>88.12</td>\n", "      <td>1.14</td>\n", "      <td>17.26</td>\n", "      <td>311.52</td>\n", "    </tr>\n", "    <tr>\n", "      <th>16</th>\n", "      <td>202.15</td>\n", "      <td>2.43</td>\n", "      <td>0.28</td>\n", "      <td>47.98</td>\n", "      <td>0.70</td>\n", "      <td>7.95</td>\n", "      <td>142.81</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                   Runtime Program / s  Unaccounted Time / s  \\\n", "Virtual Processes                                              \n", "8                               420.42                  2.09   \n", "16                              202.15                  2.43   \n", "\n", "                   Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n", "Virtual Processes                                                         \n", "8                                        0.29                     88.12   \n", "16                                       0.28                     47.98   \n", "\n", "                   Min. Init. Time / s  Presim. Time / s  Sim. Time / s  \n", "Virtual Processes                                                        \n", "8                                 1.14             17.26         311.52  \n", "16                                0.70              7.95         142.81  "]}, "execution_count": 93, "metadata": {}, "output_type": "execute_result"}], "source": ["df[[\"Runtime Program / s\", \"Unaccounted Time / s\", *cols]].head(2)"]}, {"cell_type": "code", "execution_count": 94, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "-"}}, "outputs": [{"data": {"image/png": 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11Wo+/HAu8+cvpnjxe/jss6zv/apf/3E+//wLzp07x4IFc5k582MWLFhKgQIFmTfvEwASEhKoW/cx5s1bTM2awaxcueyGWYMH/4sePZ7nqadas23bzzRp0sylc7rWihVLAfjssxW89tobnDp1KtsZV507l8Djjzdi8eKVAPz443/54IPZvPRSX5Yt+xyACRPG8MorA1iwYDFDhoxgzJjhmXISEuIpUqQo3t7eTts/+eRDpk2bydy5i7j33vv4/ffjOa7VFbriLCIiIvnGunVf0axZSwD++c/mvPXWKPr0eYWQkCZs2vQ9devW4/z581SrVp1PP51DaOhTmEwmgoJKU7t2XZeOYbFYGDFiDAMGvEy9eg0c23fu3M7Jk38QFtYTAKs1nSpVMl85vV61atUB+PXXHTRs+AT+/sUAaNfuKSIi3nKMuzpN4YEHHmTXrp03zLp2qsbKlcsYNCicRYuWu3ReV/366w7atbvyQIjy5e/lkUdqZGv/69Wv3xCAoKDS1KhRC4BSpYJITk7i4sWL7N//GxMnjsNkArsdLl26xPnzfzreB4Cff97i9F5f1bDhE7z8ci+eeOIfhIQ0pVKlKrdVa1bUOIuIiEi+cO7cObZs+YkDB/azfPkS7HY7yclJ/PDDRlq0aM3s2R+SnJxE8+atADCbvbDbc7ZS3QMPVHRM2bgqI8NG06bNeO21wQB/LRz392rIdrsdk8mE1Wp1yvL1vfIkkmtXTv5rDzIyMpzGWa22m67YfL2WLVszbdo7nD9/PtM+GRnWW+xpcnpfvLy8sjzWrVx7lfj6LJvNho+PL/PnL3as+BcbG0PRov5O46Kjo244V/u1197g8OH2bNmymfHjR/HSS31p06btbdV7K5qqISIiIvnC+vXrqF37MVatWseKFV+xcuVaund/iS+//ILq1R8hPj6eDRvW0aLFkwDUrVuP//znW+x2O/HxcezcuQOTyeTy8a5O2di3bw8AwcG1+fHHH0hMPIfdbmfq1AiWLPkMgGLFinHs2BHsdjubN/94w7zg4Nps3vwjSUnnAVizZjXBwTl/UseOHVsJDCxFsWLF8PcvxrFjRwH47be9TqtAX69Oncf47rsN2Gw2zp49w549u3NcQ1YKFy5MuXLl2bDhygJ627ZF8+qrzvOYMzIyiIuLJSjI+WZMq9VKly5PUaxYMbp160mrVm343/8O5lqtoCvOIiIiYhBrWupfj44zPtcVX3+9hj59XnHa1rHjMyxevIATJ47zz3825+eft1C2bDngylSIQ4f+R/fuz1KiREmCgko7rv5269aFKVOmU7JkwE2Pd3XKRq9e3QCoVKkyPXv2YcCAftjtdipVqkL37lembfTrF86QIQO5554S1KhR64Y33FWsWIlu3XoSHt4Xq9VKlSoPMXjwMJfO/arBg/+FxeKNzZaBt7cPb701EYBmzVqwadP3vPDCM1SpUpXKlW8+paFjx2c4duwIXbt2IiioNA888GC2asiuMWPeZsqUiSxevACLxZtx4yY6/QGzb98eHn74kUz7WSwWevUK47XXXsHXtwCFCxdh5MixuVqrye7KtX43SkhIcfqqIiCgCDOH9bzlPuER84iLS872sQICiuRov9zM8sSajMxSTe7N8dQs1eT+LNXk/qy7oaY9e/YSFFThtrOufkXv7pyoqM3Y7XYaNnyClJQUevbsypw5Cyha1P+O1ZTXsvJCTWfPnsj0OTWbTZQoUTj72bddnYiIiEgedN999zN+/Gg++eRDAHr3Dss0t1bkWmqcRURE5K5UpkxZPvxwzp0uQ/IQ3RwoIiIiIuICNc4iIiIiIi5Q4ywiIiIi4gLNcRYRERFDFCnmSwFvH8NzL6enkfyna4+kE8lNLjXO06dPZ8OGDZhMJjp16kTPnj0ZNmwYO3bsoGDBggCEh4fTvHlzoqKiiIiIIDU1lSeffJKBAwfm6gmIiIiIZyjg7UPnpS8bnrvs2Q9J5taN85kzp3nmmXa0a/cUQ4aMcGw/dOggPXt2ZfjwMbRuHUqPHs8zf/7ibNdw5sxpnnuuI/fd94DT9tDQDjz9dGenbevWfcXOnTsYMWJsto9zrQkTxrJjxzaKFvXHbrfh7e3NwIFDefjh6jfdJz4+jkmTxhMZOYMJE8YSHFyb1q1DncbMmTMLs9lEz55/LzTy9ddrWL58CQDHjx+lXLnyWCzePPJITdq0CWX16pW8+eao2zqf66WlpfH66+HMnPmxobm5KcvGeevWrURHR7NmzRqsViutW7cmJCSEvXv3smjRIgIDAx1jL1++zPDhw1m4cCGlS5cmLCyMTZs2ERISkqsnISIiIuLvX4yff95CRkaGY2nnjRu/o1ix4o4xOWmarypZMuC29s+J3r37ORrfn37axL///Q6ffLLgpuNLlgwgMnJGto/Tpk072rRpB0CnTqFMmTKd0qXLOF5/881q2c7Myq5dv1Cr1qOG5+amLBvnxx57jAULFmCxWIiJiSEjIwNfX19Onz7NqFGjOH36NM2bNyc8PJzdu3dToUIFypcvD0BoaCjr169X4ywiIiK5rmDBglSqVJldu3by6KNXlqreujWaOnUec4xp1KgOmzdvZ86cWcTHx/HHH78TE3OWtm3b8+KLvXJ87PXrv+bTT+dQqFBhgoKCKFjQD4AdO7YzdepkvLy8ePjhGhw/fpSZMz/m5Mk/iIyMICnpPL6+BRg4cDCVK1e95TFSUlIoXrwEAL/8sp25cz92XK29enU5OLg2/fuHsWLFV077Ll68gDVrVuHvX4wiRYrc8qr19a49Vnh4XypXrsL27VtJTU1l0KChLF36OceOHeHZZ5/n2We7cvHiRd59dzJHjx7BZrPRtWt3mjdvlSk3OjqKJk2aOW07fPgQ77wzgYyMDHx8fBg+fAzly9/rcq25zaWpGt7e3syYMYO5c+fSqlUrMjIyqF+/PuPGjcPPz4+wsDBWrFiBn58fAQF/L00ZGBhITExMtgrKySoucGUFI3ful5tZnliTkVmqyb05npqlmtyfpZrcn5XfazKbzVgs7nnOQFbH8fK68vqVpaU38thjj/Hbb/uoVKkSdvuVleKuZlgsZsxmE0eOHGbWrDkkJyfTqVM7OnfuQpEif78/1x7Ty8tMfHwcPXs+73TcMWPG4+9fjA8/fI8FCz7H39+fQYMG4OdXCMjgrbdGMXXqdCpVqsy7707BZLpSx4QJY3njjaFUqVKVY8eOMnToIJYtW+WUbTKZmDNnFsuXf86lS5eIiTnLlCnTsFjMeHmZHVlXx5rNJsf7YLGYHdsOHTrA11+vYcGCzzGZTPTu/WKW76mX19+/22uPdTVz8eLlzJ49i3ffncyiRUtJTEyke/fn6Nq1GwsXzuWhh6oxdux4LlxIoU+fntSoUcOx1PlVv/22jwEDBjpqBli+fDFdu3bjn/9sznffbeDAgb3cf/99t/zd38i152Y2mw373Lt8c+CAAQPo06cP/fr1Y8uWLbz//vuO17p168bq1atp1SrzXxPXrjXuihstue0KLbmdN7JUk3tzPDVLNbk/SzW5P+tuqMlmsxm23HJWsjpORsaV1xs0aMRHH31AWpqVb7/dQJMmzdm48VtsNrsjw2q1YbPZCQ6ujcnkRdGixShSpCjnzydRsGAhIPOyzRkZNkqWDGDevMxTNf773/9Qvfoj+PtfmRLSvPmT7NixjYMHD1G8eHHuv78iVquNJ58M5X//iyQpKYX9+/cxfvxYR8bFixdJSDiHv38xxza73U6vXmGOqRpHjvyPl1/uw/z5i8nIsGG3/31Odrsdm83ueB+sVptj27Zt26hfvyE+PgUAaNKkGXb7rX93GRl/v37tsex2O4899jhWq43AwCAefvgRLBZfAgKCSE5Oxmq1sXXrz6SmXuarr74E4NKlSxw6dJhSpf6e+nHmzGkCA0tht5scx7FYzNSv35DIyMlERf3E448/QdOmTbP9Gbv+d2ez2TJ97nNtye0jR46QlpbGQw89RMGCBWnRogXr1q2jWLFitGzZErjyy7JYLJQqVYr4+HjHvrGxsU5zoEVERERyk59fISpWrMTu3b/yyy/b6NcvnI0bv73hWB+fv58AYjKZsNvtNxyXFZPJ5HTR7+r8arPZfMNMm82Gj4+v03zp2NiYLJf7rlKlKmXLluXgwf1O87YBrFbrLeuz2/9uJL28vG7rDx6L5e/20csrcytps2UwatR4qlS5MvXk3LmETOcWHR1F/fqPZ9q3SZNmVK9eg59++j+WL/+c6OifGDp0ZI5rNVqW36+cPHmSkSNHkpaWRlpaGhs3bqRu3bpMnDiR8+fPk56eztKlS2nevDk1a9bk2LFjnDhxgoyMDNauXUvjxo3dcR4iIiIiADRt2oyPPppJlSrVnJq83FKjRi1++20PcXGx2Gw2vv/+OwDuu+9+kpKSOHLkMADffbcek8lE4cKFKVeuPBs2rANg27ZoXn21703zrzpz5jRnzpymYsXK+PsX4/TpU6SmppKUdJ5du3bedL86deoSFbWZlJQUUlNT+fHH/xpw1jf36KN1Wb16BQDx8fG8+OJzxMScdRqzdesW6tVrkGnf0aOH8dtv++jQ4Wl69+7HwYMHcrXW7Mry0xQSEsKuXbvo0KEDXl5etGjRgvDwcIoXL85zzz2H1WqlRYsWtG3bFoBJkybRv39/UlNTCQkJueH0DREREcl/LqensezZD3MlNzsaNmzMpEnj6d27X7aPFR8fxxtv/ItFi5bc8LUePZznONeqFcxrrw3+679XKFCgIPfddz9w5R6xsWPf5u23R2Mymbn33gr4+voCMGbM20yZMpHFixdgsXgzbtzEG05vnT37I5Yt+xyAtLRUXn31NcfNcg0aNKRbt86ULl2GmjWDb3pOlSpV4ZlnnqN37+4UKVKEUqVKZ/t9yY6XXurD1KmT6datMzabjVdeGeA0vzktLY3k5GTuuadEpn27devJ5Mlv8+mns/Hy8qJ/f896rLHJntPvJXLJjeY4zxzW85b7hEfM0xznPJKlmtyb46lZqsn9WarJ/Vl3Q0179uwlKKjCbWddPyf1TucYlWWz2Zg1ayY9evShYMGCLFmyiLi4uBw3g552fkbmGJl1fc7ZsycyfU5zbY6ziIiIiGSf2WzG39+fPn26Y7F4U7p0acMXERH3UuMsIiIikku6d+/J88+/eKfLEIO45+GLIiIiIiJ5nBpnEREREREXqHEWEREREXGBGmcRERERERfo5kARERExRPEiPlgK+Bqea72cSmJy9p7lLJIb1DiLiIiIISwFfPmp/dOG5zb8ciVk0TifOXOaZ55pR7t2TzFkyAjH9kOHDtKzZ1eGDx9D69ah9OjxvNNS19nRqFEdNm/eftPX4+PjmDRpPJGRM9i8+UdOnvydF17oftPxw4a9wZkzp7l06SLnziVQtmx5AF5+uT979uyiatWHaNQoJEe13syGDetISUnh2We7GJp7t1DjLCIiIvmCv38xfv55CxkZGXh5eQGwceN3FCtW3DEmp02zK0qWDCAycgYABw/uz3J8REQkAL/8sp25cz9m5syPHa/daDlqI0RHR+VoRUW5Qo2ziIiI5AsFCxakUqXK7Nq1k0cfrQPA1q3R1KnzmGPM1avGc+bMIj4+jj/++J2YmLO0bdueF1/s5dJxfvllOwsXzqNAgQIcP36MBx+syJgxE4iPj6N//zCmTJnOl19+AUDZsmVo1So02+cyYcJYgoNrExxcm2HD3qBMmbIcPXqYKlUeIji4Nt98s5bk5CQmTozkvvvuZ//+fcyY8S6pqZfx9y/G4MHDKVOmrFOmzWbj7NnTTstfAyxZsohvvvkas9nEQw897HTFXpzp5kARERHJN5o0ac5//7sRgP3791GxYiW8vb1vOPbw4UNMm/Y+H388n0WLPiU52fXlyPfu3c3AgUP47LMVxMSc5eeftzheu//+B2jfviPt23ekbdv2t3dCwJEjh+jRoxfLlq3iwIHfOHv2DLNmzaNZs5asWfMF6enpTJr0NmPGTGDu3M/o0uUFJk+ekCln//7fqFr1YadtVquVRYvmM2fOQubMWYTZbCYuLva2a86vdMVZRERE8o1GjZ7gk08+xGazsXHjdzRt2pyNG7+94dgxBRekAAAgAElEQVRHH62Dt7c3xYvfQ9GiRblwIYUiRYq4dJz773+QwMBSAFSocD/JyUmGncP17rmnBJUrV8VsNhMQEEjt2nUBCAoqzc6dp/njjxOcPn2SN9983bHPhQsXMuVER/9E/fqPO22zWCxUr16D3r2788QTIXTs+AwBAYG5di55nRpnERERyTf8/ApRsWIldu/+lV9+2Ua/fuE3bZx9fHwc/zaZTNjtdpePczv7Ztf1V8yvzt++KiPDRpkyZR3ztzMyMkhMPJcp59dff+GFF3pk2h4RMZV9+/YQHR3FoEEDGD16PMHBtY07gXxEUzVEREQkX2natBkffTSTKlWqYbHcmWuEXl5eZGRkuOVYFSrcR1JSErt27QTg66/XMHas8zzlP//8k4IF/fD1dX5cYGJiIl27duKBByrSu3c/6tatx5Ejh9xSd16kK84iIiJiCOvl1CuPjsuF3Oxo2LAxkyaNz9HTI+Lj43jjjX+xaNGSbO97rVq1HmXChLGULFmSatWqM3v2R44nbhjNx8eH8eMnMX16JGlpafj5FWLkyLecxmzduoV69epn2rd48eK0b9+RPn264+tbgFKlgmjdOvs3M94tTPbc/G4hBxISUrDZ/i4pIKAIM4f1vOU+4RHziItzfUL/tdk52S83szyxJiOzVJN7czw1SzW5P0s1uT/rbqhpz569BAVVuO0si8WM1WrzmBwjszyxJiOz8kJNZ8+eyPQ5NZtNlChRONvZLk3VmD59Oq1bt6ZNmzbMmzcPgKioKEJDQ2nRogXTpk1zjN2/fz9PP/00LVu2ZMSIEVit1mwXJSIiIiLiabJsnLdu3Up0dDRr1qxh5cqVLFy4kAMHDjB8+HA++OAD1q1bx969e9m0aRMAgwcPZtSoUWzYsAG73c6yZcty/SRERERERHJblo3zY489xoIFC7BYLCQkJJCRkUFSUhIVKlSgfPnyWCwWQkNDWb9+PadOneLy5cvUqlULgI4dO7J+/fpcPwkRERERkdzm0s2B3t7ezJgxg7lz59KqVStiY2MJCAhwvB4YGEhMTEym7QEBAcTExGSroJzMN7lyLNeeu2jUfrmZ5Yk1GZmlmtyb46lZqsn9WarJ/Vn5vSaz2YzFYswDujwtx8gsT6zJyCxPr+nK86+N+dy7/FSNAQMG0KdPH/r168fx48czvX6zZxiaTKZsFXSjmwNdoZsD80aWanJvjqdmqSb3Z6km92fdDTXZbDaPusEsL9yolt+y8kJNNpst0+c+pzcHZtk4HzlyhLS0NB566CEKFixIixYtWL9+vdPDt2NjYwkMDKRUqVLEx8c7tsfFxREYqNVnRERE7gb+RQvi42v8k27TUq2cT7pkeK5IdmX56T558iQzZszg888/B2Djxo106dKFd955hxMnTlCuXDnWrl3L008/TdmyZfH19WXHjh3Url2b1atX07hx41w/CREREbnzfHwtjBu01vDc0VPbZjnmzJnTPPdcR+677wFMJkhPt1KyZEmGDx/jWBo7p2bP/oiqVR+iUaOQHGccOXKY8eNHAxATc5aCBQtStKg/3t7efPLJp/To8bxj5T8jvf56f8aNi6Bw4ZxNhRVnWTbOISEh7Nq1iw4dOuDl5UWLFi1o06YN99xzD/379yc1NZWQkBBatWoFQGRkJCNHjuTChQtUq1aN7t275/pJiIiIiJQsGeDUfH700UymTZtCRETkbeXmZCGV6z34YEVHbRMmjCU4uLbTQiO50TRfvHgRmy1DTbOBXPo+ZcCAAQwYMMBpW4MGDVizZk2msVWrVmXFihXGVCciIiKSQzVrBrN5848AdOoUSrVq1Tl06CAffDCb6Ogoli//HJvNTpUqVXn99aF4eXkREfEWR48ewWQy0aFDJ9q1e8rR6AYH12bYsDcoU6YsR48epkqVhwgOrs0336wlOTmJiRMjue+++3NUa6NGddi8eTtz5swiJuYshw8f4s8/E+nT52V27NjGb7/tpVKlyowdOxGTycTChfP573+/IyPDRr169Xn55QGZ7iv75Zdt1K5d12nbhQspjB07gnPnErDb4aWX+tzWlfS7jXG3QYqIiIh4CKvVyvfff8cjj9R0bKtf/3E+//wLEhMT+eqr1Xz44Vzmz19M8eL38PnnC9mzZxdJSUnMm7eY9977kD17dmXKPXLkED169GLx4pUcOPAbZ8+eYdaseTRr1pI1a74wpPajR4/w8cfzGTVqHBER4+ja9UUWLFjKwYMHOHz4ENHRURw8uJ9PPlnAvHmfERcXx7fffpMpJzo6ivr1H3fa9uOPPxAUVIZPP13M6NHj2bXrV0NqvlsYP4NfRERE5A6Ij4+jR4/nAUhPT+Ohhx7m5ZfDHa9Xq1YdgJ07t3Py5B+EhfUEwGpNp3Llqjz1VCd+//0Er78eTsOGjXj55f6ZjnHPPSWoXLkqAAEBgY4rukFBpdm587Qh51G3bj0sFgtBQaUpUaIk99//gON4yclJbN++ld9+20uvXt0ASE29TKlSQZlyDh8+RMWKlZ22Va9eg1mz3ichIY769RvSo0cvQ2q+W6hxFhERkXzh+jnO1/P19QUgI8NG06bNeO21wcCVucAZGRkUKVKEhQuXsW3bz/z8cxQvvfQCCxc6r4Ds7e3t9PO1TxkzisXyd3t2o3ybLYPOnZ+jS5cXAEhOTs407vjxY9x7b4VM0zfKl7+XxYtXsG1bND/+uIklSxbx2Wcrsv344LuVpmqIiIjIXSU4uDY//vgDiYnnsNvtTJ0awbJli9m8eRPjxo3i8ccb8frrgylYsCCxsdlbyM0dHn20Lhs2rOPixYtYrVaGDRvEDz9sdBoTHf0T9es3zLTvypVLmTNnFv/8Z3MGDXqTxMREUlJS3FV6nqcrziIiImKItFSrS4+Oy0mukSpVqkzPnn0YMKAfdrudSpWq8MILPfDy8uK//91It26d8fHxJSSkKQ8+WDHb+bNnf0TJkiXp0KGToXVf1ahRYw4f/h99+/bAZsugXr3HefJJ5/d927atjB07IdO+rVq1YezYEXTt2hkvLy9eeqkvRYoYt5pkfmey32i5vzvoRisHzhzW85b7hEfM08qBeSRLNbk3x1OzVJP7s1ST+7Puhpr27NlLUFCF2866m1axy29ZeaGms2dPZPqc5nTlQE3VEBERERFxgRpnEREREREXqHEWEREREXGBGmcREREREReocRYRERERcYEaZxERERERF+g5ziIiImII/6I++Py1Op+R0lJTOZ+UZniuSHapcRYRERFD+Pj6Zrn2Qk6ER8wDsm6cv//+P8yfP5eMjAzsdhutWrXh+ee7A/DGGwN4881RlCwZkOM6vv56DcuXLwHg+PGjlCtXHovFm0ceqUmbNqGsXr2SN98cleP8G0lLS+P118OZOfNjQ3MlZ9Q4i4iISJ4XFxfLjBnvMmfOIvz9i3Hx4kXCw/ty770VaNQohMjIGbd9jDZt2tGmTTsAOnUKZcqU6ZQuXcbx+ptvVrvtY1xv165fqFXrUcNzJWdcapxnzpzJN998A0BISAhDhgxh2LBh7Nixg4IFCwIQHh5O8+bNiYqKIiIigtTUVJ588kkGDhyYe9WLiIiIAH/++SdWq5XLly/j7w9+fn6MHDkWH58rU0c6dQrlvfdmsXPnDqKiNhMfH0dsbAydOz9HTEwMv/yyjaJF/YmMnIFvDqab/PLLdubO/ZiZMz8mPLwvlStXYfv2raSlpfKvfw1mxYqlHDt2hGeffZ5nn+3KxYsXeffdyRw9egSbzUbXrt1p3rxVptzo6CiaNGnmtO3w4UO8884EMjIy8PHxYfjwMZQvf2/O3jjJliwb56ioKDZv3syqVaswmUz07t2b7777jr1797Jo0SICAwMdYy9fvszw4cNZuHAhpUuXJiwsjE2bNhESEpKrJyEiIiJ3t0qVKvPEE/+gc+f2VK5cheDgOjRv3opy5cpnGrt//z4WLFhCcnIynTqFMnXqe/zrX4Po3z+MrVu38MQT/zCkpgULljJ//if8+99T+PTTJfz5ZyI9elxpnD/9dA5VqjzEyJFvceFCCv36vUS1atUpW7acU8Zvv+3llVf+5bRt2bLFdOnyAk2bNmPjxm/Zt2+PGmc3yfKpGgEBAbz55pv4+Pjg7e3Ngw8+yOnTpzl9+jSjRo0iNDSUGTNmYLPZ2L17NxUqVKB8+fJYLBZCQ0NZv369O85DRERE7nJDhw5nxYqv6NChEzExZwgL68mmTd9nGvfIIzUpVKgwQUGlAahduy4AQUGlSU5ONqSW+vUbOjIffvgRChQoQFBQaVJSruRv376VL79cSY8ez/Pqq325fPkyx44ddco4c+Y0gYFBeHl5OW1v0KAh06a9Q0TEOCwW7xteqZbckeUV50qVKjn+ffz4cdatW8fixYvZunUr48aNw8/Pj7CwMFasWIGfnx8BAX9Pug8MDCQmJiZ3KhcRERH5S1TUZlJTL9GkSXPHXOQ1a1axdu2XhIQ0dRrr7e3t9LPFYvwtX9dmXt/4AthsGYwaNZ4qVaoCcO5cAkWL+juNiY6Oon79xzPt26RJM6pXr8FPP/0fy5d/TnT0TwwdOtLgM5AbcfmTcujQIcLCwhg6dCgPPPAA77//vuO1bt26sXr1alq1yvwXj8lkylZBJUoUztb4qwICirh1v9zM8sSajMxSTe7N8dQs1eT+LNXk/qz8XpPZbMZicc+SEFkdp1Chgvz731N4+OFHKFOmDHa7nd9/P0aVKlUd+3p5mTGbTZhMJqe8q/82mUyYzX+/ltUxvbzMTtlXc00mk9NrNzpenTqP8eWXKxk+fBTx8XG8+OJzfPLJPKepJdu2RTN06IhM+44YMZTmzVvSqdMzPPjgA/z731Nz9Hsw6ndn5GcgN2oym82Gfe5dapx37NjBgAEDGD58OG3atOHgwYMcP36cli1bAmC327FYLJQqVYr4+HjHfrGxsU5zoF2RkJCCzWZ3/OzqicbFZf+rlYCAIjnaLzezPLEmI7NUk3tzPDVLNbk/SzW5P+tuqMlms2G12hzb0lJT/3p0nLHSUlOdjnMjNWvWplevvgwaNACr1QpAvXoNePHF3o59MzJs2Gx27Ha7U97Vf9vtdmy2K68NHvwvevUKo2rVmz8pIyPD5pR9Nddutzu9dqPj9ejRm6lTJ/Pcc52w2Wy88soAgoLKOsalpaWRlJSEv39xxzaLxYzVauOFF3oyefLbzJ37CV5eXoSHD8zy/bne1azbZVSOkVnX59hstkyfe7PZlKOLtVk2zmfOnOHVV19l2rRpNGjQALjyAZg4cSL169fHz8+PpUuX8tRTT1GzZk2OHTvGiRMnKFeuHGvXruXpp5/OdlEiIiKS91xZpCT7C5UY1TC1aRNKy5ZtbvjaihVfAVC6dBlatw51bN+8ebvj3yNGjHX8e9q0925Z09W8qx59tA6PPloHwOmZy23btqNVq7aZjleoUGFGjx5/03wfH5+bPru5UqXKzJ694Kb7Su7JsnGeM2cOqampTJo0ybGtS5cu9O3bl+eeew6r1UqLFi1o2/bKh2LSpEn079+f1NRUQkJCbjh9Q0REREQkr8mycR45ciQjR954wnnXrl0zbWvQoAFr1qy5/cpERERERDyIe2b0i4iISL5kt9uzHiRyhxj9+VTjLCIiIjlisfhw4UKSmmfxSHa7nQsXkrBYfAzLNP7BhSIiInJXKF48gMTEOFJS/rytHLPZjM12+zcHGpVjZJYn1mRklqfXZLH4ULx4QBZ7uE6Ns4iIiOSIl5eFkiVL33aOpz5uL7/WZGRWfq/pepqqISIiIiLiAjXOIiIiIiIuUOMsIiIiIuICNc4iIiIiIi5Q4ywiIiIi4gI1ziIiIiIiLlDjLCIiIiLiAjXOIiIiIiIuUOMsIiIiIuICNc4iIiIiIi5Q4ywiIiIi4gI1ziIiIiIiLnCpcZ45cyZt2rShTZs2vPPOOwBERUURGhpKixYtmDZtmmPs/v37efrpp2nZsiUjRozAarXmTuUiIiIiIm6UZeMcFRXF5s2bWbVqFatXr2bfvn2sXbuW4cOH88EHH7Bu3Tr27t3Lpk2bABg8eDCjRo1iw4YN2O12li1blusnISIiIiKS27JsnAMCAnjzzTfx8fHB29ubBx98kOPHj1OhQgXKly+PxWIhNDSU9evXc+rUKS5fvkytWrUA6NixI+vXr8/1k3CFf1EfAgKKOP0HZNrmX9TnDlcqIiIiIp7IktWASpUqOf59/Phx1q1bR7du3QgICHBsDwwMJCYmhtjYWKftAQEBxMTEZKugEiUKZ2v838cqkuWYmcN6ZjkmPGIeAQG+txxjTU/H4u19yxpuNsYVrpxLXs5STe7N8dQs1eT+LNXk/izV5P4s1eT+rPxe07WybJyvOnToEGFhYQwdOhSLxcKxY8ecXjeZTNjt9kz7mUymbBWUkJCCzfZ3jqsnHheXfMvXs/MGupKVVRMeHjEvy5ybZedkv7ySpZrcm+OpWarJ/Vmqyf1Zqsn9WarJ/Vl5tSaz2ZSji7Uu3Ry4Y8cOevTowaBBg3jqqacoVaoU8fHxjtdjY2MJDAzMtD0uLo7AwMBsFyUiIiIi4mmybJzPnDnDq6++SmRkJG3atAGgZs2aHDt2jBMnTpCRkcHatWtp3LgxZcuWxdfXlx07dgCwevVqGjdunLtnICIiIiLiBllO1ZgzZw6pqalMmjTJsa1Lly5MmjSJ/v37k5qaSkhICK1atQIgMjKSkSNHcuHCBapVq0b37t1zr3oRERERETfJsnEeOXIkI0eOvOFra9asybStatWqrFix4vYrExERERHxIFo5UERERETEBWqcRURERERcoMZZRERERMQFapxFRERERFygxllERERExAVqnEVEREREXKDGWURERETEBWqcRURERERcoMZZRERERMQFapxFRERERFygxllERERExAVqnEVEREREXGC50wXc7fyL+uDj6+u0LSCgiNPPaampnE9Kc2dZIiIiInIdNc53mI+vLzOH9bzlmPCIeYAaZxEREZE7SVM1RERERERcoMZZRERERMQFLjfOKSkptG3blpMnTwIwbNgwWrRoQfv27Wnfvj3fffcdAFFRUYSGhtKiRQumTZuWO1WLiIiIiLiZS3Ocd+3axciRIzl+/Lhj2969e1m0aBGBgYGObZcvX2b48OEsXLiQ0qVLExYWxqZNmwgJCTG8cBERERERd3LpivOyZcsYM2aMo0m+ePEip0+fZtSoUYSGhjJjxgxsNhu7d++mQoUKlC9fHovFQmhoKOvXr8/VExARERERcQeXrjhPmDDB6eeEhATq16/PuHHj8PPzIywsjBUrVuDn50dAQIBjXGBgIDExMdkqqESJwtkaf9X1j3C7HUZleUJNnlBDbuUYmaWa3J+lmtyfpZrcn6Wa3J+lmtyfld9rulaOHkdXvnx53n//fcfP3bp1Y/Xq1bRq1SrTWJPJlK3shIQUbDa742dXTzwuLvmWr2fnDTQqK6sco7NulJ2T/XIzSzW5N8dTs1ST+7NUk/uzVJP7s1ST+7Pyak1msylHF2tz9FSNgwcPsmHDBsfPdrsdi8VCqVKliI+Pd2yPjY11mgMtIiIiIpJX5ahxttvtTJw4kfPnz5Oens7SpUtp3rw5NWvW5NixY5w4cYKMjAzWrl1L48aNja5ZRERERMTtcjRVo2rVqvTt25fnnnsOq9VKixYtaNu2LQCTJk2if//+pKamEhIScsPpG2K8Gy3dDVq+W0RERMQo2Wqcv//+e8e/u3btSteuXTONadCgAWvWrLn9yiRbXFm6G7R8t4iIiEhOaeVAEREREREXqHEWEREREXGBGmcREREREReocRYRERERcYEaZxERERERF6hxFhERERFxgRpnEREREREXqHEWEREREXGBGmcREREREReocRYRERERcYEaZxERERERF6hxFhERERFxgRpnEREREREXqHEWEREREXGB5U4XkBVrehrhEfOyHCMiIiIikptcbpxTUlLo0qULH330EeXKlSMqKoqIiAhSU1N58sknGThwIAD79+9n5MiRpKSkUKdOHd566y0slpz35xZvH8YNWnvLMaOntgVSc3wMEREREZGsuDRVY9euXTz33HMcP34cgMuXLzN8+HA++OAD1q1bx969e9m0aRMAgwcPZtSoUWzYsAG73c6yZctyrXgREREREXdxqXFetmwZY8aMITAwEIDdu3dToUIFypcvj8ViITQ0lPXr13Pq1CkuX75MrVq1AOjYsSPr16/PvepFRERERNzEpTkUEyZMcPo5NjaWgIAAx8+BgYHExMRk2h4QEEBMTEy2CipRonC2xv99rCI52i83szyxptvJys/vi2pyf5Zqcn+WanJ/lmpyf5Zqcn9Wfq/pWjmafGy32zNtM5lMN92eHQkJKdhsf+e4euJxccm3fD07b6BRWVnlGJll5PndLD8n++VWjpFZqsn9WarJ/Vmqyf1Zqsn9WarJ/Vl5tSaz2ZSji7U5apxLlSpFfHy84+fY2FgCAwMzbY+Li3NM75C8w7+oDz6+vpm2X9ucp6Wmcj5JTzMRERGRu0eOGueaNWty7NgxTpw4Qbly5Vi7di1PP/00ZcuWxdfXlx07dlC7dm1Wr15N48aNja5ZcpmPry8zh/W85ZgrjwhU4ywiIiJ3jxw1zr6+vkyaNIn+/fuTmppKSEgIrVq1AiAyMpKRI0dy4cIFqlWrRvfu3Q0tWERERETkTshW4/z99987/t2gQQPWrFmTaUzVqlVZsWLF7Vf2l/T0jL+e03zrMSIiIiIiucnjVw709vai89KXbzlm2bMfuqkayQ5X5kqD5kuLiIhI3uDxjbPkXa7MlQbNlxYREZG8waUFUERERERE7nZqnEVEREREXKCpGpIn6NnSIiIicqepcZY8wchnS9+oCdcNiyIiIpIVNc5y19ECLyIiIpITmuMsIiIiIuICNc4iIiIiIi5Q4ywiIiIi4gI1ziIiIiIiLtDNgSI5pCXFRURE7i5qnEVySEuKi4iI3F00VUNERERExAV3zRVna3raX1f+sh4nIiIiInK9u6Zxtnj7MG7Q2izHjZ7aFki95RhXmnA14JLXaZlzERERZ7fVOHfv3p2EhAQslisx48aN4/fff+fDDz8kPT2dHj160LVrV0MK9SSuNOGuNOAiRjPyhkWtsCgiIuIsx42z3W7n6NGj/PDDD47GOSYmhoEDB/LFF1/g4+NDly5dqFevHhUrVjSsYBG5Od2wKCIiknty3DgfPXoUk8lEnz59SEhIoHPnzhQqVIj69etTrFgxAFq2bMn69esJDw83rOCcSk+z/nUVOOtxIiIiIiLXy3HjnJSURIMGDRg7diyXL1+me/fuPPnkkwQEBDjGBAYGsnv37mzllihROEf1XP9V9PUy0ly7umbGlmVWenpGlk14enpGljng+nxpV7JcZVSWajIuy5qejsXb+5b73GhMbtaU2zme+LvyxJqMzFJN7s9STe7PUk3uz8rvNV0rx41zcHAwwcHBAPj5+dGpUyciIiLo16+f0ziTyZSt3ISEFGw2u+NnV088Li75lq8HBBSh89KXs8xZ9uyHhmS5knM1y5X50q7U5CqjsvJiTUZmGX1+rswnzqvv+Y2yc7JfbmZ5Yk1GZqkm92epJvdnqSb3Z+XVmsxmU44u1ua4cd6+fTvp6ek0aNAAuDLnuWzZssTHxzvGxMbGEhgYmNNDiEg+caObFnNyw6JWaxQRkTspx41zcnIyM2bMYMmSJaSnp7Nq1SqmTJnC4MGDOXfuHAULFuTbb79l/PjxRtab77gy91rzriWvM+oJHbr5UURE7qQcN85NmjRh165ddOjQAZvNxvPPP0/t2rUZOHAg3bt3Jz09nU6dOlGjRg0j6813zHabIWO0wItI9hh1FVxERO4et/Uc59dee43XXnvNaVtoaCihoaG3VdTdxMvXx6X50lk9E9rIBV5E7gZ6TrWIiGTXXbNyoIhIbtC8axGRu4ca53zClUfkXR0nIsbRvGsRkbuHGud8wtvby+XH7YmIZ3Ll6rW7r1zrirqIyN/UOEsmri7KIiLGMnLetVE3P+qKuojI39Q4SyZ2u9mQMXrSh8ido5sfRUSMp8ZZMjHqEXl60oeIXEuPABSRvE6Ns2Ri1CPyjLxh0cjpI5qKInJnGHUV3Mh51544r1xEPJcaZ8k1Rt6waMfLkDHg2pVwV66CayqKyJ1h5LxrT5xXrhsyRTyXGmfJE1xpwl19YogrV8JduQruaqPu6jgjGNnM68q8SPZ44tLymh4jYiw1znLXMaoJ98RHABrZzBt5lV9E7gxNjxExlhpnEQ9g1NVdI5t5I6/yG3V+mh4jcmdoesztZekqf/6hxlnEA1i8jbkh01O5+wq3K+M0FUUk7/PE6TGeeJVfjKPGWURynSdOjzGqUc/v88p1lV/kzvDUxYfu9ivqapxF5K5kVDPvyrcFf2fd+hsDI6+6G9WEG3l+njhlxxP/WBExmpHzyu/2xZXUOIuIeAgj55V74vQfT5yy44l/rOT3Pww8sab8zhOb3bw6FUWNs4iIuIUnTtkx8o8VU9YLqro0xhO/xTCymdcfK/rDAPLuYxdzpXH+6quv+PDDD0lPT6dHjx507do1Nw4jHi7Nmu7S/+GkWdPdUI2ISO4yatVVI3nilCRP/GYlv/+x4omr7xp5fu68om544xwTE8O0adP44osv8PHxoUuXLtSrV4+KFSsafahsMbKJcyXL1WbQqKw0a5qL5+e+v3J9LN6EDvoyy3FfTW0PXM79giRbjPycG8UTP+eeWJOI3Dme+MeKkVO3jMrKq09JMtntdrshSX9ZtWoV27ZtY+LEiQC8//772O12wsPDXdo/MfECNtvfJRXx98XH4n3LfdKs6SSfv/UvqESJwvR6+9ssjz9nZAsSElJuO8uVHCOzihf1weztk+XxbOlpJJZQ2ZgAABiSSURBVGbxVYUnvudG1WRklis5Rma5uyYjP+dGnZ8nfs49sSYjs/L751zved6tycgsTzw/T6zJyKw7fX5ms4nixQtlefzrGd44z5o1i4sXLzJw4EAAli9fzu7duxk/fryRhxERERERcSuz0YE36sNNJpPRhxERERERcSvDG+dSpUoRHx/v+Dk2NpbAwECjDyMiIiIi4laGN86PP/44W7Zs4dy5c1y6dIlvv/2Wxo0bG30YERERERG3MvypGqVKlWLgwIF0796d9PR0OnXqRI0aNYw+jIiIiIiIWxl+c6CIiIiISH5k+FQNEREREZH8SI2ziIiIiIgL1DiLiIiIiLhAjbOIiIiIiAvUOIuIiIiIuMDwx9EZbcuWLRQoUIDg4GDmzp3L1q1bqV69On379sXHx+dOlyciIiIidwmvsWPHjr3TRdzMO++8wxdffMEPP/zATz/9RExMDJ07d2bfvn388MMPNG3a9E6XKLdgtVpZuHAh69evp0CBApQpU8bx2nvvvUe9evXcmuOpNYl4Kn3ORSQvSkxMpGDBgrmS7dFTNf7v//6PJUuWsHjxYn7++WciIyMJCQlh7Nix7N69O1tZVquVJUuWkJCQQFpaGjNnziQsLIwZM2aQmpp6R7KMrOl6r7/++m3tb0TW6NGj2b9/P4GBgQwZMoSPPvrI8dr333/v9hxPrclms7FkyRJefPFFWrVqRevWrenRowdz584lPT3d5ZyUlBSmT5/OrFmzOH/+PGFhYQQHB9O9e3dOnTqVrZqMyvLEmozOup4n/G/PqCxP/JwbmZWcnMzUqVM5e/YsSUlJDBs2jLZt2zJ06FDOnTuXrZqMyvLEmozOyisuXrzokVn5waVLl4iMjKRZs2Y88sgj1KxZk+bNmzN+/HiSk5OzlXXmzBkGDx7M6NGj+eOPPwgNDaV169Y0b96cAwcOGF67Ry+A0rZtWz777DMuXrxIq1at+OGHHyhe/P/bu/e4nu89gOOvRNJxzbSZHi7nMM3CkNpxKYyU7uVUIuKRU0dzOS7lEnKkYmZum0c0nFy21FJoPWyKTYXK8XBbbpHZyi3XdO/3OX84/R5M7Pfju/od+zz/WX1//V69f79VPr/6/j6/NpSVleHu7k5qaqrGrVmzZgEQGhrK+vXrn2o+ePCATz75pN5bSnV8fX3R09N76tiZM2cwNzcHIDY2VtObpmjL2dmZPXv2AHDnzh38/Pxwd3fHz88PV1dXkpKS6rWjqzMtXLgQlUqFm5sbJiYmANy8eZPk5GT1DxdNBAYG0qVLF8rKyjhy5AhjxozBy8uLAwcOsHv3bjZv3qzxTEq1dHEmJVu6+r2nVEsXv86VbPn7+9OjRw/+/ve/s2TJEkxNTXF0dCQtLY3s7GxiYmI0nkmpli7OpHRLpVKxa9cuUlNTuXHjBo0aNcLExARra2t8fX1p0qSJRp2SkhK++OILDA0N8fb2Jjg4mOzsbHr27ElkZCQdOnTQeKa6uLm5sXv37ldqvEpLqfsJHi8uly5dyvXr1xk+fDgBAQHo6+sDEBAQQHR0dL12goKCeO+993B3d6ddu3YA3Lp1i6SkJI4fP86mTZs0vm3jx4/H1taW0tJStm7dSlhYGLa2thw/fpxPPvmEnTt3atzSiNBhSUlJwtLSUvTv319s27ZNuLq6iuXLlwtXV1exadMmrVqOjo7qt52cnIRKpVK/b29v3yAtpTrbt28X1tbWIjExURw7dkwcPXpUjBgxQhw7dkwcO3ZM447SLUdHR/Ho0SP1+9evXxdDhw4Ve/bsEa6urvXe0dWZRo4c+dzLRo0apXHHxcVF/fagQYOeuszZ2VmrmZRq6eJMSrZ09XtPqZYufp0r2Xry/7WTk9NTlz3587k+W7o4k9Kt0NBQMX/+fJGTkyOuXr0qrl69KnJyckRoaKiYNWuWxp2AgAARFRUlFi9eLGxtbcWWLVtEaWmp2LNnj5g4caJWM5mbmwszMzNhZmYmunfvrv5v7dsN0VLqfhJCCD8/P5GQkCBOnz4tAgIChL+/v6iqqhJCPP3zsL46L1rjODg4aNz59ecdPHjwU5dp+2+DJnT6VA0XFxe+//57Dh48yLhx44iKiqJt27bMnj0bf39/rVpGRkZcvHgRgI4dO1JUVATAjRs3tH6SoVItpTpjx47liy++ICEhgcLCQqysrPjTn/6EpaUllpaWGneUbo0bNw43NzeOHDkCwJtvvklMTAyrVq0iPz+/3ju6OlPz5s3rPPXoxIkTGBkZadxp3LgxGRkZpKam8ujRI86cOQNAQUGBVvMo2dLFmZRs6er3nlItX1/fZ77ON23a1KBf50q2WrZsSWZmJgA9evTgxx9/BOD8+fMYGhpqNZNSLV2cSelWTk4Oy5Ytw8LCgo4dO9KxY0csLCxYunQpeXl5GneuX79OSEgIYWFhlJaW4ufnR7NmzXBycqK4uFirmeLi4nj//fdZs2YN586dIy8vDzMzM/XbDdFS6n4CuHfvHh4eHpibm7NhwwZatGjBnDlztGoo2TE2NiY1NRWVSqU+JoQgJSWFNm3aaNVq3rw5X331FTExMdTU1HDw4EEA/vOf/9C0aVOtZ/stOn2qhpJOnDjB1KlT6du3L82aNSMzM5PevXtz9uxZlixZgo2NTb23lJwJoLKyklWrVlFYWEh+fj4pKSlaXf/3aF25cgVDQ0Pat29PfHw858+fp2fPnty9exc/P7967+jiTHl5eQQHB1NRUaH+k9Xt27cxMDDg448/xszMTKPO2bNniYiIQKVSMXfuXBYsWIChoSHXr18nIiICa2trjWdSqqWLMyndAt383lOiFR4ejouLC2+88Qbt27dXH3/06BHx8fEN8nWuZOvy5ctMmTIFIyMj2rVrR3Z2Np06deLOnTusX7+eXr16aTyTUi1dnEnp1ujRo1m0aNEz1zlx4gQRERHEx8dr3JkxYwYPHz5kwYIFxMbGYm5uTkFBAdOnTyc5OVnjmQDKy8uJioqioqKC0NBQxo0b99KnaijRUup+qm1FRkbSrVs3AKqqqvD396dTp06cOHGCvXv31munqKiIJUuWkJOTQ4sWLYDH59H379+fRYsWPfVE5N/yyy+/sGbNGlQqFdOnTyckJITLly9jaGjIunXr6Nmzp8YtTfxhFs7w+HyorKwsrl69Sk1NDW+88QaDBg3irbfearCWUp0DBw5QVFSEjY0NP//8M/v27SMiIoK4uDi8vLwapLV161a2bduGSqXigw8+oKioiBEjRpCenk6fPn346KOP6rWjqzPVKiws5ObNmwghiI+PJyIiQuvGkyoqKpg8eTKfffaZ+gdTQ7d0caZXbR0+fJjevXvTsmVL4uPjOXHiBP369cPDw0PrOXStZWFhQdu2bZk1axa2trZaz/BrR44coXHjxjRp0oSMjAyys7OxtLR8qe1FlWp9++236t2bAN555x2WLFlCnz59tJpHyZYuzqRkq64HPrdu3aJp06ZaPfD58ccfWbZsWZ0PgCMjIxk8eLBWc9VKT09nw4YN3L9/n2+//falGkq0XnQ/rVy5ku7du2vcOn78OHPmzOGf//wnTk5OwOMnLM6ePZuDBw9q/BtspTq1qquruXv3LkIIjI2NadxYmV2S79y5g7GxsSKtX/vDLJwLCwtfeLk2j26UainVWblyJWfOnOEvf/kLqamphISE4OLiAmj/hAQlW05OTiQkJHD79m0cHR05evQoTZs2pbKyktGjR6ufdFRfHV2dad68ec8cS09PV2+3GBkZWa+d130mJVvLli0jLy+PTz/9lJ07d3Lq1Ck+/PBDfvjhB0xNTQkNDdV4Jl1subq6snLlSsLCwqioqGDixIkMGzZM6z/Nw+PtRXNzc6mursbU1JRGjRrh5uZGeno6NTU1hIeH13srOjqakydPMmjQINLT07GwsMDQ0JCEhATGjx+Pp6enxjMp1dLFmZRu1XrylwVvvvmmVv8O16WiooILFy7QqVMnWrZs+UqtW7ducejQIf72t7+9UkeJlpL3U1VV1TNPKszLy+Pdd99tkM6TZs6cyapVq7S+XnV1NUlJSTRr1gxbW1uioqLUTxINDg6mdevWLz1TXXT+BVCUEhAQQEFBASYmJtQ+VtDT00MIgZ6eHmlpafXeUqrz/fffs3v3bho3boyvry+TJk3CwMAAe3t7tH1cpGRLpVJhYGBAhw4dmDRp0lPnGtXU1NR7R1dnat26NUlJSQQGBqp/2B89elTr81p/3RFCvFRHyZYuzqRkKzMzk71796Kvr8/BgwfZtWsXBgYGeHl54ejo+H/f0tPTo2vXrmzfvp2srCzi4uJYtmwZnTt35q233tJqN6LDhw+TnJxMZWUlNjY2ZGRk0KRJE6ytrdUPzuu79c0337B7924aNWqEh4cHkydPJjY2Fg8PDzw9PbVaDCrV0sWZlG5VV1ezY8cOioqK+PDDD+nfv7/6snXr1jF16lStO8OHD8fCwkL9Z3ltOnVp164dR44cUWTh/LKt6upqEhISGDFiBD169GDjxo2cPn1a/SJw2py/q1KpiI2NJS0tjVu3btGkSRM6duyIg4MDo0aNqvfO83b+GT9+PKDdLkKhoaGUlpZSWVnJtm3b6NWrF59++ikHDhxg0aJFrF27VuOWJv4wC+cvv/wSHx8fFi9eTL9+/XSipVSndqEN0LlzZ6Kjo5k4cSLGxsbPfGHWZ8vW1pZx48YRGxur/gF27tw5QkNDsbe3r/eOrs4UEhKCtbU1q1evZubMmVhZWfHvf/8bNze3V+7ExsZq3VGypYszKdkyNDSkuLgYExMT2rZtS2lpKQYGBpSVlWn9J0ddbD35YHnAgAEMGDCAqqoqzp8/z7Vr17SaSQjBw4cPKS0tpby8nJKSEtq0aUN5ebnW+zgr1aqoqKC8vBwjIyPKy8u5d+8e8PiJ240aaffceaVaujiT0q1FixahUql45513CAkJwdPTk8DAQODxX340XfA+2QkODn7pDii7kFOqFRISAsDIkSNZvnw5paWl+Pj4cOjQIebPn6/VA9eoqCj1+cj79+/HzMyM9u3bExsby5UrVwgKCqrXjp2dHRs3bmTGjBl06NABIQQLFy58qdMcz549y969e6mpqcHGxoavvvoKgK5du2r9oFwjiu/TocNOnjwpQkNDdaqlRGfdunVizJgx4uTJk+pjubm54oMPPhB9+/ZtsJYQQmRnZz/1fn5+vjh06FCDdXR1JiGEuHv3rpg2bZqIiorSenun36Pzus+kRCstLU0MHjxYREVFifDwcOHo6CiWLVsm7O3txddff/1/39q1a5dWn/dFlNxeVKlWdHS0cHZ2FitWrBDOzs5iy5Yt4ueffxYuLi7i888/12ompVq6OJPSrSe3sysuLhZOTk5iy5YtQgjttjRTqiOE7m4HWetVt9F98r6qqakR3t7eQgghKioqXri94+/VEUKIixcvCh8fH7F7924hhNB6i8tazs7O4vLly+LkyZOiV69e4tq1a0KIx18T2m51qYk/1ML5dZaVlSUuXbr01LHCwkIRHh7eoC1Je7t27dJ6D9Lfs6NkSxdnetXWTz/9JDZv3iwWL14sQkNDxerVq5964Pk6tJRSVlYmSkpKhBBCnDt3TsTExIiMjIwGbWVlZYmYmBiRlZUlhBCipKREnDt37qVmUqqlizMp2dLF/fSFUG4hp1TL09NTXLhwQQghRFBQkPjll1+EEI9vp7YPDEaNGiVu376tvn7tPCUlJVrtm6xUp1ZFRYWIjIwUU6dOfelF7uHDh4WNjY0YPHiw+O6774SdnZ2YNm2aGDp0qPr+V9If5smBkiRJkiQ1vLi4ODZv3kxYWBh//etfAcjPz8ff35/i4uI69+j+PTtP0qXtIJXcsjYxMZE1a9bQp08fTp48yaxZs+jZsycTJkxg6tSpGu+2o1QHlN8NrLCwkCFDhmBkZERubi7dunUjNzdX69ZvkQtnSZIkSZLqVUFBAQYGBk/tEFFSUkJCQoJW+4Mr1fm1zMxMUlJSXnlb0FdtKbmN7pUrVzh//jxmZmZ07tyZyspKSktLtd51QomO0ruBnT17lj//+c+v3NKEXDhLkiRJklRvdG1LV11tvc4zOTk5qXfwKigoYNKkScyZMwd7e3tcXV1JSkrSqKN0SxN/mF01JEmSJElqeHVtxVrrVbd0fZnO81qvy5a1z2vVaoj7XOjobmCafkJJkiRJkqR68fDhQ+Hk5CRyc3N1oqOrrdd5Jl3eDey36IeFhYUpvxyXJEmSJEl6loGBAe+99x6JiYnqV+tsyI6utl7nmSwtLXn77bdp06aN+qWx3377bRwcHCgvL8fa2rpBWpqQ5zhLkiRJkiRJkga0e7kfSZIkSZIkSfqDkgtnSZIkSZIkSdKAXDhLkiQBvr6+REdHP3N88+bNBAYGcvr0aaZNm1bndU+dOsWiRYte6fMPGzaM06dP1znXsGHDcHFxwdXVFQcHB0JCQigrK3ulzydJkiRpTy6cJUmSgLFjx5KYmPjM8V27djFu3Dh69uzJ2rVr67zupUuXuHHjxu82W3BwMMnJySQlJbFv3z7KysqeO4skSZL0+5ELZ0mSJGD48OGUlpaSm5urPpadnY0QgoEDB3Ls2DEcHR0BmDt3LoGBgTg4ODB//nzWrl1Lbm4u8+bNe+rjgKfev337NlOmTMHLy4thw4bh6+tLcXGxVnPq6elhZWXF5cuXATA3N2f69OmMHDmS06dPk5ubi6enJ05OTri7u/PDDz+orxsdHY2dnR2Ojo4EBQXx8OFDAOLj43F3d8fV1RU/Pz/y8/MByM3NZfTo0bi7u+Pu7s7+/ftfeLyyspKIiAjc3NxwdnZm7ty5lJSUALBz506cnZ3x8PDAx8eHS5cuaXW7JUmSdIFcOEuSJAGNGzfGy8uLhIQE9bG4uDh8fHzq3ES/vLxc/TK606ZNw8LCgsjIyBd+jpSUFN5//33i4uJIS0vD0NCQ5ORkrea8f/8+qampWFlZAVBVVcXQoUPZv38/pqamTJs2jQULFrB3716WL1/OnDlzuHbtGmlpaSQmJhIXF8e+ffswNTVl+/btZGdnk5SUxI4dO0hKSsLf35+pU6cCsG7dOiZOnEhiYiIREREcPXr0hcc3btyIvr4+iYmJ7NmzBxMTE1auXElNTQ0RERHExMTw9ddf4+npyfHjx7W63ZIkSbpAvnKgJEnS/3h6euLg4EBJSQnV1dVkZGTwvK3u+/Xrp3V/woQJ5ObmsmXLFgoKCrh48SK9e/f+zeutWLGCDRs2qF+pa+jQoYwfP159uYWFBfD4XOuOHTuqm926daNv375kZ2eTl5eHnZ0drVq1AmDevHnq9tWrV/H29lb37t+/z71797C3t+df//oX6enpDBgwgJkzZwI89/ihQ4d4+PAhWVlZwONFfdu2bdHX18fOzg5vb2+GDBnCwIEDcXJy0vr+kyRJamhy4SxJkvQ/JiYmDBgwgG+++YbS0lJGjhxJixYt6vxYIyOjOo/XviRuraqqKvXbH3/8MadOncLDwwMrKyuqq6ufednaugQHB2NnZ/fcy2tnUalUz1wmhKC6uhp9ff2nfnP+4MEDHjx4gEqlwsXFhTlz5qgbN2/epFWrVnh7ezN06FAyMzM5fPgw69evZ8+ePc89rlKpmD9/PjY2NgA8evSIiooKAFauXMmFCxfIyspi06ZNJCQksGHDht+87ZIkSbpEnqohSZL0BB8fH/bu3UtSUhJjx47V6Dr6+vpUV1cDYGxsTGFhIcXFxQghOHDggPrjMjIymDBhAq6urrRt25asrCxqamoUm713795cuXKFU6dOAXDx4kVycnKwtLRkwIABfPfdd+pzjtetW8fWrVsZOHAgKSkp3Lx5E4Avv/ySCRMmAODt7U1eXh7u7u4sXbqUBw8ecP/+/eceHzRoEDt27KCyshKVSsXChQtZtWoVd+7cwcbGhtatW+Pn58eMGTM4f/68YrdbkiSpvsjfOEuSJD3BysqK8PBwWrVqRffu3TW6Tp8+fVi9ejVBQUF89tlneHt74+HhQbt27RgyZIj644KCglixYgWff/45+vr69O3bl59++kmx2Y2NjVmzZg1Lly6lvLwcPT09IiMj6dKlC126dOHSpUuMGTMGgK5du7J06VKaN2/O5MmTmTRpEnp6ejRv3pz169ejp6fH7NmziYiIYPXq1TRq1IiPPvoIU1PT5x6fMmUKy5cvx83NjZqaGt59913mzp1L8+bN+cc//oGfnx+Ghobo6+sTHh6u2O2WJEmqL/IltyVJkiRJkiRJA/JUDUmSJEmSJEnSgFw4S5IkSZIkSZIG5MJZkiRJkiRJkjQgF86SJEmSJEmSpAG5cJYkSZIkSZIkDciFsyRJkiRJkiRpQC6cJUmSJEmSJEkD/wWuO0zueGt2dQAAAABJRU5ErkJggg==\n", "text/plain": ["<Figure size 864x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df[[\"Unaccounted Time / s\", *cols]].plot(kind=\"bar\", stacked=True, figsize=(12, 4));"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["* Make it relative to the total program run time\n", "* **Slight complication**: Our virtual processes as indexes are not unique; we need to find new unique indexes\n", "* Let's use a multi index!"]}, {"cell_type": "code", "execution_count": 95, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th>id</th>\n", "      <th>Runtime Program / s</th>\n", "      <th>Scale</th>\n", "      <th>Plastic</th>\n", "      <th>Avg. Neuron Build Time / s</th>\n", "      <th>Min. Edge Build Time / s</th>\n", "      <th>Max. Edge Build Time / s</th>\n", "      <th>Min. Init. Time / s</th>\n", "      <th>Max. Init. Time / s</th>\n", "      <th>Presim. Time / s</th>\n", "      <th>Sim. Time / s</th>\n", "      <th>Virt. Memory (Sum) / kB</th>\n", "      <th>Local Spike Counter (Sum)</th>\n", "      <th>Average Rate (Sum)</th>\n", "      <th>Number of Neurons</th>\n", "      <th>Number of Connections</th>\n", "      <th>Min. Delay</th>\n", "      <th>Max. Delay</th>\n", "      <th>Unaccounted Time / s</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Nodes</th>\n", "      <th>Tasks/Node</th>\n", "      <th>Threads/Task</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th rowspan=\"3\" valign=\"top\">1</th>\n", "      <th rowspan=\"2\" valign=\"top\">2</th>\n", "      <th>4</th>\n", "      <td>5</td>\n", "      <td>420.42</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.29</td>\n", "      <td>88.12</td>\n", "      <td>88.18</td>\n", "      <td>1.14</td>\n", "      <td>1.20</td>\n", "      <td>17.26</td>\n", "      <td>311.52</td>\n", "      <td>46560664.0</td>\n", "      <td>825499</td>\n", "      <td>7.48</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>2.09</td>\n", "    </tr>\n", "    <tr>\n", "      <th>8</th>\n", "      <td>5</td>\n", "      <td>202.15</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.28</td>\n", "      <td>47.98</td>\n", "      <td>48.48</td>\n", "      <td>0.70</td>\n", "      <td>1.20</td>\n", "      <td>7.95</td>\n", "      <td>142.81</td>\n", "      <td>47699384.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>2.43</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <th>4</th>\n", "      <td>5</td>\n", "      <td>200.84</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.15</td>\n", "      <td>46.03</td>\n", "      <td>46.34</td>\n", "      <td>0.70</td>\n", "      <td>1.01</td>\n", "      <td>7.87</td>\n", "      <td>142.97</td>\n", "      <td>46903088.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>3.12</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <th>2</th>\n", "      <th>4</th>\n", "      <td>5</td>\n", "      <td>164.16</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.20</td>\n", "      <td>40.03</td>\n", "      <td>41.09</td>\n", "      <td>0.52</td>\n", "      <td>1.58</td>\n", "      <td>6.08</td>\n", "      <td>114.88</td>\n", "      <td>46937216.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>2.45</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <th>2</th>\n", "      <th>12</th>\n", "      <td>6</td>\n", "      <td>141.70</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.30</td>\n", "      <td>32.93</td>\n", "      <td>33.26</td>\n", "      <td>0.62</td>\n", "      <td>0.95</td>\n", "      <td>5.41</td>\n", "      <td>100.16</td>\n", "      <td>50148824.0</td>\n", "      <td>813743</td>\n", "      <td>7.27</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>2.28</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                               id  Runtime Program / s  Scale  Plastic  \\\n", "Nodes Tasks/Node Threads/Task                                            \n", "1     2          4              5               420.42     10     True   \n", "                 8              5               202.15     10     True   \n", "      4          4              5               200.84     10     True   \n", "2     2          4              5               164.16     10     True   \n", "1     2          12             6               141.70     10     True   \n", "\n", "                               Avg. Neuron Build Time / s  \\\n", "Nodes Tasks/Node Threads/Task                               \n", "1     2          4                                   0.29   \n", "                 8                                   0.28   \n", "      4          4                                   0.15   \n", "2     2          4                                   0.20   \n", "1     2          12                                  0.30   \n", "\n", "                               Min. Edge Build Time / s  \\\n", "Nodes Tasks/Node Threads/Task                             \n", "1     2          4                                88.12   \n", "                 8                                47.98   \n", "      4          4                                46.03   \n", "2     2          4                                40.03   \n", "1     2          12                               32.93   \n", "\n", "                               Max. Edge Build Time / s  Min. Init. Time / s  \\\n", "Nodes Tasks/Node Threads/Task                                                  \n", "1     2          4                                88.18                 1.14   \n", "                 8                                48.48                 0.70   \n", "      4          4                                46.34                 0.70   \n", "2     2          4                                41.09                 0.52   \n", "1     2          12                               33.26                 0.62   \n", "\n", "                               Max. Init. Time / s  Presim. Time / s  \\\n", "Nodes Tasks/Node Threads/Task                                          \n", "1     2          4                            1.20             17.26   \n", "                 8                            1.20              7.95   \n", "      4          4                            1.01              7.87   \n", "2     2          4                            1.58              6.08   \n", "1     2          12                           0.95              5.41   \n", "\n", "                               Sim. Time / s  Virt. Memory (Sum) / kB  \\\n", "Nodes Tasks/Node Threads/Task                                           \n", "1     2          4                    311.52               46560664.0   \n", "                 8                    142.81               47699384.0   \n", "      4          4                    142.97               46903088.0   \n", "2     2          4                    114.88               46937216.0   \n", "1     2          12                   100.16               50148824.0   \n", "\n", "                               Local Spike Counter (Sum)  Average Rate (Sum)  \\\n", "Nodes Tasks/Node Threads/Task                                                  \n", "1     2          4                                825499                7.48   \n", "                 8                                802865                7.03   \n", "      4          4                                802865                7.03   \n", "2     2          4                                802865                7.03   \n", "1     2          12                               813743                7.27   \n", "\n", "                               Number of Neurons  Number of Connections  \\\n", "Nodes Tasks/Node Threads/Task                                             \n", "1     2          4                        112500             1265738500   \n", "                 8                        112500             1265738500   \n", "      4          4                        112500             1265738500   \n", "2     2          4                        112500             1265738500   \n", "1     2          12                       112500             1265738500   \n", "\n", "                               Min. Delay  Max. Delay  Unaccounted Time / s  \n", "Nodes Tasks/Node Threads/Task                                                \n", "1     2          4                    1.5         1.5                  2.09  \n", "                 8                    1.5         1.5                  2.43  \n", "      4          4                    1.5         1.5                  3.12  \n", "2     2          4                    1.5         1.5                  2.45  \n", "1     2          12                   1.5         1.5                  2.28  "]}, "execution_count": 95, "metadata": {}, "output_type": "execute_result"}], "source": ["df_multind = df.set_index([\"Nodes\", \"Tasks/Node\", \"Threads/Task\"])\n", "df_multind.head()"]}, {"cell_type": "code", "execution_count": 96, "metadata": {"slideshow": {"slide_type": "subslide"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1008x432 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df_multind[[\"Unaccounted Time / s\", *cols]]\\\n", "    .divide(df_multind[\"Runtime Program / s\"], axis=\"index\")\\\n", "    .plot(kind=\"bar\", stacked=True, figsize=(14, 6), title=\"Relative Time Distribution\");"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## Next Level: Hierarchical Data\n", "\n", "* `MultiIndex` only a first level\n", "* More powerful:\n", "    - Grouping: `.groupby()` ([API](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html))\n", "    - Pivoting: `.pivot_table()` ([API](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot_table.html)); also `.pivot()` ([API](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot.html))"]}, {"cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>id</th>\n", "      <th>Tasks/Node</th>\n", "      <th>Threads/Task</th>\n", "      <th>Runtime Program / s</th>\n", "      <th>Scale</th>\n", "      <th>Plastic</th>\n", "      <th>Avg. Neuron Build Time / s</th>\n", "      <th>Min. Edge Build Time / s</th>\n", "      <th>Max. Edge Build Time / s</th>\n", "      <th>Min. Init. Time / s</th>\n", "      <th>...</th>\n", "      <th>Presim. Time / s</th>\n", "      <th>Sim. Time / s</th>\n", "      <th>Virt. Memory (Sum) / kB</th>\n", "      <th>Local Spike Counter (Sum)</th>\n", "      <th>Average Rate (Sum)</th>\n", "      <th>Number of Neurons</th>\n", "      <th>Number of Connections</th>\n", "      <th>Min. Delay</th>\n", "      <th>Max. Delay</th>\n", "      <th>Unaccounted Time / s</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Nodes</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>5.333333</td>\n", "      <td>3.0</td>\n", "      <td>8.0</td>\n", "      <td>185.023333</td>\n", "      <td>10.0</td>\n", "      <td>True</td>\n", "      <td>0.220000</td>\n", "      <td>42.040000</td>\n", "      <td>42.838333</td>\n", "      <td>0.583333</td>\n", "      <td>...</td>\n", "      <td>7.226667</td>\n", "      <td>132.061667</td>\n", "      <td>4.806585e+07</td>\n", "      <td>816298.000000</td>\n", "      <td>7.215000</td>\n", "      <td>112500.0</td>\n", "      <td>1.265738e+09</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>2.891667</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>5.333333</td>\n", "      <td>3.0</td>\n", "      <td>8.0</td>\n", "      <td>73.601667</td>\n", "      <td>10.0</td>\n", "      <td>True</td>\n", "      <td>0.168333</td>\n", "      <td>19.628333</td>\n", "      <td>20.313333</td>\n", "      <td>0.191667</td>\n", "      <td>...</td>\n", "      <td>2.725000</td>\n", "      <td>48.901667</td>\n", "      <td>4.975288e+07</td>\n", "      <td>818151.000000</td>\n", "      <td>7.210000</td>\n", "      <td>112500.0</td>\n", "      <td>1.265738e+09</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>1.986667</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>5.333333</td>\n", "      <td>3.0</td>\n", "      <td>8.0</td>\n", "      <td>43.990000</td>\n", "      <td>10.0</td>\n", "      <td>True</td>\n", "      <td>0.138333</td>\n", "      <td>12.810000</td>\n", "      <td>13.305000</td>\n", "      <td>0.135000</td>\n", "      <td>...</td>\n", "      <td>1.426667</td>\n", "      <td>27.735000</td>\n", "      <td>5.511165e+07</td>\n", "      <td>820465.666667</td>\n", "      <td>7.253333</td>\n", "      <td>112500.0</td>\n", "      <td>1.265738e+09</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>1.745000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>5.333333</td>\n", "      <td>3.0</td>\n", "      <td>8.0</td>\n", "      <td>31.225000</td>\n", "      <td>10.0</td>\n", "      <td>True</td>\n", "      <td>0.116667</td>\n", "      <td>9.325000</td>\n", "      <td>9.740000</td>\n", "      <td>0.088333</td>\n", "      <td>...</td>\n", "      <td>1.066667</td>\n", "      <td>19.353333</td>\n", "      <td>5.325783e+07</td>\n", "      <td>819558.166667</td>\n", "      <td>7.288333</td>\n", "      <td>112500.0</td>\n", "      <td>1.265738e+09</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>1.275000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>5</th>\n", "      <td>5.333333</td>\n", "      <td>3.0</td>\n", "      <td>8.0</td>\n", "      <td>24.896667</td>\n", "      <td>10.0</td>\n", "      <td>True</td>\n", "      <td>0.140000</td>\n", "      <td>7.468333</td>\n", "      <td>7.790000</td>\n", "      <td>0.070000</td>\n", "      <td>...</td>\n", "      <td>0.771667</td>\n", "      <td>14.950000</td>\n", "      <td>6.075634e+07</td>\n", "      <td>815307.666667</td>\n", "      <td>7.225000</td>\n", "      <td>112500.0</td>\n", "      <td>1.265738e+09</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>1.496667</td>\n", "    </tr>\n", "    <tr>\n", "      <th>6</th>\n", "      <td>5.333333</td>\n", "      <td>3.0</td>\n", "      <td>8.0</td>\n", "      <td>20.215000</td>\n", "      <td>10.0</td>\n", "      <td>True</td>\n", "      <td>0.106667</td>\n", "      <td>6.165000</td>\n", "      <td>6.406667</td>\n", "      <td>0.051667</td>\n", "      <td>...</td>\n", "      <td>0.630000</td>\n", "      <td>12.271667</td>\n", "      <td>6.060652e+07</td>\n", "      <td>815456.333333</td>\n", "      <td>7.201667</td>\n", "      <td>112500.0</td>\n", "      <td>1.265738e+09</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>0.990000</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>6 rows \u00d7 21 columns</p>\n", "</div>"], "text/plain": ["             id  Tasks/Node  Threads/Task  Runtime Program / s  Scale  \\\n", "Nodes                                                                   \n", "1      5.333333         3.0           8.0           185.023333   10.0   \n", "2      5.333333         3.0           8.0            73.601667   10.0   \n", "3      5.333333         3.0           8.0            43.990000   10.0   \n", "4      5.333333         3.0           8.0            31.225000   10.0   \n", "5      5.333333         3.0           8.0            24.896667   10.0   \n", "6      5.333333         3.0           8.0            20.215000   10.0   \n", "\n", "       Plastic  Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n", "Nodes                                                                  \n", "1         True                    0.220000                 42.040000   \n", "2         True                    0.168333                 19.628333   \n", "3         True                    0.138333                 12.810000   \n", "4         True                    0.116667                  9.325000   \n", "5         True                    0.140000                  7.468333   \n", "6         True                    0.106667                  6.165000   \n", "\n", "       Max. Edge Build Time / s  Min. Init. Time / s  ...  Presim. Time / s  \\\n", "Nodes                                                 ...                     \n", "1                     42.838333             0.583333  ...          7.226667   \n", "2                     20.313333             0.191667  ...          2.725000   \n", "3                     13.305000             0.135000  ...          1.426667   \n", "4                      9.740000             0.088333  ...          1.066667   \n", "5                      7.790000             0.070000  ...          0.771667   \n", "6                      6.406667             0.051667  ...          0.630000   \n", "\n", "       Sim. Time / s  Virt. Memory (Sum) / kB  Local Spike Counter (Sum)  \\\n", "Nodes                                                                      \n", "1         132.061667             4.806585e+07              816298.000000   \n", "2          48.901667             4.975288e+07              818151.000000   \n", "3          27.735000             5.511165e+07              820465.666667   \n", "4          19.353333             5.325783e+07              819558.166667   \n", "5          14.950000             6.075634e+07              815307.666667   \n", "6          12.271667             6.060652e+07              815456.333333   \n", "\n", "       Average Rate (Sum)  Number of Neurons  Number of Connections  \\\n", "Nodes                                                                 \n", "1                7.215000           112500.0           1.265738e+09   \n", "2                7.210000           112500.0           1.265738e+09   \n", "3                7.253333           112500.0           1.265738e+09   \n", "4                7.288333           112500.0           1.265738e+09   \n", "5                7.225000           112500.0           1.265738e+09   \n", "6                7.201667           112500.0           1.265738e+09   \n", "\n", "       Min. Delay  Max. Delay  Unaccounted Time / s  \n", "Nodes                                                \n", "1             1.5         1.5              2.891667  \n", "2             1.5         1.5              1.986667  \n", "3             1.5         1.5              1.745000  \n", "4             1.5         1.5              1.275000  \n", "5             1.5         1.5              1.496667  \n", "6             1.5         1.5              0.990000  \n", "\n", "[6 rows x 21 columns]"]}, "execution_count": 97, "metadata": {}, "output_type": "execute_result"}], "source": ["df.groupby(\"Nodes\").mean()"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["### Pivoting\n", "\n", "* Combine categorically-similar columns\n", "* Creates hierarchical index\n", "* Respected during plotting!\n", "* A pivot table has three *layers*; if confused, think about these questions\n", "    - `index`: \u00bbWhat's on the `x` axis?\u00ab\n", "    - `values`: \u00bbWhat value do I want to plot?\u00ab\n", "    - `columns`: \u00bbWhat categories do I want [to be in the legend]?\u00ab\n", "* All can be populated from base data frame\n", "* Might be aggregated, if needed"]}, {"cell_type": "code", "execution_count": 98, "metadata": {"slideshow": {"slide_type": "subslide"}}, "outputs": [], "source": ["df_demo[\"H\"] = [(-1)**n for n in range(5)]"]}, {"cell_type": "code", "execution_count": 99, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th>H</th>\n", "      <th>-1</th>\n", "      <th>1</th>\n", "    </tr>\n", "    <tr>\n", "      <th>F</th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>-3.918282</th>\n", "      <td>NaN</td>\n", "      <td>7.389056</td>\n", "    </tr>\n", "    <tr>\n", "      <th>-2.504068</th>\n", "      <td>NaN</td>\n", "      <td>1.700594</td>\n", "    </tr>\n", "    <tr>\n", "      <th>-1.918282</th>\n", "      <td>NaN</td>\n", "      <td>0.515929</td>\n", "    </tr>\n", "    <tr>\n", "      <th>-0.213769</th>\n", "      <td>0.972652</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>0.518282</th>\n", "      <td>2.952492</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["H                -1         1\n", "F                            \n", "-3.918282       NaN  7.389056\n", "-2.504068       NaN  1.700594\n", "-1.918282       NaN  0.515929\n", "-0.213769  0.972652       NaN\n", " 0.518282  2.952492       NaN"]}, "execution_count": 99, "metadata": {}, "output_type": "execute_result"}], "source": ["df_pivot = df_demo.pivot_table(\n", "    index=\"F\",\n", "    values=\"G\",\n", "    columns=\"H\"\n", ")\n", "df_pivot"]}, {"cell_type": "code", "execution_count": 100, "metadata": {"slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"image/png": 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Y5vUkSRJMAx4DZBnWvCVcbiEiIVjmPqBpkARjz1Fw/VII50/bRcchojDEMvcRfad7oElqDVv+J5CrK0THIaIw43GZv/7665gxY4Y/s4Q0SaOpeZCFwwrbto9FxyGiMONRmW/fvh05OTn+zhLytPFNYOiWBefxAjhO7BYdh4jCiNsyv3TpEt566y1MmTIlEHlCniE1A5rEW2DbuhSKrVJ0HCIKE5Li5vSLadOmYdy4cTh79iwKCgrw2muvBSpbyLKdPY4zi/+E6M53ImnYVNFxiCgM6G72w5UrVyIlJQV9+vTBmjVr6jRAWZkFsizmdD2zOQalpVcCP7DODEPXIbDs+xKupt2ga9pJfCY3mMtzwZgJYC5vBWMuszkGZWUWJCZGe/3amy6z5ObmYtu2bcjOzsaCBQvw7bffYu7cuXUOGk4M3bKhiW1Uc6m/wyo6DhGp3E2PzBcvXnz139esWYOCggLMnDnT76HUQNIZYEyfhOp1c2ErWAlT30dERyIiFeN55n6ka3Qr9B3vhuPAJjjPHREdh4hUzOMyHzlyJD/8rANjr9GQYhrCuuUDKE676DhEpFI8MvczSW+Cqf8EKJfPwb57reg4RKRSLPMA0DXtBH27/rDv3wDb2WOi4xCRCrHMA8TYeyykiAYo/eL/QZGdouMQkcqwzANEMkbB2G887CUnYd+XKzoOEakMyzyA9C26IapDX9j3rIOr/IzoOESkIizzAGs4eBIkvQnWLYugyLLoOESkEizzANNGxcLY9yHIJcfhKNooOg4RqQTLXABd697QNusK287VkCtKRMchIhVgmQsgSRJM/ScAGm3NvVv43FAiqieWuSCaqHgYez8AV/EhOH7cIjoOEYU4lrlA+tvSoW3cHrYdKyBbLoqOQ0QhjGUukCRJMA14DFBkWPOWcLmFiOqMZS6YpkESjD1HwfVLIZw/bRcdh4hCFMs8COg73gNNchvY8j+BXF0hOg4RhSCWeRCQNBqYBkyE4rDCtu1j0XGIKASxzIOENr4xDN2y4DxeAMeJ3aLjEFGIYZkHEUNqBjSJzWDbuhSKrVJ0HCIKISzzICJpdDClT4JivQLr9hWi4xBRCGGZBxltw+YwdM2A88hWOE8XiY5DRCHCozJ/5513kJGRgczMTCxevNjfmcKeoVsWNHEpNZf6O6yi4xBRCHBb5gUFBdixYwfWrVuH1atX46OPPsLx48cDkS1sSTpDzdktlouwFawUHYeIQoDbMu/VqxeWLl0KnU6HsrIyuFwuREZGBiJbWNM2uhX6ToPgOLAJznNHRMchoiDn0TKLXq/HggULkJmZiT59+iA5OdnfuQiAsecoSDENYd3yARSnXXQcIgpikuLFDUGqq6sxZcoUZGRk4IEHHvBnLvq3qp8Lce6TVxDbZzgS73pEdBwi8rMfT17Ebc0TvH6dzt0Gx44dg91uR/v27REREYHBgwfj8OHDHg9QVmaBLIu5gZTZHIPS0itCxr4RrzNFt4K+3QBc3rEOjkap0JpbBEeuAAnGXMGYCWAubwVjrq1F5/Dltp+xaNZgr1/rdpnl9OnTmDVrFux2O+x2OzZt2oTu3bvXKSjVjbH3A5AiGtQ8N9TlFB2HiPxg465TWPzFQXRp3bBOr3db5unp6UhPT8fw4cMxatQopKWlITMzs06DUd1IxiiY+j0K+eIp2Au/FB2HiHxs894zWP7NUfTu1AjjBrWp0z7cLrMAwLRp0zBt2rQ6DUC+oWuRBl3r22Hfsw66Fj2gTWgiOhIR+UDe/rNY+t+H0aV1Il54pAcqLlfXaT+8AjSEGO94CJIhEtbvFkGRZdFxiKiedhw4h8W5h9ChRTymjugEvU5b532xzEOIJqIBjHc8BLnkOBxFX4uOQ0T1sOvHEvzri0Noe0scnhrVpV5FDrDMQ46u9e3QNkuFbecayJfPi45DRHWw7+gF/Ne6A2jVuAGeHtMFRn39ihxgmYccSZJg6v8ooNHW3LtF4XILUSgpOl6Gd9f+gFuSovHMmK4wGTz66NItlnkI0kTFw9hnLFxnf4Tj0BbRcYjIQ4dOluMfa35A48QoPPtAKiJNvilygGUesvTtBkDbpANs338K2VImOg4RuXHk1CW8s6oQSXEReHZsKqIj9D7dP8s8RNUstzwGKDKsW5fAi7syEFGAHSu+jLdXFiI+xoTnxqaiQaTB52OwzEOYpoEZxp6j4Tq1H86ftouOQ0TXcfLcFfz900LEROrxwrg0xEYb/TIOyzzE6TsOgia5Daz5yyBXXRYdh4h+43SJBfNX7EWkUYvnx6UhPsY/RQ6wzEOepNHANGAi4LDBlv+x6DhE9G/FFyoxb8Ve6HUaPD8uDQ1jI/w6HstcBbTxjWHong3n8Z1w/LxbdByisHf+YhXmrdgLSZLw/Lg0JMX7/4E+LHOVMHQdAk1iM9jylkKxVYqOQxS2Si9V443le+FyKXh+bCpSEqMCMi7LXCUkjQ6m9ElQrFdg3b5cdByisHSxwop5y/fCZnfhubGpaGKODtjYLHMV0TZsDkPXDDiP5MF56gfRcYjCyiWLDfOW70Wl1YE/jk1Fs+SYgI7PMlcZQ7csaOJSYN36IRR73W6lSUTeqai0Y97yvbhksWP6mFS0TGkQ8Awsc5WRdIaa5RbLRdgKVomOQ6R6lmoH5q/Yi7LLVjwzpgvaNI0VkoNlrkLa5DbQdxoEx8FNcJ71/HmtROSdKqsDb67Yh3MXq/HUqC5o1yxeWBaWuUoZe46GFGOG9bsPoDjtouMQqU61zYm/f1aI06UWTB3RCR1bJgjNwzJXKUlvhGnAY1Aun4d991rRcYhUxWZ34Z2VhThx9gqmZHdC1zZ1ewizL7HMVUzXpAP0tw2Aff9XcJX+LDoOkSrYHS4sWL0fR89cxhNZHdC9nVl0JAAelvnChQuRmZmJzMxMvPHGG/7ORD5k7D0WUkQsrFsWQXE5RcchCmkOp4yFOT/gx5PlmJTZHr3aJ4uOdJXbMs/Pz0deXh5ycnKwdu1aHDhwABs3bgxENvIByRAJU/9HIV88Dfu+L0XHIQpZTpeM99YWoej4RTw65Dbc0SlFdKRruC1zs9mMGTNmwGAwQK/Xo3Xr1iguLg5ENvIRXfM06Fr3hn3vOrgunhYdhyjkuGQZ/1x3APt+uoCH7mmLAV0bi45Ui9syv/XWW5GamgoAOHHiBHJzc5Genu73YORbxjsehGSIhHXLB1BkPjeUyFOyrGDRF4ew63ApHrirDe7u3lR0pOuSFA8fUXP06FE8+eSTeOqppzBixAh/5yI/sBzIQ8nat5Bw96OI650lOg5R0JNlBf/4bB++2fkLHhnSHvcPais60g159DTR3bt3Y9q0aZg5cyYyMzO9GqCszAJZFvNIM7M5BqWlV4SMfSMiMynmLtA1T8PFzZ/A1rADNLH/++FNMM4VEJy5gjETwFzecpdLURR89PURbN57Bll9W2Bg1xS//znM5hiUlVmQmOj9DbrcLrOcPXsWU6dOxfz5870ucgoukiTB2G88oNXVXEykcLmF6HoURcHyTUexee8ZDLm9GbL7tRQdyS23Zb5o0SLYbDa89tpryM7ORnZ2NpYv5y1WQ5UmKh7G3mPhOnsYjkObRcchCjqKomDVlmP4ZtdpDOrRFKPvbA1JkkTHcsvtMsusWbMwa9asQGShANG3GwDnsQLYvv8MumZdoYlOFB2JKGh8nvczvtrxC+5Ma4Jxd98aEkUO8ArQsCRJEkz9JwCKDOvWJfDwM3Ai1fty+wms23YCfTs3wsOD24ZMkQMs87ClaWCGsdcYuE7th/Novug4RMJ9XfALVm85jt4dkvHYkPbQhFCRAyzzsKbveDc0yW1g3f4JnFcuio5DJMym3aex4tuf0L2dGZOGtodGE1pFDrDMw5okaWBKnwg4HTizeAZc538SHYko4L4rLMayjUeQ2qYhnszqCK0mNGsxNFOTz2jjGiMyeyYkrRZV6/8Ge9FGrqFT2MgvOoslX/2ITq0S8LvhnaDThm4lhm5y8hltwxZoMnEetE07w5a/DNZv/xOKwyo6FpFfbd13Bou+PIR2zeLwhxGdodeFdh2GdnryGW1ENCLunQZDz9FwHi9AVc4rcJXzhmqkTnuOlGL+st1o0yQWT4/uCoNeKzpSvbHM6SpJ0sCYNhQRmS9AsVlQlfMyHD/tEB2LyKf2H7uA99YW4damcXhmTFcYDaFf5ADLnK5D17g9Ike+DG1iM1i//U9Yt33MB1uQKhw4cREL1xShqTkac57ogwijR7enCgksc7ouTVQ8Iob9CfrO98Jx4BtUrf8bZAtPX6TQdfiXcvxj1X40SojAH8emIjpCLzqST7HM6YYkjQ6mPuNgGjQVcvkZVK2ZDefpItGxiLz20+nLeHvlfiTGmvDc2DTVFTnAMicP6Fv1RNSI2ZAiGqA6903Y9nzOOy5SyPj5bAXeWrkPsdEGPDc2DQ2iDKIj+QXLnDyiiUtB5PC/QNemN+y7clC94W0oVovoWEQ39cv5K/j7p/sQZdLjhXFpiI8xio7kNyxz8pikN8I08AkY+42H68xBVK6ZDVfpz6JjEV3XmVIL5q/YB4Nei+fHpSGhgUl0JL9imZNXJEmCocNdiMyaCQCo+vxV2A/+D68apaBy7mIV5q3YB61Gwgvj0mCOixAdye9Y5lQn2qRWiBr5MrRN2sOWtwTWze9DcdpExyJCSXkV5i3fC0VR8Ny4NCQnRIqOFBAsc6ozyRSNiPumw9B9BJxHt6Mq5z8gXzonOhaFsQuXqzFv+V7YHS48NzYNTRpGiY4UMCxzqhdJ0sDYPRsRGX+EUnUJlTlz4Ph5l+hYFIbKr9gwf/k+VNlqivyWJO8fihzKWObkE7qmnRA56mVo4hvDunEhrNuXQ5F51SgFxmWLDfOW78XlKjuefaArmjeKER0p4Fjm5DOa6EREDpsJfce74fjhv1H9xRuQK8tFxyKVu1Jlx/wV+3DxihXTx3RF68axoiMJ4XGZWywWDB06FKdPn/ZnHgpxklYHU99HYLprClwXTtRcNVp8SHQsUilLtQNvrtiHkkvVeHpUF7S9JU50JGE8KvPCwkKMGzcOJ06c8HMcUgt9m96IHDEbkjEK1V++Adu+L3jVKPlUldWJtz7bh+KySvxhZGe0b5EgOpJQHpX5Z599htmzZyMpKcnfeUhFtPFNaq4abdkT9oJVsH79Dyi2StGxSAWsdifeXlmIX85b8LvhndC5VaLoSMJJihdXe9x1111YunQpmjZt6s9MpDKKoqBiVy7KvlkCXYOGSB71PIyNWoqORSHK6ZLxl//ajgM/l+GFh3ugb9fGoiMFBb/fzLeszAJZFnN1oNkcg9LSK0LGvpFgzAQEIFeLAYgcmoLqTe/izIcvwtT3EehvGyA+Vx0EYyYgfHJdsthw6nwFJme2R9vGdd93MM6X2RyDsjILEhO9P62SZ7NQwGgb3Vrz0ItGt8L63QewblkExWkXHYtCTFy0EW9O7YveHRuJjhJUWOYUUJqIBogY8hwMacPgOLwVVZ//FXJFiehYFGIkSRIdIeiwzCngJI0Gxp6jEHHfM5AtZahcMxuOE3tExyIKaV6V+bfffssPP8lndM1SETVyDjQNkmH9egFs338GRXaJjkUUknhkTkJpYsyIzJoJffs7YS/MRfWX8yBXXRYdiyjksMxJOElngKn/BJjufByukuM1V42eOyI6FlFIYZlT0NC37YvI4S8BOiOq178G+/4NfOgFkYdY5hRUtIm3IGrkbOiap8G2YwVK1syHYq8WHYso6LHMKehIhkiY7vkDjL0fQOXhAlTmzIHr4inRsYiCGsucgpIkSTB0GYKUh+cAdiuqcv4DjiPbRMciCloscwpqEc06InLUy9AmtYR18/uwbv2QV40SXQfLnIKeJjIOEZkvwNA1A45Dm1G1bi5ky0XRsYiCCsucQoKk0cJ4+/0wDZ4GueI8HEe55EL0W36/ayKRL+lbdIPu4XcAicchRL/FMqeQI+kMoiMQBR0e3hARqQDLnIhIBVjmREQqwDInIlIBljkRkQqwzImIVMDvpyZqNGKf1Sd6/OsJxkwAc3kjGDMBzOWtYMxV10ySwhtGExGFPC6zEBGpAMuciEgFWOZERCrAMideCc73AAAGzUlEQVQiUgGWORGRCrDMiYhUgGVORKQCLHMiIhVgmRMRqYBqnjS0a9cuzJ07Fw6HA02aNMHrr7+O2NjYa7YpLi5GZmYmmjVrBgBo2LAhFi1aJDyX3W7Hn//8ZxQVFcFkMmH+/Plo3bq1X3Pt3r0bc+fOhdPpRFxcHObOnYsmTZpcs02g58uTTCLm6lfvvPMONBoNnnrqqVo/E/He8iSXiPkqLi7G888/j7KyMrRs2RLz589HVFRUrW0CMV/r16/He++9B4fDgQkTJuChhx665ueHDh3CrFmzYLFY0KNHD7z88svQ6fxfi+5yLVy4EKtXr0aDBg0AAPfff3+tbWpRVGLQoEHK0aNHFUVRlHnz5ilvvvlmrW02bNigvPTSS0GX61//+tfVXAUFBcro0aP9nmvgwIHKoUOHFEVRlJUrVypTpkyptU2g58uTTCLmqqKiQnnxxReVLl26KAsWLLjuNiLeW57kEjFfTzzxhPLFF18oiqIoCxcuVN54441a2wRivs6dO6cMHDhQKS8vVyorK5Vhw4Zd/X/xV5mZmcrevXsVRVGUF198UVm2bJlfM3ma68knn1T27Nnj1X5Vs8ySm5uLNm3awOFw4Pz581d/o/3WDz/8gCNHjmDkyJEYP348Dh8+HBS5Nm/ejKysLABAz549UV5ejuLiYr9lstvtePrpp3HbbbcBANq1a4ezZ8/W2i6Q8+VppkDPFQBs2rQJLVq0wGOPPXbDbUS8tzzJFej5cjgc2LlzJ+69914AwMiRI7Fhw4Za2wVivvLz89G7d2/ExcUhMjIS99577zVZzpw5A6vVitTU1JtmDXQuACgqKsL777+PYcOG4ZVXXoHNZnO7X9WUuV6vx+HDh5Geno7vv/8emZmZtbYxGo0YPnw41qxZg0mTJmHq1Kmw2+3Cc5WUlMBsNl/92mw249y5c37LZDAYkJ2dDQCQZRkLFy7EoEGDam0XyPnyNFOg5woAhg8fjieeeAJarfaG24h4b3mSK9DzVV5ejujo6KtLFWazGefPn6+1XSDm6//+2ZOSkq7Jcr25uV5WX3OXq7KyEu3bt8ef/vQn5OTkoKKiAu+++67b/YbcmvlXX32Fv/3tb9d8r1WrVvjwww/Rrl075OfnY8WKFZg+fTpWrFhxzXa/XVNMT0/Hm2++iePHj189GhSV63o0Gt/8nr1ZLrvdjhkzZsDpdOLJJ5+s9Vp/zVd9Ml1PIObKHVHvrbrw53y1aNGi1naSVPuWrv6cr18p17kh7G+zuPu5v7gbNyoqCu+///7VrydOnIiZM2di+vTpN91vyJX5kCFDMGTIkGu+Z7PZ8M0331w9ksvKysLrr79e67UfffQRhg4divj4eAA1k+qrDzvqkyspKQmlpaVo3rw5AKC0tBRJSUl+ywXU/Pb/3e9+h7i4OLz33nvQ6/W1tvHXfNUnk4i58kSg31ueCvR8ORwO3H777XC5XNBqtTccz5/z9avk5GTs2rXr6tclJSXXZElOTsaFCxeufu3LualPruLiYuTn52P06NEAPJ8bVSyz6HQ6vPzyyygqKgJQc8TQrVu3Wtvt3LkTq1atAgAUFBRAlmW0atVKeK709HR8/vnnAGrOfjEajWjcuLHfcgHA888/j+bNm+Odd96BwWC47jaBni9PMomYK08Eeq48Fej50uv16NGjB3JzcwEAa9euxYABA2ptF4j5uuOOO7B9+3ZcvHgR1dXV+Prrr6/J0qRJExiNRuzevfumWX3NXS6TyYR58+bh1KlTUBQFy5Ytwz333ON+xz74cDYo7Ny5UxkxYoSSlZWlPP7448rZs2cVRVGUTz75RHn77bcVRan5FHnChAlKZmamMnLkyKtnTojOZbValRdeeEHJyMhQhg8frhQVFfk104EDB5S2bdsqGRkZSlZWlpKVlaVMnjy5Vq5AzpenmQI9V7+1YMGCa84aEf3e8iSXiPk6ffq08vDDDytDhgxRJk6cqFy6dKlWrkDN17p165TMzExl8ODByj//+U9FURRl8uTJyv79+xVFUZRDhw4po0aNUu677z7l2WefVWw2m19yeJtrw4YNV38+Y8YMj3LxSUNERCqgimUWIqJwxzInIlIBljkRkQqwzImIVIBlTkSkAiF30RBRfbRr1w5t27a95irITp064dVXXxWYiqj+WOYUdpYsWYKEhATRMYh8isssREQqwIuGKKxcb5nlgw8+QGJiosBURPXHZRYKO1xmITXiMgsRkQqwzImIVIBlTkSkAvwAlIhIBXhkTkSkAixzIiIVYJkTEakAy5yISAVY5kREKsAyJyJSAZY5EZEKsMyJiFTg/wMW5QNDIsf0iQAAAABJRU5ErkJggg==\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df_pivot.plot();"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 7\n", "<a name=\"task7\"></a>\n", "\n", "* Create a pivot table based on the NEST `df` data frame\n", "* Let the `x` axis show the number of nodes; display the values of the simulation time `\"Sim. Time / s\"` for the tasks per node and threas per task configurations\n", "* Please plot a bar plot\n", "* Done? [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 101, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 864x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df.pivot_table(\n", "    index=[\"Nodes\"],\n", "    columns=[\"Tasks/Node\", \"Threads/Task\"],\n", "    values=\"Sim. Time / s\",\n", ").plot(kind=\"bar\", figsize=(12, 4));"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "source": ["<a name=\"taskb\"></a>\n", "\n", "* Bonus task\n", "    - Same pivot table as before (that is, `x` with nodes, and columns for Tasks/Node and Threads/Task)\n", "    - But now, use `Sim. Time / s` and `Presim. Time / s` as values to show\n", "    - Show them as a stack of those two values inside the pivot table"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## The End\n", "\n", "* Pandas works on data frames\n", "* Slice frames to your likings\n", "* Plot frames\n", "    - Together with Matplotlib, Seaborn, others\n", "* Pivot tables are next level greatness\n", "* Remember: ***Pandas as early as possible!***\n", "* Thanks for being here! \ud83d\ude0d"]}, {"cell_type": "markdown", "metadata": {"exercise": "task"}, "source": ["<span class=\"feedback\">Tell me what you think about this tutorial! <a href=\"mailto:a.herten@fz-juelich.de\">a.herten@fz-juelich.de</a></span>\n", "\n", "Next slide: Further reading"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["## Further Reading\n", "\n", "* [Pandas User Guide](https://pandas.pydata.org/pandas-docs/stable/user_guide/index.html)\n", "* [Matplotlib and LaTeX Plots](http://sbillaudelle.de/2015/02/23/seamlessly-embedding-matplotlib-output-into-latex.html)\n", "* towardsdatascience.com:\n", "    * [Pandas DataFrame: A lightweight Intro](https://towardsdatascience.com/pandas-dataframe-a-lightweight-intro-680e3a212b96)\n", "    * [Introduction to Data Visualization in Python](https://towardsdatascience.com/introduction-to-data-visualization-in-python-89a54c97fbed)\n", "    * [Basic Time Series Manipulation with Pandas](https://towardsdatascience.com/basic-time-series-manipulation-with-pandas-4432afee64ea)\n", "    * [An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning](https://towardsdatascience.com/an-introduction-to-scikit-learn-the-gold-standard-of-python-machine-learning-e2b9238a98ab)\n", "    * [Mapping with Matplotlib, Pandas, Geopandas and Basemap in Python](https://towardsdatascience.com/mapping-with-matplotlib-pandas-geopandas-and-basemap-in-python-d11b57ab5dac)"]}, {"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "subslide"}}, "source": ["## Poll Results\n", "\n", "![Poll Results](img/poll-results.png)"]}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.2"}}, "nbformat": 4, "nbformat_minor": 2}
\ No newline at end of file
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "tags": [
+     "task"
+    ]
+   },
+   "source": [
+    "# Data Analysis and Plotting in Python with Pandas\n",
+    "\n",
+    "_Andreas Herten, J\u00fclich Supercomputing Centre, Forschungszentrum J\u00fclich, 27 May 2021_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "onlypresentation",
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Version: Slides**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## My Motivation\n",
+    "\n",
+    "* I like Python\n",
+    "* I like plotting data\n",
+    "* I like sharing\n",
+    "* I think Pandas is awesome and you should use it too\n",
+    "* \u2026_but I'm no Python expert!_\n",
+    "\n",
+    "<span style=\"color: #023d6b\"><em>Motto: <strong>\u00bbPandas as early as possible!\u00ab</strong></em></span>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "## Task Outline\n",
+    "\n",
+    "* [Task 1](#task1)\n",
+    "* [Task 2](#task2)\n",
+    "* [Task 3](#task3)\n",
+    "* [Task 4](#task4)\n",
+    "* [Task 5](#task5)\n",
+    "* [Task 6](#task6)\n",
+    "* [Task 7](#task7)\n",
+    "* [Bonus Task](#taskb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Tutorial Setup\n",
+    "\n",
+    "* 3 hours, including break around 10:30\n",
+    "* Alternating between lecture and hands-on\n",
+    "* Please give status of hands-ons via \ud83d\udc4d as BigBlueButton status"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* Please now open Jupyter Notebook of this session: https://go.fzj.de/jsc-pd21\n",
+    "* Give thumbs up! \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## About Pandas\n",
+    "\n",
+    "<img style=\"float: right; max-width: 200px;\" width=\"200px\" src=\"img/adorable-animal-animal-photography-1661535.jpg\" />\n",
+    "\n",
+    "* Python package (~~Python 2,~~ Python 3)\n",
+    "* For data analysis and manipulation\n",
+    "* With data structures (multi-dimensional table; time series), operations\n",
+    "* Name from \u00bb**Pan**el **Da**ta\u00ab\u00a0(multi-dimensional time series in economics)\n",
+    "* Since 2008\n",
+    "* https://pandas.pydata.org/\n",
+    "* Install [via PyPI](https://pypi.org/project/pandas/): `pip install pandas`\n",
+    "* *Cheatsheet: https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Pandas Cohabitation\n",
+    "\n",
+    "* Pandas works great together with other established Python tools\n",
+    "    * [Jupyter Notebooks](https://jupyter.org/)\n",
+    "    * Plotting with [`matplotlib`](https://matplotlib.org/)\n",
+    "    * Numerical analysis with [`numpy`](https://numpy.org/)\n",
+    "    * Modelling with [`statsmodels`](https://www.statsmodels.org/stable/index.html), [`scikit-learn`](https://scikit-learn.org/)\n",
+    "    * Nicer plots with [`seaborn`](https://seaborn.pydata.org/), [`altair`](https://altair-viz.github.io/), [`plotly`](https://plot.ly/)\n",
+    "    * Performance enhancement with [Cython](https://cython.org/), [Numba](numba.pydata.org/), \u2026\n",
+    "* Tools building up on Pandas: [cuDF](https://github.com/rapidsai/cudf) (GPU-accelerated DataFrames in [Rapids](https://rapids.ai/)), \u2026"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## First Steps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.2.4'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mClass docstring:\u001b[0m\n",
+       "    pandas - a powerful data analysis and manipulation library for Python\n",
+       "    =====================================================================\n",
+       "    \n",
+       "    **pandas** is a Python package providing fast, flexible, and expressive data\n",
+       "    structures designed to make working with \"relational\" or \"labeled\" data both\n",
+       "    easy and intuitive. It aims to be the fundamental high-level building block for\n",
+       "    doing practical, **real world** data analysis in Python. Additionally, it has\n",
+       "    the broader goal of becoming **the most powerful and flexible open source data\n",
+       "    analysis / manipulation tool available in any language**. It is already well on\n",
+       "    its way toward this goal.\n",
+       "    \n",
+       "    Main Features\n",
+       "    -------------\n",
+       "    Here are just a few of the things that pandas does well:\n",
+       "    \n",
+       "      - Easy handling of missing data in floating point as well as non-floating\n",
+       "        point data.\n",
+       "      - Size mutability: columns can be inserted and deleted from DataFrame and\n",
+       "        higher dimensional objects\n",
+       "      - Automatic and explicit data alignment: objects can be explicitly aligned\n",
+       "        to a set of labels, or the user can simply ignore the labels and let\n",
+       "        `Series`, `DataFrame`, etc. automatically align the data for you in\n",
+       "        computations.\n",
+       "      - Powerful, flexible group by functionality to perform split-apply-combine\n",
+       "        operations on data sets, for both aggregating and transforming data.\n",
+       "      - Make it easy to convert ragged, differently-indexed data in other Python\n",
+       "        and NumPy data structures into DataFrame objects.\n",
+       "      - Intelligent label-based slicing, fancy indexing, and subsetting of large\n",
+       "        data sets.\n",
+       "      - Intuitive merging and joining data sets.\n",
+       "      - Flexible reshaping and pivoting of data sets.\n",
+       "      - Hierarchical labeling of axes (possible to have multiple labels per tick).\n",
+       "      - Robust IO tools for loading data from flat files (CSV and delimited),\n",
+       "        Excel files, databases, and saving/loading data from the ultrafast HDF5\n",
+       "        format.\n",
+       "      - Time series-specific functionality: date range generation and frequency\n",
+       "        conversion, moving window statistics, date shifting and lagging."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%pdoc pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## DataFrames\n",
+    "### It's all about DataFrames\n",
+    "\n",
+    "<img style=\"float: right; max-width: 200px;\" width=\"200px\" src=\"img/buzz-dataframes.jpg\" />\n",
+    "\n",
+    "* Data containers of Pandas:\n",
+    "    - Linear: `Series`\n",
+    "    - Multi Dimension: `DataFrame`\n",
+    "* `Series` is *only* special (1D) case of `DataFrame`\n",
+    "* \u2192 We use `DataFrame`s as the more general case here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## DataFrames\n",
+    "### Construction\n",
+    "\n",
+    "* To show features of `DataFrame`, let's construct one and show by example!\n",
+    "* Many construction possibilities\n",
+    "    - From lists, dictionaries, `numpy` objects\n",
+    "    - From CSV, HDF5, JSON, Excel, HTML, fixed-width files\n",
+    "    - From pickled Pandas data\n",
+    "    - From clipboard\n",
+    "    - *From Feather, Parquest, SAS, SQL, Google BigQuery, STATA*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## DataFrames\n",
+    "\n",
+    "### Examples, finally"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ages  = [41, 56, 56, 57, 39, 59, 43, 56, 38, 60]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    0\n",
+       "0  41\n",
+       "1  56\n",
+       "2  56\n",
+       "3  57\n",
+       "4  39\n",
+       "5  59\n",
+       "6  43\n",
+       "7  56\n",
+       "8  38\n",
+       "9  60"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(ages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    0\n",
+       "0  41\n",
+       "1  56\n",
+       "2  56"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_ages = pd.DataFrame(ages)\n",
+    "df_ages.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "* Let's add names to ages; put everything into a `dict()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Name': ['Liu', 'Rowland', 'Rivers', 'Waters', 'Rice', 'Fields', 'Kerr', 'Romero', 'Davis', 'Hall'], 'Age': [41, 56, 56, 57, 39, 59, 43, 56, 38, 60]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = {\n",
+    "    \"Name\": [\"Liu\", \"Rowland\", \"Rivers\", \"Waters\", \"Rice\", \"Fields\", \"Kerr\", \"Romero\", \"Davis\", \"Hall\"],\n",
+    "    \"Age\": ages\n",
+    "}\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Liu</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Rowland</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Rivers</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Waters</td>\n",
+       "      <td>57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Name  Age\n",
+       "0      Liu   41\n",
+       "1  Rowland   56\n",
+       "2   Rivers   56\n",
+       "3   Waters   57"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_sample = pd.DataFrame(data)\n",
+    "df_sample.head(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* Automatically creates columns from dictionary\n",
+    "* Two columns now; one for names, one for ages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Name', 'Age'], dtype='object')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_sample.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "* First column is _index_\n",
+    "* `DataFrame` always have indexes; auto-generated or custom"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=10, step=1)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_sample.index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* Make `Name` be index with `.set_index()`\n",
+    "* `inplace=True` will modifiy the parent frame (*I don't like it*)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Liu</th>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rowland</th>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rivers</th>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Waters</th>\n",
+       "      <td>57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rice</th>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fields</th>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Kerr</th>\n",
+       "      <td>43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Romero</th>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Davis</th>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hall</th>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Age\n",
+       "Name        \n",
+       "Liu       41\n",
+       "Rowland   56\n",
+       "Rivers    56\n",
+       "Waters    57\n",
+       "Rice      39\n",
+       "Fields    59\n",
+       "Kerr      43\n",
+       "Romero    56\n",
+       "Davis     38\n",
+       "Hall      60"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_sample.set_index(\"Name\", inplace=True)\n",
+    "df_sample"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "* Some more operations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>10.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>50.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>9.009255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>38.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>41.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>56.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>56.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>60.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Age\n",
+       "count  10.000000\n",
+       "mean   50.500000\n",
+       "std     9.009255\n",
+       "min    38.000000\n",
+       "25%    41.500000\n",
+       "50%    56.000000\n",
+       "75%    56.750000\n",
+       "max    60.000000"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_sample.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Name</th>\n",
+       "      <th>Liu</th>\n",
+       "      <th>Rowland</th>\n",
+       "      <th>Rivers</th>\n",
+       "      <th>Waters</th>\n",
+       "      <th>Rice</th>\n",
+       "      <th>Fields</th>\n",
+       "      <th>Kerr</th>\n",
+       "      <th>Romero</th>\n",
+       "      <th>Davis</th>\n",
+       "      <th>Hall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>41</td>\n",
+       "      <td>56</td>\n",
+       "      <td>56</td>\n",
+       "      <td>57</td>\n",
+       "      <td>39</td>\n",
+       "      <td>59</td>\n",
+       "      <td>43</td>\n",
+       "      <td>56</td>\n",
+       "      <td>38</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Name  Liu  Rowland  Rivers  Waters  Rice  Fields  Kerr  Romero  Davis  Hall\n",
+       "Age    41       56      56      57    39      59    43      56     38    60"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_sample.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Liu', 'Rowland', 'Rivers', 'Waters', 'Rice', 'Fields', 'Kerr',\n",
+       "       'Romero', 'Davis', 'Hall'],\n",
+       "      dtype='object', name='Name')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_sample.T.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "* Also: Arithmetic operations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Liu</th>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rowland</th>\n",
+       "      <td>112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rivers</th>\n",
+       "      <td>112</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Age\n",
+       "Name        \n",
+       "Liu       82\n",
+       "Rowland  112\n",
+       "Rivers   112"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_sample.multiply(2).head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LiuLiu</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>RowlandRowland</td>\n",
+       "      <td>112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>RiversRivers</td>\n",
+       "      <td>112</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Name  Age\n",
+       "0          LiuLiu   82\n",
+       "1  RowlandRowland  112\n",
+       "2    RiversRivers  112"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_sample.reset_index().multiply(2).head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Liu</th>\n",
+       "      <td>20.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rowland</th>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rivers</th>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Age\n",
+       "Name         \n",
+       "Liu      20.5\n",
+       "Rowland  28.0\n",
+       "Rivers   28.0"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(df_sample / 2).head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Liu</th>\n",
+       "      <td>1681</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rowland</th>\n",
+       "      <td>3136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rivers</th>\n",
+       "      <td>3136</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Age\n",
+       "Name         \n",
+       "Liu      1681\n",
+       "Rowland  3136\n",
+       "Rivers   3136"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(df_sample * df_sample).head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Logical operations allowed as well"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Liu</th>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rowland</th>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rivers</th>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Waters</th>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rice</th>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fields</th>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Kerr</th>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Romero</th>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Davis</th>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hall</th>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Age\n",
+       "Name          \n",
+       "Liu       True\n",
+       "Rowland   True\n",
+       "Rivers    True\n",
+       "Waters    True\n",
+       "Rice     False\n",
+       "Fields    True\n",
+       "Kerr      True\n",
+       "Romero    True\n",
+       "Davis    False\n",
+       "Hall      True"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_sample > 40"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Task 1\n",
+    "<a name=\"task1\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "* Create data frame with\n",
+    "    - 6 names of dinosaurs, \n",
+    "    - their favourite prime number, \n",
+    "    - and their favorite color.\n",
+    "* Play around with the frame\n",
+    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "happy_dinos = {\n",
+    "    \"Dinosaur Name\": [],\n",
+    "    \"Favourite Prime\": [],\n",
+    "    \"Favourite Color\": []\n",
+    "}\n",
+    "#df_dinos = "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Dinosaur Name</th>\n",
+       "      <th>Aegyptosaurus</th>\n",
+       "      <th>Tyrannosaurus</th>\n",
+       "      <th>Panoplosaurus</th>\n",
+       "      <th>Isisaurus</th>\n",
+       "      <th>Triceratops</th>\n",
+       "      <th>Velociraptor</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Favourite Prime</th>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>15</td>\n",
+       "      <td>16</td>\n",
+       "      <td>23</td>\n",
+       "      <td>42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Favourite Color</th>\n",
+       "      <td>blue</td>\n",
+       "      <td>white</td>\n",
+       "      <td>blue</td>\n",
+       "      <td>purple</td>\n",
+       "      <td>violet</td>\n",
+       "      <td>gray</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Dinosaur Name   Aegyptosaurus Tyrannosaurus Panoplosaurus Isisaurus  \\\n",
+       "Favourite Prime             4             8            15        16   \n",
+       "Favourite Color          blue         white          blue    purple   \n",
+       "\n",
+       "Dinosaur Name   Triceratops Velociraptor  \n",
+       "Favourite Prime          23           42  \n",
+       "Favourite Color      violet         gray  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "happy_dinos = {\n",
+    "    \"Dinosaur Name\": [\"Aegyptosaurus\", \"Tyrannosaurus\", \"Panoplosaurus\", \"Isisaurus\", \"Triceratops\", \"Velociraptor\"],\n",
+    "    \"Favourite Prime\": [\"4\", \"8\", \"15\", \"16\", \"23\", \"42\"],\n",
+    "    \"Favourite Color\": [\"blue\", \"white\", \"blue\", \"purple\", \"violet\", \"gray\"]\n",
+    "}\n",
+    "df_dinos = pd.DataFrame(happy_dinos).set_index(\"Dinosaur Name\")\n",
+    "df_dinos.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Some more `DataFrame` examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-2.718282</td>\n",
+       "      <td>This</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>1.718282</td>\n",
+       "      <td>column</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-1.304068</td>\n",
+       "      <td>has</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>0.986231</td>\n",
+       "      <td>entries</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-0.718282</td>\n",
+       "      <td>entries</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A          B         C        D     E\n",
+       "0  1.2 2018-02-26 -2.718282     This  Same\n",
+       "1  1.2 2018-02-26  1.718282   column  Same\n",
+       "2  1.2 2018-02-26 -1.304068      has  Same\n",
+       "3  1.2 2018-02-26  0.986231  entries  Same\n",
+       "4  1.2 2018-02-26 -0.718282  entries  Same"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo = pd.DataFrame({\n",
+    "    \"A\": 1.2,\n",
+    "    \"B\": pd.Timestamp('20180226'),\n",
+    "    \"C\": [(-1)**i * np.sqrt(i) + np.e * (-1)**(i-1) for i in range(5)],\n",
+    "    \"D\": pd.Categorical([\"This\", \"column\", \"has\", \"entries\", \"entries\"]),\n",
+    "    \"E\": \"Same\"\n",
+    "})\n",
+    "df_demo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-2.718282</td>\n",
+       "      <td>This</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-1.304068</td>\n",
+       "      <td>has</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-0.718282</td>\n",
+       "      <td>entries</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>0.986231</td>\n",
+       "      <td>entries</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>1.718282</td>\n",
+       "      <td>column</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A          B         C        D     E\n",
+       "0  1.2 2018-02-26 -2.718282     This  Same\n",
+       "2  1.2 2018-02-26 -1.304068      has  Same\n",
+       "4  1.2 2018-02-26 -0.718282  entries  Same\n",
+       "3  1.2 2018-02-26  0.986231  entries  Same\n",
+       "1  1.2 2018-02-26  1.718282   column  Same"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo.sort_values(\"C\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>entries</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-0.72</td>\n",
+       "      <td>entries</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A          B     C        D     E\n",
+       "3  1.2 2018-02-26  0.99  entries  Same\n",
+       "4  1.2 2018-02-26 -0.72  entries  Same"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo.round(2).tail(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "A                     6.0\n",
+       "C                   -2.03\n",
+       "E    SameSameSameSameSame\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo.round(2).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{lrlrll}\n",
+      "\\toprule\n",
+      "{} &    A &          B &     C &        D &     E \\\\\n",
+      "\\midrule\n",
+      "0 &  1.2 & 2018-02-26 & -2.72 &     This &  Same \\\\\n",
+      "1 &  1.2 & 2018-02-26 &  1.72 &   column &  Same \\\\\n",
+      "2 &  1.2 & 2018-02-26 & -1.30 &      has &  Same \\\\\n",
+      "3 &  1.2 & 2018-02-26 &  0.99 &  entries &  Same \\\\\n",
+      "4 &  1.2 & 2018-02-26 & -0.72 &  entries &  Same \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df_demo.round(2).to_latex())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Reading External Data\n",
+    "\n",
+    "(Links to documentation)\n",
+    "* [`.read_json()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_json.html#pandas.read_json)\n",
+    "* [`.read_csv()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html#pandas.read_csv)\n",
+    "* [`.read_hdf5()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_hdf.html#pandas.read_hdf)\n",
+    "* [`.read_excel()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html#pandas.read_excel)\n",
+    "\n",
+    "Example:\n",
+    "\n",
+    "```json\n",
+    "{\n",
+    "    \"Character\": [\"Sawyer\", \"\u2026\", \"Walt\"],\n",
+    "    \"Actor\": [\"Josh Holloway\", \"\u2026\", \"Malcolm David Kelley\"],\n",
+    "    \"Main Cast\": [true,  \"\u2026\", false]\n",
+    "}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Actor</th>\n",
+       "      <th>Main Cast</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Character</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Hurley</th>\n",
+       "      <td>Jorge Garcia</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Jack</th>\n",
+       "      <td>Matthew Fox</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Kate</th>\n",
+       "      <td>Evangeline Lilly</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Locke</th>\n",
+       "      <td>Terry O'Quinn</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sawyer</th>\n",
+       "      <td>Josh Holloway</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Walt</th>\n",
+       "      <td>Malcolm David Kelley</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          Actor  Main Cast\n",
+       "Character                                 \n",
+       "Hurley             Jorge Garcia       True\n",
+       "Jack                Matthew Fox       True\n",
+       "Kate           Evangeline Lilly       True\n",
+       "Locke             Terry O'Quinn       True\n",
+       "Sawyer            Josh Holloway       True\n",
+       "Walt       Malcolm David Kelley      False"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_json(\"data-lost.json\").set_index(\"Character\").sort_index()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Task 2\n",
+    "<a name=\"task2\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "* Read in `data-nest.csv` to `DataFrame`; call it `df`  \n",
+    "  *(Data was produced with [JUBE](http://www.fz-juelich.de/ias/jsc/EN/Expertise/Support/Software/JUBE/_node.html))*\n",
+    "* Get to know it and play a bit with it\n",
+    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "exercise": "task"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "id,Nodes,Tasks/Node,Threads/Task,Runtime Program / s,Scale,Plastic,Avg. Neuron Build Time / s,Min. Edge Build Time / s,Max. Edge Build Time / s,Min. Init. Time / s,Max. Init. Time / s,Presim. Time / s,Sim. Time / s,Virt. Memory (Sum) / kB,Local Spike Counter (Sum),Average Rate (Sum),Number of Neurons,Number of Connections,Min. Delay,Max. Delay\n",
+      "5,1,2,4,420.42,10,true,0.29,88.12,88.18,1.14,1.20,17.26,311.52,46560664.00,825499,7.48,112500,1265738500,1.5,1.5\n",
+      "5,1,4,4,200.84,10,true,0.15,46.03,46.34,0.70,1.01,7.87,142.97,46903088.00,802865,7.03,112500,1265738500,1.5,1.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat nest-data.csv | head -3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>Nodes</th>\n",
+       "      <th>Tasks/Node</th>\n",
+       "      <th>Threads/Task</th>\n",
+       "      <th>Runtime Program / s</th>\n",
+       "      <th>Scale</th>\n",
+       "      <th>Plastic</th>\n",
+       "      <th>Avg. Neuron Build Time / s</th>\n",
+       "      <th>Min. Edge Build Time / s</th>\n",
+       "      <th>Max. Edge Build Time / s</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Max. Init. Time / s</th>\n",
+       "      <th>Presim. Time / s</th>\n",
+       "      <th>Sim. Time / s</th>\n",
+       "      <th>Virt. Memory (Sum) / kB</th>\n",
+       "      <th>Local Spike Counter (Sum)</th>\n",
+       "      <th>Average Rate (Sum)</th>\n",
+       "      <th>Number of Neurons</th>\n",
+       "      <th>Number of Connections</th>\n",
+       "      <th>Min. Delay</th>\n",
+       "      <th>Max. Delay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>420.42</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>88.12</td>\n",
+       "      <td>88.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>17.26</td>\n",
+       "      <td>311.52</td>\n",
+       "      <td>46560664.0</td>\n",
+       "      <td>825499</td>\n",
+       "      <td>7.48</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>200.84</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>46.03</td>\n",
+       "      <td>46.34</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.01</td>\n",
+       "      <td>7.87</td>\n",
+       "      <td>142.97</td>\n",
+       "      <td>46903088.0</td>\n",
+       "      <td>802865</td>\n",
+       "      <td>7.03</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>202.15</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>47.98</td>\n",
+       "      <td>48.48</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>7.95</td>\n",
+       "      <td>142.81</td>\n",
+       "      <td>47699384.0</td>\n",
+       "      <td>802865</td>\n",
+       "      <td>7.03</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>89.57</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>20.41</td>\n",
+       "      <td>23.21</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.04</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>60.31</td>\n",
+       "      <td>46813040.0</td>\n",
+       "      <td>821491</td>\n",
+       "      <td>7.23</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>164.16</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>40.03</td>\n",
+       "      <td>41.09</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.58</td>\n",
+       "      <td>6.08</td>\n",
+       "      <td>114.88</td>\n",
+       "      <td>46937216.0</td>\n",
+       "      <td>802865</td>\n",
+       "      <td>7.03</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows \u00d7 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id  Nodes  Tasks/Node  Threads/Task  Runtime Program / s  Scale  Plastic  \\\n",
+       "0   5      1           2             4               420.42     10     True   \n",
+       "1   5      1           4             4               200.84     10     True   \n",
+       "2   5      1           2             8               202.15     10     True   \n",
+       "3   5      1           4             8                89.57     10     True   \n",
+       "4   5      2           2             4               164.16     10     True   \n",
+       "\n",
+       "   Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n",
+       "0                        0.29                     88.12   \n",
+       "1                        0.15                     46.03   \n",
+       "2                        0.28                     47.98   \n",
+       "3                        0.15                     20.41   \n",
+       "4                        0.20                     40.03   \n",
+       "\n",
+       "   Max. Edge Build Time / s  ...  Max. Init. Time / s  Presim. Time / s  \\\n",
+       "0                     88.18  ...                 1.20             17.26   \n",
+       "1                     46.34  ...                 1.01              7.87   \n",
+       "2                     48.48  ...                 1.20              7.95   \n",
+       "3                     23.21  ...                 3.04              3.19   \n",
+       "4                     41.09  ...                 1.58              6.08   \n",
+       "\n",
+       "   Sim. Time / s  Virt. Memory (Sum) / kB  Local Spike Counter (Sum)  \\\n",
+       "0         311.52               46560664.0                     825499   \n",
+       "1         142.97               46903088.0                     802865   \n",
+       "2         142.81               47699384.0                     802865   \n",
+       "3          60.31               46813040.0                     821491   \n",
+       "4         114.88               46937216.0                     802865   \n",
+       "\n",
+       "   Average Rate (Sum)  Number of Neurons  Number of Connections  Min. Delay  \\\n",
+       "0                7.48             112500             1265738500         1.5   \n",
+       "1                7.03             112500             1265738500         1.5   \n",
+       "2                7.03             112500             1265738500         1.5   \n",
+       "3                7.23             112500             1265738500         1.5   \n",
+       "4                7.03             112500             1265738500         1.5   \n",
+       "\n",
+       "   Max. Delay  \n",
+       "0         1.5  \n",
+       "1         1.5  \n",
+       "2         1.5  \n",
+       "3         1.5  \n",
+       "4         1.5  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"data-nest.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Read CSV Options\n",
+    "\n",
+    "* See also full [API documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)\n",
+    "* Important parameters\n",
+    "    - `sep`: Set separator (for example `:` instead of `,`)\n",
+    "    - `header`: Specify info about headers for columns; able to use multi-index for columns!\n",
+    "    - `names`: Alternative to `header` \u2013\u00a0provide your own column titles\n",
+    "    - `usecols`: Don't read whole set of columns, but only these; works with any list (`range(0:20:2)`)\u2026\n",
+    "    - `skiprows`: Don't read in these rows\n",
+    "    - `na_values`: What string(s) to recognize as `N/A` values (which will be ignored during operations on data frame)\n",
+    "    - `parse_dates`: Try to parse dates in CSV; different behaviours as to provided data structure; optionally used together with `date_parser`\n",
+    "    - `compression`: Treat input file as compressed file (\"infer\", \"gzip\", \"zip\", \u2026)\n",
+    "    - `decimal`: Decimal point divider \u2013\u00a0for German data\u2026\n",
+    "    \n",
+    "```python\n",
+    "pandas.read_csv(filepath_or_buffer, sep=<object object>, delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='\"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, dialect=None, error_bad_lines=True, warn_bad_lines=True, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None, storage_options=None)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Slicing of Data Frames\n",
+    "\n",
+    "* Pandas documentation: [Detailed documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html), [short documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html#selection)\n",
+    "\n",
+    "### Quick Slices\n",
+    "\n",
+    "* Use square-bracket operators to slice data frame quickly: `[]`\n",
+    "    * Use column name to select column\n",
+    "    * Use numerical value to select row\n",
+    "* Example: Select only columnn `C` from `df_demo`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-2.718282</td>\n",
+       "      <td>This</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>1.718282</td>\n",
+       "      <td>column</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-1.304068</td>\n",
+       "      <td>has</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A          B         C       D     E\n",
+       "0  1.2 2018-02-26 -2.718282    This  Same\n",
+       "1  1.2 2018-02-26  1.718282  column  Same\n",
+       "2  1.2 2018-02-26 -1.304068     has  Same"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0   -2.718282\n",
+       "1    1.718282\n",
+       "2   -1.304068\n",
+       "3    0.986231\n",
+       "4   -0.718282\n",
+       "Name: C, dtype: float64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo['C']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0   -2.718282\n",
+       "1    1.718282\n",
+       "2   -1.304068\n",
+       "3    0.986231\n",
+       "4   -0.718282\n",
+       "Name: C, dtype: float64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo.C"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "* Select more than one column by providing `list` to slice operator `[]`\n",
+    "* Example: Select list of columns `A` and `C`, `['A', 'C']` from `df_demo`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>-2.718282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>1.718282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>-1.304068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>0.986231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>-0.718282</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A         C\n",
+       "0  1.2 -2.718282\n",
+       "1  1.2  1.718282\n",
+       "2  1.2 -1.304068\n",
+       "3  1.2  0.986231\n",
+       "4  1.2 -0.718282"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_slice = ['A', 'C']\n",
+    "df_demo[my_slice]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "* Use numerical values in brackets to slice along rows\n",
+    "* Use ranges just like with Python lists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>1.718282</td>\n",
+       "      <td>column</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-1.304068</td>\n",
+       "      <td>has</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A          B         C       D     E\n",
+       "1  1.2 2018-02-26  1.718282  column  Same\n",
+       "2  1.2 2018-02-26 -1.304068     has  Same"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo[1:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>1.718282</td>\n",
+       "      <td>column</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>0.986231</td>\n",
+       "      <td>entries</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A          B         C        D     E\n",
+       "1  1.2 2018-02-26  1.718282   column  Same\n",
+       "3  1.2 2018-02-26  0.986231  entries  Same"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo[1:6:2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "* Attention: location might change after re-sorting!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>1.718282</td>\n",
+       "      <td>column</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-1.304068</td>\n",
+       "      <td>has</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A          B         C       D     E\n",
+       "1  1.2 2018-02-26  1.718282  column  Same\n",
+       "2  1.2 2018-02-26 -1.304068     has  Same"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo[1:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-1.304068</td>\n",
+       "      <td>has</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-0.718282</td>\n",
+       "      <td>entries</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A          B         C        D     E\n",
+       "2  1.2 2018-02-26 -1.304068      has  Same\n",
+       "4  1.2 2018-02-26 -0.718282  entries  Same"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo.sort_values(\"C\")[1:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Slicing of Data Frames\n",
+    "\n",
+    "### Better Slicing\n",
+    "\n",
+    "* `.iloc[]` and `.loc[]`: Faster slicing interfaces with more options"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>1.718282</td>\n",
+       "      <td>column</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-1.304068</td>\n",
+       "      <td>has</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A          B         C       D     E\n",
+       "1  1.2 2018-02-26  1.718282  column  Same\n",
+       "2  1.2 2018-02-26 -1.304068     has  Same"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo.iloc[1:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
+   "source": [
+    "* Also slice rows (second argument)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>1.718282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>-1.304068</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A         C\n",
+       "1  1.2  1.718282\n",
+       "2  1.2 -1.304068"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo.iloc[1:3, [0, 2]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "* `.iloc[]`: Slice by **position** (_numerical/integer_)\n",
+    "* `.loc[]`: Slice by **label** (_named_)\n",
+    "* See difference with a *proper* index (and not the auto-generated default index from before)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>E</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>This</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-2.718282</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>column</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>1.718282</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>has</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-1.304068</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>entries</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>0.986231</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>entries</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-0.718282</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           A          B         C     E\n",
+       "D                                      \n",
+       "This     1.2 2018-02-26 -2.718282  Same\n",
+       "column   1.2 2018-02-26  1.718282  Same\n",
+       "has      1.2 2018-02-26 -1.304068  Same\n",
+       "entries  1.2 2018-02-26  0.986231  Same\n",
+       "entries  1.2 2018-02-26 -0.718282  Same"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo_indexed = df_demo.set_index(\"D\")\n",
+    "df_demo_indexed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>E</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>entries</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>0.986231</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>entries</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-0.718282</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           A          B         C     E\n",
+       "D                                      \n",
+       "entries  1.2 2018-02-26  0.986231  Same\n",
+       "entries  1.2 2018-02-26 -0.718282  Same"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo_indexed.loc[\"entries\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>has</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>-1.304068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>entries</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>0.986231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>entries</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>-0.718282</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           A         C\n",
+       "D                     \n",
+       "has      1.2 -1.304068\n",
+       "entries  1.2  0.986231\n",
+       "entries  1.2 -0.718282"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo_indexed.loc[[\"has\", \"entries\"], [\"A\", \"C\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Slicing of Data Frames\n",
+    "### Advanced Slicing: Logical Slicing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Slice can also be array of booleans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>1.718282</td>\n",
+       "      <td>column</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>0.986231</td>\n",
+       "      <td>entries</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A          B         C        D     E\n",
+       "1  1.2 2018-02-26  1.718282   column  Same\n",
+       "3  1.2 2018-02-26  0.986231  entries  Same"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo[df_demo[\"C\"] > 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    False\n",
+       "1     True\n",
+       "2    False\n",
+       "3     True\n",
+       "4    False\n",
+       "Name: C, dtype: bool"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo[\"C\"] > 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-0.718282</td>\n",
+       "      <td>entries</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A          B         C        D     E\n",
+       "4  1.2 2018-02-26 -0.718282  entries  Same"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo[(df_demo[\"C\"] < 0) & (df_demo[\"D\"] == \"entries\")]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Adding to Existing Data Frame\n",
+    "\n",
+    "* Add new columns with `frame[\"new col\"] = something` or `.insert()`\n",
+    "* Add new rows with `frame.append()`\n",
+    "* Combine data frames\n",
+    "    - *Concat*: Combine several data frames along an axis\n",
+    "    - *Merge*: Combine data frames on basis of common columns; database-style\n",
+    "    - (Join)\n",
+    "    - See user guide [on merging](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-2.718282</td>\n",
+       "      <td>This</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>1.718282</td>\n",
+       "      <td>column</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-1.304068</td>\n",
+       "      <td>has</td>\n",
+       "      <td>Same</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A          B         C       D     E\n",
+       "0  1.2 2018-02-26 -2.718282    This  Same\n",
+       "1  1.2 2018-02-26  1.718282  column  Same\n",
+       "2  1.2 2018-02-26 -1.304068     has  Same"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "      <th>F</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-2.718282</td>\n",
+       "      <td>This</td>\n",
+       "      <td>Same</td>\n",
+       "      <td>-3.918282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>1.718282</td>\n",
+       "      <td>column</td>\n",
+       "      <td>Same</td>\n",
+       "      <td>0.518282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-1.304068</td>\n",
+       "      <td>has</td>\n",
+       "      <td>Same</td>\n",
+       "      <td>-2.504068</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A          B         C       D     E         F\n",
+       "0  1.2 2018-02-26 -2.718282    This  Same -3.918282\n",
+       "1  1.2 2018-02-26  1.718282  column  Same  0.518282\n",
+       "2  1.2 2018-02-26 -1.304068     has  Same -2.504068"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo[\"F\"] = df_demo[\"C\"] - df_demo[\"A\"]\n",
+    "df_demo.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_demo.insert(df_demo.shape[1] - 1, \"E2\", df_demo[\"C\"] ** 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "      <th>E2</th>\n",
+       "      <th>F</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-1.304068</td>\n",
+       "      <td>has</td>\n",
+       "      <td>Same</td>\n",
+       "      <td>1.700594</td>\n",
+       "      <td>-2.504068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>0.986231</td>\n",
+       "      <td>entries</td>\n",
+       "      <td>Same</td>\n",
+       "      <td>0.972652</td>\n",
+       "      <td>-0.213769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-0.718282</td>\n",
+       "      <td>entries</td>\n",
+       "      <td>Same</td>\n",
+       "      <td>0.515929</td>\n",
+       "      <td>-1.918282</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A          B         C        D     E        E2         F\n",
+       "2  1.2 2018-02-26 -1.304068      has  Same  1.700594 -2.504068\n",
+       "3  1.2 2018-02-26  0.986231  entries  Same  0.972652 -0.213769\n",
+       "4  1.2 2018-02-26 -0.718282  entries  Same  0.515929 -1.918282"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo.tail(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>E</th>\n",
+       "      <th>E2</th>\n",
+       "      <th>F</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-2.718282</td>\n",
+       "      <td>This</td>\n",
+       "      <td>Same</td>\n",
+       "      <td>7.389056</td>\n",
+       "      <td>-3.918282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>1.718282</td>\n",
+       "      <td>column</td>\n",
+       "      <td>Same</td>\n",
+       "      <td>2.952492</td>\n",
+       "      <td>0.518282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-1.304068</td>\n",
+       "      <td>has</td>\n",
+       "      <td>Same</td>\n",
+       "      <td>1.700594</td>\n",
+       "      <td>-2.504068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>0.986231</td>\n",
+       "      <td>entries</td>\n",
+       "      <td>Same</td>\n",
+       "      <td>0.972652</td>\n",
+       "      <td>-0.213769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2018-02-26</td>\n",
+       "      <td>-0.718282</td>\n",
+       "      <td>entries</td>\n",
+       "      <td>Same</td>\n",
+       "      <td>0.515929</td>\n",
+       "      <td>-1.918282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.3</td>\n",
+       "      <td>2018-02-27</td>\n",
+       "      <td>-0.777000</td>\n",
+       "      <td>has it?</td>\n",
+       "      <td>Same</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>23.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A          B         C        D     E        E2          F\n",
+       "0  1.2 2018-02-26 -2.718282     This  Same  7.389056  -3.918282\n",
+       "1  1.2 2018-02-26  1.718282   column  Same  2.952492   0.518282\n",
+       "2  1.2 2018-02-26 -1.304068      has  Same  1.700594  -2.504068\n",
+       "3  1.2 2018-02-26  0.986231  entries  Same  0.972652  -0.213769\n",
+       "4  1.2 2018-02-26 -0.718282  entries  Same  0.515929  -1.918282\n",
+       "5  1.3 2018-02-27 -0.777000  has it?  Same       NaN  23.000000"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_demo.append(\n",
+    "    {\"A\": 1.3, \"B\": pd.Timestamp(\"2018-02-27\"), \"C\": -0.777, \"D\": \"has it?\", \"E\": \"Same\", \"F\": 23},\n",
+    "    ignore_index=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Combining Frames\n",
+    "\n",
+    "* First, create some simpler data frame to show `.concat()` and `.merge()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Key</th>\n",
+       "      <th>Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>First</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Second</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Key  Value\n",
+       "0   First      1\n",
+       "1  Second      1"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_1 = pd.DataFrame({\"Key\": [\"First\", \"Second\"], \"Value\": [1, 1]})\n",
+    "df_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Key</th>\n",
+       "      <th>Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>First</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Second</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Key  Value\n",
+       "0   First      2\n",
+       "1  Second      2"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_2 = pd.DataFrame({\"Key\": [\"First\", \"Second\"], \"Value\": [2, 2]})\n",
+    "df_2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "* Concatenate list of data frame vertically (`axis=0`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Key</th>\n",
+       "      <th>Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>First</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Second</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>First</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Second</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Key  Value\n",
+       "0   First      1\n",
+       "1  Second      1\n",
+       "0   First      2\n",
+       "1  Second      2"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([df_1, df_2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* Same, but re-index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Key</th>\n",
+       "      <th>Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>First</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Second</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>First</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Second</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Key  Value\n",
+       "0   First      1\n",
+       "1  Second      1\n",
+       "2   First      2\n",
+       "3  Second      2"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([df_1, df_2], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "* Concat, but horizontally"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Key</th>\n",
+       "      <th>Value</th>\n",
+       "      <th>Key</th>\n",
+       "      <th>Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>First</td>\n",
+       "      <td>1</td>\n",
+       "      <td>First</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Second</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Second</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Key  Value     Key  Value\n",
+       "0   First      1   First      2\n",
+       "1  Second      1  Second      2"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([df_1, df_2], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* Merge on common column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Key</th>\n",
+       "      <th>Value_x</th>\n",
+       "      <th>Value_y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>First</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Second</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Key  Value_x  Value_y\n",
+       "0   First        1        2\n",
+       "1  Second        1        2"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(df_1, df_2, on=\"Key\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Task 3\n",
+    "<a name=\"task3\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "* Add a column to the Nest data frame form Task 2 called `Threads` which is the total number of threads across all nodes (i.e. the product of threads per task and tasks per node and nodes)\n",
+    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>Nodes</th>\n",
+       "      <th>Tasks/Node</th>\n",
+       "      <th>Threads/Task</th>\n",
+       "      <th>Runtime Program / s</th>\n",
+       "      <th>Scale</th>\n",
+       "      <th>Plastic</th>\n",
+       "      <th>Avg. Neuron Build Time / s</th>\n",
+       "      <th>Min. Edge Build Time / s</th>\n",
+       "      <th>Max. Edge Build Time / s</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Presim. Time / s</th>\n",
+       "      <th>Sim. Time / s</th>\n",
+       "      <th>Virt. Memory (Sum) / kB</th>\n",
+       "      <th>Local Spike Counter (Sum)</th>\n",
+       "      <th>Average Rate (Sum)</th>\n",
+       "      <th>Number of Neurons</th>\n",
+       "      <th>Number of Connections</th>\n",
+       "      <th>Min. Delay</th>\n",
+       "      <th>Max. Delay</th>\n",
+       "      <th>Threads</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>420.42</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>88.12</td>\n",
+       "      <td>88.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.26</td>\n",
+       "      <td>311.52</td>\n",
+       "      <td>46560664.0</td>\n",
+       "      <td>825499</td>\n",
+       "      <td>7.48</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>200.84</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>46.03</td>\n",
+       "      <td>46.34</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.87</td>\n",
+       "      <td>142.97</td>\n",
+       "      <td>46903088.0</td>\n",
+       "      <td>802865</td>\n",
+       "      <td>7.03</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>202.15</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>47.98</td>\n",
+       "      <td>48.48</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.95</td>\n",
+       "      <td>142.81</td>\n",
+       "      <td>47699384.0</td>\n",
+       "      <td>802865</td>\n",
+       "      <td>7.03</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>89.57</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>20.41</td>\n",
+       "      <td>23.21</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>60.31</td>\n",
+       "      <td>46813040.0</td>\n",
+       "      <td>821491</td>\n",
+       "      <td>7.23</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>164.16</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>40.03</td>\n",
+       "      <td>41.09</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.08</td>\n",
+       "      <td>114.88</td>\n",
+       "      <td>46937216.0</td>\n",
+       "      <td>802865</td>\n",
+       "      <td>7.03</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows \u00d7 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id  Nodes  Tasks/Node  Threads/Task  Runtime Program / s  Scale  Plastic  \\\n",
+       "0   5      1           2             4               420.42     10     True   \n",
+       "1   5      1           4             4               200.84     10     True   \n",
+       "2   5      1           2             8               202.15     10     True   \n",
+       "3   5      1           4             8                89.57     10     True   \n",
+       "4   5      2           2             4               164.16     10     True   \n",
+       "\n",
+       "   Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n",
+       "0                        0.29                     88.12   \n",
+       "1                        0.15                     46.03   \n",
+       "2                        0.28                     47.98   \n",
+       "3                        0.15                     20.41   \n",
+       "4                        0.20                     40.03   \n",
+       "\n",
+       "   Max. Edge Build Time / s  ...  Presim. Time / s  Sim. Time / s  \\\n",
+       "0                     88.18  ...             17.26         311.52   \n",
+       "1                     46.34  ...              7.87         142.97   \n",
+       "2                     48.48  ...              7.95         142.81   \n",
+       "3                     23.21  ...              3.19          60.31   \n",
+       "4                     41.09  ...              6.08         114.88   \n",
+       "\n",
+       "   Virt. Memory (Sum) / kB  Local Spike Counter (Sum)  Average Rate (Sum)  \\\n",
+       "0               46560664.0                     825499                7.48   \n",
+       "1               46903088.0                     802865                7.03   \n",
+       "2               47699384.0                     802865                7.03   \n",
+       "3               46813040.0                     821491                7.23   \n",
+       "4               46937216.0                     802865                7.03   \n",
+       "\n",
+       "   Number of Neurons  Number of Connections  Min. Delay  Max. Delay  Threads  \n",
+       "0             112500             1265738500         1.5         1.5        8  \n",
+       "1             112500             1265738500         1.5         1.5       16  \n",
+       "2             112500             1265738500         1.5         1.5       16  \n",
+       "3             112500             1265738500         1.5         1.5       32  \n",
+       "4             112500             1265738500         1.5         1.5       16  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Threads\"] = df[\"Nodes\"] * df[\"Tasks/Node\"] * df[\"Threads/Task\"]\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "exercise": "solution"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['id', 'Nodes', 'Tasks/Node', 'Threads/Task', 'Runtime Program / s',\n",
+       "       'Scale', 'Plastic', 'Avg. Neuron Build Time / s',\n",
+       "       'Min. Edge Build Time / s', 'Max. Edge Build Time / s',\n",
+       "       'Min. Init. Time / s', 'Max. Init. Time / s', 'Presim. Time / s',\n",
+       "       'Sim. Time / s', 'Virt. Memory (Sum) / kB', 'Local Spike Counter (Sum)',\n",
+       "       'Average Rate (Sum)', 'Number of Neurons', 'Number of Connections',\n",
+       "       'Min. Delay', 'Max. Delay', 'Threads'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Aside: Plotting without Pandas\n",
+    "\n",
+    "### Matplotlib 101\n",
+    "\n",
+    "* Matplotlib: de-facto standard for plotting in Python\n",
+    "* Main interface: `pyplot`; provides MATLAB-like interface\n",
+    "* Better: Use object-oriented API with `Figure` and `Axis`\n",
+    "* Great integration into Jupyter Notebooks\n",
+    "* Since v. 3: Only support for Python 3\n",
+    "* \u2192 https://matplotlib.org/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x = np.linspace(0, 2*np.pi, 400)\n",
+    "y = np.sin(x**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(x, y)\n",
+    "ax.set_title('Use like this')\n",
+    "ax.set_xlabel(\"Numbers\");\n",
+    "ax.set_ylabel(\"$\\sqrt{x}$\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "* Plot multiple lines into one canvas\n",
+    "* Call `ax.plot()` multiple times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "y2 = y/np.exp(y*1.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(x, y, label=\"y\")\n",
+    "ax.plot(x, y2, label=\"y2\")\n",
+    "ax.legend()\n",
+    "ax.set_title(\"This plot makes no sense\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "* Matplotlib can also plot DataFrame data\n",
+    "* Because DataFrame data is _only_ array-like data with stuff on top"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(df_demo.index, df_demo[\"C\"], label=\"C\")\n",
+    "ax.legend()\n",
+    "ax.set_title(\"Nope, no sense at all\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Task 4\n",
+    "<a name=\"task4\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "\n",
+    "* Sort the data frame by threads\n",
+    "* Plot `\"Presim. Time / s\"` and `\"Sim. Time / s\"` of our data frame `df` as a function of threads\n",
+    "* Use a dashed, red line for `\"Presim. Time / s\"`, a blue line for `\"Sim. Time / s\"` (see [API description](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot))\n",
+    "* Don't forget to label your axes and to add a legend _(1st rule of plotting)_\n",
+    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df.sort_values([\"Threads\", \"Nodes\", \"Tasks/Node\", \"Threads/Task\"], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "exercise": "solution"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(df[\"Threads\"], df[\"Presim. Time / s\"], linestyle=\"dashed\", color=\"red\", label=\"Presim. Time / s\")\n",
+    "ax.plot(df[\"Threads\"], df[\"Sim. Time / s\"], \"-b\", label=\"Sim. Time / s\")\n",
+    "ax.set_xlabel(\"Virtual Process\")\n",
+    "ax.set_ylabel(\"Time / s\")\n",
+    "ax.legend(loc='best');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Plotting with Pandas\n",
+    "\n",
+    "* Each data frame hast a `.plot()` function (see [API](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html))\n",
+    "* Plots with Matplotlib\n",
+    "* Important API options:\n",
+    "    - `kind`: `line` (default), `bar[h]`, `hist`, `box`, `kde`, `scatter`, `hexbin`\n",
+    "    - `subplots`: Make a sub-plot for each column (good together with `sharex`, `sharey`)\n",
+    "    - `figsize`\n",
+    "    - `grid`: Add a grid to plot (use Matplotlib options)\n",
+    "    - `style`: Line style per column (accepts list or dict)\n",
+    "    - `logx`, `logy`, `loglog`: Logarithmic plots\n",
+    "    - `xticks`, `yticks`: Use values for ticks\n",
+    "    - `xlim`, `ylim`: Limits of axes\n",
+    "    - `yerr`, `xerr`: Add uncertainty to data points\n",
+    "    - `stacked`: Stack a bar plot\n",
+    "    - `secondary_y`: Use a secondary `y` axis for this plot\n",
+    "    - Labeling\n",
+    "        * `title`: Add title to plot (Use a list of strings if `subplots=True`)\n",
+    "        * `legend`: Add a legend\n",
+    "        * `table`: If `true`, add table of data under plot\n",
+    "    - `**kwds`: Every non-parsed keyword is passed through to Matplotlib's plotting methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "* Either slice and plot\u2026"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_demo[\"C\"].plot(figsize=(10, 2));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* \u2026 or plot and select"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_demo.plot(y=\"C\", figsize=(10, 2));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* I prefer slicing first, as it allows for further operations on the sliced data frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_demo[\"C\"].plot(kind=\"bar\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* There are pseudo-sub-functions for each of the plot `kind`s\n",
+    "* I prefer to just call `.plot(kind=\"smthng\")`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_demo[\"C\"].plot.bar();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_demo[\"C\"].plot(kind=\"bar\", legend=True, figsize=(12, 4), ylim=(-1, 3), title=\"This is a C plot\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Task 5\n",
+    "<a name=\"task5\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "Use the NEST data frame `df` to:\n",
+    "\n",
+    "1. Make the threads the index of the data frame (`.set_index()`)\n",
+    "2. Plot `\"Presim. Program / s\"` and `\"Sim. Time / s`\" individually\n",
+    "3. Plot them onto one common canvas!\n",
+    "4. Make them have the same line colors and styles as before\n",
+    "5. Add a legend, add missing axes labels\n",
+    "6. Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df.set_index(\"Threads\", inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "exercise": "solution"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Presim. Time / s\"].plot(figsize=(10, 3));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "exercise": "solution"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Sim. Time / s\"].plot(figsize=(10, 3));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Presim. Time / s\"].plot();\n",
+    "df[\"Sim. Time / s\"].plot();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot();\n",
+    "ax.set_ylabel(\"Time / s\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## More Plotting with Pandas\n",
+    "### Recap: Our first proper Pandas plot\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* **That's why I think Pandas is great!**\n",
+    "* It has great defaults to quickly plot data; basically publication-grade already\n",
+    "* Plotting functionality is very versatile\n",
+    "* Before plotting, data can be *massaged* within data frames, if needed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## More Plotting with Pandas\n",
+    "### Some versatility"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_demo[[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_demo[df_demo[\"F\"] < 0][[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_demo[df_demo[\"F\"] < 0][[\"A\", \"C\", \"F\"]]\\\n",
+    "    .plot(kind=\"barh\", subplots=True, sharex=True, title=\"Subplots Demo\", figsize=(12, 4));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3wqJFMH68bysoaatRA775Bs45B7p29X/UiOQkStBFRETC2Dvv+FaKzzwDPXoEHU3OUakSzJkDF14I/fr5i0hFcgq1WRQREQlj113nZ9FvuCHoSHKe0qXh88/h//4PLroo6GhEMk5dXERERMLQypX+RjzlygUdSe6RmAj/+Y8vGSpcOOhoJJyoi4uIiIik6bffoGNHuPrqoCPJXebOhTvugE6d4I8/go5GJHVK0EVERMLIoUNwxRWwa5duuJPV2raFsWNh3jxo08a3rhQJR0rQRUREwoQZ3HILfPutTyQbNw46otynb1+YOhXWrYOWLWH16qAjkpzAOTfaObfdOfdTKuv7OOd+dM4td85945xrdNz6/M65pc65TzNyPCXoIiIiYeL1131LwCeegJ49g44m9+rQAWbP9n8QaRZdMugdoFMa69cDbc0sGngCGHnc+nuBVRk9mLq4iIiIhImrr4Y9e+Chh4KOJPdr2hTWrv3rYtGYGIiMDDIiCWdm9rVzLjKN9d8c9fRbICL5iXMuAugKPAX8X0aOpxl0ERGRgG3cCPHxvmvLP/8JzgUdUd6QnJxPnQq1aqlXumSZm4DPj3o+DHgQSMzoDpSgi4iIBGj7dn/B4o03Bh1J3tW2LVxyia///9e/fOmL5DkFnHOLjnoMPJWdOOfa4xP0wUnPuwHbzWzxSQVzKgcXERGRzDt8GK68ErZtg3vuCTqavKtECZgyxSfojz4Kmzf76wEKKEvKS+LNrFlmduCcawiMAjqb2e9Jiy8AejjnugBFgFLOuXFm1jetfWkGXUREJABmcPvtvuXfO+9As0ylBpJZBQvC22/DP/4BI0f6O5CKZJRzrirwIdDPzNYkLzezv5tZhJlFAr2Amekl56AZdBERkUC88opPCB95BK69NuhoBHzt/1NPQbduvgUj+D+kdE2AOOcmAO2A8s65WOBRoCCAmf0beAQoB7zh/IDJ1Iy8szAutCpevLjt378/6DBERESy3NKlMHq0T9Tz6fPssLR4sb/z6PvvQ9WqQUcjp5Nz7oCZFQ86jmQ6JYiIiGSjPXv8v+eeC8OHKzkPZwcOwM8/+9n05cuDjkbyEp0WREREssnOnb7/9mOPBR2JZMSFF8Lcub7E5cILYc6coCOSvEIJuoiISDY4cgSuusp3COncOehoJKOio+Gbb6BSJX8H0u++CzoiyQt0kaiIiMhpZgZ33eVnYMeNg+bNg45ITkbVqr7bzosvQpMmQUcjeYFm0EVERE6z4cPhzTfh73+HPn2CjkZORdmy8PTTvh3jtm3wwgu6oZGcPplK0J1zVzvnVjjnEp1zqbaScc51cs797Jz7xTk3JDPHFBERyWkqVoTeveHJJ4OORLLCu+/Cgw/CgAEQFxd0NJIbZarNonOuHpAI/AcYZGaLUtgmP7AGuBSIBb4HepvZyvT2rzaLIiKSk8XF+RlXyV3M/B9bjzwCHTvCBx/4u5FKzpWr2iya2Soz+zmdzc4HfjGzdWZ2BJgIXJaZ44qIiIS7Xbt8K8Xx44OORLKac/Dww75safp0aNcOtm8POirJTbKjBr0ysOmo57FJy1LknBvonFvknFsUHx9/2oMTERHJanFxcM01sHYtREYGHY2cLjffDB9/7GfU8+cPOhrJTdLt4uKc+wo4O4VVD5nZ5KwOyMxGAiPBl7hk9f5FREROt/vvhxkz4O234YILgo5GTqfu3aFrV3/DqcOH4ZdfoEGDoKOSnC7dBN3MLsnkMTYDVY56HpG0TEREJNcZMQJefx0GDfIXEUrul3w32L//HUaOhEmTfG26yKnKjhKX74FazrnqzrlCQC9gSjYcV0REJNtt3w7dusGzzwYdiWS3Bx6AmjX9z3/s2KCjkZwss11crgCGA2cCe4BlZtbROVcJGGVmXZK26wIMA/IDo83sqYzsX11cREQkpzDzFw8CJCSoJjmv+uMP6NkTZs6E55/3n6QkjwsJX+HWxSVTCfrppgRdRERygj174LLL4JlnoFWroKORoB0+7MubPv8cVq6ESpWCjkjSE24Jero16CIiIpK6+Hjo1Qu++UY3rRGvcGHfXvOXX3xybubHiXriS0ZlRw26iIhIrvXAA/Dll/7i0LZtg45GwkW+fFC7tv/6xRehQwdf/iKSEUrQRURETtGoUTBsGNx7r++JLZKSSpVg3jy48ELYrD52kgGqQRcRETkFZnDttbB3L3z6KRRQ0aikYdo0uPJKKFsWvvgC6tULOiI5WrjVoCtBFxEROUWJiXDwIBQPm1/rEs6WLIHOnf0fd2vXQunSQUckycItQVeJi4iIyEnYuxd694aNG32dsZJzyagmTWDBAhg6VMm5pE0JuoiISAYlJMB118H778OvvwYdjeRENWpAnz7+688/hzffDDYeCU9K0EVERDJoyBCYOhWGD4f27YOORnK6t9+GgQPh8cd92YtIMl3SIiIikgHvvOPb5d1xB9x+e9DRSG4wfrwvkXrsMdiyBV5/XRcbi6eLREVERNKRkADNm8MZZ/iyBN1wRrKKGTz8MDz1FPTo4cunChUKOqq8J9wuEtXfaSIiIunInx9mzdLdICXrOQdPPgkVK8Ly5Rpf4mkGXUREJBX79vnk6ZFHoFixoKOR3M7MJ+xr1kDhwlCtWtAR5R3hNoOui0RFRERSkJgIffv6uvPFi4OORvIC5/66AVarVn5GXfImJegiIiIp+Oc/YfJk37P6wguDjkbyCudgzBj/b+vWMHt20BFJEJSgi4iIHGfcOHjmGd8C7667go5G8proaH9Do4gI6NjRXzgqeYtq0EVERI5y8CDUqgU1a8K0aeqoIcHZtQsuu8zPps+e7e9cK6dHuNWgK0EXERE5zrp1UKoUlC8fdCSS1x08CIcOQZkycOAAFC3qE3bJWuGWoOtvMREREWD/fvjPf/xFejVqKDmX8FC0qE/O4+Kga1cYMMB/LbmbEnQREcnzEhOhf39/l9AlS4KORuREBQrAxRf7C0i7d/ctQCX3UoIuIiJ53uOPw6RJ8MIL0LRp0NGInMg531lo1Cj46ito1w62bQs6KjldVIMuIiJ52n//C716wQ03wFtvqb5Xwt+nn8I110Dz5v4Ot5J54VaDrgRdRETyrN27/d0aGzaEGTP83RtFcoJvv4WSJaFBg6AjyR2UoJ8EJegiInK6zZkD9epBhQpBRyJy8sxg8GBfn96xY9DR5FzhlqCrBl1ERPKcgwdh+nT/ddu2Ss4l59q3z/fr79YNxo4NOhrJKkrQRUQkTzGDG2+Ezp3h11+DjkYkc0qWhK+/hjZt4Prr4bnn/BiXnE0JuoiI5ClPPQUTJ/p/zzkn6GhEMq9UKfjsM3+x85AhvuRFcrYCQQcgIiKSXSZNgocfhn794MEHg45GJOsULgzjx0NEBLRuHXQ0klm6SFRERPKETZugbl3fsWXWLChSJOiIRE6vqVN9sl66dNCRhL/0LhJ1zo0GugHbzSwqhfV9gMGAA/4EbjezH5xzVYAxwFmAASPN7JX04lGJi4iI5AlVqsCrr8JHHyk5l9zvt9/g6qvhwgth8+ago8kV3gE6pbF+PdDWzKKBJ4CRScvjgb+ZWX2gBXCnc65+egdTgi4iIrnaoUOwerX/+qab4Oyzg41HJDucfTZMngzr10OrVrBqVdAR5Wxm9jWwK43135jZ7qSn3wIRScu3mtmSpK//BFYBldM7nhJ0ERHJtczgllvg/PNh+/agoxHJXpde6vv8HzrkS12++SboiMJaAefcoqMeAzOxr5uAz49f6JyLBM4FvktvB5lK0J1zVzvnVjjnEp1zzdLYLsY5t9w5t8w5tygzxxQREcmo55+HceP8BaHqdS55UZMmsGABlC0Ls2cHHU1YizezZkc9Rqb/khM559rjE/TBxy0vAUwC7jOzventJ7NdXH4CegL/ycC27c1sZyaPJyIikiFTpsDf/+5bzz30UNDRiASnRg1YvNj3TAfYsQPOPDPYmHIj51xDYBTQ2cx+P2p5QXxyPt7MPszIvjI1g25mq8zs58zsQ0REJKv9/DNcdx00bQqjR4NzQUckEqxSpfz7YP16383o8cd1Q6Os5JyrCnwI9DOzNUctd8BbwCozezmj+8uuGnQDpjnnFqdX0+OcG5hc/xMfH59N4YmISG5SvTrceae/SK5o0aCjEQkfERHQowc89hjceiso1coY59wEYAFQxzkX65y7yTl3m3PutqRNHgHKAW8cV9J9AdAPuChp+TLnXJd0j5deH3Tn3FdASte8P2Rmk5O2mQ0MMrMU68udc5XNbLNzrgIwHbg76WrYNKkPuoiInIzDh2HfPihXLuhIRMKXmb9h11NP+WR9wgQoVizoqIKVXh/07JbuDLqZXWJmUSk8Jmf0IGa2Oenf7cBHwPmnHrKIiMiJzOD2233Hln37go5GJHw5B08+Ca+/Dp98As8+G3REcrzMXiSaLudccSCfmf2Z9HUH4F+n+7giIpK3DB0Kb78NjzwCJUoEHY1I+LvjDl+P3qpV0JHI8TLbZvEK51ws0BKY6pz7Mml5JefcZ0mbnQXMc879ACwEpprZF5k5roiIyNE++wweeACuugoefTToaERyjosu8nfW3b0bOnaEH38MOiKBDNSgB0k16CIikp6VK6FFC6hZE+bOheJhU0UqknP8/DNcfDH8+ae/uLpdu6Ajyl45rgZdREQknJ15pp/5mzxZybnIqapTx9/QKCLCv5/efz/oiPI2zaCLiEiOFBfn/y1YMNg4RHKTXbvgsstg/nwYOxb69Ak6ouyhGXQREZFMMoO77vIzfcmJuohkXtmyMG2a74jUvn3Q0eRdStBFRCTHGT4cRo70teeaQRfJWkWL+haMlSr5GxkNH64/hLObEnQREclRpk2D++/3H8M/+WTQ0YjkbtOnwz33QLdu/gJSyR5K0EVEJMdYvRquuQaiomDcOMin32Iip1XnzjBqFMyY4Utetm0LOqK8Qac2ERHJMeLioHZtmDJFNyMSyS433QQff+xbml5wAfz6a9AR5X5K0EVEJOwlJvp/o6Phu++gWrVg4xHJa7p1g5kz4cgR2LEj6GhyP7VZFBGRsHfXXf7f4cPBuWBjEcnLDh+GwoX91+vWQY0awcaTVdRmUURE5CSMGOE7ShQtquRcJGjJyfl//+tvbjR2bLDx5FZK0EVEJGzNnAl33w1du8KzzwYdjYgk69wZ2raF66+H557z9yaQrKMSFxERCUtr10Lz5r4X8zffQKlSQUckIkc7cgQGDIAJE3wrxqFDc25npXArcSkQdAAiIiIp+eUXKFnSd2xRci4SfgoV8u1OK1aEl1+Gyy/X3UezimbQRUQkbB19QZqIhK+FC+H88/3XZjnvepFwm0HPoR9EiIhIbvXggzBypP9ayblIzpCcnM+dCy1awObNwcaT0ylBFxGRsDFqFLzwgr8hiojkPEeO+Pdvq1awalXQ0eRcKnEREZGwMGcOXHIJXHwxfPopFNBVUiI50pIl0KWLv/PvJ5/4ZD3chVuJixJ0EREJ3Lp1/iPy8uXh22/hjDOCjkhEMmPdOujUCTZt8nf/bdgw6IjSFm4JuuYnREQkcNOn+wvLPvlEyblIblCjBsyf728yFhUVdDQ5j2bQRUQkLOzc6WfQRST32bgR/vc/+NvfwrPDS7jNoOsiURERCcxTT/muD6DkXCQ3Gz0aHngAbr0V4uODjib8KUEXEZFAvPMO/POf8MEHQUciIqfbo4/CQw/Bm2/ClVfCgQNBRxTeVOIiIiLZbv58uOgiuPBC+PxzKFgw6IhEJDu88QbcdZfvlf7JJ1CuXNAReSpxERGRPG3DBrjiCqha1dekKjkXyTvuuMN/amam935alKCLiEi2GjHC38zkk0+gbNmgoxGR7Nazp/8UrVQp2L9fNyZLiRJ0ERHJVk8/DQsXQt26QUciIkHJl5SB3ncftGzp69MjI/3yyEgYPz7A4MKAatBFRCRbvP46dO3qf/mKiIC/kVGLFrBly7HLixWDkSOhT5/siUM16CIikueMH+8vDHvttaAjEZFwUqXKX7PpRztwwM+q51WaQRcRkdPqu++gbVs/SzZtGhQqFHREIhJO8uXzF40ezzlITMyeGDSDLiIiecamTXDZZVC5MkyapORcRE5UterJLc8LlKCLiMhp89BD/qPqKVPCp9+xiISXp57yNedHK1bML8+rMpWgO+decM6tds796Jz7yDl3RirbdXLO/eyc+8U5NyQzxxQRkZzjjTdg+nRo0CDoSEQkXPXp4y8IrVbNl7VUq5a9F4iGo0zVoDvnOgAzzSzeOfccgJkNPm6b/MAa4FIgFvge6G1m6Xa9VA26iEjO9L//QZcuUKJE0JGIiKQvV9Wgm9k0M4tPevotEJHCZucDv5jZOjM7AkwELsvMcUVEJHz9979w7bXw0ktBRyIikjNlZQ36jcDnKSyvDGw66nls0rIUOecGOucWOecWxcfHp7aZiIiEoUWLYMAAaN0ahqigUUTklBRIbwPn3FfA2SmsesjMJidt8xAQD2T6vk9mNhIYCb7EJbP7ExGR7LFli+/YctZZvmNL4cJBRyQikjOlm6Cb2SVprXfODQC6ARdbygXtm4EqRz2PSFomIiK5yC23wB9/wDffQIUKQUcjIpJ1nHOj8fnudjOLSmF9H2Aw4IA/gdvN7IekdZ2AV4D8wCgzeza942W2i0sn4EGgh5kdSGWz74FazrnqzrlCQC9gSmaOe1pt3ervqPHbb0FHIiJycgI+f732Gnz0ETRsGMjhJTvod6ScTuE9vt4BOqWxfj3Q1syigSdIqgZJapbyOtAZqA/0ds7VT+9gma1Bfw0oCUx3zi1zzv07KZhKzrnPAJIuIr0L+BJYBfzPzFZk8rinzxNPwLx58K9/BR2JiMjJCej89fXX/m5/1avDpZdm66Elu+l3pJxOYTy+zOxrYFca678xs91JT49unHJKzVIy1WbxdMvWNotFi8KhQycuz5cPLrwwe2IQETkVc+emfD/sbDh/7dgJK1ZArZr+bqGSSwU4xiQPSG18FSkCBw9mSwgZabPonIsEPk2pxOW47QYBdc3sZufcVUAnM7s5aV0/oLmZ3ZXWPnQn0WTr1sF110GBpLL8fPl8EWXz5sHGJSKSnvPP9+erfEmn9Gw6f/25D1atglIloWLF03ooCVpAY0zyiOPHV7Fi/i5F69dnZxQFkrsIJj0GnspOnHPtgZvw9einHkxmXpyrVKwIpUr5v+CKFIEjR+DKK/1t8EREwt3tt/tb72XT+eu33/zvVKsE338P+VLq9SW5SzaPMcljjh5fhw75nOzsbD2xxJtZs8zswDnXEBgFdDaz35MWn1KzFM2gH23bNrjtNvj2W/9veF6kICJyomw8f5n5GxH9/jtMmZLdv0MlMPodKadTDh9fzrmqwIdAPzNbc9SqU2qWohp0ERE5aXPmwJ49vu+5iEhOl14NunNuAtAOKA9sAx4FCgKY2b+dc6OAK4ENSS8Jzcg757oAw/BtFkeb2VPpxqMEXUREMmrtWqhVK+goRESyVkYuEs1OKnEREZEMmTIF6taFDz8MOhIRkdxNCbqIiKTrxx99o6smTaBz56CjERHJ3ZSgi4hImnbsgB49fFOFjz/2t40QEZHTR20WRUQkVfHx0LOnb7Dw9de6GZGISHbQDLqIiKSqQAHo2xfeeQfOOy/oaERE8gZ1cRERkRTt2gVlywYdhYjI6acuLiIiEvY++wwiI+Gbb4KOREQk71GCLiIix1i5Enr1gpo1oVGjoKMREcl7lKCLiEjIzp3QvTsUKwaTJ0PxsPnAV0Qk71AXFxERAeDIEbjqKti8GWbPhipVgo5IRCRv0gy6iIgA4BzUrw+jRkGLFkFHIyKSd6mLi4iIcOQIFCoUdBQiIsFQFxcREQkr06b5mfO1a4OOREREQAm6iEie9vPPcM01/mLQihWDjkZEREAJuohInrVrl+/YUqgQTJkCJUoEHZGIiIC6uIiI5ElxcX7mPCYGZs6EatWCjkhERJJpBl1EJA86cAASEmDkSGjdOuhoRETkaOriIiKSx5j5looJCZA/f9DRiIgET11cREQkMDNnwiWX+DuGKjkXEQlPStBFRPKIX37xdwr97Tf1PBcRCWdK0EVE8oA9e3zHlnz54JNPoFSpoCMSEZHUqIuLiEguFx8PvXr5GfSvvoIaNYKOSERE0qIZdBGRXO633/wNiUaMgLZtg45GRETSoy4uIiJ5wP79/m6hIiJyInVxERGRbPH113Dnnf6mRErORURyDtWgi4jkQuvXQ8+eUL68vylR6dJBRyQiIhmVqQTdOfcC0B04AvwK3GBme1LYLgb4E0gA4s2sWWaOKyIiqdu713dsSUz0HVuUnIuI5CyZLXGZDkSZWUNgDfD3NLZtb2aNlZyLiGS98eMhMtK3UTz7bFi5Ej74AGrVCjoyERE5WZlK0M1smpnFJz39FojIfEgiInIyxo+HgQNhwwYwg4MHoWBB2Lo16MhERORUZFkXF+fcJ8B/zWxcCuvWA7sBA/5jZiPT2M9AYCBAoUKFmh4+fDhL4hMRya2qVYONG1NeHhOT7eGIiOQ44dbFJd0adOfcV8DZKax6yMwmJ23zEBAPjE9lN63NbLNzrgIw3Tm32sy+TmnDpOR9JPg2ixn4P4iI5Dlm8P338O67KSfnkPpyEREJb+km6GZ2SVrrnXMDgG7AxZbKdLyZbU76d7tz7iPgfCDFBF1ERNK2Zw+0agWrVkGRIlCsmO/UcryqVbM9NBERyQKZqkF3znUCHgR6mFkKvx7AOVfcOVcy+WugA/BTZo4rIpKXHDgA770HL7zgn59xBlxwAbz5pr9L6MiRPkk/WrFi8NRT2R6qiIhkgUzVoDvnfgEKA78nLfrWzG5zzlUCRplZF+dcDeCjpPUFgPfMLEO/NnQnURHJq8xg/nxfwvK///nWiXXrwk8/Qf78J24/fjw89JAva6la1Sfnffpkf9wiIjlRuNWgZ9lFoqeDEnQRyauefton3MWLw1VXQf/+0Latb6MoIiJZK9wSdN1JVEQkYH/+CZMm+dnyIUOgY0e45hqIiPB3Ay1RIugIRUQkOylBFxEJQGIizJrlk/JJk3ydec2avoc5+K9r1gw2RhERCYZKXEREstEff0Dp0pCQ4PuU//kn9OrlS1hatgTngo5QRCTvUYmLiEges2ePv9Dz3Xf9jYM2bIACBeCzz6BWLShaNOgIRUQknOhyIxGR02TpUj87fvbZcOutPlG/916Ii/PrGzZUci4iIifSDLqISBZasQJKlYIqVWDnTpg+HW65xZewNG2qEhYREUmfZtBFRDJp504YPhyaNYOoKHjlFb/84othy5a/1ik5FxHJmZxzo51z251zKd5s0zlX1zm3wDl32Dk36Lh19zvnVjjnfnLOTXDOFUnveErQRUQyoV8/qFQJ7rnHd2YZNgwefNCvy5cPChcONDwREcka7wCd0li/C7gHePHohc65yknLm5lZFJAf6JXewZSgi4hkkBksWQLPPvvXsgoV4O674Ycf/Lp77/XLREQk9zCzr/FJeGrrt5vZ90BcCqsLAEWdcwWAYsCW9I6nGnQRkXT89huMHw/vvAM//QSFCsF110HVqvDSS0FHJyIiWaCAc27RUc9HmtnIzO7UzDY7514ENgIHgWlmNi3dYDJ7YBGR3Gz6dOjc2fctb94c3ngDrr0WypYNOjIREclC8WbWLKt36pwrA1wGVAf2AO875/qa2bi0XqcEXUQkiRl8953vV96kie++0rIlDB7sa83r1g06QhERyWEuAdab2Q4A59yHQCtACbqISFo2bYKxY2HMGPj5Z9+bPLmOvEQJeOqpYOMTEZEcayPQwjlXDF/icjGwKO2XgDOz0x3YKStevLjt378/6DBEJBeKi4OCBf3Xl1wCM2bAhRfCgAFw1VW+l7mIiOQNzrkDZlY8jfUTgHZAeWAb8ChQEMDM/u2cOxufeJcCEoF9QH0z2+ucexy4FogHlgI3m9nhNONRgi4ieUViIsyb5y/2/PhjWLnS3+Vz2TKfkNeoEXCAIiISiPQS9OymEhcRyfV27PAXd777Lqxf78tWrr4aDifNXzRuHGh4IiIix9AMuojkSn/+6e/wWb06bNzoZ8fbt4f+/eGKK6B42MyTiIhI0DSDLiJymiQkwMyZfqb8ww99Qj51qu9XvnUrnHlm0BGKiIikTwm6iOQKr7/u7/AZGwulS8P11/sLPpMpORcRkZwiX9ABiIicit274T//gYMH/fMDB6BhQ/jvf/2dP//9b2jRItgYRUREToVq0EUkx4iPhy+/9CUskyfDkSP+3x49go5MRERyMtWgi4icgs2boWlT2LYNypeH227zF3yee27QkYmIiGQtJegiEpZ27ID33vMlLEOGQKVK/gZCl14KnTtDoUJBRygiInJ6qMRFRMLGkSO+68q77/p/4+Phoovgq6/AuaCjExGR3CrcSlx0kaiIBMrMPwD+8Q/o2RO++w7uvx9++glmzFByLiIieYtm0EUkEFu3wrhxfrZ8xAi48EL4+WdYt86XsRRQAZ6IiGSTcJtB169AEck2cXEwaZJPyqdNg8REaNnS/wtQp45/iIiI5GWaQReR08rMz5ZXquRrzCtVgmLFoF8/fzMhJeQiIhI0zaCLSJ6wcSOMGeMf8fHwyy++88qCBXDOOZBPV8CIiIikSAm6iGSp2bPhiSdg1iw/e962LQwY4MtY8uWDWrWCjlBERCS8KUEXkUxJTISvv4aaNSEiAvbsgZgYeOwxX8ZSvXrAAYqIiOQwmf6Q2Tn3hHPuR+fcMufcNOdcpVS26++cW5v06J/Z44pIsH75BR55xJertG8Pb73ll/fo8dc6JeciIiInL9MXiTrnSpnZ3qSv7wHqm9ltx21TFlgENAMMWAw0NbPdae1bF4mKhJ/ERN8GceZM35/80kuhf3+4/HJ/8aeIiEhOk+suEk1OzpMUxyfgx+sITDezXQDOuelAJ2BCZo8vIqdXQoK/k+eCBb5sJV8+OPdcn5j37evLWkRERCTrZEkNunPuKeB64A+gfQqbVAY2HfU8NmlZSvsaCAwEKFSoUFaEJyKnYNUq36987FjYsgXKloV77vH/vvhi0NGJiIjkXhmqQXfOfeWc+ymFx2UAZvaQmVUBxgN3ZSYgMxtpZs3MrFkB3UpQJBD//S/Ur+8T8SZN4P33/0rSRURE5PTKUAZsZpdkcH/jgc+AR49bvhlod9TzCGB2BvcpIqdRXBx88YWfLe/WzbdEvPRSePlluO46OOusoCMUERHJW7Kii8vRXY0vA1ansNmXQAfnXBnnXBmgQ9IyEQnIDz/A//2fryHv0cO3Sty3z68rWxbuv1/JuYiISBCyoobkWedcHSAR2ADcBuCcawbcZmY3m9ku59wTwPdJr/lX8gWjIpJ99u2DEiX817fdBosXQ/fuvgtL585QsGCw8YmIiEgWtFk8ndRmUSTzDh+GTz7xJSyzZsGmTVCmDPz0E1SsCOXKBR2hiIhIsHJdm0URCU8xMfDCCzBhAuzeDZUqwZ13Qny8Xx8VFWh4IiIikgol6CK5yObNcOAA1KrlZ87fftvfQKh/f7jkEsifP+gIRUREJD0qcRHJ4Q4ehI8/hnfe8TcU6tnTt0WEY2vORUREJGUqcRGRLPPoozBsGOzdC9WqwUMPwfXX/7VeybmIiEjOowRdJAeJiYGJE+Fvf/MdV4oWhSuu8CUsbdtCvkw3ThUREZGgqcRFJMz9+SdMmuRLWObM8cvmzoXWrQMNS0REJNdQiYuIZNjq1dC0qb/ws2ZNeOIJ6NfPl7OIiIhI7qQEXSSMrF3r+5UXLw5//zvUru1bI15+ObRsCc4FHaGIiIicbkrQRQK2Zw/873++hGXBAl9H3qePX5cvHzz/fJDRiYiISHbTJWUiAUhIgOTLPx54AG69Ff74A557zt/pc8yYYOMTERGR4OgiUZFstGKFL2EZNw4++cTXl69e7fuVN22qEhYREZEg6CJRkTxm/34YPdon5osXQ4EC0KXLXy0R69YNNj4REREJL5pBFzkNjhzxpSrnnOM7sJx9tv96wADo3RsqVAg6QhEREUmmGXSRXMoMli71M+XvveeT8J9+gmLFfBlLpUpBRygiIiI5gRJ0kSzw4Yfw6KM+IS9UCC67zN/dM5mScxEREckodXEROQWHDsH778PWrX89L14c3njDL/vf/6BrV130KSIikhs450Y757Y7535KZX1d59wC59xh59yg49ad4Zz7wDm32jm3yjnXMt3jqQZdJGPM4LvvfAnLxIm+f/nQoXDffX6dknEREZGcKb0adOdcG2AfMMbMolJYXwGoBlwO7DazF49a9y4w18xGOecKAcXMbE9a8ajERSQDDh/2bRBXrICiRaFnT1/CctFFfr2ScxERkdzLzL52zkWmsX47sN051/Xo5c650kAbYEDSdkeAI+kdTwm6SAr274ePPoKVK+Hpp6FwYejWDe6/H66+GkqVCjpCERERyQGqAzuAt51zjYDFwL1mlmaJiGrQRZIkJsKcOXDjjb4tYr9+vpb84EG//tln4aablJyLiIjkQgWcc4uOegzMqv0CTYARZnYusB8YkpEXieRpyfXj//433HknlCjhZ8n794cLL/zrhkIiIiKSa8WbWbPTsN9YINbMvkt6/gFK0EVStncvfPABvPMO3Hor9Onj68pLlYIrrvAdWUREREQyw8x+c85tcs7VMbOfgYuBlem9Tgm65BlmMGOGT8o//NCXrtSuDQWS3gVnnw19+wYaooiIiIQh59wEoB1Q3jkXCzwKFAQws387584GFgGlgETn3H1AfTPbC9wNjE/q4LIOuCHd46nNouR2O3bAmWf6BL1RI9i4EXr39iUszZurA4uIiEhel16bxeymGXTJlXbv9r3K333Xt0bcutXXln/wAVStCkWKBB2hiIiISMp0+ZvkKj/9BNdc48tV7rjDt0t89FE/ew6+pEXJuYiIiIQzzaBLjvfjj75PeZ06EB8Ps2bBbbf5EpZzz1UJi4iIiOQsqkGXHGnHDnjvPV/CsnQpDBgAb7/t18XFQcGCgYYnIiIiOYhq0EUyaeBAn4zHx0PTpvDqq/6iz2RKzkVERCQnU4IuYc0MliyBSZPgySf9TYMiI+G++3wJS1RU0BGKiIiIZC2VuEhY2roVxo37qwtL4cKwaJESchEREcl6uarExTn3BHAZkAhsBwaY2ZYUtksAlic93WhmPTJzXMndvvsOWrWCxERo2RL+/W/fmaVMmaAjExERETn9MjWD7pwrlXSHJJxz9+DvmHRbCtvtM7MSJ7t/zaDnfmawYIGfKa9eHYYM8bXlTz8N117rO7OIiIiInE65agY9OTlPUhwI33oZCSsbNsDYsTBmDKxdC8WKwZ13+nUFCsAjjwQbn4iIiEhQMn2RqHPuKeB64A+gfSqbFXHOLQLigWfN7OM09jcQGAhQqFChzIYnYeTgQSha1H89eDD897/Qrh384x9w5ZVQsmSg4YmIiIiEhXRLXJxzXwFnp7DqITObfNR2fweKmNmjKeyjspltds7VAGYCF5vZr+kFpxKXnC8xEb7+2pewfPABLFwI9erBmjVQqJDvyCIiIiISpBxX4mJml2RwX+OBz4ATEnQz25z07zrn3GzgXCDdBF1yrl27YNgwX8YSE+Nnx6+99q8e5bVrBxmdiIiISPjKl5kXO+dqHfX0MmB1CtuUcc4VTvq6PHABsDIzx5Xw9McfsGqV/zp/fnj5ZahVy7dL/O03GDUKatYMNkYRERGRcJfZGvRnnXN18G0WNwC3ATjnmgG3mdnNQD3gP865RPwfBM+amRL0XCIhAb76ypewfPQRREf7MpbSpWHzZv+viIiIiGScblQkp+zNN+Gxx2DLFt+jvHdvf3fP888POjIRERGRjMtxNegiyX7/HSZO9LXk5cv7evImTeCVV6B7d3+3TxERERHJHM2gS5ri4uCLL3wJy5Qp/vmYMdCvX9CRiYiIiGQNzaBLjrF3r++2sm0bnHmmv5FQ//7QuHHQkYmIiIjkXkrQJWTbNnjvPdi6FZ5/HkqVgltugfPOg86d/2qRKCIiIiKnj0pc8rjDh+GTT3wJy+ef+64srVr5mwvlzx90dCIiIiKnX7iVuGSqD7rkTGb+Dp8AL74IV18NS5bA3/4GK1bA/PlKzkVERESCohn0PGTzZn/ToHffhSefhJ49/bKffoJLLlFSLiIiInlTuM2gqwY9l0tIgP/9zyfl06f7mfMLLoCSJf36ypX9Q0RERETCg2bQcyEziImB6tX917VqQXw8XH+9f9SsGXSEIiIiIuFDM+hy2sTE+B7lY8bArl2+G0vhwjBrlp8lz6crDkRERETCnlK2XOCbb6B9ez9j/uijULUqDB361/oqVZSci4iIiOQUmkHPgRIT/ax4RATUqeOXxcbCE0/4O3xWqxZsfCIiIiJy6lSDnoOsXesv9hw7FjZuhHvugVde8XXmAM4FG5+IiIhITqQadDkl3brB1Km+VKVDB3juObjsMr9OibmIiIhI7qEEPQwlJPiWiF984WvJnfOtEdu0gb59oVKloCMUERERkdNFJS5hZMUKX8IybpzvwFK2rL/Dp2rKRURERE4flbhIir78Ejp1ggIFoEsXGDAAunaFQoWCjkxEREREspNm0ANw5Ah8/rmfLW/RAh58EA4dgpEjoVcvqFAh6AhFRERE8g7NoOdhS5fCO+/Ae+/Bzp1w1lm+thygSBHflUVERERE8jbNoJ9mu3dDmTL+6x49fCnLZZdB//7QsaMvaRERERGR4ITbDLoS9NPg0CH45BM/Wz5tmu9fHhkJv/7qk/WyZYOOUERERESShVuCrvnbLLR5Mzz5JEycCHv2QOXKMGgQFC7s159zTqDhiYiIiEgOoAQ9k2JjYdcuaNgQChb09eXdu/sSlosugvz5g45QRERERHISJein4MAB+PBD34Vlxgx/A6HZs333lW3b/AWfIiIiIiKnIl/QAeQ0zzwDZ58N/frBL7/AI4/AW2/9tV7JuYiIiIhkhhL0o4wf7y/mzJfP/zt+PKxbB48/Dnv3+m3OPBOuusrPmP/6Kzz2mGrLRURERHIz59xo59x259xPqayv65xb4Jw77JwblML6/M65pc65TzN0PHVx8caPh4EDfflKsnz5IDERnPNdWbp2zZZQRERERCQbpdfFxTnXBtgHjDGzqBTWVwCqAZcDu83sxePW/x/QDChlZt3Si0cz6EkeeujY5Bx8cn7GGbBhg5JzERERkbzKzL4GdqWxfruZfQ/EHb/OORcBdAVGZfR4StCTbNyY8vI//oAqVbI3FhERERHJVgWcc4uOegzMwn0PAx4EEjMcTBYePEerWtXPlKe0XERERERytXgza5bVO3XOdQO2m9li51y7jL5OM+hJnnoKihU7dlmxYn65iIiIiMgpuADo4ZyLASYCFznnxqX3oixL0J1zf3POmXOufCrr+zvn1iY9+mfVcbNKnz4wciRUq+YvCq1WzT/v0yfoyEREREQkJzKzv5tZhJlFAr2AmWbWN73XZUkXF+dcFXzhe12gqZntPG59WWAR/upVAxYnbbc7rf1mZxcXEREREcmbMtDFZQLQDigPbAMeBQoCmNm/nXNn43PdUvha831AfTPbe9Q+2gGDMtLFJatq0Ifii98np7K+IzDdzHYlBTgd6ARMyKLji4iIiIicFmbWO531vwER6WwzG5idkeNlusTFOXcZsNnMfkhjs8rApqOexyYtExERERGRo2RoBt059xVwdgqrHgL+AXTIqoCS2toMBChUqFBW7VZEREREJEfIUIJuZpektNw5Fw1UB35wzoGf2l/inDs/aao/2WZ83U6yCFKZ4jezkcBI8DXoGYlPRERERCS3yJKLREM78y1kmqVykehioEnSoiX4i0RTvSMT6CJRERERETn90rtINLudtj7ozrlmzrlRAEmJ+BPA90mPf6WXnIuIiIiI5EVZOoOe1TSDLiIiIiKnW56ZQRcRERERkZOnBF1EREREJIwoQRcRERERCSNhXYPunAvf4CRPc84Rzu8dybs0NiWcaXxKODMzF3QMyTLUBz1IeiNn3hdffMG9995LQkICN998M0OGDAk6pBxPv2Qyb9OmTVx//fVs27YN5xwDBw7k3nvvDTqsHE9jM2scOnSINm3acPjwYeLj47nqqqt4/PHHgw4rx9P4zDoJCQk0a9aMypUr8+mnnwYdTo6XdD+fsBH2M+jhHF9OkJCQQO3atZk+fToRERGcd955TJgwgfr16wcdWo6mXzKZt3XrVrZu3UqTJk34888/adq0KR9//LHGZiZpbGYNM2P//v2UKFGCuLg4WrduzSuvvEKLFi2CDi1H0/jMOi+//DKLFi1i7969StCzQNLYDJssXTXoudzChQupWbMmNWrUoFChQvTq1YvJkycHHZYIFStWpEkTf++ykiVLUq9ePTZv3hxwVCKec44SJUoAEBcXR1xcXNjNsEneFRsby9SpU7n55puDDkVOEyXoudzmzZupUqVK6HlERISSIAk7MTExLF26lObNmwcdikhIQkICjRs3pkKFClx66aUanxI27rvvPp5//nny5VMal1vpJysigdq3bx9XXnklw4YNo1SpUkGHIxKSP39+li1bRmxsLAsXLuSnn34KOiQRPv30UypUqEDTpk2DDkVOIyXouVzlypXZtGlT6HlsbCyVK1cOMCKRv8TFxXHllVfSp08fevbsGXQ4Iik644wzaN++PV988UXQoYgwf/58pkyZQmRkJL169WLmzJn07ds36LAki+ki0VwuPj6e2rVrM2PGDCpXrsx5553He++9R4MGDYIOLUfThU6ZZ2b079+fsmXLMmzYsKDDyTU0NrPGjh07KFiwIGeccQYHDx6kQ4cODB48mG7dugUdWo6m8Zm1Zs+ezYsvvqiLRLNAuF0kGvZtFiVzChQowGuvvUbHjh1JSEjgxhtvVHIuYWH+/PmMHTuW6OhoGjduDMDTTz9Nly5dgg1MBN9lqH///iQkJJCYmMg111yj5FxEso1m0EVOgWaBJFxpbEo40/iUcBVuM+iqQRcRERERCSNK0EVEREREwkhgNehFixb97dChQ2eltU2RIkV0YwgJSxqbEq40NiWcaXxKuCpSpEhi0DEcLbAZ9EOHDp1lZpgZH330EQCrVq0ieZmZcejQoWOe66FH8uOGG27gzDPPpEGDBimuHzduHNHR0URFRdGyZUuWLVuWpcfX2NQjrUeQ41NjU4+0Hjp36hHOj4DPnWFVVRIWwUyYMIHWrVszYcKEoEORHGLAgAFp9iSuXr06c+bMYfny5Tz88MMMHDgwG6OTvE7jU8KVxqaEM43PvwTWxSW5Q8u+ffuoU6cOs2bNonv37vz8889Hb0NQ8Un4i4mJoVu3bune3W/37t1ERUWxefPmLDu2xqakJ6jxqbEp6dG5U8JZwOfOsKm/CnwGffLkyXTq1InatWtTrlw5Fi9eHHRIksu89dZbdO7cOegwRFKk8SnhSmNTwlluH5+B36howoQJ3HvvvQD06tWLCRMm0LRp04Cjktxi1qxZvPXWW8ybNy/oUEROoPEp4UpjU8JZXhifgSbou3btYubMmSxfvhznHAkJCTjneOGFF3SVt2Tajz/+yM0338znn39OuXLlgg5H5BganxKuNDYlnOWV8RloicsHH3xAv3792LBhAzExMWzatInq1aszd+7cIMOSXGDjxo307NmTsWPHUrt27aDDETmGxqeEK41NCWd5aXwGepFou3btGDx4MJ06dQotf/XVV1m1ahUjRozQxSSSqt69ezN79mx27tzJWWedxeOPP05cXBwAt912GzfffDOTJk2iWrVqABQoUIBFixZl2fE1NiUtQY5PjU1Ji86dEs7C4NwZNuUbgXdxSWcbvZElLGlsSrjS2JRwpvEp4SrcEvTAu7iIiIiIiMhflKCLiIiIiISRwLq4FClSJNE5l+YfCEWKFFE3FwlLGpsSrjQ2JZxpfEq4KlKkSGLQMRwtsBn0Q4cO5TMz8uXLR6NGjUKP9evXY2aYGYcOHQp9nZMeH3/8MdHR0TRq1IimTZsyd+7cFLebOHEi0dHR1K9fnwcffDC0fM6cOZx77rnkz5+f999//5jXPPDAA9SvX5+6dety9913k5iYyP79++nSpQt16tShfv36DB48OLT9hg0baNeuHY0bNyY6OpqpU6diZuzcuZN27dpRvHhx7rzzzmOO8d577xEVFUV0dDQdO3Zkx44dmBmDBg2iTp06REdHc/nll7N7927MjCNHjnD99dcTFRVF3bp1efrpp4/ZX3x8PI0bN6Zr166hZf379ycyMjL0c1+6dClmxq5du7j88suJjo7mvPPOY/ny5aHXfP7559SuXZtzzjmHZ5555oTv5913303x4sVDz99++23Kly8fOsabb76ZZT/jnDo2U3uMGzeO6OhooqKiaNmyJcuWLUtxu9R+bomJidx9992cc845REdHs3jx4mNe98cff1C5cuVjxtqiRYuIiorinHPOCY3l5HWvvvpqaDw/8MADaY6zjRs30q5dO+rVq0f9+vUZNmzYMcdOaV9mxg8//ECLFi2oX78+UVFRHDx4MM24li5dSvPmzUPv6++++w4zY9asWZQqVSr0PXn88cdDxxg2bBgNGjSgfv36DB06NLT80UcfpVKlSqHXJL8vc9PYTG9MmFma567UzoMxMTGce+65NGrUiPr16zNixAjMjL179x7zu6RcuXLce++9odf997//DY2R3r17h5andE41S/38fN9994WOUatWLUqXLh1a9+CDD9KgQQMaNGjAxIkTQ8uHDx/OOeecAxA6n6b3PUptX1999VXo/3/BBRewdu1azIwRI0YQFRUVWr5ixQrMjGnTptGkSROioqJo0qQJM2bMOOX3YG4anyf7WLVqFS1atKBQoUK88MILqW43Y8YMzj33XBo0aMD1119PXFxcuq9/+eWXqV+/Pg0aNKBXr16hc9F1111H7dq1adCgATfccANHjhzBzNizZw/dunWjYcOG1K9fn9GjR4f21bFjR0qXLn3M79u0xs1LL71EvXr1iI6O5qKLLiImJibd90byo3v37jRo0CD0PLXz2uHDhxkwYABRUVE0bNiQWbNmZWgMmhkvvvjiMe+btM63WTA2w6uqJKjB7g9tVrx4cUtN8jY5zZ9//mmJiYlmZvbDDz9YnTp1Tthm586dVqVKFdu+fbuZmV1//fX21VdfmZnZ+vXr7YcffrB+/frZ+++/H3rN/PnzrVWrVhYfH2/x8fHWokULmzVrlu3fv99mzpxpZmaHDx+21q1b22effWZmZrfccou98cYbZma2YsUKq1atmpmZ7du3z+bOnWsjRoywO++8M3SMuLg4O/PMM23Hjh1mZvbAAw/Yo48+amZmX375pcXFxZmZ2YMPPmgPPvigmZmNHz/err32WjMz279/v1WrVs3Wr18f2udLL71kvXv3tq5du4aW9e/f/5j/W7JBgwbZY489ZmZmq1atsosuusjMzOLj461GjRr266+/2uHDh61hw4a2YsWK0Ou+//5769u37zHj6e233z7m/5aVcurYTM38+fNt165dZmb22Wef2fnnn5/idqn93KZOnWqdOnWyxMREW7BgwQmvv+eee6x3797H/DzOO+88W7BggSUmJlqnTp1CY3bmzJl28cUX26FDh8zMbNu2bWaW+jjbsmWLLV682MzM9u7da7Vq1QqNjdT2FRcXZ9HR0bZs2TIz8+/H+Pj4NOO69NJLQ19PnTrV2rZta2Zms2bNOmZsJ1u+fLk1aNDA9u/fb3FxcXbxxRfb2rVrzczs0UcftRdeeCHF73FmhcvYTG9MmFma567UzoOHDx8O/Tz//PNPq1atmm3evPmEfTdp0sTmzJljZmZr1qyxxo0bh8Z48jhI7Zya1vn5aK+++qrdcMMNZmb26aef2iWXXGJxcXG2b98+a9asmf3xxx9mZrZkyRJbv369VatWLXRuTet7lNa+atWqZStXrjQzs9dff9369+9vZhZab2Y2efJk69ixY+jYyd+f5cuXW6VKlULbnex7MCuEy/g8Wdu2bbOFCxfaP/7xj1TfuwkJCRYREWE///yzmZk9/PDDNmrUqDRfHxsba5GRkXbgwAEzM7v66qvt7bffNjM/PhITEy0xMdF69eoV+l3+1FNPhX7/bt++3cqUKWOHDx82M7OvvvrKpkyZcsI5KbVxM3PmTNu/f7+Zmb3xxht2zTXXmFnq741kkyZNst69e1uDBg1Cy1I7r7322ms2YMCA0PehSZMmlpCQYGapj0Ezs40bN1qHDh2satWqofdNaufbrJA0NgP/YzD5EV5/LeQSJUqUCH2Et3///hQ/zlu3bh21atXizDPPBOCSSy5h0qRJAERGRtKwYUPy5Tv2x+Oc49ChQxw5coTDhw8TFxfHWWedRbFixWjfvj0AhQoVokmTJsTGxoZes3fvXgD++OMPKlWqBEDx4sVp3bo1RYoUOeYYyQNj//79mPlZqeTXdOjQgQIFfFVUixYtjjnG/v37iY+P5+DBgxQqVIhSpUoBEBsby9SpU7n55psz9L1buXIlF110EQB169YlJiaGbdu2sXDhQmrWrEmNGjUoVKgQvXr1YvLkyQAkJCTwwAMP8Pzzz2foGHKiVq1aUaZMGeDYn21GTZ48meuvvx7nHC1atGDPnj1s3boVgMWLF7Nt2zY6dOgQ2n7r1q3s3buXFi1a4Jzj+uuv5+OPPwZgxIgRDBkyhMKFCwNQoUIFIPVxVrFiRZo0aQJAyZIlqVevHps3b05zX9OmTaNhw4Y0atQIgHLlypE/f/4040rtvZSaVatW0bx5c4oVK0aBAgVo27YtH3744Ul9X3OytMZEsrTOXamdBwsVKhT6eR4+fJjExBM/lV6zZg3bt2/nwgsvBODNN9/kzjvvDI3xo8dUSufUtM7PR5swYQK9e/cG/LmrTZs2FChQgOLFi9OwYUO++OILAM4991wiIyMz/D1Ka1+pjcPkcy4c+3vn3HPPDW3ToEEDDh48yOHDh0/pPZiXVahQgfPOO4+CBQumus3vv/9OoUKFQv25L7300tC4Sev1yee0+Ph4Dhw4EPp5denSBecczjnOP//8Y37n/vnnn5gZ+/bto2zZsqHfzRdffDElS5Y84RipjZv27dtTrFgx4MTf6ym9NwD27dvHyy+/zD//+c8Mfe+O/r1eoUIFzjjjDBYtWpTmGAS4//77ef755/NsSVTgCfrBgwdp3LgxjRs35oorrgg6nCzz0UcfUbduXbp27cro0aNPWF+zZk1+/vlnYmJiiI+P5+OPP2bTpk1p7rNly5a0b9+eihUrUrFiRTp27Ei9evWO2WbPnj188sknXHzxxQA89thjjBs3joiICLp06cLw4cPTPEbBggUZMWIE0dHRVKpUiZUrV3LTTTedsN3o0aPp3LkzAFdddRXFixenYsWKVK1alUGDBlG2bFkA7rvvPp5//vkTfskCPPTQQzRs2JD777+fw4cPA9CoUaNQErNw4UI2bNhAbGwsmzdvpkqVKqHXRkREhJKw1157jR49elCxYsUTjjFp0iQaNmzIVVddle73V7y33nor9LNNSUo/t9R+PomJifztb38LfUyZbPPmzURERJywPfjkau7cuTRv3py2bdvy/fffA2mPs2QxMTGhUpS09rVmzRqcc3Ts2JEmTZqE/rhLK65hw4bxwAMPUKVKFQYNGsQzzzwT2m7BggU0atSIzp07s2LFCgCioqKYO3cuv//+OwcOHOCzzz47Zgy+9tprNGzYkBtvvJHdu3en/gPJodJ6z6bk+HNXWjZt2kTDhg2pUqUKgwcPPuGPpYkTJ3LttdeGfrGvWbOGNWvWcMEFF9CiRYtQspvaOTUj5+cNGzawfv36UOLRqFEjvvjiCw4cOMDOnTuZNWtWuuec1L5Hae1r1KhRdOnShYiICMaOHcuQIUNCr3/99dc555xzePDBB3n11VdPON6kSZNo0qQJhQsXPqX3oKStfPnyxMfHh/pyf/DBB+mOgcqVKzNo0CCqVq1KxYoVKV269DGTGQBxcXGMHTs2dM+Yu+66i1WrVlGpUiWio6N55ZVXUvwde7S0xk2yo8/9aeUbDz/8MH/7299Cif3RUjqvNWrUiClTphAfH8/69etZvHgxmzZtSnMMTp48mcqVK4cmUY6W0vk2Nwo8QS9atCjLli1j2bJlfPTRR0GHk2WuuOIKVq9ezccff8zDDz98wvoyZcowYsQIrr32Wi688EIiIyPJnz9/mvv85ZdfWLVqVShhnTlz5jF3XY2Pj6d3797cc8891KhRA/AzPAMGDCA2NpbPPvuMfv36pTjjlCwuLo4RI0awdOlStmzZQsOGDY9JRACeeuopChQoQJ8+fQCfSOfPn58tW7awfv16XnrpJdatW8enn35KhQoVaNq06QnHeeaZZ1i9ejXff/89u3bt4rnnngNgyJAh7Nmzh8aNGzN8+PBQDWpqtmzZwvvvv8/dd999wrru3bsTExPDjz/+yKWXXkr//v3T+O4KwKxZs3jrrbdCP4/jpfZzS80bb7wR+qWQUfHx8ezatYtvv/2WF154gWuuuQYzS3WcJdu3bx9XXnklw4YNC80mprav+Ph45s2bx/jx45k3bx4fffQRM2bMSDOuESNGMHToUDZt2sTQoUNDf7g2adKEDRs28MMPP3D33Xdz+eWXA1CvXj0GDx5Mhw4d6NSpE40bNw6N5dtvv51ff/2VZcuWUbFiRf72t79l+PuTG6V07kpLlSpV+PHHH/nll19499132bZt2zHrJ06cGJrZTt7/2rVrmT17NhMmTOCWW25hz549qZ5TM3J+njhxIldddVVoeYcOHejSpQutWrWid+/etGzZMt1zemrS2tfQoUP57LPPiI2N5YYbbuD//u//Qq+78847+fXXX3nuued48sknj9nnihUrGDx4MP/5z3/SPX5q7xtJm3OOiRMncv/993P++edTsmTJdMfA7t27mTx5MuvXr2fLli3s37+fcePGHbPNHXfcQZs2bUKfCH355Zc0btyYLVu2sGzZMu66667Q7Hhq0ho3AOPGjWPRokU88MADQOr5xrJly/j1119TnFBN7bx24403EhERQbNmzbjvvvto1apVmt+XAwcO8PTTT/Ovf/3rhHWpnW9zo8AT9Nzi9ddfD30SsGXLltDyNm3asG7dOnbu3HnCa7p37853333HggULqFOnTrq3rf3oo49o0aIFJUqUoESJEnTu3JkFCxaE1g8cOJBatWpx3333hZa99dZbXHPNNYD/i/jQoUMpxpJs2bJlAJxzzjk457jmmmv45ptvQuvfeecdPv30U8aPHx+anXrvvffo1KkTBQsWpEKFClxwwQUsWrSI+fPnM2XKFCIjI+nVqxczZ86kb9++AFSsWBHnHIULF+aGG25g4cKFgP+Y9u2332bZsmWMGTOGHTt2UKNGDSpXrnzMTERsbCyVK1dm6dKl/PLLL9SsWZPIyEgOHDhAzZo1AV+2kPwR7c0338zixYvT/P7mNceP2R9//JGbb76ZyZMnU65cuRRfk9rPLbWfz4IFC3jttdeIjIxk0KBBjBkzhiFDhlC5cuVjymiStwc/i9KzZ8/Qx7r58uVj586dqY4z8H9YXnnllfTp04eePXuG9pvaviIiImjTpg3ly5enWLFidOnShSVLlqQZ17vvvhva99VXX33MmC1RogTgP5KOi4sLvcduuukmFi9ezNdff02ZMmVC7/GzzjqL/Pnzky9fPm655ZbQvnK6o8dUxYoVUxwTKUnp3JURlSpVCn1SkeyHH34gPj7+mImBiIgIevToQcGCBalevTq1a9dm7dq1aZ5T0zs/H/9HAPhPl5YtW8b06dMxs3TP6am9b1Lb144dO/jhhx9CnxBde+21x5yfk/Xq1euYUoHY2FiuuOIKxowZE7pY9VTeg3lNar/X09KyZUvmzp3LwoULadOmTbpj4KuvvqJ69eqceeaZFCxYkJ49ex7zM3388cfZsWMHL7/8cmjZ22+/Hfr51KxZk+rVq7N69epUj5HeuPnqq6946qmnmDJlSuh3ZmrvjQULFrBo0SIiIyNp3bo1a9asoV27dkDq57UCBQowdOhQli1bxuTJk9mzZw+1a9dOdQz++uuvrF+/nkaNGhEZGUlsbCxNmjTht99+S/N8m9soQc8id955Z+iTgAMHDoRmG5YsWcLhw4dTTHi2b98O+L+g33jjjXTrtKtWrcqcOXOIj48nLi6OOXPmhD5y+uc//8kff/zBsGHDTnhN8szgqlWrOHToUKiuMiWVK1dm5cqV7NixA4Dp06eHjvHFF1/w/PPPM2XKlGM+2qpatSozZ84EfO3jt99+S926dXnmmWeIjY0lJiaGiRMnctFFF4VmBpJrUc1815uoqCjAf8x95MgRwH8k16ZNG0qVKsV5553H2rVrWb9+PUeOHGHixIn06NGDrl278ttvvxETE0NMTAzFihXjl19+OeYYAFOmTDmhHCivO3rMxsfH07NnT8aOHZvmL5TUfm49evRgzJgxmBnffvstpUuXpmLFiowfP56NGzcSExPDiy++yPXXX8+zzz5LxYoVKVWqFN9++y1mxpgxY7jssssAuPzyy5k1axbgP2o/cuQI5cuXT3WcmRk33XQT9erVO2FWKLV9dezYkeXLl3PgwAHi4+OZM2cO9evXTzOuSpUqMWfOHABmzpxJrVq1APjtt99C7/eFCxeSmJgYer8nv8c3btzIhx9+yHXXXXfM9xH8L8Lk72NOd/SYuvzyy1McE8dL7dyVmtjYWA4ePAj4c+e8efOoU6dOaP3RdeHJLr/8cmbPng3Azp07WbNmDTVq1EjznJrW+Xn16tXs3r2bli1bhpYlJCTw+++/A/Djjz/y448/nlCqcLzU3jep7atMmTL88ccfrFmzBjj2/Lx27drQfqdOnRoan3v27KFr1648++yzXHDBBaFtTuU9mNccPZ7Tu+YkWfK4OXz4MM899xy33XZbmttXrVqVb7/9NpQ3zJgxI/QzHTVqFF9++SUTJkw4poTl6N/r27Zt4+eff07zk6e0xs3SpUu59dZbmTJlyjHXGqT23rj99tvZsmULMTExzJs3j9q1a4feW6md1w4cOMD+/ftDxy5QoECa59vo6Gi2b98e+r0eERHBkiVLOPvss9M83+Y6QV2dSi7u4vLss89a/fr1rVGjRtaiRQubO3duaF2jRo1CX/fq1cvq1atn9erVswkTJoSWL1y40CpXrmzFihWzsmXLWv369c3MdzIZOHCg1a1b1+rVq2f333+/mZlt2rTJAKtbt641atTIGjVqZG+++aaZ+c4trVq1soYNG1qjRo3syy+/DB2nWrVqVqZMGStevLhVrlw51PlixIgRVrduXYuOjrZu3brZzp07zczsnHPOsYiIiNAxbr31VjPznRSuuuoqq1+/vtWrV8+ef/75E74nx1953b59e4uKirIGDRpYnz597M8//zQzs2+++cZq1apltWvXtiuuuCLUdcHMX9Feq1Ytq1Gjhj355JMpfu+PHk9Dhgyx+vXrW8OGDa1du3a2atWqVH9mJyunjs3U3HTTTXbGGWeEfrZNmzYNrevcuXOoC0RqP7fExES74447rEaNGhYVFWXff//9Ccc4vqvO999/bw0aNLAaNWrYnXfeGep8dPjwYevTp481aNDAzj33XJsxY4aZpT7O5s6da4BFR0eH4p86dWqa+zIzGzt2rNWvX98aNGhgDzzwQLpxzZ0715o0aWINGza0888/3xYtWmRmZsOHDw+Ns+bNm9v8+fND+2rdurXVq1fPGjZseEwXkL59+1pUVJRFR0db9+7dbcuWLSf9M0tNuIzNtMZE8nkwrXNXaufBadOmWXR0tDVs2NCio6PtP//5zzHHrV69+gnv9cTERLv//vutXr16FhUVFTrfpnZONUv9/Gzmu1UMHjz4mGUHDx4Mbd+8eXNbunRpaN0rr7xilStXtvz581vFihXtpptuSvN7lNa+PvzwQ4uKirKGDRta27Zt7ddffzUz3ykp+fdOu3bt7KeffjIzsyeeeMKKFSsW+v42atQo1JXlZN+DWSFcxufJ2rp1q1WuXNlKlixppUuXtsqVK4c65xx9jhw0aJDVrVvXateubUOHDs3Q6x955BGrU6eONWjQwPr27RvqnpM/f36rUaNG6Of2+OOPm5nZ5s2b7dJLLw2di8eOHRs6TuvWra18+fJWpEgRq1y5sn3xxRdmlvq4ufjii61ChQqhY3Tv3t3M0n5vJFu/fv0xXVxSO6+tX7/eateubXXr1rWLL77YYmJiQq9JbQwe7ejuR2mdbzOLMOvi4iygujLnnKV3bOec6t4kLGlsSrjS2JRwpvEp4SppbIZNyxiVuIiIiIiIhBEl6CIiIiIiYaRAUAcuUqTINufcWelsk+ic0x8REnY0NiVcaWxKONP4lHBVpEiRbelvlX0Cq0EXEREREZET6a9YEREREZEwogRdRERERCSMKEEXEREREQkjStBFRERERMKIEnQRERERkTDy/3v9p+6aKdDBAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 864x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_demo.loc[df_demo[\"F\"] < 0, [\"A\", \"F\"]]\\\n",
+    "    .plot(\n",
+    "        style=[\"-*r\", \"--ob\"], \n",
+    "        secondary_y=\"A\", \n",
+    "        figsize=(12, 6),\n",
+    "        table=True\n",
+    "    );"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_demo.loc[df_demo[\"F\"] < 0, [\"A\", \"F\"]]\\\n",
+    "    .plot(\n",
+    "        style=[\"-*r\", \"--ob\"], \n",
+    "        secondary_y=\"A\", \n",
+    "        figsize=(12, 6),\n",
+    "        yerr={\n",
+    "            \"A\": df_demo[df_demo[\"F\"] < 0][\"C\"], \n",
+    "            \"F\": 0.2\n",
+    "        }, \n",
+    "        capsize=4,\n",
+    "        title=\"Bug: style is ignored with yerr\",\n",
+    "        marker=\"P\"\n",
+    "    );  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Combine Pandas with Matplotlib\n",
+    "\n",
+    "* Pandas shortcuts very handy\n",
+    "* But sometimes, one needs to access underlying Matplotlib functionality\n",
+    "* No problemo!\n",
+    "* **Option 1**: Pandas always returns axis\n",
+    "    - Use this to manipulate the canvas\n",
+    "    - Get underlying `figure` with `ax.get_figure()` (for `fig.savefig()`)\n",
+    "* **Option 2**: Create figure and axes with Matplotlib, use when drawing\n",
+    "    - `.plot()`: Use `ax` option"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Option 1: Pandas Returns Axis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = df_demo[\"C\"].plot(figsize=(10, 4))\n",
+    "ax.set_title(\"Hello There!\");\n",
+    "fig = ax.get_figure()\n",
+    "fig.suptitle(\"This title is super (literally)!\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Option 2: Draw on Matplotlib Axes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 4))\n",
+    "df_demo[\"C\"].plot(ax=ax)\n",
+    "ax.set_title(\"Hello There!\");\n",
+    "fig.suptitle(\"This title is super (still, literally)!\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "* We can also get fancy!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(12, 4))\n",
+    "for ax, column, color in zip([ax1, ax2], [\"C\", \"F\"], [\"blue\", \"#b2e123\"]):\n",
+    "    df_demo[column].plot(ax=ax, legend=True, color=color)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Aside: Seaborn\n",
+    "\n",
+    "* Python package on top of Matplotlib\n",
+    "* Powerful API shortcuts for plotting of statistical data\n",
+    "* Manipulate color palettes\n",
+    "* Works well together with Pandas\n",
+    "* Also: New, well-looking defaults for Matplotlib (IMHO)\n",
+    "* \u2192 https://seaborn.pydata.org/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "sns.set()  # set defaults"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_demo[[\"A\", \"C\"]].plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Seaborn Color Palette Example\n",
+    "\n",
+    "* [Documentation](https://seaborn.pydata.org/tutorial/color_palettes.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABECAYAAACF4e8fAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAClklEQVR4nO3Zv0tWUQDG8WP4M0h7ExuDJic3g1pcgv6NaCiCgpbIMMcIgpaoQXpp6F+oOQLX2moPGpU30yB/INwWF4dy6bwnHj6f5Qz3Dg9cLnzhjHRd1xUAgGCnWg8AAKhN8AAA8QQPABBP8AAA8QQPABBP8AAA8UZPeuHBi/Uy2N4bxpahe716rXx7ebv1jGou3F0rd949aj2jihtvB2Wxv1Y+3cz8fov9tfL88fvWM6q5t3q1vHl6v/WMaq4vPytfX31sPaOai7culX6/33pGFZfnN8vC0kr5vP6k9ZQqFpZWysMPX1rPqKI3OVaWr8z/8fmJwTPY3isbW7v/dNT/5HB7s/WEqjZ/fW89oYr9jc1jZ6Lt4P+ulFJ+/hi0nlDV4c5+6wlV7ezstJ5QxcHe1rEz0WD3oPWEJlxpAQDxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxBA8AEE/wAADxRk96YXZmchg7mhmdmWs9oaq50+daT6hi4nx3dOZ+v5neVOsJVZ05O9t6QlWj0xOtJ1Q1PT3dekIV45P7R2ev8ZJ6ZqfGW0+oojc59tfnI13XdUPaAgDQhCstACCe4AEA4gkeACCe4AEA4gkeACCe4AEA4v0G2LZVRfld9xMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x72 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.palplot(sns.color_palette())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x72 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.palplot(sns.color_palette(\"hls\", 10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABGoAAABECAYAAADZXtNTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAADw0lEQVR4nO3bv45VVRjG4RdBGoiEBA2SCXtXWqk3YLwFrNQL0EpttPUujJVWVsZKb4F4A2Kl1dqEgH8mMRhoMHgsDqGT07hmfbPyPM0q5hTvV53MLzNndrvdLgAAAAAM99zoAQAAAADsCTUAAAAARQg1AAAAAEUINQAAAABFCDUAAAAARQg1AAAAAEWcO/iJT95Mju+cwJQBvm7JV+voFf2835Jf1tErurj7SnItLXezjp7SxbW0fDPpbUnyXlo+mPi+L9Py6sT3/ZyWNe+OntHJW2n5MGu+GD2ki/1tP42e0U3La1l/Hb2in3Y1Wb8dvaKf9k6yfjR6RSc3k3YrWV8fPaSPdit5ex29op/vWvL5OnpFPx+35Md19Ip+3mhJ1j9Hz+jkZtJuJOv3o4f00W4k66ejV/RxdDn54bP//PHhUHN8J/lt+z8n1fLXxLclyd9z3vf46TvnfUnyYOLbkuT3ye/bpr9v1t+G7ydJtifvjLY8Gj2hq+3x4c+cZtuD0Qv62v4YvaCT2/tnuz12Rk/35v7ay/3J73s0+X3Z/hm9oJOH+2d7OHZGT9vx6AVD+NcnAAAAgCKEGgAAAIAihBoAAACAIoQaAAAAgCKEGgAAAIAihBoAAACAIoQaAAAAgCKEGgAAAIAihBoAAACAIoQaAAAAgCKEGgAAAIAihBoAAACAIoQaAAAAgCKEGgAAAIAihBoAAACAIoQaAAAAgCKEGgAAAIAihBoAAACAIoQaAAAAgCKEGgAAAIAihBoAAACAIoQaAAAAgCKEGgAAAIAihBoAAACAIoQaAAAAgCKEGgAAAIAihBoAAACAIoQaAAAAgCKEGgAAAIAihBoAAACAIoQaAAAAgCKEGgAAAIAihBoAAACAIoQaAAAAgCKEGgAAAIAihBoAAACAIoQaAAAAgCKEGgAAAIAihBoAAACAIoQaAAAAgCKEGgAAAIAihBoAAACAIoQaAAAAgCKEGgAAAIAihBoAAACAIoQaAAAAgCKEGgAAAIAihBoAAACAIoQaAAAAgCKEGgAAAIAihBoAAACAIs4d/MSVoxOYMdALy+gFfT0/531nn75z3pckFye+LUlemvy+Zfr7ro6e0MmlJMny5J3RkvOjJ3S1nD38mdNsuTh6QV/Li6MXdHJ9/yzXx87o6eW5v/ZyafL7zk9+X5ZZ/z7hwv5ZLoyd0dNyZfSCPo4uP/PHZ3a73e6EpgAAAADwDLOmRQAAAIBTR6gBAAAAKEKoAQAAAChCqAEAAAAoQqgBAAAAKEKoAQAAACjiX5EbY3y4VKb8AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1440x72 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.palplot(sns.color_palette(\"hsv\", 20))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x72 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.palplot(sns.color_palette(\"Paired\", 10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 576x72 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.palplot(sns.color_palette(\"cubehelix\", 8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x72 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.palplot(sns.color_palette(\"colorblind\", 10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Seaborn Plot Examples\n",
+    "\n",
+    "* Most of the time, I use a regression plot from Seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "with sns.color_palette(\"hls\", 2):\n",
+    "    sns.regplot(x=\"C\", y=\"F\", data=df_demo);\n",
+    "    sns.regplot(x=\"C\", y=\"E2\", data=df_demo);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "* A *joint plot* combines two plots relating to distribution of values into one\n",
+    "* Very handy for showing a fuller picture of two-dimensionally scattered variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = np.random.multivariate_normal([0, 0], [[1, -.5], [-.5, 1]], size=300).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.jointplot(x=x, y=y, kind=\"reg\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Task 6\n",
+    "<a name=\"task6\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "* To your `df` NEST data frame, add a column with the unaccounted time (`Unaccounted Time / s`), which is the difference of program runtime, average neuron build time, minimal edge build time, minimal initialization time, presimulation time, and simulation time.  \n",
+    "(*I know this is technically not super correct, but it will do for our example.*)\n",
+    "* Plot a stacked bar plot of all these columns (except for program runtime) over the threads\n",
+    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "cols = [\n",
+    "    'Avg. Neuron Build Time / s', \n",
+    "    'Min. Edge Build Time / s', \n",
+    "    'Min. Init. Time / s', \n",
+    "    'Presim. Time / s', \n",
+    "    'Sim. Time / s'\n",
+    "]\n",
+    "df[\"Unaccounted Time / s\"] = df['Runtime Program / s']\n",
+    "for entry in cols:\n",
+    "    df[\"Unaccounted Time / s\"] = df[\"Unaccounted Time / s\"] - df[entry]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Runtime Program / s</th>\n",
+       "      <th>Unaccounted Time / s</th>\n",
+       "      <th>Avg. Neuron Build Time / s</th>\n",
+       "      <th>Min. Edge Build Time / s</th>\n",
+       "      <th>Min. Init. Time / s</th>\n",
+       "      <th>Presim. Time / s</th>\n",
+       "      <th>Sim. Time / s</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Threads</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>420.42</td>\n",
+       "      <td>2.09</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>88.12</td>\n",
+       "      <td>1.14</td>\n",
+       "      <td>17.26</td>\n",
+       "      <td>311.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>202.15</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>47.98</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>7.95</td>\n",
+       "      <td>142.81</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Runtime Program / s  Unaccounted Time / s  \\\n",
+       "Threads                                              \n",
+       "8                     420.42                  2.09   \n",
+       "16                    202.15                  2.43   \n",
+       "\n",
+       "         Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n",
+       "Threads                                                         \n",
+       "8                              0.29                     88.12   \n",
+       "16                             0.28                     47.98   \n",
+       "\n",
+       "         Min. Init. Time / s  Presim. Time / s  Sim. Time / s  \n",
+       "Threads                                                        \n",
+       "8                       1.14             17.26         311.52  \n",
+       "16                      0.70              7.95         142.81  "
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[[\"Runtime Program / s\", \"Unaccounted Time / s\", *cols]].head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Ofo4rVy7lyIqLi4WLy1CMGjUEffs6wMzMDHXq1FPrnN61atUyODg4Ydu23WjatLnG+7+rYkVz7NrlhwYNLLFr1zasXLkOc+YsxK5dWwEAa9b4omfPXti+fTd8fFZi+fIlSEl5nS1DLpfj5cuXqFjRPNv27ds3w8NjJjZv3okOHTrj//7vXqFqBdgcExERUQlz/PgRdO3aDQDw1Vd2OH78KGrVqoP09DQ8ffoEFy6cQ/v2nSCTyXD1ajC6d3cAANjafonSpdV7+FjlypUxfvxELFmyAAqFQrU9JCQYgYF/wcVlKMaPd0FsbDQePnyQZ5aZWXlUqfIZAODatatwcHCEvr4+jIyMYGfXA6GhwQCA2rXrwMKiEvT09FCjRi0kJSXmyMqaVrF9+x4cPfonypUzw+rVy9U6p3ddvx6Kr76yAwDY2/dQPQG5ILKmQlSqVBktWrSEVCpF5cpVkJT09sEcISHB2LRpA0aMGIzvv58EuVyOZ8+eZsu4ceN/uTbpHTt2xqxZHli5cilq1qyJNm3aFbjOLJxWQURERCVGQsILXLp0Effu3cX+/XshCAKSkhJx/vwZ2Nu/vXp869YNDBs2CsDbRwu/+2ReTfTq1Rfnzv2J337brtqmUCjx7beTYGvbBQCQnPwKBgaGuHPndrZ95XK56mtDw/9W98hZi6BqvmXvrALyoacTv8vAwABdunTFr7+uV+2T2/FzJ1HVIpFI8ryhLz/vTv3IbY62QqHE2rU/o3x5M8jlSsTFxcLMLPvUiMuXg9C+fc5504MGDUOHDp0RFPQ31q9fiy++uI2vvx5X4FoBXjkmIiKiEuTUqeNo1aoNDh48Dn//I/j996MYOXIMDh06AHv77jh79g88efIEzZtbAQBat26LP/44CQC4dOkikpM1e7x21vSKLK1aWePw4QDI5XKkpKTA1fVr3L59C6am5RAZ+QxpaWlITHz1wakWrVpZ48SJY1AoFEhNTcXp0ydhZWVdwHcDCA0NQf36lgAAU9NyCA9/Ow3kr7/O5bmftXUbnDr1du71hQtnkZ5edMvotWpljQMH9gMAwsMfYdSowUhLS8025tatG2jSpFmOfceNG4WUlNcYOHAoBg4cKsq0Cl45JiIiItHI09Myl10TP1cdx48fwfjxE7Ntc3YegN27dyA1NRWmpuXQuHFT1VXU776bhkWL5uHw4QOoW7e+alrF3bt3sGHDevj6rs3zeFnTK5Yt8wIA9OnTH0+fPsHo0UOhUCjg6NgbLVu+bW5tbDpgxIiBqFLlM1Vz/r7evfvhyZN/4eIyBHK5HN26OcDW9ktcu6beMnFZc46BtzfkVatWHTNmeAIAhg0bCS+v+Th27DBsbb/MM2fq1OlYtGguDh8+AEvLRjAxKaXW8QtiypTpWLbMC8OGDYQgCPD0XJjteDEx0ShfvkKuUztcXSfCy2sB9PX1YWhoCA+PmYWuRyLkd01ey+Ljk7P9SsHcvAzWzRyd5z5u3lsRG6vZT3pZ2QXZryizdLEmMbNYk3ZzdDWLNWk/izVpP+tTqen588eoXLlGobKkUj3I5cpC11SQnP3798Laug1q1aqN+/fvYenSxdiyZddHrak4ZRWHmnL7jOrpSVChQunc9y90BURERETFVLVq1TF//mzo6UkgkxmqrrLSp4vNMREREX2ybGw6wMamw8cug3QIb8gjIiIiIsrE5piIiIiIKBObYyIiIiKiTJxzTERERKIpU84QRgYy0XNTM9KR9FK95dyICkOt5njNmjU4deoUJBIJ+vfvj9GjR2PmzJkIDQ2FsbExAMDNzQ12dnYICgqCt7c30tLS0KNHD0yZMqVIT4CIiIh0h5GBDAP3fSN6rt+gn5GEvJvjyMhIODs7olevvpg+fbZq+z//3Mfo0cMwa9Y8ODg4wcVlKLZt251HUu6ioiIxZIgzatasnW27k1Mf9Os3MNu248eP4Pr1UMybt1Dj47zLy2s+QkOvomxZUwiCElKpAaZOnYHGjZt8cJ+4uFj4+CyCr+9aeHnNh5VVKzg4OGUbs3HjL1AqBXz9tatq24oVS3HzZhjk8gw8ffpEdZ4DBgzGuXN/4ocf5qBiRfNCnc/7fHwWYeDAoahdu46ouYWRb3McHByMy5cv4/Dhw5DL5XBwcICtrS1u3bqFXbt2wcLCQjU2NTUVs2bNws6dO1GlShW4urriwoULsLW1LdKTICIiIgIAU1NTXLlyCQqFQvWo4jNn/kC5cmaqMQVpjLNUrGheqP0LYuzYCXBwcIJUqoezZ89i9epl2LhxxwfHV6xonu/DS3IzbdoMAG9/CHB3d812nj179tK8cDWEhz/SqcYYUKM5btOmDXbs2AGpVIro6GgoFAoYGhoiMjISc+bMQWRkJOzs7ODm5oYbN26gRo0aqF69OgDAyckJJ0+eZHNMREREWmFsbIJ69eojLOy66sl0wcGXYW3dRjWmY0drBAaGYPPmDYiLi8WTJ/8iOvo5HB17Y9Sorwt87JMnj2H79s0oVao0KleuDGNjEwDAtWshWL16OfT19dG4cTNERDzCunW/4unTJ/D19UZi4isYGhphyhQP1aOeP+T162SYmVVQ5W7Z8ivWrfsVAFRXia2sWsHd3RX+/key7bt79w4cPnwQpqblULZsWVhaNlL73Pr3d8KPP27A9euhCAoKRFxcLGJiojF48DBERUXh2rW3V7d9fdfC0NAQJ04cxf79e6BUCmjQwBJTp86AoaFhtswHD/5BnTp1s22Ty+Xw9l6AR4/ePua6b98B6NWrr9p1ikGtaRUGBgZYu3YttmzZgu7du0OhUKBdu3ZYuHAhTExM4OrqCn9/f5iYmMDc/L/L7RYWFoiOjtaooA89rSQ/5uZltLpfUWbpYk1iZrEm7eboahZr0n4Wa9J+1qdQU0yMHqRS7dzfr+5xuna1x4ULZ9CmTRvcuXMb9erVgyC8fSpaVoZUqgc9PQkePnyADRs2IykpCf3798LAgYNRpkyZXI+nr6+HuLhYjB49NNv2efMWwdS0HH7++Ufs2LEHpqammDZtEkxMSkEuz8DixfOwYsUa1KtXHytXLodE8rYOL6/5+P77GWjQwBLh4Y8wY8Y0+PkdzJYtkUiwefMG7N+/B2/evEF09HMsX74KUqke9PX1VFlZY/X0JNDX/+8cs7b98889HDt2GDt27IFEIsHYsaPQqFHjXN/Td/d/f7uengT37t3Gb7/5ITExEX37OmL16nWYNs0D3347HqGhV1CtWnUcPRqAjRu3wdDQEOvX/4h9+37DmDFjs+UFB19Chw4dsx3nzp2bSEpKws6de/Hq1UusWbMKzs791Ppzf9e7mXp6ehp97tW+IW/SpEkYN24cJkyYgEuXLuGnn35SvTZixAgEBASge/fuOfbLena5unJ7fLQ6+Pjo4pHFmrSbo6tZrEn7WaxJ+1mfSk1KpVK0xwfnR93j2Nh0xC+/rEd6uhynT5/Cl1/a4cyZ01AqBVWGXK6EUinAyqoVJBJ9lC1bDmXKlMWrV4kwNi6V66OMFQolKlY0x9atOadVnDv3J5o0aQpT07fTN+zseiA09CoePHiAcuXMUKtWXcjlSvTo4YT/+z9fJCYm4+7d21i0aL4qIyUlBfHxL2BqWk61TRDezgvOmlZx584duLm9nfKgUCghCP+dkyAIUCoFKBT/nWPWtqtXr6Jduw6QyYwAAF99ZYeMDHmu7+m7+7+/XakU0KRJcxgamsDc/O2V8RYtrCGXK1GpUmW8fPkKUVFRePLkCb7+elRmTgbq17fMkXf16hX06dNPtV0q1UONGrXw+HEEJk36Fu3adcCECW4af77e/7NTKpU5PveFenz0w4cPkZ6ejoYNG8LY2Bj29vY4fvw4ypUrh27dugF4+4chlUpRqVIlxMXFqfaNiYnJNieZiIiIqKiZmJRC3br1cOPG/3Dt2lVMmOCGM2dO5zpWJvtvZQ2JRAJBEHIdlx+JRJLt4l7WfGd9fX0IQs7mTqlUQiYzzDavNyYmGmXLmuZ5nPr1LVG1alXcv3832zxq4O2UhLzqe7cOfX19ZGR8eHxeDAwMsn0vlWZvJxUKJbp06YrJkz0AvG36FQpFtjGvXydDIpHAxKRUtu2mpuWwc6cfrl69gkuXLmLMmOHYudNPdTVfG/L9/cTTp0/h6emJ9PR0pKen48yZM2jdujWWLFmCV69eISMjA/v27YOdnR2aN2+O8PBwPH78GAqFAkePHkXnzp21cR5EREREKl26dMUvv6xDgwaNcjRvRaFZsxa4c+cmYmNjoFQqcfbsHwCAmjVrISkpCQ8fPgAA/PHHSUgkEpQuXRrVqlXHqVPHAQBXr17GxInj8z3O8+dRiIqKRN269WFqWg6Rkc+QlpaGxMRXCAu7/sH9rK1bIygoEMnJyUhLS8OFC+dEOOvcWVm1wl9/nUdCwgsIgoAVK7zh55f9antISHC2eeBZAgMvYOHCOWjfviMmT/4exsbGiInRbIpuYeX7abG1tUVYWBj69OkDfX192Nvbw83NDWZmZhgyZAjkcjns7e3h6OgIAPDx8YG7uzvS0tJga2ub61QLIiIiKplSM9LhN+jnIsnVRIcOneHjswhjx07Q+FhxcbHw8Pgu1+kTcXGxcHHJPue4RQsrTJ7skfm/b2FkZIyaNWsBeHuVdc6cRVi8eC4kEj18/nkN1Y1p8+YtxvLlS7B79w5IpQZYuHBJrtNRN236BX5+eyCRvF0ZbOLEyahe/XMAgI1NB4wYMRBVqnyG5s2tPnhO9eo1wIABQzB27EiUKVMGVapU0fh9UVe9evUxevQ4TJo0AYIgoF69Bhg+3CXbmMuXgzBgwOAc+7Zr1wHnzp3BiBEDIZPJYGvbJcdNe0VNIhT09wdFJLc5x+tmjs5zHzfvrZxzXEyyWJN2c3Q1izVpP4s1aT/rU6np+fPHqFy5RqGycpvf+zFzxMzS0wPWrVuD0aPHw9jYGHv37kJsbCzc3TV/DoQunl9xqCm3z2ih5hwTERERUcHo6emhTBlTjBs3ElKpAapUqYIffpjzscuiPLA5JiIiIipCI0a4YMQIl49dBqlJOwsTEhEREREVA2yOiYiIiIgysTkmIiIiIsrE5piIiIiIKBNvyCMiIiLRmJWRQWpkKHquPDUNCUmarXVMVBBsjomIiEg0UiNDXOzdT/TcDod+B/JpjiMjI+Hs7Ihevfpi+vTZqu3//HMfo0cPw6xZ8+Dg4AQXl6HZHtusiY4drREYGPLB1+PiYuHjswi+vmsRGPgXIiOfYuDAobmOffXqJb777lsAwIsX8QCA8uUrAAA8PGbhyJGDoi/7lp6ejqlT3fDLL5tEzS1J2BwTERFRiWFqaoorVy5BoVBAX18fAHDmzB8oV85MNaagjbE6KlY0h6/vWgDA/ft3oaeX84l3/9VaTlXL5s0bAABff+2qer1x4yai1xcWdg0tWrQUPbckYXNMREREJYaxsQnq1auPsLDraNnSGgAQHHwZ1tZtVGOyrv5u3rwBcXGxePLkX0RHP4ejY2+MGvW1Wse5di0EO3duhZGRESIiwlGnTl3Mm+eFuLhYuLu7YvnyNTh06AAAwMKiMnr27KXReVy7FoItW37FunW/ws1tPOrXb4DQ0KtITU3F5Mke8Pffh/Dwhxg0aCgGDRqGlJQUrFy5FI8ePYRSqcSwYSNhZ9c9R+7ly0H48suu2bY9ePAPli3zgkKhgEwmw6xZ81SPp/4UsTkmIiKiEuXLL+1w7twZtGxpjbt3b6Nu3XoQBCHXsQ8e/IP16zchOTkJAwf2gbPzQJQpU0at49y6dQO//eaPihXN4erqgitXLqFOnboAgFq1aqN3b2fo6Uk0bow/5Lff/PDrr79g9erl2L59L16+TICLy9vmePv2zWjQoCE8PRfg9etkTJgwBo0aNUHVqtWyZdy5cwvffvtdtm1+frsxePBwdOnSFWfOnMbt2zfZHBMRERGVFB07dsLGjT9DqVTizJk/0KWLHc6cOZ3r2JYtrWFgYAAzs/IoW7YsXr9OVrs5rlWrDiwsKgEAatSohaSkRNHO4X3t2nUAAFSuXAWNGzeFkZERKleuguTkJABASEgw0tJScezYYQBAamoqwsMfZWuOo6IiYWFRWTXdJIuNTQesXLkMV64EoX37Tvjii6+K7DyKAzbHREREVKKYmJRC3br1cOPG/3Dt2lVMmOD2weZYJpOpvpZIJB+8wiz2vpqSSv9r2d5vbgFAqVRgzpxFaNDAEsDbG/zKljXNNuby5SC0a9c+x75fftkVTZo0w8WLf2P//j24fPkiZszwFPkMig+uc0xEREQlTpcuXfHLL+vQoEGjbI2lNunr60OhUGjlWC1btkZAgD8AIC4uDqNGDUF09PNsY4KDL6FtW5sc+86dOxN37txGnz79MHbsBNy/f08rNesqXjkmIiIi0chT094uu1YEuZro0KEzfHwWYezYCRofKy4uFh4e32Hr1sKtatGiRUt4ec1HuXJmaNKkGTZt+kW1koXYxowZhxUrlmLEiIFQKpX49ttJ2aZUpKenIykpSbVU3LtGjBiNpUsXY/v2TdDX14e7+5QiqbG4kAhF+TuAAoiPT4ZS+V9J5uZlsG7m6Dz3cfPeitjYJI2PZW5epkD7FWWWLtYkZhZr0m6OrmaxJu1nsSbtZ30qNT1//hiVK9coVJZUqge5XFnomsTKETNLF2sSM6s41JTbZ1RPT4IKFUrnur9a0yrWrFkDBwcH9OzZE1u3bgUABAUFwcnJCfb29li1apVq7N27d9GvXz9069YNs2fPhlwu1/ikiIiIiIg+hnyb4+DgYFy+fBmHDx/G77//jp07d+LevXuYNWsW1q9fj+PHj+PWrVu4cOECAMDDwwNz5szBqVOnIAgC/Pz8ivwkiIiIiIjEkG9z3KZNG+zYsQNSqRTx8fFQKBRITExEjRo1UL16dUilUjg5OeHkyZN49uwZUlNT0aJFCwCAs7MzTp48WdTnQEREREQkCrVuyDMwMMDatWuxZcsWdO/eHTExMTA3N1e9bmFhgejo6Bzbzc3NER0drVFBH5r/kR9zc/XWJBRrv6LM0sWaxMxiTdrN0dUs1qT9LNak/axPoaaYGD1IpYVf/EqMDDFzxMzSxZrEzNL1mvT09DT63Ku9WsWkSZMwbtw4TJgwARERETle/9D6fhLJh58pnpvcbshTB2/IKx5ZrEm7ObqaxZq0n8WatJ/1qdSkVCoLfRPVp3RzWEnLKg41KZXKHJ/7vG7Iy7c5fvjwIdLT09GwYUMYGxvD3t4eJ0+ezLYAdUxMDCwsLFCpUiXExcWptsfGxsLCwkLjkyIiIqLiybSsMWSG4q8Um54mx6vEN6LnEr0v30/v06dPsXbtWuzZswcAcObMGQwePBjLli3D48ePUa1aNRw9ehT9+vVD1apVYWhoiNDQULRq1QoBAQHo3LlzkZ8EERER6QaZoRQLpx0VPXfuCsd8x0RGRmLgwD6oWbM2JBIgI0OOihUrYtasearHPBfUpk2/wNKyITp2tC1wxrFjh7F//14AQETEI1SrVh1SqQGaNm0OU1PTQufn5tSp40hOTka/fgNFzS3J8m2ObW1tERYWhj59+kBfXx/29vbo2bMnypcvD3d3d6SlpcHW1hbdu3cHAPj6+sLT0xOvX79Go0aNMHLkyCI/CSIiIiIAqFjRHNu2/ffwjl9+WYdVq5bD29u3ULkFeZjI+3r27IWePXsBAPr3d8Ly5WtQpcpnhc7Ny+XLQaLU/ilR6/cekyZNwqRJk7Jts7GxweHDh3OMtbS0hL+/vzjVERERERVC8+ZWCAz8C8DbhrRRoyb455/7WL9+Ey5fDsL+/XugVApo0MASU6fOgL6+Pry9FyA8/BEEQUDfvgPQq1dfeHnNh5VVK1hZtcLMmd/js8+q4tGjB2jQoCGsrFrhxImjSEpKxJIlvqhZs5bGdaqTn5ycBC+v5ahZsxbu3r2NtWtXIi0tFaam5eDhMQuffVY1W6ZSqcTz55HZnpQHAHv37sLJk8cgkUjQsGFjTJ8+u+BvcAkk3u2FRERERDpELpfj7Nk/0LRpc9W2du3aY8+eA0hISMCRIwH4+ect2LZtN8zMymPPnp24eTMMiYmJ2LFjD1avXo+bN8Ny5D58+A9cXL7G7t2/4969O3j+PAobNmxF167dcPjwgULX/aF8O7u3+RkZGfDxWYx587ywZctvGDx4OJYu9cqRc/fuHVhaNs7xnuzatQ3btu3C5s27oKenh9jYmELXXJKIP2OeiIiI6COJi4uFi8tQAEBGRjoaNmyMb75xU73eqFETAMD16yF4+vQJXF1HAwDk8gzUr2+Jvn37499/H+O7775F27Yd8M037jmOUb58BdSvbwkAMDe3QKtWrQEAlStXwfXrkYU+h7zynz17hidPHiMy8il++GGqap/Xr1/nyLl8+SLatWufbZtUKkWTJs0wevQIdOxoC2fnATA35+IJ72JzTERERCXG+3OO32doaAgAUCiU6NKlKyZP9gAApKSkQKFQoEyZMti50w/XrgUjMDAQY8YMx86d2Z/2a2BgkO37d1fwEkN++QqFEp99VlV1ngqFAgkJL3Lk/O9/1zB8uEuO7d7eK3Dv3i1cvHgR06ZNwty5i2Bl1Uq8EyjmOK2CiIiIPjlWVq3w11/nkZDwAoIgYMUKb/j57UZg4AUsXDgHHTp0wuTJ38PY2BgxMZo90Kyo1ahRE4mJiQgLuw7g7SoY8+dnnzf88uVLGBubqH4YyJKQkIBhw/qjTp16GDt2Alq3bouHD//RWu3FAa8cExERkWjS0+RqLbtWkFwx1atXH6NHj8OkSRMgCALq1WuA4cNdoK+vj3PnzmDIkP6QyWSwte2COnXqapy/adMvqFixIvr3F38JNZlMhkWLfLBmjS/S09NhYlIKnp4Lso0JDr6Etm3b5djXzMwMvXs7Y/To4TA0NEKlSpXh4OAkeo3FmUTI7bF2H1FuT8hbN3N0nvu4eW/lE/KKSRZr0m6OrmaxJu1nsSbtZ30qNT1//hiVK9coVNan9LS2kpZVHGrK7TOa1xPyOK2CiIiIiCgTm2MiIiIiokxsjomIiIiIMrE5JiIiIiLKxOaYiIiIiCgTm2MiIiIiokxc55iIiIhEY1pWBtl7D54QQ3paGl4lpoueS/Q+NsdEREQkGpmhYb7PJygIN++tAPJvjs+d+xM7d26DQqGAICjRvXtPDB06EgDw/feT8MMPc1CxonmB61ixYilu3gyDXJ6Bp0+foGbN2gCAAQMG49y5Pwudnxsfn0UYOHAo6tevJ2ou5Y7NMREREZUIMTExWLduNbZs2QVT03JISUmBm9t4fP55DXTsaAtf37WFPsa0aTMAAFFRkXB3d8W2bbtVr/Xs2avQ+bkJD3+E2rXrFEk25aRWc7xu3TqcOHECAGBra4vp06dj5syZCA0NhbGxMQDAzc0NdnZ2CAoKgre3N9LS0tCjRw9MmTKl6KonIiIiyvTq1UvI5XKkpqbC1BQwMTGBp+d8yGRvp3n07++EH3/cgOvXQxEUFIi4uFjExERj4MAhiI6OxrVrV1G2rCl8fddCKjXW+Pi55cfGxmDAgME58g0NDXHixFHs378HSqWABg0sMXXqDBi+NyXlwYN/cjy+Wi6Xw9t7AR49eggA6Nt3AHr16lvAd43el+8NeUFBQQgMDMTBgwcREBCA27dv448//sCtW7ewa9cuHDp0CIcOHYKdnR1SU1Mxa9YsrF+/HsePH8etW7dw4cIFbZwHERERfeLq1auPTp1sMXBgb4wbNxLr16+FQqFEtWrVc4y9e/c2VqxYi/XrN2HdutVo1649tm/fCwAIDr5U6Fqy8n/5Jff8R48e4siRAPz88xZs27YbZmblsWfPzhw5ly9fRLt2HbJtu3kzDImJidi6dTdWr16PmzfDCl0v/Sff5tjc3Bw//PADZDIZDAwMUKdOHURGRiIyMhJz5syBk5MT1q5dC6VSiRs3bqBGjRqoXr06pFIpnJyccPLkSW2cBxERERG+/34m/P2PoE+f/oiOjoKr62hcuHA2x7imTZujVKnSqFy5CgCgVavWAIDKlasgKSmp0HVk5Vep8lmu+devh+Dp0ydwdR0NF5ehCAy8gH//fZwjJzT0KqytW2fbVrt2Hfz772NMneqGU6dO4Jtv3AtdL/0n32kV9er9N/k7IiICx48fx+7duxEcHIyFCxfCxMQErq6u8Pf3h4mJCczN/5uEbmFhgejo6KKpnIiIiOgdFy/+jeTk1/jqK3v07NkLPXv2wuHDB3H06CHY2nbJNtbAwCDb91KpuLdh5ZevUCjRpUtXTJ7sAQBISUmBQqHINub162RIJBKYmJTKtt3UtBx27vTD1atXcOnSRYwZMxw7d/qhTJkyop7Dp0rtT8I///wDV1dXzJgxA7Vr18ZPP/2kem3EiBEICAhA9+7dc+wnkUg0KqhChdIajc9ibl6wD0RB9yvKLF2sScws1qTdHF3NYk3az2JN2s/6FGqKidGDVKqdxybkdxwjIyOsWLEMTZs2w2effQZBEPDvv+Fo0MBSta++vh709CSQSCTZ8rK+lkgk0NOT5Hs8fX29XMeom9+6dWtMm7YLY8aMg5mZGVau9EHVqtUwbtwE1T7Xrl1FmzZtc+T89dcFnDx5DF5eS9GhQwdcu3YV8fExMDMzzfP9eZ9Yf25i/vkXRU16enoafe7Vao5DQ0MxadIkzJo1Cz179sT9+/cRERGBbt26AQAEQYBUKkWlSpUQFxen2i8mJgYWFhZqFwMA8fHJUCoF1ffqnkxsrOa/AjE3L1Og/YoySxdrEjOLNWk3R1ezWJP2s1iT9rM+lZqUSiXkcqVqe3paWuaya+JKT0vLdpzctGrVGqNHj8O0aZMgl8sBAG3b2mDUqLGqfRUKJZRKAYIgZMvL+loQBFUfMnmyG8aOnQBLy0Y5jqVQKLPt9+72d/OzmrT382vVqovRo8dh4sTxEAQB9eo1wNCho7LlBQVdxIABg1XbpFI9yOVKtGljgzNn/sCQIf0hk8nQufOXqFmzTr7vz7uysgpLrBwxs97PUSqVOT73enqSD16Qzbc5joqKwsSJE7Fq1SrY2NgAePsHu2TJErRr1w4mJibYt28f+vbti+bNmyM8PByPHz9GtWrVcPToUfTr168w50dERETFyNsHdWj2sA4xG6wePRzRo4djrq/5+x8BAFSp8hkcHJxU2wMDQ1Rfz549X/V1Xku/VanymSqvoPlOTn3g5NTng8eYMcMz1+1SqRRz5iz84H5UOPk2x5s3b0ZaWhp8fHxU2wYPHozx48djyJAhkMvlsLe3h6Pj2w+ij48P3N3dkZaWBltb21ynWhARERER6aJ8m2NPT094eub+k8uwYcNybLOxscHhw4cLXxkRERERkZZpZwY9ERERlViCIOQ/iOgjKMhnk80xERERFZhUKsPr14lskEnnCIKA168TIZXKNNpP3EX9iIiI6JNiZmaOhIRYJCe/LHCGnp4elMrC35AnVo6YWbpYk5hZul6TVCqDmZl5Pntkx+aYiIiICkxfX4qKFasUKkOXl6rTlRxdzSqJNXFaBRERERFRJjbHRERERESZ2BwTEREREWVic0xERERElInNMRERERFRJjbHRERERESZ2BwTEREREWVic0xERERElInNMRERERFRJjbHRERERESZ2BwTEREREWVic0xERERElEmt5njdunXo2bMnevbsiWXLlgEAgoKC4OTkBHt7e6xatUo19u7du+jXrx+6deuG2bNnQy6XF03lREREREQiy7c5DgoKQmBgIA4ePIiAgADcvn0bR48exaxZs7B+/XocP34ct27dwoULFwAAHh4emDNnDk6dOgVBEODn51fkJ0FEREREJIZ8m2Nzc3P88MMPkMlkMDAwQJ06dRAREYEaNWqgevXqkEqlcHJywsmTJ/Hs2TOkpqaiRYsWAABnZ2ecPHmyqM9BLaZlZTA3L5PtfwBybDMtK/vIlRIRERHRxyLNb0C9evVUX0dEROD48eMYMWIEzM3NVdstLCwQHR2NmJiYbNvNzc0RHR2tUUEVKpTWaPx/xyqT75h1M0fnO8bNeyvMzQ3zHCPPyIDUwCDPGj40Rh3qnEtxzmJN2s3R1SzWpP0s1qT9LNak/SzWpP2sklZTvs1xln/++Qeurq6YMWMGpFIpwsPDs70ukUggCEKO/SQSiUYFxccnQ6n8L0fdk4uNTcrzdU3eJHWy8mu03by35pvzoeyC7FdcsliTdnN0NYs1aT+LNWk/izVpP4s1aT+ruNakpyf54AVZtW7ICw0NhYuLC6ZNm4a+ffuiUqVKiIuLU70eExMDCwuLHNtjY2NhYWGhziGIiIiIiD66fJvjqKgoTJw4Eb6+vujZsycAoHnz5ggPD8fjx4+hUChw9OhRdO7cGVWrVoWhoSFCQ0MBAAEBAejcuXPRngERERERkUjynVaxefNmpKWlwcfHR7Vt8ODB8PHxgbu7O9LS0mBra4vu3bsDAHx9feHp6YnXr1+jUaNGGDlyZNFVT0REREQkonybY09PT3h6eub62uHDh3Nss7S0hL+/f+ErIyIiIiLSMj4hj4iIiIgoE5tjIiIiIqJMbI6JiIiIiDKxOSYiIiIiysTmmIiIiIgoE5tjIiIiIqJMbI6JiIiIiDKxOSYiIiIiysTmmIiIiIgoE5tjIiIiIqJMbI6JiIiIiDKxOSYiIiIiyiT92AV86kzLyiAzNMy2zdy8TLbv09PS8CoxXZtlEREREX2S2Bx/ZDJDQ6ybOTrPMW7eWwGwOSYiIiIqapxWQURERESUic0xEREREVEmtZvj5ORkODo64unTpwCAmTNnwt7eHr1790bv3r3xxx9/AACCgoLg5OQEe3t7rFq1qmiqJiIiIiIqAmrNOQ4LC4OnpyciIiJU227duoVdu3bBwsJCtS01NRWzZs3Czp07UaVKFbi6uuLChQuwtbUVvXAiIiIiIrGpdeXYz88P8+bNUzXCKSkpiIyMxJw5c+Dk5IS1a9dCqVTixo0bqFGjBqpXrw6pVAonJyecPHmySE+AiIiIiEgsal059vLyyvZ9fHw82rVrh4ULF8LExASurq7w9/eHiYkJzM3NVeMsLCwQHR2tUUEVKpTWaHyW95c/KwyxsnShJl2ooahyxMxiTdrPYk3az2JN2s9iTdrPYk3azyppNRVoKbfq1avjp59+Un0/YsQIBAQEoHv37jnGSiQSjbLj45OhVAqq79U9udjYpDxf1+RNEisrvxyxs3LLLsh+RZnFmrSbo6tZrEn7WaxJ+1msSftZrEn7WcW1Jj09yQcvyBZotYr79+/j1KlTqu8FQYBUKkWlSpUQFxen2h4TE5NtTjIRERERkS4rUHMsCAKWLFmCV69eISMjA/v27YOdnR2aN2+O8PBwPH78GAqFAkePHkXnzp3FrpmIiIiIqEgUaFqFpaUlxo8fjyFDhkAul8Pe3h6Ojo4AAB8fH7i7uyMtLQ22tra5TrUg8eX2GGqAj6ImIiIi0oRGzfHZs2dVXw8bNgzDhg3LMcbGxgaHDx8ufGWkEXUeQw3wUdREREREeeET8oiIiIiIMrE5JiIiIiLKxOaYiIiIiCgTm2MiIiIiokxsjomIiIiIMrE5JiIiIiLKxOaYiIiIiCgTm2MiIiIiokxsjomIiIiIMrE5JiIiIiLKxOaYiIiIiCgTm2MiIiIiokxsjomIiIiIMrE5JiIiIiLKJP3YBeRHnpEON++t+Y4hIiIiIiostZvj5ORkDB48GL/88guqVauGoKAgeHt7Iy0tDT169MCUKVMAAHfv3oWnpyeSk5NhbW2NBQsWQCoteA8uNZBh4bSjeY6Zu8IRQFqBj0FEREREBKg5rSIsLAxDhgxBREQEACA1NRWzZs3C+vXrcfz4cdy6dQsXLlwAAHh4eGDOnDk4deoUBEGAn59fkRVPRERERCQmtZpjPz8/zJs3DxYWFgCAGzduoEaNGqhevTqkUimcnJxw8uRJPHv2DKmpqWjRogUAwNnZGSdPniyy4omIiIiIxKTWfAcvL69s38fExMDc3Fz1vYWFBaKjo3NsNzc3R3R0tEYFVahQWqPx/x2rTIH2K8osXaypMFkl+X1hTdrPYk3az2JN2s9iTdrPYk3azyppNRVoMrAgCDm2SSSSD27XRHx8MpTK/3LUPbnY2KQ8X9fkTRIrK78cMbPEPL8P5Rdkv6LKETOLNWk/izVpP4s1aT+LNWk/izVpP6u41qSnJ/ngBdkCNceVKlVCXFyc6vuYmBhYWFjk2B4bG6uaikHFh2lZGWSGhjm2v9uAp6el4VUiVwkhIiKikqVAzXHz5s0RHh6Ox48fo1q1ajh69Cj69euHqlWrwtDQEKGhoWjVqhUCAgLQuXNnsWumIiYzNMS6maPzHPN2eT02x0RERFSyFKg5NjQ0hI+PD9zd3ZGWlgZbW1t0794dAODr6wtPT0+8fv0ajRo1wsiRI0UtmIiIiIioqGjUHJ89e1b1tY2NDQ4fPpxjjKWlJfz9/QtfWaaMDEXmOsZ5jyEiIiIiKiydf0KegYE+Bu77Js8xfoN+1lI1pAl15i4DnL9MREREukPnm2MqvtSZuwxw/jIRERHpDrUeAkJERERE9Clgc0xERERElInTKqhY4NrLREREpA1sjqlYEHPt5dwabd4kSERERACbY/oE8SEnRERE9CGcc0xERERElInNMRERERFRJjbHRERERESZ2BwTEREREWXiDXlEBcTHYxMREZU8bI6JCoiPxyYiIip5OK2CiIiIiCjTJ3PlWJ6RnnkFL/9xRERERPRp+mSaY6mBDAunHc133NwVjgDS8hyjTqPNJpuKOz6ym4iIPkWFao5HjhyJ+Ph4SKVvYxYuXIh///0XP//8MzIyMuDi4oJhw4aJUqguUafRVqfJJhKbmDcJ8kmCRET0KSpwcywIAh49eoTz58+rmuPo6GhMmTIFBw4cgEwmw+DBg9G2bVvUrVtXtIKJ6MN4kyAREVHhFLg5fvToESQSCcaNG4f4+HgMHDgQpUqVQrt27VCuXDkAQLdu3XDy5Em4ubmJVW+BZaTLM6/m5j+OiIiIiD5NBW6OExMTYWNjg/nz5yM1NRUjR45Ejx49YG5urhpjYWGBGzduaJRboULpAtXz/q+N36dIV+8qmR6U+WZlZCjybbQzMhT55gDqz19WJ0tdYmWxJvGy5BkZkBoY5LlPbmOKsqaiztHFPytdrEnMLNak/SzWpP0s1qT9rJJWU4GbYysrK1hZWQEATExM0L9/f3h7e2PChAnZxkkkEo1y4+OToVQKqu/VPbnY2KQ8Xzc3L4OB+77JN8dv0M+iZKmTk5WlzvxldWpSl1hZxbEmMbPEPj915vcW1/c8t+yC7FeUWbpYk5hZrEn7WaxJ+1msSftZxbUmPT3JBy/IFrg5DgkJQUZGBmxsbAC8nYNctWpVxMXFqcbExMTAwsKioIcgohIitxsFC3KTIJ9KSERERa3AzXFSUhLWrl2LvXv3IiMjAwcPHsTy5cvh4eGBFy9ewNjYGKdPn8aiRYvErLfEUWcuNOdBU3En1soXvOGQiIiKWoGb4y+//BJhYWHo06cPlEolhg4dilatWmHKlCkYOXIkMjIy0L9/fzRr1kzMekscPUEpyhg+5IRIM2JdzSYiopKlUOscT548GZMnT862zcnJCU5OToWJ/aToG8rUmr+c35rJYj7khOhTwHWciYgoN5/ME/KIiIoC50ETEZUsbI5LCHWWl8saR0Ti4TxoIqKShc1xCWFgoK/2UnVEpJvUuQqt7SvQvDJORJ8aNseUg7oPJiEicYk5D1qsGw55ZZyIPjVsjikHQdATZQxX0CD6eHjDIRFRwbA5phzEWl6OK2gQ0bu4fB4RFQdsjikHsZaXE/MmQTGnenDaCNHHIdbVbDHnQeviPG8i+rjYHFOREfMmQQH6oowB1Luirc7VbE4bIfo4xJwHrYvzvHkTJNHHxeaYigV1Gm11V+JQ54q2Olez1W3G1R0nBjEbdl5hJ9KMLj4mnVNZiDTH5pg+OWI12rq4fJ6YDbuYV+uJ6OPgVBYizbE5JtIBYl2lFbNhF/NqvVjnx6ksRB8Hp7IULotX64sXNsdEOkBqIM5NkLpK21eq1RnHaSNExZ8uTmXRxav1pBk2x0RU5HRxKotYzXhJn+fNq/VEH4euPoDnU7gyzuaYiD5JYjXs6lz1/y8r7yv/Yl49F6vRFvP8dHF6jS7+QEIkNjHneX8KDxhic0xEpCPEnOeti1N1dHF6jS7+QFLSm39drKmk08WGVpenjbA5JiIirdDF6TVi/kAiyf/BoWqN0cXfRojZsPMHEjb/gG4vWVgkzfGRI0fw888/IyMjAy4uLhg2bFhRHIZ0XLo8Q63/qKTLM7RQDRFR0RLr6aJi0sXpQ7r4G5KS/gOJLj5lVszzE/vKuOjNcXR0NFatWoUDBw5AJpNh8ODBaNu2LerWrSv2oTQiZqOmTpa6DZ9YWenydDXPT3s/rcqkBnCadijfcUdW9AaQWvQFkUbE/JyLRRc/57pYExF9PLr4A4mY06zEytLl1YckgiAIao9Ww8GDB3H16lUsWbIEAPDTTz9BEAS4ubmptX9Cwmsolf+VVMbUEDKpQZ77pMszkPQq7z+EChVK4+vFp/M9/mZPe8THJxc6S50cMbPMysqgZyDL93jKjHQk5PNrBV18z8WqScwsdXLEzNJ2TWJ+zsU6P138nOtiTWJmlfTPOd/z4luTmFm6eH66WJOYWR/7/PT0JDAzK5XrWNGb4w0bNiAlJQVTpkwBAOzfvx83btzAokWLxDwMEREREZHo9MQOzK3XlkgkYh+GiIiIiEh0ojfHlSpVQlxcnOr7mJgYWFhYiH0YIiIiIiLRid4ct2/fHpcuXcKLFy/w5s0bnD59Gp07dxb7MEREREREohN9tYpKlSphypQpGDlyJDIyMtC/f380a9ZM7MMQEREREYlO9BvyiIiIiIiKK9GnVRARERERFVdsjomIiIiIMrE5JiIiIiLKxOaYiIiIiCgTm2MiIiIiokyiL+UmtkuXLsHIyAhWVlbYsmULgoOD0aRJE4wfPx4ymexjl0dEREREJYhOXzletmwZVq1ahUWLFmHSpEkICwvDkCFDEBsbi4ULF37s8igfcrkc27dvh4+PD0JCQrK99uOPP2o9R1drItJV/JwTUXGUkJBQqP11ujn++++/sXfvXuzevRtXrlyBr68vbG1tMX/+fNy4cUOjLLlcjr179yI+Ph7p6elYt24dXF1dsXbtWqSlpX2ULDFret/UqVMLtb8YWXPnzsXdu3dhYWGB6dOn45dfflG9dvbsWa3n6GpNSqUSe/fuxahRo9C9e3c4ODjAxcUFW7ZsQUZGhto5ycnJWLNmDTZs2IBXr17B1dUVVlZWGDlyJJ49e6ZRTWJl6WJNYme9Txf+7omVpYufczGzkpKSsGLFCjx//hyJiYmYOXMmHB0dMWPGDLx48UKjmsTK0sWaxM4qLlJSUnQyqyR48+YNfH190bVrVzRt2hTNmzeHnZ0dFi1ahKSkJI2yoqKi4OHhgblz5+LJkydwcnKCg4MD7OzscO/evQLVp9MPAXF0dMRvv/2GlJQUdO/eHefPn4eZmRnevHkDZ2dnnDhxQu2sadOmAQA8PT2xbt26bJmJiYlYsWKF1rPEyhkxYgQkEkm2bbdu3UKTJk0AADt27FD31ETN6tWrFw4fPgwAePHiBVxcXODs7AwXFxf06dMHAQEBWs3R1ZrmzJkDpVKJvn37wsLCAgAQExODQ4cOqf4BUceECRNQq1YtvHnzBpcuXcKQIUMwaNAg/Pnnnzh48CC2bNmidk1iZeliTWJm6erfPbGydPFzLmbW2LFj0ahRI4wfPx4LFixAtWrV4OjoiDNnziA4OBibNm1SuyaxsnSxJrGzlEol/Pz8cOLECURHR0NPTw8WFhbo3LkzRowYAQMDA7VykpOTsXnzZhgZGWHw4MGYPn06goOD0bRpU3h7e6Nq1apq15Sbvn374uDBg4XKKEyWWO8T8LaBXLRoEZ4/f46uXbvC1dUV+vr6AABXV1ds2LBBqzkTJ05E48aN4ezsDHNzcwBAbGwsAgICEBoaio0bN6p9biNHjoS9vT1SUlKwbds2zJ8/H/b29ggNDcWKFSuwe/dutbNUBB0WEBAgtGnTRmjdurWwc+dOoU+fPsLSpUuFPn36CBs3btQoy9HRUfW1k5OToFQqVd/36NHjo2SJlbNr1y6hc+fOwoEDB4QrV64Ily9fFuzs7IQrV64IV65cUTtH7CxHR0fh9evXqu+fP38ufPnll8Lhw4eFPn36aD1HV2vq1q3bB19zcHBQO6d3796qrzt27JjttV69emlUk1hZuliTmFm6+ndPrCxd/JyLmfXun7WTk1O2197991mbWbpYk9hZnp6ewqxZs4SrV68Kjx8/Fh4/fixcvXpV8PT0FKZNm6Z2jqurq+Dj4yPMmzdPsLe3F7Zu3SqkpKQIhw8fFkaPHq1RTU2aNBEsLS0FS0tLoUGDBqr/z/r6Y2SJ9T4JgiC4uLgI/v7+ws2bNwVXV1dh7NixQkZGhiAI2f891FZOXj1Oz5491c55/7idOnXK9pqm/23IotPTKnr37o0LFy7g3LlzGD58OHx8fFChQgV8//33GDt2rEZZJiYm+OeffwAAn3/+OaKiogAA0dHRGt/YJ1aWWDnDhg3D5s2b4e/vj8jISLRt2xalSpVCmzZt0KZNG7VzxM4aPnw4+vbti0uXLgEAKlWqhE2bNmHlypV4+PCh1nN0tabSpUvnOk3o+vXrMDExUTtHKpUiMDAQJ06cwOvXr3Hr1i0AQEREhEb1iJmlizWJmaWrf/fEyhoxYkSOz/nGjRs/6udczKyyZcvi4sWLAIBGjRrhzp07AID79+/DyMhIo5rEytLFmsTOunr1Kry8vGBtbY3PP/8cn3/+OaytrbFo0SLcvXtX7Zznz59jxowZmD9/PlJSUuDi4gJjY2M4OTkhPj5eo5r27duHFi1aYM2aNbh37x7u3r0LS0tL1dcfI0us9wkAXr58iX79+qFJkyb4+eefUaZMGXh4eGiUIWZO+fLlceLECSiVStU2QRBw7NgxmJmZaZRVunRp7N27F5s2bYJCocC5c+cAANeuXYOhoaHGtQE6Pq1CTNevX4e7uztatmwJY2NjXLx4Ec2bN8ft27exYMEC2Nraaj1LzJoAID09HStXrkRkZCQePnyIY8eOabR/UWSFh4fDyMgIVapUwf79+3H//n00bdoUCQkJcHFx0XqOLtZ09+5dTJ8+HWlpaapfL8XFxUEmk2H58uWwtLRUK+f27dtYsmQJlEolfvjhB8yePRtGRkZ4/vw5lixZgs6dO6tdk1hZuliT2FmAbv7dEyNr8eLF6N27NypWrIgqVaqotr9+/Rr79+//KJ9zMbMePXqEb7/9FiYmJjA3N0dwcDBq1KiBFy9eYN26dWjWrJnaNYmVpYs1iZ3Vv39/zJ07N8c+169fx5IlS7B//361cyZPnoykpCTMnj0bO3bsQJMmTRAREYHvvvsOhw4dUrsmAEhNTYWPjw/S0tLg6emJ4cOHF3hahRhZYr1PWVne3t6oV68eACAjIwNjx45FjRo1cP36dRw5ckSrOVFRUViwYAGuXr2KMmXKAHg7r71169aYO3cuPvvsM7XP7dmzZ1izZg2USiW+++47zJgxA48ePYKRkRF+/PFHNG3aVO2sLJ9Mcwy8nZ8UFBSEx48fQ6FQoGLFiujYsSMqV6780bLEyvnzzz8RFRUFW1tbPH36FEePHsWSJUuwb98+DBo06KNkbdu2DTt37oRSqUS7du0QFRUFOzs7nD17FlZWVnBzc9Nqjq7WlCUyMhIxMTEQBAH79+/HkiVLNM54V1paGsaNG4effvpJ9Y/Px87SxZoKm/X333+jefPmKFu2LPbv34/r16+jVatW6Nevn8Z16FqWtbU1KlSogGnTpsHe3l7jGt536dIlSKVSGBgYIDAwEMHBwWjTpk2BluYUK+v06dM4cOAAzp8/DwCoX78+FixYACsrK43qETNLF2sSMyu3H25iY2NhaGio0Q83d+7cgZeXV64/5Hp7e6NTp04a1ZXl7Nmz+Pnnn/Hq1SucPn26QBliZOX1Pvn6+qJBgwZqZ4WGhsLDwwNTpkyBk5MTgLc3CX7//fc4d+6c2leixcrJIpfLkZCQAEEQUL58eUil4qww/OLFC5QvX77A+38yzXFkZGSer2vyU4pYWWLl+Pr64tatW6hTpw5OnDiBGTNmoHfv3gA0vwlAzCwnJyf4+/sjLi4Ojo6OuHz5MgwNDZGeno7+/furbvTRVo6u1jRz5swc286ePYsuXboAALy9vbWaU9JrEjPLy8sLd+/exapVq7B7927cuHEDX331Ff766y9Uq1YNnp6eateki1l9+vSBr68v5s+fj7S0NIwePRpdunTR+NfowNulOUNCQiCXy1GtWjXo6emhb9++OHv2LBQKBRYvXqz1rA0bNiAsLAwdO3bE2bNnYW1tDSMjI/j7+2PkyJEYOHCg2jWJlaWLNYmdleXdCwKVKlXS6L/DuUlLS8P//d//oUaNGihbtmyhsmJjY3H+/HkMGDCgUDliZIn5PmVkZOS4ke/u3bto2LDhR8l519SpU7Fy5UqN95PL5QgICICxsTHs7e3h4+OjujFz+vTpKFeunMaZOv8QELG4uroiIiICFhYWyPp5QCKRQBAESCQSnDlzRutZYuVcuHABBw8ehFQqxYgRIzBmzBjIZDL06NEDmv7sI2aWUqmETCZD1apVMWbMmGxzfxQKhdZzdLWmcuXKISAgABMmTFD9g3758mWN55m+nyMIQoFyxMzSxZrEzLp48SKOHDkCfX19nDt3Dn5+fpDJZBg0aBAcHR2LfZZEIkHdunWxa9cuBAUFYd++ffDy8kLNmjVRuXJljVb5+fvvv3Ho0CGkp6fD1tYWgYGBMDAwQOfOnVU/gGs76/jx4zh48CD09PTQr18/jBs3Djt27EC/fv0wcOBAjRo+sbJ0sSaxs+RyOX777TdERUXhq6++QuvWrVWv/fjjj3B3d9c4p2vXrrC2tlb9Cl2TnNyYm5vj0qVLojTHBc2Sy+Xw9/eHnZ0dGjVqhF9//RU3b95UPQhNk/m0SqUSO3bswJkzZxAbGwsDAwN8/vnn6NmzJxwcHLSe86EVdUaOHAlAs9V5PD09kZKSgvT0dOzcuRPNmjXDqlWr8Oeff2Lu3LlYu3at2llZPpnmeM+ePRg6dCjmzZuHVq1a6USWWDlZzTQA1KxZExs2bMDo0aNRvnz5HB8+bWbZ29tj+PDh2LFjh+ofqXv37sHT0xM9evTQeo6u1jRjxgx07twZq1evxtSpU9G2bVts374dffv2LXTOjh07NM4RM0sXaxIzy8jICPHx8bCwsECFChWQkpICmUyGN2/eaPzrQV3MevcH4vbt26N9+/bIyMjA/fv38eTJE41qEgQBSUlJSElJQWpqKpKTk2FmZobU1FSN1zkWKystLQ2pqakwMTFBamoqXr58CeDtzdJ6eprdry5Wli7WJHbW3LlzoVQqUb9+fcyYMQMDBw7EhAkTALz9DY66Te27OdOnTy9wDiBusyZW1owZMwAA3bp1w9KlS5GSkoKhQ4fi/PnzmDVrlkY/nPr4+KjmB586dQqWlpaoUqUKduzYgfDwcEycOFGrOd27d8evv/6KyZMno2rVqhAEAXPmzCnQlMTbt2/jyJEjUCgUsLW1xd69ewEAdevW1fgHb5UCrXFRTIWFhQmenp46lSVGzo8//igMGTJECAsLU20LCQkR2rVrJ7Rs2fKjZQmCIAQHB2f7/uHDh8L58+c/Wo6u1iQIgpCQkCBMmjRJ8PHx0XhppKLIKek1iZF15swZoVOnToKPj4+wePFiwdHRUfDy8hJ69Ogh/P7778U+y8/PT6Pj5kXMpTnFytqwYYPQq1cvYdmyZUKvXr2ErVu3Ck+fPhV69+4trF+/XqOaxMrSxZrEznp3Kbj4+HjByclJ2Lp1qyAImi0HJlaOIOjuUopZCrsE7bvvlUKhEAYPHiwIgiCkpaXluTRiUeUIgiD8888/wtChQ4WDBw8KgiBovDxkll69egmPHj0SwsLChGbNmglPnjwRBOHtZ0LTZSKzfFLNcUkWFBQkPHjwINu2yMhIYfHixR81izTn5+en8RqdRZkjZpYu1lTYrH///VfYsmWLMG/ePMHT01NYvXp1th8uS0KWWN68eSMkJycLgiAI9+7dEzZt2iQEBgZ+1KygoCBh06ZNQlBQkCAIgpCcnCzcu3evQDWJlaWLNYmZpYvrzQuCeM2aWFkDBw4U/u///k8QBEGYOHGi8OzZM0EQ3p6nps2/g4ODEBcXp9o/q57k5GSN1hUWKydLWlqa4O3tLbi7uxe4kf37778FW1tboVOnTsIff/whdO/eXZg0aZLw5Zdfqt5/TX0yN+QRERHRx7dv3z5s2bIF8+fPh42NDQDg4cOHGDt2LOLj43Ndw7ooc96lS0spirnc64EDB7BmzRpYWVkhLCwM06ZNQ9OmTTFq1Ci4u7urvYqNWDmA+KtsRUZG4osvvoCJiQlCQkJQr149hISEaJwFfEKrVRAREZFuiIiIgEwmy7byQnJyMvz9/TVaP1usnPddvHgRx44dK/SSmoXNEnMJ2vDwcNy/fx+WlpaoWbMm0tPTkZKSovFqDmLkiL3K1u3bt1G7du1CZ2Vhc0xERERao2vLoepqVkmuycnJSbUyVkREBMaMGQMPDw/06NEDffr0QUBAgFo5Ymdl+WRWqyAiIqKPL7dlTLMUdjnUguR8KKukLPf6oawsH+M9F3R0la13Q4mIiIi0IikpSXBychJCQkJ0IkdXs0pyTbq8ypYgCIJmixMSERERFULp0qWxePHiAv26uyhydDWrJNfk5uYGd3d3lCpVSrWtVatWOHDgAJydnT9aVhbOOSYiIiIiysQrx0REREREmdgcExERERFl4moVRERasnjxYly9ehXA24cVVK1aFUZGRgCAe/fu4dKlSyhfvnyR1/HixQvY2Njg/v37RX4sIqLihs0xEZGWeHp6qr7u0qULfH190bRpUwBAgwYNPlZZRET0DjbHREQ64scff0RYWBhevnyJr7/+GsOGDcOBAwfg7++PN2/eoHTp0ti5cyf279+PPXv2QKlUoly5cpgzZw7q1KmD8PBwLFy4ECkpKYiJiYGlpSVWr14NQ0NDnD59GqtWrYKxsTGaNGmiOmZsbCxmzJiBhIQEAICtrS0mT578kd4BIqKPj80xEZGOqF69OubNm4c7d+5g0KBBGDhwIADgwYMHOHv2LEqXLo3g4GAEBATgt99+g7GxMQIDA+Hu7o7jx4/Dz88Pffr0Qe/evZGRkQFnZ2ecP38erVq1wqxZs7B3717UrVsXGzZsUB3Tz88P1apVw5YtW5CSkoLZs2cjKSkJZcqU+VhvAxHRR8XmmIhIRzg6OgIAGjZsiPT0dCQnJwN4O+WidOnSAIDz58/j8ePHGDx4sGq/V69e4eXLl/Dw8MDFixexceNGREREICYmBikpKQgNDUX9+vVRt25dAMCgQYOwcuVKAECnTp0wfvx4REVFoX379pg2bRobYyL6pLE5JiLSEVLp23+Ssx55mrUMvYmJiWqMUqlE79694eHhofo+JiYGpqammDJlChQKBXr06IEvvvgCUVFRqkervrukfdZxAKBZs2Y4c+YMLl26hMuXL2PAgAH46aef0LJlyyI/XyIiXcSl3IiIipEOHTrg2LFjiImJAQDs2bMHo0aNAgAEBgZi4sSJcHBwgEQiQVhYGBQKBaytrfHgwQPcu3cPAHDgwAFVnq+vL9avX4+uXbti9uzZqFu3LiIiIrR+XkREuoJXjomIipFOnTph3LhxGDNmDCQSCUqXLo1169ZBIpFgypQpmDhxIkxNTWFsbIzWrVvj33//Rfny5eHr64vvv/8eBgYGaN26tSpv1KhR+OGHH+Do6AiZTIYGDRqopncQEX2K+PhoIiIiIqJMnFZBRERERJSJzTERERERUSY2x0REREREmdgcExERERFlYnNMRERERJSJzTERERERUSY2x0REREREmf4f1p+hjZ22kfMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 864x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[[\"Unaccounted Time / s\", *cols]].plot(kind=\"bar\", stacked=True, figsize=(12, 4));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "* Make it relative to the total program run time\n",
+    "* **Slight complication**: Our threads as indexes are not unique; we need to find new unique indexes\n",
+    "* Could be anythig, but we use a **multi index**!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>Runtime Program / s</th>\n",
+       "      <th>Scale</th>\n",
+       "      <th>Plastic</th>\n",
+       "      <th>Avg. Neuron Build Time / s</th>\n",
+       "      <th>Min. Edge Build Time / s</th>\n",
+       "      <th>Max. Edge Build Time / s</th>\n",
+       "      <th>Min. Init. Time / s</th>\n",
+       "      <th>Max. Init. Time / s</th>\n",
+       "      <th>Presim. Time / s</th>\n",
+       "      <th>Sim. Time / s</th>\n",
+       "      <th>Virt. Memory (Sum) / kB</th>\n",
+       "      <th>Local Spike Counter (Sum)</th>\n",
+       "      <th>Average Rate (Sum)</th>\n",
+       "      <th>Number of Neurons</th>\n",
+       "      <th>Number of Connections</th>\n",
+       "      <th>Min. Delay</th>\n",
+       "      <th>Max. Delay</th>\n",
+       "      <th>Unaccounted Time / s</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Nodes</th>\n",
+       "      <th>Tasks/Node</th>\n",
+       "      <th>Threads/Task</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
+       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>420.42</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>88.12</td>\n",
+       "      <td>88.18</td>\n",
+       "      <td>1.14</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>17.26</td>\n",
+       "      <td>311.52</td>\n",
+       "      <td>46560664.0</td>\n",
+       "      <td>825499</td>\n",
+       "      <td>7.48</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>2.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>5</td>\n",
+       "      <td>202.15</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>47.98</td>\n",
+       "      <td>48.48</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>7.95</td>\n",
+       "      <td>142.81</td>\n",
+       "      <td>47699384.0</td>\n",
+       "      <td>802865</td>\n",
+       "      <td>7.03</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>2.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>200.84</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>46.03</td>\n",
+       "      <td>46.34</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>1.01</td>\n",
+       "      <td>7.87</td>\n",
+       "      <td>142.97</td>\n",
+       "      <td>46903088.0</td>\n",
+       "      <td>802865</td>\n",
+       "      <td>7.03</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>3.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <th>2</th>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>164.16</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>40.03</td>\n",
+       "      <td>41.09</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>1.58</td>\n",
+       "      <td>6.08</td>\n",
+       "      <td>114.88</td>\n",
+       "      <td>46937216.0</td>\n",
+       "      <td>802865</td>\n",
+       "      <td>7.03</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>2.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>12</th>\n",
+       "      <td>6</td>\n",
+       "      <td>141.70</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>32.93</td>\n",
+       "      <td>33.26</td>\n",
+       "      <td>0.62</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>5.41</td>\n",
+       "      <td>100.16</td>\n",
+       "      <td>50148824.0</td>\n",
+       "      <td>813743</td>\n",
+       "      <td>7.27</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>2.28</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               id  Runtime Program / s  Scale  Plastic  \\\n",
+       "Nodes Tasks/Node Threads/Task                                            \n",
+       "1     2          4              5               420.42     10     True   \n",
+       "                 8              5               202.15     10     True   \n",
+       "      4          4              5               200.84     10     True   \n",
+       "2     2          4              5               164.16     10     True   \n",
+       "1     2          12             6               141.70     10     True   \n",
+       "\n",
+       "                               Avg. Neuron Build Time / s  \\\n",
+       "Nodes Tasks/Node Threads/Task                               \n",
+       "1     2          4                                   0.29   \n",
+       "                 8                                   0.28   \n",
+       "      4          4                                   0.15   \n",
+       "2     2          4                                   0.20   \n",
+       "1     2          12                                  0.30   \n",
+       "\n",
+       "                               Min. Edge Build Time / s  \\\n",
+       "Nodes Tasks/Node Threads/Task                             \n",
+       "1     2          4                                88.12   \n",
+       "                 8                                47.98   \n",
+       "      4          4                                46.03   \n",
+       "2     2          4                                40.03   \n",
+       "1     2          12                               32.93   \n",
+       "\n",
+       "                               Max. Edge Build Time / s  Min. Init. Time / s  \\\n",
+       "Nodes Tasks/Node Threads/Task                                                  \n",
+       "1     2          4                                88.18                 1.14   \n",
+       "                 8                                48.48                 0.70   \n",
+       "      4          4                                46.34                 0.70   \n",
+       "2     2          4                                41.09                 0.52   \n",
+       "1     2          12                               33.26                 0.62   \n",
+       "\n",
+       "                               Max. Init. Time / s  Presim. Time / s  \\\n",
+       "Nodes Tasks/Node Threads/Task                                          \n",
+       "1     2          4                            1.20             17.26   \n",
+       "                 8                            1.20              7.95   \n",
+       "      4          4                            1.01              7.87   \n",
+       "2     2          4                            1.58              6.08   \n",
+       "1     2          12                           0.95              5.41   \n",
+       "\n",
+       "                               Sim. Time / s  Virt. Memory (Sum) / kB  \\\n",
+       "Nodes Tasks/Node Threads/Task                                           \n",
+       "1     2          4                    311.52               46560664.0   \n",
+       "                 8                    142.81               47699384.0   \n",
+       "      4          4                    142.97               46903088.0   \n",
+       "2     2          4                    114.88               46937216.0   \n",
+       "1     2          12                   100.16               50148824.0   \n",
+       "\n",
+       "                               Local Spike Counter (Sum)  Average Rate (Sum)  \\\n",
+       "Nodes Tasks/Node Threads/Task                                                  \n",
+       "1     2          4                                825499                7.48   \n",
+       "                 8                                802865                7.03   \n",
+       "      4          4                                802865                7.03   \n",
+       "2     2          4                                802865                7.03   \n",
+       "1     2          12                               813743                7.27   \n",
+       "\n",
+       "                               Number of Neurons  Number of Connections  \\\n",
+       "Nodes Tasks/Node Threads/Task                                             \n",
+       "1     2          4                        112500             1265738500   \n",
+       "                 8                        112500             1265738500   \n",
+       "      4          4                        112500             1265738500   \n",
+       "2     2          4                        112500             1265738500   \n",
+       "1     2          12                       112500             1265738500   \n",
+       "\n",
+       "                               Min. Delay  Max. Delay  Unaccounted Time / s  \n",
+       "Nodes Tasks/Node Threads/Task                                                \n",
+       "1     2          4                    1.5         1.5                  2.09  \n",
+       "                 8                    1.5         1.5                  2.43  \n",
+       "      4          4                    1.5         1.5                  3.12  \n",
+       "2     2          4                    1.5         1.5                  2.45  \n",
+       "1     2          12                   1.5         1.5                  2.28  "
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_multind = df.set_index([\"Nodes\", \"Tasks/Node\", \"Threads/Task\"])\n",
+    "df_multind.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_multind[[\"Unaccounted Time / s\", *cols]]\\\n",
+    "    .divide(df_multind[\"Runtime Program / s\"], axis=\"index\")\\\n",
+    "    .plot(kind=\"bar\", stacked=True, figsize=(14, 6), title=\"Relative Time Distribution\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Next _Level_: Hierarchical Data\n",
+    "\n",
+    "* `MultiIndex` only a first level\n",
+    "* More powerful:\n",
+    "    - Grouping: `.groupby()` (\"Split-apply-combine\", [API](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html), [User Guide](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html))\n",
+    "    - Pivoting: `.pivot_table()` ([API](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot_table.html), [User Guide](https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html)); also `.pivot()` (specialized version of `.pivot_table()`, [API](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot.html))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>Tasks/Node</th>\n",
+       "      <th>Threads/Task</th>\n",
+       "      <th>Runtime Program / s</th>\n",
+       "      <th>Scale</th>\n",
+       "      <th>Plastic</th>\n",
+       "      <th>Avg. Neuron Build Time / s</th>\n",
+       "      <th>Min. Edge Build Time / s</th>\n",
+       "      <th>Max. Edge Build Time / s</th>\n",
+       "      <th>Min. Init. Time / s</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Presim. Time / s</th>\n",
+       "      <th>Sim. Time / s</th>\n",
+       "      <th>Virt. Memory (Sum) / kB</th>\n",
+       "      <th>Local Spike Counter (Sum)</th>\n",
+       "      <th>Average Rate (Sum)</th>\n",
+       "      <th>Number of Neurons</th>\n",
+       "      <th>Number of Connections</th>\n",
+       "      <th>Min. Delay</th>\n",
+       "      <th>Max. Delay</th>\n",
+       "      <th>Unaccounted Time / s</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Nodes</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5.333333</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>185.023333</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.220000</td>\n",
+       "      <td>42.040000</td>\n",
+       "      <td>42.838333</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.226667</td>\n",
+       "      <td>132.061667</td>\n",
+       "      <td>4.806585e+07</td>\n",
+       "      <td>816298.000000</td>\n",
+       "      <td>7.215000</td>\n",
+       "      <td>112500.0</td>\n",
+       "      <td>1.265738e+09</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>2.891667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.333333</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>73.601667</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.168333</td>\n",
+       "      <td>19.628333</td>\n",
+       "      <td>20.313333</td>\n",
+       "      <td>0.191667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.725000</td>\n",
+       "      <td>48.901667</td>\n",
+       "      <td>4.975288e+07</td>\n",
+       "      <td>818151.000000</td>\n",
+       "      <td>7.210000</td>\n",
+       "      <td>112500.0</td>\n",
+       "      <td>1.265738e+09</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.986667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.333333</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>43.990000</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.138333</td>\n",
+       "      <td>12.810000</td>\n",
+       "      <td>13.305000</td>\n",
+       "      <td>0.135000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.426667</td>\n",
+       "      <td>27.735000</td>\n",
+       "      <td>5.511165e+07</td>\n",
+       "      <td>820465.666667</td>\n",
+       "      <td>7.253333</td>\n",
+       "      <td>112500.0</td>\n",
+       "      <td>1.265738e+09</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.745000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.333333</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>31.225000</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.116667</td>\n",
+       "      <td>9.325000</td>\n",
+       "      <td>9.740000</td>\n",
+       "      <td>0.088333</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.066667</td>\n",
+       "      <td>19.353333</td>\n",
+       "      <td>5.325783e+07</td>\n",
+       "      <td>819558.166667</td>\n",
+       "      <td>7.288333</td>\n",
+       "      <td>112500.0</td>\n",
+       "      <td>1.265738e+09</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.275000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5.333333</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>24.896667</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.140000</td>\n",
+       "      <td>7.468333</td>\n",
+       "      <td>7.790000</td>\n",
+       "      <td>0.070000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.771667</td>\n",
+       "      <td>14.950000</td>\n",
+       "      <td>6.075634e+07</td>\n",
+       "      <td>815307.666667</td>\n",
+       "      <td>7.225000</td>\n",
+       "      <td>112500.0</td>\n",
+       "      <td>1.265738e+09</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.496667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>5.333333</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>20.215000</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.106667</td>\n",
+       "      <td>6.165000</td>\n",
+       "      <td>6.406667</td>\n",
+       "      <td>0.051667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.630000</td>\n",
+       "      <td>12.271667</td>\n",
+       "      <td>6.060652e+07</td>\n",
+       "      <td>815456.333333</td>\n",
+       "      <td>7.201667</td>\n",
+       "      <td>112500.0</td>\n",
+       "      <td>1.265738e+09</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.990000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6 rows \u00d7 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             id  Tasks/Node  Threads/Task  Runtime Program / s  Scale  \\\n",
+       "Nodes                                                                   \n",
+       "1      5.333333         3.0           8.0           185.023333   10.0   \n",
+       "2      5.333333         3.0           8.0            73.601667   10.0   \n",
+       "3      5.333333         3.0           8.0            43.990000   10.0   \n",
+       "4      5.333333         3.0           8.0            31.225000   10.0   \n",
+       "5      5.333333         3.0           8.0            24.896667   10.0   \n",
+       "6      5.333333         3.0           8.0            20.215000   10.0   \n",
+       "\n",
+       "       Plastic  Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n",
+       "Nodes                                                                  \n",
+       "1         True                    0.220000                 42.040000   \n",
+       "2         True                    0.168333                 19.628333   \n",
+       "3         True                    0.138333                 12.810000   \n",
+       "4         True                    0.116667                  9.325000   \n",
+       "5         True                    0.140000                  7.468333   \n",
+       "6         True                    0.106667                  6.165000   \n",
+       "\n",
+       "       Max. Edge Build Time / s  Min. Init. Time / s  ...  Presim. Time / s  \\\n",
+       "Nodes                                                 ...                     \n",
+       "1                     42.838333             0.583333  ...          7.226667   \n",
+       "2                     20.313333             0.191667  ...          2.725000   \n",
+       "3                     13.305000             0.135000  ...          1.426667   \n",
+       "4                      9.740000             0.088333  ...          1.066667   \n",
+       "5                      7.790000             0.070000  ...          0.771667   \n",
+       "6                      6.406667             0.051667  ...          0.630000   \n",
+       "\n",
+       "       Sim. Time / s  Virt. Memory (Sum) / kB  Local Spike Counter (Sum)  \\\n",
+       "Nodes                                                                      \n",
+       "1         132.061667             4.806585e+07              816298.000000   \n",
+       "2          48.901667             4.975288e+07              818151.000000   \n",
+       "3          27.735000             5.511165e+07              820465.666667   \n",
+       "4          19.353333             5.325783e+07              819558.166667   \n",
+       "5          14.950000             6.075634e+07              815307.666667   \n",
+       "6          12.271667             6.060652e+07              815456.333333   \n",
+       "\n",
+       "       Average Rate (Sum)  Number of Neurons  Number of Connections  \\\n",
+       "Nodes                                                                 \n",
+       "1                7.215000           112500.0           1.265738e+09   \n",
+       "2                7.210000           112500.0           1.265738e+09   \n",
+       "3                7.253333           112500.0           1.265738e+09   \n",
+       "4                7.288333           112500.0           1.265738e+09   \n",
+       "5                7.225000           112500.0           1.265738e+09   \n",
+       "6                7.201667           112500.0           1.265738e+09   \n",
+       "\n",
+       "       Min. Delay  Max. Delay  Unaccounted Time / s  \n",
+       "Nodes                                                \n",
+       "1             1.5         1.5              2.891667  \n",
+       "2             1.5         1.5              1.986667  \n",
+       "3             1.5         1.5              1.745000  \n",
+       "4             1.5         1.5              1.275000  \n",
+       "5             1.5         1.5              1.496667  \n",
+       "6             1.5         1.5              0.990000  \n",
+       "\n",
+       "[6 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby(\"Nodes\").mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Pivoting\n",
+    "\n",
+    "* Combine categorically-similar columns\n",
+    "* Creates hierarchical index\n",
+    "* Respected during plotting with Pandas!\n",
+    "* A pivot table has three *layers*; if confused, think about the related questions\n",
+    "    - `index`: \u00bbWhat's on the `x` axis?\u00ab\n",
+    "    - `values`: \u00bbWhat value do I want to plot [on the `y` axis]?\u00ab\n",
+    "    - `columns`: \u00bbWhat categories do I want [to be in the legend]?\u00ab\n",
+    "* All can be populated from base data frame\n",
+    "* Might be aggregated, if needed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_demo[\"H\"] = [(-1)**n for n in range(5)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>H</th>\n",
+       "      <th>-1</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>-3.918282</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.389056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-2.504068</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.700594</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-1.918282</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.515929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-0.213769</th>\n",
+       "      <td>0.972652</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.518282</th>\n",
+       "      <td>2.952492</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "H                -1         1\n",
+       "F                            \n",
+       "-3.918282       NaN  7.389056\n",
+       "-2.504068       NaN  1.700594\n",
+       "-1.918282       NaN  0.515929\n",
+       "-0.213769  0.972652       NaN\n",
+       " 0.518282  2.952492       NaN"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_pivot = df_demo.pivot_table(\n",
+    "    index=\"F\",\n",
+    "    values=\"E2\",\n",
+    "    columns=\"H\"\n",
+    ")\n",
+    "df_pivot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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bhpycHH9nCRm2QeMgRcRA3boEuuYVHYeITKLJwr5w4QIWLFiA2bNnByJPSJAUO+zD74V2rhju/Z+LjkNEJqE0tcH8+fMxZ84clJaWXtMOYmIifNrO6Yy8pvc3Kz02HacOfwn16xzED7kdljbtGvx5qI2HLzgmDXE8LhfsY9JoYWdnZyMhIQEpKSlYvXr1Ne2gvLwKmtb440WdzkiUlV28pvc3M2nIvdBWzkfJR4vhuPXBSx8P1fFoDMekIY7H5YJlTGRZuuqBbqOFvX79epSVlSErKwsVFRWoqanB73//e8ybN88vQUONJboTrDeMgrvwY1j73AZLh+6iIxGRgTVa2P/4xz8u/f/q1auxc+dOlnUrsw/OgufQNtRtXYLwic9CknilJRFdGdtBMMkWBvvwe6CVHYXn4BbRcYjIwHwu7EmTJuGll17yZ5aQpfRMgSWuF9Sd2dDVatFxiMigeIRtAJIk1a+0rlZBzef17kR0ZSxsg7DEdoU1KR3ufblQTx8THYeIDIiFbSD2myZBsrVB+Sd/40rrRHQZFraBSI4I2IZORt2JffAc2SE6DhEZDAvbYKyJt8IW3wPq9uXQXbWi4xCRgbCwDUaSZcSOfQh6zQW4dq8THYeIDISFbUCOTr2h9L4Frj0fQ7twbc9wIaLgw8I2KPvQyYBiQ13eUp6AJCIALGzDksPbwX7TJHhPFsFzfJfoOERkACxsA7P2vR1ydGeoecuge1yi4xCRYCxsA5NkS/0dkFXlcH3zoeg4RCQYC9vglI59oPQYBlfBh9Aqy0THISKBWNgmYB92DyBZoG5/V3QUIhKIhW0CckR72AZPgOfYLniKC0XHISJBWNgmYbthNKR28fWX+XndouMQkQAsbJOQLFY4UqdDrzgN155PRMchIgFY2CaidBkApesguHa9D636vOg4RBRgLGyTsadMB3Qv1O3viY5CRAHGwjYZua0TtuRMeI5sh6fkgOg4RBRAPhX2woULkZGRgczMzAYrqZMYtoGZkCJjoW5dAl3zio5DRAHSZGHv3LkT27dvx/vvv49Vq1bhnXfewdGjRwORja5CUmywD58G7fxJuPd9JjoOEQVIk4U9dOhQvP3221AUBeXl5fB6vQgPDw9ENmqE0m0wLJ37Q/1qNbSaCtFxiCgAfJoSsVqteP3115GZmYmUlBTExcX5Oxc1QZIkOFLvA7wuqDtXio5DRAEg6c142HJtbS1mz56NjIwM3HPPPf7MRT4q/+wdVGxbg46z/gBHp96i4xCFPI9XQ+HhsxjYywlZllr1vZWmNjhy5AhcLheSkpIQFhaG0aNH4+DBgz7voLy8CprW+O8EpzMSZWUXfX7PYNec8dATx0Aq2IRTH7yJ8InzIcnBeeEPf0Ya4nhczghjous6/rHhALYUluL3jwxHfPvmTx/LsoSYmIgr/1lTn3zy5Ek8++yzcLlccLlcyM3NxY033tjsEOQfki0M9uH3Qjt7DO6DX4qOQxTS3t96DFsKSzE+tds1lXVTmjzCHjFiBAoKCjBx4kRYLBaMHj0amZmZrR6Erp3SYxgs+z+Ha+dKWK+/CZLjyr+dich/NheWYO2W75DWPx4Tb7neL/to1hz2teCUSPNdy3h4y4tRs3o+rEnpcNw800/JxOHPSEMcj8uJHJOio+X4U3YhkrpG4ekpyVAs1z412aIpETIHS0wXWPuOhHv/5/CePS46DlHIOH7qIt5YU4TOzjZ4/K4bWlTWTWFhBxH7TXdBskfU3wHJldaJ/O7shVr8KbsAEQ4FT09JRpi9yVnmFmFhBxHJ3gb2oVPgPX0InsPbRMchCmpVtW4syC6A26Ph51OSER1p9/s+WdhBRkm8GbKzO9Tt70F31YqOQxSU3B4vFq0qRNmFWjx59w3o5AzMiX4WdpCRJBmOtBnQayuh7lorOg5R0NF0HW99sB/fnqzAQ+P6IvG66IDtm4UdhCwdusPa5xa492yE93yJ6DhEQWXFZ4eRf+AMpqb3xNCkwD6mg4UdpGxDJgNWO9S8pTwBSdRKNn5VjE++KsbIGztjzNAuAd8/CztIyWFtYR8yCd7v98LzXb7oOESml3/gDJbnHsLg3k5MG9kLktS6zwnxBQs7iFmT0iG37wJ127vQParoOESmdejkBfz3un3o3qktHhnft9Uf6uQrFnYQk2QL7GkzoFefg2v3B6LjEJlSaXk1Xl9ZiJh2Djx19wDYrBZhWVjYQU5JSITSMwWugg3QKs+IjkNkKhVVKhasKIBFljBnajIiw21C87CwQ4B92FTAoqAub5noKESmUefy4E8rC1FZ48LTU5LRISpMdCQWdiiQ20TDPjgL3hPfwHPiG9FxiAzPq2n465q9OHH6Ih7L6o/rE9qKjgSAhR0yrP1HQY5KQF3eMugel+g4RIal6zre+fgg9hwtx/1jEpHcM1Z0pEtY2CFCsiiwp94HvfIMXHs+Fh2HyLDW5R3DlwWlGJfaDbcN7CQ6TgMs7BCidO4PpduNcO1eB62qXHQcIsPZUliKNZu/Q2r/eNzlp0UIWoKFHWLsKdMAXYe6/T3RUYgMpei7cvzzowPo2y0as+7sI+TGmKawsEOMHBkL26Bx8BzdCc/3+0THITKEE6cv4o2cIiTEtMETfl6EoCWMmYr8yjbgTkiRTqh5S6BrHtFxiIQ6W1GLBdkFaONQMGeq/xchaAkWdgiSFBscKdOhnS+BuyhXdBwiYarr3FiwogAut4Y5AVqEoCV8KuxFixYhMzMTmZmZeOWVV/ydiQLA0nUgLF0GQP06B1rNBdFxiALO7dHw51V76hchmBS4RQhaosnCzsvLw5YtW5CTk4M1a9Zg79692LhxYyCykR9JkgRH6nTA64G6M1t0HKKA0nQdf/twH74tvoAHM5PQp2vgFiFoiSYL2+l0Yu7cubDZbLBarejRowdKSvhQ/GAgt4uHbcBYeL7dCu+pQ6LjEAXMys+PYOf+M5iS3gPD+8aLjuOzJgu7V69eGDhwIADg2LFjWL9+PUaMGOHvXBQgtkHjIbVpj7qtS6Brmug4RH63Mb8YH+08gdsHd8LYodeJjtMsku7jciSHDh3Co48+iieffBJ33XWXv3NRAFXt24ozOf8PsWMfQdsbx4iOQ+Q3eYUleOntrzC0bzyemTUUFkHPtb5WPl2/8vXXX+Opp57CvHnzkJmZ2awdlJdXQdMa/53gdEairOxis943mAV6PPTYG2DpmISzny9FXYcBkBzGO/nCn5GGOB6Xa2pMDp+swGvLd6N7QlvMGpuIc+VVAUznO1mWEBNz5b+DTU6JlJaW4oknnsBrr73W7LImc5AkCfbU+wBXLdSvVoqOQ9TqSsursXBlAdpH2vHU5AGwC1yEoCWaPML+29/+BlVV8dJLL1362L333otp06b5NRgFlqV9Z1j73QF30UZYk26DJbab6EhEraKi2oUFKwogG2QRgpbweQ77WnFKpPlEjYfuqkH1e3Mhte2A8AnzIEnGua+KPyMNcTwud6UxUV1evLxsF0rOVuNX0weje0djPNe6MS2aEqHQIdnCYR82Fdrpw/AcyhMdh6hFvJqGv64twvHTFzE7q78pyropLGxqQOmVCrlDD6g7VkB31YiOQ3RN6hch+BaFR8px/+hEDOxlnEUIWoKFTQ1IkgxH2v3Qay9Czc8RHYfomnyw7Ti+LChBZkpX3DbIWIsQtAQLmy5jcXaDtW863EUboe7Mhq7zhhoyj617SpHz5VGk9IvDpFu7i47Tqoz7HEESyp46HdA0uL75EFrFaTjSH4akGPtJZkR7vzuHxRsOIKlrNB7ISDLkIgQtwcKmK5JkBfZbfgI5Kh7q9vdQU1WOsDFPQw6PEh2N6Iq+K6nAGzl7kBATbuhFCFoi+L4iajWSJME2YCzCRj8F7XwJanJegLf8hOhYRJcpr6jD829tR5hdwc+nJCPcEZzHoixsapLSbRDCJ8wDoKNm7YvwHN8tOhLRJdV1bizILoDq8mDO1GS0b+sQHclvWNjkE0tsV4RPnA85KgG1H78OV+HH8PM9V0RNcns0LFq1B6fP1WDeA0PR2QSLELQEC5t8JreJRvj4Z6B0Gwx1+7tQt/yTa0KSMD8sQnCw+AJ+mpmEAT2doiP5HQubmkWy2uEY9QRsyRlw79+E2g0LoKvVomNRCFq1qX4Rgsm39cDwfuZZhKAlWNjUbJIkwz5sKhwjfgpvyQHUrH0RWuUZ0bEohOR+fRIbdpxA+uBOuHOYuRYhaAkWNl0za+ItCMv8P9BqK1Cz5rfwcJkxCoBd35Zh2cZvMbBnLO67o3fQXWvdGBY2tYjSMQltsn4N2MNR+8HLcPOhUeRHh7+vwJvv78X1Hdvi0ax+kE22YkxLsbCpxeSoeLTJ+jUscT1Q9/l/Q81fzStIqNWdOleD11cWItrkixC0BAubWoXkiEBYxi+h9L4Frl3voy73r9A9LtGxKEhUVruwYMU3AIA5U5PR1sSLELREcN4OREJIFgWOEQ/CFRUP187s+tvZRz8FObyd6GhkYqrLi4UrC1BR5cIvpw9CXHS46EjC8AibWpUkSbAPzIRj1M+glRejZs0L8J47KToWmZRX0/Bfa4tw7NRFPJrVDz06hvYvfxY2+YX1+psQPuEZQPOiZu3v4CkuFB2JTEbXdSz95FsUHCnHjFG9MahX8N8Y0xQWNvmNxXl9/e3sbTug9qMFcO39VHQkMpH1249j0zclyBjeFemDO4uOYwg+F3ZVVRXGjRuHkyf5z1vynRzRHuET5sHSJRnq1iWo27oEuuYVHYsMLq+oFKu+OIrh/eIwaURwLULQEj4VdkFBAaZNm4Zjx475OQ4FI8nqQNjop2C9YQzcez9F7ccLobtqRccig9p37Bz+sf4A+lwXhQczkiCH0I0xTfGpsFesWIHnnnsOHTp08HceClKSLMORMg32W2bBe7Ko/nb2i2dFxyKDKT5ThTdy9iA+Jhw/mxScixC0hKQ34w6H22+/HW+//TY6d+Z8El27mu8KcGbVa5AUG+Km/CccnXqLjkQGcK6yDr/40xcAgFefvBXO6DDBiYzH79dhl5dXQdMa/53gdEairOyiv6OYRtCPR0R3OCY8i9qPFqDknflw3PYwrD2GNvopQT8mzRSM47H/+Hnouo6nJycDHk+zv75gGRNZlhATc+XnevPfGySEJbojwu+aD0tsN9Tl/gXqrvd5O3uIS+oajVcfS0WXDsG9CEFLsLBJGNkRibBxv4LSMwWu/NWo2/QWdK9bdCwSKJSevHcteGs6CSVZrHCkP1J/O3t+DmovnoVj9JOQHZGioxEZTrOOsD/77DOecKRWJ0kS7IOz4Bj5GLxlR+tXZ79QIjoWkeFwSoQMw9pjGMLHzQU8av2CCCf3io5EZCgsbDIUS1xPhE/8NeQ2Majd8Ee49m8SHYnIMFjYZDhypBPhWf8Xls79oG5ejLpt7/J2diKwsMmgJFsYwsb8HNZ+I+He8zFOr3wVurtOdCwioXiVCBmWJFvgSLsfcrsE1GxbBvncaYSN+TnkiPaioxEJwSNsMjxb/zsQP/UZaJVn6hdEKDsmOhKRECxsMoXwnoMRnvUsIFtQ8/7v4f4uX3QkooBjYZNpWNp3rl8QIaYz6jYugqtgg+hIRAHFwiZTkcPbIXzcXCjdh0LdtZa3slNI4UlHMh1JscEx8jHAo0KyWEXHIQoYFjaZkiRJgNUhOgZRQHFKhIjIJFjYREQmwcImIjIJFjYRkUmwsImITIKFTURkEn6/rE+WfVujzdftQgXH43Ick4Y4HpcLhjFp7GuQdC5VTURkCpwSISIyCRY2EZFJsLCJiEyChU1EZBIsbCIik2BhExGZBAubiMgkWNhERCbBwiYiMglDFfa+ffvQv39/0TEMIT8/H5MmTcL48eMxe/ZsVFRUiI4k1Ndff427774bWVlZ+MlPfoLvv/9edCTDWLhwIf785z+LjiHMunXrkJGRgVGjRmHp0qWi4/iXbhA1NTX6Pffco/fu3Vt0FEO444479EOHDum6ruuvvvqq/sc//lFwIrHS09P1/fv367qu69nZ2frs2bMFJxKvsrJSf+aZZ/QBAwbor7/+uug4Qpw6dUpPT0/Xz58/r1dXV+vjx4+/9PcmGBnmCPull17CrFmzRMcwjPXr16Nnz55wu904ffo02rZtKzqSMC6XC08//TT69OkDAEhMTERpaangVOLl5uaiW7dueOCBB0RHESYvLw/Dhw9HVFQUwsPDMWbMGHz00UeiY/mNIQo7NzcXdXV1GDt2rOgohmG1WnHw4EGMGDECO3bsQGZmpuhIwthsNmRlZQEANE3DokWLcMcddwhOJd7EiRPxyCOPwGKxiI4izJkzZ+B0Oi+97tChA06fPi0wkX8FdNX0DRs24A9/+EODj3Xv3h1VVVVYvHhxIKMYxtXGZPHixUhMTEReXh6WL1+OOXPmYPny5YJSBk5j4+FyuTB37lx4PB48+uijghIGXmNjEur0KzxsVJLM/4jVqxH+eNXs7Gy8+eabaNOmDQDgwIED6NOnD5YuXYqIiAiR0YRRVRWbN2++dBRZU1ODtLQ07N69W3Aycaqrq/HYY48hKioKr732Gmw2m+hIhvHDCccnn3xScJLAy8nJQX5+Pl588UUAwBtvvAFd1/Gzn/1McDL/ED4lMmXKFHz66adYu3Yt1q5dCwBYu3ZtyJY1ACiKgt/85jcoKioCUH+ENXjwYMGpxPrlL3+Jrl27YuHChSxruiQ1NRXbtm3DuXPnUFtbi08++QS33nqr6Fh+E9ApEfKNxWLBggULMH/+fHi9XsTFxV06gghF+/btQ25uLnr27ImJEycCqJ+rfOutt8QGI+Hi4uIwZ84czJw5E263G5MnT8aAAQNEx/Ib4VMiRETkG+FTIkRE5BsWNhGRSbCwiYhMgoVNRGQSLGwiIpPgZX0UUhITE9G7d2/I8r+OVfr37x/Sl02SebCwKeT885//RPv27UXHIGo2TokQEZkEb5yhkHKlKZG///3viImJEZiKyDecEqGQwykRMitOiRARmQQLm4jIJFjYREQmwZOOREQmwSNsIiKTYGETEZkEC5uIyCRY2EREJsHCJiIyCRY2EZFJsLCJiEyChU1EZBL/C6H9dS95/Po7AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_pivot.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Task 7\n",
+    "<a name=\"task7\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "* Create a pivot table based on the NEST `df` data frame\n",
+    "* Let the `x` axis show the number of nodes; display the values of the simulation time `\"Sim. Time / s\"` for the tasks per node and threads per task configurations\n",
+    "* Please plot a bar plot\n",
+    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.pivot_table(\n",
+    "    index=\"Nodes\",\n",
+    "    columns=[\"Tasks/Node\", \"Threads/Task\"],\n",
+    "    values=\"Sim. Time / s\",\n",
+    ").plot(kind=\"bar\", figsize=(12, 4));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "<a name=\"taskb\"></a>\n",
+    "\n",
+    "* Bonus task\n",
+    "    - Same pivot table as before (that is, `x` with nodes, and columns for Tasks/Node and Threads/Task)\n",
+    "    - But now, use `Sim. Time / s` and `Presim. Time / s` as values to show\n",
+    "    - Show them as a stack of those two values inside the pivot table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "* Pandas works with and on **data frames**, which are central\n",
+    "* **Slice** frames to your likings\n",
+    "* **Plot** frames\n",
+    "    - Together with Matplotlib, Seaborn, others\n",
+    "* **Pivot** tables are next level greatness\n",
+    "* Remember: ***Pandas as early as possible!***\n",
+    "* Thanks for being here! \ud83d\ude0d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task"
+   },
+   "source": [
+    "<span class=\"feedback\">Feedback to <a href=\"mailto:a.herten@fz-juelich.de\">a.herten@fz-juelich.de</a></span>\n",
+    "\n",
+    "Next slide: Further reading"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Further Reading\n",
+    "\n",
+    "* [Pandas User Guide](https://pandas.pydata.org/pandas-docs/stable/user_guide/index.html)\n",
+    "* [Matplotlib and LaTeX Plots](http://sbillaudelle.de/2015/02/23/seamlessly-embedding-matplotlib-output-into-latex.html)\n",
+    "* towardsdatascience.com:\n",
+    "    * [Pandas DataFrame: A lightweight Intro](https://towardsdatascience.com/pandas-dataframe-a-lightweight-intro-680e3a212b96)\n",
+    "    * [Introduction to Data Visualization in Python](https://towardsdatascience.com/introduction-to-data-visualization-in-python-89a54c97fbed)\n",
+    "    * [Basic Time Series Manipulation with Pandas](https://towardsdatascience.com/basic-time-series-manipulation-with-pandas-4432afee64ea)\n",
+    "    * [An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning](https://towardsdatascience.com/an-introduction-to-scikit-learn-the-gold-standard-of-python-machine-learning-e2b9238a98ab)\n",
+    "    * [Mapping with Matplotlib, Pandas, Geopandas and Basemap in Python](https://towardsdatascience.com/mapping-with-matplotlib-pandas-geopandas-and-basemap-in-python-d11b57ab5dac)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  },
+  "toc-autonumbering": false,
+  "toc-showcode": true,
+  "toc-showmarkdowntxt": false,
+  "toc-showtags": true
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/Introduction-to-Pandas--slides.pdf b/Introduction-to-Pandas--slides.pdf
index 8121204361ef3eeb6c2065cfd148974d942f529f..1478c8bbb2b2435950960f614833b2dbaaca3e0d 100644
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-{"cells": [{"cell_type": "markdown", "metadata": {"exercise": "task"}, "source": ["# *Introduction to* Data Analysis and Plotting with Pandas\n", "## JSC Tutorial\n", "\n", "Andreas Herten, Forschungszentrum J\u00fclich, 26 February 2019"]}, {"cell_type": "markdown", "metadata": {"exercise": "onlysolution", "slideshow": {"slide_type": "skip"}}, "source": ["**Version: Solutions**"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "source": ["## Task Outline\n", "\n", "* [Task 1](#task1)\n", "* [Task 2](#task2)\n", "* [Task 3](#task3)\n", "* [Task 4](#task4)\n", "* [Task 5](#task5)\n", "* [Task 6](#task6)\n", "* [Task 7](#task7)\n", "* [Bonus Task](#taskb)"]}, {"cell_type": "code", "execution_count": 2, "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["import pandas as pd"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 1\n", "<a name=\"task1\"></a>\n", "\n", "* Create data frame with\n", "    - 10 names of dinosaurs, \n", "    - their favourite prime number, \n", "    - and their favourite color\n", "* Play around with the frame\n", "* Tell me on poll when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "markdown", "metadata": {"exercise": "nopresentation", "slideshow": {"slide_type": "skip"}}, "source": ["Jupyter Notebook 101:\n", "\n", "* Execute cell: `shift+enter`\n", "* New cell in front of current cell: `a`\n", "* New cell after current cell: `b`"]}, {"cell_type": "code", "execution_count": 21, "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["happy_dinos = {\n", "    \"Dinosaur Name\": [],\n", "    \"Favourite Prime\": [],\n", "    \"Favourite Color\": []\n", "}\n", "#df_dinos = "]}, {"cell_type": "code", "execution_count": 22, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th>Dinosaur Name</th>\n", "      <th>Aegyptosaurus</th>\n", "      <th>Tyrannosaurus</th>\n", "      <th>Panoplosaurus</th>\n", "      <th>Isisaurus</th>\n", "      <th>Triceratops</th>\n", "      <th>Velociraptor</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Favourite Prime</th>\n", "      <td>4</td>\n", "      <td>8</td>\n", "      <td>15</td>\n", "      <td>16</td>\n", "      <td>23</td>\n", "      <td>42</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Favourite Color</th>\n", "      <td>blue</td>\n", "      <td>white</td>\n", "      <td>blue</td>\n", "      <td>purple</td>\n", "      <td>violet</td>\n", "      <td>gray</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["Dinosaur Name   Aegyptosaurus Tyrannosaurus Panoplosaurus Isisaurus  \\\n", "Favourite Prime             4             8            15        16   \n", "Favourite Color          blue         white          blue    purple   \n", "\n", "Dinosaur Name   Triceratops Velociraptor  \n", "Favourite Prime          23           42  \n", "Favourite Color      violet         gray  "]}, "execution_count": 22, "metadata": {}, "output_type": "execute_result"}], "source": ["happy_dinos = {\n", "    \"Dinosaur Name\": [\"Aegyptosaurus\", \"Tyrannosaurus\", \"Panoplosaurus\", \"Isisaurus\", \"Triceratops\", \"Velociraptor\"],\n", "    \"Favourite Prime\": [\"4\", \"8\", \"15\", \"16\", \"23\", \"42\"],\n", "    \"Favourite Color\": [\"blue\", \"white\", \"blue\", \"purple\", \"violet\", \"gray\"]\n", "}\n", "df_dinos = pd.DataFrame(happy_dinos).set_index(\"Dinosaur Name\")\n", "df_dinos.T"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 2\n", "<a name=\"task2\"></a>\n", "\n", "* Read in `nest-data.csv` to `DataFrame`; call it `df`  \n", " *Data was produced with [JUBE](http://www.fz-juelich.de/ias/jsc/EN/Expertise/Support/Software/JUBE/_node.html), Pandas works **very** well together with JUBE*\n", "* Get to know it and play a bit with it\n", "* Tell me when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 30, "metadata": {"exercise": "task"}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["id,Nodes,Tasks/Node,Threads/Task,Runtime Program / s,Scale,Plastic,Avg. Neuron Build Time / s,Min. Edge Build Time / s,Max. Edge Build Time / s,Min. Init. Time / s,Max. Init. Time / s,Presim. Time / s,Sim. Time / s,Virt. Memory (Sum) / kB,Local Spike Counter (Sum),Average Rate (Sum),Number of Neurons,Number of Connections,Min. Delay,Max. Delay\n", "5,1,2,4,420.42,10,true,0.29,88.12,88.18,1.14,1.20,17.26,311.52,46560664.00,825499,7.48,112500,1265738500,1.5,1.5\n", "5,1,4,4,200.84,10,true,0.15,46.03,46.34,0.70,1.01,7.87,142.97,46903088.00,802865,7.03,112500,1265738500,1.5,1.5\n"]}], "source": ["!cat nest-data.csv | head -3"]}, {"cell_type": "code", "execution_count": 31, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>id</th>\n", "      <th>Nodes</th>\n", "      <th>Tasks/Node</th>\n", "      <th>Threads/Task</th>\n", "      <th>Runtime Program / s</th>\n", "      <th>Scale</th>\n", "      <th>Plastic</th>\n", "      <th>Avg. Neuron Build Time / s</th>\n", "      <th>Min. Edge Build Time / s</th>\n", "      <th>Max. Edge Build Time / s</th>\n", "      <th>...</th>\n", "      <th>Max. Init. Time / s</th>\n", "      <th>Presim. Time / s</th>\n", "      <th>Sim. Time / s</th>\n", "      <th>Virt. Memory (Sum) / kB</th>\n", "      <th>Local Spike Counter (Sum)</th>\n", "      <th>Average Rate (Sum)</th>\n", "      <th>Number of Neurons</th>\n", "      <th>Number of Connections</th>\n", "      <th>Min. Delay</th>\n", "      <th>Max. Delay</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>2</td>\n", "      <td>4</td>\n", "      <td>420.42</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.29</td>\n", "      <td>88.12</td>\n", "      <td>88.18</td>\n", "      <td>...</td>\n", "      <td>1.20</td>\n", "      <td>17.26</td>\n", "      <td>311.52</td>\n", "      <td>46560664.0</td>\n", "      <td>825499</td>\n", "      <td>7.48</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>4</td>\n", "      <td>4</td>\n", "      <td>200.84</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.15</td>\n", "      <td>46.03</td>\n", "      <td>46.34</td>\n", "      <td>...</td>\n", "      <td>1.01</td>\n", "      <td>7.87</td>\n", "      <td>142.97</td>\n", "      <td>46903088.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>2</td>\n", "      <td>8</td>\n", "      <td>202.15</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.28</td>\n", "      <td>47.98</td>\n", "      <td>48.48</td>\n", "      <td>...</td>\n", "      <td>1.20</td>\n", "      <td>7.95</td>\n", "      <td>142.81</td>\n", "      <td>47699384.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>4</td>\n", "      <td>8</td>\n", "      <td>89.57</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.15</td>\n", "      <td>20.41</td>\n", "      <td>23.21</td>\n", "      <td>...</td>\n", "      <td>3.04</td>\n", "      <td>3.19</td>\n", "      <td>60.31</td>\n", "      <td>46813040.0</td>\n", "      <td>821491</td>\n", "      <td>7.23</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>5</td>\n", "      <td>2</td>\n", "      <td>2</td>\n", "      <td>4</td>\n", "      <td>164.16</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.20</td>\n", "      <td>40.03</td>\n", "      <td>41.09</td>\n", "      <td>...</td>\n", "      <td>1.58</td>\n", "      <td>6.08</td>\n", "      <td>114.88</td>\n", "      <td>46937216.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>5 rows \u00d7 21 columns</p>\n", "</div>"], "text/plain": ["   id  Nodes  Tasks/Node  Threads/Task  Runtime Program / s  Scale  Plastic  \\\n", "0   5      1           2             4               420.42     10     True   \n", "1   5      1           4             4               200.84     10     True   \n", "2   5      1           2             8               202.15     10     True   \n", "3   5      1           4             8                89.57     10     True   \n", "4   5      2           2             4               164.16     10     True   \n", "\n", "   Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n", "0                        0.29                     88.12   \n", "1                        0.15                     46.03   \n", "2                        0.28                     47.98   \n", "3                        0.15                     20.41   \n", "4                        0.20                     40.03   \n", "\n", "   Max. Edge Build Time / s  ...  Max. Init. Time / s  Presim. Time / s  \\\n", "0                     88.18  ...                 1.20             17.26   \n", "1                     46.34  ...                 1.01              7.87   \n", "2                     48.48  ...                 1.20              7.95   \n", "3                     23.21  ...                 3.04              3.19   \n", "4                     41.09  ...                 1.58              6.08   \n", "\n", "   Sim. Time / s  Virt. Memory (Sum) / kB  Local Spike Counter (Sum)  \\\n", "0         311.52               46560664.0                     825499   \n", "1         142.97               46903088.0                     802865   \n", "2         142.81               47699384.0                     802865   \n", "3          60.31               46813040.0                     821491   \n", "4         114.88               46937216.0                     802865   \n", "\n", "   Average Rate (Sum)  Number of Neurons  Number of Connections  Min. Delay  \\\n", "0                7.48             112500             1265738500         1.5   \n", "1                7.03             112500             1265738500         1.5   \n", "2                7.03             112500             1265738500         1.5   \n", "3                7.23             112500             1265738500         1.5   \n", "4                7.03             112500             1265738500         1.5   \n", "\n", "   Max. Delay  \n", "0         1.5  \n", "1         1.5  \n", "2         1.5  \n", "3         1.5  \n", "4         1.5  \n", "\n", "[5 rows x 21 columns]"]}, "execution_count": 31, "metadata": {}, "output_type": "execute_result"}], "source": ["df = pd.read_csv(\"nest-data.csv\")\n", "df.head()"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "subslide"}}, "source": ["## Task 3\n", "<a name=\"task3\"></a>\n", "\n", "* Add a column to the Nest data frame called `Virtual Processes` which is the total number of threads across all nodes (i.e. the product of threads per task and tasks per node and nodes)\n", "* Remember to tell me when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 54, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>id</th>\n", "      <th>Nodes</th>\n", "      <th>Tasks/Node</th>\n", "      <th>Threads/Task</th>\n", "      <th>Runtime Program / s</th>\n", "      <th>Scale</th>\n", "      <th>Plastic</th>\n", "      <th>Avg. Neuron Build Time / s</th>\n", "      <th>Min. Edge Build Time / s</th>\n", "      <th>Max. Edge Build Time / s</th>\n", "      <th>...</th>\n", "      <th>Presim. Time / s</th>\n", "      <th>Sim. Time / s</th>\n", "      <th>Virt. Memory (Sum) / kB</th>\n", "      <th>Local Spike Counter (Sum)</th>\n", "      <th>Average Rate (Sum)</th>\n", "      <th>Number of Neurons</th>\n", "      <th>Number of Connections</th>\n", "      <th>Min. Delay</th>\n", "      <th>Max. Delay</th>\n", "      <th>Virtual Processes</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>2</td>\n", "      <td>4</td>\n", "      <td>420.42</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.29</td>\n", "      <td>88.12</td>\n", "      <td>88.18</td>\n", "      <td>...</td>\n", "      <td>17.26</td>\n", "      <td>311.52</td>\n", "      <td>46560664.0</td>\n", "      <td>825499</td>\n", "      <td>7.48</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>8</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>4</td>\n", "      <td>4</td>\n", "      <td>200.84</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.15</td>\n", "      <td>46.03</td>\n", "      <td>46.34</td>\n", "      <td>...</td>\n", "      <td>7.87</td>\n", "      <td>142.97</td>\n", "      <td>46903088.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>16</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>2</td>\n", "      <td>8</td>\n", "      <td>202.15</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.28</td>\n", "      <td>47.98</td>\n", "      <td>48.48</td>\n", "      <td>...</td>\n", "      <td>7.95</td>\n", "      <td>142.81</td>\n", "      <td>47699384.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>16</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>5</td>\n", "      <td>1</td>\n", "      <td>4</td>\n", "      <td>8</td>\n", "      <td>89.57</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.15</td>\n", "      <td>20.41</td>\n", "      <td>23.21</td>\n", "      <td>...</td>\n", "      <td>3.19</td>\n", "      <td>60.31</td>\n", "      <td>46813040.0</td>\n", "      <td>821491</td>\n", "      <td>7.23</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>32</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>5</td>\n", "      <td>2</td>\n", "      <td>2</td>\n", "      <td>4</td>\n", "      <td>164.16</td>\n", "      <td>10</td>\n", "      <td>True</td>\n", "      <td>0.20</td>\n", "      <td>40.03</td>\n", "      <td>41.09</td>\n", "      <td>...</td>\n", "      <td>6.08</td>\n", "      <td>114.88</td>\n", "      <td>46937216.0</td>\n", "      <td>802865</td>\n", "      <td>7.03</td>\n", "      <td>112500</td>\n", "      <td>1265738500</td>\n", "      <td>1.5</td>\n", "      <td>1.5</td>\n", "      <td>16</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>5 rows \u00d7 22 columns</p>\n", "</div>"], "text/plain": ["   id  Nodes  Tasks/Node  Threads/Task  Runtime Program / s  Scale  Plastic  \\\n", "0   5      1           2             4               420.42     10     True   \n", "1   5      1           4             4               200.84     10     True   \n", "2   5      1           2             8               202.15     10     True   \n", "3   5      1           4             8                89.57     10     True   \n", "4   5      2           2             4               164.16     10     True   \n", "\n", "   Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n", "0                        0.29                     88.12   \n", "1                        0.15                     46.03   \n", "2                        0.28                     47.98   \n", "3                        0.15                     20.41   \n", "4                        0.20                     40.03   \n", "\n", "   Max. Edge Build Time / s  ...  Presim. Time / s  Sim. Time / s  \\\n", "0                     88.18  ...             17.26         311.52   \n", "1                     46.34  ...              7.87         142.97   \n", "2                     48.48  ...              7.95         142.81   \n", "3                     23.21  ...              3.19          60.31   \n", "4                     41.09  ...              6.08         114.88   \n", "\n", "   Virt. Memory (Sum) / kB  Local Spike Counter (Sum)  Average Rate (Sum)  \\\n", "0               46560664.0                     825499                7.48   \n", "1               46903088.0                     802865                7.03   \n", "2               47699384.0                     802865                7.03   \n", "3               46813040.0                     821491                7.23   \n", "4               46937216.0                     802865                7.03   \n", "\n", "   Number of Neurons  Number of Connections  Min. Delay  Max. Delay  \\\n", "0             112500             1265738500         1.5         1.5   \n", "1             112500             1265738500         1.5         1.5   \n", "2             112500             1265738500         1.5         1.5   \n", "3             112500             1265738500         1.5         1.5   \n", "4             112500             1265738500         1.5         1.5   \n", "\n", "   Virtual Processes  \n", "0                  8  \n", "1                 16  \n", "2                 16  \n", "3                 32  \n", "4                 16  \n", "\n", "[5 rows x 22 columns]"]}, "execution_count": 54, "metadata": {}, "output_type": "execute_result"}], "source": ["df[\"Virtual Processes\"] = df[\"Nodes\"] * df[\"Tasks/Node\"] * df[\"Threads/Task\"]\n", "df.head()"]}, {"cell_type": "code", "execution_count": 55, "metadata": {"exercise": "solution"}, "outputs": [{"data": {"text/plain": ["Index(['id', 'Nodes', 'Tasks/Node', 'Threads/Task', 'Runtime Program / s',\n", "       'Scale', 'Plastic', 'Avg. Neuron Build Time / s',\n", "       'Min. Edge Build Time / s', 'Max. Edge Build Time / s',\n", "       'Min. Init. Time / s', 'Max. Init. Time / s', 'Presim. Time / s',\n", "       'Sim. Time / s', 'Virt. Memory (Sum) / kB', 'Local Spike Counter (Sum)',\n", "       'Average Rate (Sum)', 'Number of Neurons', 'Number of Connections',\n", "       'Min. Delay', 'Max. Delay', 'Virtual Processes'],\n", "      dtype='object')"]}, "execution_count": 55, "metadata": {}, "output_type": "execute_result"}], "source": ["df.columns"]}, {"cell_type": "code", "execution_count": 56, "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["import matplotlib.pyplot as plt\n", "%matplotlib inline"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 4\n", "<a name=\"task4\"></a>\n", "\n", "* Sort the data frame by the virtual proccesses\n", "* Plot `\"Presim. Time / s\"` and `\"Sim. Time / s\"` of our data frame `df` as a function of the virtual processes\n", "* Use a dashed, red line for `\"Presim. Time / s\"`, a blue line for `\"Sim. Time / s\"` (see [API description](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot))\n", "* Don't forget to label your axes and to add a legend\n", "* Submit when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 61, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["df.sort_values([\"Virtual Processes\", \"Nodes\", \"Tasks/Node\", \"Threads/Task\"], inplace=True)"]}, {"cell_type": "code", "execution_count": 62, "metadata": {"exercise": "solution"}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots()\n", "ax.plot(df[\"Virtual Processes\"], df[\"Presim. Time / s\"], linestyle=\"dashed\", color=\"red\")\n", "ax.plot(df[\"Virtual Processes\"], df[\"Sim. Time / s\"], \"-b\")\n", "ax.set_xlabel(\"Virtual Process\")\n", "ax.set_ylabel(\"Time / s\")\n", "ax.legend();"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 5\n", "<a name=\"task5\"></a>\n", "\n", "Use the NEST data frame `df` to:\n", "\n", "1. Make the virtual processes the index of the data frame (`.set_index()`)\n", "2. Plot `\"Presim. Program / s\"` and `\"Sim. Time / s`\" individually\n", "3. Plot them onto one common canvas!\n", "4. Make them have the same line colors and styles as before\n", "5. Add a legend, add missing labels\n", "\n", "* Done? Tell me! [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 68, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "subslide"}}, "outputs": [], "source": ["df.set_index(\"Virtual Processes\", inplace=True)"]}, {"cell_type": "code", "execution_count": 69, "metadata": {"exercise": "solution"}, "outputs": [{"data": {"image/png": 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qtwO/kt7F+FpgJCK2VLNfMzMzs1ZT7d2Li4E1Su76ywH/FBFfl/Q+gIi4AbgDuAx4BtgD/GqV+zQzMzNrOVWFroh4DjinTPkNJdMBXF3NfszMzMxaXT0eGWFmZmZ23HPoMjMzM6sDhy4zMzOzOnDoMjMzM6sDhy4zMzOzOnDoMjMzM6sDhy4zMzOzOnDoKiN5tJiZmZlZ7VT7RPq2ks0IgHfccD8L53SwYE4ni+Z0smBOB4vmdrJwbicL53SwcE5n8je3Mymf00H3rA4y6fpmZmZmkzl0lfjJ1yziT96+kq0j+9i+Z4wde8bYvnuM51/ew8Mv7OCVPWOM58v3gmUEPbM72LF3nPUfeTNzu3xozczM7CAngxJduSzvfu2rKy6PCHaP5XlldxLGXtmT/G3fPc6OPWN86pvPAPDNJ7bxc+f01avZZmZm1gIcuo6CJOZ15ZjXlWPZojmHLS+Grlf2jNW7aWZmZtbkPJC+hj52+VkAvO7UExvcEjMzM2s2Mw5dkpZJukfSY5I2SPrtMnUulDQi6eH074+qa25z65nd0egmmJmZWZOq5vLiBPC7EbFW0nzgIUl3RcRjk+p9JyLeWsV+zMzMzFrejHu6ImJLRKxNp3cCjwP9tWqYmZmZWTupyZguScuBHwf+o8zin5L0iKSvSTrrCNu4StKgpMHh4eFaNKthRveON7oJZmZm1mSqDl2S5gFfAq6JiNFJi9cCr46Ic4BPAf9SaTsRcWNEDETEQG9vb7XNaoiTF84hmxHv+swDfPhLj/Lc8K5GN8nMzMyaRFWhS1IHSeC6NSK+PHl5RIxGxK50+g6gQ1Lb3tr3E69eyLc+dCGrz1/Gmu8P8cZPfJurb13L+qGRRjfNzMzMGkwzfc+gJAG3ANsj4poKdZYAWyMiJJ0PfJGk5+uIOx0YGIjBwcEZtatZDO/czz/c9wP+8f7n2bl/gjesOJH3X3gKP/WaE0gOnZmZmbUDSQ9FxMCU9aoIXa8HvgOsAwpp8R8ArwKIiBskfQB4P8mdjnuB34mI/zfVttshdBWN7hvn1gd+yGe/+wNe2rWfVcsW8P4LT+FNZyz2uxrNzMzawDEPXcdSO4Wuon3jeb740CY+c++zvLB9L6eeNI/3/fQpXL6qj46sn1FrZmbWqhy6mtREvsBX123h+m89yxMv7qSvZxb/9YLXsPq8VzG7MzutbUQEO/aMs3XnPl4c2cfW0X1sHd3PZ779LLvH8jz7Py8j6140MzOzuphu6PK7F+ssl81w+ap+3nZOH996cpi//dYzfPRfH+NT33yG9/yn5aw+bxl7x/NJmNq5n60j+3hxtBisitP7GZsoVNzHvvE8c7t8as3MzJqJ/2VuEElcdPpJXHT6STy4cTvXf+tZPnHXU3zirqcOqzurI8OS7lks7p7Fjy9byJKeZHpxd9eB8pO6u/iH+zZy7deeIOOB+mZmZk3HoasJnLd8Eee9ZxGPbxnl3qeGWTS3k8Xdsw6Eq+5ZuaO64/GprTuZ25UjmxFZiUwGMhLZjA58ZiWUgeyk8ozw3ZVmZmbHgENXEzljaTdnLO2e8fqzcsmA/Ms/fV9V7ZBIw1oSworTB8KaRDZzaHlG4gcv7QZg47VvqWr/zW7/RHL5d/OOfbw4upfNO/axZWQvL47s522r+njbOX2NbqKZmTUhh6428s7zlrF0wWzG8wXyhaAQQb4AhQgKhSBf/CwEhSBdfrC8EJSsl5RHWlYsP7DNQ9YL8sGB0PXrtzxI9+wOumd10DP74F/37EPne2Z3MKsj01Q9a2MTBbaO7mPLSBKktozsY8uOvWweKQarfby0a+yw9RbM6WDP/jwQDl1mZlaWQ1cbmdOZ481nLWnY/left4zrvvkMm3fs4/EtOxndO87O/RNHXKczm6F7du5AIFswu4NFc7s4YV4ni+Ymfycc+Oxi0bxO5nZmZxTUJvIFtu7cz5Ydew+Eqs07kjtAt4wkweqlXfuZfENv96wcS3tms3TBLM7uX0BfT3Lpt2/BbJam03M6c7zlb75z1G0yM7Pjh0OX1czrTj2R15166Fue8oVg575xRvYe+je6d2LS/Dij+8YZ3rWfJ1/cycu7x9hf4Q7NzlzmQBA7GMqSoNY9K8eiuV0He6lKgtW2nfsoTApU87pyLO2ZxdIFszl9STdLF8yir2d2GqpmsaRnNvN8J6iZmdWA/zWxYyqbEQvmdLJgTudRrRcR7BnLs333GC/vHmP77v28vGuM7bvHSsqSz40v7+blXWPsGcsfso3ZHdkDIeoNK05kadoztTTtpVrSM4vuWR21/LpmZmYVOXRZU5LE3K4cc7tyLFs0Z1rr/PDlPTw7vCvppeqZTffso7vrs1obNo+yYfMo//jA82SU3DFavBu0OJ2R0IGy8nVUYZ3MUdRJpqeuc9h2qe54ZbMilxGd2Yxfc2VmNolDl7WNV50wh1edML2Adiz9j39Z3+gmNIWMoCOboTObIZcVHdlM+idy6XTngenDl3dmM+QyoiOXoSOTLi+ZLq7XmcuQyyT7iAjG88mNHxOFIF8oMFEIJvKHzufT+YlCctPJRLrOeLFOyTYmJs3nC3HgZpXSsnwhyGZELg2eHen3zmWS71E8Bsl0+j3TdndkDh6j4jZKy8qukxXZTHI8ctnJ2ymtl2yzuKzctrMZNdUNLWbtyqHLrEae+bNL2bF3nIjk8mjxDtFCehdooaRsqjox6bNSncO3e+g+yu+7fDvykwe8HaUguat1LJ+ElPF8gfFCgfGJZHqiUGBsIgkx4/lDp8fzwa79EwfWO2Qb6edEOj2Wr/w2hqkUA0bx80DIySjtpTsYQnJpqMmly7s6MswpWd5Rsrz4OJXSkJZ85zS4pd9n18TEocvS71Rc50BZ+lnlKTkqHZMCYi5bPtAdLD8Y7HKZJBAe2puafmZKe1or9/AWg1/FXuKMDj7OZvJ+yqx7oAc3c+R9ZzNT9yxnMlP1PB/8DkdaPrlN5Y5XsRfb2pNDl1mN5LIZTpzX1ehmtL2IYm9TpKHuYDArBqhcaZgq+Wy1f8wKheQ7TuST3rjxkkCXLxRD66HLSkNbMdAVj1fpskrrHOwZLFRY5+D0nokJJkq2HRwa9POF0tBf5j8k0uX5Csub8NXAdVEMYtkKoaw0BKqknkjmVRrgSKYplpVsH0qDXulyTZpPy8rUP2QbHBocS/d1aFlxOMPBtomDQyeK9ZlcVtK2yfXLbiM9mJlJy8ptQxw69KO0naXbUHHoxqT601VV6JJ0CfDXQBb4+4i4dtLyLuBzwE8ALwPvioiN1ezTzI5vUrEnBmYzvZfEt6pMRnRlshyvN9CW9ubmy/XaFg4PdPni8ikC35SBsOQ5hVP1GBf3d6TlceB5icX9TOqVLhy6bn6K5aXtLH6XgAO916UBuDhNaQ83xXaVr1967EvrHzgelPTIl9QPgkKhTFmUzpeum9RnclnaNjj03FTcRgCT9lXPnuLpmvFPWVIW+DTwJmAT8KCk2yPisZJq7wVeiYhTJa0GPg68q5oGm5nZ8eFAjwvyZRmbscnDNYLi0IpJgRCIwuFlBwPrwXUPDs9IypZ/fHptqeZ/x+cDz0TEcwCS/hm4HCgNXZcDH0mnvwhcJ0kRx2unsZmZmdVTcgkWqPLu7FrIVLFuP/BCyfymtKxsnYiYAEaAE8ptTNJVkgYlDQ4PD1fRLDMzM7PmU03oqqmIuDEiBiJioLe3t9HNMTMzM6upakLXELCsZP7ktKxsHUk5oIdkQL2ZmZnZcaWa0PUgsELSj0jqBFYDt0+qcztwZTr9DuCbHs9lZmZmx6MZD6SPiAlJHwC+QfLIiJsiYoOkjwGDEXE78FngHyU9A2wnCWZmZmZmxx01Y8eTpGHg+Ua3o02cCLzU6EbYAT4fzcXno3n4XDQXn4+j8+qImHJAelOGLqsdSYMRMdDodljC56O5+Hw0D5+L5uLzcWw0zd2LZmZmZu3MocvMzMysDhy62t+NjW6AHcLno7n4fDQPn4vm4vNxDHhMl5mZmVkduKfLzMzMrA4cuszMzMzqwKGrzUjaKGmdpIclDaZliyTdJenp9HNho9vZriTdJGmbpPUlZWWPvxJ/I+kZSY9KOrdxLW8/Fc7FRyQNpb+PhyVdVrLs99Nz8aSkNzem1e1L0jJJ90h6TNIGSb+dlvv3UWdHOBf+fRxjDl3t6aKIWFXyjJUPA3dHxArg7nTejo2bgUsmlVU6/pcCK9K/q4Dr69TG48XNHH4uAD6Z/j5WRcQdAJLOJHljxlnpOn8rKVu3lh4fJoDfjYgzgdcCV6fH3b+P+qt0LsC/j2PKoev4cDlwSzp9C/D2BralrUXEvSSvvCpV6fhfDnwuEg8ACyQtrU9L21+Fc1HJ5cA/R8T+iPgB8Axw/jFr3HEoIrZExNp0eifwONCPfx91d4RzUYl/HzXi0NV+ArhT0kOSrkrLFkfElnT6RWBxY5p23Kp0/PuBF0rqbeLI/8dntfGB9HLVTSWX2n0u6kjScuDHgf/Av4+GmnQuwL+PY8qhq/28PiLOJemav1rSBaULI3lGiJ8T0iA+/g13PXAKsArYAvxVY5tz/JE0D/gScE1EjJYu8++jvsqcC/8+jjGHrjYTEUPp5zZgDUkX8NZit3z6ua1xLTwuVTr+Q8Cyknonp2V2jETE1ojIR0QB+DsOXiLxuagDSR0k/8jfGhFfTov9+2iAcufCv49jz6GrjUiaK2l+cRq4GFgP3A5cmVa7EvhKY1p43Kp0/G8HfiW9S+u1wEjJZRY7BiaNCfp5kt8HJOditaQuST9CMnj7e/VuXzuTJOCzwOMR8YmSRf591Fmlc+Hfx7GXa3QDrKYWA2uS3xM54J8i4uuSHgS+IOm9wPPAOxvYxrYm6TbgQuBESZuAPwaupfzxvwO4jGRQ6h7gV+ve4DZW4VxcKGkVySWsjcBvAETEBklfAB4jubPr6ojIN6Ldbex1wLuBdZIeTsv+AP8+GqHSubjCv49jy68BMjMzM6sDX140MzMzqwOHLjMzM7M6cOgyMzMzqwOHLjMzM7M6cOgyMzMzqwOHLjOriqR7JL15Utk1kq6X1CfpixXWWy7pP1e5729JGqhQ/qSkRyTdJ+m0avZjZlYLDl1mVq3bgNWTylYDt0XE5oh4x+QVJOWA5UBVoWsKvxwR55C8RPkvyrQhewz3bWZ2GIcuM6vWF4G3SOqEAy/Q7QO+k/ZmrU/L3yPpdknfBO4meSjmGyQ9LOmD6fLrihuV9G+SLkynr5c0KGmDpI8eZfvuBU5Nt7NR0sclrQV+SdIqSQ+kL/hdU3zBr6RTJf172lO2VtIpafl/k/RgWv+jadlcSV9N666X9K60/FpJj6V1/zIt65X0pXQbD0p6XVr+0+lxeFjS94tvljCz9uIn0ptZVSJiu6Tvkbxk/SskvVxfiIhI345Q6lzgx9J1LgQ+FBFvhSSUHWE3f5iukwXulvRjEfHoNJv4c8C6kvmX05fCI+lR4Dcj4tuSPkby1PprgFuBayNijaRZQEbSxSSvPzkfEHC7khfK9wKbI+It6TZ7JJ1A8hqV09PjsCDd918Dn4yI70p6FfAN4AzgQyRP+b5PyUuI903zu5lZC3FPl5nVQuklxtXpfDl3RcT2GWz/nWnv1PeBs4Azp7HOrekrTl5HEmqKPg9JOAIWRMS30/JbgAvSXqb+iFgDEBH7ImIPybtML07bsBY4nSSErQPelPagvSEiRoARkuD0WUm/QPIaG4CfBa5L23U70J2GrPuAT0j6rbRNE0d7gMys+bmny8xq4SvAJyWdC8yJiIcq1Nt9hG1McOh/CM4CSF+w+yHgvIh4RdLNxWVT+OWIGDzKNhyJgP8VEZ85bEHyvS8D/lTS3RHxMUnnA28E3gF8APgZku/32oiY3JN1raSvptu4T9KbI+KJGbbTzJqUe7rMrGoRsQu4B7iJyr1ck+0ESscubQRWScpIWkZyGQ+gmyQojUhaTHIZsxZtHgFekfSGtOjdwLcjYiewSdLbASR1SZpDcinw19KeKST1SzpJUh+wJyL+D8mA/XPTOj0RcQfwQeCcdB93Ar9ZbEP6cmEknRIR6yLi48CDJL1oZtZm3NNlZrVyG7CGw+9krORRIC/pEeBm4H9AKxUnAAAAsElEQVQDPwAeAx4nuYRHRDwi6fvAE8ALJJfiauVK4IY0VD0H/Gpa/m7gM+k4r3HglyLiTklnAPenY9V2Af+FZJD+X0gqpHXfTxImv5KOBxPwO+l2fwv4dDqWLEcyyP99wDWSLgIKwAbgazX8jmbWJBQRjW6DmZmZWdvz5UUzMzOzOnDoMjMzM6sDhy4zMzOzOnDoMjMzM6sDhy4zMzOzOnDoMjMzM6sDhy4zMzOzOvj/r1L+SfYYynAAAAAASUVORK5CYII=\n", "text/plain": ["<Figure size 720x216 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df[\"Presim. Time / s\"].plot(figsize=(10, 3));"]}, {"cell_type": "code", "execution_count": 70, "metadata": {"exercise": "solution"}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x216 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df[\"Sim. Time / s\"].plot(figsize=(10, 3));"]}, {"cell_type": "code", "execution_count": 71, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "subslide"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df[\"Presim. Time / s\"].plot();\n", "df[\"Sim. Time / s\"].plot();"]}, {"cell_type": "code", "execution_count": 72, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["ax = df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot();\n", "ax.set_ylabel(\"Time / s\");"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 6\n", "<a name=\"task6\"></a>\n", "\n", "* To your `df` NEST data frame, add a column with the unaccounted time (`Unaccounted Time / s`), which is the difference of program runtime, average neuron build time, minimal edge build time, minimal initialization time, presimulation time, and simulation time.  \n", "(*I know this is technically not super correct, but it will do for our example.*)\n", "* Plot a stacked bar plot of all these columns (except for program runtime) over the virtual processes\n", "* Remember: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 92, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["cols = [\n", "    'Avg. Neuron Build Time / s', \n", "    'Min. Edge Build Time / s', \n", "    'Min. Init. Time / s', \n", "    'Presim. Time / s', \n", "    'Sim. Time / s'\n", "]\n", "df[\"Unaccounted Time / s\"] = df['Runtime Program / s']\n", "for entry in cols:\n", "    df[\"Unaccounted Time / s\"] = df[\"Unaccounted Time / s\"] - df[entry]"]}, {"cell_type": "code", "execution_count": 93, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "subslide"}}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Runtime Program / s</th>\n", "      <th>Unaccounted Time / s</th>\n", "      <th>Avg. Neuron Build Time / s</th>\n", "      <th>Min. Edge Build Time / s</th>\n", "      <th>Min. Init. Time / s</th>\n", "      <th>Presim. Time / s</th>\n", "      <th>Sim. Time / s</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Virtual Processes</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>8</th>\n", "      <td>420.42</td>\n", "      <td>2.09</td>\n", "      <td>0.29</td>\n", "      <td>88.12</td>\n", "      <td>1.14</td>\n", "      <td>17.26</td>\n", "      <td>311.52</td>\n", "    </tr>\n", "    <tr>\n", "      <th>16</th>\n", "      <td>202.15</td>\n", "      <td>2.43</td>\n", "      <td>0.28</td>\n", "      <td>47.98</td>\n", "      <td>0.70</td>\n", "      <td>7.95</td>\n", "      <td>142.81</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                   Runtime Program / s  Unaccounted Time / s  \\\n", "Virtual Processes                                              \n", "8                               420.42                  2.09   \n", "16                              202.15                  2.43   \n", "\n", "                   Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n", "Virtual Processes                                                         \n", "8                                        0.29                     88.12   \n", "16                                       0.28                     47.98   \n", "\n", "                   Min. Init. Time / s  Presim. Time / s  Sim. Time / s  \n", "Virtual Processes                                                        \n", "8                                 1.14             17.26         311.52  \n", "16                                0.70              7.95         142.81  "]}, "execution_count": 93, "metadata": {}, "output_type": "execute_result"}], "source": ["df[[\"Runtime Program / s\", \"Unaccounted Time / s\", *cols]].head(2)"]}, {"cell_type": "code", "execution_count": 94, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "-"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 864x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df[[\"Unaccounted Time / s\", *cols]].plot(kind=\"bar\", stacked=True, figsize=(12, 4));"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 7\n", "<a name=\"task7\"></a>\n", "\n", "* Create a pivot table based on the NEST `df` data frame\n", "* Let the `x` axis show the number of nodes; display the values of the simulation time `\"Sim. Time / s\"` for the tasks per node and threas per task configurations\n", "* Please plot a bar plot\n", "* Done? [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 101, "metadata": {"exercise": "solution", "slideshow": {"slide_type": "fragment"}}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 864x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df.pivot_table(\n", "    index=[\"Nodes\"],\n", "    columns=[\"Tasks/Node\", \"Threads/Task\"],\n", "    values=\"Sim. Time / s\",\n", ").plot(kind=\"bar\", figsize=(12, 4));"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "source": ["<a name=\"taskb\"></a>\n", "\n", "* Bonus task\n", "    - Same pivot table as before (that is, `x` with nodes, and columns for Tasks/Node and Threads/Task)\n", "    - But now, use `Sim. Time / s` and `Presim. Time / s` as values to show\n", "    - Show them as a stack of those two values inside the pivot table"]}, {"cell_type": "markdown", "metadata": {"exercise": "task"}, "source": ["<span class=\"feedback\">Tell me what you think about this tutorial! <a href=\"mailto:a.herten@fz-juelich.de\">a.herten@fz-juelich.de</a></span>\n", "\n", "Next slide: Further reading"]}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.2"}}, "nbformat": 4, "nbformat_minor": 2}
\ No newline at end of file
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "tags": [
+     "task"
+    ]
+   },
+   "source": [
+    "# Data Analysis and Plotting in Python with Pandas\n",
+    "\n",
+    "_Andreas Herten, J\u00fclich Supercomputing Centre, Forschungszentrum J\u00fclich, 27 May 2021_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "onlysolution",
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Version: Solutions**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "## Task Outline\n",
+    "\n",
+    "* [Task 1](#task1)\n",
+    "* [Task 2](#task2)\n",
+    "* [Task 3](#task3)\n",
+    "* [Task 4](#task4)\n",
+    "* [Task 5](#task5)\n",
+    "* [Task 6](#task6)\n",
+    "* [Task 7](#task7)\n",
+    "* [Bonus Task](#taskb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Task 1\n",
+    "<a name=\"task1\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "* Create data frame with\n",
+    "    - 6 names of dinosaurs, \n",
+    "    - their favourite prime number, \n",
+    "    - and their favorite color.\n",
+    "* Play around with the frame\n",
+    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "nopresentation",
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Jupyter Notebook 101:\n",
+    "\n",
+    "* Execute cell: `shift+enter`\n",
+    "* New cell in front of current cell: `a`\n",
+    "* New cell after current cell: `b`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "happy_dinos = {\n",
+    "    \"Dinosaur Name\": [],\n",
+    "    \"Favourite Prime\": [],\n",
+    "    \"Favourite Color\": []\n",
+    "}\n",
+    "#df_dinos = "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Dinosaur Name</th>\n",
+       "      <th>Aegyptosaurus</th>\n",
+       "      <th>Tyrannosaurus</th>\n",
+       "      <th>Panoplosaurus</th>\n",
+       "      <th>Isisaurus</th>\n",
+       "      <th>Triceratops</th>\n",
+       "      <th>Velociraptor</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Favourite Prime</th>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>15</td>\n",
+       "      <td>16</td>\n",
+       "      <td>23</td>\n",
+       "      <td>42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Favourite Color</th>\n",
+       "      <td>blue</td>\n",
+       "      <td>white</td>\n",
+       "      <td>blue</td>\n",
+       "      <td>purple</td>\n",
+       "      <td>violet</td>\n",
+       "      <td>gray</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Dinosaur Name   Aegyptosaurus Tyrannosaurus Panoplosaurus Isisaurus  \\\n",
+       "Favourite Prime             4             8            15        16   \n",
+       "Favourite Color          blue         white          blue    purple   \n",
+       "\n",
+       "Dinosaur Name   Triceratops Velociraptor  \n",
+       "Favourite Prime          23           42  \n",
+       "Favourite Color      violet         gray  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "happy_dinos = {\n",
+    "    \"Dinosaur Name\": [\"Aegyptosaurus\", \"Tyrannosaurus\", \"Panoplosaurus\", \"Isisaurus\", \"Triceratops\", \"Velociraptor\"],\n",
+    "    \"Favourite Prime\": [\"4\", \"8\", \"15\", \"16\", \"23\", \"42\"],\n",
+    "    \"Favourite Color\": [\"blue\", \"white\", \"blue\", \"purple\", \"violet\", \"gray\"]\n",
+    "}\n",
+    "df_dinos = pd.DataFrame(happy_dinos).set_index(\"Dinosaur Name\")\n",
+    "df_dinos.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Task 2\n",
+    "<a name=\"task2\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "* Read in `data-nest.csv` to `DataFrame`; call it `df`  \n",
+    "  *(Data was produced with [JUBE](http://www.fz-juelich.de/ias/jsc/EN/Expertise/Support/Software/JUBE/_node.html))*\n",
+    "* Get to know it and play a bit with it\n",
+    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "exercise": "task"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "id,Nodes,Tasks/Node,Threads/Task,Runtime Program / s,Scale,Plastic,Avg. Neuron Build Time / s,Min. Edge Build Time / s,Max. Edge Build Time / s,Min. Init. Time / s,Max. Init. Time / s,Presim. Time / s,Sim. Time / s,Virt. Memory (Sum) / kB,Local Spike Counter (Sum),Average Rate (Sum),Number of Neurons,Number of Connections,Min. Delay,Max. Delay\n",
+      "5,1,2,4,420.42,10,true,0.29,88.12,88.18,1.14,1.20,17.26,311.52,46560664.00,825499,7.48,112500,1265738500,1.5,1.5\n",
+      "5,1,4,4,200.84,10,true,0.15,46.03,46.34,0.70,1.01,7.87,142.97,46903088.00,802865,7.03,112500,1265738500,1.5,1.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat nest-data.csv | head -3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>Nodes</th>\n",
+       "      <th>Tasks/Node</th>\n",
+       "      <th>Threads/Task</th>\n",
+       "      <th>Runtime Program / s</th>\n",
+       "      <th>Scale</th>\n",
+       "      <th>Plastic</th>\n",
+       "      <th>Avg. Neuron Build Time / s</th>\n",
+       "      <th>Min. Edge Build Time / s</th>\n",
+       "      <th>Max. Edge Build Time / s</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Max. Init. Time / s</th>\n",
+       "      <th>Presim. Time / s</th>\n",
+       "      <th>Sim. Time / s</th>\n",
+       "      <th>Virt. Memory (Sum) / kB</th>\n",
+       "      <th>Local Spike Counter (Sum)</th>\n",
+       "      <th>Average Rate (Sum)</th>\n",
+       "      <th>Number of Neurons</th>\n",
+       "      <th>Number of Connections</th>\n",
+       "      <th>Min. Delay</th>\n",
+       "      <th>Max. Delay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>420.42</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>88.12</td>\n",
+       "      <td>88.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>17.26</td>\n",
+       "      <td>311.52</td>\n",
+       "      <td>46560664.0</td>\n",
+       "      <td>825499</td>\n",
+       "      <td>7.48</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>200.84</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>46.03</td>\n",
+       "      <td>46.34</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.01</td>\n",
+       "      <td>7.87</td>\n",
+       "      <td>142.97</td>\n",
+       "      <td>46903088.0</td>\n",
+       "      <td>802865</td>\n",
+       "      <td>7.03</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>202.15</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>47.98</td>\n",
+       "      <td>48.48</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>7.95</td>\n",
+       "      <td>142.81</td>\n",
+       "      <td>47699384.0</td>\n",
+       "      <td>802865</td>\n",
+       "      <td>7.03</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>89.57</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>20.41</td>\n",
+       "      <td>23.21</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.04</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>60.31</td>\n",
+       "      <td>46813040.0</td>\n",
+       "      <td>821491</td>\n",
+       "      <td>7.23</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>164.16</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>40.03</td>\n",
+       "      <td>41.09</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.58</td>\n",
+       "      <td>6.08</td>\n",
+       "      <td>114.88</td>\n",
+       "      <td>46937216.0</td>\n",
+       "      <td>802865</td>\n",
+       "      <td>7.03</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows \u00d7 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id  Nodes  Tasks/Node  Threads/Task  Runtime Program / s  Scale  Plastic  \\\n",
+       "0   5      1           2             4               420.42     10     True   \n",
+       "1   5      1           4             4               200.84     10     True   \n",
+       "2   5      1           2             8               202.15     10     True   \n",
+       "3   5      1           4             8                89.57     10     True   \n",
+       "4   5      2           2             4               164.16     10     True   \n",
+       "\n",
+       "   Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n",
+       "0                        0.29                     88.12   \n",
+       "1                        0.15                     46.03   \n",
+       "2                        0.28                     47.98   \n",
+       "3                        0.15                     20.41   \n",
+       "4                        0.20                     40.03   \n",
+       "\n",
+       "   Max. Edge Build Time / s  ...  Max. Init. Time / s  Presim. Time / s  \\\n",
+       "0                     88.18  ...                 1.20             17.26   \n",
+       "1                     46.34  ...                 1.01              7.87   \n",
+       "2                     48.48  ...                 1.20              7.95   \n",
+       "3                     23.21  ...                 3.04              3.19   \n",
+       "4                     41.09  ...                 1.58              6.08   \n",
+       "\n",
+       "   Sim. Time / s  Virt. Memory (Sum) / kB  Local Spike Counter (Sum)  \\\n",
+       "0         311.52               46560664.0                     825499   \n",
+       "1         142.97               46903088.0                     802865   \n",
+       "2         142.81               47699384.0                     802865   \n",
+       "3          60.31               46813040.0                     821491   \n",
+       "4         114.88               46937216.0                     802865   \n",
+       "\n",
+       "   Average Rate (Sum)  Number of Neurons  Number of Connections  Min. Delay  \\\n",
+       "0                7.48             112500             1265738500         1.5   \n",
+       "1                7.03             112500             1265738500         1.5   \n",
+       "2                7.03             112500             1265738500         1.5   \n",
+       "3                7.23             112500             1265738500         1.5   \n",
+       "4                7.03             112500             1265738500         1.5   \n",
+       "\n",
+       "   Max. Delay  \n",
+       "0         1.5  \n",
+       "1         1.5  \n",
+       "2         1.5  \n",
+       "3         1.5  \n",
+       "4         1.5  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"data-nest.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Task 3\n",
+    "<a name=\"task3\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "* Add a column to the Nest data frame form Task 2 called `Threads` which is the total number of threads across all nodes (i.e. the product of threads per task and tasks per node and nodes)\n",
+    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>Nodes</th>\n",
+       "      <th>Tasks/Node</th>\n",
+       "      <th>Threads/Task</th>\n",
+       "      <th>Runtime Program / s</th>\n",
+       "      <th>Scale</th>\n",
+       "      <th>Plastic</th>\n",
+       "      <th>Avg. Neuron Build Time / s</th>\n",
+       "      <th>Min. Edge Build Time / s</th>\n",
+       "      <th>Max. Edge Build Time / s</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Presim. Time / s</th>\n",
+       "      <th>Sim. Time / s</th>\n",
+       "      <th>Virt. Memory (Sum) / kB</th>\n",
+       "      <th>Local Spike Counter (Sum)</th>\n",
+       "      <th>Average Rate (Sum)</th>\n",
+       "      <th>Number of Neurons</th>\n",
+       "      <th>Number of Connections</th>\n",
+       "      <th>Min. Delay</th>\n",
+       "      <th>Max. Delay</th>\n",
+       "      <th>Threads</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>420.42</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>88.12</td>\n",
+       "      <td>88.18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.26</td>\n",
+       "      <td>311.52</td>\n",
+       "      <td>46560664.0</td>\n",
+       "      <td>825499</td>\n",
+       "      <td>7.48</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>200.84</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>46.03</td>\n",
+       "      <td>46.34</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.87</td>\n",
+       "      <td>142.97</td>\n",
+       "      <td>46903088.0</td>\n",
+       "      <td>802865</td>\n",
+       "      <td>7.03</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>202.15</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>47.98</td>\n",
+       "      <td>48.48</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.95</td>\n",
+       "      <td>142.81</td>\n",
+       "      <td>47699384.0</td>\n",
+       "      <td>802865</td>\n",
+       "      <td>7.03</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>89.57</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>20.41</td>\n",
+       "      <td>23.21</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>60.31</td>\n",
+       "      <td>46813040.0</td>\n",
+       "      <td>821491</td>\n",
+       "      <td>7.23</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>164.16</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>40.03</td>\n",
+       "      <td>41.09</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.08</td>\n",
+       "      <td>114.88</td>\n",
+       "      <td>46937216.0</td>\n",
+       "      <td>802865</td>\n",
+       "      <td>7.03</td>\n",
+       "      <td>112500</td>\n",
+       "      <td>1265738500</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows \u00d7 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id  Nodes  Tasks/Node  Threads/Task  Runtime Program / s  Scale  Plastic  \\\n",
+       "0   5      1           2             4               420.42     10     True   \n",
+       "1   5      1           4             4               200.84     10     True   \n",
+       "2   5      1           2             8               202.15     10     True   \n",
+       "3   5      1           4             8                89.57     10     True   \n",
+       "4   5      2           2             4               164.16     10     True   \n",
+       "\n",
+       "   Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n",
+       "0                        0.29                     88.12   \n",
+       "1                        0.15                     46.03   \n",
+       "2                        0.28                     47.98   \n",
+       "3                        0.15                     20.41   \n",
+       "4                        0.20                     40.03   \n",
+       "\n",
+       "   Max. Edge Build Time / s  ...  Presim. Time / s  Sim. Time / s  \\\n",
+       "0                     88.18  ...             17.26         311.52   \n",
+       "1                     46.34  ...              7.87         142.97   \n",
+       "2                     48.48  ...              7.95         142.81   \n",
+       "3                     23.21  ...              3.19          60.31   \n",
+       "4                     41.09  ...              6.08         114.88   \n",
+       "\n",
+       "   Virt. Memory (Sum) / kB  Local Spike Counter (Sum)  Average Rate (Sum)  \\\n",
+       "0               46560664.0                     825499                7.48   \n",
+       "1               46903088.0                     802865                7.03   \n",
+       "2               47699384.0                     802865                7.03   \n",
+       "3               46813040.0                     821491                7.23   \n",
+       "4               46937216.0                     802865                7.03   \n",
+       "\n",
+       "   Number of Neurons  Number of Connections  Min. Delay  Max. Delay  Threads  \n",
+       "0             112500             1265738500         1.5         1.5        8  \n",
+       "1             112500             1265738500         1.5         1.5       16  \n",
+       "2             112500             1265738500         1.5         1.5       16  \n",
+       "3             112500             1265738500         1.5         1.5       32  \n",
+       "4             112500             1265738500         1.5         1.5       16  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Threads\"] = df[\"Nodes\"] * df[\"Tasks/Node\"] * df[\"Threads/Task\"]\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "exercise": "solution"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['id', 'Nodes', 'Tasks/Node', 'Threads/Task', 'Runtime Program / s',\n",
+       "       'Scale', 'Plastic', 'Avg. Neuron Build Time / s',\n",
+       "       'Min. Edge Build Time / s', 'Max. Edge Build Time / s',\n",
+       "       'Min. Init. Time / s', 'Max. Init. Time / s', 'Presim. Time / s',\n",
+       "       'Sim. Time / s', 'Virt. Memory (Sum) / kB', 'Local Spike Counter (Sum)',\n",
+       "       'Average Rate (Sum)', 'Number of Neurons', 'Number of Connections',\n",
+       "       'Min. Delay', 'Max. Delay', 'Threads'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Task 4\n",
+    "<a name=\"task4\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "\n",
+    "* Sort the data frame by threads\n",
+    "* Plot `\"Presim. Time / s\"` and `\"Sim. Time / s\"` of our data frame `df` as a function of threads\n",
+    "* Use a dashed, red line for `\"Presim. Time / s\"`, a blue line for `\"Sim. Time / s\"` (see [API description](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot))\n",
+    "* Don't forget to label your axes and to add a legend _(1st rule of plotting)_\n",
+    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df.sort_values([\"Threads\", \"Nodes\", \"Tasks/Node\", \"Threads/Task\"], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "exercise": "solution"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(df[\"Threads\"], df[\"Presim. Time / s\"], linestyle=\"dashed\", color=\"red\", label=\"Presim. Time / s\")\n",
+    "ax.plot(df[\"Threads\"], df[\"Sim. Time / s\"], \"-b\", label=\"Sim. Time / s\")\n",
+    "ax.set_xlabel(\"Virtual Process\")\n",
+    "ax.set_ylabel(\"Time / s\")\n",
+    "ax.legend(loc='best');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Task 5\n",
+    "<a name=\"task5\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "Use the NEST data frame `df` to:\n",
+    "\n",
+    "1. Make the threads the index of the data frame (`.set_index()`)\n",
+    "2. Plot `\"Presim. Program / s\"` and `\"Sim. Time / s`\" individually\n",
+    "3. Plot them onto one common canvas!\n",
+    "4. Make them have the same line colors and styles as before\n",
+    "5. Add a legend, add missing axes labels\n",
+    "6. Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df.set_index(\"Threads\", inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "exercise": "solution"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+JC2U1JxfBq4CtozpdjfwruSqx9cARyJidzH7NTMzM5urir3acRlwV3L39jrgyxFxr6T3AkTEbcA9wLXAdqAPeE+R+zQzMzObs4oKXxHxPHDxOO23FSwHcHMx+zEzMzOrFuW41YSZmZmZJRy+zMzMzMrI4cvMzMysjBy+zMzMzMrI4cvMzMysjBy+zMzMzMrI4cvMzMysjBy+yiiISpdgZmZmFVbsHe5titIpcd9Te/nFj9xHa1MjSxY2sHRhA0ubGpPXBpYsbBhZ19TAkgUN1KWdj83MzKqJw1eZ/NVvvpIfbt/PgWMD7O/t5+CxAV480MdjPz/MwWP9ZCeYFDtjfj1Hjg/y5EeuonlefXmLNjMzs5Jz+CqTS89ZzKXnLB53XTYbHD4+yMFj/ezvHeDgsQEO9OaW/+GBZwH43pY9vK1rZTlLNjMzsxng8DULpFJiycLcz47nnzl63b889BwnBrMc6B2oTHFmZmZWUtM+oUjSSkkPSnpa0lOS3j9OnyskHZG0Ofn7UHHl1p6P/MZFALyu88xJepqZmdlcUMzM1xDwxxHxmKRmYJOkjRHx9Jh+/xERby5iPzUtf56XVOFCzMzMrCSmPfMVEbsj4rFk+SjwDLC8VIWZmZmZVaOS3MdA0irgl4CHx1l9maTHJX1P0kWl2J+ZmZnZXFX0CfeSmoBvAB+IiJfHrH4MOCcieiVdC3wLWD3B56wH1gOcffbZxZZVdY71D1W6BDMzMyuBoma+JNWTC15fiohvjl0fES9HRG+yfA9QL6l1vM+KiNsjoisiutra2oopq6osXzyfdEr87mce5qPf28rhPl/1aGZmNpcVc7WjgM8Bz0TEJybo0570Q9K6ZH8HprvPWrR2ZQsP/NGv8aaLlvEvDz3Hr37sQf7+//6UoycGK12amZmZTYMipve8QUmXA/8BPAlkk+Y/A84GiIjbJN0CvI/clZHHgT+KiB9O9tldXV3R3d09rbqq2bY9R/nkxp9y71N7aFlQz3t/7Tzeddk5LGjw7drMzMwqRdKmiOiacv/phq+Z5PB1ak/uPMLHN27j37f10NrUyC1XnseNrz6bxrp0pUszMzOrOQ5fNeTRFw7yd/dt4+GfHeSsM+bxh69fzVsvXUG9H8ZtZmZWNg5fNSYi+M/tB/jb+7fx+I7DrFq6gA+84QJ+4+KzSKcmvzPrUCbLvqP97D5ynF2HT7D78HG+88Runtx1hJ/8+RtZvLChDN/CzMxs7jrd8OWTheY4SVy+upVfOX8pDzyzj7+7fxsf+Opm/vnft/NHb7yArlVL2H34BC8dOc5Lh4+z+8iJUa/7jvaTyY4fwHcc6nP4MjMzKzGHryohiTe8Yhmv6zyTe7bs5hMbf8p7//Wxk/o11KU464x5dJwxn8vOW8rylvl0nDGfjpZ5yfI8HtzWwx/e+ROfyG9mZjYD/P+uVSaVEm9+1VlcfVE792zZw4HefjrOmJ8LVi3zWLqwAU3xQZHP7j0KQF1KpFMilRJpiVQK0hrdlk6J1PArU96HmZlZrXH4qlJ16RRvufisaW07ry53wv77vnTyzNlUpQTpJLTlAtuY5eHwlgtyLxzoA6D7f72B1qbGae93NjkxmOGlw8fZdfg4Ow8dZ+ehPnYdOk5K4m+ufxV1vjDCzKwmOXzZSa7sPJMNv9dF30CGTDbIRjCUyb1mspCJIJuN4XWZbBS0FazPrxvTd3ibLMPL+fD1B//6GCsWz6dlQQMtC+pZvKCeMxY0sHhBPS3zc20tC+ppaqyr+Oza8YEMuw73sePQcXYdygWsXNDqY+eh4/Qc7R/VP50SCxrSHD0xxB+/aQ3LW+ZXqHIzM6skhy87SX06xes6l5V1n//jTWv40Le30NPbz8M/O8iR44P0nuJ5lnUpJUGsgZb59SxZ2EBrcyOtTY20NTXQ2tQ48r65kYUN6dMOa739Q0mo6jtp9mrnoeMcODb6UU/1aXFWy3xWLJ7PlWvaWLF4AcuT98sXz6d90Ty++dgu/uQbT0xrjMzMrDo4fNmssHLJAv7Pe9aNahsYynLk+CCH+wY4fHyQQ8dyr4f7BjjcN8ihvkGOHB/g0LFBXjhwjO4XD3Gob4Dx7p4yrz6VC2RN+UCWC2iNdSkuWNY8atYqH7QO941+hFNDXYoVLbkgddVZi1ixeEEuWLXMZ8XiBbQ1N07p9h5mZlbbHL5s1mqoS9HWnJu5mqqhTJaDxwbo6e1nf+8A+4/2s7+3n57kdX/vADsP9bF5xyH2946euZpfnx6epVq7soXlLblwlW9rXdhIyuHKzMyK5PBlVaUuneLMRfM4c9G8SfseH8jww+f209bcyPKW+Sw5jStBp+uzP3gegFu/v51XLl9ESiNXh+aXUxJKXnNXkJ56/cj2uatd8+/TyfrhvimmtL+Rfklfpj8m9XWiIZ0inVLFz9EzM5stHL6sZs1vSPP6C8t7bts7L1vFn39rC3c+8vOy7rfSpNy5hA3pFPVpUZ9OUZ9O0ViXe62vG2nL92moK3w/0qchnRpeVz+mbzolMtlgKJNlKJu7UGQwmyWTCQaT9kw2GMwEmWx2uC3fdyibTV6Tv0x2pL1gu+E+mSzZgLrkO9WlNFxTfTo13D68ri5FfUrUFdQ+3C+VGl5uSF7r0ika0qIulRqzbcHn5rety/Ub2Tb/OUn/VMozt2azhMOXWRm98zXn8JaLz2JgKEtEkI3cFZ/ZCGJ4OXnNnnr98PZJv8LPy+TXZ0e2mWx/ESNXoOb7TvT0g6nIRi6gDA5lGcxkGcgEg5lkeSjLQCbLYKZwfe61b2Ao117QNjgUo7YbyGTHPbdvMnWpXCipSyUBJTVmOQlJw32S5YX1daSTvvXp3G1S8mEvJYaD2VD+O2WyDGVzdfcODeVCYP675Ptlk36ZYCCTHQ5yMymVhOB88KtL5wJdfV1hcCwMjfnvnFvOz5Lm7+snMTzDOnbGNJUaPbuan5Ud6XNyv/wM6djlVDKTm5+hza0bWR4125saPfM7vC518vJEs73jthcspyWUGr8u3+fQpqKo8CXpauAfgDTw2Yj46Jj1jcAXgEuBA8DbI+KFYvZpNtedMb++0iVUhUy2IKAloWwoE8OhqDAk5W8UPNv/TzH/nYZDazLLNlgY6pLZvMGhXL/89x7KjIzBYGZkVm/8bXOzeaPCYEFAHErqODGYZSgzxEDSJ8iH/ZFwPhzUx4T2fMAvXB75D4NKj/TMOylMFpwGMBJec6cVpJT7cV8FQW74ldzMcWEQzX3+6L5K+haehjDSNnlfOPk0BApqGP685FSE3HuGv0duuXDd+H3zn1nYlzHvpTFjwejac31zwf2kcWP0furT4rq1y8t/AExi2uFLUhr4FPBGYCfwqKS7I+Lpgm43AYci4nxJNwAfA95eTMFmZpC/iW+aefXpSpdSMvnvBEB13Gt4XJEPZnFySBuZzR09Uztue8HscC4Ijpn5TbbJFIa/7Ojl/KxwpmCGOJOdeEa4cN2EM9Wj+ow/k52/b2L+MwOIZP9Bfn/JazJm2SwE+TrH78uY96PCcMG6k7cd+d+lsC9j3kfBvrNRWM+Ybcd8p8K+xMg45PvMlHn1qeoKX8A6YHtEPA8g6SvAdUBh+LoO+Eiy/HXgVkmKqIX/7jEzs/EMz1QUcTGHVZfhgFYQ1PJBb1T4y44OfBP2TX7Dn61po5jwtRzYUfB+J/DqifpExJCkI8BSYH8R+zUzM7MqkjtPD6iRQD5rHi4nab2kbkndPT09lS7HzMzMbEYUE752ASsL3q9I2sbtI6kOOIPcifcniYjbI6IrIrra2tqKKMvMzMxs9iomfD0KrJZ0rqQG4Abg7jF97gbenSxfD3zf53uZmZlZLZv2OV/JOVy3APeRu9XEhoh4StJfAt0RcTfwOeCLkrYDB8kFNDMzM7OaVdR9viLiHuCeMW0fKlg+AfxOMfswMzMzqyaajb8CSuoBXpxC11Z85WS5eKzLx2NdHh7n8vFYl4fHuXzGjvU5ETHlE9ZnZfiaKkndEdFV6Tpqgce6fDzW5eFxLh+PdXl4nMun2LGeNbeaMDMzM6sFDl9mZmZmZTTXw9ftlS6ghnisy8djXR4e5/LxWJeHx7l8ihrrOX3Ol5mZmdlcM9dnvszMzMzmlDkbviRdLWmbpO2SPljpeqqNpBckPSlps6TupG2JpI2Snk1eF1e6zrlG0gZJ+yRtKWgbd1yV84/JMf6EpEsqV/ncM8FYf0TSruS43izp2oJ1f5qM9TZJb6pM1XOPpJWSHpT0tKSnJL0/afdxXWKnGGsf1yUkaZ6kRyQ9nozzXyTt50p6OBnPryZP90FSY/J+e7J+1WT7mJPhS1Ia+BRwDfAK4EZJr6hsVVXpyohYW3A57QeBByJiNfBA8t5Ozx3A1WPaJhrXa4DVyd964NNlqrFa3MHJYw3wyeS4XpvcKJrk348bgIuSbf45+XfGJjcE/HFEvAJ4DXBzMp4+rktvorEGH9el1A+8LiIuBtYCV0t6DfAxcuN8PnAIuCnpfxNwKGn/ZNLvlOZk+ALWAdsj4vmIGAC+AlxX4ZpqwXXA55PlzwO/WblS5qaIeIjco7YKTTSu1wFfiJwfAy2SOspSaBWYYKwnch3wlYjoj4ifAdvJ/Ttjk4iI3RHxWLJ8FHgGWI6P65I7xVhPxMf1NCTHZm/ytj75C+B1wNeT9rHHdP5Y/zrwekk61T7mavhaDuwoeL+TUx+AdvoCuF/SJknrk7ZlEbE7Wd4DLKtMaVVnonH1cT4zbkl+7tpQ8NO5x7oEkp9bfgl4GB/XM2rMWIOP65KSlJa0GdgHbASeAw5HxFDSpXAsh8c5WX8EWHqqz5+r4ctm3uURcQm5nwhulvTawpWRu0zWl8qWmMd1xn0aOI/cTwm7gY9XtJoqIqkJ+AbwgYh4uXCdj+vSGmesfVyXWERkImItsILcbGFnKT9/roavXcDKgvcrkjYrkYjYlbzuA+4id/Dtzf88kLzuq1yFVWWicfVxXmIRsTf5RzULfIaRn2A81kWQVE8uDHwpIr6ZNPu4ngHjjbWP65kTEYeBB4HLyP1EXpesKhzL4XFO1p8BHDjV587V8PUosDq58qCB3AmFd1e4pqohaaGk5vwycBWwhdwYvzvp9m7g25WpsOpMNK53A+9Krg57DXCk4Gccm4Yx5xb9FrnjGnJjfUNy1dK55E4Gf6Tc9c1FybktnwOeiYhPFKzycV1iE421j+vSktQmqSVZng+8kdz5dQ8C1yfdxh7T+WP9euD7MclNVOtOtXK2ioghSbcA9wFpYENEPFXhsqrJMuCu5HzBOuDLEXGvpEeBr0m6CXgReFsFa5yTJN0JXAG0StoJfBj4KOOP6z3AteROku0D3lP2guewCcb6Cklryf0E9gLw+wAR8ZSkrwFPk7ui7OaIyFSg7LnoV4B3Ak8m58gA/Bk+rmfCRGN9o4/rkuoAPp9cGZoCvhYR35H0NPAVSX8N/IRcECZ5/aKk7eQu8rlhsh34DvdmZmZmZTRXf3Y0MzMzm5McvszMzMzKyOHLzMzMrIwcvszMzMzKyOHLzMzMrIzm5K0mzKz6SVpK7oHMAO1ABugBVgEvJQ8XnukaeiOiaab3Y2a1xTNfZjYrRcSBiFibPOLjNuCTyfJaIDvZ9gV3ojYzm1UcvsxsLkpL+oykpyTdn9yFGkn/LunvJXUD75d0qaT/lzwg/r6Cx938d0mPSnpc0jckLUjaz5X0I0lPJjdSJGnvkPSQpM2Stkj61Yp8azOrCg5fZjYXrQY+FREXAYeBtxasa4iILuAfgX8Cro+IS4ENwP9O+nwzIv5LRFxM7rEhNyXt/wB8OiJ+kdwDivN+F7gvmXm7GNg8E1/KzGqDp+XNbC76WURsTpY3kTsPLO+ryesa4JXAxuRRWWlGAtUrk5mtFqCJ3KPKIPf4lnyQ+yLwsWT5UWBD8lDjbxXs28zstHnmy8zmov6C5Qyj/0PyWPIq4Kn8eWMR8YsRcVWy7g7glmSG6y+AeQXbn/TMtYh4CHgtsAu4Q9K7SvM1zKwWOXyZWbXaBrRJugxAUr2ki5J1zcDuZCbrHQXb/CcjD8Udbpd0DrA3Ij4DfBa4ZKaLN7Pq5fBlZlUpIgaA64GPSXqc3Hlav5ys/nPgYXJha2vBZu8Hbpb0JLC8oP0K4HFJPwHeTu7cMDOzaVHESTPsZmZmZjZDPPNlZmZmVkYOX2ZmZmZl5PBlZmZmVkYOX2ZmZmZl5PBlZmZmVkYOX2ZmZmZl5PBlZmZmVkYOX2ZmZmZl9P8BhuBqHFeby1AAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Presim. Time / s\"].plot(figsize=(10, 3));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "exercise": "solution"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Sim. Time / s\"].plot(figsize=(10, 3));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Presim. Time / s\"].plot();\n",
+    "df[\"Sim. Time / s\"].plot();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot();\n",
+    "ax.set_ylabel(\"Time / s\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Task 6\n",
+    "<a name=\"task6\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "* To your `df` NEST data frame, add a column with the unaccounted time (`Unaccounted Time / s`), which is the difference of program runtime, average neuron build time, minimal edge build time, minimal initialization time, presimulation time, and simulation time.  \n",
+    "(*I know this is technically not super correct, but it will do for our example.*)\n",
+    "* Plot a stacked bar plot of all these columns (except for program runtime) over the threads\n",
+    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "cols = [\n",
+    "    'Avg. Neuron Build Time / s', \n",
+    "    'Min. Edge Build Time / s', \n",
+    "    'Min. Init. Time / s', \n",
+    "    'Presim. Time / s', \n",
+    "    'Sim. Time / s'\n",
+    "]\n",
+    "df[\"Unaccounted Time / s\"] = df['Runtime Program / s']\n",
+    "for entry in cols:\n",
+    "    df[\"Unaccounted Time / s\"] = df[\"Unaccounted Time / s\"] - df[entry]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Runtime Program / s</th>\n",
+       "      <th>Unaccounted Time / s</th>\n",
+       "      <th>Avg. Neuron Build Time / s</th>\n",
+       "      <th>Min. Edge Build Time / s</th>\n",
+       "      <th>Min. Init. Time / s</th>\n",
+       "      <th>Presim. Time / s</th>\n",
+       "      <th>Sim. Time / s</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Threads</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>420.42</td>\n",
+       "      <td>2.09</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>88.12</td>\n",
+       "      <td>1.14</td>\n",
+       "      <td>17.26</td>\n",
+       "      <td>311.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>202.15</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>47.98</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>7.95</td>\n",
+       "      <td>142.81</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Runtime Program / s  Unaccounted Time / s  \\\n",
+       "Threads                                              \n",
+       "8                     420.42                  2.09   \n",
+       "16                    202.15                  2.43   \n",
+       "\n",
+       "         Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n",
+       "Threads                                                         \n",
+       "8                              0.29                     88.12   \n",
+       "16                             0.28                     47.98   \n",
+       "\n",
+       "         Min. Init. Time / s  Presim. Time / s  Sim. Time / s  \n",
+       "Threads                                                        \n",
+       "8                       1.14             17.26         311.52  \n",
+       "16                      0.70              7.95         142.81  "
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[[\"Runtime Program / s\", \"Unaccounted Time / s\", *cols]].head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[[\"Unaccounted Time / s\", *cols]].plot(kind=\"bar\", stacked=True, figsize=(12, 4));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Task 7\n",
+    "<a name=\"task7\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "* Create a pivot table based on the NEST `df` data frame\n",
+    "* Let the `x` axis show the number of nodes; display the values of the simulation time `\"Sim. Time / s\"` for the tasks per node and threads per task configurations\n",
+    "* Please plot a bar plot\n",
+    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.pivot_table(\n",
+    "    index=\"Nodes\",\n",
+    "    columns=[\"Tasks/Node\", \"Threads/Task\"],\n",
+    "    values=\"Sim. Time / s\",\n",
+    ").plot(kind=\"bar\", figsize=(12, 4));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "<a name=\"taskb\"></a>\n",
+    "\n",
+    "* Bonus task\n",
+    "    - Same pivot table as before (that is, `x` with nodes, and columns for Tasks/Node and Threads/Task)\n",
+    "    - But now, use `Sim. Time / s` and `Presim. Time / s` as values to show\n",
+    "    - Show them as a stack of those two values inside the pivot table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task"
+   },
+   "source": [
+    "<span class=\"feedback\">Feedback to <a href=\"mailto:a.herten@fz-juelich.de\">a.herten@fz-juelich.de</a></span>\n",
+    "\n",
+    "Next slide: Further reading"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  },
+  "toc-autonumbering": false,
+  "toc-showcode": true,
+  "toc-showmarkdowntxt": false,
+  "toc-showtags": true
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/Introduction-to-Pandas--tasks.ipynb b/Introduction-to-Pandas--tasks.ipynb
index fecfe20ca7ea6ea711f1806b0861b66d2693e564..344b85f70398391fc38ea9813a5aa046b275ff8b 100644
--- a/Introduction-to-Pandas--tasks.ipynb
+++ b/Introduction-to-Pandas--tasks.ipynb
@@ -1 +1,328 @@
-{"cells": [{"cell_type": "markdown", "metadata": {"exercise": "task"}, "source": ["# *Introduction to* Data Analysis and Plotting with Pandas\n", "## JSC Tutorial\n", "\n", "Andreas Herten, Forschungszentrum J\u00fclich, 26 February 2019"]}, {"cell_type": "markdown", "metadata": {"exercise": "onlytask", "slideshow": {"slide_type": "skip"}}, "source": ["**Version: Tasks**"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "source": ["## Task Outline\n", "\n", "* [Task 1](#task1)\n", "* [Task 2](#task2)\n", "* [Task 3](#task3)\n", "* [Task 4](#task4)\n", "* [Task 5](#task5)\n", "* [Task 6](#task6)\n", "* [Task 7](#task7)\n", "* [Bonus Task](#taskb)"]}, {"cell_type": "code", "execution_count": 2, "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["import pandas as pd"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 1\n", "<a name=\"task1\"></a>\n", "\n", "* Create data frame with\n", "    - 10 names of dinosaurs, \n", "    - their favourite prime number, \n", "    - and their favourite color\n", "* Play around with the frame\n", "* Tell me on poll when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "markdown", "metadata": {"exercise": "nopresentation", "slideshow": {"slide_type": "skip"}}, "source": ["Jupyter Notebook 101:\n", "\n", "* Execute cell: `shift+enter`\n", "* New cell in front of current cell: `a`\n", "* New cell after current cell: `b`"]}, {"cell_type": "code", "execution_count": 21, "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["happy_dinos = {\n", "    \"Dinosaur Name\": [],\n", "    \"Favourite Prime\": [],\n", "    \"Favourite Color\": []\n", "}\n", "#df_dinos = "]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 2\n", "<a name=\"task2\"></a>\n", "\n", "* Read in `nest-data.csv` to `DataFrame`; call it `df`  \n", " *Data was produced with [JUBE](http://www.fz-juelich.de/ias/jsc/EN/Expertise/Support/Software/JUBE/_node.html), Pandas works **very** well together with JUBE*\n", "* Get to know it and play a bit with it\n", "* Tell me when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 30, "metadata": {"exercise": "task"}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["id,Nodes,Tasks/Node,Threads/Task,Runtime Program / s,Scale,Plastic,Avg. Neuron Build Time / s,Min. Edge Build Time / s,Max. Edge Build Time / s,Min. Init. Time / s,Max. Init. Time / s,Presim. Time / s,Sim. Time / s,Virt. Memory (Sum) / kB,Local Spike Counter (Sum),Average Rate (Sum),Number of Neurons,Number of Connections,Min. Delay,Max. Delay\n", "5,1,2,4,420.42,10,true,0.29,88.12,88.18,1.14,1.20,17.26,311.52,46560664.00,825499,7.48,112500,1265738500,1.5,1.5\n", "5,1,4,4,200.84,10,true,0.15,46.03,46.34,0.70,1.01,7.87,142.97,46903088.00,802865,7.03,112500,1265738500,1.5,1.5\n"]}], "source": ["!cat nest-data.csv | head -3"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "subslide"}}, "source": ["## Task 3\n", "<a name=\"task3\"></a>\n", "\n", "* Add a column to the Nest data frame called `Virtual Processes` which is the total number of threads across all nodes (i.e. the product of threads per task and tasks per node and nodes)\n", "* Remember to tell me when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "code", "execution_count": 56, "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "outputs": [], "source": ["import matplotlib.pyplot as plt\n", "%matplotlib inline"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 4\n", "<a name=\"task4\"></a>\n", "\n", "* Sort the data frame by the virtual proccesses\n", "* Plot `\"Presim. Time / s\"` and `\"Sim. Time / s\"` of our data frame `df` as a function of the virtual processes\n", "* Use a dashed, red line for `\"Presim. Time / s\"`, a blue line for `\"Sim. Time / s\"` (see [API description](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot))\n", "* Don't forget to label your axes and to add a legend\n", "* Submit when you're done: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 5\n", "<a name=\"task5\"></a>\n", "\n", "Use the NEST data frame `df` to:\n", "\n", "1. Make the virtual processes the index of the data frame (`.set_index()`)\n", "2. Plot `\"Presim. Program / s\"` and `\"Sim. Time / s`\" individually\n", "3. Plot them onto one common canvas!\n", "4. Make them have the same line colors and styles as before\n", "5. Add a legend, add missing labels\n", "\n", "* Done? Tell me! [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 6\n", "<a name=\"task6\"></a>\n", "\n", "* To your `df` NEST data frame, add a column with the unaccounted time (`Unaccounted Time / s`), which is the difference of program runtime, average neuron build time, minimal edge build time, minimal initialization time, presimulation time, and simulation time.  \n", "(*I know this is technically not super correct, but it will do for our example.*)\n", "* Plot a stacked bar plot of all these columns (except for program runtime) over the virtual processes\n", "* Remember: [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "slide"}}, "source": ["## Task 7\n", "<a name=\"task7\"></a>\n", "\n", "* Create a pivot table based on the NEST `df` data frame\n", "* Let the `x` axis show the number of nodes; display the values of the simulation time `\"Sim. Time / s\"` for the tasks per node and threas per task configurations\n", "* Please plot a bar plot\n", "* Done? [pollev.com/aherten538](https://pollev.com/aherten538)"]}, {"cell_type": "markdown", "metadata": {"exercise": "task", "slideshow": {"slide_type": "fragment"}}, "source": ["<a name=\"taskb\"></a>\n", "\n", "* Bonus task\n", "    - Same pivot table as before (that is, `x` with nodes, and columns for Tasks/Node and Threads/Task)\n", "    - But now, use `Sim. Time / s` and `Presim. Time / s` as values to show\n", "    - Show them as a stack of those two values inside the pivot table"]}, {"cell_type": "markdown", "metadata": {"exercise": "task"}, "source": ["<span class=\"feedback\">Tell me what you think about this tutorial! <a href=\"mailto:a.herten@fz-juelich.de\">a.herten@fz-juelich.de</a></span>\n", "\n", "Next slide: Further reading"]}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.2"}}, "nbformat": 4, "nbformat_minor": 2}
\ No newline at end of file
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "tags": [
+     "task"
+    ]
+   },
+   "source": [
+    "# Data Analysis and Plotting in Python with Pandas\n",
+    "\n",
+    "_Andreas Herten, J\u00fclich Supercomputing Centre, Forschungszentrum J\u00fclich, 27 May 2021_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "onlytask",
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Version: Tasks**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "## Task Outline\n",
+    "\n",
+    "* [Task 1](#task1)\n",
+    "* [Task 2](#task2)\n",
+    "* [Task 3](#task3)\n",
+    "* [Task 4](#task4)\n",
+    "* [Task 5](#task5)\n",
+    "* [Task 6](#task6)\n",
+    "* [Task 7](#task7)\n",
+    "* [Bonus Task](#taskb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Task 1\n",
+    "<a name=\"task1\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "* Create data frame with\n",
+    "    - 6 names of dinosaurs, \n",
+    "    - their favourite prime number, \n",
+    "    - and their favorite color.\n",
+    "* Play around with the frame\n",
+    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "nopresentation",
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Jupyter Notebook 101:\n",
+    "\n",
+    "* Execute cell: `shift+enter`\n",
+    "* New cell in front of current cell: `a`\n",
+    "* New cell after current cell: `b`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "happy_dinos = {\n",
+    "    \"Dinosaur Name\": [],\n",
+    "    \"Favourite Prime\": [],\n",
+    "    \"Favourite Color\": []\n",
+    "}\n",
+    "#df_dinos = "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Task 2\n",
+    "<a name=\"task2\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "* Read in `data-nest.csv` to `DataFrame`; call it `df`  \n",
+    "  *(Data was produced with [JUBE](http://www.fz-juelich.de/ias/jsc/EN/Expertise/Support/Software/JUBE/_node.html))*\n",
+    "* Get to know it and play a bit with it\n",
+    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "exercise": "task"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "id,Nodes,Tasks/Node,Threads/Task,Runtime Program / s,Scale,Plastic,Avg. Neuron Build Time / s,Min. Edge Build Time / s,Max. Edge Build Time / s,Min. Init. Time / s,Max. Init. Time / s,Presim. Time / s,Sim. Time / s,Virt. Memory (Sum) / kB,Local Spike Counter (Sum),Average Rate (Sum),Number of Neurons,Number of Connections,Min. Delay,Max. Delay\n",
+      "5,1,2,4,420.42,10,true,0.29,88.12,88.18,1.14,1.20,17.26,311.52,46560664.00,825499,7.48,112500,1265738500,1.5,1.5\n",
+      "5,1,4,4,200.84,10,true,0.15,46.03,46.34,0.70,1.01,7.87,142.97,46903088.00,802865,7.03,112500,1265738500,1.5,1.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat nest-data.csv | head -3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Task 3\n",
+    "<a name=\"task3\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "* Add a column to the Nest data frame form Task 2 called `Threads` which is the total number of threads across all nodes (i.e. the product of threads per task and tasks per node and nodes)\n",
+    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Task 4\n",
+    "<a name=\"task4\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "\n",
+    "* Sort the data frame by threads\n",
+    "* Plot `\"Presim. Time / s\"` and `\"Sim. Time / s\"` of our data frame `df` as a function of threads\n",
+    "* Use a dashed, red line for `\"Presim. Time / s\"`, a blue line for `\"Sim. Time / s\"` (see [API description](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot))\n",
+    "* Don't forget to label your axes and to add a legend _(1st rule of plotting)_\n",
+    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Task 5\n",
+    "<a name=\"task5\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "Use the NEST data frame `df` to:\n",
+    "\n",
+    "1. Make the threads the index of the data frame (`.set_index()`)\n",
+    "2. Plot `\"Presim. Program / s\"` and `\"Sim. Time / s`\" individually\n",
+    "3. Plot them onto one common canvas!\n",
+    "4. Make them have the same line colors and styles as before\n",
+    "5. Add a legend, add missing axes labels\n",
+    "6. Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Task 6\n",
+    "<a name=\"task6\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "* To your `df` NEST data frame, add a column with the unaccounted time (`Unaccounted Time / s`), which is the difference of program runtime, average neuron build time, minimal edge build time, minimal initialization time, presimulation time, and simulation time.  \n",
+    "(*I know this is technically not super correct, but it will do for our example.*)\n",
+    "* Plot a stacked bar plot of all these columns (except for program runtime) over the threads\n",
+    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Task 7\n",
+    "<a name=\"task7\"></a>\n",
+    "<span style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
+    "\n",
+    "* Create a pivot table based on the NEST `df` data frame\n",
+    "* Let the `x` axis show the number of nodes; display the values of the simulation time `\"Sim. Time / s\"` for the tasks per node and threads per task configurations\n",
+    "* Please plot a bar plot\n",
+    "* Tell me when you're done with status icon in BigBlueButton: \ud83d\udc4d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "<a name=\"taskb\"></a>\n",
+    "\n",
+    "* Bonus task\n",
+    "    - Same pivot table as before (that is, `x` with nodes, and columns for Tasks/Node and Threads/Task)\n",
+    "    - But now, use `Sim. Time / s` and `Presim. Time / s` as values to show\n",
+    "    - Show them as a stack of those two values inside the pivot table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "exercise": "task"
+   },
+   "source": [
+    "<span class=\"feedback\">Feedback to <a href=\"mailto:a.herten@fz-juelich.de\">a.herten@fz-juelich.de</a></span>\n",
+    "\n",
+    "Next slide: Further reading"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  },
+  "toc-autonumbering": false,
+  "toc-showcode": true,
+  "toc-showmarkdowntxt": false,
+  "toc-showtags": true
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/Makefile b/Makefile
index 4067a36b2f8c525c50e18a597ffa296aecd266a2..128c1f084e5bfd1cc6b6a941862bebb5e2922348 100644
--- a/Makefile
+++ b/Makefile
@@ -4,7 +4,10 @@ SUBNOTEBOOKS = Introduction-to-Pandas--slides.ipynb Introduction-to-Pandas--task
 
 MASTER_NOTEBOOK = Introduction-to-Pandas--master.ipynb
 
-DEP_PRESENTATION = reveal_options.txt fzj.js custom.css Makefile
+DEP_PRESENTATION = fzj-reveal.js/ Makefile patch-00--add-fzj-reveal-subdir.patch
+
+_TMPFILE1 := .tmp1
+_TMPFILE2 := .tmp2
 
 .PHONY: all presentation subnotebooks presentation-pdf
 all: presentation subnotebooks
@@ -13,14 +16,19 @@ presentation-pdf: $(SLIDES:html=pdf)
 subnotebooks: $(SUBNOTEBOOKS)
 
 %.html: %.ipynb $(DEP_PRESENTATION)
-	jupyter nbconvert --to=slides --reveal-prefix=reveal.js --stdout $< \
-	| awk '/reveal.js"/ { print "     " $$1 ","; print "     \"fzj.js\""; next }1' \
-	| sed '/transition/r reveal_options.txt' \
-	> $@
+	jupyter nbconvert --to=slides --reveal-prefix=reveal.js --stdout $< > $(_TMPFILE1)
+	gsed -i 's#<script src=#\n<script src=#g' $(_TMPFILE1)
+	gsed -i '/require.min.js/d' $(_TMPFILE1)
+	patch --silent --unified --input=fzj-reveal.js/patch-00--new-revealjs-api.patch --output=$(_TMPFILE2) $(_TMPFILE1) && mv $(_TMPFILE2) $(_TMPFILE1)
+	patch --silent --unified --input=fzj-reveal.js/patch-01--add-fzj-js.patch --output=$(_TMPFILE2) $(_TMPFILE1) && mv $(_TMPFILE2) $(_TMPFILE1)
+	patch --silent --unified --input=fzj-reveal.js/patch-02--add-fzj-css.patch --output=$(_TMPFILE2) $(_TMPFILE1) && mv $(_TMPFILE2) $(_TMPFILE1)
+	patch --silent --unified --input=fzj-reveal.js/patch-03--modify-canvas.patch --output=$(_TMPFILE2) $(_TMPFILE1) && mv $(_TMPFILE2) $(_TMPFILE1)
+	patch --silent --unified --input=patch-00--add-fzj-reveal-subdir.patch --output=$(_TMPFILE2) $(_TMPFILE1) && mv $(_TMPFILE2) $(_TMPFILE1)
+	mv $(_TMPFILE1) $@
 
 %.pdf: %.html $(DEP_PRESENTATION)
 	# This needs to have artificially large paper size in order to fix bug https://github.com/astefanutti/decktape/issues/151#issuecomment-456166075
-	decktape --size "2560x1440" reveal $< $@
+	docker run --rm -t -v $(PWD):/slides astefanutti/decktape --size "2560x1440" reveal $< $@
 
 Introduction-to-Pandas--slides.ipynb: $(MASTER_NOTEBOOK)
 	notebook-splitter --keep task --keep solution --keep onlypresentation --remove onlytask --remove onlysolution --remove nopresentation -o $@ $< 
diff --git a/custom.css b/custom.css
deleted file mode 100644
index e1abfac4d9e2b2ce700188af87894754fbd048a7..0000000000000000000000000000000000000000
--- a/custom.css
+++ /dev/null
@@ -1,103 +0,0 @@
-/*
-    2018: First version by Jan Meinke;
-    2019: Appended by Andreas Herten;
-*/
-:root {
-  --color-fzjblue: #023d6b;
-  --color-fzjlightblue: #adbde3;
-  --logo-fzj: url("img/fzjlogo.svg");
-  --logo-helmholtz: url("img/helmholtz.svg");
-}
-body {
-    font-family: "Source Sans Pro", "Arial", "sans-serif";
-    background: var(--logo-fzj), var(--logo-helmholtz);
-    background-size: 35.5%, 18%;
-    background-position: 100% 104%, 1.5% 95%;
-    background-repeat: no-repeat, no-repeat;
-}
-
-section {
-    margin: 0;
-}
-
-.reveal .slides {
-    margin: 0 1vmin;
-}
-.reveal h1,
-.reveal h2,
-.reveal h3 {
-    font-family: "Source Sans Pro", "Arial", "sans-serif";
-    text-transform: uppercase;
-    color: var(--color-fzjblue);
-}
-
-.reveal h1 {
-    color: white;
-}
-
-.reveal section[data-background-image='img/background_title_slide.svg'] * {
-    color: white;
-}
-
-.reveal h2 + h3 {
-    text-transform: unset;
-    font-size: 90%;
-}
-
-.controls {
-    visibility: hidden;
-}
-
-.reveal .progress {
-    position: absolute;
-    bottom: 1px;
-}
-
-.prompt {
-    min-width: 0;
-    width: 0;
-    visibility: hidden;
-}
-
-div.dateauthor {
-    padding-top: 4em;
-    color: white;
-}
-
-div.prompt {
-    width:0;
-}
-
-div#footer {
-    position: fixed;
-    bottom: 0;
-    width: 100%;
-    z-index: 10;
-font-size: 0.5em; font-weight: bold; padding: 0 1vmin; height: 20vmin; background: #fff}
-#footer h1 {
-    position: absolute; 
-    bottom: 3.2vmin; 
-    display: block; 
-    padding: 0 1em; 
-    font-size: 1.7vmin;
-    font-weight: bold;
-    text-transform: unset;
-    color: var(--color-fzjblue);
-}
-#footer h2 {display: block; padding: 0.em 1em 0;}
-
-img.fzjlogo {
-    position: fixed;
-    bottom: 0;
-    right: 0;
-    height: 24vmin; /* The height of the svg is about 3 times the height of the logo */
-    margin-bottom: -3vmin; /* Baseline of logo should be about 5% of short side above edge. */
-}
-
-.rendered_html img, svg {
-    max-height: 440px;
-}
-
-.reveal table.dataframe {
-    font-size: 0.7em;
-}
diff --git a/lost.json b/data-lost.json
similarity index 100%
rename from lost.json
rename to data-lost.json
diff --git a/nest-data.csv b/data-nest.csv
similarity index 100%
rename from nest-data.csv
rename to data-nest.csv
diff --git a/fzj-reveal.js b/fzj-reveal.js
index c2f88a17606130cb362f59c31172d87d51193351..a31cc55bef923f4388693b0340af0607ff1427bf 160000
--- a/fzj-reveal.js
+++ b/fzj-reveal.js
@@ -1 +1 @@
-Subproject commit c2f88a17606130cb362f59c31172d87d51193351
+Subproject commit a31cc55bef923f4388693b0340af0607ff1427bf
diff --git a/fzj.js b/fzj.js
deleted file mode 100644
index db3e15512e2419735375965d7a9245e291bc5994..0000000000000000000000000000000000000000
--- a/fzj.js
+++ /dev/null
@@ -1 +0,0 @@
-document.getElementsByTagName("section")[0].getElementsByTagName("section")[0].setAttribute("data-background-image", "img/background_title_slide.svg");
diff --git a/img/buzz-dataframes.jpg b/img/buzz-dataframes.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..577f841540d1791e4d6a403678948ac50224b633
Binary files /dev/null and b/img/buzz-dataframes.jpg differ
diff --git a/patch-00--add-fzj-reveal-subdir.patch b/patch-00--add-fzj-reveal-subdir.patch
new file mode 100644
index 0000000000000000000000000000000000000000..115aa304bdd769e16ccb865b8e3e7aecedb281c2
--- /dev/null
+++ b/patch-00--add-fzj-reveal-subdir.patch
@@ -0,0 +1,26 @@
+--- /dev/null	2021-05-25 11:39:23.000000000 +0200
++++ /dev/null	2021-05-25 11:36:47.000000000 +0200
+@@ -11,9 +11,9 @@
+ <script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
+ 
+ <!-- General and theme style sheets -->
+-<link rel="stylesheet" href="reveal.js/dist/reveal.css">
+-<link rel="stylesheet" href="reveal.js/dist/theme/simple.css" id="theme">
+-<link rel="stylesheet" href="custom.css" id="custom">
++<link rel="stylesheet" href="fzj-reveal.js/reveal.js/dist/reveal.css">
++<link rel="stylesheet" href="fzj-reveal.js/reveal.js/dist/theme/simple.css" id="theme">
++<link rel="stylesheet" href="fzj-reveal.js/custom.css" id="custom">
+ 
+ <!-- If the query includes 'print-pdf', include the PDF print sheet -->
+ <script>
+@@ -21749,8 +21749,8 @@
+ </body>
+ 
+ 
+-<script src="reveal.js/dist/reveal.js"></script>
+-<script src="fzj.js"></script>
++<script src="fzj-reveal.js/reveal.js/dist/reveal.js"></script>
++<script src="fzj-reveal.js/fzj.js"></script>
+ <script>
+   Reveal.initialize({
+     controls: true,
diff --git a/reveal_options.txt b/reveal_options.txt
deleted file mode 100644
index 719dab8983d3cc69949cec9dc7ae7564eea0e446..0000000000000000000000000000000000000000
--- a/reveal_options.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-            width: 1280,
-            height: 720,
-            center: false,
-            controls: false,