diff --git a/Introduction-to-Pandas--master.ipynb b/Introduction-to-Pandas--master.ipynb
index 79c9ecfc9e6b68cd15747aff230b5df33a6eb7d7..6c28d733e4119fb1d656155f7dda08e13ffe356e 100644
--- a/Introduction-to-Pandas--master.ipynb
+++ b/Introduction-to-Pandas--master.ipynb
@@ -11,9 +11,7 @@
    "source": [
     "# Data Analysis and Plotting in Python with Pandas\n",
     "\n",
-    "_Andreas Herten, Jülich Supercomputing Centre, Forschungszentrum Jülich, 14 June 2022_\n",
-    "\n",
-    "_MATSE Version_"
+    "_Andreas Herten, Jülich Supercomputing Centre, Forschungszentrum Jülich, 4 September 2023_"
    ]
   },
   {
@@ -104,9 +102,10 @@
    "source": [
     "## Course Setup\n",
     "\n",
-    "* Originally, a 4 h course (incleaing break)\n",
-    "* This is the two-day MATSE lecture, with plenty of space and some new material\n",
-    "* Alternating between lecture and hands-on"
+    "* 3½ hours, including break around 10:30\n",
+    "* Alternating between lecture and hands-on\n",
+    "* Please give status of hands-ons via 👍 as BigBlueButton status\n",
+    "* TAs and me in the room can help with issues, either in public chat or in 1:1 chat"
    ]
   },
   {
@@ -117,7 +116,8 @@
     }
    },
    "source": [
-    "* Please now open Jupyter Notebook of this session from Sciebo"
+    "* Please now open Jupyter Notebook of this session: https://go.fzj.de/jsc-pd\n",
+    "* Give thumbs up! 👍"
    ]
   },
   {
@@ -137,6 +137,7 @@
     "* 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",
+    "* Now at Pandas 2.0\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*"
@@ -175,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -188,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {
     "exercise": "task",
     "slideshow": {
@@ -202,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -212,10 +213,10 @@
     {
      "data": {
       "text/plain": [
-       "'1.4.2'"
+       "'2.0.3'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -226,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -339,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -352,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -442,7 +443,7 @@
        "9  60"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -453,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -508,7 +509,7 @@
        "2  56"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -531,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -556,7 +557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -621,7 +622,7 @@
        "3   Waters   57"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -645,7 +646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -654,7 +655,7 @@
        "Index(['Name', 'Age'], dtype='object')"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -677,7 +678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -686,7 +687,7 @@
        "RangeIndex(start=0, stop=10, step=1)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -709,7 +710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -804,7 +805,7 @@
        "Hall      60"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -827,7 +828,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -907,7 +908,7 @@
        "max    60.000000"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -918,7 +919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -947,7 +948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1010,7 +1011,7 @@
        "Age    41       56      56      57    39      59    43      56     38    60"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1021,7 +1022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1037,7 +1038,7 @@
        "      dtype='object', name='Name')"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1059,7 +1060,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 17,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1119,7 +1120,7 @@
        "Rivers   112"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1130,7 +1131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 18,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1189,7 +1190,7 @@
        "2    RiversRivers  112"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1200,7 +1201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 19,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1260,7 +1261,7 @@
        "Rivers   28.0"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1271,7 +1272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 20,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1331,7 +1332,7 @@
        "Rivers   3136"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1342,7 +1343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 21,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1413,7 +1414,7 @@
        "Rice     1521"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1428,7 +1429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 22,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1441,7 +1442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 23,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1512,7 +1513,7 @@
        "Rice     1521"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1535,7 +1536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 24,
    "metadata": {
     "tags": []
    },
@@ -1628,7 +1629,7 @@
        "Hall      True"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1639,7 +1640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 25,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1710,7 +1711,7 @@
        "Rice     True"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1758,7 +1759,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 26,
    "metadata": {
     "exercise": "task",
     "slideshow": {
@@ -1777,7 +1778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 27,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -1847,7 +1848,7 @@
        "Favourite Color      violet         gray  "
       ]
      },
-     "execution_count": 31,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1875,7 +1876,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1960,7 +1961,7 @@
        "4  1.2 2018-02-26 -0.718282  entries  Same"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1978,7 +1979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2067,7 +2068,7 @@
        "1  1.2 2018-02-26  1.718282   column  Same"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2078,7 +2079,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -2140,7 +2141,7 @@
        "4  1.2 2018-02-26 -0.72  entries  Same"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2151,42 +2152,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/f5/swf8tg5j5r7bqbwftt3zn6b00000gn/T/ipykernel_95838/1325867503.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
-      "  df_demo.round(2).sum()\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "A                     6.0\n",
-       "C                   -2.03\n",
-       "E    SameSameSameSameSame\n",
-       "dtype: object"
+       "A    6.00\n",
+       "C   -2.03\n",
+       "dtype: float64"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_demo.round(2).sum()"
+    "df_demo.round(2)[[\"A\", \"C\"]].sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2199,25 +2191,17 @@
      "text": [
       "\\begin{tabular}{lrlrll}\n",
       "\\toprule\n",
-      "{} &    A &          B &     C &        D &     E \\\\\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",
+      "0 & 1.200000 & 2018-02-26 00:00:00 & -2.720000 & This & Same \\\\\n",
+      "1 & 1.200000 & 2018-02-26 00:00:00 & 1.720000 & column & Same \\\\\n",
+      "2 & 1.200000 & 2018-02-26 00:00:00 & -1.300000 & has & Same \\\\\n",
+      "3 & 1.200000 & 2018-02-26 00:00:00 & 0.990000 & entries & Same \\\\\n",
+      "4 & 1.200000 & 2018-02-26 00:00:00 & -0.720000 & entries & Same \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/f5/swf8tg5j5r7bqbwftt3zn6b00000gn/T/ipykernel_95838/3396683912.py:1: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
-      "  print(df_demo.round(2).to_latex())\n"
-     ]
     }
    ],
    "source": [
@@ -2253,7 +2237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2336,7 +2320,7 @@
        "Walt       Malcolm David Kelley      False"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2366,7 +2350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 34,
    "metadata": {
     "exercise": "task"
    },
@@ -2394,7 +2378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 35,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -2618,7 +2602,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2651,7 +2635,7 @@
     "    - `decimal`: Decimal point divider – for German data…\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",
+    "pandas.read_csv(filepath_or_buffer, *, sep=_NoDefault.no_default, delimiter=None, header='infer', names=_NoDefault.no_default, index_col=None, usecols=None, 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=None, infer_datetime_format=_NoDefault.no_default, keep_date_col=False, date_parser=_NoDefault.no_default, date_format=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, encoding_errors='strict', dialect=None, on_bad_lines='error', delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None, storage_options=None, dtype_backend=_NoDefault.no_default)\n",
     "```"
    ]
   },
@@ -2688,7 +2672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -2755,7 +2739,7 @@
        "2  1.2 2018-02-26 -1.304068     has  Same"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2766,7 +2750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2785,7 +2769,7 @@
        "Name: C, dtype: float64"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2808,7 +2792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 38,
    "metadata": {
     "tags": []
    },
@@ -2824,7 +2808,7 @@
        "Name: C, dtype: float64"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2855,7 +2839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2926,7 +2910,7 @@
        "4  1.2 -0.718282"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2950,7 +2934,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -3008,7 +2992,7 @@
        "2  1.2 2018-02-26 -1.304068     has  Same"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3019,7 +3003,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3082,7 +3066,7 @@
        "3  1.2 2018-02-26  0.986231  entries  Same"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3104,7 +3088,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -3162,7 +3146,7 @@
        "2  1.2 2018-02-26 -1.304068     has  Same"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3173,7 +3157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -3231,7 +3215,7 @@
        "4  1.2 2018-02-26 -0.718282  entries  Same"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3258,7 +3242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 44,
    "metadata": {
     "tags": []
    },
@@ -3318,7 +3302,7 @@
        "2  1.2 2018-02-26 -1.304068     has  Same"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3341,7 +3325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -3390,7 +3374,7 @@
        "2  1.2 -1.304068"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3414,7 +3398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3505,7 +3489,7 @@
        "entries  1.2 2018-02-26 -0.718282  Same"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3517,7 +3501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -3580,7 +3564,7 @@
        "entries  1.2 2018-02-26 -0.718282  Same"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3591,7 +3575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3657,7 +3641,7 @@
        "entries  1.2 -0.718282"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3687,7 +3671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -3745,7 +3729,7 @@
        "3  1.2 2018-02-26  0.986231  entries  Same"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3756,7 +3740,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -3770,7 +3754,7 @@
        "Name: C, dtype: bool"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3781,7 +3765,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3834,7 +3818,7 @@
        "4  1.2 2018-02-26 -0.718282  entries  Same"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3854,7 +3838,6 @@
     "## 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",
@@ -3864,7 +3847,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3935,7 +3918,7 @@
        "2  1.2 2018-02-26 -1.304068     has  Same"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3946,7 +3929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4022,7 +4005,7 @@
        "2  1.2 2018-02-26 -1.304068     has  Same -2.504068"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4047,7 +4030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4127,7 +4110,7 @@
        "2  1.2 2018-02-26 -1.304068     has  Same  1.700594 -2.504068"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4139,7 +4122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -4219,7 +4202,7 @@
        "4  1.2 2018-02-26 -0.718282  entries  Same  0.515929 -1.918282"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4229,23 +4212,27 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 60,
+   "cell_type": "markdown",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
-    },
-    "tags": []
+    }
+   },
+   "source": [
+    "## Combining Frames\n",
+    "\n",
+    "* First, create some simpler data frame to show `.concat()` and `.merge()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/f5/swf8tg5j5r7bqbwftt3zn6b00000gn/T/ipykernel_95838/363555535.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-      "  df_demo.append(\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -4267,179 +4254,20 @@
        "  <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",
+       "      <th>Key</th>\n",
+       "      <th>Value</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",
+       "      <td>First</td>\n",
+       "      <td>1</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": 60,
-     "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": "fragment"
-    },
-    "tags": []
-   },
-   "source": [
-    "`.append()` seems to be deprecated; one needs to use something like the following in thef future:\n",
-    "```python\n",
-    "pd.concat(\n",
-    "    [\n",
-    "        df_demo, \n",
-    "        pd.DataFrame({\"A\": 1.3, \"B\": pd.Timestamp(\"2018-02-27\"), \"C\": -0.777, \"D\": \"has it?\", \"E\": \"Same\", \"F\": 23}, index=[0])\n",
-    "    ], ignore_index=True\n",
-    ")\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": 61,
-   "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",
+       "      <td>Second</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -4451,7 +4279,7 @@
        "1  Second      1"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4463,7 +4291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -4512,7 +4340,7 @@
        "1  Second      2"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4535,7 +4363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -4596,7 +4424,7 @@
        "1  Second      2"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4618,7 +4446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -4679,7 +4507,7 @@
        "3  Second      2"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4701,8 +4529,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
+   "execution_count": 60,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -4756,7 +4590,7 @@
        "1  Second      1  Second      2"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4768,9 +4602,11 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": []
    },
    "source": [
     "* Merge on common column"
@@ -4778,8 +4614,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
+   "execution_count": 61,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -4830,7 +4672,7 @@
        "1  Second        1        2"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4842,28 +4684,25 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "exercise": "task",
+    "editable": true,
     "slideshow": {
-     "slide_type": "subslide"
-    }
+     "slide_type": "fragment"
+    },
+    "tags": []
    },
    "source": [
-    "## Task 3\n",
-    "<a name=\"task3\"></a>\n",
-    "<span class=\"task\" 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: 👍"
+    "`.concat()` can also be used to append rows to a DataFrame:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 62,
    "metadata": {
-    "exercise": "solution",
+    "editable": true,
     "slideshow": {
-     "slide_type": "fragment"
-    }
+     "slide_type": ""
+    },
+    "tags": []
    },
    "outputs": [
     {
@@ -4887,71 +4726,221 @@
        "  <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",
+       "      <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>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",
+       "      <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>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>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": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat(\n",
+    "    [\n",
+    "        df_demo, \n",
+    "        pd.DataFrame({\"A\": 1.3, \"B\": pd.Timestamp(\"2018-02-27\"), \"C\": -0.777, \"D\": \"has it?\", \"E\": \"Same\", \"F\": 23}, index=[0])\n",
+    "    ], ignore_index=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Task 3\n",
+    "<a name=\"task3\"></a>\n",
+    "<span class=\"task\" 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: 👍"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "editable": true,
+    "exercise": "solution",
+    "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>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",
@@ -5075,7 +5064,7 @@
        "[5 rows x 22 columns]"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5087,7 +5076,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 64,
    "metadata": {
     "exercise": "solution"
    },
@@ -5105,7 +5094,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5136,7 +5125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 65,
    "metadata": {
     "exercise": "task",
     "slideshow": {
@@ -5151,7 +5140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -5165,7 +5154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5174,14 +5163,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5207,7 +5194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5220,19 +5207,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5259,19 +5244,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5285,10 +5268,12 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "slide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "## Task 4\n",
@@ -5305,12 +5290,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 71,
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -5319,26 +5306,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 72,
    "metadata": {
-    "exercise": "solution"
+    "editable": true,
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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TNQc5sUfLwrFHi4iIiFoCIYCiInmaEHXLMT8/HwcPHsTf/vY3PPLII/D398eAAQMQHR1tVFBUHTqo63363//+h4cffhhOTk7o378/Lly4gBMnTqBfv35QKpUYMWIErl27Vqc8bG1toVar9U2pVMLOzs7oNicnpzuGDnbs2BHvvfcepk6dCqVSCX9/f2zduhXXrl3D2LFjoVQqERISgpMnTxo93qFDh/S5+/r6Ys6cOSgqKqo1x5KSEuzZs8dkoXXq1Ck88sgjcHV1hZubG/r27XvH41o7FloWTjfFe0pK3T8EiIiIiKxNcTGgVMrTiovrlqNSqYRSqcTmzZtRWlpar+17++238eabb+Knn36CnZ0dnn76abz66qv47LPPcPDgQaSlpWHRokUNeObqZ8WKFRg4cCB+/vlnjBo1Cs8++yymTp2KZ555Bj/99BM6d+6MqVOnQvzxxTM9PR2RkZGYMGECUlJSsH79ehw6dAizZ8+u9XHi4uLQvn17dO/evcblU6ZMQYcOHXDixAkkJSXhtddeg729vdm3V04stCxc9+6AnZ006+DVq3JnQ0RERNRy2dnZISYmBmvWrIFKpcLAgQPx+uuvIyUl5a73XbBgASIiItCjRw/89a9/RVJSEt566y0MHDgQ999/P2bMmIH9+/c3+jaMHDkSzz//PLp27YpFixZBo9Ggf//+mDhxIgIDA7Fw4UKcP38eubm5AIClS5diypQpmDt3Lrp27YoHH3wQK1euxH/+8x+UlJSYfJy7DRvMzMxEeHg4unfvjq5du2LixIno1auX2bdXTiy0LJyDA9Ctm3SZwweJiIiouXJ2BgoL5WnOznXPc8KECcjKysLWrVsRGRmJ+Ph49OnTBzExMbXeL0Q3TAmAl5cXACBYd4zIH7fl5eXV6zlriLrkAUCfy6lTpxATE6PvzVMqlYiIiIBWq0VGRkaNjyGEwLZt22ottObPn48///nPCA8Px4cffoj09PR73jZLw0LLCuhe+3X4sYSIiIjIKikUgIuLPE2hqF+urVq1wqOPPoq33noLR44cwfTp0/H222/Xep+qw+IUfzxg9du0Wm39EmmAuuQBQJ9LYWEhnn/+eSQnJ+vbqVOncPHiRXTu3LnGxzh+/DgqKirw4IMPmsxj8eLFOHv2LEaNGoV9+/YhKCgImzZtuuftsyQstKyA7ocH9mgRERERWZ6goKC7Tg5hrfr06YNz586hS5cudzQHB4ca77NlyxaMGjUKtra2ta47MDAQ8+bNw549ezB+/HisXr26MTZBNiy0rABnHiQiIiKS340bNzB06FB8++23SElJQUZGBr7//nssW7YMY8eONetjbdq0yeREEk1p4cKFOHLkCGbPno3k5GRcvHgRW7ZsqXUyjK1bt9Y6bPD27duYPXs24uPjcfnyZRw+fBgnTpxAjx49GmMTZMPzaFkBXaH1yy9AWZl03BYRERERNS2lUonQ0FCsWLEC6enpKC8vh6+vL2bOnInXX3/drI9VUFCA1NRUs66zIUJCQpCQkIA33ngDDz/8MIQQ6Ny5MyZNmlRjfHp6OtLS0hAREWFynba2trhx4wamTp2K3NxctG3bFuPHj8eSJUsaazNkoRCCk4ZrNBq4u7ujoKAAbm5ucqdzByEAlQrQaKTjtKocr0hERERklUpKSpCRkYGAgAC0atVK7nTITJYvX469e/dix44dcqdSq9pef+aqDTh00AooFBw+SERERESWr0OHDoiOjpY7DYvAoYNWIjgYOHyYhRYRERERWa4nn3xS7hQshqw9WkuXLkX//v3h6uoKT09PjBs37o6xqEOGDIFCoTBqL7zwglFMZmYmRo0aBWdnZ3h6euKVV15BRUVFU25Ko2OPFhERERGR9ZC1RyshIQFRUVHo378/Kioq8Prrr2P48OE4d+4cXFxc9HEzZ87EO++8o7/uXOWscpWVlRg1ahTUajWOHDmC7OxsTJ06Ffb29vjggw+adHsak26Kd55Li4iIiIjI8slaaO3atcvoekxMDDw9PZGUlIRBgwbpb3d2doZara5xHXv27MG5c+ewd+9eeHl5oXfv3nj33XexcOFCLF682OT8/tamZ0/p75UrQH6+NDkGERERkbXjvGwkh6Z43VnUZBgFBQUAAA8PD6Pb165di7Zt26Jnz56Ijo5GcXGxflliYiKCg4Ph5eWlvy0iIgIajQZnz56t8XFKS0uh0WiMmqVTqQBfX+nymTOypkJERER0z+zt7QHA6HsdUVPRve50r8PGYDGTYWi1WsydOxcDBw5ET133DYCnn34a/v7+8PHxQUpKChYuXIjU1FRs3LgRAJCTk2NUZAHQX8/JyanxsZYuXWqV8/QHB0s9WikpwEMPyZ0NERERUcPZ2tpCpVIhLy8PgDSCSaFQyJwVNXdCCBQXFyMvLw8qlQq2traN9lgWU2hFRUXhzJkzOHTokNHts2bN0l8ODg6Gt7c3hg0bhvT0dHTu3LlBjxUdHY358+frr2s0GvjquossWEgIsGMHJ8QgIiKi5kF3aIiu2CJqKiqVyuShSeZiEYXW7NmzsX37dhw4cAAdOnSoNTY0NBQAkJaWhs6dO0OtVuP48eNGMbm5uQBg8slzdHSEo6OjGTJvWpx5kIiIiJoThUIBb29veHp6ory8XO50qIWwt7dv1J4sHVkLLSEEXnrpJWzatAnx8fEICAi4632Sk5MBAN7e3gCAsLAwvP/++8jLy4OnpycAIDY2Fm5ubggKCmq03OVQtdASQjqRMREREZG1s7W1bZIvvkRNSdZCKyoqCuvWrcOWLVvg6uqqP6bK3d0dTk5OSE9Px7p16zBy5Ei0adMGKSkpmDdvHgYNGoSQP+Y7Hz58OIKCgvDss89i2bJlyMnJwZtvvomoqCir7LWqTbdugL09oNEAmZmAv7/cGRERERERUU1knXVw1apVKCgowJAhQ+Dt7a1v69evBwA4ODhg7969GD58OLp3746XX34ZEyZMwLZt2/TrsLW1xfbt22Fra4uwsDA888wzmDp1qtF5t5oLBwege3fpMocPEhERERFZLtmHDtbG19cXCQkJd12Pv78/duzYYa60LFpwsFRknT4NjB4tdzZERERERFQTizqPFt2d7jitlBR58yAiIiIiItNYaFmZPw5N49BBIiIiIiILxkLLyuh6tFJTgbIyeXMhIiIiIqKasdCyMh06AO7uQEUF8MsvcmdDREREREQ1YaFlZRQKw/BBHqdFRERERGSZWGhZoaonLiYiIiIiIsvDQssKsdAiIiIiIrJsLLSsEAstIiIiIiLLxkLLCvXsKf29ehX4/Xd5cyEiIiIiojux0LJC7u6Av790mb1aRERERESWh4WWleLwQSIiIiIiy8VCy0rpCi1O8U5EREREZHlYaFkp3bm02KNFRERERGR5WGhZKV2P1pkzgBDy5kJERERERMZYaFmpwEDA3h64dQu4fFnubIiIiIiIqCoWWlbK3h4ICpIu8zgtIiIiIiLLwkLLinHmQSIiIiIiy8RCy4qx0CIiIiIiskwstKwYCy0iIiIiIsvEQsuK6aZ4T00FSkvlzYWIiIiIiAxYaFkxHx+gdWugshI4f17ubIiIiIiISIeFlhVTKDh8kIiIiIjIEslaaC1duhT9+/eHq6srPD09MW7cOKSmphrFlJSUICoqCm3atIFSqcSECROQm5trFJOZmYlRo0bB2dkZnp6eeOWVV1BRUdGUmyIbXaHFKd6JiIiIiCyHrIVWQkICoqKicPToUcTGxqK8vBzDhw9HUVGRPmbevHnYtm0bvv/+eyQkJCArKwvjx4/XL6+srMSoUaNQVlaGI0eOYM2aNYiJicGiRYvk2KQmpztOiz1aRERERESWQyGEEHInoXPt2jV4enoiISEBgwYNQkFBAdq1a4d169bhiSeeAAD88ssv6NGjBxITE/HAAw9g586dGD16NLKysuDl5QUA+PLLL7Fw4UJcu3YNDg4Od31cjUYDd3d3FBQUwM3NrVG30dwSE4EHH5SO1/rtN7mzISIiIiKybuaqDSzqGK2CggIAgIeHBwAgKSkJ5eXlCA8P18d0794dfn5+SExMBAAkJiYiODhYX2QBQEREBDQaDc6ePVvj45SWlkKj0Rg1a9Wzp/Q3Kwu4cUPeXIiIiIiISGIxhZZWq8XcuXMxcOBA9PyjesjJyYGDgwNUKpVRrJeXF3JycvQxVYss3XLdsposXboU7u7u+ubr62vmrWk6rq5AQIB0mcMHiYiIiIgsg8UUWlFRUThz5gy+++67Rn+s6OhoFBQU6NuVK1ca/TEbE2ceJCIiIiKyLBZRaM2ePRvbt2/H/v370aFDB/3tarUaZWVlyM/PN4rPzc2FWq3Wx1SfhVB3XRdTnaOjI9zc3IyaNWOhRURERERkWWQttIQQmD17NjZt2oR9+/YhQDcG7g99+/aFvb094uLi9LelpqYiMzMTYWFhAICwsDCcPn0aeXl5+pjY2Fi4ubkhKCioaTZEZiy0iIiIiIgsi52cDx4VFYV169Zhy5YtcHV11R9T5e7uDicnJ7i7u2PGjBmYP38+PDw84ObmhpdeeglhYWF44IEHAADDhw9HUFAQnn32WSxbtgw5OTl48803ERUVBUdHRzk3r8lUneJdqwVsLKKfkoiIiIio5WrQV/Jdu3bh0KFD+utffPEFevfujaeffhq///57ndezatUqFBQUYMiQIfD29ta39evX62NWrFiB0aNHY8KECRg0aBDUajU2btyoX25ra4vt27fD1tYWYWFheOaZZzB16lS88847Ddk0q9S1K+DoCBQVAZcuyZ0NERERERE16DxawcHB+Nvf/oaRI0fi9OnT6N+/P+bPn4/9+/eje/fuWL16dWPk2mis+TxaOvffDyQnA5s3A2PHyp0NEREREZF1kvU8WhkZGfrjn3744QeMHj0aH3zwAb744gvs3LmzwclQw+mGD6akyJsHERERERE1sNBycHBAcXExAGDv3r0YPnw4AOlEw9Z88l9rxgkxiIiIiIgsR4Mmw3jooYcwf/58DBw4EMePH9cfU3XhwgWj6dmp6bDQIiIiIiKyHA3q0fr73/8OOzs7bNiwAatWrUL79u0BADt37kRkZKRZE6S60RVaFy4AJSXy5kJERERE1NI1aDKM5qY5TIYhBNCuHXDjBpCUBPTpI3dGRERERETWR9bJMMjyKBQcPkhEREREZClYaDUjLLSIiIiIiCwDC61mhIUWEREREZFlYKHVjPBcWkRERERElqFehZafnx9mz56NPXv2oKKiorFyoga67z7pb04OcP26vLkQEREREbVk9Sq0vvnmGzg6OiIqKgpt27bFpEmTsHbtWuTn5zdSelQfSiXQqZN0mcMHiYiIiIjkU69Ca/Dgwfjkk09w8eJFHD58GL1798bnn38OtVqNoUOH4tNPP8Wvv/7aWLlSHXD4IBERERGR/Bp8jNZ9992H6OhoHD16FJcuXcJTTz2FuLg49OzZEz179sSPP/5ozjypjjghBhERERGR/OzMsRK1Wo2ZM2di5syZKC4uxu7du+Ho6GiOVVM9sdAiIiIiIpKfWQqtqpydnfH444+be7VUR7pC68wZQKsFbDivJBERERFRk+PX8GamSxegVSuguBjg4XJERERERPJgodXM2NkBQUHSZQ4fJCIiIiKSBwutZojHaRERERERyeueCq20tDTs3r0bt2/fBgAIIcySFN0bFlpERERERPJqUKF148YNhIeHIzAwECNHjkR2djYAYMaMGXj55ZfNmiDVH8+lRUREREQkrwYVWvPmzYOdnR0yMzPh7Oysv33SpEnYtWuX2ZKjhtH1aKWlAX90NhIRERERURNq0PTue/bswe7du9GhQwej27t27YrLly+bJTFqOC8voG1b4Pp14Nw5oG9fuTMiIiIiImpZGtSjVVRUZNSTpXPz5k2eqNgCKBQcPkhEREREJKcGFVoPP/ww/vOf/+ivKxQKaLVaLFu2DI888kid13PgwAGMGTMGPj4+UCgU2Lx5s9Hy6dOnQ6FQGLXIyEijmJs3b2LKlClwc3ODSqXCjBkzUFhY2JDNalY4IQYRERERkXwaNHRw2bJlGDZsGE6ePImysjK8+uqrOHv2LG7evInDhw/XeT1FRUXo1asX/vSnP2H8+PE1xkRGRmL16tX669V7zKZMmYLs7GzExsaivLwczz33HGbNmoV169Y1ZNOaDRZaRERERETyaVCh1bNnT1y4cAF///vf4erqisLCQowfPx5RUVHw9vau83pGjBiBESNG1Brj6OgItVpd47Lz589j165dOHHiBPr16wcA+PzzzzFy5Eh8/PHH8PHxqftGNTMstIiIiIiI5NOgQgsA3N3d8cYbb5gzlxrFx8fD09MTrVu3xtChQ/Hee++hTZs2AIDExESoVCp9kQUA4eHhsLGxwbFjx/D444/XuM7S0lKUlpbqr2s0msbdCBncd590rFZuLpCXB3h6yp0REREREVHL0eBCq6SkBCkpKcjLy4NWqzVa9thjj91zYoA0bHD8+PEICAhAeno6Xn/9dYwYMQKJiYmwtbVFTk4OPKtVEHZ2dvDw8EBOTo7J9S5duhRLliwxS46WysUF6NxZmuL99Glg2DC5MyIiIiIiajkaVGjt2rULU6dOxfXr1+9YplAoUFlZec+JAcDkyZP1l4ODgxESEoLOnTsjPj4ew+6hcoiOjsb8+fP11zUaDXx9fe8pV0sUHMxCi4iIiIhIDg2adfCll17CxIkTkZ2dDa1Wa9TMVWTVpFOnTmjbti3S0tIAAGq1Gnl5eUYxFRUVuHnzpsnjugDpuC83Nzej1hxxinciIiIiInk0qNDKzc3F/Pnz4eXlZe58anX16lXcuHFDP+FGWFgY8vPzkZSUpI/Zt28ftFotQkNDmzQ3S6SbEKPK00NERERERE2gQYXWE088gfj4+Ht+8MLCQiQnJyM5ORkAkJGRgeTkZGRmZqKwsBCvvPIKjh49ikuXLiEuLg5jx45Fly5dEBERAQDo0aMHIiMjMXPmTBw/fhyHDx/G7NmzMXny5BY946DOffdJf1NSgPJyeXMhIiIiImpJFEIIUd87FRcXY+LEiWjXrh2Cg4Nhb29vtHzOnDl1Wk98fHyNJzieNm0aVq1ahXHjxuHnn39Gfn4+fHx8MHz4cLz77rtGPWk3b97E7NmzsW3bNtjY2GDChAlYuXIllEplnbdHo9HA3d0dBQUFzWoYYVERoHsa8vMBd3dZ0yEiIiIisnjmqg0aVGh9/fXXeOGFF9CqVSu0adMGCoXCsEKFAr/++muDE5JDcy20SkoAJyfpskYDuLrKmw8RERERkaUzV23QoFkH33jjDSxZsgSvvfYabGwaNPqQiIiIiIio2WpQlVRWVoZJkyaxyCIiIiIiIqpBgyqladOmYf369ebOhYiIiIiIqFlo0NDByspKLFu2DLt370ZISMgdk2EsX77cLMkRERERERFZowYVWqdPn8b9998PADhz5ozRsqoTYxAREREREbVEDSq09u/fb+48iIiIiIiImg3OZkFERERERGRmde7RGj9+PGJiYuDm5obx48fXGrtx48Z7ToyIiIiIiMha1bnQcnd31x9/5e7u3mgJERERERERWTuFEELUNfidd97BggUL4Ozs3Jg5NTlznf3Z0pSUAE5O0mWNBnB1lTcfIiIiIiJLZ67aoF7HaC1ZsgSFhYUNfjAiIiIiIqKWoF6FVj06v4iIiIiIiFqses86yPNkERERERER1a7e59EKDAy8a7F18+bNBidERERERERk7epdaC1ZsoSzDhIREREREdWi3oXW5MmT4enp2Ri5EBERERERNQv1OkaLx2dZryVLgLw8ubMgIiIiImoZOOtgM+boCAwfLl3+5BMgIABYsADIyZE3LyIiIiKi5q5ehZZWq+WwQSuiUAC7dgHbtgH9+wPFxYaCa+5cICtL7gyJiIiIiJqnek/vTtZFoQBGjwaOHQN27gQeeAAoKQE++wzo1Al46SXg6lW5syQiIiIial5YaLUQCgUQGQkcOQLs2QMMHAiUlgJ//zvQuTPw4otAZqbcWRIRERERNQ8stFoYhQJ49FHg4EEgLg4YPBgoKwO+/BLo0gWYNQu4dEnuLImIiIiIrBsLrRZKoQCGDgXi46U2dChQXg78619A167AjBlAerrcWRIRERERWSdZC60DBw5gzJgx8PHxgUKhwObNm42WCyGwaNEieHt7w8nJCeHh4bh48aJRzM2bNzFlyhS4ublBpVJhxowZKCwsbMKtsH6DB0u9WwcPSrMUVlQA//430K0bMH06cOGC3BkSEREREVkXWQutoqIi9OrVC1988UWNy5ctW4aVK1fiyy+/xLFjx+Di4oKIiAiUlJToY6ZMmYKzZ88iNjYW27dvx4EDBzBr1qym2oRm5aGHgN27gcREYMQIoLISWLMG6NEDeOYZ4Px5uTMkIiIiIrIOCmEhJ8dSKBTYtGkTxo0bB0DqzfLx8cHLL7+MBQsWAAAKCgrg5eWFmJgYTJ48GefPn0dQUBBOnDiBfv36AQB27dqFkSNH4urVq/Dx8anTY2s0Gri7u6OgoABubm6Nsn3W6MQJ4J13gO3bpesKBTBpEvDWW0BQkLy5ERERERE1BnPVBhZ7jFZGRgZycnIQHh6uv83d3R2hoaFITEwEACQmJkKlUumLLAAIDw+HjY0Njh07ZnLdpaWl0Gg0Ro3u1L+/dA6upCRg3DhACOC774C+fYHcXLmzIyIiIiKyXBZbaOXk5AAAvLy8jG738vLSL8vJybnjBMp2dnbw8PDQx9Rk6dKlcHd31zdfX18zZ9+89OkDbNoEJCcDSqV0Hi4WWkREREREpllsodWYoqOjUVBQoG9XrlyROyWr0KuXVGgREREREVHtLLbQUqvVAIDcal0nubm5+mVqtRp5eXlGyysqKnDz5k19TE0cHR3h5uZm1IiIiIiIiMzFYgutgIAAqNVqxMXF6W/TaDQ4duwYwsLCAABhYWHIz89HUlKSPmbfvn3QarUIDQ1t8pyJiIiIiIgAwE7OBy8sLERaWpr+ekZGBpKTk+Hh4QE/Pz/MnTsX7733Hrp27YqAgAC89dZb8PHx0c9M2KNHD0RGRmLmzJn48ssvUV5ejtmzZ2Py5Ml1nnGQiIiIiIjI3GQttE6ePIlHHnlEf33+/PkAgGnTpiEmJgavvvoqioqKMGvWLOTn5+Ohhx7Crl270KpVK/191q5di9mzZ2PYsGGwsbHBhAkTsHLlyibfFiIiIiIiIh2LOY+WnHgerbrz9gZycoBTp4CQELmzISIiIiIyr2Z/Hi0iIiIiIiJrxUKLiIiIiIjIzFhoERERERERmRkLLSIiIiIiIjNjoUVERERERGRmLLSIiIiIiIjMjIUWERERERGRmbHQIiIiIiIiMjMWWkRERERERGbGQouIiIiIiMjMWGgRERERERGZGQstIiIiIiIiM2OhRUREREREZGYstIiIiIiIiMyMhRYREREREZGZsdAiIiIiIiIyMzu5EyDrNH480K0b0Lkz0KmToQUEAC4ucmdHRERERCQvFlpULwMHAj/8AKSnS60mXl7GxVfV5uMD2LAflYiIiIiaOYUQQsidhNw0Gg3c3d1RUFAANzc3udOxaEIAGRnAr7/W3H7/vfb7OzoCHTsa94T16wc8/HCTpE9EREREVCtz1Qbs0aJ6USgMBVJNfv+95kIsPR24fBkoLQVSU6VWVWIi8MADjZ8/EREREVFTYKFFZtW6tdT69LlzWUUFcOWKcQH29dfAtWtATk7T50pERERE1FhYaFGTsbOTJssICACGDZNuS0iQCi0iIiIioubEoqclWLx4MRQKhVHr3r27fnlJSQmioqLQpk0bKJVKTJgwAbm5uTJmTEREREREZOGFFgDcd999yM7O1rdDhw7pl82bNw/btm3D999/j4SEBGRlZWH8+PEyZktERERERGQFQwft7OygVqvvuL2goABff/011q1bh6FDhwIAVq9ejR49euDo0aN4gDMrEBERERGRTCy+R+vixYvw8fFBp06dMGXKFGRmZgIAkpKSUF5ejvDwcH1s9+7d4efnh8TERLnSJSIiIiIisuwerdDQUMTExKBbt27Izs7GkiVL8PDDD+PMmTPIycmBg4MDVCqV0X28vLyQc5cp7EpLS1FaWqq/rtFoGiN9IiIiIiJqoSy60BoxYoT+ckhICEJDQ+Hv74///e9/cHJyavB6ly5diiVLlpgjRSIiIiIiojtY/NDBqlQqFQIDA5GWlga1Wo2ysjLk5+cbxeTm5tZ4TFdV0dHRKCgo0LcrV640YtZERERERNTSWFWhVVhYiPT0dHh7e6Nv376wt7dHXFycfnlqaioyMzMRFhZW63ocHR3h5uZm1IiIiIiIiMzFoocOLliwAGPGjIG/vz+ysrLw9ttvw9bWFk899RTc3d0xY8YMzJ8/Hx4eHnBzc8NLL72EsLAwzjhohTZsALp0AXr2lDsTIiIiIqJ7Z9E9WlevXsVTTz2Fbt264cknn0SbNm1w9OhRtGvXDgCwYsUKjB49GhMmTMCgQYOgVquxceNGmbOm+ujWTfq7di0QHAz07g188gmQlSVrWkRERERE90QhhBByJyE3jUYDd3d3FBQUcBhhEysrA7ZtA775BtixAygvl25XKIBhw4BnnwUefxxwdZU3TyIiIiJqGcxVG7DQAgstS3HjBvD998C33wKHDxtud3ICxo2Tiq5HHwXsLHrAKxERERFZMxZaZsRCy/L8+qs0nPDbb4ELFwy3e3oCkycDzzwD9Osn9XwREREREZmLuWoDiz5Gi1quTp2At94CfvkFOH4cmDMHaNcOyMsDVq4EBgwAevQA3nsPyMhovDwOHpSKuR9/bLzHICIiIqLmhz1aYI+WtSgvB2JjpV6uzZuB27cNywYOlIYWTpwIeHjceV+tFtBogN9/N7T8fOPrNbW0NMM6+E4hIiIiav44dNCMWGhZn1u3gE2bpKIrLk4qpADA3h4YMkQqiqoWTAUFhpiGUKmk9RARERFR82au2oDTCpBVcnUFpk6VWlYW8N//SkVXcrLU62VKq1ZA69Z1ayoVsHy51Hv27LNNtGFERERE1Cyw0CKr5+MDvPyy1M6cAQ4dApRKqVCqXjy1alW/de/ZIxVaRERERET1wUKLmpWePaVGRERERCQnzjpIVAelpUBJCSfEICIiIqK6YY8WUR38859Ss7UFXFyMm1J5522mbq8t1sFB7q0kIiIiInNhoUVUi0cfBVatAm7ckK5XVkrTxGs05n8sO7uGFXCurkBEhHQyZyIiIiKyDCy0iGoxaBBw/TpQUQEUFd3ZCgvv/bbycumxKiqkaegLCuqf59Ch0jT3RERERGQZWGgR1YGdHeDuLjVzKy+vX5FW9frly8CBA8C+fdJsi5WVd28VFXePadMGCAyUWps2gEJh/u0mIiIias5YaBHJzN5emopepar/fePjgUcekS4//LAZk6qidWtD0VW1de0qDV0kIiIiojux0LJE2dmAt7fcWZAV6N9fGjZ46ZI0UYc5mo0NkJcHXLgAZGYCv/8OHDsmterat6+5CAsIkApIIiIiopZKIQQnrNZoNHB3d0dBQQHc3NzkTSY/H2jXDujWDRg3Tmp9+3LsFsmiuBhIT5eKrurt+nXT97O1BTp1MhReDz0EREZKQzDt7KRijoiIiMgSmas2YKEFCyu09u4FRoyQDqTR6dABGDtWKroGD2ZXAVmEmzeBixeB1FTjAuziRalAq42NjVRw2dsbii/dZXPfVpf7dOggzTDJApCIiIhYaJmRRRVagDRWa8cOYPNmYOdOadYDnVWrgBdekC01orvRaoGsLEPh9eKLcmdUN3v3AsOGyZ0FERERyY2FlhlZXKFVVUmJNKXc5s3A1q1AUpJ0YAwAfP01sHGj1NM1ZgygVsuZKVGNhABKS6VO2ooKaZbFqn9NXW7s5brLmzYZcvX1rfmYNVPHslnr7brbqv7VterX6xJjayudcNvRUbpMRERkzVhomZFFF1pVabXGY5siIoA9e6TLCgUQFmY4rqtrVzkyJLI6f/0rsHKl3Fk0H/b2QKtWhubkZHz9XpfVdh97+5oPZ71+HTh5Uhoq6uAgxdnbG182dd3WlofIEhG1NCy0zMhqCq3qzp2Tero2bwZOnDBeFhIi9X7ZcWJJotoIAZw/L/W6VT+fmFZb83nGrP32ykppu3Uxulbf65b238PGpuYC7Ny5e1tvTYVYXYo0U9dNLbO1NRxDqLtc/a+5b6u6zMaGRSUREcBCy6ysttCq6rffpKGFmzdLQw2HDAFiY+XOioiaMSEMBVh5uTTS+fZt6W9NrSHL7naf0tK65+vvLxVe5eVSKyszXNZd12ob7/myBrrhpQ0t1sxR8NX3NoUCWLgQaNv27kNpuYyT/hDVBQstM2oWhVZV+fnAtWuG4YMrVkhH+ut+NnVwMLQJE6Tp1gDp/F1ff21YVj02JAS47z7ZNouIqDqtViqQ7lagBQYCnTvXbX3Vi7CaCrLGWKbrbdQdQ6i7XNttDVnW0otJaprCrjEKRYXC0POqa1Wv1+WyueOaez661tKYqzZoNuPKvvjiC3z00UfIyclBr1698Pnnn2PAgAFypyUPlUpqlZVAx47A1aumYwMDDYVWZibw1lumY5csYaFFRBal6nBBc63P0VFqzZVu2GhjF3R3W2bOdZWXS6dpePbZ2ofMtoRldSmkdfFEddVUhd+rrwLPPy/31ppPsyi01q9fj/nz5+PLL79EaGgoPv30U0RERCA1NRWenp5ypycfW1tg/37g2DHDT6a6prv+4IOG+LZtgZkzjZdXvaz7Ofitt4A1a2r+yUOhkIYu+vpKsZ98Avz736bfWf/7n7TeoiKp0NN9w2nVyvCXx5kREZmNQmEYfkfNT9UhvXIXfeZeJoRh+2q6XNuyhsRZ2robch9zvq6aojjPz2/8x2hKzeJjdvny5Zg5cyaee+45AMCXX36JH3/8Ef/+97/x2muvyZydzLp0kVpddO4M/POfd4+7cQO4csX08qrvxJyc2o9ELymR/p48KR1XVhMbG+BvfwMWLJCunz4NPPGEoRirWpg5OgLPPAM8/rgUe+mSdD/dJ0/Vo/i1WmDiROCP1w1++036ObTqp9TBg9IyPz+pgNQVmz/9BGzbZnq7nnwS6NFDunzmjPEc4tWNGwcEB0uXU1OB7783Xl61z37kSOD++6XLGRnAunXAe+8Bw4dLU/xXLXgHDwY6dZJir1wBDhyouTBWKIB+/YCAACk2NxdITDT9c1OPHtLBLoD0ifjTT6Zj/f0NpyMoLJReC1Wf/6qXO3aUGgDcugUcOnRnrK5162Z4zm7dko5NrGmdWi3QsycwcKAUW1QE/Otfd8bY2AAzZgDu7tIPFIB08E9tBwA5Oxu+qep+lDCl6g8Guh8uTHF0NMRWVNQe6+BgiK2srD1Wd4ZmwDA+zhTdwS/3Gqt77er+6sYEAYbn31Rs9cvWpuq21fRtR/c8AMYnqNepuu1VY2talzU/T2R2CoVhqB1R1cLMGopKPz+5nzEzE1autLRU2Nraik2bNhndPnXqVPHYY4/VeJ+SkhJRUFCgb1euXBEAREFBQRNk3AxcvizEiRNCHDsmxNGjQiQmCnHkiBCHDglx8KAQt28bYi9eFCIuToi9e4WIjRVi924hdu0SYudOIXbsEEKjkeL27xfCw0MIFxch7Oyqfy4IsWKFYZ2HDt25vGp75x1D7KlTtccuXGiITUurPXb3bkPsP/9Ze+zGjYbYtWtrj/32W0Ps5s21x/7jH4bYPXtqj123zhC7YUPtsV99ZYjdubP22M8+M8QeOFB77AcfGGITE+XZby+9ZIjNzq499qefDLHvv1977MGDhthPP637a+df/zLfa+ebbxr22omNrT12+XJD7OHDtce++64hNjm59tjoaEPshQu1x86ZY4j97TfjZQqFodnYCDFzpiE2P1/6DKmp2dsL8cwzhtiyMiFsbQ3Nxsa4jRsnjDg4mM43PNw4VqUyHRsWZhzr42M6NiTEOLZrV9OxnTsbx95/v/HzpGu2tkJ06GAcO3iw9PxUbQ4OUmvXzjh29GghWrUybk5OUnN1NY6dPFn6XHdxEUKpNDRXV6mVlBhi//xnIdzdjZtKZWi//26InTtX+p9RtbVpY2i//WaIfeMNIdq2Nd3S0gyxH3wghKen6ZaSYoj99FMhvLxMt6NHDbH//KcQarXptn+/IXbtWuk1Yart2GGI3bhRiPbtTbcffjDE7t4thK+v6bZ2rSH24EEh/PxMt3/9yxB78qQQHTuabp9/bog9d06IgADT7aOPDLEZGUJ06mTcOnc2tKr/M3JyjJdVb1U/ezQaIbp0Md3mzTPElpdL7zlT7YUXhJH77hMiMLDmNn26cWz//kJ061ZzmzzZOHbwYCG6d6+5jR1rHBsZKUSPHjW3iAjj2PHjhQgKqrkNGWIc+8wz0vbV1EJDjWNnzhSiZ8+aW69exrF//asQwcFSq/oalFlBQYEwR21g9T1a169fR2VlJby8vIxu9/Lywi+//FLjfZYuXYolS5Y0RXrNk59f3X9yqGuP2pAhUk+ZTmWl1JtQUiL9VSoNy3r2BBISDMuqxpWWAn36GGLbtwe++MLQw6Kbckl3WdcrAgBeXsB//2u8/Pp14KuvpF4k3XBIQOrVefFF09uj6x3SPQe1DTiu+vz4+0vDN03p3t1w2ccHmDZNGsapUgEPP2z8tcvHxxDr6QmEh5v+Wlv1ZNcqlXRONt2y6j83VR2O6+wsHbdnKtbDwxDbqpXUY1XTfrCxAdq0MV5vnz7GZ8WtGqvr+dLFDh9uvLzqfaruYycn4KmnjJf/5z+mn2+yTEIYX656Hai5h0in+tiX2sbCVD/YpfrjWJrqPVtVn5vquVffbt2sHDWp3rOr+8ytSfXxiCUlUk9yXXIuLgYKCkzHVt2GoiLg5k3TsVX3XVGR9FluStXnoqgIyMszHVv1tVVUJI0AMKXq81lcLI3wMKVqb3RxMZCVZTq26nN/+7Y0GsOU4mLj2NpGoxQWGj9GZqbp2Fu3DJdLS6XRI6ZUHQtWViaNxjCl6j6tqAB+/dV0bNV9WlkJpKebjq26T7VaIC3NdGz1/XTxoulY3cgVnQsXTL+Pqn6H0K3X1Di5qv87ASlfU/vZwcH4enq66Zyrj3r49VfTo45+/934ekYGcPZszbHVJ4u4dEkazVOT6l2tly9LI5UA4++BzYTVzzqYlZWF9u3b48iRIwgLC9Pf/uqrryIhIQHHjh274z6lpaUorfKPQ6PRwNfXt/nMOkhE9VNWJn2Js7c3zH2sO+reFAcH49jahtfpznyri63rcMCKitqHL+pmBwUM86ub4uho+IdcUWH8BaymWN1sEPWJraw0/rJW/d+Lo6NU7OpidV+qayoGWrUy/MBSWWn4B1y9yAKkdbq7S5e1WsMXpeqxCoUUq/sSI8SdX6qqfvF3dARatzZcr/4FvGqsvb30I4VO9S/2VWPt7Az5AtKXrZq2C5BeY1XXe/PmnScx0122sZGOta2aQ9XXcPX1Vv2B8to149dl1ViFwjAEGJCeh5IS0zlX/SEkJ8f49VP9NdGpk+G5yckx/gJfPbZzZ8P7KDtbev2YKiS7djW8N3Jyai/KOnc2vIZzc6XnorZY3Ws4L6/2QqtTJ8DFRbp87VrtxVOnToCrq3T5bsPzO3Y0vCZu3qy9yPH3N/yIlZ9fezHi5we0aydd1mikosGUDh0MP9DduiWdjNCU9u0Nr5+iItNfwAHA29vwQ+7t20BysulYtdrwo2ZZmXQIgimenoYfNSsqpGPXTWnbVhqeDkivq8OHTcd6eABBQYbrhw6Z/kFGpTL+4e/IEdP/Y9zcgN69DdcTE03/33BxkYb/6xw/Lj13NWnVCggNNVw/edL4M7sqBwfj4/h/+sn0DyG2tsCgQYbrycm1v+eGDjVcTkkxvOcCA+8sSGXC6d3/UFZWBmdnZ2zYsAHjxo3T3z5t2jTk5+djy5Ytd11Hs5venYiIiIiIGsRctYHVn7bOwcEBffv2RVxcnP42rVaLuLg4ox4uIiIiIiKipmL1x2gBwPz58zFt2jT069cPAwYMwKeffoqioiL9LIRERERERERNqVkUWpMmTcK1a9ewaNEi5OTkoHfv3ti1a9cdE2QQERERERE1Bas/RssceIwWEREREREBPEaLiIiIiIjIYrHQIiIiIiIiMjMWWkRERERERGbWLCbDuFe6w9Q0Go3MmRARERERkZx0NcG9TmXBQgvArT/ORO9rIWejJiIiIiIied26dQvu7u4Nvj9nHYR0guOsrCy4urpCoVDU674ajQa+vr64cuUKZyxsQbjfWx7u85aJ+71l4n5vmbjfW6aa9rsQArdu3YKPjw9sbBp+pBV7tADY2NigQ4cO97QONzc3vilbIO73lof7vGXifm+ZuN9bJu73lqn6fr+XniwdToZBRERERERkZiy0iIiIiIiIzIyF1j1ydHTE22+/DUdHR7lToSbE/d7ycJ+3TNzvLRP3e8vE/d4yNeZ+52QYREREREREZsYeLSIiIiIiIjNjoUVERERERGRmLLSIiIiIiIjMjIUWERERERGRmbHQugdffPEFOnbsiFatWiE0NBTHjx+XOyUyo8WLF0OhUBi17t2765eXlJQgKioKbdq0gVKpxIQJE5CbmytjxtQQBw4cwJgxY+Dj4wOFQoHNmzcbLRdCYNGiRfD29oaTkxPCw8Nx8eJFo5ibN29iypQpcHNzg0qlwowZM1BYWNiEW0H1dbf9Pn369Dve/5GRkUYx3O/WZenSpejfvz9cXV3h6emJcePGITU11SimLp/rmZmZGDVqFJydneHp6YlXXnkFFRUVTbkpVA912e9Dhgy54/3+wgsvGMVwv1uXVatWISQkRH8S4rCwMOzcuVO/vKne6yy0Gmj9+vWYP38+3n77bfz000/o1asXIiIikJeXJ3dqZEb33XcfsrOz9e3QoUP6ZfPmzcO2bdvw/fffIyEhAVlZWRg/fryM2VJDFBUVoVevXvjiiy9qXL5s2TKsXLkSX375JY4dOwYXFxdERESgpKREHzNlyhScPXsWsbGx2L59Ow4cOIBZs2Y11SZQA9xtvwNAZGSk0fv/v//9r9Fy7nfrkpCQgKioKBw9ehSxsbEoLy/H8OHDUVRUpI+52+d6ZWUlRo0ahbKyMhw5cgRr1qxBTEwMFi1aJMcmUR3UZb8DwMyZM43e78uWLdMv4363Ph06dMCHH36IpKQknDx5EkOHDsXYsWNx9uxZAE34XhfUIAMGDBBRUVH665WVlcLHx0csXbpUxqzInN5++23Rq1evGpfl5+cLe3t78f333+tvO3/+vAAgEhMTmyhDMjcAYtOmTfrrWq1WqNVq8dFHH+lvy8/PF46OjuK///2vEEKIc+fOCQDixIkT+pidO3cKhUIhfvvttybLnRqu+n4XQohp06aJsWPHmrwP97v1y8vLEwBEQkKCEKJun+s7duwQNjY2IicnRx+zatUq4ebmJkpLS5t2A6hBqu93IYQYPHiw+Otf/2ryPtzvzUPr1q3FV1991aTvdfZoNUBZWRmSkpIQHh6uv83Gxgbh4eFITEyUMTMyt4sXL8LHxwedOnXClClTkJmZCQBISkpCeXm50Wuge/fu8PPz42ugGcnIyEBOTo7RfnZ3d0doaKh+PycmJkKlUqFfv376mPDwcNjY2ODYsWNNnjOZT3x8PDw9PdGtWze8+OKLuHHjhn4Z97v1KygoAAB4eHgAqNvnemJiIoKDg+Hl5aWPiYiIgEaj0f9STpat+n7XWbt2Ldq2bYuePXsiOjoaxcXF+mXc79atsrIS3333HYqKihAWFtak73U7821Gy3H9+nVUVlYaPfkA4OXlhV9++UWmrMjcQkNDERMTg27duiE7OxtLlizBww8/jDNnziAnJwcODg5QqVRG9/Hy8kJOTo48CZPZ6fZlTe913bKcnBx4enoaLbezs4OHhwdfC1YsMjIS48ePR0BAANLT0/H6669jxIgRSExMhK2tLfe7ldNqtZg7dy4GDhyInj17AkCdPtdzcnJq/DzQLSPLVtN+B4Cnn34a/v7+8PHxQUpKChYuXIjU1FRs3LgRAPe7tTp9+jTCwsJQUlICpVKJTZs2ISgoCMnJyU32XmehRWTCiBEj9JdDQkIQGhoKf39//O9//4OTk5OMmRFRY5s8ebL+cnBwMEJCQtC5c2fEx8dj2LBhMmZG5hAVFYUzZ84YHXdLzZ+p/V712Mrg4GB4e3tj2LBhSE9PR+fOnZs6TTKTbt26ITk5GQUFBdiwYQOmTZuGhISEJs2BQwcboG3btrC1tb1jdpLc3Fyo1WqZsqLGplKpEBgYiLS0NKjVapSVlSE/P98ohq+B5kW3L2t7r6vV6jsmwamoqMDNmzf5WmhGOnXqhLZt2yItLQ0A97s1mz17NrZv3479+/ejQ4cO+tvr8rmuVqtr/DzQLSPLZWq/1yQ0NBQAjN7v3O/Wx8HBAV26dEHfvn2xdOlS9OrVC5999lmTvtdZaDWAg4MD+vbti7i4OP1tWq0WcXFxCAsLkzEzakyFhYVIT0+Ht7c3+vbtC3t7e6PXQGpqKjIzM/kaaEYCAgKgVquN9rNGo8GxY8f0+zksLAz5+flISkrSx+zbtw9arVb/z5qs39WrV3Hjxg14e3sD4H63RkIIzJ49G5s2bcK+ffsQEBBgtLwun+thYWE4ffq0UZEdGxsLNzc3BAUFNc2GUL3cbb/XJDk5GQCM3u/c79ZPq9WitLS0ad/r5prJo6X57rvvhKOjo4iJiRHnzp0Ts2bNEiqVymh2ErJuL7/8soiPjxcZGRni8OHDIjw8XLRt21bk5eUJIYR44YUXhJ+fn9i3b584efKkCAsLE2FhYTJnTfV169Yt8fPPP4uff/5ZABDLly8XP//8s7h8+bIQQogPP/xQqFQqsWXLFpGSkiLGjh0rAgICxO3bt/XriIyMFPfff784duyYOHTokOjatat46qmn5NokqoPa9vutW7fEggULRGJiosjIyBB79+4Vffr0EV27dhUlJSX6dXC/W5cXX3xRuLu7i/j4eJGdna1vxcXF+pi7fa5XVFSInj17iuHDh4vk5GSxa9cu0a5dOxEdHS3HJlEd3G2/p6WliXfeeUecPHlSZGRkiC1btohOnTqJQYMG6dfB/W59XnvtNZGQkCAyMjJESkqKeO2114RCoRB79uwRQjTde52F1j34/PPPhZ+fn3BwcBADBgwQR48elTslMqNJkyYJb29v4eDgINq3by8mTZok0tLS9Mtv374t/vKXv4jWrVsLZ2dn8fjjj4vs7GwZM6aG2L9/vwBwR5s2bZoQQpri/a233hJeXl7C0dFRDBs2TKSmphqt48aNG+Kpp54SSqVSuLm5ieeee07cunVLhq2huqptvxcXF4vhw4eLdu3aCXt7e+Hv7y9mzpx5xw9p3O/Wpab9DUCsXr1aH1OXz/VLly6JESNGCCcnJ9G2bVvx8ssvi/Ly8ibeGqqru+33zMxMMWjQIOHh4SEcHR1Fly5dxCuvvCIKCgqM1sP9bl3+9Kc/CX9/f+Hg4CDatWsnhg0bpi+yhGi697pCCCHq3fdGREREREREJvEYLSIiIiIiIjNjoUVERERERGRmLLSIiIiIiIjMjIUWERERERGRmbHQIiIiIiIiMjMWWkRERERERGbGQouIiIiIiMjMWGgREZFVi4+Ph0KhQH5+vtyp6HXs2BGffvqp3GkQEZGMWGgREZHFUigUtbbFixfLnSIREVGN7OROgIiIyJTs7Gz95fXr12PRokVITU3V36ZUKnHy5Ml6r7esrAwODg5myZGIiKgm7NEiIiKLpVar9c3d3R0KhcLoNqVSqY9NSkpCv3794OzsjAcffNCoIFu8eDF69+6Nr776CgEBAWjVqhUAID8/H3/+85/Rrl07uLm5YejQoTh16pT+funp6Rg7diy8vLygVCrRv39/7N271yjHvLw8jBkzBk5OTggICMDatWuNlgshsHjxYvj5+cHR0RE+Pj6YM2dOYzxdRERkQVhoERFRs/DGG2/gk08+wcmTJ2FnZ4c//elPRsvT0tLwww8/YOPGjUhOTgYATJw4EXl5edi5cyeSkpLQp08fDBs2DDdv3gQAFBYWYuTIkYiLi8PPP/+MyMhIjBkzBpmZmfr1Tp8+HVeuXMH+/fuxYcMG/N///R/y8vL0y3/44QesWLEC//jHP3Dx4kVs3rwZwcHBjf+EEBGRrDh0kIiImoX3338fgwcPBgC89tprGDVqFEpKSvS9V2VlZfjPf/6Ddu3aAQAOHTqE48ePIy8vD46OjgCAjz/+GJs3b8aGDRswa9Ys9OrVC7169dI/xrvvvotNmzZh69atmD17Ni5cuICdO3fi+PHj6N+/PwDg66+/Ro8ePfT3yczMhFqtRnh4OOzt7eHn54cBAwY0yXNCRETyYY8WERE1CyEhIfrL3t7eAGDUs+Tv768vsgDg1KlTKCwsRJs2baBUKvUtIyMD6enpAKQerQULFqBHjx5QqVRQKpU4f/68vkfr/PnzsLOzQ9++ffXr7d69O1Qqlf76xIkTcfv2bXTq1AkzZ87Epk2bUFFR0SjPARERWQ72aBERUbNgb2+vv6xQKAAAWq1Wf5uLi4tRfGFhIby9vREfH3/HunSF0oIFCxAbG4uPP/4YXbp0gZOTE5544gmUlZXVOS9fX1+kpqZi7969iI2NxV/+8hd89NFHSEhIMMqZiIiaFxZaRETUIvXp0wc5OTmws7NDx44da4w5fPgwpk+fjscffxyAVJxdunRJv7x79+6oqKhAUlKSfuhgamrqHef0cnJywpgxYzBmzBhERUWhe/fuOH36NPr06dMYm0ZERBaAhRYREbVI4eHhCAsLw7hx47Bs2TIEBgYiKysLP/74Ix5//HH069cPXbt2xcaNGzFmzBgoFAq89dZbRr1k3bp1Q2RkJJ5//nmsWrUKdnZ2mDt3LpycnPQxMTExqKysRGhoKJydnfHtt9/CyckJ/v7+cmw2ERE1ER6jRURELZJCocCOHTswaNAgPPfccwgMDMTkyZNx+fJleHl5AQCWL1+O1q1b48EHH8SYMWMQERFxRy/U6tWr4ePjg8GDB2P8+PGYNWsWPD099ctVKhX+9a9/YeDAgQgJCcHevXuxbds2tGnTpkm3l4iImpZCCCHkToKIiIiIiKg5YY8WERERERGRmbHQIiIiIiIiMjMWWkRERERERGbGQouIiIiIiMjMWGgRERERERGZGQstIiIiIiIiM2OhRUREREREZGYstIiIiIiIiMyMhRYREREREZGZsdAiIiIiIiIyMxZaREREREREZsZCi4iIiIiIyMz+H8hDV7WTraFSAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "fig, ax = plt.subplots()\n",
+    "fig, ax = plt.subplots(figsize=(10, 3))\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(\"Threads\")\n",
@@ -5349,9 +5339,11 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "## Plotting with Pandas\n",
@@ -5390,7 +5382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5399,14 +5391,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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jONFwimcyD/JWznEsRvfUlB9OjeLBa0cT4uecd29FBirDMNh+9CQZ2cV8eKDCurCmv7c7P0iK5I6UaDWHoXtdtx1HT5KZV0FWftUZSaDJBJOiAkhLDGVWQiixIT6a7is2pyRH5BIrPdnKvRk5HChvxNXFxK+uj+euK0fogu+ADlY28Yf1BWTlVwHg7e7Kf141gnuuHomvFmwVcWiNbZ2szS0jI7uYQ1XN1u0TowJIT43hu5cNU8fFczAMg/wTTacbF1Sy93jDGftjhg7qruNJCGXq8CG4DaCnX2I/lOSIXEKfFtVw/6u51LV2EjjYgxfumMS0UUG2Dksu0vYjtSxbX8CuknoAAgd78PNrYrkjJUZrS4g4mP1lDazeXsy7u8tp7TAD3Tcw5k4KZ35KDOMi/G0coeOpaGizJjzbimrpMH/Zntrf251r4rvreK4eE6QbRNJvlOSIXAKGYfB/Hx9h2boCLEb3qtIvLUgiIsDb1qHJJWIYBh8eqOAP6ws5UtMCQHTgIH45O47vjh+mjkMidqyt08x7e0+QkV3M7tJ66/bRIT6kp8Zwy+QI/PTh+5Joae/i40PVZOZVsbGgkrrWTus+d1cTqSOHcl1iKNcmhBKu90jpQ0pyRC5Sa0cXj761l/f2ngDg+5Mj+f0t4zTNwUl1mi2s2VnKs1mHrGtLjI/wZ/H18VwRq6d2IvbkaE0Lq7OLeSv3OPWnP2y7u5qYM24Y6SnRJI8I1FTiPmS2GOQU11m7tR09fYPoC2PD/UhLCGVWYihjw/30fyGXlJIckYtQXNvCwlU5FFQ04eZi4r9uTGRBaowu1ANAa0cXf/34KCu2HqH5dMehq8cE89icOMaGa7qLiK10mS1k5VeSkV3CJ0U11u0RAd7ckRLNbVOi1AnMRg5XN5OV1z2tbWdxHV/9VDnM38u6Hk/qyEA83XSjUC6OkhyRC7SpsIoHXttFY1sXQT6e/Hn+ZJJHBNo6LOlntc3tPL+xiNXbi+k0G5hMMHdiBA/PGkNUoNqFi/SXioY2XttRwuufl1DZ2P2U1WSCGWOCWXB5DNPHhOCqaaV2o7a5nY0FVWTlV7L1YA2nOs3WfYM9XJkeF0xaQigz40IYMtjDhpGKo1KSI9JLFovBnzcX8cfMgxhGdyeel9KTnHZhOOmZ4toWntpwkH/vKQfAw9WFBZfHcP/MWL1Bi/QRi8Xg08M1ZGQXk5VfZV24cuhgD26bGsUdydG62eAA2jrNfHa4lsz8SrLyKqk6PRUYulv4T4kZwqzE7m5tw4MG2zBScSRKckR6oamtk1+s2cOGvEoAbk+O5rc3JeqxuljtPV7PsnUFbDtcC4Cvlxv3zRjFj6eN0OrgIpdIXUsHb+Uc59UdJWfUeSQPD2R+ajRzxoXpuuygLBaDfWUN1jqegoqmM/bHhvicruMJYWLUED2dk3NSkiPSQ4erm7nnHzs5XN2Ch6sL/33zWOYlR9s6LLFDhmGw9VANy9YVkH+iEYAwPy8emjWa70+O1JoRIhfAMAx2ldaTkV3Me3tP0NHV3abYx9ON702OYH5KDHFhvjaOUi610pOt1vbU24+cpMvy5UfRIB8Pa3vqK0cHacFtOYOSHJEe2HCggofX7KG5vYswPy9eTJ/MpOghtg5L7JzFYvDunjKe+vCgdXXw0SE+PDonnrSEEDWoEOmB1o4u3t1dTkZ2MQfKG63bE4f5kZ4aw80TwxnsqQ+3A0HDqU62HKwmK6+STYVVNLV1Wfd5urlwZWwQsxJDuSYhhBBfTSEf6JTkiJyHxWLwbNZBnttYBHRPhVg+f7I680ivtHeZWfVZMS9sKrK2sZ06fAiLr48nKUbNKkTO5lBlExnZxfwzt4ym0x0MPdxc+O5lw0hPjWFSVIBuFAxgnWYLO46eJPN0t7bjdafO2D8xKsBaxzMm1EfnygCkJEfkHBpOdfLQG7vZWFAFwI+mDec3NyTgrqlGcoEaTnXy0pbD/O2To7Sfnmoze2woj8yOJzbEx8bRidheR5eF9QcqyMguZsfRk9btw4cOYn5KDLcmRaqRh3yDYRgUVjaRldddx7PneMMZ+6MCva3r8UwdHqj38QFCSY7IWRysbOKef+zkWG0rnm4u/O8t4/l+UqStwxInUdHQxjOZB3kzpxSL0d096LYpUTyUNpoQP02xkIGn9GQrr+0oYc3OUmqaO4Duv4u0hBDSU2O4YlQQLiowlx6qbGzjo/zu9tSfFNVY67cA/LzcmHm6jmd6XDB+Xu42jFT6kpIcka/5YN8JfvnmHlo7zEQEePNSehLjI7W4o1x6hyqbeGJ9IVn53d36vN1duevKESycPhJfvfGKkzNbDLYcrCIju4RNhVXWhSFD/TyZNzWaeclRDPP3tm2Q4vBaO7r4+FANWXmVfFRQxcmWDus+d1cTqSOHkpYQyrUJIUQOUbtxZ2J3Sc7y5ct58sknqaioYMKECTz//PMkJyd/6+uU5MjFMlsMnvywkJe2HAZg2qihPH/7JIb6qP5G+tbnx06y9IN8ckvqAQgc7MH9M2OZnxqtNrjidGqa23nj81Je21FyRh3FlbFBpKdGc21CqKYTSZ8wWwx2ldRZ1+M5XN1yxv6EYX7MSgghLTGU8RH+quNxcHaV5Lzxxhv8x3/8By+99BIpKSk8++yzvPnmmxQWFhISEnLe1yrJkYtR19LBz1/fxceHagC4+6oRPDYnXq1+pd8YhsGHByr5w4cFHDn9xhsV6M0vr4vjxsvCNVVHHJphGOw4epKM7SWs33+CTnP3xwl/b3duTYpkfko0I4NVlyb960h1c3d76rwqdhaf5CvdqQn18yQtIZS0xFAuHzkUL3fdcHI0dpXkpKSkMHXqVF544QUALBYLUVFR/OxnP2Px4sVnHNve3k57+5cr4jY2NhIVFaUkR3rtQHkDC1flcLzuFF7uLvzh1gncNCHc1mHJANVltrBm53GeyTpI9elVv8dF+LF4TgJXjg6ycXQivdPY1sna3DJWby/mYGWzdfuEqADSU6K5cUK4PjyKXTjZ0sGmgu46ni0Hq2ntMFv3DfJw5erRwaQlhnJNfAiBan7hEOwmyeno6GDQoEG89dZbzJ0717r9zjvvpL6+nnffffeM43/729/y+OOPf+P7KMmR3nhnVxmL/7mXtk4L0YGDWLEgiYRhOn/E9lo7uvjbJ0d5acsRmk+3z71qdBCPzYlnXIRqxMS+7S9rYPX2Yt7dXW79sOjt7srNE8NJT43ROSx2ra3TTPaRWmt76srGL2+qu5hgSkwgaYndzQv0BNJ+2U2SU15eTkREBNu2bePyyy+3bn/00UfZsmUL27dvP+N4PcmRi9FptrD0gwL+9ulRAK4eE8xz8yYSMEh3Z8S+1Da388KmIjKyi61TfOZODOcX18URFagiWbEfbZ1m3tt7gozsYnaX1lu3x4b4kJ4SzfeSItXJShyOYRjsL2u01vHknWg8Y//I4MHMSgxlVkIok6KH4KqpxXbDYZOcr1NNjvRUTXM7i1bnsv30GgyLZo7i4VlxujCJXSupbeWPmYW8u7scAA9XF9JTY7j/mlhNnRCbOlbTwurtxbyZc9y62K27q4nZY8NIT40hZUSgCrjFaRyva7W2p84+Umu9+QTdTWOuOd2e+qrRQQz2dLNhpGI3SU5vp6t9nZIc6Yk9pfXcm5HDiYY2Bnu48sfbJjBn3DBbhyXSY/vLGli2roBPirqbZPh6unHvjFH85IoReHuotkH6R5fZQlZ+Fau3F1sbtgBEBHhzR0o0t02JIthXnSnFuTW2dbL1YDVZeZVsLKiisa3Lus/DzYUrRg1lVmIY1yaEEKo10Pqd3SQ50N14IDk5meeffx7objwQHR3N/fff/43GA1+nJEe+zZqdpfy/d/bT0WVhZNBgVixIYnSor63DErkgWw9Ws2xdgXXqRIivJw/NGsMPkiLVFVD6TGVjG6/tKOH1HaVUNLYBYDLBjDHBpKfGMCMuRE/FZUDqNFv4/NhJsvKqyMyvoPTkqTP2T4j0t3Zriw/z1dPNfmBXSc4bb7zBnXfeyYoVK0hOTubZZ59lzZo1FBQUEBoaet7XKsmRc+nosvC79/JYlV0MQFpCCE//cKLmhovDs1gM/rWnnKc2FFrXG4kN8eHR2XHMSgzVm6hcEhaLwbbDtWRkF5OZX4n5dJ/doYM9uG1qFHckR6s+TOQrDMPgUFUzmXmVZOZVnlGjBhA5xJu0hFBmJYaSPCJQ60L1EbtKcgBeeOEF62KgEydO5LnnniMlJeVbX6ckR86mqrGNn67OZWdxHQAPpY3hZ9fEas0RcSrtXWYyskt4YeMh6k7XREyJGcLi6+OZMjzQxtGJo6pv7eCtnOOs3l7C0ZovF01MHh7I/NRo5owL02K1Ij1Q1dTGxtN1PB8fqqG9y2Ld5+vlxoy4ENISQpgRF4K/t27AXip2l+RcKCU58nU5xXXcl5FDVVM7vp5uPDtvItcmnP+JoIgja2zrZMWWw/z1k6O0dXa/ic5KDOWxOXHEhmhqpnw7wzDYXVpPRnYJ7+0tt34Y8/F045ZJEaSnxhAXpnNJ5EKd6jDzSVENmXkVfJRfRW1Lh3Wfm4uJlJGB3dPaEkL1hPQiKckRp2MYBq/uKOG3/zpAp9lgdIgPKxYkqZe9DBgVDW08m3WQNTtLsRjd6zr8cGoUD6aNUfGrnFVrRxfv7i4nI7uYA+VftshNGOZHemo0cydGqFOUyCVmtnTfVMg63Z76UFXzGfvjw3yZldid8IyP8NcslF5SkiNOpa3TzJJ3D/DGzlIArh8XxpM/mICP3pxlACqqauKJ9YVk5lUC4OXuwl1XjmDh9FGqSRMADlU2kZFdzD9zy2g6veish5sL3x0/jPmpMUyODlBtl0g/OVrTwkf53XU8nx87ieUrn7pDfD25NiGUWYkhTBsVhJe7pop+GyU54jRONJzi3oxc9pTWYzLBI7PjuG/6KL1By4C389hJlq4rIOd0bdqQQe7cf81o0lOjVVMxAHV0WVh/oIKM7GJ2nF4vDCBm6CDmp0Tzg6QohmjtJRGbqmvpYPPBKrLyqthcWEVLh9m6z9vdlavHBJGWEMo18SEM9VG79rNRkiNOYfuRWha9mktNcwf+3u48d/skpo8JtnVYInbDMAwy8yp5Yn0Bh6u7i8gjh3jzy+viuGlCuKZBDADH61p5bUcJb3xeSk1zdx2AiwnSEkJJT43hytggnQcidqi9y0z2kZNk5VWSlV/JiYY26z6TCZKih5B2elpbbIim5n9BSY44NMMwWLntGL9/P58ui0F8mC8vL5hC9FAV64mcTZfZwls5x3km6yCVje0AJA7zY/H18VytGwNOx2wx2HqwmozsYjYWVvHFu3iIryfzkqO5PTmKYf7etg1SRHrMMAwOlDeSdXpa21dr6ABGBg22JjyTowMG9LppSnLEYZ3qMPObtfv4564yAG6aEM6y749nkIfqb0S+zakOM3/79CgvbT5srcW4MjaIxdfHMy7C38bRycWqaW5nzc5SXt1eYl1DCeCK2KGkp8SQlhiqtTlEnEB5/anuOp78Kj47XEOn+cuP6kMGuTMzPoTrEkO5anTwgGseoiRHHFLpyVbuzcjhQHkjri4mfnV9PHddOUL1NyK9dLKlgxc2FrEq+5j1zfHmieH88ro4tS91MIZh8PmxOjKyi1m3/4T1/9Pf251bkyKZnxKtLpMiTqyprZOPD9WQlVfJxsIq6k+vmwbg4erCtNih1vbUYf7O32lTSY44nE8O1fCz13Kpa+0kcLAHL9wxiWmjgmwdlohDKz3Zyh83FPLO7nIA3F1NpKfGcP/MWBW12rmmtk7W7ipjdXYJhZVN1u0TogJIT4nmxgnh6sQkMsB0mS3sLK4jK6+SzPxKimtbz9g/PsKftIRQZiWGkjDM1ylvEivJEYdhGAYvbz3CE+sLsBjdf6AvLUgiIkDzyUUulf1lDTyxvoCPD9UA3YtA3jt9JD+5coSmgtqZA+UNZGSX8O7uMlpPd17ydnfl5onhpKfGaNqhiADdn58OVzezIa97PZ5dpfV89RN9RIA3aQkhpCWGkjJiKB5uzjGVVUmOOITWji4eeWsv7+89AcCtSZH8z9xxujsp0kc+OVTD0nX51qLWEF9PHkwbw21TIgd0IauttXWaeX/vCTK2F7OrpN66PTbEh/SUaG6ZHIm/t9ZAEpFzq25qZ1NBFZn5lXx8qJq2Tot1n6+nG1fHBXNdYigzxoTgP8hxrydKcsTuHatpYeGqHAorm3BzMbHkxkTSU2Oc8tGqiD2xWAz+vbecpzYUUnqyu3h9ZPBgHp0dz+yxofob7EfHalpYvb2YN3OOW+fZu7uamD02jPTUGFJGBOr/Q0R6ra3TzCeHasjKryQrv4qa5nbrPlcXE8nDA0lLDGVWQqjDda5VkiN2bVNhFQ+8tovGti6CfDx5MX0yU4cH2joskQGlvcvM6uwSnt94iLrTH7CTYobwq+vjmaK/xz7TZbaQlV/F6u3F1umD0D215I6UaG6bEkWwr+qlROTSsFgM9hyvt7anPljZfMb+uFBf0hJDSEsIZUJkgN2vq6UkR+ySxWLw581F/DHzIIYBk6IDeHF+0oDoBiJirxrbOnl5yxH+8skR6/SGtIRQHpsTx+hQXxtH5zwqG9t4bUcJr+8opaKxe9E/kwmmjwlmQWoMM+JCcLXzDxci4viKa1vIyq8iK6+SHcdOYrZ8mQYE+Xh21/EkhHLl6CC7LB9QkiN2p6mtk1+s2cOGvEoA7kiJZsmNiXi62d8fkMhAVNnYxrNZh1izsxSzxcDFBD9IiuKhWWN0I+ICGYbBtsO1ZGQXsyGv0vphInCwB7dNiWJ+SrRaeouIzTS0drL5YBUb8irZUlhN8+n11QC83F24anQwsxJCmRkfYjdPmJXkiF05XN3MPf/YyeHqFjxcXfjvm8cyLzna1mGJyFkUVTXz5IcFfHig+4aEl7sLP7liBAunj1Lxew81tHbyZk73op1Halqs26cOH0J6agxzxoXpBo+I2JWOLgvbj9aSldddx1NW/+WCwyYTTIoK4E/zJtn8xoySHLEbGw5U8PCaPTS3dxHm58WL6ZOZFD3E1mGJyLfIKT7J0g8K2FlcB0DAIHfunxnLgstj9AH9LAzDYM/xBjKyi/n3nnLau7qn/vl4unHLpAjmp0YTH6b3MRGxf4ZhkH+iicy8SrLyK9lX1sAgD1dy/79ZNp/CpiRHbM5iMXg26yDPbSwCIHl4IMvnT7abx50i8u0MwyArv4on1hdQVNVdrBoR4M0vZ4/h5gkRdl+g2h9aO7r41+5yMrYXs7+s0bo9YZgf6anR3DwxAh9PrUUkIo7rRMMpCiuamBEXYutQlOSIbTWc6uShN3azsaAKgB9NG85vbkjAXetwiDikLrOFt3OP83TmQSobu1uRJgzzY/H18Vw9OmhAtjkuqmoiI7uEt3OP09TWPY/dw82F744fxvzUGCZHBwzIcRER6UtKcsRmCiuaWLhqJ8dqW/F0c+F/bxnP95MibR2WiFwCpzrMvLLtKC9uOkzT6QLVK2KHsnhOAuMj/W0cXd/r6LLw4YEKMrKL2X70pHV7zNBBzE+J5takKAIHe9gwQhER56YkR2zi/b0neOStPbR2mIkI8GbFgiTGRTj/Bx+RgaaupYMXNhWx6rNiOszdtSc3TgjnkeviHG5huZ44XtfKaztKeOPz49ZF9VxM3a2201NjuDI2SFP3RET6gV0kOb///e95//332b17Nx4eHtTX1/f6eyjJcQxmi8EfPixgxZYjQPed3edvn6w7miJOrvRkK09nHuSd3WUYBri7mpifEsPProllqI9j19+ZLQZbD1aTkV3MpsIqvlhKIsTXk3nJ0dyeHMUwf2/bBikiMsDYRZKzZMkSAgICOH78OH/961+V5DipupYOfv76LuvK3fdcPZJHZ8fhpvobkQHjQHkDT6wvZOvBaqC7o9jCq0dy11UjGOThWEX3Nc3trNnZ3f75eN2XLVSviB1KekoMaYmhqi8UEbERu0hyvrBy5UoefPBBJTlO6EB5AwtX5XC87hTe7q48cetl3DQh3NZhiYiNfFpUw9J1+dYuY8G+njyYNprbpkTZdWJgGAY7i+vIyC5m3b4K6xQ8f293bk2K5I6UaEYF+9g4ShER6U1uYFe32Nrb22lvb7f+u7Gx8TxHiy29s6uMxf/cS1unhejAQaxYkETCMCWiIgPZFbFB/GvRlby37wRPfVhIyclWfrN2P3/9+CiPzolj9tgwu+o41tTWyTu7ysjILqGwssm6fUJUAOkp0dw4Idzma0KIiMiFsaskZ+nSpTz++OO2DkPOo9NsYekHBfzt06MATB8TzJ/mTSRgkOpvRARcXEzcNCGcOWPDeHV7Mc9tLOJITQv3ZuQyKTqAX12fQPKIQJvGmFfeSMb2Yt7dVUZLhxkAL3cX5k6MYH5KzIDoFCci4ux6NV1t8eLFPPHEE+c9Jj8/n/j4eOu/ezNd7WxPcqKiojRdzU7UNLezaHWutXXqopmjeHhWHK7qKiQi59DU1snLW4/wl4+PcqqzO6FISwjhsTnxjA717bc42jrNfLDvBBnZxeSW1Fu3jwoeTHpqDN+bHIm/t3u/xSMiIr3XZzU51dXV1NbWnveYkSNH4uHx5V191eQ4hz2l9dybkcOJhjYGe7jyx9smMGfcMFuHJSIOoqqxjWc/OsQbn5dithi4mODWpEgemjWmT7uUFde2sHp7CW/uLKWutRMANxcTs8eFkZ4SQ+rIQLuaQiciIufWZzU5wcHBBAcHX1Rw4njW7Czl/72zn44uCyODBrNiQVK/3oEVEccX4ufF/94ynruuHMGT6wtZf6CCNTuP8+7ucn58xQjumzHqkj1J6TJb+KigiozsYmvnR4CIAG9uT47itqlRhPh6XZKfJSIi9qnPanJKSko4efIkJSUlmM1mdu/eDUBsbCw+PupS4wg6uiz87r08VmUXA91TTJ7+4UT8vDSlQ0QuzKhgH15akEROcR3L1uXz+bE6XtpymNc/L+H+mbGkp8ZccLF/ZWMbr+8o5fXPSzjR0AaAydRdO5ieEsPM+BBNrxURGSD6rIX0j370I/7+979/Y/umTZuYMWNGj76HpqvZTlVjGz9dncvO4joAHkobw8+uidWq3iJyyRiGwUf5VTyxvoBDVc1A99OWX1w3hpsnRvQoITEMg22Ha8nILiYzr5Ku06t2Bg724LYpUdyRHE300EF9+nuIiEj/sKt1ci6GkhzbyCk+yX0ZuVQ1tePr6caz8yZybUKorcMSESdlthi8nXOcpzMPUtHY/QQmPsyXxdfHM31M8FlrZhpaO3kzp3vRziM1LdbtU4cPIT01hjnjwvB0U/tnERFnoiRHLohhGKzeXsLj/z5Ap9lgdIgPL//HFEYEDbZ1aCIyAJzqMLNy2zH+vLmIprYuAKaNGsri6+O5LDIA6G6Csiq7mH/vKae9q3vRTh9PN26ZFMH81Gjiw/ReISLirJTkSK+1dZr5r3f3s2bncQC+Mz6MJ2+dwGBPu1pKSUQGgLqWDv68uYi/byumw9ydyMweG0p5fRv7yhqsxyUM8yM9NZqbJ0bgo2uViIjTU5IjvVJef4r7MnLYc7wBFxM8Mjuee6ePVFtVEbGp43WtPL3hIGt3l/HFO5WHmwvfHT+M+akxTI4O0HVKRGQAUZIjPZZ9pJZFq3OpbenA39ud52+fxNVj1CZcROxHXnkja3aWEh7gxa1JUQQO9vj2F4mIiNPps3VyxHkYhsHKbcf4n/fzMVsMEob5sSI9SV2IRMTuJIb78dubxto6DBERcSBKcgagUx1mfr12H2t3lQFw88Rwln3vMrw91IlIRERERByfkpwBpvRkK/dm5HCgvBFXFxO/uj6eu64coXntIiIiIuI0lOQMIJ8cquFnr+VS19pJ4GAPXrhjEtNGBdk6LBERERGRS0pJzgBgGAYvbz3CE+sLsBgwPsKflxYkERHgbevQREREREQuOSU5Tq61o4tH3trL+3tPAHBrUiT/M3ccXu6qvxERERER56Qkx4kdq2lh4aocCiubcHMxseTGRNJTY1R/IyIiIiJOTUmOk9pUWMUDr+2isa2LIB9PXkyfzNThgbYOS0RERESkzynJcTIWi8HyTUU8nXUQw4BJ0QG8OD+JMH8vW4cmIiIiItIvlOQ4kaa2Tn6xZg8b8ioBuCMlmiU3JuLppvobERERERk4lOQ4iaKqZhau2snh6hY8XF3475vHMi852tZhiYiIiIj0OyU5TmDDgQoeXrOH5vYuwvy8eDF9MpOih9g6LBERERERm1CS48AsFoNnsw7y3MYiAJKHB7J8/mSCfT1tHJmIiIiIiO0oyXFQDac6efD1XWwqrAbgR9OG85sbEnB3dbFxZCIiIiIitqUkxwEVVjSxcNVOjtW24unmwtLvjed7kyNtHZaIiIiIiF1QkuNg3t97gkfe2kNrh5mIAG9WLEhiXIS/rcMSEREREbEbSnIchNli8IcPC1ix5QgAV8QO5fnbJxM42MPGkYmIiIiI2Jc+K+A4duwYd911FyNGjMDb25tRo0axZMkSOjo6+upHOq26lg5+9MoOa4Jzz9Uj+fuPk5XgiIiIiIicRZ89ySkoKMBisbBixQpiY2PZv38/d999Ny0tLTz11FN99WOdzoHyBhauyuF43Sm83V154tbLuGlCuK3DEhERERGxWybDMIz++mFPPvkkL774IkeOHOnR8Y2Njfj7+9PQ0ICfn18fR2d/3tlVxuJ/7qWt00J04CBWLEgiYdjAGwcRERERkd7kBv1ak9PQ0EBgYOA597e3t9Pe3m79d2NjY3+EZXc6zRb+94N8Xvn0GADTxwTzp3kTCRik6WkiIiIiIt+m3xZVKSoq4vnnn2fhwoXnPGbp0qX4+/tbv6KiovorPLtR09xO+l+2WxOc+2fG8rcfTVWCIyIiIiLSQ71OchYvXozJZDrvV0FBwRmvKSsrY86cOfzgBz/g7rvvPuf3/tWvfkVDQ4P1q7S0tPe/kQPbU1rPjc9/wvajJxns4cpL6Un8cnYcri4mW4cmIiIiIuIwel2TU11dTW1t7XmPGTlyJB4e3U8eysvLmTFjBqmpqaxcuRIXl57nVQOpJmfN56X8v3f309FlYWTwYF5ekERsiK+twxIRERERsQt9WpMTHBxMcHBwj44tKytj5syZJCUl8corr/QqwRkoOros/Pd7B8jILgEgLSGUp384AT8vdxtHJiIiIiLimPqs8UBZWRkzZswgJiaGp556iurqauu+sLCwvvqxDqWqsY37VueSU1yHyQQPpY3h/pmxuGh6moiIiIjIBeuzJCczM5OioiKKioqIjIw8Y18/dq22WznFJ7kvI5eqpnZ8vdz407yJXBMfauuwREREREQcXr+uk9NbzliTYxgGq7eX8Pi/D9BpNhgd4sPL/zGFEUGDbR2aiIiIiIjdstt1cga6tk4z//XuftbsPA7Ad8aH8eStExjsqf8GEREREZFLRZ+u+0l5/Snuy8hhz/EGXEzwyOx47p0+EpNJ9TciIiIiIpeSkpx+kH2klkWrc6lt6cDf253nb5/E1WN61qFORERERER6R0lOHzIMg5XbjvE/7+djthgkDPNjRXoS0UMH2To0ERERERGnpSSnj5zqMPPrtftYu6sMgJsnhrPse5fh7eFq48hERERERJybkpw+UHqylYWrcsg70Yiri4lffyeBn1wxXPU3IiIiIiL9QEnOJfbxoWp+9tou6ls7GTrYgxfumMzlo4baOiwRERERkQFDSc4lYhgGK7Ye4Q/rC7AYcFmkPy+lJxEe4G3r0EREREREBhQlOZdAS3sXj769l/f3ngDgB0mR/G7uOLzcVX8jIiIiItLflORcpGM1LSxclUNhZRNuLiaW3DSW9JRo1d+IiIiIiNiIkpyLsKmgigde30VjWxfBvp78ef5kpg4PtHVYIiIiIiIDmpKcC2CxGCzfVMTTWQcxDJgcHcCL6UmE+nnZOjQRERERkQFPSU4vNbV18os1e9iQVwnAHSnRLLkxEU831d+IiIiIiNgDJTm9UFTVzMJVOzlc3YKHqwv/ffNY5iVH2zosERERERH5CiU5PbThQAUPr9lDc3sXYX5evJg+mUnRQ2wdloiIiIiIfI2SnB4wDIM1O0tpbu8ieXggy+dPJtjX09ZhiYiIiIjIWSjJ6QGTycQfb5vI37cd474Zo3B3dbF1SCIiIiIicg5KcnrI39udn1872tZhiIiIiIjIt9AjCRERERERcSpKckRERERExKkoyREREREREadi1zU5hmEA0NjYaONIRERERETElr7ICb7IEc7HrpOcpqYmAKKiomwciYiIiIiI2IOmpib8/f3Pe4zJ6EkqZCMWi4Xy8nJ8fX0xmUw2jaWxsZGoqChKS0vx8/OzaSzOSmPctzS+fUvj27c0vn1L49u3NL59S+Pbt+xpfA3DoKmpifDwcFxczl91Y9dPclxcXIiMjLR1GGfw8/Oz+X+ws9MY9y2Nb9/S+PYtjW/f0vj2LY1v39L49i17Gd9ve4LzBTUeEBERERERp6IkR0REREREnIqSnB7y9PRkyZIleHp62joUp6Ux7lsa376l8e1bGt++pfHtWxrfvqXx7VuOOr523XhARERERESkt/QkR0REREREnIqSHBERERERcSpKckRERERExKkoyREREREREaeiJEdERERERJyKkpyvWL58OcOHD8fLy4uUlBR27Nhx3uPffPNN4uPj8fLyYvz48XzwwQf9FKlj6s34rly5EpPJdMaXl5dXP0brWLZu3cqNN95IeHg4JpOJd95551tfs3nzZiZPnoynpyexsbGsXLmyz+N0VL0d382bN3/j/DWZTFRUVPRPwA5m6dKlTJ06FV9fX0JCQpg7dy6FhYXf+jpdg3vmQsZX1+Cee/HFF7nsssusq8FffvnlrFu37ryv0bnbc70dX527F2fZsmWYTCYefPDB8x7nCOewkpzT3njjDR5++GGWLFlCbm4uEyZMYPbs2VRVVZ31+G3btnH77bdz1113sWvXLubOncvcuXPZv39/P0fuGHo7vgB+fn6cOHHC+lVcXNyPETuWlpYWJkyYwPLly3t0/NGjR7nhhhuYOXMmu3fv5sEHH+Q///M/+fDDD/s4UsfU2/H9QmFh4RnncEhISB9F6Ni2bNnCokWLyM7OJjMzk87OTq677jpaWlrO+Rpdg3vuQsYXdA3uqcjISJYtW0ZOTg47d+7kmmuu4eabb+bAgQNnPV7nbu/0dnxB5+6F+vzzz1mxYgWXXXbZeY9zmHPYEMMwDCM5OdlYtGiR9d9ms9kIDw83li5detbjb7vtNuOGG244Y1tKSoqxcOHCPo3TUfV2fF955RXD39+/n6JzLoCxdu3a8x7z6KOPGmPHjj1j2w9/+ENj9uzZfRiZc+jJ+G7atMkAjLq6un6JydlUVVUZgLFly5ZzHqNr8IXryfjqGnxxhgwZYvzlL3856z6duxfvfOOrc/fCNDU1GaNHjzYyMzON6dOnGw888MA5j3WUc1hPcoCOjg5ycnJIS0uzbnNxcSEtLY3PPvvsrK/57LPPzjgeYPbs2ec8fiC7kPEFaG5uJiYmhqioqG+9ayO9o/O3f0ycOJFhw4Yxa9YsPv30U1uH4zAaGhoACAwMPOcxOocvXE/GF3QNvhBms5nXX3+dlpYWLr/88rMeo3P3wvVkfEHn7oVYtGgRN9xwwzfOzbNxlHNYSQ5QU1OD2WwmNDT0jO2hoaHnnENfUVHRq+MHsgsZ37i4OP72t7/x7rvvkpGRgcViYdq0aRw/frw/QnZ65zp/GxsbOXXqlI2ich7Dhg3jpZde4u233+btt98mKiqKGTNmkJuba+vQ7J7FYuHBBx/kiiuuYNy4cec8TtfgC9PT8dU1uHf27duHj48Pnp6e3Hvvvaxdu5bExMSzHqtzt/d6M746d3vv9ddfJzc3l6VLl/boeEc5h91sHYDI2Vx++eVn3KWZNm0aCQkJrFixgt/97nc2jEzk28XFxREXF2f997Rp0zh8+DDPPPMMq1atsmFk9m/RokXs37+fTz75xNahOKWejq+uwb0TFxfH7t27aWho4K233uLOO+9ky5Yt5/wgLr3Tm/HVuds7paWlPPDAA2RmZjpdgwYlOUBQUBCurq5UVlaesb2yspKwsLCzviYsLKxXxw9kFzK+X+fu7s6kSZMoKirqixAHnHOdv35+fnh7e9soKueWnJysD+7f4v777+e9995j69atREZGnvdYXYN7rzfj+3W6Bp+fh4cHsbGxACQlJfH555/zpz/9iRUrVnzjWJ27vdeb8f06nbvnl5OTQ1VVFZMnT7ZuM5vNbN26lRdeeIH29nZcXV3PeI2jnMOarkb3H09SUhIfffSRdZvFYuGjjz4655zPyy+//IzjATIzM887R3SgupDx/Tqz2cy+ffsYNmxYX4U5oOj87X+7d+/W+XsOhmFw//33s3btWjZu3MiIESO+9TU6h3vuQsb363QN7h2LxUJ7e/tZ9+ncvXjnG9+v07l7ftdeey379u1j9+7d1q8pU6Ywf/58du/e/Y0EBxzoHLZ15wN78frrrxuenp7GypUrjby8POOee+4xAgICjIqKCsMwDGPBggXG4sWLrcd/+umnhpubm/HUU08Z+fn5xpIlSwx3d3dj3759tvoV7Fpvx/fxxx83PvzwQ+Pw4cNGTk6OMW/ePMPLy8s4cOCArX4Fu9bU1GTs2rXL2LVrlwEYTz/9tLFr1y6juLjYMAzDWLx4sbFgwQLr8UeOHDEGDRpkPPLII0Z+fr6xfPlyw9XV1Vi/fr2tfgW71tvxfeaZZ4x33nnHOHTokLFv3z7jgQceMFxcXIysrCxb/Qp27b777jP8/f2NzZs3GydOnLB+tba2Wo/RNfjCXcj46hrcc4sXLza2bNliHD161Ni7d6+xePFiw2QyGRs2bDAMQ+fuxert+OrcvXhf767mqOewkpyveP75543o6GjDw8PDSE5ONrKzs637pk+fbtx5551nHL9mzRpjzJgxhoeHhzF27Fjj/fff7+eIHUtvxvfBBx+0HhsaGmp85zvfMXJzc20QtWP4omXx17++GNM777zTmD59+jdeM3HiRMPDw8MYOXKk8corr/R73I6it+P7xBNPGKNGjTK8vLyMwMBAY8aMGcbGjRttE7wDONvYAmeck7oGX7gLGV9dg3vuJz/5iRETE2N4eHgYwcHBxrXXXmv9AG4YOncvVm/HV+fuxft6kuOo57DJMAyj/54biYiIiIiI9C3V5IiIiIiIiFNRkiMiIiIiIk5FSY6IiIiIiDgVJTkiIiIiIuJUlOSIiIiIiIhTUZIjIiIiIiJORUmOiIiIiIg4FSU5IiIiIiLiVJTkiIiIiIiIU1GSIyIiIiIiTkVJjoiIiIiIOJX/HyEKMFQV2+NKAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5427,7 +5417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5436,14 +5426,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5461,7 +5449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -5470,14 +5458,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5495,7 +5481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5504,14 +5490,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5521,7 +5505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -5530,14 +5514,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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n54wzzsjixYvTu3fvDB8+PFOnTi3ZvEI4AAAAm6V+/frlsssuy2WXXVbYnG5HBwAAgIII4QAAAFAQIRwAAAAKIoQDAABAQYRwAAAANkpzc3N7l1BybfUzWh0dAACA96RTp04pKyvLK6+8kt69e6esrKy9SyqJ5ubmvPLKKykrK0unTp026lxCOAAAAO9Jx44dM2DAgPz1r3/NwoUL27uckiorK8uAAQPSsWPHjTqPEA4AAMB7ttVWW2XHHXfMihUr2ruUkurUqdNGB/BECAcAAGAjdezYsU0C6uagpAuz1dbW5qMf/Wi6d++ePn36ZPz48XnmmWfWOmbGjBkpKytrtXXp0qWUZQIAAEAhShrC77nnnpx66ql58MEHM2fOnKxYsSIHH3xwli1bttZxFRUVWbx4ccv24osvlrJMAAAAKERJb0e/7bbbWu3PmDEjffr0yfz58/Pxj398jePKyspSVVVVytIAAACgcIW+J7y+vj5J0rNnz7X2e+ONN7Lddtuluro6hx56aP74xz+usW9jY2MaGhpabQAAALApKiyENzU15fTTT8/HPvax7Lrrrmvst/POO+eqq67KjTfemJ/97GdpamrKfvvtl7/+9a+r7V9bW5vKysqWrbq6ulQ/AgAAAGyUsubm5uYiJjr55JPzm9/8Jvfdd18GDBiw3uNWrFiRXXbZJUcddVTOO++8VY43NjamsbGxZb+hoSHV1dWpr69PRUVFm9QOAAAAa9LQ0JDKysr1yqGFvKJs0qRJufnmm3PvvfduUABP3n0X25577pnnnntutcfLy8tTXl7eFmUCAABASZX0dvTm5uZMmjQpN9xwQ+66664MHjx4g8+xcuXKPPHEE+nXr18JKgQAAIDilPRK+KmnnpqZM2fmxhtvTPfu3VNXV5ckqaysTNeuXZMkEyZMyLbbbpva2tokybe//e3su+++2WGHHbJ06dJceOGFefHFF/OlL32plKUCAABAyZU0hE+dOjVJMnLkyFbtP/nJTzJx4sQkyaJFi9Khw78vyP/zn//MCSeckLq6umy99dYZPnx4HnjggQwdOrSUpQIAAEDJFbYwW1E25IF4AAAA2FgbkkMLfU84AAAAbM6EcAAAACiIEA4AAAAFEcIBAACgIEI4AAAAFEQIBwAAgIII4QAAAFAQIRwAAAAKIoQDAABAQYRwAAAAKIgQDgAAAAURwgEAAKAgQjgAAAAURAgHAACAggjhAAAAUBAhHAAAAAoihAMAAEBBhHAAAAAoiBAOAAAABRHCAQAAoCBCOAAAABRECAcAAICCCOEAAABQECEcAAAACiKEAwAAQEGEcAAAACiIEA4AAAAFEcIBAACgICUN4bW1tfnoRz+a7t27p0+fPhk/fnyeeeaZdY677rrrMmTIkHTp0iW77bZbbr311lKWCQAAAIUoaQi/5557cuqpp+bBBx/MnDlzsmLFihx88MFZtmzZGsc88MADOeqoo3L88cfn8ccfz/jx4zN+/Pg8+eSTpSwVAAAASq6subm5uajJXnnllfTp0yf33HNPPv7xj6+2z5FHHplly5bl5ptvbmnbd999s8cee2TatGmr9G9sbExjY2PLfkNDQ6qrq1NfX5+Kioq2/yEAAADgf2loaEhlZeV65dBCnwmvr69PkvTs2XONfebNm5dRo0a1ahs9enTmzZu32v61tbWprKxs2aqrq9uuYAAAAGhDhYXwpqamnH766fnYxz6WXXfddY396urq0rdv31Ztffv2TV1d3Wr7T5kyJfX19S3bSy+91KZ1AwAAQFvZoqiJTj311Dz55JO577772vS85eXlKS8vb9NzAgAAQCkUEsInTZqUm2++Offee28GDBiw1r5VVVVZsmRJq7YlS5akqqqqlCUCAABAyZX0dvTm5uZMmjQpN9xwQ+66664MHjx4nWNqamoyd+7cVm1z5sxJTU1NqcoEAACAQpT0Svipp56amTNn5sYbb0z37t1bnuuurKxM165dkyQTJkzItttum9ra2iTJaaedlhEjRuSiiy7K2LFjM2vWrDz66KOZPn16KUsFAACAkivplfCpU6emvr4+I0eOTL9+/Vq2a6+9tqXPokWLsnjx4pb9/fbbLzNnzsz06dMzbNiwXH/99Zk9e/ZaF3MDAACA94NC3xNehA15PxsAAABsrE32PeEAAACwORPCAQAAoCBCOAAAABRECAcAAICCCOEAAABQECEcAAAACiKEAwAAQEGEcAAAACiIEA4AAAAFEcIBAACgIEI4AAAAFEQIBwAAgIII4QAAAFAQIRwAAAAKskV7FwBsfgadeUt7l7DZWXjB2PYuAQCAuBIOAAAAhRHCAQAAoCBCOAAAABRECAcAAICCCOEAAABQECEcAAAACiKEAwAAQEGEcAAAACiIEA4AAAAFEcIBAACgIEI4AAAAFEQIBwAAgIKUNITfe++9GTduXPr375+ysrLMnj17rf3vvvvulJWVrbLV1dWVskwAAAAoRElD+LJlyzJs2LBcfvnlGzTumWeeyeLFi1u2Pn36lKhCAAAAKM4WpTz5mDFjMmbMmA0e16dPn/To0aPtCwIAAIB2tEk+E77HHnukX79+Oeigg3L//fevtW9jY2MaGhpabQAAALAp2qRCeL9+/TJt2rT88pe/zC9/+ctUV1dn5MiReeyxx9Y4pra2NpWVlS1bdXV1gRUDAADA+itrbm5uLmSisrLccMMNGT9+/AaNGzFiRAYOHJif/vSnqz3e2NiYxsbGlv2GhoZUV1envr4+FRUVG1MyUCKDzrylvUvY7Cy8YGx7lwAA8IHV0NCQysrK9cqhJX0mvC3svffeue+++9Z4vLy8POXl5QVWBAAAAO/NJnU7+uosWLAg/fr1a+8yAAAAYKOV9Er4G2+8keeee65l/4UXXsiCBQvSs2fPDBw4MFOmTMnf/va3XHPNNUmSiy++OIMHD86HP/zhvP3227nyyitz11135Y477ihlmQAAAFCIkobwRx99NJ/4xCda9idPnpwkOfbYYzNjxowsXrw4ixYtajm+fPnynHHGGfnb3/6Wbt26Zffdd8+dd97Z6hwAAADwflXYwmxF2ZAH4oH2YWG24lmYDQCgdDYkh27yz4QDAADAB4UQDgAAAAURwgEAAKAgQjgAAAAURAgHAACAggjhAAAAUBAhHAAAAAoihAMAAEBBhHAAAAAoiBAOAAAABRHCAQAAoCBCOAAAABRECAcAAICCCOEAAABQECEcAAAACiKEAwAAQEGEcAAAACiIEA4AAAAFEcIBAACgIEI4AAAAFEQIBwAAgIII4QAAAFAQIRwAAAAKIoQDAABAQYRwAAAAKIgQDgAAAAURwgEAAKAgQjgAAAAUpKQh/N577824cePSv3//lJWVZfbs2escc/fdd+cjH/lIysvLs8MOO2TGjBmlLBEAAAAKU9IQvmzZsgwbNiyXX375evV/4YUXMnbs2HziE5/IggULcvrpp+dLX/pSbr/99lKWCQAAAIXYopQnHzNmTMaMGbPe/adNm5bBgwfnoosuSpLssssuue+++/LDH/4wo0ePXu2YxsbGNDY2tuw3NDRsXNEAAABQIpvUM+Hz5s3LqFGjWrWNHj068+bNW+OY2traVFZWtmzV1dWlLhMAAADek5JeCd9QdXV16du3b6u2vn37pqGhIW+99Va6du26ypgpU6Zk8uTJLfsNDQ2COABAAQadeUt7l7DZWXjB2PYuAdhIm1QIfy/Ky8tTXl7e3mUAAADAOm1St6NXVVVlyZIlrdqWLFmSioqK1V4FBwAAgPeTTSqE19TUZO7cua3a5syZk5qamnaqCAAAANpOSUP4G2+8kQULFmTBggVJ3n0F2YIFC7Jo0aIk7z7PPWHChJb+J510Up5//vl8/etfz9NPP50rrrgiv/jFL/LVr361lGUCAABAIUoawh999NHsueee2XPPPZMkkydPzp577pmzzjorSbJ48eKWQJ4kgwcPzi233JI5c+Zk2LBhueiii3LllVeu8fVkAAAA8H5S0oXZRo4cmebm5jUenzFjxmrHPP744yWsCgAAANrHJvVMOAAAAHyQCeEAAABQECEcAAAACiKEAwAAQEGEcAAAACiIEA4AAAAFEcIBAACgIEI4AAAAFEQIBwAAgIII4QAAAFAQIRwAAAAKIoQDAABAQYRwAAAAKIgQDgAAAAURwgEAAKAgQjgAAAAURAgHAACAggjhAAAAUBAhHAAAAAoihAMAAEBBhHAAAAAoiBAOAAAABRHCAQAAoCBCOAAAABRECAcAAICCCOEAAABQECEcAAAAClJICL/88sszaNCgdOnSJfvss08efvjhNfadMWNGysrKWm1dunQpokwAAAAoqZKH8GuvvTaTJ0/O2WefncceeyzDhg3L6NGj8/LLL69xTEVFRRYvXtyyvfjii6UuEwAAAEqu5CH8Bz/4QU444YQcd9xxGTp0aKZNm5Zu3brlqquuWuOYsrKyVFVVtWx9+/YtdZkAAABQciUN4cuXL8/8+fMzatSof0/YoUNGjRqVefPmrXHcG2+8ke222y7V1dU59NBD88c//nGNfRsbG9PQ0NBqAwAAgE1RSUP4q6++mpUrV65yJbtv376pq6tb7Zidd945V111VW688cb87Gc/S1NTU/bbb7/89a9/XW3/2traVFZWtmzV1dVt/nMAAABAW9jkVkevqanJhAkTsscee2TEiBH51a9+ld69e+fHP/7xavtPmTIl9fX1LdtLL71UcMUAAACwfrYo5cm32WabdOzYMUuWLGnVvmTJklRVVa3XOTp16pQ999wzzz333GqPl5eXp7y8fKNrBQAAgFIr6ZXwzp07Z/jw4Zk7d25LW1NTU+bOnZuampr1OsfKlSvzxBNPpF+/fqUqEwAAAApR0ivhSTJ58uQce+yx2WuvvbL33nvn4osvzrJly3LcccclSSZMmJBtt902tbW1SZJvf/vb2XfffbPDDjtk6dKlufDCC/Piiy/mS1/6UqlLBQAAgJIqeQg/8sgj88orr+Sss85KXV1d9thjj9x2220ti7UtWrQoHTr8+4L8P//5z5xwwgmpq6vL1ltvneHDh+eBBx7I0KFDS10qAAAAlFRZc3Nzc3sX0ZYaGhpSWVmZ+vr6VFRUtHc5wGoMOvOW9i5hs7PwgrHtXQLwAeT7vHi+z2HTtCE5dJNbHR0AAAA+qIRwAAAAKIgQDgAAAAURwgEAAKAgQjgAAAAURAgHAACAggjhAAAAUBAhHAAAAAoihAMAAEBBhHAAAAAoiBAOAAAABRHCAQAAoCBCOAAAABRECAcAAICCCOEAAABQECEcAAAACiKEAwAAQEGEcAAAACiIEA4AAAAFEcIBAACgIEI4AAAAFEQIBwAAgIII4QAAAFAQIRwAAAAKIoQDAABAQYRwAAAAKIgQDgAAAAURwgEAAKAghYTwyy+/PIMGDUqXLl2yzz775OGHH15r/+uuuy5DhgxJly5dsttuu+XWW28tokwAAAAoqZKH8GuvvTaTJ0/O2WefncceeyzDhg3L6NGj8/LLL6+2/wMPPJCjjjoqxx9/fB5//PGMHz8+48ePz5NPPlnqUgEAAKCkSh7Cf/CDH+SEE07Icccdl6FDh2batGnp1q1brrrqqtX2v+SSS3LIIYfkv/7rv7LLLrvkvPPOy0c+8pFcdtllq+3f2NiYhoaGVhsAAABsirYo5cmXL1+e+fPnZ8qUKS1tHTp0yKhRozJv3rzVjpk3b14mT57cqm306NGZPXv2avvX1tbm3HPPbbOa29ugM29p7xI2OwsvGNveJWx2/JmzOfB9XjzfLcXzZ87mwPd58T7o3y0lvRL+6quvZuXKlenbt2+r9r59+6aurm61Y+rq6jao/5QpU1JfX9+yvfTSS21TPAAAALSxkl4JL0J5eXnKy8vbuwwAAABYp5JeCd9mm23SsWPHLFmypFX7kiVLUlVVtdoxVVVVG9QfAAAA3i9KGsI7d+6c4cOHZ+7cuS1tTU1NmTt3bmpqalY7pqamplX/JJkzZ84a+wMAAMD7RclvR588eXKOPfbY7LXXXtl7771z8cUXZ9myZTnuuOOSJBMmTMi2226b2traJMlpp52WESNG5KKLLsrYsWMza9asPProo5k+fXqpSwUAAICSKnkIP/LII/PKK6/krLPOSl1dXfbYY4/cdtttLYuvLVq0KB06/PuC/H777ZeZM2fmW9/6Vr7xjW9kxx13zOzZs7PrrruWulQAAAAoqUIWZps0aVImTZq02mN33333Km2HH354Dj/88BJXBQAAAMUq6TPhAAAAwL8J4QAAAFAQIRwAAAAKIoQDAABAQYRwAAAAKIgQDgAAAAURwgEAAKAgQjgAAAAURAgHAACAggjhAAAAUBAhHAAAAAoihAMAAEBBhHAAAAAoiBAOAAAABRHCAQAAoCBCOAAAABRECAcAAICCCOEAAABQECEcAAAACiKEAwAAQEGEcAAAACiIEA4AAAAFEcIBAACgIEI4AAAAFEQIBwAAgIII4QAAAFAQIRwAAAAKUtIQ/tprr+Xoo49ORUVFevTokeOPPz5vvPHGWseMHDkyZWVlrbaTTjqplGUCAABAIbYo5cmPPvroLF68OHPmzMmKFSty3HHH5cQTT8zMmTPXOu6EE07It7/97Zb9bt26lbJMAAAAKETJQvhTTz2V2267LY888kj22muvJMmll16aT37yk/n+97+f/v37r3Fst27dUlVVVarSAAAAoF2U7Hb0efPmpUePHi0BPElGjRqVDh065KGHHlrr2J///OfZZpttsuuuu2bKlCl5880319i3sbExDQ0NrTYAAADYFJXsSnhdXV369OnTerIttkjPnj1TV1e3xnFf+MIXst1226V///75wx/+kP/+7//OM888k1/96ler7V9bW5tzzz23TWsHAACAUtjgEH7mmWfmu9/97lr7PPXUU++5oBNPPLHln3fbbbf069cvBx54YP7yl7/kQx/60Cr9p0yZksmTJ7fsNzQ0pLq6+j3PDwAAAKWywSH8jDPOyMSJE9faZ/vtt09VVVVefvnlVu3vvPNOXnvttQ163nufffZJkjz33HOrDeHl5eUpLy9f7/MBAABAe9ngEN67d+/07t17nf1qamqydOnSzJ8/P8OHD0+S3HXXXWlqamoJ1utjwYIFSZJ+/fptaKkAAACwSSnZwmy77LJLDjnkkJxwwgl5+OGHc//992fSpEn5/Oc/37Iy+t/+9rcMGTIkDz/8cJLkL3/5S84777zMnz8/CxcuzE033ZQJEybk4x//eHbfffdSlQoAAACFKFkIT95d5XzIkCE58MAD88lPfjL7779/pk+f3nJ8xYoVeeaZZ1pWP+/cuXPuvPPOHHzwwRkyZEjOOOOMfO5zn8uvf/3rUpYJAAAAhSjZ6uhJ0rNnz8ycOXONxwcNGpTm5uaW/erq6txzzz2lLAkAAADaTUmvhAMAAAD/JoQDAABAQYRwAAAAKIgQDgAAAAURwgEAAKAgQjgAAAAURAgHAACAggjhAAAAUBAhHAAAAAoihAMAAEBBtmjvAgAAADZVCy8Y294l8AHjSjgAAAAURAgHAACAggjhAAAAUBAhHAAAAAoihAMAAEBBhHAAAAAoiBAOAAAABRHCAQAAoCBCOAAAABRECAcAAICCCOEAAABQECEcAAAACiKEAwAAQEGEcAAAACiIEA4AAAAFEcIBAACgIEI4AAAAFKRkIfz888/Pfvvtl27duqVHjx7rNaa5uTlnnXVW+vXrl65du2bUqFF59tlnS1UiAAAAFKpkIXz58uU5/PDDc/LJJ6/3mO9973v50Y9+lGnTpuWhhx7KlltumdGjR+ftt98uVZkAAABQmC1KdeJzzz03STJjxoz16t/c3JyLL7443/rWt3LooYcmSa655pr07ds3s2fPzuc///nVjmtsbExjY2PLfn19fZKkoaFhI6pvP02Nb7Z3CZud9+tnBdi0+T4vnu9zANrLv/4Oam5uXmffkoXwDfXCCy+krq4uo0aNammrrKzMPvvsk3nz5q0xhNfW1rYE/v+turq6ZLXywVJ5cXtXAEBb8H0OQHt7/fXXU1lZudY+m0wIr6urS5L07du3VXvfvn1bjq3OlClTMnny5Jb9pqamvPbaa+nVq1fKyspKUyytNDQ0pLq6Oi+99FIqKirauxwoCZ9zNgc+52wOfM7ZHPicF6+5uTmvv/56+vfvv86+GxTCzzzzzHz3u99da5+nnnoqQ4YM2ZDTbpTy8vKUl5e3alvfheBoWxUVFf4j5wPP55zNgc85mwOfczYHPufFWtcV8H/ZoBB+xhlnZOLEiWvts/3222/IKVtUVVUlSZYsWZJ+/fq1tC9ZsiR77LHHezonAAAAbEo2KIT37t07vXv3LkkhgwcPTlVVVebOndsSuhsaGvLQQw9t0ArrAAAAsKkq2SvKFi1alAULFmTRokVZuXJlFixYkAULFuSNN95o6TNkyJDccMMNSZKysrKcfvrp+c53vpObbropTzzxRCZMmJD+/ftn/PjxpSqTNlBeXp6zzz57lccC4IPE55zNgc85mwOfczYHPuebtrLm9VlD/T2YOHFirr766lXaf/vb32bkyJHvTl5Wlp/85Cctt7g3Nzfn7LPPzvTp07N06dLsv//+ueKKK7LTTjuVokQAAAAoVMlCOAAAANBayW5HBwAAAFoTwgEAAKAgQjgAAAAURAgHAACAggjhAAAAHzDW3950bdHeBfD+8+qrr+aqq67KvHnzUldXlySpqqrKfvvtl4kTJ6Z3797tXCEAAGzeysvL8/vf/z677LJLe5fC/+EVZWyQRx55JKNHj063bt0yatSo9O3bN0myZMmSzJ07N2+++WZuv/327LXXXu1cKZTWSy+9lLPPPjtXXXVVe5cC79lbb72V+fPnp2fPnhk6dGirY2+//XZ+8YtfZMKECe1UHbSNp556Kg8++GBqamoyZMiQPP3007nkkkvS2NiYY445JgcccEB7lwgbZfLkyattv+SSS3LMMcekV69eSZIf/OAHRZbFWgjhbJB99903w4YNy7Rp01JWVtbqWHNzc0466aT84Q9/yLx589qpQijG73//+3zkIx/JypUr27sUeE/+/Oc/5+CDD86iRYtSVlaW/fffP7NmzUq/fv2SvPvL1f79+/uM875222235dBDD81WW22VN998MzfccEMmTJiQYcOGpampKffcc0/uuOMOQZz3tQ4dOmTYsGHp0aNHq/Z77rkne+21V7bccsuUlZXlrrvuap8CWYUQzgbp2rVrHn/88QwZMmS1x59++unsueeeeeuttwquDNrWTTfdtNbjzz//fM444wwBhfetz3zmM1mxYkVmzJiRpUuX5vTTT8+f/vSn3H333Rk4cKAQzgfCfvvtlwMOOCDf+c53MmvWrJxyyik5+eSTc/755ydJpkyZkvnz5+eOO+5o50rhvbvgggsyffr0XHnlla1+odSpU6f8/ve/X+VOJ9qfEM4GGTx4cM4999w13p54zTXX5KyzzsrChQuLLQzaWIcOHVJWVrbWRU3KysoEFN63+vbtmzvvvDO77bZbknfvZjrllFNy66235re//W223HJLIZz3vcrKysyfPz877LBDmpqaUl5enocffjh77rlnkuTJJ5/MqFGjWta4gferRx55JMccc0zGjRuX2tradOrUSQjfhFkdnQ3yta99LSeeeGJOO+203HTTTXnooYfy0EMP5aabbsppp52Wk046KV//+tfbu0zYaP369cuvfvWrNDU1rXZ77LHH2rtE2ChvvfVWttji3+uzlpWVZerUqRk3blxGjBiRP//5z+1YHbSdfz0+16FDh3Tp0iWVlZUtx7p37576+vr2Kg3azEc/+tHMnz8/r7zySvbaa688+eSTqzw6yqbD6uhskFNPPTXbbLNNfvjDH+aKK65ouULSsWPHDB8+PDNmzMgRRxzRzlXCxhs+fHjmz5+fQw89dLXH13WVHDZ1Q4YMyaOPPrrKqrmXXXZZkuTTn/50e5QFbWrQoEF59tln86EPfShJMm/evAwcOLDl+KJFi1rWQYD3u6222ipXX311Zs2alVGjRrmTaRPmdnTesxUrVuTVV19NkmyzzTbp1KlTO1cEbed3v/tdli1blkMOOWS1x5ctW5ZHH300I0aMKLgyaBu1tbX53e9+l1tvvXW1x0855ZRMmzYtTU1NBVcGbWfatGmprq7O2LFjV3v8G9/4Rl5++eVceeWVBVcGpfXXv/418+fPz6hRo7Llllu2dzn8H0I4AAAAFMQz4QAAAFAQIRwAAAAKIoQDAABAQYRwAAAAKIgQDgAAAAURwgEAAKAgQjgAAAAU5P8DLAzn1c6PH1oAAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 864x288 with 1 Axes>"
+       "<Figure size 1200x400 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5570,7 +5552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 78,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -5584,21 +5566,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 79,
    "metadata": {
     "exercise": "solution"
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5608,21 +5588,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 80,
    "metadata": {
-    "exercise": "solution"
+    "editable": true,
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5632,66 +5615,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 81,
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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fMxufWQZOAFYCS4GFcbWFwN3lBikiIpVTTlfPZODHZpb5Of/p7v/fzH4DLDGzs4E/AGeUH2ZtjR8f5jfdBLNnw7hxtY1HRKQcw0787v4scGSe8leBOeUEVW9mzoTLLoOvfhWefhruuisM4CYi0oh0524JzODLX4af/SyM19PZGQZwExFpREr8Q3DiidDVBR0d8MEPwqWXwp49tY5KRGRolPiH6JBD4Je/hDPPhEsugZNPhi0lDkjx2GPw3HNVDU9EpKhyr+NPpXHj4Pbb4V3vgs9+NnT9/PjHA0fx3LULnn0Wurth7Vr4whdCeR08AkFEUkyJf5jM4NOfhlmz4LTTwtU+n/98OPpfuzZML7ygriARqT9K/GU65pjwZK6PfhSuuAJaW8NTut79bjjrrLCcmWbPDt8ORERqSYm/AiZPhgcfhO3bYd99w7cBEZF6pcRfIWaw3361jkJEpDhd1bOX9fbq5K6I1JaO+PeiUaNgyRJYuhSmTIE3vSlMmeX+ZfvvD83NtY5aRJJGiX8vuv12eOgheOkl2LAhzJ95JpRt3jxwfTNoaxu4gzjqqHAyWURkOJT496LOzsJX9ezaBS+/HHYGuTuG/juJDRvgz3+G+fNhzJi9G7+IJIMSf50YPRre/OYwDebqq+FLX4LLL4eRI8MO489/DvPc5Y4OOPVUOPpoXWUkIn0p8TeYzI7hiivCvLk57DRGjcrOR42CO++Eq66CAw+ED38YPvIROPZYaGqqXewiUh/M6+ASk87OTu/q6hrem59+GlpaQoZLiTfeCAl81KjCR/NbtsBPfxp2APfdB3/6UzhZfMopYSfwvvfpxLFIozOzx919yLeFNnbid4f3vAdWrYLrrw8jp6lfY4DXXw/DSN95ZxhaeseOsNOYPz/sAMaNg7Fjwzx3uf98zBj9ekXqSToTP8Dvfw9///fwyCMhk910U7iVVvLauTMk/9NPH977x47tu0M47TS48srKxigipUlv4odwV9TXvw4XXxy6fR58EI44omLxJVFvbzjy37kzdB1l5rnLxea33RZ+1g03ZM8t5J5nyJ3yleeWNTfr24TIUA038Sfj5G5TUxjz+MQTw2Uvhx0Wyt2VTQpoagrjCu277/B/xn77wXXXhVFKK6G5efCdx8iRYZ3+U6Z8/HiYODFMEyZkl3Nfjx+vPwmRZBzx57N1K3zgA+FBuSedVNmfLUDYr27ZArt3Zy8lzZ3ylZVT3tMTPqv/1NMT6l97LdwIt2tX4ZibmgrvGP73f+Gpp0JPYUsL7LPP8Odjx4ZzIqNHh/lgJ+JFhivdR/z5vPJK6I/4u78Lyf+SS2odUeKYhaRZb3buDDuAzLRlS+HXL70Ukv2WLbBtW3j/cceFE+I7doT5q6/2fb1jx/CeszBmTN+dQe5UStlQ37drFyxenP0mle/bVLEuuMFe69LgxpXcxP+Wt4QH5B59dLjiZ+JE+Kd/CnVdXeG/vKUlfPcfPz5cDqpDskQYOxamTg3TUPT0hPnIIv8V7uHy2NwdQf/5n/6Uf9q1q3D59u2wcWPhdevgy3kfI0YMfWcxnB1Mvi6/ESPCjqf/VKnypKeC5Hb1ZFx1FVx0URjk5o9/DGUf+lC4yL3/ehdcUJ0YRMrkHrq1Cu08Cu1QWlrCuE6Z9w/Wrda/rNjrSq2Teb17d61/y31Vc8eSW3766eHCxOGou64eM5sH/BvQBHzb3b9Wrc8a1Je+BF/8YvgvyLj22pDkX3stTEuWwKRJ4ZDr5JOzWyUzP+ssWLAgfOfXBe1SA2bZI97hvn/06DDVq9ydU6Gdw65doZutt3fgVKnySv6sfOX92/Daa3v/d12VxG9mTcA3gbnAOuA3ZrbU3Z+qxucVNWJESNgZmWchZmSGusx08mbOVma23BtvhPLjj4eVK8N/X2trmObOhX//91B/wQVhpLWenvDenp4wTsLnPhfqzcJ0+eXZ1x/+cLgK6dln4Yc/zMaU2bGccUYYeGfNGviv/4Lnnw9dVZ/+dNgpnXxy6KpasybcyDZiRN8d1/vfH/7bn3sO1q3L1vX0hPJ3vjN8zjPPhPrc2CF8OwL4n/8JT43v6cmuM3o0fPKT4Xf0+usDY898V8/9HebWZ67j3LOn7445I/O9fs+e8F/fX3NzaEtvb9/DxczvOdMnsGdPtp8kU1fOjjv3W7JZ+PmZTv/cupEjs/W57dZBQ165O7eWllpHk3DuXvEJOAa4L+f1RcBFhdZ/xzve4Q3hO99xv/JK9wsucF+0yP2MM9wvuyxbf+SR7h0d7oce6n7YYe4zZrh/8YvZ+pAW+k533hnq7r03f/3994f6JUvy1z/7bKi/8sr89Rs3hvqLLspf39sb6s85Z2Dd2LHZ2D/xiYH1++8f6l56Kf/PvvrqUL92bf76G24I9U88kb/+u98N9Q89lL/+Jz8J9T/7Wf76Bx4I9d//fv76Rx8N9d/+tvuIEe5NTX2n1atD/bXX5n//iy+G+ksvzV+/dWuoP//8/PU9PaH+U5/q+/kjR7rvt1/2d79wofvo0e5jxoRp7Njwd5axYIH7+PFh2nffMM2ala0/+WT3CROy08SJ7n/zN9n6uXPd29r6Th/8YLb+2GPDts6dPv7xbP2sWe5velOYpkwJ0znnZOsPO8z9gAPCNHVqmM4/P1t/0EHu06b1nS69NNTt2BHqc6eODvdrrgn1mza5H3zwwOmmm0L9c8+5H3LIwOk//iPUr1rl/pa3DJwyf1uPPeY+ffrAadmyUP+LX4T29Z9+9avs3+bb3jZwWrEi1D/zjJcD6PJh5OhqdfVMBV7Meb0OeFfuCma2CFgE8OZiQ1LWizPPHLx++fLB6/fsyR6ZZo4MM2cS587NHhXnHjVmvpufemr4TugerljKHGVmxig6++xwBVPmO2Rm3tqarZ8zJ/v9ctOmbB2EbyUf+1iIJ3fKuPbaMDJcU9PA+pYWuOaagbEfd1yYT5oE//qvA+tnzw7zAw4I51j6mzUrzDs64F/+ZWD9X/1VmB92WLY+k1YBDjkkzI84Ai67rG/ahezZ3yOPDOeBMu/PHJFPmpSN8ytfCcu5R+2ZmyCOPz78/P5H9Jltd8IJ2UPY3M/PrDdvXvbyqEx97kBK8+Zl70bP1OceEv/t34aHNWR+rnt4kENu/UEHZety2w7h7yL3GzDAoYdml+fOHXhD5OGH963furXvtj3qqOzyCSeES61y63PfP2fOwDPXmXiamsLvNyOzXkdHmDc3h2Fb+su0b+xYePe7B9Znfp/jxoULQPrLbPuWlvxjqWf+d8aPh5kzB9Zntk9rK7z97QPrx44N8xr1vVXl5K6ZnQbMc/d/jK/PBN7l7uflW7+qJ3dFRBJquCd3q/XM3fXAtJzXB8YyERGpsWol/t8A083sYDMbBSwAllbps0REZAiq0sfv7j1mdh5wH+FyztvcfVU1PktERIamatfxu/s9wD3V+vkiIjI81erqERGROqXELyKSMkr8IiIpo8QvIpIydTE6p5ltAv5QoLoNeGUvhrO3qF2NJ6ltU7saT6ZtB7l7+1DfXBeJfzBm1jWcO9PqndrVeJLaNrWr8ZTbNnX1iIikjBK/iEjKNELiv7nWAVSJ2tV4kto2tavxlNW2uu/jFxGRymqEI34REakgJX4RkZSp28RvZvPMbI2ZdZvZhbWOp1xm9ryZ/c7MlptZVyybaGbLzGxtnE+odZzFmNltZrbRzFbmlOVthwXXx224wsyOKvyTa6tAuy4xs/Vxmy03s5Ny6i6K7VpjZh+oTdTFmdk0M3vQzJ4ys1Vm9plYnoRtVqhtDb3dzGyMmT1mZk/Gdl0ayw82s0dj/HfEIe8xs9HxdXes7yj6IcN5XmO1J8JQzr8HDgFGAU8CM2odV5lteh5o61d2NXBhXL4QuKrWcZbQjuOAo4CVxdoBnATcCxgwG3i01vEPsV2XAOfnWXdG/JscDRwc/1abat2GAu2aAhwVl8cDz8T4k7DNCrWtobdb/N23xOVm4NG4LZYAC2L5t4BPxeVPA9+KywuAO4p9Rr0e8R8NdLv7s+7+Z+AHwPwax1QN84HFcXkxcErtQimNuz8MbO5XXKgd84HvePBroNXMpuyVQIeoQLsKmQ/8wN13uftzQDfhb7buuPsGd38iLr8GrCY8EzsJ26xQ2wppiO0Wf/evx5fNcXLg/cCPYnn/bZbZlj8C5phlHuicX70m/nwPax9sgzYCB+43s8fjg+YBJrv7hrj8EjC5NqGVrVA7krAdz4tdHrfldMU1ZLtiF8AswhFkorZZv7ZBg283M2sys+XARmAZ4dvJVnfviavkxv6XdsX6bcCkwX5+vSb+JHqPux8FnAica2bH5VZ6+J7W8NfWJqUd0Y3AocBMYANwTU2jKYOZtQB3Ap919+25dY2+zfK0reG3m7v3uvtMwvPKjwbeVsmfX6+JP3EPa3f39XG+EfgxYWO+nPkaHecbaxdhWQq1o6G3o7u/HP8B9wC3kO0WaKh2mVkzITF+z93visWJ2Gb52paU7Qbg7luBB4FjCN1umacm5sb+l3bF+v2AVwf7ufWa+BP1sHYz28fMxmeWgROAlYQ2LYyrLQTurk2EZSvUjqXAWfFKkdnAtpzuhbrXr2/7VMI2g9CuBfFqioOB6cBjezu+UsS+3luB1e5+bU5Vw2+zQm1r9O1mZu1m1hqXxwJzCecvHgROi6v132aZbXka8N/xW1xhtT6DPciZ7ZMIZ+l/D1xc63jKbMshhKsJngRWZdpD6Id7AFgL/ByYWOtYS2jL9wlfn3cT+hnPLtQOwtUJ34zb8HdAZ63jH2K7vhvjXhH/uabkrH9xbNca4MRaxz9Iu95D6MZZASyP00kJ2WaF2tbQ2w14O/DbGP9K4Cux/BDCjqob+CEwOpaPia+7Y/0hxT5DQzaIiKRMvXb1iIhIlSjxi4ikjBK/iEjKKPGLiKSMEr+ISMqMLL6KSOMws8xligBvAnqBTUAH8Ed3n7EXYnjd3Vuq/Tkiw6UjfkkUd3/V3Wd6uN39W8B1cXkmsKfY+3PujBRJLCV+SZMmM7sljnF+f7wrEjP7hZl93cJzEj5jZu8ws4figHr35Qxt8Ekz+00cJ/1OMxsXyw82s19ZeN7C5ZkPM7MpZvZwHBN+pZm9tyatFulHiV/SZDrwTXc/HNgKfCSnbpS7dwLXA98ATnP3dwC3AVfEde5y93e6+5GEW+jPjuX/Btzo7kcQ7v7N+DhwX/zGcSThzlKRmtPXWkmT59x9eVx+nNDvn3FHnB8G/DWwLA5p3kQ2mf91PKJvBVqA+2L5sWR3It8FrorLvwFuiwOJ/STns0VqSkf8kia7cpZ76XvgsyPODViVOU/g7ke4+wmx7nbgvHhkfylhjJSMAWOfeHi4y3GE0RNvN7OzKtMMkfIo8Yv0tQZoN7NjIAz7a2aHx7rxwIZ4BP+JnPf8kjCCLLnlZnYQ8LK73wJ8m/BoR5GaU+IXyeHhUZ+nAVeZ2ZOEfvl3x+ovE57w9Evg6Zy3fYbwcJ3f0feJTscDT5rZb4GPEs4FiNScRucUEUkZHfGLiKSMEr+ISMoo8YuIpIwSv4hIyijxi4ikjBK/iEjKKPGLiKTM/wFB0toRUodpRQAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df[\"Presim. Time / s\"].plot(style=\"--r\");\n",
-    "df[\"Sim. Time / s\"].plot(style=\"-b\");"
+    "df[\"Presim. Time / s\"].plot(style=\"--r\", figsize=(10,3));\n",
+    "df[\"Sim. Time / s\"].plot(style=\"-b\", figsize=(10,3));"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 82,
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "ax = df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot(style=[\"--b\", \"-r\"]);\n",
+    "ax = df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot(style=[\"--b\", \"-r\"], figsize=(10,3));\n",
     "ax.set_ylabel(\"Time / s\");"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "## More Plotting with Pandas\n",
@@ -5700,29 +5685,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
+   "execution_count": 83,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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3aR11WblypX75y1+aWoOZ6DnytMBQKaCTJEM6/o3Z1QAAADQdw5CKT5szGUa9SszNzdUXX3yhP/zhD7r++usVFRWlESNGKDEx0S0EVL6srqKX58MPP9Q111wjf39/DR8+XN9++62+/vprDRs2TIGBgRo3bpyOHz9erzqsVqvCwsJcU2BgoLy9vd3m+fv7n3dZXbdu3fTCCy/o7rvvVmBgoKKiovTpp5/q+PHjmjRpkgIDAzVo0CBt377dbX9ffvmlq/bIyEg99NBDOn36dK01FhYW6vPPP68xHO3cuVPXX3+9OnTooKCgIA0dOvS8/bZ29Bw1BftlUkaKc8S6S68wuxoAAICmUXJG+n2EOft+8qjk277OZoGBgQoMDNQnn3yiK6+8Un5+fvXexTPPPKNXXnlFXbt21X333ac777xTHTp00KuvvqqAgADdeuutmjdvnhYvXtyYI6nTokWL9Pvf/15PP/20Fi1apLvuuktXXXWV7rvvPr300kt6/PHHdffdd2vv3r2yWCw6dOiQ4uPj9cILL+j//u//dPz4cc2ePVuzZ8/WkiVLatzP+vXrdemll6pfv37VLp82bZqGDBmixYsXy2q1aseOHfLx8WmqwzYFPUdNgRHrAAAAWgRvb28tXbpUy5Ytk81m06hRo/Tkk09q165dda47d+5cxcXFqX///vrtb3+rtLQ0Pf300xo1apSGDBmiGTNmaOPGjU1+DOPHj9f999+v3r17a968ecrPz9fw4cN1yy23qE+fPnr88ce1f/9+ZWdnS5IWLFigadOm6eGHH1bv3r111VVX6bXXXtNf//pXFRYW1rifui6py8zMVGxsrPr166fevXvrlltu0eDBgz1+vGai56gpuAZl4FlHAADgIuYT4OzBMWvf9TR16lRNmDBBX3zxhbZs2aI1a9Zo4cKFevPNNzV9+vQa1xs0aJDrvd1ulyQNHDjQbV5OTk7Da2+g+tQhSTk5OQoLC9POnTu1a9cuvfPOO642hmHI4XAoIyND/fv3P28fhmFo1apV+vDDD2usY86cOfrVr36lv/3tb4qNjdUtt9yinj17Nvr4WhJ6jpqCnXAEAADaAIvFeWmbGZPF0qBS27VrpxtvvFFPP/20Nm/erOnTp+uZZ56pdZ3Kl4xZyvdXdZ7D4WhQHReiPnVIctVSUFCg+++/Xzt27HBNO3fu1IEDB2oMM9u2bVNpaamuuuqqGuuYP3++9u7dqwkTJmjDhg2Kjo7WihUrGn18LQk9R02hc39JFunMT1JBjnOQBgAAALQY0dHRLeK5Rk3hiiuu0L59+9SrV696r7Ny5UpNmDBBVqu11nZ9+vRRnz599Mgjj+iOO+7QkiVLdNNNNzW25BaDnqOm4BsghfRwvqf3CAAAwDQnTpzQDTfcoLffflu7du1SRkaGli9froULF2rSpEke3deKFStqHMygOT3++OPavHmzZs+erR07dujAgQNauXKlZs+eXeM6n376aa33G509e1azZ89WcnKyvv/+e3311Vf6+uuvq71ErzWj56ip2KOlk4ecgzL0vN7sagAAANqkwMBAjRw5UosWLdKhQ4dUUlKiyMhIzZw5U08++aRH95WXl6f09HSPbvNCDBo0SCkpKfqv//ovXXPNNTIMQz179tRtt91WbftDhw7p4MGDiouLq3GbVqtVJ06c0N13363s7Gx16tRJU6ZM0bPPPttUh2EKi2HUc5D4FiQ/P1/BwcHKy8tTUFCQ2eVUL/lFKXmBdPk0afJfzK4GAACgUQoLC5WRkaHu3burXbt2ZpcDD3r55Ze1bt06ffbZZ2aXUqPavv88mQ24rK6pMGIdAAAAWoEuXbooMTHR7DJaBC6rayoVzzo6/o3kKJO8ar+5DQAAADDDrbfeanYJLUaDeo4WLFig4cOHq0OHDgoNDdXkyZPPu67yuuuuk8VicZt+/etfu7XJzMzUhAkTFBAQoNDQUD322GMqLS1t/NG0JJd0k7z9pdJC6WSG2dUAAAAAqEODwlFKSooSEhK0ZcsWJSUlqaSkRGPHjtXp06fd2s2cOVPHjh1zTQsXLnQtKysr04QJE1RcXKzNmzdr2bJlWrp0qebNm+eZI2opvKxSaPnoHdl7zK0FAAAAQJ0adFnd2rVr3T4vXbpUoaGhSktL0+jRo13zAwICFBYWVu02Pv/8c+3bt0/r1q2T3W7X5Zdfrueff16PP/645s+fL19f3ws4jBbKHi0d/ZdzxLrLJptdDQAAQKO1wrG8cBForu+7Rg3IkJeXJ0kKCQlxm//OO++oU6dOGjBggBITE3XmzBnXstTUVA0cOFB2u901Ly4uTvn5+dq7t/rBC4qKipSfn+82tQqh5fcdMSgDAABo5Xx8fCTJ7f91QHOp+L6r+D5sKhc8IIPD4dDDDz+sUaNGacCAAa75d955p6KiohQREaFdu3bp8ccfV3p6uj7++GNJUlZWllswkuT6nJWVVe2+FixY0DrHULczYh0AALg4WK1W2Ww25eTkSHJeKWSxWEyuChc7wzB05swZ5eTkyGazyWpt2kHOLjgcJSQkaM+ePfryyy/d5s+aNcv1fuDAgQoPD9eYMWN06NAh9ezZ84L2lZiYqDlz5rg+5+fnKzIy8sIKb0728tD482Gp+LTk297UcgAAABqj4raJioAENBebzVbjbTuedEHhaPbs2Vq9erU2bdqkLl261Np25MiRkqSDBw+qZ8+eCgsL07Zt29zaZGdnS1KNB+zn5yc/P78LKdVc7TtJ7UOl0zlSzjdSl6FmVwQAAHDBLBaLwsPDFRoaqpKSErPLQRvh4+PT5D1GFRoUjgzD0IMPPqgVK1YoOTlZ3bt3r3OdHTt2SJLCw8MlSTExMfrd736nnJwchYaGSpKSkpIUFBSk6OjoBpbfCtijpe9ypJy9hCMAAHBRsFqtzfafVaA5NSgcJSQk6N1339XKlSvVoUMH1z1CwcHB8vf316FDh/Tuu+9q/Pjx6tixo3bt2qVHHnlEo0eP1qBBgyRJY8eOVXR0tO666y4tXLhQWVlZeuqpp5SQkNA6e4fqYh8gfZfMfUcAAABAC9eg0eoWL16svLw8XXfddQoPD3dNH3zwgSTJ19dX69at09ixY9WvXz89+uijmjp1qlatWuXahtVq1erVq2W1WhUTE6P/+I//0N13363nnnvOs0fWUoQyKAMAAADQGjT4srraREZGKiUlpc7tREVF6bPPPmvIrluvyiPWGYbEqC4AAABAi9So5xyhHjr3kyxe0tmTUkG22dUAAAAAqAHhqKn5+Esh5UOYc2kdAAAA0GIRjppDxaV1OfvMrQMAAABAjQhHzSH0MudrNuEIAAAAaKkIR83BXhGO9phbBwAAAIAaEY6aQ8VldcfTpbJSc2sBAAAAUC3CUXOwdZN82ktlRdLJ78yuBgAAAEA1CEfNwctLCu3nfJ/DiHUAAABAS0Q4ai6u+44IRwAAAEBLRDhqLoxYBwAAALRohKPm4nrWET1HAAAAQEtEOGouFT1HPx+Wik6ZWgoAAACA8xGOmkv7jlJgmPN9zjfm1gIAAADgPISj5sSldQAAAECLRThqTqHl4YhBGQAAAIAWh3DUnOwDnK8M5w0AAAC0OISj5lT5sjrDMLcWAAAAAG4IR82pU1/JYpXO/iydyjK7GgAAAACVEI6ak087qWNP53sGZQAAAABaFMJRc7OXP++I+44AAACAFoVw1NwqHgbLiHUAAABAi0I4am486wgAAABokRoUjhYsWKDhw4erQ4cOCg0N1eTJk5Wenu7WprCwUAkJCerYsaMCAwM1depUZWdnu7XJzMzUhAkTFBAQoNDQUD322GMqLS1t/NG0BhXPOjqeLpWVmFsLAAAAAJcGhaOUlBQlJCRoy5YtSkpKUklJicaOHavTp0+72jzyyCNatWqVli9frpSUFB09elRTpkxxLS8rK9OECRNUXFyszZs3a9myZVq6dKnmzZvnuaNqyWxRkm+gVFYsnThkdjUAAAAAylkM48IfuHP8+HGFhoYqJSVFo0ePVl5enjp37qx3331XN998syTpm2++Uf/+/ZWamqorr7xSa9as0S9+8QsdPXpUdrtdkvTGG2/o8ccf1/Hjx+Xr61vnfvPz8xUcHKy8vDwFBQVdaPnmeTNW+uFr6eb/kwZMNbsaAAAAoNXyZDZo1D1HeXl5kqSQkBBJUlpamkpKShQbG+tq069fP3Xt2lWpqamSpNTUVA0cONAVjCQpLi5O+fn52ru3+vtwioqKlJ+f7za1ahWX1jEoAwAAANBiXHA4cjgcevjhhzVq1CgNGDBAkpSVlSVfX1/ZbDa3tna7XVlZWa42lYNRxfKKZdVZsGCBgoODXVNkZOSFlt0y2J1fL4bzBgAAAFqOCw5HCQkJ2rNnj95//31P1lOtxMRE5eXluaYjR440+T6bFCPWAQAAAC3OBYWj2bNna/Xq1dq4caO6dOnimh8WFqbi4mLl5ua6tc/OzlZYWJirTdXR6yo+V7Spys/PT0FBQW5Tq1ZxWV1uplR0ytxaAAAAAEhqYDgyDEOzZ8/WihUrtGHDBnXv3t1t+dChQ+Xj46P169e75qWnpyszM1MxMTGSpJiYGO3evVs5OTmuNklJSQoKClJ0dHRjjqX1CAiROoQ73+fsN7cWAAAAAJIk74Y0TkhI0LvvvquVK1eqQ4cOrnuEgoOD5e/vr+DgYM2YMUNz5sxRSEiIgoKC9OCDDyomJkZXXnmlJGns2LGKjo7WXXfdpYULFyorK0tPPfWUEhIS5Ofn5/kjbKnsl0mnjknZe6TIEWZXAwAAALR5Deo5Wrx4sfLy8nTdddcpPDzcNX3wwQeuNosWLdIvfvELTZ06VaNHj1ZYWJg+/vhj13Kr1arVq1fLarUqJiZG//Ef/6G7775bzz33nOeOqjVgxDoAAACgRWnUc47M0uqfcyRJO9+XVtwvRY2S7v3M7GoAAACAVqnFPOcIjWC/zPmavUdqffkUAAAAuOgQjszSqY9ksUqFeVL+UbOrAQAAANo8wpFZvP2kTr2d73O47wgAAAAwG+HITK5BGXgYLAAAAGA2wpGZXPcdEY4AAAAAsxGOzFQRjrisDgAAADAd4chMFZfVHU+XykrMrQUAAABo4whHZrJ1lXw7SI4S6cRBs6sBAAAA2jTCkZksFsnOoAwAAABAS0A4Mhsj1gEAAAAtAuHIbAzKAAAAALQIhCOzMZw3AAAA0CIQjswW2t/5mndEKswztxYAAACgDSMcmc3/EinoUuf7nP3m1gIAAAC0YYSjloBBGQAAAADTEY5aAu47AgAAAExHOGoJGLEOAAAAMB3hqCVwXVa3TzIMc2sBAAAA2ijCUUvQqY/k5S0V5Un5P5pdDQAAANAmEY5aAm9fZ0CSuO8IAAAAMAnhqKVgxDoAAADAVISjlsJeHo4YlAEAAAAwBeGopbAPcL7ScwQAAACYosHhaNOmTZo4caIiIiJksVj0ySefuC2fPn26LBaL2xQfH+/W5uTJk5o2bZqCgoJks9k0Y8YMFRQUNOpAWr2Ky+p++lYqLTa3FgAAAKANanA4On36tAYPHqzXX3+9xjbx8fE6duyYa3rvvffclk+bNk179+5VUlKSVq9erU2bNmnWrFkNr/5iEtxF8guWHKXSiQNmVwMAAAC0Od4NXWHcuHEaN25crW38/PwUFhZW7bL9+/dr7dq1+vrrrzVs2DBJ0p/+9CeNHz9ef/zjHxUREdHQki4OFosU2l86ssX5vKOKB8MCAAAAaBZNcs9RcnKyQkND1bdvXz3wwAM6ceKEa1lqaqpsNpsrGElSbGysvLy8tHXr1mq3V1RUpPz8fLfpolQRiLL3mFsHAAAA0AZ5PBzFx8frr3/9q9avX68//OEPSklJ0bhx41RWViZJysrKUmhoqNs63t7eCgkJUVZWVrXbXLBggYKDg11TZGSkp8tuGRixDgAAADBNgy+rq8vtt9/uej9w4EANGjRIPXv2VHJyssaMGXNB20xMTNScOXNcn/Pz8y/OgBRa0XNEOAIAAACaW5MP5d2jRw916tRJBw8elCSFhYUpJyfHrU1paalOnjxZ431Kfn5+CgoKcpsuShU9R/k/SGd/NrcWAAAAoI1p8nD0ww8/6MSJEwoPD5ckxcTEKDc3V2lpaa42GzZskMPh0MiRI5u6nJatXbAUXN4jlrPf3FoAAACANqbB4aigoEA7duzQjh07JEkZGRnasWOHMjMzVVBQoMcee0xbtmzR4cOHtX79ek2aNEm9evVSXFycJKl///6Kj4/XzJkztW3bNn311VeaPXu2br/99rY7Ul1leUecr+/eZm4dAAAAQBvT4HC0fft2DRkyREOGDJEkzZkzR0OGDNG8efNktVq1a9cu/fKXv1SfPn00Y8YMDR06VF988YX8/Pxc23jnnXfUr18/jRkzRuPHj9fVV1+t//mf//HcUbVmlvJT0rGXuXUAAAAAbYzFMAzD7CIaKj8/X8HBwcrLy7v47j9aPUfa/pZ0XaJ03RNmVwMAAAC0aJ7MBk1+zxEAAAAAtAaEIwAAAAAQ4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4ajl2r9K+i5ZMgyzKwEAAADaBMJRS9MrVrL6Stl7pL9Okv4vXjq4npAEAAAANDHCUUvTb7z00A5pxP2S1U86skV6e4r0Zqz07eeEJAAAAKCJWAyj9f1vOz8/X8HBwcrLy1NQUJDZ5TSdU1nSV69K2/9PKi10zosYIl37uNQnXrJYzK0PAAAAMJknswHhqDU4lS2l/kn6+i2p5IxzXtgg6dr/lPpOkLzoAAQAAEDbRDhqa+GoQsFxKfXP0rb/lUpOO+fZB0ijH5P6/5KQBAAAgDaHcNRWw1GF0yekLa9LW/9HKj7lnNe5v3TtY1L0ZMnLamp5AAAAQHPxZDZocFfDpk2bNHHiREVERMhiseiTTz5xW24YhubNm6fw8HD5+/srNjZWBw4ccGtz8uRJTZs2TUFBQbLZbJoxY4YKCgoadSBtSvuO0ph50sO7nPcf+QVLx/dLH90n/SVG2rVccpSZXSUAAADQqjQ4HJ0+fVqDBw/W66+/Xu3yhQsX6rXXXtMbb7yhrVu3qn379oqLi1NhYaGrzbRp07R3714lJSVp9erV2rRpk2bNmnXhR9FWBYRI1z/pDEnXPSm1C5Z+Spc+/pX0+ghpx3tSWanZVQIAAACtQqMuq7NYLFqxYoUmT54sydlrFBERoUcffVRz586VJOXl5clut2vp0qW6/fbbtX//fkVHR+vrr7/WsGHDJElr167V+PHj9cMPPygiIqLO/bb5y+pqUpgvbfsf531JZ392zrukuzR6rjTodsnqbW59AAAAgIeZelldbTIyMpSVlaXY2FjXvODgYI0cOVKpqamSpNTUVNlsNlcwkqTY2Fh5eXlp69at1W63qKhI+fn5bhOq0S7IGYQe3i3FzpcCOko/Z0grE6RNL5ldHQAAANCieTQcZWVlSZLsdrvbfLvd7lqWlZWl0NBQt+Xe3t4KCQlxtalqwYIFCg4Odk2RkZGeLPvi49dBuvoRZ0gaeKtzXm6muTUBAAAALVyrGPs5MTFReXl5runIkSNml9Q6+LaXwgaYXQUAAADQKng0HIWFhUmSsrOz3eZnZ2e7loWFhSknJ8dteWlpqU6ePOlqU5Wfn5+CgoLcJgAAAADwJI+Go+7duyssLEzr1693zcvPz9fWrVsVExMjSYqJiVFubq7S0tJcbTZs2CCHw6GRI0d6shwAAAAAqLcGD19WUFCggwcPuj5nZGRox44dCgkJUdeuXfXwww/rhRdeUO/evdW9e3c9/fTTioiIcI1o179/f8XHx2vmzJl64403VFJSotmzZ+v222+v10h1AAAAANAUGhyOtm/fruuvv971ec6cOZKke+65R0uXLtV//ud/6vTp05o1a5Zyc3N19dVXa+3atWrXrp1rnXfeeUezZ8/WmDFj5OXlpalTp+q1117zwOEAAAAAwIVp1HOOzMJzjhrgq1elpHnS4DulmxabXQ0AAADgUS32OUcAAAAA0FoRjgAAAABAhCMAAAAAkEQ4AgAAAABJhCMAAAAAkEQ4AgAAAABJhCMAAAAAkEQ4AgAAAABJhCMAAAAAkEQ4AgAAAABJhCMAAAAAkEQ4AgAAAABJhCMAAAAAkEQ4AgAAAABJhCMAAAAAkEQ4AgAAAABJkrfZBaCZ7P9UOvmddEk35xTS/dz7QLtksZhbHwAAAGAywtHFLmKIZPWVigukI1ucU1Xe/tIlUefCUuXJFiX5BjRryQAAAIAZLIZhGGYX0VD5+fkKDg5WXl6egoKCzC6n5Tub6+w1+jlD+vmw+5T3g2Q4al8/0O4emEJ6SH3HS+342gMAAMBcnswG9By1Bf426dIrnFNVZSVS3pHzQ9PPh6WTh6WiPKkg2zkd2Xpuvctukm5Z2vS1AwAAAM2EcNTWWX2cPUEhPapffvZn6WSlHqfDX0qH1ksFOc1ZJQAAANDkCEeonf8l0qWXnOt1CunhDEcAAADARcbjQ3nPnz9fFovFberXr59reWFhoRISEtSxY0cFBgZq6tSpys7O9nQZAAAAANAgTfKco8suu0zHjh1zTV9++aVr2SOPPKJVq1Zp+fLlSklJ0dGjRzVlypSmKAMAAAAA6q1JLqvz9vZWWFjYefPz8vL01ltv6d1339UNN9wgSVqyZIn69++vLVu26Morr2yKcgAAAACgTk3Sc3TgwAFFRESoR48emjZtmjIzMyVJaWlpKikpUWxsrKttv3791LVrV6WmpjZFKQAAAABQLx7vORo5cqSWLl2qvn376tixY3r22Wd1zTXXaM+ePcrKypKvr69sNpvbOna7XVlZWTVus6ioSEVFRa7P+fn5ni4bAAAAQBvn8XA0btw41/tBgwZp5MiRioqK0ocffih/f/8L2uaCBQv07LPPeqpEAAAAADhPk1xWV5nNZlOfPn108OBBhYWFqbi4WLm5uW5tsrOzq71HqUJiYqLy8vJc05EjR5q4agAAAABtTZOHo4KCAh06dEjh4eEaOnSofHx8tH79uefkpKenKzMzUzExMTVuw8/PT0FBQW4TAAAAAHiSxy+rmzt3riZOnKioqCgdPXpUzzzzjKxWq+644w4FBwdrxowZmjNnjkJCQhQUFKQHH3xQMTExjFTX2pzMkPaukPqMk3zamV0NAAAA0GgeD0c//PCD7rjjDp04cUKdO3fW1VdfrS1btqhz586SpEWLFsnLy0tTp05VUVGR4uLi9Je//MXTZaCphHSXLFbp1FFp+XTJL0iKniQNuk2KGiV5NXlnJAAAANAkLIZhGGYX0VD5+fkKDg5WXl4el9iZ4Xi6tPM9addyKf+Hc/ODukiDbnEGpdD+5tUHAACANsOT2YBwhAvncEiZm6VdH0h7V0pFeeeWhQ10hqSBt0gdah5sAwAAAGgMwhHhqOUpKZS+XSvt+lA68LnkKHHOt3hJ3a91BqX+EyW/QHPrBAAAwEWFcEQ4atnOnHQO1rDrA+nI1nPzfQKkfhOcQanH9ZLV47e8OS0aILWzSQ982TTbBwAAQItBOCIctR4nM6Tdy51B6cTBc/Pbd5YGTHUGpYghksVy/rolhVJhrnQ2t5bXPPd5x7+RDIdz/aeOS96+TXhwAAAAMBvhiHDU+hiGdPRfzsvudn8knfnp3LKOvaWQHs5wU5h3LuiUFjZun0//JFl9GrcNAAAAtGiezAZNdF0TUIXFIl061DmNfUE6tNHZm/TNP6QTB5xTtet5Se2Cyyeb5G+r/VWS/ja5YuUmPCAAAABcbAhHaH5WH6nPWOdUdEr69p9S8enqA49vh4Y9O+nsz01SMgAAAC5+hCOYy6+DNPBms6sAAAAA1IA/yQOtTHGB81lMAAAAQD3Qc4SL1x+inK/e/pJvgOTT3vnq2945rLjrNUDyDTz3vqKdT3tnG7d55W19A5zbbcglfwAAAGjRCEe4uLSzST1vcA74oPKBGEvPOied8Pz+fALqCFiVwlTlQNaptxR1lefrAQAAwAUjHOHiYrFId61wDh1eclYqOeMc7KHkjFR8xnmpXY3zzkglp8vnnT73vqJ9xTolZ87tr+LzmZpLqtGvv5LCBnjs0AEAANA4hCNcnCyW8t6cAKl9J89u2+Fw9kRVG6xOu4es6uYdXCcV5Uv7P3U+18kokxxl5a+OKp/LnA+1dftczXwvb+mSblLHXpKtK893AgAAuAA8BBZobm9cI2XtarrtVw5KHXtJHXuee98h3BkcAQAALhI8BLYFSfv+pE4VliqmZ0f5eVvNLgetwdDpUuqfne8tVsnLWv7qVeVzbfO93D+XFkonD0snDjp7tU4cdE5V+bSXOvaoFJwqBSj/S5rzqwAAANDi0HPUSDP/ul1J+7IV6Oet6/p21tjLwnRd384KasdlTTCBwyGdOnYuHFWefv7eeQleTQI6ngtLIT2ky6c5A5OXd3kQo8cJAAC0PJ7MBoSjRnp+9T6t2nlUOaeKXPN8rBbF9OyksdF23Rhtlz2onYkVAuVKi6Xc76uEpkPO11PH6l7fy1vy8jkXlqw+leZZne+tFe8rlnlLVu8GrOvtPtW0rtVP6hUrBXZu+q8bAABo0QhHLSgcSZLDYWjnD7n6fF+2Pt+bpUPHT7stvzzSphuj7Yq7zK6enQNl4S/waGmKCqST5UFp6/9IR7aYXVHdesdJ0z40uwoAAGAywlELC0dVHTpeoKTyoPSvzFy3ZT06tdeNl9k1NjpMQyJt8vIiKKEFKiuVyoolR4lzRDxHqVRW4nytPJVVWu6otLystIa21c0vrbJ+WS37KpXS1zjbSlLQpZXuy6p6b1b556r3Z7k+t8L2rveW8vaV2lRM5y2vrk35PO92zp44/mADAGjFCEctPBxVlpNfqHX7c/T5vixtPnhCxWUO17JOgX66MdqusZfZdRUDOgD1c+KQ9PoIZ1BC41m8nCHJ26+G13Y1L/fxr9Kuchv/mrfpU97e6ucMalU5HNIP25zD4Ft9JKuv89JKa/klllbfGt77lF+CWc02AQAXLcJRKwpHlZ0qLFHKt8f1+d5sbfwmR6eKzv3nrr2vVdf1C9XYaLuu6xuqYH8GdABqVHDceZ9U5WdDVfs8KIdzfnXPiGrS9kY188rnV/vMqvq2r9ifcW6/rnmOSttyVL/ccNT9tW1uVt/zw9iJA43bpsV6LihZK4Umq7d70Ko1dPm4b6O6bVW+Z87V4+d9bp7Fq9I9dJ6cV/HZShAEABGOWm04qqy41KEt353Q5/uylLQvW9n55wZ08PX20tLpw3VVLw8/vBQAKgKdo0QqLSqfCiu9Flb5XGl+SU3LipxDyFc3v6Tq/LMNC2md+zkv8Swrv/TS7X3JuUss2yyLe2CqLqSdN6/y54bM86oUymqaZ60S3mqYl/6Z83lvPgHVXGpa8Wqp5TEGNVyGet6yai5jre0RCTXWUtOy6i7prWvZBRwDl74CtSIcXQThqDKHw9CuH/OUtC9Lr2885Jof2z9UPlYveVu95GO1yNfqJW+rRT5WL/lavcqXnfvstszbIm8vZ5sO7bw1snuIvK38hRFAC1BWWkMQq/Tey1vqGuPsqamNYZy7J62suNL9aeXhyfW+Sqiqtk1d26jyvuJ+O6PivruyKvMqz688r2J+NfNqWg9t23lhqaEBz4NBraFh02KRZKnlffln1/sa5te5jfpsu4b91Lrtqu9Vj21Xfa8G7qfKevXZT+VttMEwfdE8BPb111/XSy+9pKysLA0ePFh/+tOfNGLECDNLMoWXl0WXR9p0eaRNft5WvZz0rSRp3f4cj+3jwRt66dGxfT22PQC4YFZvyRoo+QU2flsWy7nL3RTQ+O21RBWXStYZqqoLaDXNc1zgtqquV8e82rZVmCdd/Yjk26EBl7Je4GWxrvWqti+rZlnVbTuqae/hWur8HqjU4wvUS31DZUXAUv2CV9WQGNBRmrXRpGNsGqaFow8++EBz5szRG2+8oZEjR+qVV15RXFyc0tPTFRoaalZZprv/2h7qF9ZBpwpLVVLmUInDUEmpw/m+zKGSMqPG98VlDpVWml9c6tDWjJOSpD9tOKiP//Wj6w8KFlnKXyWv8jcWSRaLpfy1fH6VecdPFbme6bT24Wvka/WSn4+1/NXZa+Xn7cVw5QDgKZUvL5Ov2dWgKTQ4qHkqxNVyz6OnwqYMyVD5a/k9k+e9L//s9l7nz69zG/XddtX2F7qf2rbtOHfs9d62J+8Lrdif6hfAL1TJmabbtklMu6xu5MiRGj58uP785z9LkhwOhyIjI/Xggw/qiSeeqHXdi+2yuqa0bl+2fvXX7c2+X1+rl3y9nZNf+WvlAOWcb3W18bJYZBiG8wqZyq+S23xHxe8Zt3mG63eVIUNfH/5ZkvTbMb3Pq6umzOaMf9XMr2Z2TbGvxm1XWfDFgePa82O+nhzfX15Vw2qlIFp5fsV2XMsqhVtL+V9vvCzVhNtKAbhiPa+KP/yUb+PcsvL1LVJJqUMBvt6ur6+j/B82hyE5HOXnofz8VCx3OE+A22ejvJ2jyvl0GIbz31FVtKtlnWq+J04VlmjcgHBFday9p6A+Ib2uFvXJ+TV9/zRkG/XRlH9zqHwMlfdTeZeVv57u86tv05JV/aev6r+EVf9hrM8/lfX5+lRtBwDncQ2s05AQVhGEaglejQl7Na3nZZUuvaLZv0RVtfp7joqLixUQEKCPPvpIkydPds2/5557lJubq5UrV7q1LyoqUlHRuQEL8vPzFRkZSTiqp8wTZ5R7trg8PJz7j6ZknJvnqPiPqzNgOP+T63xf0cYwDOWcKtJ/frRLktQp0FdFJQ4VlfdSAUBtGhq6KqsrrJy/vKHVme+8EOW2rOavkfvXteaN1BrY6hGOa62jvrXX0q6qukJk7evWumoda9e+fmP+oNKYP6TUvd8LD9217reOzXr6j0MNPYqGHneDv0pNXL/U9MfQ8HNQ/xVsAT764P6YBlbkea3+nqOffvpJZWVlstvtbvPtdru++eab89ovWLBAzz77bHOVd9Hp2jFAXT14Lf6twyLPm2cYzsv6iksdKip1vlZ+X1Ra5nwtc6ioxKHiMoeKSsrKXx0yJHlV7sWwlPeElPd0VPSCeJX3jHh5nbvsz8tica3nZZFW7TymolKHwoPbudd43n+fKmqv/jirm13zf7jqv+1N3x7X0bxCXd+3s7PHTOd6YSoHUelcWK3cM1bRRtWEV4drmeEWhCt6XSq26dYrV7Gf8vffnzijdj5euiTAt9L5cP86V/RGnfu6Wyq1OdcD5fa5Yp3zzl3F+ufOYcW2ZanSRtKXB39yXdqJ1qXyz4NR04I2rNbeq3p/jfhaAmg+nQIvvkt9TR2Qob4SExM1Z84c1+eKniO0HBaLRX7eVvl5W9XB5FriB4SbXAGamsNhqMRRe2+lJ/6/XZ9t1BS6G7aN+tTSdP/prbxl127cgoxx/vLz1jNqmF/Ndmrchnvbqn+9rK1H4/yei/r3VtS3V6bqditrzNfo/HWq30B916nP/qu2a+h2a9t2Tee06tK6vqXr+o6ve/3G/Ww29fbr0uTHV+f+6z6Axp6jhm3twrbbkBIatN0GNG6yGhqy5Sb6mnl7XXyXCZsSjjp16iSr1ars7Gy3+dnZ2QoLCzuvvZ+fn/z8/JqrPAAtnJeXRX5eVrPLAAAAFxlTHnzj6+uroUOHav369a55DodD69evV0yM+dctAgAAAGh7TLusbs6cObrnnns0bNgwjRgxQq+88opOnz6te++916ySAAAAALRhpoWj2267TcePH9e8efOUlZWlyy+/XGvXrj1vkAYAAAAAaA6mPeeoMXjOEQAAAADJs9nAlHuOAAAAAKClIRwBAAAAgAhHAAAAACCplTwEtqqK26Ty8/NNrgQAAACAmSoygSeGUmiV4ejUqVOSpMjISJMrAQAAANASnDp1SsHBwY3aRqscrc7hcOjo0aPq0KGDLBZLg9bNz89XZGSkjhw5wkh3bQjnvW3ivLdNnPe2h3PeNnHe26bqzrthGDp16pQiIiLk5dW4u4ZaZc+Rl5eXunTp0qhtBAUF8YPUBnHe2ybOe9vEeW97OOdtE+e9bap63hvbY1SBARkAAAAAQIQjAAAAAJDUBsORn5+fnnnmGfn5+ZldCpoR571t4ry3TZz3todz3jZx3tumpj7vrXJABgAAAADwtDbXcwQAAAAA1SEcAQAAAIAIRwAAAAAgiXAEAAAAAJLaYDh6/fXX1a1bN7Vr104jR47Utm3bzC4JHjJ//nxZLBa3qV+/fq7lhYWFSkhIUMeOHRUYGKipU6cqOzvbxIpxITZt2qSJEycqIiJCFotFn3zyidtywzA0b948hYeHy9/fX7GxsTpw4IBbm5MnT2ratGkKCgqSzWbTjBkzVFBQ0IxHgYaq67xPnz79vJ//+Ph4tzac99ZlwYIFGj58uDp06KDQ0FBNnjxZ6enpbm3q83s9MzNTEyZMUEBAgEJDQ/XYY4+ptLS0OQ8FDVCf837ddded9/P+61//2q0N5711Wbx4sQYNGuR6sGtMTIzWrFnjWt6cP+ttKhx98MEHmjNnjp555hn961//0uDBgxUXF6ecnByzS4OHXHbZZTp27Jhr+vLLL13LHnnkEa1atUrLly9XSkqKjh49qilTpphYLS7E6dOnNXjwYL3++uvVLl+4cKFee+01vfHGG9q6davat2+vuLg4FRYWutpMmzZNe/fuVVJSklavXq1NmzZp1qxZzXUIuAB1nXdJio+Pd/v5f++999yWc95bl5SUFCUkJGjLli1KSkpSSUmJxo4dq9OnT7va1PV7vaysTBMmTFBxcbE2b96sZcuWaenSpZo3b54Zh4R6qM95l6SZM2e6/bwvXLjQtYzz3vp06dJFL774otLS0rR9+3bdcMMNmjRpkvbu3SupmX/WjTZkxIgRRkJCgutzWVmZERERYSxYsMDEquApzzzzjDF48OBql+Xm5ho+Pj7G8uXLXfP2799vSDJSU1ObqUJ4miRjxYoVrs8Oh8MICwszXnrpJde83Nxcw8/Pz3jvvfcMwzCMffv2GZKMr7/+2tVmzZo1hsViMX788cdmqx0Xrup5NwzDuOeee4xJkybVuA7nvfXLyckxJBkpKSmGYdTv9/pnn31meHl5GVlZWa42ixcvNoKCgoyioqLmPQBckKrn3TAM49prrzV++9vf1rgO5/3icMkllxhvvvlms/+st5meo+LiYqWlpSk2NtY1z8vLS7GxsUpNTTWxMnjSgQMHFBERoR49emjatGnKzMyUJKWlpamkpMTt/Pfr109du3bl/F9EMjIylJWV5Xaeg4ODNXLkSNd5Tk1Nlc1m07Bhw1xtYmNj5eXlpa1btzZ7zfCc5ORkhYaGqm/fvnrggQd04sQJ1zLOe+uXl5cnSQoJCZFUv9/rqampGjhwoOx2u6tNXFyc8vPzXX+RRstW9bxXeOedd9SpUycNGDBAiYmJOnPmjGsZ5711Kysr0/vvv6/Tp08rJiam2X/WvT1zGC3fTz/9pLKyMrcvmiTZ7XZ98803JlUFTxo5cqSWLl2qvn376tixY3r22Wd1zTXXaM+ePcrKypKvr69sNpvbOna7XVlZWeYUDI+rOJfV/ZxXLMvKylJoaKjbcm9vb4WEhPC90IrFx8drypQp6t69uw4dOqQnn3xS48aNU2pqqqxWK+e9lXM4HHr44Yc1atQoDRgwQJLq9Xs9Kyur2t8HFcvQslV33iXpzjvvVFRUlCIiIrRr1y49/vjjSk9P18cffyyJ895a7d69WzExMSosLFRgYKBWrFih6Oho7dixo1l/1ttMOMLFb9y4ca73gwYN0siRIxUVFaUPP/xQ/v7+JlYGoKndfvvtrvcDBw7UoEGD1LNnTyUnJ2vMmDEmVgZPSEhI0J49e9zuI8XFr6bzXvlewYEDByo8PFxjxozRoUOH1LNnz+YuEx7St29f7dixQ3l5efroo490zz33KCUlpdnraDOX1XXq1ElWq/W8kS2ys7MVFhZmUlVoSjabTX369NHBgwcVFham4uJi5ebmurXh/F9cKs5lbT/nYWFh5w3CUlpaqpMnT/K9cBHp0aOHOnXqpIMHD0rivLdms2fP1urVq7Vx40Z16dLFNb8+v9fDwsKq/X1QsQwtV03nvTojR46UJLefd8576+Pr66tevXpp6NChWrBggQYPHqxXX3212X/W20w48vX11dChQ7V+/XrXPIfDofXr1ysmJsbEytBUCgoKdOjQIYWHh2vo0KHy8fFxO//p6enKzMzk/F9EunfvrrCwMLfznJ+fr61bt7rOc0xMjHJzc5WWluZqs2HDBjkcDtc/sGj9fvjhB504cULh4eGSOO+tkWEYmj17tlasWKENGzaoe/fubsvr83s9JiZGu3fvdgvGSUlJCgoKUnR0dPMcCBqkrvNenR07dkiS28875731czgcKioqav6fdU+MJtFavP/++4afn5+xdOlSY9++fcasWbMMm83mNrIFWq9HH33USE5ONjIyMoyvvvrKiI2NNTp16mTk5OQYhmEYv/71r42uXbsaGzZsMLZv327ExMQYMTExJleNhjp16pTx73//2/j3v/9tSDJefvll49///rfx/fffG4ZhGC+++KJhs9mMlStXGrt27TImTZpkdO/e3Th79qxrG/Hx8caQIUOMrVu3Gl9++aXRu3dv44477jDrkFAPtZ33U6dOGXPnzjVSU1ONjIwMY926dcYVV1xh9O7d2ygsLHRtg/PeujzwwANGcHCwkZycbBw7dsw1nTlzxtWmrt/rpaWlxoABA4yxY8caO3bsMNauXWt07tzZSExMNOOQUA91nfeDBw8azz33nLF9+3YjIyPDWLlypdGjRw9j9OjRrm1w3lufJ554wkhJSTEyMjKMXbt2GU888YRhsViMzz//3DCM5v1Zb1PhyDAM409/+pPRtWtXw9fX1xgxYoSxZcsWs0uCh9x2221GeHi44evra1x66aXGbbfdZhw8eNC1/OzZs8ZvfvMb45JLLjECAgKMm266yTh27JiJFeNCbNy40ZB03nTPPfcYhuEczvvpp5827Ha74efnZ4wZM8ZIT09328aJEyeMO+64wwgMDDSCgoKMe++91zh16pQJR4P6qu28nzlzxhg7dqzRuXNnw8fHx4iKijJmzpx53h++OO+tS3XnW5KxZMkSV5v6/F4/fPiwMW7cOMPf39/o1KmT8eijjxolJSXNfDSor7rOe2ZmpjF69GgjJCTE8PPzM3r16mU89thjRl5entt2OO+ty3333WdERUUZvr6+RufOnY0xY8a4gpFhNO/PusUwDKNhfU0AAAAAcPFpM/ccAQAAAEBtCEcAAAAAIMIRAAAAAEgiHAEAAACAJMIRAAAAAEgiHAEAAACAJMIRAAAAAEgiHAEATJCcnCyLxaLc3FyzS3Hp1q2bXnnlFbPLAACYiHAEAPAoi8VS6zR//nyzSwQAoFreZhcAALi4HDt2zPX+gw8+0Lx585Senu6aFxgYqO3btzd4u8XFxfL19fVIjQAAVIeeIwCAR4WFhbmm4OBgWSwWt3mBgYGutmlpaRo2bJgCAgJ01VVXuYWo+fPn6/LLL9ebb76p7t27q127dpKk3Nxc/epXv1Lnzp0VFBSkG264QTt37nStd+jQIU2aNEl2u12BgYEaPny41q1b51ZjTk6OJk6cKH9/f3Xv3l3vvPOO23LDMDR//nx17dpVfn5+ioiI0EMPPdQUXy4AQAtCOAIAmOa//uu/9N///d/avn27vL29dd9997ktP3jwoP7+97/r448/1o4dOyRJt9xyi3JycrRmzRqlpaXpiiuu0JgxY3Ty5ElJUkFBgcaPH6/169fr3//+t+Lj4zVx4kRlZma6tjt9+nQdOXJEGzdu1EcffaS//OUvysnJcS3/+9//rkWLFun//b//pwMHDuiTTz7RwIEDm/4LAgAwFZfVAQBM87vf/U7XXnutJOmJJ57QhAkTVFhY6OolKi4u1l//+ld17txZkvTll19q27ZtysnJkZ+fnyTpj3/8oz755BN99NFHmjVrlgYPHqzBgwe79vH8889rxYoV+vTTTzV79mx9++23WrNmjbZt26bhw4dLkt566y3179/ftU5mZqbCwsIUGxsrHx8fde3aVSNGjGiWrwkAwDz0HAEATDNo0CDX+/DwcEly68GJiopyBSNJ2rlzpwoKCtSxY0cFBga6poyMDB06dEiSs+do7ty56t+/v2w2mwIDA7V//35Xz9H+/fvl7e2toUOHurbbr18/2Ww21+dbbrlFZ8+eVY8ePTRz5kytWLFCpaWlTfI1AAC0HPQcAQBM4+Pj43pvsVgkSQ6HwzWvffv2bu0LCgoUHh6u5OTk87ZVEW7mzp2rpKQk/fGPf1SvXr3k7++vm2++WcXFxfWuKzIyUunp6Vq3bp2SkpL0m9/8Ri+99JJSUlLcagYAXFwIRwCAVuOKK65QVlaWvL291a1bt2rbfPXVV5o+fbpuuukmSc5AdfjwYdfyfv36qbS0VGlpaa7L6tLT08975pK/v78mTpyoiRMnKiEhQf369dPu3bt1xRVXNMWhAQBaAMIRAKDViI2NVUxMjCZPnqyFCxeqT58+Onr0qP7xj3/opptu0rBhw9S7d299/PHHmjhxoiwWi55++mm33qi+ffsqPj5e999/vxYvXixvb289/PDD8vf3d7VZunSpysrKNHLkSAUEBOjtt9+Wv7+/oqKizDhsAEAz4Z4jAECrYbFY9Nlnn2n06NG699571adPH91+++36/vvvZbfbJUkvv/yyLrnkEl111VWaOHGi4uLizuvtWbJkiSIiInTttddqypQpmjVrlkJDQ13LbTab/vd//1ejRo3SoEGDtG7dOq1atUodO3Zs1uMFADQvi2EYhtlFAAAAAIDZ6DkCAAAAABGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAECS9P8BM/bjH3mjmEYAAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot();"
+    "df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot(figsize=(10,3));"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "* **That's why I think Pandas is great!**\n",
     "* It has great defaults to quickly plot data; basically publication-grade already\n",
@@ -5744,56 +5739,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
+   "execution_count": 84,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df_demo[[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True);"
+    "df_demo[[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True, figsize=(10,3));"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 85,
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df_demo[df_demo[\"F\"] < 0][[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True);"
+    "df_demo[df_demo[\"F\"] < 0][[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True, figsize=(10,3));"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 86,
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -5802,41 +5802,39 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 864x288 with 3 Axes>"
+       "<Figure size 1000x400 with 3 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "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 Demo\", figsize=(12, 4));"
+    "    .plot(kind=\"barh\", subplots=True, sharex=True, title=\"Subplots Demo\", figsize=(10, 4));"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 87,
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 864x432 with 2 Axes>"
+       "<Figure size 1200x600 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5852,23 +5850,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 88,
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 864x432 with 2 Axes>"
+       "<Figure size 1200x600 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5879,7 +5877,7 @@
     "        secondary_y=\"A\", \n",
     "        figsize=(12, 6),\n",
     "        yerr={\n",
-    "            \"A\": df_demo[df_demo[\"F\"] < 0][\"C\"], \n",
+    "            \"A\": abs(df_demo[df_demo[\"F\"] < 0][\"C\"]), \n",
     "            \"F\": 0.2\n",
     "        }, \n",
     "        capsize=4,\n",
@@ -5921,19 +5919,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x288 with 1 Axes>"
+       "<Figure size 1000x400 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5957,19 +5953,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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sDWV+6KGHeOmll5gzZw5PPPHEFQfqjYuLY/v27ZddsAKgXbt2VKpUSXt0RKRAqeSIiFN45JFH+PTTT/n444+5ePEioaGh3H///YwYMQKr9do7rSdPnsxTTz3Fs88+S0ZGBsOHD79qyfnggw/o378/r776KpcuXaJnz55XLDnw55e/cuXK8dZbb/Huu++Snp5O+fLladmyZZ6vJPaXevXq0aFDB3788UdOnDiBl5cX9erVY8GCBTlXx7qR1w0bNoyzZ8/yww8/8N1339GpUycWLFhAmTJlrjjfb7/9lmHDhjFkyBBcXFwYNGgQ7777bq5phgwZQrVq1Rg7dmzOOTHh4eHcdtttdO7cOV/v/+/c3Nx4+OGH+f777xk9enTO4wMGDODLL7/kgw8+IDk5mbCwMJ5++mleffXVnGkaN27MG2+8wSeffMLChQux2+3ExsbeUMlJTk7G09Mz17g/18vV1ZXp06czdOhQHn/8cbKyspg6dSoRERG0adOGNWvW8MYbbzB+/HiSk5MJDQ2lSZMmDBgwIE/z/+qrr3jqqaeYMGEChmFw2223sWDBAsqVK3fdmUNCQrjtttv45Zdf6NGjxxWnmT17Nu7u7nTv3v26lyMikh8W41pn8oqIiPzNiBEjGDlyJGfPnjX1BPJDhw5Ro0YNFixYQLt27UzLAX9+yf/rBPySqmvXrsTExHDgwIErPt+gQQPatGnD2LFjCzmZiJRUOidHRESKncqVK9O3b1/eeustU3Ps3LmTS5cu8Z///MfUHGY6deoUP//881X34ixcuJD9+/czdOjQQk4mIiWZDlcTEZFiaeLEiWZHoHbt2iQmJpodwxSxsbGsWrWKyZMn4+rqetVD5jp27HjFiyWIiDiS9uSIiIhIvi1fvpwePXoQGxvL9OnTrzgelIiIWXROjoiIXLdevXqxbNkyDh8+nPOYxWJh+PDhjBgxolCzVKpUiTp16vDTTz8V6nJFRKTo0Z4cEREnN23aNCwWCxs3brzi823atLnq1eTM1KtXLywWy7/eevXqZXbUa1q2bBkWi0WXSBYRKUQ6J0dERIqkAQMG0L59+5z7sbGxDBs2jP79+9OyZcucxyMjI82IJyIiRZhKjoiIFElNmzaladOmOfc3btzIsGHDaNq0KY888ogpmbKysrDb7bi5uZmyfBERyRsdriYiIlc0c+ZMGjZsiKenJ4GBgTzwwAMcO3bsuua1ZcsWOnXqhJ+fHz4+PrRr1461a9cWcOI/rVy5kujoaDw8PKhcuTJffPHFZdNcvHiRZ555hvDwcNzd3alSpQpvv/02drs9Z5rDhw9jsVh47733+PDDD4mMjMTd3Z1du3YBsGfPHu69914CAwPx8PCgUaNGzJ8/3yHvSURE8kd7ckRESoiEhATOnTt32eOZmZmXPTZq1Chee+01unfvTr9+/Th79iwfffQRrVq1YsuWLQQEBOR5uTt37qRly5b4+fnx0ksv4erqyqRJk2jTpg3Lly+nSZMmN/K2cjlw4AD33nsvffv2pWfPnnz++ef06tWLhg0bUrt2bQBSU1Np3bo1J06cYMCAAVSoUIHVq1czdOhQTp06xYcffphrnlOnTiUtLY3+/fvj7u5OYGAgO3fupHnz5pQvX54hQ4bg7e3Nd999R5cuXZg1axZdu3YtsPckIiLXwRAREac2depUA7jmrXbt2jnTHz582LDZbMaoUaNyzScmJsZwcXHJ9XjPnj2NihUr5poOMIYPH55zv0uXLoabm5tx8ODBnMdOnjxp+Pr6Gq1atcrz+9iwYYMBGFOnTr3i8xUrVjQAY8WKFTmPxcXFGe7u7sbzzz+f89gbb7xheHt7G/v27cv1+iFDhhg2m804evSoYRiGERsbawCGn5+fERcXl2vadu3aGVFRUUZaWlrOY3a73WjWrJlRtWrVXNMuXbrUAIylS5fm+b2KiMiN0eFqIiIlxIQJE1i8ePFlt7p16+aabvbs2djtdrp37865c+dybqGhoVStWpWlS5fmeZnZ2dn8+uuvdOnShcqVK+c8XrZsWR566CFWrlxZoINp1qpVK9dFCYKDg6levTqHDh3Keez777+nZcuWlCpVKtf7a9++PdnZ2axYsSLXPO+55x6Cg4Nz7p8/f57ff/+d7t27k5SUlPP6+Ph4OnTowP79+zlx4kSBvScREck/Ha4mIlJCREdH06hRo8se/+vL/l/279+PYRhUrVr1ivNxdXXN8zLPnj1Lamoq1atXv+y5mjVrYrfbOXbsWM6hZDeqQoUKlz1WqlQpLly4kHN///79bN++PVdx+bu4uLhc9yMiInLdP3DgAIZh8Nprr/Haa69ddR7ly5fPb3wRESkgKjkiIpKL3W7HYrGwYMECbDbbZc/7+PiYkCpvrpQXwPjbuNd2u51bb72Vl1566YrTVqtWLdd9T0/PXPf/ujjBCy+8QIcOHa44jypVquQ5s4iIFDyVHBERySUyMhLDMIiIiLjsC39+BQcH4+Xlxd69ey97bs+ePVitVsLDw29oGfkVGRlJcnJyrjF48uOvw+5cXV3zNI82bdrkKlkiIuJ4OidHRERy6datGzabjZEjR1725dwwDOLj4/M8L5vNxm233ca8efM4fPhwzuNnzpzhq6++okWLFvj5+RVU9Dzp3r07a9asYdGiRZc9d/HiRbKysq75+jJlytCmTRsmTZrEqVOnLnv+7Nmzue4nJCSwZ88eUlNTbyy4iIjkmfbkiIhILpGRkbz55psMHTqUw4cP06VLF3x9fYmNjWXOnDn079+fF154Ic/ze/PNN1m8eDEtWrTgySefxMXFhUmTJpGens4777zjwHdyZS+++CLz58/nzjvvzLm8dEpKCjExMfzwww8cPnyYoKCga85jwoQJtGjRgqioKB577DEqV67MmTNnWLNmDcePH2fbtm05086ZM4fevXuzdOlS2rRp4+B3JyIioJIjIiJXMGTIEKpVq8bYsWMZOXIkAOHh4dx222107tw5X/OqXbs2f/zxB0OHDmXMmDHY7XaaNGnCzJkzC3SMnLzy8vJi+fLljB49mu+//54vvvgCPz8/qlWrxsiRI/H39//XedSqVYuNGzcycuRIpk2bRnx8PGXKlKFBgwYMGzasEN6FiIhci8XQgcIiIiIiIuJEdE6OiIiIiIg4FZUcERERERFxKio5IiIiIiLiVFRyRERERETEqajkiIiIiIiIUynSl5C22+2cPHkSX19fLBaL2XFERERERMQkhmGQlJREuXLlsFqvva+mSJeckydPEh4ebnYMEREREREpIo4dO0ZYWNg1pynSJcfX1xf48434+fmZnEZERERERMySmJhIeHh4Tke4liJdcv46RM3Pz08lR0RERERE8nQaiy48ICIiIiIiTkUlR0REREREnIpKjoiIiIiIOBWVHBERERERcSoqOSIiIiIi4lRUckRERERExKmo5IiIiIiIiFNRyREREREREaeikiMiIiIiIk5FJUdERERERJyKSo5ICXAkPoVnv93KqgPnzI4iIiIi4nAqOSJOLttuMPibrczZcoLe0zaw9lC82ZFEREREHEolR8TJfbnuCFuPXQQgI8vOY9M3suNEgrmhRERERBxIJUfEiZ1OSOOdhXsBeOX2mkRHBJKUnkXPz9dz6GyyyelEREREHEMlR8SJjZi/k+T0LOqHB9CnRQSTezaidjk/4lMy6DFlPacSLpkdUURERKTAqeSIOKnFu86wcOdpXKwWxnSLwma14OfhyvQ+0UQEeXPi4iUenbKeCykZZkcVERERKVAqOSJOKDk9i2HzdgDQr2Vlapb1y3kuyMedL/pEE+rnwf64ZHpP20BKepZZUUVEREQKnEqOiBN6/9e9nEpIIzzQk8Htql72fHigFzP6RhPg5crWYxd5fOYm0rOyTUgqIiIiUvBUckSczPbjF5m++jAAo7pE4elmu+J0VUN8mdqrMV5uNv7Yf47nvt1Gtt0oxKQiIiIijqGSI+JEsrLtDJkVg92Au+uXo1W14GtO36BCKSb1aIirzcLPMad4de4ODENFR0RERIo3lRwRJ/L5qlh2nUrE39OV1+6slafXtKwazIf3N8Biga/XH+W9X/c6OKWIiIiIY6nkiDiJY+dTGbt4PwAv316DIB/3PL/2jrplGdUlCoAJSw8y+Y9DDskoIiIiUhhUckScgGEYvDZvB5cys4mOCKR7o/B8z+OhJhV4sUN1AN78eTc/bDpe0DFFRERECoVKjogT+Gn7KZbtPYubzcrorlFYLJbrms+TbSLp1yICgP/M2s6vO08XZEwRERGRQqGSI1LMJaRmMvLHXQA80SaSKmV8rnteFouFV+6oyb0Nw8i2Gwz6egtrDsYXVFQRERGRQqGSI1LMvbVwD+eS06kc7M2TbSNveH4Wi4W3ukXRvmYIGVl2HvtiIztOJBRAUhEREZHCoZIjUoxtOHyer9cfBWB01yjcXa48Jk5+udisjH+oAU0iAklOz6Ln5+s5dDa5QOYtIiIi4mgqOSLFVEaWnZdnxwBwf6Nwbq5cukDn7+FqY3LPRtQp70d8SgY9pqznVMKlAl2GiIiIiCOo5IgUU5OWH2R/XDJBPm4Mvb2GQ5bh6+HKtN7RVA7y5sTFS/SYsp4LKRkOWZaIiIhIQVHJESmGDp1N5qOlBwB47c5aBHi5OWxZQT7ufNE3mlA/Dw7EJdNr2gaS07MctjwRERGRG6WSI1LMGIbBK3N2kJFlp2XVIDrXK+fwZYaV8mJG32gCvFzZduwij8/YRHpWtsOXKyIiInI9VHJEiplZm0+w5lA8Hq5WRnW5/jFx8qtqiC/Tekfj5WZj5YFzPPvtVrLtRqEsW0RERCQ/VHJEipHzKRmM+vnPMXEGt6tGhdJehbr8+uEBfNqjEa42C7/EnObVuTEYhoqOiIiIFC0qOSLFyJs/7+JCaiY1Qn3p1zLClAwtqgYx7oEGWCzw9fpjvLtoryk5RERERK5GJUekmFh14ByzN5/AYoEx3aJwtZn3v+/tUWUZ3TUKgI+XHeSzFYdMyyIiIiLyTyo5IsVAWmY2r8z5c0ycHjdXpEGFUiYnggejK/BSx+oAjPplN99tPGZyIhEREZE/qeSIFAPjfz/A4fhUQvzcebFDdbPj5HiidSSP/e+wuSGztrNo52mTE4mIiIio5IgUefvOJPHJ8oMAjOxcG18PV5MT/T+LxcLLt9fkvoZh2A146ustrDkYb3YsERERKeFUckSKMLvdYOjsGLLsBu1rhtChdqjZkS5jsVgY0y2KW2uFkJFl57EvNhJzPMHsWCIiIlKCqeSIFGFfbzjKpiMX8Haz8frdtQttTJz8crFZ+ejBBtxcOZDk9Cx6Tl3PwbPJZscSERGREkolR6SIiktM460FewB4/rbqlAvwNDnRtXm42vjs0UbUKe/H+ZQMHp2ynpMXL5kdS0REREoglRyRImrkT7tISsuibpg/PZtVMjtOnvh6uDKtdzSVg7w5cfESPaas43xKhtmxREREpIRRyREpgpbuiePn7aewWS2M7hqFzVo0D1O7kiAfd2b0a0JZfw8Onk2h99T1JKdnmR1LREREShCVHJEiJjUji1fn7gCgT/NK1Cnvb3Ki/Csf4MmMvtGU8nJl2/EEBszYSHpWttmxREREpIRQyREpYsYu3seJi5coH+DJs7dWMzvOdatSxpepvaPxcrOx6kA8z3yzlWy7YXYsERERKQFUckSKkB0nEpiyMhaAN7vUwcvNxeREN6Z+eACf9miEm83Kgh2neWVODIahoiMiIiKOpZIjUkRkZdsZOjsGuwF31C1L2xplzI5UIFpUDWLcA/WxWuCbDcd4Z9FesyOJiIiIk1PJESkipq85QsyJBHw9XBh+Vy2z4xSoTlFlGd01CoCJyw7y6YqDJicSERERZ6aSI1IEnLh4ifd//XMPx5BONSjj62FyooL3QHQF/tOxBgCjf9nDdxuOmZxIREREnJVKjojJDMNg+LwdpGZk06hiKR5sXMHsSA7zeOvK9G9VGYAhs7ezaOdpkxOJiIiIM1LJETHZwh2n+W13HK42C6O7RWEtRmPi5JfFYmFopxp0bxSG3YCnvtrC6oPnzI4lIiIiTkYlR8REiWmZDJ+/E4ABrSKpFuJrciLHs1j+HOD0tlohZGTbeWz6RrYfv2h2LBEREXEiKjkiJnp34V7iktKJCPJm0C1VzI5TaFxsVv77YAOaVi5NSkY2vaZu4EBcstmxRERExEk4tOSsWLGCu+66i3LlymGxWJg7d64jFydSrGw6coGZ644AMKpLHTxcbSYnKlwerjY+fbQhUeX9OZ+SwaNT1nHy4iWzY4mIiIgTcGjJSUlJoV69ekyYMMGRixEpdjKz7bw8OwbDgHtuCqNZlSCzI5nC18OVab0bUznYm5MJafSYso7zKRlmxxIREZFizqElp1OnTrz55pt07drVkYsRKXY+++MQe88kUcrLlVfuqGl2HFOV9nFnRt8mlPX34ODZFHpNXU9yepbZsURERKQYK1Ln5KSnp5OYmJjrJuJsjsSnMO63/QC8ekctAr3dTE5kvvIBnszoG00pL1e2H0+g/xcbSc/KNjuWiMgNy8y2M21VLAtiTmEYhtlxREqMIlVyxowZg7+/f84tPDzc7EgiBcowDF6du4P0LDvNq5Sm203lzY5UZFQp48u03tF4u9lYfTCewV9vJduuLwQiUnxdSMng0SnrGfHjLp74cjMPT16ni6yIFJIiVXKGDh1KQkJCzu3YMY2ILs5l3taT/LH/HG4uVt7sEoXF4rxj4lyPeuEBfPpoI9xsVhbuPP2/85ZUdESk+Nl7OonOE1ay5lA8Xm423F2srD4YT6dxK3h30R4uZWhvtYgjFamS4+7ujp+fX66biLO4mJrBGz/tAuDpW6oQEeRtcqKiqXmVIP77YH2sFvh24zHeXrjX7EgiIvmyeNcZun28imPnLxEe6MnsJ5vx23OtuaVGGTKzDSYsPcitY5ezZPcZs6OKOK0iVXJEnNnoX3YTn5JBtRAf+reKNDtOkdaxTlnGdIsC4JPlB5m0/KDJiURE/p1hGExYeoD+MzaSkpHNzZUDmTewBTVC/QgP9GJKz0ZM6tGQcv4eHL9wib7TN/LYFxs5fiHV7OgiTsfFkTNPTk7mwIEDOfdjY2PZunUrgYGBVKhQwZGLFilS1h6K57uNxwEY3TUKNxf9vvBv7m9cgQupmby1YA9jFuwhwMuV+xvrc0NEiqZLGdm8+MM2ftp+CoAeN1dk2F21cLX9/+e9xWKhQ+1QWlYNYtyS/Uz5I5bFu86wcv85nm5Xlb4tIvTvg0gBsRgOPOB92bJltG3b9rLHe/bsybRp0/719YmJifj7+5OQkKBD16TYSs/KptO4Pzh0NoWHmlRgdNcosyMVK2MW7GbS8kNYLfDxww3pWCfU7EgiIrmcvHiJ/jM2suNEIi5WCyM61+aRmyv+6+v2nUni1bk7WB97HoCqZXx4o0sdbq5c2tGRRYql/HQDh5acG6WSI85g7OJ9jFuyn2Bfd357rjX+nq5mRypWDMNgyKwYvt14DDeblWm9G5fYwVNFpOjZdOQCA2Zs4lxyOqW8XJn4SMN8lRTDMJi9+UTOIc0A3RqUZ+jtNQn2dXdUbJFiKT/dQPtERRzoQFwyE5f9eT7J8LtqqeBcB4vFwqiudehQO4SMbDuPfbGR7ccvmh1LRITvNx7jwU/Xci45nRqhvswf1CLfe2EsFgv3NAzj9+fb8MjNFbBYYPaWE9zy/jJmrDmsS+mLXCeVHBEHsdsNXp4TQ0a2nbbVg7kjqqzZkYotF5uVcQ80oFlkaVIysuk1dYPGmhAR02Rl23nzp128+MN2MrLtdKgdwqwnmhEe6HXd8/T3cuXNLlHMfbI5UeX9SUrL4rV5O+n68Sr9sCNyHVRyRBzk+03HWB97Hk9XG6/fXUdj4twgD1cbnz7aiLph/pxPyaDHlHWcuHjJ7FgiUsIkXMqkz/SNTF4ZC8DT7aoy8eGGeLsXzLWc6oUHMHdgc16/uza+7i5sP57A3RNW8drcHSRcyiyQZYiUBCo5Ig5wLjmd0b/sAeC5W6vd0K978v983F2Y1juaysHenEpIo8eUdcQnp5sdS0RKiINnk+k6YRUr9p3Fw9XKhIdu4rlbq2G1FuyPWDarhUebVmLJC63pUr8chgEz1h6h3fvLmLPluAZJFskDlRwRB3jjp10kXMqkdjk/ejevZHYcpxLo7cbMvk0o5+/BobMp9J62geT0LLNjiYiTW7Y3ji4TVnHoXArl/D344fFm3FHXsYchl/H14MMHGvDVY02IDPbmXHIGz367jQc/W8uBuCSHLlukuFPJESlgy/edZd7Wk1gtMKZbFC42/W9W0MoFePJF3yYEerux/XgC/b/YSFpmttmxRMQJGYbBZysO0WfaBpLSsmhUsRTzBrWgTnn/QsvQLDKIBYNb8WKH6ni4Wll76Dydxv3B2wv3cClDn30iV6JvXyIF6FJGNq/OjQGgZ7NK1A0LMDeQE6tSxodpvRvj7WZj9cF4Bn+zhaxsu9mxRMSJpGVm88L32xn1y27sBtzfKJwvH2tiyqWd3VysDGxbhcXPtqZ9zTJkZhtMXHaQ9h8sZ/GuM4WeR6SoU8kRKUDjluzn2PlLlPX34Pnbqpsdx+nVDQvgs0cb4WazsmjnGV6eE6Nj1UWkQMQlpvHgZ2uZtfk4NquFEXfV4q17onB3sZmaKzzQi8k9G/PZo40oH+DJiYuXeOyLjfSbvoFj51NNzSZSlKjkiBSQXScT+eyPQwC8fncdfAroSjtybc2qBPHfBxtgtcB3G4/z1sI9ZkcSkWJu+/GLdB6/ii1HL+Lv6cr03tH0ah5RpK6SeWutEBY/14on2kTiYrXw2+44bh27nAlLD5CRpb3aIio5IgUg224wdE4M2XaDjrVDubVWiNmRSpSOdUJ5q1tdACYtP8Qnyw+anEhEiqt5W09w3ydrOJ2YRpUyPswd2JwWVYPMjnVFXm4u/KdjDRYMbsnNlQNJy7Tz7qK9dBq3gtUHz5kdT8RUKjkiBWDm2iNsO3YRH3cXRnSubXacEql743CGdqoBwFsL9vDN+qMmJxKR4sRuN3hn4R4Gf7OV9Cw7t9Qow+wnmxER5G12tH9VNcSXrx+7mbH31yPIx42DZ1N46LN1PPPNFuKS0syOJ2IKlRyRG3Q6IY13F+0F4KWO1Qn19zA5Uck1oHUkj7eOBODlOTEs3HHK5EQiUhwkpWXSf8ZGPl72517gx1tH8tmjjfDzcDU5Wd5ZLBa6NghjyfNt6HFzRSwWmLv1JO3eX84Xaw6Tbdf5ilKyqOSI3KDh83eQnJ5F/fAAHm5S0ew4Jd5/Olbn/kbh2A14+uutrDqgQzZE5OqOxKfQ7ePV/LY7DjcXKx/eX58hnWpgK+ABPguLv6crb3Spw7yBzYkq709SWhbD5u2ky4RVbDt20ex4IoVGJUfkBvy68zSLdp7BxWphTLeoYvuPojOxWCyM6lqHjrVDyci20/+LjfqHXUSuaNWBc3Qev4r9ccmU8XXn+wFN6dKgvNmxCkTdsADmDmzOG3fXxtfDhZgTCXT5eBWvzo0hITXT7HgiDqeSI3KdktOzGD5/JwD9WlamZlk/kxPJX1xsVsY9WJ/mVUqTkpFNr6nrNTq4iOQwDIPpqw/z6OfrSbiUSb0wf358qgX1wgPMjlagbFYLPZpW4vfn29CtQXkMA2auPcot7y9j1qbjuuS+ODWVHJHr9N6ivZxKSKNCoBeD21U1O478g7uLjUk9GlEvzJ8LqZn0mLKeExcvmR1LREyWkWXn5TkxDJ+/k2y7QdcG5fl2QFNC/Jz3fMpgX3c+uL8+Xz92M1XK+BCfksHz32/j/k/Xsu+MfgAS56SSI3Idth27yPQ1hwEY1bUOnm7mDg4nV+bj7sLU3tFEBntzKiGNHlPWEZ+cbnYsETHJueR0Hp68lq/XH8NigaGdavBB93p4uJaMz/CmkaX55emW/KdjDTxcrayPPc/t4/7grQV7SM3IMjueSIFSyRHJp6xsO0Nnx2AY0KV+OVpWDTY7klxDoLcbM/o2oZy/B4fOptBr6gaS0nQ8ukhJs/NkAnePX8WGwxfwdXfh856NGdA6skgN8FkY3FysPNEmkt+ea82ttULIsht8svwgt36wgkU7T+sQNnEaKjki+fT5qlh2nUokwMuVV++sZXYcyYNyAZ7M6NeEQG83Yk4k0P+LTaRlZpsdS0QKyYKYU9w7cQ0nLl6iUmkv5gxsRtsaZcyOZaqwUl589mgjJj/aiPIBnpy4eIkBMzbRb/pGjp1PNTueyA1TyRHJh2PnUxm7eD8AL3eqSZCPu8mJJK8ig32Y3jsabzcbaw7F8/TXW8jKtpsdS0QcyG43GLt4H098uZlLmdm0rBrEvIEtqFLG1+xoRUb7WiH89lxrBraNxNVmYcmeOG4du5wJSw+QnqUfg6T4UskRySPDMHht3g4uZWbTJCKQ+xqFmR1J8ikqzJ/PejbCzcXKr7vO/O+wQx2aIeKMUtKzePLLzYxb8ucPU32aRzC1V2P8vYrPAJ+FxdPNxosdarBgcCuaVi5NWqaddxftpdO4PzTWmBRbKjkiefTT9lMs23sWN5uV0d2iStxx3M6iWWQQHz3YAKsFvt90nLcW7DE7kogUsGPnU7ln4moW7jyNq83CO/fUZdhdtXCx6WvPtVQp48NXjzVh3AP1CfJx59DZFB6evI6nv95CXGKa2fFE8kX/t4vkQUJqJiN/3AXAk20jiQz2MTmR3IgOtUN56566AExacYiJyw6anEhECsr62PPcPWEVe04nEeTjxteP3Uz3xuFmxyo2LBYLd9cvz5LnW9OzaUWsFpi/7STt3l/OtFWxZNu191uKB5UckTx4a+EeziWnExnszRNtIs2OIwWge6NwXr69BgBvL9zD1+uPmpxIRG7U1+uP8vDktZxPyaB2OT/mDWpBo0qBZscqlvw9XRl5dx3mDWxBvTB/ktKzGPHjLjqPX8nWYxfNjifyr1RyRP7FhsPnc74Aj+4ahbtLyRhPoSTo3yoyp7S+MieGBTGnTE4kItcjM9vO8Hk7GDo7hsxsgzvqluWHx5tRPsDT7GjFXlSYP7OfbM6bXerg5+HCzpOJdP14FS/PiSEhVZfjl6JLJUfkGjKy7Lw8OwaA+xuF06RyaZMTSUF7qUN1HowOx27A4G+2snK/TrIVKU4upGTQ8/P1TF9zBIAXbqvG+AcbaJDmAmSzWnjk5or8/kIb7rkpDMOAr9Yd5Zb3l/HDpuO6gIsUSSo5ItcwaflB9sclE+TjxtD/HdokzsVisfBmlyg61QklI9tO/xkbdSiGSDGx70wSXT5exeqD8Xi52ZjUoyGDbqmqC8M4SJCPO+93r8e3/W+mahkf4lMyeOH7bdw/aS17TyeZHU8kF5Uckas4dDaZj5YeAOC1O2sR4OVmciJxFJvVwocP1Kd5ldKkZmTTe+p6DsTpH2yRouy3XWfo9vFqjsSnElbKk9lPNqND7VCzY5UITSqX5pfBLRnSqQaerjbWHz7PHf/9gzG/7CYlPcvseCKASo7IFRmGwStzdpCRZadl1SA61ytndiRxMHcXG5N6NKJemD8XUjN5ZPJ6jl/QqN8iRY1hGHy87ACPzdhIcnoWTSICmT+oBTVC/cyOVqK42qw83jqS355vTYfaIWTZDSatOMStHyxn4Y7TOoRNTKeSI3IFszafYM2heDxcrYzqojFxSgofdxem9o6mShkfTiem8eiU9ZxLTjc7loj8T1pmNoO/2co7C/diGPDIzRWY2a8Jgd7a026W8gGeTOrRiCk9GxFWypOTCWk8PnMTfaZt4Gi8figS86jkiPzD+ZQMRv3855g4g9tVo0JpL5MTSWEK9HZjRt9oygd4cuhcCr2mricpTVcQEjHbqYRL3PfJGuZvO4mL1cIbXerwZpcoXDXAZ5HQrmYIi59tzaC2VXC1WVi69yy3jl3OR0v2k56VbXY8KYH0ySDyD2/+vIsLqZnUCPWlX8sIs+OICcr6ezKjbzSlvd3YcSKRx77YSFqm/pEWMcvmoxfoPH4VMScSKOXlyoy+Tehxc0WzY8k/eLrZeKFDdRY+04pmkaVJz7Lz/uJ9dPrwD125UgqdSo7I36w6cI7Zm09gscCYbvqFsCSrHOzDtN7R+Li7sPbQeZ76egtZ2XazY4mUOD9sOs4Dk9ZyNimd6iG+zB/UgqaRupx/URYZ7MOX/Zow7oH6BPu6c+hcCo9MWcegrzZzJjHN7HhSQugbnMj/pGVm88qcP8fE6XFzRRpUKGVyIjFbVJg/nz3aCDcXK4t3nWHI7BidTCtSSLLtBqN+3sUL328jI9vObbVCmPVkM8IDdQhxcWCxWLi7fnmWPN+aXs0qYbXAT9tP0e795Xy+MlY/GonDqeSI/M9Hv+/ncHwqIX7uvNihutlxpIhoGlma8Q82wGr58xfl0b/sVtERcbCES5n0mbaBz/6IBeDpW6rwySMN8XF3MTmZ5JefhysjOtdm/qAW1AsPIDk9i9d/2kXn8avYfPSC2fHEiankiAB7TycxafkhAEZ2ro2vh6vJiaQoua12KG/fUxeAz/6IZeLygyYnEnFeB88m03XCKpbvO4uHq5XxDzXguduqY7XqKpfFWZ3y/sx5ohmju0bh7+nKrlOJ3DNxNUNnx3AxNcPseOKEVHKkxLPbDV6eE0OW3aB9zRANJidXdF+jcF69oyYA7yzcy9frj5qcSMT5LN93li4TVnHoXApl/T344fFm3FlX45Q5C6vVwkNNKrDk+dbc2zAMw4Cv1x/llveX893GY9jt2ksuBUclR0q8r9YfZdORC3i72Xj97toaE0euql/LyjzZJhKAV+bE8EvMKZMTiTgHwzCY/Mchek9dT1JaFg0rlmL+oBbUKe9vdjRxgCAfd967rx7fDWhKtRAfzqdk8NIP27n/0zXsOZ1odjxxEio5UqLFJabx9sI9ADx/W3XKBXianEiKuhc7VOfB6ArYDRj8zRb+2H/W7EgixVpaZjYvfL+dN3/ejd2A+xqG8dVjTQj2dTc7mjhYdEQgPz/dkpdvr4GXm40Nhy9wx39XMvqX3aSkZ5kdT4o5lRwp0Ub+uIuktCzqhvnTs1kls+NIMWCxWHizSx1ujwolM9tgwIxNbNHJsyLXJS4xjQc/W8uszcexWmDYnbV45966uLvYzI4mhcTVZqV/q0h+e641HWuHkm03+HTFIdq9v5wFMad0oRe5bio5UmL9vucMP8ecwma1MLprFDad1Cp5ZLNaGHt/fVpWDSI1I5ve0zaw/0yS2bFEipXtxy/Sefwqthy9iJ+HC9N6R9OnRYQOGS6hygV48kmPhkzt1ZgKgV6cTkzjiS8302vqBo7Ep5gdT4ohlRwpkVLSs3ht7k4A+jSvpOO+Jd/cXWx88khD6oUHcDE1kx5T1nP8QqrZsUSKhfnbTnLfJ2s4nZhGZLA38wa1oFW1YLNjSRHQtkYZfn22FU/fUgU3m5Xl+85y69gVjPttP2mZ2WbHk2JEJUdKpLGL93Hi4iXKB3jy7K3VzI4jxZS3uwvTejWmShkfTiem0WPKes4lp5sdS6TIstsN3l20h6e/3kJ6lp221YOZM7A5EUHeZkeTIsTD1cZzt1Vn4TMtaVEliIwsO2N/20encX/oPEjJM5UcKXF2nEjg81V/DjD3Ztc6eLlpcDm5fqW83ZjRN5ryAZ7Enkuh5+frSUrLNDuWSJGTlJZJ/xkbmbD0z3GmBrSuzOSejfHTuGRyFZWDfZjRN5qPHmxAGV93Ys+l0GPKegZ+tZkziWlmx5MiTiVHSpSsbDtDZ8dgN+DOumVpW72M2ZHECZT192RG32hKe7ux82Qi/aZv1GEVIn9zND6Veyau5rfdcbi5WBl7fz2GdqqpcyHlX1ksFu6qV44lz7emT/MIrBb4efsp2r2/nCkrY8nKtpsdUYoolRwpUaavOULMiQT8PFwYdlcts+OIE6kc7MP0PtH4uLuwLvY8g77aon98RYDVB8/RecJK9p1JpoyvO98NaErXBmFmx5JixtfDlWF31eLHp1rQoEIAyelZvPHTLu78aCWbjpw3O54UQSo5UmKcuHiJ93/dC8CQTjUp4+thciJxNnXK+zO5ZyPcXKz8tvsM/5kVoxG8pcQyDIMZaw7TY8p6LqZmUi/Mn/mDWlA/PMDsaFKM1S7nz6zHmzGmWxT+nq7sOZ3EPRPXMGTWdi6kZJgdT4oQlRwpEQzDYPi8HaRmZNOoYikeaBxudiRxUjdXLs2Eh27CZrUwa/NxRv+yW+M8SImTkWXnlbk7eG3eTrLtBl3ql+PbAU0J9dePS3LjrFYLD0ZX4PfnW9O90Z97Bb/ZcIxb3l/GdxuO6cclAVRypIRYuOM0v+2Ow9VmYUy3KKw6Dlwc6NZaIbx9T10AJq+M5eNlB01OJFJ44pPTeWTKOr5adxSLBYZ0qsHY++vj4aoBPqVglfZx55176/H9402pHuLLhdRMXpq1nfsmrWH3qUSz44nJVHLE6SWmZTJ8/p9j4jzeOpKqIb4mJ5KS4N6GYbx6R00A3l20l6/WHTU5kYjj7T6VSOfxq1gfex4fdxem9GzE460jNcCnOFTjSoH89HQLXrm9Jl5uNjYducCdH63kzZ92kZyeZXY8MYlKjji9dxfuJS4pnYggbwa2rWJ2HClB+rWszMC2kQC8MjeGn7efMjmRiOMs3HGKeyau5sTFS1Qq7cXcgc24pUaI2bGkhHC1WXmsVWWWPN+a26NCybYbTF4ZS/v3l/NLzCkdNlwCqeSIU9t05AIz1x0BYFSXOjpcQgrdC7dV56EmFTAMeObbLRrITpyO3W4w7rf9PD5zM6kZ2bSoEsTcgc2pUkZ7zaXwlfX35OOHGzKtd2MqlvbidGIaT365mZ5TN3D4XIrZ8aQQFUrJmTBhApUqVcLDw4MmTZqwfv36wlislHCZ2XZenh2DYcA9N4XRrEqQ2ZGkBLJYLLxxdx3uiCpLZrbBgBmb2HL0gtmxRApEakYWg77ezNjf9gHQu3klpvVuTICXm8nJpKRrU70Mi55pxeB2VXGzWVmx7yy3fbiCsYv3aRyzEsLhJefbb7/lueeeY/jw4WzevJl69erRoUMH4uLiHL1oKeE+++MQe88kUcrLlVf+d26EiBlsVgsf3F+PllWDSM3Ipve0Dew7k2R2LJEbcvxCKvdMXMMvMadxtVl4+54oht9VGxebDhKRosHD1cazt1Zj0bOtaFk1iIwsO+OW7KfDhytYvk971Z2dxXDwQYpNmjShcePGjB8/HgC73U54eDhPPfUUQ4YMueZrExMT8ff3JyEhAT8/P0fGFCdzJD6F28auID3Lzvv31eOehhp4TsyXkp7Fw5PXsfXYRUL83Pnh8WaEB3qZHUsk3zYcPs/jMzYRn5JBkI8bnzzSkEaVAs2OJXJVhmHwS8xpXv9pJ2cS0wG4PSqUYXfW1qXNi5H8dAOH/tySkZHBpk2baN++/f8v0Gqlffv2rFmz5rLp09PTSUxMzHUTyS/DMHh17g7Ss+w0r1KabjeVNzuSCADe7i5M7dWYqmV8OJOYzqOfr+dsUrrZsUTy5Zv1R3nos7XEp2RQq6wf8wa1UMGRIs9isXBH3bL89lxr+raIwGa18EvMadq9v4zJfxwiK9tudkQpYA4tOefOnSM7O5uQkNxXVwkJCeH06dOXTT9mzBj8/f1zbuHhGrBR8m/e1pP8sf8cbi5W3uwSpUuXSpFSytuNGX2bUD7Ak9hzKfSaup7EtEyzY4n8q6xsOyPm72TI7Bgysw3uiCrLD080pXyAp9nRRPLM18OV1+6sxY+DWnBThQBSMrJ58+fd3PnRSjYePm92PClARerA2aFDh5KQkJBzO3bsmNmRpJi5mJrBGz/tAuDpW6oQEeRtciKRy4X6ezCzXxNKe7ux82Qi/aZv1ImwUqRdTM2g59T1TFt9GIDnb63G+Ica4OXmYm4wketUq5wfPzzejLfviSLAy5U9p5O495M1vPTDNs6nZJgdTwqAQ0tOUFAQNpuNM2fO5Hr8zJkzhIaGXja9u7s7fn5+uW4i+TH6l93Ep2RQLcSH/q0izY4jclURQd5M7xONr7sL62PPM+irzTpcQoqk/WeSuHvCKlYdiMfLzcYnjzTkqXZVtZdcij2r1cL9jSvw+/NtuL/Rn0cPfbfxOLe8v4xv1h/FbtfYOsWZQ0uOm5sbDRs2ZMmSJTmP2e12lixZQtOmTR25aCmB1hyM57uNxwEY3TUKN5citaNS5DJ1yvszuWcj3F2s/LY7jv/MitE/qlKkLNl9hq4fr+ZIfCrlAzyZ9UQzOta5/EdKkeIs0NuNt++ty6wnmlIj1JeLqZkMmR3DvZ+sZtdJnR9eXDn8W+Bzzz3HZ599xvTp09m9ezdPPPEEKSkp9O7d29GLlhIkLTObV+bEAPBQkwo6CVaKjSaVSzP+oZuwWS3M2nycUb/s1sjcYjrDMJi47CD9vthIcnoW0RGBzB/UnJpldYSFOK+GFQP56akWvHpHTbzdbGw+epE7P/qD13/cRZLOnSx2HF5y7r//ft577z2GDRtG/fr12bp1KwsXLrzsYgQiN+LjZQc5dC6FYF93/tOxhtlxRPLl1lohvHNPXQCmrIzl42UHTU4kJVlaZjbPfLuVtxfuwTD+/OFoZt8mlPZxNzuaiMO52Kz0a1mZJc+34Y6ostgN+HxVLO0/WM5P20/qR6hixOHj5NwIjZMjeXEgLolO4/4gM9tg/EMNuLNuObMjiVyXKStjcy6c8WaXOjxyc0WTE0lJczohjf4zNrL9eAI2q4URnWvTQ9uhlGDL951l+LwdHI5PBaBl1SBev7uOLmxkkiIzTo6Io9ntBi/P3kFmtkHb6sHcEVXW7Egi161viwgGta0CwGvzdvDT9pMmJ5KSZMvRC9w1fiXbjycQ4OXKjL7RKjhS4rWuFszCZ1rxTPuquLlY+WP/OTqMXcEHi/fpqphFnEqOFGvfbTzG+sPn8XS18frddXS1Hyn2nr+tGg83qYBhwLPfbmXFvrNmR5ISYNam49z/6VrOJqVTPcSX+QNb0CwyyOxYIkWCh6uNZ9pX49dnWtGqWjAZ2Xb+u2Q/t41dwdK9cWbHk6tQyZFi62xSOqN/2Q3Ac7dWIzzQy+REIjfOYrHw+t11uKNuWTKzDQbM2MTmoxfMjiVOKttuMPqX3Tz//TYysuy0rxnCrCebUaG0Pk9F/qlSkDfTezdm4sM3EernwdHzqfSeuoEnZm7i5MVLZseTf1DJkWLrjZ92kZiWRe1yfvRuXsnsOCIFxma1MLZ7fVpWDeJSZja9p25g35kks2OJk0lMy6Tv9A18uuIQAIPaVuHTHg3xcdcAnyJXY7FY6BRVlt+eb81jLSOwWS0s2HGa9h8s59MVB8nUeGdFhkqOFEvL9sYxf9tJrBYY0y0KF5s2ZXEubi5WJvVoSIMKASRcyqTHlHUcO59qdixxEofOJtNlwiqW7T2Lh6uVjx5swAsdqmO16pBfkbzwcXfhlTtq8dNTLWhYsRSpGdmM/mUPd/53JRsOnzc7nqCSI8XQpYxsXpu3A4CezSpRNyzA3EAiDuLl5sLUXo2pFuLDmcR0ekxZx9mkdLNjSTG3Yt9ZukxYxaGzKZT19+D7Ac24q56uSilyPWqW9eP7AU155966lPJyZe+ZJO77ZA0vfr+N+GR9XptJJUeKnQ+X7OPY+UuU8/fg+duqmx1HxKECvNz4ok8Twkp5cjg+lZ6frydRg9LJdTAMgykrY+k1dT2JaVncVCGAeYOaExXmb3Y0kWLNarXQvVE4vz/fhgejwwH4ftNxbnl/OV+tO4rdXmRHa3FqKjlSrOw6mcjkP2IBeP3uOjp2XEqEUH8PZvRtQpCPG7tOJdJv2kZdulTyJT0rm5d+2M4bP+3CbsC9DcP4uv/NlPH1MDuaiNMo5e3GmG51mfVEM2qW9SPhUiYvz4mh28TV7DiRYHa8EkclR4qNbLvB0DkxZNsNOtUJpX2tELMjiRSaiCBvpvWOxtfdhfWHzzPoq806wVXyJC4pjQc/Xcv3m45jtcBrd9bi3Xvr4u5iMzuaiFNqWLEUPw5qzrA7a+Hj7sLWYxfpPH4lI+bvJEl74guNSo4UGzPXHmHbsYv4urswonNts+OIFLo65f2Z3LMR7i5Wftsdx39+2K7DIOSadpxI4O7xq9h89CJ+Hi5M6x1N3xYRGlNMxMFcbFb6tIhgyfOtubNuWewGTFt9mHbvL2f+tpMYhj67HU0lR4qF0wlpvLtoLwAvdaxOiJ8OsZCSqUnl0kx46CZsVguzt5zgzZ936x9LuaIft53k3k9WcyohjcrB3swd2JxW1YLNjiVSooT4eTD+oZuY0TeaiCBv4pLSefrrLfSYsp5DZ5PNjufUVHKkWBg+fwfJ6Vk0qBDAw00qmh1HxFTta4Xw7r11Afh8VSwTlh4wOZEUJXa7wXuL9vLU11tIy7TTpnowcwc2p3Kwj9nRREqsllWDWTC4Jc/dWg03FysrD5yj44d/8P6ve3WOpYOo5EiR9+vO0yzaeQYXq4Ux3aI0joMI0O2mMIbdWQuA937dx4y1R0xOJEVBcnoWA2ZuYvz/iu+AVpWZ0rMxfh6uJicTEQ9XG0+3q8riZ1vRpnowGdl2Pvr9ALeOXc7SPXFmx3M6KjlSpCWnZzF8/k4AHmtVmRqhfiYnEik6+rSI4KlbqgAwbN4Oftx20uREYqaj8al0+3gVi3edwc3Fygfd6zH09prY9MOQSJFSsbQ3U3s15pNHbqKsvwfHzl+i97QNDJixkRMXL5kdz2mo5EiR9t6ivZxKSKNCoBeD21U1O45IkfPcrdV45OYKGAY8991Wlu87a3YkMcHqg+foPGEl+84kU8bXnW/730y3m8LMjiUiV2GxWOhYpyy/PdeaAa0q42K1sGjnGdq/v5xJyw/q6pkFQCVHiqxtxy4yfc1hAEZ1rYOHqy53KvJPFouFkZ3rcGfdsmRmGzw+YxObjlwwO5YUohlrDtNjynoupmZSN8yf+YNa0KBCKbNjiUgeeLu7MPT2mvz8dEsaVyrFpcxsxizYwx3//YN1h+LNjlesqeRIkZSVbWfo7BgMA7rUL0fLqroikMjV2KwWPuhen1bVgrmUmU2faRvYezrJ7FjiYBlZdl6ZE8Nr83aSbTe4u345vhvQlFB/XX1SpLipHurLdwOa8u69dQn0dmPfmWTu/3Qtz3+3jXPJ6WbHK5ZUcqRI+nxVLLtOJRLg5cqr/zu5WkSuzs3FyieP3ESDCgEkXMqkx5R1HDufanYscZD45HR6TFnHl+uOYrHAfzrW4MP762uPt0gxZrFYuK9ROL8/35qHmlTAYoFZm4/T7v3lfLnuiMZFyyeVHClyjp1PZezi/QC83KkmQT7uJicSKR683FyY2qsx1UN8iUtK55Ep6zibpF8Anc3uU4ncPWEV62LP4+PuwuRHG/FEm0gN8CniJAK83BjdNYpZTzSjVlk/Ei5l8sqcHXSduJodJxLMjldsqORIkWIYBq/N28GlzGyaRARyXyOdOCuSHwFebnzRN5qwUp4ciU/l0c/Xk3Ap0+xYUkAW7jjNPRNXc/zCJSqW9mLOk81oVzPE7Fgi4gA3VSjF/EHNGX5XLXzcXdh27CKdx69kxPydJKbpc/3fqORIkfLT9lMs23sWN5uV0d2i9MukyHUI8fNgZt8mBPm4sftUIo9N38ilDA02V5wZhsF/l+zn8ZmbSM3IpnmV0swb2JyqIb5mRxMRB3KxWendPILfn29N53rlsBswbfVh2r2/nHlbT2AYOoTtalRypMhISM1k5I+7AHiybSSRGp1b5LpVCvJmep9ofN1dWH/4PIO+2qxLkhZTqRlZDPpqCx8s3gdAr2aVmNY7mgAvN5OTiUhhKePnwX8fbMCX/ZpQOcibs0npDP5mKw9PXseBuGSz4xVJKjlSZLy1cDfnktOJDPbmiTaRZscRKfZql/NnSq/GuLtYWbInjpd+2K4TV4uZExcvce/ENfwccwpXm4Ux3aIY0bk2rjb98y1SEjWvEsSCZ1rywm3VcHexsvpgPJ3GreDdRXu0x/4f9CkpRcL62PN8vf4YAKO7RuHuoisEiRSE6IhAPn74JmxWC3O2nOD1n3bp8IZiYsPh83T+aCW7TiVS2tuNL/vdzIPRFcyOJSImc3exMeiWqix+tjVtqweTmW0wYelBbh27nCW7z5gdr8hQyRHTpWdl8/KcGADubxROk8qlTU4k4lza1QzhvfvqAn8eyz3+9wMmJ5J/8+2Gozz02VriUzKoWdaPeYOaEx0RaHYsESlCKpT24vNejZnUoyHl/D04fuESfadvpP8XGzlx8ZLZ8UynkiOmm7T8EAfikgnycWPo7TXMjiPilLo2CGP4XX+OOfX+4n3MWHvE5ERyJVnZdkb+uJP/zIohM9ugU51QZj3RlLBSXmZHE5EiyGKx0KF2KIufa82A1pVxsVr4ddcZ2r+/nInLDpKRVXLPxVTJEVMdOpvM+KV//qr82p21dCKtiAP1bh7B07dUAWDYvB3M33bS5ETydxdTM+g1dQNTVx0G4Nn21Zjw0E14ubmYG0xEijxvdxeGdqrJL4NbEh0RyKXMbN5euIc7/vsHaw/Fmx3PFCo5YhrDMHhlzg4ysuy0rBpE53rlzI4k4vSevbUaPW6uiGHAc99uZdneOLMjCXAgLokuE1ax8sA5PF1tfPLITQxuXxWrVZfRF5G8qxbiy7f9b+b9++pR2tuN/XHJPPDpWp77dmuJGxxaJUdM88Om46w5FI+Hq5VRXTQmjkhhsFgsjOxcm7vqlSPLbvD4zE1sOnLe7Fgl2u97ztBlwmoOx6dSPsCTWU80o2OdsmbHEpFiymKxcE/DMH5/vg0PN6mAxQKzt5yg3fvLmLH2CNkl5CqbKjliivjkdEb9shuAwe2qUaG0jjcXKSxWq4X376tH62rBpGXa6T11A3tOJ5odq8QxDINPlh+k7/SNJKdnER0RyPxBzalVzs/saCLiBPy9XBnVNYo5TzanTnk/EtOyeG3uDrp9vIqY4wlmx3M4lRwxxaifd3MxNZMaob70axlhdhyREsfNxcrER27ipgoBJKZl8eiU9RyNTzU7VomRlpnNs99u5a0FezAMeDC6AjP7NqG0j7vZ0UTEydQPD2DewBaM7FwbX3cXth1PoPOElQybt4OES5lmx3MYlRwpdCv3n2P2lhNYLDCmW5QGtRMxiZebC5/3akz1EF/iktLp8fk64pLSzI7l9E4npHH/pDXM3XoSm9XCG3fXZnTXOri56LNQRBzDZrXQs1kllrzQmrvrl8Mw4Is1R2j3/nLmbjnhlOOn6RNVClVaZjavzP1zTJweN1ekQYVSJicSKdkCvNz4om804YGeHIlPpefnG5z6lz2zbT12kc7jV7LteAIBXq7M6BNNj6aVdE6iiBSKMr4ejHugAV/1a0LlYG/OJafzzLdbefCztRyISzI7XoFSyZFC9dHv+zkSn0qonwcvdqhudhwRAUL8PJjRpwlBPu7sPpVIv+kbuJSRbXYspzNny3G6T1pDXFI61UJ8mD+wBc2qBJkdS0RKoGZVglg4uBUvdqiOh6uVtYfO02ncH7y9cI/TfP6r5Eih2Xs6iUnLDwEwonNtfD1cTU4kIn+pFOTNF32i8fVwYcPhCwz8ajOZ2SV3ELmClG03GPPLbp79dhsZWXba1wxh9pPNdcEVETGVm4uVgW2rsPjZ1rSrUYbMbIOJyw7S/oPlLN51xux4N0wlRwqF3W7w8pwYsuwGt9YKoWOdULMjicg/1Crnx+e9GuPuYuX3PXG8+P027CXkUqOOkpiWSb/pG5i04s8feAa1rcKnPRri464BPkWkaAgP9GJKr8Z82qMh5QM8OXHxEo99sZF+0zdy7HzxvSCNSo4Uiq/WH2XTkQt4u9kY2bm22XFE5CoaVwpk4iM34WK1MHfrSV7/aZdTnpBaGGLPpdB1wiqW7j2Lu4uV/z7YgBc6VNcAnyJSJN1WO5TFz7XiiTaRuFgt/Lb7DLeOXc7Hyw6QkVX89uyr5IjDxSWm8fbCPQC80KE65QI8TU4kItdyS40Q3ruvHgDTVh/mo98PmJyo+Plj/1nuHr+Sg2dTCPXz4IfHm9G5XjmzY4mIXJOXmwv/6ViDBYNb0iQikLRMO+8s3EuncSs4EJdsdrx8UckRhxv54y6S0rKoF+bPo00rmR1HRPKgS4PyjLirFgAfLN7HjDWHzQ1UTBiGwecrY+n5+XoS07JoUCGA+U81JyrM3+xoIiJ5VjXEl2/638wH3esR5ONGSno2of4eZsfKFx0ULA71+54z/BxzCpvVwuhuUdh0mIZIsdGreQQXUjMZt2Q/w+bvxM/Tlbvrlzc7VpGVnpXNa3N38N3G4wDc2zCMUV3r4O5iMzmZiEj+WSwWut0URrsaIRw5n1LsziUsXmmlWElJz+K1uTsB6Nsigtrl9EumSHHzTPuqXEjN4Is1R3j+u234ebrStnoZs2MVOWeT0nl85iY2HbmA1QIv316Tvi0iNP6NiBR7/l6u1PUKMDtGvulwNXGYsYv3ceLiJcoHePJM+6pmxxGR62CxWBhxV2061ytHlt3giZmb2HTkvNmxipQdJxLoPH4lm45cwNfDham9o+nXsrIKjoiIiVRyxCF2nEjg81WxALzZtQ5ebtppKFJcWa0W3ruvHm2qB5OWaaf31A3sOZ1odqwi4aftJ7n3k9WcSkijcrA38wY2p3W1YLNjiYiUeCo5UuCysu0MnR2D3YA765bVoS0iTsDNxcrEhxvSsGIpEtOy6DFlPUfji+/4CTfKbjd4/9e9DPpqC2mZdlpXC2bOk82pHOxjdjQREUElRxxg+pojxJxIwM/DhWH/uzqTiBR/nm42Pu/ZmBqhvpxNSueRKeuIS0ozO1ahS07PYsDMTTmX1n6sZQSf92qMv6eryclEROQvKjlSoE5cvMT7v+4FYEinmpTxLV6XGxSRa/P3cuWLPtGEB3py9Hwqj05ZT8KlTLNjFZpj51O55+PVLN51Bjeblffuq8crd9TSlSNFRIoYlRwpMIZhMHzeDlIzsmlUsRQPNA43O5KIOEAZPw9m9m1CsK87e04n0XfaBi5lZJsdy+HWHIyn8/iV7D2TRLCvO98MuJl7G4aZHUtERK5AJUcKzMIdp/ltdxyuNgtjukVh1S+bIk6rYmlvvugTja+HCxuPXODJLzeRmW03O5bDzFx7hB5T1nEhNZOo8v7MH9ScmyqUMjuWiIhchUqOFIjEtEyGz/9zTJzHW0dSNcTX5EQi4mg1y/oxtVdjPFytLN17lhe+34bdbpgdq0BlZtt5dW4Mr87dQZbdoHO9cnz/eFPK+nuaHU1ERK5BJUcKxDsL9xCXlE5EkDcD21YxO46IFJJGlQKZ+HBDXKwW5m09ycgfd2IYzlF0zqdk0GPKOmauPYrFAi91rM64B+rj4WozO5qIiPwLlRy5YZuOXODLdUcBGNWljr4AiJQwbWuU4f3u9YA/r644bsl+kxPduD2nE+k8fiVrD53H283GZz0a8WSbKhrgU0SkmNAIjXJDMrPtvDw7BsOAe24Ko1mVILMjiYgJ7q5fnoupfx62+uFv+ynl5UbPZpXMjnVdFu08zbPfbiU1I5sKgV5M7tmIajoEV0SkWHHYnpxRo0bRrFkzvLy8CAgIcNRixGSfrjjE3jNJlPJy5ZU7apodR0RM1LNZJZ5pXxWA4fN3Mm/rCZMT5Y9hGHy0ZD8DZmwiNSObZpGlmTewuQqOiEgx5LCSk5GRwX333ccTTzzhqEWIyY7Ep/Df/x2W8uodtQj0djM5kYiYbXC7qvRsWhGA57/bxtI9cSYnyptLGdkM+noL7y/eB0CvZpWY3ieaUvpcExEplhx2uNrIkSMBmDZtmqMWISYyDINX5uwgPctO8yql6XZTebMjiUgRYLFYGH5XbS5eymTe1pM88eUmZvZtQqNKgWZHu6qTFy/x2Bcb2XkyEVebhdfvrsOD0RXMjiUiIjegSF14ID09ncTExFw3KZrmbj3BygPncHOx8maXKJ2MKyI5rFYL791XjzbVg0nLtNNn2gZ2nyqan+ebjpyn8/iV7DyZSKC3G1/2u1kFR0TECRSpkjNmzBj8/f1zbuHh4WZHkiu4kJLBGz/tBuDpW6oQEeRtciIRKWpcbVYmPtyQRhVLkZiWxaOfr+dIfIrZsXL5bsMxHvh0LeeSM6gR6sv8Qc2Jjii6e5xERCTv8lVyhgwZgsViueZtz5491x1m6NChJCQk5NyOHTt23fMSxxn9y27Op2RQLcSH/q0izY4jIkWUp5uNKb0aUyPUl7NJ6fSYsp64xDSzY5GVbef1H3fx0qztZGYbdKoTyqwnmhFWysvsaCIiUkDydU7O888/T69eva45TeXKla87jLu7O+7u7tf9enG8NQfj+X7TcQBGd43CzaVI7QwUkSLG39OVL/pEc+8nazh6PpVHP1/Pt/2b4u/lakqehNRMBn29mT/2nwPgmfZVefqWqlitOuRWRMSZ5KvkBAcHExwc7KgsUsSlZWbzypwYAB5qUqFIn0gsIkVHGT8PZvZtwj2frGbP6ST6TN/AzL5N8HQr3IGDD8Ql0W/6Rg7Hp+LpauOD7vXoFFW2UDOIiEjhcNjP8EePHmXr1q0cPXqU7Oxstm7dytatW0lOTnbUIsXBPl52kEPnUgj2dec/HWuYHUdEipEKpb34ok80fh4ubDpygSe+3ERmtr3Qlr90TxxdJ6zmcHwq5QM8mfVEMxUcEREn5rCSM2zYMBo0aMDw4cNJTk6mQYMGNGjQgI0bNzpqkeJAB+KSmLjsAAAj7qqNv6c5h5qISPFVs6wfn/dqjIerlWV7z/LC99uw2w2HLtMwDCYtP0if6RtISs8iulIg8wY1p1Y5P4cuV0REzOWwkjNt2jQMw7js1qZNG0ctUhzEbjd4efYOMrMNbqlRhtujQs2OJCLFVKNKgUx8pCEuVgvztp5k5I87MQzHFJ20zGye+24bYxbswTDgwehwZvZrQpCPzv0UEXF2Omtc/tV3G4+x/vB5PF1tvH53bY2JIyI3pG31MrzfvR4WC0xfc4QPf9tf4Ms4k5jG/Z+uZc6WE9isFl6/u7YuliIiUoLk68IDUvKcTUpn9C9/jonz/G3VdIlVESkQd9cvT8KlTIbN28m4Jfsp5eVKr+YRBTLvrccu0v+LjcQlpRPg5crHD91EsypBBTJvEREpHlRy5Jre+GkXiWlZ1CnvR69mlcyOIyJO5NGmlbiQksnY3/Yx4sddBHi50aVB+Rua55wtx/nPrBgysuxULePD5J6NqFhaAxaLiJQ0KjlyVcv2xjF/20msFhjTtS4uNh3mISIF6+l2VbiQmsG01Yd54ftt+Hu60rZGmXzPJ9tu8M6iPUxafgiA9jXLMPb++vh66CIpIiIlkb61yhVdysjmtXk7AOjVLIKoMH+TE4mIM7JYLAy7sxZd6pcjy27w+MxNbDh8Pl/zSEzLpN/0DTkF58k2kXzao5EKjohICaaSI1f04ZJ9HDt/iXL+Hjx/WzWz44iIE7NaLbx7Xz1uqVGG9Cw7faZtYNfJxDy99vC5FLp9vJqle8/i7mJl3AP1ealjDaxWXSBFRKQkU8mRy+w6mcjkP2IBeP3uOni766hGEXEsV5uVCQ/dRONKpUhKy+LRz9dzJD7lmq9Zuf8cd09YxYG4ZEL9PPj+8abcXf/GzukRERHnoJIjuWTbDYbOiSHbbtCpTijta4WYHUlESghPNxuTezamRqgv55LTeWTKOuIS0y6bzjAMpq6KpefU9SRcyqR+eADzBzWnblhA4YcWEZEiSSVHcpm59gjbjl3E192FEZ1rmx1HREoYf09XvugbTcXSXhw7f4keU9aTkJqZ83x6VjZDZsUw8sddZNsNut1Unm/630wZPw8TU4uISFGjkiM5Tiek8e6ivQC81LE6IfrSICImKOPrwcy+TSjj687eM0n0mb6B1IwsziWn8/Bn6/h24zGsFnj1jpq8f189PFxtZkcWEZEiRidbSI7h83eQnJ5FgwoBPNykotlxRKQECw/04ou+0XT/ZA2bjlygz7QNHI1P5WRCGr4eLnz0YAPaVM//paZFRKRk0J4cAWDRztMs2nkGF6uFMd2idGUiETFdjVA/pvZujIerlbWHznMyIY3KQd7MHdhcBUdERK5JJUdISstk+LydADzWqjI1Qv1MTiQi8qeGFQP55JGG+Hm40L5mGeY82ZzIYB+zY4mISBGnw9WE93/dx+nENCoEejG4XVWz44iI5NKmehm2DLsNm/Ywi4hIHmlPTgm39dhFpq85DMCornV0Aq+IFEkqOCIikh8qOSVYZradobNjMAzoUr8cLasGmx1JREREROSGqeSUYJ+vjGX3qUQCvFx59c5aZscRERERESkQKjkl1LHzqYz9bR8AL3eqSZCPu8mJREREREQKhkpOCWQYBq/O3UFapp0mEYHc1yjM7EgiIiIiIgVGJacE+nH7KZbvO4ubzcroblFYLDqhV0RERESch0pOCZOQmsnrP/45Js6TbSM13oSIiIiIOB2VnBLmrYW7OZecQWSwN0+0iTQ7joiIiIhIgVPJKUHWx57n6/XHABjdNQp3F42JIyIiIiLORyWnhEjPyublOTEA3N8onCaVS5ucSERERETEMVRySohJyw9xIC6ZIB83ht5ew+w4IiIiIiIOo5JTAhw6m8z4pQcAeO3OWgR4uZmcSERERETEcVRynJxhGLwyZwcZWXZaVQumc71yZkcSEREREXEolRwn98Om46w5FI+Hq5VRXepoTBwRERERcXoqOU4sPjmdUb/sBuCZ9tUID/QyOZGIiIiIiOOp5DixUT/v5mJqJjVCfenbIsLsOCIiIiIihUIlx0mt3H+O2VtOYLHAW/fUxdWmP7WIiIiIlAz65uuE0jKzeWXun2PiPHpzReqHB5gbSERERESkEKnkOKGPft/PkfhUQv08eKFDdbPjiIiIiIgUKpUcJ7P3dBKTlh8CYETn2vh6uJqcSERERESkcKnkOBG73eDlOTFk2Q1urRVCxzqhZkcSERERESl0KjlO5Kv1R9l05ALebjZGdq5tdhwREREREVOo5DiJM4lpvL1gDwAvdKhOuQBPkxOJiIiIiJhDJcdJjPxxJ0npWdQL8+fRppXMjiMiIiIiYhqVHCewZPcZfok5jc1qYXS3KGxWi9mRRERERERMo5JTzKWkZzFs3k4A+raIoHY5f5MTiYiIiIiYSyWnmPtg8T5OXLxE+QBPnmlf1ew4IiIiIiKmU8kpxmKOJzB1VSwAb3atg5ebi8mJRERERETMp5JTTGVl2xk6Zzt2A+6sW5a21cuYHUlEREREpEhQySmmpq0+zI4Tifh5uDDsrlpmxxERERERKTJUcoqhExcv8cHifQAM6VSTMr4eJicSERERESk6VHKKGcMwGDZ3B6kZ2TSqWIoHGoebHUlEREREpEhRySlmFuw4zZI9cbjaLIzpFoVVY+KIiIiIiOSiklOMJKZlMmL+n2PiPN46kqohviYnEhEREREpelRyipF3Fu4hLimdiCBvBratYnYcEREREZEiSSWnmNh05AJfrjsKwKgudfBwtZmcSERERESkaFLJKQYys+28PDsGw4B7bgqjWZUgsyOJiIiIiBRZKjnFwKcrDrH3TBKB3m68ckdNs+OIiIiIiBRpDis5hw8fpm/fvkRERODp6UlkZCTDhw8nIyPDUYt0SkfiU/jvkv0AvHpHTQK93UxOJCIiIiJStLk4asZ79uzBbrczadIkqlSpwo4dO3jsscdISUnhvffec9RinYphGLwyZwfpWXaaVylN1wblzY4kIiIiIlLkWQzDMAprYe+++y4TJ07k0KFDeZo+MTERf39/EhIS8PPzc3C6omfOluM8++023F2sLHqmFZWCvM2OJCIiIiJiivx0A4ftybmShIQEAgMDr/p8eno66enpOfcTExMLI1aRdCElgzd+2g3A0+2qquCIiIiIiORRoV144MCBA3z00UcMGDDgqtOMGTMGf3//nFt4eHhhxStyRv+ym/MpGVQL8eGxlpXNjiMiIiIiUmzku+QMGTIEi8VyzduePXtyvebEiRN07NiR++67j8cee+yq8x46dCgJCQk5t2PHjuX/HTmBNQfj+X7TcQDGdIvCzUUXwRMRERERyat8H672/PPP06tXr2tOU7ny/+95OHnyJG3btqVZs2Z8+umn13ydu7s77u7u+Y3kVNIys3llTgwADzepQMOKVz+8T0RERERELpfvkhMcHExwcHCepj1x4gRt27alYcOGTJ06FatVeyT+zcfLDnLoXArBvu681LGG2XFERERERIodh1144MSJE7Rp04aKFSvy3nvvcfbs2ZznQkNDHbXYYu1AXBITlx0AYMRdtfH3dDU5kYiIiIhI8eOwkrN48WIOHDjAgQMHCAsLy/VcIV61utiw2w1enr2DzGyDW2qU4fYoFUERERERkevhsOPHevXqhWEYV7zJ5b7beIz1h8/j6Wrj9btrY7FYzI4kIiIiIlIs6SSZIiAuKY3Rv/w5Js7zt1UjrJSXyYlERERERIovlZwi4I2fdpOYlkWd8n70albJ7DgiIiIiIsWaSo7Jlu2N48dtJ7FaYEzXurjY9CcREREREbkR+kZtotSMLF6duwOAXs0iiArzNzmRiIiIiEjxp5JjonG/7ef4hUuU8/fg+duqmR1HRERERMQpqOSYZOfJBCavjAXg9bvr4O3usKt5i4iIiIiUKCo5Jsi2G7w8O4Zsu0GnOqG0rxVidiQREREREaehkmOCGWsOs+14Ar7uLozoXNvsOCIiIiIiTkUlp5CdSrjEu4v2AvBSx+qE+HmYnEhERERExLmo5BSy4fN2kpKRTYMKATzcpKLZcUREREREnI5KTiFatPM0v+46g4vVwphuUVitFrMjiYiIiIg4HZWcQpKUlsnweTsBeKxVZWqE+pmcSERERETEOankFJL3f93H6cQ0KgR6MbhdVbPjiIiIiIg4LZWcQrD12EWmrzkMwKiudfBwtZkbSERERETEiankOFhmtp2hs2MwDOhSvxwtqwabHUlERERExKmp5DjY5ytj2X0qkQAvV169s5bZcUREREREnJ5KjgMdO5/K2N/2AfDy7TUJ8nE3OZGIiIiIiPNTyXEQwzB4de4O0jLt3Fw5kPsahpkdSURERESkRFDJcZAft59i+b6zuNmsjOoahcWiMXFERERERAqDSo4DJKRm8vqPf46JM7BtFSKDfUxOJCIiIiJScqjkOMBbC3dzLjmDyGBvHm9T2ew4IiIiIiIlikpOAVsfe56v1x8DYEy3uri7aEwcEREREZHCpJJTgNKzsnl5TgwADzQOJzoi0OREIiIiIiIlj0pOAZq0/BAH4pIJ8nFjaKeaZscRERERESmRVHIKyKGzyYxfegCA1+6shb+Xq8mJRERERERKJpWcAmAYBi/PiSEjy06rasF0rlfO7EgiIiIiIiWWSk4B+H7TcdYeOo+Hq5VRXepoTBwREREREROp5Nyg+OR0Rv+yG4Bn2lcjPNDL5EQiIiIiIiWbSs4NevPn3VxMzaRGqC99W0SYHUdEREREpMRTybkBf+w/y5wtJ7BY4K176uJq0+oUERERETGbvpVfp7TMbF6duwOAR2+uSP3wAHMDiYiIiIgIoJJz3f67ZD9H4lMJ9fPghQ7VzY4jIiIiIiL/o5JzHfacTuTTFYcAGNG5Nr4eGhNHRERERKSoUMnJJ7vd4OXZMWTZDW6tFULHOqFmRxIRERERkb9RycmnL9cfZfPRi3i72RjZubbZcURERERE5B9UcvLhTGIa7yzYA8ALHapTLsDT5EQiIiIiIvJPKjn5MPLHnSSlZ1EvzJ9Hm1YyO46IiIiIiFyBSk4eLdl9hl9iTmOzWhjdLQqb1WJ2JBERERERuQKVnDya9L+rqfVtEUHtcv4mpxERERERkatxMTtAcTGtd2Mm/xFLv5YRZkcREREREZFrUMnJIy83F55uV9XsGCIiIiIi8i90uJqIiIiIiDgVlRwREREREXEqKjkiIiIiIuJUVHJERERERMSpqOSIiIiIiIhTUckRERERERGnopIjIiIiIiJORSVHREREREScikqOiIiIiIg4FZUcERERERFxKi5mB7gWwzAASExMNDmJiIiIiIiY6a9O8FdHuJYiXXKSkpIACA8PNzmJiIiIiIgUBUlJSfj7+19zGouRlypkErvdzsmTJ/H19cVisZiaJTExkfDwcI4dO4afn5+pWZyV1rFjaf06ltavY2n9OpbWr2Np/TqW1q9jFaX1axgGSUlJlCtXDqv12mfdFOk9OVarlbCwMLNj5OLn52f6H9jZaR07ltavY2n9OpbWr2Np/TqW1q9jaf06VlFZv/+2B+cvuvCAiIiIiIg4FZUcERERERFxKio5eeTu7s7w4cNxd3c3O4rT0jp2LK1fx9L6dSytX8fS+nUsrV/H0vp1rOK6fov0hQdERERERETyS3tyRERERETEqajkiIiIiIiIU1HJERERERERp6KSIyIiIiIiTkUlR0REREREnIpKzt9MmDCBSpUq4eHhQZMmTVi/fv01p//++++pUaMGHh4eREVF8csvvxRS0uIpP+t32rRpWCyWXDcPD49CTFu8rFixgrvuuoty5cphsViYO3fuv75m2bJl3HTTTbi7u1OlShWmTZvm8JzFVX7X77Jlyy7bfi0WC6dPny6cwMXMmDFjaNy4Mb6+vpQpU4YuXbqwd+/ef32dPoPz5nrWrz6D827ixInUrVs3ZzT4pk2bsmDBgmu+Rttu3uV3/WrbvTFvvfUWFouFZ5555prTFYdtWCXnf7799luee+45hg8fzubNm6lXrx4dOnQgLi7uitOvXr2aBx98kL59+7Jlyxa6dOlCly5d2LFjRyEnLx7yu34B/Pz8OHXqVM7tyJEjhZi4eElJSaFevXpMmDAhT9PHxsZyxx130LZtW7Zu3cozzzxDv379WLRokYOTFk/5Xb9/2bt3b65tuEyZMg5KWLwtX76cgQMHsnbtWhYvXkxmZia33XYbKSkpV32NPoPz7nrWL+gzOK/CwsJ466232LRpExs3buSWW27h7rvvZufOnVecXttu/uR3/YK23eu1YcMGJk2aRN26da85XbHZhg0xDMMwoqOjjYEDB+bcz87ONsqVK2eMGTPmitN3797duOOOO3I91qRJE2PAgAEOzVlc5Xf9Tp061fD39y+kdM4FMObMmXPNaV566SWjdu3auR67//77jQ4dOjgwmXPIy/pdunSpARgXLlwolEzOJi4uzgCM5cuXX3UafQZfv7ysX30G35hSpUoZkydPvuJz2nZv3LXWr7bd65OUlGRUrVrVWLx4sdG6dWtj8ODBV522uGzD2pMDZGRksGnTJtq3b5/zmNVqpX379qxZs+aKr1mzZk2u6QE6dOhw1elLsutZvwDJyclUrFiR8PDwf/3VRvJH22/hqF+/PmXLluXWW29l1apVZscpNhISEgAIDAy86jTahq9fXtYv6DP4emRnZ/PNN9+QkpJC06ZNrziNtt3rl5f1C9p2r8fAgQO54447Lts2r6S4bMMqOcC5c+fIzs4mJCQk1+MhISFXPYb+9OnT+Zq+JLue9Vu9enU+//xz5s2bx8yZM7Hb7TRr1ozjx48XRmSnd7XtNzExkUuXLpmUynmULVuWTz75hFmzZjFr1izCw8Np06YNmzdvNjtakWe323nmmWdo3rw5derUuep0+gy+Pnldv/oMzp+YmBh8fHxwd3fn8ccfZ86cOdSqVeuK02rbzb/8rF9tu/n3zTffsHnzZsaMGZOn6YvLNuxidgCRK2natGmuX2maNWtGzZo1mTRpEm+88YaJyUT+XfXq1alevXrO/WbNmnHw4EHGjh3LjBkzTExW9A0cOJAdO3awcuVKs6M4pbyuX30G50/16tXZunUrCQkJ/PDDD/Ts2ZPly5df9Yu45E9+1q+23fw5duwYgwcPZvHixU53gQaVHCAoKAibzcaZM2dyPX7mzBlCQ0Ov+JrQ0NB8TV+SXc/6/SdXV1caNGjAgQMHHBGxxLna9uvn54enp6dJqZxbdHS0vrj/i0GDBvHTTz+xYsUKwsLCrjmtPoPzLz/r95/0GXxtbm5uVKlSBYCGDRuyYcMGxo0bx6RJky6bVttu/uVn/f6Ttt1r27RpE3Fxcdx00005j2VnZ7NixQrGjx9Peno6Npst12uKyzasw9X483+ehg0bsmTJkpzH7HY7S5Ysueoxn02bNs01PcDixYuveYxoSXU96/efsrOziYmJoWzZso6KWaJo+y18W7du1fZ7FYZhMGjQIObMmcPvv/9ORETEv75G23DeXc/6/Sd9BueP3W4nPT39is9p271x11q//6Rt99ratWtHTEwMW7duzbk1atSIhx9+mK1bt15WcKAYbcNmX/mgqPjmm28Md3d3Y9q0acauXbuM/v37GwEBAcbp06cNwzCMHj16GEOGDMmZftWqVYaLi4vx3nvvGbt37zaGDx9uuLq6GjExMWa9hSItv+t35MiRxqJFi4yDBw8amzZtMh544AHDw8PD2Llzp1lvoUhLSkoytmzZYmzZssUAjA8++MDYsmWLceTIEcMwDGPIkCFGjx49cqY/dOiQ4eXlZbz44ovG7t27jQkTJhg2m81YuHChWW+hSMvv+h07dqwxd+5cY//+/UZMTIwxePBgw2q1Gr/99ptZb6FIe+KJJwx/f39j2bJlxqlTp3JuqampOdPoM/j6Xc/61Wdw3g0ZMsRYvny5ERsba2zfvt0YMmSIYbFYjF9//dUwDG27Nyq/61fb7o3759XVius2rJLzNx999JFRoUIFw83NzYiOjjbWrl2b81zr1q2Nnj175pr+u+++M6pVq2a4ubkZtWvXNn7++edCTly85Gf9PvPMMznThoSEGLfffruxefNmE1IXD39dsvift7/Wac+ePY3WrVtf9pr69esbbm5uRuXKlY2pU6cWeu7iIr/r9+233zYiIyMNDw8PIzAw0GjTpo3x+++/mxO+GLjSugVybZP6DL5+17N+9Rmcd3369DEqVqxouLm5GcHBwUa7du1yvoAbhrbdG5Xf9att98b9s+QU123YYhiGUXj7jURERERERBxL5+SIiIiIiIhTUckRERERERGnopIjIiIiIiJORSVHREREREScikqOiIiIiIg4FZUcERERERFxKio5IiIiIiLiVFRyRERERETEqajkiIiIiIiIU1HJERERERERp6KSIyIiIiIiTuX/AImmotsqRsMLAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x288 with 1 Axes>"
+       "<Figure size 1000x400 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5993,7 +5987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6002,14 +5996,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 864x288 with 2 Axes>"
+       "<Figure size 1200x400 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -6039,7 +6031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6053,14 +6045,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {},
+   "execution_count": 93,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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xdkIDb1GdRSQlnMhRogv/RWztR+UvBLIJnv99vG36pHTpZ0+d5oQG3Yrd7btYK4qIffYRdvF2IjOfxVj0Fv72Q/G17YfhT68hjSKVQU2OSJJZS98hvvFjMDyEhozBrFngdqSMY5gmgfMvx6zdlMiHz5PYvpzSCQ+WP6ejletEJElONvPG16Yv/vMvxwzWqLQcZm5dgr2vInDeKKzVM4itmoZztJjogteILn0H/7kD8bUfjJlVq9IyiVR1anJEkii28WOsJRMACPS9Bm/Dtu4GynC+lj0wa9YnPOVxnMO7KRv/IKFBt+Bt2sntaCKS5r5u5o23fmvXMhnBHAJdLsFfOJzY+nlYyyfjHN6D9en7WMs/wNe6F77C4fqwRwQ1OSJJk9i7icis5wDwFQ7H3/YClxNVD566zckafT+RaX8hsXsd4Q/G4e/+HfwdL9LDuSJSYRWZeeMWw+vHf05/fG36Ed/6Cdbyydh7NhBb+xGxtR/hbdYZX8cL8dZv5XZUEddUjf9bRdKcfbS4fCW1RAxP044Eul/udqRqxcyqSeiiXxKd9zKxNbOwFr6JvX8bwf43YHgDbscTkTQR3/YpkbkVn3njFsM08bU4D1+L84jvXk9s2STiWz859sssaFm+SEGzzlqcRaodNTkiZ8iJRQgXjcMJH8bMb0xo4I/1l4kLDI+XYN8fYdZuSnTuK8Q3LaTs8B5CQ8dU2R9QRKRqSNbMGzd567fCW/9OEod2EVv+AbF187D3bCAy9QmMmgX4OwzH17o3htfvdlSRSqEmR+QMOI5NZOaz2MXbMEK55SupaZUbV/nPHYiZ14jI1Cexi7dSNv4BgkN+grdBG7ejiUgVk6qZN27y1GqIp9/1+LteRmzlNKzVM3AO7yE6559YS8bjazcY/7kDMYI5bkcVSSk1OSJnwFr0FvEtS8HjJTT0Dl0xqCK8DdqQNfp+wlMexy7eRvj9PxLofSX+cwe6HU1EqojEng1EZv8T+8B2IDUzb9xkZtUi0P27+DtdROyzj7CWF+GUHsBa/DbWp+/ja9MPf+EwzBp13Y4qkhJqckROU2zdHKxPJwIQ7Hc9noKWLieSrzJr1CHr0nuJzHqe+KaFROe8iF28jUCvqzA8OvWJVFflM2/+TWzth+UvVNLMG7cY/hD+DsPwtRtEfOPC8kUKircTWzWN2OoZeM/qhr/jCDx1mrsdVSSp9De9yGmI715H5KN/AODvPBJfq14uJ5KTMbwBgoNuxarTFGvhW8TWzMI+uIvg4Nsxs2q6HU9EKtHJZt54W/cl0KNyZ964xTC9+Fr1wtuyJ4mdq7CWTSaxcxXxjR8T3/gxnobn4O94IZ7G7bUypWQENTkiFWSX7CMy5QmwE3hbdMXfdbTbkeQbGIZBoNPFePIbE57+DInd6ygb/wChoXfgqdvc7XgiUglOOvOmzzXV8lk9wzDwNm6Pt3F7Evu3Yi2fTHzjQhK71hDetQYzvwn+wuF4W55fZZbMFjkdmXddViSFHCtMuGgsTuQIZp3mBAfclJG3N2Qib9NOZI++D6NmfZzSA5S9+xCxDfPdjiUiKeTEo0QXvknZm/eVNzgeP/7u3yPrsgeqZYPz3zx1mhEa+GOyf/BHfO2HgDeAfWA7kVl/o/S1X2It/wDHCrsdU+S0qEUXOUWOnSA8/WnsgzsxsmoRGnanZrCkGbNWA7JH/S/hGc+Q2L6cyIxnsIu34+/2XS37LZJh/nvmjadpJ4K9r9SD9idh1qhDsNeVBLpcirVmJrGVU3FKDxBd8DrRpe/gP2cAvg5DMbNquR1V5JSpyRE5RdEFb5DYvhw8fkLD7sLMznM7kpwGI5BNaNhdWIvfwvp0ItaySSQObC+fbxTIdjueiJyhTJh54xYjmEOg80j8HYYRWz+v/ErO4d1YyyZhrZiCr1VPfIUj8OQ1dDuqyLdSkyNyCqw1s4itnAJAcMBNepYjzRmmSaD79zDzmxD58O8ktq+gdML/ERp2J55a+stbJB2dOPPGxNdhWFrPvHGL4fXjP6c/vrb9iG/9lNiyyST2rCf22Wxin83G07Rj+SIF9VtrkQKpstTkiHyL+M7VROe8BIC/62X4zurmciJJFl/LHpi16hMuehzn8B7Kxj9IaOAteJt1cjuaiFRAps+8cYthmPiad8HXvAuJ3evLFynY8gmJbcsIb1uGWe8s/IUj8DY/T7f8SpWjJkfkG9iHdhOe+iQ4Cbwte+LvPNLtSJJknjrNybrst0SmPkli9zrCRePwd7sMf6eL9QmlSBV3spk3gfMvx9emrxaFSTJP/VaE6rfCPvQ51vIPiK2fi713E5Fpf8HILcBfOAxf6z4YXr/bUUUANTkiX8uJllJW9GewyjALWhLsd51+6M1QZiiX0EW/JDrvFWJrZmItegu7eDvBC27A8GlxCZGqprrPvHGTWasBwX7X4e96GbFV07BWz8Ap2UN0zotYi8fjazcYf7tBGMEct6NKNacmR+QkHDtOeOqTOIf3YOTUJjRkjD6dynCGx0uw77WYtZsQnfsK8U0LKTu8m9DQOzBr1HE7noh8QTNvqgYzqyaBbt/B3+kiYms/wlpRhHO0GGvJeKxlE/G16Yu/w3DMXK1mJ+5QkyPyXxzHITr3ZRK71oAvWL6SWlZNt2NJJfGfOxAzrxGRqU9iF2+jbPwDBAffjrdhW7ejiVRrTjyKtfQ9rGWTwUmUz7w571L8HYZhePTjjFsMXxB/h6H42g0ivmkR1rLJ2MVbia2aTmz1DLwtupUvUqAFe6SS6awg8l9iK6cSWzMLMAgNvAVPbT24Wt14G7Qh67LfEp7yOPb+rYQnPkKg95X4zhmgWxZFXKCZN1WfYXrwteyB9+zzSexcjbV8MokdK4lvWkh800I8Dc/BXzgCT5MOOo9KpVCTI/IV8W3LiS54DYBAj8vxNuvsciJxi5lTm6xL/ofIh/8gvnEB0TkvYu/fSqD31frUWKSS2EcPEJ33imbepBHDMPA2boe3cTsSxduwlk0mvvFjErvWEN61BjOvMf6OI/Cefb7OpZJSOrpEvpA4sJPw9KfAcfC16Yevw3C3I4nLDG+A4MBbsGo3xfpiBSf74C6CQ36iWxhFUsixE+Uzb5ZMgFhEM2/SlKd2U0IDb8Hu/l2sFVO+OIfuIDLrbxiL3sTffii+c/pj+ENuR5UMpCZHBLDDJYSLxkIsgqdBGwJ9rtHldAHKP5UMdLoQT34jwjP+SmLPesrGP0Bo6Bg8dVu4HU8k4yT2bCAy55/YxeUzb8yClgT7XKtbh9OYmVObYM8fEuhyCdaamcRWTMUpPUj04zeILn0X/7kD8LUfgpmd53ZUySBqcqTacxIxIlOfxDmyDyO3XvlKarqELv/F27Qj2aPuI1w0Dvvwbsre/T3Bftfha9XL7WgiGaF85s2bxNbOKn9BM28yjhHIJtDpYvwdhhFbP4/Y8g/K5+4sm4S1oqh8Hl3hCDz5jdyOKhlAP8lJteY4DpHZL5DYvQ78IULD79La/vK1zFoNyBp9H+EZz5DYtozIzGdJFG8n0P17gH4IEzkd5TNv5hFd8PpXZt70IXD+5ZihXJfTSSoYHh/+thfga9OXxNZl5YsU7F5HfN0c4uvm4GnasbzZadBGd1XIaVOTI9WatWwS8XVzwTAJDb4dT62GbkeSKs7wZxEaeifW4rexPn2f2PLJ2Ad34BlyG5DtdjyRtJI4uIvonH9+ZeZNQwJ9rtXMm2rCMEy8zTvjbd6ZxJ4N5YsUbFlKYtsywtuWYdY9q3yRgubnYZj6IEkqRk2OVFuxLUuwFr4JQKDXFXgbt3c5kaQLwzQJdP8uZu0mRGY9T2L7Co689Vtq/OB/wKN7ykW+zbGZN8sng62ZNwKegpaEho7BPrQba8UHxNbNwd63ici0v2Dk1sPfYRi+Nn0wvAG3o0qa0JlEqqXE/q1EZjwDOPjOHYS/3WC3I0ka8p19PmbN+uXzdA7vYec/fkXW4B9jNu7odjSRKuvEmTcdCfa+SjNvBACzVn2CfX+Ev+tlxFZNw1o1HadkL9G5L2EtmYCv3SB87QZhBmu4HVWqODU5Uu3YZYcIF42DuIWncXsCva5wO5KkMU+dZmSNvp/otL8Q//wzSieNxd/tMvydLta95CJfcfKZN1fibdZF/6/ICcxQLoGul+HveBGxzz7CWlGEc2Q/1pIJWJ9OwtemD/7C4Zi59dyOKlWUmhypVpy4RbjocZzSA5i1GhAadCuG6XE7lqQ5M5RLziX3YC96g5KlRViL3sIu3kbwghsxfLq1Qqq3k8+8GUrgvFGaeSPfyvAF8Lcfgu/cgcQ3L8ZaNgl7/1Ziq2cQWzMTb4uuhDpfBHkd3I4qVYyaHKk2HMchMus57H2bIJBNaNhdGAE9KC7JYXi81BlxM/EaDSmb/SLxTYsoO7yb0NA7dBuOVFuaeSPJYpgefGefj/es7iR2rSlfkW37CuKbFnFk0yJiTdvh7TAcGrbXlUEB1ORINWItfYf4poVgeggNGYNZs8DtSJKBAu0G4NRsQGTqk9jF2yl7+wGCQ27H2/Act6OJVBrNvJFUMQwDb6Nz8TY6l0Txdqzlk4lv/JjItlWwbRVmXiP8hcPxtuypRSyquaSfaWzb5vHHH6dv37506tSJm266ie3btyd7NyIVEtv4MdaSCQAE+lyDt2FbdwNJRvPWb03W6Psx6zTDiR4lPPERrFXTcBzH7WgiKeU4DrF1cyn916+PNTje1n3IvvwP+NteoAZHkspTuwmhATdT88pHqXn+JeALYh/cSeTD5yl9/RdYyybhWGVuxxSXJP1s89RTT/Hqq6/y4IMP8vrrr2PbNjfeeCOWZSV7VyKnJLF3E5FZzwHgKxyOv+0FLieS6sDMqU3WJf+Dt2UPcGyic18mOvsfOImY29FEUiJxcBfh9x8mMutvOJEjmHkNCY38NaH+N2qop6SUWaM2tQdfS81r/oy/+/cwsmrhlB4k+vG/OPrK3UQWvI599IDbMaWSJfU6nmVZ/P3vf+fnP/85/fv3B+DPf/4zffv2ZcqUKVx88cXJ3J3It7KPFpevpJaI4WnakUD3y92OJNWI4Q0QHHALsdpNiX78b2JrPyJxcBehIT/BzKrldjyRpNDMG6kqzEA2gU4X4e8wlPiGBVjLJmMf2kVs+QfEVk7F27IH/sIRePIbux1VKkFSzz5r166ltLSUnj17HnstNzeXc889l0WLFqnJkUrlxCKEi8bhhA9j5jchNPDHmpgslc4wDPwdL8TMb0x4+tPYezZQNv4BQkPvwFO3hdvxRM6IZt5IVWR4fPja9MXbujeJbcvLFyn4/DPi6+YSXzcXT5NC/B1H4GnQVosUZLCkNjm7d+8GoEGDBse9Xq9evWNfE6kMjmMTmfksdvE2jFAuoWF3YvhDbseSaszbpJDsUfcTnjIO+9DnlL37e4L9rsPXqpfb0UQqTDNvJB0Yhom3WSe8zTqR2LsRa9lk4puXkNi+nPD25Zh1W+AvHIG3xXkaJ5GBktrkhMNhAPx+/3GvBwIBDh8+fEbv7fW6/wm8x2Me909JrmTWNzz/TeJbloLHR86IO/HmaViYjt/UOqX61mmI7zv3UzrtaWJblxGZ+SzOwe2EenxfVxm/hY7f1DrV+jp2gujyKYQXjT828ybQcRihbqM18+Yb6PhNrVOpr7dhKwINW5E4vIfop5OJrp2NvW8zkelPYebWJdBxOIG2/TTb7CTS9fhNapMTDJaf4CzLOvbvANFolFDo9D9FN02DvLyqM88kN1dXBFLpTOt7ZPlMIp+8D0Ddi2+jxjmdkpAqc+j4Ta1vr282eVfcy8EPX+fQvLeJfjoZs2QX9UbdjSeUUykZ05mO39T6pvpGdq5j/6RnsPZuASDQqA11RtxMoKB55YTLADp+U+uU6pt3FjS/nUTpVRxePJmSJR9gl+wjPPsloosnkHvecGp2HYEnu2bqA6eZdDt+k9rkfHmb2t69e2natOmx1/fu3UubNm1O+31t26GkxP0lAD0ek9zcECUlYRIJ2+04GScZ9Y1//hlHJj4NQPC8S4g3Oo+DB0uTGTNt6fhNrYrW1+g0iuzs+pTOfI7wpmVsf/6X5Iy4C09+o0pIm350/KbWN9XXjhwlvODfWKtnAQ5GIJtQz+/jP6cfZYZJmc6x30rHb2qdXn29GIUjyT1nKNG1s4ku+wC7ZC+H5vybQ/MnEGjbl0CnEXg0U6/KHb+5uaFTuqqU1Canbdu25OTk8PHHHx9rckpKSli9ejVXXXXVGb13PO5+Ub+USNhVKk+mOd362iV7KZv8ONgJvC264u0ySn9OJ6HjN7UqUl+zRXeyahQQnvI49uE9lLz1AKGBt+Bt1jnFKdOXjt/U+mp9Hcchvn4e0QWv40SOAOUzbwLnX44ZyiWRANCfRUXo+E2t06qv4cN7zkA8bS4gvnkJ1vLJ2Ps2E101g+iqmXhbnFe+SEG9s1MTOo2k2/Gb1CbH7/dz1VVX8eijj5Kfn0+jRo145JFHqF+/PkOHDk3mrkSO41hhwkVjy2cz1GlOcMBNGjonacFTpxlZo+8nMu0vJD7/jHDR4/i7jsbfeaQe4BbXJA7uIjrnRRKfrwXAzGtIoM+1eBuc/l0ZIlWZYXrwnd0d71ndSHy+FmvZZBLblxPfvJj45sV4GrQpX366aaF+vkgTSV/A/o477iAej/Ob3/yGSCRCt27deP755/H5fMnelQhQ/iBsePrT2Ad3YWTVKl9JzasHByV9mKFcQhf9gui814itno61+G3s4m0E+9+oh7mlUjmxKNFF7/zXzJtL8HcYrpk3Ui0YhoG34Tl4G55D4sAOrOWTiW9YUP4h1OefYdZqiL9wON5WPTE8+tm2KjMcx3HcDvFtEgmbAwfcv+fX6zXJy8vm4MHStLpcly5Ot76Rea8QWzkVPH6yLvkfPHWbpy5kGtPxm1rJqq+1ZhbRuS+BnSif7zT0DsxczRzR8ZtaXq9JoHgteyf/DbtkH6CZN8mk4ze1Ul1f++gBrJVTiK2ZVb6qIGBk1cLXfgj+c/pjBKrO4lipUNWO3/z87Mp/JkekslmrZ5Y3OEBwwE1qcCTt+c/pj5nXiMjUJ7APbKds/AMEh9yOt+E5bkeTDGUfPcDRBa9xcNMiQDNvRP6bmZNPsMcPCHS5hNiaWVgrpuCUHcJa+G+sT97D1/YC/B2GYubUdjuqfIWaHElb8Z2ric59GQB/18vwndXN5UQiyeGt34qs0fcTnvIE9v4thCc+QqDnFfjaDdIPnZI0jp0gtnIa0SXHz7zxdb5Ut0mKnIThz8Lf8UJ87YcS3zAfa/kH2Ad3EltRRGzlNLwtzy9fpCC/idtRBTU5kqbsQ7sJT30SnATelj3xdx7pdiSRpDJzapN1yf8Q+egfxDfMJzrvZezibQT6XK37wOWMJfZsIDLnn9jF2wHwFLSk/shbKfPXrRK3o4hUZYbHi69NX7yt+5DYvrx8kYLP1xJfP4/4+nl4mnQoX6Sg4Tn6YMpFanIk7TiRo5QV/RmsMsyClgT7XaeTiGQkw+snOOBmYrWbEl34L2KffUTi0C5CQ36CmVXL7XiShpzIUaIL3yS29kPAgUA2gfMvJ9TuAgL5NTTzRqQCDMPA27Qj3qYdSezdVL5IwebFJLavILx9BWadZvg7Xoi3RVcM0+N23GpHTY6kFceOE572F5zDezByahMaMgbD63c7lkjKGIaBv+MIzPxG5asI7tlA2fgHCA0Zg6feWW7HkzTxbTNvtCSuyJnx1DuL0ODbsUv2Yi0vIvbZbOz9W4lMfxqjRh38HYbha9MPw6fVXyuLmhxJG47jEJ37Molda8AXJDT8Lsysmm7HEqkU3iaFZI+6n/CUcdiHPqfsvd8T7Hsdvta93Y4mVdwJM29qNSTQ5xq8Ddu6nEwk85i59Qj2uRp/11HEVk0ntmo6zpH9ROe9QnTJBPztBuFrNxgzlOt21IynJkfSRmzl1PLlGzEIDfyxHuyTasesVZ+sUfcRnvEMiW2fEpn1NxIHthPo/j3dCiEncOJRrKXvaeaNiAvMYA0C543C33EEsXVzsZZNxjmyD2vpu1jLJuNr3Rt/4XDMmvXdjpqxdJaTtBDftpzogtcACPS4HG+zTu4GEnGJ4Q8RGnYH1uLxWJ+8R2z5B9gHdhAadGvGz2qQUxfftozI3Jdxjnxl5k2vqzRzSaSSGd4A/nMH4mvbn/iWJVjLJmHv20xszSxiaz7E27xL+YpsBS3djppx1ORIlZc4sJPw9KfAcfC16Yevw3C3I4m4yjBMAt2+g1m7CZFZz5HYsZLS8Q8QGnonnvxGbscTF9lHDxCd/yrxzYuBL2be9LoSb3PNvBFxk2Ga+M7qhrdFVxKff4a1fDKJbcuIb1lCfMsSPPVblzc7TTvqGbkkUZMjVZodLiFcNBZiETwN2hDoc43+ohb5gu+s7pg16xMuGodTspeydx4kOOBmfM27uB1NKtnJZt74OgwlcN4ozbwRqUIMw8DbsC3ehm1JHNiJtfwD4hvmkdi9jvDudZi1GuArHI6vVS+NCzhDanKkynISMSJTnsA5sg8jt175Smq6j1zkOJ7aTckafT+RaU+R+HwtkSmPY3cdjb/zSH0aWE3898wbs6AlwT7X4qmt5xZFqjJPfiNC/W/A7nYZsZVTsVbPxD70OdGP/oG16G18HYbgP2eAbkU+TfqJUaokx3GIfPQCiT3rwR8iNPwujGCO27FEqiQzlEvoop8Tnf8asVXTsRaPxy7eTrD/jfoUP4OddOZN9+/ha9tPDa5IGjGz8wicfzn+ziOJrZ2FtWIKTulBrIVvYn3yPr62F+DvMBQzp7bbUdOKmhypkqxlk4ivnwuGSWjw7XhqNXQ7kkiVZphegr2vxqzdlOicF4lvXkzZ4d2Eht6ph80zzMln3vQmcP73tSytSBoz/CH8hSPwtRtCfOPHWMsmYx/cQWxFEbGV0/Ce3b38uZ3aTd2OmhbU5EiVY21agrXwTYDyB2Ybt3c5kUj68Le9AE+thoSnPoF9YAdl4x8gOPg2vI3OdTuaJEHi0C6iszXzRiSTGR4vvta98bbqRWLHCqxlk0nsWkN8w3ziG+bjadwef+EIPI3O1XPK30BNjlQp0d2bKJ32NODgO3cQ/naD3I4kknY89VuRNfq35Y3Ovs2EJz1KoOcP8bUbrL8Q05QTt7A+eQ9r2STNvBGpJgzDwNukEG+TQhL7tpTf5bJ5EYkdKwnvWIlZuxn+jiPwntVNs9JOQmdGqTLs0kPsfvthiFt4Grcn0OsKtyOJpC0zJ5+skb8m8tE/iG+YT3TeK9jF28pXKNSKPWlFM29ExFO3OaHBt2GX7MVaUURs7Wzs4q1EZvwVY+G/8RcOx9emr57D/Ao1OVIlOHGLo5PHkjhSjFmrQflgQ30qIXJGDK+f4ICbidVpSvTjfxH7bDaJg7sIDR2DmVXL7XjyLTTzRkT+m5lbj2DvqwmcNxpr9XRiK6fhHC0mOu8VoksmlA8ebTcYM6um21FdpyZHXOc4TvlAw72bMEM55Fx0N46WSxRJCsMw8BeOwMxvQnjaU9h7N1L29m8JDb0DT72z3I4nJ6GZNyLybYxgDoEul+IvHEFs3Rys5UU4JXvKb2tdPhlfqz74C4dj1qrvdlTXqMkR11lL3yG+aSGYHgq+80siuQXE47bbsUQyirdxe7JH30+4aBz2oV2Uvfd7gn2vw9e6t9vR5Cs080ZEKsLw+suv3rTtT3zrUqxlk7D3biK2dhaxtR/ibd65fJGC+q3cjlrp1OSIq2IbP8ZaMgGArAt+RKhZOyIHS90NJZKhzJoFZI36XyIznyW+9RMis/5GongbgfMv1+2hLnOipUQX/pvYGs28EZGKM0wTX4uueJufR2L3uvIV2bZ9SnzLUuJbluIpaIWv4wi8zTpVm3OKmhxxTWLvJiKzngPAVzicwDkXuJxIJPMZ/hDBoWOwlkzAWvousRVF2Ad2lD8Hp4G7lU4zb0QkmQzDwNugDd4GbUgc3EVs+WRi6+eT2LOexJT1mDXr4yscjq9VLwyv3+24KaUmR1xhHy0mXDQWEjE8TTsS6H6525FEqg3DMAl0vQwzv0n51Zydqygd/wChYXfhyW/kdrxqQzNvRCSVPHkN8VxwA/5u3yG2cirW6hnYh3cTnf0C1uK38bUfgv/cgRgZ+hy0mhypdE4sQrhoLE64BDO/CaGBP8Ywq8elU5GqxHdWN8ya9QlPGYdzZB9l7zxIcMBN+Jqf53a0jHbizBsf/i6X4i/UzBsRST4zqxaB7t/D3+liYms/wlpRhFN6AGvRW1ifvI+vbT/8HYZh1qjjdtSk0tlUKpXj2ERmPotdvB0jlEto2J0Y/pDbsUSqLU/tJmSNvp/ItKdI7FpDZMoT2OeNxt9lZLW5b7synTDzpkkhwd5Xa+aNiKSc4Q/hLxyGr/0g4hsXYi2bjH1gO7GVU4mtmo737O7lixTUaeZ21KRQkyOVylr4JvEtS8HjJTT0joz71EAkHZnBGoQu/BnR+a8TWzUNa8l47OJtBAfcpCWLk+TkM2+uwNv8PM28EZFKZZhefK164W3Zk8SOlVjLJ5PYuZr4hgXENyzA06gd/o4j8DRql9bnJzU5Umli6+aU354BBPtdj6egpcuJRORLhukl2PsqzNpNiM55kfiWJZRN2ENo2B2YufXcjpe2Tjrzpv2Q8pk3uootIi4yDANvkw54m3QgsX8L1rLJxDctIrFzFeGdqzBrN8VfOBxP6x5uRz0tanKkUsR3ryPy0T8A8Hceia9VL5cTicjJ+NtegKdWQ8JTn8A+uKN8QYLBt+NtdK7b0dLOCTNv6p1NsO+1eGo3dTmZiMjxPHWaExp0K3b372KtmEJs7YfYxduIzHwWa9FbeHqNwjm7n9sxK0Q3XEvK2SV7iUx5AuwE3hZd8Xcd7XYkEfkGnvqtyBr9W8y6LSBaSnjSo1grp+I4jtvR0oITLSUy+wXK3nmovMEJZBPo+yOyLr1XDY6IVGlmjboEe11JzhWP4e96GUYoF/toMcVTnie+fYXb8SpEV3IkpRyrrHwltcgRzDrNy+/x18PMIlWemZNP1shfE5n9Qvkcl3mvkNi/jWDfazA8PrfjVUmaeSMimcII5hDocgn+wuHYG+fjLduDUXA2ttvBKkBNjqSMYycIT38a++AujKxa5SupeQNuxxKRU2R4/QT730SsdjOiH79OfN1syg7tJDRkDGZ2ntvxqhTNvBGRTGR4/QTaDSAvL5uDB0ux4+nT5qjJkZSJLnidxPYV4PETGnaXfigSSUOGYeAvHIaZ36j8Q4u9mygb/wChoWPw1Dvb7Xiu08wbEZGqSfcNSUpYq2cSWzkVgOCAm/DUbe5uIBE5I97G7ckedR9mXkOcskOUvfsHYuvmuB3LVfFtyyn9971Yn7wHdgJPk0Kyv/d7Ap0vVoMjIuIynYUl6eI7VxOd+xIA/q6X4Turm8uJRCQZzJoFZF36v0RmPkt86ydEZj1HYv82Aj2+j2F63I5XaezSg0TnvaKZNyIiVZiaHEkq+9BuwlOfBMfG27In/s4j3Y4kIklk+EMEh47BWvIO1tJ3iK2cgn1wB6FBt2EEc9yOl1KOnSC2ahrRxZp5IyJS1anJkaRxIkcpK/ozWGWYBS0J9rtOn2qKZCDDMAl0HY2Z37j8as7O1eXzdIbdiSe/sdvxUiKxdyOR2f/ELt4GaOaNiEhVpyZHksKx44Sn/QXn8B6MnNqEhozB8PrdjiUiKeQ7qxtmzfqEp4zDObKPsgkPEhxwM74W57kdLWmcaCnRhW8SWzMLcMpn3nT/Hr62/bQcvohIFZbSM/R9993Hr371q1TuQqoAx3GIznmZxK414AsSGn4XZlZNt2OJSCXw1G5C1uj78TQ8B+JRIlOfILpkAo6TPsuMnozjOMTWz6P0X78mtmYm4OBt1Zvsy/+A/5z+anBERKq4lJylbdvmscce44033kjF20sVE1s5ldjaWYBBaOCP8eQ3cTuSiFQiM1iD0IU/w9d+CADWkglEpv4Fxwq7nOz0JA7tIjzxj0RmPosTLsGs1YDQxfcQGnCThnqKiKSJpN+utnHjRu699162bt1Kw4YNk/32UsXEty0nuuA1AAI9vo+3WSd3A4mIKwzTS7DXlXhqNyUy+5/Etyyh7J09hIbdgZlbz+14p+TkM28uwV84QktCi4ikmaRfyVmwYAFnn30277//Po0bZ+YDqFIucWAH4elPgePga9MPX4dhbkcSEZf52vQla+SvMEI1sQ/uoHT8A8R3rHI71rc6+cybhwh0HqkGR0QkDSX9zH3llVcm+y0B8Hrdv//Z4zGP+2d1ZodLKC0aC7EI3oZtye7/IwzPmc3JUH1TS/VNLdX3P7yNWuP73gMc/eBxEns3EZ78KKFePyRQOOy0V1xMVX3towcom/sKsY2LADCy88jqcxW+s7pWq9UhdfymluqbWqpvaqVrfSvU5OzYsYNBgwZ97dfnz59Pfn7+GYf6b6ZpkJeXnfT3PV25udV7HoITj7Hr3Sexj+zHm1efRt//FZ6sGkl7/+pe31RTfVNL9f1CXjZ51z3E/knPcHTFLMJzX8VzZBd1RtyCeQYrLyarvo6doGTxZA59+BqOVT7zpmb3i8jr+33MQPX9M9Txm1qqb2qpvqmVbvWtUJNTUFDApEmTvvbrNWumZkUt23YoKSlLyXtXhMdjkpsboqQkTCKR3isHnS7HcSib8SzWjrUY/iyyht9FSdSEaOkZv7fqm1qqb2qpvifn7XMdodyGhOe9xtHlswjv3kbOiDsxs/Mq9D7JrG98z0bKPvwHif3lM288BWeTdcF1mHWacrjMhrIzP5+lGx2/qaX6ppbqm1pVrb65uaFTuqpUoSbH5/Nx9tlnn3aoMxGPu1/ULyUSdpXKU5min07E+mwuGCbBwbfh1Kif9FpU5/pWBtU3tVTfE3nbDSVUsxHh6U+R2LuJkn/fT2jIT/AUtKzwe51Jfb9p5g2GqT83dPymmuqbWqpvaqVbfdPr5jpxVWzLEqyFbwIQ6HUl3sbtXU4kIunC27gd2aPvx8xrhFN2iLL3Hib22exK2bdm3oiIVD9aMkZOSWL/ViIzngEcfO0G4W/39c9miYicjJlbj6xLf0Nk1t+Ib1lK5MPnSRRvI9DjBxjmmS1c8nUSh3YRnfNS+bBiwKzVgECfa/A2PCcl+xMRkapBTY58K7vsEOGicRC38DRuT6DnFW5HEpE0ZfhDBIf8BGvJO1hL3yG2cir2gR2EBt+OEcxJ2n4080ZEpHpL6Zn+pZdeSuXbSyVw4hbhonE4pQfKp34PujVln7iKSPVgGCaBrqMxazchMvNvJHatoXT8A4SG3YEnv8kZv398+3Iic17CObIPAE+TQoK9r0qboaQiInLm9HGWfC3HcYjMeg5732YIZBMa/lOMQNVZyltE0puvRVfMmgWEix7HObKPsgm/IzjgJnwtup7W+9mlB4nOe4X45sVA+cybQM8r8LaoXjNvRERETY58A2vJBOKbFoLpITRkjD4FFZGk8+Q3IXv0/eUrr+1cTWTqk9hdLsV/3qWnvCCAYyeIrZpOdPHbECufeeNrP4TAeaMw/Ok110FERJJDTY6cVGzDAqyl7wAQ7HMt3oZtXU4kIpnKCOYQGvEzogveILZyCtbSd7APbCfY/6ZvbVISezcSmf1P7OLymTdmvbMI9rkWT51mlRFdRESqKDU5coLE3k1EPnweAF/h8PIZEiIiKWSYHoK9rsBTuwmR2f8kvmUpZe/8jtCwO096FfmEmTf+rPKZN+dcoCWhRURETY4czz5aTLhoLCRieJp2ItD9crcjiUg14mvTF7NWA8JTn8Q+uLN8QYJBt+JtXgj8Z+ZNdMHrOOESALytehHo8QPMUK6b0UVEpApRkyPHOLEI4aKxOOESzPwmhAbegmHqE1ERqVyegpZkjb6f8NQnsPduIjz5T9Drh1jtz+fo+38lvlMzb0RE5JupyREAHMcmMuMZ7OLtGKFcQsPu1AO7IuIaMzuPrIt/RWTOP4mvm0t47qvsmPsa4GjmjYiIfCv97SAAWAvfJL71E/B4CQ29A7NGHbcjiUg1Z3j9BC+4kVjtZkQXvA6OjbdpIYFemnkjIiLfTE2OEPtsdvlUcCDY73o8BS1dTiQiUs4wDPwdhuJv3JYcv004twWJhON2LBERqeLU5FRz8d3riMx+AQB/55H4WvVyN5CIyEl46zYnlJdN5GApoCZHRES+mZ4qr8bskr1EpjwBdgJvi674u452O5KIiIiIyBlTk1NNOVZZ+UpqkSOYdZoTHHCTZkuIiIiISEbQT7XVkGMnCE9/GvvgLoysWuUrqXkDbscSEREREUkKNTnVUHTB6yS2rwCPn9CwuzCz89yOJCIiIiKSNGpyqhlr9UxiK6cCEBx4M566zd0NJCIiIiKSZGpyqpH4ztVE574EgL/bd/C16OpyIhERERGR5FOTU03Yhz4nPPXJ8mF6LXvi73Sx25FERERERFJCTU414ESOUlY0FqwyzIKWBPtdh2EYbscSEREREUkJNTkZzrHjhKf9BefwHoyc2oSG3oHh9bsdS0REREQkZdTkZDDHcYjOeZnErjXgCxIafhdmKNftWCIiIiIiKaUmJ4PFVk4ltnYWYBAa+GM8+U3cjiQiIiIiknJqcjJUfNtyogteAyDQ4/t4m3VyN5CIiIiISCVRk5OBEgd2EJ7+FDgOvrb98HUY5nYkEREREZFKoyYnw9jhEsJFYyEWwdOgDYHe12glNRERERGpVtTkZBAnESMy5QmcI/sxcgsIDRmD4fG6HUtEREREpFKpyckQjuMQ+egFEnvWgz9EaPidGMEct2OJiIiIiFQ6NTkZwlo2kfj6uWCYhAbfjqdWQ7cjiYiIiIi4Qk1OBohtXoK18E0AAr2uxNu4vcuJRERERETcowc20lxi/1YiM58BwNduEP52g1xOJCIiIiJnwrZtEom42zEAsG2DSMSDZUVJJJyU7svj8WKaybkGoyYnjdllhwgXjYO4hadxewI9r3A7koiIiIicJsdxKCk5QDh81O0ox9m/38S27UrZVyiUQ25u/hmvDqwmJ005cYtw0Tic0gOYtRoQGnQrhulxO5aIiIiInKYvG5ycnDz8/kCVGQPi8Rgpv4rjOA6WFeXo0YMA1KxZ+4zeT01OGnIch8is57D3bcYI5BAa/lOMQLbbsURERETkNNl24liDk5OT63ac43i9JvF46q/k+P0BAI4ePUiNGnlndOuaFh5IQ9aSCcQ3LQTTQ3DoGMzcem5HEhEREZEzkEgkgP/8oF9dffn9n+kzSWpy0kxswwKspe8AEOxzLd4GbVxOJCIiIiLJUlVuUXNLsr5/NTlpJLF3I5EPnwPAVzgcX9t+LicSEREREal6kt7kfP7559x999307t2bbt26ccMNN7B+/fpk76basY8Wl6+klojjbdaZQPfL3Y4kIiIiIlIlJbXJsSyLm2++mX379vHXv/6VV199lezsbK699loOHDiQzF1VK04sQrhoLE64BDO/CcEBN2MkaQ1xEREREZFUKS09yqBBvRk5cijxeOXN/knqT8qLFy9m3bp1PProo3To0IFWrVrxyCOPUFZWxowZM5K5q2rDcWwiM57BLt6OEcolNOxODH/I7VgiIiIiIt9q2rQp5OXlU1p6lA8/rLx+IKlNTqtWrXj22WcpKCj4zw6+uOJQUlKSzF1VG9bCN4lv/QQ8XkJD78CsUcftSCIiIiIip2TixHfp0aMXXbp05Z133q60/SZ1Tk7dunW54IILjnvtpZdeIhKJ0Lt37zN6b6/X/duzPB7zuH+mWnTtbKxlkwDIHnAj/katK2W/bqns+lY3qm9qqb6ppfqmluqbWqpvamVKfW375KuKOY6DFUv9jJqvE/CX19UwwKngPNAtWzazevVKrrzyGo4cKeHhh3/Htm1badq02bf+Xo/HOKOf/yvU5OzYsYNBgwZ97dfnz59Pfn7+sf+eOnUqf/rTn/jRj35Emzanv9SxaRrk5VWdYZe5uam/XSy8bTUHZ/0DgFq9v0v++UNSvs+qojLqW52pvqml+qaW6ptaqm9qqb6ple71jUQ87N9vHvfDveM4/O6fS1i/47BruVo1rslvru16Wk3k5MnvkpWVRe/efYhGozz66MO8++7b3HXXz77299i2gWma1KyZRTAYPO3cFWpyCgoKmDRp0td+vWbNmsf+/bXXXuPBBx/kkksu4Ze//OVpBwSwbYeSkrIzeo9k8HhMcnNDlJSESSRS11EnDu/lyFv/D+w4vrO7QeHFHDxYmrL9VRWVVd/qSvVNLdU3tVTf1FJ9U0v1Ta1Mqa9lRbFtm0TCIR4v/z4cx6GCF09SJpGwK3QlJx6PM3nyJHr37ofX68fr9dO9e08mTXqfm266jUDg5ENPEwkH27Y5fLiMcDhxwtdzc0On1HBVqMnx+XycffbZ37rdI488wnPPPcd1113HPffck5ShPl/+YVcFiYSdsjyOVUbZpMdwIkcx67YgcMGNlA/ArTrff6qlsr6i+qaa6ptaqm9qqb6ppfqmVrrXN5E4sYMwDINfX9nF1dvV/D4TwzBwKniv2oIFczlwoJhBg4Yee23w4GHMmzebmTOnMXz4Rd/4+7/a7J2OpD6TA/9pcO655x6uv/76ZL99RnPsBOHpT2Mf3IWRVYvQ0DswvCfvckVEREQk8xmGQcDvcTtGhU2c+B4A9977ixO+9s47b31rk3OmktrkfPzxxzz33HNcffXVjBw5kn379h37WlZWFtnZVee5mqoouuB1EttXgMdPaNhdmNl5bkcSEREREamQgwcPMH/+HC68cCQ/+MGVx33tjTdeZeLEd9m0aQNnndUyZRmSugzF+++/D5SvqNanT5/jfv39739P5q4yjrV6BrGVUwEIDrwZT93m7gYSERERETkNRUWTSCQSXHXVtZx1Vsvjfl1zzfWYpsmECW+lNENSr+Q8+OCDPPjgg8l8y2ohvnM10bkvA+Dv9h18Lbq6nEhERERE5PRMmvQeXbt2p2nT5id8rVGjxvTtewFTpkzm1lvvIBRKzap4SX8mRyrGPvQ54alPgmPjbdkTf6eL3Y4kIiIiInLaXnzxjW/8+kMPPZLyDOk9NSnNOZGjlBWNBasMs6AlwX7XJWUlOhERERGR6kxNjkscO0542l9wDu/ByKn9xUpqfrdjiYiIiIikPTU5LnAch+icl0nsWgO+IKHhd2GGct2OJSIiIiKSEdTkuCC2cgqxtbMAg9DAH+PJb+J2JBERERGRjKEmp5LFty0juuB1AAI9vo+3WSd3A4mIiIiIZBg1OZUocWAH4elPg+Pga9sPX4dhbkcSEREREck4anIqiR0uIVw0FmIRPA3aEOh9jVZSExERERFJATU5lcBJxIhMeQLnyH6M3AJCQ8ZgeDSiSEREREQkFdTkpJjjOEQ+eoHEnvXgDxEafidGMMftWCIiIiIiGUuXE1LMWjaR+Pq5YJiEBt+Op1ZDtyOJiIiIiKRcPB7n7bf/TVHRJLZt20og4KdVqzZcffV1dOnSNaX7VpOTQrHNS7AWvglAoNeVeBu3dzmRiIiIiEjqRaNRfvrT29mzZzc33vhj2rcvJBqNMnHiu9x112385jf/x9Chw1O2fzU5KZLYv5XIzGcA8LUbhL/dIJcTiYiIiIhUjuef/ysbN67nxRffoKCg/rHX77zzZ5SWHmXcuEfo06cfWVlZKdm/mpwUsMsOES4aB3ELT+P2BHpe4XYkEREREZFKEY/Hef/9d7nwwkuOa3C+dPPNtzF69HcJBAIpy6AmJ8mcuEW4aBxO6QHMWg0IDboVw/S4HUtERERE0pDjOBC33Avg9Vf4t+zatYOSksN06NDxpF+vU6cuderUPdNk30hNThI5jk1k1nPY+zZjBHIIDf8pRiDb7VgiIiIikoYcx6Hs3Yew92xwLYOnoBXey35Tod9TUlICQI0aNVIR6ZRoCekkspa8Q3zTQjA9BIeOwcyt53YkEREREUljBuk3PL5WrTwASkoOu5ZBV3KSJLZhAdbSdwAI9rkWb4M2LicSERERkXRmGAahS/7H9dvVDMMAnFP+LQ0bNiI/vzYrVixj0KChJ3x9y5bNjBv3KGPG3M1ZZ52dxLD/oSYnCRJ7NxL58DkAfIXD8bXt53IiEREREckEhmGAL3UP6KeCaZpcdNElvPXWv/jhD68+YfGBV199kTVrVtOgQermR+p2tTNkHy0uX0ktEcfbrDOB7pe7HUlERERExFXXXnsDTZo05bbbbuSDDyayc+cO1qxZxe9//wAffDCRe+65l1AolLL960rOGXBiEcJFY3HCJZj5TQgOvAXDVN8oIiIiItVbMBjkySef5bXXXuLll//Jnj2fEwgEad26LU888QwdO3ZO6f7V5Jwmx7GJzHgGu3g7RiiX0PC7MHxBt2OJiIiIiFQJoVCI66+/meuvv7nS963LDqfJWvgm8a2fgMdLaOgdmDm13Y4kIiIiIiKoyTkt0bWzsZZNAiB4wQ14Clq6nEhERERERL6kJqeCwttWUzbr7wD4u1yCr2VPlxOJiIiIiMhX6ZmcCkgc3suet/8IdgJvi674zxvldiQREREREfkvanJOkZOIUzrpz9jhI3jqtiA44CYMQxfCRERERESqGv2Ufoqc0oPYB3fiyckn58K7MLzpNZRJRERERKo+x3HcjuCqZH3/upJziszcutT4zv3kN21OSdSDHbfdjiQiIiIiGcLj8QBgWVH8/ur7YbplRQHweM6sTVGTUwHegrPxZGVDtNTtKCIiIiKSQUzTQyiUw9GjBwHw+wMYhuFyqnK2bZBIpPYKk+M4WFaUo0cPEgrlYJpndsOZmhwRERERkSogNzcf4FijU1WYpoltV85dTKFQzrE6nAk1OSIiIiIiVYBhGNSsWZsaNfJIJOJuxwHA4zGoWTOLw4fLUn41x+PxnvEVnC+pyRERERERqUJM08Q0/W7HAMDrNQkGg4TDCeJp9Ey6VlcTEREREZGMoiZHREREREQyipocERERERHJKGpyREREREQkoxhOGoxVdRwH264aMT0ek0QifR66Sjeqb2qpvqml+qaW6ptaqm9qqb6ppfqmVlWqr2kapzQ/KC2aHBERERERkVOl29VERERERCSjqMkREREREZGMoiZHREREREQyipocERERERHJKGpyREREREQko6jJERERERGRjKImR0REREREMoqaHBERERERyShqckREREREJKOoyRERERERkYyiJkdERERERDKKmhwREREREckoanK+wrZtHn/8cfr27UunTp246aab2L59+9duf/DgQX72s5/RrVs3unfvzgMPPEA4HK7ExOmlovV99913adOmzQm/duzYUYmp09MzzzzD1Vdf/Y3b6Pg9fadSXx2/FXPo0CHuu+8++vXrR5cuXfjhD3/I4sWLv3b7HTt2cMstt9ClSxf69OnD2LFjSSQSlZg4vVS0vk8//fRJj185ueLiYn7xi1/Qo0cPOnfuzM0338zGjRu/dnudfyumovXV+ff0bd68mc6dO/P2229/7Tbpcvx63Q5QlTz11FO8+uqrPPzww9SvX59HHnmEG2+8kffeew+/33/C9nfccQfhcJgXXniBkpIS7r33XsrKyvh//+//uZC+6qtofT/77DO6d+/OY489dtzr+fn5lRU5Lb3yyiuMHTuWrl27fuN2On5Pz6nWV8dvxdx9993s27ePxx57jNq1a/PSSy9xww03MH78eM4666zjto3FYtxwww00b96c119/nW3btnHvvfdimiZ33HGHS99B1VaR+kL58XvppZfyi1/8woW06ef222/Htm2effZZsrOzGTduHD/60Y+YMmUKoVDohO11/q2YitZX59/TE4vF+PnPf05ZWdk3bpc2x68jjuM4TjQadTp37uy88sorx147fPiwU1hY6Lz33nsnbL906VKndevWzoYNG469Nnv2bKdNmzbO7t27KyVzOqlofR3HcW688UbnwQcfrKyIaW/37t3OLbfc4nTq1MkZPny4c9VVV33ttjp+K64i9XUcHb8VsWXLFqd169bO4sWLj71m27YzePBgZ+zYsSds/9577znt27d3Dh06dOy1119/3enSpYsTjUYrJXM6qWh9HcdxRowY4fzjH/+opITp7dChQ87dd9/tfPbZZ8deW7NmjdO6dWtn2bJlJ2yv82/FVLS+jqPz7+n605/+5FxzzTVO69atnbfeeuuk26TT8avb1b6wdu1aSktL6dmz57HXcnNzOffcc1m0aNEJ2y9evJi6dety9tlnH3ute/fuGIbBkiVLKiVzOqlofaH8k5iv1le+2apVq/D5fLz77rt07NjxG7fV8VtxFakv6PitiLy8PJ599lk6dOhw7DXDMDAMg5KSkhO2X7x4Me3ataNmzZrHXuvRowdHjx5lzZo1lZI5nVS0vpZlsWXLlpNe4ZET1axZkz/96U+0bt0agAMHDvDCCy9Qv359WrZsecL2Ov9WTEXrCzr/no5Fixbxxhtv8PDDD3/jdul0/KrJ+cLu3bsBaNCgwXGv16tX79jXvmrPnj0nbOv3+6lVqxaff/556oKmqYrW9/Dhw+zZs4fFixczcuRI+vTpw2233cbmzZsrJW86GjhwIE888QRNmjT51m11/FZcReqr47dicnNzueCCC467bbWoqIitW7fSt2/fE7bfvXs39evXP+61evXqAej4PYmK1nfDhg0kEgmKiooYNmwY/fv35xe/+AV79+6tzNhp6X//93/p2bMnEydO5KGHHiIrK+uEbXT+PX2nUl+dfyuupKSEX/7yl/zmN7854dj8b+l0/KrJ+cKXD0z997MhgUCAaDR60u1P9hzJ121f3VW0vuvXrwfAcRz+8Ic/MHbsWKLRKFdccQX79+9PfeAMp+M3tXT8npmlS5fy61//mqFDh9K/f/8Tvh6JRE56LgF0/J6Cb6vvunXrAAiFQowbN46HHnqITZs2cc011xCJRCo5bXq59tpreeutt7j44ou5/fbbWbVq1Qnb6Px7+k6lvjr/Vtxvf/tbOnfuzMiRI79123Q6frXwwBeCwSBQfpn+y3+H8r8wT/ZQWzAYxLKsE16PRqMn/WShuqtofbt27cr8+fPJy8vDMAwAnnzySfr378/bb7/NzTffXDnBM5SO39TS8Xv6pk2bxs9//nO6dOnCo48+etJtTnb8fvmXq47fb3Yq9R01ahT9+vU77iHtVq1a0a9fP2bMmMGFF15YWXHTzpe3Tz300EMsW7aMl19+mT/84Q/HbaPz7+k7lfrq/FsxEyZMYPHixbz33nuntH06Hb+6kvOFLy+9/ffl+L1791JQUHDC9vXr1z9hW8uyOHTo0LHbJuQ/KlpfKF8F5csTFJR/qti4cWP27NmTuqDVhI7f1NPxW3Evv/wyY8aMYcCAAfz1r389dnXmv53s+P3yv7/ufCKnXl84cRWqevXqUatWrZPeXlzdHThwgIkTJxKPx4+9ZpomLVu2POktfjr/VkxF6ws6/1bEW2+9RXFxMf3796dz58507twZgPvvv58bb7zxhO3T6fhVk/OFtm3bkpOTw8cff3zstZKSElavXk23bt1O2L5bt27s3r2brVu3Hntt4cKFAJx33nmpD5xmKlrfN954g/PPP/+4ZQyPHj3Kli1bvvZBQzl1On5TS8dvxb366qs8+OCDXHnllTz22GMnvR3iS926dWP16tUcPXr02GsLFiwgOzubtm3bVkbctFOR+v75z39m2LBhOI5z7LUdO3Zw8OBBHb8nsX//fu6++27mz59/7LVYLMbq1atP+vC7zr8VU9H66vxbMY8++iiTJk1iwoQJx35B+TLRDz300Anbp9PxqybnC36/n6uuuopHH32U6dOns3btWn76059Sv359hg4dSiKRYN++fcfuR+7YsSNdunThpz/9KcuXL2fBggXcd999jBo1Sp8knkRF69uvXz9s2+aXv/wl69evZ8WKFYwZM4b8/Hwuu+wyl7+b9KPjN7V0/J6ZzZs38/vf/54hQ4Zwyy23sH//fvbt28e+ffs4cuQIlmWxb9++Y7dIDB48mLp163LXXXexdu1apk2bxmOPPcb111//jT+8V1cVre+QIUPYuXMnv/3tb9m8eTOLFi1izJgxdOnS5aQLFVR3rVu3pl+/fvzud79j0aJFrFu3jl/96leUlJTwox/9SOffM1TR+ur8WzEFBQU0a9bsuF8AtWvXpqCgIL2PX3dXsK5a4vG488c//tHp0aOH06lTJ+emm25ytm/f7jiO42zfvv2EdcP379/vjBkzxunUqZNz/vnnO/fff78TiUTcil/lVbS+K1eudK677jrnvPPOc7p06eKMGTPG2bVrl1vx08o999xz3BwXHb/JdSr11fF76p5++mmndevWJ/11zz33OAsWLHBat27tLFiw4Njv2bJli3Pdddc5HTp0cPr06eOMHTvWSSQSLn4XVdfp1HfevHnO97//fadTp05O9+7dnV//+tfHzSWS45WUlDj333+/07t3b6ewsNC5/vrrnXXr1jmOo/NvMlS0vjr/npmv1jOdj1/Dcb5yPVpERERERCTN6XY1ERERERHJKGpyREREREQko6jJERERERGRjKImR0REREREMoqaHBERERERyShqckREREREJKOoyRERERERkYyiJkdERERERDKKmhwREREREckoanJERERERCSjqMkREREREZGMoiZHREREREQyyv8HtQatnYty2MQAAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -6068,15 +6066,17 @@
     }
    ],
    "source": [
-    "df_demo[[\"A\", \"C\"]].plot();"
+    "df_demo[[\"A\", \"C\"]].plot(figsize=(10,3));"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "### Seaborn Color Palette Example\n",
@@ -6086,14 +6086,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABECAYAAACF4e8fAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAClklEQVR4nO3Zv0tWUQDG8WP4M0h7ExuDJic3g1pcgv6NaCiCgpbIMMcIgpaoQXpp6F+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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxoAAABhCAYAAABRTdfNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAEbklEQVR4nO3dzWpcZRzH8f+ZM0lDm5M0mbSKFLRuRN30Alx4F11lY9e9AUHoxlIw0IWI7oq4ElzYTW9AhOILuOvGWlSiIXbMy7SZSTJnjpfQI/7lMO3ns34Wv8Uzc+YLB6ZomqYJAACARL2uBwAAAM8foQEAAKQTGgAAQDqhAQAApBMaAABAOqEBAACkExoAAEA6oQEAAKQTGgAAQLp+24NN08Tj/XHUM38k3tZGfxzlyiDqw2E0s7rrOXOh6JVRrgxieLQXdTPres7cOD+OWNwYxMnjYTS1u9ZGUZaxuDGIw/1JzGbuWlv9chLLq+vx5ODvmPlea6XXK2N5dT2mo+MIV621ce8kqqqK0WjkM9pSr9eLqqridLIfjWdoa0+LKtaWFmJvchp143duG+tLi1H2imeeax0aRVHEh3e+i4fbB/9p2Ivk07e+iUvXtmLnq1txsvOo6zlzYfHly3Hp2lZ89O1n8Wjv967nzI3375+LK7e34sHNW/H0F3etjXOvX44rt7fiyzvfx872Yddz5sYbr/0QV6/fiHtffBx//fFr13PmwoVXXo2r12/En18/iOPdp13PmRv3X3oUm5ubcffu3djd3e16zly4ePFibG5uxs8/fR7j0XbXc+bGvbPvxQfvvBmf/Pgwfjscdz1nLtx89+24cPbMM895dQoAAEgnNAAAgHRCAwAASCc0AACAdEIDAABIJzQAAIB0QgMAAEgnNAAAgHRCAwAASCc0AACAdEIDAABIJzQAAIB0QgMAAEgnNAAAgHRCAwAASCc0AACAdEIDAABIJzQAAIB0QgMAAEgnNAAAgHRCAwAASCc0AACAdEIDAABIJzQAAIB0QgMAAEgnNAAAgHRCAwAASCc0AACAdEIDAABIJzQAAIB0QgMAAEgnNAAAgHRCAwAASCc0AACAdEIDAABIJzQAAIB0QgMAAEgnNAAAgHRCAwAASCc0AACAdEIDAABIJzQAAIB0QgMAAEgnNAAAgHRCAwAASCc0AACAdEIDAABIJzQAAIB0QgMAAEgnNAAAgHRCAwAASCc0AACAdEIDAABIJzQAAIB0QgMAAEgnNAAAgHRCAwAASCc0AACAdEIDAABIJzQAAIB0QgMAAEgnNAAAgHRCAwAASCc0AACAdEIDAABIJzQAAIB0QgMAAEgnNAAAgHRCAwAASCc0AACAdEIDAABIJzQAAIB0QgMAAEgnNAAAgHRF0zRN28OP98cxrWf/557nysbCJPorg5geDqOpp13PmQtF2Y/+yiCGR3sxndVdz5kb54+LODMYxPFwGM2pu9ZGsdCPM4NBHO6Po/a91tpC/ySWV9fiycFe1PVp13PmQlkuxPLqWkxHx9HUrR+5L7xJeRpVVcVoNIq69jxooyzLqKoqTiYH0cw8C9o66lWxtrQYe5OTmM58RttYX1qMslc889y/Cg0AAIA2vDoFAACkExoAAEA6oQEAAKQTGgAAQDqhAQAApBMaAABAOqEBAACkExoAAEA6oQEAAKT7ByHC1VIPhmY7AAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x72 with 1 Axes>"
+       "<Figure size 1000x100 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -6106,14 +6106,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x72 with 1 Axes>"
+       "<Figure size 1000x100 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -6126,14 +6126,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 1440x72 with 1 Axes>"
+       "<Figure size 2000x100 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -6146,7 +6146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -6155,9 +6155,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x72 with 1 Axes>"
+       "<Figure size 1000x100 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -6170,14 +6170,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 576x72 with 1 Axes>"
+       "<Figure size 800x100 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -6190,14 +6190,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x72 with 1 Axes>"
+       "<Figure size 1000x100 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -6223,7 +6223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6232,9 +6232,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -6261,7 +6261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6270,14 +6270,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x432 with 3 Axes>"
+       "<Figure size 600x600 with 3 Axes>"
       ]
      },
      "metadata": {},
@@ -6309,7 +6309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 103,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -6332,7 +6332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 104,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -6422,7 +6422,7 @@
        "16                      0.70              7.95         142.81  "
       ]
      },
-     "execution_count": 108,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6433,7 +6433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 105,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -6443,9 +6443,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 864x288 with 1 Axes>"
+       "<Figure size 1200x400 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -6471,7 +6471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6621,183 +6621,440 @@
        "      <th>2</th>\n",
        "      <th>4</th>\n",
        "      <td>5</td>\n",
-       "      <td>164.16</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": 106,
+     "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": 107,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3b9/G9u3b8eeffwIAzp8/j5EjR8LY2BgODg44ceKE3GPybG/loExWVhZSUlIglUqrzFNWVobExMQqvyMPKvc2q9cT9aaYmBiYmZmhbdu2AAAzMzO5xU1LS4OrqysePHgATU1NbN26FdOmTYO6ujr09fVx9uxZ/PLLL9i7dy+6du0qt7gxMTFvnVb5kKfKNw2NGDFCbnHfdaIWEhICExMTYeRTniOg4eHhiIiIwOTJkyGRSLBnzx44ODggIiIC+vr6wnzyvuCM136VnJwMV1dXPH/+HBoaGtiwYQPmzJkDHR0d9OvXD7dv38bYsWOxfft2uT5gEgAePXoELy8vXLt2DZ06dcKWLVvg4eGBBw8eoGPHjjh06BDCw8Oxe/duuW5jXrkWW9z169fj/Pnz8Pf3h0Qiwfbt2+Ho6Ijg4GD069dPmE/etbRv3z4sW7YMw4cPR7169bBr1y6cOHEC27ZtE/ajiooKuX8Z5NVHA/z6aV5xeeVabMdhXsdDMdYSr1zzOgcQW3uDg4PfOi09PR0RERHCK5W9vLzkFpdXngF+ueZ17sErrtjyHBcXh2nTpqFJkyYoLCzEkiVL4O/vjyFDhuCbb77B7du3MXv2bKxbtw62trZyi8urvQCQn5+PWbNmIT4+HgDQpk0bLFu2DNbW1jLzTJgwATdv3pR7fJW7zUpfX1/YUO9qmkQikWtC3dzcUL9+fcyaNQv79u3DwYMH0atXL/z4449QU1MDYwy+vr7Izs7GTz/9JLe4xsbGKC4uBqDc9urq6r51VLHy0rHK/8oz7uDBgzF79mwMGTIEAPD8+XN4enrizp072L17Nzp37ownT56gX79+co3La78aP348OnbsCB8fH+zduxfr16/HyJEjsWzZMmGedevWISEhAbt375ZbXADw9vbGgwcP4ObmhpiYGFy7dg2fffYZQkJC0KxZMxQVFcHb2xsaGhpYv3693OLyyrXY4trY2GDZsmXCQa68vBzz5s3D6dOn8dNPP6Fnz54KqaVhw4bByckJo0aNAvBqYHjKlCkoLi7Gnj170KZNG4XE5dVHA/z6aV5xeeVabMdhXsdDMdYSr1zzOgcQW3v79euHJ0+eoHXr1mjQoIHMtIcPH6Jt27aoX78+JBIJTp48Kbe4vPIM8Ms1r3MPXnHFlmcHBwcMGDAAM2bMwJEjRzB37lxMnToVM2bMEOaJjIzEgQMHEBUVJbe4vNoLAL6+vkhOTsaSJUsgkUgQFhaGM2fOYMWKFfj6668BvLo6y9LSErdu3ZJrbACq9zarxMREZm1tzZydndmdO3dYVlYWy8rKYpmZmaxHjx4sISFB+EyeTE1N2d27dxljrx72p6+vX+VJ1ampqczc3FyucdPS0piDgwNzdnau8lC0Hj16sIyMDLnGq3TgwAFmamrKnJ2d2YULF9ilS5fYpUuXWHx8PDMyMmIHDx4UPpOn6tr04sULNnLkSGZtbc2ys7PZ48ePma6urlzj8tqvjI2NhfZKpVKmr6/Prl+/LjPPvXv3WM+ePeUalzHGevXqxW7evMkYY+z58+dMT0+PJSYmysxz8+ZNZmFhIde4vHIttrg9evRg9+/fl/msvLycubi4MDMzM/b3338rpJaqq+HHjx+z//znP8ze3p49f/5cIXF59dGM8eunecXllWuxHYd5HQ/FWEu8cs3rHEBs7c3NzWUeHh7Mzs6uygNcVXG/YozvvsXr3INHXDHmOTMzU/hZX19f+C5RKSMjgxkbG8s9Lo/2MsZYv3792OXLl2U+W716NdPX12fHjh1jjDGFxWaMMZV7Zo6pqSkOHjwITU1NeHh4ICcnB9ra2mjfvj0AQEtLC9ra2tDW1pZr3IYNG+L58+cAgMLCQkilUhQVFcnM8+LFC6irq8s1bseOHbF3714YGhpi+PDhOHLkiFyX/zb29vaIiYlBaWkp1q1bh3bt2sHc3By9evVCvXr10KNHD5ibm8Pc3Fyucdu3by9cxlapcePG2LJlCxhjcHV1RW5urlxjAvz2qxYtWiArKwvAq78OSaXSKs8EyM7ORrNmzeQaF3j1l83Xr75ijEFNTa3KPBUVFXKNyyvXYovbuXNnHD16VOaz+vXrY/369WjXrh1cXV2RlpYm15gA0LZt2yr3Mbdu3Rpbt25FdnY2PDw8hL+8yxOvPhrg10/zissr12I7DvM6Hoqxlnjlmtc5gNjaq6mpic2bN8PJyQnOzs7YunWrXJf/NrzyDPDLNa9zD15xxZbnNm3aCFe+3L59G1KpFKmpqTLz/P3332jTpo1c4/JqLwAUFRUJt/dW8vHxgb29PebOnYvLly8rJK5AIUNEtURMTAwzNzdnP/zwAysrK1Po6PrSpUuZnZ0d27lzJ3N0dBT+0l75l6PHjx+zsWPHsrlz5yokPmOMJSQkMGtrazZnzhxWUFCg0PZWqqioYJs2bWJmZmbsl19+YYwp9q8Yv/76KzMwMGB+fn7s3r17MtNSU1NZ3759mbm5ucJGPxlT7n61bt06ZmlpyVavXs2GDBnChg4dyhwdHVlSUhIrKSlhV69eZV999RVbvny53GN7e3szR0dHdvr0aTZjxgzWu3dv5unpKbzKr6SkhHl6ejIPDw+5x66kzFyLLe7JkyeZvr4+mzx5Mrt165bMtEePHrEhQ4YwQ0NDuddSWFgYMzc3Z1u2bKnyl/XLly8zY2NjNnDgQLnHrQ19NGN8+mllx+WV69qwjZWZZ17Hw9qQZ8bEkWte5wBia+/rKq/+GjdunHCFrKrtV4zxyzWvcw9eccWW5//+97/M2NiYTZ8+nfXp04eNHTuWjR49mh08eJClpaWx2NhY1q9fP7Zu3Tq5xuXVXsYYGz9+PAsICKjyeVlZGZs4cSLr2bMnO3z4sMK+m6r0YA5jjGVmZrKxY8eykSNHMkNDQ4V1yC9evGA+Pj7M1NSUDRo0iF2+fJnt3LmT6erqsr59+zIDAwM2ePBglpOTo5D4lfLz85m3tzezsrJiBgYGSvmSwBhjycnJbNCgQczd3Z0ZGRkpNO6BAwfY2LFj2Z9//lllWmZmJhs/frxCB3Mq4yhjvyovL2cbNmxgw4cPZ5MnT2apqans6NGjrFu3bkxXV5fp6uoyR0dHVlBQIPfYubm5bPz48UxHR4eZmJiw48ePs/Xr1zNzc3M2evRo1qtXL9a7d2/hcntFUVauxRg3MTGRzZ07t8olsIy96kt8fHyYkZGRXGNWVFSwzZs3MysrK3bx4sUq05OTkxUymFNb+mjG+PXTyorLK9e1ZRsrc/vyOB7Wljwzpvq55nkOILb2vq6srIwFBQUxU1NTldyvGOObax7nHrziijHPUVFRzNPTk/n5+bFHjx6xxMRE1qtXL6arq8t0dHSYt7c3KykpkXtcXu1NSkpiPXr0YEOGDKlyy3FhYSFzdHQUtrUiqNwDkKtTUVGBkJAQREdHY+fOnWjXrp3SYv/111/4888/oaWlBWtrazRq1EgpcWNiYhAdHY2goCDhTTiK9vLlS6xYsQLR0dE4ceIEPv/8c6XErc6jR48U3m6e+1V2djaSk5OhpaUFIyMjub7i7k2VT+CvvGT+yJEjSEpKwqeffoqRI0fK/VLJ6vDKtdjivm1d6tVTzB257P8/JP1NUqkUV69ehbGxsULivo5XHw3w6ad5xuWVazEdh6ujjOMhIM5aepOycq3Mc4B3UfX2JiQkYP/+/Zg3b16V2yiUSVl5BmrPvqXIc4/aEFdseX758iXu3r0LLS0ttG7dWuHx3kZR7U1PT8eBAwcwfPhwdOzYUWZaWVkZQkNDcejQIRw7dkzusUUxmEMIIYQQQgghhBCiKlTuAciEEEIIIYQQQgghqkzt/bMQUnssXLiwxvOuWrVKgWtCSN3m5ORU48t6IyMj5RY3ODi4xvN6eXnJLS4hqoaOh8ojtlyLrb28iDHPvM49eMXlhdr7dqrQ3tfRYA6pUxo0aIBffvkFn332mdxf1UyImFhaWmL9+vXo2LEjjIyMlBb3r7/+wvnz59GsWTM0adLkrfNJJBIazCHkHeh4qDxiy7XY2suLGPPM69yDV1xeqL3KY2NjU+OBpJMnT8o9Pj0zh9Q5P/zwA6KiovDbb79xfSAdIXXdrl27sHbtWhw8eBDt27dXSkzGGBYuXIg///wTMTExaNy4sVLiEqKK6HioPGLLtdjay4sY88zj3INnXF6ovcpx6NAhfPfdd+jUqRP+85//vHNeRfyRUpSDOWVlZfjrr7+goaEBAwMD1K9fX6XjlpSUIDY2FhoaGrCwsEDz5s2VEreiogLZ2dlo2LChXA9QjDE4OjqiS5cuWLFihdyW+7HEtn0BxW3j9xFbDSsy7tSpU6Guro4NGzbIbZnvU1paiq+//hr9+/fH3LlzlRb3fXhtX4BfHfOKq4q19C6KyjMdD6sSW66pvcrps4qKihAREYGGDRti4MCB+Ne//iWX5dbWPAOKzTWPcw+ecd9FFfP8LoqqJYBfe3///XfMnj0b+/btg66urlJji/IByPn5+XByckJMTAzGjx8PqVSq0nGfP3+OBQsW4MGDB3BxcUF5eblS4ubl5cHGxgaLFi2Cp6en3JYrkUjg7+8PPT09uS1THsS2fQHFbeP3EVsNKzLu8uXLMXLkSLktrybU1dWxfPlyqKnVrjt9eW1fgF8d84qrirX0LorKMx0PqxJbrqm9yumzioqKEBwcjPbt22PBggUqn2dAsbnmce7BM+67qGKe30VRtQTwa6+trS3s7Ozw/fffKz22aK/MuXLlCszNzZGZmYnPP/9cpeNWVFTg4cOH0NbWRmFhIZo2baqUuCUlJThy5AhGjhyJy5cvw8TEBPXqqe74odi2L8BvG4uthnnFFRueeeZVx7ziiq2WePbTPIixlnih9iq/vYyxGj8joy6rDbkWAzHnWZVqqbCwEFlZWUq/MkeUgzmEEEIIIYQQQgghdZVKXiZx7949bNy4Ef7+/jhz5kyV6YWFhR/0OsDaHvdt3Nzc8OjRI4Ut/+nTp4iOjsb27dvx999/V5leeRmdqhDb9gVq3zZeunQp8vLyFLZ86jtUW23Ms6LrODExEQsWLICHhwd+/vnnKre75OfnY8KECQqL/yZVreG3UUY/zUNtyzOgurl+G1Vtb23rs4YNG4aHDx8qLV5toKr7Vm0jtjyLsZaUQeWuzElKSoKLiwvatm0LiUSCjIwMDBo0CIGBgVBXVwcAPHnyBP369cPNmzfrfNyYmJi3TluyZAlmzpwpPJh2xIgRcoubmpqKiRMnoqioCMCr222cnZ0xf/58YR5FtJcXsW1fgN82TkxMfOu0KVOmICAgAG3btgUAmJmZyS2u2PoOseGZZ151fOrUKXh5ecHc3Bz16tVDfHw8unfvjtDQUOEBi1TDH49nP82DGGuJF7G1l1ef9a4/SoWGhmLs2LFCfEW8jYYHse1bvIgtz2KsJd5UbjDH0dERenp68PPzAwAcO3YMixYtgrGxMUJDQ6GmpqaQAwGvuMbGxiguLgbw6r7Dt5FIJHKN6+rqisaNGyMoKAj16tVDZGQkfvjhB9jb22PlypUAVOsLqNi2L8BvG+vr6wttVWabxdZ3iA3PPPOqYwcHBwwcOBAeHh4AgKtXr8LLywutW7dGZGQkmjZtSjUsBzz7aR7EWEu8iK29vPqsfv364cmTJ2jdujUaNGggM+3hw4do27Yt6tevD4lEgpMnT8otLk9i27d4EVuexVhL3DEVY2Jiwu7duyfzWVJSEuvRowfz9vZmjDH2+PFjpqurqxJx09LSmIODA3N2dmbZ2dky03r06MEyMjLkGq+SmZkZS01Nlfns+PHjTF9fn33//feMMcW0lxexbV/G+G3jxMREZm1tzZydndmdO3dYVlYWy8rKYpmZmaxHjx4sISFB+EyexNZ3iA3PPPOq4+qWfffuXda7d282ceJEVlpaSjUsBzz7aR7EWEu8iK29vPqs3Nxc5uHhwezs7FhKSsp710kViG3f4kVseRZjLfGmcs/Madq0KXJzc2U+MzExQWBgIGJjY7Fq1SqVituxY0fs3bsXhoaGGD58OI4cOaKQOG9SV1dHSUmJzGcDBw6Er68vIiIisH37dqWsR3XKysqQmJiIq1evyu11qGLbvgC/bWxqaoqDBw9CU1MTHh4eyMnJgba2Ntq3bw8A0NLSgra2NrS1teUaV2x9R02UlJQgJiYGsbGxyM/PV1rciooKPHjwQK7PVuGZZ151rKmpifv378t81qlTJ2zatAlXrlzB/PnzFfLKaLHVMM9++n1U6XgIiC/XYmsvrz5LU1MTmzdvhpOTE5ydnbF161a5x/inFJFnoHbvW7zOPRQRV2x5rs21xGu/Ujjeo0ny5ufnx+zt7dmVK1dYaWmpzLQdO3YwHR0dNn36dLmP6vOK+7qEhARmbW3N5syZwwoKChQ6Ajp79mzm5OTEHj16VGXamjVrmK6uLgsICOByNcHjx4+Zjo4OW7ZsGRszZgwrLy//6GWKbfsyVju2cUxMDDM3N2c//PADKysrU2ibxdx3vE1lLYWHh7Ovv/6alZWVKTWuu7s78/DwkMsya0uelVnHQUFBzNramh06dIjl5+fLTDt27BgzMDBgo0ePphqWI2X30++jqsdDxsSR69eJob21oc+qvJJi3LhxwtWEqpbnN9XWfYvXuYei4ootz7W1lpS9XzHG2IsXL9jGjRtZWFhYlStbP5bKDeY8e/aMTZo0ienq6rK4uLgq03ft2sUMDAzkfiDgFfdN+fn5zNvbm1lZWTEDAwOFFc3Dhw/Z0KFDma6uLjtz5kyV6WvWrGE6OjpcvoCWlpayS5cuMcaY3Novtu3LWO3ZxpmZmWzs2LFs5MiRzNDQUGFtFnvfUR2pVCrcCvP8+XOlxS0uLmbR0dGMsVe37Eil0o9eZm3Ks7LquLi4mH333XesW7du7Pz581Wmnzx5kpmamlINy5ky++n3UeXjIWOqn+s3qXp7a0ufVVZWxoKCgpipqalK5rk6tWnf4nXuoYy4YstzbaolXvsVY/83kBQbG8vGjBkj14EklRvMqXT//n1WUFBQ7bS0tDQWGhqqUnHftH//fubk5MRycnIUFqOkpIRduHCBPX78uNrp8fHxzM/PT2HxeRDT9mWs9mxjqVTKgoODmY2NDXvw4IFCY4m971B1tSnPyqrjly9fsuLi4mqn5efns99++02h8RkTRw2/SVnbl5fakmfGVD/Xb1L19taGPosxxi5dusQWLFjAcnNzlRKvNlD1fau2EFuexVhLb1NRUSHX5anc26yI6rt37x5+++035Ofnw8rKClZWVjLTCwsLERAQwPVZI4TUZW5ubvD39xdeHy1vT58+xenTp1FQUIC+ffuia9euMtOLiooQERFBr60k5D3oeMjf0qVLMWPGDOH1wqqC9i3loDz/H0WfeyQmJiIqKkrI9ahRo1C/fn1hen5+PqZPn47IyEiFxK8tFJ3n2kbV20uDOaROSUpKgouLC9q2bQuJRIKMjAwMGjQIgYGBUFdXB6Bar0QnRFFiYmLeOm3JkiWYOXOm8OVkxIgRcoubmpqKiRMnoqioCMCrB9I5Oztj/vz5wjxUw4S8Hx0PlScxMfGt06ZMmYKAgADhi4KZmZmyVkthaN9SDjHmmde5x6lTp+Dl5QVzc3PUq1cP8fHx6N69O0JDQ9G8eXMAqpVrXnnmpba2d9iwYdi6dSvatWunsBg0mEPqFEdHR+jp6cHPzw8AcOzYMSxatAjGxsYIDQ2FmpqaSnXGhCiKsbExiouLAQDvOgxIJBK51pKrqysaN26MoKAg1KtXD5GRkfjhhx9gb2+PlStXAlCtEypCFIWOh8qjr68v9JPK7C95oX1LOcSYZ17nHg4ODhg4cCA8PDwAAFevXoWXlxdat26NyMhING3aVKVyzSvPvPBsb3Bw8FunhYaGYuzYscKAoUKuOJfrTVuEKJiJiUmVp4AnJSWxHj16MG9vb8bYq4dM8XhYLCF1SeVbBpydnVl2drbMNEW+ccDMzIylpqbKfHb8+HGmr6/Pvv/+e8YY1TAhNUHHQ+VJTExk1tbWzNnZmd25c4dlZWWxrKws4Q0tCQkJwmeqgPYt5RBjnnmde1S37Lt377LevXuziRMnstLSUpXKNa8888KzvZaWlkxXV5dZWloya2trmX+6urrMysqKWVtbMxsbG4XEryf/4SFCFKdp06bIzc2V+czExASBgYGIjY0VxT3FhMhDx44dsXfvXhgaGmL48OE4cuSIUuKqq6ujpKRE5rOBAwfC19cXERER2L59u1LWg5C6jo6HymNqaoqDBw9CU1MTHh4eyMnJgba2Ntq3bw8A0NLSgra2NrS1tTmvqXzQvqUcYswzr3MPTU1N3L9/X+azTp06YdOmTbhy5Qrmz58PqVSqlHVRBl555oVnew8cOABra2s0b94cmzZtwqlTp4R/DRs2xM6dO3Hq1CmcPHlSMSugkCGiWq64uJjt37+fHTlyhD179oziKkhpaSlLSEhgycnJrLy8XC7L9PPzY/b29uzKlSustLRUZtqOHTuYjo4Omz59OpeRdbFtX8YUs41rc1xV3cYJCQnM2tqazZkzhxUUFCj0rxizZ89mTk5O7NGjR1WmrVmzhunq6rKAgABR1TDP2C9evGAbN25kYWFhVf5CrEhUwx+Pjod8YsfExDBzc3P2ww8/sLKyMu5/5RbbvsWrz1JE3NqcZ8ZU69wjKCiIWVtbs0OHDrH8/HyZaceOHWMGBgZs9OjRKtlfKjPPNaHoGubV3j179jBzc3O2ZcsW4TNlxBbllTnPnz/HggUL8ODBA7i4uKC8vJziKkB+fj6cnJwQExOD8ePHy2XEe86cOWjVqhXGjh2LixcvykwbP348Fi9ejFOnTn10nH9CbNsXUMw2rs1xVXUbm5mZISYmBowxDB06FGVlZXJd/uvmz5+Pp0+fwsrKCmfPnpWZNm/ePEyePJnbmyR41hKv2EVFRQgODkb79u2xYMGCOn18qAlVqmE6HvKJPXz4cERFRSExMRGjRo3i/td8se1bvPosRcStzXkGVOvcw8vLCxYWFliwYAGuX78uM23QoEHYsGED7t69q7D476JKea4JRdcwr/aOHj0ae/bswbFjxzB+/HhkZWUpJa4or8yRSqXCfc3Pnz+nuApSWlrKLl26xBhjch+VvH//PisoKKh2WlpaGgsNDZVrvJoQ2/ZlTLHbuDbGFcM23r9/P3NycmI5OTkKi1FSUsIuXLjAHj9+XO30+Ph45ufnp7D4b8OzlnjGrlRRUaG0WFTD8kPHQz6xpVIpCw4OZjY2NuzBgwcKi1OT9RDTvvU6ZfZZioxbW/OsaucejDH28uVLVlxcXO20/Px89ttvvyk0fnVUMc81pega5tHesrIyFhQUxExNTZmBgYHCz3HobVaEEEIIIYQQQgghcpCQkID9+/dj3rx5wmvRFUGUt1kR+bh37x42btwIf39/nDlzpsr0wsJCLFy4kMOaKUZJSQmuX78uvPru5s2b+O677+Dq6orvv/8e2dnZnNdQ/nht49q2b7m5ueHRo0cKWXZMTAxKS0tlPouPj4ebmxvs7e0xZ84cbpf+qhqeNcwrdnJyMrZu3Sr8HB8fj6lTp2Lo0KHw9PTE5cuXFRJXTDVcG+MqEs8+S4z9pdjOPXj1Wbzi1kaq2G/xIsY+i2rpFXNzc6xatUqhAzkAQFfmkH8kKSkJLi4uaNu2LSQSCTIyMjBo0CAEBgZCXV0dAPDkyRP069cPN2/e5Ly2Hy8tLQ3Ozs549OgRPvvsM/j7+8PT0xPa2tro0qULUlJSUFBQgN27d6Nz5868V1cueG1jXnFjYmLeOm3JkiWYOXOm0CGPGDFCbnH19PRw7tw5tGrVCgBw9uxZuLm5wdLSEl27dsW1a9dw9epVbNu2DSYmJnKLKzY8a5hX7KNHj8Lb2xsWFhYIDw/H6dOn4enpCSsrK3Tp0gV37tzBhQsXEBwcDGtra7nFFVsN84rLC88+S2z9pdjOPXj1Wbzi8iS2fosXsfVZYqwl3lRuMMfJyQkSiaRG88rzIZtii+vo6Ag9PT34+fkBAI4dO4ZFixbB2NgYoaGhUFNTU6nBHHd3d2hoaMDT0xPbt29HbGws7OzsEBAQAIlEgvLycvj4+CA/Px/h4eFyi8tr+wL8tjGvuMbGxsJfPt/VLUokErnG1dXVxfnz54UDvaOjI7p37w4fHx9hnlWrVuHatWvYvXu33OKKDa8a5hl76NChGDp0KKZOnQoAGDVqFPr27YuZM2cK84SEhOD48ePYv3+/3OKKrYZ5xeWFZ58ltv6SZ7/FA68+i1dcnsTWb/Eitj5LjLXEm8rdZmVpaYnLly8jNzcX2tra7/xHcf+527dvY8KECcLPgwcPRlhYGJKSkmQ6KFWRkJCAWbNmQVdXF/Pnz0dJSQnGjx8vDLSoqanB3d0dSUlJco3La/sC/LYxr7jR0dHQ19dH7969ERcXh1u3bgn/GjVqhBMnTuDWrVsKP6m5f/8+hg0bJvPZ6NGjkZKSotC4qo5XDfOMnZGRATs7O+HnrKwsDB48WGaeoUOHyv0Sb7HVcG3pO3jh2Wepen/Js9/igVefxSsuT2Lvt3hR9T5LjLXEmxrvFZA3d3d3NG3aFGvXrsWWLVvQvn17iqsATZs2RW5uLv71r38Jn5mYmCAwMBAzZsxA69atMWXKFKWsy5tKSkoQGxsLDQ0NWFhYoHnz5h+9zIYNG+Lly5cAAE1NTYwaNQoaGhoy8xQUFOCTTz756Fiv47V9AX7bmFfcjh07Yu/evdiwYQOGDx+OxYsX46uvvpJ7nDe9eeVVx44dUVhYKPNZXl6e3PetmlBELdVEWVkZ/vrrL2hoaMDAwAD169f/6GXyqmGesT///HOcP38eY8aMAfDqcu9bt25BV1dXmOfq1av49NNP5RpXbDXMK25NKKKGefZZtbm/VLV+630UsW/x6rN4xa0JRR2HxdZv1URRUREiIiLQsGFDDBw4UOYY9k/V5j5LlWq4JnjtVwqn0HdlceTu7s6mT59OcRXEz8+P2dvbsytXrrDS0lKZaTt27GA6Ojps+vTpTFdXV2nrVOnx48dMR0eHhYeHs6+//pqVlZV99DLnzp3LRo8ezf7+++8q06RSKTt37hwbPHgwW7Zs2UfHqg6P/YrXNq4N+1ZCQgKztrZmc+bMYQUFBaxHjx4Ke7Wgjo4O6969Oxs+fDjz9vZmEydOZCNHjmQlJSXCugwdOpQtWrRIIfHfRRG19CFxly1bxsaMGcPKy8s/epk8a5hX7P379zMDAwMWGBjIbt68yS5fvsxsbW3Znj17WFJSEouIiGA9e/Zk27Ztk2tcsdVwbYj7NoqoYZ59Vl3oL1Wl33ofRexbvPosXnFrQhnHYTH0Wx8SNzY2lo0ZM6bO95fvo0o1XBO89ivGGHvx4gXbuHEjCwsLY/fu3ZPrslV2MCcnJ4edOnWK4irIs2fP2KRJk5iuri6Li4urMn3Xrl3MwMCAy2COVCplWVlZjDHGnj9/Lpdl5ubmstGjR7M5c+ZUmXb48GGmo6PD3Nzc5BbvTTz2K17buLbsW/n5+czb25tZWVkxAwMDhZ3YZGdns7i4OBYWFsbmzp3L7O3tWbdu3VhRURFjjDETExP27bffstzcXIXEfxdF1FJNlJaWskuXLjHGmNzyzrOGecaOiYlhNjY2TEdHh+nq6jIdHR3hn4mJCdu8ebPcY4qthmtL3OooooZ59lm1ub9UtX7rfRR1fODRZ/GM+z7KOg6rer/1oSoqKuSynNrcZ6laDb8Pz/1KEQOFlVTuAchEuTIyMtCyZctqLw9MT0/H8ePH4e7uzmHNFKOgoADNmjWT+ezp06d48uQJunbtymmtFIvXNq4t+1ZMTAyio6MRFBSEtm3bKjweAEilUuES/dTUVHTu3LnGD8Im78azhnnGTk9PR3p6OgoLC6GmpgYtLS0YGBhUuWVDnsRcwzzj8sCzzxJDfynGcw8efRbPuLWFmPotXsTQZwFUS2/DGJPv9pbbsFAtcePGjQ/+nWvXrlHcOhKXFzHmWWxtFltcntLT09mGDRvYihUrqr1y4/nz52zBggVyjUm1RHFVLS5jjBUXF7Nr166xly9fMsYYS0lJYYsWLWIuLi5s9erV7OHDh3KJ8zox1hJj4uu33mbKlCksJydHIcsWWw3v379fuN2m0sWLF9mUKVPYsGHDmLe3N0tNTf3oONURW7/FGGN//fUX27Jli/DzxYsXmbu7O7Ozs2MeHh4sMTFR7jGpht+vrtcSbyp3Zc63336LLl26wNXVFZ07d37nvDdu3MC2bduQnp6OqKgoilsH4vIixjyLrc1ii8tLUlISXFxc0LZtW0gkEmRkZGDQoEEIDAyEuro6ACjktdVUSxRX1eKmpaXB2dkZjx49wmeffQZ/f394enpCW1sbXbp0QUpKCgoKCrB79+73rteHEGMtia3fiomJeeu0JUuWYObMmdDU1AQAjBgx4qNivU5sNaynp4dz584Jr60+e/Ys3NzcYGlpia5du+LatWu4evUqtm3bBhMTk4+K9Sax9VtHjx6Ft7c3LCwsEB4ejtOnT8PT0xNWVlbo0qUL7ty5gwsXLiA4OBjW1tZyi0s1/HaqUksAkJycjEuXLsHNzQ0AEB8fj+3btyMrKwsdOnTA5MmTYWpqKve4AKBygzlSqRTh4eEICwtDu3bt0L9/f3zxxRdo1aoVpFIp8vLykJKSgvj4eDx48ACTJk2Cm5sbGjRoQHHrQFwnJ6caX5oWGRn5UbFeJ7Y8i7HNYovLq5YcHR2hp6cHPz8/AMCxY8ewaNEiGBsbIzQ0FGpqagr5UkS1RHFVLa67uzs0NDTg6emJ7du3IzY2FnZ2dggICIBEIkF5eTl8fHyQn5+P8PDwj4r1OjHWktj6LWNjYxQXFwN4dUvA20gkEpVoL6+4urq6OH/+vPAF1NHREd27d4ePj48wz6pVq3Dt2jXs3r37o2K9SWz91tChQzF06FBMnToVADBq1Cj07dsXM2fOFOYJCQnB8ePHsX//frnFpRpW/VriNVBYSeUGcyoVFhZiz549OHnyJK5du4by8nIAQIMGDWBkZARbW1s4ODjI/bVkFFexcbds2YL169ejY8eOMDIyeue8q1atkkvM14klz7UhNsVVzVrq2bMnoqOjZV75+eeff8LFxQU2NjZYu3atQr4UVaJaoriqEtfY2BhRUVHo1KkT8vLyYGFhgejoaOjr6wvz3LlzB6NHj8aVK1fkEvN1YqolsfVb6enpmDt3Lpo1a4bVq1fLvEbY2NgYBw8exOeffy6XWNURSw2/+QW0b9++CAsLk6nhtLQ0ODg44K+//pJLzDeJpd8yMjLC4cOHhf3WwsICERERMq/MzszMhJ2dHa5evSq3uJWohlW3lngNFAr43eGlPBUVFSw3N5c9ffqU4qpA3J07dzJjY2OWmZmp0Djvo+p5rk2xKa5i8KglKysrlpSUVOXzEydOMD09PbZy5Ur2+PFjpbwJj2qJ4tbluL1792bXr18Xfvbz86vyTIDExETWr18/ha1DJVWvJTH2W2VlZWzt2rWsV69e7PDhw8Lnyn5ttSrXsK6uLnvy5Inw87hx44Q3olVKTExklpaWCluH16lyv/XVV1+xn3/+Wfh58uTJbP/+/TLzHDp0iNna2so1bnWohuWPZy0ZGhrK5LNPnz7s5s2bMvNkZGQwQ0NDucdmTAWfmUPEYerUqVBXV8eGDRt4rwohdZqya2nx4sVITk7GsmXLYGBgIHNp7c6dO+Hv749BgwbhxIkTCvkLNyGqYt68ecjMzIS/vz+6dOkiM62iogIXL17EihUrYGFhgcWLF3NaS9Ug5n4rMTERPj4+MDExwZIlS2BlZaXwv+qLha6uLho2bIh///vf6Ny5M3Jzc1FQUIA9e/ZAXV0diYmJWL58OYyMjBAQEMB7deWCV78VExMDX19fODs7Y+jQoXjx4gUWLFgAV1dXdO3aFcnJydi0aRO8vLzg7Owst7i1gRhqmGct2dnZwcnJCWPGjAEAuLi4YNiwYTLPIjp8+DDWrVuHEydOyDU2oMK3WRHV9ujRI9y4cUMh9x4SIibKrqX8/HzMnj0bFy9exJYtW2BlZSUzfffu3Vi5ciWkUqnKfSkiRJ7y8vLg6emJ9u3bIygoSGbakSNH4O3tjf79+2Pt2rVo2rQpp7VUDWLvtwoKCrBs2TJcvnwZubm5iI2NVakvgrzk5OTg9u3buHPnjvDftLQ0JCQkoFGjRujZsyc6d+6M0NBQ4WG1dR3PfuvAgQPYsGED/ve//0Eikcg8S6ZJkyZwdXWFh4eHXGPWFqpewzxrifdAIQ3mEEIIUbqMjAy0bNkSn3zySZVp6enpOH78ONzd3TmsGSF1S0FBAZo1aybz2dOnT/HkyRN07dqV01qpJrH3WzExMYiOjkZQUBDatm3Le3VUklQqRf369QEAqamp6Ny5c41fVlCX8Oy30tPTkZ6ejsLCQqipqUFLSwsGBgbQ0NBQaNzaQEw1rMxa4jlQSIM5pE5JSUmReZhVTVy/fh3dunVT0BoRUjfxqiWqYULkg2pJecSWa7G1lxcx5pnOPZSD2vt+imgvj4FCGswhdcq3336LLl26wNXVFZ07d37nvDdu3MC2bduQnp6OqKgoJa0hIXUDr1qiGiZEPqiWlEdsuRZbe3kRY57p3EM5qL1vJ+/28h5IosEcUqdIpVKEh4cjLCwM7dq1Q//+/fHFF1+gVatWkEqlyMvLQ0pKCuLj4/HgwQNMmjQJbm5uMg8rJITwqyWqYULkg2pJecSWa7G1lxcx5pnOPZSD2qu89vIeOKPBHFInFRYWYs+ePTh58iSuXbuG8vJyAECDBg1gZGQEW1tbODg4oHnz5pzXlJDajVctUQ0TIh9US8ojtlyLrb28iDHPdO6hHNRexbeX98AZDeaQOo8xhqdPn6JevXpo0aIF79UhpM7iVUtUw4TIB9WS8ogt12JrLy9izDOdeygHtVexeA2c0WAOIYQQQgghhBBCyEdS5kASDeYQQgghhBBCCCGE1CH1eK8AIYQQQgghhBBCCKk5GswhhBBCCCGEEEIIqUNoMIcQQgghhBBCCCGkDqHBHEIIIeQjODk5QV9fH9euXat2uo2NDRYsWPBRMbKysqCjo4Po6OiPWs772NjYQEdH553/Nm7c+NFxoqOjoaOjg6ysrI9elr+/P3x9fQEAOjo6GDBgAAoLC6vMJ88cymP9K5fxvn8AsHHjRuH/a5sFCxbAxsbmg3/vxIkTGDRoEBYsWPDeHDg5OX30eiqrhgghhBBlUeO9AoQQQkhdJ5VKsXDhQkRHR0NdXZ336vxjwcHBKC0tFX728vKCvr4+PD09hc+0tLR4rNpbnTlzBvPmzRN+fvjwIVavXg1/f3+Oa/V+AwYMwN69e4Wf//jjD4SEhCA4OBht2rThuGbKERcXBysrK0yYMAFjxowRPt+8eTNSUlIQHBwsfNa0aVMeq0gIIYTUajSYQwghhHykTz75BH///Tc2bdqE2bNn816df0xfX1/mZ3V1dWhqaqJHjx58Vug97t+/jwcPHqBPnz7CZ82aNcO+ffvw5Zdfom/fvhzX7t00NTWhqakp/JyWlgYA0NPTQ/v27XmtltKcPXsW/v7+6NChAzp06CB8rqmpCXV19Vq7zxFCCCG1Bd1mRQghhHwkPT09jBgxAuHh4bh+/fpb55NKpdi1axeGDRsGIyMjDBgwAEFBQSgpKZGZ7/jx47C3t4eRkRFGjhyJW7duVVnWs2fPsHjxYlhYWMDQ0BCjRo3CxYsXZeY5f/48Ro0aBWNjY5iZmcHDwwN37979qLYWFxdj7dq1GDRoELp16wYTExNMmjQJN2/eFObJy8vDnDlz0LdvXxgaGmL48OGIiYl56zILCgowfPhw2NjY4MGDBzVe97i4OJiYmMhcuTF69Gh07NgRvr6+1d5u9brnz59j1apVsLW1haGhIYYOHYpff/1VZp6Kigps3rwZAwYMQPfu3eHp6Yn8/Pwqy7pz5w7c3d1hYmICExMTTJs2DZmZme+M/6H++OMP2Nvbw9DQEIMHD5bJ6aVLl6Cjo4M9e/bA2toaJiYmOH/+PADg8uXLGD9+PLp37w5zc3P4+PggLy9PZtmJiYlwcXGBmZkZunXrBhsbG2zcuBEVFRXCPPn5+Vi4cCHMzc1hZmaGwMBAmekAkJGRgalTp6JXr17o3r07Ro8ejbi4OJl5bt26hfz8fJibm9e47fv27YODgwN69OgBIyMjDB8+HLGxscL0iooK/Pjjj7CxsRHWf+3atSgrK6t2eYwxLFy4EEZGRjh37lyN14MQQgipLWgwhxBCCJGDRYsWoWXLlli4cKHMrUqvW7x4sTB4EBISgnHjxmHnzp3w9PQEYwwAcOrUKcyYMQM6OjrYtGkTvvzyS5nbiACgpKQEEydOxMmTJzF79mwEBwdDS0sLrq6uwoBOZmYmPD090a1bN4SEhCAgIADp6elwc3Or8gX8Q8yfPx9RUVFwc3NDREQEFi5ciL///htz5swR2jBv3jzcvXsXy5YtQ1hYGPT19eHj44P4+Pgqy3vx4gWmTJmCgoICREZG4rPPPqvxusfFxaF///4yy9PQ0MCqVauQnZ2NNWvWvLUdxcXFcHR0xG+//QZXV1ds3rwZPXv2xHfffYfQ0FBhvsDAQGzatAnffPMNgoOD0aJFC6xdu1ZmWenp6RgzZgxyc3Px/fffIyAgAJmZmRg7dixyc3P/UZ6rs3jxYjg7OyMkJARaWlpYsGBBlYG+4OBg+Pj4YPHixTA2NkZiYiKcnZ3RsGFDrFu3DosWLUJCQgImTJiA4uJiAK8GV5ydndGiRQv8+OOPCAkJgampKYKDg4UBk4qKCri6uiIuLg4+Pj5YvXo1/vzzTxw5ckSIXVFRAXd3d7x8+RJr1qzB5s2b0aJFC3h4eOD+/fvCfGfOnEGvXr2goaFRo3bv2rULixcvhq2tLbZs2YKgoCCoq6tj7ty5yM7OBgCEhYXh559/xrRp0xAREYGxY8fip59+QkhISLXL9Pf3x6FDhxAcHAxLS8uabwRCCCGklqDbrAghhBA5aN68OZYvXw4PD49qb7dKTU3Fr7/+ijlz5sDNzQ0A0LdvX7Rt2xbz58/HmTNn0L9/f2zatAlGRkYIDAwEAPTr1w8AZAYQDhw4gFu3buGXX35B9+7dAQBWVlZwcnJCUFAQoqKicPXqVRQXF8Pd3R2ffvopgFfPuzl58iSKior+0XNISktL8eLFC/j6+uKrr74CAJibm6OwsBCrV6/GkydP0KZNGyQkJGDatGmwtbUV5mnRokWV5wmVlJTAw8MDOTk52LFjh3B7UU3Wvbi4GImJiVi4cGGV9TQ2NsbEiROxbds2DBkyBBYWFlXmiY6Oxp07d7Bnzx4YGxsLuS4vL8fmzZsxZswY1KtXDzt27MCkSZPg5eUlzPPo0SOcPXtWWFZwcDAaNWqE7du3C3nt06cPbG1tER4eDh8fnw/OdXX8/f1hZWUFAOjQoQMGDhyIhIQE6OrqCvM4OjpiyJAhws9r165Fx44dsWXLFtSvXx8A0L17d9jZ2SEqKgrjxo3DrVu3YGFhgcDAQNSr9+rvfH379sWpU6dw6dIl2NnZ4cyZM7h69SrCwsKEdejTp4/Mw49zc3ORlpYGT09PYZDNyMioyrOYzpw5gy+//LLG7c7MzISLi4vMs5u0tbXh4OCApKQk2NnZISEhAd26dcPXX38N4NU+16hRI3zyySdVlrd27Vrs3bsXwcHBQlsIIYSQuoYGcwghhBA5sbGxgb29PcLDwzFo0CAYGBgI0xISEgAAdnZ2Mr9jZ2eHhQsX4tKlS+jVqxdu3LiBmTNnyszz5ZdfygzmXLx4EW3atIGBgQHKy8uFz62trbFmzRrk5+eje/fu0NDQwDfffIMhQ4bAysoKvXr1gpGR0T9un7q6On766ScAQE5ODtLT03Hv3j2cPn0aAIQv7L169cLGjRuRkpKCfv36oX///tUOaMyfPx/Xr1/HypUr8fnnnwuf12Td4+Pj0bp1a3Tp0qXadZ01axZOnz4NX19fHDx4sMr0hIQEaGtrCwM5lezt7fHrr78iOTkZEokEZWVlsLa2lpnnyy+/lBnMiY+Ph7m5ORo2bChsj6ZNm8LU1BQXLlx4e0I/kKmpqfD/lQNfBQUFMvPo6ekJ///y5UskJyfDxcUFjDFh3T7//HN07twZ58+fx7hx4zBixAiMGDECJSUlSE9Px/3793Hz5k1IpVLhNqXLly+jQYMGwuAiADRu3Bj9+/dHYmIiAAjbw8/PD+fOnYOlpSWsrKxkBtyeP3+OK1euYNWqVTVud+Xb4AoKCpCWlob79+/j0qVLAGT3ubVr18LR0RE2NjYYMGAAxo8fX2VZu3btwvXr1zFixAgMGDCgxutACCGE1DY0mEMIIYTIka+vLy5evIiFCxciKipK+LzyOStvvqlITU0NLVu2xPPnz5Gfnw/GGFq2bCkzT9u2bWV+fvbsGR4/fiwzWPS6x48fo0uXLti5cye2bt2KX3/9FZGRkWjWrBkcHR0xa9YsSCSSf9S+s2fPYuXKlUhLS0OTJk2gq6uLxo0bA4Bwm9WPP/6I0NBQxMbG4tixY6hXrx4sLCywfPlyaGtrC8vKycmBgYEBNm3ahCFDhqBJkyYAXg1UvG/d4+LiZAYW3tSwYUOsXLkS48ePx5o1a4SroSrl5+dX+9ao1q1bA5AdJHlze7z5e8+ePcORI0dkbjmq9PpDjj9WZZ4BCFfQVOa8unkKCgpQUVGBsLAwhIWFVVle5W1OxcXFWLFiBQ4cOIDy8nK0b98exsbGUFNTE5afn5+PFi1aVNlvXs+FRCJBREQEQkJCcOLECcTExKBBgwawtbXFsmXL0Lx5c5w7dw4dOnSQGbx7n4yMDCxevBgXL15EgwYN0KlTJ+FqpMr1c3V1RZMmTRAVFYWgoCAEBgaia9eu8PX1Re/evYVl3bp1C5aWljh06BAmTpxY5aHfhBBCSF1BgzmEEEKIHDVv3hxLly7FtGnTsHnzZpnPgVcDLa8PaJSVleHp06do2bIlWrRogXr16uHJkycyy3z27JnMz5988gn+/e9/IygoqNp1qLxq4/VbXJKSkrB3716EhoZCV1f3g25zqZSRkSHcPrVlyxZ8/vnnkEgk2LVrl8yVKp988gnmzZuHefPmIS0tDSdPnsTmzZuxbNkybN26VZiv8vYkBwcH/Pjjj/D19RWmvW/dz5w5g+++++6d69uzZ084OTkhMjIShoaGMtOaN28u8xyXSo8fPwYgO4CTm5uLTp06CT9Xtz0sLCwwadKkKstTU+N3qtWkSRNIJBI4OztXuSIMABo1agQACAgIwLFjx7Bu3TpYWFgIA0KvvyWsZcuWePr0KaRSqXC7FlA1F59++imWLl2KJUuW4NatWzh69CjCwsLQsmVLLFmyRLidsKYqKirg5uaGBg0a4Ndff4Wenh7U1NSQmpqKAwcOCPPVq1cP48aNw7hx45Cbm4u4uDiEhoZi+vTpwoOgAWDmzJmYMGEC7Ozs4Ovri3379sm0hxBCCKkr6AHIhBBCiJzZ2tpi6NCh2Lp1q/DWoMo39xw+fFhm3sOHD0MqlaJnz57Q0NCAsbExjh8/LnPFxalTp2R+x9zcHA8fPkSrVq1gaGgo/Dt//jzCw8NRv359bN++HdbW1igtLYW6ujr69OmDFStWAIDwxqgPdf36dZSUlMDNzQ0dOnQQrtKoHMhhjOF///sf+vfvj6NHjwIAOnXqhClTpsDCwqJK3NatW0NHRwfOzs7YtWsXkpOTAeC963737l08evRI5oqLt/H29kaHDh3w/fffy3xuZmaG//3vf7hy5YrM5wcPHkSDBg1gZGQEY2NjNGzYUGhLpcrbyiqZm5sjNTUVenp6wrbo1q0btm/fjhMnTrx3HRWladOm0NfXR1pamsx+0rVrV2zcuFG4VSkpKQm9evWCra2tMJBz/fp15OXlCQ+c7tOnD8rLy/H7778Lyy8tLZUZKLly5QosLCxw9epVSCQS6OnpYfbs2fjiiy/w4MEDMMZw9uzZD3pOzdOnT5Geno5vvvkGhoaGwuDYmTNnAEBYvzFjxsDf3x8A0KpVKzg4OGDcuHEoKCiQeatZ69at0bBhQyxevBg3btzAtm3bPjivhBBCSG1AV+YQQgghCuDn54f4+HjhKpsuXbpg5MiR2LBhA16+fAkzMzPcvHkTwcHB6NWrl3DLkLe3NyZOnAgvLy+MHj0a6enpMm9XAgAHBwfs3LkTkyZNwtSpU9GuXTtcuHABYWFhGD9+PBo0aIDevXsjKCgI06ZNw/jx41G/fn3s2bMH6urqwjNg8vLykJGRgS5dutTogcgGBgZQU1NDYGAgJk+ejNLSUkRHR+OPP/4AABQVFUFHRwdaWlrw9/dHYWEhOnTogOvXryMuLg7u7u7VLtfLywuxsbHw9fVFdHT0e9c9Li4OZmZmMrcUvc3rt1u9mcPdu3dj2rRpmDFjBtq3b49Tp04hKioKXl5eaNasGQDA09MT69atQ6NGjdC7d2/ExcVVGczx9PTEmDFj4O7ujrFjx0JDQwN79+7F77//jg0bNgjzpaSkQF1d/a3P+VEEb29vuLm5Yc6cObC3t4dUKkVERASSk5OFBwobGRkhNjYWP//8Mzp37oxbt24hJCQEEokEL1++BPBqMMfS0hK+vr7Izc2FtrY2IiMjkZeXh1atWgEA9PX10bBhQ8yfPx/Tp09H69atceHCBdy8eRMTJkxASkoKXrx4IfPsn/dp1aoVtLW1sWvXLmhpaaFZs2Y4e/YsIiMjAUBYPzMzM0RERKB169YwNjZGTk4Otm3bBnNzc2hqaqKoqEhmuf3798eQIUOwceNGDB48+INu+yKEEEJqA7oyhxBCCFGAFi1aYOnSpTKfBQQEYNq0afjtt9/g5uaGXbt2YcKECQgLCxOegWJqaoqwsDDk5OTAy8sLe/fuxcqVK2WW07hxY+zatQs9e/ZEYGAgpkyZguPHj2POnDnCw2Z1dXURGhqKwsJCeHt7w8vLC8+ePUNERIRwy9Aff/yB0aNH48aNGzVq07/+9S+sXbsWOTk58PDwwOLFiwEAO3bsgEQiweXLlwG8un2qX79+WL9+PSZPnoyff/4ZXl5emDZtWrXLbdSoERYvXow7d+5g69at7133M2fOfNDVHaamplUGcxo1aoQdO3bA2toa69evh4eHB5KSkhAQEIDp06cL87m7u2PRokU4evQoPDw8cPv27SoPc9bV1cWuXbsgkUgwf/58zJgxA48fP8amTZswaNAgYT4vLy8sW7asxustD5aWlvjpp5+QnZ2NGTNmYP78+ahfvz62bduGHj16AHj1gGFbW1usW7cO7u7u2LdvHzw8PDBq1ChcuXIFUqkUwKvtam9vjw0bNmDWrFnQ0tLCqFGjhFgaGhqIiIhA165dERAQABcXF5w8eRLLly+Hg4MDzpw5gz59+lR5q9n7bN68GZ9++ikWLFiAWbNmITk5GSEhIejUqZOwz82cORNTp05FVFQUXF1dsXr1alhaWsoMpr1p0aJFUFNTg5+f3wdmlRBCCOFPwt58ch4hhBBCRGP+/PlwdHQUvtgTxcnMzMTSpUuFN4IRQgghhPxTdGUOIYQQIlKpqalITk7GF198wXtVRCE0NBR9+/blvRqEEEIIUQF0ZQ4hhBAiUnl5eSgpKUG7du14r4oopKSkQE9P7x+/Fp4QQgghpBIN5hBCCCGEEEIIIYTUIXSbFSGEEEIIIYQQQkgdQoM5hBBCCCGEEEIIIXUIDeYQQgghhBBCCCGE1CE0mEMIIYQQQgghhBBSh9BgDiGEEEIIIYQQQkgdQoM5hBBCCCGEEEIIIXUIDeYQQgghhBBCCCGE1CE0mEMIIYQQQgghhBBSh9BgDiGEEEIIIYQQQkgd8v8A06JYQYfwQ/AAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1400x600 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": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "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": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Grouping\n",
+    "\n",
+    "* Group a frame by common values of column(s)\n",
+    "* Use operations on this group\n",
+    "* Grouped frame is not _directly_ a new frame, but only through an applied operation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: [8, 16, 16, 24, 32, 48], 2: [16, 32, 32, 48, 64, 96], 3: [24, 48, 48, 72, 96, 144], 4: [32, 64, 64, 96, 128, 192], 5: [40, 80, 80, 120, 160, 240], 6: [48, 96, 96, 144, 192, 288]}"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby(\"Nodes\").groups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "editable": true,
+    "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>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>Unaccounted 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",
+       "      <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>32</th>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>66.58</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>18.86</td>\n",
+       "      <td>19.65</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.35</td>\n",
+       "      <td>43.38</td>\n",
+       "      <td>47361344.0</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>1.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>34.09</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>0.14</td>\n",
+       "      <td>10.60</td>\n",
+       "      <td>10.83</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.25</td>\n",
+       "      <td>20.96</td>\n",
+       "      <td>47074752.0</td>\n",
+       "      <td>818198</td>\n",
+       "      <td>7.33</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",
+       "      <td>1.03</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",
+       "      <th>64</th>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>32.49</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>0.09</td>\n",
+       "      <td>9.98</td>\n",
+       "      <td>10.31</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>20.12</td>\n",
+       "      <td>48081056.0</td>\n",
+       "      <td>818198</td>\n",
+       "      <td>7.33</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",
+       "      <td>1.09</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>3 rows × 22 columns</p>\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",
+       "         id  Nodes  Tasks/Node  Threads/Task  Runtime Program / s  Scale  \\\n",
+       "Threads                                                                    \n",
+       "32        5      4           2             4                66.58     10   \n",
+       "64        5      4           2             8                34.09     10   \n",
+       "64        5      4           4             4                32.49     10   \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",
+       "         Plastic  Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n",
+       "Threads                                                                  \n",
+       "32          True                        0.13                     18.86   \n",
+       "64          True                        0.14                     10.60   \n",
+       "64          True                        0.09                      9.98   \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",
+       "         Max. Edge Build Time / s  ...  Presim. Time / s  Sim. Time / s  \\\n",
+       "Threads                            ...                                    \n",
+       "32                          19.65  ...              2.35          43.38   \n",
+       "64                          10.83  ...              1.25          20.96   \n",
+       "64                          10.31  ...              1.12          20.12   \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",
+       "         Virt. Memory (Sum) / kB  Local Spike Counter (Sum)  \\\n",
+       "Threads                                                       \n",
+       "32                    47361344.0                     821491   \n",
+       "64                    47074752.0                     818198   \n",
+       "64                    48081056.0                     818198   \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",
+       "         Average Rate (Sum)  Number of Neurons  Number of Connections  \\\n",
+       "Threads                                                                 \n",
+       "32                     7.23             112500             1265738500   \n",
+       "64                     7.33             112500             1265738500   \n",
+       "64                     7.33             112500             1265738500   \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",
+       "         Min. Delay  Max. Delay  Unaccounted Time / s  \n",
+       "Threads                                                \n",
+       "32              1.5         1.5                  1.70  \n",
+       "64              1.5         1.5                  1.03  \n",
+       "64              1.5         1.5                  1.09  \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  "
+       "[3 rows x 22 columns]"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_multind = df.set_index([\"Nodes\", \"Tasks/Node\", \"Threads/Task\"])\n",
-    "df_multind.head()"
+    "df.groupby(\"Nodes\").get_group(4).head(3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
-   "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",
+   "execution_count": 110,
    "metadata": {
+    "editable": true,
     "slideshow": {
-     "slide_type": "slide"
-    }
+     "slide_type": "fragment"
+    },
+    "tags": []
    },
-   "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": 112,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -7075,7 +7332,7 @@
        "[6 rows x 21 columns]"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7087,9 +7344,11 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "### Pivoting\n",
@@ -7107,11 +7366,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 111,
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -7120,11 +7381,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 112,
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
@@ -7197,7 +7460,7 @@
        " 0.518282  2.952492       NaN"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7213,18 +7476,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 113,
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": []
    },
    "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",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -7232,16 +7497,18 @@
     }
    ],
    "source": [
-    "df_pivot.plot();"
+    "df_pivot.plot(figsize=(10,3));"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "slide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "## Task 7\n",
@@ -7256,19 +7523,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 114,
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 864x288 with 1 Axes>"
+       "<Figure size 1200x400 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -7286,6 +7555,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "subslide"
@@ -7306,6 +7576,100 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Pandas 2\n",
+    "\n",
+    "* [Pandas 2.0](https://pandas.pydata.org/docs/dev/whatsnew/v2.0.0.html) was released in April 2023\n",
+    "* Only limited deprecations (i.e. _an upgrade is probably safe_)\n",
+    "* Key new feature: Apache Arrow support (via PyArrow)\n",
+    "* Fine-grained installation options `python3 -m pip install 'pandas[performance, excel]'`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "* Get a reasonably large data source (larger would be better, though)\n",
+    "* Example: [Train stations as provided by Deutsche Bahn](https://download-data.deutschebahn.com/static/datasets/stationsdaten/DBSuS-Uebersicht_Bahnhoefe-Stand2020-03.csv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "data_db = 'db-bahnhoefe.csv'  # source: https://download-data.deutschebahn.com/static/datasets/stationsdaten/DBSuS-Uebersicht_Bahnhoefe-Stand2020-03.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8.97 ms ± 320 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit pd.read_csv(data_db, sep=';')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.54 ms ± 84.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit pd.read_csv(data_db, sep=';', engine='pyarrow', dtype_backend='pyarrow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -7316,12 +7680,14 @@
     "\n",
     "* Pandas can read data directly in `tar` form\n",
     "* Pandas can read data directly from online resource\n",
-    "* Let's combine that to an advanced task!"
+    "* Let's combine that to an advanced task!\n",
+    "* It works also with the PyArrow backend (remember to download the online resource when testing; there is no cache!)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "slide"
@@ -7329,7 +7695,7 @@
     "tags": []
    },
    "source": [
-    "## Task 8 (_MATSE Bonus_)\n",
+    "## Task 8 (Super Bonus)\n",
     "<a name=\"task8\"></a>\n",
     "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
     "\n",
@@ -7339,6 +7705,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "fragment"
@@ -7363,6 +7730,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "subslide"
@@ -7372,7 +7740,7 @@
    "source": [
     "```python\n",
     "df_names = pd.read_table('imdb-data/name.basics.tsv.gz', sep=\"\\t\", low_memory=False, na_values=[\"\\\\N\",\"nan\"])\n",
-    "df_ratings = pd.read_table('https://datasets.i\n",
+    "df_ratings = pd.read_table('https://datasets.imdbws.com/title.ratings.tsv.gz', sep=\"\\t\", low_memory=False, na_values=[\"\\\\N\",\"nan\"])\n",
     "                           \n",
     "df_names_i = df_names.set_index('nconst')\n",
     "df_ratings_i = df_ratings.set_index('tconst')\n",
@@ -7391,6 +7759,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "subslide"
@@ -7429,6 +7798,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "slide"
@@ -7436,11 +7806,11 @@
     "tags": []
    },
    "source": [
-    "## Task 8B (B, like <em>B</em>onus)\n",
+    "## Task 8B (<em>B</em>onuseption)\n",
     "<a name=\"task8b\"></a>\n",
     "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
     "\n",
-    "All of the following are unique sub-tasks, which can be done individually\n",
+    "All of the following are ideas for unique sub-tasks, which can be done individually\n",
     "* In addition to Task 8, restrict the top titles to those with more than 10000 votes\n",
     "* For 30 top-rated actors, plot rating vs. age\n",
     "* For 30 top-rated actors, plot rating vs. average runtime of the known-for-titles (using `title.basics.tsv.gz`)"
@@ -7449,6 +7819,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -7503,9 +7874,11 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "## Further Reading\n",
@@ -7517,7 +7890,8 @@
     "    * [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)"
+    "    * [Mapping with Matplotlib, Pandas, Geopandas and Basemap in Python](https://towardsdatascience.com/mapping-with-matplotlib-pandas-geopandas-and-basemap-in-python-d11b57ab5dac)\n",
+    "    * [Whats new in Pandas 2](https://towardsdatascience.com/pandas-2-0-a-game-changer-for-data-scientists-3cd281fcc4b4)"
    ]
   }
  ],
@@ -7537,7 +7911,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.11.5"
   },
   "toc-autonumbering": false,
   "toc-showcode": false,
diff --git a/Introduction-to-Pandas--slides.html b/Introduction-to-Pandas--slides.html
index ecfa3b37777ab55d478e48d6f1f7935e8c2cd28d..03c2fba49c402c436ccd350f813d3fa0a10af74a 100644
--- a/Introduction-to-Pandas--slides.html
+++ b/Introduction-to-Pandas--slides.html
@@ -1,18 +1,18 @@
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-<html>
-<head><meta charset="utf-8" />
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-
-<meta name="apple-mobile-web-app-capable" content="yes" />
-<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent" />
-
 
+<html lang="en">
+<head><meta charset="utf-8"/>
+<meta content="chrome=1" http-equiv="X-UA-Compatible"/>
+<meta content="yes" name="apple-mobile-web-app-capable"/>
+<meta content="black-translucent" name="apple-mobile-web-app-status-bar-style"/>
 <title>Introduction-to-Pandas--slides slides</title>
 <script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
-
+<script type="module">
+  import mermaid from 'https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.3.1/mermaid.esm.min.mjs';
+  mermaid.initialize({ startOnLoad: true });
+</script>
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+  border-right: 0 solid transparent;
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 .jp-scrollbar-tiny::-webkit-scrollbar-track:vertical {
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+
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+  display: block;
+  transform-origin: top left;
+  transform: rotate(-90deg) translate(-100%);
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+
+/*
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+ * Distributed under the terms of the Modified BSD License.
+ */
+
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@@ -386,32 +317,37 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
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-	top: 0;
-	bottom: 0;
-	margin: auto;
-	padding: 7px 0;
-	display: none;
-	vertical-align: middle;
+  z-index: 1;
+  right: 3%;
+  top: 0;
+  bottom: 0;
+  margin: auto;
+  padding: 7px 0;
+  display: none;
+  vertical-align: middle;
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-	height: 15px;
-	text-align: center;
-	color:#000;
-	font-weight: normal;
-	font-size: 12px;
-	cursor: pointer;
+  content: 'X';
+  display: block;
+  width: 15px;
+  height: 15px;
+  text-align: center;
+  color: #000;
+  font-weight: normal;
+  font-size: 12px;
+  cursor: pointer;
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+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
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+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
 /*-----------------------------------------------------------------------------
 | Copyright (c) Jupyter Development Team.
 | Copyright (c) 2014-2017, PhosphorJS Contributors
@@ -521,8 +430,6 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
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+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
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 | Copyright (c) 2014-2017, PhosphorJS Contributors
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-.p-MenuBar-itemLabel,
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 .lm-MenuBar-itemIcon,
 .lm-MenuBar-itemLabel {
   display: inline-block;
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+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
 /*-----------------------------------------------------------------------------
 | Copyright (c) Jupyter Development Team.
 | Copyright (c) 2014-2017, PhosphorJS Contributors
@@ -640,8 +528,6 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
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-
-/* <DEPRECATED> */ .p-ScrollBar-thumb, /* </DEPRECATED> */
 .lm-ScrollBar-thumb {
   box-sizing: border-box;
   position: absolute;
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+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
 /*-----------------------------------------------------------------------------
 | Copyright (c) Jupyter Development Team.
 | Copyright (c) 2014-2017, PhosphorJS Contributors
@@ -698,26 +575,18 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
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 .lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle:after {
   top: 50%;
   min-height: 8px;
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+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
 /*-----------------------------------------------------------------------------
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 | Copyright (c) 2014-2017, PhosphorJS Contributors
@@ -772,8 +630,6 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
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-
-/* <DEPRECATED> */ .p-TabBar[data-orientation='horizontal'], /* </DEPRECATED> */
 .lm-TabBar[data-orientation='horizontal'] {
   flex-direction: row;
   align-items: flex-end;
 }
 
-
-/* <DEPRECATED> */ .p-TabBar[data-orientation='vertical'], /* </DEPRECATED> */
 .lm-TabBar[data-orientation='vertical'] {
   flex-direction: column;
   align-items: flex-end;
 }
 
-
-/* <DEPRECATED> */ .p-TabBar-content, /* </DEPRECATED> */
 .lm-TabBar-content {
   margin: 0;
   padding: 0;
@@ -806,95 +656,61 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
   list-style-type: none;
 }
 
-
-/* <DEPRECATED> */
-.p-TabBar[data-orientation='horizontal'] > .p-TabBar-content,
-/* </DEPRECATED> */
 .lm-TabBar[data-orientation='horizontal'] > .lm-TabBar-content {
   flex-direction: row;
 }
 
-
-/* <DEPRECATED> */
-.p-TabBar[data-orientation='vertical'] > .p-TabBar-content,
-/* </DEPRECATED> */
 .lm-TabBar[data-orientation='vertical'] > .lm-TabBar-content {
   flex-direction: column;
 }
 
-
-/* <DEPRECATED> */ .p-TabBar-tab, /* </DEPRECATED> */
 .lm-TabBar-tab {
   display: flex;
   flex-direction: row;
   box-sizing: border-box;
   overflow: hidden;
+  touch-action: none; /* Disable native Drag/Drop */
 }
 
-
-/* <DEPRECATED> */
-.p-TabBar-tabIcon,
-.p-TabBar-tabCloseIcon,
-/* </DEPRECATED> */
 .lm-TabBar-tabIcon,
 .lm-TabBar-tabCloseIcon {
   flex: 0 0 auto;
 }
 
-
-/* <DEPRECATED> */ .p-TabBar-tabLabel, /* </DEPRECATED> */
 .lm-TabBar-tabLabel {
   flex: 1 1 auto;
   overflow: hidden;
   white-space: nowrap;
 }
 
-
 .lm-TabBar-tabInput {
   user-select: all;
   width: 100%;
-  box-sizing : border-box;
+  box-sizing: border-box;
 }
 
-
-/* <DEPRECATED> */ .p-TabBar-tab.p-mod-hidden, /* </DEPRECATED> */
 .lm-TabBar-tab.lm-mod-hidden {
   display: none !important;
 }
 
-
 .lm-TabBar-addButton.lm-mod-hidden {
   display: none !important;
 }
 
-
-/* <DEPRECATED> */ .p-TabBar.p-mod-dragging .p-TabBar-tab, /* </DEPRECATED> */
 .lm-TabBar.lm-mod-dragging .lm-TabBar-tab {
   position: relative;
 }
 
-
-/* <DEPRECATED> */
-.p-TabBar.p-mod-dragging[data-orientation='horizontal'] .p-TabBar-tab,
-/* </DEPRECATED> */
 .lm-TabBar.lm-mod-dragging[data-orientation='horizontal'] .lm-TabBar-tab {
   left: 0;
   transition: left 150ms ease;
 }
 
-
-/* <DEPRECATED> */
-.p-TabBar.p-mod-dragging[data-orientation='vertical'] .p-TabBar-tab,
-/* </DEPRECATED> */
 .lm-TabBar.lm-mod-dragging[data-orientation='vertical'] .lm-TabBar-tab {
   top: 0;
   transition: top 150ms ease;
 }
 
-
-/* <DEPRECATED> */
-.p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging,
-/* </DEPRECATED> */
 .lm-TabBar.lm-mod-dragging .lm-TabBar-tab.lm-mod-dragging {
   transition: none;
 }
@@ -902,10 +718,15 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
 .lm-TabBar-tabLabel .lm-TabBar-tabInput {
   user-select: all;
   width: 100%;
-  box-sizing : border-box;
+  box-sizing: border-box;
   background: inherit;
 }
 
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
 /*-----------------------------------------------------------------------------
 | Copyright (c) Jupyter Development Team.
 | Copyright (c) 2014-2017, PhosphorJS Contributors
@@ -915,18 +736,19 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
 | The full license is in the file LICENSE, distributed with this software.
 |----------------------------------------------------------------------------*/
 
-
-/* <DEPRECATED> */ .p-TabPanel-tabBar, /* </DEPRECATED> */
 .lm-TabPanel-tabBar {
   z-index: 1;
 }
 
-
-/* <DEPRECATED> */ .p-TabPanel-stackedPanel, /* </DEPRECATED> */
 .lm-TabPanel-stackedPanel {
   z-index: 0;
 }
 
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
 /*-----------------------------------------------------------------------------
 | Copyright (c) Jupyter Development Team.
 | Copyright (c) 2014-2017, PhosphorJS Contributors
@@ -936,10559 +758,3020 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
 | The full license is in the file LICENSE, distributed with this software.
 |----------------------------------------------------------------------------*/
 
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-  -webkit-box-sizing:border-box;
-          box-sizing:border-box; }
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-  line-height:1.28581;
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-  font-family:-apple-system, "BlinkMacSystemFont", "Segoe UI", "Roboto", "Oxygen", "Ubuntu", "Cantarell", "Open Sans", "Helvetica Neue", "Icons16", sans-serif; }
-
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-  margin-bottom:10px;
-  margin-top:0; }
-
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-  font-size:12px; }
-
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-  font-weight:600; }
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-  margin:0 0 10px;
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-  .bp3-dark a,
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-.bp3-running-text pre, .bp3-code-block{
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-  .bp3-button:not([class*="bp3-intent-"]){
-    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));
-    -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);
-    color:#182026; }
-    .bp3-button:not([class*="bp3-intent-"]):hover{
-      background-clip:padding-box;
-      background-color:#ebf1f5;
-      -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-button:not([class*="bp3-intent-"]):active, .bp3-button:not([class*="bp3-intent-"]).bp3-active{
-      background-color:#d8e1e8;
-      background-image:none;
-      -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); }
-    .bp3-button:not([class*="bp3-intent-"]):disabled, .bp3-button:not([class*="bp3-intent-"]).bp3-disabled{
-      background-color:rgba(206, 217, 224, 0.5);
-      background-image:none;
-      -webkit-box-shadow:none;
-              box-shadow:none;
-      color:rgba(92, 112, 128, 0.6);
-      cursor:not-allowed;
-      outline:none; }
-      .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{
-    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));
-    -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);
-    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{
-      background-color:#106ba3;
-      -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); }
-    .bp3-button.bp3-intent-primary:active, .bp3-button.bp3-intent-primary.bp3-active{
-      background-color:#0e5a8a;
-      background-image:none;
-      -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); }
-    .bp3-button.bp3-intent-primary:disabled, .bp3-button.bp3-intent-primary.bp3-disabled{
-      background-color:rgba(19, 124, 189, 0.5);
-      background-image:none;
-      border-color:transparent;
-      -webkit-box-shadow:none;
-              box-shadow:none;
-      color:rgba(255, 255, 255, 0.6); }
-  .bp3-button.bp3-intent-success{
-    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));
-    -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);
-    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{
-      background-color:#0d8050;
-      -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); }
-    .bp3-button.bp3-intent-success:active, .bp3-button.bp3-intent-success.bp3-active{
-      background-color:#0a6640;
-      background-image:none;
-      -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); }
-    .bp3-button.bp3-intent-success:disabled, .bp3-button.bp3-intent-success.bp3-disabled{
-      background-color:rgba(15, 153, 96, 0.5);
-      background-image:none;
-      border-color:transparent;
-      -webkit-box-shadow:none;
-              box-shadow:none;
-      color:rgba(255, 255, 255, 0.6); }
-  .bp3-button.bp3-intent-warning{
-    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));
-    -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);
-    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{
-      background-color:#bf7326;
-      -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); }
-    .bp3-button.bp3-intent-warning:active, .bp3-button.bp3-intent-warning.bp3-active{
-      background-color:#a66321;
-      background-image:none;
-      -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); }
-    .bp3-button.bp3-intent-warning:disabled, .bp3-button.bp3-intent-warning.bp3-disabled{
-      background-color:rgba(217, 130, 43, 0.5);
-      background-image:none;
-      border-color:transparent;
-      -webkit-box-shadow:none;
-              box-shadow:none;
-      color:rgba(255, 255, 255, 0.6); }
-  .bp3-button.bp3-intent-danger{
-    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));
-    -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);
-    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{
-      background-color:#c23030;
-      -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); }
-    .bp3-button.bp3-intent-danger:active, .bp3-button.bp3-intent-danger.bp3-active{
-      background-color:#a82a2a;
-      background-image:none;
-      -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); }
-    .bp3-button.bp3-intent-danger:disabled, .bp3-button.bp3-intent-danger.bp3-disabled{
-      background-color:rgba(219, 55, 55, 0.5);
-      background-image:none;
-      border-color:transparent;
-      -webkit-box-shadow:none;
-              box-shadow: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-height:40px;
-    min-width:40px;
-    font-size:16px;
-    padding:5px 15px; }
-    .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-height:24px;
-    min-width: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{
-      margin:0;
-      position:absolute; }
-    .bp3-button.bp3-loading > :not(.bp3-button-spinner){
-      visibility:hidden; }
-  .bp3-button[class*="bp3-icon-"]::before{
-    font-family:"Icons16", sans-serif;
-    font-size:16px;
-    font-style:normal;
-    font-weight:400;
-    line-height:1;
-    -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-"]){
-    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));
-    -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);
-    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{
-      background-color:#30404d;
-      -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:not([class*="bp3-intent-"]):active, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-active{
-      background-color:#202b33;
-      background-image:none;
-      -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); }
-    .bp3-dark .bp3-button:not([class*="bp3-intent-"]):disabled, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-disabled{
-      background-color:rgba(57, 75, 89, 0.5);
-      background-image:none;
-      -webkit-box-shadow:none;
-              box-shadow: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{
-      background-image:none;
-      -webkit-box-shadow:none;
-              box-shadow: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{
-    background:none;
-    -webkit-box-shadow:none;
-            box-shadow:none; }
-    .bp3-button.bp3-minimal:hover{
-      background:rgba(167, 182, 194, 0.3);
-      -webkit-box-shadow:none;
-              box-shadow:none;
-      color:#182026;
-      text-decoration:none; }
-    .bp3-button.bp3-minimal:active, .bp3-button.bp3-minimal.bp3-active{
-      background:rgba(115, 134, 148, 0.3);
-      -webkit-box-shadow:none;
-              box-shadow:none;
-      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;
-      color:rgba(92, 112, 128, 0.6);
-      cursor:not-allowed; }
-      .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{
-      background:none;
-      -webkit-box-shadow:none;
-              box-shadow: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{
-        background:none;
-        -webkit-box-shadow:none;
-                box-shadow: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;
-        color:rgba(167, 182, 194, 0.6);
-        cursor:not-allowed; }
-        .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{
-        background:none;
-        -webkit-box-shadow:none;
-                box-shadow: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{
-        background:none;
-        -webkit-box-shadow:none;
-                box-shadow: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); }
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-.bp3-button.bp3-align-left .bp3-button-text, .bp3-button.bp3-align-right .bp3-button-text,
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-  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-bottom-left-radius:0;
-    border-top-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){
-    border-bottom-right-radius:0;
-    border-top-right-radius:0;
-    margin-right:-1px; }
-  .bp3-button-group.bp3-minimal .bp3-button{
-    background:none;
-    -webkit-box-shadow:none;
-            box-shadow:none; }
-    .bp3-button-group.bp3-minimal .bp3-button:hover{
-      background:rgba(167, 182, 194, 0.3);
-      -webkit-box-shadow:none;
-              box-shadow:none;
-      color:#182026;
-      text-decoration:none; }
-    .bp3-button-group.bp3-minimal .bp3-button:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-active{
-      background:rgba(115, 134, 148, 0.3);
-      -webkit-box-shadow:none;
-              box-shadow:none;
-      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;
-      color:rgba(92, 112, 128, 0.6);
-      cursor:not-allowed; }
-      .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{
-      background:none;
-      -webkit-box-shadow:none;
-              box-shadow: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{
-        background:none;
-        -webkit-box-shadow:none;
-                box-shadow: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;
-        color:rgba(167, 182, 194, 0.6);
-        cursor:not-allowed; }
-        .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{
-        background:none;
-        -webkit-box-shadow:none;
-                box-shadow: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{
-        background:none;
-        -webkit-box-shadow:none;
-                box-shadow: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{
-        background:none;
-        -webkit-box-shadow:none;
-                box-shadow: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{
-        background:none;
-        -webkit-box-shadow:none;
-                box-shadow: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-align:stretch;
-        -ms-flex-align:stretch;
-            align-items:stretch;
-    -webkit-box-orient:vertical;
-    -webkit-box-direction:normal;
-        -ms-flex-direction:column;
-            flex-direction:column;
-    vertical-align:top; }
-    .bp3-button-group.bp3-vertical.bp3-fill{
-      height:100%;
-      width:unset; }
-    .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{
-  font-size:14px;
-  line-height:1.5;
-  background-color:rgba(138, 155, 168, 0.15);
-  border-radius:3px;
-  padding:10px 12px 9px;
-  position:relative;
-  width:100%; }
-  .bp3-callout[class*="bp3-icon-"]{
-    padding-left:40px; }
-    .bp3-callout[class*="bp3-icon-"]::before{
-      font-family:"Icons20", sans-serif;
-      font-size:20px;
-      font-style:normal;
-      font-weight:400;
-      line-height:1;
-      -moz-osx-font-smoothing:grayscale;
-      -webkit-font-smoothing:antialiased;
-      color:#5c7080;
-      left:10px;
-      position:absolute;
-      top:10px; }
-  .bp3-callout.bp3-callout-icon{
-    padding-left:40px; }
-    .bp3-callout.bp3-callout-icon > .bp3-icon:first-child{
-      color:#5c7080;
-      left:10px;
-      position:absolute;
-      top:10px; }
-  .bp3-callout .bp3-heading{
-    line-height:20px;
-    margin-bottom:5px;
-    margin-top:0; }
-    .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{
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-  .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;
-    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);
-    width:auto; }
-    .bp3-control.bp3-switch .bp3-control-indicator::before{
-      background:#ffffff;
-      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);
-      height:calc(1em - 4px);
-      left:0;
-      margin:2px;
-      position:absolute;
-      -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);
-      width:calc(1em - 4px); }
-  .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{
-    background:#394b59;
-    -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-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{
-    font-size:0.7em;
-    text-align:center; }
-  .bp3-control.bp3-switch .bp3-control-indicator-child:first-child{
-    line-height:0;
-    margin-left:0.5em;
-    margin-right:1.2em;
-    visibility:hidden; }
-  .bp3-control.bp3-switch .bp3-control-indicator-child:last-child{
-    line-height:1em;
-    margin-left:1.2em;
-    margin-right:0.5em;
-    visibility:visible; }
-  .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator .bp3-control-indicator-child:first-child{
-    line-height:1em;
-    visibility:visible; }
-  .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator .bp3-control-indicator-child:last-child{
-    line-height:0;
-    visibility:hidden; }
-  .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{
-      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));
-      -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 .bp3-control-indicator{
-      background-color:#30404d; }
-    .bp3-dark .bp3-control input:not(:disabled):active ~ .bp3-control-indicator{
-      background:#202b33;
-      -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); }
-    .bp3-dark .bp3-control input:disabled ~ .bp3-control-indicator{
-      background:rgba(57, 75, 89, 0.5);
-      -webkit-box-shadow:none;
-              box-shadow:none;
-      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{
-  cursor:pointer;
-  display:inline-block;
-  height:30px;
-  position:relative; }
-  .bp3-file-input input{
-    margin:0;
-    min-width:200px;
-    opacity:0; }
-    .bp3-file-input input:disabled + .bp3-file-upload-input,
-    .bp3-file-input input.bp3-disabled + .bp3-file-upload-input{
-      background:rgba(206, 217, 224, 0.5);
-      -webkit-box-shadow:none;
-              box-shadow:none;
-      color:rgba(92, 112, 128, 0.6);
-      cursor:not-allowed;
-      resize:none; }
-      .bp3-file-input input:disabled + .bp3-file-upload-input::after,
-      .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after{
-        background-color:rgba(206, 217, 224, 0.5);
-        background-image:none;
-        -webkit-box-shadow:none;
-                box-shadow:none;
-        color:rgba(92, 112, 128, 0.6);
-        cursor:not-allowed;
-        outline:none; }
-        .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{
-        background:rgba(57, 75, 89, 0.5);
-        -webkit-box-shadow:none;
-                box-shadow:none;
-        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{
-          background-color:rgba(57, 75, 89, 0.5);
-          background-image:none;
-          -webkit-box-shadow:none;
-                  box-shadow: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{
-  -webkit-appearance:none;
-     -moz-appearance:none;
-          appearance:none;
-  background:#ffffff;
-  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);
-  color:#182026;
-  font-size:14px;
-  font-weight:400;
-  height:30px;
-  line-height:30px;
-  outline:none;
-  padding:0 10px;
-  -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);
-  vertical-align:middle;
-  overflow:hidden;
-  text-overflow:ellipsis;
-  white-space:nowrap;
-  word-wrap:normal;
-  color:rgba(92, 112, 128, 0.6);
-  left:0;
-  padding-right:80px;
-  position:absolute;
-  right:0;
-  top:0;
-  -webkit-user-select:none;
-     -moz-user-select:none;
-      -ms-user-select:none;
-          user-select:none; }
-  .bp3-file-upload-input::-webkit-input-placeholder{
-    color:rgba(92, 112, 128, 0.6);
-    opacity:1; }
-  .bp3-file-upload-input::-moz-placeholder{
-    color:rgba(92, 112, 128, 0.6);
-    opacity:1; }
-  .bp3-file-upload-input:-ms-input-placeholder{
-    color:rgba(92, 112, 128, 0.6);
-    opacity:1; }
-  .bp3-file-upload-input::-ms-input-placeholder{
-    color:rgba(92, 112, 128, 0.6);
-    opacity:1; }
-  .bp3-file-upload-input::placeholder{
-    color:rgba(92, 112, 128, 0.6);
-    opacity:1; }
-  .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{
-    background:rgba(206, 217, 224, 0.5);
-    -webkit-box-shadow:none;
-            box-shadow:none;
-    color:rgba(92, 112, 128, 0.6);
-    cursor:not-allowed;
-    resize:none; }
-  .bp3-file-upload-input::after{
-    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));
-    -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);
-    color:#182026;
-    min-height:24px;
-    min-width:24px;
-    overflow:hidden;
-    text-overflow:ellipsis;
-    white-space:nowrap;
-    word-wrap:normal;
-    border-radius:3px;
-    content:"Browse";
-    line-height:24px;
-    margin:3px;
-    position:absolute;
-    right:0;
-    text-align:center;
-    top:0;
-    width:70px; }
-    .bp3-file-upload-input::after:hover{
-      background-clip:padding-box;
-      background-color:#ebf1f5;
-      -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-file-upload-input::after:active, .bp3-file-upload-input::after.bp3-active{
-      background-color:#d8e1e8;
-      background-image:none;
-      -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); }
-    .bp3-file-upload-input::after:disabled, .bp3-file-upload-input::after.bp3-disabled{
-      background-color:rgba(206, 217, 224, 0.5);
-      background-image:none;
-      -webkit-box-shadow:none;
-              box-shadow:none;
-      color:rgba(92, 112, 128, 0.6);
-      cursor:not-allowed;
-      outline:none; }
-      .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{
-    background-clip:padding-box;
-    background-color:#ebf1f5;
-    -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-file-upload-input:active::after{
-    background-color:#d8e1e8;
-    background-image:none;
-    -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); }
-  .bp3-large .bp3-file-upload-input{
-    font-size:16px;
-    height:40px;
-    line-height:40px;
-    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-height:30px;
-      min-width:30px;
-      line-height:30px;
-      margin:5px;
-      width:85px; }
-  .bp3-dark .bp3-file-upload-input{
-    background:rgba(16, 22, 26, 0.3);
-    -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);
-    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{
-      background:rgba(57, 75, 89, 0.5);
-      -webkit-box-shadow:none;
-              box-shadow:none;
-      color:rgba(167, 182, 194, 0.6); }
-    .bp3-dark .bp3-file-upload-input::after{
-      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));
-      -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);
-      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{
-        background-color:#30404d;
-        -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-file-upload-input::after:active, .bp3-dark .bp3-file-upload-input::after.bp3-active{
-        background-color:#202b33;
-        background-image:none;
-        -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); }
-      .bp3-dark .bp3-file-upload-input::after:disabled, .bp3-dark .bp3-file-upload-input::after.bp3-disabled{
-        background-color:rgba(57, 75, 89, 0.5);
-        background-image:none;
-        -webkit-box-shadow:none;
-                box-shadow: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{
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-    .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{
-      background:rgba(57, 75, 89, 0.5);
-      -webkit-box-shadow:none;
-              box-shadow:none;
-      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{
-    background:rgba(16, 22, 26, 0.3);
-    -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);
-    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{
-      background:rgba(57, 75, 89, 0.5);
-      -webkit-box-shadow:none;
-              box-shadow:none;
-      color:rgba(167, 182, 194, 0.6); }
-label.bp3-label{
-  display:block;
-  margin-bottom:15px;
-  margin-top:0; }
-  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{
-    font-weight:400;
-    vertical-align:top;
-    width:100%; }
-  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;
-  min-height:0;
-  padding:0;
-  width:30px; }
-  .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;
-  border:none;
-  border-radius:3px;
-  cursor:pointer;
-  font-size:14px;
-  -webkit-box-pack:center;
-      -ms-flex-pack:center;
-          justify-content:center;
-  padding:5px 10px;
-  text-align:left;
-  vertical-align:middle;
-  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));
-  -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);
-  color:#182026;
-  -moz-appearance:none;
-  -webkit-appearance:none;
-  border-radius:3px;
-  height:30px;
-  padding:0 25px 0 10px;
-  width:100%; }
-  .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{
-    background-clip:padding-box;
-    background-color:#ebf1f5;
-    -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-html-select select:active,
-  .bp3-select select:active, .bp3-html-select select.bp3-active,
-  .bp3-select select.bp3-active{
-    background-color:#d8e1e8;
-    background-image:none;
-    -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); }
-  .bp3-html-select select:disabled,
-  .bp3-select select:disabled, .bp3-html-select select.bp3-disabled,
-  .bp3-select select.bp3-disabled{
-    background-color:rgba(206, 217, 224, 0.5);
-    background-image:none;
-    -webkit-box-shadow:none;
-            box-shadow:none;
-    color:rgba(92, 112, 128, 0.6);
-    cursor:not-allowed;
-    outline:none; }
-    .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{
-  background:none;
-  -webkit-box-shadow:none;
-          box-shadow:none; }
-  .bp3-html-select.bp3-minimal select:hover,
-  .bp3-select.bp3-minimal select:hover{
-    background:rgba(167, 182, 194, 0.3);
-    -webkit-box-shadow:none;
-            box-shadow:none;
-    color:#182026;
-    text-decoration:none; }
-  .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{
-    background:rgba(115, 134, 148, 0.3);
-    -webkit-box-shadow:none;
-            box-shadow:none;
-    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;
-    color:rgba(92, 112, 128, 0.6);
-    cursor:not-allowed; }
-    .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{
-    background:none;
-    -webkit-box-shadow:none;
-            box-shadow: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{
-      background:none;
-      -webkit-box-shadow:none;
-              box-shadow: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;
-      color:rgba(167, 182, 194, 0.6);
-      cursor:not-allowed; }
-      .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{
-      background:none;
-      -webkit-box-shadow:none;
-              box-shadow: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{
-      background:none;
-      -webkit-box-shadow:none;
-              box-shadow: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{
-      background:none;
-      -webkit-box-shadow:none;
-              box-shadow: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; }
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-  content:""; }
-
-.bp3-icon-add-column-left::before{
-  content:""; }
-
-.bp3-icon-add-column-right::before{
-  content:""; }
-
-.bp3-icon-add-row-bottom::before{
-  content:""; }
-
-.bp3-icon-add-row-top::before{
-  content:""; }
-
-.bp3-icon-add-to-artifact::before{
-  content:""; }
-
-.bp3-icon-add-to-folder::before{
-  content:""; }
-
-.bp3-icon-airplane::before{
-  content:""; }
-
-.bp3-icon-align-center::before{
-  content:""; }
-
-.bp3-icon-align-justify::before{
-  content:""; }
-
-.bp3-icon-align-left::before{
-  content:""; }
-
-.bp3-icon-align-right::before{
-  content:""; }
-
-.bp3-icon-alignment-bottom::before{
-  content:""; }
-
-.bp3-icon-alignment-horizontal-center::before{
-  content:""; }
-
-.bp3-icon-alignment-left::before{
-  content:""; }
-
-.bp3-icon-alignment-right::before{
-  content:""; }
-
-.bp3-icon-alignment-top::before{
-  content:""; }
-
-.bp3-icon-alignment-vertical-center::before{
-  content:""; }
-
-.bp3-icon-annotation::before{
-  content:""; }
-
-.bp3-icon-application::before{
-  content:""; }
-
-.bp3-icon-applications::before{
-  content:""; }
-
-.bp3-icon-archive::before{
-  content:""; }
-
-.bp3-icon-arrow-bottom-left::before{
-  content:"↙"; }
-
-.bp3-icon-arrow-bottom-right::before{
-  content:"↘"; }
-
-.bp3-icon-arrow-down::before{
-  content:"↓"; }
-
-.bp3-icon-arrow-left::before{
-  content:"←"; }
-
-.bp3-icon-arrow-right::before{
-  content:"→"; }
-
-.bp3-icon-arrow-top-left::before{
-  content:"↖"; }
-
-.bp3-icon-arrow-top-right::before{
-  content:"↗"; }
-
-.bp3-icon-arrow-up::before{
-  content:"↑"; }
-
-.bp3-icon-arrows-horizontal::before{
-  content:"↔"; }
-
-.bp3-icon-arrows-vertical::before{
-  content:"↕"; }
-
-.bp3-icon-asterisk::before{
-  content:"*"; }
-
-.bp3-icon-automatic-updates::before{
-  content:""; }
-
-.bp3-icon-badge::before{
-  content:""; }
-
-.bp3-icon-ban-circle::before{
-  content:""; }
-
-.bp3-icon-bank-account::before{
-  content:""; }
-
-.bp3-icon-barcode::before{
-  content:""; }
-
-.bp3-icon-blank::before{
-  content:""; }
-
-.bp3-icon-blocked-person::before{
-  content:""; }
-
-.bp3-icon-bold::before{
-  content:""; }
-
-.bp3-icon-book::before{
-  content:""; }
-
-.bp3-icon-bookmark::before{
-  content:""; }
-
-.bp3-icon-box::before{
-  content:""; }
-
-.bp3-icon-briefcase::before{
-  content:""; }
-
-.bp3-icon-bring-data::before{
-  content:""; }
-
-.bp3-icon-build::before{
-  content:""; }
-
-.bp3-icon-calculator::before{
-  content:""; }
-
-.bp3-icon-calendar::before{
-  content:""; }
-
-.bp3-icon-camera::before{
-  content:""; }
-
-.bp3-icon-caret-down::before{
-  content:"⌄"; }
-
-.bp3-icon-caret-left::before{
-  content:"〈"; }
-
-.bp3-icon-caret-right::before{
-  content:"〉"; }
-
-.bp3-icon-caret-up::before{
-  content:"⌃"; }
-
-.bp3-icon-cell-tower::before{
-  content:""; }
-
-.bp3-icon-changes::before{
-  content:""; }
-
-.bp3-icon-chart::before{
-  content:""; }
-
-.bp3-icon-chat::before{
-  content:""; }
-
-.bp3-icon-chevron-backward::before{
-  content:""; }
-
-.bp3-icon-chevron-down::before{
-  content:""; }
-
-.bp3-icon-chevron-forward::before{
-  content:""; }
-
-.bp3-icon-chevron-left::before{
-  content:""; }
-
-.bp3-icon-chevron-right::before{
-  content:""; }
-
-.bp3-icon-chevron-up::before{
-  content:""; }
-
-.bp3-icon-circle::before{
-  content:""; }
-
-.bp3-icon-circle-arrow-down::before{
-  content:""; }
-
-.bp3-icon-circle-arrow-left::before{
-  content:""; }
-
-.bp3-icon-circle-arrow-right::before{
-  content:""; }
-
-.bp3-icon-circle-arrow-up::before{
-  content:""; }
-
-.bp3-icon-citation::before{
-  content:""; }
-
-.bp3-icon-clean::before{
-  content:""; }
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
 
-.bp3-icon-clipboard::before{
-  content:""; }
+.jp-Collapse {
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+}
 
-.bp3-icon-cloud::before{
-  content:"☁"; }
+.jp-Collapse-header {
+  padding: 1px 12px;
+  background-color: var(--jp-layout-color1);
+  border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
+  color: var(--jp-ui-font-color1);
+  cursor: pointer;
+  display: flex;
+  align-items: center;
+  font-size: var(--jp-ui-font-size0);
+  font-weight: 600;
+  text-transform: uppercase;
+  user-select: none;
+}
 
-.bp3-icon-cloud-download::before{
-  content:""; }
+.jp-Collapser-icon {
+  height: 16px;
+}
 
-.bp3-icon-cloud-upload::before{
-  content:""; }
+.jp-Collapse-header-collapsed .jp-Collapser-icon {
+  transform: rotate(-90deg);
+  margin: auto 0;
+}
 
-.bp3-icon-code::before{
-  content:""; }
+.jp-Collapser-title {
+  line-height: 25px;
+}
 
-.bp3-icon-code-block::before{
-  content:""; }
+.jp-Collapse-contents {
+  padding: 0 12px;
+  background-color: var(--jp-layout-color1);
+  color: var(--jp-ui-font-color1);
+  overflow: auto;
+}
 
-.bp3-icon-cog::before{
-  content:""; }
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
 
-.bp3-icon-collapse-all::before{
-  content:""; }
+/* This file was auto-generated by ensureUiComponents() in @jupyterlab/buildutils */
 
-.bp3-icon-column-layout::before{
-  content:""; }
+/**
+ * (DEPRECATED) Support for consuming icons as CSS background images
+ */
 
-.bp3-icon-comment::before{
-  content:""; }
+/* Icons urls */
 
-.bp3-icon-comparison::before{
-  content:""; }
-
-.bp3-icon-compass::before{
-  content:""; }
-
-.bp3-icon-compressed::before{
-  content:""; }
+:root {
+  --jp-icon-add-above: url(data:image/svg+xml;base64,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);
+  --jp-icon-add-below: url(data:image/svg+xml;base64,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);
+  --jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-bell: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE2IDE2IiB2ZXJzaW9uPSIxLjEiPgogICA8cGF0aCBjbGFzcz0ianAtaWNvbjIganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMzMzMzMzIgogICAgICBkPSJtOCAwLjI5Yy0xLjQgMC0yLjcgMC43My0zLjYgMS44LTEuMiAxLjUtMS40IDMuNC0xLjUgNS4yLTAuMTggMi4yLTAuNDQgNC0yLjMgNS4zbDAuMjggMS4zaDVjMC4wMjYgMC42NiAwLjMyIDEuMSAwLjcxIDEuNSAwLjg0IDAuNjEgMiAwLjYxIDIuOCAwIDAuNTItMC40IDAuNi0xIDAuNzEtMS41aDVsMC4yOC0xLjNjLTEuOS0wLjk3LTIuMi0zLjMtMi4zLTUuMy0wLjEzLTEuOC0wLjI2LTMuNy0xLjUtNS4yLTAuODUtMS0yLjItMS44LTMuNi0xLjh6bTAgMS40YzAuODggMCAxLjkgMC41NSAyLjUgMS4zIDAuODggMS4xIDEuMSAyLjcgMS4yIDQuNCAwLjEzIDEuNyAwLjIzIDMuNiAxLjMgNS4yaC0xMGMxLjEtMS42IDEuMi0zLjQgMS4zLTUuMiAwLjEzLTEuNyAwLjMtMy4zIDEuMi00LjQgMC41OS0wLjcyIDEuNi0xLjMgMi41LTEuM3ptLTAuNzQgMTJoMS41Yy0wLjAwMTUgMC4yOCAwLjAxNSAwLjc5LTAuNzQgMC43OS0wLjczIDAuMDAxNi0wLjcyLTAuNTMtMC43NC0wLjc5eiIgLz4KPC9zdmc+Cg==);
+  --jp-icon-bug-dot: url(data:image/svg+xml;base64,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);
+  --jp-icon-bug: url(data:image/svg+xml;base64,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);
+  --jp-icon-build: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE0LjkgMTcuNDVDMTYuMjUgMTcuNDUgMTcuMzUgMTYuMzUgMTcuMzUgMTVDMTcuMzUgMTMuNjUgMTYuMjUgMTIuNTUgMTQuOSAxMi41NUMxMy41NCAxMi41NSAxMi40NSAxMy42NSAxMi40NSAxNUMxMi40NSAxNi4zNSAxMy41NCAxNy40NSAxNC45IDE3LjQ1Wk0yMC4xIDE1LjY4TDIxLjU4IDE2Ljg0QzIxLjcxIDE2Ljk1IDIxLjc1IDE3LjEzIDIxLjY2IDE3LjI5TDIwLjI2IDE5LjcxQzIwLjE3IDE5Ljg2IDIwIDE5LjkyIDE5LjgzIDE5Ljg2TDE4LjA5IDE5LjE2QzE3LjczIDE5LjQ0IDE3LjMzIDE5LjY3IDE2LjkxIDE5Ljg1TDE2LjY0IDIxLjdDMTYuNjIgMjEuODcgMTYuNDcgMjIgMTYuMyAyMkgxMy41QzEzLjMyIDIyIDEzLjE4IDIxLjg3IDEzLjE1IDIxLjdMMTIuODkgMTkuODVDMTIuNDYgMTkuNjcgMTIuMDcgMTkuNDQgMTEuNzEgMTkuMTZMOS45NjAwMiAxOS44NkM5LjgxMDAyIDE5LjkyIDkuNjIwMDIgMTkuODYgOS41NDAwMiAxOS43MUw4LjE0MDAyIDE3LjI5QzguMDUwMDIgMTcuMTMgOC4wOTAwMiAxNi45NSA4LjIyMDAyIDE2Ljg0TDkuNzAwMDIgMTUuNjhMOS42NTAwMSAxNUw5LjcwMDAyIDE0LjMxTDguMjIwMDIgMTMuMTZDOC4wOTAwMiAxMy4wNSA4LjA1MDAyIDEyLjg2IDguMTQwMDIgMTIuNzFMOS41NDAwMiAxMC4yOUM5LjYyMDAyIDEwLjEzIDkuODEwMDIgMTAuMDcgOS45NjAwMiAxMC4xM0wxMS43MSAxMC44NEMxMi4wNyAxMC41NiAxMi40NiAxMC4zMiAxMi44OSAxMC4xNUwxMy4xNSA4LjI4OTk4QzEzLjE4IDguMTI5OTggMTMuMzIgNy45OTk5OCAxMy41IDcuOTk5OThIMTYuM0MxNi40NyA3Ljk5OTk4IDE2LjYyIDguMTI5OTggMTYuNjQgOC4yODk5OEwxNi45MSAxMC4xNUMxNy4zMyAxMC4zMiAxNy43MyAxMC41NiAxOC4wOSAxMC44NEwxOS44MyAxMC4xM0MyMCAxMC4wNyAyMC4xNyAxMC4xMyAyMC4yNiAxMC4yOUwyMS42NiAxMi43MUMyMS43NSAxMi44NiAyMS43MSAxMy4wNSAyMS41OCAxMy4xNkwyMC4xIDE0LjMxTDIwLjE1IDE1TDIwLjEgMTUuNjhaIi8+CiAgICA8cGF0aCBkPSJNNy4zMjk2NiA3LjQ0NDU0QzguMDgzMSA3LjAwOTU0IDguMzM5MzIgNi4wNTMzMiA3LjkwNDMyIDUuMjk5ODhDNy40NjkzMiA0LjU0NjQzIDYuNTA4MSA0LjI4MTU2IDUuNzU0NjYgNC43MTY1NkM1LjM5MTc2IDQuOTI2MDggNS4xMjY5NSA1LjI3MTE4IDUuMDE4NDkgNS42NzU5NEM0LjkxMDA0IDYuMDgwNzEgNC45NjY4MiA2LjUxMTk4IDUuMTc2MzQgNi44NzQ4OEM1LjYxMTM0IDcuNjI4MzIgNi41NzYyMiA3Ljg3OTU0IDcuMzI5NjYgNy40NDQ1NFpNOS42NTcxOCA0Ljc5NTkzTDEwLjg2NzIgNC45NTE3OUMxMC45NjI4IDQuOTc3NDEgMTEuMDQwMiA1LjA3MTMzIDExLjAzODIgNS4xODc5M0wxMS4wMzg4IDYuOTg4OTNDMTEuMDQ1NSA3LjEwMDU0IDEwLjk2MTYgNy4xOTUxOCAxMC44NTUgNy4yMTA1NEw5LjY2MDAxIDcuMzgwODNMOS4yMzkxNSA4LjEzMTg4TDkuNjY5NjEgOS4yNTc0NUM5LjcwNzI5IDkuMzYyNzEgOS42NjkzNCA5LjQ3Njk5IDkuNTc0MDggOS41MzE5OUw4LjAxNTIzIDEwLjQzMkM3LjkxMTMxIDEwLjQ5MiA3Ljc5MzM3IDEwLjQ2NzcgNy43MjEwNSAxMC4zODI0TDYuOTg3NDggOS40MzE4OEw2LjEwOTMxIDkuNDMwODNMNS4zNDcwNCAxMC4zOTA1QzUuMjg5MDkgMTAuNDcwMiA1LjE3MzgzIDEwLjQ5MDUgNS4wNzE4NyAxMC40MzM5TDMuNTEyNDUgOS41MzI5M0MzLjQxMDQ5IDkuNDc2MzMgMy4zNzY0NyA5LjM1NzQxIDMuNDEwNzUgOS4yNTY3OUwzLjg2MzQ3IDguMTQwOTNMMy42MTc0OSA3Ljc3NDg4TDMuNDIzNDcgNy4zNzg4M0wyLjIzMDc1IDcuMjEyOTdDMi4xMjY0NyA3LjE5MjM1IDIuMDQwNDkgNy4xMDM0MiAyLjA0MjQ1IDYuOTg2ODJMMi4wNDE4NyA1LjE4NTgyQzIuMDQzODMgNS4wNjkyMiAyLjExOTA5IDQuOTc5NTggMi4yMTcwNCA0Ljk2OTIyTDMuNDIwNjUgNC43OTM5M0wzLjg2NzQ5IDQuMDI3ODhMMy40MTEwNSAyLjkxNzMxQzMuMzczMzcgMi44MTIwNCAzLjQxMTMxIDIuNjk3NzYgMy41MTUyMyAyLjYzNzc2TDUuMDc0MDggMS43Mzc3NkM1LjE2OTM0IDEuNjgyNzYgNS4yODcyOSAxLjcwNzA0IDUuMzU5NjEgMS43OTIzMUw2LjExOTE1IDIuNzI3ODhMNi45ODAwMSAyLjczODkzTDcuNzI0OTYgMS43ODkyMkM3Ljc5MTU2IDEuNzA0NTggNy45MTU0OCAxLjY3OTIyIDguMDA4NzkgMS43NDA4Mkw5LjU2ODIxIDIuNjQxODJDOS42NzAxNyAyLjY5ODQyIDkuNzEyODUgMi44MTIzNCA5LjY4NzIzIDIuOTA3OTdMOS4yMTcxOCA0LjAzMzgzTDkuNDYzMTYgNC4zOTk4OEw5LjY1NzE4IDQuNzk1OTNaIi8+CiAgPC9nPgo8L3N2Zz4K);
+  --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,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIj4KICAgIDxwYXRoIGQ9Ik05IDE2LjE3TDQuODMgMTJsLTEuNDIgMS40MUw5IDE5IDIxIDdsLTEuNDEtMS40MXoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --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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);
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+  --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-dot: url(data:image/svg+xml;base64,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);
+  --jp-icon-filter-list: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEwIDE4aDR2LTJoLTR2MnpNMyA2djJoMThWNkgzem0zIDdoMTJ2LTJINnYyeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-filter: url(data:image/svg+xml;base64,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);
+  --jp-icon-folder-favorite: url(data:image/svg+xml;base64,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);
+  --jp-icon-folder: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMTAgNEg0Yy0xLjEgMC0xLjk5LjktMS45OSAyTDIgMThjMCAxLjEuOSAyIDIgMmgxNmMxLjEgMCAyLS45IDItMlY4YzAtMS4xLS45LTItMi0yaC04bC0yLTJ6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-home: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjRweCIgdmlld0JveD0iMCAwIDI0IDI0IiB3aWR0aD0iMjRweCIgZmlsbD0iIzAwMDAwMCI+CiAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPjxwYXRoIGNsYXNzPSJqcC1pY29uMyBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xMCAyMHYtNmg0djZoNXYtOGgzTDEyIDMgMiAxMmgzdjh6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-html5: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDUxMiA1MTIiPgogIDxwYXRoIGNsYXNzPSJqcC1pY29uMCBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiMwMDAiIGQ9Ik0xMDguNCAwaDIzdjIyLjhoMjEuMlYwaDIzdjY5aC0yM1Y0NmgtMjF2MjNoLTIzLjJNMjA2IDIzaC0yMC4zVjBoNjMuN3YyM0gyMjl2NDZoLTIzbTUzLjUtNjloMjQuMWwxNC44IDI0LjNMMzEzLjIgMGgyNC4xdjY5aC0yM1YzNC44bC0xNi4xIDI0LjgtMTYuMS0yNC44VjY5aC0yMi42bTg5LjItNjloMjN2NDYuMmgzMi42VjY5aC01NS42Ii8+CiAgPHBhdGggY2xhc3M9ImpwLWljb24tc2VsZWN0YWJsZSIgZmlsbD0iI2U0NGQyNiIgZD0iTTEwNy42IDQ3MWwtMzMtMzcwLjRoMzYyLjhsLTMzIDM3MC4yTDI1NS43IDUxMiIvPgogIDxwYXRoIGNsYXNzPSJqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiNmMTY1MjkiIGQ9Ik0yNTYgNDgwLjVWMTMxaDE0OC4zTDM3NiA0NDciLz4KICA8cGF0aCBjbGFzcz0ianAtaWNvbi1zZWxlY3RhYmxlLWludmVyc2UiIGZpbGw9IiNlYmViZWIiIGQ9Ik0xNDIgMTc2LjNoMTE0djQ1LjRoLTY0LjJsNC4yIDQ2LjVoNjB2NDUuM0gxNTQuNG0yIDIyLjhIMjAybDMuMiAzNi4zIDUwLjggMTMuNnY0Ny40bC05My4yLTI2Ii8+CiAgPHBhdGggY2xhc3M9ImpwLWljb24tc2VsZWN0YWJsZS1pbnZlcnNlIiBmaWxsPSIjZmZmIiBkPSJNMzY5LjYgMTc2LjNIMjU1Ljh2NDUuNGgxMDkuNm0tNC4xIDQ2LjVIMjU1Ljh2NDUuNGg1NmwtNS4zIDU5LTUwLjcgMTMuNnY0Ny4ybDkzLTI1LjgiLz4KPC9zdmc+Cg==);
+  --jp-icon-image: url(data:image/svg+xml;base64,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);
+  --jp-icon-info: url(data:image/svg+xml;base64,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);
+  --jp-icon-inspector: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaW5zcGVjdG9yLWljb24tY29sb3IganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMjAgNEg0Yy0xLjEgMC0xLjk5LjktMS45OSAyTDIgMThjMCAxLjEuOSAyIDIgMmgxNmMxLjEgMCAyLS45IDItMlY2YzAtMS4xLS45LTItMi0yem0tNSAxNEg0di00aDExdjR6bTAtNUg0VjloMTF2NHptNSA1aC00VjloNHY5eiIvPgo8L3N2Zz4K);
+  --jp-icon-json: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtanNvbi1pY29uLWNvbG9yIGpwLWljb24tc2VsZWN0YWJsZSIgZmlsbD0iI0Y5QTgyNSI+CiAgICA8cGF0aCBkPSJNMjAuMiAxMS44Yy0xLjYgMC0xLjcuNS0xLjcgMSAwIC40LjEuOS4xIDEuMy4xLjUuMS45LjEgMS4zIDAgMS43LTEuNCAyLjMtMy41IDIuM2gtLjl2LTEuOWguNWMxLjEgMCAxLjQgMCAxLjQtLjggMC0uMyAwLS42LS4xLTEgMC0uNC0uMS0uOC0uMS0xLjIgMC0xLjMgMC0xLjggMS4zLTItMS4zLS4yLTEuMy0uNy0xLjMtMiAwLS40LjEtLjguMS0xLjIuMS0uNC4xLS43LjEtMSAwLS44LS40LS43LTEuNC0uOGgtLjVWNC4xaC45YzIuMiAwIDMuNS43IDMuNSAyLjMgMCAuNC0uMS45LS4xIDEuMy0uMS41LS4xLjktLjEgMS4zIDAgLjUuMiAxIDEuNyAxdjEuOHpNMS44IDEwLjFjMS42IDAgMS43LS41IDEuNy0xIDAtLjQtLjEtLjktLjEtMS4zLS4xLS41LS4xLS45LS4xLTEuMyAwLTEuNiAxLjQtMi4zIDMuNS0yLjNoLjl2MS45aC0uNWMtMSAwLTEuNCAwLTEuNC44IDAgLjMgMCAuNi4xIDEgMCAuMi4xLjYuMSAxIDAgMS4zIDAgMS44LTEuMyAyQzYgMTEuMiA2IDExLjcgNiAxM2MwIC40LS4xLjgtLjEgMS4yLS4xLjMtLjEuNy0uMSAxIDAgLjguMy44IDEuNC44aC41djEuOWgtLjljLTIuMSAwLTMuNS0uNi0zLjUtMi4zIDAtLjQuMS0uOS4xLTEuMy4xLS41LjEtLjkuMS0xLjMgMC0uNS0uMi0xLTEuNy0xdi0xLjl6Ii8+CiAgICA8Y2lyY2xlIGN4PSIxMSIgY3k9IjEzLjgiIHI9IjIuMSIvPgogICAgPGNpcmNsZSBjeD0iMTEiIGN5PSI4LjIiIHI9IjIuMSIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-julia: url(data:image/svg+xml;base64,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);
+  --jp-icon-jupyter-favicon: url(data:image/svg+xml;base64,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);
+  --jp-icon-jupyter: url(data:image/svg+xml;base64,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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-launch: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMzIgMzIiIHdpZHRoPSIzMiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIj4KICAgIDxwYXRoIGQ9Ik0yNiwyOEg2YTIuMDAyNywyLjAwMjcsMCwwLDEtMi0yVjZBMi4wMDI3LDIuMDAyNywwLDAsMSw2LDRIMTZWNkg2VjI2SDI2VjE2aDJWMjZBMi4wMDI3LDIuMDAyNywwLDAsMSwyNiwyOFoiLz4KICAgIDxwb2x5Z29uIHBvaW50cz0iMjAgMiAyMCA0IDI2LjU4NiA0IDE4IDEyLjU4NiAxOS40MTQgMTQgMjggNS40MTQgMjggMTIgMzAgMTIgMzAgMiAyMCAyIi8+CiAgPC9nPgo8L3N2Zz4K);
+  --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,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGNsYXNzPSJqcC1pY29uMiBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xOSA1djE0SDVWNWgxNG0xLjEtMkgzLjljLS41IDAtLjkuNC0uOS45djE2LjJjMCAuNC40LjkuOS45aDE2LjJjLjQgMCAuOS0uNS45LS45VjMuOWMwLS41LS41LS45LS45LS45ek0xMSA3aDZ2MmgtNlY3em0wIDRoNnYyaC02di0yem0wIDRoNnYyaC02ek03IDdoMnYySDd6bTAgNGgydjJIN3ptMCA0aDJ2Mkg3eiIvPgo8L3N2Zz4K);
+  --jp-icon-markdown: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8cGF0aCBjbGFzcz0ianAtaWNvbi1jb250cmFzdDAganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjN0IxRkEyIiBkPSJNNSAxNC45aDEybC02LjEgNnptOS40LTYuOGMwLTEuMy0uMS0yLjktLjEtNC41LS40IDEuNC0uOSAyLjktMS4zIDQuM2wtMS4zIDQuM2gtMkw4LjUgNy45Yy0uNC0xLjMtLjctMi45LTEtNC4zLS4xIDEuNi0uMSAzLjItLjIgNC42TDcgMTIuNEg0LjhsLjctMTFoMy4zTDEwIDVjLjQgMS4yLjcgMi43IDEgMy45LjMtMS4yLjctMi42IDEtMy45bDEuMi0zLjdoMy4zbC42IDExaC0yLjRsLS4zLTQuMnoiLz4KPC9zdmc+Cg==);
+  --jp-icon-move-down: url(data:image/svg+xml;base64,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);
+  --jp-icon-move-up: url(data:image/svg+xml;base64,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);
+  --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,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtbm90ZWJvb2staWNvbi1jb2xvciBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiNFRjZDMDAiPgogICAgPHBhdGggZD0iTTE4LjcgMy4zdjE1LjRIMy4zVjMuM2gxNS40bTEuNS0xLjVIMS44djE4LjNoMTguM2wuMS0xOC4zeiIvPgogICAgPHBhdGggZD0iTTE2LjUgMTYuNWwtNS40LTQuMy01LjYgNC4zdi0xMWgxMXoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-numbering: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjIiIGhlaWdodD0iMjIiIHZpZXdCb3g9IjAgMCAyOCAyOCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CgkJPHBhdGggZD0iTTQgMTlINlYxOS41SDVWMjAuNUg2VjIxSDRWMjJIN1YxOEg0VjE5Wk01IDEwSDZWNkg0VjdINVYxMFpNNCAxM0g1LjhMNCAxNS4xVjE2SDdWMTVINS4yTDcgMTIuOVYxMkg0VjEzWk05IDdWOUgyM1Y3SDlaTTkgMjFIMjNWMTlIOVYyMVpNOSAxNUgyM1YxM0g5VjE1WiIvPgoJPC9nPgo8L3N2Zz4K);
+  --jp-icon-offline-bolt: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyIDIuMDJjLTUuNTEgMC05Ljk4IDQuNDctOS45OCA5Ljk4czQuNDcgOS45OCA5Ljk4IDkuOTggOS45OC00LjQ3IDkuOTgtOS45OFMxNy41MSAyLjAyIDEyIDIuMDJ6TTExLjQ4IDIwdi02LjI2SDhMMTMgNHY2LjI2aDMuMzVMMTEuNDggMjB6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --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-pdf: url(data:image/svg+xml;base64,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);
+  --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-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --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,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);
+  --jp-icon-settings: url(data:image/svg+xml;base64,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);
+  --jp-icon-share: 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-table-rows: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMSw4SDNWNGgxOFY4eiBNMjEsMTBIM3Y0aDE4VjEweiBNMjEsMTZIM3Y0aDE4VjE2eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-tag: url(data:image/svg+xml;base64,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);
+  --jp-icon-terminal: url(data:image/svg+xml;base64,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);
+  --jp-icon-text-editor: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtdGV4dC1lZGl0b3ItaWNvbi1jb2xvciBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xNSAxNUgzdjJoMTJ2LTJ6bTAtOEgzdjJoMTJWN3pNMyAxM2gxOHYtMkgzdjJ6bTAgOGgxOHYtMkgzdjJ6TTMgM3YyaDE4VjNIM3oiLz4KPC9zdmc+Cg==);
+  --jp-icon-toc: url(data:image/svg+xml;base64,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);
+  --jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --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-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-users: url(data:image/svg+xml;base64,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);
+  --jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-word: url(data:image/svg+xml;base64,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);
+  --jp-icon-yaml: url(data:image/svg+xml;base64,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);
+}
 
-.bp3-icon-confirm::before{
-  content:""; }
+/* Icon CSS class declarations */
 
-.bp3-icon-console::before{
-  content:""; }
+.jp-AddAboveIcon {
+  background-image: var(--jp-icon-add-above);
+}
 
-.bp3-icon-contrast::before{
-  content:""; }
+.jp-AddBelowIcon {
+  background-image: var(--jp-icon-add-below);
+}
 
-.bp3-icon-control::before{
-  content:""; }
+.jp-AddIcon {
+  background-image: var(--jp-icon-add);
+}
 
-.bp3-icon-credit-card::before{
-  content:""; }
+.jp-BellIcon {
+  background-image: var(--jp-icon-bell);
+}
 
-.bp3-icon-cross::before{
-  content:"✗"; }
+.jp-BugDotIcon {
+  background-image: var(--jp-icon-bug-dot);
+}
 
-.bp3-icon-crown::before{
-  content:""; }
+.jp-BugIcon {
+  background-image: var(--jp-icon-bug);
+}
 
-.bp3-icon-cube::before{
-  content:""; }
+.jp-BuildIcon {
+  background-image: var(--jp-icon-build);
+}
 
-.bp3-icon-cube-add::before{
-  content:""; }
+.jp-CaretDownEmptyIcon {
+  background-image: var(--jp-icon-caret-down-empty);
+}
 
-.bp3-icon-cube-remove::before{
-  content:""; }
+.jp-CaretDownEmptyThinIcon {
+  background-image: var(--jp-icon-caret-down-empty-thin);
+}
 
-.bp3-icon-curved-range-chart::before{
-  content:""; }
+.jp-CaretDownIcon {
+  background-image: var(--jp-icon-caret-down);
+}
 
-.bp3-icon-cut::before{
-  content:""; }
+.jp-CaretLeftIcon {
+  background-image: var(--jp-icon-caret-left);
+}
 
-.bp3-icon-dashboard::before{
-  content:""; }
+.jp-CaretRightIcon {
+  background-image: var(--jp-icon-caret-right);
+}
 
-.bp3-icon-data-lineage::before{
-  content:""; }
+.jp-CaretUpEmptyThinIcon {
+  background-image: var(--jp-icon-caret-up-empty-thin);
+}
 
-.bp3-icon-database::before{
-  content:""; }
+.jp-CaretUpIcon {
+  background-image: var(--jp-icon-caret-up);
+}
 
-.bp3-icon-delete::before{
-  content:""; }
+.jp-CaseSensitiveIcon {
+  background-image: var(--jp-icon-case-sensitive);
+}
 
-.bp3-icon-delta::before{
-  content:"Δ"; }
+.jp-CheckIcon {
+  background-image: var(--jp-icon-check);
+}
 
-.bp3-icon-derive-column::before{
-  content:""; }
+.jp-CircleEmptyIcon {
+  background-image: var(--jp-icon-circle-empty);
+}
 
-.bp3-icon-desktop::before{
-  content:""; }
+.jp-CircleIcon {
+  background-image: var(--jp-icon-circle);
+}
 
-.bp3-icon-diagnosis::before{
-  content:""; }
+.jp-ClearIcon {
+  background-image: var(--jp-icon-clear);
+}
 
-.bp3-icon-diagram-tree::before{
-  content:""; }
+.jp-CloseIcon {
+  background-image: var(--jp-icon-close);
+}
 
-.bp3-icon-direction-left::before{
-  content:""; }
+.jp-CodeCheckIcon {
+  background-image: var(--jp-icon-code-check);
+}
 
-.bp3-icon-direction-right::before{
-  content:""; }
+.jp-CodeIcon {
+  background-image: var(--jp-icon-code);
+}
 
-.bp3-icon-disable::before{
-  content:""; }
+.jp-CollapseAllIcon {
+  background-image: var(--jp-icon-collapse-all);
+}
 
-.bp3-icon-document::before{
-  content:""; }
+.jp-ConsoleIcon {
+  background-image: var(--jp-icon-console);
+}
 
-.bp3-icon-document-open::before{
-  content:""; }
+.jp-CopyIcon {
+  background-image: var(--jp-icon-copy);
+}
 
-.bp3-icon-document-share::before{
-  content:""; }
+.jp-CopyrightIcon {
+  background-image: var(--jp-icon-copyright);
+}
 
-.bp3-icon-dollar::before{
-  content:"$"; }
+.jp-CutIcon {
+  background-image: var(--jp-icon-cut);
+}
 
-.bp3-icon-dot::before{
-  content:"•"; }
+.jp-DeleteIcon {
+  background-image: var(--jp-icon-delete);
+}
 
-.bp3-icon-double-caret-horizontal::before{
-  content:""; }
+.jp-DownloadIcon {
+  background-image: var(--jp-icon-download);
+}
 
-.bp3-icon-double-caret-vertical::before{
-  content:""; }
+.jp-DuplicateIcon {
+  background-image: var(--jp-icon-duplicate);
+}
 
-.bp3-icon-double-chevron-down::before{
-  content:""; }
+.jp-EditIcon {
+  background-image: var(--jp-icon-edit);
+}
 
-.bp3-icon-double-chevron-left::before{
-  content:""; }
+.jp-EllipsesIcon {
+  background-image: var(--jp-icon-ellipses);
+}
 
-.bp3-icon-double-chevron-right::before{
-  content:""; }
+.jp-ErrorIcon {
+  background-image: var(--jp-icon-error);
+}
 
-.bp3-icon-double-chevron-up::before{
-  content:""; }
+.jp-ExpandAllIcon {
+  background-image: var(--jp-icon-expand-all);
+}
 
-.bp3-icon-doughnut-chart::before{
-  content:""; }
+.jp-ExtensionIcon {
+  background-image: var(--jp-icon-extension);
+}
 
-.bp3-icon-download::before{
-  content:""; }
+.jp-FastForwardIcon {
+  background-image: var(--jp-icon-fast-forward);
+}
 
-.bp3-icon-drag-handle-horizontal::before{
-  content:""; }
+.jp-FileIcon {
+  background-image: var(--jp-icon-file);
+}
 
-.bp3-icon-drag-handle-vertical::before{
-  content:""; }
+.jp-FileUploadIcon {
+  background-image: var(--jp-icon-file-upload);
+}
 
-.bp3-icon-draw::before{
-  content:""; }
+.jp-FilterDotIcon {
+  background-image: var(--jp-icon-filter-dot);
+}
 
-.bp3-icon-drive-time::before{
-  content:""; }
+.jp-FilterIcon {
+  background-image: var(--jp-icon-filter);
+}
 
-.bp3-icon-duplicate::before{
-  content:""; }
+.jp-FilterListIcon {
+  background-image: var(--jp-icon-filter-list);
+}
 
-.bp3-icon-edit::before{
-  content:"✎"; }
+.jp-FolderFavoriteIcon {
+  background-image: var(--jp-icon-folder-favorite);
+}
 
-.bp3-icon-eject::before{
-  content:"⏏"; }
+.jp-FolderIcon {
+  background-image: var(--jp-icon-folder);
+}
 
-.bp3-icon-endorsed::before{
-  content:""; }
+.jp-HomeIcon {
+  background-image: var(--jp-icon-home);
+}
 
-.bp3-icon-envelope::before{
-  content:"✉"; }
+.jp-Html5Icon {
+  background-image: var(--jp-icon-html5);
+}
 
-.bp3-icon-equals::before{
-  content:""; }
+.jp-ImageIcon {
+  background-image: var(--jp-icon-image);
+}
 
-.bp3-icon-eraser::before{
-  content:""; }
+.jp-InfoIcon {
+  background-image: var(--jp-icon-info);
+}
 
-.bp3-icon-error::before{
-  content:""; }
+.jp-InspectorIcon {
+  background-image: var(--jp-icon-inspector);
+}
 
-.bp3-icon-euro::before{
-  content:"€"; }
+.jp-JsonIcon {
+  background-image: var(--jp-icon-json);
+}
 
-.bp3-icon-exchange::before{
-  content:""; }
+.jp-JuliaIcon {
+  background-image: var(--jp-icon-julia);
+}
 
-.bp3-icon-exclude-row::before{
-  content:""; }
+.jp-JupyterFaviconIcon {
+  background-image: var(--jp-icon-jupyter-favicon);
+}
 
-.bp3-icon-expand-all::before{
-  content:""; }
+.jp-JupyterIcon {
+  background-image: var(--jp-icon-jupyter);
+}
 
-.bp3-icon-export::before{
-  content:""; }
+.jp-JupyterlabWordmarkIcon {
+  background-image: var(--jp-icon-jupyterlab-wordmark);
+}
 
-.bp3-icon-eye-off::before{
-  content:""; }
+.jp-KernelIcon {
+  background-image: var(--jp-icon-kernel);
+}
 
-.bp3-icon-eye-on::before{
-  content:""; }
+.jp-KeyboardIcon {
+  background-image: var(--jp-icon-keyboard);
+}
 
-.bp3-icon-eye-open::before{
-  content:""; }
+.jp-LaunchIcon {
+  background-image: var(--jp-icon-launch);
+}
 
-.bp3-icon-fast-backward::before{
-  content:""; }
+.jp-LauncherIcon {
+  background-image: var(--jp-icon-launcher);
+}
 
-.bp3-icon-fast-forward::before{
-  content:""; }
+.jp-LineFormIcon {
+  background-image: var(--jp-icon-line-form);
+}
 
-.bp3-icon-feed::before{
-  content:""; }
+.jp-LinkIcon {
+  background-image: var(--jp-icon-link);
+}
 
-.bp3-icon-feed-subscribed::before{
-  content:""; }
+.jp-ListIcon {
+  background-image: var(--jp-icon-list);
+}
 
-.bp3-icon-film::before{
-  content:""; }
+.jp-MarkdownIcon {
+  background-image: var(--jp-icon-markdown);
+}
 
-.bp3-icon-filter::before{
-  content:""; }
+.jp-MoveDownIcon {
+  background-image: var(--jp-icon-move-down);
+}
 
-.bp3-icon-filter-keep::before{
-  content:""; }
+.jp-MoveUpIcon {
+  background-image: var(--jp-icon-move-up);
+}
 
-.bp3-icon-filter-list::before{
-  content:""; }
+.jp-NewFolderIcon {
+  background-image: var(--jp-icon-new-folder);
+}
 
-.bp3-icon-filter-open::before{
-  content:""; }
+.jp-NotTrustedIcon {
+  background-image: var(--jp-icon-not-trusted);
+}
 
-.bp3-icon-filter-remove::before{
-  content:""; }
+.jp-NotebookIcon {
+  background-image: var(--jp-icon-notebook);
+}
 
-.bp3-icon-flag::before{
-  content:"⚑"; }
+.jp-NumberingIcon {
+  background-image: var(--jp-icon-numbering);
+}
 
-.bp3-icon-flame::before{
-  content:""; }
+.jp-OfflineBoltIcon {
+  background-image: var(--jp-icon-offline-bolt);
+}
 
-.bp3-icon-flash::before{
-  content:""; }
+.jp-PaletteIcon {
+  background-image: var(--jp-icon-palette);
+}
 
-.bp3-icon-floppy-disk::before{
-  content:""; }
+.jp-PasteIcon {
+  background-image: var(--jp-icon-paste);
+}
 
-.bp3-icon-flow-branch::before{
-  content:""; }
+.jp-PdfIcon {
+  background-image: var(--jp-icon-pdf);
+}
 
-.bp3-icon-flow-end::before{
-  content:""; }
+.jp-PythonIcon {
+  background-image: var(--jp-icon-python);
+}
 
-.bp3-icon-flow-linear::before{
-  content:""; }
+.jp-RKernelIcon {
+  background-image: var(--jp-icon-r-kernel);
+}
 
-.bp3-icon-flow-review::before{
-  content:""; }
+.jp-ReactIcon {
+  background-image: var(--jp-icon-react);
+}
 
-.bp3-icon-flow-review-branch::before{
-  content:""; }
+.jp-RedoIcon {
+  background-image: var(--jp-icon-redo);
+}
 
-.bp3-icon-flows::before{
-  content:""; }
+.jp-RefreshIcon {
+  background-image: var(--jp-icon-refresh);
+}
 
-.bp3-icon-folder-close::before{
-  content:""; }
+.jp-RegexIcon {
+  background-image: var(--jp-icon-regex);
+}
 
-.bp3-icon-folder-new::before{
-  content:""; }
+.jp-RunIcon {
+  background-image: var(--jp-icon-run);
+}
 
-.bp3-icon-folder-open::before{
-  content:""; }
+.jp-RunningIcon {
+  background-image: var(--jp-icon-running);
+}
 
-.bp3-icon-folder-shared::before{
-  content:""; }
+.jp-SaveIcon {
+  background-image: var(--jp-icon-save);
+}
 
-.bp3-icon-folder-shared-open::before{
-  content:""; }
+.jp-SearchIcon {
+  background-image: var(--jp-icon-search);
+}
 
-.bp3-icon-follower::before{
-  content:""; }
+.jp-SettingsIcon {
+  background-image: var(--jp-icon-settings);
+}
 
-.bp3-icon-following::before{
-  content:""; }
+.jp-ShareIcon {
+  background-image: var(--jp-icon-share);
+}
 
-.bp3-icon-font::before{
-  content:""; }
+.jp-SpreadsheetIcon {
+  background-image: var(--jp-icon-spreadsheet);
+}
 
-.bp3-icon-fork::before{
-  content:""; }
+.jp-StopIcon {
+  background-image: var(--jp-icon-stop);
+}
 
-.bp3-icon-form::before{
-  content:""; }
+.jp-TabIcon {
+  background-image: var(--jp-icon-tab);
+}
 
-.bp3-icon-full-circle::before{
-  content:""; }
+.jp-TableRowsIcon {
+  background-image: var(--jp-icon-table-rows);
+}
 
-.bp3-icon-full-stacked-chart::before{
-  content:""; }
+.jp-TagIcon {
+  background-image: var(--jp-icon-tag);
+}
 
-.bp3-icon-fullscreen::before{
-  content:""; }
+.jp-TerminalIcon {
+  background-image: var(--jp-icon-terminal);
+}
 
-.bp3-icon-function::before{
-  content:""; }
+.jp-TextEditorIcon {
+  background-image: var(--jp-icon-text-editor);
+}
 
-.bp3-icon-gantt-chart::before{
-  content:""; }
+.jp-TocIcon {
+  background-image: var(--jp-icon-toc);
+}
 
-.bp3-icon-geolocation::before{
-  content:""; }
+.jp-TreeViewIcon {
+  background-image: var(--jp-icon-tree-view);
+}
 
-.bp3-icon-geosearch::before{
-  content:""; }
+.jp-TrustedIcon {
+  background-image: var(--jp-icon-trusted);
+}
 
-.bp3-icon-git-branch::before{
-  content:""; }
+.jp-UndoIcon {
+  background-image: var(--jp-icon-undo);
+}
 
-.bp3-icon-git-commit::before{
-  content:""; }
+.jp-UserIcon {
+  background-image: var(--jp-icon-user);
+}
 
-.bp3-icon-git-merge::before{
-  content:""; }
+.jp-UsersIcon {
+  background-image: var(--jp-icon-users);
+}
 
-.bp3-icon-git-new-branch::before{
-  content:""; }
+.jp-VegaIcon {
+  background-image: var(--jp-icon-vega);
+}
 
-.bp3-icon-git-pull::before{
-  content:""; }
+.jp-WordIcon {
+  background-image: var(--jp-icon-word);
+}
 
-.bp3-icon-git-push::before{
-  content:""; }
+.jp-YamlIcon {
+  background-image: var(--jp-icon-yaml);
+}
 
-.bp3-icon-git-repo::before{
-  content:""; }
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
 
-.bp3-icon-glass::before{
-  content:""; }
+/**
+ * (DEPRECATED) Support for consuming icons as CSS background images
+ */
 
-.bp3-icon-globe::before{
-  content:""; }
+.jp-Icon,
+.jp-MaterialIcon {
+  background-position: center;
+  background-repeat: no-repeat;
+  background-size: 16px;
+  min-width: 16px;
+  min-height: 16px;
+}
 
-.bp3-icon-globe-network::before{
-  content:""; }
+.jp-Icon-cover {
+  background-position: center;
+  background-repeat: no-repeat;
+  background-size: cover;
+}
 
-.bp3-icon-graph::before{
-  content:""; }
+/**
+ * (DEPRECATED) Support for specific CSS icon sizes
+ */
 
-.bp3-icon-graph-remove::before{
-  content:""; }
+.jp-Icon-16 {
+  background-size: 16px;
+  min-width: 16px;
+  min-height: 16px;
+}
 
-.bp3-icon-greater-than::before{
-  content:""; }
+.jp-Icon-18 {
+  background-size: 18px;
+  min-width: 18px;
+  min-height: 18px;
+}
 
-.bp3-icon-greater-than-or-equal-to::before{
-  content:""; }
+.jp-Icon-20 {
+  background-size: 20px;
+  min-width: 20px;
+  min-height: 20px;
+}
 
-.bp3-icon-grid::before{
-  content:""; }
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
 
-.bp3-icon-grid-view::before{
-  content:""; }
+.lm-TabBar .lm-TabBar-addButton {
+  align-items: center;
+  display: flex;
+  padding: 4px;
+  padding-bottom: 5px;
+  margin-right: 1px;
+  background-color: var(--jp-layout-color2);
+}
 
-.bp3-icon-group-objects::before{
-  content:""; }
+.lm-TabBar .lm-TabBar-addButton:hover {
+  background-color: var(--jp-layout-color1);
+}
 
-.bp3-icon-grouped-bar-chart::before{
-  content:""; }
+.lm-DockPanel-tabBar .lm-TabBar-tab {
+  width: var(--jp-private-horizontal-tab-width);
+}
 
-.bp3-icon-hand::before{
-  content:""; }
+.lm-DockPanel-tabBar .lm-TabBar-content {
+  flex: unset;
+}
 
-.bp3-icon-hand-down::before{
-  content:""; }
+.lm-DockPanel-tabBar[data-orientation='horizontal'] {
+  flex: 1 1 auto;
+}
 
-.bp3-icon-hand-left::before{
-  content:""; }
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
 
-.bp3-icon-hand-right::before{
-  content:""; }
+/**
+ * Support for icons as inline SVG HTMLElements
+ */
 
-.bp3-icon-hand-up::before{
-  content:""; }
+/* recolor the primary elements of an icon */
+.jp-icon0[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
 
-.bp3-icon-header::before{
-  content:""; }
+.jp-icon1[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
 
-.bp3-icon-header-one::before{
-  content:""; }
+.jp-icon2[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
 
-.bp3-icon-header-two::before{
-  content:""; }
+.jp-icon3[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
 
-.bp3-icon-headset::before{
-  content:""; }
+.jp-icon4[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
 
-.bp3-icon-heart::before{
-  content:"♥"; }
+.jp-icon0[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
 
-.bp3-icon-heart-broken::before{
-  content:""; }
+.jp-icon1[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
 
-.bp3-icon-heat-grid::before{
-  content:""; }
+.jp-icon2[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
 
-.bp3-icon-heatmap::before{
-  content:""; }
+.jp-icon3[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
 
-.bp3-icon-help::before{
-  content:"?"; }
+.jp-icon4[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
 
-.bp3-icon-helper-management::before{
-  content:""; }
+/* recolor the accent elements of an icon */
+.jp-icon-accent0[fill] {
+  fill: var(--jp-layout-color0);
+}
 
-.bp3-icon-highlight::before{
-  content:""; }
+.jp-icon-accent1[fill] {
+  fill: var(--jp-layout-color1);
+}
 
-.bp3-icon-history::before{
-  content:""; }
+.jp-icon-accent2[fill] {
+  fill: var(--jp-layout-color2);
+}
 
-.bp3-icon-home::before{
-  content:"⌂"; }
+.jp-icon-accent3[fill] {
+  fill: var(--jp-layout-color3);
+}
 
-.bp3-icon-horizontal-bar-chart::before{
-  content:""; }
+.jp-icon-accent4[fill] {
+  fill: var(--jp-layout-color4);
+}
 
-.bp3-icon-horizontal-bar-chart-asc::before{
-  content:""; }
+.jp-icon-accent0[stroke] {
+  stroke: var(--jp-layout-color0);
+}
 
-.bp3-icon-horizontal-bar-chart-desc::before{
-  content:""; }
+.jp-icon-accent1[stroke] {
+  stroke: var(--jp-layout-color1);
+}
 
-.bp3-icon-horizontal-distribution::before{
-  content:""; }
+.jp-icon-accent2[stroke] {
+  stroke: var(--jp-layout-color2);
+}
 
-.bp3-icon-id-number::before{
-  content:""; }
+.jp-icon-accent3[stroke] {
+  stroke: var(--jp-layout-color3);
+}
 
-.bp3-icon-image-rotate-left::before{
-  content:""; }
+.jp-icon-accent4[stroke] {
+  stroke: var(--jp-layout-color4);
+}
 
-.bp3-icon-image-rotate-right::before{
-  content:""; }
+/* set the color of an icon to transparent */
+.jp-icon-none[fill] {
+  fill: none;
+}
 
-.bp3-icon-import::before{
-  content:""; }
+.jp-icon-none[stroke] {
+  stroke: none;
+}
 
-.bp3-icon-inbox::before{
-  content:""; }
+/* brand icon colors. Same for light and dark */
+.jp-icon-brand0[fill] {
+  fill: var(--jp-brand-color0);
+}
 
-.bp3-icon-inbox-filtered::before{
-  content:""; }
+.jp-icon-brand1[fill] {
+  fill: var(--jp-brand-color1);
+}
 
-.bp3-icon-inbox-geo::before{
-  content:""; }
+.jp-icon-brand2[fill] {
+  fill: var(--jp-brand-color2);
+}
 
-.bp3-icon-inbox-search::before{
-  content:""; }
+.jp-icon-brand3[fill] {
+  fill: var(--jp-brand-color3);
+}
 
-.bp3-icon-inbox-update::before{
-  content:""; }
+.jp-icon-brand4[fill] {
+  fill: var(--jp-brand-color4);
+}
 
-.bp3-icon-info-sign::before{
-  content:"ℹ"; }
+.jp-icon-brand0[stroke] {
+  stroke: var(--jp-brand-color0);
+}
 
-.bp3-icon-inheritance::before{
-  content:""; }
+.jp-icon-brand1[stroke] {
+  stroke: var(--jp-brand-color1);
+}
 
-.bp3-icon-inner-join::before{
-  content:""; }
+.jp-icon-brand2[stroke] {
+  stroke: var(--jp-brand-color2);
+}
 
-.bp3-icon-insert::before{
-  content:""; }
+.jp-icon-brand3[stroke] {
+  stroke: var(--jp-brand-color3);
+}
 
-.bp3-icon-intersection::before{
-  content:""; }
+.jp-icon-brand4[stroke] {
+  stroke: var(--jp-brand-color4);
+}
 
-.bp3-icon-ip-address::before{
-  content:""; }
+/* warn icon colors. Same for light and dark */
+.jp-icon-warn0[fill] {
+  fill: var(--jp-warn-color0);
+}
 
-.bp3-icon-issue::before{
-  content:""; }
+.jp-icon-warn1[fill] {
+  fill: var(--jp-warn-color1);
+}
 
-.bp3-icon-issue-closed::before{
-  content:""; }
+.jp-icon-warn2[fill] {
+  fill: var(--jp-warn-color2);
+}
 
-.bp3-icon-issue-new::before{
-  content:""; }
+.jp-icon-warn3[fill] {
+  fill: var(--jp-warn-color3);
+}
 
-.bp3-icon-italic::before{
-  content:""; }
+.jp-icon-warn0[stroke] {
+  stroke: var(--jp-warn-color0);
+}
 
-.bp3-icon-join-table::before{
-  content:""; }
+.jp-icon-warn1[stroke] {
+  stroke: var(--jp-warn-color1);
+}
 
-.bp3-icon-key::before{
-  content:""; }
+.jp-icon-warn2[stroke] {
+  stroke: var(--jp-warn-color2);
+}
 
-.bp3-icon-key-backspace::before{
-  content:""; }
+.jp-icon-warn3[stroke] {
+  stroke: var(--jp-warn-color3);
+}
 
-.bp3-icon-key-command::before{
-  content:""; }
+/* icon colors that contrast well with each other and most backgrounds */
+.jp-icon-contrast0[fill] {
+  fill: var(--jp-icon-contrast-color0);
+}
 
-.bp3-icon-key-control::before{
-  content:""; }
-
-.bp3-icon-key-delete::before{
-  content:""; }
-
-.bp3-icon-key-enter::before{
-  content:""; }
+.jp-icon-contrast1[fill] {
+  fill: var(--jp-icon-contrast-color1);
+}
 
-.bp3-icon-key-escape::before{
-  content:""; }
+.jp-icon-contrast2[fill] {
+  fill: var(--jp-icon-contrast-color2);
+}
 
-.bp3-icon-key-option::before{
-  content:""; }
+.jp-icon-contrast3[fill] {
+  fill: var(--jp-icon-contrast-color3);
+}
 
-.bp3-icon-key-shift::before{
-  content:""; }
+.jp-icon-contrast0[stroke] {
+  stroke: var(--jp-icon-contrast-color0);
+}
 
-.bp3-icon-key-tab::before{
-  content:""; }
+.jp-icon-contrast1[stroke] {
+  stroke: var(--jp-icon-contrast-color1);
+}
 
-.bp3-icon-known-vehicle::before{
-  content:""; }
+.jp-icon-contrast2[stroke] {
+  stroke: var(--jp-icon-contrast-color2);
+}
 
-.bp3-icon-lab-test::before{
-  content:""; }
+.jp-icon-contrast3[stroke] {
+  stroke: var(--jp-icon-contrast-color3);
+}
 
-.bp3-icon-label::before{
-  content:""; }
+.jp-icon-dot[fill] {
+  fill: var(--jp-warn-color0);
+}
 
-.bp3-icon-layer::before{
-  content:""; }
+.jp-jupyter-icon-color[fill] {
+  fill: var(--jp-jupyter-icon-color, var(--jp-warn-color0));
+}
 
-.bp3-icon-layers::before{
-  content:""; }
+.jp-notebook-icon-color[fill] {
+  fill: var(--jp-notebook-icon-color, var(--jp-warn-color0));
+}
 
-.bp3-icon-layout::before{
-  content:""; }
+.jp-json-icon-color[fill] {
+  fill: var(--jp-json-icon-color, var(--jp-warn-color1));
+}
 
-.bp3-icon-layout-auto::before{
-  content:""; }
+.jp-console-icon-color[fill] {
+  fill: var(--jp-console-icon-color, white);
+}
 
-.bp3-icon-layout-balloon::before{
-  content:""; }
+.jp-console-icon-background-color[fill] {
+  fill: var(--jp-console-icon-background-color, var(--jp-brand-color1));
+}
 
-.bp3-icon-layout-circle::before{
-  content:""; }
+.jp-terminal-icon-color[fill] {
+  fill: var(--jp-terminal-icon-color, var(--jp-layout-color2));
+}
 
-.bp3-icon-layout-grid::before{
-  content:""; }
+.jp-terminal-icon-background-color[fill] {
+  fill: var(
+    --jp-terminal-icon-background-color,
+    var(--jp-inverse-layout-color2)
+  );
+}
 
-.bp3-icon-layout-group-by::before{
-  content:""; }
+.jp-text-editor-icon-color[fill] {
+  fill: var(--jp-text-editor-icon-color, var(--jp-inverse-layout-color3));
+}
 
-.bp3-icon-layout-hierarchy::before{
-  content:""; }
+.jp-inspector-icon-color[fill] {
+  fill: var(--jp-inspector-icon-color, var(--jp-inverse-layout-color3));
+}
 
-.bp3-icon-layout-linear::before{
-  content:""; }
+/* CSS for icons in selected filebrowser listing items */
+.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
+  fill: #fff;
+}
 
-.bp3-icon-layout-skew-grid::before{
-  content:""; }
+.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
+  fill: var(--jp-brand-color1);
+}
 
-.bp3-icon-layout-sorted-clusters::before{
-  content:""; }
+/* stylelint-disable selector-max-class, selector-max-compound-selectors */
 
-.bp3-icon-learning::before{
-  content:""; }
+/**
+* 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;
+}
 
-.bp3-icon-left-join::before{
-  content:""; }
+.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);
+}
 
-.bp3-icon-less-than::before{
-  content:""; }
+/* stylelint-enable selector-max-class, selector-max-compound-selectors */
 
-.bp3-icon-less-than-or-equal-to::before{
-  content:""; }
+/* CSS for icons in status bar */
+#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
+  fill: #fff;
+}
 
-.bp3-icon-lifesaver::before{
-  content:""; }
+#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
+  fill: var(--jp-brand-color1);
+}
 
-.bp3-icon-lightbulb::before{
-  content:""; }
+/* 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);
+}
 
-.bp3-icon-link::before{
-  content:""; }
+/* not sure what to do with this one, used in filebrowser listing */
+.jp-DragIcon {
+  margin-right: 4px;
+}
 
-.bp3-icon-list::before{
-  content:"☰"; }
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
 
-.bp3-icon-list-columns::before{
-  content:""; }
+/**
+ * Support for alt colors for icons as inline SVG HTMLElements
+ */
 
-.bp3-icon-list-detail-view::before{
-  content:""; }
+/* alt recolor the primary elements of an icon */
+.jp-icon-alt .jp-icon0[fill] {
+  fill: var(--jp-layout-color0);
+}
 
-.bp3-icon-locate::before{
-  content:""; }
+.jp-icon-alt .jp-icon1[fill] {
+  fill: var(--jp-layout-color1);
+}
 
-.bp3-icon-lock::before{
-  content:""; }
+.jp-icon-alt .jp-icon2[fill] {
+  fill: var(--jp-layout-color2);
+}
 
-.bp3-icon-log-in::before{
-  content:""; }
+.jp-icon-alt .jp-icon3[fill] {
+  fill: var(--jp-layout-color3);
+}
 
-.bp3-icon-log-out::before{
-  content:""; }
+.jp-icon-alt .jp-icon4[fill] {
+  fill: var(--jp-layout-color4);
+}
 
-.bp3-icon-manual::before{
-  content:""; }
+.jp-icon-alt .jp-icon0[stroke] {
+  stroke: var(--jp-layout-color0);
+}
 
-.bp3-icon-manually-entered-data::before{
-  content:""; }
+.jp-icon-alt .jp-icon1[stroke] {
+  stroke: var(--jp-layout-color1);
+}
 
-.bp3-icon-map::before{
-  content:""; }
+.jp-icon-alt .jp-icon2[stroke] {
+  stroke: var(--jp-layout-color2);
+}
 
-.bp3-icon-map-create::before{
-  content:""; }
+.jp-icon-alt .jp-icon3[stroke] {
+  stroke: var(--jp-layout-color3);
+}
 
-.bp3-icon-map-marker::before{
-  content:""; }
+.jp-icon-alt .jp-icon4[stroke] {
+  stroke: var(--jp-layout-color4);
+}
 
-.bp3-icon-maximize::before{
-  content:""; }
+/* alt recolor the accent elements of an icon */
+.jp-icon-alt .jp-icon-accent0[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
 
-.bp3-icon-media::before{
-  content:""; }
+.jp-icon-alt .jp-icon-accent1[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
 
-.bp3-icon-menu::before{
-  content:""; }
+.jp-icon-alt .jp-icon-accent2[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
 
-.bp3-icon-menu-closed::before{
-  content:""; }
+.jp-icon-alt .jp-icon-accent3[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
 
-.bp3-icon-menu-open::before{
-  content:""; }
+.jp-icon-alt .jp-icon-accent4[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
 
-.bp3-icon-merge-columns::before{
-  content:""; }
+.jp-icon-alt .jp-icon-accent0[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
 
-.bp3-icon-merge-links::before{
-  content:""; }
+.jp-icon-alt .jp-icon-accent1[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
 
-.bp3-icon-minimize::before{
-  content:""; }
+.jp-icon-alt .jp-icon-accent2[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
 
-.bp3-icon-minus::before{
-  content:"−"; }
+.jp-icon-alt .jp-icon-accent3[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
 
-.bp3-icon-mobile-phone::before{
-  content:""; }
+.jp-icon-alt .jp-icon-accent4[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
 
-.bp3-icon-mobile-video::before{
-  content:""; }
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
 
-.bp3-icon-moon::before{
-  content:""; }
+.jp-icon-hoverShow:not(:hover) .jp-icon-hoverShow-content {
+  display: none !important;
+}
 
-.bp3-icon-more::before{
-  content:""; }
+/**
+ * Support for hover colors for icons as inline SVG HTMLElements
+ */
 
-.bp3-icon-mountain::before{
-  content:""; }
+/**
+ * regular colors
+ */
 
-.bp3-icon-move::before{
-  content:""; }
+/* recolor the primary elements of an icon */
+.jp-icon-hover :hover .jp-icon0-hover[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
 
-.bp3-icon-mugshot::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon1-hover[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
 
-.bp3-icon-multi-select::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon2-hover[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
 
-.bp3-icon-music::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon3-hover[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
 
-.bp3-icon-new-drawing::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon4-hover[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
 
-.bp3-icon-new-grid-item::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon0-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
 
-.bp3-icon-new-layer::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon1-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
 
-.bp3-icon-new-layers::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon2-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
 
-.bp3-icon-new-link::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon3-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
 
-.bp3-icon-new-object::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon4-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
 
-.bp3-icon-new-person::before{
-  content:""; }
+/* recolor the accent elements of an icon */
+.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
+  fill: var(--jp-layout-color0);
+}
 
-.bp3-icon-new-prescription::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
+  fill: var(--jp-layout-color1);
+}
 
-.bp3-icon-new-text-box::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
+  fill: var(--jp-layout-color2);
+}
 
-.bp3-icon-ninja::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
+  fill: var(--jp-layout-color3);
+}
 
-.bp3-icon-not-equal-to::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
+  fill: var(--jp-layout-color4);
+}
 
-.bp3-icon-notifications::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
+  stroke: var(--jp-layout-color0);
+}
 
-.bp3-icon-notifications-updated::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
+  stroke: var(--jp-layout-color1);
+}
 
-.bp3-icon-numbered-list::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
+  stroke: var(--jp-layout-color2);
+}
 
-.bp3-icon-numerical::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
+  stroke: var(--jp-layout-color3);
+}
 
-.bp3-icon-office::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
+  stroke: var(--jp-layout-color4);
+}
 
-.bp3-icon-offline::before{
-  content:""; }
+/* set the color of an icon to transparent */
+.jp-icon-hover :hover .jp-icon-none-hover[fill] {
+  fill: none;
+}
 
-.bp3-icon-oil-field::before{
-  content:""; }
+.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
+  stroke: none;
+}
 
-.bp3-icon-one-column::before{
-  content:""; }
+/**
+ * inverse colors
+ */
 
-.bp3-icon-outdated::before{
-  content:""; }
+/* inverse recolor the primary elements of an icon */
+.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
+  fill: var(--jp-layout-color0);
+}
 
-.bp3-icon-page-layout::before{
-  content:""; }
+.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
+  fill: var(--jp-layout-color1);
+}
 
-.bp3-icon-panel-stats::before{
-  content:""; }
+.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
+  fill: var(--jp-layout-color2);
+}
 
-.bp3-icon-panel-table::before{
-  content:""; }
+.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
+  fill: var(--jp-layout-color3);
+}
 
-.bp3-icon-paperclip::before{
-  content:""; }
+.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
+  fill: var(--jp-layout-color4);
+}
 
-.bp3-icon-paragraph::before{
-  content:""; }
+.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
+  stroke: var(--jp-layout-color0);
+}
 
-.bp3-icon-path::before{
-  content:""; }
+.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
+  stroke: var(--jp-layout-color1);
+}
 
-.bp3-icon-path-search::before{
-  content:""; }
+.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
+  stroke: var(--jp-layout-color2);
+}
 
-.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:""; }
+.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
+  stroke: var(--jp-layout-color3);
+}
 
-.bp3-icon-print::before{
-  content:"⎙"; }
+.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
+  stroke: var(--jp-layout-color4);
+}
 
-.bp3-icon-projects::before{
-  content:""; }
+/* 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);
+}
 
-.bp3-icon-properties::before{
-  content:""; }
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
 
-.bp3-icon-property::before{
-  content:""; }
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
 
-.bp3-icon-publish-function::before{
-  content:""; }
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
 
-.bp3-icon-pulse::before{
-  content:""; }
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
 
-.bp3-icon-random::before{
-  content:""; }
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
 
-.bp3-icon-record::before{
-  content:""; }
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
 
-.bp3-icon-redo::before{
-  content:""; }
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
 
-.bp3-icon-refresh::before{
-  content:""; }
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
 
-.bp3-icon-regression-chart::before{
-  content:""; }
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
 
-.bp3-icon-remove::before{
-  content:""; }
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
 
-.bp3-icon-remove-column::before{
-  content:""; }
+.jp-IFrame {
+  width: 100%;
+  height: 100%;
+}
 
-.bp3-icon-remove-column-left::before{
-  content:""; }
+.jp-IFrame > iframe {
+  border: none;
+}
 
-.bp3-icon-remove-column-right::before{
-  content:""; }
+/*
+When drag events occur, `lm-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;
+}
 
-.bp3-icon-remove-row-bottom::before{
-  content:""; }
+body.lm-mod-override-cursor .jp-IFrame::before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: transparent;
+}
 
-.bp3-icon-remove-row-top::before{
-  content:""; }
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
 
-.bp3-icon-repeat::before{
-  content:""; }
+.jp-HoverBox {
+  position: fixed;
+}
 
-.bp3-icon-reset::before{
-  content:""; }
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
 
-.bp3-icon-resolve::before{
-  content:""; }
+.jp-FormGroup-content fieldset {
+  border: none;
+  padding: 0;
+  min-width: 0;
+  width: 100%;
+}
 
-.bp3-icon-rig::before{
-  content:""; }
+/* stylelint-disable selector-max-type */
 
-.bp3-icon-right-join::before{
-  content:""; }
+.jp-FormGroup-content fieldset .jp-inputFieldWrapper input,
+.jp-FormGroup-content fieldset .jp-inputFieldWrapper select,
+.jp-FormGroup-content fieldset .jp-inputFieldWrapper textarea {
+  font-size: var(--jp-content-font-size2);
+  border-color: var(--jp-input-border-color);
+  border-style: solid;
+  border-radius: var(--jp-border-radius);
+  border-width: 1px;
+  padding: 6px 8px;
+  background: none;
+  color: var(--jp-ui-font-color0);
+  height: inherit;
+}
 
-.bp3-icon-ring::before{
-  content:""; }
+.jp-FormGroup-content fieldset input[type='checkbox'] {
+  position: relative;
+  top: 2px;
+  margin-left: 0;
+}
 
-.bp3-icon-rotate-document::before{
-  content:""; }
+.jp-FormGroup-content button.jp-mod-styled {
+  cursor: pointer;
+}
 
-.bp3-icon-rotate-page::before{
-  content:""; }
+.jp-FormGroup-content .checkbox label {
+  cursor: pointer;
+  font-size: var(--jp-content-font-size1);
+}
 
-.bp3-icon-satellite::before{
-  content:""; }
+.jp-FormGroup-content .jp-root > fieldset > legend {
+  display: none;
+}
 
-.bp3-icon-saved::before{
-  content:""; }
+.jp-FormGroup-content .jp-root > fieldset > p {
+  display: none;
+}
 
-.bp3-icon-scatter-plot::before{
-  content:""; }
+/** copy of `input.jp-mod-styled:focus` style */
+.jp-FormGroup-content fieldset input:focus,
+.jp-FormGroup-content fieldset select:focus {
+  -moz-outline-radius: unset;
+  outline: var(--jp-border-width) solid var(--md-blue-500);
+  outline-offset: -1px;
+  box-shadow: inset 0 0 4px var(--md-blue-300);
+}
 
-.bp3-icon-search::before{
-  content:""; }
+.jp-FormGroup-content fieldset input:hover:not(:focus),
+.jp-FormGroup-content fieldset select:hover:not(:focus) {
+  background-color: var(--jp-border-color2);
+}
 
-.bp3-icon-search-around::before{
-  content:""; }
+/* stylelint-enable selector-max-type */
 
-.bp3-icon-search-template::before{
-  content:""; }
+.jp-FormGroup-content .checkbox .field-description {
+  /* Disable default description field for checkbox:
+   because other widgets do not have description fields,
+   we add descriptions to each widget on the field level.
+  */
+  display: none;
+}
 
-.bp3-icon-search-text::before{
-  content:""; }
+.jp-FormGroup-content #root__description {
+  display: none;
+}
 
-.bp3-icon-segmented-control::before{
-  content:""; }
+.jp-FormGroup-content .jp-modifiedIndicator {
+  width: 5px;
+  background-color: var(--jp-brand-color2);
+  margin-top: 0;
+  margin-left: calc(var(--jp-private-settingeditor-modifier-indent) * -1);
+  flex-shrink: 0;
+}
 
-.bp3-icon-select::before{
-  content:""; }
+.jp-FormGroup-content .jp-modifiedIndicator.jp-errorIndicator {
+  background-color: var(--jp-error-color0);
+  margin-right: 0.5em;
+}
 
-.bp3-icon-selection::before{
-  content:"⦿"; }
+/* RJSF ARRAY style */
 
-.bp3-icon-send-to::before{
-  content:""; }
+.jp-arrayFieldWrapper legend {
+  font-size: var(--jp-content-font-size2);
+  color: var(--jp-ui-font-color0);
+  flex-basis: 100%;
+  padding: 4px 0;
+  font-weight: var(--jp-content-heading-font-weight);
+  border-bottom: 1px solid var(--jp-border-color2);
+}
 
-.bp3-icon-send-to-graph::before{
-  content:""; }
+.jp-arrayFieldWrapper .field-description {
+  padding: 4px 0;
+  white-space: pre-wrap;
+}
 
-.bp3-icon-send-to-map::before{
-  content:""; }
+.jp-arrayFieldWrapper .array-item {
+  width: 100%;
+  border: 1px solid var(--jp-border-color2);
+  border-radius: 4px;
+  margin: 4px;
+}
 
-.bp3-icon-series-add::before{
-  content:""; }
+.jp-ArrayOperations {
+  display: flex;
+  margin-left: 8px;
+}
 
-.bp3-icon-series-configuration::before{
-  content:""; }
+.jp-ArrayOperationsButton {
+  margin: 2px;
+}
 
-.bp3-icon-series-derived::before{
-  content:""; }
+.jp-ArrayOperationsButton .jp-icon3[fill] {
+  fill: var(--jp-ui-font-color0);
+}
 
-.bp3-icon-series-filtered::before{
-  content:""; }
+button.jp-ArrayOperationsButton.jp-mod-styled:disabled {
+  cursor: not-allowed;
+  opacity: 0.5;
+}
 
-.bp3-icon-series-search::before{
-  content:""; }
+/* RJSF form validation error */
 
-.bp3-icon-settings::before{
-  content:""; }
+.jp-FormGroup-content .validationErrors {
+  color: var(--jp-error-color0);
+}
 
-.bp3-icon-share::before{
-  content:""; }
+/* Hide panel level error as duplicated the field level error */
+.jp-FormGroup-content .panel.errors {
+  display: none;
+}
 
-.bp3-icon-shield::before{
-  content:""; }
+/* RJSF normal content (settings-editor) */
 
-.bp3-icon-shop::before{
-  content:""; }
+.jp-FormGroup-contentNormal {
+  display: flex;
+  align-items: center;
+  flex-wrap: wrap;
+}
 
-.bp3-icon-shopping-cart::before{
-  content:""; }
+.jp-FormGroup-contentNormal .jp-FormGroup-contentItem {
+  margin-left: 7px;
+  color: var(--jp-ui-font-color0);
+}
 
-.bp3-icon-signal-search::before{
-  content:""; }
+.jp-FormGroup-contentNormal .jp-FormGroup-description {
+  flex-basis: 100%;
+  padding: 4px 7px;
+}
 
-.bp3-icon-sim-card::before{
-  content:""; }
+.jp-FormGroup-contentNormal .jp-FormGroup-default {
+  flex-basis: 100%;
+  padding: 4px 7px;
+}
 
-.bp3-icon-slash::before{
-  content:""; }
+.jp-FormGroup-contentNormal .jp-FormGroup-fieldLabel {
+  font-size: var(--jp-content-font-size1);
+  font-weight: normal;
+  min-width: 120px;
+}
 
-.bp3-icon-small-cross::before{
-  content:""; }
+.jp-FormGroup-contentNormal fieldset:not(:first-child) {
+  margin-left: 7px;
+}
 
-.bp3-icon-small-minus::before{
-  content:""; }
+.jp-FormGroup-contentNormal .field-array-of-string .array-item {
+  /* Display `jp-ArrayOperations` buttons side-by-side with content except
+    for small screens where flex-wrap will place them one below the other.
+  */
+  display: flex;
+  align-items: center;
+  flex-wrap: wrap;
+}
 
-.bp3-icon-small-plus::before{
-  content:""; }
+.jp-FormGroup-contentNormal .jp-objectFieldWrapper .form-group {
+  padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
+  margin-top: 2px;
+}
 
-.bp3-icon-small-tick::before{
-  content:""; }
+/* RJSF compact content (metadata-form) */
 
-.bp3-icon-snowflake::before{
-  content:""; }
+.jp-FormGroup-content.jp-FormGroup-contentCompact {
+  width: 100%;
+}
 
-.bp3-icon-social-media::before{
-  content:""; }
+.jp-FormGroup-contentCompact .form-group {
+  display: flex;
+  padding: 0.5em 0.2em 0.5em 0;
+}
 
-.bp3-icon-sort::before{
-  content:""; }
+.jp-FormGroup-contentCompact
+  .jp-FormGroup-compactTitle
+  .jp-FormGroup-description {
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color2);
+}
 
-.bp3-icon-sort-alphabetical::before{
-  content:""; }
+.jp-FormGroup-contentCompact .jp-FormGroup-fieldLabel {
+  padding-bottom: 0.3em;
+}
 
-.bp3-icon-sort-alphabetical-desc::before{
-  content:""; }
+.jp-FormGroup-contentCompact .jp-inputFieldWrapper .form-control {
+  width: 100%;
+  box-sizing: border-box;
+}
 
-.bp3-icon-sort-asc::before{
-  content:""; }
+.jp-FormGroup-contentCompact .jp-arrayFieldWrapper .jp-FormGroup-compactTitle {
+  padding-bottom: 7px;
+}
 
-.bp3-icon-sort-desc::before{
-  content:""; }
+.jp-FormGroup-contentCompact
+  .jp-objectFieldWrapper
+  .jp-objectFieldWrapper
+  .form-group {
+  padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
+  margin-top: 2px;
+}
 
-.bp3-icon-sort-numerical::before{
-  content:""; }
+.jp-FormGroup-contentCompact ul.error-detail {
+  margin-block-start: 0.5em;
+  margin-block-end: 0.5em;
+  padding-inline-start: 1em;
+}
 
-.bp3-icon-sort-numerical-desc::before{
-  content:""; }
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
 
-.bp3-icon-split-columns::before{
-  content:""; }
+.jp-SidePanel {
+  display: flex;
+  flex-direction: column;
+  min-width: var(--jp-sidebar-min-width);
+  overflow-y: auto;
+  color: var(--jp-ui-font-color1);
+  background: var(--jp-layout-color1);
+  font-size: var(--jp-ui-font-size1);
+}
 
-.bp3-icon-square::before{
-  content:""; }
+.jp-SidePanel-header {
+  flex: 0 0 auto;
+  display: flex;
+  border-bottom: var(--jp-border-width) solid var(--jp-border-color2);
+  font-size: var(--jp-ui-font-size0);
+  font-weight: 600;
+  letter-spacing: 1px;
+  margin: 0;
+  padding: 2px;
+  text-transform: uppercase;
+}
 
-.bp3-icon-stacked-chart::before{
-  content:""; }
+.jp-SidePanel-toolbar {
+  flex: 0 0 auto;
+}
 
-.bp3-icon-star::before{
-  content:"★"; }
+.jp-SidePanel-content {
+  flex: 1 1 auto;
+}
 
-.bp3-icon-star-empty::before{
-  content:"☆"; }
+.jp-SidePanel-toolbar,
+.jp-AccordionPanel-toolbar {
+  height: var(--jp-private-toolbar-height);
+}
 
-.bp3-icon-step-backward::before{
-  content:""; }
+.jp-SidePanel-toolbar.jp-Toolbar-micro {
+  display: none;
+}
 
-.bp3-icon-step-chart::before{
-  content:""; }
+.lm-AccordionPanel .jp-AccordionPanel-title {
+  box-sizing: border-box;
+  line-height: 25px;
+  margin: 0;
+  display: flex;
+  align-items: center;
+  background: var(--jp-layout-color1);
+  color: var(--jp-ui-font-color1);
+  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+  box-shadow: var(--jp-toolbar-box-shadow);
+  font-size: var(--jp-ui-font-size0);
+}
 
-.bp3-icon-step-forward::before{
-  content:""; }
+.jp-AccordionPanel-title {
+  cursor: pointer;
+  user-select: none;
+  -moz-user-select: none;
+  -webkit-user-select: none;
+  text-transform: uppercase;
+}
 
-.bp3-icon-stop::before{
-  content:""; }
+.lm-AccordionPanel[data-orientation='horizontal'] > .jp-AccordionPanel-title {
+  /* Title is rotated for horizontal accordion panel using CSS */
+  display: block;
+  transform-origin: top left;
+  transform: rotate(-90deg) translate(-100%);
+}
 
-.bp3-icon-stopwatch::before{
-  content:""; }
+.jp-AccordionPanel-title .lm-AccordionPanel-titleLabel {
+  user-select: none;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+  overflow: hidden;
+}
 
-.bp3-icon-strikethrough::before{
-  content:""; }
+.jp-AccordionPanel-title .lm-AccordionPanel-titleCollapser {
+  transform: rotate(-90deg);
+  margin: auto 0;
+  height: 16px;
+}
 
-.bp3-icon-style::before{
-  content:""; }
+.jp-AccordionPanel-title.lm-mod-expanded .lm-AccordionPanel-titleCollapser {
+  transform: rotate(0deg);
+}
 
-.bp3-icon-swap-horizontal::before{
-  content:""; }
+.lm-AccordionPanel .jp-AccordionPanel-toolbar {
+  background: none;
+  box-shadow: none;
+  border: none;
+  margin-left: auto;
+}
 
-.bp3-icon-swap-vertical::before{
-  content:""; }
+.lm-AccordionPanel .lm-SplitPanel-handle:hover {
+  background: var(--jp-layout-color3);
+}
 
-.bp3-icon-symbol-circle::before{
-  content:""; }
+.jp-text-truncated {
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
 
-.bp3-icon-symbol-cross::before{
-  content:""; }
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
 
-.bp3-icon-symbol-diamond::before{
-  content:""; }
+.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;
+}
 
-.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:""; }
+.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;
+}
 
-.bp3-icon-tick::before{
-  content:"✓"; }
+.jp-SpinnerContent::before {
+  width: 50%;
+  height: 50%;
+  background: #f37626;
+  border-radius: 100% 0 0;
+  position: absolute;
+  top: 0;
+  left: 0;
+  content: '';
+}
 
-.bp3-icon-tick-circle::before{
-  content:""; }
+.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;
+}
 
-.bp3-icon-time::before{
-  content:"⏲"; }
+@keyframes fadeIn {
+  0% {
+    opacity: 0;
+  }
 
-.bp3-icon-timeline-area-chart::before{
-  content:""; }
+  100% {
+    opacity: 1;
+  }
+}
 
-.bp3-icon-timeline-bar-chart::before{
-  content:""; }
+@keyframes load3 {
+  0% {
+    transform: rotate(0deg);
+  }
 
-.bp3-icon-timeline-events::before{
-  content:""; }
+  100% {
+    transform: rotate(360deg);
+  }
+}
 
-.bp3-icon-timeline-line-chart::before{
-  content:""; }
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
 
-.bp3-icon-tint::before{
-  content:""; }
+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: 0 12px;
+  letter-spacing: 0.8px;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+}
 
-.bp3-icon-torch::before{
-  content:""; }
+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;
+}
 
-.bp3-icon-tractor::before{
-  content:""; }
+input[type='checkbox'].jp-mod-styled {
+  appearance: checkbox;
+  -webkit-appearance: checkbox;
+  -moz-appearance: checkbox;
+  height: auto;
+}
 
-.bp3-icon-train::before{
-  content:""; }
+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);
+}
 
-.bp3-icon-translate::before{
-  content:""; }
+.jp-select-wrapper {
+  display: flex;
+  position: relative;
+  flex-direction: column;
+  padding: 1px;
+  background-color: var(--jp-layout-color1);
+  box-sizing: border-box;
+  margin-bottom: 12px;
+}
 
-.bp3-icon-trash::before{
-  content:""; }
+.jp-select-wrapper:not(.multiple) {
+  height: 28px;
+}
 
-.bp3-icon-tree::before{
-  content:""; }
+.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);
+}
 
-.bp3-icon-trending-down::before{
-  content:""; }
+select.jp-mod-styled:hover {
+  cursor: pointer;
+  color: var(--jp-ui-font-color0);
+  background-color: var(--jp-input-hover-background);
+  box-shadow: inset 0 0 1px rgba(0, 0, 0, 0.5);
+}
 
-.bp3-icon-trending-up::before{
-  content:""; }
+select.jp-mod-styled {
+  flex: 1 1 auto;
+  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: 0;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+}
 
-.bp3-icon-truck::before{
-  content:""; }
+select.jp-mod-styled:not([multiple]) {
+  height: 32px;
+}
 
-.bp3-icon-two-columns::before{
-  content:""; }
+select.jp-mod-styled[multiple] {
+  max-height: 200px;
+  overflow-y: auto;
+}
 
-.bp3-icon-unarchive::before{
-  content:""; }
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
 
-.bp3-icon-underline::before{
-  content:"⎁"; }
+.jp-switch {
+  display: flex;
+  align-items: center;
+  padding-left: 4px;
+  padding-right: 4px;
+  font-size: var(--jp-ui-font-size1);
+  background-color: transparent;
+  color: var(--jp-ui-font-color1);
+  border: none;
+  height: 20px;
+}
 
-.bp3-icon-undo::before{
-  content:"⎌"; }
+.jp-switch:hover {
+  background-color: var(--jp-layout-color2);
+}
 
-.bp3-icon-ungroup-objects::before{
-  content:""; }
+.jp-switch-label {
+  margin-right: 5px;
+  font-family: var(--jp-ui-font-family);
+}
 
-.bp3-icon-unknown-vehicle::before{
-  content:""; }
+.jp-switch-track {
+  cursor: pointer;
+  background-color: var(--jp-switch-color, var(--jp-border-color1));
+  -webkit-transition: 0.4s;
+  transition: 0.4s;
+  border-radius: 34px;
+  height: 16px;
+  width: 35px;
+  position: relative;
+}
 
-.bp3-icon-unlock::before{
-  content:""; }
+.jp-switch-track::before {
+  content: '';
+  position: absolute;
+  height: 10px;
+  width: 10px;
+  margin: 3px;
+  left: 0;
+  background-color: var(--jp-ui-inverse-font-color1);
+  -webkit-transition: 0.4s;
+  transition: 0.4s;
+  border-radius: 50%;
+}
 
-.bp3-icon-unpin::before{
-  content:""; }
+.jp-switch[aria-checked='true'] .jp-switch-track {
+  background-color: var(--jp-switch-true-position-color, var(--jp-warn-color0));
+}
 
-.bp3-icon-unresolve::before{
-  content:""; }
+.jp-switch[aria-checked='true'] .jp-switch-track::before {
+  /* track width (35) - margins (3 + 3) - thumb width (10) */
+  left: 19px;
+}
 
-.bp3-icon-updated::before{
-  content:""; }
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
 
-.bp3-icon-upload::before{
-  content:""; }
+:root {
+  --jp-private-toolbar-height: calc(
+    28px + var(--jp-border-width)
+  ); /* leave 28px for content */
+}
 
-.bp3-icon-user::before{
-  content:""; }
+.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: 8;
+  overflow-x: hidden;
+}
 
-.bp3-icon-variable::before{
-  content:""; }
+/* Toolbar items */
 
-.bp3-icon-vertical-bar-chart-asc::before{
-  content:""; }
+.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
+  flex-grow: 1;
+  flex-shrink: 1;
+}
 
-.bp3-icon-vertical-bar-chart-desc::before{
-  content:""; }
+.jp-Toolbar-item.jp-Toolbar-kernelStatus {
+  display: inline-block;
+  width: 32px;
+  background-repeat: no-repeat;
+  background-position: center;
+  background-size: 16px;
+}
 
-.bp3-icon-vertical-distribution::before{
-  content:""; }
+.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%;
+}
 
-.bp3-icon-video::before{
-  content:""; }
+/* Toolbar buttons */
 
-.bp3-icon-volume-down::before{
-  content:""; }
+/* 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: 0;
+  margin: 0;
+}
 
-.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-target.bp3-popover-open > .bp3-menu-item{ }
-
-.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{
-  background:#ffffff;
-  border-radius:3px;
-  color:#182026;
-  list-style:none;
-  margin:0;
-  min-width:180px;
-  padding:5px;
-  text-align:left; }
-
-.bp3-menu-divider{
-  border-top:1px solid rgba(16, 22, 26, 0.15);
-  display:block;
-  margin:5px; }
-  .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;
-  color:inherit;
-  line-height:20px;
-  padding:5px 7px;
-  text-decoration:none;
-  -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; }
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-      margin-right:10px;
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-  display:block;
-  margin:5px;
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-  padding-left:2px; }
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-.bp3-dark .bp3-menu{
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-.bp3-dark .bp3-menu-item{ }
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-    color:#ffb366; }
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-      color:#ffb366; }
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-    .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,
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-    color:#ff7373; }
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-      color:inherit; }
-    .bp3-dark .bp3-menu-item.bp3-intent-danger::before, .bp3-dark .bp3-menu-item.bp3-intent-danger::after,
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-      background-color:#db3737; }
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-            transform-origin:top center; }
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-    position:relative; }
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-    max-width:350px;
-    padding:20px; }
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-    margin:0 !important; }
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-      display:none; }
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-      .bp3-popover-enter > .bp3-popover.bp3-minimal.bp3-popover, .bp3-popover-appear > .bp3-popover.bp3-minimal.bp3-popover{
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-    .bp3-dark .bp3-popover .bp3-popover-arrow-border{
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-.bp3-popover-arrow::before{
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-.bp3-tether-pinned .bp3-popover-arrow{
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-.bp3-popover-backdrop{
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-.bp3-transition-container{
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-  display:-ms-flexbox;
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-.bp3-portal{
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-@keyframes linear-progress-bar-stripes{
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-.bp3-progress-bar{
-  background:rgba(92, 112, 128, 0.2);
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-.bp3-dark .bp3-progress-bar{
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-.bp3-progress-bar.bp3-intent-primary .bp3-progress-meter{
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-.bp3-progress-bar.bp3-intent-success .bp3-progress-meter{
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-.bp3-progress-bar.bp3-intent-warning .bp3-progress-meter{
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-.bp3-progress-bar.bp3-intent-danger .bp3-progress-meter{
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-  to{
-    background:rgba(92, 112, 128, 0.2);
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-    border-color:rgba(206, 217, 224, 0.2); }
-  to{
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-          animation:1000ms linear infinite alternate skeleton-glow;
-  background:rgba(206, 217, 224, 0.2);
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-  border-color:rgba(206, 217, 224, 0.2) !important;
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-.bp3-slider-track,
-.bp3-slider-progress{
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-  top:5px;
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-.bp3-slider-track{
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-.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; }
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-.bp3-slider-handle{
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-  color:#182026;
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-          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
-  cursor:pointer;
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-  left:0;
-  position:absolute;
-  top:0;
-  width:16px; }
-  .bp3-slider-handle:hover{
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-    background-color:#ebf1f5;
-    -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-slider-handle:active, .bp3-slider-handle.bp3-active{
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-    -webkit-box-shadow:none;
-            box-shadow:none;
-    color:rgba(92, 112, 128, 0.6);
-    cursor:not-allowed;
-    outline:none; }
-    .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); }
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-      color:rgba(167, 182, 194, 0.6); }
-      .bp3-dark .bp3-slider-handle:disabled.bp3-active, .bp3-dark .bp3-slider-handle.bp3-disabled.bp3-active{
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-      background:rgba(16, 22, 26, 0.5);
-      stroke:#8a9ba8; }
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-    transition-property:filter, opacity, -webkit-filter;
-    -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); }
-  .bp3-omnibar .bp3-input{
-    background-color:transparent;
-    border-radius:0; }
-    .bp3-omnibar .bp3-input, .bp3-omnibar .bp3-input:focus{
-      -webkit-box-shadow:none;
-              box-shadow:none; }
-  .bp3-omnibar .bp3-menu{
-    background-color:transparent;
-    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);
-    max-height:calc(60vh - 40px);
-    overflow:auto; }
-    .bp3-omnibar .bp3-menu:empty{
-      display:none; }
-  .bp3-dark .bp3-omnibar, .bp3-omnibar.bp3-dark{
-    background-color:#30404d;
-    -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-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-height:300px;
-  max-width:400px;
-  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-height:300px;
-  max-width:400px;
-  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-height:300px;
-  max-width:400px;
-  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 */
+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: 0 6px;
+  margin: 0;
+  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;
+}
 
-/**
- * (DEPRECATED) Support for consuming icons as CSS background images
- */
+button.jp-ToolbarButtonComponent:disabled {
+  opacity: 0.4;
+}
 
-/* Icons urls */
+button.jp-ToolbarButtonComponent > span {
+  padding: 0;
+  flex: 0 0 auto;
+}
 
-: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,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIj4KICAgIDxwYXRoIGQ9Ik05IDE2LjE3TDQuODMgMTJsLTEuNDIgMS40MUw5IDE5IDIxIDdsLTEuNDEtMS40MXoiLz4KICA8L2c+Cjwvc3ZnPgo=);
-  --jp-icon-circle-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyIDJDNi40NyAyIDIgNi40NyAyIDEyczQuNDcgMTAgMTAgMTAgMTAtNC40NyAxMC0xMFMxNy41MyAyIDEyIDJ6bTAgMThjLTQuNDEgMC04LTMuNTktOC04czMuNTktOCA4LTggOCAzLjU5IDggOC0zLjU5IDgtOCA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
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-  --jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
-  --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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);
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-  --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,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);
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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-table-rows: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMSw4SDNWNGgxOFY4eiBNMjEsMTBIM3Y0aDE4VjEweiBNMjEsMTZIM3Y0aDE4VjE2eiIvPgogICAgPC9nPgo8L3N2Zz4=);
-  --jp-icon-tag: url(data:image/svg+xml;base64,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);
-  --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-toc: url(data:image/svg+xml;base64,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);
-  --jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4=);
-  --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,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbi1jb250cmFzdDIganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjRDgxQjYwIj4KICAgIDxwYXRoIGQ9Ik03LjIgMTguNnYtNS40TDMgNS42aDMuM2wxLjQgMy4xYy4zLjkuNiAxLjYgMSAyLjUuMy0uOC42LTEuNiAxLTIuNWwxLjQtMy4xaDMuNGwtNC40IDcuNnY1LjVsLTIuOS0uMXoiLz4KICAgIDxjaXJjbGUgY2xhc3M9InN0MCIgY3g9IjE3LjYiIGN5PSIxNi41IiByPSIyLjEiLz4KICAgIDxjaXJjbGUgY2xhc3M9InN0MCIgY3g9IjE3LjYiIGN5PSIxMSIgcj0iMi4xIi8+CiAgPC9nPgo8L3N2Zz4K);
+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);
+  font-family: var(--jp-ui-font-family);
 }
 
-/* Icon CSS class declarations */
+#jp-main-dock-panel[data-mode='single-document']
+  .jp-MainAreaWidget
+  > .jp-Toolbar.jp-Toolbar-micro {
+  padding: 0;
+  min-height: 0;
+}
 
-.jp-AddIcon {
-  background-image: var(--jp-icon-add);
+#jp-main-dock-panel[data-mode='single-document']
+  .jp-MainAreaWidget
+  > .jp-Toolbar {
+  border: none;
+  box-shadow: none;
 }
-.jp-BugIcon {
-  background-image: var(--jp-icon-bug);
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-WindowedPanel-outer {
+  position: relative;
+  overflow-y: auto;
 }
-.jp-BuildIcon {
-  background-image: var(--jp-icon-build);
+
+.jp-WindowedPanel-inner {
+  position: relative;
 }
-.jp-CaretDownEmptyIcon {
-  background-image: var(--jp-icon-caret-down-empty);
+
+.jp-WindowedPanel-window {
+  position: absolute;
+  left: 0;
+  right: 0;
+  overflow: visible;
 }
-.jp-CaretDownEmptyThinIcon {
-  background-image: var(--jp-icon-caret-down-empty-thin);
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* Sibling imports */
+
+body {
+  color: var(--jp-ui-font-color1);
+  font-size: var(--jp-ui-font-size1);
 }
-.jp-CaretDownIcon {
-  background-image: var(--jp-icon-caret-down);
+
+/* Disable native link decoration styles everywhere outside of dialog boxes */
+a {
+  text-decoration: unset;
+  color: unset;
 }
-.jp-CaretLeftIcon {
-  background-image: var(--jp-icon-caret-left);
+
+a:hover {
+  text-decoration: unset;
+  color: unset;
 }
-.jp-CaretRightIcon {
-  background-image: var(--jp-icon-caret-right);
+
+/* Accessibility for links inside dialog box text */
+.jp-Dialog-content a {
+  text-decoration: revert;
+  color: var(--jp-content-link-color);
 }
-.jp-CaretUpEmptyThinIcon {
-  background-image: var(--jp-icon-caret-up-empty-thin);
+
+.jp-Dialog-content a:hover {
+  text-decoration: revert;
 }
-.jp-CaretUpIcon {
-  background-image: var(--jp-icon-caret-up);
+
+/* Styles for ui-components */
+.jp-Button {
+  color: var(--jp-ui-font-color2);
+  border-radius: var(--jp-border-radius);
+  padding: 0 12px;
+  font-size: var(--jp-ui-font-size1);
+
+  /* Copy from blueprint 3 */
+  display: inline-flex;
+  flex-direction: row;
+  border: none;
+  cursor: pointer;
+  align-items: center;
+  justify-content: center;
+  text-align: left;
+  vertical-align: middle;
+  min-height: 30px;
+  min-width: 30px;
 }
-.jp-CaseSensitiveIcon {
-  background-image: var(--jp-icon-case-sensitive);
+
+.jp-Button:disabled {
+  cursor: not-allowed;
 }
-.jp-CheckIcon {
-  background-image: var(--jp-icon-check);
+
+.jp-Button:empty {
+  padding: 0 !important;
 }
-.jp-CircleEmptyIcon {
-  background-image: var(--jp-icon-circle-empty);
+
+.jp-Button.jp-mod-small {
+  min-height: 24px;
+  min-width: 24px;
+  font-size: 12px;
+  padding: 0 7px;
 }
-.jp-CircleIcon {
-  background-image: var(--jp-icon-circle);
+
+/* Use our own theme for hover styles */
+.jp-Button.jp-mod-minimal:hover {
+  background-color: var(--jp-layout-color2);
 }
-.jp-ClearIcon {
-  background-image: var(--jp-icon-clear);
+
+.jp-Button.jp-mod-minimal {
+  background: none;
 }
-.jp-CloseIcon {
-  background-image: var(--jp-icon-close);
+
+.jp-InputGroup {
+  display: block;
+  position: relative;
 }
-.jp-CodeIcon {
-  background-image: var(--jp-icon-code);
+
+.jp-InputGroup input {
+  box-sizing: border-box;
+  border: none;
+  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);
+  padding-bottom: 0;
+  padding-top: 0;
+  padding-left: 10px;
+  padding-right: 28px;
+  position: relative;
+  width: 100%;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  appearance: none;
+  font-size: 14px;
+  font-weight: 400;
+  height: 30px;
+  line-height: 30px;
+  outline: none;
+  vertical-align: middle;
 }
-.jp-ConsoleIcon {
-  background-image: var(--jp-icon-console);
+
+.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);
 }
-.jp-CopyIcon {
-  background-image: var(--jp-icon-copy);
+
+.jp-InputGroup input:disabled {
+  cursor: not-allowed;
+  resize: block;
+  background-color: var(--jp-layout-color2);
+  color: var(--jp-ui-font-color2);
 }
-.jp-CopyrightIcon {
-  background-image: var(--jp-icon-copyright);
+
+.jp-InputGroup input:disabled ~ span {
+  cursor: not-allowed;
+  color: var(--jp-ui-font-color2);
 }
-.jp-CutIcon {
-  background-image: var(--jp-icon-cut);
+
+.jp-InputGroup input::placeholder,
+input::placeholder {
+  color: var(--jp-ui-font-color2);
 }
-.jp-DownloadIcon {
-  background-image: var(--jp-icon-download);
+
+.jp-InputGroupAction {
+  position: absolute;
+  bottom: 1px;
+  right: 0;
+  padding: 6px;
 }
-.jp-EditIcon {
-  background-image: var(--jp-icon-edit);
+
+.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);
+  font-family: var(--jp-ui-font-family);
+  height: 24px;
+  line-height: 14px;
+  padding: 0 25px 0 10px;
+  text-align: left;
+  -moz-appearance: none;
+  -webkit-appearance: none;
 }
-.jp-EllipsesIcon {
-  background-image: var(--jp-icon-ellipses);
+
+.jp-HTMLSelect.jp-DefaultStyle select:disabled {
+  background-color: var(--jp-layout-color2);
+  color: var(--jp-ui-font-color2);
+  cursor: not-allowed;
+  resize: block;
 }
-.jp-ExtensionIcon {
-  background-image: var(--jp-icon-extension);
+
+.jp-HTMLSelect.jp-DefaultStyle select:disabled ~ span {
+  cursor: not-allowed;
 }
-.jp-FastForwardIcon {
-  background-image: var(--jp-icon-fast-forward);
+
+/* Use our own theme for hover and option styles */
+/* stylelint-disable-next-line selector-max-type */
+.jp-HTMLSelect.jp-DefaultStyle select:hover,
+.jp-HTMLSelect.jp-DefaultStyle select > option {
+  background-color: var(--jp-layout-color2);
+  color: var(--jp-ui-font-color0);
 }
-.jp-FileIcon {
-  background-image: var(--jp-icon-file);
+
+select {
+  box-sizing: border-box;
 }
-.jp-FileUploadIcon {
-  background-image: var(--jp-icon-file-upload);
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Styles
+|----------------------------------------------------------------------------*/
+
+.jp-StatusBar-Widget {
+  display: flex;
+  align-items: center;
+  background: var(--jp-layout-color2);
+  min-height: var(--jp-statusbar-height);
+  justify-content: space-between;
+  padding: 0 10px;
 }
-.jp-FilterListIcon {
-  background-image: var(--jp-icon-filter-list);
+
+.jp-StatusBar-Left {
+  display: flex;
+  align-items: center;
+  flex-direction: row;
 }
-.jp-FolderIcon {
-  background-image: var(--jp-icon-folder);
+
+.jp-StatusBar-Middle {
+  display: flex;
+  align-items: center;
 }
-.jp-Html5Icon {
-  background-image: var(--jp-icon-html5);
+
+.jp-StatusBar-Right {
+  display: flex;
+  align-items: center;
+  flex-direction: row-reverse;
 }
-.jp-ImageIcon {
-  background-image: var(--jp-icon-image);
+
+.jp-StatusBar-Item {
+  max-height: var(--jp-statusbar-height);
+  margin: 0 2px;
+  height: var(--jp-statusbar-height);
+  white-space: nowrap;
+  text-overflow: ellipsis;
+  color: var(--jp-ui-font-color1);
+  padding: 0 6px;
 }
-.jp-InspectorIcon {
-  background-image: var(--jp-icon-inspector);
+
+.jp-mod-highlighted:hover {
+  background-color: var(--jp-layout-color3);
 }
-.jp-JsonIcon {
-  background-image: var(--jp-icon-json);
+
+.jp-mod-clicked {
+  background-color: var(--jp-brand-color1);
 }
-.jp-JuliaIcon {
-  background-image: var(--jp-icon-julia);
+
+.jp-mod-clicked:hover {
+  background-color: var(--jp-brand-color0);
 }
-.jp-JupyterFaviconIcon {
-  background-image: var(--jp-icon-jupyter-favicon);
+
+.jp-mod-clicked .jp-StatusBar-TextItem {
+  color: var(--jp-ui-inverse-font-color1);
 }
-.jp-JupyterIcon {
-  background-image: var(--jp-icon-jupyter);
+
+.jp-StatusBar-HoverItem {
+  box-shadow: '0px 4px 4px rgba(0, 0, 0, 0.25)';
 }
-.jp-JupyterlabWordmarkIcon {
-  background-image: var(--jp-icon-jupyterlab-wordmark);
+
+.jp-StatusBar-TextItem {
+  font-size: var(--jp-ui-font-size1);
+  font-family: var(--jp-ui-font-family);
+  line-height: 24px;
+  color: var(--jp-ui-font-color1);
 }
-.jp-KernelIcon {
-  background-image: var(--jp-icon-kernel);
+
+.jp-StatusBar-GroupItem {
+  display: flex;
+  align-items: center;
+  flex-direction: row;
 }
-.jp-KeyboardIcon {
-  background-image: var(--jp-icon-keyboard);
+
+.jp-Statusbar-ProgressCircle svg {
+  display: block;
+  margin: 0 auto;
+  width: 16px;
+  height: 24px;
+  align-self: normal;
 }
-.jp-LauncherIcon {
-  background-image: var(--jp-icon-launcher);
+
+.jp-Statusbar-ProgressCircle path {
+  fill: var(--jp-inverse-layout-color3);
 }
-.jp-LineFormIcon {
-  background-image: var(--jp-icon-line-form);
+
+.jp-Statusbar-ProgressBar-progress-bar {
+  height: 10px;
+  width: 100px;
+  border: solid 0.25px var(--jp-brand-color2);
+  border-radius: 3px;
+  overflow: hidden;
+  align-self: center;
 }
-.jp-LinkIcon {
-  background-image: var(--jp-icon-link);
+
+.jp-Statusbar-ProgressBar-progress-bar > div {
+  background-color: var(--jp-brand-color2);
+  background-image: 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%,
+    transparent
+  );
+  background-size: 40px 40px;
+  float: left;
+  width: 0%;
+  height: 100%;
+  font-size: 12px;
+  line-height: 14px;
+  color: #fff;
+  text-align: center;
+  animation: jp-Statusbar-ExecutionTime-progress-bar 2s linear infinite;
 }
-.jp-ListIcon {
-  background-image: var(--jp-icon-list);
+
+.jp-Statusbar-ProgressBar-progress-bar p {
+  color: var(--jp-ui-font-color1);
+  font-family: var(--jp-ui-font-family);
+  font-size: var(--jp-ui-font-size1);
+  line-height: 10px;
+  width: 100px;
 }
-.jp-ListingsInfoIcon {
-  background-image: var(--jp-icon-listings-info);
+
+@keyframes jp-Statusbar-ExecutionTime-progress-bar {
+  0% {
+    background-position: 0 0;
+  }
+
+  100% {
+    background-position: 40px 40px;
+  }
 }
-.jp-MarkdownIcon {
-  background-image: var(--jp-icon-markdown);
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-commandpalette-search-height: 28px;
 }
-.jp-NewFolderIcon {
-  background-image: var(--jp-icon-new-folder);
+
+/*-----------------------------------------------------------------------------
+| Overall styles
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette {
+  padding-bottom: 0;
+  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);
 }
-.jp-NotTrustedIcon {
-  background-image: var(--jp-icon-not-trusted);
+
+/*-----------------------------------------------------------------------------
+| Modal variant
+|----------------------------------------------------------------------------*/
+
+.jp-ModalCommandPalette {
+  position: absolute;
+  z-index: 10000;
+  top: 38px;
+  left: 30%;
+  margin: 0;
+  padding: 4px;
+  width: 40%;
+  box-shadow: var(--jp-elevation-z4);
+  border-radius: 4px;
+  background: var(--jp-layout-color0);
 }
-.jp-NotebookIcon {
-  background-image: var(--jp-icon-notebook);
+
+.jp-ModalCommandPalette .lm-CommandPalette {
+  max-height: 40vh;
 }
-.jp-NumberingIcon {
-  background-image: var(--jp-icon-numbering);
+
+.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {
+  display: none;
 }
-.jp-OfflineBoltIcon {
-  background-image: var(--jp-icon-offline-bolt);
+
+.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {
+  display: none;
 }
-.jp-PaletteIcon {
-  background-image: var(--jp-icon-palette);
+
+.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {
+  margin-left: 4px;
+  margin-right: 4px;
 }
-.jp-PasteIcon {
-  background-image: var(--jp-icon-paste);
+
+.jp-ModalCommandPalette
+  .lm-CommandPalette
+  .lm-CommandPalette-item.lm-mod-disabled {
+  display: none;
 }
-.jp-PdfIcon {
-  background-image: var(--jp-icon-pdf);
+
+/*-----------------------------------------------------------------------------
+| Search
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette-search {
+  padding: 4px;
+  background-color: var(--jp-layout-color1);
+  z-index: 2;
 }
-.jp-PythonIcon {
-  background-image: var(--jp-icon-python);
+
+.lm-CommandPalette-wrapper {
+  overflow: overlay;
+  padding: 0 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);
 }
-.jp-RKernelIcon {
-  background-image: var(--jp-icon-r-kernel);
+
+.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);
 }
-.jp-ReactIcon {
-  background-image: var(--jp-icon-react);
+
+.jp-SearchIconGroup {
+  color: white;
+  background-color: var(--jp-brand-color1);
+  position: absolute;
+  top: 4px;
+  right: 4px;
+  padding: 5px 5px 1px;
 }
-.jp-RedoIcon {
-  background-image: var(--jp-icon-redo);
-}
-.jp-RefreshIcon {
-  background-image: var(--jp-icon-refresh);
-}
-.jp-RegexIcon {
-  background-image: var(--jp-icon-regex);
-}
-.jp-RunIcon {
-  background-image: var(--jp-icon-run);
-}
-.jp-RunningIcon {
-  background-image: var(--jp-icon-running);
+
+.jp-SearchIconGroup svg {
+  height: 20px;
+  width: 20px;
 }
-.jp-SaveIcon {
-  background-image: var(--jp-icon-save);
+
+.jp-SearchIconGroup .jp-icon3[fill] {
+  fill: var(--jp-layout-color0);
 }
-.jp-SearchIcon {
-  background-image: var(--jp-icon-search);
+
+.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);
 }
-.jp-SettingsIcon {
-  background-image: var(--jp-icon-settings);
+
+.lm-CommandPalette-input::-webkit-input-placeholder,
+.lm-CommandPalette-input::-moz-placeholder,
+.lm-CommandPalette-input:-ms-input-placeholder {
+  color: var(--jp-ui-font-color2);
+  font-size: var(--jp-ui-font-size1);
 }
-.jp-SpreadsheetIcon {
-  background-image: var(--jp-icon-spreadsheet);
+
+/*-----------------------------------------------------------------------------
+| Results
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette-header:first-child {
+  margin-top: 0;
 }
-.jp-StopIcon {
-  background-image: var(--jp-icon-stop);
+
+.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;
 }
-.jp-TabIcon {
-  background-image: var(--jp-icon-tab);
+
+.lm-CommandPalette-header.lm-mod-active {
+  background: var(--jp-layout-color2);
 }
-.jp-TableRowsIcon {
-  background-image: var(--jp-icon-table-rows);
+
+.lm-CommandPalette-header > mark {
+  background-color: transparent;
+  font-weight: bold;
+  color: var(--jp-ui-font-color1);
 }
-.jp-TagIcon {
-  background-image: var(--jp-icon-tag);
+
+.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;
 }
-.jp-TerminalIcon {
-  background-image: var(--jp-icon-terminal);
+
+.lm-CommandPalette-item.lm-mod-disabled {
+  color: var(--jp-ui-font-color2);
 }
-.jp-TextEditorIcon {
-  background-image: var(--jp-icon-text-editor);
+
+.lm-CommandPalette-item.lm-mod-active {
+  color: var(--jp-ui-inverse-font-color1);
+  background: var(--jp-brand-color1);
 }
-.jp-TocIcon {
-  background-image: var(--jp-icon-toc);
+
+.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
+  color: var(--jp-ui-inverse-font-color0);
 }
-.jp-TreeViewIcon {
-  background-image: var(--jp-icon-tree-view);
+
+.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {
+  fill: var(--jp-layout-color0);
 }
-.jp-TrustedIcon {
-  background-image: var(--jp-icon-trusted);
+
+.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
+  color: var(--jp-ui-inverse-font-color1);
+  background: var(--jp-brand-color1);
 }
-.jp-UndoIcon {
-  background-image: var(--jp-icon-undo);
+
+.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
+  background: var(--jp-layout-color2);
 }
-.jp-VegaIcon {
-  background-image: var(--jp-icon-vega);
+
+.lm-CommandPalette-itemContent {
+  overflow: hidden;
 }
-.jp-YamlIcon {
-  background-image: var(--jp-icon-yaml);
+
+.lm-CommandPalette-itemLabel > mark {
+  color: var(--jp-ui-font-color0);
+  background-color: transparent;
+  font-weight: bold;
 }
 
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
+.lm-CommandPalette-item.lm-mod-disabled mark {
+  color: var(--jp-ui-font-color2);
+}
 
-/**
- * (DEPRECATED) Support for consuming icons as CSS background images
- */
+.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
+  margin: 0 4px 0 0;
+  position: relative;
+  width: 16px;
+  top: 2px;
+  flex: 0 0 auto;
+}
 
-.jp-Icon,
-.jp-MaterialIcon {
-  background-position: center;
-  background-repeat: no-repeat;
-  background-size: 16px;
-  min-width: 16px;
-  min-height: 16px;
+.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
+  opacity: 0.6;
 }
 
-.jp-Icon-cover {
-  background-position: center;
-  background-repeat: no-repeat;
-  background-size: cover;
+.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
+  flex: 0 0 auto;
 }
 
-/**
- * (DEPRECATED) Support for specific CSS icon sizes
- */
+.lm-CommandPalette-itemCaption {
+  display: none;
+}
 
-.jp-Icon-16 {
-  background-size: 16px;
-  min-width: 16px;
-  min-height: 16px;
+.lm-CommandPalette-content {
+  background-color: var(--jp-layout-color1);
 }
 
-.jp-Icon-18 {
-  background-size: 18px;
-  min-width: 18px;
-  min-height: 18px;
+.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;
 }
 
-.jp-Icon-20 {
-  background-size: 20px;
-  min-width: 20px;
-  min-height: 20px;
+.lm-CommandPalette-emptyMessage {
+  text-align: center;
+  margin-top: 24px;
+  line-height: 1.32;
+  padding: 0 8px;
+  color: var(--jp-content-font-color3);
 }
 
 /*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, 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);
-}
-.jp-icon1[fill] {
-  fill: var(--jp-inverse-layout-color1);
+.jp-Dialog {
+  position: absolute;
+  z-index: 10000;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  top: 0;
+  left: 0;
+  margin: 0;
+  padding: 0;
+  width: 100%;
+  height: 100%;
+  background: var(--jp-dialog-background);
 }
-.jp-icon2[fill] {
-  fill: var(--jp-inverse-layout-color2);
+
+.jp-Dialog-content {
+  display: flex;
+  flex-direction: column;
+  margin-left: auto;
+  margin-right: auto;
+  background: var(--jp-layout-color1);
+  padding: 24px 24px 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);
+  resize: both;
 }
-.jp-icon3[fill] {
-  fill: var(--jp-inverse-layout-color3);
+
+.jp-Dialog-content.jp-Dialog-content-small {
+  max-width: 500px;
 }
-.jp-icon4[fill] {
-  fill: var(--jp-inverse-layout-color4);
+
+.jp-Dialog-button {
+  overflow: visible;
 }
 
-.jp-icon0[stroke] {
-  stroke: var(--jp-inverse-layout-color0);
-}
-.jp-icon1[stroke] {
-  stroke: var(--jp-inverse-layout-color1);
-}
-.jp-icon2[stroke] {
-  stroke: var(--jp-inverse-layout-color2);
-}
-.jp-icon3[stroke] {
-  stroke: var(--jp-inverse-layout-color3);
-}
-.jp-icon4[stroke] {
-  stroke: var(--jp-inverse-layout-color4);
-}
-/* recolor the accent elements of an icon */
-.jp-icon-accent0[fill] {
-  fill: var(--jp-layout-color0);
-}
-.jp-icon-accent1[fill] {
-  fill: var(--jp-layout-color1);
-}
-.jp-icon-accent2[fill] {
-  fill: var(--jp-layout-color2);
-}
-.jp-icon-accent3[fill] {
-  fill: var(--jp-layout-color3);
-}
-.jp-icon-accent4[fill] {
-  fill: var(--jp-layout-color4);
+button.jp-Dialog-button:focus {
+  outline: 1px solid var(--jp-brand-color1);
+  outline-offset: 4px;
+  -moz-outline-radius: 0;
 }
 
-.jp-icon-accent0[stroke] {
-  stroke: var(--jp-layout-color0);
-}
-.jp-icon-accent1[stroke] {
-  stroke: var(--jp-layout-color1);
-}
-.jp-icon-accent2[stroke] {
-  stroke: var(--jp-layout-color2);
-}
-.jp-icon-accent3[stroke] {
-  stroke: var(--jp-layout-color3);
-}
-.jp-icon-accent4[stroke] {
-  stroke: var(--jp-layout-color4);
-}
-/* set the color of an icon to transparent */
-.jp-icon-none[fill] {
-  fill: none;
+button.jp-Dialog-button:focus::-moz-focus-inner {
+  border: 0;
 }
 
-.jp-icon-none[stroke] {
-  stroke: none;
-}
-/* brand icon colors. Same for light and dark */
-.jp-icon-brand0[fill] {
-  fill: var(--jp-brand-color0);
-}
-.jp-icon-brand1[fill] {
-  fill: var(--jp-brand-color1);
-}
-.jp-icon-brand2[fill] {
-  fill: var(--jp-brand-color2);
-}
-.jp-icon-brand3[fill] {
-  fill: var(--jp-brand-color3);
-}
-.jp-icon-brand4[fill] {
-  fill: var(--jp-brand-color4);
+button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus,
+button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus,
+button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
+  outline-offset: 4px;
+  -moz-outline-radius: 0;
 }
 
-.jp-icon-brand0[stroke] {
-  stroke: var(--jp-brand-color0);
-}
-.jp-icon-brand1[stroke] {
-  stroke: var(--jp-brand-color1);
-}
-.jp-icon-brand2[stroke] {
-  stroke: var(--jp-brand-color2);
-}
-.jp-icon-brand3[stroke] {
-  stroke: var(--jp-brand-color3);
-}
-.jp-icon-brand4[stroke] {
-  stroke: var(--jp-brand-color4);
-}
-/* warn icon colors. Same for light and dark */
-.jp-icon-warn0[fill] {
-  fill: var(--jp-warn-color0);
-}
-.jp-icon-warn1[fill] {
-  fill: var(--jp-warn-color1);
-}
-.jp-icon-warn2[fill] {
-  fill: var(--jp-warn-color2);
-}
-.jp-icon-warn3[fill] {
-  fill: var(--jp-warn-color3);
+button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus {
+  outline: 1px solid var(--jp-accept-color-normal, var(--jp-brand-color1));
 }
 
-.jp-icon-warn0[stroke] {
-  stroke: var(--jp-warn-color0);
-}
-.jp-icon-warn1[stroke] {
-  stroke: var(--jp-warn-color1);
-}
-.jp-icon-warn2[stroke] {
-  stroke: var(--jp-warn-color2);
-}
-.jp-icon-warn3[stroke] {
-  stroke: var(--jp-warn-color3);
-}
-/* icon colors that contrast well with each other and most backgrounds */
-.jp-icon-contrast0[fill] {
-  fill: var(--jp-icon-contrast-color0);
-}
-.jp-icon-contrast1[fill] {
-  fill: var(--jp-icon-contrast-color1);
-}
-.jp-icon-contrast2[fill] {
-  fill: var(--jp-icon-contrast-color2);
-}
-.jp-icon-contrast3[fill] {
-  fill: var(--jp-icon-contrast-color3);
+button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus {
+  outline: 1px solid var(--jp-warn-color-normal, var(--jp-error-color1));
 }
 
-.jp-icon-contrast0[stroke] {
-  stroke: var(--jp-icon-contrast-color0);
-}
-.jp-icon-contrast1[stroke] {
-  stroke: var(--jp-icon-contrast-color1);
-}
-.jp-icon-contrast2[stroke] {
-  stroke: var(--jp-icon-contrast-color2);
-}
-.jp-icon-contrast3[stroke] {
-  stroke: var(--jp-icon-contrast-color3);
+button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
+  outline: 1px solid var(--jp-reject-color-normal, var(--md-grey-600));
 }
 
-/* CSS for icons in selected items in the settings editor */
-#setting-editor .jp-PluginList .jp-mod-selected .jp-icon-selectable[fill] {
-  fill: #fff;
-}
-#setting-editor
-  .jp-PluginList
-  .jp-mod-selected
-  .jp-icon-selectable-inverse[fill] {
-  fill: var(--jp-brand-color1);
+button.jp-Dialog-close-button {
+  padding: 0;
+  height: 100%;
+  min-width: unset;
+  min-height: unset;
 }
 
-/* CSS for icons in selected filebrowser listing items */
-.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
-  fill: #fff;
-}
-.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
-  fill: var(--jp-brand-color1);
+.jp-Dialog-header {
+  display: flex;
+  justify-content: space-between;
+  flex: 0 0 auto;
+  padding-bottom: 12px;
+  font-size: var(--jp-ui-font-size3);
+  font-weight: 400;
+  color: var(--jp-ui-font-color1);
 }
 
-/* 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;
+.jp-Dialog-body {
+  display: flex;
+  flex-direction: column;
+  flex: 1 1 auto;
+  font-size: var(--jp-ui-font-size1);
+  background: var(--jp-layout-color1);
+  color: var(--jp-ui-font-color1);
+  overflow: auto;
 }
 
-#tab-manager .lm-TabBar-tab.jp-mod-active .jp-icon-selectable-inverse[fill] {
-  fill: var(--jp-brand-color1);
-}
-#tab-manager
-  .lm-TabBar-tab.jp-mod-active
-  .jp-icon-hover
-  :hover
-  .jp-icon-selectable[fill] {
-  fill: var(--jp-brand-color1);
+.jp-Dialog-footer {
+  display: flex;
+  flex-direction: row;
+  justify-content: flex-end;
+  align-items: center;
+  flex: 0 0 auto;
+  margin-left: -12px;
+  margin-right: -12px;
+  padding: 12px;
 }
 
-#tab-manager
-  .lm-TabBar-tab.jp-mod-active
-  .jp-icon-hover
-  :hover
-  .jp-icon-selectable-inverse[fill] {
-  fill: #fff;
+.jp-Dialog-checkbox {
+  padding-right: 5px;
 }
 
-/**
- * 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
- */
-#tab-manager
-  .lm-TabBar-tab.jp-mod-dirty
-  > .lm-TabBar-tabCloseIcon
-  > :not(:hover)
-  > .jp-icon3[fill] {
-  fill: none;
+.jp-Dialog-checkbox > input:focus-visible {
+  outline: 1px solid var(--jp-input-active-border-color);
+  outline-offset: 1px;
 }
-#tab-manager
-  .lm-TabBar-tab.jp-mod-dirty
-  > .lm-TabBar-tabCloseIcon
-  > :not(:hover)
-  > .jp-icon-busy[fill] {
-  fill: var(--jp-inverse-layout-color3);
+
+.jp-Dialog-spacer {
+  flex: 1 1 auto;
 }
 
-#tab-manager
-  .lm-TabBar-tab.jp-mod-dirty.jp-mod-active
-  > .lm-TabBar-tabCloseIcon
-  > :not(:hover)
-  > .jp-icon-busy[fill] {
-  fill: #fff;
+.jp-Dialog-title {
+  overflow: hidden;
+  white-space: nowrap;
+  text-overflow: ellipsis;
 }
 
-/**
-* 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;
+.jp-Dialog-body > .jp-select-wrapper {
+  width: 100%;
 }
-.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);
+
+.jp-Dialog-body > button {
+  padding: 0 16px;
 }
 
-/* CSS for icons in status bar */
-#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
-  fill: #fff;
+.jp-Dialog-body > label {
+  line-height: 1.4;
+  color: var(--jp-ui-font-color0);
 }
 
-#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
-  fill: var(--jp-brand-color1);
+.jp-Dialog-button.jp-mod-styled:not(:last-child) {
+  margin-right: 12px;
 }
-/* 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);
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-Input-Boolean-Dialog {
+  flex-direction: row-reverse;
+  align-items: end;
+  width: 100%;
 }
 
-/* not sure what to do with this one, used in filebrowser listing */
-.jp-DragIcon {
-  margin-right: 4px;
+.jp-Input-Boolean-Dialog > label {
+  flex: 1 1 auto;
 }
 
 /*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
 | Distributed under the terms of the Modified BSD License.
 |----------------------------------------------------------------------------*/
 
-/**
- * Support for alt colors for icons as inline SVG HTMLElements
- */
+.jp-MainAreaWidget > :focus {
+  outline: none;
+}
 
-/* alt recolor the primary elements of an icon */
-.jp-icon-alt .jp-icon0[fill] {
-  fill: var(--jp-layout-color0);
-}
-.jp-icon-alt .jp-icon1[fill] {
-  fill: var(--jp-layout-color1);
-}
-.jp-icon-alt .jp-icon2[fill] {
-  fill: var(--jp-layout-color2);
-}
-.jp-icon-alt .jp-icon3[fill] {
-  fill: var(--jp-layout-color3);
-}
-.jp-icon-alt .jp-icon4[fill] {
-  fill: var(--jp-layout-color4);
-}
-
-.jp-icon-alt .jp-icon0[stroke] {
-  stroke: var(--jp-layout-color0);
-}
-.jp-icon-alt .jp-icon1[stroke] {
-  stroke: var(--jp-layout-color1);
-}
-.jp-icon-alt .jp-icon2[stroke] {
-  stroke: var(--jp-layout-color2);
-}
-.jp-icon-alt .jp-icon3[stroke] {
-  stroke: var(--jp-layout-color3);
-}
-.jp-icon-alt .jp-icon4[stroke] {
-  stroke: var(--jp-layout-color4);
-}
-
-/* alt recolor the accent elements of an icon */
-.jp-icon-alt .jp-icon-accent0[fill] {
-  fill: var(--jp-inverse-layout-color0);
-}
-.jp-icon-alt .jp-icon-accent1[fill] {
-  fill: var(--jp-inverse-layout-color1);
-}
-.jp-icon-alt .jp-icon-accent2[fill] {
-  fill: var(--jp-inverse-layout-color2);
-}
-.jp-icon-alt .jp-icon-accent3[fill] {
-  fill: var(--jp-inverse-layout-color3);
-}
-.jp-icon-alt .jp-icon-accent4[fill] {
-  fill: var(--jp-inverse-layout-color4);
-}
-
-.jp-icon-alt .jp-icon-accent0[stroke] {
-  stroke: var(--jp-inverse-layout-color0);
-}
-.jp-icon-alt .jp-icon-accent1[stroke] {
-  stroke: var(--jp-inverse-layout-color1);
-}
-.jp-icon-alt .jp-icon-accent2[stroke] {
-  stroke: var(--jp-inverse-layout-color2);
-}
-.jp-icon-alt .jp-icon-accent3[stroke] {
-  stroke: var(--jp-inverse-layout-color3);
-}
-.jp-icon-alt .jp-icon-accent4[stroke] {
-  stroke: var(--jp-inverse-layout-color4);
+.jp-MainAreaWidget .jp-MainAreaWidget-error {
+  padding: 6px;
 }
 
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
-
-.jp-icon-hoverShow:not(:hover) svg {
-  display: none !important;
+.jp-MainAreaWidget .jp-MainAreaWidget-error > pre {
+  width: auto;
+  padding: 10px;
+  background: var(--jp-error-color3);
+  border: var(--jp-border-width) solid var(--jp-error-color1);
+  border-radius: var(--jp-border-radius);
+  color: var(--jp-ui-font-color1);
+  font-size: var(--jp-ui-font-size1);
+  white-space: pre-wrap;
+  word-wrap: break-word;
 }
 
-/**
- * Support for hover colors for icons as inline SVG HTMLElements
- */
-
-/**
- * regular colors
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
  */
 
-/* recolor the primary elements of an icon */
-.jp-icon-hover :hover .jp-icon0-hover[fill] {
-  fill: var(--jp-inverse-layout-color0);
-}
-.jp-icon-hover :hover .jp-icon1-hover[fill] {
-  fill: var(--jp-inverse-layout-color1);
-}
-.jp-icon-hover :hover .jp-icon2-hover[fill] {
-  fill: var(--jp-inverse-layout-color2);
-}
-.jp-icon-hover :hover .jp-icon3-hover[fill] {
-  fill: var(--jp-inverse-layout-color3);
-}
-.jp-icon-hover :hover .jp-icon4-hover[fill] {
-  fill: var(--jp-inverse-layout-color4);
-}
-
-.jp-icon-hover :hover .jp-icon0-hover[stroke] {
-  stroke: var(--jp-inverse-layout-color0);
-}
-.jp-icon-hover :hover .jp-icon1-hover[stroke] {
-  stroke: var(--jp-inverse-layout-color1);
-}
-.jp-icon-hover :hover .jp-icon2-hover[stroke] {
-  stroke: var(--jp-inverse-layout-color2);
-}
-.jp-icon-hover :hover .jp-icon3-hover[stroke] {
-  stroke: var(--jp-inverse-layout-color3);
-}
-.jp-icon-hover :hover .jp-icon4-hover[stroke] {
-  stroke: var(--jp-inverse-layout-color4);
-}
-
-/* recolor the accent elements of an icon */
-.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
-  fill: var(--jp-layout-color0);
-}
-.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
-  fill: var(--jp-layout-color1);
-}
-.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
-  fill: var(--jp-layout-color2);
-}
-.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
-  fill: var(--jp-layout-color3);
-}
-.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
-  fill: var(--jp-layout-color4);
-}
-
-.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
-  stroke: var(--jp-layout-color0);
-}
-.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
-  stroke: var(--jp-layout-color1);
-}
-.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
-  stroke: var(--jp-layout-color2);
-}
-.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
-  stroke: var(--jp-layout-color3);
-}
-.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
-  stroke: var(--jp-layout-color4);
-}
-
-/* set the color of an icon to transparent */
-.jp-icon-hover :hover .jp-icon-none-hover[fill] {
-  fill: none;
-}
-
-.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
-  stroke: none;
-}
-
 /**
- * inverse colors
+ * google-material-color v1.2.6
+ * https://github.com/danlevan/google-material-color
  */
-
-/* inverse recolor the primary elements of an icon */
-.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
-  fill: var(--jp-layout-color0);
-}
-.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
-  fill: var(--jp-layout-color1);
-}
-.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
-  fill: var(--jp-layout-color2);
-}
-.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
-  fill: var(--jp-layout-color3);
-}
-.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
-  fill: var(--jp-layout-color4);
-}
-
-.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
-  stroke: var(--jp-layout-color0);
-}
-.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
-  stroke: var(--jp-layout-color1);
-}
-.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
-  stroke: var(--jp-layout-color2);
-}
-.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
-  stroke: var(--jp-layout-color3);
-}
-.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
-  stroke: var(--jp-layout-color4);
-}
-
-/* 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);
-}
-.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
-  fill: var(--jp-inverse-layout-color1);
-}
-.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
-  fill: var(--jp-inverse-layout-color2);
-}
-.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
-  fill: var(--jp-inverse-layout-color3);
-}
-.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
-  fill: var(--jp-inverse-layout-color4);
-}
-
-.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
-  stroke: var(--jp-inverse-layout-color0);
-}
-.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
-  stroke: var(--jp-inverse-layout-color1);
-}
-.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
-  stroke: var(--jp-inverse-layout-color2);
-}
-.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
-  stroke: var(--jp-inverse-layout-color3);
-}
-.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
-  stroke: var(--jp-inverse-layout-color4);
-}
-
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
-
-.jp-switch {
-  display: flex;
-  align-items: center;
-  padding-left: 4px;
-  padding-right: 4px;
-  font-size: var(--jp-ui-font-size1);
-  background-color: transparent;
-  color: var(--jp-ui-font-color1);
-  border: none;
-  height: 20px;
-}
-
-.jp-switch:hover {
-  background-color: var(--jp-layout-color2);
-}
-
-.jp-switch-label {
-  margin-right: 5px;
-}
-
-.jp-switch-track {
-  cursor: pointer;
-  background-color: var(--jp-border-color1);
-  -webkit-transition: 0.4s;
-  transition: 0.4s;
-  border-radius: 34px;
-  height: 16px;
-  width: 35px;
-  position: relative;
-}
-
-.jp-switch-track::before {
-  content: '';
-  position: absolute;
-  height: 10px;
-  width: 10px;
-  margin: 3px;
-  left: 0px;
-  background-color: var(--jp-ui-inverse-font-color1);
-  -webkit-transition: 0.4s;
-  transition: 0.4s;
-  border-radius: 50%;
-}
-
-.jp-switch[aria-checked='true'] .jp-switch-track {
-  background-color: var(--jp-warn-color0);
-}
-
-.jp-switch[aria-checked='true'] .jp-switch-track::before {
-  /* track width (35) - margins (3 + 3) - thumb width (10) */
-  left: 19px;
-}
-
-/*-----------------------------------------------------------------------------
-| 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;
-}
-
-*,
-*::before,
-*::after {
-  box-sizing: unset;
-}
-
-body {
-  color: unset;
-  font-family: var(--jp-ui-font-family);
-}
-
-p {
-  margin-top: unset;
-  margin-bottom: unset;
-}
-
-small {
-  font-size: unset;
-}
-
-strong {
-  font-weight: unset;
-}
-
-/* Override Blueprint's _typography.scss styles */
-a {
-  text-decoration: unset;
-  color: unset;
-}
-a:hover {
-  text-decoration: unset;
-  color: unset;
-}
-
-/* Override Blueprint's _accessibility.scss styles */
-:focus {
-  outline: unset;
-  outline-offset: unset;
-  -moz-outline-radius: unset;
-}
-
-/* Styles for ui-components */
-.jp-Button {
-  border-radius: var(--jp-border-radius);
-  padding: 0px 12px;
-  font-size: var(--jp-ui-font-size1);
-}
-
-/* Use our own theme for hover styles */
-button.jp-Button.bp3-button.bp3-minimal:hover {
-  background-color: var(--jp-layout-color2);
-}
-.jp-Button.minimal {
-  color: unset !important;
-}
-
-.jp-Button.jp-ToolbarButtonComponent {
-  text-transform: none;
-}
-
-.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);
-}
-
-.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);
-}
-
-.jp-InputGroup input::placeholder,
-input::placeholder {
-  color: var(--jp-ui-font-color3);
-}
-
-.jp-BPIcon {
-  display: inline-block;
-  vertical-align: middle;
-  margin: auto;
-}
-
-/* Stop blueprint futzing with our icon fills */
-.bp3-icon.jp-BPIcon > svg:not([fill]) {
-  fill: var(--jp-inverse-layout-color3);
-}
-
-.jp-InputGroupAction {
-  padding: 6px;
-}
-
-.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;
-}
-
-/* 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);
-}
-select {
-  box-sizing: border-box;
-}
-
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
-
-.jp-Collapse {
-  display: flex;
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-  --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;
-}
-
-.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;
-}
-
-.jp-SpinnerContent:before {
-  width: 50%;
-  height: 50%;
-  background: #f37626;
-  border-radius: 100% 0 0 0;
-  position: absolute;
-  top: 0;
-  left: 0;
-  content: '';
-}
-
-.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;
-}
-
-@keyframes fadeIn {
-  0% {
-    opacity: 0;
-  }
-  100% {
-    opacity: 1;
-  }
-}
-
-@keyframes load3 {
-  0% {
-    transform: rotate(0deg);
-  }
-  100% {
-    transform: rotate(360deg);
-  }
-}
-
-/*-----------------------------------------------------------------------------
-| 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;
-}
-
-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;
-}
-
-input[type='checkbox'].jp-mod-styled {
-  appearance: checkbox;
-  -webkit-appearance: checkbox;
-  -moz-appearance: checkbox;
-  height: auto;
-}
-
-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);
-}
-
-.jp-FileDialog-Checkbox {
-  margin-top: 35px;
-  display: flex;
-  flex-direction: row;
-  align-items: end;
-  width: 100%;
-}
-
-.jp-FileDialog-Checkbox > label {
-  flex: 1 1 auto;
-}
-
-.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;
-}
-
-.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);
-}
-
-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);
-}
-
-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;
-}
-
-/*-----------------------------------------------------------------------------
-| 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 */
-}
-
-.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;
-  overflow-x: auto;
-}
-
-/* Toolbar items */
-
-.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
-  flex-grow: 1;
-  flex-shrink: 1;
-}
-
-.jp-Toolbar-item.jp-Toolbar-kernelStatus {
-  display: inline-block;
-  width: 32px;
-  background-repeat: no-repeat;
-  background-position: center;
-  background-size: 16px;
-}
-
-.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%;
-}
-
-/* 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;
-}
-
-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;
-}
-
-button.jp-ToolbarButtonComponent:disabled {
-  opacity: 0.4;
-}
-
-button.jp-ToolbarButtonComponent span {
-  padding: 0px;
-  flex: 0 0 auto;
-}
-
-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);
-}
-
-#jp-main-dock-panel[data-mode='single-document']
-  .jp-MainAreaWidget
-  > .jp-Toolbar.jp-Toolbar-micro {
-  padding: 0;
-  min-height: 0;
-}
-
-#jp-main-dock-panel[data-mode='single-document']
-  .jp-MainAreaWidget
-  > .jp-Toolbar {
-  border: none;
-  box-shadow: none;
+  --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: #a0f;
+  --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: #ff0;
+  --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: #eee;
+  --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;
 }
 
 /*-----------------------------------------------------------------------------
@@ -11499,1329 +3782,1183 @@ button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
 
 /*-----------------------------------------------------------------------------
 | 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.
+| Distributed under the terms of the Modified BSD License.
 |----------------------------------------------------------------------------*/
 
-
-/* <DEPRECATED> */ body.p-mod-override-cursor *, /* </DEPRECATED> */
-body.lm-mod-override-cursor * {
-  cursor: inherit !important;
-}
-
 /*-----------------------------------------------------------------------------
-| Copyright (c) 2014-2016, Jupyter Development Team.
-|
-| Distributed under the terms of the Modified BSD License.
+| RenderedText
 |----------------------------------------------------------------------------*/
 
-.jp-JSONEditor {
-  display: flex;
-  flex-direction: column;
-  width: 100%;
-}
-
-.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;
-}
-
-.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
-  border-color: red;
-  outline-color: red;
-}
-
-.jp-JSONEditor-header {
-  display: flex;
-  flex: 1 0 auto;
-  padding: 0 0 0 12px;
-}
-
-.jp-JSONEditor-header label {
-  flex: 0 0 auto;
+:root {
+  /* This is the padding value to fill the gaps between lines containing spans with background color. */
+  --jp-private-code-span-padding: calc(
+    (var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
+  );
 }
 
-.jp-JSONEditor-commitButton {
-  height: 16px;
-  width: 16px;
-  background-size: 18px;
-  background-repeat: no-repeat;
-  background-position: center;
+.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);
 }
 
-.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);
+.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: 0;
+  padding: 0;
 }
 
-.jp-Editor.jp-mod-dropTarget {
-  border: var(--jp-border-width) solid var(--jp-input-active-border-color);
-  box-shadow: var(--jp-input-box-shadow);
+.jp-RenderedText pre a:link {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
 }
 
-/* BASICS */
-
-.CodeMirror {
-  /* Set height, width, borders, and global font properties here */
-  font-family: monospace;
-  height: 300px;
-  color: black;
-  direction: ltr;
+.jp-RenderedText pre a:hover {
+  text-decoration: underline;
+  color: var(--jp-content-link-color);
 }
 
-/* PADDING */
-
-.CodeMirror-lines {
-  padding: 4px 0; /* Vertical padding around content */
-}
-.CodeMirror pre.CodeMirror-line,
-.CodeMirror pre.CodeMirror-line-like {
-  padding: 0 4px; /* Horizontal padding of content */
+.jp-RenderedText pre a:visited {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
 }
 
-.CodeMirror-scrollbar-filler, .CodeMirror-gutter-filler {
-  background-color: white; /* The little square between H and V scrollbars */
+/* console foregrounds and backgrounds */
+.jp-RenderedText pre .ansi-black-fg {
+  color: #3e424d;
 }
 
-/* GUTTER */
-
-.CodeMirror-gutters {
-  border-right: 1px solid #ddd;
-  background-color: #f7f7f7;
-  white-space: nowrap;
-}
-.CodeMirror-linenumbers {}
-.CodeMirror-linenumber {
-  padding: 0 3px 0 5px;
-  min-width: 20px;
-  text-align: right;
-  color: #999;
-  white-space: nowrap;
+.jp-RenderedText pre .ansi-red-fg {
+  color: #e75c58;
 }
 
-.CodeMirror-guttermarker { color: black; }
-.CodeMirror-guttermarker-subtle { color: #999; }
-
-/* CURSOR */
-
-.CodeMirror-cursor {
-  border-left: 1px solid black;
-  border-right: none;
-  width: 0;
-}
-/* Shown when moving in bi-directional text */
-.CodeMirror div.CodeMirror-secondarycursor {
-  border-left: 1px solid silver;
-}
-.cm-fat-cursor .CodeMirror-cursor {
-  width: auto;
-  border: 0 !important;
-  background: #7e7;
-}
-.cm-fat-cursor div.CodeMirror-cursors {
-  z-index: 1;
-}
-.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;
-}
-.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;
+.jp-RenderedText pre .ansi-green-fg {
+  color: #00a250;
 }
-@-moz-keyframes blink {
-  0% {}
-  50% { background-color: transparent; }
-  100% {}
+
+.jp-RenderedText pre .ansi-yellow-fg {
+  color: #ddb62b;
 }
-@-webkit-keyframes blink {
-  0% {}
-  50% { background-color: transparent; }
-  100% {}
+
+.jp-RenderedText pre .ansi-blue-fg {
+  color: #208ffb;
 }
-@keyframes blink {
-  0% {}
-  50% { background-color: transparent; }
-  100% {}
+
+.jp-RenderedText pre .ansi-magenta-fg {
+  color: #d160c4;
 }
 
-/* Can style cursor different in overwrite (non-insert) mode */
-.CodeMirror-overwrite .CodeMirror-cursor {}
+.jp-RenderedText pre .ansi-cyan-fg {
+  color: #60c6c8;
+}
 
-.cm-tab { display: inline-block; text-decoration: inherit; }
+.jp-RenderedText pre .ansi-white-fg {
+  color: #c5c1b4;
+}
 
-.CodeMirror-rulers {
-  position: absolute;
-  left: 0; right: 0; top: -50px; bottom: 0;
-  overflow: hidden;
+.jp-RenderedText pre .ansi-black-bg {
+  background-color: #3e424d;
+  padding: var(--jp-private-code-span-padding) 0;
 }
-.CodeMirror-ruler {
-  border-left: 1px solid #ccc;
-  top: 0; bottom: 0;
-  position: absolute;
+
+.jp-RenderedText pre .ansi-red-bg {
+  background-color: #e75c58;
+  padding: var(--jp-private-code-span-padding) 0;
 }
 
-/* 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;
+.jp-RenderedText pre .ansi-green-bg {
+  background-color: #00a250;
+  padding: var(--jp-private-code-span-padding) 0;
 }
 
-.CodeMirror-scroll {
-  overflow: scroll !important; /* Things will break if this is overridden */
-  /* 50px is the magic margin used to hide the element's real scrollbars */
-  /* See overflow: hidden in .CodeMirror */
-  margin-bottom: -50px; margin-right: -50px;
-  padding-bottom: 50px;
-  height: 100%;
-  outline: none; /* Prevent dragging from highlighting the element */
-  position: relative;
+.jp-RenderedText pre .ansi-yellow-bg {
+  background-color: #ddb62b;
+  padding: var(--jp-private-code-span-padding) 0;
 }
-.CodeMirror-sizer {
-  position: relative;
-  border-right: 50px solid transparent;
+
+.jp-RenderedText pre .ansi-blue-bg {
+  background-color: #208ffb;
+  padding: var(--jp-private-code-span-padding) 0;
 }
 
-/* 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;
-  outline: none;
+.jp-RenderedText pre .ansi-magenta-bg {
+  background-color: #d160c4;
+  padding: var(--jp-private-code-span-padding) 0;
 }
-.CodeMirror-vscrollbar {
-  right: 0; top: 0;
-  overflow-x: hidden;
-  overflow-y: scroll;
+
+.jp-RenderedText pre .ansi-cyan-bg {
+  background-color: #60c6c8;
+  padding: var(--jp-private-code-span-padding) 0;
 }
-.CodeMirror-hscrollbar {
-  bottom: 0; left: 0;
-  overflow-y: hidden;
-  overflow-x: scroll;
+
+.jp-RenderedText pre .ansi-white-bg {
+  background-color: #c5c1b4;
+  padding: var(--jp-private-code-span-padding) 0;
 }
-.CodeMirror-scrollbar-filler {
-  right: 0; bottom: 0;
+
+.jp-RenderedText pre .ansi-black-intense-fg {
+  color: #282c36;
 }
-.CodeMirror-gutter-filler {
-  left: 0; bottom: 0;
+
+.jp-RenderedText pre .ansi-red-intense-fg {
+  color: #b22b31;
 }
 
-.CodeMirror-gutters {
-  position: absolute; left: 0; top: 0;
-  min-height: 100%;
-  z-index: 3;
+.jp-RenderedText pre .ansi-green-intense-fg {
+  color: #007427;
 }
-.CodeMirror-gutter {
-  white-space: normal;
-  height: 100%;
-  display: inline-block;
-  vertical-align: top;
-  margin-bottom: -50px;
+
+.jp-RenderedText pre .ansi-yellow-intense-fg {
+  color: #b27d12;
 }
-.CodeMirror-gutter-wrapper {
-  position: absolute;
-  z-index: 4;
-  background: none !important;
-  border: none !important;
+
+.jp-RenderedText pre .ansi-blue-intense-fg {
+  color: #0065ca;
 }
-.CodeMirror-gutter-background {
-  position: absolute;
-  top: 0; bottom: 0;
-  z-index: 4;
+
+.jp-RenderedText pre .ansi-magenta-intense-fg {
+  color: #a03196;
 }
-.CodeMirror-gutter-elt {
-  position: absolute;
-  cursor: default;
-  z-index: 4;
+
+.jp-RenderedText pre .ansi-cyan-intense-fg {
+  color: #258f8f;
 }
-.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 */
+.jp-RenderedText pre .ansi-white-intense-fg {
+  color: #a1a6b2;
 }
-.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;
+
+.jp-RenderedText pre .ansi-black-intense-bg {
+  background-color: #282c36;
+  padding: var(--jp-private-code-span-padding) 0;
 }
-.CodeMirror-wrap pre.CodeMirror-line,
-.CodeMirror-wrap pre.CodeMirror-line-like {
-  word-wrap: break-word;
-  white-space: pre-wrap;
-  word-break: normal;
+
+.jp-RenderedText pre .ansi-red-intense-bg {
+  background-color: #b22b31;
+  padding: var(--jp-private-code-span-padding) 0;
 }
 
-.CodeMirror-linebackground {
-  position: absolute;
-  left: 0; right: 0; top: 0; bottom: 0;
-  z-index: 0;
+.jp-RenderedText pre .ansi-green-intense-bg {
+  background-color: #007427;
+  padding: var(--jp-private-code-span-padding) 0;
 }
 
-.CodeMirror-linewidget {
-  position: relative;
-  z-index: 2;
-  padding: 0.1px; /* Force widget margins to stay inside of the container */
+.jp-RenderedText pre .ansi-yellow-intense-bg {
+  background-color: #b27d12;
+  padding: var(--jp-private-code-span-padding) 0;
 }
 
-.CodeMirror-widget {}
+.jp-RenderedText pre .ansi-blue-intense-bg {
+  background-color: #0065ca;
+  padding: var(--jp-private-code-span-padding) 0;
+}
 
-.CodeMirror-rtl pre { direction: rtl; }
+.jp-RenderedText pre .ansi-magenta-intense-bg {
+  background-color: #a03196;
+  padding: var(--jp-private-code-span-padding) 0;
+}
 
-.CodeMirror-code {
-  outline: none;
+.jp-RenderedText pre .ansi-cyan-intense-bg {
+  background-color: #258f8f;
+  padding: var(--jp-private-code-span-padding) 0;
 }
 
-/* Force content-box sizing for the elements where we expect it */
-.CodeMirror-scroll,
-.CodeMirror-sizer,
-.CodeMirror-gutter,
-.CodeMirror-gutters,
-.CodeMirror-linenumber {
-  -moz-box-sizing: content-box;
-  box-sizing: content-box;
+.jp-RenderedText pre .ansi-white-intense-bg {
+  background-color: #a1a6b2;
+  padding: var(--jp-private-code-span-padding) 0;
 }
 
-.CodeMirror-measure {
-  position: absolute;
-  width: 100%;
-  height: 0;
-  overflow: hidden;
-  visibility: hidden;
+.jp-RenderedText pre .ansi-default-inverse-fg {
+  color: var(--jp-ui-inverse-font-color0);
 }
 
-.CodeMirror-cursor {
-  position: absolute;
-  pointer-events: none;
+.jp-RenderedText pre .ansi-default-inverse-bg {
+  background-color: var(--jp-inverse-layout-color0);
+  padding: var(--jp-private-code-span-padding) 0;
 }
-.CodeMirror-measure pre { position: static; }
 
-div.CodeMirror-cursors {
-  visibility: hidden;
-  position: relative;
-  z-index: 3;
+.jp-RenderedText pre .ansi-bold {
+  font-weight: bold;
 }
-div.CodeMirror-dragcursors {
-  visibility: visible;
+
+.jp-RenderedText pre .ansi-underline {
+  text-decoration: underline;
 }
 
-.CodeMirror-focused div.CodeMirror-cursors {
-  visibility: visible;
+.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
+  background: var(--jp-rendermime-error-background);
+  padding-top: var(--jp-code-padding);
 }
 
-.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; }
+/*-----------------------------------------------------------------------------
+| RenderedLatex
+|----------------------------------------------------------------------------*/
 
-.cm-searching {
-  background-color: #ffa;
-  background-color: rgba(255, 255, 0, .4);
+.jp-RenderedLatex {
+  color: var(--jp-content-font-color1);
+  font-size: var(--jp-content-font-size1);
+  line-height: var(--jp-content-line-height);
 }
 
-/* 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;
-  }
+/* Left-justify outputs.*/
+.jp-OutputArea-output.jp-RenderedLatex {
+  padding: var(--jp-code-padding);
+  text-align: left;
 }
 
-/* See issue #2901 */
-.cm-tab-wrap-hack:after { content: ''; }
+/*-----------------------------------------------------------------------------
+| RenderedHTML
+|----------------------------------------------------------------------------*/
 
-/* Help users use markselection to safely style text background */
-span.CodeMirror-selectedtext { background: none; }
+.jp-RenderedHTMLCommon {
+  color: var(--jp-content-font-color1);
+  font-family: var(--jp-content-font-family);
+  font-size: var(--jp-content-font-size1);
+  line-height: var(--jp-content-line-height);
 
-.CodeMirror-dialog {
-  position: absolute;
-  left: 0; right: 0;
-  background: inherit;
-  z-index: 15;
-  padding: .1em .8em;
-  overflow: hidden;
-  color: inherit;
+  /* Give a bit more R padding on Markdown text to keep line lengths reasonable */
+  padding-right: 20px;
 }
 
-.CodeMirror-dialog-top {
-  border-bottom: 1px solid #eee;
-  top: 0;
+.jp-RenderedHTMLCommon em {
+  font-style: italic;
 }
 
-.CodeMirror-dialog-bottom {
-  border-top: 1px solid #eee;
-  bottom: 0;
+.jp-RenderedHTMLCommon strong {
+  font-weight: bold;
 }
 
-.CodeMirror-dialog input {
-  border: none;
-  outline: none;
-  background: transparent;
-  width: 20em;
-  color: inherit;
-  font-family: monospace;
+.jp-RenderedHTMLCommon u {
+  text-decoration: underline;
 }
 
-.CodeMirror-dialog button {
-  font-size: 70%;
+.jp-RenderedHTMLCommon a:link {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
 }
 
-.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;
-}
-.CodeMirror-foldgutter {
-  width: .7em;
-}
-.CodeMirror-foldgutter-open,
-.CodeMirror-foldgutter-folded {
-  cursor: pointer;
-}
-.CodeMirror-foldgutter-open:after {
-  content: "\25BE";
+.jp-RenderedHTMLCommon a:hover {
+  text-decoration: underline;
+  color: var(--jp-content-link-color);
 }
-.CodeMirror-foldgutter-folded:after {
-  content: "\25B8";
+
+.jp-RenderedHTMLCommon a:visited {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
 }
 
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
+/* Headings */
 
-.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 */
+.jp-RenderedHTMLCommon h1,
+.jp-RenderedHTMLCommon h2,
+.jp-RenderedHTMLCommon h3,
+.jp-RenderedHTMLCommon h4,
+.jp-RenderedHTMLCommon h5,
+.jp-RenderedHTMLCommon h6 {
+  line-height: var(--jp-content-heading-line-height);
+  font-weight: var(--jp-content-heading-font-weight);
+  font-style: normal;
+  margin: var(--jp-content-heading-margin-top) 0
+    var(--jp-content-heading-margin-bottom) 0;
 }
 
-.CodeMirror pre {
-  padding: 0 var(--jp-code-padding);
+.jp-RenderedHTMLCommon h1:first-child,
+.jp-RenderedHTMLCommon h2:first-child,
+.jp-RenderedHTMLCommon h3:first-child,
+.jp-RenderedHTMLCommon h4:first-child,
+.jp-RenderedHTMLCommon h5:first-child,
+.jp-RenderedHTMLCommon h6:first-child {
+  margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
 }
 
-.jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-dialog {
-  background-color: var(--jp-layout-color0);
-  color: var(--jp-content-font-color1);
+.jp-RenderedHTMLCommon h1:last-child,
+.jp-RenderedHTMLCommon h2:last-child,
+.jp-RenderedHTMLCommon h3:last-child,
+.jp-RenderedHTMLCommon h4:last-child,
+.jp-RenderedHTMLCommon h5:last-child,
+.jp-RenderedHTMLCommon h6:last-child {
+  margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
 }
 
-/* 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;
+.jp-RenderedHTMLCommon h1 {
+  font-size: var(--jp-content-font-size5);
 }
 
-.CodeMirror-linenumber {
-  padding: 0 8px;
+.jp-RenderedHTMLCommon h2 {
+  font-size: var(--jp-content-font-size4);
 }
 
-.jp-CodeMirrorEditor {
-  cursor: text;
+.jp-RenderedHTMLCommon h3 {
+  font-size: var(--jp-content-font-size3);
 }
 
-.jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-cursor {
-  border-left: var(--jp-code-cursor-width0) solid var(--jp-editor-cursor-color);
+.jp-RenderedHTMLCommon h4 {
+  font-size: var(--jp-content-font-size2);
 }
 
-/* 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);
-  }
+.jp-RenderedHTMLCommon h5 {
+  font-size: var(--jp-content-font-size1);
 }
 
-/* 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);
-  }
+.jp-RenderedHTMLCommon h6 {
+  font-size: var(--jp-content-font-size0);
 }
 
-.CodeMirror.jp-mod-readOnly .CodeMirror-cursor {
-  display: none;
-}
+/* Lists */
 
-.CodeMirror-gutters {
-  border-right: 1px solid var(--jp-border-color2);
-  background-color: var(--jp-layout-color0);
-}
+/* stylelint-disable selector-max-type, selector-max-compound-selectors */
 
-.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;
+.jp-RenderedHTMLCommon ul:not(.list-inline),
+.jp-RenderedHTMLCommon ol:not(.list-inline) {
+  padding-left: 2em;
 }
 
-.CodeMirror-selectedtext.cm-searching {
-  background-color: var(--jp-search-selected-match-background-color) !important;
-  color: var(--jp-search-selected-match-color) !important;
+.jp-RenderedHTMLCommon ul {
+  list-style: disc;
 }
 
-.cm-searching {
-  background-color: var(
-    --jp-search-unselected-match-background-color
-  ) !important;
-  color: var(--jp-search-unselected-match-color) !important;
+.jp-RenderedHTMLCommon ul ul {
+  list-style: square;
 }
 
-.CodeMirror-focused .CodeMirror-selected {
-  background-color: var(--jp-editor-selected-focused-background);
+.jp-RenderedHTMLCommon ul ul ul {
+  list-style: circle;
 }
 
-.CodeMirror-selected {
-  background-color: var(--jp-editor-selected-background);
+.jp-RenderedHTMLCommon ol {
+  list-style: decimal;
 }
 
-.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;
+.jp-RenderedHTMLCommon ol ol {
+  list-style: upper-alpha;
 }
 
-.jp-CodeMirror-ruler {
-  border-left: 1px dashed var(--jp-border-color2);
+.jp-RenderedHTMLCommon ol ol ol {
+  list-style: lower-alpha;
 }
 
-/**
- * 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);
+.jp-RenderedHTMLCommon ol ol ol ol {
+  list-style: lower-roman;
 }
 
-/* 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;
+.jp-RenderedHTMLCommon ol ol ol ol ol {
+  list-style: decimal;
 }
 
-.cm-s-jupyter .CodeMirror-cursor {
-  border-left: var(--jp-code-cursor-width0) solid var(--jp-editor-cursor-color);
-}
-.cm-s-jupyter span.cm-keyword {
-  color: var(--jp-mirror-editor-keyword-color);
-  font-weight: bold;
-}
-.cm-s-jupyter span.cm-atom {
-  color: var(--jp-mirror-editor-atom-color);
-}
-.cm-s-jupyter span.cm-number {
-  color: var(--jp-mirror-editor-number-color);
-}
-.cm-s-jupyter span.cm-def {
-  color: var(--jp-mirror-editor-def-color);
-}
-.cm-s-jupyter span.cm-variable {
-  color: var(--jp-mirror-editor-variable-color);
-}
-.cm-s-jupyter span.cm-variable-2 {
-  color: var(--jp-mirror-editor-variable-2-color);
-}
-.cm-s-jupyter span.cm-variable-3 {
-  color: var(--jp-mirror-editor-variable-3-color);
-}
-.cm-s-jupyter span.cm-punctuation {
-  color: var(--jp-mirror-editor-punctuation-color);
-}
-.cm-s-jupyter span.cm-property {
-  color: var(--jp-mirror-editor-property-color);
-}
-.cm-s-jupyter span.cm-operator {
-  color: var(--jp-mirror-editor-operator-color);
-  font-weight: bold;
-}
-.cm-s-jupyter span.cm-comment {
-  color: var(--jp-mirror-editor-comment-color);
-  font-style: italic;
-}
-.cm-s-jupyter span.cm-string {
-  color: var(--jp-mirror-editor-string-color);
-}
-.cm-s-jupyter span.cm-string-2 {
-  color: var(--jp-mirror-editor-string-2-color);
-}
-.cm-s-jupyter span.cm-meta {
-  color: var(--jp-mirror-editor-meta-color);
-}
-.cm-s-jupyter span.cm-qualifier {
-  color: var(--jp-mirror-editor-qualifier-color);
-}
-.cm-s-jupyter span.cm-builtin {
-  color: var(--jp-mirror-editor-builtin-color);
-}
-.cm-s-jupyter span.cm-bracket {
-  color: var(--jp-mirror-editor-bracket-color);
-}
-.cm-s-jupyter span.cm-tag {
-  color: var(--jp-mirror-editor-tag-color);
-}
-.cm-s-jupyter span.cm-attribute {
-  color: var(--jp-mirror-editor-attribute-color);
-}
-.cm-s-jupyter span.cm-header {
-  color: var(--jp-mirror-editor-header-color);
-}
-.cm-s-jupyter span.cm-quote {
-  color: var(--jp-mirror-editor-quote-color);
+.jp-RenderedHTMLCommon ol,
+.jp-RenderedHTMLCommon ul {
+  margin-bottom: 1em;
 }
-.cm-s-jupyter span.cm-link {
-  color: var(--jp-mirror-editor-link-color);
+
+.jp-RenderedHTMLCommon ul ul,
+.jp-RenderedHTMLCommon ul ol,
+.jp-RenderedHTMLCommon ol ul,
+.jp-RenderedHTMLCommon ol ol {
+  margin-bottom: 0;
 }
-.cm-s-jupyter span.cm-error {
-  color: var(--jp-mirror-editor-error-color);
+
+/* stylelint-enable selector-max-type, selector-max-compound-selectors */
+
+.jp-RenderedHTMLCommon hr {
+  color: var(--jp-border-color2);
+  background-color: var(--jp-border-color1);
+  margin-top: 1em;
+  margin-bottom: 1em;
 }
-.cm-s-jupyter span.cm-hr {
-  color: #999;
+
+.jp-RenderedHTMLCommon > pre {
+  margin: 1.5em 2em;
 }
 
-.cm-s-jupyter span.cm-tab {
-  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
-  background-position: right;
-  background-repeat: no-repeat;
+.jp-RenderedHTMLCommon pre,
+.jp-RenderedHTMLCommon code {
+  border: 0;
+  background-color: var(--jp-layout-color0);
+  color: var(--jp-content-font-color1);
+  font-family: var(--jp-code-font-family);
+  font-size: inherit;
+  line-height: var(--jp-code-line-height);
+  padding: 0;
+  white-space: pre-wrap;
 }
 
-.cm-s-jupyter .CodeMirror-activeline-background,
-.cm-s-jupyter .CodeMirror-gutter {
+.jp-RenderedHTMLCommon :not(pre) > code {
   background-color: var(--jp-layout-color2);
+  padding: 1px 5px;
 }
 
-/* Styles for shared cursors (remote cursor locations and selected ranges) */
-.jp-CodeMirrorEditor .remote-caret {
-  position: relative;
-  border-left: 2px solid black;
-  margin-left: -1px;
-  margin-right: -1px;
-  box-sizing: border-box;
-}
+/* Tables */
 
-.jp-CodeMirrorEditor .remote-caret > div {
-  white-space: nowrap;
-  position: absolute;
-  top: -1.15em;
-  padding-bottom: 0.05em;
-  left: -2px;
-  font-size: 0.95em;
-  background-color: rgb(250, 129, 0);
-  font-family: var(--jp-ui-font-family);
-  font-weight: bold;
-  line-height: normal;
-  user-select: none;
-  color: white;
-  padding-left: 2px;
-  padding-right: 2px;
-  z-index: 3;
-  transition: opacity 0.3s ease-in-out;
+.jp-RenderedHTMLCommon table {
+  border-collapse: collapse;
+  border-spacing: 0;
+  border: none;
+  color: var(--jp-ui-font-color1);
+  font-size: var(--jp-ui-font-size1);
+  table-layout: fixed;
+  margin-left: auto;
+  margin-bottom: 1em;
+  margin-right: auto;
 }
 
-.jp-CodeMirrorEditor .remote-caret.hide-name > div {
-  transition-delay: 0.7s;
-  opacity: 0;
+.jp-RenderedHTMLCommon thead {
+  border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
+  vertical-align: bottom;
 }
 
-.jp-CodeMirrorEditor .remote-caret:hover > div {
-  opacity: 1;
-  transition-delay: 0s;
+.jp-RenderedHTMLCommon td,
+.jp-RenderedHTMLCommon th,
+.jp-RenderedHTMLCommon tr {
+  vertical-align: middle;
+  padding: 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
 }
 
-/*-----------------------------------------------------------------------------
-| Copyright (c) Jupyter Development Team.
-| Distributed under the terms of the Modified BSD License.
-|----------------------------------------------------------------------------*/
-
-/*-----------------------------------------------------------------------------
-| RenderedText
-|----------------------------------------------------------------------------*/
-
-:root {
-  /* This is the padding value to fill the gaps between lines containing spans with background color. */
-  --jp-private-code-span-padding: calc(
-    (var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
-  );
+.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
+.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
+  max-width: none;
 }
 
-.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);
+:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
+:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
+:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
+  text-align: right;
 }
 
-.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;
+.jp-RenderedHTMLCommon th {
+  font-weight: bold;
 }
 
-.jp-RenderedText pre a:link {
-  text-decoration: none;
-  color: var(--jp-content-link-color);
-}
-.jp-RenderedText pre a:hover {
-  text-decoration: underline;
-  color: var(--jp-content-link-color);
+.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
+  background: var(--jp-layout-color0);
 }
-.jp-RenderedText pre a:visited {
-  text-decoration: none;
-  color: var(--jp-content-link-color);
+
+.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
+  background: var(--jp-rendermime-table-row-background);
 }
 
-/* console foregrounds and backgrounds */
-.jp-RenderedText pre .ansi-black-fg {
-  color: #3e424d;
-}
-.jp-RenderedText pre .ansi-red-fg {
-  color: #e75c58;
+.jp-RenderedHTMLCommon tbody tr:hover {
+  background: var(--jp-rendermime-table-row-hover-background);
 }
-.jp-RenderedText pre .ansi-green-fg {
-  color: #00a250;
+
+.jp-RenderedHTMLCommon p {
+  text-align: left;
+  margin: 0;
+  margin-bottom: 1em;
 }
-.jp-RenderedText pre .ansi-yellow-fg {
-  color: #ddb62b;
+
+.jp-RenderedHTMLCommon img {
+  -moz-force-broken-image-icon: 1;
 }
-.jp-RenderedText pre .ansi-blue-fg {
-  color: #208ffb;
+
+/* Restrict to direct children as other images could be nested in other content. */
+.jp-RenderedHTMLCommon > img {
+  display: block;
+  margin-left: 0;
+  margin-right: 0;
+  margin-bottom: 1em;
 }
-.jp-RenderedText pre .ansi-magenta-fg {
-  color: #d160c4;
+
+/* Change color behind transparent images if they need it... */
+[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
+  background-color: var(--jp-inverse-layout-color1);
 }
-.jp-RenderedText pre .ansi-cyan-fg {
-  color: #60c6c8;
+
+[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
+  background-color: var(--jp-inverse-layout-color1);
 }
-.jp-RenderedText pre .ansi-white-fg {
-  color: #c5c1b4;
+
+.jp-RenderedHTMLCommon img,
+.jp-RenderedImage img,
+.jp-RenderedHTMLCommon svg,
+.jp-RenderedSVG svg {
+  max-width: 100%;
+  height: auto;
 }
 
-.jp-RenderedText pre .ansi-black-bg {
-  background-color: #3e424d;
-  padding: var(--jp-private-code-span-padding) 0;
+.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;
 }
-.jp-RenderedText pre .ansi-red-bg {
-  background-color: #e75c58;
-  padding: var(--jp-private-code-span-padding) 0;
+
+.jp-RenderedHTMLCommon .alert {
+  padding: var(--jp-notebook-padding);
+  border: var(--jp-border-width) solid transparent;
+  border-radius: var(--jp-border-radius);
+  margin-bottom: 1em;
 }
-.jp-RenderedText pre .ansi-green-bg {
-  background-color: #00a250;
-  padding: var(--jp-private-code-span-padding) 0;
+
+.jp-RenderedHTMLCommon .alert-info {
+  color: var(--jp-info-color0);
+  background-color: var(--jp-info-color3);
+  border-color: var(--jp-info-color2);
 }
-.jp-RenderedText pre .ansi-yellow-bg {
-  background-color: #ddb62b;
-  padding: var(--jp-private-code-span-padding) 0;
+
+.jp-RenderedHTMLCommon .alert-info hr {
+  border-color: var(--jp-info-color3);
 }
-.jp-RenderedText pre .ansi-blue-bg {
-  background-color: #208ffb;
-  padding: var(--jp-private-code-span-padding) 0;
+
+.jp-RenderedHTMLCommon .alert-info > p:last-child,
+.jp-RenderedHTMLCommon .alert-info > ul:last-child {
+  margin-bottom: 0;
 }
-.jp-RenderedText pre .ansi-magenta-bg {
-  background-color: #d160c4;
-  padding: var(--jp-private-code-span-padding) 0;
+
+.jp-RenderedHTMLCommon .alert-warning {
+  color: var(--jp-warn-color0);
+  background-color: var(--jp-warn-color3);
+  border-color: var(--jp-warn-color2);
 }
-.jp-RenderedText pre .ansi-cyan-bg {
-  background-color: #60c6c8;
-  padding: var(--jp-private-code-span-padding) 0;
+
+.jp-RenderedHTMLCommon .alert-warning hr {
+  border-color: var(--jp-warn-color3);
 }
-.jp-RenderedText pre .ansi-white-bg {
-  background-color: #c5c1b4;
-  padding: var(--jp-private-code-span-padding) 0;
+
+.jp-RenderedHTMLCommon .alert-warning > p:last-child,
+.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
+  margin-bottom: 0;
 }
 
-.jp-RenderedText pre .ansi-black-intense-fg {
-  color: #282c36;
+.jp-RenderedHTMLCommon .alert-success {
+  color: var(--jp-success-color0);
+  background-color: var(--jp-success-color3);
+  border-color: var(--jp-success-color2);
 }
-.jp-RenderedText pre .ansi-red-intense-fg {
-  color: #b22b31;
+
+.jp-RenderedHTMLCommon .alert-success hr {
+  border-color: var(--jp-success-color3);
 }
-.jp-RenderedText pre .ansi-green-intense-fg {
-  color: #007427;
+
+.jp-RenderedHTMLCommon .alert-success > p:last-child,
+.jp-RenderedHTMLCommon .alert-success > ul:last-child {
+  margin-bottom: 0;
 }
-.jp-RenderedText pre .ansi-yellow-intense-fg {
-  color: #b27d12;
+
+.jp-RenderedHTMLCommon .alert-danger {
+  color: var(--jp-error-color0);
+  background-color: var(--jp-error-color3);
+  border-color: var(--jp-error-color2);
 }
-.jp-RenderedText pre .ansi-blue-intense-fg {
-  color: #0065ca;
+
+.jp-RenderedHTMLCommon .alert-danger hr {
+  border-color: var(--jp-error-color3);
 }
-.jp-RenderedText pre .ansi-magenta-intense-fg {
-  color: #a03196;
+
+.jp-RenderedHTMLCommon .alert-danger > p:last-child,
+.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
+  margin-bottom: 0;
 }
-.jp-RenderedText pre .ansi-cyan-intense-fg {
-  color: #258f8f;
+
+.jp-RenderedHTMLCommon blockquote {
+  margin: 1em 2em;
+  padding: 0 1em;
+  border-left: 5px solid var(--jp-border-color2);
 }
-.jp-RenderedText pre .ansi-white-intense-fg {
-  color: #a1a6b2;
+
+a.jp-InternalAnchorLink {
+  visibility: hidden;
+  margin-left: 8px;
+  color: var(--md-blue-800);
 }
 
-.jp-RenderedText pre .ansi-black-intense-bg {
-  background-color: #282c36;
-  padding: var(--jp-private-code-span-padding) 0;
+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;
 }
-.jp-RenderedText pre .ansi-red-intense-bg {
-  background-color: #b22b31;
-  padding: var(--jp-private-code-span-padding) 0;
+
+.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: var(--jp-ui-font-size0);
+  line-height: 1em;
+  padding: 0.2em 0.5em;
 }
-.jp-RenderedText pre .ansi-green-intense-bg {
-  background-color: #007427;
-  padding: var(--jp-private-code-span-padding) 0;
+
+/* 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;
 }
-.jp-RenderedText pre .ansi-yellow-intense-bg {
-  background-color: #b27d12;
-  padding: var(--jp-private-code-span-padding) 0;
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*-----------------------------------------------------------------------------
+| 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.
+|----------------------------------------------------------------------------*/
+
+.lm-cursor-backdrop {
+  position: fixed;
+  width: 200px;
+  height: 200px;
+  margin-top: -100px;
+  margin-left: -100px;
+  will-change: transform;
+  z-index: 100;
 }
-.jp-RenderedText pre .ansi-blue-intense-bg {
-  background-color: #0065ca;
-  padding: var(--jp-private-code-span-padding) 0;
+
+.lm-mod-drag-image {
+  will-change: transform;
 }
-.jp-RenderedText pre .ansi-magenta-intense-bg {
-  background-color: #a03196;
-  padding: var(--jp-private-code-span-padding) 0;
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-lineFormSearch {
+  padding: 4px 12px;
+  background-color: var(--jp-layout-color2);
+  box-shadow: var(--jp-toolbar-box-shadow);
+  z-index: 2;
+  font-size: var(--jp-ui-font-size1);
 }
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+
+.cm-trailingspace {
+  background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAsElEQVQIHQGlAFr/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7+r3zKmT0/+pk9P/7+r3zAAAAAAAAAAABAAAAAAAAAAA6OPzM+/q9wAAAAAA6OPzMwAAAAAAAAAAAgAAAAAAAAAAGR8NiRQaCgAZIA0AGR8NiQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQyoYJ/SY80UAAAAASUVORK5CYII=);
+  background-position: center left;
+  background-repeat: repeat-x;
 }
-.jp-RenderedHTMLCommon .alert-danger > p:last-child,
-.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
-  margin-bottom: 0;
+
+.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;
 }
 
-.jp-RenderedHTMLCommon blockquote {
-  margin: 1em 2em;
-  padding: 0 1em;
-  border-left: 5px solid var(--jp-border-color2);
+.jp-CodeMirror-ruler {
+  border-left: 1px dashed var(--jp-border-color2);
 }
 
-a.jp-InternalAnchorLink {
-  visibility: hidden;
-  margin-left: 8px;
-  color: var(--md-blue-800);
+/* Styles for shared cursors (remote cursor locations and selected ranges) */
+.jp-CodeMirrorEditor .cm-ySelectionCaret {
+  position: relative;
+  border-left: 1px solid black;
+  margin-left: -1px;
+  margin-right: -1px;
+  box-sizing: border-box;
 }
 
-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;
+.jp-CodeMirrorEditor .cm-ySelectionCaret > .cm-ySelectionInfo {
+  white-space: nowrap;
+  position: absolute;
+  top: -1.15em;
+  padding-bottom: 0.05em;
+  left: -1px;
+  font-size: 0.95em;
+  font-family: var(--jp-ui-font-family);
+  font-weight: bold;
+  line-height: normal;
+  user-select: none;
+  color: white;
+  padding-left: 2px;
+  padding-right: 2px;
+  z-index: 101;
+  transition: opacity 0.3s ease-in-out;
 }
 
-.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;
+.jp-CodeMirrorEditor .cm-ySelectionInfo {
+  transition-delay: 0.7s;
+  opacity: 0;
 }
 
-/* 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;
+.jp-CodeMirrorEditor .cm-ySelectionCaret:hover > .cm-ySelectionInfo {
+  opacity: 1;
+  transition-delay: 0s;
 }
 
 /*-----------------------------------------------------------------------------
@@ -12852,31 +4989,36 @@ h6:hover .jp-InternalAnchorLink {
 | Distributed under the terms of the Modified BSD License.
 |----------------------------------------------------------------------------*/
 
-.jp-FileBrowser {
+.jp-FileBrowser .jp-SidePanel-content {
   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);
 }
 
 .jp-FileBrowser-toolbar.jp-Toolbar {
+  flex-wrap: wrap;
+  row-gap: 12px;
   border-bottom: none;
   height: auto;
-  margin: var(--jp-toolbar-header-margin);
+  margin: 8px 12px 0;
   box-shadow: none;
+  padding: 0;
+  justify-content: flex-start;
+}
+
+.jp-FileBrowser-Panel {
+  flex: 1 1 auto;
+  display: flex;
+  flex-direction: column;
 }
 
 .jp-BreadCrumbs {
   flex: 0 0 auto;
-  margin: 8px 12px 8px 12px;
+  margin: 8px 12px;
 }
 
 .jp-BreadCrumbs-item {
-  margin: 0px 2px;
-  padding: 0px 2px;
+  margin: 0 2px;
+  padding: 0 2px;
   border-radius: var(--jp-border-radius);
   cursor: pointer;
 }
@@ -12886,7 +5028,7 @@ h6:hover .jp-InternalAnchorLink {
 }
 
 .jp-BreadCrumbs-item:first-child {
-  margin-left: 0px;
+  margin-left: 0;
 }
 
 .jp-BreadCrumbs-item.jp-mod-dropTarget {
@@ -12898,45 +5040,18 @@ h6:hover .jp-InternalAnchorLink {
 | Buttons
 |----------------------------------------------------------------------------*/
 
-.jp-FileBrowser-toolbar.jp-Toolbar {
-  padding: 0px;
-  margin: 8px 12px 0px 12px;
-}
-
-.jp-FileBrowser-toolbar.jp-Toolbar {
-  justify-content: flex-start;
-}
-
-.jp-FileBrowser-toolbar.jp-Toolbar .jp-Toolbar-item {
+.jp-FileBrowser-toolbar > .jp-Toolbar-item {
   flex: 0 0 auto;
-  padding-left: 0px;
+  padding-left: 0;
   padding-right: 2px;
+  align-items: center;
+  height: unset;
 }
 
-.jp-FileBrowser-toolbar.jp-Toolbar .jp-ToolbarButtonComponent {
+.jp-FileBrowser-toolbar > .jp-Toolbar-item .jp-ToolbarButtonComponent {
   width: 40px;
 }
 
-.jp-FileBrowser-toolbar.jp-Toolbar
-  .jp-Toolbar-item:first-child
-  .jp-ToolbarButtonComponent {
-  width: 72px;
-  background: var(--jp-brand-color1);
-}
-
-.jp-FileBrowser-toolbar.jp-Toolbar
-  .jp-Toolbar-item:first-child
-  .jp-ToolbarButtonComponent:focus-visible {
-  background-color: var(--jp-brand-color0);
-}
-
-.jp-FileBrowser-toolbar.jp-Toolbar
-  .jp-Toolbar-item:first-child
-  .jp-ToolbarButtonComponent
-  .jp-icon3 {
-  fill: white;
-}
-
 /*-----------------------------------------------------------------------------
 | Other styles
 |----------------------------------------------------------------------------*/
@@ -12953,10 +5068,12 @@ h6:hover .jp-InternalAnchorLink {
   display: none;
 }
 
-.jp-FileBrowser-filterBox {
-  padding: 0px;
-  flex: 0 0 auto;
-  margin: 8px 12px 0px 12px;
+.jp-FileSize-hidden {
+  display: none;
+}
+
+.jp-FileBrowser .lm-AccordionPanel > h3:first-child {
+  display: none;
 }
 
 /*-----------------------------------------------------------------------------
@@ -12970,14 +5087,11 @@ h6:hover .jp-InternalAnchorLink {
   outline: 0;
 }
 
-.jp-DirListing:focus-visible {
-  border: 1px solid var(--jp-brand-color1);
-}
-
 .jp-DirListing-header {
   flex: 0 0 auto;
   display: flex;
   flex-direction: row;
+  align-items: center;
   overflow: hidden;
   border-top: var(--jp-border-width) solid var(--jp-border-color2);
   border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
@@ -12986,7 +5100,7 @@ h6:hover .jp-InternalAnchorLink {
 }
 
 .jp-DirListing-headerItem {
-  padding: 4px 12px 2px 12px;
+  padding: 4px 12px 2px;
   font-weight: 500;
 }
 
@@ -13004,10 +5118,16 @@ h6:hover .jp-InternalAnchorLink {
   text-align: right;
 }
 
+.jp-DirListing-headerItem.jp-id-filesize {
+  flex: 0 0 75px;
+  border-left: var(--jp-border-width) solid var(--jp-border-color2);
+  text-align: right;
+}
+
 .jp-id-narrow {
   display: none;
   flex: 0 0 5px;
-  padding: 4px 4px;
+  padding: 4px;
   border-left: var(--jp-border-width) solid var(--jp-border-color2);
   text-align: right;
   color: var(--jp-border-color2);
@@ -13056,6 +5176,7 @@ h6:hover .jp-InternalAnchorLink {
 .jp-DirListing-item {
   display: flex;
   flex-direction: row;
+  align-items: center;
   padding: 4px 12px;
   -webkit-user-select: none;
   -moz-user-select: none;
@@ -13063,6 +5184,40 @@ h6:hover .jp-InternalAnchorLink {
   user-select: none;
 }
 
+.jp-DirListing-checkboxWrapper {
+  /* Increases hit area of checkbox. */
+  padding: 4px;
+}
+
+.jp-DirListing-header
+  .jp-DirListing-checkboxWrapper
+  + .jp-DirListing-headerItem {
+  padding-left: 4px;
+}
+
+.jp-DirListing-content .jp-DirListing-checkboxWrapper {
+  position: relative;
+  left: -4px;
+  margin: -4px 0 -4px -8px;
+}
+
+.jp-DirListing-checkboxWrapper.jp-mod-visible {
+  visibility: visible;
+}
+
+/* For devices that support hovering, hide checkboxes until hovered, selected...
+*/
+@media (hover: hover) {
+  .jp-DirListing-checkboxWrapper {
+    visibility: hidden;
+  }
+
+  .jp-DirListing-item:hover .jp-DirListing-checkboxWrapper,
+  .jp-DirListing-item.jp-mod-selected .jp-DirListing-checkboxWrapper {
+    visibility: visible;
+  }
+}
+
 .jp-DirListing-item[data-is-dot] {
   opacity: 75%;
 }
@@ -13093,18 +5248,36 @@ h6:hover .jp-InternalAnchorLink {
   user-select: none;
 }
 
+.jp-DirListing-itemText:focus {
+  outline-width: 2px;
+  outline-color: var(--jp-inverse-layout-color1);
+  outline-style: solid;
+  outline-offset: 1px;
+}
+
+.jp-DirListing-item.jp-mod-selected .jp-DirListing-itemText:focus {
+  outline-color: var(--jp-layout-color1);
+}
+
 .jp-DirListing-itemModified {
   flex: 0 0 125px;
   text-align: right;
 }
 
+.jp-DirListing-itemFileSize {
+  flex: 0 0 90px;
+  text-align: right;
+}
+
 .jp-DirListing-editor {
   flex: 1 0 64px;
   outline: none;
   border: none;
+  color: var(--jp-ui-font-color1);
+  background-color: var(--jp-layout-color1);
 }
 
-.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon:before {
+.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon::before {
   color: var(--jp-success-color1);
   content: '\25CF';
   font-size: 8px;
@@ -13113,7 +5286,7 @@ h6:hover .jp-InternalAnchorLink {
 }
 
 .jp-DirListing-item.jp-mod-running.jp-mod-selected
-  .jp-DirListing-itemIcon:before {
+  .jp-DirListing-itemIcon::before {
   color: var(--jp-ui-inverse-font-color1);
 }
 
@@ -13125,20 +5298,11 @@ h6:hover .jp-InternalAnchorLink {
   width: 160px;
   background-color: var(--jp-ui-inverse-font-color2);
   box-shadow: var(--jp-elevation-z2);
-  border-radius: 0px;
+  border-radius: 0;
   color: var(--jp-ui-font-color1);
   transform: translateX(-40%) translateY(-58%);
 }
 
-.jp-DirListing-deadSpace {
-  flex: 1 1 auto;
-  margin: 0;
-  padding: 0;
-  list-style-type: none;
-  overflow: auto;
-  background-color: var(--jp-layout-color1);
-}
-
 .jp-Document {
   min-width: 120px;
   min-height: 120px;
@@ -13150,13 +5314,6 @@ h6:hover .jp-InternalAnchorLink {
 | Distributed under the terms of the Modified BSD License.
 |----------------------------------------------------------------------------*/
 
-/*-----------------------------------------------------------------------------
-| Private CSS variables
-|----------------------------------------------------------------------------*/
-
-:root {
-}
-
 /*-----------------------------------------------------------------------------
 | Main OutputArea
 | OutputArea has a list of Outputs
@@ -13167,16 +5324,14 @@ h6:hover .jp-InternalAnchorLink {
 }
 
 .jp-OutputArea-child {
-  display: flex;
-  flex-direction: row;
-}
-
-body[data-format='mobile'] .jp-OutputArea-child {
-  flex-direction: column;
+  display: table;
+  table-layout: fixed;
+  width: 100%;
+  overflow: hidden;
 }
 
 .jp-OutputPrompt {
-  flex: 0 0 var(--jp-cell-prompt-width);
+  width: 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);
@@ -13185,11 +5340,13 @@ body[data-format='mobile'] .jp-OutputArea-child {
   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;
@@ -13197,12 +5354,14 @@ body[data-format='mobile'] .jp-OutputArea-child {
   user-select: none;
 }
 
-body[data-format='mobile'] .jp-OutputPrompt {
-  flex: 0 0 auto;
-  text-align: left;
+.jp-OutputArea-prompt {
+  display: table-cell;
+  vertical-align: top;
 }
 
 .jp-OutputArea-output {
+  display: table-cell;
+  width: 100%;
   height: auto;
   overflow: auto;
   user-select: text;
@@ -13211,13 +5370,30 @@ body[data-format='mobile'] .jp-OutputPrompt {
   -ms-user-select: text;
 }
 
-.jp-OutputArea-child .jp-OutputArea-output {
-  flex-grow: 1;
-  flex-shrink: 1;
+.jp-OutputArea .jp-RenderedText {
+  padding-left: 1ch;
+}
+
+/**
+ * Prompt overlay.
+ */
+
+.jp-OutputArea-promptOverlay {
+  position: absolute;
+  top: 0;
+  width: var(--jp-cell-prompt-width);
+  height: 100%;
+  opacity: 0.5;
+}
+
+.jp-OutputArea-promptOverlay:hover {
+  background: var(--jp-layout-color2);
+  box-shadow: inset 0 0 1px var(--jp-inverse-layout-color0);
+  cursor: zoom-out;
 }
 
-body[data-format='mobile'] .jp-OutputArea-child .jp-OutputArea-output {
-  margin-left: var(--jp-notebook-padding);
+.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay:hover {
+  cursor: zoom-in;
 }
 
 /**
@@ -13229,7 +5405,7 @@ body[data-format='mobile'] .jp-OutputArea-child .jp-OutputArea-output {
 }
 
 /*
-When drag events occur, `p-mod-override-cursor` is added to the body.
+When drag events occur, `lm-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.
@@ -13238,7 +5414,7 @@ body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
   position: relative;
 }
 
-body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated:before {
+body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated::before {
   content: '';
   position: absolute;
   top: 0;
@@ -13252,8 +5428,8 @@ body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated:before {
 
 .jp-OutputArea-output pre {
   border: none;
-  margin: 0px;
-  padding: 0px;
+  margin: 0;
+  padding: 0;
   overflow-x: auto;
   overflow-y: auto;
   word-break: break-all;
@@ -13298,6 +5474,13 @@ body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated:before {
   margin: 0;
 }
 
+.jp-TrimmedOutputs pre {
+  background: var(--jp-layout-color3);
+  font-size: calc(var(--jp-code-font-size) * 1.4);
+  text-align: center;
+  text-transform: uppercase;
+}
+
 /* Hide the gutter in case of
  *  - nested output areas (e.g. in the case of output widgets)
  *  - mirrored output areas
@@ -13306,14 +5489,20 @@ body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated:before {
   display: none;
 }
 
+/* Hide empty lines in the output area, for instance due to cleared widgets */
+.jp-OutputArea-prompt:empty {
+  padding: 0;
+  border: 0;
+}
+
 /*-----------------------------------------------------------------------------
 | executeResult is added to any Output-result for the display of the object
 | returned by a cell
 |----------------------------------------------------------------------------*/
 
 .jp-OutputArea-output.jp-OutputArea-executeResult {
-  margin-left: 0px;
-  flex: 1 1 auto;
+  margin-left: 0;
+  width: 100%;
 }
 
 /* Text output with the Out[] prompt needs a top padding to match the
@@ -13328,12 +5517,6 @@ body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated:before {
 | The Stdin output
 |----------------------------------------------------------------------------*/
 
-.jp-OutputArea-stdin {
-  line-height: var(--jp-code-line-height);
-  padding-top: var(--jp-code-padding);
-  display: flex;
-}
-
 .jp-Stdin-prompt {
   color: var(--jp-content-font-color0);
   padding-right: var(--jp-code-padding);
@@ -13348,18 +5531,28 @@ body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated:before {
   background-color: inherit;
   width: 42%;
   min-width: 200px;
+
   /* 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;
+  padding: 0 0.25em;
+  margin: 0 0.25em;
   flex: 0 0 70%;
 }
 
+.jp-Stdin-input::placeholder {
+  opacity: 0;
+}
+
 .jp-Stdin-input:focus {
   box-shadow: none;
 }
 
+.jp-Stdin-input:focus::placeholder {
+  opacity: 1;
+}
+
 /*-----------------------------------------------------------------------------
 | Output Area View
 |----------------------------------------------------------------------------*/
@@ -13373,6 +5566,160 @@ body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated:before {
   height: 100%;
 }
 
+/*-----------------------------------------------------------------------------
+| Printing
+|----------------------------------------------------------------------------*/
+
+@media print {
+  .jp-OutputArea-child {
+    break-inside: avoid-page;
+  }
+}
+
+/*-----------------------------------------------------------------------------
+| Mobile
+|----------------------------------------------------------------------------*/
+@media only screen and (max-width: 760px) {
+  .jp-OutputPrompt {
+    display: table-row;
+    text-align: left;
+  }
+
+  .jp-OutputArea-child .jp-OutputArea-output {
+    display: table-row;
+    margin-left: var(--jp-notebook-padding);
+  }
+}
+
+/* Trimmed outputs warning */
+.jp-TrimmedOutputs > a {
+  margin: 10px;
+  text-decoration: none;
+  cursor: pointer;
+}
+
+.jp-TrimmedOutputs > a:hover {
+  text-decoration: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Table of Contents
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-toc-active-width: 4px;
+}
+
+.jp-TableOfContents {
+  display: flex;
+  flex-direction: column;
+  background: var(--jp-layout-color1);
+  color: var(--jp-ui-font-color1);
+  font-size: var(--jp-ui-font-size1);
+  height: 100%;
+}
+
+.jp-TableOfContents-placeholder {
+  text-align: center;
+}
+
+.jp-TableOfContents-placeholderContent {
+  color: var(--jp-content-font-color2);
+  padding: 8px;
+}
+
+.jp-TableOfContents-placeholderContent > h3 {
+  margin-bottom: var(--jp-content-heading-margin-bottom);
+}
+
+.jp-TableOfContents .jp-SidePanel-content {
+  overflow-y: auto;
+}
+
+.jp-TableOfContents-tree {
+  margin: 4px;
+}
+
+.jp-TableOfContents ol {
+  list-style-type: none;
+}
+
+/* stylelint-disable-next-line selector-max-type */
+.jp-TableOfContents li > ol {
+  /* Align left border with triangle icon center */
+  padding-left: 11px;
+}
+
+.jp-TableOfContents-content {
+  /* left margin for the active heading indicator */
+  margin: 0 0 0 var(--jp-private-toc-active-width);
+  padding: 0;
+  background-color: var(--jp-layout-color1);
+}
+
+.jp-tocItem {
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.jp-tocItem-heading {
+  display: flex;
+  cursor: pointer;
+}
+
+.jp-tocItem-heading:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-tocItem-content {
+  display: block;
+  padding: 4px 0;
+  white-space: nowrap;
+  text-overflow: ellipsis;
+  overflow-x: hidden;
+}
+
+.jp-tocItem-collapser {
+  height: 20px;
+  margin: 2px 2px 0;
+  padding: 0;
+  background: none;
+  border: none;
+  cursor: pointer;
+}
+
+.jp-tocItem-collapser:hover {
+  background-color: var(--jp-layout-color3);
+}
+
+/* Active heading indicator */
+
+.jp-tocItem-heading::before {
+  content: ' ';
+  background: transparent;
+  width: var(--jp-private-toc-active-width);
+  height: 24px;
+  position: absolute;
+  left: 0;
+  border-radius: var(--jp-border-radius);
+}
+
+.jp-tocItem-heading.jp-tocItem-active::before {
+  background-color: var(--jp-brand-color1);
+}
+
+.jp-tocItem-heading:hover.jp-tocItem-active::before {
+  background: var(--jp-brand-color0);
+  opacity: 1;
+}
+
 /*-----------------------------------------------------------------------------
 | Copyright (c) Jupyter Development Team.
 | Distributed under the terms of the Modified BSD License.
@@ -13380,8 +5727,8 @@ body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated:before {
 
 .jp-Collapser {
   flex: 0 0 var(--jp-cell-collapser-width);
-  padding: 0px;
-  margin: 0px;
+  padding: 0;
+  margin: 0;
   border: none;
   outline: none;
   background: transparent;
@@ -13393,10 +5740,27 @@ body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated:before {
   display: block;
   width: 100%;
   box-sizing: border-box;
+
   /* height: 100% doesn't work because the height of its parent is computed from content */
   position: absolute;
-  top: 0px;
-  bottom: 0px;
+  top: 0;
+  bottom: 0;
+}
+
+/*-----------------------------------------------------------------------------
+| Printing
+|----------------------------------------------------------------------------*/
+
+/*
+Hiding collapsers in print mode.
+
+Note: input and output wrappers have "display: block" propery in print mode.
+*/
+
+@media print {
+  .jp-Collapser {
+    display: none;
+  }
 }
 
 /*-----------------------------------------------------------------------------
@@ -13411,10 +5775,10 @@ body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated:before {
 /* Hidden by zero height by default */
 .jp-CellHeader,
 .jp-CellFooter {
-  height: 0px;
+  height: 0;
   width: 100%;
-  padding: 0px;
-  margin: 0px;
+  padding: 0;
+  margin: 0;
   border: none;
   outline: none;
   background: transparent;
@@ -13431,33 +5795,27 @@ body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated:before {
 
 /* All input areas */
 .jp-InputArea {
-  display: flex;
-  flex-direction: row;
+  display: table;
+  table-layout: fixed;
+  width: 100%;
   overflow: hidden;
 }
 
-body[data-format='mobile'] .jp-InputArea {
-  flex-direction: column;
-}
-
 .jp-InputArea-editor {
-  flex: 1 1 auto;
+  display: table-cell;
   overflow: hidden;
-}
+  vertical-align: top;
 
-.jp-InputArea-editor {
   /* This is the non-active, default styling */
   border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
-  border-radius: 0px;
+  border-radius: 0;
   background: var(--jp-cell-editor-background);
 }
 
-body[data-format='mobile'] .jp-InputArea-editor {
-  margin-left: var(--jp-notebook-padding);
-}
-
 .jp-InputPrompt {
-  flex: 0 0 var(--jp-cell-prompt-width);
+  display: table-cell;
+  vertical-align: top;
+  width: var(--jp-cell-prompt-width);
   color: var(--jp-cell-inprompt-font-color);
   font-family: var(--jp-cell-prompt-font-family);
   padding: var(--jp-code-padding);
@@ -13466,12 +5824,13 @@ body[data-format='mobile'] .jp-InputArea-editor {
   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;
@@ -13479,9 +5838,19 @@ body[data-format='mobile'] .jp-InputArea-editor {
   user-select: none;
 }
 
-body[data-format='mobile'] .jp-InputPrompt {
-  flex: 0 0 auto;
-  text-align: left;
+/*-----------------------------------------------------------------------------
+| Mobile
+|----------------------------------------------------------------------------*/
+@media only screen and (max-width: 760px) {
+  .jp-InputArea-editor {
+    display: table-row;
+    margin-left: var(--jp-notebook-padding);
+  }
+
+  .jp-InputPrompt {
+    display: table-row;
+    text-align: left;
+  }
 }
 
 /*-----------------------------------------------------------------------------
@@ -13494,21 +5863,33 @@ body[data-format='mobile'] .jp-InputPrompt {
 |----------------------------------------------------------------------------*/
 
 .jp-Placeholder {
-  display: flex;
-  flex-direction: row;
-  flex: 1 1 auto;
+  display: table;
+  table-layout: fixed;
+  width: 100%;
 }
 
 .jp-Placeholder-prompt {
+  display: table-cell;
   box-sizing: border-box;
 }
 
 .jp-Placeholder-content {
-  flex: 1 1 auto;
-  border: none;
-  background: transparent;
-  height: 20px;
+  display: table-cell;
+  padding: 4px 6px;
+  border: 1px solid transparent;
+  border-radius: 0;
+  background: none;
   box-sizing: border-box;
+  cursor: pointer;
+}
+
+.jp-Placeholder-contentContainer {
+  display: flex;
+}
+
+.jp-Placeholder-content:hover,
+.jp-InputPlaceholder > .jp-Placeholder-content:hover {
+  border-color: var(--jp-layout-color3);
 }
 
 .jp-Placeholder-content .jp-MoreHorizIcon {
@@ -13520,10 +5901,22 @@ body[data-format='mobile'] .jp-InputPrompt {
 
 .jp-Placeholder-content .jp-MoreHorizIcon:hover {
   border: 1px solid var(--jp-border-color1);
-  box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.25);
+  box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.25);
   background-color: var(--jp-layout-color0);
 }
 
+.jp-PlaceholderText {
+  white-space: nowrap;
+  overflow-x: hidden;
+  color: var(--jp-inverse-layout-color3);
+  font-family: var(--jp-code-font-family);
+}
+
+.jp-InputPlaceholder > .jp-Placeholder-content {
+  border-color: var(--jp-cell-editor-border-color);
+  background: var(--jp-cell-editor-background);
+}
+
 /*-----------------------------------------------------------------------------
 | Copyright (c) Jupyter Development Team.
 | Distributed under the terms of the Modified BSD License.
@@ -13543,7 +5936,7 @@ body[data-format='mobile'] .jp-InputPrompt {
 
 .jp-Cell {
   padding: var(--jp-cell-padding);
-  margin: 0px;
+  margin: 0;
   border: none;
   outline: none;
   background: transparent;
@@ -13557,8 +5950,9 @@ body[data-format='mobile'] .jp-InputPrompt {
 .jp-Cell-outputWrapper {
   display: flex;
   flex-direction: row;
-  padding: 0px;
-  margin: 0px;
+  padding: 0;
+  margin: 0;
+
   /* Added to reveal the box-shadow on the input and output collapsers. */
   overflow: visible;
 }
@@ -13596,17 +5990,41 @@ body[data-format='mobile'] .jp-InputPrompt {
 
 .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);
+  max-height: 24em;
   margin-left: var(--jp-private-cell-scrolling-output-offset);
+  resize: vertical;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea[style*='height'] {
+  max-height: unset;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea::after {
+  content: ' ';
+  box-shadow: inset 0 0 6px 2px rgb(0 0 0 / 30%);
+  width: 100%;
+  height: 100%;
+  position: sticky;
+  bottom: 0;
+  top: 0;
+  margin-top: -50%;
+  float: left;
+  display: block;
+  pointer-events: none;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-child {
+  padding-top: 6px;
 }
 
 .jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
-  flex: 0 0
-    calc(
-      var(--jp-cell-prompt-width) -
-        var(--jp-private-cell-scrolling-output-offset)
-    );
+  width: calc(
+    var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset)
+  );
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay {
+  left: calc(-1 * var(--jp-private-cell-scrolling-output-offset));
 }
 
 /*-----------------------------------------------------------------------------
@@ -13618,7 +6036,8 @@ body[data-format='mobile'] .jp-InputPrompt {
 |----------------------------------------------------------------------------*/
 
 .jp-MarkdownOutput {
-  flex: 1 1 auto;
+  display: table-cell;
+  width: 100%;
   margin-top: 0;
   margin-bottom: 0;
   padding-left: var(--jp-code-padding);
@@ -13628,29 +6047,113 @@ body[data-format='mobile'] .jp-InputPrompt {
   overflow: auto;
 }
 
-.jp-showHiddenCellsButton {
+/* collapseHeadingButton (show always if hiddenCellsButton is _not_ shown) */
+.jp-collapseHeadingButton {
+  display: flex;
+  min-height: var(--jp-cell-collapser-min-height);
+  font-size: var(--jp-code-font-size);
+  position: absolute;
+  background-color: transparent;
+  background-size: 25px;
+  background-repeat: no-repeat;
+  background-position-x: center;
+  background-position-y: top;
+  background-image: var(--jp-icon-caret-down);
+  right: 0;
+  top: 0;
+  bottom: 0;
+}
+
+.jp-collapseHeadingButton.jp-mod-collapsed {
+  background-image: var(--jp-icon-caret-right);
+}
+
+/*
+ set the container font size to match that of content
+ so that the nested collapse buttons have the right size
+*/
+.jp-MarkdownCell .jp-InputPrompt {
+  font-size: var(--jp-content-font-size1);
+}
+
+/*
+  Align collapseHeadingButton with cell top header
+  The font sizes are identical to the ones in packages/rendermime/style/base.css
+*/
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='1'] {
+  font-size: var(--jp-content-font-size5);
+  background-position-y: calc(0.3 * var(--jp-content-font-size5));
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='2'] {
+  font-size: var(--jp-content-font-size4);
+  background-position-y: calc(0.3 * var(--jp-content-font-size4));
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='3'] {
+  font-size: var(--jp-content-font-size3);
+  background-position-y: calc(0.3 * var(--jp-content-font-size3));
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='4'] {
+  font-size: var(--jp-content-font-size2);
+  background-position-y: calc(0.3 * var(--jp-content-font-size2));
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='5'] {
+  font-size: var(--jp-content-font-size1);
+  background-position-y: top;
+}
+
+.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='6'] {
+  font-size: var(--jp-content-font-size0);
+  background-position-y: top;
+}
+
+/* collapseHeadingButton (show only on (hover,active) if hiddenCellsButton is shown) */
+.jp-Notebook.jp-mod-showHiddenCellsButton .jp-collapseHeadingButton {
+  display: none;
+}
+
+.jp-Notebook.jp-mod-showHiddenCellsButton
+  :is(.jp-MarkdownCell:hover, .jp-mod-active)
+  .jp-collapseHeadingButton {
+  display: flex;
+}
+
+/* showHiddenCellsButton (only show if jp-mod-showHiddenCellsButton is set, which
+is a consequence of the showHiddenCellsButton option in Notebook Settings)*/
+.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton {
   margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding));
   margin-top: var(--jp-code-padding);
   border: 1px solid var(--jp-border-color2);
   background-color: var(--jp-border-color3) !important;
   color: var(--jp-content-font-color0) !important;
+  display: flex;
 }
 
-.jp-showHiddenCellsButton:hover {
+.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton:hover {
   background-color: var(--jp-border-color2) !important;
 }
 
-.jp-collapseHeadingButton {
+.jp-showHiddenCellsButton {
   display: none;
 }
 
-.jp-MarkdownCell:hover .jp-collapseHeadingButton {
-  display: flex;
-  min-height: var(--jp-cell-collapser-min-height);
-  position: absolute;
-  right: 0;
-  top: 0;
-  bottom: 0;
+/*-----------------------------------------------------------------------------
+| Printing
+|----------------------------------------------------------------------------*/
+
+/*
+Using block instead of flex to allow the use of the break-inside CSS property for
+cell outputs.
+*/
+
+@media print {
+  .jp-Cell-inputWrapper,
+  .jp-Cell-outputWrapper {
+    display: block;
+  }
 }
 
 /*-----------------------------------------------------------------------------
@@ -13667,6 +6170,10 @@ body[data-format='mobile'] .jp-InputPrompt {
 | Variables
 |----------------------------------------------------------------------------*/
 
+:root {
+  --jp-notebook-toolbar-padding: 2px 5px 2px 2px;
+}
+
 /*-----------------------------------------------------------------------------
 
 /*-----------------------------------------------------------------------------
@@ -13674,7 +6181,10 @@ body[data-format='mobile'] .jp-InputPrompt {
 |----------------------------------------------------------------------------*/
 
 .jp-NotebookPanel-toolbar {
-  padding: 2px;
+  padding: var(--jp-notebook-toolbar-padding);
+
+  /* disable paint containment from lumino 2.0 default strict CSS containment */
+  contain: style size !important;
 }
 
 .jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
@@ -13694,30 +6204,199 @@ body[data-format='mobile'] .jp-InputPrompt {
   top: 5px !important;
 }
 
+.jp-Toolbar-responsive-popup {
+  position: absolute;
+  height: fit-content;
+  display: flex;
+  flex-direction: row;
+  flex-wrap: wrap;
+  justify-content: flex-end;
+  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: var(--jp-notebook-toolbar-padding);
+  z-index: 1;
+  right: 0;
+  top: 0;
+}
+
+.jp-Toolbar > .jp-Toolbar-responsive-opener {
+  margin-left: auto;
+}
+
 /*-----------------------------------------------------------------------------
 | Copyright (c) Jupyter Development Team.
 | Distributed under the terms of the Modified BSD License.
 |----------------------------------------------------------------------------*/
 
 /*-----------------------------------------------------------------------------
-| Private CSS variables
+| 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);
+/*-----------------------------------------------------------------------------
+
+/*-----------------------------------------------------------------------------
+| Styles
+|----------------------------------------------------------------------------*/
+
+.jp-Notebook-ExecutionIndicator {
+  position: relative;
+  display: inline-block;
+  height: 100%;
+  z-index: 9997;
+}
+
+.jp-Notebook-ExecutionIndicator-tooltip {
+  visibility: hidden;
+  height: auto;
+  width: max-content;
+  width: -moz-max-content;
+  background-color: var(--jp-layout-color2);
+  color: var(--jp-ui-font-color1);
+  text-align: justify;
+  border-radius: 6px;
+  padding: 0 5px;
+  position: fixed;
+  display: table;
+}
+
+.jp-Notebook-ExecutionIndicator-tooltip.up {
+  transform: translateX(-50%) translateY(-100%) translateY(-32px);
+}
+
+.jp-Notebook-ExecutionIndicator-tooltip.down {
+  transform: translateX(calc(-100% + 16px)) translateY(5px);
+}
+
+.jp-Notebook-ExecutionIndicator-tooltip.hidden {
+  display: none;
+}
+
+.jp-Notebook-ExecutionIndicator:hover .jp-Notebook-ExecutionIndicator-tooltip {
+  visibility: visible;
+}
+
+.jp-Notebook-ExecutionIndicator span {
+  font-size: var(--jp-ui-font-size1);
+  font-family: var(--jp-ui-font-family);
+  color: var(--jp-ui-font-color1);
+  line-height: 24px;
+  display: block;
+}
+
+.jp-Notebook-ExecutionIndicator-progress-bar {
+  display: flex;
+  justify-content: center;
+  height: 100%;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+/*
+ * Execution indicator
+ */
+.jp-tocItem-content::after {
+  content: '';
+
+  /* Must be identical to form a circle */
+  width: 12px;
+  height: 12px;
+  background: none;
+  border: none;
+  position: absolute;
+  right: 0;
+}
+
+.jp-tocItem-content[data-running='0']::after {
+  border-radius: 50%;
+  border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
+  background: none;
+}
+
+.jp-tocItem-content[data-running='1']::after {
+  border-radius: 50%;
+  border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
+  background-color: var(--jp-inverse-layout-color3);
+}
+
+.jp-tocItem-content[data-running='0'],
+.jp-tocItem-content[data-running='1'] {
+  margin-right: 12px;
+}
+
+/*
+ * Copyright (c) Jupyter Development Team.
+ * Distributed under the terms of the Modified BSD License.
+ */
+
+.jp-Notebook-footer {
+  height: 27px;
+  margin-left: calc(
+    var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
+      var(--jp-cell-padding)
+  );
+  width: calc(
+    100% -
+      (
+        var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
+          var(--jp-cell-padding) + var(--jp-cell-padding)
+      )
+  );
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  color: var(--jp-ui-font-color3);
+  margin-top: 6px;
+  background: none;
+  cursor: pointer;
+}
+
+.jp-Notebook-footer:focus {
+  border-color: var(--jp-cell-editor-active-border-color);
+}
+
+/* For devices that support hovering, hide footer until hover */
+@media (hover: hover) {
+  .jp-Notebook-footer {
+    opacity: 0;
+  }
+
+  .jp-Notebook-footer:focus,
+  .jp-Notebook-footer:hover {
+    opacity: 1;
+  }
 }
 
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
 /*-----------------------------------------------------------------------------
 | Imports
 |----------------------------------------------------------------------------*/
 
+/*-----------------------------------------------------------------------------
+| CSS variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-side-by-side-output-size: 1fr;
+  --jp-side-by-side-resized-cell: var(--jp-side-by-side-output-size);
+  --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);
+}
+
 /*-----------------------------------------------------------------------------
 | Notebook
 |----------------------------------------------------------------------------*/
 
+/* stylelint-disable selector-max-class */
+
 .jp-NotebookPanel {
   display: block;
   height: 100%;
@@ -13745,17 +6424,12 @@ body[data-format='mobile'] .jp-InputPrompt {
   contain: strict;
 }
 
-.jp-Notebook-render * {
-  contain: none !important;
-}
-
 .jp-Notebook .jp-Cell {
   overflow: visible;
 }
 
 .jp-Notebook .jp-Cell .jp-InputPrompt {
   cursor: move;
-  float: left;
 }
 
 /*-----------------------------------------------------------------------------
@@ -13795,7 +6469,8 @@ body[data-format='mobile'] .jp-InputPrompt {
 .jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {
   color: var(--jp-warn-color1);
 }
-.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt:before {
+
+.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt::before {
   color: var(--jp-warn-color1);
   content: '•';
 }
@@ -13863,7 +6538,7 @@ body[data-format='mobile'] .jp-InputPrompt {
 }
 
 .jp-dragImage-singlePrompt {
-  box-shadow: 2px 2px 4px 0px rgba(0, 0, 0, 0.12);
+  box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
 }
 
 .jp-dragImage .jp-dragImage-content {
@@ -13877,7 +6552,7 @@ body[data-format='mobile'] .jp-InputPrompt {
   background: var(--jp-cell-editor-background-color);
   color: var(--jp-content-font-color3);
   text-align: left;
-  margin: 4px 4px 4px 0px;
+  margin: 4px 4px 4px 0;
 }
 
 .jp-dragImage .jp-dragImage-prompt {
@@ -13902,7 +6577,7 @@ body[data-format='mobile'] .jp-InputPrompt {
   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);
+  box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
 }
 
 /*-----------------------------------------------------------------------------
@@ -13914,40 +6589,42 @@ body[data-format='mobile'] .jp-InputPrompt {
   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;
 }
 
-.jp-NotebookTools-tool {
-  padding: 0px 12px 0 12px;
-}
-
 .jp-ActiveCellTool {
-  padding: 12px;
-  background-color: var(--jp-layout-color1);
-  border-top: none !important;
+  padding: 12px 0;
+  display: flex;
 }
 
-.jp-ActiveCellTool .jp-InputArea-prompt {
-  flex: 0 0 auto;
-  padding-left: 0px;
+.jp-ActiveCellTool-Content {
+  flex: 1 1 auto;
 }
 
-.jp-ActiveCellTool .jp-InputArea-editor {
-  flex: 1 1 auto;
+.jp-ActiveCellTool .jp-ActiveCellTool-CellContent {
   background: var(--jp-cell-editor-background);
-  border-color: var(--jp-cell-editor-border-color);
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  border-radius: 0;
+  min-height: 29px;
 }
 
-.jp-ActiveCellTool .jp-InputArea-editor .CodeMirror {
-  background: transparent;
+.jp-ActiveCellTool .jp-InputPrompt {
+  min-width: calc(var(--jp-cell-prompt-width) * 0.75);
+}
+
+.jp-ActiveCellTool-CellContent > pre {
+  padding: 5px 4px;
+  margin: 0;
+  white-space: normal;
 }
 
 .jp-MetadataEditorTool {
   flex-direction: column;
-  padding: 12px 0px 12px 0px;
+  padding: 12px 0;
 }
 
 .jp-RankedPanel > :not(:first-child) {
@@ -13961,13 +6638,19 @@ body[data-format='mobile'] .jp-InputPrompt {
 }
 
 .jp-KeySelector label,
-.jp-MetadataEditorTool label {
+.jp-MetadataEditorTool label,
+.jp-NumberSetter label {
   line-height: 1.4;
 }
 
 .jp-NotebookTools .jp-select-wrapper {
   margin-top: 4px;
-  margin-bottom: 0px;
+  margin-bottom: 0;
+}
+
+.jp-NumberSetter input {
+  width: 100%;
+  margin-top: 4px;
 }
 
 .jp-NotebookTools .jp-Collapse {
@@ -13988,6 +6671,81 @@ body[data-format='mobile'] .jp-InputPrompt {
   flex: 0 0 110px;
 }
 
+/*-----------------------------------------------------------------------------
+| Side-by-side Mode (.jp-mod-sideBySide)
+|----------------------------------------------------------------------------*/
+.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell {
+  margin-top: 3em;
+  margin-bottom: 3em;
+  margin-left: 5%;
+  margin-right: 5%;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell {
+  display: grid;
+  grid-template-columns: minmax(0, 1fr) min-content minmax(
+      0,
+      var(--jp-side-by-side-output-size)
+    );
+  grid-template-rows: auto minmax(0, 1fr) auto;
+  grid-template-areas:
+    'header header header'
+    'input handle output'
+    'footer footer footer';
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell {
+  grid-template-columns: minmax(0, 1fr) min-content minmax(
+      0,
+      var(--jp-side-by-side-resized-cell)
+    );
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader {
+  grid-area: header;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper {
+  grid-area: input;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper {
+  /* overwrite the default margin (no vertical separation needed in side by side move */
+  margin-top: 0;
+  grid-area: output;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter {
+  grid-area: footer;
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle {
+  grid-area: handle;
+  user-select: none;
+  display: block;
+  height: 100%;
+  cursor: ew-resize;
+  padding: 0 var(--jp-cell-padding);
+}
+
+.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after {
+  content: '';
+  display: block;
+  background: var(--jp-border-color2);
+  height: 100%;
+  width: 5px;
+}
+
+.jp-mod-sideBySide.jp-Notebook
+  .jp-CodeCell.jp-mod-resizedCell
+  .jp-CellResizeHandle::after {
+  background: var(--jp-border-color0);
+}
+
+.jp-CellResizeHandle {
+  display: none;
+}
+
 /*-----------------------------------------------------------------------------
 | Placeholder
 |----------------------------------------------------------------------------*/
@@ -14027,10 +6785,6 @@ body[data-format='mobile'] .jp-InputPrompt {
   background-repeat: no-repeat;
   background-size: 800px 104px;
   height: 104px;
-  position: relative;
-}
-
-.jp-Cell-Placeholder-wrapper-body div {
   position: absolute;
   right: 15px;
   left: 15px;
@@ -14072,8 +6826,7 @@ div.jp-Cell-Placeholder-content-3 {
 }
 
 </style>
-
-    <style type="text/css">
+<style type="text/css">
 /*-----------------------------------------------------------------------------
 | Copyright (c) Jupyter Development Team.
 | Distributed under the terms of the Modified BSD License.
@@ -14129,33 +6882,33 @@ all of MD as it is not optimized for dense, information rich UIs.
     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);
+  --jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color),
+    0 1px 1px 0 var(--jp-shadow-penumbra-color),
+    0 1px 3px 0 var(--jp-shadow-ambient-color);
+  --jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color),
+    0 2px 2px 0 var(--jp-shadow-penumbra-color),
+    0 1px 5px 0 var(--jp-shadow-ambient-color);
+  --jp-elevation-z4: 0 2px 4px -1px var(--jp-shadow-umbra-color),
+    0 4px 5px 0 var(--jp-shadow-penumbra-color),
+    0 1px 10px 0 var(--jp-shadow-ambient-color);
+  --jp-elevation-z6: 0 3px 5px -1px var(--jp-shadow-umbra-color),
+    0 6px 10px 0 var(--jp-shadow-penumbra-color),
+    0 1px 18px 0 var(--jp-shadow-ambient-color);
+  --jp-elevation-z8: 0 5px 5px -3px var(--jp-shadow-umbra-color),
+    0 8px 10px 1px var(--jp-shadow-penumbra-color),
+    0 3px 14px 2px var(--jp-shadow-ambient-color);
+  --jp-elevation-z12: 0 7px 8px -4px var(--jp-shadow-umbra-color),
+    0 12px 17px 2px var(--jp-shadow-penumbra-color),
+    0 5px 22px 4px var(--jp-shadow-ambient-color);
+  --jp-elevation-z16: 0 8px 10px -5px var(--jp-shadow-umbra-color),
+    0 16px 24px 2px var(--jp-shadow-penumbra-color),
+    0 6px 30px 5px var(--jp-shadow-ambient-color);
+  --jp-elevation-z20: 0 10px 13px -6px var(--jp-shadow-umbra-color),
+    0 20px 31px 3px var(--jp-shadow-penumbra-color),
+    0 8px 38px 7px var(--jp-shadow-ambient-color);
+  --jp-elevation-z24: 0 11px 15px -7px var(--jp-shadow-umbra-color),
+    0 24px 38px 3px var(--jp-shadow-penumbra-color),
+    0 9px 46px 8px var(--jp-shadow-ambient-color);
 
   /* Borders
    *
@@ -14167,6 +6920,7 @@ all of MD as it is not optimized for dense, information rich UIs.
   --jp-border-color1: var(--md-grey-400);
   --jp-border-color2: var(--md-grey-300);
   --jp-border-color3: var(--md-grey-200);
+  --jp-inverse-border-color: var(--md-grey-600);
   --jp-border-radius: 2px;
 
   /* UI Fonts
@@ -14184,9 +6938,9 @@ all of MD as it is not optimized for dense, information rich UIs.
   --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';
+  --jp-ui-font-family: system-ui, -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.
@@ -14229,7 +6983,6 @@ all of MD as it is not optimized for dense, information rich UIs.
 
   /* 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;
@@ -14240,12 +6993,10 @@ all of MD as it is not optimized for dense, information rich UIs.
   --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';
+  --jp-content-link-color: var(--md-blue-900);
+  --jp-content-font-family: system-ui, -apple-system, blinkmacsystemfont,
+    'Segoe UI', helvetica, arial, sans-serif, 'Apple Color Emoji',
+    'Segoe UI Emoji', 'Segoe UI Symbol';
 
   /*
    * Code Fonts
@@ -14256,7 +7007,7 @@ all of MD as it is not optimized for dense, information rich UIs.
   --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-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. */
@@ -14285,7 +7036,7 @@ all of MD as it is not optimized for dense, information rich UIs.
    * theme these would go from dark to light.
    */
 
-  --jp-inverse-layout-color0: #111111;
+  --jp-inverse-layout-color0: #111;
   --jp-inverse-layout-color1: var(--md-grey-900);
   --jp-inverse-layout-color2: var(--md-grey-800);
   --jp-inverse-layout-color3: var(--md-grey-700);
@@ -14298,7 +7049,6 @@ all of MD as it is not optimized for dense, information rich UIs.
   --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-900);
   --jp-accent-color1: var(--md-green-700);
   --jp-accent-color2: var(--md-green-300);
@@ -14310,17 +7060,14 @@ all of MD as it is not optimized for dense, information rich UIs.
   --jp-warn-color1: var(--md-orange-700);
   --jp-warn-color2: var(--md-orange-300);
   --jp-warn-color3: var(--md-orange-100);
-
   --jp-error-color0: var(--md-red-900);
   --jp-error-color1: var(--md-red-700);
   --jp-error-color2: var(--md-red-300);
   --jp-error-color3: var(--md-red-100);
-
   --jp-success-color0: var(--md-green-900);
   --jp-success-color1: var(--md-green-700);
   --jp-success-color2: var(--md-green-300);
   --jp-success-color3: var(--md-green-100);
-
   --jp-info-color0: var(--md-cyan-900);
   --jp-info-color1: var(--md-cyan-700);
   --jp-info-color2: var(--md-cyan-300);
@@ -14329,26 +7076,25 @@ all of MD as it is not optimized for dense, information rich UIs.
   /* 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: var(--jp-code-font-family-default);
-  --jp-cell-prompt-letter-spacing: 0px;
+  --jp-cell-prompt-letter-spacing: 0;
   --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;
@@ -14389,8 +7135,8 @@ all of MD as it is not optimized for dense, information rich UIs.
   --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-box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.24);
+  --jp-toolbar-header-margin: 4px 4px 0 4px;
   --jp-toolbar-active-background: var(--md-grey-300);
 
   /* Statusbar specific styles */
@@ -14403,7 +7149,7 @@ all of MD as it is not optimized for dense, information rich UIs.
   --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-border-color: var(--jp-inverse-border-color);
   --jp-input-active-border-color: var(--jp-brand-color1);
   --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
 
@@ -14420,15 +7166,15 @@ all of MD as it is not optimized for dense, information rich UIs.
   --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-2-color: rgb(0, 54, 109);
   --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-operator-color: #a2f;
   --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-meta-color: #a2f;
   --jp-mirror-editor-qualifier-color: #555;
   --jp-mirror-editor-builtin-color: #008000;
   --jp-mirror-editor-bracket-color: #997;
@@ -14440,6 +7186,20 @@ all of MD as it is not optimized for dense, information rich UIs.
   --jp-mirror-editor-error-color: #f00;
   --jp-mirror-editor-hr-color: #999;
 
+  /*
+    RTC user specific colors.
+    These colors are used for the cursor, username in the editor,
+    and the icon of the user.
+  */
+
+  --jp-collaborator-color1: #ffad8e;
+  --jp-collaborator-color2: #dac83d;
+  --jp-collaborator-color3: #72dd76;
+  --jp-collaborator-color4: #00e4d0;
+  --jp-collaborator-color5: #45d4ff;
+  --jp-collaborator-color6: #e2b1ff;
+  --jp-collaborator-color7: #ff9de6;
+
   /* Vega extension styles */
 
   --jp-vega-background: white;
@@ -14465,9 +7225,32 @@ all of MD as it is not optimized for dense, information rich UIs.
   --jp-icon-contrast-color1: var(--md-green-600);
   --jp-icon-contrast-color2: var(--md-pink-600);
   --jp-icon-contrast-color3: var(--md-blue-600);
+
+  /* Button colors */
+  --jp-accept-color-normal: var(--md-blue-700);
+  --jp-accept-color-hover: var(--md-blue-800);
+  --jp-accept-color-active: var(--md-blue-900);
+  --jp-warn-color-normal: var(--md-red-700);
+  --jp-warn-color-hover: var(--md-red-800);
+  --jp-warn-color-active: var(--md-red-900);
+  --jp-reject-color-normal: var(--md-grey-600);
+  --jp-reject-color-hover: var(--md-grey-700);
+  --jp-reject-color-active: var(--md-grey-800);
+
+  /* File or activity icons and switch semantic variables */
+  --jp-jupyter-icon-color: #f37626;
+  --jp-notebook-icon-color: #f37626;
+  --jp-json-icon-color: var(--md-orange-700);
+  --jp-console-icon-background-color: var(--md-blue-700);
+  --jp-console-icon-color: white;
+  --jp-terminal-icon-background-color: var(--md-grey-800);
+  --jp-terminal-icon-color: var(--md-grey-200);
+  --jp-text-editor-icon-color: var(--md-grey-700);
+  --jp-inspector-icon-color: var(--md-grey-700);
+  --jp-switch-color: var(--md-grey-400);
+  --jp-switch-true-position-color: var(--md-orange-900);
 }
 </style>
-
 <style type="text/css">
 a.anchor-link {
    display: none;
@@ -14490,7 +7273,6 @@ a.anchor-link {
   }
 }
 </style>
-
 <style type="text/css">
 /* Overrides of notebook CSS for static HTML export */
 .reveal {
@@ -14559,6 +7341,10 @@ div.jp-OutputPrompt {
   box-shadow: none;
 }
 
+main {
+  height: 100%;
+}
+
 /* Reveal navigation controls */
 
 .reveal .controls .navigate-left,
@@ -14610,12 +7396,12 @@ div.jp-OutputPrompt {
   background: #727272 !important;
 }
 </style>
-
 <!-- Load mathjax -->
-    
+
 <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML-full,Safe"> </script>
-    <!-- MathJax configuration -->
-    <script type="text/x-mathjax-config">
+<!-- MathJax configuration -->
+
+<script type="text/x-mathjax-config">
     init_mathjax = function() {
         if (window.MathJax) {
         // MathJax loaded
@@ -14645,37 +7431,33 @@ div.jp-OutputPrompt {
     }
     init_mathjax();
     </script>
-    <!-- End of mathjax configuration --><!-- Reveal Theme -->
-<link rel="stylesheet" href="fzj-reveal.js/reveal.js/dist/theme/simple.css" id="theme">
+<!-- End of mathjax configuration --><!-- Reveal Theme -->
+<link href="fzj-reveal.js/reveal.js/dist/theme/simple.css" id="theme" rel="stylesheet"/>
 <link rel="stylesheet" href="fzj-reveal.js/custom.css" id="custom">
 <link rel="stylesheet" href="custom.css" id="custom">
-
 </head>
-
 <body class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light">
-
+<main>
 <div class="reveal">
 <div class="slides"><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><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, 14 June 2022</em></p>
-<p><em>MATSE Version</em></p>
-
+</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">¶</a></h1><p><em>Andreas Herten, Jülich Supercomputing Centre, Forschungszentrum Jülich, 4 September 2023</em></p>
 </div>
 </div>
 </div>
 </div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><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>
+</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">¶</a></h2><ul>
 <li>I like Python</li>
 <li>I like plotting data</li>
 <li>I like sharing</li>
@@ -14683,18 +7465,17 @@ div.jp-OutputPrompt {
 <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>
 </div>
 </div><div class="fragment">
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</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>
+</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">¶</a></h2><ul>
 <li><a href="#task1">Task 1</a></li>
 <li><a href="#task2">Task 2</a></li>
 <li><a href="#task3">Task 3</a></li>
@@ -14706,70 +7487,69 @@ div.jp-OutputPrompt {
 <li><a href="#task8">Task 8</a></li>
 <li><a href="#task8b">Task 8B</a></li>
 </ul>
-
 </div>
 </div>
 </div>
 </div></div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h2 id="Course-Setup">Course Setup<a class="anchor-link" href="#Course-Setup">&#182;</a></h2><ul>
-<li>Originally, a 4 h course (incleaing break)</li>
-<li>This is the two-day MATSE lecture, with plenty of space and some new material</li>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="Course-Setup">Course Setup<a class="anchor-link" href="#Course-Setup">¶</a></h2><ul>
+<li>3½ hours, including break around 10:30</li>
 <li>Alternating between lecture and hands-on</li>
+<li>Please give status of hands-ons via 👍 as BigBlueButton status</li>
+<li>TAs and me in the room can help with issues, either in public chat or in 1:1 chat</li>
 </ul>
-
 </div>
 </div>
 </div>
 </div><div class="fragment">
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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+<div class="jp-Cell-inputWrapper" tabindex="0">
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 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <ul>
-<li>Please now open Jupyter Notebook of this session from Sciebo</li>
+<li>Please now open Jupyter Notebook of this session: <a href="https://go.fzj.de/jsc-pd">https://go.fzj.de/jsc-pd</a></li>
+<li>Give thumbs up! 👍</li>
 </ul>
-
 </div>
 </div>
 </div>
 </div></div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><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>
+</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">¶</a></h2><p><img alt="No description has been provided for this image" src="img/adorable-animal-animal-photography-1661535.jpg" style="float: right; max-width: 200px;" width="200px"/></p>
 <ul>
 <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>Now at Pandas 2.0</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>
 </div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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 <div class="jp-InputArea jp-Cell-inputArea"><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>
+</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">¶</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>
@@ -14781,132 +7561,101 @@ div.jp-OutputPrompt {
 </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>), <a href="https://arrow.apache.org/docs/python/index.html">pyarrow</a> (Apache Arrow bindings in Python) …</li>
 </ul>
-
 </div>
 </div>
 </div>
 </div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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 <div class="jp-InputArea jp-Cell-inputArea"><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 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">¶</a></h2>
 </div>
 </div>
 </div>
-</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span>
 </pre></div>
-
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 </div>
 </div>
 </div>
-
-</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
-<div class="jp-Cell-inputWrapper">
+</div>
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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>
+<div class="cm-editor 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="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[7]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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>
+<div class="cm-editor 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">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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-
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-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[7]:</div>
-
-
-
-
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
-<pre>&#39;1.4.2&#39;</pre>
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[3]:</div>
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+<pre>'2.0.3'</pre>
 </div>
-
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 </div>
-
 </div>
-
-</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[8]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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
+<div class="cm-editor 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="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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-<div class="jp-RenderedText jp-OutputArea-output " data-mime-type="text/plain">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
 <pre><span class="ansi-red-fg">Class docstring:</span>
     pandas - a powerful data analysis and manipulation library for Python
     =====================================================================
     
     **pandas** is a Python package providing fast, flexible, and expressive data
-    structures designed to make working with &#34;relational&#34; or &#34;labeled&#34; data both
+    structures designed to make working with "relational" or "labeled" data both
     easy and intuitive. It aims to be the fundamental high-level building block for
     doing practical, **real world** data analysis in Python. Additionally, it has
     the broader goal of becoming **the most powerful and flexible open source data
@@ -14940,21 +7689,17 @@ div.jp-OutputPrompt {
       - Time series-specific functionality: date range generation and frequency
         conversion, moving window statistics, date shifting and lagging.</pre>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div></div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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 <div class="jp-InputArea jp-Cell-inputArea"><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>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="DataFrames">DataFrames<a class="anchor-link" href="#DataFrames">¶</a></h2><h3 id="It's-all-about-DataFrames">It's all about DataFrames<a class="anchor-link" href="#It's-all-about-DataFrames">¶</a></h3><p><img alt="No description has been provided for this image" src="img/buzz-dataframes.jpg" style="float: right; max-width: 200px;" width="200px"/></p>
 <ul>
 <li>Data containers of Pandas:<ul>
 <li>Linear: <code>Series</code></li>
@@ -14964,18 +7709,17 @@ div.jp-OutputPrompt {
 <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>
-
 </div>
 </div>
 </div>
 </div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><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="Construction">Construction<a class="anchor-link" href="#Construction">&#182;</a></h3><ul>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="DataFrames">DataFrames<a class="anchor-link" href="#DataFrames">¶</a></h2><h3 id="Construction">Construction<a class="anchor-link" href="#Construction">¶</a></h3><ul>
 <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>
@@ -14986,70 +7730,57 @@ div.jp-OutputPrompt {
 </ul>
 </li>
 </ul>
-
 </div>
 </div>
 </div>
 </div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><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="Examples,-finally">Examples, finally<a class="anchor-link" href="#Examples,-finally">&#182;</a></h3>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="DataFrames">DataFrames<a class="anchor-link" href="#DataFrames">¶</a></h2><h3 id="Examples,-finally">Examples, finally<a class="anchor-link" href="#Examples,-finally">¶</a></h3>
 </div>
 </div>
 </div>
-</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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>
+<div class="cm-editor 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>
-
-     </div>
 </div>
 </div>
 </div>
-
-</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div>
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[10]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-     <div class="CodeMirror cm-s-jupyter">
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+<div class="cm-editor 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>
-
-     </div>
 </div>
 </div>
 </div>
-
+</div>
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-
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-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[6]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
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         vertical-align: middle;
     }
@@ -15063,98 +7794,84 @@ div.jp-OutputPrompt {
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-      <th>7</th>
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-    </tr>
-    <tr>
-      <th>8</th>
-      <td>38</td>
-    </tr>
-    <tr>
-      <th>9</th>
-      <td>60</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>0</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>41</td>
+</tr>
+<tr>
+<th>1</th>
+<td>56</td>
+</tr>
+<tr>
+<th>2</th>
+<td>56</td>
+</tr>
+<tr>
+<th>3</th>
+<td>57</td>
+</tr>
+<tr>
+<th>4</th>
+<td>39</td>
+</tr>
+<tr>
+<th>5</th>
+<td>59</td>
+</tr>
+<tr>
+<th>6</th>
+<td>43</td>
+</tr>
+<tr>
+<th>7</th>
+<td>56</td>
+</tr>
+<tr>
+<th>8</th>
+<td>38</td>
+</tr>
+<tr>
+<th>9</th>
+<td>60</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[11]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</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_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>
+<div class="cm-editor 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="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[7]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -15168,127 +7885,100 @@ div.jp-OutputPrompt {
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>0</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>41</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>56</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>56</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>0</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>41</td>
+</tr>
+<tr>
+<th>1</th>
+<td>56</td>
+</tr>
+<tr>
+<th>2</th>
+<td>56</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div></div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</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><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[12]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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;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>
+<div class="cm-editor 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">"Name"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Liu"</span><span class="p">,</span> <span class="s2">"Rowland"</span><span class="p">,</span> <span class="s2">"Rivers"</span><span class="p">,</span> <span class="s2">"Waters"</span><span class="p">,</span> <span class="s2">"Rice"</span><span class="p">,</span> <span class="s2">"Fields"</span><span class="p">,</span> <span class="s2">"Kerr"</span><span class="p">,</span> <span class="s2">"Romero"</span><span class="p">,</span> <span class="s2">"Davis"</span><span class="p">,</span> <span class="s2">"Hall"</span><span class="p">],</span>
+    <span class="s2">"Age"</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="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
 <div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-
-
-<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]}
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>{'Name': ['Liu', 'Rowland', 'Rivers', 'Waters', 'Rice', 'Fields', 'Kerr', 'Romero', 'Davis', 'Hall'], 'Age': [41, 56, 56, 57, 39, 59, 43, 56, 38, 60]}
 </pre>
 </div>
 </div>
-
 </div>
-
 </div>
-
-</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[13]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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>
+<div class="cm-editor 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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[13]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[9]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -15302,206 +7992,159 @@ div.jp-OutputPrompt {
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Name</th>
-      <th>Age</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>Liu</td>
-      <td>41</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>Rowland</td>
-      <td>56</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>Rivers</td>
-      <td>56</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>Waters</td>
-      <td>57</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Name</th>
+<th>Age</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>Liu</td>
+<td>41</td>
+</tr>
+<tr>
+<th>1</th>
+<td>Rowland</td>
+<td>56</td>
+</tr>
+<tr>
+<th>2</th>
+<td>Rivers</td>
+<td>56</td>
+</tr>
+<tr>
+<th>3</th>
+<td>Waters</td>
+<td>57</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div></div><div class="fragment">
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</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><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[14]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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>
+<div class="cm-editor 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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[14]:</div>
-
-
-
-
-<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 class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[10]:</div>
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+<pre>Index(['Name', 'Age'], dtype='object')</pre>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div></div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</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><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[15]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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>
+<div class="cm-editor 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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[15]:</div>
-
-
-
-
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[11]:</div>
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
 <pre>RangeIndex(start=0, stop=10, step=1)</pre>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div><div class="fragment">
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <ul>
 <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><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[16]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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>
+<div class="cm-editor 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">"Name"</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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[16]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[12]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -15515,115 +8158,100 @@ div.jp-OutputPrompt {
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Age</th>
-    </tr>
-    <tr>
-      <th>Name</th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>Liu</th>
-      <td>41</td>
-    </tr>
-    <tr>
-      <th>Rowland</th>
-      <td>56</td>
-    </tr>
-    <tr>
-      <th>Rivers</th>
-      <td>56</td>
-    </tr>
-    <tr>
-      <th>Waters</th>
-      <td>57</td>
-    </tr>
-    <tr>
-      <th>Rice</th>
-      <td>39</td>
-    </tr>
-    <tr>
-      <th>Fields</th>
-      <td>59</td>
-    </tr>
-    <tr>
-      <th>Kerr</th>
-      <td>43</td>
-    </tr>
-    <tr>
-      <th>Romero</th>
-      <td>56</td>
-    </tr>
-    <tr>
-      <th>Davis</th>
-      <td>38</td>
-    </tr>
-    <tr>
-      <th>Hall</th>
-      <td>60</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Age</th>
+</tr>
+<tr>
+<th>Name</th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>Liu</th>
+<td>41</td>
+</tr>
+<tr>
+<th>Rowland</th>
+<td>56</td>
+</tr>
+<tr>
+<th>Rivers</th>
+<td>56</td>
+</tr>
+<tr>
+<th>Waters</th>
+<td>57</td>
+</tr>
+<tr>
+<th>Rice</th>
+<td>39</td>
+</tr>
+<tr>
+<th>Fields</th>
+<td>59</td>
+</tr>
+<tr>
+<th>Kerr</th>
+<td>43</td>
+</tr>
+<tr>
+<th>Romero</th>
+<td>56</td>
+</tr>
+<tr>
+<th>Davis</th>
+<td>38</td>
+</tr>
+<tr>
+<th>Hall</th>
+<td>60</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div></div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</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><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[17]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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>
+<div class="cm-editor 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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[17]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[13]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -15637,87 +8265,74 @@ div.jp-OutputPrompt {
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Age</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>count</th>
-      <td>10.000000</td>
-    </tr>
-    <tr>
-      <th>mean</th>
-      <td>50.500000</td>
-    </tr>
-    <tr>
-      <th>std</th>
-      <td>9.009255</td>
-    </tr>
-    <tr>
-      <th>min</th>
-      <td>38.000000</td>
-    </tr>
-    <tr>
-      <th>25%</th>
-      <td>41.500000</td>
-    </tr>
-    <tr>
-      <th>50%</th>
-      <td>56.000000</td>
-    </tr>
-    <tr>
-      <th>75%</th>
-      <td>56.750000</td>
-    </tr>
-    <tr>
-      <th>max</th>
-      <td>60.000000</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Age</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>count</th>
+<td>10.000000</td>
+</tr>
+<tr>
+<th>mean</th>
+<td>50.500000</td>
+</tr>
+<tr>
+<th>std</th>
+<td>9.009255</td>
+</tr>
+<tr>
+<th>min</th>
+<td>38.000000</td>
+</tr>
+<tr>
+<th>25%</th>
+<td>41.500000</td>
+</tr>
+<tr>
+<th>50%</th>
+<td>56.000000</td>
+</tr>
+<tr>
+<th>75%</th>
+<td>56.750000</td>
+</tr>
+<tr>
+<th>max</th>
+<td>60.000000</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[18]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">info</span><span class="p">()</span>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_sample</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
 </pre></div>
-
-     </div>
 </div>
 </div>
 </div>
-
+</div>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
 <div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-
-
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
-<pre>&lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>&lt;class 'pandas.core.frame.DataFrame'&gt;
 Index: 10 entries, Liu to Hall
 Data columns (total 1 columns):
  #   Column  Non-Null Count  Dtype
@@ -15728,44 +8343,31 @@ memory usage: 160.0+ bytes
 </pre>
 </div>
 </div>
-
 </div>
-
 </div>
-
-</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[19]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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>
+<div class="cm-editor 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="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[19]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[15]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -15779,136 +8381,106 @@ memory usage: 160.0+ bytes
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th>Name</th>
-      <th>Liu</th>
-      <th>Rowland</th>
-      <th>Rivers</th>
-      <th>Waters</th>
-      <th>Rice</th>
-      <th>Fields</th>
-      <th>Kerr</th>
-      <th>Romero</th>
-      <th>Davis</th>
-      <th>Hall</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>Age</th>
-      <td>41</td>
-      <td>56</td>
-      <td>56</td>
-      <td>57</td>
-      <td>39</td>
-      <td>59</td>
-      <td>43</td>
-      <td>56</td>
-      <td>38</td>
-      <td>60</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th>Name</th>
+<th>Liu</th>
+<th>Rowland</th>
+<th>Rivers</th>
+<th>Waters</th>
+<th>Rice</th>
+<th>Fields</th>
+<th>Kerr</th>
+<th>Romero</th>
+<th>Davis</th>
+<th>Hall</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>Age</th>
+<td>41</td>
+<td>56</td>
+<td>56</td>
+<td>57</td>
+<td>39</td>
+<td>59</td>
+<td>43</td>
+<td>56</td>
+<td>38</td>
+<td>60</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[20]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">T</span><span class="o">.</span><span class="n">columns</span>
+<div class="cm-editor 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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[20]:</div>
-
-
-
-
-<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;Name&#39;)</pre>
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[16]:</div>
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+<pre>Index(['Liu', 'Rowland', 'Rivers', 'Waters', 'Rice', 'Fields', 'Kerr',
+       'Romero', 'Davis', 'Hall'],
+      dtype='object', name='Name')</pre>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div></div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</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><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[21]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>
+<div class="cm-editor 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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[21]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[17]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -15922,73 +8494,59 @@ memory usage: 160.0+ bytes
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Age</th>
-    </tr>
-    <tr>
-      <th>Name</th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>Liu</th>
-      <td>82</td>
-    </tr>
-    <tr>
-      <th>Rowland</th>
-      <td>112</td>
-    </tr>
-    <tr>
-      <th>Rivers</th>
-      <td>112</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Age</th>
+</tr>
+<tr>
+<th>Name</th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>Liu</th>
+<td>82</td>
+</tr>
+<tr>
+<th>Rowland</th>
+<td>112</td>
+</tr>
+<tr>
+<th>Rivers</th>
+<td>112</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[22]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="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>
+<div class="cm-editor 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="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[22]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[18]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -16002,73 +8560,59 @@ memory usage: 160.0+ bytes
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Name</th>
-      <th>Age</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>LiuLiu</td>
-      <td>82</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>RowlandRowland</td>
-      <td>112</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>RiversRivers</td>
-      <td>112</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Name</th>
+<th>Age</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>LiuLiu</td>
+<td>82</td>
+</tr>
+<tr>
+<th>1</th>
+<td>RowlandRowland</td>
+<td>112</td>
+</tr>
+<tr>
+<th>2</th>
+<td>RiversRivers</td>
+<td>112</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[23]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="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>
+<div class="cm-editor 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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[23]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[19]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -16082,73 +8626,59 @@ memory usage: 160.0+ bytes
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Age</th>
-    </tr>
-    <tr>
-      <th>Name</th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>Liu</th>
-      <td>20.5</td>
-    </tr>
-    <tr>
-      <th>Rowland</th>
-      <td>28.0</td>
-    </tr>
-    <tr>
-      <th>Rivers</th>
-      <td>28.0</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Age</th>
+</tr>
+<tr>
+<th>Name</th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>Liu</th>
+<td>20.5</td>
+</tr>
+<tr>
+<th>Rowland</th>
+<td>28.0</td>
+</tr>
+<tr>
+<th>Rivers</th>
+<td>28.0</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[24]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="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>
+<div class="cm-editor 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="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[24]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[20]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -16162,77 +8692,63 @@ memory usage: 160.0+ bytes
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Age</th>
-    </tr>
-    <tr>
-      <th>Name</th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>Liu</th>
-      <td>1681</td>
-    </tr>
-    <tr>
-      <th>Rowland</th>
-      <td>3136</td>
-    </tr>
-    <tr>
-      <th>Rivers</th>
-      <td>3136</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Age</th>
+</tr>
+<tr>
+<th>Name</th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>Liu</th>
+<td>1681</td>
+</tr>
+<tr>
+<th>Rowland</th>
+<td>3136</td>
+</tr>
+<tr>
+<th>Rivers</th>
+<td>3136</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[25]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="k">def</span> <span class="nf">mysquare</span><span class="p">(</span><span class="n">number</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">mysquare</span><span class="p">(</span><span class="n">number</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
     <span class="k">return</span> <span class="n">number</span><span class="o">*</span><span class="n">number</span>
 
 <span class="n">df_sample</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">mysquare</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 <span class="c1"># or: df_sample.apply(lambda x: x*x).head()</span>
 </pre></div>
-
-     </div>
 </div>
 </div>
 </div>
-
+</div>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[25]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[21]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -16246,81 +8762,67 @@ memory usage: 160.0+ bytes
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Age</th>
-    </tr>
-    <tr>
-      <th>Name</th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>Liu</th>
-      <td>1681</td>
-    </tr>
-    <tr>
-      <th>Rowland</th>
-      <td>3136</td>
-    </tr>
-    <tr>
-      <th>Rivers</th>
-      <td>3136</td>
-    </tr>
-    <tr>
-      <th>Waters</th>
-      <td>3249</td>
-    </tr>
-    <tr>
-      <th>Rice</th>
-      <td>1521</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Age</th>
+</tr>
+<tr>
+<th>Name</th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>Liu</th>
+<td>1681</td>
+</tr>
+<tr>
+<th>Rowland</th>
+<td>3136</td>
+</tr>
+<tr>
+<th>Rivers</th>
+<td>3136</td>
+</tr>
+<tr>
+<th>Waters</th>
+<td>3249</td>
+</tr>
+<tr>
+<th>Rice</th>
+<td>1521</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[27]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [23]:</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">apply</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">square</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_sample</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">square</span><span class="p">)</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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[27]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[23]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -16334,93 +8836,78 @@ memory usage: 160.0+ bytes
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Age</th>
-    </tr>
-    <tr>
-      <th>Name</th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>Liu</th>
-      <td>1681</td>
-    </tr>
-    <tr>
-      <th>Rowland</th>
-      <td>3136</td>
-    </tr>
-    <tr>
-      <th>Rivers</th>
-      <td>3136</td>
-    </tr>
-    <tr>
-      <th>Waters</th>
-      <td>3249</td>
-    </tr>
-    <tr>
-      <th>Rice</th>
-      <td>1521</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Age</th>
+</tr>
+<tr>
+<th>Name</th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>Liu</th>
+<td>1681</td>
+</tr>
+<tr>
+<th>Rowland</th>
+<td>3136</td>
+</tr>
+<tr>
+<th>Rivers</th>
+<td>3136</td>
+</tr>
+<tr>
+<th>Waters</th>
+<td>3249</td>
+</tr>
+<tr>
+<th>Rice</th>
+<td>1521</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div></div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</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="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[28]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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_sample</span> <span class="o">&gt;</span> <span class="mi">40</span>
+<div class="cm-editor 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="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[28]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[24]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -16434,101 +8921,87 @@ memory usage: 160.0+ bytes
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Age</th>
-    </tr>
-    <tr>
-      <th>Name</th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>Liu</th>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>Rowland</th>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>Rivers</th>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>Waters</th>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>Rice</th>
-      <td>False</td>
-    </tr>
-    <tr>
-      <th>Fields</th>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>Kerr</th>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>Romero</th>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>Davis</th>
-      <td>False</td>
-    </tr>
-    <tr>
-      <th>Hall</th>
-      <td>True</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Age</th>
+</tr>
+<tr>
+<th>Name</th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>Liu</th>
+<td>True</td>
+</tr>
+<tr>
+<th>Rowland</th>
+<td>True</td>
+</tr>
+<tr>
+<th>Rivers</th>
+<td>True</td>
+</tr>
+<tr>
+<th>Waters</th>
+<td>True</td>
+</tr>
+<tr>
+<th>Rice</th>
+<td>False</td>
+</tr>
+<tr>
+<th>Fields</th>
+<td>True</td>
+</tr>
+<tr>
+<th>Kerr</th>
+<td>True</td>
+</tr>
+<tr>
+<th>Romero</th>
+<td>True</td>
+</tr>
+<tr>
+<th>Davis</th>
+<td>False</td>
+</tr>
+<tr>
+<th>Hall</th>
+<td>True</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[29]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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_sample</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">mysquare</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">()</span> <span class="o">==</span> <span class="n">df_sample</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</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="o">.</span><span class="n">head</span><span class="p">()</span>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_sample</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">mysquare</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">()</span> <span class="o">==</span> <span class="n">df_sample</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</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="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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[29]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[25]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -16542,131 +9015,114 @@ memory usage: 160.0+ bytes
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Age</th>
-    </tr>
-    <tr>
-      <th>Name</th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>Liu</th>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>Rowland</th>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>Rivers</th>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>Waters</th>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>Rice</th>
-      <td>True</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Age</th>
+</tr>
+<tr>
+<th>Name</th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>Liu</th>
+<td>True</td>
+</tr>
+<tr>
+<th>Rowland</th>
+<td>True</td>
+</tr>
+<tr>
+<th>Rivers</th>
+<td>True</td>
+</tr>
+<tr>
+<th>Waters</th>
+<td>True</td>
+</tr>
+<tr>
+<th>Rice</th>
+<td>True</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div></div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><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 class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</em></span></p>
+</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">¶</a></h2><p><a name="task1"></a>
+<span class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</span></p>
 <ul>
 <li>Create data frame with<ul>
-<li>6 names of dinosaurs, </li>
-<li>their favourite prime number, </li>
+<li>6 names of dinosaurs,</li>
+<li>their favourite prime number,</li>
 <li>and their favorite color.</li>
 </ul>
 </li>
 <li>Play around with the frame</li>
 <li>Tell me when you're done with status icon in BigBlueButton: 👍</li>
 </ul>
-
 </div>
 </div>
 </div>
-</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[30]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>
-    <span class="s2">&quot;Favourite Color&quot;</span><span class="p">:</span> <span class="p">[]</span>
+<div class="cm-editor 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">"Dinosaur Name"</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">"Favourite Prime"</span><span class="p">:</span> <span class="p">[],</span>
+    <span class="s2">"Favourite Color"</span><span class="p">:</span> <span class="p">[]</span>
 <span class="p">}</span>
 <span class="c1">#df_dinos = </span>
 </pre></div>
-
-     </div>
 </div>
 </div>
 </div>
-
-</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div>
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[31]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>
-    <span class="s2">&quot;Favourite Color&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;white&quot;</span><span class="p">,</span> <span class="s2">&quot;blue&quot;</span><span class="p">,</span> <span class="s2">&quot;purple&quot;</span><span class="p">,</span> <span class="s2">&quot;violet&quot;</span><span class="p">,</span> <span class="s2">&quot;gray&quot;</span><span class="p">]</span>
+<div class="cm-editor 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">"Dinosaur Name"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Aegyptosaurus"</span><span class="p">,</span> <span class="s2">"Tyrannosaurus"</span><span class="p">,</span> <span class="s2">"Panoplosaurus"</span><span class="p">,</span> <span class="s2">"Isisaurus"</span><span class="p">,</span> <span class="s2">"Triceratops"</span><span class="p">,</span> <span class="s2">"Velociraptor"</span><span class="p">],</span>
+    <span class="s2">"Favourite Prime"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"4"</span><span class="p">,</span> <span class="s2">"8"</span><span class="p">,</span> <span class="s2">"15"</span><span class="p">,</span> <span class="s2">"16"</span><span class="p">,</span> <span class="s2">"23"</span><span class="p">,</span> <span class="s2">"42"</span><span class="p">],</span>
+    <span class="s2">"Favourite Color"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"blue"</span><span class="p">,</span> <span class="s2">"white"</span><span class="p">,</span> <span class="s2">"blue"</span><span class="p">,</span> <span class="s2">"purple"</span><span class="p">,</span> <span class="s2">"violet"</span><span class="p">,</span> <span class="s2">"gray"</span><span class="p">]</span>
 <span class="p">}</span>
-<span class="n">df_dinos</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">happy_dinos</span><span class="p">)</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;Dinosaur Name&quot;</span><span class="p">)</span>
+<span class="n">df_dinos</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">happy_dinos</span><span class="p">)</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Dinosaur Name"</span><span class="p">)</span>
 <span class="n">df_dinos</span><span class="o">.</span><span class="n">T</span>
 </pre></div>
-
-     </div>
 </div>
 </div>
 </div>
-
+</div>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[31]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[27]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -16680,98 +9136,84 @@ memory usage: 160.0+ bytes
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th>Dinosaur Name</th>
-      <th>Aegyptosaurus</th>
-      <th>Tyrannosaurus</th>
-      <th>Panoplosaurus</th>
-      <th>Isisaurus</th>
-      <th>Triceratops</th>
-      <th>Velociraptor</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>Favourite Prime</th>
-      <td>4</td>
-      <td>8</td>
-      <td>15</td>
-      <td>16</td>
-      <td>23</td>
-      <td>42</td>
-    </tr>
-    <tr>
-      <th>Favourite Color</th>
-      <td>blue</td>
-      <td>white</td>
-      <td>blue</td>
-      <td>purple</td>
-      <td>violet</td>
-      <td>gray</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th>Dinosaur Name</th>
+<th>Aegyptosaurus</th>
+<th>Tyrannosaurus</th>
+<th>Panoplosaurus</th>
+<th>Isisaurus</th>
+<th>Triceratops</th>
+<th>Velociraptor</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>Favourite Prime</th>
+<td>4</td>
+<td>8</td>
+<td>15</td>
+<td>16</td>
+<td>23</td>
+<td>42</td>
+</tr>
+<tr>
+<th>Favourite Color</th>
+<td>blue</td>
+<td>white</td>
+<td>blue</td>
+<td>purple</td>
+<td>violet</td>
+<td>gray</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div></div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h3 id="More-DataFrame-examples">More <code>DataFrame</code> examples<a class="anchor-link" href="#More-DataFrame-examples">&#182;</a></h3>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="More-DataFrame-examples">More <code>DataFrame</code> examples<a class="anchor-link" href="#More-DataFrame-examples">¶</a></h3>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[32]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="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>
-    <span class="s2">&quot;C&quot;</span><span class="p">:</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">i</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">e</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="p">(</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)],</span>
-    <span class="s2">&quot;D&quot;</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">&quot;This&quot;</span><span class="p">,</span> <span class="s2">&quot;column&quot;</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="s2">&quot;entries&quot;</span><span class="p">]),</span>
-    <span class="s2">&quot;E&quot;</span><span class="p">:</span> <span class="s2">&quot;Same&quot;</span>
+<div class="cm-editor 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">"A"</span><span class="p">:</span> <span class="mf">1.2</span><span class="p">,</span>
+    <span class="s2">"B"</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">'20180226'</span><span class="p">),</span>
+    <span class="s2">"C"</span><span class="p">:</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">i</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">e</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="p">(</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)],</span>
+    <span class="s2">"D"</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">"This"</span><span class="p">,</span> <span class="s2">"column"</span><span class="p">,</span> <span class="s2">"has"</span><span class="p">,</span> <span class="s2">"entries"</span><span class="p">,</span> <span class="s2">"entries"</span><span class="p">]),</span>
+    <span class="s2">"E"</span><span class="p">:</span> <span class="s2">"Same"</span>
 <span class="p">})</span>
 <span class="n">df_demo</span>
 </pre></div>
-
-     </div>
 </div>
 </div>
 </div>
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+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[28]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -16785,101 +9227,87 @@ memory usage: 160.0+ bytes
     }
 </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>0</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-2.718282</td>
-      <td>This</td>
-      <td>Same</td>
-    </tr>
-    <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>2</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-1.304068</td>
-      <td>has</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>
-    <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>
+<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>0</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-2.718282</td>
+<td>This</td>
+<td>Same</td>
+</tr>
+<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>2</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-1.304068</td>
+<td>has</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>
+<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>
-
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-
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-
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-
-</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
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+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[33]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [29]:</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>
+<div class="cm-editor 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">"C"</span><span class="p">)</span>
 </pre></div>
-
-     </div>
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+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[29]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -16893,101 +9321,87 @@ memory usage: 160.0+ bytes
     }
 </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>0</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-2.718282</td>
-      <td>This</td>
-      <td>Same</td>
-    </tr>
-    <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>
-    <tr>
-      <th>3</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>0.986231</td>
-      <td>entries</td>
-      <td>Same</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>1.718282</td>
-      <td>column</td>
-      <td>Same</td>
-    </tr>
-  </tbody>
+<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>0</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-2.718282</td>
+<td>This</td>
+<td>Same</td>
+</tr>
+<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>
+<tr>
+<th>3</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>0.986231</td>
+<td>entries</td>
+<td>Same</td>
+</tr>
+<tr>
+<th>1</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>1.718282</td>
+<td>column</td>
+<td>Same</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
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-
 </div>
-
 </div>
-
-</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[34]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="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>
+<div class="cm-editor 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>
-
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+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[30]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -17001,172 +9415,114 @@ memory usage: 160.0+ bytes
     }
 </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>3</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>0.99</td>
-      <td>entries</td>
-      <td>Same</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-0.72</td>
-      <td>entries</td>
-      <td>Same</td>
-    </tr>
-  </tbody>
+<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>3</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>0.99</td>
+<td>entries</td>
+<td>Same</td>
+</tr>
+<tr>
+<th>4</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-0.72</td>
+<td>entries</td>
+<td>Same</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
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-
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-
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-
-</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
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+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[35]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [31]:</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>
+<div class="cm-editor 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="s2">"A"</span><span class="p">,</span> <span class="s2">"C"</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">
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-
-
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
-<pre>/var/folders/f5/swf8tg5j5r7bqbwftt3zn6b00000gn/T/ipykernel_95838/1325867503.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with &#39;numeric_only=None&#39;) is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
-  df_demo.round(2).sum()
-</pre>
-</div>
-</div>
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[35]:</div>
-
-
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-
-<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 class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[31]:</div>
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+<pre>A    6.00
+C   -2.03
+dtype: float64</pre>
 </div>
-
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-
 </div>
-
 </div>
-
-</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[36]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="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>
+<div class="cm-editor 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="jp-Cell-outputWrapper">
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-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
 <pre>\begin{tabular}{lrlrll}
 \toprule
-{} &amp;    A &amp;          B &amp;     C &amp;        D &amp;     E \\
+ &amp; A &amp; B &amp; C &amp; D &amp; E \\
 \midrule
-0 &amp;  1.2 &amp; 2018-02-26 &amp; -2.72 &amp;     This &amp;  Same \\
-1 &amp;  1.2 &amp; 2018-02-26 &amp;  1.72 &amp;   column &amp;  Same \\
-2 &amp;  1.2 &amp; 2018-02-26 &amp; -1.30 &amp;      has &amp;  Same \\
-3 &amp;  1.2 &amp; 2018-02-26 &amp;  0.99 &amp;  entries &amp;  Same \\
-4 &amp;  1.2 &amp; 2018-02-26 &amp; -0.72 &amp;  entries &amp;  Same \\
+0 &amp; 1.200000 &amp; 2018-02-26 00:00:00 &amp; -2.720000 &amp; This &amp; Same \\
+1 &amp; 1.200000 &amp; 2018-02-26 00:00:00 &amp; 1.720000 &amp; column &amp; Same \\
+2 &amp; 1.200000 &amp; 2018-02-26 00:00:00 &amp; -1.300000 &amp; has &amp; Same \\
+3 &amp; 1.200000 &amp; 2018-02-26 00:00:00 &amp; 0.990000 &amp; entries &amp; Same \\
+4 &amp; 1.200000 &amp; 2018-02-26 00:00:00 &amp; -0.720000 &amp; entries &amp; Same \\
 \bottomrule
 \end{tabular}
 
 </pre>
 </div>
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-
-
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
-<pre>/var/folders/f5/swf8tg5j5r7bqbwftt3zn6b00000gn/T/ipykernel_95838/3396683912.py:1: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.
-  print(df_demo.round(2).to_latex())
-</pre>
-</div>
-</div>
-
 </div>
-
 </div>
-
 </div></div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><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>
+</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">¶</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>
 <li><a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html#pandas.read_csv"><code>.read_csv()</code></a></li>
@@ -17174,49 +9530,38 @@ dtype: object</pre>
 <li><a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html#pandas.read_excel"><code>.read_excel()</code></a></li>
 </ul>
 <p>Example:</p>
-<div class="highlight"><pre><span></span><span class="p">{</span><span class="w"></span>
-<span class="w">    </span><span class="nt">&quot;Character&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[</span><span class="s2">&quot;Sawyer&quot;</span><span class="p">,</span><span class="w"> </span><span class="s2">&quot;…&quot;</span><span class="p">,</span><span class="w"> </span><span class="s2">&quot;Walt&quot;</span><span class="p">],</span><span class="w"></span>
-<span class="w">    </span><span class="nt">&quot;Actor&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[</span><span class="s2">&quot;Josh Holloway&quot;</span><span class="p">,</span><span class="w"> </span><span class="s2">&quot;…&quot;</span><span class="p">,</span><span class="w"> </span><span class="s2">&quot;Malcolm David Kelley&quot;</span><span class="p">],</span><span class="w"></span>
-<span class="w">    </span><span class="nt">&quot;Main Cast&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[</span><span class="kc">true</span><span class="p">,</span><span class="w">  </span><span class="s2">&quot;…&quot;</span><span class="p">,</span><span class="w"> </span><span class="kc">false</span><span class="p">]</span><span class="w"></span>
-<span class="p">}</span><span class="w"></span>
+<div class="highlight"><pre><span></span><span class="p">{</span>
+<span class="w">    </span><span class="nt">"Character"</span><span class="p">:</span><span class="w"> </span><span class="p">[</span><span class="s2">"Sawyer"</span><span class="p">,</span><span class="w"> </span><span class="s2">"…"</span><span class="p">,</span><span class="w"> </span><span class="s2">"Walt"</span><span class="p">],</span>
+<span class="w">    </span><span class="nt">"Actor"</span><span class="p">:</span><span class="w"> </span><span class="p">[</span><span class="s2">"Josh Holloway"</span><span class="p">,</span><span class="w"> </span><span class="s2">"…"</span><span class="p">,</span><span class="w"> </span><span class="s2">"Malcolm David Kelley"</span><span class="p">],</span>
+<span class="w">    </span><span class="nt">"Main Cast"</span><span class="p">:</span><span class="w"> </span><span class="p">[</span><span class="kc">true</span><span class="p">,</span><span class="w">  </span><span class="s2">"…"</span><span class="p">,</span><span class="w"> </span><span class="kc">false</span><span class="p">]</span>
+<span class="p">}</span>
 </pre></div>
-
 </div>
 </div>
 </div>
-</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[37]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>
+<div class="cm-editor 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">"data-lost.json"</span><span class="p">)</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Character"</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="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <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 class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[33]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -17230,109 +9575,95 @@ dtype: object</pre>
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Actor</th>
-      <th>Main Cast</th>
-    </tr>
-    <tr>
-      <th>Character</th>
-      <th></th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>Hurley</th>
-      <td>Jorge Garcia</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>Jack</th>
-      <td>Matthew Fox</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>Kate</th>
-      <td>Evangeline Lilly</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>Locke</th>
-      <td>Terry O'Quinn</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>Sawyer</th>
-      <td>Josh Holloway</td>
-      <td>True</td>
-    </tr>
-    <tr>
-      <th>Walt</th>
-      <td>Malcolm David Kelley</td>
-      <td>False</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Actor</th>
+<th>Main Cast</th>
+</tr>
+<tr>
+<th>Character</th>
+<th></th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>Hurley</th>
+<td>Jorge Garcia</td>
+<td>True</td>
+</tr>
+<tr>
+<th>Jack</th>
+<td>Matthew Fox</td>
+<td>True</td>
+</tr>
+<tr>
+<th>Kate</th>
+<td>Evangeline Lilly</td>
+<td>True</td>
+</tr>
+<tr>
+<th>Locke</th>
+<td>Terry O'Quinn</td>
+<td>True</td>
+</tr>
+<tr>
+<th>Sawyer</th>
+<td>Josh Holloway</td>
+<td>True</td>
+</tr>
+<tr>
+<th>Walt</th>
+<td>Malcolm David Kelley</td>
+<td>False</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div></div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><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 class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</em></span></p>
+</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">¶</a></h2><p><a name="task2"></a>
+<span class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</span></p>
 <ul>
-<li>Read in <code>data-nest.csv</code> to <code>DataFrame</code>; call it <code>df</code><br>
+<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 with status icon in BigBlueButton: 👍</li>
 </ul>
-
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[38]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="o">!</span>head data-nest.csv
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="o">!</span>head<span class="w"> </span>data-nest.csv
 </pre></div>
-
-     </div>
 </div>
 </div>
 </div>
-
+</div>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
 <div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-
-
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
 <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
@@ -17346,45 +9677,32 @@ dtype: object</pre>
 </pre>
 </div>
 </div>
-
 </div>
-
 </div>
-
-</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[39]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>
+<div class="cm-editor 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">"data-nest.csv"</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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <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 class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[35]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -17398,173 +9716,169 @@ dtype: object</pre>
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>id</th>
-      <th>Nodes</th>
-      <th>Tasks/Node</th>
-      <th>Threads/Task</th>
-      <th>Runtime Program / s</th>
-      <th>Scale</th>
-      <th>Plastic</th>
-      <th>Avg. Neuron Build Time / s</th>
-      <th>Min. Edge Build Time / s</th>
-      <th>Max. Edge Build Time / s</th>
-      <th>...</th>
-      <th>Max. Init. Time / s</th>
-      <th>Presim. Time / s</th>
-      <th>Sim. Time / s</th>
-      <th>Virt. Memory (Sum) / kB</th>
-      <th>Local Spike Counter (Sum)</th>
-      <th>Average Rate (Sum)</th>
-      <th>Number of Neurons</th>
-      <th>Number of Connections</th>
-      <th>Min. Delay</th>
-      <th>Max. Delay</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>5</td>
-      <td>1</td>
-      <td>2</td>
-      <td>4</td>
-      <td>420.42</td>
-      <td>10</td>
-      <td>True</td>
-      <td>0.29</td>
-      <td>88.12</td>
-      <td>88.18</td>
-      <td>...</td>
-      <td>1.20</td>
-      <td>17.26</td>
-      <td>311.52</td>
-      <td>46560664.0</td>
-      <td>825499</td>
-      <td>7.48</td>
-      <td>112500</td>
-      <td>1265738500</td>
-      <td>1.5</td>
-      <td>1.5</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>5</td>
-      <td>1</td>
-      <td>4</td>
-      <td>4</td>
-      <td>200.84</td>
-      <td>10</td>
-      <td>True</td>
-      <td>0.15</td>
-      <td>46.03</td>
-      <td>46.34</td>
-      <td>...</td>
-      <td>1.01</td>
-      <td>7.87</td>
-      <td>142.97</td>
-      <td>46903088.0</td>
-      <td>802865</td>
-      <td>7.03</td>
-      <td>112500</td>
-      <td>1265738500</td>
-      <td>1.5</td>
-      <td>1.5</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>5</td>
-      <td>1</td>
-      <td>2</td>
-      <td>8</td>
-      <td>202.15</td>
-      <td>10</td>
-      <td>True</td>
-      <td>0.28</td>
-      <td>47.98</td>
-      <td>48.48</td>
-      <td>...</td>
-      <td>1.20</td>
-      <td>7.95</td>
-      <td>142.81</td>
-      <td>47699384.0</td>
-      <td>802865</td>
-      <td>7.03</td>
-      <td>112500</td>
-      <td>1265738500</td>
-      <td>1.5</td>
-      <td>1.5</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>5</td>
-      <td>1</td>
-      <td>4</td>
-      <td>8</td>
-      <td>89.57</td>
-      <td>10</td>
-      <td>True</td>
-      <td>0.15</td>
-      <td>20.41</td>
-      <td>23.21</td>
-      <td>...</td>
-      <td>3.04</td>
-      <td>3.19</td>
-      <td>60.31</td>
-      <td>46813040.0</td>
-      <td>821491</td>
-      <td>7.23</td>
-      <td>112500</td>
-      <td>1265738500</td>
-      <td>1.5</td>
-      <td>1.5</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>5</td>
-      <td>2</td>
-      <td>2</td>
-      <td>4</td>
-      <td>164.16</td>
-      <td>10</td>
-      <td>True</td>
-      <td>0.20</td>
-      <td>40.03</td>
-      <td>41.09</td>
-      <td>...</td>
-      <td>1.58</td>
-      <td>6.08</td>
-      <td>114.88</td>
-      <td>46937216.0</td>
-      <td>802865</td>
-      <td>7.03</td>
-      <td>112500</td>
-      <td>1265738500</td>
-      <td>1.5</td>
-      <td>1.5</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>id</th>
+<th>Nodes</th>
+<th>Tasks/Node</th>
+<th>Threads/Task</th>
+<th>Runtime Program / s</th>
+<th>Scale</th>
+<th>Plastic</th>
+<th>Avg. Neuron Build Time / s</th>
+<th>Min. Edge Build Time / s</th>
+<th>Max. Edge Build Time / s</th>
+<th>...</th>
+<th>Max. Init. Time / s</th>
+<th>Presim. Time / s</th>
+<th>Sim. Time / s</th>
+<th>Virt. Memory (Sum) / kB</th>
+<th>Local Spike Counter (Sum)</th>
+<th>Average Rate (Sum)</th>
+<th>Number of Neurons</th>
+<th>Number of Connections</th>
+<th>Min. Delay</th>
+<th>Max. Delay</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>5</td>
+<td>1</td>
+<td>2</td>
+<td>4</td>
+<td>420.42</td>
+<td>10</td>
+<td>True</td>
+<td>0.29</td>
+<td>88.12</td>
+<td>88.18</td>
+<td>...</td>
+<td>1.20</td>
+<td>17.26</td>
+<td>311.52</td>
+<td>46560664.0</td>
+<td>825499</td>
+<td>7.48</td>
+<td>112500</td>
+<td>1265738500</td>
+<td>1.5</td>
+<td>1.5</td>
+</tr>
+<tr>
+<th>1</th>
+<td>5</td>
+<td>1</td>
+<td>4</td>
+<td>4</td>
+<td>200.84</td>
+<td>10</td>
+<td>True</td>
+<td>0.15</td>
+<td>46.03</td>
+<td>46.34</td>
+<td>...</td>
+<td>1.01</td>
+<td>7.87</td>
+<td>142.97</td>
+<td>46903088.0</td>
+<td>802865</td>
+<td>7.03</td>
+<td>112500</td>
+<td>1265738500</td>
+<td>1.5</td>
+<td>1.5</td>
+</tr>
+<tr>
+<th>2</th>
+<td>5</td>
+<td>1</td>
+<td>2</td>
+<td>8</td>
+<td>202.15</td>
+<td>10</td>
+<td>True</td>
+<td>0.28</td>
+<td>47.98</td>
+<td>48.48</td>
+<td>...</td>
+<td>1.20</td>
+<td>7.95</td>
+<td>142.81</td>
+<td>47699384.0</td>
+<td>802865</td>
+<td>7.03</td>
+<td>112500</td>
+<td>1265738500</td>
+<td>1.5</td>
+<td>1.5</td>
+</tr>
+<tr>
+<th>3</th>
+<td>5</td>
+<td>1</td>
+<td>4</td>
+<td>8</td>
+<td>89.57</td>
+<td>10</td>
+<td>True</td>
+<td>0.15</td>
+<td>20.41</td>
+<td>23.21</td>
+<td>...</td>
+<td>3.04</td>
+<td>3.19</td>
+<td>60.31</td>
+<td>46813040.0</td>
+<td>821491</td>
+<td>7.23</td>
+<td>112500</td>
+<td>1265738500</td>
+<td>1.5</td>
+<td>1.5</td>
+</tr>
+<tr>
+<th>4</th>
+<td>5</td>
+<td>2</td>
+<td>2</td>
+<td>4</td>
+<td>164.16</td>
+<td>10</td>
+<td>True</td>
+<td>0.20</td>
+<td>40.03</td>
+<td>41.09</td>
+<td>...</td>
+<td>1.58</td>
+<td>6.08</td>
+<td>114.88</td>
+<td>46937216.0</td>
+<td>802865</td>
+<td>7.03</td>
+<td>112500</td>
+<td>1265738500</td>
+<td>1.5</td>
+<td>1.5</td>
+</tr>
+</tbody>
 </table>
 <p>5 rows × 21 columns</p>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div></div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><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>
+</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">¶</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>
 <li><code>sep</code>: Set separator (for example <code>:</code> instead of <code>,</code>)</li>
@@ -17579,35 +9893,33 @@ 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">=&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>
+<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="o">*</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="n">_NoDefault</span><span class="o">.</span><span class="n">no_default</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">'infer'</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="n">_NoDefault</span><span class="o">.</span><span class="n">no_default</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">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">None</span><span class="p">,</span> <span class="n">infer_datetime_format</span><span class="o">=</span><span class="n">_NoDefault</span><span class="o">.</span><span class="n">no_default</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="n">_NoDefault</span><span class="o">.</span><span class="n">no_default</span><span class="p">,</span> <span class="n">date_format</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">'infer'</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">'.'</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">'"'</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">encoding_errors</span><span class="o">=</span><span class="s1">'strict'</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">on_bad_lines</span><span class="o">=</span><span class="s1">'error'</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> <span class="n">dtype_backend</span><span class="o">=</span><span class="n">_NoDefault</span><span class="o">.</span><span class="n">no_default</span><span class="p">)</span>
 </pre></div>
-
 </div>
 </div>
 </div>
 </div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><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>
+</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">¶</a></h2><ul>
 <li>Slicing: Select a sub-range / sub-set of entire data frame</li>
 <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>
-
 </div>
 </div>
 </div>
 </div><div class="fragment">
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h3 id="Quick-Slices">Quick Slices<a class="anchor-link" href="#Quick-Slices">&#182;</a></h3><ul>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="Quick-Slices">Quick Slices<a class="anchor-link" href="#Quick-Slices">¶</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>Use numerical value to select row</li>
@@ -17615,43 +9927,32 @@ dtype: object</pre>
 </li>
 <li>Example: Select only columnn <code>C</code> from <code>df_demo</code></li>
 </ul>
-
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[40]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+<div class="cm-editor 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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[40]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[36]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -17665,84 +9966,69 @@ dtype: object</pre>
     }
 </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>0</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-2.718282</td>
-      <td>This</td>
-      <td>Same</td>
-    </tr>
-    <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>2</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-1.304068</td>
-      <td>has</td>
-      <td>Same</td>
-    </tr>
-  </tbody>
+<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>0</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-2.718282</td>
+<td>This</td>
+<td>Same</td>
+</tr>
+<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>2</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-1.304068</td>
+<td>has</td>
+<td>Same</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[41]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="s1">&#39;C&#39;</span><span class="p">]</span>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="s1">'C'</span><span class="p">]</span>
 </pre></div>
-
-     </div>
 </div>
 </div>
 </div>
-
+</div>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[41]:</div>
-
-
-
-
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[37]:</div>
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
 <pre>0   -2.718282
 1    1.718282
 2   -1.304068
@@ -17750,59 +10036,43 @@ dtype: object</pre>
 4   -0.718282
 Name: C, dtype: float64</pre>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div></div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <ul>
 <li>Instead of column name in quotes and square brackets: Name of column <em>directly</em></li>
 </ul>
-
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[42]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="o">.</span><span class="n">C</span>
+<div class="cm-editor 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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[42]:</div>
-
-
-
-
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[38]:</div>
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
 <pre>0   -2.718282
 1    1.718282
 2   -1.304068
@@ -17810,77 +10080,61 @@ Name: C, dtype: float64</pre>
 4   -0.718282
 Name: C, dtype: float64</pre>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <ul>
-<li>I'm not a friend, because no spaces allowed<br>
+<li>I'm not a friend, because no spaces allowed<br/>
 (And <strong>Pandas as early as possible</strong> means labelling columns well and adding spaces)</li>
 </ul>
-
 </div>
 </div>
 </div>
 </div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <ul>
 <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><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[43]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>
+<div class="cm-editor 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">'A'</span><span class="p">,</span> <span class="s1">'C'</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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[43]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<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" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -17894,98 +10148,83 @@ Name: C, dtype: float64</pre>
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>C</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1.2</td>
-      <td>-2.718282</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>1.2</td>
-      <td>1.718282</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>1.2</td>
-      <td>-1.304068</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>1.2</td>
-      <td>0.986231</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>1.2</td>
-      <td>-0.718282</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>A</th>
+<th>C</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>1.2</td>
+<td>-2.718282</td>
+</tr>
+<tr>
+<th>1</th>
+<td>1.2</td>
+<td>1.718282</td>
+</tr>
+<tr>
+<th>2</th>
+<td>1.2</td>
+<td>-1.304068</td>
+</tr>
+<tr>
+<th>3</th>
+<td>1.2</td>
+<td>0.986231</td>
+</tr>
+<tr>
+<th>4</th>
+<td>1.2</td>
+<td>-0.718282</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div></div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <ul>
 <li>Use numerical values in brackets to slice <strong>along rows</strong></li>
 <li>Use ranges just like with Python lists</li>
 </ul>
-
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[44]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
+<div class="cm-editor 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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <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 class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[40]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -17999,77 +10238,63 @@ Name: C, dtype: float64</pre>
     }
 </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>2</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-1.304068</td>
-      <td>has</td>
-      <td>Same</td>
-    </tr>
-  </tbody>
+<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>2</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-1.304068</td>
+<td>has</td>
+<td>Same</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[45]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="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 class="cm-editor 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>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[45]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[41]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -18083,91 +10308,76 @@ Name: C, dtype: float64</pre>
     }
 </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>
+<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 jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</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>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[46]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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</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="cm-editor 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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <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-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[42]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -18181,77 +10391,63 @@ Name: C, dtype: float64</pre>
     }
 </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>2</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-1.304068</td>
-      <td>has</td>
-      <td>Same</td>
-    </tr>
-  </tbody>
+<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>2</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-1.304068</td>
+<td>has</td>
+<td>Same</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[47]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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</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>
+<div class="cm-editor 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">"C"</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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[47]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[43]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -18265,91 +10461,76 @@ Name: C, dtype: float64</pre>
     }
 </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>
+<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 jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><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>
+</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">¶</a></h2><h3 id="Better-Slicing">Better Slicing<a class="anchor-link" href="#Better-Slicing">¶</a></h3><ul>
 <li><code>.iloc[]</code> and <code>.loc[]</code>: Faster slicing interfaces with more options</li>
 </ul>
-
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[48]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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</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="cm-editor 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="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[48]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<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" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -18363,91 +10544,76 @@ Name: C, dtype: float64</pre>
     }
 </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>2</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-1.304068</td>
-      <td>has</td>
-      <td>Same</td>
-    </tr>
-  </tbody>
+<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>2</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-1.304068</td>
+<td>has</td>
+<td>Same</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div><div class="fragment">
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <ul>
 <li>Also slice along columns (second argument)</li>
 </ul>
-
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[49]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="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>
+<div class="cm-editor 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="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[49]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[45]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -18461,85 +10627,70 @@ Name: C, dtype: float64</pre>
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>C</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>1</th>
-      <td>1.2</td>
-      <td>1.718282</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>1.2</td>
-      <td>-1.304068</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>A</th>
+<th>C</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>1</th>
+<td>1.2</td>
+<td>1.718282</td>
+</tr>
+<tr>
+<th>2</th>
+<td>1.2</td>
+<td>-1.304068</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div></div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <ul>
 <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><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[50]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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_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>
+<div class="cm-editor 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">"D"</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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[50]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[46]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -18553,102 +10704,88 @@ Name: C, dtype: float64</pre>
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-      <th>E</th>
-    </tr>
-    <tr>
-      <th>D</th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>This</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-2.718282</td>
-      <td>Same</td>
-    </tr>
-    <tr>
-      <th>column</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>1.718282</td>
-      <td>Same</td>
-    </tr>
-    <tr>
-      <th>has</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-1.304068</td>
-      <td>Same</td>
-    </tr>
-    <tr>
-      <th>entries</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>0.986231</td>
-      <td>Same</td>
-    </tr>
-    <tr>
-      <th>entries</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-0.718282</td>
-      <td>Same</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>A</th>
+<th>B</th>
+<th>C</th>
+<th>E</th>
+</tr>
+<tr>
+<th>D</th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>This</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-2.718282</td>
+<td>Same</td>
+</tr>
+<tr>
+<th>column</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>1.718282</td>
+<td>Same</td>
+</tr>
+<tr>
+<th>has</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-1.304068</td>
+<td>Same</td>
+</tr>
+<tr>
+<th>entries</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>0.986231</td>
+<td>Same</td>
+</tr>
+<tr>
+<th>entries</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-0.718282</td>
+<td>Same</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[51]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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_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>
+<div class="cm-editor 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">"entries"</span><span class="p">]</span>
 </pre></div>
-
-     </div>
 </div>
 </div>
 </div>
-
+</div>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
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 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[51]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[47]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -18662,81 +10799,67 @@ Name: C, dtype: float64</pre>
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>A</th>
-      <th>B</th>
-      <th>C</th>
-      <th>E</th>
-    </tr>
-    <tr>
-      <th>D</th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>entries</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>0.986231</td>
-      <td>Same</td>
-    </tr>
-    <tr>
-      <th>entries</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-0.718282</td>
-      <td>Same</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>A</th>
+<th>B</th>
+<th>C</th>
+<th>E</th>
+</tr>
+<tr>
+<th>D</th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>entries</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>0.986231</td>
+<td>Same</td>
+</tr>
+<tr>
+<th>entries</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-0.718282</td>
+<td>Same</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[52]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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_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>
+<div class="cm-editor 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">"has"</span><span class="p">,</span> <span class="s2">"entries"</span><span class="p">],</span> <span class="p">[</span><span class="s2">"A"</span><span class="p">,</span> <span class="s2">"C"</span><span class="p">]]</span>
 </pre></div>
-
-     </div>
 </div>
 </div>
 </div>
-
+</div>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[52]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[48]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -18750,103 +10873,88 @@ Name: C, dtype: float64</pre>
     }
 </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>
+<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="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><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 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">¶</a></h2><h3 id="Advanced-Slicing:-Logical-Slicing">Advanced Slicing: Logical Slicing<a class="anchor-link" href="#Advanced-Slicing:-Logical-Slicing">¶</a></h3>
 </div>
 </div>
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</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>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[53]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="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>
+<div class="cm-editor 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">"C"</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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
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 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[53]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[49]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -18860,76 +10968,61 @@ Name: C, dtype: float64</pre>
     }
 </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>
+<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 class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[54]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="s2">"C"</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span>
 </pre></div>
-
-     </div>
 </div>
 </div>
 </div>
-
+</div>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[54]:</div>
-
-
-
-
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[50]:</div>
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
 <pre>0    False
 1     True
 2    False
@@ -18937,46 +11030,32 @@ Name: C, dtype: float64</pre>
 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><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[55]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="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>
+<div class="cm-editor 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">"C"</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">"D"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"entries"</span><span class="p">)]</span>
 </pre></div>
-
-     </div>
 </div>
 </div>
 </div>
-
+</div>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[55]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[51]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -18990,46 +11069,41 @@ Name: C, dtype: bool</pre>
     }
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 <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>4</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-0.718282</td>
-      <td>entries</td>
-      <td>Same</td>
-    </tr>
-  </tbody>
+<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>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></div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><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>
+</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">¶</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>
 <li>Combine data frames<ul>
 <li><em>Concat</em>: Combine several data frames along an axis</li>
 <li><em>Merge</em>: Combine data frames on basis of common columns; database-style</li>
@@ -19038,43 +11112,32 @@ Name: C, dtype: bool</pre>
 </ul>
 </li>
 </ul>
-
 </div>
 </div>
 </div>
-</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[56]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+<div class="cm-editor 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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[56]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[52]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -19088,86 +11151,72 @@ Name: C, dtype: bool</pre>
     }
 </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>0</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-2.718282</td>
-      <td>This</td>
-      <td>Same</td>
-    </tr>
-    <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>2</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-1.304068</td>
-      <td>has</td>
-      <td>Same</td>
-    </tr>
-  </tbody>
+<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>0</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-2.718282</td>
+<td>This</td>
+<td>Same</td>
+</tr>
+<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>2</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-1.304068</td>
+<td>has</td>
+<td>Same</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[57]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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_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>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="s2">"F"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_demo</span><span class="p">[</span><span class="s2">"C"</span><span class="p">]</span> <span class="o">-</span> <span class="n">df_demo</span><span class="p">[</span><span class="s2">"A"</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>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[57]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[53]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -19181,205 +11230,90 @@ Name: C, dtype: bool</pre>
     }
 </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>
-      <th>F</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-2.718282</td>
-      <td>This</td>
-      <td>Same</td>
-      <td>-3.918282</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>1.718282</td>
-      <td>column</td>
-      <td>Same</td>
-      <td>0.518282</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-1.304068</td>
-      <td>has</td>
-      <td>Same</td>
-      <td>-2.504068</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>A</th>
+<th>B</th>
+<th>C</th>
+<th>D</th>
+<th>E</th>
+<th>F</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-2.718282</td>
+<td>This</td>
+<td>Same</td>
+<td>-3.918282</td>
+</tr>
+<tr>
+<th>1</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>1.718282</td>
+<td>column</td>
+<td>Same</td>
+<td>0.518282</td>
+</tr>
+<tr>
+<th>2</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-1.304068</td>
+<td>has</td>
+<td>Same</td>
+<td>-2.504068</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div></div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <ul>
 <li><code>.insert()</code> allows to specify position of insertion</li>
 <li><code>.shape</code> gives tuple of size of data frame, <code>vertical, horizontal</code></li>
 </ul>
-
 </div>
 </div>
 </div>
-</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[58]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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_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>
+<div class="cm-editor 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">"E2"</span><span class="p">,</span> <span class="n">df_demo</span><span class="p">[</span><span class="s2">"C"</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</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 class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-
-
-<div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[58]:</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>
-      <th>E2</th>
-      <th>F</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-2.718282</td>
-      <td>This</td>
-      <td>Same</td>
-      <td>7.389056</td>
-      <td>-3.918282</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>1.718282</td>
-      <td>column</td>
-      <td>Same</td>
-      <td>2.952492</td>
-      <td>0.518282</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-1.304068</td>
-      <td>has</td>
-      <td>Same</td>
-      <td>1.700594</td>
-      <td>-2.504068</td>
-    </tr>
-  </tbody>
-</table>
-</div>
-</div>
-
-</div>
-
-</div>
-
-</div>
-
-</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
-<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_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 class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[59]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[54]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -19393,109 +11327,79 @@ Name: C, dtype: bool</pre>
     }
 </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>
-      <th>E2</th>
-      <th>F</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>
-      <td>1.700594</td>
-      <td>-2.504068</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>
-      <td>0.972652</td>
-      <td>-0.213769</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>
-      <td>0.515929</td>
-      <td>-1.918282</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>A</th>
+<th>B</th>
+<th>C</th>
+<th>D</th>
+<th>E</th>
+<th>E2</th>
+<th>F</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-2.718282</td>
+<td>This</td>
+<td>Same</td>
+<td>7.389056</td>
+<td>-3.918282</td>
+</tr>
+<tr>
+<th>1</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>1.718282</td>
+<td>column</td>
+<td>Same</td>
+<td>2.952492</td>
+<td>0.518282</td>
+</tr>
+<tr>
+<th>2</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-1.304068</td>
+<td>has</td>
+<td>Same</td>
+<td>1.700594</td>
+<td>-2.504068</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[60]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>
+<div class="cm-editor 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 class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
-<div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-
-
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
-<pre>/var/folders/f5/swf8tg5j5r7bqbwftt3zn6b00000gn/T/ipykernel_95838/363555535.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
-  df_demo.append(
-</pre>
 </div>
 </div>
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[60]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[55]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -19509,157 +11413,93 @@ Name: C, dtype: bool</pre>
     }
 </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>
-      <th>E2</th>
-      <th>F</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-2.718282</td>
-      <td>This</td>
-      <td>Same</td>
-      <td>7.389056</td>
-      <td>-3.918282</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>1.718282</td>
-      <td>column</td>
-      <td>Same</td>
-      <td>2.952492</td>
-      <td>0.518282</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>1.2</td>
-      <td>2018-02-26</td>
-      <td>-1.304068</td>
-      <td>has</td>
-      <td>Same</td>
-      <td>1.700594</td>
-      <td>-2.504068</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>
-      <td>0.972652</td>
-      <td>-0.213769</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>
-      <td>0.515929</td>
-      <td>-1.918282</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>1.3</td>
-      <td>2018-02-27</td>
-      <td>-0.777000</td>
-      <td>has it?</td>
-      <td>Same</td>
-      <td>NaN</td>
-      <td>23.000000</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>A</th>
+<th>B</th>
+<th>C</th>
+<th>D</th>
+<th>E</th>
+<th>E2</th>
+<th>F</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>
+<td>1.700594</td>
+<td>-2.504068</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>
+<td>0.972652</td>
+<td>-0.213769</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>
+<td>0.515929</td>
+<td>-1.918282</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
-</div>
-
-</div>
-
-</div>
-
-</div><div class="fragment">
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p><code>.append()</code> seems to be deprecated; one needs to use something like the following in thef future:</p>
-<div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span>
-    <span class="p">[</span>
-        <span class="n">df_demo</span><span class="p">,</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">1.3</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">index</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">])</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></section><section>
+</div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><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>
+</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">¶</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><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[61]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>
+<div class="cm-editor 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">"Key"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"First"</span><span class="p">,</span> <span class="s2">"Second"</span><span class="p">],</span> <span class="s2">"Value"</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="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[61]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[56]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -19673,69 +11513,55 @@ Name: C, dtype: bool</pre>
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Key</th>
-      <th>Value</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>First</td>
-      <td>1</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>Second</td>
-      <td>1</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Key</th>
+<th>Value</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>First</td>
+<td>1</td>
+</tr>
+<tr>
+<th>1</th>
+<td>Second</td>
+<td>1</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[62]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>
+<div class="cm-editor 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">"Key"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"First"</span><span class="p">,</span> <span class="s2">"Second"</span><span class="p">],</span> <span class="s2">"Value"</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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[62]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[57]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -19749,82 +11575,67 @@ Name: C, dtype: bool</pre>
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Key</th>
-      <th>Value</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>First</td>
-      <td>2</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>Second</td>
-      <td>2</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Key</th>
+<th>Value</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>First</td>
+<td>2</td>
+</tr>
+<tr>
+<th>1</th>
+<td>Second</td>
+<td>2</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div></div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</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><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[63]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>
+<div class="cm-editor 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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[63]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[58]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -19838,92 +11649,77 @@ Name: C, dtype: bool</pre>
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Key</th>
-      <th>Value</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>First</td>
-      <td>1</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>Second</td>
-      <td>1</td>
-    </tr>
-    <tr>
-      <th>0</th>
-      <td>First</td>
-      <td>2</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>Second</td>
-      <td>2</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Key</th>
+<th>Value</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>First</td>
+<td>1</td>
+</tr>
+<tr>
+<th>1</th>
+<td>Second</td>
+<td>1</td>
+</tr>
+<tr>
+<th>0</th>
+<td>First</td>
+<td>2</td>
+</tr>
+<tr>
+<th>1</th>
+<td>Second</td>
+<td>2</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div><div class="fragment">
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</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><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[64]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>
+<div class="cm-editor 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>
 </pre></div>
-
-     </div>
 </div>
 </div>
 </div>
-
+</div>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[64]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[59]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -19937,92 +11733,77 @@ Name: C, dtype: bool</pre>
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Key</th>
-      <th>Value</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>First</td>
-      <td>1</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>Second</td>
-      <td>1</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>First</td>
-      <td>2</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>Second</td>
-      <td>2</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Key</th>
+<th>Value</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>First</td>
+<td>1</td>
+</tr>
+<tr>
+<th>1</th>
+<td>Second</td>
+<td>1</td>
+</tr>
+<tr>
+<th>2</th>
+<td>First</td>
+<td>2</td>
+</tr>
+<tr>
+<th>3</th>
+<td>Second</td>
+<td>2</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div></div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <ul>
 <li>Concat, but horizontally</li>
 </ul>
-
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[65]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>
+<div class="cm-editor 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>
-
-     </div>
 </div>
 </div>
 </div>
-
+</div>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[65]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[60]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -20036,88 +11817,153 @@ Name: C, dtype: bool</pre>
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Key</th>
-      <th>Value</th>
-      <th>Key</th>
-      <th>Value</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>First</td>
-      <td>1</td>
-      <td>First</td>
-      <td>2</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>Second</td>
-      <td>1</td>
-      <td>Second</td>
-      <td>2</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Key</th>
+<th>Value</th>
+<th>Key</th>
+<th>Value</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>First</td>
+<td>1</td>
+<td>First</td>
+<td>2</td>
+</tr>
+<tr>
+<th>1</th>
+<td>Second</td>
+<td>1</td>
+<td>Second</td>
+<td>2</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div><div class="fragment">
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <ul>
 <li>Merge on common column</li>
 </ul>
-
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[66]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="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>
+<div class="cm-editor 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">"Key"</span><span class="p">)</span>
 </pre></div>
-
-     </div>
 </div>
 </div>
 </div>
-
+</div>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[61]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+<div>
+<style scoped="">
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
 
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[66]:</div>
-
-
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
 
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Key</th>
+<th>Value_x</th>
+<th>Value_y</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>First</td>
+<td>1</td>
+<td>2</td>
+</tr>
+<tr>
+<th>1</th>
+<td>Second</td>
+<td>1</td>
+<td>2</td>
+</tr>
+</tbody>
+</table>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div></div><div class="fragment">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><code>.concat()</code> can also be used to append rows to a DataFrame:</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [62]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor 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="p">[</span>
+        <span class="n">df_demo</span><span class="p">,</span> 
+        <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">"A"</span><span class="p">:</span> <span class="mf">1.3</span><span class="p">,</span> <span class="s2">"B"</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">"2018-02-27"</span><span class="p">),</span> <span class="s2">"C"</span><span class="p">:</span> <span class="o">-</span><span class="mf">0.777</span><span class="p">,</span> <span class="s2">"D"</span><span class="p">:</span> <span class="s2">"has it?"</span><span class="p">,</span> <span class="s2">"E"</span><span class="p">:</span> <span class="s2">"Same"</span><span class="p">,</span> <span class="s2">"F"</span><span class="p">:</span> <span class="mi">23</span><span class="p">},</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">])</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 class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[62]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -20131,89 +11977,126 @@ Name: C, dtype: bool</pre>
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Key</th>
-      <th>Value_x</th>
-      <th>Value_y</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>First</td>
-      <td>1</td>
-      <td>2</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>Second</td>
-      <td>1</td>
-      <td>2</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>A</th>
+<th>B</th>
+<th>C</th>
+<th>D</th>
+<th>E</th>
+<th>E2</th>
+<th>F</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-2.718282</td>
+<td>This</td>
+<td>Same</td>
+<td>7.389056</td>
+<td>-3.918282</td>
+</tr>
+<tr>
+<th>1</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>1.718282</td>
+<td>column</td>
+<td>Same</td>
+<td>2.952492</td>
+<td>0.518282</td>
+</tr>
+<tr>
+<th>2</th>
+<td>1.2</td>
+<td>2018-02-26</td>
+<td>-1.304068</td>
+<td>has</td>
+<td>Same</td>
+<td>1.700594</td>
+<td>-2.504068</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>
+<td>0.972652</td>
+<td>-0.213769</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>
+<td>0.515929</td>
+<td>-1.918282</td>
+</tr>
+<tr>
+<th>5</th>
+<td>1.3</td>
+<td>2018-02-27</td>
+<td>-0.777000</td>
+<td>has it?</td>
+<td>Same</td>
+<td>NaN</td>
+<td>23.000000</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
 </div></div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><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 class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</em></span></p>
+</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">¶</a></h2><p><a name="task3"></a>
+<span class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</span></p>
 <ul>
 <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>
 </div>
-</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[67]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">"Threads"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"Nodes"</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s2">"Tasks/Node"</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s2">"Threads/Task"</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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[67]:</div>
-
-
-
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+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[63]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
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-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -20227,221 +12110,202 @@ Name: C, dtype: bool</pre>
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>id</th>
-      <th>Nodes</th>
-      <th>Tasks/Node</th>
-      <th>Threads/Task</th>
-      <th>Runtime Program / s</th>
-      <th>Scale</th>
-      <th>Plastic</th>
-      <th>Avg. Neuron Build Time / s</th>
-      <th>Min. Edge Build Time / s</th>
-      <th>Max. Edge Build Time / s</th>
-      <th>...</th>
-      <th>Presim. Time / s</th>
-      <th>Sim. Time / s</th>
-      <th>Virt. Memory (Sum) / kB</th>
-      <th>Local Spike Counter (Sum)</th>
-      <th>Average Rate (Sum)</th>
-      <th>Number of Neurons</th>
-      <th>Number of Connections</th>
-      <th>Min. Delay</th>
-      <th>Max. Delay</th>
-      <th>Threads</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>5</td>
-      <td>1</td>
-      <td>2</td>
-      <td>4</td>
-      <td>420.42</td>
-      <td>10</td>
-      <td>True</td>
-      <td>0.29</td>
-      <td>88.12</td>
-      <td>88.18</td>
-      <td>...</td>
-      <td>17.26</td>
-      <td>311.52</td>
-      <td>46560664.0</td>
-      <td>825499</td>
-      <td>7.48</td>
-      <td>112500</td>
-      <td>1265738500</td>
-      <td>1.5</td>
-      <td>1.5</td>
-      <td>8</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>5</td>
-      <td>1</td>
-      <td>4</td>
-      <td>4</td>
-      <td>200.84</td>
-      <td>10</td>
-      <td>True</td>
-      <td>0.15</td>
-      <td>46.03</td>
-      <td>46.34</td>
-      <td>...</td>
-      <td>7.87</td>
-      <td>142.97</td>
-      <td>46903088.0</td>
-      <td>802865</td>
-      <td>7.03</td>
-      <td>112500</td>
-      <td>1265738500</td>
-      <td>1.5</td>
-      <td>1.5</td>
-      <td>16</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>5</td>
-      <td>1</td>
-      <td>2</td>
-      <td>8</td>
-      <td>202.15</td>
-      <td>10</td>
-      <td>True</td>
-      <td>0.28</td>
-      <td>47.98</td>
-      <td>48.48</td>
-      <td>...</td>
-      <td>7.95</td>
-      <td>142.81</td>
-      <td>47699384.0</td>
-      <td>802865</td>
-      <td>7.03</td>
-      <td>112500</td>
-      <td>1265738500</td>
-      <td>1.5</td>
-      <td>1.5</td>
-      <td>16</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>5</td>
-      <td>1</td>
-      <td>4</td>
-      <td>8</td>
-      <td>89.57</td>
-      <td>10</td>
-      <td>True</td>
-      <td>0.15</td>
-      <td>20.41</td>
-      <td>23.21</td>
-      <td>...</td>
-      <td>3.19</td>
-      <td>60.31</td>
-      <td>46813040.0</td>
-      <td>821491</td>
-      <td>7.23</td>
-      <td>112500</td>
-      <td>1265738500</td>
-      <td>1.5</td>
-      <td>1.5</td>
-      <td>32</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>5</td>
-      <td>2</td>
-      <td>2</td>
-      <td>4</td>
-      <td>164.16</td>
-      <td>10</td>
-      <td>True</td>
-      <td>0.20</td>
-      <td>40.03</td>
-      <td>41.09</td>
-      <td>...</td>
-      <td>6.08</td>
-      <td>114.88</td>
-      <td>46937216.0</td>
-      <td>802865</td>
-      <td>7.03</td>
-      <td>112500</td>
-      <td>1265738500</td>
-      <td>1.5</td>
-      <td>1.5</td>
-      <td>16</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>id</th>
+<th>Nodes</th>
+<th>Tasks/Node</th>
+<th>Threads/Task</th>
+<th>Runtime Program / s</th>
+<th>Scale</th>
+<th>Plastic</th>
+<th>Avg. Neuron Build Time / s</th>
+<th>Min. Edge Build Time / s</th>
+<th>Max. Edge Build Time / s</th>
+<th>...</th>
+<th>Presim. Time / s</th>
+<th>Sim. Time / s</th>
+<th>Virt. Memory (Sum) / kB</th>
+<th>Local Spike Counter (Sum)</th>
+<th>Average Rate (Sum)</th>
+<th>Number of Neurons</th>
+<th>Number of Connections</th>
+<th>Min. Delay</th>
+<th>Max. Delay</th>
+<th>Threads</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>5</td>
+<td>1</td>
+<td>2</td>
+<td>4</td>
+<td>420.42</td>
+<td>10</td>
+<td>True</td>
+<td>0.29</td>
+<td>88.12</td>
+<td>88.18</td>
+<td>...</td>
+<td>17.26</td>
+<td>311.52</td>
+<td>46560664.0</td>
+<td>825499</td>
+<td>7.48</td>
+<td>112500</td>
+<td>1265738500</td>
+<td>1.5</td>
+<td>1.5</td>
+<td>8</td>
+</tr>
+<tr>
+<th>1</th>
+<td>5</td>
+<td>1</td>
+<td>4</td>
+<td>4</td>
+<td>200.84</td>
+<td>10</td>
+<td>True</td>
+<td>0.15</td>
+<td>46.03</td>
+<td>46.34</td>
+<td>...</td>
+<td>7.87</td>
+<td>142.97</td>
+<td>46903088.0</td>
+<td>802865</td>
+<td>7.03</td>
+<td>112500</td>
+<td>1265738500</td>
+<td>1.5</td>
+<td>1.5</td>
+<td>16</td>
+</tr>
+<tr>
+<th>2</th>
+<td>5</td>
+<td>1</td>
+<td>2</td>
+<td>8</td>
+<td>202.15</td>
+<td>10</td>
+<td>True</td>
+<td>0.28</td>
+<td>47.98</td>
+<td>48.48</td>
+<td>...</td>
+<td>7.95</td>
+<td>142.81</td>
+<td>47699384.0</td>
+<td>802865</td>
+<td>7.03</td>
+<td>112500</td>
+<td>1265738500</td>
+<td>1.5</td>
+<td>1.5</td>
+<td>16</td>
+</tr>
+<tr>
+<th>3</th>
+<td>5</td>
+<td>1</td>
+<td>4</td>
+<td>8</td>
+<td>89.57</td>
+<td>10</td>
+<td>True</td>
+<td>0.15</td>
+<td>20.41</td>
+<td>23.21</td>
+<td>...</td>
+<td>3.19</td>
+<td>60.31</td>
+<td>46813040.0</td>
+<td>821491</td>
+<td>7.23</td>
+<td>112500</td>
+<td>1265738500</td>
+<td>1.5</td>
+<td>1.5</td>
+<td>32</td>
+</tr>
+<tr>
+<th>4</th>
+<td>5</td>
+<td>2</td>
+<td>2</td>
+<td>4</td>
+<td>164.16</td>
+<td>10</td>
+<td>True</td>
+<td>0.20</td>
+<td>40.03</td>
+<td>41.09</td>
+<td>...</td>
+<td>6.08</td>
+<td>114.88</td>
+<td>46937216.0</td>
+<td>802865</td>
+<td>7.03</td>
+<td>112500</td>
+<td>1265738500</td>
+<td>1.5</td>
+<td>1.5</td>
+<td>16</td>
+</tr>
+</tbody>
 </table>
 <p>5 rows × 22 columns</p>
 </div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[68]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">df</span><span class="o">.</span><span class="n">columns</span>
+<div class="cm-editor 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>
 </pre></div>
-
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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;Threads&#39;],
-      dtype=&#39;object&#39;)</pre>
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[64]:</div>
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+<pre>Index(['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', 'Threads'],
+      dtype='object')</pre>
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-
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 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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+<div class="jp-Cell-inputWrapper" tabindex="0">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<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>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="Aside:-Plotting-without-Pandas">Aside: Plotting without Pandas<a class="anchor-link" href="#Aside:-Plotting-without-Pandas">¶</a></h2><h3 id="Matplotlib-101">Matplotlib 101<a class="anchor-link" href="#Matplotlib-101">¶</a></h3><ul>
 <li>Matplotlib: de-facto standard for plotting in Python</li>
 <li>Main interface: <code>pyplot</code>; provides MATLAB-like interface</li>
 <li>Better: Use object-oriented API with <code>Figure</code> and <code>Axis</code></li>
@@ -20449,248 +12313,179 @@ Name: C, dtype: bool</pre>
 <li>Since v. 3: Only support for Python 3</li>
 <li>→ <a href="https://matplotlib.org/">https://matplotlib.org/</a></li>
 </ul>
-
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 </div>
 </div>
-</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[69]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<div class="cm-editor 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>
-
-     </div>
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-
-</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
-<div class="jp-Cell-inputWrapper">
+</div>
+</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[70]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>
+<div class="cm-editor 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 class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[71]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>
+<div class="cm-editor 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&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>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Use like this'</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">"Numbers"</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">"$\sqrt</span><span class="si">{x}</span><span class="s2">$"</span><span class="p">);</span>
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"/>
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+</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>
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 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[72]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>
+<div class="cm-editor 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>
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 </div>
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+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[73]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [69]:</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>
+<div class="cm-editor 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">"y"</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">"y2"</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">"This plot makes no sense"</span><span class="p">);</span>
 </pre></div>
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"/>
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+</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>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[74]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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_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>
+<div class="cm-editor 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_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">"C"</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"C"</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;Nope, no sense at all&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">"Nope, no sense at all"</span><span class="p">);</span>
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EUedQ1NVpejvCx3z0EeeyufmeMHcu1pQzpdvznM+8XqmbmvaJD7sVfXuhpk9fbDlT0tLDSEB2aPZUdZNfO/2m11OcqLDh2p5/YFaxiYEMljl0/4xrh4R9ISo1h40zT6a5j7hAa5nzLGsGi1kwkZCYxN9X6DrO46e3QKp49I4sn3tlFxpM7qcpQXGGP4xZJ1lFTW8PTVU7p9R3HzmXlzmOfs1jD3Fg1yP7Wx6DD5+yqZY8GdnF0hIjx40Vgqa+p56v1tVpejvOA/n+3i3c37uXfmGCZlJvZoG6kJX4f5tf/WMPcWDXI/tcixx9Uga2Ka1aW0a8zAeK44aRAvfLGbHaVVVpejPGids4JH3szj3LEDuOH0ob3aVnOYJx0Lc+2v52ka5H6opr6R19YWc6GPG2T1xM/PG0VkWCgPr8i3uhTlIYeO1nPbwlySYyP445yuj4t3JDXBNc/cFearNcw9TIPcD721cR+VNQ3M9bOLnG1JjovglrOH817efj4vKLO6HNVLxhjue2U9xRU1/PXqySRGh3ts264z81NJOjZmrmHuKRrkfuhYg6yhvm+Q1RPXf2so6YlR/G55Ho1NepOQnb345W5WbNjH3ReMZurgfh7f/sCESF66+VSS4yI0zD1Ig9zP7Ck/wufby5lrYYOs7ooMC+WXF44hb+9hluQ4rS5H9dDGokP8blke00cnc/MZw7y2n+YwT4mP5AfPrcKxS8O8tzTI/cySHKdfNMjqrosmpDJlUCJ/fGcrVbUNVpejuqmypp7bFuTSLyacJ+ZO8vpJxMCESBbeNI2U+Eiu/beGeW9pkPuRxibD4pxCzhyZTJrFDbK6S8TVs7y0spa/f7jd6nJUNxhjuH/pRpwHj/KXqybTL8Zz4+IdaR3mqzXMe0yD3I98WlDG3kM1zPXTueOdmTyoL5dMSuOfn+ygqOKo1eWoLlq4yskb64r5+XmjOHmo58fFO+IaZnGF+XUa5j2mQe5Hslc76RsdxrnjUqwupcfumTkGgD+8pdMR7SBv72F+88YmzhiZxE/OGm5JDQPiXWE+QM/Me0yD3E8cqK7jnc37uHRyhl81yOqu9MQobjpjGK+tLdae1H6uuraBWxfkEh8V5pNx8Y4MiI9k4c3TGKhh3iMa5H7i1TVF1Dca5p5kr4ucbfnJ9OEkx0Xwu2Xas9xfGWN48NWN7Cqr5qkrJ5EcF2F1SRrmvaBB7geMMWQ7nEzMSGDMQP9rkNVdMRF9uPv80eTuqWDZ+r1Wl6PasDinkKVrirhjxkhOG55kdTnHNA+zNIf5qp0a5l2hQe4HNhQd8usGWT1x2dQMxqXG8+ib+dTUN1pdjmph6/5KfvXaRk4b3p/bzxlpdTnHSWkO84RIrvuPhnlXaJD7gUWrna4GWZP8t0FWd4WGuLojFlUc5blPd1pdjnI7UtfArfNziY3ow5NXTiLUT286S4mP5KWbvg7zr3aUW12SX9Mgt9jRukZeX1vMrBNT2/wcRDs7bXgS540bwN9WFlBaWWt1OQr49WubKCit4skrJpMSF2l1OR1qDvPUhEh++PxqDfMOaJBb7K1Ne6msbbDt3PHO3HfhGGobmnji3S1WlxL0XsktZHFOIbedPYLTR/rPuHhHUuJdNw1pmHdMg9xi2asLGdQvmlN8fCOGrwxLjuUHpw5h0WoneXsPW11O0CooqeLBVzdy8tB+3DnD/8bFO5Lins2SmhDJdf/RMG+LBrmFdpdX88WOcuZmZdimQVZP3DljJPFRYcxbrtMRrVBT38htC3KJDAvlL1dOpk+o/V72KXGuME9LdIX5lxrm32C/n2gAWZJTSIgNG2R1V0J0GHfOGMlnBeV8kF9idTlB5zdvbCJ/XyVPzJ3IwAT/HhfvSHOYp/eN4oca5t+gQW6RxibDkpxCzhyVTGqCvRpk9cT3pw1mWHIMv1+RR31jk9XlBI3X1haxcJWTH581nOmj7dv6oVlKXCQLbjrlWJh/sV3DHDTILfPJtlJbN8jqrrDQEB6YNZYdpdXM/3K31eUEhZ1l1dz/ygamDu7LXeePsrocj0mJc10ATe8bxfXPa5iDBrllsh1O+sWEc+7YAVaX4jPnjEnhWyP68+T72zh0pN7qcgJaTX0jt87PJaxPCH+9ajJhNhwX70hyXISGeQuB9dO1iQPVdby7eT+XTk4nvE/w/AhEhAdnj+Pw0Xqeen+b1eUEtN8vz2Pz3sP8ac5E2/W276rmMM/oG8UPn18V1GEePCniR5Y2N8gKkmGVlsamxnPFSZn874td7CitsrqcgLRiw15e+HI3N50xlBkB/o4vOS6CBTdNI7NvND98fhWfbw/ODwDXIPcxYwyLHU4mZiYyemCc1eVY4mfnjSKiTwiPvKk9yz1td3k19y5Zz6TMxGO94QNdyzC//vnVQRnmGuQ+tr7Q1SBrblZgTznsSEpcJLecPYJ3N+8Pyhedt9Q2NHLbgjWIEJDj4h1Jjotg4c0twrwguH6vgucn7ScWOZxEhoVw8cTAaZDVEzecPpT0xCjmLcujsUlvEvKER1bks6HoEI/PmUhmv2iry/G5pFhXmA/qF831/w2uMNcg96GjdY28sbaYWScEXoOs7ooMC+XeC8ewee9hXs4ptLoc23tr4z6e/3wX1502hAvGD7S6HMskxbqGWZrD/LMgCXMNch96c6O7QdZJwXeRsy0XT0hl8qBEHn9nC1W1DVaXY1vOA0e4Z8k6TkxP4L5ZwTEu3pHmMB/cL4YbgiTMNch9KNvhZHD/wG2Q1V0iwkMXjaO0spZ/fLTd6nJsqa6hidsXrsEYeObqKbb+vFdPSoqNYP5NpzC4XwzXPx/4Ye6RIBeRmSKyRUQKROSXnthmoNldXs2XOw4wNysTkcBtkNVdUwb15dsT03j24x0UVRy1uhzbefztfNY6K3js8gkM6h984+IdaQ7zIf0DP8x7HeQiEgo8A1wIjAOuEpFxvd1uoFnscDfImhK8s1Xac++FruGAP7yl0xG74/28/fzzk51cM20ws05Mtbocv+QaZvk6zD/dFphh7okz8pOBAmPMDmNMHfAScIkHthswmhtknTUq2dbd57wlPTGKG88Yymtri1mz56DV5dhCccVR7lq8jnGp8Twwe6zV5fi1/i3C/Ib/BmaYeyLI0wFni38Xuh/7BhG5WUQcIuIoLS31wG7t4+Ntpew7XMMVepGzXT+ZPoLkuAjmLc/TnuWdqG90jYvXNzTxzPemEBmm4+KdaQ7zoUmuMP9kW2BlkM8udhpjnjXGZBljspKTk321W7+QvdpJ/5hwzhkT2LdL90ZsRB9+cf4ocnYfZNn6vVaX49f+9M5WcnYf5OHvnsjQpBiry7GN/rERzL/RFeY3/tcRUGHuiSAvAlqeama4H1NAeVUt7+UFX4Osnrh8aiZjU+N59M18auobrS7HL63cUsLfP9rOVSdncsmk4974qk60DvOPtwZGmHsiWVYDI0VkqIiEA1cCr3tguwHhWIMsHVbpVGiI8NDssRRVHOXfn+20uhy/s+9QDXdlr2PMwDh+ffF4q8uxrf7ueeZDk2K46X+BEea9DnJjTANwG/A2kAdkG2M29Xa7gcAYQ7bDyaTMREYNCM4GWd112ogkzh07gL+t3E5pZa3V5fiNhsYm7nhpDTX1jTx9tY6L91a/mPBjYX5jAIS5R97rG2NWGGNGGWOGG2N+74ltBoJ1hYfYur8qKNvV9sb9s8ZQU9/IE+9utboUv/HU+9tYtfMA875zAiNSYq0uJyA0h/mwAAhzHbT1okWrmxtk6Rzf7hiWHMs1pw5m0eo95O87bHU5lvt0WxlPryxgztQMvqv3IXhUc5gPT461dZhrkHvJ0bpG3lhXzKwTU4kL8gZZPXHnjJHERYYxb1lwT0csOVzDTxetYURyLL+5RMfFvaFfTDjzbzzlWJh/ZMMw1yD3khUb9lJV28AVOqzSI4nR4dw5YySfFpSxckuJ1eVYorHJcOdLa6mqbeCZ700hOryP1SUFrH4x4Sxwh/lNNgxzDXIvyXY4GdI/mpO1QVaPXXPqYIYlxTBveR71jU1Wl+Nzf/1gG1/sKOe3l5ygF8t9oG+rMP/QRicQGuResKusmq92HmCONsjqlbDQEO6fNZYdpdUs+GqP1eX41Ofby3jq/W1cOjmdOVN1XNxXmsN8RHIsN7+QY5sw1yD3gsU5Tm2Q5SEzxqZw2vD+/Pm9rRw6Um91OT5RWlnLnS+tZWhSDPO+c4KeDPhYX/eYuZ3CXIPcwxoam1iSU8j00SnaIMsDRIQHZ4/j0NF6/vLBNqvL8bqmJsPPs9dy+Gg9z1w9hZgIHRe3QnOYj0yxR5hrkHvYJ9vK2H+4VueOe9C4tHiuyMrkf1/sYmdZtdXleNX/+2g7n2wr49cXj2dsarzV5QS1b4T5/3L8+qK7BrmHLTrWICvF6lICys/PH0V4aAiPrMizuhSvWbXzAH96ZwsXT0zjqpP1RMAfJEa7w3xALD/y4zDXIPeg5gZZ352iDbI8LSUuklvOHsE7m/fz+fbA6yd9oLqOOxauYVC/aB6+VMfF/clxYZ7vf2GuaeNBS9cU0dBkdFjFS244fSjpiVHMW5ZHY1Pg3CTUPC5+oLqOp6+eojeQ+aHmMB81MJYfveB/Ya5B7iHGGBatdjJ5UCIjdc6vV0SGhXLPzNFs3nuYl3MLrS7HY579ZAcfbinloYvGckJ6gtXlqHYkRofz4g1fh/kH+futLukYDXIPWeusYFuJNsjytm9PTGPyoEQef3sL1bUNVpfTazm7D/D421uYdeJAvj9tsNXlqE60DPMfv5DrN2GuQe4h2Q4nUWGhXDRBG2R5U/N0xNLKWv7x0Xary+mVg9V13L5gDWmJkTx62QQdF7eJxOhw5t8w7ViYv59nfZhrkHvAkboG3li3Vxtk+cjUwX25eGIaz36yg+KKo1aX0yPGGO5eso7SqlqeuXoK8fp7YysJ0WHMv2EaowfG8ZMXrQ9zDXIPWLFhn6tBln4KkM/cO3M0TQb+8Fa+1aX0yHOf7uS9vBLuu3AsEzISrS5H9UBCdBgv3nAKowfG8eMXcywNcw1yD8h2OBmaFMNJQ/paXUrQyOgbzY2nD+XVtcWsdVZYXU63rHVW8Nhb+Zw/bgA//NYQq8tRvdAc5mNT4y0Ncw3yXtpZVs2qnQeYk5WhY5w+dsvZI0iKjWDess226Vl+6Gg9ty3IJSUukscvn6i/MwEgITqMF67/Oszf2+z7MNcg76XFDiehIcLl2iDL52Ij+vCL80fh2H2Q5Rv2Wl1Op4wx3LNkHfsO1fD01ZNJiNZx8UCREB3GC+4z85/M932Ya5D3wrEGWaOSSYnXBllWmJOVyZiBcTz6Zj419Y1Wl9Oh/36+i7c37efemWOYPEiH4QJNQtQ3w/xdH4a5BnkvfLytlJLKWubqRU7LhIYID100jsKDR/nPZ7usLqddGwoP8fCKfM4Zk8INpw+1uhzlJc1hPi41nlt8GOYa5L2waLWTpFhtkGW1b41I4tyxKTyzsoDSylqryznO4Zp6bl2QS//YcP40ZyIhITouHsgSosL4X4swf2fTPq/vU4O8h0ora3k/r4TvTskgLFQPo9XumzWWmvpG/vzeVqtL+QZjDPe9vIGiiqP89arJ9I0Jt7ok5QPHwjwtgVsX5Ho9zDWBeujVYw2y9CKnPxieHMv3pw3mpVV7yN932Opyjpn/1R6Wb9jLXeePImuIfn5rMEmICuN/15/skzDXIO8BYwyLHE6mDEpkRIo2yPIXPz13JHGRYfx+eZ5fTEfcVHyI3y7bzFmjkvnxmcOtLkdZwDVm7grzW+Z7L8w1yHtgjbOCAm2Q5XcSo8O5Y8ZIPtlWxodbSi2tpaq2gdsWrKFvdBhPzNVx8WAWH+kK8/HprjD/eKvnfzc1yHsge7W7QdbENKtLUa1cM22w60OLl2+mvrHJkhqMMTywdAO7y6v5y5WT6R8bYUkdyn80h/mlk9MZn+b5j/DTIO8mV4OsYmZPSCVWPxjX74T3CeH+WWPZXlrNgq/2WFLDotVOXltbzM/OHcUpw/pbUoPyP/GRYTw+Z6JX/rBrkHfT8vV7qa5r1AZZfuzcsSmcOqw/T763lUNH6n267/x9h/n165s4fUQSt5w9wqf7VsFLg7ybFjsKGZYUQ9ZgvTPPX4kID140loqj9fz1g20+2291bQO3zs8lLjKMP18xiVAdF1c+okHeDTtKq1i16wBzsjK12ZGfG5+WwNypmfz3i13sLKv2yT4fem0jO8qqeerKSSTH6bi48h0N8m5YnFNIaIhw2ZR0q0tRXXDX+aMICw3h0TfzvL6vJTmFvJJbxO3njORbI5K8vj+lWtIg76KGxiZezink7NHaIMsuUuIjuWX6cN7etJ8vtpd7bT/b9lfy0KsbmTasH3fOGOm1/SjVHg3yLvpoq7tBls4dt5UbzxhGWkIk85ZvprHJ8zcJHa1r5NYFuUSHh/LUlZN1XFxZQoO8i1wNsiI4Wxtk2UpkWCj3XjiGTcWHeSW30OPb/7/XN7GtpIo/XzGJAfpOTVmkV0EuInNEZJOINIlIlqeK8jellbV8kF/CZVPStUGWDX17YhqTMhN5/O0tVNc2eGy7r64pYpHDyS3Th3PmqGSPbVep7uptKm0Evgt87IFa/NbSNYU0NBnm6LCKLYkID100lpLKWv7x0XaPbHN7aRX3L93ASUP68rNzR3lkm0r1VK+C3BiTZ4zZ4qli/JExhkWrnUwd3JcRKbFWl6N6aOrgflw0IZVnP9lBccXRXm2rpr6RW+fnEtEnhL9cNZk++i5NWcxnv4EicrOIOETEUVpqbUOj7sjdU8H20mptVxsA7p05hiYDj7/du3OP3y7bTP6+Sp6YO4nUhCgPVadUz3Ua5CLynohsbOPrku7syBjzrDEmyxiTlZxsn/HE7NVOosNDmT1BG2TZXWa/aG44fShL1xSx1lnRo228sa6YBV/t4UdnDtML38pvdBrkxphzjTEntPH1mi8KtFJ1bQPL1hcz+0RtkBUobpk+nKTYcOYt29ztnuW7yqq575UNTBmUyC8uGO2lCpXqPh3c68DyDdogK9DERYZx1/mjcew+yIoNXW/yX9vQyG0LcwkNEf569RSdvaT8Sm+nH14qIoXAqcByEXnbM2X5h8UOJ8OSY5iqDbICytysTMYMjOPRt/KoqW/s0nMeXp7HxqLD/HHORNITdVxc+ZfezlpZaozJMMZEGGMGGGMu8FRhVtteWsXqXQeZqw2yAk5oiPDg7HE4Dxzl+c93dbr+mxv28t8vdnPD6UM5b9wA7xeoVDfp+8N2LHa4GmR9VxtkBaTTRyYxY0wKT39QQFlVbbvr7Sk/wj0vr2diRgL3zhzjwwqV6joN8jY0NDbxcm4hZ49OISVOb7sOVPfNGktNfSN/fndrm8vrGpq4fWEuAE9fPYXwPvpyUf5JfzPb8OGWUkora/UiZ4AbkRLL96cNZuGqPWzZV3nc8kffzGdd4SEev3wCmf2iLahQqa7RIG/DIoerQdb00faZ76565s4ZI4mN6MO85d+cjvjOpn38+7OdXHvqYGaekGphhUp1ToO8lZLKGleDrKnaICsY9I0J544ZI/lkWxkfbnXdcVx48Ai/WLyOE9LjuX/2WIsrVKpzmlStLM0torHJMGeqDqsEix+cOoShSTH8frlrOuLtC9fQZODpq6YQ0SfU6vKU6pQGeQvGGBY5nGRpg6ygEt4nhPsuHENBSRWX/u1z1uyp4NHLTmRIUozVpSnVJRrkLeTuOciO0mr9FKAgdN64AUwb1o+8vYf53imDuEh76ygb0QYiLSxa7SQmPJTZE/TiVrARER67bAJLcgq59ewRVpejVLdokLu5GmTt5eIJacRog6ygNLh/DHedr82wlP3o0Irb8vV7OVLXyNyTtO+4UspeNMjdsh1OhifHMGWQNshSStmLBjlQUFKFY7c2yFJK2ZMGObA4x+lukKXDKkop+wn6IK9vbOLlnCLOGZNCclyE1eUopVS3BX2Qr8wvoayqlit07rhSyqaCPsizHYUkx2mDLKWUfQV1kJccrmHllhIum5JBH22QpZSyqaBOr1fWuBtkZelFTqWUfQVtkBtjyF7t5KQhfRmerA2ylFL2FbRBnrP7IDvKqpmjFzmVUjYXtEF+rEHWidogSyllb0EZ5FW1DSzfsJeLJ2qDLKWU/QVlkC9fX8yRukYdVlFKBYSgDPJsRyEjUmKZMijR6lKUUqrXgi7IC0oqydl9kLlZGdogSykVEIIuyBc7CukTIlw6WeeOK6UCQ1AFeX1jEy/nFmqDLKVUQAmqIP8gv4SyqjquOEkvciqlAkdQBflih5OUuAjOGqUNspRSgSNogtzVIKuUy6ZqgyylVGAJmkR7OdfdIGuqXuRUSgWWoAhyYwyLHU5OHtKPYdogSykVYIIiyB3uBllz9SKnUioABUWQL1rtJDaiD7NOHGh1KUop5XEBH+RVtQ0sX7+XiyemEh2uDbKUUoGnV0EuIo+LSL6IrBeRpSKS6KG6PGbZumKO1muDLKVU4OrtGfm7wAnGmAnAVuC+3pfkWdkOJyNTYpmcmWh1KUop5RW9CnJjzDvGmAb3P78E/GpuX0FJJbl7KpiblakNspRSAcuTY+TXA2+2t1BEbhYRh4g4SktLPbjb9mU3N8iaku6T/SmllBU6vfonIu8BbU33eMAY85p7nQeABmB+e9sxxjwLPAuQlZVlelRtN9Q3NvFKbiEzxqaQFKsNspRSgavTIDfGnNvRchG5DrgImGGM8XpAd9X7edogSykVHHo1H09EZgL3AGcZY454piTPaG6QdeZIbZCllApsvR0jfxqIA94VkbUi8ncP1NRr+w/XsHJLCZdrgyylVBDo1Rm5MWaEpwrxpJdzC2ky6NxxpVRQCLjTVVeDrEJOHtqPoUkxVpejlFJeF3BBvnrXQXaWVXOFno0rpYJEwAV5c4OsC7VBllIqSARUkFfW1LNiw14unpimDbKUUkEjoIJ82fq9HK1vZG6WX3UKUEoprwqoIM92OBk1IJZJ2iBLKRVEAibIt+2vZI02yFJKBaGACfJFq52uBlmTtUGWUiq4BESQ1zU0sXRNEeeOHUB/bZCllAoyARHkH+Tvp7xaG2QppYJTQAR5tqOQAfERnDEyyepSlFLK52wf5PsO1fChNshSSgUx2yffsQZZU3VYRSkVnGwd5K4GWU5OGdqPIdogSykVpGwd5Kt2HmBX+RG9yKmUCmq2DvJFDidxEX248IRUq0tRSinL2DbIjzXImpRGVHio1eUopZRlbBvkb6zbS019E3O177hSKsjZNsizHU5GD4hjYkaC1aUopZSlbBnkW/dXstZZwZysDG2QpZQKerYM8kWrnYSFaoMspZQCGwa5NshSSqlvsl2Qv5+3nwPVdczVueNKKQXYMMizHU4Gxkdy5shkq0tRSim/YKsg33eoho+2lnL51AxCQ/Qip1JKgc2C/FiDLP1wZaWUOsZWQZ4cF8HcrAwG99cGWUop1ayP1QV0x9ysTL2TUymlWrHVGblSSqnjaZArpZTNaZArpZTNaZArpZTNaZArpZTNaZArpZTNaZArpZTNaZArpZTNiTHG9zsVKQV29/DpSUCZB8vxFK2re7Su7tG6usdf64Le1TbYGHNcx0BLgrw3RMRhjMmyuo7WtK7u0bq6R+vqHn+tC7xTmw6tKKWUzWmQK6WUzdkxyJ+1uoB2aF3do3V1j9bVPf5aF3ihNtuNkSullPomO56RK6WUakGDXCmlbM5vg1xEZorIFhEpEJFftrE8QkQWuZd/JSJD/KSu60SkVETWur9u9EFN/xaREhHZ2M5yEZG/uGteLyJTvF1TF+uaLiKHWhyrX/morkwRWSkim0Vkk4jc2cY6Pj9mXazL58dMRCJFZJWIrHPX9Zs21vH567GLdfn89dhi36EiskZElrWxzLPHyxjjd19AKLAdGAaEA+uAca3WuQX4u/v7K4FFflLXdcDTPj5eZwJTgI3tLJ8FvAkIMA34y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-<h2 id="Task-4">Task 4<a class="anchor-link" href="#Task-4">&#182;</a></h2><p><a name="task4"></a>
-<span class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</em></span></p>
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+<h2 id="Task-4">Task 4<a class="anchor-link" href="#Task-4">¶</a></h2><p><a name="task4"></a>
+<span class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</span></p>
 <ul>
 <li>Sort the Nest 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>
@@ -20698,85 +12493,62 @@ jp-needs-light-background
 <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>
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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><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[75]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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</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>  <span class="c1"># multi-level sort</span>
+<div class="cm-editor 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">"Threads"</span><span class="p">,</span> <span class="s2">"Nodes"</span><span class="p">,</span> <span class="s2">"Tasks/Node"</span><span class="p">,</span> <span class="s2">"Threads/Task"</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="c1"># multi-level sort</span>
 </pre></div>
-
-     </div>
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 </div>
 </div>
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[76]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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;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;Threads&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">loc</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">);</span>
+<div class="cm-editor 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">3</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">"Threads"</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s2">"Presim. Time / s"</span><span class="p">],</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">"dashed"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"red"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Presim. Time / s"</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">"Threads"</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s2">"Sim. Time / s"</span><span class="p">],</span> <span class="s2">"-b"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Sim. Time / s"</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">"Threads"</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">"Time / s"</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">'best'</span><span class="p">);</span>
 </pre></div>
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-     </div>
 </div>
 </div>
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+</div>
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-<h2 id="Plotting-with-Pandas">Plotting with Pandas<a class="anchor-link" href="#Plotting-with-Pandas">&#182;</a></h2><ul>
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+<h2 id="Plotting-with-Pandas">Plotting with Pandas<a class="anchor-link" href="#Plotting-with-Pandas">¶</a></h2><ul>
 <li>Each data frame has 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="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[77]:</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;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 class="highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="s2">"C"</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 class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[78]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [74]:</div>
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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="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>
+<div class="cm-editor 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">"C"</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>
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 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <ul>
-<li>I prefer slicing first:<br>
+<li>I prefer slicing first:<br/>
 → Allows for further operations on the sliced data frame</li>
 </ul>
-
 </div>
 </div>
 </div>
-</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[79]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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_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>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="s2">"C"</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">"bar"</span><span class="p">);</span>
 </pre></div>
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-     </div>
 </div>
 </div>
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+</div>
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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>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[80]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [76]:</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>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="s2">"C"</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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-<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>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="s2">"C"</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">"bar"</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">"This is a C plot"</span><span class="p">);</span>
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"/>
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 </div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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+<div class="jp-Cell-inputWrapper" tabindex="0">
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-</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 class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</em></span></p>
+</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">¶</a></h2><p><a name="task5"></a>
+<span class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</span></p>
 <p>Use the Nest data frame <code>df</code> to:</p>
 <ol>
 <li>Make threads index of the data frame (<code>.set_index()</code>)</li>
@@ -21111,554 +12778,350 @@ jp-needs-light-background
 <li>Add a legend, add missing axes labels</li>
 <li>Tell me when you're done with status icon in BigBlueButton: 👍</li>
 </ol>
-
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 </div>
 </div>
-</div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
-<div class="jp-Cell-inputWrapper">
+</div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[82]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>
+<div class="cm-editor 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">"Threads"</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>
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-     </div>
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 </div>
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-
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[83]:</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;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> <span class="n">style</span><span class="o">=</span><span class="s2">&quot;--&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>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">"Presim. Time / s"</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> <span class="n">style</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"red"</span><span class="p">);</span>
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b5gwQL95je/0eLFi7V582Z16NBBOTk5OnHihM9jLlmyRLNnz9Zjjz2mbdu2KSMjQzk5OTp06FBTy2vZjh+XbrhB6t3bXAcAAABgGZthGMY572yzaenSpRo/frwks1cpJSVFDz74oObMmSNJqqysVGJiovLz8zVp0iSvx8nMzNTFF1+s559/XpJUX1+v1NRUff/739cjjzxy1jrsdrtiYmJUWVmp6Ojoc3071jMMKSFBOnxYKiiQhg+3uiIAAACgRfFnNvDrbHilpaUqLy9Xdna287WYmBhlZmZq48aNXvepra3V1q1bPfYJCQlRdna2z31qampkt9s9llbBZuPmtAAAAECQ8GtYKi8vlyQlJiZ6vJ6YmOjc1tDhw4dVV1fXpH3y8vIUExPjXFJTU/1QfZBw3Jx2xw5r6wAAAADauBZ5n6W5c+eqsrLSuezdu9fqkvzH0bNEWAIAAAAs5dewlJSUJEk6ePCgx+sHDx50bmsoLi5OoaGhTdonIiJC0dHRHkurQVgCAAAAgoJfw1KvXr2UlJSkVatWOV+z2+3avHmzsrKyvO4THh6u4cOHe+xTX1+vVatW+dynVRs40Hw8cEA6csTaWgAAAIA2rMk3pa2qqlJxcbHzeWlpqQoLC9WlSxd1795ds2bN0s9+9jP16dNHvXr10qOPPqqUlBTnjHmSNGbMGE2YMEEzZ86UJM2ePVtTp07ViBEjNHLkSD377LOqrq7WnXfeef7vsKXp1Mm8bql9ezMsde1qdUUAAABAm9TksFRQUKDRo0c7n8+ePVuSNHXqVOXn5+uHP/yhqqurde+996qiokKXX365li9frsjISOc+JSUlOnz4sPP5xIkT9dVXX2nevHkqLy/X0KFDtXz58tMmfWgzPv3UnBkPAAAAgGXO6z5LwaLV3GcJAAAAwHkJ2vsswc9OnbK6AgAAAKDNIiwFo//8R+rfX0pIkFp+xx8AAADQIhGWglFCglRSIn3zjVRWZnU1AAAAQJtEWApG7dpJ6enmOvdbAgAAACxBWApW3JwWAAAAsBRhKVgRlgAAAABLEZaC1aBB5uPOndbWAQAAALRRhKVg5ehZ+vJL6eRJa2sBAAAA2qAwqwuAD927S1lZUlqadPSo1KWL1RUBAAAAbQphKVjZbNKGDVZXAQAAALRZDMMDAAAAAC8IS8Gurk7av9/qKgAAAIA2h7AUzLZulTp2lC67zOpKAAAAgDaHsBTMevWSTpyQdu82J3kAAAAA0GwIS8GsSxcpJcVc535LAAAAQLMiLAU7x/2Wduywtg4AAACgjSEsBbtBg8xHepYAAACAZkVYCnb0LAEAAACWICwFuz59zMc9e6ytAwAAAGhjwqwuAGeRnCxNmyZ17Wp1JQAAAECb4veepZ49e8pms522zJgxw2v7/Pz809pGRkb6u6yWq1cv6fe/l/LyrK4EAAAAaFP83rP0j3/8Q3V1dc7nO3fu1DXXXKNbb73V5z7R0dEqKipyPrfZbP4uCwAAAACaxO9hKT4+3uP5k08+qQsvvFBXXnmlz31sNpuSkpL8XUrrcOqUeUPakBApJsbqagAAAIA2I6ATPNTW1urVV1/VXXfddcbeoqqqKvXo0UOpqam68cYb9dlnn53xuDU1NbLb7R5Lq7Vtm3lz2qFDra4EAAAAaFMCGpaWLVumiooK3XHHHT7b9OvXTy+++KLeeecdvfrqq6qvr9ell16qffv2+dwnLy9PMTExziU1NTUA1QMAAABoy2yGYRiBOnhOTo7Cw8P13nvvNXqfkydPqn///po8ebKeeOIJr21qampUU1PjfG6325WamqrKykpFR0efd91BZcsWKTNT6tlTKi21uhoAAAAgqNntdsXExPglGwRs6vA9e/Zo5cqVevvtt5u0X7t27XTRRRepuLjYZ5uIiAhFREScb4kAAAAA4FPAhuG99NJLSkhI0PXXX9+k/erq6rRjxw4lJycHqDIAAAAAOLuAhKX6+nq99NJLmjp1qsLCPDuvpkyZorlz5zqfP/744/roo4/073//W9u2bdN3vvMd7dmzR/fcc08gSgMAAACARgnIMLyVK1eqrKxMd91112nbysrKFBLiymjffPONpk2bpvLycnXu3FnDhw/Xhg0bNGDAgECUBgAAAACNEtAJHpqLPy/iCjolJdJjj0nx8dKvfmV1NQAAAEBQaxETPMBPLrxQevVVq6sAAAAA2pyA3mcJAAAAAFoqepaCXX29dOqUuR4ebm0tAAAAQBtCz1KwKyiQIiKkfv2srgQAAABoUwhLAAAAAOAFYQkAAAAAvCAsAQAAAIAXhCUAAAAA8IKwBAAAAABeEJZaijBmeQcAAACaE2Ep2HXqJIWGSu+843qtqsq6egAAAIA2grAU7NLTpdWrpQEDXK/dcos0YoT0wgvSsWPW1QYAAAC0YoSlYGezSaNGuZ4fPiytXStt3Srdc4+UkiLNmiV9+aVlJQIAAACtEWGppYmLk/bulRYskNLSpMpK6de/lvr3l66+Wlq1yuoKAQAAgFaBsNQSxcVJDz0k7dolffihdMMNUkiIOVxvzx6rqwMAAABaBcJSSxYSIo0da07+UFoqzZsnTZrk2v7b30oTJkgffSTV11tXJwAAANAC2QzDMKwu4nzZ7XbFxMSosrJS0dHRVpcTHAxDGjhQ+uIL83nv3tJ990l33CF17WppaQAAAECg+DMb0LPUWtls0ltvSd//vhQdLRUXS3PmSBdcIE2dKu3caXWFAAAAQFCjZ6ktqK6WXntNWrRI2r7dfK1LF+nIEWvrAgAAAPzMn9kgzE81IZh16CBNm2ZONb5+vVRQYM6eBwAAAMAnvw/Dmz9/vmw2m8eSnp5+xn3efPNNpaenKzIyUoMHD9YHH3zg77Igue7Z9IMfmBNDAAAAAPApINcsDRw4UAcOHHAu69ev99l2w4YNmjx5su6++25t375d48eP1/jx47WTa2oAAAAAWCggYSksLExJSUnOJS4uzmfbX//61xo7dqweeugh9e/fX0888YSGDRum559/PhCl4fhx6YUXzAUAAACATwEJS7t27VJKSorS0tJ0++23q6yszGfbjRs3Kjs72+O1nJwcbdy40ec+NTU1stvtHgsaqarKvHbpnnusrgQAAAAIan4PS5mZmcrPz9fy5cu1aNEilZaWatSoUTp69KjX9uXl5UpMTPR4LTExUeXl5T6/Rl5enmJiYpxLamqqX98DAAAAAPg9LI0bN0633nqrhgwZopycHH3wwQeqqKjQG2+84bevMXfuXFVWVjqXvXv3+u3YAAAAACA1w9ThsbGx6tu3r4qLi71uT0pK0sGDBz1eO3jwoJKSknweMyIiQhEREX6tEwAAAADcBeSaJXdVVVUqKSlRcnKy1+1ZWVlatWqVx2srVqxQVlZWoEsDAAAAAJ/8HpbmzJmjtWvXavfu3dqwYYMmTJig0NBQTZ48WZI0ZcoUzZ0719n+gQce0PLly/X000/ryy+/1Pz581VQUKCZM2f6uzQAAAAAaDS/D8Pbt2+fJk+erCNHjig+Pl6XX365Nm3apPj4eElSWVmZQkJcGe3SSy/Va6+9pp/85Cf60Y9+pD59+mjZsmUaNGiQv0sDAAAAgEazGYZhWF3E+bLb7YqJiVFlZaWio6OtLie41dRI779vrt98s7W1AAAAAH7mz2wQ8AkeEGQiIghJAAAAQCMEfIIHAAAAAGiJ6Flqa06ckJYtM9cnTbK0FAAAACCYEZbamqNHpf/OTEhYAgAAAHxjGB4AAAAAeEFYAgAAAAAvCEsAAAAA4AVhCQAAAAC8ICwBAAAAgBeEJQAAAADwgqnD25pOnaRXXrG6CgAAACDoEZbamshI6bvftboKAAAAIOgRltqq3/1O2rtXSkg4fenaVQoNtbpCAAAAwFKEpbbqtdekdeu8b4uIkI4fl2w28/mzz0r//rf3YJWQYA7tc7QFAAAAWgnCUls1aZI0eLB06JDncuSI1KWLZ/hZutR3sAoLk2prpbo6eqMAAADQqhCW2qrp072/fuqUVFHh+drdd0uXXXZ6sDp0SIqJkUL+O6liVZXUoUNAywYAAACaC2EJnsLCpLg4z9emTPHd/sQJKSrKXN+8Wbr66sDVBgAAADQj7rOE8xMZ6VqvrLSuDgAAAMDPCEvwn4wMqysAAAAA/MbvYSkvL08XX3yxOnXqpISEBI0fP15FRUVn3Cc/P182m81jiXTvsUBwc1ynxIx4AAAAaEX8HpbWrl2rGTNmaNOmTVqxYoVOnjypa6+9VtXV1WfcLzo6WgcOHHAue/bs8XdpAAAAANBofp/gYfny5R7P8/PzlZCQoK1bt+qKK67wuZ/NZlNSUpK/y0FzmDnTnD48OtrqSgAAAAC/CfhseJX/vei/S5cuZ2xXVVWlHj16qL6+XsOGDdP//u//auDAgYEuD/7w5JNWVwAAAAD4XUAneKivr9esWbN02WWXadCgQT7b9evXTy+++KLeeecdvfrqq6qvr9ell16qffv2eW1fU1Mju93usQAAAACAP9kMwzACdfDp06frww8/1Pr169WtW7dG73fy5En1799fkydP1hNPPHHa9vnz5+unP/3paa9XVlYqmqFgzW/fPumrr6Q+faSOHa2uBgAAAG2Y3W5XTEyMX7JBwHqWZs6cqffff1+rV69uUlCSpHbt2umiiy5ScXGx1+1z585VZWWlc9m7d68/Ssa5GjpUGjZMuuAC6Q9/kE6dsroiAAAA4Lz5PSwZhqGZM2dq6dKl+vjjj9WrV68mH6Ourk47duxQcnKy1+0RERGKjo72WGChP/xBSk2V7Hbp3nulAQOkN96Q6uutrgwAAAA4Z34PSzNmzNCrr76q1157TZ06dVJ5ebnKy8t1/PhxZ5spU6Zo7ty5zuePP/64PvroI/373//Wtm3b9J3vfEd79uzRPffc4+/yEAgTJkj/+pf0q19JcXHSrl3SxInSiBHSihVWVwcAAACcE7+HpUWLFqmyslJXXXWVkpOTncuSJUucbcrKynTgwAHn82+++UbTpk1T//79dd1118lut2vDhg0aMGCAv8tDoERGSrNmSf/+t/TTn0qdOknbt0urV1tdGQAAAHBOAjrBQ3Px50Vc8JPDh6Wnn5Z++EOpc2fztX/+UwoJkQYPtrY2AAAAtFotYoIHtHFxcVJenisoGYZ0//1SRob0ne+YPVAAAABAECMsoXkcOyZ162aGpj/9SerXzwxPbsMxAQAAgGBCWELz6NBBWrJE2rpVyskxpxdftEi68ELpkUekb76xukIAAADAA9cswRpr10pz50obN5rPX3lF+u53m3aM+nqzZ6qszPty553S//yPOeTPbjenN+/a1f/vBQAAAEHDn9kgzE81AU1z5ZXSJ59I770nvfqqdNttrm2ffy717i0dP+4KPnv3mo9ZWVJurqvdmSaL+OIL8/EHP5Defde8HxTT0QMAAKCRCEuwjs0m3XCDuTgcPy5de6106JB08uTp+0yf7gpL3btLoaHmtVDdu5++9O/fPO8DAAAArRJhCcGlqEiqq3MFpa5dPQPQVVe52kZHSzU1ZmACAAAA/IywhOAydKi0e7c57C452ZwY4kyaEpT++lezF+qKK6T27c3X9uwxr3sKDTWXsDDXemioGdAiIsy2VVXmrH4N2ziehzBfCgAAQGvCX3cIPhER5jVLZwtKjeUIVMuWSePGSeXlrm2//a15HdTIkdLw4eZ9oAYNMofw9e0r/etfrrZPPy0lJpq9XbGxUqdOZugKDze/xqZNrra/+Y25PTbWbJ+QYIa/1FSpZ09pwwZX29tuM4ck3n67f95va1dXZ4bpv/9d+r//kx5/XLrrLvOx5c9XAwAAggg9S2j9ZsyQvvpKOnHC/EM7MtK1LTZW6tXLfL2uzpzS3LFeVye1a+dqW19/5q/j3st1/LjZE+WL+/VYn3xiPr72mjRnjhmuunY1g5jN1ui32Wo4ZjncvVvat0+aONG17Y47zPt0nTrlfd/bbjODNgAAgB8wdTjQFIbhGabcw1VMjCtcVVRIR454D2B1dVJ6unnNlWTeZ+oXvzj9a91/v7RwobleWWlOhe4IUg2XHj3MXquWwDA8Q+Drr0urVpnhaPduc9bD2lrXdrvd7KWTpPvuk373O3PoY/fuZi9dr15mb+H995uzLPqrRxIAALRITB0OWMVmM/9QDzvLRyc21lwaY+ZMcxr00lIzYB05YoYF9/0PHZKWLvV9jPvuM2/yK5k3+B0+3Bz6l5RkDh10f+zfP/AzBR4+LBUXuwJQaalrfc8ec3vHjmbb1aulP/7Rc3/H9WI9e3qGpZ/8RPrxj6WUFCb2AAAAAUdYAqzWrZt5HygHw5Cqqz2vv+nSxexlcoQp9+Xrr81g4XD4sBlOSku9f73vfU9avNhcr6iQLr749EDlWE9PN6/dcmcY5rDGhkHol7909er8+MfS73/v+z3v2SMNHGiu5+aa13M5eol69pQuuMB7IO3WzfcxAQAA/IywBAQbm83V6+LQtas5zKwxunWT1q83e6MOHjSHqLk/DhjgaltebvYAFRd7P9b06eYkGJI5acXixWYwOn789LYzZ7oCUFqaazIL9xDkWHcfMvitb5nL+cjPN2uaONEMlgAAAH5AWAJam6go6bLLGte2e3dp3Trvoaq83OxZcqiqkr74wly32cyhcO5ByH1M8MMPm0tz+eEPzd6uK64gLAEAAL8hLAFtWfv20qhRjWv77W+bQ/YcPUOO+08Fg6++Mh+3bTPfU0iIec1WVJT5umN2Qsf9sNyX0FBzYg7ukwUAABogLAFonN69g39a7ilTXOsffiiNHWuuv/aadM89vvd76y3p5pvN9SVLzCnIGwYqx/rixdKkSWbbjz4ypzNvGMAc+8yfL02ebLbdssW8Xsxb25AQcxijY5r0L7+UZs3y3XbSJOmWW8y2e/aYMyr6csMNrhoOHjSP68u115qzLkrmdXOPP27eRywiwnx0X09Pd/Vg1tVJa9a42jTcp1MnqXNn19dpOCOiFWprzSn8HcupU671sDBzhkmHggLz++GtbceO0vXXu9q++6509KgZwMPDPR87dDD/w8HhP/8xj+XY7t42NNT67xEAgLAEoBX43vfMKcU7djTv01Rf7zlb3tnukeXeq+TY39c+7vd4On7cvCeUL3a753phoe+2N93kWv/6a+lvf/PdduhQ13plpTn9ui89erjCUlXVmdvGx7vCkt0uLVjgu+3dd7vCUnW1lJ3tu+3Eia6v67h/mXuwcg9X11wjPf+8a9/cXPMxPNx8dA8sw4dLTz7pajtypFm3e5hxrF9yiRmgHbp1c/VINjR8uBmQHG691bxWz5v0dM+w9OMfSzt3em/brZt5Q2WHW27xvJm1uy5dzAlcHCZOlDZu9B7C2rc3p993WLDA/Flz/z67t3/8cdfnY/lyc5KWhsd1rI8Z47olQlmZ+fPW8HiO9Q4dCHgAWh3CEoCWb/Fi1wx/3kybZvYs1debf6y7B6L6etdwPUkaP94MQI5tDdsnJrraXnWV+Uep+3b39hde6Gp70UVmAGr4tR3thwxxte3dW3rlldPbOtqPHOlqm5IiPfus7/c+YoRrvWtX320NwzOERUVJs2ebPTC1tVJNjed6RoarbX29NHjw6e0cz92HbNbWml+rpsZcGho0yPP5Bx/4Dq4N/zAvKvIMqO4qKz2fN5xtMTTUfK1dO8+fB8k8H5GRrmDgaNeunWcPlGQGyORkV8+V4/tw8qTnz45kBozISHN7w/fYsL7ycs+g5a7hvcXWrPEMhg397Geu9RdeMHtWfamqcoWlefOkl1/23ba83PUeH3zQPLa3cBceLr3/vjnrpaOGpUvN10NDPXtyQ0Ol//1fV9v33jPfm3uPr/vjjBmuWTM3bJA+/vj03mHH+s03u477+edmcG3YxvF4+eXmLKGSebPsHTu8f/2QEKlfP/OzJpm3cigr8902Ls41oU9Njdkr6a3WkBDzZ4LhwkCzIywBaBtsNtcfYmcSFXX6H8u+xMR4hoYz6drVHObWGAkJ0ne/27i2cXHSAw80rm1sbNPaPv1049t++mnj2kZGmn9UNwxfjnX3+4sZhjnToaONzeYZVhx/6Dq895756B5mHOsNA0VRkSsgne2P0BUrGvfepDOH9obWrnWt19W5esNqa83n7l54wZzq37HdvW1D991n/qy5Bzb347qHzEsuMb/P7qHO/dHRoyeZwykTEk5v4+De9ujR0wOqO/dw+Nln0l//6rvt3Lmu9S1bXPeU8+aWW1xhad066dFHfbe96CLXz9DKlWf+bHzwgTRunLn+t781fljv3/7m6tn15uWXXcOHP/rIHDbry6JF5rmVzPvT5eT4DoKPP+6aQXX7dmnCBN9tZ8wwe+clqaTE/N3jq+23vy3ddZfZ9uBBc19vwS401Oxtvu02s63dbt4nz2ZztXGs22zm8FTH0OKaGjMgN2zjeBwwwPV9qq+XnnvOd9uePT1/7/7pT65hwA3bJiaakwQ5/O1v5s+4t3pjYz3/I6qgwPwseGvbvr3nZEklJa62Db8f4eGev9cOH3Z9ZhseOzTUc+bckyfN99bw6+O8BSwsLVy4UE899ZTKy8uVkZGh5557TiPd/ze0gTfffFOPPvqodu/erT59+ugXv/iFrrvuukCVBwCwguOPksa2bWxolDz/0Dkbx42Og4UjyEdGet/elOsFz/QHd0MPPtj4ts89Zy7uDMP8Y6621vM/GX7+c2nOnNODleMxPt7VdtIk87YDJ0969sw61hMSXG1Hjza/Tw17cR3r7j9bGRlmr7Kvtu419OxpDqf01dbRUySZQyQvush7b3JdnecfsBERZk2+2jp67aSmDRd2hGtf3Httjx83r230xX04alWVOdzTl4sucq3b7dJf/uK7badOrrBUXX36z467u+/2DEuPP+677aRJnmHpTNdh5uZ6hqW77vL+HwySdPXVnsNZJ00y/4PCm0su8fw+jR9vXoPozeDBnv+ZdN110r/+5b1tWpoZphyuucb38O2kJM9h4KNHS598cno7m838j71vvnG9dsMN5nv1FjLDwswg7HDPPeZw3YZhzbG+c6frP0rmzjX/o8A9SLYCAQlLS5Ys0ezZs7V48WJlZmbq2WefVU5OjoqKipTg/kvvvzZs2KDJkycrLy9P3/rWt/Taa69p/Pjx2rZtmwY1HJIBAACCg83m6p1zFx/vGUbOZORIz6GlZ3L11ebSGOPGuXqDzuaGGxofMidMMBd/t73xRu9BzbHuHkYvv9wcDuirvfvfWkOGmD1yvtqmpbna9uwpLVvmu63jXnqSeX5/+1vfbYcNc7Xt0MHsWaqrMwN2fb3no/v5Dwsz7/HnrV19vZSZ6fl9mzTJd1v3CVUkM3ycPOm9rftQaMms32733tZ9iLXj+xYZ6dru3rbhfw7FxJiB21vb9u09256pZ6jhNvcb2Td8vWEQP35cOnbMe/uGn+XDh30HwYbKynwftwWzGYav7+65y8zM1MUXX6zn/3uRbn19vVJTU/X9739fj3iZtWnixImqrq7W+++/73ztkksu0dChQ7W4EUMa7Ha7YmJiVFlZqWj3e70AAAAALZlhuBb3gOXeE11VZU5o4y04GobrmjvJHAp9/Lj3wGYYnjevLykxe9h8BdJLL3X1eu7caQ4jdJ/91CL+zAZ+71mqra3V1q1bNddtjHFISIiys7O10UfX7saNGzV79myP13JycrRs2TJ/lwcAAAC0HDabqyfJ13W37sM/z8Y9OJ1Nw160M2mlo8H8HpYOHz6suro6JTbodkxMTNSXX37pdZ/y8nKv7cvLy722r6mpUY3beFy7r9mPAAAAAOActcg5KPPy8hQTE+NcUlNTrS4JAAAAQCvj97AUFxen0NBQHXSfSUPSwYMHleSj2y8pKalJ7efOnavKykrnstfXvScAAAAA4Bz5PSyFh4dr+PDhWuU2/WJ9fb1WrVqlrKwsr/tkZWV5tJekFStW+GwfERGh6OhojwUAAAAA/CkgU4fPnj1bU6dO1YgRIzRy5Eg9++yzqq6u1p133ilJmjJlii644ALl5eVJkh544AFdeeWVevrpp3X99dfr9ddfV0FBgX7/+98HojwAAAAAOKuAhKWJEyfqq6++0rx581ReXq6hQ4dq+fLlzkkcysrKFOJ2c7VLL71Ur732mn7yk5/oRz/6kfr06aNly5ZxjyUAAAAAlgnIfZaaG/dZAgAAACAF+X2WrODIe0whDgAAALRtjkzgjz6hVhGWjh49KklMIQ4AAABAkpkRYmJizusYrWIYXn19vfbv369OnTrJ5rjDcRPY7XalpqZq7969DONrIzjnbRPnvW3ivLdNnPe2ifPe9ng754Zh6OjRo0pJSfGYJ+FctIqepZCQEHXr1u28j8M05G0P57xt4ry3TZz3tonz3jZx3tuehuf8fHuUHPx+nyUAAAAAaA0ISwAAAADgBWFJUkREhB577DFFRERYXQqaCee8beK8t02c97aJ8942cd7bnkCf81YxwQMAAAAA+Bs9SwAAAADgBWEJAAAAALwgLAEAAACAF4QlAAAAAPCizYelhQsXqmfPnoqMjFRmZqa2bNlidUnwo/nz58tms3ks6enpzu0nTpzQjBkz1LVrV3Xs2FE333yzDh48aGHFOBfr1q1Tbm6uUlJSZLPZtGzZMo/thmFo3rx5Sk5OVlRUlLKzs7Vr1y6PNl9//bVuv/12RUdHKzY2Vnfffbeqqqqa8V2gKc52zu+4447TPvtjx471aMM5b3ny8vJ08cUXq1OnTkpISND48eNVVFTk0aYxv9fLysp0/fXXq3379kpISNBDDz2kU6dONedbQRM05rxfddVVp33m77vvPo82nPeWY9GiRRoyZIjzRrNZWVn68MMPndub83PepsPSkiVLNHv2bD322GPatm2bMjIylJOTo0OHDlldGvxo4MCBOnDggHNZv369c9sPfvADvffee3rzzTe1du1a7d+/XzfddJOF1eJcVFdXKyMjQwsXLvS6fcGCBfrNb36jxYsXa/PmzerQoYNycnJ04sQJZ5vbb79dn332mVasWKH3339f69at07333ttcbwFNdLZzLkljx471+Oz/+c9/9tjOOW951q5dqxkzZmjTpk1asWKFTp48qWuvvVbV1dXONmf7vV5XV6frr79etbW12rBhg15++WXl5+dr3rx5VrwlNEJjzrskTZs2zeMzv2DBAuc2znvL0q1bNz355JPaunWrCgoKdPXVV+vGG2/UZ599JqmZP+dGGzZy5EhjxowZzud1dXVGSkqKkZeXZ2FV8KfHHnvMyMjI8LqtoqLCaNeunfHmm286X/viiy8MScbGjRubqUL4myRj6dKlzuf19fVGUlKS8dRTTzlfq6ioMCIiIow///nPhmEYxueff25IMv7xj38423z44YeGzWYz/vOf/zRb7Tg3Dc+5YRjG1KlTjRtvvNHnPpzz1uHQoUOGJGPt2rWGYTTu9/oHH3xghISEGOXl5c42ixYtMqKjo42amprmfQM4Jw3Pu2EYxpVXXmk88MADPvfhvLd8nTt3Nv74xz82++e8zfYs1dbWauvWrcrOzna+FhISouzsbG3cuNHCyuBvu3btUkpKitLS0nT77berrKxMkrR161adPHnS42cgPT1d3bt352egFSktLVV5ebnHeY6JiVFmZqbzPG/cuFGxsbEaMWKEs012drZCQkK0efPmZq8Z/rFmzRolJCSoX79+mj59uo4cOeLcxjlvHSorKyVJXbp0kdS43+sbN27U4MGDlZiY6GyTk5Mju93u/F9rBLeG593hT3/6k+Li4jRo0CDNnTtXx44dc27jvLdcdXV1ev3111VdXa2srKxm/5yH+edttDyHDx9WXV2dxzdRkhITE/Xll19aVBX8LTMzU/n5+erXr58OHDign/70pxo1apR27typ8vJyhYeHKzY21mOfxMRElZeXW1Mw/M5xLr191h3bysvLlZCQ4LE9LCxMXbp04WehhRo7dqxuuukm9erVSyUlJfrRj36kcePGaePGjQoNDeWctwL19fWaNWuWLrvsMg0aNEiSGvV7vby83OvvA8c2BDdv512SbrvtNvXo0UMpKSn69NNP9fDDD6uoqEhvv/22JM57S7Rjxw5lZWXpxIkT6tixo5YuXaoBAwaosLCwWT/nbTYsoW0YN26cc33IkCHKzMxUjx499MYbbygqKsrCygAE0qRJk5zrgwcP1pAhQ3ThhRdqzZo1GjNmjIWVwV9mzJihnTt3elyHitbP13l3v95w8ODBSk5O1pgxY1RSUqILL7ywucuEH/Tr10+FhYWqrKzUW2+9palTp2rt2rXNXkebHYYXFxen0NDQ02bOOHjwoJKSkiyqCoEWGxurvn37qri4WElJSaqtrVVFRYVHG34GWhfHuTzTZz0pKem0iV1OnTqlr7/+mp+FViItLU1xcXEqLi6WxDlv6WbOnKn3339fq1evVrdu3ZyvN+b3elJSktffB45tCF6+zrs3mZmZkuTxmee8tyzh4eHq3bu3hg8frry8PGVkZOjXv/51s3/O22xYCg8P1/Dhw7Vq1Srna/X19Vq1apWysrIsrAyBVFVVpZKSEiUnJ2v48OFq166dx89AUVGRysrK+BloRXr16qWkpCSP82y327V582bnec7KylJFRYW2bt3qbPPxxx+rvr7e+Q8uWrZ9+/bpyJEjSk5OlsQ5b6kMw9DMmTO1dOlSffzxx+rVq5fH9sb8Xs/KytKOHTs8wvKKFSsUHR2tAQMGNM8bQZOc7bx7U1hYKEken3nOe8tWX1+vmpqa5v+c+2N2ipbq9ddfNyIiIoz8/Hzj888/N+69914jNjbWY+YMtGwPPvigsWbNGqO0tNT45JNPjOzsbCMuLs44dOiQYRiGcd999xndu3c3Pv74Y6OgoMDIysoysrKyLK4aTXX06FFj+/btxvbt2w1JxjPPPGNs377d2LNnj2EYhvHkk08asbGxxjvvvGN8+umnxo033mj06tXLOH78uPMYY8eONS666CJj8+bNxvr1640+ffoYkydPtuot4SzOdM6PHj1qzJkzx9i4caNRWlpqrFy50hg2bJjRp08f48SJE85jcM5bnunTpxsxMTHGmjVrjAMHDjiXY8eOOduc7ff6qVOnjEGDBhnXXnutUVhYaCxfvtyIj4835s6da8VbQiOc7bwXFxcbjz/+uFFQUGCUlpYa77zzjpGWlmZcccUVzmNw3luWRx55xFi7dq1RWlpqfPrpp8Yjjzxi2Gw246OPPjIMo3k/5206LBmGYTz33HNG9+7djfDwcGPkyJHGpk2brC4JfjRx4kQjOTnZCA8PNy644AJj4sSJRnFxsXP78ePHjfvvv9/o3Lmz0b59e2PChAnGgQMHLKwY52L16tWGpNOWqVOnGoZhTh/+6KOPGomJiUZERIQxZswYo6ioyOMYR44cMSZPnmx07NjRiI6ONu68807j6NGjFrwbNMaZzvmxY8eMa6+91oiPjzfatWtn9OjRw5g2bdpp/xHGOW95vJ1zScZLL73kbNOY3+u7d+82xo0bZ0RFRRlxcXHGgw8+aJw8ebKZ3w0a62znvayszLjiiiuMLl26GBEREUbv3r2Nhx56yKisrPQ4Due95bjrrruMHj16GOHh4UZ8fLwxZswYZ1AyjOb9nNsMwzCa1hcFAAAAAK1fm71mCQAAAADOhLAEAAAAAF4QlgAAAADAC8ISAAAAAHhBWAIAAAAALwhLAAAAAOAFYQkAAAAAvCAsAQAst2bNGtlsNlVUVFhdilPPnj317LPPWl0GAMBChCUAQEDZbLYzLvPnz7e6RAAAvAqzugAAQOt24MAB5/qSJUs0b948FRUVOV/r2LGjCgoKmnzc2tpahYeH+6VGAAC8oWcJABBQSUlJziUmJkY2m83jtY4dOzrbbt26VSNGjFD79u116aWXeoSq+fPna+jQofrjH/+oXr16KTIyUpJUUVGhe+65R/Hx8YqOjtbVV1+tf/7zn879SkpKdOONNyoxMVEdO3bUxRdfrJUrV3rUeOjQIeXm5ioqKkq9evXSn/70J4/thmFo/vz56t69uyIiIpSSkqL/+Z//CcS3CwAQRAhLAICg8eMf/1hPP/20CgoKFBYWprvuustje3Fxsf7yl7/o7bffVmFhoSTp1ltv1aFDh/Thhx9q69atGjZsmMaMGaOvv/5aklRVVaXrrrtOq1at0vbt2zV27Fjl5uaqrKzMedw77rhDe/fu1erVq/XWW2/pt7/9rQ4dOuTc/pe//EW/+tWv9Lvf/U67du3SsmXLNHjw4MB/QwAAlmIYHgAgaPz85z/XlVdeKUl65JFHdP311+vEiRPOXqTa2lq98sorio+PlyStX79eW7Zs0aFDhxQRESFJ+uUvf6lly5bprbfe0r333quMjAxlZGQ4v8YTTzyhpUuX6t1339XMmTP1r3/9Sx9++KG2bNmiiy++WJL0wgsvqH///s59ysrKlJSUpOzsbLVr107du3fXyJEjm+V7AgCwDj1LAICgMWTIEOd6cnKyJHn08PTo0cMZlCTpn//8p6qqqtS1a1d17NjRuZSWlqqkpESS2bM0Z84c9e/fX7GxserYsaO++OILZ8/SF198obCwMA0fPtx53PT0dMXGxjqf33rrrTp+/LjS0tI0bdo0LV26VKdOnQrI9wAAEDzoWQIABI127do51202mySpvr7e+VqHDh082ldVVSk5OVlr1qw57ViOsDNnzhytWLFCv/zlL9W7d29FRUXplltuUW1tbaPrSk1NVVFRkVauXKkVK1bo/vvv11NPPaW1a9d61AwAaF0ISwCAFmvYsGEqLy9XWFiYevbs6bXNJ598ojvuuEMTJkyQZAas3bt3O7enp6fr1KlT2rp1q3MYXlFR0Wn3fIqKilJubq5yc3M1Y8YMpaena8eOHRo2bFgg3hoAIAgQlgAALVZ2draysrI0fvx4LViwQH379tX+/fv117/+VRMmTNCIESPUp08fvf3228rNzZXNZtOjjz7q0VvVr18/jR07Vt/73ve0aNEihYWFadasWYqKinK2yc/PV11dnTIzM9W+fXu9+uqrioqKUo8ePax42wCAZsI1SwCAFstms+mDDz7QFVdcoTvvvFN9+/bVpEmTtGfPHiUmJkqSnnnmGXXu3FmXXnqpcnNzlZOTc1pv0EsvvaSUlBRdeeWVuummm3TvvfcqISHBuT02NlZ/+MMfdNlll2nIkCFauXKl3nvvPXXt2rVZ3y8AoHnZDMMwrC4CAAAAAIINPUsAAAAA4AVhCQAAAAC8ICwBAAAAgBeEJQAAAADwgrAEAAAAAF4QlgAAAADAC8ISAAAAAHhBWAIAAAAALwhLAAAAAOAFYQkAAAAAvCAsAQAAAIAXhCUAAAAA8OL/AQN59k0eNvl7AAAAAElFTkSuQmCC"/>
 </div>
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[84]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [80]:</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;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> <span class="n">style</span><span class="o">=</span><span class="s2">&quot;-b&quot;</span><span class="p">);</span>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">"Sim. Time / s"</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> <span class="n">style</span><span class="o">=</span><span class="s2">"-b"</span><span class="p">);</span>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[85]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [81]:</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;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">style</span><span class="o">=</span><span class="s2">&quot;--r&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="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">style</span><span class="o">=</span><span class="s2">&quot;-b&quot;</span><span class="p">);</span>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s2">"Presim. Time / s"</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="s2">"--r"</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>
+<span class="n">df</span><span class="p">[</span><span class="s2">"Sim. Time / s"</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="s2">"-b"</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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"/>
 </div>
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 </div>
-
 </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><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[86]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [82]:</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">style</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;--b&quot;</span><span class="p">,</span> <span class="s2">&quot;-r&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>
+<div class="cm-editor 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">"Presim. Time / s"</span><span class="p">,</span> <span class="s2">"Sim. Time / s"</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">"--b"</span><span class="p">,</span> <span class="s2">"-r"</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>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Time / s"</span><span class="p">);</span>
 </pre></div>
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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="Recap:-Our-first-proper-Pandas-plot">Recap: Our first proper Pandas plot<a class="anchor-link" href="#Recap:-Our-first-proper-Pandas-plot">&#182;</a></h3>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="More-Plotting-with-Pandas">More Plotting with Pandas<a class="anchor-link" href="#More-Plotting-with-Pandas">¶</a></h2><h3 id="Recap:-Our-first-proper-Pandas-plot">Recap: Our first proper Pandas plot<a class="anchor-link" href="#Recap:-Our-first-proper-Pandas-plot">¶</a></h3>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[87]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [83]:</div>
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+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[[</span><span class="s2">"Presim. Time / s"</span><span class="p">,</span> <span class="s2">"Sim. Time / s"</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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 <ul>
 <li><strong>That's why I think Pandas is great!</strong></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>
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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>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="More-Plotting-with-Pandas">More Plotting with Pandas<a class="anchor-link" href="#More-Plotting-with-Pandas">¶</a></h2><h3 id="Some-versatility">Some versatility<a class="anchor-link" href="#Some-versatility">¶</a></h3>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[88]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [84]:</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>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[[</span><span class="s2">"A"</span><span class="p">,</span> <span class="s2">"C"</span><span class="p">,</span> <span class="s2">"F"</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">"bar"</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">10</span><span class="p">,</span><span class="mi">3</span><span class="p">));</span>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[89]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [85]:</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>
+<div class="cm-editor 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">"F"</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">][[</span><span class="s2">"A"</span><span class="p">,</span> <span class="s2">"C"</span><span class="p">,</span> <span class="s2">"F"</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">"bar"</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">10</span><span class="p">,</span><span class="mi">3</span><span class="p">));</span>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[90]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [86]:</div>
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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>
+<div class="cm-editor 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">"F"</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">][[</span><span class="s2">"A"</span><span class="p">,</span> <span class="s2">"C"</span><span class="p">,</span> <span class="s2">"F"</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">"barh"</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">"Subplots Demo"</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>
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"/>
 </div>
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 </div>
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-</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[91]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>\
+<div class="cm-editor 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">"F"</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">"A"</span><span class="p">,</span> <span class="s2">"F"</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> 
+        <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s2">"-*r"</span><span class="p">,</span> <span class="s2">"--ob"</span><span class="p">],</span> 
+        <span class="n">secondary_y</span><span class="o">=</span><span class="s2">"A"</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">6</span><span class="p">),</span>
         <span class="n">table</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>
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"/>
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+</div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[92]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [88]:</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>\
+<div class="cm-editor 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">"F"</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">"A"</span><span class="p">,</span> <span class="s2">"F"</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> 
+        <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s2">"-*r"</span><span class="p">,</span> <span class="s2">"--ob"</span><span class="p">],</span> 
+        <span class="n">secondary_y</span><span class="o">=</span><span class="s2">"A"</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">6</span><span class="p">),</span>
         <span class="n">yerr</span><span class="o">=</span><span class="p">{</span>
-            <span class="s2">&quot;A&quot;</span><span class="p">:</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;C&quot;</span><span class="p">],</span> 
-            <span class="s2">&quot;F&quot;</span><span class="p">:</span> <span class="mf">0.2</span>
+            <span class="s2">"A"</span><span class="p">:</span> <span class="nb">abs</span><span class="p">(</span><span class="n">df_demo</span><span class="p">[</span><span class="n">df_demo</span><span class="p">[</span><span class="s2">"F"</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">][</span><span class="s2">"C"</span><span class="p">]),</span> 
+            <span class="s2">"F"</span><span class="p">:</span> <span class="mf">0.2</span>
         <span class="p">},</span> 
         <span class="n">capsize</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
-        <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Bug: style is ignored with yerr&quot;</span><span class="p">,</span>
-        <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;P&quot;</span>
+        <span class="n">title</span><span class="o">=</span><span class="s2">"Bug: style is ignored with yerr"</span><span class="p">,</span>
+        <span class="n">marker</span><span class="o">=</span><span class="s2">"P"</span>
     <span class="p">);</span>  
 </pre></div>
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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>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="Combine-Pandas-with-Matplotlib">Combine Pandas with Matplotlib<a class="anchor-link" href="#Combine-Pandas-with-Matplotlib">¶</a></h2><ul>
 <li>Pandas shortcuts very handy</li>
 <li>But sometimes, one needs to access underlying Matplotlib functionality</li>
 <li>No problemo!</li>
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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>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="Option-1:-Pandas-Returns-Axis">Option 1: Pandas Returns Axis<a class="anchor-link" href="#Option-1:-Pandas-Returns-Axis">¶</a></h3>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[93]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [89]:</div>
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-<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>
+<div class="cm-editor cm-s-jupyter">
+<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">"C"</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">"Hello There!"</span><span class="p">);</span>
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-<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>
+<span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s2">"This title is super (literally)!"</span><span class="p">);</span>
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"/>
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-<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><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">¶</a></h3>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[94]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [90]:</div>
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-<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>
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+<span class="n">df_demo</span><span class="p">[</span><span class="s2">"C"</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">"Hello There!"</span><span class="p">);</span>
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 <ul>
 <li>We can also get fancy!</li>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[95]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [91]:</div>
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-     <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>
+<div class="cm-editor 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">"C"</span><span class="p">,</span> <span class="s2">"F"</span><span class="p">],</span> <span class="p">[</span><span class="s2">"blue"</span><span class="p">,</span> <span class="s2">"#b2e123"</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>
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-<h2 id="Aside:-Seaborn">Aside: Seaborn<a class="anchor-link" href="#Aside:-Seaborn">&#182;</a></h2><ul>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="Aside:-Seaborn">Aside: Seaborn<a class="anchor-link" href="#Aside:-Seaborn">¶</a></h2><ul>
 <li>Python package on top of Matplotlib</li>
 <li>Powerful API shortcuts for plotting of statistical data</li>
 <li>Manipulate color palettes</li>
@@ -21873,570 +13274,378 @@ jp-needs-light-background
 <li>Also: New, well-looking defaults for Matplotlib (IMHO)</li>
 <li>→ <a href="https://seaborn.pydata.org/">https://seaborn.pydata.org/</a></li>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[96]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [92]:</div>
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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>
+<div class="cm-editor cm-s-jupyter">
+<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>
 <span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">()</span>  <span class="c1"># set defaults</span>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[97]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [93]:</div>
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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="s2">&quot;A&quot;</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>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[[</span><span class="s2">"A"</span><span class="p">,</span> <span class="s2">"C"</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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-<h3 id="Seaborn-Color-Palette-Example">Seaborn Color Palette Example<a class="anchor-link" href="#Seaborn-Color-Palette-Example">&#182;</a></h3><ul>
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+<h3 id="Seaborn-Color-Palette-Example">Seaborn Color Palette Example<a class="anchor-link" href="#Seaborn-Color-Palette-Example">¶</a></h3><ul>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[102]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [98]:</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>
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 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[103]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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="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>
+<div class="cm-editor 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">"colorblind"</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
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-
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-<h3 id="Seaborn-Plot-Examples">Seaborn Plot Examples<a class="anchor-link" href="#Seaborn-Plot-Examples">&#182;</a></h3><ul>
+</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">¶</a></h3><ul>
 <li>Most of the time, I use a regression plot from Seaborn</li>
 </ul>
-
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[104]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [100]:</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;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>
+<div class="cm-editor 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">"hls"</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">"C"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"F"</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">"C"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"E2"</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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"/>
 </div>
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 </div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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+<div class="jp-Cell-inputWrapper" tabindex="0">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <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>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
-<div class="jp-Cell-inputWrapper">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[105]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [101]:</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="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="mf">.5</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">.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>
+<div class="cm-editor cm-s-jupyter">
+<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="mf">.5</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mf">.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>
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 </div>
 </div>
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[106]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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>
+<div class="cm-editor cm-s-jupyter">
+<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">"reg"</span><span class="p">);</span>
 </pre></div>
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-     </div>
 </div>
 </div>
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+</div>
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-<img 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-<h2 id="Task-6">Task 6<a class="anchor-link" href="#Task-6">&#182;</a></h2><p><a name="task6"></a>
-<span class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</em></span></p>
+</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">¶</a></h2><p><a name="task6"></a>
+<span class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</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>
+<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 threads</li>
 <li>Tell me when you're done with status icon in BigBlueButton: 👍</li>
 </ul>
-
 </div>
 </div>
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 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[107]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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">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> 
-    <span class="s1">&#39;Min. Init. Time / s&#39;</span><span class="p">,</span> 
-    <span class="s1">&#39;Presim. Time / s&#39;</span><span class="p">,</span> 
-    <span class="s1">&#39;Sim. Time / s&#39;</span>
+<div class="cm-editor 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">'Avg. Neuron Build Time / s'</span><span class="p">,</span> 
+    <span class="s1">'Min. Edge Build Time / s'</span><span class="p">,</span> 
+    <span class="s1">'Min. Init. Time / s'</span><span class="p">,</span> 
+    <span class="s1">'Presim. Time / s'</span><span class="p">,</span> 
+    <span class="s1">'Sim. Time / s'</span>
 <span class="p">]</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="s1">&#39;Runtime Program / s&#39;</span><span class="p">]</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">"Unaccounted Time / s"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Runtime Program / s'</span><span class="p">]</span>
 <span class="k">for</span> <span class="n">entry</span> <span class="ow">in</span> <span class="n">cols</span><span class="p">:</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="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>
+    <span class="n">df</span><span class="p">[</span><span class="s2">"Unaccounted Time / s"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"Unaccounted Time / s"</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>
 </pre></div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[108]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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;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>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[[</span><span class="s2">"Runtime Program / s"</span><span class="p">,</span> <span class="s2">"Unaccounted Time / s"</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>
 </pre></div>
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+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[104]:</div>
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-<style scoped>
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         vertical-align: middle;
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@@ -22450,155 +13659,121 @@ class="
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Runtime Program / s</th>
-      <th>Unaccounted Time / s</th>
-      <th>Avg. Neuron Build Time / s</th>
-      <th>Min. Edge Build Time / s</th>
-      <th>Min. Init. Time / s</th>
-      <th>Presim. Time / s</th>
-      <th>Sim. Time / s</th>
-    </tr>
-    <tr>
-      <th>Threads</th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>8</th>
-      <td>420.42</td>
-      <td>2.09</td>
-      <td>0.29</td>
-      <td>88.12</td>
-      <td>1.14</td>
-      <td>17.26</td>
-      <td>311.52</td>
-    </tr>
-    <tr>
-      <th>16</th>
-      <td>202.15</td>
-      <td>2.43</td>
-      <td>0.28</td>
-      <td>47.98</td>
-      <td>0.70</td>
-      <td>7.95</td>
-      <td>142.81</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>Runtime Program / s</th>
+<th>Unaccounted Time / s</th>
+<th>Avg. Neuron Build Time / s</th>
+<th>Min. Edge Build Time / s</th>
+<th>Min. Init. Time / s</th>
+<th>Presim. Time / s</th>
+<th>Sim. Time / s</th>
+</tr>
+<tr>
+<th>Threads</th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>8</th>
+<td>420.42</td>
+<td>2.09</td>
+<td>0.29</td>
+<td>88.12</td>
+<td>1.14</td>
+<td>17.26</td>
+<td>311.52</td>
+</tr>
+<tr>
+<th>16</th>
+<td>202.15</td>
+<td>2.43</td>
+<td>0.28</td>
+<td>47.98</td>
+<td>0.70</td>
+<td>7.95</td>
+<td>142.81</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
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-
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-
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[109]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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</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>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[[</span><span class="s2">"Unaccounted Time / s"</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">"bar"</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>
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cJZBAUFAgBu3gzDlCnT8dtv/oiOfo4rVy7lyIqLi4WLy1CMGjUEffs6wMzMDHXq1FPrnN61atUyODg4Ydu23WjatLnG+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+jVatW6Nevn8Z16FqWtbU1KlSog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 <ul>
 <li>Make it relative to the total program run time</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>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[110]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [106]:</div>
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-<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>
+<div class="cm-editor 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">"Nodes"</span><span class="p">,</span> <span class="s2">"Tasks/Node"</span><span class="p">,</span> <span class="s2">"Threads/Task"</span><span class="p">])</span>
 <span class="n">df_multind</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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@@ -22612,240 +13787,217 @@ class="
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-    <tr style="text-align: right;">
-      <th></th>
-      <th></th>
-      <th></th>
-      <th>id</th>
-      <th>Runtime Program / s</th>
-      <th>Scale</th>
-      <th>Plastic</th>
-      <th>Avg. Neuron Build Time / s</th>
-      <th>Min. Edge Build Time / s</th>
-      <th>Max. Edge Build Time / s</th>
-      <th>Min. Init. Time / s</th>
-      <th>Max. Init. Time / s</th>
-      <th>Presim. Time / s</th>
-      <th>Sim. Time / s</th>
-      <th>Virt. Memory (Sum) / kB</th>
-      <th>Local Spike Counter (Sum)</th>
-      <th>Average Rate (Sum)</th>
-      <th>Number of Neurons</th>
-      <th>Number of Connections</th>
-      <th>Min. Delay</th>
-      <th>Max. Delay</th>
-      <th>Unaccounted Time / s</th>
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-      <th>Nodes</th>
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-      <th></th>
-      <th></th>
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-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th rowspan="3" valign="top">1</th>
-      <th rowspan="2" valign="top">2</th>
-      <th>4</th>
-      <td>5</td>
-      <td>420.42</td>
-      <td>10</td>
-      <td>True</td>
-      <td>0.29</td>
-      <td>88.12</td>
-      <td>88.18</td>
-      <td>1.14</td>
-      <td>1.20</td>
-      <td>17.26</td>
-      <td>311.52</td>
-      <td>46560664.0</td>
-      <td>825499</td>
-      <td>7.48</td>
-      <td>112500</td>
-      <td>1265738500</td>
-      <td>1.5</td>
-      <td>1.5</td>
-      <td>2.09</td>
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-    <tr>
-      <th>8</th>
-      <td>5</td>
-      <td>202.15</td>
-      <td>10</td>
-      <td>True</td>
-      <td>0.28</td>
-      <td>47.98</td>
-      <td>48.48</td>
-      <td>0.70</td>
-      <td>1.20</td>
-      <td>7.95</td>
-      <td>142.81</td>
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-      <td>802865</td>
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-      <td>1265738500</td>
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-      <td>200.84</td>
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-      <td>True</td>
-      <td>0.15</td>
-      <td>46.03</td>
-      <td>46.34</td>
-      <td>0.70</td>
-      <td>1.01</td>
-      <td>7.87</td>
-      <td>142.97</td>
-      <td>46903088.0</td>
-      <td>802865</td>
-      <td>7.03</td>
-      <td>112500</td>
-      <td>1265738500</td>
-      <td>1.5</td>
-      <td>1.5</td>
-      <td>3.12</td>
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-      <th>2</th>
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-      <td>6.08</td>
-      <td>114.88</td>
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-      <td>1265738500</td>
-      <td>1.5</td>
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-      <td>1.5</td>
-      <td>1.5</td>
-      <td>2.28</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th></th>
+<th></th>
+<th>id</th>
+<th>Runtime Program / s</th>
+<th>Scale</th>
+<th>Plastic</th>
+<th>Avg. Neuron Build Time / s</th>
+<th>Min. Edge Build Time / s</th>
+<th>Max. Edge Build Time / s</th>
+<th>Min. Init. Time / s</th>
+<th>Max. Init. Time / s</th>
+<th>Presim. Time / s</th>
+<th>Sim. Time / s</th>
+<th>Virt. Memory (Sum) / kB</th>
+<th>Local Spike Counter (Sum)</th>
+<th>Average Rate (Sum)</th>
+<th>Number of Neurons</th>
+<th>Number of Connections</th>
+<th>Min. Delay</th>
+<th>Max. Delay</th>
+<th>Unaccounted Time / s</th>
+</tr>
+<tr>
+<th>Nodes</th>
+<th>Tasks/Node</th>
+<th>Threads/Task</th>
+<th></th>
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+<tr>
+<th>8</th>
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+<td>2.43</td>
+</tr>
+<tr>
+<th>4</th>
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+<td>1265738500</td>
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+<td>3.12</td>
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+<td>112500</td>
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+<td>1.5</td>
+<td>1.5</td>
+<td>2.45</td>
+</tr>
+<tr>
+<th>1</th>
+<th>2</th>
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+<td>6</td>
+<td>141.70</td>
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+<td>1265738500</td>
+<td>1.5</td>
+<td>1.5</td>
+<td>2.28</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[111]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [107]:</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>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_multind</span><span class="p">[[</span><span class="s2">"Unaccounted Time / s"</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">"Runtime Program / s"</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="s2">"index"</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">"bar"</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">"Relative Time Distribution"</span><span class="p">);</span>
 </pre></div>
-
-     </div>
 </div>
 </div>
 </div>
-
+</div>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
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 <div class="jp-OutputArea jp-Cell-outputArea">
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 <div class="jp-OutputArea-child">
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-    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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"/>
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-
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-
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-
 </div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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-</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>
+</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">¶</a></h2><ul>
 <li><code>MultiIndex</code> only a first level</li>
 <li>More powerful:<ul>
 <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>
@@ -22853,43 +14005,240 @@ class="
 </ul>
 </li>
 </ul>
-
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div></section><section>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="Grouping">Grouping<a class="anchor-link" href="#Grouping">¶</a></h3><ul>
+<li>Group a frame by common values of column(s)</li>
+<li>Use operations on this group</li>
+<li>Grouped frame is not <em>directly</em> a new frame, but only through an applied operation</li>
+</ul>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[112]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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>
+<div class="cm-editor 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">"Nodes"</span><span class="p">)</span><span class="o">.</span><span class="n">groups</span>
 </pre></div>
-
-     </div>
 </div>
 </div>
 </div>
-
+</div>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[108]:</div>
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+<pre>{1: [8, 16, 16, 24, 32, 48], 2: [16, 32, 32, 48, 64, 96], 3: [24, 48, 48, 72, 96, 144], 4: [32, 64, 64, 96, 128, 192], 5: [40, 80, 80, 120, 160, 240], 6: [48, 96, 96, 144, 192, 288]}</pre>
+</div>
+</div>
+</div>
+</div>
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [109]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor 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">"Nodes"</span><span class="p">)</span><span class="o">.</span><span class="n">get_group</span><span class="p">(</span><span class="mi">4</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 class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[109]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+<div>
+<style scoped="">
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
 
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[112]:</div>
-
-
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
 
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>id</th>
+<th>Nodes</th>
+<th>Tasks/Node</th>
+<th>Threads/Task</th>
+<th>Runtime Program / s</th>
+<th>Scale</th>
+<th>Plastic</th>
+<th>Avg. Neuron Build Time / s</th>
+<th>Min. Edge Build Time / s</th>
+<th>Max. Edge Build Time / s</th>
+<th>...</th>
+<th>Presim. Time / s</th>
+<th>Sim. Time / s</th>
+<th>Virt. Memory (Sum) / kB</th>
+<th>Local Spike Counter (Sum)</th>
+<th>Average Rate (Sum)</th>
+<th>Number of Neurons</th>
+<th>Number of Connections</th>
+<th>Min. Delay</th>
+<th>Max. Delay</th>
+<th>Unaccounted Time / s</th>
+</tr>
+<tr>
+<th>Threads</th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>32</th>
+<td>5</td>
+<td>4</td>
+<td>2</td>
+<td>4</td>
+<td>66.58</td>
+<td>10</td>
+<td>True</td>
+<td>0.13</td>
+<td>18.86</td>
+<td>19.65</td>
+<td>...</td>
+<td>2.35</td>
+<td>43.38</td>
+<td>47361344.0</td>
+<td>821491</td>
+<td>7.23</td>
+<td>112500</td>
+<td>1265738500</td>
+<td>1.5</td>
+<td>1.5</td>
+<td>1.70</td>
+</tr>
+<tr>
+<th>64</th>
+<td>5</td>
+<td>4</td>
+<td>2</td>
+<td>8</td>
+<td>34.09</td>
+<td>10</td>
+<td>True</td>
+<td>0.14</td>
+<td>10.60</td>
+<td>10.83</td>
+<td>...</td>
+<td>1.25</td>
+<td>20.96</td>
+<td>47074752.0</td>
+<td>818198</td>
+<td>7.33</td>
+<td>112500</td>
+<td>1265738500</td>
+<td>1.5</td>
+<td>1.5</td>
+<td>1.03</td>
+</tr>
+<tr>
+<th>64</th>
+<td>5</td>
+<td>4</td>
+<td>4</td>
+<td>4</td>
+<td>32.49</td>
+<td>10</td>
+<td>True</td>
+<td>0.09</td>
+<td>9.98</td>
+<td>10.31</td>
+<td>...</td>
+<td>1.12</td>
+<td>20.12</td>
+<td>48081056.0</td>
+<td>818198</td>
+<td>7.33</td>
+<td>112500</td>
+<td>1265738500</td>
+<td>1.5</td>
+<td>1.5</td>
+<td>1.09</td>
+</tr>
+</tbody>
+</table>
+<p>3 rows × 22 columns</p>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [110]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor 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">"Nodes"</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[110]:</div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -22903,221 +14252,217 @@ class="
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>id</th>
-      <th>Tasks/Node</th>
-      <th>Threads/Task</th>
-      <th>Runtime Program / s</th>
-      <th>Scale</th>
-      <th>Plastic</th>
-      <th>Avg. Neuron Build Time / s</th>
-      <th>Min. Edge Build Time / s</th>
-      <th>Max. Edge Build Time / s</th>
-      <th>Min. Init. Time / s</th>
-      <th>...</th>
-      <th>Presim. Time / s</th>
-      <th>Sim. Time / s</th>
-      <th>Virt. Memory (Sum) / kB</th>
-      <th>Local Spike Counter (Sum)</th>
-      <th>Average Rate (Sum)</th>
-      <th>Number of Neurons</th>
-      <th>Number of Connections</th>
-      <th>Min. Delay</th>
-      <th>Max. Delay</th>
-      <th>Unaccounted Time / s</th>
-    </tr>
-    <tr>
-      <th>Nodes</th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>1</th>
-      <td>5.333333</td>
-      <td>3.0</td>
-      <td>8.0</td>
-      <td>185.023333</td>
-      <td>10.0</td>
-      <td>1.0</td>
-      <td>0.220000</td>
-      <td>42.040000</td>
-      <td>42.838333</td>
-      <td>0.583333</td>
-      <td>...</td>
-      <td>7.226667</td>
-      <td>132.061667</td>
-      <td>4.806585e+07</td>
-      <td>816298.000000</td>
-      <td>7.215000</td>
-      <td>112500.0</td>
-      <td>1.265738e+09</td>
-      <td>1.5</td>
-      <td>1.5</td>
-      <td>2.891667</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>5.333333</td>
-      <td>3.0</td>
-      <td>8.0</td>
-      <td>73.601667</td>
-      <td>10.0</td>
-      <td>1.0</td>
-      <td>0.168333</td>
-      <td>19.628333</td>
-      <td>20.313333</td>
-      <td>0.191667</td>
-      <td>...</td>
-      <td>2.725000</td>
-      <td>48.901667</td>
-      <td>4.975288e+07</td>
-      <td>818151.000000</td>
-      <td>7.210000</td>
-      <td>112500.0</td>
-      <td>1.265738e+09</td>
-      <td>1.5</td>
-      <td>1.5</td>
-      <td>1.986667</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>5.333333</td>
-      <td>3.0</td>
-      <td>8.0</td>
-      <td>43.990000</td>
-      <td>10.0</td>
-      <td>1.0</td>
-      <td>0.138333</td>
-      <td>12.810000</td>
-      <td>13.305000</td>
-      <td>0.135000</td>
-      <td>...</td>
-      <td>1.426667</td>
-      <td>27.735000</td>
-      <td>5.511165e+07</td>
-      <td>820465.666667</td>
-      <td>7.253333</td>
-      <td>112500.0</td>
-      <td>1.265738e+09</td>
-      <td>1.5</td>
-      <td>1.5</td>
-      <td>1.745000</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>5.333333</td>
-      <td>3.0</td>
-      <td>8.0</td>
-      <td>31.225000</td>
-      <td>10.0</td>
-      <td>1.0</td>
-      <td>0.116667</td>
-      <td>9.325000</td>
-      <td>9.740000</td>
-      <td>0.088333</td>
-      <td>...</td>
-      <td>1.066667</td>
-      <td>19.353333</td>
-      <td>5.325783e+07</td>
-      <td>819558.166667</td>
-      <td>7.288333</td>
-      <td>112500.0</td>
-      <td>1.265738e+09</td>
-      <td>1.5</td>
-      <td>1.5</td>
-      <td>1.275000</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>5.333333</td>
-      <td>3.0</td>
-      <td>8.0</td>
-      <td>24.896667</td>
-      <td>10.0</td>
-      <td>1.0</td>
-      <td>0.140000</td>
-      <td>7.468333</td>
-      <td>7.790000</td>
-      <td>0.070000</td>
-      <td>...</td>
-      <td>0.771667</td>
-      <td>14.950000</td>
-      <td>6.075634e+07</td>
-      <td>815307.666667</td>
-      <td>7.225000</td>
-      <td>112500.0</td>
-      <td>1.265738e+09</td>
-      <td>1.5</td>
-      <td>1.5</td>
-      <td>1.496667</td>
-    </tr>
-    <tr>
-      <th>6</th>
-      <td>5.333333</td>
-      <td>3.0</td>
-      <td>8.0</td>
-      <td>20.215000</td>
-      <td>10.0</td>
-      <td>1.0</td>
-      <td>0.106667</td>
-      <td>6.165000</td>
-      <td>6.406667</td>
-      <td>0.051667</td>
-      <td>...</td>
-      <td>0.630000</td>
-      <td>12.271667</td>
-      <td>6.060652e+07</td>
-      <td>815456.333333</td>
-      <td>7.201667</td>
-      <td>112500.0</td>
-      <td>1.265738e+09</td>
-      <td>1.5</td>
-      <td>1.5</td>
-      <td>0.990000</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>id</th>
+<th>Tasks/Node</th>
+<th>Threads/Task</th>
+<th>Runtime Program / s</th>
+<th>Scale</th>
+<th>Plastic</th>
+<th>Avg. Neuron Build Time / s</th>
+<th>Min. Edge Build Time / s</th>
+<th>Max. Edge Build Time / s</th>
+<th>Min. Init. Time / s</th>
+<th>...</th>
+<th>Presim. Time / s</th>
+<th>Sim. Time / s</th>
+<th>Virt. Memory (Sum) / kB</th>
+<th>Local Spike Counter (Sum)</th>
+<th>Average Rate (Sum)</th>
+<th>Number of Neurons</th>
+<th>Number of Connections</th>
+<th>Min. Delay</th>
+<th>Max. Delay</th>
+<th>Unaccounted Time / s</th>
+</tr>
+<tr>
+<th>Nodes</th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>1</th>
+<td>5.333333</td>
+<td>3.0</td>
+<td>8.0</td>
+<td>185.023333</td>
+<td>10.0</td>
+<td>1.0</td>
+<td>0.220000</td>
+<td>42.040000</td>
+<td>42.838333</td>
+<td>0.583333</td>
+<td>...</td>
+<td>7.226667</td>
+<td>132.061667</td>
+<td>4.806585e+07</td>
+<td>816298.000000</td>
+<td>7.215000</td>
+<td>112500.0</td>
+<td>1.265738e+09</td>
+<td>1.5</td>
+<td>1.5</td>
+<td>2.891667</td>
+</tr>
+<tr>
+<th>2</th>
+<td>5.333333</td>
+<td>3.0</td>
+<td>8.0</td>
+<td>73.601667</td>
+<td>10.0</td>
+<td>1.0</td>
+<td>0.168333</td>
+<td>19.628333</td>
+<td>20.313333</td>
+<td>0.191667</td>
+<td>...</td>
+<td>2.725000</td>
+<td>48.901667</td>
+<td>4.975288e+07</td>
+<td>818151.000000</td>
+<td>7.210000</td>
+<td>112500.0</td>
+<td>1.265738e+09</td>
+<td>1.5</td>
+<td>1.5</td>
+<td>1.986667</td>
+</tr>
+<tr>
+<th>3</th>
+<td>5.333333</td>
+<td>3.0</td>
+<td>8.0</td>
+<td>43.990000</td>
+<td>10.0</td>
+<td>1.0</td>
+<td>0.138333</td>
+<td>12.810000</td>
+<td>13.305000</td>
+<td>0.135000</td>
+<td>...</td>
+<td>1.426667</td>
+<td>27.735000</td>
+<td>5.511165e+07</td>
+<td>820465.666667</td>
+<td>7.253333</td>
+<td>112500.0</td>
+<td>1.265738e+09</td>
+<td>1.5</td>
+<td>1.5</td>
+<td>1.745000</td>
+</tr>
+<tr>
+<th>4</th>
+<td>5.333333</td>
+<td>3.0</td>
+<td>8.0</td>
+<td>31.225000</td>
+<td>10.0</td>
+<td>1.0</td>
+<td>0.116667</td>
+<td>9.325000</td>
+<td>9.740000</td>
+<td>0.088333</td>
+<td>...</td>
+<td>1.066667</td>
+<td>19.353333</td>
+<td>5.325783e+07</td>
+<td>819558.166667</td>
+<td>7.288333</td>
+<td>112500.0</td>
+<td>1.265738e+09</td>
+<td>1.5</td>
+<td>1.5</td>
+<td>1.275000</td>
+</tr>
+<tr>
+<th>5</th>
+<td>5.333333</td>
+<td>3.0</td>
+<td>8.0</td>
+<td>24.896667</td>
+<td>10.0</td>
+<td>1.0</td>
+<td>0.140000</td>
+<td>7.468333</td>
+<td>7.790000</td>
+<td>0.070000</td>
+<td>...</td>
+<td>0.771667</td>
+<td>14.950000</td>
+<td>6.075634e+07</td>
+<td>815307.666667</td>
+<td>7.225000</td>
+<td>112500.0</td>
+<td>1.265738e+09</td>
+<td>1.5</td>
+<td>1.5</td>
+<td>1.496667</td>
+</tr>
+<tr>
+<th>6</th>
+<td>5.333333</td>
+<td>3.0</td>
+<td>8.0</td>
+<td>20.215000</td>
+<td>10.0</td>
+<td>1.0</td>
+<td>0.106667</td>
+<td>6.165000</td>
+<td>6.406667</td>
+<td>0.051667</td>
+<td>...</td>
+<td>0.630000</td>
+<td>12.271667</td>
+<td>6.060652e+07</td>
+<td>815456.333333</td>
+<td>7.201667</td>
+<td>112500.0</td>
+<td>1.265738e+09</td>
+<td>1.5</td>
+<td>1.5</td>
+<td>0.990000</td>
+</tr>
+</tbody>
 </table>
 <p>6 rows × 21 columns</p>
 </div>
 </div>
-
 </div>
-
 </div>
-
 </div>
-
-</div></section><section>
+</div></div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</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>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="Pivoting">Pivoting<a class="anchor-link" href="#Pivoting">¶</a></h3><ul>
 <li>Combine categorically-similar columns</li>
 <li>Creates hierarchical index</li>
 <li>Respected during plotting with Pandas!</li>
@@ -23130,64 +14475,51 @@ class="
 <li>All can be populated from base data frame</li>
 <li>Might be aggregated, if needed</li>
 </ul>
-
 </div>
 </div>
 </div>
-</div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
-<div class="jp-Cell-inputWrapper">
+</div></section><section><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[113]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [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_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>
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="p">[</span><span class="s2">"H"</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 class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div class="jp-Cell-inputWrapper">
+</div>
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[114]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [112]:</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;E2&quot;</span><span class="p">,</span>
-    <span class="n">columns</span><span class="o">=</span><span class="s2">&quot;H&quot;</span>
+<div class="cm-editor 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">"F"</span><span class="p">,</span>
+    <span class="n">values</span><span class="o">=</span><span class="s2">"E2"</span><span class="p">,</span>
+    <span class="n">columns</span><span class="o">=</span><span class="s2">"H"</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="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
-
-
 <div class="jp-OutputArea jp-Cell-outputArea">
-
-<div class="jp-OutputArea-child">
-
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[114]:</div>
-
-
-
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[112]:</div>
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-      <th></th>
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-      <th>-3.918282</th>
-      <td>NaN</td>
-      <td>7.389056</td>
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-    <tr>
-      <th>-2.504068</th>
-      <td>NaN</td>
-      <td>1.700594</td>
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-      <th>-1.918282</th>
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-      <td>0.515929</td>
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+<thead>
+<tr style="text-align: right;">
+<th>H</th>
+<th>-1</th>
+<th>1</th>
+</tr>
+<tr>
+<th>F</th>
+<th></th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>-3.918282</th>
+<td>NaN</td>
+<td>7.389056</td>
+</tr>
+<tr>
+<th>-2.504068</th>
+<td>NaN</td>
+<td>1.700594</td>
+</tr>
+<tr>
+<th>-1.918282</th>
+<td>NaN</td>
+<td>0.515929</td>
+</tr>
+<tr>
+<th>-0.213769</th>
+<td>0.972652</td>
+<td>NaN</td>
+</tr>
+<tr>
+<th>0.518282</th>
+<td>2.952492</td>
+<td>NaN</td>
+</tr>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[115]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [113]:</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>
+<div class="cm-editor 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><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>
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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>
-<span class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</em></span></p>
+</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">¶</a></h2><p><a name="task7"></a>
+<span class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</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 threads per task configurations</li>
 <li>Please plot a bar plot</li>
 <li>Tell me when you're done with status icon in BigBlueButton: 👍</li>
 </ul>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[116]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [114]:</div>
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-     <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="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>
+<div class="cm-editor 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="s2">"Nodes"</span><span class="p">,</span>
+    <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">"Tasks/Node"</span><span class="p">,</span> <span class="s2">"Threads/Task"</span><span class="p">],</span>
+    <span class="n">values</span><span class="o">=</span><span class="s2">"Sim. Time / s"</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">"bar"</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>
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"/>
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-
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-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h2 id="Task-7B-(like-Bonus)">Task 7B (like <em>B</em>onus)<a class="anchor-link" href="#Task-7B-(like-Bonus)">&#182;</a></h2><p><a name="task7b"></a>
-<span class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</em></span></p>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="Task-7B-(like-Bonus)">Task 7B (like <em>B</em>onus)<a class="anchor-link" href="#Task-7B-(like-Bonus)">¶</a></h2><p><a name="task7b"></a>
+<span class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</span></p>
 <ul>
 <li>Same pivot table as before (that is, <code>x</code> with nodes, and columns for Tasks/Node and Threads/Task)</li>
 <li>But now, use <code>Sim. Time / s</code> and <code>Presim. Time / s</code> as values to show</li>
 <li>Show them as a <strong>stack</strong> of those two values inside the pivot table</li>
 <li>Use Panda's functionality as much as possible!</li>
 </ul>
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+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="Pandas-2">Pandas 2<a class="anchor-link" href="#Pandas-2">¶</a></h2><ul>
+<li><a href="https://pandas.pydata.org/docs/dev/whatsnew/v2.0.0.html">Pandas 2.0</a> was released in April 2023</li>
+<li>Only limited deprecations (i.e. <em>an upgrade is probably safe</em>)</li>
+<li>Key new feature: Apache Arrow support (via PyArrow)</li>
+<li>Fine-grained installation options <code>python3 -m pip install 'pandas[performance, excel]'</code></li>
+</ul>
+</div>
+</div>
+</div>
+</div></section><section>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<ul>
+<li>Get a reasonably large data source (larger would be better, though)</li>
+<li>Example: <a href="https://download-data.deutschebahn.com/static/datasets/stationsdaten/DBSuS-Uebersicht_Bahnhoefe-Stand2020-03.csv">Train stations as provided by Deutsche Bahn</a></li>
+</ul>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [115]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">data_db</span> <span class="o">=</span> <span class="s1">'db-bahnhoefe.csv'</span>  <span class="c1"># source: https://download-data.deutschebahn.com/static/datasets/stationsdaten/DBSuS-Uebersicht_Bahnhoefe-Stand2020-03.csv</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [116]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">timeit</span> pd.read_csv(data_db, sep=';')
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>8.97 ms ± 320 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div></div><div class="fragment"><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [117]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">timeit</span> pd.read_csv(data_db, sep=';', engine='pyarrow', dtype_backend='pyarrow')
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>2.54 ms ± 84.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div></div></section></section><section><section>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h2 id="Large-Data-&amp;-Mangling">Large Data &amp; Mangling<a class="anchor-link" href="#Large-Data-&amp;-Mangling">&#182;</a></h2><ul>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="Large-Data-&amp;-Mangling">Large Data &amp; Mangling<a class="anchor-link" href="#Large-Data-&amp;-Mangling">¶</a></h2><ul>
 <li>Pandas can read data directly in <code>tar</code> form</li>
 <li>Pandas can read data directly from online resource</li>
 <li>Let's combine that to an advanced task!</li>
+<li>It works also with the PyArrow backend (remember to download the online resource when testing; there is no cache!)</li>
 </ul>
-
 </div>
 </div>
 </div>
 </div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h2 id="Task-8-(MATSE-Bonus)">Task 8 (<em>MATSE Bonus</em>)<a class="anchor-link" href="#Task-8-(MATSE-Bonus)">&#182;</a></h2><p><a name="task8"></a>
-<span class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</em></span></p>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="Task-8-(Super-Bonus)">Task 8 (Super Bonus)<a class="anchor-link" href="#Task-8-(Super-Bonus)">¶</a></h2><p><a name="task8"></a>
+<span class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</span></p>
 <ul>
 <li>Create bar chart of top 10 actors (on <code>x</code>) and average ratings of their top movies (<code>y</code>) based on IMDb data (only if they play in at least two movies)</li>
 </ul>
-
 </div>
 </div>
 </div>
 </div><div class="fragment">
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <ul>
 <li>IMDb provides data sets at <a href="https://datasets.imdbws.com">datasets.imdbws.com</a></li>
-<li>Can directly be loaded like<div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">&#39;https://datasets.imdbws.com/dataset.tsv.gz&#39;</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">low_memory</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">na_values</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;</span><span class="se">\\</span><span class="s2">N&quot;</span><span class="p">,</span><span class="s2">&quot;nan&quot;</span><span class="p">])</span>
+<li>Can directly be loaded like</li>
+</ul>
+<div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">'https://datasets.imdbws.com/dataset.tsv.gz'</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">"</span><span class="se">\t</span><span class="s2">"</span><span class="p">,</span> <span class="n">low_memory</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">na_values</span><span class="o">=</span><span class="p">[</span><span class="s2">"</span><span class="se">\\</span><span class="s2">N"</span><span class="p">,</span><span class="s2">"nan"</span><span class="p">])</span>
 </pre></div>
-</li>
+<ul>
 <li>Needed:<ul>
 <li><code>name.basics.tsv.gz</code> (for names of actors and movies they are known for)</li>
 <li><code>title.ratings.tsv.gz</code> (for ratings of titles)</li>
@@ -23438,98 +14825,94 @@ class="
 </ul>
 </li>
 </ul>
-
 </div>
 </div>
 </div>
 </div></div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<div class="highlight"><pre><span></span><span class="n">df_names</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">&#39;imdb-data/name.basics.tsv.gz&#39;</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">low_memory</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">na_values</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;</span><span class="se">\\</span><span class="s2">N&quot;</span><span class="p">,</span><span class="s2">&quot;nan&quot;</span><span class="p">])</span>
-<span class="n">df_ratings</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">&#39;https://datasets.i</span>
-
-<span class="n">df_names_i</span> <span class="o">=</span> <span class="n">df_names</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">&#39;nconst&#39;</span><span class="p">)</span>
-<span class="n">df_ratings_i</span> <span class="o">=</span> <span class="n">df_ratings</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">&#39;tconst&#39;</span><span class="p">)</span>
-
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div class="highlight"><pre><span></span><span class="n">df_names</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">'imdb-data/name.basics.tsv.gz'</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">"</span><span class="se">\t</span><span class="s2">"</span><span class="p">,</span> <span class="n">low_memory</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">na_values</span><span class="o">=</span><span class="p">[</span><span class="s2">"</span><span class="se">\\</span><span class="s2">N"</span><span class="p">,</span><span class="s2">"nan"</span><span class="p">])</span>
+<span class="n">df_ratings</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="s1">'https://datasets.imdbws.com/title.ratings.tsv.gz'</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">"</span><span class="se">\t</span><span class="s2">"</span><span class="p">,</span> <span class="n">low_memory</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">na_values</span><span class="o">=</span><span class="p">[</span><span class="s2">"</span><span class="se">\\</span><span class="s2">N"</span><span class="p">,</span><span class="s2">"nan"</span><span class="p">])</span>
+                           
+<span class="n">df_names_i</span> <span class="o">=</span> <span class="n">df_names</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">'nconst'</span><span class="p">)</span>
+<span class="n">df_ratings_i</span> <span class="o">=</span> <span class="n">df_ratings</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">'tconst'</span><span class="p">)</span>
+                           
 <span class="n">df_names_i</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span>
     <span class="p">[</span>
         <span class="n">df_names_i</span><span class="p">,</span> 
-        <span class="n">df_names_i</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">line</span><span class="p">:</span> <span class="n">valid_and_avg_rating</span><span class="p">(</span><span class="n">line</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">result_type</span><span class="o">=</span><span class="s1">&#39;expand&#39;</span><span class="p">)</span>
+        <span class="n">df_names_i</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">line</span><span class="p">:</span> <span class="n">valid_and_avg_rating</span><span class="p">(</span><span class="n">line</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">result_type</span><span class="o">=</span><span class="s1">'expand'</span><span class="p">)</span>
     <span class="p">]</span>
     <span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span>
 <span class="p">)</span>
-<span class="n">df_names_i</span><span class="p">[</span><span class="n">df_names_i</span><span class="p">[</span><span class="s1">&#39;toPlot&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="kc">True</span><span class="p">]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">&#39;avgRating&#39;</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">&#39;primaryName&#39;</span><span class="p">)[</span><span class="s1">&#39;avgRating&#39;</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="s1">&#39;bar&#39;</span><span class="p">)</span>
+<span class="n">df_names_i</span><span class="p">[</span><span class="n">df_names_i</span><span class="p">[</span><span class="s1">'toPlot'</span><span class="p">]</span> <span class="o">==</span> <span class="kc">True</span><span class="p">]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'avgRating'</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">'primaryName'</span><span class="p">)[</span><span class="s1">'avgRating'</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="s1">'bar'</span><span class="p">)</span>
 </pre></div>
-
 </div>
 </div>
 </div>
 </div></section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">valid_and_avg_rating</span><span class="p">(</span><span class="n">row</span><span class="p">):</span>
     <span class="n">rating</span> <span class="o">=</span> <span class="mi">0</span>
     <span class="n">ntitles</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="n">_titles</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">&#39;knownForTitles&#39;</span><span class="p">]</span>
-    <span class="n">_professions</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">&#39;primaryProfession&#39;</span><span class="p">]</span>
+    <span class="n">_titles</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'knownForTitles'</span><span class="p">]</span>
+    <span class="n">_professions</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'primaryProfession'</span><span class="p">]</span>
     <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">_titles</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
         <span class="n">_titles</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">_titles</span><span class="p">)</span>
     <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">_professions</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
         <span class="n">_professions</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">_professions</span><span class="p">)</span>
-    <span class="n">titles</span> <span class="o">=</span> <span class="n">_titles</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>
-    <span class="n">professions</span> <span class="o">=</span> <span class="n">_professions</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>
+    <span class="n">titles</span> <span class="o">=</span> <span class="n">_titles</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">','</span><span class="p">)</span>
+    <span class="n">professions</span> <span class="o">=</span> <span class="n">_professions</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">','</span><span class="p">)</span>
     <span class="k">for</span> <span class="n">title</span> <span class="ow">in</span> <span class="n">titles</span><span class="p">:</span>
         <span class="k">if</span> <span class="n">title</span> <span class="ow">in</span> <span class="n">df_ratings_i</span><span class="o">.</span><span class="n">index</span><span class="p">:</span>
-            <span class="n">rating</span> <span class="o">+=</span> <span class="n">df_ratings_i</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">title</span><span class="p">][</span><span class="s1">&#39;averageRating&#39;</span><span class="p">]</span>
+            <span class="n">rating</span> <span class="o">+=</span> <span class="n">df_ratings_i</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">title</span><span class="p">][</span><span class="s1">'averageRating'</span><span class="p">]</span>
             <span class="n">ntitles</span> <span class="o">+=</span> <span class="mi">1</span>
     <span class="k">if</span> <span class="n">ntitles</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
         <span class="n">plot</span> <span class="o">=</span> <span class="kc">False</span>
         <span class="k">if</span> <span class="n">ntitles</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="k">if</span> <span class="s1">&#39;actor&#39;</span> <span class="ow">in</span> <span class="n">professions</span><span class="p">:</span>
+            <span class="k">if</span> <span class="s1">'actor'</span> <span class="ow">in</span> <span class="n">professions</span><span class="p">:</span>
                 <span class="n">plot</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;toPlot&#39;</span><span class="p">:</span> <span class="n">plot</span><span class="p">,</span> <span class="s1">&#39;avgRating&#39;</span><span class="p">:</span> <span class="n">rating</span> <span class="o">/</span> <span class="n">ntitles</span><span class="p">}</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s1">'toPlot'</span><span class="p">:</span> <span class="n">plot</span><span class="p">,</span> <span class="s1">'avgRating'</span><span class="p">:</span> <span class="n">rating</span> <span class="o">/</span> <span class="n">ntitles</span><span class="p">}</span>
     <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;toPlot&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span> <span class="s1">&#39;avgRating&#39;</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">NA</span><span class="p">}</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s1">'toPlot'</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span> <span class="s1">'avgRating'</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">NA</span><span class="p">}</span>
 </pre></div>
-
 </div>
 </div>
 </div>
 </div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h2 id="Task-8B-(B,-like-Bonus)">Task 8B (B, like <em>B</em>onus)<a class="anchor-link" href="#Task-8B-(B,-like-Bonus)">&#182;</a></h2><p><a name="task8b"></a>
-<span class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</em></span></p>
-<p>All of the following are unique sub-tasks, which can be done individually</p>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="Task-8B-(Bonuseption)">Task 8B (<em>B</em>onuseption)<a class="anchor-link" href="#Task-8B-(Bonuseption)">¶</a></h2><p><a name="task8b"></a>
+<span class="task" style="padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;">TASK</span></p>
+<p>All of the following are ideas for unique sub-tasks, which can be done individually</p>
 <ul>
 <li>In addition to Task 8, restrict the top titles to those with more than 10000 votes</li>
 <li>For 30 top-rated actors, plot rating vs. age</li>
 <li>For 30 top-rated actors, plot rating vs. average runtime of the known-for-titles (using <code>title.basics.tsv.gz</code>)</li>
 </ul>
-
 </div>
 </div>
 </div>
 </div></section></section><section><section>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper">
+<div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h2 id="Random-Features-Not-Shown">Random Features Not Shown<a class="anchor-link" href="#Random-Features-Not-Shown">&#182;</a></h2><p>This are all links:</p>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="Random-Features-Not-Shown">Random Features Not Shown<a class="anchor-link" href="#Random-Features-Not-Shown">¶</a></h2><p>This are all links:</p>
 <ul>
 <li><a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop.html"><code>df.drop()</code></a></li>
 <li><a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.corr.html"><code>df.corr()</code></a></li>
@@ -23544,18 +14927,17 @@ class="
 </ul>
 </li>
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-<h2 id="Conclusion">Conclusion<a class="anchor-link" href="#Conclusion">&#182;</a></h2><ul>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="Conclusion">Conclusion<a class="anchor-link" href="#Conclusion">¶</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>
@@ -23563,34 +14945,32 @@ class="
 </ul>
 </li>
 <li><strong>Pivot</strong> tables are next level greatness</li>
-<li>Remember: <strong><em>Pandas as early as possible!</em></strong></li>
+<li>Remember: <em><strong>Pandas as early as possible!</strong></em></li>
 <li>Thanks for being here! 😍</li>
 </ul>
-
 </div>
 </div>
 </div>
 </div>
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+</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><em>Next slide: Further reading</em></p>
-
 </div>
 </div>
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-<h2 id="Further-Reading">Further Reading<a class="anchor-link" href="#Further-Reading">&#182;</a></h2><ul>
+</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">¶</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>
 <li>towardsdatascience.com:<ul>
@@ -23599,24 +14979,18 @@ class="
 <li><a href="https://towardsdatascience.com/basic-time-series-manipulation-with-pandas-4432afee64ea">Basic Time Series Manipulation with Pandas</a></li>
 <li><a href="https://towardsdatascience.com/an-introduction-to-scikit-learn-the-gold-standard-of-python-machine-learning-e2b9238a98ab">An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning</a></li>
 <li><a href="https://towardsdatascience.com/mapping-with-matplotlib-pandas-geopandas-and-basemap-in-python-d11b57ab5dac">Mapping with Matplotlib, Pandas, Geopandas and Basemap in Python</a></li>
+<li><a href="https://towardsdatascience.com/pandas-2-0-a-game-changer-for-data-scientists-3cd281fcc4b4">Whats new in Pandas 2</a></li>
 </ul>
 </li>
 </ul>
-
 </div>
 </div>
 </div>
 </div></section></section>
 </div>
 </div>
+</main>
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diff --git a/Introduction-to-Pandas--slides.ipynb b/Introduction-to-Pandas--slides.ipynb
index 13233639a0531477597b0fef7a1741663ffa7cd1..ec30be18fb58e401bcda1c71157c5f4e675c1f3e 100644
--- a/Introduction-to-Pandas--slides.ipynb
+++ b/Introduction-to-Pandas--slides.ipynb
@@ -11,9 +11,7 @@
    "source": [
     "# Data Analysis and Plotting in Python with Pandas\n",
     "\n",
-    "_Andreas Herten, J\u00fclich Supercomputing Centre, Forschungszentrum J\u00fclich, 14 June 2022_\n",
-    "\n",
-    "_MATSE Version_"
+    "_Andreas Herten, J\u00fclich Supercomputing Centre, Forschungszentrum J\u00fclich, 4 September 2023_"
    ]
   },
   {
@@ -80,9 +78,10 @@
    "source": [
     "## Course Setup\n",
     "\n",
-    "* Originally, a 4 h course (incleaing break)\n",
-    "* This is the two-day MATSE lecture, with plenty of space and some new material\n",
-    "* Alternating between lecture and hands-on"
+    "* 3\u00bd 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\n",
+    "* TAs and me in the room can help with issues, either in public chat or in 1:1 chat"
    ]
   },
   {
@@ -93,7 +92,8 @@
     }
    },
    "source": [
-    "* Please now open Jupyter Notebook of this session from Sciebo"
+    "* Please now open Jupyter Notebook of this session: https://go.fzj.de/jsc-pd\n",
+    "* Give thumbs up! \ud83d\udc4d"
    ]
   },
   {
@@ -113,6 +113,7 @@
     "* 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",
+    "* Now at Pandas 2.0\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*"
@@ -151,7 +152,7 @@
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     {
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@@ -484,7 +485,7 @@
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@@ -630,7 +631,7 @@
        "Index(['Name', 'Age'], dtype='object')"
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@@ -653,7 +654,7 @@
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@@ -1095,7 +1096,7 @@
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@@ -1176,7 +1177,7 @@
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@@ -1236,7 +1237,7 @@
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       ]
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+     "execution_count": 19,
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@@ -1307,7 +1308,7 @@
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@@ -1920,7 +1921,7 @@
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       ]
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@@ -1938,7 +1939,7 @@
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@@ -2027,7 +2028,7 @@
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@@ -2100,7 +2101,7 @@
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       ]
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@@ -2111,42 +2112,33 @@
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-      "  df_demo.round(2).sum()\n"
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-       "A                     6.0\n",
-       "C                   -2.03\n",
-       "E    SameSameSameSameSame\n",
-       "dtype: object"
+       "A    6.00\n",
+       "C   -2.03\n",
+       "dtype: float64"
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-     "execution_count": 35,
+     "execution_count": 31,
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-    "df_demo.round(2).sum()"
+    "df_demo.round(2)[[\"A\", \"C\"]].sum()"
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-   "execution_count": 36,
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       "\\begin{tabular}{lrlrll}\n",
       "\\toprule\n",
-      "{} &    A &          B &     C &        D &     E \\\\\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",
+      "0 & 1.200000 & 2018-02-26 00:00:00 & -2.720000 & This & Same \\\\\n",
+      "1 & 1.200000 & 2018-02-26 00:00:00 & 1.720000 & column & Same \\\\\n",
+      "2 & 1.200000 & 2018-02-26 00:00:00 & -1.300000 & has & Same \\\\\n",
+      "3 & 1.200000 & 2018-02-26 00:00:00 & 0.990000 & entries & Same \\\\\n",
+      "4 & 1.200000 & 2018-02-26 00:00:00 & -0.720000 & entries & Same \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\n"
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-     "text": [
-      "/var/folders/f5/swf8tg5j5r7bqbwftt3zn6b00000gn/T/ipykernel_95838/3396683912.py:1: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
-      "  print(df_demo.round(2).to_latex())\n"
-     ]
     }
    ],
    "source": [
@@ -2213,7 +2197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2296,7 +2280,7 @@
        "Walt       Malcolm David Kelley      False"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2326,7 +2310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 34,
    "metadata": {
     "exercise": "task"
    },
@@ -2354,7 +2338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 35,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -2578,7 +2562,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2611,7 +2595,7 @@
     "    - `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",
+    "pandas.read_csv(filepath_or_buffer, *, sep=_NoDefault.no_default, delimiter=None, header='infer', names=_NoDefault.no_default, index_col=None, usecols=None, 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=None, infer_datetime_format=_NoDefault.no_default, keep_date_col=False, date_parser=_NoDefault.no_default, date_format=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, encoding_errors='strict', dialect=None, on_bad_lines='error', delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None, storage_options=None, dtype_backend=_NoDefault.no_default)\n",
     "```"
    ]
   },
@@ -2648,7 +2632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -2715,7 +2699,7 @@
        "2  1.2 2018-02-26 -1.304068     has  Same"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2726,7 +2710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2745,7 +2729,7 @@
        "Name: C, dtype: float64"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2768,7 +2752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 38,
    "metadata": {
     "tags": []
    },
@@ -2784,7 +2768,7 @@
        "Name: C, dtype: float64"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2815,7 +2799,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2886,7 +2870,7 @@
        "4  1.2 -0.718282"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2910,7 +2894,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -2968,7 +2952,7 @@
        "2  1.2 2018-02-26 -1.304068     has  Same"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2979,7 +2963,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3042,7 +3026,7 @@
        "3  1.2 2018-02-26  0.986231  entries  Same"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3064,7 +3048,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -3122,7 +3106,7 @@
        "2  1.2 2018-02-26 -1.304068     has  Same"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3133,7 +3117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -3191,7 +3175,7 @@
        "4  1.2 2018-02-26 -0.718282  entries  Same"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3218,7 +3202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 44,
    "metadata": {
     "tags": []
    },
@@ -3278,7 +3262,7 @@
        "2  1.2 2018-02-26 -1.304068     has  Same"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3301,7 +3285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -3350,7 +3334,7 @@
        "2  1.2 -1.304068"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3374,7 +3358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3465,7 +3449,7 @@
        "entries  1.2 2018-02-26 -0.718282  Same"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3477,7 +3461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -3540,7 +3524,7 @@
        "entries  1.2 2018-02-26 -0.718282  Same"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3551,7 +3535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3617,7 +3601,7 @@
        "entries  1.2 -0.718282"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3647,7 +3631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -3705,7 +3689,7 @@
        "3  1.2 2018-02-26  0.986231  entries  Same"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3716,7 +3700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -3730,7 +3714,7 @@
        "Name: C, dtype: bool"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3741,7 +3725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3794,7 +3778,7 @@
        "4  1.2 2018-02-26 -0.718282  entries  Same"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3814,7 +3798,6 @@
     "## 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",
@@ -3824,7 +3807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3895,7 +3878,7 @@
        "2  1.2 2018-02-26 -1.304068     has  Same"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3906,7 +3889,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3982,7 +3965,7 @@
        "2  1.2 2018-02-26 -1.304068     has  Same -2.504068"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4007,7 +3990,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4087,7 +4070,7 @@
        "2  1.2 2018-02-26 -1.304068     has  Same  1.700594 -2.504068"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4099,7 +4082,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -4179,7 +4162,7 @@
        "4  1.2 2018-02-26 -0.718282  entries  Same  0.515929 -1.918282"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4189,23 +4172,27 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 60,
+   "cell_type": "markdown",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
-    },
-    "tags": []
+    }
+   },
+   "source": [
+    "## Combining Frames\n",
+    "\n",
+    "* First, create some simpler data frame to show `.concat()` and `.merge()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/f5/swf8tg5j5r7bqbwftt3zn6b00000gn/T/ipykernel_95838/363555535.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-      "  df_demo.append(\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -4227,179 +4214,20 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
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-       "      <th>B</th>\n",
-       "      <th>C</th>\n",
-       "      <th>D</th>\n",
-       "      <th>E</th>\n",
-       "      <th>E2</th>\n",
-       "      <th>F</th>\n",
+       "      <th>Key</th>\n",
+       "      <th>Value</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.2</td>\n",
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-       "      <td>Same</td>\n",
-       "      <td>7.389056</td>\n",
-       "      <td>-3.918282</td>\n",
+       "      <td>First</td>\n",
+       "      <td>1</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": 60,
-     "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": "fragment"
-    },
-    "tags": []
-   },
-   "source": [
-    "`.append()` seems to be deprecated; one needs to use something like the following in thef future:\n",
-    "```python\n",
-    "pd.concat(\n",
-    "    [\n",
-    "        df_demo, \n",
-    "        pd.DataFrame({\"A\": 1.3, \"B\": pd.Timestamp(\"2018-02-27\"), \"C\": -0.777, \"D\": \"has it?\", \"E\": \"Same\", \"F\": 23}, index=[0])\n",
-    "    ], ignore_index=True\n",
-    ")\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": 61,
-   "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",
+       "      <td>Second</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -4411,7 +4239,7 @@
        "1  Second      1"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4423,7 +4251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -4472,7 +4300,7 @@
        "1  Second      2"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4495,7 +4323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -4556,7 +4384,7 @@
        "1  Second      2"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4578,7 +4406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -4639,7 +4467,7 @@
        "3  Second      2"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4661,8 +4489,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
+   "execution_count": 60,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -4716,7 +4550,7 @@
        "1  Second      1  Second      2"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4728,9 +4562,11 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": []
    },
    "source": [
     "* Merge on common column"
@@ -4738,8 +4574,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
+   "execution_count": 61,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -4790,7 +4632,7 @@
        "1  Second        1        2"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4802,28 +4644,25 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "exercise": "task",
+    "editable": true,
     "slideshow": {
-     "slide_type": "subslide"
-    }
+     "slide_type": "fragment"
+    },
+    "tags": []
    },
    "source": [
-    "## Task 3\n",
-    "<a name=\"task3\"></a>\n",
-    "<span class=\"task\" 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"
+    "`.concat()` can also be used to append rows to a DataFrame:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 62,
    "metadata": {
-    "exercise": "solution",
+    "editable": true,
     "slideshow": {
-     "slide_type": "fragment"
-    }
+     "slide_type": ""
+    },
+    "tags": []
    },
    "outputs": [
     {
@@ -4847,71 +4686,221 @@
        "  <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",
+       "      <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>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",
+       "      <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>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>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": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat(\n",
+    "    [\n",
+    "        df_demo, \n",
+    "        pd.DataFrame({\"A\": 1.3, \"B\": pd.Timestamp(\"2018-02-27\"), \"C\": -0.777, \"D\": \"has it?\", \"E\": \"Same\", \"F\": 23}, index=[0])\n",
+    "    ], ignore_index=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "exercise": "task",
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Task 3\n",
+    "<a name=\"task3\"></a>\n",
+    "<span class=\"task\" 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": 63,
+   "metadata": {
+    "editable": true,
+    "exercise": "solution",
+    "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>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",
@@ -5035,7 +5024,7 @@
        "[5 rows x 22 columns]"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5047,7 +5036,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 64,
    "metadata": {
     "exercise": "solution"
    },
@@ -5065,7 +5054,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5096,7 +5085,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 65,
    "metadata": {
     "exercise": "task",
     "slideshow": {
@@ -5111,7 +5100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -5125,7 +5114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5134,14 +5123,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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zWS3NHoqHMC95rYVc2fF8CaCMyYYiU6zgiw8ew+sv3ixdWuGaC8YAaJLhG4EX9Oa63esYkx1DMSzkyp4NeWrHiRa5CYE47naoa07/9r+2H0Lk9WbSznom89kSfIRT5tAIGRUnPJOlnPY70ZzLEDkamYPoFrLlU7wfp1DGZANxxyOTyJaq+A1JuZJmxvuiuGhLH+7dIMbk+ZkshuKhU+LdrRBG++h8d3knJ1a0EtxWbBnwyJjontBaHazVvIWzJdbz2TIG4yH4mwoqBuPOh7maPROxnsyKrnSxgr5ocP0TbaKMyQahXmd8/idHcem2flyyrd+RNa69YAyPHl925D9et3FwLtuQ/OhEo4PbJW0pI9TqjJMrxdNyEwJxfMrB/opWzGVKCPrptBtfJOhHIhxw/PdqIVs6reIp6PdhMB5yyDPRjUmT1yA++0pBniebLlSQUsZEIYv7D87j0HwO737ZDsfW+LlzRsAM/PjgvGNrdAPMjIOzWUPNntv1HpSjHvVttGI+W0K1zhhvY0wS4QCS4YDjYaW1zGVKGEmEWyrmOlHltJZ2uYUhB3o/AC1nkowEEGxSlRbGJC3JmNTrjEypilTEeYF4ZUw2CJ974AiGEyG87qJNjq1x4ZY+pCIBPHDQvWFGXjCfLWOlUMHZI+sbk2jIj02pCI50URJehK+2tAlzAVqoac5lD3MuW2or9T6cCGPe4YKAdlVP/bEglh1Q8l3KlxthLYFszyRbroIZyjNRyOHYQh7fPTCLd+zdjnDAuVpzv4/w0rOG8KOD82d0ibBQtt09ZkyGZsdwzDUJdSNML4uqqdaeCaAlgt2u5prPtDcmQ4mQ9JLZtSxky6dUcgn6oiEsSww7CRZz5VOS/dpa2k1f1nrCw9lQxoSIriOiA0R0kIg+0OL1jxPRo/rPc0S03PRarem1u1zdeA/w+QePwE+Ed1wx4fhaLz97GFPLha4sh5WFKAs+y4BnAgA7huI42kXGRISvNnXoMxpJOu8JrGVdz8TBMFe1VkemVEV/7PSb7kAs6Ii+WrpYPS0/JNaX5ZmkC1pVWCrivDHpikmLROQH8EkA1wKYBLCPiO5i5qfFOcz8h03n/x6Ay5reosDMl7q03Z6iUK7hy/uO47UXbsKmPjlNip246mxtdvyPX5jHjmFnJ7t5xbGFHMIBX8ebcTMTQ3HMZ8vIFCtIuvCfej3mslqiu9WNUzCSCOOHLhqTep2xkC2d0jDYzFAijKV82bHJlaK7vlXVk1Nhrkyxgq1r8lbRoB9BP0kzJisNz2Tj5Ez2AjjIzIeYuQzgNgDXdzj/7QC+5MrOepy7n5hGuljFO13wSgCtemm8L4IHXjhz8ybHFwvYNhhrq8m1lp36hLtu8dZm0+0T3YKRZBiZUhWFcs2VPaWLFdT51MqmU/aTCIF5tZxW+vr6Tbe1MQmhUKmhWJH7d5EpVpFckxgn0qrZpHkmen+TG55JtxiTLQCONz2f1I+dBhFNANgJ4LtNhyNEtJ+IHiSiN7a57ib9nP1zc3OStt39fHnfcewYiuHKXYOurEdEuHzHIPYfWTxj8ybHFvONKi0jbNPPlT30yCqdwkkCJ5v1WtGqG7yZQb1kVzT6yWalozGRX64LANkWxgTQ8hvywlztP5dsusWYmOEGAHcwc/PXhAlm3gPgHQA+QURnrb2ImW9m5j3MvGdkxPxUwV7khbksHj6yiLddvr3jt1DZ7NkxgJl0ydE5EF7BzDhu0pgIFdoTLs3kWI+5DolugXh91qVQl5h7vjYhLWgkph3yTFY6JKr7oyF9bXnGpFKro1CptQx79kWD0kqDRfhuI3kmUwC2NT3fqh9rxQ1YE+Ji5in9z0MAvo9T8ykbltv3HUfAR3jTS1o6eY6xZ0LzgvYfXXR1XTdYzleQKVUb3oYR+mNBRII+nPBAibcVhoxJQ5PKHWPSUNBtY0yEd+BEVRXQ2TMZ0NdekmjIsvpNvpVnIjXMpb9PYgP1mewDsJuIdhJRCJrBOK0qi4jOAzAA4CdNxwaIKKw/HgZwFYCn11670ShX6/jqTyfx6vNHT5mX4AbnbkoiGQ5g/5ElV9d1g2N6qMqMZ0JE2NwfdVyo0AjVWh0LuVLDWLRD6GO51WsiPJOBNkUBTnSGN9PJmPQJQybRM8k0jElrz0RmziQZDpwiEeMUXVHNxcxVInofgHsA+AHcwsxPEdGHAexnZmFYbgBwG58ajD8fwL8RUR2acfxocxXYRuW7z85gPlvGDZdvd31tv49w2cTAGW1Mtg2279Foxea+aGP4kpcs5spgBkbWqUQbjIdA5IFn0kbrrJG3cKCqCuicqBahN5khNrFeK88kEQ40PBe75EpVxMPu3Oa7wpgAADPfDeDuNcf+as3zD7W47gEAFzm6uR7kSw8fx6ZUBFef401+6PKJAfzTfc9hJV9pfLM7E2gYkwHjngmgyaj/8HnvCz9EDmQ9zyTg92EoHnLNmCzlywj4CMk2N76E/u16ueBcziTk9yESPD1YI6RIMhKHczU8kxafNxEOICtpsmO2VHUlxAV0T5hLIZGp5QJ++Pwc3rpnqyvubStePDEAZuCxyWVP1neKyaU8hhMh09/2NvdHMZspoVytO7QzY4iw1Xo5E0BrFHTPmFTQHwu2LRQhIvRHnen3AFbFEFutHw8FQKT1hchCGItWYa5EOIBStY5Kzf7vSrZUc80zUcbkDOQr+7Uq67fs2bbOmc5x4ZY+AMDjZ5gxObaYN5V8F2zuj4DZ+Zkc6zGXbj0zpBVu6nMt50+XFllLXyzoaAK+r01jn89HSIQCUscGZzqEucTNX8bc+VypikTY2XG9AmVMzjBqdcZX9k/i5WcPW7rpyaIvGsSu4Tgem1zxbA9OYLbHRCB0sKY9Lg8245m4KamylKu0Tb4L+qNB53ImhWpH/apkRF7oCWhOwLcOcwGQsl62WEU8pDwThQV+dHAeU8sFvO1y77wSwcVb+84oz6RWZ5xYLmLrgLnkO7A6I8Triq65TAnJSACR4PrfVkd0sUc3mk+XDHgm/bGQozmTTo19yUhQaphr1TM5fc1Vz8R+x73KmSgs8+V9xzAQC+JafYyul1y8tR8z6ZLnoR1ZzGVKqNW5o9puO8RUQ68ruoz0mAhGEmGUa3Wp4Z12LOXLhjwTp3Im6xmThAOeSTjgQyhw+i04roelZKyXK1cbno7TKGNyBrGQLeHep2fwyy/e6qjUvFEu2ablTR47vuztRiRxQvcq2o277UQsFEBfNOh546IYQGUEJ0piW8HMWMpX2jYsCmTKjKxlfc8kILWaK12sthX9lBXmYmZXS4OVMTmD+M9HT6BSY7zVw8R7MxeM98HvIzx+huRNjMwB6UQ3NC7OZoqGPZPVzm9nbuCCQqWGcrVuIMwVRKZYRVVClVMz9TojU6x0lBxJhOUaE01Buk0ZdEROAl6rCGPlmSjMwcy4fd9xXLKtH+duSnq9HQDalMHdowk8MXWGGBPhmVg1Jn0Rz8NcC9lyW5n3tYgGQjGr3CnW634X9IuRtpLDbtlyFXXuLIao5UzkrZsttRZ5BNBImNv1TIQxiodUNZfCBI9NruDATAZv6xKvRHDB5hSemU57vQ0pnFguIhbyW54NMZoKuyac2IpyVRsAtXZUbDtE2EmmJlUrhLEykoAH5IfdRIVYJ2OSigQkJ+DbG5OEpNJgkcBPuDRDRxmTM4Tb9x9HJOjDL14y7vVWTuGC8RRmMyXXpMydZHqlgPG+iGUF5pFkBAu5kvQwjVGEUTBuTNwJc4mk+nr76nNI7LGTYrBANBLKajrNFCtIhluvJ6vPRHg2qs9EYZhCuYavP3oCv3DRuCtS02Y4fzwFAGeEd3Jipdgo8bXCaDIMZmDB4bBRO8TMEKPGJBUJwkduhLmEYrCxMJfsXpOGLlcHj1N4EbIqujp5JqGADyG/DxlJxkQl4BWGufuJaWRK1a5JvDdzJhmT6eWC4VG9rRBd57Npb7y0JZPGxOcj9MdCzoe58ibDXJJ7TYwMkBKVV7JCXZkO1VyAloS3H+ZSxkRhktv3a9MUr9jpzjRFMwzGQ9iUiuCZ6YzXW7FFuVrHXLaEcTueiW6IZjPeJOEXTBoTwLn5582I6YmdZtIDq56J7P10kp8XJCSKPdbqvG4zYTzst920mCuLMJcyJgoDHJnP4aHDi3jLnm2uTlM0w/njyZ73TGYzRTBrFVlWGXV5euFaltYZQNWKwVioER5zinSxgnjIj6C/8+0o5aExSUo0JuImn+pkTEL2myTzZc0YRQ2oHchAGZMe5/b9x+Ej4E0v3ur1Vtpy/ngKB2ezKFXty0N4hdDUsuOZiJJcr8JcjTnrJkYCuBHmEoq96+H3EVKRgPTGxUyxCiJ01LASyXIZYa5OulyCRNh+mKsgjIkqDVasR7WmTVN85bmj2GTjG7PTnD+eQrXOeH4m6/VWLCM61+14JqGAD4PxkGdhrsVcGX3RIALreADNDLgQ5uqUjF5LXywovTQ4W9LEEH0dxjXITMB30uUSxCXMNClUNGMS22jGhIiuI6IDRHSQiD7Q4vV3E9EcET2q//xG02s3EtHz+s+N7u7cO374/Bxm0qWuTLw3cyYk4WV4JoAW6vIqzLWYK2PIRL4E0PIri/myo2KP6XW6z5tJSW4eBMQ0ws43XJlhLqOeiawwV8QlaaWumLRIRH4AnwRwLYBJAPuI6K4W43e/zMzvW3PtIIC/BrAHAAN4RL/2zJsZu4bb901iKB7Cq84b9XorHdk5HEck6OvpJPz0cgHJSMB2MnPEQ2OylC+3HYvbjv5YCOVqHYVKDTGHpMzTxQpGk8Y8PtkaWYDW3LdexdNqAt6+l2akZFdOmKuKSNDX0eOSSbd4JnsBHGTmQ8xcBnAbgOsNXvtaAPcy86JuQO4FcJ1D++wa5rMl3PfMDH75xVtaKo92E34f4dxNvd0Jf2KlaFlGpZnRZARzHqkoL2TLppLvADAYd75x0UyYKxkJNvpCZJEtra+sGw74EQrY7/0AVnMZnXI08XDAdjVXvuzcF4BWdMtdaAuA403PJ/Vja3kTET1ORHcQkYjtGLqWiG4iov1EtH9uzvtZ3Hb52k+nUK13j6jjelwwnsTT02lXZmM4wfRKQUpeajSlTS/04u9hKW8+zCV6O5xsXEwXjIe5kpIFFwE9zGXgpitrbeFxdMplJMJ+ZEtV1OvWf08KlZprlVxA9xgTI3wdwA5mvhia93GrmYuZ+WZm3sPMe0ZGRhzZoFswM27bdwyXbe/H7rHuEHVcj3PGklgpVFwbAyub6eWiJen5tYwmw6jU2HGJkrUwszbN0KQxcVqfi5lNeiZyNbIAPQFvIHwpK8RmJDEu9pOvWPdOCuWaa8l3oHuMyRSA5q/YW/VjDZh5gZnFnejTAF5i9NozjYcOL+KFuRzesXe711sxzO5Rzegd7MGKrmKlhoVc2bL0fDMiN+B2RVe2VEW5VreQgHc2zFWo1FCts6HSYEALc2VLVamenTZAav2bbjISRFaCIRPhq04GTIY+V75cc60sGOgeY7IPwG4i2klEIQA3ALir+QQialYwfAOAZ/TH9wB4DRENENEAgNfox85YvvjQMaQiAfzixZu93ophdo8lAADPz/aeMTkpKrkkhbkA93tNRJe5lQS8dr0znomRyqZmkpEA6rxaqSQDIwl4QN5Mk3xZ62sJd8h1yihFdjvM1RXVXMxcJaL3QTMCfgC3MPNTRPRhAPuZ+S4Av09EbwBQBbAI4N36tYtE9LfQDBIAfJiZF13/EC4xny3hW09O451XTrj6rcMuo8kwkpEAnp/tvYquRlmwBM9ETDmcc7mia1EPU5nOmUSFZ+KMMRG6WIZzJg2NLHkTBI0k4LW1Azi6kLe9Xr5cQzwU6KhYIXI4djyTQrmG4YS5f287dIUxAQBmvhvA3WuO/VXT4w8C+GCba28BcIujG+wSvrJ/EpUa41eu6J0QFwAQEXaPJnqycVGEpDb1GRsq1Ykh/T+30xIla1nMacbLrGcS8PuQjAQca1xMm/RMmkt0ZRREVGqarLwhz0TSHPh8ubpuLkPsJ2vDE8qXq4iG7H8BMkq3hLkUBqjXGf/+8FFcsXMQZ4/2RuK9mXPGkjjYg2EuEZIaMdgL0YlEOIBQwOf6fJdFPcw1aLI0GNA0q5yavb4q/27UMwno18mp6DKjrJuSVJacK62fGJcxB75YqSMa3HilwQoD3H9wHscXC3jnlRNeb8USZ48msJArY6HHKrpmM0VEgr6OwnxGISKMJMKYz7rrmQgJkv64+Xk3ThoTkYMw3gEvd66ImQFS8bAf+XLNdvLfSP+H6MgXopDW1lnfA5KJMiY9xBcePIqheAivfdEmr7diCVHG3GveyUy6hNGk9QmLaxlKhLCQc9egLucr8PsISQt5hr5osJHbkM1qzsR40yIgb66IkcoqQSwUQK3OKNmctmjkJr/qmVgvNNio1VyKdZheKeA7z8zgrZdv6/qO93bsHu3Niq7ZTBFjKfv5EsFQPIQFlz2TpXwZ/dGgJYOYinRPmEvcZGU1LpqZRhjXb8x2K8ny5Rpi66wnjEDBomdS142ealpUnMaXHj4OBvD2y3sr8d7MeF8E8ZC/5zyTWd0zkcVQIux6qG+5UGnMUDeL02GuoJ86lsk2k5SokQWs5kyMVHPFJPR+AJpnEl/HYxBhMKuGy23FYEAZk56gVK3h3x86ileeM4LtQzGvt2MZIsLZY8meKw+ezZQa/SEyGEqEMJ9zVol3LSv5SqPM1yx9Mfl6WAIhpWLUY9JKau1VOTXTSMAbkFOJ27zBr665fvjJ7yOEAr6GjpdZ8i7PMgGUMekJvv7YNOazZbzn5bu83opteq08OFeqIluqSvVMhuNhlKt1aUlkIywXzIs8CvqiQRQrdUeGm5mRUgG0ufSJcEBaNVfWhGcikuIy5owYMV6xkN+y4SpW3J2yCChj0vUwM2750WGcM5bAVWcPeb0d2+weTWA2U8KKy9pUVhFy8VJzJnqviZt5k+W89TCXSI47EepKF41NWWxGptjjammwkWou4ZnYWztXMlZlFQtaNybiuo2oGqxow0OHF/H0dBq/ftXOrp3xbgYhq3JwrjdCXTO6XLzsnAkAVyu6tDCXNc9E3OzTBfmelFnPBNAquqTlTMpmqrn0cl0bFVaiGszITT4a8jc8DLMIg6dyJooGt/zoMAZiQbzxslaK/L3HWSOaMXlhLufxTozhiGeid6G71WtSqdWRKVXRbyMBDzjkmZiQnxfIHJCVLVUR8BkrAFjNmdjr/QCMeUKxUMDyWiIBH1FhLgUAHFvI495nZvArV0y4+kvhJFv6owj6CYfne8SYOOCZDAvPxCVjIno5rBqTVc9EvjGx5pnIkTUBxMjezjpZglijkdBe7wdgLDEetZEzKZRVNZeiiVt+fBh+IvzqS3uz470VAb8P2wdjONxDnkk44EMqKi/2PBgXORN3wlzLuhHos1rNJYyJAxVdZua/CxISw1xGRR6B1SS9XVl4wFj1WCzkb3gYVtdRxkSBhWwJX953HG+4dDPGUvK+FXcDO4cTPeOZzKSLGE2FpearQgFNmmXBJbFHIdLYb6OaC5Af5qrW6siXa+YT8BLDXJpnYuyGGwn4QQTkbRgTYYiMeCZ2qrlUmEvR4LMPHEGxWsPvvPIsr7cinV0jcRxeyNkaSeoWs+kSxiSGuATDibBrYo8rBV2Xy6JnIjwH2RV4ZmeZCOQaE2OzTACtLDkW9NsKc4mbvBHPJBoMWO4zUWEuBQCtu/ezDxzBdS/a1JPqwOuxcziOcrWOEysFr7eyLjOZotSGRcFwIuxazmTVM7FmTEIBH6JBv/QwV0NKxWSYKxUJolyrW650asZMmAvQuuDtJOAb898NJeD9ltfa0KXBRHQdER0gooNE9IEWr7+fiJ4moseJ6DtENNH0Wo2IHtV/7lp7ba/x+QePIlOs4ndeebbXW3GEncNxAMChHsibzEmWUhEMJUKueSYNY2KxNBhwRlLFqmciQ55dkCtVDXkJgnjIb6s02Ewuw1YCXje0RmVqZNAVxoSI/AA+CeB1AC4A8HYiumDNaT8DsIeZLwZwB4B/aHqtwMyX6j9vcGXTDlEo1/CZ+w/j6nNGcNHWPq+34wi7RjRj0u15k3y5ikyp6ohnoikHu+SZFCogMn/TbiYVDUg3Jg3FYAs5E0CO2KOo5jJK3KZnYiYBHw36UarWUbMQDi6Uq4gG/fD53OtN6wpjAmAvgIPMfIiZywBuA3B98wnM/D1mFjMzHwSw1eU9usLt+49jIVfG+37+zPRKAG10bSIc6HpjIoZiOZEzGYqHsZQvo1qzJ2duhJV8GX3RoK0biyZDL7dp0eyURYFMGXotzGU8rxAP2StLFobIaAIegKWKLm1mirvtBN1iTLYAON70fFI/1o73APivpucRItpPRA8S0RtbXUBEN+nn7J+bm7O9YScoVmr4l+8fxOU7BrB356DX23EMIsLO4TgOdbkxaXS/O5IzCYEZWHJBVma5YF3kUeBEmMtqzkRWmIuZkSsbT8ADWq7DjtBjY36KwdJgwFqTZKFcc703rVuMiWGI6J0A9gD4WNPhCWbeA+AdAD5BRKeVQDHzzcy8h5n3jIyMuLRbc3z+J0cxky7hj19zrtdbcZydw3Ecnu9uwcfV7ncncibuSapoulzW8yWAMzNNzE5ZFAhPxq5ysAghmQpzhQK2+kwK5SqIgEhw/VtvVDc4Viq6CpWN65lMAdjW9HyrfuwUiOgaAH8B4A3M3PhfyMxT+p+HAHwfwGVObtYJMsUK/uX7B3H1OSO4YlfvCzqux87hOCaXCo4o0cpiVZfLgZxJ3D2xRxmeSSoqX4Ze5EwSHiXgzSgGC+z0fgBa93ws6DfWcW9jGNdGDnPtA7CbiHYSUQjADQBOqcoiossA/Bs0QzLbdHyAiML642EAVwF42rWdS+IzPzqMpXwFf7IBvBJAS8Iza5Ix3cpcpoRQwGe5c7wTwjNxo6JrOV+2XBYs6IsGkSlWLSWD25EpVpEMB+A3mctJSJoDv6oYbC4Bb7cDfr0pi4KoDWOyYcNczFwF8D4A9wB4BsDtzPwUEX2YiER11scAJAB8ZU0J8PkA9hPRYwC+B+CjzNxTxmQ2U8Sn7z+M11246Yyt4FqLKA/uZsHHmXQRo0m53e+CYRdl6JdtDMYSiIorWTImgJYzsVJhJmt076pnYiIBr+dMrA42MzL/XRALitG9vRHmcq+jZR2Y+W4Ad6859ldNj69pc90DAC5ydnfO8o/3HECpWsOfXnee11txDWFMurmiazZTckzKJhUJIuAjx3MmtTojXbSfM+lrkqG3KsuyloyFWSaA1jsR9JMEz8S4/LwgFgqgqsvIW/nmnyvVDDcSxmyoFGtGy92prF3hmWxknphcwVcemcSvXbWzcYPdCCQjQYwkw12dhBeeiRP4fITBeAjzGWc9k0yxAmbrUioCJ/S50gXzisGAVg2YCAdsJ+DNzH8XxG2EngCgUFl//rsgaqM0uFBefzSwbJQx8RBmxt98/SkMxkJ436vO3L6SdmgVXRvTMwE09WCnGxftSqkInJi2qIW5rO0rIUGG3lIC3qZysJH574217ORMKjVXR/YCyph4yn8+OoX9R5fwx68913R55JnAri42JoVyDZliFSMOeSaA1gW/6HCYa9nmLBOBGPkrs6IrW7LmmQBAIhy0nTOxkoBPNEb3WhdgNCrfoqq5FIZYyJbw4a8/jRdv78db92xb/4IzkJ3Dccxny45M8LPLbEYrC3bSMxmKh7HouGeivX+fDV0u7Xr5YS4rg7EEyXAA2ZK9vWQtGJPG6F6Lkio5Ewn4RpjL5FpiNLAKc20QPvyNp5EtVfHRN11sujTyTKGbk/AzupSKUzkTwJ0w14okz6QhQy/JmDAzMh6HuVa70c1Uc9kLc2mlwcbWC/l98PvItGci1JRVmGsD8N1nZ3Dnoyfwuz9/Ns4ZO/Mk5o0iBB+PdKExccczCSFTrKJcdU6fa1Ux2J4xiYX8CPhI2ujeUrWOSo1thLkkJODLVUSCPgT8xm+DDc/EonJwvmxcpZhIm59i1ph4MWURUMbEdeYyJfzpHY/j3LEkfvsMHHxlhq0DMRABR7uwcdEVz0TvNXEy1CWMid3GSyKSqs/VkJ83EWJqRlYC3kzyHVjV1LJSrlurM4oVc+GnaMhvus9EnB91cZYJoIyJq9TrjPff/igyxSr+zzsuQzjg7jeHbiMS9GNTKoKjC93pmYT8PtvhoU40JFUcTMIvF8pIhgOmvn23IyXVmGjvYzXMlQzbn7ZoVn4eaApzWUqK6zkaEzd5K3PgCyrMdebzqfsP4f7n5/FX/+2CDR3eamZiKIaji93nmcymSxhxqPtdICRVnPRMVvIV9MflGERNn0uODL3VwViCRDiAUrVuK0RodjAWgMa8eCtz4Fc9BjOeScBCmEuf5qjCXGcm3z8wi7//1rN43YWb8I69273eTtcwMRjvyjDXbKaIMQek55sZjLsQ5ipUbE1YbMaRMJeNBDxgPREOWAtzRQJ+EFnzTMQ1cRPyLZpnYu4zWjFaMlDGxAWen8ng9/79Zzh3Uwr/+JZLHP2222tMDMcwny1JGcEqkxmHxvU244ZysAyRR0EqEpCWgBdlvWZv5gIZysG5Us3UjR3QlAtiQb8lI7bqMTirUqzCXGcoM+kifv3WfYiE/PjMjXtMx2jPdCYGtYqublMPnk0775mkIkH4HdbnWi5UpKkea9MW5RgTq1MWBTJG91rJmQBaF7w1vSzzVVbRoPkEfNdWcxFR9w6c6HJOrhRxw80PYjFbxqfftQeb+6Neb6nrmBjSxOi6KQlfrNSQLlYx6mBZMKB9yx2IhZzPmcjyTPSZJlYVc5uxOhhLkAhr19nxTKyEubS1A5ZKg4U347hn0sVhLhWTscDUcgE33PwTzGVK+Nx79uKSbf1eb6kr2S6MSRcl4WddKAsWDMVDjoW5mFlqziQVCaJS08pb7SKqucyGmQSrM02se0qWPZOQ3/IoXXG9Uawk4EWYy4zRkoGR1eRNw9kg/OzYEn7zc4+gVKnh1l/fi5dMDHi9pa4lFQliMB7qqiT8TEbMfnfWMwGEPpczxiRb0oZZyfNMtNtFulix/a03W9RkRayWLNudaVKvm5//LtBG99pIwJu4yWthLnOfURifrs2ZENH/JpU57ki9zrj5hy/grf/2E8RCfvzH77xMGRIDTAzFuirMJTwTp3MmgFbR5ZQxkdWwKBAhKRl5Ezu6XEDTHHirsib6t3czg7EEsbBVz6TauN7wWiE/8hVzw7jMzJmXiZnVMgDuIqI4ABDRa4nox7I2QkTXEdEBIjpIRB9o8XqYiL6sv/4QEe1oeu2D+vEDRPRaWXsywyNHF/FL//Jj/M+7n8WrzhvFnb97FXarXhJDTAzGusozEVIqTldzAXqYy2FjImuYlRhkJUM5OFOyrssFNFVzWfRMrCgGC+Iha933OUthLj+YNfkZowj5ebe/+xv+m2TmvySidwD4PhGVAWQBnHbTtwIR+QF8EsC1ACYB7COiu9aM330PgCVmPpuIbgDw9wDeRkQXQJsZ/yIAmwHcR0TnMLPjhQMr+Qq+8+wMbtt3HA8fXsRYKoxPvO1SXH/pZlX+a4KJoTjufOwEStVaV6gCzKRLCPoJAw52vwsG42GsFCqo1OoISuhSb2a5oBkpWWGu5mmLdskUrSW/BbGQ1u9h1TOxMstEIEb3miVf0j0GE7/jzTL0Ric75svuzzIBTBgTIno1gN8EkAMwDuDXmfmApH3sBXCQmQ/pa90G4HoAzcbkegAf0h/fAeCf9bDb9QBuY+YSgMNEdFB/v59I2luD+WwJf33nU0gXK5haKuDwQg7MwJb+KP7y9efj7Xu3q9JfC0wMxcAMTC4VcNZIwuvtYDZTxGgy4soXAqHPtZQrS8/RyBJ5FIgBWVI8E5thLjFt0WrOpOGZWEhSx0IBi30m2k3eZ0IlfNWYVBtNruvhxZRFwNwM+L8A8D+Y+UdEdBGALxPR+5n5uxL2sQXA8abnkwCuaHcOM1eJaAXAkH78wTXXblm7ABHdBOAmANi+3VoHuo8Iz55Moy8axO6xBK6/dAuuPmcYl2ztN/ULojgVUR58bCHfHcYkXcKoC/kSoFmfywFjouc2+iSWBgNyZOgzxQq22CyV12aa2PNMLIW5dM+EmU194ciVjc9/FwixRjO9Jl4MxgLMhble1fT4CSJ6HYCvAniZExuTDTPfDOBmANizZ4+lCrXBeAjf+aNXytyWAlqYCwCOdEkSfjZTbMxacZohByVVVhqDseQYE+FJdEMCHtCVgy17JiIBb80zqdYZ5VrdVFg2X66aLoWOBc1PW/RiZC9go8+EmacBvFrSPqYANI8b3Kofa3kOEQUA9AFYMHitoosZiocQD/m7Jgk/k3Z29nszQ4lVz0Q2y/kKYiG/tDxUOOBHJOiTIvZotWGwmYQNz2Q1AW/+70YM08qbLA+2ksuwMrrXqzCXEWPyBSL6cyKaWPsCMxck7WMfgN1EtJOIQtAS6netOecuADfqj98M4Lus1cvdBeAGvdprJ4DdAB6WtC+FCxARJobiXVEeXKzUsFKouGZMBuNaOG0hK19SRWtYlFtEkIrYl1Sp1urIl2u2qrkAIBEJIuNJAt5aWbLmmZgNc+mje02IPeYrVdcbFgEDYS5mfhcRbYJWObUdwBMAvsLM0v7n6zmQ9wG4B4AfwC3M/BQRfRjAfma+C8BnAHxeT7AvQjM40M+7HVqyvgrgd92o5FLIZWIohgMzGa+34Wr3O6Alx33kTJhrOV9Bn6SyYIGQVLGDuAnbDXMlwwFMLVnzZm2VBofFgCzznolZ4xULmV+r0M3VXMx8EsD/BgAiOg/AHxBRFMD3mfk+GRth5rsB3L3m2F81PS4CeEuba/8OwN/J2IfCG7YPxXDfMzOo1Rl+D4sZZlwY19uM0OdyIsy1UihL90w0sUd7YS67s0wEdsNcRNbEEMU1pj2TUs30l5QzLcy1lhMAZgC8AsBfE1FPJOAV3c2OoTgqNcb0iqzIqTVm0u4aE0CXVHFAn2tZosijIBUJ2PZMpBkTGwn4bKmGeChgqfx71TMxt3bOxPx3QSPMZaaaq+JNNZcpY0JErwHwIwCbALybmV/BzA84sjPFhmJicLU82EvcDnMBzkmqLBccMCYSBmTZHdkrSIQDyJVrqNXNF2dqIo/WbrjCIJjV57LiMZzJnsl+aEbkI8x82IkNKTYmE8OiPNhbYzLjwuz3tQzFw9JnmjAzVvIV9ElSDBbISMDL8kzE9TkLOllZC8lwQWN0rxXPxMJkRwCGxR5rdUapWu/enImAmRehJb8VCqlsSkUQ8vtwdNHbii7RsOimHM6gA/pchUoN5VrdAc8kgHSxarphrxk7lVTNNOtzmZ2LkrNRmiwMgpku+Fpdk+43e5P3+QjRoHH5llX5+e73TBQKR/D7CNsGozg677Fnki66mi8BNGOynK+gWrM/J0QgW0pFkIoEUauzJW0qgbQwlw3l4FzJfP5C0AhzmWwkBKz1tWhz4A0ak8ZgLPdLg5UxUXQNE0Nxz4dkzbgwrnctonFxKS9nJC7QrBgsP2cC2NPnsjuyV2Bnpkm2ZG2WCaBJu/vInGeStzBlURANGR/dW/BolgmgjImii9g+GMOxhZyUsbBWmU2XXJGeb2ZIb1yUmYRfbkipyM+ZAPaUgzPFKkJ+n2EV3HbYmWmihbmsrU9Epgdk2ZnLbmZ0b74ijJYyJooNzI6hGHLlGuYdGmO7HvlyFZlS1TWRR8FgQ+xRXhJeeDkDcfl9JoA9zyRbqjRCVHZozIG34JlYHdkriIX9pjwTUSRgzTMJNIZ5rYdX898BZUwUXYQQfDzmURK+MWHRbc9E6HNJNKJilsmA9A54+2KPMkQeAXtz4O1qg8XDAVNVZLY8k6C/ESZbDxXmUiiwKkV/xKMkvBcNi8CqZyI3zCV3ZK9AhLns9JpIMyYWcybVWh2lat2WZxI3OdNEGBOrCXjDYS4bRssuypgouoatAzH4CJ4l4Wcy7s1+b2YgFgKRXOXgpVwZ0aDfdl5iLY0EvC1jUkEybN/IJSwKLopchy1jEvabquayk4CPhQPGq7lUabBCAYQCPoz3RXHMI/XgWd0zkT2kaj38PkJ/NIhFyTkTJ8YON2aa2JChzxSrUnImfh8hFvKbzplk9LCY1QQ8YN0zsRzmMhhSE2Eu2V8ijKCMiaKr2DEc86wLfiZdRCToa4yndZOhRFh6NVe/5HwJAAT9PsRC/q7ImQDWxB7leCYBU702eVsJeL/h2Sl21rGLMiaKrsLLuSYzelmwm93vgsF4SHICviK9kkuQitiToc8UK6Y71tuRiARMzzSR0YEfN13NZbM0uFIzVDJfqNQtr2MXZUwUXcXEYAxL+YqUOeNmmc2437AoGJIsqbKUL6Nfco+JIBUNWO4zYWZkS/I8k2TYvHJwToYxsRjmslJlFQ8HUNPHBK9HoaxJ64cD7t/alTFRdBWN8mAPQl2aLpe7+RKBbOVgJ+TnBX02BmTlyzXU2b4ulyARsRLmsj4YSxALa70fdYOKxflSFbGQHz4Ls3qEATLSBS9GA3vhXXtuTIhokIjuJaLn9T8HWpxzKRH9hIieIqLHiehtTa99logOE9Gj+s+lrn4AhVR2DOvlwR6EumbSRdd7TARD8RCW8mVLcuprqdcZy/my9B4TQSpiXYZ+VTFYUpjLgmciJcwV8oMZhqus7MwYMSNDX/BolgnQBcYEwAcAfIeZdwP4jv58LXkA72LmFwG4DsAniKi/6fU/YeZL9Z9Hnd6wwjm263NN3M6bZEtV5Mo178JciTCYV2VQ7JApVVFn+bpcAjuje1dFHmUl4IOmPRM5ORNz8veaZ2JtvWjIuOS9V7NMgO4wJtcDuFV/fCuAN649gZmfY+bn9ccnAMwCGHFrgwr3iIUCGEuFcdTlMJdXDYsCmY2LwiA5Uc0F6NMWLeZMRLJcRmkwoBmljEnDJiPMJZoPjepz5crWPYa4iTnwIszlBd1gTMaYeVp/fBLAWKeTiWgvgBCAF5oO/50e/vo4EbX8aklENxHRfiLaPzc3J2XjCmfQKrq8MSZu63IJhhr6XPaNSUOXy0HPJFOsGM4XNCPCXLLKr0VpsBlx0EypilDAh5CNJPXqtEXj/R9uhbm8kJ8HXDImRHQfET3Z4uf65vNY+41o+1tBROMAPg/g15hZlDZ8EMB5AC4HMAjgz1pdy8w3M/MeZt4zMqKcmm5mYjDmes5kdVyvR56JRH2uJcc9kyDqbG3CoaxZJoJEJIC6idwFoAlDJm0WAJgdkGVlyqLAzBz4QrmG2JnsmTDzNcx8YYufOwHM6EZCGIvZVu9BRCkA3wTwF8z8YNN7T7NGCcD/A7DX+U+kcJIdw3HMZkqmx6LaYTYjwlzeeCarYS77XfAizOWcZ2K9C17WyF5B87RFo9hVDAZWjYnhCYg2wk+xkPH8TL5S3dA5k7sA3Kg/vhHAnWtPIKIQgK8B+Bwz37HmNWGICFq+5UknN6twHiH46GaoayZdQizkl1ayahZReSUjzLU6GMsZz6TPhj6XuOnL+nsWRslM46JdxWBAq+YS72UEO56JmTBXfoMn4D8K4Foieh7ANfpzENEeIvq0fs5bAVwN4N0tSoC/SERPAHgCwDCAj7i6e4V0dui9Jm5WdIlxvV7U5wOaTEl/LCglAb+Ur4BIvmKwYHVAlnljkilqe7M6MnctVjyTbMm+NtiqZ2K0mst+zsRImKvoYZjLm69hTTDzAoBXtzi+H8Bv6I+/AOALba5/laMbVLjOdg88E23CojchLsGgpC745XwZqUgQfgsNckYQysFWek3SRc0rsNK81worysHZUtV2bmw1AW9cGt66MTFRzVXZ2J6JQnEKqUgQQ/GQq4KPM5miZ93vgqF4CAtZ+zkTpxSDBQ3PxGLORJYuF7BaYmxmpkm2aD/MFWuUBq+/bq3OejOh9ZnzRJpUynps9DCXQnEa24diroW5mBnTK0WM93lrTGRJqizny+hzKF8C2Ju2mC1VpOalxFwUc55JzXYCPujXSouNzDSxO2OEiBALrj8/pVZnlKt1xIJncGmwQmGWHS72mizmyihX611gTOTI0C877JkIY2ClC16m/DzQNLrXxF6ypYqUPcRDxpSDG4OxbBiwaGh9yXsvB2MBypgoupSJoRhOrBRQNNE/YJXpFa0seLwv6vhandD0uaw1Azaz5KAuFwAE/D4kwta64GUbE9GJbtQzqdbqKFbqUgoAjM6BX5VvsX6Tj4X864a5RDFARBkThWKVHUNxMAOTS857JyeWCwDguWcylAihVmfb8vtOKgYLUpGARc+kgoTEnEk44Eco4DNcGiwS5jLkXIzK0DeGcdkwYEbmwBfL+iyTM7lpUaEwi+g1OTLvvDE5qUupjPd7Heay32tSrtaRLVUd9UwAXezRYjVXX1RuTN/MTJNs2b6XIIiH17/BA3KEJY0YE+ElxSV8NisoY6LoShq9JotueCZFBP2E4bi3pcFD+vp2KrqEV+O4Z2JBOZhZ87pk97+YmWmy2jRpfw9xgyODpcxPCQXW7WnxcmQvoIyJokvpjwWRjARcqeiaXilgU19EWu+DVWQoBzutGCzQZpqYHEpVrqFWZ6mlwYC5mSbZkmYAZYW5jMxmX/UY7CTg1/dMshJm29tBGRNFV0JE2DEUd6XXZHq5iPGUt8l3QMuZAPbCXE4rBgu00b3mPBPhNUn3TMLG58CLG66MMFcs7DeVgLdTeBAL+dcVs8yXVJhLoWjJhEu9JtPpguf5EmBVn8uOZ7LUEHl01jPpi5qftph2yJgkIyY8E5lhLsMJeFlhrvVyJvYT/XZQxkTRtewYimNyqYBKrb7+yRap1xknV4qelwUDQCjgQyoSsGVMVvLO3LDX0h8NIVuqmvq3cdIzMSy4KPHbu1YabCQBr/d/2KiyioX8Dc+jHas5E+WZKBSnMDEUQ63OmFoqOLbGfK6ESo2xuQs8E0Ab3ztvIwHf8EziznomIsFvJtQljEnKwwS8CIclpXgmfpSr9XUNaq5URTzkt5WTi4X8yFdqHYeAZSV4QHZQxkTRtewYdr6i66TesLjJY10ugV1JlaV8BUE/NSTSnUIYE5GjMYJznknQdJhLlmcCYN0kvIz5KdGQH8xAqdrecOVLNfh9hLCNCZJ2UMZE0bVMDAr1YOfyJieWNWOyud/7MBdg35gs5koYjIccl9IX1WIrBeN7TTvkmSQjAZRrdZSqxiqrIkEfAn77t75G9/06SXg581PWVw7OlauIhfyejVFQxkTRtYwkw4gG/Y42Lk6vdEf3u2DIpgz9Qrbc6Fdxkn7dICyb8EzSBW2Wid2RuWtJNvS51vdOMhIUgwWrnknndWV5JuK92pEv1TxLvgNdYEyIaJCI7iWi5/U/B9qcV2sajHVX0/GdRPQQER0koi/rUxkVZwBEhIkhZ+fBT68UEQr4Gj0eXjOUCGEpV+4YG+/EQq7cKDF2EhHmMmNMVgoVpCJB6f08Zmaa5CR4CQJx415v3VypZjus1hiQ1aE8OFuuNqTxvcBzYwLgAwC+w8y7AXxHf96KAjNfqv+8oen43wP4ODOfDWAJwHuc3a7CTXaNxHFoLuvY+wvpea9CA2sZjIdRrbMlEUUAWMiVMOSCYeyPamssm0zApyRLqQCrxsTITBMZUxYFRufAywxzdfZMqhvbMwFwPYBb9ce3Qpvjbgh97vurAIi58KauV3Q/u4YTOL5UQLlD4tEO08uFrglxAWgYgvmctYquxWwZgy6EuZKRAHy02nFvBCekVIAmGXoDnkm2KO+GGzM4B97O/HeBuL7TZMdc2b4HZIduMCZjzDytPz4JYKzNeREi2k9EDxLRG/VjQwCWmVn8a04C2NLqYiK6Sb9+/9zcnKy9Kxxm10gctTrjmEMVXdMrRWzugh4TgR1JlWKlhly55kqYy+cj9EWD5nImxaojxqQxIMuoZyIpzJVoeCbO50yMhPLyZW89E1dWJqL7AGxq8dJfND9hZiaidsHiCWaeIqJdAL5LRE8AWDG6B2a+GcDNALBnzx57AyMUrrFrJAEAODSXxdmjCanvXaszZtLFruh+FzSUg7PmjYlI3LsR5gK0ii6zYa6xlNx/Q8CcZ5IuVnDupqSUdVdH9zof5koa+Iy5Ug2x4TPcmDDzNe1eI6IZIhpn5mkiGgcw2+Y9pvQ/DxHR9wFcBuCrAPqJKKB7J1sBTEn/AArP2DWi9ZocmpefhJ/PllCtMzZ1kWcivAornolQGx5KuKN+rHkmXRDmEjkTI8ZE4h6M5DFkDeMSnk2niZKiOdIruiHMdReAG/XHNwK4c+0JRDRARGH98TCAqwA8zVrJy/cAvLnT9YreJRUJYjgRdiQJP6l31m/pQs9k0ULORHgmblWm9cfM6XOJai7ZGC0NrtcZmVIVKUkJeK2no7MxaQzGspnLENd3km/Jl2ueyc8D3WFMPgrgWiJ6HsA1+nMQ0R4i+rR+zvkA9hPRY9CMx0eZ+Wn9tT8D8H4iOggth/IZV3evcBytoku+ZyKmOG4biEl/b6uEA34kwwHMWwhzLerXDLuQMwG0XpMlg55JsVJDuVqX3rAIAOGADwEfNeTl25EpVcEsr2mSiLTxxR2M2OowLns3+cZEyTZrMTNy5aoUNWSreGfGdJh5AcCrWxzfD+A39McPALiozfWHAOx1co8KbzlrJI57npqR/r7CM9naRcYEAAYT1rrgF3Rvxj3PJGQ4Ae+UYjCg39QNKAc70YGfigQ7liTLUAwWJMOBtgazWKmDGYh5pMsFdIdnolB0ZNdwAou5sqn4vBGOL+YxnAg1uou7Ba0L3lqYKxTwSatWWo/+mHYjrRpQDnZKl0tgZKZJQ2hSYqgtGQl0nDgpY2SvIN5hCFhD5HGD50wUio7s1AUfX5Ac6ppcKnSdVwIAo8kIZtMWjEm2jCEXdLkEQlKlU5hH4JRisMDItEVx05dp0FLRYEflZJmeiSa13zpn4vXIXkAZE0UP0KjokpyEP76Ux7bB7jMmY6kwZtJF09ctuiSlIhBij0byJk57JslIYN0O+NUwl7wbbioS7GhMZc5P0aT2WxsuWYl+Oyhjouh6tg3GEPCR1PLgWp1xYrmArQPdUxYsGE1FkC5WUVxnTOtaFrIlV7rfBX0m9LmcNibaTb3zPoREjVTPJBJApmOYS4wJlpUzaW248hLmzNtFGRNF1xP0+7B9KCbVM5lJF1GpcVdVcglGk5pBMBvqWsiVMeyiYOWACRl6p2fT98XW78Z3ItTmZpjLSM5EhbkUinXYNZzAYYmeyXFdnmXbYPd5JmP6oK6ZjLlQ10K27Kr6sRkZ+qVcGX4fOdJnAmiGbb0CjXRRk8BPSLzhpiJa4r9eby2qITMBr4W52uVMVJhLoTDEWaNxHJnPG6ocMsLxLi0LBpqMiYm8SaFcQ6FSw6CrORPNMBgpY17IlTEQky8/39hLNIhcudZREDTtgAR+MhIEc/sBWblSFQFJ0w87lQY3PCDlmSgUnTlnNIlyrS5thO/kUh5E6JrZ782MpbQw14yJMNes7sWMJt37PKlIEAEfGTImYgKkU/THhSR++704IYEv3q9d8l+IPMqosIuHAyhWWs+cF55JTJUGKxSdEeJ8z53MSHm/44sFbEpFEA50V48JoCWIQwFfw0AYQRgeYYjcwOcjDMZDhkQpl3KVRo7FCUTIbaVDyM0J1WIRtmuXN8k4oFLcSr4lKzE3YxVlTBQ9wdmjCRABB2bkGJNji7muLAsGtI7u0WTYVAJehMREiMwthhJhQw2WC7mSo2XLA40y5fbGxAltMJHMb2dM0oWqtIR/J3XkfLkKv6RwmlWUMVH0BJGgHzuG4nhOkjE5PJ/DLr0ZshsZS0VM5UxmM7pn4mKYC9B0wOYMeCaLOWeLA1bHCLffi0zFYIEQmWzXa5IuVqQJSyY7zDTJlWq68KR3E0OVMVH0DOeMJfCshDBXuljBfLaMHV1sTEaT5hoXZ9NFhAM+R8bidmI4EW5I37ejVmcsFyoYdDLMZaDnRbuxOxPmatdrki5UpHkmqzL0rT0TL5PvgDImih7i3LEkjsznTDfzreWIXmK8s4uNyVgq0vA2jDCTLmI0FXb9m+mQgZzJcr4MZmcFKEU3vvsJ+HVyJsWqNAMmwlytNMhypVpjWJdXKGOi6BnO2ZREnYEXbDYvHu4BYzKaCiNTrK47ElYwky65HuICtJxJoVLruE8htzLo4NCueMiPoJ/a5kxK1RqKlbo3YS5JBizZIQHvhNdlFmVMFD3DuWN6RZfNvMnh+RyIgO1dmoAHVnMfRpPwM5mi68l3YHUyZCfvRLzmZJiLiNAXbS+JL6RUZAtNBv0+RIP+lmGuep2RLcn3TFqFudLFasOweYUyJoqeYcdwHEE/4cBJ+57J5r4oIsHuKwsWCMNw0mDeZC5dwqiLZcGCEd3bmOuQN1l0aQLkQKz9GGEnFIMFqWigYayakT2MK94hAZ8pysvNWMVzY0JEg0R0LxE9r/850OKcnyeiR5t+ikT0Rv21zxLR4abXLnX7MyjcIej34ayRhG3P5Mh8rqFE3K2M682UJ5YL656bK1WRKVVdbVgUGPFMFvUbvNOKxv0d9LnSDswyESTbiEyKNWV5DCLB3qpBMl2Q5wFZxXNjAuADAL7DzLsBfEd/fgrM/D1mvpSZLwXwKgB5AN9uOuVPxOvM/KgLe1Z4xDljSRywUdHFzDg0n8OOoe42Jlv6Nc2wqaX1jUmjLNgDz2RI90w6VXSJccL9Dok8CvpjISy3SYQ7OU8l1WZAljgm6ybv9xHiIX+HnIkKc10P4Fb98a0A3rjO+W8G8F/MLEdXQ9FTnLspianlQkfZ704s5srIFKtdnXwHtL6a4US4MVq4E141LAJaNRegaW+1YyFXRjIccFxtoD/aPsy1KoEv/4abirYe3buap5G3pib2eOpaxYqmSbbhw1wAxph5Wn98EsDYOuffAOBLa479HRE9TkQfJ6KWX8+I6CYi2k9E++fm5mxuWeEVq0l4a3kTMa1xZ5eHuQBgy0AUUwbCXF56JpGgH4lwAHMdypiX8mVXBCg7hbmWdGPnhKRLMtJahl62ZwJoeZO1pcHCkG0Iz4SI7iOiJ1v8XN98HjMzgNZaztr7jAO4CMA9TYc/COA8AJcDGATwZ62uZeabmXkPM+8ZGRmx+5EUHnH+5hQA4OkTK5auP3AyDWDVKHUzW/sNGhPdMxn1wDMBtC74Tp7JYq7sqC6XoD8WQqFSa9mHtJgrg2i1H0UmWpjrdM/EiYFgyRYzTYTRSnqcM3HFlDHzNe1eI6IZIhpn5mndWMx2eKu3AvgaMze+BjR5NSUi+n8A/ljKphVdyea+CIbiITwxZc2YPHsyg2QkgPG+7lMLXsvWgSjufWYG9Tp3lE2fSRcRCfoafQhuM7ROF/xiroxNLhg6kZNZKVROq9Rb0A2a3wEJfDEgi5lPaRpteEMSq9hS0WDDSAkanonL6gdr6YYw110AbtQf3wjgzg7nvh1rQly6AQJp/4pvBPCk/C0qugUiwoVb+vD4pFXPJIPzNiU91TAyypaBKMrVOubXEVI8sVzE5r6oZ59pvS74xVxZ6g21HQMdZtI7qQ2WigRRrXNDBl6wlK8g5PchLlEWvj8WOs2YOFmpZoZuMCYfBXAtET0P4Br9OYhoDxF9WpxERDsAbAPwgzXXf5GIngDwBIBhAB9xY9MK77hoSx+en82allVhZhyYyTTk7LsdUdG1XhJ+crmALR7Osh9KhDHfxjOp1xlzmVJjFLGTdJr8uOCgMRmMtx4StpQroz8WlGrk+6PB04yl8Ey8DnN5bkyYeYGZX83Mu5n5GmZe1I/vZ+bfaDrvCDNvYeb6mutfxcwXMfOFzPxOZpY3KFzRlVy0tQ+1OuPp6bSp606sFJEpVnHuppRDO5OLmAK5Xnnw1FK+YXi8YCQZxmK+3HJo02K+jGqdXak0G+zQ87KYKzcqz6SvGw831mhmKS/fgA3EtDBX85jgRqJfhbkUCnNctKUPAPCkybyJSL6f1yueie5tdErCFys1zGfL2OqhZzLeFwEzWgpTnlwRZcvOeyaNbvwWQ8WcDHOJ921lTGQXHvTHQmDGKX0twhNzorvfDMqYKHqO8b4IhhMh03kTIV9/Tg9UcgHaZL2+aLCjZyJCYF6GuTbpxQwnV07fZ2OcsAueyUAshICPTpN2qdUZS3nnPJN2vTZarkjuDV4UGTQLWi7mSogEfYgpCXqFwhxEhIu29OGx48umrjtwMoPNfRHPv8GZYUt/FJNL7ftzhdeypd870UpRGTe9crpHIMYJu1HN5fMRhhPh03penJbAF+G1pTXGZDkvf1SxeL/m5szFnLOzYoyijImiJ9mzYxDPz2ZPCy104onJFVywuTfyJYLtgzEcXWxvTI4taE2Y2wa9DHNpa59sYUzEsREXEvBinbXhNvF8xCHtsmQ4gKCfTvFM6ro3JNuAtRoC5lZT6HooY6LoSfbuHAQA7DuyaOj8pVwZh+ZzePHEaTqiXc2ukTiOLeRbJrcB4PB8HpGgz5NZJoJUJIBYyN/SM5nNFDGcCCHod+dWM5I83TMRcjOb+pwxaESEwXgIi00l3JliFXWW3yTZagjYgktNoeuhjImiJ7l4ax9CAR/2HTZmTH52fAkA8OLtvWZMEqjWGcfbeCdHFjTRyk5NjU5DRNjUF8F0i5zJiWV356yMtAhzCWPipKryYDx8ipcseoNk52kGRM4k1+SZOFhcYAZlTBQ9STjgx6Xb+vGwQc/kp0eX4fcRLt7a5/DO5CIEKQ/pmmJrOTKf6wrRyi390ZaFAseX8q4OIRtNaT0vtabS2ZMrQrvMOWMyFA9hvqkkeWbFGfHNVCQIH51aOeZkpZoZlDFR9CxX7BzEk1MrLYcFreWnx5Zw/njS84oXs5ylC1Iemj+9fapaq+PYYh47usCYtMrt1OuMyaUCtrlqTCKoM07xTk6mixiKhxAKOHe7G02FGxppgDb5EpBfEu3zkZ4X0t6/VK0hW6qqBLxCYYe9OwdR5/XzJtVaHY8eX8ZLeizEBWgx8uFEuKVK8rHFPKp17grPZGIohuV8BStNieG5bAnlah3bXCxbFv02zRVwM+mi46XJm/uimMmsekROekNjqUijSk6Eu9yQq1kPZUwUPcueiUFEgj5895lO2qDAgZkM8uVazyXfBeePJ/FMi25/0Tdzfhd09G8f1Aza0cXVcJzI87jpmWzTVQOaJWhm0kVscrhpcrw/glqdGx7DTLqIZDjQGLUrk9FkpJEHWnAoN2MFZUwUPUs05MfVu0dw3zMz0KYXtObHB+cBrFaA9RoXjKfw/Ez2tIquZ6fT8BGweyzh0c5WmRjSbuJHF1Y9gmMeGJNWnsnUcgHjDsvNbNbLo08srxqTMYeUqcdSq+XPYr1NXaCCrYyJoqe59oIxTK8U8eRUe52uHzw3h3PHko1+iF7j/PEUyrU6Xpg7NdT1zMkMdo0kTpNb9wJhTI7Mr3omh+dz8PvIVamXtRMql3JlLOcr2OVwKHBTo3FTW/dkuuiYhMxYKoLFXBmlag1TutH0UgFBoIyJoqd59flj8BFw79MnW76eKVaw7/ASfu7c3h2IduEWLYz1+PFT5WOePpHuGp2xWCiAbYPRRugNAJ6ZzmDXcNzxcb1r2ToQbRiTQ7pxczqvtHlN4+bJlaJjvT/CSM2mS5haLiAU8GE47v6UzbUoY6LoaQbjIeyZGMR/PXmyZajr20/NoFyr47oLN3mwOzmcNZLAQCx4Shn05FIeU8sFvKSL8kAvGu87Rcn52ZNpnDfufj5n53AcB2c1L054Sk5XvKWiAcRDfkwuFZAtVTG9UsQuh0ZDi2KCmXQRU8sFbOmPetpnJFDGRNHzvPGyLXh+NoufHls+7bU7HzuBLf1RXLat3/V9yYKIsGfHIB5uatAUFWzdlAe6YHMKRxZyyJaqSBcrmFwqeOI5XTCewsl0EQvZUiPUJhLzTkFE2D2WxLMn0w1DttshQdGdQ5qRemEui6mlgqfjB5pRxkTR81x/6WYkwgHc8uPDpxx/YS6LHz43hze/ZGtPTFbsxJW7hnBsMd/4pv2TFxaQjARwXhdUcgletDkFZm00wBO6ovMFHngmL9L11546kcYz02lMDMUc7TERXLA5hadPpPHcjBbq2z3qTGHE9sEYYiE/npnOYFIZk1WI6C1E9BQR1YloT4fzriOiA0R0kIg+0HR8JxE9pB//MhF5XyOncJV4OIB3vXQC33x8Gk+dWM0rfPK7BxHy+/DOKyc83J0cRJju64+dQLFSw389eRLXnD/myExzq1y+cxBBP+F7z87iu8/OIuT3eeI5vWizpnLw6PFlPHR4ES/dNeTKuuePp5AuVvGD5+YQCvgc6/z3+QjnbkriW0+exEKu3DXipZ4bE2gz238ZwA/bnUBEfgCfBPA6ABcAeDsRXaC//PcAPs7MZwNYAvAeZ7er6EZuunoXhuIh/NHtj2E5X8a3njyJ//jZFH7z6p2uKdY6yZb+KPbuGMRt+47jSw8fQ6ZYxS9dtsXrbZ1CKhLElbuG8I3Hp/GtJ0/iyrOGHOmzWI++WBDnbUrin+59DtlSFVedPezKusIj+ubj07hoSx8CDopbnq+H8gDg5bvd+Xzr4bkxYeZnmPnAOqftBXCQmQ8xcxnAbQCuJy128SoAd+jn3QrgjY5tVtG19MdC+Ke3XYpDczlc8T+/g/d+4RFcuq0f7/v53V5vTRp//NpzMbVcwN98/WlcvmPAtZukGd6+dzumlguYWi7gHXu3ebaPX7tqR+PxVWe58/d0ydb+RvPgjS/b0flkm/zCheONx06XPRulV4SKtgA43vR8EsAVAIYALDNztel4y69rRHQTgJsAYPv27c7tVOEZP3fOCO747ZfiK/snMZYK48aX7UA05H0Phiz27hzEzb/6Ejx5Io1fvXKiq0Jcgl+4aBz/+JZLUKnVcV3TDc9tfumyrVjIlXHdizahL+bOMDS/j3DHb78MX3/sBH7B4erBl+8exld/+2UA0DX5QOrUOSxtEaL7ALT62/0LZr5TP+f7AP6Ymfe3uP7NAK5j5t/Qn/8qNGPyIQAP6iEuENE2AP/FzBd22s+ePXt4//7TllEoFApFB4joEWZumdt2xTNh5mtsvsUUgGafeat+bAFAPxEFdO9EHFcoFAqFi3ieMzHIPgC79cqtEIAbANzFmlv1PQBv1s+7EcCdHu1RoVAoNiyeGxMi+iUimgTwUgDfJKJ79OObiehuANC9jvcBuAfAMwBuZ+an9Lf4MwDvJ6KD0HIon3H7MygUCsVGx5WcSbehciYKhUJhnk45E889E4VCoVD0PsqYKBQKhcI2ypgoFAqFwjbKmCgUCoXCNhsyAU9EcwCO2niLYQDzkrbjBb2+f6D3P4Pav/f0+mfwYv8TzNxy0tyGNCZ2IaL97SoaeoFe3z/Q+59B7d97ev0zdNv+VZhLoVAoFLZRxkShUCgUtlHGxBo3e70Bm/T6/oHe/wxq/97T65+hq/avciYKhUKhsI3yTBQKhUJhG2VMFAqFQmEbZUxMQETXEdEBIjpIRB/wej9mIaJbiGiWiJ70ei9WIKJtRPQ9InqaiJ4iov/u9Z7MQkQRInqYiB7TP8PfeL0nKxCRn4h+RkTf8HovZiGiI0T0BBE9SkQ9qfhKRP1EdAcRPUtEzxDRSz3fk8qZGIOI/ACeA3AttPHA+wC8nZmf9nRjJiCiqwFkAXxuvWmU3QgRjQMYZ+afElESwCMA3thj/wYEIM7MWSIKAvgRgP/OzA96vDVTENH7AewBkGLmX/R6P2YgoiMA9jBzzzYsEtGtAO5n5k/rM55izLzs5Z6UZ2KcvQAOMvMhZi4DuA3A9R7vyRTM/EMAi17vwyrMPM3MP9UfZ6DNttni7a7MwRpZ/WlQ/+mpb3REtBXA6wF82uu9bESIqA/A1dBnNzFz2WtDAihjYoYtAI43PZ9Ej93IziSIaAeAywA85PFWTKOHiB4FMAvgXmbutc/wCQB/CqDu8T6swgC+TUSPENFNXm/GAjsBzAH4f3qo8dNEFPd6U8qYKHoOIkoA+CqAP2DmtNf7MQsz15j5UgBbAewlop4JORLRLwKYZeZHvN6LDV7OzC8G8DoAv6uHf3uJAIAXA/i/zHwZgBwAz3O4ypgYZwrAtqbnW/VjChfR8wxfBfBFZv4Pr/djBz008T0A13m8FTNcBeANet7hNgCvIqIveLslczDzlP7nLICvQQth9xKTACabPNo7oBkXT1HGxDj7AOwmop16wusGAHd5vKcNhZ68/gyAZ5j5n7zejxWIaISI+vXHUWgFHc96uikTMPMHmXkrM++A9n/gu8z8To+3ZRgiiuvFG9BDQ68B0FPVjcx8EsBxIjpXP/RqAJ4XoQS83kCvwMxVInofgHsA+AHcwsxPebwtUxDRlwC8EsAwEU0C+Gtm/oy3uzLFVQB+FcATes4BAP6cme/2bkumGQdwq14d6ANwOzP3XHltDzMG4Gva9xIEAPw7M3/L2y1Z4vcAfFH/YnsIwK95vB9VGqxQKBQK+6gwl0KhUChso4yJQqFQKGyjjIlCoVAobKOMiUKhUChso4yJQqFQKGyjjIlCYREiYiL6X03P/5iIPiTpvT9LRG+W8V4KhRsoY6JQWKcE4JeJaNjrjTRDRKp/TOE6ypgoFNapQpvD/YdrX1jrWRBRVv/zlUT0AyK6k4gOEdFHiehX9BknTxDRWU1vcw0R7Sei53RNLCES+TEi2kdEjxPRbzW97/1EdBeAp/VO72/qc1OeJKK3OfkXoVCobzAKhT0+CeBxIvoHE9dcAuB8aOMADgH4NDPv1Yd9/R6AP9DP2wFNN+osAN8jorMBvAvACjNfTkRhAD8mom/r578YwIXMfJiI3gTgBDO/HmjIlisUjqE8E4XCBrpq8ecA/L6Jy/bps1lKAF4AIIzBE9AMiOB2Zq4z8/PQjM550LSk3qXLyTwEYAjAbv38h5n5cNN7XUtEf09Er2DmFfOfTqEwjjImCoV9PgHgPQCaZ0pUof//IiIfgFDTa6Wmx/Wm53WcGi1Yq3XEAAjA7zHzpfrPTmYWxijXOJH5OWieyhMAPkJEf2XhcykUhlHGRKGwCTMvArgdmkERHAHwEv3xG6BNVDTLW4jIp+dRdgE4AE1o9Ld1KX4Q0TmtBiMR0WYAeWb+AoCPoQskyhVnNipnolDI4X8BeF/T808BuJOIHgPwLTR5DSY4BuBhACkA72XmIhF9Gloo7Ke6JP8cgDe2uPYiAB8jojqACoDftrC+QmEYpRqsUCgUCtuoMJdCoVAobKOMiUKhUChso4yJQqFQKGyjjIlCoVAobKOMiUKhUChso4yJQqFQKGyjjIlCoVAobPP/AxQmSFG10rQYAAAAAElFTkSuQmCC\n",
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FLgRBwC/OGAYAePurWi5/o1ebI20efHGwDWaTgB+fOBCnn1ACAPhodyOMPgu07Ug7fMEQirJsmD9lAADgm6NOdPmDOp+sb7YfDRd8Z44txdjKPADAN7V8eWKDIRF7GsJtsJOHFQIA1u9p0vNIAIBv610AgBPKsgEAWw62otGlX4vWsIVTR0cHtm7diq1btwIIxw1s3boVBw+GWypLly7FwoUL5ftfc801+O6773Drrbdi586d+Mtf/oJ//vOf+PWvfy3fZ8mSJXj66aexatUq7NixA9deey3cbjeuvPJKTZ8boR4b9jbj3v/sAAD85pwxuHBa7540QRBw1w/HYergAri8Adz+6leGf/NJF0RRxOtbw4XTj6Q3UQCYObIYOQ4LGlxefH6gVa/j6cYXB8PPeVxlLkpy7DhpSCEcVhPqnV7sbjC2SZz9fU4dXICBBRkozrYjEBLlooRnWEEyvCQbYyvCnYlvjvJVOB1q6USXPwS7xYQFJ4V/NrLvJz050hpuu08dXIgfnzgAv5o9EioN1sWFYQunzz//HFOmTMGUKVMAhIueKVOm4I477gAA1NbWykUUAAwdOhRvv/021qxZg0mTJuEPf/gD/vrXv2Lu3LnyfRYsWICHHnoId9xxByZPnoytW7di9erVxxnGCWPS3unHkn9uRUgEfnziAFw1c2hcj7NZTHjowklwWE34eE8zXovqtRP6sf2oEweaO5FpM2POuMi/UbvFjLnjwu31t786qtfxdOOLg20AgClV+QAAh9WM6qFFAID/7dZfPUiFz/eH38RPGlIIQRAwWXqO7DnzDJtsHF6ajXGV4cJpO2eF0y5J2RlRmo1hxWF152hbV28P0YTDbeHCaWBBBh6+aDJurDkBRdl23c5jvBAMiVmzZvX6m393qeCzZs3CF1980et1Fy9ejMWLF6d6PIJD7nn7G9S2d2FocRZ+N398QjkqQ4uzcMPskXhg9S48+M4unDW+Ahk2s4qnJfqCtRBOGV6ETFvsj7I5Y8vw8ubDXLQZtIYpBJMH5cu3TRtcgA+/bcT2I/wrM72xTTr/FOm5TRmUj/d21OOLQ236HSoOAsEQ9jeF2/wjSrKRn2EFwF+rbrdUOI0qy0FlfgYAoN7VBX8wBKtZP52FKU4DCzJ0O0M0hlWcCCIRPtvfgpc3H4YgAA9dOOm4N9p4+NmpQzEgPwO17V1YtWG/8ockEuKTvc0AgBnDi4/73PShYX/G3ka3ocbVU8UXCGGbpGJMqSqQbx9VngMgPCVlVDy+oBwZMrwkrIawltdezluQh1s98AXDLbAB+RkYXR4+d6PLixa3T+fTRdhVH34dTyjPQVGWDTaLCaII1LXrqzodlgqnAflUOBGEJgSCIfz29XBi/MUnVWHq4II+HtE9DqsZvz7zBADAX//3nSEMqemKLxDCZ9K0z6kjio77fH6mDaPKwsXC5/v1nwrSip11TvgCIRRkWjG4KFO+nRVOexo7EDBoOOhBaTAj12FBfmZYsRkkPcdDLZ1cew9Zm25YSTZMJgEZNjOKpVYTT7EZR1rDr/HgwkyYTAIq8xwAgKNt+p0xEAzJBfPAgsw+7q0NVDgRac+zn+zHzjoXCjKtuHXu6JSudd7kSgwsyEBThw8vUraTbmw91AaPP4iirEiBdCxMddq0T39zq1bslhSD0eW5Ma3oqoJMZNrM8AVCXI7AxwM795DiLPm5MQXC7Qtypdwci+xvKsmSb6vMDxcleqs50TRIk2qlueGijrXrjupY3NW7vAiGRFjNAkpz9PM1RUOFE5HWtHf68ScpMO+2eaNRkGVL6XpWswm/OGM4AGDVhgP9ctydB7YeijUJdwcrnD7rR4rTd01M2ciKud1kEjCyzNjtuoPNkhpSFHluDqsZ5bnhAuRQKz/KzbGwVlO0CsjOfZSTwkkURXnEvzQnfDa5cNLRIM78TRV5GartqEsUKpyItObp/30HV1cAo8pycFEf0QPx8uMpA5Bjt2Bfkxv/64fmYx748nDYJDyxKq/H+7CJq111rn6zu25vQ1iVYR6gaEZLhdO3Bi2cmOI0uDC2XTNI+vNBjjPW6qVWU5lULAFARR5TnPgo+JxdAXgD4X8nJTmxitMRHVt1h6X2IS/GcIAKJyKNae7wyvuglsw5QbHfVrLsFlwwbSAA4P+RSVwXvjrcBgCYOCC/x/sMLMhAjt0CXzAkZ+ikOz0pTgAwUgoP3GPQhb8HZMUptnCqKoz4nHil4RglBwDK88KFQC0nihNTm3IcFjis4YnhAfn6e5zYaxdddOoNFU5E2vL4ur3o9AUxcWAe5oxVNovr0upBAIAPdjXqmmDbH2l1+3CoJfyDfMLAnhUnQRAwppLPoEE1CIZEeeS9O8VpkFxg8KFwJMqBlojHKZqqwnABwlp5PNLgZG/+EY9ORHHio3BqcIXPEe0jkhUnHdugrZ1h71pBZmo2CyWhwolISxpcXfh/nx4AANw0Z1RCmU3xMKI0B5Or8hEMifj3VgrE1JKvpCyfocVZyJPycHpCTmjmLC9HDQ63dsIXDMFmMclveNHIE2it/BYYPREIhuQ370E9tOp4fV6iKEaKktxoxYmvwon9AlgSVTgxlUfPSI82tx8AUJDZ+791LaHCiUhLVn2yH95ACFMG5eP0kcfn/CjBBVPD7bpXtlDhpCUsBHHCgJ7VJkYkodnYwY/x8F1jWJEZVpzV7e7FKmmUu63TD2eXX9OzpUqz24eQCJhNAkqOSYyu4rxwau30wx8MD5FEn50pTrXtXVxEKRxrDAcgxz60efwI6jQIwxSn/BQHe5SECici7XB7A/j7p+GogF+cPlxxtYnxg4kVMJsE7Kh1Yn+TMUe8jQhb+Dm6ovsYgmjGRrXqeHhzUpMDzDxd1H3WTZbdgkLpzYdnP1B3MHN1aY79OK8im05rcHq5/DtmZy+UAiUZTM3x+INwegK6nC2a7hQn1h4TRcDp0afYbuskxYkgVOelzw6h3ePH0OIsnKmwtyma/EwbZgwLhy++s71Ota9DxLKrLrIWoi+Gl2RDEMITQ00d/Ob8KMEReZ9XzyGBVdJkktF8TvVOli90vEGYvdF7AyE4u/QvQI4luuiLxmE1y4WsnjlJjIiBPXJOq9mEHEd4y0JLpz7/fsjjRBAq4w+G8Mz68CTdotOGdduyUJK548PLZFdT4aQJ/mBIbkmdEEfh5LCa5THmvQadJosXVjj1tpaCtbUOc9rW6gl5nL+bAESH1Sy/uTe6+PALRdPbVFhxdrgY4CG8s/GY8EsGK1hadTpjq6Q45ZPiRBDqsHpbHY60eVCcbcOPTxyg+tebO7YMghDezs7T6oR05UCzG75gCFk2c9x7q9iEGSu40hVmnh7QS95NlQEyj7qjuxykaJhK0sDhhGtDD4oTEClKeCqcSrJjX2MWGswKGC0RRRFtpDgRhLo8J+UqXVo9WM4iUZPSXAdOHBTefffu9nrVv15/Z1ddWDUaWZYTdy4XK5xIcYoYxI3qcSrLPb74ACKGZh6jQeqdPStOhXJRon/hxFpxBVmxyg7zFumhOHV4AwhIpnQqnAhCBXbUOvHZ/lZYTIKcs6QFZ7F23TZq16nNrvr4/U2M/lA4dfmDsoert8KJqVG8hC7GS28eJyDic2J5STzRnemawdScZp39d6Ioot3DWmKxBUphpn7FHTOG2y0mZNjU/0U4XqhwItKG5zaEc5vmji/v8QesGswdFy6cNu5r1s0H0F9gxc+I0uMDHnuCpWinc6uOJTtn2sy9ekGYYsMUHKMQb6uuUce8oZ6Qzc3djNMXcaI4dflD8EnrVo7NRmPn1sMczqMxHKDCiUgT2j1+vP5FOE9p4cmDNf3aVYWZOKEsGyER+GRvs6Zfu7+xj2UVdbNSpCeY4nSotRNd/qAq59Kb6DZdb/EbbHS/tdNvqNciYrDuoVWXyxQn/grCyJv/8QUtLx4npjZZTAKyjlF22LlZEKWW8GgMB6hwItKEVzYfhscfxKiyHEwfWqj51585ogQAsH5Po+Zfu78giiL2SXlZQ4vjL5yKs23IsVsgisabJouXeIzhQFhNYFlCPLa1usMbCMqFRVlOH606Dj1OrXIO0fGqCS8epzZP+OvnZViPK7z1VJx4NIYDVDgRaYAoinjxs3Dg5WUzBqsWeNkbp0np5P/b3cRlCF86UO/0wuMPwmwS5OmweBAEwbDTZPHCWnXdrVqJRhAEWXWq53B0vzuYd8tmNvWoPPBqDo+ZCuumVScXJTqoOdG0S8VdXi+qmB42BPY1jzWs6w0VToTh+fJwO76t74DdYsJ5kyt1OUP1sEJYzQIOt3rkLe6EsnzXFPY3DSrMhNWc2I8uts+M50WwqcDM0xVxePtY4cTLjrS+aJZ8S4VZth5/KeI1jsDtC8rrVrpr1RXqnJHEaJNadd3tfizQ0RweadWR4kQQivLPzw8BAM6eUIFchz6/mWTaLHIswfo9TbqcId1Jpk3HYAtuDxosMTte6uUlst17gKIpNZhBvFkqKgp72VVWLO2Aa/f4ZZMzD7CCyGYxIaObeBSmpLS4fboq1fJEXTeFU6GOOU69nUtPqHAiDI3HF8SbW48CAC6cNlDXs8wcEW7Xrd9NhZMaMGN4MoVTurfqGvoY149GbtUZpHBqkVp1Rdk9F055GVawWK82DjKRGNF71rpTy4qywgWfLxiC26efWV9u1XWrOEnm8E4fQhov+u3whlfo5Oj0C3FPUOFEGJr/bquFyxtAVWEGTh5apOtZZko+p0/2Num2STydSUlxKjRm8GO8NLjYSpI4Cqc8qVVnEHN4SxyKk8kkyO0cPZSRnmjpw9ycYTPDYQ2/DevZrmPm8O5aYrlSMRUSgQ6ftrsAO6Tdg9nSSh1eoMKJMDSsTXfh1Kq4k6TVYuLAfOQ6LHB2BfDV4TZdz5KOHJYmxwYlYAxnDIpSnNLNvO8PhmQDdU/j+tGUGkxxiqdVB0SUEb1H+6OJZyqskINIgvZePE4Oqxk2yVPo0niJsqw42alwIghFONDsxqfftUAQgPOn6tumAwCzScDJw8Kq18Z9LTqfJr0QRVGOEhjYx8h9d4TzjQBPVMJ2usAmySwmIa6xbcO16tzh51fUR+HEy2h/NPFMhek57s9o66VVB0Beouzq0lbNc0mFUzYVTgShDC9vPgwg7C2Kd+Gr2pw0JJwh9fn+Vp1Pkl60dvplD0hfI/fdYbOY5ImzdPM5sUmy0hx7XKoryzxq4mwCrSda3D2P80eTr+P0V0/EMxXGil09vVmRdSvdF07ZcuGkdasufK4catURROoEQ6JcOC04qUrn00SYOiQ8Wbf5QEvatYT0hKlNZbn2pJc3R/a0pddkHVOO4l0zVCyZrN2+IDw6GpLjhRVOfSpOnIz2R8OKocJeCqfcjHBR4PRoW5RE01urDogULh06terI40QQCrDxu2bUtnch12FBzZgyvY8jM74yD3aLCa2dfuxN491oWsP8TQMLEvc3MZhSxcIi04UGeY9b3/4mINz2YOnhTRzudjuWiDm89+fHS5hkNPGsDGERKk6PfufuS3HKsUtn1LhVxwo19vV5gQonwpC8vjW8l+6ciRVJKxBqYLOYMGlgPoCw6kQoQyr+JkZFHiucjOHtiRcWflkax0QdEE4PL5aKjGaO1JmeSNQczlMcQTxLatnUmtZFSTTxe5y0U5yCIVFuz5PiRBAp0uUP4r9f1wEA5k8eoPNpjoe168jnpByHWpjilHzhNCA/XFikneLEwi9z4lOcAKBYum8z54qTLxCS36z7atXxYLI+lra4FCd9W3WiKMqm754ChFmOkpaFE2vTAUCWnZ9fjgEqnAgD8v7OBri8AVTmOWQzNk9MG8x8TlQ4KQVTnKqUaNWlmceJTQmWJFA4sSKE91YdU2zMJqFHNYTBo8eJqUi9nV1vxanTFwSLnespaFKPqTpWONksJtgtVDgpxooVKzBkyBA4HA5UV1dj06ZNPd531qxZEAThuI9zzjlHvs8VV1xx3OfnzZunxVMhEuC1L8JtuvOmDNA9u6k7pkqF03dNbu5/ozcKynqc0qtVx77H2NqReGD35T2aobmDtbqsff5bZyP/PAVgMoUmt7fCyaFv4cQKFLNJkMM4jyVXh1ZdxN/EV5sOMHDh9NJLL2HJkiVYtmwZtmzZgkmTJmHu3LloaGjo9v6vvvoqamtr5Y9t27bBbDbjwgsvjLnfvHnzYu73wgsvaPF0iDhp6/Rh3a7w3zGPbTogPHo8ojQbAKlOShDOcEq9VVcpeZxa3D5DTJPFS1McK0mOpSibter4Lpzi8QgxCjhTnERRlA3fve3Q1HuqjhVD2XZLj0uUI606LRWn8Nfizd8EGLhwevjhh7Fo0SJceeWVGDt2LJ544glkZmZi5cqV3d6/sLAQ5eXl8seaNWuQmZl5XOFkt9tj7ldQUKDF0yHi5D9f18EfFDGmIhejynP0Pk6PULtOOVrcPnj8QQgCUJEfnwG6O3IzLMiyhSX/dGnXiaIot9sSU5yM0aqL7Hrru3Bi5nGXN8DFol+PP4iA1APrLYeIF8Wpt5BJPXKcogs63jBk4eTz+bB582bU1NTIt5lMJtTU1GDDhg1xXeOZZ57BxRdfjKys2L1X69atQ2lpKUaNGoVrr70Wzc3NvV7H6/XC6XTGfBDq8brUpps/uVLnk/TOiVLhtPVQm74HSQMOSWpTea4jJa+DIAhpF0ng9gXhlYqERBQnVmQ1uzkvnKQdar21uhi5Dr4W/TIFyWwSkGnr+ftW9jjpFEcgt8R6Ke70mKqLp6DTC0MWTk1NTQgGgygri83vKSsrQ11dXZ+P37RpE7Zt24af//znMbfPmzcPzz33HNauXYv7778fH374Ic466ywEgz3L+suXL0deXp78UVXFTxhjunG4tROb9odXrPyQ88Jp4sA8AMC2I+2abxRPN5SIImBU5LMQzPTwObH070ybGZm2+N9gWJHFe6uur3yhaExRBvI2HTORGJFJtZ5bYOHPM8UpoEtortwS66VAydFBFYunoNML/k6kAc888wwmTJiA6dOnx9x+8cUXy/8/YcIETJw4EcOHD8e6deswe/bsbq+1dOlSLFmyRP6z0+mk4kkl3vyyFgBw8tAiOZOHV0aUZMNhNcHtC+K7JrfseSISRwljOION7DcaZN1IXzDFKBG1CYg2h/P9OrSzcf44FCcgrN60dvp1DZNksCKjL7WMeZyCIRGdviCyNFZY5JZYHIpTdESA2pDipDDFxcUwm82or6+Pub2+vh7l5eW9PtbtduPFF1/EVVdd1efXGTZsGIqLi7Fnz54e72O325GbmxvzQajDW18dBQCcO4lvtQkALGYTxlWGVaevj7TpexiDo6TixNK1jbLgti+YMTwRfxMQKbRa3D4EOVZE48lBioYpTu08FE6e+BSTDKsZFqnHqIfPKZ4CRY+pungKOr0wZOFks9kwdepUrF27Vr4tFAph7dq1mDFjRq+P/de//gWv14vLLrusz69z+PBhNDc3o6KiIuUzE6mxr8mN7UedMJsEzBvfe3HMCxMGSIXTYfK9pQILv0wlw4nB0rUbnHwrLfHCFKOiPtaRHAszW4dEfVd99AXzOPWV4cTQ22gdjbOPUEmGIAhRPiftJ+tccXmcwufr8GrXTowUdHytWwEMWjgBwJIlS/D0009j1apV2LFjB6699lq43W5ceeWVAICFCxdi6dKlxz3umWeewfz581FUVBRze0dHB2655RZ8+umn2L9/P9auXYvzzjsPI0aMwNy5czV5TkTPvC2pTaeOKO5z9QIvyIUTKU4poYri5EoPxalZDr9M7N+E1WyS3yh5Sto+Fnn5bBxTdUCU4sRBllM8UQQMOT2cU8WJfa+wdqIm5yKPk/IsWLAAjY2NuOOOO1BXV4fJkydj9erVsmH84MGDMJli68Jdu3Zh/fr1ePfdd4+7ntlsxldffYVVq1ahra0NlZWVmDNnDu655x7Y7Yn9Nkcoz1tfhf1NP5hoHPUvYhB3IhgSYeYwrJN3RFHEEWkCboAChVNJmilOzUkqTkB4fN/VFeBiAq0n2pLwOAFAu06ZSNE4E3jj13OyLjL23/NrnGE1w2wSEAyJcHUFNPFh8exx4u9ECbB48WIsXry428+tW7fuuNtGjRrVo8yYkZGBd955R8njEQqxp8GFnXUuWM0C5o41RpsOAIaVZCPTZkanL4i9jR04oYzf3Cleaff40eUPj9uX5Saf4cRgilOjywtRFHuddjICTe7Ewy8Z+Zk2HGjuRItbf3WmJxKZqgOiwiR5atXFGaUQ/RgtkQuUXgo8QRCQbbeg3eOHq8uP8rzU/y3Gey6tzfLxYNhWHdF/YNN0p40sQV6cP0B5wGwSMF4yiH91uF3n0xiTOsnEXZhlg8Oa+r4qts/NFwzJaoaRYXEERQmawwGgUPq3xEvSdnewv6N4PU48msPjatXpmB7eIRVrfa02YcqPVq06lu6f1UsGll5Q4URwjSiK8jSdkdp0jAkDmUG8Td+DGBSWt1SugNoEAHaLGQVSwZAOPie2kqQoCd+fvKKE01adNxCExx9+88zPSMzjxIPhPaI49a2Y5Nj1O3c8ihMAOcTTrVEkgdsX/joZVDgRRGLsrHNhb6MbNosJZ44t6/sBnMF8Tl8dIcUpGepZ4aRgayCdJutYmy2elSTHUiAVW7yaw5lqJAjxG4SZusOD4hSZVutbcWJFS4dPv6m6vrxEmdLn3VorTtSqI4jEYGrTrBNK4voBxBssy2lnrYvrvBxeYYqTEv4mRmmaZDmJoiirRclMmjLlrY1TjxObjMt1WGGKc7CCr1ZdJDm8L1hxoJWaE028ilO2Paz8dGpU3MmKkwIteqWhwonglnCbLuxvOseAbToAGFqcBYfVBI8/iAPNbr2PYzhYcVOhhuJk8PRwZ1dALsbjNU9Hw7vi1JagMRyIGLG1DGrsiUTM4cxf1KHDuVnh1JfHia30cXu1UZw6SXEiiMTZftSJA82dcFhNqBljvDYdEDaIj5Km6XbWuXQ+jfFQ2uMExE7WGRlm6s6ymZMyzhcyjxOn5vBEowgA3hSn+OMIWHHQoVFRwhBFUS7W+lKcsjT0OIliJC+qtwXJekGFE8Etb0ptuu+PLuXyt454GV0eXsOzs5YSxBOFKU7KepzSo1XHlKKCJANh8zk3h7PiJx7FhsEKpw5vAIFgSJVzxQtbnpsTR/K17HHyalvweQMhBCTVMn6Pk/qFkzcQktVUKpwIIk5EUcTbcugl/7vpemN0RVhx+qaWFKdEqVOhcGJ+KaO36phSlGySPntcK6exDM4kCqdodUfPdl0gGJLzx7Lsfb/xy606jT1O0a9Rlo2fOAJP1NfI7ONcekCFE8ElWw+14XCrB5k2M743qlTv46TEmApJcaojxSkRuvxBuV1D5vDjaZEKp2Qm6sKPk8zhnT6EOBxcYG/q8ZirGVazSW4p6dmu6/RH3vjjUcsj5nBtW3Ws7ZZlM/dpwGfKjxbFHVO17BYTlxsXqHAiuISZwmePKeMyxyMRRpeHFafDrR4uEo2NQp3kb8q0mRN68+yLaHO4VgtL1SCViTog0qoLiXwkbR+LiwUzJjhNK2c56ficWEFiMQmwW/p+m2VqjtYqGStQMuMp7iTlp1ODwsnDsb8JoMKJ4JBQSMR/vjbebrqeyM+0yVNh35JBPG6ijeFKrkaR08MDIS5MxMmSSoYTANgsJvkNu4VDg7icg5SgvzGXA4O4O2pdSDzfu9k6xRF0JpDOnaVhjpNbLpz4a9MBVDgRHLLlYCtq27uQbbfgjBNK9D6OIrB23Q4yiMeNGsZwAHBYzfKIu5F9ThGPU/L5ZjxNoR2Li5mrE1Qb2f31GO1nsOm4eBfUMnO4xx/U1NTemUCBwrxaWhR3LCuKFCeCiJO3JbXpzLFliuwn4wHWrttBilPcyMZwBf1NjHSYrEt1qg6IqDNODnKPjiWR5O1o9Gp7RRNRnOL7+RV9P62SuYFI2y2ec8o5Thqcr1MqPONpIeoBFU4EV0S36c6ZYPw2HWN0BUUSJEqdCutWGPJknYHXrrD2WmGSrToAyJP2qPGoODm74s9BioYVWnp6nDqiWnXxYLeYYTObYh6rBYm0xFhxpYXHSfZecfqLMxVOBFdsPtiKeqcXOXYLTjuhWO/jKMZYKZJgV52LywkmHlGzcGI+JyMv+k01jgDgvFWXpDlcbtXpsL6EwRSneFt1gLatMAZricWjOMnmcA3jCOJV7LSGCieCK1h205ljy2C38PmPJhmGFGXBZjHB7QvicKtH7+MYglqn8nvqGOmgOLGpuvwUFCe2FNfJZeGUnOLE/EJctOoSMDfrcW4Wf5CI4qRNHEH4XBlkDieI3olp06XBNF00FrMJI0qyAQC7G8jnFA/17crvqWOUZIcVp6YOYxZOoZAoq0TJ7KljGEFxyk1QcWL3d+naqkt8z1qWTfvJOllxisOEnSkrTlrEEcR/Lj2gwonghs8PtKLB5UWOw4KZI9OnTccYWcYKpw6dT8I/gWAIDS71zOFF2WGVprmDvzH8eOjwBcA6vnkJJGsfi1w4cZYe7o9K3k56qo6LVl38b/x6nNudgAmbFYH+oAhfQN3JP4ojIIg4+c/X6dmmY4wslQqneiqc+qKpw4eQGA4QLJLUISUpygpfs9ltTMWJFToOqymlydO8TP2N1N0R3a7qa/nssfAwVZeoORyInFvLwikxxSlq8k/lM1IAJkHEQTDNQi+7Y0Rp2CC+h1p1fcKiCEpz7KqsXGCKE4/Bj/HAVtHkZyTvbwL4bdWxNluG1QyrObG3qchUnf6KU0KtOlY4aelxSkDZsZpNsEkp6Gov+mWvXyaZwwmiZz7f3xJp041Ij9DLY4lu1Rl51YcWNEiFU4kKbTogtnAKGnDKkRU6qbTpAD5StrsjWWM4EKXc6LlyJQmPjh6tukRynADtFv3KwZwUR0AQPcPUprnjyuXfatKNwYWZsJoFdPqCONpu3DF4LWCJ3iyoUmkKo/a0tXUaT3Vq84TPnJeCMRyIGKl5K5ycXcmlhkc/Rt9WnTHM4XJeUpxeItY6U/uMnQns0NOD9HyHIgxFMCTiP9vqAKRX6OWxWMwmDCtmPidq1/VGo1Q4lahUOFnMJnkardmA7bpIqy61wonfVl1yqeFA9FSd/q26RHKc5DgCTT1OieUlRYo7dRUnN3mcCKJ3PtvfgkaXF7kOC04dkX7TdNGMkNp1e2iyrlfUVpwAoEgKjjRiJIESUQRApHBydQW4almm1KrTae9bNEl5nKSixKPhyhXZSxSn4pQhFTJqRxKQOZwg+qA/tOkYNFkXH2orTgDkaT0jGsSV8jhFP17PpbjHkmyGExBbbOkVSZDMVF2GRm2waGTFKcFWncevtscpsYJOa9L7XYrgnvA0ndSmS9NpumhGSpN1FILZO40uNlWnjjkcAIoNnOXUpkBqOADYLCZkSAZcntp1qShOVrMJDqsp5jpawwqSZFauqF2URJPo9Br7XulS+YwswyuDzOEEcTyb9rWgqcOLvAxr2rfpAJqsi5dGTVp1UpaTAVt1zOOUquIUfQ2+CqfkzeEAkG3X1+fUkeC0GgBkWLU1h4uimLDi5JBbdeoWTqx4zKBWHUEcz9tfHwUAzB1XlnBeixEZUpQFs0mAqysg+3iIWERRRGOH+q06thy3yYCtujaFPE4AkJsRftPkq3BK3hwOALnyZJ32z8kfDMnJ2skoTlos0QUAXzCEgORri1dxYvEAaqtizONEihNBHEMwJGI1m6abWKnzabTBZjFhSFEmAPI59URrpx/+YPgHerEKqeGMSKvOeAWsUyGPU/Q1+CycklOc9IwkiFaMEvE4ZWqk5jA6oybj4s1LYgpQl4pnFEVRLsxSScVXEyqcCN3YuK8ZTR0+5GdaccrwIr2PoxkjmEGcfE7dwtp0BZlWVYcFmDncmB4nZZLDAT4Lp0iOU3KFYbaO++rY17RZTAmp6JElutoUTizDyW4xwRLnOZkCpOYZvVF78KhVRxDH8PZX0jTd2PJ+0aZjDCsJF07fNbp1PgmfNGhgDAcicQRGnKpjAZjKtOr4K5xSVZwi++q0f04s4yiRNh0QrThpU+xFMpwSn/xTs1UXHcfg4HTKms9TxcmKFSswZMgQOBwOVFdXY9OmTT3e99lnn4UgCDEfDkfsD2ZRFHHHHXegoqICGRkZqKmpwe7du9V+Gv2SQDCEd7b3n2m6aIYVZwEA9jVR4dQdWkQRABHFyWg5Tl3+oDx1lGpyOBBRnHha9JuqOZwVA24NM5EYyRjDgYji5PEHEdIgUyuS4ZSIgV2Dwkm6ts0cvxKmNXyeKg5eeuklLFmyBMuWLcOWLVswadIkzJ07Fw0NDT0+Jjc3F7W1tfLHgQMHYj7/wAMP4NFHH8UTTzyBjRs3IisrC3PnzkVXF63HUJrwNJ0PBZlWzOhHbTogWnEij1N3aBF+CUQUJ2dXQDbzGgHmbzKbBOQosJKCx7UrTHFKJscJiKg9WmYiMeTwywQziFgBI4pAV0D9gq8ziZBJWXFSsSBl5+K1TQcYuHB6+OGHsWjRIlx55ZUYO3YsnnjiCWRmZmLlypU9PkYQBJSXl8sfZWVl8udEUcQjjzyC3/zmNzjvvPMwceJEPPfcczh69Chef/11DZ5R/+ItKfRy3vj+1aYDIorT0fYuzWR5I6GV4pSXYYXZJAAwVruOTdTlOiwQBCHl6/HocUq1VccUJz08TsmsWwFiJ8jUXmkCRBcoCbTqNFCcWEYUrxN1gEELJ5/Ph82bN6Ompka+zWQyoaamBhs2bOjxcR0dHRg8eDCqqqpw3nnnYfv27fLn9u3bh7q6uphr5uXlobq6utdrer1eOJ3OmA+idwLBEN6RpunOTuPddD1RkGVDgdRioXbd8TRoVDiZTEIkksBA7TrZGJ5i+CVDbtVxUjgFgiH5jTlpc7iOilMyqeFA+PtRLkw0aDGy1zjeiTpAG8WJ9wwnwKCFU1NTE4LBYIxiBABlZWWoq6vr9jGjRo3CypUr8e9//xt///vfEQqFcMopp+Dw4cMAID8ukWsCwPLly5GXlyd/VFVVpfLU+gUf721Gs9uHoiwbZgzrX206BmvXUeF0PA3OcGtc7cIJMKZBnKWGKxFFEH0dXhSnaJUoacVJXl+ivccpWcUJiPii3Boo0R7payRSoGjicfLxHUUAGLRwSoYZM2Zg4cKFmDx5Ms444wy8+uqrKCkpwZNPPpnSdZcuXYr29nb549ChQwqdOH15Y2s49PLsCRXcmv/UZqjUrqPJuuNh4ZdqT9UBQBHLcnIbR3FSasEvgxnMeSmcWJvOYU1snD+aTD1bdfK0WuJv/FpGEiTjJdJUcbLy+97A78l6obi4GGazGfX19TG319fXo7y8PK5rWK1WTJkyBXv27AEA+XGJXtNutyM3Nzfmg+iZLn8Q70rTdD+c3D9CL7tjWAkrnMggfiyNTqlwytVCcTJelpNSC34ZvLXqUs1wAozZqgO0jSSQzeGJtOq09DhRq05ZbDYbpk6dirVr18q3hUIhrF27FjNmzIjrGsFgEF9//TUqKsIem6FDh6K8vDzmmk6nExs3boz7mkTfrNvVAJc3gMo8B6YOKtD7OLoxrJhadd3h8QXhkt54NGnVZTOPk3EKp0j4pTKFE5tcc3YFuNifmKoxHODDHJ7oVB2gbXo4K1B4m6rjfd0KAKQ+y6oTS5YsweWXX45p06Zh+vTpeOSRR+B2u3HllVcCABYuXIgBAwZg+fLlAIC7774bJ598MkaMGIG2tjY8+OCDOHDgAH7+858DCE/c3Xjjjfjd736HkSNHYujQofjtb3+LyspKzJ8/X6+nmXa88WW4TXfupEqYTKlPBBmViOLkhiiKikxHpQNsos5hNSkyat8XxdnGW/TLwi/zFDaHB0MiOryBlJQeJUh1Tx0AZGu89y2a1BQn1qrTTnFyJFA4ZVojWVNqwfu6FcDAhdOCBQvQ2NiIO+64A3V1dZg8eTJWr14tm7sPHjwIkykiqLW2tmLRokWoq6tDQUEBpk6dik8++QRjx46V73PrrbfC7Xbj6quvRltbG2bOnInVq1cfF5RJJIery4+1O8I5W+dO6r9tOgAYXJQJkwC4vAE0dng18fMYgcaOiDFci2LSiObwdk/4TVUpxclhNcFmNsEXDKHd4+egcIrELSRLFgc5TtlJeZy0K/girbr4X2eHLfye6vEHVfuFz2OAOALDFk4AsHjxYixevLjbz61bty7mz3/84x/xxz/+sdfrCYKAu+++G3fffbdSRySiWPNNPbyBEIaVZGFcZf/2gtktZgwsyMTBlk581+imwkmiwamdMRxAJI7AQIWT0lN1giAgN8OKpg4v2j1+DNS5g65Iq86mZ6su8VUmDLlw0mAakE3VJZMcLorhnXJqqEJdSQRzao0hPU6EMWFtuh9OqqTWFCKTdeRziiBnOGWr728Cohf9GqdVp/RUHQDkZVhirq0n8roVe+rmcG8ghEBQ21T4lFp1ds6n6qIKJbV8Tsm0ELWGCidCE1rcPqzf3QQgXDgRNFnXHSyIUgtjOBBRnJjh2ghEAjCVLJzYZJ3+SfZKmsMB7bOcmD8pmRwnNuGmhcfJk4Q53GIOt3WjH6/WuXhu1VHhRGjCf76uRSAkYvyAXDn8sb9Dy36Ph023FWukOLEE9w5vAF4N9oMpQSSOQBlzOADkchRJ4FTAHG6zRN7gOzRea5RSq05DxSnZ6TWHlK+k1hmpcCIICRZ6SWpThMFF4cLpQHOnzifhB9YyYzEBapPrsIINdxpBdQqGRDnnSCmPExAdSaD/ayC36lJQnICoFG6NfU4dCpjDtUgOT3aZLpv861JJcaIcJ4IAcLTNg037WwAAP5hIhRNjCCucWjoRCumfn8MDzW6mOGlTOJlMgrzzrbWTf4O4q8sPFrWkZOHEihSm9uhJKitLosnUwSAuimIkxymZlStamsPlVl2Cy4ht6oZg0soVggDw1ldhtWn6kEJU5mfofBp+qMx3wGIS4AuEUO/q0vs4XNAkK07atOqASLvOCJEErE2XaTPDZlHuxzdri3VwUTgl3+qKRo/0cG8ghID0S1BKOU4q5iQxPElOrzms6kYmUKuO6PeIoohXNh8BAJw3hdSmaCxmEwYWhAvJ/U3UrgMiq09YvpIWGMkgzszbSqpNQERxcnHQqotMpaX2xqlHqy76a6WUHK7BmTuTWPILRM6o1lSdxx+egqTCiei3fFPrxK56F2wWE34wgQqnYxkktesOtpBBvMsflN80izWaqgMgt+qMoDg55XBItQonDhSnFKbSoomEYGpn+mdfK8NqhjmJzQiamsOTVHbY/VXzOCXpvdISKpwIVXl1S1htOnNMmbyFnYgwpCgTALCfDOKyv8lm1mbdCqMwkylOBiicpFZdboayr49cOHn1V5xS8QhFI7fqNJyqS1Ut02rJrz8Ygj8oxnzNeNGqVUceJ6Jf4g+G8O+t4cLpxycO0Pk0fDKoMFw4HaTCKWaiTsuA1Pws5nHSv2joC6c8caaw4mTnx+PUoZA5XI9Fvx4/S+NO7uxarVyJvn7SrTqVc5woOZzol/xvdyOaOnwoyrLh9BNK9D4Ol7DJuv3N1KqT/U0aTdQxjKU4hd+YU9nj1h28tOoCwRC6JI+LEc3hnSmuC4ks+VW3cGJtNrNJkPOu4kXtVh1N1RH9mlekNt0PJ1fCmuA/zv7C4KKI4iSK/TuSoJEpTlna+ZsAoIB5nAxQOMkLcBU3h7McJ30LJ3dUwaCcOVw7j1Oy2UiMrKhWnZo/Dzqjwi8TVXczVG4ndsmtOn7fM/g9GWFo2j1+rPmmHgBw/okDdT4Nv1RJrTqXN2AIc7Ka6KU4FUhTda0GeP1ZYaOeOVzfdiVTh6xmAXZLqoWT9q06VkwkM1EHRIqSkLREVy2SnaiLfozHp/z5AsFInANN1RH9jre/qoUvEMKoshyMq8zV+zjc4rCaUZHnABAOwuzPMI+TVgt+GYWSx6nVEHEE6prDvYEQfCq+YfeFUsZwIFK86NGqS1ZxivZGqdmuSzbDCYgUNGp4nLqivveoVUf0O17dchhA2BSupdHXiDCD+IF+7nNiU3VaK05ycrghFCd1zOHRRmwtFZpjkafSklRsotHFHJ6ix8lsEmCXgk3VLPg6k9xTB0TnOCl/vmjflF3BgFel4fdkhGE50OzG5wdaYRKA+VNomq4vhtDOOgBRqeEae5yYOdzlDcAf1E9tiYeIOVzZwsliNslviHq265gfKdWJuvA1tA/ATNUcDkQKPlUVpxQm1xxqKk7SNe0WE9e/cFPhRCgOy246dUQxynIdOp+GfwYVMcWpvxdO+ihOuRlWsJ/RvO+rkwMwFW7VAXxM1imVGh6+hnZhkoyIkpP8348WWU6eFFqKkVad8r9ksIlKntt0ABVOhMKEQiJe/SLcpiNTeHxEFKd+3qqTFKdijT1OZpOAfGlKjfe1Ky6VzOFAROVx6qo4Kehx0qVVx3Kckn/j1yLLKZIanvxaGDVbdTxP1AFUOBEK8+l3zTjU4kG23YI548r0Po4hGEyKE0IhUZ4q1FpxAqIiCTj3OTFzeI7COU7ha+ofgqnUupXoaxjJHA5ok+WUiuLkUDEAs8sAqeEAFU6Ewrzw2SEA4eymZNNz+xusVdfs9ulqzNUTZ5dfHkPW2uMEGCOSIBgS4ZK+P5TOcQJ4a9UppzhpmuOkQOq1Jq06WXFKvASQW3UqFHZyqy7FKAq1ocKJUIxWtw/vbKsDAFxy0iCdT2Mcch1WFEpv3P21Xcf8TbkOC2w6TNMwxYnnSIJoJUgNxYm1//Q1hyuoOEm/uPmC2kUsdCowFaiF4tSV5IJfILpVp6bixHdpwvfpCEPx6hdH4AuGMK4yFxMG5ul9HEPR39t1evmbGAWZLMuJX8WJeY8cVlPK4ZDdwYPixNQhZczhkWto1a5TplWn/jSgvNaEuxwnaaqOWnVEf0AURbz02UEAwMUnVel8GuMxuLB/F056TdQxCg3QqpMn6lQwhgMRlYeLHCcFFCeL2SRnAWn1nJRYUMsKPjUUHQYrUJJpiakbR0BTdUQ/YsvBNnxb3wGH1YQfTqbspkQZ3M8n65rd+mQ4MfINsK+OZTip0aYLX1f/fXVKtuqAKJ+Tin6haJRQnNikm1tVc3i4QEnmnKwo7PKHEAopu09PbtVxHH4JUOFEKARTm86eUIE8FYyr6U5/b9XprzjxH0fgVGnBL4OHfXVKJocD0Yt+NVKc5ADM5M8fUZzUO3MqHqfoYospV0pBU3VEv8HV5cebX9YCAC6ZTqbwZOj3ipPOHqd8A8QRyHvqVGrV8eFxUq5VB0Tvq9Nmsq5TgRwnVpioqjilUDhFt/eUbieyxcY8L/gFqHAiFODfW4/C4w9ieEkWpg0u0Ps4hoQpTrXOrph9Tf2FZklxKtbZ49TGcatODr9UTXHiYapOuZUr0dfR3Byewhs/K/bU9DilYg43mQR56k3pyT+aqiP6BaIo4v9tOAAA+En1YK73C/FMUZYNWTYzRBE40ubR+ziaI++p022qzgCKU5d64ZfR1+XDHK6M4qBlengwJMqKiTKKkxY5Tsmdkz1O6V/yqFVH9As27mvBrnoXMm1mXDCVVqwkiyAIGFgQVp0OtfQ/n1MzSw3P0kdxYnEEzq4AApwu+lVrwS+Di1adgsnh0dfRQnGKDqxMpdXIFKdOFduLqXicoh+n9GQdm6qjOAIirXluw34AwPwpA8gUniJVhRkAgEOtpDhpTV7Mol8+DeJqLvgFolt1aeRxsqvvF2Kw9pcgQI5BSIZM6cydfg0UJ1ty58xQaZ+eh1p1RLpT2+7BO9vrAQALZwzW+TTGhylOh1v7l+LkDQTlN2u9PE4Ws0lWcto9fLbrXCrnOEW36oIKj5nHgzcQhD8Y/rrKFU7atepYEZFpNadkWciU1BY1FSfZ45Ss4qTSvrpIHAEpTqqxYsUKDBkyBA6HA9XV1di0aVOP93366adx2mmnoaCgAAUFBaipqTnu/ldccQUEQYj5mDdvntpPw7A8v/EggiER1UMLMbo8V+/jGJ4qKQTzcEv/UpyYr8hiEnRVLfMz+Y4kkFt1KscRANrlHkUTPfmWlYJHKBptW3VMxUmt6NNkya9SHifFzeEUgKkqL730EpYsWYJly5Zhy5YtmDRpEubOnYuGhoZu779u3Tpccskl+OCDD7BhwwZUVVVhzpw5OHLkSMz95s2bh9raWvnjhRde0OLpGA5vIIgXNoWzmy4/ZYi+h0kTBhawVl3/UpyaozKc9BwuyM/gvHBS2Rxut5hhM4ffEvRo17HixmE1wWJW5q2JFSFaKE4ef+pRBIB6ak40Xn/yAZjhx6lT3HkD1KpTlYcffhiLFi3ClVdeibFjx+KJJ55AZmYmVq5c2e39//GPf+C6667D5MmTMXr0aPz1r39FKBTC2rVrY+5nt9tRXl4ufxQU0Hh9d/z36zo0dfhQnuvAmWPL9D5OWlDVT83hjR36poYz8qTJujYP34WTWq06QN8QzA6FU8PD11K/7cWQW3VKFU4qKU6BYAg+aQAi2ZZYhlTYqNaqI8VJeXw+HzZv3oyamhr5NpPJhJqaGmzYsCGua3R2dsLv96OwsDDm9nXr1qG0tBSjRo3Ctddei+bm5l6v4/V64XQ6Yz7SHVEU8bdP9gMALq0eBKtCvx32d5g5vLXTr+tIuNY065waziiQW3V8epxYqy5PJXM4oO9kndLGcCCiOGnRelRi3QoQ8Tj5giFVJjy7ApFrJq04qRZHwFp1fL+n8H26HmhqakIwGERZWazSUVZWhrq6uriucdttt6GysjKm+Jo3bx6ee+45rF27Fvfffz8+/PBDnHXWWQgGe/7mWL58OfLy8uSPqqr0X3C7aV8LvjzUBpvFhEuqKSlcKXIcVtln059UJ71Twxk8t+pEUVTdHA5EJus6dCiclF63AkSm6tT0CzE8CitOgDrtumglK9npP7VadUYxh6v3qwvH3HfffXjxxRexbt06OBwO+faLL75Y/v8JEyZg4sSJGD58ONatW4fZs2d3e62lS5diyZIl8p+dTmfaF09PffQdAOCCqQN1f7NLN6oKMtHW2Y7DrR6Mqegfhnu9M5wYkVYdf4qT2xcEG3TL0aBV59ShVad0ajgQpThpoOC65XUrqZ3fbjFBEABRDBc5Sv99R2c4JespZIqT4oWT5HGiHCcVKC4uhtlsRn19fczt9fX1KC8v7/WxDz30EO677z68++67mDhxYq/3HTZsGIqLi7Fnz54e72O325Gbmxvzkc7srndh7c4GCALw85lD9T5O2iFnOfUjxYllOBXnkOLUE2xPndUsqNrGYEWLvq065d405YW5GqwxUkpxEgQhEkmgglIWyXBKJd08/D2oVquOdtWpgM1mw9SpU2OM3czoPWPGjB4f98ADD+Cee+7B6tWrMW3atD6/zuHDh9Hc3IyKigpFzp0OPP2/sNp05pgyDCvJ1vk06YecHt6PJuuaOvhQnFibtJ1Dc3i0MVzNyUM9QzA71PQ4GcgcDkRaYWq26lIpTlgrTb2VK3yXJnyfrheWLFmCp59+GqtWrcKOHTtw7bXXwu1248orrwQALFy4EEuXLpXvf//99+O3v/0tVq5ciSFDhqCurg51dXXo6OgAAHR0dOCWW27Bp59+iv3792Pt2rU477zzMGLECMydO1eX58gbDc4uvP7FUQDAL84YpvNp0pMqFknQj7KcuPE4cZzjpHaGE0PPqTq3ClN18voSLc3h1tTPzxQdNRWnVIoTplYpXTh5DZLjZFiP04IFC9DY2Ig77rgDdXV1mDx5MlavXi0bxg8ePAiTKfKN8fjjj8Pn8+GCCy6Iuc6yZctw5513wmw246uvvsKqVavQ1taGyspKzJkzB/fccw/sdvLxAMDfPtkPXzCEqYMLMHVwYd8PIBJmYGH/Sw/nZaouX/I4tXI4VRcxhqv7IztXx0W/HT4VFKcoc3goJMJkUk+t8/iUyXECgEyp+FIjkkCJVp1DhV11wZAYiUmgwkk9Fi9ejMWLF3f7uXXr1sX8ef/+/b1eKyMjA++8845CJ0s/OrwB/P3TAwCAq08ntUktorOcRFHUNRBSC0RRRLNb3z11DOZxaudRcZLDL9VWnPRr1akRRxA9oefxBxW99rEoFUcQfQ01WnVdSrTq5DgC5eISWPhl+Pp8N8P4Ph3BDas+2Q9XVwDDSrJw5hgKvFQLlh7u9gW5bBkpjbMrIO8n09/jFP76Lm8AfhXyc1Ih0qpT93fdbF1znNhUnXJqg8Nqkpc3q53l1OlX0OMkm8OVP7NHgZDJDBUUp2h1jfc4AiqciD5xdfnlCIIbvj9SVbm7v+OwmlEqTZf1B4M48zfl2C26y/PRbTAnZwZxdh41M5wAPpLDlVSFBEGI+JxUNogrNVUXfQ01WnVKTK4xRcirYOHEgjltZhP37zFUOBF9suqT/Wj3+DGsJAvnTqrU+zhpD1v22x8M4k2c+JsAwGI2yYUDb2tX5Kk6lc3hzJith8dJDXM4EClC1Fac2PlTzXEC1G3V8ao4MaO5nfM2HUCFE9EHri4/nv7fPgDAr2aPhJnz3wTSgf607JcpTnr7mxgFLASTM4M4a53lqOjRASKKk56Fk5LJ4UBEwVI7PdyjSqtODcUpdY+TXcXCSW/lOR6ocCJ65dmPw2rT8JIs/GAiqU1a0J+W/TZxkhrO4DWSQDvFiYOVK2opTioXg0qaw9Vs1XkUOGeGCuZwo+ypA6hwInrB2eXHX9eH1aYbSG3SDDk9vDX9W3W8KU55nKaHa2UO13fJr/IrV4DoLCetPE5KtOpUDMBUolXHcpwUfE29BtlTB1DhRPTCyvX70O7xY0RpNqlNGsIUp/6Q5cQynEo48DgBkck6bj1OKpvD2VSdLxiKGQ/XAjVWrgCRLCf1FSflcpzUbNV5FGjVMVWoS8HvEXYtatURhqXR5ZUn6W6sIbVJS6rkEEwPQmyza5rSxJniFMly4svjJE/Vqdyqi/YXadmuE0VRNm8bVXHqVCAfiRFp1Sn/dyDnONlSSA6XnqM/KCoW3UGtOsLwPPLet+j0BTGpKh/nTKBdfVpSkeeA2STAFwihUSos0hVeUsMZsseJM8VJNoernBxuNgnIkt60tTSIe/xBsN8RVPM4qThVFwyJ8Erj9MrsqlN/qk6JAExAubUrZA4nDM2ehg68+NkhAMD/nTU67dOrecNiNqEizwEg/Q3iTSw1PIsTxUleu8JP4SSKomatOkCf9HBWpAmCMoVHNPJUnYo5TtEFjiIeJw1adakUKHZLpHRQyiDeZZA9dQAVTkQ33PffnQiGRNSMKUP1sCK9j9Mv6S+RBExxKuZFcZLN4fy06rr8ITldXe1WHaBPejgzhmfZLIr/oqaF4tQZVfgp0WrifapOEISIz4kUJ6K/88GuBry3ox4Wk4Dbzxqt93H6LbJBPI1DMH2BENqllhg3HiepVdfOUauOqU0mAXIbTU30CMFUyxgevqb6ihNThjKtZkUKP1V31SnQqot+vGKFEzOHW/gvS/g/IaEZ3kAQd72xHQBw5alDMKI0W+cT9V8GSIrTkbb0LZxaJVXHbBJkpUdveMxxckVlOGnRNo+EYGr3GqiV4QRopDjJKo4y55eTuVVcuZKqsqN0eji16ghD8tf/7cP+5k6U5Nhxw+yReh+nXzMgP/0Lp0ZX2N9UmGXjZjdVXgZ/yeHtHm2M4Qw9spzUWrcCaDNV5/ErF0UQvo56Z1bC4xT9eOU8Tuxc/Jcl/J+Q0IRDLZ348/t7AAD/39ljZIMooQ+y4pTGIZjNnKWGA0CBpDg5uwIIKDRmnSpaGsOBSPGihzlc6XUrgDY5Tp0KLvgF+J+qAyKFk3KKE3mcCAMhiiKWvvo1PP4gqocW4rzJFHapNwPzwx6nI20eiGJ6Zjmx1PBiTvxNQCQ5HAgXTzwgZzhpVjhJa1c09ThJ5nCDKk5KrluJvo4qrTqFzsqUIaXOSIUTYSj+tfkw1u9pgt1iwn3nT6T4AQ4oz3NAEABvIISmDn7aRkrCW4YTEI6CYIt0eWnXsQJO7XUrDDZVp2UAZqRVp/ybZoYGHiePwopTplQ8+IIhxZVPpRQn9roqlTDPWn52MocTvFPv7MLv3voGALDkzBMwtDhL5xMRAGCzmFCeG85ySlefE28ZTow8zkIwmTlcq/Z5ruxxSg9zuKw4aTBVl2FVyBweVYAp2a7zB0MISEmjSk3VkeJE9CtCIRE3/fNLOLsCmDgwD1fNHKr3kYgomEE8XXfWNbmkDKccfhQnICqSgJPJOnnBr8YeJz3iCNQwh8seJ1Wn6pSNU7BbTGDCv5LtuugizJHCyhUAsCseR0BTdYQBeGb9Pqzf0wSH1YSHL5oMi5m+HXgi3Q3izZLiVMyZ4lQgL/rlpVXH4gi0bdVpOlXn00Bx8gVV8wsqbQ4XBEFu1ynpzWL+JpMA2FL8eR+JI6CpOqKfsO1IOx54ZycAYNm54yiziUPSPZKAR48TEDGIt7p5UZz0marTUnHqUNEczhSn6H1ySqN0qw6IZEIp2aqL9jel6mVVOjncq5D3SguocOqHtLp9uObvm+EPipg7rgwXn1Sl95GIbkh7xUmaquMlNZzB26LfiDlcm8JJj111aprDM6PeiNWarPP4lM1xAoAMqZWm5JnlwkmJRcRKt+ooAJPglUAwhF++8AUOt3owqDATD5w/iaboOCWdFSdRFNHEYY4TAORLIZjtnEzVRczh2gZgaqs4qdeqs5hN8qSWWllOSscRAECmpF4p6XFSsjhRPMcpQK06glPu++9OrN/ThEybGU8tnCpPEBH8MTCNFacObwA+qW3CU44TwKHipFerTgfFSY3CKfq6agRKAkCnX1mPE6BOCCYrwpQsnJRe8mu3pJHiZDbz/2SI3nlm/T78df0+AMCDF0zC6PJcnU9E9EalpDi5vAGuls4qAcumyrKZFf0tXQmYx4mXfXVa5zgxxckXDCmW0dMXak7VAVH76lRSnJTOcYq+VqeC04BKLfiNvoZy5vA0bNWla3pxf+HfW4/gHimv6dZ5o3DOxAqdT0T0RabNgkKpjZVuqhOv/iYgaqqOk1ad1opT9NoTrXxOsjlchZUr0ddVy+PEihullvwC6iz6VSr8ElBecfKk41Qd+WCMy9tf1eKmf34JALjy1CG49ozhOp+IiJd09Tk1cTpRB/DVqvMGgvIkmFaFk8kkaN6uU11xUnlfHStusozSqlPCHG5TbqouFBLl1n1aKU7H4nK5lDwHoRIvbz6MX76wBYGQiB9PGYDfnjOWimADIRdOaRaC2cxpajgQVThx0KqLVnyyNTKHA9pGEgRDolwcKBUgeSxqK05uNczhcqtODcUpdVXHYVFOEYuOiUjrwum0005DXV2dkmchFEQURaz4YA9u/teXCInAJdOr8OCFk2AyUdFkJORIgjRTnFiGUzGHilOeNFXn7PIjGNLXosDadDl2C8wa/tvN0TAEM9rDo5Y5PFPlfXURjxPfrTolPU5MtepSwAcXrVo50nlX3ZQpU1BdXY2dO3fG3L5161acffbZKR+MSJ5OXwCLn/8CD76zCwBw1cyhuPdHEzT9wUsoQ/q26qTUcA49TswcLora7mvrDq0znBjZGu6rc0v+JotJUG3BKyvI1NpX16lKjpMKAZgKKmNKKk6s+LKYBENssEj6hH/7299wxRVXYObMmVi/fj2+/fZbXHTRRZg6dSpN4OnIxu+acdaf/oe3v66F1Sxg+Y8n4Lc/oPacUUnXSAJeU8OB8IJl1qrSu10nK04atukAbVt10RlOav2cUltxiiSHG6NVp0Q7jBVfXQpM1Rlpog5IMcfprrvuwpIlS3DmmWdi/PjxcLlc2LBhA958802lztcrK1aswJAhQ+BwOFBdXY1Nmzb1ev9//etfGD16NBwOByZMmID//Oc/MZ8XRRF33HEHKioqkJGRgZqaGuzevVvNp6AYde1dWPrq11jw1Kc40NyJijwHnl90Mi6ZPkjvoxEpkK6tuiaOp+qAqLUrOk/WsVaZVsZwhpYhmGobw4EoxUkFj1P0KhdFFSe5Vafc34GSU3VKJocbaU8dkELhVF9fj1/96lf43e9+h7Fjx8JqteKKK67A9OnTlTxfj7z00ktYsmQJli1bhi1btmDSpEmYO3cuGhoaur3/J598gksuuQRXXXUVvvjiC8yfPx/z58/Htm3b5Ps88MADePTRR/HEE09g48aNyMrKwty5c9HV1aXJc0qGHbVO3PnGdpz+4Ad4YdNBAMDFJ1XhnV+fjpOGFOp8OiJVBuZnAghPoSnpddCbZik1vJiz1HAGL5N1Wi/4ZeTYtVu7Egm/VE9tUDPHKbqVpqjHSYWpOkU9TgruqjNS+CUAJP23PHToUIwaNQr/+te/cM4552D16tVYsGABDh48iFtuuUXJM3bLww8/jEWLFuHKK68EADzxxBN4++23sXLlStx+++3H3f9Pf/oT5s2bJ5/tnnvuwZo1a/DnP/8ZTzzxBERRxCOPPILf/OY3OO+88wAAzz33HMrKyvD666/j4osvVv059YQoimhx+9Da6Udbpw9H2jz44mAbPv2uGTvrItON04cU4qY5J6B6WJFuZyWUJTfDgmy7BR3eAI60edJmGTPPOU5ApHBq56RVp7XilK2hOVzNdSsMNafqmL9JEJRVTNRo1bGWmJK76jz+IERRTKnNquS5tCDp79SVK1fGFBPz5s3DBx98gB/84AfYv38/VqxYocgBu8Pn82Hz5s1YunSpfJvJZEJNTQ02bNjQ7WM2bNiAJUuWxNw2d+5cvP766wCAffv2oa6uDjU1NfLn8/LyUF1djQ0bNvRYOHm9Xni9XvnPTqcz2afVKycvXwt/8PgJH5vZhNljSvGT6kGYOaKYvExphiAIGJCfgV31rrQpnALBEFqlgoRHjxMQ2VendwhmRHHSuHCSPU4amMN96rfq1MxxkifqrGZFf/6qEoApXcuugOLErhESwynzqahFRtpTB6RQOHVXSJx44on45JNPcNZZZ6V0qL5oampCMBhEWVlZzO1lZWXHTfkx6urqur0/i1Rg/+3tPt2xfPly3HXXXQk/h0QQBAEFmTZ4/EEUZNpQnG3DxIH5mDIoH2ecUIL8TD7ffAhlGFAgFU5pYhBvkdp0JiGS0s0bTHFq1V1xCr/Ra20Olz1OmihO6qaGR19bDcWJTQUqmRoevp4KAZgqeJyAsGKUSuHkZR6ndG/V9cSQIUPwySefKH1Zblm6dGmMkuV0OlFVVaX419mwdDbFCfRTIpEE6RGCyVLDC7Ns3H5Py606nT1OLA5AL3O4lh6nTC08TipM1Xn8ykcRhK8nxRFwunLFahZgNgkIhkR0+YPyQEUypO1UXSK76goKCpI6TLwUFxfDbDajvr4+5vb6+nqUl5d3+5jy8vJe78/+m8g1AcButyM3NzfmQw14fYMh1GdAmkUS8JwazuCnVaeP4pTNzOHpNlWnQo5TpwoLfoFIcaOsx4mpY6m3xARBkMMqUzWIp+1U3SOPPIIzzjgDy5Ytw969e9U8U5/YbDZMnToVa9eulW8LhUJYu3YtZsyY0e1jZsyYEXN/AFizZo18/6FDh6K8vDzmPk6nExs3buzxmgShBekWgslzhhMjj5epOunrp/LbfDJka9qqU98crqbi1KnCupXo66myq04hZUepM7LHK+G90oK4C6cbbrgBH374Ib73ve9h+fLlOPvss/Hkk0+ivb1dzfP1yJIlS/D0009j1apV2LFjB6699lq43W55ym7hwoUx5vFf/epXWL16Nf7whz9g586duPPOO/H5559j8eLFAMLV84033ojf/e53eOONN/D1119j4cKFqKysxPz58/V4igQBIP0UJ94znAAgP4OPfXX6m8M1WLkiqUBGzXHyqKQ4sevx2qoDIvEBqYZgyq26dPU4zZo1C7NmzUJXVxf+/e9/42c/+xnsdjsuueQSnH322Zqlhi9YsACNjY244447UFdXh8mTJ2P16tWyufvgwYMwmSJ14SmnnILnn38ev/nNb/B///d/GDlyJF5//XWMHz9evs+tt94Kt9uNq6++Gm1tbZg5cyZWr14Nh8OhyXMiiO4YKClOdc4u+IMhWA2wkqA3mMepiNMMJwDywIXeHidmDtfa45Sr4cqVDkkFylJxFF3NHKdIarjC5nCpuPEFQwgEQ4qsIom06hRWnFIs7ozWqkv6b9rhcGDBggVyAfPCCy/gnHPOwejRo/HTn/4UU6dOVfKc3bJ48WJZMTqWdevWHXfbhRdeiAsvvLDH6wmCgLvvvht33323UkckiJQpzrbDZjbBFwyhrr0LVYWZeh8pJViGU0kOx4oTa9Xp7nHSJwAzOyo5PNWMnr5wa5jj5A0oV4Qw1NhTB8QWNx5/EDkKnNmj8GoYpUIwI3EEaao4dcfBgwexb98+fPnll9iyZQsAaFI4EUR/wGQSUJnvwP7mThxp8xi/cHIbQHHKiEzVhUIiTDoMZ/iDIVnNyNE6AFMqYvzB8DoRNd/QtDCHR0/sdfqDyFWwcGLFiNLJ53aLCSYhnJPk8QVT/h4QRVHxVp1Sa1e88lSdMRSnlE75wAMPYPTo0Zg1axYaGxvxzDPPoLa2Fo888ohCxyMIAgAGFoSLpXTwOfGeGg5EzOEhUZvJsu6INmZrPVWXZbOAiUxq+5zkHCcVCyeb2QSLVPwqPVnnVqlVJwiCopN1/qCIkDQc71BIHXNYlTGHd/WnHKfPP/8cS5cuxQUXXICsrCylzkQQxDGk02RdkwGm6uwWMzJtZnT6gmjv9Gs+1QZE2nSZNrPmvjaTSUC2zQKXNwBXVwDFKha5WrTqBEFAhs0MV1dA8ck6j0qtOiAcqun2BRWZrIu+hlIFisOqlDm8H7Xqbr75ZgwfPpyKJoJQmXSZrBNFUc5xKuY4xwkIt+s6fUG0eXwYBO3bo3oZwxnZjnDhpHYkgRatOiCsorm6AoorTmrFEQDK7qtjxYnZJMBqVqb1nKGY4iS16tJ9Vx0ATJ8+XalzEATRC+miOLl9QfmHJM+KEwDkZdpwtL1Lt7UrehnDGayQcam8ry6S46Tum6a8r05hxanTr04cAaDsvrpoY7hSZn/FzeGWfuBxIghCG5jidLjV2GtXmL8pw2pWtTWjBJEsJ30m61j4pZ6KE6BuCGYgGII3EC6k1dxVF339TsVbdSoWTgqGYHpUaIcpZQ43WquOCieCMABMcTra1oVQKP71R7xhBH8TQ+99dXqFXzLYFJea++rcUW0ztQvpSJaT0q268Ouj9JJfILpVl/rfgUfBdSsMh0KKWNruqiMIQj/K8xwwCeEwvCbJI2REjDBRx4hkOelUOHn02VPHyNEgPZyFX9rMJthUbtNE0sNVUpxUeNNXslXXpXCGExBlDg/0rwBMY5ySIPo5VrMJ5bnhBHsjG8SZ4lTMcYYTI09e9KtP4cRSu3Vr1WlQOLk18jcBaipOUuGkwnNQo1WnZOEUSQ7vX1N1VDgRhEGQJ+sMbBBnipOa4+1KIStOHp08TlKLTDdzuLx2RYvCSf3nyDxOSi7NBaIKJ1VbdXx6nJiZO3XFyVi76qhwIgiDUJlv/EiCyIJf/hUnOT1ct1advopTjgb76twaLPhlyFN1Citoaq1cAVSaqlPwnOxaXal6nALUqiMIQgXSIZJAbtUZQnGSWnX91ByuRauuQwfFSQn1JppOFbxDjAwFVbIuFVp1iieHU6uOIAglSYcQzEbWquN4wS9D70W/egdg5mgQR6Blq04NxSkYEuU4BTUUJ2UDMMPnVMUcnkLhJIqifDY7KU4EQShJOihOssfJAOZw3afqdA/AlOII1DSH+1hquPpKgxqKU7TSoobHKdKqUy6OwK5CjpMnhZUrrPAESHEiCEJhBqaBOVxu1RlBccqItOpEUfvsLOZxytHd46RBq07l8EsgaqpOwTgC5m8SBHX8ObxP1bFCx5vC+bxRRReZwwmCUBRmDnd1BWQ1wkj4AiE5TNIYHqdwwRIMiar6fHpCnqrTKcdJTg5XceWKplN1LMdJwTgCNdaYRKPoVJ1sDlfubV+JXXXMGG4SoNgOPbWhwokgDEKmzYIC6c3ciD6nFndYbTKbBHlijWccVjPs0ri11u266GJNt+RwuxYep/CbpqY5TooqTupFEQAKB2Cqojilvqsu+lxqFJ9qQIUTQRgIIxvEWRRBYZYNJpMxfkDqtXYluljRKzk8ojgFVGtVajpVZ1fe46RmFAHA/646JabqjLZuBaDCiSAMhZEN4o0GCr9k5OuUHs5asQ6rCXadfB/MW+UPijEGXiVhrTpNcpxsyk/Vdaq44Dd8XSmOgNMcp8hUXSjp4tpoUQQAFU4EYSgG5GcCAI4asHBqcrHCif+JOkaeTunh7TqHXwLh3Wusc6KWQVxLc7gaU3WdKhQj0bC2mpLJ4WqsXAGQdHHdJU/7GaccMc5JCYKQW3WHDVg4NUsepxIDKU4FOkUS6B1+CQAmk4Bsm7ohmKwg0DTHyadc69GjsuKkZKtODWXHEbWYOVlVrCtgrHUrABVOBGEoBuQbd9EvU5yMsG6FwVp1WnucWPilXv4mRrbKIZhatuqY4iSKEV9NqkRSw9U5f6ZNOXO4rDgpWORZzCZ5Ei7ZfXXsuRll3QpAhRNBGArWqjOix6nJiB4nSXFqdWvbqpMVJx1bdYD6++oi5nD11YboFpVSk3Wqm8OlM/uCIQSCqRV7sh9LYS+RI8XJP2+APE4EQagIa9U1urwpjQDrAWvVGalwinictFWcmKdIz1YdEFGC1EoP11JxMpmESC6SQllOWrXqAAX2wankx0p1sk6NmAS1ocKJIAxEQaZV/gFT296l82kSo9FlnD11DN2m6mRzuN6tunDhpl6rTjuPExCZUlNMcfKrm+Nkt5jAkjtSbdd1qlSgZERN1iWD3KpTqfhUAyqcCMJACIIgq05Gm6xj61aKDLCnjhHJcdKpVaez4iSHYKqgOPkCIfik9pNWhRNrCXYqVTh51W3VCYIgF2WpTtapEUcApB6CyfbckTmcIAjVkLOcDGQQD4VEtLjDilOJoRQnnabqPGzdSvp6nKLzlLI0UhtkxUmhVp3acQSAMiGTwVAki0s9xSnFVp2Cq2DUxjgnJQgCQGRnnZEiCVo7fQhJE+CFBlKc9PI4RRQnnVt1KnqcmIplt5hgMWvzVpRlU1hx8qvrcYq+diqKU3RRo3RbkTxOBEFwz0ADrl1hbbr8TCusGr1JKkF+phRH0OlXbe1IdzCPU47OipOacQTMZ6SFMZyRaVdWcVLbHB597VQ8TtFFl92i7L8/R4oeJ0oOJwhCdSJrVzp1Pkn8GDGKAIgEYPqCIUVCCOPFyabq9DaHq+hxcmu4p46huOIkxxGo9xyU2QcXUXWU3hOZkeL51NihpzZUOBGEwYiYw40zVRcpnIzTpgPCbwo2SSHT0ufk4sQczjxWaqxc6dB4og6IeJHcCq1diSSfa9GqS/7vQE0vlmwOT/I1ZeZwatWpTEtLCy699FLk5uYiPz8fV111FTo6Onq9/y9/+UuMGjUKGRkZGDRoEG644Qa0t7fH3E8QhOM+XnzxRbWfDkEkBFOcats9CIW0ax+lAmvVGU1xEgRB9jm1dmo3WefkYFcdoG6rrlPOcNLuDVPeV6eQgub2qq84KdGqU2NPHYMVY6maw42kOOmrAyfJpZdeitraWqxZswZ+vx9XXnklrr76ajz//PPd3v/o0aM4evQoHnroIYwdOxYHDhzANddcg6NHj+Lll1+Oue/f/vY3zJs3T/5zfn6+mk+FIBKmLNcBs0mAPyiiweVFeZ5D7yP1iVFbdUB4sq7R5UW7RopTKCTKZuz+YA7XUnGK7KtTWHHivFXH1Cp1FCeFzOEGmqozXOG0Y8cOrF69Gp999hmmTZsGAHjsscdw9tln46GHHkJlZeVxjxk/fjxeeeUV+c/Dhw/H73//e1x22WUIBAKwWCIvQ35+PsrLy9V/IgSRJGaTgPJcB460eXCkrdMQhVOzQVt1QCTLSavJug5fAMyHzo3i5FUvjkBbjxPLRFJYcdKkVZe6x0kNE3uq5nA5X8pAipNxSjyJDRs2ID8/Xy6aAKCmpgYmkwkbN26M+zrt7e3Izc2NKZoA4Prrr0dxcTGmT5+OlStX9jlJ4/V64XQ6Yz4IQm2Yz+mwQSbrjNqqA4A8jdPDWZvOZjHp3r7IlXOc1JiqC79hZquo1hwLKxyUznFSd6ou/Pqk1KrzSSGTarTqUlWcpF11dgMVToZTnOrq6lBaWhpzm8ViQWFhIerq6uK6RlNTE+655x5cffXVMbfffffd+P73v4/MzEy8++67uO6669DR0YEbbrihx2stX74cd911V+JPhCBSYGB+BjbBOMt+Dd2qkxUnbTxOkfBL/X88Z9sjK1dEUYQgKDeRpUerjn0tJRQnXyCEgOQx5H2qTs1lxMwc7k12qo4Up+S5/fbbuzVnR3/s3Lkz5a/jdDpxzjnnYOzYsbjzzjtjPvfb3/4Wp556KqZMmYLbbrsNt956Kx588MFer7d06VK0t7fLH4cOHUr5jATRF0Zbu9Ik7akrMmKrTpps08rjJIdf6tymAyKtukBU8rRSuHUwhyupOEUXX0YJwFTFHJ6yx8l4U3X6/0ojcdNNN+GKK67o9T7Dhg1DeXk5GhoaYm4PBAJoaWnp05vkcrkwb9485OTk4LXXXoPV2vsPpurqatxzzz3wer2w27v/Tdlut/f4OYJQCyOtXRFFEU1u47bqZMVJo8KJtcVydI4iAIBMqxmCAIhi+FxKtnp0UZwU9DixVqPNYlI11DUyVcdnHIFdIXO43m3pROCmcCopKUFJSUmf95sxYwba2tqwefNmTJ06FQDw/vvvIxQKobq6usfHOZ1OzJ07F3a7HW+88QYcjr4NtVu3bkVBQQEVRgR3VMohmPwXTi5vAD5JrTDSnjoGSw/XrlXHFCf9fzybTAKy7Ra4ugJwdfkV/fvTwxyeaU9dvWGwSAO19+wp0apTNY4gxV11ap5NLfT/l5kgY8aMwbx587Bo0SI88cQT8Pv9WLx4MS6++GJ5ou7IkSOYPXs2nnvuOUyfPh1OpxNz5sxBZ2cn/v73v8eYuEtKSmA2m/Hmm2+ivr4eJ598MhwOB9asWYN7770XN998s55PlyC6ZUDU2hWlvSdKw9p02XaLoX6rZGitODk5Cb9k5EiFk9Lp4W45AFOHHCcFCie3bAxX921UiVadmj6iSKsu8VauKIqR5HCKI1CXf/zjH1i8eDFmz54Nk8mE888/H48++qj8eb/fj127dqGzM7ySYsuWLfLE3YgRI2KutW/fPgwZMgRWqxUrVqzAr3/9a4iiiBEjRuDhhx/GokWLtHtiBBEnrFXn9gXR7vHLqgiPNEttOiP6mwAgX/OpOmYO56NwynZYgHblQzDlVp2GU3VZco5T6s9FVpxULvyUDMBUM44gGXO4LxiSozeM9EuVIQunwsLCHsMuAWDIkCExMQKzZs3qM1Zg3rx5McGXBMEzDqsZxdk2NHX4cLjVw3XhxBQnI/qbAB2m6mTFiY8fz2qFYEbM4VrGEbDkcCXM4cw3pO75lZmqY6qOGsnhYaUomfN1+SIqlZFadcbRxgiCiIGpTrxP1hl1Tx0jL0PjVh0n61YYOSrtq9MzANMXDMm+u2RhqpXaHidFcpyY4qRCcWK3JK+IsXNZTIKqBnulMc5JCYKIQfY5cV44NXawVp2xFSdvIJS0ATYRInEEnChO8r46ZQtHt7wgV/slv0BqhQgQHX5pII+TKopT8uZwI07UAVQ4EYRhqcwzRiRBs4HDL4FwK8lsCpvvtVCdZI8TR+ZwAIqaw0VR1KVVZ7OYYJOUjVR9Tm6NPE6KTNWp2FZMZeWKhwongiC0xCiKE2vVlRi0VScIghyCqYXPyeXlq1WnhsfJG5W6reVUHRAdSZDa89FacUpFIevUII7AFwwhGOrdS3wsHgMu+AWocCIIwzLAIFlOTQZv1QFAnoaRBBHFiY9WnRoeJ3dUEablVF3010s1PZwVTup7nCKFSSCYnC+rS8WdetHFWKLtOrlVZyHFiSAIDYjOcuKZRmmqrtSA4ZeMgkztIgl4WrkCRHuclCycIm/kJpO2GWTy2pWUFSf19r9FE93GSrZd1+kPHHctpbBbImVEsoWTGt4rNaHCiSAMysD8TADhnCQtTMvJIIoiGlxdAIyZGs6Q99Wp3KoTRTEyVZfGHic91q0wMu3KRBLIxZ/Kz8FuMYHVlsm26zzS2L8aRZ7JJMjFU6KFHTsXKU4EQWhCboZF9p/w2q7r8AZk06iRCyfWqmtVWXFy+4JgNpEc7qbqFFScfNobwxlZCitOarfqBEGI5E8lWTipueQXiDaIJ9mqI8WJIAgtEAQBlfnhnYu8tusaotatqG2iVROt0sPbJbXJaha4CQRkBZxTwTgCpjip3ebqjlSLEIZWK1eA1CbrRFFUva2YkeRkXWRPnbFKEWOdliCIGHg3iKeDvwmIZDmp3apr6wxfPy/Dxs3+wWwVWnV6hF8y5LUrKT4frVauAKllOfmCIVnFVEvZYR6lRAs7ynEiCEJzeDeIM8WpOE0KJ60UpzxOJuqAiOKkRuGkR6tOKcVJqziC8NdIIZ076jFqqZjM45S0OZwKJ4IgtGKAZBAnxUldtFq70i5dn6fdg9n2SBxBXzs/46XDq31qOENpj5MW7cZUWnXsMVazemtNMpIs7CgAkyAIzeE9BJNN1JXmOHQ+SWqwQqbNo5XixMdEHRBRnIIhMal06O5gRnM9DPCKTdXpoDglE9opL/hVsThhU3FdCe7/k6fqqHAiCEIrBnBuDmeKk5En6oCoOIJOlT1OUuGUz1HhlGkzg9mtWKp5qrgko7kehZNiipMOHqdUWnVqKmPyvroEz9cVoFYdQRAaw1p1dc6upFOF1SRdWnUFWitOmfwUToIgRAziCkUSsBTyHIPmOIVCorzGhPepOo8GPiKHNbkcpy55h56xShFjnZYgiBhKc+ywmgUEQyLqpSKFJxqc6aE4sUKm0xeEN6Be2CjzUPHUqgMiBY5Sa1eY0TxHh3R0JRSnrkAQzO7F+1Sdmgt+GUnnOAXI40QQhMaYTAIq8vidrGuUFvyW5hq7cMqxW2CW4pvVNIg7OWzVAZECR6nJOpYJZdSpOvZYQdAm9ZqdOZlWHTurmllJGUkqYh4N/FdqQIUTQRicSJZTp84nicUXCKHFHfYElRh4wS8QLlALJNWJPSc1aJNyonhq1QGR9HDlFSc9CqfUc5xYmy/Dqs2uvVRadV0atBQdKQdgUuFEEISGsMm6o21dOp8klmZ3WG2ymATZI2Rk2GRdq4qFU7usOPH1eikdgil7nPRo1dmTb3sx3HIUgTaFXyqtOi2m6jKSXrlCU3UEQehApaQ4HeasVcf8TcXZdk1+K1ebQlY4qdiqY21AXhb8MiKKk/Gn6iKtuhQUJ592E3VA9FRd4mf2+NWfqpPN4YlO1ZHiRBCEHgzkdO2KPFFncH8ToyBLatWpGEkgK06ctepyFV70q2eOU5ZUOLlTmKpjj9VKcUppqk4qttSdqmM5TskFYNJUHUEQmjJQatUdbuHL48TWrRjd38QozFK3VRcMiXILi7epOiVbdcGQKIdH6mIOt0eKkGAouSR01v7K0mhJcUpTdXJxon7hlKziZNfAYK8kVDgRhMGpKgxnOR1u9SCU5BuBGqSb4sQ8TmqZw51RGVH8FU7S2hUFCqdo1UqfOIJIsZaMggNErVvRqPBLLQAz7CNSs3CSPU4JJ4erX9SpARVOBGFwKvIcMJsE+IIhWeXhAbZuJW0UJxaCqVKrjoVrZtnMqu0USxYlp+pY+rjdYoLNov3zdFhNchJ6Z5KFoFtjxSm1AEz1W3VJJ4dL5nDyOBEEoSkWswkVeeHVK4da+WnXyetWco29p45RILXqWlQyh0f8TXxN1AERL1KHAuZwl47+JiCchJ6VYpYTK7i0UkpSyXHSYuUKM4cn4nEKhkT4gjRVRxCETlQVhNt1hzjyOaWbx4nlOKnlcWJKFm8TdUAkOVwJj5OeqeGMzBTTwyOKE8URRF87kcIuOrqAFCeCIDSnqjBsED/Uws9kXbp5nGTFSaXCqZ3T1HBA4VadjqnhjCy7MopTpkZxBEzZSmVXnbqKU+Lni76vXYeWbSoY67QEQXSLrDhx0qoTRTHSqksTxUltj5O84JfDwompQ8oUTvq26oBIEZGsgsYW/GqlOGUkObUW/RhVPU5JJIdHJupMhst5o8KJINIANlnHS6uurdMv+xeMvuCXwRQnty+YcEJyPLR38pnhBCgbR8BD4cQUp2TXrsiKk8ZxBL5gCIFgkmtNtJiqS+DfRZcG51ILKpwIIg1grTpe0sPrnOGJuoJMq+GMnz2R61B30W8b14pTpHASxdQiL1jhxCIO9CA7xcJJ9jhp1G6MLi6SXaSrSQBmIq06KSZBiyXJSkOFE0GkAaxVV9vugT/B30jVoF4qnMrSZKIOCE9jqbnoV27Vcaw4BUNiwotcj0XPdSsM9nySbT3KOU4aqSU2swmsm5Vou86jwZJfVpQFQmLcP3/YBB4pThrR0tKCSy+9FLm5ucjPz8dVV12Fjo6OXh8za9YsCIIQ83HNNdfE3OfgwYM455xzkJmZidLSUtxyyy0IBJRZMUAQalKSY4fdYkJIBI5ysHqFFU7leelTOAGQlxWr4XNiKhZvC36BcIHA3rhT3VcXmarjoVWXXMtV65UrgiBE7dhL7Mydcsikem/3dmvk2vEqYh4Npv3UwpCF06WXXort27djzZo1eOutt/DRRx/h6quv7vNxixYtQm1trfzxwAMPyJ8LBoM455xz4PP58Mknn2DVqlV49tlncccdd6j5VAhCEQRBkFev8DBZV9ceNoaXp5HiBERnOSlfODk5btUJghBRaVL0OfHgcYq0HpMrAuUlvxqqJclO1kU8Tuq93nZL4ooYO5fDarwyxHAn3rFjB1avXo2//vWvqK6uxsyZM/HYY4/hxRdfxNGjR3t9bGZmJsrLy+WP3Nxc+XPvvvsuvvnmG/z973/H5MmTcdZZZ+Gee+7BihUr4POpt9STIJQisnpFf4N4XRq26gB1s5zaPOFr8mgOByKTdaku+o206vT3OHUkqTgxFUerlStApB2WiOIUDInwBdRP505GEZPN4aQ4qc+GDRuQn5+PadOmybfV1NTAZDJh48aNvT72H//4B4qLizF+/HgsXboUnZ2RN5gNGzZgwoQJKCsrk2+bO3cunE4ntm/f3uM1vV4vnE5nzAdB6AFPkQTp6HECIot+W9zKm8N5jiMAlJusi5jD9W/VJR1HoPHKFSC5fXWdUQGfavuxMuSQzvheUyMXTvp95yZJXV0dSktLY26zWCwoLCxEXV1dj4/7yU9+gsGDB6OyshJfffUVbrvtNuzatQuvvvqqfN3oogmA/Oferrt8+XLcddddyT4dglAMnkIw69qZxyk9oggYzOPUqqLHidfCKUcOwUytaOSiVZfiVF2HxitXor9WIq06VuCZTYLqIZNZNjMakUCrzsAeJ24Kp9tvvx33339/r/fZsWNH0teP9kBNmDABFRUVmD17Nvbu3Yvhw4cnfd2lS5diyZIl8p+dTieqqqqSvh5BJAspTurDFCelC6cufxBeqaXC41QdoFx6OE/m8GTajv5gSG5/5WgYqRBp1cV/ZndU3pQgqBsymZFoqy5gzD11AEeF00033YQrrrii1/sMGzYM5eXlaGhoiLk9EAigpaUF5eXlcX+96upqAMCePXswfPhwlJeXY9OmTTH3qa+vB4Ber2u322G3p9dv1YQxiYRg6qs4+QIhNEseoHQzh7MFvErHEbA2ndkkyGoIb6Q6ws/gwuPkSL5VF61SZWm0cgVItlWnXcJ5ovv0PBpM+6kFN/9CS0pKUFJS0uf9ZsyYgba2NmzevBlTp04FALz//vsIhUJyMRQPW7duBQBUVFTI1/3973+PhoYGuRW4Zs0a5ObmYuzYsQk+G4LQHqY4NXV44fEFdctHaXCF1Sab2SQrNOlCYZZkDldYcWJtulyHRXVlIFnY8mFnCq06URS5aNVl25NfucLO77CaYDFr96bPFJ1EWnVuDXfqZSbpcaIATA0YM2YM5s2bh0WLFmHTpk34+OOPsXjxYlx88cWorKwEABw5cgSjR4+WFaS9e/finnvuwebNm7F//3688cYbWLhwIU4//XRMnDgRADBnzhyMHTsWP/3pT/Hll1/inXfewW9+8xtcf/31pCgRhiA3wyKrFXpO1rE2XWmundsiIFlkj5PC5nB5wW8mv4Um816xsyaDNxBCIBROHtfTHM5Sy5PxOLl9+pjbM6Sx/USm6rRUnBKd+qOVKxrzj3/8A6NHj8bs2bNx9tlnY+bMmXjqqafkz/v9fuzatUuemrPZbHjvvfcwZ84cjB49GjfddBPOP/98vPnmm/JjzGYz3nrrLZjNZsyYMQOXXXYZFi5ciLvvvlvz50cQySAIAgYW6u9zStcMJ0A9czgL1Mzl1BgOAPmscEph3QxTqwRBuwW53cFabMlkUnXoNBUYGfdPwOOkYcJ5oq3ETjKHa0thYSGef/75Hj8/ZMiQmH1KVVVV+PDDD/u87uDBg/Gf//xHkTMShB5UFWRgR61TV5+TnOGUZqnhQCQAs1Na9KvUD33Wqivk1BgOKKM4yVEENgtMJv3USGbq9gXCRm9bAhNnrL2n1Z46Biv2Ekk77/Rqt1OPZVrFqzh1yqtgjFc4GVJxIgiieyIGcf1bdemoOEUv+lVSdWJm+gKOPWFKFE4dHPibgFhTd6LtOv0Kp8QjFDRVnFirzh/f+TwathGVhgongkgjqtjaFV1bdSyKIP28geFFv8r7nFgRVpjmHqeIMVxfZc1iNsmrPhI1iLPCRevpx6wkdtXJCeccturcOmRhKQUVTgSRRvAQSZCu61YYbO2KkpEE7FqF2RwXTtLzbktFcZJ2w2XrrDgBEYN4ooUTK/70UpwSOW8kx0kDc3iChZ2HWnUEQfBAFQfm8IY0btUBkRDMZrdXsWuy3Xfprjg5OWnVAZFIgkRbdcxjpHXxl8x55ak6DuMIImqY/t8LiUKFE0GkEQOlVp2rK5DS9FOyiKIoK07laWgOB4DinHALsrmjf3qcfIGQPEqeKDzsqWPISegJe5wk1YwUpxgyEgzA7PRq579SGiqcCCKNyLRZUJwdfmM/0OLW/Os7PQF0+cOrFNK1VVeshuIkeZyKOC6csu0RY3xbkkV5ByceJyDiGUrcHK6PqTkzBY+TFsuIE/Vg0VQdQRDcMKQo3K470Kx9u46pTfmZVkPms8QDK0ybXMp7nHhWnARBSLldF1m3or/ixM6Q6L46pvho36pLvNBjbbNMLeIIksxx0uJsSkOFE0GkGYOLsgAAB5q1V5zq0tzfBABFUuGklOLkC4TkFhbPHicgdZ+TPFXHwZtldhKtLyBSuGRruKcOiPiUEmrVaZkcnoDHKRC1KDnTgL9gUeFEEGnGYElx2q+D4lTbFp7mq0hTfxMAFEmTb00KeZxYarhJiBQmvMLO15ZkhhV70+dBcWKKkTNZxcmu7d8VK/S8gRACwVBcj4koTnzFEXRGeeQojoAgCN1hhdNBHQqnI1LhVJmfofnX1gq5VdehjOLUIhUhBZk2XdO04yFVxckpt+r0LxDZGVwJLi3ukOMItH3DjzZ4u+M2YGunOMlTdXEMDrDiymwSYE8gtZ0XjHdigiB6ZYjUqtuvQ6uOFU4DCtK5cJLM4QopTi0d/PubGKkWTuxxPChrTPVyJag4sTRurVUzm8UEmzn8lh2vz0nL5HA5xymOlTDytJ/VbMhF4FQ4EUSawQqnBpc3oYWgSnCkVSqc+oHi5PEHE57I6g6mOBUaoHDKz1SocOJgJ1/qipP27casBLOctNxVxyb3fMG+W4nMGG7ENh1AhRNBpB15mVb5DU7rybqj7elfOGXazPK6DiVUJyOEXzLSSXHKTVJxknfV6RDcyNp18RjERVGUFSct4giii6C+2nWRYE79vW7JQIUTQaQhgwu1jyQIhkR5T106e5wEQUBRluRzUmCyrkXaeZfurbpQSISTo8IpmVadPxiCV5oG08Pgzgzi8WQleQMhhMTw/2sx8m8zm+Scr74M4kwJzzDgRB1AhRNBpCV6RBI0urzwB0WYTQJKc9JvwW80LD28yaVE4RS+Bs/hl4xceaou8cKpwxeQ38j5KJwSb9VFt8j0bNXFozhFn1WLAkUQBDlaoK/CzqPh8mE1oMKJINKQITpEEjBjeHmuAxZzev9oiaSHp96qa+k0juKUn4LixNQmm8XERThqMooTu6/dYoJVh+/xrARCMFnx4rBGlCC1yYzTg+U2cPglQIUTQaQleihOR9vS39/EiKSHp644yR6nLP1VmL5gSpEzicKJJ38TEK04xV84sTiFXJ2eg7wmJo5WnR55U/GuXfH4IlN1RoQKJ4JIQ4YUa+9ximQ4pW/4JYOFYCqhOMkLfo1gDpeGDtpSKJxyOQi/BCKKky8Y/9Jip0ffAM9EFCc9wkbjPV8nteoIguCNQYVhxelouwfeQHKb7BPlaD/IcGIUKRiCyRQnZjjnmfyMcHHX7vFDFMWEHsuTMRwAsm0WsAiheFUn5ofK1SnAMzuBOAIWm5CtYTssXg9WpFVHhRNBEJxQnG1Dls0MUQQOtXg0+ZoswymdJ+oYxfLaldQKJ1EUI8nhBmrVBUNi3OnVDN5adSaTgGwb8znFp6DJu/Z0VpziMYe7vNoXTvEuIpZbdTpEOigBFU4EkYYIgqC5z+lIP/Q4pZrj5PYF5WWnRgjAdFgj6dWJGsR5K5yAxA3ieitOCbXqmOKkQ6uur8JODsAkjxNBEDzBfE77mrQpnPqjOTxVjxNr0zmsJkP89i0IQsTnlOCiXz4Lp8QM4mwhcG6GPn9XEUUnHnO4P+YxWpAV5/kiAZhUOBEEwRHDirMBAN9pUDi5uvzym0p/aNUxc3hrpy/uTfXd0WKg1HBGsiGYzFjNV+GUaKtO3yXF7LzOOM6rh8dJLuz6WPUkB2Aa4JeF7qDCiSDSlGEl4Vbd3oYO1b/W0bZwYnh+ptWwaxQSoSDTBpMAiGJk11wyRPxNxiucEo0kkKfquCyc4m3VSR4nnb7HWWESz3llj5OWrbo4V8LIU3XUqiMIgieGl2inOLE2XWVe+qtNAGA2CbInqcmVQuHUYZwFv4y8JNPDeW7VxaPgRN9Pr+IvkbRztw7m8HiXEHfoUNQpCRVOBJGmMMWp0eWN+40hWQ639Z+JOgaLD2hOYV9da6fxCqdk08P5LJySVJx0esNP5Lx65DjFO1XHPq+XcpcqVDgRRJqS47CiRNqp9l2juqoTiyIY0A/CLxnFOVIIZgqTdS0GCr9k5KZR4cSeS/yKEytG9HkOuQmY2V265DjF16pj/iujtvWpcCKINGa4pDp916iuz+lQSzihvKowU9WvwxNMcUoly4kVXUZY8MuQW3UJFk48qmuJGt1dOqefM/XI4w/2OZTQoWurrvepOmrVEQTBLcMkn9NelQun/VJW1BApO6o/wCbrGlMonNhjmTJoBPIzE1ecgiFRvn8+R+paokZ3vRWn6EIjXlVHD3N4b606URR1KeqUhAongkhjZIO4iq06URRxUNqJN7io/yhOpTnhtmRjCot+mwxcOCWS4xRe0RL7eB7IT9DoHokj0OcN32o2wWE1SWeJz4Cdo+WS3zhadR5/ECHpe4EKJ4IguEOOJFBRcWpx++DyBiAI/atVV5YbLnYanCkoTi7jFU7Mj9Xijl9xYm26HLsFVjM/bzuJtOq8gSC8Usq7npEK8U4C6qE4xWMOZ0WVINCSX01paWnBpZdeitzcXOTn5+Oqq65CR0fPbwz79++HIAjdfvzrX/+S79fd51988UUtnhJBqMJwKQRzf3MngqHElrLGywHJ31SR64DDoLksyVCWG1ac6p1dST0+FBINqTgxb1dLAtOEbZzmVSVidI9WePRUSuKZrBNFER0+ZsDW7t+knBzuCyLUw88buaCzWSCwLcsGw5CF06WXXort27djzZo1eOutt/DRRx/h6quv7vH+VVVVqK2tjfm46667kJ2djbPOOivmvn/7299i7jd//nyVnw1BqMeAggzYLCb4AiF58k1p2C68Qf2oTQcApVKx05Bkq67d44c/GH5zYcWIEWDLiFvdfohifMU4U6cKOGrTAVFtxwQKp2y7BWaTfm/4OXGEYHb6gnJrVMtWXXRB2env3iBudGM4ABju5Dt27MDq1avx2WefYdq0aQCAxx57DGeffTYeeughVFZWHvcYs9mM8vLymNtee+01XHTRRcjOzo65PT8//7j7EoRRMZsEDC3Kwq56F/Y2dqhS3OxvCitO/ckYDgClkuLU7vGjyx9MWG1jxvD8TCtsFuP8DsuKPF8whA5vIC6jNGvV8WQMByKtOl8g1Offod7+JkY8IZisODGbBNkTpQUOqwkmAQiJ4XZdd8qc0Y3hgAEVpw0bNiA/P18umgCgpqYGJpMJGzdujOsamzdvxtatW3HVVVcd97nrr78excXFmD59OlauXNnnb1RerxdOpzPmgyB4Qm2f08EWZgzvX4VTrsMivykl43NqYv6mbOOoTQCQYTPLW+1b4lxyLLfqOFOcotWjvgzi7PN651Cxwq03A3a0OqZlO0wQhD4N4kbPcAIMWDjV1dWhtLQ05jaLxYLCwkLU1dXFdY1nnnkGY8aMwSmnnBJz+913341//vOfWLNmDc4//3xcd911eOyxx3q91vLly5GXlyd/VFVVJfaECEJlhsuRBOpM1rEogv40UQeE3yTYZF29K3GfkxGjCBgsiynewqlVKjp48zgJghC3QbxNjlPgo3DqrVWnp6rTl0GcLQDWW7lLBW4Kp9tvv71HAzf72LlzZ8pfx+Px4Pnnn+9Wbfrtb3+LU089FVOmTMFtt92GW2+9FQ8++GCv11u6dCna29vlj0OHDqV8RoJQkhGl4cJpd71Llesf6IdRBIxUJuuMOFHHSLhw4jghPbJ7r/fn0s7ajRn6Pod4puo6dEgNZ8gG8R5CMPU8m1Jwc/KbbroJV1xxRa/3GTZsGMrLy9HQ0BBzeyAQQEtLS1zepJdffhmdnZ1YuHBhn/etrq7GPffcA6/XC7u9+x9udru9x88RBA+MKs8BAOyqd0EURUWle2eXX37z7G+tOiDic0pmsq7RoK06IFI4NcetOPHZqgPijyRgrTq9FafsOMzheq636atV5/Iav1XHzclLSkpQUlLS5/1mzJiBtrY2bN68GVOnTgUAvP/++wiFQqiuru7z8c888wx++MMfxvW1tm7dioKCAiqMCEMzvCQbFpMAV1cAte1dii7iZcGXxdk2Q/8GmSxlqbTqpMKp2MCKU2uCrTrezOFA/IUTL88hnlYdey565E3lyh6s7l9PN5nDtWfMmDGYN28eFi1ahE2bNuHjjz/G4sWLcfHFF8sTdUeOHMHo0aOxadOmmMfu2bMHH330EX7+858fd90333wTf/3rX7Ft2zbs2bMHjz/+OO6991788pe/1OR5EYRa2CwmDC0Oq0G76pRt10X8Tf1PbQKA0lRadR3GV5wSbdXxtKeOEe8KmTYPmwzUV3HKjWOqTk/FSc7G6sFs39FFHidd+Mc//oHRo0dj9uzZOPvsszFz5kw89dRT8uf9fj927dqFzs7OmMetXLkSAwcOxJw5c467ptVqxYoVKzBjxgxMnjwZTz75JB5++GEsW7ZM9edDEGrD2nU7FS6c+rO/CQDKpVZdXXviilOt9JjyPIeiZ9KCxFt1fLS5uiNexYkVAvmcTNXx2qrLlT1YPUzVSd4natVpTGFhIZ5//vkePz9kyJBuYwTuvfde3Hvvvd0+Zt68eZg3b55iZyQInhhdnoO3vqrFrjpl4zJY+OXgwv6pOFVIRU9te+LhovVpUDjF06oLhUTZeM2j4pQX5746Xqbq8uLYFaiv4hQuK3panMxaeNSqIwiCa0aV5wJQT3EaUtw/FSfmFzva3hV3ijYQNs4ykyxTrYxEIopTm8ePQIjfhPT4PU58hHiyqb7ezuuUCyfti5PcPqb+KACTIAhDMFpq1e1t7IA/GFLsuiwbqr+lhjPKch0QhHDydLxtKyDS2suxWwzZsiiSC6e+vV1sH19eBp8J6awQau0zjoAPxYmtvGnr7Hnljaw46XBW5nFyenpv1VHhRBAE1wzIz0CWzQx/UMS+JmWCMNs7/fKb4vDS7D7unZ7YLCYUS+bu2rb4fU4svqDMgG06IJI91ejy9qm0sYT04mz+2nRApAjszeguimKkVadzjhP7+oGQ2OPIv56tur4UvA5JiTLiLwwMKpwIoh9gMgk4QWGD+J7G8HUq8xyG/u0xVSql4udoAj4npjhVGLxw6vKHel39AUSmB4s5nR6MZ0KwwxtAUGo36q04OawmWbnryZelrzlc8jj10KpjpnG9V9ekAhVOBNFPYO26bxUqnHbXh3ff9Ve1iVGRF/Y51bYlUDgxxcmA/iYAyLRZ5GK5wdV7u66pI1yQ8JpXFe3X6kk9YwWKw2pKeJmz0giCIAeJ9qTqMOO4nnEE3RVOoihy0/JMBSqcCKKfMKpMYcWpIVw4jSzNUeR6RqUin03Wxd+qY4qTEY3hjNKodl1vNHGeV1UktRB9gRDcvu7XhMip4Tq36RjsHN35skIhUR480CcAs2ePk8cfhE/yWJLiRBAE90Qm65SJJNjTGC6cRvRzxakyLzJZFy91Bvc4AREFqU/FiXOPU6bNAoc1/FbY3NH9c+El/JIRiSQ4XtVxdQXAhDM9PU7OLj9CoVgFj53XahaQadNXuUsFKpwIop8wtiJcOB1u9cS9KqM3WKuu3xdO+Um06pjHqR8pTrx6nIBITEJPk5EswJMXlYS16tq6adWx9p3DaoLdon1xwgI6RRHo8MWqTm3y62hTdGem1lDhRBD9hLxMq7x65asj7Sldy9XlxxGpUDihrH8XTgMKwoXT4db4C6ej0mtnxPBLRknchZPkceK5cJLUsJaO7gunZs6KPznLqZtWnZ7GcABwWM2wS+b1Y0MweVPukoUKJ4LoR0wamAcA+PJQW0rXYTvvKvIcugcC6s2gwnD4Z52zC13+7j0y0bi9AVnZqCo0bnBoqbTguKGPBcey4sSpORzoe7KukbN2Iys8Wrtp1eldOAE9Zzk5OTibElDhRBD9iIkD8wGkXjjtqA37pNikXn+mINOKLMmvcSSOdh27T67DYug3kHgUJ1EU0SwrTnwUHd3RVxI6b+1G9stKdx4nHgqnvB4m69o42feXKlQ4EUQ/YlJVPgDgy8PtCa0IOZZvasOK0xjJN9WfEQRBVo4OtnT2cW/gkHSfgQXGVZuA+AonpycgT1HxUnR0RyQEs/vnwtqNJZyoZvlyHAF/rTogkuV0bFxCm46J5kpChRNB9CPGVebCYhLQ1OFNaArsWGTFiQonAJF23aE4CifmhaoqzFD1TGoTjzm81hl+rvmZVt3zj3qjsA9zOHeKU0bPrTp2Vj33AkZadT0pTvyqj/FAhRNB9CMcVjNGSe21ZNt1oZAoe5zGVlCrDogUTgeb+4/ixMI7m92+Hr1dbA0NCwnllb7WrsiRCtwoTqxVd/x5I54y/YoTluV0rOLEFDIjt6gBKpwIot8ht+uSLJy+a3LD4w/CYTX12+W+xzKoKP5WHVOcBhbwXUz0RUGmFRmSilTXg3rJ1tBUcj49KHucupmqE0UxajKQD6WELfrtrtDjQR3ryTPGCimaqiMIwlBMZgbxw21JPf4r6XHjK/NgMdOPEACJeZxaw/epMrjiJAgCKqXU9J5M8bLilM934cTUs+4mBHn0abEU9tZOP3yBUMznmlz6xz8wL9ixgaJtabBuBaDCiSD6HUxx+vpwu7y4NBGYUsUm9IioVl1LZ5+me1lxMrjHCQAGSMXfkR4yrJjixHurjuVpNbi88AdjCxG2pDjHYeHGp1WQaYPFFA6QbDqmOJFX3OjYVmTKXNMxCl4bZ0GiyUKFE0H0M0aUZiPTZobbF8TuhsT31n15OByeOakqT+mjGZZBhZmwmAR0+oK97qxrcfvkdsUgA2c4MQbEqThVcq44FWXZYDULEMXjze487tozmQS5MKp3xn6/NXLQqmPG9GOLOh4m/pSACieC6GeYTQJOHFQAANi0ryWhx/oCIXxzNDxRN4kUJxmr2YTBks+JLT/ujr3Sfr8B+RnItFk0OZuaDJDWzfRUOLGdfLwrTiaTILfrji18I+GX/BROQGSqMXpXYJc/CFdXOHRSz0KPmeibXMe26lhyOB9esWShwokg+iEnDysEAHz6XXNCj9tR64QvGEJehlUuFIgwbGdfr4WT9LlhJelhqmfrZrpr1YmiKK+WqeDcHA5Eznis0Z2H1ld3lOZG2osMdlab2YTcDP0K8+hWHWtdd/mDcPvC05fMPG5UqHAiiH7IycOKAACfftdy3Abz3mAK1bTBBYZe0qkGcuHU2LfilC6LkSslJYl5maJp7fTDKxmXjbCTr1x6LrXHPBdWSHFXOLEcrahWHfMUFWXru0SXqXO+YAhOSQFjr2OG1SwHZBoVKpwIoh8ycWA+MqxmtLh9+DYBn9PGfWGFqlpSrIgII0vDmVa9t+rcAIDhJelRODHFqbat67gCnKlNxdk22C18mKp7oyfFSZ6C5MyTFtkVGKU4cdJWdFjNyLaHiyM2WcfatuV5DsP/0kWFE0H0Q2wWE6YNCfuc1u9uiusxwZAoK07VQ4tUO5tRYSrS3l4KJ1ZUpUvhVJbrgNkkwBcMof6YUf7vmsJFolGyvsqZx+kYs/WhFinpnbPcrdLc4z1OkQwn/Vthx07WMRN7WS5fyl0yUOFEEP2UWaNKAQAf7GqI6/4765xwdgWQbbdgXCWtWjmWYSVZEIRw6N+x00RA2OPB1IvhpcYoJvrCajZhcGH3pvi9BisS+1KcBnHm6YuYw6Nbdfz4sZjqxc7EXldWoBoZKpwIop/y/dHhwmnTvhZ0eAN93v+jb8PK1LQhBRR82Q2ZNguGFYcLoq+lyIZodtW5IIrhxG2eRttTZWRZuDDaXX9M4dRoLCN8eTeFk6vLL2cP8RZYKrfqnJEine2fLOOgOCmSFafYVl2ZAfxufUE//QiinzK0OAtDijLhD4pYv7uxz/u/v7MeADBbKriI42Hholu7WWfDktonDsw3vMcjGubt2n2s4mQwPxfbHVjb7pF377E2XWGWDVl2vgzNrFXX1OGVg2z3S+3RwRy0R2XFSWolslYdKU4EQRiamjFlAIC3v67r9X6tbh82H2gFAHyPCqceYdlWX3WzzubLQyw4NF+7A2kAU5z2RA0ZhEIivpMUp+EGmSAszrYhP9OKkBhRyyLrcfjyNwHhwsRuMSEkRhZHs8JpaLH+6hhTxFguFrXqCIJIC344uRIAsOabul7bdR/sakBIBEaX58i/mRPHM3FgOE39y8Ptx61eYYrT5DRLXGem+G/rO+TnfKTNA28gBKtZ4LLo6A5BEHACU8+ktiMrSAZyNlEHhINsmZq3p6EDXf6g3KrjwZA/VGrRsiK0XmopUquOIAhDM2FAHoYWZ6HLH8K723tWnV7dcgQAMHdcuVZHMyRjKnJhNQtocfvkNg8Q9sqwN5B02/E3vCQbghBep8EmqNhzHVKUZSg/HFPPvq0Pq2escOLN38RgRevuhg4caA6fNcdh4SJgcoRU1O1tdCMUEqlVRxBEeiAIAs6TVKe/f3qg2/scaunEx3vDxvALpg7U7GxGxGE1y+26D6N8Y5/vb4UohleU6J2xozQOq1lWOLYdCbcjWVvXaNOXJ5SFFSdWOLG9jKPK+Ww3jpQLJxf2yW26LC48dGzKtN3jx446JwIhEYLAx8RfqlDhRBD9nJ9MHwSb2YQtB9vkN7xo/vn5IYgicOqIIu5CAHlktuQbe++bevm2d78Jq3nfG12iy5nUpnpoOBD1f1Im2Ia94aDUGcONlfcVUZzCra/tR8OF07TBfAa+svPubejA/ma+crMcVrOs1DHFenhJNqwGUiB7wpDP4Pe//z1OOeUUZGZmIj8/P67HiKKIO+64AxUVFcjIyEBNTQ12794dc5+WlhZceumlyM3NRX5+Pq666ip0dPQcZkcQ6UBprkNWnR5duzvGm9Pc4cXfPt4PALi0erAexzMcZ44Nm+c37G1GhzeAYEjEu9vDRdS8cRV6Hk01ThsZLgjX72lEpy8gTxXOGFas46kSZ5SkOB1q7cTHe5rgD4oozbFjIKc+rej9iMyMP6SYj8IJiJzvpc8OAQBOkkJ3jY4hCyefz4cLL7wQ1157bdyPeeCBB/Doo4/iiSeewMaNG5GVlYW5c+eiqyuS2XHppZdi+/btWLNmDd566y189NFHuPrqq9V4CgTBFdfOGg6b2YQPv23E21/Xyrc/9O636PAGMH5ALuaRvykuhpdkY2hxFnzBEP699Qg27G1Gs9uHvAxr2q6qOXVEEQQhrNS89VUtAiERA/IzUFXIZ8HRE0XZdgwvyYIoAne9+Q0AYCrHexkHF2XBYhLg9gXxz88PAwAmDeRn+IAVTmzwhFflLlEMWTjddddd+PWvf40JEybEdX9RFPHII4/gN7/5Dc477zxMnDgRzz33HI4ePYrXX38dALBjxw6sXr0af/3rX1FdXY2ZM2fisccew4svvoijR4+q+GwIQn+GlWTj2lnDAQC3vvwVXvviMB5+dxde2HQQALD0rDEwmfh88+ANQRBw2clhde7/e20bLntmIwDg3EkVadGm6I78TJtsel/27+0AgJkjirktOHrjomlVAICDkjF86mB+VRKr2YQzToi0f0tz7DF/1pvR5Tkxfz5pCBVOhmHfvn2oq6tDTU2NfFteXh6qq6uxYcMGAMCGDRuQn5+PadOmyfepqamByWTCxo0be7y21+uF0+mM+SAII3Ld94bjtJHF6PQF8euXvsSj7+8BANwydxROHWGsloveLJwxGMOjErNLcuy4ec4oHU+kPr+uGQmTAHj8QeQ6LPjl7BF6HykpfnxiZAAiP9OKH06q1PE0fXPz3Mj31XmTK7maYjx7QgXGVIQHBIqz7YZTIHuCn1dYRerqwsbMsrKymNvLysrkz9XV1aG0NDbYz2KxoLCwUL5Pdyxfvhx5eXnyR1VVlcKnJwhtsFvMeObyk/CLM4ZhWEkWRpfn4IHzJ+I6SYki4sdqNuHJn07DORMrMH1oIf508WTkZ+o/Iq4ms0aV4r4fT8Swkiw8eskUw+Z9leTYceu8UTjjhBK89cuZKOV8fH5MRS5umD0SYypycdXMYXofJwaH1Yx/XTMDV80ciuU/nmBIBbI7uMmQv/3223H//ff3ep8dO3Zg9OjRGp0oPpYuXYolS5bIf3Y6nVQ8EYbFZjFh6VljsPSsMXofxfCMKM3Gip+cqPcxNOWik6pw0UnG//l33awRuG6W3qeInyVnnoAlZ56g9zG6JdtuwW9/MFbvYygKN4XTTTfdhCuuuKLX+wwbllw1XV4eNrXW19ejoiIy1VJfX4/JkyfL92loiN0SHwgE0NLSIj++O+x2O+x24+dSEARBEATRN9wUTiUlJSgpUcfUNnToUJSXl2Pt2rVyoeR0OrFx40Z5Mm/GjBloa2vD5s2bMXXqVADA+++/j1AohOrqalXORRAEQRCEsTCkx+ngwYPYunUrDh48iGAwiK1bt2Lr1q0xmUujR4/Ga6+9BiA85XLjjTfid7/7Hd544w18/fXXWLhwISorKzF//nwAwJgxYzBv3jwsWrQImzZtwscff4zFixfj4osvRmUl3+ZAgiAIgiC0gRvFKRHuuOMOrFq1Sv7zlClTAAAffPABZs2aBQDYtWsX2tvb5fvceuutcLvduPrqq9HW1oaZM2di9erVcDgixr9//OMfWLx4MWbPng2TyYTzzz8fjz76qDZPiiAIgiAI7hHEY1d4EynhdDqRl5eH9vZ25OYaa08TQRAEQfRX4n3/NmSrjiAIgiAIQg+ocCIIgiAIgogTKpwIgiAIgiDihAongiAIgiCIOKHCiSAIgiAIIk6ocCIIgiAIgogTKpwIgiAIgiDihAongiAIgiCIOKHCiSAIgiAIIk4MuXKFZ1gQu9Pp1PkkBEEQBEHEC3vf7muhChVOCuNyuQAAVVVVOp+EIAiCIIhEcblcyMvL6/HztKtOYUKhEI4ePYqcnBwIgqDYdZ1OJ6qqqnDo0CHagdcN9Pr0Dr0+PUOvTe/Q69M79Pr0jpFeH1EU4XK5UFlZCZOpZycTKU4KYzKZMHDgQNWun5uby/03n57Q69M79Pr0DL02vUOvT+/Q69M7Rnl9elOaGGQOJwiCIAiCiBMqnAiCIAiCIOKECieDYLfbsWzZMtjtdr2PwiX0+vQOvT49Q69N79Dr0zv0+vROOr4+ZA4nCIIgCIKIE1KcCIIgCIIg4oQKJ4IgCIIgiDihwokgCIIgCCJOqHAiCIIgCIKIEyqcDMKKFSswZMgQOBwOVFdXY9OmTXofiQs++ugjnHvuuaisrIQgCHj99df1PhI3LF++HCeddBJycnJQWlqK+fPnY9euXXofixsef/xxTJw4UQ7mmzFjBv773//qfSwuue+++yAIAm688Ua9j8INd955JwRBiPkYPXq03sfihiNHjuCyyy5DUVERMjIyMGHCBHz++ed6H0sRqHAyAC+99BKWLFmCZcuWYcuWLZg0aRLmzp2LhoYGvY+mO263G5MmTcKKFSv0Pgp3fPjhh7j++uvx6aefYs2aNfD7/ZgzZw7cbrfeR+OCgQMH4r777sPmzZvx+eef4/vf/z7OO+88bN++Xe+jccVnn32GJ598EhMnTtT7KNwxbtw41NbWyh/r16/X+0hc0NrailNPPRVWqxX//e9/8c033+APf/gDCgoK9D6aIlAcgQGorq7GSSedhD//+c8Awvvwqqqq8Mtf/hK33367zqfjB0EQ8Nprr2H+/Pl6H4VLGhsbUVpaig8//BCnn3663sfhksLCQjz44IO46qqr9D4KF3R0dODEE0/EX/7yF/zud7/D5MmT8cgjj+h9LC6488478frrr2Pr1q16H4U7br/9dnz88cf43//+p/dRVIEUJ87x+XzYvHkzampq5NtMJhNqamqwYcMGHU9GGI329nYA4eKAiCUYDOLFF1+E2+3GjBkz9D4ON1x//fU455xzYn7+EBF2796NyspKDBs2DJdeeikOHjyo95G44I033sC0adNw4YUXorS0FFOmTMHTTz+t97EUgwonzmlqakIwGERZWVnM7WVlZairq9PpVITRCIVCuPHGG3Hqqadi/Pjxeh+HG77++mtkZ2fDbrfjmmuuwWuvvYaxY8fqfSwuePHFF7FlyxYsX75c76NwSXV1NZ599lmsXr0ajz/+OPbt24fTTjsNLpdL76PpznfffYfHH38cI0eOxDvvvINrr70WN9xwA1atWqX30RTBovcBCIJQn+uvvx7btm0jD8YxjBo1Clu3bkV7eztefvllXH755fjwww/7ffF06NAh/OpXv8KaNWvgcDj0Pg6XnHXWWfL/T5w4EdXV1Rg8eDD++c9/9vtWbygUwrRp03DvvfcCAKZMmYJt27bhiSeewOWXX67z6VKHFCfOKS4uhtlsRn19fczt9fX1KC8v1+lUhJFYvHgx3nrrLXzwwQcYOHCg3sfhCpvNhhEjRmDq1KlYvnw5Jk2ahD/96U96H0t3Nm/ejIaGBpx44omwWCywWCz48MMP8eijj8JisSAYDOp9RO7Iz8/HCSecgD179uh9FN2pqKg47pePMWPGpE0rkwonzrHZbJg6dSrWrl0r3xYKhbB27VryYhC9IooiFi9ejNdeew3vv/8+hg4dqveRuCcUCsHr9ep9DN2ZPXs2vv76a2zdulX+mDZtGi699FJs3boVZrNZ7yNyR0dHB/bu3YuKigq9j6I7p5566nHRJ99++y0GDx6s04mUhVp1BmDJkiW4/PLLMW3aNEyfPh2PPPII3G43rrzySr2PpjsdHR0xv+Ht27cPW7duRWFhIQYNGqTjyfTn+uuvx/PPP49///vfyMnJkT1xeXl5yMjI0Pl0+rN06VKcddZZGDRoEFwuF55//nmsW7cO77zzjt5H052cnJzjvHBZWVkoKioij5zEzTffjHPPPReDBw/G0aNHsWzZMpjNZlxyySV6H013fv3rX+OUU07Bvffei4suugibNm3CU089haeeekrvoymDSBiCxx57TBw0aJBos9nE6dOni59++qneR+KCDz74QARw3Mfll1+u99F0p7vXBYD4t7/9Te+jccHPfvYzcfDgwaLNZhNLSkrE2bNni++++67ex+KWM844Q/zVr36l9zG4YcGCBWJFRYVos9nEAQMGiAsWLBD37Nmj97G44c033xTHjx8v2u12cfTo0eJTTz2l95EUg3KcCIIgCIIg4oQ8TgRBEARBEHFChRNBEARBEEScUOFEEARBEAQRJ1Q4EQRBEARBxAkVTgRBEARBEHFChRNBEARBEEScUOFEEARBEAQRJ1Q4EQRBJMD+/fshCAK2bt2q91EIgtABKpwIgjAcV1xxBQRBwH333Rdz++uvvw5BEHQ6FUEQ/QEqnAiCMCQOhwP3338/Wltb9T6KIvh8Pr2PQBBEHFDhRBCEIampqUF5eTmWL1/e7efvvPNOTJ48Oea2Rx55BEOGDJH/fMUVV2D+/Pm49957UVZWhvz8fNx9990IBAK45ZZbUFhYiIEDB+Jvf/vbcdffuXMnTjnlFDgcDowfPx4ffvhhzOe3bduGs846C9nZ2SgrK8NPf/pTNDU1yZ+fNWsWFi9ejBtvvBHFxcWYO3cuRFHEnXfeiUGDBsFut6OyshI33HBD8i8SQRCKQ4UTQRCGxGw2495778Vjjz2Gw4cPJ32d999/H0ePHsVHH32Ehx9+GMuWLcMPfvADFBQUYOPGjbjmmmvwi1/84rivccstt+Cmm27CF198gRkzZuDcc89Fc3MzAKCtrQ3f//73MWXKFHz++edYvXo16uvrcdFFF8VcY9WqVbDZbPj444/xxBNP4JVXXsEf//hHPPnkk9i9ezdef/11TJgwIennRhCE8lDhRBCEYfnRj36EyZMnY9myZUlfo7CwEI8++ihGjRqFn/3sZxg1ahQ6Ozvxf//3fxg5ciSWLl0Km82G9evXxzxu8eLFOP/88zFmzBg8/vjjyMvLwzPPPAMA+POf/4wpU6bg3nvvxejRozFlyhSsXLkSH3zwAb799lv5GiNHjsQDDzyAUaNGYdSoUTh48CDKy8tRU1ODQYMGYfr06Vi0aFHSz40gCOWhwokgCENz//33Y9WqVdixY0dSjx83bhxMpsiPwrKyshiVx2w2o6ioCA0NDTGPmzFjhvz/FosF06ZNk8/w5Zdf4oMPPkB2drb8MXr0aADA3r175cdNnTo15poXXnghPB4Phg0bhkWLFuG1115DIBBI6nkRBKEOVDgRBGFoTj/9dMydOxdLly6Nud1kMkEUxZjb/H7/cY+3Wq0xfxYEodvbQqFQ3Gfq6OjAueeei61bt8Z87N69G6effrp8v6ysrJjHVVVVYdeuXfjLX/6CjIwMXHfddTj99NO7PTdBEPpAhRNBEIbnvvvuw5tvvokNGzbIt5WUlKCuri6meFIye+nTTz+V/z8QCGDz5s0YM2YMAODEE0/E9u3bMWTIEIwYMSLm49hi6VgyMjJw7rnn4tFHH8W6deuwYcMGfP3114qdmyCI1KDCiSAIwzNhwgRceumlePTRR+XbZs2ahcbGRjzwwAPYu3cvVqxYgf/+97+Kfc0VK1bgtddew86dO3H99dejtbUVP/vZzwAA119/PVpaWnDJJZfgs88+w969e/HOO+/gyiuvRDAY7PGazz77LJ555hls27YN3333Hf7+978jIyMDgwcPVuzcBEGkBhVOBEGkBXfffXdMO23MmDH4y1/+ghUrVmDSpEnYtGkTbr75ZsW+3n333Yf77rsPkyZNwvr16/HGG2+guLgYAFBZWYmPP/4YwWAQc+bMwYQJE3DjjTciPz8/xk91LPn5+Xj66adx6qmnYuLEiXjvvffw5ptvoqioSLFzEwSRGoJ4rAmAIAiCIAiC6BZSnAiCIAiCIOKECieCIAiCIIg4ocKJIAiCIAgiTqhwIgiCIAiCiBMqnAiCIAiCIOKECieCIAiCIIg4ocKJIAiCIAgiTqhwIgiCIAiCiBMqnAiCIAiCIOKECieCIAiCIIg4ocKJIAiCIAgiTqhwIgiCIAiCiJP/H6iZ6OjnCeZSAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5167,7 +5154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5180,19 +5167,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5219,19 +5204,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5245,10 +5228,12 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "slide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "## Task 4\n",
@@ -5265,12 +5250,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 71,
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -5279,26 +5266,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 72,
    "metadata": {
-    "exercise": "solution"
+    "editable": true,
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "fig, ax = plt.subplots()\n",
+    "fig, ax = plt.subplots(figsize=(10, 3))\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(\"Threads\")\n",
@@ -5309,9 +5299,11 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "## Plotting with Pandas\n",
@@ -5350,7 +5342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5359,14 +5351,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5387,7 +5377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5396,14 +5386,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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s8mZ8/yrO19XuyhMeKRKCRrNJC5kdJjlEw0gIgU9Ol+P3/8mFWiMQ4uWEj5bEciHLYXSuogVr9xbgm9L+zkbOdlZ4dlYIfjF9DNegIKIBKaprR0q6Aruyq9B+pc2zrZUFfjLBF8kJcsTI3IzuKUlDRy8O5SuRml+H48X16FF/t76Ik60VZoZ7YnakFLPCPblkAZkFJjlEw6RHrcHqPbn4LLMCADBvnDfeejiaRaEGIITA0aJ6rN1bgILadgCAt4sdXpwdhp/F+rNlNxH9SG+fBvsu1mJbejlOX27SbQ/0cMTieDkeivU3meSgR63ByZKGK93alKhv/27dQUsLCaaMGYXZUd6YHSmFfDSn9ZJpYpJDNAxqWrvxdEo2zlW0QCIBXpkTjmdmBhvdnb6RRqsV2J1Thb8eKEJVSzcAINTLCb+ZG4G7I734+yEiVDR14ZPT5dhxpgKNnSoA/YlAUqQXkhMCMD3Yw6RrKbVagXOVLVfW41GisK79qv1hUqcrdTxSxPi7mfTPSiMLkxwiPcsobcTyT7LR0KGCq701Plg0ETPDPA0dFn1Pj1qDrd8osP5wiW7l8bgx7lh1bwQmyUcZODoiGm4arcCRQiVS0hU4UlSvq2WRuthi4RQ5FsXJ4e1qZ9gg9aS8sQtp+XVIzavD6ctNutbXAODhZKtbj2d6iAfsbTjFl4wXkxwiPRFCYMupy/jzV/no0wpEeDvjoyWT+ejfiLV2q7HxyCVsPlmG3ivdkeaO9cYrc8MRzLopIrNX396LHZkV+CSjXPd0FwBmhHggOUGOuyOlsB5BC222dqlxpKh/PZ6jhfW6+iMAsLO2wIwQT8yO8sJdEVJ4OtsaMFKiH2OSQ6QHPWoN/uffF/Dvs1UAgPujfbH2Z+PhYMP6G1NQ09qNd1OLsDOrElrRPzXl0SkyrLw7FF4u5nn3lmikEkIgo6wJKekK7M+t1S2s6WpvjYdj/fFYvJzNYdC/rtvpsiak5tUiLV95VRIokQATZW5IipJidqQUIV5OnO5LBsckh2iIVTR14emULORWt8HSQoLX5kXgiRmB/MA3QUV17XhzXwHS8pUAAHtrS/zqjkA8dWcQnLlgK5FJa+tRY1d2FVLSFShWdui2x8jckJwQgJ9M8GHHxesQQiC/pv1K44I6nK9svWp/wGiH/jqeSCmmjBkFqxH09IuMB5McoiF0sqQBKz7JRnOXGu6ONlj/2ERMC/YwdFh0mzJKG7F2XwHOlrcAANwdbfDru0LwWHwA15YgMjEXq1qxLUOBPTnV6FJpAPTfwFgw0ReL4wMwzs/VwBGantrWHl3Cc6qkESrNd+2pXe2tcVdEfx3PnWEevEFEw4ZJDtEQEELg/46XYu3eAmhF/6rSHy6JhZ+bvaFDoyEihMD+3Fq8ua8QpQ2dAAC5uwNenhOOn4z3YcchIiPWo9bgy/M1SElXIKeiRbc91MsJyQkBeHCSH1x48T0kOnv7cLy4Hql5ShwqqENzl1q3z9pSgoSg0bgnSoq7I6Xw5d9I0iMmOUS3qUvVh1d3nseX52sAAD+b5I8/PziO0xzMlFqjxY7MCryXVqxbW2K8nytWzYvA9BA+tSMyJmUNndiWrsDO7Eq0XLnYtraUYO44HyTHyxEX6M6pxHqk0QpkKZp13drKrtwg+tZYXxckRUoxO0qKsb4u/F3QkGKSQ3QbFI2dWLY1CwW17bCykOB386OwJCGAH9QjQJeqD/84XoZNx0rRcaXj0J1hnvjN3HCM9eV0FyJD6dNokZZfh5T0cpwoadBt93Ozx2PxcjwyWcZOYAZyqb4DaXn909oyFc34/lWlj6udbj2ehCB32FrxRiHdHiY5RLfocKESz396Fm09ffBwssXfFk9CXKC7ocOiYdbY0Yt1h0qwLUMBtUZAIgEWxPjhxdlhkLmzXTjRcKlt7cGnp8ux/Uw56tr6n7JKJMCsME8smRqAmWFesOS0UqPR2NGLQwVKpOXX4VhRA7rVGt0+RxtLzAz3RFKkFInhXhjlaGPASMlUMckhGiStVuBvR0rw19QiCNHfiefD5FizXRiOBkbR2Im3DxThv+eqAQA2lhZYMjUAKxJD+AeaSE+0WoGTlxqQkq5AWr5St3DlaEcbPDJFhsfi5LzZYAJ61Bp8c6kRqfl1SMurg/LKVGCgv4X/5IBRmB3V361tjIejASMlU8Ikh2gQ2nvUeGnHORzIqwMALIqT4/f3R/GxOumcr2zB2r0FOHWpEQDgbGeFZ2YF4xfTArk6ONEQae5UYWdWJT45XX5VnUfcGHcsTpBj7jhvfi6bKK1W4EJVq66Op6C2/ar9IV5OV+p4vBAjG8Wnc3RdTHKIBuhSfQee+lcmLtV3wsbSAn94YCwWxskNHRYZISEEjhU3YO3eAuTXtAEAvF3s8MLsUPxskj/XjCC6BUIInK1oQUq6Al+er4Gqr79NsZOtFX46yQ+L4wMQ7u1s4ChpqFU0denaU2eUNqFP+92lqIeTja499YxQDy64TVdhkkM0AAdya/HijnPo6O2Dt4sdNiZPwkT5KEOHRUZOqxXYc64Kb+8v0q0OHurlhFfnRiAp0osNKogGoEvVhz051UhJVyC3uk23PcrHBckJAXggxheOtry4HQlau9U4WlSPtLw6HC5Uor2nT7fP1soCM0I8MDtKirsiveDlzCnkIx2THKIb0GoF3ksrwgeHSgD0T4XYsHgSO/PQoPT2abD1GwXWHy7RtbGdMmYUVs2LQGwAm1UQXUtxXTtS0hX4d3YV2q90MLSxssBPJvggOSEAE2VuvFEwgqk1Wpwua0LqlW5tlc3dV+2Pkbnp6njCpE48V0YgJjlE19HarcYLn+XgUIESAPD4tDH47X2RsOZUI7pFrd1qfHj0Ej4+UYbeK1Nt5oyV4pU5EQjxcjJwdESGp+rTYl9uLVLSFThd1qTbPma0AxbHB+ChWH828qAfEUKgsK4daXn9dTznKluv2i9zt9etxzNljDv/jo8QTHKIrqGorh1P/SsTlxu7YGtlgb88OB4/i/U3dFhkJmpbe/BuahE+z6qAVvR3D3pksgwvJIXCy4VTLGjkqWjqwqeny7EjswINHSoA/f8vkiK9kJwQgOnBHrBggTkNUF1bDw7m97enPlHSoKvfAgAXOyskXqnjmRnuCRc7awNGSvrEJIfoB76+UIOXPz+HLpUGfm72+DA5FuP9ubgjDb3iuna8sa8Qafn93frsrS3xxIxALJsZBGf+4SUzp9EKHC1SIiW9HIcLlbqFIaUutlg4RY6FcTL4uNobNkgyeV2qPhwvbkBaXh0OFijR1KnS7bO2lCAhaDSSIqW4O9IL/qPYbtycGF2Ss2HDBrz11luora1FdHQ01q1bh7i4uJu+jkkO3S6NVuCt/YX48OglAMC04NFYt2giRjux/ob068zlJqz5Oh/Z5S0AAHdHG6xIDMHiBDnb4JLZaejoxWdnKvDp6fKr6ihmhHggOUGOuyOlnE5EeqHRCpwtb9atx3OpvvOq/ZE+Lpgd6YWkKCnG+7myjsfEGVWS89lnn+HnP/85PvzwQ8THx+O9997D559/jsLCQnh5ed3wtUxy6HY0d6rw6+1ncby4AQDw5B2B+M3cCLb6pWEjhMD+3Dq8ub8ApVf+8Mrc7fHyPeGYP8GXU3XIpAkhcLqsCSkZ5dh3sQZqTf/lhKu9NR6K9cfieDmCPFmXRsOrtL6jvz11nhKZiiZ8rzs1pC62SIqUIilKiqlBo2FnzRtOpsaokpz4+HhMmTIF69evBwBotVrIZDI899xzWLVq1VXH9vb2orf3uxVx29raIJPJmOTQoOVWt2LZ1ixUNnfDztoCbz4UjfujfQ0dFo1QfRotdmRW4t20ItRfWfV7nJ8LVs2NxIxQDwNHRzQ4bT1q7MquwrYMBYrqOnTbo2VuSI6XY360Ly8eySg0dapwuKC/judoUT26VBrdPgcbS9wZ6omkKCnuivCCO5tfmASjSXJUKhUcHBywc+dOLFiwQLd96dKlaGlpwZ49e646/ve//z1ef/31H30fJjk0GLvPVmHVv8+jR62F3N0Bm5bEItKH5w8ZXpeqDx+fKMOHR0vRcaV97h2hHvjN3AiM82ONGBm3i1Wt2JahwJ6cat3For21JR6I8UVyQgDPYTJqPWoN0ksbde2p69q+u6luIQEmB7gjKaq/eQGfQBovo0lyqqur4efnh1OnTmHq1Km67a+++iqOHj2KjIyMq47nkxy6HWqNFmu+LsDHJ8sAAHeGeeKDhTFwc+DdGTIujR29WH+4BCnpCt0UnwUxvnjpnnDI3FkkS8ajR63Bl+drkJKuQE5Fi257iJcTkuPl+GmsPztZkckRQuBiVZuujievpu2q/UGejpgdJcXsSCkmykfBklOLjYbJJjk/xJocGqiGjl4s35aNjCtrMCxPDMaLs8P5wURGrbyxC39NLcSenGoAgI2lBZITArDirhBOnSCDutzQiW0ZCnyeValb7NbaUoI5Y72RnBCA+EB3FnCT2ahs7tK1p04vbdTdfAL6m8bcdaU99R2hHnC0tTJgpGQ0Sc5gp6v9EJMcGohzFS14OiULNa09cLSxxF8ficbccT6GDotowC5WtWLt3gKcKOlvkuFsa4WnZwXjl9MDYW/D2gYaHn0aLdLyldiWodA1bAEAPzd7PBYvxyOTZfB0ZmdKMm9tPWocK6pHWl4dDhUo0dbTp9tnY2WB6cGjMTvKG3dHekHKNdCGndEkOUB/44G4uDisW7cOQH/jAblcjhUrVvyo8cAPMcmhm9mRWYH/3X0Rqj4tgjwcsWlJLEKlzoYOi+iWHCuqx9q9BbqpE17OtnhhdhgejvVnV0DSm7q2Hnx6uhzbT1egtq0HACCRALPCPJGcEIBZ4V58Kk4jklqjxZnLTUjLUyI1vxYVTd1X7Y/2d9V1a4vwdubTzWFgVEnOZ599hqVLl2LTpk2Ii4vDe++9hx07dqCgoABSqfSGr2WSQ9ej6tPij1/mYWu6AgCQFOmFdx6N4dxwMnlarcB/zlXj7QOFuvVGQryc8OqccMyOkvKPKA0JrVbg1KVGpKQrkJpfB82VPrujHW3wyBQZHouTsz6M6HuEEChWdiA1rw6peXVX1agBgP8oeyRFSjE7Soq4QHeuC6UnRpXkAMD69et1i4HGxMTggw8+QHx8/E1fxySHrkXZ1oNnt2UjU9EMAHghKQzP3RXCNUfIrPT2aZCSXo71h4rRfKUmYnLAKKyaF4HJY9wNHB2ZqpYuFXZmVWJbRjnKGr5bNDFujDsWJ8gxd5w3F6slGgBlew8OXanjOV7cgN4+rW6fs50VZoV7ISnSC7PCveBqzxuwQ8XokpxbxSSHfihL0YxnUrKgbO+Fs60V3lsYg7sjb/xEkMiUtfWosenoJfzjRBl61P1/RGdHSfGbueEI8eLUTLo5IQRyKlqQkl6OL89X6y7GnGyt8OBEPyQnBCDcm+cS0a3qVmlwoqQBqXm1OJivRGOnSrfPykKC+CD3/mltkVI+Ib1NTHLI7Agh8Mnpcvz+P7lQawRCvZywaUkse9nTiFHb2oP30oqwI7MCWtG/rsOjU2RYmRTG4le6pi5VH/bkVCMlXYHc6u9a5Eb6uCA5QY4FMX7sFEU0xDTa/psKaVfaUxcrO67aH+HtjNlR/QnPeD9XzkIZJCY5ZFZ61Bqs3pOLzzIrAADzxnnjrYej4cQ/zjQClSjb8ca+QqTm1QEA7Kwt8MSMQCybGcyaNAIAFNe1IyVdgX9nV6H9yqKzNlYW+Ml4HyxOCMAkuRtru4iGSVlDJw7m99fxnLncBO33rrq9nG1xd6QUs6O8MC3YA3bWnCp6M0xyyGzUtHbj6ZRsnKtogUQCvDInHM/MDOYfaBrxMi83Yc3eAmRdqU0b5WCNFXeFIjlBzpqKEUjVp8W+3FqkpCtw+sp6YQAQMNoBi+PleDhWhlFce4nIoJo7VThSpERanhJHCpXoVGl0++ytLXFnmAeSIqW4K8ILo53Yrv1amOSQWcgobcTyT7LR0KGCq701Plg0ETPDPA0dFpHREEIgNa8Ob+wrwKX6/iJy/1H2ePmecNwf7ctpECNAZXMXPj1djs/OVKCho78OwEICJEVKkZwQgBkhHjwPiIxQb58G6aVNSMurQ1p+HWpae3T7JBIgVj4KSVemtYV4cWr+t5jkkEkTQmDLqcv481f56NMKRHg746MlkyEfzWI9omvp02ixM6sS76YVoa6tFwAQ5eOCVfMicCdvDJgdjVbgWFE9UtIVOFSoxLd/xb2cbbEwTo5FcTL4uNobNkgiGjAhBHKr25B2ZVrb92voACDIw1GX8EySu43oddOY5JDJ6lZp8NtdF/Dvs1UAgPujfbH2Z+PhYMP6G6Kb6VZp8PHJMnx45JKuFmNGiAdWzYvAOD9XA0dHt6uhoxc7MivwSUa5bg0lAJgeMhrJ8QFIipJybQ4iM1Dd0t1fx5OvxDeXGqDWfHepPsrBGokRXrgnSoo7Qj1HXPMQJjlkkiqauvB0ShZyq9tgaSHBa/Mi8MSMQNbfEA1SU6cK6w+VYGv6Zd0fxwdifPHyPeFsX2pihBA4c7kZKekK7L1Yo/t9utpb46FYfyyOl7PLJJEZa+9R43hxA9Ly6nCoUImWK+umAYCNpQWmhYzWtaf2djX/TptMcsjknChuwHOfZqO5Sw13Rxusf2wipgV7GDosIpNW0dSFvx4oxO6cagCAtaUEyQkBWJEYwqJWI9feo8aus1XYll6Owrp23fZomRuS4+WYH+3LTkxEI0yfRotMRTPS8uqQml8HRWPXVfvH+7kiKVKK2VFSRPo4m+VNYiY5ZDKEEPjoWCne2FcArej/D/rhklj4uXE+OdFQuVjVijf2FeB4cQOA/kUgn54ZhF/OCORUUCOTW92KlPRy7MmpQteVzkv21pZ4IMYXyQkBnHZIRAD6r58u1XfgQF7/ejxnK1rw/St6Pzd7JEV6ISlKivjA0bCxMo+prExyyCR0qfrwys7z+Op8DQDgoVh//GnBON6dJNKTE8UNWLM3X1fU6uVsi5VJYXhksv+ILmQ1tB61Bl+dr0FKhgJny1t020O8nJAcL8eDk/zhas81kIjo+urbe3G4QInU/DocL65Hj1qr2+dsa4U7wz1xT5QUs8K84Opgup8nTHLI6F1u6MSyrVkorGuHlYUEq+dHITkhwCwfrRIZE61W4L/nq/H2gUJUNPUXrwd5OuLVORGYM1bK/4PD6HJDJ7ZlKPB5VqVunr21pQRzxnojOSEA8YHu/H0Q0aD1qDU4UdyAtPw6pOUr0dDRq9tnaSFB3Bh3JEVJMTtSanKda5nkkFE7XKjE85+eRVtPHzycbLExeRKmjHE3dFhEI0pvnwbb0sux7lAxmq9cYMcGjMJr8yIwmf8f9aZPo0VavhLbMhS66YNA/9SSx+LleGSyDJ7OrJcioqGh1Qqcq2zRtacuquu4an+41BlJUV5IipQi2t/N6NfVYpJDRkmrFfjbkRL8NbUIQgAT5W7YuDh2RHQDITJWbT1qfHS0FH8/Uaqb3pAUKcVv5oYjVOps4OjMR11bDz49XY7tpytQ29a/6J9EAswM88SShADMCveCpZFfXBCR6VM0diItX4m0vDqcvtwEjfa7NMDDyba/jidSihmhHkZZPsAkh4xOe48aL+04hwN5dQCAx+LlWD0/CrZWxvcfiGgkqmvrwXtpxdiRWQGNVsBCAjwcK8MLs8N4I+IWCSFw6lIjUtIVOJBXp7uYcHe0wSOTZVgcL2dLbyIymNYuNY4UKXEgrw5HC+vRcWV9NQCws7bAHaGemB0pRWKEl9E8YWaSQ0blUn0HnvpXJi7Vd8LG0gJ/eGAsFsbJDR0WEV1DibIDb+0vwP7c/hsSdtYW+OX0QCybGczi9wFq7VLj86z+RTtLGzp126eMGYXkhADMHefNGzxEZFRUfVpklDUiLa+/jqeq5bsFhyUSYKLMDe8vnGjwGzNMcshoHMitxYs7zqGjtw/eLnbYmDwJE+WjDB0WEd1ElqIJa74uQKaiGQDg5mCNFYkhWDI1gBfo1yCEwLnKVqSkK/Dfc9Xo7euf+udka4UHJ/phcYIcEd78O0ZExk8IgfyadqTm1SEtvw4XqlrhYGOJ7P832+BT2JjkkMFptQLvpRXhg0MlAIC4Me7YsHiS0TzuJKKbE0IgLV+JN/YVoETZX6zq52aPl+eE4YFoP6MvUB0OXao+/CenGikZClysatNtj/RxQXKCHA/E+MHJlmsREZHpqmntRmFtO2aFexk6FCY5ZFit3Wq88FkODhUoAQCPTxuD394XCWuuw0Fkkvo0WnyRXYl3UotQ19bfijTSxwWr5kXgzlCPEdnmuETZjpT0cnyRXYn2nv557DZWFvjJeB8sTgjAJLnbiBwXIiJ9YpJDBlNY245lWzNxubELtlYW+MuD4/GzWH9Dh0VEQ6BbpcHmU2XYePgS2q8UqE4PGY1VcyMx3t/VwNHpn6pPi/25tUhJVyCjrEm3PWC0AxbHy/FQrAzujjYGjJCIyLwxySGD+Op8DV7ZeQ5dKg383OyxaUksxvmZ/4UP0UjT3KnC+sMl2PqNAipNf+3J/GhfvHJPuMktLDcQlc1d+PR0OT47U6lbVM9C0t9qOzkhADNCPDh1j4hoGBhFkvPnP/8ZX331FXJycmBjY4OWlpZBfw8mOaZBoxV4c38BNh0tBdB/Z3fdokm8o0lk5iqauvBOahF251RBCMDaUoLF8QF47q4QjHYy7fo7jVbgWFE9UtIVOFyoxLdLSXg522JhnByL4mTwcbU3bJBERCOMUSQ5q1evhpubGyorK/GPf/yDSY6Zau5U4dfbz+pW7n7qziC8OiccVqy/IRoxcqtb8ca+QhwrqgfQ31Fs2Z1BeOKOQDjYmFbRfUNHL3Zk9rd/rmz+roXq9JDRSI4PQFKUlPWFREQGYhRJzre2bNmClStXMskxQ7nVrVi2NQuVzd2wt7bEGw9NwP3RvoYOi4gM5GRJA9bszdd1GfN0tsXKpFA8Mllm1ImBEAKZimakpCuw90Ktbgqeq701Hor1x2PxcgR7Ohk4SiIiGkxuYFS32Hp7e9Hb26v7d1tb2w2OJkPafbYKq/59Hj1qLeTuDti0JBaRPkxEiUay6SEe+M/yGfjyQg3e3l+I8qYu/HbXRfzjeBlenRuOOWO9jarjWHuPGrvPViElvRyFde267dEyNyTHyzE/2tfga0IQEdGtMaokZ82aNXj99dcNHQbdgFqjxZqvC/DxyTIAwMwwT7y/MAZuDqy/ISLAwkKC+6N9MXesNz7JUOCDQyUobejE0ynZmCh3w2vzIhEX6G7QGPOq25CSocCes1XoVGkAAHbWFlgQ44fF8QEjolMcEZG5G9R0tVWrVuGNN9644TH5+fmIiIjQ/Xsw09Wu9SRHJpNxupqRaOjoxfJt2brWqcsTg/Hi7HBYsqsQEV1He48aHx0rxd+Pl6Fb3Z9QJEV64TdzIxAqdR62OHrUGnx9oQYp6Qpkl7fotgd7OiI5IQA/neQPV3vrYYuHiIgGT281OfX19WhsbLzhMUFBQbCx+e6uPmtyzMO5ihY8nZKFmtYeONpY4q+PRGPuOB9Dh0VEJkLZ1oP3DhbjszMV0GgFLCTAQ7H+eGF2mF67lCkaO7EtoxyfZ1aguUsNALCykGDOOG8kxwcgIcjdqKbQERHR9emtJsfT0xOenp63FRyZnh2ZFfjf3Reh6tMiyMMRm5bEDusdWCIyfV4udvjLg+PxxIxAvLWvEPtya7EjsxJ7cqrxi+mBeGZW8JA9SenTaHGwQImUdIWu8yMA+LnZY1GcDI9MkcHL2W5I3ouIiIyT3mpyysvL0dTUhPLycmg0GuTk5AAAQkJC4OTELjWmQNWnxR+/zMPWdAWA/ikm7zwaAxc7TukgolsT7OmED5fEIkvRjLV783HmcjM+PHoJ28+UY0ViCJITAm652L+urQfbT1dg+5ly1LT2AAAkkv7aweT4ACRGeHF6LRHRCKG3FtKPP/44/vnPf/5o++HDhzFr1qwBfQ9OVzMcZVsPnt2WjUxFMwDghaQwPHdXCFf1JqIhI4TAwXwl3thXgGJlB4D+py0v3ROGB2L8BpSQCCFw6lIjUtIVSM2rQ9+VVTvdHW3wyGQZHouTQz7aQa8/BxERDQ+jWifndjDJMYwsRROeScmGsr0XzrZWeG9hDO6OlBo6LCIyUxqtwBdZlXgntQi1bf1PYCK8nbFqXgRmhnles2amtUuNz7P6F+0sbejUbZ8yZhSSEwIwd5w3bK3Y/pmIyJwwyaFbIoTAtoxyvP7fXKg1AqFeTvjo55MR6OFo6NCIaAToVmmw5dRl/O1ICdp7+gAA04JHY9W8CEzwdwPQ3wRla7oC/z1Xjd6+/kU7nWyt8OBEPyxOkCPCm38riIjMFZMcGrQetQa/23MROzIrAQD3jvfGWw9Fw9HWqJZSIqIRoLlThb8dKcE/Tymg0vQnMnPGSlHd0oMLVa264yJ9XJCcIMcDMX5w4mcVEZHZY5JDg1Ld0o1nUrJwrrIVFhLglTkReHpmENuqEpFBVTZ34Z0DRdiVU4Vv/1LZWFngJ+N9sDghAJPkbvycIiIaQZjk0ICllzZi+bZsNHaq4GpvjXWLJuLOMLYJJyLjkVfdhh2ZFfB1s8NDsTK4O9rc/EVERGR29LZODpkPIQS2nLqMP32VD41WINLHBZuSY9mFiIiMTpSvC35//1hDh0FERCaESc4I1K3S4H92XcCus1UAgAdifLH2pxNgb8NORERERERk+pjkjDAVTV14OiULudVtsLSQ4LV5EXhiRiDntRMRERGR2WCSM4KcKG7Ac59mo7lLDXdHG6x/bCKmBXsYOiwiIiIioiHFJGcEEELgo2OleGNfAbQCGO/nig+XxMLPzd7QoRERERERDTkmOWauS9WHV3aex1fnawAAD8X6408LxsHOmvU3RERERGSemOSYscsNnVi2NQuFde2wspBg9fwoJCcEsP6GiIiIiMwakxwzdbhQiec/PYu2nj54ONliY/IkTBnjbuiwiIiIiIj0jkmOmdFqBTYcLsE7aUUQApgod8PGxbHwdrUzdGhERERERMOCSY4Zae9R46Ud53Agrw4A8Fi8HKvnR8HWivU3RERERDRyMMkxEyXKDizbmolL9Z2wsbTAHx4Yi4VxckOHRUREREQ07JjkmIEDubV4ccc5dPT2wdvFDhuTJ2GifJShwyIiIiIiMggmOSZMqxV4L60IHxwqAQDEjXHHhsWT4Olsa+DIiIiIiIgMh0mOiWrtVmPl9rM4XFgPAHh82hj89r5IWFtaGDgyIiIiIiLDYpJjggpr27FsayYuN3bB1soCa346Hj+d5G/osIiIiIiIjAKTHBPz1fkavLLzHLpUGvi52WPTkliM83M1dFhEREREREaDSY6J0GgF3txfgE1HSwEA00NGY92iSXB3tDFwZERERERExkVvBRyXL1/GE088gcDAQNjb2yM4OBirV6+GSqXS11uareZOFR7ffFqX4Dx1ZxD++Ys4JjhERERERNegtyc5BQUF0Gq12LRpE0JCQnDx4kU8+eST6OzsxNtvv62vtzU7udWtWLY1C5XN3bC3tsQbD03A/dG+hg6LiIiIiMhoSYQQYrje7K233sLGjRtRWlo6oOPb2trg6uqK1tZWuLi46Dk647P7bBVW/fs8etRayN0dsGlJLCJ9Rt44EBERERENJjcY1pqc1tZWuLu7X3d/b28vent7df9ua2sbjrCMjlqjxV++zsfmk5cBADPDPPH+whi4OXB6GhERERHRzQzboiolJSVYt24dli1bdt1j1qxZA1dXV92XTCYbrvCMRkNHL5L/nqFLcFYkhuDjx6cwwSEiIiIiGqBBJzmrVq2CRCK54VdBQcFVr6mqqsLcuXPx8MMP48knn7zu937ttdfQ2tqq+6qoqBj8T2TCzlW0YP66E8goa4KjjSU+TI7Fy3PCYWkhMXRoREREREQmY9A1OfX19WhsbLzhMUFBQbCx6X/yUF1djVmzZiEhIQFbtmyBhcXA86qRVJOz40wF/nfPRaj6tAjydMRHS2IR4uVs6LCIiIiIiIyCXmtyPD094enpOaBjq6qqkJiYiNjYWGzevHlQCc5IoerT4g9f5iIlvRwAkBQpxTuPRsPFztrAkRERERERmSa9NR6oqqrCrFmzEBAQgLfffhv19fW6fd7e3vp6W5OibOvBM9uykaVohkQCvJAUhhWJIbDg9DQiIiIiolumtyQnNTUVJSUlKCkpgb+//1X7hrFrtdHKUjThmZRsKNt74WxnhfcXxuCuCKmhwyIiIiIiMnnDuk7OYJljTY4QAtsyyvH6f3Oh1giEejnho59PRqCHo6FDIyIiIiIyWka7Ts5I16PW4Hd7LmJHZiUA4N7x3njroWg42vLXQEREREQ0VHh1PUyqW7rxTEoWzlW2wkICvDInAk/PDIJEwvobIiIiIqKhxCRnGKSXNmL5tmw0dqrgam+NdYsm4s6wgXWoIyIiIiKiwWGSo0dCCGw5dRl/+iofGq1ApI8LNiXHQj7awdChERERERGZLSY5etKt0uB/dl3ArrNVAIAHYnyx9qcTYG9jaeDIiIiIiIjMG5McPaho6sKyrVnIq2mDpYUE/3NvJH45fQzrb4iIiIiIhgGTnCF2vLgez316Fi1daox2tMH6xyZhavBoQ4dFRERERDRiMMkZIkIIbDpWijf3FUArgAn+rvgwORa+bvaGDo2IiIiIaERhkjMEOnv78OoX5/HV+RoAwMOx/vjjgnGws2b9DRERERHRcGOSc5suN3Ri2dYsFNa1w8pCgtX3j0VyvJz1N0REREREBsIk5zYcLlDi+e1n0dbTB09nW/xt8SRMGeNu6LCIiIiIiEY0Jjm3QKsV2HC4BO+kFUEIYJLcDRuTYyF1sTN0aEREREREIx6TnEFq71HjpR3ncCCvDgDwWLwcq+dHwdaK9TdERERERMaASc4glCg7sGxrJi7Vd8LG0gJ/eGAsFsbJDR0WERERERF9D5OcATqQW4sXd5xDR28fvF3ssDF5EibKRxk6LCIiIiIi+gEmOQMghMCOzAp09PYhbow7NiyeBE9nW0OHRURERERE18AkZwAkEgn++kgM/nnqMp6ZFQxrSwtDh0RERERERNfBJGeAXO2t8eu7Qw0dBhERERER3QQfSRARERERkVlhkkNERERERGaFSQ4REREREZkVo67JEUIAANra2gwcCRERERERGdK3OcG3OcKNGHWS097eDgCQyWQGjoSIiIiIiIxBe3s7XF1db3iMRAwkFTIQrVaL6upqODs7QyKRGDSWtrY2yGQyVFRUwMXFxaCxmCuOsX5xfPWL46tfHF/94vjqF8dXvzi++mVM4yuEQHt7O3x9fWFhceOqG6N+kmNhYQF/f39Dh3EVFxcXg/+CzR3HWL84vvrF8dUvjq9+cXz1i+OrXxxf/TKW8b3ZE5xvsfEAERERERGZFSY5RERERERkVpjkDJCtrS1Wr14NW1tbQ4ditjjG+sXx1S+Or35xfPWL46tfHF/94vjql6mOr1E3HiAiIiIiIhosPskhIiIiIiKzwiSHiIiIiIjMCpMcIiIiIiIyK0xyiIiIiIjIrDDJISIiIiIis8Ik53s2bNiAMWPGwM7ODvHx8Th9+vQNj//8888REREBOzs7jB8/Hl9//fUwRWqaBjO+W7ZsgUQiuerLzs5uGKM1LceOHcP8+fPh6+sLiUSC3bt33/Q1R44cwaRJk2Bra4uQkBBs2bJF73GaqsGO75EjR350/kokEtTW1g5PwCZmzZo1mDJlCpydneHl5YUFCxagsLDwpq/jZ/DA3Mr48jN44DZu3IgJEyboVoOfOnUq9u7de8PX8NwduMGOL8/d27N27VpIJBKsXLnyhseZwjnMJOeKzz77DC+++CJWr16N7OxsREdHY86cOVAqldc8/tSpU1i0aBGeeOIJnD17FgsWLMCCBQtw8eLFYY7cNAx2fAHAxcUFNTU1ui+FQjGMEZuWzs5OREdHY8OGDQM6vqysDPfddx8SExORk5ODlStX4le/+hX279+v50hN02DH91uFhYVXncNeXl56itC0HT16FMuXL0d6ejpSU1OhVqtxzz33oLOz87qv4WfwwN3K+AL8DB4of39/rF27FllZWcjMzMRdd92FBx54ALm5udc8nufu4Ax2fAGeu7fqzJkz2LRpEyZMmHDD40zmHBYkhBAiLi5OLF++XPdvjUYjfH19xZo1a655/COPPCLuu+++q7bFx8eLZcuW6TVOUzXY8d28ebNwdXUdpujMCwCxa9euGx7z6quvirFjx1617dFHHxVz5szRY2TmYSDje/jwYQFANDc3D0tM5kapVAoA4ujRo9c9hp/Bt24g48vP4NszatQo8fe///2a+3ju3r4bjS/P3VvT3t4uQkNDRWpqqpg5c6Z4/vnnr3usqZzDfJIDQKVSISsrC0lJSbptFhYWSEpKwjfffHPN13zzzTdXHQ8Ac+bMue7xI9mtjC8AdHR0ICAgADKZ7KZ3bWhweP4Oj5iYGPj4+GD27Nk4efKkocMxGa2trQAAd3f36x7Dc/jWDWR8AX4G3wqNRoPt27ejs7MTU6dOveYxPHdv3UDGF+C5eyuWL1+O++6770fn5rWYyjnMJAdAQ0MDNBoNpFLpVdulUul159DX1tYO6viR7FbGNzw8HB9//DH27NmDlJQUaLVaTJs2DZWVlcMRstm73vnb1taG7u5uA0VlPnx8fPDhhx/iiy++wBdffAGZTIZZs2YhOzvb0KEZPa1Wi5UrV2L69OkYN27cdY/jZ/CtGej48jN4cC5cuAAnJyfY2tri6aefxq5duxAVFXXNY3nuDt5gxpfn7uBt374d2dnZWLNmzYCON5Vz2MrQARBdy9SpU6+6SzNt2jRERkZi06ZN+OMf/2jAyIhuLjw8HOHh4bp/T5s2DZcuXcK7776LrVu3GjAy47d8+XJcvHgRJ06cMHQoZmmg48vP4MEJDw9HTk4OWltbsXPnTixduhRHjx697oU4Dc5gxpfn7uBUVFTg+eefR2pqqtk1aGCSA8DDwwOWlpaoq6u7antdXR28vb2v+Rpvb+9BHT+S3cr4/pC1tTUmTpyIkpISfYQ44lzv/HVxcYG9vb2BojJvcXFxvHC/iRUrVuDLL7/EsWPH4O/vf8Nj+Rk8eIMZ3x/iZ/CN2djYICQkBAAQGxuLM2fO4P3338emTZt+dCzP3cEbzPj+EM/dG8vKyoJSqcSkSZN02zQaDY4dO4b169ejt7cXlpaWV73GVM5hTldD/3+e2NhYHDx4ULdNq9Xi4MGD153zOXXq1KuOB4DU1NQbzhEdqW5lfH9Io9HgwoUL8PHx0VeYIwrP3+GXk5PD8/c6hBBYsWIFdu3ahUOHDiEwMPCmr+E5PHC3Mr4/xM/gwdFqtejt7b3mPp67t+9G4/tDPHdv7O6778aFCxeQk5Oj+5o8eTIWL16MnJycHyU4gAmdw4bufGAstm/fLmxtbcWWLVtEXl6eeOqpp4Sbm5uora0VQgixZMkSsWrVKt3xJ0+eFFZWVuLtt98W+fn5YvXq1cLa2lpcuHDBUD+CURvs+L7++uti//794tKlSyIrK0ssXLhQ2NnZidzcXEP9CEatvb1dnD17Vpw9e1YAEO+88444e/asUCgUQgghVq1aJZYsWaI7vrS0VDg4OIhXXnlF5Ofniw0bNghLS0uxb98+Q/0IRm2w4/vuu++K3bt3i+LiYnHhwgXx/PPPCwsLC5GWlmaoH8GoPfPMM8LV1VUcOXJE1NTU6L66urp0x/Az+NbdyvjyM3jgVq1aJY4ePSrKysrE+fPnxapVq4REIhEHDhwQQvDcvV2DHV+eu7fvh93VTPUcZpLzPevWrRNyuVzY2NiIuLg4kZ6erts3c+ZMsXTp0quO37FjhwgLCxM2NjZi7Nix4quvvhrmiE3LYMZ35cqVumOlUqm49957RXZ2tgGiNg3ftiz+4de3Y7p06VIxc+bMH70mJiZG2NjYiKCgILF58+Zhj9tUDHZ833jjDREcHCzs7OyEu7u7mDVrljh06JBhgjcB1xpbAFedk/wMvnW3Mr78DB64X/7ylyIgIEDY2NgIT09Pcffdd+suwIXguXu7Bju+PHdv3w+THFM9hyVCCDF8z42IiIiIiIj0izU5RERERERkVpjkEBERERGRWWGSQ0REREREZoVJDhERERERmRUmOUREREREZFaY5BARERERkVlhkkNERERERGaFSQ4REREREZkVJjlERERERGRWmOQQEREREZFZYZJDRERERERm5f8DoXYnMw7/FOkAAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5421,7 +5409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -5430,14 +5418,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiIAAAGYCAYAAAB/DYmkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAARm0lEQVR4nO3dX2yV9f3A8c+p1aMIxSAdSCgyNAa96RS2KVkimGZiiDO7cLsxillw+CdxAbPQXUhMZsqF8U+ckxnDyh3bxWZINM6Fbc4LplIUdVunuBEqBIQsa1llB0Of38VCNwL2J4znfM7peb2SJ+E5z+n5fpoH0nee8xxaKYqiCACABG3ZAwAArUuIAABphAgAkEaIAABphAgAkEaIAABphAgAkEaIAABp2rMHmMjY2Fjs378/pk2bFpVKJXscAOBzKIoijhw5EnPmzIm2tomveTR0iOzfvz+6urqyxwAAzsLQ0FDMnTt3wuc0dIhMmzYtIv79jXR0dCRPAwB8HiMjI9HV1TX+c3wiDR0iJ96O6ejoECIA0GQ+z20VblYFANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgjRABANIIEQAgTXv2APDf5q97MXuEc2LPhhXZIwA0BVdEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0QgQASCNEAIA0pYbI73//+7j11ltjzpw5UalU4oUXXihzOQCgyZQaIqOjo9Hd3R3PPPNMmcsAAE2qvcwXv+WWW+KWW24pcwkAoImVGiJnqlarRa1WG98fGRlJnAYAKFtD3aza19cX06dPH9+6urqyRwIAStRQV0R6e3tjzZo14/sjIyNiBCAi5q97MXuE/9meDSuyR6ABNVSIVKvVqFar2WMAAHXSUG/NAACtpdQrIv/85z9j9+7d4/t/+9vf4u23344ZM2bEvHnzylwaAGgCpYbIjh07YtmyZeP7J+7/uOuuu6K/v7/MpQGAJlBqiCxdujSKoihzCQCgiblHBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDRCBABII0QAgDR1CZFnnnkm5s+fHxdeeGF89atfjTfeeKMeywIADa70EPnZz34Wa9asifXr18fOnTuju7s7br755vj444/LXhoAaHClh8jjjz8eq1atirvvvjuuueaa2LhxY0yZMiU2bdpU9tIAQIMrNUSOHTsWAwMD0dPT858F29qip6cntm/fXubSAEATaC/zxQ8fPhzHjx+PWbNmnfT4rFmzYnBw8JTn12q1qNVq4/sjIyNljgcAJCs1RM5UX19fPPLII3Vfd/66F+u+Zhn2bFiRPcL/bDJ8D5OFfxeNZbJ8H5PBZPi30Uh/n0p9a2bmzJlx3nnnxcGDB096/ODBgzF79uxTnt/b2xvDw8Pj29DQUJnjAQDJSg2RCy64IBYtWhTbtm0bf2xsbCy2bdsWN9xwwynPr1ar0dHRcdIGAExepb81s2bNmrjrrrti8eLF8ZWvfCWefPLJGB0djbvvvrvspQGABld6iHz729+OQ4cOxcMPPxwHDhyIL33pS/Hyyy+fcgMrANB66nKz6gMPPBAPPPBAPZYCAJqI3zUDAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAmvbsAQCgmezZsCJ7hEnFFREAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSCBEAII0QAQDSlBYijz76aCxZsiSmTJkSl1xySVnLAABNrLQQOXbsWNx+++1x7733lrUEANDk2st64UceeSQiIvr7+8taAgBocqWFyNmo1WpRq9XG90dGRhKnAQDK1lA3q/b19cX06dPHt66uruyRAIASnVGIrFu3LiqVyoTb4ODgWQ/T29sbw8PD49vQ0NBZvxYA0PjO6K2ZtWvXxsqVKyd8zoIFC856mGq1GtVq9ay/HgBoLmcUIp2dndHZ2VnWLABAiyntZtW9e/fG3//+99i7d28cP3483n777YiIuPLKK2Pq1KllLQsANJHSQuThhx+OzZs3j+9fe+21ERHx29/+NpYuXVrWsgBAEyntUzP9/f1RFMUpmwgBAE5oqI/vAgCtRYgAAGmECACQRogAAGmECACQRogAAGmECACQRogAAGmECACQRogAAGmECACQRogAAGmECACQRogAAGmECACQRogAAGnaswcAGtOeDSuyRwBagCsiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApBEiAEAaIQIApCktRPbs2RPf+c534otf/GJcdNFFccUVV8T69evj2LFjZS0JADSZ9rJeeHBwMMbGxuInP/lJXHnllfHee+/FqlWrYnR0NB577LGylgUAmkhpIbJ8+fJYvnz5+P6CBQviL3/5Szz77LNCBACIiBJD5HSGh4djxowZn3m8VqtFrVYb3x8ZGanHWABAkrrdrLp79+54+umn47vf/e5nPqevry+mT58+vnV1ddVrPAAgwRmHyLp166JSqUy4DQ4OnvQ1+/bti+XLl8ftt98eq1at+szX7u3tjeHh4fFtaGjozL8jAKBpnPFbM2vXro2VK1dO+JwFCxaM/3n//v2xbNmyWLJkSTz33HMTfl21Wo1qtXqmIwEATeqMQ6SzszM6Ozs/13P37dsXy5Yti0WLFsVPf/rTaGvz35YAAP9R2s2q+/bti6VLl8bll18ejz32WBw6dGj82OzZs8taFgBoIqWFyK9//evYvXt37N69O+bOnXvSsaIoyloWAGgipb1XsnLlyiiK4rQbAECE3zUDACQSIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAGiECAKQRIgBAmvbsARrBng0rskcAgJbkiggAkEaIAABphAgAkEaIAABphAgAkEaIAABphAgAkEaIAABphAgAkEaIAABphAgAkEaIAABphAgAkEaIAABp2rMHmEhRFBERMTIykjwJAPB5nfi5feLn+EQaOkSOHDkSERFdXV3JkwAAZ+rIkSMxffr0CZ9TKT5PriQZGxuL/fv3x7Rp06JSqWSPc9ZGRkaiq6srhoaGoqOjI3ucluZcNA7nonE4F41lMpyPoijiyJEjMWfOnGhrm/gukIa+ItLW1hZz587NHuOc6ejoaNq/VJONc9E4nIvG4Vw0lmY/H//flZAT3KwKAKQRIgBAGiFSB9VqNdavXx/VajV7lJbnXDQO56JxOBeNpdXOR0PfrAoATG6uiAAAaYQIAJBGiAAAaYQIAJBGiABAA2m1z5A09P+s2qwOHz4cmzZtiu3bt8eBAwciImL27NmxZMmSWLlyZXR2diZPCECjqlarsWvXrrj66quzR6kLH989x9588824+eabY8qUKdHT0xOzZs2KiIiDBw/Gtm3b4pNPPolf/epXsXjx4uRJiYgYGhqK9evXx6ZNm7JHmfSOHj0aAwMDMWPGjLjmmmtOOvavf/0rfv7zn8edd96ZNF1r+fOf/xx/+MMf4oYbboiFCxfG4OBgPPXUU1Gr1eKOO+6Im266KXvElrBmzZrTPv7UU0/FHXfcEZdeemlERDz++OP1HKvuhMg5dv3110d3d3ds3LjxlF/UVxRFrF69Ot55553Yvn170oT8t127dsV1110Xx48fzx5lUnv//ffj61//euzduzcqlUp87Wtfiy1btsRll10WEf8O9Tlz5jgPdfDyyy/HbbfdFlOnTo1PPvkkfvnLX8add94Z3d3dMTY2Fq+++mq88sorYqQO2traoru7Oy655JKTHn/11Vdj8eLFcfHFF0elUonf/OY3OQPWiRA5xy666KJ46623YuHChac9Pjg4GNdee20cPXq0zpO1pq1bt054/K9//WusXbvWD8CSffOb34xPP/00+vv74x//+Ed873vfiz/96U/xu9/9LubNmydE6mjJkiVx0003xQ9/+MPYsmVL3HfffXHvvffGo48+GhERvb29MTAwEK+88krypJPfhg0b4rnnnovnn3/+pPA7//zzY9euXadcOZy0Cs6p+fPnF5s3b/7M45s3by4uv/zy+g3U4iqVStHW1lZUKpXP3Nra2rLHnPS+8IUvFO+88874/tjYWLF69epi3rx5xYcfflgcOHDAeaiTjo6O4oMPPiiKoiiOHz9etLe3Fzt37hw//u677xazZs3KGq/lvPHGG8VVV11VrF27tjh27FhRFEXR3t5e/PGPf0yerH58auYce+ihh+Kee+6JBx98MLZu3Rqvv/56vP7667F169Z48MEHY/Xq1fH9738/e8yWcdlll8UvfvGLGBsbO+22c+fO7BFbwtGjR6O9/T/3xlcqlXj22Wfj1ltvjRtvvDHef//9xOlaz4m3jdva2uLCCy886de1T5s2LYaHh7NGazlf/vKXY2BgIA4dOhSLFy+O995775S39Sc7n5o5x+6///6YOXNmPPHEE/HjH/94/FLzeeedF4sWLYr+/v741re+lTxl61i0aFEMDAzEbbfddtrjlUql5T4ql2HhwoWxY8eOUz4F8KMf/SgiIr7xjW9kjNWS5s+fHx988EFcccUVERGxffv2mDdv3vjxvXv3jt+7Q31MnTo1Nm/eHFu2bImenp6We4vSPSIl+vTTT+Pw4cMRETFz5sw4//zzkydqPa+99lqMjo7G8uXLT3t8dHQ0duzYETfeeGOdJ2stfX198dprr8VLL7102uP33XdfbNy4McbGxuo8WevZuHFjdHV1xYoVK057/Ac/+EF8/PHH8fzzz9d5MiIiPvrooxgYGIienp64+OKLs8epCyECAKRxjwgAkEaIAABphAgAkEaIAABphAgAkEaIAABphAgAkEaIAABp/g+lbphmmqNPYAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5455,7 +5441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5464,14 +5450,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5481,7 +5465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -5490,14 +5474,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 864x288 with 1 Axes>"
+       "<Figure size 1200x400 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5530,7 +5512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 78,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -5544,21 +5526,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 79,
    "metadata": {
     "exercise": "solution"
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5568,21 +5548,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 80,
    "metadata": {
-    "exercise": "solution"
+    "editable": true,
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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IkiR1jIkGrgR+EhF3RcSKsm1uZm4ql58C5k7wNyRJkjraRC+a/+PM3BgR/wVYFRG/bd6YmRkRQ94GWQa0FQBvectbJliGJEnS5DWhEa7M3FjOtwA/Ak4ANkfE4QDlfMswn708M5dl5rLe3t6JlCFJkjSpjTtwRcQBEXFQYxn4ILAWWAmcXe52NnDjRIvsNtOmwfr18Fd/BWvW+HogSZI63URGuOYCt0XEPcAdwP/NzJuBi4APRMTvgPeX6xqDv/1bOOUUuPRSOO644pldF10Ejz9ed2WSJGk8fNL8JPbMM/Bv/wb/+q/wy18Wbe97H/zZn8EZZ/jqH0mS6jCeJ81X8VgItcihh8LnPge33QYPPQRf/zps3gwrVsCb3gQf/SjccANs3z7x39qwAe6/f+LfI0mS9uYIV4fJhLvvLka9fvCDIoDNng0f+xh88pPw3vcW14AB7NgBzz1XTNu27Vkequ1HP9rz/ZIkaXjjGeEycHWwXbvgZz8rwtcNN8DLL8PcudDTU4SoV17Z9+f33784LTl7NqxbV7RNgsNBkqRJzZdXd5meHvjgB4vpssvgxhvhP/8TZswogtTgafbs1y/PmrXnu049tRjxkiRJrWfgmiIOOAA+8YlikiRJk4uBS3/w+OPw5S8X4e2AA+DAA/csD54a22bNgoi6K5ckaXIzcAmAE04o7oa88MKxXcc1bdrwYWxfQa0xnXgi+GYnSdJU50Xzep1MePXV4gL85umll/ZuG+u2V17ZO8y9//2walU9f6skSePhRfOasIji7sX994dWv+JycJj7xCfgpz+FZctgYGBsUwQsWABHHw2LFhXzxjRnTmvrliRpogxcapvBYe5zn4M3vhGmTx/7NDAAjzxSPJPs+uuL9YZDD907hB19NBx1VHEaU5KkdvOUojrejh3w6KPw4IN7Txs3vn7fefP2PCi2p6eYZszYe3motoks77efNxdI0lThKUV1pZkz94xiDfbyy8VrixoB7JJLioe8Nh702i4rVsA//3N7f1OSNHk4wqWutHt38aT+xrRz58SXh9t+/vnFb+63396nRqdNq6dtNPtOm7ZnvbE80vrMmfCGN+x72m+/Yj5r1p7XUElSJ3GESxqlRjiYObP63zr+eLj55r0v/N+9e2JtO3fCa6+19jsHBtr7eqfhAlojlI00XXfdnv8dZ8zYM29MVa03ThlPn/76+VBtQ+1j0JS6j4FLqthJJxVTp8gswlcjgDWWB68Pta0RApunV1/du220+2zbNvw+O3bsqfnUU4vf3rGj2LZz5571xvJQ67t21dbNI4aykYLbWEPeWPe55poiXO5rlHS821r9fe38LcOyxquywBURpwL/AEwH/iUzL6rqtyS1TsSe/7jMmFF3NcPbvRu2by9GusZ7Q0Lm6wPYaELajh1FUBsYeP18qLbx7DPW7xuuntF+z86d++6jk04q9tuxY3QjpWPZNgmuaBmXVoS7adOK47Yxb0zN6524bTLU9fa3wzvfWfdRsrdKAldETAe+DXwAeBK4MyJWZub9VfyepO4zbVpx6nEiItp3ankyawSgwWFszpxq767NbG2AG8u2ur9v9+7i72+MKDeWB683RpCH27avz1W5bSzf0e5g/ed/3kWBCzgB2JCZDwNExDXAcsDAJUmTTOOmh3aPaEbsOZ2pqa2dIfGgg+r+a4dW1WE+D3iiaf1JYBLmTUmSVLXmU37dqrY/PSJWRMTqiFi9devWusqQJEmqXFWBayNwRNP6/LLtDzLz8sxclpnLelv90j5JkqRJpKrAdSewKCIWRsRM4CxgZUW/JUmSNKlVcg1XZu6KiPOAH1M8FuLKzGzzy1QkSZImh8ruDcnMm4Cbqvp+SZKkTjEp3qUYEVuBx0a5+2HA0xWWoz3s6/awn9vHvm4P+7l97Ov2ae7rt2bmmC5AnxSBaywiYvVYXxip8bGv28N+bh/7uj3s5/axr9tnon3dxU/EkCRJag8DlyRJUsU6MXBdXncBXcS+bg/7uX3s6/awn9vHvm6fCfV1x13DJUmS1Gk6cYRLkiSpo3RU4IqIUyPigYjYEBEX1F3PVBIRj0bEfRGxJiJWl21zImJVRPyunB9Sd52dKCKujIgtEbG2qW3Ivo3CpeUxfm9EHF9f5Z1lmH7+akRsLI/rNRFxetO2L5b9/EBEfKieqjtTRBwREbdGxP0RsS4iPl+2e1y30D762eO6xSLiDRFxR0TcU/b118r2hRFxe9mn15ZvzyEiZpXrG8rtC0b6jY4JXBExHfg2cBqwGPh4RCyut6op508yc2nTba8XALdk5iLglnJdY3cVcOqgtuH69jRgUTmtAC5rU41TwVXs3c8Al5TH9dLygcyU/3acBSwpP/NP5b8xGp1dwPmZuRg4ETi37FOP69Yarp/B47rVtgMnZ+Y7gKXAqRFxInAxRV8fBTwHnFPufw7wXNl+SbnfPnVM4AJOADZk5sOZuQO4Blhec01T3XLg6nL5auDD9ZXSuTLzF8Czg5qH69vlwHez8GtgdkQc3pZCO9ww/Tyc5cA1mbk9Mx8BNlD8G6NRyMxNmXl3ufwisB6Yh8d1S+2jn4fjcT1O5bH5Urk6o5wSOBn4Ydk++JhuHOs/BE6JiNjXb3RS4JoHPNG0/iT7PvA0Ngn8JCLuiogVZdvczNxULj8FzK2ntClpuL71OG+988rTWFc2nRa3n1ukPJVyHHA7HteVGdTP4HHdchExPSLWAFuAVcBDwLbM3FXu0tyff+jrcvvzwKH7+v5OClyq1h9n5vEUQ//nRsT7mjdmcTurt7RWwL6t1GXA2yhOEWwCvllrNVNMRBwIXA98ITNfaN7mcd06Q/Szx3UFMnMgM5cC8ylGBo9p5fd3UuDaCBzRtD6/bFMLZObGcr4F+BHFwba5MexfzrfUV+GUM1zfepy3UGZuLv8R3Q18hz2nV+znCYqIGRQh4PuZeUPZ7HHdYkP1s8d1tTJzG3Ar8C6K09895abm/vxDX5fbDwae2df3dlLguhNYVN4xMJPiwsCVNdc0JUTEARFxUGMZ+CCwlqJ/zy53Oxu4sZ4Kp6Th+nYl8Onyrq4TgeebTtFojAZdJ/QRiuMain4+q7zTaCHFxdx3tLu+TlVeq3IFsD4z/75pk8d1Cw3Xzx7XrRcRvRExu1zeD/gAxTVztwJnlLsNPqYbx/oZwM9yhAeb9uxr42SSmbsi4jzgx8B04MrMXFdzWVPFXOBH5fV+PcD/zsybI+JO4LqIOAd4DDizxho7VkT8ADgJOCwingS+AlzE0H17E3A6xcWurwCfaXvBHWqYfj4pIpZSnNp6FPgsQGaui4jrgPsp7gQ7NzMHaii7U70H+BRwX3nNC8CX8LhuteH6+eMe1y13OHB1eVfnNOC6zPyPiLgfuCYivgH8hiIAU86/FxEbKG7WOWukH/BJ85IkSRXrpFOKkiRJHcnAJUmSVDEDlyRJUsUMXJIkSRUzcEmSJFWsYx4LIWnqi4hDKV56DPAmYADYCiwAfl++xLfqGl7KzAOr/h1J3cURLkmTRmY+k5lLy9dr/C/gknJ5KbB7pM83PRFakiYVA5ekTjE9Ir4TEesi4ifl06CJiJ9HxP+MiNXA5yPijyLi/5UvYv9x06tm/ntE3BkR90TE9RGxf9m+MCJ+FRH3lQ83pGw/PCJ+ERFrImJtRLy3lr9a0pRg4JLUKRYB387MJcA24KNN22Zm5jLgUuBbwBmZ+UfAlcCF5T43ZOZ/zcx3ULyy45yy/R+AyzKzj+JFwA2fAH5cjrC9A1hTxR8lqTs4/C6pUzySmWvK5bsorutquLacvx04FlhVvqpqOntC1LHlCNZs4ECK14RB8fqURnj7HnBxuXwncGX58uD/0/TbkjRmjnBJ6hTbm5YHeP3/YXy5nAewrnEdWGb2ZeYHy21XAeeVI1lfA97Q9Pm93nGWmb8A3gdsBK6KiE+35s+Q1I0MXJKmkgeA3oh4F0BEzIiIJeW2g4BN5YjVJ5s+80v2vHj2D+0R8VZgc2Z+B/gX4Piqi5c0dRm4JE0ZmbkDOAO4OCLuobju6t3l5i8Dt1MErN82fezzwLkRcR8wr6n9JOCeiPgN8N8orvWSpHGJzL1G0iVJktRCjnBJkiRVzMAlSZJUMQOXJElSxQxckiRJFTNwSZIkVczAJUmSVDEDlyRJUsUMXJIkSRX7/+uD/EGCih7cAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5592,66 +5575,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 81,
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df[\"Presim. Time / s\"].plot(style=\"--r\");\n",
-    "df[\"Sim. Time / s\"].plot(style=\"-b\");"
+    "df[\"Presim. Time / s\"].plot(style=\"--r\", figsize=(10,3));\n",
+    "df[\"Sim. Time / s\"].plot(style=\"-b\", figsize=(10,3));"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 82,
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "ax = df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot(style=[\"--b\", \"-r\"]);\n",
+    "ax = df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot(style=[\"--b\", \"-r\"], figsize=(10,3));\n",
     "ax.set_ylabel(\"Time / s\");"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "## More Plotting with Pandas\n",
@@ -5660,29 +5645,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
+   "execution_count": 83,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot();"
+    "df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot(figsize=(10,3));"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "* **That's why I think Pandas is great!**\n",
     "* It has great defaults to quickly plot data; basically publication-grade already\n",
@@ -5704,56 +5699,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
+   "execution_count": 84,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df_demo[[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True);"
+    "df_demo[[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True, figsize=(10,3));"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 85,
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
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+      "image/png": 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Fzp49e+K73/1uzJs3L6666qrun5966qlZLAcAANAtk9PVnn322di0aVNUVFTEGWecEcOHD48LL7zwkDs5HR0d0d7evs8DAACgJzLZyXn11VcjIuKGG26I22+/PUaPHh233XZbnHvuufHyyy/HkCFDDvh7zc3NceONN2YxEgAAJOW0+08r6nrrrlhX1PUOR492cubMmRO5XO6gj9bW1ujq6oqIiB/84Adx0UUXxbhx4+K+++6LXC4Xv/3tbz/w/ZuamqKtra37sXHjxsP76wAAgJJoaGg4YC+sX78+87V7tJMze/bsaGhoOOgx9fX18dZbb0XEvtfg5PP5qK+vjzfeeOMDfzefz0c+n+/JSAAAQC81ZcqUuO+++/b5WW1tbebr9ihyamtrP9RQ48aNi3w+Hy+99FKcc845ERHR2dkZGzZsiOOPP/6jTQoAABxV8vl8DBs2rOjrZnJNzqBBg2LGjBkxd+7cqKuri+OPPz7mzZsXEREXX3xxFksCAABERIbfkzNv3ryorKyMb37zm7Fr166YMGFCPPnkk3HsscdmtSQAANCLPProozFgwIDu5xdeeOFBr9E/UjKLnKqqqpg/f37Mnz8/qyUAAIBe7LzzzouFCxd2P+/fv39R1s0scgAAgPLWv3//OOmkk4q+biZfBgoAAFAqIgcAAEiK09UAAOAotO6KdaUeodcSOQAAwBG3ZMmSkq3tdDUAACApIgcAAEiKyAEAAJLimpwicFEYAAAUj50cAAA4ChQKhVKPkLkj9TeKHAAA6MX69OkTERG7d+8u8STZ27lzZ0REVFVVHdb7OF0NAAB6scrKyujXr19s27YtqqqqoqIivX2KQqEQO3fujLfffjsGDx7cHXYflcgBAIBeLJfLxfDhw+O1116L119/vdTjZGrw4MExbNiww34fkQMAAL3cMcccEyeffHLSp6xVVVUd9g7O+0QOAAAcBSoqKqJv376lHuOokN4JfQAAQFkTOQAAQFJEDgAAkJRefU3O+18G1N7eXuJJAACAUnq/CT7MF4b26sjZvn17RETU1dWVeBIAAKA32L59e9TU1Bz0mFzhw6RQiXR1dcXmzZtj4MCBkcvlSj1OWWpvb4+6urrYuHFjDBo0qNTjQEn4HIDPAfgMlF6hUIjt27fHiBEjDvmFqL16J6eioiJGjhxZ6jGIiEGDBvlAU/Z8DsDnAHwGSutQOzjvc+MBAAAgKSIHAABIisjhoPL5fMydOzfy+XypR4GS8TkAnwPwGTi69OobDwAAAPSUnRwAACApIgcAAEiKyAEAAJIicgAA4ENwKfvRo1d/GSjF984778S9994bq1evji1btkRExLBhw2LixInR0NAQtbW1JZ4QAKA08vl8rF27Nj7xiU+UehQOwd3V6PbXv/41Jk+eHP369YtJkybFcccdFxERW7dujZUrV8bOnTvj8ccfj/Hjx5d4UsjWrl27oqWlJYYMGRKnnnrqPq/95z//iQcffDAuv/zyEk0HxfHiiy/GM888E2eddVaMGTMmWltb44477oiOjo6YNm1anH/++aUeETIza9asA/78jjvuiGnTpsXHPvaxiIi4/fbbizkWPSBy6PbZz342xo4dG4sWLYpcLrfPa4VCIWbMmBHPP/98rF69ukQTQvZefvnluOCCC+KNN96IXC4X55xzTixdujSGDx8eEf+N/hEjRsTevXtLPClkZ/ny5fGVr3wlBgwYEDt37oyHH344Lr/88hg7dmx0dXXFqlWrYsWKFUKHZFVUVMTYsWNj8ODB+/x81apVMX78+Ojfv3/kcrl48sknSzMghyRy6FZdXR3PPfdcjBkz5oCvt7a2xhlnnBG7du0q8mRQPF/72teis7MzlixZEu+++25cd9118c9//jOeeuqpGDVqlMihLEycODHOP//8uOmmm2Lp0qVxzTXXxNVXXx0333xzREQ0NTVFS0tLrFixosSTQjZuueWWuPvuu+Oee+7ZJ+arqqpi7dq1++3y0/u48QDdhg0bFmvWrPnA19esWdN9Chuk6i9/+Us0NzfH0KFD46STTopHHnkkJk+eHJ/73Ofi1VdfLfV4UBT/+Mc/oqGhISIiLrnkkti+fXt8/etf7379sssui+eff75E00H25syZE8uWLYurr746rr/++ujs7Cz1SPSQGw/Q7frrr49vf/vb0dLSEl/84hf3uybnl7/8ZcyfP7/EU0K2du3aFZWV//N/jblcLhYuXBgzZ86ML3zhC/HAAw+UcDoonvdPW66oqIi+fftGTU1N92sDBw6Mtra2Uo0GRfGZz3wmWlpaorGxMcaPHx+//vWv9zudn95L5NCtsbExhg4dGj/5yU/i5z//effpOH369Ilx48bFkiVL4pJLLinxlJCtMWPGxN/+9rf97pyzYMGCiIj48pe/XIqxoKhGjx4dr7zySpx44okREbF69eoYNWpU9+tvvPFG93VqkLIBAwbE/fffH0uXLo1JkyY5Vfko4pocDqizszPeeeediIgYOnRoVFVVlXgiKI7m5ub485//HH/84x8P+Po111wTixYtiq6uriJPBsWzaNGiqKuri6lTpx7w9e9///vx9ttvxz333FPkyaB03nzzzWhpaYlJkyZF//79Sz0OhyByAACApLjxAAAAkBSRAwAAJEXkAAAASRE5AABAUkQOAACQFJEDAAAkReQAAABJETkAAEBS/j8nsx6UoYdWigAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df_demo[df_demo[\"F\"] < 0][[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True);"
+    "df_demo[df_demo[\"F\"] < 0][[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True, figsize=(10,3));"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 86,
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
     },
@@ -5762,41 +5762,39 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 864x288 with 3 Axes>"
+       "<Figure size 1000x400 with 3 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "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 Demo\", figsize=(12, 4));"
+    "    .plot(kind=\"barh\", subplots=True, sharex=True, title=\"Subplots Demo\", figsize=(10, 4));"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 87,
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 864x432 with 2 Axes>"
+       "<Figure size 1200x600 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5812,23 +5810,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 88,
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 864x432 with 2 Axes>"
+       "<Figure size 1200x600 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5839,7 +5837,7 @@
     "        secondary_y=\"A\", \n",
     "        figsize=(12, 6),\n",
     "        yerr={\n",
-    "            \"A\": df_demo[df_demo[\"F\"] < 0][\"C\"], \n",
+    "            \"A\": abs(df_demo[df_demo[\"F\"] < 0][\"C\"]), \n",
     "            \"F\": 0.2\n",
     "        }, \n",
     "        capsize=4,\n",
@@ -5881,19 +5879,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x288 with 1 Axes>"
+       "<Figure size 1000x400 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5917,19 +5913,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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r+Gn1Pq/sV0RE/JMKKfFJOTmWJ6YnEhkWwnM9Gnl135HhoYzuH0vTKiW474vl/Lkxyav7FxER/6FCSnzSl0t2snjrYZ66rgHloiK9vv+iEWFMGNiaWtFFGTZpKUu3H/Z6BhER8X0qpMTn7E9O59Uf19G2dhluiq3qWI4SRcKZPCSOCiUiGTh+CWv2HHMsi4iI+CYVUuJznv12NSezc3ilVxPHb44ZHRXBlKFxREWEMWDsYjYnpTqaR0REfIsKKfEpsxL38vOa/Tx4dV1qlC3qdBwAKpcszJShcRgD/cbEs+tImtORRETER6iQEp9xLC2TZ2euoVGl4gxtX9PpOP+lVnQxJg2O43hGFv3GxHMgJd3pSCIi4gNUSInPeHXWOg4fP8nrfZoSFup7P5oNKxVn/KDWHEjJoP+YxRxNO+l0JBERcZjvfVpJUFq4+SBfLtnJ0A41aVy5hNNxzqpl9VJ8OiCWrQePc/v4JaRmZDkdSUREHKRCShyXnpnNk9MTqV6mCA9cWdfpOOfVrk5ZPry1Oat3H+OOiQmkZ2Y7HUlERBzikULKGDPOGHPAGLPaE9uT4PLunI1sO5TGq72aULhQqNNx8qRzowq8dWMz/tp6iOGfLyMzO8fpSCIi4gBPnZGaAHTx0LYkiKzefYxP/9zCTbFVaFunrNNxLsj1zSvzYs/GzFl3gIenriQ7xzodSUREvMwjkxZba+cZY2p4YlsSPLKycxgxfRWlihTiqWsbOh0nX/q1qU5Kehav/7SeohFhvNKrseP3vhIREe/xSCGVF8aYYcAwgGrVqnlrt+LDxs7fyurdyXx0WwtKFAl3Ok6+/ePy2qSkZ/LR3M0UjwxjRNf6KqZEvGRLUiqPT1tFo0olePDqupQo7L/vJeKfvFZIWWtHA6MBYmNj1QcS5LYdPM7bszdwdcPydG1cwek4F+3Ra+qRmpHFJ/O2EBUZxvArYpyOJBLwflu/n/u/XAEWlm4/wncr9/B41/rc0KIKISH6Y0a8Q1ftiddZa3lyRiKFQkN4sWdgdIUZY3iueyN6N6/Mm79sYMKCrU5HEglY1lpG/r6JIRMTqFa6CLMe6MB397anRtmiPPb1Km4YtZDVuzU3pniHCinxun8v3cXCzYcYcW19KpSIdDqOx4SEGP51Q1M6NyzPc9+t5eulu5yOJBJwjmdkcc/ny3jj57/p3rQSX9/VliqlitCoUgn+feelvHljM7YfSqPHh/N55pvVHEvLdDqyBDhP3f7gC2ARUM8Ys8sYM8QT25XAcyAlnZe+X0vrGqXp2yrwxsqFhYbwwa3NaV+nLI99vZJZiXudjiQSMLYfOk7vjxby0+p9PHVtA9675ZL/umVKSIjhhpZV+O2RyxlwaQ0+i99Op7fmMnXJTnJ0Va0UEGOt93+4YmNjbUJCgtf3K86757NlzF67n1kPdKB2dDGn4xSYtJOuOfkSdx9jzO2t6Fg32ulIIn7tz41JDP98OQAf9G3OZXn4nVq7J5lnv11NwvYjNK9Wkhd7NvbpmRPEdxljllprY8+0TF174jW/rNnHD4l7ue/KOgFdRAEUKRTG+EGtiSkXxZ2TE1iy7bDTkUT8krWW0fM2c/u4xVQoHsnM4e3yVESBa37Mf991KW/d2Iydh0/Q/cP5PP1NoubJFI/SGSnxiuT0TK5++w9KFSnEd/e2J9wHJyUuCAdTM7jpk0UkJWfwxbA2+mtY5AKcOJnNiOmr+HbFHq5tUoE3bmhG0Yj8XWyenJ7JO7M3MHHhNkoWKcRj19TjptiqurpP8kRnpMRxr89aT1JKBq/1aRo0RRRA2WIRTBkSR/HC4QwYt5hNB1KcjiTiF3YdSaPPxwuZuXIPj15Tj5G3tsh3EQVQPDKcf3ZvxA/3daB2dFFGTE+k98cLSdylq/vk4gTPJ5o4ZvHWw3wWv4NB7WpySdWSTsfxukolCzNlaBwhxtBvzGJ2Hk5zOpKIT1u4+SA9PlzAziNpjLu9Ffd0quOx26Q0qFicqXdeyjs3N2PXkRP0GDmfJ2ckcuS4uvskf1RISYFKz3Sdmq9SqjAPd67rdBzH1CxblClDW3MiM5t+Y+M5kJzudCQRn2OtZfyCrfQfu5jSRQvx7T3t6FS/nMf3Y4yhV/Mq/PZIRwa1rclXS3ZyxVtz+WLxDl3dJxdMhZQUqJG/b2JL0nFe6dWEIoW8diN9n1S/QnEmDGpFUkoG/cbG6y9gkVzSM7N55N+reP67tXSqV44Zd7elVgFflFI8Mpxnuzfkh/vaE1M+iiemJ9LrowWs3Hm0QPcrgUWFlBSY9fuS+XjuZnq3qJznq2wCXfNqpRhzeyzbDqUxcPxiUjOynI4k4ri9x05w8yeLmLZsF/dfGcPo/i2JivTenHn1KxTnq2FtePfmS9hzLJ3rP1rAE9PV3Sd5o0JKCkR2juXxaYmUKBzOM9c1dDqOT2lbuywf3dqCNXuSGTJhCemZ2U5HEnHMkm2H6f7BfDYdSOWT/i158Oq6jlxJZ4zh+uaV+e3hjgxpV5OpCTvp9NZcPovfTra6++QcVEhJgZiwcBsrdx7l2e4NKVW0kNNxfM5VDcvz1k3NWLztMHd/toyTWTlORxLxKmstU/7aTt/RfxEVGc4397TjmkbOT2AeFRnO090a8uN9HahXPoqnZqym10cLWKHuPjkLFVLicTsPp/Hmz39zRf1y9GhWyek4PqvnJZV5+fom/Lb+AA9NXaG/eiVoZGRl8+SMRJ7+ZjXtY8ryzT3tiCkf5XSs/1KvQhRfDmvDe7dcwr5j6fT6aAEjpq3isLr75DTBPfpXPM5ay1PfrCbEwIvXN/bYJcuB6ta4aqSkZ/LqrPUUiwjj1d5N9JpJQDuQnM5dU5aybMdR7ulUm4eurkeoj94U0xhDz0sqc0X9crz/60bGL9jGrNX7ePSaevRtXc1nc4t36YyUeNSM5buZtyGJx7rUp3LJwk7H8Qt3dqzN8E51+HLJTl7+YR1OzDYg4g3Ldhyh2wfzWbc3hZG3tuDRa+r7RTESFRnOU9c1ZNb9HWhYsThPf7Oa60cuYPmOI05HEx+gQko85mBqBi98v5YW1UrSr011p+P4lYc712Vg2xqMmb+V93/d5HQcEY+bumQnt3zyFxHhIUy/uy3XNa3odKQLFlM+is/viOP9vs05kJJOr48W8vjXqziUmuF0NHGQuvbEY178fi3HM7J4rU9Tv/gr05cYY3i2W0NSM7J4Z84GoiLDGNy+ptOxRC5aZnYOL36/lkmLttO+Tlk+6Nvcry9AMcbQo1ml/3T3jZu/lZ/W7OORznW5Na663vuCkM5IiUf8vv4A367Ywz2d6lDXxwaN+ouQEMNrvZvQtXEFXvh+LVOX7HQ6kshFOZiawW1j4pm0aDvDLqvFhEGt/LqIyq1YRBhPXtuAWfd3oFGl4jzz7Rp6jpzPMnX3BR0VUnLRUjOyeGpGIjHlivGPy2s7HcevhYWG8O4tl9Ahpiwjpq/ih1V7nY4kki+Ju47R44P5rNx5lPduuYQnr21AWABOWB5TPorPhsbxQd/mHEw5Se+PFvLov1dyUN19QSPwfqrF6978+W/2JqfzWp+mRISFOh3H70WEhfJJ/5a0qFaKB75azty/DzgdSeSCTF+2ixtGLcQYw7R/tKXnJZWdjlSgjDF0b1aJXx/uyJ0dazFj+W6ueHMukxZt021NgoAKKbkoS7cfYeKibQxoU52W1Us5HSdgFCkUxrhBrahbPoq7pixl8dbDTkcSOa8s93ioh6aupHm1kswc3o7GlUs4HctrikaE8UTXBvz0QAeaVCnBs9+uofsH81m6Xb+/gUyFlOTbyawcRkxbRcXikTzapb7TcQJO8chwJg1uTeWShRk8YQmJu445HUnkrA4fP8mAcYsZO38rA9vWYPKQOMoUi3A6liPqlItiypA4Rt7agiNpJ+nz8SIeUXdfwFIhJfn20dxNbDyQyku9GlMsQheAFoQyxSKYMjSOEoXDGTAuno37U5yOJPI/1u5JpseH80nYfoQ3bmjKcz0aER6A46EuhDGG65pWZM5DHbmrY22+XbGbTm/OZcKCrWRla0qoQBLcP+mSbxv3pzDy903uy4DLOx0noFUsUZjPhsYRFhpCv7Hx7Dyc5nQkkf/4buUeen+8gKxsy9Q7L+XG2KpOR/IpRSPCGNG1Pj89cBmXVC3Jc9+tpfuHC0jYpu6+QKFCSi5YTo7l8WmrKBoRxrPdGzodJyjUKFuUKUPiyMjK4dYxf7HvWLrTkSTIZedYXpu1nnu/WE7jSiWYeW87Lqla0ulYPqt2dDEmDW7NR7e14FjaSW4YtYiHpq4gKUXdff5OhZRcsCnx21m24yjPdmtI2SAdA+GEehWimDioNYdTT9JvbLwmTxXHHEvLZNCEJYz6YzO3xVXj8zvaUC4q0ulYPs8Yw7VNKjLn4Y7cfXltvlu5hyvenMt4dff5NRVSckF2Hz3B67PW0yGmLL2aB/Ylzb6oWdWSjLm9FTsPp3H7uMUkp2c6HUmCzIb9KfQYOZ9Fmw/ySq8mvNyrCYXC9FFyIYoUCuOxLu7uvmolef67tXT7YL6uzvVT+umXPLPW8vSMRHIsvNKrCcZoKgQnXFq7DKP6tWTd3mSGTkjgxMlspyNJkPhp9V6uH7mAtJPZfDmsDbfGVXM6kl871d03ql8LUtKzuOmTRTz01QoOpKjr3p+okJI8+27VXn7/O4mHO9elaukiTscJap3ql+Odmy9hyfbD3DVlKSez1C0gBScnx/L2L39z15RlxJSP4rvh7WlZvbTTsQKCMYYujSsy+6HLuKdTbb5ftZcr3/yDsfPV3ecvVEhJnhw5fpLnZ66hWZUSDGqnyXR9QfdmlXi1VxP+2JDEg1+t0B2UpUAkp2dyx6QE3v9tEze2rMJXw9pQoYTGQ3lakUJhPHpNfX5+8DKaVy/Fi9+7uvvitxxyOpqchwopyZOXfljHsROZvNanqWY39yG3tK7G09c14IfEvTwxfRU5KqbEgzYnpXL9yAX8sSGJF3o24l83NCUyXNNAFaSaZYsycVArRvVrSUp6FjeP/osHvlzOgWR19/kq3UVRzmvehiSmLdvF8E51aFCxuNNx5DRDO9QiOT2L93/dSLGIcJ7p1kDj1+SizVm7nwe/WkGhsBCmDI2jTa0yTkcKGq7uvgp0rBvNR3M38ckfW5iz7gAPXBXD7W1rBP3NTn2NjoacU9rJLJ6ckUit6KIMv6KO03HkLB68KoZB7WowbsFW3p2z0ek44sdycizv/7qRoZMSqF62CDPvba8iyiGFC4XycOd6/PzgZcTWKMVLP6yj2/vz+UvdfT5FhZSc09u/bGDXkRO81lun9H2ZMYZnrmvIjS2r8N6vGxnz5xanI4kfSs3I4u7PlvH27A30al6Zr+9qS+WShZ2OFfRqli3K+IGtGN2/JakZWdwy+i/u/3I5+9Xd5xPUtSdntXLnUcYt2MptcdVoXVNX6Pi6kBDDa32acvxkFi/9sI6oyDBubqXL0yVvth08zrDJCWw6kMrT1zVgSPua6iL2IcYYOjeqQIeYaD6eu4lR87YwZ+1+HriqLgPbqbvPSXrl5Ywys3N4fNoqoqMieLxrfafjSB6Fhhjevbk5HetGM2J6It+v2uN0JPEDf2xIoseH8zmQksGkwXEM7VBLRZSPKlwolIc612P2g5fRumZpXv5xHde+9yeLNqu7zykqpOSMRs/bwvp9KbzYszHFI8OdjiMXoFBYCKP6taRV9dI88OUKfl9/wOlI4qOstYz6YzODxi+mUsnCfDe8Pe1jyjodS/KgepmijBvYik8HxHIiM5u+n/7FvV8s1zycDlAhJf9jc1Iq7/26keuaVKRzowpOx5F8KFwolDEDY6lfMYq7pizV4FT5H2kns7j3i+W8Nms9XZtUZPrdbXWjXT9jjOHqhuWZ81BH7rsyhp/X7OPKt+Yyet5mMnUzT6/xSCFljOlijPnbGLPJGDPCE9sUZ+TkWJ6YnkhkWAj/7NHQ6ThyEYpHhjNpcBzVShdhyIQlrNx51OlI4iN2Hk6jz8eL+CFxL493qc+HfZtTpJCGzPqryPBQHrq6LrMfvIw2tcrwyo/r6frenyzcdNDpaEHhogspY0woMBLoCjQE+hpj9Ansp75YsoPFWw/z9HUNNZt7AChdtBBThsZRulghbh+/mL/3pTgdSRy2YNNBun84n91H0hg/sBX/uLy2xkMFiOplijJ2YCvGDIglIyubW8fEM/zzZeruK2CeOCPVGthkrd1irT0JfAn09MB2xcv2HUvntR/X07Z2GW6MreJ0HPGQ8sUj+WxIGyLCQug3Np7th447HUkcYK1l7PytDBi3mOhiEXw7vD2X1yvndCwpAFc1LM/sBzvywFUxzF67nyvemsuoPzZrTs4C4olCqjKwM9f3u9yPiR+x1vLMt6s5mZ3Dq72b6C/UAFOtTBGmDIkjKzuH28bEs/fYCacjiRelZ2bz8NSVvPj9Wq6sX44Z97SjZtmiTseSAhQZHsoDV9Vl9oMdaVu7jGss3HvzWKDuPo/z2mBzY8wwY0yCMSYhKSnJW7uVPPpp9T5mr93PQ1fXpXoZvcEGopjyUUwaHMfRtEz6jYnnUGqG05HEC/YcPcGNoxYxffluHrq6LqP6taRYhMZDBYtqZYow5vZWjBsYS2a25bYx8dzz2TL9MeVBniikdgNVc31fxf3Yf7HWjrbWxlprY6Ojoz2wW/GUY2mZPDtzDY0qFWdI+5pOx5EC1KRKCcbeHsuuIycYMG4xyemZTkeSAhS/5RDdP5jP1oPH+XRALPddGUOIJh0PSlfUL88vD17Gg1fVZc66/Vz51h98PFfdfZ7giUJqCRBjjKlpjCkE3ALM9MB2xUte+XEdh4+f5PU+TQnT3XEDXlytMozq35IN+1MYMmEJJ05mOx1JPMxay+RF27htTDwlCofzzT3tuLpheadjicMiw0O5/6oY5jzUkXZ1yvL6T+vp8t48/tyoXqKLcdGfmtbaLGA48DOwDphqrV1zsdsV71i46SBfJexkaIeaNK5cwuk44iWd6pXj3Zubs3T7Ee6cspSMLBVTgSIjK5sR0xJ55ts1XFY3mm+Gt6NOuWJOxxIfUrV0ET4dEMv4ga3IzrH0H7uYuz9byp6j6u7LD2Ot9fpOY2NjbUJCgtf3K/8tPTOba96dB8DPD1ymSYmD0NQlO3ls2iq6Nq7AB32b64ykn9ufnM6dk5eyYudR7r2iDg9eVVddeXJO6ZnZfDpvCyPnbsJgGH5FHYZ2qElEmD4PcjPGLLXWxp5pmd41g9i7czay/VAar/ZuoiIqSN3UqirPdGvIrNX7GDE9kZwc7/9hJZ6xdPsRun0wnw37U/j4thY83Lmeiig5r8jwUO69MobZD3akQ0xZ3vj5b7q++yfzNqi7L69USAWp1buP8emfW7g5tipta2turWA2pH1NHrgqhq+X7uKF79fixFlquThfLt7BLaMXUTg8lBl3t6Nrk4pORxI/U7V0EUYPiGXCoFbkWMuAcYu5a/JSdqu777x0DWwQysrO4fFpqyhdtBBPXtvA6TjiA+6/MoaU9CzGzt9KVGQYD3eu53QkyYOTWTm88P0apvy1gw4xZfmgb3NKFinkdCzxY5fXK8fPD5bh03lb+PD3Tcx96wD3XhGj7r5zUCEVhMbO38qaPcl8fFsLShQJdzqO+ABjDE9f14DjGVl88NsmoiLDGHZZbadjyTkkpWRw92dLWbLtCHd2rMVj19QnVF154gERYaEMvyKG65tX5qXv1/HGz3/z9dJd/LN7Q90N/wzUtRdkth08ztuzN9C5YXm6NK7gdBzxIcYYXu7VhG5NK/LKj+v5PH6H05HkLFbuPEr3D+aTuPsY7/dtzhNdG6iIEo+rUqoIo/q3ZOLg1gAMHL+EOycnsOtImsPJfIsKqSBireXJGYkUCg3hhZ6NNQ2M/I/QEMPbN11Cp3rRPPVNIt+u+J9764rDvl66ixs/WURoiGHaP9rSo1klpyNJgOtYN5qfHujAo9fUY96Gg1z19h988OtG0jN12xRQIRVU/p2wi4WbDzHi2vpUKBHpdBzxUYXCQvi4X0ta1yjNw1NX8uu6/U5HEiAzO4fnZq7hkX+vpGW1Unx3b3saVdK938Q7IsJCuadTHeY83JFO9crx1uwNdHl3Hr//fcDpaI5TIRUkDqSk89IPa2ldszR9W1VzOo74uMjwUMbcHkujSsX5x2fLWLhZE5066VBqBgPGLmbCwm0MbleTyUNaU7qoBpWL91UuWZiP+7Vk8pDWhBjDoPFLuGNSAjsPB293nwqpIPH8zLWkZ+Xwau8mureM5ElUZDgTBrWmRpki3DExgeU7jjgdKSit3n2MHh8uYOmOI7x1YzOe7d5QN04Vx3WIiWbWAx14rEs95m90dfe9H6TdffptDAK/rNnHD4l7uf/KGGpHa6oIybtSRQsxZUgcZYpFMHD8EtbvS3Y6UlD5dsVubhi1kBxr+fquS+nTsorTkUT+IyIslLsvr8OvD3fkqgbleXv2Bq55dx6/rQ+u4QAqpAJccnomz3y7mvoVohh2WS2n44gfKlc8ks+GxlE4PJR+Yxaz7eBxpyMFvOwcy6s/ruP+L1fQtHJJZg5vT9MqJZ2OJXJGlUoWZuRtLZgyJI7QEMPgCQkMnRg83X0qpALc67PWk5SSwet9mhKu7gDJp6qlizBlaGtyrOW2MfGa3LQAHU07ycDxi/lk3hYGXFqdKUPjiI6KcDqWyHm1jynLT/dfxoiu9Vm42dXd9+6cDQHf3adP1gC2eOthPovfweB2NWlWtaTTccTP1SkXxaTBrUk+kUm/sfEcTM1wOlLAWb8vmR4fLiB+y2Fe692EF3o2plCY3qbFfxQKC+GujrVd3X0Ny/PunI10fmdeQF/9q9/QAJWemc2IaauoUqowD3Wu63QcCRCNK5dg3KBW7Dl6ggFjF3PsRKbTkQLGj4l76f3RQtIzs/liWBtuaa2ra8V/VSxRmJG3tuCzoXEUCgthyMQEhkxYwo5Dgdfdp0IqQH342ya2HDzOK72aUKSQZgISz2lVozSf9I9l44EUBk9YQtrJLKcj+bXsHMsbP6/n7s+WUa9CFN/d256W1Us5HUvEI9rVKcuP93Xgia71WbTlEFe98wfvzA6s7j4VUgFo3d5kRv2xmT4tqnBZ3Win40gA6lg3mvdvac7yHUe4c/JSMrIC503Rm46dyOSOSQmM/H0zN8dW5cthbShfXDfLlcBSKCyEOzvW5reHL+eaRhV479eNXP3OH8xZGxjdfSqkAkx2jmXEtFWUKBzO09c1cDqOBLCuTSryrxua8efGg9z3xXKysnOcjuRXNh1IodfIBczbkMSL1zfmtT5NiAgLdTqWSIGpUCKSD/o25/M74ogIC2XopAQGT1jC9kP+fSWwCqkAM37BVlbuOsY/ezSilO58LAXshpZVeK57Q35es5/Hvl5FTo51OpJfmL12P9ePXEhyeiaf39GG/m2qa+5LCRpta5dl1v0deOraBsRvOcTV78zj7V/+5sRJ/zyzrcEzAWTn4TTe+mUDV9QvR/emFZ2OI0FiYLuapKRn8dbsDRSLDOP5Ho1UFJxFTo7l/d828u6cjTStUoJR/VpSqWRhp2OJeF14aAh3XFaLHpdU4uUf1vH+b5uYvnw3z3ZryNUNy/vVe4jOSAUIay1PzkgkxMBL1zf2qx9C8X/Dr6jDsMtqMWnRdt785W+n4/iklPRM7pyylHfnbKR3i8pMvfNSFVES9MoXj+T9vs354o42FCkUyrDJSxk0YYlf3fhXhVSAmLF8N39uPMjjXevrzVm8zhjDE13r07d1NUb+vplRf2x2OpJP2ZKUSq+PFvLb+gM8260hb93YjMhwjYcSOeXS2mX44b4OPH1dAxK2HaHzO/N4y0+6+9S1FwAOpmbwwvdraVGtJP3iqjsdR4KUMYaXrm9MakYWr81aT7GIMPq10c/j7+sPcN+XywkLMUwe3Jq2dco6HUnEJ4WHhjC0Qy16NKvEKz+u44PfNjF92W6e7d6Qzj7c3aczUgHghe/WkpaRzet9mhIS4ps/aBIcQkMMb9/UjCvrl+OZb1fz7YrdTkdyjLWWj+ZuYvDEJVQtVYSZw9uriBLJg3LFI3n3luZ8OawNxSLCuHPyUgaOX8JWH+3uUyHl535bv5+ZK/dwT6c6xJSPcjqOCOGhIYy8rQVtapbhoakrmR0g94q5EGknsxj++XL+9dPfdG9aiWn/aEvV0kWcjiXiV9rUKsP397XnmW4NWbr9CNe8M483fl7vczcBNtZ6/3Ll2NhYm5CQ4PX9BprUjCw6v/0HxSLD+P7eDpqTS3xKakYWt42JZ93eZMYPbEW7IDkbs+NQGsMmJ7Bhfwojutbnjg61fLZLQsRfHEhO59VZ65mxfDeVSxbmmW4NuKZRBa/9bhljllprY8+0TJ+8fuzNn/9mb3I6r/ZuqiJKfE6xiDAmDmpFzTJFuWNSAku3H3E6UoGbv/EgPUbOZ++xdCYMas2wy2qriBLxgHLFI3nn5kuYeuelREWGcdeUZQwYt5gtSalOR1Mh5a+Wbj/CxEXbuP3SGpqXS3xWySKFmDykNeWiIhg0fjFr9yQ7HalAWGv5dN4WBoyLp3xUJDOHt9P0TCIFoHXN0nx/b3ue7daQFTuOcs278/hh1V5HM6mQ8kMZWdk8Pm0VFYtH8sg19ZyOI3JO5YpHMmVoHEUjwhgwLt4n/oL0pBMns3ngqxW8/OM6OjeswPS721K9TFGnY4kErLDQEAa3r8mvj3TkhpZVaFXD2ZMJKqT80MdzN7PpQCov925CsQjdwUJ8X5VSRZgyNA5rod+YeHYfPeF0JI/YdSSNG0YtZObKPTzSuS4f92tBUf1OinhFuahIXu3dlHIOT/StQsrPbNyfwsjfN9Hzkkp0qlfO6TgieVY7uhiThrQmJSOLfmPiSUrJcDrSRVm0+RA9PlzAjkNpjL09luFXxGg8lEgQUiHlR7JzLI9PW0WxiDCe7dbQ6TgiF6xRpRJMGNSKfcfS6T82nmNpmU5HumDWWiYu3Ea/sfGUKhLON8PbcUX98k7HEhGHqJDyI1P+2s6yHUd5pltDyhSLcDqOSL60rF6a0QNasiXpOAMnLOZ4hm/dE+Zc0jOzefTrVfxz5ho61SvHN/e0o3Z0MadjiYiDVEj5id1HT/Cvn9ZzWd1oejWv7HQckYvSISaa9/s2Z9WuYwybnEB6pu/Pp7X32Alu/mQRXy/dxf1XxjC6f0uiIsOdjiUiDlMh5QestTw9I5EcCy9f31jjMCQgdGlcgX/1acqCTYe494vlZGbnOB3prBK2Hab7BwvYdCCVT/q35MGr62o6JhEBVEj5hZkr9/D730k8ck09TTMhAaVPyyq80LMRs9fu57GvV5GT4/2ZFs7ns/jt9P30L4pFhDLjnnZc06iC05FExIfoOl0fd/j4SZ7/bi3NqpZkYNsaTscR8bgBl9YgJT2LN37+m6IRobzY0zfOup7MyuGfM9fwxeIdXF4vmvdubk6JIurKE5H/dlGFlDHmRuA5oAHQ2lqrCfQ87KUf1pJ8IpPX+zQhVF0JEqDuvrw2yemZfPLHFqIiw3m8S31H8xxITucfny1j6fYj3H15bR7uXE+/fyJyRhd7Rmo10Bv4xANZ5DTzNiQxfdlu7r2iDvUrFHc6jkiBMcYwokt9UtOz+HjuZopFhHFPpzqOZFmx8yh3Tk4g+UQWH97anG5NKzmSQ0T8w0UVUtbadYBPnIYPNMczsnhyRiK1oos69oEi4k3GGF7s2ZjjGa5uvqjIMAZcWsOrGaYm7OTpGaspXyKC6Xe3pUFF/QEjIufmtTFSxphhwDCAatWqeWu3fuvt2RvYdeQEU++8lMjwUKfjiHhFSIjhjRubcfxkNs9+u4ZiEWH0blGlwPebmZ3DS9+vZeKi7bSrU4YP+7agVNFCBb5fEfF/571qzxgzxxiz+gz/el7Ijqy1o621sdba2OhozYp+Lit2HmX8gq30a1ON1jVLOx1HxKvCQ0P4oG9z2tUpw6Nfr+Kn1fsKdH8HUzO4bUw8Exdt544ONZk4qLWKKBHJs/OekbLWXuWNIOKSmZ3DiGmrKBcVyWMOD7gVcUpkeCij+8fSb2w8932xnLEDY+kQ4/k/wBJ3HePOyQkcOn6Sd25uRq/mBX/2S0QCi+4j5WNGz9vC+n0pvHh9Y4rrrskSxIpGhDFhYGtqRRdl2KSlLN1+2KPb/2b5bm4YtRCAaf9oqyJKRPLlogopY0wvY8wu4FLgB2PMz56JFZw2J6Xy3q8bua5JRa5uqElQRUoUCWfykDgqlIhk4PglrNlz7KK3meUeD/XAVyu4pGpJZt7bnsaVS3ggrYgEo4sqpKy1M6y1Vay1Edba8tbaazwVLNjk5FiemJZI4fBQnuvRyOk4Ij4jOiqCKUPjiIoIY8DYxWxOSs33to4cP8nA8UsYM38rA9vWYMrQOMpqAnARuQjq2vMRXyzZweJth3nqugZER+mNXSS3yiULM2VoHMZAvzHx7DqSdsHbWLsnmR4j57N462H+dUNTnuvRiPBQvQWKyMXRu4gP2Hcsndd+XE/b2mW4saXGaYicSa3oYkwaHMfxjCz6jYnnQEp6np/7/ao99Pl4ISezcvjqzjbcFFu1AJOKSDBRIeUway3PfLuazJwcXu3dRDc3FTmHhpWKM35Qaw6kZDBg7GKOpp085/rZOZbXf1rP8M+X07BScb67tz3Nq5XyUloRCQYqpBw2a/U+Zq/dz0NX16V6maJOxxHxeS2rl2J0/1i2JB1n4PglpGZknXG9Y2mZDJ6whI/nbubWuGp8cUcbykVFejmtiAQ6FVIOOpaWybPfrqFx5eIMblfT6TgifqN9TFk+vLU5ibuPccfEBNIzs/9r+Yb9KfQcOZ+Fmw/ycq/GvNKrCYXC9HYnIp6ndxYHvfLjOo6kneS13k0J06BXkQvSuVEF3ryxKX9tPcTwz5eRmZ0DwM9r9tFr5AJSM7L54o423BZX3eGkIhLIvDbXnvy3hZsO8lXCTu7qWFv3sBHJp17Nq3A8I5unv1nNw1NXUqNsUd7/dSPNqpbkk34tqVBCXXkiUrBUSDngxMlsnpiRSI0yRXjgqhin44j4tX5tqpOSnsXrP60H4IaWVXjp+saa7FtEvEKFlAPe/XUD2w+l8fkdcXqzF/GAf1xem2IRoUSEhXJjbBVd/SoiXqNCystW7z7GmD+3ckurqrStXdbpOCIBo/+lNZyOICJBSCOcvSgrO4fHp62idNFCPNG1gdNxRERE5CLpjJQXjZm/lTV7kvn4thaUKBLudBwRERG5SDoj5SXbDh7nndkbuKZRebo2qeh0HBEREfEAFVJeYK3liemJFAoN4YWejZ2OIyIiIh6iQsoL/p2wi0VbDvHEtQ0oX1z3tREREQkUKqQK2IHkdF76YS2ta5bmllaacV5ERCSQqJAqYM99t4b0rBxe692EkBDd20ZERCSQqJAqQD+v2cePifu4/8oYakUXczqOiIiIeJgKqQKSnJ7Js9+upn6FKIZdVsvpOCIiIlIAdB+pAvLarPUkpWTw6YBYwkNVr4qIiAQifcIXgPgth/g8fgdD2tekaZWSTscRERGRAqJCysPSM7N5YnoiVUsX5sGr6zodR0RERAqQuvY87MPfNrHl4HEmD2lNkUJ6eUVERAKZzkh50Lq9yYz6YzN9WlShQ0y003FERESkgKmQ8pDsHMvj01ZRskg4T1/XwOk4IiIi4gUqpDxk/IKtrNp1jH92b0SpooWcjiMiIiJeoELKA3YeTuOtXzZwZf1ydGta0ek4IiIi4iUqpC6StZYnZyQSGmJ48frGGKNpYERERIKFCqmLNH3Zbv7ceJDHu9SjUsnCTscRERERL1IhdREOpmbw4g9raVm9FLfFVXc6joiIiHiZCqmL8MJ3a0nLyOa13k0ICVGXnoiISLBRIZVPv63fz8yVe7inUx1iykc5HUdEREQcoEIqH1Izsnhqxmrqli/GPy6v7XQcERERcYgKqXx446f17EtO57U+TSkUppdQREQkWKkKuEBLtx9m0l/buf3SGrSoVsrpOCIiIuIgFVIXICMrm8enJVKpRGEevaae03FERETEYRdVSBlj3jDGrDfGrDLGzDDGlPRQLp/00e+b2XQglZd6NaZoRJjTcURERMRhF3tGajbQ2FrbFNgAPHHxkXzThv0pfDR3E9dfUolO9co5HUdERER8wEUVUtbaX6y1We5v/wKqXHwk35OdY3l82iqKRYTxTLeGTscRERERH+HJMVKDgVlnW2iMGWaMSTDGJCQlJXlwtwVvyl/bWb7jKM92b0iZYhFOxxEREREfcd6BPsaYOUCFMyx6ylr7rXudp4As4LOzbcdaOxoYDRAbG2vzldYBu4+e4F8/reeyutFcf0llp+OIiIiIDzlvIWWtvepcy40xA4FuwJXWWr8pkPLCWsvTMxKxwCu9GmOMpoERERGR/3exV+11AR4Delhr0zwTyXfMXLmH3/9O4pHO9ahSqojTcURERMTHXOwYqQ+BKGC2MWaFMWaUBzL5hMPHT/L8d2tpVrUkt7et4XQcERER8UEXdTMka20dTwXxNS99v5bkE5m83qcJoSHq0hMREZH/pTubn8EfG5KYvnw3d19em/oVijsdR0RERHyUCqnTHM/I4snpidSOLso9VwTsCTcRERHxAM1zcpq3Z29g99ET/PuuS4kIC3U6joiIiPgwnZHKZcXOo4xfsJV+barRqkZpp+OIiIiIj1Mh5XYyK4cR01ZRLiqSx7vUdzqOiIiI+AF17bmNnreZ9ftS+HRALFGR4U7HERERET+gM1LApgOpvP/rJq5rWpGrG5Z3Oo6IiIj4iaAvpHJyLE9OT6RwoVCe697I6TgiIiLiR4K+kPp88Q4WbzvM09c1IDoqwuk4IiIi4keCupDadyyd12atp12dMtzQsorTcURERMTPBG0hZa3l6W9Wk5WTwyu9mmCMpoERERGRCxO0hdSs1fuYs24/D11dl+plijodR0RERPxQUBZSR9NO8uy3a2hSuQSD29V0Oo6IiIj4qaC8j9QrP67jSNpJJg5uRVhoUNaSIiIi4gFBV0Us2HSQqQm7GHZZLRpVKuF0HBEREfFjQVVInTiZzZMzEqlZtij3XxnjdBwRERHxc0HVtffunA1sP5TGl8PaEBke6nQcERER8XNBc0Zq9e5jfPrnFvq2rkqbWmWcjiMiIiIBICgKqczsHB77ehVlikUwomsDp+OIiIhIgAiKrr2x87eydm8yo/q1oEThcKfjiIiISIAI+DNSWw8e553ZG+jSqAJdGld0Oo6IiIgEkIAupKy1PDF9FYXCQni+ZyOn44iIiEiACehCamrCTv7acpgnr21A+eKRTscRERGRABOwhdSB5HRe/mEdcTVLc3NsVafjiIiISAAK2ELqnzPXkJ6Vw6u9mxASYpyOIyIiIgEoIAupn1bvY9bqfTxwVQy1oos5HUdEREQCVEAWUsUiwriqQTnu6FDL6SgiIiISwALyPlLtY8rSPqas0zFEREQkwAXkGSkRERERb1AhJSIiIpJPKqRERERE8kmFlIiIiEg+qZASERERyScVUiIiIiL5pEJKREREJJ9USImIiIjkk7HWen+nxiQB2wt4N2WBgwW8D18WzO0P5rZDcLdfbQ9ewdz+YG47eKf91a210Wda4Egh5Q3GmARrbazTOZwSzO0P5rZDcLdfbQ/OtkNwtz+Y2w7Ot19deyIiIiL5pEJKREREJJ8CuZAa7XQAhwVz+4O57RDc7Vfbg1cwtz+Y2w4Otz9gx0iJiIiIFLRAPiMlIiIiUqD8vpAyxnQxxvxtjNlkjBlxhuURxpiv3MvjjTE1HIhZIPLQ9oHGmCRjzAr3v6FO5CwIxphxxpgDxpjVZ1lujDHvu1+bVcaYFt7OWJDy0P7LjTHHch37Z72dsaAYY6oaY343xqw1xqwxxtx/hnUC8vjnse2BfOwjjTGLjTEr3e1//gzrBOR7fh7bHrDv+QDGmFBjzHJjzPdnWObccbfW+u0/IBTYDNQCCgErgYanrXM3MMr99S3AV07n9mLbBwIfOp21gNp/GdACWH2W5dcCswADtAHinc7s5fZfDnzvdM4CantFoIX76yhgwxl+9gPy+Oex7YF87A1QzP11OBAPtDltnUB9z89L2wP2Pd/dvoeAz8/08+3kcff3M1KtgU3W2i3W2pPAl0DP09bpCUx0f/01cKUxxngxY0HJS9sDlrV2HnD4HKv0BCZZl7+AksaYit5JV/Dy0P6AZa3da61d5v46BVgHVD5ttYA8/nlse8ByH89U97fh7n+nD/QNyPf8PLY9YBljqgDXAWPOsopjx93fC6nKwM5c3+/if99U/rOOtTYLOAaU8Uq6gpWXtgP0cXdtfG2MqeqdaD4hr69PILvU3Q0wyxjTyOkwBcF9+r45rr/Ocwv443+OtkMAH3t3984K4AAw21p71mMfYO/5eWk7BO57/rvAY0DOWZY7dtz9vZCSc/sOqGGtbQrM5v+rdQl8y3BNadAM+AD4xtk4nmeMKQZMAx6w1iY7ncebztP2gD721tpsa+0lQBWgtTGmscORvCYPbQ/I93xjTDfggLV2qdNZzsTfC6ndQO6Ku4r7sTOuY4wJA0oAh7ySrmCdt+3W2kPW2gz3t2OAll7K5gvy8rMRsKy1yae6Aay1PwLhxpiyDsfyGGNMOK5C4jNr7fQzrBKwx/98bQ/0Y3+KtfYo8DvQ5bRFgfqe/x9na3sAv+e3A3oYY7bhGsZyhTFmymnrOHbc/b2QWgLEGGNqGmMK4RpgNvO0dWYCt7u/vgH4zbpHo/m587b9tDEhPXCNpwgWM4EB7qu32gDHrLV7nQ7lLcaYCqfGBxhjWuP6XQ+IDxN3u8YC66y1b59ltYA8/nlpe4Af+2hjTEn314WBq4H1p60WkO/5eWl7oL7nW2ufsNZWsdbWwPVZ95u1tt9pqzl23MO8sZOCYq3NMsYMB37GdRXbOGvtGmPMC0CCtXYmrjedycaYTbgG597iXGLPyWPb7zPG9ACycLV9oGOBPcwY8wWuq5PKGmN2Af/ENfgSa+0o4EdcV25tAtKAQc4kLRh5aP8NwD+MMVnACeCWQPgwcWsH9AcS3eNFAJ4EqkHAH/+8tD2Qj31FYKIxJhRXgTjVWvt9MLznk7e2B+x7/pn4ynHXnc1FRERE8snfu/ZEREREHKNCSkRERCSfVEiJiIiI5JMKKREREZF8UiElIiIikk8qpERERETySYWUiIiISD6pkBIRERHJp/8DhPkMw3YBYOsAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x288 with 1 Axes>"
+       "<Figure size 1000x400 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5953,7 +5947,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5962,14 +5956,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 864x288 with 2 Axes>"
+       "<Figure size 1200x400 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -5999,7 +5991,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6013,14 +6005,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {},
+   "execution_count": 93,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -6028,15 +6026,17 @@
     }
    ],
    "source": [
-    "df_demo[[\"A\", \"C\"]].plot();"
+    "df_demo[[\"A\", \"C\"]].plot(figsize=(10,3));"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "### Seaborn Color Palette Example\n",
@@ -6046,14 +6046,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x72 with 1 Axes>"
+       "<Figure size 1000x100 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -6066,14 +6066,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x72 with 1 Axes>"
+       "<Figure size 1000x100 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -6086,14 +6086,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 1440x72 with 1 Axes>"
+       "<Figure size 2000x100 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -6106,7 +6106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -6115,9 +6115,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x72 with 1 Axes>"
+       "<Figure size 1000x100 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -6130,14 +6130,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 576x72 with 1 Axes>"
+       "<Figure size 800x100 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -6150,14 +6150,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x72 with 1 Axes>"
+       "<Figure size 1000x100 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -6183,7 +6183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6192,9 +6192,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -6221,7 +6221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6230,14 +6230,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x432 with 3 Axes>"
+       "<Figure size 600x600 with 3 Axes>"
       ]
      },
      "metadata": {},
@@ -6269,7 +6269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 103,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -6292,7 +6292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 104,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -6382,7 +6382,7 @@
        "16                      0.70              7.95         142.81  "
       ]
      },
-     "execution_count": 108,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6393,7 +6393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 105,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -6403,9 +6403,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 864x288 with 1 Axes>"
+       "<Figure size 1200x400 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -6431,7 +6431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6581,183 +6581,440 @@
        "      <th>2</th>\n",
        "      <th>4</th>\n",
        "      <td>5</td>\n",
-       "      <td>164.16</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": 106,
+     "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": 107,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 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": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "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": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Grouping\n",
+    "\n",
+    "* Group a frame by common values of column(s)\n",
+    "* Use operations on this group\n",
+    "* Grouped frame is not _directly_ a new frame, but only through an applied operation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: [8, 16, 16, 24, 32, 48], 2: [16, 32, 32, 48, 64, 96], 3: [24, 48, 48, 72, 96, 144], 4: [32, 64, 64, 96, 128, 192], 5: [40, 80, 80, 120, 160, 240], 6: [48, 96, 96, 144, 192, 288]}"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby(\"Nodes\").groups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "editable": true,
+    "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>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>Unaccounted 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",
+       "      <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>32</th>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>66.58</td>\n",
+       "      <td>10</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>18.86</td>\n",
+       "      <td>19.65</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.35</td>\n",
+       "      <td>43.38</td>\n",
+       "      <td>47361344.0</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>1.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>34.09</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>0.14</td>\n",
+       "      <td>10.60</td>\n",
+       "      <td>10.83</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.25</td>\n",
+       "      <td>20.96</td>\n",
+       "      <td>47074752.0</td>\n",
+       "      <td>818198</td>\n",
+       "      <td>7.33</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",
+       "      <td>1.03</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",
+       "      <th>64</th>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>32.49</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>0.09</td>\n",
+       "      <td>9.98</td>\n",
+       "      <td>10.31</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>20.12</td>\n",
+       "      <td>48081056.0</td>\n",
+       "      <td>818198</td>\n",
+       "      <td>7.33</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",
+       "      <td>1.09</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>3 rows \u00d7 22 columns</p>\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",
+       "         id  Nodes  Tasks/Node  Threads/Task  Runtime Program / s  Scale  \\\n",
+       "Threads                                                                    \n",
+       "32        5      4           2             4                66.58     10   \n",
+       "64        5      4           2             8                34.09     10   \n",
+       "64        5      4           4             4                32.49     10   \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",
+       "         Plastic  Avg. Neuron Build Time / s  Min. Edge Build Time / s  \\\n",
+       "Threads                                                                  \n",
+       "32          True                        0.13                     18.86   \n",
+       "64          True                        0.14                     10.60   \n",
+       "64          True                        0.09                      9.98   \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",
+       "         Max. Edge Build Time / s  ...  Presim. Time / s  Sim. Time / s  \\\n",
+       "Threads                            ...                                    \n",
+       "32                          19.65  ...              2.35          43.38   \n",
+       "64                          10.83  ...              1.25          20.96   \n",
+       "64                          10.31  ...              1.12          20.12   \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",
+       "         Virt. Memory (Sum) / kB  Local Spike Counter (Sum)  \\\n",
+       "Threads                                                       \n",
+       "32                    47361344.0                     821491   \n",
+       "64                    47074752.0                     818198   \n",
+       "64                    48081056.0                     818198   \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",
+       "         Average Rate (Sum)  Number of Neurons  Number of Connections  \\\n",
+       "Threads                                                                 \n",
+       "32                     7.23             112500             1265738500   \n",
+       "64                     7.33             112500             1265738500   \n",
+       "64                     7.33             112500             1265738500   \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",
+       "         Min. Delay  Max. Delay  Unaccounted Time / s  \n",
+       "Threads                                                \n",
+       "32              1.5         1.5                  1.70  \n",
+       "64              1.5         1.5                  1.03  \n",
+       "64              1.5         1.5                  1.09  \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  "
+       "[3 rows x 22 columns]"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_multind = df.set_index([\"Nodes\", \"Tasks/Node\", \"Threads/Task\"])\n",
-    "df_multind.head()"
+    "df.groupby(\"Nodes\").get_group(4).head(3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
-   "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",
+   "execution_count": 110,
    "metadata": {
+    "editable": true,
     "slideshow": {
-     "slide_type": "slide"
-    }
+     "slide_type": "fragment"
+    },
+    "tags": []
    },
-   "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": 112,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -7035,7 +7292,7 @@
        "[6 rows x 21 columns]"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7047,9 +7304,11 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "### Pivoting\n",
@@ -7067,11 +7326,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 111,
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -7080,11 +7341,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 112,
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
@@ -7157,7 +7420,7 @@
        " 0.518282  2.952492       NaN"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7173,18 +7436,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 113,
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -7192,16 +7457,18 @@
     }
    ],
    "source": [
-    "df_pivot.plot();"
+    "df_pivot.plot(figsize=(10,3));"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "slide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "## Task 7\n",
@@ -7216,19 +7483,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 114,
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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8U6cubNq04bpvP77VKTkWERERcUEnT/6Ej48PPj4Fb3oLDn6MWrVqAbBx43qaNbvHvu16zGYz4eFzePnlVzGZCs+0rVXrNmrW9OLkyZ/L7wSqKCXHIiIiIi4oISEBP7+6RcojIzewbdtWwsJmFbv/6tUrCA5+jLvuanzd7fXr1+fcubNOidWV6IE8ERERERdkNBpwc3MrVLZs2dvExe1n6dIV+PsXTZx/64svPqdGDXc+/ng7ly+nAFCzZk0GDSp4u7HJZMJorH7jqEqORURERFxQQEADLl68YP8eGbmBb789wvLlq/D19S1x/w0b/vPA3apV7wHYE2OA8+d/ISCgoRMjdg1KjkVERETKIDvHzI43Q8ul3eI0bXo3qalXycjIwNvbm9Wr/xdvb2/GjBlurxMR8TbJyZdYufJvRES84/Cx09PTycjIoGnTZjcdv6tSciwiIiJSBulpWSWuR1weDAYD/foNZPfuj+nTpz+xsf+4br06de7Az8+/2Laee254oe+7dsXQv/9Ap8XqSqrfRBIRERGRW0SvXn355puvyc7OvmGdpKREunTp5nCbmZmZHD58iJ49+zgjRJejkWMRERERF2UymQgPX1Rsnbp161G3bj2H2/Ty8mLhwsWYTEbMZktZQ3Q5GjkWEREREbFRciwiIiIiYqPkWERERETERsmxiIiIiIiNHsgTERERKYPbb3PH5O7h9HbNuTlcSc0ttk5+fj7Tp0/htdfmkJGRwfz5s7h8OQWj0cCoUeN46KHWxe6/YcM6du7cAUCPHqEMGPA0mZmZzJ07g9dfXwgYnHU6LkPJsYiIiEgZmNw9ODXP+cueNZ62GSg+OY6OjiIwsA2enp4sXDiPdu3a06dPf86ePcPo0cPZunVnkVdM/+rcuQS2bo3i/fcjsVisPP10P9q3D6ZBg4a0ahXI1q2b6dmzr9PPq6rTtAoRERERF2S1WomK2kSnTl0BCA7uSOfOBesZBwQ0JDc3h6ysrBvub7FYyMvLIycnl7y8XKxWKyZTwbhpp05d2LRpA1artfxPpIrRyLGIiIiICzp58id8fHzw8fEBIDj4Mfu2jRvX06zZPfZt1/P73zeiU6eu9O0bgtVqJSQklHr16gNQq9Zt1KzpxcmTP9Os2d3leyJVjEaORURERFxQQkICfn51i5RHRm5g27athIXNKnb/gwfjOHHiONHRu4iO3snx4//H559/at9ev359zp076/S4qzolxyIiIiIuyGg0FJlPvGzZ22zfHs3SpStKfCve/v1f8eijj+Hl5YW3tw+dOnXln//81r7dZDJhNFa/VNGhM16yZAndu3ene/fuLFy4EIBXX32VLl26EBoaSmhoKJ9+WnCnERcXR0hICF26dGHx4sXlF7mIiIhINRYQ0ICLFy/Yv0dGbuDbb4+wfPkq/P2Ljij/t6ZNm7F//z7y8/Mxm818/XUcLVrca99+/vwvBAQ0LJfYq7IS5xzHxcWxb98+tm7disFg4Pnnn+fTTz/l+++/5/3338ff399eNzs7m6lTp7J+/Xrq16/P8OHD2bt3L8HBweV6EiIiIiLVTdOmd5OaepWMjAy8vb1Zvfp/8fb2ZsyY4fY6ERFvk5x8iZUr/0ZExDuF9g8J6cnZs2cYMuQp3NzcCApqT7duPQBIT08nIyODpk2bVeg5VQUlJsd+fn5MmTIFd3d3AJo0acL58+c5f/4806dP5/z583Tu3JnRo0dz7NgxGjVqRMOGBXcZISEhxMbGKjkWERGRW5Y5N8e27Jrz2y2OwWCgX7+B7N79MX369Cc29h/XrVenzh34+fkXKTcajYwZM4ExYyYU2bZrVwz9+w+8ucBdXInJcbNm/7ljOHPmDDt37mTDhg0cOnSI2bNn4+XlxfDhw4mKisLLyws/Pz97fX9/fxITE8snchEREZEqoOBFHcWvR1xeevXqS1jYK3TvHoqnp+d16yQlJdKlSzeH28zMzOTw4UMsXPims8J0KQ4v5fbzzz8zfPhwJk+eTOPGjVm6dKl925AhQ4iOjubxxx8vsp/BULo3q9Spc+MlRyqLn59vlWxLKpb6zrWp/1yb+s91laXvkpKMmEzV74Gw0jCZ3ImIeKvYOgEBdxIQcKfDbdaq5cOiRW+XMbKqw2g0lurn0KHk+MiRI4wdO5apU6fSvXt3Tpw4wZkzZ+jatWDR6V8Xja5bty7Jycn2/ZKSkgrNSXZESkoGFkvFLDjt6IW6dCndacdzVltSsdR3rk3959rUf66rrH1nsVgwmy1OjEhKw2Qy3hLX32KxFPk5NBoNNxyQLfF27MKFC4waNYqIiAi6d+8OFCTD8+fPJzU1lby8PDZt2kTnzp25//77OX36NPHx8eTn5xMTE0OHDh2ccFoiIiIiIuWvxJHjVatWkZOTQ3h4uL1swIABvPjiiwwcOBCz2UyXLl3o0aPg6cbw8HDGjBlDTk4OwcHB151qISIiIiJSFZWYHIeFhREWFnbdbYMHDy5SFhQUxPbt28semYiIiIhIBXP4gTwRERERKcq3tgeeNdyd3m52Xi7pV4tfzk2cT8mxiIiISBl41nDnqU0jnd5uZP/lpFN8cpyfn8/06VN47bU5ZGRkMH/+LC5fTsFoNDBq1Dgeeqh1ice5dCmJ558fwrZtu+1lq1ev4B//+AyrFdq2bcdf//oSmZmZzJ07gzlzwou8tvpWovVRRERERFxUdHQUgYFt8PT0ZNmyt2nXrj1r1mxg5sx5zJoVRn5+frH7HziwjzFjRpCSkmIv++abr/nmm4OsW7eRNWs2cOLEj+zd+w+8vLxo1SqQbdu2lPdpVSolxyIiIiIuyGq1EhW1iU6dCpbWDQ7uSOfOBS/7CAhoSG5uDllZWcW2EROzjfnzFxYqq1PnDkaNGk+NGjUwmUw0avQHEhMvAtCpUxciIzditVbMsruVQdMqRERERFzQyZM/4ePjg49PwXq9wcGP2bdt3LieZs3usW+7kXnz3ihS1rhxE/vnhISz7NnzKcuXrwagVq3b8PKqycmTP9Os2d3OOI0qRyPHIiIiIi4oISEBP7+6RcojIzewbdtWwsJmlan9U6f+zfjxoxg1ahwNG/7eXl63bn3OnTtbprarMo0ci4iIiLggo9FQ5MG4ZcveJi5uP0uXrsDfv2ji7Khjx/5JWNhkxo6dYJ+28SuTyYTReOuOryo5FhEREXFBAQENuHjxgv17ZOQGvv32CMuXr8LX1/em201MvMjUqS8zd244LVu2KrL9woXzBAQ0vOn2qzolxyIiIiJlkJ2XS2T/5eXSbnGaNr2b1NSrZGRk4O3tzerV/4u3tzdjxgy314mIeJvk5EusXPk3IiLecei4Gze+T05OLm+/vYhfn7vr2bM3PXv2JT09nYyMDJo2bXbT51XVKTkWERERKYP0qzklrkdcHgwGA/36DWT37o/p06c/sbH/uG69OnXuwM/Pv9i29u07bP88btzLjBv3MiaTEbPZUqjerl0x9Os3oOzBV2G37oQRERERkVtcr159+eabr8nOzr5hnaSkRLp06VbmY2VmZnL48CFCQ3uXua2qTCPHIiIiIi7KZDIRHr6o2Dp169ajbt16ZT6Wl5cXCxcuLnM7VZ1GjkVEREREbJQci4iIiIjYKDkWEREREbFRciwiIiIiYqPkWERERKQMbvd1x8/P1+n/3e7rXuKx8/PzmTp1UqHVKjIzr/HUU6F8++3hYvYsbOnSt5k3byYAFouFV199mczMzFJfi1uBVqsQERERKQOTpwf7Q/s4vd122zZDevEvAomOjiIwsA2enp72skWLFpKenu7wcQ4fPsSuXTsICmoPgNFo5Mkne7Jq1QpGjhx7c8G7MI0ci4iIiLggq9VKVNQmOnXqai/7/PNP8PLyokmTpg61kZaWyooVyxgy5JlC5YGBQezdu4dr1zKcGrMrUHIsIiIi4oJOnvwJHx8ffHx8ALh48SKRkRsZNeolh9tYuHA+L774V3x9axUqd3Nzo0mTZqWamnGrUHIsIiIi4oISEhLw86sLFMwTDg+fzfjxr+Dh4VnCngV27Iimbt26tGoVeN3t9evXJyEhwWnxugrNORYRERFxQUajATc3NwDi489w9mw84eFzAPjllwQWLJjL5MlhPPhgq+vu//nnn5CSksywYYNIS0slKyuLd955k7FjJwLg5mbCaDRUzMlUIUqORURERFxQQEADLl68AMBddzVmy5aP7dtGj36RZ5998YaJMcBbby2zf965cwfffXfEnhgDXLhwnv/5n/vLIfKqTdMqRERERFxQ06Z3k5p6lYyM4h+aS06+xLBhg0rVdn5+PidO/EirVg+XJUSXpJFjERERkTIwZ+cULLtWDu0Wx2Aw0K/fQHbv/pg+ffoX2rZkyQr75zvu8OOee5oX29YTT4TwxBMh9u9xcV/x6KMdCy0RV104lBwvWbKEXbt2ARAcHMwrr7xCXFwcr7/+Ojk5OXTr1o3x48cDcPz4ccLCwsjIyKBVq1bMmjULk0k5uIiIiNyarqTnlrgecXnp1asvYWGv0L176A0T2aysLNq2fcThNi0WCzEx25g9e56zwnQpJU6riIuLY9++fWzdupXo6Gh++OEHYmJimDp1KsuWLWPnzp18//337N27F4BJkyYxffp0du/ejdVqJTIystxPQkRERKQ6MplMhIcvKnaEt2bNmgQHd3S4TaPRyIIFi/H29nFGiC6nxOTYz8+PKVOm4O7uTo0aNWjSpAlnzpyhUaNGNGzYEJPJREhICLGxsfzyyy9kZ2fTsmVLAHr37k1sbGx5n4OIiIiIiFOUON+hWbNm9s9nzpxh586dDBkyBD8/P3u5v78/iYmJJCUlFSr38/MjMTGxVAHVqVP17lL8/HyrZFtSsdR3rk3959rUf66rLH2XlGTEZNLaAZXpVrj+RqOxVD+HDk8G/vnnnxk+fDiTJ0/GZDJx+vTpQtsNBgNWq7XIfgZD6dbHS0nJwGIp2k55cPRCXbrk+PvJSzqes9qSiqW+c23qP9em/nNdZe07i8WC2WxxYkRSGiaT8Za4/haLpcjPodFouOGArEO3A0eOHGHYsGFMnDiRXr16UbduXZKTk+3bk5KS8Pf3L1J+6dIl/P39b+Y8REREREQqXIkjxxcuXGDUqFEsXryYoKAgAO6//35Onz5NfHw8DRo0ICYmhj59+hAQEICHhwdHjhzhoYceIjo6mg4dOpT7SYiIiIhUlttq1cTdw/krc+XmmElNy3J6u1K8Enty1apV5OTkEB4ebi8bMGAA4eHhjBkzhpycHIKDg3n88ccBiIiIICwsjGvXrnHvvfcydOjQ8oteREREpJK5e5iYPTHG6e2+9maPEuvk5+czffoUXnttjn3FiszMawwbNogpU6YX+4Y8gN27d7J+/RoA2rRpy+jR47BYLEyb9gqzZs3F3V3rHBcRFhZGWFjYdbdt3769SFnz5s2Jiooqe2QiIiIiUqzo6CgCA9sUWspt0aKFpKeXPNc7Ozubt96KYOPGLfj4+DBy5HN8883XtG79ME8+2ZNVq1YwcuTY8gy/SnL9RxBFREREqiGr1UpU1CY6depqL/v880/w8vKiSZOmJe5vseRjtVrIzs4iP99Mfr4ZDw8PAAIDg9i7dw/XrhX/aupbkZJjERERERd08uRP+Pj44ONTsOrCxYsXiYzcyKhRLzm0v5eXN88/P4JBg/rSs+cT1Kt3J//zP/cD4ObmRpMmzfj228PlFn9VpeRYRERExAUlJCTg51cXKFiuLDx8NuPHv4KHh2PzhE+e/JmPP97O5s072LYtFqPRyMaN6+3b69evT0JCQrnEXpUpORYRERFxQUajATc3NwDi489w9mw84eFzGDZsECdOHGfBgrnFjvweOnSAhx4K5Pbbf4e7uztPPBHCd98dsW93czNhNJbufRW3AuevOyIiIiIi5S4goAEXL14A4K67GrNly8f2baNHv8izz75Y7GoVTZvezbJl75CVlYWnpyf7939J8+b32rdfuHDePs2iOlFyLCIiIlIGuTlmh5Zdu5l2i9O06d2kpl4lIyPDPu/4epKTL/Hyyy+xZs2GQuWBgW346acfee65pzGZTLRo8UeefnoYULBE3IkTPzJ16syynobLUXIsIiIiUgaV9aIOg8FAv34D2b37Y/r06V9o25IlK+yf77jDj3vuaX7dNp5+epg9If6tuLivePTRjoWWiKsuNOdYRERExEX16tWXb775muzs7BvWycrKom3bRxxu02KxEBOzjWeffcEZIbocjRyLiIiIuCiTyUR4+KJi69SsWZPg4I4Ot2k0GlmwYDEmkxGz2VLWEF2ORo5FRERERGyUHIuIiIiI2Cg5FhERERGxUXIsIiIiImKj5FhERESkDG6r5Y6fn6/T/7utlnuJx87Pz2fq1EmFVqvIzLzGU0+FFvt2vN+6di2DIUOe4sKF8/aybdu2MGhQP4YO7c/8+bPIy8vDYrHw6qsvk5mZWfqL5EK0WoWIiIhIGbh7eLDk1Wec3u7o1/8O5BZbJzo6isDANoXWI160aCHp6ekOHeOHH75n4cK5JCSctZedPRvPxo3rWbv2A9zdazJv3ky2bImkf//BPPlkT9as+V/++teXbuqcXIFGjkVERERckNVqJSpqE506dbWXff75J3h5edGkSVOH2tixYysTJkzmjjv87GXu7u5MnDgFb28fDAYDjRs3JTHxIgCBgUHs3fsPrl3LcO7JVCFKjkVERERc0MmTP+Hj42N/dfTFixeJjNzIqFGOj+pOmTKd++9/oFBZvXr1ad36YQCuXLnCli2RtG8fDICbmxtNmjRzeMqGK1JyLCIiIuKCEhIS8POrCxS81S48fDbjx7+Ch4dzXvmclJTEuHEj6dEjlAcfbGUvr1evHgkJCU45RlWk5FhERETEBRmNBtzc3ACIjz/D2bPxhIfPYdiwQZw4cZwFC+be9AhvfPwZXnzxGR5/vAfDhj1faJvJZMJoNJQ5/qpKD+SJiIiIuKCAgAZcvHgBgLvuasyWLR/bt40e/SLPPvtioRFfR2VmXmP8+FGMHDmazp27Fdl+/vx57ruv5U3HXdVp5FhERETEBTVtejepqVfJyCj+4bjk5EsMGzbI4XZ37IjmypXLbNiwnmHDBjFs2CBWrvwbULB03E8//UirVg+XKfaqTCPHIiIiImWQm5NjW3bN+e0Wx2Aw0K/fQHbv/pg+ffoX2rZkyQr75zvu8OOee5oX21ZU1A775/79B9O//2BMJiNms6VQvbi4rwgOfqzQ0nG3GiXHIiIiImWQmpZLSesRl5devfoSFvYK3buH3jBhzcrKom3bR8p8LIvFQkzMNl57bU6Z26rKlByLiIiIuCiTyUR4+KJi69SsWZPg4I5lPpbRaGTBgsVlbqeqc3jOcUZGBj169ODcuXMAvPrqq3Tp0oXQ0FBCQ0P59NNPAYiLiyMkJIQuXbqwePGtfwFFRERE5Nbh0Mjx0aNHCQsL48yZM/ay77//nvfffx9/f397WXZ2NlOnTmX9+vXUr1+f4cOHs3fvXoKDg50euIiIiNz6rFYrBsOtu2yYlC+r1VrqfRwaOY6MjGTGjBn2RDgzM5Pz588zffp0QkJCeOedd7BYLBw7doxGjRrRsGFDTCYTISEhxMbGljooEREREZPJnWvX0m4qwRGxWq1cu5aGyeReqv0cGjmeN29eoe8pKSm0adOG2bNn4+XlxfDhw4mKisLLyws/v/+8m9vf35/ExMRSBVSnjk+p6lcEPz/fKtmWVCz1nWtT/7k29Z/rKkvf1a7tSUJCApcunXNiRFKd1KzpSdOmd1GjRg2H97mpB/IaNmzI0qVL7d+HDBlCdHQ0jz/+eJG6pf1TSEpKBhZLxdwhOvoLe+lSutOO56y2pGKp71yb+s+1qf9clzP6ztfXD1/dG1WKW+V37+rVbCC7UJnRaLjhgOxNvQTkxIkT7N692/7darViMpmoW7cuycnJ9vKkpKRCc5JFRERERKqym0qOrVYr8+fPJzU1lby8PDZt2kTnzp25//77OX36NPHx8eTn5xMTE0OHDh2cHbOIiIiISLm4qWkVzZs358UXX2TgwIGYzWa6dOlCjx49AAgPD2fMmDHk5OQQHBx83akWIiIiIiJVUamS4z179tg/Dx48mMGDBxepExQUxPbt28seWRVhMec6NDfZnJvDldTi345jyS25LXN2DlfSK+ctOyIiIiLVnd6QVwKjyZ1T8/qUWK/xtM2U9OpIo7s7+0OLb6vdts2g5FhERESkUtzUnGMRERERkVuRkmMRERERERslxyIiIiIiNkqORURERERslByLiIiIiNgoORYRERERsVFyLCIiIiJio+RYRERERMRGybGIiIiIiI2SYxERERERGyXHIiIiIiI2So5FRERERGyUHIuIiIiI2Cg5FhERERGxUXIsIiIiImKj5FhERERExEbJsYiIiIiIjamyAxC5Wb61auLpUfyPcHaOmfS0rAqKSERERFydkmNxWZ4eJkImbiu2zo43Q0mvoHhERETE9WlahYiIiIiIjZJjEREREREbJcciIiIiIjZKjkVEREREbBxKjjMyMujRowfnzp0DIC4ujpCQELp06cLixYvt9Y4fP06fPn3o2rUr06ZNw2w2l0/UIiIiIiLloMTk+OjRowwcOJAzZ84AkJ2dzdSpU1m2bBk7d+7k+++/Z+/evQBMmjSJ6dOns3v3bqxWK5GRkeUavIiIiIiIM5WYHEdGRjJjxgz8/f0BOHbsGI0aNaJhw4aYTCZCQkKIjY3ll19+ITs7m5YtWwLQu3dvYmNjyzV4ERERERFnKnGd43nz5hX6npSUhJ+fn/27v78/iYmJRcr9/PxITEx0YqhVW25+Hn5+vpUdhoiIiIiUQalfAmK1WouUGQyGG5aXVp06PqXepypwd6vBU5tGFlsnsv9yh9pSku1czrqe6hfXpv5zbeo/16W+c23Vsf9KnRzXrVuX5ORk+/ekpCT8/f2LlF+6dMk+FaM0UlIysFiKJtrloap2+KVLeqebIxztP2dcTz8/X/WLC1P/uTb1n+tS37m2W7n/jEbDDQdkS72U2/3338/p06eJj48nPz+fmJgYOnToQEBAAB4eHhw5cgSA6OhoOnToULbIRUREREQqUKlHjj08PAgPD2fMmDHk5OQQHBzM448/DkBERARhYWFcu3aNe++9l6FDhzo9YBERERGR8uJwcrxnzx7756CgILZv316kTvPmzYmKinJOZCIiIiIiFUxvyBMRERERsVFyLCIiIiJio+RYRERERMRGybGIiIiIiI2SYxERERERGyXHIiIiIiI2So5FRERERGyUHIuIiIiI2Cg5FhERERGxUXIsIiIiImKj5FhERERExEbJsYiIiIiIjZJjEREREREbJcciIiIiIjZKjkVEREREbJQci4iIiIjYKDkWEREREbFRciwiIiIiYmOq7ABEpPrxrVUTT4/i//nJzjGTnpZVQRGJiIgUUHIsIhXO08NEyMRtxdbZ8WYo6RUUj4iIyK80rUJERERExEYjx1WMOS8fPz/fYuvk5phJ1Z+bRURERJxOyXEVY6rhxuyJMcXWee3NHhUUjYiIiEj1omkVIiIiIiI2ZRo5Hjp0KCkpKZhMBc3Mnj2bs2fPsnz5cvLy8hg2bBiDBw92SqAiIiIiIuXtppNjq9XKqVOn+OKLL+zJcWJiIuPHj2fLli24u7szYMAAHn74YZo2beq0gEVEREREystNJ8enTp3CYDDwwgsvkJKSwlNPPYW3tzdt2rShdu3aAHTt2pXY2FhGjx7trHhFSsVizi3xAUdzbg5XUnOLbye35HYAzNk5XEkvvi0RERGpum46OU5LSyMoKIiZM2eSnZ3N0KFD6datG35+fvY6/v7+HDt2zCmBitwMo8mdU/P6FFun8bTNQPEJrdHdnf2hxbcD0G7bZlByLCIi4rJuOjl+4IEHeOCBBwDw8vKib9++vP7664wYMaJQPYPBUKp269TxudmQqhVHRjHFcc68nuob51G/yK/Uf65LfefaqmP/3XRyfPjwYfLy8ggKCgIK5iAHBASQnJxsr5OUlIS/v3+p2k1JycBisd5sWKXiyh1+6ZLeHebM/ivpepbmWOqbkjl6PZ11Lf38fNUvLkz957rUd67tVu4/o9FwwwHZm17KLT09nYULF5KTk0NGRgZbt27ljTfe4MCBA1y+fJmsrCw++eQTOnTocNOBi4iIiIhUpJseOe7YsSNHjx6lZ8+eWCwWBg0axEMPPcT48eMZOnQoeXl59O3bl/vuu8+Z8YqIiIiIlJsyrXM8btw4xo0bV6gsJCSEkJCQsjQrIiJVmG+tmnh6FP+/j+wcM+l6zb2IuCC9PlpERErF08NEyMRtxdbZ8WYot+ZMRRG51en10SIiIiIiNkqORURERERslByLiIiIiNhozrFUe7n5eS695rWIiIg4j5JjF2TOcyyZy83JITVNrzIuibtbDZ7aNLLYOpH9l1dQNCIiIlKZlBy7IFONGix59ZkS641+/e+AkmMRESngyDJ8oKX4pHpTciwiIlJNOLIMH2gpPqnelByLSJVkMec6NH3InJvDlVT9hURERJxDybGIVElGkzun5vUpsV7jaZspafqQJbfkRNucncOVdCXZIiLVnZJjEbnlGd3d2R9afKLdbttmUHIsIlLtKTkWERERcQGOPFCphynLTsmxiIiIiAtw5IFKPUxZdkqORZzInJdf4tzW3BwzqbqrFxERqZKUHIs4kamGG7MnxhRb57U3e1RQNNWD3nBYNTmy2ohWGhGRqkjJsYi4NL3hsGpyZLURR1YaAa02IiIVS8mxiIhUaVptREQqkpJjERERKUTTYqQ6U3IsUsHMeSXPkc3NySE1Tf/TEZHK4axpMY5MiQFNi5GqRcmxSAUz1ajBklefKbbO6Nf/jiNzMcV5HFlpBLTaiDPpYcpbnyNTYkDTYpzJkVF/0Mh/cZQci4jg2EojoNVGnMmRhylBD1SKlIYjo/7gvJH/W3HUX8mxiIi4PK0xXvE08u/aHO2/6vgwrJJjEZFS0JzxqklrjFc8Zy6jqJubiqdlMG9MybGISClozrjrcuTGBnRzUxl0c+O6bsUbGyXHIiJSLThyYwO6uamq9FebqulWvLEpl+R4x44dLF++nLy8PIYNG8bgwYPL4zAiIiJSTeivNq7L1f5q4/TkODExkcWLF7Nlyxbc3d0ZMGAADz/8ME2bNnX2oURERESkinO1v9o4PTmOi4ujTZs21K5dG4CuXbsSGxvL6NGjHdrfaDQ4O6Ri+d9es8Q6ptv8HGrLz+t3Jdbx8C+5rdsciMm3dh2HYqro61nRnNV/zuo7cF7/qe+q5u8eqP+gYn/3QP92OpMjfQf6t7OqctV/O6va715xxzFYrVarMw/23nvvkZmZyfjx4wH46KOPOHbsGHPmzHHmYUREREREnM7o7Aavl2sbDLf2XZyIiIiI3BqcnhzXrVuX5ORk+/ekpCT8/f2dfRgREREREadzenLctm1bDhw4wOXLl8nKyuKTTz6hQ4cOzj6MiIiIiIjTOf2BvLp16zJ+/HiGDh1KXl4effv25b777nP2YUREREREnM7pD+SJiIiIiLgqp0+rEBERERFxVUqORURERERslByLiIiIiNgoORYRERERsVFyLCIiIiJio+RY5Do+++wz1q9fz9mzZwuVb9q0qZIiktI4c+YMiYmJQMEr7OfOncvOnTsrOSq5GeHh4ZUdgjjo2LFj9s8HDhwgPDyciIgIjh49WolRiaO++uor0tLSAIiOjmb27Nls3ry5kqOqHFrKTeS/RERE8P3339OkSRN27drF5MmTCQ0NBaBXr15s3bq1kiOU4qxZs4b169djsVho06YNFy5coHPnzuzZs4cHH3yQUaNGVXaIcgOvvvpqkbI9e/bw2GOPAfD6669XdEhSCr/++/jBBx/w4Ycf0qdPHwC2bt1Kv379ePrppys5QrmRefPmcfz4cRYvXswHH3zAv/71L/785z/z5Zdf0qBBA8LCwio7xArl9JeASIHz588Xu/3OO++soEiktPbu3cvWrVsxmUwMGTKEZ599Fnd3d7p164buJau+zZs3s3PnTpKTk+nRowcHDx7Ew8ODfv360bdvXyXHVVjt2rWJjo5mxIgR1KpVC4CDBw8SGBhYyZFJaURGRrJu3Tpuv/12APr27Uvfvn2VHFdh+/fvZ8eOHbi5ufHFF18QGRmJu7s7/fv3p0ePHpUdXoVTclxOhg8fzpkzZ/D39y+SUBkMBj7//PNKikxKYrVaMRgMAPzhD3/gvffe45lnnuF3v/udvVyqLovFgru7OwEBATz77LN4eHjYt+Xn51diZFKSyZMn06FDB9566y0mTJjAww8/zNq1a+nVq1dlhyYOMJvNWCwW6tSpg5eXl73c3d0do1GzOKsyT09PUlJS8Pf3p06dOmRmZuLu7k5WVhYmU/VLFTWtopxkZGQwaNAgZsyYwUMPPVTZ4UgpLFmyhLi4OKZMmWJ/9fmRI0cYPXo0ubm5HDlypJIjlOK8/fbbHDp0iHXr1uHm5gbAjz/+SFhYGI8++iijR4+u5AilJFevXmXGjBnceeed7Nu3jx07dlR2SOKAoUOHcvr0aQwGA23btiU8PJwDBw7wxhtv8OijjzJ27NjKDlFuYM+ePcycOZPu3btjNps5ePAgQUFB7Nu3j+eff57evXtXdogVSslxOTp27BgfffQRc+bMqexQpJQOHDiAv78/TZo0sZdduHCB1atXM23atEqMTBzxzTff0Lp1a/v3U6dOkZCQQHBwcCVGJaX10UcfsWvXLlavXl3ZoUgpnDp1irS0NFq2bMmRI0dIT0/n0UcfreywpAQJCQl89tlnxMfHk5+fzx133EHHjh3tg0TViZJjEREREREbTQISEREREbFRciwiIiIiYqPkWESkEp07d4577rmHjz76qFD5qlWrmDJlisPtXL58mXvuucfZ4YmIVDtKjkVEKpnRaGTBggWcPn26skMREan2qt/idSIiVYynpyfPPPMMEydO5MMPP8Td3d2+LT09nVmzZvHjjz9iMBh45JFHmDBhAiaTiU8++YTFixdTs2ZN/vSnPxVq86OPPmLjxo1YLBZq167N9OnTadKkCYcPHyY8PByLxQIUrMnetWvXCj1fEZGqTCPHIiJVwMiRI6lZsyaLFy8uVD537lxq167Njh072Lx5MydOnGD16tUkJyczdepU3n33XbZs2UJAQIB9n0OHDhEdHc0HH3xAdHQ0zz//PGPGjAHg3Xff5ZlnnmHLli3Mnz+fgwcPVuh5iohUdRo5FhGpAoxGI2+88Qa9evWiffv29vIvv/ySjRs3YjAYcHd3Z8CAAaxdu5ZGjRpx991307RpUwD69+/PokWLAPjiiy+Ij49nwIAB9nZSU1O5evUq3bp1Y/bs2ezZs4e2bdsyYcKEij1REZEqTsmxiEgVceeddzJz5kwmT55Mz549AezTH35lsVgwm80YDIZCr6b/7SteLRYLoaGhTJo0yf49KSmJ2267jQEDBtCxY0f279/PV199xZIlS9i+fTu+vr7lf4IiIi5A0ypERKqQbt260aFDB9auXQtA+/bt+eCDD7BareTm5hIZGUnbtm1p1aoVJ0+e5McffwRgy5Yt9jbatWvHxx9/TFJSEgAbN27kL3/5CwADBgzg+PHj9O7dmzlz5pCWlkZqamoFn6WISNWlkWMRkSomLCyMI0eO2D/PnTuXkJAQ8vLyeOSRRxgxYgTu7u5ERETw8ssvU6NGjUKvy37kkUd44YUXePbZZzEYDPj4+LBkyRIMBgMvv/wy8+fP56233sJoNDJ69GgaNGhQWacqIlLl6PXRIiIiIiI2mlYhIiIiImKj5FhERERExEbJsYiIiIiIjZJjEREREREbJcciIiIiIjZKjkVEREREbJQci4iIiIjYKDkWEREREbH5f1VBIlm2N1SMAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<Figure size 864x288 with 1 Axes>"
+       "<Figure size 1200x400 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -7246,6 +7515,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "subslide"
@@ -7266,6 +7536,100 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Pandas 2\n",
+    "\n",
+    "* [Pandas 2.0](https://pandas.pydata.org/docs/dev/whatsnew/v2.0.0.html) was released in April 2023\n",
+    "* Only limited deprecations (i.e. _an upgrade is probably safe_)\n",
+    "* Key new feature: Apache Arrow support (via PyArrow)\n",
+    "* Fine-grained installation options `python3 -m pip install 'pandas[performance, excel]'`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "* Get a reasonably large data source (larger would be better, though)\n",
+    "* Example: [Train stations as provided by Deutsche Bahn](https://download-data.deutschebahn.com/static/datasets/stationsdaten/DBSuS-Uebersicht_Bahnhoefe-Stand2020-03.csv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "data_db = 'db-bahnhoefe.csv'  # source: https://download-data.deutschebahn.com/static/datasets/stationsdaten/DBSuS-Uebersicht_Bahnhoefe-Stand2020-03.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8.97 ms \u00b1 320 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit pd.read_csv(data_db, sep=';')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.54 ms \u00b1 84.8 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit pd.read_csv(data_db, sep=';', engine='pyarrow', dtype_backend='pyarrow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -7276,12 +7640,14 @@
     "\n",
     "* Pandas can read data directly in `tar` form\n",
     "* Pandas can read data directly from online resource\n",
-    "* Let's combine that to an advanced task!"
+    "* Let's combine that to an advanced task!\n",
+    "* It works also with the PyArrow backend (remember to download the online resource when testing; there is no cache!)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "slide"
@@ -7289,7 +7655,7 @@
     "tags": []
    },
    "source": [
-    "## Task 8 (_MATSE Bonus_)\n",
+    "## Task 8 (Super Bonus)\n",
     "<a name=\"task8\"></a>\n",
     "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
     "\n",
@@ -7299,6 +7665,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "fragment"
@@ -7323,6 +7690,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "subslide"
@@ -7332,7 +7700,7 @@
    "source": [
     "```python\n",
     "df_names = pd.read_table('imdb-data/name.basics.tsv.gz', sep=\"\\t\", low_memory=False, na_values=[\"\\\\N\",\"nan\"])\n",
-    "df_ratings = pd.read_table('https://datasets.i\n",
+    "df_ratings = pd.read_table('https://datasets.imdbws.com/title.ratings.tsv.gz', sep=\"\\t\", low_memory=False, na_values=[\"\\\\N\",\"nan\"])\n",
     "                           \n",
     "df_names_i = df_names.set_index('nconst')\n",
     "df_ratings_i = df_ratings.set_index('tconst')\n",
@@ -7351,6 +7719,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "subslide"
@@ -7389,6 +7758,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "slide"
@@ -7396,11 +7766,11 @@
     "tags": []
    },
    "source": [
-    "## Task 8B (B, like <em>B</em>onus)\n",
+    "## Task 8B (<em>B</em>onuseption)\n",
     "<a name=\"task8b\"></a>\n",
     "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
     "\n",
-    "All of the following are unique sub-tasks, which can be done individually\n",
+    "All of the following are ideas for unique sub-tasks, which can be done individually\n",
     "* In addition to Task 8, restrict the top titles to those with more than 10000 votes\n",
     "* For 30 top-rated actors, plot rating vs. age\n",
     "* For 30 top-rated actors, plot rating vs. average runtime of the known-for-titles (using `title.basics.tsv.gz`)"
@@ -7409,6 +7779,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
     },
@@ -7463,9 +7834,11 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "slideshow": {
      "slide_type": "slide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "## Further Reading\n",
@@ -7477,7 +7850,8 @@
     "    * [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)"
+    "    * [Mapping with Matplotlib, Pandas, Geopandas and Basemap in Python](https://towardsdatascience.com/mapping-with-matplotlib-pandas-geopandas-and-basemap-in-python-d11b57ab5dac)\n",
+    "    * [Whats new in Pandas 2](https://towardsdatascience.com/pandas-2-0-a-game-changer-for-data-scientists-3cd281fcc4b4)"
    ]
   }
  ],
@@ -7497,7 +7871,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.11.5"
   },
   "toc-autonumbering": false,
   "toc-showcode": false,
diff --git a/Introduction-to-Pandas--slides.pdf b/Introduction-to-Pandas--slides.pdf
index 5ce15378448f3f10fdbe2dc3a56a2af792bc0add..54482f3026ac140bafc1fa53afd2fa2ff88719da 100644
Binary files a/Introduction-to-Pandas--slides.pdf and b/Introduction-to-Pandas--slides.pdf differ
diff --git a/Introduction-to-Pandas--solution.ipynb b/Introduction-to-Pandas--solution.ipynb
index 117f49a24ccba4def9f81642d2a531b5165a71af..e25301fe351bd4a660664142a2295defc192d6d1 100644
--- a/Introduction-to-Pandas--solution.ipynb
+++ b/Introduction-to-Pandas--solution.ipynb
@@ -11,9 +11,7 @@
    "source": [
     "# Data Analysis and Plotting in Python with Pandas\n",
     "\n",
-    "_Andreas Herten, J\u00fclich Supercomputing Centre, Forschungszentrum J\u00fclich, 14 June 2022_\n",
-    "\n",
-    "_MATSE Version_"
+    "_Andreas Herten, J\u00fclich Supercomputing Centre, Forschungszentrum J\u00fclich, 4 September 2023_"
    ]
   },
   {
@@ -53,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {
     "exercise": "task",
     "slideshow": {
@@ -104,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 26,
    "metadata": {
     "exercise": "task",
     "slideshow": {
@@ -123,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 27,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -193,7 +191,7 @@
        "Favourite Color      violet         gray  "
       ]
      },
-     "execution_count": 31,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -229,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 34,
    "metadata": {
     "exercise": "task"
    },
@@ -257,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 35,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -481,7 +479,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -494,10 +492,12 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "## Task 3\n",
@@ -510,12 +510,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 63,
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
@@ -727,7 +729,7 @@
        "[5 rows x 22 columns]"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -739,7 +741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 64,
    "metadata": {
     "exercise": "solution"
    },
@@ -757,7 +759,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -768,7 +770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 65,
    "metadata": {
     "exercise": "task",
     "slideshow": {
@@ -784,10 +786,12 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "slide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "## Task 4\n",
@@ -804,12 +808,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 71,
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -818,26 +824,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 72,
    "metadata": {
-    "exercise": "solution"
+    "editable": true,
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
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\n",
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TNQc5sUfLwrFHi4iIiFoCIYCiInmaEHXLMT8/HwcPHsTf/vY3PPLII/D398eAAQMQHR1tVFBUHTqo63363//+h4cffhhOTk7o378/Lly4gBMnTqBfv35QKpUYMWIErl27Vqc8bG1toVar9U2pVMLOzs7oNicnpzuGDnbs2BHvvfcepk6dCqVSCX9/f2zduhXXrl3D2LFjoVQqERISgpMnTxo93qFDh/S5+/r6Ys6cOSgqKqo1x5KSEuzZs8dkoXXq1Ck88sgjcHV1hZubG/r27XvH41o7FloWTjfFe0pK3T8EiIiIiKxNcTGgVMrTiovrlqNSqYRSqcTmzZtRWlpar+17++238eabb+Knn36CnZ0dnn76abz66qv47LPPcPDgQaSlpWHRokUNeObqZ8WKFRg4cCB+/vlnjBo1Cs8++yymTp2KZ555Bj/99BM6d+6MqVOnQvzxxTM9PR2RkZGYMGECUlJSsH79ehw6dAizZ8+u9XHi4uLQvn17dO/evcblU6ZMQYcOHXDixAkkJSXhtddeg729vdm3V04stCxc9+6AnZ006+DVq3JnQ0RERNRy2dnZISYmBmvWrIFKpcLAgQPx+uuvIyUl5a73XbBgASIiItCjRw/89a9/RVJSEt566y0MHDgQ999/P2bMmIH9+/c3+jaMHDkSzz//PLp27YpFixZBo9Ggf//+mDhxIgIDA7Fw4UKcP38eubm5AIClS5diypQpmDt3Lrp27YoHH3wQK1euxH/+8x+UlJSYfJy7DRvMzMxEeHg4unfvjq5du2LixIno1auX2bdXTiy0LJyDA9Ctm3SZwweJiIiouXJ2BgoL5WnOznXPc8KECcjKysLWrVsRGRmJ+Ph49OnTBzExMbXeL0Q3TAmAl5cXACBYd4zIH7fl5eXV6zlriLrkAUCfy6lTpxATE6PvzVMqlYiIiIBWq0VGRkaNjyGEwLZt22ottObPn48///nPCA8Px4cffoj09PR73jZLw0LLCuhe+3X4sYSIiIjIKikUgIuLPE2hqF+urVq1wqOPPoq33noLR44cwfTp0/H222/Xep+qw+IUfzxg9du0Wm39EmmAuuQBQJ9LYWEhnn/+eSQnJ+vbqVOncPHiRXTu3LnGxzh+/DgqKirw4IMPmsxj8eLFOHv2LEaNGoV9+/YhKCgImzZtuuftsyQstKyA7ocH9mgRERERWZ6goKC7Tg5hrfr06YNz586hS5cudzQHB4ca77NlyxaMGjUKtra2ta47MDAQ8+bNw549ezB+/HisXr26MTZBNiy0rABnHiQiIiKS340bNzB06FB8++23SElJQUZGBr7//nssW7YMY8eONetjbdq0yeREEk1p4cKFOHLkCGbPno3k5GRcvHgRW7ZsqXUyjK1bt9Y6bPD27duYPXs24uPjcfnyZRw+fBgnTpxAjx49GmMTZMPzaFkBXaH1yy9AWZl03BYRERERNS2lUonQ0FCsWLEC6enpKC8vh6+vL2bOnInXX3/drI9VUFCA1NRUs66zIUJCQpCQkIA33ngDDz/8MIQQ6Ny5MyZNmlRjfHp6OtLS0hAREWFynba2trhx4wamTp2K3NxctG3bFuPHj8eSJUsaazNkoRCCk4ZrNBq4u7ujoKAAbm5ucqdzByEAlQrQaKTjtKocr0hERERklUpKSpCRkYGAgAC0atVK7nTITJYvX469e/dix44dcqdSq9pef+aqDTh00AooFBw+SERERESWr0OHDoiOjpY7DYvAoYNWIjgYOHyYhRYRERERWa4nn3xS7hQshqw9WkuXLkX//v3h6uoKT09PjBs37o6xqEOGDIFCoTBqL7zwglFMZmYmRo0aBWdnZ3h6euKVV15BRUVFU25Ko2OPFhERERGR9ZC1RyshIQFRUVHo378/Kioq8Prrr2P48OE4d+4cXFxc9HEzZ87EO++8o7/uXOWscpWVlRg1ahTUajWOHDmC7OxsTJ06Ffb29vjggw+adHsak26Kd55Li4iIiIjI8slaaO3atcvoekxMDDw9PZGUlIRBgwbpb3d2doZara5xHXv27MG5c+ewd+9eeHl5oXfv3nj33XexcOFCLF682OT8/tamZ0/p75UrQH6+NDkGERERkbXjvGwkh6Z43VnUZBgFBQUAAA8PD6Pb165di7Zt26Jnz56Ijo5GcXGxflliYiKCg4Ph5eWlvy0iIgIajQZnz56t8XFKS0uh0WiMmqVTqQBfX+nymTOypkJERER0z+zt7QHA6HsdUVPRve50r8PGYDGTYWi1WsydOxcDBw5ET133DYCnn34a/v7+8PHxQUpKChYuXIjU1FRs3LgRAJCTk2NUZAHQX8/JyanxsZYuXWqV8/QHB0s9WikpwEMPyZ0NERERUcPZ2tpCpVIhLy8PgDSCSaFQyJwVNXdCCBQXFyMvLw8qlQq2traN9lgWU2hFRUXhzJkzOHTokNHts2bN0l8ODg6Gt7c3hg0bhvT0dHTu3LlBjxUdHY358+frr2s0GvjquossWEgIsGMHJ8QgIiKi5kF3aIiu2CJqKiqVyuShSeZiEYXW7NmzsX37dhw4cAAdOnSoNTY0NBQAkJaWhs6dO0OtVuP48eNGMbm5uQBg8slzdHSEo6OjGTJvWpx5kIiIiJoThUIBb29veHp6ory8XO50qIWwt7dv1J4sHVkLLSEEXnrpJWzatAnx8fEICAi4632Sk5MBAN7e3gCAsLAwvP/++8jLy4OnpycAIDY2Fm5ubggKCmq03OVQtdASQjqRMREREZG1s7W1bZIvvkRNSdZCKyoqCuvWrcOWLVvg6uqqP6bK3d0dTk5OSE9Px7p16zBy5Ei0adMGKSkpmDdvHgYNGoSQP+Y7Hz58OIKCgvDss89i2bJlyMnJwZtvvomoqCir7LWqTbdugL09oNEAmZmAv7/cGRERERERUU1knXVw1apVKCgowJAhQ+Dt7a1v69evBwA4ODhg7969GD58OLp3746XX34ZEyZMwLZt2/TrsLW1xfbt22Fra4uwsDA888wzmDp1qtF5t5oLBwege3fpMocPEhERERFZLtmHDtbG19cXCQkJd12Pv78/duzYYa60LFpwsFRknT4NjB4tdzZERERERFQTizqPFt2d7jitlBR58yAiIiIiItNYaFmZPw5N49BBIiIiIiILxkLLyuh6tFJTgbIyeXMhIiIiIqKasdCyMh06AO7uQEUF8MsvcmdDREREREQ1YaFlZRQKw/BBHqdFRERERGSZWGhZoaonLiYiIiIiIsvDQssKsdAiIiIiIrJsLLSsEAstIiIiIiLLxkLLCvXsKf29ehX4/Xd5cyEiIiIiojux0LJC7u6Av790mb1aRERERESWh4WWleLwQSIiIiIiy8VCy0rpCi1O8U5EREREZHlYaFkp3bm02KNFRERERGR5WGhZKV2P1pkzgBDy5kJERERERMZYaFmpwEDA3h64dQu4fFnubIiIiIiIqCoWWlbK3h4ICpIu8zgtIiIiIiLLwkLLinHmQSIiIiIiy8RCy4qx0CIiIiIiskwstKwYCy0iIiIiIsvEQsuK6aZ4T00FSkvlzYWIiIiIiAxYaFkxHx+gdWugshI4f17ubIiIiIiISIeFlhVTKDh8kIiIiIjIEslaaC1duhT9+/eHq6srPD09MW7cOKSmphrFlJSUICoqCm3atIFSqcSECROQm5trFJOZmYlRo0bB2dkZnp6eeOWVV1BRUdGUmyIbXaHFKd6JiIiIiCyHrIVWQkICoqKicPToUcTGxqK8vBzDhw9HUVGRPmbevHnYtm0bvv/+eyQkJCArKwvjx4/XL6+srMSoUaNQVlaGI0eOYM2aNYiJicGiRYvk2KQmpztOiz1aRERERESWQyGEEHInoXPt2jV4enoiISEBgwYNQkFBAdq1a4d169bhiSeeAAD88ssv6NGjBxITE/HAAw9g586dGD16NLKysuDl5QUA+PLLL7Fw4UJcu3YNDg4Od31cjUYDd3d3FBQUwM3NrVG30dwSE4EHH5SO1/rtN7mzISIiIiKybuaqDSzqGK2CggIAgIeHBwAgKSkJ5eXlCA8P18d0794dfn5+SExMBAAkJiYiODhYX2QBQEREBDQaDc6ePVvj45SWlkKj0Rg1a9Wzp/Q3Kwu4cUPeXIiIiIiISGIxhZZWq8XcuXMxcOBA9PyjesjJyYGDgwNUKpVRrJeXF3JycvQxVYss3XLdsposXboU7u7u+ubr62vmrWk6rq5AQIB0mcMHiYiIiIgsg8UUWlFRUThz5gy+++67Rn+s6OhoFBQU6NuVK1ca/TEbE2ceJCIiIiKyLBZRaM2ePRvbt2/H/v370aFDB/3tarUaZWVlyM/PN4rPzc2FWq3Wx1SfhVB3XRdTnaOjI9zc3IyaNWOhRURERERkWWQttIQQmD17NjZt2oR9+/YhQDcG7g99+/aFvb094uLi9LelpqYiMzMTYWFhAICwsDCcPn0aeXl5+pjY2Fi4ubkhKCioaTZEZiy0iIiIiIgsi52cDx4VFYV169Zhy5YtcHV11R9T5e7uDicnJ7i7u2PGjBmYP38+PDw84ObmhpdeeglhYWF44IEHAADDhw9HUFAQnn32WSxbtgw5OTl48803ERUVBUdHRzk3r8lUneJdqwVsLKKfkoiIiIio5WrQV/Jdu3bh0KFD+utffPEFevfujaeffhq///57ndezatUqFBQUYMiQIfD29ta39evX62NWrFiB0aNHY8KECRg0aBDUajU2btyoX25ra4vt27fD1tYWYWFheOaZZzB16lS88847Ddk0q9S1K+DoCBQVAZcuyZ0NERERERE16DxawcHB+Nvf/oaRI0fi9OnT6N+/P+bPn4/9+/eje/fuWL16dWPk2mis+TxaOvffDyQnA5s3A2PHyp0NEREREZF1kvU8WhkZGfrjn3744QeMHj0aH3zwAb744gvs3LmzwclQw+mGD6akyJsHERERERE1sNBycHBAcXExAGDv3r0YPnw4AOlEw9Z88l9rxgkxiIiIiIgsR4Mmw3jooYcwf/58DBw4EMePH9cfU3XhwgWj6dmp6bDQIiIiIiKyHA3q0fr73/8OOzs7bNiwAatWrUL79u0BADt37kRkZKRZE6S60RVaFy4AJSXy5kJERERE1NI1aDKM5qY5TIYhBNCuHXDjBpCUBPTpI3dGRERERETWR9bJMMjyKBQcPkhEREREZClYaDUjLLSIiIiIiCwDC61mhIUWEREREZFlYKHVjPBcWkRERERElqFehZafnx9mz56NPXv2oKKiorFyoga67z7pb04OcP26vLkQEREREbVk9Sq0vvnmGzg6OiIqKgpt27bFpEmTsHbtWuTn5zdSelQfSiXQqZN0mcMHiYiIiIjkU69Ca/Dgwfjkk09w8eJFHD58GL1798bnn38OtVqNoUOH4tNPP8Wvv/7aWLlSHXD4IBERERGR/Bp8jNZ9992H6OhoHD16FJcuXcJTTz2FuLg49OzZEz179sSPP/5ozjypjjghBhERERGR/OzMsRK1Wo2ZM2di5syZKC4uxu7du+Ho6GiOVVM9sdAiIiIiIpKfWQqtqpydnfH444+be7VUR7pC68wZQKsFbDivJBERERFRk+PX8GamSxegVSuguBjg4XJERERERPJgodXM2NkBQUHSZQ4fJCIiIiKSBwutZojHaRERERERyeueCq20tDTs3r0bt2/fBgAIIcySFN0bFlpERERERPJqUKF148YNhIeHIzAwECNHjkR2djYAYMaMGXj55ZfNmiDVH8+lRUREREQkrwYVWvPmzYOdnR0yMzPh7Oysv33SpEnYtWuX2ZKjhtH1aKWlAX90NhIRERERURNq0PTue/bswe7du9GhQwej27t27YrLly+bJTFqOC8voG1b4Pp14Nw5oG9fuTMiIiIiImpZGtSjVVRUZNSTpXPz5k2eqNgCKBQcPkhEREREJKcGFVoPP/ww/vOf/+ivKxQKaLVaLFu2DI888kid13PgwAGMGTMGPj4+UCgU2Lx5s9Hy6dOnQ6FQGLXIyEijmJs3b2LKlClwc3ODSqXCjBkzUFhY2JDNalY4IQYRERERkXwaNHRw2bJlGDZsGE6ePImysjK8+uqrOHv2LG7evInDhw/XeT1FRUXo1asX/vSnP2H8+PE1xkRGRmL16tX669V7zKZMmYLs7GzExsaivLwczz33HGbNmoV169Y1ZNOaDRZaRERERETyaVCh1bNnT1y4cAF///vf4erqisLCQowfPx5RUVHw9vau83pGjBiBESNG1Brj6OgItVpd47Lz589j165dOHHiBPr16wcA+PzzzzFy5Eh8/PHH8PHxqftGNTMstIiIiIiI5NOgQgsA3N3d8cYbb5gzlxrFx8fD09MTrVu3xtChQ/Hee++hTZs2AIDExESoVCp9kQUA4eHhsLGxwbFjx/D444/XuM7S0lKUlpbqr2s0msbdCBncd590rFZuLpCXB3h6yp0REREREVHL0eBCq6SkBCkpKcjLy4NWqzVa9thjj91zYoA0bHD8+PEICAhAeno6Xn/9dYwYMQKJiYmwtbVFTk4OPKtVEHZ2dvDw8EBOTo7J9S5duhRLliwxS46WysUF6NxZmuL99Glg2DC5MyIiIiIiajkaVGjt2rULU6dOxfXr1+9YplAoUFlZec+JAcDkyZP1l4ODgxESEoLOnTsjPj4ew+6hcoiOjsb8+fP11zUaDXx9fe8pV0sUHMxCi4iIiIhIDg2adfCll17CxIkTkZ2dDa1Wa9TMVWTVpFOnTmjbti3S0tIAAGq1Gnl5eUYxFRUVuHnzpsnjugDpuC83Nzej1hxxinciIiIiInk0qNDKzc3F/Pnz4eXlZe58anX16lXcuHFDP+FGWFgY8vPzkZSUpI/Zt28ftFotQkNDmzQ3S6SbEKPK00NERERERE2gQYXWE088gfj4+Ht+8MLCQiQnJyM5ORkAkJGRgeTkZGRmZqKwsBCvvPIKjh49ikuXLiEuLg5jx45Fly5dEBERAQDo0aMHIiMjMXPmTBw/fhyHDx/G7NmzMXny5BY946DOffdJf1NSgPJyeXMhIiIiImpJFEIIUd87FRcXY+LEiWjXrh2Cg4Nhb29vtHzOnDl1Wk98fHyNJzieNm0aVq1ahXHjxuHnn39Gfn4+fHx8MHz4cLz77rtGPWk3b97E7NmzsW3bNtjY2GDChAlYuXIllEplnbdHo9HA3d0dBQUFzWoYYVERoHsa8vMBd3dZ0yEiIiIisnjmqg0aVGh9/fXXeOGFF9CqVSu0adMGCoXCsEKFAr/++muDE5JDcy20SkoAJyfpskYDuLrKmw8RERERkaUzV23QoFkH33jjDSxZsgSvvfYabGwaNPqQiIiIiIio2WpQlVRWVoZJkyaxyCIiIiIiIqpBgyqladOmYf369ebOhYiIiIiIqFlo0NDByspKLFu2DLt370ZISMgdk2EsX77cLMkRERERERFZowYVWqdPn8b9998PADhz5ozRsqoTYxAREREREbVEDSq09u/fb+48iIiIiIiImg3OZkFERERERGRmde7RGj9+PGJiYuDm5obx48fXGrtx48Z7ToyIiIiIiMha1bnQcnd31x9/5e7u3mgJERERERERWTuFEELUNfidd97BggUL4Ozs3Jg5NTlznf3Z0pSUAE5O0mWNBnB1lTcfIiIiIiJLZ67aoF7HaC1ZsgSFhYUNfjAiIiIiIqKWoF6FVj06v4iIiIiIiFqses86yPNkERERERER1a7e59EKDAy8a7F18+bNBidERERERERk7epdaC1ZsoSzDhIREREREdWi3oXW5MmT4enp2Ri5EBERERERNQv1OkaLx2dZryVLgLw8ubMgIiIiImoZOOtgM+boCAwfLl3+5BMgIABYsADIyZE3LyIiIiKi5q5ehZZWq+WwQSuiUAC7dgHbtgH9+wPFxYaCa+5cICtL7gyJiIiIiJqnek/vTtZFoQBGjwaOHQN27gQeeAAoKQE++wzo1Al46SXg6lW5syQiIiIial5YaLUQCgUQGQkcOQLs2QMMHAiUlgJ//zvQuTPw4otAZqbcWRIRERERNQ8stFoYhQJ49FHg4EEgLg4YPBgoKwO+/BLo0gWYNQu4dEnuLImIiIiIrBsLrRZKoQCGDgXi46U2dChQXg78619A167AjBlAerrcWRIRERERWSdZC60DBw5gzJgx8PHxgUKhwObNm42WCyGwaNEieHt7w8nJCeHh4bh48aJRzM2bNzFlyhS4ublBpVJhxowZKCwsbMKtsH6DB0u9WwcPSrMUVlQA//430K0bMH06cOGC3BkSEREREVkXWQutoqIi9OrVC1988UWNy5ctW4aVK1fiyy+/xLFjx+Di4oKIiAiUlJToY6ZMmYKzZ88iNjYW27dvx4EDBzBr1qym2oRm5aGHgN27gcREYMQIoLISWLMG6NEDeOYZ4Px5uTMkIiIiIrIOCmEhJ8dSKBTYtGkTxo0bB0DqzfLx8cHLL7+MBQsWAAAKCgrg5eWFmJgYTJ48GefPn0dQUBBOnDiBfv36AQB27dqFkSNH4urVq/Dx8anTY2s0Gri7u6OgoABubm6Nsn3W6MQJ4J13gO3bpesKBTBpEvDWW0BQkLy5ERERERE1BnPVBhZ7jFZGRgZycnIQHh6uv83d3R2hoaFITEwEACQmJkKlUumLLAAIDw+HjY0Njh07ZnLdpaWl0Gg0Ro3u1L+/dA6upCRg3DhACOC774C+fYHcXLmzIyIiIiKyXBZbaOXk5AAAvLy8jG738vLSL8vJybnjBMp2dnbw8PDQx9Rk6dKlcHd31zdfX18zZ9+89OkDbNoEJCcDSqV0Hi4WWkREREREpllsodWYoqOjUVBQoG9XrlyROyWr0KuXVGgREREREVHtLLbQUqvVAIDcal0nubm5+mVqtRp5eXlGyysqKnDz5k19TE0cHR3h5uZm1IiIiIiIiMzFYgutgIAAqNVqxMXF6W/TaDQ4duwYwsLCAABhYWHIz89HUlKSPmbfvn3QarUIDQ1t8pyJiIiIiIgAwE7OBy8sLERaWpr+ekZGBpKTk+Hh4QE/Pz/MnTsX7733Hrp27YqAgAC89dZb8PHx0c9M2KNHD0RGRmLmzJn48ssvUV5ejtmzZ2Py5Ml1nnGQiIiIiIjI3GQttE6ePIlHHnlEf33+/PkAgGnTpiEmJgavvvoqioqKMGvWLOTn5+Ohhx7Crl270KpVK/191q5di9mzZ2PYsGGwsbHBhAkTsHLlyibfFiIiIiIiIh2LOY+WnHgerbrz9gZycoBTp4CQELmzISIiIiIyr2Z/Hi0iIiIiIiJrxUKLiIiIiIjIzFhoERERERERmRkLLSIiIiIiIjNjoUVERERERGRmLLSIiIiIiIjMjIUWERERERGRmbHQIiIiIiIiMjMWWkRERERERGbGQouIiIiIiMjMWGgRERERERGZGQstIiIiIiIiM2OhRUREREREZGYstIiIiIiIiMyMhRYREREREZGZsdAiIiIiIiIyMzu5EyDrNH480K0b0Lkz0KmToQUEAC4ucmdHRERERCQvFlpULwMHAj/8AKSnS60mXl7GxVfV5uMD2LAflYiIiIiaOYUQQsidhNw0Gg3c3d1RUFAANzc3udOxaEIAGRnAr7/W3H7/vfb7OzoCHTsa94T16wc8/HCTpE9EREREVCtz1Qbs0aJ6USgMBVJNfv+95kIsPR24fBkoLQVSU6VWVWIi8MADjZ8/EREREVFTYKFFZtW6tdT69LlzWUUFcOWKcQH29dfAtWtATk7T50pERERE1FhYaFGTsbOTJssICACGDZNuS0iQCi0iIiIioubEoqclWLx4MRQKhVHr3r27fnlJSQmioqLQpk0bKJVKTJgwAbm5uTJmTEREREREZOGFFgDcd999yM7O1rdDhw7pl82bNw/btm3D999/j4SEBGRlZWH8+PEyZktERERERGQFQwft7OygVqvvuL2goABff/011q1bh6FDhwIAVq9ejR49euDo0aN4gDMrEBERERGRTCy+R+vixYvw8fFBp06dMGXKFGRmZgIAkpKSUF5ejvDwcH1s9+7d4efnh8TERLnSJSIiIiIisuwerdDQUMTExKBbt27Izs7GkiVL8PDDD+PMmTPIycmBg4MDVCqV0X28vLyQc5cp7EpLS1FaWqq/rtFoGiN9IiIiIiJqoSy60BoxYoT+ckhICEJDQ+Hv74///e9/cHJyavB6ly5diiVLlpgjRSIiIiIiojtY/NDBqlQqFQIDA5GWlga1Wo2ysjLk5+cbxeTm5tZ4TFdV0dHRKCgo0LcrV640YtZERERERNTSWFWhVVhYiPT0dHh7e6Nv376wt7dHXFycfnlqaioyMzMRFhZW63ocHR3h5uZm1IiIiIiIiMzFoocOLliwAGPGjIG/vz+ysrLw9ttvw9bWFk899RTc3d0xY8YMzJ8/Hx4eHnBzc8NLL72EsLAwzjhohTZsALp0AXr2lDsTIiIiIqJ7Z9E9WlevXsVTTz2Fbt264cknn0SbNm1w9OhRtGvXDgCwYsUKjB49GhMmTMCgQYOgVquxceNGmbOm+ujWTfq7di0QHAz07g188gmQlSVrWkRERERE90QhhBByJyE3jUYDd3d3FBQUcBhhEysrA7ZtA775BtixAygvl25XKIBhw4BnnwUefxxwdZU3TyIiIiJqGcxVG7DQAgstS3HjBvD998C33wKHDxtud3ICxo2Tiq5HHwXsLHrAKxERERFZMxZaZsRCy/L8+qs0nPDbb4ELFwy3e3oCkycDzzwD9Osn9XwREREREZmLuWoDiz5Gi1quTp2At94CfvkFOH4cmDMHaNcOyMsDVq4EBgwAevQA3nsPyMhovDwOHpSKuR9/bLzHICIiIqLmhz1aYI+WtSgvB2JjpV6uzZuB27cNywYOlIYWTpwIeHjceV+tFtBogN9/N7T8fOPrNbW0NMM6+E4hIiIiav44dNCMWGhZn1u3gE2bpKIrLk4qpADA3h4YMkQqiqoWTAUFhpiGUKmk9RARERFR82au2oDTCpBVcnUFpk6VWlYW8N//SkVXcrLU62VKq1ZA69Z1ayoVsHy51Hv27LNNtGFERERE1Cyw0CKr5+MDvPyy1M6cAQ4dApRKqVCqXjy1alW/de/ZIxVaRERERET1wUKLmpWePaVGRERERCQnzjpIVAelpUBJCSfEICIiIqK6YY8WUR38859Ss7UFXFyMm1J5522mbq8t1sFB7q0kIiIiInNhoUVUi0cfBVatAm7ckK5XVkrTxGs05n8sO7uGFXCurkBEhHQyZyIiIiKyDCy0iGoxaBBw/TpQUQEUFd3ZCgvv/bbycumxKiqkaegLCuqf59Ch0jT3RERERGQZWGgR1YGdHeDuLjVzKy+vX5FW9frly8CBA8C+fdJsi5WVd28VFXePadMGCAyUWps2gEJh/u0mIiIias5YaBHJzN5emopepar/fePjgUcekS4//LAZk6qidWtD0VW1de0qDV0kIiIiojux0LJE2dmAt7fcWZAV6N9fGjZ46ZI0UYc5mo0NkJcHXLgAZGYCv/8OHDsmterat6+5CAsIkApIIiIiopZKIQQnrNZoNHB3d0dBQQHc3NzkTSY/H2jXDujWDRg3Tmp9+3LsFsmiuBhIT5eKrurt+nXT97O1BTp1MhReDz0EREZKQzDt7KRijoiIiMgSmas2YKEFCyu09u4FRoyQDqTR6dABGDtWKroGD2ZXAVmEmzeBixeB1FTjAuziRalAq42NjVRw2dsbii/dZXPfVpf7dOggzTDJApCIiIhYaJmRRRVagDRWa8cOYPNmYOdOadYDnVWrgBdekC01orvRaoGsLEPh9eKLcmdUN3v3AsOGyZ0FERERyY2FlhlZXKFVVUmJNKXc5s3A1q1AUpJ0YAwAfP01sHGj1NM1ZgygVsuZKVGNhABKS6VO2ooKaZbFqn9NXW7s5brLmzYZcvX1rfmYNVPHslnr7brbqv7VterX6xJjayudcNvRUbpMRERkzVhomZFFF1pVabXGY5siIoA9e6TLCgUQFmY4rqtrVzkyJLI6f/0rsHKl3Fk0H/b2QKtWhubkZHz9XpfVdh97+5oPZ71+HTh5Uhoq6uAgxdnbG182dd3WlofIEhG1NCy0zMhqCq3qzp2Tero2bwZOnDBeFhIi9X7ZcWJJotoIAZw/L/W6VT+fmFZb83nGrP32ykppu3Uxulbf65b238PGpuYC7Ny5e1tvTYVYXYo0U9dNLbO1NRxDqLtc/a+5b6u6zMaGRSUREcBCy6ysttCq6rffpKGFmzdLQw2HDAFiY+XOioiaMSEMBVh5uTTS+fZt6W9NrSHL7naf0tK65+vvLxVe5eVSKyszXNZd12ob7/myBrrhpQ0t1sxR8NX3NoUCWLgQaNv27kNpuYyT/hDVBQstM2oWhVZV+fnAtWuG4YMrVkhH+ut+NnVwMLQJE6Tp1gDp/F1ff21YVj02JAS47z7ZNouIqDqtViqQ7lagBQYCnTvXbX3Vi7CaCrLGWKbrbdQdQ6i7XNttDVnW0otJaprCrjEKRYXC0POqa1Wv1+WyueOaez661tKYqzZoNuPKvvjiC3z00UfIyclBr1698Pnnn2PAgAFypyUPlUpqlZVAx47A1aumYwMDDYVWZibw1lumY5csYaFFRBal6nBBc63P0VFqzZVu2GhjF3R3W2bOdZWXS6dpePbZ2ofMtoRldSmkdfFEddVUhd+rrwLPPy/31ppPsyi01q9fj/nz5+PLL79EaGgoPv30U0RERCA1NRWenp5ypycfW1tg/37g2DHDT6a6prv+4IOG+LZtgZkzjZdXvaz7Ofitt4A1a2r+yUOhkIYu+vpKsZ98Avz736bfWf/7n7TeoiKp0NN9w2nVyvCXx5kREZmNQmEYfkfNT9UhvXIXfeZeJoRh+2q6XNuyhsRZ2robch9zvq6aojjPz2/8x2hKzeJjdvny5Zg5cyaee+45AMCXX36JH3/8Ef/+97/x2muvyZydzLp0kVpddO4M/POfd4+7cQO4csX08qrvxJyc2o9ELymR/p48KR1XVhMbG+BvfwMWLJCunz4NPPGEoRirWpg5OgLPPAM8/rgUe+mSdD/dJ0/Vo/i1WmDiROCP1w1++036ObTqp9TBg9IyPz+pgNQVmz/9BGzbZnq7nnwS6NFDunzmjPEc4tWNGwcEB0uXU1OB7783Xl61z37kSOD++6XLGRnAunXAe+8Bw4dLU/xXLXgHDwY6dZJir1wBDhyouTBWKIB+/YCAACk2NxdITDT9c1OPHtLBLoD0ifjTT6Zj/f0NpyMoLJReC1Wf/6qXO3aUGgDcugUcOnRnrK5162Z4zm7dko5NrGmdWi3QsycwcKAUW1QE/Otfd8bY2AAzZgDu7tIPFIB08E9tBwA5Oxu+qep+lDCl6g8Guh8uTHF0NMRWVNQe6+BgiK2srD1Wd4ZmwDA+zhTdwS/3Gqt77er+6sYEAYbn31Rs9cvWpuq21fRtR/c8AMYnqNepuu1VY2talzU/T2R2CoVhqB1R1cLMGopKPz+5nzEzE1autLRU2Nraik2bNhndPnXqVPHYY4/VeJ+SkhJRUFCgb1euXBEAREFBQRNk3AxcvizEiRNCHDsmxNGjQiQmCnHkiBCHDglx8KAQt28bYi9eFCIuToi9e4WIjRVi924hdu0SYudOIXbsEEKjkeL27xfCw0MIFxch7Oyqfy4IsWKFYZ2HDt25vGp75x1D7KlTtccuXGiITUurPXb3bkPsP/9Ze+zGjYbYtWtrj/32W0Ps5s21x/7jH4bYPXtqj123zhC7YUPtsV99ZYjdubP22M8+M8QeOFB77AcfGGITE+XZby+9ZIjNzq499qefDLHvv1977MGDhthPP637a+df/zLfa+ebbxr22omNrT12+XJD7OHDtce++64hNjm59tjoaEPshQu1x86ZY4j97TfjZQqFodnYCDFzpiE2P1/6DKmp2dsL8cwzhtiyMiFsbQ3Nxsa4jRsnjDg4mM43PNw4VqUyHRsWZhzr42M6NiTEOLZrV9OxnTsbx95/v/HzpGu2tkJ06GAcO3iw9PxUbQ4OUmvXzjh29GghWrUybk5OUnN1NY6dPFn6XHdxEUKpNDRXV6mVlBhi//xnIdzdjZtKZWi//26InTtX+p9RtbVpY2i//WaIfeMNIdq2Nd3S0gyxH3wghKen6ZaSYoj99FMhvLxMt6NHDbH//KcQarXptn+/IXbtWuk1Yart2GGI3bhRiPbtTbcffjDE7t4thK+v6bZ2rSH24EEh/PxMt3/9yxB78qQQHTuabp9/bog9d06IgADT7aOPDLEZGUJ06mTcOnc2tKr/M3JyjJdVb1U/ezQaIbp0Md3mzTPElpdL7zlT7YUXhJH77hMiMLDmNn26cWz//kJ061ZzmzzZOHbwYCG6d6+5jR1rHBsZKUSPHjW3iAjj2PHjhQgKqrkNGWIc+8wz0vbV1EJDjWNnzhSiZ8+aW69exrF//asQwcFSq/oalFlBQYEwR21g9T1a169fR2VlJby8vIxu9/Lywi+//FLjfZYuXYolS5Y0RXrNk59f3X9yqGuP2pAhUk+ZTmWl1JtQUiL9VSoNy3r2BBISDMuqxpWWAn36GGLbtwe++MLQw6Kbckl3WdcrAgBeXsB//2u8/Pp14KuvpF4k3XBIQOrVefFF09uj6x3SPQe1DTiu+vz4+0vDN03p3t1w2ccHmDZNGsapUgEPP2z8tcvHxxDr6QmEh5v+Wlv1ZNcqlXRONt2y6j83VR2O6+wsHbdnKtbDwxDbqpXUY1XTfrCxAdq0MV5vnz7GZ8WtGqvr+dLFDh9uvLzqfaruYycn4KmnjJf/5z+mn2+yTEIYX656Hai5h0in+tiX2sbCVD/YpfrjWJrqPVtVn5vquVffbt2sHDWp3rOr+8ytSfXxiCUlUk9yXXIuLgYKCkzHVt2GoiLg5k3TsVX3XVGR9FluStXnoqgIyMszHVv1tVVUJI0AMKXq81lcLI3wMKVqb3RxMZCVZTq26nN/+7Y0GsOU4mLj2NpGoxQWGj9GZqbp2Fu3DJdLS6XRI6ZUHQtWViaNxjCl6j6tqAB+/dV0bNV9WlkJpKebjq26T7VaIC3NdGz1/XTxoulY3cgVnQsXTL+Pqn6H0K3X1Di5qv87ASlfU/vZwcH4enq66Zyrj3r49VfTo45+/934ekYGcPZszbHVJ4u4dEkazVOT6l2tly9LI5UA4++BzYTVzzqYlZWF9u3b48iRIwgLC9Pf/uqrryIhIQHHjh274z6lpaUorfKPQ6PRwNfXt/nMOkhE9VNWJn2Js7c3zH2sO+reFAcH49jahtfpznyri63rcMCKitqHL+pmBwUM86ub4uho+IdcUWH8BaymWN1sEPWJraw0/rJW/d+Lo6NU7OpidV+qayoGWrUy/MBSWWn4B1y9yAKkdbq7S5e1WsMXpeqxCoUUq/sSI8SdX6qqfvF3dARatzZcr/4FvGqsvb30I4VO9S/2VWPt7Az5AtKXrZq2C5BeY1XXe/PmnScx0122sZGOta2aQ9XXcPX1Vv2B8to149dl1ViFwjAEGJCeh5IS0zlX/SEkJ8f49VP9NdGpk+G5yckx/gJfPbZzZ8P7KDtbev2YKiS7djW8N3Jyai/KOnc2vIZzc6XnorZY3Ws4L6/2QqtTJ8DFRbp87VrtxVOnToCrq3T5bsPzO3Y0vCZu3qy9yPH3N/yIlZ9fezHi5we0aydd1mikosGUDh0MP9DduiWdjNCU9u0Nr5+iItNfwAHA29vwQ+7t20BysulYtdrwo2ZZmXQIgimenoYfNSsqpGPXTWnbVhqeDkivq8OHTcd6eABBQYbrhw6Z/kFGpTL+4e/IEdP/Y9zcgN69DdcTE03/33BxkYb/6xw/Lj13NWnVCggNNVw/edL4M7sqBwfj4/h/+sn0DyG2tsCgQYbrycm1v+eGDjVcTkkxvOcCA+8sSGXC6d3/UFZWBmdnZ2zYsAHjxo3T3z5t2jTk5+djy5Ytd11Hs5venYiIiIiIGsRctYHVn7bOwcEBffv2RVxcnP42rVaLuLg4ox4uIiIiIiKipmL1x2gBwPz58zFt2jT069cPAwYMwKeffoqioiL9LIRERERERERNqVkUWpMmTcK1a9ewaNEi5OTkoHfv3ti1a9cdE2QQERERERE1Bas/RssceIwWEREREREBPEaLiIiIiIjIYrHQIiIiIiIiMjMWWkRERERERGbWLCbDuFe6w9Q0Go3MmRARERERkZx0NcG9TmXBQgvArT/ORO9rIWejJiIiIiIied26dQvu7u4Nvj9nHYR0guOsrCy4urpCoVDU674ajQa+vr64cuUKZyxsQbjfWx7u85aJ+71l4n5vmbjfW6aa9rsQArdu3YKPjw9sbBp+pBV7tADY2NigQ4cO97QONzc3vilbIO73lof7vGXifm+ZuN9bJu73lqn6fr+XniwdToZBRERERERkZiy0iIiIiIiIzIyF1j1ydHTE22+/DUdHR7lToSbE/d7ycJ+3TNzvLRP3e8vE/d4yNeZ+52QYREREREREZsYeLSIiIiIiIjNjoUVERERERGRmLLSIiIiIiIjMjIUWERERERGRmbHQugdffPEFOnbsiFatWiE0NBTHjx+XOyUyo8WLF0OhUBi17t2765eXlJQgKioKbdq0gVKpxIQJE5CbmytjxtQQBw4cwJgxY+Dj4wOFQoHNmzcbLRdCYNGiRfD29oaTkxPCw8Nx8eJFo5ibN29iypQpcHNzg0qlwowZM1BYWNiEW0H1dbf9Pn369Dve/5GRkUYx3O/WZenSpejfvz9cXV3h6emJcePGITU11SimLp/rmZmZGDVqFJydneHp6YlXXnkFFRUVTbkpVA912e9Dhgy54/3+wgsvGMVwv1uXVatWISQkRH8S4rCwMOzcuVO/vKne6yy0Gmj9+vWYP38+3n77bfz000/o1asXIiIikJeXJ3dqZEb33XcfsrOz9e3QoUP6ZfPmzcO2bdvw/fffIyEhAVlZWRg/fryM2VJDFBUVoVevXvjiiy9qXL5s2TKsXLkSX375JY4dOwYXFxdERESgpKREHzNlyhScPXsWsbGx2L59Ow4cOIBZs2Y11SZQA9xtvwNAZGSk0fv/v//9r9Fy7nfrkpCQgKioKBw9ehSxsbEoLy/H8OHDUVRUpI+52+d6ZWUlRo0ahbKyMhw5cgRr1qxBTEwMFi1aJMcmUR3UZb8DwMyZM43e78uWLdMv4363Ph06dMCHH36IpKQknDx5EkOHDsXYsWNx9uxZAE34XhfUIAMGDBBRUVH665WVlcLHx0csXbpUxqzInN5++23Rq1evGpfl5+cLe3t78f333+tvO3/+vAAgEhMTmyhDMjcAYtOmTfrrWq1WqNVq8dFHH+lvy8/PF46OjuK///2vEEKIc+fOCQDixIkT+pidO3cKhUIhfvvttybLnRqu+n4XQohp06aJsWPHmrwP97v1y8vLEwBEQkKCEKJun+s7duwQNjY2IicnRx+zatUq4ebmJkpLS5t2A6hBqu93IYQYPHiw+Otf/2ryPtzvzUPr1q3FV1991aTvdfZoNUBZWRmSkpIQHh6uv83Gxgbh4eFITEyUMTMyt4sXL8LHxwedOnXClClTkJmZCQBISkpCeXm50Wuge/fu8PPz42ugGcnIyEBOTo7RfnZ3d0doaKh+PycmJkKlUqFfv376mPDwcNjY2ODYsWNNnjOZT3x8PDw9PdGtWze8+OKLuHHjhn4Z97v1KygoAAB4eHgAqNvnemJiIoKDg+Hl5aWPiYiIgEaj0f9STpat+n7XWbt2Ldq2bYuePXsiOjoaxcXF+mXc79atsrIS3333HYqKihAWFtak73U7821Gy3H9+nVUVlYaPfkA4OXlhV9++UWmrMjcQkNDERMTg27duiE7OxtLlizBww8/jDNnziAnJwcODg5QqVRG9/Hy8kJOTo48CZPZ6fZlTe913bKcnBx4enoaLbezs4OHhwdfC1YsMjIS48ePR0BAANLT0/H6669jxIgRSExMhK2tLfe7ldNqtZg7dy4GDhyInj17AkCdPtdzcnJq/DzQLSPLVtN+B4Cnn34a/v7+8PHxQUpKChYuXIjU1FRs3LgRAPe7tTp9+jTCwsJQUlICpVKJTZs2ISgoCMnJyU32XmehRWTCiBEj9JdDQkIQGhoKf39//O9//4OTk5OMmRFRY5s8ebL+cnBwMEJCQtC5c2fEx8dj2LBhMmZG5hAVFYUzZ84YHXdLzZ+p/V712Mrg4GB4e3tj2LBhSE9PR+fOnZs6TTKTbt26ITk5GQUFBdiwYQOmTZuGhISEJs2BQwcboG3btrC1tb1jdpLc3Fyo1WqZsqLGplKpEBgYiLS0NKjVapSVlSE/P98ohq+B5kW3L2t7r6vV6jsmwamoqMDNmzf5WmhGOnXqhLZt2yItLQ0A97s1mz17NrZv3479+/ejQ4cO+tvr8rmuVqtr/DzQLSPLZWq/1yQ0NBQAjN7v3O/Wx8HBAV26dEHfvn2xdOlS9OrVC5999lmTvtdZaDWAg4MD+vbti7i4OP1tWq0WcXFxCAsLkzEzakyFhYVIT0+Ht7c3+vbtC3t7e6PXQGpqKjIzM/kaaEYCAgKgVquN9rNGo8GxY8f0+zksLAz5+flISkrSx+zbtw9arVb/z5qs39WrV3Hjxg14e3sD4H63RkIIzJ49G5s2bcK+ffsQEBBgtLwun+thYWE4ffq0UZEdGxsLNzc3BAUFNc2GUL3cbb/XJDk5GQCM3u/c79ZPq9WitLS0ad/r5prJo6X57rvvhKOjo4iJiRHnzp0Ts2bNEiqVymh2ErJuL7/8soiPjxcZGRni8OHDIjw8XLRt21bk5eUJIYR44YUXhJ+fn9i3b584efKkCAsLE2FhYTJnTfV169Yt8fPPP4uff/5ZABDLly8XP//8s7h8+bIQQogPP/xQqFQqsWXLFpGSkiLGjh0rAgICxO3bt/XriIyMFPfff784duyYOHTokOjatat46qmn5NokqoPa9vutW7fEggULRGJiosjIyBB79+4Vffr0EV27dhUlJSX6dXC/W5cXX3xRuLu7i/j4eJGdna1vxcXF+pi7fa5XVFSInj17iuHDh4vk5GSxa9cu0a5dOxEdHS3HJlEd3G2/p6WliXfeeUecPHlSZGRkiC1btohOnTqJQYMG6dfB/W59XnvtNZGQkCAyMjJESkqKeO2114RCoRB79uwRQjTde52F1j34/PPPhZ+fn3BwcBADBgwQR48elTslMqNJkyYJb29v4eDgINq3by8mTZok0tLS9Mtv374t/vKXv4jWrVsLZ2dn8fjjj4vs7GwZM6aG2L9/vwBwR5s2bZoQQpri/a233hJeXl7C0dFRDBs2TKSmphqt48aNG+Kpp54SSqVSuLm5ieeee07cunVLhq2huqptvxcXF4vhw4eLdu3aCXt7e+Hv7y9mzpx5xw9p3O/Wpab9DUCsXr1aH1OXz/VLly6JESNGCCcnJ9G2bVvx8ssvi/Ly8ibeGqqru+33zMxMMWjQIOHh4SEcHR1Fly5dxCuvvCIKCgqM1sP9bl3+9Kc/CX9/f+Hg4CDatWsnhg0bpi+yhGi697pCCCHq3fdGREREREREJvEYLSIiIiIiIjNjoUVERERERGRmLLSIiIiIiIjMjIUWERERERGRmbHQIiIiIiIiMjMWWkRERERERGbGQouIiIiIiMjMWGgREZFVi4+Ph0KhQH5+vtyp6HXs2BGffvqp3GkQEZGMWGgREZHFUigUtbbFixfLnSIREVGN7OROgIiIyJTs7Gz95fXr12PRokVITU3V36ZUKnHy5Ml6r7esrAwODg5myZGIiKgm7NEiIiKLpVar9c3d3R0KhcLoNqVSqY9NSkpCv3794OzsjAcffNCoIFu8eDF69+6Nr776CgEBAWjVqhUAID8/H3/+85/Rrl07uLm5YejQoTh16pT+funp6Rg7diy8vLygVCrRv39/7N271yjHvLw8jBkzBk5OTggICMDatWuNlgshsHjxYvj5+cHR0RE+Pj6YM2dOYzxdRERkQVhoERFRs/DGG2/gk08+wcmTJ2FnZ4c//elPRsvT0tLwww8/YOPGjUhOTgYATJw4EXl5edi5cyeSkpLQp08fDBs2DDdv3gQAFBYWYuTIkYiLi8PPP/+MyMhIjBkzBpmZmfr1Tp8+HVeuXMH+/fuxYcMG/N///R/y8vL0y3/44QesWLEC//jHP3Dx4kVs3rwZwcHBjf+EEBGRrDh0kIiImoX3338fgwcPBgC89tprGDVqFEpKSvS9V2VlZfjPf/6Ddu3aAQAOHTqE48ePIy8vD46OjgCAjz/+GJs3b8aGDRswa9Ys9OrVC7169dI/xrvvvotNmzZh69atmD17Ni5cuICdO3fi+PHj6N+/PwDg66+/Ro8ePfT3yczMhFqtRnh4OOzt7eHn54cBAwY0yXNCRETyYY8WERE1CyEhIfrL3t7eAGDUs+Tv768vsgDg1KlTKCwsRJs2baBUKvUtIyMD6enpAKQerQULFqBHjx5QqVRQKpU4f/68vkfr/PnzsLOzQ9++ffXr7d69O1Qqlf76xIkTcfv2bXTq1AkzZ87Epk2bUFFR0SjPARERWQ72aBERUbNgb2+vv6xQKAAAWq1Wf5uLi4tRfGFhIby9vREfH3/HunSF0oIFCxAbG4uPP/4YXbp0gZOTE5544gmUlZXVOS9fX1+kpqZi7969iI2NxV/+8hd89NFHSEhIMMqZiIiaFxZaRETUIvXp0wc5OTmws7NDx44da4w5fPgwpk+fjscffxyAVJxdunRJv7x79+6oqKhAUlKSfuhgamrqHef0cnJywpgxYzBmzBhERUWhe/fuOH36NPr06dMYm0ZERBaAhRYREbVI4eHhCAsLw7hx47Bs2TIEBgYiKysLP/74Ix5//HH069cPXbt2xcaNGzFmzBgoFAq89dZbRr1k3bp1Q2RkJJ5//nmsWrUKdnZ2mDt3LpycnPQxMTExqKysRGhoKJydnfHtt9/CyckJ/v7+cmw2ERE1ER6jRURELZJCocCOHTswaNAgPPfccwgMDMTkyZNx+fJleHl5AQCWL1+O1q1b48EHH8SYMWMQERFxRy/U6tWr4ePjg8GDB2P8+PGYNWsWPD099ctVKhX+9a9/YeDAgQgJCcHevXuxbds2tGnTpkm3l4iImpZCCCHkToKIiIiIiKg5YY8WERERERGRmbHQIiIiIiIiMjMWWkRERERERGbGQouIiIiIiMjMWGgRERERERGZGQstIiIiIiIiM2OhRUREREREZGYstIiIiIiIiMyMhRYREREREZGZsdAiIiIiIiIyMxZaREREREREZsZCi4iIiIiIyMz+H8hDV7WTraFSAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "fig, ax = plt.subplots()\n",
+    "fig, ax = plt.subplots(figsize=(10, 3))\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(\"Threads\")\n",
@@ -870,7 +879,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 78,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -884,21 +893,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 79,
    "metadata": {
     "exercise": "solution"
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -908,21 +915,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 80,
    "metadata": {
-    "exercise": "solution"
+    "editable": true,
+    "exercise": "solution",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x216 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -932,57 +942,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 81,
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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2zdq+AAAAADBFtaY4JydHS5cu1TvvvKOuXbt6zhFKSEhQbGysEhISdOutt2rOnDlKSkqSw+HQL3/5S2VlZemCCy6QJI0dO1ZDhw7VTTfdpIULF8rpdOrhhx9WTk6O7HZ78NcwRI0YIRUXm+cd/Ss3AgAAALBQq/YcLVq0SC6XS5deeqnS0tI87fXXX/fUPPPMM7ryyis1depUjR49WqmpqXr77bc9yyMjI7VixQpFRkYqKytLN954o2bMmKHHH388eGsVBjjvCAAAAAgtp3SdI6uE+3WOJOmvf5VmzJBGj5Zyc63uDQAAABCeQuY6Rzh5vsN5h188BQAAADoewpFFBg+WIiOl8nLpn/+0ujcAAAAACEcWsdulQYPMac47AgAAAKxHOLIQgzIAAAAAoYNwZCHf844AAAAAWItwZCH2HAEAAAChg3BkIXc42rlTqquzti8AAABAZ0c4slCfPlLXrmYw+uYbq3sDAAAAdG6EIwvZbN69R5x3BAAAAFiLcGQxzjsCAAAAQgPhyGKEIwAAACA0EI4sxnDeAAAAQGggHFls2DDzZ1GR5HJZ2xcAAACgMyMcWez006WePc3pbdus7QsAAADQmRGOQgDnHQEAAADWIxyFAM47AgAAAKxHOAoB7DkCAAAArEc4CgG+4cgwrO0LAAAA0FkRjkLA4MFSVJQ5Wt3331vdGwAAAKBzIhyFgJgYMyBJnHcEAAAAWIVwFCI47wgAAACwFuEoRBCOAAAAAGsRjkIEw3kDAAAA1mp1OPr00081adIkpaeny2azafny5X7Lb775ZtlsNr82fvx4v5pDhw5p+vTpcjgcSkxM1K233qrKyspTWpFw595ztGuXVFtrbV8AAACAzqjV4ejIkSMaOXKknn/++YA148ePV0lJiae9+uqrfsunT5+u7du3a9WqVVqxYoU+/fRTzZo1q/W970B69ZISEqT6eqmgwOreAAAAAJ1PVGvvMGHCBE2YMKHFGrvdrtTU1GaX7dy5Ux9++KG+/PJLjRo1SpL03HPP6YorrtBTTz2l9PT01napQ7DZpGHDpM8+M887cu9JAgAAANA+2uSco3Xr1ik5OVmDBg3SnXfeqYMHD3qW5eXlKTEx0ROMJCk7O1sRERHauHFjs49XU1OjiooKv9YRcd4RAAAAYJ2gh6Px48frpZde0po1a/Tf//3fys3N1YQJE9TQ0CBJcjqdSk5O9rtPVFSUkpKS5HQ6m33MBQsWKCEhwdN69eoV7G6HBEasAwAAAKzT6sPqjmfatGme6eHDh2vEiBHq37+/1q1bpzFjxpzUY86dO1dz5szx3K6oqOiQAYlwBAAAAFinzYfy7tevn7p37649e/ZIklJTU1VWVuZXU19fr0OHDgU8T8lut8vhcPi1jsgdjvbvl3780dq+AAAAAJ1Nm4ej77//XgcPHlRaWpokKSsrS+Xl5crPz/fUrF27Vo2NjcrMzGzr7oS0hASpd29zets2a/sCAAAAdDatPqyusrLSsxdIkvbu3avNmzcrKSlJSUlJmj9/vqZOnarU1FQVFhbqgQce0IABAzRu3DhJ0pAhQzR+/HjdfvvtWrx4serq6jR79mxNmzat045U56uoyPx55ZWSy2VtXwAAAIDOpNV7jjZt2qRzzjlH55xzjiRpzpw5OuecczRv3jxFRkZqy5Ytuuqqq3TmmWfq1ltvVUZGhv7+97/Lbrd7HuOVV17R4MGDNWbMGF1xxRW66KKL9H//93/BW6swFvGvLTJokLX9AAAAADobm2EYhtWdaK2KigolJCTI5XJ1uPOP7rpLWrRIeuwx6dFHre4NAAAAENqCmQ3a/JwjAAAAAAgHhCMAAAAAEOEIAAAAACQRjgAAAABAEuEIAAAAACQRjgAAAABAEuEIAAAAACQRjgAAAABAEuEIAAAAACQRjgAAAABAEuEIAAAAACQRjgAAAABAEuEIAAAAACQRjgAAAABAEuEIAAAAACQRjgAAAABAEuEoZL39trRmjWQYVvcEAAAA6BwIRyFm/HgpJkbaskXKzpYuvlj6+GNCEgAAANDWCEch5qqrpMJC6Ze/lOx26bPPpHHjpKws6YMPCEkAAABAWyEchaCePaVnn5X27pXuvls67TRp40Zp4kTp/POl994jJAEAAADBRjgKYWlp0jPPmCHpvvukuDhp0yZz71JGhrRsmdTYaHUvAQAAgI6BcBQGUlOlJ580Q9KDD0pdukhffSVNmSKdc4705puEJAAAAOBUEY7CSHKy9MQT0r590n/8h9S1qzlww3XXSSNGSK+/LjU0WN1LAAAAIDy1Ohx9+umnmjRpktLT02Wz2bR8+XK/5YZhaN68eUpLS1NsbKyys7O1e/duv5pDhw5p+vTpcjgcSkxM1K233qrKyspTWpHOpHt36Te/MUPSvHlSQoK0fbs0bZo0fLi0dCkhCQAAAGitVoejI0eOaOTIkXr++eebXb5w4UI9++yzWrx4sTZu3KguXbpo3Lhxqq6u9tRMnz5d27dv16pVq7RixQp9+umnmjVr1smvRSeVlCTNn2+GpPnzpcREaedOafp0aehQ6aWXpPp6q3sJAAAAhAebYZz8uGc2m03Lli3T5MmTJZl7jdLT03XvvffqvvvukyS5XC6lpKRoyZIlmjZtmnbu3KmhQ4fqyy+/1KhRoyRJH374oa644gp9//33Sk9PP+7vraioUEJCglwulxwOx8l2v8OpqJD+53+k3/5WOnTInNe/v/Sf/ynddJMUFWVt/wAAAIBgC2Y2COo5R3v37pXT6VR2drZnXkJCgjIzM5WXlydJysvLU2JioicYSVJ2drYiIiK0cePGZh+3pqZGFRUVfg3HcjjMc5H27TPPTere3bxm0i9+If3Xf1ndOwAAACC0BTUcOZ1OSVJKSorf/JSUFM8yp9Op5ORkv+VRUVFKSkry1DS1YMECJSQkeFqvXr2C2e0Op2tXc1S7ffvMQ+wkcxoAAABAYGExWt3cuXPlcrk8bf/+/VZ3KSx06SKNHGl1LwAAAIDwENRwlJqaKkkqLS31m19aWupZlpqaqrKyMr/l9fX1OnTokKemKbvdLofD4dcAAAAAIJiCGo769u2r1NRUrVmzxjOvoqJCGzduVFZWliQpKytL5eXlys/P99SsXbtWjY2NyszMDGZ3AAAAAOCEtXr8ssrKSu3Zs8dze+/evdq8ebOSkpLUu3dv3X333fqv//ovDRw4UH379tUjjzyi9PR0z4h2Q4YM0fjx43X77bdr8eLFqqur0+zZszVt2rQTGqkOAAAAANpCq8PRpk2b9LOf/cxze86cOZKkmTNnasmSJXrggQd05MgRzZo1S+Xl5brooov04Ycf6rTTTvPc55VXXtHs2bM1ZswYRUREaOrUqXr22WeDsDoAAAAAcHJO6TpHVuE6RyfuySelBx6QZs6UliyxujcAAABAcIXsdY4AAAAAIFwRjgAAAABAhCMAAAAAkEQ4AgAAAABJhCMAAAAAkEQ4AgAAAABJhCMAAAAAkEQ4AgAAAABJhCMAAAAAkEQ4AgAAAABJhCMAAAAAkEQ4AgAAAABJhCMAAAAAkEQ4AgAAAABJhCMAAAAAkEQ4AgAAAABJUpTVHUD7eOstac8eqV8/s/Xv751OTZVsNqt7CAAAAFiLcNTBjRolxcRIlZXSZ5+ZranYWKlvX29Y8m19+0pxce3fbwAAAKC9EY46uJ/9TCotNfcaFRZK337r34qKpKoqaccOszUnNdU/MA0YIF19teRwtO+6AAAAAG3JZhiGYXUnWquiokIJCQlyuVxy8An9lNTVmQGpaWj69lszTLlczd/v3/5Nev319u0rAAAA0FQwswF7jjq56Gjz/KP+/Ztf/uOP/nuc1q2TPvpIcjrbtZsAAABAmyMcoUWnn26etzRqlHl7wAAzHAEAAAAdTdCH8n7sscdks9n82uDBgz3Lq6urlZOTo27duik+Pl5Tp05VaWlpsLsBAAAAAK3SJtc5Ouuss1RSUuJp69ev9yy755579N577+mNN95Qbm6uiouLNWXKlLboBgAAAACcsDY5rC4qKkqpqanHzHe5XPrzn/+spUuX6rLLLpMkvfDCCxoyZIg+//xzXXDBBW3RHQAAAAA4rjbZc7R7926lp6erX79+mj59uoqKiiRJ+fn5qqurU3Z2tqd28ODB6t27t/Ly8tqiKwAAAABwQoK+5ygzM1NLlizRoEGDVFJSovnz5+viiy/Wtm3b5HQ6FRMTo8TERL/7pKSkyNnC8Gc1NTWqqanx3K6oqAh2twEAAAB0ckEPRxMmTPBMjxgxQpmZmerTp4/+9re/KTY29qQec8GCBZo/f36wuggAAAAAx2iTw+p8JSYm6swzz9SePXuUmpqq2tpalZeX+9WUlpY2e46S29y5c+VyuTxt//79bdxrAAAAAJ1Nm4ejyspKFRYWKi0tTRkZGYqOjtaaNWs8ywsKClRUVKSsrKyAj2G32+VwOPwaAAAAAART0MPRfffdp9zcXO3bt08bNmzQNddco8jISN1www1KSEjQrbfeqjlz5uiTTz5Rfn6+brnlFmVlZTFSXZgpLJTeeEOqrra6JwAAAEBwBP2co++//1433HCDDh48qB49euiiiy7S559/rh49ekiSnnnmGUVERGjq1KmqqanRuHHj9Ic//CHY3UAb6d9fioyU/vlP6d/+TXI4pOuuk268URo9Wopo832RAAAAQNuwGYZhWN2J1qqoqFBCQoJcLheH2Flg507ppZekV16RfE//6tVLmj7dDEpnnWVd/wAAANB5BDMbEI5w0hobpb//XXr5ZfMQO5fLu+zss82Q9POfS2lplnURAAAAHRzhiHAUcqqrpRUrzKD0wQdSXZ05PyJCGjPGDEpTpkjx8db2EwAAAB0L4YhwFNIOHjT3JP31r9KGDd75cXHS5MlmULr8cikq6Ge8mfr0kU4/Xdq8uW0eHwAAAKGDcEQ4Chvffmuem/Tyy9I333jnJydL06ZJN90kZWRINtux962uln78seVWXu5/e8cO83A/SaqpkWJi2mU1AQAAYBHCEeEo7BiGtGmTGZJefVU6cMC7bNAgacCAY8POqQ4TXlsrRUef2mMAAAAgtBGOCEdhra5OWrXKDErLl0tVVYFrIyKkxESznX768ZtkHrLn/j1tdegeAAAAQkMwswEfHdHuoqOlK64w2+HD0vvvS5WVzYedrl1bd+2kH39su34DAACgYyMcwVJdu5rnHgEAAABWa8V38kB4qaz0Ds4AAAAAHA97jtBhuc9Bio2VunTxtvh4/9uB5h2vNja2dYf8AQAAILQRjtChJCZKY8eaAz64hxqpqjLbDz8E//fFxZ1cwBo0SLr44uD3BwAAACePcIQOxWaTPvrIDEZVVdKRI/6tsvLU5x096v19R4+azXdo8hP19dfSiBHBW3cAAACcGsIROiSbzdyrExcn9egR3MdubPQGr6Yh6kSC1sqVUkWF9NZb5nWdGhpOvUVFSf36SWeeKZ1xBtd3AgAAOBlc5whoZ+eeK331Vds9vm9QatrS083gCAAA0FFwnaNQc/CgOSZ1TIzVPUEYmDVL+u1vzenIyOC06mqpsFD65htzr9Y335itqS5dpIEDmw9O7gEsAAAAOiv2HAXDHXdIr75qXtV08mRpwgQpFPqFTqexUSou9oajggLv9N695iF4gXTv7g1KAwdKN98sJSWZe6IiI9njBAAAQlMwswHhKBgyMqR//MN7OzpaGjNGuvpq6aqrzGOZAIvV1poByR2WfFtx8fHvHxVlPrWjovynrZhnt0vjx0vJyW3/dwMAAKGNcBRq4aixUfryS2n5crPt2uVdNnBg88c3ASGkslLavdt8qj73nPTZZ1b36PgmTpRWrLC6FwAAwGqEo1ALR03t2iW9844ZlC6+WFq40JxfWytdeKF02WXm4XeZmVxFFCGpvt58utbVmdP19c1PH295a2pPdPm775rTktSzp/e8q4iI5s/H6kjz3dMREd52vNuBaiIjpdNOM/fEccgkACCcEY5CPRz5amz0BqCPPjKPBXJLTTUPu5s82QxMdrslXQTCye7d0tChZlDCqYuIMENSoBYb23bL7Pbmvx9qbJTy8syh76OjzbFuoqO9zfd202XR0XznBACdDeEonMKRr8OHzYvcLF8uvf++ebEbt/h46YUXpGuvtax7QLgoKzPPk/K91lNjY/PXgLJifls9dmOjf2s673i3Gxut3nLHiok5NjgVFJzaY0ZGBg5PLQWr1t6OjjZ/l/t8OPd005/Bnuf7kyAIAISj8A1HvmprpXXrzKD0zjvmJ70tW6Thw63uGYAOzB2S6uqkmhpzGHjfVlV17LxTWdZ0flVV60La0KHeQzzr6vyn3a0zs9lOLWC1VXA73rx33zWv9xYXF/gSBYEONe1syyIiOPQVOB7CUUcIR74aG83/Eueea74DFhZKd99tTsfEeL+ujImRunWTnnjCe98XXzSDlbvOtzYuTpoyxbLVAoDm1NcfP2xFRUkXXWS+nbXEMLznpDUNTi2FqkDLWlPX0GD+bvdP3+mW5p3MMnRuzZ17GGohLtAym81s7pDXdLqlZSdTF2qPbVV/OpsOE46ef/55Pfnkk3I6nRo5cqSee+45nX/++ce9X4cLR76eeEKaP9/8dNCcn/xE+v577+2f/tQ8OL85CQlSeXnQuwgAaD+G4T1M8mQCV7ACWrB/T3m59NBD5jXUT/RQ0466DAi2EwlawQhr3btLX3xh9doGNxtEBalPrfb6669rzpw5Wrx4sTIzM/W73/1O48aNU0FBgZI788VL7rlHGjJEcrm8X1PW1npbXJx//aRJZn1Ltd99Z46a5/ss9312z5ghPfKIWfvDD9IllwR+ZVx9tTRvnllbWGi+w9vtZnOfYW23d86vLQCgDdhs3m/jY2Ks7g3agm9YsjqoBXuZYXhbY2Pz0y0tO5m69rpPWzx2sLgfU2rbAH7kSNs9tlUsC0dPP/20br/9dt1yyy2SpMWLF+v999/XX/7yFz300ENWdct6drsZQE7U3LnHr6mrk/bvD7z8wAH/2h07AteefbZ3+rrrzMMBm9Ozp//v/PnPpa+/9g9Q7ulu3aQ//9lbO3++ef6V+53C/c7R2Gh+Onj3XW/tww+bF+XxfYdZv95c9te/Sjfe6K1dsMAMjc3p3Vv613NRkvT004Ff8Wlp0m23eW//z//476HzDYbdukl33OG9/cc/SkuXSvn50lNPecdWttmkHj2kK6/01i5fbvahuaCakCCNHeutXbPGvFhRc1/rxMWZgddt0yZzcJDmamNipPPO89bu2GHW+m4L9985MtI87sntiy/McO27vXxHAZg61Vu7bp1UVNT8Nm5slGbN8p5pvnKlOTy+b43LZQ5eMniweQa/ZC47fLj5bSaZx2n5frlQWRm4NjLS+7hSy+/+kZHmc9nt6NHAte6h4dyqq73/vZqy2fxra2sD10r+o12ebK3vc9c97Xtcm+9/2OZqm06HE99PEu7bvnxP/Ag0uoV7uW9tc9shXP9GaDPufwXHO4wUnUMohbXj1UVZliTakGGBmpoaIzIy0li2bJnf/BkzZhhXXXXVMfXV1dWGy+XytP379xuSDJfL1U49DnNVVYbx5ZeG8cUXhrFxo2Hk5RnGhg2G8dlnhrF+vWHs2eOtra42jDVrDGP1asNYtcowPv7YMD76yDA+/NAwPvjAMLZs8dZefLFhOByGYbf7vnbM1quXfx/OP//YGndLTPSvHTMmcG10tH/tpEmBay+/3L82Pj5w7UUX+dempASuPfdc/9p+/QLXDhrkXztsWODaUaP8a3v3Dlw7dKh/7ZAhgWv79PGvzcgIXNu9u3/tBRe0zXa76qrAtZJh1NV5a2+4IXDdjTd666qqWn7MKVP8+xAREbh23Dj/2lB47gwfHri2Z0//2szMwLWnn+5f+7OfBa612/1rr7yy5b9xfb23dto073ybzdsiIszm+/59222GERV1bIuONltJibf2V78yjMhIb3M/nrv5vp89+GDL/fV9P3v88ZZr8/K8tU891XLt6tXe2kWLWq595x1v7UsvNf+3cq/ra695a99+2/v3cbeYGG/7y1+8tR99ZBinnebfYmO97fnnvbXr1xtGly7eFh9vtq5dzfbb33prv/rKMBIS/Ftiorf95jfe2oICw0hKOrZ162a2hx/21n7/vfleFKjde6+39tAhw0hODtzuustbW1Vlvj4DtV/8wvCTnm4YqanNtxtu8K8980yzvrk2ebJ/7dlnG8ZPftJ8Gz/ev/bCC83/p821Sy/1r738cvP/RnMtM9O/dvJkwzjjjObb2Wf7106fbhh9+zbfBg/2r73tNvN9zbf17+9tvu8Rv/qV/7Km7fBhb+1DDxnGgAGBW1mZt/bxxw1j4MDA7bvvvLVPPmluu0Bt1y5v7fPPm+/LgdpXX3lr//IX828TqG3Y4K197TXzf3igtnatt/add8z//4Ha++97az/+2DDOOitwe+stb+3f/25+PgnUXn7ZW7tpk/n/aPjwY5+vFnK5XEawsoElee+HH35QQ0ODUlJS/OanpKRo165dx9QvWLBA8+fPb6/udTynnSaNGnVitXa7ec2lE/Hpp95pwzC/gXYPf9V0H+7//q906JC5rKbGW1dTY97X169+Ze5lcH/76vszMtK/9v77penT/Wtee8183Msv96+97TZzfnP69/e/PWOG/1Drvnr18r89bZr/3jdfTZ7jmjLF3Mv0/ffSFVeY/XV/TDrzTP/aiy+WSku9y32/sjnjDP/as8+WEhOb/1onLe3Yda2q8v+I5q5NSvKvTU83f5fvFUTdf+euXf1rBw0y161pXXNfh55zjrktfGt87+f7nLjwQv+61avNQUgQenz3iPhuw+amfWvdJ6GcCPfxOh1F071Igf5uTW+7hxwMxHfPVmNj4PNYJf+/Z0NDy3tKffe+NzSYe3ED8f2djY3m/4BAfPe4Njaae6ED8d3r29hoju0fSNP38dLSwLVNz9F1OgMf59R0XYqLA++NbrouJSWB+9H0f0ZJSeAjP5oeZl9SYu6Rb07T9Sgpkfbta7626f+BkhJp797ma5ten9HplL79tvnapkpLzcPzA/F9vpeVSXv2BK71fQ6XlZkXxAvE93Xzww/SN98ErvV9vh882PJ1Bnyf74cOmUc8BOL7fP/xR2nnzsC1vs8rl6vlo3t8n+8VFdL27YFrf/zR/3ds2xa41vf5fvSotHWrOd3S0RphzJIBGYqLi/WTn/xEGzZsUFZWlmf+Aw88oNzcXG3cuNGvvqamRjU+H2orKirUq1evjjkgA4Djc38w9A1ehhE4/Epmre8JG1VVLdf6/tNvzaFyR48e+6HWzX2Yo1tlZcu18fH+tYE+qNls/mH1yJGWA4Tv+2ZlpX9t0/4kJnqnDx82/+6BPsB36+b9sO9yHfvlh+90crL30Mkff/QGdt9a92Olpnq/GHG5jt0evgGje3fvcR6VlS3Xnn66t/bIkWOfE761Dof3ueYeas+Xb9+7dvXWVlV5w4a7xrc2IcH7XKuq8g8bzW0L9+GeVVX+H1iaq3U/f6qqvF/gNNeHpCTvc6KqyvwwHOhxk5LMv5tk/h18P7g3V9u9uzldU+P9gB2oD6mp5nRdXcsfbhMTzS9uJDNUt/QhNCHB+4VWY2PLHxYdDqlPH+/tr78OXBsf7/+l2pYtgV+fXbpIAwf61wb6MiA21jyP2G3r1sCHg9vt0rBh3tvbtwd+X4uOlkaO9K8NFIIjI6WMDO/tnTsDf1los0m+A2nt2uX/obupCy7wvq4KCszAEUhmpvd1v3t3yyH4vPO87++Fhf7P4aYyMryvo717/Qe5aurcc83tJ5nnb3/3XeDas8/2vo727285JI4Y4X0dFRe3/Hw/6yzv68jpbPn5PmSIN2CXlbX8fB80yPs6Oniw5ef7wIHe11F5uXlqgGT+77vwwsD3a0dhP1pdbW2t4uLi9Oabb2ry5Mme+TNnzlR5ebneeeedFu/foUerAwAAAHDCgpkNLLm2dkxMjDIyMrRmzRrPvMbGRq1Zs8ZvTxIAAAAAtBfLxpiYM2eOZs6cqVGjRun888/X7373Ox05csQzeh0AAAAAtCfLwtH111+vAwcOaN68eXI6nTr77LP14YcfHjNIAwAAAAC0B0vOOTpVnHMEAAAAQOoA5xwBAAAAQKghHAEAAACACEcAAAAAIMnCARlOhfs0qYpAFyUDAAAA0Cm4M0EwhlIIy3B0+PBhSVIv99V6AQAAAHRqhw8fVkJCwik9RliOVtfY2Kji4mJ17dpVNputVfetqKhQr169tH//fka660TY7p0T271zYrt3Pmzzzont3jk1t90Nw9Dhw4eVnp6uiIhTO2soLPccRUREqGfPnqf0GA6HgxdSJ8R275zY7p0T273zYZt3Tmz3zqnpdj/VPUZuDMgAAAAAACIcAQAAAICkThiO7Ha7Hn30Udntdqu7gnbEdu+c2O6dE9u982Gbd05s986prbd7WA7IAAAAAADB1un2HAEAAABAcwhHAAAAACDCEQAAAABIIhwBAAAAgKROGI6ef/55nXHGGTrttNOUmZmpL774wuouIUgee+wx2Ww2vzZ48GDP8urqauXk5Khbt26Kj4/X1KlTVVpaamGPcTI+/fRTTZo0Senp6bLZbFq+fLnfcsMwNG/ePKWlpSk2NlbZ2dnavXu3X82hQ4c0ffp0ORwOJSYm6tZbb1VlZWU7rgVa63jb/eabbz7m9T9+/Hi/GrZ7eFmwYIHOO+88de3aVcnJyZo8ebIKCgr8ak7kfb2oqEgTJ05UXFyckpOTdf/996u+vr49VwWtcCLb/dJLLz3m9X7HHXf41bDdw8uiRYs0YsQIz4Vds7KytHLlSs/y9nytd6pw9Prrr2vOnDl69NFH9Y9//EMjR47UuHHjVFZWZnXXECRnnXWWSkpKPG39+vWeZffcc4/ee+89vfHGG8rNzVVxcbGmTJliYW9xMo4cOaKRI0fq+eefb3b5woUL9eyzz2rx4sXauHGjunTponHjxqm6utpTM336dG3fvl2rVq3SihUr9Omnn2rWrFnttQo4Ccfb7pI0fvx4v9f/q6++6rec7R5ecnNzlZOTo88//1yrVq1SXV2dxo4dqyNHjnhqjve+3tDQoIkTJ6q2tlYbNmzQiy++qCVLlmjevHlWrBJOwIlsd0m6/fbb/V7vCxcu9Cxju4efnj176oknnlB+fr42bdqkyy67TFdffbW2b98uqZ1f60Yncv755xs5OTme2w0NDUZ6erqxYMECC3uFYHn00UeNkSNHNrusvLzciI6ONt544w3PvJ07dxqSjLy8vHbqIYJNkrFs2TLP7cbGRiM1NdV48sknPfPKy8sNu91uvPrqq4ZhGMaOHTsMScaXX37pqVm5cqVhs9mMf/7zn+3Wd5y8ptvdMAxj5syZxtVXXx3wPmz38FdWVmZIMnJzcw3DOLH39Q8++MCIiIgwnE6np2bRokWGw+Ewampq2ncFcFKabnfDMIxLLrnE+H//7/8FvA/bvWM4/fTTjT/96U/t/lrvNHuOamtrlZ+fr+zsbM+8iIgIZWdnKy8vz8KeIZh2796t9PR09evXT9OnT1dRUZEkKT8/X3V1dX7bf/DgwerduzfbvwPZu3evnE6n33ZOSEhQZmamZzvn5eUpMTFRo0aN8tRkZ2crIiJCGzdubPc+I3jWrVun5ORkDRo0SHfeeacOHjzoWcZ2D38ul0uSlJSUJOnE3tfz8vI0fPhwpaSkeGrGjRuniooKzzfSCG1Nt7vbK6+8ou7du2vYsGGaO3eujh496lnGdg9vDQ0Neu2113TkyBFlZWW1+2s9KjirEfp++OEHNTQ0+P3RJCklJUW7du2yqFcIpszMTC1ZskSDBg1SSUmJ5s+fr4svvljbtm2T0+lUTEyMEhMT/e6TkpIip9NpTYcRdO5t2dzr3L3M6XQqOTnZb3lUVJSSkpJ4LoSx8ePHa8qUKerbt68KCwv1H//xH5owYYLy8vIUGRnJdg9zjY2Nuvvuu3XhhRdq2LBhknRC7+tOp7PZ9wP3MoS25ra7JP385z9Xnz59lJ6eri1btujBBx9UQUGB3n77bUls93C1detWZWVlqbq6WvHx8Vq2bJmGDh2qzZs3t+trvdOEI3R8EyZM8EyPGDFCmZmZ6tOnj/72t78pNjbWwp4BaGvTpk3zTA8fPlwjRoxQ//79tW7dOo0ZM8bCniEYcnJytG3bNr/zSNHxBdruvucKDh8+XGlpaRozZowKCwvVv3//9u4mgmTQoEHavHmzXC6X3nzzTc2cOVO5ubnt3o9Oc1hd9+7dFRkZeczIFqWlpUpNTbWoV2hLiYmJOvPMM7Vnzx6lpqaqtrZW5eXlfjVs/47FvS1bep2npqYeMwhLfX29Dh06xHOhA+nXr5+6d++uPXv2SGK7h7PZs2drxYoV+uSTT9SzZ0/P/BN5X09NTW32/cC9DKEr0HZvTmZmpiT5vd7Z7uEnJiZGAwYMUEZGhhYsWKCRI0fq97//fbu/1jtNOIqJiVFGRobWrFnjmdfY2Kg1a9YoKyvLwp6hrVRWVqqwsFBpaWnKyMhQdHS03/YvKChQUVER278D6du3r1JTU/22c0VFhTZu3OjZzllZWSovL1d+fr6nZu3atWpsbPT8g0X4+/7773Xw4EGlpaVJYruHI8MwNHv2bC1btkxr165V3759/ZafyPt6VlaWtm7d6heMV61aJYfDoaFDh7bPiqBVjrfdm7N582ZJ8nu9s93DX2Njo2pqatr/tR6M0STCxWuvvWbY7XZjyZIlxo4dO4xZs2YZiYmJfiNbIHzde++9xrp164y9e/can332mZGdnW10797dKCsrMwzDMO644w6jd+/extq1a41NmzYZWVlZRlZWlsW9RmsdPnzY+Oqrr4yvvvrKkGQ8/fTTxldffWV89913hmEYxhNPPGEkJiYa77zzjrFlyxbj6quvNvr27WtUVVV5HmP8+PHGOeecY2zcuNFYv369MXDgQOOGG26wapVwAlra7ocPHzbuu+8+Iy8vz9i7d6+xevVq49xzzzUGDhxoVFdXex6D7R5e7rzzTiMhIcFYt26dUVJS4mlHjx711Bzvfb2+vt4YNmyYMXbsWGPz5s3Ghx9+aPTo0cOYO3euFauEE3C87b5nzx7j8ccfNzZt2mTs3bvXeOedd4x+/foZo0eP9jwG2z38PPTQQ0Zubq6xd+9eY8uWLcZDDz1k2Gw24+OPPzYMo31f650qHBmGYTz33HNG7969jZiYGOP88883Pv/8c6u7hCC5/vrrjbS0NCMmJsb4yU9+Ylx//fXGnj17PMurqqqMu+66yzj99NONuLg445prrjFKSkos7DFOxieffGJIOqbNnDnTMAxzOO9HHnnESElJMex2uzFmzBijoKDA7zEOHjxo3HDDDUZ8fLzhcDiMW265xTh8+LAFa4MT1dJ2P3r0qDF27FijR48eRnR0tNGnTx/j9ttvP+aLL7Z7eGlue0syXnjhBU/Nibyv79u3z5gwYYIRGxtrdO/e3bj33nuNurq6dl4bnKjjbfeioiJj9OjRRlJSkmG3240BAwYY999/v+Fyufweh+0eXn7xi18Yffr0MWJiYowePXoYY8aM8QQjw2jf17rNMAyjdfuaAAAAAKDj6TTnHAEAAABASwhHAAAAACDCEQAAAABIIhwBAAAAgCTCEQAAAABIIhwBAAAAgCTCEQAAAABIIhwBACywbt062Ww2lZeXW90VjzPOOEO/+93vrO4GAMBChCMAQFDZbLYW22OPPWZ1FwEAaFaU1R0AAHQsJSUlnunXX39d8+bNU0FBgWdefHy8Nm3a1OrHra2tVUxMTFD6CABAc9hzBAAIqtTUVE9LSEiQzWbzmxcfH++pzc/P16hRoxQXF6ef/vSnfiHqscce09lnn60//elP6tu3r0477TRJUnl5uW677Tb16NFDDodDl112mb7++mvP/QoLC3X11VcrJSVF8fHxOu+887R69Wq/PpaVlWnSpEmKjY1V37599corr/gtNwxDjz32mHr37i273a709HT96le/aos/FwAghBCOAACW+c///E/99re/1aZNmxQVFaVf/OIXfsv37Nmjt956S2+//bY2b94sSbruuutUVlamlStXKj8/X+eee67GjBmjQ4cOSZIqKyt1xRVXaM2aNfrqq680fvx4TZo0SUVFRZ7Hvfnmm7V//3598sknevPNN/WHP/xBZWVlnuVvvfWWnnnmGf3v//6vdu/ereXLl2v48OFt/wcBAFiKw+oAAJb5zW9+o0suuUSS9NBDD2nixImqrq727CWqra3VSy+9pB49ekiS1q9fry+++EJlZWWy2+2SpKeeekrLly/Xm2++qVmzZmnkyJEaOXKk53f8+te/1rJly/Tuu+9q9uzZ+uabb7Ry5Up98cUXOu+88yRJf/7znzVkyBDPfYqKipSamqrs7GxFR0erd+/eOv/889vlbwIAsA57jgAAlhkxYoRnOi0tTZL89uD06dPHE4wk6euvv1ZlZaW6deum+Ph4T9u7d68KCwslmXuO7rvvPg0ZMkSJiYmKj4/Xzp07PXuOdu7cqaioKGVkZHged/DgwUpMTPTcvu6661RVVaV+/frp9ttv17Jly1RfX98mfwMAQOhgzxEAwDLR0dGeaZvNJklqbGz0zOvSpYtffWVlpdLS0rRu3bpjHssdbu677z6tWrVKTz31lAYMGKDY2Fhde+21qq2tPeF+9erVSwUFBVq9erVWrVqlu+66S08++aRyc3P9+gwA6FgIRwCAsHHuuefK6XQqKipKZ5xxRrM1n332mW6++WZdc801ksxAtW/fPs/ywYMHq76+Xvn5+Z7D6goKCo655lJsbKwmTZqkSZMmKScnR4MHD9bWrVt17rnntsWqAQBCAOEIABA2srOzlZWVpcmTJ2vhwoU688wzVVxcrPfff1/XXHONRo0apYEDB+rtt9/WpEmTZLPZ9Mgjj/jtjRo0aJDGjx+vf//3f9eiRYsUFRWlu+++W7GxsZ6aJUuWqKGhQZmZmYqLi9PLL7+s2NhY9enTx4rVBgC0E845AgCEDZvNpg8++ECjR4/WLbfcojPPPFPTpk3Td999p5SUFEnS008/rdNPP10//elPNWnSJI0bN+6YvT0vvPCC0tPTdckll2jKlCmaNWuWkpOTPcsTExP1xz/+URdeeKFGjBih1atX67333lO3bt3adX0BAO3LZhiGYXUnAAAAAMBq7DkCAAAAABGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAEAS4QgAAAAAJBGOAAAAAECS9P8B2k3f3GPUoJ0AAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df[\"Presim. Time / s\"].plot(style=\"--r\");\n",
-    "df[\"Sim. Time / s\"].plot(style=\"-b\");"
+    "df[\"Presim. Time / s\"].plot(style=\"--r\", figsize=(10,3));\n",
+    "df[\"Sim. Time / s\"].plot(style=\"-b\", figsize=(10,3));"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 82,
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1000x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "ax = df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot(style=[\"--b\", \"-r\"]);\n",
+    "ax = df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot(style=[\"--b\", \"-r\"], figsize=(10,3));\n",
     "ax.set_ylabel(\"Time / s\");"
    ]
   },
@@ -1007,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 103,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -1030,7 +1040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 104,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -1120,7 +1130,7 @@
        "16                      0.70              7.95         142.81  "
       ]
      },
-     "execution_count": 108,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1131,7 +1141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 105,
    "metadata": {
     "exercise": "solution",
     "slideshow": {
@@ -1141,9 +1151,9 @@
    "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",
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",
       "text/plain": [
-       "<Figure size 864x288 with 1 Axes>"
+       "<Figure size 1200x400 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1157,10 +1167,12 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "slide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "## Task 7\n",
@@ -1175,19 +1187,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 114,
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 864x288 with 1 Axes>"
+       "<Figure size 1200x400 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1205,6 +1219,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "subslide"
@@ -1225,6 +1240,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "slide"
@@ -1232,7 +1248,7 @@
     "tags": []
    },
    "source": [
-    "## Task 8 (_MATSE Bonus_)\n",
+    "## Task 8 (Super Bonus)\n",
     "<a name=\"task8\"></a>\n",
     "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
     "\n",
@@ -1242,6 +1258,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "fragment"
@@ -1266,6 +1283,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "subslide"
@@ -1275,7 +1293,7 @@
    "source": [
     "```python\n",
     "df_names = pd.read_table('imdb-data/name.basics.tsv.gz', sep=\"\\t\", low_memory=False, na_values=[\"\\\\N\",\"nan\"])\n",
-    "df_ratings = pd.read_table('https://datasets.i\n",
+    "df_ratings = pd.read_table('https://datasets.imdbws.com/title.ratings.tsv.gz', sep=\"\\t\", low_memory=False, na_values=[\"\\\\N\",\"nan\"])\n",
     "                           \n",
     "df_names_i = df_names.set_index('nconst')\n",
     "df_ratings_i = df_ratings.set_index('tconst')\n",
@@ -1294,6 +1312,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "solution",
     "slideshow": {
      "slide_type": "subslide"
@@ -1332,6 +1351,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "slide"
@@ -1339,11 +1359,11 @@
     "tags": []
    },
    "source": [
-    "## Task 8B (B, like <em>B</em>onus)\n",
+    "## Task 8B (<em>B</em>onuseption)\n",
     "<a name=\"task8b\"></a>\n",
     "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
     "\n",
-    "All of the following are unique sub-tasks, which can be done individually\n",
+    "All of the following are ideas for unique sub-tasks, which can be done individually\n",
     "* In addition to Task 8, restrict the top titles to those with more than 10000 votes\n",
     "* For 30 top-rated actors, plot rating vs. age\n",
     "* For 30 top-rated actors, plot rating vs. average runtime of the known-for-titles (using `title.basics.tsv.gz`)"
@@ -1377,7 +1397,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.11.5"
   },
   "toc-autonumbering": false,
   "toc-showcode": false,
diff --git a/Introduction-to-Pandas--tasks.ipynb b/Introduction-to-Pandas--tasks.ipynb
index 2541ec1b0eb73c0399fbdcdf31d4d869713d1a10..0e253594f28bbbaa77e2b579f6b284bb51bd6e99 100644
--- a/Introduction-to-Pandas--tasks.ipynb
+++ b/Introduction-to-Pandas--tasks.ipynb
@@ -11,9 +11,7 @@
    "source": [
     "# Data Analysis and Plotting in Python with Pandas\n",
     "\n",
-    "_Andreas Herten, J\u00fclich Supercomputing Centre, Forschungszentrum J\u00fclich, 14 June 2022_\n",
-    "\n",
-    "_MATSE Version_"
+    "_Andreas Herten, J\u00fclich Supercomputing Centre, Forschungszentrum J\u00fclich, 4 September 2023_"
    ]
   },
   {
@@ -53,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {
     "exercise": "task",
     "slideshow": {
@@ -104,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 26,
    "metadata": {
     "exercise": "task",
     "slideshow": {
@@ -142,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 34,
    "metadata": {
     "exercise": "task"
    },
@@ -171,10 +169,12 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "## Task 3\n",
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 65,
    "metadata": {
     "exercise": "task",
     "slideshow": {
@@ -203,10 +203,12 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "slide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "## Task 4\n",
@@ -266,10 +268,12 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "slide"
-    }
+    },
+    "tags": []
    },
    "source": [
     "## Task 7\n",
@@ -285,6 +289,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "subslide"
@@ -305,6 +310,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "slide"
@@ -312,7 +318,7 @@
     "tags": []
    },
    "source": [
-    "## Task 8 (_MATSE Bonus_)\n",
+    "## Task 8 (Super Bonus)\n",
     "<a name=\"task8\"></a>\n",
     "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
     "\n",
@@ -322,6 +328,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "fragment"
@@ -346,6 +353,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
     "exercise": "task",
     "slideshow": {
      "slide_type": "slide"
@@ -353,11 +361,11 @@
     "tags": []
    },
    "source": [
-    "## Task 8B (B, like <em>B</em>onus)\n",
+    "## Task 8B (<em>B</em>onuseption)\n",
     "<a name=\"task8b\"></a>\n",
     "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n",
     "\n",
-    "All of the following are unique sub-tasks, which can be done individually\n",
+    "All of the following are ideas for unique sub-tasks, which can be done individually\n",
     "* In addition to Task 8, restrict the top titles to those with more than 10000 votes\n",
     "* For 30 top-rated actors, plot rating vs. age\n",
     "* For 30 top-rated actors, plot rating vs. average runtime of the known-for-titles (using `title.basics.tsv.gz`)"
@@ -391,7 +399,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.11.5"
   },
   "toc-autonumbering": false,
   "toc-showcode": false,
diff --git a/Makefile b/Makefile
index c663bdf8a5848a672782e165355da1efcae8ecc8..191bb090e669afbc209ca53ab36d84f6921cf735 100644
--- a/Makefile
+++ b/Makefile
@@ -17,14 +17,17 @@ subnotebooks: $(SUBNOTEBOOKS)
 
 %.html: %.ipynb $(DEP_PRESENTATION)
 	jupyter nbconvert --to=slides --reveal-prefix=reveal.js --stdout $< > $(_TMPFILE1)
-	gsed -i 's#<script src=#\n<script src=#g' $(_TMPFILE1)
+	gsed -i 's#<script #\n<script #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) $(_TMPFILE1)
 	patch --silent --unified --input=fzj-reveal.js/patch-04--add-dependencies.patch --output=$(_TMPFILE2) $(_TMPFILE1) && mv $(_TMPFILE2) $(_TMPFILE1)
-	patch --silent --unified --input=patch-00--add-fzj-reveal-subdir.patch --output=$(_TMPFILE2) $(_TMPFILE1) && mv $(_TMPFILE2) $(_TMPFILE1)
+# 	patch --silent --unified --input=patch-00--add-fzj-reveal-subdir.patch --output=$(_TMPFILE2) $(_TMPFILE1) && mv $(_TMPFILE2) $(_TMPFILE1)
+	gsed -i 's#="reveal.js#="fzj-reveal.js/reveal.js#g' $(_TMPFILE1)
+	gsed -i 's#="custom.css#="fzj-reveal.js/custom.css#g' $(_TMPFILE1)
+	gsed -i 's#="fzj.js#="fzj-reveal.js/fzj.js#g' $(_TMPFILE1)
 	patch --silent --unified --input=patch-01--add-ah-css.patch --output=$(_TMPFILE2) $(_TMPFILE1) && mv $(_TMPFILE2) $(_TMPFILE1)
 	mv $(_TMPFILE1) $@
 
diff --git a/patch-01--add-ah-css.patch b/patch-01--add-ah-css.patch
index 31e1e4d0a114ae47582c0e22a37c83ca36a58d27..47aebf961e6c384ad203ada05d7a6f2ddb20f441 100644
--- a/patch-01--add-ah-css.patch
+++ b/patch-01--add-ah-css.patch
@@ -1,10 +1,10 @@
 --- /dev/null	2021-06-02 15:02:18.000000000 +0200
 +++ /dev/null	2021-06-02 15:02:43.000000000 +0200
-@@ -14,6 +14,7 @@
- <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">
+@@ -7432,6 +7432,7 @@
+ <!-- End of mathjax configuration --><!-- Reveal Theme -->
+ <link href="fzj-reveal.js/reveal.js/dist/theme/simple.css" id="theme" rel="stylesheet"/>
  <link rel="stylesheet" href="fzj-reveal.js/custom.css" id="custom">
 +<link rel="stylesheet" href="custom.css" id="custom">
- 
- <!-- If the query includes 'print-pdf', include the PDF print sheet -->
- <script>
+ </head>
+ <body class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light">
+ <main>