diff --git a/Introduction-to-Pandas--master-2023.ipynb b/Introduction-to-Pandas--master-2023.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6c28d733e4119fb1d656155f7dda08e13ffe356e --- /dev/null +++ b/Introduction-to-Pandas--master-2023.ipynb @@ -0,0 +1,7923 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "exercise": "task", + "tags": [ + "task" + ] + }, + "source": [ + "# Data Analysis and Plotting in Python with Pandas\n", + "\n", + "_Andreas Herten, Jülich Supercomputing Centre, Forschungszentrum Jülich, 4 September 2023_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "exercise": "onlypresentation", + "slideshow": { + "slide_type": "skip" + } + }, + "source": [ + "**Version: Slides**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "exercise": "onlysolution", + "slideshow": { + "slide_type": "skip" + } + }, + "source": [ + "**Version: Solutions**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "exercise": "onlytask", + "slideshow": { + "slide_type": "skip" + } + }, + "source": [ + "**Version: Tasks**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## My Motivation\n", + "\n", + "* I like Python\n", + "* I like plotting data\n", + "* I like sharing\n", + "* I think Pandas is awesome and you should use it too\n", + "* …_but I'm no Python expert!_\n", + "\n", + "<span style=\"color: #023d6b\"><em>Motto: <strong>»Pandas as early as possible!«</strong></em></span>" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "exercise": "task", + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "## Task Outline\n", + "\n", + "* [Task 1](#task1)\n", + "* [Task 2](#task2)\n", + "* [Task 3](#task3)\n", + "* [Task 4](#task4)\n", + "* [Task 5](#task5)\n", + "* [Task 6](#task6)\n", + "* [Task 7](#task7)\n", + "* [Task 7B](#task7b)\n", + "* [Task 8](#task8)\n", + "* [Task 8B](#task8b)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Course Setup\n", + "\n", + "* 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" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "* Please now open Jupyter Notebook of this session: https://go.fzj.de/jsc-pd\n", + "* Give thumbs up! 👍" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## About Pandas\n", + "\n", + "<img style=\"float: right; max-width: 200px;\" width=\"200px\" src=\"img/adorable-animal-animal-photography-1661535.jpg\" />\n", + "\n", + "* Python package (~~Python 2,~~ Python 3)\n", + "* For data analysis and manipulation\n", + "* With data structures (multi-dimensional table; time series), operations\n", + "* Name from »**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*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Pandas Cohabitation\n", + "\n", + "* Pandas works great together with other established Python tools\n", + " * [Jupyter Notebooks](https://jupyter.org/)\n", + " * Plotting with [`matplotlib`](https://matplotlib.org/)\n", + " * Numerical analysis with [`numpy`](https://numpy.org/)\n", + " * Modelling with [`statsmodels`](https://www.statsmodels.org/stable/index.html), [`scikit-learn`](https://scikit-learn.org/)\n", + " * Nicer plots with [`seaborn`](https://seaborn.pydata.org/), [`altair`](https://altair-viz.github.io/), [`plotly`](https://plot.ly/)\n", + " * Performance enhancement with [Cython](https://cython.org/), [Numba](numba.pydata.org/), …\n", + "* Tools building up on Pandas: [cuDF](https://github.com/rapidsai/cudf) (GPU-accelerated DataFrames in [Rapids](https://rapids.ai/)), [pyarrow](https://arrow.apache.org/docs/python/index.html) (Apache Arrow bindings in Python) …" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## First Steps" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "import pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "exercise": "task", + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.0.3'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mClass docstring:\u001b[0m\n", + " pandas - a powerful data analysis and manipulation library for Python\n", + " =====================================================================\n", + " \n", + " **pandas** is a Python package providing fast, flexible, and expressive data\n", + " structures designed to make working with \"relational\" or \"labeled\" data both\n", + " easy and intuitive. It aims to be the fundamental high-level building block for\n", + " doing practical, **real world** data analysis in Python. Additionally, it has\n", + " the broader goal of becoming **the most powerful and flexible open source data\n", + " analysis / manipulation tool available in any language**. It is already well on\n", + " its way toward this goal.\n", + " \n", + " Main Features\n", + " -------------\n", + " Here are just a few of the things that pandas does well:\n", + " \n", + " - Easy handling of missing data in floating point as well as non-floating\n", + " point data.\n", + " - Size mutability: columns can be inserted and deleted from DataFrame and\n", + " higher dimensional objects\n", + " - Automatic and explicit data alignment: objects can be explicitly aligned\n", + " to a set of labels, or the user can simply ignore the labels and let\n", + " `Series`, `DataFrame`, etc. automatically align the data for you in\n", + " computations.\n", + " - Powerful, flexible group by functionality to perform split-apply-combine\n", + " operations on data sets, for both aggregating and transforming data.\n", + " - Make it easy to convert ragged, differently-indexed data in other Python\n", + " and NumPy data structures into DataFrame objects.\n", + " - Intelligent label-based slicing, fancy indexing, and subsetting of large\n", + " data sets.\n", + " - Intuitive merging and joining data sets.\n", + " - Flexible reshaping and pivoting of data sets.\n", + " - Hierarchical labeling of axes (possible to have multiple labels per tick).\n", + " - Robust IO tools for loading data from flat files (CSV and delimited),\n", + " Excel files, databases, and saving/loading data from the ultrafast HDF5\n", + " format.\n", + " - Time series-specific functionality: date range generation and frequency\n", + " conversion, moving window statistics, date shifting and lagging." + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%pdoc pd" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## DataFrames\n", + "### It's all about DataFrames\n", + "\n", + "<img style=\"float: right; max-width: 200px;\" width=\"200px\" src=\"img/buzz-dataframes.jpg\" />\n", + "\n", + "* Data containers of Pandas:\n", + " - Linear: `Series`\n", + " - Multi Dimension: `DataFrame`\n", + "* `Series` is *only* special (1D) case of `DataFrame`\n", + "* → We use `DataFrame`s as the more general case here" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## DataFrames\n", + "### Construction\n", + "\n", + "* To show features of `DataFrame`, let's construct one and show by example!\n", + "* Many construction possibilities\n", + " - From lists, dictionaries, `numpy` objects\n", + " - From CSV, HDF5, JSON, Excel, HTML, fixed-width files\n", + " - From pickled Pandas data\n", + " - From clipboard\n", + " - *From Feather, Parquet, SAS, SQL, Google BigQuery, STATA*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## DataFrames\n", + "\n", + "### Examples, finally" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "ages = [41, 56, 56, 57, 39, 59, 43, 56, 38, 60]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>0</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>41</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>56</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>56</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>57</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>39</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>59</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>43</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>56</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>38</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>60</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " 0\n", + "0 41\n", + "1 56\n", + "2 56\n", + "3 57\n", + "4 39\n", + "5 59\n", + "6 43\n", + "7 56\n", + "8 38\n", + "9 60" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(ages)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>0</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>41</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>56</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>56</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " 0\n", + "0 41\n", + "1 56\n", + "2 56" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ages = pd.DataFrame(ages)\n", + "df_ages.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "* Let's add names to ages; put everything into a `dict()`" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Name': ['Liu', 'Rowland', 'Rivers', 'Waters', 'Rice', 'Fields', 'Kerr', 'Romero', 'Davis', 'Hall'], 'Age': [41, 56, 56, 57, 39, 59, 43, 56, 38, 60]}\n" + ] + } + ], + "source": [ + "data = {\n", + " \"Name\": [\"Liu\", \"Rowland\", \"Rivers\", \"Waters\", \"Rice\", \"Fields\", \"Kerr\", \"Romero\", \"Davis\", \"Hall\"],\n", + " \"Age\": ages\n", + "}\n", + "print(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Name</th>\n", + " <th>Age</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Liu</td>\n", + " <td>41</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Rowland</td>\n", + " <td>56</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Rivers</td>\n", + " <td>56</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Waters</td>\n", + " <td>57</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Name Age\n", + "0 Liu 41\n", + "1 Rowland 56\n", + "2 Rivers 56\n", + "3 Waters 57" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sample = pd.DataFrame(data)\n", + "df_sample.head(4)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "* Automatically creates columns from dictionary\n", + "* Two columns now; one for names, one for ages" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Name', 'Age'], dtype='object')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sample.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "* First column is _index_\n", + "* `DataFrame` always have indexes; auto-generated or custom" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=10, step=1)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sample.index" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "* Make `Name` be index with `.set_index()`\n", + "* `inplace=True` will modifiy the parent frame (*I don't like it*)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Age</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Name</th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Liu</th>\n", + " <td>41</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rowland</th>\n", + " <td>56</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rivers</th>\n", + " <td>56</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Waters</th>\n", + " <td>57</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rice</th>\n", + " <td>39</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Fields</th>\n", + " <td>59</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Kerr</th>\n", + " <td>43</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Romero</th>\n", + " <td>56</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Davis</th>\n", + " <td>38</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Hall</th>\n", + " <td>60</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Age\n", + "Name \n", + "Liu 41\n", + "Rowland 56\n", + "Rivers 56\n", + "Waters 57\n", + "Rice 39\n", + "Fields 59\n", + "Kerr 43\n", + "Romero 56\n", + "Davis 38\n", + "Hall 60" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sample.set_index(\"Name\", inplace=True)\n", + "df_sample" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "* Some more operations" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Age</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>count</th>\n", + " <td>10.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>mean</th>\n", + " <td>50.500000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>std</th>\n", + " <td>9.009255</td>\n", + " </tr>\n", + " <tr>\n", + " <th>min</th>\n", + " <td>38.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25%</th>\n", + " <td>41.500000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>50%</th>\n", + " <td>56.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>75%</th>\n", + " <td>56.750000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>max</th>\n", + " <td>60.000000</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Age\n", + "count 10.000000\n", + "mean 50.500000\n", + "std 9.009255\n", + "min 38.000000\n", + "25% 41.500000\n", + "50% 56.000000\n", + "75% 56.750000\n", + "max 60.000000" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sample.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<class 'pandas.core.frame.DataFrame'>\n", + "Index: 10 entries, Liu to Hall\n", + "Data columns (total 1 columns):\n", + " # Column Non-Null Count Dtype\n", + "--- ------ -------------- -----\n", + " 0 Age 10 non-null int64\n", + "dtypes: int64(1)\n", + "memory usage: 160.0+ bytes\n" + ] + } + ], + "source": [ + "df_sample.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th>Name</th>\n", + " <th>Liu</th>\n", + " <th>Rowland</th>\n", + " <th>Rivers</th>\n", + " <th>Waters</th>\n", + " <th>Rice</th>\n", + " <th>Fields</th>\n", + " <th>Kerr</th>\n", + " <th>Romero</th>\n", + " <th>Davis</th>\n", + " <th>Hall</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Age</th>\n", + " <td>41</td>\n", + " <td>56</td>\n", + " <td>56</td>\n", + " <td>57</td>\n", + " <td>39</td>\n", + " <td>59</td>\n", + " <td>43</td>\n", + " <td>56</td>\n", + " <td>38</td>\n", + " <td>60</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + "Name Liu Rowland Rivers Waters Rice Fields Kerr Romero Davis Hall\n", + "Age 41 56 56 57 39 59 43 56 38 60" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sample.T" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Liu', 'Rowland', 'Rivers', 'Waters', 'Rice', 'Fields', 'Kerr',\n", + " 'Romero', 'Davis', 'Hall'],\n", + " dtype='object', name='Name')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sample.T.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "* Also: Arithmetic operations" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " 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"fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Name</th>\n", + " <th>Age</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>LiuLiu</td>\n", + " <td>82</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>RowlandRowland</td>\n", + " <td>112</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>RiversRivers</td>\n", + " <td>112</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Name Age\n", + "0 LiuLiu 82\n", + "1 RowlandRowland 112\n", + "2 RiversRivers 112" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sample.reset_index().multiply(2).head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Age</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Name</th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Liu</th>\n", + " <td>20.5</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rowland</th>\n", + " <td>28.0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rivers</th>\n", + " <td>28.0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Age\n", + "Name \n", + "Liu 20.5\n", + "Rowland 28.0\n", + "Rivers 28.0" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(df_sample / 2).head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Age</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Name</th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Liu</th>\n", + " <td>1681</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rowland</th>\n", + " <td>3136</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rivers</th>\n", + " <td>3136</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Age\n", + "Name \n", + "Liu 1681\n", + "Rowland 3136\n", + "Rivers 3136" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(df_sample * df_sample).head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Age</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Name</th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Liu</th>\n", + " <td>1681</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rowland</th>\n", + " <td>3136</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rivers</th>\n", + " <td>3136</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Waters</th>\n", + " <td>3249</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rice</th>\n", + " <td>1521</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Age\n", + "Name \n", + "Liu 1681\n", + "Rowland 3136\n", + "Rivers 3136\n", + "Waters 3249\n", + "Rice 1521" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def mysquare(number: float) -> float:\n", + " return number*number\n", + "\n", + "df_sample.apply(mysquare).head()\n", + "# or: df_sample.apply(lambda x: x*x).head()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Age</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Name</th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Liu</th>\n", + " <td>1681</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rowland</th>\n", + " <td>3136</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rivers</th>\n", + " <td>3136</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Waters</th>\n", + " <td>3249</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rice</th>\n", + " <td>1521</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Age\n", + "Name \n", + "Liu 1681\n", + "Rowland 3136\n", + "Rivers 3136\n", + "Waters 3249\n", + "Rice 1521" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sample.apply(np.square).head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "Logical operations allowed as well" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Age</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Name</th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Liu</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rowland</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rivers</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Waters</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rice</th>\n", + " <td>False</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Fields</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Kerr</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Romero</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Davis</th>\n", + " <td>False</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Hall</th>\n", + " <td>True</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Age\n", + "Name \n", + "Liu True\n", + "Rowland True\n", + "Rivers True\n", + "Waters True\n", + "Rice False\n", + "Fields True\n", + "Kerr True\n", + "Romero True\n", + "Davis False\n", + "Hall True" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sample > 40" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Age</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Name</th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Liu</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rowland</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rivers</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Waters</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rice</th>\n", + " <td>True</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Age\n", + "Name \n", + "Liu True\n", + "Rowland True\n", + "Rivers True\n", + "Waters True\n", + "Rice True" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sample.apply(mysquare).head() == df_sample.apply(lambda x: x*x).head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "exercise": "task", + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Task 1\n", + "<a name=\"task1\"></a>\n", + "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n", + "\n", + "* Create data frame with\n", + " - 6 names of dinosaurs, \n", + " - their favourite prime number, \n", + " - and their favorite color.\n", + "* Play around with the frame\n", + "* Tell me when you're done with status icon in BigBlueButton: 👍" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "exercise": "nopresentation", + "slideshow": { + "slide_type": "skip" + } + }, + "source": [ + "Jupyter Notebook 101:\n", + "\n", + "* Execute cell: `shift+enter`\n", + "* New cell in front of current cell: `a`\n", + "* New cell after current cell: `b`" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "exercise": "task", + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "happy_dinos = {\n", + " \"Dinosaur Name\": [],\n", + " \"Favourite Prime\": [],\n", + " \"Favourite Color\": []\n", + "}\n", + "#df_dinos = " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "exercise": "solution", + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th>Dinosaur Name</th>\n", + " <th>Aegyptosaurus</th>\n", + " <th>Tyrannosaurus</th>\n", + " <th>Panoplosaurus</th>\n", + " <th>Isisaurus</th>\n", + " <th>Triceratops</th>\n", + " <th>Velociraptor</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Favourite Prime</th>\n", + " <td>4</td>\n", + " <td>8</td>\n", + " <td>15</td>\n", + " <td>16</td>\n", + " <td>23</td>\n", + " <td>42</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Favourite Color</th>\n", + " <td>blue</td>\n", + " <td>white</td>\n", + " <td>blue</td>\n", + " <td>purple</td>\n", + " <td>violet</td>\n", + " <td>gray</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + "Dinosaur Name Aegyptosaurus Tyrannosaurus Panoplosaurus Isisaurus \\\n", + "Favourite Prime 4 8 15 16 \n", + "Favourite Color blue white blue purple \n", + "\n", + "Dinosaur Name Triceratops Velociraptor \n", + "Favourite Prime 23 42 \n", + "Favourite Color violet gray " + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "happy_dinos = {\n", + " \"Dinosaur Name\": [\"Aegyptosaurus\", \"Tyrannosaurus\", \"Panoplosaurus\", \"Isisaurus\", \"Triceratops\", \"Velociraptor\"],\n", + " \"Favourite Prime\": [\"4\", \"8\", \"15\", \"16\", \"23\", \"42\"],\n", + " \"Favourite Color\": [\"blue\", \"white\", \"blue\", \"purple\", \"violet\", \"gray\"]\n", + "}\n", + "df_dinos = pd.DataFrame(happy_dinos).set_index(\"Dinosaur Name\")\n", + "df_dinos.T" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### More `DataFrame` examples" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>D</th>\n", + " <th>E</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-2.718282</td>\n", + " <td>This</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>1.718282</td>\n", + " <td>column</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-1.304068</td>\n", + " <td>has</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>0.986231</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-0.718282</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A B C D E\n", + "0 1.2 2018-02-26 -2.718282 This Same\n", + "1 1.2 2018-02-26 1.718282 column Same\n", + "2 1.2 2018-02-26 -1.304068 has Same\n", + "3 1.2 2018-02-26 0.986231 entries Same\n", + "4 1.2 2018-02-26 -0.718282 entries Same" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demo = pd.DataFrame({\n", + " \"A\": 1.2,\n", + " \"B\": pd.Timestamp('20180226'),\n", + " \"C\": [(-1)**i * np.sqrt(i) + np.e * (-1)**(i-1) for i in range(5)],\n", + " \"D\": pd.Categorical([\"This\", \"column\", \"has\", \"entries\", \"entries\"]),\n", + " \"E\": \"Same\"\n", + "})\n", + "df_demo" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>D</th>\n", + " <th>E</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-2.718282</td>\n", + " <td>This</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-1.304068</td>\n", + " <td>has</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-0.718282</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>0.986231</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>1.718282</td>\n", + " <td>column</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A B C D E\n", + "0 1.2 2018-02-26 -2.718282 This Same\n", + "2 1.2 2018-02-26 -1.304068 has Same\n", + "4 1.2 2018-02-26 -0.718282 entries Same\n", + "3 1.2 2018-02-26 0.986231 entries Same\n", + "1 1.2 2018-02-26 1.718282 column Same" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demo.sort_values(\"C\")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>D</th>\n", + " <th>E</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>0.99</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-0.72</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A B C D E\n", + "3 1.2 2018-02-26 0.99 entries Same\n", + "4 1.2 2018-02-26 -0.72 entries Same" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demo.round(2).tail(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "A 6.00\n", + "C -2.03\n", + "dtype: float64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demo.round(2)[[\"A\", \"C\"]].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{tabular}{lrlrll}\n", + "\\toprule\n", + " & A & B & C & D & E \\\\\n", + "\\midrule\n", + "0 & 1.200000 & 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" + ] + } + ], + "source": [ + "print(df_demo.round(2).to_latex())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Reading External Data\n", + "\n", + "(Links to documentation)\n", + "* [`.read_json()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_json.html#pandas.read_json)\n", + "* [`.read_csv()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html#pandas.read_csv)\n", + "* [`.read_hdf5()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_hdf.html#pandas.read_hdf)\n", + "* [`.read_excel()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html#pandas.read_excel)\n", + "\n", + "Example:\n", + "\n", + "```json\n", + "{\n", + " \"Character\": [\"Sawyer\", \"…\", \"Walt\"],\n", + " \"Actor\": [\"Josh Holloway\", \"…\", \"Malcolm David Kelley\"],\n", + " \"Main Cast\": [true, \"…\", false]\n", + "}\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Actor</th>\n", + " <th>Main Cast</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Character</th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Hurley</th>\n", + " <td>Jorge Garcia</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Jack</th>\n", + " <td>Matthew Fox</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Kate</th>\n", + " <td>Evangeline Lilly</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Locke</th>\n", + " <td>Terry O'Quinn</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Sawyer</th>\n", + " <td>Josh Holloway</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Walt</th>\n", + " <td>Malcolm David Kelley</td>\n", + " <td>False</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Actor Main Cast\n", + "Character \n", + "Hurley Jorge Garcia True\n", + "Jack Matthew Fox True\n", + "Kate Evangeline Lilly True\n", + "Locke Terry O'Quinn True\n", + "Sawyer Josh Holloway True\n", + "Walt Malcolm David Kelley False" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.read_json(\"data-lost.json\").set_index(\"Character\").sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "exercise": "task", + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Task 2\n", + "<a name=\"task2\"></a>\n", + "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n", + "\n", + "* Read in `data-nest.csv` to `DataFrame`; call it `df` \n", + " *(Data was produced with [JUBE](http://www.fz-juelich.de/ias/jsc/EN/Expertise/Support/Software/JUBE/_node.html))*\n", + "* Get to know it and play a bit with it\n", + "* Tell me when you're done with status icon in BigBlueButton: 👍" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "exercise": "task" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "id,Nodes,Tasks/Node,Threads/Task,Runtime Program / s,Scale,Plastic,Avg. Neuron Build Time / s,Min. Edge Build Time / s,Max. Edge Build Time / s,Min. Init. Time / s,Max. Init. Time / s,Presim. Time / s,Sim. Time / s,Virt. Memory (Sum) / kB,Local Spike Counter (Sum),Average Rate (Sum),Number of Neurons,Number of Connections,Min. Delay,Max. Delay\n", + "5,1,2,4,420.42,10,true,0.29,88.12,88.18,1.14,1.20,17.26,311.52,46560664.00,825499,7.48,112500,1265738500,1.5,1.5\n", + "5,1,4,4,200.84,10,true,0.15,46.03,46.34,0.70,1.01,7.87,142.97,46903088.00,802865,7.03,112500,1265738500,1.5,1.5\n", + "5,1,2,8,202.15,10,true,0.28,47.98,48.48,0.70,1.20,7.95,142.81,47699384.00,802865,7.03,112500,1265738500,1.5,1.5\n", + "5,1,4,8,89.57,10,true,0.15,20.41,23.21,0.23,3.04,3.19,60.31,46813040.00,821491,7.23,112500,1265738500,1.5,1.5\n", + "5,2,2,4,164.16,10,true,0.20,40.03,41.09,0.52,1.58,6.08,114.88,46937216.00,802865,7.03,112500,1265738500,1.5,1.5\n", + "5,2,4,4,77.68,10,true,0.13,20.93,21.22,0.16,0.46,3.12,52.05,47362064.00,821491,7.23,112500,1265738500,1.5,1.5\n", + "5,2,2,8,79.60,10,true,0.20,21.63,21.91,0.19,0.47,2.98,53.12,46847168.00,821491,7.23,112500,1265738500,1.5,1.5\n", + "5,2,4,8,37.20,10,true,0.13,10.08,11.60,0.10,1.63,1.24,23.29,47065232.00,818198,7.33,112500,1265738500,1.5,1.5\n", + "5,3,2,4,96.51,10,true,0.15,26.54,27.41,0.36,1.22,3.33,64.28,52256880.00,813743,7.27,112500,1265738500,1.5,1.5\n" + ] + } + ], + "source": [ + "!head data-nest.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "exercise": "solution", + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>id</th>\n", + " <th>Nodes</th>\n", + " <th>Tasks/Node</th>\n", + " <th>Threads/Task</th>\n", + " <th>Runtime Program / s</th>\n", + " <th>Scale</th>\n", + " <th>Plastic</th>\n", + " <th>Avg. Neuron Build Time / s</th>\n", + " <th>Min. Edge Build Time / s</th>\n", + " <th>Max. Edge Build Time / s</th>\n", + " <th>...</th>\n", + " <th>Max. Init. Time / s</th>\n", + " <th>Presim. Time / s</th>\n", + " <th>Sim. Time / s</th>\n", + " <th>Virt. Memory (Sum) / kB</th>\n", + " <th>Local Spike Counter (Sum)</th>\n", + " <th>Average Rate (Sum)</th>\n", + " <th>Number of Neurons</th>\n", + " <th>Number of Connections</th>\n", + " <th>Min. Delay</th>\n", + " <th>Max. Delay</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>5</td>\n", + " <td>1</td>\n", + " <td>2</td>\n", + " <td>4</td>\n", + " <td>420.42</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.29</td>\n", + " <td>88.12</td>\n", + " <td>88.18</td>\n", + " <td>...</td>\n", + " <td>1.20</td>\n", + " <td>17.26</td>\n", + " <td>311.52</td>\n", + " <td>46560664.0</td>\n", + " <td>825499</td>\n", + " <td>7.48</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>5</td>\n", + " <td>1</td>\n", + " <td>4</td>\n", + " <td>4</td>\n", + " <td>200.84</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.15</td>\n", + " <td>46.03</td>\n", + " <td>46.34</td>\n", + " <td>...</td>\n", + " <td>1.01</td>\n", + " <td>7.87</td>\n", + " <td>142.97</td>\n", + " <td>46903088.0</td>\n", + " <td>802865</td>\n", + " <td>7.03</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>5</td>\n", + " <td>1</td>\n", + " <td>2</td>\n", + " <td>8</td>\n", + " <td>202.15</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.28</td>\n", + " <td>47.98</td>\n", + " <td>48.48</td>\n", + " <td>...</td>\n", + " <td>1.20</td>\n", + " <td>7.95</td>\n", + " <td>142.81</td>\n", + " <td>47699384.0</td>\n", + " <td>802865</td>\n", + " <td>7.03</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>5</td>\n", + " <td>1</td>\n", + " <td>4</td>\n", + " <td>8</td>\n", + " <td>89.57</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.15</td>\n", + " <td>20.41</td>\n", + " <td>23.21</td>\n", + " <td>...</td>\n", + " <td>3.04</td>\n", + " <td>3.19</td>\n", + " <td>60.31</td>\n", + " <td>46813040.0</td>\n", + " <td>821491</td>\n", + " <td>7.23</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>5</td>\n", + " <td>2</td>\n", + " <td>2</td>\n", + " <td>4</td>\n", + " <td>164.16</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.20</td>\n", + " <td>40.03</td>\n", + " <td>41.09</td>\n", + " <td>...</td>\n", + " <td>1.58</td>\n", + " <td>6.08</td>\n", + " <td>114.88</td>\n", + " <td>46937216.0</td>\n", + " <td>802865</td>\n", + " <td>7.03</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>5 rows × 21 columns</p>\n", + "</div>" + ], + "text/plain": [ + " id Nodes Tasks/Node Threads/Task Runtime Program / s Scale Plastic \\\n", + "0 5 1 2 4 420.42 10 True \n", + "1 5 1 4 4 200.84 10 True \n", + "2 5 1 2 8 202.15 10 True \n", + "3 5 1 4 8 89.57 10 True \n", + "4 5 2 2 4 164.16 10 True \n", + "\n", + " Avg. Neuron Build Time / s Min. Edge Build Time / s \\\n", + "0 0.29 88.12 \n", + "1 0.15 46.03 \n", + "2 0.28 47.98 \n", + "3 0.15 20.41 \n", + "4 0.20 40.03 \n", + "\n", + " Max. Edge Build Time / s ... Max. Init. Time / s Presim. Time / s \\\n", + "0 88.18 ... 1.20 17.26 \n", + "1 46.34 ... 1.01 7.87 \n", + "2 48.48 ... 1.20 7.95 \n", + "3 23.21 ... 3.04 3.19 \n", + "4 41.09 ... 1.58 6.08 \n", + "\n", + " Sim. Time / s Virt. Memory (Sum) / kB Local Spike Counter (Sum) \\\n", + "0 311.52 46560664.0 825499 \n", + "1 142.97 46903088.0 802865 \n", + "2 142.81 47699384.0 802865 \n", + "3 60.31 46813040.0 821491 \n", + "4 114.88 46937216.0 802865 \n", + "\n", + " Average Rate (Sum) Number of Neurons Number of Connections Min. Delay \\\n", + "0 7.48 112500 1265738500 1.5 \n", + "1 7.03 112500 1265738500 1.5 \n", + "2 7.03 112500 1265738500 1.5 \n", + "3 7.23 112500 1265738500 1.5 \n", + "4 7.03 112500 1265738500 1.5 \n", + "\n", + " Max. Delay \n", + "0 1.5 \n", + "1 1.5 \n", + "2 1.5 \n", + "3 1.5 \n", + "4 1.5 \n", + "\n", + "[5 rows x 21 columns]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv(\"data-nest.csv\")\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Read CSV Options\n", + "\n", + "* See also full [API documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)\n", + "* Important parameters\n", + " - `sep`: Set separator (for example `:` instead of `,`)\n", + " - `header`: Specify info about headers for columns; able to use multi-index for columns!\n", + " - `names`: Alternative to `header` – provide your own column titles\n", + " - `usecols`: Don't read whole set of columns, but only these; works with any list (`range(0:20:2)`)…\n", + " - `skiprows`: Don't read in these rows\n", + " - `na_values`: What string(s) to recognize as `N/A` values (which will be ignored during operations on data frame)\n", + " - `parse_dates`: Try to parse dates in CSV; different behaviours as to provided data structure; optionally used together with `date_parser`\n", + " - `compression`: Treat input file as compressed file (\"infer\", \"gzip\", \"zip\", …)\n", + " - `decimal`: Decimal point divider – for German data…\n", + " \n", + "```python\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", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Slicing of Data Frames\n", + "\n", + "* Slicing: Select a sub-range / sub-set of entire data frame\n", + "* Pandas documentation: [Detailed documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html), [short documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html#selection)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "source": [ + "### Quick Slices\n", + "\n", + "* Use square-bracket operators to slice data frame quickly: `[]`\n", + " * Use column name to select column\n", + " * Use numerical value to select row\n", + "* Example: Select only columnn `C` from `df_demo`" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>D</th>\n", + " <th>E</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-2.718282</td>\n", + " <td>This</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>1.718282</td>\n", + " <td>column</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-1.304068</td>\n", + " <td>has</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A B C D E\n", + "0 1.2 2018-02-26 -2.718282 This Same\n", + "1 1.2 2018-02-26 1.718282 column Same\n", + "2 1.2 2018-02-26 -1.304068 has Same" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demo.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 -2.718282\n", + "1 1.718282\n", + "2 -1.304068\n", + "3 0.986231\n", + "4 -0.718282\n", + "Name: C, dtype: float64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demo['C']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "* Instead of column name in quotes and square brackets: Name of column _directly_" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 -2.718282\n", + "1 1.718282\n", + "2 -1.304068\n", + "3 0.986231\n", + "4 -0.718282\n", + "Name: C, dtype: float64" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demo.C" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* I'm not a friend, because no spaces allowed \n", + " (And **Pandas as early as possible** means labelling columns well and adding spaces)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "* Select more than one column by providing `list` to slice operator `[]`\n", + "* Example: Select list of columns `A` and `C`, `['A', 'C']` from `df_demo`" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>C</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>1.2</td>\n", + " <td>-2.718282</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1.2</td>\n", + " <td>1.718282</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>1.2</td>\n", + " <td>-1.304068</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>1.2</td>\n", + " <td>0.986231</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>1.2</td>\n", + " <td>-0.718282</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A C\n", + "0 1.2 -2.718282\n", + "1 1.2 1.718282\n", + "2 1.2 -1.304068\n", + "3 1.2 0.986231\n", + "4 1.2 -0.718282" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_slice = ['A', 'C']\n", + "df_demo[my_slice]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "* Use numerical values in brackets to slice **along rows**\n", + "* Use ranges just like with Python lists" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>D</th>\n", + " <th>E</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>1.718282</td>\n", + " <td>column</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-1.304068</td>\n", + " <td>has</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A B C D E\n", + "1 1.2 2018-02-26 1.718282 column Same\n", + "2 1.2 2018-02-26 -1.304068 has Same" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demo[1:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>D</th>\n", + " <th>E</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>1.718282</td>\n", + " <td>column</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>0.986231</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A B C D E\n", + "1 1.2 2018-02-26 1.718282 column Same\n", + "3 1.2 2018-02-26 0.986231 entries Same" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demo[1:6:2]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "* Attention: location might change after re-sorting!" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>D</th>\n", + " <th>E</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>1.718282</td>\n", + " <td>column</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-1.304068</td>\n", + " <td>has</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A B C D E\n", + "1 1.2 2018-02-26 1.718282 column Same\n", + "2 1.2 2018-02-26 -1.304068 has Same" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demo[1:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>D</th>\n", + " <th>E</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-1.304068</td>\n", + " <td>has</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-0.718282</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A B C D E\n", + "2 1.2 2018-02-26 -1.304068 has Same\n", + "4 1.2 2018-02-26 -0.718282 entries Same" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demo.sort_values(\"C\")[1:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "## Slicing of Data Frames\n", + "\n", + "### Better Slicing\n", + "\n", + "* `.iloc[]` and `.loc[]`: Faster slicing interfaces with more options" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>D</th>\n", + " <th>E</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>1.718282</td>\n", + " <td>column</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-1.304068</td>\n", + " <td>has</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A B C D E\n", + "1 1.2 2018-02-26 1.718282 column Same\n", + "2 1.2 2018-02-26 -1.304068 has Same" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demo.iloc[1:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "source": [ + "* Also slice along columns (second argument)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>C</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1.2</td>\n", + " <td>1.718282</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>1.2</td>\n", + " <td>-1.304068</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A C\n", + "1 1.2 1.718282\n", + "2 1.2 -1.304068" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demo.iloc[1:3, [0, 2]]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "* `.iloc[]`: Slice by **position** (_numerical/integer_)\n", + "* `.loc[]`: Slice by **label** (_named_)\n", + "* See difference with a *proper* index (and not the auto-generated default index from before)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>E</th>\n", + " </tr>\n", + " <tr>\n", + " <th>D</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>This</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-2.718282</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>column</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>1.718282</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>has</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-1.304068</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>entries</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>0.986231</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>entries</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-0.718282</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A B C E\n", + "D \n", + "This 1.2 2018-02-26 -2.718282 Same\n", + "column 1.2 2018-02-26 1.718282 Same\n", + "has 1.2 2018-02-26 -1.304068 Same\n", + "entries 1.2 2018-02-26 0.986231 Same\n", + "entries 1.2 2018-02-26 -0.718282 Same" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demo_indexed = df_demo.set_index(\"D\")\n", + "df_demo_indexed" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>E</th>\n", + " </tr>\n", + " <tr>\n", + " <th>D</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>entries</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>0.986231</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>entries</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " 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<th>A</th>\n", + " <th>C</th>\n", + " </tr>\n", + " <tr>\n", + " <th>D</th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>has</th>\n", + " <td>1.2</td>\n", + " <td>-1.304068</td>\n", + " </tr>\n", + " <tr>\n", + " <th>entries</th>\n", + " <td>1.2</td>\n", + " <td>0.986231</td>\n", + " </tr>\n", + " <tr>\n", + " <th>entries</th>\n", + " <td>1.2</td>\n", + " <td>-0.718282</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A C\n", + "D \n", + "has 1.2 -1.304068\n", + "entries 1.2 0.986231\n", + "entries 1.2 -0.718282" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demo_indexed.loc[[\"has\", \"entries\"], [\"A\", \"C\"]]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "## Slicing of Data Frames\n", + "### Advanced Slicing: Logical Slicing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Slice can also be array of booleans" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>D</th>\n", + " <th>E</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>1.718282</td>\n", + " <td>column</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " 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"<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>D</th>\n", + " <th>E</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-0.718282</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A B C D E\n", + "4 1.2 2018-02-26 -0.718282 entries Same" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demo[(df_demo[\"C\"] < 0) & (df_demo[\"D\"] == \"entries\")]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Adding to Existing Data Frame\n", + "\n", + "* Add new columns with `frame[\"new col\"] = something` or `.insert()`\n", + "* Combine data frames\n", + " - *Concat*: Combine several data frames along an axis\n", + " - *Merge*: Combine data frames on basis of common columns; database-style\n", + " - (Join)\n", + " - See user guide [on merging](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " 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"metadata": { + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "* `.insert()` allows to specify position of insertion\n", + "* `.shape` gives tuple of size of data frame, `vertical, horizontal`" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>D</th>\n", + " <th>E</th>\n", + " <th>E2</th>\n", + " <th>F</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-2.718282</td>\n", + " <td>This</td>\n", + " <td>Same</td>\n", + " <td>7.389056</td>\n", + " <td>-3.918282</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>1.718282</td>\n", + " <td>column</td>\n", + " <td>Same</td>\n", + " <td>2.952492</td>\n", + " <td>0.518282</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-1.304068</td>\n", + " <td>has</td>\n", + " <td>Same</td>\n", + " <td>1.700594</td>\n", + " <td>-2.504068</td>\n", + " </tr>\n", + " </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" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demo.insert(df_demo.shape[1] - 1, \"E2\", df_demo[\"C\"] ** 2)\n", + "df_demo.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>D</th>\n", + " <th>E</th>\n", + " <th>E2</th>\n", + " <th>F</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-1.304068</td>\n", + " <td>has</td>\n", + " <td>Same</td>\n", + " <td>1.700594</td>\n", + " <td>-2.504068</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>0.986231</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " <td>0.972652</td>\n", + " <td>-0.213769</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-0.718282</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " <td>0.515929</td>\n", + " <td>-1.918282</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A B C D E E2 F\n", + "2 1.2 2018-02-26 -1.304068 has Same 1.700594 -2.504068\n", + "3 1.2 2018-02-26 0.986231 entries Same 0.972652 -0.213769\n", + "4 1.2 2018-02-26 -0.718282 entries Same 0.515929 -1.918282" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_demo.tail(3)" + ] + }, + { + "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": 56, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Key</th>\n", + " <th>Value</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>First</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Second</td>\n", + " <td>1</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Key Value\n", + "0 First 1\n", + "1 Second 1" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_1 = pd.DataFrame({\"Key\": [\"First\", \"Second\"], \"Value\": [1, 1]})\n", + "df_1" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Key</th>\n", + " <th>Value</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>First</td>\n", + " <td>2</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Second</td>\n", + " <td>2</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Key Value\n", + "0 First 2\n", + "1 Second 2" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_2 = pd.DataFrame({\"Key\": [\"First\", \"Second\"], \"Value\": [2, 2]})\n", + "df_2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "* Concatenate list of data frame vertically (`axis=0`)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " 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"execute_result" + } + ], + "source": [ + "pd.concat([df_1, df_2])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "* Same, but re-index" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Key</th>\n", + " <th>Value</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>First</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Second</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>First</td>\n", + " <td>2</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Second</td>\n", + " <td>2</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Key Value\n", + "0 First 1\n", + "1 Second 1\n", + "2 First 2\n", + "3 Second 2" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.concat([df_1, df_2], ignore_index=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "* Concat, but horizontally" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + 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"cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "source": [ + "* Merge on common column" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "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>Key</th>\n", + " <th>Value_x</th>\n", + " <th>Value_y</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>First</td>\n", + " <td>1</td>\n", + " <td>2</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Second</td>\n", + " <td>1</td>\n", + " <td>2</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Key Value_x Value_y\n", + "0 First 1 2\n", + "1 Second 1 2" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.merge(df_1, df_2, on=\"Key\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "source": [ + "`.concat()` can also be used to append rows to a DataFrame:" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>D</th>\n", + " <th>E</th>\n", + " <th>E2</th>\n", + " <th>F</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-2.718282</td>\n", + " <td>This</td>\n", + " <td>Same</td>\n", + " <td>7.389056</td>\n", + " <td>-3.918282</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>1.718282</td>\n", + " <td>column</td>\n", + " <td>Same</td>\n", + " <td>2.952492</td>\n", + " <td>0.518282</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-1.304068</td>\n", + " <td>has</td>\n", + " <td>Same</td>\n", + " <td>1.700594</td>\n", + " <td>-2.504068</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>0.986231</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " <td>0.972652</td>\n", + " <td>-0.213769</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-0.718282</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " <td>0.515929</td>\n", + " <td>-1.918282</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>1.3</td>\n", + " <td>2018-02-27</td>\n", + " <td>-0.777000</td>\n", + " <td>has it?</td>\n", + " <td>Same</td>\n", + " <td>NaN</td>\n", + " <td>23.000000</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A B C D E E2 F\n", + "0 1.2 2018-02-26 -2.718282 This Same 7.389056 -3.918282\n", + "1 1.2 2018-02-26 1.718282 column Same 2.952492 0.518282\n", + "2 1.2 2018-02-26 -1.304068 has Same 1.700594 -2.504068\n", + "3 1.2 2018-02-26 0.986231 entries Same 0.972652 -0.213769\n", + "4 1.2 2018-02-26 -0.718282 entries Same 0.515929 -1.918282\n", + "5 1.3 2018-02-27 -0.777000 has it? Same NaN 23.000000" + ] + }, + "execution_count": 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", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>16</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>5</td>\n", + " <td>1</td>\n", + " <td>2</td>\n", + " <td>8</td>\n", + " <td>202.15</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.28</td>\n", + " <td>47.98</td>\n", + " <td>48.48</td>\n", + " <td>...</td>\n", + " <td>7.95</td>\n", + " <td>142.81</td>\n", + " <td>47699384.0</td>\n", + " <td>802865</td>\n", + " <td>7.03</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>16</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>5</td>\n", + " <td>1</td>\n", + " <td>4</td>\n", + " <td>8</td>\n", + " <td>89.57</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.15</td>\n", + " <td>20.41</td>\n", + " <td>23.21</td>\n", + " <td>...</td>\n", + " <td>3.19</td>\n", + " <td>60.31</td>\n", + " <td>46813040.0</td>\n", + " <td>821491</td>\n", + " <td>7.23</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>32</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>5</td>\n", + " <td>2</td>\n", + " <td>2</td>\n", + " <td>4</td>\n", + " <td>164.16</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.20</td>\n", + " <td>40.03</td>\n", + " <td>41.09</td>\n", + " <td>...</td>\n", + " <td>6.08</td>\n", + " <td>114.88</td>\n", + " <td>46937216.0</td>\n", + " <td>802865</td>\n", + " <td>7.03</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>16</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>5 rows × 22 columns</p>\n", + "</div>" + ], + "text/plain": [ + " id Nodes Tasks/Node Threads/Task Runtime Program / s Scale Plastic \\\n", + "0 5 1 2 4 420.42 10 True \n", + "1 5 1 4 4 200.84 10 True \n", + "2 5 1 2 8 202.15 10 True \n", + "3 5 1 4 8 89.57 10 True \n", + "4 5 2 2 4 164.16 10 True \n", + "\n", + " Avg. Neuron Build Time / s Min. Edge Build Time / s \\\n", + "0 0.29 88.12 \n", + "1 0.15 46.03 \n", + "2 0.28 47.98 \n", + "3 0.15 20.41 \n", + "4 0.20 40.03 \n", + "\n", + " Max. Edge Build Time / s ... Presim. Time / s Sim. Time / s \\\n", + "0 88.18 ... 17.26 311.52 \n", + "1 46.34 ... 7.87 142.97 \n", + "2 48.48 ... 7.95 142.81 \n", + "3 23.21 ... 3.19 60.31 \n", + "4 41.09 ... 6.08 114.88 \n", + "\n", + " Virt. Memory (Sum) / kB Local Spike Counter (Sum) Average Rate (Sum) \\\n", + "0 46560664.0 825499 7.48 \n", + "1 46903088.0 802865 7.03 \n", + "2 47699384.0 802865 7.03 \n", + "3 46813040.0 821491 7.23 \n", + "4 46937216.0 802865 7.03 \n", + "\n", + " Number of Neurons Number of Connections Min. Delay Max. Delay Threads \n", + "0 112500 1265738500 1.5 1.5 8 \n", + "1 112500 1265738500 1.5 1.5 16 \n", + "2 112500 1265738500 1.5 1.5 16 \n", + "3 112500 1265738500 1.5 1.5 32 \n", + "4 112500 1265738500 1.5 1.5 16 \n", + "\n", + "[5 rows x 22 columns]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"Threads\"] = df[\"Nodes\"] * df[\"Tasks/Node\"] * df[\"Threads/Task\"]\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "exercise": "solution" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['id', 'Nodes', 'Tasks/Node', 'Threads/Task', 'Runtime Program / s',\n", + " 'Scale', 'Plastic', 'Avg. Neuron Build Time / s',\n", + " 'Min. Edge Build Time / s', 'Max. Edge Build Time / s',\n", + " 'Min. Init. Time / s', 'Max. Init. Time / s', 'Presim. Time / s',\n", + " 'Sim. Time / s', 'Virt. Memory (Sum) / kB', 'Local Spike Counter (Sum)',\n", + " 'Average Rate (Sum)', 'Number of Neurons', 'Number of Connections',\n", + " 'Min. Delay', 'Max. Delay', 'Threads'],\n", + " dtype='object')" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Aside: Plotting without Pandas\n", + "\n", + "### Matplotlib 101\n", + "\n", + "* Matplotlib: de-facto standard for plotting in Python\n", + "* Main interface: `pyplot`; provides MATLAB-like interface\n", + "* Better: Use object-oriented API with `Figure` and `Axis`\n", + "* Great integration into Jupyter Notebooks\n", + "* Since v. 3: Only support for Python 3\n", + "* → https://matplotlib.org/" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "exercise": "task", + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "x = np.linspace(0, 2*np.pi, 400)\n", + "y = np.sin(x**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.plot(x, y)\n", + "ax.set_title('Use like this')\n", + "ax.set_xlabel(\"Numbers\");\n", + "ax.set_ylabel(\"$\\sqrt{x}$\");" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "* Plot multiple lines into one canvas\n", + "* Call `ax.plot()` multiple times" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "y2 = y/np.exp(y*1.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.plot(x, y, label=\"y\")\n", + "ax.plot(x, y2, label=\"y2\")\n", + "ax.legend()\n", + "ax.set_title(\"This plot makes no sense\");" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "* Matplotlib can also plot DataFrame data\n", + "* Because DataFrame data is _only_ array-like data with stuff on top" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.plot(df_demo.index, df_demo[\"C\"], label=\"C\")\n", + "ax.legend()\n", + "ax.set_title(\"Nope, no sense at all\");" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "exercise": "task", + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## Task 4\n", + "<a name=\"task4\"></a>\n", + "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n", + "\n", + "\n", + "* Sort the Nest data frame by threads\n", + "* Plot `\"Presim. Time / s\"` and `\"Sim. Time / s\"` of our data frame `df` as a function of threads\n", + "* Use a dashed, red line for `\"Presim. Time / s\"`, a blue line for `\"Sim. Time / s\"` (see [API description](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot))\n", + "* Don't forget to label your axes and to add a legend _(1st rule of plotting)_\n", + "* Tell me when you're done with status icon in BigBlueButton: 👍" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "editable": true, + "exercise": "solution", + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "df.sort_values([\"Threads\", \"Nodes\", \"Tasks/Node\", \"Threads/Task\"], inplace=True) # multi-level sort" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "editable": true, + "exercise": "solution", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "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", + "ax.set_ylabel(\"Time / s\")\n", + "ax.legend(loc='best');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## Plotting with Pandas\n", + "\n", + "* Each data frame has a `.plot()` function (see [API](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html))\n", + "* Plots with Matplotlib\n", + "* Important API options:\n", + " - `kind`: `'line'` (default), `'bar[h]'`, `'hist'`, `'box'`, `'kde'`, `'scatter'`, `'hexbin'`\n", + " - `subplots`: Make a sub-plot for each column (good together with `sharex`, `sharey`)\n", + " - `figsize`\n", + " - `grid`: Add a grid to plot (use Matplotlib options)\n", + " - `style`: Line style per column (accepts list or dict)\n", + " - `logx`, `logy`, `loglog`: Logarithmic plots\n", + " - `xticks`, `yticks`: Use values for ticks\n", + " - `xlim`, `ylim`: Limits of axes\n", + " - `yerr`, `xerr`: Add uncertainty to data points\n", + " - `stacked`: Stack a bar plot\n", + " - `secondary_y`: Use a secondary `y` axis for this plot\n", + " - Labeling\n", + " * `title`: Add title to plot (Use a list of strings if `subplots=True`)\n", + " * `legend`: Add a legend\n", + " * `table`: If `true`, add table of data under plot\n", + " - `**kwds`: Non-parsed keyword passed to Matplotlib's plotting methods" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "* Either slice and plot…" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x200 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_demo[\"C\"].plot(figsize=(10, 2));" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "* … or plot and select" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x200 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_demo.plot(y=\"C\", figsize=(10, 2));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* I prefer slicing first: \n", + " → Allows for further operations on the sliced data frame" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_demo[\"C\"].plot(kind=\"bar\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* There are pseudo-sub-functions for each of the plot `kind`s\n", + "* I prefer to just call `.plot(kind=\"smthng\")`" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_demo[\"C\"].plot.bar();" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1200x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_demo[\"C\"].plot(kind=\"bar\", legend=True, figsize=(12, 4), ylim=(-1, 3), title=\"This is a C plot\");" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "exercise": "task", + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Task 5\n", + "<a name=\"task5\"></a>\n", + "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n", + "\n", + "Use the Nest data frame `df` to:\n", + "\n", + "1. Make threads index of the data frame (`.set_index()`)\n", + "2. Plot `\"Presim. Time / s\"` and `\"Sim. Time / s`\" individually\n", + "3. Plot them onto one common canvas!\n", + "4. Make them have the same line colors and styles as before\n", + "5. Add a legend, add missing axes labels\n", + "6. Tell me when you're done with status icon in BigBlueButton: 👍" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "exercise": "solution", + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "df.set_index(\"Threads\", inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "exercise": "solution" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df[\"Presim. Time / s\"].plot(figsize=(10, 3), style=\"--\", color=\"red\");" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "editable": true, + "exercise": "solution", + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df[\"Sim. Time / s\"].plot(figsize=(10, 3), style=\"-b\");" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "editable": true, + "exercise": "solution", + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "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": 82, + "metadata": { + "editable": true, + "exercise": "solution", + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "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", + "### Recap: Our first proper Pandas plot\n" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot(figsize=(10,3));" + ] + }, + { + "cell_type": "markdown", + "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", + "* Plotting functionality is very versatile\n", + "* Before plotting, data can be *massaged* within data frames, if needed" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## More Plotting with Pandas\n", + "### Some versatility" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_demo[df_demo[\"F\"] < 0][[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True, figsize=(10,3));" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x400 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_demo[df_demo[\"F\"] < 0][[\"A\", \"C\", \"F\"]]\\\n", + " .plot(kind=\"barh\", subplots=True, sharex=True, title=\"Subplots Demo\", figsize=(10, 4));" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1200x600 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_demo.loc[df_demo[\"F\"] < 0, [\"A\", \"F\"]]\\\n", + " .plot(\n", + " style=[\"-*r\", \"--ob\"], \n", + " secondary_y=\"A\", \n", + " figsize=(12, 6),\n", + " table=True\n", + " );" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1200x600 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_demo.loc[df_demo[\"F\"] < 0, [\"A\", \"F\"]]\\\n", + " .plot(\n", + " style=[\"-*r\", \"--ob\"], \n", + " secondary_y=\"A\", \n", + " figsize=(12, 6),\n", + " yerr={\n", + " \"A\": abs(df_demo[df_demo[\"F\"] < 0][\"C\"]), \n", + " \"F\": 0.2\n", + " }, \n", + " capsize=4,\n", + " title=\"Bug: style is ignored with yerr\",\n", + " marker=\"P\"\n", + " ); " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Combine Pandas with Matplotlib\n", + "\n", + "* Pandas shortcuts very handy\n", + "* But sometimes, one needs to access underlying Matplotlib functionality\n", + "* No problemo!\n", + "* **Option 1**: Pandas always returns axis\n", + " - Use this to manipulate the canvas\n", + " - Get underlying `figure` with `ax.get_figure()` (for `fig.savefig()`)\n", + "* **Option 2**: Create figure and axes with Matplotlib, use when drawing\n", + " - `.plot()`: Use `ax` option" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Option 1: Pandas Returns Axis" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = df_demo[\"C\"].plot(figsize=(10, 4))\n", + "ax.set_title(\"Hello There!\");\n", + "fig = ax.get_figure()\n", + "fig.suptitle(\"This title is super (literally)!\");" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Option 2: Draw on Matplotlib Axes" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "df_demo[\"C\"].plot(ax=ax)\n", + "ax.set_title(\"Hello There!\");\n", + "fig.suptitle(\"This title is super (still, literally)!\");" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "* We can also get fancy!" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1200x400 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(12, 4))\n", + "for ax, column, color in zip([ax1, ax2], [\"C\", \"F\"], [\"blue\", \"#b2e123\"]):\n", + " df_demo[column].plot(ax=ax, legend=True, color=color)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Aside: Seaborn\n", + "\n", + "* Python package on top of Matplotlib\n", + "* Powerful API shortcuts for plotting of statistical data\n", + "* Manipulate color palettes\n", + "* Works well together with Pandas\n", + "* Also: New, well-looking defaults for Matplotlib (IMHO)\n", + "* → https://seaborn.pydata.org/" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "sns.set() # set defaults" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "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", + "\n", + "* [Documentation](https://seaborn.pydata.org/tutorial/color_palettes.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "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 1000x100 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.palplot(sns.color_palette())" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 1000x100 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.palplot(sns.color_palette(\"hls\", 10))" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 2000x100 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.palplot(sns.color_palette(\"hsv\", 20))" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 1000x100 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.palplot(sns.color_palette(\"Paired\", 10))" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 800x100 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.palplot(sns.color_palette(\"cubehelix\", 8))" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 1000x100 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.palplot(sns.color_palette(\"colorblind\", 10))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Seaborn Plot Examples\n", + "\n", + "* Most of the time, I use a regression plot from Seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "image/png": 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OSentxIK3IJ9hh6g5NV242bFjB97ylrfgIx/5CN7ylreEPZyWoErbyLXG2YEqT9aYctApTW8dcl0sdf5y3lrschzsqjqMsF/KcoVnKB7DqcUYFH9gUIQEFyd7zT+5OJmomTVVuCkWi7jxxhsxH2hQSWsnJSVOkRKnBLapm0CV52BgAXNmmcMIp4zBVKGAXwNALgfMZNBRWssTPIFZa8RZ5aEI4OJkoubWVOHmrrvuQldXV9jDaGtSCKzXGut15ZdO7jhVnoOuC2eJ51mwFnscB3uqGov2+VWeYPDp41oeChEXJxM1n6YJN0888QS2b9+OBx54AK997WtX/Xxar81Bdsratpxy6VISXTGNLVic1jLWYsp1Men4ocdxMOk6OOouXeWZNgbThQJ+E7gWE8Cg0t7UVvk/hQQPI6wLpUTgLf9Na2UA5KyFsNZbnCwtYkJCA9DCm+5dTeBRSla8pWjgfWkOTRFuMpkMbrrpJnz4wx/G8PDwqp9PSoG+vlQdRnYsUXSglpmiaTe9QMXiZQDIuQb7CwXsXyhi70IRBwoF7FsoLt1Y1AITjoOJ6rU8WmG4I46NHTFsjMcx3BHDuphmY9GTlOpKhD2ElmABOPAO0ZQQiEm/27kQUMI7r2ollch0OrlWQ6VV4H2JtqYINx/96Efx8pe/HJdffnldns8Yi0xmbdbtzFmLrLPUkltSSiDVlcCgC6wTGmcnNJBIwliLo67BpOvgQNGr8Ew6DqaXqfJMOS6mnByeyubK12KAt4YnUOEZ1BpJVnmWVLon2bk8XLf9qo6NIoS3+0oKr7KjhYQCoIR3dEN1lUcpiXQ6iUwmB3eZ7wNqLN6X8KTTyZorZpEPNw888ACefPJJfO9736vr8zpO/b8ohfC3l/ILfhneF6brHvvv1CMEenQML9GLC5gX/MaiwdOXD7ouFpaq8gDY6zjYW1XlSUtZXsNTWs8zoBSrPACWuye0NrxVZt76HeFvQ5cAtPSmtbzzd7ypXWMMXNdwLU/EuK5Zk58jVB/CRvwQh2uuuQb/8i//gnigx9L8/Dzi8TjOP/983HPPPSt+Ttc1mJrK1nOYALwXKVZulqeURLoniczMyf/WY63FjN9YtBR4Jh0HUyucDlQA1ledvjyoFDrbrMpTj3tC9SUEoJVCOp3A/Fwe0niBR8OLosr7KHABc+NpLdHXl8L0dJbhpsH6+1OtU7m54447kM/nK65deumluOGGG3DFFVeENCoKkxACvUqhVymcXtVYtNxqInAgYX6ZxqIHXBcHqhqLdvvn/gQDz4BSUKzyUINYfyu6AVDwK2rlKg8EhABifpVH+icsS2u5Y4vIF/lwMzg4eNzrAwMDS76P2lNMCIzEYhgJnMtjrUWmqspz0G8sutTr/6y1mC0W8UJVY9H1VS0nBrVGqs2qPBQe/5xB70/+tnQAfujxAo+EH3qEKK/nUbb0UQw91D4iH26IVkMIgR6l0KMUXlJV5Tlc1W5i0nWRW6ax6KRfEfpVoOVESoiKLuoblMJ6VnmogUrn8MB61ciiH3pkoMqjpPRaSgSntgQPIaTW1ZTh5plnngl7CNTkYkJgWGsMBw4jtNZiztpjAs+RZRqLZq3Fi8UiXqyq8qwLVHlKb7tWuAWYaDVMVZVnAahYwCywOLVV2qaurBeIWOWhZteU4YZoLQgh0C0EuuNxnBq47pSqPIGTlycdB9llqjwH/emvfw1UeTpLLScCgWe9UtAMPNQg5akt/2u3emqr3FOrdC4PUP6PU1vUTBhuiE5AC4EhrTGkNRBoLDpnTMU6nkm/sehSVZ55a7HTcbAzsE1dABio7rGlFNJsOUENFGwxAb9TevUC5uA29dICZk5tUVQx3BCdpC4p0RWPV5zA7AYaiwant+aWePW3AA67Lg67Lp4KVHkSQhwTeNazsSg10MkuYJac2qIIYLghqiPlbyPfoDXODlR5soHGosEqz1InIuWtxS7Hwa7qlhPBxqJ+8OlhlYcaaCULmKW/gNkLPl6VBwBDD605hhuiBkhJiVOkxCmBberGr/IcDKznmXRdZJY5jHDKGEwVCvh14FpHaS1PsNLDKg812MoXMAsoa1nloTXBcEMUEikE1muN9VrjpYHrueNUeQ66LpwlnmfBWuxxHOxxHGBhoXy9L1jl8QNPH6s81EC1LGAuhx4uYKY6YrghipiklNgsJTZXVXmmjTkm8BxdpsozbQymCwX8JnCt3Fg0sGtrWMSQXru/DtExKhcwe1Nbyy5ghhd4JBcwU40YboiagBQCA34biLMC1/PG4NBxFjAXl3ieisaigSpP/9GjWC+9hcul9Tx9UrKxKDXM8guYj99clGfz0FIYboiaWEJKjEmJsaqWExVVHn89z/Rya3kcF1NwETweMwZgfWC3VqnSk2TLCWqg4NRWcAGzt1vLX8Dsb0+vqPJ4HwWGnvbEcEPUYoQQ6FcK/UrhzMD1hUBj0eD5PAtLvPIXAexzHOyr2rGVlvKYbeoDSrHKQw1lLeCWqjywNS1gZnPRtWesxYTjIGsMUlJiVOtQXhsYbojaRIcQGIvFjqnyzApgNi6xIzOPA0UHk46DqWWqPBljkDEGzwVaTmj4jUWr1vN0sspDDXSiBcxsLrq2nikU8Eg2i0nHgQPvdWFQa/xuKoXTA739GoHhhqiNCSHQpyQ2dyWxyRVwXS/UFKzFoapprUnXRX6JV34HwH7XxX638uSebv/cn2ClZ4CNRanBajmbhwuYV+eZQgHbMxnkjUGnlOiE97qwt1jE9kwGf5RONzTgMNwQ0THiQmAkFsNIVZUnU71N3W8sutRr/6y1mC0W8UJVY9H1wR5b/p9TrPJQg1WfzQOceAGz9nM58/kiYy0eyWaRN6biUNE4vCrZjDF4JJvFabFYw6aoGG6IqCZCCPQohR6lcFrgN7BiqbFoVTf13DKNRSf9YBSUEqLi9OUNSmEdG4tSgy23gFlAQFsLp1BE3lhAgGfzAJhwvOnszuOcoyWEQKeUmHQcTDgONgV+YVpLDDdEtCoxITCsNYb14suJtRazfpUnGHiOLNNYNGstXiwW8WJVlae6seig1ugSgocRUkN5s1oWrrEoWous48K4puJsnpiU5VYTuo0WMGeNgQMgLQTiUiImBFxYzPvT3BpAzv+4RmG4IaK6E0IgrRTSSuHUwHWnVOVxXRzyA8+k4yC7TJXnkOvikOsCgcainaWWE4FKz3pWeajB2r25qPftJtCjFYZjuvx3tAA0BOb9j3PgVbYaOfXMcENEDaOFwJDWGNIaCDQWnTOmvGi5NL11aJkqz7y12Ok42BnYpi5wnCqPUuhmywlqsJU0F1XBthMRXsBcCjIWFq4QcKyFay0K1iAOb/x7FxaQEBIprdDlBxlrLeaNwUgshlHduMjBcENEoeuSEl3xOLYErrl+Y9Hq05fnlnjltwAOuy4Ouy6eClR5EkIccy7PBq0RY+ChBlttc1Fg7Tuqi/KCaQHXWhgBuNaruhZh4RoDY73QEhzKKzoSOFB0MGcMOqyEsRYFP9gkpMTvplINPe+G4YaIIkn528g3aI2zA1WebGktT6DSc8h14S7xPHlrsctxsKvqMML+YGNRP/j0sMpDDVZrc1Htr+eRfqWnFIBWWulZ/PL2KkilAGOsNw1srIUDwPErp/Y4QeZ4tsRiuKyrC4/lcshZg6wfbkZiMZ5zQ0R0IikpcYqUOKWqseiR0uLlQPDJLNdywhhMFQr4deBaR2ktT6DSs15rdDDwUIMdr7kocOxW9eB6Humv5/FCj/csFoArBIy/+8v6bx1rYPzQUv5YixOGmOVsicUwrjWOWIO4EOiA4AnFREQnSwqB9VpjvdZ4aeB6rrrK4//ZWeJ5FqzFHsfBnqrGon1SVkxrlRqLsspDjXa8reqlwFOqxgh43xP1Di+1kEJgSGr0aQUZ4tohhhsiallJKbFZSmyuqvKUGouWws5B18XRZao808ZgulA4prFodbuJQaXQwcMIqcGqd20tXm1fDDdE1FakEBjw20CcFbierzqXp/S2uMTzFAHsdRzsrary9Ep5zDb1PinZWJSogRhuiIgAJKTEJikrTlC1fpUn2EX94Akaix41BkeNwbNVjUU3qGMbiyZZ5SFaEww3RERLEEKgXyn0K4UzAtcL1pansyYDwWdhmcai+1wX+6paTqSlLC9eLk1rDSjFKg/RKjHcEBGtUFwIjMZiGK2q8swEGouW1vMcWabKkzEGGWPwXKDKo+A1Fh2KaWw2RfQ4FuuFRCerPEQ1Y7ghIqoDIQR6lUKvUnhJVWPRQ8dpLJpfosrjAjjgujjguvhFfnEtT9cSjUUVqzxEx2C4ISJaQzEhsFFrbDxOY9HJqnN5jrjukntc5qzFXLGIF6oai64PbE8vTXF1scpDbY7hhoiowYKNRU8LXC+WGos6Dg4agykYTOQLyC3TWLQUkH4VaDmR8k93Dh5GuI6NRamNMNwQEUVETAgMa41hraGURLoniZmj85hxKqe1Dvo9tJZazZO1FjuKReyoqvIc01hUa3QJwcMIqeU0Rbg5evQoPvnJT+LHP/4x5ubmcPrpp+NDH/oQzj333LCHRkS0poQQ6JYS3fE4Tg1cd/wqT/W5PNllqjyH/G7rCFR5kn5j0eB6nvWs8lCTa4pw88EPfhCHDh3CJz/5SQwMDOC+++7Dtddei//xP/4HtmzZcuInICJqMVoIDGmNIa2BQGPRuapzeSYdB4eXaSyasxY7HQc7A41FBbwqT2l7ein4pNlygppE5MPNrl278JOf/AT3338/XvnKVwIAPvKRj+Cf//mf8b3vfQ/vf//7Qx4hEVF0dEmJrngcwV/7XL+xaHDx8kHHwewSVR4L4LA/9fV04Hqi1Fg00Gdrg9aIMfBQxEQ+3PT19eGLX/wizj777PI14c8RZzKZk35erddmN4GyFmol/efbjFIi8JY7OqKA9ySa6nlfFIBhrTBcdT1b6rHlOJgsretxnCWrPHlrsdtxsNupbD06oBQGtR96/P96W7TKw++XE5NCQGkJFeKPQmFt8/0k/uEPf4gbbrgBX/jCF/Da1752xY+31q7ZN93RooP5ZQ7tIiKKMtdaHC462LdQwL6FIvYXvLdHnaUiz/F1SIGN8TiGO2LY2BHDxngcQx0xJLhNveUJAAMxjXiI97rpws2//Mu/4LrrrsOFF16Iu+6666Sew3UNMplcnUfmmbMW2RW+CLQTpQRSXQlk5/Jw3ab60mtZvCfRFLX7kitXedxAtceBc+KHVuhT0p/W0hiKeW97VfM0Fo3afYkiKQT6Y6rulZt0OgmlagtMkZ+WCnrkkUdw44034hWveAXuuOOOVT2X49S/uiKE91uP67JyszTvC9N1+e8UZKzFAddFzhgkpcRQQ/sL8Z5EU7TuSxzAmNIYU4sLmI3fWLR6m/rRZarX067BtFvAb7C4YysOLPbX8t9uUCqiVZ5o3ZdIEgKuEAizdNI04eZrX/saPvaxj+ENb3gD/vqv/xrxwPHmRM3sxWIRj+VyOOyvdVAA1mmNC5JJbAn0LiKKGikEBvxmn2cFruf9HlvV29SLSzxPAcCE42DCcYCFxZYTvVJWdFPfoDX6ZfNUeSg8TRFu7r//ftx222245pprcOutt7bkIjVqTy8Wi3hobg4FY5CQEgl4vYUmi0U85Lq4rKuLAYeaTkJKbJISm6oaix4tTW35weeg42BqmSrPUWNw1Bg8GziMUAMVgadU6UlGsspDYYl8uNmxYwf+6q/+Cq9//evxp3/6pzh8+HD5fYlEAt3d3SGOjujkGWvxWC6HgjHoCuwskQC0lJgzBo/lchjXmr+pUtMTQqBPKfQphTMC1wvW4mBVU9GDrouFJeY0HAD7XBf73Mq1jWkpy9Wd0vk8Aw2d3qUoiXy4+eEPf4hisYiHH34YDz/8cMX7rrzySnz84x8PaWREq3PAdXHYcZA4zpZZIQQSUuKw4+CA61Y0XSRqJXEhMBqLYbSqyjNTaizqB5+DjoMjy1R5MsYgYwyeC1R5FLzGosHTlweVQierPC0v8q+Y119/Pa6//vqwh0FUdzlj4AJILPF+BSDvfxxROxFCoFcp9CqF0wPrK4uBKk+w0pNfosrjwvsl4kBVladLiIrAs8FvLKpY5WkZkQ83RK0qKSUUvBfg4/0eWVpczLUERJ6YEBiJxTBSVeXJ+AuYgycwH3FdLLVZZ85azBWLeKGqsej6UsuJQPDp4vdfU2K4IQrJkFJYpzUmi0Xoqqkpay3yxmAwFsOQUiGOkijahBDoUQo9SuG0wPViqbGoH3ZK01vzyzQWnfQD0q8CjUVTQlTs1hqOxdBpeL5N1DHcEIVECoELkkk85LqY83dLlSo5eWMQlxIXJJNcEEl0EmJCYFhrDAfWq1lrMedPbQUDz2HXxVKTv1lrsaNYxI5glWd6GusCvbVKu7a6/NZAFD6GG6IQbYnFcFlXV/mcmzy8qajBWIzn3BDVmRAC3UKgOx7H1sB1p1TlqTqXJ7tMlae07geBKk9SiIrt6YNaY51SbCwaAoYbopBticUwrnWIJxQTtTctBIa0xpBePH0ZAOaMWeyi7geew667ZGPRnLXY6TjYGWgsKuA1Fi1tTy8Fn3SLNhaNCoYbogiQQnC7N1HEdEmJrngcW4IXpUA+qfHC0SwOFIrl6a25Jao8FsBhf+rr6cD1hBAV29NLU1ys8tQHX02JiIhqpITAcEccqYSLl8YWt6nPl3ZslSo9/vTWUlWevLXY7TjY7VS2Hu2X8pht6r2s8qwYww0REdEqdUqJcSkxHlgnZ6zFkdKUVmDnVmaZs6umjMFUoYBfB67F/bU8wUrPeq3RwcCzJIYbIiKiNSCFwHqtsV5rvDRwPReo8gSDj7PE8xSsxR7HwZ6qxqJ9Ui5uU/eDTx+rPAAYboiIiJZlrC0v+O/SGt1LrK+pVVJKbJYSm6uqPFP+Aubg9NbMMlWeaWMwXSjgmcC1GFBxLk8p+CTa7DBChhsiIqIlvFgslo9qcAEoAQzl5/GqRAKbZf1+hEohsM5vA3FW4HreGBzyqzulwHPIcVBY4nmKAPY6DvZWVXl6pKxcwKw1+qVs2V2ZDDdERETH8WKxiIfm5lDwD9lMADBCYN9CEd9bKOKyrq41P4sqISXGpMRYVcuJo8ZUdFGfdBxML1PlmTEGM1WNRTVwzEGEG5RqiZYvDDdERERVjLV4LJdDwRh0BdaxKCGQkBLTjoPHcjmMa93w6ocQAn1KoU8pnBG4Xgicvhw8n2dhiWk0B8A+18W+qsaiaSnL1Z3S+TwDTXb2FsMNERFRlQOui8OOg8TxFugKb93MYcfBAdeNzBlVcSEwGothtKrKM2NMZeBxHBxZpsqTMQaZqiqPgtdYNLhNfVApdEa0yhONO0JERBQhOWPgAkgs8f5SH7jcMiEhCoQQ6FUKvUrh9PjiuTxFa721PFWVnvwSVR4XXuA7UFXl6RLimHN5NkQg7IU/AiIioohJBhrZHq824cILOM26PiXmn4oerDpZazFbqvIEgs8R18VS+8PmrMVcsYgXgo1FAfx2MoGrutOhbUtnuCEiIqoypBTWaY3JYhG6emrKehWbQa0xpFR4g6wzIQTSSiGtFE4LXHf8Kk+wm/qk6yK3TGPRn+byuCjZWTFF1kgMN0RERFWkELggmcRDros5f7eUgveDe9YYxP33N9Mi25OlhcCw1hiuqvLMWVvRRb1U5THwdmGtDzH4MdwQUUMFD0RjB3SKsi2xGC7r6iqfc5MHoGCxsSNe93Numo0QAt1CoDsex6mB60XXxdzUYYzlMtD5LOzGMYgQpu7a984QUcMdcyAagHVa44Jkcs3PCyE6GVtiMYxrXXFC8ekDXZjN5OG60V5M3GjOrhdQ+Ok/I5mbg+MU4Bw5Arl+CImL3wB96pkNHUtzroQioqZTOhBtslhETAikhEBMCEz6118MLEgkihLpL77dGo9jY0yzd9NxOLtewMKPvgt76ABEPA7R2Q3R0QGzfwK5v/8anOd/feInqSOGGyJac9UHosWEgPTDTZeUKBiDx3I5mFX27CGixrPGYOGn/wy7sADR1Q0IBQhAxOIQPb2w+Tzyj/4AtoHb5jktRURrbrkD0YR/4mvUDkQjohrMzQL/+nMkdjwLXSxAH9gL6TowyU7I/gEIIYHOFMyhAzD79kCNbm7IsPgqQkRrrpYD0fKI/oFoRG0tn4PYuxtyYhfExC7IiZ0QR6eO/7G5eaDYA8TjgNZALguTnUWj9k8x3BDRmmv1A9GIWk6xALFvwgswE7sg9u6GODwJUePUsejrB0qbBBwHUBoy1b2GA67EcENEa265A9Gstcgbg8FYrKUORCNqGq4LMbkPYmIn5MRuiImd3v+vsZJqu7rhxDtQtBZuVzfQ049eJYAjh2GthZ3PQg6PQm4cW+O/yCKGGyJac0sdiOYCyBuDuJRtcyAaUaiM8SowE7v86aWdEPsnIBynpofbRBJ2dDPM6GbYEe8tevrg7H7R2y21sIAYLKwBUCjAzmchEgkkLn5DQ8+7YbghooY4/oFowGAsxnNuiNaCtcD0kXKIkRO7IPbtgVjI1/bwWBx2ZBPMyCbY0c2wo+Ow/euA44QUvXkrcOkV3q6p3BysUwAKC5DDo6Gcc8NwQ0QNU30gGk8oJqqjzEx5jYyc2AWxdxfEfLamh1qlYIdHy9UYO7oZdv0QsIKpYr15K9TYKRBHDiGxMA+l45A8oZiI2kHpQDQiWoX5rBdi9u5aDDOZozU91AoBDA7DjGyGGfGDzPAIoFdfPRVSQm0YgtYKMsRjq5riFcYYg8985jP49re/jdnZWZx33nn4i7/4C4yNNW5xEhERUSgW8hD79gS2YO+CmDpU88PtwPqKNTJ24xjQsdTBDK2hKcLN3Xffjfvvvx8f//jHMTQ0hNtvvx3XXXcdvve97yEej4c9PCIiovooFiEO7F1cIzOxC+LQgZq3YNuePtjRTTCjm2FGxmFHNgGdqTUedPREPtwUCgXce++9uPHGG/Ha174WAPCpT30Kr3nNa/CjH/0Ib3rTm8IdIBER0clwXYiD+yt3Lk3ug3Ddmh5uO1Owo+PlNTJmZDOQ7lnjQTeHyIeb3/zmN8hms7jgggvK19LpNM466yw88cQTJx1utF6bBU7KWij2x1mSUiLwlge2RQHvSTTxvkTTSd8XY4AjhyD27PRCzJ5dwL7dEDU2jLUdCWB0k7djaWwcdnQz0DcA+IvxBdCw039PRAoBpSUU19ws7cCBAwCA4eHhiusbNmwov2+lpBTo61ubMp0oOlA8Qv6EUl2tPd/bjFZyT4J7m6pfv0rvkwAEBCxs+WPscT6elsfvlQgwBoX9e+Fms1CpFNA5sux9sdbCTh+B2bkDZueL3tvdO72WBLWIxSBHN0GOb4HcfArk+CkQg8Oh7Do6GQJAOqYRD3G8kQ83uVwOAI5ZW9PR0YGZmZmTek5jLDKZGr/IVmjOWmSd2kqK7UgpgVRXAtm5PFyXP+aiYKl7IuD9UiggIASgBaCEhAYghfd+A+8oDRde528lBGIC0EJAWpRPIi4FHAMg6xrkjQELnMvj90o0OLteQP7//CPMkUOwxoVQCvENg4i96v+FGtvifdBsxlsbs2cnUGpXMDdb0/NbKYHhEa8iM7oZdmwcGNx47Bbs2YW6/r3WkhQCsZiqe+UmnU5CqdoCU+TDTSLhpeNCoVD+MwAsLCwgmUye9PM6Tv2rK0IArrVwXVZuluZ9Ybou/52iQkB61RYDCGsR808PVkJAwbtjCgCsAKytCCXK/y/ul2us8d5pYLHU3U0JQAqJrHHhGv7QXhq/V8Lm7HqhfOquSCYhlIYoLMDsehHuC89B9fRBTR2GmJmu6fmsEMD6QX+xb2kL9igQO87GmGa+50LAFSLUX2AiH25K01EHDx7Epk2bytcPHjyI008/PaxhETWdYBVGAtDSq8LEtURfTKMjpmBd6b8gVYYYG/jf41nJi5i1QFIAMaWQgYsiAw5FkDUGC//nx1CZGe97ZXYGOj8PVSgsftDU4eWfo3+dvwV7E8zouLcFO3Hyv5RT7SIfbs444wx0dXXh8ccfL4ebTCaDp59+Gm9961tDHh1RtFRPJQmgshIjBKQ/fWT9RKIg0CEl5i0aVkmx1qv49CmFWWmx4LpgxqFQOY63BXuvv3Npx/PoOHKwYn3Zcmx3urxzqVSVQaprTYdMS4t8uInH43jrW9+KO+64A/39/RgZGcHtt9+OoaEhXHrppWEPjyg05SAjBCS8EKNLU0kCUNYLOeUqjEW5xGKisuDFAmkhkNMaWZfTVNQgxnhnx0zsKrcrEPv3Qrg1No/UGk5nFxylgPMuhHzFq4B0b3nnEoUv8uEGAG644QY4joMPf/jDyOfzOO+887Bt2zbE2GiP2og8TpCJwauAVIQYoDyDZJtgb1J5mkprzLouCs281oCix1rgyKFyr6Vy88hCbQt0rZRwU11wEp1w4nE4iSRkIgEnvwBbLCB55suAnr41/kvQSglra/8VrlAo4Mc//jF2796N0047DRdddNExHzM5OYlvf/vbeO9731vXgdaT6xpMTdXWTGwlhOBuqRNRSiLdk0RmJsdFksvw1sV4U0sxKRHzKzIaKE8p1av4orVEX18K09PZNVlovyL+91Ce01T8XjkZ1h6/eWSNW7Ct0rAbR2BHxmFGxjD/r7+EMzsDke6pqMooKeDMHIVYN4TO/+9tTbNFu1GUEOhbg95S/f2p+u+WOnLkCN7+9rfjueeeA+D9BnnmmWfi05/+NEZHR8sfd+DAAXz2s5+NdLghihJveklACm+Rbwzei4P2p5aAygW7kZlSWgsW6BYCmtNUVIvsnB9kdkPs9dsVzGZqeqiV0mseOTrur5HZBDs4AgSauur1Q3B/9F3Y2QxEIum9z3HgFvMQ8Q50vOo1DDYRVXO4ueOOO5DNZvGtb30LW7ZswcMPP4y/+Zu/wVVXXYWvfvWrGB8fX8NhErWG4DoZJQTi/lstBJS1x6yRaccf7dXTVEXXtOW/A1XJ5/zmkYGqzPSRmh9u120INI/0dy6doDeh3rwVuPQKLPz0n2GnDgELOQipEB8chv7t10CMnrLavxWtkZrDzWOPPYYPfOADOOeccwAAV155JV75ylfibW97G97xjndg+/btWL9+/ZoNlKgZLbVORgL+6TKlIFPabM0f44C/m8pa9EqJOSE4TdVuigWI/ROBnku7IA5P1t48srff67VU2rk0sglIdp7UUPTmrVBjp8AcPACTyyLW1YW+07YgM7vA6cIIqzncZDKZY8LLpk2bsG3bNlx11VV45zvfia9//et1HyBRs6heJ6PhndS71DoZBpkaWKBbSGgtOE3VqlzXaxZZ3TyyxjY2NtUNO7a4/dqMbga60nUdopASamijd6SCkgCnoiKv5nBTmor6nd/5nYrrW7duxac//Wlce+21+LM/+zNcf/31dR8kUdQE18kof52MPt46Gf/PLb1OZo1ZazlN1SqM8SowE7shJvw1MvsnIJwam0cmkn73601+kBn3dipxCzZVqTncXHvttfjABz6A/fv349/+23+Liy++uPy+888/Hx//+Mdx880348///M/XYpxEoak+TybuTy9xnUzjcJqqCVkLTB8pV2Pk3t0Qe3dDLORre3gs5p3sO+Ktk7Gjm2EH1rNqQjWpOdz83u/9Howx+MIXvoAnnniiItwAwJve9CZ0d3fj1ltvrfsgiRpJBk74LQcZeN8sgutkwuVPU8W0wBynqaKltAV77+7yW5Gdq+mhVinYIa95ZKkqYzcMH9s8kqhGKzrE741vfCPe+MY3YqmjcS666CI88sgj+OY3v1mXwRGtNQGvg231Ohnlv+U6meix1iIhAM1pqvDMZ701Mnt3La6VyRyt6aFWCGDDcFXzyBFA81BWqp+aw82rX/1qfOlLX8KZZ54J4c9vfvnLX8bv//7vo7+/v/xxzzzzDP76r/8ab3/72+s+WKLVKE0vSX/7daw0tRRoVWCt5TqZJsBpqgZayPtbsANBZupQzQ+3A+sDW7A3e1uwOxJrOGCiFYSbw4cPo1hcXPTlui7+5m/+Br/9279dEW6IouK427CXWCcDsCrTlALTVFnXhcOEszpO0euxVK7K7IQ4eKD2Ldg9vYtbsDf6zSM7U2s8aKJjraq31Ao6NxCtqZrbFXCdTMsJTlPN+b2peHdr4LoQB/cvrpGZ2OV1xXZrax9jO1N+F+xN5YPxkO5Z2zET1agpGmcSBbFdAVUrTVP1cJrq+IyBOHLICzDB5pHFQk0Ptx0Jb+dSYHoJfQPcgk2RxXBDkcZ2BbQinKbykt7MdLnnUnnnUj5X28O1ht045rUoKO1cWjfILdjUVBhuKFLYroBWa3GaSmHONa0/TTWXWWxRUForMzdb00OtlMDQiL9zaRPs6Djs4EZuwaamt+pwI1iWpJO01DZstiug1fKmqdB601T53LFbsI9O1fRQKwSwfrDca8lrHjkKxJZvHknUjFYUbt7znvcgXtVF9frrr0cstng+QaFQ2xwutZfSOhklvamlLq28fi0C0Nb7CG7Dprpr5mmqQgHu83sgfvMM9B7/lN/DB2t+uO0b8NbIjAa6YCeSazhgouioOdxceeWVazkOaiHLtSvoUAJ98RimRQGOH2ZKE0xEa6EppqkcB2Jyb2UX7IP7sWAMapkgst1pf+fS5nLLAnR1r/mwiaKq5nDzX//rf13LcVATW1G7AslpTGq84DRVVgjkwpymMgbi0AE/yHiLfsWBCQjHqenhNtlZPkumvHMp3cudS0QBXFBMK7Jcu4LSb5hcJ0ORZYEuIaEbNU1lLTB1uHyOTHkLdmGhtsd3dMBuHIM7EggyA+sZZIhOgOGGlhQ8T6aWdgWMMdQM1nSaauZoZZDZuwsiN1/buJSG3TgCO+JNL4lN4+g+bQtmZxfguqZeIyRqCww3VMZ2BdQujp2mMitfxJ6dC4SYnZATuyFmZ2r7/EIAgxu9Ldj+ol87OALoxZdkpSQEz5YhOikMN22K7QqIUJ6mimmBueWmqRbygTYF/tvpI7V/mnUbAmtk/J1LcW7BJlorDDdtgO0KiJZmrUUcQK9WmHUNCvkFYP+eyp1Lhydrbx7Z21feuVQ6UwbJzrX9SxBRBYabFsN2BUS1s64Dc2Af3ImdcPd4u5fi+ycAU9saF5vqhh0rhRh/wW93eo1HTUQnwnDT5NiugKg21hiYw5Nw9+xc/G/fHsAp1vb4RKIcYEoH46GnjzuXiCKI4aaJLLcNW7NdAVGZtRZ2+gjcPTvhTPhBZu9uoMbmkYjFoUbG4I6Ow9m4CWZ0E+zABjaPJGoSDDcRVbEN218nU9qGzXYFRJVMZsafWvL/m9gJm52r7cFSQW0cgRo9BWpsM9ToOOTgRgilAAHMW4t513jfb0TUFBhuImC5dgVLr5PhCy21JzufhTOxC+6eHYtBZuZobQ8WAmrDMNTYOOTouBdmhscgAv3xKj8ZkPIP/Vt2NxURRQrDTQhW1K4AnF6i1mKNgdm3ByY7C5nqhtw4tuR5LnYhD3fv7nKIcffshDlyqObPJQfWQ42NQ42Oe283boJIJFY23uBuKmNRcFx+RxJFHMPNGmO7AqJFzvO/Rv7RH8AcOgC4DqA05PohJC5+A9T4qXD3T1Qs+DUH91d+gyxDpHuhx8YDVZlxyM5U3cYuDdAjBOa1wvzJHPpHRA3TFOFm//79uP322/H444+jUCjgnHPOwS233ILTTjst7KEdl5aC7QqIqjjP/xq5v/8abD4PdHYCNgYs5OHueA7Zbc96269r3IItUl1Qo5sXqzKj45A9vWv7FwA4TUXUJCIfbgqFAt71rneht7cXn//855FIJHDXXXfhj//4j/Hggw+iv78/7CGWWQskpUSnEFXbsP33M85QG7LWwj10APMPbPfWxggBZGdrrsigowN6dDPU6CmQY5uhx8Yh+tZBhLQFm9NURNEX+XDz5JNP4tlnn8U//dM/YXBwEABw++234/zzz8ejjz6KP/zDPwx5hJWk4ToZal/WWtijU3AndnnbsPfshLt3F1Bj80goDTWyya/IbIYaOwVy/WAkeyxxmooouiIfbk477TR88YtfLAcbAJD+C10mkznp59U6ei+W7UApWfGWwreae2JmM3B274DjBxln907Yudq/L0UiCdGRgFUSNr+ArqvegY6XvmzF4whTjwASWmLOGBTd+gUcpUTgLb9fooL35cSkEFBaltv7hCHy4Wb9+vW46KKLKq7dd999yOfzuPDCC0/qOaUU6Our30JDWrl0Ohn2EKjKie6JOz+PhZ0vYmHHC8jveAELO16Ec+Rwzc8v4nGIRBKyowNCae//+7+omMICLIDe0SEkmvR7s99YzDgOCstUcLzJ6pVJda1sdxc1Bu/L0gSAdEwjHmLFNfRwMzExgUsuuWTJ9z/22GMV62oefvhhfOITn8Db3/52nH766Sf1OY2xyGRqLJNTXSklkU4nkcnk4Lq1LR6ltXW8e2ILC3AmdgcqMjtgDk3W/JyyfwB67BTosXHoTeOQG8eQ/eoX4OyfgIknvPUyxgLG9aayZmehh0cx370euensWv1V15wS3utL9TSVEgIJJREXQNYYFGqo8CglkOpKIDuXh1vHihCtDu/LiUkhEIupuldu0ulkzRXm0MPN4OAgHnrooSXf39PTU/7zN77xDdx222244oorcNNNN63q8zoOf7CGyXUN70FEWMdBfucBZJ96GoVd3sF4ZnJf7TuXutPeFuzRccjS267uys8BIP66N8D9+695i4o7U4DWgOPAzmchEgnv/QY1f96oSggBKQRmjYGFt8kgIQWU8TYYdAuBOQHkXRfLb7byXsRd1/IXgUjhfTkhIeAKUfOegbUQeriJxWLYunXrCT/u9ttvxz333IM/+ZM/wc033xzaTgmiZmaNgTl0oLJ55P49mHKcmh4vkp2Li339s2RET19N34/61DOR/IO3Lp5zk8t659wMjyJx8RugTz1ztX+9SLDWIgZvN5UAIC1gTWA6ygLd/nbyrOvC5XZyoroLPdzUohRsbr75ZrzjHe8IezhETcFaC3PkUHnnkjvh71xaWKjtCeJx6JHN/qF4/s6lgfWr+sVCn3omUltOr/mE4mYm/V/qjxddrLVICiCmNWZdF0XXcH8lUR1FPtw8/vjjuOeee3DNNdfg8ssvx6FDi0evd3Z2IpVqzsWHRPVmZo76IabUc2kX7HyN61e0ht44BumHGTU2DrlheE1Ch5DSq/zU/Zmbi7WAsha9UmJOiBqmqYioVpEPNw8++CAAb4fUfffdV/G+9773vXjf+94XxrCI6mol/ZYAwGTnqrpg74LNHK3tkwkBNbTRWxszOo6O8S1Yd9ZLcHSuwHVQYfCnqWL+NBVPPSZaPWFt+5085boGU1PNuyOjmWkt0deXwvR0lj9Ifcv1W9Knngmbz8Pduzi15OzZCTtV+xZsuW5DZfPIkU0Q8Y7y+3lPokEIwBHAnGtQcA2kkkj3JJGZ4c7CKFG8LyekhECfVpB1Thf9/anm2S1F1M6C/ZZEKgUrO4GFHNxdLyL7lbshkp2wM9M1tyqQvf1QY5vL/ZbU6GaIOjaPpLXjTVMBPVIiKwQKXIVDdNIYbohCYopF5L7/AMxsBlAaduoIUCxUfIxdyC/5eNHVHWgc6QUame5Z8uOpSVigS0g4yjsMjYhWjuGGqAGsMTCHDy7uWvLXycCtcQt2IukHGG/Xkhobh+jt55EILcpaiw5IpGIa80og5678ZGOidsZwQ1Rn1lrY6SOLjSMndsLduxvI52p7AiEgkp1APA5bWEDisj9E/LwLW3K7NC0vLiV6lYLUYHNOohVguCFaJZOZCVRjvLc2O1fz40UiAZFIAkp7/8ViEELAFgoABPTIJgabdmaBlH/o3xx3UxHVhOGGaAXsfBbuxC44gW3Ydma6tgcLAbVhuHwonhzZjPwPHoCd3AekuiummKy1sPNZyOFRyI1ja/S3oWZhrUUc3qnHs8ai4LicpiJaBsMN0RJsYSFwuu8uuBM7YQ4frPnxcmC9v0bGX/Q7shkiUdlJWPzuZd5uqSX6LSUufgOrNlQmDdAjBOa1Qs41cDlNRXRcDDdEAKxThLt/YjHIlJpH1vjDQ6R7veaRY5sh/W3YMtV1wse1S78lqiN/miqmBWY5TUV0XAw31HasMTAH9weaR+6Au39v7TuXOlP+gXiboUa9nUuyp/ekx9NO/ZaoPoLNOeeMxQKnqYgqMNxQS7PWeluwgwt+J3Yfc57Mkjo6oEtnyIyOQ4+NQ/Svq/sWbPZbopMRnKaa5zQVURnDDbUMay3szHTFriVnYheQm6/tCbSG2rjJXyPjd8NeP8QKCkWatUCnENBaYc51UeQ0FRHDDTUvMzd7bPPI2ZnaHiwl1NCIF2BGNkOPjUMOjUBofktQ87EW3jSVUpiTFnnXrXW5GFFL4is5NQWbmy83j3T8Bb92+kjNj5cbhrwAM+LvXto4VtE8kqgVCAukhYBWnKai9sZwQ5FjiwW4e3dXVGTMoQM1P172DVRMLamRzd6Jv0RtoDRNFdMKs5ymojbFcEOhsq4Ds3+v36ZgF9w9O7wt2MbU9HjRnfa2YPsLftXYZsiu9BqPmijarPVe3PuUwqy0WHBdMONQO2G4oYaxxsA5cACZp/Zh7jfPwNm9A+6+PYBT4xbsZGfloXhj4xA9fWweSbQUf5oqpzWyrguXCYfaBMMNrQlrLezUYa8iU24euQtYWKjtCeJxaH99jCxtxV63gUGGaIWsBZICiCmFDDhNRe2B4YbqwswcPXbn0nyNzSOVgto4Vq7GlLdgK576QlQP1gIKnKai9sFwQytmsnP+YXi7FptHZo7W9mAhEB8Zhdi4GWLEP1NmeBRCx9Z0zEQETlNR22C4oWXZfL68Bbvcc2nqUM2Pl+s2lHcsqbFxdGweR//QAKans3Cc2hYNE1H9lKeptPZ2U7mGrRuo5TDcUJktFuHu3xOYWtoJc/BAzc0jZW/f4mLfUW8rtuhMVXyM0Dztlyhs1gLKWvRKiTkhkOc0FbUYhps2ZV0XZnJfOcg4Ezth9u8FjFvT40Wqq2LXkhodh0z3rPGoiaiuLNDtdxjPssM4tRCGmzZgjfGaRwZ6Lrl7dwNOsbYn6Ej4Z8lsXtyC3TfAnUtELcBai4QAtNaYc10UOE1FLYDhpsVYa2GPTnnVmD07FoNMPlfbE+gY9Ogm70C80qF46wbZPJKohZWmqXo4TUUtguGmyZnZDNw9Oyp3LmVna3uwVFDDIxXTS3JwI7dgE7UrTlNRi2C4aSI2N1/RONKd2Al7dKq2Bwux2DzSX+yrNo5BxOJrO2giaiqL01QKc67hNBU1JYabiLKFBa95pN9vyd2zE+bwwZofLwfWV7YqGNkEkUiu4YiJqFV401RAj5TICoGca2DYYZyaCMNNnVhjYPbtgcnOQqa6ITeO1bxOxToO3P0TFQt+zeS+mrdgi3Svt+B3bHN5rYxMda3mr0NEBFigy5+mmuM0FTURhps6cJ7/NfKP/gDm0AHAdQClIdcPIXHxG6BPPbPiY60xMAf3L54ls2cH3P17vcfVQHSmygt91egp3jqZnt41+FsREXnTVHEAvVph1lgUHJfTVBR5TRdunnzySVxzzTX4yle+gvPPPz/s4cB5/tfI/f3XYPN5iFQKUCnAdWD2T2D+7+5D4vVvAqRarMpM7AaKhdqePN4BPVpqHjkOPTYO0b+OW7CJqOGkAXqEwLxWmOc0FUVcU4Wb2dlZ3HTTTTAmGsf2W2OQf/QHXrDp6QWMARbysIUF2GIBmMsg980v1/ZkWkNt3LS4TqbUPJJbsIkoKiyQEhKa01QUcU0Vbj760Y9ibGwMe/fuDXsoAOCtsTl0ACKVgs3Nw04dru2BUkINbqyoyMihEQjdVLeDiNpQcJpqzlgscJqKIqhpfpp+5zvfwc9//nN87nOfwxVXXBH2cAAAJjvrr7FJwc4cXfLjRG8/YltO8xb7jo17W7DjHY0bKBFRnUnjdRif1wo518DlNBVFSFOEm4mJCXzsYx/D3XffjVQqdeIH1EDXo4FjOo280oDrQHZ1wxQLgJQQyU4IHYM1BrAG6Xe8B3psfPWfrwUoJSveUvh4T6KpWe5LjwASWmLOGBTd1g84SonA22jfm7BIIaC0hArxyyH0cDMxMYFLLrlkyff/5Cc/wX/4D/8Bf/RHf4Rzzz0XExMTq/6cUgr09a0+JNmeM1EYGUFhYjdUbz9E1+L2a2st3KNTiI9uwrr/50yunamSTvPMnajhPYmmZrkvA8biqOOgaG1bTFOluhJhDyGyBIB0TCMe4s+90MPN4OAgHnrooSXf/81vfhO5XA7ve9/76vY5jbHIZObr8lyxi/4NFr71VRSnjkAkU4DWgOPA5rIQHQnELvo3ODpTY1+nNqCURDqdRCaTg+tGY2F4u+M9iaZmvC8KQN7alt5NpZRAqiuB7FwebhtUqk6GFAKxmKp75SadTtZcyRTWRvsr8OKLL8bBgwcRi8UAeBWRXC6Hjo4OvPnNb8Z/+S//ZcXP6boGU1PZuo1xJefctDutJfr6UpiezsJxmuMFu9XxnkRTs94XIYACgDnXRbEFd1MpJZHuSSIz0zyhs9GUEOjTCrLOt7+/P1VzuAm9cnMi9913Hxxn8YC7yclJXHPNNfjLv/xLXHjhhSGObJE+9Uyktpx+0icUExG1CmuBGIBepTAnLfKuW+th60R1E/lwMzIyUvH/ld+xenBwEAMDA2EM6biElN4ZNWEPhIgoAoT1dlNp5R36x91U1EgsLRAR0ZqwFugUAj1aISZ5sjo1TuQrN9VGR0fxzDPPhD0MIiKqgbXeD5o+pTArLRZcFy24FIcihpUbIiJae/40VZfWUKzi0BpjuCEiooawFkjCW2zMaSpaSww3RETUMNYCynrTVJ1agRmH1gLDDRERNZ4FujlNRWuE4YaIiEJRnqbSGnElwYhD9cJwQ0REobEWUMaiV0okOU1FdcJwQ0RE4bNAt5Do1hqaCYdWieGGiIgiwVqLBIAerdDBaSpaBYYbIiKKDG+aCujhNBWtAsMNERFFD6epaBUYboiIKJI4TUUni+GGiIgiKzhN5R36x4hDJ8ZwQ0RE0WeBLiGR1orTVHRCDDdERNQUrLWIW6BXK3RoxWkqWhLDDRERNRVpgB4hkOI0FS2B4YaIiJqPBVKcpqIlMNwQEVFTCk5TJThNRQEMN0RE1NSkAdJCoEsrKE5TERhuiIioFVigUwhOUxEAhhsiImoR1gIxC/RxmqrtMdwQEVFLEf5uKk5TtS+GGyIiajk2ME0V4zRV22G4ISKillSapupVCkmtwCJO+2C4ISKiliast5uqW2soVnHaAsMNERG1PGuBJLwqDqepWh/DDRERtQVrAWWBPn+aihmndTHcEBFRe7GlQ/84TdWqGG6IiKjtlKeptEZc8Udhq+EdJSKitmQtoIxFr5To5DRVS2G4ISKi9maBbk5TtZSmCDfbtm3DJZdcgnPOOQdvectb8NOf/jTsIRERUQupnqZixGlukQ83d999Nz7zmc/gQx/6EL773e/it37rt/Dud78be/bsCXtoRETUQoLTVNxN1dwiHW7m5+fxpS99CTfeeCMuu+wyjI+P49Zbb8XY2Bh+9rOfhT08IiJqRRboFhLdWrPDeJPSYQ9gOT/72c+Qy+Xwxje+sXxNKYXvfve7IY6KiIhanbUWCQForTDnGhRcAxv2oKhmkQ43O3bsQE9PD5555hnceeed2LlzJ0499VR84AMfwCte8YpVPbfWkS5atSzlb7lU3HoZGbwn0cT7Eg0KQIeSmDMWOdeF8Cs5SglEfPIjNFIIKC2hQkyDwlob2qefmJjAJZdcsuT73//+9+MLX/gChoaG8KEPfQgbN27E9u3b8cADD+CBBx7A1q1bT+rzWmsh2EGNiIhWIOu4mHNduGEPJOIEgIGYRlyGF/5CDTfFYhG7d+9e8v3/83/+T3ziE5/AF7/4RVx00UUAvGBy+eWX47zzzsN/+k//6aQ+r+saZDK5k3osrY5SEul0EplMDq5rwh4OgfckqnhfoskqCTehMTObg+Nyoup4pBDoj6m6V27S6WTNlcxQp6Visdiy1ZennnoKAHD66aeXrwkhsHXrVkxMTKzqczsOXyzC5LqG9yBieE+iifclWjS8qkReSBSNAxNefSC6hIArBML8p4n0hOG5554LIQR+8YtflK9Za/H8889j8+bN4Q2MiIjalhQC3VIgrRV3U0VUpBcUb9y4EX/wB3+Av/zLv0QymcSmTZtw3333YWJiAldddVXYwyMiojZlLRC3QK9WmDUWBcflbqoIiXS4AYCPfvSj+MxnPoMPf/jDmJmZwVlnnYV7770XW7ZsCXtoRETU5qQBeoTAvFaYdw2nqSIi1AXFYXFdg6mpbNjDaEtaS/T1pTA9neU6gojgPYkm3pdoWuq+CCFQgMWs68IxbfdjtYISAn1aQdb5n6G/P1XzguJIr7khIiJqBtZaxPxpqoRW7E0VMoYbIiKiOpEGSAuBLq2geJ5aaBhuiIiI6skCncLbTRXjbqpQMNwQERHVmbXwpqmUQpLTVA3HcENERLRGhOU0VRgYboiIiNaQ9aepejhN1TAMN0RERGvMWkBboK80TcWMs6YYboiIiBrFn6bq1hqKVZw1w3BDRETUQNYCSXiLjTlNtTYYboiIiBrMWkAFpqmYceqL4YaIiCgs5d1UnKaqJ4YbIiKiEJWnqbRGXEmeiVMHDDdEREQhsxZQxqJXSk5T1QHDDRERUVRYIC0lp6lWieGGiIgoQoyxnKZaJYYbIiKiiOE01eow3BAREUWVBbqFRLfW0Ew4NWO4ISIiijBrLRIAerRCB6epasJwQ0REFHHeNBXQIyU6OU11Qgw3REREzcICXZymOiGGGyIioiZSmqbq1QodWnGa6jgYboiIiJqMtYA0QI8QSGkFKRhxghhuiIiImpUFUkIirRWnqQIYboiIiJqYtRZxy2mqIIYbIiKiFhCcplJtPk3FcENERNQqOE0FgOGGiIiopVhrEfOnqRJtOk3FcENERNSCpAHSQqCrDaepGG6IiIhalQU6hUBaK8TaaJqK4YaIiKiFWQtvmkopJNtkmqopwk02m8V//s//Ga9+9atx7rnn4p3vfCdeeOGFsIdFRETUNIRtn2mqpgg3t912Gx5//HF8+tOfxvbt26GUwnXXXYeFhYWwh0ZERNQ0rD9N1dPi01RNEW4eeeQR/Pt//+/xile8Alu3bsWf//mfY9++fXj++efDHhoREVFTsRbQFugrTVO1YMZpinAzMDCAhx56CEeOHEGhUMDf/d3fobe3F5s2bQp7aERERM3Jn6bq1hqqxao4OuwB1OJjH/sYbrrpJvzO7/wOlFLo7OzEvffei+7u7pN+Tq2bIte1HKVkxVsKH+9JNPG+RFMr3pduAXQogVljUHTtqp9PCgGlJdTqn+qkCWttiJ8emJiYwCWXXLLk+x977DF8//vfx49+9CP86Z/+KTo7O/GlL30Jv/rVr7B9+3YMDw+v+HNaayFasQ5HRER0khxrkSk6WLAWqwkGAsBATCMuwwuAoYebYrGI3bt3L/n+o0eP4uqrr8ajjz6KjRs3lh/ze7/3e3jd616HW2+9dcWf03UNMpncSY+ZTp5SEul0EplMDq5rwh4Ogfckqnhfoqkd7kvOWmRdA/ck44EUAv0xVffKTTqdrLliFvq0VCwWw9atW5d8/7Zt2zAwMFAONqXHnHXWWdi1a9dJf17Hac0vymbhuob3IGJ4T6KJ9yWaWvm+xAUgpcSs66LompVXcYSAKwTCLJ1EftJwaGgI09PTOHjwYPmaMQbPP/88xsfHwxsYERFRC7IWUMaiV0oktUIzrjWOfLh53eteh7GxMdxwww345S9/iRdeeAEf+chHsH//frztbW8Le3hEREStyQLdQqKrCXdTRT7cdHZ24qtf/SpGRkbwnve8B//u3/077N+/H9/4xjcwOjoa9vCIiIhalrUWSQC9WiOuZNO0bgh9zU0tBgcH8YlPfCLsYRAREbUdawFlvWmqOSGQd12YULcinVjkKzdEREQUAf40VbfW0BGfpmK4ISIioppYa5EA0KMVOiI8TcVwQ0RERDXzdlMBPVKiUyvICB6Ky3BDREREK2eBLiGR1ipy01QMN0RERHRSrLWIW6BXK3RoFZlpKoYbIiIiWhVpgB4hkNIKMgJVnKbYCk5EREQRZ4GUkNAi/H3iDDdERERUF9ZaxAEIgVV1Fl8tTksRERFRXYXZNBNguCEiIqIWw3BDRERELYXhhoiIiFoKww0RERG1FIYbIiIiaikMN0RERNRSGG6IiIiopTDcEBERUUthuCEiIqKWwnBDRERELYXhhoiIiFoKww0RERG1FIYbIiIiainC2rB7dzaetRbGtN1fOzKUknBdE/YwKID3JJp4X6KJ9yUcUgoIIWr62LYMN0RERNS6OC1FRERELYXhhoiIiFoKww0RERG1FIYbIiIiaikMN0RERNRSGG6IiIiopTDcEBERUUthuCEiIqKWwnBDRERELYXhhoiIiFoKww0RERG1FIYbIiIiaikMN0RERNRSGG4oFPv378cHP/hBXHjhhTjvvPNw7bXX4rnnngt7WBTwF3/xF7jlllvCHkbbMcbg05/+NF7zmtfgt37rt/DOd74Te/bsCXtYFPCFL3wB11xzTdjDoGUw3FDDFQoFvOtd78KhQ4fw+c9/Hvfffz9SqRT++I//GFNTU2EPr+0ZY/DJT34S27dvD3sobenuu+/G/fffj9tuuw3f/OY3YYzBddddh0KhEPbQCMDXv/513HnnnWEPg06A4YYa7sknn8Szzz6LO+64A2effTZOO+003H777Zifn8ejjz4a9vDa2gsvvICrrroK3/72t7Fx48awh9N2CoUC7r33Xtxwww147WtfizPOOAOf+tSncODAAfzoRz8Ke3htbXJyEtdffz3uuOMOjI+Phz0cOgGGG2q40047DV/84hcxODhYvial96WYyWTCGhYB+OlPf4qtW7fiwQcfxOjoaNjDaTu/+c1vkM1mccEFF5SvpdNpnHXWWXjiiSdCHBk99dRTiMVi+O53v4uXvexlYQ+HTkCHPQBqP+vXr8dFF11Uce2+++5DPp/HhRdeGNKoCACuvvrqsIfQ1g4cOAAAGB4erri+YcOG8vsoHBdffDEuvvjisIdBNWK4obqbmJjAJZdcsuT7H3vsMfT395f//8MPP4xPfOITePvb347TTz+9EUNsSyu9L9R4uVwOABCPxyuud3R0YGZmJowhETUlhhuqu8HBQTz00ENLvr+np6f852984xu47bbbcMUVV+Cmm25qxPDa1kruC4UjkUgA8NbelP4MAAsLC0gmk2ENi6jpMNxQ3cViMWzduvWEH3f77bfjnnvuwZ/8yZ/g5ptvhhCiAaNrX7XeFwpPaTrq4MGD2LRpU/n6wYMHWdUkWgEuKKZQlILNzTffjFtuuYXBhgjAGWecga6uLjz++OPla5lMBk8//TTOO++8EEdG1FxYuaGGe/zxx3HPPffgmmuuweWXX45Dhw6V39fZ2YlUKhXi6IjCE4/H8da3vhV33HEH+vv7MTIygttvvx1DQ0O49NJLwx4eUdNguKGGe/DBBwF4O6Tuu+++ive9973vxfve974whkUUCTfccAMcx8GHP/xh5PN5nHfeedi2bRtisVjYQyNqGsJaa8MeBBEREVG9cM0NERERtRSGGyIiImopDDdERETUUhhuiIiIqKUw3BAREVFLYbghIiKilsJwQ0RERC2F4YaIiIhaCk8oJqKW8Ktf/Qpf/epX8cQTT2BqagobNmzABRdcgHe9610YGxsLe3hE1EA8oZiImt7Xv/51/NVf/RXOP/98XHnlldiwYQN27dqFbdu24ejRo/hv/+2/4Ywzzgh7mETUIAw3RNTUfvazn+Gaa67B1VdfjVtvvbXifVNTU3jzm9+MdevW4b//9/8e0giJqNE4LUVETW3btm3o7u7GBz/4wWPe19/fj1tuuQU7duzA/Pw8Ojs7QxghETUaww0RNS1rLf73//7fuPjii5FMJo/7MZdddlmDR0VEYeNuKSJqWtPT01hYWMDo6GjYQyGiCGG4IaKmpZQCALiuG/JIiChKGG6IqGn19PQglUph3759S37M/Pw8ZmZmGjgqIgobww0RNbVXv/rVePzxx7GwsHDc93/rW9/Cq171Kjz11FMNHhkRhYXhhoia2jve8Q4cPXoUd9555zHvO3ToEO69916ceuqpeOlLX9r4wRFRKHjODRE1vc997nO48847cdFFF+HNb34z+vr68Nxzz2Hbtm3I5/O4//77sXXr1rCHSUQNwnBDRC3hH//xH/H1r38dTz/9NGZmZjA8PIwLLrgA119/PYaHh8MeHhE1EMMNERERtRSuuSEiIqKWwnBDRERELYXhhoiIiFoKww0RERG1FIYbIiIiaikMN0RERNRSGG6IiIiopTDcEBERUUthuCEiIqKWwnBDRERELYXhhoiIiFrK/w/+UWeHS7muMwAAAABJRU5ErkJggg==", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with sns.color_palette(\"hls\", 2):\n", + " sns.regplot(x=\"C\", y=\"F\", data=df_demo);\n", + " sns.regplot(x=\"C\", y=\"E2\", data=df_demo);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "* A *joint plot* combines two plots relating to distribution of values into one\n", + "* Very handy for showing a fuller picture of two-dimensionally scattered variables" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "x, y = np.random.multivariate_normal([0, 0], [[1, -.5], [-.5, 1]], size=300).T" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x600 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.jointplot(x=x, y=y, kind=\"reg\");" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "exercise": "task", + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Task 6\n", + "<a name=\"task6\"></a>\n", + "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n", + "\n", + "* To your `df` Nest data frame, add a column with the unaccounted time (`Unaccounted Time / s`), which is the difference of program runtime, average neuron build time, minimal edge build time, minimal initialization time, presimulation time, and simulation time. \n", + "(*I know this is technically not super correct, but it will do for our example.*)\n", + "* Plot a stacked bar plot of all these columns (except for program runtime) over the threads\n", + "* Tell me when you're done with status icon in BigBlueButton: 👍" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "exercise": "solution", + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "cols = [\n", + " 'Avg. Neuron Build Time / s', \n", + " 'Min. Edge Build Time / s', \n", + " 'Min. Init. Time / s', \n", + " 'Presim. Time / s', \n", + " 'Sim. Time / s'\n", + "]\n", + "df[\"Unaccounted Time / s\"] = df['Runtime Program / s']\n", + "for entry in cols:\n", + " df[\"Unaccounted Time / s\"] = df[\"Unaccounted Time / s\"] - df[entry]" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "exercise": "solution", + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Runtime Program / s</th>\n", + " <th>Unaccounted Time / s</th>\n", + " <th>Avg. Neuron Build Time / s</th>\n", + " <th>Min. Edge Build Time / s</th>\n", + " <th>Min. Init. Time / s</th>\n", + " <th>Presim. Time / s</th>\n", + " <th>Sim. Time / s</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Threads</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>420.42</td>\n", + " <td>2.09</td>\n", + " <td>0.29</td>\n", + " <td>88.12</td>\n", + " <td>1.14</td>\n", + " <td>17.26</td>\n", + " <td>311.52</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16</th>\n", + " <td>202.15</td>\n", + " <td>2.43</td>\n", + " <td>0.28</td>\n", + " <td>47.98</td>\n", + " <td>0.70</td>\n", + " <td>7.95</td>\n", + " <td>142.81</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Runtime Program / s Unaccounted Time / s \\\n", + "Threads \n", + "8 420.42 2.09 \n", + "16 202.15 2.43 \n", + "\n", + " Avg. Neuron Build Time / s Min. Edge Build Time / s \\\n", + "Threads \n", + "8 0.29 88.12 \n", + "16 0.28 47.98 \n", + "\n", + " Min. Init. Time / s Presim. Time / s Sim. Time / s \n", + "Threads \n", + "8 1.14 17.26 311.52 \n", + "16 0.70 7.95 142.81 " + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[[\"Runtime Program / s\", \"Unaccounted Time / s\", *cols]].head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "exercise": "solution", + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th>id</th>\n", + " <th>Runtime Program / s</th>\n", + " <th>Scale</th>\n", + " <th>Plastic</th>\n", + " <th>Avg. Neuron Build Time / s</th>\n", + " <th>Min. Edge Build Time / s</th>\n", + " <th>Max. Edge Build Time / s</th>\n", + " <th>Min. Init. Time / s</th>\n", + " <th>Max. Init. Time / s</th>\n", + " <th>Presim. Time / s</th>\n", + " <th>Sim. Time / s</th>\n", + " <th>Virt. Memory (Sum) / kB</th>\n", + " <th>Local Spike Counter (Sum)</th>\n", + " <th>Average Rate (Sum)</th>\n", + " <th>Number of Neurons</th>\n", + " <th>Number of Connections</th>\n", + " <th>Min. Delay</th>\n", + " <th>Max. Delay</th>\n", + " <th>Unaccounted Time / s</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Nodes</th>\n", + " <th>Tasks/Node</th>\n", + " <th>Threads/Task</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th rowspan=\"3\" valign=\"top\">1</th>\n", + " <th rowspan=\"2\" valign=\"top\">2</th>\n", + " <th>4</th>\n", + " <td>5</td>\n", + " <td>420.42</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.29</td>\n", + " <td>88.12</td>\n", + " <td>88.18</td>\n", + " <td>1.14</td>\n", + " <td>1.20</td>\n", + " <td>17.26</td>\n", + " <td>311.52</td>\n", + " <td>46560664.0</td>\n", + " <td>825499</td>\n", + " <td>7.48</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>2.09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>5</td>\n", + " <td>202.15</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.28</td>\n", + " <td>47.98</td>\n", + " <td>48.48</td>\n", + " <td>0.70</td>\n", + " <td>1.20</td>\n", + " <td>7.95</td>\n", + " <td>142.81</td>\n", + " <td>47699384.0</td>\n", + " <td>802865</td>\n", + " <td>7.03</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>2.43</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <th>4</th>\n", + " <td>5</td>\n", + " <td>200.84</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.15</td>\n", + " <td>46.03</td>\n", + " <td>46.34</td>\n", + " <td>0.70</td>\n", + " <td>1.01</td>\n", + " <td>7.87</td>\n", + " <td>142.97</td>\n", + " <td>46903088.0</td>\n", + " <td>802865</td>\n", + " <td>7.03</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>3.12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <th>2</th>\n", + " <th>4</th>\n", + " <td>5</td>\n", + " <td>164.16</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.20</td>\n", + " <td>40.03</td>\n", + " <td>41.09</td>\n", + " <td>0.52</td>\n", + " <td>1.58</td>\n", + " <td>6.08</td>\n", + " <td>114.88</td>\n", + " <td>46937216.0</td>\n", + " <td>802865</td>\n", + " <td>7.03</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>2.45</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <th>2</th>\n", + " <th>12</th>\n", + " <td>6</td>\n", + " <td>141.70</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.30</td>\n", + " <td>32.93</td>\n", + " <td>33.26</td>\n", + " <td>0.62</td>\n", + " <td>0.95</td>\n", + " <td>5.41</td>\n", + " <td>100.16</td>\n", + " <td>50148824.0</td>\n", + " <td>813743</td>\n", + " <td>7.27</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>2.28</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " id Runtime Program / s Scale Plastic \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 5 420.42 10 True \n", + " 8 5 202.15 10 True \n", + " 4 4 5 200.84 10 True \n", + "2 2 4 5 164.16 10 True \n", + "1 2 12 6 141.70 10 True \n", + "\n", + " Avg. Neuron Build Time / s \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 0.29 \n", + " 8 0.28 \n", + " 4 4 0.15 \n", + "2 2 4 0.20 \n", + "1 2 12 0.30 \n", + "\n", + " Min. Edge Build Time / s \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 88.12 \n", + " 8 47.98 \n", + " 4 4 46.03 \n", + "2 2 4 40.03 \n", + "1 2 12 32.93 \n", + "\n", + " Max. Edge Build Time / s Min. Init. Time / s \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 88.18 1.14 \n", + " 8 48.48 0.70 \n", + " 4 4 46.34 0.70 \n", + "2 2 4 41.09 0.52 \n", + "1 2 12 33.26 0.62 \n", + "\n", + " Max. Init. Time / s Presim. Time / s \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 1.20 17.26 \n", + " 8 1.20 7.95 \n", + " 4 4 1.01 7.87 \n", + "2 2 4 1.58 6.08 \n", + "1 2 12 0.95 5.41 \n", + "\n", + " Sim. Time / s Virt. Memory (Sum) / kB \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 311.52 46560664.0 \n", + " 8 142.81 47699384.0 \n", + " 4 4 142.97 46903088.0 \n", + "2 2 4 114.88 46937216.0 \n", + "1 2 12 100.16 50148824.0 \n", + "\n", + " Local Spike Counter (Sum) Average Rate (Sum) \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 825499 7.48 \n", + " 8 802865 7.03 \n", + " 4 4 802865 7.03 \n", + "2 2 4 802865 7.03 \n", + "1 2 12 813743 7.27 \n", + "\n", + " Number of Neurons Number of Connections \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 112500 1265738500 \n", + " 8 112500 1265738500 \n", + " 4 4 112500 1265738500 \n", + "2 2 4 112500 1265738500 \n", + "1 2 12 112500 1265738500 \n", + "\n", + " Min. Delay Max. Delay Unaccounted Time / s \n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 1.5 1.5 2.09 \n", + " 8 1.5 1.5 2.43 \n", + " 4 4 1.5 1.5 3.12 \n", + "2 2 4 1.5 1.5 2.45 \n", + "1 2 12 1.5 1.5 2.28 " + ] + }, + "execution_count": 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>0.14</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>1.03</td>\n", + " </tr>\n", + " <tr>\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>0.09</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>1.09</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>3 rows × 22 columns</p>\n", + "</div>" + ], + "text/plain": [ + " 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", + " 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. 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", + " 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", + " 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", + " Min. Delay Max. 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populated from base data frame\n", + "* Might be aggregated, if needed" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "df_demo[\"H\"] = [(-1)**n for n in range(5)]" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "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>H</th>\n", + " <th>-1</th>\n", + " <th>1</th>\n", + " </tr>\n", + " <tr>\n", + " <th>F</th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>-3.918282</th>\n", + " <td>NaN</td>\n", + " <td>7.389056</td>\n", + " </tr>\n", + " <tr>\n", + " <th>-2.504068</th>\n", + " <td>NaN</td>\n", + " <td>1.700594</td>\n", + " </tr>\n", + " <tr>\n", + " <th>-1.918282</th>\n", + " <td>NaN</td>\n", + " <td>0.515929</td>\n", + " </tr>\n", + " <tr>\n", + " <th>-0.213769</th>\n", + " <td>0.972652</td>\n", + " <td>NaN</td>\n", + " </tr>\n", + " <tr>\n", + " <th>0.518282</th>\n", + " <td>2.952492</td>\n", + " <td>NaN</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + "H -1 1\n", + "F \n", + "-3.918282 NaN 7.389056\n", + "-2.504068 NaN 1.700594\n", + "-1.918282 NaN 0.515929\n", + "-0.213769 0.972652 NaN\n", + " 0.518282 2.952492 NaN" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_pivot = df_demo.pivot_table(\n", + " index=\"F\",\n", + " values=\"E2\",\n", + " columns=\"H\"\n", + ")\n", + "df_pivot" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_pivot.plot(figsize=(10,3));" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "exercise": "task", + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## Task 7\n", + "<a name=\"task7\"></a>\n", + "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n", + "\n", + "* Create a pivot table based on the Nest `df` data frame\n", + "* Let the `x` axis show the number of nodes; display the values of the simulation time `\"Sim. Time / s\"` for the tasks per node and threads per task configurations\n", + "* Please plot a bar plot\n", + "* Tell me when you're done with status icon in BigBlueButton: 👍" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": { + "editable": true, + "exercise": "solution", + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1200x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.pivot_table(\n", + " index=\"Nodes\",\n", + " columns=[\"Tasks/Node\", \"Threads/Task\"],\n", + " values=\"Sim. Time / s\",\n", + ").plot(kind=\"bar\", figsize=(12, 4));" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "exercise": "task", + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "## Task 7B (like <em>B</em>onus)\n", + "<a name=\"task7b\"></a>\n", + "<span class=\"task\" style=\"padding: 2px 8px; color: white; background-color: #b9d25f; float: right; text-weight: bolder;\">TASK</em></span>\n", + "\n", + "- Same pivot table as before (that is, `x` with nodes, and columns for Tasks/Node and Threads/Task)\n", + "- But now, use `Sim. Time / s` and `Presim. Time / s` as values to show\n", + "- Show them as a **stack** of those two values inside the pivot table\n", + "- Use Panda's functionality as much as possible!" + ] + }, + { + "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" + }, + "tags": [] + }, + "source": [ + "## Large Data & Mangling\n", + "\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!\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" + }, + "tags": [] + }, + "source": [ + "## 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", + "* Create bar chart of top 10 actors (on `x`) and average ratings of their top movies (`y`) based on IMDb data (only if they play in at least two movies)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "exercise": "task", + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "source": [ + "* IMDb provides data sets at [datasets.imdbws.com](https://datasets.imdbws.com)\n", + "* Can directly be loaded like\n", + "```python\n", + "pd.read_table('https://datasets.imdbws.com/dataset.tsv.gz', sep=\"\\t\", low_memory=False, na_values=[\"\\\\N\",\"nan\"])\n", + "```\n", + "* Needed:\n", + " * `name.basics.tsv.gz` (for names of actors and movies they are known for)\n", + " * `title.ratings.tsv.gz` (for ratings of titles)\n", + "* Strategy _suggestions_:\n", + " * Use `df.apply()` with custom function\n", + " * Custom function: Compute average rating and determine if this entry is eligible for plotting (this _can_ be done at once, but does not need to be)\n", + " * Average rating: Look up title IDs as listed in `knownForTitles` in titles dataframe" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "exercise": "solution", + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "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.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", + " \n", + "df_names_i = pd.concat(\n", + " [\n", + " df_names_i, \n", + " df_names_i.apply(lambda line: valid_and_avg_rating(line), axis=1, result_type='expand')\n", + " ]\n", + " , axis=1\n", + ")\n", + "df_names_i[df_names_i['toPlot'] == True].sort_values('avgRating', ascending=False).iloc[0:10].reset_index().set_index('primaryName')['avgRating'].plot(kind='bar')\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "exercise": "solution", + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "```python\n", + "def valid_and_avg_rating(row):\n", + " rating = 0\n", + " ntitles = 0\n", + " _titles = row['knownForTitles']\n", + " _professions = row['primaryProfession']\n", + " if not isinstance(_titles, str):\n", + " _titles = str(_titles)\n", + " if not isinstance(_professions, str):\n", + " _professions = str(_professions)\n", + " titles = _titles.split(',')\n", + " professions = _professions.split(',')\n", + " for title in titles:\n", + " if title in df_ratings_i.index:\n", + " rating += df_ratings_i.loc[title]['averageRating']\n", + " ntitles += 1\n", + " if ntitles > 0:\n", + " plot = False\n", + " if ntitles > 2:\n", + " if 'actor' in professions:\n", + " plot = True\n", + " return {'toPlot': plot, 'avgRating': rating / ntitles}\n", + " else:\n", + " return {'toPlot': False, 'avgRating': pd.NA}\n", + "\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "exercise": "task", + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## 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 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`)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## Random Features Not Shown\n", + "\n", + "This are all links:\n", + "* [`df.drop()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop.html)\n", + "* [`df.corr()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.corr.html)\n", + "* [`df.boxplot()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.boxplot.html)\n", + "* [`pd.read_sql_query(\"SELECT * FROM purchases\", con)`](https://pandas.pydata.org/docs/reference/api/pandas.read_sql.html)\n", + "* [`df.duplicated()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.duplicated.html) and [`df.drop_duplicates()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop_duplicates.html)\n", + "* Aliases for [categorical data](https://pandas.pydata.org/docs/user_guide/categorical.html#categorical)\n", + "* Working with [time](https://pandas.pydata.org/docs/user_guide/timeseries.html#timeseries)\n", + " - `ts.tz_convert`\n", + " - `pd.period_range()`\n", + " - `pd.period_range().asfreq()`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Conclusion\n", + "\n", + "* Pandas works with and on **data frames**, which are central\n", + "* **Slice** frames to your likings\n", + "* **Plot** frames\n", + " - Together with Matplotlib, Seaborn, others\n", + "* **Pivot** tables are next level greatness\n", + "* Remember: ***Pandas as early as possible!***\n", + "* Thanks for being here! 😍" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "exercise": "task" + }, + "source": [ + "<span class=\"feedback\">Feedback to <a href=\"mailto:a.herten@fz-juelich.de\">a.herten@fz-juelich.de</a></span>\n", + "\n", + "_Next slide: Further reading_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## Further Reading\n", + "\n", + "* [Pandas User Guide](https://pandas.pydata.org/pandas-docs/stable/user_guide/index.html)\n", + "* [Matplotlib and LaTeX Plots](http://sbillaudelle.de/2015/02/23/seamlessly-embedding-matplotlib-output-into-latex.html)\n", + "* towardsdatascience.com:\n", + " * [Pandas DataFrame: A lightweight Intro](https://towardsdatascience.com/pandas-dataframe-a-lightweight-intro-680e3a212b96)\n", + " * [Introduction to Data Visualization in Python](https://towardsdatascience.com/introduction-to-data-visualization-in-python-89a54c97fbed)\n", + " * [Basic Time Series Manipulation with Pandas](https://towardsdatascience.com/basic-time-series-manipulation-with-pandas-4432afee64ea)\n", + " * [An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning](https://towardsdatascience.com/an-introduction-to-scikit-learn-the-gold-standard-of-python-machine-learning-e2b9238a98ab)\n", + " * [Mapping with Matplotlib, Pandas, Geopandas and Basemap in Python](https://towardsdatascience.com/mapping-with-matplotlib-pandas-geopandas-and-basemap-in-python-d11b57ab5dac)\n", + " * [Whats new in Pandas 2](https://towardsdatascience.com/pandas-2-0-a-game-changer-for-data-scientists-3cd281fcc4b4)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, + "toc-autonumbering": false, + "toc-showcode": false, + "toc-showmarkdowntxt": false, + "toc-showtags": true + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Introduction-to-Pandas--master.ipynb b/Introduction-to-Pandas--master.ipynb index b801e61094f9439e00aa9ee33ece3c83704b6549..dc4ac9a9a4ecd75b35d0bda9316285365a28bdb7 100644 --- a/Introduction-to-Pandas--master.ipynb +++ b/Introduction-to-Pandas--master.ipynb @@ -176,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 118, "metadata": { "slideshow": { "slide_type": "fragment" @@ -189,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 119, "metadata": { "exercise": "task", "slideshow": { @@ -203,20 +203,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 120, "metadata": { "slideshow": { "slide_type": "-" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'2.1.4'" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pd.__version__" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 121, "metadata": { "slideshow": { "slide_type": "fragment" @@ -282,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 122, "metadata": { "slideshow": { "slide_type": "fragment" @@ -295,26 +306,167 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 123, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>0</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>41</td>\n", + " </tr>\n", + " <tr>\n", + " 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"source": [ "df_ages = pd.DataFrame(ages)\n", "df_ages.head(3)" @@ -333,13 +485,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 125, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Name': ['Liu', 'Rowland', 'Rivers', 'Waters', 'Rice', 'Fields', 'Kerr', 'Romero', 'Davis', 'Hall'], 'Age': [41, 56, 56, 57, 39, 59, 43, 56, 38, 60]}\n" + ] + } + ], "source": [ "data = {\n", " \"Name\": [\"Liu\", \"Rowland\", \"Rivers\", \"Waters\", \"Rice\", \"Fields\", \"Kerr\", \"Romero\", \"Davis\", \"Hall\"],\n", @@ -350,13 +510,76 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 126, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", 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"df_sample = pd.DataFrame(data)\n", "df_sample.head(4)" @@ -376,9 +599,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 127, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Name', 'Age'], dtype='object')" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_sample.columns" ] @@ -397,9 +631,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 128, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=10, step=1)" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_sample.index" ] @@ -418,13 +663,106 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 129, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", 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<td>43</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Romero</th>\n", + " <td>56</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Davis</th>\n", + " <td>38</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Hall</th>\n", + " <td>60</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Age\n", + "Name \n", + "Liu 41\n", + "Rowland 56\n", + "Rivers 56\n", + "Waters 57\n", + "Rice 39\n", + "Fields 59\n", + "Kerr 43\n", + "Romero 56\n", + "Davis 38\n", + "Hall 60" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_sample.set_index(\"Name\", inplace=True)\n", "df_sample" @@ -443,54 +781,221 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 130, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + 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}, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_sample.apply(np.square).head()" ] @@ -617,25 +1489,186 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 141, "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Age</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Name</th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Liu</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rowland</th>\n", + " 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"execute_result" + } + ], "source": [ "df_demo.round(2).tail(2)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 148, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "A 6.00\n", + "C -2.03\n", + "dtype: float64" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_demo.round(2)[[\"A\", \"C\"]].sum()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 149, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{tabular}{lrlrll}\n", + "\\toprule\n", + " & A & B & C & D & E \\\\\n", + "\\midrule\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" + ] + } + ], "source": [ "print(df_demo.round(2).to_latex())" ] @@ -824,13 +2190,94 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 150, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Actor</th>\n", + " <th>Main Cast</th>\n", + " </tr>\n", + " 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Neuron Build Time / s Min. Edge Build Time / s \\\n", + "0 0.29 88.12 \n", + "1 0.15 46.03 \n", + "2 0.28 47.98 \n", + "3 0.15 20.41 \n", + "4 0.20 40.03 \n", + "\n", + " Max. Edge Build Time / s ... Max. Init. Time / s Presim. Time / s \\\n", + "0 88.18 ... 1.20 17.26 \n", + "1 46.34 ... 1.01 7.87 \n", + "2 48.48 ... 1.20 7.95 \n", + "3 23.21 ... 3.04 3.19 \n", + "4 41.09 ... 1.58 6.08 \n", + "\n", + " Sim. Time / s Virt. Memory (Sum) / kB Local Spike Counter (Sum) \\\n", + "0 311.52 46560664.0 825499 \n", + "1 142.97 46903088.0 802865 \n", + "2 142.81 47699384.0 802865 \n", + "3 60.31 46813040.0 821491 \n", + "4 114.88 46937216.0 802865 \n", + "\n", + " Average Rate (Sum) Number of Neurons Number of Connections Min. Delay \\\n", + "0 7.48 112500 1265738500 1.5 \n", + "1 7.03 112500 1265738500 1.5 \n", + "2 7.03 112500 1265738500 1.5 \n", + "3 7.23 112500 1265738500 1.5 \n", + "4 7.03 112500 1265738500 1.5 \n", + "\n", + " Max. 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Memory (Sum) / kB', 'Local Spike Counter (Sum)',\n", + " 'Average Rate (Sum)', 'Number of Neurons', 'Number of Connections',\n", + " 'Min. Delay', 'Max. Delay', 'Threads'],\n", + " dtype='object')" + ] + }, + "execution_count": 181, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.columns" ] @@ -1551,7 +5071,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 182, "metadata": { "exercise": "task", "slideshow": { @@ -1566,7 +5086,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 183, "metadata": { "slideshow": { "slide_type": "subslide" @@ -1580,13 +5100,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 184, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:5: SyntaxWarning: invalid escape sequence '\\s'\n", + "<>:5: SyntaxWarning: invalid escape sequence '\\s'\n", + "/tmp/ipykernel_106956/3587136147.py:5: SyntaxWarning: invalid escape sequence '\\s'\n", + " ax.set_ylabel(\"$\\sqrt{x}$\");\n" + ] + }, + { + "data": { + 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(x, y)\n", @@ -1609,7 +5150,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 185, "metadata": { "slideshow": { "slide_type": "-" @@ -1622,9 +5163,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 186, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(x, y, label=\"y\")\n", @@ -1648,9 +5200,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 187, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(df_demo.index, df_demo[\"C\"], label=\"C\")\n", @@ -1661,7 +5224,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "exercise": "task", "slideshow": { "slide_type": "slide" @@ -1683,9 +5245,8 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 188, "metadata": { - "editable": true, "exercise": "solution", "slideshow": { "slide_type": "fragment" @@ -1699,16 +5260,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 189, "metadata": { - "editable": true, "exercise": "solution", "slideshow": { "slide_type": "" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "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", @@ -1721,7 +5292,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "slide" }, @@ -1764,13 +5334,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 190, "metadata": { "slideshow": { "slide_type": "-" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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PB2/M4KYViWzfW8mB0kYOnWzi6OlmcnNs3LluHqHB2htDREYZhkHluS7yC+wcOtnEyPmNGQP9fbh+SRy5OTYSY0JcPMrxCQ32Z/3SeNYvjWdo2MHJ6vax5bE7e4Y4crqZI6ebsVosZCSGjc4LyohmzjR8KCTiKvrG5xq5W8frbTwtX3tLL8+8fpzG9n58fax86dYFrF8a7+phXZGn5Xutahq7eW1XBSVVbcDoycxtq5O5dWUyAf5Tv0P6TMt3uilfc82kfAeHHBw4MbrRaE1jz9jx5NgQcpfbWLN4DoH+U/tZsavydRoG1Q3dFJSNLpVd19xz0e3xUcHkZMSQnRFNanzouOcsuZuZVL+u4G756lK3aeJuL7y38aR8j5xq4sV3TjI45CAyNIDH780iJX765pNMhiflO5VOnG3j1V0VVDd0AxA2y5+71qewYWn8lH4zN1PznS7K11wzIV97cw+7Cs7xSWn92HxAXx8rKxfGkrfcRlpCqGkLArhLvs0d/WP7BZ2p7cDh/MtpYWiwH0vTR+cFLU6JJMBv6j8gMou75Out3C1fNT7TxN1eeG/jCfk6nQbb91by9qfVACxMDucb9yzxiEuoPCFfszgNg8Mnm9i2p4LmjgEA5kQGc9/GVFYsiJmSk52ZnO90UL7m8tZ8RxxOjp5uJr/AzpnajrHjseFB5ObYWL80flrmvLhjvn0DwxyvHN0vqLiydawZBPDztZI5L5LsjGiWpUW53ZzVv+aO+XoTd8tXc3xEpkFP/zC/eLN07NKpW1Ym8UBemnbR9gBWi4XVi+ewYkEMuwrsvLn/LI1tfTy3o4S0hFAeyEtnflK4q4cpIlOkpaOf3UXn2Ft0jq6+0b1vLBbITo8mb7mNxfMiXbbnl7sIDvRjzeI41iyOY8Th5Extx/mlslto7RoYmyMEkJoQen6p7GgSomfNqKWyxXPpG59r5G4dr7dx53xrGrt5ZlsxLZ0D+Pta+bvbF7ImM87Vw5oQd853uvUPjvDewRreP1wztvpRdno0921KxTbJyczK11zK11zekK/TaVBc2Up+gZ3iitax5Z3DQvzZtCyBjcsSXLbJpyflaxgG9uZeCsqaKSxvoaq++6LbY8IDyU4fnReUkRjmFov5eFK+nsjd8tWlbtPE3V54b+Ou+R440cBv3jnF0IiT6LBAntiaRfKc2a4e1oS5a76u1NEzyJv7z7Kn8BxOw8BigXVZ8dyzPmXCJ0jK11zK11yenG9X7xB7j59jd+E5WjoHxo4vmhtBXo6N7Ixol5+ce3K+7d2DFFWMzgs6cbZ9bPU7gFmBvmSlRZGdHk1WahRBAa65uMiT8/UE7pavGp9p4m4vvLdxt3wdTiev5lfwweFaADJTIvn7uzI9dg8Ed8vXndS39rJtTyVHTzcDo9e333RdIpvXzCU4cHyvt/I1l/I1l6flaxgGZ2o7yC+wc/R089gk/VmBvqzLiic3x0ZcpPss1exp+V7JwNAIpVXtFJY3U1TeSk//8NhtPlYLC5PDyc6IYVl6FNFhE9/odbK8JV935W75qvGZJu72wnsbd8q3q2+In+8o4VRNBwB3rJnL1o2pHrvUJ7hXvu6qwt7Jq/nlnKnrBEZPojavnceNK2z4+X7+CkfK11zK11yekm/fwAifljawq8COveUv5wUp8aHk5dhYtSgWfzdcjcxT8p0Ip9Og4lzn2Lyghra+i25Pjg0Z2y9o7pzZps4L8sZ83Ym75avGZ5q42wvvbdwl37MNXTyzrZi2rkEC/Hz46uZFXLcw1mXjmSrukq+7MwyDovJWXttdwbnzJ1ZRoQHcsyGVtZlxV2x+la+5lK+53D3f6oZu8gvsHDzRyODw6Opj/n5W1iyeQ15OInPj3PvyY3fPdyrUt/ZSVN5KYVkzZfZOPnvGGTE7gGXnF0dYmByBn+/UXno4E/J1JXfLV6u6iUyRfcfrefn904w4nMyJCOKJrVmTnuwunslisZCdEc3StCj2F9ezY18VrV2DvPj2Sd4/VMsDeWksSYnUqkYiJhsadnD4VBP5BXYqz3WNHY+PCiYvx8b1S+LGfSmqmC8+ahbxUbO4bXUy3X1DHK8YXSq7pKqN9u5BdhXY2VVgJ8DfhyUpkWSnj77PzvaA7SDEM6nxEbmCEYeTV3aW8fExOwDL0qL4+p2L9ZfqDGa1WtiwLIHVi+fw0dE63v60mrrmHn76pyIWJofzQF66229aK+KJGtv72FVgZ9/xenoHRoDR+SMrFsSQl2NjflK4Pnhwc7OD/VmXFc+6rHiGRxycrO44v3FqMx09Qxw93czR081YLJBhCyM7I4acjGjmuNG8LPF8utTtGrnbV33exlX5dvYM8tyOEsrOz+u4a9087lqf4nV7PKh+r01P/zBvf3qWnUfrGHGMvpWuXBjL1k2pzIkIVr4mU77mcnW+DqeTwrJWdhXUUXq2fex4VGgAm7JtbFiWQNgsz/1mwNX5ugvDMDjb0E1h2egeQbVNPRfdHh8VTHb66LygtISwcc+rVb7mcrd8NcdnmrjbC+9tXJFvhb2TZ7cX09EzRFCAD1/bspicjJhpee7ppvqdGi2d/WzfU8WB0gYMRj+Jzs22ce+mVOYmRihfk6h+zeWqfNu7B9lTdI49Redo7x4EwAJkpUWRm2NjaWqURy8qc4Hq9/JaOvspKm+loKyZ0zUdY6vzAcwO9mNZ2mgTlDkvkgD/Ky9aoXzN5W75qvGZJu72wnub6c53V6Gd339wBofTID4qmCe2ZhEfNcv053UV1e/Uqmns5rXdFZRUtgEQ6O/D1rwMcpfF4Wt1/YZ+3kb1a67pzNdpGJysbmfXMTsFZS04z5+ahAT5sWFZPLnZNmLCp28p5Omg+r26voERSqpG5wUdr2ilb3Bk7DZfHyuL50WMrhKXHk14SMBFj1W+5nK3fNX4TBN3e+G9zXTlOzzi5PcfnmFP0TkAVsyP4SubF7ls47Xpovo1x8mzbby6q4KzDaO7m4fN8ueudfPYsCzB5ZsmehPVr7mmI9+e/mH2F9ezq8BOY3v/2PGMxDDycmysWBA75at9uQvV78SMOJyU1V1YKrv5oo1pYXT58uyMaHLSo7HFzMLPz0f5msjd6tetGp+qqip+8IMfcPToUYKCgti8eTNPPfUUgYET2wX9s9T4zAzTkW979yDPbi+m8lwXFmDrplTuWDN3RkyUVf2ax2kYFJS1sG13JfWto+9VcyKCuG9TGisWxMyI+jKb6tdcZuVrGAaV9V3sOmbn0Kkmhs//7EB/H9YuiSMv20ZirPevnKn6nTzDMLC39I7NC/rsCn8A0WGBLF8Qw8blSSREBIJHfsTv3tytft2m8enq6mLLli0kJCTw2GOP0dbWxtNPP82GDRv48Y9/POmfq8ZnZjA73zO1HTy3o4Su3iGCA3z5+7szyUqNmvLncVeqX3P5+loJmR3EjvwzbN9TSXff6I7mqQmhPJCbxoLkCBeP0LOpfs011fkODjk4cKKB/AI7NY1/mcCeFBtCXo6N1YvneP237J+l+p06HT2DFJW3UFjWwonq9rFmGiA4wJestCiy06PJSo3UyqxTxN3q12328XnllVfo6upix44dREZGAuDj48NTTz3FN7/5TdLS0swegsglDMPg42N2XtlZhsNpkBgziye2ZhEboWUzZWr5+Vq56bokVi+aw/uHanj/UC2V57r4P/9dwNK0KO7PTSNR+0KJF7O39LLrmJ1PSuvpHxzdaNTXx8rKhbHkLbeRlhCqb0DlmoSHjK70tynbxuCQgxNn2yiqaKWoooXOniEOnmjk4IlGfKwW5ieFk3N+XlC0l80bk6szvfHZs2cPa9euHWt6AG699Va+973vsXv3bjU+Mu2Ghh28/P5pPilpAGDVolgeuX3R564OI3KtggJ8uWdDKnk5Nt7cf5bdhec4XtFKcWUr65bEc8+GFCJDJ3/5r4g7GXE4OXammfxjdk7Xdowdjw0PIjfHxrqsOG1SKaYI8PchZ34MKxfPITQsmKMl5zhyuonCshbqW/s4Wd3Oyep2/vujMhJjQkbnBWVEMzduttdtWSGXMr3xqaio4L777rvomL+/P8nJyVRUVJj99CIXaens59ltJVQ3dmOxwAO56dy6KkmfNsq0CQsJ4Iu3LuDmlUls213BkdPN7Cuu5+DJRm5akcgda+cyS5diiIdq6exnd+E59h6vp6t3CACLBbLTo8lbbmPxvEidXMq08bFayEgKJyU+lAdy02ls6zu/aWoLZ+o6qGvuoa65h7c+OUt4iP/YfkGL5kbg56sPQ72R6Y1PV1cXoaGX7mQeGhpKZ2fnNf1sXzdY6eXC9YTjua5QJm4q8z1R1cYz24rp6R8mJMiPx7dmkZkSefUHejHVr7k+L9/E2BCefGAZFfZOXtlZxumaDt49WMOeonPcuS6Fm1Ym4q+/eD+X6tdc483X6TQormxl59E6ispbuDBzODzEn9wcG7k5Nn2beRmqX3NdLl9bbAi22BA2Xz+P7r4hjpe3cqysmeKKVjp6hthVeI5dhecI8PNhSWoky+fHsCw9mlAP3ijXLJ5avy6bRWgYxjV9ym61WoiIcJ/9VUJDdZ2oma4lX8Mw2LG7gt+8VYrTgLTEML735VXERmo+zwWqX3N9Xr7XRcxiRWY8R0428tu3T1Dd0M0rO8v46GgdD9+2kNwVSfh4wWaNZlL9mutK+XZ0D/LhoWreO1BNU1vf2PFlGdHcfn0KqzPjtHz7OKh+zXWlfCMiZpFsi2DLpnSGRxwUl7dyoLSeQ6UNtHYOcPR0M0dPN2O1wMJ5kazOjGNVZhyJsbOn+Tdwb55Wv6av6rZ27Vruu+8+nnrqqYuOb968mezsbH74wx9O6uc6HE66uvqvfkeT+fhYCQ0NoqurH4fD9ataeJtrzXdwyMGLb53gwIlGANZlxfPIHQvx99Mn6aD6NdtE83U6DfYX1/P6rgrazu9YnxQbwhduSGdpWpQuyfwrql9zXS5fwzA4U9vBzqN1HD7ZhMM5egoxK9CX9csSuGG5zas3fZ5Kql9zTTZfwzCobujm2JlmCspaqD6/H9sFcZHBLF8QQ05GDBmJYVhn6AdT7la/oaFB7rGqW1pa2iVzeYaGhqipqblk7s9EucPyeRc4HE63Go+3mUy+Te19PLOtmLrmXnysFh68MYMbltuwWCx6rf6K6tdcE8l3bWYcK+bHsPNoHW9/Wk1tUw//95VCFiaH80BeOinxl146PNOpfs3lcDjp7h3ik5IGdhXasTf/ZSuJlPhQ8nJsrFoUO/aBkl6LiVH9mmsy+SbGhJAYE8Jd61Jo7RwYnRdU3sKp6nYa2vp459Nq3vm0mpAgP5alRZGdEU1mSiSB/jNnOfYLPK1+TX+FNm7cyPPPP097ezsREaN7Vnz44YcMDQ2xadMms59eZqjiylZeeKOUvsERQoP9eOzeLOYnhbt6WCLj4u/nw+1r5rJhWQLvfFrNR0frOFXTwf/32yNctzCW+zalMkdLr8s0qLR3siO/jE9KGhgcHl2K2t/XyprMOeTm2JgXp0ZcvFtUWCA3rkjkxhWJ9A+OUFLVRmFZM8crWunpH2Z/SQP7Sxrw9bGyeF4E2enRLEuPJmJ2gKuHLpcxbRuY2mw2HnvsMVpbW/nRj37E+vXrtYGpXNVE8zUMg7c/rWb7nkoMRjeKfPzeLL0BXYHq11xTlW9r5wA79lbySUkDBqMrFW3MTuCudSmEzeBJt6pfcwyPODh0soldheeosP9lEaL4qODRpaiXxGkTyCmg+jWX2fmOOJyU13VSWN5CQVkzzR0DF90+L272+aWyY0iMmeV1lyq7W/2OdwNT0xsfgKqqKn7wgx9w9OhRAgMD2bJlC0899RSBgZNf5UWNz8wwkXz7B0d46e2THD3TDMDGZQk8dPN8/Nxg9T93pfo111TnW9vUw2u7KiiubAVG96u4bVUyt6xMmlE73l+g+p1aje197Cqws+94Pb0DI8Bok71iYSy5yxJYkBzudSdvrqT6Ndd05msYBuda+ygsa6awvIVKexefPbmOCg0kO2N0qewFSeFeseiHu9WvWzU+ZlDjMzOMN9/61l6e2VZMfWsfPlYLD90yn9xs2zSO1DOpfs1lVr4nq9t5bVc5VfWjk25Dg/24a30KG5cleMVfqOOl+r12DqeTovJW8gvslFa1jR2PCg0gb3kid21KB4dD+ZpA9WsuV+bb2TNIUUUrhWUtnDjbxtBnnj8owJes1EiyM6JZmhrlsd+eulv9qvGZJu72wnub8eRbWNbCL98qpX/QQXiIP4/dm0W6LWyaR+qZVL/mMjNfwzA4crqZ13dX0NQ+usJlbEQQ921K47oFMTPik3nV7+S1dw+yt+gcu4vO0X5+BUELsCQ1irwcG0vTovD391G+JlL9mstd8h0cdnDibBuFZS0UlbfQ1Tc8dpuP1cL8pPCxjVNjwj1naWh3yfeC8TY+M+/aCPEaTsPgzX1VvLn/LAAZiWE8ds8SwkI0n0e8n8ViYeXCWHIyotlTdI4391XR1N7P8ztKzu9SnsbCuRGuHqa4EcMwOFndTn6BnYIzLTjPf+4ZEuTHhmXxbMq2EetBJ14iniDAz4ecjNHlr52GQdW5rtFV4spasLf0crK6nZPV7fxhZxmJMbNGL4lLj2Fe/GysM+ADrOmmb3yukbt1vN7mSvn2DYzwyz+XUlQxOtfhhuU2HrwxY0Zd5jMVVL/mms58+wdHeP9QDe8fqh1bfWtpWhT3b0ojMTbE1Od2FdXv+PQODLP/eD35hedo/MxGoxmJYeTl2FixIPaycyGVr7mUr7k8Id+m9j4Ky0aXyj5T2zn2YQRA2Cx/lqVHk5MRzaK5EW63/6C75atL3aaJu73w3uZy+dpbennm9eM0tvfj62PlS7cuYP3SeBeP1DOpfs3linw7e4d4c38VewrP4XAaWIDrl8Rxz4ZUosImv6CMO1L9XplhGFTVd5NfUMehk00Mn88n0N+HtUviyMu2XbUhVr7mUr7m8rR8e/qHKa4cnRdUXNnKwJBj7DZ/PyuZ80bnBS1LiybUDVbzdLd8dambeKUjp5p48e2TDA47iAwN4PF7s7Sho8hnhM3y54u3LOCW65J4fU8lR041sb+kgYMnm7hpRSKbr5/LLA+dTCtXNzjk4ODJRvKP2alu/MuO80mxIeTl2Fi9eM6MXAFQxN2FBPmxNjOOtZlxDI84OV3bPvZtUFvXIAVlLRSUtWAB0mxh55fKjiYuMnhGzOmcKvrG5xq5W8frbS7k29raw58+LuedA9UALEwO5xv3LCE02PWfengy1a+53CHfynNdvJpfzunaDgCCA3zZfP1cblye6HaXTkyUO+TrLs619JJfYOeTkgb6B0eXovb1sbJyYSx5y22kJYRO+ORI+ZpL+ZrLW/I1DIPaph4Kzzc+n/1AA2BORND5eUHRpCeG4WOdnkv+3S1fXeo2Tdzthfc2vr5WfAP8+LdfH6SkcnSp1VtXJXF/btq0/eH2Zqpfc7lLvoZhUFzZyqu7KrA3j75vRoYGcM/6VK5fEofV6pmfFrpLvq4y4nBy7Ewz+cfsY40tQGx4EJtyElifFc/sa/hwaKbnazblay5vzbeta4Ci8hYKyls4Vd3OiOMvp/EhQX4sTYsiOz2azJRIU7/ddbd81fhME3d74b3NudZe/vP1Yhrb+vD3tfJ3dyxkzeI4Vw/La6h+zeVu+TqdBp+WNrB9byVtXaNLGNtiZnH/pjSWpkV53OUS7pbvdGntHGB3kZ09RfV09Q4BYLFAdno0eTk2FqdETslqUDM13+mifM01E/LtHxyhtKqNgrIWjle0jG08DODrY2Hh3Ahy0qNZlh5NZOjUzvF0t3zV+EwTd3vhvcmB0gZ+8+4phkacxIQH8fi9S0ieM9vVw/Iqql9zuWu+wyMOPjpax9ufVNN3/rKoBUnh3J+XRlqC5+yB5a75msFpGJRUtrGrwE5RRQsX/uYOC/Fn49IENmUneP2JjbdRvuaaafk6nE7K6zopLB+9JO7C/m4XzI2bTc75/YKSYkOu+YMud8tXjc80cbcX3hs4nE5eza/gg8O1AOTMj+Hrdy4m0MPnI7gj1a+53D3f3oFh3v60mo+O1DHiGB3fdQtiuG9TGnMig108uqtz93ynQlffEPuO17OrwE5L58DY8UVzI8jLsZGdEW3aMv4zIV9XUr7mmsn5GoZBfWvf2H5BFfZOPnuyHxkaQHZ6NDkZMSxIDp/Ue4i75avGZ5q42wvv6br6hvj5jhJO1XQAcOf18/jqvUvp6uxTviZQ/ZrLU/Jt6xpg+95KPiluwGB0N/GNyxK4a30KYW6wbOqVeEq+E2UYBmV1newqsHPkdNPYNfzBAb6sy4onNyeB+KhZpo/DW/N1F8rXXMr3L7p6hyiqGG2CSqvaGPpMHoH+PmSlRpGdEU1WahQhQeNb9dPd8lXjM03c7YX3ZGcbunhmWzFtXYME+Pnw1c2LWLMkTvmaSPVrLk/Lt66ph9d2V3D8/MbAAX4+3LoqiVtXJbvlEsielu/V9A+O8GlpA/kF9rFFKABS4meTm2Nj1aI5BEzjN9/elq+7Ub7mUr6XNzTs4ET16FLZReUtdJ6fJwhgtViYnxRGdkYM2RnRxIYHXfHnuFu+anymibu98J5q3/F6Xn7/NCMOJ3Mignhiaxa2mBDlazLlay5Pzfd0TTt/yq+gqr4LgNBgP+5cl8Km7ATTLquaDE/N96/VNHazq8DOpycaGTy/aaG/r5XVi+eQt9zGvDjX7FXmLfm6K+VrLuV7dU7D4Gx9N4XlzRSUtVz0gQuALXrW2FLZKQmhFy2a4m75qvGZJu72wnuaEYeTV3aW8fExOwDL0qL4+p2LCT6/waLyNZfyNZcn52sYBkdPN/P67goaz0+SjQ0PYuumVFYujHWLFeA8Od/hEQeHTzWRX2Cnwt41djw+KpjcHBvrlsSNvQ+6iifn6wmUr7mU78Q1dfRTdH7T1NM1HTg/0yKEzvInOz2K7PQYFs2LYFaQn1vlq8ZnmugP1uR19gzy3I4Syuo6Abhr3TzuWp/i1p8oeBvlay5vyHfE4WRv0Tne2H92bOnkeXGzeSAvnUVzI1w6Nk/Mt6m9j10F59hXXE9P/zAwOqdq+fwY8nJsLEgOd4umEjwzX0+ifM2lfK9N78AwxRWtFJa3UFzZSv+gY+w2f18rmamRbFqeRHZaJE6H61sJNT7TRH+wJqfC3smz24vp6BkiKMCHr21ZTE5GzCX3U77mUr7m8qZ8B4ZG+OBQLe8eqhm7HCsrNYr7c9NIig1xyZg8JV+H08nx8lY+LrBTWtU2djwyNIBN2TY2Lo0nLCTAhSO8PE/J11MpX3Mp36kz4nByuraDwrIWCsuaaT2/DxzAI3csYsPSeBeObtR4Gx/3m60qXm9XoZ3ff3AGh9MgPiqYJ7ZmTcsKRSIyeYH+vty1PoVNOTb+vL+K3YXnKK5spaSylbVL4rh3QypRYVO7j4yn6+gZZE/ROXYXnqO9e/REwQJkpkaSl2NjWVo0Vqt7fLsjInIlvj5WMudFkjkvkr+9KYPaph6OV7bS0jnIkpRIVw9vQtT4yLQZHnHy+w9Ps6eoHoAV82P4yuZFbrlalIhcXtgsfx6+ZQE3r0xi2+5KDp9q4pOSBg6dbOLGFTY2r5037uVQvZFhGJyqbie/wE5BWQsO5+hFFSFBfmxYGs+mHNvnrpQkIuLOLBYLyXNmk2oL88hv1HTGKdOirWuA53aUUHmuCwuwdVMqd6yZ6zbXsovIxMyJCOab9yzhtvouXs0v51RNB+8fqmVPUT2b187lphWJ+M+gTYd7B4bZX9zArgI7DW19Y8fTE8PIy7Fx3YJY/HzdZ0U8EZGZyPTGZ//+/Wzbto2ioiJqa2t56KGH+Jd/+Rezn1bcyOmadp7fUUJX3zCzAn159K5MslKjXD0sEZkCKfGh/NP/yKG4so3XdpVT19zLa7sq2Hm0jns2pLBuSbxXX85VVd9F/jE7h042jm0KGODvw/WZceTl2Eh00fwnERG5lOmNz549ezh58iQrV66ks7PT7KcTN2IYBjuP1vHHj8txOA0SY0J44r4sXeYh4mUsFgtL06JYkhLJp6UN7NhbSWvXIL9+5xQfHKrlvtw0lqVFec03vIPDDg6eaCS/wE51Q/fY8cSYEPKW21izeI4u4RURcUOmvzN/+9vf5rvf/S4ABw8eNPvpxE0MDTt4+f3TfFLSAMCqRbE8cvsiAvxnzqUvIjON1WphXVY8qxbFsvOonbc/PYu9pZefvXac+UnhPJCbRpotzNXDnLRzLb3sKrCzv6SB/sERAHx9LKxcGEteTiJptlCvae5ERLyR6Y2P1aprmmeals5+nt1WQnVjNxYLfCEvnVtWJumEQGSG8PP14bbVyWxYFs87B6r56EgdZ2o7+OHvjrJiQQxbN6Z6zEqOIw4nx840s6vAzqmajrHjMeGB5ObYWJ8Vz+xgf9cNUERExk3fxcuUOnm2jeffKKWnf5iQID++eXcmi+Z51lKHIjI1ZgX68UBuOjcuT2TH3ir2l9Rz9HQzBWda2JidwN3r5rnl/jUArZ0D7C6ys6eofmzjVosFstOjycuxsTgl8qLNlkVExP15dOPj6wYr5FzYLGk8myZ5M8MweO9gDa/sLMMwRnd2f/L+pURf43we5Wsu5Wsu5TsqNjKYR+/O5I61c/lTfjmFZS3sKrDzSUk9t6+eyx1r505qTsxU5+s0DEoqW9l5pI7C8hYubO8dFuJPbraN3BzbjNqrSPVrLuVrLuVrLk/N12IYF97ax6e7u5umpqar3i8pKQl//4u//r/hhhvIzc2dklXdDMPQpVNuYmBwhP/8UyF7Cu0A3HBdEo/dv4yAGbSUrYiMX0lFC7956wSna9qB0cbiwZsXcOuaeS5Z8rmzZ5CPDtXw3oGzNLT+ZSnqpenR3HF9CquXxOHrYX+5i4jIpSb8EduHH344tljB59mxYweLFi2a1KDGw+k06Orqu/odTebjYyU0NIiurn4cDs/ZwGmqNLb18bPXjlPb1IOP1cLf3jyfm65LpK9ngKl4dWZ6vmZTvuZSvpdniwzie19czpFTTbyaX0FDWx8vbC9m+65y7s9NY9XiOeO6jOxa8jUMg7K6Tj4+Wsehk42MOEY/AwwO9GX90nhuWJ5IQvToPKTurv6J/5JeQPVrLuVrLuVrLnfLNzQ0aFzfPk248dm6dStbt26d1KCmmjvtFOtwON1qPNOhuLKVF94opW9whNBgPx67N4v5SeE4HAYwoS8Sr2om5judlK+5lO/l5WTEkJUaxd7j9byxr4qm9n6e217C259W84XctHHPD5xIvv2DIxwobSC/wE5dc+/Y8Xlxs8nLsbFq8Zyxb6v1mo1S/ZpL+ZpL+ZrL0/L16Dk+4hqGYfD2p9Vs31OJAaQmhPL4vVlEzHbPScoi4r58fazk5dhYmzmHDw7X8u7BGqobuvmPVwpZkhLJ/blpJM+Zfc3PU9vUQ36BnU9LGxgccgDg72tl1eI55OXYSIkPvebnEBER92Z642O32ykuLgagv7+fmpoa3nvvPQBuu+02s59eplj/4Agvvn2SY2eaAdi4LIGHbp7vkuvyRcR7BPr7cte6FHKzbfz5k7PsKrBTUtVGaVUbazLjuHdjCtFhE1ssZXjEwZFTzeQX2Cm3/2UD7fioYHKzbVyfFcesQL+p/lVERMRNmd74HDx48KI5QXv37mXv3r0AnD592uynlylU39rLM9uKqW/tw9fHwkM3z2dTts3VwxIRLxI6y5+Hbp7Pzdclsm1PJYdONvFpaQOHTzVyw/JEtlw/j5Cgz29Wmtr72FV4jn3H6+npHwbAx2ohZ34MeTk2FiaHa3EcEZEZyPTGx53mBMnkFZQ186u3TtA/6CA8xJ/H783y6B3YRcS9xUYE8427l3Drqi5e21XByep2Pjhcy97j9dyxJpmbr0u6aEsDh9PJ8fJW8s9/U3RBZGgAm5YlsGFZAuFuumeQiIhMD83xkc/lNAze3FfFm/vPApCRGMZj9yxx200HRcS7pMSH8tSD2ZRWtfHqrgpqm3p4fXclHx+zs3VjKuuXJ/HnvZXkH7PT3j0IgAXITI0kL8fG0rQofKy6FFdERNT4yOfoGxjml38+QVFFKwA3Lk/kb25M134WIjKtLBYLS1KjWJwSyYHSBrbvqaS1a5AX3z7Ji2+fHLtfSJAfG5bGsynHRuw1bp4sIiLeR42PXJa9uYdnthXT2N6Pr4+VL9+2gHVZ8a4elojMYFaLheuXxLNyYSwfH7Pz1qfV9PYPk5EYRm6OjesWxGqhFRERuSI1PnKJI6eaePHtkwwOO4gKDeDxrVnMi9NSryLiHvx8fbh1VTI3rUzCP9Afi8PhUftIiIiIa6jxkTFOp8G2PZW8c6AagIXJ4XzjniWEBvu7eGQiIpcK8PMhIjSQ9vbeq99ZRERmPDU+AkBP/zAvvFlK6fnVkG5dlcT9uWmaFCwiIiIiXkGNj1DT2M0z24pp6RzA39fK392xkDWL41w9LBERERGRKaPGZ4Y7UNrAb949xdCIk+iwQJ7YmkXynNmuHpaIiIiIyJRS4zNDOZxOXs2v4IPDtQBkpkTy93dlXnVHdBERERERT6TGZwbq6hvi5ztKOFXTAcDmtXO5d0MqVqvFtQMTERERETGJGp8Zpqq+i2e3F9PWNUiAvw9fvWMR1y2MdfWwRERERERMpcZnBtl3vJ6X3z/NiMPJnIggnrhvKbboWa4eloiIiIiI6dT4zAAjDid/2FlG/jE7ANnp0Xxty2KCA/Xyi4iIiMjMoDNfL9fZM8izO0oor+sE4O71Kdy5bh5Wi+bziIiIiMjMocbHi5XbO3l2ezGdPUMEBfjw9S2ZZGdEu3pYIiIiIiLTTo2Pl9pVaOf3H5zB4TSIjwrmH+5bSlxksKuHJSIiIiLiEmp8vMzwiJPff3iaPUX1AKyYH8NXNi8iKEAvtYiIiIjMXDob9iJtXQM8t6OEynNdWICtm1K5Y81cLJrPIyIiIiIznKmNj8Ph4KWXXmL37t2Ul5fjcDiYP38+TzzxBGvXrjXzqWec0zXtPL+jhK6+YWYF+vLoXZlkpUa5elgiIiIiIm7BauYPHxgY4IUXXmDhwoU8/fTT/OQnP2HOnDk88sgj5Ofnm/nUM4ZhGHx0pJYfv1JIV98wiTEh/K+/W6mmR0RERETkM0z9xicwMJCdO3cSFhY2dmz9+vWcPXuWl156iby8PDOf3usNDTt4+f3TfFLSAMCqRbE8cvsiAvx9XDwyERERERH3Ymrj4+Pjc1HTA2CxWFi4cCFHjx4186m9XktnP89uK6G6sRuLBb6Ql84tK5M0n0dERERE5DKmfXEDp9NJQUEBaWlp0/3UXuPk2Taef6OUnv5hQoL8+ObdmSyaF+nqYYmIiIiIuK1pb3x+97vfUVVVxb/+679e88/y9TV1itK4+PhYL/q3mQzD4N0DNfzx4zIMA+bFz+bJ+5cRHRZo+nO7ynTmOxMpX3MpX3MpX3MpX3MpX3MpX3N5ar4WwzCMiTygu7ubpqamq94vKSkJf3//i44dOnSIr3zlK3zpS1/in//5nyc20r9iGMaMuqxrYHCEn/2pkL2FdgBuuC6Jx+5fRoCf5vOIiIiIiFzNhBufbdu28d3vfveq99uxYweLFi0a+/9Tp07x8MMPs379en76059ec9PicDjp6uq/pp8xFXx8rISGBtHV1Y/D4TTlORrb+vjZa8epberBx2rhoVvmc+OKxBnR+E1HvjOZ8jWX8jWX8jWX8jWX8jWX8jWXu+UbGho0rm+fJnyp29atW9m6deuEHlNTU8PXvvY1Fi9ezL//+79P2Qn7yIjrg77A4XCaMp7iylZeeKOUvsERQmf589g9S5ifFI7DYQAT6lk9mln5yijlay7lay7lay7lay7lay7lay5Py9f0OT7Nzc185StfITo6mueee+6Sy9/k8gzD4O1Pq9m+pxIDSE0I5fF7s4iYHeDqoYmIiIiIeBxTG5+BgQG+9rWv0drayne+8x3Ky8svuj07O9vMp/dY/YMjvPj2SY6daQZgU3YCf3vTfPzcYDEHERERERFPZGrj09LSwqlTpwB4/PHHL7n99OnTZj69R6pv7eWZbcXUt/bh62PhoZvnsynb5uphiYiIiIh4NFMbn8TERDU3E1BQ1syv3jpB/6CD8BB/Hr83izRb2NUfKCIiIiIin2va9/GRSzkNgzf3VfHm/rMAZCSG8dg9SwgL0XweEREREZGpoMbHxfoGhvnln09QVNEKwI3LE/mbG9Px9bANoURERERE3JkaHxeyN/fwzLZiGtv78fWx8uXbFrAuK97VwxIRERER8TpqfFzkyKkmXnz7JIPDDqJCA3h8axbz4kJdPSwREREREa+kxmeaOZ0G2/ZU8s6BagAWzY3g7+/OJDRY+xuJiIiIiJhFjc806ukf5oU3SymtagPg1lVJ3J+bho9V83lERERERMykxmea1DR288y2Ylo6B/D3tfLIHYtYvXiOq4clIiIiIjIjqPGZBgdKG/jNu6cYGnESEx7IE1uXkhQb4uphiYiIiIjMGGp8TORwOnk1v4IPDtcCsCQlkkfvyiQkyM/FIxMRERERmVnU+Jikq3eIn79RwqmaDgA2r53LvRtSsVotrh2YiIiIiMgMpMbHBFX1XTy7vZi2rkEC/H342uZFrFgQ6+phiYiIiIjMWGp8pti+4/W8/P5pRhxO5kQE8cR9S7FFz3L1sEREREREZjQ1PlNkxOHkdx+cJv+YHYDs9Gi+tmUxwYGKWERERETE1XRWPgXauwZ4+ndHKavrBODu9SncuW4eVovm84iIiIiIuAM1PteovK6TZ7YV09Y1QFCAD1/fkkl2RrSrhyUiIiIiIp+hxucaDA07+PEfCugbHCEhehZPbM0iLjLY1cMSEREREZG/osbnGvj5WsnOiCZ0diD3rp+Hn4/V1UMSEREREZHLUONzDSwWC9+4ZwkREbNob+9lZMTp6iGJiIiIiMhl6CsKERERERHxemp8RERERETE66nxERERERERr6fGR0REREREvJ7FMAzD1YOYDMMwcDrdY+g+PlYcDi1sYBblay7lay7lay7lay7lay7lay7lay53ytdqtWCxWK56P49tfERERERERMZLl7qJiIiIiIjXU+MjIiIiIiJeT42PiIiIiIh4PTU+IiIiIiLi9dT4iIiIiIiI11PjIyIiIiIiXk+Nj4iIiIiIeD01PiIiIiIi4vXU+IiIiIiIiNdT4yMiIiIiIl5PjY+IiIiIiHg9NT4iIiIiIuL11Ph8jqqqKr761a+SnZ3N2rVr+cEPfsDAwMC4Hrt9+3Zuu+02srKy2LJlC++++67Jo/U8k833i1/8IgsWLLjkn4qKimkYteeorq7mX/7lX7j77rtZvHgxW7ZsGfdjVb9XN9l8Vb9X9+677/LYY4+xadMmsrOzufPOO/nv//5vnE7nVR+r2r26year2h2fvXv38vDDD7NmzRqWLFnCjTfeyNNPP013d/dVH6v6vbrJ5qv6nZze3l42btzIggULKC4uvur93b2GfV09AHfV1dXFl7/8ZRISEvjZz35GW1sbTz/9NB0dHfz4xz/+3Me+9957fOc73+HRRx9l3bp1fPTRR/zjP/4js2fPZv369dP0G7i3a8kXYPny5Xz729++6FhiYqJZw/VIZWVl7N69m2XLluF0OjEMY1yPU/2Oz2TzBdXv1fz6178mISGBf/7nfyYqKoqDBw/ywx/+kNra2kty+yzV7vhMNl9Q7Y5HZ2cnOTk5fPnLXyY0NJSysjL+8z//k7KyMl566aUrPk71Oz6TzRdUv5Px3HPP4XA4xnVfj6hhQy7rhRdeMJYtW2a0traOHXvzzTeN+fPnG+Xl5Z/72Ntuu8148sknLzr2la98xXjggQdMGasnupZ8H374YePRRx81e4gez+FwjP33t7/9bWPz5s3jepzqd3wmm6/q9+o++75wwb/9278ZWVlZxuDg4BUfp9odn8nmq9qdvD/+8Y/G/PnzjYaGhiveR/U7eePJV/U7ceXl5UZ2drbxhz/8wZg/f75x/Pjxz72/J9SwLnW7gj179rB27VoiIyPHjt166634+/uze/fuKz6utraWysrKSy572bJlC8ePH6etrc20MXuSyeYr42e1TvyPt+p3/CaTr4zPZ98XLli0aBGDg4N0dHRc9jGq3fGbTL5ybcLDwwEYGRm57O2q32tztXxlcn74wx/y4IMPkpKSctX7ekoN62/uK6ioqCAtLe2iY/7+/iQnJ3/u9aCVlZUApKamXnQ8LS0NwzDGbp/pJpvvBYcOHSI7O5usrCwefvhhDh8+bNZQZxTV7/RQ/U7c0aNHCQ8PJyoq6rK3q3avzdXyvUC1O34Oh4PBwUFKS0t59tlnycvLw2azXfa+qt+Jm0i+F6h+x++9997j1KlTPP744+O6v6fUsOb4XEFXVxehoaGXHA8NDaWzs/OKj7tw218/Niws7KLbZ7rJ5guwcuVK7r77bubNm0dTUxMvvvgijzzyCL/73e/Iyckxa8gzgurXfKrfiSsuLmbbtm08/vjj+Pj4XPY+qt3JG0++oNqdqLy8PBobGwHYsGEDP/nJT654X9XvxE0kX1D9TkR/fz8/+tGP+J//838SEhIyrsd4Sg2r8ZkgwzCwWCxXvd9f38c4P/F5PI+dycaT75NPPnnR/+fm5rJlyxaee+45fvnLX5o5vBlD9Wse1e/ENDc38+STT5KVlcXXv/71q95ftTsxE8lXtTsxv/jFL+jr66O8vJznnnuOb3zjG/z617/+3OZS9Tt+E81X9Tt+zz//PFFRUWzdunXCj3X3GtalblcQGhpKV1fXJce7u7sv+03FBVfqbC/8rM977Ewy2XwvJzg4mE2bNlFaWjpVw5uxVL/TT/V7Zd3d3Xz9618nMDCQ559/Hj8/vyveV7U7cRPJ93JUu59v4cKFLF++nC984Qs888wzHDx4kA8//PCy91X9TtxE8r0c1e/l2e12XnrpJZ588kl6enro6uqir68PgL6+Pnp7ey/7OE+pYTU+V5CWlnbJXJOhoSFqamoumZvyWReubfzraxkrKiqwWCyXXPs4U0023ysxJrCUsFyZ6tc1VL+XGhwc5Jvf/CYtLS386le/IiIi4nPvr9qdmInmeyWq3fFZtGgRPj4+1NTUXPZ21e+1uVq+V6L6vVRdXR3Dw8M8+uijrFy5kpUrV/KNb3wDgC996Us88sgjl32cp9SwGp8r2LhxIwcOHKC9vX3s2IcffsjQ0BCbNm264uOSkpJITU3lnXfeuej4W2+9xdKlSy+7ms5MNNl8L6evr4/du3eTlZU11cOccVS/00/1e6mRkRG+9a1vcerUKX71q19ddcIyqHYnYjL5Xo5qd/wKCgpwOBxX3DNG9Xttrpbv5ah+L2/RokW8/PLLF/3z3e9+F4Dvf//7/O///b8v+zhPqWHN8bmCBx98kP/6r//iscce47HHHqO1tZUf/ehH3HnnnRd9I/G9732PHTt2cOLEibFjTz75JP/4j/9IcnIy119/PTt37mT//v386le/csWv4pYmm++RI0d48cUXufnmm0lISKCpqYlf//rXNDc38//+3/9z1a/jlvr7+8eWBrfb7fT09PDee+8BsGrVKiIjI1W/12Ay+ap+x+df//Vfyc/P55/+6Z8YGBigsLBw7Lb09HRCQkJUu9dgMvmqdsfviSeeYMmSJSxYsIDAwMCxBnPBggXcdNNNgM4drsVk8lX9jl9oaCirV6++7G2ZmZlkZmYCnlvDanyuIDQ0lN/+9rf84Ac/4B/+4R8IDAxky5YtPPXUUxfdz+l0XrKj7e23387AwAA///nPefHFF5k7dy4//elP3WfXWjcw2XxjYmIYGhriJz/5CR0dHQQFBZGTk8P3v/99li5dOt2/hltrbW3lW9/61kXHLvz/yy+/zOrVq1W/12Ay+ap+x2ffvn0A/Md//Mclt6l2r91k8lXtjt/SpUt55513+MUvfoFhGNhsNr7whS/w1a9+FX9/f0DnDtdiMvmqfqeep9awxdAFjiIiIiIi4uU0x0dERERERLyeGh8REREREfF6anxERERERMTrqfERERERERGvp8ZHRERERES8nhofERERERHxemp8RERERETE66nxERERERERr6fGR0REREREvJ4aHxERERER8XpqfERERERExOup8REREREREa/3/wMqcnEoatZJ4gAAAABJRU5ErkJggg==", + "text/plain": [ + "<Figure size 1000x200 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo[\"C\"].plot(figsize=(10, 2));" ] @@ -1788,13 +5369,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 191, "metadata": { "slideshow": { "slide_type": "-" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x200 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo.plot(y=\"C\", figsize=(10, 2));" ] @@ -1809,13 +5401,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 192, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo[\"C\"].plot(kind=\"bar\");" ] @@ -1830,26 +5433,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 193, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo[\"C\"].plot.bar();" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 194, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1200x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo[\"C\"].plot(kind=\"bar\", legend=True, figsize=(12, 4), ylim=(-1, 3), title=\"This is a C plot\");" ] @@ -1879,7 +5504,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 195, "metadata": { "exercise": "solution", "slideshow": { @@ -1893,43 +5518,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 196, "metadata": { "exercise": "solution" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df[\"Presim. Time / s\"].plot(figsize=(10, 3), style=\"--\", color=\"red\");" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 197, "metadata": { - "editable": true, "exercise": "solution", "slideshow": { "slide_type": "" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df[\"Sim. Time / s\"].plot(figsize=(10, 3), style=\"-b\");" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 198, "metadata": { - "editable": true, "exercise": "solution", "slideshow": { "slide_type": "subslide" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df[\"Presim. Time / s\"].plot(style=\"--r\", figsize=(10,3));\n", "df[\"Sim. Time / s\"].plot(style=\"-b\", figsize=(10,3));" @@ -1937,16 +5593,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 199, "metadata": { - "editable": true, "exercise": "solution", "slideshow": { "slide_type": "fragment" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ax = df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot(style=[\"--b\", \"-r\"], figsize=(10,3));\n", "ax.set_ylabel(\"Time / s\");" @@ -1955,7 +5621,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "slide" }, @@ -1968,15 +5633,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 200, "metadata": { - "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot(figsize=(10,3));" ] @@ -1984,7 +5659,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "" }, @@ -2011,45 +5685,75 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 201, "metadata": { - "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo[[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True, figsize=(10,3));" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 202, "metadata": { - "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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e50n66st/pjry7/e8FjlOTsLbSVrwefrpp1VTU6PJkyfrscceS9awAFKYDV+6Pc9XS0v6Bh8AQOrLpMvbjqa7/v1Jmc46Fovpnnvu0cKFC5WdnZ2MIQEAAACgVVLO+Nx333067bTTVF5erq1bt3bbdrOy0vMxRK6bnnXbiM8CiTqy77APIZPRB0C4feD7xz7T4TgRY5eLm7piw3UjXfr+H3rwef/997V69Wo9++yz3bpdx4mooKB3t24TmScazTVdAtIc+xBAHwBSOH3Q1ORq/36n3Rf+SCSiPn16Gvulg+f5qq9vUhAkFn6uuuoK/eMf27R06W919tnnnHB934/IcRzl5/dSz549ExpTSiD41NfXa+/evSdcb/DgwcrOztaiRYt0xRVXqLS0NKECj8X3A8Vi6TlJwpGbomFeut+YDnNsmdwA6Ar6AAi3D+Lx5v//4NKgzf2kWVmOXNdR5eNva9fn9d065okM6t9H86edrSAIErrH9ZNP/ql//GObJOnFF1/Qt7414oTv8bxAvu+rrq5BjY1eu+XRaG6HQmCng09VVZUWLlx4wvXWrFmjjz/+WNXV1aqsrFQsFpMkNTc3Szp830+PHj3Uo0fis1RwQzG6ihvT0VXsQwB9AEjh9IHnHf+Myq7P61W9u65bxwzbunVr5bquhg8/W6+88pJuvPGmDs8B8H8DYGd1OvhMmjRJkyZN6tC669evV11dnSZMmNBu2ciRIzVr1izNnz+/syUAAAAASENVVS9qxIhzdNlll2vBghv02mt/0bhx45Mydqj3+PzkJz/Rt7/97TavPfvss3rhhRf00EMPaeDAgWEODwAAACBFbN26RZ9+ultXXz1TI0eOVt++fbVu3Vo7gs+gQYM0aNCgNq+9+eabcl1Xo0aNCnNoAAAAACmkqmqtcnJy9N3vTlBWVpbKy7+vP/3pOX3xxSH17p0X+vjMPwkAAAAgVJ7n6eWX12vMmHOVl3c45Jx//oWKx5u1YcMrSakh6cFn3rx52rx5c7KHBQAAAGDI//7vG6qpOahzzx2n+vp61dfX66STTlZxcX+tW7c2KTUk5QGmAAAAADJXVdXhcPNf/3WbpNvaLNu/f58OHNivoqJ+odZA8AEAAAAQmqamJm3cuEHf+c54TZkytc2y2tpa/ed/3qyXXlqnyy67ItQ6CD4AAABAGhrUv09ajLlp0wY1NjZoypSpGjHinHbLn3xymNate5HgAwAAAOArvh/I83zNn3a2kfE9z5fvH//hqv9q3boX1b//AJ111tHrvfDCS/SrXy3Wjh2faMiQf+uuMtsh+AAAAABpxPcD1dY2yHEixsbvTPBZvPhXx10+efJlmjz5sq6WdUIEHwAAACDNdDZ8gOf4AAAAAMgABB8AAAAA1iP4AAAAALAewQcAAACA9Qg+AAAAAKxH8AEAAABgPYIPAAAAAOsRfAAAAABYjweYAgAAAGnGcSJynIiRsdP14akEHwAAACCNOE5EBX1z5biukfF9z1NNbWPC4ee11zbpj3/8vbZte08NDV+oqKifxow5T5dffqVKSgZ1c7VfIfgAAAAAacRxInJcV3vX3Kf4gV1JHTunaJCKf3yDHCeSUPD57W+X6dFHl2vcuHItWLBQBQWF+uyzT/Xii3/SDTfM0R//+FwIVR9G8AEAAADSUPzALsX3bDddRoe9/vqrevTR5fr3f5+u2bPntr4+fPgIXXTRJdq0aWOo4zO5AQAAAIDQPfnkKhUWFmnmzNlHXX7eeeNCHZ/gAwAAACBULS0t2rLlbzrnnG8rK8vMRWcEHwAAAAChisXqFI83q7i4v7EaCD4AAAAAQhUEhydCiETMTMEtEXwAAAAAhCw/v69ycnro88/3GKuB4AMAAAAgVFlZWTrzzG/prbfeVEtLi5EaCD4AAAAAQjd16pU6ePCAVqx46KjL//KXP4c6Ps/xAQAAANJQTtGgtBpz9OixuuqqGVq58mF98sl2TZx4gQoKCrVnz2f6n/95QTt37tC5536nG6tti+ADAAAApBHfD+R7nop/fIOZ8T1Pvh8k9N7/+I85Ov30M7V69VO6665fqKHhC/Xr9zWNHDlK8+bd2M2VtkXwAQAAANKI7weqqW2U45iZIc33g4SDjySNHXuexo49rxsr6hiCDwAAAJBmuho+MhGTGwAAAACwHsEHAAAAgPUIPgAAAACsR/ABAAAAYD2CDwAAAADrJSX4NDU16d5771V5eblOP/10TZgwQffff38yhgYAAACA8Kez9jxPs2fP1p49e1RRUaGSkhJ9+umn+uyzz8IeGgAAAAAkJSH4rF69Wu+9957Wrl2rfv36hT0cAAAAALSTlOBz0UUXEXoAAACAbuI4ETlOxMjYiT489eGHf6MVKx5q9/qQIf+mJ554ujtKO65Qg088Htd7772n8vJyLViwQOvWrZPruiovL9ctt9yigoKCLm0/Kys952Zw3fSs20Z8FkjUkX2HfQiZjD4Awu0D3z96sHGciPoW5Mp13G4fsyM831NtTWNC4adHjx767/9+sN1rHeG6kS59/w81+NTW1qqlpUUPPfSQRo0apaVLl2rfvn1avHixbrzxRq1YsSLhbTtORAUFvbuxWmSiaDTXdAlIc+xDAH0ASOH0QVOTq/37nXZf+F3Xkeu4WvL6cu2O7en2cY+nJDpAFaNnKDvblef5nXrv4bNUjoYP/1an3uf7h9+Xn99LPXv27NR7/1Wng099fb327t17wvUGDx6sIDicAqPRqJYsWaKcnBxJUu/evTVv3jz9/e9/15lnntnZEiQdPsUWizUk9F7TXNfhIJEiYrHGTjctIH3Vx+xDyGT0ARBuH8TjzfJ9X54XqKWl/bZ3x/Zoe83Obh2zozzPP2pNx3PkDFFn3+d5gXzfV11dgxobvXbLo9HcDp1x63Twqaqq0sKFC0+43po1a3TSSSdJkkaMGNEaeiRp9OjRkqQPP/ww4eAjdf6HBvxfiTQt8K/YhwD6AJDC6QPP6/ylZOmgpaWlzd9d11UkcuL7lY4VADuq08Fn0qRJmjRpUofXLykpOeYyx+GaYAAAACBTNDY2avz40W1eu/XWRbrggotDHzv0Wd3Gjx+v9evXKx6Pt571efXVVyVJQ4cODXt4AAAAACmiR48eWrq07cxuAwce+0RJdwo9+MycOVPPPfec5s6dqyuvvFJ79+7VPffco4kTJ6qsrCzs4QEAAACkCMdxNHToMDNjhz1ASUmJHnnkETU2NmrevHmqrKzU+eefr7vuuivsoQEAAABAUhLO+EjS6aefrlWrViVjKAAAAABoJynBBwAAAED3KokOyIgxuwvBBwAAAEgjvh/I8z1VjJ5hZHzP91qfyZNOCD4AAABAGvH9QLU1jXKcEz/7JqzxEwk+M2fO1syZs0OoqGMIPgAAAECaSTR8ZDKeIAoAAADAegQfAAAAANYj+AAAAACwHvf4GJTO0wGmO372AAAgXQRBZt/L013/foKPAaanIMRh6ToVIwAAyAyu60qS4vFm5eT0MFyNOfF4syTJdbsWXQg+BpiegrA7uK6jaDRXsVijPM83XU5CmA0FAACkMsdxlZubp0OHaiRJOTk9FImk7/fHzgqCQPF4sw4dqlFubp4cp2t36RB8DLHlS7fn+WppSc/gAwAAkOqi0UJJag0/mSg3N6/159AVBB8AAAAgRUUiEeXnF6lPnwJ5XovpcpLOdbO6fKbnCIIPAAAAkOIcx5Hj5JguI60xnTUAAAAA6xF8AAAAAFiP4AMAAADAegQfAAAAANaLBGn6KNggsGM66HTmuk7aPsMH6A70AEAfABJ9YJrjRDr0fKO0DT4AAAAA0FFc6gYAAADAegQfAAAAANYj+AAAAACwHsEHAAAAgPUIPgAAAACsR/ABAAAAYD2CDwAAAADrEXwAAAAAWI/gAwAAAMB6BB8AAAAA1iP4AAAAALAewQcAAACA9Qg+AAAAAKxH8AEAAABgPYIPAAAAAOsRfAAAAIAENTQ0aOrUqXr//fdNl4ITyDJdAFLfxx9/rI0bN6q6ulp1dXWSpPz8fJWWlmrcuHE6+eSTDVcIhO/LL79UXV2dioqKFIlE2i0/dOiQ3n//fY0cOdJAdUBy7Nu3Ty0tLfr6178uSQqCQFVVVfrkk080ZMgQfe9731NWFl8tYJ933333mMsaGhr0zjvvaOvWrfJ9X5J02mmnJas0dEIkCILAdBFITc3Nzbr11lv1/PPPKzs7W0OGDFE0GlUQBKqvr9eOHTv05Zdf6tJLL9Xtt9+unJwc0yUD3S4IAlVWVurxxx9Xc3Oz8vPzNX36dF1zzTVyXbd1vb/97W/8xg/WOnTokK6//nq9+uqrkqQJEybonnvu0ezZs/XGG2/IdV15nqeysjKtWrVKvXv3Nlwx0L2GDh3a+kuvIAja/QLsyGtH/uRYkJr4tQyOqbKyUhs3btTdd9+t888/v12wicfjqqqq0h133KHKykr9/Oc/N1QpEJ6nnnpKK1eu1JVXXqmysjK99dZb+vWvf62NGzdq2bJlys/PN10iELr7779f7777rhYtWqT8/HwtW7ZMFRUV2rFjh55++mmVlZXpr3/9q2644QatWLFC1113nemSgW5VXFws3/dVUVGhk046qc2yL774Qtdee61uvvlmlZWVmSkQHcIZHxzT2LFjddNNN+lHP/rRcddbs2aNFi9e3PqbQMAml156qb7//e9r3rx5ra9t2bJFFRUV6t27t373u99pwIABnPGB1SZOnKjp06dr2rRpkg5f9jN58mTdfvvtmjJlSut6K1as0DPPPKPnn3/eVKlAKBoaGrR06VI98cQTuuKKKzRnzpzWM5v19fUaOXKkHnvsMS53TnFMboBjamxsVL9+/U64Xr9+/dTY2JiEioDk27lzp0aNGtXmtTPOOEN/+MMflJWVpcsuu0wffvihoeqA5Pj888916qmntv79m9/8Zps/jxg6dKh2796d1NqAZOjVq5cWLFig1atXa9u2bbrgggv07LPPmi4LnUTwwTGNGDFCS5cubZ3Q4Gjq6uq0bNkynX322UmsDEie/Px87d+/v93rX/va17Rq1SoNGTJE06ZN09tvv22gOiA58vLy2hwLsrKy1L9/f/Xq1avNes3NzXIcvlrAXqWlpXr44Yd1yy23aMmSJZoyZYo2b9581ElvkHq4xwfHdOutt+qqq67S+PHjNXbsWJWWlqpPnz6KRCKKxWKqrq7Wa6+9pmg0qpUrV5ouFwjFaaedpvXr1+viiy9utywvL0/Lly9XRUWFFi9ezIEP1jrllFO0ZcsWTZw4UZLkOI42bNjQbr0PPvhAQ4YMSXZ5QNJdeOGFGj9+vJYtW6a5c+eaLgcdxD0+OK76+no9+eST+vOf/6zq6mrFYjFJUjQabZ3OeurUqerTp4/hSoFwrF27Vo888ogefPBBFRQUHHUdz/N02223adOmTXr55ZeTXCEQvk2bNqmurk4/+MEPjrvevHnzdOaZZ2rWrFlJqgww79NPP9WuXbs0bNgw5eXlmS4Hx0HwAQAAAGA9LsQFAAAAYD2CDwAAAADrEXwAAAAAWI/gAwAAAMB6BB8AAAAA1iP4AAAAALAewQcAAACA9f4fQug7EVXF5C0AAAAASUVORK5CYII=", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo[df_demo[\"F\"] < 0][[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True, figsize=(10,3));" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 203, "metadata": { - "editable": true, "slideshow": { "slide_type": "fragment" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x400 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo[df_demo[\"F\"] < 0][[\"A\", \"C\", \"F\"]]\\\n", " .plot(kind=\"barh\", subplots=True, sharex=True, title=\"Subplots Demo\", figsize=(10, 4));" @@ -2057,15 +5761,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 204, "metadata": { - "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1200x600 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo.loc[df_demo[\"F\"] < 0, [\"A\", \"F\"]]\\\n", " .plot(\n", @@ -2078,15 +5792,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 205, "metadata": { - "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1200x600 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo.loc[df_demo[\"F\"] < 0, [\"A\", \"F\"]]\\\n", " .plot(\n", @@ -2136,9 +5860,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 206, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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t2zYyMjLYuXNnmWsB8OOPP/LNN9/QuXNnGjduTGBgIEeOHGHFihV4eHhcdgHKc958802Sk5Pp0qULDRs2xN3dnd27d7N+/XoaNGjA0KFDz7/25ptvZsqUKXz88cfEx8cTHh7O4cOHWbNmDQMGDDh/j9Gf1a1bl+LiYoYNG0bfvn2xWq0sWrSIlJQUbrvttvNTWcPZS/dee+01XnjhBYYNG0aPHj1o2rQpVquV48ePs2XLFoKCgli0aFG5avZnAwcOZMqUKaxZs6bEWcpKy9fXl/bt27Np0yaefvppmjZtitlspm/fvkRGRtK9e3eeeuop3nnnHQYNGkTPnj0JCwsjLy+P48ePs2nTJjp06FDq6afNZjPjx4/nk08+Yfjw4fTr14/i4mI2bNhAZmbm+XV4KsLYsWN54403zv//5axdu5aAgAC6det20XNLly7lueee46abbuLNN9+skGwiUnOp8RERlxYdHc19993Hxo0bWbNmDZmZmQQHBxMVFcX48ePp1atXqfYzatQojh8/zk8//cRnn32G1WqlS5cul218AgMDeffdd3n//feZO3fu+Zvrb7jhhsverN29e3d++OEHpkyZwq+//srmzZtxd3cnJCSEbt26MWjQoDLV4I+GDRtGUVER27ZtY8+ePRQUFBAaGsrQoUO58847adWq1RX3cd999/HLL7+wa9cu1q1bh8lkokGDBtx///1MnDjxgqnHg4ODmT59Ov/85z/ZtGkTmzZtIjo6milTpnDs2LFLNj4eHh588cUXvPPOO/z000+kp6fTqFEj7r333hLXehkxYgSRkZF88cUXbNiwgV9//RUfHx9CQkIYNGgQQ4YMKXfN/iw2NpY2bdrw448/8vTTT5c4CUNp/fOf/+SNN95gzZo1LFiwAIfDQb169c7f33XvvffSoUMHpk+fzpYtW1i+fDl+fn6EhoYyevTo8xMIlNa56bvnzJnDrFmz8Pf355prruHxxx8vcRbB8rrpppt46623cHd3v+y/kUOHDhEXF8eECRNKnGJdRKQimRwOh8PoECIiIuf07dsXgOXLlxuc5NIWLFjAU089xeTJkxkwYIDRcaqcdevWcccddzBixAj++c9/XvJ1b775Jl999RULFy6kUaNGlZhQRGoi3eMjIiJSRkOHDqV9+/a899576PPDi3322WcAl50e/fTp08yYMYPx48er6RGRSqFL3URERMrIZDLx8ssvs3TpUk6fPk1oaKjRkQwXHx/PsmXL2L17N7/++iv9+vW77AKxycnJTJo0qVT3lYmIVAQ1PiIiIuUQGRnptLWWqqM9e/bw7rvv4ufnx/XXX8/f//73y74+NjaW2NjYSkonIqJ7fEREREREpAbQPT4iIjXQ3LlziYiIuOS02Pfdd9/5SQbK6tlnn71o2759+/Lss8+Wa39/dC73lf6cO/6zzz5bJc8qHDt2jIiIiFJPRS0iIldPl7qJiEi10bt3b2bNmnXBY2PGjGHQoEHcdddd5x/74yKqIiIioMZHRESqkeDgYIKDgy96vE6dOsTExFR+oN8VFBTg5eVl2PFFROTKdKmbiIiUisPh4Ouvv2bEiBG0a9eOzp078+ijj3L06NFy7e/48eM8/fTTdO/enejoaIYMGcKUKVOw2+0VnBySkpKYNGkSsbGx9OrVizfffJOioqILXlNUVMQHH3zA4MGDiY6Oplu3bjz33HOkpaVd8Lq+ffty3333sWTJEm688Ubatm3L5MmTAUhJSeGll16iZ8+eREdH07dvXyZPnozVaq3wr0lERMpGZ3xERGowu91e4pvykua9eemll5g3bx7jx4/n6aefJjMzk/fff5+xY8fy/fffU6dOnVIfNy0tjbFjx1JcXMxjjz1Gw4YNWblyJW+99RZHjhzh//7v/67my7pAcXExDzzwALfccgt33XUXmzZt4oMPPsDPz4+HH34YOFuHBx98kC1btnD33XfToUMHkpOTee+999ixYwfffffdBWd0du/eTWJiIg888ABhYWF4e3uTkpLCqFGjMJvNPPTQQzRu3Jht27bx4YcfkpyczBtvvFFhX5OIiJSdGh8RkRps9OjRl3yuYcOG5/8/Li6O2bNn8+yzz3LnnXeef7xTp04MGjSIL774gmeeeabUx/3iiy84deoUc+bMOb/WS48ePbDZbMycOZOJEyfSrFmzcnxFFysuLuaRRx5hyJAhAHTv3p1du3axYMGC843PwoULWbNmDe+99x4DBw48v21kZCS33HILc+fO5bbbbjv/eFpaGj/99NMFGV966SUyMzP56aefaNCgwfljeXl58dZbb3H33XfTokULANzd3QFwc9OvYRGRyqJL3UREarC33nqLb7/99qI/HTt2vOB1K1aswGQyccMNN2C1Ws//qVOnDpGRkWzcuLFMx12/fj0tWrS4aIHLkSNH4nA4WL9+/VV/beeYTKaLZpmLiIjg+PHj5/++YsUKAgIC6NOnzwVfX+vWralbt+5FX19ERMRFjdnKlSvp2rUrISEhF+yjZ8+eABfsw8/P74L/ioiI8+mjJhGRGiw8PJy2bdte9Li/vz8nT548//fU1FQcDgfXXHNNiftp1KhRmY6bkZFxwRmlc0JCQs4/X1G8vb3x9PS84DEPDw8KCwvP/z01NZWsrCyio6NL3Ed6evoFf69bt+5Fr0lNTWXFihVERUVdcR++vr64ubmp8RERqURqfERE5IqCgoIwmUx8/fXXJU4VXdbpo2vVqkVKSspFj58+ffr88SpTUFAQtWrV4rPPPivxeV9f3wv+bjKZStxHREQEjz/+eIn7ONfUnbN79+7yhRURkXJR4yMiIlfUu3dvPvnkE06dOsX1119/1fvr3r07H3/8Mbt3777gDMn8+fMxmUx07dr1qo9RFr179+ann37CbrfTvn37cu9j1apVNG7cmMDAwApOKCIiV0uNj4iIXFHHjh0ZM2YMzz//PLt27aJz587nZzLbsmULrVq1uuDm/yu54447mD9/Pvfddx+PPvooDRo0YOXKlXzzzTfceuutFTaxQWkNHTqUH3/8kXvvvZfx48fTrl073N3dOXnyJBs2bKBfv34MGDDgsvt49NFH+e233xg7dizjx4+nWbNmFBUVcezYMVavXs0//vEP6tWrB0BycjIDBgzgmWeeuWCyCBERcR41PiIiUiovv/wy7du3Z9asWcyYMQO73U5ISAgdOnS4aJKCKwkODmbmzJm8/fbbvP322+Tm5hIWFmZYI2CxWPjwww+ZNm0a33//PZ988gkWi4V69erRuXNnWrVqdcV9hISE8O233/LBBx/w+eefc+rUKXx9fWnYsCE9evQgICDg/GsdDgc2m80paxaJiEjJTI6SFmsQERERERFxIZrOWkREREREXJ4aHxERERERcXlqfERERERExOWp8REREREREZenxkdERERERFyeGh8REREREXF5anxERERERMTlVdsFTB0OB3Z71ViCyGw2VZksrkj1dS7V17lUX+dSfZ1L9XUu1de5VF/nqkr1NZtNmEymK76u2jY+druDtLRco2Pg5mYmKMiXrKw8rFatwF3RVF/nUn2dS/V1LtXXuVRf51J9nUv1da6qVt/gYF8slis3PrrUTUREREREXJ4aHxERERERcXlqfERERERExOWp8REREREREZenxkdERERERFyeGh8REREREXF5anxERERERMTlqfERERERERGXp8ZHRERERERcnhofERERERFxeW5GBxAR4+QXWnHPLzY6hoiIiIjTqfERqaFy8ov5vy82kltg5emxMYQ3CDQ6koiIiIjT6FI3kRpq9ooDpGUVUlhk4z+ztnPkVLbRkUREREScRo2PSA2UcCSdX3ecAKBJPX/yCq28M3s7p9LzDE4mIiIi4hxqfERqmGKrnamLEgDo06Ehbz7cg0YhfmTlFvH2zDjSswsNTigiIiJS8dT4iNQwP607zMm0PAJ9PRjdtwV+3u48c1ssIbW8OZNZwDuz48gt0IQHIiIi4lrU+IjUICdSc/l5fRIAt/Zvia+XOwC1/Dx5amwMgX4eJKfk8t852yksshkZVURERKRCqfERqSHsDgdTF8ZjtTloF16bzpEhFzxft5Y3T42OwcfTjcTkLN6fvxOrzW5QWhEREZGKpcZHpIb4dccJ9h3LxMPdzLgBrTCZTBe9JizEj8dHt8fD3cyug2l8tmAPdrvDgLQiIiIiFUuNj0gNkJlbxOzlBwC48brm1KnlfcnXtmgYyEM3tcViNrFx72m+/mUfDoeaHxEREane1PiI1AAzl+0nr9BK41A/BnQOu+Lr2zavzT3D2mACVmxN5vtfDzk/pIiIiIgTqfERcXE7D6ayYc8pTCaYODgSi7l0/+y7tgll3MBWAPyw9jBLNx91ZkwRERERp1LjI+LCCottTF98ds2efh3DaFY/oEzb9+kQxo09mgEw45f9rNt1ssIzioiIiFQGNT4iLuyHXw9xJrOA4ABPburRvFz7GH5NU/p3PHt53Oc/7WX7gTMVGVFERESkUqjxEXFRR05ls3jj2cvTxg2IwNvTrVz7MZlMjO3fku5RodgdDj6Yv4t9RzMqMKmIiIiI86nxEXFBdruDqYsSsDscdIyoS0zLOle1P7PJxJ3Xt6Z9eG2KrXb+9+12jpzKrqC0IiIiIs6nxkfEBa3YlsyhE1l4e1q4rX+rCtmnm8XMAzdG0yoskPxCG+/M3s6p9LwK2beIiIiIs6nxEXEx6dmFfLcqEYCbe4UT5O9ZYfv2cLfw6C3taBTiR1ZuEW/PjCM9u7DC9i8iIiLiLGp8RFzM10v3UVBkI7xBAL1jG1b4/n283HlydHtCanlzJrOAd2bHkZNfXOHHEREREalIanxEXMi2fSls3ZeCxWxi4uBIzCaTU44T6OfJU2NjCPTzIDkll/99u53CIptTjiUiIiJSEdT4iLiI/EIrXy3dB8CgLo0JC/Fz6vHq1vLmqTEx+Hq5kZicxfvzdmK12Z16TBEREZHyUuMj4iLmrT5IenYhdWt5ccO1TSvlmGF1/XhsVHs83M3sOpTGZwv2YLc7KuXYIiIiImWhxkfEBRw6kcWyLccAmDAoEg93S6Udu0XDQB6+qS0Ws4mNe0/z1dJ9OBxqfkRERKRqUeMjUs3Z7HamLozHAXSLCiWqWXClZ4huXptJw9tgAlZuS2b+mkOVnkFERETkctT4iFRzSzcd48jpHHy93Bjbt6VhObq0DmXcwLNrBv3422GWbjpqWBYRERGRP1PjI1KNncnIZ/6vBwEY3acFAb4ehubp0yGMm3o0A2DGsv38tuuEoXlEREREzlHjI1JNORwOpi/ZR1GxnYhGtbiuXX2jIwEw7JqmDOjUCIApP8UTt/+MwYlERERE1PiIVFub4k+z82AqbhYTEwZHYHLSmj1lZTKZGNOvBd2j6mF3OPjw+10kHEk3OpaIiIjUcGp8RKqh3IJivvllPwBDuzelfm1fgxNdyGwycef1kcS0qEOx1c673+3gyKlso2OJiIhIDabGR6Qa+nZlIlm5RdSv7cP13ZoYHadEbhYz94+IolVYIPmFNt6ZFceptDyjY4mIiEgNpcZHpJrZdzSDVXHHAZgwKAJ3t6r7z9jD3cKjt7SncYgfWXnF/HtmHOnZhUbHEhERkRqo6r5jEpGLWG12pi1OAKBHu/pENA4yONGV+Xi58cSYGEKCvEnNKuCdWXHk5BcbHUtERERqGDU+ItXIwvVJHD+TS4CPO6P6tDA6TqkF+nrw9JgYavl5kHwml//N2U5hkc3oWCIiIlKDqPERqSZOpuXx429JAIzt1xI/b3eDE5VNnVrePDkmBl8vNxKPZzF53k6sNrvRsURERKSGUOMjUg04HA6mLYrHarMT1SyYrm1CjY5ULmF1/Xh8VHs83M3sPpTGpz/uwW53GB1LREREagA1PiLVwNqdJ4k/koGHm5nxg6rOmj3lEd4wkIdHtsViNrEp/jRfLUnA4VDzIyIiIs6lxkekisvKK2LW8rNr9txwXTNCankbnOjqRTerzaThbTABK+OOM2/NIaMjiYiIiItT4yNSxc1adoDcAithdf0Y2LmR0XEqTJfWoYwbFAHAgt8Os2TTUYMTiYiIiCtT4yNShe0+nMa63ScxAROHROBmca1/sn1iG3JTz+YAzFy2n7U7TxicSERERFyVa72LEnEhRcU2pi86u2ZPnw4NCW8QaHAi5xjWvcn5M1lf/BzPtv0pBicSERERV6TGR6SK+vG3w5zOyCfI35Obe4UbHcdpTCYTo/u24NroetgdDj6cv5uEI+lGxxIREREX4/TGJykpiZdeeokRI0bQpk0bhg0b5uxDilR7x1JyWLThCAC39W+Ft6ebwYmcy2wyccf1kcS0qIPVZufd73aQdDLb6FgiIiLiQpze+Ozfv59Vq1bRpEkTwsNd91NrkYpidziYtigBm91BbMs6dIyoa3SkSmExm7l/RBStGtUiv9DGf2bHcSotz+hYIiIi4iKc3vj07duXVatW8e677xIVFeXsw4lUe6vijnMgORNPDwu3D2hldJxK5eFu4dGb29E41I+svGL+PTOO9OxCo2OJiIiIC3B642M26zYikdLKyCnk25WJAIzs2ZzgAC+DE1U+Hy83nhwdQ2iQN6lZBbw9K46c/GKjY4mIiEg1p65EpAr55pf95BdaaVbfn34dwoyOY5gAXw+eGhNDkL8nx8/k8t852ykoshodS0RERKqxan3HtJub8X2b5fd1VSwutr5KVVGT6hu3/wyb409jNpm4a2gbPDwsTj9mVa5vvTq+/OW2WF6dtoWDx7P4YN4unhgTg3sV+HdfWlW5vq5A9XUu1de5LBYzDodD9XUSjV/nqq71rbaNj9lsIijI1+gY5wUEeBsdwaW5en3zC61MX3J2zZ4RvcKJaV2vUo9fVesbFOTLPyZ1428f/cauQ2lMWRjPM+M6YTGbjI5WJlW1vq5C9XUu1bfiFRRZef/b7WzYdYIRPVtwS7+WeLo7/8Oumkjj17mqW32rbeNjtzvIyjJ+xieLxUxAgDdZWfnYbHaj47icmlLfb5buIyU9nzqBXlzfpRHp6bmVctzqUN+QAE8evaUdb8+MY+3243hYtnDHkEhMpqrf/FSH+lZnqq9zqb7OkZpZwP/mbOfw71P2z1yawPLNR5gwOIJ24XUMTuc6NH6dq6rVNyDAu1Rnn6pt4wNgtRpf6HNsNnuVyuNqXLm+SSezWbzx7Jo94wZGYDGbKv1rrer1jWwcxL03RPHR/F2s2JqMr5cbI3tWn+nxq3p9qzvV17lU34pz4Fgmk+ftJCu3CH8fd27p25L5qxI5nZ7Pv2fE0TGiLrf2a1kjJ7ZxFo1f56pu9a3WjY9IdWez2/lyUTwOB3RpHUK78NpGR6qyOkeGkDsogmmLE1jwWxJ+Xu4M7NLY6FgiIqWyZsdxpi9OwGpz0CjEjydGt6dlszp0ax3CdysT+WXzMbYkpLDrYBojrmtG/05huFWz+ydEqjqnNz75+fmsWrUKgOTkZHJycli0aBEAXbp0ITg42NkRRKqsZVuSSTqZjY+nG7f2a2l0nCqvd2xDcvKLmbv6IDOXH8DX251r29Y3OpaIyCXZ7HbmrEhkyaajAHSMqMs9Q9vg6+MOgLenG2P7teSa6HpMX5JAYnIWs1cc4LddJxg/KIKWYbUMTC/iWpze+KSmpvLYY49d8Ni5v0+bNo2uXbs6O4JIlZSaWcC81QcBuKVPOIF+ngYnqh6Gdm9CTn4xSzYd5Yuf4/HxciO2ZV2jY4mIXCS3oJiPvt/N7kNpANx4XTOGXdsUcwn3KDYO9ee5cR35dccJ5qw4wLGUXN74aivXta3PqD7h+Pt4VHZ8EZfj9MYnLCyMhIQEZx9GpFpxOBx8vXQfhcU2WoQF0rN9A6MjVRsmk4nRfVuQm1/M2l0n+XD+bp4a056IxkFGRxMROe9Eai7vfruDU+n5eLibmTSsDR0jQi67jdlkomf7BsS2rMO3KxNZs+MEv+48wbb9KdzSO5we7RuU2DSJSOno4lERA2xJSCHuwBksZhMTB0fqF1kZmU0m7rg+kpgWdbDa7Pzv2x0k/T5DkoiI0XYkpvLqtM2cSs+ndoAXz4/reMWm54/8fTy48/rWPD+uI2F1/cgtsDJ1UQKvT9+in3UiV0GNj0glyyuw8vUv+wAY0q0JDetUnfWoqhOL2cz9I6KIaFSLgiIb78yO42Sa8VPci0jN5XA4WLThCP+bs538QhutwgJ5cWInGof6l2t/LcIC+fudnRjbryWeHhYOHs/i5amb+OaXfeQXWis4vYjrU+MjUsm+W51IZk4RoUHeDL+midFxqjUPdwuP3NyOxqF+ZOcV8/bMbaRlFRgdS0RqoGKrjc8W7GX2igM4gJ7tG/D0rbEE+F7dvTkWs5mBnRvx+qRudI4MweGAXzYf4/lP17Nx7ykcDkfFfAEiNYAaH5FKdCA5k5VbkwGYMCgCdzet1H21fLzceHJ0DKFB3qRmFfLO7O3k5BcbHUtEapD07ELe/Hob63afxGwycfuAVkwcHFGh01EH+XvywI3RPDmmPSFB3mTmFPHR97t5e5bOdouUlhofkUpitdmZuigeB3BtdD1aN9VU7hUlwNeDp8bGEOTvyfEzufx3znYKinQZiIg436ETWbwydROHTmTh6+XGU2Pa069jGCYn3bsZ3aw2r9zdhRuva4abxcyew+m89PkG5q0+SFGxzSnHFHEVanxEKsnijUdITsnFz9ud0X1bGB3H5dQJ9ObJMTH4erlx8HgW78/dSXE1Wk1aRKqfdbtP8sZXW8nIKaJBHV9enNipUj7UcnezcMN1zXjlni5ENwvGanPw42+HefHzDexITHX68UWqKzU+IpXgdHoeP6w9DMCYvi20HoOTNKzjy+Oj2+PpbmH34XQ+XbAHu13Xv4tIxbLbHcxZeYBPf9yD1WYnpkUdXhjfkZAgn0rNERrkwxOj2/PgjdEE+XuSklHAf+ds5/25O3W/o0gJ1PiIOJnD4WDa4gSKrXZaNwnimuh6RkdyaeENAnl4ZFssZhOb408zfUmCbv4VkQqTX2jlve92sHD9EeDsosoP39wWb0+nL41YIpPJRKfIEF69pyuDujTCbDKxZV8KL3y6gYUbkrDadOZb5Bw1PiJOtn73KfYcTsfNYmbCoAinXfct/19Us2DuuyEKE7Aq7jhzVx80OpKIuIBT6Xm8Om0z2xNTcXczc+8Nbbi5V3iVWIvN29ONMX1b8n93dqZFWCCFxTbmrEjkH19sYt/RDKPjiVQJanxEnCgnv5gZy/YDMPzapoQGV+5lEDVZp8gQJgyOAOCndUks3njE4EQiUp3tOZzGq1M3cyI1jyB/T569vQPd2lS9M/hhIX48e3sH7rw+Ej9vd5LP5PLm11v5fMEesvKKjI4nYihjzsuK1BCzlx8gJ7+YhnV8GdK1sdFxapxeMQ3JyS/mu1UHmbX8AL5e7lzXrr7RsUSkGnE4HCzbcoyZyw5gdzgIbxDAQyPbUsvP0+hol2Q2mejRrgGxLevy7cpEVm8/ztpdJ4k7cIabe4XTM6ZBlThLJVLZdMZHxEnik9L5decJACZU8HoOUnrXd2vCoC6NAPhyYTzb9qUYnEhEqotzyxB888t+7A4H10bX4y+3xVbppueP/LzduWNIJC+M70ijED9yC6xMW5zAa9O2kHQy2+h4IpVO78REnKDYamPq4gQAesc2pGVYLWMD1WAmk4nRfVpwbdt62B0OPvx+N/FJ6UbHEpEqLiu3iH/N2Mbq7Scwmc7OyHnX0NbVcuHp8IaBvHRHJ27t1xIvDwuHTmTx8tRNfL10H3kFWvNMag41PiJO8NO6JE6l5RHo68EtvZobHafGM5lM3DEkktiWdbDa7Lz73Q592ikil3TkVDavTN3E/mOZeHu68fio9gzq0rhaT05jMZsZ0LkRr03qRpfWITgcsGzLMV74dD3r95zU7JdSI6jxEalgx8/k8tO6JABuG9AKHy93gxMJnP2lf/+IKCIb16KgyMY7s+M4mZZndCwRqWI2x5/m9a+2kJpVSGiwD3+b0JG2zWsbHavCBPl7cv+IaJ4aE0NokDeZuUV88sMe/j0zjhOpuUbHE3EqNT4iFcjucDBtUTw2u4N24bXpFFHX6EjyB+5uFh65uR1NQv3Jzivm7ZnbtMifiABnf37PX3OQD+bvoqjYTnSzYF6c0JH6tX2NjuYUUc2CefnurtzUoxnubmb2JqXz0ucbmbs6kcJim9HxRJxCjY9IBfp1xwn2HcvEw93MuIGtqvVlEa7K29ONJ0a3JzTYh9SsQt6eFUe2pngVqdEKiqx8OG8XP6w9DMCgLo14fFR7lz9j7+5mZvi1zXjlnq60bV4bm93Bgt+SePGzDWw/cMboeCIVTo2PSAXJzC1i9vIDANzUozl1Ar0NTiSXEuDrwVNj2hPk78mJ1Dz+O2c7+YW6wVekJjqTkc/r07eyZV8KbhYTdw9tzZi+LTGba84HVyG1vHl8VDseuimaIH9PzmQW8L9vd/DedztIzdRZcXEdanxEKsjMZfvJK7TSJNSf/p3CjI4jV1An0JunxsTg5+3OoRPZvD9vJ8VWu9GxRKQSJRxJ5+WpmzmWkkOgrwd/va0D17atmWt9mUwmOkaE8Nqkrgzu2hiL2cS2/Wd44bP1LFyfhNWmn49S/anxEakAOw+msmHPKUwmmDgkAotZ/7SqgwZ1fHl8VHs83S3sOZzOpz/uxm7XzEYiNcHKuGT+PTOOnPximtTz58WJnQhvGGh0LMN5ebgxuk8L/n5nZ1qGBVJUbGfOykT+74tNJBzRUgBSvendmchVKiy2Mf33NXv6d2xE03oBBieSsmjeIICHb26Lm8XE5oQUpi1O0LSuIi7MarPz1ZIEpi1KwGZ30KV1CM/d3oHgAC+jo1UpYXX9ePb2Dtw9tDV+3u4cP5PLW99s47MFe8jK1X2RUj2p8RG5Sj/8eogzmQUEB3hyU89mRseRcohqGsy9w6MwmWD19uPMXX3Q6Egi4gQ5+cW8MyuO5VuTMQE392rOfTdE4eFe/RYlrQwmk4lr29bn9Xu70TumASbgt10nef6T9azYekxnyKXaUeMjchWOnMpm8cajAIwbEIGXh5vBiaS8OkWGMGFQBHB2AdpFG44YnEhEKtKxlBxe/nIT8Ucy8PSw8PDNbRnavalm3ywFP293JgyO5PkJHWkc6kdeoZXpS/bx2vTNHD6ZZXQ8kVJT4yNSTna7g6mLErA7HHSMqEtMyzpGR5Kr1CumIbf0Dgdg9ooDrNlx3OBEIlIRtu1P4bXpWziTWUDdWl78bXxHYltqnbWyCm8QyIsTO3Fb/5Z4e1o4dCKbV6Zu5usl+8grKDY6nsgV6eNpkXJasS2ZQyey8Pa0cFv/VkbHkQoypGtjcvKKWbTxCF8ujMfXy50OrfQGSaQ6cjgc/LQuiXmrD+IAIhvX4sGb2uLn7drr8ziTxWymf6dGdIoMYdbyA2zYc4plW4+xKeE0Y/q2oFubUJ1FkypLZ3xEyiE9u5DvViUCcHOvcIL8PQ1OJBXFZDIxqk8417Wrj8MBH32/m71JmslIpLopLLbx8Q+7mft709OvQxhP/j6FvVy9Wn6e3HdDFE+PjSE02Ies3CI+/XEP/5qxjeNnco2OJ1IiNT4i5fD10n0UFNkIbxBA79iGRseRCmYymZg4OILYlnWw2uy8990OXccuUo2kZRXw5ldb2bj3NBaziQmDI7h9YCvcLHrbU9HaNA3m5bu6cFPP5ri7mYk/ksHfp2zku1WJFBbbjI4ncgH9BBApo237Uti6LwWL2cTEwZGYdUrfJVnMZu4fEUVk41oUFNl4Z9Z2TqTqU0yRqu5AciYvT91M0qls/LzdeXpsDL1j9AGVM7m7mRl+TVNevacr7cJrY7OfvcTwb59uIG7/GaPjiZynxkekDPILrXy1dB8Ag7o0JizEz+BE4kzubhYeubkdTer5k5NfzNuz4kjLKjA6lohcwq87TvDPb7aSlVtEWF0/XprYiYjGQUbHqjHq1vLmsVva8fDItgQHeJKaVcC73+3g3W93cCYz3+h4Imp8RMpi3uqDpGcXUreWFzdc29ToOFIJvD3deGJ0e+oF+5CWVcjbs+LIztPifSJVic1uZ+ay/Uz5eS9Wm4OOrery/PgO1KnlbXS0GsdkMtGhVV1eu6cbQ7o2xmI2EXfgDH/7dAM/rTuM1WY3OqLUYGp8RErp4PEslm05BsCEQZFa8K4GCfDx4KkxMQT5e3IiNY//ztlOfqHV6FgiAuQVFPO/OTtYsunsmmo3XNuUB26K1rpqBvP0sDCqTwv+787OtGpUiyKrne9WHeTvUzYSrwljxCBqfERKwWqzM3VRPA6gW1QoUc2CjY4klax2oBdP/T4j1KET2Uyeu5Niqz65FDHSidRcXpm2hV2H0vBwN/PgjdHc2KO57r2sQhrW9eOvt8Vy99DW+Pu4cyI1j3/O2ManP+4mM1dnz6VyqfERKYWlm49y9HQOvl5ujO3b0ug4YpAGdXx5YnR7PD0s7E1K55Mfd2O3O4yOJVIj7TyYyqvTtnAqLY/aAZ48P64jnSJDjI4lJTCZTFzbtj6v39uNPrENMQHrdp/i+U/Ws3zrMf0clUqjxkfkClIy8vl+zSEARvdpQYCvh8GJxEjN6gfwyMi2uFlMbElIYdrieBwO/dIWqSwOh4NFG46cv+S0ZVggL07sTONQf6OjyRX4erkzflAEf5vYiSb1/M9OGLRkH69M28yhE1oyQJxPjY/IZTgcDqYvSaDIaieiUS2ua1ff6EhSBbRpGsx9N0RhMsHq7Sf4btVBoyOJ1AjFVhtTftrL7BUHcDigZ/v6PHNrrD6Qqmaa1Q/gxQmduH1AK7w9LSSdzObVqZuZviSBvIJio+OJC1PjI3IZG/eeZtfBNNwsZxfAM+m6cfldx4gQJg6OBODn9Uks2nDE4EQiri0jp5B/frONtbtOYjaZuH1AKyYOjtSipNWU2WyiX8cwXp/UjW5RoTiAFVuTef6T9azbdVJn0sUp9NNC5BJyC4qZ8cvZNXuGdm9K/dq+BieSqqZn+waM6h0OwOwVB1iz/bjBiURc06ETWbwydTOJx7Pw9XLjyTHt6dcxTB9GuYBAP0/uHR7FM7fGUr+2D1l5xXy6YA///GYbyWe0aLRULDU+IpcwZ0UiWXnF1K/tw/XdmhgdR6qoId2aMLhrYwC+XBTPloQUgxOJuJb1e07y5tdbSc8upEEdX16c2Ik2TTWzpqtp3SSIf9zVhZt7NcfDzUzC0Qz+b8pGvl2ZSGGRzeh44iLU+IiUYN/RDFb//un9hEERuLvpn4pc2qje4fRoVx+HAz7+YRd7tUaFyFWzOxx8tyqRT37YQ7HVTvvw2rwwviMhQT5GRxMncbOYGdq9Ka/e05WYFnWw2R38vD6Jv322nm379KGSXD29mxP5k2Lr2TV74OyNsxGNgwxOJFWdyXT2HrAOrepitTl497sdmqFI5CrkF1qZ/N1OflqXBMDQ7k145OZ2eHtqUdKaoE4tbx69pR2P3NyW2gGepGYV8t7cnbz77Q7OZOQbHU+qMTU+In+ycEMSJ1LzCPBxZ1SfFkbHkWrCYjZz3w1taN0kiMIiG/+ZvZ3juj5dpMxOp+fx2vQtxB04g7ubmXuHt+HmXuGYzbqfp6aJbVmXV+/pxvXdmmAxm4g7cIa/fbaBBb8dxmrTAtJSdmp8RP7gZFoeC347+wnj2P4t8fVyNziRVCfubhYeHtmWpvX8yckv5p/fbCUlXZ9OipTWnsNpvDJ1M8fP5FLLz4Nnb+9At6h6RscSA3l6WLildzj/d1cXIhvXoshqZ+7qg/x9ykb2Hk4zOp5UM2p8RH7ncDiYtigeq81OdLNgurYONTqSVEPenm48Pro99YJ9SMsq5KVPfiM7r8joWCJVmsPhYNmWY7wzazu5BVaaNwjgpTs606x+gNHRpIpoWMeXZ26NZdKwNgT4uHMiNY9/zYzjkx93k5lTaHQ8qSbU+Ij8bu3Ok8QfycDDzcz4QVqzR8ovwMeDp8fGEBzgybHTOfx7Rhz5hVajY4lUSVabnamLEvh66T7sDgfdo+rx19tiqeXnaXQ0qWJMJhPdo+vx+r3d6NOhISZg/e5TPP/pepZtOYbdrrV/5PLU+IgAWXlFzFq+H4AR1zWjbi1vgxNJdRcc4MVfbuuAv48Hh05kMXnuToqtuiZd5I+ycov414xtrN5+HJMJRvdpwT3DWuPuZjE6mlRhPl7ujB8Ywd8mdqJJPX/yC218vXQfr0zdzMHjmlhGLk2Njwgwa9kBcgushNX1Y0DnRkbHERfRoI4v/7i3G14eFvYmpfPJD7ux2dX8iAAcOZXNK1M3sf9YJt6eFh67pT2DuzbW2XYptWb1A3hxQifGDWyFt6cbSaeyeW3aZqYtTiA3v9joeFIFqfGRGm/34TTW7T6JCbhjSCRuFv2zkIrTslEQj49qj5vFxJZ9KUxblIDDocsxpGbbHH+a17/aQmpWIaFB3vxtQifahdc2OpZUQ2azib4dwnj93m50j6qHA1i5LZm/fPgbyzcf0c9buYDe4UmNVlRsY/qiBAD6dgijeQPdSCsVr02zYO67IRqTCdbsOMG3KxONjiRiCLvDwfw1B/lg/i6Kiu1ENQvmbxM7Ub+2r9HRpJoL9PVg0vA2/OXWWOrX9iE7r5j/zNjG69O3kJySY3Q8qSLU+EiN9uNvhzmdkU+QvycjezU3Oo64sI4RdbljcCQACzccYeH6JIMTiVSugiIrH87bxQ9rDwMwsHMjHh/VTssGSIWKbBLEP+7qwui+LfBwt5BwJIP/+2ITc1YcoLDIZnQ8MZgaH6mxjqXksGjDEQBu699KK4KL0/Vo34BRfcIBmLMykdXbjxucSKRynMnI5/XpW9myLwU3i4k7r49kbL+WWMx6GyIVz81iZtg1TfnwL33p0KouNruDhRuO8MJn69m6L0WXv9VglfIT59ChQ9x9993ExMTQvXt3Xn31VQoKCirj0CIlsjscTFuUgM3uILZlHTpG1DU6ktQQQ7o2YUjXxgBMXRTPloTTBicSca59RzN4eepmjqXkEODrwV9u7UCPdg2MjiU1QEiwD4+Pbs+jN7ejdoAXaVmFTJ67k/99u4PTGVpcuiZy+kfcWVlZTJw4kQYNGvDuu++SlpbGG2+8QUZGBv/+97+dfXiREq2KO86B5Ew8PSzcPqCV0XGkhrmldzg5+cWs2XGCj3/YzROj3GjdNNjoWCIVblVcMl8t2YfN7qBJqD+P3NyW4AAvo2NJDRPTsg6tmwax4LfDLNpwhB2JqexN2sCw7k0Y3LUJ7m4681hTOL3xmTlzJllZWcyfP5/g4LO/2C0WC08//TQPPPAA4eHhzo4gcoGMnMLzN5eP7Nlcv4Sl0plMJiYMjiCvwMqWfSm8O3cnf7k1VqvUi8uw2uzMWnaAZVuPAdCldQh3Xt8aT3etzyPG8HS3cHOvcK6Jrsf0xQnEH8lg3ppD/Lb7FOMHtqKNPnyqEZze4q5evZru3bufb3oABg0ahIeHB6tWrXL24UUu8s0v+8kvtNKsvj/9OoQZHUdqKIvZzL03RNG6SRCFRTb+M3s7J1JzjY4lctVy8ov5z+zt55uekT2bc98NUWp6pEqoX9uXZ26N5d7hbQjw9eBUWh7/nhnHxz/sJiOn0Oh44mROb3wSExMvOqvj4eFB48aNSUzUlK5SubYfOMPm+NOYTSYmDo7EbNZCeWIcdzczD49sS9N6/uTkF/PvmXGkZur+R6m+klNyeHXqZvYmpePpYeGRkW0Zdk1TLUoqVYrJZKJbVD1en9SVfh3CMJlgw55TvPDpen7ZfFQLTbuwSrnHJyDg4ss3AgICyMzMvKp9u1WBazItvy92adGil05RkfUtKLLy1ZJ9AAzq2pjmDQOvep/Vncavc5Wmvv5uHjxzWyyvTt3MidQ83pkdxwsTOhHg61FZMastjV/nKmt9t+1L4cP5uygoslG3ljdPjG5PWIifMyNWaxq/zlWa+gb4eTLx+kh6xjZg6sJ4Dh7P4ptf9rN210nuGBJJuN4nXFJ1Hb+Gzd/rcDiu6hMgs9lEUFDVWfAsIMDb6AgurSLq+9n3u0jNKiAkyJu7bojGS9NXn6fx61xXqm9QkC+vPXAdf5m8hhOpefx3znZee+BafLS+Salo/DrXlerrcDj4dvl+pi/ci8MB7VrU4a8TOqt5LyWNX+cqTX2DgnxpH1mPJesPM/XnvSSdzOblLzcxsGsTJg5tg7+PxvKlVLfx6/R3fgEBAWRlZV30eHZ29lVNbGC3O8jKyruaaBXCYjETEOBNVlY+NptOjVa0iqrvoRNZ/LDm7KWV4wdFkJ9XSH6eruXV+HWustTXDXjm1hhenbqZA8cy+cen63hybAwebrov4lI0fp2rNPUtKrbx2YI9rN99CoD+ncK4bUArbEXFpBcVV2bcakfj17nKU99urUNo3SiQmcsOsHbnCRavT+K3HccZ268l17Wrr0s2/6Cqjd+AAO9SnX1yeuMTHh5+0b08RUVFHDlyhJtvvvmq9m21Gl/oc2w2e5XK42qupr42u50pC85+EtmldQhRTYP1vfoTjV/nKm196waevTzorW+2sedwOh/M3cUDN0Zpkccr0Ph1rkvVNy2rgPfm7iTpZDYWs4nbB7Sid2xDcFSt389Vncavc5W1vr5e7tw9tDXXta3H9CX7OH4ml09/3MPKbcmMHxRBWF1dvvlH1W38Ov23ac+ePVm/fj3p6ennH1u6dClFRUX06tXL2YcXYdnmYySdysbH041b+7U0Oo7IZTWtF8CjN7fDzWJi674Upi5K0CrjUuUkJmfyytTNJJ3Mxs/bnafHxpxtekRcRETjIP7vzs6M6h2Oh7uZ/ccy+b8pm5i9/AAFRVaj40k5Ob3xGTt2LP7+/jz44IOsWbOG+fPn88orrzB8+HCt4SNOl5pZwLw1hwC4pU84gX6eBicSubLWTYK4f0Q0JhP8uuMEc1ZqBkypOtbuPMFb32wlM7eIsLq+vDSxExGNg4yOJVLh3CxmhnRrwmv3dKNDq7rYHQ4WbTzCC59uYEvCaX0oVQ05vfEJCAhg6tSp+Pj48Mgjj/Dmm28ybNgwXn31VWcfWmo4h8PBV0sSKCy20SIskJ7tGxgdSaTUOrSqyx1DIgFYtOEIC9cnGZxIajq73cGs5fv5/Ke9WG0OOrSqy/PjO1KnVvW6uVmkrGoHevHwyLY8eks76gR6kZ5dyPvzdvHfOTs4nW78/eZSepUyrVWzZs34/PPPK+NQIudtSUhhe2IqFvPva/bopkSpZnq0a0BuvpXZKw4wZ2Uivt7uauDFEHkFxXz0w252HUwD4IZrm3LDdc30c1VqlJgWdWjdJIif1h1m4foj7DyYyoufpzO0exOGdG2CexVYZkUuT/P5ikvKK7Dy9S9n1+wZ0q0JDetUnanPRcpicNfG5OQX8/P6JKYuisfXy42OESFGx5Ia5ERqLu/M2s6ptDw83MzcPawNnSM1BqVm8nS3MLJnON2j6vHVkn3sTUpn/ppDrNt1knEDI4hqFmx0RLkMtabikr5blUhmThGhQd4Mv6aJ0XFErsrNvZrTs319HA74+Ifd7DmcZnQkqSG2xp/mH1M2cSotj+AAT54b11FNjwhQv7YvT4+N4d4b2hDo68Gp9HzenhXHR9/vIj1by2VUVWp8xOUcSM5k5bZkACYMjsRd66BINWcymZgwKJKOEXWx2hy8N3cnh05cvD6aSEVxOBwsXJ/EPz5bR16hlRZhgbw4sTNN6vkbHU2kyjCZTHRrU4/XJnWjf8cwTCbYuPc0L3y6nqWbjmKzV59pnmsKNT7iUqw2O1MXxeMArm1bj9ZNNNOQuAaz2cS9w6No3SSIwiIb/5m9neNnco2OJS6o2Gpjyk97mfHLfuwO6BXTgGfGxhLoq9XrRUri4+XGbQNa8dLEzjSrH0BBkY0Zy/bzypebSUzONDqe/IEaH3EpizceITklFz9vd8b01Zo94lrc3cw8PLItzer7k5NfzNuz4kjNLDA6lriQjJxC/vnNNtbuOonZZOLeG9ty19DWumlbpBSa1PPnhQkdmTA4Al8vN46czuG16Vv4cmE8OfnFRscT1PiICzmdnscPaw8DMLZfC/y83Y0NJOIE3p5uPD6qPfVr+5CeXcjbs+LIyisyOpa4gEMnsnhl6mYSj2fh6+XG07fGMLxHc0yauU2k1MwmE71jGvLapG5c27YeAKu3H+f5T9azZvtx7Fr7x1BqfMQlOBwOpi1OoNhqp3WTILpH1TM6kojT+Pt48NSYGGoHeHIyLY//zN5OfqFWEpfy27DnFG9+vZX07ELq1/bhbxM6Ed28ttGxRKqtAF8P7h7ahmdv70DDOr7k5BfzxcJ43vx6K0dP5xgdr8ZS4yMuYf3uU+w5nI67m5kJgyP0CaW4vOAAL54cE4O/jztJJ7N577sdFFttRseSasbucPDdqkQ+/mE3xVY77cJr88L4ToQG+xgdTcQltGpUi7/f2ZnRfVrg6W7hwLFM/vHFJmYu268PrAygxkeqvZz8YmYs2w/A8GuaEhqkX9hSM9Sv7csTo9vj5WEh/kgGH32/W7MISanlF1qZ/N1OflqXBMCQbo159OZ2+HhpiT+RiuRmMTO4a2Nem9SVjq3qYnc4WLLpKH/7bAOb40/j0OVvlUaNj1R7s5cfICe/mIZ1fBnctbHRcUQqVdN6ATx6czvcLGa27T/D1IUJ+iUqV3Q6I5/Xp28h7sAZ3CxmJg1vw6jeLTCbdbZcxFmCA7x4aGRbHh/Vjrq1vEjPLuSD+bv4z5ztnErPMzpejaDGR6q1+KR0ft15AoCJgyNxs2hIS80T2SSI+0dEYTLBrztPMGdFopofuaS9Sem88uUmks/kUsvPg+fGddB9kSKVqF14HV65uyvDr2mKm8XEroNpvPjZRr7/9ZAuWXYyvUuUaqvYamPq4gQAesc2pEVYoMGJRIzToVVd7hgSCcCijUdYuOGIwYmkqnE4HCzfeoy3Z8aRW2ClWf0AXvx93RERqVwe7hZu6tmcl+/uSpumQVhtdr7/9RAvfr6RXYdSjY7nstT4SLX107okTqXlEejrwS29mhsdR8RwPdo1YHSfFgB8uzKRVXHJBieSqsJqszN9cQJfLdmH3eGge1Q9nr09liB/T6OjidRo9YJ9eGpMDPePiCLQz4PT6fm8M2s7H8zfRXp2odHxXI7uYJRq6fiZ3PM35N42oBU+XlqzRwRgcNfG5BYU89O6JKYtTsDXy51OkSFGxxIDZeUV8cG8Xew7moEJGNWnBYO6NNLslyJVhMlkokvrUNo2r828NQdZtuUYm+NPs/NgKjdd14x+ncKwmHWuoiKo8ZFqx+5wMG1RPDa7g3bhtekUUdfoSCJVysiezcnOK2b19uN88uNuvL3ciGoabHQsMcDR0zm8++0OUrMK8Pa0cN8NUbQLr2N0LBEpgbenG7f1b8V1beszfXECicezmLn8AL/uPMmEQRG6pL8CqH2UaufXHSfYdywTD3cz4wa20qeWIn9iMpmYMCiCThF1sdocTP5uJwePZxkdSyrZloTTvD59C6lZBYQGefO3CZ3U9IhUA41D/XlufEcmDo7A18uNYyk5vP7VFr74eS85+cVGx6vW1PhItZKZW8Ts5QcAuKlHc+oEehucSKRqMptNTBoeRZumQRQW2/jP7DiOn8k1OpZUArvDwQ+/HuL9ebsoLLYR1TSIv03sRP3avkZHE5FSMptM9IppyGv3duO6dvUBWLPjBM9/sp7V249j18yd5aLGR6qVmcv2k1dopUmoP/07hRkdR6RKc3cz8/DItjSrH0BugZW3Z8VxJjPf6FjiRIVFNj6av4v5vx4CYECnRjw+uj2+ug9SpFoK8PHgrutb8+ztHWhY15ec/GK+XBjPG19t4cipbKPjVTtqfKTa2HkwlQ17TmEywcQhEbrRT6QUvDzceGJ0e+rX9iE9u5C3Z20nK7fI6FjiBGcy83n9qy1sTkjBYjZx55BIbu3fUj8rRVxAq0a1+PsdnRndpwWe7hYSk7N4+cvNzFy2n/xCq9Hxqg39NJRqobDIxvTf1+zp37ERTetp3QmR0vLzduepMTHUDvDkVFoe/5m9Xb8oXcy+oxm8MnUzR0/nEODjzl9ui6VH+wZGxxKRCuRmMTO4a2Nem9SVThF1sTscLNl0lBc+Xc/Gvae0cHUpqPGRamHemoOcySwgOMCTm3o2MzqOSLUTHODFU2Nj8fdxJ+lUNu99t0MrhLuI1duP868Z28jOK6ZxqB8vTuxMy7BaRscSEScJDvDiwZva8sTo9oTU8iYjp4iPvt/NO7O3cyotz+h4VZoaH6nyDiZnsmj92VXoxw2IwMtDs7CLlEe9YB+eHB2Dl4eF+CMZfPT9bmx2u9GxpJxsdjtfL93HlwvPTu/fOTKE58Z1pHagl9HRRKQStG1em5fv7sIN1zbFzWJi96E0Xvx8I/PXHNQHW5egxkeqNLvdweQ5cdgdDjpG1CWmpaZiFbkaTer589gt7XCzmNm2/wxfLozX5RHVUE5+Me/M2s6yLccAuKlnc+4fEYWnu8XgZCJSmTzcLdzYozmv3N2VqGbBWG12flh7mBc/28jOg6lGx6ty1PhIlfbLlqPsP5qBt6eF2/q3MjqOiEuIaBzEAzdGYTaZWLvzJLNXHFDzU40kn8nl1amb2ZuUjqe7hYdHtmX4NU21pplIDRYa7MOTo9vzwI3R1PLz4HRGPv+ZvZ0P5u0kLavA6HhVhhofqbLSsgr4dkUiAKP6tCDI39PgRCKuI7ZlXe68PhKAxRuP8vP6JIMTSWnEHTjDa9M2czojnzqBXrwwviMdWtU1OpaIVAEmk4nOkSG8NqkbAzs3wmwysTkhhRc+3cCiDUew2nRpsxofqbK+XrqPgiIbEU2C6NtRa/aIVLRr29ZnTN8WAHy36iCr4pINTiSX4nA4+Hl9Eu99u+Psz8VGtXhxYifCQvyMjiYiVYy3pxtj+7XkpTs6Ed4wgMJiG7NXHODlLzex/1iG0fEMpcZHqqSt+1LYtv8MFrOJh0fFYNYlHCJOMahLY4Z2bwLAtMUJbI4/bXAi+bOiYhuf/riHb1cm4gD6xDbkqbEx+Pt4GB1NRKqwxqH+PDeuI3cMicTXy41jKbm88dVWpvy0l+y8mrmem6bHkionv9DK10v3ATCkWxOa1g8gPT3X4FQirmtkz+bk5hezMu44H/+wG29PN6KaBRsdS4D07ELe+24Hh09mYzGbuK1/S/p00BlwESkds8lEz/YNiG1Zh29XJrJmxwl+3XmCbftTuKV3OD3aN6hRHy7rjI9UOXNXHyQ9u5C6tby4sYfW7BFxNpPJxLiBEXSKDMFmdzB57k4Sj2caHavGSzyeyctfbuLwyezzi9Cq6RGR8vD38eDO61vz/LiOhNX1I7fAytRFCbwxfQtHTmUbHa/SqPGRKuXg8SyW/z4964RBkXhoalaRSmE2m5g0rA1RTYMoLLbx39nbST6jM61GWbvzBG99vY3M3CIa1vXlxYmdiGwSZHQsEanmWoQF8vc7OzG2bws8PSwkHs/iH19u4ptf9pFfaDU6ntOp8ZEqw2qzM3VRPA6ge1SoLrURqWTubmYeGtmW5g0CyC2w8s6sOM5k5hsdq0ax2x3MXn6Az3/ai9VmJ7ZlHZ4f15G6tbyNjiYiLsJiNjOwS2Nen9SNTpEhOBzwy+ZjPP/pejbuPeXSyxuo8ZEqY+nmoxw9nYOvlxtj+rU0Oo5IjeTl4cbjo9rToI4v6dmFvD0zjqzcmnkTbGXLK7Dyv293sGjjEQCGX9OUh0a2xdtTt+OKSMUL8vfkwRujeXJ0e0KCvMnMKeKj73fz9qw4TqblGR3PKdT4SJWQkpHP92sOATC6bwsCNFuRiGHO3U9SO8CLU+n5vDM7rkZcAmGkk2l5vDptMzsPpuLhZub+EVHc1LN5jbrpWESMEd28Nq/c3YUR1zXDzWJmz+F0Xvp8A/NWH6So2GZ0vAqlxkcM53A4mL4kgSKrncjGtbiubX2jI4nUeEH+njw9NgZ/H3eOnMrh3W93UGx1rV+AVcWuQ6m8OnUzJ9PyCA7w5LlxHenSOtToWCJSg7i7WRhxXTNeuacL0c2Csdoc/PjbYV78fAM7ElONjldh1PiI4TbuPc2ug2m4WUyMHxSBSZ9wilQJocE+PDk6Bm9PCwlHM/hw/m5sdq38XVEcDgdLNh3lP7O3k1dopUXDQF6c2Jkm9fyNjiYiNVRokA9PjG7PgzdGE+TvSUpGAf+ds5335+4kLavA6HhXTY2PGCq3oJgZv5xds2dY96bUr+1rcCIR+aMm9fx59OZ2uLuZiTtwhi9/jsfuwje+VpZiq50vfo5n5rL9OBxwXbv6PHNrLIG+usxXRIxlMpnoFBnCq/d0ZVCXRphNJrbsS+GFTzewaMMRrLbq+wGYGh8x1JwViWTlFVO/tg9DujUxOo6IlCCicRD3j4jCbDKxdtdJZi8/4NKz/jhbZk4h/5yxlV93nsBkglv7t+TOIZG4u+lXsohUHd6ebozp25K/39mZFg0DKSy2MXvFAf7xxSYSjqQbHa9c9FNWDLPvaAartx8HYOJg/dIXqcpiW9blzusjAViy6Sg/r08yOFH1lHQym5enbiYxOQsfTzeeHB3DgE6NdImviFRZjUL8eHZcB+68PhI/b3eSz+Ty2rQt/GfGVgqr2eQHmiNTDFFsPbtmD0DP9vVp1aiWsYFE5IqubVuf3AIrM5ft57tVB/H1dqd3TEOjY1UbG/eeYspPeymy2qlf24dHb25HaLCP0bFERK7IbDLRo10DYlvW5duViazefpzlm4/SuK4vPds3MDpeqanxEUMs3JDEidQ8AnzcGdWnhdFxRKSUBnZuRE5+EQt+S2L6ogR8vdzpHBlidKwqze5wMH/NQRb8dvYsWbvw2tw7PAofL/0KFpHqxc/bnTuGRNIrtgHxRzPpEFHX6Ehlop+6UulOpuWdfwMwtn9LfL3cDU4kImVxU4/m5OQVszLuOJ/8sBsfTzeimgUbHatKyi+08tmCPWzbfwaAIV0bc3OvcMxmXdomItVXy7BadGnbkPT0XKzW6jPZgW6qkErlcDiYtigeq81OdLNgumqtCpFqx2QyMW5gBJ0jQ7DZHUyeu5PE45lGx6pyTmfk8/r0LWzbfwY3i5lJw9owqk8LNT0iIgZR4yOVau3Ok8QfycDDzaw1e0SqMbPZxKThbYhqFkxhsY3/zt5OckqO0bGqjL1J6bzy5SaSz+QS6OfBs7d3oHt0PaNjiYjUaGp8pNJk5RUxa/l+AEZc14y6tbwNTiQiV8PNYubhm9oS3iCA3AIrb8+K40xGvtGxDLd86zHenhlHboGVZvX9eWliZ5o3CDA6lohIjafGRyrNrGX7yS2wElbXjwGdGxkdR0QqgKeHhcdGtadhHV8ycop4e1YcmblFRscyhNVmZ9riBL5asg+7w0G3qFD+elsHgvw9jY4mIiKo8ZFKsvtQGut2n8IE3DEkEjeLhp6Iq/DzdufJMTHUCfTiVHo+/5kdR16B1ehYlSorr4i3Z8axclsyJmBU73AmDWuDh7vF6GgiIvI7vfsUpysqtjF9cQIAfTuE6ZIPERcU5O/JU2NiCPBx58ipHN79bgdF1Wxhu/I6ejqHV6duJuFoBl4eFh69pR1DujXRPYwiIlWMGh9xuh9/O8zpjHyC/D0Z2au50XFExElCg314YnQM3p4W9h3N4KPvd2OzV59pTstjS0IKr0/fwpnMAkKCvPnbhE60b1HH6FgiIlICNT7iVMdO57BowxEAbuvfCm9PLR0l4sqa1PPn0Zvb4e5mJu7AGb78OR67w2F0rArncDj4Ye0h3p+3k8JiG22aBvG3CZ1oUMfX6GgiInIJanzEaewOB1MXx2OzO4htWYeO1Wx1XxEpn4jGQTwwIhqzycTaXSeZvfwADhdqfgqLbHz4/W7mrzkEQP9OYTwxuj1+3lqMWUSkKlPjI06zalsyiclZeHpYuH1AK6PjiEglimlZh7uGRgKwZNNRflqXZHCiipGaWcAbX21hc/xpLGYTdwyJ5Lb+rbCY9etURKSqc/p1R2vXrmXu3Lls376do0ePcvvtt/PSSy85+7BisPTsQr5dlQjAyJ7NCQ7wMjiRiFS2a6Lrk5tvZcay/cxdfRA/b3d6xzY0Ola57TuawfvzdpKdV0yAjzsPjWxLy7BaRscSEZFScnrjs3r1avbu3Uvnzp3JzMx09uGkipjxyz7yC200q+9Pvw5hRscREYMM6NyI7PxiFvx2mOmLE/DxcqNL61CjY5XZ6u3Hmb44AZvdQeMQPx65uR21A/WBjohIdeL0xuevf/0rzz33HAAbNmxw9uGkCog7cIbNCSmYTSYmDo7EbNaUriI12U09mpGTX8zKbcl8+uMefLzciG5W2+hYpWKz25m1/AC/bD4GQKfIEO6+vjWeHlqfR0SkunH6RclmXfdcoxQUWfl6ydk1ewZ2bkTjUH+DE4mI0UwmE+MGtKJL6xBsdgeT5+4kMbnqXwGQk1/Mf2ZvP9/03NSjGQ+MiFLTIyJSTVXruYXd3IxvqiwW8wX/rem+X3GY1KxC6gR6cXPv8Kv+Hqm+zqX6Opfqe6H7b4wmv3A7Ow+m8t8523lhYifC6vqVe3/OrG/ymVz+OyuOU+n5eLpbuG9EFJ0iQyr8OFWZxq9zqb7Opfo6V3Wtb7VtfMxmE0FBVWe9hIAAb6MjGO7A0QyWbjy7Zs/Do2OoFxpQYftWfZ1L9XUu1ff/e+mebvzt499ISErn3zPi+OcjPQgN9rmqfVZ0fTftOcm/vtpCfqGVkGAfXryrK03rV9zPs+pG49e5VF/nUn2dq7rVt8yNT3Z2NqdPn77i6xo1aoSHh0e5QpWG3e4gKyvPafsvLYvFTECAN1lZ+dhsrr1C+eXY7Hb+N3Mrdgd0axNK81A/0tNzr3q/qq9zqb7OpfqW7LFb2vHatM0kp+Tywge/8reJnQj08yzzfiq6vg6Hg5/XJZ1ddwiIbFyLR25ph7+XpUJ+nlU3Gr/Opfo6l+rrXFWtvgEB3qU6+1Tmxmfp0qXnJyu4nPnz59O6deuy7r5MrFbjC32OzWavUnkq25KNRzh8MhsfTzfG9GtZ4bWo6fV1NtXXuVTfC3m5W3hydAxvfLWFU+n5/Oubbfzltg74eJXvIoSKqG9RsY0vF8WzfvcpAHrHNOC2Aa1ws5hr/PdO49e5VF/nUn2dq7rVt8y/ZUaOHMnIkSOdkUWqqdTMAub9voL5qD7hBPo670yfiLiGIH9PnhobwxvTt3DkdA7vfrudJ8fE4OFe+RMHpGcXMnnuDg6dyMZsMnH7gJb00TT8IiIup3rdkSRVjsPh4KslCRQW22gZFkiP9g2MjiQi1URokA9PjonB29PCvmOZfPT9bqyVfMnEweNZvDx1E4dOZOPr5cZTY2PU9IiIuCinNz7JycksWrSIRYsWkZ+fz5EjR87/Xaq/LQkpbE9MxWL+fc0ek9bsEZHSaxzqz2O3tMfdzUzcgTN88XM8doejUo69btdJ3vx6K5k5RTSs68uLd3SmdZOgSjm2iIhUPqfP6rZhw4YL7glas2YNa9asASAhIcHZhxcnyiuw8vUv+wC4vlsTGtSpOrPsiUj10apRLR64MZrJ3+1k3e6T+Hm7M7ZfC0xO+iDFbnfw7apEFm04OwtlTIs6TBreBm/PajvRqYiIlILTf8rrniDX9d2qRDJziggN9mHYNU2MjiMi1VhMizrcPbQ1ny7Yw9LNR/HzcWf4NU0r/Dh5BVY++XE3OxJTARh2TRNu7NFcZ6tFRGoAfbwl5XIgOZOV25IBmDAoAnc3rWQuIlene3Q9cgqKmfHLfuatPoiftzt9YhtW2P5PpeXx7nc7OJGah4ebmbuGtqZL69AK27+IiFRtanykzKw2O1MXxeMArm1bT9fEi0iFGdCpETl5xfz422G+WpyAr5dbhTQnuw+l8eH8XeQVWgny9+SRm9vStF7NXZRURKQmUuMjZbZ44xGSU3Lx83ZnTN+WRscRERdzY49m5BQUs2JrMp/+uAcfTzeim9cu174cDge/bD7GzOX7cTggvGEAD9/UtlwLpoqISPWm6aylTE6n5/HD2sMAjO3XAj9vd2MDiYjLMZlM3D6gFV1ah2CzO5g8bycHkjPLvJ9iq50vFsYzY9nZpufatvX4y60d1PSIiNRQanyk1BwOB9MWJ1BstdO6SRDdo+oZHUlEXJTZZOKeYW2Ibh5MUbGd/83ZzrGUnFJvn5lbxL9mbOPXHScwmWBsv5bcdX1r3N30a09EpKbSbwAptXW7T7LncDrubmYmDI5w2lSzIiIAbhYzD93YlvCGAeQWWHl7VhwpGflX3C7pZDYvf7mJA8mZ+Hi68cTo9gzs3Eg/s0REajg1PlIq2XlFzFx2AIDh1zQlNMjH4EQiUhN4elh47Jb2NKzrS2ZOEW/PiiMzt+iSr9+49xRvfLWF9OxC6gX78LeJnYhuVr77g0RExLWo8ZFSmb3iADn5xTSs48vgro2NjiMiNYiftztPjo6hTqAXp9PzeWdWHHkFxRe8xu5wMHf1QT76fjdFVjttm9fmbxM6US9YH9KIiMhZanzkivYmpbN250kAJg6OxM2iYSMilSvI35OnxsYQ4OvB0dM5vPvtDgqLbQDkF1p5f+5OFvx2GIDBXRvz2C3t8PHSxKUiIvL/6R2sXFax1ca0RfEA9I5tSIuwQIMTiUhNFRrkw5Oj2+Pt6ca+Y5m8P3cnySk5vPLlJrbtP4Obxcw9w1ozuk8LzGbdzyMiIhdS4yOXteC3JE6l5xPo68EtvZobHUdEarjGof48dks73N3MxO0/w4P/XM6xlFwC/Tz46+2xXBNd3+iIIiJSRanxkUtKPpPLz+uTALhtQCt8vLRmj4gYr1WjWjx4YzQWswm73UGz+gG8NLEz4Q10RlpERC5NF0BLiewOB9MWxWOzO2gXXptOEXWNjiQicl77FnV45tZYTmYUcF10KGZNVS0iIlegxkdKtGb7cfYfy8TD3cy4ga20/oWIVDltmgVzbZAv6em5WK12o+OIiEgVp0vd5CKZOYXMWZEIwE09mlMn0NvgRCIiIiIiV0eNj1xkxrL95BVaaRLqT/9OYUbHERERERG5amp85AI7D6ayce9pTCaYOCQCi1lDRERERESqP72rlfMKi2xMX5wAQP+OjWhaL8DgRCIiIiIiFUONj5z3/dpDnMksoHaAJzf1bGZ0HBERERGRCqPGRwA4ciqbJRuPAnD7wAi8PDThn4iIiIi4DjU+gt3uYOqieOwOB50i6hLToo7RkUREREREKpQaH2H51mMcOpGNt6eFW/u3MjqOiIiIiEiFU+NTw6VlFfDd6oMA3NIrnCB/T4MTiYiIiIhUPDU+NdzXS/dRWGQjvGEAvWIbGh1HRERERMQp1PjUYFv3pbBt/xksZhMTB0diNpmMjiQiIiIi4hRqfGqo/EIrXy/dB8Dgro0Jq+tncCIREREREedR41NDzV19kPTsQkJqeTP8mqZGxxERERERcSo1PjXQweNZLN9yDIDxgyPwcLcYnEhERERExLnU+NQwVpudqYvicQDdo0KJahpsdCQREREREadT41PDLN18lKOnc/D1cmNMv5ZGxxERERERqRRqfGqQlIx8vl9zCIDRfVsQ4ONhcCIRERERkcqhxqeGcDgcTF+cQJHVTmTjWlzXtr7RkUREREREKo0anxpiw95T7DqUhpvFxPhBEZi0Zo+IiIiI1CBqfGqAnPxiZv6yH4Bh3ZtSv7avwYlERERERCqXGp8a4NuVB8jKK6Z+bR+GdGtidBwRERERkUqnxsfF7TuawertJwCYODgSdzd9y0VERESk5tG7YBdWbD27Zg9Az/b1adWolrGBREREREQMosbHhS1cn8SJ1DwCfNwZ1aeF0XFERERERAyjxsdFnUjNZcG6wwCM7d8SXy93YwOJiIiIiBhIjY8LOrdmj9XmILpZMF1bhxodSURERETEUGp8XNCvO08QfyQDDzez1uwREREREUGNj8vJyiti9vIDAIy4rhl1a3kbnEhERERExHhqfFzMrGX7yS2wElbXjwGdGxkdR0RERESkSlDj40J2H0pj3e5TmIA7hkTiZtG3V0REREQE1Pi4jKJiG9MXJwDQt2MYzRsEGJxIRERERKTqUOPjIn787TCnM/IJ8vdkZM/mRscREREREalS1Pi4gGOnc1i04QgAtw9ohbenm8GJRERERESqFjU+1Zzd4WDq4nhsdgexLevQoVVdoyOJiIiIiFQ5anyquVXbkklMzsLLw8LtA1oZHUdEREREpEpS41ONpWcX8u2qRABG9mxOcICXwYlERERERKomp94MYrPZmDJlCqtWreLAgQPYbDZatWrFww8/TPfu3Z156Bphxi/7yC+00ax+AH07hBkdR0RERESkynLqGZ+CggI+/vhjIiMjeeONN3jnnXcIDQ3lzjvvZMWKFc48tMuLO3CGzQkpmE0mJg6OwGw2GR1JRERERKTKcuoZHy8vL5YtW0ZgYOD5x6677joOHz7MlClT6NOnjzMP77IKiqx8veTsmj0DuzSicai/wYlERERERKo2p57xsVgsFzQ9ACaTicjISE6fPu3MQ7u0+WsOkZpVSJ1AL0Zc28zoOCIiIiIiVV6lL/hit9vZtm0b4eHhV70vNzfj52awWMwX/NfZDp3IYunmowDccX0kvj7ulXJco1R2fWsa1de5VF/nUn2dS/V1LtXXuVRf56qu9a30xmf69OkcOnSIl19++ar2YzabCAryraBUVy8gwNvpx7DZ7Ez7chMOB/SMaUivTk2cfsyqojLqW5Opvs6l+jqX6utcqq9zqb7Opfo6V3Wrb5kbn+zs7FJdptaoUSM8PDwueGzjxo3861//4q677qJz585lPfQF7HYHWVl5V7WPimCxmAkI8CYrKx+bze7UYy1cn0TisUx8vNwY1bs56em5Tj1eVVCZ9a2JVF/nUn2dS/V1LtXXuVRf51J9nauq1TcgwLtUZ5/K3PgsXbqU55577oqvmz9/Pq1btz7/9/j4eB588EH69+/PM888U9bDlshqNb7Q59hsdqfmOZOZz3e/r9kzqnc4vl7uVerrdzZn17emU32dS/V1LtXXuVRf51J9nUv1da7qVt8yNz4jR45k5MiRZdrmyJEj3HPPPbRp04Z//vOfmEyaerksHA4HXy3ZR1GxnZZhgfRo38DoSCIiIiIi1YrT70hKSUnhrrvuok6dOnzwwQcXXf4mV7Y5IYUdialYzCYmDo7ErMZRRERERKRMnDq5QUFBAffccw+pqak8++yzHDhw4ILnY2JinHl4l5BXUMw3S/cBcH23JjSoU3UmdBARERERqS6c2vicOXOG+Ph4AB566KGLnk9ISHDm4V3Ct6sOkplbRGiwD8OuqTmzuImIiIiIVCSnNj5hYWFqbq7CgWOZrNyWDMCEQRG4u1kMTiQiIiIiUj1Vr1WHahCrzc7URWfPll3bth6tmwQZnEhEREREpPpS41NFLdpwhOQzufh5uzOmb0uj44iIiIiIVGtqfKqgU+l5/LD2MABj+7XAz9vd2EAiIiIiItWcGp8qxuFwMG1RAlabndZNgugeVc/oSCIiIiIi1Z4anypm3e6T7E1Kx93NzITBEVrsVURERESkAqjxqUKy84qYuezsWkfDr2lKaJCPwYlERERERFyDGp8qZPaKA+TkF9Owji+DuzY2Oo6IiIiIiMtQ41NF7E1KZ+3OkwBMHByJm0XfGhERERGRiqJ311VAsdXGtN/X7OkT25AWYYEGJxIRERERcS1qfKqABb8lcSo9n0A/D27uFW50HBERERERl6PGx2DJZ3L5eX0SALf3b4WPl5vBiUREREREXI8aHwPZHQ6mLYrHZnfQPrw2HSPqGh1JRERERMQlqfEx0Jrtx9l/LBNPdwvjBmrNHhERERERZ1HjY5DMnELmrEgE4KYezagd6GVwIhERERER16XGxyAzlu0nr9BKk1B/+nUKMzqOiIiIiIhLU+NjgJ0HU9m49zQmE9wxJBKLWd8GERERERFn0jvuSlZYZGP64gQABnRqRJN6/gYnEhERERFxfWp8Ktn3aw9xJrOA2gGe3NijmdFxRERERERqBDU+lejIqWyWbDwKwO0DI/Dy0Jo9IiIiIiKVQY1PJbHbHXy5MB67w0GniLrEtKhjdCQRERERkRpDjU8lWbb1GIdPZuPtaeHW/q2MjiMiIiIiUqOo8akEaVkFzF19EIBbeoUT5O9pcCIRERERkZpFjU8l+HrpPgqLbIQ3DKBXbEOj44iIiIiI1DhqfJxsS0IK2/afwWI2MXFwJGaTyehIIiIiIiI1jhofJ8ovtPLNL/sAGNy1MWF1/QxOJCIiIiJSM6nxcaK5qw6Snl1ISC1vhl/T1Og4IiIiIiI1lhofJ0k8nsnyrccAGD84Ag93i8GJRERERERqLjU+TmC12Zm6MAEH0D0qlKimwUZHEhERERGp0dT4OMHSTUc5lpKDr5cbY/q1NDqOiIiIiEiNp8angqVk5PP9r4cAGN23BQE+HgYnEhERERERNT4VyOFwMH1xAkVWO5GNa3Fd2/pGRxIREREREdT4VKgNe0+x61AabhYT4wdFYNKaPSIiIiIiVYIanwqSk1/MzF/2AzCse1Pq1/Y1OJGIiIiIiJyjxqeCzFq2n6y8YurX9mFItyZGxxERERERkT9Q41MBdh9MZVXccQAmDo7E3U1lFRERERGpSvQO/SoVW+1MnhMHQM/2DWjVqJaheURERERE5GJqfK7ST78d5tjpHAJ9PRjVJ9zoOCIiIiIiUgI1Pleh2Grnx7WHAbh9YCt8vdyNDSQiIiIiIiVyMzpAdWYxm+gaFUqdIB+6tgnFZnMYHUlEREREREqgxucqmM0m7r0hiqAgX9LTcwE1PiIiIiIiVZEudRMREREREZenxkdERERERFyeGh8REREREXF5anxERERERMTlqfERERERERGXp8ZHRERERERcnhofERERERFxeWp8RERERETE5anxERERERERl6fGR0REREREXJ4aHxERERERcXkmh8PhMDpEeTgcDuz2qhHdYjFjs9mNjuGyVF/nUn2dS/V1LtXXuVRf51J9nUv1da6qVF+z2YTJZLri66pt4yMiIiIiIlJautRNRERERERcnhofERERERFxeWp8RERERETE5anxERERERERl6fGR0REREREXJ4aHxERERERcXlqfERERERExOWp8REREREREZenxkdERERERFyeGh8REREREXF5anxERERERMTlqfERERERERGXp8bnMg4dOsTdd99NTEwM3bt359VXX6WgoKBU286bN4/BgwfTtm1bhg0bxsKFC52ctvopb33Hjx9PRETERX8SExMrIXX1kZSUxEsvvcSIESNo06YNw4YNK/W2Gr9XVt76avxe2cKFC3nwwQfp1asXMTExDB8+nG+++Qa73X7FbTV2r6y89dXYLZ01a9Ywbtw4unXrRnR0NP369eONN94gOzv7ittq/F5Zeeur8Vs+ubm59OzZk4iICHbu3HnF11f1MexmdICqKisri4kTJ9KgQQPeffdd0tLSeOONN8jIyODf//73ZbddtGgRzz77LPfeey/XXnstv/zyC0888QT+/v5cd911lfQVVG1XU1+ADh068Ne//vWCx8LCwpwVt1rav38/q1aton379tjtdhwOR6m20/gtnfLWFzR+r+SLL76gQYMG/OUvf6F27dps2LCB1157jaNHj15Utz/S2C2d8tYXNHZLIzMzk9jYWCZOnEhAQAD79+/nvffeY//+/UyZMuWS22n8lk556wsav+XxwQcfYLPZSvXaajGGHVKijz/+2NG+fXtHamrq+cd++OEHR6tWrRwHDhy47LaDBw92PProoxc8dtdddzlGjRrllKzV0dXUd9y4cY57773X2RGrPZvNdv7///rXvzqGDh1aqu00fkunvPXV+L2yP/5cOOf11193tG3b1lFYWHjJ7TR2S6e89dXYLb9Zs2Y5WrVq5Th58uQlX6PxW36lqa/Gb9kdOHDAERMT45gxY4ajVatWjh07dlz29dVhDOtSt0tYvXo13bt3Jzg4+PxjgwYNwsPDg1WrVl1yu6NHj3Lw4MGLLnsZNmwYO3bsIC0tzWmZq5Py1ldKz2wu+z9vjd/SK099pXT++HPhnNatW1NYWEhGRkaJ22jsll556itXp1atWgBYrdYSn9f4vTpXqq+Uz2uvvcbYsWNp1qzZFV9bXcawfnNfQmJiIuHh4Rc85uHhQePGjS97PejBgwcBaN68+QWPh4eH43A4zj9f05W3vuds3LiRmJgY2rZty7hx49i0aZOzotYoGr+VQ+O37LZs2UKtWrWoXbt2ic9r7F6dK9X3HI3d0rPZbBQWFrJ7927ef/99+vTpQ8OGDUt8rcZv2ZWlvudo/JbeokWLiI+P56GHHirV66vLGNY9PpeQlZVFQEDARY8HBASQmZl5ye3OPffnbQMDAy94vqYrb30BOnfuzIgRI2jatCmnT5/m888/584772T69OnExsY6K3KNoPHrfBq/Zbdz507mzp3LQw89hMViKfE1GrvlV5r6gsZuWfXp04dTp04B0KNHD955551Lvlbjt+zKUl/Q+C2L/Px83nzzTZ588kn8/PxKtU11GcNqfMrI4XBgMpmu+Lo/v8bx+43Ppdm2JitNfR999NEL/t67d2+GDRvGBx98wKeffurMeDWGxq/zaPyWTUpKCo8++iht27Zl0qRJV3y9xm7ZlKW+Grtl88knn5CXl8eBAwf44IMPuP/++/niiy8u21xq/JZeWeur8Vt6H374IbVr12bkyJFl3raqj2Fd6nYJAQEBZGVlXfR4dnZ2iWcqzrlUZ3tuX5fbtiYpb31L4uPjQ69evdi9e3dFxauxNH4rn8bvpWVnZzNp0iS8vLz48MMPcXd3v+RrNXbLriz1LYnG7uVFRkbSoUMHRo8ezeTJk9mwYQNLly4t8bUav2VXlvqWROO3ZMnJyUyZMoVHH32UnJwcsrKyyMvLAyAvL4/c3NwSt6suY1iNzyWEh4dfdK9JUVERR44cuejelD86d23jn69lTExMxGQyXXTtY01V3vpeiqMMUwnLpWn8GkPj92KFhYU88MADnDlzhs8++4ygoKDLvl5jt2zKWt9L0dgtndatW2OxWDhy5EiJz2v8Xp0r1fdSNH4vduzYMYqLi7n33nvp3LkznTt35v777wdgwoQJ3HnnnSVuV13GsBqfS+jZsyfr168nPT39/GNLly6lqKiIXr16XXK7Ro0a0bx5c37++ecLHl+wYAHt2rUrcTadmqi89S1JXl4eq1atom3bthUds8bR+K18Gr8Xs1qtPPbYY8THx/PZZ59d8YZl0Ngti/LUtyQau6W3bds2bDbbJdeM0fi9Oleqb0k0fkvWunVrpk2bdsGf5557DoB//OMf/P3vfy9xu+oyhnWPzyWMHTuWr776igcffJAHH3yQ1NRU3nzzTYYPH37BGYnnn3+e+fPns2fPnvOPPfroozzxxBM0btyYa665hmXLlrF27Vo+++wzI76UKqm89d28eTOff/45AwYMoEGDBpw+fZovvviClJQU/ve//xn15VRJ+fn556cGT05OJicnh0WLFgHQpUsXgoODNX6vQnnqq/FbOi+//DIrVqzgmWeeoaCggLi4uPPPtWjRAj8/P43dq1Ce+mrslt7DDz9MdHQ0EREReHl5nW8wIyIi6N+/P6D3DlejPPXV+C29gIAAunbtWuJzUVFRREVFAdV3DKvxuYSAgACmTp3Kq6++yiOPPIKXlxfDhg3j6aefvuB1drv9ohVthwwZQkFBAR999BGff/45TZo04T//+U/VWbW2CihvfevWrUtRURHvvPMOGRkZeHt7Exsbyz/+8Q/atWtX2V9GlZaamspjjz12wWPn/j5t2jS6du2q8XsVylNfjd/S+fXXXwH417/+ddFzGrtXrzz11dgtvXbt2vHzzz/zySef4HA4aNiwIaNHj+buu+/Gw8MD0HuHq1Ge+mr8VrzqOoZNDl3gKCIiIiIiLk73+IiIiIiIiMtT4yMiIiIiIi5PjY+IiIiIiLg8NT4iIiIiIuLy1PiIiIiIiIjLU+MjIiIiIiIuT42PiIiIiIi4PDU+IiIiIiLi8tT4iIiIiIiIy1PjIyIiIiIiLk+Nj4iIiIiIuDw1PiIiIiIi4vL+H3CSYN3escyKAAAAAElFTkSuQmCC", + "text/plain": [ + "<Figure size 1000x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ax = df_demo[\"C\"].plot(figsize=(10, 4))\n", "ax.set_title(\"Hello There!\");\n", @@ -2159,9 +5894,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 207, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(figsize=(10, 4))\n", "df_demo[\"C\"].plot(ax=ax)\n", @@ -2182,13 +5928,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 208, "metadata": { "slideshow": { "slide_type": "-" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1200x400 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(12, 4))\n", "for ax, column, color in zip([ax1, ax2], [\"C\", \"F\"], [\"blue\", \"#b2e123\"]):\n", @@ -2215,7 +5972,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 209, "metadata": { "slideshow": { "slide_type": "fragment" @@ -2229,15 +5986,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 210, "metadata": { - "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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RfVjrZpL6eCnOwW0k5j6KkdeMcI8JhDoNxQhGqvW4ItlEjY9IDbFWvEp6x4cQCBIbPxWzfhO/I2UNIxgmOuIGUk3akXz3JdJbllF6ZE9ZnXUzsIh4wE0nsT74J9aameDaEAwT7nsx4Z7jMUzv3l6ZDc8iOuwbhM+/hNRHc7E2zMctPkBy2QtYq6YT6jaGULcxVTrTJJKt1PiI1IDU5iVYa94GIDriBgLNNaG7uhmGQbj7OMz8VsTnPopzcDulr/2C2PjbVW8RqVb/PZMn2K4PkcFX1egvtMycBkT6X0a492RSmxdjrZuFe+wQ1qrpWKvfJtRpKOGeEzUmQeQ/qPER8Vh672YSS/4CQLjPRYQ6DvI3UJYLtupaNu9n1kM4RbsofeN+okOvIdR5uN/RRCTDnW4mT+jsvr5lMkIRwt3HEeo6mvS297HWzsA5tIPUhvmkNi4g2L5f2UYIzc7xLaNIbaHGR8RDTvEBErMfBscu++HT7xK/I9UJZl4zci7+XxIL/kz601UkFj+DXVhAZNCVnl6CIiLZqSIzefximAFCHQcS7HAB9p6NWGvext61nvS290lve59Ai86Ee00i0KanNn+ROkvvAEQ84lqlZTu4JY9jNm1PdNQ3K7SdqVSNEYoSHXcr1gdvYK2aRuqjuThFu4mOvUXXvotIudn7t5JY8hzO4Z3AqWfy1CaGYRBs1ZVgq67YhTux1s4gvfU97L2biO/dhJnfinDPiQQ7DsII6G2g1C1a8SIecB2b+LzHcYr2YOQ0JDZ+qnba8YFhmETOn4LZuDWJBX/G3rOR0mn3EBt/B4HGtfNNi4jUDmUzeV4htWlR2QcqOZPHT4HGbYiNugmn/2VY6+eQ2rgQp2g3iUVPY6x8rewSuS4jMcI5fkcVqRFqfEQ8kFz+MvbOdRAIE5twp3YW81no7PMxpzQnPutB3GMHKX39l0RHfpPQOf39jiYitYxXM3n8ZNZrTHTglUT6XIS1cSGp9XNwS4pIvvd/JD/4J6EuIwl3H49Zr5HfUUU8pcZHpJpZGxaQWj8HgOjomwg0PdvfQAJAoFHrsk0P5j2OvfsjEnMfxelzEeF+l2TMb29FxFs1NZPHL0Ykl0jvCwn3GE9663KsNTNwjuwhtXYmqfVzCHYcWLYRQqPWfkcV8YQaH5FqlN69geSyFwAI97+MUPt+PieS/2RE6xGb9F2S7/0fqXWzsD58A+fwLqKjbtIwWZE6rGwmzxtYa2aUzeQJhAmf7/1MHr8YgRChTsMInjcEu2Bt2UYI+z4m/fEy0h8vI9CmZ9lGCC06ayMEySrZ97dZxCfOkb3E5zwCrkOw4yDCvSf7HUlOwTADRAd9lUDjNiSW/IX0jg8pff2XxMbfgdmgud/xRKSGpQtWk1j275k8gba9iQ65CrN+U5+Tec8wTILtehNs1xv7wCdYa2aQ3r4Ke+da4jvXYjZtT7jnJILtz8cwA37HFakyNT4i1cBNHKd01gNglWI270h0+HX6LVktFzpvKGbDlsRnP4RTtIeSafcQG/Ntgm16+B1NRGpAbZzJ46dAsw7Ext2Gc3Qf1tpZpD5einNwO4l5j2HUb0q45wRCnYZpox7JaLqwXaSKXCdNfO6juEf3Y9Rr/K8d3MJ+x5JyCDQ7h5xLf4HZrANYpcRn/gFr7Qxc1/U7moh4xHXSWGtnUPJ/Py1regyTUM9J5F7x6zrb9Pwns8FZRIddS+7X/h/hvlMwIvVwjx0kuexFSv76PZIrp+HEi/2OKVIpOuMjUgWu65Jc+iL2no0QihKbeCdmLM/vWFIBZk5Dci76MYklz5P+eAnJ5X/HPlRQdtZODaxIVsm0mTx+MmN5RPpdQrjXl0htXoK1bhbusYNYH7yOtebtsp3uekzQJcKSUdT4iFRBav0cUpsWAgax0d/SD88MZQRCREdcT6pJW5LvvkR667uUHt1HbNzt2t5VJAucaiZP5IIryi7d0q6OX8gIRQh3H0uo6yjS21dhrZ2Bc3A7qQ3zSW1YQLD9+WUbITTr4HdUkTNS4yNSSemCtSSXvwRAZOD/EGzX299AUiWGYRDuPg4zvxXxuY/iHNxO6bRfEBt3O4GzzvU7nohUwqlm8gTPG0ZkYObO5PGLYQYIdRhA8Jz+2Hs3Ya2Zgb1zLentK0lvX0mgRaeyrbDb9lQzKbWWGh+RSrAP7yI+7zFwXUKdhxPqMcHvSFJNgq26ls37mf0QzuFdlL55P5Gh1xDuPMLvaCJSAaecyTP0GoItOvmcLLMZhkGwZReCLbtgH96FtXYG6S3LsfduJr53M2bDloR7TiR47iCMQMjvuCInUOMjUkFOvJj4rAcglSDQojORIddoB7csY+Y1I2fK/5JY+BTp7StJLn4Wp7CAyKCvZuVMD5FscuqZPFMI95iAEdDf3+oUaNSa2Mhv4vS7DGv9bFIbF+Ic2UNi8TMYK18j1H0c4S4jMSK5fkcVAdT4iFSIa6dIzH4Y99ghjLzmxMbdph+kWcoIRYmOvQXrwzewVk4j9dE8nMO7iY69RRtYiNRSdXkmj5/Meo2IDrySSN8vY21YSGr9bNzSI1grXsH68A1CXUYS7j5e90yK7/SOTaScXNclsfgv2Pu3QDhGbOIdGNF6fscSDxmGSaTvFMxGbUgseBJ77yZKp91DbMIdBBq39TueiPyLZvLUDkY4h0jvLxHuMZ701nex1s7EKdpNau1MUuvmEOx4QdlGCNoISHyixkeknKw1b5HesgwMk9jYWwk0bOl3JKkhobP7Yk75OfHZD+IWH6D09V8RHXkjoXMG+B1NpE5znTSp9XNIrpwO6WTZTJ4eE4icPwUjFPU7Xp1lBIKEOg0jeN4Q7J1ryzZC2LuZ9JZ3SG95h0CbHmUbIbTsokvFpUap8REph9T2VVgrXgUgMvgqgq27+5xIalqgUStyL76L+LzHsXd/RGLuYzh9dhLud4l2MBLxgWby1H6GYRJs25tg297YB7aVbYSwfSX2znXEd67DbNKOcK8vEWzfD8MM+B1X6gA1PiJnYB/aQWLBEwCEuo0h3G2Mz4nEL0a0HrFJ3y2bB7J2JtaHb2AX7iQ2+maMcMzveCJ1gmbyZKZAs3OIjb0Vp/gA1tqZpDYvxTm0g8S8xzHqNyHcYwKhTsMxQhG/o0oWU+Mj8gWc0iPEZz0IaYtA6+5EBn3N70jiM8MMEB14JYFGbUgseRa7YDWl039JbMJUzAZn+R1PJGuVzeR5h+TylzWTJ4OZec2IDr2GcL9LSH00j9RH83CPHSL5zl9JrppOuNsYQt3GahMZ8YQaH5HTcNMW8VkP4pYcxmzYktjYW3QqXj4XOm8IZsMWZfN+juyhZNq9xMZ8m2CbHn5HE8k6dtEekkuf+4+ZPC2JDL1WM3kymBmtT+T8iwn3mkRq81KstTNxjx3E+uCfWGtmEDpvCOGeE/ULJalWanxETsF1XRILn8I5uB0jUo/YxDsxwjl+x5JaJtDsHHIu/QXxOY/g7N9KfOYfiAy4glDPibphV6QafD6TZ+0McDSTJxsZwUjZWZ4uo0h/ugprzds4B7eT2riQ1MZFBM/uW7YTXPOOfkeVLKDvGiKnYK2aTnrbCjADRMffjpnXzO9IUkuZOQ3JmfwjkktfILV5Mcn3/o5dWEB0+HUQ1K5SIpV18kyeXkSHXK2ZPFnKME1C5/Qn2L4f9t7NWGtnYBesIf3pKtKfriJw1nllDVDbXrqXSypNjY/If0ltXY71wesARHUphZSDEQgRGX4dZuO2JN/9G+mt71J6ZC/1J90B+ZpYLlIRzvHDJN/563/N5LmKYLu+OpNaBxiGQbBlZ4ItO2Mf3l22E9zWd7H3fUx838eYDVsQ6jmR0LmDMQIhv+NKhlHjI/If7AOfkFj0FEDZN9bOw31OJJnCMAzC3cdiNmpFYs6jOIc+pfjVu8m9/EdQT9vripyJ69hlM3lWTYdU4l8zecYTOf9izeSpowKNWhEbeSNO/8tIrZ+DtWEBzpG9JBc/i/X+a4R6jCPcZRRGRL9gkvJR4yPyL87xwrId3Ow0wXZ9iAy4wu9IkoGCLbuQc8ndZZseHN7JnhfvJmf41wmcN8LvaCK1lr1/K4mlz+EUls3kMZt3JDr0WgKN9UsDATM3n8gFVxDucxGpjQux1s/GLSnCWvEq1odvEuo8gnCP8Zj1GvsdVWo5NT4igJtKEJ/1AG68GLNRG6Kjb8YwdQ2xVI6Z15ScKT8juehpUtvep3Ths4QOFBAZ/FUMU992RT5TNpPnVVKbFpZ9QDN55AsY4RjhXpMIdR9H+pP3sNbMwCnaRWrdLFLr5xLsMKDsPqDGbf2OKrWUfgJLnee6Don5T+AU7sSI5ZXt4KbLKqSKjFCU3Am3YWyYSdGil0htmIdTtIvo2Fs1n0LqvFPP5BlK5IIr9PdDzsgIBAmdN4TguYOxd64r2whhz0bSW98lvfVdAq27E+vzJdyGA/yOKrWMGh+p86wVr5Le8SEEgsTGT9Wpcqk2hmGQP/QrWDnNKZn7J+y9mymddg+x8VMJNGnndzwRX2gmj1QXwzAItu1JsG1P7IPbsdbMIL39fexd6zm+az3WivZl4wXa9dMcPgHU+Egdl9q8BGvN2wBER9ygOQHiiXD7vrgX/5z4rIdwi/dT+vp9REfeSKiDfhspdYdm8oiXAk3bExt7C07xAax1s8p+vu/fjjXncYx6jQn3nFh2CaWu6KjTauQC2u3bt3PDDTfQu3dvBg0axK9+9SsSiURNHFrktNJ7N5NY8hcAwn2/TKjjIH8DSVYL5Lci95K7CLTuDrZFYt5jJFe8ius6fkcT8Vy6YDUlr/wMa/Wb4NgE2vYi94r7iPS+UE2PVCszrxnRIV+nwTV/JH/4lRjR+rjHC0m+81eO/+17JN//B07pUb9jik88/25TXFzMtddeS8uWLXnooYc4fPgwv/nNbzhy5Ai///3vvT68yCk5xQdIzH4YHJtg+36Ez7/Y70hSBxiRXGITv0NyxSuk1s7EWv0m9uGdxEbfjBHO8TueSLXTTB7xixmtT/6wy3E7jyW+YTHW2pm4xQewPiw76xg6dyjhnhMxG57ld1SpQZ43Pi+//DLFxcVMnz6dRo0aARAIBPj+97/Pt7/9bTp06OB1BJETuFZp2Q5uyeOYTdsTHfVN7R4kNcYwA0QHXkmgcVsSi5/BLlhD6fRfEht/h34AS9bQTB6pLYxgmHDX0YQ6jyT96SqstTNwDmwjtWkhqU2LCJ7dh3DPSQTOOtfvqFIDPG98Fi9ezKBBgz5vegAmTJjAT3/6UxYtWqTGR2qU69jE5z2OU7QHIzef2PipGMGI37GkDgqdOxizYYuyeT9H9lIy/R5iY75NsE1Pv6OJVIlm8khtZJgmoXP6E2zfD3vfx1hrZmAXrCb96QekP/2AQPNzCfWaRLBdb/0yNIt53vh88sknXHbZZSd8LBwO07ZtWz755BOvDy9yguTyl7F3roNAmNiEOzBz8/2OJHVYoGn7smGncx7B2b+V+Mw/EhlwOaGek3QZkGQczeSRTGAYBsEWnQi26IRdtJvU2pmktryLvX8L9uwtmA3OItRzIqFzB2MEw37HlWpWI/f45OWdvCd/Xl4eR49W7eayYND/b6SBgHnCP6V6VWd9k+vnk1o/B4DcsTcTPuucKr9mptP69Va56pvXiNDFP6F08fNYGxeRfO//cA/vJGfUDfqhewZav94qb31d18XavIz4uy/hxstm8oQ7DyM26H80k+cLaP16qzz1DTZtQ2TMN3EGXk5y7WySH83HObqP5JK/YK2cRqTnOCLdxmBGc2sqdsbI1PXr21YqrutW6TeapmmQn197FmJeXszvCFmtqvWNb19L0ZLnAcgf+TXy+42shlTZQ+vXW+Wpb/4lt1Pc9lwKZz+DteVdOLafsy7/EcG8JjWQMLNp/Xrri+prHdrFoRlPkij4CIBQk9Y0mXQTsbbdaipextP69Va56pufC62vwxlzJcWr53D0vTexjxWSeO9Vkh+8Sf0+Y2kw4EJCDZp5HzjDZNr69bzxycvLo7i4+KSPHzt2rEr39ziOS3FxaVWiVYtAwCQvL0ZxcRzb1ra01a066msX7eXYa78D1yF83mDcLhMoKiqp5qSZSevXWxWub4fh1PtyU0pmPYy1bxs7n/oB9SbersGOp6H1660vqq+bSpJY9U8Sq98um8kTDBPrN4VIr0kkAkES+h57Rlq/3qp0fc8bQ/0OI7C2vkfyw7exD++keMWbFL//NuGOFxDp8yWCGkBd69ZvXl6sXGefPG98OnTocNK9PJZlUVBQcNK9PxWVTvtf6M/YtlOr8mSbytbXTRyn5O0/4CZLMZt3JDz0G9i2C7jVHzKDaf16qyL1NZp3KrvvZ9ZDOId3cuz1+4kM+TrhLiO9DZnBtH699d/1TResIbHsRdxjBwEItO1FdMjVmPWbYruA/l9UiNavtypXX5NAh0HEzhmIvWs91toZ2Ls3YG15F2vLuwRadSPcaxKBVt3q/P2YmbZ+PW98hg8fzuOPP05RURH5+WU3ks+ZMwfLshgxYoTXh5c6zHXSxOc+int0P0a9xv/awU33TEjtZ9ZvSs6U/yWx6CnS294nueQvOIUFRAZ9TcMexTfO8cMk3/0b6e0rAc3kkexnGAbBNj0ItumBfehTrDUzSG9bgb37I+K7P8Js3JZwz4kEOwzAMPW9ORMYrut6+qvv4uJiJk+eTKtWrbjlllsoLCzk/vvvZ+jQoVUaYGrbDocP+38qPRg0yc/PpaioJKM63kxR2fq6rktyyXNluwuFouRM+RmBRtpK9b9p/XqrqvV1XRdr9ZtY778GuARadCI69lbdMP4vWr/e+qy+hwuLia+eTXLVNM3kqUZav97yqr5O8UGsdbNIbV4MaQsAo15jwj3GE+o8os78naht67dRo9xyXermeeMDsH37dn71q1+xatUqotEokydP5vvf/z7RaOUXhxqfuqGy9bXWzSL57kuAQWziHQTb9vYsYybT+vVWddU3veND4vOfgFTi87OXAV1jrvXrsWDQJFa6m/1vPI5dWABoJk910vr1ltf1dRPHsTbMJ/XRXNz4v+5lD+eUDUvtPhYzp2G1H7M2qW3rt1Y1Pl5Q41M3VKa+6YI1xGc9AK5LZOCVhHtO9DZkBtP69VZ11tcu2kN81oO4xfshECY68gZCHS6opqSZSevXO27iONbKf2BtWAi4msnjAa1fb9VUfd20RWrLO1hrZ+Ae3V/2QTNI6LzBhHpOJNCwpWfH9lNtW7/lbXx0QaJkFfvwLuLzHgfXJdR5OKEeE/yOJFItAvktyb3kLuLzHsfetZ7EvMdxCncS7ncphqk3olI9XNclveUdkstfxk38eyZPqP/lusRS5BSMYJhwl5GEOg0nveNDrLUzcPZvJbVpMalNiwm260Oo15cInnWu31EFNT6SRZx4cdmZnlSCQIvORIZcoxtuJasYkVxiE79LcsUrpNbOwFr9JvbhncRG34wRzvE7nmQ4u2gPyaXPY+/dBICZ34rmk28mUf/sWvEbXZHazDBNQu3PJ9T+fNL7tpBa8zbpHR9+/sds3pFwz0kEz+6js6Y+UuMjWcG1UyRmP4x77BBGXnNi427T7leSlQzTJDrwfwg0bkNi8bPYBWsonXYvsQl3YDZs4Xc8yUBuOon1wRtYa2eUzeQJhAmf/2Vy+nyJWJMGmskjUkHBs84leNYd2Ef2kFo7k9TH7+Ds30pizsMYDc4i3HMioXMHa6dZH+idoWQ813VJLP4L9v4tEI4Rm3gHRrSe37FEPBU6dzBmwxbEZz+Mc3QfJdPvJTb62wTb9vQ7mmSQL5rJY5TjenkROb1Aw5YEhl9PuN+lpNbPxdowH/foPpJL/oK18jVC3cYS7jpa71lqkBofyXjWmrdIb1kGhkls7G1ZeyOhyH8LNG1PziV3EZ/zCM7+rcRn/pHwgMsJ95qkyzzlC2kmj0jNMXMaEhnwFcK9LyS1eTHWutm4xwuxVr6GtfotQp2HE+4xHrN+U7+jZj01PpLRUttXYa14FYDIkKsJtu7mcyKRmmXmNCRn8o9ILnuR1KZFWCv+D6ewgOiI6zCCEb/jSS3jOjap9XM1k0fEB0Y4RrjHBELdxpD+ZEXZRgiFO0mtn0Pqo3kEz+lPuNckAk3O9jtq1lLjIxnLPrSDxIInAAh1G0O462ifE4n4wwiEiAz7BmaTdiSX/ZX0J8spPbqX2PipmPUa+x1Pagl7/1YSS5/DKdwJaCaPiF8MM0jo3MEEOw7C3v0R1poZ2Ls/Iv3Je6Q/eY9Aq66Ee04i0Lq7zsBWMzU+kpGc0iNlO7ilLQKtuxMZ9DW/I4n4yjAMwl1HYzZsSWLuoziHdlA67R6iY28l2KKT3/HER27iOMkVr5LatIjPZ/IMuJxQ5+HaXUrER4ZhEGzdnWDr7tiHdmCtnUH6kxXYuzcQ370Bs1Ebwr0mEewwAMPUW/bqoO94knHctFU2yLGkCLNhS2Jjb8EwA37HEqkVgi07k3PJ3ZiN2+DGi4m/+VusDQv8jiU+cF2X1MfLKPm/n5DatBBwCZ43hNwrfkO4y0g1PSK1SKBJO2Kjv0Xulb8l1H0cBCM4h3eSWPAkJS/9EGvtTFwr7nfMjKf2UTKK6zokFj6Fc3A7RqQesYl3an6JyH8x6zch58v/S2LR06S3rSC59DmcwgIig6/SNu91xEkzeRq2JDL0GoItO/ucTES+iFm/CdHBVxHpOwVr4wJS6+fglhwmufxlkh+8TrjraELdx2HmNPQ7akbST0DJKNaq10lvWwFmgOj42zHzmvkdSaRWMkIRomO+jdW4Ldb7/yC1cQFO0W6i427DjOX5HU88crqZPOEeE9X0imQQI1qPSJ+LCPeYQGrLO2VnfI7uw1r9FtbaWYTOHUSo5yQC+drJtiL0XVAyRmrrcqwPXgcgOuwbum9B5AwMwyDSZzKBRq2Jz/8T9r6PKX3tF8Qm3EGgSTu/40k1O+VMnsFXY+Zpi1yRTGUEw4S7jCTUeTjpHatJrZmBvX8Lqc1LSG1eQqBt77Kd4M46TxshlIMaH8kI6f2fkFj0FAChnpMIdRrmcyKRzBFs15uci+8iPvtB3KP7KX39PqIjrifUcaDf0aQanHImz+CrCJ6tmTwi2cIwTEJn9yV0dl/sfVvKNkL49EPsgtXEC1ZjNjuHcK8vlc3iMnX/3umo8ZFaL330IMfffgDsNMF2fYgMuNzvSCIZJ5DfktyL7yo787NzHYn5f8I5vJNwv8v0QzJDaSaPSN0UOOtcYmedi3NkL9baWaS2LMU5sI3EnEcw8poT7jmB0HlDMYJhv6PWOmp8pFZzUwn2vX4/bvwoZqM2REffrDdpIpVkRHKJTfgO1vuvYq15G2v1W9iFO4mN+ZY2CckwmskjImbDFkSHf4Nwv0tIfTQXa8N83OL9JJc+j7VyGqHuYwl3HYMRred31FpDjY/UWq7rUDL3T6QOfIoRa1C2g5t+iylSJYZpErngCszGbUgsegZ751pKp91LbMIdmA1b+B1PzsBNlpBc8QqpjZrJIyJlzJwGRPpfRrj3haQ2LcZaNwv3eCHWymlYq98i1GkY4R4Tdb8fanykFrNWvEpq+wcYgRD1Jt0BmkAvUm1CHQdhNmhBfPZDOEf3UTLtXmJjvkWwbS+/o8kpuK5Less7JJe/jJs4BkDwvCFELvgf7dInIgAYoSjhHuMJdRtDetv7WGtm4BTuIPXRPFIb5hNs359wry8RaHq231F9o8ZHaqXU5iVYa94GoOnkW0md1ZF02vE5lUh2CTQ9m5xL7iYx91HsfR8Tn/kA4QGXEe51oW6Kr0XsI3tILtFMHhEpH8MMEOo4kGCHC7B3b8BaOwN713rS21aQ3raCQMsuZTvBte5R577Xq/GRWie9dzOJJX8BINpvCvW6D6OoqMTfUCJZysxpQOzCH5Jc9iKpTQuxVryKc6iA6MgbMIIRv+PVaW7awvrwjbJfAmkmj4hUkGEYBFt3I9i6G3ZhAdaaGaQ/eQ97z0biezZiNmpNuOckgh0uqDPfU+rGVykZwyk+QGL2w+DYBM/pT7T/JX5HEsl6RiBIdPg3MJu0Jbnsr6S3raD06H5iE6Zi6hJTX5w0k6dNT6JDvq5r9EWkUgKN2xIbfTPOgK9grZtNatMinMO7SCz8M8b7r5ZdItd5JEY45ndUT6nxkVrDtUqJz3wAN3kcs2l7oiNv1M26IjUo3HU0Zn4rEnMewSncQelrvyA67jYNC65Bp57J8zWCZ59f5y5JEZHqZ9ZrTHTQV4n0/TLWxgWk1s3BLSkiufzvJFf9k3DXUYS6j8PMzfc7qifU+Eit4Do28bmP4RzZg5GbT2z8VF1mI+KDYItO5Fxyd9mmB4UFxN/8LZEhVxHuOtrvaFntlDN5uo8rm8mT5b+BFZGaZ0RyifSeTLjHBFJb3iG1dmbZXKA1b2Otm0Ww42DCvSYSyG/ld9RqpcZHaoXk8pexd62HYLhsW90s/U2DSCYw6zchZ8rPSCx8mvS2FSSXPo9TWEBk8NV15jrwmnTSTJ5mHYgOu5ZA47Y+JxORbGcEQoQ7jyDUaRh2wRqsNTOw931M+uMlpD9eQqBtr7Kd4M46LyvOOusnmPjO2jCf1Po5AERH3USgydn+BhIRjGCE6JhvYzVpi7XiH6Q2LsQp2kN07K2YOQ38jpcVNJNHRGoLwzAJtutDsF0f7P1byzZC+PQD7II1xAvWYDY9h3CvSWWX3WbwIHk1PuKr9K6PSC57EYBw/8sIte/ncyIR+YxhGER6TybQqDXxeU9g7/uY0mn3EJswVb+gqALN5BGR2izQvCOx8bfjHNmHtW4mqY+X4hzcRmLuoxh5zQj3nEig63Ag1++oFabGR3zjHNlLfO6j4DoEzx1MuPdkvyOJyCkE2/Ym55KfE5/1EO7RfZS+fh/RETcQ6jjQ72gZRzN5RCRTmA3PIjrsG4T7XUrqo7lYH83DLT5AcunzWCunYQz4Em6XCUDmnAFS4yO+cBPHKZ31AFilmM07Eh1+XVZcOyqSrQINW5J78c+Jz38Ce+daEvP/hFNYQLj/VzL6soeacvJMnhDhvlMI99RMHhGp3cxYHpF+lxLudSGpzYux1s3CPXaIosV/J8eIEeg00u+I5abvtlLjXCdNfO6juEf3Y9RrXLaDWyDkdywROQMjkktswp1YK/+BtfotrDVvYx/eRWz0zRiRzLvkoaZoJo+IZAMjFCHcfRyhrqNxd6zCLNyK2a43jt/BKkCNj9Qo13VJLn0Be89GCEWJTbxT17SLZBDDNIkMuByzURsSi57B3rmWkum/LLvvp2FLv+PVKk5JEcl3/qqZPCKSVQwzQOjcgeQPGENRUQlOOnNaHzU+UqNS68umBWMYxMZ8i0CjNn5HEpFKCHUciNnwrH/f9zPtl8RG30ywXW+/o/nOdWxSH80luVIzeUREahM1PlJj0gVrSC5/GYDIBVcSbNvb30AiUiWBJmeTc+kvSMx5BHvfx8RnPUi4/2WEe19YZ89o2Ac+IbHkOZzCAkAzeUREahM1PlIj7MO7iM97HFyXUOfhhHqM9zuSiFQDM5ZH7MIfknznr6Q2LsB6/1WcwgKiI27ACEX8jldjymbyvEpq40I0k0dEpHZS4yOec+LFxGc9AKkEgRadiQy5ps7+NlgkGxmBINFh12I2bkNy2V9Jb1tB6dF9xMZPxazfxO94nnJdl/TWd8tm8sSLAQieO4TIQM3kERGpbdT4iKdcO0Vi9sO4xw5h5DUnNu42bd0qkqXCXUdj5rciMecRnMICSqfdQ3TsrVk7o8Y+suffm7UAZsMW/5rJ08XnZCIiciqen39ftmwZ3/ve9xg7diydOnXi3nvv9fqQUku4rkti8bPY+7dAOEZs4h0Y0Xp+xxIRDwVbdCLn0l9gNm6HmzhG/K3fYW2Yj+u6fkerNm7aIvn+Pyh99edlTU8gRLj/ZeRc9ks1PSIitZjnv3pfvHgxGzdupH///hw9etTrw0ktYq15i/SWd8AwiY29TVvditQRZr3G5Ez5KYlFz5D+5D2SS5/HOVRAZMjVGX/GN12wlsSyF/5rJs/VmHnNfE4mIiJn4vlPoB/96Ef85Cc/AeC9997z+nBSS6S2r8Ja8SoAkSFXE2zdzedEIlKTjGCE6OhvYTVui7XiVVKbFuIU7SY67jbMnAZ+x6uwk2fy5BMZfJVm8oiIZBDPGx/T1G42dY19aAeJBU8AEOo2lnDX0T4nEhE/GIZBpPeFBBq1Jj7/T9j7t1A67R5i46cSaHq23/HKRTN5RESyR2ZfcyC1jlNSVLaDW9oi0Lo7kUFf9TuSiPgs2LYXuRffRXzWgzhH91H6z/uIjrieUMdBfkf7QprJIyKSXTK68QkG/T+bFAiYJ/yzLnNTSUpnP4RbUoSZ35L6E27DCIeq9Jqqr7dUX2+pvv+hSStCX/kFx+c8TrpgDYn5T+Ae3kls4BUYlbwywKv6OokS4u+9gvXRAsDFiOQSG3gF4a4j6tRMHq1fb6m+3lJ9vZWp9a1w43Ps2DEOHDhwxse1adOGcDhcqVDlYZoG+fm5nr1+ReXl1e1LHlzX4cC0P2Ef3I4Zq0+rr/6MUH7Tanv9ul5fr6m+3lJ9P5NL/lU/o2jRyxx55zWSq9/GLN5Ls4vvJBCr/I6P1VVf13U5vn4xh+c9h11SthlPvR4jaTzmGgK5mXdfUnXR+vWW6ust1ddbmVbfCjc+c+bM+Xyzgi8yffp0unTxbltPx3EpLi717PXLKxAwycuLUVwcx7Ydv+P4Jr7iNRIb3wEzQM6E2zlOfSgqqfLrqr7eUn29pfqemtH7YnJzz6JkwVPEt33Izqd/SL1JdxJo1KpCr1Od9bWL9lC6+DnSu/89kydnxDcItepCsQVYVf9+lmm0fr2l+npL9fVWbatvXl6sXGefKtz4XHrppVx66aWVClXd0mn/C/0Z23ZqVZ6alNq6nMTK6QBEh30Do9l51V6LulzfmqD6ekv1PZnZfgA59ZsTn/0QztH9FP/jHmKjv0WwXe8Kv1ZV6uumLawP38Ba8zY4dtlMnr5fJtxzEkYgqP9vaP16TfX1lurrrUyrb0bf4yP+sw98QmLRUwCEek4i1GmYz4lEJFMEmrQj55K7Scx5BHvfx8RnPUi4/6WEe0+ukS2iNZNHRKRu8bzx2b17N+vWrQMgHo9TUFDAzJkzAZg4caLXhxcPOccLic96EOw0wXZ9iAy43O9IIpJhzFgesQt/SPLdv5HaMB/r/X/gFO4kOuIGjFDEk2NqJo+ISN3keePz3nvvnXBP0JIlS1iyZAkAmzdv9vrw4hE3lSA+6wHceDFmozZER99c6Z2ZRKRuMwJBokOvwWzUhuSyF0lvW0Hp0b3Exk/FrF99m6RoJo+ISN3meeNTm+4Jkurhug6J+U/gFO7EiOURm3gnRijqdywRyXDhrqMwG7UiMecRnMKdlE67l+jYWwm27Fzl1z55Js85RIdeS6BJuyq/toiIZAbd4yMVZq14lfSODyEQLPuNbL3GfkcSkSwRPOs8ci65u2zTg0M7iL/1OyKDv0ao6+hKXYbmJktIrniV1MaFgAvhHCIDLifUpW7N5BERETU+UkGpzUvKdj8CoiNuINC8o8+JRCTbmPUak/Pln5JY/CzprctJLnsBp7CAyJCvYwTK92PLdV3SW98lufxl3HgxAMFzBxMZeCVmLM/L+CIiUkup8ZFyS+/dTGLJXwAI9/0yoY6D/A0kIlnLCEaIjrqZVOO2JN97hdSmRdhFu4mNuw0zp+EXPtc+sofk0hew9/x7Jk9k6DUEW3o3W05ERGo/NT5SLk7xARKzHwbHJnhOf8LnX+x3JBHJcoZhEO71Jcz81sTnP46zfyul0+4hNn4qgabtT3r8mWbyiIhI3aYLnOWMXKuU+MwHcJPHMZu2JzryRl0bLyI1Jti2J7kX343ZsAVuSRGl//w1qS3vnPCY9M61lLzyM6wP3wDHJtCmJ7mX30ekz0VqekREBNAZHzkD17GJz30M58gejNx8YuOnYgS9ma0hInI6ZsOzyLn458TnP4FdsIbEgidxi3ZSf+gUjs96mtQn7wP/mskz6GsE2/fTTB4RETmBGh/5QsnlL2PvWg/BMLEJd2Dm5vsdSUTqKCOcQ2z8HVgrX8Na/SbJ1TMoWD0TcDWTR0REzkiNj5yWtWE+qfVzAIiOuolAk7P9DSQidZ5hmkQGfAWzcRsSi56GtEWgeQciQ67RTB4REflCanzklNK7PiK57EUAwv0vI9S+n8+JRET+LdThAsLN2xNLF5Fs3Anb9juRiIjUdmp85CTOkb3E5z4KrkPw3MGEe0/2O5KIyEkCDc8iN78DVlEJ4PgdR0REajltzSUncBPHKZ31AFilmM07Eh1+nW4QFhEREZGMp8ZHPuc6aeJzH8U9uh+jXuOyHdwCIb9jiYiIiIhUmRofAcB13X9POg9FiU28EzOW53csEREREZFqocZHAEitn01q0yIwDGJjvkWgURu/I4mIiIiIVBs1PkK6YA3J5S8DELngSoJte/sbSERERESkmqnxqePsw7uIz3scXJdQ5xGEeoz3O5KIiIiISLVT41OHOfFi4jP/CKkEgRadiQz5unZwExEREZGspManjnLtFPHZD+EeL8TIa05s3G0YAY11EhEREZHspManDnJdl8TiZ3H2b4VwjJyJd2JE6/kdS0RERETEM2p86iBr9Vukt7wDhkls7G2YDVv4HUlERERExFNqfOqY1PaVWO+/CkBkyNUEW3fzOZGIiIiIiPfU+NQh9qEdJBY8CUCo21jCXUf7nEhEREREpGao8akjnJIi4rMegLRFoHV3IoO+6nckEREREZEao8anDnDTybId3EqKMBu2JDb2Fgwz4HcsEREREZEao8Yny7muQ2LhUzgHt2NE6hGbeCdGOMfvWCIiIiIiNUqNT5azVr1Oetv7YAaIjr8dM6+Z35FERERERGqcJlZmsdTW5VgfvA5AdNg3CLbo5HMiEREREakMx3Gw7bTfMQBwHINEIoBlJbFt19NjBQJBTLN6ztWo8clS9oFPSCx6CoBQz0mEOg3zOZGIiIiIVJTruhQXHyYeP+53lBMcOmTiOE6NHCsWq0deXiMMw6jS66jxyULO8ULisx4EO02wXR8iAy73O5KIiIiIVMJnTU+9evmEw5Eqv/mvLoGA4fnZHtd1sawkx48XAdCgQeMqvZ4anyzjphLEZz2AGy/GbNyG6OibMarp9KCIiIiI1BzHsT9veurVy/M7zgmCQZN02vszPuFwBIDjx4uoXz+/Spe96R1xFnEdh8T8J3AKd2LE8ohNuBMjFPU7loiIiIhUgm3bwL/f/NdVn339Vb3HSY1PFrHef5X0jg8hECQ2fipmvaqdDhQRERER/9WWy9v8Ul1fvxqfLJHavARrzdsAREfcSKB5R58TiYiIiIjUHmp8skB672YSS/4CQLjvlwl1HOhvIBERERGRWkaNT4Zzig+QmP0wODbBc/oTPv9ivyOJiIiIiJzW9ddfxdCh/fjgg5U1elxPGx/btvnzn//M1VdfzcCBA+nfvz9XXXUV7777rpeHrTNcq5T4zAdwk8cxm7YnOvJGDEO9rIiIiIjUTjt2fMrHH28GYM6cmTV6bE/fJScSCZ544gk6d+7Mb37zG/7whz/QvHlzrrvuOhYsWODlobOe69jE5z6Gc2QPRm4+sfFTMYJ1e8cPEREREandZs+eQSAQ4PzzB7BgwTxSqVSNHdvTOT7RaJR58+bRoEGDzz82dOhQPv30U5555hlGjRrl5eGzWvLdl7B3rYdgmNiEOzBz8/2OJCIiIiLyhebMmUnfvv244oqv8oMf3Mm77y5j+PCRNXJsTxufQCBwQtMDZdvRde7cmVWrVnl56KxmbZhP6qO5AERH3USgydn+BhIRERGRGuO6LlbK++GhpxMOVe6isfXr17Fnz26uvfYG+vcfSMOGDZk9e0Z2ND6n4jgOH374IR06dKjyawWD/t/PEgiYJ/zTa6md60kuexGA6AWXEzt3QI0c1y81Xd+6RvX1lurrLdXXW6qvt1Rfb2VLfR3n5Pk1ruvymxc/YOvuoz4kKnNu6wb877X9MAxw3fI/b86cGYTDYUaMGE0wGGTUqHG89dY/KSk5Tm5uvTM+PxAwqvT+v8YbnxdeeIHt27dz7733Vul1TNMgPz+3mlJVXV5ezPNjWIW72TPnUXAd6vUYQdMx/1NnBlrVRH3rMtXXW6qvt1Rfb6m+3lJ9vZXp9U0kAhw6ZJ7wht91XWrL27+KNJa2bTN//lwGDx5Kw4Z5AEya9CWmTXuFJUsWMnnyl0/7XMcxME2TBg1yiEajlc5b4cbn2LFjHDhw4IyPa9OmDeFw+ISPrVixgt/97ndcf/319O/fv6KHPoHjuBQXl1bpNapDIGCSlxejuDiObXt3ytFJHOfYP+7DSZQQOOtcgoOv4cgR/79+r9VUfesq1ddbqq+3VF9vqb7eUn29lS31tawkjuNg2y7p9L+/jh9f1dfXS90iYRPDMLBtp9xnfJYvf5eiosMMHjyMoqKys1Vt2pxNs2bNmTnzbSZOnHza59q2i+M4HD1aSjxun/T5vLxYuZqwCjc+c+bM4Sc/+ckZHzd9+nS6dOny+X9v2rSJW265hbFjx/KDH/ygooc9pf9cAH6zbcezPK6TJj7zYZyj+zHqNyE67nZsNwC16Ov3mpf1FdXXa6qvt1Rfb6m+3lJ9vZXp9bXtU3cVhmEQCQdqOM3JKnqZG8Cvf30PcM8Jnzt06CCFhYdo3LjJF77GfzeAFVXhxufSSy/l0ksvrdBzCgoKuPHGG+natSu//e1v68zlWdXBdV2SS1/A3rMRQlFiE+7EjOX5HUtEREREpFwSiQSLFy9i2LCRXH75lSd87siRI9x114+ZN282V1zxNU9zeH6Pz8GDB7n++utp0qQJjz322EmXv8kXS62bTWrTIjAMYmO+RaBRa78jiYiIiIiU29Kli4jHS7n88ivp27ffSZ9/6aWuzJ490/PGx/MBpjfeeCOFhYXcdtttbN26ldWrV3/+R75YumA1yfdeBiBywZUE2/b2N5CIiIiISAXNnj2T5s3Pok+f80/5+YkTJ7Np0wYKCnZ4msPTMz6HDh1i06ZNANx6660nfX7z5s1eHj6j2Yd3EZ/3J3BdQp1HEOox3u9IIiIiIiIV9tvf/vELP3/ZZVdw2WVXeJ7D08andevWam4qwYkXE5/5R0glCLToTGTI13VflIiIiIhIFWT2VKcs5KYt4rMfwj1eiJHXnNi42zACNT5uSUREREQkq6jxqUVc1yWx5C84+7dCOIeciXdiRM88xVZERERERL6YGp9axFr9Fukt74BhEht7K2bDFn5HEhERERHJCmp8aonU9pVY778KQGTI1QRbd/M5kYiIiIhI9lDjUwvYhz4lseBJAELdxhLuOtrnRCIiIiIi2UWNj8+ckiLisx6EtEWgdXcig77qdyQRERERkayjxsdHbjpZtoNbSRFmw5bExt6CYQb8jiUiIiIiknXU+PjEdR0SC5/CObgdI1KP2MQ7McI5fscSEREREclKGhDjE2vV66S3vQ9mgOj42zHzmvkdSURERETEc+++u5RXXvk7mzZtoLS0hMaNmzBo0FC++tWradWqtWfHVePjg9TW5VgfvA5AdNg3CLbo5HMiERERERHvPfnkYzz//DMMHz6KH/zgJ+TnN2Lv3j3MnPkWd955C6+88k/Pjq3Gp4bZ+7eSWPQUAKGekwh1GuZzIhERERER7y1f/g7PP/8MX//6ddx8862ff7x3775MmjSZpUsXe3p83eNTg5zjhcRnPwR2mmC7PkQGXO53JBERERGRGvHSSy/SqFFjbrjh5lN+fujQ4Z4eX41PDXFTCeKzHsCNF2M2bkN09M0YpsovIiIiItkvnU6zbt0a+vUbQDDoz0VnutStBriOQ2L+EziFOzFiecQm3IkRivodS0REREQykOu6kLb8CxAMV/gpxcVHsawkzZo19yBQ+ajxqQHJFa+Q3vEhBILExk/FrNfY70giIiIikoFc16X0n/fh7N/qW4ZA83MJXvq/FXqO67oAGIbhRaRy0bVWHkttXkJq7QwAoiNuJNC8o8+JRERERCSTGfjXPFRWgwYNCYcj7N+/z7cMOuPjofTezSSW/AWAcN8phDoO9DeQiIiIiGQ0wzCIffmnvl/qVnbmxi3/U4JBevbsxcqVK0in077c56MzPh5xig+QmP0wODbBc/oTPn+K35FEREREJAsYhoERivj3p5KXq1155dUcPlzIs8/++ZSfX7ZsSVXKckY64+MB1yolPvMB3ORxzKbtiY68EcNQjykiIiIiddfAgYO55prree65p9mxYztjx04gP78R+/btZdast9m5s4AhQ7ybcanGp5q5jk187mM4R/Zg5OYTm3AHRjDidywREREREd/ddNMtdO/ek1dffZn/7/+7j9LSEpo0aUr//hdw++3f9fTYanyqWfLdl7B3rYdgmNiEOzFzGvodSURERESk1hg8eCiDBw+t8ePq+qtqZG2YT+qjuQBER91EoEk7nxOJiIiIiAio8ak2qZ3rSS57EYBw/68Qat/P50QiIiIiIvIZNT7VwCrcTcnsR8B1CJ47mHDvC/2OJCIiIiIi/0H3+FSRkzjGvmm/xk2WYjbvSHT4db5OpBURERERkZPpjE8Vlcx+jHTRPsz6TYiNn4oRCPkdSURERERE/ovO+FSBm7ZI7/0YIxyj3pe+ixvL8zuSiIiIiGQZ13X9juCr6vr61fhUgREMk/eVe2jQNJ/jTi7ptON3JBERERHJEoFAAADLShIO1925kJaVBCAQqFrrosanigKNWxNqkAtFJX5HEREREZEsYpoBYrF6HD9eBEA4HKk195I7joFte3smynVdLCvJ8eNFxGL1MM2q3aWjxkdEREREpJbKy2sE8HnzU1uYponj1MzVTrFYvc/rUBVqfEREREREainDMGjQoDH16+dj22m/4wAQCBg0aJDD0aOlnp/1CQSCVT7T8xk1PiIiIiIitZxpmphm2O8YAASDJtFolHjczqh73LWdtYiIiIiIZD01PiIiIiIikvXU+IiIiIiISNZT4yMiIiIiIlnPcDN0FKzrujhO7YgeCJjYdubc2JVpVF9vqb7eUn29pfp6S/X1lurrLdXXW7WpvqZplGu+UcY2PiIiIiIiIuWlS91ERERERCTrqfEREREREZGsp8ZHRERERESynhofERERERHJemp8REREREQk66nxERERERGRrKfGR0REREREsp4aHxERERERyXpqfEREREREJOup8RERERERkaynxkdERERERLKeGh8REREREcl6any+wPbt27nhhhvo3bs3gwYN4le/+hWJRKJcz502bRoTJ06kR48eTJ48mRkzZnicNvNUtr5f//rX6dSp00l/PvnkkxpInTl27NjBXXfdxZQpU+jatSuTJ08u93O1fs+ssvXV+j2zGTNmcMsttzBixAh69+7NRRddxN/+9jccxznjc7V2z6yy9dXaLZ8lS5Zw9dVXM3DgQLp3786YMWP4zW9+w7Fjx874XK3fM6tsfbV+K6ekpIThw4fTqVMn1q1bd8bH1/Y1HPQ7QG1VXFzMtddeS8uWLXnooYc4fPgwv/nNbzhy5Ai///3vv/C5M2fO5Mc//jE33XQTQ4YMYe7cuXznO9+hfv36DB06tIa+gtqtKvUF6Nu3Lz/60Y9O+Fjr1q29ipuRtmzZwqJFi+jVqxeO4+C6brmep/VbPpWtL2j9nsmzzz5Ly5Yt+eEPf0jjxo157733uO+++9i5c+dJdftPWrvlU9n6gtZueRw9epQ+ffpw7bXXkpeXx5YtW3j44YfZsmULzzzzzGmfp/VbPpWtL2j9VsZjjz2GbdvlemxGrGFXTumJJ55we/Xq5RYWFn7+sX/+85/ueeed527duvULnztx4kR36tSpJ3zs+uuvdy+//HJPsmaiqtT36quvdm+66SavI2Y827Y///cf/ehH7oUXXliu52n9lk9l66v1e2b/+X3hM7/+9a/dHj16uMlk8rTP09otn8rWV2u38v7+97+75513nrtv377TPkbrt/LKU1+t34rbunWr27t3b/ell15yzzvvPHft2rVf+PhMWMO61O00Fi9ezKBBg2jUqNHnH5swYQLhcJhFixad9nk7d+5k27ZtJ132MnnyZNauXcvhw4c9y5xJKltfKT/TrPhfb63f8qtMfaV8/vP7wme6dOlCMpnkyJEjp3yO1m75Vaa+UjUNGzYEIJ1On/LzWr9Vc6b6SuXcd999XHnllbRv3/6Mj82UNayf3KfxySef0KFDhxM+Fg6Hadu27RdeD7pt2zYAzjnnnBM+3qFDB1zX/fzzdV1l6/uZFStW0Lt3b3r06MHVV1/N+++/71XUOkXrt2Zo/VbcqlWraNiwIY0bNz7l57V2q+ZM9f2M1m752bZNMpnko48+4tFHH2XUqFG0atXqlI/V+q24itT3M1q/5Tdz5kw2bdrErbfeWq7HZ8oa1j0+p1FcXExeXt5JH8/Ly+Po0aOnfd5nn/vv5zZo0OCEz9d1la0vQP/+/ZkyZQpnn302Bw4c4Omnn+a6667jhRdeoE+fPl5FrhO0fr2n9Vtx69at47XXXuPWW28lEAic8jFau5VXnvqC1m5FjRo1iv379wMwbNgw/vCHP5z2sVq/FVeR+oLWb0XE43Huv/9+vvvd71KvXr1yPSdT1rAanwpyXRfDMM74uP9+jPuvG5/L89y6rDz1nTp16gn/PXLkSCZPnsxjjz3Gn//8Zy/j1Rlav97R+q2YgwcPMnXqVHr06ME3v/nNMz5ea7diKlJfrd2KefLJJyktLWXr1q089thjfOtb3+LZZ5/9wuZS67f8Klpfrd/ye/zxx2ncuDGXXnpphZ9b29ewLnU7jby8PIqLi0/6+LFjx055puIzp+tsP3utL3puXVLZ+p5KTk4OI0aM4KOPPqqueHWW1m/N0/o9vWPHjvHNb36TaDTK448/TigUOu1jtXYrriL1PRWt3S/WuXNn+vbtyxVXXMEjjzzCe++9x5w5c075WK3fiqtIfU9F6/fUdu/ezTPPPMPUqVM5fvw4xcXFlJaWAlBaWkpJSckpn5cpa1iNz2l06NDhpHtNLMuioKDgpHtT/tNn1zb+97WMn3zyCYZhnHTtY11V2fqejluBrYTl9LR+/aH1e7JkMsm3v/1tDh06xFNPPUV+fv4XPl5rt2IqWt/T0dotny5duhAIBCgoKDjl57V+q+ZM9T0drd+T7dq1i1QqxU033UT//v3p378/3/rWtwC45ppruO666075vExZw2p8TmP48OEsX76coqKizz82Z84cLMtixIgRp31emzZtOOecc3j77bdP+Pibb75Jz549T7mbTl1U2fqeSmlpKYsWLaJHjx7VHbPO0fqteVq/J0un09xxxx1s2rSJp5566ow3LIPWbkVUpr6norVbfh9++CG2bZ92ZozWb9Wcqb6novV7al26dOH5558/4c9PfvITAO655x7uvvvuUz4vU9aw7vE5jSuvvJIXX3yRW265hVtuuYXCwkLuv/9+LrroohPOSPz0pz9l+vTpbNiw4fOPTZ06le985zu0bduWwYMHM2/ePJYtW8ZTTz3lx5dSK1W2vitXruTpp59m3LhxtGzZkgMHDvDss89y8OBBHnzwQb++nFopHo9/vjX47t27OX78ODNnzgRgwIABNGrUSOu3CipTX63f8rn33ntZsGABP/jBD0gkEqxevfrzz3Xs2JF69epp7VZBZeqrtVt+t912G927d6dTp05Eo9HPG8xOnToxduxYQO8dqqIy9dX6Lb+8vDwuuOCCU36uW7dudOvWDcjcNazG5zTy8vJ47rnn+NWvfsXtt99ONBpl8uTJfP/73z/hcY7jnDTRdtKkSSQSCf70pz/x9NNP065dO/74xz/Wnqm1tUBl69u0aVMsy+IPf/gDR44cIRaL0adPH+655x569uxZ019GrVZYWMgdd9xxwsc+++/nn3+eCy64QOu3CipTX63f8lm6dCkAv/vd7076nNZu1VWmvlq75dezZ0/efvttnnzySVzXpVWrVlxxxRXccMMNhMNhQO8dqqIy9dX6rX6ZuoYNVxc4ioiIiIhIltM9PiIiIiIikvXU+IiIiIiISNZT4yMiIiIiIllPjY+IiIiIiGQ9NT4iIiIiIpL11PiIiIiIiEjWU+MjIiIiIiJZT42PiIiIiIhkPTU+IiIiIiKS9dT4iIiIiIhI1lPjIyIiIiIiWU+Nj4iIiIiIZL3/HwSUQKqDGKpmAAAAAElFTkSuQmCC", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo[[\"A\", \"C\"]].plot(figsize=(10,3));" ] @@ -2245,7 +6012,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "subslide" }, @@ -2259,58 +6025,124 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 211, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 1000x100 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.palplot(sns.color_palette())" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 212, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 1000x100 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.palplot(sns.color_palette(\"hls\", 10))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 213, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 2000x100 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.palplot(sns.color_palette(\"hsv\", 20))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 214, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 1000x100 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.palplot(sns.color_palette(\"Paired\", 10))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 215, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 800x100 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.palplot(sns.color_palette(\"cubehelix\", 8))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 216, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 1000x100 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.palplot(sns.color_palette(\"colorblind\", 10))" ] @@ -2330,13 +6162,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 217, "metadata": { "slideshow": { "slide_type": "-" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "with sns.color_palette(\"hls\", 2):\n", " sns.regplot(x=\"C\", y=\"F\", data=df_demo);\n", @@ -2357,7 +6200,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 218, "metadata": {}, "outputs": [], "source": [ @@ -2366,9 +6209,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 219, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x600 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.jointplot(x=x, y=y, kind=\"reg\");" ] @@ -2394,7 +6248,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 220, "metadata": { "exercise": "solution", "slideshow": { @@ -2417,28 +6271,126 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 221, "metadata": { "exercise": "solution", "slideshow": { "slide_type": "subslide" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Runtime Program / s</th>\n", + " <th>Unaccounted Time / s</th>\n", + " <th>Avg. Neuron Build Time / s</th>\n", + " <th>Min. Edge Build Time / s</th>\n", + " <th>Min. Init. Time / s</th>\n", + " <th>Presim. Time / s</th>\n", + " <th>Sim. Time / s</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Threads</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>420.42</td>\n", + " <td>2.09</td>\n", + " <td>0.29</td>\n", + " <td>88.12</td>\n", + " <td>1.14</td>\n", + " <td>17.26</td>\n", + " <td>311.52</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16</th>\n", + " <td>202.15</td>\n", + " <td>2.43</td>\n", + " <td>0.28</td>\n", + " <td>47.98</td>\n", + " <td>0.70</td>\n", + " <td>7.95</td>\n", + " <td>142.81</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Runtime Program / s Unaccounted Time / s \\\n", + "Threads \n", + "8 420.42 2.09 \n", + "16 202.15 2.43 \n", + "\n", + " Avg. Neuron Build Time / s Min. Edge Build Time / s \\\n", + "Threads \n", + "8 0.29 88.12 \n", + "16 0.28 47.98 \n", + "\n", + " Min. Init. Time / s Presim. Time / s Sim. Time / s \n", + "Threads \n", + "8 1.14 17.26 311.52 \n", + "16 0.70 7.95 142.81 " + ] + }, + "execution_count": 221, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df[[\"Runtime Program / s\", \"Unaccounted Time / s\", *cols]].head(2)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 222, "metadata": { "exercise": "solution", "slideshow": { "slide_type": "-" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 1200x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df[[\"Unaccounted Time / s\", *cols]].plot(kind=\"bar\", stacked=True, figsize=(12, 4));" ] @@ -2458,13 +6410,282 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 223, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th>id</th>\n", + " <th>Runtime Program / s</th>\n", + " <th>Scale</th>\n", + " 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Delay</th>\n", + " <th>Unaccounted Time / s</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Nodes</th>\n", + " <th>Tasks/Node</th>\n", + " <th>Threads/Task</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th rowspan=\"3\" valign=\"top\">1</th>\n", + " <th rowspan=\"2\" valign=\"top\">2</th>\n", + " <th>4</th>\n", + " <td>5</td>\n", + " <td>420.42</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.29</td>\n", + " <td>88.12</td>\n", + " <td>88.18</td>\n", + " <td>1.14</td>\n", + " <td>1.20</td>\n", + " <td>17.26</td>\n", + " <td>311.52</td>\n", + " <td>46560664.0</td>\n", + " <td>825499</td>\n", + " <td>7.48</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>2.09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>5</td>\n", + " <td>202.15</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.28</td>\n", + " <td>47.98</td>\n", + " <td>48.48</td>\n", + " <td>0.70</td>\n", + " <td>1.20</td>\n", + " <td>7.95</td>\n", + " <td>142.81</td>\n", + " <td>47699384.0</td>\n", + " <td>802865</td>\n", + " <td>7.03</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>2.43</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <th>4</th>\n", + " <td>5</td>\n", + " <td>200.84</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.15</td>\n", + " <td>46.03</td>\n", + " <td>46.34</td>\n", + " <td>0.70</td>\n", + " <td>1.01</td>\n", + " <td>7.87</td>\n", + " <td>142.97</td>\n", + " <td>46903088.0</td>\n", + " <td>802865</td>\n", + " <td>7.03</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>3.12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <th>2</th>\n", + " <th>4</th>\n", + " <td>5</td>\n", + " <td>164.16</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.20</td>\n", + " <td>40.03</td>\n", + " <td>41.09</td>\n", + " <td>0.52</td>\n", + " <td>1.58</td>\n", + " <td>6.08</td>\n", + " <td>114.88</td>\n", + " <td>46937216.0</td>\n", + " <td>802865</td>\n", + " <td>7.03</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>2.45</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <th>2</th>\n", + " <th>12</th>\n", + " <td>6</td>\n", + " <td>141.70</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.30</td>\n", + " <td>32.93</td>\n", + " <td>33.26</td>\n", + " <td>0.62</td>\n", + " <td>0.95</td>\n", + " <td>5.41</td>\n", + " <td>100.16</td>\n", + " <td>50148824.0</td>\n", + " <td>813743</td>\n", + " <td>7.27</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>2.28</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " id Runtime Program / s Scale Plastic \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 5 420.42 10 True \n", + " 8 5 202.15 10 True \n", + " 4 4 5 200.84 10 True \n", + "2 2 4 5 164.16 10 True \n", + "1 2 12 6 141.70 10 True \n", + "\n", + " Avg. Neuron Build Time / s \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 0.29 \n", + " 8 0.28 \n", + " 4 4 0.15 \n", + "2 2 4 0.20 \n", + "1 2 12 0.30 \n", + "\n", + " Min. Edge Build Time / s \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 88.12 \n", + " 8 47.98 \n", + " 4 4 46.03 \n", + "2 2 4 40.03 \n", + "1 2 12 32.93 \n", + "\n", + " Max. Edge Build Time / s Min. Init. Time / s \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 88.18 1.14 \n", + " 8 48.48 0.70 \n", + " 4 4 46.34 0.70 \n", + "2 2 4 41.09 0.52 \n", + "1 2 12 33.26 0.62 \n", + "\n", + " Max. Init. Time / s Presim. Time / s \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 1.20 17.26 \n", + " 8 1.20 7.95 \n", + " 4 4 1.01 7.87 \n", + "2 2 4 1.58 6.08 \n", + "1 2 12 0.95 5.41 \n", + "\n", + " Sim. Time / s Virt. Memory (Sum) / kB \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 311.52 46560664.0 \n", + " 8 142.81 47699384.0 \n", + " 4 4 142.97 46903088.0 \n", + "2 2 4 114.88 46937216.0 \n", + "1 2 12 100.16 50148824.0 \n", + "\n", + " Local Spike Counter (Sum) Average Rate (Sum) \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 825499 7.48 \n", + " 8 802865 7.03 \n", + " 4 4 802865 7.03 \n", + "2 2 4 802865 7.03 \n", + "1 2 12 813743 7.27 \n", + "\n", + " Number of Neurons Number of Connections \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 112500 1265738500 \n", + " 8 112500 1265738500 \n", + " 4 4 112500 1265738500 \n", + "2 2 4 112500 1265738500 \n", + "1 2 12 112500 1265738500 \n", + "\n", + " Min. Delay Max. Delay Unaccounted Time / s \n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 1.5 1.5 2.09 \n", + " 8 1.5 1.5 2.43 \n", + " 4 4 1.5 1.5 3.12 \n", + "2 2 4 1.5 1.5 2.45 \n", + "1 2 12 1.5 1.5 2.28 " + ] + }, + "execution_count": 223, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_multind = df.set_index([\"Nodes\", \"Tasks/Node\", \"Threads/Task\"])\n", "df_multind.head()" @@ -2472,13 +6693,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 224, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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AM8GRGNSbYbnlG/c1WW17KLzyf5XYvanu9LLJN8tqU+R0PAAAADSwcDis1atflSQdOLBftbW1Wr58mSRp0KALlJub26R5KDE4a+5eY2Tmd/lwDfMz/yHv6C/L3XdCRpxrCQAAgDrHjh3VL37x04/c9sHH9933sHJzhzZpHkoMzomV31lZJbMUfuUPiu94U5FX/1eJqm3yjbtWhovzPQEAADJB+/Yd9Oqr65yOcRrXxOCcGZ6AfFNukWf4lZJhKP7+KgUX3qXkiQNORwMAAEAGosSgQRiGIe+gS+S/9DYZ/hwlj+5WbekcxXasdzoaAAAAMgwlBg3K1aGvAiVzZLXtKcVCCr94vyJr/iE7mXA6GgAAADIEJQYNzszKlX/aT+QecLEkKbphsULP/VbJ4AmHkwEAACATUGLQKAzTJd+oa+SbfJPk9imxv1zB0lmKH3jf6WgAAABNxrZtpyOklIb6elBi0KjcRcMVmHmnzFYdZAePK7To14pueoG/0AAAIKNZliVJikYjDidJLR98PSzr3JYks2IZjc5q1UGBmXcqXPZHxSvWKLL6r3VrmIuvk+HxOx0PAACgwZmmJb8/WzU1xyRJHo+3Wb+Pnm3bikYjqqk5Jr8/W6Z5bsdSKDFoEobbJ9/EGxRr20OR1X9TfPsbCh7ZLd9Ft8jK7eh0PAAAgAaXk5MnSaeLDCS/P/v01+VcUGLQZAzDkOe8KbJad1No2YNKnjig4IK58hV/Q+4eI52OBwAA0KAMw1DLlvlq0SJXiUTc6TiOsyzXOR+B+QAlBk3OattDgZI5Cr80T4l9WxR++WElDlbIO+IqGed4fiQAAECqMU1TpulxOkZG4cJ+OML058h/yY/lGXSZJCn2zlIFn/21kjVHHU4GAACAVEeJgWMM05R3+Bflv/jfJY9fyaptdWuY977rdDQAAACkMEoMHOfqOlhZJXNk5neRHT6p0OLfKvLWs7LtpNPRAAAAkIIoMUgJZk4bBS7/uVy9xkm2regbTyr0wn2yI7VORwMAAECKocQgZRguj/wTvilv8Tcky6XErrdVWzpbicM7nY4GAACAFEKJQcrx9BmvwPSfy2hRIPvkIQWfvkuxrSudjgUAAIAUQYlBSrJaFypr5mxZnc+XEjGFV/yPwmV/lB2POh0NAAAADqPEIGUZvmz5p35fnqElkgzFylco+MyvlKw+5HQ0AAAAOIgSg5RmGKa8Q6bLf8mPZHizlTy8U7ULZiu+a4PT0QAAAOAQSgzSgqvTeQpcMUdm6yIpUqvQkv9SZF2p7CRrmAEAAJobSgzShpmdr8D02+XuN1GSFH3zGYWe/52S4ZMOJwMAAEBTosQgrRiWW76x18p34bcly6PE3s0KPjVLiYMVTkcDAABAE6HEIC25e45WYOYvZLRsK7v2qILP/Ieim1+SbdtORwMAAEAjo8QgbVl5nZU1c7ZchRdIyYQiq+YrvPwR2bGI09EAAADQiCgxSGuGxy/flO/KO/IqyTAV37ZawYW/VPL4AaejAQAAoJFQYpD2DMOQ5/wvyH/pbTL8OUoe26PaBbMVq1zndDQAAAA0AkoMMoarQx8Frpgrq10vKRZWeOkDCr/+N9nJhNPRAAAA0IAoMcgoZqCV/JfdJvf5UyVJsY1LFHruHiWDx50NBgAAgAZDiUHGMUyXfCOvlm/yzZLbp8T+rQo+NUvx/VudjgYAAIAGQIlBxnIXDVPWzFkyczvKDp1Q6NnfKLpxCWuYAQAA0hwlBhnNbNVegRl3ytVjpGQnFXn9bwove1B2NOR0NAAAAJwlSgwynuH2ynfhd+Qd81XJtBSvXKfaBXOUOLrH6WgAAAA4C5QYNAuGYcjTf5IC038mIytP9okDCi6cq9j7rzkdDQAAAPVEiUGzYrXprkDJbFkd+0vxqMLLH1H41fmyE3GnowEAAOAMUWLQ7Jj+HPm/8CN5Bk+TJMXefUnBRf+hZM0Rh5MBAADgTFBi0CwZpinvsCvkn/p9yRNQ8uB2BUtnK75ns9PRAAAA8DnOqcTU1taquLhYvXv31qZNmxoqE9BkXF0GKatkjsz8rrLDJxVafK8ibz4j2046HQ0AAACf4pxKzEMPPaREItFQWQBHmDmtFbj8Drn7FEuyFV1XqtAL/092pNbpaAAAAPgEZ11iKioq9Je//EW33HJLQ+YBHGG4PPIVXydf8XWS5VJi1wbVls5S4vAOp6MBAADg/zjrEvOrX/1KV199tbp169aQeQBHufsUK3D5L2S0aC375GEFn75L0fIVTscCAADAv3CdzSctWbJE5eXluu+++7R5c8NcCO1yNeyOAcsyP/Jv4Ey52nWT+9/mKvjS7xXb+bYiZX+UfbBCgeJrZbg8n/v5zB6cwuzBKcwenMDcNW/1LjGhUEi//vWv9cMf/lDZ2dkNEsI0DeXmZjXIY/1fOTn+RnlcZLos5X35Dh1/bYGOrfibouVl0rHdanvFrXLntjujR2D24BRmD05h9uAE5q55qneJmTdvnvLz81VSUtJgIZJJW9XVwQZ7PKmulefk+FVdHVIiwaYpnKV+U5XdopNql85TtKpSe/7nxwpM+o48hYM/9VOYPTiF2YNTmD04gbnLTDk5/jM6ulavErN371499thjevDBB1VTUyNJCgaDp/9dW1urrKyzO6ISjzfO8CUSyUZ7bDQPRvt+CpTMUWjZg0oerFDt4v9SbPA0eS6YKcP89L9kzB6cwuzBKcwenMDcNU/1KjF79uxRLBbTt7/97Y/dd+2112rgwIH6xz/+0WDhgFRhZucpMO12RV7/m2Kblyn61iIlDlbIN/EGmf4cp+MBAAA0K/UqMX379tXjjz/+kdu2bNmiu+++W3PmzNGAAQMaNByQSgzLJd+Yr8hq20PhsseU2PuugqWz5Z98k6y2PZyOBwAA0GzUq8Tk5ORoxIgRn3hf//791b9//wYJBaQyd4+RMvM6K7z0fiVPHFBw0d3yjrxG7v6TZBiG0/EAAAAyHjvpgLNg5XVUYOYsuboNlZIJRV57QuGXfy87FnE6GgAAQMY75xIzYsQIbd26lVPJ0OwYHr98k2+Wd+Q1kmEqXvG6ggvnKnFsv9PRAAAAMhpHYoBzYBiGPOdfLP9lP5ERaKXksb2qfnKWarasdjoaAABAxqLEAA3A1b63AiWzZbXvLcXCOlh6r4Kr/iI7GXc6GgAAQMahxAANxAy0kv/S2+QdfKkkKbJhiULP3qNk7TGHkwEAAGQWSgzQgAzTUmDUVWp7xW2Sx6/EgfcULJ2l+L5yp6MBAABkDEoM0Aiy+oxQzhfnyMztJDtUrdBz9yi6YbFs23Y6GgAAQNqjxACNxGrVToEZv5CrxyjJTiqy5h8KL31AdjTodDQAAIC0RokBGpHh9sp34bflHXutZLoU37FetaVzlDiy2+loAAAAaYsSAzQywzDk6TdRgek/k5GdL7u6SsGFv1TsvVVORwMAAEhLlBigiVhtiurWMHc6T0pEFX7lUYVX/q/sRMzpaAAAAGmFEgM0IdPXQv6pP5RnyOWSpNiW5Qo+8x9K1hxxOBkAAED6oMQATcwwTXmHzpR/6g8kb5aShyoVfGqW4rs3OR0NAAAgLVBiAIe4ugxUVslsmQWFsiM1Cj3/n4qsf1q2nXQ6GgAAQEqjxAAOMlu0VmD6z+TuM0GSrej6BQot+W/Z4RqnowEAAKQsSgzgMMPlka/46/KN/6ZkuZXYvVG1pbOUOLTD6WgAAAApiRIDpAh373EKXP5zGTltZNccUfDpuxTd8ops23Y6GgAAQEqhxAApxCroqqyZs+TqOlhKxhVZ+SeFV/yP7HjE6WgAAAApgxIDpBjDmyXfRbfIM/zfJMNQ/L1XFXz6LiVPVDkdDQAAICVQYoAUZBimvIMulf/S22T4c5Q8slu1C2YrtuNNp6MBAAA4jhIDpDBXh74KlMyR2baHFA0p/OJ9iqz9p+xkwuloAAAAjqHEACnOzMpV4LKfyn3eFElS9O3nFFp8r5KhaoeTAQAAOIMSA6QBw3LJN/rL8k26UXJ5ldi3RcGn7lTiwPtORwMAAGhylBggjbi7j1Bg5iyZrdrLDh5XcNGvFX1nKWuYAQBAs0KJAdKMldtBgRl3ylU0XLITirz2Z4Vfmic7FnY6GgAAQJOgxABpyPD45Zt0o7yjviQZluLb1yq4YK4Sx/Y5HQ0AAKSRrbuO6dFFm7Wx4rDTUerF5XQAAGfHMAx5Blwks3U3hZc9qOTxfQoumCPf+G/K3X240/EAAEAKq9xfrdKy7dpcefTULYbO717gaKb6oMQAac7VrqcCJXMUfvlhJfZtUfilh5Soel/eEVfJsPgrDgAAPrT3UI0WrKzUm+8dkiRZpqFxAzto5rhuDierH17hABnADLSU/5JbFV1Xqujbzyn2zlIlDlXKP/lmmVm5TscDAAAOqzoW1NOvVmrN5irZkgxDGtW/naaP7aY2rfxOx6s3SgyQIQzTknf4v8ls013h5Y8qWbVNwdJZ8k28Qa6O/ZyOBwAAHHC0OqxFr+3Qyg37lTy1zfSC3q01Y1yROhZkOZzu7FFigAzjLhwiq2S2QksfUPLoboUW/1aeYV+UZ+AlMgzD6XgAAKAJVNdG9dzqnVr+1l7FE0lJ0oCifJUUF6lruxYOpzt3lBggA5kt2yow4+cKr3xc8fdXKbr2n0pWbZNvwrdkeNP3ty4AAOCz1YZjWrJml5at26NILCFJ6tW5lUqKi9SrcytnwzUgSgyQoQyXV74J31KsXU9FVj2h+M63VLtgjvxTvisrv4vT8QAAQAMKR+Natm6PlqzZpWAkLkkqbNdCJeOL1L8wL+POxqDEABnMMAx5+k6QVdBVoaUPyK4+qODCX8o37mty9xrrdDwAAHCOYvGElr+1T4tX71B1MCZJ6tg6SzPHFWlwz4KMKy8foMQAzYDVupuySuYotPz3SuzepPArf1DiwDZ5R39JhsvjdDwAAFBP8URSqzbt1zOrdujYyYgkqU0rv2aM66bhfdvKNDOzvHyAEgM0E4YvW/6pP1D0zUWKrl+oWPkrShzeIf+Um2W2aO10PAAAcAaSSVtrtlTp6ZWVOng8JEnKbeHV9DGFGjOgvVyW6XDCpkGJAZoRwzDlveByWW2KFHr5YSUP71Bt6Wz5J35Hrs7nOx0PAAB8Ctu29eZ7h7Vw5XbtPVwrScoJuHXpqEJNGNxBbpflcMKmRYkBmiFX5wF1p5cte1DJQ5UKPf9f8gyZLs+Qy2WYzeM3OAAApAPbtrV5x1EtKNuuyv0nJUkBr0tTR3TR5KGd5PM0z5fzzfO/GoDMFgUKTP+ZIqv/qti7Lyv65tNKHKyQb+J3ZPrSf388AADp7r3dx1Vatl3v7T4uSfK6LU0Z1klTh3dRwOd2NpzDKDFAM2ZYbvnGXiurTXeFV/6vEnveUbB0tvyTb5bVpsjpeAAANEs7D5xUadl2bdp+RJLkskxNHNJRl4zsqpwsFvJIlBgAkty9xsjM73JqDXOVgs/8h7yjvyR33wszdjUjAACpZu/hWi1cuV3rtx6SJFmmobHnt9e00YXKy/E5nC61UGIASJKs/M7KKpml8Ct/UHzHm4q8+rgSVRXyjbtWhsvrdDwAADLWweMhPb2yUq+/e0C2LRmSRvZvq8vHdlOb3IDT8VISJQbAaYYnIN+UWxTb+Lwia/+p+PurFDyyU/4p35XZsp3T8QAAyCjHTka06LUdWrlhnxJJW5I0pFdrzRjXTZ1aZzucLrVRYgB8hGEY8gy8RGbrbgq/NE/Jo3tUWzpHvgu/JXfhBU7HAwAg7VUHo1q8eqeWv7VXsXhSknRetzzNLC5St/Y5DqdLD5QYAJ/I1aGvAiVzFF72kBJV7yv84v1KDrxEnmFXyDCb1y56AAAaQjAc1wtrd+nFdbsViSYkST07tVRJcZF6d8l1OF16ocQA+FRmVq78036iyJp/KrbpBUU3LK5bwzzpRpmBVk7HAwAgLUSiCS1bv1tL1uxSbTguSeraroVKiot0Xrc8luicBUoMgM9kmC75Rl0jq213hVc8psT+rQqWzpZv8k1ytevldDwAAFJWLJ7UK2/v1XOrd6q6NipJ6lCQpZnjumlIr9aUl3NAiQFwRtxFw2XmdVJ46QNKHtun0KJfyzviKrkHXMQ3YQAA/kUimdSqTQe0aFWljlRHJEmtW/l0+dhuGtmvnUyTn5vnihID4IxZrTooMONOhcv+pHjF64q8/lclqt6Xb/w3ZXj8TscDAMBRSdvW2i1VenplpaqOhSRJuS28mja6UGPPby+XZTqcMHNQYgDUi+H2yTfxO4q17aHI6r8qXrlOwaN75Jtyi6y8jk7HAwCgydm2rbe3HdaCsu3ac6hWkpTtd+uyUV01YXBHedwsxGlo9Soxzz//vBYtWqTNmzfrxIkT6ty5s6655hpdffXVMk2aJdBcGIYhz3mTZbUuVGjZg0qeOKDgwjnyFV8nd4+RTscDAKBJ2Latd3ceU+mK7arcXy1J8ntdmjq8syYP7Sy/l+MFjaVeX9k//vGP6tChg2677Tbl5+drzZo1+tWvfqXdu3frJz/5SWNlBJCirLY96tYwvzRPiX1bFH75YSWqtsk78moZFt+4AQCZa9ueEyotq1D5ruOSJI/b1JShnXXx8C7K9rudDdcM1OtVxsMPP6y8vLzTH48cOVLBYFB//vOf9YMf/EAej6fBAwJIbaY/R/5LfqzoulJF335Wsc3LlDhUKf/km2Vm533+AwAAkEZ2VZ1Uadl2baw4IklyWYYmDOqoS0cXqmUWr4WbSr1KzL8WmA/07dtXkUhEx48fV5s2bRosGID0YZimvMO/KKttd4WWP6LkwQoFS2fJN+lGuTr2czoeAADnbP+RWi1YWal15QclSaZhaOz57TRtdDflt/Q5nK75OefzPdavX69WrVopPz//3IK4GvaaGuvU9geLLRBoYs159lzdL5C7YK5qX7hficO7FFr8W/mGXyHfkMtkGM3v69HUmvPswVnMHpzQVHN36FhIC1Zu16pN+2XbkiFpZP92mjm+SO3yAo363Ph0hm3b9tl+8qZNm3T11Vfr5ptv1k033XTWIWzb5n0mgAySjEV0eMkfVLPxZUlSoOdQtZ52iyx/tsPJAAA4M0erw/r70q16cc1OxRN1L5dH9G+nr3yhrwrb5zicDmddYg4dOqQrr7xSbdu21fz58+V2n/0FTIlEUtXVobP+/E9iWaZycvyqrg4pkUg26GMDn4XZ+1Dk3RUKrnxcSsRk5rRR1tRb5Cro6nSsjMXswSnMHpzQWHN3MhjVc6t3atkbuxWN1z1u/255+uKE7uresWWDPQ8+WU6O/4yOrp3V6WQnT57U9ddfL5/Pp3nz5p1TgflAPN443/QSiWSjPTbwWZg9yeo1ToG8zgotfUDJ6oM6+dRc+cZeK3fvcU5Hy2jMHpzC7MEJDTV3oUhcL6zdpRff2K1wNCFJ6tGxpUqKi9Sna66kxnu9ivqrd4mJRCK68cYbdfjwYf39739Xbm5uY+QCkCGsgkJlzZyt0PJHlNi9UeEV/6NE1fvyjv6KDBdbXAAAzorEEnr5zT1avHqnasNxSVKXNtkqGV+kAUX5XPKQoupVYuLxuP793/9d5eXleuKJJ9SxI+/ODeDzGb5s+ad+X9G3nlV03QLFysuUOLxT/snflZnT2ul4AIBmKJ5IasXb+/Tsazt0ojYqSWqfH9CMcUW6oHdrmZSXlFavEjN37lwtX75cP/7xjxUOh/X222+fvq9Hjx7KzuaiXQCfzDBMeYdMl9WmSOGXHlby8E7VLpgt/4XflqvLQKfjAQCaiUQyqdfeOaBnXt2hI9VhSVJBS58uH9tNI/u3lWWyZS8d1OvC/okTJ2rv3r2feN/jjz+uESNGnFWIRCKpo0drz+pzP43LZSo3N0vHjtVy/iKaFLP3+ZI1RxRa+qCSh7ZLkjxDpsszZIYMfnCcE2YPTmH24IT6zl3StrWu/KAWrqzUgaNBSVLLbI+mjS5U8cAOcrEiPCXk5WU1/IX9L7/88lkHAoAPmNn5Cky/XZHVf1Ps3ZcUffMZJaoq5Jt0g0xfC6fjAQAyiG3b2lBxRAvKtmv3wRpJUrbfrUtGdtWFQzrK67YcToizcc5vdgkAZ8Ow3PKN/aqstt0VLvuTEns3K/jULPnGXSur0/kclQEAnLMtO4+ptKxCFXurJUk+j6Wpw7toyrDO8nt5GZzO+L8HwFHunqNl5tetYbZPVCm05L9lZOfL3Wus3L3HyWxR4HREAECaqdh3QqUrtmvLzmOSJI/L1KQLOukLI7sq23/ubw0C51FiADjOyuusrJmzFVm3QLH3V8muOaLom08r+uYzsjr1l7tPsVxdB8uw+MEDAPh0uw/WaEHZdr297bAkyTINTRjUUZeO7qpW2V6H06EhUWIApATD45dv9JfkHf5FxXe8qVj5CiX2bVFizztK7HlHhq+FXD1Hy927WFYe690BAB86cDSohSu3a+2Wg5Ikw5DGnNde08cUqqCV3+F0aAyUGAApxXB55O4xUu4eI5WsPqjY1pWKbV0pO3hcsU0vKLbpBZlte8jde5zc3UfIcPucjgwAcMjBo0H977Pv6tWN+5U8tXB3eN82unxsN7XPz3I4HRpTvVYsNxZWLCOTMHsNz04mlNizSbHyMsV3bpDsRN0dbp/cRcPl7lMss033Zv+uyswenMLsoamdqInoudd36pW39imeqJu5QT0KNGNcN3Vpy5bLdNYoK5YBwAmGacnVZZBcXQYpGTyu2HuvKbZ1hewTVYptLVNsa5nM3I5y9y6Wq9do1jQDQIaqCcX0/JqdemndHkVPFeZ+hbmaOa5I3Tu2dDgdmhJHYoAGxuw1Ddu2lTjwXt3Rme1vSIlo3R2mJVfhELn7jJfVsZ8Mo/msamb24BRmD40tFIlr6Ru79cIbuxSK1B2N796xpb4xrb+6FASYuwzCkRgAGc0wDLna95arfW/ZY76s2LbXFSsvU/LwDsW3v6H49jfqVjX3Hle3qjk73+nIAIB6isYSevnNvVr8+k7VhGKSpE6ts1UyvkgX9G6tvLxsHTvWsL8IR3qgxABIe4YnIE+/ifL0m6jE4Z11p5i9v7puVfP6hYquf/rUqubxp1Y1860PAFJZPJHUyg37tOi1HTpeU3ekvW1eQDPHddPQPm1kGkazvw6yueMnOYCMYhV0lVXwVXlHXKX4jvWKlZd98qrmPsWyclnVDACpJJm0tXrzAT39aqUOnwhLkvJzfJo+tlCjz2sny2w+pwjjs1FiAGSkulXNo+TuMapuVXN5mWLvvfqxVc2e3sVydR/OqmYAcFDStvXm1kNasHK79h8JSpJaZnl02ehCFQ/sILeL8oKPosQAyHhmTht5h39RnqEzldi9SbHyFYrv2qBk1TaFq7ZJq/8id/fhcvcZL7N1EacoAEATsW1bm7YfUWnZdu2qqpEkZflcumRkV028oJO8bsvhhEhVlBgAzYZhWnJ1HSRX1w9WNa9SrLxMdnVV3ZGa8lOrmvsUy9WTVc0A0Ji27jqmp8q2a9ueE5Ikr8fSxcM666JhXRTw8RIVn40JAdAsmYFW8g66VJ6Blyixf6tiW+tWNSeP7VVk9V8VWfPPU6uai5vdqmYAaEyV+6tVuqJCm3cckyS5XaYmDemkL4zsohYBj8PpkC4oMQCaNcMw5OrQR64OfWSPPrWqeWuZkod3Kr59reLb155a1Vwsd++xrGoGgLO052CNFqzcrrfePyxJskxDxYM66LJRhcpt4XU4HdINJQYATjG8WfL0nyRP/0l1q5rLyxTb9tqpVc0LFF2/UFbnAXL3HseqZgA4Q1XHgnp6ZaXWvFslW5JhSKP7t9P0sd3UupXf6XhIU/wEBoBPYBV0lTX2q/KOvErxynV1q5r3lyuxe6MSuzfWrWruNUbu3sWycjs4HRcAUs7R6rCeWVWpVzceUNK2JUlD+7TRjLHd1KEgy+F0SHeUGAD4DIbLI3fP0XL3HK3kiSrFtq5UbOtK2aETim1cotjGJbLa9qxbBlA0XIabUyIANG8naqN67rUdeuXtvYon6srL+d3zNXNckbq2Y2EKGgYlBgDOkNmy7YermndtrFsGsGuDElXvK1H1vvTan+XuPuLUquZurGoG0KzUhmNasmaXlq7brWgsKUnq06WVSoq7q0enlg6nQ6ahxABAPRmmJVfhYLkKB59a1fzqqVXNBxUrX6FY+QqZeZ3qlgH0HC3Dl+10ZABoNKFIXMvW7daStbsVisQlSd3a56hkfJH6dc3lFzpoFJQYADgHdauaL/twVXN5meKV65Q8ukeR1X9RZM0/Tq1qHi+rY19WNQPIGLF4Qsvf3KvnXt+pk8GYJKlT6yzNLC7SoB4FlBc0KkoMADQAwzDl6tBXrg59ZUe+oti21YqVlyl5ZNeHq5pbFMjde5zcvcbJzM5zOjIAnJVk0lbZhn1a9NoOHTsZkSS1zfXr8nHdNLxvW5mUFzQBSgwANLC6Vc2T5ek/WYnDO06tal4t++RhRdedWtXcaUDdMoAug1jVDCCtLH9rr/689D1JUl6OV9PHdNOYAe1kmRxpRtPhJycANCKroFDW2MK6Vc3b1ym2tUyJ/Vs/XNXsz5Gr52i5+xTLasWqZgCpr2u7FurduZWG9G6tCYM6yu2ivKDpGbZ9anG3gxKJpI4erW3Qx3S5TOXmZunYsVrF48kGfWzgszB7+DzJEwdOrWp+VXboxOnbz3VVM7MHpzB7cAJzl5ny8rJkWZ9fjDkSAwBNzGzZTt7h/ybP0JmK79pY90aau//vquaRcvcpZlUzAACfgBIDAA4xTJfchUPkLhyiZO2xD1c1nzykWPkripW/Urequc94uXuMYlUzAACnUGIAIAWYWbnyDp4mz6BLT61qXvHhqubX/qzImr/LVXhB3armDn1Y1QwAaNYoMQCQQj66qrlWsfdXK7Z1hZJHditesUbxijUyWrQ+tap5LKuaAQDNEiUGAFKU4c2S57zJcvefpOThnYqVr1Bs2+uyTx5SdF2pousXyOp8vty9i+XqOlCSx+nIAAA0CUoMAKQ4wzBktS6U1bpQ3lFXf3RV864NSuzaIMOfI0/vscoaOVUyWzkdGQCARkWJAYA0Yri8cvcaI3evMUoe31+3qvm9V2WHqhV5e7H2vL1Yrva95eo9Tq5uw85qVTMAAKmO94kBGhizh6ZmJ+OK79qgxNaViu3aKNmn5s7tl7vHCLn7jJdZUMiqZjQavu/BCcxdZuJ9YgCgmahb1XyB/D2GqYUV1sE1LyqyZUXdquYtryi25RWZ+Z3l7l3MqmYAQEagxABABnHl5Ms/dLpcAy9RYl+5YuVliu9Yp+SR3f+yqnmo3H2KWdUMAEhblBgAyECGYcrVsZ9cHfvJDtcotm21YuVlSh7drXjF64pXvH56VbOn/yQZ3iynIwMAcMYoMQCQ4QxftjznTZG7/2QlD+/42Krm5OEd8l/0PadjAgBwxigxANBM1K1q7iardTd5R16jeOUbileul6twiNPRAACoF0oMADRDhtsrd6+xcvca63QUAADqjSs6AQAAAKQVSgwAAACAtEKJAQAAAJBWKDEAAAAA0golBgAAAEBaocQAAAAASCuUGAAAAABpxbBt23Y6hG3bSiYbPoZlmUokkg3+uMDnYfbgFGYPTmH24ATmLvOYpiHDMD73z6VEiQEAAACAM8XpZAAAAADSCiUGAAAAQFqhxAAAAABIK5QYAAAAAGmFEgMAAAAgrVBiAAAAAKQVSgwAAACAtEKJAQAAAJBWKDEAAAAA0golBgAAAEBaocQAAAAASCuUGAAAAABphRIDAAAAIK00yxLzzjvvqG/fvho8eLDTUZDhHnvsMc2YMUNDhw7VoEGDNG3aND3xxBOybdvpaMhgiURCjz76qL7yla9o5MiRGjZsmL785S9r9erVTkdDM7Bq1Sr96Ec/0uTJk9W7d2/NnTvX6UjIQJWVlfrmN7+pQYMGadSoUbrrrrsUDoedjoUm5HI6QFOzbVu//OUvlZeXp2Aw6HQcZLiTJ0/qsssuU8+ePeV2u7V69Wrdddddqqmp0Q033OB0PGSocDis3//+95oxY4a++c1vyuVyacGCBfrGN76hefPm6cILL3Q6IjJYWVmZtmzZomHDhunEiRNOx0EGqq6u1te+9jV16NBB9913n44ePaq7775bx48f17333ut0PDQRw25mvxJ+8skn9cgjj2jq1KmaP3++3nrrLacjoZn50Y9+pHfeeUcvvPCC01GQoRKJhGpqatSyZcvTt9m2rSuuuEJZWVmaP3++g+mQ6ZLJpEyz7kSPiRMnasKECbrzzjsdToVM8sgjj+ihhx7Syy+/rLy8PEnSokWLdOutt2rx4sXq3r27wwnRFJrV6WTV1dX63e9+p9tvv11ut9vpOGimcnNzFYvFnI6BDGZZ1kcKjCQZhqE+ffro4MGDDqVCc/FBgQEaS1lZmUaNGnW6wEjSxRdfLI/HoxUrVjiYDE2pWX2n+e///m/179+fUynQ5OLxuGpra/XKK69o4cKFuvbaa52OhGYmmUzqrbfe4jeUANJeRUXFx76XeTwedenSRRUVFQ6lQlNrNtfEbNmyRU8++aQWLFjgdBQ0Mzt37tRFF110+uMbb7xRX//6150LhGZp/vz5qqys5CJrAGmvurpaOTk5H7s9JyeH67CakbQtMSdPnjyj0yI6d+4st9utuXPn6ktf+hK/hcQ5qc/ceTweSVL79u315JNPKhgM6o033tCjjz4q0zT1ve99r7HjIoOczex9YO3atfrtb3+r6667TsOGDWusiMhQ5zJ7QFOybVuGYTgdA00kbUvM0qVLdfvtt3/un1u4cKG2b9+uiooK3XvvvaqurpYkRSIRSXVt3uv1yuv1NmpeZIb6zF3fvn0l1R3iHjBggCRpxIgRCgQCuvfee3XNNdeodevWjZoXmeNsZk+SysvLddNNN2ny5Mn68Y9/3JgRkaHOdvaAxpKTk3P69dy/OnnyJL+sbkbStsSUlJSopKTkjP7ssmXLdOLECU2cOPFj9w0bNkzXX3+9br311oaOiAxUn7n7NP3791cikdDevXspMThjZzN7u3bt0re+9S3169dP99xzD7+hxFlpiO97QEPq3r37x659iUaj2rVrl6644gqHUqGppW2JqY+ZM2dq+PDhH7ltwYIFWrx4sR599FF16NDBoWRojtavXy/DMNSpUyenoyCDHTp0SNddd50KCgr00EMPcZoPgIxRXFysefPm6dixY8rNzZVUd8QwGo1q/PjxDqdDU2kWJaZTp04fe8G4du1aWZalESNGOJQKme7kyZO6/vrrNX36dHXt2lXxeFyvv/665s+fr6uuukoFBQVOR0SGCofD+ta3vqUjR47opz/9qbZt2/aR+wcNGuRMMDQLe/fu1aZNmyRJoVBIu3bt0pIlSyRJU6dOdTIaMsTVV1+tJ554QjfddJNuuukmHTlyRL/+9a81bdo0TidrRprdm11+4P7779djjz3Gm12i0USjUc2aNUvr169XVVWVfD6funTpoquvvlozZsyQZVlOR0SG2rNnjyZNmvSp92/durUJ06C5KS0t/dRraJg9NJTKykrdddddWr9+vXw+ny677DLdeuut8vl8TkdDE2m2JQYAAABAempWb3YJAAAAIP1RYgAAAACkFUoMAAAAgLRCiQEAAACQVigxAAAAANIKJQYAAABAWqHEAAAAAEgrlBgAAAAAacXldAAAQPP1We/uft111+knP/lJEycCAKQDSgwAwHF33323ioqKPnJbmzZtHEoDAEh1lBgAgON69uypAQMGOB0DAJAmuCYGAAAAQFrhSAwAwHHJZFLxePwjt7lc/IgCAHwyfkIAABx35ZVXfuy2zZs3U2QAAJ+Inw4AAMf95je/Uffu3T9yGwUGAPBp+AkBAHBc9+7dubAfAHDGuLAfAAAAQFqhxAAAAABIK5QYAAAAAGmFEgMAAAAgrRi2bdtOhwAAAACAM8WRGAAAAABphRIDAAAAIK1QYgAAAACkFUoMAAAAgLRCiQEAAACQVigxAAAAANIKJQYAAABAWqHEAAAAAEgrlBgAAAAAaYUSAwAAACCtUGIAAAAApJX/D0Pfazi7O8GVAAAAAElFTkSuQmCC", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_pivot.plot(figsize=(10,3));" ] @@ -2641,7 +7433,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "exercise": "task", "slideshow": { "slide_type": "slide" @@ -2661,16 +7452,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 231, "metadata": { - "editable": true, "exercise": "solution", "slideshow": { "slide_type": "fragment" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1200x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df.pivot_table(\n", " index=\"Nodes\",\n", @@ -2682,7 +7483,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "exercise": "task", "slideshow": { "slide_type": "subslide" @@ -2703,7 +7503,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "slide" }, @@ -2723,7 +7522,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "subslide" }, @@ -2736,9 +7534,8 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 232, "metadata": { - "editable": true, "slideshow": { "slide_type": "" }, @@ -2751,30 +7548,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 233, "metadata": { - "editable": true, "slideshow": { "slide_type": "fragment" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 ms ± 239 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": null, + "execution_count": 234, "metadata": { - "editable": true, "slideshow": { "slide_type": "fragment" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'pyarrow'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[234], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpyarrow\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(pyarrow\u001b[38;5;241m.\u001b[39m__version__)\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pyarrow'" + ] + } + ], "source": [ "import pyarrow\n", "print(pyarrow.__version__)" @@ -2809,7 +7624,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "slide" }, @@ -2827,7 +7641,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "exercise": "task", "slideshow": { "slide_type": "slide" @@ -2845,7 +7658,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "exercise": "task", "slideshow": { "slide_type": "fragment" @@ -2870,7 +7682,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "exercise": "solution", "slideshow": { "slide_type": "subslide" @@ -2899,7 +7710,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "exercise": "solution", "slideshow": { "slide_type": "subslide" @@ -2938,7 +7748,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "exercise": "task", "slideshow": { "slide_type": "slide" @@ -2959,7 +7768,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "slide" }, @@ -3014,7 +7822,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "slide" }, @@ -3037,7 +7844,7 @@ ], "metadata": { "kernelspec": { - "display_name": "venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/Introduction-to-Pandas--slides.html b/Introduction-to-Pandas--slides.html index 2683dd50eda3f04f05991290964d3a8de2dc26d7..0da8642a16f8a37b351cd391e9de95fbcc84cad5 100644 --- a/Introduction-to-Pandas--slides.html +++ b/Introduction-to-Pandas--slides.html @@ -14806,7 +14806,7 @@ div.jp-OutputPrompt { <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 [2]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [118]:</div> <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> <div class="CodeMirror cm-s-jupyter"> <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> @@ -14822,7 +14822,7 @@ div.jp-OutputPrompt { <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 [12]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [119]:</div> <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> <div class="CodeMirror cm-s-jupyter"> <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> @@ -14833,12 +14833,12 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [120]:</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> @@ -14849,12 +14849,35 @@ div.jp-OutputPrompt { </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[120]:</div> + + + + +<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain"> +<pre>'2.1.4'</pre> +</div> + +</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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [121]:</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 @@ -14924,7 +14947,7 @@ div.jp-OutputPrompt { <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 [5]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [122]:</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> @@ -14935,12 +14958,12 @@ div.jp-OutputPrompt { </div> </div> -</div></div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div></div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [123]:</div> <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> <div class="CodeMirror cm-s-jupyter"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ages</span><span class="p">)</span> @@ -14951,12 +14974,98 @@ div.jp-OutputPrompt { </div> </div> -</div></div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[123]:</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>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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [124]:</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> @@ -14968,6 +15077,64 @@ div.jp-OutputPrompt { </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[124]:</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>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"> @@ -14982,12 +15149,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [125]:</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> @@ -15002,12 +15169,33 @@ div.jp-OutputPrompt { </div> </div> -</div></div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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>{'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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [126]:</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> @@ -15019,6 +15207,73 @@ div.jp-OutputPrompt { </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[126]:</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>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"> @@ -15034,12 +15289,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [127]:</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> @@ -15050,6 +15305,29 @@ div.jp-OutputPrompt { </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[127]:</div> + + + + +<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain"> +<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"> @@ -15065,12 +15343,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [128]:</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> @@ -15081,6 +15359,29 @@ div.jp-OutputPrompt { </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[128]:</div> + + + + +<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain"> +<pre>RangeIndex(start=0, stop=10, step=1)</pre> +</div> + +</div> + +</div> + +</div> + </div><div class="fragment" > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -15096,12 +15397,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div></div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div></div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [129]:</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">"Name"</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> @@ -15113,6 +15414,96 @@ div.jp-OutputPrompt { </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[129]:</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>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"> @@ -15127,12 +15518,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [130]:</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> @@ -15143,12 +15534,90 @@ div.jp-OutputPrompt { </div> </div> -</div></div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[130]:</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>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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [131]:</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> @@ -15159,12 +15628,40 @@ div.jp-OutputPrompt { </div> </div> -</div></div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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><class 'pandas.core.frame.DataFrame'> +Index: 10 entries, Liu to Hall +Data columns (total 1 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Age 10 non-null int64 +dtypes: int64(1) +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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [132]:</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> @@ -15175,12 +15672,80 @@ div.jp-OutputPrompt { </div> </div> -</div></div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[132]:</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>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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [133]:</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> @@ -15191,6 +15756,31 @@ div.jp-OutputPrompt { </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[133]:</div> + + + + +<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain"> +<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"> @@ -15205,12 +15795,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [134]:</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> @@ -15221,31 +15811,77 @@ div.jp-OutputPrompt { </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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> -<div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> -<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> - <div class="CodeMirror cm-s-jupyter"> -<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_sample</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> -</pre></div> - </div> + +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child jp-OutputArea-executeResult"> + + <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[134]:</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>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 class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div> + +</div></div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [135]:</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=" 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> @@ -15253,12 +15889,152 @@ div.jp-OutputPrompt { </div> </div> -</div></div></section><section ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> -<div class="jp-Cell-inputWrapper"> -<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> </div> -<div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> + + +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child jp-OutputArea-executeResult"> + + <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[135]:</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>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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [136]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> + <div class="CodeMirror cm-s-jupyter"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="p">(</span><span class="n">df_sample</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> +</div> + +<div 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[136]:</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>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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [137]:</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> @@ -15269,12 +16045,74 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[137]:</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>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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [138]:</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">-></span> <span class="nb">float</span><span class="p">:</span> @@ -15289,12 +16127,82 @@ div.jp-OutputPrompt { </div> </div> -</div></div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[138]:</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>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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [140]:</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> @@ -15305,6 +16213,76 @@ div.jp-OutputPrompt { </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[140]:</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>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"> @@ -15317,12 +16295,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [141]:</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="mi">40</span> @@ -15333,12 +16311,102 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[141]:</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>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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [142]:</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> @@ -15349,6 +16417,76 @@ div.jp-OutputPrompt { </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[142]:</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>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"> @@ -15377,7 +16515,7 @@ div.jp-OutputPrompt { <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 [26]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [143]:</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> @@ -15393,12 +16531,12 @@ div.jp-OutputPrompt { </div> </div> -</div></div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div></div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [144]:</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> @@ -15415,6 +16553,75 @@ div.jp-OutputPrompt { </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[144]:</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>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"> @@ -15426,12 +16633,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [145]:</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> @@ -15449,12 +16656,102 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[145]:</div> + + + +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html"> +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>A</th> + <th>B</th> + <th>C</th> + <th>D</th> + <th>E</th> + </tr> + </thead> + <tbody> + <tr> + <th>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> + +</div> + +</div> + +</div> + +</div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [146]:</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">"C"</span><span class="p">)</span> @@ -15465,12 +16762,102 @@ div.jp-OutputPrompt { </div> </div> -</div></div></section><section ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[146]:</div> + + + +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html"> +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>A</th> + <th>B</th> + <th>C</th> + <th>D</th> + <th>E</th> + </tr> + </thead> + <tbody> + <tr> + <th>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> + +</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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [147]:</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> @@ -15481,12 +16868,78 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[147]:</div> + + + +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html"> +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>A</th> + <th>B</th> + <th>C</th> + <th>D</th> + <th>E</th> + </tr> + </thead> + <tbody> + <tr> + <th>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> + +</div> + +</div> + +</div> + +</div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [148]:</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="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> @@ -15497,12 +16950,37 @@ div.jp-OutputPrompt { </div> </div> -</div></div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[148]:</div> + + + + +<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain"> +<pre>A 6.00 +C -2.03 +dtype: float64</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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [149]:</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> @@ -15513,6 +16991,38 @@ div.jp-OutputPrompt { </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>\begin{tabular}{lrlrll} +\toprule + & A & B & C & D & E \\ +\midrule +0 & 1.200000 & 2018-02-26 00:00:00 & -2.720000 & This & Same \\ +1 & 1.200000 & 2018-02-26 00:00:00 & 1.720000 & column & Same \\ +2 & 1.200000 & 2018-02-26 00:00:00 & -1.300000 & has & Same \\ +3 & 1.200000 & 2018-02-26 00:00:00 & 0.990000 & entries & Same \\ +4 & 1.200000 & 2018-02-26 00:00:00 & -0.720000 & entries & Same \\ +\bottomrule +\end{tabular} + +</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"> @@ -15538,12 +17048,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [150]:</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">"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> @@ -15554,6 +17064,88 @@ div.jp-OutputPrompt { </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[150]:</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>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"> @@ -15573,12 +17165,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [151]:</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<span class="w"> </span>data-nest.csv @@ -15589,12 +17181,42 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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>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 +5,1,2,8,202.15,10,true,0.28,47.98,48.48,0.70,1.20,7.95,142.81,47699384.00,802865,7.03,112500,1265738500,1.5,1.5 +5,1,4,8,89.57,10,true,0.15,20.41,23.21,0.23,3.04,3.19,60.31,46813040.00,821491,7.23,112500,1265738500,1.5,1.5 +5,2,2,4,164.16,10,true,0.20,40.03,41.09,0.52,1.58,6.08,114.88,46937216.00,802865,7.03,112500,1265738500,1.5,1.5 +5,2,4,4,77.68,10,true,0.13,20.93,21.22,0.16,0.46,3.12,52.05,47362064.00,821491,7.23,112500,1265738500,1.5,1.5 +5,2,2,8,79.60,10,true,0.20,21.63,21.91,0.19,0.47,2.98,53.12,46847168.00,821491,7.23,112500,1265738500,1.5,1.5 +5,2,4,8,37.20,10,true,0.13,10.08,11.60,0.10,1.63,1.24,23.29,47065232.00,818198,7.33,112500,1265738500,1.5,1.5 +5,3,2,4,96.51,10,true,0.15,26.54,27.41,0.36,1.22,3.33,64.28,52256880.00,813743,7.27,112500,1265738500,1.5,1.5 +</pre> +</div> +</div> + +</div> + +</div> + +</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell "> <div class="jp-Cell-inputWrapper"> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [152]:</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">"data-nest.csv"</span><span class="p">)</span> @@ -15606,6 +17228,193 @@ div.jp-OutputPrompt { </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[152]:</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>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"> @@ -15668,12 +17477,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [153]:</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> @@ -15684,12 +17493,86 @@ div.jp-OutputPrompt { </div> </div> -</div></div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[153]:</div> + + + +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html"> +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>A</th> + <th>B</th> + <th>C</th> + <th>D</th> + <th>E</th> + </tr> + </thead> + <tbody> + <tr> + <th>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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [154]:</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">'C'</span><span class="p">]</span> @@ -15700,6 +17583,34 @@ div.jp-OutputPrompt { </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[154]:</div> + + + + +<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain"> +<pre>0 -2.718282 +1 1.718282 +2 -1.304068 +3 0.986231 +4 -0.718282 +Name: C, dtype: float64</pre> +</div> + +</div> + +</div> + +</div> + </div></div></section><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -15714,12 +17625,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [155]:</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> @@ -15730,6 +17641,34 @@ div.jp-OutputPrompt { </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[155]:</div> + + + + +<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain"> +<pre>0 -2.718282 +1 1.718282 +2 -1.304068 +3 0.986231 +4 -0.718282 +Name: C, dtype: float64</pre> +</div> + +</div> + +</div> + +</div> + </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -15760,12 +17699,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [156]:</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">'A'</span><span class="p">,</span> <span class="s1">'C'</span><span class="p">]</span> @@ -15777,6 +17716,78 @@ div.jp-OutputPrompt { </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[156]:</div> + + + +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html"> +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>A</th> + <th>C</th> + </tr> + </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"> @@ -15792,12 +17803,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [157]:</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> @@ -15808,12 +17819,78 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[157]:</div> + + + +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html"> +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>A</th> + <th>B</th> + <th>C</th> + <th>D</th> + <th>E</th> + </tr> + </thead> + <tbody> + <tr> + <th>1</th> + <td>1.2</td> + <td>2018-02-26</td> + <td>1.718282</td> + <td>column</td> + <td>Same</td> + </tr> + <tr> + <th>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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [158]:</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> @@ -15824,6 +17901,72 @@ div.jp-OutputPrompt { </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[158]:</div> + + + +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html"> +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>A</th> + <th>B</th> + <th>C</th> + <th>D</th> + <th>E</th> + </tr> + </thead> + <tbody> + <tr> + <th>1</th> + <td>1.2</td> + <td>2018-02-26</td> + <td>1.718282</td> + <td>column</td> + <td>Same</td> + </tr> + <tr> + <th>3</th> + <td>1.2</td> + <td>2018-02-26</td> + <td>0.986231</td> + <td>entries</td> + <td>Same</td> + </tr> + </tbody> +</table> +</div> +</div> + +</div> + +</div> + +</div> + </div></div></section><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -15838,12 +17981,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [159]:</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> @@ -15854,12 +17997,78 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[159]:</div> + + + +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html"> +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>A</th> + <th>B</th> + <th>C</th> + <th>D</th> + <th>E</th> + </tr> + </thead> + <tbody> + <tr> + <th>1</th> + <td>1.2</td> + <td>2018-02-26</td> + <td>1.718282</td> + <td>column</td> + <td>Same</td> + </tr> + <tr> + <th>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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [160]:</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">"C"</span><span class="p">)[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span> @@ -15870,6 +18079,72 @@ div.jp-OutputPrompt { </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[160]:</div> + + + +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html"> +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>A</th> + <th>B</th> + <th>C</th> + <th>D</th> + <th>E</th> + </tr> + </thead> + <tbody> + <tr> + <th>2</th> + <td>1.2</td> + <td>2018-02-26</td> + <td>-1.304068</td> + <td>has</td> + <td>Same</td> + </tr> + <tr> + <th>4</th> + <td>1.2</td> + <td>2018-02-26</td> + <td>-0.718282</td> + <td>entries</td> + <td>Same</td> + </tr> + </tbody> +</table> +</div> +</div> + +</div> + +</div> + +</div> + </div></section><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -15884,20 +18159,86 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> -<div class="jp-Cell-inputWrapper"> -<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div><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 [161]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> + <div class="CodeMirror cm-s-jupyter"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span> +</pre></div> + + </div> +</div> +</div> +</div> + +<div 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[161]:</div> + + + +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html"> +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>A</th> + <th>B</th> + <th>C</th> + <th>D</th> + <th>E</th> + </tr> + </thead> + <tbody> + <tr> + <th>1</th> + <td>1.2</td> + <td>2018-02-26</td> + <td>1.718282</td> + <td>column</td> + <td>Same</td> + </tr> + <tr> + <th>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 class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> -<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> - <div class="CodeMirror cm-s-jupyter"> -<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span> -</pre></div> - </div> </div> + </div> + </div> </div><div class="fragment" > @@ -15914,12 +18255,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [162]:</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> @@ -15930,6 +18271,63 @@ div.jp-OutputPrompt { </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[162]:</div> + + + +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html"> +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>A</th> + <th>C</th> + </tr> + </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"> @@ -15946,12 +18344,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [163]:</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">"D"</span><span class="p">)</span> @@ -15963,12 +18361,103 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[163]:</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>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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [164]:</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">"entries"</span><span class="p">]</span> @@ -15979,12 +18468,82 @@ div.jp-OutputPrompt { </div> </div> -</div></div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[164]:</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>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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [165]:</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">"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> @@ -15995,6 +18554,73 @@ div.jp-OutputPrompt { </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[165]:</div> + + + +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html"> +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>A</th> + <th>C</th> + </tr> + <tr> + <th>D</th> + <th></th> + <th></th> + </tr> + </thead> + <tbody> + <tr> + <th>has</th> + <td>1.2</td> + <td>-1.304068</td> + </tr> + <tr> + <th>entries</th> + <td>1.2</td> + <td>0.986231</td> + </tr> + <tr> + <th>entries</th> + <td>1.2</td> + <td>-0.718282</td> + </tr> + </tbody> +</table> +</div> +</div> + +</div> + +</div> + +</div> + </div></div></section><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -16020,12 +18646,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [166]:</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">"C"</span><span class="p">]</span> <span class="o">></span> <span class="mi">0</span><span class="p">]</span> @@ -16036,12 +18662,78 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[166]:</div> + + + +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html"> +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>A</th> + <th>B</th> + <th>C</th> + <th>D</th> + <th>E</th> + </tr> + </thead> + <tbody> + <tr> + <th>1</th> + <td>1.2</td> + <td>2018-02-26</td> + <td>1.718282</td> + <td>column</td> + <td>Same</td> + </tr> + <tr> + <th>3</th> + <td>1.2</td> + <td>2018-02-26</td> + <td>0.986231</td> + <td>entries</td> + <td>Same</td> + </tr> + </tbody> +</table> +</div> +</div> + +</div> + +</div> + +</div> + +</div><div 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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [167]:</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">"C"</span><span class="p">]</span> <span class="o">></span> <span class="mi">0</span> @@ -16052,12 +18744,40 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[167]:</div> + + + + +<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain"> +<pre>0 False +1 True +2 False +3 True +4 False +Name: C, dtype: bool</pre> +</div> + +</div> + +</div> + +</div> + +</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell "> <div class="jp-Cell-inputWrapper"> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [168]:</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">"C"</span><span class="p">]</span> <span class="o"><</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&</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> @@ -16068,6 +18788,64 @@ div.jp-OutputPrompt { </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[168]:</div> + + + +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html"> +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>A</th> + <th>B</th> + <th>C</th> + <th>D</th> + <th>E</th> + </tr> + </thead> + <tbody> + <tr> + <th>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"> @@ -16089,12 +18867,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [169]:</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> @@ -16105,12 +18883,86 @@ div.jp-OutputPrompt { </div> </div> -</div></div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[169]:</div> + + + +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html"> +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>A</th> + <th>B</th> + <th>C</th> + <th>D</th> + <th>E</th> + </tr> + </thead> + <tbody> + <tr> + <th>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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [170]:</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">"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> @@ -16122,6 +18974,84 @@ div.jp-OutputPrompt { </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[170]:</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>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"> @@ -16137,12 +19067,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [171]:</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">"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> @@ -16154,12 +19084,94 @@ div.jp-OutputPrompt { </div> </div> -</div></div></section><section ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[171]:</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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [172]:</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> @@ -16170,6 +19182,88 @@ div.jp-OutputPrompt { </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[172]:</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>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></section><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -16184,16 +19278,90 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><div class="fragment" ><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 [173]:</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">"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 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[173]:</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>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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [174]:</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">"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> +<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> @@ -16201,21 +19369,61 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> -<div class="jp-Cell-inputWrapper"> -<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +<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[174]:</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>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 class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</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">"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></section><section > @@ -16232,12 +19440,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [175]:</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> @@ -16248,6 +19456,73 @@ div.jp-OutputPrompt { </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[175]:</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>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"> @@ -16262,12 +19537,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [176]:</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> @@ -16278,6 +19553,73 @@ div.jp-OutputPrompt { </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[176]:</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>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"> @@ -16292,12 +19634,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [177]:</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> @@ -16308,6 +19650,69 @@ div.jp-OutputPrompt { </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[177]:</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>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"> @@ -16322,12 +19727,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [178]:</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">"Key"</span><span class="p">)</span> @@ -16338,6 +19743,66 @@ div.jp-OutputPrompt { </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[178]:</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>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"> @@ -16350,12 +19815,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [179]:</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> @@ -16371,6 +19836,118 @@ div.jp-OutputPrompt { </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[179]:</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> + <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"> @@ -16388,12 +19965,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [180]:</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">"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> @@ -16405,12 +19982,199 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[180]:</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>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> +</div> + +</div> + +</div> + +</div> + +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [181]:</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> @@ -16421,6 +20185,36 @@ div.jp-OutputPrompt { </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[181]:</div> + + + + +<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain"> +<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> +</div> + +</div> + +</div> + +</div> + </div></div></section></section><section ><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -16445,7 +20239,7 @@ div.jp-OutputPrompt { <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 [65]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [182]:</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> @@ -16462,7 +20256,7 @@ div.jp-OutputPrompt { <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 [66]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [183]:</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> @@ -16474,12 +20268,12 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [184]:</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> @@ -16494,6 +20288,45 @@ div.jp-OutputPrompt { </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><>:5: SyntaxWarning: invalid escape sequence '\s' +<>:5: SyntaxWarning: invalid escape sequence '\s' +/tmp/ipykernel_106956/3587136147.py:5: SyntaxWarning: invalid escape sequence '\s' + ax.set_ylabel("$\sqrt{x}$"); +</pre> +</div> +</div> +<div class="jp-OutputArea-child"> + + <div class="jp-OutputPrompt jp-OutputArea-prompt"></div> + + + + +<div class="jp-RenderedImage jp-OutputArea-output "> +<img 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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + </div></div></section><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -16514,7 +20347,7 @@ div.jp-OutputPrompt { <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 [68]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [185]:</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> @@ -16525,12 +20358,12 @@ div.jp-OutputPrompt { </div> 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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + </div></section><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -16560,23 +20419,49 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</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">"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">"Nope, no sense at all"</span><span class="p">);</span> -</pre></div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [187]:</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">"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">"Nope, no sense at all"</span><span class="p">);</span> +</pre></div> + + </div> +</div> +</div> +</div> + +<div 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4rrcfDMFanLISJSrP/7v/cREhKK++57qM+Pz5gxy6nHZzght5GTV939/zfvL4F1ELsbEhF5OovFgtOnT2HixMnQaKRpsLCtQ26hw2LFycIaAIBGrUJVvRFH8qqRPXqYxJURkScTRRHmDun+UPL2Ug14ea/B0ASTyYSIiEgnVXVjDCfkFs4U16PNZEVIoBZLp4/AX3fmY+uBEkxJj4RK5dn7DhCRNERRxAt/PY7CiibJakiKCcKz3x8/oIAiip3b1ku5ZwvbOuQWcvI7WzqTRkVixcyR8PPR4HKdEUcLqm/wSiIiJ1Lg30ZBQcHQarWoqpLu2j3OnJDimTusOHGhFgAwJT0Sfj5eWDw5Dp/svYgt+0swMS0CKg/ftZGIXE8QBDz7/fGKa+toNBpkZo7F0aNHYLFYJLnuhDMnpHhniuthMlsRqtMiMbrzhlsLJ8fCV6tGRW0rjhfUSFwhEXkqQRCg9VZL9t9gWzP/8R9rUF9fhw0b3uzz4/v37xvKp+WGOHNCimdv6UxMi+j+RvT38cKCCbHYcqAEm/eXYHxqOGdPiIj6KTt7Ou666168997bKC0txoIFi6DXh+DKlcvYtWs7ysvLMH36TKcdn+GEFM3cYcXJws6WzqS03leWL5wUi8+PluNSTQtOXajFuJRwKUokIlKkBx9chzFjMvHxx3/Hb3/7PIzGVoSFhWPSpCl47LEfOvXYDCekaKcv2ls6PhgxPLDXxwJ8vbBgQgy2HSzF5v0lGJsc5vF3DCUiGohp02Zg2rQZLj8urzkhRcvJrwIATOrR0unppkmx0HqpUVrVjNyiOleXR0REg8BwQopl6rDiVGFn4JiUHtHncwL9vDFvfDQAYPP+4u71+0REJF8MJ6RYp4vqYOqwIizIBwnDAq/5vEWT4+CtUaH4cjPOFNe7sEIiIhoMhhNSrO6N167R0rHT+Xtjzriu2ZNvOHtCRCR3DCekSCazFaeKulbpXKOl09PiKXHw0qhQVGnAudIGZ5dHRERDwHBCipR7sQ7mDhvCg30QH3ntlo5dcIAWs7OiAHD2hIicx9N/tjhq/AwnpEg5efZVOpH9Xh68ZGo8NGoBFy41oaCs0YnVEZGnUavVAACz2SRxJdKyj1+tHtpOJdznhBTHZLZ2LwuelHbjlo6dPlCLmVlR+Op4BTbvL0ZavN5ZJRKRh1Gp1PD1DUBLS2fb2Ntbq+h9lWw2AVZr/2dBRFGE2WxCS0sDfH0DoFINbe6D4YQU51RRLcwWGyL0voiLDBjQa5dOicfek5XIL2vE+fJGpMQGO6dIIvI4Ol0IAHQHFCVTqVSw2QZ+w0Jf34Duz8NQMJyQ4vR3lU5fQoN8MCNzOPacrMSW/cX40R3jnFEiEXkgQRAQFBSKwEA9rFaL1OUMmlotICjID01NxgHNnqjVmiHPmNgxnJCitJstg2rp9LRsajy+yb2MsyUNKKxoQlJ0kCNLJCIPp1KpoFJ5S13GoGk0Kvj4+KCtzQqLZeCzJ47AC2JJUU4V1qHDYkOk3hexEQNr6diFBfsie8wwAMCW/SUOrI6IiByB4YQUpbulkz7wlk5Py7PjoRIEnL5Yh+LLBkeVR0REDsBwQorRZurZ0okc0ntF6P2QPbrzPTh7QkQkLwwnpBinCmthsdowLMQPMeH+Q36/ZdMSIAjAycJalF5pdkCFRETkCAwnpBhDWaXTl2EhfpgyqnP2ZPP+4iG/HxEROQbDCSlCm8mC0xc77yjcn3vp9Nfy7AQIAE5cqEVZFWdPiIjkgOGEFOFkV0tneKgfosOG3tKxiwrz7w47Ww+UOOx9iYho8BhOSBFy8hzb0ulp+bQEAMDRghpcqmlx6HsTEdHAMZyQ7BnbLThTPLSN164nJjwAE1LDAXD2hIhIDhhOSPZOFtbAYhURFeaP6PDBbbx2Iyu6Zk9y8qpxua7VKccgIqL+YTgh2evZ0nGWuMhAjEsOgwjOnhARSY3hhGTN2N6BM8Wdq3QmOjGcAMCK6QkAgEPnqlBVb3TqsYiI6NoYTkjWTlyohdUmIjrc36GrdPqSMEyHzMRQiCKw9WCJU49FRETXxnBCstZz4zVXuHn6CADAwTNVqG5sc8kxiYioN4YTkq3W9g6c7WrpuCqcjIzSYczIENhEEds5e0JEJAmGE5KtE+c7Wzox4f4YHurclk5P9tmT/aevoLaJsydERK7GcEKy5eqWjl1SdBBGJehhtYnYfrDUpccmIiKGE5KplrYOnCtxzSqdvthnT/blXka9od3lxyci8mQMJyRLJ87XwGoTERsR4NKWjl1KbDDS4oI7Z08OcfaEiMiVGE5IlqRq6fS0omv2ZO+pSjQ0mySrg4jI0zCckOx0tnQaAEgbTtLigpEcEwSLVcSOw5w9ISJyFYYTkp3j52tgE0XERQYgMsRPsjoEQei+9mTPyUo0tXD2hIjIFRhOSHbk0NKxG5WgR2KUDh0WG3YeKZO6HCIij8BwQrLSbDQjTwYtHTtBELqvPfnqRAUMrWaJKyIicn8MJyQr9pZOfGQgIvTStXR6yhgZgoRhgTB32LArh7MnRETOxnBCstLd0kmXftbErue1J/86VoFmI2dPiIicieGEZMNgNCOvtLOlI8XGa9eTlRSKuMgAmDqs2H20XOpyiIjcGsMJycbxghqIIpAwLBARwb5Sl9NLz9mTL45eQmt7h8QVERG5L6eHk9LSUjz33HNYuXIlRo0aheXLlzv7kKRQcmzp9DQ2OQwx4QFoN1uxO4ezJ0REzuL0cHLhwgXs2bMH8fHxSExMdPbhSKGaWs3IL+tapZMqz3CiEgTcPD0BALD76CUYOXtCROQUTg8n8+bNw549e7B+/XqMHj3a2YcjhTpeUA1RBEYM1yFMZi2dnsanhiMqzB9tJgu+OHZJ6nKIiNyS08OJSsXLWujG5LTx2vWoBAErpiUAAHbnlKPNZJG2ICIiN6SRuoCh0GgcG3zUalWv/3VHchxjY4sJBeWNAICpoyOHdF5dMb7sMcOweX8xLtcZ8fXJiu5N2lxBjufP0dx9jByf8rn7GOUwPsWGE5VKgF7v75T31unk21ZwFDmN8cC5zpZOapweySPCHPKezh7f9xal4fcfHMfOw+VYvTANvlrXfivJ6fw5i7uP0V3HV11vxK4vz2Ph5Hjo9e45Rjt3PYd2Uo5PseHEZhNhMBgd+p5qtQo6nS8MhjZYrTaHvrdcyHGMXx/t3HV1fEoYGhpah/RerhpfRkIwIvW+qGpowz++LMCy7ASnHasnOZ4/R3P3Mbrz+DosNvzPW4dRWduKvccv4b/umgAfb8X+mrkmdz6HgHPHp9P59mtGRtFfNRaLc74orFab095bLuQyxsYWEwrKGgEA45PDHVaTK8a3LDsB72zPw/aDpZgzNhpaL7VTj9eTXM6fM7n7GN1xfJ/tvYjK2s4/MEquNOPVj0/j8dsyoHbTaw/d8Rz2JOX43PMrhhTjWEENRACJ0TqEBvlIXc6ATB0dibAgHzQbO7DnRIXU5RBJqqyqGdsPlQIAVs9PhrdGhdMX6/DXz89DFEWJqyOlYTghSeXkVQEAJqVFSlzJwGnUKizvWrmz43AZzB1WaQsikojVZsOGHfmw2kRMSA3HnUvS8cgtYyAA2HOysju0EPWX08NJW1sbdu7ciZ07d6KiogItLS3d/66vr3f24UnGGppNuHCpCQAwMTVc4moGZ9qYYQjVadHUasbeU5VSl0Mkic9zylF6pRl+Wg3uXpwGQRAwITUC31uQDAD4x56LOHT2isRVkpI4/ZqTuro6PPHEE70es/9748aNmDJlirNLIJk6WlANEUBSTBBCdMpq6dhp1Cosy07Axl0F2H6oFLPHRsFL47prT4ikdqXeiM/2FQMAvjs/CcGB2u6PLZgYi9qmdnyeU453tudBH6hFapxeqlJJQZweTmJiYlBQUODsw5ACHVXIxms3Mj1jOLYcKEFDswnf5F7G3PExUpdE5BI2UcS7O/LRYbFhdIIeMzKGX/Wc2+cloc7QjmMFNXj1H6fxX3dOQFSYc7aBIPfBa05IEvaWjgBgokzvpdNfXhoVlk6NBwBsO1QKixsuLSTqy54TFThf3gitl7q7nfNtKkHAA8tHITFaB6PJgpc/OoWmFpME1ZKSMJyQJOyzJkkxQdD3mAZWqllZwxEU4I16gwnfnL4sdTlETlfX1I6Pvi4CAKyaPfK698Ty9lLjsVszEaH3RZ2hHX/4OBcmMy8gp2tjOCFJKOVeOv3lpVFj6ZSu2ZMDnD0h9yaKIjbuKoDJbEVSdBDm96OVqfPzxlO3ZyHA1wulV5rx53+egdXG7xPqG8MJuVy9oR2FFZ0tnQkKb+n0NGtsFHT+3qgztOPgGa5MIPd16GwVTl+sg0Yt4J4laVCprm7n9CVS74fHb8uEl0aFU0V1+GD3Be6BQn1iOCGXs7d0kmOD3aKlY6f1UmPx5DgAwNaDJfyrkNySodWMD744DwBYMX3EgC9uTYoOwgPLR0EA8NWJCuw8UuaEKknpGE7I5dytpdPT3HHRCPD1Qk1jOw6drZK6HCKH++CL82httyA2IgBLpsQN6j0mpkXgu/OSAACbvirCkTx+r1BvDCfkUrVNbSiqNHSt0lHmxmvXo/VWY3HXD+ytB0pgs3HKmtzHifM1OJJXDZUg4N6l6dD04wZu17JwUiwWTOi8VuWtredwvrzRQVWSO2A4IZc6ml8DAEiNC0ZQgPu0dHqaOy4a/j4aVDW08S9CchvG9g5s/Lxzz6pFU2IRPyxwSO8nCALumJ+McclhsFhFvPqPXFyuG9pdycl9MJyQSx0tcN+Wjp2vVoObuq492cLZE3ITH/6rEE0tZkSG+GHl9BEOeU+VSsCDN4/GiOE6tLZ37YHSanbIe5OyMZyQy9Q2tuFipQGCAIx3o1U6fZk/PgZ+Wg0u1xm7AxmRUp0rqce+3M79e9YuSYO3l+Nu0aD1UuOJ2zIRHuyD2qZ2rP84FybeRNPjMZyQyxwt6GrpxAYjyN9b4mqcy89Hg5smxQLomj3hcklSKJPZind35AMA5o2PRkpssMOPofP3xpOrs+Dvo0HxZQP+svksZxw9HMMJuUxOfuf1F5PSIyWuxDUWTIyBr1aNippWnDhfI3U5RIPyyd6LqG1qR4hOi1tnJzrtOMND/fHYrZnQqFU4caEWf/uSe6B4MoYTcomaxjYUX26GIAATUtxvlU5f/Hy8sGBC5+zJ5v0l/EFLilNU0YQvjpYDAO5enAZfrXPvFZsSG4z7l6cDAL48dgm7c8qdejySL4YTcgn7xmtpcXro3Lyl09PCSbHQeqtRXt2Ck4W1UpdD1G8dFhs27MiHCGDamGHIGBnqkuNOTo/E7XM790D58F+F3T87yLMwnJBLHLFvvJbu3hfCfluAr1f3Xg6bv+HsCSnH1gMlqKxthc7PC3fMT3bpsRdNjsXc8dEQAby59RwKLzW59PgkPYYTcrrqBiNKrzRDJQgY7yEtnZ5umhQLrZcapVXNyC2qk7ocohsqr27B9kOlAIDv35SKAF8vlx5fEAT8x4JkjE0KQ4fFhvX/yEVVvdGlNZC0GE7I6ezb1afHB0Pn5zktHbtAP2/MHR8NgNeekPxZbTa8sz0PVpuI8Snhku3krFap8NDNo5EwLBAtbR14edMpGIzcA8VTMJyQ09l3hfWUVTp9WTQ5Dt4aFYovG3C2uF7qcoiu6fOccpReaYafVoM1N6VAEPp3x2Fn0Hp37oESFuSD6oY2vPpxLszcA8UjMJyQU1U1GFFa1dnSGZccJnU5kgny98accZ2zJ//cX8zZE5KlqnojPttXDAD47vwkBMvgFhNBAVo8uToLfloNiioNeHPLOe6B4gEYTsip7FfapyfoEeiBLZ2eFk+Jg0atQlGFAXmlDVKXQ9SLTRSxYUc+Oiw2jErQY0bGcKlL6hYV5o/Hbs2ARi3g2PkafPRVodQlkZMxnJBT5eS5/710+is4QIvZY6MAdF57QiQne05W4nx5I7y9VLh7cZqk7Zy+pMbpce+yzj1QPs8px+6j3APFnTGckNNcqTeirLoFapVnrtLpy5IpcdCoBZwvb0RBGWdPSB7qDe3Y1DUbcevsRIQH+0pcUd+mjhqGW2ePBAD8/YsLOM6dl90Wwwk5TU6Plo6rlyLKVYjOBzOzOHtC8iGKIjbuKkC72YrEaB3mj4+RuqTrWjo1HnPGRkEE8Mbmsyiq5B4o7ojhhJyGLZ2+LZ0SD7VKQF5pA86XN0pdDnm4Q+eqkFtUB41awNol6VCp5NXO+TZBEPD9m1KQmRjauQfKx7mobuAeKO6G4YSc4nJdKy7VsKXTl9AgH8zI7LzYcMuBEmmLIY9maDXjb19cAACsmD4CUWH+ElfUP2qVCg+vHI34yEA0Gzvw8ken0NLWIXVZ5EAMJ+QU9pbO6BEh8PdhS+fblk3tnD05W1yPogpOS5M0PvjiPFraOhAbEYAlU+KkLmdAfLw1eGJ1JkJ1WlQ1tGH9P3LRYeEeKO6C4YScwr6EmC2dvoUF+yJ7zDAAnD0haZw4X4MjedVQCQLuXZoOjVp5vw6Cu/ZA8dVqUHipCW9uzYONewi5BeV9NZLsVda24lJNK9Qqz9547UaWZ8dDJQjILapD8WWD1OWQBzG2d2Dj5wUAgEVTYhE/LFDiigYvOjwAj67KgFol4Gh+NT7+ukjqksgBGE7I4Y72aOn4saVzTRF6P0wd3bml/xau3CEX+uirQjS1mBGp98XK6SOkLmfI0uP1uHdp5x4oOw+X4ctjlySuiIaK4YQcLoctnX5blh0PQQBOFtai9Eqz1OWQBzhXUo+9py4DANYuTYe3l1riihwje8ww3DKzM2h98MV5nLxQK3FFNBQMJ+RQFTUtqKhthUbNlk5/DA/1x5SuGyLy2hNyNpPZind35AMA5o6PRkpssLQFOdjyaQmYmTkcogj8efMZtksVjOGEHMo+azJmRChbOv20bFoCBADHz9egvLpF6nLIjX267yJqm9oRotPittmJUpfjcIIg4M5FqRgzIgTmDhte2XQKNY1tUpdFg8BwQg4jiiJbOoMQHeaPiV2fL86ekLMUVTRhd07n/WjuWpQGX61G4oqcQ6NW4ZHvjEFsRAAMxg78YRP3QFEihhNymIraVlyuM0KjVmEsWzoDsmJaAgDgWH41Kmo4e0KO1WGxYcOOfIgAskcPQ2ZiqNQlOZWvVoMnV2dBH6jF5TojXvvkNDosNqnLogFgOCGHsa/SyRgZ4rZ/lTlLTEQAJqSEQwSw9WCp1OWQm9l2sASVta3Q+XnhewuSpS7HJfSBWjy1Ogu+WjXOlzfi7W3nuAeKgjCckEOwpTN0K6YnAACOnKvC5bpWaYsht1Fe3YJtXYH3+zeletRNOGMiArDuls49UI7kVeOTPRelLon6ieGEHKKi5t8tnawktnQGIy4yEOOSwzpnTw5w9oSGzmqzYcP2PFhtIsYlh2Fiqufd52p0QgjuWZIGANh+qBRfn6iQuCLqD4YTcogjbOk4hH325NC5K6jinVZpiHbnXELJlWb4ajW4c1EqBEHedxx2lukZw7FyRuceKO9/XoDcIu6BIncMJzRkvVo66WzpDEXCMB0yE0MhisA2zp7QEFTVG/Hpvs42xh3zkhAcoJW4ImndPD0B0zOGQRSBP312FiVXuAeKnDGc0JCVV7egqt4IL40KWYls6QyVffbkwJkr3KOBBsUminh3Rz46LDaMStBjRuZwqUuSnCAIuHtxGkYl6GHqsOKVTbmobeL3l1wxnNCQ2WdNMkeGsqXjAIlRQRgzIgQ2UcS2gyVSl0MKtPdkJQrKG+HtpcLdi9M8tp3zbRq1Cuu+k4GYcH80tZrxh025MLZzDxQ5YjihIWFLxzlu7roZ2/7TV/jXHQ1IvaEdH31VCAC4dVYiwoN9Ja5IXvx8OvdACQ7wRmVtK/dAkSmGExqSsqoWVDe0wVujcvuNnVwpKSYI6fF6WG0ith8qk7ocUghRFLFxVwHazVYkRuswf0KM1CXJUojOB0+uzoKPtxr5ZY14d0ceRO6BIisMJzQkRwu6WjqJofDxZkvHkW7uuvZk36lK1BvapS2GFOHQuSrkFtVBoxZwz5J0qFRs51xLXGQg1n1nDFSCgINnq/DpvmKpS6IeGE5o0ERRRE6evaUTKXE17ic1To/U2GBYbSJ2cPaEbsDQasbfvrgAoPN2CNFh/hJXJH9jRobirsWpAICtB0qw91SlxBWRHcMJDVpZVQuqG7taOiPZ0nEG++zJnlOVaGg2SVsMydoHX5xHS1sHYsIDsGRqvNTlKMasrCgs77q31cadBThzsU7agggAwwkNwZH8KgBAZlIYtN5qiatxT2nxeiTFBMFitWHnYc6eUN9OXKjBkbxqCAJw77I0aNT80T4Qt8wcgezRw2ATRfzxszMoq2qWuiSPx69gGpSeLZ3JvJeO0wiCgJVdK3e+PlmBxhbOnlBvxvYOvL+rAACweHIcEobpJK5IeQRBwNqlaUiLC4bJbMUfNp3idV4SYzihQSm50ozapnZ4e6mQwVU6TjUqQY/EKB06LDZee0JX+eirIjS2mBGp9+3eop0GTqNW4dFVGYgK80djixkvbzoFY7tF6rI8FsMJDYp9b5OxSWHQerGl40yCIGBF1+zJl8fK0cTZE+qSV1LffRHnPUvS4M3vxSHx8/HCk6szEeTvjYqaVvzx09OwWLkHihQYTmjAeq3SYUvHJTJGhiBhWCDMHTZ8tqdI6nJIBkxmK97dmQ8AmDs+Gqlxeokrcg9hQb54cnUWtF5q5JU24L0d+dwDRQIMJzRgxZebUWdoh9ZLjQyu0nEJQRC6d43dtv8imo1miSsiqX267yJqGtsRotPittmJUpfjVuKHBeKRrj1Q9p+5gn9+wz1QXI3hhAbsqL2lkxzGaWQXykoKRVxkANpMVuw6Ui51OSShosom7D7a+TVw16I03tPKCTITQ7FmUQoAYPP+EnyTe1niijwLwwkNSOe9dDqXELOl41qCIOA7M0cCAHbnlKGVNyzzSB0WGzZsz4coAtmjh/G2EU40Z2w0lmV37hnz3s58nC2pl7giz8FwQgNy8bIBdQYTtN5qjBkRInU5Hmd8ajgShuvQZrJidw5nTzzRtoMlqKxtRaCfF763IFnqctzeLbNGYsqoSFhtIv74yWmUV7dIXZJHYDihAbFfCDsuiS0dKagEAd9d2DnVvPvoJS519DDl1S3YdrAUAPD9hSkI8PWSuCL3pxIE3Ls0HamxwWjnHiguw3BC/WYTxe4b/bGlI51pGVGIDvNHm8mCL49x9sRTWG02bNieB6tNxLjkMH4PupCXRoVHb83A8FA/NDSb8PsPT8LItqpTMZxQv12sNKDeYIKPtxpjRrKlIxWVSsDNXZttfZ5TjjYTZ088we6cSyi50gxfrQZrbkqFIPCOw67k7+OFJ1dnQefvjbKqFvzmvRzugeJEDCfUb90tneQweGnY0pHSlFGRiAzxQ2u7Bf86fknqcsjJqhqM+HTfRQDAHfOSoA/USlyRZwoP9sUTt2XC20uFE+dr8O527oHiLAwn1C+9WzqREldDKpWAFdM6VxHsOlKOdjNnT9yVTRTx7vZ8dFhsSI/XY0bmcKlL8mgjhuvwg1syoBKAvacqsfVAidQluSWGE+qXooomNDSb4KtVYzRX6cjClFGRiND7oqWtA1+fqJS6HHKSvScrUVDeCG8vFe5ZksZ2jgyMSwnHg7dkAgA+3VeMA2e4B4qjMZxQv9jvpTMuORxeGn7ZyIFapcLy7AQAwM7DpTB1WKUtiByu3tCOj74qBADcOisR4cG+EldEdsumj8DSrj1QNmzPRx73QHEol/yWKS4uxn333YexY8ciOzsbv/rVr9DezqVYSmETxe5dYblCQF6mjo5EWJAPDMYO7DnJ2RN3IooiNu4qQLvZisQoHeZPiJG6JPqW2+clYVJaBKw2Ea99egYVNdwDxVGcHk4MBgPuvvtutLa2Yv369XjmmWewZcsW/PSnP3X2oclBCi81obHFDF+thi0dmdGoVVg+LQEAsONQKcycPXEbh89VIbeoDhq1gHuWpkOlYjtHblSCgPuXpyM5JghtJgte3nQKDc28a7gjOD2c/P3vf4fBYMDrr7+OWbNm4Tvf+Q5++tOfYsuWLSgq4t1VlcDe0hmfHAaNmi0duZk2ZhhCdVo0tZqxj/f/cAsGoxkffHEBALBiWgKiw/wlroiuxUujxmO3ZiIyxA/1BhNe+fgUL1B3AKf/ptm7dy+ys7MREvLvv7gXLVoEb29v7Nmzx9mHpyGy2Xqs0klnS0eONGoVlnZde7L9UCk6LNx7Qek+2H0eLW0diAkPwJKp8VKXQzcQ4OuFp27PQqCfF8qqWvCnz87CauP34VA4PZwUFRUhMbH37by9vb0RFxfHmRMFuHCpEU0tZvhpNRiVwJaOXM3IGA59oBYNzSZ8k8trT5TsxIUaHMmrhiAA9y5L42ylQkQE++Lx2zLhrVHh9MU6/PXz89wDZQicfp9tg8EAnU531eM6nQ5NTU1Dem+Ng1eNqLt+CKjd+IfBQMd47HwNAGBCWjh8FHBbdnc/h9can0bTee3J+7sKsO1QKeZOiFHsLzVPPYcAYGy34K+fnwcALJ0aj6SYYFeW5hDufv6Aa48xNU6PR24Zg/WbcrHnZCUi9H5YMT1BggqHRg7nULLfNqIoDmm9vkolQK93Th9Wp3P/5Xr9GaPVJuJoQWc4mT853mmfb2dw93PY1/i+MzcZ2w6WoN5gwvHCOiyamuD6whzIE8/h/206iYZmE6LC/LF2ZQa0Cr65prufP6DvMS6YOgJtHSL+8tlpbPqqEHFRQZgzXpkrraQ8h04PJzqdDgaD4arHm5ubr2r3DITNJsJgMA6ltKuo1SrodL4wGNpgddN7JgxkjHkl9WhsNsHfR4O4MD80NLS6qMrBc/dzeKPxLZ4Sjw92n8ffPy/A+KRQRc6eeOo5PFdcj12HOu84fM+SNBhb2uHYn3Cu4e7nD7jxGGeMiUTp5SbsOlyGV/5+HFoVkBavl6DSwXHmOdTpfPs1I+P0cJKYmHjVtSVmsxllZWW49dZbh/TeFidd+Ge12pz23nLRnzEeOlcFABifEg6Izvt8O4O7n8NrjW9m5nBs3V+M2qZ27DtViZmZURJU5xiedA5NZive3nYOADB3XDSSooMUP3Z3P3/A9ce4ek4iahvbcKygBn/46BT+684JiFLYqispz6HT/6yaNWsWDh06hIaGhu7Hdu/eDbPZjNmzZzv78DRINpuIY9x4TXG0XmosntK5umPbgVKuGFCIT/ddRE1jO0J0Wtw2Z/AzyiQfKkHAA8tHITFaB6PJgpc/OoWmFu6B0l9ODyd33HEHAgMDsW7dOuzbtw+fffYZ/t//+39YsWLFkNo65FwF5Y0wGDvg76NR1HQkdf7lHeDrherGNhzumv0i+SqqbMLuo+UAgLsWpcJXAReeU/94e6nx+K2ZiND7os7Qjj98nAuTmRsl9ofTw4lOp8N7770HPz8/PPbYY/jNb36D5cuX41e/+pWzD01DYN94bUJquCKvW/BkWm81Fk+JAwBsOVAKm43LGeWqw2LDu9vzIYpA9uhIZCaGSV0SOVignzeeuj0LAb5eKL3SjD//8wxnNPvBJRF9xIgRePvtt11xKHIAq82GY/aN19IiJa6GBmPuuGjsOFSKqnojjuRXYeqoYVKXRH3YdrAEFbWtCPTzwvcWpEhdDjlJpN4Pj9+WiRf/dgKniurwwe4LWHNTCu8wfR38k5iuUlDWiGZjBwJ8vZAWHyx1OTQIvloNbprcNXuyvwQ2bgYlO+XVLdh2sHN1zvcXpiDA10viisiZkqKD8MDyURAAfHWiAjuPlEldkqwxnNBVerZ01Cp+iSjV/PEx8NNqcLnOiGNd+9WQPFitNry99RysNhHjksN40bmHmJgWge/OSwIAbPqqCEfyeE3YtfA3D/XS2dLp/EXGH5jK5uejwcJJsQCALfuLOXsiI5v3XcTFSgN8tRqsuSmV0/seZOGkWCyY0Lkp21tbz+F8eaO0BckUwwn1kl/aiJa2DgT6eSE1LljqcmiIFkyMga9WjUs1rThxvlbqcghAVb0Rf92ZDwD47rwk6AO1EldEriQIAu6Yn4xxyWGwWEW8+o9cXK6T/waXrsZwQr38u6UTwZaOG/D38cL8Cf+ePeGNyKRlE0W8sy0P5g4rRiWEYGbmcKlLIgmoVAIevHk0RgzXobW9aw+UVrPUZckKf/tQN4vVhuNdN/qblBoucTXkKDdNioXWW42y6hacLOTsiZT2nqpEXmkDtN5q3Lssje0cD6b1UuOJ2zIRHuyD2qZ2rP84F6YO7oFix3BC3fLLGtDS1gGdnxdS2NJxGwG+XpjfdeOxzftLOHsikXpDOz76VyEA4M4l6YjQ+0lcEUlN5++Np24fC38fDYovG/CXzWe5L1EXhhPqlpPHlo67umlyLLy9VCi90ozTF+ukLsfjiKKIjbsK0G62IjE6CMtnjJS6JJKJYSF+eOzWTGjUKpy4UIu/fXmBf0CA4YS69GrpcJWO29H5eWPeOM6eSOVwXhVyi+qgUQu4f3k61Cq2c+jfUmKD8cCKUQCAL49dwu6ccokrkh7DCQEA8kob0Npugc7fGymxwVKXQ06waEocvDUqXKw04GxJvdTleAyD0YwPdl8AACyfloDo8ACJKyI5mpQWgdvndu6B8uG/CnG0a3GCp2I4IQD/bulMTA2Hin/VuaUgf2/MGRcNANj8DWdPXOVvX1xAS1sHYsIDsHRqvNTlkIwtmhyLueOjIQJ4c+s5FF5qkrokyTCcEFs6HmTxlDho1CoUVjQhv7RB6nLc3skLtTh8rgqCAKxdmsabaNJ1CYKA/1iQjLFJYeiw2LD+H7moqjdKXZYk+J1COFfSAKPJgqAAbyTHBEtdDjlRcIAWs8dGAei89oScx9huwfufFwAAFk2Ow4jhOokrIiVQq1R46ObRSBgWiJa2Dry86RQMRs/bA4XhhJCT33l/h4mpEWzpeIAlU+KgUQsoKG9EQRlnT5xl09eFaGg2IULvi+/MGCF1OaQgWu/OPVDCgnxQ3dCGVz/OhdnD9kBhOPFwnS2dzo252NLxDCE6H8zM5OyJM+WVNmDPyUoAwNolafD2UktcESlNUIAWT67Ogr+PBkWVBry55ZxH7YHCcOLhzhbXo62rpZMUEyR1OeQiS6fGQ60SkFfagAuXGqUux62YOqx4d0ceAGDuuGikxuklroiUKirMH4+uyoBGLeDY+Rp89FWh1CW5DMOJh7PfS2dSagRU3ErbY4QG+WB6Rud9XbZw9sShPtt3ETWN7dAHanHbnESpyyGFS43T495l6QCAz3PKsfuoZ+yBwnDiwTosNpy40LVKJ50tHU+zLDseKkHAmeJ6FFV67pJFR7pYacDnXRto3b04Fb5ajcQVkTuYOmoYbp3duavw37+40L260p0xnHiwzpaOFfpALRKj2dLxNOHBvpg2ZhgAzp44gsVqw4bteRBFIHt0JDITw6QuidzI0qnxmDM2CiKANzafdfs/KBhOPFivVTps6XikZdPiIQhAblEdii8bpC5H0bYdLEVFbSsC/bxwx/xkqcshNyMIAr5/UwoyE0M790D5OBfVDe67BwrDiYcyW6w4caFrlQ5bOh4rUu+HqaM4ezJUl2pasPVACQDg+wtTEOjnLW1B5JbUKhUeXjka8ZGBaDZ24OWPTqGlrUPqspyC4cRDnblYj3azFSE6LUZGcXMoT7a8a/bkZGEtSq80S12O4thsIjZsz4PVJmJcchiX5JNT+Xhr8MTqTITqtKhqaMP6f+Siw+J+e6AwnHioI+fY0qFOw0P9MSU9EgC6//qn/vs8pxzFl5vhq9VgzU2pEPj9RE4W3LUHiq9Wg8JLTXhzax5sbnavLIYTD2TqsPJeOtTLsmkJEAAcO1+DS9UtUpejGFUNRny27yIA4LvzkqAP1EpcEXmK6PAAPLoqA2qVgKP51fj46yKpS3IohhMPdDy/Gu1mK0LZ0qEu0WH+mNgVVLdw9qRfRFHEezvyYbbYkB6vx8zM4VKXRB4mPV6Pe5d27oGy83AZvjx2SeKKHIfhxAN9c6oCADAxLYJT0NRtxbQEAMDR/GpU1LZKW4wC7DlVifyyRnh7qXD3kjR+L5EksscMwy2zOvdA+eCL8zjZtdBB6RhOPIy5w4ojZ68AACalRUpcDclJTEQAJqSEQwSwjbMn11VvaMemrq3EV81KRESwr8QVkSdbnh2PWVnDIYrAnzefcYttARhOPMypojq0m60IC/LBiOGBUpdDMrNiegIA4HBeFS7XcfakL6Io4v1dBWgzWTEySocFE2KkLok8nCAIWHNTKsaMCIG5w4ZXNp1CTWOb1GUNCcOJh7Gv0pk8KpLT0HSVuMhAjE0Kgyh2bipGVzucV4VTRXVQqwSsXZIGlYrfRyQ9jVqFR74zBrERATAYO/CHTcreA4XhxIOYOqzd99KZnM6WDvXNPnty6GwVqtx4B8rBMBjN+GD3BQCdn6fo8ACJKyL6N1+tBk+uzoI+UIvLdUa89slpdFhsUpc1KAwnHuR0UR3MHTZEhvixpUPXNGK4DpmJobCJIrYd4OxJT3//4gJa2joQE+6PpVPjpS6H6Cr6QC2eWp0FX60a58sb8fa2c4rcA4XhxIPk5FcDAGZkRbGlQ9dlX7lz4MwVxfeuHeVkYS0OnauCIABrl6ZDo+aPT5KnmIgArLulcw+UI3nV+GTPRalLGjB+d3kIk9mKU0WdS8xmZEVLXA3JXWJ0EEaPCOmcPeG1JzC2W/D+rgIAwKLJcRgxnPsDkbyNTgjBPUvSAADbD5Xi6xMVElc0MAwnHiL3YmdLJyLYF4kxQVKXQwpwc9e1J/tPX0Ztk2fPnmz6uhANzSZE6H2xcsYIqcsh6pfpGcO7v17f/7wAuUXK2QOF4cRD5ORxlQ4NTHJMMNLj9bDaROw4VCZ1OZLJK23AnpOVAIC1S9Kg9VJLXBFR/908PQHTM4ZBFIE/fXYWJVeUsQcKw4kHaDdbkFtUBwCYPIr30qH+s8+e7MutRL2hXdpiJGDqsOK9HfkAgDnjopEap5e4IqKBEQQBdy9Ow6gEPUwdVryyKVcRM6EMJx4gt6gOZosNEXpfxEdylQ71X2qcHqmxwbBYRew47HmzJ5/tu4jqxjboA7VYPSdR6nKIBkWjVmHddzIQE+6PplYz/rApF8Z2ee+BwnDiAXLyOlfpTOK9dGgQ7LMne05WorHFJG0xLnSx0oDPc8oBAHctSoWvViNxRUSD5+fTuQdKcIA3KmtbZb8HCsOJm2s3W5B7sbOlMymNLR0auLR4PZJigmCx2rDTQ2ZPLFYbNuzIgygCU0dHIispTOqSiIYsROeDJ1dnwcdbjfyyRry7Iw+iTPdAYThxc6cK69Bh6dx4LTaCu1nSwAmC0D178vWJCjS1mqUtyAW2HSxFRU0rAv288L35yVKXQ+QwcZGBWPedMVAJAg6ercKn+4qlLqlPDCduzr7xGls6NBSjE0IwMkoHs8WGXUfce/bkUk0Ltnbdlfn7C1MQ6OctbUFEDjZmZCjuWpwKANh6oAR7T1VKXNHVGE7cWJvp36t02NKhoeg5e/Kv45dgMLrn7InNJmLD9nxYbSLGJoXx+4bc1qysKCzv2gl6484CnOlq/8sFw4kbO1VYC4vVhmEhfogJ95e6HFK4jJGhiB8WCHOHDbu7LhR1N7uPlqP4sgG+Wg3uXJTK2UZya7fMHIHs0cNgE0X88bMzKKtqlrqkbgwnbowtHXKknrMnXxy7pOjbsfelusGIT/d23oPku/OSoA/USlwRkXMJgoC1S9OQFhcMk9mKP2w6JZv9jBhO3FSbyYLT9lU66ZyaJscYmxSG2IgAmMzW7mW27kAURby7Ix9miw3p8XrMzBwudUlELqFRq/DoqgxEh/mjscWMlzedgrHdInVZDCfu6uSFWlisIoaH+iE6jC0dcoyesydfHitHq8w3cuqvvacqkV/WCG+NCncvSeNMI3kUPx8vPLk6C0EB3qioacX6j3Ml3wOF4cRNsaVDzjIuJRzR4f5oM1nxxdFLUpczZA3NJnz0VSEAYNWskYgI9pW4IiLXCw3ywZO3ZUHrpca5knr88eOTktbDcOKGjO0WnCnmKh1yDpUg4ObpnXc63Z1TLosp4MESRRHv7ypAm8mKkVE6LJgYK3VJRJKJHxaIR7r2QPkypxxNEu4IzXDihk4W1sBiFREd5o/ocG68Ro43ITUcUWH+MJos+PK4cmdPjuRV42RhLdQqAWuXpEGl4iwjebbMxFD8+Htj8citmQgKkO6icIYTN9TzXjpEzqASBCyfFg8A+PxIGdpMyps9aTaa8X+7zwMAVkxLYJAn6jJmZCiWThshaQ0MJ27G2N6BM8X1AICJDCfkRJPTIhEZ4ofWdgu+OlEhdTkD9rcvLqClrQMx4f5Ymh0vdTlE1APDiZs5caEWVpuI6HB/RHGVDjmRSiVgRdfsyc7DZTCZrRJX1H8nC2tx6FwVBAFYuzQdGjV/FBLJCb8j3UzPVTpEzjZlVCQign3R0tahmNkTY7sF7+8qAAAsmhSHEcN1EldERN/GcOJGWts7cLarpcNwQq6gVqmwzD57cqQMpg75z558/HUhGppNiND7YuVMafvqRNQ3hhM3cvx8Daw2ETHhARgeypYOuUb26GEIC/KBodWMvSfld3fTnvJLG/B1V41rl6RB66WWuCIi6gvDiRvpbulwu3pyIY1ahWVdF5RuP1yKDos8Z09MHVa8uyMfADBnXDRS4/QSV0RE18Jw4iZa2jqQV9IAgC0dcr3pGcMRotOiqcWMvacuS11On/65rxjVjW3QB2qxek6i1OUQ0XUwnLiJE10tnbiIAAwL8ZO6HPIwGrUKy6Z2zZ4cKpX8vhzfVnzZgF05ZQCAuxalwlerkbgiIroehhM3YW/pcG8TksqMzCjoA7VoaDbhm9PymT2xWG3YsD0PoghMHR2JrKQwqUsiohtgOHEDLW0dOMeWDknMS6PCUvvsycESWKzymD3ZfrAUl2paEeDrhe/NT5a6HCLqB4YTN3D8fA1sooi4yABEsqVDEpqVNRxBAd6oM5hw4MwVqcvBpZoWbDlQAgD4/sIUBPp5S1sQEfULw4kbyMmrAsBZE5Kel0aNJVM6Z0+2HpB29sRmE7Fhez6sNhFjk8IwmavYiBSD4UThDEYz8kobATCckDzMHhsFnZ8XapvacehslWR1fHG0HMWXDfDVqnHnolQIAu84TKQUTg8n+/fvx49+9CMsWLAAqamp+OUvf+nsQ3oUe0snflggIvRs6ZD0tF5qLLbPnhwsgdXm+tmT6gYjPtl7EQDw3XnJ0AdKd+t3Iho4p4eTvXv3Ii8vD5MmTYJOx3tYOFpOXucqncmcNSEZmTMuCgG+XqhuaMORc9UuPbYoinh3Rz7MFhvS4oIxM3O4S49PREPn9HDyzDPPYPv27XjhhRcQGBjo7MN5FEOrGfllnat0uISY5MTHW4NFk2MBAFsOlMBmE1127H25l5Ff1ghvjQr3LEljO4dIgZweTlQqXtbiLMfP10AUgRHDAxEe7Ct1OUS9zBsfA38fDa7UG7v34XG2hmYTPvzXBQDAqlkj2eokUigmBwXjxmskZ75aDW6a1GP2RHTu7Ikoinh/VwHaTFaMGK7DgomxTj0eETmPovdw1mgcm63UalWv/5WzphZTd0tn6uhh/f5cKGmMg8HxycuiKfHYdaQclbWtOFlYi8npkTd8zWDHeOjsFZwsrIVaJeCBm0fB21uedxxW2jkcKHcfH+D+Y5TD+AYcTpqbm1FdfeMp2tjYWHh7O2/DI5VKgF7v75T31unk3yI5mFcNUQRS4oKRMmLg23ErYYxDwfHJgx7AytmJ+NvnBdh6oBQLp46AStW/a0AGMsamFhP++vl5AMB3F6YiI+XGIUhqSjmHg+Xu4wPcf4xSjm/A4WT37t149tlnb/i8zz77DOnp6YMqqj9sNhEGg9Gh76lWq6DT+cJgaINVJltvX8vXR8sBABNSwtHQ0Nrv1ylpjIPB8cnPrIxh+PTrQpRcNuDLwyU3bEMOZox//uwMDK1mxEYEYMH4qAF9T7iaEs/hQLj7+AD3H6Mzx6fT+fZrRmbA4WTVqlVYtWrVoIpyNIuT7nxqtdqc9t6O0NRiQn5pZ0tnXHLYoGqV+xiHiuOTD62XGgsmxmDrgVJ8tvcishJD+7WCpr9jPFVYiwNnrkAQgHuWpAGi8342OJKSzuFguPv4APcfo5Tjc8+GmZs7WlADEUBilA5hQe49rUju4aZJcdB6q1FW3YJThXUOe982kwUbdxUAABZNisOI4dxLicgdOP2C2IqKCpw+fRoA0NbWhrKyMuzcuRMAsHjxYmcf3i3ZV+lwu3pSigBfL8wfH4Pth0qxeX8xspL6N3tyI5u+LkJDswkRel+snDnCAZUSkRw4PZwcPny41zUq+/btw759+wAABQUFzj6822lsMeFCeSMALiEmZblpciy+OFaOkivNOH2xHpmJoUN6v/zSBnx9ogIAcM/iNGi95Lk6h4gGzunhRE7XqLiDY10tnaToIITofKQuh6jfdH7emDcuBjuPlGHz/mJkjAwZ9OyJqcOKd3fmAwDmjI1CWrzekaUSkcR4zYnC5OR13uWVsyakRIumxMFLo8LFSgPOlTQM+n3++U0xqhvaoA/UYvXcJAdWSERywHCiIA3NJly41AQAmJgaLnE1RAMX5O+NOWOjAQD/3F8McRC7xhZfNmDXkTIAwF2LUuGrVfRekkTUB4YTBTlaUN3Z0olhS4eUa/GUOGjUKhReakJ+WeOAXmux2rBhex5EEZg6KhJZSQPfgJCI5I/hREG4SofcgT5Qi9lZUQCALfuLB/Ta7QdLcammFQG+XvjegmRnlEdEMsBwohD1hnYUXmqCAGBiKsMJKduSqXHQqAXklzWioKx/155U1LRgy4ESAMD3F6Yg0M95t8cgImkxnCjE0YIaAEByTBD0gVqJqyEamhCdD2Zkds2edAWO67HZRGzYkQ+rTcTYpDBMTmdAJ3JnDCcKkZPfuUpnUj/u6kqkBEunxkGtEnCupAGFXRd6X8sXR8txsdIAX60ady5KdcgGbkQkXwwnClBvaEdRhQECgAlcpUNuIizIF9MzhgEANl/n2pPqxjZ8svciAOD2uUmcOSTyAAwnCnC060LYlNhgBAfwBzO5j2XZCVAJAs4U16Oo8urZE1EU8d6OfJgtNqTFBWNW14W0ROTeGE4UwL5KhxuvkbsJD/bFtDGdsydb9pdc9fF9uZeRV9oAb40K9yxJYzuHyEMwnMhcbVMbiioNXat02NIh97NsWjwEAcgtqkPJFUP34/WGdnz4rwsAgFtmjUSE3k+qEonIxRhOZO5ofucqndS4YASxpUNuKFLvh6mjes+eiKKI93bmo81kxYjhOiycGCthhUTkagwnMseN18gTLJ8WDwHAiQu1KL3SjG9OVuLE+VqoVQLWLk2DSsV2DpEn4U0pZKy2sQ3Flw0QBGA8N14jNzY81B+TR0Xi8LkqfPjlBZTXtAAAlk9LQEx4gMTVEZGrceZExnIKOmdN0uL0CPLnbpjk3pZnd86enCmuR1OLGTHh/liWHS91WUQkAYYTGcvJY0uHPEd0eAAmdH2tqwTg/hWjoFHzRxSRJ2JbR6ZqGttQcqW5q6XDVTrkGVbNGonK2lYsmhqPkVFBsFhsUpdERBJgOJEp+8Zr6fF66HiDM/IQw0L88JuHs6HX+6OhoVXqcohIIpwzlakj3HiNiIg8FMOJDFU3GFF6pRkqQcD4FLZ0iIjIszCcyFBOd0snmC0dIiLyOAwnMtS98Vp6pMSVEBERuR7DicxU1RtRVtXClg4REXkshhOZsc+ajErQI8DXS+JqiIiIXI/hRGZ4Lx0iIvJ0DCcycqXeiPLqFqhVAsaxpUNERB6K4URG/t3SCWFLh4iIPBbDiYzY76UzMY2zJkRE5LkYTmTicl0rLtV0tnS4SoeIiDwZw4lM2Fs6o0eEwN+HLR0iIvJcDCcywVU6REREnRhOZKCithUVNa2dq3SSw6Quh4iISFIMJzJwtGvWZMyIEPixpUNERB6O4UQG/n0vHbZ0iIiIGE4kVlHTgsraVmjUAsYmcZUOERERw4nEcrpbOqHw89FIXA0REZH0GE4kJIoiV+kQERF9C8OJhCpqW3G5zgiNWoWxXKVDREQEgOFEUvbt6jNGhsBXy5YOERERwHAiGbZ0iIiI+sZwIpFLNa24Ut/Z0slKYkuHiIjIjuFEIjn5VQCAzMRQtnSIiIh6YDiRgCiK3debsKVDRETUG8OJBMqrW1DV0AYvjQpZSaFSl0NERCQrDCcSsF8ImzkyFD7ebOkQERH1xHDiYr1W6fBeOkRERFdhOHGxsqoWVDe0wVujQmYiWzpERETfxnDiYt0tnUS2dIiIiPrCcOJCnS2dziXEk9IjJa6GiIhInhhOXKi0qhk1je3w9lIhcyRbOkRERH1hOHEh+94mWYlh0HqrJa6GiIhInhhOXIT30iEiIuofhhMXKbnSjNqmdmi91MjgKh0iIqJrYjhxEfusSVZSKLRebOkQERFdC8OJC/BeOkRERP3HcOICxZebUWfoaulwlQ4REdF1MZy4gH1vk7HJYfBmS4eIiOi6GE6cjKt0iIiIBobhxMkuVhpQbzBB661GxsgQqcshIiKSPYYTJ7PPmoxLDoOXhi0dIiKiG2E4cSKbKOJoAVs6REREA8Fw4kT2lo6vVo0xI9jSISIi6g+GEyey720yNoktHSIiov7SOPPNrVYr3nnnHezZsweFhYWwWq1ISUnBo48+iuzsbGceWnK9WzqREldDRESkHE6dOWlvb8cbb7yBtLQ0vPDCC/j973+PyMhIrF27Fl999ZUzDy25ooomNDR3tnRGs6VDRETUb06dOfHx8cGXX36JoKCg7sdmzJiBkpISvPPOO5g7d64zDy8pe0tnXHI4vDTsnhEREfWXU39rqtXqXsEEAARBQFpaGqqrq515aEnZRBE5XKVDREQ0KC7/k95ms+HEiRNITEx09aFdpvBSE5pazPDVatjSISIiGiCntnX68v7776O4uBi//OUvh/xeGge3S9RqVa//HSz7hbATU8Pho3X5p/i6HDVGueL4lM/dx8jxKZ+7j1EO4xvwb87m5uZ+tWRiY2Ph7e3d67EjR47gxRdfxL333otJkyYN9NC9qFQC9Hr/Ib3Hteh0voN+rdUm4vj5GgDAvMnxTqtxqIYyRiXg+JTP3cfI8Smfu49RyvENOJzs3r0bzz777A2f99lnnyE9Pb373/n5+Vi3bh0WLFiAp59+eqCHvYrNJsJgMA75fXpSq1XQ6XxhMLTBarUN6j3ySxtQbzDB30eD+HA/NDS0OrTGoXLEGOWM41M+dx8jx6d87j5GZ45Pp/Pt14zMgMPJqlWrsGrVqgG9pqysDPfffz9GjRqF//3f/4UgCAM9bJ8sFud8UVittkG/96GzVwB0rtKB6Lwah2ooY1QCjk/53H2MHJ/yufsYpRyf0xtKNTU1uPfeexEWFobXX3/9qlaPO7HZRBwt6GzpTErnKh0iIqLBcOrVmu3t7bj//vtRV1eHn/zkJygsLOz18bFjxzrz8C53vrwRhlYz/H00SI/XS10OERGRIjk1nNTW1iI/Px8A8IMf/OCqjxcUFDjz8C6Xk995ofD4lHBo3PQqbiIiImdzajiJiYlxuwByLVabDcfsG6+xpUNERDRo/PPeQc6XNcJg7ECArxfS4tjSISIiGiyGEwdhS4eIiMgx+FvUAaw2G46d5yodIiIiR2A4cYCCskY0d7d0gqUuh4iISNEYThzA3tKZkBoOtYqfUiIioqHgb9Ih6lyl09XSSWNLh4iIaKgYToYov7QRLW0dCPTzQipbOkREREPGcDJEOflVAIAJqRFs6RARETkAf5sOgcXKlg4REZGjMZwMQX5pA1rbLdD5eSE1NljqcoiIiNwCw8kQHLGv0kmLgEolSFwNERGRe2A4GSSL1YYTXRuvTWZLh4iIyGEYTgYpz97S8fdGckyw1OUQERG5DYaTQcrJ62zpTEwNZ0uHiIjIgRhOBsFiteH4ea7SISIicgaGk0E4V1IPo8mCoAC2dIiIiByN4WQQ/t3S4SodIiIiR2M4GaAOiw3HL9QCYEuHiIjIGRhOBuhsST3aTBYEB3gjKSZI6nKIiIjcDsPJAHW3dNIioBLY0iEiInI0hpMB6LDYcLLQvvFapMTVEBERuSeGkwE4W1yPNpMV+kAtRkbrpC6HiIjILTGcDEBOfhWArlU6bOkQERE5BcNJP3VYrDhhX6WTzlU6REREzsJw0k9nLtaj3WxFiE6LkVFs6RARETkLw0k/5eT32HiNLR0iIiKnYTjpB3OHFScK2dIhIiJyBYaTfjh9sR4msxWhOi1GDmdLh4iIyJkYTvrhaEFnS2dSWiQEtnSIiIiciuHkBswdVpzkKh0iIiKXYTi5gdMX62DqsCIsyAcJwwKlLoeIiMjtMZzcQPcqnbQItnSIiIhcgOHkOkwdVpy0r9JJY0uHiIjIFRhOruN0UR3MHTa2dIiIiFyI4eQ6jnS1dCals6VDRETkKgwn12AyW5Hb1dKZnBYpcTVERESeg+HkGk4V1cJssSEi2BdxkQFSl0NEROQxGE6u4ShbOkRERJJgOOlDu9mC3KI6AFylQ0RE5GoMJ304eaGrpaP3RWwEWzpERESuxHDShyN5VQA6Z03Y0iEiInIthpNvMbZ34FQhWzpERERSYTj5lpxzVeiw2BAZ4seWDhERkQQYTr7lm1MVANjSISIikgrDSQ9tJguOdS0hnsyWDhERkSQYTno4cb4GHRYbhof6ITrcX+pyiIiIPBLDSQ/HztcAAKaMimRLh4iISCIMJz2EB/siOFCLmVlRUpdCRETksTRSFyAnd8xPxiO3jUVDQyssFpvU5RAREXkkzpwQERGRrDCcEBERkawwnBAREZGsMJwQERGRrDCcEBERkawwnBAREZGsMJwQERGRrDCcEBERkawwnBAREZGsMJwQERGRrDCcEBERkawwnBAREZGsMJwQERGRrDCcEBERkawIoiiKUhcxGKIowmZzfOlqtQpWq83h7ysn7j5Gjk/53H2MHJ/yufsYnTU+lUqAIAg3fJ5iwwkRERG5J7Z1iIiISFYYToiIiEhWGE6IiIhIVhhOiIiISFYYToiIiEhWGE6IiIhIVhhOiIiISFYYToiIiEhWGE6IiIhIVhhOiIiISFYYToiIiEhWGE6IiIhIVjwmnBQXF+O+++7D2LFjkZ2djV/96ldob2/v12s//fRTLF68GBkZGVi+fDl27Njh5GoHbrDju/POO5GamnrVf0VFRS6ouv9KS0vx3HPPYeXKlRg1ahSWL1/e79cq4fwBgx+jUs7hjh07sG7dOsyePRtjx47FihUr8MEHH8Bmu/GdT5VwDgc7PqWcv3379mHNmjWYOnUqxowZg/nz5+OFF15Ac3PzDV+rhPMHDH6MSjmH39ba2opZs2YhNTUVp0+fvuHzXXkeNU57ZxkxGAy4++67ERUVhfXr16O+vh4vvPACGhsb8bvf/e66r925cyd+8pOf4MEHH8T06dPxxRdf4KmnnkJgYCBmzJjhohFc31DGBwDjx4/HM8880+uxmJgYZ5U7KBcuXMCePXuQlZUFm82G/t5MWwnnz26wYwSUcQ43bNiAqKgo/Od//idCQ0Nx+PBhPP/88ygvL7+q9p6Ucg4HOz5AGeevqakJ48aNw9133w2dTocLFy7g1VdfxYULF/DOO+9c83VKOX/A4McIKOMcftvrr78Oq9Xar+e6/DyKHuCNN94Qs7KyxLq6uu7HNm/eLKakpIiFhYXXfe3ixYvFxx9/vNdj9957r7h69Wqn1DoYQxnfmjVrxAcffNDZJQ6Z1Wrt/v/PPPOMuGzZsn69Tgnnz26wY1TKOez59Wn361//WszIyBBNJtM1X6eUczjY8Snl/PXlww8/FFNSUsQrV65c8zlKOX/X0p8xKvEcFhYWimPHjhX/9re/iSkpKWJubu51n+/q8+gRbZ29e/ciOzsbISEh3Y8tWrQI3t7e2LNnzzVfV15ejosXL141vb58+XLk5uaivr7eaTUPxGDHpyQq1cC/VJVy/uwGM0Yl6fn1aZeeng6TyYTGxsY+X6OkcziY8SldcHAwAMBisfT5cSWdv2u50RiV6vnnn8cdd9yBESNG3PC5UpxH9/5p2KWoqAiJiYm9HvP29kZcXNx1e4IXL14EAIwcObLX44mJiRBFsfvjUhvs+OyOHDmCsWPHIiMjA2vWrEFOTo6zSnUppZw/R1DqOTx27BiCg4MRGhra58eVfg5vND47JZ0/q9UKk8mEs2fP4o9//CPmzp2L6OjoPp+r1PM3kDHaKekc7ty5E/n5+fjBD37Qr+dLcR495poTnU531eM6nQ5NTU3XfJ39Y99+bVBQUK+PS22w4wOASZMmYeXKlUhISEB1dTXefvttrF27Fu+//z7GjRvnrJJdQinnb6iUeg5Pnz6NTz75BD/4wQ+gVqv7fI6Sz2F/xgco7/zNnTsXVVVVAICZM2fi97///TWfq9TzN5AxAso6h21tbfjNb36DH/7whwgICOjXa6Q4jx4RTq5FFEUIgnDD5337OWLXhYr9ea2U+jO+xx9/vNe/58yZg+XLl+P111/Hm2++6czyXEap56+/lHgOa2pq8PjjjyMjIwMPPPDADZ+vtHM4kPEp7fz95S9/gdFoRGFhIV5//XU8/PDD2LBhw3UDmNLO30DHqKRz+Kc//QmhoaFYtWrVgF/ryvPoEW0dnU4Hg8Fw1ePNzc19zjjYXSsV2t/req91pcGOry9+fn6YPXs2zp4966jyJKOU8+docj+Hzc3NeOCBB+Dj44M//elP8PLyuuZzlXgOBzK+vsj9/KWlpWH8+PG4/fbb8dprr+Hw4cPYvXt3n89V4vkDBjbGvsj1HFZUVOCdd97B448/jpaWFhgMBhiNRgCA0WhEa2trn6+T4jx6RDhJTEy86toLs9mMsrKyq67V6MneX/t2P62oqAiCIFzVf5PKYMd3LeIAlrDKmVLOnzPI9RyaTCY88sgjqK2txVtvvQW9Xn/d5yvtHA50fNci1/P3benp6VCr1SgrK+vz40o7f3250RivRY7n8NKlS+jo6MCDDz6ISZMmYdKkSXj44YcBAHfddRfWrl3b5+ukOI8eEU5mzZqFQ4cOoaGhofux3bt3w2w2Y/bs2dd8XWxsLEaOHInt27f3enzr1q3IzMzs8+p8KQx2fH0xGo3Ys2cPMjIyHF2myynl/DmaXM+hxWLBE088gfz8fLz11ls3vMAQUNY5HMz4+iLX89eXEydOwGq1XnM/DyWdv2u50Rj7ItdzmJ6ejo0bN/b679lnnwUA/OIXv8DPfvazPl8nxXn0iGtO7rjjDvz1r3/FunXrsG7dOtTV1eE3v/kNVqxY0Wtm4b/+67/w2Wef4dy5c92PPf7443jqqacQFxeHadOm4csvv8T+/fvx1ltvSTGUPg12fEePHsXbb7+NhQsXIioqCtXV1diwYQNqamrwyiuvSDWcPrW1tXUvi66oqEBLSwt27twJAJg8eTJCQkIUe/7sBjNGJZ3DX/7yl/jqq6/w9NNPo729HSdPnuz+WFJSEgICAhR9DgczPiWdv0cffRRjxoxBamoqfHx8ukNYamoqFixYAEC5P0PtBjNGJZ1DnU6HKVOm9Pmx0aNHY/To0QDkcR49IpzodDq89957+NWvfoXHHnsMPj4+WL58OX784x/3ep7NZrtqt7wlS5agvb0df/7zn/H2228jPj4eL7/8sqx2Nhzs+MLDw2E2m/H73/8ejY2N8PX1xbhx4/CLX/wCmZmZrh7GddXV1eGJJ57o9Zj93xs3bsSUKVMUe/7sBjNGJZ3Db775BgDw4osvXvUxdziHgxmfks5fZmYmtm/fjr/85S8QRRHR0dG4/fbbcd9998Hb2xuAcn+G2g1mjEo6h/0lh/MoiHJsjBEREZHH8ohrToiIiEg5GE6IiIhIVhhOiIiISFYYToiIiEhWGE6IiIhIVhhOiIiISFYYToiIiEhWGE6IiIhIVhhOiIiISFYYToiIiEhWGE6IiIhIVhhOiIiISFb+P1kQoxiW/ET0AAAAAElFTkSuQmCC" +class=" +" +> +</div> - </div> </div> + </div> + </div> </div></section></section><section ><section > @@ -16604,7 +20489,7 @@ div.jp-OutputPrompt { <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 [71]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [188]:</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">"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> @@ -16615,12 +20500,12 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [189]:</div> <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> <div class="CodeMirror cm-s-jupyter"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> @@ -16636,6 +20521,32 @@ div.jp-OutputPrompt { </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-RenderedImage jp-OutputArea-output "> +<img src="data:image/png;base64,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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + </div></div></section></section><section ><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -16686,12 +20597,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [190]:</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">"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> @@ -16702,6 +20613,32 @@ div.jp-OutputPrompt { </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-RenderedImage jp-OutputArea-output "> +<img 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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + </div><div class="fragment" > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -16716,12 +20653,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [191]:</div> <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> <div class="CodeMirror cm-s-jupyter"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_demo</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s2">"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> @@ -16732,6 +20669,32 @@ div.jp-OutputPrompt { </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-RenderedImage jp-OutputArea-output "> +<img 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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -16747,12 +20710,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div></div></section><section ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [192]:</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">"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> @@ -16763,6 +20726,32 @@ div.jp-OutputPrompt { </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-RenderedImage jp-OutputArea-output "> +<img src="data:image/png;base64,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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -16778,12 +20767,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [193]:</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">"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> @@ -16794,12 +20783,38 @@ div.jp-OutputPrompt { </div> </div> -</div></div></section><section ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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-RenderedImage jp-OutputArea-output "> +<img src="data:image/png;base64,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" +class=" +" +> +</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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [194]:</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">"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> @@ -16810,6 +20825,32 @@ div.jp-OutputPrompt { </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-RenderedImage jp-OutputArea-output "> +<img 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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + </div></section></section><section ><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -16837,7 +20878,7 @@ div.jp-OutputPrompt { <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 [78]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [195]:</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">"Threads"</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> @@ -16848,12 +20889,12 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [196]:</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">"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> @@ -16864,12 +20905,38 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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-RenderedImage jp-OutputArea-output "> +<img 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" 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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> @@ -16880,12 +20947,38 @@ div.jp-OutputPrompt { </div> </div> -</div></section><section ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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-RenderedImage jp-OutputArea-output "> +<img 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" +class=" +" +> +</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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [198]:</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">"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> @@ -16897,12 +20990,38 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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-RenderedImage jp-OutputArea-output "> +<img 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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + +</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell "> <div class="jp-Cell-inputWrapper"> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [199]:</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">"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> @@ -16914,6 +21033,32 @@ div.jp-OutputPrompt { </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-RenderedImage jp-OutputArea-output "> +<img 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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + </div></div></section></section><section ><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -16925,12 +21070,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [200]:</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">"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> @@ -16941,6 +21086,32 @@ div.jp-OutputPrompt { </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-RenderedImage jp-OutputArea-output "> +<img 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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + </div> <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -16969,31 +21140,100 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [201]:</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">"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> </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-RenderedImage jp-OutputArea-output "> +<img 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" +class=" +" +> +</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 [202]:</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">"F"</span><span class="p">]</span> <span class="o"><</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> +</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-RenderedImage jp-OutputArea-output "> +<img src="data:image/png;base64,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" +class=" +" +> </div> + </div> + </div> -</div></section><section ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div> + +</div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [203]:</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">"F"</span><span class="p">]</span> <span class="o"><</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> +<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"><</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> </pre></div> </div> @@ -17001,29 +21241,38 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> -<div class="jp-Cell-inputWrapper"> -<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +<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-RenderedImage jp-OutputArea-output "> +<img 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" 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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> -</pre></div> - </div> </div> + </div> + </div> -</div></div></section><section ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [204]:</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">"F"</span><span class="p">]</span> <span class="o"><</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>\ @@ -17040,12 +21289,38 @@ div.jp-OutputPrompt { </div> </div> -</div></section><section ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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-RenderedImage jp-OutputArea-output "> +<img 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" +class=" +" +> +</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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [205]:</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">"F"</span><span class="p">]</span> <span class="o"><</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>\ @@ -17068,6 +21343,32 @@ div.jp-OutputPrompt { </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-RenderedImage jp-OutputArea-output "> +<img 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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + </div></section></section><section ><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -17104,12 +21405,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [206]:</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_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> @@ -17123,6 +21424,32 @@ div.jp-OutputPrompt { </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-RenderedImage jp-OutputArea-output "> +<img 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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + </div></section><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -17134,12 +21461,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [207]:</div> <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> <div class="CodeMirror cm-s-jupyter"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span> @@ -17153,6 +21480,32 @@ div.jp-OutputPrompt { </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-RenderedImage jp-OutputArea-output "> +<img 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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + </div></section><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -17167,12 +21520,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [208]:</div> <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> <div class="CodeMirror cm-s-jupyter"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span> @@ -17185,6 +21538,32 @@ div.jp-OutputPrompt { </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-RenderedImage jp-OutputArea-output "> +<img 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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + </div></section></section><section ><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -17209,7 +21588,7 @@ div.jp-OutputPrompt { <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 [92]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [209]:</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">seaborn</span> <span class="k">as</span> <span class="nn">sns</span> @@ -17221,12 +21600,12 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [210]:</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">"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> @@ -17237,6 +21616,32 @@ div.jp-OutputPrompt { </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-RenderedImage jp-OutputArea-output "> +<img 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a2IDLuW4FnnVetxAs07Eht/O86RfVjrZpL6eCnOwW0k5j6KkdeMcI8JhDoNxQhGqvW4ItlEjY9IDbFWvEp6x4cQCBIbPxWzfhO/I2UNIxgmOuIGUk3akXz3JdJbllF6ZE9ZnXUzsIh4wE0nsT74J9aameDaEAwT7nsx4Z7jMUzv3l6ZDc8iOuwbhM+/hNRHc7E2zMctPkBy2QtYq6YT6jaGULcxVTrTJJKt1PiI1IDU5iVYa94GIDriBgLNNaG7uhmGQbj7OMz8VsTnPopzcDulr/2C2PjbVW8RqVb/PZMn2K4PkcFX1egvtMycBkT6X0a492RSmxdjrZuFe+wQ1qrpWKvfJtRpKOGeEzUmQeQ/qPER8Vh672YSS/4CQLjPRYQ6DvI3UJYLtupaNu9n1kM4RbsofeN+okOvIdR5uN/RRCTDnW4mT+jsvr5lMkIRwt3HEeo6mvS297HWzsA5tIPUhvmkNi4g2L5f2UYIzc7xLaNIbaHGR8RDTvEBErMfBscu++HT7xK/I9UJZl4zci7+XxIL/kz601UkFj+DXVhAZNCVnl6CIiLZqSIzefximAFCHQcS7HAB9p6NWGvext61nvS290lve59Ai86Ee00i0KanNn+ROkvvAEQ84lqlZTu4JY9jNm1PdNQ3K7SdqVSNEYoSHXcr1gdvYK2aRuqjuThFu4mOvUXXvotIudn7t5JY8hzO4Z3AqWfy1CaGYRBs1ZVgq67YhTux1s4gvfU97L2biO/dhJnfinDPiQQ7DsII6G2g1C1a8SIecB2b+LzHcYr2YOQ0JDZ+qnba8YFhmETOn4LZuDWJBX/G3rOR0mn3EBt/B4HGtfNNi4jUDmUzeV4htWlR2QcqOZPHT4HGbYiNugmn/2VY6+eQ2rgQp2g3iUVPY6x8rewSuS4jMcI5fkcVqRFqfEQ8kFz+MvbOdRAIE5twp3YW81no7PMxpzQnPutB3GMHKX39l0RHfpPQOf39jiYitYxXM3n8ZNZrTHTglUT6XIS1cSGp9XNwS4pIvvd/JD/4J6EuIwl3H49Zr5HfUUU8pcZHpJpZGxaQWj8HgOjomwg0PdvfQAJAoFHrsk0P5j2OvfsjEnMfxelzEeF+l2TMb29FxFs1NZPHL0Ykl0jvCwn3GE9663KsNTNwjuwhtXYmqfVzCHYcWLYRQqPWfkcV8YQaH5FqlN69geSyFwAI97+MUPt+PieS/2RE6xGb9F2S7/0fqXWzsD58A+fwLqKjbtIwWZE6rGwmzxtYa2aUzeQJhAmf7/1MHr8YgRChTsMInjcEu2Bt2UYI+z4m/fEy0h8vI9CmZ9lGCC06ayMEySrZ97dZxCfOkb3E5zwCrkOw4yDCvSf7HUlOwTADRAd9lUDjNiSW/IX0jg8pff2XxMbfgdmgud/xRKSGpQtWk1j275k8gba9iQ65CrN+U5+Tec8wTILtehNs1xv7wCdYa2aQ3r4Ke+da4jvXYjZtT7jnJILtz8cwA37HFakyNT4i1cBNHKd01gNglWI270h0+HX6LVktFzpvKGbDlsRnP4RTtIeSafcQG/Ntgm16+B1NRGpAbZzJ46dAsw7Ext2Gc3Qf1tpZpD5einNwO4l5j2HUb0q45wRCnYZpox7JaLqwXaSKXCdNfO6juEf3Y9Rr/K8d3MJ+x5JyCDQ7h5xLf4HZrANYpcRn/gFr7Qxc1/U7moh4xHXSWGtnUPJ/Py1regyTUM9J5F7x6zrb9Pwns8FZRIddS+7X/h/hvlMwIvVwjx0kuexFSv76PZIrp+HEi/2OKVIpOuMjUgWu65Jc+iL2no0QihKbeCdmLM/vWFIBZk5Dci76MYklz5P+eAnJ5X/HPlRQdtZODaxIVsm0mTx+MmN5RPpdQrjXl0htXoK1bhbusYNYH7yOtebtsp3uekzQJcKSUdT4iFRBav0cUpsWAgax0d/SD88MZQRCREdcT6pJW5LvvkR667uUHt1HbNzt2t5VJAucaiZP5IIryi7d0q6OX8gIRQh3H0uo6yjS21dhrZ2Bc3A7qQ3zSW1YQLD9+WUbITTr4HdUkTNS4yNSSemCtSSXvwRAZOD/EGzX299AUiWGYRDuPg4zvxXxuY/iHNxO6bRfEBt3O4GzzvU7nohUwqlm8gTPG0ZkYObO5PGLYQYIdRhA8Jz+2Hs3Ya2Zgb1zLentK0lvX0mgRaeyrbDb9lQzKbWWGh+RSrAP7yI+7zFwXUKdhxPqMcHvSFJNgq26ls37mf0QzuFdlL55P5Gh1xDuPMLvaCJSAaecyTP0GoItOvmcLLMZhkGwZReCLbtgH96FtXYG6S3LsfduJr53M2bDloR7TiR47iCMQMjvuCInUOMjUkFOvJj4rAcglSDQojORIddoB7csY+Y1I2fK/5JY+BTp7StJLn4Wp7CAyKCvZuVMD5FscuqZPFMI95iAEdDf3+oUaNSa2Mhv4vS7DGv9bFIbF+Ic2UNi8TMYK18j1H0c4S4jMSK5fkcVAdT4iFSIa6dIzH4Y99ghjLzmxMbdph+kWcoIRYmOvQXrwzewVk4j9dE8nMO7iY69RRtYiNRSdXkmj5/Meo2IDrySSN8vY21YSGr9bNzSI1grXsH68A1CXUYS7j5e90yK7/SOTaScXNclsfgv2Pu3QDhGbOIdGNF6fscSDxmGSaTvFMxGbUgseBJ77yZKp91DbMIdBBq39TueiPyLZvLUDkY4h0jvLxHuMZ701nex1s7EKdpNau1MUuvmEOx4QdlGCNoISHyixkeknKw1b5HesgwMk9jYWwk0bOl3JKkhobP7Yk75OfHZD+IWH6D09V8RHXkjoXMG+B1NpE5znTSp9XNIrpwO6WTZTJ4eE4icPwUjFPU7Xp1lBIKEOg0jeN4Q7J1ryzZC2LuZ9JZ3SG95h0CbHmUbIbTsokvFpUap8REph9T2VVgrXgUgMvgqgq27+5xIalqgUStyL76L+LzHsXd/RGLuYzh9dhLud4l2MBLxgWby1H6GYRJs25tg297YB7aVbYSwfSX2znXEd67DbNKOcK8vEWzfD8MM+B1X6gA1PiJnYB/aQWLBEwCEuo0h3G2Mz4nEL0a0HrFJ3y2bB7J2JtaHb2AX7iQ2+maMcMzveCJ1gmbyZKZAs3OIjb0Vp/gA1tqZpDYvxTm0g8S8xzHqNyHcYwKhTsMxQhG/o0oWU+Mj8gWc0iPEZz0IaYtA6+5EBn3N70jiM8MMEB14JYFGbUgseRa7YDWl039JbMJUzAZn+R1PJGuVzeR5h+TylzWTJ4OZec2IDr2GcL9LSH00j9RH83CPHSL5zl9JrppOuNsYQt3GahMZ8YQaH5HTcNMW8VkP4pYcxmzYktjYW3QqXj4XOm8IZsMWZfN+juyhZNq9xMZ8m2CbHn5HE8k6dtEekkuf+4+ZPC2JDL1WM3kymBmtT+T8iwn3mkRq81KstTNxjx3E+uCfWGtmEDpvCOGeE/ULJalWanxETsF1XRILn8I5uB0jUo/YxDsxwjl+x5JaJtDsHHIu/QXxOY/g7N9KfOYfiAy4glDPibphV6QafD6TZ+0McDSTJxsZwUjZWZ4uo0h/ugprzds4B7eT2riQ1MZFBM/uW7YTXPOOfkeVLKDvGiKnYK2aTnrbCjADRMffjpnXzO9IUkuZOQ3JmfwjkktfILV5Mcn3/o5dWEB0+HUQ1K5SIpV18kyeXkSHXK2ZPFnKME1C5/Qn2L4f9t7NWGtnYBesIf3pKtKfriJw1nllDVDbXrqXSypNjY/If0ltXY71wesARHUphZSDEQgRGX4dZuO2JN/9G+mt71J6ZC/1J90B+ZpYLlIRzvHDJN/563/N5LmKYLu+OpNaBxiGQbBlZ4ItO2Mf3l22E9zWd7H3fUx838eYDVsQ6jmR0LmDMQIhv+NKhlHjI/If7AOfkFj0FEDZN9bOw31OJJnCMAzC3cdiNmpFYs6jOIc+pfjVu8m9/EdQT9vripyJ69hlM3lWTYdU4l8zecYTOf9izeSpowKNWhEbeSNO/8tIrZ+DtWEBzpG9JBc/i/X+a4R6jCPcZRRGRL9gkvJR4yPyL87xwrId3Ow0wXZ9iAy4wu9IkoGCLbuQc8ndZZseHN7JnhfvJmf41wmcN8LvaCK1lr1/K4mlz+EUls3kMZt3JDr0WgKN9UsDATM3n8gFVxDucxGpjQux1s/GLSnCWvEq1odvEuo8gnCP8Zj1GvsdVWo5NT4igJtKEJ/1AG68GLNRG6Kjb8YwdQ2xVI6Z15ScKT8juehpUtvep3Ths4QOFBAZ/FUMU992RT5TNpPnVVKbFpZ9QDN55AsY4RjhXpMIdR9H+pP3sNbMwCnaRWrdLFLr5xLsMKDsPqDGbf2OKrWUfgJLnee6Don5T+AU7sSI5ZXt4KbLKqSKjFCU3Am3YWyYSdGil0htmIdTtIvo2Fs1n0LqvFPP5BlK5IIr9PdDzsgIBAmdN4TguYOxd64r2whhz0bSW98lvfVdAq27E+vzJdyGA/yOKrWMGh+p86wVr5Le8SEEgsTGT9Wpcqk2hmGQP/QrWDnNKZn7J+y9mymddg+x8VMJNGnndzwRX2gmj1QXwzAItu1JsG1P7IPbsdbMIL39fexd6zm+az3WivZl4wXa9dMcPgHU+Egdl9q8BGvN2wBER9ygOQHiiXD7vrgX/5z4rIdwi/dT+vp9REfeSKiDfhspdYdm8oiXAk3bExt7C07xAax1s8p+vu/fjjXncYx6jQn3nFh2CaWu6KjTauQC2u3bt3PDDTfQu3dvBg0axK9+9SsSiURNHFrktNJ7N5NY8hcAwn2/TKjjIH8DSVYL5Lci95K7CLTuDrZFYt5jJFe8ius6fkcT8Vy6YDUlr/wMa/Wb4NgE2vYi94r7iPS+UE2PVCszrxnRIV+nwTV/JH/4lRjR+rjHC0m+81eO/+17JN//B07pUb9jik88/25TXFzMtddeS8uWLXnooYc4fPgwv/nNbzhy5Ai///3vvT68yCk5xQdIzH4YHJtg+36Ez7/Y70hSBxiRXGITv0NyxSuk1s7EWv0m9uGdxEbfjBHO8TueSLXTTB7xixmtT/6wy3E7jyW+YTHW2pm4xQewPiw76xg6dyjhnhMxG57ld1SpQZ43Pi+//DLFxcVMnz6dRo0aARAIBPj+97/Pt7/9bTp06OB1BJETuFZp2Q5uyeOYTdsTHfVN7R4kNcYwA0QHXkmgcVsSi5/BLlhD6fRfEht/h34AS9bQTB6pLYxgmHDX0YQ6jyT96SqstTNwDmwjtWkhqU2LCJ7dh3DPSQTOOtfvqFIDPG98Fi9ezKBBgz5vegAmTJjAT3/6UxYtWqTGR2qU69jE5z2OU7QHIzef2PipGMGI37GkDgqdOxizYYuyeT9H9lIy/R5iY75NsE1Pv6OJVIlm8khtZJgmoXP6E2zfD3vfx1hrZmAXrCb96QekP/2AQPNzCfWaRLBdb/0yNIt53vh88sknXHbZZSd8LBwO07ZtWz755BOvDy9yguTyl7F3roNAmNiEOzBz8/2OJHVYoGn7smGncx7B2b+V+Mw/EhlwOaGek3QZkGQczeSRTGAYBsEWnQi26IRdtJvU2pmktryLvX8L9uwtmA3OItRzIqFzB2MEw37HlWpWI/f45OWdvCd/Xl4eR49W7eayYND/b6SBgHnCP6V6VWd9k+vnk1o/B4DcsTcTPuucKr9mptP69Va56pvXiNDFP6F08fNYGxeRfO//cA/vJGfUDfqhewZav94qb31d18XavIz4uy/hxstm8oQ7DyM26H80k+cLaP16qzz1DTZtQ2TMN3EGXk5y7WySH83HObqP5JK/YK2cRqTnOCLdxmBGc2sqdsbI1PXr21YqrutW6TeapmmQn197FmJeXszvCFmtqvWNb19L0ZLnAcgf+TXy+42shlTZQ+vXW+Wpb/4lt1Pc9lwKZz+DteVdOLafsy7/EcG8JjWQMLNp/Xrri+prHdrFoRlPkij4CIBQk9Y0mXQTsbbdaipextP69Va56pufC62vwxlzJcWr53D0vTexjxWSeO9Vkh+8Sf0+Y2kw4EJCDZp5HzjDZNr69bzxycvLo7i4+KSPHzt2rEr39ziOS3FxaVWiVYtAwCQvL0ZxcRzb1ra01a066msX7eXYa78D1yF83mDcLhMoKiqp5qSZSevXWxWub4fh1PtyU0pmPYy1bxs7n/oB9SbersGOp6H1660vqq+bSpJY9U8Sq98um8kTDBPrN4VIr0kkAkES+h57Rlq/3qp0fc8bQ/0OI7C2vkfyw7exD++keMWbFL//NuGOFxDp8yWCGkBd69ZvXl6sXGefPG98OnTocNK9PJZlUVBQcNK9PxWVTvtf6M/YtlOr8mSbytbXTRyn5O0/4CZLMZt3JDz0G9i2C7jVHzKDaf16qyL1NZp3KrvvZ9ZDOId3cuz1+4kM+TrhLiO9DZnBtH699d/1TResIbHsRdxjBwEItO1FdMjVmPWbYruA/l9UiNavtypXX5NAh0HEzhmIvWs91toZ2Ls3YG15F2vLuwRadSPcaxKBVt3q/P2YmbZ+PW98hg8fzuOPP05RURH5+WU3ks+ZMwfLshgxYoTXh5c6zHXSxOc+int0P0a9xv/awU33TEjtZ9ZvSs6U/yWx6CnS294nueQvOIUFRAZ9TcMexTfO8cMk3/0b6e0rAc3kkexnGAbBNj0ItumBfehTrDUzSG9bgb37I+K7P8Js3JZwz4kEOwzAMPW9ORMYrut6+qvv4uJiJk+eTKtWrbjlllsoLCzk/vvvZ+jQoVUaYGrbDocP+38qPRg0yc/PpaioJKM63kxR2fq6rktyyXNluwuFouRM+RmBRtpK9b9p/XqrqvV1XRdr9ZtY778GuARadCI69lbdMP4vWr/e+qy+hwuLia+eTXLVNM3kqUZav97yqr5O8UGsdbNIbV4MaQsAo15jwj3GE+o8os78naht67dRo9xyXermeeMDsH37dn71q1+xatUqotEokydP5vvf/z7RaOUXhxqfuqGy9bXWzSL57kuAQWziHQTb9vYsYybT+vVWddU3veND4vOfgFTi87OXAV1jrvXrsWDQJFa6m/1vPI5dWABoJk910vr1ltf1dRPHsTbMJ/XRXNz4v+5lD+eUDUvtPhYzp2G1H7M2qW3rt1Y1Pl5Q41M3VKa+6YI1xGc9AK5LZOCVhHtO9DZkBtP69VZ11tcu2kN81oO4xfshECY68gZCHS6opqSZSevXO27iONbKf2BtWAi4msnjAa1fb9VUfd20RWrLO1hrZ+Ae3V/2QTNI6LzBhHpOJNCwpWfH9lNtW7/lbXx0QaJkFfvwLuLzHgfXJdR5OKEeE/yOJFItAvktyb3kLuLzHsfetZ7EvMdxCncS7ncphqk3olI9XNclveUdkstfxk38eyZPqP/lusRS5BSMYJhwl5GEOg0nveNDrLUzcPZvJbVpMalNiwm260Oo15cInnWu31EFNT6SRZx4cdmZnlSCQIvORIZcoxtuJasYkVxiE79LcsUrpNbOwFr9JvbhncRG34wRzvE7nmQ4u2gPyaXPY+/dBICZ34rmk28mUf/sWvEbXZHazDBNQu3PJ9T+fNL7tpBa8zbpHR9+/sds3pFwz0kEz+6js6Y+UuMjWcG1UyRmP4x77BBGXnNi427T7leSlQzTJDrwfwg0bkNi8bPYBWsonXYvsQl3YDZs4Xc8yUBuOon1wRtYa2eUzeQJhAmf/2Vy+nyJWJMGmskjUkHBs84leNYd2Ef2kFo7k9TH7+Ds30pizsMYDc4i3HMioXMHa6dZH+idoWQ813VJLP4L9v4tEI4Rm3gHRrSe37FEPBU6dzBmwxbEZz+Mc3QfJdPvJTb62wTb9vQ7mmSQL5rJY5TjenkROb1Aw5YEhl9PuN+lpNbPxdowH/foPpJL/oK18jVC3cYS7jpa71lqkBofyXjWmrdIb1kGhkls7G1ZeyOhyH8LNG1PziV3EZ/zCM7+rcRn/pHwgMsJ95qkyzzlC2kmj0jNMXMaEhnwFcK9LyS1eTHWutm4xwuxVr6GtfotQp2HE+4xHrN+U7+jZj01PpLRUttXYa14FYDIkKsJtu7mcyKRmmXmNCRn8o9ILnuR1KZFWCv+D6ewgOiI6zCCEb/jSS3jOjap9XM1k0fEB0Y4RrjHBELdxpD+ZEXZRgiFO0mtn0Pqo3kEz+lPuNckAk3O9jtq1lLjIxnLPrSDxIInAAh1G0O462ifE4n4wwiEiAz7BmaTdiSX/ZX0J8spPbqX2PipmPUa+x1Pagl7/1YSS5/DKdwJaCaPiF8MM0jo3MEEOw7C3v0R1poZ2Ls/Iv3Je6Q/eY9Aq66Ee04i0Lq7zsBWMzU+kpGc0iNlO7ilLQKtuxMZ9DW/I4n4yjAMwl1HYzZsSWLuoziHdlA67R6iY28l2KKT3/HER27iOMkVr5LatIjPZ/IMuJxQ5+HaXUrER4ZhEGzdnWDr7tiHdmCtnUH6kxXYuzcQ370Bs1Ebwr0mEewwAMPUW/bqoO94knHctFU2yLGkCLNhS2Jjb8EwA37HEqkVgi07k3PJ3ZiN2+DGi4m/+VusDQv8jiU+cF2X1MfLKPm/n5DatBBwCZ43hNwrfkO4y0g1PSK1SKBJO2Kjv0Xulb8l1H0cBCM4h3eSWPAkJS/9EGvtTFwr7nfMjKf2UTKK6zokFj6Fc3A7RqQesYl3an6JyH8x6zch58v/S2LR06S3rSC59DmcwgIig6/SNu91xEkzeRq2JDL0GoItO/ucTES+iFm/CdHBVxHpOwVr4wJS6+fglhwmufxlkh+8TrjraELdx2HmNPQ7akbST0DJKNaq10lvWwFmgOj42zHzmvkdSaRWMkIRomO+jdW4Ldb7/yC1cQFO0W6i427DjOX5HU88crqZPOEeE9X0imQQI1qPSJ+LCPeYQGrLO2VnfI7uw1r9FtbaWYTOHUSo5yQC+drJtiL0XVAyRmrrcqwPXgcgOuwbum9B5AwMwyDSZzKBRq2Jz/8T9r6PKX3tF8Qm3EGgSTu/40k1O+VMnsFXY+Zpi1yRTGUEw4S7jCTUeTjpHatJrZmBvX8Lqc1LSG1eQqBt77Kd4M46TxshlIMaH8kI6f2fkFj0FAChnpMIdRrmcyKRzBFs15uci+8iPvtB3KP7KX39PqIjrifUcaDf0aQanHImz+CrCJ6tmTwi2cIwTEJn9yV0dl/sfVvKNkL49EPsgtXEC1ZjNjuHcK8vlc3iMnX/3umo8ZFaL330IMfffgDsNMF2fYgMuNzvSCIZJ5DfktyL7yo787NzHYn5f8I5vJNwv8v0QzJDaSaPSN0UOOtcYmedi3NkL9baWaS2LMU5sI3EnEcw8poT7jmB0HlDMYJhv6PWOmp8pFZzUwn2vX4/bvwoZqM2REffrDdpIpVkRHKJTfgO1vuvYq15G2v1W9iFO4mN+ZY2CckwmskjImbDFkSHf4Nwv0tIfTQXa8N83OL9JJc+j7VyGqHuYwl3HYMRred31FpDjY/UWq7rUDL3T6QOfIoRa1C2g5t+iylSJYZpErngCszGbUgsegZ751pKp91LbMIdmA1b+B1PzsBNlpBc8QqpjZrJIyJlzJwGRPpfRrj3haQ2LcZaNwv3eCHWymlYq98i1GkY4R4Tdb8fanykFrNWvEpq+wcYgRD1Jt0BmkAvUm1CHQdhNmhBfPZDOEf3UTLtXmJjvkWwbS+/o8kpuK5Less7JJe/jJs4BkDwvCFELvgf7dInIgAYoSjhHuMJdRtDetv7WGtm4BTuIPXRPFIb5hNs359wry8RaHq231F9o8ZHaqXU5iVYa94GoOnkW0md1ZF02vE5lUh2CTQ9m5xL7iYx91HsfR8Tn/kA4QGXEe51oW6Kr0XsI3tILtFMHhEpH8MMEOo4kGCHC7B3b8BaOwN713rS21aQ3raCQMsuZTvBte5R577Xq/GRWie9dzOJJX8BINpvCvW6D6OoqMTfUCJZysxpQOzCH5Jc9iKpTQuxVryKc6iA6MgbMIIRv+PVaW7awvrwjbJfAmkmj4hUkGEYBFt3I9i6G3ZhAdaaGaQ/eQ97z0biezZiNmpNuOckgh0uqDPfU+rGVykZwyk+QGL2w+DYBM/pT7T/JX5HEsl6RiBIdPg3MJu0Jbnsr6S3raD06H5iE6Zi6hJTX5w0k6dNT6JDvq5r9EWkUgKN2xIbfTPOgK9grZtNatMinMO7SCz8M8b7r5ZdItd5JEY45ndUT6nxkVrDtUqJz3wAN3kcs2l7oiNv1M26IjUo3HU0Zn4rEnMewSncQelrvyA67jYNC65Bp57J8zWCZ59f5y5JEZHqZ9ZrTHTQV4n0/TLWxgWk1s3BLSkiufzvJFf9k3DXUYS6j8PMzfc7qifU+Eit4Do28bmP4RzZg5GbT2z8VF1mI+KDYItO5Fxyd9mmB4UFxN/8LZEhVxHuOtrvaFntlDN5uo8rm8mT5b+BFZGaZ0RyifSeTLjHBFJb3iG1dmbZXKA1b2Otm0Ww42DCvSYSyG/ld9RqpcZHaoXk8pexd62HYLhsW90s/U2DSCYw6zchZ8rPSCx8mvS2FSSXPo9TWEBk8NV15jrwmnTSTJ5mHYgOu5ZA47Y+JxORbGcEQoQ7jyDUaRh2wRqsNTOw931M+uMlpD9eQqBtr7Kd4M46LyvOOusnmPjO2jCf1Po5AERH3USgydn+BhIRjGCE6JhvYzVpi7XiH6Q2LsQp2kN07K2YOQ38jpcVNJNHRGoLwzAJtutDsF0f7P1byzZC+PQD7II1xAvWYDY9h3CvSWWX3WbwIHk1PuKr9K6PSC57EYBw/8sIte/ncyIR+YxhGER6TybQqDXxeU9g7/uY0mn3EJswVb+gqALN5BGR2izQvCOx8bfjHNmHtW4mqY+X4hzcRmLuoxh5zQj3nEig63Ag1++oFabGR3zjHNlLfO6j4DoEzx1MuPdkvyOJyCkE2/Ym55KfE5/1EO7RfZS+fh/RETcQ6jjQ72gZRzN5RCRTmA3PIjrsG4T7XUrqo7lYH83DLT5AcunzWCunYQz4Em6XCUDmnAFS4yO+cBPHKZ31AFilmM07Eh1+XVZcOyqSrQINW5J78c+Jz38Ce+daEvP/hFNYQLj/VzL6soeacvJMnhDhvlMI99RMHhGp3cxYHpF+lxLudSGpzYux1s3CPXaIosV/J8eIEeg00u+I5abvtlLjXCdNfO6juEf3Y9RrXLaDWyDkdywROQMjkktswp1YK/+BtfotrDVvYx/eRWz0zRiRzLvkoaZoJo+IZAMjFCHcfRyhrqNxd6zCLNyK2a43jt/BKkCNj9Qo13VJLn0Be89GCEWJTbxT17SLZBDDNIkMuByzURsSi57B3rmWkum/LLvvp2FLv+PVKk5JEcl3/qqZPCKSVQwzQOjcgeQPGENRUQlOOnNaHzU+UqNS68umBWMYxMZ8i0CjNn5HEpFKCHUciNnwrH/f9zPtl8RG30ywXW+/o/nOdWxSH80luVIzeUREahM1PlJj0gVrSC5/GYDIBVcSbNvb30AiUiWBJmeTc+kvSMx5BHvfx8RnPUi4/2WEe19YZ89o2Ac+IbHkOZzCAkAzeUREahM1PlIj7MO7iM97HFyXUOfhhHqM9zuSiFQDM5ZH7MIfknznr6Q2LsB6/1WcwgKiI27ACEX8jldjymbyvEpq40I0k0dEpHZS4yOec+LFxGc9AKkEgRadiQy5ps7+NlgkGxmBINFh12I2bkNy2V9Jb1tB6dF9xMZPxazfxO94nnJdl/TWd8tm8sSLAQieO4TIQM3kERGpbdT4iKdcO0Vi9sO4xw5h5DUnNu42bd0qkqXCXUdj5rciMecRnMICSqfdQ3TsrVk7o8Y+suffm7UAZsMW/5rJ08XnZCIiciqen39ftmwZ3/ve9xg7diydOnXi3nvv9fqQUku4rkti8bPY+7dAOEZs4h0Y0Xp+xxIRDwVbdCLn0l9gNm6HmzhG/K3fYW2Yj+u6fkerNm7aIvn+Pyh99edlTU8gRLj/ZeRc9ks1PSIitZjnv3pfvHgxGzdupH///hw9etTrw0ktYq15i/SWd8AwiY29TVvditQRZr3G5Ez5KYlFz5D+5D2SS5/HOVRAZMjVGX/GN12wlsSyF/5rJs/VmHnNfE4mIiJn4vlPoB/96Ef85Cc/AeC9997z+nBSS6S2r8Ja8SoAkSFXE2zdzedEIlKTjGCE6OhvYTVui7XiVVKbFuIU7SY67jbMnAZ+x6uwk2fy5BMZfJVm8oiIZBDPGx/T1G42dY19aAeJBU8AEOo2lnDX0T4nEhE/GIZBpPeFBBq1Jj7/T9j7t1A67R5i46cSaHq23/HKRTN5RESyR2ZfcyC1jlNSVLaDW9oi0Lo7kUFf9TuSiPgs2LYXuRffRXzWgzhH91H6z/uIjrieUMdBfkf7QprJIyKSXTK68QkG/T+bFAiYJ/yzLnNTSUpnP4RbUoSZ35L6E27DCIeq9Jqqr7dUX2+pvv+hSStCX/kFx+c8TrpgDYn5T+Ae3kls4BUYlbwywKv6OokS4u+9gvXRAsDFiOQSG3gF4a4j6tRMHq1fb6m+3lJ9vZWp9a1w43Ps2DEOHDhwxse1adOGcDhcqVDlYZoG+fm5nr1+ReXl1e1LHlzX4cC0P2Ef3I4Zq0+rr/6MUH7Tanv9ul5fr6m+3lJ9P5NL/lU/o2jRyxx55zWSq9/GLN5Ls4vvJBCr/I6P1VVf13U5vn4xh+c9h11SthlPvR4jaTzmGgK5mXdfUnXR+vWW6ust1ddbmVbfCjc+c+bM+Xyzgi8yffp0unTxbltPx3EpLi717PXLKxAwycuLUVwcx7Ydv+P4Jr7iNRIb3wEzQM6E2zlOfSgqqfLrqr7eUn29pfqemtH7YnJzz6JkwVPEt33Izqd/SL1JdxJo1KpCr1Od9bWL9lC6+DnSu/89kydnxDcItepCsQVYVf9+lmm0fr2l+npL9fVWbatvXl6sXGefKtz4XHrppVx66aWVClXd0mn/C/0Z23ZqVZ6alNq6nMTK6QBEh30Do9l51V6LulzfmqD6ekv1PZnZfgA59ZsTn/0QztH9FP/jHmKjv0WwXe8Kv1ZV6uumLawP38Ba8zY4dtlMnr5fJtxzEkYgqP9vaP16TfX1lurrrUyrb0bf4yP+sw98QmLRUwCEek4i1GmYz4lEJFMEmrQj55K7Scx5BHvfx8RnPUi4/6WEe0+ukS2iNZNHRKRu8bzx2b17N+vWrQMgHo9TUFDAzJkzAZg4caLXhxcPOccLic96EOw0wXZ9iAy43O9IIpJhzFgesQt/SPLdv5HaMB/r/X/gFO4kOuIGjFDEk2NqJo+ISN3keePz3nvvnXBP0JIlS1iyZAkAmzdv9vrw4hE3lSA+6wHceDFmozZER99c6Z2ZRKRuMwJBokOvwWzUhuSyF0lvW0Hp0b3Exk/FrF99m6RoJo+ISN3meeNTm+4Jkurhug6J+U/gFO7EiOURm3gnRijqdywRyXDhrqMwG7UiMecRnMKdlE67l+jYWwm27Fzl1z55Js85RIdeS6BJuyq/toiIZAbd4yMVZq14lfSODyEQLPuNbL3GfkcSkSwRPOs8ci65u2zTg0M7iL/1OyKDv0ao6+hKXYbmJktIrniV1MaFgAvhHCIDLifUpW7N5BERETU+UkGpzUvKdj8CoiNuINC8o8+JRCTbmPUak/Pln5JY/CzprctJLnsBp7CAyJCvYwTK92PLdV3SW98lufxl3HgxAMFzBxMZeCVmLM/L+CIiUkup8ZFyS+/dTGLJXwAI9/0yoY6D/A0kIlnLCEaIjrqZVOO2JN97hdSmRdhFu4mNuw0zp+EXPtc+sofk0hew9/x7Jk9k6DUEW3o3W05ERGo/NT5SLk7xARKzHwbHJnhOf8LnX+x3JBHJcoZhEO71Jcz81sTnP46zfyul0+4hNn4qgabtT3r8mWbyiIhI3aYLnOWMXKuU+MwHcJPHMZu2JzryRl0bLyI1Jti2J7kX343ZsAVuSRGl//w1qS3vnPCY9M61lLzyM6wP3wDHJtCmJ7mX30ekz0VqekREBNAZHzkD17GJz30M58gejNx8YuOnYgS9ma0hInI6ZsOzyLn458TnP4FdsIbEgidxi3ZSf+gUjs96mtQn7wP/mskz6GsE2/fTTB4RETmBGh/5QsnlL2PvWg/BMLEJd2Dm5vsdSUTqKCOcQ2z8HVgrX8Na/SbJ1TMoWD0TcDWTR0REzkiNj5yWtWE+qfVzAIiOuolAk7P9DSQidZ5hmkQGfAWzcRsSi56GtEWgeQciQ67RTB4REflCanzklNK7PiK57EUAwv0vI9S+n8+JRET+LdThAsLN2xNLF5Fs3Anb9juRiIjUdmp85CTOkb3E5z4KrkPw3MGEe0/2O5KIyEkCDc8iN78DVlEJ4PgdR0REajltzSUncBPHKZ31AFilmM07Eh1+nW4QFhEREZGMp8ZHPuc6aeJzH8U9uh+jXuOyHdwCIb9jiYiIiIhUmRofAcB13X9POg9FiU28EzOW53csEREREZFqocZHAEitn01q0yIwDGJjvkWgURu/I4mIiIiIVBs1PkK6YA3J5S8DELngSoJte/sbSERERESkmqnxqePsw7uIz3scXJdQ5xGEeoz3O5KIiIiISLVT41OHOfFi4jP/CKkEgRadiQz5unZwExEREZGspManjnLtFPHZD+EeL8TIa05s3G0YAY11EhEREZHspManDnJdl8TiZ3H2b4VwjJyJd2JE6/kdS0RERETEM2p86iBr9Vukt7wDhkls7G2YDVv4HUlERERExFNqfOqY1PaVWO+/CkBkyNUEW3fzOZGIiIiIiPfU+NQh9qEdJBY8CUCo21jCXUf7nEhEREREpGao8akjnJIi4rMegLRFoHV3IoO+6nckEREREZEao8anDnDTybId3EqKMBu2JDb2Fgwz4HcsEREREZEao8Yny7muQ2LhUzgHt2NE6hGbeCdGOMfvWCIiIiIiNUqNT5azVr1Oetv7YAaIjr8dM6+Z35FERERERGqcJlZmsdTW5VgfvA5AdNg3CLbo5HMiEREREakMx3Gw7bTfMQBwHINEIoBlJbFt19NjBQJBTLN6ztWo8clS9oFPSCx6CoBQz0mEOg3zOZGIiIiIVJTruhQXHyYeP+53lBMcOmTiOE6NHCsWq0deXiMMw6jS66jxyULO8ULisx4EO02wXR8iAy73O5KIiIiIVMJnTU+9evmEw5Eqv/mvLoGA4fnZHtd1sawkx48XAdCgQeMqvZ4anyzjphLEZz2AGy/GbNyG6OibMarp9KCIiIiI1BzHsT9veurVy/M7zgmCQZN02vszPuFwBIDjx4uoXz+/Spe96R1xFnEdh8T8J3AKd2LE8ohNuBMjFPU7loiIiIhUgm3bwL/f/NdVn339Vb3HSY1PFrHef5X0jg8hECQ2fipmvaqdDhQRERER/9WWy9v8Ul1fvxqfLJHavARrzdsAREfcSKB5R58TiYiIiIjUHmp8skB672YSS/4CQLjvlwl1HOhvIBERERGRWkaNT4Zzig+QmP0wODbBc/oTPv9ivyOJiIiIiJzW9ddfxdCh/fjgg5U1elxPGx/btvnzn//M1VdfzcCBA+nfvz9XXXUV7777rpeHrTNcq5T4zAdwk8cxm7YnOvJGDEO9rIiIiIjUTjt2fMrHH28GYM6cmTV6bE/fJScSCZ544gk6d+7Mb37zG/7whz/QvHlzrrvuOhYsWODlobOe69jE5z6Gc2QPRm4+sfFTMYJ1e8cPEREREandZs+eQSAQ4PzzB7BgwTxSqVSNHdvTOT7RaJR58+bRoEGDzz82dOhQPv30U5555hlGjRrl5eGzWvLdl7B3rYdgmNiEOzBz8/2OJCIiIiLyhebMmUnfvv244oqv8oMf3Mm77y5j+PCRNXJsTxufQCBwQtMDZdvRde7cmVWrVnl56KxmbZhP6qO5AERH3USgydn+BhIRERGRGuO6LlbK++GhpxMOVe6isfXr17Fnz26uvfYG+vcfSMOGDZk9e0Z2ND6n4jgOH374IR06dKjyawWD/t/PEgiYJ/zTa6md60kuexGA6AWXEzt3QI0c1y81Xd+6RvX1lurrLdXXW6qvt1Rfb2VLfR3n5Pk1ruvymxc/YOvuoz4kKnNu6wb877X9MAxw3fI/b86cGYTDYUaMGE0wGGTUqHG89dY/KSk5Tm5uvTM+PxAwqvT+v8YbnxdeeIHt27dz7733Vul1TNMgPz+3mlJVXV5ezPNjWIW72TPnUXAd6vUYQdMx/1NnBlrVRH3rMtXXW6qvt1Rfb6m+3lJ9vZXp9U0kAhw6ZJ7wht91XWrL27+KNJa2bTN//lwGDx5Kw4Z5AEya9CWmTXuFJUsWMnnyl0/7XMcxME2TBg1yiEajlc5b4cbn2LFjHDhw4IyPa9OmDeFw+ISPrVixgt/97ndcf/319O/fv6KHPoHjuBQXl1bpNapDIGCSlxejuDiObXt3ytFJHOfYP+7DSZQQOOtcgoOv4cgR/79+r9VUfesq1ddbqq+3VF9vqb7eUn29lS31tawkjuNg2y7p9L+/jh9f1dfXS90iYRPDMLBtp9xnfJYvf5eiosMMHjyMoqKys1Vt2pxNs2bNmTnzbSZOnHza59q2i+M4HD1aSjxun/T5vLxYuZqwCjc+c+bM4Sc/+ckZHzd9+nS6dOny+X9v2rSJW265hbFjx/KDH/ygooc9pf9cAH6zbcezPK6TJj7zYZyj+zHqNyE67nZsNwC16Ov3mpf1FdXXa6qvt1Rfb6m+3lJ9vZXp9bXtU3cVhmEQCQdqOM3JKnqZG8Cvf30PcM8Jnzt06CCFhYdo3LjJF77GfzeAFVXhxufSSy/l0ksvrdBzCgoKuPHGG+natSu//e1v68zlWdXBdV2SS1/A3rMRQlFiE+7EjOX5HUtEREREpFwSiQSLFy9i2LCRXH75lSd87siRI9x114+ZN282V1zxNU9zeH6Pz8GDB7n++utp0qQJjz322EmXv8kXS62bTWrTIjAMYmO+RaBRa78jiYiIiIiU29Kli4jHS7n88ivp27ffSZ9/6aWuzJ490/PGx/MBpjfeeCOFhYXcdtttbN26ldWrV3/+R75YumA1yfdeBiBywZUE2/b2N5CIiIiISAXNnj2T5s3Pok+f80/5+YkTJ7Np0wYKCnZ4msPTMz6HDh1i06ZNANx6660nfX7z5s1eHj6j2Yd3EZ/3J3BdQp1HEOox3u9IIiIiIiIV9tvf/vELP3/ZZVdw2WVXeJ7D08andevWam4qwYkXE5/5R0glCLToTGTI13VflIiIiIhIFWT2VKcs5KYt4rMfwj1eiJHXnNi42zACNT5uSUREREQkq6jxqUVc1yWx5C84+7dCOIeciXdiRM88xVZERERERL6YGp9axFr9Fukt74BhEht7K2bDFn5HEhERERHJCmp8aonU9pVY778KQGTI1QRbd/M5kYiIiIhI9lDjUwvYhz4lseBJAELdxhLuOtrnRCIiIiIi2UWNj8+ckiLisx6EtEWgdXcig77qdyQRERERkayjxsdHbjpZtoNbSRFmw5bExt6CYQb8jiUiIiIiknXU+PjEdR0SC5/CObgdI1KP2MQ7McI5fscSEREREclKGhDjE2vV66S3vQ9mgOj42zHzmvkdSURERETEc+++u5RXXvk7mzZtoLS0hMaNmzBo0FC++tWradWqtWfHVePjg9TW5VgfvA5AdNg3CLbo5HMiERERERHvPfnkYzz//DMMHz6KH/zgJ+TnN2Lv3j3MnPkWd955C6+88k/Pjq3Gp4bZ+7eSWPQUAKGekwh1GuZzIhERERER7y1f/g7PP/8MX//6ddx8862ff7x3775MmjSZpUsXe3p83eNTg5zjhcRnPwR2mmC7PkQGXO53JBERERGRGvHSSy/SqFFjbrjh5lN+fujQ4Z4eX41PDXFTCeKzHsCNF2M2bkN09M0YpsovIiIiItkvnU6zbt0a+vUbQDDoz0VnutStBriOQ2L+EziFOzFiecQm3IkRivodS0REREQykOu6kLb8CxAMV/gpxcVHsawkzZo19yBQ+ajxqQHJFa+Q3vEhBILExk/FrNfY70giIiIikoFc16X0n/fh7N/qW4ZA83MJXvq/FXqO67oAGIbhRaRy0bVWHkttXkJq7QwAoiNuJNC8o8+JRERERCSTGfjXPFRWgwYNCYcj7N+/z7cMOuPjofTezSSW/AWAcN8phDoO9DeQiIiIiGQ0wzCIffmnvl/qVnbmxi3/U4JBevbsxcqVK0in077c56MzPh5xig+QmP0wODbBc/oTPn+K35FEREREJAsYhoERivj3p5KXq1155dUcPlzIs8/++ZSfX7ZsSVXKckY64+MB1yolPvMB3ORxzKbtiY68EcNQjykiIiIiddfAgYO55prree65p9mxYztjx04gP78R+/btZdast9m5s4AhQ7ybcanGp5q5jk187mM4R/Zg5OYTm3AHRjDidywREREREd/ddNMtdO/ek1dffZn/7/+7j9LSEpo0aUr//hdw++3f9fTYanyqWfLdl7B3rYdgmNiEOzFzGvodSURERESk1hg8eCiDBw+t8ePq+qtqZG2YT+qjuQBER91EoEk7nxOJiIiIiAio8ak2qZ3rSS57EYBw/68Qat/P50QiIiIiIvIZNT7VwCrcTcnsR8B1CJ47mHDvC/2OJCIiIiIi/0H3+FSRkzjGvmm/xk2WYjbvSHT4db5OpBURERERkZPpjE8Vlcx+jHTRPsz6TYiNn4oRCPkdSURERERE/ovO+FSBm7ZI7/0YIxyj3pe+ixvL8zuSiIiIiGQZ13X9juCr6vr61fhUgREMk/eVe2jQNJ/jTi7ptON3JBERERHJEoFAAADLShIO1925kJaVBCAQqFrrosanigKNWxNqkAtFJX5HEREREZEsYpoBYrF6HD9eBEA4HKk195I7joFte3smynVdLCvJ8eNFxGL1MM2q3aWjxkdEREREpJbKy2sE8HnzU1uYponj1MzVTrFYvc/rUBVqfEREREREainDMGjQoDH16+dj22m/4wAQCBg0aJDD0aOlnp/1CQSCVT7T8xk1PiIiIiIitZxpmphm2O8YAASDJtFolHjczqh73LWdtYiIiIiIZD01PiIiIiIikvXU+IiIiIiISNZT4yMiIiIiIlnPcDN0FKzrujhO7YgeCJjYdubc2JVpVF9vqb7eUn29pfp6S/X1lurrLdXXW7WpvqZplGu+UcY2PiIiIiIiIuWlS91ERERERCTrqfEREREREZGsp8ZHRERERESynhofERERERHJemp8REREREQk66nxERERERGRrKfGR0REREREsp4aHxERERERyXpqfEREREREJOup8RERERERkaynxkdERERERLKeGh8REREREcl6any+wPbt27nhhhvo3bs3gwYN4le/+hWJRKJcz502bRoTJ06kR48eTJ48mRkzZnicNvNUtr5f//rX6dSp00l/PvnkkxpInTl27NjBXXfdxZQpU+jatSuTJ08u93O1fs+ssvXV+j2zGTNmcMsttzBixAh69+7NRRddxN/+9jccxznjc7V2z6yy9dXaLZ8lS5Zw9dVXM3DgQLp3786YMWP4zW9+w7Fjx874XK3fM6tsfbV+K6ekpIThw4fTqVMn1q1bd8bH1/Y1HPQ7QG1VXFzMtddeS8uWLXnooYc4fPgwv/nNbzhy5Ai///3vv/C5M2fO5Mc//jE33XQTQ4YMYe7cuXznO9+hfv36DB06tIa+gtqtKvUF6Nu3Lz/60Y9O+Fjr1q29ipuRtmzZwqJFi+jVqxeO4+C6brmep/VbPpWtL2j9nsmzzz5Ly5Yt+eEPf0jjxo157733uO+++9i5c+dJdftPWrvlU9n6gtZueRw9epQ+ffpw7bXXkpeXx5YtW3j44YfZsmULzzzzzGmfp/VbPpWtL2j9VsZjjz2GbdvlemxGrGFXTumJJ55we/Xq5RYWFn7+sX/+85/ueeed527duvULnztx4kR36tSpJ3zs+uuvdy+//HJPsmaiqtT36quvdm+66SavI2Y827Y///cf/ehH7oUXXliu52n9lk9l66v1e2b/+X3hM7/+9a/dHj16uMlk8rTP09otn8rWV2u38v7+97+75513nrtv377TPkbrt/LKU1+t34rbunWr27t3b/ell15yzzvvPHft2rVf+PhMWMO61O00Fi9ezKBBg2jUqNHnH5swYQLhcJhFixad9nk7d+5k27ZtJ132MnnyZNauXcvhw4c9y5xJKltfKT/TrPhfb63f8qtMfaV8/vP7wme6dOlCMpnkyJEjp3yO1m75Vaa+UjUNGzYEIJ1On/LzWr9Vc6b6SuXcd999XHnllbRv3/6Mj82UNayf3KfxySef0KFDhxM+Fg6Hadu27RdeD7pt2zYAzjnnnBM+3qFDB1zX/fzzdV1l6/uZFStW0Lt3b3r06MHVV1/N+++/71XUOkXrt2Zo/VbcqlWraNiwIY0bNz7l57V2q+ZM9f2M1m752bZNMpnko48+4tFHH2XUqFG0atXqlI/V+q24itT3M1q/5Tdz5kw2bdrErbfeWq7HZ8oa1j0+p1FcXExeXt5JH8/Ly+Po0aOnfd5nn/vv5zZo0OCEz9d1la0vQP/+/ZkyZQpnn302Bw4c4Omnn+a6667jhRdeoE+fPl5FrhO0fr2n9Vtx69at47XXXuPWW28lEAic8jFau5VXnvqC1m5FjRo1iv379wMwbNgw/vCHP5z2sVq/FVeR+oLWb0XE43Huv/9+vvvd71KvXr1yPSdT1rAanwpyXRfDMM74uP9+jPuvG5/L89y6rDz1nTp16gn/PXLkSCZPnsxjjz3Gn//8Zy/j1Rlav97R+q2YgwcPMnXqVHr06ME3v/nNMz5ea7diKlJfrd2KefLJJyktLWXr1q089thjfOtb3+LZZ5/9wuZS67f8Klpfrd/ye/zxx2ncuDGXXnpphZ9b29ewLnU7jby8PIqLi0/6+LFjx055puIzp+tsP3utL3puXVLZ+p5KTk4OI0aM4KOPPqqueHWW1m/N0/o9vWPHjvHNb36TaDTK448/TigUOu1jtXYrriL1PRWt3S/WuXNn+vbtyxVXXMEjjzzCe++9x5w5c075WK3fiqtIfU9F6/fUdu/ezTPPPMPUqVM5fvw4xcXFlJaWAlBaWkpJSckpn5cpa1iNz2l06NDhpHtNLMuioKDgpHtT/tNn1zb+97WMn3zyCYZhnHTtY11V2fqejluBrYTl9LR+/aH1e7JkMsm3v/1tDh06xFNPPUV+fv4XPl5rt2IqWt/T0dotny5duhAIBCgoKDjl57V+q+ZM9T0drd+T7dq1i1QqxU033UT//v3p378/3/rWtwC45ppruO666075vExZw2p8TmP48OEsX76coqKizz82Z84cLMtixIgRp31emzZtOOecc3j77bdP+Pibb75Jz549T7mbTl1U2fqeSmlpKYsWLaJHjx7VHbPO0fqteVq/J0un09xxxx1s2rSJp5566ow3LIPWbkVUpr6norVbfh9++CG2bZ92ZozWb9Wcqb6novV7al26dOH5558/4c9PfvITAO655x7uvvvuUz4vU9aw7vE5jSuvvJIXX3yRW265hVtuuYXCwkLuv/9+LrroohPOSPz0pz9l+vTpbNiw4fOPTZ06le985zu0bduWwYMHM2/ePJYtW8ZTTz3lx5dSK1W2vitXruTpp59m3LhxtGzZkgMHDvDss89y8OBBHnzwQb++nFopHo9/vjX47t27OX78ODNnzgRgwIABNGrUSOu3CipTX63f8rn33ntZsGABP/jBD0gkEqxevfrzz3Xs2JF69epp7VZBZeqrtVt+t912G927d6dTp05Eo9HPG8xOnToxduxYQO8dqqIy9dX6Lb+8vDwuuOCCU36uW7dudOvWDcjcNazG5zTy8vJ47rnn+NWvfsXtt99ONBpl8uTJfP/73z/hcY7jnDTRdtKkSSQSCf70pz/x9NNP065dO/74xz/Wnqm1tUBl69u0aVMsy+IPf/gDR44cIRaL0adPH+655x569uxZ019GrVZYWMgdd9xxwsc+++/nn3+eCy64QOu3CipTX63f8lm6dCkAv/vd7076nNZu1VWmvlq75dezZ0/efvttnnzySVzXpVWrVlxxxRXccMMNhMNhQO8dqqIy9dX6rX6ZuoYNVxc4ioiIiIhIltM9PiIiIiIikvXU+IiIiIiISNZT4yMiIiIiIllPjY+IiIiIiGQ9NT4iIiIiIpL11PiIiIiIiEjWU+MjIiIiIiJZT42PiIiIiIhkPTU+IiIiIiKS9dT4iIiIiIhI1lPjIyIiIiIiWU+Nj4iIiIiIZL3/HwSUQKqDGKpmAAAAAElFTkSuQmCC" +class=" +" +> +</div> + +</div> + +</div> + +</div> + </div></div></section><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -17251,12 +21656,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [211]:</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> @@ -17267,12 +21672,38 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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-RenderedImage jp-OutputArea-output "> +<img src="data:image/png;base64,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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [212]:</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">"hls"</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span> @@ -17283,12 +21714,38 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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-RenderedImage jp-OutputArea-output "> +<img src="data:image/png;base64,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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [213]:</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">"hsv"</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span> @@ -17299,12 +21756,38 @@ div.jp-OutputPrompt { </div> </div> -</div></section><section ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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-RenderedImage jp-OutputArea-output "> +<img src="data:image/png;base64,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" +class=" +" +> +</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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [214]:</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">"Paired"</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span> @@ -17315,36 +21798,114 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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-RenderedImage jp-OutputArea-output "> +<img src="data:image/png;base64,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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [215]:</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">"cubehelix"</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span> </pre></div> - </div> -</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-RenderedImage jp-OutputArea-output "> +<img src="data:image/png;base64,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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + +</div><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 [216]:</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">"colorblind"</span><span class="p">,</span> <span class="mi">10</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-RenderedImage jp-OutputArea-output "> +<img src="data:image/png;base64,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" +class=" +" +> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> -<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 [ ]:</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">"colorblind"</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span> -</pre></div> - </div> -</div> </div> + </div> </div></section><section > @@ -17361,12 +21922,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [217]:</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">"hls"</span><span class="p">,</span> <span class="mi">2</span><span class="p">):</span> @@ -17379,6 +21940,32 @@ div.jp-OutputPrompt { </div> </div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> + + +<div 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CxsbbfQxu/41MRqHYhY2RlewPLeB3w7EK2sSAeePXv24O/+7u/wyU9+Ev/1v/7XoIfTEtqkxIiUGPHNBhljcFrripmg47aNyQVmg85ojTNa46ViEcgBSE/BwtzmquvYXJWaVKkOyF8IzQMRiRon0oHnL/7iL3DNNddg8+bNQQ+lpQkh0KsUepXC+fF4+XrBGEyUApBvRmimxjduG8ARx8GRqhm7LinLW+ZLS2Jr2FyVmkw9ByK2wcA2BkK4H2MGIqpfZAPPj370I+zatQtf/vKX8fzzz6/Ia1pWY2YSlDFQLfidqR3ARktho++aMQZnvJ1i7j9uEDq5QHPVKa0xpTV2+2qDFIC1loUByyuS9v5hc9WVoZTwPfLvNEgGQNEYFAEUtIYpFDEDAyUFYsI9msJiIXSglJIVjxROkQw8uVwOn/vc5/Dxj38cnZ2dK/KaUgr09iZX5LWqiaINtcDyTqvpBipCEOB+Iz9WsHF4poDDhQKOzBRxeKaI6Rp/bw6Ao7aNo7YNYHanWJeSGGyLY0M85j62xTAQj7G56hIlOxNBD4GqaACx9nj53w0ADYG4dE+Fjkn3TCDujlx9qVR70EOgBUQy8Nx7771Ys2YNrrrqqhV7Ta0N0unpFXs9v4wxyNosrK5FKYFkZwJ9tkY3FLbF24F4O0ynwZTW5Vmg0j8LNVedcjRenM7jxel8+ZqE11zVa9cx4M0MdXLLfE2le5LN5OE4nDYIi7Pdl1IdkJKAhVJbDHdLPDvDN45SEqlUO9LpHByHv9yuplSqve6ZtcgFnvHxcdx333245557kMlkAADT09Plx2w2i2RyaTM1tr3yX6hCeIWJfBMswP1idZy5f09JCGyxLGzxdZkvNVf1F0kfs21M1/huroHyzrLf+M4N6vA1Vy09rlWKs0EAFronFKT67ktp8bfWgYgW2Bi1ERxHN+TnCK2MyAWesbExFItFfOQjH5nzseuuuw4XXXQRHnrooQBGRqulVnPVzDynSE8sMBs0bQz22zb2VzVXXVN1ZtA6NleliFqoEFrxQERqMZELPNu2bcP9999fcW3nzp347Gc/i09/+tM8eLCFdUqJzngcW33XHG82qLq5amaB5qonHAcnHAfPF2ZPF0r4ZoP8O8ZiDEEUIdWd4f0HIlZ0hhfukhg7w1MziVzgSaVSuPjii+f92Pbt27F9+/Z5P0atSQlR3sH1Ct9sULa6nYbXXLVWpVXe31zVp0/KilYaA0qhm7NBFCELdYafPRCRneEp+iIXeIhWQlJKbJYSm6vaaZws1Qb5wlC6juaqO33X2nzNVUshaK1loY0hiCJgoc7w5QMR2RmeIqgpAs/FF1+MF198MehhUMRJIbDWsrDWqnxb5HynSB+vo7nqjDE4ZNs4VNVctVfKcgAqhaFezgZRyFV3hnfrgGZngCygXAzNQmgKs6YIPESN1C4lRqXE6DzNVf11QcccB2cWmA06pTVOFQrwR/NSc9UB326xdWyuSiFWXQgN+DrD++uA4C4psw6IwoKBh2gJ/M1VX+67nq/RXLVY43Uqmqv6ZoN6fM1VS2GoV0pIzgZRCPnrgNgZnsKKgYdoBSWkxEYpsbGqueoprWcbq9bRXPW01jitNX7ra6dhARUhqPTYztkgCplaneGFAOK+zvCsA6LVxMBD1GBCCPQphT6lcL7vesGY2bogXxhaqLnqYcfB4armqqlSc9XSAYrezBNngygsquuA2BmegsDAQxSQuBAYjsUwXDUbdMYrki7VBx23bZxcYDYorTXSWuOl6uaqVadIDyiFDs4GUQjU0xne8kIQC6FppTDwEIWIEAI9SqFHKbwsHi9fLxqDiarDE485DvI1fgo4AI46Do5WzQZ1eucS+ZfG+pWC4mwQBaj6QMQ8ahyICDcIsRCaloKBhygCYkJg0LIw6Nsyb4zXXLWqSPqk46DWz4KMMcgUi9jjmw0qN1f1haAN8Ti6+BOFArTQgYjxciH0bB0QZ4HobBh4iCJKCIGUUkgphXN9121vNshfJF1vc1X42ml0nj6FtVJVtNJgc1UKynwHIpbqgErLYMqbEZLGsDcYzcHAQ9RkLCGwwbKwoWo2KGtMxVb5Y17PsFrVQRlHI+No7PPNBgm4s0HVfcW6eIAirbJ564DghiB3Nsg7GFEIKACqvC2eQahVMfAQtQAhBDqFmLe56kRVK41jto3sAs1VJ7wu9P7mqu3VzVW92SA2V6XVVApBMChviQe8eiC4QcjyTodWpcMR4RZLsy6ocbQxGLNtZLVGUkoMW1Ygu0gZeIhamBIC6y0L6y0L8DVXzQkg06aw90wWR4s2jtk2TizQXDVnDA7YNg74mqsKAH1qdkmsVCzN5qq02rQBgNlt8UBpNmj2gER/ECrNBrE2aPleLBTweDaLY147HgvAgGXhimSyYmPGamDgIaI5OqXEYEcC64sGjuMuejlec9Xj/i3zCzRXNQBOekXUL/iutwlREYJKzVXjDEG0iko7w/wHJAJeCPKKo2WpUWppWcz7h8ti9XmxUMCD6TTyWqNDSnTAPU9svFjEg+k03pdKrWroYeAhorooIdzDDS0LF/hmg6a9dhr+LfMTjrNgc9WDto2DVe00+rzmqv4g1MPZIFpl7oqYKc8KFeCbDcJs13i3LsjbKeZ9ifJLdZY2Bo9ns8hrXTGrG4f793dGazyezeLcWGzVlrcYeIhoWTpqNFedrGquevwszVUntcZkoYBdvmvxUm2Qv6WGUmjjAYq0isqzQfMuiwlYWsKeKSDnaEiBcpG0bOFlsTHbXQrvmOeXFiEEOqTEMdvGmG1XtOJpJAYeIlpxUgj0e4cabvddLzVXrT5JulZz1YJX7DhWo7mq/yRpNlel1VYqknaMgQ0g52g4XugpzQYpb8u89BVJl7rIA83bSDWrNWwAHXB3jrZJ972Z9ZbILQA57/NWCwMPEa2ahZqr+rfMH3ccnFpkc9UYgLW+WaBSGGJzVVpt1UXSM6gskhbwtsxj9uygZjtAMSklUlKiU0kkpEDpryTrfdyGG/6Sq/j+ZOAhokD5m6tu812fMQYTvq3ypUMUCzV+GhQBHLZtHLYrq4f8zVUHvCDUx9kgWmX+Imlg7rKYOysUzbOD3LeSO6OljYEWwPqYhaF4DGOFAqZtIKYkOr1wY4zBtNYYisUwbK1eDGHgIaJQaqvRXPW0r0i6NCM0ucjmqhbc5qrrfOcGsbkqBcF/gCLgP0W6nrOD3FdYjSBUCjWAu6vNwD3rSMPdwenA3dGpvT+UgcG5Vgwv5WdQ0BprpIA2BgUv7CSkxBXJ5Kr+4sHAQ0SRIYRAr1LorWquWvBmg0r1QaVDFGs1V7UBHHEcHKlqrtpV2onm2ym2hs1VaZUtXCQ9uyxm+XaLVc4ILa42aPbL2w00ujQG9z/hAF6dkkFR6/JONu+Mx5q2xGJ4R2cnns7lkDMaWS/wDMViPIeHiGgp4kJgKBbDUNVsULpqNuiY42BygeaqU8Zgqqq5qoLXXLUUhLwZoU7OBtEqq7UsBsw2VxW+5qruTJC7W0x5M0KlZSdHuDMuDrxlKAC20dDGwJe1gDqCzUK2xGIYtSycNBpxIdAGwZOWiYhWkhAC3UqhWymc6/tNsmgMTlQtiR1zHORq/ErsAOWw5JcUohyCSkti/WyuSgEpNVctzQiVZoNKtTUS7nuiFGj8y2GNXhGTQmC9tNBrKcgA65AYeIiopcRqNFfNzNNc9eQCzVWzxmBvsYi9vtkgCWBN1blBA5aFTiF4gCKtquplMad8tXUx8BBRyxNCoEsIdMXjOMd33S7NBvm2yy/UXFVjtrkqqpqrDlSdG7SWs0FEq4qBh4ioBqtGc9WM1uVZIH87jVqzQTljsN+2sb+queqa0plBvjCUYjsNooZg4CEiWqROKdEZj2OL71qpuWr1KdJTNWaDDIATjoMTVc1VE6V2Gr5DFNlclWj5GHiIiFaAv7nqK2o0V/UvjTk1Xifvb67q0ycl1scsbLQL6HEM+oVkc1WiRWDgISJqoFrNVUuzQcd9IeiszVVnCnhhZrY2KO7VBvlbaayzLLQxBBHNwcBDRLTKpBBYa1lYW3Wsfs6bDTruOzdo4izNVQ/ZNg5VNVftrdFclbNB1MoYeIiIQqJdSmySEpuqZoNKzVUntMakMBjLzeD0ArNBp7TGKa3xYlVz1epTpNcphQQPUKQWEbnA88Mf/hDf+9738Pzzz+PMmTMYGRnBH/zBH+Caa66B5BuXiJqMFAJrSi0ulESqux3pMzlki+7OsOoi6UKN1ykCGLdtjFfNBnXPMxvE5qrUjCIXeP72b/8Wg4OD+I//8T9izZo1+PnPf46/+Iu/wKFDh/DJT34y6OEREa2KhJQYkRIj8zRXrQ5BCzVXPaM1ztRorjpQNSPUzl8qKcIiF3i++tWvoq+vr/zfl1xyCaanp/G//tf/wn/4D/8B8VVuRkZEFBb+5qrnz9Nc1X9u0DHHwcxim6tKOadIul8pzgZRJEQu8PjDTsm2bdswMzOD06dPY926dQGMiogovGo1Vz3j2zJfepzUunZzVa0xpTV2VzVXXetrpVEKQ0nOBlHIRC7wzOfZZ59FT08P1qxZs6zXsazGvEGVMVA1fpMiQCnhe+Q3yTDgPQmnlb4va6CwBjFs810rzwbZNo7Z3myQbS/YXPWo4+Bo1WxQp5QYsLzlMO+fZm2uyvfL2UkhoCwJxeahS/eb3/wGjzzyCD760Y9CKbXk15FSoLc3uYIjmyWKNtQCa+jkSnYmgh4CVeE9CadG35d+oCIEGWNwxnFwZKaIwzMFHJ4p4kihiOOFYs12GhmtkSlo7ClUzgati8cw2BbDhrY4BuMxDLbF0aWaY8s83y+1CQCpmIV4gDN/wpjoTj1MTEzgve99LwYGBrBjxw7EfNO1i+U4Gul0bgVHNytjDLJ2rXNVSSmBZGcC2UwejhPZL8emwnsSTmG7L0VjcMK2cdT2zg6ybRy1bUwv8sdKhxAVM0HrLYV+y0IsIiEobPcljKQQ6IupFZ/hSaXaoVR9ISqyMzxTU1P48Ic/jEQigXvvvXdZYafEtld+FkYIt8eO43CGpzb3i9Vx+Pfkp43BUcdBTmu0S4n1q1ocynsSTuG6LxLAOqmwLq4Ar0jaGIOsMRV1Qce8nmG1RjxtDPYVi9jnqw0qNVcd8NUHDSiFrlAeoBiu+xJKQsARAkFOsUQy8MzMzOBP/uRPcOLECTz44IPo7e0NekhEK2pvsYinczmcsG04cJcC+i0Ll7a3Y8sKhHuiRhFCoFMIdMbj2Oq77hiDE75dYqXHTB3NVZ8vzJ4ulPDaafi3zK9VKjKzQRScyAUe27bxsY99DLt27cIDDzyAoaGhoIdEtKL2Fov4QSaDgtZISIkE3MLQY8UifuA4eEdnJ0MPRY7yLVv5m6tm59kpNnGW5qoHbBsHfM1VBdzmquWdYt5jdyhngygokQs8d9xxB3784x/jtttuQz6fx7/+67+WP3bOOeegs7MzuMERLZM2Bk/ncihojU7fN2sJwJISGa3xdC6HUcvi2SfUFJJSYrOU2FyjuWr5AEXHQbrG5g8D4KTWOFkoYKfvepsQ7ixQVXPVON87LSlygef//b//BwD4/Oc/P+dj999/Py6++OLVHhLRijnqODhh20jM85upEAIJKd0iUcfBoBW5ty9RXfzNVS/wzQblfLNBx3xd5u0arzOzUHNVX13QOjZXbQmR+475xBNPBD0EoobJaQ0HQK3NrQpA3vs8olZTq7nqpNblDvOlEHTW5qqFAl70XYtj/uaqbTxAsWlELvAQNbN2KaHg1uzM9222VMDMnkZELikE+r0WFy/3Xc97s0HVM0LFGq9TADBm2xirmg3qmae5ai+bq0YSAw9RiKxX7vkjx4pFWFVT7MYY5LXGQCyG9cs4ZJOoFSSkxEYpsbGqncYprSsKpI87Dk4tMBt0Wmuc1hq/rWquus5XHL0hHsM5TlvN16BwYOAhChEpBC5tb8cPHAcZb5dWacYnrzXiUuLS9nb+dkm0BEII9CmFPqUqTpKe8TdX9TVZXai56mHHweFSO41pAKfPIFVqrlqaDVIKa9hcNTQYeIhCZksshnd0dpbP4cnDXcYaiMV4Dg9RA7QJgeFYDMPzNFf1h6BSc9Va0lojrTVemqe56kBVfVAHl6VXHQMPUQhticUwalkBnrRM1NqEEOhRCj1K4WXeKdKA205joqou6JjjIL/Y5qreuUT++qB+paD4Hm8YBh6ikJJCcOs5UcjEvPel/70ppYBOxrHnVAZHinY5DE06Dmp1UsgYg0yxiD2+2SAJoL/q3KABy0InZ4NWBL+bEhERLYMQAr0xC+e1tWGrNbssViy10/AtiR1zHORqzAZpoLyz7De+dhpJIWZPkfZqhNYqBYuzQYvCwENERNQAMSGwwbKwwTcbZIxBxmuu6l8SO7lAc9Vsjeaq/V4A8p8bFM7mquHAwENERLRKhBDoEgJd8TjO8V23fc1V/TNC2QWaq044Diaqmqu2e81VyydJWxb62VwVAAMPERHRomljypsKOi0LXTWCSb0sIbDesrDesgBfO42Md4r0cV9vsRMLNFfNGYP9to391c1VS200fDNCqRabDWLgISIiWoS9xWL52AgHgBLA+vw0LkkksEmu7I/VTinRGY9ji++a4zVXrThF2nEwtVBzVW/Z7AXf9USpuarv3KC1TdxclYGHiIioTnuLRfwgk0HBOxg0AUALgcMzRXxvpoh3dHY2/Kws5RUxr6tqrjrta65aepxYoLlq3hgctG0ctCs/o8/XXLX02NMEs0EMPERERHXQxuDpXA4FrdHpCwBKCCSkxCnbxtO5HEYtK5AzszqkxKiUGJ2nuWp1h/kzCxygOKk1JgsF7PJdi5dmg3wzQlFrrsrAQ0REVIejjoMTto3EfLMdwm3qe8K2cdRxQnOGlr+56nbf9XzVKdKlYumazVWNOWtz1dKMUF9IZ4PCcUeIiIhCLqc1HACJGh8v9b3LLTB7EhYJKbFJSmyqmg065RVJ+8PQ6UU2V40BFf3EBrxi7F4E2/SYgYeIiKgO7b5mvvMt5DhwQ097hJZ5/KQQWOM1PK1orurVBlUUSds2CjVepwhg3LYxXlUbdFFbG27o7g6sRQ4DDxERUR3WK4V+y8KxYhFW9bKNcWd2BiwL61WwMxkrrU1KjEiJkarmqqe9ZbFSkfTxszRX/dXMDA7bdkWT1tXEwENERFQHKQQubW/HDxwHGW+XloLbEmJKa8S9j7dCk18hBHqVQq9SON/XXLVgDCZ8dUGlGaEZYzDs7SwLCgMPEQXOf4gbO8NTmG2JxfCOzs7yOTx5AAoGg23xhpzDEzVxITAUi2HIXxvkOJiZPIH1U2moXAZmcAQigGW/1r4zRBS4OYe4Aei3LFza3t7w80yIlmJLLIZRy6o4afllazoxlc7DccJfsLya7AN7MPOzp6ByGczYBcycPAm5dj0Sl78N1jnbzv4CKyialVVE1BRKh7gdKxYREwJJIRATAse863uLtTbJEgVLCoFBy8LWeByDMSuU27CDZh/Yg5l/+geYiaMQ8ThERxdEWxv0kTFMP7wD9u6dqzoeBh4iCkT1IW4xISC9wNMpJQpa4+lcDnqZPYqIaPUZrTHzs6dgZmYgOrsgtAFgACsGpLph8nnkn/gRzCpu4eeSFhEFYqFD3IR3cm3YDnEjorPQGuLkBPD8v6J9z05YxSKswwchjIFuS0CuWQspJUyyE3riKPThQ1DDm1ZlaPwuQkSBqOcQtzyicYgbUUsyBjg9CTl+AGLsAOTYAYjxgxD53PyfP5MHbBuIxwHLAnJZ6OzUqh1HyMBDRIFo9kPciJrOVLocbtyAsx8im6nrqQYCsr8fKG1EsG1AWZDJrgYOuBIDDxEFYqFD3IwxyGuNgVis6Q5xI4qE3DTE+EF31mZsv/t45lRdTzVCAP0DKAIoOjacrhTQswa90MDJEzDGwExnITcMQw6ONPbP4cPAQ0SBqHWImwO3sWFcypY5xI0oUIUZiMNj7oxNafbm5PG6n25610APb4IZ3gQ9PAozOAIk2md3ac3MIObYbhAqFGCmsxCJBBKXv21Vz+Nh4CGiwMx/iBswEIvxHB6iRrBtiKPjEONezc3YAYhjhyHq3A1pulIww6NuwBnaCD20Ceicf1nK2rQVeMu73N1auQyMXQAKM5AbhgM5h4eBh4gCVX2IG09aJlohWkMcP+LO2JRqb46MQzj22Z8LwLR3eLM2m2CG3EekeoBFvDetTVuhRjZDnJxAYmYayopD8qTl+u3btw+f+cxn8Oyzz6K9vR2/93u/h1tvvRWJRK39HkQUZqVD3IhoiYwBTk5U7pg6fAiiMFPf0+Nt5RkbM7wRengU6OtfVLipRUgJtW49LEtBBnisVuS+w6TTafzhH/4hBgcH8eUvfxmTk5P47Gc/i9OnT+MLX/hC0MMjIiJqLGOA9OnykpS7HfwARG66vqcrC2ZwuDxrY4Y3waxdDzT5jsjIBZ7/83/+D9LpNB599FH09fUBAJRSuPXWW/Enf/In2Lp1a8AjJCIiWkHZzGxBcan2Zipd11ONlMDABujhUejBEZiRUZiBIfccnBYTuT/xT37yE1x66aXlsAMAb33rW/Gf/tN/wpNPPsnAQ0RE0ZXPzW4HL4WbUyfrfrrpH4Ae8WpuhjbBDA4D8bYGDjg6Ihd49uzZg6uvvrriWjwex8aNG7Fnz55lvbZlNWY6TxkDxX5ANSklfI/NPaUaFatxT6QABASkBBxtoPkWOSu+V8JpyfelWAAOH4I45J51Iw7th5g4VvfTTU8fMOLumMLIKMzQJqC9vfzxMH2FSCGgLAnFGp76pdNppFKpOddTqRTOnDmz5NeVUqC3N7mcodUkijYUj8c/q2Qni87Dpt57Ul3WWP09TXj/SAAxr1GoJQQs6T5mHQcZ24Gz/CG3BL5XQkBrFI6Mw8lmoZJJoGNowftiHBtmfAx6/z44B/ZC798Hc3gMqPdnQ1cKcnQz1KYtkKObITdtgZjnZ2FYCQCpmIV4gHVCkQs8tRhj5jQgXAytDdLp+gq+FitjDLI2v5XXopRAsjOBbCYPx+Gv+WFQ655IAQACQgBKAJZwDwtUAlAQgHC7oGsAjgE0UP64JQQkBACDogGKVf9PSwB5R2NGa8721MD3SjjYB/Yg/9MnoU9OwGgHQinE1w0gdsnroUa2uCFm4tjsrM3YAXcmx65/OziGN8F4S1NmZBTo7q3cMWUAnKnRsyqEpBCIxdSKz/CkUu1Qqr4QFbnAk0qlkE7PLdaamppadv2Oba/8LIwQgGMMHIczPLW5X6yOw7+nMBAApJCQAISBOxsDb0oabniRBhBGAMagtFprvHkdAS/kwP36L33cgTnrDE5SCCghkdUObKaeefC9EjT/6cGivR1CKsiZHLB3N/Rvd8HqSEKenKh/O3gs7m0H3+julhoehVmzdu52cG0wd+40QoSAIwSCrO6IXODZunXrnFqdQqGAgwcPzqntIaLaBAAh3NkaAXepyQ00AnEl0BuLoU0VvZmE2WCDqoCzkMV+czPGoA1AzFLIaIMZ24nyt3hqMkZrFJ76Z1inTyEmAHX6JKx8DtLxRfkF+k0ZpWA2DLuH+Q35toOzX9yqiFzgef3rX497770Xp06dQm9vLwDgscceQ6FQwBve8IaAR0cUPsIrDhbCnR+wpIQFN9goIaCMgSz/5uUGGwsCMXf9CiaAX8mkBlJCYNpSmHY0NIv+KQjZjLdjaj/E+EGIA3uRyE7V9VQjxOx28KGN7tLUhiHAYruUoEQu8FxzzTV44IEHcNNNN+Gmm27CyZMn8bnPfQ6///u/zy3p1PKkL9yUioMlZpehSjU0pjQ77gWJUAYKAySFhGUJZBwucVGDzeR93cG9VgyTJ+p+uk60w27vQFEIiNe9CerfXsrt4CETucCTSqXw7W9/G5/5zGdwyy23IJFI4J3vfCduvfXWoIdGtKrcGZvKcFOqnbGEgDFLW4YKE2MM4gB6LIUpbVDgEhethGIR4siYd0rxfveU4oljdTfQ1LE4nGQn7EQCxXgbnEQ7VDwOO5eHKRbQvvk8hp0QWlLg+cUvfoGjR4/inHPOwXnnnTfn48eOHcN3vvMd3Hzzzcse4Hw2b96Mb37zmw15baIwKtXbSOEuScXgLklZAuVdD/7v1aGcsVkGqYFuLnHRUjiO10Bz/2x38KPjEHVuBzfJznIDTT24Edlf/Bz69CREV6qisNgYAzOTg+hfD7lufaP+NLQMiwo8hUIBN954I55++unyNvA3v/nN+MxnPlNxNs7Ro0dxzz33NCzwEDUzN9y4AUcJMXtmjVdvI6qWpVrmRz+XuOhstIY4cbyyBcPhQxB29SEI8zOJxGx/qVJ38J6+imATT3a6u7Sm0hCJdrdFg23DKeYh4m1ou+R3A+kETme3qMDz7W9/G88++yxuu+02XHjhhXj66afxzW9+E9dccw2+9a1vYd26dY0aJ1HTKp847C1NWd42cAuoCjfuD/ioLUutJC5xUZkxwOnJco8ptxXDQYiZfH1Pj8VgBkdmd0sNb4JZs+6sDTStTVuBt7wLMz97CmZyApjJQUiF+MAGWK/5XYjhzSvxp6MGWFTg+Yd/+Ad85CMfwQ033AAA+J3f+R28+c1vxo033ohrr70W999/P9av51QeUS3uGTezdTcW3Hqbeetu0NrhZiFc4mpBU2dml6RKRcXZTF1PNVICG4bdZalSwFm3Ycnbwa1NW6FGNkMfPwqdyyLW2Ynec7cgPTXD85FCbFGBZ2xsDL/zO79Tce3888/HAw88gOuuuw7XXnstvv3tb6/oAImiyl93o7y6G0sI91Ri487eGDO7JsUf2ovEJa7mNZ2d3Q4+dgBy/CDEAufb+BkhgHXrfeFmFGb9EBBb2e3gQkqo9YPuRgElzzozRMFbVOBJJpOYnp7bfmF4eBg7duzABz/4QXzwgx/Exz/+8RUbIFFUSK/uptQvyvLqb+aruwE4e7MSKpa4HI2Co/m3GjUzeYjDh2a3g48dgJicqPvppq8fenjUO8xvI8zQRqCNvcZorkUFns2bN+OZZ57BZZddNudjQ0ND5dDzn//zf16xARKFUa0t4aUD/cpLU6y7WRVSA91SIisEco7DXlxhZRchjoy7O6bGD7q9po4frXs7uEl1wwyPzs7eDG0Ekp0NHjQ1i0UFnte+9rXYsWMHPvaxjyGRmJugh4aGystb4+PjKzZIoiC1+pbwyDBAp7fEleUSV/DK28G9epvSdnCnvkbKpiNZ3g5e3jGV6mnsmKmpLSrwfPCDH8QVV1yx4OcMDg7ioYcewu7du5c1MKIgVG8Jj3uPLb8lPCKMMUgIwLIUMlziWj1aQ5ycqNoOfhCiWOd28LaE20DTH25618xtoEm0DIsKPDt37sTLX/7yeWd3qh08eBCvec1rljwwotUwb90N3J5TklvCI8kYd+aNS1wNYgxw5pRXUOzrM5XP1fd0y3K3gw+PwpQ6hPcPsOiXGm5Rgee6667Dgw8+iAsvvBAAoLXGhRdeiIceeggvf/nLy5936NAh/Jf/8l/w7ne/e2VHS7QMtbaEz6m78TDcRByXuFZGJj13O3imzgaaUgLrhyq3gw8Msjs4BWJRgae6a7IxBrZtB9JNmWghC20Jt4z7GdwS3vy4xLVI+dzsTqlSK4bFbAdfO+ArKN4EMzgMxOINHjRRfSLXPJSomr/uRgKIl04rXrDuhj/2WoV/iSsjBPJc4nIVZiAOj5XPuhHjByBPHK/76aZ3jVtzM7zJXZ4aHAES7Q0cMNHyMPBQ5JRaMQjhCzdgKwY6CwN0CYlYKy5x2TbEsXHf7M0BiGOH698O3pUqbwc3QxuhhzYBnV0NHjTRymLgoVCbb0u4/7RiAKy7obrNLnFZmHIcFJtxiUtriImjXrjxZm+OjEM4dl1PN+0d828H544pirhFB569e/dCeQVnjneewt69e+d8DtFiuUXF3BJOjeUucRn0NMMSlzHAyQmvcaavO3hhpr6nx9tghkagh0ZhhjdCD48Cff0MN9SUhFlExfH5558PUfVGMMbUvLZz586VGeUqcByNycnsir+uEEDGGGTt+g7baiWlcBOzJPpSHchP5SG0mbslnFadZUn09iZx6lQWtt28zRCFEMjBIOs4cMKeeoyByqTRMXkY+d++BBzcDzF+ACI3t93PvE9XFszgcHnWxgxvglm7ntvBV4BSEqnudqTP5Ng8tAYlBHotBbnCb7O+vqTby6wOi5rh+exnP7ukAREt1IohrhR6YhZOCQGbdTe0iowxaBdALIxLXNlM+awbMe7tmJpKowD3nKiFGCmBgQ3Qw6Nuf6nhTTADQ4DFKgZqXYv66r/yyisbNQ5qIottxcBjDShIoVjiyue87uC+s25Onaz76aZ/AHpktubGbBgB4twOTuTHuE/LUt2KIebV3LDuhiLHAF1CwLKsxi5xFQtzt4NPHKv76aJvDfTgRjj+BprtHY0ZK1ETYeChRSltCZdithUDt4RTszAGFUtcheXWYziO2zBzvGo7uK7vdU1nl7cdfCPM0CjEplGkhgZYK0K0BAw8VFOtVgzKe2QrBmpG/iWuKSEwU+8Sl9YQJ45VnFQsjoxB2IvYDl7dQLO7t2LHVL3FmUQ0FwMPAWArBqI5DJASArn5lriMAU6drGzBMH6w/u3gsVj5AD8zvAlmeBSmr587pogaiIGnRc3bJZytGIgqlJa4VGYKmQN7oA8dKJ95I6brO8bCKAWzYdg9zG/Itx2cDTSJVhUDTwtYaEt4RZdw1t0QQWczcMb2wznk/TN2ACZ92jsfamFGiMrt4EObYDYMAVZsNYZORAtg4Gkyi90SzqUpamUmn4czfsALNvthH9oPM3mi/uevWQs9MurW3AxtdBtotiUaOGIiWioGngir3hLOVgxEtZliEc6RQ7MzN4f2Q08cRb3HecueXqiRUaiRUWB4FNMbhlFMcDs4UVQw8ETIvHU3wNxWDFyaohZnHBv66GE4Y+7sjT22H/rIOKDra/EiOrvccDO8CWp4FGp4FDLVXfE5bQKYMgZ5x2ELFKIIYOAJKW4Jp1ZitIY+fAg6OwWZ7IIcHIGoc8eS0Rr6xDE4hw7M1t6MHwTsYl3PF4l2L9hsghrZDDUyCtHTN6dH4Nz/sbuLK2ZZyNoOHKYeolCLVOBxHAf33XcfnnzySezevRuO4+C8887DzTffjEsvvTTo4S0Zt4RTK7N370T+iR+5y0uODSgLcu16JC5/G6xztlV8rjEG5tTJ8qxNOdzkc/X9z6wYrOGNkN6sjdo4CrlmXd3hqlppF5dlKbcXV9gbkBK1sEgFnnw+j6997Wv49//+3+OP/uiPYFkWvvvd7+L666/Hvffei8suuyzoIZ6Vv+5GAoiXTivmlnBqQfbuncj9/QMw+TxEMgmoJODY0EfGkPv7B9D29ishrFi5qNg5tB8mm6nvxaWC2jDkLU25tTdyYAOEWtlve8a430h7lEJGcomLKKwiFXgSiQT++Z//Gd3ds2vpr3vd67B//37cd999oQ48lhSz4QZsxUBktEb+iR+5Yae7BzAaKMzAFAowdhHm5BRyO75W34sJAbVugxtqhje5IWfDCERs9baDC2+Jy1IK047mEhdRyEQq8CilKsIO4M6UnH/++Xj22WcDGtXCjAGSQqDTO2SMdTdEgJnJo/ivz8A5tA8wBuboYXc5q05yzdqKmRs1uBEiEfx2cGOADiFgWQoZLnERhUqkAs98tNb45S9/ia1btwY9lNq4JZxamLGLcA6PVRzmp48fqXs7uEh2who9x5u98ZamOpINHvXSGQPE4Fvish2+/4lCIPKBZ8eOHdi3bx/uuOOOZb+WZbGPTRBKDRHZGDE8lnpPjOPAOXYY9kH3ED/74D44R8YAp77t4FAKor0DIhaH0QYwGqkP3QJrZHSRf4Jw6AUwrSSyjl6RzQZKCd8j3y9hwftydlIIKEuWD8ANQuCBZ2pqCsePHz/r542MjCAej1dce+aZZ/D5z38eN9xwA1796lcvaxxSCvT2hve3xlaQSrUHPQSqstA9MVqjePwoZvbuRX7fHszs34uZA/tgCoW6Xlsk2iHjcRjHhujohIrHAcuC8I5dcE5PIj6yEf0XbFvyLqow6AWQczTSto35Yp9bybc4yc7gl+9oLt6X2gSAVMxCPMD3cuCB57HHHsPtt99+1s979NFHsW3b7BbVXbt24aabbsIVV1yB2267bdnj0NognZ5e9uvQ4iklkUq1I53OwXF00MMhzL0nxhjo05OwD+6DfdBdlrIP7Yepezu4BWt4E6yRTbBGNkNtHIVaux72nheReeh+6HwORkj3J79tw+SyEG0JxN7wVpw+U+f/I+QsAeQcB0Xj/ntMSFjC/UGQ1RoF5+yxRymBZGcC2UweTh2fT6uD9+XspBCIxdSKz/CkUu11z0QLY6K3leDgwYN4//vfjy1btuB//s//OWfmZykcR2Nysr7ux7SyLEuitzeJU6eysG0GnjCQuQwSk0dweucuFA/uc7eDZ6bqfLJ0t4MP+7aDrx+suR18MefwRJ0Qbqar2KEJAALIeKc2L1TnrJREqrsd6TP85SBMeF/OTgmBXktBrnDi6OtL1h14Ap/hWayJiQnccMMN6O/vx1e+8pUVCTtErczkpuGMHYDtP+vm9GR9TxYCcu0ArJHN7nbw4VGooRGIWP3vS+ucbUhuedmST1qOklLAmbND0wBdQsCyLGQdBw53dxGtuEgFnnw+jw996EM4efIkPvWpT2H37t0VH3/lK18ZzMCIIsIUZuCMH/KCjTtzo0+cvYauRPb1V24HH9oIkVh+7ZWQ0m3tsOxXiq7Sqc0xy3JPbXY0d3cRraBIBZ4TJ05g165dAICPfvSjcz7+4osvrvaQiELL2Db00XHYXrBxDu2HPna47u3gqrvHXY4a2uRtB98Emexq8KhbmzGAMgY9UiIjxFmXuIiofpEKPMPDwww11DIW01DTaA19/Eg52Dhj++EcHqv7MD/R3uE1znSXpeKjW9C/eZh1VUExQJeQiFkCWceBzdRDtGyRCjxErWKhQl619XzokxPuktTYgdkGmoWZ+l483gbLa79QqruRa9ZWdAdXPJMqcMYYJARgWRYyjoMCi2GJloWBhyhk/A010dEBiDYgn4ezfzey990FCFl/uFEW1NCIt2PKCznrNjRlQXAzKi1xdUuJrBAosKqHaMkYeIhCxJlKI/e970CnzwBSACcygK7zN3spoQYGyy0YrJFRyPVDEBbf5pFngE4hYSv33B4iWjx+JyQKiMlNwxk/6B7iV9oOfupk3c93t4N7/aVK28HjbQ0cMQXJGIM2SCRjFrJKIO+wRx/RYjDwEK0CUyy44aZUc3Nov1ufU69YHKK9HVAWTD6H9ndfh/iF/7ZxA6bQikuJXqWQtoDpFerRRdQKGHiIVphxbOijh8vtF5xD+9zt4PUuTSkFkWh3Z2uUcsOOck+oMYUCYAxUX38D/wQUegZICgnLEshwFxdRXRh4iJbBaA09cXR2K/ih/XAOHwLsOreDJ9rdA/y8w/zk0EZMf+d+mKPjQEeyYueUMQZmOgu5YRhycKRRfySKCGMM4gB6LIUpbVCwHS5xES2AgYeoTsYYmMkT3qyNF3DGDwAzde6YisVhDW90g00p4KxZO2fHVPub3u7u0jpzGuhIApblNtSczkIkEkhc/jbusqIyqYFuITBtKS5xES2AgYeoBn3m9OyszaH9cMYOwExn6nuyUlCDI+5W8FIDzXUbyktTC7HO2Yb2q6+dPYcnl3XP4dkw3JQNNWkFcImL6KwYeIgA6GzGDTe+omKTPl3fk4XwbQd3z7pRG4YhrNiSx9NKDTVpZfiXuDLaYIZLXEQVGHio5Zh8Hs74AV/dzQHoyYm6ny/718020Bze5DbQbEus+DjZUJOWQmogJQRy3hKXwyUuIgAMPNTkTLEI58ih2WWp0nbwOn8IyJ5eX7hxA47oSDZ41ETLZIAOIWBZClNc4iICwMBDTaS8HdxblrLH9kMfGQe0U9fzRbJzNtyUiopT3Q0eNVFjGAPEAPQqhSnJJS4iBh6KJKM19Ilj5WLicgNNu1jX80Wi3Ssoni0qFr1rKraBEzUDYSp3cXGJi1oVAw+FnjEG5tTJihYMzvhBIJ+r7wWsGKzhjbMtGEZGIfvXsQCYWobxlrhi3hJXkUtc1IIYeCh0dPrM7HbwUo+pbJ3bwaWCGhyarbkZGYUcGKxrOzhRMzPG/YbfoxQy0iDvOPWWshE1BQYeCpSZzqJw5CAmJ8Yx9dvfwj64zz1wrx5CQK3bULUdfAQitvTt4ETNThh3F1fMspB1HDic7aEWwcBDq8YUZmbrbbyZG31yEdvB16yt2g6+CSKx8tvBiZqdMUC7AGJKIQ0ucVFrYOChhjB2Ec7hsYqTivXxI3VvBxepHlhejynpBRyZ7GzwqIlahzGAgm8Xl+OAuYeaGQMPLZtxHOhjRyrrbo6MAU6d28E7kmjfcg7M4AjEoLtrSnb3NHbQROQypYMKucRFzY2BhxbFaA198vjc7eDFQn0v0JbwGmhuhhzZBGtkFLG169DX14lTp7Kwbd3YPwARzVFe4rIsdxeXo3lmDzUdBh6qyRgDc3qyPGtjeyGn/u3gFtTQRrfuZmgT1MhmyLUDc7aD8+wbouAZAyhj0CMlMkIgzyUuajIMPFSmM2k4hw7AObRvdjt4Zqq+J0sJtaFqO/j6QQjFLzGiSDFAlxCwuMRFTYY/jVqUyU3DGTvgzdp44eb0ZH1PFgJy7QCskc3udvDhUaihEYhYvLGDJqJVwSUuakYMPC3AFGbgjB/ygs0+d8fUieN1P1/29Vf2mBraCJFob+CIiShoXOKiZsPA02SMbUMfHYftBRvn0H7oY4fr3w7e1T13O3hnV4NHTUShZYAuIRGzBLLsvE4RxsATYUZr6ONHysHGGdsP5/AY4Nh1PV+0d7gzNr7ZG5HqYRExEVUwxiAhAMtSyDgaBS5xUQQx8ESEMQb65IRXUOzbDl6Yqe8F4m2wvO7g0gs4cs1ahhsiqou7xAV0S4msEMhxiYsihoGngYzW0IcPQWenIJNdkIMjdXXoNsbAnDnla8FwAM7YfpjcdH3/Y2VBDQ5DjWx2WzCMjEKu28Du4ES0fAboFBIWl7goYiIdeJ577jm85z3vQSKRwC9/+cugh1PB3r0T+Sd+BD1x1F1iUhbk2vVIXP42WOdsq/hcnZ3yLUu5szdm6kx9/yMhoNYPumfclHZMbRiGsCJ9a4koxEpLXDFLYUobFGyHS1wUepH9qWiMwX//7/8dfX19mJ6uc+Zjldi7dyL39w/A5PMQySSgkoBjQx8Zw/R37kfba98IaAO7tB381Mm6X9vdDl4qKPa2g8fbGveHISKahzGANEC3EJi2FKYdDV3n5giiIEQ28Pz93/89Tp06hauvvho7duwIejhlRmvkn/iRG3a6e4BiESabAYoFmMIMkEkj//2H63ot2dtXsSylhjZBtHc09g9ARLQYBkh6S1wZLnFRiEUy8KTTaXzxi1/EX/7lX+K5554LejgV9OFD0BNHIZJJmMwUzJlTdT1PdHbBGtkMNbIJcth77Ew1eLRERMtnjEEcQA+XuCjEIhl4/vqv/xrbt2/HZZddFr7Ak53yanaSMDP5+T9JSqjBEVjnvdzrMTUK0dPHHVNEFGlSzy5x5RwNh0tcFCKRCzw7d+7Eww8/jO9+97sr/tqWtQK7mFIp5JUFODZkqhtaawilINoSMJYFaAM4Njrfex2skdHl//+agFKy4pGCx3sSTlG5L90CSFgSGa1RdJo/9CglfI/hvjdBkUJAWRIqwC+HwAPP1NQUjh8/e5uDkZERxGIx3HHHHXj/+9+PrVu3rug4pBTo7U0u+3VM9zYUhoZQGDsI1dMH0TFbc2OMgXN6EvHhjei/YBu3iVdJpdiuImx4T8IpKvelTxucsW0UWmSmJ9mZCHoIoSUApGIW4gH+3As88Dz22GO4/fbbz/p5jz76KPbu3Ys9e/bgC1/4AtLpNABgZsY9eC+dTqOtrQ1tbUvbsaS1QTq9Mru9Ym94K2Yeuh/FyZMQ7UnAsgDbhsllIdoSiL3hrTh9Jrci/69moJREKtWOdDoHx9FBD4fAexJWUbwvCoA2pql3cSklkOxMIJvJw2mBGa2lkEIgFlMrPsOTSrXXPeMpjInOV+Bdd92Fu+++u+bHP/zhD+PWW29d0ms7jsbkZHapQ5tjMefwtDrLkujtTeLUqSxsOxrfxJsd70k4RfW+CAEUAGQcB8Um3MWllESqux3pM9EJoqtNCYFeS0Gu8O3v60vWHXgCn+FZjCuvvBKvec1rKq5997vfxQ9+8AN84xvfwODgYEAjm8s6ZxuSW162pJOWiYiaiTFADECPUshIgzx3cVEAIhV4hoeHMTw8XHHtmWeegVIKF198cUCjqk1I6Z6hE/RAiIhCQBggJQQs76BC7uKi1cTpBiIiWjXGAB1CoNtSiEkexUGrJ/KB55ZbbgldHy0iIqrNGMAyQK9SaLcUeAQZrYbIBx4iIooob4mry7KgONtDDcbAQ0REgTEGaIdb0MwlLmokBh4iIgqUMYDyLXEx91AjMPAQEVE4eEtcnVziogZg4CEiotAoL3FZFuJKgrGHVgoDDxERhYoxgNIGPVJyiYtWDAMPERGFkwG6hOQSF60IBh4iIgotYwyXuGhFMPAQEVGocYmLVgIDDxERRYO3xNVlWbCYemiRGHiIiCgyjDFIAOi2FNq4xEWLwMBDRESR4i5xAd1SosNSkGzGRXVg4CEiomgyQKeQSFmKS1x0Vgw8REQUWcYYxA3QYym0WYpLXFQTAw8REUWe1EC3EEhyiYtqYOAhIqLmYIAkl7ioBgYeIiJqGlzioloYeIiIqOn4l7gUl7gIDDxERNSsuMRFPgw8RETUtIwxiHlLXAkucbU0Bh4iImp6UgMpIdDJJa6WxcBDREStwQAdQiBlKcS4xNVyGHiIiKhlGAN3iUsptFsKnOxpHQw8RETUcoTxlrgUl7haBQMPERG1JOMtcXVziaslMPAQEVHLMgawDNDrLXEx9zQvBh4iIqLSEpdlQTH1NCUGHiIiIrizPe1wC5q5xNV8GHiIiIg8xgDKW+Lq4BJXU4lk4Mnn8/jSl76Eyy67DBdccAEuv/xy3H333UEPi4iImoUBurjE1VSsoAewWI7j4I//+I9x9OhR/Omf/imGhoZw+PBhHDlyJOihERFREzEGaBdAzLIw5TgoOhom6EHRkkUu8Dz88MN44YUX8MMf/hD9/f1BD4eIiJqYu8Rl0CMlMkIg7zjQTD2RFLklrYcffhhvf/vbGXaIiGj1GKBLSHRZFjuvR1SkAk+hUMALL7yA9evX47bbbsNFF12EV73qVfjEJz6BU6dOBT08IiJqYsYYJAB0WxbalGTn9YiJ1JLW6dOnYds2vvGNb+Diiy/GPffcg4mJCfzVX/0VPv7xj+Nv//Zvl/X6lhWp/Nc0lJIVjxQ83pNw4n0JBwWgTVmY0gZ5x4HwZnyUEojYPMKqkUJAWRIqwOXAwAPP1NQUjh8/ftbPGxkZgTHu31QqlcKXv/xlxONxAEAymcQtt9yCX//617jwwguXNA4pBXp7k0t6Lq2MVKo96CFQFd6TcOJ9CYdeAFnbwZTjQANIdiaCHlJoCQCpmIW4DC4QBh54HnvsMdx+++1n/bxHH30Uo6OjAIBXvepV5bADAJdccgkA4KWXXlpy4NHaIJ2eXtJzaXmUkkil2pFO5+A4OujhEHhPwor3JZziSsJJWDgzlYPtsKJ5PlIIxGJqxWd4Uqn2umc8Aw88V111Fa666qq6P39oaKjmx+Qyk6Nt8xtIkBxH8x6EDO9JOPG+hIsFYE3MQl5IFLUNbRh65hACjhAI8q8mcouNb3zjG/Hss8+iUCiUr/30pz8FAJx//vlBDYuIiFqYFAJdUiBlKe7iCqnIBZ4/+qM/wvT0ND760Y/iySefxHe+8x38t//233DFFVdg27ZtQQ+PiIhalDFA3AA9lkKbpbiLK2QiF3iGhobwrW99C7lcDrfccgu+8IUv4C1veQvuvPPOoIdGREQEqYFuIZC0FKRg7AmLwGt4luKCCy7AAw88EPQwiIiI5meApJCwLIGM48Dm8cyBi9wMDxERURQYY8pLXAkucQWOgYeIiKiBpAZS3hKX4hJXYBh4iIiIGs1b4uIuruAw8BAREa0CYwxiBujlElcgGHiIiIhWkfB2cXVyiWtVMfAQERGtMmOADuEeVBjjEteqYOAhIiIKgDFAzAA9SqHdUuBkT2Mx8BAREQVIGHcXV5dlQXG2p2EYeIiIiAJmDNAOoFtxiatRGHiIiIhCwBjAMkCvt8TF3LOyGHiIiIjCxFvi6uQS14pi4CEiIgqZ0hJXj2VxiWuFMPAQERGFkDGA0ga9SqGDS1zLxsBDREQUZgbo4hLXsjHwEBERhZx/iSuuJNtSLAEDDxERUQSUlrh6pOQuriVg4CEiIooSA3QJiS7LYuf1RWDgISIiihhjDBIAui2FNi5x1YWBh4iIKILcJS6gm0tcdWHgISIiijIucdWFgYeIiCjiuMR1dgw8RERETcC/xOUeVMjY48fAQ0RE1EwM0CkkUpbiEpcPAw8REVGTMcYgboAeS6HNUlziAgMPERFR05Ia6BYCSS5xMfAQERE1NQMkucTFwENERNTs/EtciRZd4mLgISIiahFSAykh0GkpqBZb4mLgISIiaiUG6BCi5Za4rKAHsFi2beO+++7DI488giNHjmDNmjW4/PLL8ad/+qdIpVJBD4+IiCj0jAFiAHqVwpQ0mLEdmKAH1WCRCzz33HMPvv71r+OWW27BK1/5SuzZswf/43/8D4yNjeGrX/1q0MMjIiKKDGHcXVzTlsK0o+GY5o09kQs83//+9/HOd74TN954IwDgkksuwfT0NL70pS9henoaHR0dAY+QiIgoOoy3xBWzFKYcB0XdnKEncjU8tm2jq6ur4loqlYIxBqaJkykREVGjGANYBuhRCu2WQjPWM0cu8Lzvfe/D//2//xc//elPkc1m8Zvf/Ab33XcfrrzySiSTyaCHR0REFFnCuLu4uiwLqskKmiO3pHXjjTfCtm3ccMMN5Rmdt7zlLbjjjjuW/dqWFbn81xSUkhWPFDzek3DifQmnZrwvXQJoUwJTWqPoLH/1RAoBZUmoABdihAl4HWhqagrHjx8/6+eNjIwgHo/jgQcewF//9V/j5ptvxvbt27Fv3z78zd/8DV73utfhzjvvXPI4jDEQzTiHR0REtES2MUgXbcwYs6xdXALAmpiFuAwuFAYeeB555BHcfvvtZ/28Rx99FOvXr8frX/963HbbbbjuuuvKH3v88cfx0Y9+FI888gi2b9++pHE4jkY6nVvSc2l5lJJIpdqRTufgODro4RB4T8KK9yWcWuG+5IxBdhm7uKQQ6IupFZ/hSaXa655ZC3xJ66qrrsJVV11V1+f++te/RqFQwLZt2yqul/774MGDSw48AGDbzfmFGhWOo3kPQob3JJx4X8Kpme9LXABSSncXl6MXP9sjBBwhEOQUS6QWHAcHBwEAzz//fMX15557DgAwNDS06mMiIiJqdsYAShv0SIl2SyGK9cyBz/AsRn9/P9761rfib/7mb2DbNi644ALs3bsXd911F/7Nv/k3uOCCC4IeIhERUfMyQEpKWEIg6zhwInRmT+A1PIuVyWRw77334rHHHsPRo0fR39+P3/3d38XHPvYx9PX1Lfl1HUdjcjK7giOlelmWRG9vEqdOZZt2OjhqeE/CifclnFrxvggB2ELUvcSlhECvpSBXOHH09SXrruGJXOBpFAae4LTiN4uw4z0JJ96XcGrp+yKAjDHIOw4WmuwJQ+CJVA0PERERhYgBuoREl2WFvvM6Aw8REREtmTEGCQDdlkKbkghr7GHgISIiomVxd3EB3VKiI6S7uBh4iIiIaGUYoFNIpEK4xMXAQ0RERCvGGIO4AXoshTZLhWaJi4GHiIiIVpzUQLcQSFoKMgSzPZE6eJCIiIgixABJIWGJ4E/AYeAhIiKihjHGIA73sMIgYw+XtIiIiKjhgj7mmIGHiIiImh4DDxERETU9Bh4iIiJqegw8RERE1PQYeIiIiKjpMfAQERFR02PgISIioqbHwENERERNj4GHiIiImh4DDxERETU9Bh4iIiJqegw8RERE1PQYeIiIiKjpCWOC7l8aDsYYaM2/iqAoJeE4OuhhkA/vSTjxvoQT70swpBQQQtT1uQw8RERE1PS4pEVERERNj4GHiIiImh4DDxERETU9Bh4iIiJqegw8RERE1PQYeIiIiKjpMfAQERFR02PgISIioqbHwENERERNj4GHiIiImh4DDxERETU9Bh4iIiJqegw8RERE1PSsoAdAVOI4Du677z48+eST2L17NxzHwXnnnYebb74Zl156adDDa2n/8i//gkceeQS/+tWvcOjQIXzgAx/An//5nwc9rJaxb98+fOYzn8Gzzz6L9vZ2/N7v/R5uvfVWJBKJoIfW0g4cOIBvfvOb+NWvfoWXXnoJW7Zswfe///2gh0U1cIaHQiOfz+NrX/sazj//fHz2s5/Fl770JQwMDOD666/Hj3/846CH19J+8pOfYOfOnXj1q1+NVCoV9HBaSjqdxh/+4R8im83iy1/+Mj75yU/ie9/7Hv7sz/4s6KG1vJdeeglPPvkkNm3ahK1btwY9HDoLYYwxQQ+CCHBneDKZDLq7u8vXjDG4+uqrkUwmsWPHjgBH19q01pDS/f3o8ssvxxvf+EbO8KySr3/96/jKV76CJ554An19fQCA733ve7j11lvxgx/8gD9oA+R/X3zqU5/Cc889xxmeEOMMD4WGUqoi7ACAEALnn38+jh8/HtCoCED5mzqtvp/85Ce49NJLy2EHAN761rciHo/jySefDHBkxPdFtPBuUahprfHLX/6Sv8VSy9qzZ8+cr/94PI6NGzdiz549AY2KKHoYeCjUduzYgX379uH6668PeihEgUin0/PWTaVSKZw5cyaAERFFE3dpUUNNTU3VtRw1MjKCeDxece2ZZ57B5z//edxwww149atf3aghtqTl3BcKB2MMhBBBD4MoMhh4qKEee+wx3H777Wf9vEcffRTbtm0r//euXbtw00034YorrsBtt93WyCG2pKXeF1p9qVQK6XR6zvWpqSku9RItAgMPNdRVV12Fq666alHPOXjwID70oQ/h5S9/Of7qr/6Kv8U2wFLuCwVj69atc2p1CoUCDh48iKuvvjqgURFFD2t4KFQmJiZwww03oL+/H1/5yle4nEIt7/Wvfz1+9rOf4dSpU+Vrjz32GAqFAt7whjcEODKiaOEMD4VGPp/Hhz70IZw8eRKf+tSnsHv37oqPv/KVrwxmYITx8XH85je/AQDkcjkcPHgQP/rRjwAAb3vb24IcWtO75ppr8MADD+Cmm27CTTfdhJMnT+Jzn/scfv/3f59LWgHL5XLlowHGx8eRyWTK74vXvOY1FUcJUPB48CCFxtjYGN70pjfV/PiLL764iqMhv0ceeaRmzQ/vS+P5W0skEgm8853vZGuJEFjoe9b999+Piy++eJVHRAth4CEiIqKmxxoeIiIianoMPERERNT0GHiIiIio6THwEBERUdNj4CEiIqKmx8BDRERETY+Bh4iIiJoeT1omoqa1a9cufPvb38bPf/5zTExMwLIsjI6O4h3veAfe8573oKenJ+ghEtEq4cGDRNSUHnroIXz605/G5s2b8Qd/8Ac455xzYNs2nnvuOTz00EM4//zzcc899wQ9TCJaJQw8RNR0fvnLX+IDH/gAXvva187bhLZQKOCpp55asJUJETUXBh4iajo33ngjnnrqKTz++OPYsGFD0MMhohBg0TIRNRXHcfCzn/0M27dvZ9ghojIGHiJqKqdOnUIul8Pw8HDQQyGiEGHgISIioqbHwENETaW3txft7e0YGxsLeihEFCIMPETUVJRSuOSSS/D888/j6NGjQQ+HiEKCgYeIms4f//EfwxiDP/uzP0OhUJjz8WKxiCeeeCKAkRFRULgtnYiaUungwS1btuCaa67BueeeC9u28cILL+Chhx7Cueeey4MHiVoIAw8RNa1du3bhW9/6Vrm1RCwWw+joKC677DJce+216OvrC3qIRLRKGHiIiIio6bGGh4iIiJoeAw8RERE1PQYeIiIianoMPERERNT0GHiIiIio6THwEBERUdNj4CEiIqKmx8BDRERETY+Bh4iIiJoeAw8RERE1PQYeIiIianoMPERERNT0/j+kUyeaI2vj1QAAAABJRU5ErkJggg==" +class=" +" +> +</div> + +</div> + +</div> + +</div> + </div></section><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -17399,7 +21986,7 @@ div.jp-OutputPrompt { <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 [101]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [218]:</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> @@ -17410,12 +21997,12 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [219]:</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">"reg"</span><span class="p">);</span> @@ -17426,6 +22013,32 @@ div.jp-OutputPrompt { </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-RenderedImage jp-OutputArea-output "> +<img 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" 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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[221]:</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>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> + +</div> + +</div> + +</div> + +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [222]:</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">"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> @@ -17502,6 +22197,32 @@ div.jp-OutputPrompt { </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 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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + </div></section><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -17518,12 +22239,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [223]:</div> <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> <div class="CodeMirror cm-s-jupyter"> <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_multind</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">([</span><span class="s2">"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> @@ -17535,12 +22256,219 @@ div.jp-OutputPrompt { </div> </div> -</div></div></section><section ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[223]:</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></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> + <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 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> + </tr> + <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> + <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>2.43</td> + </tr> + <tr> + <th>4</th> + <th>4</th> + <td>5</td> + <td>200.84</td> + <td>10</td> + <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> + </tr> + <tr> + <th>2</th> + <th>2</th> + <th>4</th> + <td>5</td> + <td>164.16</td> + <td>10</td> + <td>True</td> + <td>0.20</td> + <td>40.03</td> + <td>41.09</td> + <td>0.52</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> + <td>2.45</td> + </tr> + <tr> + <th>1</th> + <th>2</th> + <th>12</th> + <td>6</td> + <td>141.70</td> + <td>10</td> + <td>True</td> + <td>0.30</td> + <td>32.93</td> + <td>33.26</td> + <td>0.62</td> + <td>0.95</td> + <td>5.41</td> + <td>100.16</td> + <td>50148824.0</td> + <td>813743</td> + <td>7.27</td> + <td>112500</td> + <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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [224]:</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">"Unaccounted Time / s"</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">]]</span>\ @@ -17553,6 +22481,32 @@ div.jp-OutputPrompt { </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-RenderedImage jp-OutputArea-output "> +<img 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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + </div></section></section><section ><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -17588,12 +22542,12 @@ div.jp-OutputPrompt { </div> </div> </div> -</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [225]:</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">"Nodes"</span><span class="p">)</span><span class="o">.</span><span class="n">groups</span> @@ -17604,12 +22558,35 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[225]:</div> + + + + +<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain"> +<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"> <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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [226]:</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">"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> @@ -17620,12 +22597,175 @@ div.jp-OutputPrompt { </div> </div> -</div></div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[226]:</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>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"> <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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [227]:</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">"Nodes"</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> @@ -17636,6 +22776,241 @@ div.jp-OutputPrompt { </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[227]:</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>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></div></section><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -17665,7 +23040,7 @@ div.jp-OutputPrompt { <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 [111]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [228]:</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">"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> @@ -17676,12 +23051,12 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [229]:</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> @@ -17697,12 +23072,89 @@ div.jp-OutputPrompt { </div> </div> -</div></div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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[229]:</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>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> + </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 class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> </div> <div class="jp-InputArea jp-Cell-inputArea"> -<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [230]:</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><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> @@ -17713,6 +23165,32 @@ div.jp-OutputPrompt { </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-RenderedImage jp-OutputArea-output "> +<img 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" 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" +class=" +" +> +</div> + +</div> + +</div> + +</div> + </div></div></section><section > <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell"> <div class="jp-Cell-inputWrapper"> @@ -17811,7 +23315,7 @@ div.jp-OutputPrompt { <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 [13]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [232]:</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_db</span> <span class="o">=</span> <span class="s1">'db-bahnhoefe.csv'</span> <span class="c1"># source: https://web.archive.org/web/20231208211825/https://download-data.deutschebahn.com/static/datasets/stationsdaten/DBSuS-Uebersicht_Bahnhoefe-Stand2020-03.csv</span> @@ -17822,12 +23326,12 @@ div.jp-OutputPrompt { </div> </div> -</div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +</div><div class="fragment" ><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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [233]:</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">timeit</span> pd.read_csv(data_db, sep=';') @@ -17838,12 +23342,33 @@ div.jp-OutputPrompt { </div> </div> -</div></div><div class="fragment" ><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> +<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>10 ms ± 239 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"> <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 [ ]:</div> +<div class="jp-InputPrompt jp-InputArea-prompt">In [234]:</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">pyarrow</span> @@ -17855,6 +23380,33 @@ div.jp-OutputPrompt { </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> +<span class="ansi-red-fg">---------------------------------------------------------------------------</span> +<span class="ansi-red-fg">ModuleNotFoundError</span> Traceback (most recent call last) +Cell <span class="ansi-green-fg">In[234], line 1</span> +<span class="ansi-green-fg">----> 1</span> <span class="ansi-bold" style="color: rgb(0,135,0)">import</span> <span class="ansi-bold" style="color: rgb(0,0,255)">pyarrow</span> +<span class="ansi-green-intense-fg ansi-bold"> 2</span> <span style="color: rgb(0,135,0)">print</span>(pyarrow<span style="color: rgb(98,98,98)">.</span>__version__) + +<span class="ansi-red-fg">ModuleNotFoundError</span>: No module named 'pyarrow'</pre> +</div> +</div> + +</div> + +</div> + </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs "> <div class="jp-Cell-inputWrapper"> <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> diff --git a/Introduction-to-Pandas--slides.ipynb b/Introduction-to-Pandas--slides.ipynb index bf3d5bdff18e772f77a0e8ef848a78fef351d3bb..5a4a1d680f13e1ae5e6f346bd290f0630b1b3d2a 100644 --- a/Introduction-to-Pandas--slides.ipynb +++ b/Introduction-to-Pandas--slides.ipynb @@ -152,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 118, "metadata": { "slideshow": { "slide_type": "fragment" @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 119, "metadata": { "exercise": "task", "slideshow": { @@ -179,20 +179,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 120, "metadata": { "slideshow": { "slide_type": "-" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'2.1.4'" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pd.__version__" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 121, "metadata": { "slideshow": { "slide_type": "fragment" @@ -258,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 122, "metadata": { "slideshow": { "slide_type": "fragment" @@ -271,26 +282,167 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 123, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "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", 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-326,13 +486,76 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 126, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Name</th>\n", + " <th>Age</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Liu</td>\n", + " <td>41</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Rowland</td>\n", + " <td>56</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Rivers</td>\n", + " <td>56</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Waters</td>\n", + " <td>57</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Name Age\n", + "0 Liu 41\n", + "1 Rowland 56\n", + "2 Rivers 56\n", + "3 Waters 57" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_sample = pd.DataFrame(data)\n", "df_sample.head(4)" @@ -352,9 +575,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 127, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Name', 'Age'], dtype='object')" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_sample.columns" ] @@ -373,9 +607,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 128, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=10, step=1)" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_sample.index" ] @@ -394,13 +639,106 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 129, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Age</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Name</th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Liu</th>\n", + " <td>41</td>\n", + " </tr>\n", + " <tr>\n", + " 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"df_sample.set_index(\"Name\", inplace=True)\n", "df_sample" @@ -419,54 +757,221 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 130, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Age</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>count</th>\n", + " <td>10.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>mean</th>\n", + " <td>50.500000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>std</th>\n", + " <td>9.009255</td>\n", + " </tr>\n", + " <tr>\n", + " <th>min</th>\n", + " <td>38.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25%</th>\n", + " <td>41.500000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>50%</th>\n", + " <td>56.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>75%</th>\n", + " <td>56.750000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>max</th>\n", + " <td>60.000000</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Age\n", + "count 10.000000\n", + "mean 50.500000\n", + "std 9.009255\n", + "min 38.000000\n", + "25% 41.500000\n", + "50% 56.000000\n", + "75% 56.750000\n", + "max 60.000000" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_sample.describe()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 131, "metadata": { "slideshow": { "slide_type": "fragment" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<class 'pandas.core.frame.DataFrame'>\n", + "Index: 10 entries, Liu to Hall\n", + "Data columns (total 1 columns):\n", + " # Column Non-Null Count Dtype\n", + "--- ------ -------------- -----\n", + " 0 Age 10 non-null int64\n", + "dtypes: int64(1)\n", + "memory usage: 160.0+ bytes\n" + ] + } + ], "source": [ "df_sample.info()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 132, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th>Name</th>\n", + " <th>Liu</th>\n", + " 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139, "metadata": { "slideshow": { "slide_type": "skip" @@ -567,14 +1371,82 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 140, "metadata": { "slideshow": { "slide_type": "fragment" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Age</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Name</th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Liu</th>\n", + " <td>1681</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rowland</th>\n", + " <td>3136</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rivers</th>\n", + " <td>3136</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Waters</th>\n", + " <td>3249</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rice</th>\n", + " <td>1521</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Age\n", + "Name \n", + "Liu 1681\n", + "Rowland 3136\n", + "Rivers 3136\n", + "Waters 3249\n", + "Rice 1521" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_sample.apply(np.square).head()" ] @@ -593,25 +1465,186 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 141, "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Age</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Name</th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Liu</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rowland</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rivers</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Waters</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rice</th>\n", + " <td>False</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Fields</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Kerr</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Romero</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Davis</th>\n", + " <td>False</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Hall</th>\n", + " <td>True</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Age\n", + "Name \n", + "Liu True\n", + "Rowland True\n", + "Rivers True\n", + "Waters True\n", + "Rice False\n", + "Fields True\n", + "Kerr True\n", + "Romero True\n", + "Davis False\n", + "Hall True" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_sample > 40" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 142, "metadata": { "slideshow": { "slide_type": "fragment" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Age</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Name</th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Liu</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rowland</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rivers</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Waters</th>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Rice</th>\n", + " <td>True</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Age\n", + "Name \n", + "Liu True\n", + "Rowland True\n", + "Rivers True\n", + "Waters True\n", + "Rice True" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_sample.apply(mysquare).head() == df_sample.apply(lambda x: x*x).head()" ] @@ -639,7 +1672,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 143, "metadata": { "exercise": "task", "slideshow": { @@ -658,14 +1691,81 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 144, "metadata": { "exercise": "solution", "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th>Dinosaur Name</th>\n", + " <th>Aegyptosaurus</th>\n", + " <th>Tyrannosaurus</th>\n", + " <th>Panoplosaurus</th>\n", + " <th>Isisaurus</th>\n", + " <th>Triceratops</th>\n", + " <th>Velociraptor</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Favourite Prime</th>\n", + " <td>4</td>\n", + " <td>8</td>\n", + " <td>15</td>\n", + " <td>16</td>\n", + " <td>23</td>\n", + " <td>42</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Favourite Color</th>\n", + " <td>blue</td>\n", + " <td>white</td>\n", + " <td>blue</td>\n", + " <td>purple</td>\n", + " <td>violet</td>\n", + " <td>gray</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + "Dinosaur Name Aegyptosaurus Tyrannosaurus Panoplosaurus Isisaurus \\\n", + "Favourite Prime 4 8 15 16 \n", + "Favourite Color blue white blue purple \n", + "\n", + "Dinosaur Name Triceratops Velociraptor \n", + "Favourite Prime 23 42 \n", + "Favourite Color violet gray " + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "happy_dinos = {\n", " \"Dinosaur Name\": [\"Aegyptosaurus\", \"Tyrannosaurus\", \"Panoplosaurus\", \"Isisaurus\", \"Triceratops\", \"Velociraptor\"],\n", @@ -689,9 +1789,96 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 145, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>D</th>\n", + " <th>E</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-2.718282</td>\n", + " <td>This</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>1.718282</td>\n", + " <td>column</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-1.304068</td>\n", + " <td>has</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>0.986231</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-0.718282</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A B C D E\n", + "0 1.2 2018-02-26 -2.718282 This Same\n", + "1 1.2 2018-02-26 1.718282 column Same\n", + "2 1.2 2018-02-26 -1.304068 has Same\n", + "3 1.2 2018-02-26 0.986231 entries Same\n", + "4 1.2 2018-02-26 -0.718282 entries Same" + ] + }, + "execution_count": 145, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_demo = pd.DataFrame({\n", " \"A\": 1.2,\n", @@ -705,52 +1892,231 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 146, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>D</th>\n", + " <th>E</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-2.718282</td>\n", + " <td>This</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-1.304068</td>\n", + " <td>has</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-0.718282</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>0.986231</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>1.718282</td>\n", + " <td>column</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A B C D E\n", + "0 1.2 2018-02-26 -2.718282 This Same\n", + "2 1.2 2018-02-26 -1.304068 has Same\n", + "4 1.2 2018-02-26 -0.718282 entries Same\n", + "3 1.2 2018-02-26 0.986231 entries Same\n", + "1 1.2 2018-02-26 1.718282 column Same" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_demo.sort_values(\"C\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 147, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>A</th>\n", + " <th>B</th>\n", + " <th>C</th>\n", + " <th>D</th>\n", + " <th>E</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>0.99</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>1.2</td>\n", + " <td>2018-02-26</td>\n", + " <td>-0.72</td>\n", + " <td>entries</td>\n", + " <td>Same</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " A B C D E\n", + "3 1.2 2018-02-26 0.99 entries Same\n", + "4 1.2 2018-02-26 -0.72 entries Same" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_demo.round(2).tail(2)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 148, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "A 6.00\n", + "C -2.03\n", + "dtype: float64" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_demo.round(2)[[\"A\", \"C\"]].sum()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 149, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{tabular}{lrlrll}\n", + "\\toprule\n", + " & A & B & C & D & E \\\\\n", + "\\midrule\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" + ] + } + ], "source": [ "print(df_demo.round(2).to_latex())" ] @@ -784,13 +2150,94 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 150, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Actor</th>\n", + " <th>Main Cast</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Character</th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Hurley</th>\n", + " <td>Jorge Garcia</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Jack</th>\n", + " <td>Matthew Fox</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Kate</th>\n", + " <td>Evangeline Lilly</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Locke</th>\n", + " <td>Terry O'Quinn</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Sawyer</th>\n", + " <td>Josh Holloway</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>Walt</th>\n", + " <td>Malcolm David Kelley</td>\n", + " <td>False</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Actor Main Cast\n", + "Character \n", + "Hurley Jorge Garcia True\n", + "Jack Matthew Fox True\n", + "Kate Evangeline Lilly True\n", + "Locke Terry O'Quinn True\n", + "Sawyer Josh Holloway True\n", + "Walt Malcolm David Kelley False" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pd.read_json(\"data-lost.json\").set_index(\"Character\").sort_index()" ] @@ -816,25 +2263,263 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 151, "metadata": { "exercise": "task" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "id,Nodes,Tasks/Node,Threads/Task,Runtime Program / 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Delay', 'Threads'],\n", + " dtype='object')" + ] + }, + "execution_count": 181, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.columns" ] @@ -1511,7 +5031,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 182, "metadata": { "exercise": "task", "slideshow": { @@ -1526,7 +5046,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 183, "metadata": { "slideshow": { "slide_type": "subslide" @@ -1540,13 +5060,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 184, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:5: SyntaxWarning: invalid escape sequence '\\s'\n", + "<>:5: SyntaxWarning: invalid escape sequence '\\s'\n", + "/tmp/ipykernel_106956/3587136147.py:5: SyntaxWarning: invalid escape sequence '\\s'\n", + " ax.set_ylabel(\"$\\sqrt{x}$\");\n" + ] + }, + { + "data": { + 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(x, y)\n", @@ -1569,7 +5110,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 185, "metadata": { "slideshow": { "slide_type": "-" @@ -1582,9 +5123,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 186, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(x, y, label=\"y\")\n", @@ -1608,9 +5160,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 187, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(df_demo.index, df_demo[\"C\"], label=\"C\")\n", @@ -1621,7 +5184,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "exercise": "task", "slideshow": { "slide_type": "slide" @@ -1643,9 +5205,8 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 188, "metadata": { - "editable": true, "exercise": "solution", "slideshow": { "slide_type": "fragment" @@ -1659,16 +5220,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 189, "metadata": { - "editable": true, "exercise": "solution", "slideshow": { "slide_type": "" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "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", @@ -1681,7 +5252,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "slide" }, @@ -1724,13 +5294,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 190, "metadata": { "slideshow": { "slide_type": "-" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x200 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo[\"C\"].plot(figsize=(10, 2));" ] @@ -1748,13 +5329,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 191, "metadata": { "slideshow": { "slide_type": "-" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x200 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo.plot(y=\"C\", figsize=(10, 2));" ] @@ -1769,13 +5361,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 192, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo[\"C\"].plot(kind=\"bar\");" ] @@ -1790,26 +5393,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 193, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo[\"C\"].plot.bar();" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 194, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1200x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo[\"C\"].plot(kind=\"bar\", legend=True, figsize=(12, 4), ylim=(-1, 3), title=\"This is a C plot\");" ] @@ -1839,7 +5464,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 195, "metadata": { "exercise": "solution", "slideshow": { @@ -1853,43 +5478,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 196, "metadata": { "exercise": "solution" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df[\"Presim. Time / s\"].plot(figsize=(10, 3), style=\"--\", color=\"red\");" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 197, "metadata": { - "editable": true, "exercise": "solution", "slideshow": { "slide_type": "" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df[\"Sim. Time / s\"].plot(figsize=(10, 3), style=\"-b\");" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 198, "metadata": { - "editable": true, "exercise": "solution", "slideshow": { "slide_type": "subslide" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df[\"Presim. Time / s\"].plot(style=\"--r\", figsize=(10,3));\n", "df[\"Sim. Time / s\"].plot(style=\"-b\", figsize=(10,3));" @@ -1897,16 +5553,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 199, "metadata": { - "editable": true, "exercise": "solution", "slideshow": { "slide_type": "fragment" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ax = df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot(style=[\"--b\", \"-r\"], figsize=(10,3));\n", "ax.set_ylabel(\"Time / s\");" @@ -1915,7 +5581,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "slide" }, @@ -1928,15 +5593,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 200, "metadata": { - "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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o6A/l0hkoJQUsnoiIiIi8zIIFi7F48XysXPkMbrnldgQHB6Oo6BK+/PIw7r47HcnJo/CHP6xBcLAe11yThODgYBw9+g3y80+1OsJce61duwoffPA35OYe7pTtt0d09CA88MBkrF69Eg899DASEhJhsVhQUHAOR478G2vX/q/T9b755gjM5lqH+7ycOX36FF5+ORO33HI7+vcfgIqKCuzcuQN9+/ZD//7eMWp1h+95otZJhgF1xdOPQEzLXxoiIiIi6lpJScOxefN2ZGe/grVrf4fa2lpERERi1KjrMWDAQHub999/F3/96x7U1NSgX7/+WLRoCdLS7uuUmKyjIMqtN+xiv/zlU4iOHoT33nsHf/zjdvj7ByA6ehBSU53fBwUABw8eQHLydU2e8dpYeHg4wsPDsXPnDhQXFyEwMAjDh4/AypW/hyR5x2Wkgmp7uFIPIcsKSkoq27WuRiMiLCwQV65UunWUDvOxj2A6uAuaQSMRcMdit22XOq6zck7ejXn3Tcy7b2LeW1Zba8blyxcQHt4XWq3O0+G4VXP3PFHXmzr1AUyZ8hAeeGBy643byPYd7tOnHyIjDQ7nusEQ6H1DlVPrbCPuySUFHo6EiIiIiKhrvfnmO54OwW3cW4qRU7YR99TyYqjmag9HQ0RERERE7cHiqQsI/kEQeoUCAJQr5z0bDBERERERtQuLpy5Sf+keH5ZLRERERNQdsXjqIrbiSbnM+56IiIiIiLojFk9dxHbfk3KFPU9ERERERN0Ri6cu0vCyvR42OjwRERERkU9g8dRFxNC+gCACpkqoVaWeDoeIiIiIiNqIxVMXETQ6iCFRAHjfExERERFRd8SH5HYh0TAASulPkEt+hCb6Wk+HQ0RERNSj/P3v+5GTsxvnzp2FqgIRERFIShqOX/xiIcLCDACAxx9/DL169cIf/vBSp8eTl/dvPPHEvFbb5eS8j0WLfoHx4yfgySef7vS4WlNVVYl77rkVL720GcOHj/R0OF6FxVMXEg0DgDNfQClhzxMRERGRO+3c+UdkZW3ClCk/x+zZ8yCKAk6fPoW///0DFBcX2YunX/1qOSSpay6+io8fiq1bd9inv//+BDIyXsCzz/4PoqOvss8PD++N5557EcHB+i6JqzVffHEIAQG9kJjIP/Y3xuKpC9lH3OOznoiIiIjc6i9/eQt33ZWGRYuWAAA0GhGjR4/Dz38+A4qi2NtdffXgLospMDAIiYlJ9mmz2QQAGDw4BkOHJji0jYsb2mVxtebgwc8wZsw4SJLk6VC8Du956kL2Zz2V/gRVsXg4GiIiIqKeo6KiHOHhvZ0uE8X6X3kff/wxLFv2S/t0dvYruO22iThx4jvMnTsTqak34NFHf44TJ76DyWTCunVrcdddqbj//rvx9ttvdFr8Dz6YjoyMF+zTa9b8Fg8/PAWHD3+OGTN+htTUG7BgwRz89NN5GI1lWLnyGdx+ewqmTLkX//jH35ts7+DBz+zHk5Z2K9atW4vq6upW41AUBZ9//i/ccMPEZtuUl5fjhRdW47777kJq6ng88MA9+J//eaZ9B97NsOepCwnB4YDWH6itgVJaCMnQ39MhERERETWhqipgMXtm5xodBEFo82rx8cPw3nvvoF+//hg/fgIiI/u4vK7FYsHatavws5/9HGFhYdiyZSN+/euncO21I2AwGLBq1XM4cCAXGzZkYNiwa5CUNLzN8bXH5cuXsWXLRsycOQcajYSXXlqHVat+g4CAAAwfPhLp6ffi/ff3YNWq3+Caa5IQFdUXAPDJJx/jf/7nWdx9dzpmz/4FLl8uxtatL6O83Ijf/W5ti/s8fvxbGI1lGDNmfLNtNm7MwOHDBzFv3iJERfXF5cvFOHTooFuP3VuxeOpCgiBaB40oPA2lpIDFExEREXkdVVVR9f4aKIWnPbJ/KXIIAv7fs20uoH71q6fx7LNP4YUXVgNAXRE1ET/72c/Rt2+/Ftetra3F/PmLMHastWBQFBVPP70EiiJj0aInAQDJydfjk0/+gU8++bjLiqfyciM2b96Oq666GgBQXFyEzMwXMW3aTDzyyBwAwNCh1+DTTz/Bp5/+H6ZMeQiqqmLTpvVITb0Ny5f/xr4tg8GAZcuWYObMORg8OKbZfR48eABJScMRHBzcbJvvvvsWt956J+66K80+79Zb7+jo4XYLvGyvi0lhdZfu8b4nIiIi8lIC2t7z42mDB8di58638eKLL2Hy5IcQFBSEP//5Tcyc+RBOnTrZ4rqiKOK66663Tw8cGA0AGDVqjH2eJEno338ALl0q7JwDcKJ37wh74WSNa1BdXKPt84KDgxEaGmaPq6DgLC5evIDU1NtgsVjs/0aMuA6CIODkye9a3OfBg5/hhhtubLFNXNxQ7N+/F2+8sRNnznimyPYU9jx1Mdt9TzJH3CMiIiIvJAgCAv7fs93usj0A0Gq1GDduAsaNmwCNRsS//vUvLFv2S+zYsR3PPfdis+v5+flBq9U6bAcAgoKCHEPTaGA2d93n4mz/AJr0Cmm1WvtgFKWlpQCAZ59d6nSbhYUXm91fYeFFnD79PVateq7FuJYsWQa9/hW89dafsHnzevTpE4mHH34U99//YIvr9QQsnrqYfdAI9jwRERGRlxIEAdD6eTqMDhszZhxiYobg7NkfPB1Kl9HrQwBYC5xrrklssrx374hm1z148DMMGDDQYRh1Z4KCgrB48a+wePGvkJ9/Gjk5u/G///s8rr56MEaMSO5Q/N6Ol+11MamueFIrLkM1V3k4GiIiIqKeoaTkcpN5JlMNLl0qhMEQ7oGIPGPQoKvQp08kfvrpPIYOTWjyr+Xi6UCLo+w5ExMTiyeesN4XdvbsfzsSerfQpp6n/fv3469//Su+/fZblJWVYeDAgXjooYcwdepU+xCQy5cvx7vvvttk3W3btuHGGx2vn8zOzsauXbtQVFSEuLg4LFu2DGPGjGmybk8i+AdBCAyDWnkFcsl5aKKGeDokIiIiom5vxoypuOGGiRg9ehx69+6NkpJivP32mygrK8XkyQ91yj7379+L55//PV56aTNGjryuU/bRVoIg4PHHl+B3v/s1amqqMW7cBAQEBODixQv4/PPP8NhjCxEdPajJejU1Nfjqq39j6tTpre5j/vxZmDjxZgweHANJEvHBB3+DVqvF8OEjO+OQvEqbiqcdO3agX79+WLZsGcLDw3H48GGsWbMGBQUFePrpp+3tBg4ciHXr1jmsGxPjOKpHdnY2MjMzsWTJEiQkJCAnJwdz585FTk4O4uPjO3BI3k80DIBceQVKSQHA4omIiIiow2bNegz/+tcBvPxyJkpLryA0NBSDBw/B+vVbkJw8qlP2qaoqZFm2Du3uRVJTb0VwcBBee+1V/P3v+wEAUVF9MWbM+GZ74f7978PQajUuFUBJScPx4Yd/w08//QRRFDB4cCxeeCHTYXCLnkpQ25DtkpISGAwGh3lr167F7t278e9//xs6nQ7Lly/HsWPHsHfv3ma3YzabMX78eEyZMgXLli0DAMiyjPT0dMTHxyMzM7OdhwPIsoKSksp2ravRiAgLC8SVK5WwWJTWV2inmkNvofY/+6FNSIX/hBmdth9qXVflnLwL8+6bmHffxLy3rLbWjMuXLyA8vC+0Wp2nw3ErjUZkztvghRfWoKKiHL///fOeDqVNbN/hPn36ITLS4HCuGwyBkCT33qXUpp6nxoUTAAwbNgwmkwmlpaXo08e1h5Hl5eWhvLwcaWn1Y8NLkoS7774br776KlRVbfcoK92BFD4QteCgEURERETkHZ5++teeDqFb6PBoe1999RVCQ0MRHl7fBXju3DmMGjUKNTU1iIuLw4IFC3Drrbfal+fn5wMABg8e7LCtmJgYVFZWorCwEFFRUe2OSaNpX4Vpq0zdXaE2ERGNGliLJ0kSenSh6O26LOfkVZh338S8+ybmvWWK0jN/B7H9aiUIgJddUUedpKvO9Q4VT0ePHsU777yDhQsXQpIkANaeqKSkJMTGxqK8vBy7d+/GwoULsX79etx5550AAKPRCJ1OB39/f4fthYRYh1YsLS1td/EkigLCwgI7cFSAXh/QofVbowbHolwQoZqroNfUQKPv3an7o9Z1ds7JOzHvvol5903Mu3M1NRKKi0VIktDuPz57MxbNPZ+iCBBFEUFB1rqis8/1dhdPRUVFeOKJJ5CUlIS5c+fa58+cOdOhXWpqKqZOnYoNGzbYiycATntbbLdfdaQnRlFUGI3tGwJckkTo9QEwGqshy517jawY1hdKyXmUnPke2kH8ge4pXZlz8h7Mu29i3n0T894ys9kERVEgy2qPuj9IEKy5l2WFPU89nCyrUBQFFRU18Pf3dzjX9foAz97zZFNeXo65c+fC398fW7ZscXgic2OiKOL222/Hiy++iJoa60Hp9XqYTCaYTCb4+dU/gM1oNAKo74Fqr46e/LKsdPoPEDFsAJSS8zAXnYXQP6lT90Wt64qck/dh3n0T8+6bmHfnZLlnVha2gomFk++wFUydfa63uRQzmUyYP38+iouLsX37doSFhbW6TuMB/WzDltvufbLJz89HYGAgIiMj2xpWtyPWPSyXg0YQERGRp3nbUNtErqr/7nbN/XttKp4sFgsWL16MEydOYPv27ejfv3+r6yiKgg8//BBDhgyx3+OUnJyM4OBg7Nu3z95OlmXs378fKSkpPjGAgsTiiYiIiDzMds+62WzycCRE7WP77mo0Upfsr02X7a1atQqffPIJnnrqKdTU1ODrr7+2L4uNjUVZWRmWL1+OtLQ0REdHo6ysDLt378axY8ewceNGe1udTof58+cjMzMTBoPB/pDcgoICZGRkuO3gvJloGAgAUEovQJUtEKQOD3xIRERE1CaiKCEgIAgVFVcAADqdX4/5I7aiCD32skSy9jiZzSZUVFxBQEAQRNELi6fPPvsMAPDiiy82Wfb6668jPj4eQUFB2LRpE0pKSqDVapGYmIht27Zh4sSJDu1nzZoFVVWxc+dOFBcXIy4uDllZWYiPj+/A4XQfQlA4oA0AaquhlF2AVFdMEREREXUlvd76HE9bAdVTiKIIReF9bj1dQECQ/TvcFQS1h13kKssKSkoq27VuVz+FvPK91VAKT8M/9RfQxo7r9P1RU3zyvG9i3n0T8+6bmHfXWUfds3g6DLeQJAEhIb1QVlbF3qceTJI0EEXrXUjOznWDIdA7Rtsj95AMA6EUnuZ9T0RERORxoihCFHWeDsMtNBoR/v7+qK6WWTSTW/HJYR5kG3FPZvFEREREROT1WDx5kH248ssFHo6EiIiIiIhaw+LJg2zDlauVJVBN7btPi4iIiIiIugaLJw8S/AIhBFpHB5GvnPdwNERERERE1BIWTx7GS/eIiIiIiLoHFk8eZrt0jyPuERERERF5NxZPHiayeCIiIiIi6hZYPHmYGD4QgHW48h72vGIiIiIioh6FxZOHiSF9AUECaquhVlz2dDhERERERNQMFk8eJkgaiKF9AfDSPSIiIiIib8biyQuI4db7nmQWT0REREREXovFkxfgoBFERERERN6PxZMXqB+unM96IiIiIiLyViyevIBosI64p5RehCpbPBwNERERERE5w+LJCwiBBkAXAKgylNILng6HiIiIiIicYPHkBQRBgGTrfeKle0REREREXonFk5fgoBFERERERN6NxZOXsBVPHK6ciIiIiMg7sXjyEvZBI1g8ERERERF5JRZPXkIy9AcAqJUlUE2VHo6GiIiIiIgaa1PxtH//fixYsAApKSkYMWIE0tPT8cYbb0BRFId2ubm5uO+++5CUlITbbrsNu3btcrq97OxspKamIikpCZMmTcLhw4fbfyTdnKDrBSEoHAAv3SMiIiIi8kZtKp527NgBnU6HZcuWYevWrbj11luxZs0avPjii/Y2R44cwYIFC5CQkIBt27bh/vvvx+rVq5GTk+OwrezsbGRmZmLatGnIysrCoEGDMHfuXJw8edI9R9YNiXxYLhERERGR19K0pfHWrVthMBjs02PHjkVVVRV27dqFJUuWQKfTYdOmTUhISMBzzz1nb3PhwgWsX78ekyZNgiiKMJvN2LJlC2bMmIHZs2cDAEaPHo309HRs3boVmZmZbjzE7kMyDIR87hve90RERERE5IXa1PPUsHCyGTZsGEwmE0pLS2E2m3Ho0CHcc889Dm3S09NRVFSE48ePAwDy8vJQXl6OtLQ0extJknD33XcjNzcXqqq251i6PY64R0RERETkvdrU8+TMV199hdDQUISHh+OHH35AbW0tBg8e7NAmNjYWAJCfn4/ExETk5+cDQJN2MTExqKysRGFhIaKiotodk0bTvnEwJEl0eO1qQkQ0amAdcU+SBAiC4JE4fImnc06ewbz7JubdNzHvvol59z1dlfMOFU9Hjx7FO++8g4ULF0KSJJSVlQEA9Hq9QzvbtG250WiETqeDv7+/Q7uQkBAAQGlpabuLJ1EUEBYW2K51bfT6gA6t316qPgZGUQPU1iBYrII2tI9H4vBFnso5eRbz7puYd9/EvPsm5t33dHbO2108FRUV4YknnkBSUhLmzp3rsKy5HpOG8521sV2u15EeF0VRYTRWtWtdSRKh1wfAaKyGLCutr9AJpLC+kC8XoOSH76G7qmNFILXOG3JOXY95903Mu29i3n0T8+57nOVcrw9we09Uu4qn8vJyzJ07F/7+/tiyZQu0Wi2A+p4jWw+TjdFoBFDfA6XX62EymWAymeDn59eknW077WWxdOwkkWWlw9toLyFsAHC5ALWXzkIcMNwjMfgiT+acPId5903Mu29i3n0T8+57OjvnbS7FTCYT5s+fj+LiYmzfvh1hYWH2ZdHR0dBqtThz5ozDOqdPnwZgvaep4avt3ieb/Px8BAYGIjIysq1h9RiiYSAAcMQ9IiIiIiIv06biyWKxYPHixThx4gS2b9+O/v37OyzX6XQYO3Ys9u/f7zB/7969iIiIQEJCAgAgOTkZwcHB2Ldvn72NLMvYv38/UlJSfHqgBMn2rKcrLJ6IiIiIiLxJmy7bW7VqFT755BM89dRTqKmpwddff21fFhsbi6CgICxcuBDTp0/HihUrkJ6ejry8POTk5GDVqlUQRWutptPpMH/+fGRmZsJgMCAhIQE5OTkoKChARkaGWw+wu7E/KLf0IlS5FoKk9XBEREREREQEtLF4+uyzzwAAL774YpNlr7/+OsaMGYORI0di8+bNyMjIwJ49exAVFYUVK1Zg8uTJDu1nzZoFVVWxc+dOFBcXIy4uDllZWYiPj+/A4XR/QmAYoOsFmKugXPkJUu9Bng6JiIiIiIjQxuLpn//8p0vtUlJSkJKS0mIbQRAwZ84czJkzpy0h9HiCIEAKHwj5wknr855YPBEREREReQU+OcwLiWHWS/dkDhpBREREROQ1WDx5Ift9TyUFHo6EiIiIiIhsWDx5ISmcw5UTEREREXkbFk9eSAyzDgGvVpVCranwcDRERERERASwePJKgi4AQnBvALzviYiIiIjIW7B48lK2QSN43xMRERERkXdg8eSleN8TEREREZF3YfHkpWwj7vGyPSIiIiIi78DiyUvVD1f+I1RV8XA0RERERETE4slLiSGRgKgBLCao5cWeDoeIiIiIyOexePJSgqiBGNYPAC/dIyIiIiLyBiyevFjDS/eIiIiIiMizWDx5McnA4cqJiIiIiLwFiycvJho4XDkRERERkbdg8eTF7JftlRVCtZg9HA0RERERkW9j8eTFhF6hgF8goCpQSi94OhwiIiIiIp/G4smLCYLA+56IiIiIiLwEiycvZ7vvicOVExERERF5FosnL8fhyomIiIiIvAOLJy8nsXgiIiIiIvIKbS6ezp49i5UrV+Lee+9FQkIC0tLSmrRZvnw54uPjm/z79NNPm7TNzs5GamoqkpKSMGnSJBw+fLh9R9JD2Xqe1KpSKDXlHo6GiIiIiMh3adq6wqlTp5Cbm4vhw4dDURSoquq03cCBA7Fu3TqHeTExMQ7T2dnZyMzMxJIlS5CQkICcnBzMnTsXOTk5iI+Pb2toPZKg9YcQHAG1vAhKyY8Q+w3zdEhERERERD6pzcVTamoqbr31VgDWHqZjx445befv748RI0Y0ux2z2YwtW7ZgxowZmD17NgBg9OjRSE9Px9atW5GZmdnW0HosyTAAlrriCSyeiIiIiIg8os2X7Ymie26TysvLQ3l5ucNlf5Ik4e6770Zubm6zPVq+yD5oxGUOV05ERERE5CmdNmDEuXPnMGrUKCQmJuKBBx7Axx9/7LA8Pz8fADB48GCH+TExMaisrERhYWFnhdbtiOEcrpyIiIiIyNPafNmeK4YNG4akpCTExsaivLwcu3fvxsKFC7F+/XrceeedAACj0QidTgd/f3+HdUNCQgAApaWliIqKatf+NZr21YSSJDq8egshIho1AJQr5yFJgCB4V3zdmbfmnDoX8+6bmHffxLz7Jubd93RVzjuleJo5c6bDdGpqKqZOnYoNGzbYiycAEAShybq2y/WcLXOFKAoICwts17o2en1Ah9Z3NzVkMMolLVSLCcFCJbRh7SsqqXnelnPqGsy7b2LefRPz7puYd9/T2TnvlOKpMVEUcfvtt+PFF19ETU0N/P39odfrYTKZYDKZ4OfnZ29rNBoB1PdAtZWiqDAaq9q1riSJ0OsDYDRWQ5aVdm2js4hh/SAXn0XJmZPQDQ72dDg9hjfnnDoP8+6bmHffxLz7Jubd9zjLuV4f4PaeqC4pngA0GQDCNmx5fn4+EhIS7PPz8/MRGBiIyMjIdu/LYunYSSLLSoe34W6CYQBQfBa1RQUQo5M9HU6P4405p87HvPsm5t03Me++iXn3PZ2d8y65EFRRFHz44YcYMmSI/R6n5ORkBAcHY9++ffZ2sixj//79SElJafdlez2VFGYdcc/81bsejoSIiIiIyDe1ueepuroaubm5AIDz58+joqICH3zwAQDrc5qqq6uxfPlypKWlITo6GmVlZdi9ezeOHTuGjRs32rej0+kwf/58ZGZmwmAw2B+SW1BQgIyMDDcdXs+hVJd5OgQiIiIiIp/W5uLp8uXLWLx4scM82/Trr7+O+Ph4BAUFYdOmTSgpKYFWq0ViYiK2bduGiRMnOqw3a9YsqKqKnTt3ori4GHFxccjKykJ8fHwHDqln0kSPQO1/PvB0GEREREREPqvNxdOAAQNw8uTJFtts2bLFpW0JgoA5c+Zgzpw5bQ3D99RdxiiGcKQ9IiIiIiJP4OD3RERERERELmDxRERERERE5AIWT0RERERERC5g8UREREREROQCFk9EREREREQuYPFERERERETkAhZPRERERERELmDxRERERERE5AIWT0RERERERC5g8UREREREROQCFk9EREREREQuYPFERERERETkAhZPRERERERELmDxRERERERE5AIWT0RERERERC5g8dTNqHItVNni6TCIiIiIiHwOi6duQtD6AwDUisuofHMZzMf/CVWu9XBURERERES+g8VTNyGGR8NvwgwIvUKhVpbA9Nnr1iLq2MdQLWZPh0dERERE1ONpPB0AuUYQBOgSUqGNm4DaE5/C/M3foFZegengn2D+ei90w++CdthNEDR+ng6ViIiIiKhHYvHUzQgaHXSJt0I7LAW1Jw/AfGSvtSfq890wf/23uiIqFYKWRRQRERERkTuxeOqmBElr7YmKvxG1338G89d7oZYXw3ToLZi/3gfttXdBd02q/V4pIiIiIiLqmDbf83T27FmsXLkS9957LxISEpCWlua0XW5uLu677z4kJSXhtttuw65du5y2y87ORmpqKpKSkjBp0iQcPny4rSH5NEHSQDfsJgT+7Hn43zgLQnAE1JpymL94G5VvLIXpyF6o5mpPh0lERERE1O21uXg6deoUcnNzMWjQIMTExDhtc+TIESxYsAAJCQnYtm0b7r//fqxevRo5OTkO7bKzs5GZmYlp06YhKysLgwYNwty5c3Hy5Mn2HY0PE0QNtENvtBZRN82FEBIJ1VQB85d/RsXupTDlvQfVXOXpMImIiIiIui1BVVW1LSsoigJRtNZcy5cvx7Fjx7B3716HNnPmzEFZWZlDsfSb3/wGn3zyCT799FOIogiz2Yzx48djypQpWLZsGQBAlmWkp6cjPj4emZmZ7TogWVZQUlLZrnU1GhFhYYG4cqUSFovSrm14C1WRYck/DHPe+1DKLlpn6gKgS7wduqTbIfgFejZAL9GTck6uY959E/Pum5h338S8+x5nOTcYAiFJ7h1cvM1bsxVOzTGbzTh06BDuueceh/np6ekoKirC8ePHAQB5eXkoLy93uOxPkiTcfffdyM3NRRtrOmpEECVoh4xHr8nPwT91HsSwfoC5Gua891DxxlKYvvwL1JoKT4dJRERERNRtuH3AiHPnzqG2thaDBw92mB8bGwsAyM/PR2JiIvLz8wGgSbuYmBhUVlaisLAQUVFR7YpBo2lfhWmrTN1doXqWCO3Q8fCPH4vaM/9GzZfvQS4pgPnIX2E+9hECRj8A/+F3ejpIj+mZOafWMO++iXn3Tcy7b2LefU9X5dztxVNZWRkAQK/XO8y3TduWG41G6HQ6+Ps7jgYXEhICACgtLW1X8SSKAsLCOnZJml4f0KH1vZbhZqjXpaDq5Je48lkOzIU/oOaLv6DvTZM8HZnH9dicU4uYd9/EvPsm5t03Me++p7Nz3mlDlQuC0Op8Z21sl+s1t35rFEWF0di+gREkSYReHwCjsRqy3IOvj41MRMDtfWHeuQSqLOPKlfbdI9YT+EzOyQHz7puYd9/EvPsm5t33OMu5Xh/g9p4otxdPtp4jWw+TjdFoBFDfA6XX62EymWAymeDn59eknW077dHRGwNlWenxNxcqcv09ZT39WF3hCzmnpph338S8+ybm3Tcx776ns3Pu9osCo6OjodVqcebMGYf5p0+fBgD78Oa2V9u9Tzb5+fkIDAxEZGSku0MjIiIiIiJqN7cXTzqdDmPHjsX+/fsd5u/duxcRERFISEgAACQnJyM4OBj79u2zt5FlGfv370dKSkq7L9sjIiIiIiLqDG2+bK+6uhq5ubkAgPPnz6OiogIffPABAGD06NEwGAxYuHAhpk+fjhUrViA9PR15eXnIycnBqlWr7EOd63Q6zJ8/H5mZmTAYDEhISEBOTg4KCgqQkZHhxkMkIiIiIiLquDYXT5cvX8bixYsd5tmmX3/9dYwZMwYjR47E5s2bkZGRgT179iAqKgorVqzA5MmTHdabNWsWVFXFzp07UVxcjLi4OGRlZSE+Pr4Dh0REREREROR+bS6eBgwYgJMnT7baLiUlBSkpKS22EQQBc+bMwZw5c9oaBhERERERUZfik8OIiIiIiIhcwOKJiIiIiIjIBSyeiIiIiIiIXMDiiYiIiIiIyAUsnoiIiIiIiFzA4omIiIiIiMgFLJ6IiIiIiIhcwOKJiIiIiIjIBSyeiIiIiIiIXMDiiYiIiIiIyAUsnoiIiIiIiFzA4omIiIiIiMgFLJ6IiIiIiIhcwOKJiIiIiIjIBSyeiIiIiIiIXMDiydepCizn/gOl9CJUudbT0RAREREReS2NpwMgD9Fora+qguoPMupmChACwyDqIyAE94Goj7D+C46AoO8DwT8YgiB4LGQiIiIiIk9i8eSjRP9g+E2YAbngKJTyIijGIsBiglpZArmyBLhwsulKGj+HYkoMjoCotxZZQlA4BI2u6w+EiIiIiKiLsHjyYbqEVCAhFQCgqirUmnKoxkv2YkoxFkEtv2R9rbwCWExQSn6EUvKjk61Ze600A6+F38SZ7KEiIiIioh6HxRMBAARBgBCgBwL0kCJjmyxX5Vqo5ZehlF+CYqwrqGxFVnkRUFsDtbIEtSf+D7pR90HoFdr1B0FERERE1IlYPJFLBEkLITQKYmhUk2W2XqvKP/0SUBVAVbs+QCIiIiKiTtYpo+298847iI+Pb/Jv3bp1Du1yc3Nx3333ISkpCbfddht27drVGeFQJxMEAWKAHgAv1SMiIiKinqtTe562b9+O4OBg+3RkZKT9/ZEjR7BgwQLce++9WL58OfLy8rB69WrodDpMnjy5M8MiIiIiIiJqs04tnq655hoYDAanyzZt2oSEhAQ899xzAICxY8fiwoULWL9+PSZNmgRR5COoiIiIiIjIe3ikQjGbzTh06BDuueceh/np6ekoKirC8ePHPREWERERERFRszq15yktLQ1XrlxBv379MGXKFMyZMweSJOHcuXOora3F4MGDHdrHxlpHecvPz0diYmK796vRtK8mlCTR4ZXaSACgWj9/sZ056GrMuW9i3n0T8+6bmHffxLz7nq7KeacUTxEREVi0aBGGDx8OQRDwz3/+Ey+99BIKCwuxcuVKlJWVAQD0er3DerZp2/L2EEUBYWGB7Q8egF4f0KH1fdWVuteQkF7QBHcsB12NOfdNzLtvYt59E/Pum5h339PZOe+U4mnixImYOHGifXrChAnw8/PDa6+9hnnz5tnnN/cg1Y48YFVRVBiNVe1aV5JE6PUBMBqrIctKu2PwdWVlVRAtfp4OwyXMuW9i3n0T8+6bmHffxLz7Hmc51+sD3N4T1WXPebrrrrvw6quv4rvvvkP//v0BNO1hMhqNAJr2SLWVxdKxk0SWlQ5vwyfVPd7JYlEgdrPPjzn3Tcy7b2LefRPz7puYd9/T2Tn3yIWg0dHR0Gq1OHPmjMP806dPAwBiYmI8ERa5Cx+SS0REREQ9UJcVT/v27YMkSUhISIBOp8PYsWOxf/9+hzZ79+5FREQEEhISuioscqe64eWr3v0tag7uglz0A1QWUkRERETUQ3TKZXuzZ8/G2LFjERcXBwD4xz/+gbfffhszZsxAREQEAGDhwoWYPn06VqxYgfT0dOTl5SEnJwerVq3iM566Kb/rH4T5671Qq42oPfYRao99BDEkCpoh46GNHQdRH+HpEImIiIiI2k1QO6FrYPXq1Thw4AAuXrwIRVFw1VVXYfLkyXj44YcdBoPIzc1FRkYG8vPzERUVhUcffRTTpk3r0L5lWUFJSWW71tVoRISFBeLKlUpeH9tOqmKB/OO3qD31OSz/zQNks32ZFBUHTew4aAdfD8E/yINR1mPOfRPz7puYd9/EvPsm5t33OMu5wRDo9gEjOqV48iQWT95DNVfD8t+vUHvqc8jnj8M+ooQoQRM9HJrYcdBED4eg0XksRubcNzHvvol5903Mu29i3n1PVxVPXTbaHvkeQRcAbdwEaOMmQKm8AsvpQ6g9fRDK5QJY/ptn7ZnS9YJ28PXQDBkPKWoIBKHzLtlUVRVqxWWIwb07bR9ERERE1HOxeKIuIQaGQTf8LuiG3wW5pACWU5+j9vQhqJUlqD2Ri9oTuRCCwqGNHWctpML6tXtfqiJDNVcBpkqopiqo5iqopkrU/GMLAMBv4iPQDbvJTUdGRERERL6CxRN1OckwENKYgdCNfhDyhZOwnDqI2jP/hlpxGeav98L89V6IvQdBGzse0oAEoNbkUASppkrH4shUCdVc/x61NS3u33L2axZPRERERNRmLJ7IYwRBhKbfMGj6DYPfDQ/Dcu5r1H5/EHLBUSjFZ2EqPtuxHWj8IPgFQvDrBcEvEPKFk9bZ0de6IXoiIiIi8jUsnsgrCBodtINHQzt4NJSacljyv0Dt6c+hXPkJgi6grggKhKCzFkLw62V/37BAEnR1y/x6QRAdv95V+9ZB/vEYBI2fh46SiIiIiLozFk/kdUT/YOiuuQW6a27xdChERERERHZ8Gi0REREREZEL2PNEPqf2zBdQyoshaHWQdQEoDw2G2SxAEXXW+6Q0Oghaf0DrZ73ET+MHQeTfGYiIiIh8HYsn8hmCLgAAIJ/7BvK5b+zzq1xZWdJA0DQoqLTWIgta//oCS6urW+bfqI0fBI0/BK2tnR9gK9A0uk59thURERERuQ+LJ/IZfmOmQAzrD9VcbR3+3GKCIJuhgQXmqkqodfNsy2AxAapqXVm2QJUrAFMFVHcHJunqCyr/IPiN+zk0fePdvRciIiIi6iAWT+QzxOAI+F13n8M8jUZEWFggrlyphMWiOCxTVRWQax0LqmZe7e9rrUWXwzLbPPsyM1BrAmxlmGyGKput+6y4DMv3n7F4IiIiIvJCLJ6ImiEIgrU3SKMD/IPdum1rYWZuUFiZUfvdJ6j99mPUnjwApaoMUBXrP8X6qtqmVdU+z/ZPbdAOqtpgmQqhVyjEkEiIIVEQQ6OsryFREPyD3HpMRERERD0diyciD7AWZn4Oz5xSeg9Cbd17ueA/btuXWlMOpaSg6QK/QHshJYZE1hdW+kjrZYRERERE5IDFUyepqK7Fro++R2iQDiOHRCC2fwhEUfB0WOTFNLHj4K/1h2qusg4iIYiAWPda908QREAUHObZ2gkO8wTrugDUihIoZRehlBVaX0svQq0sAUyVUC7lQ7mU3yQWIdBQ31sVEgUx1PpeCO7d5OHDRERERL6CvwV1kh8vVeDw8UIAwIdfFCC4lxYjYntjZFwEEgaFQaeVPBwheRtB0kA7+Hr3b9gwEMBwh1mqxQSl7FJdUXXRXlyppRehmiqgVpZAriyB/NN3jaMEBBG6kfcAgmQt2kSp7r11GqJUN0+smydBaLTcOk8ERE1dgVi3ToPlEOq2bZ+WAEGw9toREREReQCLp04ydFAYfjn5Whw+fgnfnC5GeVUtDvznAg785wJ0WhFJV4djZFxvXBvTG0EBWk+HSz5G0PhBCh8IKXxgk2VqTYVjT5Xt3+UCACqgyjDnvd/1Qds0KtaEBsUWRA2k3oPgf8s8DgFPREREbsfiqRNdG2MtjiyyglMFpcg7VYwjp4pQYjThq++L8NX3RRAFAfHRoRg5pDdGDolAeIi/p8MmHyf4B0Hyj4UUGeswX1UV1B79EEpFSd3gFDKgyFAbvIeiQFUs9QNdKBbrvAbLoch100rdPNk66EVdW+u0XD9MfGOKDEAG5Lq4Gi22GAthDusHMSTS8TJGof7SRlUjoao0ALWVZsiKbZnQ9LJHoeHlkILDZZL1y5yvB7CXjIiIqKcRVLW531C6J1lWUFJS2a51Wxq22l1UVcW5wgrkfV+EI6eK8GORY6yDIoOthVRcBAZEBPKXr07WFTmn9qkfQbBB0WUvtmyFmuywvGrPKk+H7Uhwcn+aINRdsig2Ke7sRVyzyxquJzTdpiAAsN3vJjRo17BAFACIDdoIDbZVtx1JY33+mKQFNFrra92/JvMavLcu0wGixit/dvF8903Mu29i3n2Ps5wbDIGQJPdeicLiqQFPnGiXSqvx9fdFyDtVjFM/ljr8sb13iD+S4yIwckhvDBkQygEnOgF/uPYstScPoPbMl47DvEOt6/2qH9pdUFVIEmCpres9azS8e8P1VWfzbdPUPKfFlq5psWV7r2mmOJO01scFSFoIkgamvPet06Km/v46UWo6LTVdLmk0CAgKQLVJgaKKLbatn9YAUt19enXzG7+HqIEg8jJRb8Wf876Jefc9LJ7aqbsVTw0Zq8z45nQxjnxfjG//W4LaBjEEBWgxPDYct18fjYF9+Hwed/F0zskz3JF3a8GlArD1kNUXV2rDYqvRM7mgqvUFWZP1VKeFn+NzvpxtW4UKtckzvuqnVeu20WC7jZY77F+2WB8QLdfWPSja7DBtnVdbP6/ufdOLKH2J4FiIORsxU5SsRZYg2gdcsQ+MIgj19+7Z5zVeX6wbfKV+e47tG2zD1rbhNuwDtTjGZF3fMaam8drWazCvwf7tI342irut9x6qqgql6AeoNcYWRhV1NuKoAKeX2AoiNFoNQsOCUFZWDYsCJz3B/MNkT8T/331PVxVPvOfJi+h76TDx2n6YeG0/mMwyjv1QgiOnivDN6WJUVNfiX0cv4uCxi/jdo6MhSQI0kghJtL5qJAFS3bQk8j8Dos7meAlco2UeicizVFW1XlLZsMiymBsUYA2LLcdirGEBZnvvUKg1LOIsZkAXAF1CquOlnIqlflq2OF0mQIFOAkw1JqiyxaGttb3z91BkqHLDaYuzT6BuPxbblPPPqdMy4K2ERsWe4LwArCv2lNKfOiWKshZDtJ3HjkWXvdBE40tpG1xe26b1nBR4YqP1nC7r6vVcuQe06Xot3QPKAXyoJ/Foz9MPP/yA1atX46uvvkJAQADuueceLF26FP7+7R80oTv3PDVHVhTs+/ws3j3wg8vr2IopjVj3KgnQiCIkSYAk1k1LIkbFR+D20dGdGL1389acU+di3n2Tu/Ju76FzVmjZijKH3kXbQCn17x0uC3XSXm3QxjqAilI3OIvjNu29ko3aq4pqX895DI3mNdhPfQyN4nUWl20/nUAMH+TQa2uPs0GPrENvrtNlPL+9RpOewbp7NAXUvQoQGt6Hifr59raA43SDbQiNpiGI0GhEyLIKFY7z67ddP91k37Z2DvtqOi241K75+dYX2/G1dPxNYxKaicmhXXOfnW39Bp+/q/ELLuxL1PeB1GcwulKP73kyGo2YOXMm+vXrhw0bNqCkpARr165FaWkp1q1b56mwvJIkirh9dDROnCvFxZIqyLICWVFhkVVY6t43Zl0mw9TKtk+fL4M+UGf/wWP97gv2v5wLdSeVaP+5Y/shVdeuwXlXXlmL8BB/xA0Mdd/BExF5GesvHHX3O8H+a4lPU1ss6tRGBaOzoq5BG0GAFDkEguSeX1EkCQgLCcCVknJYLLLj5a7NXCbrcKlsa5fXOlz6qrSyzNnlvGrnr9ewKHZ2D6fSOP7m1mu8rO69K32qLhSzHflrvrN1zR3YHnVc4M9esI5828N4rHh68803YTQasWfPHhgMBgCAJElYunQp5s+fj5iYGE+F5pX8tBKeemik02WqqkJWVMiyCouiwCKrkGUFFqXuVVYhN5wvqygx1mDH/hMAgKy/HndrrPEDQxEe4g+NJEKrEaGVrH/90UoCtBrJ2utVN7/hcmftJVGEqqpQ645TVWF/r1jfWP9frutAtS5vMK/utg5FVWGqlVFjlnH90D5uPV4iIl9nHaXR8a+73lJUCoIIQdJA0OggwPGXd2+JsburLygbF2XNDbrT4L5L2O7ZhH0aDe/LRIP59mnFPl9tZr4kCQgK9ENFeTVkWXGybeueW95X8zGoDfZVv21n00rd2wbH22i6/tXZfMd9q23cl3W6fr7aaNqhnb24bfSZNN53o/nOpoXg3hCCwtETeax4+vTTTzFu3Dh74QQAd9xxB5599lnk5uayeGoDQRDqLsMD/CC5vF5RWTXO/GRscG5a39iLEjQ8t2wFjHVGwyLGds4VXKoAAJwsKAUK3HRwnWALrJc11hNgPYKm/402d+tYs//hNtveyYJGs0zm+ktf+oQGNLiaQYC9Vx8NegZty+HYA+jYO9hgeYNlYqPt2rr+HXoeBUC0za/br6qqOF9ciejI4PpitsF3Q6374dpw2vZ9UtCgAFbhZH3HQlhR679fir1wVptZ1/q+ssZ6v8mdY6LtsTsjiQL8A7Soqa6176+tXLutsPVGrbXw9O2LDe+fdAhFcPK2mbbNnkcubbttH4Da8O/PjVIriAICAnSorjZDUVS0dNV640VqCwtb+wY5HpvQ7LKWcu3KZ2WdbH4jgrOctRKT0zw7iamFzTW7ryZrt7Cvpvtw/XshiQJ6BfqhqtIEWVFb3nYrm3X1822PFvPfemDtWeTCui2v3ZFjFhpeTuZK+zZuX5IEBKr+qKysgSy3/nO+zcdi/0+5bc1d3nzbmrfrLwFt/fnaluYRIf7o76beY2/jsaPKz8/HpEmTHObpdDpER0cjPz+/Q9vWaNp3baPtmkh3XxvpraakDnHr9n4qrsSJs1dgkRXUygosFgW1FgW1sopaiwyLrNZN1y+zta21NGxfN9+i2P+js18iaPvlvsEv/mJd5WD/Rb/Be1Gov564sKTKHqvF4Qep2ujV8y6VVns6hBYVl9V4OoQWfXD4nKdDICIi8mnrFo5Hn7BeXba/rvo93qP3POn1+ibz9Xo9ysrK2r1dURQQFhbYkdCg1wd0aH1fFRYWiGuGePclcVfKnf8FytkfoNXmiqm2zW7xr9uNXbpSBZ1GcuhlsW3D3gtom+/QE9NC+8Y9Qw23ozTYhm29hr2LjdYvLKlEeEiAvYgVG/RUCYJgHVip4bQgWO8LbnA/nXVgqkZtGhTG9dOCvRBuON1Sm1XbD+PC5Urce2PzPdfN5tWxUUcW23PgNftpJ7WZCYc/PTjZf8NZzcXn0KbBFps7HNt2nPURt9YL0bQnxPVe5ub20Z6/uDfpzXLhswFc+3wab69JM4ecON92k3YtxNr8d6NRu1by2Vo75+u2sryVM6ejp0xr55xLm2/tGDq6D49/Rh3bvztjaX673SyGNm24LU0753PoY+iFqwcaoNO6fkWUu3T27/Fe15+mqmqbuuMbUxQVRmNV6w2dkCQRen0AjMa662OpR2p4Grcr523u2ne96cDwrvsLTfv09nQALXph/jiX2vFc903Mu29i3n0T8+55lRU1aN/41+3jLOd6fUDPGW1Pr9fDaDQ2mV9eXt7h+506OvSwXHdZGfkO5tw3Me++iXn3Tcy7b2LefU9n59xjN/fExMQ0ubfJbDbj3LlzHCyCiIiIiIi8jseKpxtvvBGHDh3ClStX7PM++ugjmM1mpKSkeCosIiIiIiIipzxWPE2dOhXBwcFYsGABDhw4gD179uD3v/890tPT2fNERERERERex6P3PL322mtYvXo1Fi1aBH9/f6SlpWHp0qWeComIiIiIiKhZHh1t7+qrr0Z2drYnQyAiIiIiInKJbzwNloiIiIiIqIMEtTOfsOgBqqpCUdp/SJIk8nkAPoY5903Mu29i3n0T8+6bmHff0zjnoih06PmxzvS44omIiIiIiKgz8LI9IiIiIiIiF7B4IiIiIiIicgGLJyIiIiIiIheweCIiIiIiInIBiyciIiIiIiIXsHgiIiIiIiJyAYsnIiIiIiIiF7B4IiIiIiIicgGLJyIiIiIiIheweCIiIiIiInIBiyciIiIiIiIXsHgiIiIiIiJyAYsnIiIiIiIiF7B4AvDDDz9g9uzZGDFiBMaNG4fVq1ejpqbG02GRm7zzzjuIj49v8m/dunUO7XJzc3HfffchKSkJt912G3bt2uWhiKmtzp49i5UrV+Lee+9FQkIC0tLSnLZzNcfZ2dlITU1FUlISJk2ahMOHD3dm+NROruR9+fLlTs//Tz/9tElb5t377d+/HwsWLEBKSgpGjBiB9PR0vPHGG1AUxaEdz/WexZW881zveQ4cOIDp06dj7NixSExMxC233IK1a9eivLzcoV1Xn++adq3VgxiNRsycORP9+vXDhg0bUFJSgrVr16K0tLTJL9fUvW3fvh3BwcH26cjISPv7I0eOYMGCBbj33nuxfPly5OXlYfXq1dDpdJg8ebInwqU2OHXqFHJzczF8+HAoigJVVZu0cTXH2dnZyMzMxJIlS5CQkICcnBzMnTsXOTk5iI+P78rDola4kncAGDhwYJOf5zExMQ7TzHv3sGPHDvTr1w/Lli1DeHg4Dh8+jDVr1qCgoABPP/00AJ7rPZEreQd4rvc0ZWVlGDlyJGbOnAm9Xo9Tp05h48aNOHXqFF599VUAHjrfVR/3yiuvqMOHD1cvX75sn/f++++rcXFx6unTpz0YGbnLX/7yFzUuLs4hx43Nnj1bffDBBx3mrVixQr3hhhtUWZY7O0TqoIY5evrpp9V77rmnSRtXcmwymdTrrrtOfeGFF+xtLBaLetddd6m//OUvOyl6ai9X8t7c/IaY9+7D2c/x5557Tk1KSlJNJpOqqjzXeyJX8s5z3Te89dZbalxcnHrx4kVVVT1zvvv8ZXuffvopxo0bB4PBYJ93xx13QKfTITc314ORUVcxm804dOgQ7rnnHof56enpKCoqwvHjxz0UGblKFFv+UeZqjvPy8lBeXu5w+ZckSbj77ruRm5vbbM8GeUZreXcV8959NPy/2mbYsGEwmUwoLS3lud5DtZZ3VzHv3V9oaCgAwGKxeOx89/niKT8/v0mXrk6nQ3R0NPLz8z0UFXWGtLQ0DBs2DLfccgteeeUVyLIMADh37hxqa2sxePBgh/axsbEAwO9BD+Bqjm2vjdvFxMSgsrIShYWFXRAtudu5c+cwatQoJCYm4oEHHsDHH3/ssJx5796++uorhIaGIjw8nOe6D2mYdxue6z2TLMswmUz49ttvsWnTJtx8883o37+/x8533vNkNEKv1zeZr9frUVZW5oGIyN0iIiKwaNEiDB8+HIIg4J///CdeeuklFBYWYuXKlfY8N/4e2Kb5Pej+XM2x0WiETqeDv7+/Q7uQkBAAQGlpKaKiojo7XHKjYcOGISkpCbGxsSgvL8fu3buxcOFCrF+/HnfeeScA5r07O3r0KN555x0sXLgQkiTxXPcRjfMO8FzvyW6++WZ7gTNx4kRkZGQA8Nz/7T5fPDVHVVUIguDpMMgNJk6ciIkTJ9qnJ0yYAD8/P7z22muYN2+efX5z+eb3oOdwJcfO2ti69Pld6H5mzpzpMJ2amoqpU6diw4YN9l+oAOa9OyoqKsITTzyBpKQkzJ0712EZz/Weq7m881zvubKyslBVVYXTp09j8+bNmDdvHnbs2GFf3tXnu89ftqfX62E0GpvMLy8vd9ojRT3DXXfdBVmW8d1339n/8tC4h8n2veD3oPtzNcd6vR4mkwkmk8lpO9t2qPsSRRG333478vPz7Y+kYN67n/LycsydOxf+/v7YsmULtFotAJ7rPV1zeXeG53rPMXToUCQnJ2PKlCl4+eWXcfjwYXz00UceO999vniKiYlpck+L2WzGuXPnmtwLRT1TdHQ0tFotzpw54zD/9OnTAJoOc0rdj6s5tr02/pmQn5+PwMBAh+HtqftqfHMw8969mEwmzJ8/H8XFxdi+fTvCwsLsy3iu91wt5b05PNd7nmHDhkGSJJw7d85j57vPF0833ngjDh06hCtXrtjnffTRRzCbzUhJSfFgZNSZ9u3bB0mSkJCQAJ1Oh7Fjx2L//v0Obfbu3YuIiAgkJCR4KEpyF1dznJycjODgYOzbt8/eRpZl7N+/HykpKbykowdQFAUffvghhgwZYr/+nXnvPiwWCxYvXowTJ05g+/bt6N+/v8Nynus9U2t5d4bnes905MgRyLKMAQMGeOx89/l7nqZOnYo//elPWLBgARYsWIDLly/j+eefR3p6OnsceojZs2dj7NixiIuLAwD84x//wNtvv40ZM2YgIiICALBw4UJMnz4dK1asQHp6OvLy8pCTk4NVq1a5bThk6jzV1dX2RwucP38eFRUV+OCDDwAAo0ePhsFgcCnHOp0O8+fPR2ZmJgwGg/1BegUFBfYbVMl7tJb36upqLF++HGlpaYiOjkZZWRl2796NY8eOYePGjfbtMO/dx6pVq/DJJ5/gqaeeQk1NDb7++mv7stjYWAQFBfFc74Fay3tZWRnP9R7o8ccfR2JiIuLj4+Hv728vnuPj43HrrbcCcO33N3fnXVA5qD1++OEHrF69Gl999RX8/f2RlpaGpUuXNhmVg7qn1atX48CBA7h48SIURcFVV12FyZMn4+GHH3b4a0Nubi4yMjKQn5+PqKgoPProo5g2bZoHIydX/fjjj7jlllucLnv99dcxZswYAK7lWFVVZGdnY9euXSguLkZcXByeeuopjB07ttOPg9qmtbzHx8fjmWeewbfffouSkhJotVokJibisccecxhEBmDeu4vU1FScP3/e6TKe6z1Xa3nnud4zZWVlYd++fTh37hxUVUX//v1x2223Yfbs2QgKCrK36+rzncUTERERERGRC3g9EhERERERkQtYPBEREREREbmAxRMREREREZELWDwRERERERG5gMUTERERERGRC1g8ERERERERuYDFExERERERkQtYPBEREREREblA4+kAiIioZ4mPj3ep3euvvw4AmDFjBtavX48777yzM8Nyi4cffhgAsHPnTg9HQkREnsDiiYiI3Oqtt95ymN68eTMOHz6M1157zWF+bGwsvv32264MjYiIqENYPBERkVuNGDHCYdpgMEAUxSbz3aG6uhoBAQFu3y4REZEzvOeJiIg8zmKxIDMzExMmTEBycjIeeeQRnDlzxqHNww8/jLS0NHz55ZeYOnUqhg8fjmeffRYAUFFRgRdeeAGpqalITEzExIkTsWbNGlRVVTlsY9euXZg2bRrGjRuHESNGID09Hdu2bUNtba1DO1VVsW3bNtx8881ISkrC/fffj9zc3CZxK4qCzZs344477sC1116LUaNGIT09vUkvGxER9QzseSIiIo/LyMhAcnIy1qxZg4qKCqxbtw7z58/Hvn37IEmSvV1RURGeeuopzJkzB0uWLIEoiqiursb06dNx8eJFzJs3D/Hx8Th16hQ2bNiA77//Hn/84x8hCAIA4Ny5c0hLS8OAAQOg1Wpx4sQJbN26FWfOnMHatWvt+3n55Zfx8ssv48EHH8Qdd9yBixcv4je/+Q0URcHVV19tb7d9+3a8/PLLmD9/PkaNGgWLxYIzZ86gvLy86z48IiLqMiyeiIjI42JjY7Fu3Tr7tCiK+OUvf4mjR486XO5XWlqKl156CePGjbPPy8rKwsmTJ/H2228jKSkJADBu3DhERkbiiSeewKeffoqUlBQAwDPPPGNfT1EUjBo1CqGhoXj22WexfPlyhISEwGg0Ytu2bbjtttuwZs0ahxgfeughh+IpLy8PcXFxWLRokX3exIkT3ffBEBGRV+Fle0RE5HGpqakO07YR+3766SeH+SEhIQ6FEwB88sknGDJkCIYNGwaLxWL/N2HCBAiCgC+++MLe9vjx45g3bx7GjBmDYcOG4ZprrsHTTz8NWZbx3//+FwBw5MgRmEwmpKenO+wnOTkZ/fv3d5iXlJSEEydO4Le//S0OHDiAioqKDn0ORETk3djzREREHhcaGuowrdPpAAA1NTUO8yMiIpqse/nyZZw9exbXXHON021fuXIFgLUQmzZtGq6++mo8++yz6N+/P/z8/PCf//wHq1atsu+rtLQUANC7d+8m22o87xe/+AV69eqF999/H2+++SYkScKoUaOwdOlSey8YERH1HCyeiIio27Ddu9RQWFgY/Pz88NxzzzldJywsDADw8ccfo6qqChs3bnToQTpx4oRDe1shV1xc3GRbxcXFDutqNBo8+uijePTRR2E0GnHw4EFkZmZizpw5+L//+z+OBEhE1MPwsj0iIurWbrrpJhQUFCA0NBRJSUlN/g0YMABAfeFl69UCrKPqvf322w7bGzFiBPz8/PDXv/7VYX5eXh7Onz/fbBx6vR533nknfv7zn6O0tLTFtkRE1D2x54mIiLq1mTNn4u9//zumT5+ORx55BPHx8VAUBRcuXMBnn32GWbNmYfjw4Rg/fjy0Wi2efPJJzJkzB2azGbt374bRaHTYXkhICGbNmoUtW7bg17/+Ne68805cvHgRGzdubHLZ4Lx58zBkyBAkJibCYDDg/PnzeO2119C/f38MGjSoKz8GIiLqAiyeiIioW+vVqxd27dqFrKwsvPXWW/jxxx/h7++Pvn37Yvz48fbL7GJiYrBx40a89NJLWLRoEUJDQ5GWloZHHnkEc+fOddjm4sWL0atXL7zxxht47733MHjwYPzud7/Dq6++6tBuzJgx+PDDD5GTk4OKigpERERg/PjxWLBgAbRabZd9BkRE1DUEVVVVTwdBRERERETk7XjPExERERERkQtYPBEREREREbmAxRMREREREZELWDwRERERERG5gMUTERERERGRC1g8ERERERERuYDFExERERERkQtYPBEREREREbmAxRMREREREZELWDwRERERERG5gMUTERERERGRC/4/Z9Xeo48gEyYAAAAASUVORK5CYII=", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df[[\"Presim. Time / s\", \"Sim. Time / s\"]].plot(figsize=(10,3));" ] @@ -1944,7 +5619,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "" }, @@ -1971,45 +5645,75 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 201, "metadata": { - "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo[[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True, figsize=(10,3));" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 202, "metadata": { - "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo[df_demo[\"F\"] < 0][[\"A\", \"C\", \"F\"]].plot(kind=\"bar\", stacked=True, figsize=(10,3));" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 203, "metadata": { - "editable": true, "slideshow": { "slide_type": "fragment" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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7dq1KS0t15ZVXSpIuvfRSHXfccdxSBgA4JkIMAMA1K1euVFpamsaMGSNJysjI0OjRo/Xhhx/qX//6l7vFAQCSFiEGAOCKL774Qn/+85914YUXynEcBYNBBYNBjR49WtL/zVgGAMDRWOwSAOCKxx57TE8++WSL248//nitX79epmm2YlUAAC9IcbsAAED7Y1mWXnrpJfXs2VOzZs1qsv2Pf/yjFi9erA0bNmj48OEuVAgASGaEGABAq9uwYYO+/PJLTZs2TXl5eU22n3baaVq2bJlWrFhBiAEANMGYGABAq1uxYoVSU1M1bty4Zrfn5OToW9/6lv74xz/q4MGDrVwdACDZMSYGAAAAgKdwJQYAAACApxBiAAAAAHgKIQYAAACApxBiAAAAAHgKIQYAAACApxBiAAAAAHgKIQYAAACAp6S4XYAkOY4j207ccjWG4Uvo/tE20CcIhR5BOOgThIM+QSjttUcMwyefzxfyeUkRYmzb0eHD5QnZd0qKoezsDAWDFaqrsxNyDHgffYJQ6BGEgz5BOOgThNKeeyQnJ0OmGTrEcDsZAAAAAE8hxAAAAADwlKS4nQwAAKC9MU2+S0bz6nujNXvEthM7Rj3eYgox5eXluuSSS7R//36tWLFCAwYMiFddAAAAbZLPd2TAdiCQ7nYpSHKt2SOWZau4uMIzQSamELNgwQJZlhWvWkKybSvi49m2T1VVpmpqqmVZ3vihfJVpmjIM0+0yAABAnBiGT4bh09znCrRnf6nb5QDq3qWjpl1ztqdmRIs6xOzYsUPPP/+8pk+frhkzZsSzpiYcx1EweFiVleWSIn9jDx40ZNtendnBp/T0DAUCOWFNNwcAALxhz/5S7SgscbsMwJOiDjGzZ8/W+PHjdcopp8SznmZVVparsrJMmZlZ6tAhTVJkJ/Om6fPkVRjJUXV1lcrKipWa2kHHHZfpdkEAAACA66IKMWvXrtXWrVs1b948bdmyJd41NeI4jsrKipWWlqHMzE5R7SMlxfDsHNupqR1UV1ersrJipadncDUGAAAA7V7EIaayslIPP/yw7rrrLmVmxu/KQEpK87MvWJYl27aUlnZcVPutP+f3+STHixdjJKWlHaeqqnIZBjOZJIobs4DAW+gRhIM+QTgMgy8kkZy89NkVcYhZuHChcnNzdcUVV8StCMPwKTs7o9ltVVVVMgxDfn9qi0EnHF76oRzN70+VYRjKzPQrLS3N7XLaNGaKQSj0CMJBnwDwIi99dkUUYgoLC7V48WLNnz9fZWVlkqSKioqG/5eXlysjo/kwciy27SgYrGh2W01NtWzblmU5Ud0S5vMdCTCWZXv2SoxlObJtWyUlFaqsbL3Z4NoT0zQUCKQrGKyUZXnz1kMkFj2CcNAnCEdqqqnMTL6URPJJhs+uQCA9rIsPEYWYPXv2qLa2VjfeeGOTbddee63OOOMMLV++PJJdNmgpoMQ6IL8+uHg1wHxVtEEO4bMsm/cYx0SPIBz0CY7Fy3eHoG3z0mdXRCGmT58+Wrp0aaPHPvvsM82ZM0cPPvhgqy92WT/Peijx/rCIx4qmkyZdo3/843PNm/cbnXXWOXGqDAAAAGj7IgoxgUBAeXl5zW7r16+f+vXrF5eiwmEYPmVlHefKtxmxrmj6xRf/0j/+8bkkad26tYQYAAAAIAJRrxPjNsPwyTSNVl/tNh4rmr7xxmsyTVNnnnm23nnnLd1113SlpqbGuVIAAJDMunfp6HYJgCRv9mLMISYvL0+ff/55PGqJihdXu62/+nLVVRN0zz136v3339OwYRe5XRYAAGgF9belT7vmbLdLARpYlh3zcInW5NkrMV716aef6N//LtR1103WuecOVlZWlt544zVCDAAA7YTjODIMX1LMBIXk5MZMh/EY892aCDGtbN261+T3+3XhhSOUkpKi4cO/pVde+V+Vl5cpIyN+i4cCAIDk5qWZoOAOeqRlzPHXiizL0ttvv6khQ76pzMwjgeXb3x6tmppqrV//jsvVAQAAAN5AiGlFf/7zByoqOqxvfnOYSktLVVpaqpNP/rpOOKGL3njjNbfLAwAAADyB28la0bp1R4LKQw89KOnBRtsOHjygQ4cOKje3swuVAQAAAN5BiGklVVVV2rBhvS644CJ973vjG20rLi7Wz352n9566w1dddXVLlUIAAAAeAMhppW8++56VVZW6HvfG9/s4pa//31fvfHGWkIMAAAAEILnQ0xrL84T7fHeeGOtunQ5UYMGNT8n/OjR+frv/35Eu3Z9oZ49vxZLiQAAAECb5nMcx/UJoS3L1uHD5c1uq62t0aFDe5Wbe5JSU/0NjxuGT1lZx8k0W39uAsuyVVxc0Wpzabf0HiB+UlIMZWdnqKionKkM0Sx6BOGgTxAO+gShtOceycnJCOv83rNXYmzbUXFxhQzDF/K5pmnEdaEgry0GBAAAko8bX8TCG+p7ozV7xGvntxGFmI0bN+rJJ5/U9u3bVVZWpi5dumjUqFG67bbb1LFj697WJUX2Zre3FAsAAJKTz+eTbTsKBNLdLgVJrjV7pLXvNIpVRCGmpKREgwYN0nXXXadAIKBt27bp8ccf17Zt27R48eJE1QgAANBmGIZPhuHT3OcKtGd/qdvlAOrepaOmXXO2DMPXNkNMfn6+8vPzG/6cl5cnv9+vn/70p9q/f7+6dOkS9wIBAADaoj37S7WjsMTtMgBPivlGu6ysLElSXV1drLsCAAAAgJCiGthvWZbq6uq0fft2zZ8/X8OHD1e3bt3iXVsjSTCJmmva898dAAAAOFpUIWb48OHav3+/JOmCCy7QY489FnshKc1fFDKMVElSTU21/P4OEe/X5/u//3s1C9TUVEuSOnRIlWEwk0kiuDELCLyFHkE46BOEI5yZVQE3eOmzK6oQs2jRIlVUVGj79u1asGCBbr75Zi1ZskSmaUZVhGH4lJ2d0eL26uocFRUVyzR96tChg6TI/vFbVlRlJQFH1dXVqqgoUW5ujnJzA24X1OYxUwxCoUcQDvoEgBd56bMrqhBz+umnS5LOOuss9e3bV+PGjdO6des0evToqIqwbUfBYEWL29PSAkpPr1VJyeEo9u77ykwL3rwUk5HRUWlpARUVNb8gKGJnmoYCgXQFg5VxXVMIbQc9gnDQJwhHaqqpzMw0t8sAmkiGz65AIL11Frvs06ePTNPUrl27YtpPqHVcOnbMUUZGliwrsgkETNOnTp2OU0lJhSzLeyHGNFNkGMZ/avde/V5jWTZrCuGY6BGEgz7BsXjplh20L1767Io5xHz00UeyLEvdu3ePRz3HZBiGDMMf0WtSUgylpaWpstLyzA8FAAAAQMsiCjG33Xab+vfvr969eystLU1bt27V008/rd69e2vUqFGJqhEAAKDN6d6lo9slAJK82YsRhZiBAwfq1Vdf1aJFi+Q4jrp166arrrpKkydPlt8f2RUSAACA9si2Hdm2o2nXnO12KUADy7L/M4bcG3xOEixCYlm2Dh9OzKD1lBRD2dkZKioq53YytIg+QSj0CMJBnyAc9X2SDIOokZzcmCSkPly7LScno3UG9gMAACByXhpEDXfQIy1jegwAAAAAnkKIAQAAAOAphBgAAAAAnkKIAQAAAOAphBgAAAAAnkKIAQAAAOAphBgAAAAAnkKIAQAAAOApLHYJAADggnBWJUf7VN8brdkjtu3Itp1WO16sIgoxr732ml5++WVt2bJFJSUl6tGjhyZMmKDx48fLMPiHCAAAEIrP55NtOwoE0t0uBUmuNXvEsmwVF1d4JshEFGKWLFmirl276t5771Vubq4++OADzZ49W7t379b06dMTVSMAAECbYRg+GYZPc58r0J79pW6XA6h7l46ads3ZMgxf2wwxv/nNb5STk9Pw58GDB6uiokLPPfecfvSjH8nv98e9QAAAgLZoz/5S7SgscbsMwJMiugfsqwGmXp8+fVRdXa3i4uJ41QQAAAAALYp5YH9BQYGysrKUm5sbWyEpiRlT48bAKHgPfYJQ6BGEgz5BOAzD53YJQLO89NkVU4j55JNPtGrVKt16660yTTPq/RiGT9nZGbGUEhKD5xAO+gSh0CMIB30CwIu89NkVdYg5cOCAbr/9dg0YMEBTpkyJqQjbdhQMVsS0j5aYpqFAIF3BYKUsy07IMeB99AlCoUcQDvoE4UhNNZWZmeZ2GUATyfDZFQikh3VFKKoQU1paqilTpigtLU0LFy5UampqNLtppK4usW+YZdkJPwa8jz5BKPQIwkGf4Fi8dMsO2hcvfXZFHGKqq6t1yy236ODBg3rxxReVnZ2diLoAAAAAoFkRhZi6ujrdcccd2rp1q5YtW6Zu3bolqi4AAAAAaFZEIWbmzJl65513dM8996iqqkoff/xxw7ZTTz1VmZmZ8a4PAACgTerepaPbJQCSvNmLEYWYd999V5L06KOPNtm2dOlS5eXlxacqAACANsq2Hdm2o2nXnO12KUADy7Jl247bZYQtohDz9ttvJ6oOAACAdsFxHBmGLylmgkJycmOmw/pw7RUxL3YJAACAyHlpJii4gx5pGXP8AQAAAPAUQgwAAAAATyHEAAAAAPAUQgwAAAAATyHEAAAAAPAUQgwAAAAATyHEAAAAAPAU1okBAABwgWnyXTKaV98brdkjbX6xy507d2rWrFkqKChQenq6xowZo2nTpiktLS0R9QEAALQpPp9Ptu0oEEh3uxQkudbsEcuyVVxc4ZkgE1GICQaDuu6669S1a1fNmzdPhw8f1pw5c1RcXKy5c+cmqkYAAIA2wzB8Mgyf5j5XoD37S90uB1D3Lh017ZqzZRi+thliXnjhBQWDQa1evVo5OTmSJNM0NW3aNN1yyy3q1atXQooEAABoa/bsL9WOwhK3ywA8KaIb7TZs2KAhQ4Y0BBhJuvjii+X3+7V+/fq4FwcAAAAAR4soxOzYsaPJ1Ra/36+ePXtqx44dcS0MAAAAAJoT8ZiYQCDQ5PFAIKCSktguh6akJGb2BTdmd4D30CcIhR5BOOgThMMwfG6XADTLS59dcZli2XEc+XzR/4M0DJ+yszPiUUqLmAEE4aBPEAo9gnDQJwC8yEufXRGFmEAgoGAw2OTx0tLSmAb127ajYLAi6tcfi2kaCgTSFQxWyrLshBwD3kefIBR6BOGgTxCO1FRTmZksTYHkkwyfXYFAelhXhCIKMb169Woy9qWmpka7du3SuHHjIqvwKHV1iX3DLMtO+DHgffQJQqFHEA76BMfipVt20L546bMron9Fw4YN06ZNm1RUVNTw2Lp161RTU6MLL7ww7sUBAAAAwNEiCjHjx49Xx44dNXXqVG3cuFGrV6/WL37xC40dO5Y1YgAAAAC0iojHxDzzzDOaNWuWfvjDHyotLU35+fmaNm1aouoDAABok7p36eh2CYAkb/aiz3Ecx+0iLMvW4cPlCdl3Soqh7OwMFRWVe+YeP7Q++gSh0CMIB32CcKSmmgoE0plqGUnFsmwVF1fItt2NBjk5GfEf2A8AAIDYOI4jw/AlxUxQSE5uzHRo247rASYShBgAAAAXeGkmKLiDHmkZIQYA4Lrt27dp+fLn9dFHBTp06KBM01SPHj01cuS3NXbsZQoEOrldIgAgiSTFmBjHSezlK9M0uFyLkOgThEKPJMYf/rBcv/jFL3TyySdrwoQJ6tXrVNXV1erTT7doxYo/qHfv3nr88SfcLjNs9AnCQZ8glPbaI4bhk88XerxYUoQYAED79NFHH+maa67R0KFDtWDBAvn9/kbba2pqtHHjRo0cOdKlCgEAyYgQAwBwzc0336yNGzfqzTff1EknneR2OQAAj4hosUsAAOLFsixt2rRJ/fr1I8AAACJCiAEAuKKoqEiVlZXq3r2726UAADyGEAMAAADAUwgxAABXZGdnKz09XXv27HG7FACAxxBiAACuME1TgwcP1pYtW7Rv3z63ywEAeAghBgDgmptuukmO4+gnP/mJampqmmyvra3V22+/7UJlAIBkxhTLAABXLV++XA8++KC+/vWva/z48TrttNNUV1env//971q+fLlOO+00zZ8/3+0yAQBJhBADAHDd1q1b9bvf/U4ffPCBDhw4oNTUVJ188skaPny4fvCDHygnJ8ftEgEASYQQAwAAAMBTGBMDAAAAwFMIMQAAAAA8hRADAAAAwFMIMQAAAAA8hRADAAAAwFMIMQAAAAA8hRADAAAAwFMIMQAAAAA8JcXtAiTJcRzZduLW3DQMX0L3j7aBPkEo9AjCQZ8gHPQJQmmvPWIYPvl8vpDPS4oQY9uODh8uT8i+U1IMZWdnKBisUF2dnZBjwPvoE4RCjyAc9AnCQZ8glPbcIzk5GTLN0CGG28kAAAAAeAohBgAAAICnJMXtZAAAIPEMwyfDCH2bBhLLNPkOGYhVTCGmvLxcl1xyifbv368VK1ZowIAB8aoLAADEkWH4lJ2VLsM03S4FkhzbDmvwMoDmxRRiFixYIMuy4lVLSLZty7LqInyNT1VVpmpqqmVZ3pvhwTRTZBh8YwMAiI1h+GSYpr5c/WvVHNrjdjntmj+3u0647E6uigExiDrE7NixQ88//7ymT5+uGTNmxLOmJhzHUTB4WJWVZVG9/uBBQ7bt3Zkd0tMzFQjk8I0NACBmNYf2qGbfTrfLAICYRB1iZs+erfHjx+uUU06JZz3Nqg8wmZnZ8vs7RHwyb5o+T16FcRxHNTXVKisrkiR16pTrckUAAACA+6IKMWvXrtXWrVs1b948bdmyJd41NWLbVkOAycwMRLWPlBTDs3Ns+/0dJEllZUXq2DGbW8sAAADQ7kUcYiorK/Xwww/rrrvuUmZmZvwKSWn+5Lym5sgYmPqT+UjVX7Tx+STHexdjJH31724rJYUJ5RKhfqYYZoxBS+gRhCOZ+yQZa2rvDMPX4vkP2rdk/ixJFhGfES9cuFC5ubm64oor4laEYfiUnZ3R7LaqKlMHDxpKSTFi+ofu5SawbUOGYahTp3SlpaW5XU6bFgiku10Ckhw9gnDQJwhHZia/03FsfJa0LKIQU1hYqMWLF2v+/PkqKzsyyL6ioqLh/+Xl5crIaD6MHIttOwoGK5rdVlNT/Z9ZyZyobgnz+Y4EGMuyPXslxrIc2batkpIKVVa23mxw7YlpGgoE0hUMVsqyvHnrIRKLHkE4krlP6mtD8igrq1JtLb/X0VQyf5YkWiCQHtbFh4hCzJ49e1RbW6sbb7yxybZrr71WZ5xxhpYvXx7JLhu0FFBiHZBfH1ySKcC8//67+sMfXtTWrX9XRUW5cnM7a8iQ8zVhwg/UrVv3Fl8XbZBD+CzL5j3GMdEjCAd9gnDYNr/XcWx8lrQsohDTp08fLV26tNFjn332mebMmaMHH3yw1Re7DHfl4XjfSmbbjmw7ulS0aNECLV26WMOGDdc999yv7Owc7d37b61d+4ruvHOq/vCH/41rrQAAAEBbE1GICQQCysvLa3Zbv3791K9fv7gUFQ43Vx62LUtFxZURB5lNm/6kpUsXa+LE/9JNN93a8PiZZ56lSy7J17vvboh3qQAANOLPbfmKP1oHPwMgdp6d6sqtlYe/uspupCHm979fppycXE2efFOz288/f1g8SgQAoAnbdmRblk647E63S4Ekx7ajvqsDQBxCTF5enj7//PN41BIVr6w8XFdXp08++asuumgE0yQDAFqdbTsqKq4M6zZsJFb9oG0nmQbsAh7D2XQrCQZLVFNTrRNO6OJ2KQCAdiqWMZ0AkEy8u3iKx9R/2+Lz8Q0YAAAAEAtCTCvp1ClLfn8H7d+/z+1SAAAAAE8jxLSSlJQUDRx4hj78cLPq6urcLgcAAADwLEJMKxo//gc6fPiQlix5qtnt7723sZUrAgAAALyHgf2taPDgobr22kl65pnf6osvdmrUqIuVnZ2jffv26vXXX9Xu3bv0zW9e4HaZAAAAQFLzfIhp7QWjYj3ejTdOVf/+A7VixQv65S9nq6KiXJ07H69zz83TD394V5yqBAAAANouz4YYNxftsi0rpikqhw49X0OHnh/HigAAAID2w9MhJtxFu0zTkGXZcT028+wDAAAA7vBsiJEiCxN1dfELMQAAALEyTeZXQlN8UR6eiELMxo0b9eSTT2r79u0qKytTly5dNGrUKN12223q2LFjomoEAABoM3w+nxzbViCQ7nYpSEK2ZSlYWu12GUkvohBTUlKiQYMG6brrrlMgENC2bdv0+OOPa9u2bVq8eHGiagQAAGgzDMMnn2Hoy9W/Vs2hPW6XgyTiz+2uEy67M6zhEu1dRCEmPz9f+fn5DX/Oy8uT3+/XT3/6U+3fv19dunSJe4EAAABtUc2hParZt9PtMgBPivlmzKysLElK+Cr0jtN+7w9sz393AAAA4GhRDey3LEt1dXXavn275s+fr+HDh6tbt26xFZLSfJ4yjFRJUk1Ntfz+DhHv1+f7v/97NQvU1By5L7JDh1QZBoMAE6F+cCWDLNESegThoE8QDm4VQij1PcJnScuiCjHDhw/X/v37JUkXXHCBHnvssZiKMAyfsrMzWtxeXZ2joqJimaZPHTp0kBTZP37Liqk8Fzmqrq5WRUWJcnNzlJsbcLugNo9BlgiFHkE46BMAscjMTJPEZ8mxRBViFi1apIqKCm3fvl0LFizQzTffrCVLlsg0zaiKsG1HwWBFi9vT0gJKT69VScnhKPbuk2H4/jNdnTcvxWRkdFRaWkBFReVul9JmmaahQCBdwWBlXNcUQttBjyAc9AnCkZpqNpykAs0pK6tSZmZau/wsCQTSw7oCFVWIOf300yVJZ511lvr27atx48Zp3bp1Gj16dDS7kxR6HZeOHXOUkZEly4ps7I1p+tSp03EqKamQZXkvxJhmigzD+E/t3qvfayzLZk0hHBM9gnDQJzgWbhFCKPVrxfBZ0rKYF7vs06ePTNPUrl274lHPMRmGIcPwR/SalBRDaWlpqqy0aAIAAACgDYj5q4CPPvpIlmWpe/fu8agHAAAAAI4poisxt912m/r376/evXsrLS1NW7du1dNPP63evXtr1KhRiaoRAACgzfHn8gUwGqMnwhdRiBk4cKBeffVVLVq0SI7jqFu3brrqqqs0efJk+f2R3eYFAADQHtm2I8e2dcJld7pdCpKQbVkNY2LQsohCzI033qgbb7wxUbUAAAC0eY7jyGcY7XLmKYRm2w5rCYUh5oH9AAAAiBwzT6ElhJjQmOMPAAAAgKcQYgAAAAB4CiEGAAAAgKcQYgAAAAB4CiEGAAAAgKcQYgAAAAB4CiEGAAAAgKewTgwAwBMMw5f0ayeYptHo/0Bz6A8gdhGFmNdee00vv/yytmzZopKSEvXo0UMTJkzQ+PHjZRj8gwQAJIZh+JSdlS7DNN0uJSyBQLrbJSDJObYtny+5QzmQzCIKMUuWLFHXrl117733Kjc3Vx988IFmz56t3bt3a/r06YmqEQDQzhmGT4Zp6svVv1bNoT1ulwPExJ/bXSdcdmfSX1kEkllEIeY3v/mNcnJyGv48ePBgVVRU6LnnntOPfvQj+f3+uBcIAEC9mkN7VLNvp9tlAABcFtE9YF8NMPX69Omj6upqFRcXx6smAAAAAGhRzANZCgoKlJWVpdzc3HjUAwAAAADHFNPsZJ988olWrVqlW2+9VWaMgy1TUhIzMQAzxSAc9AlCoUfcxfuOtsgwfAk7/4G38TsntKhDzIEDB3T77bdrwIABmjJlSkxFGIZP2dkZMe0jFGaKQTjoE4RCjwCIl8zMNLdLQJLjd07LogoxpaWlmjJlitLS0rRw4UKlpqbGVIRtOwoGK2LaR0tM01AgkK5gsFKWZSfkGPA++gSh0CPuqn//gbakrKxKtbWW22UgCbXn3zmBQHpYV6AiDjHV1dW65ZZbdPDgQb344ovKzs6OqsCj1dUl9gdkWXbCjwHvo08QCj0CIF5s2+HzBMfE75yWRRRi6urqdMcdd2jr1q1atmyZunXrlqi6AAAAAKBZEYWYmTNn6p133tE999yjqqoqffzxxw3bTj31VGVmZsa7PgAAAABoJKIQ8+6770qSHn300Sbbli5dqry8vPhUBQBAM/y53d0uAYgZfQzELqIQ8/bbbyeqDgAAWmTbjmzL0gmX3el2KUBcOLYt23bcLgPwrJjWiQEAoDXYtqOi4koZhs/tUo6pPc8ohPDV94njEGKAaBFiAACeYNuOZ765ZkYhAEgslgEFAAAA4CmEGAAAAACeQogBAAAA4CmEGAAAAACeQogBAAAA4CmEGAAAAACeQogBAAAA4CmsEwMkOcPwJf0Cf+2BaRqN/g80hz5BOOgPIHYRh5idO3dq1qxZKigoUHp6usaMGaNp06YpLS0tEfUB7Zph+JSdlS7DNN0uBf8RCKS7XQI8gD5BKI5ty+fjCyogWhGFmGAwqOuuu05du3bVvHnzdPjwYc2ZM0fFxcWaO3duomoE2i3D8MkwTX25+teqObTH7XIAAHHgz+2uEy67k6vsQAwiCjEvvPCCgsGgVq9erZycHEmSaZqaNm2abrnlFvXq1SshRQLtXc2hParZt9PtMgAAAJJCRDdlbtiwQUOGDGkIMJJ08cUXy+/3a/369XEvDgAAAACOFlGI2bFjR5OrLX6/Xz179tSOHTviWhgAAAAANCfiMTGBQKDJ44FAQCUlJbEVkpKYmTqYKQbhSNY+SbZ6AADxYxi+hJ3/wNuS9bwkmcRlimXHcWKaYcMwfMrOzohHKS1iphiEgz4BALSWzExmdsWxcV7SsohCTCAQUDAYbPJ4aWlpTIP6bdtRMFgR9euPxTQNBQLpCgYrZVl2Qo4B70vWPqmvCwDQ9pSVVam21nK7DCShZD0vaQ2BQHpYV6AiCjG9evVqMvalpqZGu3bt0rhx4yKr8Ch1dYn9AVmWnfBjwPvoEwBAa7Fth985OCbOS1oW0Y12w4YN06ZNm1RUVNTw2Lp161RTU6MLL7ww7sUBAAAAwNEiuhIzfvx4LVu2TFOnTtXUqVN16NAhPfzwwxo7dixrxAAJ5M/t7nYJAIA44TMdiJ3PcRwnkhfs3LlTs2bNUkFBgdLS0pSfn69p06YpLS36wWmWZevw4fKoX38sKSmGsrMzVFRUzuU4tChZ+8QwfMrOSpdhmm6XAgCII8e2VRJkTAyal6znJa0hJycj/mNiJOmUU07Rb3/726iKAhAZ23ZUVFwpw4h+9j/ER3seZInw0ScIR32fRPg9MoCviMsUywASx7Yd2Ta/6JIFgywRDvoEABKLFXQAAAAAeApXYgAArnn11Zf10EMPNrtt/Pgf6Lbb7mzdggAAnpAUIcYwfMrJyUjoMVgwEOGgTxAKPRJfGRkdJEmzZz+kr3/9lEbbjj/+hIT/bkgU+gThoE8QSnvskXDHASdFiPH5fDLNxA5cDmeWA4A+QSj0SHzV/7Lq3fsbGjBggMvVxA99gnDQJwiFHmkZ7wwAAAAAT0mKKzEAgPbNtm3V1dU1eiwlhV9RAIDm8RsCAOC6q666qsljW7ZsIcgAAJrFbwcAgOt++ctfqlevXo0eI8AAAFrCbwgAgOt69erVpgb2AwASi4H9AAAAADyFEAMAAADAUwgxAAAAADyFEAMAAADAU3yO4zhuFwEAAAAA4eJKDAAAAABPIcQAAAAA8BRCDAAAAABPIcQAAAAA8BRCDAAAAABPIcQAAAAA8BRCDAAAAABPSXG7AElyHEe2nbjlagzDl9D9o22gTxAKPYJw0CcIB32CUNprjxiGTz6fL+TzkiLE2Lajw4fLE7LvlBRD2dkZCgYrVFdnJ+QY8D76BKHQIwgHfYJw0CcIpT33SE5OhkwzdIjhdjIAAAAAnkKIAQAAAOAphBgAAAAAnpIUY2IAoK0xDJ8MI/Q9vWhbTNNo9H+gOfQHELuYQkx5ebkuueQS7d+/XytWrNCAAQPiVRcAeJZh+JSVnS7TMN0uBS4JBNLdLgFJznbssGZgAtC8mELMggULZFlWvGoBgDbBMHwyDVPzNi1WYXCf2+UASDLdAifq9sGTuFoLSZJt27KsuqMe86mqylRNTbUsq+1Ms2yaKTKM+FyJjDrE7NixQ88//7ymT5+uGTNmxKUYAGhLCoP7tLNot9tlAACSkOM4CgYPq7KyrNntBw8asu22N71yenqmAoGcmK9ERh1iZs+erfHjx+uUU06JqQAAAACgvakPMJmZ2fL7OzQ5qTdNX5u6CuM4jmpqqlVWViRJ6tQpN6b9RRVi1q5dq61bt2revHnasmVLTAUAAAAA7YltWw0BJjMz0OxzUlKMNrfQpd/fQZJUVlakjh2zY7q1LOIQU1lZqYcfflh33XWXMjMzoz5wk0JSEjNTBzPFIBz0CUKJpEfoIwDhMAxfws5/kNxqao6Mgak/qT9a/UUZn09y2s7FGElf/TvbSkmJfnh+xK9cuHChcnNzdcUVV0R90KMZhk/Z2Rlx219zmCkG4aBPEAo9AiBeMjPT3C4BLqmqMnXwoKGUFOOYQbYtfilm24YMw1CnTulKS4v+30BEIaawsFCLFy/W/PnzVVZ2ZBBSRUVFw//Ly8uVkRF5GLFtR8FgRcSvC4dpGgoE0hUMVsqy2tYlOcQPfYJQIumR+ucCwLGUlVWptpZZXtujmprq/8xK5jR7y5jPd+R3iWXZbe5KjGU5sm1bJSUVqqxs2v+BQHpY4S2iELNnzx7V1tbqxhtvbLLt2muv1RlnnKHly5dHsssGib7nz7LsNndfIeKPPkEo9AiAeLHt5k9g0fYda8B+/WLJrX0VxrYd2Xbkiem3v31SS5Y81eTxnj2/puefX9ni61oKcOGKKMT06dNHS5cubfTYZ599pjlz5ujBBx9ksUsAAAAgSm4ulmzZloqLKqMKMh06dND//M9vmjyWSBGFmEAgoLy8vGa39evXT/369YtLUQDQFnQLnOh2CQCSEJ8NaIlbiyV/dQHWaEKMYRjq3791L2ZEPyUAAKBZtu3Isi3dPniS26UASFK2Y0d1soj2gcWSQ4s5xOTl5enzzz+PRy0A0CbYtqPiokoZRmyrEcN7mCQE4ajvE6etjdhGu1ZXV9foz6ZpNlnAM564EgMACRDtAEm0DUwAAaA9qays1EUXDW702E9/OlMXX3xpwo5JiAEAAAAQtQ4dOmj+/MYzlHXt2i2hxyTEAAAAAIiaYRg6/fS+rXvMVj0aAAAAAMSIEAMAAADAUwgxAAAAADyFMTEAAABAEmntBVG9uAArIQYAAABIAm4ulmzZVlRLA0yefJMmT74pARUdGyEGAAAASAJfXSzZNI1WXTTXa+ubRRRiNm7cqCeffFLbt29XWVmZunTpolGjRum2225Tx44dE1UjAABAQhiGT4aRuFXFm2OaDElGy74aJlg0t2URhZiSkhINGjRI1113nQKBgLZt26bHH39c27Zt0+LFixNVIwAAQNwZhk9Z2ekyDbPVj207tny+1g1PQFsSUYjJz89Xfn5+w5/z8vLk9/v105/+VPv371eXLl3iXiAAAEAiGIZPpmFq3qbFKgzua7XjdgucqNsHT2r1K0BAWxLzmJisrCxJUl1dXay7AgAAaHWFwX3aWbTb7TIARCCqEGNZlurq6rR9+3bNnz9fw4cPV7du3eJdGwAAANBmOY53BtLHS7z+zlGFmOHDh2v//v2SpAsuuECPPfZY7IWkJGaQW/3gOQbR4VjoE4RCjyAc9Im3uP1zMgxfws5/kNwMI1WSVFNTLb+/Q5Pt9cOlfD6preWcmppqSVKHDqkyjOj7P6oQs2jRIlVUVGj79u1asGCBbr75Zi1ZskSmGd3AOMPwKTs7I6rXhisQSE/o/tE20CcIhR5BOOgThCMzM83tEuCi6uocFRUVyzR96tChg6TGY6Qsy526EsdRdXW1KipKlJubo9zcQEx7iyrEnH766ZKks846S3379tW4ceO0bt06jR49OqoibNtRMFgR1WtDMU1DgUC6gsHKVp1rG95CnyAUegThoE+8pf7n5ZaysirV1ra5M1WEKS0toPT0WpWUHG5m65Gpv49Mtdy2LsVkZHRUWlpARUXlzW4PBNLDukoa88D+Pn36yDRN7dq1K6b9JHoebMuymWsbIdEnCIUeQTjoE4TDth36pJ3r2DFHGRlZsqzGE2SZpk+dOh2nkpIKWVbbCTGmmSLDMP7zd4rt7xVziPnoo49kWZa6d+8e664AAACAdsUwDBmGv9FjKSmG0tLSVFlpEXRbEFGIue2229S/f3/17t1baWlp2rp1q55++mn17t1bo0aNSlSNAAAAANAgohAzcOBAvfrqq1q0aJEcx1G3bt101VVXafLkyfL7/aF3AAAAkGS6BU5s08cD2iKfkwQTVFuWrcOHmx/cE6uUFEPZ2RkqKirnchxaRJ8gFHoE4aBPvMUwfMrKTpdpRDe7aixsx1awhIH9aF57/izJyclonYH9AAAAXmTbjoqLKmUYvtBPjqP6WdGS4HtkwLMIMQAAoN2ybec/09gC8BKWiQUAAADgKYQYAAAAAJ5CiAEAAADgKYQYAAAAAJ5CiAEAAADgKYQYAAAAAJ5CiAEAAADgKawTA6BZhuFr9QXgkln96sHhrCKM9os+QTjoDyB2EYWY1157TS+//LK2bNmikpIS9ejRQxMmTND48eNlGPyDBNoKw/ApKztdpmG6XUrSCQTS3S4BHkCfIBTbseXz8UUREK2IQsySJUvUtWtX3XvvvcrNzdUHH3yg2bNna/fu3Zo+fXqiagTQygzDJ9MwNW/TYhUG97ldDgC0Kd0CJ+r2wZO42g3EIKIQ85vf/EY5OTkNfx48eLAqKir03HPP6Uc/+pH8fn/cCwTgnsLgPu0s2u12GQAAAI1EdA/YVwNMvT59+qi6ulrFxcXxqgkAAAAAWhTzQJaCggJlZWUpNzc3HvUAAAAAwDHFNDvZJ598olWrVunWW2+VacY2ADglJTETAzBTDMJBnzTG+wAAiWcYvoSd/8DbOC8JLeoQc+DAAd1+++0aMGCApkyZElMRhuFTdnZGTPsIhZliEA76BADQWjIz09wuAUmO85KWRRViSktLNWXKFKWlpWnhwoVKTU2NqQjbdhQMVsS0j5aYpqFAIF3BYKUsy07IMeB99Elj9e8HACBxysqqVFtruV0GklB7Pi8JBNLDugIVcYiprq7WLbfcooMHD+rFF19UdnZ2VAUera4usT8gy7ITfgx4H30CAGgttu3wOwfHxHlJyyIKMXV1dbrjjju0detWLVu2TN26dUtUXQAAAADQrIhCzMyZM/XOO+/onnvuUVVVlT7++OOGbaeeeqoyMzPjXR8AF3ULnOh2CQDQ5vDZCsQuohDz7rvvSpIeffTRJtuWLl2qvLy8+FQFwFW27ciyLd0+eJLbpQBAm2Q7tmzbcbsMwLMiCjFvv/12ouoAkERs21FxUaUMw+d2KUmjPQ+yRPjoE4Sjvk8chxADRCumdWIAtF227fAtYTMYZIlw0CcAkFisoAMAAADAUwgxAAAAADyFEAMAAADAUwgxAAAAADyFEAMAAADAUwgxAAAAADyFEAMAAADAUwgxAAAAADyFxS6PYhg+Vilvh0zTaPR/4Gj0CMJBnyAc9AcQu4hDzM6dOzVr1iwVFBQoPT1dY8aM0bRp05SWlpaI+lqVYfiUlZ0u0zDdLgUuCQTS3S4BSY4eQTjoE4RiO7Z8Pr40BaIVUYgJBoO67rrr1LVrV82bN0+HDx/WnDlzVFxcrLlz5yaqxlZjGD6Zhql5mxarMLjP7XIAAEAb1C1wom4fPIk7P4AYRBRiXnjhBQWDQa1evVo5OTmSJNM0NW3aNN1yyy3q1atXQopsbYXBfdpZtNvtMgAAAAA0I6KbMjds2KAhQ4Y0BBhJuvjii+X3+7V+/fq4FwcAAAAAR4voSsyOHTs0bty4Ro/5/X717NlTO3bsiK2QlMQMcotkkCUD7QAAQGsxDF/Czn/gbUwSElrEY2ICgUCTxwOBgEpKSqIuwjB8ys7OiPr14WCQJQAASCaZmd6fFAmJxflry+IyxbLjODHNsGHbjoLBiniU0oRpGgoE0hUMVsqy7LCeCwAAkGhlZVWqrbXcLgNJKJLz17YmEEgP6wpURCEmEAgoGAw2eby0tDTmQf11dYn9AVmWnfBjAAAAhMu2Hc5NcEycv7YsohvtevXq1WTsS01NjXbt2tVmZiYDAAAAkNwiCjHDhg3Tpk2bVFRU1PDYunXrVFNTowsvvDDuxQEAAADA0SK6nWz8+PFatmyZpk6dqqlTp+rQoUN6+OGHNXbs2DZ1JaZb4ES3SwAAAG0U5xlA7HyO4ziRvGDnzp2aNWuWCgoKlJaWpvz8fE2bNk1padHPsGFZtg4fLo/69ceSkmIoOztDRUXlIe8pNAyfsrLTZRpmQmoBAACQJNuxFSxhYD+aF8n5a1uTk5MR1sD+iENMIiRLiJGOBBnDiH6mNXhTe54FBOGhRxAO+gThqO+T9niCivAQYuI8O1l7YNuObNv1XAeXMAsIQqFHEA76BAASi2VAAQAAAHgKIQYAAACApyTFmBjHSewtXKZpcG8yQqJPEAo9gnDQJwgHfYJQ2muPGIZPPl/o8elJEWIAAAAAIFzcTgYAAADAUwgxAAAAADyFEAMAAADAUwgxAAAAADyFEAMAAADAUwgxAAAAADyFEAMAAADAUwgxAAAAADyFEAMAAADAUwgxAAAAADyFEAMAAADAUwgxAAAAADyl3YaYTz/9VH369NGgQYPcLgVJZvHixbrssst0zjnn6Mwzz9TYsWO1bNkyOY7jdmlIEpZl6amnntIPfvADDR48WOeee66uueYavf/++26XhiTy3nvv6e6779aoUaPUu3dvzZw50+2S4LKdO3dq8uTJOvPMMzVkyBDNmjVLVVVVbpeFJPLFF1/oZz/7mb773e+qb9++ys/Pd7ukpJXidgFucBxHv/jFL5STk6OKigq3y0GSKS0tVX5+vk477TSlpqbq/fff16xZs1RWVqabb77Z7fKQBKqqqvTkk0/qsssu0+TJk5WSkqKXXnpJ//Vf/6WFCxdq+PDhbpeIJLBhwwZ99tlnOvfcc1VSUuJ2OXBZMBjUddddp65du2revHk6fPiw5syZo+LiYs2dO9ft8pAktm3bpvXr1+uMM86Qbdt8gXoMPqcdvjsrVqzQokWLNHr0aD377LP66KOP3C4JSe7uu+/Wp59+qtdff93tUpAELMtSWVmZOnXq1PCY4zgaN26cMjIy9Oyzz7pYHZKFbdsyjCM3PIwYMUIXXXSRfvazn7lcFdyyaNEiLViwQG+//bZycnIkSS+//LKmTZumV199Vb169XK5QiSDr35u3Hffffr000+1Zs0al6tKTu3udrJgMKhf/epXuv/++5Wamup2OfCI7Oxs1dbWul0GkoRpmo0CjCT5fD6dfvrp+vLLL12qCsmm/kQEkI5cmRsyZEhDgJGkiy++WH6/X+vXr3exMiQTPjfC1+7eqV//+tfq168ft3sgpLq6OpWXl+uPf/yjVq9erWuvvdbtkpDEbNvWRx99xLepAJq1Y8eOJp8Pfr9fPXv21I4dO1yqCvCudjUm5rPPPtOKFSv00ksvuV0KktwXX3yhb3/72w1/vuWWW3T99de7VxCS3rPPPqudO3cyeBtAs4LBoAKBQJPHA4EAY6aAKHg6xJSWloZ160aPHj2UmpqqmTNn6uqrr+ab0nYmkj7x+/2SpJNOOkkrVqxQRUWF/vznP+upp56SYRi6/fbbE10uXBJNn9TbvHmzHn30UU2aNEnnnntuokqEy2LpEaAljuPI5/O5XQbgOZ4OMevWrdP9998f8nmrV6/WP//5T+3YsUNz585VMBiUJFVXV0s68u1Ihw4d1KFDh4TWC3dE0id9+vSRdOQS/4ABAyRJeXl5Ou644zR37lxNmDBBxx9/fELrhTui6RNJ2rp1q6ZOnapRo0bpnnvuSWSJcFm0PQJIR6641J9/fFVpaSlfrgJR8HSIueKKK3TFFVeE9dw333xTJSUlGjFiRJNt5557rqZMmaJp06bFu0QkgUj6pCX9+vWTZVkqLCwkxLRR0fTJrl27dMMNN6hv37565JFH+Da1jYvHZwnar169ejUZ+1JTU6Ndu3Zp3LhxLlUFeJenQ0wkLr/8cp133nmNHnvppZf06quv6qmnnlLXrl1dqgxeUFBQIJ/Pp+7du7tdCpLEgQMHNGnSJHXu3FkLFizg9iEAxzRs2DAtXLhQRUVFys7OlnTk6l5NTY0uvPBCl6sDvKfdhJju3bs3OQHdvHmzTNNUXl6eS1Uh2ZSWlmrKlCn6zne+o6997Wuqq6vTpk2b9Oyzz+r73/++Onfu7HaJSAJVVVW64YYbdOjQId13333avn17o+1nnnmmO4UhqRQWFuqTTz6RJFVWVmrXrl1au3atJGn06NFulgYXjB8/XsuWLdPUqVM1depUHTp0SA8//LDGjh3L7WRoUFlZ2TDldmFhocrKyho+N84777xGU3S3d+1ysct6jz/+uBYvXsxil2hQU1OjGTNmqKCgQPv371daWpp69uyp8ePH67LLLpNpmm6XiCSwZ88ejRw5ssXtn3/+eStWg2S1atWqFsfQ0CPt086dOzVr1iwVFBQoLS1N+fn5mjZtmtLS0twuDUniWL9fli5dyhfvX9GuQwwAAAAA72l3i10CAAAA8DZCDAAAAABPIcQAAAAA8BRCDAAAAABPIcQAAAAA8BRCDAAAAABPIcQAAAAA8BRCDAAAAABPIcQAAAAA8BRCDAAAAABPIcQAAAAA8BRCDAAAAABP+f85jqzZTFDW6wAAAABJRU5ErkJggg==", + "text/plain": [ + "<Figure size 1000x400 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo[df_demo[\"F\"] < 0][[\"A\", \"C\", \"F\"]]\\\n", " .plot(kind=\"barh\", subplots=True, sharex=True, title=\"Subplots Demo\", figsize=(10, 4));" @@ -2017,15 +5721,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 204, "metadata": { - "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1200x600 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo.loc[df_demo[\"F\"] < 0, [\"A\", \"F\"]]\\\n", " .plot(\n", @@ -2038,15 +5752,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 205, "metadata": { - "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1200x600 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo.loc[df_demo[\"F\"] < 0, [\"A\", \"F\"]]\\\n", " .plot(\n", @@ -2096,9 +5820,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 206, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ax = df_demo[\"C\"].plot(figsize=(10, 4))\n", "ax.set_title(\"Hello There!\");\n", @@ -2119,9 +5854,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 207, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(figsize=(10, 4))\n", "df_demo[\"C\"].plot(ax=ax)\n", @@ -2142,13 +5888,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 208, "metadata": { "slideshow": { "slide_type": "-" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1200x400 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(12, 4))\n", "for ax, column, color in zip([ax1, ax2], [\"C\", \"F\"], [\"blue\", \"#b2e123\"]):\n", @@ -2175,7 +5932,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 209, "metadata": { "slideshow": { "slide_type": "fragment" @@ -2189,15 +5946,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 210, "metadata": { - "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_demo[[\"A\", \"C\"]].plot(figsize=(10,3));" ] @@ -2205,7 +5972,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "subslide" }, @@ -2219,58 +5985,124 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 211, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 1000x100 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.palplot(sns.color_palette())" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 212, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 1000x100 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.palplot(sns.color_palette(\"hls\", 10))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 213, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 2000x100 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.palplot(sns.color_palette(\"hsv\", 20))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 214, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 1000x100 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.palplot(sns.color_palette(\"Paired\", 10))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 215, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 800x100 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.palplot(sns.color_palette(\"cubehelix\", 8))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 216, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 1000x100 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.palplot(sns.color_palette(\"colorblind\", 10))" ] @@ -2290,13 +6122,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 217, "metadata": { "slideshow": { "slide_type": "-" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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eB3w7EK2sSAeePXv24O/+7u/wyU9+Ev/1v/7XoIfTEtqkxIiUGPHNBhljcFrripmg47aNyQVmg85ojTNa46ViEcgBSE/BwtzmquvYXJWaVKkOyF8IzQMRiRon0oHnL/7iL3DNNddg8+bNQQ+lpQkh0KsUepXC+fF4+XrBGEyUApBvRmimxjduG8ARx8GRqhm7LinLW+ZLS2Jr2FyVmkw9ByK2wcA2BkK4H2MGIqpfZAPPj370I+zatQtf/vKX8fzzz6/Ia1pWY2YSlDFQLfidqR3ARktho++aMQZnvJ1i7j9uEDq5QHPVKa0xpTV2+2qDFIC1loUByyuS9v5hc9WVoZTwPfLvNEgGQNEYFAEUtIYpFDEDAyUFYsI9msJiIXSglJIVjxROkQw8uVwOn/vc5/Dxj38cnZ2dK/KaUgr09iZX5LWqiaINtcDyTqvpBipCEOB+Iz9WsHF4poDDhQKOzBRxeKaI6Rp/bw6Ao7aNo7YNYHanWJeSGGyLY0M85j62xTAQj7G56hIlOxNBD4GqaACx9nj53w0ADYG4dE+Fjkn3TCDujlx9qVR70EOgBUQy8Nx7771Ys2YNrrrqqhV7Ta0N0unpFXs9v4wxyNosrK5FKYFkZwJ9tkY3FLbF24F4O0ynwZTW5Vmg0j8LNVedcjRenM7jxel8+ZqE11zVa9cx4M0MdXLLfE2le5LN5OE4nDYIi7Pdl1IdkJKAhVJbDHdLPDvDN45SEqlUO9LpHByHv9yuplSqve6ZtcgFnvHxcdx333245557kMlkAADT09Plx2w2i2RyaTM1tr3yX6hCeIWJfBMswP1idZy5f09JCGyxLGzxdZkvNVf1F0kfs21M1/huroHyzrLf+M4N6vA1Vy09rlWKs0EAFronFKT67ktp8bfWgYgW2Bi1ERxHN+TnCK2MyAWesbExFItFfOQjH5nzseuuuw4XXXQRHnrooQBGRqulVnPVzDynSE8sMBs0bQz22zb2VzVXXVN1ZtA6NleliFqoEFrxQERqMZELPNu2bcP9999fcW3nzp347Gc/i09/+tM8eLCFdUqJzngcW33XHG82qLq5amaB5qonHAcnHAfPF2ZPF0r4ZoP8O8ZiDEEUIdWd4f0HIlZ0hhfukhg7w1MziVzgSaVSuPjii+f92Pbt27F9+/Z5P0atSQlR3sH1Ct9sULa6nYbXXLVWpVXe31zVp0/KilYaA0qhm7NBFCELdYafPRCRneEp+iIXeIhWQlJKbJYSm6vaaZws1Qb5wlC6juaqO33X2nzNVUshaK1loY0hiCJgoc7w5QMR2RmeIqgpAs/FF1+MF198MehhUMRJIbDWsrDWqnxb5HynSB+vo7nqjDE4ZNs4VNVctVfKcgAqhaFezgZRyFV3hnfrgGZngCygXAzNQmgKs6YIPESN1C4lRqXE6DzNVf11QcccB2cWmA06pTVOFQrwR/NSc9UB326xdWyuSiFWXQgN+DrD++uA4C4psw6IwoKBh2gJ/M1VX+67nq/RXLVY43Uqmqv6ZoN6fM1VS2GoV0pIzgZRCPnrgNgZnsKKgYdoBSWkxEYpsbGqueoprWcbq9bRXPW01jitNX7ra6dhARUhqPTYztkgCplaneGFAOK+zvCsA6LVxMBD1GBCCPQphT6lcL7vesGY2bogXxhaqLnqYcfB4armqqlSc9XSAYrezBNngygsquuA2BmegsDAQxSQuBAYjsUwXDUbdMYrki7VBx23bZxcYDYorTXSWuOl6uaqVadIDyiFDs4GUQjU0xne8kIQC6FppTDwEIWIEAI9SqFHKbwsHi9fLxqDiarDE485DvI1fgo4AI46Do5WzQZ1eucS+ZfG+pWC4mwQBaj6QMQ8ahyICDcIsRCaloKBhygCYkJg0LIw6Nsyb4zXXLWqSPqk46DWz4KMMcgUi9jjmw0qN1f1haAN8Ti6+BOFArTQgYjxciH0bB0QZ4HobBh4iCJKCIGUUkgphXN9121vNshfJF1vc1X42ml0nj6FtVJVtNJgc1UKynwHIpbqgErLYMqbEZLGsDcYzcHAQ9RkLCGwwbKwoWo2KGtMxVb5Y17PsFrVQRlHI+No7PPNBgm4s0HVfcW6eIAirbJ564DghiB3Nsg7GFEIKACqvC2eQahVMfAQtQAhBDqFmLe56kRVK41jto3sAs1VJ7wu9P7mqu3VzVW92SA2V6XVVApBMChviQe8eiC4QcjyTodWpcMR4RZLsy6ocbQxGLNtZLVGUkoMW1Ygu0gZeIhamBIC6y0L6y0L8DVXzQkg06aw90wWR4s2jtk2TizQXDVnDA7YNg74mqsKAH1qdkmsVCzN5qq02rQBgNlt8UBpNmj2gER/ECrNBrE2aPleLBTweDaLY147HgvAgGXhimSyYmPGamDgIaI5OqXEYEcC64sGjuMuejlec9Xj/i3zCzRXNQBOekXUL/iutwlREYJKzVXjDEG0iko7w/wHJAJeCPKKo2WpUWppWcz7h8ti9XmxUMCD6TTyWqNDSnTAPU9svFjEg+k03pdKrWroYeAhorooIdzDDS0LF/hmg6a9dhr+LfMTjrNgc9WDto2DVe00+rzmqv4g1MPZIFpl7oqYKc8KFeCbDcJs13i3LsjbKeZ9ifJLdZY2Bo9ns8hrXTGrG4f793dGazyezeLcWGzVlrcYeIhoWTpqNFedrGquevwszVUntcZkoYBdvmvxUm2Qv6WGUmjjAYq0isqzQfMuiwlYWsKeKSDnaEiBcpG0bOFlsTHbXQrvmOeXFiEEOqTEMdvGmG1XtOJpJAYeIlpxUgj0e4cabvddLzVXrT5JulZz1YJX7DhWo7mq/yRpNlel1VYqknaMgQ0g52g4XugpzQYpb8u89BVJl7rIA83bSDWrNWwAHXB3jrZJ972Z9ZbILQA57/NWCwMPEa2ahZqr+rfMH3ccnFpkc9UYgLW+WaBSGGJzVVpt1UXSM6gskhbwtsxj9uygZjtAMSklUlKiU0kkpEDpryTrfdyGG/6Sq/j+ZOAhokD5m6tu812fMQYTvq3ypUMUCzV+GhQBHLZtHLYrq4f8zVUHvCDUx9kgWmX+Imlg7rKYOysUzbOD3LeSO6OljYEWwPqYhaF4DGOFAqZtIKYkOr1wY4zBtNYYisUwbK1eDGHgIaJQaqvRXPW0r0i6NCM0ucjmqhbc5qrrfOcGsbkqBcF/gCLgP0W6nrOD3FdYjSBUCjWAu6vNwD3rSMPdwenA3dGpvT+UgcG5Vgwv5WdQ0BprpIA2BgUv7CSkxBXJ5Kr+4sHAQ0SRIYRAr1LorWquWvBmg0r1QaVDFGs1V7UBHHEcHKlqrtpV2onm2ym2hs1VaZUtXCQ9uyxm+XaLVc4ILa42aPbL2w00ujQG9z/hAF6dkkFR6/JONu+Mx5q2xGJ4R2cnns7lkDMaWS/wDMViPIeHiGgp4kJgKBbDUNVsULpqNuiY42BygeaqU8Zgqqq5qoLXXLUUhLwZoU7OBtEqq7UsBsw2VxW+5qruTJC7W0x5M0KlZSdHuDMuDrxlKAC20dDGwJe1gDqCzUK2xGIYtSycNBpxIdAGwZOWiYhWkhAC3UqhWymc6/tNsmgMTlQtiR1zHORq/ErsAOWw5JcUohyCSkti/WyuSgEpNVctzQiVZoNKtTUS7nuiFGj8y2GNXhGTQmC9tNBrKcgA65AYeIiopcRqNFfNzNNc9eQCzVWzxmBvsYi9vtkgCWBN1blBA5aFTiF4gCKtquplMad8tXUx8BBRyxNCoEsIdMXjOMd33S7NBvm2yy/UXFVjtrkqqpqrDlSdG7SWs0FEq4qBh4ioBqtGc9WM1uVZIH87jVqzQTljsN+2sb+queqa0plBvjCUYjsNooZg4CEiWqROKdEZj2OL71qpuWr1KdJTNWaDDIATjoMTVc1VE6V2Gr5DFNlclWj5GHiIiFaAv7nqK2o0V/UvjTk1Xifvb67q0ycl1scsbLQL6HEM+oVkc1WiRWDgISJqoFrNVUuzQcd9IeiszVVnCnhhZrY2KO7VBvlbaayzLLQxBBHNwcBDRLTKpBBYa1lYW3Wsfs6bDTruOzdo4izNVQ/ZNg5VNVftrdFclbNB1MoYeIiIQqJdSmySEpuqZoNKzVUntMakMBjLzeD0ArNBp7TGKa3xYlVz1epTpNcphQQPUKQWEbnA88Mf/hDf+9738Pzzz+PMmTMYGRnBH/zBH+Caa66B5BuXiJqMFAJrSi0ulESqux3pMzlki+7OsOoi6UKN1ykCGLdtjFfNBnXPMxvE5qrUjCIXeP72b/8Wg4OD+I//8T9izZo1+PnPf46/+Iu/wKFDh/DJT34y6OEREa2KhJQYkRIj8zRXrQ5BCzVXPaM1ztRorjpQNSPUzl8qKcIiF3i++tWvoq+vr/zfl1xyCaanp/G//tf/wn/4D/8B8VVuRkZEFBb+5qrnz9Nc1X9u0DHHwcxim6tKOadIul8pzgZRJEQu8PjDTsm2bdswMzOD06dPY926dQGMiogovGo1Vz3j2zJfepzUunZzVa0xpTV2VzVXXetrpVEKQ0nOBlHIRC7wzOfZZ59FT08P1qxZs6zXsazGvEGVMVA1fpMiQCnhe+Q3yTDgPQmnlb4va6CwBjFs810rzwbZNo7Z3myQbS/YXPWo4+Bo1WxQp5QYsLzlMO+fZm2uyvfL2UkhoCwJxeahS/eb3/wGjzzyCD760Y9CKbXk15FSoLc3uYIjmyWKNtQCa+jkSnYmgh4CVeE9CadG35d+oCIEGWNwxnFwZKaIwzMFHJ4p4kihiOOFYs12GhmtkSlo7ClUzgati8cw2BbDhrY4BuMxDLbF0aWaY8s83y+1CQCpmIV4gDN/wpjoTj1MTEzgve99LwYGBrBjxw7EfNO1i+U4Gul0bgVHNytjDLJ2rXNVSSmBZGcC2UwejhPZL8emwnsSTmG7L0VjcMK2cdT2zg6ybRy1bUwv8sdKhxAVM0HrLYV+y0IsIiEobPcljKQQ6IupFZ/hSaXaoVR9ISqyMzxTU1P48Ic/jEQigXvvvXdZYafEtld+FkYIt8eO43CGpzb3i9Vx+Pfkp43BUcdBTmu0S4n1q1ocynsSTuG6LxLAOqmwLq4Ar0jaGIOsMRV1Qce8nmG1RjxtDPYVi9jnqw0qNVcd8NUHDSiFrlAeoBiu+xJKQsARAkFOsUQy8MzMzOBP/uRPcOLECTz44IPo7e0NekhEK2pvsYinczmcsG04cJcC+i0Ll7a3Y8sKhHuiRhFCoFMIdMbj2Oq77hiDE75dYqXHTB3NVZ8vzJ4ulPDaafi3zK9VKjKzQRScyAUe27bxsY99DLt27cIDDzyAoaGhoIdEtKL2Fov4QSaDgtZISIkE3MLQY8UifuA4eEdnJ0MPRY7yLVv5m6tm59kpNnGW5qoHbBsHfM1VBdzmquWdYt5jdyhngygokQs8d9xxB3784x/jtttuQz6fx7/+67+WP3bOOeegs7MzuMERLZM2Bk/ncihojU7fN2sJwJISGa3xdC6HUcvi2SfUFJJSYrOU2FyjuWr5AEXHQbrG5g8D4KTWOFkoYKfvepsQ7ixQVXPVON87LSlygef//b//BwD4/Oc/P+dj999/Py6++OLVHhLRijnqODhh20jM85upEAIJKd0iUcfBoBW5ty9RXfzNVS/wzQblfLNBx3xd5u0arzOzUHNVX13QOjZXbQmR+475xBNPBD0EoobJaQ0HQK3NrQpA3vs8olZTq7nqpNblDvOlEHTW5qqFAl70XYtj/uaqbTxAsWlELvAQNbN2KaHg1uzM9222VMDMnkZELikE+r0WFy/3Xc97s0HVM0LFGq9TADBm2xirmg3qmae5ai+bq0YSAw9RiKxX7vkjx4pFWFVT7MYY5LXGQCyG9cs4ZJOoFSSkxEYpsbGqncYprSsKpI87Dk4tMBt0Wmuc1hq/rWquus5XHL0hHsM5TlvN16BwYOAhChEpBC5tb8cPHAcZb5dWacYnrzXiUuLS9nb+dkm0BEII9CmFPqUqTpKe8TdX9TVZXai56mHHweFSO41pAKfPIFVqrlqaDVIKa9hcNTQYeIhCZksshnd0dpbP4cnDXcYaiMV4Dg9RA7QJgeFYDMPzNFf1h6BSc9Va0lojrTVemqe56kBVfVAHl6VXHQMPUQhticUwalkBnrRM1NqEEOhRCj1K4WXeKdKA205joqou6JjjIL/Y5qreuUT++qB+paD4Hm8YBh6ikJJCcOs5UcjEvPel/70ppYBOxrHnVAZHinY5DE06Dmp1UsgYg0yxiD2+2SAJoL/q3KABy0InZ4NWBL+bEhERLYMQAr0xC+e1tWGrNbssViy10/AtiR1zHORqzAZpoLyz7De+dhpJIWZPkfZqhNYqBYuzQYvCwENERNQAMSGwwbKwwTcbZIxBxmuu6l8SO7lAc9Vsjeaq/V4A8p8bFM7mquHAwENERLRKhBDoEgJd8TjO8V23fc1V/TNC2QWaq044Diaqmqu2e81VyydJWxb62VwVAAMPERHRomljypsKOi0LXTWCSb0sIbDesrDesgBfO42Md4r0cV9vsRMLNFfNGYP9to391c1VS200fDNCqRabDWLgISIiWoS9xWL52AgHgBLA+vw0LkkksEmu7I/VTinRGY9ji++a4zVXrThF2nEwtVBzVW/Z7AXf9USpuarv3KC1TdxclYGHiIioTnuLRfwgk0HBOxg0AUALgcMzRXxvpoh3dHY2/Kws5RUxr6tqrjrta65aepxYoLlq3hgctG0ctCs/o8/XXLX02NMEs0EMPERERHXQxuDpXA4FrdHpCwBKCCSkxCnbxtO5HEYtK5AzszqkxKiUGJ2nuWp1h/kzCxygOKk1JgsF7PJdi5dmg3wzQlFrrsrAQ0REVIejjoMTto3EfLMdwm3qe8K2cdRxQnOGlr+56nbf9XzVKdKlYumazVWNOWtz1dKMUF9IZ4PCcUeIiIhCLqc1HACJGh8v9b3LLTB7EhYJKbFJSmyqmg065RVJ+8PQ6UU2V40BFf3EBrxi7F4E2/SYgYeIiKgO7b5mvvMt5DhwQ097hJZ5/KQQWOM1PK1orurVBlUUSds2CjVepwhg3LYxXlUbdFFbG27o7g6sRQ4DDxERUR3WK4V+y8KxYhFW9bKNcWd2BiwL61WwMxkrrU1KjEiJkarmqqe9ZbFSkfTxszRX/dXMDA7bdkWT1tXEwENERFQHKQQubW/HDxwHGW+XloLbEmJKa8S9j7dCk18hBHqVQq9SON/XXLVgDCZ8dUGlGaEZYzDs7SwLCgMPEQXOf4gbO8NTmG2JxfCOzs7yOTx5AAoGg23xhpzDEzVxITAUi2HIXxvkOJiZPIH1U2moXAZmcAQigGW/1r4zRBS4OYe4Aei3LFza3t7w80yIlmJLLIZRy6o4afllazoxlc7DccJfsLya7AN7MPOzp6ByGczYBcycPAm5dj0Sl78N1jnbzv4CKyialVVE1BRKh7gdKxYREwJJIRATAse863uLtTbJEgVLCoFBy8LWeByDMSuU27CDZh/Yg5l/+geYiaMQ8ThERxdEWxv0kTFMP7wD9u6dqzoeBh4iCkT1IW4xISC9wNMpJQpa4+lcDnqZPYqIaPUZrTHzs6dgZmYgOrsgtAFgACsGpLph8nnkn/gRzCpu4eeSFhEFYqFD3IR3cm3YDnEjorPQGuLkBPD8v6J9z05YxSKswwchjIFuS0CuWQspJUyyE3riKPThQ1DDm1ZlaPwuQkSBqOcQtzyicYgbUUsyBjg9CTl+AGLsAOTYAYjxgxD53PyfP5MHbBuIxwHLAnJZ6OzUqh1HyMBDRIFo9kPciJrOVLocbtyAsx8im6nrqQYCsr8fKG1EsG1AWZDJrgYOuBIDDxEFYqFD3IwxyGuNgVis6Q5xI4qE3DTE+EF31mZsv/t45lRdTzVCAP0DKAIoOjacrhTQswa90MDJEzDGwExnITcMQw6ONPbP4cPAQ0SBqHWImwO3sWFcypY5xI0oUIUZiMNj7oxNafbm5PG6n25610APb4IZ3gQ9PAozOAIk2md3ac3MIObYbhAqFGCmsxCJBBKXv21Vz+Nh4CGiwMx/iBswEIvxHB6iRrBtiKPjEONezc3YAYhjhyHq3A1pulIww6NuwBnaCD20Ceicf1nK2rQVeMu73N1auQyMXQAKM5AbhgM5h4eBh4gCVX2IG09aJlohWkMcP+LO2JRqb46MQzj22Z8LwLR3eLM2m2CG3EekeoBFvDetTVuhRjZDnJxAYmYayopD8qTl+u3btw+f+cxn8Oyzz6K9vR2/93u/h1tvvRWJRK39HkQUZqVD3IhoiYwBTk5U7pg6fAiiMFPf0+Nt5RkbM7wRengU6OtfVLipRUgJtW49LEtBBnisVuS+w6TTafzhH/4hBgcH8eUvfxmTk5P47Gc/i9OnT+MLX/hC0MMjIiJqLGOA9OnykpS7HfwARG66vqcrC2ZwuDxrY4Y3waxdDzT5jsjIBZ7/83/+D9LpNB599FH09fUBAJRSuPXWW/Enf/In2Lp1a8AjJCIiWkHZzGxBcan2Zipd11ONlMDABujhUejBEZiRUZiBIfccnBYTuT/xT37yE1x66aXlsAMAb33rW/Gf/tN/wpNPPsnAQ0RE0ZXPzW4HL4WbUyfrfrrpH4Ae8WpuhjbBDA4D8bYGDjg6Ihd49uzZg6uvvrriWjwex8aNG7Fnz55lvbZlNWY6TxkDxX5ANSklfI/NPaUaFatxT6QABASkBBxtoPkWOSu+V8JpyfelWAAOH4I45J51Iw7th5g4VvfTTU8fMOLumMLIKMzQJqC9vfzxMH2FSCGgLAnFGp76pdNppFKpOddTqRTOnDmz5NeVUqC3N7mcodUkijYUj8c/q2Qni87Dpt57Ul3WWP09TXj/SAAxr1GoJQQs6T5mHQcZ24Gz/CG3BL5XQkBrFI6Mw8lmoZJJoGNowftiHBtmfAx6/z44B/ZC798Hc3gMqPdnQ1cKcnQz1KYtkKObITdtgZjnZ2FYCQCpmIV4gHVCkQs8tRhj5jQgXAytDdLp+gq+FitjDLI2v5XXopRAsjOBbCYPx+Gv+WFQ655IAQACQgBKAJZwDwtUAlAQgHC7oGsAjgE0UP64JQQkBACDogGKVf9PSwB5R2NGa8721MD3SjjYB/Yg/9MnoU9OwGgHQinE1w0gdsnroUa2uCFm4tjsrM3YAXcmx65/OziGN8F4S1NmZBTo7q3cMWUAnKnRsyqEpBCIxdSKz/CkUu1Qqr4QFbnAk0qlkE7PLdaamppadv2Oba/8LIwQgGMMHIczPLW5X6yOw7+nMBAApJCQAISBOxsDb0oabniRBhBGAMagtFprvHkdAS/kwP36L33cgTnrDE5SCCghkdUObKaeefC9EjT/6cGivR1CKsiZHLB3N/Rvd8HqSEKenKh/O3gs7m0H3+julhoehVmzdu52cG0wd+40QoSAIwSCrO6IXODZunXrnFqdQqGAgwcPzqntIaLaBAAh3NkaAXepyQ00AnEl0BuLoU0VvZmE2WCDqoCzkMV+czPGoA1AzFLIaIMZ24nyt3hqMkZrFJ76Z1inTyEmAHX6JKx8DtLxRfkF+k0ZpWA2DLuH+Q35toOzX9yqiFzgef3rX497770Xp06dQm9vLwDgscceQ6FQwBve8IaAR0cUPsIrDhbCnR+wpIQFN9goIaCMgSz/5uUGGwsCMXf9CiaAX8mkBlJCYNpSmHY0NIv+KQjZjLdjaj/E+EGIA3uRyE7V9VQjxOx28KGN7tLUhiHAYruUoEQu8FxzzTV44IEHcNNNN+Gmm27CyZMn8bnPfQ6///u/zy3p1PKkL9yUioMlZpehSjU0pjQ77gWJUAYKAySFhGUJZBwucVGDzeR93cG9VgyTJ+p+uk60w27vQFEIiNe9CerfXsrt4CETucCTSqXw7W9/G5/5zGdwyy23IJFI4J3vfCduvfXWoIdGtKrcGZvKcFOqnbGEgDFLW4YKE2MM4gB6LIUpbVDgEhethGIR4siYd0rxfveU4oljdTfQ1LE4nGQn7EQCxXgbnEQ7VDwOO5eHKRbQvvk8hp0QWlLg+cUvfoGjR4/inHPOwXnnnTfn48eOHcN3vvMd3Hzzzcse4Hw2b96Mb37zmw15baIwKtXbSOEuScXgLklZAuVdD/7v1aGcsVkGqYFuLnHRUjiO10Bz/2x38KPjEHVuBzfJznIDTT24Edlf/Bz69CREV6qisNgYAzOTg+hfD7lufaP+NLQMiwo8hUIBN954I55++unyNvA3v/nN+MxnPlNxNs7Ro0dxzz33NCzwEDUzN9y4AUcJMXtmjVdvI6qWpVrmRz+XuOhstIY4cbyyBcPhQxB29SEI8zOJxGx/qVJ38J6+imATT3a6u7Sm0hCJdrdFg23DKeYh4m1ou+R3A+kETme3qMDz7W9/G88++yxuu+02XHjhhXj66afxzW9+E9dccw2+9a1vYd26dY0aJ1HTKp847C1NWd42cAuoCjfuD/ioLUutJC5xUZkxwOnJco8ptxXDQYiZfH1Pj8VgBkdmd0sNb4JZs+6sDTStTVuBt7wLMz97CmZyApjJQUiF+MAGWK/5XYjhzSvxp6MGWFTg+Yd/+Ad85CMfwQ033AAA+J3f+R28+c1vxo033ohrr70W999/P9av51QeUS3uGTezdTcW3Hqbeetu0NrhZiFc4mpBU2dml6RKRcXZTF1PNVICG4bdZalSwFm3Ycnbwa1NW6FGNkMfPwqdyyLW2Ynec7cgPTXD85FCbFGBZ2xsDL/zO79Tce3888/HAw88gOuuuw7XXnstvv3tb6/oAImiyl93o7y6G0sI91Ri487eGDO7JsUf2ovEJa7mNZ2d3Q4+dgBy/CDEAufb+BkhgHXrfeFmFGb9EBBb2e3gQkqo9YPuRgElzzozRMFbVOBJJpOYnp7bfmF4eBg7duzABz/4QXzwgx/Exz/+8RUbIFFUSK/uptQvyvLqb+aruwE4e7MSKpa4HI2Co/m3GjUzeYjDh2a3g48dgJicqPvppq8fenjUO8xvI8zQRqCNvcZorkUFns2bN+OZZ57BZZddNudjQ0ND5dDzn//zf16xARKFUa0t4aUD/cpLU6y7WRVSA91SIisEco7DXlxhZRchjoy7O6bGD7q9po4frXs7uEl1wwyPzs7eDG0Ekp0NHjQ1i0UFnte+9rXYsWMHPvaxjyGRmJugh4aGystb4+PjKzZIoiC1+pbwyDBAp7fEleUSV/DK28G9epvSdnCnvkbKpiNZ3g5e3jGV6mnsmKmpLSrwfPCDH8QVV1yx4OcMDg7ioYcewu7du5c1MKIgVG8Jj3uPLb8lPCKMMUgIwLIUMlziWj1aQ5ycqNoOfhCiWOd28LaE20DTH25618xtoEm0DIsKPDt37sTLX/7yeWd3qh08eBCvec1rljwwotUwb90N3J5TklvCI8kYd+aNS1wNYgxw5pRXUOzrM5XP1fd0y3K3gw+PwpQ6hPcPsOiXGm5Rgee6667Dgw8+iAsvvBAAoLXGhRdeiIceeggvf/nLy5936NAh/Jf/8l/w7ne/e2VHS7QMtbaEz6m78TDcRByXuFZGJj13O3imzgaaUgLrhyq3gw8Msjs4BWJRgae6a7IxBrZtB9JNmWghC20Jt4z7GdwS3vy4xLVI+dzsTqlSK4bFbAdfO+ArKN4EMzgMxOINHjRRfSLXPJSomr/uRgKIl04rXrDuhj/2WoV/iSsjBPJc4nIVZiAOj5XPuhHjByBPHK/76aZ3jVtzM7zJXZ4aHAES7Q0cMNHyMPBQ5JRaMQjhCzdgKwY6CwN0CYlYKy5x2TbEsXHf7M0BiGOH698O3pUqbwc3QxuhhzYBnV0NHjTRymLgoVCbb0u4/7RiAKy7obrNLnFZmHIcFJtxiUtriImjXrjxZm+OjEM4dl1PN+0d828H544pirhFB569e/dCeQVnjneewt69e+d8DtFiuUXF3BJOjeUucRn0NMMSlzHAyQmvcaavO3hhpr6nx9tghkagh0ZhhjdCD48Cff0MN9SUhFlExfH5558PUfVGMMbUvLZz586VGeUqcByNycnsir+uEEDGGGTt+g7baiWlcBOzJPpSHchP5SG0mbslnFadZUn09iZx6lQWtt28zRCFEMjBIOs4cMKeeoyByqTRMXkY+d++BBzcDzF+ACI3t93PvE9XFszgcHnWxgxvglm7ntvBV4BSEqnudqTP5Ng8tAYlBHotBbnCb7O+vqTby6wOi5rh+exnP7ukAREt1IohrhR6YhZOCQGbdTe0iowxaBdALIxLXNlM+awbMe7tmJpKowD3nKiFGCmBgQ3Qw6Nuf6nhTTADQ4DFKgZqXYv66r/yyisbNQ5qIottxcBjDShIoVjiyue87uC+s25Onaz76aZ/AHpktubGbBgB4twOTuTHuE/LUt2KIebV3LDuhiLHAF1CwLKsxi5xFQtzt4NPHKv76aJvDfTgRjj+BprtHY0ZK1ETYeChRSltCZdithUDt4RTszAGFUtcheXWYziO2zBzvGo7uK7vdU1nl7cdfCPM0CjEplGkhgZYK0K0BAw8VFOtVgzKe2QrBmpG/iWuKSEwU+8Sl9YQJ45VnFQsjoxB2IvYDl7dQLO7t2LHVL3FmUQ0FwMPAWArBqI5DJASArn5lriMAU6drGzBMH6w/u3gsVj5AD8zvAlmeBSmr587pogaiIGnRc3bJZytGIgqlJa4VGYKmQN7oA8dKJ95I6brO8bCKAWzYdg9zG/Itx2cDTSJVhUDTwtYaEt4RZdw1t0QQWczcMb2wznk/TN2ACZ92jsfamFGiMrt4EObYDYMAVZsNYZORAtg4Gkyi90SzqUpamUmn4czfsALNvthH9oPM3mi/uevWQs9MurW3AxtdBtotiUaOGIiWioGngir3hLOVgxEtZliEc6RQ7MzN4f2Q08cRb3HecueXqiRUaiRUWB4FNMbhlFMcDs4UVQw8ETIvHU3wNxWDFyaohZnHBv66GE4Y+7sjT22H/rIOKDra/EiOrvccDO8CWp4FGp4FDLVXfE5bQKYMgZ5x2ELFKIIYOAJKW4Jp1ZitIY+fAg6OwWZ7IIcHIGoc8eS0Rr6xDE4hw7M1t6MHwTsYl3PF4l2L9hsghrZDDUyCtHTN6dH4Nz/sbuLK2ZZyNoOHKYeolCLVOBxHAf33XcfnnzySezevRuO4+C8887DzTffjEsvvTTo4S0Zt4RTK7N370T+iR+5y0uODSgLcu16JC5/G6xztlV8rjEG5tTJ8qxNOdzkc/X9z6wYrOGNkN6sjdo4CrlmXd3hqlppF5dlKbcXV9gbkBK1sEgFnnw+j6997Wv49//+3+OP/uiPYFkWvvvd7+L666/Hvffei8suuyzoIZ6Vv+5GAoiXTivmlnBqQfbuncj9/QMw+TxEMgmoJODY0EfGkPv7B9D29ishrFi5qNg5tB8mm6nvxaWC2jDkLU25tTdyYAOEWtlve8a430h7lEJGcomLKKwiFXgSiQT++Z//Gd3ds2vpr3vd67B//37cd999oQ48lhSz4QZsxUBktEb+iR+5Yae7BzAaKMzAFAowdhHm5BRyO75W34sJAbVugxtqhje5IWfDCERs9baDC2+Jy1IK047mEhdRyEQq8CilKsIO4M6UnH/++Xj22WcDGtXCjAGSQqDTO2SMdTdEgJnJo/ivz8A5tA8wBuboYXc5q05yzdqKmRs1uBEiEfx2cGOADiFgWQoZLnERhUqkAs98tNb45S9/ia1btwY9lNq4JZxamLGLcA6PVRzmp48fqXs7uEh2who9x5u98ZamOpINHvXSGQPE4Fvish2+/4lCIPKBZ8eOHdi3bx/uuOOOZb+WZbGPTRBKDRHZGDE8lnpPjOPAOXYY9kH3ED/74D44R8YAp77t4FAKor0DIhaH0QYwGqkP3QJrZHSRf4Jw6AUwrSSyjl6RzQZKCd8j3y9hwftydlIIKEuWD8ANQuCBZ2pqCsePHz/r542MjCAej1dce+aZZ/D5z38eN9xwA1796lcvaxxSCvT2hve3xlaQSrUHPQSqstA9MVqjePwoZvbuRX7fHszs34uZA/tgCoW6Xlsk2iHjcRjHhujohIrHAcuC8I5dcE5PIj6yEf0XbFvyLqow6AWQczTSto35Yp9bybc4yc7gl+9oLt6X2gSAVMxCPMD3cuCB57HHHsPtt99+1s979NFHsW3b7BbVXbt24aabbsIVV1yB2267bdnj0NognZ5e9uvQ4iklkUq1I53OwXF00MMhzL0nxhjo05OwD+6DfdBdlrIP7Yepezu4BWt4E6yRTbBGNkNtHIVaux72nheReeh+6HwORkj3J79tw+SyEG0JxN7wVpw+U+f/I+QsAeQcB0Xj/ntMSFjC/UGQ1RoF5+yxRymBZGcC2UweTh2fT6uD9+XspBCIxdSKz/CkUu11z0QLY6K3leDgwYN4//vfjy1btuB//s//OWfmZykcR2Nysr7ux7SyLEuitzeJU6eysG0GnjCQuQwSk0dweucuFA/uc7eDZ6bqfLJ0t4MP+7aDrx+suR18MefwRJ0Qbqar2KEJAALIeKc2L1TnrJREqrsd6TP85SBMeF/OTgmBXktBrnDi6OtL1h14Ap/hWayJiQnccMMN6O/vx1e+8pUVCTtErczkpuGMHYDtP+vm9GR9TxYCcu0ArJHN7nbw4VGooRGIWP3vS+ucbUhuedmST1qOklLAmbND0wBdQsCyLGQdBw53dxGtuEgFnnw+jw996EM4efIkPvWpT2H37t0VH3/lK18ZzMCIIsIUZuCMH/KCjTtzo0+cvYauRPb1V24HH9oIkVh+7ZWQ0m3tsOxXiq7Sqc0xy3JPbXY0d3cRraBIBZ4TJ05g165dAICPfvSjcz7+4osvrvaQiELL2Db00XHYXrBxDu2HPna47u3gqrvHXY4a2uRtB98Emexq8KhbmzGAMgY9UiIjxFmXuIiofpEKPMPDwww11DIW01DTaA19/Eg52Dhj++EcHqv7MD/R3uE1znSXpeKjW9C/eZh1VUExQJeQiFkCWceBzdRDtGyRCjxErWKhQl619XzokxPuktTYgdkGmoWZ+l483gbLa79QqruRa9ZWdAdXPJMqcMYYJARgWRYyjoMCi2GJloWBhyhk/A010dEBiDYgn4ezfzey990FCFl/uFEW1NCIt2PKCznrNjRlQXAzKi1xdUuJrBAosKqHaMkYeIhCxJlKI/e970CnzwBSACcygK7zN3spoQYGyy0YrJFRyPVDEBbf5pFngE4hYSv33B4iWjx+JyQKiMlNwxk/6B7iV9oOfupk3c93t4N7/aVK28HjbQ0cMQXJGIM2SCRjFrJKIO+wRx/RYjDwEK0CUyy44aZUc3Nov1ufU69YHKK9HVAWTD6H9ndfh/iF/7ZxA6bQikuJXqWQtoDpFerRRdQKGHiIVphxbOijh8vtF5xD+9zt4PUuTSkFkWh3Z2uUcsOOck+oMYUCYAxUX38D/wQUegZICgnLEshwFxdRXRh4iJbBaA09cXR2K/ih/XAOHwLsOreDJ9rdA/y8w/zk0EZMf+d+mKPjQEeyYueUMQZmOgu5YRhycKRRfySKCGMM4gB6LIUpbVCwHS5xES2AgYeoTsYYmMkT3qyNF3DGDwAzde6YisVhDW90g00p4KxZO2fHVPub3u7u0jpzGuhIApblNtSczkIkEkhc/jbusqIyqYFuITBtKS5xES2AgYeoBn3m9OyszaH9cMYOwExn6nuyUlCDI+5W8FIDzXUbyktTC7HO2Yb2q6+dPYcnl3XP4dkw3JQNNWkFcImL6KwYeIgA6GzGDTe+omKTPl3fk4XwbQd3z7pRG4YhrNiSx9NKDTVpZfiXuDLaYIZLXEQVGHio5Zh8Hs74AV/dzQHoyYm6ny/718020Bze5DbQbEus+DjZUJOWQmogJQRy3hKXwyUuIgAMPNTkTLEI58ih2WWp0nbwOn8IyJ5eX7hxA47oSDZ41ETLZIAOIWBZClNc4iICwMBDTaS8HdxblrLH9kMfGQe0U9fzRbJzNtyUiopT3Q0eNVFjGAPEAPQqhSnJJS4iBh6KJKM19Ilj5WLicgNNu1jX80Wi3Ssoni0qFr1rKraBEzUDYSp3cXGJi1oVAw+FnjEG5tTJihYMzvhBIJ+r7wWsGKzhjbMtGEZGIfvXsQCYWobxlrhi3hJXkUtc1IIYeCh0dPrM7HbwUo+pbJ3bwaWCGhyarbkZGYUcGKxrOzhRMzPG/YbfoxQy0iDvOPWWshE1BQYeCpSZzqJw5CAmJ8Yx9dvfwj64zz1wrx5CQK3bULUdfAQitvTt4ETNThh3F1fMspB1HDic7aEWwcBDq8YUZmbrbbyZG31yEdvB16yt2g6+CSKx8tvBiZqdMUC7AGJKIQ0ucVFrYOChhjB2Ec7hsYqTivXxI3VvBxepHlhejynpBRyZ7GzwqIlahzGAgm8Xl+OAuYeaGQMPLZtxHOhjRyrrbo6MAU6d28E7kmjfcg7M4AjEoLtrSnb3NHbQROQypYMKucRFzY2BhxbFaA198vjc7eDFQn0v0JbwGmhuhhzZBGtkFLG169DX14lTp7Kwbd3YPwARzVFe4rIsdxeXo3lmDzUdBh6qyRgDc3qyPGtjeyGn/u3gFtTQRrfuZmgT1MhmyLUDc7aD8+wbouAZAyhj0CMlMkIgzyUuajIMPFSmM2k4hw7AObRvdjt4Zqq+J0sJtaFqO/j6QQjFLzGiSDFAlxCwuMRFTYY/jVqUyU3DGTvgzdp44eb0ZH1PFgJy7QCskc3udvDhUaihEYhYvLGDJqJVwSUuakYMPC3AFGbgjB/ygs0+d8fUieN1P1/29Vf2mBraCJFob+CIiShoXOKiZsPA02SMbUMfHYftBRvn0H7oY4fr3w7e1T13O3hnV4NHTUShZYAuIRGzBLLsvE4RxsATYUZr6ONHysHGGdsP5/AY4Nh1PV+0d7gzNr7ZG5HqYRExEVUwxiAhAMtSyDgaBS5xUQQx8ESEMQb65IRXUOzbDl6Yqe8F4m2wvO7g0gs4cs1ahhsiqou7xAV0S4msEMhxiYsihoGngYzW0IcPQWenIJNdkIMjdXXoNsbAnDnla8FwAM7YfpjcdH3/Y2VBDQ5DjWx2WzCMjEKu28Du4ES0fAboFBIWl7goYiIdeJ577jm85z3vQSKRwC9/+cugh1PB3r0T+Sd+BD1x1F1iUhbk2vVIXP42WOdsq/hcnZ3yLUu5szdm6kx9/yMhoNYPumfclHZMbRiGsCJ9a4koxEpLXDFLYUobFGyHS1wUepH9qWiMwX//7/8dfX19mJ6uc+Zjldi7dyL39w/A5PMQySSgkoBjQx8Zw/R37kfba98IaAO7tB381Mm6X9vdDl4qKPa2g8fbGveHISKahzGANEC3EJi2FKYdDV3n5giiIEQ28Pz93/89Tp06hauvvho7duwIejhlRmvkn/iRG3a6e4BiESabAYoFmMIMkEkj//2H63ot2dtXsSylhjZBtHc09g9ARLQYBkh6S1wZLnFRiEUy8KTTaXzxi1/EX/7lX+K5554LejgV9OFD0BNHIZJJmMwUzJlTdT1PdHbBGtkMNbIJcth77Ew1eLRERMtnjEEcQA+XuCjEIhl4/vqv/xrbt2/HZZddFr7Ak53yanaSMDP5+T9JSqjBEVjnvdzrMTUK0dPHHVNEFGlSzy5x5RwNh0tcFCKRCzw7d+7Eww8/jO9+97sr/tqWtQK7mFIp5JUFODZkqhtaawilINoSMJYFaAM4Njrfex2skdHl//+agFKy4pGCx3sSTlG5L90CSFgSGa1RdJo/9CglfI/hvjdBkUJAWRIqwC+HwAPP1NQUjh8/e5uDkZERxGIx3HHHHXj/+9+PrVu3rug4pBTo7U0u+3VM9zYUhoZQGDsI1dMH0TFbc2OMgXN6EvHhjei/YBu3iVdJpdiuImx4T8IpKvelTxucsW0UWmSmJ9mZCHoIoSUApGIW4gH+3As88Dz22GO4/fbbz/p5jz76KPbu3Ys9e/bgC1/4AtLpNABgZsY9eC+dTqOtrQ1tbUvbsaS1QTq9Mru9Ym94K2Yeuh/FyZMQ7UnAsgDbhsllIdoSiL3hrTh9Jrci/69moJREKtWOdDoHx9FBD4fAexJWUbwvCoA2pql3cSklkOxMIJvJw2mBGa2lkEIgFlMrPsOTSrXXPeMpjInOV+Bdd92Fu+++u+bHP/zhD+PWW29d0ms7jsbkZHapQ5tjMefwtDrLkujtTeLUqSxsOxrfxJsd70k4RfW+CAEUAGQcB8Um3MWllESqux3pM9EJoqtNCYFeS0Gu8O3v60vWHXgCn+FZjCuvvBKvec1rKq5997vfxQ9+8AN84xvfwODgYEAjm8s6ZxuSW162pJOWiYiaiTFADECPUshIgzx3cVEAIhV4hoeHMTw8XHHtmWeegVIKF198cUCjqk1I6Z6hE/RAiIhCQBggJQQs76BC7uKi1cTpBiIiWjXGAB1CoNtSiEkexUGrJ/KB55ZbbgldHy0iIqrNGMAyQK9SaLcUeAQZrYbIBx4iIooob4mry7KgONtDDcbAQ0REgTEGaIdb0MwlLmokBh4iIgqUMYDyLXEx91AjMPAQEVE4eEtcnVziogZg4CEiotAoL3FZFuJKgrGHVgoDDxERhYoxgNIGPVJyiYtWDAMPERGFkwG6hOQSF60IBh4iIgotYwyXuGhFMPAQEVGocYmLVgIDDxERRYO3xNVlWbCYemiRGHiIiCgyjDFIAOi2FNq4xEWLwMBDRESR4i5xAd1SosNSkGzGRXVg4CEiomgyQKeQSFmKS1x0Vgw8REQUWcYYxA3QYym0WYpLXFQTAw8REUWe1EC3EEhyiYtqYOAhIqLmYIAkl7ioBgYeIiJqGlzioloYeIiIqOn4l7gUl7gIDDxERNSsuMRFPgw8RETUtIwxiHlLXAkucbU0Bh4iImp6UgMpIdDJJa6WxcBDREStwQAdQiBlKcS4xNVyGHiIiKhlGAN3iUsptFsKnOxpHQw8RETUcoTxlrgUl7haBQMPERG1JOMtcXVziaslMPAQEVHLMgawDNDrLXEx9zQvBh4iIqLSEpdlQTH1NCUGHiIiIrizPe1wC5q5xNV8GHiIiIg8xgDKW+Lq4BJXU4lk4Mnn8/jSl76Eyy67DBdccAEuv/xy3H333UEPi4iImoUBurjE1VSsoAewWI7j4I//+I9x9OhR/Omf/imGhoZw+PBhHDlyJOihERFREzEGaBdAzLIw5TgoOhom6EHRkkUu8Dz88MN44YUX8MMf/hD9/f1BD4eIiJqYu8Rl0CMlMkIg7zjQTD2RFLklrYcffhhvf/vbGXaIiGj1GKBLSHRZFjuvR1SkAk+hUMALL7yA9evX47bbbsNFF12EV73qVfjEJz6BU6dOBT08IiJqYsYYJAB0WxbalGTn9YiJ1JLW6dOnYds2vvGNb+Diiy/GPffcg4mJCfzVX/0VPv7xj+Nv//Zvl/X6lhWp/Nc0lJIVjxQ83pNw4n0JBwWgTVmY0gZ5x4HwZnyUEojYPMKqkUJAWRIqwOXAwAPP1NQUjh8/ftbPGxkZgTHu31QqlcKXv/xlxONxAEAymcQtt9yCX//617jwwguXNA4pBXp7k0t6Lq2MVKo96CFQFd6TcOJ9CYdeAFnbwZTjQANIdiaCHlJoCQCpmIW4DC4QBh54HnvsMdx+++1n/bxHH30Uo6OjAIBXvepV5bADAJdccgkA4KWXXlpy4NHaIJ2eXtJzaXmUkkil2pFO5+A4OujhEHhPwor3JZziSsJJWDgzlYPtsKJ5PlIIxGJqxWd4Uqn2umc8Aw88V111Fa666qq6P39oaKjmx+Qyk6Nt8xtIkBxH8x6EDO9JOPG+hIsFYE3MQl5IFLUNbRh65hACjhAI8q8mcouNb3zjG/Hss8+iUCiUr/30pz8FAJx//vlBDYuIiFqYFAJdUiBlKe7iCqnIBZ4/+qM/wvT0ND760Y/iySefxHe+8x38t//233DFFVdg27ZtQQ+PiIhalDFA3AA9lkKbpbiLK2QiF3iGhobwrW99C7lcDrfccgu+8IUv4C1veQvuvPPOoIdGREQEqYFuIZC0FKRg7AmLwGt4luKCCy7AAw88EPQwiIiI5meApJCwLIGM48Dm8cyBi9wMDxERURQYY8pLXAkucQWOgYeIiKiBpAZS3hKX4hJXYBh4iIiIGs1b4uIuruAw8BAREa0CYwxiBujlElcgGHiIiIhWkfB2cXVyiWtVMfAQERGtMmOADuEeVBjjEteqYOAhIiIKgDFAzAA9SqHdUuBkT2Mx8BAREQVIGHcXV5dlQXG2p2EYeIiIiAJmDNAOoFtxiatRGHiIiIhCwBjAMkCvt8TF3LOyGHiIiIjCxFvi6uQS14pi4CEiIgqZ0hJXj2VxiWuFMPAQERGFkDGA0ga9SqGDS1zLxsBDREQUZgbo4hLXsjHwEBERhZx/iSuuJNtSLAEDDxERUQSUlrh6pOQuriVg4CEiIooSA3QJiS7LYuf1RWDgISIiihhjDBIAui2FNi5x1YWBh4iIKILcJS6gm0tcdWHgISIiijIucdWFgYeIiCjiuMR1dgw8RERETcC/xOUeVMjY48fAQ0RE1EwM0CkkUpbiEpcPAw8REVGTMcYgboAeS6HNUlziAgMPERFR05Ia6BYCSS5xMfAQERE1NQMkucTFwENERNTs/EtciRZd4mLgISIiahFSAykh0GkpqBZb4mLgISIiaiUG6BCi5Za4rKAHsFi2beO+++7DI488giNHjmDNmjW4/PLL8ad/+qdIpVJBD4+IiCj0jAFiAHqVwpQ0mLEdmKAH1WCRCzz33HMPvv71r+OWW27BK1/5SuzZswf/43/8D4yNjeGrX/1q0MMjIiKKDGHcXVzTlsK0o+GY5o09kQs83//+9/HOd74TN954IwDgkksuwfT0NL70pS9henoaHR0dAY+QiIgoOoy3xBWzFKYcB0XdnKEncjU8tm2jq6ur4loqlYIxBqaJkykREVGjGANYBuhRCu2WQjPWM0cu8Lzvfe/D//2//xc//elPkc1m8Zvf/Ab33XcfrrzySiSTyaCHR0REFFnCuLu4uiwLqskKmiO3pHXjjTfCtm3ccMMN5Rmdt7zlLbjjjjuW/dqWFbn81xSUkhWPFDzek3DifQmnZrwvXQJoUwJTWqPoLH/1RAoBZUmoABdihAl4HWhqagrHjx8/6+eNjIwgHo/jgQcewF//9V/j5ptvxvbt27Fv3z78zd/8DV73utfhzjvvXPI4jDEQzTiHR0REtES2MUgXbcwYs6xdXALAmpiFuAwuFAYeeB555BHcfvvtZ/28Rx99FOvXr8frX/963HbbbbjuuuvKH3v88cfx0Y9+FI888gi2b9++pHE4jkY6nVvSc2l5lJJIpdqRTufgODro4RB4T8KK9yWcWuG+5IxBdhm7uKQQ6IupFZ/hSaXa655ZC3xJ66qrrsJVV11V1+f++te/RqFQwLZt2yqul/774MGDSw48AGDbzfmFGhWOo3kPQob3JJx4X8Kpme9LXABSSncXl6MXP9sjBBwhEOQUS6QWHAcHBwEAzz//fMX15557DgAwNDS06mMiIiJqdsYAShv0SIl2SyGK9cyBz/AsRn9/P9761rfib/7mb2DbNi644ALs3bsXd911F/7Nv/k3uOCCC4IeIhERUfMyQEpKWEIg6zhwInRmT+A1PIuVyWRw77334rHHHsPRo0fR39+P3/3d38XHPvYx9PX1Lfl1HUdjcjK7giOlelmWRG9vEqdOZZt2OjhqeE/CifclnFrxvggB2ELUvcSlhECvpSBXOHH09SXrruGJXOBpFAae4LTiN4uw4z0JJ96XcGrp+yKAjDHIOw4WmuwJQ+CJVA0PERERhYgBuoREl2WFvvM6Aw8REREtmTEGCQDdlkKbkghr7GHgISIiomVxd3EB3VKiI6S7uBh4iIiIaGUYoFNIpEK4xMXAQ0RERCvGGIO4AXoshTZLhWaJi4GHiIiIVpzUQLcQSFoKMgSzPZE6eJCIiIgixABJIWGJ4E/AYeAhIiKihjHGIA73sMIgYw+XtIiIiKjhgj7mmIGHiIiImh4DDxERETU9Bh4iIiJqegw8RERE1PQYeIiIiKjpMfAQERFR02PgISIioqbHwENERERNj4GHiIiImh4DDxERETU9Bh4iIiJqegw8RERE1PQYeIiIiKjpCWOC7l8aDsYYaM2/iqAoJeE4OuhhkA/vSTjxvoQT70swpBQQQtT1uQw8RERE1PS4pEVERERNj4GHiIiImh4DDxERETU9Bh4iIiJqegw8RERE1PQYeIiIiKjpMfAQERFR02PgISIioqbHwENERERNj4GHiIiImh4DDxERETU9Bh4iIiJqegw8RERE1PSsoAdAVOI4Du677z48+eST2L17NxzHwXnnnYebb74Zl156adDDa2n/8i//gkceeQS/+tWvcOjQIXzgAx/An//5nwc9rJaxb98+fOYzn8Gzzz6L9vZ2/N7v/R5uvfVWJBKJoIfW0g4cOIBvfvOb+NWvfoWXXnoJW7Zswfe///2gh0U1cIaHQiOfz+NrX/sazj//fHz2s5/Fl770JQwMDOD666/Hj3/846CH19J+8pOfYOfOnXj1q1+NVCoV9HBaSjqdxh/+4R8im83iy1/+Mj75yU/ie9/7Hv7sz/4s6KG1vJdeeglPPvkkNm3ahK1btwY9HDoLYYwxQQ+CCHBneDKZDLq7u8vXjDG4+uqrkUwmsWPHjgBH19q01pDS/f3o8ssvxxvf+EbO8KySr3/96/jKV76CJ554An19fQCA733ve7j11lvxgx/8gD9oA+R/X3zqU5/Cc889xxmeEOMMD4WGUqoi7ACAEALnn38+jh8/HtCoCED5mzqtvp/85Ce49NJLy2EHAN761rciHo/jySefDHBkxPdFtPBuUahprfHLX/6Sv8VSy9qzZ8+cr/94PI6NGzdiz549AY2KKHoYeCjUduzYgX379uH6668PeihEgUin0/PWTaVSKZw5cyaAERFFE3dpUUNNTU3VtRw1MjKCeDxece2ZZ57B5z//edxwww149atf3aghtqTl3BcKB2MMhBBBD4MoMhh4qKEee+wx3H777Wf9vEcffRTbtm0r//euXbtw00034YorrsBtt93WyCG2pKXeF1p9qVQK6XR6zvWpqSku9RItAgMPNdRVV12Fq666alHPOXjwID70oQ/h5S9/Of7qr/6Kv8U2wFLuCwVj69atc2p1CoUCDh48iKuvvjqgURFFD2t4KFQmJiZwww03oL+/H1/5yle4nEIt7/Wvfz1+9rOf4dSpU+Vrjz32GAqFAt7whjcEODKiaOEMD4VGPp/Hhz70IZw8eRKf+tSnsHv37oqPv/KVrwxmYITx8XH85je/AQDkcjkcPHgQP/rRjwAAb3vb24IcWtO75ppr8MADD+Cmm27CTTfdhJMnT+Jzn/scfv/3f59LWgHL5XLlowHGx8eRyWTK74vXvOY1FUcJUPB48CCFxtjYGN70pjfV/PiLL764iqMhv0ceeaRmzQ/vS+P5W0skEgm8853vZGuJEFjoe9b999+Piy++eJVHRAth4CEiIqKmxxoeIiIianoMPERERNT0GHiIiIio6THwEBERUdNj4CEiIqKmx8BDRERETY+Bh4iIiJoeT1omoqa1a9cufPvb38bPf/5zTExMwLIsjI6O4h3veAfe8573oKenJ+ghEtEq4cGDRNSUHnroIXz605/G5s2b8Qd/8Ac455xzYNs2nnvuOTz00EM4//zzcc899wQ9TCJaJQw8RNR0fvnLX+IDH/gAXvva187bhLZQKOCpp55asJUJETUXBh4iajo33ngjnnrqKTz++OPYsGFD0MMhohBg0TIRNRXHcfCzn/0M27dvZ9ghojIGHiJqKqdOnUIul8Pw8HDQQyGiEGHgISIioqbHwENETaW3txft7e0YGxsLeihEFCIMPETUVJRSuOSSS/D888/j6NGjQQ+HiEKCgYeIms4f//EfwxiDP/uzP0OhUJjz8WKxiCeeeCKAkRFRULgtnYiaUungwS1btuCaa67BueeeC9u28cILL+Chhx7Cueeey4MHiVoIAw8RNa1du3bhW9/6Vrm1RCwWw+joKC677DJce+216OvrC3qIRLRKGHiIiIio6bGGh4iIiJoeAw8RERE1PQYeIiIianoMPERERNT0GHiIiIio6THwEBERUdNj4CEiIqKmx8BDRERETY+Bh4iIiJoeAw8RERE1PQYeIiIianoMPERERNT0/j+kUyeaI2vj1QAAAABJRU5ErkJggg==", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "with sns.color_palette(\"hls\", 2):\n", " sns.regplot(x=\"C\", y=\"F\", data=df_demo);\n", @@ -2317,7 +6160,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 218, "metadata": {}, "outputs": [], "source": [ @@ -2326,9 +6169,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 219, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x600 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.jointplot(x=x, y=y, kind=\"reg\");" ] @@ -2354,7 +6208,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 220, "metadata": { "exercise": "solution", "slideshow": { @@ -2377,28 +6231,126 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 221, "metadata": { "exercise": "solution", "slideshow": { "slide_type": "subslide" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Runtime Program / s</th>\n", + " <th>Unaccounted Time / s</th>\n", + " <th>Avg. Neuron Build Time / s</th>\n", + " <th>Min. Edge Build Time / s</th>\n", + " <th>Min. Init. Time / s</th>\n", + " <th>Presim. Time / s</th>\n", + " <th>Sim. Time / s</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Threads</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>420.42</td>\n", + " <td>2.09</td>\n", + " <td>0.29</td>\n", + " <td>88.12</td>\n", + " <td>1.14</td>\n", + " <td>17.26</td>\n", + " <td>311.52</td>\n", + " </tr>\n", + " <tr>\n", + " <th>16</th>\n", + " <td>202.15</td>\n", + " <td>2.43</td>\n", + " <td>0.28</td>\n", + " <td>47.98</td>\n", + " <td>0.70</td>\n", + " <td>7.95</td>\n", + " <td>142.81</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Runtime Program / s Unaccounted Time / s \\\n", + "Threads \n", + "8 420.42 2.09 \n", + "16 202.15 2.43 \n", + "\n", + " Avg. Neuron Build Time / s Min. Edge Build Time / s \\\n", + "Threads \n", + "8 0.29 88.12 \n", + "16 0.28 47.98 \n", + "\n", + " Min. Init. Time / s Presim. Time / s Sim. Time / s \n", + "Threads \n", + "8 1.14 17.26 311.52 \n", + "16 0.70 7.95 142.81 " + ] + }, + "execution_count": 221, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df[[\"Runtime Program / s\", \"Unaccounted Time / s\", *cols]].head(2)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 222, "metadata": { "exercise": "solution", "slideshow": { "slide_type": "-" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 1200x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df[[\"Unaccounted Time / s\", *cols]].plot(kind=\"bar\", stacked=True, figsize=(12, 4));" ] @@ -2418,13 +6370,282 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 223, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th>id</th>\n", + " <th>Runtime Program / s</th>\n", + " <th>Scale</th>\n", + " 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Delay</th>\n", + " <th>Unaccounted Time / s</th>\n", + " </tr>\n", + " <tr>\n", + " <th>Nodes</th>\n", + " <th>Tasks/Node</th>\n", + " <th>Threads/Task</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th rowspan=\"3\" valign=\"top\">1</th>\n", + " <th rowspan=\"2\" valign=\"top\">2</th>\n", + " <th>4</th>\n", + " <td>5</td>\n", + " <td>420.42</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.29</td>\n", + " <td>88.12</td>\n", + " <td>88.18</td>\n", + " <td>1.14</td>\n", + " <td>1.20</td>\n", + " <td>17.26</td>\n", + " <td>311.52</td>\n", + " <td>46560664.0</td>\n", + " <td>825499</td>\n", + " <td>7.48</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>2.09</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>5</td>\n", + " <td>202.15</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.28</td>\n", + " <td>47.98</td>\n", + " <td>48.48</td>\n", + " <td>0.70</td>\n", + " <td>1.20</td>\n", + " <td>7.95</td>\n", + " <td>142.81</td>\n", + " <td>47699384.0</td>\n", + " <td>802865</td>\n", + " <td>7.03</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>2.43</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <th>4</th>\n", + " <td>5</td>\n", + " <td>200.84</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.15</td>\n", + " <td>46.03</td>\n", + " <td>46.34</td>\n", + " <td>0.70</td>\n", + " <td>1.01</td>\n", + " <td>7.87</td>\n", + " <td>142.97</td>\n", + " <td>46903088.0</td>\n", + " <td>802865</td>\n", + " <td>7.03</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>3.12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <th>2</th>\n", + " <th>4</th>\n", + " <td>5</td>\n", + " <td>164.16</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.20</td>\n", + " <td>40.03</td>\n", + " <td>41.09</td>\n", + " <td>0.52</td>\n", + " <td>1.58</td>\n", + " <td>6.08</td>\n", + " <td>114.88</td>\n", + " <td>46937216.0</td>\n", + " <td>802865</td>\n", + " <td>7.03</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>2.45</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <th>2</th>\n", + " <th>12</th>\n", + " <td>6</td>\n", + " <td>141.70</td>\n", + " <td>10</td>\n", + " <td>True</td>\n", + " <td>0.30</td>\n", + " <td>32.93</td>\n", + " <td>33.26</td>\n", + " <td>0.62</td>\n", + " <td>0.95</td>\n", + " <td>5.41</td>\n", + " <td>100.16</td>\n", + " <td>50148824.0</td>\n", + " <td>813743</td>\n", + " <td>7.27</td>\n", + " <td>112500</td>\n", + " <td>1265738500</td>\n", + " <td>1.5</td>\n", + " <td>1.5</td>\n", + " <td>2.28</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " id Runtime Program / s Scale Plastic \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 5 420.42 10 True \n", + " 8 5 202.15 10 True \n", + " 4 4 5 200.84 10 True \n", + "2 2 4 5 164.16 10 True \n", + "1 2 12 6 141.70 10 True \n", + "\n", + " Avg. Neuron Build Time / s \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 0.29 \n", + " 8 0.28 \n", + " 4 4 0.15 \n", + "2 2 4 0.20 \n", + "1 2 12 0.30 \n", + "\n", + " Min. Edge Build Time / s \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 88.12 \n", + " 8 47.98 \n", + " 4 4 46.03 \n", + "2 2 4 40.03 \n", + "1 2 12 32.93 \n", + "\n", + " Max. Edge Build Time / s Min. Init. Time / s \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 88.18 1.14 \n", + " 8 48.48 0.70 \n", + " 4 4 46.34 0.70 \n", + "2 2 4 41.09 0.52 \n", + "1 2 12 33.26 0.62 \n", + "\n", + " Max. Init. Time / s Presim. Time / s \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 1.20 17.26 \n", + " 8 1.20 7.95 \n", + " 4 4 1.01 7.87 \n", + "2 2 4 1.58 6.08 \n", + "1 2 12 0.95 5.41 \n", + "\n", + " Sim. Time / s Virt. Memory (Sum) / kB \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 311.52 46560664.0 \n", + " 8 142.81 47699384.0 \n", + " 4 4 142.97 46903088.0 \n", + "2 2 4 114.88 46937216.0 \n", + "1 2 12 100.16 50148824.0 \n", + "\n", + " Local Spike Counter (Sum) Average Rate (Sum) \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 825499 7.48 \n", + " 8 802865 7.03 \n", + " 4 4 802865 7.03 \n", + "2 2 4 802865 7.03 \n", + "1 2 12 813743 7.27 \n", + "\n", + " Number of Neurons Number of Connections \\\n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 112500 1265738500 \n", + " 8 112500 1265738500 \n", + " 4 4 112500 1265738500 \n", + "2 2 4 112500 1265738500 \n", + "1 2 12 112500 1265738500 \n", + "\n", + " Min. Delay Max. Delay Unaccounted Time / s \n", + "Nodes Tasks/Node Threads/Task \n", + "1 2 4 1.5 1.5 2.09 \n", + " 8 1.5 1.5 2.43 \n", + " 4 4 1.5 1.5 3.12 \n", + "2 2 4 1.5 1.5 2.45 \n", + "1 2 12 1.5 1.5 2.28 " + ] + }, + "execution_count": 223, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_multind = df.set_index([\"Nodes\", \"Tasks/Node\", \"Threads/Task\"])\n", "df_multind.head()" @@ -2432,13 +6653,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 224, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x300 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_pivot.plot(figsize=(10,3));" ] @@ -2601,7 +7393,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "exercise": "task", "slideshow": { "slide_type": "slide" @@ -2621,16 +7412,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 231, "metadata": { - "editable": true, "exercise": "solution", "slideshow": { "slide_type": "fragment" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1200x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df.pivot_table(\n", " index=\"Nodes\",\n", @@ -2642,7 +7443,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "exercise": "task", "slideshow": { "slide_type": "subslide" @@ -2663,7 +7463,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "slide" }, @@ -2683,7 +7482,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "subslide" }, @@ -2696,9 +7494,8 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 232, "metadata": { - "editable": true, "slideshow": { "slide_type": "" }, @@ -2711,30 +7508,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 233, "metadata": { - "editable": true, "slideshow": { "slide_type": "fragment" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 ms \u00b1 239 \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": null, + "execution_count": 234, "metadata": { - "editable": true, "slideshow": { "slide_type": "fragment" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'pyarrow'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[234], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpyarrow\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(pyarrow\u001b[38;5;241m.\u001b[39m__version__)\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pyarrow'" + ] + } + ], "source": [ "import pyarrow\n", "print(pyarrow.__version__)" @@ -2769,7 +7584,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "slide" }, @@ -2787,7 +7601,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "exercise": "task", "slideshow": { "slide_type": "slide" @@ -2805,7 +7618,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "exercise": "task", "slideshow": { "slide_type": "fragment" @@ -2830,7 +7642,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "exercise": "solution", "slideshow": { "slide_type": "subslide" @@ -2859,7 +7670,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "exercise": "solution", "slideshow": { "slide_type": "subslide" @@ -2898,7 +7708,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "exercise": "task", "slideshow": { "slide_type": "slide" @@ -2919,7 +7728,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "slide" }, @@ -2974,7 +7782,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "slide" }, @@ -2997,7 +7804,7 @@ ], "metadata": { "kernelspec": { - "display_name": "venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" },