See More

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Visualization in Python with [matplotlib](https://matplotlib.org/) and [pandas](https://pandas.pydata.org/)\n", "----\n", "## Table of Contents\n", "\n", "[1. Intro Slides and Plot Anatomy](#S1) \n", "[2. Getting Started](#S2) \n", "[3. pylab, object oriented, and Pandas approaches](#S3) \n", "[4. Style Basics](#S4) \n", "[5. Style Presets](#S5) \n", "[6. Subplots](#S6) \n", "[7. Legends](#S7) \n", "[8. Pandas: Dates, groupby](#S8) \n", "[9. Annotations, Layers & Mathtext](#S9) \n", "[10. Statistical Visualization in Seaborn](#S10) \n", "[11. Images and 3D plots](#S11) \n", "[12. Palettable](#S12) \n", "[13. Documentation and Resources](#S13) \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 1. Intro & Plot Anatomy\n", "__[A few intro slides ](https://gitpitch.com/jimbopants/pythonvisualization#/)__\n", "\n", "\n", "### Major packages used in this class:\n", "* **Matplotlib**: Main plotting library in python. It’s very powerful/flexible but sometimes requires fiddling to make things look perfect\n", "* **Pandas**: Adds support to python for SQL/spreadsheet like data structures called dataframes. We will use this to speed up some data manipulation. It also has it's own plotting functionality that we will mostly ignore. \n", "* **Seaborn**: This is a wrapper for matplotlib that prettifies the default settings, allows you to quickly specify options/style and adds a few new plot types/statistical overlays for data. \n", "\n", "### Figure Anatomy\n", "* A **Figure** is a high-level container where axes are drawn. It can contain multiple multiple Axes, a subtitle (which is a centered title for the figure), a legend, a color bar, etc.\n", "* An **Axes** is the area on which we plot our data and any labels/ticks associated with it. Each Axes has an X-Axis and a Y-Axis\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

\n", " \"Drawing\" \n", " \"Drawing\" \n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 2. Getting Started\n", "\n", "* Import libraries \n", "* Tell the notebook to show matplotlib plots inline. \n", "* Use `%matplotlib notebook` instead for interactive plots; note that with interactive plots, you'll need to use `plt.close()` to close them afterward." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": false }, "outputs": [], "source": [ "# Array and dataframe manipulation\n", "import pandas as pd\n", "import numpy as np\n", "\n", "# Plotting interfaces, usuaully just import the pyplot module\n", "import matplotlib.pyplot as plt\n", "\n", "import datetime as dt\n", "import matplotlib.dates as dates\n", "\n", "# Seaborn\n", "import seaborn as sns\n", "\n", "# Options for jupyter notebooks\n", "%matplotlib inline\n", "\n", "# Usually we don't need the next line, but we'll set a standard size for all our plots. \n", "# Make sure to run this line after the %matplotlib inline line\n", "import matplotlib \n", "matplotlib.rcParams['figure.figsize'] = (8, 5)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Seaborn used to not be part of the Anaconda distribution, so if the above cell errors you'll need to install it. Copy the command below into a new cell and run it (only the first time you need to use it):\n", "\n", "```\n", "!pip3 install seaborn\n", "```\n", "\n", "Note that if you're running multiple versions of Python on your system, you might need to install the package from the command line instead into the correct environment. On mac/linux: Via the terminal, if you're using Anaconda, use `conda install seaborn`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "### 2b. Loading Example Data\n", "Read in [earthquakes data](http://www.ldeo.columbia.edu/~felixw/NCAeqDD/). It's on the large side, so it will take a minute to load.\n", "\n", "Note that there's a bunch of extra info at the start of the file, so we'll need to skip that. Also, some columns are combined under a single header, so we'll manually specify the header." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "From the file:\n", "\n", "FORMAT DESCRIPTION:\n", "DATE......... Year, Month, Day\n", "TIME......... Hour, Minute, Second\n", "LAT/LON...... Location\n", "DEP.......... Depth [km]\n", "EH1/EH2/AZ... Horizontal major/minor axis [km] and azimuth of the 95% \n", " relative location error ellipses. (-1 if not available).\n", "EV........... Vertical relative location error [km] at the 95%\n", " confidence level. (-1 if not available).\n", "MAG.......... Magnitude, ML, as listed in the NCSN catalog. \n", "ID........... CUSP event ID." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YEARMONTHDAYHOURMINUTESECONDLATLONDEPEH1EH2AZEVMAGID
019841111911.31736.08781-120.2286910.8970.0200.01096.00.0221.81109386
11984111582.42036.87608-120.906692.6610.0710.01855.00.4210.0346
219841115927.12436.87000-120.908891.5920.1100.01666.00.7271.51109389
31984112284.24037.51546-118.754857.7030.0200.00837.00.0241.21109391
41984113858.04440.57227-124.5593320.4071.0090.083103.00.0802.01109392
519841131536.69037.56065-118.8444910.4040.0330.01638.00.0391.11109393
619841144638.70838.80774-122.847611.8240.0090.005175.00.0111.11109395
71984115153.59436.24686-120.393856.8360.0590.016106.00.0401.4357
81984117831.98636.49920-121.0785513.0200.0420.020100.00.1021.41109397
919841171721.37337.54467-118.866787.2450.0420.01574.00.0430.7363
\n", "
" ], "text/plain": [ " YEAR MONTH DAY HOUR MINUTE SECOND LAT LON DEP EH1 \\\n", "0 1984 1 1 1 19 11.317 36.08781 -120.22869 10.897 0.020 \n", "1 1984 1 1 1 58 2.420 36.87608 -120.90669 2.661 0.071 \n", "2 1984 1 1 1 59 27.124 36.87000 -120.90889 1.592 0.110 \n", "3 1984 1 1 2 28 4.240 37.51546 -118.75485 7.703 0.020 \n", "4 1984 1 1 3 8 58.044 40.57227 -124.55933 20.407 1.009 \n", "5 1984 1 1 3 15 36.690 37.56065 -118.84449 10.404 0.033 \n", "6 1984 1 1 4 46 38.708 38.80774 -122.84761 1.824 0.009 \n", "7 1984 1 1 5 1 53.594 36.24686 -120.39385 6.836 0.059 \n", "8 1984 1 1 7 8 31.986 36.49920 -121.07855 13.020 0.042 \n", "9 1984 1 1 7 17 21.373 37.54467 -118.86678 7.245 0.042 \n", "\n", " EH2 AZ EV MAG ID \n", "0 0.010 96.0 0.022 1.8 1109386 \n", "1 0.018 55.0 0.421 0.0 346 \n", "2 0.016 66.0 0.727 1.5 1109389 \n", "3 0.008 37.0 0.024 1.2 1109391 \n", "4 0.083 103.0 0.080 2.0 1109392 \n", "5 0.016 38.0 0.039 1.1 1109393 \n", "6 0.005 175.0 0.011 1.1 1109395 \n", "7 0.016 106.0 0.040 1.4 357 \n", "8 0.020 100.0 0.102 1.4 1109397 \n", "9 0.015 74.0 0.043 0.7 363 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# if you are on google colab replace \"./datasets/NCAeqDD.v201112.1\" with \"url\"\n", "\n", "url = 'http://www.ldeo.columbia.edu/~felixw/NCAeqDD/NCAeqDD.v201112.1.gz'\n", "df = pd.read_csv(\"./datasets/NCAeqDD.v201112.1\", \n", " delim_whitespace=True, skiprows=79, na_values=[-1],\n", " names=[\"YEAR\", \"MONTH\", \"DAY\", \"HOUR\", \"MINUTE\", \"SECOND\",\n", " \"LAT\", \"LON\", \"DEP\", \"EH1\", \"EH2\", \"AZ\", \"EV\", \"MAG\", \"ID\"])\n", "df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 3. Interfaces: Pylab vs. Pyplot vs. Pandas.plot\n", "> There should be one-- and preferably only one --obvious way to do it. \n", "> Zen of Python\n", "\n", "* Matplotlib (MPL) started as a port of MATLAB's state-based plotting interface (Pylab) that was then completely duplicated using a pythonic \"object oriented\" API (Pyplot).\n", "* Pyplot makes it easy to work with subplots and making more than 1 plot at a time.\n", "* Pandas replicates Matplotlib functions and adds support for labeled datasets and statistics. \n", "\n", "---\n", "The next 3 cells demonstrate 3 different methods for creating a scatter plot of the magnitude vs depth for the first 50 earthquake observations with the data we just loaded. \n", "\n", "Throughout the class, we're going to use the matplotlib and pandas methods see the differences and similarities. The method (pandas or matplotlib) is marked for the cells so you can keep track of the different methods.\n", "\n", "We will mostly use matplotlib.pyplot (the 2nd method shown and the object oriented approach) directly. `plt` is the alias for `matplotlib.pyplot` that we imported above. See https://matplotlib.org/api/pyplot_api.html. `plot` will make a line plot. We use the `scatter` function for a scatter plot." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.1 Pylab / Functional Interface: (Forget this Immediately)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(df.loc[:50,'MAG'], df.loc[:50,'DEP'])\n", "plt.xlabel('Magnitude');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2 Preferred pyplot/object oriented approach:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots() # Create a named figure and axis instance\n", "ax.scatter(df.loc[:50,'MAG'], df.loc[:50,'DEP']) # Pylab functions are implemented as axis.method() \n", "ax.set_xlabel('Magnitude');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.3 Pandas pd.DataFrame.plot approach. \n", "Note that axis names are automatically added from dataframe columns." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAFp5JREFUeJzt3X+MXFd5xvHnnfVk12XTZFk7qbHjOtQglYBt0m1I5YqmpKkCrRxaJyWRgFBRub9ogVLZlFbQ5h+IIaCWVkWGRKSIBqIs1C5KWmhCG0FLYI3sTdwADRDIOhZ2FifxhvVmd+ftH3PXnl3PzJ3dmfvzfD+S5Zk71553rz3z3HPuOeeauwsAEK5K1gUAALJFEABA4AgCAAgcQQAAgSMIACBwBAEABI4gAIDAEQQAEDiCAAACtyrrAjqxZs0a37RpU9ZlAEChHDx48Cl3Xxu3XyGCYNOmTRobG8u6DAAoFDP7QSf70TUEAIEjCAAgcAQBAASOIACAwBEEABA4ggBAT01OzejwE09rcmom61LQoUIMHwVQDPsPHdWe0XFVKxXN1mrau3OLdmxbn3VZiEGLAEBPTE7NaM/ouE7P1nRqZk6nZ2vaPTpOy6AACAIAPTFxclrVyuKvlGqloomT0xlVhE4RBAB6YsPQas3Waou2zdZq2jC0OqOK0CmCAEBPDA/2a+/OLRqoVnR+/yoNVCvau3OLhgf7sy4NMbhYDKBndmxbr+2b12ji5LQ2DK0mBAqCIADQU8OD/QRAwdA1BACBIwgAIHAEAQAEjiAAgMARBAAQOIIAAAJHEABA4AgCAAgcQQAAgSMIACBwBAEABI4gAIDAEQQAEDiCAEgRN3ZHHrEMNZASbuyOvKJFAKSAG7sjzxILAjO7xMy+bGaPmtkRM3t7tP2FZvYlM/u/6PehpGoA8oIbuyPPkmwRzEl6l7v/vKQrJf2xmb1M0rsl3e/uL5F0f/QcKDVu7I48SywI3P2Yu38zenxK0qOS1ku6TtKd0W53Snp9UjUAecGN3ZFnqVwsNrNNkl4p6SFJF7v7MakeFmZ2UYs/s0vSLknauHFjGmUCieLG7sirxIPAzAYljUp6h7s/a2Yd/Tl33ydpnySNjIx4chUC6eHG7sijREcNmVlV9RD4tLt/Ltr8IzNbF72+TtLxJGsAALSX5Kghk3S7pEfd/cMNLx2QdHP0+GZJ+5OqAQAQL8muoe2S3iTpYTM7FG17j6QPSLrbzN4q6YeSbkiwBgBAjMSCwN2/IqnVBYGrk3pfAMDyMLMYAAJHEABA4AgCAAgcQQAAgSMIACBwBAEABI4gAIDAEQQAEDiCAAACRxAAQOAIAgAIHEEAAIEjCAAgcAQBAASOIACAwBEEABA4ggAAAkcQoBAmp2Z0+ImnNTk1k3UpQOkkec9ioCf2HzqqPaPjqlYqmq3VtHfnFu3Ytj7rsoDSoEWAXJucmtGe0XGdnq3p1MycTs/WtHt0nJYB0EMEAXJt4uS0qpXF/02rlYomTk5nVBFQPgQBcm3D0GrN1mqLts3WatowtDqjioDyIQiQa8OD/dq7c4sGqhWd379KA9WK9u7couHB/qxLA0qDi8XIvR3b1mv75jWaODmtDUOrCQGgxwgCFMLwYD8BkJLJqRlCNzAEAVLBl0sxMFQ3TAQBEseXSzE0DtU9rfoF+t2j49q+eQ3hXXJcLEaimAdQHAzVDRdBgETx5VIcDNUNF0GARPHlUhwM1Q0X1wiQqIUvl91LrhHw5ZJPDNUNE0GAxPHlUiwM1Q0PQYBU8OUC5BfXCAAgcAQBAAQusSAwszvM7LiZPdKw7a/N7KiZHYp+vS6p9wcAdCbJFsEnJV3bZPtH3H1b9OveBN8fANCBxILA3R+U9OOk/n4AQG9kcY3gbWY2HnUdDWXw/gCABmkHwT9K+jlJ2yQdk3Rbqx3NbJeZjZnZ2IkTJ9KqDwCCk2oQuPuP3H3e3WuSPi7pijb77nP3EXcfWbt2bXpFAkBgUg0CM1vX8PS3JD3Sal8AQDoSm1lsZndJukrSGjObkPQ+SVeZ2TZJLulxSb+f1PsDADqTWBC4+01NNt+e1PsBAFaGmcUAEDiCAAACRxAAQOAIAgAIHEEAAIEjCAAgcAQBAASOIACAwBEEABA4ggAAAkcQoBAmp2Z0+ImnNTk1k3UpQOkkttYQ0Cv7Dx3VntFxVSsVzdZq2rtzi3ZsW591WUBp0CJArk1OzWjP6LhOz9Z0amZOp2dr2j06TssA6CGCALk2cXJa1cri/6bVSkUTJ6czqggoH4IAubZhaLVma7VF22ZrNW0YWp1RRUD5EATIteHBfu3duUUD1YrO71+lgWpFe3du0fBgf9alAaXBxWLk3o5t67V98xpNnJzWhqHVhADQY22DwMwGJP2BpM2SHpZ0u7vPpVEY0Gh4sJ8AABIS1zV0p6QR1UPgtZJuS7wiAECq4rqGXubur5AkM7td0teTLwkAkKa4FsHswgO6hACgnOJaBFvN7NnosUlaHT03Se7uP51odQCAxLUNAnfvS6sQAGGYnJphBFjOxI0aeo27PxA9vtTdv9/w2m+7++eSLhBAebBuVD7FXSP4UMPj0SWv/VWPawFQYqwblV9xQWAtHjd7DgAtsW5UfsUFgbd43Ow5UGjc8yBZrBuVX3Gjhl5sZgdUP/tfeKzo+aWJVgakiL7r5C2sG7V7yXHmgnH2zL31ib2Z/Uq7P+zu/9XzipoYGRnxsbGxNN4KAZqcmtH2Wx/Q6dmzZ6sD1Yq+uuc1fEklgFFD6TGzg+4+Erdf3PDRM1/0ZrY22nai+/KA/Fjouz6ts0Gw0Hfd+EXFF1hvsG5U/sQNHzVJ75X0J6p3B1XMbE7SR939lhTqAxLXSd81XUcos7iLxe+Q9MuSftHdh919SNKrJG03s3cmXh2Qgrh7HjDsEWUXd7H4zZKucfenFja4+/fM7I2SvijpI0kWB6Sl3T0POu06AooqLgiqjSGwwN1PmFk1oZpQQGXoP2/Vd82wR5RdXBA8v8LXEJCy958z7BFlt5zVRxuZpIEE6um5Mpyp5llj//lC18nu0XFt37ymVMeb22WizBJbfdTM7pD0m5KOu/vLo20vlPRZSZskPS7pd9z95ErfI07Zz1TzIKT+c4Y9oqziRg1145OSrl2y7d2S7nf3l0i6P3qeCEZ6pIP+c6D4EgsCd39Q0o+XbL5O9fsgK/r99Um9PwtcpSNu6CWA/Iu7RtBrF7v7MUly92NmdlFSb8SZanroPweKLcmuoa6Y2S4zGzOzsRMnlr+qBWeq6Roe7NfWSy7k+AIFlHaL4Edmti5qDayTdLzVju6+T9I+qb7o3ErejDNVAIiXdovggKSbo8c3S9qf9BtypooF3G8AaC6xFoGZ3SXpKklrzGxC0vskfUDS3Wb2Vkk/lHRDUu8PNGIoMdBaYkHg7je1eOnqpN4TaCaUSW/ASuX2YjHQKwwlBtojCNATee5/Zygx0F7ao4ZQQnnvf2fROKA9ggBdKUr/O0OJgdYIAnRl4uS0vLZ4mofXPJeLzrFoHNAc1whKIK5/Psn++xec16eZ+cVBMDPvesF5K164FkDKaBEUXFz/fNL99889P6+BakWnZ89ejB2oVvTc8/PL+nu4bwSwWJqfCYKgwOL659Pov2818mY5I3LyfrEZSFvanwm6hgosbnx8GuPnu13cj/tGAItl8ZmgRVBgcePj0xo/382InJDucAZ0IovPBC2CAos7G09zKe6VLu7HZC9gsSw+E+a+ohWeUzUyMuJjY2NZl5FbcReV8n4h9sCho+dM9kqiPzTvxwFY0KvPhJkddPeR2P0IAuRB0l/SXJBG0fTiM9FpEHCNAKmI+0+d5GSvosx+BhqlOQGSIEBHujk76eRsPMkWARekgfYIAsTqplulk7PxpLttuCANtMeoIbTV7ZjmuLkMaYyZTnP0FFBEtAhKIM/dKnFn42l127D6KNAaQVBwaXSrTM/OLdo2PTvXcbdK3L0A0uy2YfVRoDmCoMDSGg1jZpJ8yfPOtTsb56YxQPYIggJLo1tl4uS0Blb1aXb+bKtgYFXfst+j3dk43TZAtgiCAuu226bT90ij6yau24ZZwUByCIKC67bbJk4eum6YFQwkiyAosF5128TJsuuGWcFohhZibxEEBRbCiBtmBWMpWoi9x4SyAgthohSzgtGIGxklgxZBwZV9xE0erlEgP2ghJoMgKIFuu23y3t9a9rBD52ghJoMgCFxR+luZFQyJFmJSCIKAMSIHRUQLsfcIgoDR34qiooXYW4waChj9rQAkgiBoIQw/BRCPrqHA0d8KgCBAT/pb8z4EFUBrBAG6/hIvyhBUAM1lEgRm9rikU5LmJc25+0gWdaD7L/FOh6DSYgDyK8sWwa+6+1MZvn/wejGPoJMhqJ2EDUEBZIeuoYD1Yh5B3BDUTsKGriUgW1kNH3VJXzSzg2a2K6MaCmNyakaHn3i65yss9mIeQdwQ1IWwabQQNhKrSQJ5kFWLYLu7P2lmF0n6kpl9y90fbNwhCohdkrRx48YsasyFJM+We7VuS7shqHFhU6bZzXRvoagyCQJ3fzL6/biZfV7SFZIeXLLPPkn7JGlkZMTP+UsCkMZaQL2aR9BqCGpc2JRldjPdWyiy1IPAzF4gqeLup6LHvy7plrTrKIK0zpaTXrelXdiUYTVJFu9D0WXRIrhY0uejm6yvkvTP7v5vGdSRe2U5W5bah03RZzeXqXsLYUo9CNz9e5K2pv2+RVSGs+UFcf3nRV5NMs3ALsN1iDL8DGXD8NGcK/rZslTvP999z7j6Kqb5muuD15er/zytwC7DdYgy/AxlZO75vw47MjLiY2NjWZeRW3k+w5qcmtGV779fs/Nn/59V+0xf+4urc1drt5L8d5icmtH2Wx/Q6dmzLY+BakVf3fOawhzHMvwMRWNmBztZuYEWQcHl/QzryJPPLAoBSZqddx158hm9+qUXZVRVMpLs3irDdYgy/Axlxf0ICqwYk7FsmdvzK6mJfZ0ow8CBMvwMZUUQFFjcrN08eNEFA8vanlf7Dx3V9lsf0Bs/8ZC23/qADhw6mur7l+EmQmX4GcqKrqECK8IZ1nPPz2ugWjmnX/i55+czrGp58jJPoAwDB8rwM5QRLYICK8IZVqtQylNYxclTy2t4sF9bL7kwV//Gy1WGn6FsaBEUXN7PsMowF6IILS+gGwRBCeR9MlbewypOGcIMaIcgQCryHlZxih5mQDsEAdChoocZ0AoXiwFgiSznjGSBFgEANMj7bP0klL5FEFqyA1i5YszW771StwhCTHYAKxfqekilbRGEmuwAVi7UOSOlDYI8zQZFPtBNiDhFmK2fhNJ2DYWa7GiObkJ0KsQ5I6VtEYSa7DgX3YRYrtDWQypti0AKM9lxrlAvAAKdKnUQSMwGzYssb6dJNyHQXmm7hpCudhdiuakLkG+lbxEgefsPHdXuew6rzyqa95o+eP3WMxdiuakLkH+0CNCVyakZvevuQ5qZc/1kdl4zc64/u/vQmZZBmsN444aHhnYBEOgULQJ05ciTz2pucfe75mr17a9+6drU+ucZHgqsHC0CdMnbbk+jf57hoUB3aBGgK5e96AJV+0yz82cDodpnuuxFF5x5nnT/PMNDge7QIkBXhgf7ddsNW9W/qqKfOq9P/asquu2Gral+ATM8FOgOLQJ0Le6MP+n+e+4pDHSHIEBPtJq4l9bwUYaHAitHECBRafbfM4scWBmuESBR9N8jj1iSfDFaBEgU/ffIG+acnIsgQOLov0de5GXJk7whCJAK+u+RB8w5aY5rBACCwTWr5jIJAjO71sy+bWaPmdm7s6gBQHhYkry51LuGzKxP0j9IukbShKRvmNkBd//ftGsBEB6uWZ0ri2sEV0h6zN2/J0lm9hlJ10kiCACkgmtWi2XRNbRe0hMNzyeibQCADGQRBNZk2zlrGZvZLjMbM7OxEydOpFAWAIQpiyCYkHRJw/MNkp5cupO773P3EXcfWbt2bWrFAUBosgiCb0h6iZldambnSbpR0oEM6gAAKIOLxe4+Z2Zvk/Tvkvok3eHuR9KuAwBQZ+6tbjWYH2Z2QtIPuvgr1kh6qkflJIUae4Mae4MaeyPrGn/W3WP71gsRBN0yszF3H8m6jnaosTeosTeosTeKUKPEEhMAEDyCAAACF0oQ7Mu6gA5QY29QY29QY28UocYwrhEAAFoLpUUAAGihNEEQt7S1mfWb2Wej1x8ys005rPEtZnbCzA5Fv34vgxrvMLPjZvZIi9fNzP4u+hnGzezyHNZ4lZk903Ac35tyfZeY2ZfN7FEzO2Jmb2+yT6bHscMasz6OA2b2dTM7HNX4N032yfRz3WGNmX+uY7l74X+pPjHtu5JeLOk8SYclvWzJPn8k6WPR4xslfTaHNb5F0t9nfCxfLelySY+0eP11ku5Tfc2oKyU9lMMar5L0hQyP4TpJl0ePz5f0nSb/1pkexw5rzPo4mqTB6HFV0kOSrlyyT9af605qzPxzHferLC2CM0tbu/vzkhaWtm50naQ7o8f3SLrazJotgJdljZlz9wcl/bjNLtdJ+iev+5qkC81sXTrV1XVQY6bc/Zi7fzN6fErSozp3hd1Mj2OHNWYqOjZT0dNq9GvpRc1MP9cd1ph7ZQmCTpa2PrOPu89JekbScCrVLXn/SKvlt3dGXQX3mNklTV7PWlGWEf+lqLl+n5ldllURUVfFK1U/U2yUm+PYpkYp4+NoZn1mdkjScUlfcveWxzGjz3UnNUo5/1yXJQg6Wdq6o+WvE9TJ+/+rpE3uvkXSf+jsmU6eZH0cO/FN1afWb5X0UUn/kkURZjYoaVTSO9z92aUvN/kjqR/HmBozP47uPu/u21RfpfgKM3v5kl0yP44d1Jj7z3VZgqCTpa3P7GNmqyRdoHS7F2JrdPdJd5+Jnn5c0i+kVNtydLSMeJbc/dmF5rq73yupamZr0qzBzKqqf8F+2t0/12SXzI9jXI15OI4NtTwt6T8lXbvkpaw/12e0qrEIn+uyBEEnS1sfkHRz9Ph6SQ94dCUnLzUu6SPeoXq/bd4ckPTmaNTLlZKecfdjWRfVyMx+ZqGf2MyuUP3/+WSK72+Sbpf0qLt/uMVumR7HTmrMwXFca2YXRo9XS/o1Sd9aslumn+tOaizC5zqLexb3nLdY2trMbpE05u4HVP9P/ykze0z1M4Ybc1jjn5rZDklzUY1vSbNGSTKzu1QfLbLGzCYkvU/1C2By949Julf1ES+PSfqJpN/NYY3XS/pDM5uTNC3pxpRDf7ukN0l6OOo7lqT3SNrYUGPWx7GTGrM+jusk3WlmfaqH0N3u/oU8fa47rDHzz3UcZhYDQODK0jUEAFghggAAAkcQAEDgCAIACBxBAACBIwiAJszMzexTDc9XRStIfmHJfvvN7H+a/Pk3RksKHImWaPjEwnhzIG8IAqC55yS9PJokJEnXSDrauEP0xX656gvGXdqw/VpJ75T0Wne/LNrnvyVdnEbhwHIRBEBr90n6jejxTZLuWvL6TtXXkfmMFk9k+ktJf+7uR6Uza9Hc4e7fTrheYEUIAqC1z0i60cwGJG3RuatzLoTDXdHjBZepvmAbUAgEAdCCu49L2qT6l/y9ja+Z2cWSNkv6irt/R9Jck1UnZWaviO5K9V0ze0MKZQPLRhAA7R2Q9CGd2y30BklDkr5vZo+rHhgL3UNHVL8uIHd/OFqi+D5JqwXkEEEAtHeHpFvc/eEl22+SdK27b3L3TaovLbwQBO+X9CEz29CwPyGA3CrF6qNAUtx9QtLfNm6L7ui1UdLXGvb7vpk9a2avcvd7zWytpPuiVSmflvSI6ivPArnD6qMAEDi6hgAgcAQBAASOIACAwBEEABA4ggAAAkcQAEDgCAIACBxBAACB+3+I4nwyzjp4VwAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df[:50].plot(x='MAG', y='DEP', kind='scatter') # df.plot has a keyword called kind that controls plot type. (bar, line, etc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise On Plotting:\n", "\n", "Plot which magnitudes occurred during which hour of the day using an object orientated approach and DataFrame approach.\n", "(Remember to only look at the first 50 data points)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.4 Interface Notes\n", "* All 3 plots are similar. \n", "\n", "* In the pyplot example (3.2), we used `plt.subplots()` to return an axis and figure instance. `plt.subplots()` is a convenience function that returns both figure and axes, instead of doing:\n", "```\n", "fig = plt.figure(figsize=(10, 3.5))\n", "ax = plt.subplot(1,1,1) # or plt.subplot(111) is equivalent\n", "```\n", "* Remember, when use the pyplot interface, we get to specify an axes to plot on or change properties on using the method calls on that axis i.e (`ax.plot`, `ax.scatter`, etc.) This is really useful for making multiple plots and for incrementally adding to an existing plot. \n", "\n", "* For pandas:\n", " * Line plot is the default (see https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html), but we can change that with the [`kind`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html) parameter. \n", "\n", "* Also note that we can omit the `Text(0.5,0,'Magnitude')` type output by putting a ; at the end of the plotting line. (Similar to MATLAB)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ---\n", "## 4. Style Options\n", "In the next 2 examples, we will go over data aesthetics and axis options. The axis options are the same for any plot type but each plotting function has slightly different data aesthetic options. Luckily the [matptlotlib documentation](https://matplotlib.org/api/pyplot_summary.html#the-object-oriented-api) shows all the arguments for a given plot type. \n", "\n", "It can be annoying to figure out how to get everything perfect when you're learning matplotlib. Remember:\n", "* If you don't know how to do something, you don't have to read the documentation! All you need is a rough idea of the plot anatomy you want to manipulate and google like so: `site:stackoverflow.com matplotlib \"thing I can't do\"` or simply `matplotlib \"thing I can't do\"`.\n", "* If you really want a label/arrow/tick mark RIGHT THERE but you have no idea how to do that in matplotlib, save the plot and add that feature in gimp/illustrator/photoshop/powerpoint instead of spending hours doing it in python.\n", "\n", "### In matplotlib we can style the plot data as well as the visible parts of the axis:\n", "#### Data aesthetics:\n", "* color\n", "* size\n", "* scatter marker\n", "* edge color\n", "* And more for different plot types!\n", "\n", "#### Axis options: \n", "* labels\n", "* limits\n", "* ticks\n", "* ticklabels\n", "* grids\n", "* spines\n", "\n", "\n", " \"Drawing\" \n", "\n", "\n", "We're aiming to make something like this plot:\n", "![colorbar](Images/colorbar.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1 Data Aesthetics:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Data aesthetics are passed to plotting functions for each dataset as either a constant or variable with the same length\n", "f,ax = plt.subplots(figsize=(10, 3.5))\n", "ax.scatter(range(0,100,2), y=[0]*50, s=range(0,200,4), label='Array-like of same length as data');\n", "ax.scatter(range(0,100,2), y=[.5]*50, s=90, label='Constant aesthetic');\n", "ax.set_ylim([-1,1])\n", "\n", "# Let's also introduce legend, a function that will put a box with line/markers matching all the calls to plot with \n", "ax.legend()\n", "\n", "ax.set_title('Data aesthetics can either be constant or an iterable the same length (and order) as data');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Marker sizes don't have to be a constant. They can be a function of other variables. \n", "We could, for instance, make each marker size a function of the magnitude (not a great idea from a visualization perspective...but we'll do a better example next) \n", "We can also change the [plot marker](https://matplotlib.org/api/markers_api.html). We can add the `marker` option to either pandas' `DataFrame.plot` or matplotlib's `scatter`. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'DEP')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Matplotlib\n", "plt.scatter(df['MAG'][:50], df['DEP'][:50], marker=\"v\", \n", " s=[10+100*df['MAG'][:50]]);\n", "plt.xlabel('MAG')\n", "plt.ylabel('DEP')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "### 4.2 A simple plot with constant and variable data aesthetics\n", "\n", "[plot.scatter documentation](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.scatter.html)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": false }, "outputs": [], "source": [ "# Subset only large eathquakes:\n", "bigones = df[df['MAG'] >= 5]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Matplotlib\n", "f, ax = plt.subplots()\n", "\n", "pts = ax.scatter(bigones['MAG'], bigones['DEP'], # data\n", " # s = marker size\n", " s=50, \n", " \n", " # Marker = [s, ., o, *, v, ...]\n", " marker='s', \n", " \n", " # Adding an edge around the line and a bit of transparency can make it easier to distinguish overlapping points\n", " edgecolors = 'k', lw = 1, alpha = .75,\n", " \n", " # c = Color\n", " c=bigones['YEAR'], # Here we use the year column to color the points\n", " cmap=\"winter\") # color scheme: https://matplotlib.org/users/colormaps.html\n", "\n", "# Add colormap \n", "clrbar = plt.colorbar(pts); # Here we pass the scatter variable as the keyword argument\n", "#print(type(clrbar.ax)) # By default, calling plt.colorbar on a collection of paths makes a new axis we can access that has the same methods as any other axis object. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise On Data Aesthetics:\n", "1. Color by a different variable instead: `EV`, which is an error estimate on depth (I think). Copy code from above, and make the changes you need. Also, go to https://matplotlib.org/users/colormaps.html and find your favorite colormap and apply it to the data.\n", "2. In the plot above: Change the marker size to reflect the depth of the earthquake. For aesthetic reasons, make sure the points are large enough. \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "### 4.3 Demonstrating Axis Options in Matplotlib\n", "\n", "Matplotlib has options for all of the plot anatomy features we described before. Let's replot the last example while demonstrating what some of those are.\n", "\n", "* Keep in mind this is an example of what's possible, not a style guide. Please do not make yellow titles.\n", "* For every `ax.set_xVAR()` there's an `ax.set_yVAR()` as well\n", "* Note that to edit the colorbar title, we call a method on the `clrbar` axis we saved. \n", "* Calling the ax.method() only changes that property on the current axis. We'll talk about `seaborn.set_style` and rcParams next.\n", "* Many matplotlib functions have an optional fontdict argument. I demonstrate one use of it below but a full description is available [here](https://matplotlib.org/users/text_props.html)\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots()\n", "colors = sns.color_palette('Set1') # A set of colors that look good together\n", "\n", "pts = ax.scatter(bigones['MAG'], bigones['DEP'], # Python will ignore line breaks after a comma so I like to group my scatter, line, and color options on different lines for readability.\n", " s=50, marker='s', alpha = .75,\n", " edgecolors = 'k', lw = 1, \n", " c=bigones['YEAR'], cmap=\"winter\")\n", "\n", "# Add colormap \n", "clrbar = plt.colorbar(pts); # Here we pass the scatter variable as the keyword argument\n", "\n", "\n", "#######\n", "# Axis options: \n", "\n", "# Labels with some basic text options:\n", "ax.set_xlabel('Magnitude') # Default text settings are inferred from rcParams\n", "ax.set_ylabel('Depth', labelpad=20) # Adds space between Label and axis\n", "ax.set_title('Major Northern California Earthquakes', \n", " fontsize=13, color=colors[5], # Change size and color, 'colors[]' accesses different colors\n", " fontdict={'family':'fantasy'}) # Dictionary of font-properties like {'param':'val'} \n", "\n", "# Setting options on the colorbar (Note the colorbar object is NOT an axis, but it has a .ax attribute that is.)\n", "clrbar.ax.set_title('Year as a... color?')\n", "clrbar.ax.set_ylabel('Years going up')\n", "\n", "\n", "# Limits:\n", "ax.set_xlim([4.5,8]) # Overrides defaults\n", "\n", "\n", "# Spines: Removing top and right spines\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n", "\n", "\n", "# Grid\n", "ax.grid(True)\n", "\n", "\n", "## Ticks: (Changing frequency and location can either be done with intervals or lists.\n", "#1. List:\n", "exact_y_ticks = [0, 10, 20,30]\n", "ax.yaxis.set_ticks(exact_y_ticks)\n", "\n", "# 2. Iterator\n", "loc = matplotlib.ticker.MultipleLocator(base=10) \n", "ax.yaxis.set_major_locator(loc)\n", "\n", "# 3. Changing the tick labels to strings (Useful for categorical variables)\n", "ax.xaxis.set_ticks([5,6,7,8])\n", "ax.set_xticklabels(['five', 'six', 'Seven', '8'], rotation=45);\n", "\n", "# Hide left tickmarks but keep labels:\n", "#ax.tick_params(left=False);\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise Set 2. \n", "Use axis and data options to change the above plot to have the same theme as the plot at the beginning of section 4 also shown below:\n", "\n", "![colorbar](Images/colorbar.png)\n", "\n", "Hint: The colormap is called 'cool'\n", "\n", "\n", "### Exercise 2B.\n", "Use google to change the grid frequency and add minorticklocators in the above plot.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 5. Styles and Context Presets in Seaborn & Matplotlib\n", "\n", "* Manually setting style options on a per axis basis is super annoying. There are several ways around this.\n", "* Seaborn has a number of pre-built styles and contexts that can save a lot of time.\n", "* **Context** controls readability by changing text and plot element size \n", "* `sns.set(context)` changes context between: **\"notebook\", “paper”, “talk”, and “poster”**\n", "* **Style** controls background color, appearance of grids, ticks and spines\n", "* `sns.set_style()` switches the axis style more quickly then manually setting evreything. Changes will apply to all plots made afterwards, although options can still be specified. Options are: **\"whitegrid\", \"darkgrid\", \"white\", \"dark\", \"ticks\"**\n", "* You can change the default style of matplotlib plots. A list of available styles:\n", "\n", "Matplotlib has it's own styles as well based on things like *ggplot* and *538* graph settings. You can see examples at the end of this section.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.1 Seaborn Contexts" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot Function\n", "def sinplot(flip=1):\n", " x = np.linspace(0, 14, 100)\n", " for i in range(1, 7):\n", " plt.plot(x, np.sin(x + i * .5) * (7 - i) * flip)\n", "\n", "# Possible contexts\n", "contexts = ['paper', 'notebook', 'talk', 'poster']\n", "\n", "ind=1\n", "for context in contexts: # Iterate \n", " plt.subplot(2,2,ind) # Note using Ind here to change were we plot the function on the subplot\n", " sns.set(context)\n", " sinplot()\n", " plt.gca().set_title(context) # plt.gca() = GetCurrentAxis. \n", " # You can assign the axis to a new variable or set axis options on the fly\n", " ind+=1\n", "\n", "# Adjust horizontal spacing so that labels don't overlap\n", "plt.subplots_adjust(hspace=.4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.2 Seaborn Styles" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "seaborn_styles = [\"whitegrid\", \"darkgrid\", \"white\", \"dark\", \"ticks\"]\n", "sns.set('notebook')\n", "\n", "ind=1\n", "for style in seaborn_styles:\n", " sns.set_style(style)\n", " plt.subplot(3,2,ind)\n", " sinplot()\n", " plt.gca().set_title(style) \n", " ind+=1\n", "\n", "plt.subplots_adjust(hspace=.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.3 Matplotlib Style sheets\n", "\n", "* Matplotlib has its own style presets that are often combinations of seaborn styles, contexts, and colormaps without the need to import the seaborn library, or emulate other plotting styles like 538, ggplot, etc.\n", "* You can see a gallery of what plots look like in the styles at https://tonysyu.github.io/raw_content/matplotlib-style-gallery/gallery.html\n", "* You can also write your own styles: https://matplotlib.org/users/style_sheets.html\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', 'tableau-colorblind10', '_classic_test']\n" ] } ], "source": [ "print(matplotlib.style.available)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* To just affect a single plot without changing future plots, indent the plot call in a `with plt.style.context(\"style\"):` block \n", "* Seaborn styles can be accessed analogously using `with sns.axes_style(\"style\")`" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with plt.style.context(('fivethirtyeight')):\n", " pts = plt.scatter(bigones['MAG'], bigones['DEP'], # data\n", " s=50, # marker size\n", " cmap=\"cool\", # color scheme: https://matplotlib.org/users/colormaps.html\n", " c=1.0*(bigones['YEAR']-1983)/28) # specific color for each point (range between 0 and 1)\n", " plt.ylabel(\"Depth\")\n", " plt.xlabel(\"Magnitude\")\n", " plt.title(\"Large Earthquakes\")\n", " clrbar = plt.colorbar(pts, ticks=[1.0/28, 27.0/28]) # data takes values from 1983=1/28, 2010=27/28 (none in 2011)\n", " clrbar.ax.set_yticklabels(['1984', '2010'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Introducing Other Plot Types:\n", "We don't have to use the scatter function. These are some common plot types supported in matplotlib or seaborn and what they are good for:\n", "\n", "### Linear relationships:\n", "* scatter\n", "* line \n", "### Categorical data:\n", "* bar\n", "* area\n", "* pie (Don't use these, they are hard to read, & bar charts can be more information dense)\n", "### Distributions:\n", "* histogram\n", "* kernel plots\n", "* violin (Use these instead of histograms to get published in high impact journals)\n", "\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
caratcutcolorclaritydepthtablepricexyz
00.23IdealESI261.555.03263.953.982.43
10.21PremiumESI159.861.03263.893.842.31
20.23GoodEVS156.965.03274.054.072.31
30.29PremiumIVS262.458.03344.204.232.63
40.31GoodJSI263.358.03354.344.352.75
\n", "
" ], "text/plain": [ " carat cut color clarity depth table price x y z\n", "0 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43\n", "1 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31\n", "2 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31\n", "3 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63\n", "4 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Loading some data:\n", "diamonds = sns.load_dataset(\"diamonds\")\n", "diamonds.head()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAf0AAAD8CAYAAAB9532ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3Xd0FUX7wPHvvUlueoU0CCIEktAV6VWkI6GKvIjACwh2BAXpRWmW0ASpPwsqFgSUojQD0ov0EElCSYX03m6f3x95uRKTQAIpJJnPOTknd8vsc8vus7M7O6MQQggkSZIkSarylBUdgCRJkiRJ5UMmfUmSJEmqJmTSlyRJkqRqQiZ9SZIkSaomZNKXJEmSpGpCJn1JkiRJqiaqfdK/dOkSo0aNwt/fn/79+/PKK69w/fp1AIKCgpg0aVKpbcvX15eUlJRiL3/mzBn69+9f6LxVq1bx66+/3nf9NWvW8Mcff5QoxsfFtWvX6NGjB0OGDCEmJqbA/MOHDzNq1CgGDhzI888/z+TJk4mNjS3VGObMmcPVq1eLvXxgYCCLFi0q1Rik4omJiaFRo0YMHDjQ9DdgwAC2bdtW0aExbty4Eu33ZeH48eN069aNF154AbVanW+er68v/v7+ps9syJAhbNmyxTT/hx9+YOPGjeUdcpFSUlLw9fUt0TolPfYW5mGPOVeuXGHevHmPtO1SJaoxjUYj2rRpI65evWqa9uuvv4quXbsKvV5f6tvz8fERycnJxV7+9OnT4vnnn3/o7b388sti7969D71+RVq9erWYNWtWofN27dol+vbtKyIiIoQQQhiNRrF+/XrRo0cPodFoSi2Gbt26iStXrpRaeVLZiY6OFk899VS+aXFxcaJVq1bi2rVrFRRVnpLu92VhxowZ4vPPPy903r/jS05OFsOGDRNffPFFeYVXIsnJycLHx6dE6zzqd/Aox5zt27eLiRMnPvS2S5t5RZ90VKTc3FwyMzPJyckxTRswYAB2dnYYDAbOnTvHwoUL2bNnDzNmzMDKyoqwsDCSk5N57rnncHJy4vDhwyQmJrJo0SLat2/PjBkzsLS0JCQkhOTkZDp27MicOXOwsLDIt+2ff/6ZH374AaPRiJOTE3PnzsXb27tAjDk5OUyZMoVbt26h0WhYtGgRrVq1YsaMGTRs2JDx48fz2WefcfDgQSwsLHB2dmbp0qUcPHiQq1ev8sknn2BmZka7du344IMPCAkJQaFQ0LlzZ959913Mzc05cuQIAQEBKJVKGjVqxMmTJ/n+++85e/Ys27ZtIzc3Fzs7OzZs2MCCBQuIjIwkLS0NW1tbAgICqF+/PqNGjaJJkyZcunSJlJQUXnzxRZKSkjh79iy5ubmsXLmy0LPzzz//nN9++w0zMzPq1avH3LlzOXXqFD/88AMGgwG1Ws2yZcvyrbNixQoWLlxI3bp1AVAoFEycOBFPT0+0Wi16vf6+cTo6OnLr1i1GjBhBs2bN+PTTT9FqtSQmJtKhQweWLFnCihUrSEhIYOrUqXzyySe0aNHCtP3ExESmT59OamoqAF27dmXy5Mns2LGD/fv3s3btWoYMGWJaPj09neTkZM6cOYPBYGDx4sWEhYWh0+lo374977//Pubm5oV+j25ubg/xy5YA3N3dqVu3LhEREfj5+RW5z82YMYO0tDSio6N59tlneeONN1i0aBEXLlzAzMyMHj16MGXKFHQ6HQEBAfz1118YDAYaN27MnDlzsLOz47nnnmPw4MGcOnWK2NhYBg4cyOTJk5k5cyYAY8aMYePGjYSEhLBhwwa0Wi0pKSkMGjSIyZMnA7Bx40a2bduGra0trVq1IjAwkEOHDqHVaovc7r10Oh0fffQRp06dwszMjObNmzNz5kx+/PFHAgMDsbS0JDMzk+nTp9/3c3NxcWHGjBlMmjSJsWPHsmbNGlJTU5k3bx6HDx8uNP4zZ86wfPlyPD09CQ8Px9ramokTJ/Ltt98SHh5Or169mDVrFgA//fQT3377LUqlkpo1azJ37lzq1avHjBkzsLOzIzQ0lLi4OHx9ffn444+xtbXlwIEDrFixAmtra5o2bWqKtah9sTArV64kKCgIo9HI5MmT6datG2PHjqVv3768+OKLAKxdu5a0tDRTrHc96JizZ88e9u/fz4YNGwBMx4IFCxbw2WefkZmZycyZM1m6dOmDf7hlraLPOiral19+KZo3by6ee+45MXXqVPHzzz+LnJwcIUT+mvb06dPFsGHDhFarFQkJCcLHx0d88803Qgghvv76azF27FjTcoMGDRJZWVlCo9GIkSNHim+//VYI8c/Z5pkzZ8RLL71k2s6xY8dEnz59CsR2+vRp0ahRI3Hp0iUhhBBfffWVGD16tGk7//d//yfu3LkjWrZsaTrb/OKLL8TBgweFEPlr+u+//75YuHChMBqNQqPRiHHjxokNGzaIlJQU0aZNG1NtaMeOHcLHx0dER0eL7du3i9atW4vMzEwhhBB79+4VCxcuNMU3d+5c8eGHH5q29dZbbwkhhLh06ZLw8fERgYGBQgghFi9eLObMmVPg/W3btk0MHz5cZGdnCyGE+Oyzz8S4ceNM/3/wwQcF1klJSRE+Pj6mz64wD4pz5syZpnlTpkwRp0+fFkIIkZWVJdq2bSuCgoKEEEXX9NesWSPmzp0rhBAiOztbTJ48WWRkZBR6Rp+amir69esn9uzZI4TIq3Hd/d3o9XoxdepUsXHjxvt+j9KDFVbTv3DhgmjdurW4c+fOffe56dOnizFjxpjWW7JkiZgyZYrQ6/Wmffj06dNi9erV4qOPPhJGo1EIIcSyZcvE/PnzhRB5v5WPPvpICJF3haFZs2YiKipKCPHPfm80GsXLL78swsPDTcs1atRIJCcni6NHj4revXuL9PR0YTQaxcyZM0W3bt2EEOK+273XqlWrxFtvvSW0Wq0wGAxixowZpt/p3eNFYQqrBWdnZ5um390X7xf/3WNVcHCwEEKI8ePHi+HDhwuNRiOSk5NFkyZNRFxcnDh58qTo0aOHaXvbt28Xffv2FUajUUyfPt20jlarFYMGDRLbtm0TiYmJ4plnnhHXr18XQgixfv16U02/qH2xsPe4YcMGIYQQoaGhok2bNiI5OVkcPHhQDB06VAghhMFgEN26dRM3b97Mt25xjjn/3vfvfS1r+o+ZsWPHMmzYMP766y/++usvNm3axKZNmwq9F9itWzcsLCxwdXXFxsaGzp07A/DEE0+QlpZmWm7w4MHY2toCMHDgQAIDA3n55ZdN8//8808iIyP5z3/+Y5qWkZFBWloaTk5O+bZZp04dUy3Tz8+P7du355vv7u6On58fgwcPpkuXLnTp0oX27dsXiP3o0aP88MMPKBQKVCoV//nPf9i8eTP16tXD29sbPz8/U+z33pf29fU11Sj69OlDnTp1+Pbbb4mMjOTs2bM8/fTTpmV79uxpihnI9/mcPXu20JiGDBmCjY0NAKNHj2b9+vVotdoCy96lVOY1QzEajUUu86A4W7VqZfr/o48+4ujRo6xfv950NeXeKz+F6dy5MxMnTiQ2NpYOHTrw3nvvYW9vX2A5tVrNa6+9ZroHCHnffVBQkOn3dff+anG/R6loarWagQMHAmAwGHB2dubTTz/F09PT9FsobJ8DeOaZZ0zTT548ycyZMzEzM8PMzIzvvvsOgE8//ZTMzExOnjwJ5NWsa9SoYVqve/fuQN53WaNGDdLT0037AuTVDtevX8+ff/7Jnj17uHnzJkIIcnNzOXLkCH369MHBwQGAkSNHcvr0aSDvN3O/7d519OhRpkyZYrqqOGrUKN58882H+iwVCgUAlpaWxYofwMvLi8aNGwN5+7y9vT0qlQoXFxdsbW1JT0/n2LFj9OvXDxcXFwCGDBnC4sWLTe12OnfujEqlAsDHx4f09HTOnz+Pj48PDRo0AGD48OEsX77ctHxx9kWAESNGmMr19vbm4sWLdOvWjcWLFxMSEkJ8fDxeXl7Ur18/33rFOeZUJtU66Z8/f56LFy/yyiuv0K1bN7p168a7775L//79OXHiBM7OzvmWv/tjvMvcvPCPz8zMzPS/EML0o7nLaDQycOBApk2bZnqdkJCAo6NjgbLuvS2gUCgQ/xoqQalU8t133xEUFMSpU6dYsmQJnTt35v333y+wzbs78t3Xer0eMzOzQsu8625CBvj+++/ZunUrI0eOxN/fHycnp3yN7P79+fz7lsa/FRXT/Tg6OvLkk09y+fJlOnTokG/eO++8w+uvv86FCxfuG+e97+nll1/G19eXzp0707dvXy5fvlzg8/i35s2bExgYyKlTpzh9+jTDhg1j06ZN+ZYxGAy89957+Pj4MHHixHzvcdWqVaZbORkZGSgUimJ/j1LRrKys2LlzZ6HzHrTP3fubMDc3z/e7jI2NxcrKCqPRyKxZs+jatSsA2dnZaDQa03L/TpD//h3l5OQwePBgevToQatWrRg6dCh//PEHQgjMzc3zLX/vMeRB2713uX/vTzqdrqiP676CgoLw8vIyVV4eFD8U7/hYWOIUQpj2eysrK9P0ez/Dez+be8stal+89xbAXfce14xGI+bm5piZmTF8+HC2bdtGQkJCvpPCu4pzzPn39/2wn3t5qNat911cXFi3bh3nzp0zTUtMTCQrKwsfH5+HLnfv3r1otVo0Gg2//PIL3bp1yze/U6dO/PbbbyQkJAB5rWPHjBnzUNsKCQmhf//+eHt78+qrr/Lf//6XoKAgIO/AcXdn6tSpE9999x1CCLRaLVu3bqVDhw60bNmSiIgIQkJCANi/f78pEf3b8ePHGTx4MMOGDaNevXocOnQIg8HwUHFD3ln69u3bTTXrb7/9ltatWxc4ePzbW2+9xeLFi4mMjATyEuzatWsJCQmhfv36xY4zIyODoKAgpk6dSq9evYiLiyMqKsp0YLr387tXQEAAa9eupUePHsyePZsGDRqYnvi468MPP0Sv1xdotdupUye+/vpr0/fw+uuv89133933e5QeXUn2ufbt2/PLL79gNBrRarVMmjSJv/76i06dOrFlyxa0Wi1Go5G5c+eaapz3c/d3FBkZSVZWFpMnT+a5557jzJkzprK6du3KgQMHyMzMBMh3pbG42+3cuTM//PADOp0Oo9HIli1b6NixY4k/q/j4eAICAhg3bly+6feLv7g6d+7M77//bmpJv337dpycnEz3ygvTunVrbty4YTpG7dixwzSvOPviXb/88gsAwcHBREVFma6gDhs2jD/++IPg4GDT1cp/e9Axx8XFhevXr6PRaNDpdOzfv9+0blHHkYpSrWv69erV4/PPP2fFihXExcVhaWmJvb09S5YsoX79+iQmJj5UuVZWVrz00ktkZGTQu3dvhg4dmm9+p06dmDBhAuPGjUOhUGBnZ8eaNWsKTbQP4ufnR9++fRk6dCg2NjZYWVkxZ84cAJ577jmWL1+OTqdjzpw5LFq0CH9/f3Q6HZ07d+a1115DpVKxfPlypk+fjlKppGnTppibm2NtbV1gW+PGjWPevHmmA9JTTz1FWFjYQ3xCeV544QViY2MZNmwYRqORunXrEhAQ8MD1/P39EULw7rvvotfr0Wg0NGnShM2bN6NSqYodp4ODAxMnTmTw4MHY2Njg7u5Oy5YtiYyMpH379vTs2ZNp06axYMECOnXqZFpvzJgxzJgxg/79+6NSqfD19eX5559nz549AFy8eJEff/wRX19fXnjhBVMNYNGiRcyePZvFixebvocOHTrwyiuvYGFhUeT3KD26kuxzdw/wAwcOxGAw0K9fP3r16kWXLl34+OOPGTx4MAaDgUaNGjFjxowHbrtPnz6MGjWKVatW8eyzz9K3b19UKpXpknVkZCSdO3fmxRdfZPjw4VhZWdGwYUPTPvjGG28Ua7uvv/46H3/8MYMGDUKv19O8eXPmzp1brM9nzJgxKJVK0xWGoUOHMnLkyHzL+Pr6Fhn/g07U7+rYsSP//e9/GTNmDEajERcXFzZs2FDgaui9XFxcCAgIYOrUqVhYWNC6det8cRe2LxYmOjqaQYMGoVAoWL58uelWao0aNWjatCne3t5FXp180DGnY8eOtG7dmr59++Lq6krbtm0JDQ0F8o4/n3/+OW+99RZr1qwp1udUlhTiQdcypRK5t1V9ZZCVlcXatWt5++23sba2Jjg4mFdffZVjx4491EmIJEklFxQUxMWLFxk9ejQAX331FZcvX2blypUVHFnVl5KSwgsvvMCWLVvw9PSs6HDKXLWu6UtgZ2eHhYUFL7zwAubm5pibm7Ny5UqZ8CWpHNWrV49NmzaxdetWFAoFnp6eLFy4sKLDqvK2bt3K8uXLefvtt6tFwgdZ05ckSZKkaqNaN+STJEmSpOpEJn1JkiRJqiYq5T19tVrN1atXcXV1zfc8qyRJ+RkMBhITE2natGm+Z6AlSaqeKmXSv3r1aoHHSSRJKtqWLVvy9UQoSVL1VCmTvqurK5B3IPPw8KjgaCTp8RUXF8fIkSNN+4wkSdVbpUz6dy/pe3h44OXlVcHRSNLjT94GkyQJZEM+SZIkSao2ZNKXJEmSpGpCJn1JkiRJqiZk0pckSZKkaqJSNuSTJEmqrmJiYujTpw/e3t75pq9fv77Q/uN/+OEHAEaMGFEu8UmPN5n0JUmSKhk3Nzd27txZrGVlspfuVaWTvlZnQGVR9KNKD5ovSZJUWYSFhbFw4UJycnJISUlh4sSJjBgxgtWrVwPw9ttv065dO5o2bUpiYiLbtm0rcvx4qeqq0klfZWGG/3tFnw3vXjawHKORJEkqHQkJCQwc+M/xy9/fn/j4eN544w3at29PdHQ0AwYMKFDLT01NZcKECbRt27a8Q5YeE1U66UuSJFVFhV3eNxgMHDt2jA0bNhAWFkZOTk6h67Zo0aI8QpQeU7L1viRJUhUwefJkDh48iLe3N5MnTy5yOTnwUvUma/qSJElVwIkTJ9i7dy/u7u5s2bIFyKv9S9K9yrymn5WVRf/+/YmJiSkw748//mDgwIEMGDCAN954g/T09LIOR5IkqUp6++23eemll+jXrx/nz5+ndu3ahR53peqtTGv6ly9fZs6cOURERBSYl5WVxYIFC9i+fTvu7u6sWrWK1atXM2fOnLIMSZIkqVLz8vLi0KFDBaaPHTuWsWPHFpj+9ttvm/4PDQ0t09ikx1+Z1vS3bt3K/PnzcXNzKzBPp9Mxf/583N3dAfD19SU2NrYsw5EkSSoRvV7PunXr6Nu3L/369aN3796sX78eIcQjlz1q1CjOnDlTClFKUvGVaU1/8eLFRc5zdnamZ8+eAKjVajZu3MioUaMKLJeRkUFGRka+aXFxcaUbqCRJUiE++OADkpKS+Omnn3BwcCArK4s333wTe3t7Ro4cWdHhSVKJVXhDvszMTN588038/PwYPHhwgfmbN29mzZo1FRCZJEnVWVxcHLt27eLo0aM4ODgAYGdnx7x587hx4wZJSUnMnj2bO3fuYG5uzpQpU+jSpQu5ubnMmTOH0NBQFAoF48ePZ9CgQWi1WmbPns3Vq1epXbs2qampFfwOpeqoQpN+QkIC48ePp127dsyaNavQZcaMGVPgZCAuLk6eZUuSVKauXLmCt7c3jo6O+aZ7e3vj7e3NO++8Q7t27Rg7dizR0dGMGDGCX3/9lS+//BJnZ2f27NlDSkoKw4YNw8/PjxMnTgCwd+9eIiIiGDBgQLm+H73BiEab15rfwlyJUqkgO1dHZo6OjGwN6VkaNDojBoORerUccLW34mZIIkozBebmSuzsLbG1s8TaVoWlVV7q0OuNCKPA3FyJuezdtFKosKRvMBh47bXX6Nu3L2+88UaRyzk4OJjOsiVJksqTQqEw/b9v3z7WrVuH0WhEpVIRExPDokWLAKhTpw4tWrTg8uXLnD59miVLlgDg4uJC9+7dOXv2LGfPnmX48OEAPPnkkzz99NNlFrdRp0EYDChVVmi1ei7eSCYsKpWouEziU3JITs8lM0dX5Pqj+jai1RPO7PrpcpHLqCzNsbO3xNHZGjcPe2rXdcbTywEnFxv0eiMIgUpljkKpKLIMqfyVe9KfMGECkyZNIi4ujr///huDwcD+/fsBaNq06X3bAUiSJJWXpk2bcvPmTbKysrCzs6NPnz706dOHmJgYRo8eXaAxnxACg8FQ5HSFQpFvnrl56R1+hV6HMOhAoSQ3+hq5N86juXMDc0dXbJ4bz+Kvzpbatu7SavSkaPSkJGUTfj0JjoUDoFCAS01b3Gs74O3jSoNGblhZWSCEQGVZ4XeUq71y+Qbufbxk06ZNADRr1oyQkJDy2LwkSVKJ1apViwEDBjB9+nSWLl2Kg4MDer2eP//8E6VSSbt27di2bZvp8v6FCxdYsGCBafqcOXNISUkhMDCQ1atXo1Qq2b17N926dSM2NpYLFy48UnxGrRqF0gxNQiTZoWdQh19GExcOwmhaRp+Zgqut9aN+FCUiBCQnZpOcmM3fl/KeyHJ0tqa+jyt+zTyoW98FIcBCpUSplJ3Cljd52iVJklSEBQsW8NVXXzF69GgMBgPZ2dm0bduWTZs2YWNjw7x589ixYwcAixYtws3NjTfffJMFCxbg7+9vuo3ZpEkTfHx8uH79On379qV27dr4+PiUOB6jVg1KJZr4SLIuB5IdchpjbmaRyxsyk0EYqV/LkVt3Kq7zs/TUXC6eieLimSgUCniingst29fFr6kHRqMRSys52l95kUlfkiSpCEqlkvHjxzN+/PhC52/YsKHANDs7OwICAgpMt7CwMLUBKAkhjBi1GgzZ6WSc+53saycxZBW/5b86IZo2TdwrNOnfSwiIvJVC5K0UzM2V+Db1oFXHutSu44QQAguVTEtlSX66kiRJjyGjXgfCSE54EBlndqGOCn6ocrS3Q2lcr00pR1c69HojwZfuEHzpDrb2ljzdpg4dunmjVCrk/f8yIj9VSZKkx4hRp0EYjWSc30fG+X0YMpIeqTxN7A3qencqpejKTnamhuOBNzh15BYtWnnRtbcPKpW56fFAqXTIVhSSJEmPAaNWg0GdQ+rJX4hePZHUw989csIH0MZH4GBnWQoRlg+D3siF01GsXBjI7p8vk5KUjVajL7XyR4wYwW+//ZZvWk5ODm3btiUlJaXUtgOQkpLC7Nmz6dmzJ3379mXw4MEEBgaWWvm+vr4lXkeeQkmSJFUgo16LMBhIO7eXzNO/YlRnl2r5uuQ7mFlYUMPRiuR0damWXZaEUfD3pVj+vhSLTxN3nn+hGZaW5o982X/o0KHs3r2b559/3jTtwIEDtG3bFhcXl0cN20Sr1TJmzBh69+7Nvn37MDMz49atW4wfP57atWvj5+dXatsqCZn0JUmSKoAQAoMmh+ybl0j746sSNc4r2YaMaJLjaNvEg99PRpTNNspYWHA8N0MSaf9sfTr1aICZUomZ+cNdqO7bty+ffPIJaWlpODk5AbBr1y7GjBkD5PXEuHTpUtRqNc7OznzwwQfUqVOHUaNG4ejoyPXr1xk+fDjBwcEsW7YMgNWrV2NpacnEiRNN29m/fz+Wlpa89dZbpmn169dnwYIFGAx5PSNeunSJxYsXo9FocHZ25sMPP6Ru3bqEh4czb9480tLSsLGxYfbs2TRv3pyYmBimTZtGTk4OLVq0eKj3Ly/vS5IklTNdThaalDgStn1C8q/Lyy7h/4/mznWaN3At022UNYPByPHAG6xZepiQq3HotAU7QioOW1tbunfvzr59+wCIj48nPDycTp06odVqmTNnDsuWLeOXX35h7NixzJ0717Sur68v+/fvZ/jw4Zw6dYqsrCwA9uzZw8CBA/Nt5/Lly7Ru3brA9rt27UqTJk3QarW8++67zJ07l127dvGf//yHd999F4Bp06YxatQodu/ezcyZM3nnnXfQarUsXLiQIUOGsHPnTlq2bFni9w4y6UuSJJUbg06LXp1D+qlfuLNxMurIq+WyXe2dMOq7W5XLtspaVoaG7d9e4Jt1p0hNyUH3v/EESmLIkCHs2bMHgN27dzNgwADMzMyIiIggOjqa119/nYEDBxIQEEB0dLRpvebNmwN5Jw5du3bl4MGDnDt3jjp16piGiS9KQEAAAwcOpHfv3ixatIiIiAgcHBxMZfbt25eoqCgyMzOJioqiV69eADz11FM4Ojpy69Ytzp49S9++fQEYMGAAFhYl799AXt6XJEkqB+rMNAyZyST/ugJ9amy5blsbH0FN+6rVAc7tqDTWfXKEXv6NeapNHSxUxR/wp3Xr1iQmJhIbG8uuXbtMI7kajUa8vLzYuXMnkDdGTFLSP40praz+OXEaOnQo69atw8vLiyFDhhTYRtOmTfnxxx9Nr6dOncrUqVPZsWMHZ8+exWg0FlhHCEFmZsHOlu525Xz3f8gbF+JhejSUNX1JkqQyZNDr0eVmk33pIPGbZ5Z7wgfQJkSisrLGSlW1DvkGvZG9v1zlxy//Ijdbi15f/Fr/oEGDWLduHY6OjjzxxBNA3j339PR0zp07B8D27duZOnVqoeu3atWKuLg4zpw5Q48ePQrM79evH7m5uaxbtw6dLm9wo8zMTM6cOYNSqaR+/fqkpaVx5coVAH7//Xdq1apFrVq18PLy4sCBA0Deff+kpCQaNmxIhw4d2LVrF5DX+FCj0RT7/d4la/qSJEllJDcrA4VeQ/LOFWhiQissDqHXostKpVUjD45fvlNhcZSV8OtJrPnoMINeepq69V2K1cJ/yJAhPPfcc/kGeVOpVKxatcrUuM7Ozo6PP/64yDJ69uxJWloaKpWqwDyVSsU333zDypUrGTRoEJB35aB379688sorqFQqVqxYwcKFC8nNzcXR0ZEVK1YA8Omnn7JgwQJWr16NhYUFq1evRqVSMW/ePKZNm8ZPP/1E06ZNsbW1LelHhUI8TEuIChYTE0P37t0JDAzEy8vrvsv6v7ezyHm7lw0scp4kVQUl2Vek0iOEIDstGWNyNCm/LseoyanokHAd8j6nMjxYvfXSA5e9O7TudxvOlENkpevptnXoM6hpiS73l5QQAp1Ox9ixY5k1axZNmjQps22Vtqp1rUeSJKmC6XU61FnpaEJOkrR1yWOR8AE0t0NoWKvkNcPK5uKZaL7dcBrLCPaeAAAgAElEQVR1rg6DoeB989KQmJhIx44dadGiRaVK+CAv70uSJJWa3JwczDCScfhbsoP+rOhw8tHFR+Depmo15itKTEQq6wOOMPr19jg4WmFuUbq1fjc3N/76669SLbO8yJq+JElSKUhPTcbMqCNx6+LHLuEDaOLDsbKxoboMYZ+Rpmbj8mPciU5Hqy29bnwru2ry9UuSJJWd1ORErMyVxH83F83tsIoOp1DG3EyMWjVN6tWo6FDKjVaj55t1pwgLji/V/vsrs2qd9LW6oh/vuN88SZKku2LvxKC0tkRYWaO0dazocO5LEx9Bq8b370SmqjEaBTu2XCQkKE4mfqr5PX2VhVmRrftly35Jkh4kPDoCla0l7/+xhDa1n2Lk8NkkfTsXbdytig6tUJqYEPzqPFvRYZQ/Ab/+eIkhZk/j09j9kQftqcyqdU1fkiTpYV2PuIGVgzULjq0kNTed/TeOsOPaPmq+/AHmNWpXdHiF0sbfwsulejTmK0DAL1sucj0koVrX+Ms86WdlZdG/f39iYmIKzLt27RpDhgyhd+/ezJ49G72++n4RkiRVHqE3w6jhWpNFx1aTrs4wTf81ZD/7bh7F9b9LMHeoWYERFk4bF46tbdXog/9hCAE7vr3AzdDEatu4r0yT/uXLlxkxYgQRERGFzp82bRrz5s1j//79CCHYunVrWYYjSZL0yG5FhuPp6clHxz8nMTu5wPzvg3ZyNOocNV/5FKXN43WPX5+eiEKhoI67XUWHUmGEgG3fXiDyZspDDdZT2ZVp0t+6dSvz58/Hzc2twLzbt2+jVqt56qmngLwuEe8OdShJkvQ4iomNoaZrTVac+YKItIJXL+/64uJPnIv7G7eJy1GqbMoxwn/sCknm1Z3XeW3XdT44FElabl7NVpMYQ9smHgBkxV8j4shywg9/wp3z32LQqQFISYzlzUn/ZdehpdyM+qdXvlsx57gcurf830wpE0bBz5vPkZ6WW2Yd+DyuyjTpL168mFatWhU6LyEhAVfXf8Z3dnV1JT4+vsByGRkZxMTE5PuLi4srs5glSZIKE5+YgI2dLV9e2kpwwoP70f/8r2+4mhKO66srwLxg3+xl6XpyLtuDk1jetz7rBzSkloOKby7lHV81t0NpUq8Gek0W8Ze3UuuZUdTr9j4WNjVICslL6GeO/sawoSN5vus0rt4IBECnVxMWcYImDbqX63spK3qdke82nK529/crrCGf0WhEoVCYXgsh8r2+a/PmzXTv3j3f38iRI8szVEmSqrmUtFQycjI4EnWGk9HnirWOEIIVp/6PmzmJuE9cAcryazHesIY1Xwz2wVZlhtZgJDlHj4NlXq902tgbPOmqIicxDCunOqjs8ipfTnXbkXn7IkIIzMwtyFXnojdoUZB3XL4SdoDG9Z/F3Kx8T2DKUkaamu83na1Wl/krLOl7eHiQmJhoep2UlFTobYAxY8YQGBiY72/Lli3lGaokSdWYVqcl9FYoWpWBn4J3l2hdgzDy8bG1xOizcXslgPI85JorFZyMymDUtlCuxmfTs4EzAJq4cBztLNGr0zG3+qfNgbmVI0a9GqNeQ/tn/Tl6LJA/Tq3l6cb9Sc+MJz0zjidqtSi3+MvL7ag0ft9+pdo07KuwpF+7dm0sLS05f/48ADt37qRLly4FlnNwcMDLyyvfn4eHR3mHK0lSNSSE4Nzl8/j6+LLy7JcYRcnv/+qMehYdXU2SObiN+6gMoixahycc+Gl4I0a2cGPOHxEYhUCXfBtzlSVWFkoo5OqqQqHE3tGFpYtW8XzXqdTxaMb5v3fxTOMB3I7/m8Nn/4/jF75Fo308BhIqDZfP3ebSmehqkfjLPelPmDCBoKAgAAICAli6dCl9+vQhJyeH0aNHl3c4kiRJRfr7+jWaNGrCyjNf5ns0r6Q0eg0LDq8gw9YW15c/fODyO68l88qvYby5+wYfHY0ms4j7zkIIAo7HsC04yTTtTqaGib+GMWpbCPuvpwLQq4Ez8Vk6vjgXB0YD2tR4Gvk8if6e96RXZ6C0sEb5r/YHkXcu42jnhqO9B+f/3kXnZ0ZT260xIeFHHuajeGwd2P03mekahLHSjTZfIuWS9A8dOmQay3vTpk00a9YMAD8/P7Zt28a+fftYtmwZKlXVuVckSVLlFp+YgIWlit9uBBKSdOORy8vR5TL30DLUNTyo+eLMIpe7HJfFz8GJLO1Zj8/9G9C6th2rTt0psFxUmpqZByM4HpWeb/qekBQ6P+mIUQi+v5IAwP7rKajMFIx6Oq8LXvWdG/Tp1Q11ahTarLzbrGmRp7Fzzz9MrN6g5drNwzT36Q2AEIa8e/wKBXqD7uE/jMeQ0ZDXol+vr9qt+WWPfJIkSf+i0Wo4f/UCZrYW7Ar9o9TKzdRkMfdQAAavhtQYOLnQZa4nq3na0w5X27ye8zo+4ciZmEx0/3q0bE9oCr0bONO5bv6+ACzMFNS0sWBw45qk5Op5c/cNtgYnMf4ZD6LSNLy5+wa6O2E87VsL9xbDuHP+OyL+DECbGYtr4/75yrp6PRCfJztiYZHXoU+j+s+y58inhNw6gu+THUvpU3l8JMRmcvSP61W6RX/17YBYkiSpCMfPnqJz204sOLrioe7j309KbhpzAwNY2nM6Ln0mkrJvY775fjWt2XUtmfgsLe52Kg7cTEVvFGRqDLjY/FNPe6NtLQAuxGblW3+gXw0+PR5DusbAzC518HJQsfFcHP5+eaPrfe7fAE1cODU6qbBzb4Sde6MiY33Kr2++1z5PdsSnCib7e508dIPGzT1x97RHaVb16sUy6UuSJN0jJjYGN7ea7L91hOj0gpfVS0NCdhLzApexqMc0nDU5pB7+zjSvqbstI1u4svDPKJQK6OXtjL3KDHNlwUZ3hXGxsWBpr3qm13P/iGBCKw/OxmSyJzQZGwsz3upkTi1ra1TmSrRV/HJ2SQkBP28+z2tTu6Cqgkm/6r0jSZKkh6TX67kQdAlnVxe2XyvbnuduZ8bxweEVWLXug1PHoabpOToDzdxtWdO/AZ8934B2TzgAYP+/5+xL4lhEOnUcLanrZMXGc7HM6voEbbzs2XH5NvrsdJ72K/iYtARpKTkcORhWJS/zy5q+JEnS/xz/6wSdO3Qi4PRGDMay77AlIi2GxUfWMKfr2xjVOWSc30tKjp4ZB8PZMKAhtiozfrySyLP1HAvtvOx+1Hoj24KTWNLzSQAMRoFSAQpAYzCijgvnGV83zlwt3R5Ow2PO8/fNwygUCszMLGjVZDAujrW5eO037iRcA4UCB1tX2jR7ASvLoscACLl1lBvRZ+jfdRoAmdlJHDu/Gb1eS5MGz+H9RFsgr2vgzOxEWvj2LbKsh3H2aATtutSvcsPwypq+JEkSkJicSLY6h9Dkm1xPDi+37YYl3+KT4+ux7zEKu2Zd8XK05MWmrkzZe4tXfg1DZzQy/hkPwpJyeXN38Z8i+Ckokf5+Ltiq8q4QDG1ck1d3XmfH30n4+9ZAGxOCj5d9qb6XjKwELlzbzXNtJ9Kvy3s0bdCTo+e+5mb0WVLSY+jb+V36d52GnU0NLvy9q8hyElLC+fvm4XzTwiJO0Ni7W7l1DWwwGPl9+9UqV9uvWqcwkiRJD+mP44fx79WPmYc+KfdtX00IZeWpL5nc71WMmhwG8BcD/tfw7i6fmtZ87t+gwLrvdfQqtMwx/3s8767+fjXof0+Z2vhwPJ6xKIXo/6FUmtOu+YtYW+Xdkqjh5IVak4m9rSstG/tjZmb+v+l1CIs4UWgZuZpMzl3dwdON+hN881C+svV6bbl2DRx6NY6khCw8azuiKGabisedrOlLklTtRcZE4enhwYnoc8RnJT54hTJw/s4V1v/1Hc6DpmD1ZPMy3542PhxrG+tSLdPOxoXa7o2BvI6Dzv+9i9ruTXCv4Y2LY97JiUabQ9D1gzzhWbBLX6MwcuLCdzzdqD82VvkfRfSr15nI2Evl3jXwnp+vVKln92XSlySpWhNCcPjUEZ5p3pKf//69QmM5EXWOry/9jMuL01HValim2zJkpyP0WvyedC71svV6DccvfENmdhLtWrxomp6ZncTBU5/j6lyv0Ef/Ll37Dbca9fF09S0wz9rKge7tXiv3roHjbmcQGhxXZYbglUlfkqRqLeRGKH4Nfdl3408yNJkVHQ6Bt06w9eoeao6cj4XrE2W6LXVCFG0al+5YJtm5qew/sRqFQkmP9m+gssi7mhCXdIP9J1ZT36sVbZu/UGjDxPDb54mODeL3o8s4fWUrWdlJ/H50WYHlyrtr4MN7QzFWke55ZdKXJKnaMhgM/HH8EM0aNeX3G4cfvEI52RMWyO6wQGqOWYS5k/uDV3hI2pgQ/Oo6lVp5Or2aP06tpY5nMzq1HIW5WV6bgZT0GI6e+4oOT42gsXe3Itcf2nMBz3edSr8u79Gu+YvY2dakX5f38i1TEV0DpybnEHEjuUokftmQT5Kkaiv0Vhg+3g05HvkX2Y/ZqHE/B/+GjbkV3cZ9TMLGdzFmpZT6NrRxt6jTqOgkXFKhESfIzkklJu4qMXFXTdMtVbYAXAr5jUshvwFga+1C19ZjiYm7yvXIU3RrO6FY2yiqa2CVhTWdnym7QduOHgijrrcLKlXlTpuVO3pJkqSHJITg0Ik/GTN8FLMqoMV+cWy+vB1rC2vaTQggYd0kjOqsB69UApr4cGrYWpVaeU0bdKdpCR+f8/JoipdH0wLT3Ws2MD2jf6+K6hr4dlQayQnZeHo5Pnjhx5i8vC9JUrUUfScGt5quhCbdrLAW+8Wx4dwWLidex3XiClCVXoIG0KfGozQzw7OGTamWW1Ud2R+KppI/ty+TfhnQ6oruyet+8yRJKj9/njrKsx27sius9EbRKwsCwaozXxGWeRv3iStAWZoXaAWapNu0bepZimVWXWHXElDnVu4hheXl/TKgsjDD/72dhc7bvWxgOUcjSdK/JaemkJqRil5hICSp+L3cVRSjMPLpiY3M6fI2dScuJ379ZKB0HiHTxITR1Ls5vx65WSrlVWkCzh6P4NlePlioSj4WwuNA1vQlSap2/rp8jmeat+RI1JmKDqXY9EY9S46tIQ4dbq98Wmrl6uJuUM/VstTKq+quXrhNCYdBeKzIpC9JUrViNBo5ff4MTzVtwfHIsxUdToloDTo+/HMVqZYqXMcsKZUyNXHhONmVTTe2VVFmupqEuIrvz+FhyaQvSVK1Enk7Ci/P2sRmxJOYU/qPwZW1XL2a+YeXk+PoQs0Rcx+5PG1SNBaWVthZybu9xXX+VFSlHYhHJn1JkqqVC0EXaflUS/6sRJf2/y1Lm83cQwHoPOpSY8jURyvMoEeXnkQb2Ziv2K5diUVZSQfgKdOkv3v3bvr160evXr3YsmVLgfnBwcEMHTqUAQMG8Oqrr5KRkVGW4UiSVM1pdVouXL1E4wZ+nI4+X9HhPJI0dQZzAwOgXlNqPP/GI5WlvnODpxq6llJkVZ86V0dUeGpFh/FQyizpx8fHs2LFCr7//nt+/fVXfvrpJ27cyN9KdvHixUyaNIldu3ZRr149vvjii7IKR5IkiZsRt/DyqEVMeiyZ2uyKDueRJeWkMC8wALNG7XDu8d+HLkd7JwzvWvJZ/ZK4cj4GjbryPb5XZkn/5MmTtGvXDicnJ2xsbOjduzf79u3Lt4zRaCQ7O2/Hy83NxcqqYMcTGRkZxMTE5PuLi4srq7AlSarCroRcxbehL5fi/67oUEpNbFYC8w8vx/LpHjh3Gf5QZWjjwnG1tyjlyKq2iBtJKM0q3x3yMmu5kZCQgKvrP5eL3NzcuHLlSr5lZsyYwbhx41iyZAnW1tZs3bq1QDmbN29mzZo1ZRWmJEnVhBCCqyHBvPbfCay/8n1Fh1OqotPvsPDIKuY/OxmDOoeMs7tLtL4mIQIrGxvMlVCFho4vUxlpatS5OiwsKtfz+mV2mmI0GvMNnSiEyPdarVYze/Zsvv76a44fP85LL73E9OnTC5QzZswYAgMD8/0V1j5AkiTpfhKTExHCiIujCzeSwys6nFJ3MyWSpcfWYvfsCOyf6lGidYUmB0NuFi183MoouqrpVlgiQlSukfeKlfTffvttTp48WaKCPTw8SEz8pz/rxMRE3Nz++UGFhYVhaWlJ8+bNARg+fDhnzxZ8ZtbBwQEvL698fx4epTv+syRJVV9EdCQN6zUkJOE6BlE1q7PXEq+z7ORGHHqPw7ZRhxKtq467xTONym4Y36ro+rWEStcXf7GSfs+ePVm7di29e/fmiy++IC0t7YHrdOjQgVOnTpGSkkJubi4HDhygS5cupvl169YlLi6OW7duARAYGEizZs0e8m1IkiTd35WQq/g0bMiVxJCKDqVMXY77mzVnNuM04C2svZ8u9nqa26H4eDmUYWRVT+SNZMwr2X39YkU7YMAAvvvuO9auXUtycjIvvPAC06ZNK3CP/l7u7u5MmTKF0aNHM2jQIPr370/z5s2ZMGECQUFBODo6snTpUiZPnoy/vz/bt29nyZLS6WFKkiTpXnq9ntCboXjV8uJGSkRFh1PmzsRc5P/O/4jL0KlYejUq1jrauFvUdpId9JREdpaW7CxtRYdRIsX+ho1GI5GRkURERGAwGKhRowYLFizg2WefZdKkSYWu4+/vj7+/f75pmzZtMv3ftWtXunbt+pChS5IkFU9iShIIcHN2JSI1pqLDKRdHIk5jZW7JyBGzSfpmLtr4+7dj0MZHYGNrXU7RVR2xMek4Oleez61YSX/FihXs2LGDOnXq8NJLL7Fq1SosLCzIycmhW7duRSZ9SZKkx0F8YjyuNV1JyUlFY6hcNbNHsf/GEWwsrBg06kMSv5qBPvl2kcsaMlMQRgPetR3LMcLKLyYylQaN3DA3rxyX+YuV9FNSUti0aRN+fn75ptvY2LBs2bIyCUySJKm0RN2OorZHLaLS7lR0KOXul2v7sTa3ovd/l5C46T30GUlFLqtJiKZtE9lQ+kHs7C1x9bCjprs99X1qotfpMTevHIMWFSvpGwyGAgl/0qRJfPbZZ3Tq1KlMApMkSSot4dGRPNW0BREZ0RUdSoX4Pmgn1hbWdH7lUxLWv4Mxp/AuzzUxITR6sh1h0enlHOHjyd7BClcPO1zd7XGvbY9HLUdcatqiUIAmOxd9WirGxEgs6laeRuj3Tfrz588nPj6e8+fPk5Lyz2hUer2e6OjqufNIklS5CCG4HXeHHs9250J8aEWHU2G+vPATNhZWPDNhOQnrJmHU5hRYRht3kycadiasmh3eHZyscHW3x9XDHo//JXfnmrYIo0Cbk4shNRltTCSZv4dx++Jl1LfvuU2iUNB+248VF3wJ3Tfpv/DCC1y/fp3Q0FB69+5tmm5mZsZTTz1V5sFJkiQ9qvTMDHR6HQ729qREVM5BUkqDQLDm7GamdphI41dXEL/ubdDnb9+gjY+ghl3B7tCrBAU4OVvj6m5PTXc7PGs74F7LAecathiNRrTZecldE32LjF9Dib58CXVs/IPLFQJtSipWbpVjwKL7Jv1mzZrRrFkzOnbsiLu77LRBkqTKJzMrAwUK7G3tSc55cB8jVZkQguWn/o9Znd+k/sQVxK9/B4z/dC6jS4lFaW6BnXXl7YdfoQAnFxtcPexxdbfD43/J3cnFBoPeiDYrB2NqMpro66SfDiXiwkW0iUW3cygOdWxs1Uj677zzDqtWreKVV14pdP7u3SXr31mSJKm8ZWZngQLsrGxJVct71QajgY+Or2N+18nUeuVTEjZO+WemMKJNvkNdT3vQP97dyyqUCpzvSe6eXg64ezrg6GyDXmdAm52NISUJTWQIqUdDCL94Ce09t6lLk7YYHdY9Lu6b9CdMmADA3LlzyyUYSZKk0paVnY2djS052lwMRkNFh/NY0Bl0LDz6GQufew+3sR+T8NU/456ob4fh4d2J9PisCozwH0qlApeattR0t8PNwx5PLwfcPB1wcLJGp9Wjy8rGkJxIbuRVkgJDuH7xEvr08j2506VVnpPJ+yb9pk2bAtCmTRuio6OpU6cOf/75J8HBwYwePbpcApQkSXoUKWkpODk6k5ZbeQ7M5UGj1/DB4ZUs7vE+riM/IHHLfAB0sTdwatKF8v60zMyUuLja4upuh6uHPZ6185K7vaMVOo0O7f+SuzriMgn7rxF24RL6rMfjxESfkYEwGFCYPf4j7hXrkb158+YBeSPezZkzh86dOzNr1ixWr15dpsFJkiQ9quTUZBzt7cnQZFd0KI+dbF0O8w4t46Oe06kxbAbJP3+ENj4cpeLB6z4sM3MlNVxtcfWwx83DDs/ajrh62GPnYIVOrUOblYUhKYHc8AvE7fmb0CtX0Gc93t+dPjsbo16PWVVJ+levXmXbtm1s3LiRwYMH89577zFkyJCyjk2SJOmRpaSl4uLkjN6gq+hQHksZmkzmBAbwUc8ZuPhPInXvepRmj94Hv7mFkppueR3YuHvY4eGVl9xt7SzRqrXoMrMwJMaTE3aWO78Ek3Y5CKNaXQrvqPzps7MRhspx66hY36wQAqVSyYkTJ3jttdcAUFfSL0eSpOpFo9GgslChM1auIVDLU0puGnMDA1jSYzpO2tHoczOhmLV9C5UZNd3s8i7Le9pTy8sRV3d7rG1VaHO16DIzMSTEkXPtb2K2BpN+JQijtmp1hWzIzgHxeDd8vKtYSf+JJ55gwoQJxMTE0KZNG9577z18fX3LOjZJkqRHpjfoMTc3l0n/AeKzk5h3eBmLuk9DKRQoyH9lRGVpZnrG3c3TPu+yvLsdVjYqNLkadBmZGOJjyblyhcigYNKCroK+enzm+uzH+/bDvYqV9JcuXcrBgwd55plnsLCwoFWrVgwaNKisY5MkSXpkOr3uf0lfXt5/kNsZcXz450o+fG4qjs429B7UGM/ajtR0s8PS2gJNjgZdRgaGuDtkX7hA+JVg0q9dqzbJvSiV6cpFsZK+jY0NrVq1Ij09neDgYJo3b86tW7do0qRJWccnSZL0SPR6Axbm5ugM1TsxFVd4ajT7bxyh9xOdaWCVSvbZc9y4fIXMkFAwGis6vMeSUqWCynF1v3hJf9WqVXz55ZfUqFHDNE2hUBAYGFhmgVU0rc6AyqLolpgPmi9J0uNBr9cBChTFvUktcTk2mN7urQlZtKSiQ6kUlJaWxW4DUdGKlfR37tzJgQMHqlVXvCoLM/zf21nk/N3LBpZjNJIkPSyD0YBOp8PBoor2KV8GrqdEonKwB6VS1u6LwcyycgyrC6AszkKenp7VKuFLklR1WJirUGvU2JjLpF9car0ag1aLZc0aD15Y+l9Nv3JU9YtV02/fvj2ffPIJ3bt3x8rqnx1H3tOXJOlxZ2VpSa5GjZWs6ZeIRqvGyt0dTUJiRYfy2FOqVFUr6e/YsQOAffv2maYV557+7t27WbduHXq9njFjxjBy5Mh882/dusX8+fNJT0/H1dWV5cuX4+joWNL3IEmSVCRrK2vU6lysZU2/RLJ1uVi6uVV0GJWC0tIShbJYF84rXLGS/qFDh0pccHx8PCtWrGDHjh2oVCr+85//0LZtWxo0aADkdfjz+uuvM3v2bLp06UJAQAAbN25k2rRpJd6WJElSUaysLMnNzcXKwrKiQ6lUEg2ZOHjI27rFYWZpWSn63Ydi3tPPzs7mww8/ZMyYMaSlpTFv3jyyH9AZwcmTJ2nXrh1OTk7Y2NjQu3fvfFcKgoODsbGxoUuXLgC89tprBa4ESJIkPSobKxvSszJxsLKv6FAqlbisRKy8ald0GJWCpbsbSvNH77q4PBQr6S9atAh7e3uSk5OxtLQkKyvLNAhPURISEnB1dTW9dnNzIz4+3vQ6KiqKmjVrMmvWLAYPHsz8+fOxsbEpUE5GRgYxMTH5/uLi4or7/iRJquZsrG3JzskCIbBT2VZ0OJVGZPptrD09KzqMSsHmiScqOoRiK1bSv3btGlOmTMHc3Bxra2sCAgK4du3afdcxGo0o7mnYIITI91qv13P27FlGjBjBL7/8Qp06dfjoo48KlLN582a6d++e709eEZAkqbhca9REq9WSmpWOm61sjV5cN5IjsHStWdFhVApWleg2SLGuRyj/1UDBYDAUmPZvHh4enDt3zvQ6MTERt3sahbi6ulK3bl2aNWsGQP/+/Zk0aVKBcsaMGcPgwYPzTYuLi5OJX5KkYqnh7AIKBekZ6bjb1eRWalRFh1Qp3EqLwtzaGoWFBUInuzAuklKJysmpoqMotmLV9Fu3bs2nn36KWq3m2LFjvPXWW7Rt2/a+63To0IFTp06RkpJCbm4uBw4cMN2/B3j66adJSUkhJCQEyGssWNgjgA4ODnh5eeX78/DwKMl7lCSpGnO0d0ShUJCZkYmbray5FpfRaESvVmPl5vrghasxlYsLxkp0UlSspD916lRsbGywt7dn5cqV+Pn58f777993HXd3d6ZMmcLo0aMZNGgQ/fv3p3nz5kyYMIGgoCCsrKz4/PPPmTNnDs8//zxnzpxhxowZpfKmJEmS7nKwdwAhyMjMwMNWJrCSUGvV8rG9B7D29EAYDBUdRrE98PL+wYMH+eKLLwgNDcXKygpfX19atmyJpeWDH3/x9/fH398/37RNmzaZ/m/RogXbtm17iLAlSZKKx8HOHgEkJiXSxvf+Vyil/DL1uVjJ3ljvy8rDo9I8rgcPSPp79+5lxYoVTJo0CT8/PxQKBUFBQSxevBiNRkOvXr3KK05JkqSHYmZmRg0nFyLvRDHEaTBmSjMMxspTM6tICbp0anrK26n3Y/Nk3bxueCuJ+yb9b775hq+//ppatWqZpnl7e9OiRQtmzZolk74kSZVCvSfqceXaFVIz03jCsTbhpdSYL/1aIlHb/6bZnK4AxB+NIPVSHMIocG7ujnu3evmeWrqrqOXSQ5K4s+86SnMldQY3wqa2AwDRv17DqZk79t4upRJ3ccVmJVBHPqt/X07NmxX6HT+u7ntPPzs7O1/Cv89E1DkAACAASURBVKtevXpoNJoyC0qSJKk0NXzSG41WS3xiPA1cniyVMjXJOdzZf8P0OiMsibSrCTR8rTW+b7YhKzyN9OCEAuvdb7n4w7doML4lXgP9SDgWCUBOTAYGjaHcEz5ARFpMpXocrbwpLCywqlW5+jK4b9I3u899CiFEqQcjSZJUFjzdPVAqlCTEJ+DjUu+RyzNqDURt+5tafRqYpqVfS8S5uTtmKjOUFma4tPQk9XJ8gXXvt5zCTIlRa8Cg1qMwUyKE4M6BG9Tq3aBAOeXhenI4ljVk3wZFsfOuj1GjregwSqRyjBAgSZL0CNxd3REI7sTfwafGoyf9mN0huLSuhbW7nWmaLl2DheM/g/pYOFiiy1AXWPd+y3n2akDk1mDij0Tg/uyTpJy/g319Z1ROFTNYUExGLEoLC8ys5WBFhXFo2gSlyqKiwyiR+97TDw0NpWXLlgWmCyHQaivX2Y0kSdWXykKFp5sHEbejcLC0o4aNM8k5qQ9VVtLZGFAqqNGyFtrUXNP0Alc/BYUOt3q/5eyedMLn9dYA6HN1pJyPxXvc08QfiSA7Kh0rd1tq9SrfWr9OnYulmzs5kZHlut3KwKVNa5QWVSjpHzx4sLzikCRJKlONGvjx5+mj/H97dx4XZb33f/w1M8wM+yYMCO6ioCJKKLgk7qgg4q5pasuxRctzPP3qeNui1emurHPslNXJHt3dVlpqd0pmiZrZcStzyV0CWQRkRzZZZpiZ3x8mSeAWDMPA5/l4+JC5rovvvIeH8rmu7/W9vt+UjDTuah/Mrgv7/lA7RcezMRtMJL5zGLPRhMlgJPGdwzi0d6Gm7LexToayatRu9Ud1a9zsb+u4nG9T0A3vjKGkmvKUy3S/P5SMrecou1DUrPf3q/RV2PvopOj/jkKtxrlb43uNmttNi76/v4zaFEK0DkEBgew5sJf09HQG9Ar5w0W/58MDa7/WX64k8e3DBC4Kp+R8Abl7U/Ec4I9CqaDoeDaeofUHebkGed/yuMqcMgzFVbgFeVOZWw7KX3sMFApMBtMfyv1HFRsr0PrIBD2/5xoUiElvaF1X+kII0Vp08u+ISqUkOf0CYyJHoVapMRibbvpUtyAvqnLLSXrvCGajCbcgLzz6X33GveCnLCqzSuk4uddNj7vm0o5kOkwMBMDBxxm1s4ZzbxzCXueEa4/mHcWfpy/Gv4GnuNo6r+HDbHKsgxR9IUSboNVoCQoI4kJ6CjmFeQTrenI8+0yj2tR4ONQ+ow/gM7wLPsO71DvOa6A/DPS/5XHXdL8vtM7rTtN6NypnY2SV5hDg3zRF/1BJMd8UFaAANEoFc3R+dHVw4PnUZPRmM3a/jm0Y5OrGhHb1p0xOrLjC5rwc9GYzjkolD7TvgE6jIU9fzdtZGVSbTMS082aYuwcAB0suk6PXM9W7aR87VKhUeA0diuIWC8+1RLaXWAgh/qD+ffpRWVlJWnoaEX6ht/4GQerljCZ5Vj+7uppNeTn8tWNnnu8aQGw7HW9nXaTaZCLPoOf5rgG1fxoq+EUGA2syLzLP148XugYQ5uLKJ7mXANhzuYgJ7bx4sWsAXxVenfOg0mhkz+UiYhpoq7Hc+/fj6ghM2yNFXwjRZnTv3A2Ac0nniegYikZlW/djreGXwgtoPTwa3Y5aoeC+9v642139mXexd6CkpoakiivYK5Wszkjj2dQkPs3NRm+qP27hSFkJfZ2d6WzvAMAId0/u0V0dC2GnUFBtMlFtNqHgam9BfEEe4z290Frgatx7zChUDg5N3m5zkKIvhGgzPNzc6eTfkUs5l7iUm01EB7nav5XCimLMgJ2LS6Pa8dJo6Od8tQ2z2cxnedn0d3HBYDYT5OjEo/4dea5zd4oMBj7Prz+pUa5ej1ap5N9ZGaxMTebdSxmofr0dMMazHYdLS1h1MY2ZOl8uVVdxSV/NAFe3RmVuiFKrxTPsLpuaevd6UvSFEG1K5KBhlF0p51ziOcZ0GWrtODbBUFWJfRON4K82mXj3UgZ5ej33+/oT6uLKQr+OOKvsUCuVxHh5c7ystN73Gc1mjpeVMcVbx8quAfRydObtrKtrKLjbqXmyU1de6BrAXS6ufJaXwyydLyfKy3gjI41/Z2VQbqxpkvye4QNtaind35OiL4RoU3r36IWdyo7E5F/o6O6Pt5NMM3srFYYqtE2wxG6hQc9L6SkoUfBUp644qlT8XFZKYsWV2mPMZmqv4K/nbmdHDwdHfDRX5zSIdPcgo7qq3q2An0pL8NNo8dfa81leNo/6d6Kfsws7iwobnR9AFzUGO0fHJmnLGqToCyHaFEcHB8JCQskryud80nlGdhli7Ugt3mXjlUZf6Vcajbx6MZUwF1ce8e+I5td77ZdrDGzKy0FvMmEym9l5uYCBDXTL3+XiSlJlBfm/zgZ7tKwEf422th242ouwo6iAOK+rWY1mM0pAAQ2OE7hT9u3b4xrYs9HtWJM8sieEaHPC+w/kx+OHOX3uDFMmTmbLuW8wmJqm+7c1yq0qolsjJ2vbU1xEocHAsbJSjl3Xff9kpy7kGww8n3YB46/39yf9OuL+eFkpe4uLWNqxC53sHZjn2541WRcxms04qlQ86t+xzntsL8xnlIcnDr8uFjfe04tnUpNwVKpY5N+pUfkB/KZNQXGThehsgRR9IUSLEhgYSGJiYr3ty5YtIzw8nKlTp95xm6NGjeKjjz6iQ4cOAHTt2AV3Vw/Ssy6SV5DPsM4R7Ek90OjsrVVmSTZ9/Hs0qo2Ydt43fHxups6XmTrfettDXVwJdXGtfR3m4kaYy40H5/3+efxRHu0Y5dE0t2/UHu7oht2N0s62y6Z07wsh2hylUklU5BguF1/m2IljTA6MstnR2M0h5fJF7HVN/7y7LWkfNwmF0vb/jUjRF0K0SGazmZdffplx48Yxb948Ll68WLtv69atTJkyhbi4OJYvX0519dUFbD755BNmzJjBxIkTmTJlCikpKTdsPzS4P/Zae5LTLmCo0jNIHt+7ocTCFNTu7g2uGtgWqJwcaT8+CqVGY+0ojWbRor9t2zaio6OJiopi/fr1Nzxu7969jBo1ypJRhBA2JiEhgbNnz/LVV1/xr3/9q7boJyUlsWnTJj777DPi4+Np164dH3zwAeXl5ezevZuPP/6Yr776ihEjRtz0946DvT2j7h5JfmEBh4/+xIxeE2sndhF1XdFXYDYY0DTBJD22yDd6QqvpCbLYzYnc3FxWr17NF198gUajYfbs2URERBAQUHct6IKCAl599VVLxRBC2KjDhw8TFRWFWq3G09OTyMhIAH788UfS09OZOXMmAAaDgd69e+Ps7Mw//vEPtm/fTlpaGvv27aNXr143fY8hYYPY9Z/dJKb8QsSACAZ3DONgxhGLfzZbVF1dhdZHh76oyNpRmpVSo8F/8iRU9ra3uE5DLHalf/DgQQYNGoS7uzuOjo6MGzeOHTt21DvumWee4bHHHrNUDCGEjVIoFJjNv81vbvfrACqj0ciECROIj48nPj6ezZs389xzz5Gdnc2sWbMoKysjMjKSKVOm1Pn+hjg7OTNyyEjyC/LZ/8N+5oVMlal5b+BKTSX2TfCsvq3xnzkdhY0P3ruexYp+Xl4e3t6/DfzQ6XTk5tadWvGjjz6id+/e9OvX74btlJaWkpmZWedPTk6OpWILIVqIwYMH880336DX6ykpKWHfvn0AREREsGvXLgoLCzGbzaxcuZJ169Zx6tQpOnfuzH333Uffvn3ZvXs3xtuYOW1Y+BDUag3JaSnk5uYyKXCspT+aTSqsKW+yWflshX379vhNjMaulVzlgwW7900mU517IGazuc7rX375hZ07d/K///u/Ny3i69atY82aNZaKKYRoocaMGcOpU6eYOHEiXl5edO/eHYCgoCAee+wxFixYgMlkolevXjz00EPU1NTw6aefEh0djdlsZuDAgSQlJd3yfVycXZg0NobN2/+P/T8cYO60e9iTepCiymJLf0SbklNRSO8OjXtW39Z0eWQhCnXr6vmxWNH39fXlyJHf7o3l5+ej0/12lrhjxw7y8/OZNm0aBoOBvLw85syZw4YNG+q0s2DBAqZMmVJnW05ODnPnzrVUdCGEFV3/jP7SpUtZunRpvWNmzJjBjBkz6mzTarV8+OGHDba5Z8+em75nRGg4ew/9h4zsTE6cPcm9IVN488eG22qrMkqyCPPrb+0YzcYzIhyXgO6oWlHXPliwe3/IkCEcOnSIoqIiKisr2blzZ+1AHIAlS5aQkJBAfHw8a9euRafT1Sv4AK6urnTo0KHOH1/f+pM4NDe9wXYXXBBC1KVWq5kxcRolpcUcPvYTfb2DCPIKuPU3tiHJRelo28iz+kqNhq4PL0Tj7GztKE3OYqcwPj4+LF26lPnz52MwGJg+fTohISEsXLiQJUuW0LdvX0u9dbPQqFXEPhHf4L5t/4hr5jRCiMbq2a0HwYHBJKcls2ffdzw+dAF/3fl3qmuqrR2tRbhQmIrayQmFSmXTq8zdDr8Z01Daa60dwyIs2m8RGxtLbGxsnW3vv/9+veM6dOhwy+43IYSwJIVCQVxULK+++zrnks7TvWs3FoRMZ+2xGz/r35boTTXUVFej8fKiOrf+evethWPnTvjFxqB2cLB2FIuQGfmEEOJXPt46JkdNIjsvh+8OfE+YbzD9fftYO1aLoddXteoR/EqNhh5PPYGylQ3eu54UfSGEuM7QgYMJ7N6TrOwsdu7dxaMD7sVJY7vrpzelsprKVl30O92/AKWzc6sbvHc9KfpCCHEdlUrFPXEzMZnN/JKSzIWUCzw6YJ61Y7UI+TWlaFvAQGpL8IwIx2vYUBzd3Rvcn5mZSXBwMHFxcUyePJmYmBjuv//+Jp835umnn+bUqVNN2ub1pOg3s1uN+penAoSwvnYe7ZgZM428gjz+88N+/LTeTO013tqxrC67PB+HVvisvr2vL90eX4Tayemmx+l0OuLj49m6dSvbt28nMDCQVatWNWmWl156yaID3VtvH0YLdbNR/yAj/4VoKQb2H8CZX85x6vwptiVsZ87U2aQWZ3I8+7S1o1lNenEWQ9oPtXaMJqXUaOjxX09hUihQKu/sOjgiIoJ//vOfjBo1ipCQEM6dO8eGDRvYt28f69atw2Qy0adPH1asWIFWq2Xo0KGMHj2akydP4uXlxbRp0/j444/JycnhlVdeITw8nHnz5tVOTb9mzRo+/vhjAJYtW0Z4eDjh4eEsXryYbt26kZycTO/evQkNDWXLli2UlJTw9ttv105k1eDn/eM/KiGEaL0UCgWzJs3Aq5036ZkX+WrX1zw2cAF+Lm1v/vlrkotSsfdqZ+0YTUehoOvji1G6uOBwh8/kGwwGEhIS6N//6oRFkZGRJCQkUFRU1OAqkHB1gbnIyEi2bt1KdXU1u3fvZsOGDTz++OOsW7futt87MTGRhQsXEh8fz7Fjx8jKymLjxo1MnDiRjRs33vR7pegLIcQNODo48KfZ92E2mUhKTebAjwf529BFOKhbz1zsdyL9chZKrX2rWFceoPOD9+HYKxDndp63dXxeXh5xcXHExcUxadIkzGYzTzzxBEDtGjLXrwIZFxfHt99+S0pKSm0b1yap8/f3Z9CgQQD4+flRWlp627m9vLzo3bs3SqUSX19fBg8efNvtSPe+EELchM5Lx/2zFvDOR+9x/PTPeHl58V9DF/P3fW+iNxqsHa9ZmTBRU12JVudNZWaWteM0it/0qbgOisC53e33XFy7p98QrfbqZD7XVoF85plnALhy5UqdhZ80150wqVSqG77X71eZNBh++7em+d1J183a+T250hdCiFsICghkyvhJXMrNZs++76gp1fPkkIdRKW//l21rUaWvsvkldr3HjkYXE429q+sd38e/lRutAnmnPDw8yMjIoLq6muLiYo4ePdok+aToCyHEbRg+KJLhg4aReSmTHXsSsNdr+EvEgygVbevXaGlNJVobflbfc1A4Hefdi8reHo226afavX4VyJiYGEwmEw899NAdt9OjRw+GDx9OTEwMf/7znwkLC2uSfNK9L4QQt0GpVDJlfBzVej0/Hj/M9l1fM3lCHI8OmMc7P32EGfOtG2kF8gwl+LRvb+0Yf4hrcB+6PbaIGsDJ8c6m2b3ZdPG/397QKpBQdwXJV155pfbriIgIIiIiAGpH6wO88MILt3y/64+fOnUqU6dOvdnHkCt9IYS4XSqVilmx0+nfJ4T0zIt8ueNLujl24OGwuW3miv9SaS72NvisvkfYXfRY9iR6owknFxdrx7GatvGvVAghmoidnR1zp9xDUPdA0jIu8n/bt9DZ3o//N/gh1KrWO2f7NWnFGTj42tY9fd3oUXT982NUVFbi4u5m7ThWJUVfCCHukFaj5b6Z8+nRNYDUi2ls/Toeh2o1z0UuwVHdOldnuyapMAXtHYx4tzb/mdPxnTOL0vIreOpsdyxCU5Gi38LcbBpemaJXiJbDwd6ehXMfIDS4P+kZ6Xy162tKc4r5+6gn8XBovVeT2eX5KJRKVE4tfBEipZIujzyEy4hIDICPv5+1E7UIMpCvhbnZNL0yRa8QLYtGrWHe1Dm4ODqz99D37N73HYPDInh59N94/eBakovSrB3RIgxVldjrdFxJTbN2lAYpNRq6P/EXjDpvNM7OuLi13pOwOyVX+kII0QgqlYqp0ZOJHRvDpZxLHPjpIP/Zv4//unsx47pHWjueRVQaqlrsY3sO/v70fu1lKt1c8fD1lYL/O3KlL4QQjaRQKBgbOQY3Vzc++3IzR04epaCogIlRMQR6dee9I+upNuqtHbPJFBsrsNe1vMF8utGj6HjfPBLPnqVPWBh26tY/sPJOyZW+EEI0AYVCQURoOH9duAQ7lR3nkhL5dMtGXKsdeWXMsla1UE9u1WW0/i3nWX2VoyMBT/4Vz2mTOXv6NH3Dw6Xg34AUfSGEaEId/Try/x5eSlBAT1LSU9m++2tOnTjF30c9SVxgVKt4nj+zNAcH/5bxrL5zzx70feMf5NnZUWkwcNeQIU0+tW5rYtGfzLZt24iOjiYqKor169fX27979+7a1YoWLVpESUmJJeMIIUSzcHZy5oFZ9zFpbAw5ubkcOHyQT7/YyGDvUF4e/Tc6utn2SPLUyxdxsPL8+wo7O/xnzaDns8s5deY03fsG06FLF6tmsgUWK/q5ubmsXr2aDRs2sHXrVjZu3EhycnLt/vLyclauXMnatWv58ssvCQwM5K233rJUHCGEaFYqlYqxkWNY8uBi1Go1pxPPsGnrZhJPn+f5EX9lZp+JNrtgT2JhChoPd6u9v1u/EELeegNjcG8uJCURMXIkjk5OVstjSyxW9A8ePMigQYNwd3fH0dGRcePGsWPHjtr9BoOBFStW4PPr2WJgYCDZ2dmWiiOEEFbRvXM3nnr0CcbcPYrs3Bz2/bCfTzavJ8SlJ/+Meo672gdbO+IdK6kqxWwyoW7mkfFab28CnnqCTo8v5sDBg7h6exMcFoZCoWjWHLbMYqP38/Ly8Pb2rn2t0+k4efJk7WsPDw/Gjh0LQFVVFWvXrmXevHn12iktLaW0tLTOtpycHAulFkKIpmevtSd2bAz9eoewfsunnEtOpKj4MoHde/LAkFlM7DGGj0/9H6mXM6wd9bbpq68+tmdohtuyKgcH/KZPxWd8FEcOHMD+/DlGx02SwXp/gMWKvslkqnP2ZTabGzwbKysrY/HixQQFBTFlypR6+9etW8eaNWssFVMIIZpNJ/+OPPHwX/jPj/v55rsEDh75gZSMVPr3DuHpux/nbH4Sn56OJ7s8z9pRb6nCUIm9j47yX5Is9h4qR0d8xkfRPi6WpHPnOb39a4aOHYObp6fF3rO1s1jR9/X15ciRI7Wv8/Pz0f1u3uO8vDwefPBBBg0axPLlyxtsZ8GCBfVOBnJycpg7d27ThxZCCAvTqDWMuXsUA0LC2LE3gUNHfyS/IJ8ziWcJ7RvKS6Of4lxeEl8m7SKxIMXacW+oyHgFrYXmste088Q3bhI+Y0aRfPYsX2+NZ2DkMIIHRUhXfiNZrOgPGTKEt956i6KiIhwcHNi5cycvvvhi7X6j0cgjjzzChAkTWLRo0Q3bcXV1xdXV1VIxhRDCKtxd3Zg9aSaREcPY/u03nDp/hku5ORw7eZy+vYL5y8AHKdaXEp+4i8NZP2Mym6wduY7cykICOnRo0jYdO3ei/dTJeAwcwMnDh/n2vbUMGB5J3Lx7Ualsc9BjS2Oxou/j48PSpUuZP38+BoOB6dOnExISwsKFC1myZAk5OTmcPXsWo9FIQkICAMHBwbz00kuWiiSEEC2On097/nTP/aRnXuTb/Xs4df40mdmZHDl5jKDuPZnebzwL+k1jd8p+9l38ibwrBdaODEBGSTZ9/YMa3Y5So8Fj4AC8osbg2LULP37/HzI/+IDwESO5d8nj2NnJxLFNyaI/zdjYWGJjY+tse//99wHo27cv58+ft+TbCyGETVAoFHTp2JkH77mfvII8Dhw5xP6fDpKTl8Op86fp2rELvQJ7ETNmNNlleXyf/gOHMo5Spr9itcwpRenY9/tjawso1Go87grFc/gwPEP7k52ezsGjx6j+6SciRo1kROxE6ca3EDmFEkKIFkTnpWPK+DjGDhvNTyeOsOfA9xw5eYyfz57E29OLHl0DGNyjP3NDppCYf4GjOac4mXOu2Qf//VKYisbVFZRKMN361oNCrcY9pC+ekcPwHDiA/EtZHDv+M4nffENAn94MGjUSnZ9tT1pkC6ToCyFEC+Ts5MzIISOIjBhGetZFjp48xk8njpKdt58ff/4JHy9venTtwYCOfZg2fAJGTJzMPcfPuWc4k5dEaXWZRfNV1lRhNOjReHqiL6h/y8HOxRmXoCBcevfCObgPLp07UZB1iRPHjpH46ip0fu0JiQhnREwMDk6OFs0qfiNFXwghWjCVSkW3Tl3p1qkrceNiSU67wI/HDnPml7NkXMoCM7i6uNDJvxNdOnYmpsNIHg67F71RT/rlTC4UXyS1OIO04kzyygswY26ybNXVVdj76DAbDNj7tcfB3x/nXoG49O6F1tOTvIsZpKamkr5rJ5fSLuLXuRP9BkUwbPw4HJ2dmyyHuH1S9NsIvcGIRt3w6Neb7RNCtBwatYbePXrRu0cv9AY9WTmXSEpN5sTZk5w+f5rT58+AApwcHfHTtcfP1w+fdt708Q/Ap58OZwcnSipLuVxRQmHlZfIriyisvEy5vgKDsQaDyVDnb5VSib2dFns7e+zttDio7XGw0+KmccVL44adnYY+L6zAUF1NWWEhRQUFnEpLI2P9BnIvXcLZxYVuQUH0HzyYSffeK2vbtwBS9G1IYwq3Rq0i9on4Bvdt+0dck+QTQjQfjVpD145d6NqxC1GRYyi/Us7FSxlkZmeRkp5CeuZFfklNRqlQYDKbsbOzw8neEU9PTzzdPXF3dcPV2QVfp25onLSoVCpUKhV2v/6tsrPDbDKhNxgwGPTo9QYMBgPVVVXkpGRz9ue9nHZ2paiwiOrKCjCb8fZrT/devRkeE43Ozw8XNzcZkNfCSNG3IVK4hRA34uzkXNsLAFdnQS0tLyWvIJ/8wnwKigrJLyqgqLiIc7+co6KqEqVCAfWKsgLFtZsAZkBxtS2z2YwCcHVxw93FDQc3F/qEDsC9nSeuHh64t2uH1t6+eT+0uGNS9IUQohVSKBS4ubjh5uJGj64B9fYbDAauVFZQU2PAaDRRY6zBaDRSY6zBZDKhUWtQq9Vo1GrUdmo0Gg0atUbWqrdxUvSFEKINUqvVuKvlHntbI6dsQgghRBshRV8IIYRoI6ToCyGEEG2EFH0hhBCijZCiL4QQQrQRUvSFEEKINkKKvhBCCNFGSNEXQggh2ggp+kIIIUQbIUVfCCGEaCOk6Av0BmOj9gshhLANMvd+K3GrpXVv5mar94Gs4CeEEK2FRYv+tm3bePfdd6mpqWHBggXMnTu3zv5z587x9NNPc+XKFQYMGMDzzz+PnZ2ch/wRUriFEELcisW693Nzc1m9ejUbNmxg69atbNy4keTk5DrHPPnkkzz33HMkJCRgNpvZtGmTpeKIRrhZ9750/QshhO2w2GX1wYMHGTRoEO7u7gCMGzeOHTt28NhjjwGQlZVFVVUV/fv3B2Dq1Km8+eabzJkzp047paWllJaW1tmWlZUFQE5Ozi1zGCqKbrgvMzPzhvtvtq8x32tr7QLk5Wbzp5d2Nbjv3b+NRm3X8LmjocZ0w32ieVz7P2I0ysmZEAIUZrPZbImG33vvPSoqKli6dCkAmzdv5uTJk7z44osAHD9+nFWrVvHpp58CkJ6ezkMPPURCQkKddt566y3WrFljiYhCtBnr169nwIAB1o4hhLAyi13pm0wmFApF7Wuz2Vzn9a32X7NgwQKmTJlSZ5terycjIwN7e3vmz5/P+vXr8fX1tcCn+GNycnKYO3eu5LoDLTVbS80Ft5fNaDSSn59PcHBwM6cTQrREFiv6vr6+HDlypPZ1fn4+Op2uzv78/Pza1wUFBXX2X+Pq6oqrq2u97d26dSMzM7O2rQ4dOjRl/CYhue5cS83WUnPBrbN17ty5GdMIIVoyi91wHTJkCIcOHaKoqIjKykp27txJZGRk7X5/f3+0Wi1Hjx4FID4+vs5+IYQQQjQtixV9Hx8fli5dyvz585k8eTITJ04kJCSEhQsXcurUKQBef/11Xn75ZcaPH09FRQXz58+3VBwhhBCizbPoQ/GxsbHExsbW2fb+++/Xfh0UFMTnn39uyQhCCCGE+JVq5cqVK60dojG0Wi0RERFotVprR6lDct25lpqtpeaClp1NCNHyWOyRPSGEEEK0LDJzihBCCNFGSNEXQggh2gibLfrbtm0jOjqaqKgo1q9fb+04dZSXlzNx4sTaeQRaijVr1hATE0NMTAyrVq2ydpxa//rXv4iOjiYmJoYPP/zQ2nHq4IvqigAABdZJREFUefXVV1m2bJm1Y9Qxb948YmJiiIuLIy4ujhMnTlg7khDCBtjkknbXFvP54osv0Gg0zJ49m4iICAICAqwdjRMnTvDMM8+QlpZm7Sh1HDx4kP3797NlyxYUCgV/+tOf2LVrF2PHjrVqrsOHD/PDDz/w5ZdfUlNTQ3R0NMOHD6dbt25WzXXNoUOH2LJlCyNGjLB2lFpms5m0tDS+++47WZVSCHFHbPJK//rFfBwdHWsX82kJNm3axIoVKxqcXdCavL29WbZsGRqNBrVaTffu3bl06ZK1YxEeHs5HH32EnZ0dhYWFGI1GHB0drR0LgOLiYlavXs0jjzxi7Sh1pKSkAPDAAw8wadIkPvnkEysnEkLYCpu8TMjLy8Pb27v2tU6n4+TJk1ZM9JuXXnrJ2hEa1KNHj9qv09LS+Oabb2oXO7I2tVrNm2++yf/8z/8wfvx4fHx8rB0JgOeee46lS5eSnZ1t7Sh1lJaWMnjwYJ599lkMBgPz58+na9euDB061NrRhBAtnE1e6d/uYj2ivqSkJB544AGeeuopunTpYu04tZYsWcKhQ4fIzs5m06ZN1o7D5s2bad++PYMHD7Z2lHpCQ0NZtWoVLi4ueHp6Mn36dL7//ntrxxJC2ACbvNK/1WI+omFHjx5lyZIlLF++nJiYGGvHAeDChQvo9Xp69eqFg4MDUVFRJCYmWjsWX3/9Nfn5+cTFxVFSUkJFRQX//d//zfLly60djSNHjmAwGGpPSMxms9zbF0LcFpu80r/VYj6ivuzsbBYvXszrr7/eYgo+QGZmJs888wx6vR69Xs+3335LWFiYtWPx4Ycf8tVXXxEfH8+SJUsYNWpUiyj4AGVlZaxatYrq6mrKy8vZsmWL1QdkCiFsg01eHly/mI/BYGD69OmEhIRYO1aL9sEHH1BdXc0rr7xSu2327Nncc889VkwFw4cP5+TJk0yePBmVSkVUVFSLOilpiUaOHMmJEyeYPHkyJpOJOXPmEBoaau1YQggbINPwCiGEEG2ETXbvCyGEEOLOSdEXQggh2ggp+kIIIUQbIUVfCCGEaCOk6AshhBBthBR9cUNGo5EPP/yQqVOnEhcXR3R0NK+99hp6vb5J2i8rK2P+/PlN0pYQQohbk6IvbmjlypUcP36cdevWER8fz+eff05qaipPP/10k7RfUlLCqVOnmqQtIYQQtybP6YsGZWZmMnHiRPbv34+zs3Pt9vz8fI4dO0bPnj154YUXuHLlCvn5+QQFBfHGG2+g1WoJDg5m9OjRnD9/ntdff53ExEQ2btyIwWCgpKSEhQsXMmfOHObNm8eRI0fo2bMnX3zxBSqVyoqfWAghWj+50hcNOnPmDAEBAXUKPlxdonfcuHFs2rSJyZMns2nTJnbu3ElmZiZ79+4FwGAwMHLkSBISEujWrRubN29m7dq1bN26ldWrV/Paa68B8PLLL2Nvb098fLwUfCGEaAY2OQ2vsDylUonJZLrh/ieffJIDBw7w/vvvk5aWRl5eHhUVFbX7BwwYAICTkxP//ve/+f7770lLS+P8+fN1jhNCCNF8pOiLBoWEhJCSkkJ5eXmdq/3c3FyeffZZHB0dMRqNTJgwgREjRpCdnc31d4ocHR0ByMnJYdasWcycOZOwsDDGjx/Pd9991+yfRwghhHTvixvw8fEhNjaW5cuXU15eDkB5eTkrV67E3d2d/fv3s3jxYqKjowE4ceIERqOxXjunT5/G09OTRYsWcffdd9cWfKPRiJ2dHUajERlWIoQQzUOu9MUNrVixgnfeeYfZs2ejUqnQ6/WMGTOGxx9/nM2bN7N48WIcHR1xdnZm4MCBXLx4sV4bQ4cO5fPPP2f8+PEoFArCw8Px9PQkPT2dzp07ExISQkxMDOvXr8fDw8MKn1IIIdoOGb0vhBBCtBHSvS+EEEK0EVL0hRBCiDZCir4QQgjRRkjRF0IIIdoIKfpCCCFEGyFFXwghhGgjpOgLIYQQbYQUfSGEEKKN+P/tSoeEO1NLuQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Basic Plots 1\n", "with plt.rc_context({'figure.figsize' : (8.0, 3.5)}): # Locally set options for just one figure:\n", "\n", " # Make NxM array of subplots (don't worry about this for now )\n", " f,axs = plt.subplots(1,2)\n", " axs.ravel() # Flatten the array\n", " \n", " # Histogram Example:\n", " axs[0].hist(diamonds['carat'], bins=25, density=True)\n", " axs[0].set_xlabel('Carat')\n", " axs[0].set_ylabel('Density');\n", " axs[0].set_title('Simple histogram of Carat sizes')\n", " \n", " # Pie Chart Example:\n", " #Use group by function to sort data\n", " groups = diamonds.groupby('cut')\n", " counts = [len(x[1]) for x in groups ] # Grabs the amount in each group\n", " names = [x[0] for x in groups] # Grabs the labels of the groupby method\n", " \n", " axs[1].pie(counts, labels=names, autopct='%1.1f%%', shadow=True, startangle=90) #Notice house were using axs[1]\n", " axs[1].set_title('Percentage of Diamonds by Cut')\n", " " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\David Delgado\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Violin plots show distributions + categorical data\n", "iris_data = sns.load_dataset('iris')\n", "with plt.rc_context({'figure.figsize' : (10.0, 6.5)}): # Locally set options for just one figure:\n", " ax = sns.violinplot(x=\"species\", y=\"sepal_length\", data=iris_data, palette=\"Set2\", cut=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise on contexts and different types of graphs\n", "\n", "Change the above graph to have a \"poster\" context and a 'dark' style. Also change the palette to \"Set3.\" If you get done early feel free to explore different types of plots and styles." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 6. Subplot and Figure Options\n", "We used the subplot function above, but not to create multiple plots. Let's actually make more than one. \n", "\n", "The syntax we've been using to get axes is `f,ax = plt.subplots()`. In this example, we are going to add arguments to return more than 1 axis. \n", "\n", "We can pass (nrows, ncols, index) to `plt.subplots` to create an array of axes and access the one located at the index location.\n", "\n", "\n", "![multiple](Images/twoplots.png)\n", "\n", "The marker/line type specification shortcuts used below can be seen at https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Function and arrays to generate some data\n", "def f(t):\n", " return np.exp(-t) * np.cos(2*np.pi*t)\n", "\n", "t1 = np.arange(0.0, 5.0, 0.1)\n", "t2 = np.arange(0.0, 5.0, 0.02)\n", "\n", "# create a figure\n", "plt.figure(1)\n", "\n", "plt.subplot(211) # 211 = 2 rows, 1 column, first plot\n", "plt.plot(t1, f(t1), 'bo', \n", " t2, f(t2), 'k') # bo = blue, o marker, Makes a line through the dots\n", "\n", "plt.subplot(212) # 212 = 2 rows, 1 column, second plot\n", "plt.plot(t2, np.cos(2*np.pi*t2), 'r--'); # r-- = red, dashed line" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise on Subplots\n", "\n", "Copy the code below and expand it to four subplots with the same two plots like the image below. Feel free to also change the markers,lines, etc. \n", "\n", "![colorbar](Images/Quad_subplot.png)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Object-orientated approach to subplots \n", "\n", "Now that we have worked with the `plt.subplot()` method a bit more, let's take a object orientated approach. This will allow us to cleanly organize multiple subplot figures and each of the graphs in those subplots. We'll first go through a single subplot and then extend into multiple subplots. " ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def f(t):\n", " return np.exp(-t) * np.cos(2*np.pi*t)\n", "\n", "t1 = np.arange(0.0, 5.0, 0.1)\n", "t2 = np.arange(0.0, 5.0, 0.02)\n", "\n", "fig, axes = plt.subplots(2,1)\n", "\n", "axes[0].plot(t1, f(t1), 'bo', \n", " t2, f(t2), 'k')\n", "\n", "axes[1].plot(t2, np.cos(2*np.pi*t2), 'r--');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alright, let's now go to two subplots" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def f(t):\n", " return np.exp(-t) * np.cos(2*np.pi*t)\n", "\n", "t1 = np.arange(0.0, 5.0, 0.1)\n", "t2 = np.arange(0.0, 5.0, 0.02)\n", "\n", "fig, axes = plt.subplots(2,1)\n", "\n", "axes[0].plot(t1, f(t1), 'bo', \n", " t2, f(t2), 'k')\n", "axes[1].plot(t2, np.cos(2*np.pi*t2), 'r--');\n", "\n", "def f(t):\n", " return np.exp(-t) * np.cos(4*np.pi*t)\n", "\n", "fig2, axes2 = plt.subplots(2,2)\n", "\n", "# Note that since we've gone to a 2 by 2 plot we need to tell the axes[] two numbers for its location \n", "axes2[0,0].plot(t1, f(t1), 'bo', \n", " t2, f(t2), 'k')\n", "\n", "axes2[0,1].plot(t2, np.cos(4*np.pi*t2), 'r--');\n", "\n", "axes2[1,0].plot(t1, f(t1), 'bo', \n", " t2, f(t2), 'k')\n", "axes2[1,1].plot(t2, np.cos(4*np.pi*t2), 'r--');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise on object orientated subplots\n", "\n", "Make a 3 by 1 and 1 by 3 plots all within one cell. If you get done early do some customization of the color schemes. Use what you have learned and google to add y and x labels to the axises. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 7. Legends\n", "\n", "* Legends can usually be handled by adding a `label = 'str'` keyword argument to a matplotib.pyplot method call and then calling `ax.legend()` afterwards. Basics:\n", " * `ax.legend()` looks for labeled data plotted on a given axis and adds a legend to that axis. \n", " * `ax.legend()` has a `loc=` keyword that handles placement that takes 0-9 for default placements or a tuple of x,y coords\n", "* If this is insufficient, there are methods to directly make [custom legends](https://matplotlib.org/gallery/text_labels_and_annotations/custom_legends.html)\n", "* This is often useful for multiple plots or when there are multiple data aesthetics to explain at once\n", "\n", "* Legends are also objects and we can move them outside the axes if we want. If legends or other plot options made this way are cut off when saving, you can save the legend as a variable and manually add them when you save figures using `fig.savefig('path_to_file', bbox_extra_artists=(lgd,), bbox_inches='tight')`\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
\n", "
" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "0 5.1 3.5 1.4 0.2 setosa\n", "1 4.9 3.0 1.4 0.2 setosa\n", "2 4.7 3.2 1.3 0.2 setosa\n", "3 4.6 3.1 1.5 0.2 setosa\n", "4 5.0 3.6 1.4 0.2 setosa" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Quick legend example with the iris dataset:\n", "iris_data = sns.load_dataset('iris')\n", "iris_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7.1 Basic Legend Example" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "versicolor\n", "setosa\n", "virginica\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Standard legend creation and placement example:\n", "colors = sns.color_palette('Set1')\n", "spec_list = list(set(iris_data['species'])) #List to break data down by \n", "\n", "fig,axs = plt.subplots(1,2)\n", "axs= axs.ravel() # Ravel flattens an array into a 1D iterable\n", "\n", "for species in spec_list: # Sets contain each unique value one time\n", " print(species)\n", " species_subset = iris_data[iris_data['species'] == species]\n", " axs[0].scatter(species_subset['sepal_length'], species_subset['sepal_width'], label=species, marker='s')\n", " axs[1].scatter(species_subset['petal_length'], species_subset['petal_width'], label=species)\n", " \n", "axs[0].legend(frameon=True, loc=0) # 0 = best, MPL tries to find a place where it doesn't overlap data\n", "axs[1].legend(frameon= False, loc=4); # 4 = lower right, can manually set to any corner or midpoint using 1-9. Removed legend box." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7.2 More legend customization " ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# A bit more customization:\n", "from matplotlib.patches import Patch\n", "from matplotlib.lines import Line2D\n", "\n", "fig,axs = plt.subplots(1,2)\n", "axs= axs.ravel() # Ravel flattens an array into a 1D iterable\n", "\n", "for species in list(set(iris_data['species'])): # Sets contain each unique value one time\n", " species_subset = iris_data[iris_data['species'] == species] #Use loop to subset the data you are using. \n", " axs[0].scatter(species_subset['sepal_length'], species_subset['sepal_width'], label=species, marker='s')\n", " axs[1].scatter(species_subset['petal_length'], species_subset['petal_width'], label=species)\n", "\n", " \n", "\n", "species_legend_elements = [Patch(facecolor=colors[0], label=spec_list[0]),\n", " Patch(facecolor=colors[1], label=spec_list[1]),\n", " Patch(facecolor=colors[2], label=spec_list[2]),\n", " ]\n", "\n", "# First two arguments is to fill the x and y data\n", "anatomy_legend_elements = [Line2D( [0], [0], marker='s', color='w', label='Sepal',\n", " markerfacecolor='k', markersize=10),\n", " Line2D([0], [0], marker='o', color='w', label='Petal',\n", " markerfacecolor='k', markersize=10)]\n", "\n", "# Handles are a group of keywords for more control over what is displayed in the legend i.e. patches\n", "species_legend = axs[0].legend(handles=species_legend_elements, loc=(2.25,.65), title='Species', frameon=True)\n", "anatomy_legend = axs[1].legend(handles=anatomy_legend_elements, loc=(1.05,.35), title='Anatomy Type',frameon=True)\n", "\n", "\n", "# Saving:\n", "fig.savefig('./legend_cut_off.png') # Will cut off objects outside the axes.\n", "# Add extra artists explicitly when saving:\n", "fig.savefig('./legend_example.png', bbox_extra_artists=(species_legend,anatomy_legend), bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example of the legend being cut off:\n", "![colorbar](legend_cut_off.png)\n", "\n", "What we actually want to go into our papers:\n", "![colorbar](legend_example.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise on Legends\n", "\n", "Move the top legends in the figure above into the frame of the left hand side plot. Make sure the legend isn't overlapping with the data and turn off the box around the text.\n", "\n", "![colorbar](Images/Plant_plot_1.png)\n", "\n", "\n", "If you get done early, try to add the the top outside legend to both figures in different locations as shown in the example below. This may require some googling which is just as, if not more, important skill to learn. \n", "\n", "![colorbar](Images/Plant_plot_2.png)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 8. Working with Dates, Aggregating, & Other Nifty Pandas Tricks\n", "Matplotlib can understand dates as a datatype and automatically adjust the axis labels to handle dates. \n", "For this example, we will use the groupby feature of pandas to group observations by a daterange.\n", "Key points:\n", "* **groupby** groups dataframe observations by the values in a particular column and returns an iterable of tuples composed of the groupname and a dataframe for each group.\n", "\n", "\n", "* **datetime** objects and indices can be parsed using date ranges and **resampled** by pandas into day, month, or year ranges.\n", "* The datetime object represented by the string '2018-06-12 15:07:01' has the format '%Y-%m-%d %H-%M:%S' (This is what we talked about in the earlier section)\n", "* You can convert from strings to datetimes and vice versa using `datetime.datetime.strptime(string, format)` and `datetime.datetime.strftime(datetime, format)`\n", "\n", "\n", "* When plotting data with date as the x-axis, use `ax.xaxis_date()` to automatically label the axis correctly\n", "* We can alter the text style of a date axis using the `ax.xaxis.set_major_formatter(dates.DateFormatter('%format'))` syntax.\n", "\n", "\n", "### Monthly Earthquake Counts\n", "We will make the below plot of monthly earthquake counts for the last 4 years of the data.\n", "\n", "![monthly](Images/monthly_earthquake.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a proper date column (instead of the current multiple columns for each part of the date) in the data frame so that we can plot by date. Setting the new date column as an index lets us group by month easily." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "scrolled": false }, "outputs": [], "source": [ "df['datetime'] = pd.to_datetime(df[df.columns[range(6)]]) # First 6 columns are year, month, day, hour, min, sec\n", "df = df.set_index(\"datetime\")" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YEARMONTHDAYHOURMINUTESECONDLATLONDEPEH1EH2AZEVMAGID
datetime
1984-01-01 01:19:11.31719841111911.31736.08781-120.2286910.8970.0200.01096.00.0221.81109386
1984-01-01 01:58:02.4201984111582.42036.87608-120.906692.6610.0710.01855.00.4210.0346
1984-01-01 01:59:27.12419841115927.12436.87000-120.908891.5920.1100.01666.00.7271.51109389
1984-01-01 02:28:04.2401984112284.24037.51546-118.754857.7030.0200.00837.00.0241.21109391
1984-01-01 03:08:58.0441984113858.04440.57227-124.5593320.4071.0090.083103.00.0802.01109392
\n", "
" ], "text/plain": [ " YEAR MONTH DAY HOUR MINUTE SECOND LAT \\\n", "datetime \n", "1984-01-01 01:19:11.317 1984 1 1 1 19 11.317 36.08781 \n", "1984-01-01 01:58:02.420 1984 1 1 1 58 2.420 36.87608 \n", "1984-01-01 01:59:27.124 1984 1 1 1 59 27.124 36.87000 \n", "1984-01-01 02:28:04.240 1984 1 1 2 28 4.240 37.51546 \n", "1984-01-01 03:08:58.044 1984 1 1 3 8 58.044 40.57227 \n", "\n", " LON DEP EH1 EH2 AZ EV MAG \\\n", "datetime \n", "1984-01-01 01:19:11.317 -120.22869 10.897 0.020 0.010 96.0 0.022 1.8 \n", "1984-01-01 01:58:02.420 -120.90669 2.661 0.071 0.018 55.0 0.421 0.0 \n", "1984-01-01 01:59:27.124 -120.90889 1.592 0.110 0.016 66.0 0.727 1.5 \n", "1984-01-01 02:28:04.240 -118.75485 7.703 0.020 0.008 37.0 0.024 1.2 \n", "1984-01-01 03:08:58.044 -124.55933 20.407 1.009 0.083 103.0 0.080 2.0 \n", "\n", " ID \n", "datetime \n", "1984-01-01 01:19:11.317 1109386 \n", "1984-01-01 01:58:02.420 346 \n", "1984-01-01 01:59:27.124 1109389 \n", "1984-01-01 02:28:04.240 1109391 \n", "1984-01-01 03:08:58.044 1109392 " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you don't have data in this format, you can use the [strptime](http://strftime.org/) function in the datetime module:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Object `dt.datetime.strptime()` not found.\n" ] } ], "source": [ "?dt.datetime.strptime()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2000-01-01 00:00:00\n", "1984-01-01 01:19:11.317000\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YEARMONTHDAYHOURMINUTESECONDLATLONDEPEH1EH2AZEVMAGID
datetime
1984-01-01 01:19:11.31719841111911.31736.08781-120.2286910.8970.0200.01096.00.0221.81109386
1984-01-01 01:58:02.4201984111582.42036.87608-120.906692.6610.0710.01855.00.4210.0346
1984-01-01 01:59:27.12419841115927.12436.87000-120.908891.5920.1100.01666.00.7271.51109389
1984-01-01 02:28:04.2401984112284.24037.51546-118.754857.7030.0200.00837.00.0241.21109391
1984-01-01 03:08:58.0441984113858.04440.57227-124.5593320.4071.0090.083103.00.0802.01109392
1984-01-01 03:15:36.69019841131536.69037.56065-118.8444910.4040.0330.01638.00.0391.11109393
1984-01-01 04:46:38.70819841144638.70838.80774-122.847611.8240.0090.005175.00.0111.11109395
1984-01-01 05:01:53.5941984115153.59436.24686-120.393856.8360.0590.016106.00.0401.4357
1984-01-01 07:08:31.9861984117831.98636.49920-121.0785513.0200.0420.020100.00.1021.41109397
1984-01-01 07:17:21.37319841171721.37337.54467-118.866787.2450.0420.01574.00.0430.7363
\n", "
" ], "text/plain": [ " YEAR MONTH DAY HOUR MINUTE SECOND LAT \\\n", "datetime \n", "1984-01-01 01:19:11.317 1984 1 1 1 19 11.317 36.08781 \n", "1984-01-01 01:58:02.420 1984 1 1 1 58 2.420 36.87608 \n", "1984-01-01 01:59:27.124 1984 1 1 1 59 27.124 36.87000 \n", "1984-01-01 02:28:04.240 1984 1 1 2 28 4.240 37.51546 \n", "1984-01-01 03:08:58.044 1984 1 1 3 8 58.044 40.57227 \n", "1984-01-01 03:15:36.690 1984 1 1 3 15 36.690 37.56065 \n", "1984-01-01 04:46:38.708 1984 1 1 4 46 38.708 38.80774 \n", "1984-01-01 05:01:53.594 1984 1 1 5 1 53.594 36.24686 \n", "1984-01-01 07:08:31.986 1984 1 1 7 8 31.986 36.49920 \n", "1984-01-01 07:17:21.373 1984 1 1 7 17 21.373 37.54467 \n", "\n", " LON DEP EH1 EH2 AZ EV MAG \\\n", "datetime \n", "1984-01-01 01:19:11.317 -120.22869 10.897 0.020 0.010 96.0 0.022 1.8 \n", "1984-01-01 01:58:02.420 -120.90669 2.661 0.071 0.018 55.0 0.421 0.0 \n", "1984-01-01 01:59:27.124 -120.90889 1.592 0.110 0.016 66.0 0.727 1.5 \n", "1984-01-01 02:28:04.240 -118.75485 7.703 0.020 0.008 37.0 0.024 1.2 \n", "1984-01-01 03:08:58.044 -124.55933 20.407 1.009 0.083 103.0 0.080 2.0 \n", "1984-01-01 03:15:36.690 -118.84449 10.404 0.033 0.016 38.0 0.039 1.1 \n", "1984-01-01 04:46:38.708 -122.84761 1.824 0.009 0.005 175.0 0.011 1.1 \n", "1984-01-01 05:01:53.594 -120.39385 6.836 0.059 0.016 106.0 0.040 1.4 \n", "1984-01-01 07:08:31.986 -121.07855 13.020 0.042 0.020 100.0 0.102 1.4 \n", "1984-01-01 07:17:21.373 -118.86678 7.245 0.042 0.015 74.0 0.043 0.7 \n", "\n", " ID \n", "datetime \n", "1984-01-01 01:19:11.317 1109386 \n", "1984-01-01 01:58:02.420 346 \n", "1984-01-01 01:59:27.124 1109389 \n", "1984-01-01 02:28:04.240 1109391 \n", "1984-01-01 03:08:58.044 1109392 \n", "1984-01-01 03:15:36.690 1109393 \n", "1984-01-01 04:46:38.708 1109395 \n", "1984-01-01 05:01:53.594 357 \n", "1984-01-01 07:08:31.986 1109397 \n", "1984-01-01 07:17:21.373 363 " ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "date_from_string = dt.datetime.strptime('2000-01-01', '%Y-%m-%d' )\n", "print(date_from_string)\n", "print(df.index[0])\n", "df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot number of earthquakes by month with a bar chart, limit to recent years (the last 48 observations are the last 4 years (4 years * 12 months). We need to use a grouper to tell pandas how to group the data by month." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YEARMONTHDAYHOURMINUTESECONDLATLONDEPEH1EH2AZEVMAGID
datetime
1984-01-31142714271427142714271427142714271427139613961396139614271427
1984-02-29143114311431143114311431143114311431139913991399139914311431
1984-03-31162416241624162416241624162416241624159715971597159716241624
1984-04-30196219621962196219621962196219621962194219421942194219621962
1984-05-31204120412041204120412041204120412041200720072007200720412041
\n", "
" ], "text/plain": [ " YEAR MONTH DAY HOUR MINUTE SECOND LAT LON DEP EH1 \\\n", "datetime \n", "1984-01-31 1427 1427 1427 1427 1427 1427 1427 1427 1427 1396 \n", "1984-02-29 1431 1431 1431 1431 1431 1431 1431 1431 1431 1399 \n", "1984-03-31 1624 1624 1624 1624 1624 1624 1624 1624 1624 1597 \n", "1984-04-30 1962 1962 1962 1962 1962 1962 1962 1962 1962 1942 \n", "1984-05-31 2041 2041 2041 2041 2041 2041 2041 2041 2041 2007 \n", "\n", " EH2 AZ EV MAG ID \n", "datetime \n", "1984-01-31 1396 1396 1396 1427 1427 \n", "1984-02-29 1399 1399 1399 1431 1431 \n", "1984-03-31 1597 1597 1597 1624 1624 \n", "1984-04-30 1942 1942 1942 1962 1962 \n", "1984-05-31 2007 2007 2007 2041 2041 " ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby(pd.Grouper(freq='M')).count().head()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "datetime\n", "2008-01-31 1844\n", "2008-02-29 1881\n", "2008-03-31 2115\n", "2008-04-30 2730\n", "2008-05-31 2034\n", "Freq: M, Name: ID, dtype: int64" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counts = df.groupby(pd.Grouper(freq='M')).count()['ID'][-48:] # Do the aggregation by month\n", "counts.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we tried to just use the month variable, we'd end up with aggregates across all the months:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "MONTH\n", "1 40840\n", "2 33769\n", "3 42063\n", "4 41818\n", "5 43717\n", "6 40274\n", "7 47537\n", "8 44285\n", "9 41316\n", "10 45632\n", "11 46809\n", "12 45414\n", "Name: ID, dtype: int64" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby(df.MONTH).count()['ID'][-48:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another way to get the counts is to use `resample()` instead of a grouper.\n", "This is the most intuitive way to do it IMO if you have a datetime index already but as you can see, there are many options:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "datetime\n", "2008-01-31 1844\n", "2008-02-29 1881\n", "2008-03-31 2115\n", "2008-04-30 2730\n", "2008-05-31 2034\n", "Freq: M, Name: ID, dtype: int64" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc['2008-01-01':'2012-01-01'].resample('M').count()['ID'].head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'counts' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Matplotlib\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Specify a plot size, use subplots to get axes because we'll need it later\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcounts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcounts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# If we don't set the bar width, some bars might not be visible with this many\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis_date\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Monthly Earthquake Count for Northern California\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'counts' is not defined" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Matplotlib\n", "fig, ax = plt.subplots(figsize=(10, 3.5)) # Specify a plot size, use subplots to get axes because we'll need it later\n", "ax.bar(counts.index, counts, width=15) # If we don't set the bar width, some bars might not be visible with this many\n", "ax.xaxis_date()\n", "plt.title(\"Monthly Earthquake Count for Northern California\")\n", "plt.ylabel(\"Number of Earthquakes\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 8.2 Hourly Counts on a Busy Day\n", "\n", "Let's plot hourly earthquakes on a busy day, October 18, 1989 using the same approach of:\n", "\n", "1. resample the datetime index\n", "2. bar plot \n", "\n", "![hourly](Images/hourly_earthquakes.png)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "datetime\n", "1989-10-18 00:00:00 64\n", "1989-10-18 01:00:00 90\n", "1989-10-18 02:00:00 86\n", "1989-10-18 03:00:00 59\n", "1989-10-18 04:00:00 60\n", "Freq: H, Name: ID, dtype: int64" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counts_hourly = df.loc['1989-10-18'].groupby(pd.Grouper(freq='H')).count()['ID']\n", "\n", "# Another way to do the above:\n", "df.loc['1989-10-18'].resample('1H').count()['ID']; # '1H' stands for each hour\n", "\n", "counts_hourly.head() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can plot using the new resampled index and the hourly counts. " ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Matplotlib\n", "fig,ax = plt.subplots()\n", "\n", "ax.bar(counts_hourly.index, \n", " counts_hourly, width=.03, color='g', align='center')\n", "ax.xaxis.set_major_formatter(dates.DateFormatter('%H'));\n", "#ax.xaxis_date()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks OK, but we can make it nicer. Also let's export the plot as pdf (type is determined by file name extension). With `%matplotlib inline` turned on, the save statement needs to be in the same cell of the notebook with the plot creation to work." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Matplotlib\n", "fig, ax = plt.subplots() ## in one line instead of two above, just to show this\n", "plt.bar(counts_hourly.index, counts_hourly, width=.03, \n", " color='g', align='center')\n", "ax.xaxis.set_major_formatter(dates.DateFormatter('%H:00'))\n", "#plt.ylim(0, 95) # Could modify ylim manually, but below works even if data changes\n", "ax.margins(0.025) # Control extra space at beginning and end of x-axis\n", "plt.title(\"Northern California Earthquakes, 18 October 1989\")\n", "plt.xlabel(\"Hour of the Day\")\n", "plt.ylabel(\"Number of Earthquakes\");\n", "plt.savefig(\"plot1.pdf\") # save the plot too" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise on time series data\n", "Plot earthquake variability across some category (year, month, time of day, or spatial range)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# First resample the data into a dataframe" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Plot the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 9. Some Matplotlib Features: Layered Plots, Annotations, MathTex" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 9.1 Layered plots\n", "* In this example, we plot a histogram of some random values with `randn` as well as a theoretical fit to the data.\n", "* `numpy.random.randn` generates normally distributed random numbers\n", "* `scipy.stats.norm.pdf` fits normal probability distributions to data. \n", "* By saving bins (the output of the histogram plot) we can pass to the pdf function to estimate the true probability distribution.\n", "\n", "![normal](Images/normal.png)\n", "\n", "Info on named colors at https://matplotlib.org/examples/color/named_colors.html" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.stats import norm\n", "from numpy.random import randn\n", "\n", "# Plot with matplotlib\n", "\n", "# Keep the values returned from calling the histogram plot to use with the legend and next plot\n", "n, bins, patches = plt.hist(randn(1000), bins=40, \n", " density=True, histtype='stepfilled', \n", " color=\"lightblue\")\n" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot with matplotlib\n", "\n", "# Keep the values returned from calling the histogram plot to use with the legend and next plot\n", "n, bins, patches = plt.hist(randn(1000), bins=40, \n", " density=True, histtype='stepfilled', \n", " color=\"lightblue\")\n", "\n", "# Add another plot on top of the histogram\n", "# Save the value here too so we can use it for the legend\n", "l, = plt.plot(bins, norm.pdf(bins, 0.0, 1.0), \n", " 'g--', linewidth=3)\n", "# Comma above in l, means unpack the returned tuple to give you just the line object\n", "\n", "plt.legend([l, patches[0]], ['theory', 'data']);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 9.2 Annotating Plots\n", "Let's add to our ability to re-sample a few sections ago and add some annotations to our plots. We'll start out with something simple and then get a bit more advanced later on. Remember, if you feel your are spending way too much time figuring out how to annotate in python, save the figure and open it in illustrator/photoshop/powerpoint and manually add it in. " ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Pandas with matplotlib adjustments\n", "counts = df.resample('D').count()['ID'];\n", "counts.plot() # Plot the Series directly\n", "plt.title(\"Daily Earthquake Count for Northern California\")\n", "plt.ylabel(\"Number of Earthquakes\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Then annotate one of the spikes:" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "counts.plot() \n", "plt.title(\"Daily Earthquake Count for Northern California\")\n", "plt.ylabel(\"Number of Earthquakes\")\n", "\n", "plt.text(dt.datetime(1998,3,1), 925, \"Big One!\"); # Use date to position on x-axis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Or with an arrow" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "counts.plot() \n", "plt.title(\"Daily Earthquake Count for Northern California\")\n", "plt.ylabel(\"Number of Earthquakes\")\n", "\n", "plt.annotate(\"Big One!\", xy=(dt.datetime(1998,3,1), 925),\n", " xytext=(dt.datetime(2000,3,1), 800),\n", " arrowprops=dict(color='black', shrink=0.01, width=.1,\n", " headwidth=5));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise on Subplots\n", "\n", "Add another annotation labeled \"another one\" at one of the other big spikes. If you get done early, try to also add a different colored and size arrow to that second annotation. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 9.3 Mathtext and Mathematical Functions \n", "\n", "Let's make a plot like the following:\n", "\n", "![mathplot](Images/mathfunction.png) \n", "\n", "This example intrdouces some new things, namely:\n", "* Moving the spines to be 0 centered\n", "* Using mathtext to make the pi symbol\n", "* Annotating plots with lines and text\n", "\n", "For math text used in the labels, see https://matplotlib.org/users/mathtext.html\n", "\n", "#### We'll take it step by step:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Generate data\n", "sns.set_style('ticks') # set a global style\n", "x = np.arange(-2*np.pi, 2*np.pi, 0.01) # Make array of numbers in range\n", "y = np.sin(x)\n", "\n", "# Create empty plot\n", "fig = plt.figure(figsize=(4, 5));\n", "ax = fig.add_subplot(111);\n", "# Basic plot\n", "# Plot the sin wave\n", "ax.plot(x, y,\n", " linewidth=3, # Line thickness\n", " alpha=0.3); # Line opacity -- just setting so we can see the labels and exis through it" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Adjust spines to look like a mathemetical graph: " ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPkAAAEqCAYAAADXkvalAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJztnXl0VGWa/7+1L9kTsiCRTVuhEQXE7tbuAyrQbAkoP8dBPaLjclBpUeYHDosjwjTNgGBjN2DjMkM3Mk4z0gHiYXGkR3t+YrcEFINGQHBJAqESslZqu9vvj0rdurdSWSp1l/dWvZ9zPKaqblW91L3f+zzv8z7v85gEQRBAoVBSFrPeA6BQKOpCRU6hpDhU5BRKikNFTqGkOFTkFEqKQ0VOoaQ4VOQUSopDRU6hpDhU5BRKikNFTqGkOFTkFEqKQ0WeZrAsi7q6OrAsq/dQKBpBRZ5mNDQ0YMqUKWhoaNB7KBSNoCKnUFIcKnIKJcWhIqdQUhwqcgolxaEip1BSHCpyCiXFoSKnUFIcKnIKJcWhItcZr9eLsrIy1NXVdXutpqYG8+bNw/Tp07Fq1SoxS+3ixYt44IEHMGPGDDz55JPo7OzUetgUA2FokQuCgBDDgeN4vYcyIE6dOoX77rsP3377bdzXly1bhhdeeAFHjhyBIAjYs2cPAGDNmjW4//77cfjwYdxwww3Yvn27hqPuG47jEWI4vYdB6cKwIvc0+/DZ2UZ8drYRJ8548HVtq+EurD179mD16tUoKirq9lp9fT0CgQDGjRsHAJg3bx4OHz4MhmFw/PhxTJ8+XfZ8PNrb21FXVyf7T8101iDD4ez3LThxxoPPzjbi0zMeNLb4Vfs+Sv+w6j2AgfBdQzsuX/FFnxCA5vYAvP4QRg3Ph9NujH/WunXrenzN4/GgsLBQfFxYWIjLly+jpaUFmZmZsFqtsufj8fvf/x5bt25VdtA9EAiy+PKbZrASr4pheXxzsQ2BEIuri7M0GQelO8ZQgwRPs08mcJMJiPSACTE8ztW2YsyIApjNJp1GqAw8z8Nkiv4bBEGAyWQS/y8l9nGEhx56CHfffbfsuYaGBjzwwAOKjpXjBZz5vkUmcLPZBJ4Pn5hLTZ1wO60oyHEp+r2U/mEokYcYDt9f7hAf52Y5cE1pLjo6QzhX2wJBAPwBFhebvCgtMrblKCkpQWNjo/i4qakJRUVFyM/PR0dHBziOg8ViQWNjY1x3HwCys7ORnZ2t+ljrPB0IhsJTJbPZhB9cnYtMtx1f17agzRsCAHx7qR3ZGQ7YrIadIRoWQ/3itZc7ROvgdFhwTWkuLGYTcrMcMnew4YrPcPPzWIYMGQKHw4ETJ04AAPbv349JkybBZrNh4sSJOHjwIABg3759mDRpkm7jDARZXG6OelbDSrKRk+mAxWzCtaW5cNgsAACOE1Df6NVrmGmNYUQeCLG40h4QHw8fnAOLxCUvznfD7Qw7Jjwv4FKTMZeVHn/8cVRXVwMANm3ahPXr12PGjBnw+XxYsGABAGD16tXYs2cPZs2ahaqqKjz77LO6jffSlU6ga7qU5bajMC/qklssZgwtid58G1uMf/M1IiajdDX95mKbGKnNzrBj1PD8bse0eYM4810LgLDbOO66QlgthrmPaUJdXR2mTJmCo0ePorS0NKnPCjEcPjvXKIp81PB8ZGfYux33xYUr6PQzAIDBgzJoEE5jDKEAjuNxpS1qxQcPyoh7XE6mAy5H1JrT5Rt1aWr1iwLPdNviChwArpKcr8YWvzjlomiDIUTe3B6UzcVzMh09Hltc4Bb/bmz19XgcJTkEQX4TLc5393hsbpYDdlv4UmM5Hq3eoOrjo0QxhMibWqMX06Dc3pdhCnJc4vJZIMiJbiJFWdo7Qwh2za8tFhNys5w9HmsymWTnrbGF3ny1hHiRBxkOHb7wMgxMwKA+1loj0fYIV9qoy64GzZIgaEGOSxYEjYdU5G2dITCsMVORjQjxIm+RXEzZGXbYu5ZkekN6I5BejBRlEAQBLR1Rl7sgp2crHsFptyLDZev6AKC1g7rsWkG8yKUXQ34vLqGU7Aw7LJawZQkxPHwB6rIridfPgO2yxFarGZkR8fZBXnbUw2rpoDdfrSBa5CzHoz3iqgMyN7w3zGaTLDhHrYaySL2jvCxHj2m1sUhv0m3eIDgaZdcEokXe5g2KSzQZLlu/XPUIeVlSq0FFriTSm2ZeP70rAHA6rOISpyAA7Z30vGgB4SKPWvG8flrxCDmZDqDLwHT6GTAszbRSgiDDiXnqJhN6XBvviZzM6PHtkvNLUQ/CRR690yd6MVkt8rlieye9oJRAek6yMuwJ7/aTTqPaqCXXBGJF7g+y4jKLxWKKRmYTQHpjoCJXBunvmJORmHcFAJluOyJT+ECQo7nsGkCsyKUXU5bb3u/gjpRsyUVIRZ48giDIXGyp691fLGYTstzR97VRl111iBW51C3sLY21NzJdNtFqBEOcmKFFGRj+ICsWhrBazGIQLVGypfNy6rKrDpEiFwQhmuWGxOfjEcxmE7Ik7+2g1jwpOnzRfIPsjIF5V+H3Rm/a0vNMUQciRR4IceC48NpZMhYDALLddF6uFFJBZroTj5FEcDusYsAuxNDKrmpDpMiVupgAyCy5109Fngxe2XkZmHcFhD2sDGf0vFJrri5Eily6cyxZkbudNnG9PBDkZMUGKf0nxHAIMeHfzmw2wZ2EdwXIz6uX7hRUFSJFLr2zZyVhMYBwNFdqNbw+ekENBJl35bIlXQ1XJnJ6TlSFOJEzLI9AMJpR5XYmZ8kByJJiqMs+MKRCHEjOQizSm3dngKF57CpCnMilInQ7bX3uU+4P1DVMHiW9KyAmoCqAFvdQEfJE7lNuPh5Banm8PgYGqV1JDDwvwB9kxcdKnRfp51CRqwdxzRWke7/7u0+5L5x2K6xWM1iWFy9YJaYByVJZWYlXX30VLMvioYceknU2qampwfLly8XHzc3NyMnJwbvvvouKigps3rwZBQUFAIDbb78dS5YsUW2cviArdqlx2C2KVcDNcNrQiHDlHipy9SBO5J3+qMVQUoiZLpu4RdIX0F/kly9fxq9//Wv86U9/gt1ux/z58/HjH/8Y1157LQBg9OjR2L9/PwDA7/fj7/7u7/Diiy8CAE6fPo3ly5ejrKxMk7H6JALMUPB3c0tu4p20sIdqEOWuB5noEpfZbILT3v/9432RQdgFdezYMfzkJz9Bbm4u3G43pk+f3mN30h07duCWW27BxIkTAQDV1dWoqKhAeXk5li5dira2NlXHKv29lAi6RXA7rLK0Y7q8qQ5EiTzWYgw0bTIe0nVdn8Rb0IvYrqVFRUVxu5N2dHRgz549+MUvfiE+V1hYiKeeegoHDhzA4MGDsXbt2rjfoVTrYmmwMtKlRgnMZpMsm5G67OpAlLsuu5hcyg4t1pLH6w6qJT11LY3lwIEDmDp1qjj/BoBt27aJfz/22GOYNm1a3O9QonVxbNBNSUse+TxfIPz5vgA74M1IlJ4hSuRqBN0i2G3hgBHLhYNvwRAHZ5JZW8lQUlKCqqoq8XFP3Unff/99LFy4UHzc0dGBvXv34uGHHwYQvjlYLPGnNUq0LvYFWbEEl5JBtwg0+KY+RLnrnQF1gm7iZ0q8A73n5bfddhs+/vhjNDc3w+/347333uvWnVQQBHzxxRcYP368+Jzb7cYbb7yBU6dOAQDeeuutHi15dnY2SktLZf+VlJQkNE6p8JS24gANvmkBMZY8xHBimV+lg24RMpw2seiBL8CiIEfxr+g3xcXFWLJkCRYsWACGYXDPPffgxhtvxOOPP47Fixdj7NixaG5uhs1mg8MRdWEtFgu2bNmCF198EYFAAMOHD8fGjRtVG6fUu1Iysh4hEnwThGjwjTapVBZiupq2dARw7vtWAOGMqtEjunctTZbm9gC+rg1/R3amHaOGKf8dpJNoV1NpR9Lrh+WpMmeuPt8Ef5cXN3pEviIZdZQoxNwy/TJXXR0HQ/q5JETYSUcQ5EE31c6LdOUjQM+L0hAjcp/kYkqmSERvOO3RYgUsR4sV9EUwxIndZK1WM2xW5adQgDz+QrvdKA8xItfCksd+ttRKUbrj08CKx342teTKQ4TIeV6AP6S+JY/9bHpB9Y5fA+8q9rP9QZZuIFIYIkQeCEnWYm0WWFSMrsZeUJSekbrObod6uf6RHAYgfMOnVXWVhQiRyyyGim4hQOd/ieDTaAoV+/l+6mEpChEil15MarqFsZ9PXcOe4bqyAgEAJqieHUg9LPUgROTqbICIh81qhs0a/mdHEjAo3ZEKzWm3KFKhpzdcNPimGkSIXKsAj/gd0guKWo24SG+8WpwT2TQqSKdRSqK7yMPr1eF0VpMpvJatNtQ17Bt5Eoz6BTak5yQgWZ+nJI/uIpe5hZLOGmoijRTT4Ft8ZHkLGlhyi9kEh60r2UboWnGhKIL+Itcw6CZ+D53/9YnWUyhAfl6oh6Uc+os8pK3FAOQXbZChrmEsHMeLveFNpvA+ci2Q7jykIlcO/UUe465rgcVsgt3W9U+nrmE3/JIVB6fdqlkFHen5jzTYoCSP7iIP6OAWAjEXFF1GkyGdQjkd2lhxQO7J+emNVzF0FTnHC2JkHSZEAy8aIIvmUtdQhtSz0WK1Q/yumHNCE5WUQVeRS8XlsFk0iaxHcNlpkKcntNhDHg+rxQwrTVRSHH1FrtHOs3g46Vp5j+hlyQHqsquBriLXY5km+n3RqQGdk0fheUH2e2hd0ZbefJWHGJFrbTFsVgsslvD0gG5vjCLd9mu3mVXPWY9FuoxGI+zKoPOcPHoSXRpGcaPfSYNvsehpxQGacqwGuolcEAT53I9eUESg15JmvO+k50QZdBN5MMSJ7XBtVrMutbalUwQ95uWVlZWYNWsWfv7zn2P37t3dXt+6dSvuuOMOzJ07F3PnzhWPqampwbx58zB9+nSsWrUKLKucGGQFNTWeQgHhKjGRVRaeF2ixTQXQrbmCVjXdekOa6KG1u95X62Ig3KL45ZdflnVQAYBly5bhl7/8JcaNG4eVK1diz549uP/++xUZV0CHDMRYXA6rWOvdH2Rh1zB/IhXRzZJL5+O6XUw6rpX3p3Xx6dOnsWPHDpSXl2Pt2rUIBoOor69HIBDAuHHjAADz5s3rseVxooSnUNI4iX4ij0BXPpJHN5HHVh7RA4fdIvbHZlhe0/7YfbUu7uzsxOjRo7Fs2TJUVFSgvb0d27dv7/a+wsLCuC2PgcRbF4dYXtysY7GYxAo6WiOLsNO18qTRz13XOcADACaTCU67VRxLIMQh06XNhd1X6+KMjAy8/vrr4uNHHnkEK1euxKRJk/rV8hhIvHVxQMclTSnSXW806y15dDuTQZ2XaiI47BZR5MEQq3jL5J7oq3XxxYsXcezYMdxzzz0AwmK2Wq0oKSlBY2OjeFxTU1PclsdA4q2LZedEJ+8KoBF2pdHFH2O5qGtsMgF2ndxCQL8Lqq/WxU6nEy+99BJqa2shCAJ2796NadOmYciQIXA4HDhx4gQAYP/+/d1aHkdItHUxCcFQAHBIvIggw9GNKkmiy5kM6LRfOR4Ouz7prf1pXbx27Vo8+eSTYBgGEyZMwD/8wz8AADZt2oTnn38eXq8XY8aMwYIFCxQZk9SSa1UoIh4WczgewLA80LVRRU9vz+jo0rr4Spsf5+vaAAC5WQ5cNzRP6yGItHeG8NW3zQCADJcNY0YW6DYWLeitdfHnXzeKqx5jRhYgQ6OpSzxqvmlGhy/cS/66oXnIzVK+ZXK6oIufHCBk7hf7/ekcyRUEgZg5ORCTw5DG50UJ9BE5IVFcQJ5hxXGCWNss3QjPfcN/W61mVfvR9Qe9sxFTCd0tuZ5zv3hjSFerQZIVjx1Dup4TpdBJ5PqnTkqh2xu7B0P1JrYUFGXgaC5yluPBcWG/UO/lswhy1zA9Lyh5NRj9LbnDZgG6Fl1CDE/LZieB5gojaflMOo4I6ZphRcLGFClmswkOK3XZlUBzkQclJ4uE+ThAI7kAWSseERwOmt6qBJqLnISc9VicOiXEkIIgyMtfOQiYkwMxuwTT9OarBLq666RYcptVXqiAYdNL6MEQJ9Z1s1m1r+vWE3SjijLo4K6TFcWNkM7WnLTIegRZQDRNVz2UQAdLTlYUN4IzjZstyJc0STonNFaiBJqKnGGjy2dms4mosj7ONA7ykLIxJRaHPbqMxrA8OLqMNiA0FTmJkfUI6bxW7texY0pvmEwmWX+8YJqdF6XQVOQkLtNESOc5eTBI7nmhwbfk0VjkZFoMIH0vJp4XEGTJWz6LILPktDzzgNDNkpPmrndfRkuP3Wghhszlswh0N1ryaDwnJ3OpJoLcmqfH/E9qHUk8JzTCnjw6ipwsSw6kp2tIamQ9QrpOo5REM5FzMcUb9arp3RvpGHwLELziAchvvCFa1HFAaKY0eW60hYjdZ7E40nA3mtxdJ0/kFosZ1i6DIAjp42EpiXYil7qFBCXBSEnH+Z/Rzku63HyVRDORyyPr5AV4gJg5eZpcTCSveERIx/OiJLq46yS6hUC4qKOWaZR9tS5+//33MXfuXMyZMwdPPfUU2trCZawrKirws5/9TGxp/Otf/3pA389Iep+ZzSbYrGSeF1nwjbrrCaOZSZUFeAh1CyPVSCIXUjDEwu1Up/Z4X62LvV4vXnzxRezduxfFxcV45ZVX8Nvf/hbPP/88Tp8+jeXLl6OsrCypMcTGSUglnVOOlUCfOTnBF5SsGomKVqOv1sUMw2D16tUoLi4GAFx//fW4dOkSAKC6uhoVFRUoLy/H0qVLRQufKEED3HgBuoyWLJqIXBCEcGZVF0RfUBrN//pqXZyXl4dp06YBAAKBAF577TVMnToVQLhd8VNPPYUDBw5g8ODBWLt2bdzv6Kt1sVFuvOm4tKkkmrjrIZYnqnB/b2hlNfpqXRyho6MDixYtwqhRo8QOpdu2bRNff+yxx8SbQSx9tS4OGuTGG0k55nlBTDkmMc+CVDQRuRGWaSI4YzpqqkVfrYuBsLV/9NFH8ZOf/AQrV64EEBb93r178fDDDwMI3xwslvi/aV+ti0lPM5bisFvgD0RbTNusdp1HZBw0uR2SnlUlRXoTUjPI01frYo7j8MQTT2DmzJlYtWqVaOXdbjfeeOMNnDp1CgDw1ltv9WjJ+2pdbBR3HUjPlGOloJY8htjEi57c6GTpq3VxQ0MDvvzyS3AchyNHjgAAbrjhBqxbtw5btmzBiy++iEAggOHDh2Pjxo0Jf78gxGwxNdB5ofPyxNCkdfHXda1obgsAAEZclYPCPJfaX5kUJ7/yiHn2464rJKpMVbJEWhcfPPQervjD7YBtVjPGX1/Uxzv1xdPsw7eX2gEAg3JdGDkkR+cRGQdN3HUjuYVAbAPE1LQaIdbI54SulSeC5iInNdtNSjpcUCEmWhSD9KAbQFNbk0F1kRthi2ks6XBBkVxUMx5apxynEqorzghbTGPRahlNT6TuuhG8q9gGiOlSuUcJ1Be5gSLrEdIhjVLqrhvmvKRxbfxkUF3kRthiGks67F82WjAUoGvlA0VTd90IbiEQjhtEZhWsJKaQSvAC+VtMY6F95AeGBpbcGDudpJhMprRw2QHjnBOA7isfKNrOyQ1iyYH0CL4BRjsnqb+0qQaqitxIW0xj0SqHXW+MJHJ7zNImrdzaP1QVuZG2mMZC3XXysFrMsFqilVvTpctNsqiqOiMun0VIl/mfEbLdpKRDyrHSqCpyI20xjSUdst4AA56XNPGwlIRa8h5wxATeUmX+F/vvMNx5ka2Vp26sREnUFTnhzfR6w2I2RfPsU6hzhzTTzWY1i51cjQJ11xNHO0tuMLcQSE3X0GhbTGOhCTGJo5nIjZLtJiUV0yiNtsU0lnQJiCqJaiI34hbTWJyO1LMaIYM0VOgJuzTlmOXBpWDKsdKopjwjbjGNJRUj7NJgldGCbkA45diegh6WmqgncgNH1iOkYoUYubtuzPOSDrsElUQ1kRtxi2ksqTknN7a7DsivJxph7xtN3HWjWgy7zSIuMXGcYPgtpywXLZtkNhlni2ksThp8SwgV3XVjz/0iqDkv76t1cU1NDebNm4fp06dj1apVYNnwb3rx4kU88MADmDFjBp588kl0dnb26/ukVs/IZabl5yQ1plFqopG7buALSqV5eaR18X/8x39g3759+OMf/4ivv/5adsyyZcvwwgsv4MiRIxAEAXv27AEArFmzBvfffz8OHz6MG264Adu3b+/Xd0oFYbcZb7UjAk2ISQxVzrSRt5jGota6bF+ti+vr6xEIBDBu3DgAwLx583D48GEwDIPjx49j+vTpsuf7g3T8qWLJQymUcqwWCUfEWJaVtb+NB8Py8Fy+Ev4CixmXLhnXpWpu9aOx0QsA4IJO8IEsRT733LlzcDgcqKurAwDYbDZ89dVX4uMvvvgC2dnZ4mOO41BXV4cvv/wSLpdLPAccx6GhoUE8TorX64XX6xUfX7rix3e14fe1tjTFfY9RaLlyRYyRfOMOwW7APIwInhYfOv0MBhdkyHIzeqKkpARWa/+lm3CbpEibHQqFog9Hjx5FaWlpv49PWOT9seQAwHICfAEGmW4bzAolwkTa7u7evVvWnVNNAiEOZ75rBgDYrRaMHpGvyPiOHDmC6upqLF26FACwa9cuCIKABQsWAAjP2ZcuXYpdu3YBAKqrq7Fz505s2LABd911Fw4cOACz2QyPx4MlS5bEDdzFWvLzF724cP48dv5uIza+tBk3TxiX2I+hAf39Db9raEdrRxAAUFqUhYIcp1ZDVPQ6bG4LoNbTAQDIzXRg2ODsPt+TqCVP2F23Wq0J3UXUoKSkRLMxcLyA5q7GgDABV11V3OfOrf6Mr6ysDLt374bb7YbL5cJf//pX/Mu//Iv4vtLSUmRkZODy5cu4+eabsWPHDkybNg3Dhw/Hj370I5w6dQrl5eWorKzEnXfe2ef38bwA3n0ZTFcyzODBxbqfx97o8ze0d8DWGF5VyB+UgdJiZaZRiaDIdWjvQACZAIDBKv07jDuR0YjYLafSXVzJIG1dfNddd6GsrExsXVxdXQ0A2LRpE9avX48ZM2bA5/OJVn716tXYs2cPZs2ahaqqKjz77LN9fl+I5QCJz6aUd6UXDltq7CvQYqemMVPRNMZht4j1xIIhTrHdW+Xl5SgvL5c99/rrr4t/jxo1Cu+880639w0ZMkR04/uLkYUQD6cjNRJighqsQlFL3g9SYaOKUcfdE6lwTgBtLLmhRJ6dnY1f/OIXyM7uOzihJLItp71YDb3G1x8i43a6XACAzMxMPYfTI/39DfXscqPUeeZ4IVpx1qSeJU84up6ONLX6caG+DQCQl+3AD67O03lEifN1bSua2wNo9FzCPz4xP+FlGBL5/OtGBILhm9eYkQXIcNl0HlFi+AIMTp8P55M4bBbcdF2hKt9jKEuuF6lQBipVtspKMXqXm9iaC2pBRd4PUmHXk1HH3RtG73KjVQ1E4qPrFRUV2Lx5MwoKCgAAt99+O5YsWSI7pr29HUuXLkVtbS3y8/OxZcsWFBYq5/rYrOEtpzwviFtOI508Tpw4gfXr14NhGOTm5uJXv/oVhgwZInt/fX09ysrKMHToUADAoEGD8Oabbyo2vp6orKzEq6++CpblMXXuw7j99ttlS2c1NTVYtWoVOjs7MXHiRKxZsyahJItk2bp1Kw4dOgQAmDx5Mp577rlur+/du1ec+95777144IEHxNfV9rAefPBBNDc3i7/J2rVrcdNNN4mvHzt2DOvXr0cwGMTMmTO7XZd9oZUlh0A4a9euFSorK3s9Zs2aNcKOHTsEQRCEiooK4ZlnnlF8HJ+faxT+dvqS8LfTl4QOX0h8/o477hBqamoEQRCE//qv/xKeeOKJbu89fPiw8M///M+Kj6k3GhoahDvuuENoaWkRGq+0CmXznxIq/vuk8P7/+1y47rrrhNraWmH27NnCp59+KgiCIKxYsULYvXu3ZuP76KOPhL//+78XgsGgEAqFhAULFgjvvfee7JiFCxcKJ0+e7PEzmtv94jmp+faKouPjeV742c9+JjAME/d1v98vTJ48Wfj+++8FhmGERx55RPjggw8S+o4z3zWL429q9Skx7LgQ765XV1ejoqIC5eXlWLp0Kdra2rod88EHH4jrzWVlZfjLX/4ChmEUHYfcaoRdw1AohGeeeQajRo0CAFx//fW4dOlS3H/D2bNnMXfuXCxYsABnzpxRdGzxkO5yM1sdmDB+Ak6ePCFuMb18+XLcXW5aUVhYiOXLl8Nut8Nms+Gaa67BxYsXZcecPn0aO3bsQHl5OdauXYtgMCh7XbaMFlTWkl+4cAEA8Mgjj2DOnDl46623ZK9//vnnGDZsGK6++mpYrVaUl5cn/PsFNaqeRLzICwsL8dRTT+HAgQMYPHgw1q5d2+0Yj8cjuudWqxWZmZlobm5WdBzxtpza7XbMnTsXAMDzPLZu3YqpU6d2f6/DgTlz5qCiogKPPvooFi1ahFAopOj4YpH+JoEQi5zcHLS2tIpbTJuammRTmsLCQly+fFnVMUn5wQ9+IN5gvv32Wxw6dAiTJ08WX+/s7MTo0aOxbNkyVFRUoL29vdu+eVmXG1bZLaft7e249dZbsW3bNuzcuRP/+Z//iY8++kh8Xfr7AkBRUVHCv58WiTAAQXPyQ4cOYf369bLnRo4ciZ07d4qPH3vsMUybNq3PzxIEAWazsvevvx77f9jx7+GiDXyoA7y/URxfKBTC8uXLwbIsFi5c2O29Tz/9tPj35MmTsXnzZly4cEH0ANSA53mxQm64za+80qkgCLIKurGPteLcuXNYuHAhnnvuOQwfPlx8PiMjQ5b998gjj2DlypWyeW8k5ZhhebHLjVLZiOPHj8f48ePFx/fccw8+/PBD/PSnPwUg/32BxH8/huXAR0pxSVOnVYAYkc+cORMzZ86UPdfR0YGdO3fi4YcfBhD+IS2W7ne8oqIiNDU1oaSkBCzLorOzE7m5uYqOb/q0KRhx/QQAQHaGHaOGh3ejdXZ24sknn0Rubi5effVV2Gzd12p37dqFsrIy5OXlif8OtQNcJSUlqKqqAhC++Nvb2pCTmyO664WFhWhoMCCWAAAWyklEQVRsbBSPb2pqQlFRkapjiuXEiRNYvHgxVq5cidmzZ8teu3jxIo4dO4Z77rkHQM+/mVopx1VVVWAYBrfeemvc7y8pKZH9fo2NjQn9flp2FyLaXXe73XjjjTdw6tQpAMBbb70V15JPnjwZ+/btAwAcPHgQEydOjCu2ZOgpkrts2TIMGzYMW7Zsgd1uj/ve48ePiznon3zyCXiex8iRIxUdXyy33XYbPv74YzQ3N6O9w4dPP/0UY8bcAEdX8cbi4mI4HA6cOHECALB//35MmjRJ1TFJuXTpEhYtWoRNmzZ1EzgAOJ1OvPTSS6itrYUgCNi9e3fcc69WemtHRwc2btyIYDAIr9eLiooK2fffdNNN+Oabb/Ddd9+B4zi8++67Cf1+WhY6JT7jraqqCuvWrUMgEMDw4cOxceNGZGVl4ZVXXkFRURHuu+8+tLa2Yvny5aitrUVWVhY2bdqkeDYXzwuoqumac5mAiaOK8dVXNbj77rtx7bXXinf5oqIivP7663j77bfh8XjwzDPP4PLly1i+fDkaGxvhcDiwbt06VV31CJWVldixYwdY51X46W0/xfTp0/Gbf12G4598gqNHj8Lr9eL555+H1+vFmDFjsH79+h5vVErzy1/+Env37hWXFQFg/vz5+POf/4zFixdj7NixOHLkCH7729+CYRhMmDABa9as6Ta++kYv6j3hPfODB2XgagW3am7ZsgVHjhwBz/O4//778dBDD2Hu3Ll47bXXUFxcjI8//lhcQps8eTJWrFjRb5f9YqMXdV3jLilwY2iJeqnQxIucJD494xFdwxuvHdSvUj16Ewix+PxcE4BwvndhRghTpkxJibRWwLgpxxfq29DU6gcADBucjeJ8t2rfRbS7ThpGbLZn9M6yfWHUlGMtOwxRkSeAEdvmarVMoxdGTTmW9qRTe05ORZ4ARuyNluqWPJJyDBinyw3PC7KedGqXx6YiTwAjWg15j3jyYwgDQb5RhfzzEpLVvzf3WTMwWajIE8CI1Ug02wShI/FSjklGPoVS/8ZLRZ4ARmzPE0iRnnS9YbTgm9ZTKCryBJDO/3hp6R5CYTkeHBdeITWZoGrqpJ4YrcW07MZLRU4eRlpGi7UYeuSma4HRVj20buudmpEYFXHYLPAHwnfiYIhF5gDril28eBHLli3DlStXMGLECGzatAkZGRmyYzweD1asWIGmpiaYzWY899xzuPXWW8EwDH784x/j6quvFo/905/+1C2vX34xpe6pNtqqh5Zr5AC15Amj1PyvP+2HN27ciDvvvBP79+/H5s2bsXTpUnAchzNnzmD8+PHYv3+/+F+8jTtaX0x6IetyyvLi7i5S0ToYSkWeIErM//rbfnjatGkoKysDAAwbNgzBYBA+nw/V1dVobm7GvHnzcO+99+KTTz6J+z1az/30wmw2RfutC/IlKtKQxknCW0ypu04cSsz/WlpakJmZKW5q6algQ+QmAABvvvkmRo8ejaysLJhMJkyZMgULFy7EuXPn8Pjjj6OyshL5+fJmjM2t7WhqCud1Z1j9YP22fjWrNCIOmxUhJlyII8hwxO4rCOjgXZH5SxBMovO/eMUwhg0b1i0I1ltQbOfOnfjjH/8oliCaP3+++NoPf/hD3HjjjTh58mS3qjSV7x7CgcqDAADflQsQOHn5pFTCYbegwxf+OxDikKPvcHokqIN3RUWeIPHmf71lLMUrhhEJnHEcB4vF0mvBgY0bN+LDDz+Utcndt28fJkyYIG7TFASh2/55QRBw+51T8aMf3wYAuGFEDsxmk9h2N9VwGiQhRp6BqI3I6Zw8QcyxXU4HMP+z2WyYOHEiDh4MW9l9+/bFLTiwc+dO/O1vf8Pbb78t64N95swZ/Nu//RuAcMHBmpoa3HzzzbL3hlgebpcbgwYNQklJEYYOvRqlpaWa9XXXGqMkKgU0Kt4ohVryAeC0W8Gwyc3/Vq9ejeXLl+PVV1/F4MGD8fLLLwOAWGxi8eLF2LZtGzIzM/Hggw+K73vttdewaNEirFy5EmVlZTCZTNiwYUO33maBYOpnukkxSkKMdIqnlSWnIh8ASsz/emo/fN9994l/Hz9+vMf3/+Y3v+n189MhZ12KwyAJMXrsCqTu+gAwwvxPj7mfntisZuJTjrXqYhoLFfkAMEJqq9wtTA+HjfTdaMGYzUJapRlTkQ8AI+xfDqSZJQfI3++vl3dFRT4AjLC1UasWPCRB+n7/gE4FPKjIBwDpW06l3TksFnW7c5CE9GZGooelV5pxepx9FSB5Xq5H6iQJOAn3sPSqt0dFPkDkriFZQZ5AUB+3UG/ksRKyzgmgX709KvIBQvK8PF12n8Vit1mAroA1Q9iWU54XEGT18bCoyAeIbDcaYe56OlRojYfZbBJ7vQFknZcQwwFd9xwtKrRKoSIfIDLXMEjOxQTErJE70seSA+R6WHpF1gEq8gEjX5Mla/6XjmvkEUgtBaXnFIqKfIBIu16EGHLmf9K5qFaVR0iC1FUPPTvZUJEPEFnJIZBzQelRlIAkSE2I0TPNmIo8CZyy5AsyXMN0dtUBcnejaV2GWQoVeRKQGORJx40pUkjMXxcEQdfKuVTkSeCSFIvwB8mz5OnorlstZlgs0pRj/YUeYnkIXSEbq9UMi0Vb2VGRJwGJljxd18ilkLZLUFqlR48pFBV5ErjsJFpyfS8oEpDFSgjIYdBzjRygIk+K2DRKTudltNjC/Wo3tycV0tbKZZZch+QkKvIkMJtNRG1USff5eARprIQEkfslY3BRS248SAq+pVuF1p6QVs8lwV0PBvW9+VKRJwlJwTfpTcZFaJsgLXDGuOuCoN80iucF3bvLpu+VoBADDb71p3VxfX09ysrKxE4pgwYNwptvvolQKIRVq1bh9OnTcDqd2LRpE6655pq0XyOPYLWYYbWawXYtXQUZTrffIxBTvFHL3WcRqCVPkoF27uhP6+LTp0+jvLxcbE/85ptvAgB27doFl8uFQ4cOYeXKlVixYkW373el2e6zWFyERNhlkXWdzgkVeZIMJMjT39bF1dXVOHv2LObOnYsFCxbgzJkzAIAPPvgAc+bMAQDccsstaG5uRn19fUwUN30tOSAXlJ7BNxK8q/S+EhTA3uWC8bwAjgsXdeyrcGJ/Wxc7HA7MmTMH8+fPx//+7/9i0aJFOHjwIDweDwoLC8XjCgsLUVt/CeaMUgBd7qrFjPb2drS3t8s+M1VbF8fiJKSoo6wUl043XipyBXDYLfAHwnfsYIiFzWoXX0umdfHTTz8t/j158mRs3rwZFy5cgCAIsuMFQQDHm0S3LGLFfv/732Pr1q1J/duMisyS67jqQUJyEhW5ArjsVlHk/iCHTHf0tWRaF+/atQtlZWXIy8sDEBaz1WpFcXExPB6PGJBrampCZnYegl1B5MgU4qGHHsLdd98t+8xUbV0cCynZiCRYcjonV4BEM6z627r4+PHjeOeddwAAn3zyCXiex8iRIzF58mTs378fAFBVVQWHw4GcvEHi+yKuanZ2NkpLS2X/pWrr4lhIyEZkWB4sF67JbzabYNep/j0VuQIMJPi2evVq7NmzB7NmzUJVVRWeffZZAOHWxa+88goAYNWqVTh27BjKysqwYcMGbN68GWazGQ8++CBCoRBmz56NdevWYePGjbqnTpIGCdmIsQU8tOp9FotJ0DNTIEXw+kL48ptmAIDLacXYawb18Q7l+fSMR+zkcuO1g3p0Devq6jBlyhQcPXoUpaWlWg5Rc85+34LWjiAA4JrSHBTkuDT9/sYWP7652AYAyM924tqrczX9/gjUkiuAPI1S+wwrjuNFgZtM6Z23LsU5wBwGpSCl/j0VuQJEMqwAhDOsNL6gYjem6OUWkoZT530FUpHrmWZMRa4QbukFpfH8j4SECxKR7yvXXuSk7CWgIlcIPa0GKRcTabgc+rnrPC/EpLRSkRse2ZbTgMaWPE0bHPaFzWqBtaueWuxuMLUJhFixLZLDZoFFh40pEajIFUK+jKat1ZBbchp0k6LXzddP0JImFblCSMXl1zDCHnYLqbveEy6nPtMokqZQVOQKEesahrqWtNQmyHBiuV+7Tftyv6SjV+Ue6RTK5bBp9r3xoFeEgkjdMq1cQ1+AEf/W22KQiF4il1lyJ7XkKYMeFxRJbiGJxJ4TLaZR3aZQOicnUZEriB5VQuUi19ctJBGb1ax5hJ20KRQVuYJIRe7TyF0nyS0kFa09LD9hFXqoyBVE64spNuGCuuvx0TrCTtoUiopcQew2i6zZXkhl15CkhAuS0XqtnIo8xdHSZaeuev/Q3F2XnHc3FXnqIRN5kOnlyOSR3kRIsBikEpuNqGaEnecFeVskAs4LFbnCZDijEW4tLbmbWvIesVmjW4F5XlB1K7A/KJlC2S26R9YBKnLFkYpN7fmfn1ryfiM9Lz4VXfZOSXISKTdeKnKFcUksuT/EglepgCDL8eKar8lEd5/1hXRu3OlXbxolm487ychboCJXGIvZFC31I6gX6JFOBZwOqy49toxEhkty81XRkvsIC7oBVOSqIHXTpO6bkkhz1jMIsRgk49LIkkuDrW4XGeeFilwFpKJTa14usxiEzP1IxiXxdhg2WvhSSYIMB44LT88sFhMxPeLp1aEC0jXrniLs/Wld/MQTT+DSpUsAAJ7ncfbsWbzzzjsYNWoU7n/wYRQVDwkfGPCg4p23YbGQcVGRiMlkgsthFa24L8AgJ9Oh6HdIvSs3QfsIqMhVQBpw6WmtPNK6ePbs2di2bRu2b9+OZcuWyY753e9+J/79yiuvYNy4cRg7diw+r67GtT8YhcWLnwEATBhVRMRSDem4nVKRs4qL3E+od0WvDBVwSNJbOa77zqf+ti6OcOHCBezbtw//9E//BAD47NRpdHi9+NWvfoUN/7oOJ09UqfQvSS2k0yg1YiXSzyQpA5GckaQYGU4b2jtDAMKBHun8rL+tiyNs374djz76KDIzMwEADGfGTTfdhBnTZ8Db6sGSJU+jsrIS+fn5svelc+vieKidwyAN6JEUDKUiV4kMV1jkJ06cwHNLXgcfaBZf62/rYgBoa2vDRx99hHXr1onPTf35TFxu9gEAxvzwOtx44404efIkpk6dKntvOrcujkdsDgPHC4pt6mFYHiEm2sWGpOQkckaSYkTWZW+++WbcMelWjBoetbL9bV0MAB9++CEmTZoEhyM6fzz03p9x9bBrUFhYiAynDYIgwGbrbjnSuXVxPCxmE5wOS7j+mhAOlGW57X2/sR9IrbjbaSMqb4HOyVVCmnzh9TOyTRH9bV0MAJ999hkmTpwoPuZ5Ad98V4f/fv+/AQCehlrU1NTg5ptv7vbedG5d3BOZrqiovT7l5uXS+XgmIevjEajIVcJhs8g2RcTWYu9P62IAqK2tRXFxsfjYF2Qxa/ZseDu8WLvmBfzff1yCDRs2iPN1Su9kym6+IcU+VzYfJ0zktHWxikhb5464KgeFecm3zm240onvGzoAAPk5Tlxbmlg73HRqXRwPX4DB6fNXAITrr427Lv40KVFOnvGA7UqwGXvtIKLm5NSSq4jUaii1ZOP1k+sWGgFp5luI4RWp3hNkOFHgZrNJ1jKZBKjIVUTqtimVL91JRZ4UJpNJvrdAgfPi9UXd/kyXjbjW0VTkKpIRY8m5JLedMiwvFjwwmcjZymg0MmOCoskiDeCRNh8HqMhVxWoxR+dmgvyOPxCkgSLSlmmMRKZb2Qh7u+S8ZmcosySnJFTkKpPpjt7ZO5IUeUenxC10k2cxjEJshD2Zwh4sx0f3p5vInEJRkauM9M6erNXokLw/W6EkjnTEbrOIhT0EIbmgaKefEWu6uR1WIjcKkTeiFEOaUeX1MwO2GhzHyy7GLALdQiMhvfm2dw7cw5K+V6nsOaWhIlcZu80ibk7heUG25zgRvBKL4XJaxf5elIEhE7l34CKXTsFInULRK0UDshSwGtL3UVc9eeQe1sDm5RzHy6LzJAbdACpyTZBeUG0DtBoyt5DQi8lIxM7LB7KU1t4ZknlXNitZSTARqMg1IDcrKsoOfwgcl1h9MYblokkbJnIthtGQ/o5t3mDC72+T3HhzFa4yoyRU5Bpgs1qiWVZC4i671Ppnumx0Pq4Q0vJPrQMRueQ9JN946dWiEclcUNLjSbYYRiMnw45IBqo/wHYr09UbgRArZh+azSZiI+sAFblm5GZJRN7Rf5HzvCCzGEoXH0xnLBazLL6RyHlpaY8em+UmO/uQilwjMl02sbgjw/L9TnHt8IXEWt42q5nI3Ggjk5flFP9u6Qj0+33N7dFj87KdvRypP1TkGmEymZAvuRiutPfvgpJeTPk5ZF9MRkTqYXV0hvrVdCHEyAOh0hsFiVCRa4hU5M1tgT77ZPO8IBN5AeEWw4g4bBbROxIE+U21J6THZGfYYbOSLSOyR5diZGfYxcg4w/J9RtlbvUHRVbfbzLLdUxTlGJQbrdjT1Orv8/jGlugx+Qa48VKRa4jJZEKBxOWOlFXuCU9L9PWCnORLR1Hik5/tFKPsnX6m19Rjry8k7jozm02G8K6oyDWmKN8t/t3qDfa4bOMPstGcapP8fRRlsVnNsrl5w5Web74NzdIbr5PIXWexkD/CFMPlsEYTJwTgUlNn3OMuNnrFv3MzHcR0yExVSgqizSavtPnj1n4LBFnZfLwwzxg3XipyHZBeUJ4WHwJBecseX4CRRd8HD5J3O6UoT5bbLu4iEwSgzuPtdkydxyvmqmdn2oksEBEPKnIdyM1yiBlS+/fvx8bfvClG2nlewIX6NkAAGJbBH/59B+79P3Nx99134/z58wAAQRCwYcMGzJgxA7NmzcKJEyd0+7ekEkMKo7Xrm1r9siSklvaAzIpLjyUdKnKdyMsA/vCHP+Do+0fB8Bacr2+DL8Dg67pWsaf5B//zP8hxCTh06BBWrlyJFStWAACOHDmC8+fP4+DBg9i2bRtWrFgBllW+gV+6kZPpQF52dG7+dV0rWjoCaGkP4Hx9m/h8QY6T6DTWWMipAJ9m/PXYXzDy6gLk5E4BEF43b26Tr9F+8ekx/OPTjwEAbrnlFjQ3N+PixYv48MMPMWvWLJjNZowYMQKDBw/Gp59+iltuuUX2ftrVNHGGD86G13cFDMuD4wSc+75V9rrdZsbQkmydRjcwqMh14q677oIgCFj/8g744xjh4gI3rjR8g8LCQvG5wsJCNDQ0wOPxyBokRp6PhXY1TRyb1YLrhubhzPctYsOE6GtmXDc0j/jkl1ioyFXm0KFDWL9+vey5kSNHYufOnTCZTMiyM7CZeWS4bAiGODgdFpQUZCA/2wlBEGSF+gVBgNlsBs/zcZ+PhXY1HRgZLhtuGFmA+kavOC/PyXSgtCiT2MIQvUFFrjIzZ87EzJkzez3GaeUwZmRBt+eLi4vh8XgwdOhQAEBTUxOKiopQUlICj8cjHhd5Ppbs7GxkZxvLtSQFu82CEVfl6D0MRTCW35FmTJ48Gfv37wcAVFVVweFw4KqrrsKkSZNQWVkJjuPw3Xff4dtvv8XYsWN1Hi2FVKglJ4y3334bHo8HzzzzDB588EG88MILmD17Nux2OzZu3AgAmDFjBj7//HPMmTMHALBu3To4neSnV1L0gbYuTjPSvXVxOkLddQolxaEip1BSHCpyCiXFoSKnUFIcKnIKJcWhIqdQUhwqcgolxaEip1BSHJoMk2awLIuGhgaUlJTAaqUJj+kAFTmFkuJQd51CSXGoyCmUFIeKnEJJcajIKZQUh4qcQklxqMgplBSHipxCSXGoyCmUFIeKnEJJcajIKZQU5/8DhgewwwOTfn4AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(4, 5));\n", "ax = fig.add_subplot(111);\n", "ax.plot(x, y,linewidth=3, alpha=0.3);\n", "\n", "# Deal with bounding box \n", "# Lose top and right\n", "# Move bottom and left to the center like a mathematical graph\n", "ax.spines['top'].set_color('none')\n", "ax.spines['right'].set_color('none')\n", "\n", "ax.spines['left'].set_position('center')\n", "ax.spines['bottom'].set_position('center')\n", "\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.yaxis.set_ticks_position('left')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Annotate with reference lines:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPkAAAEqCAYAAADXkvalAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJztnXmYFPWd/999H3MywxzIIIhGMYqiQowmD6hAuGZAWGNUIrgejwfxYH/gcrgibAgLQoIGMBjdJUHWDYsZYHw4EslqdsVEBg0OikjAgxkYZoY5e/qq6/dHTVdX9fTM9FHHt7q/r+eph+nuquovXfWpz+f7+X4OiyAIAigUSsZiNXoAFApFW6iQUygZDhVyCiXDoUJOoWQ4VMgplAyHCjmFkuFQIadQMhwq5BRKhkOFnELJcKiQUygZDhVyCiXDoUKeZbAsi/r6erAsa/RQKDpBhTzLaGxsxMSJE9HY2Gj0UCg6QYWcQslwqJBTKBkOFXIKJcOhQk6hZDhUyCmUDIcKOYWS4VAhp1AyHCrkFEqGQ4XcYHw+HyorK1FfX9/rsxMnTmDOnDmYMmUKli9fLkWpnTt3DnPnzsXUqVPx+OOPo7u7W+9hU0yEqYVcEASEGQ4cxxs9lJQ4duwY7r33Xnz11VdxP1+8eDGef/55HDx4EIIgYOfOnQCAlStX4r777sOBAwdw7bXXYsuWLTqOemA4jkeY4YweBqUHu9EDSJWmVj8amn1gWB6wAEV5blxangenw2b00BJm586dWLFiBZ599tlenzU0NCAYDGLMmDEAgDlz5uDll1/GD3/4Qxw5cgSbN2+W3v/xj3+MxYsX9zpHZ2cnOjs7Fe9Fwll3fbYLhS2F0vvXlFyDcUPHgeEY7Kjb0etcY8rHYEz5GPgZP3Z+urPX52MvGYtvDboax7+ux54vdqNy+I/gsFtRUZqHkkGeJH4VitqYUsi/buzEhYv+6BsC0NoZhC8QxqgRRXA7zfHfWr16dZ+fNTU1oaSkRHpdUlKCCxcuoK2tDbm5ubDb7Yr34/Gb3/wGmzZtUnfQfRAKs/j09EV0BEMY88e/YvBgoOXOH+HLcx0IhlkMK8vTZRyU3phDGmQ0tfoVAm6xAJEeMGGGx6mz7bjmsmJYrRaDRqgOPM/DYon+HwRBgMVikf6VE/s6wvz58zF79mzFe42NjZg7dy7u+vZdqKio6HWMw+bAA2Me6HNcXoe31+ccL+D46RaEOA65jnyMGHwz5EM639INr9uO4gKq0Y3AVEIeZjh8c6FLel2Y58LlFYXo6g7j1Nk2CAIQCLI41+JDRam5NUd5eTmam5ul1y0tLSgtLUVRURG6urrAcRxsNhuam5tRWloa9xz5+fnIz8/XfKz1TV0IhcU5uNVqQXmxF26nHeFcJzp8YQDAV+c7kZ/jgsNuajeQKTHVL372Qhd4XlTbbpcNl1cUwma1oDDPpTAHGy/6Te/4GTp0KFwuF44ePQoA2LNnD8aPHw+Hw4GxY8di3759AIDdu3dj/Pjxho0zGGJxoTVqWQ0vz4fX7YDVasEVFYVw9fhIOE5AQ7PPqGFmNaYR8mCYxcXOoPR6xJAC2GQmeVmRF163aJjwvIDzLeZcVnrkkUdQV1cHAFi/fj3WrFmDqVOnwu/3Y968eQCAFStWYOfOnZg+fTpqa2vxzDPPGDbe8xe7gZ7pUp7XqXCy2WxWXFoeffg2t5n/4WtGLGbpavrluQ40twUAAPk5TowaUdRrnw5fCCe/bgMgmo1jriyB3Waa55gu1NfXY+LEiTh06FDcOXkyhBkOfzvVLAn5qBFFyM9xAkeOiG+MGwcA+PTMRXQHGADAkME51AmnM6aQAI7jcbEjqsWHDM6Ju19BrgseV1SbRx4KFG1oaQ9IAp7rdYgCDojC3SPgAHCJ7Ho1twWkKRdFH0wh5K2dIcVcvCDX1ee+ZcVe6e/mdn+f+1HSQxCUD9GyoujvDoYRtx4K81xwOsRbjeV4tPtCuo2TYhIhb2mP3kyDC/tfhiku8EjLZ8EQJ5mJFHXp7A4j1DO/ttksKMxzRz/csUPcerBYLIrr1txGH756QryQhxgOXX5xGQYWYPAAa60Rb3uEix3UZNeCVpkTtLjAo3CCxkMu5B3dYTFSkaILxAt5m+xmys9xJhS2Kn8QyG9GijoIgoC2rqjJXVzg7mdvEbfTjhyPo+cEQHsXNdn1gnghl98MRXkD30yA+DCw2UTNEmZ4+IPUZFcTX4AB26OJ7XYrciPCOwCD8qMWVlsXffjqBdFCznI8OiOmOqAww/vDarUonHNUa6iL3DoalOfqM6w2FvlDusMXAke97LpAtJB3+ELSEk2Ox5FUhtmgPLnWoEKuJh0y7/igeNbVmDHiFoPbZZeWOAUB6Oym10UPiI5dj8Q9A0qhTYSCXBdgASAA3QEGDMvBYTdPGiqphBgOwZDoVbdYEF0blxNHwCMU5DoRCInFLzp94fgPCYqqkK/Je4h7M/WD3aacK3Z2h/vZm5Io8muSl+OMn+3n94tbHOTTqA6qyXWBWCEPhFhpmcVms0Q9s0kgfzBQIVcH+e9YkNOHdbVzp7jFIdfrlNJQgyGOxrLrALFCLr+Z8rzOhJ07cvJlNyEV8vQRBAGdsilUQW5y1hUgxjHkeaPHyadkFG0gVsjlZmF/Yaz9ketxSFojFOakCC1KagRCLNieenp2m1VyoiVLfq7cwqImu9YQKeSCIESj3JD8fDyC1WpBnuzYLqrN06LLH403yM9JzboSj40+tOXXmaINRAp5MMyB48S1s3Q0BgDke+m8XC3kApnrTd5HEsHrsksOuzBDK7tqDZFLaGrdTAAUmtwXoEKeDj7FdenHuho7tt/zWK0W5Lgd0nXu8odp/TcNIVLI5Zlj6Qq51+2Q1suDIQ4sx9NCEikQZjiEGXE+brVa4O3Purr22gHPl+uNCrkvwFAh1xAi73a5Js/rT2MkgK1Ha0Tw+WkceyoorCuPo/9quB0d4tYP8oc3vSbaQpyQMyyviKjyutPT5AAUQTHUZE8NuSAOGLNQXS1u/SB/eHcHGRrHriHECblcCL1ux4B5yomg0Bq0iERKqGldATEOVQG0uIeGkCfkfvXm4xHkmsfnZ2CS2pXEwPOCFG8OqHdd5OehQq4dxDne5LnfieYpD4TbaYfdbgXL8tINq8Y0IF1qamrwyiuvgGVZzJ8/H3PnzpU+O3HiBJYsWSK9bm1tRUFBAd5++21UV1djw4YNKC4uBgDcdtttWLhwoWbj9IdYqUuNy2lTzXGZ43agGWLlHirk2kGckHcHohpDTUHM9TikvHJ/0Hghv3DhAn7xi1/g97//PZxOJ+655x7cfPPNuOKKKwAAV199Nfbs2QMACAQC+OEPf4gXXngBAHD8+HEsWbIElZWVuozVLxPAHBV/N6/sId5NC3toBlHmeojhpLBJq9UCt1O91NAcwm6ow4cP47vf/S4KCwvh9XoxZcoUHDhwIO6+W7duxbhx4zC2Z/25rq4O1dXVqKqqwqJFi9AxgCc7XeS/V0KJQrfcIm4D4HXZFWHHrElbUJMOUUIeqzFSDZuMh3xd1y+zFowitmtpaWlp3O6kXV1d2LlzJ37yk59I75WUlOCJJ57A3r17MWTIEKxatSrud3R2dqK+vl6xRVoXJ4PcWRnpUtMvV10lbgNgtVoU0YzUZNcGosx1xc3kUXdosZo8XndQPemra2kse/fuxaRJk6T5NwCpNzkAPPzww5g8eXLc71CjdXGs0y0hTd7SIv47ePCAu+Z4HPAHxfP7g2zKyUiUviFLk2vgdIvgdEQdRjwvSF04jSK2a2lf3UnfeecdTJ8+XXrd1dWFbdu2Sa8FQYDNFn9aM3/+fBw6dEix7ZDVQ08Ef4iVSnAl7HR7+21xSwD5HJ9qcm0gSsi7g9o43aRzyqwDo+flt956Kz744AO0trYiEAjgD3/4Q6/upIIg4NNPP8UNN9wgvef1evHaa6/h2LFjAIA33nijT02en5+PiooKxVZeXp7UOOWCl0rhjoGgzjftIcZcDzOcVOZXbadbhBy3Qyp64A+yKC5Q/SsSpqysDAsXLsS8efPAMAzuuusuXHfddXjkkUfw1FNPYfTo0WhtbYXD4YDLFTVhbTYbNm7ciBdeeAHBYBAjRozAunXrNBun3LpS07MeIeJ8E4So843mFqgLMULeHdTO6SadlzCtUVVVhaqqKsV7v/71r6W/i4uL8f777/c6buzYsageIGxULfwK60r928VqtcDtsiPQ8z2BEKtKRB0lCjGPzIDGN1PseUnwsJOOICidbppdF/nKR5BeF7UhRpP7ZTdTOkUi+sPtFIsV8LwAlhOLFSRTyz3bCIU5qZus3W5NvKR1jG9hILxuh9Samna7UR9ihFwPTR45dyQ+PhBiqZD3gz9VLT5yZFLfo7CwqCZXHSLMdZ4XEAhrr8ljz01vqP4JpGpdNTaKW4LIzx0IsTSBSGWIEPJgWLYW67DBpqF3NfaGovSN3HT2upLwrB84IG4J0iuGgdZ8UxUihFyhMTQ01QHl+jud//WP1p51OfLzB6iFpSpECLn8ZtLSVI89PzUN+4aTRwVaxGaFWkItLO0gRMiTTIBIA4fdCodd/G9HAjAovZELmttpU6VCT394qPNNM4gQ8pQdPCmiuKGo1oiL/MGrxzVRTKNCdBqlJoYvoYnr1WI4q8UirmVrjcdll8JbqWkYH2UQTJLhrBMnJv198gdJsGd9vt+KsJSEMVzIFWahrLOGlsg9xdT5Fh9F3EKymnzYsKS/z2a1wOWwiZ51QVxxMbp6T6ZguLke0NHpJn0Pnf8NSFpTqLNnxS1J5NeFWljqYbyQh9PQGCkiv2lDTDR0kyLCcbzUG95iEfPIk+LQIXFLEnnmIRVy9TBeyGPMdT2wWS1wOnr+6z2mISVKQLbi4HbadaugI7/+kQYblPQxXMiDOnvWI7hjHD2UKPIplNulX2y/3JIL0Aevahgq5BwvSJ51WMSQVr1QeHOpaahAbtnosdohfVfMNaGBSupgqJDLhcvlsOm6ZOJxUidPX+iRQx4Pu80KOw1UUh1Dl9CCOmWexcNNwyj7JG1NPnVqyt/tddnRyfbEMIRZ3fw0mYyhmlzvSDc5Htlck87Jo/C8oPg9UhKy8nJxSwH68FUfYoRcz7kfADjsNths4vSApjdGkaf9Oh3W1GLWz5wRtxSQL6NRD7s6GGuuyy6iR0cvbvQ7o1VigiFWV8cfqaStxQHgz38W/02yQgxAs9G0wDBNLgiCcu5nwNyL3lC9MWpJM9530muiDoYJeSjMSe1wHXarIbW25VMEI+blNTU1mD59On7wgx/E7WyyadMm3H777Zg1axZmzZol7XPixAnMmTMHU6ZMwfLly8Gy6gmDoqCmzlMoQKwSE1ll4XkBYTqNShvDzHW9arr1hzzQQ++18oFaFwNii+Kf//znig4qALB48WL89Kc/xZgxY7Bs2TLs3LkT9913nyrjChoQgRiLx2WXOrfQYpvpY5gml8/HDbuZDFwrT6R18fHjx7F161ZUVVVh1apVCIVCaGhoQDAYxJgxYwAAc+bM6bPlcbKIUyi5n8Q4IY9AVz7SxzhNHlN5xAhcTpvUoodheV1b9MRrXfzJJ59Ir7u7u3H11Vdj8eLFGD58OJYsWYItW7bgtttuUxxXUlISt+UxILYu7uzsVLzXX+viMMtLyTo2m0WqoJM0lZWpHdeDwsNOw1vThgghN0pjWCwWuJ12aSzBMIdcjz5CPlDr4pycHEXLpAcffBDLli3D+PHjE2p5DCTfujio1pJmAi2L+0Oe9Uaj3tLHMCEPqbFUowIup00S8lCYVb1lcl+Ul5ejtrZWeh3buvjcuXM4fPgw7rrrLgCiMNvt9l4tj1taWuK2PAbE1sWzZ89WvNfY2Ii5c+fG3V9xTdKxrk6eFP+96qqUDqcednUxZE7OcqJpDIj5ys5UzUIVMOqGGqh1sdvtxosvvoizZ89CEATs2LEDkydPxtChQ+FyuXD06FEAwJ49e3q1PI6QbOti1ZyhH3wgbiniklkRIYajiSppYogKDRqUrxwPl9OY8NZEWhevWrUKjz/+OBiGwY033oh//Md/BACsX78ezz33HHw+H6655hrMmzdPlTHJNXnShSJUxGYV/QEMywM9iSo0hj11DPnlQjKNYeTNBCjnnnrP/wZqXTxlyhRMmTKl13GjRo3Crl27VB+PUSmm8XA77WB6ElWCVMjTwhA7OajW3E8FqCdXRBAE9ebkKqCIYcji66IGxgi5gYkpscgjrDhOkGqbZRvi3Ff82263atqPLhGMjkbMJAyfkxttrkfGECl5FAyzcNidBo9If1TV4jEe/VSgFpZ6GGSuGx86KYemN/Z2hqZFQYG4pUFsKShK6ugu5CzHg+NEu9Do5bMIStMwO28opdMtTU1+/Li4pYHLYQN6Fl3CDE/LZqeB7hJG0vKZfBwRsjXCStXElNpacUsDq9UCl52a7Gqgu5CTtHwWgXpyyVrxiOBy0fBWNdBdyEmIWY/FbVBADCkIgrL8lcvgFY8IiizBLH34qoGh5jopmtxhVxYqYNjsEvRQmJPqujnsKdZ10wCaqKIOBpjrKnpxVSSbtbmqnnUVUThEs3TVQw10v6KqenFVxO20Sx1OAyEWed7sWStXLmmqcE3uvjv9c4CulauFrkLOsNHlM6vVQlRZH3cWO3lUT0zxetM/B3rGYgHQU9SD4wViphJmQldznUTPeoRsXisPqJ2Y8re/iVuaWCwWRZnsUJZdF7XQVchJXKaJkM1z8lBI5euikpAD1PmmBjoLOTmJKbFk683E8wJCLHnLZxEUmpyWZ04JwzQ5aeZ672W07MhGCzNkLp9FoNlo6aPznJzMpZoISm2eHfM/uXYk8ZpQD3v6GCjkZGlyIDtNQ1JKPvVFtk6j1ES3RzcXU7wx5ZreGpKNzregFisefVSDTQX5gzfcU9SRhKQmM6GbpCljo21EXihXFmajKc11lYTc4RA3FbDZrLD3KARByB4LS030E3K5WUhQEIycbJz/aXJdjhwRN5VwU5M9LXQTcqVnnTwHDxAzJ8+Sm0mTFY9PPxU3lcjG66ImhpjrJDrdALGoY6QaSSSMUksGal38zjvvYNasWZg5cyaeeOIJdHR0AACqq6vx/e9/X2pp/Itf/CKl72dkvc+sVgscdjKvi8L5Rs31pNFNpSocPISa65FqJJEbKRRm4XVr0zZpoNbFPp8PL7zwAt566y2UlZXhpZdewi9/+Us899xzOH78OJYsWYLKNBsLxvpJSCWbQ47VwJg5OcE3lKIaiYZaY6DWxQzDYMWKFSgrKwMAXHXVVTh//jwAoK6uDtXV1aiqqsKiRYskDZ8sIRM8eAG6jJYuugi5IAhiZFUPRN9QOs3/4rUulrcgHjRoECZPngwACAaDePXVVzFp0iQAYrviJ554Anv37sWQIUOwatWquN/R2dmJ+vp6xSZvXWyWB282Lm2qiS7mepjliSrc3x96aY2BWhdH6OrqwoIFCzBq1CipQ+nmzZulzx9++GHpYRDLQK2LQ1o9eB94QL1zIRpyzPOCFHJMYpwFqegi5GZYPovgjumoqRUDtS4GRG3/0EMP4bvf/S6WLVsGQBT6t956Cw/0CJIgCLDZ4v+mA7UuJj3MWI68AUYoSxtgpIouj0NNoqo0Qv4Q0tLJM1DrYo7j8Nhjj2HatGlYvny5pOW9Xi9ee+01HDt2DADwxhtv9KnJB2pdrJm5fviwuKlINoYcqwXV5DHEBl5oFUY5UOvixsZGfPbZZ+A4DgcPHgQAXHvttVi9ejU2btyIF154AcFgECNGjMC6deuS/n5BiEkxVfO6fPGF+O+tt6p2SjovTx19hJzwTCc5NpsVdpsVLCf6ERiW16xMVX+ti0ePHo3PP/887nFjx45FdXV1Wt8dZnhFiqmVsBTTWGgDjNTRxVw3ixc3gisLtEaYNfM1oWvlyaC7kJMa7SYnG26oMBMtikG6dQXQ0NZ00FzIzZBiGks23FCaFtW028VNRfQOOc4kNH+EmyHFNBa9ltGMJMxycPasQqluXf34x+qeD/qGHGcamqtVM3nWI2RDGKXcXDfNdcni2vjpoLmQmyHFNJZsyF/W1Bn63nvipjJ0rTw1tNfkJkgxjcVhtyIyq2BlPoVMghc0TDH98ktxUxm6jJYaOmhyc2Q6ybFYLFlhsgPmuSYAzStPFX3n5CbR5EB2ON8As12TzF/a1AJNhdxMKaax6BXDbjRmEnJnzNKmINBltETQVMjNlGIaCzXX08DjETeVsfeEHAOQQo4pA6Opu9uMy2cRsmX+p0m0249+pP45e3A5bWADonAHwxxR7a9JRVPVaqYU01iyIeoNMOF1yRILS000FXJza3Kl4y1T5n+x/w9Nrss774ibBijXyjPXV6Im2prrJkoxjcVmtcBht4rzvp7OHWb7P8RDHummWYppfb365+whGzIE1UY/TW4ysxDITNPQbCmmsdCAmOTRTcjNEu0mJxPDKM2WYhpLtjhE1UQzITdjimksblfmaY2wSRoq9IVTHnLM8uAyMORYbTSTPDOmmMaSiR52ubNKM2dofr64aYDFYlEGxVBtPiCa2Wtm9qxHyMQKMUpzXaPrMmeONuftwe20SfdXKMzRvPIB0EyTmzHFNJbMnJOb21wHlPcT9bAPjC7muhmdboAYKx1ZYuI4wfQppywXLZtktWjYxfTAAXHTCDd1viWFdkJuwhTTeGg5Lx+odfGJEycwZ84cTJkyBcuXLwfLir/puXPnMHfuXEydOhWPP/44uru7E/o+udbTNBy0sVHcNEJ5TTJjGqUlOpnrJhZyjeblkdbF//mf/4ndu3fjd7/7Hf7+978r9lm8eDGef/55HDx4EIIgYOfOnQCAlStX4r777sOBAwdw7bXXYsuWLQl9p1wgnA7zrXZEoAExyaHJlTZzimksWq3LDtS6uKGhAcFgEGPGjAEAzJkzBwcOHADDMDhy5AimTJmieD8R5OM3c2KH/H4KZ1DIsVYk7RFjWVbR/jYeDMuj6cJF8QtsVpw/b16TqrU9gOZmHwCAC7nBB/NUOe+pU6fgcrlQ3xMC6nA48Pnnn0uvP/30U+Tn50uvOY5DfX09PvvsM3g8HukacByHxsZGaT85Pp8PPp9Pen3+YgBfnxWPa29riXuMKrS3i/9qGN7advGi5CP50huG04RxGBGa2vzoDjAYUpyjiM3oi/LyctiTKHltEZJ8DNbX12PixInJHEKhUFTk0KFDqKioSHj/pIU8EU0OACwnwB9kkOt1wKpSIEyk7e6OHTsU3Tm1JBjmcPLrVgCA027D1ZcVqTK+gwcPoq6uDosWLQIAbN++HYIgYN68eQDEOfuiRYuwfft2AEBdXR22bduGtWvX4s4778TevXthtVrR1NSEhQsXxnXcxWry0+d8OHP6NLb9ah3WvbgBN904JrkfQwcS/Q2/buxEe1cIAFBRmofiArdeQ1T1PmztCOJsUxcAoDDXheFDBg4iSlaTJ22u2+32pJ4iWlBeXq7bGDheQGvAJb6wAJdcUjZg5lYi46usrMSOHTvg9Xrh8Xjwl7/8Bf/6r/8qHVdRUYGcnBxcuHABN910E7Zu3YrJkydjxIgR+M53voNjx46hqqoKNTU1uOOOOwb8Pp4XwHsvgOkJhhkypMzw69gfA/6Gzi44msVVhaLBOagoU2calQyq3IfOLgSRCwAYotH/w7wTGZ2IpJwCAARlFlc6yFsX33nnnaisrJRaF9fV1QEA1q9fjzVr1mDq1Knw+/2Sll+xYgV27tyJ6dOno7a2Fs8888yA3xdmOamLKQDVrCujcDkyI69Aj0xNc4ai6YzLaZPqiYXC6uWV99e6GABGjRqFXbt29Tpu6NChkhmfKGYWhHi4XZkREBPSYRWKavIEyIREFbOOuy8y4ZoA+mhyUwl5fn4+fvKTnyBfowynvlCknPajNYwaXyJExu3uqaKam5tr5HD6JNHf0MguN2pdZ44XohVnLdpp8qS969lIS3sAZxo6AACD8l341rBBBo8oef5+th2tnUE0N53HPz12T9LLMCTyyd+bEQyJD69rRhYjx2OubDR/kMHx02I8icthw/VXlmjyPabS5EaRCWWgMiVVVo7Zu9zE1lzQCirkCZAJWU9mHXd/mL3LjV41EIn3rldXV2PDhg0oLi4GANx2221YuHChYp/Ozk4sWrQIZ8+eRVFRETZu3IiSEvVMH4ddTDnleUFKOY108jh69CjWrFkDhmFQWFiIn/3sZxg6dKji+IaGBlRWVuLSSy8FAAwePBivv/66auPri5qaGrzyyitgWR6TZj2A2267TbF0duLECSxfvhzd3d0YO3YsVq5cmVSQRbps2rQJ+/fvBwBMmDABzz77bK/P33rrLWnue/fdd2Pu3LnS51pbWPfffz9aW1ul32TVqlW4/vrrpc8PHz6MNWvWIBQKYdq0ab3uy4HQS5NDIJxVq1YJNTU1/e6zcuVKYevWrYIgCEJ1dbXw9NNPqz6OT041C389fl746/HzQpc/LL1/++23CydOnBAEQRD++7//W3jsscd6HXvgwAHhX/7lX1QfU380NjYKt99+u9DW1iY0X2wXKu95Qqj+40fCO//3iXDllVcKZ8+eFWbMmCF8/PHHgiAIwtKlS4UdO3boNr73339f+NGPfiSEQiEhHA4L8+bNE/7whz8o9nn00UeFjz76qM9ztHYGpGty4quLqo6P53nh+9//vsAwTNzPA4GAMGHCBOGbb74RGIYRHnzwQeHdd99N6jtOft0qjb+l3a/GsONCvLleV1eH6upqVFVVYdGiRejo6Oi1z7vvviutN1dWVuLPf/4zGIZRdRxKrSGahuFwGE8//TRGjRoFALjqqqtw/vz5uP+HL774ArNmzcK8efNw8uRJVccWD3mWm9Xuwo033IiPPjoqpZheuHAhbpabXpSUlGDJkiVwOp1wOBy4/PLLce7cOcU+x48fx9atW1FVVYVVq1YhFAopPlcso4XU1eRnzpwBADz44IOYOXMm3njjDcXnn3zyCYYPH45hw4bBbrejqqoq6d8vpFP1JOKFvKSkBE888QT27t2LIUOGYNWqVb3fYo8IAAAYy0lEQVT2aWpqksxzu92O3NxctLa2qjqOeCmnTqcTs2bNAgDwPI9NmzZh0qRJvY91uTBz5kxUV1fjoYcewoIFCxAOh1UdXyzy3yQYZlFQWID2tnYpxbSlpUUxpSkpKcGFCxc0HZOcb33rW9ID5quvvsL+/fsxYcIE6fPu7m5cffXVWLx4Maqrq9HZ2dkrb17R5YZVN+W0s7MTt9xyCzZv3oxt27bhv/7rv/D+++9Ln8t/XwAoLS1N+vfTIxAGIGhOvn//fqxZs0bx3siRI7Ft2zbp9cMPP4zJkycPeC5BEGC1qvv8+svh/8PW/xCLNvDhLvCBZml84XAYS5YsAcuyePTRR3sd++STT0p/T5gwARs2bMCZM2ckC0ALeJ6XKuSKbX6VlU4FQVBU0I19rRenTp3Co48+imeffRYjRoyQ3s/JyVFE/z344INYtmyZYt6rZZebG264ATfccIP0+q677sJ7772H733vewCUvy+Q/O/HsBz4SCkueei0BhAj5NOmTcO0adMU73V1dWHbtm144IEHAIg/pM3W+4lXWlqKlpYWlJeXg2VZdHd3o7CwUNXxTZk8EZdddSMAID/HiVEjxGy07u5uPP744ygsLMQrr7wCh6P3Wu327dtRWVmJQYMGSf8PrR1c5eXlqK2tBSDe/J0dHSgoLJDM9ZKSEjQ3N0v7t7S0oLS0VNMxxXL06FE89dRTWLZsGWbMmKH47Ny5czh8+DDuuusuAH3/ZlqFHNfW1oJhGNxyyy1xv7+8vFzx+zU3Nyf1++nZXYhoc93r9eK1117DsWPHAABvvPFGXE0+YcIE7N69GwCwb98+jB07Nq6wpUNfntzFixdj+PDh2LhxI5xOZ9xjjxw5IsWgf/jhh+B5HiNHjlR1fLHceuut+OCDD9Da2orOLj8+/vhjXHPNtXD1FG8sKyuDy+XC0aNHAQB79uzB+PHjNR2TnPPnz2PBggVYv359LwEHALfbjRdffBFnz56FIAjYsWNH3GuvVXhrV1cX1q1bh1AoBJ/Ph+rqasX3X3/99fjyyy/x9ddfg+M4vP3220n9fnoWOiU+4q22tharV69GMBjEiBEjsG7dOuTl5eGll15CaWkp7r33XrS3t2PJkiU4e/Ys8vLysH79etWjuXheQO2JnjmXBRg7qgyff34Cs2fPxhVXXCE95UtLS/HrX/8ab775JpqamvD000/jwoULWLJkCZqbm+FyubB69WpNTfUINTU12Lp1K1j3Jfjerd/DlClT8PK/LcaRDz/EoUOH4PP58Nxzz8Hn8+Gaa67BmjVr+nxQqc1Pf/pTvPXWW9KyIgDcc889+NOf/oSnnnoKo0ePxsGDB/HLX/4SDMPgxhtvxMqVK3uNr6HZh4YmMWd+yOAcDFMxVXPjxo04ePAgeJ7Hfffdh/nz52PWrFl49dVXUVZWhg8++EBaQpswYQKWLl2asMl+rtmH+p5xlxd7cWm5dqHQxAs5SXx8skkyDa+7YnBCpXqMJhhm8cmpFgBivHdJThgTJ07MiLBWwLwhx2caOtDSHgAADB+Sj7Iir2bfRbS5ThpmbLZn9s6yA2HWkGM9OwxRIU8CM7bN1WuZxijMGnIs70mn9ZycCnkSmLE3WqZr8kjIMWCeLjc8Lyh60mldHpsKeRKYUWsoe8ST70NIBWWiCvnXJayof28dsGZgulAhTwIzViPRLQnCQOKFHJOMcgql/YOXCnkSmLE9TzBDetL1h9mcb3pPoaiQJ4F8/sfLS/cQCsvx4DhxhdRigaahk0ZithbTigcvFXLyMNMyWqzGMCI2XQ/Mtuqhd1vvzPTEaIjLYUMgKD6JQ2EWuSnWFTt37hwWL16Mixcv4rLLLsP69euRk5Oj2KepqQlLly5FS0sLrFYrnn32Wdxyyy1gGAY333wzhg0bJu37+9//vldcv/JmytxLbbZVDz3XyAGqyZNGrflfIu2H161bhzvuuAN79uzBhg0bsGjRInAch5MnT+KGG27Anj17pC1e4o7eN5NRKLqcsryU3UUqejtDqZAniRrzv0TbD0+ePBmVlZUAgOHDhyMUCsHv96Ourg6tra2YM2cO7r77bnz44Ydxv0fvuZ9RWK2WaL91QblERRpyP4mYYkrNdeJQY/7X1taG3NxcKamlr4INkYcAALz++uu4+uqrkZeXB4vFgokTJ+LRRx/FqVOn8Mgjj6CmpgZFRcpmjK3tnWhpEeO6c+wBsAFHQs0qzYjLYUeYEQtxhBiO2LyCoAHWFZm/BMEkO/+LVwxj+PDhvZxg/TnFtm3bht/97ndSCaJ77rlH+uzb3/42rrvuOnz00Ue9qtLUvL0fe2v2AQD8F89A4JTlkzIJl9OGLr/4dzDMocDY4fRJyADrigp5ksSb//UXsRSvGEbEccZxHGw2W78FB9atW4f33ntP0SZ39+7duPHGG6U0TUEQeuXPC4KA2+6YhO/cfCsA4NrLCmC1WqS2u5mG2yQBMcoIRH2EnM7Jk8Qa2+U0hfmfw+HA2LFjsW+fqGV3794dt+DAtm3b8Ne//hVvvvmmog/2yZMn8e///u8AxIKDJ06cwE033aQ4Nszy8Hq8GDx4MMrLS3HppcNQUVGhW193vTFLoFJQp+KNcqgmTwG30w6GTW/+t2LFCixZsgSvvPIKhgwZgp///OcAIBWbeOqpp7B582bk5ubi/vvvl4579dVXsWDBAixbtgyVlZWwWCxYu3Ztr95mwVDmR7rJMUtAjHyKp5cmp0KeAmrM//pqP3zvvfdKfx85cqTP419++eV+z58NMetyXCYJiDEiK5Ca6ylghvmfEXM/I3HYrcSHHOvVxTQWKuQpYIbQVqVZmB0GG+nZaKGYZCG9woypkKeAGfKXg1mmyQHy8/2Nsq6okKeAGVIb9WrBQxKk5/sHDSrgQYU8BUhPOZV357DZtO3OQRLyhxmJFpZRYcbZcfU1gOR5uRGhkyTgJtzCMqreHhXyFFGahmQ5eYIhY8xCo1H6Ssi6JoBx9faokKcIyfPybMk+i8XpsAE9DmuGsJRTnhcQYo2xsKiQp4giG40wcz0bKrTGw2q1SL3eALKuS5jhgJ5njh4VWuVQIU8RhWkYIudmAmLWyF3Zo8kBci0sozzrABXylFGuyZI1/8vGNfIIpJaCMnIKRYU8ReRdL8IMOfM/+VxUr8ojJEHqqoeRnWyokKeIouQQyLmhjChKQBKkBsQYGWZMhTwN3IrgCzJMw2w21QFys9H0LsMshwp5GpDo5MnGxBQ5JMavC4JgaOVcKuRp4JEViwiEyNPk2Wiu221W2GzykGPjBT3M8hB6XDZ2uxU2m75iR4U8DUjU5Nm6Ri6HtCxBeZUeI6ZQVMjTwOMkUZMbe0ORgMJXQkAMg5Fr5AAV8rSIDaPkDF5Giy3cr3Vze1Ihba1cockNCE6iQp4GVquFqESVbJ+PR5D7SkgQ8oBsDB6qyc0HSc63bKvQ2hfy6rkkmOuhkLEPXyrkaUKS803+kPEQ2iZID9wx5rogGDeN4nnB8O6y2XsnqESqzrdEWhc3NDSgsrJS6pQyePBgvP766wiHw1i+fDmOHz8Ot9uN9evX4/LLL8/6NfIIdpsVdrsVbM/SVYjhDPs9gjHFG/XMPotANXmapNq5I5HWxcePH0dVVZXUnvj1118HAGzfvh0ejwf79+/HsmXLsHTp0l7f78my7LNYPIR42BWedYOuCRXyNEnFyZNo6+K6ujp88cUXmDVrFubNm4eTJ08CAN59913MnDkTADBu3Di0traioaEhxoubvZocUAqUkc43Eqyr7L4TVMDZY4LxvACOE4s6DlQ4MdHWxS6XCzNnzsQ999yD//3f/8WCBQuwb98+NDU1oaSkRNqvpKQEZxvOw5pTAaDHXLVZ0dnZic7OTsU5M7V1cSxuQoo6KkpxGfTgpUKuAi6nDYGg+MQOhVk47E7ps3RaFz/55JPS3xMmTMCGDRtw5swZCIKg2F8QBHC8RTLLIlrsN7/5DTZt2pTW/82sKDS5gaseJAQnUSFXAY/TLgl5IMQh1xv9LJ3Wxdu3b0dlZSUGDRoEQBRmu92OsrIyNDU1SQ65lpYW5OYPQqjHiRyZQsyfPx+zZ89WnDNTWxfHQko0IgmanM7JVSDZCKtEWxcfOXIEu3btAgB8+OGH4HkeI0eOxIQJE7Bnzx4AQG1tLVwuFwoGDZaOi5iq+fn5qKioUGyZ2ro4FhKiERmWB8uJNfmtVgucBtW/p0KuAqk431asWIGdO3di+vTpqK2txTPPPANAbF380ksvAQCWL1+Ow4cPo7KyEmvXrsWGDRtgtVpx//33IxwOY8aMGVi9ejXWrVtneOgkaZAQjRhbwEOv3mexWAQjIwUyBJ8/jM++bAUAeNx2jL588ABHqM/HJ5ukTi7XXTG4T9Owvr4eEydOxKFDh1BRUaHnEHXni2/a0N4VAgBcXlGA4gKPrt/f3BbAl+c6AABF+W5cMaxQ1++PQDW5CijDKPWPsOI4XhJwiyW749bluFOMYVALUurfUyFXgUiEFQAxwkrnGyo2McUos5A03AbnFciF3MgwYyrkKuGV31A6z/9ICLggEWVeuf5CTkouARVylTBSa5ByM5GGx2Wcuc7zQkxIKxVy06NIOQ3qrMmztMHhQDjsNth76qnFZoNpTTDMSm2RXA4bbAYkpkSgQq4SymU0fbWGUpNTp5scox6+AYKWNKmQq4RcuAI6ethFs5Ca633hcRszjSJpCkWFXCViTcNwz5KW1oQYTir363ToX+6XdIyq3COfQnlcDt2+Nx70jlARuVmml2noDzLS30ZrDBIxSsgVmtxNNXnGYMQNRZJZSCKx10SPaVSvKZTBwUlUyFXEiCqhSiE31iwkEYfdqruHnbQpFBVyFZELuV8nc50ks5BU9LawAoRV6KFCriJ630yxARfUXI+P3h520qZQVMhVxOmwKZrthTU2DUkKuCAZvdfKqZBnOHqa7NRUTwzdzXXZdfdSIc88FEIeYvrZM33kDxESNAapxEYjaulh53lB2RaJgOtChVxlctxRD7eemtxLNXmfOOzRVGCeFzRNBQ6EZFMop81wzzpAhVx15MKm9fwvQDV5wsivi19Dk71bFpxEyoOXCrnKeGSaPBBmwWtUQJDleGnN12Kh2WcDIZ8bdwe0m0Yp5uNuMuIWqJCrjM1qiZb6EbRz9MinAm6X3ZAeW2YixyN7+Gqoyf2EOd0AKuSaIDfT5Oabmshj1nMI0Rgk49FJk8udrV4PGdeFCrkGyIVOq3m5QmMQMvcjGY/M2mHYaOFLNQkxHDhOnJ7ZbBZiesTTu0MD5GvWfXnYE2ld/Nhjj+H8+fMAAJ7n8cUXX2DXrl0YNWoU7rv/AZSWDRV3DDahetebsNnIuKlIxGKxwOOyS1rcH2RQkOtS9Tvk1pWXoDwCKuQaIHe49LVWHmldPGPGDGzevBlbtmzB4sWLFfv86le/kv5+6aWXMGbMGIwePRqf1NXhim+NwlNPPQ0AuHFUKRFLNaTjdcuFnFVdyAOEWlf0ztAAlyy8leN6Zz4l2ro4wpkzZ7B792788z//MwDgb8eOo8vnw89+9jOs/bfV+OhorUb/k8xCPo3SwlciPydJEYjkjCTDyHE70NkdBiA6euTzs0RbF0fYsmULHnroIeTm5gIAGM6K66+/HlOnTIWvvQkLFz6JmpoaFBUVKY7L5tbF8dA6hkHu0CPJGUqFXCNyPKKQHz16FM8u/DX4YKv0WaKtiwGgo6MD77//PlavXi29N+kH03Ch1Q8AuObbV+K6667DRx99hEmTJimOzebWxfGIjWHgeEG1pB6G5RFmol1sSApOImckGUZkXfamm27C7eNvwagRUS2baOtiAHjvvfcwfvx4uFzR+eP+P/wJw4ZfjpKSEuS4HRAEAQ5Hb82Rza2L42GzWuB22cT6a4LoKMvzOgc+MAHkWtzrdhAVt0Dn5BohD77wBRhFUkSirYsB4G9/+xvGjh0rveZ5AV9+XY8/vvNHAEBT41mcOHECN910U69js7l1cV/keqJC7fOrNy+Xz8dzCVkfj0CFXCNcDpsiKSK2FnsirYsB4OzZsygrK5Ne+0Msps+YAV+XD6tWPo//908LsXbtWmm+TumfXMXDN6zaeRXzccKEnLYu1hB569zLLilAyaD0W+c2XuzGN41dAICiAjeuqEiuHW42tS6Ohz/I4PjpiwDE+mtjrow/TUqWj042ge0JsBl9xWCi5uRUk2uIXGuotWTjC5BrFpoBeeRbmOFVqd4TYjhJwK1Wi6JlMglQIdcQudmmVrx0NxXytLBYLMrcAhWui88fNftzPQ7iWkdTIdeQnBhNzqWZdsqwvFTwwGIhJ5XRbOTGOEXTRe7AI20+DlAh1xS7zRqdmwnKJ34qyB1FpC3TmIlcr7oe9k7Zdc3PUWdJTk2okGtMrjf6ZO9KU8i7umVmoZc8jWEWYj3s6RT2YDk+mp9uIXMKRYVcY+RP9nS1Rpfs+HyVgjiyEafDJhX2EIT0nKLdAUaq6eZ12YlMFCJvRBmGPKLKF2BS1hocxytuxjwCzUIzIX/4dnanbmHJj1Urek5tqJBrjNNhk5JTeF5Q5Bwng0+mMTxuu9Tfi5IaCiH3pS7k8ikYqVMoeqfoQJ4KWkN+HDXV00dpYaU2L+c4XuGdJ9HpBlAh1wX5DdWRotZQmIWE3kxmInZenspSWmd3WGFdOexkBcFEoEKuA4V5UaHsCoTBccnVF2NYLhq0YSFXY5gN+e/Y4QslfXyH7MFbqHKVGTWhQq4DDrstGmUlJG+yy7V/rsdB5+MqIS//1J6KkMuOIfnBS+8WnUjnhpLvT7LGMBsFOU5EIlADQbZXma7+CIZZKfrQarUQ61kHqJDrRmGeTMi7EhdynhcUGkPt4oPZjM1mVfg3krkubZ3RffO8ZEcfUiHXiVyPQyruyLB8wiGuXf6wVMvbYbcSGRttZgbluaW/27qCCR/X2hndd1C+u589jYcKuU5YLBYUyW6Gi52J3VDym6mogOybyYzILayu7nBCTRfCjNIRKn9QkAgVch2RC3lrR3DAPtk8LyiEvJhwjWFGXA6bZB0JgvKh2hfyffJznHDYyRYjskeXYeTnOCXPOMPyA3rZ230hyVR3OqyK7CmKegwujFbsaWkPDLh/c1t0nyITPHipkOuIxWJBsczkjpRV7oumtujnxQXpl46ixKco3y152bsDTL+hxz5/WMo6s1otprCuqJDrTGmRV/q73Rfqc9kmEGKjMdUW5XEUdXHYrYq5eePFvh++ja3yB6+byKyzWMgfYYbhcdmjgRMCcL6lO+5+55p90t+FuS5iOmRmKuXF0WaTFzsCcWu/BUOsYj5eMsgcD14q5AYgv6Ga2vwIhpQte/xBRuF9HzJY2e2Uoj55XqeURSYIQH2Tr9c+9U0+KVY9P9dJZIGIeFAhN4DCPJcUIbVnzx6se/l1ydPO8wLONHQAAsCwDH77H1tx9z/MwuzZs3H69GkAgCAIWLt2LaZOnYrp06fj6NGjhv1fMomhJdHa9S3tAUUQUltnUKHF5fuSDhVygxiUA/z2t7/FoXcOgeFtON3QAX+Qwd/r26We5u/+z/+gwCNg//79WLZsGZYuXQoAOHjwIE6fPo19+/Zh8+bNWLp0KVhW/QZ+2UZBrguD8qNz87/Xt6OtK4i2ziBON3RI7xcXuIkOY42FnArwWcZfDv8ZI4cVo6BwIgBx3by1Q7lG++nHh/FPTz4MABg3bhxaW1tx7tw5vPfee5g+fTqsVisuu+wyDBkyBB9//DHGjRunOJ52NU2eEUPy4fNfBMPy4DgBp75pV3zudFhxaXm+QaNLDSrkBnHnnXdCEASs+flWBOIo4bJiLy42fomSkhLpvZKSEjQ2NqKpqUnRIDHyfiy0q2nyOOw2XHnpIJz8pk1qmBD9zIorLx1EfPBLLFTINWb//v1Ys2aN4r2RI0di27ZtsFgsyHMycFh55HgcCIU5uF02lBfnoCjfDUEQFIX6BUGA1WoFz/Nx34+FdjVNjRyPA9eOLEZDs0+alxfkulBRmktsYYj+oEKuMdOmTcO0adP63cdt53DNyOJe75eVlaGpqQmXXnopAKClpQWlpaUoLy9HU1OTtF/k/Vjy8/ORn28u05IUnA4bLrukwOhhqIK57I4sY8KECdizZw8AoLa2Fi6XC5dccgnGjx+PmpoacByHr7/+Gl999RVGjx5t8GgppEI1OWG8+eabaGpqwtNPP437778fzz//PGbMmAGn04l169YBAKZOnYpPPvkEM2fOBACsXr0abjf54ZUUY6Cti7OMbG9dnI1Qc51CyXCokFMoGQ4Vcgolw6FCTqFkOFTIKZQMhwo5hZLhUCGnUDIcKuQUSoZDg2GyDJZl0djYiPLyctjtNOAxG6BCTqFkONRcp1AyHCrkFEqGQ4WcQslwqJBTKBkOFXIKJcOhQk6hZDhUyCmUDIcKOYWS4VAhp1AyHCrkFEqG8/8BL2Ej24esXb0AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the reference lines\n", "fig = plt.figure(figsize=(4, 5));\n", "ax = fig.add_subplot(111);\n", "ax.plot(x, y,linewidth=3, alpha=0.3);\n", "ax.spines['top'].set_color('none')\n", "ax.spines['right'].set_color('none')\n", "ax.spines['left'].set_position('center')\n", "ax.spines['bottom'].set_position('center')\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.yaxis.set_ticks_position('left')\n", "\n", "# Notice how were just using \"plot\" to add the annotation lines in\n", "ax.plot([0, np.pi/2], [1, 1], ls=\"--\", \n", " color=\"green\", alpha=0.5)\n", "ax.plot([np.pi/2, np.pi/2], [1, 0], \n", " ls=\"--\", color=\"red\", alpha=0.5);\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Add mathtext: \n", "(See above for link to documentation, but a lot of common math notation like subscripts, superscripts, integrals, etc. are available)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(4, 5));\n", "ax = fig.add_subplot(111);\n", "ax.plot(x, y,linewidth=3, alpha=0.3);\n", "ax.spines['top'].set_color('none')\n", "ax.spines['right'].set_color('none')\n", "ax.spines['left'].set_position('center')\n", "ax.spines['bottom'].set_position('center')\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.yaxis.set_ticks_position('left')\n", "ax.plot([0, np.pi/2], [1, 1], ls=\"--\", \n", " color=\"green\", alpha=0.5)\n", "ax.plot([np.pi/2, np.pi/2], [1, 0], \n", " ls=\"--\", color=\"red\", alpha=0.5);\n", "\n", "# axis limits\n", "ax.set_xlim(-2*np.pi, 2*np.pi)\n", "ax.set_ylim(-1.5, 1.5)\n", "\n", "# axis labels\n", "xticker = np.arange(-np.pi-np.pi/2, np.pi+np.pi, np.pi/2)\n", "xlabels = [r\"$\\frac{-3\\pi}{2}$\", r\"${-\\pi}$\",\n", " r\"$\\frac{-\\pi}{2}$\",\"\",r\"$\\frac{pi}{2}$\",\n", " r\"${\\pi}$\",r\"$\\frac{3\\pi}{2}$\"]\n", "\n", "ax.set_xticks(xticker)\n", "ax.set_xticklabels(xlabels, size=17)\n", "\n", "yticker = np.arange(-1, 2, 1)\n", "ax.set_yticks(yticker);\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Text annotation:" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(4, 5));\n", "ax = fig.add_subplot(111);\n", "ax.plot(x, y,linewidth=3, alpha=0.3);\n", "ax.spines['top'].set_color('none')\n", "ax.spines['right'].set_color('none')\n", "ax.spines['left'].set_position('center')\n", "ax.spines['bottom'].set_position('center')\n", "ax.xaxis.set_ticks_position('bottom')\n", "ax.yaxis.set_ticks_position('left')\n", "ax.plot([0, np.pi/2], [1, 1], ls=\"--\", \n", " color=\"green\", alpha=0.5)\n", "ax.plot([np.pi/2, np.pi/2], [1, 0], \n", " ls=\"--\", color=\"red\", alpha=0.5);\n", "ax.set_xlim(-2*np.pi, 2*np.pi)\n", "ax.set_ylim(-1.5, 1.5)\n", "xticker = np.arange(-np.pi-np.pi/2, np.pi+np.pi, np.pi/2)\n", "xlabels = [r\"$\\frac{-3\\pi}{2}$\", r\"${-\\pi}$\",\n", " r\"$\\frac{-\\pi}{2}$\",\"\",r\"$\\frac{pi}{2}$\",\n", " r\"${\\pi}$\",r\"$\\frac{3\\pi}{2}$\"]\n", "ax.set_xticks(xticker)\n", "ax.set_xticklabels(xlabels, size=17)\n", "yticker = np.arange(-1, 2, 1)\n", "ax.set_yticks(yticker);\n", "\n", "# Annotation of function line\n", "ax.text(np.pi, 1.1, \"y=sin(x)\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 10. Statistical visualization with [Seaborn](https://seaborn.pydata.org)\n", "\n", "[Seaborn](https://seaborn.pydata.org) is one option for moving beyond the basic plots. It's focus is statistical visualization.\n", "\n", "\n", "### 10.1 Density/Bivariate Distribution Plots\n", "When we have a lot of data, scatter plots can get difficult to read. Let's do a density plot instead to explore a bivariate distribution. Seaborn has several options, both for the density plot and supplementary plots along the axes. http://seaborn.pydata.org/tutorial/distributions.html#plotting-bivariate-distributions" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\David Delgado\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set() # Use Seaborn default styling\n", "sns.jointplot(x=df['MAG'][:1000], \n", " y=df['DEP'][:1000], \n", " kind=\"hex\", \n", " color=\"r\"); # Color r is red" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`jointplot` takes care of the overall plot. If you want more control over the core and axes plots, you can add a density plot and axes plots to an existing matplotlib figure." ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(6, 6))\n", "sns.kdeplot(df['MAG'][:1000], df['DEP'][:1000], ax=ax)\n", "#sns.rugplot(df['MAG'][:1000], color=\"g\", ax=ax)\n", "#sns.rugplot(df['DEP'][:1000], vertical=True, ax=ax);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 10.2 Regression and Trend Lines\n", "Seaborn has many functions for visualizing linear relationships and even interaction effect between (or conditional effects of) variables: http://seaborn.pydata.org/tutorial/regression.html\n", "\n", "First, a standard regression plot" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.regplot(x=\"MAG\", y=\"DEP\", data=df[:1000]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This includes a 95% confidence interval on the slope coefficient in the plot by default (you can turn it off with the `ci` parameter). We can also plot the residuals (errors) from a regression to check fit and adherence to model assumptions: " ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsvXmUHOV97/19qqr37tlHIwkhlsFCOEQYLhyBIUaYg4yNwcHRTYiFHYeb1+Qmdl75OEevw7VjLjExUWLjOF4TR/HBmIt9FQvLGBRxEOBIimXEJgQaZEkMg9BsPVtPr7U97x/VVV3d9VRP10zv83zOkWaqprrqqeru3+95fiuhlFJwOBwOh2NDaPQAOBwOh9N8cOXA4XA4HAdcOXA4HA7HAVcOHA6Hw3HAlQOHw+FwHHDlwOFwOBwHXDlwOBwOxwFXDhwOh8NxwJUDh8PhcBxw5cDhcDgcB1w5cDgcDscBVw4cDofDccCVA4fD4XAcSI0egFdmZlLQde+FZHt7o5iaStZgRM0Jv9/2ZTndK8Dvd6kIAkF3d8Tz61pOOeg6XZRyMF+7nOD3274sp3sF+P02Am5W4nA4HI4Drhw4HA6H44ArBw6Hw+E44MqBw+FwOA5aziHNaSxHT8Wx9/AI4nNZ9HUGcfPGtdgw2NfoYXE4nCrDlUObYQrv6aSMnqi/qsL76Kk4fvTUCYiigHBQwmxKxo+eOgEAXEFwOG0GNyu1Eabwnk3JiIUKwvvoqXhVzr/38AhEUUDAJ4IQgoBPhCgK2Ht4pCrn53A4zQNXDm1ErYV3fC4Lv1T8kfFLAuJz2aqcn8PhNA9cObQRtRbefZ1ByKpetE9WdfR1Bqtyfg6H0zzUVDl885vfxC233IJbbrkFO3bsAAAcOnQIt956KzZv3owHH3ywlpdfdtRaeN+8cS00TUdO0UApRU7RoGk6bt64tirn53A4zUPNlMOhQ4dw4MAB7N69G4899hhee+01PP7447jnnnvw7W9/G0888QSOHTuG5557rlZDWHbUWnhvGOzD1pvWoSviRzqroivix9ab1nFnNIfThtQsWqm/vx+f//zn4ff7AQCDg4MYHh7Geeedh3PPPRcAcOutt2Lv3r24/vrrazWMZYUppPceHsFMUkZ3laOVzGtwZcDhtD81Uw7vete7rN+Hh4fx5JNP4s4770R/f7+1f8WKFRgfH/d03t7e6KLH1N8fW/RrW4Ub+2O48eoLGj2MhrAc3l+T5XSvAL/fRlDzPIff/OY3uPvuu7F9+3aIoojh4WHrb5RSEEI8nW9qKrmoioX9/TFMTs57fl2rwu+3fVlO9wrw+10qgkAWNamuqUP6hRdewCc/+Ul87nOfw+23346VK1dicnLS+vvk5CRWrFhRyyFwOBwOZxHUTDmMjo7iz//8z/EP//APuOWWWwAAl112Gd5880289dZb0DQNjz/+ON73vvfVaggcDofDWSQ1Myv967/+K3K5HB544AFr3x133IEHHngAn/nMZ5DL5XD99dfj5ptvrtUQOBwOh7NICKW08S2HPMB9DpXB77d9WU73CvD7XSpN6XPgcDgcTmvClQOHw+FwHHDlwOFwOBwHvJ8Dh1MG3tyIs1zhyoHDcYE3N+IsZ7hZicNxgTc34ixnuHLgcFzgzY04yxmuHDgcF3hzI85yhisHDscF3tyIs5zhDmkOxwV7fwwercRZbnDlwOGUgTc34ixXuFmJw+FwOA64cuBwOByOA25W4rQEPFOZw6kvba8cTKEynZTRE/VzodKC8ExlDqf+tLVZyRQqsykZsVBBqBw9FW/00Dge4JnKHE79aeuVA0uo5PL7+YyzdYjPZREOFn9UeabywnBTHGcptPXKgZc/aA94prJ37KtmuymOr5o5ldLWyqGvM4hESsbYdBrDowmMTaeRSMlcqLQYN29ci3RWxWg8hTMT8xiNp5DOqjxTuQzcFMdZKm2tHNav7UIiLUNVdQgEUFUdibSM9Wu7Gj00jlesVuekZJvDgq+aOUulrZXD0MgsOiMB+CQBOiXwSQI6IwEMjcw2emgcD+w9PIJwyIdVfRGsWRHFqr4IwiEfnwWXgZviOEulrZVDfC6LWNiHgZ4wzl8Vw0BPGLGwj8+eWgw+C/YOLxrIWSptHa3U1xnEbEpGwCda+/jsqfXg76N3eNFAzlJpa+Vw88a1+NFTJ5ADIImEz55aFPv76JcEyKrO38cK4EUDOUuhrZWDffY0k5TRzTOkWxI+C+Zw6k/NlUMymcQdd9yB7373u1izZg3+6q/+Ci+88AJCoRAA4NOf/jRuuummml3fnD3198cwOTlfs+twagufBXM49aWmyuGVV17BF77wBQwPD1v7jh07hocffhgrVqyo5aXrBs9C5XA47UhNo5V+8pOf4Etf+pKlCDKZDM6ePYt77rkHt956K77xjW9A1/UFztK88CxUDofTrtR05XD//fcXbcfjcVx99dX40pe+hFgshrvvvhu7du3C7//+79dyGDXDnoUKgNdu4jDhq0tOK1JXh/S5556Lb33rW9b2xz/+cTz22GOelENvb3TR1+/vjy36tSymk0a1V0KItU8SCWaSctWvtRiaYQzV4sjxcfz02ZMYn05joCeMj266CFdeMlB0TDPe75Hj43j06ZOQJILOqB/JrIJHnz6Jzs6wY/xeaMZ7rSX8futPXZXDG2+8geHhYXzgAx8AAFBKIUnehjA1lYSuey+dUAuHdE/U74i/zykauqP+hju/l3q/zTTbtfdzCPoETM6k8e1dL2PrTeusMTVrwMGP9w0BBBAFAapGjZ9Ew4/3DeG8vvCiztms91or+P0uDUEgi5pU1zVDmlKKv/3bv8Xc3BwURcGPf/zjmkYq1Zp2zUJtNl9KKxeR49ndnFalriuH9evX41Of+hT+8A//EKqqYvPmzfjwhz9czyFUlXaNv282X0or93Pg2d2cVqUuymH//v3W71u3bsXWrVvrcdm60I7x980mjFtZwPLsbk6r0taF9ziLo9kqeray+W7DYB+23rQOXRE/0lkVXRF/ka+Ew2lW2rp8Rj1oJsdttajHbNfLc9sw2Ifh0QT2PX8GWVlF0C9h81VrWuY5t+PqktP+cOWwBOxRNHbHLYCaC4NqKaVy56mV0vP63I6eiuPgsTF0RP3ok4xVzcFjYzh/VQcXuhxOjeDKYQk0ynFbLaW00HlqdQ9en1uzOcg5nOUAVw5LoFGO28UIS9YKoZpCd8+B0w6zz23XXcg81utzazYHOYezHODKYQksJoqmGuYgr8LSbYWQkzV0xQIVn8eNPQdOY8+hYRAQiILRN2PPoWEAYCoIr8+tHtFK7eg74nCWAo9WWgJeo2iqlVzmNZrILYlM1WhVopL2PX/GUgyE5H+CYN/zZ5jHe31utY5WarakPw6nGeArhyXg1XFbLTNOuWgicwY8nZTRk29u5LbSEEViCF3GebyQlVWIAinaJxBjPwuvz63WDvJW92nwVQ+nFnDlsES8OG6rZTt3E5YALPNRLFSYAQd9huAvNcuc0xexfA9LESxBv4ScokG06QedGvvL3YOX67gdz1KGtTbTNRONjJjjtDdcOdSRatrOWcJyxyMvOsxHOQAgBJqqMVcI1YhK2nzVGuw5NAxNN1YMOgUoKDZftWZJ510Iu2C0K0PAm2Bs5QzsVl/1cJoXrhzqSCXmoFo4qtNZFXduXlcz04PpdGZFK9XS5MHypVTbTNfstPKqh9PccOVQRyoxBy3FNFBuBlzrLN3brrvQEZlUa5NHrc10rTDzbuVVD6e54cqhzixkDgKq46iWRNLwGkS1NnnU2kzXCrTyqofT3PBQ1iagWjX/7UXekpnGF3mrdS+DVi7IVy14YT9OreArhyagFjPgZuieVWuTh90cNJOU0b3IaKVWp1VXPZzmhiuHJqBdTQP1uK9mUoYcTjvBlUMT0MoOUZNGVHflcDi1o+2VQzWSpGoxnlJh2cqmgaOn4tj5i+PIyho0nSKRkrHzF8dx1y2XtPR9cTjLmbZWDtVKkqrFeCoN7WyF0gi7njmJVFYFIWYSHEUqq2LXMyddx+qliiuHw6k/bR2t5FZwbu/hkZYYT6sUhBufyeYVg1F4z/hp7GdhVnHNKVpRFdc9B07XeeQcDseNtl45NFv2aHzOEKLj8zmomg5JFBAL+1zH0zqlESgoBWCrrUQpQAhlHm2v4goAIgE03dh//qqOpl8pcTjLgbZeOXgtbV1rgn4R04ksVI1CIASqRjGdyCLoF5nH1zpPoFoM9IQBUOh5XWD8pPn9TrKyipIirhAIkJHVllgpcTjLgbZWDk2XJGVNrykopQDy25Q9w2425ebGlk2DiIT8EAig6ToEAkRCfmzZNMg8PuiXoOkUiqZDVnUomg5Np8ZqoonMgBzOcqatzUrNliSVVXR0x/yYz6hQVR2SZDjKs4rOPL4Z8x/cHOR3fWh9xeagywZ78F+vT1jblBpq0ifSllgpcTjLgbZWDkBzJUmZGcMre/zWvpyioSvidxW6w6MJR1RPo5TbQtFWlY5rJikjHBCRkbW8bwII+UUAhNl3otlWStWiFSLROMuXmpuVkskkPvzhD+PMGaNl5KFDh3Drrbdi8+bNePDBB2t9+abCzcy1fm0X09a+58BpHDw2ho6oH2tWRNER9ePgsbGG2eCrFf0Vn8siEvIh4BMhicZ5IiEfRJEgnVUxGk/hzMQ8RuMppLNqXVZKR0/FseORF7H9O4ew45EXa/6MWyUSjbN8qenK4ZVXXsEXvvAFDA8PAwCy2Szuuece/PCHP8SqVatw991347nnnsP1119fszE0KgnOS8awW1TSvufPoCPqb5popWpFfwX9IkanUiBEKHLMd8eCkBUtf1TeY+3ij6kmjeim1jqRaJzlSk2Vw09+8hN86Utfwvbt2wEAR48exXnnnYdzzz0XAHDrrbdi7969NVMOjUqC82p+eXjfCabQzcoq+qSgY3+jbPBVK6RX5Jg3dxIkUjJ6OoPo7iicL6doNReYjRDUzRZmzeGUUlPlcP/99xdtT0xMoL+/39pesWIFxsfHa3b9anUKW8p1gYWFjZvQDfqlmtvgvdi9q+Ugzyo6wkERyYxq+RyiIQmpjNoQh3QjBDVv0sNpdurqkNZ1HYQUAtwppUXbldDbG6342OmkjFhIsq7hkwRIIsFMUkZ/f8zTdb1Qel0AZa/7B5vX43s/PQpNNxRBTtEACvzupkHsf/5tx/4/2Ly+ovEvdMyR4+N49OmTkCSCzqgfyayCR58+ic7OMK68ZMBx/I39Mbx8agq/fPksdJ1CEAje957VuPHqCyp4KgUiYR9mxnOQBAEkH8mbzmrw+UTolCJoE5hZWcWq/ijeiqfx02dPYnw6jYGeMD666SLHGCt5JkeOjzvOs6o/iplEhnndWn1O3N7zar237Qa/3/pTV+WwcuVKTE5OWtuTk5NYsWKFp3NMTSWh65XZoXuifmt25pMEKKrhDO6O+msauWS/rkm5657XF8YdN17EnMGv6Ag49p/XF3Ydvxcfy4/3DQEEEAUBqkaNn0TDj/cN4bw+ZwLbngOn8cuX3wEBgU8k0Cnwy5ffQVfY56kukqbqACiMzAYjjBWgiAYl5GQNqkaLViaDK6P49q6XIYoCgj4BkzNpfHvXy0VNbSqJRrOb++znufbSlTgYTzque+Plq2v2OXF7z8u9tybNEHlXT/j9Lg1BIJ4m1SZ1VQ6XXXYZ3nzzTbz11ltYs2YNHn/8cfze7/1eza5388a12PnEEKbnstB0ClEgCAYk3PH+i2p2TfO6Xs0vbqGgXkJEvfpYvJpTypW98KIcsrKGno4g5tOKrYxIEJQCW29aV7HD3oySqlQZ7j08AkXVkUgrVp5JyC9iaGSWeV2vpkevoam8Yi2nmamrcggEAnjggQfwmc98BrlcDtdffz1uvvnm2l407/G0LDx1iH5pVB8Drz4Wr3bvrKxaisFEIMZ+L5jXtZfXMPM9vDjs34mnXJUh4Hz+78RTSOdUkPy4NU3HfEaHFk8tWVA3IuKJw6kldVEO+/fvt36/5pprsGfPnnpcFnsPjyAc8qG7I1hkVqpHuGAjZoVeVwJeVzhBv2RUUrXpB50a+73g9bpuSkzTKFMZ7nrmJHKq7hDUOdmw6wt5BUcAaDqFpi19wsBDUzntRlvXVmqVwnXVwmstpg2Dfbj20pVIJGWcmUgikZRx7aUrXYXZ5qvWgIJC043aUJpOQUGx+ao1nsa5YbAPW29ah66IH+msiq6Iv8h/UIpb8qAkEub7Oz5jFDecnc/hbDyF2fkcVM0YL6tAoCR6C4pgsdw+a5z2p63LZyy3cEH7jFwSyYKFBo+eimP/S2ehajoIAVRNx/6XzuL8VR1MQW36FarRpMdtZeWljMjQyCzz/dUpRSKVK0qyS6RyAAg6IgFkcqrl6wgFAhjoDnkefyn1+Kw1W1dDTnvT1srBq7CsB16dll46ptmF6MSMEZpZrhbTrmdPIZWRQYgAURCgUyCVkbHr2VOur7ntugtr1rHNzW4/PJqwyoj0Scbq6OCxMSPK6NiY4/2VRAJVg1UWXCCARgkk0fjXFQssKU+D9R7Wukhis3U15LQ/bW1WspsvkpmFzRe1xms9Ha8d046eiltC9IJVsQVrMY1PpwGQIiEKkPz++uNWu2nf82eY+4dGZi2z2Juj85ZZzO8TgXy7UkopdEoBAvh9oidzFoujp+LY+cQQTp9NYGY+i9NnE9j5xBAALPnci3k2vJw5p1a09coBaK6qrG6hlG5OS6+ho94zwomjWxvJK4hG4OZQz8oqwqqImflcUanzd+IpTCWy6Ij6sSooIZVVcfDYGDrDPogCQUbWip7zyp7wkgMFyq227vsfG3m5DU7b0PbKoZl4J55CMqMA+f4FuqxBVrS8o9SJ19BRrwJkoDuI0ekMdFArGY1SYGVPY3wybnZ7SRQwMy/n+1QbIagz83LeTCRgPp1DfDYLSSQIBSQEfAQ+SUAwIFXdxMNabWm09qutvs4gxmcyeX8Jte61Gv4SDodFW5uVmo1cvn+BqQpMYZyTNebxQb+EUr1RLnS0rzOIRErG2HQaw6MJjE2nkUjJrk7RLTdchEhQgkCMbGeBEESCErbcUNskQTfcopI6In6YXfOMkiSGKlNUHYlULp/dDcvxnEjJNTTxEJRWfKnHamv92i7MpXJQVB0CMe59LpXD+rVdNb0uZ/nCVw51xL5CKJSNgOvKYfNVa7Dn0DA03Zih6hRlQ0fXr+3CiTOzeVMUoKo6EqqGTe9ZzTx+w2Af7rrlkqZpOOOWPPjwvhPMjOr4bBaEEMtXIhAKjRrRSbXKM2nUamtoZBYdYT8yct7pbsvuvq2mV+YsV7hyqCMCASgpCBRCYGXrsvAaOjo0MotOK1STwicZpodyAqTZSjiwxtPXOcLMqDYXETqlEADL8SxWIW/BjS03XISdvziOrKzZSrKINV9txeey6Ij40RklVkInpZT7HDg1gyuHOjLQE8boVAoCEQorAaoXCb1SvISOxueyiIV96Ij420qAuIWJru4NI5lVrdm0KBYcz7WiUaut5Zazw2k8ZZXD8PAwPve5z+HNN9/Exo0b8eUvfxm9vb31GlvbsWXTIP55z+vGzB7GqiEUkLBl02BVzt9IAVLLfshu5iYA+NFTJxAMSIjko5XqkcfSiNVWM+bscNqbssrhvvvuw+23345rrrkGu3btwo4dO/B3f/d39RpbWyKJhlnANElUo3SDSaMESDWLzrkpmXICee/hEcwkZXS3cdawXUG2+71ymoOyyiEej+POO+8EAPzlX/4lPvKRj9RlUO2KvRCgSTULATZKgFSr6NxilEwz5bHUmuV0r5zGU1Y5SFLhz6IoFm23Cs1Uj6YeiUyNECDVuq9K+jY0Q1QVh7McKCvtKS3Nnm1M5uxiOXoqju/97Biyig5KgcmZNN4aS+Duj1zaEMFSTZ+Al5pLtabcfXnxRbgpGXvfBt4rgcOpD2WVw9jYGL785S+7bn/hC1+o3ciqwA/3DiEjF0pYUwpkZB0/3DuEv//z6+o+nmoVZ9tz4DQeOzBsbadzqrXdCAXhdl/r13Z5Eurl+jZ4KTvC4XCWTlnlsHXr1rLbzc70vAwgn7uaz1iitv31ZjEd4lgz758fGmYe+/NDww1RDm735dUX4aZkKNWRSKvQ83pekzUoqnvZkXalmUyknPanrHL49Kc/Xa9x1AQ30dFIkbLYntD2mbems493298oyvkiypmbSvd/57FjlmIws5J1HZAVdtmRdoSX7ObUm7LK4fOf/zweeOABAMDu3btx++23W3/bsmULdu3aVdvRLZGAT0BO0ZFv+FW0vxVwm3kvhmrlIbDOAwA7nxhCNqdC03UkUjJ2PjGEjpAEWdUdZqKgTyhrbiodl+rSxtNtfzviveIuh7M0ykrJoaEh6/eHHnqo6G+q6q2pfCO44l3sL43b/mbDrfWkG27hAl77SLjhdp6H951AKiNDpygqY51VdKQzCkbjKZyZSGI0nkI6owCEeOpNIBCjVhTJlx4hBBAFY/9ygbch5dSbiqfQrRi59PZE0iEwSX5/K+DWE9qtFlMoIDL3V6tRjNt5puayYDUNmk5kUShhmv/8EIK5lOxJ0A10B4F8XwufaPa3IPn9ywOv/cE5nKVSsXJoBWVQyvhMFmK+CX3AJ8IvCRBFgvGZ1phtuZWwXt0XQTQkFQnjaEjC2oEY8zyLmXUePRXHjkdexPbvHMKOR17E0VNx1/OYs3k7hBjRYaJAIAhGqW1BMAS7plFPgq7ZSos3ArfPAi+fwakVZX0OragQiqGgRhuAwh4KR/ezZqVcTaGdTwxBEgVoug5RECAIgqug8Jpf4eYID/oEJFKyo8OaP1/kTwOF/XETAiRSORAiQCDE6rcQ8ImGoENlIb3NVlq8EfDyGZx6U1Y5vP322/jTP/1Tx+8AcObMmdqOrAqYVVB1SiASmm+cQzHQE2n00CqG5aA9eioORVGhaEZyn051iIpq/a003NFrfoWbIzyr6JhLyTAtjFq+k93Fazrxxpk5a7+5khAEAkrh6JoGEGy9aZ0nYd9spcUbQauXz/AaFMFDdxsLoaXOBBu7d+8uHJhfRdgPt0cv1YupqST0CuPbzWbwRhSNWXtfwl0fWt/SH7K//v6v8E7caEtpn6n3xPwQRAGiKBRVKd160zoA7PwKVqb1gVfHEA5KRStHSinemUwZPROo7brEUDjhoC/fR8JoxhMKSJhL5kAEgkLvNuNfOCDhH//id6r6TFpVYC6GVrxX+2rUPkFx69BnP770s9zK391KqPb7KwgEvb1Rz68ru3K4/fbb8dRTT+H73/8+Tpw4gVAohHXr1uGP//iPcf311y96sPViw2Af7vrQ+pZeirNmW2en0kW5GubvU/MyVvaGmeGO2z92heO+9xw4jT2HhvOd44wqrnsODaMnZnRdKxX2uk4Nv4GtkqymU+QUHR2RYhOklHcc+30iMjk17yswSpSf09e4lZvb7LWWJcc53osz8tDdxlNWOTz55JN48MEH8Rd/8RdYv349CCE4evQo7r//fuRyOWzevHlRF/34xz+O6elpq5Dffffdh8suu2xR51qIVlmKu+UPsGz/5RZOmqZjfD5nNaGPhX2ujud9z5+xFAMAiMRIpJuZz5odmyEQQFF1yGrO1fEMANOJbJFvYTqRRSToQzKrgBACn2A0N8rIKtav7WqIMHbzpQyPJnDw2Biv3VRDvBZnrEeRSk55yiqHhx56CD/4wQ+wenWhB/Hg4CDe85734J577lmUcqCUYnh4GM8880xLVXmtpTBzE1oBSfCcBGcKaVGAJaRX9bJn6lnZ8FMoGi1qW6pToCvqdziezfajpf2TRcHMztZLzq9ZfY/t5znyxmRDhPHewyPMGk37nj+Djqh/ySXHOe54DYrgne8aT9lQ1lQqVaQYTC644ALkcovL1T19+jQA4K677sJtt92Ghx9+eFHnqSfVSiJzwy1/YHxmMUlwhtimVlp4PqaUgSQK+Valxrbh3DZ+74j4sbInjDUroljZE0ZHxBCerJBS0SXxQlZ15nnGp9NIpGSMTaUxMp7E2JSx7TXvwivvxFOYzyjQNCNXRNN0zGcUpHMqTzCrMV5DcXnobuMpO3UXRXZSFeBMiquURCKBa665Bl/84hehKAo+8YlP4IILLsC1115b0esX41gx6e9n5wEsxNO7jiLgFxH0G4/L7xORlVU8/dJZ3Hj1BZ7OdeT4OH767EmMT6cx0BPGRzddhOmkjFio2AEsiQRTBNApRdA2e8rKKiSRQNepNXM3Z/wUQH9XEHMpGYqqwycJ6Iz4oejsew8HJchJZxFCSSRIZRWks6p1nnBQwnmrO/HRTRfhp8+exMR0Givy4/+bnYcBFJuczI8Ha/yKRoGS0hc5Rcebo4lFv0fAwu+vqfhEoaAIVF2HIBDmOFf1R5c0nlrSrONy48b+GDo7w47PzpWXDFTl+HajGd7futt1Lr/8clx++eXW9pYtW/Dcc89VrBy8RCvZWYrPYXQyiXBQgmJL3BIIwehk0vWcC/kQgj4BkzNpfHvXywj4RKSyatESOqdoGOgOISdrUDVaFOGxsjuEVE5zOIxzsgYQghXdYfjyuQc5RUN3xM8cZyqjMMeu6xQz8znL5yArOnJKDr/z26twXl8Yn92yoeh4c6JAaUFJWffBGL8bOUX39DztJp9K3l+RGIPUdAqB5JVFXimwxnnj5aub0k/V7P4zN1ifnXL3YR5vv99WvG+vtES00htvvIErrrjCsZ9SClleXNnrI0eOQFEUXHPNNda5mt33UK0kMlcfAqVIZ1VMz2WtpDYz5BZgJ8H96KkT6IoFioTZ9VetwcFjY8we0izhatexdqGuUyASkJDJqVD0QpTR0MgsbmPcb8gvISurjpVM0C/h2ktXOkJl7b0oKqFaPapX90UwPpMpUaoBDHSHsH5tl2Oc3N/AWc6UlcpPPfVU1S84Pz+Pb3zjG3j00UehKAp2796N//2//3fVr1NNFpNExnJ8zs7LWNUXLjrWLwn5WbpJ/rf8bLxc8hdrJj0+ncbh45PQKYVACDZe0g+AHfVEbHP80rVYVtGMsNVE+jEhAAAgAElEQVT837KKhnfiKaaS2XzVGuw5NAwBBEI+KomC4rLBHhw8NoaOqB99klEb6OCxsYqfu/0+q9Gj2nwfS5Xq+rVdzHGev6qDKwjOsqWscjjnnHOqfsEbbrgBr7zyCn73d38Xuq7jYx/7WJGZqRnx2qTnnXgK6ZwKAtgcnzqoTpnlJzSNoiPqR3dHYSWSUzTPvZP3HDiNw0MTIITALxJoOnB4aAIn3p6Fzy85hCsFsRSEPZmOAtAY5bBzssZUMltvWofb3nu+Y+Y9NDLLFOqF6KZiOsI+5n0tJqzRS7+IaikfDqedaIg9Z9u2bdi2bVtdrmVlACsagj5x0b2WvZRv0DQjhVgQCvFDWj6BLJGWLVu+qupIqBoCksCMljnr0jt5eDSB//j1iNUbeyqRxVtjCegUVt4CIQSiQKHpRue7aFjH+LRqmX2Mwn0EoZCIdLawP5zPRmXFG8iq7ipEb964FmsHopbQPX9VBw68OgbA8F+YyjAWkhAO+iArGnJKQUMEfALuuuUS5vOspEe1vcQCwF4pub2PD+87wWPqOZwSmtvYv0TsGcCSACsDGKhtr2XD3k+hU1JwfIKCUoLOSMCyefukgiOZ1RRH1SgEhnnq54eGi2beZm9soKCITExfwnxaLTp+Pq0i4BOMFY4tSS2dYysGEy9KjOo6ZlOKUWcpv4KamZexqieEK9efw7Txs2b8lfSotndHC+SzstPZQmZ2OCi5rgQaFVPPs7I5zUxbKwd7BrB9Jr3v+TM1VQ5ujs9kWkEs7ENHxG8dSymFpunMKqU61TGf0RzmqXLtQEvleum23fEsq7qR3CZr0HRj5RDyi0jnNObx5mtYSizMWFEkUrJRi0kvrsWUVXTsf+ksVE0HIYCqGdsAmMlxW29axyzU51Zi4Ww8ZTnbzaS+ZEbFyDg7AsSrT6kalHOyA976jHM4taCtlUNWVh0JWgIpZAbXCjfH50A3u2bR6r6IJezsAuGf97wOXVOtPDZSKqk9IJqO4vx5hHypjJyqG8ozf+pcPlxXIHBEH/kkgRlVJYrEVrbDuK9Y2AdZ1YtWIWZenpHFjXwmd6Fz3JOHR9DdEWSarVi1odzMQXbFYP6kALIyW6t69SlVAzc/x65nThrvCS/lwWkwba0cglaIJXWEWFYLL47P4dGEZeay1yy6/rJVzHNTWtz/epF5h8zXWtsM34hdkVjHEqO96mvDM/kXFqKqAj6RWVvJrshKQ2VFQhylvHOK7ilTua8ziJHxeWRkzXp/Q/7Cqqb0cellHmC9S4K7OdlH42n0drEVJFcOnHrS1srhssEe/NfrE9Z2vto0Lhvsqcr5F4q/L/0y7z084lpraP9LZ/OlxXUkUjJ2PjGUdzAXC7lKFg/EdqBdIJuYz0ESAFWnRT4KAAgHRFBKLYc3IUDQJ+DtyRTCIZ8jqmp6LgtKAU23JwkWj9Nh3mIU8CMAc2U10B1i3md31I+hkYIJjFIgndMM5abDseIKBZrn4+7m5wAoL+XBaQqa59tSA96eTHna75VyIZCHXxtz5BvE57LoiPjRGWX0SdALIaW6rkNRZUO4lVyTwojsEfK2e7vwPm8ghqG354pWG8a4DAd5KW6J5rKioa87jBUlGdvj02msKim37ZcEKFrBGU5RmQIzXBHFBfwiIQlzqVzFK6tXTk0zzV8EABHyVWXNVRCh2HzVmgVGVRnVcCS7+TkGesJMv85inOOsXh219LVx2ou2Vg7j02lrFm2fQY5Pp6ty/vicYTsvtbW/OZrAkC1MU6cU//X6BKIhCZIkOL74pTN36vjFyd0fuZRpthp6e85xLEsxGONin1vVAVXVHCGoAGEKLgJilNAoox3su3yigFBARFbWbE2YRHSEfZBEwbGycsvMdvMd6RS47Vpn3sVt1124ZMF+9FQcO39x3Bp7IiVj5y+O465bLvF0nnItYKvhHHfr1QHUNlKP0z60tXLQ8sXpSm32pcJ4sQT9It6JpyzzjaZp+SqSxt/tlhMKIJVVIQiCw6HrFUXVmWarb+8+tuh7KWVqzqi6SwHosoYpWUNPzM+MqhIFCkUD0zci5E081LYdDIh4/xVGKKuqqZBEY/vAq2PMlZWbSUUShbwppnBdmh/bbddd6BCC1SjDseuZk0hlVcupr1OKVFbFrmdOel49eM1+94Jbr45aR+px2oe2Vg4+SbASrYpmrmXKXnshU5ITUGrOKYWa9g9rRMBCXmbWykcUCNNkYApKls/BK0x/ASG4aHWHw1z2yikViuacxQuEoCPsc6wEoiEfs1xF0Cd4Mql0RPyW4rC/v/ZQYTvVyIQen8nmFUPBia+DYnzGu0/AbRVTDed4oyL1OO1DWyuHgE80BCYtjrO3C5+lkEjJTOFtCwRywHLoJtLs6qgWJScM+EVXk0EtmUrkrPIcPsHo6XB4aAIEhHm84RimoKY/RaeGb4VSdgFCQqCpmieTSmfEh/m0YiW7xfIlOFiCtzrdxagVvWXtoQAh3tRwtYoJuhH0S8gpGmwdXaFTYz9PvuNUQlsrh+JkNKNtZrnoF+8QiGJhFgkYZgaVUZcIMISXl+Y9JnbdQABkcyqQd+iayWumI5aax1W4avAaDcUyVbiZ6RSNImQ9m/zoCcFcWkF3LFB0rD+fQ3HdbzuruLoJLjPipysWLCpRLgmEKXjLrUwqFZgDPWGMTqUc2e8DPd76Yte6npNZDFHTYY3TLIZYS6XEaR+qY19pUm7euBaSSNAVC+C8lVF0xQKQRFK1zNeB7mC+exoFpTT/05gxsyi1kQNmxrFgCXggL+iJ4bhFyX4Qw2HM6uBGAYQDUpFfOLyATyMalqywUkKM7XJQSqFoOmRVh6LpCzZ9CgclrOqLYM2KGFb1RRAOStA0ynwOQZ/AzJx267jn1i3MvjKxd9YDIczjzTIclXT627JpEJGQP59EaHSUi4T82LJpsOxzKCU+x+7yV62Q1duuuxC3vfd8BHwiNN3IRbntvedjJikzn02tu/BxWg/x3nvvvbfRg/BCJiNXnAw20BPGQHcIZyaSSKQVdEf9+Oj7LqzaDKm3M4jX3pyGplMjsUsgCAckKKoRgSOJAkTBLN8BK29hdj6HuaSMVEYBpRSbrzoXZ6fSeUFLIYkCIiGjOB0hxecByrspBlfHEE/kXLdLUWxRVQRGY59ysEJry9HTESjqcCcKBHK+HLj5zEzzkapRJFI5IL860SkgKyrenkjihiucYagDPWFksgpeH55BfC4LTaO44fLVOD06D0opphM5zCVzyORU+CQBqkZx5+Z1ODORxFxKRk8sgI++70L86vVxa0VhPm+dAmcmkrj2t1c5rrm6L4yphJHbcU5/BP/9hkHPn6mXfjOJVE6FZJtJyKqOnljAcc1SIpEA0umF+6lcvLYbH7rmPNx23QX40DXn4eK13fjZgTcR9IuO92QuJeOmq871dA/1otL7bReqfb+EEITDbD9cOdrarGSnOvFJxWwY7MNdt1ziMEd866evGuaGErs0AFv2V8HMcv6qDrwfyEfvGCGx7798NX7xXyNQdR2aWhh9afJYKaWhrKzQVjvlEtWqgZdyId/66av5VZAO1Za34BZ6fPRU3HJsr8pXkz14bAyU0qJoK03TkJM1nNMfqUpV1mo4jBtRzwloXJFBTuvR1srBHpOuU4rZRHZRMenlYAkKN7u0lLfz2u3tOUWz6umURu8IAgXViq+3lBIaLArjY28vFS9JbTplZ3K7+XrdCu/NzGeYSi+TZTv+GyEwNwz2YXg0Uffuc41SSpzWo62VQ3FMOllSTLoXtmwaxD/9+1GotvKpogD4fSIzuWwqqSAa9mE+nSuaYctqLebyxdiL1FFUVzEAQNDnrPp6ZGiCWX3VzX9BQF2jjwpJiEbAQSzsg6pREFKsSAkBEml2GGejqrJ67T7H6l2xmNwKgFd95SxMWysHe0w6IQQCFh+T7oXDr405ymprulGWIm1LoDL7G+i6YWu3F65LpHJVXyWUo1aXYlV9PTuVRmc04FCG5TK23SJszIJ/ooBCwT+wCw26Fd7zKjCrEQrqNVrJHvpq711hH3+l1LvIIKc1aWvlUK2YdK8cPj4JFF/WEIyKno9kEmwzW93Kkyi1tTcSv0SKVi6l26WwZup5/7qj6qtOgdlkzpFZDrBLhVNqCP5SZZLKKDDXPJQuXNlpIX9NJVQrP8FrzoWbCY1Xa+XUirZWDnbbv0joomPSvVKuNHQ0ZCRtqbakrURKgX2hYdray8ESotU0CSkluRql26WUJgOaIlpjVH0FihUJtf4zIisE2+tp/o+z804HMwjQ2xHAfEY1ynjkZ9XxOXZ0llv+iReBX638hL7OIMam047s8ZU9YebxbiY0Xq2VUyvaWjls2TSI7+15DVlZg6rlq5f6Rc8x6V4x/RssUTSXKjhFdWpsL2ZCa5imhEKCE9XhEwBFW/i1lcAyy5RLkGM5kgM+oahPdCVEgpKjIF86ayhT6/y2XyRJxMoef1ESnBtuz9mLwK9OljWwfm0XTpyZdfQT3/Se1czjg34Ro1MphwltVW9tJzqNpBo+Fs7iaeskOMDI6LWnGLuVeqgm69Z0eDp+MRP+joiR0KdTYxbZEQmg1m+n2zgFl0fqVTEQAHfdcgkuXN2B7lgAF67uwF23XJIv7F04xn7ddEbBaDyF4dEERuMppDOKq/nIbb+XhLS+ziAzgc9rZNPQyCw6IwH4JAEURr2vzkgAQyOz7BdY9lFqraaspVobYq7mZlNykY/FLSGSU33aeuWw69lTyCkqRNGYbWk6kFNU7Hr2VNVmICznJAhxzJoXM4suRzqrWOdTNc3KXG4EZl2jxZi57KsRQSBMZ6lAAHM9UFTqw+g1auw3ZScxEuhYJiS3goteQlmrFdkUn8sy+4m7rUCyio7umN9hQstW8TPVKFjfIe5jaTxtrRzGp9P5jmC6ZVaqZj8HN1t1VtYQ8IlQVN0SnAGfWFXlUHquap57MbDMSpVgP25VT4gpKDoifkwxsrwJqJU3YjcrJdMyM3/D71Jw0YvAr1YoqNfcir7OIMZnMjBd+4Zz3yjh0sqU+w6x6m9xH0v9aGvlUOt+Dm626kwuVzRz1SkWrrzKQTTkYwoKQojD32FsG7WSSp20okAQCTlLhbs5e70mpDUiQ9ruoxAFQFFp2YTCVsHtO6RpatU64nmFV601aGvlYO/nULq/Grg5J+2KYaEqp8sVlhnqxJkEOqN+R8hqImW0TDUrzRYioygzz6E7FjSCDwJSRYL36Kk4s+BfuYS0peJ1BTI0Mmv1H9e0hbvklaOZhJ/bd0gSidVYShKJVSSx1pnctS6l3ko0RDn8/Oc/x3e+8x2oqoo/+qM/wtatW2tyndJmJwvt94p7k/h8LwNaEH5Cvrw1C0kwEr1qSa3LZFQDnbKTATXdeM9EW3MCI1/C0BaabpgNAeO+gj4BW264qGIBuOvZU0hl5LySMYrupTIydj17CoBTgLNWGeW6q1WjqY+9/7hpQivnoyg3lloLPy/Kx+07ZK+/NZOU0V2naKVal1JvJequHMbHx/Hggw/ipz/9Kfx+P+644w5s3LgRF110UQ2uRpgz1GqlmLmZBvySAE2n8JUIM59IoOq6I1lMkgSEJRHJTKFpTTTkq5opiuUcbrRicPdRECv6SSCAZia3EUN5WCsxAlDdfIUt/S1vwvMieA0flPO6o1NphyA1Q6MFUllv5moJ42rVf6q18PN6v+XMa+Z72N8fw+Tk/JLHVgnVClVuB+oeynro0CFcffXV6OrqQjgcxgc+8AHs3bu3JtcSRVIU7WdGtNhnoEthw2Aftt60Dl0RP9JZFV0RP7betA4funotNN3oWWD+03SKjrCEkF8s6p8Q8oug1Kheah/nfFox+jlUgVrogZoFBJPi/hjIr7oCeYWraEZSXUAyVheCYMykAz4RPkmA4BKptNBFS8NcCTG62JVGzGRlLd+zwyjJYpQGIdj3/BnmmVlRN4vpn+DWu2IxUVK17CPh9X7dvkONmqVXK1S5Hai7cpiYmEB/f7+1vWLFCoyPj1f8+gMHngMAqKqK7du3Yf/+pwAA2WwW27dvw3PP7QcApFJJjD7/b0iPHzdmzkoaUy8/hMzECXRG/Jiensb27dtw5MivAQCTkxPYvn0bXnrpBQDA6OhZbN++DUePvgwAOHNmBNu3b8Prrx8DAAwPv4nt27choMax/WNX4O7NKxB/+SFEMIvzV3VAyI5j6uWHoKQmQAAImbM4efD7SM5NQBQItMRbmHr5IaTmp5BTdGRnTiP+8kPQsnOgADLTJzH50g/gQxqEANn4CUy9/BD8xPgSZyaPY+rlh6Cr+e2J1zD18kOgmrHaSI8fNbZ1w96SHnsFUy8/ZD3H9OiLmHrlYWs79c4RTB99pLB95jCmjz1qbSff/i/MvPZ/LSE6P3IQM6//e2H7rV9i5vhu6/j5N5/F7NAeaztx+mnMnni8sH3qKcz95klre+7kfyBx8j8QC/kgigKm3ngCc6eeQizkQ3csgNFXf4b50/vhEwkEQjB6dDfmh5+xlMnksX/HzOnnLOX/la/ch5/8pHA/X/7yX2PXrh/j6Kk4djzyIn7/k/8Tn/ni13H0VBwD3UFMvPwjzL39a0spTbz0Q6RHX7QE6Wv7v4uJN48Yqxxdw9gLP0By9Khxcl3B24d3Fn32tm/fhoMHf2lkNmsZvLb/u5h553XjeCWFXz/xTU+fvYe/92VcO0jRFfFjcvwM3jz4fbxvnYANg304deoktm/fhlOnTgIA3nhjCNu3b8Pw8JsAgNdfP4bt27fhzJkR9HUGMTV6Eq/t/y6yySkAQPzsCbx9eCcmJycAAEeO/Brbt2/D9PQ0AOBXvzqE7du3YW7OKP9+8OAvsX37NqRSSQDAc8/tx/bt25DNZhGfyyLxzst4bf93oec/e3Nvv4hfP/FN67148snH8Vd/9Tlre+T4Acwf/zF2/M/3YvvHrsDpV5/Fvff+L+vvP/zhD/HlL/+1tf2TnzyCr3zlPmv7kUcewo4d91vbDz20E1/72t9Z2//2b/+Cf/zHf7C2/+VfvoNvfevr1vZ3v/tNfPe7xvhu3rgWIy8+htMv/txSwm+98O+gY/9pHf+1r/0dHnpop7W9Y8f9eOSRwnfL7bNncu+9/wuPPbbL2v7iF/8/PP74Y9b2n/3Zn+HJJwvfle3bt+Gpp4wJdCVyz/zsAcDc3FzRs/JC3c1Kuq4XNRqhlBZtL0QkEkB/fwyqqsLnExGLBdHfH0M264PPJ6KjI4T+/hhCISMGnpCSmTMBRElAb28EPp+Izk7jeE1LFW3ncsbfu7rC6O+PIZUq3p6bC8PnEzE2l8UTu45i+PRJTE6mMDqbwfNvphHwixAEAlEQEAxIgCZaphRRECAIC+tlTQeCPgkyROSMRmYI+CXI7r17as4fbr4YP/vlaaSI0dnuDzdfjP+z742qnJvC6HEcDfuQC/ugEwlBv4RgQIJPEqDBKOMhmA2QRAGhgIh0RoWmUwiUIhQQcf7qTtARyfqsAIDfL2EmJePRp09CkozVRk7V8OjTJ/H+q87F6780ViIUsBor9XSGoFOKoE+EQABJKP48GWkWBDqMHA37Z8/8LK7qD2BiMgshr7R8koAsKPyL+Oxd+Vur8D8uuwwnT57Ejh3/iasuXY3+/himp43PYne3cfzERPF2V5e5HcEfbO7DP/zzEIwS8gSabvRY7+8Oo6cngv7+GDo7Q/D5RPT2RtDXV9ju64uiqyuGjg5zO4ZoNGpt9/fHsKo/it+MGas9YyUngObv13wvYrEg/H7J2o5GWduF4833z9yORAIIBIq3g0Ff0XYyWdgOh/1QFH/RtijSom0A6O+P4cb+GJ4Y7MPweBo5RceKnjAiF/Ti/HO6rOODQV/RZ6t0OxBwfvaiUfu2iGg0WPL3YNH9mnINQJGcq0Tu2bd9Pg0+lxDuhSB0oT6PVWb37t04cuQI7r/f0PTf+ta3QCnFpz/96YpePzWVNJrUV8D/+43/xDzDbh8L+/CPf/E7lQ/aBbt91W4vTaZlIzmpJLrGKEKHvMO1UPaiXO5aV9TvCMmcTVavS1R3LOBoxjMz7659tv33DQ5n49f/79GqjYd1/p2/OI5UVnE8N0kUoGi6Fd6p6Uaf5Nveez7T/r/jkRcddvucoqEr4mc2HwLgeH/nkjnL52Dvzex2TbfPyFJMJ0u1wdcyWqkZ77fVqPb9CgJBb2/U8+vqvnJ473vfi3/6p3/C9PQ0QqEQ9u3bh7/5m7+pybXcmru47feKm3MvqxSczpYDNe8Q74iUCuNAWWE8nzFqL5klvucz1Q1rEoWCw57Cvf+1iemQpRSYSmTx1vi8JQRqheFDcDqMFZWiM2o+TwqfRBAKSK7hneWcjeUc2HZBesf7L/IUrdSM/RNqWbK7Ge+XszjqrhwGBgbw2c9+Fp/4xCegKAq2bNmCDRs21ORabvKqWnLMTdg4Ko6av1NDwOu6UR9H1ym0BUpe6FpxAb9qO4Jn5mVHf4lyZHKFwnaUGtuVrForzfdgRbroVGdGK+k6tUpQVBLeuZiIH5Yg3TDYVzZ0tZJztDPL7X7blYbkOdx666249dZbG3HpqlIuz8EVRg/pcpQK1MXaAF37LYDC0V/CI+UqwZrnpSXbbrBWYgIREJAoMvkVixnlBYl4yqKtdcc3N3NNMyWdcVqPRn1+2jpD2q3YXcBXnSAtN2FTLsFMFIzwS52SvMO69lViAXYJboDdX8JeVrwW13VbRYgCYYZZAhRZJW/nzycXZhUNG9evwMmziYqzaKvVt5lZbBHsVc/waAL7XzqLbE6FputIpGTsfGIId31ofU07zXHag0ZmbLd1ye4r3sV+eG77veIWo10u+iqRMuoA2TOAGwWBkTBmz6+odg0ogoIpzPy9XEltVow5QBAOSPnKs0YORDggYSYpW88/mVk4Rt7et3nNiig6on4cPDbmqQy0vZS0/cu669lTUDWjKdHZeAqz+XpPTx4eQSojQ6dgZl9Xen5eqnp5Uq08mcXQ1iuHmaSMSFBCJqdamcehvFCpFm4lpqlrCWuXDOAGYC9KyNquBn1dQWNlknfAx8I+TM5mmYX0TB9M6UqMUh2ZnA5BECDlV2WZnIqz8ZSnLNpqZAfvPTwCRdWRSCtFEWTTiRwAyiz9Yawki1vAulUGLjdG8++8+c3yoZEZ222tHOJzWfR2BkHI0urReGWgJ4x34qmiJkM07/RlOVbrgVutp0IRu0JJ82rqB0XVi6KhlPzKgOVLIQC23rTOYVL55z2vA6AOpVouE5plmlnMF630PMNjCSgadUSQmZ3rWIrfNDGa5T0ojCKCXsZ4Np6yzAv25jfA8isIt5yoVtmUxdDWyqFRD/bKi/vxTjxl1w0gALrzOQuljtWMrtW8oZc9KIpSwJSrYj7j2ESntKzQZa3EzO1SfCJBIi07WmGKArHKpttXED5JYK7ERJEAqlOpupVBcbPTBv2iJwc26zw5WbfGao5fs0n/coq/eKVEPY1R1SjCDWp+w30gjaPWQRTlaGvlcPPGtdj5xBCm57K2nsQS7nh/LYr8FTBaQDqT10AI07FaLcUQ8AmQlcLM3LL1uzjIhfyKQUdBoC00lnBQQiziL/qgZnIq81hFo4iGJKSzKpS8eSUclJDJaUWrFHP1EnCJiT2nL4Kx6XTF/RncTDOgbLOV2xeNdR5zlWUqRz1vixMFghijh0QiJVvmOvu96pQdmeU2RlFkO+sXswr2Iux5CevG0si8kbZWDgCQycpWqKWmU1BaPX+DG/byyiaUUozG0wgHnMIymWEL13KsP7cTQ2/PFW3ffPV52PmL48jKGnRqOL2DftHVyUwpEPALVlIbIUYje3suQykss0+5DGn7venU2BYFgq6oMzN7oDvEPIc5e2L1Z2A1oXczzaSzKq777ZUVRyu5nUdRdUgiKUpkjARE5FTdMcbVfREkM4pDaaQyKlPYp7Mq7tzsfMZ7D49UZRXsVdjzEtaNp1F5I22tHH64d8gRg69oxv6///PranZdN3OWTikyOdXhWF0MZ6fSVmlwUSA4O2U4OO+65RJHDXw34W3MaJ2xpqWKx+Sad6+oygeVUqOuT1csUNEM3m32BIBph3czzQR9ghWt1CcZ1TcPHhtzberT1xl0rFgk0Yg4Kh37lhsuKjvGUqUx0B10NXG5PWPTvLCU5jdehT0vYb18aWvlMO2S7eu2v1q42Qkl0Sj5UBq5Ajhj/xeKYTJ7PwDGikjVdOx65iTu+5OrPdXAz8gFZwSlxvaZySTz2AEXM45XdMpegZRTOiyBueORF6FqFPPpHOKzWUiiUT4jILFNM1I+DLBSwWhvzWn6TBRQbFy/AjNJmTn2Skpw2JVGpSYuu4JcSvMbr8K+kQ5RTmNpa+XgJlxrHTjqNtP93s+OuTa5cc9gZsNq3jM6nanK+JNZjams9j1/Buev6mAKOq9UYwVyNp6yCitSAJoG5GQNmubD+684x2E+OvDqGAhBvud0IbTWTTAavqOC+csnCVYo9PaPXVGVe12MglxKYTavwr6RDlFOY2lr5VBN9hw47ak15PBoAiPjSWRlFemsiuHRBAgRwCpPYdivi6X9Yjq1aTrFjh+94PBFLAZWqGk6pzLt1b58ddRy2JVNwCd4joBhHZ+VteLaVfmf6azCNB8BsHpOm7kI04ksVvVGmNeMz2Wt2k3WNRbZmnOpbUKrhVdhzwvpLV+4cqiAPQdO47EDw9Z2Oqda2ywFsefAaew5NJwvJW1rJeki8FWNei6d7Uapr4DlO1gKLLOMprMVgyQYIbOlK6Qr3tWH7/3smFW9diqRxVtjCdz9kUsBlPct2JWSalNIduWj6uxxTs9lrSMLqzL3JVpfZxDjM5mKHedeymoAjYn2WYyw54X0lidcOVTAE79ip6o/8asRpnLY9/wZUB2ghBqJZoywTXvSGWCsHoifBgUAABv4SURBVOyUbjcLqqphZj5nOWhjIcm9jpQo4Kp39eHw8UkremrjJf048fYs09fx/cdfRyggOQRpwMVXQPPhpKxMdFYkkKrp6OkIYD6jFo0/y6i/BTh9DoqqQ1ZzuP6yVY5j3aKA3MbeyGgfLuw5ldDWysEnsiuGem2MZNb7sYtrCvcKrGkzAslWs6gIWvyTELa5oxnxUuI7p+h47a3ZfMcxI6rqtbdmkUgZryl9nsmMimjY7xCk49NpdMUCDl+BIBjaVSBCodkP1RGQBCRSsiN8NOiXIEkiVvb4bWM0mv2wGBqZRUfYma/C6hfhFgU0Pp3Gqr5isxWP9uG0Am1deO/Ki1d42u+G2xzebb+ZcUxs/+yU6AYrGU3V9Hw2rF7zjOlyuK1aOsI+mOsfo7jgwnWhWEXnysGa8evUUJ72goXTiSy6on5EQn4jkkgzymtEQn5c8a4+JNIyVFUvZGanZVw22GNEMSma1R+4nL3dzFdZ2RPGmhVRrOwJoyPiZwr2+FwWmqZjfDqNdyaTGJ9O53t1EN6wntOStPXK4cXfsCtZuu13o7cziPhc1jILUdt+FkG/gHROcxzP+p3CbG9ZzGIc0l5xE+1Bn4Ck5lxy5RQNkaAPyUyhxHc05CtKsis9p9GiuNg3YFJ6bbMqa2kkjVFuw3iF3VcQ8ov4+A0XOcI79x4ecY0y8hJC6yWyJ+gXMTqVcqz+uqIBT1nZrQQvq9HetLVyMHs5mHZ+0+7P6vFQjjs3r8M/73kdmZxqCfxQQMKdm9cxj187EMOZySTS2UINonBQQsqWLcyKsqk3btdNZtkZ0jnFqPEjCARi/vWprOp4vna/iptFjcU5vWHkVKcgDfhFBHxCWV+B/bzlooy82Ns9RfbQwlSgVIFtySuwdhKivKxG+9PWysFtZuzV1bthsA+fuu3dFX/BzZpOkihA03WIggBBECrvldnEaIwlTcAnIBz0LTnaamI2iw9dvdYRMjw0MouxkhLXqkYRDYrsDGmfgPm0wowy8jLb9RLZk1V0hIMikhnVco5H8wqsHR3AvKxG+9PWyqGnI4CpRM7Rp6CnI+D5XJ6/4NRuQDK2G+lHqCWUUiTTOcv5r2oaNIZZqhSWg3//S2ehajoIMXww+186i986z5mpnFA1SKIASXJWKs0qOmaTOet5q5qGnKJh/bmdnme7lb7vQZ+AmXmj4qw5B0hnNXSG2c5uoLXNMrysRvvT1g7pj3/gYoQCotV5jBAgFBDx8Q9cXNPr7j08AkEgIPni/iTfGrRdkVXKrGG1ENT2z2Q+LUNRdWi6ETo6n5bx4m/i6IwE4JMEUBjlsjsjAcwlc0wH9sx8DqDFpUlAgRdOxGvXVcuKSy5x1ru0vWv1jm99nUEkUjLGptM4M5HE2HQaiZTMHe1tRFuvHDYM9uEDV51rmCkUDUGfuKiewYC3Wd478RTSOdXREAaofXOdVsec7VsWOGr4OjoirDwQYjMfUau2kq5To/xI/vXI5z/Iqo5URsb4dLHpR3UJSfZCVtbQ01Ha9S6IrMzWkq1ulmHVnUqoGja9Z3Wjh8apEm2tHOw9g1cFJaSyatkqnOXO48UcoWmGxDdXC2ZDGFGw5pZWPwdKKOjSZVNLsVDdKFYxQlYeSDjow1wql89EBxSVQlZzjr4Udsf4fFot2j+fVuGPLX0BbVZwNa9NYax83HpOLKUrXTO0CXWrO8XKAeG0Jm1tVtp7eMRq+v7WWNJq+u7VjOC1ybckEug6zWfU6lBUHbpOEQn6sHH9ClBqNMKhFNi43lvORTuwkO+FZW4qRAKZfyHIyRo6wn5I+VwISRLQEfYXWXIqyVFJZhdXNt3O+rVdzNyK9Wu7mMf3dQY95T/YzVB253ujzFBmRNhATxjn9Ecx0BMuW8SQ03q0tXI4G08hkTIUgijAavp+Np7ydJ74XNZTF66OsK9g1kBelBGjbebJswms6Alh7UAUK3pCOHk2sYg7qz325D1WIl89rysQQCBG+1JFo/mEN6NMuZmkdv6qDitJDSiU0jADTE2FIQqw3htCkF9xLH3pZs6kS/0iQyOzzONv3rjWU0Ke1wlKrfGq3DitR1srB6PSqdn0vfCzXI9kFp6/CISAEEASBfjzDWIIMWaorC94OfwSKbtdCQEf+zXlztTbGYDfL0IUCPx+Eb2d3iO8FkMs7INPEiAKBD7JKJPh9wmuDm/W+xLyS+iKBhDwi5BEgoBfRFc0kM9cJ/Dl3xefKAAgCPqXbl31OpPeMNiHrTetQ1fEj3RWRVfEj603rSvbla5abUKrgVflxmk96u5z2L17N7761a+it7cXALBp0yZ89rOfrcm17I3pBRg/yzWmd8NrmWM35+SUyxc8HBCRZrTmXH9uJ068MwdRIFa/YnWB1OmATyhK8gv4BNcaUOXOxKpBVGtEgVid7CppQ6pTWNnH9u5om69ag/0vnYWuG2YoXafQdIqNl/Tj8NAENL3Q/5mCYvNVa1zHVGkgwmKa4ngJj262pju8lHf7U3flcOzYMXz+85/Hhz/84Zpfy96Y3mjSXr4xvRtevwjmF9neOS2naAj6JWZ5iLUDMaxf28VM/mKWgSiTXDbQEwYhxqxbUXVQSjEybnR2K1e6ohRWyQc3JRYOiFBUrWh27xON0tlu/gVWpdpVvd47zZnlMOzlMwAAL75jXsn4QSk2/tZKAMDh45NQdLNK7ArXvhxeAhHMxMfpuayV+BgMSLjj/Re5jt1LBJx9grKUNqHVpB2T+zgF6q4cXn31VQwPD+N73/seLr74Ynzxi19EZ+fiGtIshL0xfSQfrbTYL1Q1yi5svmoNDh4bY65ANgz2OYTUge8csiKczAiYBaxQzNDOxXDR6g5Hqe23J5LI5NKODnGqRtHbFS5SejlFw/hUmtnhTiAEkZAP2ZxaJEivvLjfVRi7weqOtuORFxEO+dDdUZhV5xQNu545iZyqY0VPyHr+J88mcPRUnPneeg43ZSQ+uuE1Aq5abUI5nEqpu3Lo7+/HXXfdhSuuuAJf+9rXcN999+GrX/1qTa5VzS9UNcsulK4Q3M4T9AkYnc5UXCIbQElmsLliEZCVdU/5FL86PmGdR6cUvzo+gYBPQDQsOUpEpDIqM3/AdAKXKpNVPSFmvSE3YWwU3nOO3qgS68QtTHQ0nkZvV7BiYe8l3NQcuyDohkIViOUwdlM8Zv9re4mPcnkO1WgTCrR2ZjanftRMOTz55JP4yle+UrTvwgsvxA9+8ANr+0/+5E9w0003eTpvb2/U0/E39sdw49UXeHpNKUeOj+PRp09Ckgg6o34kswoeffokOjvDuPKSgYqve+T4OH71+gR6OgMI+MLIKRp+9foELlu/knkeyWe8PYQUfA6UUuv3Ulj7KQUCfhFZ2el3ME1PLEonvZQCOVmHolJIgmCtCNJZDaDs/IFwQEA6V3x+nQLv+2/n4sarL3A8n0eePolYSMpnGOefgUiQCfuQk7WihLKgX8RnP/bf0N8fs/aZv6/qj2ImkUHQtpLJyipAgEjQef6ZpFx0HhO380TDfjy46yjGp9MY6Anjo5suwth0BvNp2VAKeWWWSMmgFMxzez2+lEqOYbGYz3IzsNj7bVWa4X5rphw++MEP4oMf/GDRvvn5efzgBz/AJz/5SQCGoBNFb513pqaS0BdRz3ops60f7xsyHNmCkA+LFaASDT/eN4Tz+iq3k5c7z9xc2jGbS6ZlRIISkhkFikuJbDtuj2UuyT7eawgnhbGK0HVa5CuwY09cK1UMJv9x6E2s6Ag47rcn6nc4XXOKhlU9YaZP5ry+sPWe2t/fGy9fjZ2/OI4JOWM1GQr6RQx0h5DKqo7zd0f9zM/GjZevzrcjpZYZKp1VoSgZnJ00EhtnE1k8+MgLlsOeIJ/9nj+HrGjMc8sej7fTDJ/lerLUlVKrUe37FQTieVIN1NmsFA6H8f3vfx+XX345LrvsMjz88MOeVw6NoFpFxtzOczaewrd2vwpFpdZxp87OoSPsRyqrOkpkN5KizGPrP8MXYqxsUDCDueieqXmZaW+/9tKVTJ/M+rVdVqZ7n2SEFS+Y6W6tDsz6GQRXXtzv6vMpZ2qx71dkFSmVWveoU4pUVoWuUwgigU5pQUHmI+NY57ZH0pUeX0t4wTxOpdRVOYiiiK9//eu49957kc1mcf7552PHjh31HMKiqFYYodt5kmkFpXJUUSmmErm8A1qwOXabo9aGs/o4gc8m2AwfgfsKj2VvHxqZZTbj8eoY3nt4BOGghO5YITcjp2iu5wdQ1jlsv8bdf/+s5VQ3n4PpzYmFfI6WotGQj3nuzrAPokAcx3uNpPNKs4XEcpqXujukr7zySuzevbvel10SXvMcvJ6nnLhn5UtMzjZ+llcq9nVKoamFvS7FSC0SqVxRraREKgdNY/c+eHjfCU+z3XKzY9b5dzzyogflQwt9fcw9+dWSTzKiruzvLShlnhv5cOPS4xcTSbfYkNh260zHqS5tXXivWlQr4cftPG5JXgAgioIjX6LZIMSIrMoquiUogz4BGYYT3IRSQKc6VFoodeGWue51tuv1eC+mloGeMEanUtApsQUAUKzui2DLpkHHe+um2NJZFXdurrxlqRvlQmIB988sj1biLARXDhVSrYQf1nncqpQSsJPRmg1Kgc5YECtKHL2ZqbTra+zOczOHg7qUp/U62y13PGuW3dcZxPhMhtk5rpQtmwax84mh4hyNoB9bNg0yx1JOUVXjM+Vmctv17CnkFK0iUxmHw6Ktayu1Cldfwq7MevW7V+DaS1cikZRxZiKJRFLGtZeurMuYRKG4NtRCyXepjIyR8Xm8NTaPkfF5pDIyfC6BaG4WJ7doK691iNyOB8BssNMd9WMulYOi6iAworjmUjlmRdUNg32460PrceHqDnTHgrhwdQfu+tB613OvX9tV0xpEbjWXxqfTTVWoj9N68JVDE/D/3HYpgGOOjOSNv7USP3rqhCNKxytmZI1h4zejinTXaCIA6LCV7TBm0oGyZTtYeQ69MT/6u0IYenvO+tv6cztx4swcc6W0mOqoXnocuM2yXzk1jY6w3+EcdutN4MVv4eYEr9bM3W1lApCmKtTHaT24cmgSBnrCCPrFfBy/iIGeMPYeHkEmpyKdVaHn8xxK7delhAMSMjnVykMIBST0dASQyqqehL0kEnTFAksyZ80kZSj5HAEz3+DsVLpMvaXyLTVLTSTDowkcPDYGURSKehzY99uPz8kaAn4R4/M5m4Pfh6ysIhwsrjoricSTIPXqBK8Wbia0ge4gs44Xj0riVAo3KzUBew6cxs8ODiOdM5RAOqfiZweHcfLMLJIZYx+BMeNPZtzzHAgBPnXbu3Hx2i4M9IRx8doufOq2d2PLpkFL2K/ui6ArFsi32WQjEFTFnKVTIy9Dt7K6jZyA0nIaJm5jcutlsO/5M5725xQN04lsvh9EoaOcQICZeRmapheVKQn6Kv96NKq/gZsJbcsNF/GS2pwlwVcOTcCTh0ccvZMpBVTbPvvf3JCE8vV3Sk0bP9w7hClGraZwQGQmnS0Gt5yAQslsWNt+FyeF26w8K6sIqyJm5nNW1d1YSEJWVtEnBR3HK6qeX51Q2+rF7ApE4cgnWSge10YjQ0TLrUx4VBJnsXDl0ASY/RfclABLIZQeQwD4JPe3kyVAQkEfSFJ2lM5WNIoow37eG/MzlUk5mDkBADoiThv/yp6wazQRy64uiQJm5mVLAZkzfl9eOJceLwoCOqM+zGdU67qxkITpRA69nc58Enstp4VoxhBRHpXEWQpcOTQB5VYEpsA28wdMQQ4CkBKhXs5UxCIra+hzCEWfa1MiVRXwu9ed76hx9PNDbzGrphpQR05Ab2cQsqKB5l9DdaNe0/q1XZ7KanRE/JiZz8Kc8YMaa5NoyM8MAR7oDkKlcDQxCvolZj5JV6RwXCVwYcxpJ7hyaAJ6OgKYSuQcCiLkF5BTdRAQCGbtIkIRC/own3EW0+vwKMzKNSVKpGTmzP626y509J048sYk3omnHCuQnlgAikYdOQHX/fZK7H/pbP7VhdpHR96YdI34ufbSlQ6ldODVMVsGudG/IhYOglKULZPhpc8GC7eMZK+lsGtdOpuX5uYsBfHee++9t9GD8EImI5froeJKJBJAOu3NJFIvVvaE8cqpuDX7JgQIBUTc/ZFLMdAdwltjSSiqjqBfwoeuXoupuWyhIJ9AQAQCAoJYSMINVxgtLyu531jYh1dOxqFTo2eCKRTffV4XTo8mQHWzgJ7h0Lz20pW4eG234zy9nUG8/v+3d68xTax5GMCftlAuynXFirIbtSqa9ZDlGC+oERFBbJHiRhO8QFYk0Y0JhpgYQrx80igxIOpXr8E0QQMCLhJQjNGAVxSWvbiKcDYgR1FQQSoUmP2ApytOCxQZBsrzS4zOdBif0cB/3nfeed/6VghC33xKTiolJrk54y/r5yN47hS8//QFggDM8JuEzWFa3P/nG/QIArw9XOA5yQUe7moolQo0t5rgPdml36gllVKB5g8mvH7fAbev8yU5OSnxy5s2uKpVgEIBr8ku+J2XK9xcnNAr9BWlP4dqseInf0Qs/j1W/OQPja973y8fNzS8bcfHz119x62ajVV/mmF1v7UfpL+NnOrq7oWrWoXPnd2oevkOpi9m/O3+L6L9Gh+3fsV3sPPYOv5bQ/m//ZHzjzVj+XtXCiN9vQqFAu7u9t04Amw5jAlB2inYFfNHq3d5VleI+/uv8PFQi/rOv5jtG25qq5+8+MF/rS5POtC4/0TdfJt3qUOdKwlQWH1W0N0jwN3a/ESCYHUNaSlX+rP1rkTJowZ4TlbbNTmgXavM2Unq85PjY3EYI4az2Pz3fef29pHb+nuzS/4DD3fnft1UgiAMOO5/OPm/LwIaH1d0doufFahU1l/o+nZ+oqGs9Gfv0pzWDDRyytoIqeFMDmirO6jg3qu+rjVzD1ydVYhcHGBz/WtOzU0/isVhHJJ62KTU0zrbyh/3dYoLay2ZweYnGsoCKSNxN23r38ZV7WTXS2e2z6OyWsAe/ONXPPj3WyiggJOy72agoLweAKwWCE7NTT+KL8GNQ/bONWSvqKV/kPQFqoHyB2mnYP/Wn5H+1+XYv/VnBGmnjFgeW/MQ2XM3bStL5OIAuzLaOs+3U3x/+wLfg381Q4Gvz5gUyr7foUDJowa7zs+X4Gio2HIYp6QcNjkaY/btyT9SeUbibnqgLDP9PYec0dZ5bD2P6RUEOCv7D1VWKr6ujW1nTqKhYHEgq8bamP2RyDNS3XG2stib0drxU7ysd6EpFYq+UWXf1IdeAXBV2/fiI9FQsVuJJgypu+NGgq3uoKUL/CBAQE+v8HVGXQECBEQuDpA7MjkothxoQhnrd9MDdQdpfIc+WonoR7E4EI0xtgrYb2+nD2VkFtGPYrcSERGJsDgQEZEIiwMREYmwOBARkci4eyCtVNq3ZsFIfe14xOt1XBPpWgFerxznUgjCcCbAJiIiR8ZuJSIiEmFxICIiERYHIiISYXEgIiIRFgciIhJhcSAiIhEWByIiEmFxICIiERYHIiISmRDFobCwEDqdDpGRkbh8+bLccSTX3t6O6OhoNDRYX3zekZw5cwZ6vR56vR7p6elyx5FcVlYWdDod9Ho9zp8/L3ecUXH8+HGkpqbKHUNy8fHx0Ov1MBgMMBgMqKqqkjXPuJtbyV5v3rxBZmYmcnNzoVarERcXh6VLl2LOnDlyR5NEVVUVDhw4gPr6ermjSK68vBz37t1DXl4eFAoFkpKSUFpaioiICLmjSeLhw4e4f/8+CgoK0N3dDZ1Oh9DQUMye7birwVVUVCAvLw+rV6+WO4qkBEFAfX09bt++DSensfFj2eFbDuXl5Vi2bBm8vb3h7u6OdevWobi4WO5YksnJycHhw4cxdepUuaNIzs/PD6mpqVCr1XB2doZWq8Xr16/ljiWZJUuW4NKlS3BycsL79+/R09MDd3d3uWNJ5sOHD8jMzMTu3bvljiK5V69eAQASExMRExOD7OxsmRNNgJbD27dv4efnZ9meOnUqqqurZUwkrSNHjsgdYdTMnTvX8uf6+nrcuHEDRqNRxkTSc3Z2xqlTp3Du3DlERUVBo9HIHUkyhw4dQkpKCpqamuSOIrlPnz4hJCQEBw8ehNlsRkJCAmbNmoUVK1bIlsnhWw69vb1QKP4/Za0gCP22afx78eIFEhMTsX//fsycOVPuOJJLTk5GRUUFmpqakJOTI3ccSVy5cgX+/v4ICQmRO8qoCA4ORnp6Ojw8PODr64tNmzbhzp07smZy+JbDtGnT8PjxY8t2c3PzhOhymSiePHmC5ORkpKWlQa/Xyx1HUrW1tejq6sKCBQvg5uaGyMhIPH/+XO5YkigqKkJzczMMBgM+fvyIjo4OHD16FGlpaXJHk8Tjx49hNpstxVAQBNmfPTh8y2H58uWoqKhAS0sLTCYTSkpKsGrVKrlj0QhoamrCnj17cOLECYcvDADQ0NCAAwcOoKurC11dXbh16xYWLVokdyxJnD9/HtevX0d+fj6Sk5OxZs0ahy0MANDW1ob09HR0dnaivb0deXl5sg+scPiWg0ajQUpKChISEmA2m7Fp0yYEBQXJHYtGwNmzZ9HZ2Yljx45Z9sXFxWHLli0yppJOaGgoqqurERsbC5VKhcjIyAlRFCeCsLAwVFVVITY2Fr29vdi6dSuCg4NlzcSV4IiISMThu5WIiMh+LA5ERCTC4kBERCIsDkREJMLiQEREIiwORFY0NDQgMDAQ27dvF32WmpqKwMBAtLS0AADMZjNWrlyJpKQkq+e6evUqNm/eDJ1Oh7Vr12LHjh2yz7hJNBiHf8+BaLhcXFxQV1eHxsZGzJgxAwDQ0dGBysrKfseVlpZi/vz5qKmpQW1tLbRareWzjIwMPHr0CCdPnrSco6KiArt27UJubi6mT58+ehdEZAe2HIhsUKlUWL9+PQoLCy37SkpKEB4e3u84o9GI8PBw6HQ6XLx40bL/3bt3uHjxIrKysiyFAQBCQkKQmpoKk8kk/UUQDROLA9EAYmNjkZ+fb9m+du0aNm7caNl++fIlnj59iqioKMuxra2tAIBnz55Bq9VancsrNja2XwuDaKxhcSAawMKFC6FSqVBTU4OmpiZ8/vwZ8+bNs3xuNBoRFhYGHx8fBAUFISAgwDJT6veTD7S3t1tW+YqIiEBGRsaoXguRPfjMgWgQMTExKCgogK+vLwwGg2W/yWRCfn4+1Go11qxZA6CvAGRnZyMxMRFBQUGoq6tDa2srfHx8MHnyZEsr5PTp05YWBtFYxJYD0SAMBgOKi4tRVFSE6Ohoy/6ioiJ4e3vj7t27KCsrQ1lZGW7evImOjg4UFxdDo9EgISEBe/fu7bdCXWNjIyorK6FU8tuPxi62HIgGodFooNVq4eHhAW9vb8t+o9GInTt3QqVSWfZ5enoiPj4eFy5cwIYNG5CSkoKCggLs27cPJpMJbW1t8PLygk6nw7Zt2+S4HKIh4aysREQkwnYtERGJsDgQEZEIiwMREYmwOBARkQiLAxERibA4EBGRCIsDERGJsDgQEZHI/wCd7Hr+52vyWwAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.residplot(\"MAG\",\"DEP\", df[:1000]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are further options for variations like polynomial fits, logistic regression, loess smoothing, and robust regression (eliminates/down-weights outliers).\n", "\n", "In addition to `regplot`, there's also the slightly more powerful `lmplot` that let's us condition the slope and intercept on a third variable." ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ0AAAFcCAYAAAATa2OzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsvXt0XOV57/9592VG95sl2fIdG7BNwIAJF0OISQLGYAwh5HAghKz2lIY0ha626eEQl19Yp20uzUpX2qSc05xf0pPGoesXQghxSWziOIWADU6wAcWALxjLsrEsS7Kuo7ntvd/fH3tmNGNrZI2suUh6PmuBpT2z97yzZ7S/+32e7/s8SmutEQRBEIQCYBR7AIIgCMLMQURHEARBKBgiOoIgCELBENERBEEQCoaIjiAIglAwRHQEQRCEgiGiIwiCIBQMER1BEAShYFj5PPg//dM/8fzzz6OU4pOf/CR/+Id/yBe/+EV2795NeXk5AA899BA33XRTPochCIIglAh5E53f/va3vPrqq2zevBnHcbj11ltZs2YNe/fu5Yc//CHNzc35emlBEAShRMlbeO2qq67iBz/4AZZl0dPTg+u6lJWVcfz4cTZu3MiGDRv41re+hed5+RqCIAiCUGLkNadj2zbf+ta3WL9+PatXr8ZxHK655hq+8pWv8NRTT/Haa6/x9NNP53MIgiAIQgmhClHwMxwO87nPfY5bb72V//pf/2tq+7Zt23j22Wd54okn8j0EQRAEoQTIW07n0KFDxGIxVqxYQXl5OWvXruUXv/gFdXV13HzzzQBorbGs3IbQ0zOE5xW/MHZTUzVdXYPFHkZRkXMg5wDkHCRJnoempupiD6WkyVt47dixYzz22GPEYjFisRjbt2/nyiuv5Ctf+Qr9/f3E43F+9KMfiXNNEARhBpG3mc6aNWtobW3l4x//OKZpsnbtWh566CHq6+u59957cRyHtWvXctttt+VrCIIgCEKJUZCczmQi4bXSQc6BnAOQc5BEwmvjQyoSCIIgCAVDREcQBEEoGCI6giAIQsEQ0REEQRAKhoiOIAiCUDBEdARBEISCIaIjCIIgFIy89tMRBOHciLe3Em/dgjfYhVHdhL3yFuyFK4s9LEGYMDLTEYQSJd7eSnTHJrxQHwQq8UJ9RHdsIt7eWuyhCcKEEdERhBIl3roFDAtlB1FKoewgGJa/XRCmKCI6glCieINdYAUyN1oBf7sgTFFEdAShRDGqm8CJZW50Yv52QZiiiJFAEEoUe+UtRHdsQsfxZzxODDwHe+UtxR5aXhDTxMxAZjqCUKLYC1cSvO5+jMo6iIUwKusIXnf/tLwQi2li5iAzHUEoYeyFK6elyJxOumkCADuIjvvbZ8L7n0nITEcQhKIjpomZg4iOIAhFR0wTMwcRHUEQio698hbwHHQ8itYaHY9Oa9PETEZERxCEojOTTBMzHTESCIJQEswU08RMR2Y6giAIQsEQ0REEQRAKhoiOIAiCUDBEdARBEISCIaIjCIIgFAwRHUEQBKFgiOgIgiAIBUNERxAEQSgYIjqCIAhCwcir6PzTP/0Tt956K+vXr+f//t//C8DOnTvZsGEDa9eu5Zvf/GY+X14QBEEoMfJWBue3v/0tr776Kps3b8ZxHG699VZWr17Nxo0b2bRpEy0tLTz44IO8+OKLrFmzJl/DEARBEEqIvM10rrrqKn7wgx9gWRY9PT24rsvAwACLFi1iwYIFWJbFhg0b2Lp1a76GIAiCIJQYeQ2v2bbNt771LdavX8/q1as5efIkTU0j/TGam5vp7OzM5xAEQRCEEiLvVab/7M/+jD/+4z/mc5/7HG1tbSilUo9prTN+Hw+zZlVN9hAnTFNTdbGHUHTkHMg5ADkHSeQ8nJ28ic6hQ4eIxWKsWLGC8vJy1q5dy9atWzFNM/Wcrq4umpubczpuT88Qnqcne7g509RUTVfXYLGHUVTkHMg5ADkHSZLnQYRnbPIWXjt27BiPPfYYsViMWCzG9u3bueeeezh8+DBHjhzBdV2ee+45PvzhD+drCIIgCEKJkbeZzpo1a2htbeXjH/84pmmydu1a1q9fT0NDAw8//DDRaJQ1a9awbt26fA1BEARBKDGU1rr4saockPBa6SDnQM4ByDlIIuG18SEVCQRBEISCIaIjCIIgFAwRHUEQBKFgiOgIgiAIBUNERxAEQSgYIjqCIAhCwRDREQRBEAqGiI4gCIJQMER0BEEQhIKR9yrTgiBMnHh7K/HWLXiDXRjVTdgrb8FeuLLYwxKECSMzHUEoUeLtrUR3bMIL9UGgEi/UR3THJuLtrcUemiBMGBEdQShR4q1bwLBQdhClFMoOgmH52wVhiiKiIwglijfYBVYgc6MV8LcLwhRFcjpCyTLT8xlGdZMfWrODIxudGEZ1U/adBKHEkZmOUJJIPgPslbeA56DjUbTW6HgUPMffLghTFBEdoSSRfAbYC1cSvO5+jMo6iIUwKusIXnf/jJrtCdMPCa8JJYk32AWBysyNMzCfYS9cKSIjTCtkpiOUJEZ1EzixzI2SzxCEKY+IjlCSSD5DEKYnIjpCSSL5DEGYnkhORyhZJJ8hCNMPmekIgiAIBUNERxAEQSgYIjqCIAhCwZCcjjDjmenldgShkMhMR5jRSLkdQSgsIjrCjEbK7QhCYRHREWY00j5AEAqLiI4wo5FyO4JQWPJqJPjnf/5ntmzxwxRr1qzhkUce4Ytf/CK7d++mvLwcgIceeoibbropn8MQhKzYK28humMTOo4/43FiUm5HEPJI3kRn586dvPzyy/z0pz9FKcUDDzzAtm3b2Lt3Lz/84Q9pbm7O10sLwrjxXWr3i3tNEApE3kSnqamJRx99lEDAj5cvXbqU48ePc/z4cTZu3EhnZyc33XQTDz30EIYhUT6heEi5HUEoHEprrfP9Im1tbdx77708+eST/MM//AOPP/441dXVPPjgg9x2223cfffd+R6CIAiCUALkXXQOHjzIgw8+yMMPP8ydd96Z8di2bdt49tlneeKJJ8Z9vJ6eITwv7zp5VpqaqunqGiz2MIqKnAM5ByDnIEnyPDQ1VRd7KCVNXuNau3fv5g/+4A/4whe+wJ133sn+/ft5/vnnU49rrbEsKYogCIIwU8ib6HR0dPCnf/qnfOMb32D9+vWALzJf+cpX6O/vJx6P86Mf/Uica4IgCDOIvE0zvve97xGNRvna176W2nbPPffw2c9+lnvvvRfHcVi7di233XZbvoYgCIIglBgFMRJMJpLTKR3kHMg5ADkHSSSnMz7EqywIgiAUDBEdQRAEoWCIdUwQJkCyB097qAddOUuqGAjCOJGZjiDkSHoPHlVWJT14BCEHZKYjCDkyWg8eHfe3l8JsRzqhCqWMzHQEIUdKuQePdEIVSh0RHUHIkVLuwSOdUIVSR0RHEHLEXnkLeA46HkVrjY5HS6YHTynPwgQBRHQEIWfshSsJXnc/RmUdOjKEUVlH8Lr7SyJvUsqzMEEAMRIIwoRI9uAptdX40glVKHVkpiMI04j0WRixUEnNwgQBZKYjCNMO6YQqlDIy0xEEQRAKhsx0hBmDLJoUhOIjMx1hRiCLJgWhNJCZjjAjSF80CcA5lq6Rgp+CMDFkpiPMCCZz0aQU/BSEiSMzHWFakS1vY1Q3+aG15EwHJrxostQLfgpCKSOiI0wbkjMQDCsjbwP3T+qiSW+wCwKVmRvzVGpGzA/CdEPCa8K0Yaxil5O5aLJQpWbE/CBMR2SmI0wbzjYDmaxFk+mzJm2W563g52SbHwShFBDREYpO66Futu5qp7s/QmNtGeuuXsjKpY05H2cy8zZj4V/w7yfeugUd6sHIk3utkGE8QSgUIjpCUWk91M2T2w5gmgYVZRZ9oRhPbjsAkLPwFLLYZSEKfhZKRAWhkEhORygqW3e1Y5oGQdtEKUXQNjFNg6272nM+1nQrdlnKfXsEYaLITEcoKt39ESrKMr+GAcuguz8yoeNNp2KX6WE8ca8J0wURHaGoNNaW0ReKEbTN1LaY49FYW1bEUZUO00lEBQFEdIQis+7qhTy57QBR/BlOzPFwXY91Vy8s9tDGRMrgCMLEENERikrSLDAZ7rVCkb4I1Sqrwk1fhCrCIwhjIqIjFJ2VSxuLKjK5rvqXMjiCMHHy6l7753/+Z9avX8/69ev5+te/DsDOnTvZsGEDa9eu5Zvf/GY+X14QzspEVv1PZvFQQZhp5G2ms3PnTl5++WV++tOfopTigQce4LnnnuMb3/gGmzZtoqWlhQcffJAXX3yRNWvW5GsYgjAmE1n1f7b1M1IvTRCyk7eZTlNTE48++iiBQADbtlm6dCltbW0sWrSIBQsWYFkWGzZsYOvWrfkagiCclYnMWsZaPyP10gRhbPI207ngggtSP7e1tbFlyxY+/elP09Q0spq6ubmZzs7OnI47a1bVpI3xXGlqqi72EIrOVD8H8YY5OEO9GNaIRduLR7Aa5mR/b03XEaorp/+Vn+H0nSRY10zt6juoXLqK4z98HDMQwLATx7MtvHgE3vklTVdcV4B3VBym+vdgspDzcHbybiQ4ePAgDz74II888gimadLW1pZ6TGuNUiqn4/X0DOF5epJHmTv5LH8yVZgW52DFWtwdm3AdL6N0jrVi7djvreYC7Jv/irmJczAMDHcNEj11wp/hOG7qqRoL99SJqX+usjAtvgeTQPI8iPCMTV6NBLt37+YP/uAP+MIXvsCdd97JnDlz6OoaCVt0dXXR3NyczyEIwphMdumcQrU9EISpSt5mOh0dHfzpn/4p3/zmN1m9ejUAl156KYcPH+bIkSPMnz+f5557jrvuuitfQxCEDLIl+Cdz1X8hi44KwlQkb6Lzve99j2g0yte+9rXUtnvuuYevfe1rPPzww0SjUdasWcO6devyNQRBSBFvbyXy4vcgFgbPxR0ewH3xe7DmjybVWSb10gRhbJTWuvgJkhyQnE7pMJXOQejp/wev9zgYBqAADZ6HUT+Xyk/+7YSPO5XOQb6Qc+AjOZ3xIRUJciTZcOzUUIyGqkDJl2wRfLz+E6CU/x8A/s9e/4mijksQZhoiOjmQ3nCsuvzcGo4JgiDMRKSJWw5MZsMxobCo2tmgNWgv419VO7vYQxOmEcpz8GLRYg+jpBHRyYHu/ggBK/OUnUvDMaFwBK+6G4KVgOELDgYEK/3tgnCOKO3iDPbiDZ1Cu/FiD6ekEdHJgcbaMmKOl7FNGo5NDeyFKym74QHM2UtRlfWYs5dSdsMD4ioTzgmlHYgO4A324MXCaM87+04zHMnp5EB6wzHLVETj7pRoOCb4SBdOYbJQ2kXHh/EiIjS5IqKTA+kNx3qHYtSLe00QZhQKF2IiNueCiE6OJBuOydoEQZg5KDxfbKLDaFfE5lwQ0REEQciKRsWH8SIhEZtJQkRHEAThDDRGPIIbGcJz3bM/XRg3IjqCIAhpGE4ENxrCiYv1OR+I6AiCIKAx3BheZEjEJs+I6AiCMKNRXhwdGfTFZmrVP56SiOgIgjAjUdpFR4dwoxERmwIioiPMeLI1dxOmJyn7c2RY1toUASmDI8xo4u2tRHdswgv1QaASL9RHdMcm4u2txR6akAeMeBhvsAd3eEgEp0iI6AgzmnjrFjAslB1EKYWyg2BY/nZhWqAUKDcKoR6cUD9aLNBFRcJrwozGG+yCQGXmRivgbxemPL5JYAgvHpO8TYkgoiPMaIzqJj+0ZgdHNjoxjOqm4g1KOGeU56BjITEJlCBjhtfa2tq46667WLVqFX/yJ39CT09PocYlCAXBXnkLeA46HkVrjY5HwXP87cKUQ2nXbzUwdAovEhbBKUHGFJ2/+Zu/4c477+THP/4xixcv5utf/3qhxiUIBcFeuJLgdfdjVNZBLIRRWUfwuvvFvTbFUHio2KDf1yYsrrRSZszwWnd3N5/+9KcB+Ku/+ivuuOOOggxKEAqJ9NmZykhBzqnGmDMdyxrRJNM0M34XBEEoHhojHkYPduOGBkVwzsL27dv5yU9+AsBHP/pRQqFQ0cYyporo0+KhSqm8DkaYGrQe6mbrrnZODcVomAaN7JLvp7s/QmNt2ZR/P9MbjeFE8aQgZ068/fbb1NfXF3sYwFlE58SJE/zd3/1d1t8fe+yx/I2sRJluF9xcaT3UzZPbDmCaBtXlFn2hGE9uOwBQ8udhNHEBUu+nomxqvZ+ZhFKAE0XP8IKczzzzDDt37qSzs5OOjg7+8i//kssuu4xHH32Uvr4+Wlpa+OpXv8rw8DCf//znsSyL+fPns3v3bkzTZMmSJQD83d/9HXv37qW5uZknnniCsrKygr2HMUXnvvvuG/P3mcZUvuBOFlt3tWOaBkHbRClF0DaJJraX8jlI/+zSxSVoGan3A0yZ9zNTSIlNNIQnBTkBePfdd3nqqac4cuQIDz30EEuXLuWee+7h1ltv5cknn+Qb3/gGn//852lra+M3v/kNdXV1fPvb36a+vp5rr70WgGuvvZavfvWr/MVf/AXbt29n/fr1BRv/mKLz0EMPFWocU4KpesGdTLr7I1SUZX5tApZBd3+kSCMaH+mfHYyIS2dvhJbGioznToX3M91RCnBj6EhIFnaexhVXXEEgEOD888+nv7+f1tZWnnjiCQDuuOMOvv/97wMwf/586urqRj3GmjVrAFiyZAl9fX0FGXeSMY0Ejz76aOrnn/70pxmPffKTn8zPiEqY7v4IASvzlM20C1RjbRkxJzNpG3M8GmsLNz2fCNk+O9BT8v1MR1oPdfOPP36Db/z779j07C7eeecwXiw6puDEO/YTfuH/EPr53xN+4f8Q79hfwBEXh0AgAPg5dq11Ru5da42bKPMzVsjMNM2MfQrJmKKzb9++1M8/+MEPMh5zHGdcLzA0NMRtt93GsWPHAPjiF7/I2rVrueOOO7jjjjvYtm1brmMuGlP1gjuZrLt6Ia7rEY27aK2Jxl1c10vlR0qVbJ/d7IaKKfl+pht7D/ewbdd7lLkhKtwBTnT28rOXDrHvaG/WfeId+4nteRZveBDscrzhQWJ7np0RwpPOJZdcwpYtfq3AzZs3c/nll5/xHNM0x33Nzjfj9kBPxMn25ptv8thjj9HW1pbatnfvXn74wx/S3Nw8/lGWCOuuXsiT2w4QBSxTzcgLVDKMuHVXO71DMeqniJki/bMLWAYxx8N1Pe756IUA4l4rIko7vPn797BjA0QT1xnbNiEOL715nOULRnddOftfBGWhbP/OHzuAjvvb7ZZlhRp+0fnrv/5rNm7cyP/6X/+LxsZG/v7v/574aWaLK664go0bNzJ//vwijXKEcYvOROzSTz31FI8//jiPPPIIAOFwmOPHj7Nx40Y6Ozu56aabeOihhzCMqVHseqpecCdKNitx8r+mpmq6ugaLPcxxkf7ZjSYu0/UzzCfn6uRUuIm+NmGOvt9FedBKJHN8bMugdzCadX8v1At2eeZGy/a3T1M+8YlPZPy+a9cuADZt2nTGc5955pnUz1dffTXbt28H4MYbb0xtf/jhh/MxzDEZU3TOdV3Ol7/85Yzfu7u7ueaaa3j88ceprq7mwQcf5Omnn+buu+8+p9cpJFPxgjsRsrm9YOpeoJOfnXDunJuTU6PiYbzIUGpRZ311kMHhuD/DSRB3POqrg9kOglFZnwitBUY2OnGMytJYjyKMzpiic/ToUT73uc+d8TOQytHkwoIFC1IuC4D777+fZ599NifRmTWrKufXzRdNTdXFHkLe2P50K8GASVnA/4oEbJNIzGH768f52DXnpZ43nc/BeJmJ5+D070dVuT3q9yMdrTVeLIwXHkIbLgRHZim3XreEH23bj+d5qfBncnt9Q+Woxyv74Fr6Xvox6DjKDKDdGCiPug+upTzLPvkm21iFEcYUnb/+679O/bxu3TpgJLdz88035/xi+/fvp62tLbWv1jrn0jo9PUN4XvHtk9N9ptPRNURFmUU8LfluKEVH11DqfU/3cwBnr1YwE87BaKR/P2zLIO54Z3w/kows7EzYn0dhXn05t65exEtvHqd3MEp9dZDrL53LvPpyek9lKdlStRjzsttx9r+IG+rFqKzHWraGSNViItn2ySP1DZX0ngrRWFFT8NeeSox5xb/zzjvZtm0b3/3udzlw4ADl5eVceOGF/OEf/mHK550LWmu+8pWvcM0111BRUcGPfvQj7rzzzgkPXsgfjbVl9IViqXUtMPOcetMxxDhZjPf7kUsTteUL6rOaBrJhtyybUaaB6cCYorNlyxa++c1v8md/9mcsX74cpRStra18+ctfJhqNsnbt2pxebPny5Xz2s5/l3nvvxXEc1q5dy2233XZOb0DID9ncXjPJqZdtQelMWgycjbM5Of0makO40bHX2Uw3vPAA0FLsYZQ0Y4rOD37wA77//e8zd+7c1LalS5dy2WWXsXHjxnGLzq9//evUz/fdd9+ML6czFTib22smMFWrL0w2Y4UY052ct16ziEvOq0VHB/Ai4YIvOiwWOhbGOfp7Tv7mDWIn3oO//LdiD6mkGVN0QqFQhuAkOe+884hGs1sZhelBsd1exa7+LCHGs4cYk07O7q4+3/48eGpGNFDTnovbeRCnbQ/u+2+DVxoLL7Nx7Ngx1q1bx9KlSzO2/8u//AstLWfOzL797W8D+bFUjyk66aUSTmem3MWcTry9lXjrFtpDPejKWdgrb5EGYHmgFPIpEmIcT4hR4w77HTtnQk8bt/c4zpE9OEfegOjQyAOmTcWSy3BbLine4M5Cc3MzP/vZz4o9jPEvDhV8wYnu2ASGhVVWhRvq839H2htPNqWQT5EQ4+ghxqBtEI07qY6drlE2rQXHCw/gtr+B07YHr/9E2iMKo3kJ1qLLseZfQsPshuxOu3Hw2judPPPCu3SeGmZ2QwWfuOF8Prhi9rm/gTE4cOAAf/u3f8vw8DCnTp1K5dyTxONxNm7cyMGDBwH41Kc+xd133013dzdf+tKXOHHiBEopvvCFL6QqWJ+NMUVn//79rFq16oztWmtisdGtj9OZeOsWMCyUHUQphbKD6Li/XURncimVfEqxQ4zF5vQQY0XQpNJ0qTKjuKGBIo8uf2gnhnv8bT981nkwwwyhqpuwFq/CWng5RuXoVZxz5bV3OvnOM61YlqK63KJ3IMx3nmmFT6ycNOE5efIkd9xxR+r3DRs20NnZyec//3lWr17N0aNHuf322zNE5/XXX6e/v59nn32Wzs5O/uEf/oG7776bL3/5y9x111187GMf4+TJk3zqU5/i2Wefparq7OsoxxSdqVSMsxB4g10QOG3xlxXwt88gCtHITvIppUEyxGgELZorFW50iFBfiBtWLy720CYdrT28rjY/fHb09+Ck5a0DFVgLL8VadDlGw4JJ76L8zAvvYlkqtdi2LGARweGZF96dNNEZLbzmui4vvfQS3/nOdzhw4ADDw8MZj19wwQUcPnyYP/qjP+LDH/5wqqTZzp07ee+99/jWt74F+AWgjx49yooVK846jjFFZ968eTm9qemOUd2EF+oDO600hxPDqG4q3qAKTKEa2ZVKPqXYZoZic+n5jZRbHrtef48TJ/uorrBZv3pxzutpShlvsDuRp3kdnV63zTAxW5ZhLVqF2bIcZeYvG9F5apjq8tPDmCYnTw1n2WNy+PM//3Nqamr4yEc+wq233spzzz2X8Xh9fT0///nP2bFjBy+++CJ33nknP//5z/E8j3/7t39L9es5efIks2bNGtdrSk4nB+yVtxDdsQkdB22Wo+NR8BzslbcUe2hnZSIXz9H2KVQju3zkU5ImEG+wC6O66awmkFIwMxQTpePocIildZqlNywu9nAmFR0dxjnainNkD15Pe8ZjRsMCrEWrsBauRAULU9ZmdkMFvQPh1EwHIBp3aW6oGGOvc2fHjh1s2bKF2bNn8+STTwKk+vEAbN++nc2bN/OP//iPXH/99bzyyit0dHRwzTXX8O///u98/vOf59133+W+++5j+/bt5x5eEzLxL1D3E2/dgg71YEwR99pELp7Z9onGXOpOK8KYr1zLWPmUXEU03QRCoBJvHCaQUjAzFAN/YWcILxqZVi5V7bm4J/b7eZrj74A3cnFVFbW+0Cy6HKOm8G1XPnHD+XznmVYiOP73LO7iOJpP3HB+Xl/34Ycf5lOf+hTBYJDly5czb968jLqaH/7wh/nlL3/J+vXrCQaD3H777SxbtozHHnuML33pS2zYsAGAr3/96+MSHAClp9i3Smqv5c7X/33PGfmRaNylrjLAI5860ygy1j4DQzECtkE45q9AN02D8oDJnIaKrMeabNIFMT30dt9NF2YVg+Hn/h4v1IdKC43qeBSjso6K2/7HqPs88r93UlFmZcTvtdYMRxy+/ie+U2cqfQ/OhtIuOj6MjoRzWmuTrDlWimit8Xrf98Nn7W9CNG2cVgBr/sVYi1ZhNC9BqXNrsZKqvTa/BbM898LESffayVPDNBfIvVYMZKYzA5iIEyzbPp72GBh2UChMAxzHY8BxueGyMxcR54uJzEAmYgKZKWYGhZfoazM8bRZ2esP9OO1v4LTtRg+cTHtEYTQv9d1n8y9GWYGsxyg0H1wxe1qKzOmI6ORIIZxbk81ELp7Z9jGUQW2lTTjq4Lga21KUBy32tfdxe17fxQgTEdGJmEBKxcyQjVxzVGeiU2ttpsM6G+3EcN9/i3jbHrzOd4E0m3NN80j4rKK2eIMURHRyoVDOrclmIhfPbPtYpqK6wqamMpAqaa+1Luj6mYmIaLoJBCsATixlAhmrQypMnpnh3EUi81i55qhGSDZRC+GlJY2nIlp7eCff88Nnx/b6n2uSYCXWgkuxFq/CqJ836TZnYWKI6ORAoZxbk81ELp7Z9tm6q73oIaeJiGi6CST9ov9OfO5Za4tNxmd7biIxyvHSFir7b3A8C5U1hhPFjYbw4vEJv5dSwBs4idO2B6f9dfRw/8gDhonZshxr8RWYLctQRvZSXkJxENHJAf9uXtM7GE0l0avLrSlRdXgiF89s+4xV0r4QTHQGYi9cecYFeeu/7ymIQ21iIpGdXHJU6U3UnCxN1KYCOhrCaU/YnE8dzXjMmLXQD58tWIkK5tdmLJwbIjo5UGYbdJwKo5TfRdN1PXoHY7S2ADipAAAgAElEQVQ0lJ995xIlV+vxaCXti5HXmqwZSKHK7Ux2NYvx5qiU56CjQ3ixqdnXRrvOiM25Y99pNuc6X2gWr8KoLt1Ig5CJiE4uKIWfnDQSPyrAS2yfekx08WN6SfupbhculENtsqtZjJWjAlC46GhoSva10VrjnTo2YnOOpa3Kt4JYCy7xbc5Ni8/Z5iwUHhGdHIjEXBpqyhgcjuO4OpFULyMSm5rJ2Jm6+DGdQjnUziYSOR8vS47KXngJKh6ako40b7gP58jrOG170OkzQKUwZ1+AtehyzHkfKCmb81Tgf/7P/8mePXuIx+O0t7eneup85jOf4a677ir4eER0ciB5Vzy7oSLl3EouspyKlEol52JSqPYF9sKVtHXcjrP3eSqdU4SsWqyLb+WCc6hmkZmj0hjxCO5g95RypOl4FOf9vX7bgJPvkWFzrp09YnMuryneIAtE6NAe+l/5GU7fSay6ZmpX30Hl0nNfcP34448DfiO3z3zmM0XvqSOikwNn6ws/1Zgpix/PRiHaF7Qe6ubJ101M8zYCgcSM6nWP+xq7Wbm08ZwKixpOBC8awpkijjTteXhdh3z32bG94KaNO1jlV3NefAVGXcuMsTmHDu2hZ+t3wbRQZVU4Q73+7+semBThGY1vf/vbvPHGG3R0dPDpT3+aLVu28NBDD3H11VenBOrXv/71OfXOGQ0RnRxYubSRto4Bfvm7Y5zsDVNmm6y9cv6UDUWV+uLH6cRYoUxgQrm1qSY2Xn/nSDXncFovHsPCnHcR1uJVmLMvmJE25/5XfgamhWH7N3zKLsMjQv8rP8ub6ADEYjF+8YtfALBly5ZRn3MuvXNGQ0QnB1oPdbNj7wlqqgK0lFmEIg479p5gcUvNlBQe6YxZOMYKZeaaW5tKYqMjQzjtb/o25973Mx4zGhcnbM6XoAJT1wE6GTh9J1FlmRdxZQVx+k5m2WNyWLny7OHdc+mdMxoiOjkwVReHjkWhOmMe3PVSIp/Rn8hn3MwFV18/4eNNtT43Y4Uyx5db0xhuDC8yVPJio10Ht+OdhM15P+gRQ4OqbPDbOy9ehVE1vv4rMwGrrhlnqBdlj4S2tRPFqstvxeuyssxQetLp6DhOatu59M4ZDRGdHJDE+8Q4uOslAm88haVMoipImTuE8cZTHIQJCc9k97k5l15D463BN1Yoc6wqD0oBbsyfMcTjJbvWRmuN19Oe6LrZCrHwyIN2GdaClb7NuXHRjMnT5ELt6jvo2fpdPCIoK4h2ouA61K6+4+w7TxL19fW8++67XHPNNfzqV79KbT+X3jmjIaKTA5J4nxjO3uexlImjbP93bKzEdiYgOmfLj+QiIOfaa2i8NfjOFso8XZAU8PFrF6DDfXjR0l3Y6YVOMXD4JcLvvIoe6hl5QBmYcy5IlKNZgbLs4g1yClC5dBWseyAv7rXx8sADD/Doo4/yk5/8hI997GOp7efSO2c0pJ9ODrQe6uaVbb/iWqOVejVIr65mp7eS1TfdWNKhnXwx3sWh7d95mKgKZi6i1ZqgjrLwwW/n/LrZ+tz0DUYJBsyc+uxMtNfQiVPDk9pTKDlz6h2Msqi5kpsubWBJY6AkWw3oeATn6O/9PE3X4YzHjLoWv73zwsswyquLNMLicK79dGYKMtPJgRX2ceZX/JbBqGZYB6lRYf5LxW+pti8CZp7ojJeQVUuZO4TDyN2uhUPImliJ+WwzTsfVVOS42HUiIdP3u0MMRx0UI+WQBsMebvfEG5mtXNrIyiWzUM5Iq4FSEhztebgn3/XzNO+/lWFzNipqMOZfir14FUZdSxFHKUwFRHRyIN66BdvQNFhhcB0wLTDKJ1y0caZgXXwzxhtP+SE1LCwcDO1iXXzzhI6XLT9imoqAlVkW5WwCMpGQqetq0GAYCqUUBuB62t/OxFoYGPEwbmSo5BZ2en0nRmzOkbRZrWljzvsA1uJVNC5fSV+f5DWF8SGikwPuqWN+HSilQBngORAZwD3lnH3nGcwFV1/PQZg099pktl2YyFolf2GwxtMKU2n8aK9fFimXFgZKgXKiJedI05FB3+bcthuvryPjMaPpvBGbc3JNyQxcVyNMHBGdXEhWuFUGCoXGAO1mVL4VRueCq6/P2TSw+eX3+OXvjhGJOZQFLNZeOZ/bP7QE8EOd59dsw1OJ2YR9C0xAQCayVmluYyWdveFU91TLVJQHg8yuLx9XC4NUq4FoCLdEHGnajeMeT9icTxzItDlXzUpUc74co7KhiKMUpgN5FZ2hoSHuuece/uVf/oX58+ezc+dOvvrVrxKNRrnlllv4i7/4i3y+/ORjWnixCJ7n4qEw0P5/pmj3ZLP55ffYvLMNhcI0/JJDm3e2AXDLwqFRZxMrrruf+266MGf7c65rlZKzo7rqIJWJRcJJcfN+O3YLA+XF/erPJdBqQGuN131kxOYcTwuR2WV+OZpFqzBmLRzV5rzvaC8vvXmc/uE4tRU21186l+UL6gv4DoSpSN6ulm+++SaPPfYYbW1tAEQiETZu3MimTZtoaWnhwQcf5MUXX2TNmjX5GsKkEy5rIjrsUq5imLi4mAzpAMGyJmaWTyf//PJ3x1KCA2AqcD1/+419r2SdTay87X/k3Uk4Vjmk4XfObGGgAHP2Eoj04WaxP8c79uPsfxEv1ItRWY+1bA12y7K8jN8b6hmp5hw6NfKAMjBblvnus7krUGPcTO072st/7DiMaRhUBk0Gh+P8x47DcB0iPMKY5E10nnrqKR5//HEeeeQRAFpbW1m0aBELFiwAYMOGDWzdunVKic724Q+wWp+k160ghkkAF9vw2D78Ae4t9uA4t0WOpbayPxJzUoKTxFD+9sluiJYrY5VDWpHWwkAFK1GWhdIuxvyVeJHRk+3xjv3E9jwLygK7HG940P991ccnTXh0LIJzNNF1s7st4zGjfp4fPlt46RmlWLLx0pvHMQ0DO1Gdw7ZNiPvbRXSEscib6Hz5y1/O+P3kyZM0NY00rGpubqazszNfL58XXu5p4Ji+io+WvcUsY5Aer5pfhz5A23BD0UXnXBc5TsbK/smkLGARjbuYabrjaX+7Ud1EuK+Hvgg4rodlGtSVQXndxBqi5cqY5ZA+tQrUZ3APvIQe7kMrhbH0Q1jN52c9nrP/RVAWyk60yLAD6Li//VxER3subudBP09z/G3fcZlAldf45WgWrcKonZ3zsXsHo5QHMy8ftmXQOxid8HinBVJt4awULBnhed4Zi/kmUg5j1qziLbqKOpp3vHnsc+altmkNhqFpaipugG37060EAyZlAf8jDdgmkZjD9teP87Frzpu0fU4nX+/74zcs5UfbDuBq7YfW9Mj2PruM+I5NaM/A1SamF2fI9Yhe9hEWFeBzODUUo7p8ZHGqbRlYpmJgOE5jQzluxQfwFp837pxNZLgPK1iR+fdhBNHDfdQ3VI6x5+jEuo8xfOC3hA++hhcesTkrK0D5eZdSceFVBOddiDIm3nWzqaGSgaEIwYRF3TYV0bhHU0PlhMY8dVEow0BZAZRl0VTdINUXzkLBRGfOnDl0dY2EP7q6umhuzr2YXTErEiSLN2jtx+l12vZit23u6Bqioswi7oy4jgyl6Ogayjq2ieyTTj7bVd94+TwOH+1l1ztdxLTGUIqrVzRx4+Xz+NL3jvGByBJuCL5D0IgT1TYvRFbw1i7N31yc/8+hoSqQsmbbloHramwLls+26TpyJOeOnbqiDnd4cGSmA+h4DKOijt5T41tw6oUHcNvf8Juh9Z/IeMxoXoK1+AqseRej7CBhINwXHv1A42T1Rc38x47DuK6mPGgSjrq4nsfqi5rHPeYph/LXZSnL9v8zbbRhgrbQzsjfQ1OTlMUai4KJzqWXXsrhw4c5cuQI8+fP57nnnitKq9RzoTxgEYk5aBLCo3zxSc4UislEFjmWci251kPdvHt8gOaG8pT9+d3jA7Qe6qZu4ABXlR1iQJcT11XYuFwVOMT7A420Hlqa97xW0r0WU1BbaRPQMdzIEKsWt4wpOEm3V+9glPrqYMrtZS1bQ2jXM4SGokS1QVB5VAYUlZePne/UTgz3+Nt++KzzYMbMSlU3JbpuXoZROfk5luUL6uE6P4czMBynZrq515ICY5pg2hiWjTYstOHPcLVO63FafMf7lKJgV8tgMMjXvvY1Hn74YaLRKGvWrGHdunWFevlJYe2V89m8sw0DhWn6biqNZu2V84s9NNZdvZB//fk7nOqP4Hoa01CUBUzu+Wj2XMLZ9immyWCsop43Bt7C0SZx5X9941ig4YbAWzy5bWne81orlzZimYrX9rYz2N+HhccNl7eMecFNd3uVB60Mtxc080ZkJZcb+6lVIfqp5KXIMi5zmll+2nG09vC62hI259+Dk5ZDCVQkbM6XYzQsyHs15+UL6lm+oD5Vc2wqo5QCQyXCZLZvxzdtUAZaQ/qtRAksq5rS5F10fv3rX6d+Xr16NZs3b873S+aN2z+0hM5Tw37Ix0mGfJpTCxYLxWhiAKQlMTWgxpfUzLJP66Fu/vUX+4hEHVzPYyAU419/sY//duvyggjPWDXRmuwQA46dcYcZw6TBHMqpGRqMLW6j76MxnCgXNcHyD7VQ33D+uC646W4vIMPtBTBozKPLHlnEGo+7DKY5wbzB7pFyNKHekQMb5ojNuWX5mDZnIUFqFmOhbBtl2GjTQiduYjI0RQRm0pFvaA6kh3ySiwKTIZ9CzQCy3ZkHLf/3+uqR9SHRuHvWC262fYbCcULhGEoZmIaBpyEUjvH0C4cK8l7HCv1pbxaBgV5iaV/fAC59XhWBYPbaa6OJ9fgLfp5bEzXf1aXpD8VSlakry0x6B/176NGcYMODA8TffdW3Ofe0ZzxuNCzw3WcLL0UFZ1LifgKk52LswBmzGNGVwiKikwNbd7XjuJrB4SjdfZFE+ROroJ1Ds92Zd/ZGaGmsyHju2Ypd+o9pegejOI6HZfn9Ybr7I/QPRf2wgvZw0vJXnaeGx2xgNllrhcaqibbj5ZVca/4nynOIaZOA8tdLbYmuJOZ4owpVsi3FLUYr9eYgvUPV7Ny2kjJ7cdZ9IK1kTSSE44yUrMl1NX7QMujqj6Qmk/7s0aOptoyKcpvB4bi/5kV7zNfHWeoeYpF5nNietHI05bUjXTdr8ttRckqjFCo9VGYG0MpCGSozNCZqUxREdHLgeHeIUCSeuPsHx9UMhKK4rlew/Ee2O3PQDIRihGNuSkCSPV6yUWYbdJwKkwhn47oevYMxWhrK6dWQbhJM3RF6OmsDM4B//fk7RGIurueP519//g7/bb3fS3208zNWGC9bSZsf/nIOEfvDXGW8Qa0eoF/V8JK6jEPMocz1RhWqt17+DbcYO3AxCROkimFuMXbwK22y3513xj7rr12E8vyOnd5p9dH2He3lJy++RzTm4HmagaEoP3nxPe5asyS78Cjln08vFcj0/6cU169s4dWde1geb+d8jlBOWp7GCmDNu9gXmuYlKDVxm/O0JSUyQQw7kEj4+7blDI0RkSkJRHRywHH9y4WRuGIYSuNqvy5YoRZZZgs71VQG6B2KohLjcxyPAcflhsvmZj+YShq/DZRK/lF6advPxNMQdzwGhuN094VTDcy27mpnaDhGKOKkRMzTmlDEYdPWfRimMer5efqFQwwOxyAhap7nEXf9MN7f/NHVo56/xtoyDoYW0G4vTm2Lxl3mNQZS1abPEP9tr+Eamd1L0fBB93U+eNNHUvvMbaxg/VVzuaDZxB3sHfVK9fyudsKROCi/TI+rIRyJ8/yu9qyiMxQ+MyRXrcIsj73Lwre3s8A4mdqugWjtImqWXY01/2KUFThj35mMUgosC2XaGFYg5SqDkYR/qVbaEER0csI0FTj+xdTA/xflX4RzTWBPlGxhp/KghadJVD72sC3fJbWvvY/bsxwrEnNpqCljcDieWtlfXVFGJOaOeVc4GI6P2sAsEnXxPJ0hVwo4NRhj9qyKUc9PR89wxmslugTQ0TOc9cKx7uqF/Osv9iVcdx6mYVAWtLjno+dnLd45yxwipAOkWyviWMwyh5iX2Ed5DjoWwotG8KJjhSX9NS7pNx+eHtk+Gq6rMRQETZcLrONcbB1lsXnSF/uBxLmqaU7YnC+nqmJiDe6mHaetjcFMOMsM8wxXWZJSrrQhiOjkxLzGylHbFIfCTs7NwyZKerHJ9JL/L//+BNUVNjWVaQsMtR5XA7PZaSG4ZKvmMceepYGZ43pnzI+Sv7uuR+dgNE3cbLoTVu3RcD09ZqhuRKnUab+Pjl3XhHWqG4dAalZn6TiBWXNQ2kXHh/Ei4XF261Sg9BmbyJC0EbT2WGR1caF9mGVWB0E1Uo5mWAeoufCDfjma+nl5tzmXOsowwDQTs5gz18akGOPjzt2RKBQSEZ0cSM4yyoJWRkn72fVlYyajJ5P0YpONlv+6O/aeoCxgjjmGbMn6bDOGfe19Y4xi9AZmZI/KcWogglIGhlI4rubUQISWWWO7rkYL1T39n+9SVRGgotymvmbk/CZddzB67qj+6juwXvg+g1GHiGdQbilqa+upuvJWvMGenFpDN9YE6eqP4KFTY0NDY20w43newEm/mvORPXwy0J/a7miDQ+4cDujzGKpZzAOXX+IbE3a+fcbC0WlNMhdjprnKssxicsnH9PRHqKywMQ0DwwCFv/4sHHMTC7t14r9E3lKDVhrt+d9nrf1/FVBdYUsuaJIR0cmBbCXtF7fU5Nw8bKJku4tDa9wsSfRsyfqPXj4364zBv5iOPoaayuCoDczGFipfkUb+gP3phmmorLMdX3BUam8PTWdvhEjcQynOmDkd7w6NEVZZSc+yO3He+iW1ZQ7x8kb6519Cdc0cYu+/k1NbgZuvWcRPXjhENO76oValCAZMbr5mEToawmlPVHM+dTRjvxN6FgfVYo5YiwkpG9fz2HDZ/DEXji61Thas5UFeSVsbg21jGHZGLmY8a2NU2ldUJ75LyZJYXqJMiAcsnV9L/3Acy1B4WuO5mqjjUlNh05Uo/5MsaZXMJY6GZSmqkTpqk43SemrpeDFrr6XHitNnOvfddCGQW/fJifLI/95JRZnF6cVThyMOn147utvrS9/bxftd/gLGlHMKCNj+RW40x9vQcIz3u4fP2KehOsBQxCEaH7kPDdoGf/Lxi/nHH7dmHXdjbZDBsJNhzQZFmW1wvGc4Q+CSgpeMWCVLDqH9VtGzGyro6AklZk4k7k598amvKcuY7SXDheuuXsizL71HY4XC8sIMDg7juB7/5SKPuve2EYrpzBI0V39izIt70jI9MBynrtzgxoVhZg8dwO3Yl9FJVlXUJbpuruJAnzlqGZz/97m3UpbpJPG4y4X2ST4SeN1veWDZ4MRBOwTO0vKgkL15gDMrEmQpIYNxum3ZFwXQeN7IDCMpHp6nU6LhaY2r/dlIykmZEB9SP/vsb+9l847DmKaBbRrEXf8G7PbrzmPZwvHPHi1L0VRbPu6ZzkjtNemuNRbTfqajFAxHHJTh5x9QvrtLKd8Fo1TSjeYz1hdsrJL2j3xqVdEXTWZLoh/vDmVaRxP/RuMejpc0BZBhCvij9St44plW4u7IPrYJTXXl9Bztzzh+NO6x663MIpOn43ophzAq8fvsel8Yv/OzvUTiXkpcgraBZfmNwVLXlMS/NZWBxC9nzpzijjdqbi0Ucdjz9jGaAhGccIy4p7EsP4QT2/cCg9rFUTaG8isbeNE47pu/YtYYF+pl8+u4oGII80QroYO7Yf8wKamxgljzL8ZafAVG0+KUzXl59egNzrK1CTg/uheCubU8KERvHiAjfeVp/MKXpoU2fYFxMdCYuK6HF9V42gMdw0VDIrR1ev0ynVCOc72lXLawntvhDIHPRXCE/DHtRQd8u2oyhJOe703OFoyEAJlKpRLkyiAlSMmEuQc01/t3PpapiMU9KhIVdtOn/vlkrEWT2RhzTAlTACTFwDcFtHUM4JyW5nA82H+a4CTZ9c7YDdT6EotNARzXJRp3WXNpCwAXBTq4tuxN6tUgvbqand6lHDEXMaTiqYtQ8nMrD1pEYi4VZSZDYSclVFXlFpGom5HXKguYlJkeDUGHzo4TuJ5HKOzgeB6WYVBZblGhh8BU1DGIgYuHSUgFcQb8jpqnF+m8YXkVi+Lv+V0305vGKYU5+wKsxasw516Uk825vjp45kzH8ag3QmCd5mKzbLz0MjinMe7ePBl+BeXPIBLzBq2T/wJo2joGef1gFwPDcWorAlx2YRPz5zTgGSaesvB0Jf3axo176Cgk5imAQ7FYtrBeRKZEmRGik45O/W8kruslHjlbcRPtepzs9WcZAdvA9XzHVk25TVd/GCMR7jGUwjAVhlKpGVVylmUmbvf9Vibjn2UlSc5kcgnljazBGfVd4WmVlsPx8zR+u2gywlt+XmV0vLMM/vSHtYbX9ncROvQGGwI7cTGJU04dETawk6dDDqp26Wl2bptIzKXMNujpT7ugaRgadmioCeK6HgRMmipNjPgww4MhrrhoIc9393NqIJaY6SocT9M/FCNWYdLIEBqFRmHgUcMwp7yaVK6lzHC5xH6fpcPv0bL7JPG0z8xqaMGYfznmosswymvGPAfZuP7SuX4OJ+7PcOKOh+d5WHUN4EQgreUBThyjoh5PJwNLI7M9T4M31At2GRlRc9PCG+olFItzpGOQPQe66A/FqCq3uPT8RhY2V5854wBAcfTkEC+/3YUZCGLYZRwZ9nhnVy83fbCa8+dXgoYKTOKnVdfe335mRW0RAQFmoOicC9de0sLmHYdxXA/PM1PW6Y9cNg/H0TASYBmV5GxIoVJ3mofe72fX2ycYGI7TUF3GdZfM4cIFdYnQX8KaTNqMyyBVfqcs4PdzSSbbs2FbRkYOJn08ZQHLNwVof8ZXHrSY21jJ/qN9metnJiHscTrHukJ8su41HGUQ9Uy01jjKJGhobgi8xdPmBaPaubv6wqOGCwOWwWevUnDgP7EHOgh5AcwLr2fxgnqef/VI6tmZAqgSd/gq9TNoTAMO7NnNjcZBzuMYdjJPoyBMGdUJm/OsJefT1zs89htN/3hS53FkHBfMr+XjH1rC7v0nGQjHmV1fzsrzmygjiH7zOXTM81fYe3HwPGILVzM8FD1NIHzsYC0qNghmmlC5MXSwlv2HTrH1d0eJxx08DwwDjnSGWHflAs6bW+OHoE0T07Ihsejy5VdP0eNUgKPwEg6/mOPxwuvvc/78ulHfbnpOpSxoMRCOs3nHYW6HMYVHhGpmIKKTA8sW1nNF1xAvt56gpz9C0DL50Mo5LFtYP64/mBHDjH/lOT3h2X5ykMO/7D8j4ZmccSgUB9/v4/nftmNbBlUVNpG4y+adbTiexwUL6lOOL6VAaV/c5syqSIWivDS7qKk0jqepLLf90JrWeJ7mA+fV817HQCq5m0rw5oF6Y9CvGJ12jsKuSaMVyurGe+KZvZhG0kDgi+qsKpNLgseYdWA32nGhqpxqJw5HtxNvqibmeNRVBRiOuWhXY9smlWUm5a7LEJVUEMXExcXAxaaeAT4W/VVqXC4G71sLOWIv5aiew5qmBbzxZhdDr+6hsszk0vObWDi7ipQMaNCePz5Xa7T2Uu/vdKeWRlNfE+TGKxdkCOIwSzCX34J5eCdGuA+vvA73vGtxG8/Pai10z7sW+50taGKQECrleTjnXctLu48Tjfo29EDAwDJNDNPi9SMhLly2CE9ZaAziiXGiof3kMGVBK6NguW2O3Zb6pTePY5oGAcsPFwYsk1hiezYRmahQCVMPEZ0c2N/ey+4DXVRV2jQFygjHXHYf8OP6uw905fwHM94/znSnzn/uPkYk5uJ5EI0l7zxdntvRxgMbzlz3ohSU2SZ9A1Fi7ogdujxgYhgmlQGTWNzF85KLXS0OdwxRWxkcCQ0mRCx9HCkBTbtK2qZKvUb6lTX5/NEicP2qmgozRDzNmmrjMGBU85mblvHiG8fpG4wyu76CD1/WwrIF9cyqDeJqsAxFdYVJteXghkNcFv0d2vYgmU+xbHA0sXdeYOGcjzIUdmg0lS+kHkQdh3C8ijozQjyuUTpKAJf0GesJ1cRhcwmH1UKiBIhFfFn66W8O+VZp26S73+Xw8QE+umo+582dWIgtG27TBbhNF+T0fCBDqJwl16GbL8TR+6iurcGwbaKOYsiBcMzj/fdj3Elg1KlsfXWQgXA89R0FiLteRmXy0+kdjFJ2ujFiHELluJqhSAzX8TAtgzLbHFOohKnJtBad4UicXW930t0fSRme/JTKyG1bKuSVmE6MzCpIzS6Sz3m59bgfb0+sD/ASOZ0X3zhOMGj54SHHv2A5nmbLq0eIxNyRY6fCa/4LdvaGCdgGYVenIjAaONkbZn97b2JcKmOcJ3vDBAIm0djIhVGjOdkX5nDHQCL3krlPTWXgDANAKOIQsBQ1KXEx8DzfdHGqP8xAOHsSOP19jJxKxXUXz2bH3s6Mu2KV2keRFlVMvdfnoxezvux3OFrjYGDjopTil+EPcHdlkNuvOy/j9U72JaoGeJoqyyXeF+b9iN82oKpqCFfVZM4ClIUa6uW8C2r49Z5jGKbCMgw8L84i7zjV1YpAuJ/0S6hWBu6cD/Be9Qd5/q2h1D6OF8dzNbaliERconEvZXII2gav7Ts56aIzEbzmC9FzlmEkFl0qwy8d0xmrIOa4uO7IZ+tpjUJlnalff+lcNu84TAwy7MfXX5q9pt9EhKqzN0w45qSclJ7rEXK9oi2PEPLHtF6n88xvDvHcziNnf6IwDRjJyiRRiW3+yncTrT0WGF1cGXiPywJtVBqx1HPD2ub3sQW8HjuPI7T4a0rwL5bxNBtfMtQ3GgqorQpkOiQTNxsK35EWjjr+glJDURG0MqzvSnHGjUm6SCuVLuIjucHkjZQyFEoZaKUSj6XffCjaOgZSY08sewL8HGFyoWVSRA1DMb+5ipoKv+1CV1/YdwZaBpWR+iwAACAASURBVM0NFdQmyi0pBcGg7YftEgceCMU40jmYcoR62l9nc96cGuqqgxk3dMnx73q7E9fTGcYaT4NpKK67pCXt5vC0G0NGto+818ybxdNzqRmvf9p5T7/xrCq3uOGyedhp4jkWsk5nfEzrmc6qC5t47/gAA6FYWmgoMySUHibSGdt0ZvxdQ38oOrJokZH9vEQSPv0WXydWqpcFLX9dQtrrJ4/tepq442XMcsD/Qzvdgj3qmIQ0VMICcKapokEN8UH7Pa4MHqLZHExtd7ViX3wuv4stYW9sgd/2OkW6sIys/Yk4mb+fzqmhs9mEfWnEg4iTGcorJOnfIcfVZzzmepojJwY5nbjjMXR8YEKvuf/oWBUrfE6/n3Q9zW8S3VWLgetq1l29qGivPx2Z1qKzeE4N//3eyznZG85aaiUX0pOd5YER99oVFzalcjq5roCeiGMnfZ+6qgAfSoQ6/mOUVdjrVy/m//vVQdxR3r4CyoN+Tsf1wDQgYJvcdu1iXn3rBMd7Qjhp10TLBDRnrN8BmFUToCJoMxSJ+3eGCXGMuy63qxcJumFimClRsHApq6nj/3RdjjPKddoy4eYrF3Hw6Cl0PEJQOSxpqWbOrEp+/9obLHf34SmFg4WJi9Ka16JLcLVihf0+1WqYCiNGjRFhnpV5sRsua6an9iK6a1fgWJVcBFyUeCxZYgUNr7zVSWW8m/NUB2VECRPksG7haDjzTjb91N70wfmZ27UvX6/v7yLiuJhp/XBczyNgmVx2QeOo+TKSMxbDAKXQykQrhdYGHkmTB6fd0DByk5P2nOR4Tg1EeL9riEjMJRgwmddYxb4jyVBu2ntK7HPlimb6BqN09AwTjbsEbZPZDeXUVAZJ3gQFgxbRqJNhlDk955cxxjTXXfJ5R074szA3UXEg6dwMWAbzm6rSbOGZ+2UYN7Ice/QxnH6TeeaNZ3nQZMXiBoTJZVqLzmSTvtI5vWPksoX1zGuqmpDdcyKL2Ebb57v/8RaWZabi6EHDIKZcXtl7ItFrJ3bGcUxDUVFuU1c9Ujgz5ri8tu8kDdVB2k+GMp6fFCDT8GcUycmdoRTDERfTMAhYiuGYg+dqLMugqszkPKeDsBHk9IxOZbSPFQs/RmfvMJHYiJJVBi1aGoIEuvbySf06FU4XA1TyRvtyjPoPclvNW+ihboLK8Rd0apMIFvVqkFeiF9JoDrHM7sBWaXmvQBXO3Etw5l4K1bOZBcw6y3nu/P0ubih7HRcDBwsLh6t5l184K3nPmZ0hNgo/9HbV+aPndN7ed4zqchOlRt6n1ppYLMr1KxIWecPwS/ibtr+2BhOtDH8NUZp4TDZf+u6rqVBWkmSoa8WiejbvOEwwaFJVYRN3PXoGIlx3SUvqO9jQUMmpU6Fsh88g203WZJWumUxyLYMjjA8RnRy5yH6flTW/wgqcwilrwLFvxKW+6CugewejoKBnOIbnad9SXW4TisR9a3XM9e+cEzFtM1F5YVZNWWr9TzIuHo25nBqM0Vxfnla1YSQenl5wMXl3qICm2iAn+yJ+mSBL42oYjrr0GdVUqeEzHGo9bhU3X7WQH7/wLlUVfj27MksTJEZLeD+XDr2Ep0ziKkAVET6sd/P7Q0FWxHrxVMy/20dhKJdqHKrsCOfZr6Rew8HksJ7HPhazYe1HsZSJmRhzRjmW5A5pd8kAVwUO4HoKR1mJ41mg43yo4l3eG5yNgT8J8RLdQK9Ynn2Bbk1lgFNDUVytMZWBHbQJBsuY21yJWVWfEBcTlxG7cnJM2QKqZsderAO/wgj14FXOwrnwRtyWi8fzdclgVm05XX3hVJg4GRSYVVs+IftzNs5mi5bSNTMDEZ0cMDv2EnjjKb/OVKASFekn8MZTxGBCf+ynk56A9n8cyZamEsgJcTBJ1I0zfMFYMLuaUwMRKsv92mSu5//XWFdGNOYyt7GCwYhfcNMwFJVBk/6QQyxRr8xxQOMRifnVeNs6/Lh98qJ8tjtty1QjPYQS+yTfzy+ti/hkxS5M7dc2C+BiKJdtwxfx3xc3cPWK2bz+znHCvf1E3RhXXNjIvM49xA0DV/sXOxcTE82CgTch6JLM3hh6JPmcPEftzix+H1/IPmcuMWxMZXBPRVqbidPSPrHj+3H3v4g31IuqrMe88HqMlmXYdpT/v703D5KrOu/+P+fcpZfpWTWj0YIGYQkJIRiZxQhJ2BgLCwkZC8c4sYMhb0jqzfo65peqFOWqJJW4kkr8VpnYTv3y1u9NHDu2wcaENUGyMJsBCWEQSBYItCAhoXXWnun93nvO74/b3dM90z3SjMWMlvOpEqiX2336dut873Oe53yfZMHBlsU8DBqtHGZF8nxsdgfvHh4kIEzAXzqnkZVXzKla2ikn96XFogWzeHXvAJ4vSPuQz2i8IcUn5jSGYjzBq+nK36J247/Wb3Ht8i4efuE9CgWfQIXO327EYe3yLp546cCEy5/rcSoBm+4LN8PUYERnAth7fh4Kjh0ptsyNoP08zp6fo+dcOUowRqpiRIU9Ti1ft8qNnKVIpLLMWoowSS5HTZaVIjAwlOVo71i3ZtcSJOIug+kCMdeG4haWvBeQiFqkMoXiRDCyAfPzn/gIuw8OFstpT48gCButVbkYFP+z25vLw5nlfCr6FjPkMH2qkefzV3AiOp99B4+xb+8BYkEBYYOHYPveHi6XaXJUd/r0seiQSZTvIUpiU1FwUdAW/zf1KQZV9X6lWGxU4r9ijN6xd/FKBpluDJ0dxn/zCVx5O27LDKID/QznQsGVUtAY0QTRFg73pmhMuMSjNrmCT3+qwEAqzyVzmsPlMdtFWzZKWGgs3n2ln2HfKVaIaRwbLEvy3tEhbv6YqI6yTqNYpPK3GN4R/hbtPT+fsOgs7mrljhs/UjPKaG08OuHy53pMZv+O4fzjghSd0SWtldFEaUOkJUYiiZJIqGxfGOEIgW1JQKNlBJHtp7E5WlF6KYrWNKOqgn6NteHSslY9BtNeObqo/H8y43HHTQtrmoR+cZyWDFFXkskHVa91KnKF+pVYu7257PbmYluCxphNS6Nitszx3JbdDCWz5ApB2Ygz6loMywZiOoMvbBwCouSJ6EIoNMVBlaIwH0kOl74gMUZwIBTYevjvvgBBAH4aAh8sG+wo/rsvcKLtapzjG7EQ+FhI7VPIa7Z68/HROI5ECEkkFsfH4aX9WRZdvmhsl0vg7QP9gCjaFpXsjcKJeGZLrMopotRYTFF0gyg2F1OBxtcKrTQy3QduvPp7sdzw/klQL8qYzD6dekxm/47h/OOCEJ2GmFNhvBmKjCzX649EE8WbZUZPHJmmmaj0IMKJFPcLCPALyKaOqiRseOzUZh+DICzRtqzqZHAQ6FOahNYyC+3qbOSDnhSZnF9e649HbVJ1No3KUc3YRgtVxLFoikmaXEWQGyY94JHWmkws9H6rNOJMZT126XmsjvwKodJYpdbQRUE/plpI6DQ57eALG1sHWELxSv5SpCgtB45EjUGt0r0iavAEeNnik2XYCyefQg0GPN7r0JjtZrm7l2Y7S0q2sEUt5hBzaG5OMJAOeD8VWgm5jo9SPjBWcKCU91Fob+TB0sZMKOXMxsaVO/f3svmXh+kfytPREmXNdfO44pIZpFvC3yL2iJOA9gvQ1E5DzEEFoWCFIlW/sutUnMlcy5kUMMO5y3kvOlpDPHL6H3O8f4xO9zryL/8A7YG2YmgvD8rH6V53Bkb662FbgrxX2zF6MpRaKCTiblV0BNQUnpaEGy6vjZr04xGb5pgkJjy8XIqhVLWXd1BhzROhwGXOEZbah5ln91UkhiCvbd4Jumi//lZ+/ItjXKSPcn1kL00iw5CO80ruUvb7nViyugpLKV11ZT0GXTLyLC3BifA+KUjnfDKxS3jKXYwSNhkP0p4mW1C8n8ygkVgy7A2kM2rc9tudrVGO9WdRxVxU6RzNaqvf0ryyaaDrSHqSOf5j07vc+elFLOlex/Dz32N4KEdOSaJS0RgRNK5YjxMLCzYqNzyWlvWqoimKEZSuaNVcdNoIGGmYtmR+W1g6PMElwNGYYgEDXACicyZxurqBu/B2bkSn+5ANM3C61+F0dbNzf++UdA6tx5z2Bg4cGxrT0XNOe0PV5DW6jTPUjoDqRUcPP7ePef77VfmZZ3NLGXYvJRaJc6Q3jQDiMZsZMYFDjlw6z5BXO0KyLM0CjnOFc5hF9jHsipLiYRVllzePt4MuTgZNoDUd2/uQlmB/vpO9qc7yc6UExw4FQCldbueggVVXzqp/4qSFkA7CjSAsF2wLISxwIqTTLWQ9hZ/V+EXrGKU1fqCL1X8arUet90HN38IdNy3kxY2bWSl30ipSDOgEW1Q3H79pTd2hbdp2iIXyA1ayg2ZviKRoYotcxqZtUVjexdbMdcXXC/sQbcp0s8KbQ3fx+Mq9LCMOBqe+CKl+SqVgjUTwlR0+m+Iuft4rLgWG5z/QYeO2UpRVOs4UCxiM6EwQp6sbp6u7bHkBjDupT5XwtCZc3hnVviDvKVoTblXHU6Dc8fTh5/aR91XdcdfqRLpl889Zl3gVT0kyOkKzzPKbiVfZ6DnMvfI6hjJ52mIC4WXJpXLkAlUjetR0Wkm63cN0R44QI1d+pKAt3lNz2Z69iPeDDlRxuUqIMELoTWZpb46RHhVtKQUzW2JccUkbL//qeHkj46orZ7H6mnlVzxXhzkOE7eJ2XYka6kFnh9BeHh1otHSxYs04jkXPUH5McYYU0NroMpwN25VbVth+O+epur+F378q4EtNrzGc1+RUhDaZ50uR12h0rgBq/0aakntYa23B15IsEeI6zVpe4r+Tik3bYNCaz3FnQcX3HZDcdujX/s1Vf1+VggVjSv+AxgaXXKa6GKDSUaOUrwojqYpcVVHERrejLp3v8qZOs0/mvMKIzhmg3qS+6QxMAKfLjv39XO4c4aaKCOS53FJ27LeJR8M/lbi25Fhvhhkt0QmN+0Z3Fyqw0LaDKwRaW2ipuTl2kFcGF3NRrMDgUAbPV0gRdu/MFQsSEiLHFe5hrnQP02mNWKloDUfFTPbLj7BPzSUnLAaDQtVkX4pahIZcoXbUlCv4rL5m3liRkRJhFbtpWqH5JTJsVy0XrMR7+QfhffE4+AVEccn02pYEj710sOq1lIYZTRFs22JWm1tuulbq9VPvt+Dvego3EaE9MZI0114eb+fGYgRd+1z7gcQX4XKZjwPa40Z3F/+e7Kr5nfYmc7VeasrRVUIdRlgWwDgrnSWhKolR5VKgpqKgQpXEqbQ8+OvlrQxTy7SIzl133UV/fz+2Hb793/7t37Js2bLpGEpNxlsqKz3WnyrQlnBZu7yL3mRu2ieA+ep9Ph/fhq8t0jpCk8jy+fg2/jMDqnkpg+lClblkaPyoce3qcuLKcb+0cTOJA8/RIoYY1E2kLrmJy500Jz0LpRXScrHjMYRt0c4QBw8eJZX1RhLbGvxCnivc43RHDnOxPFFV9p0UTbQvXcH77kd44Z1UeZ3/08vm8MOfvVs3aTCUrt3jdSjt4R3fQ7B/KxQyWM2zsBfdgHXR5SCs6kmo+PfKJVM13INs7Cgvmb62+ZWaQn5CX1Lu9RPm0oJyr58fbt5T87fQUEiCPcpSxXZRw/XbfHc6aXp8q1xVWSoL73QytMeiNb/T9ub6OaKzndL3E37eiS0FVtrklAyBS2JVMjMNc1cjf1dalTf2hq8x+XyV4fSZctHRWnPw4EGee+65suicTZwq/7H16Z+zrrSOnmpky9PdRN1LGM54YQfOYmvlWMSmszU2ZeNeHXsLX1sUil9pARt0eL+1fF3NkunOtnjdcb+0cTNzDz6Bj0VGR2ggQ/PBJ0i5MaIRCxVJILUmyGfQqWG8llbyhZL/luZiq48r3UMscY8SESORSQ6X/XSxW13M8pXXMKerjUuBSxee/metnJzCJLuF5dhcEh1C7d8CgULbUby+I3gv/geRVXfVjSZgZMl0NC3D+/hcfFt4DojQJEMhfzQnuPmza9m07RADqQKtxYuP7gXttDcfqikGabuZNr8ATkV5sF9ANnbUHVe0bRbtg30M5ih/Py1Ri2jLDNYu6ar5na5d3nX6J/I8YHQ0U2oVfyoq9awqsiJcqkVr9ORqcAynYMpn/ffeew+Ae+65h8HBQX7zN3+TL3/5y1M9jLqMt1Q2zzvIOvkyARY5oiTIsE6+zFN5zWvpTkptpT1fUfDz3Lhs9inf70wVIMywUgwHbtV9BSxmWGm66hQFHDw2xBNbDtYcd2LnT/Gx8EoWMDKCE3HQkRhRlSWf7ifvBTgiwJKardlLadTDfDy+u+h7NpJfCrQg37aQ19JzeCvbTnNjnI8vm8Nl88KE8juHx/pxiWLupLISLnTttrBsGyVtpOVQUJJhD3J5zXrxC4KUQhQndmHZaBWMu4Q1Hp90d+HrkXPgFYX8k+4uuhd8me4F7VW5PRip+hstBnb3LfDeE2iPsMzZL5yy8tHpXod6+Qd0Ntpgx6qO6e4avwz+TOId2lkzEjyXqRSrMZGVrP08w5lhykVnaGiIFStW8Jd/+Zd4nsfdd9/NJZdcwqpVq07r+BkzEh/q+PpTBRpjdlWjN9sSDKQKrOM1FBaBDCf3QLgIVeA6drC/8TYyOR/PV7hOGCXtP54at7fGa7tP8ONn9mHbguaESyrn8eNn9tHcHOfaJZ11j6vFr0QTcZkmr+xypXFEBiRFIx0djazuaGT19ZdUHfO1f3mZ1sZIzXF3i2Ey2kXaEexoDMu2ULk0dvIY/5XrZrmzl2Yrx3AQ5WTQzGK9g5saqzcmelpS0BZP55bxp7/1OyyqMe5d+3t5auv7WJagMe6Qyfs8tfV9WhojDA4XcJywpbIs5mNamhv5oC9LMl3A8wKUHtn42RZNYkdnVn132oqh032n/B4eeX4fJ/ozdLbF+Y1PLuTaJZ3MdNLlVtqlGjUPi5lOmvd7MzWPWd3RSHNznEee38fJ/gwzKx7b8VyE4VefJO73k7FbaLzucyy7Zpzffccq0i0xklsfxx88id0yk+YVG2hYcDVAze/0TJPev52+V36EsGzseBM6P4T/yo9obomVxwGYHjJFzHk4NVMuOldddRVXXXVV+fYdd9zBCy+8cNqiM5EmbpOhLeGOWR7JewGtCZe2dIq0dhFaF3edawrYtIkUDVGHRGwk0tBac6wnVXUVPJqfbH4HBFhSFstwJb4I+Mnmd5iT2Tehq8tdkWtZlX8OKYOyI7KFYlfkWq7sGa4ZUR3rqT3u3oEM6fgsmlyNFyhULk1hOIeDz6COccDrQGjFlc5hLnWO0yX6y8cHWpDVLhnlEmBh4/Ox+CEG6rgQP/Xye+QLATkv7LAai7jEYhEs1yUiFakCZD0oZAP8wGPD4ia27+uv+Vq9QQJ3IMlgdiQ6aolBc/vMut9D5XJq1JH0DGT4fx9+kzs/vYhZrTOJ9PWS13a582xEBqj4DO5/4PWwbbjWDA7luP+B17ln/RK6F7RzcXuce++o/q6eeeUAP3rJx7LW4zrFCOglnztjB8aNTnb2tLKp71Ph9xZEWdvTSndT/d/UmSbzi0dQWiKEE9ajCwetFb2/eIRMU9gae3S0d6FimridHlMuOq+99hqe57FixQognOTOptxOveWRtcu7cLZ3EOs/TowClg4IsMhql6TVHHZVnGBSt14BQlNyD/mXXw8rqtwGVHqQ/Ms/AMLcxN5tL+Lv+hkNfpK03Yx9xS0sveETbHy6EO7bYJgBGtmiullxwyfq5qmixckv4ljhhO9auFLRHhPs7pvPx5KvgArNNh18XBFQUDb/q3FTVdfNgrbYE8zlsmgvfQUHVVoMF6CEzUw3X3MJ7bKuNpIZH+E4NMQTFJTFgAfHhjSe5xGL2GQLAb6vsG1Jc9TlnUP1G4E9m1vKHXIbtrbwsLB1QC4bsN+6ihvqHDPecuq9N2zAfv57DOdVxQZMi8cyl5PO+eUlP6U16ZzPw8/tqysgk6lwPBtK8dVwD7ijNr2eogDCYBiPKZ/th4eH+fa3v82Pf/xjPM/j0Ucf5W/+5m+mehh1Gc8yJjfYTWHgQOgsjMAioFlkUZes4uL9B1mpR034y28G6q+JtzfXrkC60d2FVgHkhkf8wNw43s6NHDyWpOHNH4W9ZISiIciSf/NH8NE7WXpxG8EBjS8UgdYsvaSN7gXtfOOB7TUnPITAEhB3Ba1RjZcdJpPKctXlF/PEkTYywTKuFW8zwxrGFhpXBDRbx4Ewkjjgd/Arr4s93mykE6F7zhu0DZxEezmkDlDCQjhRgmgr/7XlIFHXpq2lgUDYPLd7CKexjWHRxGDGIzcUEARhtFMqHepsc2lOVCyVaU1vMlee6CvzvJrQ3+3VwgI+GdlNRHjktcPz+SW8+E6UG+qkTnqTOfKez4l+vxwdJWI2vq9wulbS+Mn/QbT83c3C6V7Htp/0I4QuG7hKwr0nJwbqVytOpsLxbCjFl40dod3OBAogDIbxmHLRuemmm9ixYwe33347Sil++7d/u2q57Wyg1qZIAHXsHYQbw/ayoVWKkODEmJF8my/EMwznNWnl0iSzfCH+Ko3O5XiHjoZRSo2oZe3yLr7737vpT+bKlvJR12JW4yCkUxVv7IOXI1A+1rHHiItC2NgLgUQTFwX8HT9hnhIURLhps0FkaH3/SfZui9GbtKomPCkFrQkXB59rG0/SdvwVEsl+kirOibarWTz7cq6KHWFh7i06xGBVpU9v0MiRoJU2MUyrlWYZ75PD5ag1F9nxEZyTB4o+ZhbCshC24G3rEhJt7QRYJAMRtjzOejy65Qj9w3myhcrGZsUxCupGj5fObWLr2yfHlLUucY5wnbufIR3D0wkcAq5z93M4U13uXnkxoZViODNSXac1DGd83Mb6LakpluFWql5pA2s96l1gjBcNnw2l+E73OnIv/Bs61Rf60kkL3BjOii9N2RgM5xfTsq711a9+la9+9avT8da/FkH/B+DnQs8VbECBn0MPHsNtnFlz4x8A0i5XVOFE0B7hY0t+r6J2sziLCYH060wqhQwtSocti4szXmmaa9YpTqrmsMJKgKdt0D75HRtpb76DoYxHU4NDg6OJWgH5dJK2zPvMG3yVQEuy2LTIFLOTz5B69BlW6qBcxRNoQV7beFqyl4tY4hzCV4KcckjIHLfEdvKGG0P1vI9onolEI6QNQqCEJDbwPjEauU6/SbMO7VxeFR9ld3IuQY3W1xCeltJemNHLnOEFwS627e5B6TDiWL6kg4998HTNarObY2/XXapKpkeWCStNSlM5H+/QTnLP/yt4OdABQWaI4Pl/ZXnLxxkYznFT5C1mWCn6ggTP5ZeSbK5VKhEy3rJtPSYjVB8KlTXqlbcNhklw9iRTzgXUiDmkQKCRYcSjVFgGW0nlunedNfFN2w4Rj9pV1u55LwBde8c9gR/6gqGwAIFGI1CE84GHVdHtDXzh0uz43L5yHk9v24/0hyDrM5D1CJTi48EubOnTZHlYRbsToLw5LtClT6mJCA+Nw1VyHxkdQVkODqCEBbbk4+0nkdLGCxrwUr2Qz6AQiGgTHSrLrWwmio9A06hTtOuTROPreHZ4xIer2placOenF9UtCV6+dBYDqUL5seVLZzGrL0tv1qra2edhMSeW5Xt1fMy2qnYsWdqrQblU2/MV+VcfQudTBFoUnbYDLJVig7uFVNynoCVp5dIos9wRf5XCFfPr/nS6F7QT692Nv+spGgrFXFz3LVw6zjLZZITqTOPt3IiINCASIxtbT+WkYDCMhxGdiWDZUMiACqqXdqQV7qGos+5db02893Dt5RNRoJbFFQA62oSdHajoAaSxNHjaIiFyxF2N4zhoN06eCAM5yaJWhX95c5jIT+XpTMDqWQM0vdc30jagiNJQ0BItBFFGypElEBdeaB0TSSClwnYctB1B42AXBknnFfbw0XLbCAnobB8RIZFaVY05pj3W8AueFxtYXGPX/151EbHe3dyW/RkNMkk624zdewss+HjdqOXPZ8ygVQyOql4TJNpn03R4D+vl87jKwxKaqMqwXvQx6KxiT3ARzqiWEFHXIhg8HjpIF9tfKEIXZic/SCLRwWAObKWR0iYRhVjPFuDjNb8379BOZr/3BCRssNvCjaLvPYE3u7nu5H2qlhRTgSkkMJxpjOhMhHql2tIB5dfd+FdqhzD6sfYhv+byiSdsXMLeLCNosCM4thzZKa0BKZGRKDhxZtg2SgUEhTyk+okEBY6KK1ikNYvnNLJQgn/wLYJj78CBoKrrpgICSklxiwijrGakhXSjCDeKa7loP4/y8pAZRCiNbp1NLjVEEyM+aVBaoVNjG+wIcPODLG/p5SY/tO/JMGLfs5NB3Df3YAuLvIgQDVLINx9iL7Bpf0PNBPszmaXcFtlKY8wec65vPvo9YjqPFmFkKIUmRp7Pxrfzv4fDZb5SSwiNZs3HLkK9rano8Vfhd6mJxGLMigts28L3g9C4cpyJ2Nu5se4y63gRQ7384lRhCgkMZxojOhMhn6p9v58nsuqPx9lXU9vba+3yXg4+/SDX61/h4lHA4RV1JakFn6LtwNOMnqWdZbfivf4o0gqt+KUTAynRfh63kGZwWBDReSyhCLQkT4TuxEny2x/HP7QjjNJK2BE8z0MUO7yU2olpFKooPgiJjMaRbvF9CjlUNoUK/NClWQhKaiIQxHWWgFBoShoTAJYuf4QRNGgBn2l5h+E+q5x/8YSNFLBC/oo08SqzSxvwd/2M3sKtNSPENzKzuOOm2ue6jWT5c1JxZjusIT67cj6bf/kBuYJP1LVZ87GL+OwNH2Hvrmba5UDRQLL0OTUFbROdoKXNuRoxVPaQOl0nBYNhPIzoTIh6CVRd178L6nt7LRp8mfnyDQLC/IkjfD4p38Bt2wBtt+P96mdhEjvSgLtsHdHuW9D7t4BWKC9PkEmG3SKLRBtmMpyNolRAXBZoFAVEZQE75QAAHqpJREFU+iT+vpPhE4TA6rwUe/7VWHMu552ffocmhokJryxUWR0l5cygqSGLsBx0PkuQGkQHxchHgGicCbkhtPKL7sytaC9L+QmjQxr02OXC4u24n8SLxShkRtptR2Mx3NwQSVH98/SxafCTtDdHaU3tY6XYUS5M2KKXMdC8kN3eHDYNfTpcjtJR1pb6y9T/6vjsDR/hszd8ZMxDr8Vu4Mb800QoIAlQSHJEedvp5nr13oSa+Z2rEcN4hqgGw2QwojONeL/6GUIKbFnh964CvF2bia3+I3T/EsgPI+OtyMQM1HAffrInbEpSAzfIMsMqgC72NikVHTV34lx8DdbFH0XGmsrP3x+9kqsK2xhWNj4u0aiLE4vynn0dF6U3oof6q9eWdPgXIS1E86yRpSUvj2yYwVAyS5tIh35pxcOsijhhNEpDzm4mP9SLZbllJ+V8LosjHGz80M6/iI1P2m7mcwvSuG++iMIq95lZo1/kcGsL3/3vNLlCQKA0Q+kC3/3v3dyzfgmNsoUW3V8cyUjUMihaaasxNoClN3yCJ2ttuL3xZiLO0ZrN/OpxLkcM411QGQwTxYjORIgkai+xRSbpB+flQISCI2wHYUcQloOQgsJrj6CD0GaFoffg2B7cq28P+8CU6owrZnMBkK+wIhECLBf7yltwF66o8iMrsfDaj/HiFsU1iSM0Ox69OYuX+hexcs11OIMehdcfHRO0WAuvR5/YV/MqXx37PkqlK6b1Yq5ISyyhKn0UUcCAnMErmaWsEs8h8PC1jYOPJRS/ZBlX6V/RRAqJQiHJaxv/is8zt2cL2YYYgzlQgUJZLk1RaDn8PPP8JdwUe4s2maJfhaXMDz/ncFf37WTffICIqIhadBR/2e11v54wl3IzG7ctqpHIbx/TzG88TMRgMIQY0ZkAzhWfxnv90Zr3TwaZaAsnaDeKVj7aK6AyyXAiT7QjHDcUE8dFe+C/+wKploU09O4uX7BXdxa2wI0CAtHUgXPZJ3FmL6753kJKIg2NHIxexi8HF5LKeuXNqSuBPS2rOKgOcb2szjfNv/jzLFlY+yq/JaIYzCZIiDwWoU1QSkdI2IpC4BMRhbIYFbSLv+x23thukXM+MWYPT94LWBlz0AUPtEYITSzi0DG7mfx7PURiDcyKV7sVtA4f5zdipVYEYSnzb8S28eiw4NLlX2YvkBxlH3Tp8trVZiXOZCLfRAwGgxGdCeEf+GXoQlC5vCVkeP81G07rNYTyEcpHBQXsS1fg7fo5Oj1YjChUuL7kRMB2qg+UEjV4jEjBG9PbKkCQEglmb/h/EO74PXyEJZHRBnCiPL5xJ6kCtDRGaCnuFcp7AZu2HQJg0PoYbzjXl4/NewHvbDtE929fXfMqP9o2i3zvSXqykSrDzWgiRm6wH88PsIQm0AJp28yf3Ux7s8/e9DwOOfOr3ucu979QdoxBPxb2kpGSFjus9qqXH7GFJqNqtyIAQoGpITJnqr2EwWA4NUZ0JoBOnqDkGlCeVRHF+8ciBKACROCh/ALay4UO2cUd3c7CVah8Fn/PS+DlwYlgL7oB1fMeKjOMtp1wCa6QCf8P5QxHgCSHSw4XhYWrChT2vjzmtSJLQ/836TjISBxlR8sVXMf7s6F1jDfWKQCYsAXLkY6VuEceImpZZafrQj5gSNjkAgdfNpRPnR0UUNseZ+3yP6q5AXKGnaI3bSGEREpBoDS9aUWHOkHjJ/9HzfyIkhaesqgoNsPDosOp7XANZ4eppsFwIWFEZyJoFToQlG8Xi4KLG2dCs8ogNOkMPLSXRwcBhSO78d99AZUeQDa0Yi++sbzsJdvmIVvnlB8TrRch7QjqrafB96hKqlgOmcBGUsAhIE6OKDnyOATCxX/rmbLvGX6B4N0X8WMJosvWoawIo8sProofZ1X+F6FbdjEhv1r/gmj8Jg478+tasJRcrg8HSVLWyDLVo/sbSPgrWSF20CpSDOgEW/Uy1mdfplFmiRCUP05eWBQGdd2d+n27EkRFCl/I8rl1CDjhJWirkx/JbHu83IqgJDwRERBpnVn3K91Ux6lg07bo+C0HarQtLz1/Mk3PpjvaOh8btRnOTozoTAZpIWwX6UQQtgOWA9kkSvkQhBsFS9GMd+xdCtsfA2GDE0NlhsPbV4cJ7MK2h0au1DNJ1MkDjKnvFRI593Ki1/0WqRceIFbM6WjCPTEx7aGEFy79SQvhxpDROEJrCu+8iHv17TUnldXxt0jlJL5wEAI87aC1ZnX8LY5f/YmaEcjK1l4a3ny0mJBXxIMM+TcfYC9wpBcy+Vm8yaxy7kYDv5HIE6kUayBCgFJZvEM7mbXnYSALMqCVPOx5mGcLC7naGgLtlaMmG8ULhStYAhw8lsQ/MUyDnyedGcY+lmT+8tqtCBqX11/6bEruYa21BV/LsvCu5SX+O6mAq2seUxkdNcaqo6MlTn2D13qT+HRHW96hnRMes8EwWYzonCZCgBVvQhQ91rSfDxP/uTQ68FD5bM3j/HdfAGEjnKI3W0VRgMoNFzdsFkWm0khRWmBHIZoApVADR/B73iMxdKDsSCBKy0gCLCGR0UZkLIFWfjiufGhUWW9Sifl5ZGMjyYwX5k0sSVM8QdRP1o1A7J0/Iy6y5Qo1SYAlsuR3PkYQbOBm980xrQVicpS7QZGo9Ci8+lPIDhXv0WGjsKxHt32QN/UilrOzXMiwjW6Gmhexd9uLuG8+NMat4CC/ybylN+KU9jc5UZylt4w7cd7o7sIPZNUmVLTHjcU8UC0qWw4IIapaDixserq+wSvUjCYmG22dKSbrlmAwTAYjOnUI8zE+IvBRfiH8U8iiM0Mjxp+ngUoPgDOS3A+joADVdxiC2pMxAPG2EaGyKAsVXm6kYk1QdA1oQEbiaN/DTw+Ey3vhu4MTrTupEKSJuopYW3xkfF4e2dBRNwIJVBJZUchQEp8W1c9q9w1usneiEfgIXOFzS3RnPRs5BKAGj1Id2YV/b9N9dKsCyVKbAhHQLd7lsgXL8Hf9jKgOiOsstlD4Osxv2TsfIxPRDHsRcipG1FM0vvUCVsclOF3dNaO9TidNj2+Fn6OYbypoi04nU2PEIeO1HFCitvNA0P8Bqk400ZTcw2esF3C1h0QR01k+w3M8Pk60dSZRwz3kdITkcKZ88dEcd4ie5W4JhnMTIzplNEIFCOWhAg/te2jfD0Wi9IzCxPuYyIbWsChACshnwsimzubOKkZXr9lOKGBlBDKWCIsDCln8od6aYuhceQv+nhdrToRIq65nXOHVn6KywyiKDss6QGaHqwSn6nMKzSrnrdAQtOiCHe7TqSc5pSfUFnAB+MiwAo2wEk3i03zoeXyvh6jwyjY7UigayCFUhoFsM75wkRLyWhJkC/jbHqcVGH7+ewzndbj0ljpOY//3iMQTtMs8gznKE25L1CLaMqPusMdrOVCvsi7sRVM7mlhjnwx94Sp6JMXIs8b+JfDFuuM4U3mYjN1Mqr+XQLjloo2h4RSqrZ1J7kAzGOoyXqeq8xohQOgAEeQhP4xO9REM9+GnkqhsBuV5VYIDhJN0Lercr3PDiEQ7ZPph6GS4sbQoOKKps7wxtCa+N+a2bGgFBDKawG6ZiZASP9mDGif6CpLHQ6uVCruc8PUKWG0XEVl1F7KhBQppZEMLkVXhOr4/eIxAh6IhhEAhCEqnY7SlTNHlJkKxRULxbkHoSFC+UevPOATSwbEljiNxbEkgHbzBnuJ7aGwULgobVWo0gRSKNgZp1320MYgUCm+wh4FtjzOYDchrGynDjaaD2YBs3iNiaTobLea2N9DZaBGx9LhOAWuXd4W9frwwf5f3gpGW5t3rikKeR2td3jyLZddtfzFDJFG6JNCiLPQzRJKd+3v5xgPb+Yt/2cI3HtjOzv29wEgeRqUHqyIn79DO8U9qDZ7JLMUWCodwT5SDhy0Uz2SWTvi1DIZTcUFEOuGyiR7ZI+N76MBDBwFanUbUUULatSd3OXIate8RHNuNf3A7wfE9Y/b0iHgz9uWrcS+5lvTjXw+jn9Hb/m0XdCkCcUIBEhr3yjX4rz+K9vP4Q33hZHYK1L5XiK69d8IWLEqFGzItgpG+PVV2y9VDFtJCBePENaO9cEq3pVXznGoNjqi2wXHw6QsSdJIti1npZSw0CmghXY4YLBQtpOkJWogP9iCFpo1BLF3cuCqi5DMZmm/5/boRQ61oontB+NimbYcYSBVorapea6dWZZ23c2Nd7zWdSYIMRb1UiW8VQ8p6BQYLd5+5PMwbmVk1N+juzszC9Ac1nGnOW9EJBcZDBX64VBb4oaXMr9P1sIaVTImg5yD++9vxD+8s76kBwIlidy3Dvvhq5IyuKjsa2dyJGu4DLwtBAJYFTgzZOAN78Y3lYgNnxlycpauxLrqS3LP/3+ktz5XRdS1YgLpVS0MqRqtIVfioaSzCjai2qKxNC6/OZcscgv7DpzOcCjcFgWhoRQ/3jnlaVsSQOkAEmoK2cEWAlIq3YtfSmd0UtidgRMeK8SOCarsdDdiWIBPYdMjBmoJUj/GquroXdNO9oL2mDU4954F6wq9f/SkMHEVaFQZCStOrm2u2cNi07RB/XCd3NBnX6vbmaM0Nuu3Nbv2DDIZJcs6LTjnhr4IweilGMZWbMM/cm9VZDvML5J77PxXPk1izF4duzrMvQ1hOzcPsxTeG5dPR5pGIRvvhPp45lxG5+EpEpAFtuZSnVyda3aLgNKk1EWb+6x/rXi170iXQY9sUDIsErW4AhYpqPTeKe90X8Df9UyiINSKaquKH8qCi4Z8aLg/KaeSn/VfyqciIj9qz2aUsWvpRxNsbkbo6PpSlNxsVUQkNbSJJv9UcLncVFU8XRTMmC3WFxdu5kXwgGEwH+EG6mO8RyElEE6fyXsu98G/hOVVBGP1FE/ws9THcaPUKeKlgQc47c67VZ0OHUsOFwzkoOhqhvHCZzAurylBqbP7lwyCWgEKt3e3he8vWi7DnX4U9bxkieuoUrDN7MVx9e/XG0ctuItK1FBFpADsyRjdl1zLUvq1n4MOM3+MlgseAStAocuUKsWEdJWb5YdM6yykKjCwvL0rbQfuhw3WFYUMY3bkNI0UUQoIbx5oxLxxDw4zQrDTwwteNNKLTOY5FPsK/5rvKCf5YxEYdGuQTtoXyvDHGorLW8p0AqX2ao4p81iYuRnJlGe3QaOVANtYU3lz/cXqzsqYrQpyJUy8Ccrq64cbfGyNIQy/5FOoULJxJ1+qzoUOp4cLhnBMdneon8LwzH8WM954qIDixFzLJ2k+INBD75P9ENndO+LWd2YtH3AkcJxSrktjU+IgiMxi6Wo+awMmnax9g1V8iGa/Hy0AySUKm6VXN4dsIiEgfSxdARkHKcE+NDDekejs3ghNBFEWnaiXSiYWecNHGMROkt3MjQe+h8D5d9J4TGfqCZhrjDk0NI+PXWtObzGElXJRfINBiJKcjNKLSc2HUSqhjW9hFwSkJYlx4CCXqJvhPeA31XRHqntXJUUuQ1i7vrRuBOF21c0eT3Vcz3R1KDRcO557oKDUlgqO1Rg0eC/M0779Zo6WBKC6DNIZ/nYTglF/JcbAiDSg7Ei7/jPPx1HAPIjEDIUYmCK01Wquay27OR9fXfa3xrpbf6tvHqvxzCCsoOwJYxSoxckPF8j8ZFjPkhgj6fWS8BZUdZnRhhEzMwL3uCzUnyMK+rVDYPfJ0raCQJm/PpeCrmlf5WaeDfCYgJgplN+u0dmkW6fqFDLnhkaq6ck6KYm127S6gLwxezHprS11XhA+bU0UgxrXacC5yzonOh43KDhEcehPv4HZ08njFIyKcmKQDsQSObeMHCu0VkPHGSb2XdBykG0c50fH3s1QeUyc6sdovRs6+bKTbqBPFufIWouO4X4+XZ1h6wxw21mhg9lvu86DCCEuUCpV1ACpAFbLUqqdWhdpuDQDq0A40MqyP0xotBALBpfIQQUHVvMp/5qWlrBInGaZhRBBFgIfEHeMwR7g5188Xc3KqYu2vmEuqs1dpaMhnU0qO6VA61LzwtL6rM4GJQAznG0Z0AO0XCI6+HZY5n9hbFUmJxg7si6/CvvhqgqETYeLf99GWhfYK5cT/RBCOgzVBsSkxXnTidHWPKzI1X6/O1XK9BmbWli2QKy5vCsL/a8J9KJmBMa8DQKqvbrJeF3J4GhBWWNoOoBWOX+DOTy+qeZX/w821S3zzXsCdzs+rS8mlTXT1H5F79v+EwiMrijpUAE6MyKrawhsub+U4YHXhOkXhU4o7TYLdYJg0F6zoaK1Q5TLnX4UTUgk3jj2vG3v+Nci2i8plzrKhpZz415lBZLylyjH6VAjLwoomJiU2JaayA2Wtq+zM7rkEyRNQyKCVHwpJtBGruZPg6DvFZ43O6Ou6VXJ5HOyiu0DpSCEgj1P3Kr9eiW9Ls0v0hstrnpvgylvwtj9e3BMkKUU8zpW3nEJ4TYLdYDiTXHCio4Z7i3maN9CVtjLSKpY5X4M1azHCqn1qSon/1rYGBvrr92mppBzZ2FHUOHt9TpfpXMt3uteFHmLxVuxoDD+XLUdawbF3qVsBUSdZvyW4ghut7YBCFxfsBJotwRXUi9nGK/F1utprnptSBDiR5Uc4/5a3TAsDw3RzQYiOzmfwD+/Ef387qu9Q1WOy7SLsi6/B7uoOy5TPFCLsjimLBQKTjWzONiojrdHtqgutc1H9HxSfWbFhprQMWCNZ/65ehUyJMW7S7zavrDuGyUYg0Ws2THj58XzCtDAwnA2ct6KjVUBw7F3897cTHN1dZbUi4s3YF1+NffFVyKb6Db4mhRBh6XNxU+f5IjaVlCKt0bvx3eu+MHaToxvDufxT+HteqpmHWuvN4UdP59hqXVsVtZwqb3K+RSBTgWlhYDgbOK9ER2sd9p15fzv+oR3FvStFbBf7oivCPE3HJQhxhr1OhUA6LiISr7mp80Kg3iZHp6sbr+OSmveXprqzNW9yPi1HjbcZ2GCYKqZFdJ588kn+5V/+Bd/3+Z3f+R3uvPPOX+v1VCaJ//4b+O9vRw+drHhEIDsX4sy/Gmvu0nIDtjOKEEg3Ei7NWU7dlMaFznh5qLM1ajnbl6MmKojjbQY2GKaKKRedEydOcP/99/PII4/gui5f/OIXWb58OQsXTmzvg/YLBEfewjv4OurEfipnetE0E3v+NdhdH0XGm8/wJyi/CzIaDcVGGrGBs3+Snihn83LUZM71mbTOMRgmy5SLzpYtW7j++utpaQndfW+55RY2bdrEn/7pn57W8arvEPl3X8L/YFd1j5hIw4ibc+vcKjfnM4kQAhGJYje1gSoU95V8KG91znE2T9KT4WxejprMuZ7KcnuDoR5TLjonT56ko2MknJ85cyY7d55+46n8Kw/iJ4v/6KVNbP4VxBddR3Te5QhrnKZoZwDpRMJuncV/6B0dkVMccf7T0THixnAo3YcdTVQJvrZi6HRf1fPOFby2WfipAaQdLd+nvBx226yqzzMdn23S57pjFVyz6oyP51z8fj8MzHk4NVMuOkqp6n8oWk84KpEzusLqs3ndiEicHJBLTryV9GkhBNJ2ENEG0C46XwAKNfuoXGiMPge6YQZBenDk6hvQXh7ZMOPcPFdL1hC8/AMCX1UtR9lL1pQ/z3T9Ds6mc23+LYSUzoMRnvGZ8nbVs2bNoqdnZHmip6eHmTNPv2w5+sn/SWz1H+MsvD6sFPuwKFajWYkWaGhFWxdmRdpEqNeq+VzNGThd3XXbeU8359u5Nlw4THmks3LlSr7zne/Q399PLBZj8+bNfP3rXz/t40VDK9o/dZvmSVOMbGS0AWW5xaZfhtPhfMwZnK1OzufjuTZcGEy56HR2dnLvvfdy991343ked9xxB93dZ8E/lIpNndgRlIlqJsXZOkmfj5hzbTgXmZZ9Orfddhu33XbbdLz1WEaJjSl9NhgMhg+P88qRYELUchAwYmMwGAwfKhee6JTEJtoAlmvExmAwGKaQC0d0Ssto0YQRG4PBYJgmLgjRKYuNWUYzGAyGaeW8Fh1hSaxoI8qJGbsag8FgOAs4L0VHSImMxkIjxKnf/2owGAyGOpxfolNuM5BAy/ProxkMBsP5wPkxM9cqEjAYDAbDWce5LTqVZpymSMBgMBjOes5N0akQG13yRzNiYzAYDGc955zoSMdBxBpHxMZgMBgM5wznnOgQa0ar6R6EwWAwGCbDOVhPbKIbg8FgOFc5B0XHYDAYDOcqRnQMBoPBMGUY0TEYDAbDlGFEx2AwGAxThhEdg8FgMEwZRnQMBoPBMGUY0TEYDAbDlGFEx2AwGAxThhEdg8FgMEwZRnQMBoPBMGWcc95rUp49Njhn01imC3MOzDkAcw5KmPNwaoTWpuWZwWAwGKYGs7xmMBgMhinDiI7BYDAYpgwjOgaDwWCYMozoGAwGg2HKMKJjMBgMhinDiI7BYDAYpgwjOgaDwWCYMozoGAwGg2HKMKJjMBgMhinDiM4EefLJJ7n11ltZs2YNP/rRj6Z7ONNGKpXiM5/5DB988MF0D2Xa+Od//mfWr1/P+vXr+cY3vjHdw5kWvvWtb3Hrrbeyfv16/v3f/326hzOt/OM//iP33XffdA/jrMeIzgQ4ceIE999/Pw888ACPPfYYP/nJT9i3b990D2vK2bFjB1/60pc4ePDgdA9l2tiyZQsvvfQSjz76KI899hhvvfUWTz/99HQPa0p59dVXeeWVV3jiiSf4z//8T37wgx/w3nvvTfewpoWtW7fy6KOPTvcwzgmM6EyALVu2cP3119PS0kI8HueWW25h06ZN0z2sKeehhx7ir//6r5k5c+Z0D2Xa6Ojo4L777sN1XRzHYcGCBRw9enS6hzWlXHfddfzHf/wHtm3T19dHEATE4/HpHtaUMzg4yP33388f/uEfTvdQzgnOOZfp6eTkyZN0dHSUb8+cOZOdO3dO44imh7/7u7+b7iFMO5deemn57wcPHmTjxo08+OCD0zii6cFxHL797W/z3e9+l7Vr19LZ2TndQ5py/uqv/op7772XY8eOTfdQzglMpDMBlFIIMWJdrrWuum248Ni7dy/33HMPf/EXf8H8+fOnezjTwle+8hW2bt3KsWPHeOihh6Z7OFPKT3/6U2bPns2KFSumeyjnDCbSmQCzZs3itddeK9/u6em5oJeYLnRef/11vvKVr/C1r32N9evXT/dwppz9+/dTKBRYsmQJsViMNWvW8O677073sKaUp556ip6eHjZs2EAymSSTyfD3f//3fO1rX5vuoZ21GNGZACtXruQ73/kO/f39xGIxNm/ezNe//vXpHpZhGjh27Bh/8id/wv3333/BXuV+8MEHfPvb3y4vKz7zzDN8/vOfn+ZRTS2VFXuPPPIIr776qhGcU2BEZwJ0dnZy7733cvfdd+N5HnfccQfd3d3TPSzDNPBv//Zv5PN5/uEf/qF83xe/+EW+9KUvTeOoppYbb7yRnTt3cvvtt2NZFmvWrLkgIz7DxDCdQw0Gg8EwZZhCAoPBYDBMGUZ0DAaDwTBlGNExGAwGw5RhRMdgMBgMU4YRHYPBYDBMGUZ0DOcNH3zwAYsXL+bLX/7ymMfuu+8+Fi9eTH9/PwCe53HDDTfw+7//+zVf6+GHH+YLX/gCt956KzfffDO/+7u/y44dOz7U8RsMFwJmn47hvCISiXDgwAGOHDnC3LlzAchkMmzfvr3qeU8//TSXXXYZu3btYv/+/SxYsKD82De/+U1++ctf8k//9E/l19i6dSt/8Ad/wCOPPMKcOXOm7gMZDOcZJtIxnFdYlsW6det48skny/dt3ryZ1atXVz3vwQcfZPXq1dx66618//vfL9/f29vL97//fb71rW+VBQdgxYoV3HfffWSz2Q//QxgM5zFGdAznHbfffjuPP/54+fZjjz3G5z73ufLtffv28cYbb7B27drycwcGBgB48803WbBgQU1Pvdtvv70qIjIYDBPHiI7hvOOKK67Asix27drFsWPHSKfTLFq0qPz4gw8+yE033URrayvd3d1cdNFFZXfk0QYdqVSKDRs2sGHDBj796U/zzW9+c0o/i8FwvmFyOobzks9+9rM88cQTtLW1sWHDhvL92WyWxx9/HNd1+dSnPgWEwvLDH/6Qe+65h+7ubg4cOMDAwACtra0kEoly1PSd73ynHBEZDIbJYSIdw3nJhg0b2LRpE0899RSf+cxnyvc/9dRTtLS08OKLL/Lss8/y7LPP8vOf/5xMJsOmTZvo7Ozk7rvv5s/+7M+qOoEeOXKE7du3I6X5J2Mw/DqYSMdwXtLZ2cmCBQtobGykpaWlfP+DDz7I7/3e72FZVvm+pqYm7rrrLr73ve9x2223ce+99/LEE0/w53/+52SzWYaHh2lububWW2/lzjvvnI6PYzCcNxiXaYPBYDBMGWatwGAwGAxThhEdg8FgMEwZRnQMBoPBMGUY0TEYDAbDlGFEx2AwGAxThhEdg8FgMEwZRnQMBoPBMGUY0TEYDAbDlPH/A/a+LLdLUbVgAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['north'] = df['LAT'] > 38 # Create a new variable to divide earthquakes by latitude\n", "sns.lmplot(x=\"MAG\", y=\"DEP\", hue=\"north\", data=df.sample(500));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, we can now easily compare multiple variables, conditions, and fits all on one graph." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise on Seaborn\n", "\n", "Make an overlaid figure on the graph above but hued on \"south\" and with the `rugplots` along the axises as shown below:\n", "\n", "![colorbar](Images/Seaborn_overlay.png)\n", "\n", "I've started writing the plotting code. The reason is we want to make sure were grabbing the right points with `df[:1000]` as we would with the `rugplots` from before." ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "scrolled": true }, "outputs": [], "source": [ "#sns.lmplot(x=\"__\", y=\"__\", hue= ___, data=df[:1000]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 11. Image Data and 3D Surfaces\n", "\n", "Plot a MRI image, change the [color map](https://matplotlib.org/examples/color/colormaps_reference.html); [source](https://matplotlib.org/examples/pylab_examples/mri_demo.html) \n", "[Link to image data](https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/mpl-data/sample_data/s1045.ima.gz) if your cbook.get_sample_data path is broken like mine.\n", "\n", "[Colormap creation tool](http://jdherman.github.io/colormap/)\n", "\n", "* Images are just arrays of pixel values\n", "* `ax.imshow(array)` will display an array as an image.\n", "* The **scikit-image** and **cv2** packages have a lot of functionality for edge detection, thresholding, segmenting images but for this class we will just show you how to display images and a little bit of thresholding.\n", "* You can add annotations and colormaps to axes with imshow on them just like with other plot types" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": false }, "outputs": [], "source": [ "import matplotlib.cbook as cbook # For the data\n", "import matplotlib.cm as cm # For color map" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Might be necessary if the cookbook symlink is broken. Need to download image.\n", "import os\n", "home = os.path.expanduser('~')\n", "dfile = cbook.get_sample_data(home+'\\Downloads\\s1045.ima.gz')\n", "\n", "matplotlib.rcParams['figure.figsize'] = (10, 8)\n", "fig, ax = plt.subplots(num=\"MRI_demo\")\n", "\n", "# Data are 256x256 16 bit integers\n", "im = np.frombuffer(dfile.read(), np.uint16).astype(float)\n", "im.shape = (256, 256)\n", "dfile.close()\n", "\n", "ax.imshow(im, cmap=cm.gray)\n", "ax.axis('off');" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "(-0.5, 255.5, 255.5, -0.5)" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(num=\"MRI_demo\")\n", "ax.imshow(im, cmap=cm.viridis)\n", "ax.axis('off')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can quickly go over some thresholding techniques." ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "# Get relevant libray\n", "import cv2 as cv" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 256. 512. 768. 1024. 1280. 1536. 1792. 2048. 2304.\n", " 2560. 2816. 3072. 3584. 3840. 4096. 4352. 4608. 4864. 5120.\n", " 5376. 5632. 5888. 6144. 6400. 6656. 6912. 7168. 7424. 7680.\n", " 7936. 8192. 8448. 8704. 8960. 9216. 9472. 9728. 9984. 10240.\n", " 10496. 10752. 11008. 11264. 11520. 11776. 12032. 12288. 12544. 12800.\n", " 13056. 13312. 13568. 13824. 14080. 14336. 14592. 14848. 15104. 15360.\n", " 15616. 15872. 16128. 16384. 16640. 16896. 17152. 17408. 17664. 17920.\n", " 18176. 18432. 18688. 18944. 19200. 19456. 19712. 19968. 20224. 20480.\n", " 20736. 20992. 21248. 21504. 21760. 22016. 22272. 22528. 22784. 23040.\n", " 23296. 23552. 23808. 24064. 24320. 24576. 24832. 25088. 25344. 25600.\n", " 25856. 26112. 26368. 26624. 26880. 27136. 27392. 27648. 27904. 28160.\n", " 28416. 28672. 28928. 29184. 29440. 29696. 29952. 30208. 30464. 30720.\n", " 30976. 31232. 31488. 31744. 32000. 32256. 32512. 32768. 33024. 33280.\n", " 33536. 33792. 34048. 34304. 34560. 34816. 35072. 35328. 35584. 35840.\n", " 36096. 36352. 36608. 36864. 37120. 37376. 37632. 37888. 38144. 38400.\n", " 38656. 38912. 39168. 39424. 39680. 39936. 40192. 40448. 40704. 40960.\n", " 41216. 41472. 41728. 41984. 42240. 42496. 42752. 43008. 43264. 43520.\n", " 43776. 44032. 44288. 44544. 44800. 45056. 45312. 45568. 45824. 46080.\n", " 46336. 46592. 46848. 47104. 47360. 47616. 47872. 48128. 48384. 48640.\n", " 48896. 49152. 49408. 49664. 49920. 50176. 50432. 50688. 50944. 51200.\n", " 51456. 51712. 51968. 52224. 52480. 52736. 52992. 53248. 53504. 54528.\n", " 55040.]\n" ] } ], "source": [ "print(np.unique(im))" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(num=\"MRI_demo\")\n", "# Normalize image to be between 0 and 255 (since its a 256 by 256 figure)\n", "im /= im.max()/255.0\n", "# Set your threshold\n", "threshold = 130\n", "ret, thresh1 = cv.threshold(im,threshold, 255,cv.THRESH_BINARY) # ret is the threshold and thresh1 is the image\n", "\n", "ax.imshow(thresh1, 'gray')\n", "ax.axis('off')\n", "ax.grid(b=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3D Surfaces\n", "\n", "[mplot3d](https://matplotlib.org/mpl_toolkits/mplot3d/index.html) if for 3D plotting. [Example source](https://matplotlib.org/examples/mplot3d/custom_shaded_3d_surface.html) \n", "[Link to image data](https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/mpl-data/sample_data/jacksboro_fault_dem.npz)\n" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "scrolled": false }, "outputs": [], "source": [ "# Potentially required fix. If the cell below fails, download the data from the link above and then save the path like this:\n", "import os\n", "home = os.path.expanduser('~')\n", "filename = home+'/Downloads/jacksboro_fault_dem.npz'" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(45, 45)\n" ] } ], "source": [ "from mpl_toolkits.mplot3d import Axes3D\n", "from matplotlib.colors import LightSource\n", "\n", "with np.load(filename) as dem:\n", " \n", " z = dem['elevation']\n", " nrows, ncols = z.shape\n", " x = np.linspace(dem['xmin'], dem['xmax'], ncols)\n", " y = np.linspace(dem['ymin'], dem['ymax'], nrows)\n", " x, y = np.meshgrid(x, y)\n", "\n", "region = np.s_[5:50, 5:50]\n", "x, y, z = x[region], y[region], z[region]\n", "\n", "print(x.shape)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '
');\n", " var titletext = $(\n", " '
');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('');\n", "\n", " var fmt_picker = $('');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
');\n", " var button = $('');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '
');\n", " var titletext = $(\n", " '
');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('');\n", "\n", " var fmt_picker = $('');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
');\n", " var button = $('');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))\n", "\n", "ls = LightSource(270, 45)\n", "# To use a custom hillshading mode, override the built-in shading and pass\n", "# In the rgb colors of the shaded surface calculated from \"shade\".\n", "rgb = ls.shade(z, cmap=cm.gist_earth, vert_exag=0.1, blend_mode='soft') # make colors\n", "surf = ax.plot_surface(x, y, z, # 2D arrays of data\n", " rstride=1, # row step\n", " cstride=1, # column step\n", " facecolors=rgb, #the shade from above\n", " shade=False) # because we already computed what we wanted" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Interactive Plots\n", "Above we turned on the interactive plots in the notebook. Let's turn them back on to see what they do." ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '
');\n", " var titletext = $(\n", " '
');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('');\n", "\n", " var fmt_picker = $('');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
');\n", " var button = $('');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "plt.figure()\n", "pts = plt.scatter(bigones['MAG'], bigones['DEP'], # data\n", " s=50, # marker size\n", " cmap=\"cool\", # Color scheme: https://matplotlib.org/users/colormaps.html\n", " c=1.0*(bigones['YEAR']-1983)/28) # Specific color for each point (range between 0 and 1)\n", "plt.ylabel(\"Depth\")\n", "plt.xlabel(\"Magnitude\")\n", "plt.title(\"Large Earthquakes\")\n", "clrbar = plt.colorbar(pts, ticks=[1.0/28, 27.0/28]) # data takes values from 1983=1/28, 2010=27/28 (none in 2011)\n", "clrbar.ax.set_yticklabels(['1984', '2010']);\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### And now turn it back off:" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "scrolled": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 12. Paletteable\n", "* This library features all of the default matplotlib and seaborn palettes along with some other unique palettes\n", "* You can use it to create continous colormaps as well as discrete colormaps.\n", "* Also keep in mind whether you want your colormap to show categorical data, sequentially increasing data, or diverging data (extrema are more pronounced) and pick the appropriate colormap\n", "* Read the docs [here](https://jiffyclub.github.io/palettable/) \n", "#### A quick example using a Wes Anderson color palette:" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "'pip3' is not recognized as an internal or external command,\n", "operable program or batch file.\n" ] } ], "source": [ "# May be necessary if you don't have this installed. \n", "!pip3 install palettable # If this doesn't work: open a terminal, and run: conda install palettable" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Use .mpl_colors for discrete colors and .mpl_colormap for continuous ranges.\n", "import palettable\n", "WA_colors = palettable.wesanderson.Aquatic2_5.mpl_colors # All the palettes are accessed via cat.palette\n", "sns.palplot(WA_colors)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "---\n", "## 13. Documentation and Resources\n", "\n", "### Matplotlib\n", "\n", "* [Matplotlib Beginnner Tips](http://matplotlib.org/users/beginner.html)\n", "* [Pyplot Module Summary](http://matplotlib.org/api/pyplot_summary.html)\n", "* [MPL Gallery](http://matplotlib.org/gallery.html)\n", "\n", "### Other packages in this class\n", "* [Seaborn Docs](https://seaborn.pydata.org/)\n", "* [Seaborn Gallery](https://seaborn.pydata.org/examples/index.html)\n", "* [Palettable](https://jiffyclub.github.io/palettable/)\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }