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610 lines
184 KiB
610 lines
184 KiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"___\n",
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"\n",
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"<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
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"___\n",
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"# Matplotlib Figure Object"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Import the `matplotlib.pyplot` module under the name `plt` (the tidy way):"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# COMMON MISTAKE!\n",
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"# DON'T FORGET THE .PYPLOT part\n",
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"\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**NOTE: For users running .py scripts in an IDE like PyCharm or Sublime Text Editor. You will not see the plots in a notebook, instead if you are using another editor, you'll use: *plt.show()* at the end of all your plotting commands to have the figure pop up in another window.**"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"___\n",
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"### Matplotlib Object Oriented Method\n",
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"Now that we've seen the basics, let's break it all down with a more formal introduction of Matplotlib's Object Oriented API. This means we will instantiate figure objects and then call methods or attributes from that object."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### The Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 50,
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"metadata": {},
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"outputs": [],
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"source": [
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"a = np.linspace(0,10,11)\n",
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"b = a ** 4"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 51,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])"
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]
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},
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"execution_count": 51,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"a"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 52,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([0.000e+00, 1.000e+00, 1.600e+01, 8.100e+01, 2.560e+02, 6.250e+02,\n",
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" 1.296e+03, 2.401e+03, 4.096e+03, 6.561e+03, 1.000e+04])"
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]
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},
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"execution_count": 52,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"b"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"metadata": {},
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"outputs": [],
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"source": [
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"x = np.arange(0,10)\n",
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"y = 2 * x"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 54,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
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]
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},
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"execution_count": 54,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 55,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18])"
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]
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},
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"execution_count": 55,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"y"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Creating a Figure"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The main idea in using the more formal Object Oriented method is to create figure objects and then just call methods or attributes off of that object. This approach is nicer when dealing with a canvas that has multiple plots on it. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 73,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 0 Axes>"
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]
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},
|
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Creates blank canvas\n",
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"fig = plt.figure()"
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]
|
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**NOTE: ALL THE COMMANDS NEED TO GO IN THE SAME CELL!**\n",
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"\n",
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"To begin we create a figure instance. Then we can add axes to that figure:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 56,
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"metadata": {},
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"outputs": [
|
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{
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"data": {
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"image/png": "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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Create Figure (empty canvas)\n",
|
|
"fig = plt.figure()\n",
|
|
"\n",
|
|
"# Add set of axes to figure\n",
|
|
"axes = fig.add_axes([0, 0, 1, 1]) # left, bottom, width, height (range 0 to 1)\n",
|
|
"\n",
|
|
"# Plot on that set of axes\n",
|
|
"axes.plot(x, y)\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 57,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Create Figure (empty canvas)\n",
|
|
"fig = plt.figure()\n",
|
|
"\n",
|
|
"# Add set of axes to figure\n",
|
|
"axes = fig.add_axes([0, 0, 1, 1]) # left, bottom, width, height (range 0 to 1)\n",
|
|
"\n",
|
|
"# Plot on that set of axes\n",
|
|
"axes.plot(a, b)\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Adding another set of axes to the Figure\n",
|
|
"\n",
|
|
"So far we've only seen one set of axes on this figure object, but we can keep adding new axes on to it at any location and size we want. We can then plot on that new set of axes."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 58,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"matplotlib.figure.Figure"
|
|
]
|
|
},
|
|
"execution_count": 58,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"type(fig)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Code is a little more complicated, but the advantage is that we now have full control of where the plot axes are placed, and we can easily add more than one axis to the figure. Note how we're plotting a,b twice here"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 62,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Creates blank canvas\n",
|
|
"fig = plt.figure()\n",
|
|
"\n",
|
|
"axes1 = fig.add_axes([0, 0, 1, 1]) # Large figure\n",
|
|
"axes2 = fig.add_axes([0.2, 0.2, 0.5, 0.5]) # Smaller figure\n",
|
|
"\n",
|
|
"# Larger Figure Axes 1\n",
|
|
"axes1.plot(a, b)\n",
|
|
"\n",
|
|
"# Use set_ to add to the axes figure\n",
|
|
"axes1.set_xlabel('X Label')\n",
|
|
"axes1.set_ylabel('Y Label')\n",
|
|
"axes1.set_title('Big Figure')\n",
|
|
"\n",
|
|
"# Insert Figure Axes 2\n",
|
|
"axes2.plot(a,b)\n",
|
|
"axes2.set_title('Small Figure');"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Let's move the small figure and edit its parameters."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 69,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Creates blank canvas\n",
|
|
"fig = plt.figure()\n",
|
|
"\n",
|
|
"axes1 = fig.add_axes([0, 0, 1, 1]) # Large figure\n",
|
|
"axes2 = fig.add_axes([0.2, 0.5, 0.25, 0.25]) # Smaller figure\n",
|
|
"\n",
|
|
"# Larger Figure Axes 1\n",
|
|
"axes1.plot(a, b)\n",
|
|
"\n",
|
|
"# Use set_ to add to the axes figure\n",
|
|
"axes1.set_xlabel('X Label')\n",
|
|
"axes1.set_ylabel('Y Label')\n",
|
|
"axes1.set_title('Big Figure')\n",
|
|
"\n",
|
|
"# Insert Figure Axes 2\n",
|
|
"axes2.plot(a,b)\n",
|
|
"axes2.set_xlim(8,10)\n",
|
|
"axes2.set_ylim(4000,10000)\n",
|
|
"axes2.set_xlabel('X')\n",
|
|
"axes2.set_ylabel('Y')\n",
|
|
"axes2.set_title('Zoomed In');"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"You can add as many axes on to the same figure as you want, even outside of the main figure if the length and width correspond to this."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 74,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x1cd42ad2888>]"
|
|
]
|
|
},
|
|
"execution_count": 74,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Creates blank canvas\n",
|
|
"fig = plt.figure()\n",
|
|
"\n",
|
|
"axes1 = fig.add_axes([0, 0, 1, 1]) # Full figure\n",
|
|
"axes2 = fig.add_axes([0.2, 0.5, 0.25, 0.25]) # Smaller figure\n",
|
|
"axes3 = fig.add_axes([1, 1, 0.25, 0.25]) # Starts at top right corner!\n",
|
|
"\n",
|
|
"# Larger Figure Axes 1\n",
|
|
"axes1.plot(a, b)\n",
|
|
"\n",
|
|
"# Use set_ to add to the axes figure\n",
|
|
"axes1.set_xlabel('X Label')\n",
|
|
"axes1.set_ylabel('Y Label')\n",
|
|
"axes1.set_title('Big Figure')\n",
|
|
"\n",
|
|
"# Insert Figure Axes 2\n",
|
|
"axes2.plot(a,b)\n",
|
|
"axes2.set_xlim(8,10)\n",
|
|
"axes2.set_ylim(4000,10000)\n",
|
|
"axes2.set_xlabel('X')\n",
|
|
"axes2.set_ylabel('Y')\n",
|
|
"axes2.set_title('Zoomed In');\n",
|
|
"\n",
|
|
"# Insert Figure Axes 3\n",
|
|
"axes3.plot(a,b)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Figure Parameters"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 82,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x1cd42d53848>]"
|
|
]
|
|
},
|
|
"execution_count": 82,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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AAADgOfX0VFmytCNJctX89rS0lJoXcSiRDwAAAIDn9JPHnswTT27P2OFD8tbzZtQ9h8MQ+QAAAAB4TouXdiZJ3n7+jIwZPqTeMRyWyAcAAADAs3p849b85NEn01KSK+e31z2HZyHyAQAAAPCsljSu4nvNGZMz44RR9Y7hWYl8AAAAABzWMzv25Kv3rE6SLFo4u+Y1PBeRDwAAAIDD+uIdq7Jrb0/OOKktF84+oe45PAeRDwAAAIDfsLe7J9fe1pkkWbSgPaWUWvfw3EQ+AAAAAH7D9x5Yn3VduzJxzLBcevbUuufwPEQ+AAAAAH7DgRtuvOvCWRkxtLXeMTwvkQ8AAACAX3Pvqmdy94rNGdpa8p55M+uewxEQ+QAAAAD4NUuWdiRJLj17aiaNHVHzGo6EyAcAAADAL23Ysivf/vm6JMmiBbNrXsOREvkAAAAA+KXrbluRfT1VLmg/IXOnjat7DkdI5AMAAAAgSbJrb3e+sHxFkmTRwvZ6x3BURD4AAAAAkiTfuHdNNu/Ym2njR+biM6fUPYejIPIBAAAAkKqqsvjWziTJlfPb09pS6h3EURH5AAAAAMiyX2zKIxu2ZtSw1rz9/Bl1z+EoiXwAAAAAZMnSjiTJW8+dnnEjh9a8hqMl8gEAAAAMch1Pbc+/Pbwxyf6P6tJ8RD4AAACAQe5zyzpTVcmr5kzKySeOqXsOx0DkAwAAABjEtuzam6/ctSpJctWC9nrHcMxEPgAAAIBB7Mt3rsr2Pd05bdKYLDx1Yt1zOEYiHwAAAMAg1d1T5ZplnUmSRQtnp5RS7yCOmcgHAAAAMEj94KENWb15Z8aPGprLz5lW9xxeAJEPAAAAYJBafGtHkuRdF8zMyGGtNa/hhRD5AAAAAAahB9Z2ZXnH0xnSUvK7F82qew4vkMgHAAAAMAgtWdqZJLnkxSflpHEj6x3DCybyAQAAAAwyT27dnRvvXZskWbSgvd4x9AqRDwAAAGCQ+cLyFdnT3ZOXzhyfl86cUPcceoHIBwAAADCI7N7Xnc/fvjJJctWC2TWvobeIfAAAAACDyLd+ti5PbdudKW0jcsncKXXPoZeIfAAAAACDRFVVWby0I0lyxfxZGdoqDQ0U/pcEAAAAGCTu7NycB9ZuyYihLXnn+TPrnkMvEvkAAAAABonFt+6/iu/NL52eCaOH1byG3iTyAQAAAAwCq57ekZsfXJ8kWbSgvd4x9DqRDwAAAGAQuPa2zvRUyctPm5jTJo+tew69TOQDAAAAGOC27d6XL925KkmyaMHsmtfQF0Q+AAAAgAHuq3evztZd+3LyxNF55ekn1j2HPiDyAQAAAAxgPT1VrlnWmSS5ckF7WlpKvYPoEyIfAAAAwAD240c3puOp7Rk7Ykje8rLpdc+hj4h8AAAAAAPY4ls7kyTvvGBmRg8fUu8Y+ozIBwAAADBAPbpha259/Km0lOSKi2bVPYc+JPIBAAAADFBLlnYkSV531pRMnzCq5jX0JZEPAAAAYADavH1PvnbPmiTJooWza15DXxP5AAAAAAag6+9Ymd37ejJ3WlvOmzWh7jn0MZEPAAAAYIDZ292T625bkSRZtGB2Sik1L6KviXwAAAAAA8x371+f9Vt25cSxw/OGl5xU9xyOA5EPAAAAYIBZfOv+G26858JZGT6kteY1HA8iHwAAAMAAcs/Kzbl31TMZ1tqSd8+bWfccjhORDwAAAGAAWbK0M0ly2TlTM3HM8HrHcNyIfAAAAAADxLqunfnOfeuSJFctmF3zGo4nkQ8AAABggLj2thXp7qky7+QTcubUtrrncBz1euQrpQwppXyilNJRStlZSnmilPKRUkrLQWdKKeVjpZS1jTM/LqWcdcj7TCilXFdK6Wp8XVdKGX/ImReXUm5pvMeaxu/jntAAAADAoLNzT3e+eMfKJK7iG4z64kq+DyT5wyTvTXJGkj9L8n8med9BZ/4syfsbZ85Psj7J90spYw86c32Sc5K8vvF1TpLrDjxZSmlL8v0kaxvv8b4kf9p4XwAAAIBB5es/XZNnduzNjBNG5jVnTK57DsfZkD54z4uSfKOqqm83ft1ZSnlnkvOS/VfxJfnjJH9dVdXXGo/9XpINSd6V5DOllDOyP+zNq6pqeePMHyS5rZTyW1VVPZLk3UlGJLmyqqrdSe4vpZye5P2llE9VVVX1wc8GAAAA0O9UVZUlSzuSJFfOn53WFh90HGz64kq+W5O8uhHcUko5O8nCJN9pPD87yZQkNx94QSPS3ZJkfuOhi5J0HQh8jTO3J+k65Mwtjdce8L0kU5O0H25YKWV4KaXtwFeSsYc7BwAAANBMbn38qTy2cVtGD2vN286bXvccatAXV/L9bZJxSR4upXQnaU3yl1VVfbHx/JTG9w2HvG5DklkHndl4mPfeeNDrpyTpPMx7HHiu4zCv/4skH33+HwEAAACgeSy+dX8Gedt5M9I2YmjNa6hDX1zJ9ztJ3pP9H719WZLfS/KnjY/kHuzQj9OWQx473Mdtn+9MeZbHD/hk9gfIA1/SNgAAANDUfvHktvzokSdTSnLl/Pa651CTvriS7++T/E1VVV9q/Pq+Usqs7L+K7nPZf5ONZP/VdusOet2k/OpKvPVJDvdfiDzxkDNTDnl+UuP7oVcJJvnlx4J/+fFeN+IFAAAAmt3nlnUmSV49Z1LaJ46udwy16Ysr+UYl6Tnkse6Dfq+O7A90Fx94spQyLMkrkyxrPHRbknGllAsOOnNh9l99d/CZVzRee8Brs/9uu5298YMAAAAA9GddO/bmK3etTpIsWjC75jXUqS8i3zeT/GUp5Q2llPZSypuTvD/J15Okcdfbf0zywVLKm0spc5Nck2RHkusbZx5KclOSz5ZS5pVS5iX5bJJvNe6sm8bZ3UmuKaXMbfw+H0zizroAAADAoPAvd63Mzr3dmTNlbC465UV1z6FGffFx3fcluTrJP2f/x2fXJvlMkr866MzfJRnZODMhyfIkr62qautBZ96d5NP51V14b0zy3gNPVlXVVUq5OMk/JbkryeYkn2p8AQAAAAxo+7p78rllK5IkVy1o958lG+TKYL7orZTSlqSrq6srbW1tdc8BAAAAOGLfvW9d/ugL9+SE0cOy7M9flRFDW+ueRB/YsmVLxo0blyTjqqra8mzn+uLjugAAAAD0scVLO5Ik775wpsCHyAcAAADQbO5b3ZU7OzdnSEvJe+bNqnsO/YDIBwAAANBkljSu4nvjS07K5LYRNa+hPxD5AAAAAJrIxi278s2fr02SLFo4u+Y19BciHwAAAEAT+fzyldnbXeXcWRPykunj655DPyHyAQAAADSJXXu784XbVyRJFi1wFR+/IvIBAAAANIkbf7Y2m7bvydRxI/K6sybXPYd+ROQDAAAAaAJVVWXJ0s4kyRXz2zOkVdbhV/xpAAAAAGgCtz/xdB5atyUjh7bmHefPqHsO/YzIBwAAANAEFi/tSJK85dxpGT9qWM1r6G9EPgAAAIB+buWmHfnBQxuSJFfOd8MNfpPIBwAAANDPXbOsM1WVvPL0E3PqpDF1z6EfEvkAAAAA+rGtu/bmy3etSpIsWugqPg5P5AMAAADox/717tXZtntfTjlxdF5x2sS659BPiXwAAAAA/VR3T5VrlnUmSa5aMDullHoH0W+JfAAAAAD91A8f3pgVm3Zk3Mih+Q8vm1b3HPoxkQ8AAACgn1qytCNJ8o4LZmTUsCE1r6E/E/kAAAAA+qGH1m3Jsl9sSmtLyRUXtdc9h35O5AMAAADoh65Z2pkkef3cKZk2fmS9Y+j3RD4AAACAfmbTtt35+r1rkiSLFrTXO4amIPIBAAAA9DPXL1+ZPft6cvb0cXnZzAl1z6EJiHwAAAAA/ciefT257vYVSZJFC2enlFLzIpqByAcAAADQj3znvnXZuHV3Jo0dnkvmnlT3HJqEyAcAAADQT1RVlcVLO5IkV1w0K8OGSDccGX9SAAAAAPqJe1Zuzs9Xd2X4kJa884KZdc+hiYh8AAAAAP3E4ls7kySXnzMtLxozvN4xNBWRDwAAAKAfWPPMztz0wPokyVUL2+sdQ9MR+QAAAAD6gWtv60x3T5UFp74oc6a01T2HJiPyAQAAANRsx559+eLylUmSq+bPrnkNzUjkAwAAAKjZV+9Zky279mXWi0blVXMm1T2HJiTyAQAAANSop6fKNUs7kiRXzW9PS0upeRHNSOQDAAAAqNFPHnsyv3hye8YOH5K3njej7jk0KZEPAAAAoEaLl3YmSd5+/oyMGT6k3jE0LZEPAAAAoCaPb9yanzz6ZFpKcuX89rrn0MREPgAAAICaLGlcxfeaMyZnxgmj6h1DUxP5AAAAAGrwzI49+eo9q5MkixbOrnkNzU7kAwAAAKjBl+5clV17e3LmSW25cPYJdc+hyYl8AAAAAMfZ3u6efG5ZZ5LkqgXtKaXUO4imJ/IBAAAAHGffe2B91nXtysQxw3Lp2VPrnsMAIPIBAAAAHGcHbrjxrgtnZcTQ1nrHMCCIfAAAAADH0b2rnsndKzZnaGvJe+bNrHsOA4TIBwAAAHAcLVnakSS59OypmTR2RM1rGChEPgAAAIDjZMOWXfn2z9clSRYtmF3zGgYSkQ8AAADgOLnuthXZ11PlgvYTMnfauLrnMICIfAAAAADHwa693fnC8hVJkkUL2+sdw4Aj8gEAAAAcB9+4d00279ibaeNH5uIzp9Q9hwFG5AMAAADoY1VVZfGtnUmSK+e3p7Wl1DuIAUfkAwAAAOhjy36xKY9s2JpRw1rz9vNn1D2HAUjkAwAAAOhjS5Z2JEneeu70jBs5tOY1DEQiHwAAAEAf6nhqe/7t4Y1J9n9UF/qCyAcAAADQhz63rDNVlbxqzqScfOKYuucwQIl8AAAAAH1ky669+cpdq5IkVy1or3cMA5rIBwAAANBHvnznqmzf053TJo3JwlMn1j2HAUzkAwAAAOgD3T1VPndbZ5Jk0cLZKaXUuoeBTeQDAAAA6AM/eGhDVj29M+NHDc3l50yrew4DnMgHAAAA0AcW39qRJHnXBTMzclhrzWsY6EQ+AAAAgF72wNquLO94OkNaSn73oll1z2EQEPkAAAAAetmSpZ1JkktefFJOGjey3jEMCiIfAAAAQC96cuvu3Hjv2iTJogXt9Y5h0BD5AAAAAHrR9ctXZk93T146c3xeOnNC3XMYJEQ+AAAAgF6ye193rrt9RZLkqgWza17DYCLyAQAAAPSSb/1sXZ7atjtT2kbkkrlT6p7DICLyAQAAAPSCqqqyeGlHkuSK+bMytFV24fjxpw0AAACgF9zZuTkPrN2SEUNb8s7zZ9Y9h0FG5AMAAADoBYtv3X8V35tfOj0TRg+reQ2DjcgHAAAA8AKtenpHbn5wfZJk0YL2escwKIl8AAAAAC/Qtbd1pqdKXn7axJw2eWzdcxiERD4AAACAF2D77n350p2rkiSLFsyueQ2DlcgHAAAA8AJ89Z7V2bprX06eODqvPP3EuucwSIl8AAAAAMeop6fKkqWdSZIrF7SnpaXUO4hBS+QDAAAAOEY/fnRjOp7anrEjhuQtL5te9xwGMZEPAAAA4BgduIrvnRfMzOjhQ+odw6Am8gEAAAAcg0c3bM2/P/ZUWkpyxUWz6p7DICfyAQAAAByDJUs7kiSvO2tKpk8YVfMaBjuRDwAAAOAobd6+J1+7Z02SZNHC2TWvAZEPAAAA4Khdf8fK7N7Xk7nT2nLerAl1zwGRDwAAAOBo7O3uyXW3rUiSLFowO6WUmheByAcAAABwVL57//qs37IrJ44dnje85KS650ASkQ8AAADgqCy+df8NN95z4awMH9Ja8xrYT+QDAAAAOEL3rNyce1c9k2GtLXn3vJl1z4FfEvkAAAAAjtCSpZ1JksvOmZqJY4bXOwYOIvIBAAAAHIF1XTvznfvWJUmuWjC75jXw60Q+AAAAgCNw7W0r0t1TZd7JJ+TMqW11z4FfI/IBAAAAPI+de7rzxTtWJkkWuYqPfkjkAwAAAHgeX//pmjyzY29mnDAyrz5jct1z4DeIfAAAAADPoaqqLFnakSS5cv7stLaUmhfBbxL5AAAAAJ7DrY8/lcc2bsvoYa1523nT654DhyXyAQAAADyHxbfuv4rvbefNSNuIoQN5gtAAACAASURBVDWvgcMT+QAAAACexRNPbsuPHnkypSRXzm+vew48K5EPAAAA4Flcs6wzSfLqOZPSPnF0vWPgOYh8AAAAAIfRtXNv/vXu1UmSRQtm17wGnpvIBwAAAHAYX75zVXbs6c6cKWNz0SkvqnsOPCeRDwAAAOAQ+7p7fvlR3asWtKeUUu8geB4iHwAAAMAhvv/ghqx5ZmdOGD0sl50zre458LxEPgAAAIBDLFnamSR594UzM2Joa71j4AiIfAAAAAAHuW91V+7ofDpDWkreM29W3XPgiIh8AAAAAAdZsrQjSfLGl5yUyW0jal4DR0bkAwAAAGjofGp7vvnztUmSRQtn17wGjpzIBwAAAJCkqqp85MYHsre7yitOPzEvmT6+7klwxEQ+AAAAgCQ33b8+P3n0yQxrbcnH33RW3XPgqIh8AAAAwKC3bfe+fPybDyZJ/vCVJ2f2xNE1L4KjI/IBAAAAg96n/+2xrN+yKzNOGJn/47dPrXsOHDWRDwAAABjUHlm/NYtv3X9H3b9609yMGNpa8yI4eiIfAAAAMGhVVZUP33B/9vVUed1Zk/PbcybVPQmOicgHAAAADFpfu2dN7uh8OiOHtuYjl7rZBs1L5AMAAAAGpa4de/PfvvNQkuS/vPq0TBs/suZFcOxEPgAAAGBQ+vubH86m7Xty6qQx+d8Xzq57DrwgIh8AAAAw6Px89TP5wvKVSZKrL5ubYUMkEpqbP8EAAADAoNLdU+VDN9yfqkouP2dqLjrlRXVPghdM5AMAAAAGlevvWJmfr+7K2OFD8sE3nFH3HOgVIh8AAAAwaDy1bXf+/qaHkyR/+rrfyqSxI2peBL1D5AMAAAAGjU9+5+Fs2bUvZ01ty3vmzap7DvQakQ8AAAAYFJY/sSlfvWd1Skk+cfnctLaUuidBrxH5AAAAgAFvb3dPPvyN+5Mk7zh/Zl46c0LNi6B3iXwAAADAgLdkaUce3bAtJ4welj973W/VPQd6ncgHAAAADGjrunbmH3/wWJLkzy+Zkwmjh9W8CHqfyAcAAAAMaFd/68Hs2NOd82ZNyFtfNr3uOdAnRD4AAABgwPrxIxvznfvWp7Wl5OrL56bFzTYYoEQ+AAAAYEDatbc7H73xgSTJlfPbc8ZJbTUvgr4j8gEAAAAD0v+45RdZsWlHJrcNzx+/5rS650CfEvkAAACAAWfFpu355x//Ikny4TeembEjhta8CPqWyAcAAAAMKFVV5SPfeCB79vXk5adNzBtefFLdk6DPiXwAAADAgPK9B9bnlkefzLDWlnz8TWelFDfbYOAT+QAAAIABY/vuffn4Nx9MkvynV56ck08cU/MiOD5EPgAAAGDA+PS/PZZ1Xbsy44SR+c+/fWrdc+C4EfkAAACAAeHRDVvzv27tSJJ87NKzMmJoa82L4PgR+QAAAICmV1VVPnTD/dnXU+XiMyfn1WdMrnsSHFciHwAAAND0vv7TNbmj4+mMGNqSj156Zt1z4LgT+QAAAICm1rVjb/7bdx5KkvyXV5+W6RNG1bwIjj+RDwAAAGhq//3mR/LUtj055cTR+f2FJ9c9B2oh8gEAAABN677VXfn88hVJkqsvn5thQ6QOBid/8gEAAICm1N1T5UM33JeqSi47Z2rmnzKx7klQG5EPAAAAaEpfvGNlfra6K2OHD8lf/m9n1D0HaiXyAQAAAE3nqW2783c3PZwk+ZPXnp5JbSNqXgT1EvkAAACApvPJ7zycLbv25aypbXnPvFl1z4Ha9UnkK6VMK6V8vpSyqZSyo5Rybynl3IOeL6WUj5VS1pZSdpZSflxKOeuQ95hQSrmulNLV+LqulDL+kDMvLqXc0niPNaWUj5RSSl/8TAAAAED/cEfH0/nqPauT7L/ZxpBW1zBBr/8tKKVMSLI0yd4klyQ5M8mfJHnmoGN/luT9Sd6b5Pwk65N8v5Qy9qAz1yc5J8nrG1/nJLnuoN+nLcn3k6xtvMf7kvxp430BAACAAWhvd08+fMP9SZJ3XjAjL5s5oeZF0D8M6YP3/ECSVVVVXXXQY50H/qFxpd0fJ/nrqqq+1njs95JsSPKuJJ8ppZyR/WFvXlVVyxtn/iDJbaWU36qq6pEk704yIsmVVVXtTnJ/KeX0JO8vpXyqqqqqD342AAAAoEbXLO3MIxu2ZsKoofmz182pew70G31xPeubktxVSvlKKWVjKeWnjUB3wOwkU5LcfOCBRqS7Jcn8xkMXJek6EPgaZ25P0nXImVsarz3ge0mmJmk/3LBSyvBSStuBryRjD3cOAAAA6H/Wde3MP/zg0STJX1xyRiaMHlbzIug/+iLynZzkj5I8luR1Sf5Hkk+XUq5oPD+l8X3DIa/bcNBzU5JsPMx7bzzkzOHe4+Df41B/kf2h8MDX6uf6QQAAAID+4xPfeig79nTn3FkT8tZzp9c9B/qVvoh8LUnuqarqg1VV/bSqqs8k+Wz2h7+DHfpx2nLIY4f7uO3znSnP8vgBn0wy7qAv/48AAAAATeCWR5/Mt+9bl5aSXH3Z3LS0uO8mHKwvIt+6JA8e8thDSWY2/nl94/uhV9tNyq+uxFufZPJh3vvEQ84c7j2S37zCL8n+jwVXVbXlwFeSrc/2QwAAAAD9w6693fnoN/bfbOPK+bNz5tS2mhdB/9MXkW9pkt865LHTk6xo/HNH9ge6iw88WUoZluSVSZY1HrotybhSygUHnbkw+6++O/jMKxqvPeC12X+33c7e+EEAAACA+n3mlifSuWlHJo0dnv968Wl1z4F+qS8i3z8kmVdK+WAp5dRSyruS/Mck/5Qkjbve/mOSD5ZS3lxKmZvkmiQ7klzfOPNQkpuSfLaUMq+UMi/7P/L7rcadddM4uzvJNaWUuaWUNyf5YBJ31gUAAIABYsWm7fmnHz+eJPnwG8/M2BFDa14E/dOQ3n7DqqrubAS3Tyb5SPZfuffHVVV94aBjf5dkZJJ/TjIhyfIkr62q6uCPz747yafzq7vw3pjkvQf9Pl2llIuzPx7elWRzkk81vgAAAIAmV1VVPnbjA9mzrycLT52YN77kpLonQb9VBvNFb6WUtiRdXV1daWvzeX4AAADoT266f33+8PN3Z2hryU1//IqccuKYuifBcbdly5aMGzcuScY17jFxWH3xcV0AAACAF2T77n35q28+kCT5T684ReCD5yHyAQAAAP3Op3/4WNZ27cr0CSPzn3/71LrnQL8n8gEAAAD9ymMbtuZ//XtHkuTjbzorI4e11rwI+j+RDwAAAOg3qqrKh264P/t6qrzmjMl59RmT654ETUHkAwAAAPqNG+5dk+UdT2fE0JZ89NIz654DTUPkAwAAAPqFrp1789fffihJ8r5XnZYZJ4yqeRE0D5EPAAAA6Bf+r5sfyVPb9uSUE0fnD15+ct1zoKmIfAAAAEDt7lvdlc/fviJJcvVlczNsiGQBR8PfGAAAAKBW3T1VPnTDfempkjedPTXzT51Y9yRoOiIfAAAAUKsv3bkyP1vdlTHDh+RDbzij7jnQlEQ+AAAAoDZPbdudv7vpkSTJn7z29ExqG1HzImhOIh8AAABQm7/57sPp2rk3Z57Ult+dN6vuOdC0RD4AAACgFnd2Pp1/vXt1kuTqy+dmSKtMAcfK3x4AAADguNvb3ZMPff3+JMk7zp+Rc2dNqHkRNDeRDwAAADjuPresM49s2JoJo4bmA6+fU/ccaHoiHwAAAHBcre/alX/4/qNJkj+/ZE4mjB5W8yJofiIfAAAAcFxd/e0Hs31Pd142c3zedu6MuufAgCDyAQAAAMfNTx59Mt/++bq0lP0322hpKXVPggFB5AMAAACOi117u/ORb+y/2cbvzW/PWVPH1bwIBg6RDwAAADgu/udPnkjnph2ZNHZ43n/x6XXPgQFF5AMAAAD63MpNO/JPP3o8SfKhN56ZsSOG1rwIBhaRDwAAAOhTVVXlozfen937ejL/lBfl0pecVPckGHBEPgAAAKBP3fzghvzokScztLXkry6bm1LcbAN6m8gHAAAA9Jkde/bl4zc+kCT5j684OadOGlPzIhiYRD4AAACgz3z63x7P2q5dmTZ+ZN7726fVPQcGLJEPAAAA6BOPbdia/+/fn0iSfOxNZ2XksNaaF8HAJfIBAAAAva6qqnz4G/dnX0+V15wxKRefObnuSTCgiXwAAABAr/vGvWtz+xNPZ8TQlnz00rPqngMDnsgHAAAA9KqunXvziW8/lCR536tOy4wTRtW8CAY+kQ8AAADoVZ+6+ZE8tW13Tj5xdH7/5bPrngODgsgHAAAA9Jr713TluttXJEmuvmxuhg9xsw04HkQ+AAAAoFf09FT5yxvuT0+VXHr21Cw4dWLdk2DQEPkAAACAXvGlO1flZ6ueyZjhQ/KhN5xR9xwYVEQ+AAAA4AXbtG13/vamh5Mk77/49ExuG1HzIhhcRD4AAADgBfub7z6crp17c8ZJbbnioll1z4FBR+QDAAAAXpC7Op/OV+5enST5xOVzM6RVboDjzd86AAAA4Jjt6+7Jh264P0nyO+fNyLmzJtS8CAYnkQ8AAAA4Ztcs68zD67dm/Kih+cAlc+qeA4OWyAcAAAAck/Vdu/IP3380SfLnr5+TE0YPq3kRDF4iHwAAAHBMPvHtB7N9T3deOnN83n7ejLrnwKAm8gEAAABH7d8fezLf+vm6tJTk6svmpqWl1D0JBjWRDwAAADgqu/d15yPfeCBJcsVF7Zk7bVzNiwCRDwAAADgq//OWJ9Lx1PacOHZ43v/a0+ueA0TkAwAAAI7Cqqd35P/90eNJkg+94Yy0jRha8yIgEfkAAACAI1RVVT564wPZva8nF538orzp7Kl1TwIaRD4AAADgiHz/wQ354cMbM7S15OrLz0opbrYB/YXIBwAAADyvHXv25ePffDBJ8gcvPzmnThpb8yLgYCIfAAAA8Lz+nx8+njXP7My08SPzvledVvcc4BAiHwAAAPCcHt+4NZ/9yRNJko9eemZGDmuteRFwKJEPAAAAeFZVVeXDNzyQfT1VXj1nUi4+c3Ldk4DDEPkAAACAZ3Xjz9bmtic2ZfiQlnzsTW62Af2VyAcAAAAc1pZde3P1tx5KkrzvVadmxgmjal4EPBuRDwAAADisT938aJ7atjsnTxydP3jFyXXPAZ6DyAcAAAD8hvvXdOXa2zqTJH912dwMH+JmG9CfiXwAAADAr+npqfKhG+5PT5W88SUnZeFpE+ueBDwPkQ8AAAD4Nf9y16rcu+qZjBk+JB9+45l1zwGOgMgHAAAA/NKmbbvzN999OEnyXy8+PZPbRtS8CDgSIh8AAADwS39708Pp2rk3c6aMze9dNKvuOcAREvkAAACAJMndK57Ol+9anST5xOVzM6RVNoBm4W8rAAAAkH3dPfnLr9+fJHn7edNzXvsJNS+C/5+9+w7TqjDQPvycaXQGEBQRQbALFkRQo5iyiSkm0URTTFPUZNOTTdldd1M2id9mk2yautnNZ8MUNbopljSTmGywoqIiKjZQQBCQERjKMO18f4B+6mqMynCm3Pd1cQFnzozP+4czvL/rfc/hhRD5AAAAgFx4w8NZ8Ghzhg2szz++ft+q5wAvkMgHAAAAfdyja1vyravvTZL8w+v2yYhBDRUvAl4okQ8AAAD6uDN+eXc2tHbkoF2H5R2H7Fr1HOBFEPkAAACgD7v2/sdy1bzlqSm23GyjpqaoehLwIoh8AAAA0Edtbu/IFy7fcrON9x2+Wybv0ljxIuDFEvkAAACgjzrnzwuz8LENGTm4Xz519F5VzwFeApEPAAAA+qAlTRtz1jUPJEk+d8y+Gdq/vuJFwEsh8gEAAEAf9KUr78rm9s4cNnFEjj1oTNVzgJdI5AMAAIA+5nd3r8jv71mZupoiZxw3OUXhZhvQ04l8AAAA0IdsbG3Pv1xxV5Lk/UdNzB47Dql4EbAtiHwAAADQh5x9zQN5ZM2m7DJsQD72qj2qngNsIyIfAAAA9BEPrFyfc2YvTJJ84U37ZWBDXcWLgG1F5AMAAIA+oCzLfOHy+WnrKPOqfXbM0fvtVPUkYBsS+QAAAKAPuOKOZbn+wdXpV1eTf3nTJDfbgF5G5AMAAIBebl1LW8745T1Jko++co+M22FgxYuAbU3kAwAAgF7uW1ffl1XNmzNh5KB84OUTq54DdAGRDwAAAHqxu5atzQ9ueChJ8qU3T0q/utpK9wBdQ+QDAACAXqqzs8znfjE/nWVyzAE756i9RlU9CegiIh8AAAD0UpfesiS3LV6TQQ21+fwx+1U9B+hCIh8AAAD0Qk0bWvNvv1mQJPm71+yV0Y39K14EdCWRDwAAAHqhr/16QdZsbMs+o4fk5JftVvUcoIuJfAAAANDL3Prw4/nJLUuSJF85bnLqaj39h97O/+UAAADQi7R3dOZzv5ifJHnb1LGZttuIihcB24PIBwAAAL3ID254OPcsX5fGAfX5x9fvU/UcYDsR+QAAAKCXWLGuJd/63X1Jkr9/3d7ZYXC/ihcB24vIBwAAAL3EGb+8J+s3t+fAXYflndPGVT0H2I5EPgAAAOgFrnvgsVx5x7LUFMkZx05ObU1R9SRgOxL5AAAAoIfb3N6Rz1++5WYb7z1sfPYf21jxImB7E/kAAACghzt39qIsXLUhIwf3y6eO3rvqOUAFRD4AAADowZY0bcxZ19yfJPnnY/ZJ44D6ihcBVRD5AAAAoAf70pV3p6WtM4dOGJHjDtql6jlARUQ+AAAA6KF+d/eK/P6eFamrKXLGcZNTFG62AX2VyAcAAAA90KbWjvzLFXclSU6bMTF77jSk4kVAlUQ+AAAA6IHO/uP9eWTNpoxp7J+P/80eVc8BKibyAQAAQA/zwMr1+b9/Xpgk+cKbJmVgQ13Fi4CqiXwAAADQg5RlmS9eMT9tHWVeufeovHbSTlVPAroBkQ8AAAB6kCvnLc91D6xOv7qafOnNbrYBbCHyAQAAQA+xrqUtX7nq7iTJR165R8btMLDiRUB3IfIBAABAD/Ht392XVc2bs9sOA/OBoyZWPQfoRkQ+AAAA6AHuWrY2F17/UJLky8dOTv/62moHAd2KyAcAAADdXGdnmc//Yn46y+SY/XfOUXuNqnoS0M2IfAAAANDNXXbrksxdvCaDGmrz+TfuV/UcoBsS+QAAAKAba9rQmq/+ekGS5JOv3iujG/tXvAjojkQ+AAAA6Ma+/psFWbOxLXvvNCQnH7Fb1XOAbkrkAwAAgG5q7uLHc8nNS5IkZ7xlcuprPY0Hnp3vDgAAANANtXd05nM/n58kOWHq2EzbbUTFi4DuTOQDAACAbuiHNz6cu5evS+OA+pz++n2qngN0cyIfAAAAdDMr17Xkm1fflyT57Gv3zg6D+1W8COjuRD4AAADoZs745T1Zv7k9B45tzInTx1U9B+gBRD4AAADoRq5/4LFccceyFEVyxnH7p7amqHoS0AOIfAAAANBNtLZ35nOXb7nZxnsPG5/9xzZWvAjoKUQ+AAAA6CbOmb0wC1dtyMjBDfn00XtXPQfoQUQ+AAAA6AaWNG3MWdfcnyT5pzfsm8YB9RUvAnoSkQ8AAAC6gS9fdXda2jozfcKIvGXKLlXPAXoYkQ8AAAAq9vu7V+R3d69IXU2RM46bnKJwsw3ghRH5AAAAoEKbWjvyL1felSQ5dcaE7LXTkIoXAT2RyAcAAAAV+o8/PpClj2/Kzo398/FX7Vn1HKCHEvkAAACgIg+uWp/v//nBJMkX37RfBvWrq3gR0FOJfAAAAFCBsizzxcvvSltHmVfsPSqvnTS66klADybyAQAAQAWumrc81z7wWBrqavKlN09ysw3gJRH5AAAAYDtrbmnLV666O0ny4VfsnvE7DKp4EdDTiXwAAACwnX37d/dnZfPmjN9hYD748t2rngP0AiIfAAAAbEd3L1uXWdcvSpJ86c2T0r++tuJFQG8g8gEAAMB20tlZ5vOXz09nmbxh/9F5xd47Vj0J6CVEPgAAANhO/vvWpbn14cczsKE2n3/jflXPAXoRkQ8AAAC2g8c3tOarv74nSfLJV++ZnRsHVLwI6E1EPgAAANgOvv7bBXl8Y1v22mlwZh4xoeo5QC8j8gEAAEAXm7v48Vxy85IkyRnH7Z/6Wk/HgW3LdxUAAADoQu0dnfn8L+anLJPjDx6b6RNGVD0J6IVEPgAAAOhCP7rx4dy1bF2G9q/L6W/Yp+o5QC8l8gEAAEAXWfTYhnzz6vuSJJ993T4ZObhfxYuA3krkAwAAgC7w2PrNOen8OWne3J6Dxw3Lu6aPq3oS0IuJfAAAALCNbWxtz6mzbs7ipo0ZN2Jgvv/eQ1JbU1Q9C+jFRD4AAADYhto7OvORH8/NHUvXZvjA+syaOS2jhnibLtC1ujzyFUVxelEUZVEU33nKsX5FUZxVFMVjRVFsKIriiqIoxj7j88YVRXHl1o8/VhTFmUVRNDzjnJcXRXFrURQtRVEsLIrig139eAAAAOC5lGWZz18+P3+8d1X61dXk3JOmZeKowVXPAvqALo18RVFMS/KBJPOe8aHvJHlLkncmOTLJ4CRXFUVRu/XzapP8MsmgrR9/Z5Ljk3zzKV97QpJfJZmdZEqSf01yZlEUx3fhQwIAAIDndNY1D+TiOUtSUyRnnTglU8cPr3oS0EfUddUXLopicJIfJ3l/ks895XhjklOTvLcsy99vPfaeJEuSvDrJb5McnWS/JLuWZbls6zmfTjKrKIp/LstyXZIPJllcluUnt37pe4qiOCTJZ5L8tKseFwAAADyby25Zkm/9bsuddL/05kk5etLoihcBfUlXvpLvP5L88omQ9xRTk9QnufqJA1tD3vwkL9t66PAk858IfFv9Nkm/rZ//xDlX5+l+m+SQoijqn23Q1rcJD33iV5IhL/xhAQAAwNP9z32rcvrP7kySfOgVu+e9h+9W7SCgz+mSyFcUxTuzJcad/iwfHp2ktSzLx59xfMXWjz1xzoqnfnDr+a1/6Zytf69LMvI5pp2eZO1Tfi19vscCAAAAf8n8R9bmwz+6Ne2dZY47aEw+e/TeVU8C+qBtHvmKotg1yXeTvLssy5YX8qlJyqf8vXwR5xTPcfwJX03S+JRfY5/jPAAAAHheS5o2Zuasm7OhtSNH7LFDvn7CgampKZ7/EwG2sa54Jd/UJDsmubUoivaiKNqTvDzJx7f+eUWShqIonnn10R3z/1+Z92j+/yv2kiRbz6//S+ds/RrtSVY/27CyLDeXZbnuiV9Jml/MAwQAAIA1G1tz0gVzsqp5c/YZPST/+Z6paajr0vtbAjynrvju84ck+yc56Cm/bsmWm3A88ee2JK954hOKotg5yeQk1289dEOSyVuPP+HoJJuT3PqUc16Tpzs6yS1lWbZtw8cDAAAAT9PS1pHTLrwlC1dtyJjG/pk1c3qG9n/Wy8MDbBfb/O66ZVk2Z8tNNJ5UFMWGJKvLspy/9e/nJflmURSrkzQl+fckdyZ54iYdVye5O8kPi6L4bJIRW885Z+sr8JLkv5J8tCiKbyU5J1tuxHFqkhO39WMCAACAJ3R0lvnkJbfnlocfz5D+dZl1yvSMbuxf9Sygj9vmke+v9HfZ8rbaS5MMyJZX/51clmVHkpRl2VEUxTFJvpfkuiSbklyU5DNPfIGyLBcVRfGGJN9O8pEky5J8vCzLn27PBwIAAEDfUZZlvnLV3fnNXY+mobYm57zvkOy105CqZwGkKMvnukdF71cUxdAka9euXZuhQ4dWPQcAAIBu7v/++cH8668WJEnOOnFK3nTgmIoXAb3dunXr0tjYmCSNT3mH6//iiqAAAADwV7jijmVPBr5/fsO+Ah/QrYh8AAAA8DxueHB1PnPpHUmSmUfsltNmTKh4EcDTiXwAAADwF9z7aHM+8MNb0trRmddPHp3PHbNfiqKoehbA04h8AAAA8ByWr92Uky+Yk+aW9kzbbXi+/Y6DUlsj8AHdj8gHAAAAz2JdS1tmXnBzlq9tye6jBuWc9x2S/vW1Vc8CeFYiHwAAADxDa3tnPvjDW7Pg0eaMGtIvs2ZOz7CBDVXPAnhOIh8AAAA8RWdnmb//7zty/YOrM6ihNhecPC27jhhY9SyAv0jkAwAAgKf4+m/vzS9uX5a6miL/+Z6pmbxLY9WTAJ6XyAcAAABb/eCGh/Jf//NgkuTfjj8gR+01qtpBAH8lkQ8AAACS/Gb+o/niFXclST79mr1ywtSxFS8C+OuJfAAAAPR5tz7clE9cclvKMjlx+rh89FV7VD0J4AUR+QAAAOjTHly1PqdeeEs2t3fmb/bZMV85dlKKoqh6FsALIvIBAADQZ61sbsnJF8zJmo1tOXBsY85615TU1XqqDPQ8vnMBAADQJ23Y3J5TZ92SJU2bMn6HgTnv5GkZ2FBX9SyAF0XkAwAAoM9p6+jMRy6amzsfWZsRgxpy4czpGTm4X9WzAF40kQ8AAIA+pSzLfO7n8/One1elf31NzjvpkOw2clDVswBeEpEPAACAPuW7f7g/P7llSWqK5OwTD86UccOrngTwkol8AAAA9BmX3rwk3/n9/UmSrxw3Oa/eb6eKFwFsGyIfAAAAfcIf712Z039+Z5LkI6/cPe8+dHzFiwC2HZEPAACAXm/e0jX5yI/npqOzzFsP3iWfOXrvqicBbFMiHwAAAL3a4tUbc8qsm7OxtSNH7jEy//bWA1IURdWzALYpkQ8AAIBeq2lDa06+YE4eW9+afXcemv98z8FpqPNUGOh9fGcDAACgV2pp68hpF96chY9tyC7DBmTWzGkZ0r++6lkAXULkAwAAoNfp6Czz8Ytvy9zFazK0f11mzZyWnYb2r3oWQJcR+QAAAOhVyrLMl668K1ffvSINtTU596Rp2XOnIVXPAuhSIh8AAAC9yvf/vDA/uOHhFEXy7XcclOkTRlQ9CaDLiXwAAAD0Gpff/kj+7dcLkiSfO2a/HHPAzhUvAtg+RD4AAAB6hesfeCyfueyOJMmpR07IqXVXWwAAIABJREFUqUdOqHgRwPYj8gEAANDj3bN8Xf72h7emraPMMQfsnH9+w75VTwLYrkQ+AAAAerRlazZl5gU3p3lze6ZPGJFvvu3A1NQUVc8C2K5EPgAAAHqstZvacvIFc/LoupbssePgnPPeQ9K/vrbqWQDbncgHAABAj7S5vSN/+8Nbct+K9dlxSL9ceMr0NA6sr3oWQCVEPgAAAHqczs4yn7lsXm5c2JTB/eoya+b07DJsQNWzACoj8gEAANDjfO03C3LlHctSV1Pkv94zNfuNGVr1JIBKiXwAAAD0KLOuW5Tv/3lhkuTrJxyQI/ccWfEigOqJfAAAAPQYv5m/PF+66u4kyWdfu3feevDYihcBdA8iHwAAAD3CLQ815ROX3J6yTN596Lh8+BW7Vz0JoNsQ+QAAAOj2Hli5Pqf94JZsbu/Mq/fdKV8+dnKKoqh6FkC3IfIBAADQra1sbsnJF8zJmo1tOWjXYTnrxCmprRH4AJ5K5AMAAKDbWr+5PTMvuDlLH9+U3XYYmPNOOiQDGmqrngXQ7Yh8AAAAdEttHZ358I/n5q5l67LDoIZceMr07DC4X9WzALolkQ8AAIBupyzLnP6zO/Pn+1ZlQH1tzj95WsbvMKjqWQDdlsgHAABAt/Pt39+f/751aWqK5Ox3TcmBuw6rehJAtybyAQAA0K1cPGdxzvzD/UmS//OW/fM3++5U8SKA7k/kAwAAoNu4ZsGKfO4X85MkH3/VHjlx+riKFwH0DCIfAAAA3cIdS9bkIz++LR2dZU6YOjZ/95q9qp4E0GOIfAAAAFTu4dUbcsqsm7OprSMz9hyZr751/xRFUfUsgB5D5AMAAKBSq9dvzskX3JzVG1ozaczQ/Od7pqa+1tNVgBfCd00AAAAqs6m1I6deeEsWPbYhuwwbkAtOnpbB/eqqngXQ44h8AAAAVKKjs8zHLr4tty9Zk8YB9bnwlGnZcWj/qmcB9EgiHwAAANtdWZb54hXz8/t7VqShribnnnRI9thxSNWzAHoskQ8AAIDt7j//58H86MbFKYrku+84KNN2G1H1JIAeTeQDAABgu/rZ3KX5+m/uTZJ84Y375fX771zxIoCeT+QDAABgu7n2/sfy9/89L0nygaMmZuYREypeBNA7iHwAAABsF3cvW5cP/ujWtHeWedOBY/KPr9un6kkAvYbIBwAAQJd7ZM2mzJw1J+s3t+fQCSPy7287IDU1RdWzAHoNkQ8AAIAutXZjW04+f05WrNucvXYanP/7vkPSr6626lkAvYrIBwAAQJfZ3N6R9//wlty/cn12Gtovs2ZOT+OA+qpnAfQ6Ih8AAABdorOzzKcuvSNzFjVlSL+6zJo5PWOGDah6FkCvJPIBAADQJf71V/fkl/OWp762yPffOzX77jy06kkAvZbIBwAAwDZ33rWLcu61i5Ik3zjhwLxsj5EVLwLo3UQ+AAAAtqlf3bk8Z/zy7iTJP7xunxw3ZZeKFwH0fiIfAAAA28ycRU355E9uT1km7zt8fD748olVTwLoE0Q+AAAAtokHVjbn/T+4Ja3tnTl6v53yxTdNSlEUVc8C6BNEPgAAAF6yFetactL5N2ftprYcPG5YzjxxSmprBD6A7UXkAwAA4CVpbmnLzAtuziNrNmXCyEE596Rp6V9fW/UsgD5F5AMAAOBFa+vozId/PDd3L1+XkYMbcuHM6RkxqKHqWQB9jsgHAADAi1KWZf7hp/My+/7HMrChNuefPC3jdhhY9SyAPknkAwAA4EX51u/uy8/mPpLamiL/8a6Dc8DYYVVPAuizRD4AAABesB/f9HDOuuaBJMm/vmVyXrnPjhUvAujbRD4AAABekN/fvSKf/8X8JMkn/mbPvGPauIoXASDyAQAA8Fe7fcmafPTiueksk7cfMjaffPWeVU8CICIfAAAAf6WHHtuQU2bdnJa2zrx8r1H5P2/ZP0VRVD0LgIh8AAAA/BVWr9+cky6Yk6YNrZm8y9B8790Hp77WU0qA7sJ3ZAAAAP6ija3tOeXCW/Lw6o0ZO3xAzj95Wgb1q6t6FgBPIfIBAADwnNo7OvOxi27LHUvWZNjA+lx4yvTsOKR/1bMAeAaRDwAAgGdVlmW+cMVd+cOClelXV5PzTjoku48aXPUsAJ6FyAcAAMCz+t6fHsxFNy1OUSTffeeUTB0/oupJADwHkQ8AAID/5ae3Ls03fntvkuRf3jQpr5s8uuJFAPwlIh8AAABP8+f7VuUffjovSfK3L5+Yk162W7WDAHheIh8AAABPumvZ2nzoR7emvbPMmw8ck3947T5VTwLgryDyAQAAkCRZ+vjGnHzBzdnQ2pHDJ+6Qb7ztgNTUFFXPAuCvIPIBAACQNRtbc/IFN2dV8+bsM3pIvv++qelXV1v1LAD+SiIfAABAH9fS1pEP/ODWPLByfXZu7J8LZk7L0P71Vc8C4AUQ+QAAAPqwzs4yn7r09sx5qClD+tdl1szp2blxQNWzAHiBRD4AAIA+7Ixf3pNf3floGmpr8v33Ts3eo4dUPQmAF0HkAwAA6KPOnb0w51+3KEnyjbcdkJftPrLiRQC8WCIfAABAH3TVvGU545f3JElOf/0+OfagXSpeBMBLIfIBAAD0MTctXJ1P/eSOJMnJL9stHzhqYsWLAHipRD4AAIA+5L4VzXn/D25Ja0dnXjtpp3z+jfulKIqqZwHwEol8AAAAfcSja1ty8vlzsq6lPYeMH57vvnNKamsEPoDeQOQDAADoA5pb2nLyBXOybG1LJo4alHPed0j619dWPQuAbUTkAwAA6OVa2zvzwR/dmgWPNmfk4H65cOb0DB/UUPUsALYhkQ8AAKAXK8sy//DTebnugdUZ2FCbWTOnZdcRA6ueBcA2JvIBAAD0Yt/47b35+W2PpLamyPfefXAm79JY9SQAuoDIBwAA0Ev98MaH870/PZgk+epb988r9t6x4kUAdBWRDwAAoBe6+q5H88XL5ydJ/u7Ve+Xth+xa8SIAupLIBwAA0MvMXfx4Pn7Jbeksk3dO2zUf/5s9qp4EQBcT+QAAAHqRRY9tyGkX3pKWts68cu9ROeO4ySmKoupZAHQxkQ8AAKCXWNW8OSedPydNG1pzwNjGnP2ug1NX62kfQF/guz0AAEAvsLG1PadeeHMWN23MuBEDc95J0zKoX13VswDYTkQ+AACAHq69ozMf+fHczFu6NsMH1mfWzGkZNaRf1bMA2I5EPgAAgB6sLMt8/vL5+eO9q9KvribnnTwtE0cNrnoWANuZyAcAANCDnXXNA7l4zpLUFMlZJ07JweOGVz0JgAqIfAAAAD3UZbcsybd+d1+S5EvHTs7Rk0ZXvAiAqoh8AAAAPdD/3Lcqp//sziTJh16xe9572PiKFwFQJZEPAACgh5n/yNp8+Ee3pr2zzFum7JK/f+3eVU8CoGIiHwAAQA+ypGljZs66ORtaO3LEHjvka8cfkKIoqp4FQMVEPgAAgB5iSdPGnHTBnKxq3px9Rg/Jf75nahrqPK0DIKmregAAAADP79d3Ls/f/3RemlvaM6axf2bNnJ6h/eurngVANyHyAQAAdGMtbR35ylV358c3LU6STBk3LGedOCWjG/tXvAyA7kTkAwAA6KYeWNmcj150WxY82pwk+eDLd8+nj94r9bXeogvA04l8AAAA3UxZlrns1qX54uV3ZVNbR0YObsi33n5QjtprVNXTAOimRD4AAIBuZP3m9nzu53fmF7cvS5IcsccO+fY7DsqOQ7w9F4DnJvIBAAB0E/MfWZuPXjQ3D63emNqaIp96zV754Mt3T21NUfU0ALo5kQ8AAKBiZVlm1vUP5au/WpDWjs6MaeyfM0+ckkN2G1H1NAB6CJEPAACgQo9vaM1n/3tefn/PiiTJ0fvtlK+fcECGDWyoeBkAPYnIBwAAUJE5i5ryiUtuy/K1LWmorck/H7Nv3nf4+BSFt+cC8MKIfAAAANtZR2eZ7/3xgXz79/els0wmjByUs06cksm7NFY9DYAeSuQDAADYjlaua8knf3J7rn9wdZLkrVN2yZePm5zB/Tw9A+DF81MEAABgO/nTvSvz6UvvyOoNrRlQX5uvHDc5J0wdW/UsAHoBkQ8AAKCLtXV05t+vvjff/5+FSZJ9Rg/Jf7z74Ow+anDFywDoLUQ+AACALrSkaWM+dvFtuX3JmiTJ+w4fn396w77pX19b8TIAehORDwAAoIv86s7l+YefzktzS3uG9q/L1084IK+bvHPVswDohUQ+AACAbaylrSNfueru/PimxUmSg8cNy3ffOSW7jhhY8TIAeiuRDwAAYBt6YGVzPnrRbVnwaHOS5EOv2D2fes1eqa+tqXgZAL2ZyAcAALANlGWZy25Zmi9ecVc2tXVk5OCGfOvtB+WovUZVPQ2APkDkAwAAeInWb27PP//8zlx++7IkyZF7jMy33nFgdhzSv+JlAPQVIh8AAMBLcOfStfnYxXPz0OqNqa0p8qnX7JUPvXz31NQUVU8DoA8R+QAAAF6EsixzwXUP5au/vidtHWXGNPbPmSdOySG7jah6GgB9kMgHAADwAj2+oTWf/e878vt7ViZJjt5vp3z9hAMybGBDxcsA6KtEPgAAgBdgzqKmfOKS27J8bUsaamvyz8fsm/cdPj5F4e25AFRH5AMAAPgrdHSW+Y8/PpDv/P6+dJbJxJGDcta7pmTSmMaqpwGAyAcAAPB8VqxryScvuT03LFydJHnrlF3yleMmZ1A/T6kA6B78RAIAAPgL/nTvynz60juyekNrBjbU5ivHTs7xU8dWPQsAnqZmW3/BoihOL4ri5qIomouiWFkUxS+Kotj7Gef0K4rirKIoHiuKYkNRFFcURTH2GeeMK4riyq0ff6woijOLomh4xjkvL4ri1qIoWoqiWFgUxQe39eMBAAD6ptb2znz1V/fk5AtuzuoNrdl356G58mNHCnwAdEvbPPIleXmS/0hyWJLXZMurBa8uimLQU875TpK3JHlnkiOTDE5yVVEUtUmy9fdfJhm09ePvTHJ8km8+8QWKopiQ5FdJZieZkuRfk5xZFMXxXfCYAACAPmRJ08a87fs35Pt/Xpgked/h4/PzD78su48aXPEyAHh2RVmWXfsfKIpRSVYmeXlZln8uiqIxyaok7y3L8idbzxmTZEmSN5Rl+duiKF6f5Koku5ZluWzrOe9MMivJjmVZriuK4mtJ3lyW5b5P+W/9V5IDy7I8/Dm29EvS7ymHhiRZunbt2gwdOnTbPnAAAKBH+uW85fnHn85L8+b2DO1fl6+fcGBeN3l01bMA6KPWrVuXxsbGJGksy3Ldc53XFa/ke6YnbjXVtPX3qUnqk1z9xAlbQ978JC/beujwJPOfCHxb/TZbAt3Up5xzdZ7ut0kOKYqi/jm2nJ5k7VN+LX2hDwYAAOidWto68k8/vzMfuWhumje35+Bxw/KrT8wQ+ADoEbr0xhtFURRJvpXk2rIs5289PDpJa1mWjz/j9BVbP/bEOSue+sGyLB8viqL1L52z9e91SUYmWf4sk766dc8ThkToAwCAPu/+Fc356EW35d4VzSmK5EMv3z1/95q9Ul+7PV4XAQAvXVffXffsJAdky3X1nk+R5KnvHX629xE/3znFX/jclGW5OcnmJ08uimc7DQAA6CPKssxltyzNF66Yn5a2zowc3JBvv+OgzNhzVNXTAOAF6bLIVxTFWUnenOSosiyf+mq5R5M0FEUx/Bmv5tsxyfVPOefQZ3y94dnyNt8VTznnma+b3zFJe5LV2+RBAAAAvVZzS1s+94v5ufz2LVcJmrHnyHzz7QdmxyH9K14GAC/cNn/tebHF2UnemuRVZVkuesYptyZpy5Y77z7xOTsnmZz/H/luSDJ56/EnHJ0tr8K79SnnvCZPd3SSW8qybNsWjwUAAOid7ly6Nm8869pcfvuy1NYU+fvX7Z0LZ04X+ADosbrilXz/keRdSY5N0lwUxROvtltbluWmsizXFkVxXpJvFkWxOltuyPHvSe5M8vut516d5O4kPyyK4rNJRmw955yn3EXkv5J8tCiKbyU5J1tuxHFqkhO74DEBAAC9QFmWOf+6h/Jvv74nbR1ldhk2IGeeeFCmjh9R9TQAeEm6IvJ9aOvvf3rG8ZlJZm39899ly9tqL00yIMkfkpxclmVHkpRl2VEUxTFJvpfkuiSbklyU5DNPfLGyLBcVRfGGJN9O8pEky5J8vCzLn277hwQAAPR0TRta89nL7sgfFqxMkrx20k75+vEHpnFgfcXLAOClK8ryWe9R0ScURTE0ydq1a9dm6NChVc8BAAC6yE0LV+cTl9yeR9e1pKGuJp8/Zt+857DxbsYHQLe3bt26NDY2JknjU97h+r909d11AQAAKtPRWebsax7Id/9wXzrLZOLIQTnrXVMyaUxj1dMAYJsS+QAAgF5pxbqWfOKS23LjwqYkyfEHj82Xj52UQf08DQKg9/HTDQAA6HX+eO/KfPrSO9K0oTUDG2pzxnGT89aDx1Y9CwC6jMgHAAD0Gq3tnfnGbxfknNmLkiT77Tw0Z79rSiaOGlzxMgDoWiIfAADQKyxevTEfu+S23LFkTZLkpMPH5/Q37Jv+9bUVLwOArifyAQAAPd5V85bl9J/emebN7WkcUJ+vn3BAXjtpdNWzAGC7EfkAAIAea1NrR7581d25eM7iJMnU8cNz5olTssuwARUvA4DtS+QDAAB6pPtXNOcjF83NfSvWpyiSD79i9/zdq/dKXW1N1dMAYLsT+QAAgB6lLMtcesuSfPGKu9LS1pmRg/vlO+84KEfuObLqaQBQGZEPAADoMZpb2vJPP5+fK+9YliSZsefIfOvtB2XUkH4VLwOAaol8AABAjzBv6Zp87OLb8vDqjamtKfKZo/fO3x41MTU1RdXTAKByIh8AANCtlWWZ865dlK/9ZkHaOsrsMmxAzjxxSqaOH171NADoNkQ+AACg22ra0JrPXnZH/rBgZZLkdZNG52vHH5DGgfUVLwOA7kXkAwAAuqWbFq7OJy65PY+ua0lDXU0+f8y+ec9h41MU3p4LAM8k8gEAAN1KR2eZs695IN/9w33pLJOJowbl7BMPzn5jhlY9DQC6LZEPAADoNlasa8knLrktNy5sSpKcMHVsvvTmSRnUz1MXAPhL/KQEAAC6hT8uWJlPX3ZHmja0ZmBDbf7PWybnLVPGVj0LAHoEkQ8AAKhUa3tnvvHbBTln9qIkyX47D83Z75qSiaMGV7wMAHoOkQ8AAKjM4tUb87GL5+aOpWuTJCe/bLec/oZ90q+utuJlANCziHwAAEAlrrxjWf7pZ3emeXN7GgfU5xsnHJCjJ42uehYA9EgiHwAAsF1tau3Il6+6KxfPWZIkOWT88Hz3xCnZZdiAipcBQM8l8gEAANvNfSua89GL5ua+FetTFMlHXrFHPvnqPVNXW1P1NADo0UQ+AACgy5VlmZ/cvCT/cuVdaWnrzMjB/fKddxyUI/ccWfU0AOgVRD4AAKBLNbe05fSf3Zmr5i1PkszYc2S+9faDMmpIv4qXAUDvIfIBAABdZt7SNfnoRbdlcdPG1NYU+czRe+dvj5qYmpqi6mkA0KuIfAAAwDZXlmXOu3ZRvvabBWnrKLPLsAE588QpmTp+eNXTAKBXEvkAAIBtqmlDaz5z2R25ZsHKJMnrJo3O144/II0D6yteBgC9l8gHAABsMzcuXJ1PXHJbVqzbnIa6mnz+jfvlPYeOS1F4ey4AdCWRDwAAeMk6Osucdc39OfMP96ezTCaOGpSzTzw4+40ZWvU0AOgTRD4AAOAleXRtSz5xyW25aVFTkuSEqWPz5WMnZWCDpxsAsL34qQsAALxo1yxYkc9cNi9NG1ozqKE2Z7xlct4yZWzVswCgzxH5AACAF6y1vTNf/82CnHvtoiTJpDFDc9aJUzJx1OCKlwFA3yTyAQAAL8jDqzfkYxfflnlL1yZJTn7Zbjn9DfukX11txcsAoO8S+QAAgL/alXcsy+k/uzPrN7encUB9vnHCATl60uiqZwFAnyfyAQAAz2tTa0e+fNVduXjOkiTJIeOH58wTp2TMsAEVLwMAEpEPAAB4HvetaM5HL5qb+1asT1EkH33lHvnE3+yZutqaqqcBAFuJfAAAwLMqyzKX3LwkX7ryrrS0dWbUkH75zjsOyhF7jKx6GgDwDCIfAADwv6xracs//ezOXDVveZLkqL1G5ZtvOzCjhvSreBkA8GxEPgAA4GnuWLImH7v4tixu2pi6miKfee3e+cCMiampKaqeBgA8B5EPAABIknR2ljnv2kX52m8WpL2zzNjhA3LmiVNy8LjhVU8DAJ6HyAcAAGT1+s35zGV35I/3rkqSvH7y6Pzb8QekcUB9xcsAgL+GyAcAAH3cDQ+uzid/cltWrNuchrqafOGN++Xdh45LUXh7LgD0FCIfAAD0US1tHfnenx7M2dfcn84y2X3UoJz9roOz785Dq54GALxAIh8AAPQxq5o354c3PJQf3bQ4TRtakyRvmzo2Xzp2UgY2eIoAAD2Rn+AAANBH3LeiOefOXphf3LYsrR2dSZKxwwfks6/dO8cetEvF6wCAl0LkAwCAXqwsy1z7wGM5Z/ai/Pm+VU8enzJuWN4/Y2KO3m+n1NXWVLgQANgWRD4AAOiFNrd35Irbl+W8axdlwaPNSZKaInntpNE5bcbETB0/vOKFAMC2JPIBAEAv8viG1vz4podz4Q0PZ1Xz5iTJwIbavP2QXXPKERMyboeBFS8EALqCyAcAAL3AwlXrc/51i/Lfty5NS9uW6+2NHto/Jx+xW06cPi6NA+orXggAdCWRDwAAeqiyLHPToqacO3tR/rBgRcpyy/FJY4bm/TMm5g3775yGOtfbA4C+QOQDAIAepq2jM7+6c3nOnb0odz6y9snjr953x5x65MQcNnFEiqKocCEAsL2JfAAA0EOs3dSWS+YszqzrH8rytS1Jkn51NTl+6ticeuSE7D5qcMULAYCqiHwAANDNLWnamPOvW5RLb16SDa0dSZKRgxvyvsN3y3sOG58RgxoqXggAVE3kAwCAbmru4sdz3uxF+fX85encer29vXYanNOOnJg3HzQm/etrqx0IAHQbIh8AAHQjHZ1lrr7r0Zwze2HmLl7z5PEZe47MaTMm5qg9R7reHgDwv4h8AADQDWzY3J5Lb1mS869blCVNm5IkDbU1OfagMTl1xoTsM3poxQsBgO5M5AMAgAotX7sps65/KBfdtDjNLe1JkmED6/Pew8bnvYePz45D+le8EADoCUQ+AACowPxH1ubc2Qtz1bzlad96wb0JIwfllCMn5ISDx2ZAg+vtAQB/PZEPAAC2k87OMn+8d2XOmb0wNy5sevL4oRNG5P0zJuZV++yYmhrX2wMAXjiRDwAAutim1o787LalOe/aRVm4akOSpLamyBsP2DmnHTkx+49trHghANDTiXwAANBFVja35Ec3PJwf3vhwHt/YliQZ0r8u75o+Lie9bLeMGTag4oUAQG8h8gEAwDZ276PNOe/ahfnFbcvS2tGZJBk7fEBOOWJC3j5t1wzu55/hAMC25V8XAACwDZRlmdn3P5Zzr12UP9+36snjU8YNy/tnTMzR++2UutqaChcCAL2ZyAcAAC/B5vaOXH77spw3e1HuXdGcJKkpktdOGp3TZkzM1PHDK14IAPQFIh8AALwIj29ozY9ufDgX3vBwHlu/OUkysKE2bz9k15xyxISM22FgxQsBgL5E5AMAgBdg4ar1Oe/aRfnp3KVpadtyvb3RQ/vn5CN2y4nTx6VxQH3FCwGAvkjkAwCA51GWZW5a1JRzZy/MHxasTFluOT5pzNC8f8bEHHPAzql3vT0AoEIiHwAAPIe2js786s7lOXf2otz5yNonj7963x1z6pETc9jEESmKosKFAABbiHwAAPAMaze15ZI5izPr+oeyfG1LkqRfXU2Onzo2px45IbuPGlzxQgCApxP5AABgqyVNG3P+dYty6c1LsqG1I0kycnC/nHT4+Lz7sPEZMaih4oUAAM9O5AMAoM+bu/jxnDt7YX4z/9F0br3e3t47DcmpMybkzQeOSf/62moHAgA8D5EPAIA+qaOzzNV3PZpzZi/M3MVrnjw+Y8+ROW3GxBy150jX2wMAegyRDwCAPmX95vZcdsuSnH/doixp2pQkaaitybEHjcmpMyZkn9FDK14IAPDCiXwAAPQJy9duyqzrH8pFNy1Oc0t7kmT4wPq857Dxee/h47PjkP4VLwQAePFEPgAAerX5j6zNubMX5qp5y9O+9YJ7E0cOyilHTsjxB4/NgAbX2wMAej6RDwCAXqezs8w1C1bm3GsX5saFTU8eP3TCiLx/xsS8ap8dU1PjensAQO8h8gEA0Gtsau3IT+cuzfnXLsrCxzYkSepqihxzwM457ciJ2X9sY8ULAQC6hsgHAECPt7K5JT+84eH86MaH8/jGtiTJkP51edf0cTnpZbtlzLABFS8EAOhaIh8AAD3WvY8259zZC3P57cvS2tGZJBk7fEBOOWJC3j5t1wzu55+7AEDf4F89AAD0KGVZZvb9j+Wc2Qsz+/7Hnjw+ZdywvH/GxBy9306pq62pcCEAwPYn8gEA0CNsbu/I5bcvy3mzF+XeFc1Jkpoied3k0Tn1yImZOn54xQsBAKoj8gEA0K01bWjNj298OBfe8HAeW785STKwoTbvmLZrZr5sQsbtMLDihQAA1RP5AADolh5ctT7nX7soP527NC1tW663N3po/8w8Yre8c/q4NA6or3ghAED3IfIBANBtlGWZmxY15dzZC/P7e1Y+eXzSmKF5/4yJOeaAnVPvensAAP+LyAcAQOXaOjrzqzuX55zZCzP/kXVPHn/1vjvm1CMn5rCJI1IURYULAQC6N5EPAIDKrN3UlovnLM6s6x7Ko+takiT96mpywtSxOeXICdl91OCKFwIA9AwiHwAA292Spo05/7pFufTmJdnQ2pEkGTm4X06cAcpNAAAUFUlEQVQ6fHzefdj4jBjUUPFCAICeReQDAGC7ufXhx3PetQvzm/mPprPccmzvnYbk1BkT8uYDx6R/fW21AwEAeiiRDwCALtXRWea3dz2ac2cvzNzFa548PmPPkXn/jImZsedI19sDAHiJRD4AALrE+s3tufTmJbng+kVZ0rQpSdJQW5NjDxqT02ZMzN6jh1S8EACg9xD5AADYppav3ZRZ1z2Ui+YsTnNLe5Jk+MD6vOew8Xnv4eOz45D+FS8EAOh9RD4AAF6yFetacuPC1fnDPSvzqzuXp33rBfcmjhyUU46ckOMPHpsBDa63BwDQVUQ+AABesEfWbMpNC1fnpoVNuWnR6jy0euPTPn7YxBE57ciJedU+O6amxvX2AAC6msgHAMBfVJZlljRtyo2LtkS9GxeuziNrNj3tnKJI9tt5aA6dsEPeMmWX7D+2saK1AAB9k8gHAMDTlGWZhY9tePJVenMWNWX52pannVNbU2TymKE5dOIOOXTCiByy24g0DqivaDEAACIfAEAfV5Zl7l+5PjctXJ0bFzVlzqKmrGre/LRz6muLHDB2WA6dMCLTt0a9wf38UxIAoLvwLzMAgD6ms7PMgkebc9PWt9/OeagpTRtan3ZOQ21NDho3LIdNGJFDJ+6Qg8cNd+MMAIBuTOQDAOjlOjrL3L1sXW5atDo3LmzKzQ81Ze2mtqed07++JgePG55DJ+yQQyeOyEG7Dkv/elEPAKCnEPkAAHqZto7OzH9kbW5a1JSbFq7OLQ89nubN7U87Z2BDbaaOH57Dtl5T74Cxw9JQV1PRYgAAXiqRDwCgh2tt78y8pWty06Itd7699eHHs7G142nnDOlXl0N2G/7kjTIm79KY+lpRDwCgtxD5AAB6mJa2jty+ZM2Td7+du/jxtLR1Pu2cxgH1mbbbiBw2cUQOnbBD9hszNLU1RUWLAQDoaiIfAEA3t6m1I3MXP/7k3W9vX7Imre1Pj3ojBjVk+m4jcujWqLfP6CGpEfUAAPoMkQ8AoJtZv7k9tz68JerdtKgp85auSVtH+f/au/cYua77PuDfM/veJbkkRcl86GEpddy4TWxHthWkiZ2mcIEEQVq3f7goWqBFUadWU8BNYAdygiRN2sRBAUd5OEhQoKjjtmkSBDCQtgHcFk3iR0RH8iO147ZGRUsWaVEWKS4pcp+zp3/M7HBmucuHxd3hnf18gMXMPffcO78hebk73z3nnoE+R/ZN5ZGHDvdWv33NPftSilAPAGCvEvIBAAzZxaXVPPmV8zn59Pk8cep8vnB6Ie31wVDv6IHp3ii9Rx46nIeOzAn1AADoEfIBAOyyC1dW8ulT5zur3546lz8/czGbMr2cODjTHanXCfXuPzwr1AMAYFtCPgCAHXbu5eVeqPfE0+fyf85eSt0U6r36rtneKL23PHg49x6aHU6xAAA0kpAPAOA2e+HiUp44dT6fPnUuJ58+ny+/8PI1fb7p7rk88tBdeeTBzhTco/PTQ6gUAIBRIeQDAHiFzlxYzMluoHfy1PmcevHyNX1e+6r9vXvqveXBw7l7/9QQKgUAYFQJ+QAAbkGtNc+9tJgnuivfnjx1Ll89vzjQp5TkW44eGAj1Ds9NDqliAAD2AiEfAMB11FrzlXNXcnIj1Hv6XM4sLA30aZXkL5+Y7029ffOrD2d+dmJIFQMAsBcJ+QAA+tRa8/++/nKe6E69Pfn0ubxwaXmgz3ir5Fvvne8tlPGmBw5l/7RQDwCA4RHyAQB72vp6zf994VL3fnrn8ulT5/PiyysDfSbHWnn9fVdDvYcfOJTZST9GAQBw5/DTKQCwp7TXa770tYu9UXp/+pXzeenK6kCfqfFW3nj/wV6o9+33H8r0xNiQKgYAgBsT8gEAI22tvZ4vnrnYW/320185n0tLawN9ZibG8vADhzr31Hvorrz+vvlMjQv1AABoDiEfADBSVtvr+bPnFnqh3lPPvJSXlwdDvX1T43nTqw/1Vr79tnvnMzHWGlLFAADwygn5AIBGW15r5/NfXeitfvvUMy9lcbU90OfA9Hje0l359pGHDud1xw5kXKgHAMAIEfIBAI2ytNrOZ559qbdQxmefvZDltfWBPodmJwZCvb949EDGWmVIFQMAwM4T8gEAd7QrK2t56pmrod7nv7qQlfZgqHdk32Qv0Hvkwbvymnv2pSXUAwBgDxHyAQBDd2lpNWcuLOXMwmLOXNj4WsrTL17OF08vZG29DvR/1YGpgVDvm+6eSylCPQAA9i4hHwCwo1bb6zl7cakT4l1Y7AvyOtunLyxes9rtZicOznRXvu2Eeg/cNSvUAwCAPkI+AOAbVmvNwuJqTveFdp0g7+rzsxeXsmkg3pbmZyZy/OBMThyczvGDM93nM3nDfQdz3+HZnX8zAADQYEI+AGBby2vtPL+wdN0Q78pK+4bnmRxr5ej8dI53A7wT3RBvI9Q7Nj+TuSk/lgAAwDfKT9MAsEfVWnPu8kovrDvdH+J1g7yvX1q+qXMd2TeZY/MzW4Z4xw9O58jclIUwAABgBwn5AGBELa60Bxay2CrEW1lbv+F5pidaveDu2PzgVNrj3bbpibFdeEcAAMB2hHwA0EDt9ZqvX1rO6QuL+VrfQhan+0K8l66s3vA8pST37J8aCO42gryNEO/Q7IRFLgAA4A4n5AOAO9ClpdUtV6PdCPGeX1jK2k2sZjE3OZYTh2a2DfFedWA6k+OtXXhHAADAThLyAcAuW22v5+zFpV6Id3U03tXtS0trNzzPWKvk6IGri1lsXsji+MGZHJgeNwoPAAD2ACEfANxGtdYsLK5edzXasxeXchOD8HJwdiLH5juh3eYQ7/jBmdy9byrjY0bhAQAAQj4AuCXLa+08v7B0TYjXGY3X2b6y0r7heSbHWjl2cPqa+9/1j8Sbm/JtGgAAuDk+PQBAV6015y6vXHc12q9fWr6pcx3ZN9kJ7eZncuzg9ECId/zgdI7MTaXVMo0WAAC4PYR8AOwJtdYsrrZ7o+22C/FW1tZveK7pidbV0XfdEK9/NN6x+elMT4ztwrsCAADoEPIBcMeotWZ5bT2LK+1cWW1ncWUtV1baubLS7rSttHNlZS2Lq+1cXu7bv7qxf22gb6ffWu987Zu4EV4pyT37pwZWoz0+P51jfSHeodkJi1kAAAB3FCEfALekP4i7vLLWF761s7h6bSi3uLKWy33PN8K3K5vbbiGIeyXmJsdy4tDMYIh3cDrHu6vRvurAdCbHLWYBAAA0i5APYARtBHG9kW/dQK0/lOuNfFttDwZ13VBuYGTc6uAxO5zDJeksTDEzOZbZybHe4+zkePdxLDMTfc97j+OZnej2nRrv9rt67NzUWPZNjRuFBwAAjJzGh3yllEeTvDfJsSRfTPKeWuvHh1sVwI3VWrO0un5NkLYRyl1e2Xq66ubRcteMputOZ92VIG681QnQJjaCtvFe4DbX93xmciyz3VDuamDXCeXmJq8ee7XvWMbHjKYDAAC4WY0O+Uop70zyeJJHk3wyyQ8l+YNSyutqrc8OtThgV9Va016vWa/Jeq1Z799er2l329bX09tXa/rau33Wtzi+97zv+FrTbtfefeMuL2+EdIP3hOsP5frvKbcR6tVdCOKmNoK4/tCtb3TbzORYN2jbYmTcptFyc31B3IwgDgAA4I5R6m58wtwhpZSTST5Ta313X9uXkny01vrYTRx/IMnCwsJCDhw4sIOV7q7TFxazvmkIz3Z/zTVb79i+/zbtWxywfd9tdtyGWm7b+7yF89zqJXS9c/eCpJotQ6X19av76kZbN8QaCKL6+1wTfGUw1Oo/vnaDr/V6nddIr70XlPVed1Ptm4K2zmtcDeS2Cto2v7/+11tfHwzdNo7d6Xu47YbpiVYncJsY2xS0jfdGtl135NsWodzsVOd8Yy1TUwEAAJrq4sWLmZ+fT5L5WuvF7fo1diRfKWUyycNJPrBp18eSfOc2x0wlmepr2r8z1Q3XOz70ybxwaXnYZcAdp5RkrJS0WiWtjef9262SUkrGSuk+77S1Smd/q9deMtZKxlqtvvBtcGTcxvTVuYH7wo1vmqo61gv2BHEAAAC8Eo0N+ZIcSTKW5Oym9rNJjm5zzGNJfmoni7oTbIQIm20XIWx3A/ptI4dtdmzVvO25b+Ec1z3PLZx7u7Pfei1b9b095261OiHSWNkcMJW0Wum2b9OnL7i6GkJdDa1aJd0+3e3W1dCq9xr9Qdem19t8/MZrb/sa3fO3+o/v2978/q73Glud75o/n43tbY5vle3/HQEAAEDTNTnk27B5nl7Zom3Dzyf5YN/2/iTP7URRw/RH7/2rwy4BAAAAgF3U5JDvxSTtXDtq755cO7ovSVJrXU7Sm8dqVA8AAAAAo6CxyyLWWleSPJXk7Zt2vT3Jp3a/IgAAAAAYjiaP5Es6U28/Ukp5MsmfJHlXkvuT/PpQqwIAAACAXdTokK/W+tullLuS/GSSY0m+kOT7a63PDLcyAAAAANg9jQ75kqTW+mtJfm3YdQAAAADAsDT2nnwAAAAAQIeQDwAAAAAaTsgHAAAAAA0n5AMAAACAhhPyAQAAAEDDCfkAAAAAoOGEfAAAAADQcEI+AAAAAGg4IR8AAAAANJyQDwAAAAAaTsgHAAAAAA0n5AMAAACAhhPyAQAAAEDDCfkAAAAAoOGEfAAAAADQcEI+AAAAAGg4IR8AAAAANJyQDwAAAAAaTsgHAAAAAA0n5AMAAACAhhPyAQAAAEDDCfkAAAAAoOGEfAAAAADQcEI+AAAAAGg4IR8AAAAANJyQDwAAAAAaTsgHAAAAAA0n5AMAAACAhhsfdgF3gosXLw67BAAAAAC4xs3mVqXWusOl3LlKKSeSPDfsOgAAAADgBu6ttZ7ebudeD/lKkuNJLg27lttsfzrh5b0ZvfcGdwrXGewO1xrsPNcZ7DzXGey8Ub/O9ic5U68T5O3p6brdP5htE9Cm6mSXSZJLtVZzkWEHuM5gd7jWYOe5zmDnuc5g5+2B6+yG78nCGwAAAADQcEI+AAAAAGg4Id9oWk7yL7qPwM5wncHucK3BznOdwc5zncHO2/PX2Z5eeAMAAAAARoGRfAAAAADQcEI+AAAAAGg4IR8AAAAANJyQDwAAAAAaTsgHAAAAAA0n5BtBpZRHSymnSilLpZSnSinfPeyaYFSUUh4rpfxpKeVSKeWFUspHSymvHXZdMMq6110tpTw+7FpglJRSTpRS/n0p5Vwp5Uop5XOllIeHXReMilLKeCnlX3Y/my2WUp4upfxkKcXncHgFSilvLaX8finlTPdnxL+5aX8ppfx0d/9iKeUPSyl/aVj17ib/uYyYUso7kzye5F8leWOSjyf5g1LK/UMtDEbH25J8KMl3JHl7kvEkHyulzA21KhhRpZQ3J3lXkj8bdi0wSkoph5J8Mslqku9L8rokP5rkwjDrghHzY0n+SZIfTvItSd6X5L1J/tkwi4IRMJfk8+lcW1t5X5If6e5/c5Lnk/y3Usr+3SlveEqtddg1cBuVUk4m+Uyt9d19bV9K8tFa62PDqwxGUynl7iQvJHlbrfWPh10PjJJSyr4kn0nyaJKfSPK5Wut7hlsVjIZSygeS/JVaqxkfsENKKf85ydla6z/qa/u9JFdqrX9/eJXB6Cil1CTvqLV+tLtdkpxJ8nit9Re6bVNJzib5sVrrbwyt2F1gJN8IKaVMJnk4ycc27fpYku/c/YpgT5jvPp4fahUwmj6U5L/UWv/7sAuBEfSDSZ4spfxu9/YTny2l/ONhFwUj5hNJ/lop5ZuTpJTy+iTfleS/DrUqGG0PJjmavlyk1rqc5I+yB3KR8WEXwG11JMlYOgl1v7Pp/CMHbqPub4k+mOQTtdYvDLseGCWllL+Tzi+u3jTsWmBEPZTk3el8H/u5JG9J8sullOVa628OtTIYHb+Qzi+E/3cppZ3OZ7Ufr7X+1nDLgpG2kX1slYs8sMu17Doh32jaPAe7bNEGvHK/muTb0vmNLHCblFLuS/JLSf56rXVp2PXAiGolebLW+v7u9me7NyV/dxIhH9we70zy95L83SRfTPKGJI+XUs7UWj881Mpg9O3JXETIN1peTNLOtaP27sm1KTbwCpRSfiWdqU5vrbU+N+x6YMQ8nM73rqc6A2aTdEY/vLWU8sNJpmqt7WEVByPia0n+fFPbl5L87SHUAqPqXyf5QK31P3W3/1cp5YEkjyUR8sHOeL77eDSd73Ub9kQu4p58I6TWupLkqXRW/Oz39iSf2v2KYPR0l2P/1SR/K8n31lpPDbsmGEH/I8m3pjPiYePryST/IckbBHxwW3wyyWs3tX1zkmeGUAuMqtkk65va2vE5HHbSqXSCvl4u0l2/4G3ZA7mIkXyj54NJPlJKeTLJnyR5V5L7k/z6UKuC0fGhdKZc/I0kl0opGyNnF2qti8MrC0ZHrfVSkoH7XJZSLic55/6XcNv8YpJPlVLen+R30rkn37u6X8Dt8ftJfryU8mw603XfmORHkvzboVYFDVdK2ZfkL/Q1PVhKeUOS87XWZ0spjyd5fynly0m+nOT9Sa4k+Y+7X+3uKrWO/JTkPaeU8miS9yU5ls6HpH9ea/3j4VYFo6G7RPtW/mGt9d/tZi2wl5RS/jDJ52qt7xl2LTAqSik/kOTnk7wmnZEPH6y1/pvhVgWjo5SyP8nPJnlHOlMFzyT5rSQ/052FBXwDSinfk+R/brHrw7XWf9BdIPGnkvxQkkNJTib5p3vhl8VCPgAAAABoOPcCAAAAAICGE/IBAAAAQMMJ+QAAAACg4YR8AAAAANBwQj4AAAAAaDghHwAAAAA0nJAPAAAAABpOyAcAAAAADSfkAwAAAICGE/IBAAAAQMMJ+QAAAACg4f4/E73+zD5c6ZUAAAAASUVORK5CYII=\n",
|
|
"text/plain": [
|
|
"<Figure size 1200x800 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Creates blank canvas\n",
|
|
"fig = plt.figure(figsize=(12,8),dpi=100)\n",
|
|
"\n",
|
|
"axes1 = fig.add_axes([0, 0, 1, 1])\n",
|
|
"\n",
|
|
"axes1.plot(a,b)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Exporting a Figure"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 95,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig = plt.figure()\n",
|
|
"\n",
|
|
"axes1 = fig.add_axes([0, 0, 1, 1])\n",
|
|
"\n",
|
|
"axes1.plot(a,b)\n",
|
|
"axes1.set_xlabel('X')\n",
|
|
"\n",
|
|
"# bbox_inches ='tight' automatically makes sure the bounding box is correct\n",
|
|
"fig.savefig('figure.png',bbox_inches='tight')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"---"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 112,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 864x576 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Creates blank canvas\n",
|
|
"fig = plt.figure(figsize=(12,8))\n",
|
|
"\n",
|
|
"axes1 = fig.add_axes([0, 0, 1, 1]) # Full figure\n",
|
|
"axes2 = fig.add_axes([1, 1, 0.25, 0.25]) # Starts at top right corner!\n",
|
|
"\n",
|
|
"# Larger Figure Axes 1\n",
|
|
"axes1.plot(x,y)\n",
|
|
"\n",
|
|
"# Insert Figure Axes 2\n",
|
|
"axes2.plot(x,y)\n",
|
|
"\n",
|
|
"fig.savefig('test.png',bbox_inches='tight')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"----\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": "3.7.4"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 1
|
|
}
|