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610 lines
184 KiB
610 lines
184 KiB
2 years ago
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{
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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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|
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"text/plain": [
|
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|
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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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|
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"text/plain": [
|
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|
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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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|
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"execution_count": 55,
|
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|
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"metadata": {},
|
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|
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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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|
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{
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"cell_type": "markdown",
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|
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"metadata": {},
|
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"source": [
|
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|
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"## Creating a Figure"
|
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|
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]
|
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},
|
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|
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{
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|
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"cell_type": "markdown",
|
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|
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"metadata": {},
|
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"source": [
|
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|
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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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{
|
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|
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"cell_type": "code",
|
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|
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"execution_count": 73,
|
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|
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"metadata": {},
|
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|
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"outputs": [
|
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|
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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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|
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"metadata": {},
|
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|
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"output_type": "display_data"
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|
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}
|
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],
|
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|
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"source": [
|
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|
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"# Creates blank canvas\n",
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|
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"fig = plt.figure()"
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]
|
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},
|
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|
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{
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|
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"cell_type": "markdown",
|
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|
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"metadata": {},
|
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|
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"source": [
|
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|
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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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|
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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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},
|
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|
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{
|
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|
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"cell_type": "code",
|
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|
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"execution_count": 56,
|
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|
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"metadata": {},
|
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|
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"outputs": [
|
||
|
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{
|
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|
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"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
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"<Figure size 432x288 with 1 Axes>"
|
||
|
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]
|
||
|
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},
|
||
|
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"metadata": {
|
||
|
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"needs_background": "light"
|
||
|
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},
|
||
|
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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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"# Create Figure (empty canvas)\n",
|
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|
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"fig = plt.figure()\n",
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"\n",
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|
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"# Add set of axes to figure\n",
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|
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"axes = fig.add_axes([0, 0, 1, 1]) # left, bottom, width, height (range 0 to 1)\n",
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"\n",
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|
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"# Plot on that set of axes\n",
|
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|
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"axes.plot(x, y)\n",
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"\n",
|
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|
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"plt.show()"
|
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|
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]
|
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|
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},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 57,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"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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
|
||
|
|
"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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
|
||
|
|
"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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
|
||
|
|
"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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
|
||
|
|
"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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
|
||
|
|
"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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
|
||
|
|
"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
|
||
|
|
}
|