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667 lines
212 KiB
667 lines
212 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 Sub Plots"
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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": 9,
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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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"### 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": 10,
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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": 11,
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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": 12,
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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": 12,
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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": 13,
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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": 13,
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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": 14,
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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": 15,
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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": 15,
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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": 16,
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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": 16,
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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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"# plt.subplots()\n",
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"\n",
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"**NOTE: Make sure you put the commands all together in the same cell as we do in this notebook and video!**\n",
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"\n",
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"The plt.subplots() object will act as a more automatic axis manager. This makes it much easier to show multiple plots side by side.\n",
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"\n",
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"Note how we use tuple unpacking to grba both the Figure object and a numpy array of axes:"
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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": 18,
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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": [
|
|
"# Use similar to plt.figure() except use tuple unpacking to grab fig and axes\n",
|
|
"fig, axes = plt.subplots()\n",
|
|
"\n",
|
|
"# Now use the axes object to add stuff to plot\n",
|
|
"axes.plot(x, y, 'r')\n",
|
|
"axes.set_xlabel('x')\n",
|
|
"axes.set_ylabel('y')\n",
|
|
"axes.set_title('title'); #; hides Out[]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Adding rows and columns\n",
|
|
"\n",
|
|
"Then you can specify the number of rows and columns when creating the subplots() object:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"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": [
|
|
"# Empty canvas of 1 by 2 subplots\n",
|
|
"fig, axes = plt.subplots(nrows=1, ncols=2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000023521E20588>,\n",
|
|
" <matplotlib.axes._subplots.AxesSubplot object at 0x0000023521E5D8C8>],\n",
|
|
" dtype=object)"
|
|
]
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Axes is an array of axes to plot on\n",
|
|
"axes"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(2,)"
|
|
]
|
|
},
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"axes.shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Empty canvas of 2 by 2 subplots\n",
|
|
"fig, axes = plt.subplots(nrows=2, ncols=2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000023521ED5E48>,\n",
|
|
" <matplotlib.axes._subplots.AxesSubplot object at 0x0000023521F09D88>],\n",
|
|
" [<matplotlib.axes._subplots.AxesSubplot object at 0x0000023521F45308>,\n",
|
|
" <matplotlib.axes._subplots.AxesSubplot object at 0x0000023521F79D88>]],\n",
|
|
" dtype=object)"
|
|
]
|
|
},
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"axes"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(2, 2)"
|
|
]
|
|
},
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"axes.shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Plotting on axes objects\n",
|
|
"\n",
|
|
"Just as before, we simple .plot() on the axes objects, and we can also use the .set_ methods on each axes.\n",
|
|
"\n",
|
|
"Let's explore this, make sure this is all in the same cell:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"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": [
|
|
"fig,axes = plt.subplots(nrows=1,ncols=2)\n",
|
|
"\n",
|
|
"for axe in axes:\n",
|
|
" axe.plot(x,y)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x2352216ce88>]"
|
|
]
|
|
},
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"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": [
|
|
"fig,axes = plt.subplots(nrows=1,ncols=2)\n",
|
|
"\n",
|
|
"axes[0].plot(a,b)\n",
|
|
"axes[1].plot(x,y)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x2352229c648>]"
|
|
]
|
|
},
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# NOTE! This returns 2 dimensional array\n",
|
|
"fig,axes = plt.subplots(nrows=2,ncols=2)\n",
|
|
"\n",
|
|
"axes[0][0].plot(a,b)\n",
|
|
"axes[1][1].plot(x,y)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"A common issue with matplolib is overlapping subplots or figures. We ca use **fig.tight_layout()** or **plt.tight_layout()** method, which automatically adjusts the positions of the axes on the figure canvas so that there is no overlapping content:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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PpjESkdSpgpJY/Pffl/HEe6u57oyj+bcRR8UdjohkISUoybin56zm1zM+4sJh3fjROf3iDkdEstThrKjbw8zeMLPFZlZkZt8P999hZmvMbF54G5XwPTeHMzQvNbNzE/anNHuz5L63PtrIzc8t4NRjOnD3hYPq44W4IpKiwzkHVQr80N3fD2dWnmtmM8LH7nf3+xIbhzMyjwOOA7oCr5lZ3/DhB4CzCa6in21mhe6+6DBikyy0cM12rv2fuRzT6QgevHQYjRuqgBeRqh3OirrrgHXh9g4zW0ylSS0rGQM86e57gRVmtoxg5mYIZ28GMLOK2ZuVoOqQkq27uGLqbFo3a8TUK4bTsmmjuEMSkSyXln9hzSwfGMr/rk1zvZnNN7MpZtY23FfVLM01zt6c0I9mZ85BqzbvYsKU99izv4ypVw7nyNZN4w5JRHLAYScoMzsCeBb4gbt/BjwIHA0MIaiwfl3RNMm3ezX7D96p2Zlzzt/mr+Nrv3+bDTv28tBlBfTt3DLukEQkRxzWdVBm1oggOf3Z3Z8DcPf1CY//EahYs6a6WZqrm71ZctCe/WVMenERj7+7iiE9gslfe7SrN4sOikga1DpBWTD86k/AYnf/TcL+LuH5KYALgIXhdiHwuJn9hmCQRB/gPYIKqo+Z9QLWEAyk+FZt45L4Lduwg+sf/4Aln+7gO6f15kfn9KNRngZEiMihOZwK6hTg34EFZjYv3PdTYLyZDSE4TFcMfAcgnJH5KYLBD6XAde5eBmBmFbM35wFT3L3oMOKSmLg7T88p4fbCIpo3zmPqFV/i9H6d4g5LRHLU4Yzi+yfJzx9NT7Kv4nvuAu5Ksv+g2Zslt+zcW8otzy9g2ry1nNS7Pb8dN4TOrTQY4lCZ2UjgdwT/rD3k7ndXevwG4CqCf/I2Ale6+8qMByqSAZqLTw7bwjXbuf7x91m1ZRc3nN2X6844pr4uNnhYzCyPmq8J/AAocPddZnYtcA/wzcxHKxI9JSipNXdn6r+K+eX0JbRr0Zgnrz6J4b3axR1WLhtODdcEuvsbCe1nAZdmNEKRDFKCklrZtmsfNz4znxmL1nNm/07cd/Fg2rZoHHdYuS7ZNYEjqmk/EXipqgfN7GrgaoCePXumIz6RjFKCkkM2u3gL33/iAzbu3MvPvj6AK0/J15x66ZHyNYFmdilQAJxW1ZO5+2RgMkBBQUHS5xHJZkpQkrKycufBN5dx/2sf061NM5699mQGddcig2lU3bWCB5jZWcAtwGnh1GEidZISlNRof1k50xesY/Jbyyla+xlfH9SFX1x4PK00n166zaaGawLNbCjwB2Cku2/IfIgimaMEJVXavns/T763iqn/Kmbd9j307tiC+785mPOHdNMhvQi4e2myawLNbBIwx90LgXuBI4Cnw9/BKncfHVvQIhFSgpKDrN6yi4dnFvOX2av4fF8ZJ/Vuz10XDOT0vp1ooOHjkUp2TaC735awfVbGgxKJiRKUHPDBqq089PYKXlq4jgZmfGNwVyae2ouB3VrHHZqI1ENKUPVcWbkzY9GnPPT2Cuas3ErLpg359ld6c/nJ+XRp3Szu8ESkHlOCqqd27Svl6TklTJm5gpWbd9GjXTNu/8YALinoQYsmeluISPz0l6ge2Vtaxoert/P3JRt44r1VbN+9n2E923DTyP6cc9yRmp5IRLKKElQdtmd/Ge+v2sq7y7fw7orNfLBqG3tLy2lgcO5xR3LVl3tzwlFta34iEZEYKEHVIbv2lTJ35f8mpA9Xb2dfWZCQBnRtxaUnHsWJvdvzpfy2tGmuaYlEJLspQeWwHXv2MychIS0o2U5puZPXwBjYrTVXnJLPiN7tKMhvp4tqRSTnZE2CqmkdnPpob2kZn27fw5ptu1m7bQ9rt+1m3fbdrNm2hzVbd1G8eRdl5U7DBsag7q359ld6c2Lv9pxwVFuO0EAHEclxWfFXLMV1cOqEfaXl7N5Xxq79pezaV8bOPaWs2x4kn7XbdrM2TEBrt+1m446Dp1nrcEQTurVpSt/OLRl1fBdG9GrPsKPa0LxxVvwqRUTSJlv+qtW4Dk6qnp1bwu79ZV/Yl3QaZz94rxNcF1TuUF7ulLtT5o57xX6nvDzY94U25UG1s3tfGbv2lbFrfxm79wUJ6MC+faXs3l/G/rKqJ5Vu2qgBXds0o1ubZvTv14mubZrRtU1TurVpRtc2zTiydVOaNso71JdERCQnZUuCSmkdnFTWt/nlS0vYtDPaCZ7zGhgNDBqYhdtG44YNaNYoj+aN82jepCHNG+VxZKtGNGsc7mvcMNhulEezxnm0aNKQ5o3zaNG4IUe2DpJQm+aNNMediEgoWxJUSuvgpLK+zUvf/zKepGayJF0kywV5ZjQIE1BF8gluwX0lEBGRzMiWBJXSOjip6NiySVoCEolLTQOGzKwJ8ChwArAZ+Ka7F2c6TpGoNYg7gNCBdXDMrDHBOjiFMcckknEJA4bOAwYA481sQKVmE4Gt7n4McD/wq8xGKZIZWZGg3L0UqFgHZzHwlLsXxRuVSCwODBhy931AxYChRGOAR8LtZ4AzTceepQ7KlkN8SdfBqc7cuXM3mdnKKh7uAGxKS2DpkW3xQPbFlGvxHBVRv6kMGDrQJlzkcDvQnkrxJg4qAvaa2cJIIk5N3L9f9R9v//1q801Zk6AOlbt3rOoxM5vj7gWZjKc62RYPZF9Miud/u06yr/Kon0MeVBT366v+1X9tvi8rDvGJyAGpDBg60MbMGgKtgS0ZiU4kg5SgRLJLKgOGCoEJ4fZY4O/uSa48F8lxOXuIrwaT4w6gkmyLB7IvJsXDgXNKFQOG8oAp7l5kZpOAOe5eCPwJeMzMlhFUTuNSeOq4X1/1r/4PmekfLxERyUY6xCciIllJCUpERLJSTicoMxtpZkvNbJmZ3ZTk8SZm9pfw8XfNLD/CWHqY2RtmttjMiszs+0nanG5m281sXni7Lap4wv6KzWxB2NdBwzwt8Pvw9ZlvZsMijqdfws8+z8w+M7MfVGoT6WtkZlPMbEPiNUFm1s7MZpjZx+HXtlV874SwzcdmNiFZmzjF/XlIof8bzGxR+F573czSei1ZTf0ntBtrZm5maR12nUr/ZnZJ+BoUmdnjmezfzHqGf6M+CH8Ho9Lc/0GfrUqPH/rfG3fPyRvBCeRPgN5AY+BDYEClNt8F/l+4PQ74S4TxdAGGhdstgY+SxHM68GIGX6NioEM1j48CXiK4ruZE4N0M//4+BY7K5GsEfAUYBixM2HcPcFO4fRPwqyTf1w5YHn5tG263zdTrleLrGdvnIcX+zwCah9vXZrr/sF1L4C1gFlCQ4Z+/D/BBxfsG6JTh/icD14bbA4DiNL8HD/psVXr8kP/e5HIFlVVTwrj7Ond/P9zeQTBlU7co+kqjMcCjHpgFtDGzLhnq+0zgE3evajaQSLj7Wxx8zVDi++QR4Pwk33ouMMPdt7j7VmAGMDKyQA9d3J+HGvt39zfcfVd4dxbBNV7pksrPD3AnwT8ke9LYd6r9fxt4IHz/4O4bMty/A63C7dbUckLuqlTx2Up0yH9vcjlBJZsSpnJC+MKUMEDFlDCRCg+dDAXeTfLwSWb2oZm9ZGbHRRyKA6+a2VwLpr2pLJXXMCrjgCeqeCyTrxFAZ3dfB8E/GkCnJG3ifK1SEffn4VBfn4kE/02nS439m9lQoIe7v5jGflPuH+gL9DWzmWY2y4JZ6zPZ/x3ApWZWQjCt3PfS2H8qDvkzlMvXQaVtSph0MrMjgGeBH7j7Z5Uefp/gkNbO8PjvCwRlf1ROcfe1ZtYJmGFmS8L/cg6Em+R7Ir/uwIILUEcDNyd5ONOvUapiea0OQdyfh5Sf28wuBQqA09LUd439m1kDgpnfL09jnyn3H2pI8F4+naB6fNvMBrr7tgz1Px6Y6u6/NrOTCK6lG+ju5WnoPxWH/P7L5Qoq66aEMbNGBMnpz+7+XOXH3f0zd98Zbk8HGplZh6jicfe14dcNwPMEhwESpW0drkN0HvC+u6+v/ECmX6PQ+opDDeHXZIde4nqtUhX35yGl18fMzgJuAUa7ezqXvq6p/5bAQOBNMysmOAdSmMaBEqm+/tPcfb+7rwCWkr5/vlLpfyLwFIC7vwM0JZhENlMO+TOUywkqq6aECY/l/wlY7O6/qaLNkRXH/M1sOMHrvzmieFqYWcuKbeAcoPLomkLgsnB0zYnA9opDXREbTxWH9zL5GiVIfJ9MAKYlafMKcI6ZtQ1H+Z0T7ssWcX8eauw/PMT2B4LklM7zLzX27+7b3b2Du+e7ez7BObDR7l6rSUwPtf/QCwQDRQj/6epLMNgmU/2vIjj3i5kdS5CgNqap/1Qc+t+bdI7iyPSNYFTIRwSjV24J900ieONB8At4GlgGvAf0jjCWUwnK1fnAvPA2CrgGuCZscz1QRDDCZhZwcoTx9A77+TDss+L1SYzHCBbH+wRYQBpHNVUTV3OChNM6YV/GXiOCxLgO2E/wH91EgvMwrwMfh1/bhW0LCFa0rfjeK8P30jLgirje99X8bLF+HlLo/zVgfcLnozCT/Vdq+2a63+8p/PwG/AZYFH7exmW4/wHAzPCzNQ84J839J/tsHdbfG011JCIiWSmXD/GJiEgdpgQlIiJZSQlKRESykhKUiIhkJSUoERHJSkpQIiKSlZSgREQkKylBiYhIVlKCEhGRrKQEJSIiWUkJSkREspISlIiIZKXIE5SZTTGzDWZWeamHisfNzH5vZsvMbL6ZDYs6JhERyX6ZqKCmAtUtbXwewaJdfYCrgQczEJOIiGS5yBOUB0uMV7dq5xjgUQ/MAtpUrG4qIiL1V8O4AwC6AasT7peE+w5aadHMriaosmjRosUJ/fv3z0iAIpXNnTt3k7t3jDuOVHXo0MHz8/PjDkPqqdp+XrIhQVmSfUlXUXT3ycBkgIKCAp8zJ12rNYscGjNbGXcMhyI/Px99XiQutf28ZMMovhKgR8L97sDamGIREZEskQ0JqhC4LBzNdyKw3d0POrwnIiLZafe+skieNxPDzJ8A3gH6mVmJmU00s2vM7JqwyXRgObAM+CPw3ahjEhGRw1dW7vzhH5/w5Xv+zpptu9P+/JGfg3L38TU87sB1UcchIiLps2zDTn709IfMW72Nswd0pnFe+uudbBgkISIiOaKs3Hno7eX8esZHNGuUx+/GDWH04K6YJRvvdniUoEREJCWfbNzJjU9/yPurgqrprgsG0qll08j6U4ISEZFqlZU7U/65gvteXUrTRnn89ptDGDMkmqopkRKUiIhUKbFqOuvYzvzigoF0ahVd1ZRICUpERA5SuWq6/5uDOX9It8irpkRKUCIi8gXLN+7kxmfmM3flVs46thO/uOD4jFVNiZSgREQECKqmh2eu4N5X4quaEilBiYgIyzfu5MfPzGdOzFVTIiUoEZF6LLFqatKwAb+5ZDAXDI2vakqkBCUiUk8Vb/qcG5/5kNnFWzmzfyd+ceHxdI65akqkBCUiUs+UlztT/1XMPa8soXFeA3598WAuHJYdVVMiJSgRkXoksWr6av9O/DLLqqZEmZjNfKSZLTWzZWZ2U5LHe5rZG2b2gZnNN7NRUcckkgvMbIqZbTCzhQn77jCzNWY2L7zp8yIpKQ+vaxr5u7dY8ukO7rt4MH+aUJC1yQkirqDMLA94ADibYGHC2WZW6O6LEprdCjzl7g+a2QCC5Tfyo4xLJEdMBf4beLTS/vvd/b7MhyO5qnjT5/z4mfm8V7yFr/YPRugd2Tp7E1OFqA/xDQeWuftyADN7EhgDJCYoB1qF263RaroiALj7W2aWH3cckrvKy51H3inmVy8voVFeA+4dO4ixJ3TPunNNVYk6QXUDVifcLwFGVGpzB/CqmX0PaAGcVdWTmdnVwNUAPXv2TGugIjnkejO7DJgD/NDdtyZrpM9L/bZy8+fc+Mx83luxhTP6deSXFw7KiaopUdTnoJKlaa90fzww1d27A6OAx8wsaVzuPtndC9y9oGPHjmkOVSQnPAgcDQwB1gG/rqqhPi/1U3m5M3XmCkb+9m0Wr/uMe8cOYsrlX8q55ATRV1AlQI+E+905+BDeRGAkgLu/Y2ZNgQ7AhohjE8k57r6+YtvM/gi8GGM4kmUSq6bT+zGIHGAAABHMSURBVHXklxceT5fWzeIOq9aiTlCzgT5m1gtYA4wDvlWpzSrgTGCqmR0LNAU2RhyXSE4ysy7uvi68ewGwsLr2Uj+UlzuPvlPMr15eSsMGxj1jB3FxDp1rqkqkCcrdS83seuAVIA+Y4u5FZjYJmOPuhcAPgT+a2X8SHP673N0rHwYUqXfM7AngdKCDmZUAtwOnm9kQgs9KMfCd2AKUrLBq8y5ufOZD3l2xhdP6duTui3K7akoU+YW67j6dYOh44r7bErYXAadEHYdIrnH38Ul2/ynjgUhWKi93Hpu1krtfWhJUTRcN4uKC3K+aEmkmCRGRHJNYNX2lb0fuvvB4urapG1VTIiUoEZEcUV7u/M+7QdWUZ8avLjqeSwp61KmqKZESlIhIDli9JaiaZi2v21VTIiUoEZEsllg1NTDj7guP55tfqrtVUyIlKBGRLLV6yy5+/Mx83lm+mS/36cDdFw2iWx2vmhIpQYmIZJnycufP763il9MX08CMX154POPqSdWUSAlKRCSL1PeqKZESlIhIFkismgz4xQXHM354/auaEilBiYjEbPWWXfzk2fn865PNnHpMB+6+6Hi6t20ed1ixU4ISEYmJu/Pnd4OqCVQ1VaYEJSISg5KtQdU0c9lmTjmmPb+6aJCqpkqUoEREMsjdefy9Vfzib0HVdNcFA/nW8J6qmpJQghIRyZCSrbu46dkF/HPZJk45pj13XziIHu1UNVUl8gRlZiOB3xEst/GQu9+dpM0lBEu/O/Chu1deM0pEJGe5O0+8t5pfTF+Mu/Nf5w/k30aoaqpJpAnKzPKAB4CzCVbXnW1mheESGxVt+gA3A6e4+1Yz6xRlTCIimbRm225uenY+b3+8iZOPDs41qWpKTdQV1HBgmbsvBzCzJ4ExwKKENt8GHnD3rQDurqXeRSTnuTtPzl7NXX9bTLk7d54/kH8b3pMGDVQ1pSrqBNUNWJ1wvwQYUalNXwAzm0lwGPAOd3852ZOZ2dXA1QA9e/ZMe7AiIumgqik9ok5Qyf5VqLyce0OgD8HS1t2Bt81soLtvO+gb3ScDkwEKCgq0LLyIZBVVTekVdYIqAXok3O8OrE3SZpa77wdWmNlSgoQ1O+LYRLKamU0Bvg5scPeB4b52wF+AfKAYuKTi8LjEa+223dz03ALe+mgjJ/Vuzz1jVTUdrgYRP/9soI+Z9TKzxsA4oLBSmxeAMwDMrAPBIb/lEcclkgumAiMr7bsJeN3d+wCvh/clRu7OX2av4tz732JO8RbuHHMcf75qhJJTGkRaQbl7qZldD7xCcH5pirsXmdkkYI67F4aPnWNmi4Ay4EZ33xxlXCK5wN3fMrP8SrvHEBwOB3gEeBP4ScaCki9IrJpO7N2Oe8cOVmJKo8ivg3L36cD0SvtuS9h24IbwJiLV6+zu6wDcfV11l2VoUFF03J2n5qzmv15cTJk7k8Ycx6UjjtK5pjTTTBIidZQGFUVj3fbd3PTsAv4RVk33XDSYnu1VNUVBCUokt6w3sy5h9dQF0HWDGeLuPD2nhDtfXERpufPz0cfx7yeqaoqSEpRIbikEJgB3h1+nxRtO/bBu+25ufm4Bby7dyIhe7bhn7CCOat8i7rDqPCUokSxlZk8QDIjoYGYlwO0EiekpM5sIrAIuji/Cus/deXpuWDWVqWrKNCUokSzl7uOreOjMjAZST326fQ83PzefN5ZuZHivdtyrqinjlKBERBK4O8/MLWFSWDXd8Y0BXHZSvqqmGChBiYiEvlA15bfj3otVNcVJCUpE6j1359n31/Dzvxaxv6yc278xgAmqmmKnBCUi9dr6z/Zw83ML+PuSDQzPD0bo5XdQ1ZQNlKBEpF5yd54Lq6Z9ZeXc9vUBXH6yqqZsogQlIvXO+s/28NPnFvD6kg18Kb8t94wdTC9VTVlHCUpE6o3KVdPPvj6AK1Q1Za2ol9vAzEaa2VIzW2ZmVS4NYGZjzczNrCDqmESk/ln/2R6uemQOP3z6Q/p2bslL3/8KE0/tpeSUxSKtoMwsD3gAOJtgYcLZZlbo7osqtWsJ/AfwbpTxiEj94+48/8Ea7igsYm9pObd+7ViuOKUXeUpMWS/qQ3zDgWXuvhzAzJ4kWM9mUaV2dwL3AD+KOB4RqUc2fLaHnz6/gNcWb6DgqLbce7HONeWSqBNUN2B1wv0SYERiAzMbCvRw9xfNrNoEpfVtRCQVqprqhqgTVLJ3w4F1acysAXA/cHkqT6b1bUSkJkHVtJDXFq/nhKPacs/YQRzd8Yi4w5JaiDpBlQA9Eu53B9Ym3G8JDATeNDOAI4FCMxvt7nMijk1E6hB354V5a7ijcBF79pepaqoDok5Qs4E+ZtYLWAOMA75V8aC7bwc6VNw3szeBHyk5icih2LBjDz99LqiahvVsw70XD1bVVAdEmqDcvdTMrgdeAfKAKe5eZGaTgDnuXhhl/yJSt7k70+at5fbCIvbsL+OWUcdy5amqmuqKyC/UdffpwPRK+26rou3pUccjInXDhh17uOX5hcxYtJ6hPdtwn6qmOkczSYhITnF3Cj8MqqZd+8r46aj+TDy1t6qmOkgJSkRyxoYde7j1+YW8GlZN944dzDGdVDXVVUpQIjnIzIqBHUAZUOrudXqKsMpV083n9eeqL6tqquuUoERy1xnuvinuIKK2ccdebn1hAa8UrWdIj+Bck6qm+kEJSkSykqomUYISyU0OvGpmDvwhnGXlC3J5arCNO/bysxcW8nLRp2HVNIhjOrWMOyzJMCUokdx0iruvNbNOwAwzW+LubyU2yMWpwdydF+ev47ZpC/l8Xxk3ndefq07tRcO8yFcGkiykBCWSg9x9bfh1g5k9T7BywFvVf1d227RzL7c+H1RNg3u04deqmuo9JSiRHGNmLYAG7r4j3D4HmBRzWIflxflr+dkLC/l8bxk/Gdmfb39ZVZMoQYnkos7A8+EEyw2Bx9395XhDqp1NO/dy27SFTF8QVE33jR1En86qmiSgBCWSY8IFQAfHHcfhenH+Wm6bVsTOPaX8eGQ/rv5yb1VN8gVKUCKSUV+omrq35t6LB9NXVZMkEXmCMrORwO8IZjN/yN3vrvT4DcBVQCmwEbjS3VdGHZeIZN7f5q/jZ9MWqmqSlESaoMwsD3gAOJtg8cLZZlbo7osSmn0AFLj7LjO7FrgH+GaUcYlIZm3euZfbphXxtwXrGNS9NfepapIURF1BDQeWhcfMMbMngTHAgQTl7m8ktJ8FXBpxTCKSQdMXrONnLyxkx55Sbjy3H9/5iqomSU3UCaobsDrhfgkwopr2E4GXqnowl6+MF6lvNu/cy22FRfxt/jqO7xZUTf2OVNUkqYs6QSWbNCvpFe1mdilQAJxW1ZPl4pXxIvXRSwvWcesLC/lsz35VTVJrUSeoEqBHwv3uwNrKjczsLOAW4DR33xtxTCISkS2f7+O2aQt5MayaHr/4RFVNUmtRJ6jZQB8z6wWsAcYB30psYGZDgT8AI919Q8TxiEhEEqumH53Tl++cdjSNVDXJYYg0Qbl7qZldD7xCMMx8irsXmdkkYI67FwL3AkcAT4dXxq9y99FRxiUi6bPl833cXljEXz9cy8BurfjzxSPof2SruMOSOiDy66DcfTowvdK+2xK2z4o6BhGJxssLP+XWFxawffd+fnh2X645XVWTpI9mkhCRQ7b1833cFlZNx3VtxWMTR3BsF1VNkl5KUCJySBKrphvO7su1qpokIkpQIpKSreG5pkJVTZIhSlAiUqNXij7llucXsm3XPv7zrL589wxVTRI9JSgRqdLWz/dxx1+LmDZvLQO6tOLRK4czoKuqJskMJSgRSerVok/5qaomiZESlIh8gaomyRZKUCJyQGLV9IOz+nDdGceoapLYKEGJ5KCaFgI9VNt27ePnf13E8x+s4dgurXjkyi9xXNfWaYlVpLaUoERyTIoLgaZsxqL1/PT5BWz9fB/fPzOomho3VNUk8VOCEsk9NS4Emgp350dPz+fZ90vof2RLpl6hqkmyixKUSO5JaSHQmhb4NDM6tWrCf5zZh+tVNUkWUoISyT0pLQSaygKfPxnZP72RiaRR5P8ymdlIM1tqZsvM7KYkjzcxs7+Ej79rZvlRxySS41JaCFQk10WaoBJO5p4HDADGm9mASs0mAlvd/RjgfuBXUcYkUgccWAjUzBoTLARaGHNMImkXdQV14GSuu+8DKk7mJhoDPBJuPwOcaeHKhSJyMHcvBSoWAl0MPOXuRfFGJZJ+UZ+DSuVk7oE24Qq824H2wKbKT5Z40hfYa2YL0x5x6jqQJEb1X2/67xdj30kXAq3O3LlzN5nZyioejvu1jLv/bIihrvd/VG2+KeoElcrJ3JRO+MIXT/qa2Rx3Lzi88GpP/av/uPquDXfvWNVj2fBaxtl/NsRQ3/uvStSH+FI5mXugjZk1BFoDWyKOS0REslzUCSqVk7mFwIRweyzwd3dPWkGJiEj9EekhvvCcUsXJ3DxgirsXmdkkYI67FwJ/Ah4zs2UEldO4FJ9+ciRBp079q/+6Iu6fJe7+If4Y6nv/SZmKFRERyUaa20RERLKSEpSIiGSlrE5QcU+TlEL/N5jZIjObb2avm1mtxvrXtv+EdmPNzM0srcNEU+nfzC4JX4MiM3s8k/2bWU8ze8PMPgh/B6PS3P8UM9tQ1fV2Fvh9GN98MxuWzv6jlur7K8L+e4S/v8Xh++f7mY4hjCMvfA+9GEPfbczsGTNbEr4OJ8UQw3+Gr/9CM3vCzJpmOoYquXtW3ggGVXwC9AYaAx8CAyq1+S7w/8LtccBfMtz/GUDzcPvaTPcftmsJvAXMAgoy/PP3AT4A2ob3O2W4/8nAteH2AKA4ze/BrwDDgIVVPD4KeIngWr4TgXfT2X+Ut1TfXxHH0AUYFm63BD7KdAxh3zcAjwMvxtD3I8BV4XZjoE2G++8GrACahfefAi7P9OtQ1S2bK6i4p0mqsX93f8Pdd4V3ZxFc55Uuqfz8AHcC9wB70th3qv1/G3jA3bcCuPuGDPfvQKtwuzVpnjDV3d+i+mvyxgCPemAW0MbMuqQzhgil+v6KjLuvc/f3w+0dBNM2dctkDGbWHfga8FAm+w37bkXwT9CfANx9n7tvy3QcBKO5m4XXoTYniyYezuYElWyapMpv3i9MkwRUTJOUqf4TTST4bzpdauzfzIYCPdw9ikMTqfz8fYG+ZjbTzGaFy5Bnsv87gEvNrIRg2p/vpbH/VBzqeySbZFXs4eH5ocC7Ge76t8CPgfIM9wtB9boReDg8xPiQmbXIZADuvga4D1gFrAO2u/urmYyhOtmcoNI6TVJE/QcNzS4FCoB709R3jf2bWQOC2d9/mMY+U+4/1JDgMN/pwHjgITNrk8H+xwNT3b07weG2x8LXJVOifP9FLWtiN7MjgGeBH7j7Zxns9+vABnefm6k+K2lIcAj5QXcfCnwOZPRcoJm1JaicewFdgRbh37OskM0JKu5pklJac8fMzgJuAUa7+9409Z1K/y2BgcCbZlZMcA6kMI0DJVJ9/ae5+353XwEsJUhYmep/IsExc9z9HaApwaSXmZLL6zJlRexm1oggOf3Z3Z/LcPenAKPDz8+TwFfN7H8y2H8JUOLuFVXjMwQJK5POAla4+0Z33w88B5yc4RiqFvdJsGpO3jUElhNk9oqTuMdVanMdXxwk8VSG+x9KcKK5Txw/f6X2b5LeQRKp/PwjgUfC7Q4Eh4zaZ7D/lwhP6ALHEvyBtTT/HvKpepDE1/jiIIn30v0+iOp2qO+viGIw4FHgt1nwepxOPIMk3gb6hdt3APdmuP8RQBHBuScjOKf/vbh/HwfiizuAGl68UQQjez4Bbgn3TSKoViD4j/lpYBnwHtA7w/2/BqwH5oW3wkz2X6ltWhNUij+/Ab8BFgELgHEZ7n8AMDP84zoPOCfN/T9BcFx+P8F/uxOBa4BrEn7+B8L4FqT79Y/6luz1zXD/pxIcVpyf8BkaFdNrEVeCGgLMCV+DFwhHxGY4hp8DS4CFwGNAkzh+B8lumupIRESyUjafgxIRkXpMCUpERLKSEpSIiGQlJSgREclKSlAiIpKVlKBERCQrKUGJiEhW+v8sPzHege7vcAAAAABJRU5ErkJggg==\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# NOTE! This returns 2 dimensional array\n",
|
|
"fig,axes = plt.subplots(nrows=2,ncols=2)\n",
|
|
"\n",
|
|
"axes[0][0].plot(a,b)\n",
|
|
"axes[1][1].plot(x,y) \n",
|
|
"\n",
|
|
"plt.tight_layout()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Parameters on subplots()\n",
|
|
"\n",
|
|
"Recall we have both the Figure object and the axes. Meaning we can edit properties at both levels."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 45,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 864x576 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig,axes = plt.subplots(nrows=2,ncols=2,figsize=(12,8))\n",
|
|
"\n",
|
|
"# SET YOUR AXES PARAMETERS FIRST\n",
|
|
"\n",
|
|
"# Parameters at the axes level\n",
|
|
"axes[0][0].plot(a,b)\n",
|
|
"axes[0][0].set_title('0 0 Title')\n",
|
|
"\n",
|
|
"\n",
|
|
"axes[1][1].plot(x,y)\n",
|
|
"axes[1][1].set_title('1 1 Title')\n",
|
|
"axes[1][1].set_xlabel('1 1 X Label')\n",
|
|
"\n",
|
|
"axes[0][1].plot(y,x)\n",
|
|
"axes[1][0].plot(b,a)\n",
|
|
"\n",
|
|
"# THEN SET OVERALL FIGURE PARAMETERS\n",
|
|
"\n",
|
|
"# Parameters at the Figure level\n",
|
|
"fig.suptitle(\"Figure Level\",fontsize=16)\n",
|
|
"\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Manual spacing on subplots()\n",
|
|
"\n",
|
|
"Use .subplots_adjust to adjust spacing manually.\n",
|
|
"\n",
|
|
"Full Details Here: https://matplotlib.org/3.2.2/api/_as_gen/matplotlib.pyplot.subplots_adjust.html\n",
|
|
"\n",
|
|
"Example from link:\n",
|
|
"\n",
|
|
"* left = 0.125 # the left side of the subplots of the figure\n",
|
|
"* right = 0.9 # the right side of the subplots of the figure\n",
|
|
"* bottom = 0.1 # the bottom of the subplots of the figure\n",
|
|
"* top = 0.9 # the top of the subplots of the figure\n",
|
|
"* wspace = 0.2 # the amount of width reserved for space between subplots,\n",
|
|
" # expressed as a fraction of the average axis width\n",
|
|
"* hspace = 0.2 # the amount of height reserved for space between subplots,\n",
|
|
" # expressed as a fraction of the average axis height"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 52,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 864x576 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig,axes = plt.subplots(nrows=2,ncols=2,figsize=(12,8))\n",
|
|
"\n",
|
|
"# Parameters at the axes level\n",
|
|
"axes[0][0].plot(a,b)\n",
|
|
"axes[1][1].plot(x,y)\n",
|
|
"axes[0][1].plot(y,x)\n",
|
|
"axes[1][0].plot(b,a)\n",
|
|
"\n",
|
|
"# Use left,right,top, bottom to stretch subplots\n",
|
|
"# Use wspace,hspace to add spacing between subplots\n",
|
|
"fig.subplots_adjust(left=None,\n",
|
|
" bottom=None,\n",
|
|
" right=None,\n",
|
|
" top=None,\n",
|
|
" wspace=0.9,\n",
|
|
" hspace=0.1,)\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Exporting plt.subplots()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 53,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 864x576 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# NOTE! This returns 2 dimensional array\n",
|
|
"fig,axes = plt.subplots(nrows=2,ncols=2,figsize=(12,8))\n",
|
|
"\n",
|
|
"axes[0][0].plot(a,b)\n",
|
|
"axes[1][1].plot(x,y)\n",
|
|
"axes[0][1].plot(y,x)\n",
|
|
"axes[1][0].plot(b,a)\n",
|
|
"\n",
|
|
"fig.savefig('subplots.png',bbox_inches='tight')\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"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": "3.7.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 1
|
|
}
|