484 lines
116 KiB
Plaintext
484 lines
116 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f8579363",
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"metadata": {},
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"source": [
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"# **Libraries**"
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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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"id": "e4d24b04",
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"metadata": {},
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"outputs": [],
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"source": [
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"# libraries\n",
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"import scipy \n",
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"# \"SciPy\" provides algorithms for optimization, integration, interpolation, eigenvalue problems, \n",
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"# algebraic equations, differential equations, statistics and many other classes of problems.\n",
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"import numpy as np\n",
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"# Fast and versatile, the \"NumPy\" vectorization, indexing, and broadcasting concepts are the \n",
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"# de-facto standards of array computing today.\n",
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"import matplotlib.pyplot as plt\n",
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"# \"Matplotlib\" is a comprehensive library for creating static, animated, and interactive \n",
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"# visualizations in Python."
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]
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},
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{
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"cell_type": "markdown",
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"id": "4c41a20d",
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"metadata": {},
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"source": [
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"## 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": 2,
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"id": "9e9a38b3",
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"metadata": {},
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"outputs": [],
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"source": [
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"x= np.linspace(0,2*np.pi)\n",
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"y= np.sin(x)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6a8dcbb5",
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"metadata": {},
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"source": [
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"## Anatomy of a 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": 18,
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"id": "b5768c9e",
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"from PIL import Image\n",
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"img = Image.open('anatomy.webp')\n",
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"img.save(\"anatomy.png\")"
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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": 20,
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"id": "8f75cee3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<img src=\"anatomy.png\" width=\"500\" height=\"500\"/>"
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],
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"text/plain": [
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"<IPython.core.display.Image object>"
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]
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},
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"execution_count": 20,
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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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"# import image module\n",
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"from IPython.display import Image\n",
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" \n",
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"# get the image\n",
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"Image(url=\"anatomy.png\", width=500, height=500)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "82c13c31",
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"metadata": {},
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"source": [
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"## Implicit or explicit?\n",
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"**Using figures**\n",
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"- Explicitly create Figures and Axes, and call methods on them (the \"object-oriented (OO) style\").\n",
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"- Rely on pyplot to implicitly create and manage the Figures and Axes, and use pyplot functions for plotting."
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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": 6,
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"id": "0f84f0db",
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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": [
|
|
"# implicit \n",
|
|
"plt.plot(x,y,label=\"sin\")\n",
|
|
"plt.xlabel('x label')\n",
|
|
"plt.ylabel('y label')\n",
|
|
"plt.title(\"Simple Plot\")\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "3f2d03de",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.legend.Legend at 0x7fcca3b14dc0>"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x432 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# explicity\n",
|
|
"fig = plt.figure(figsize=(6,6)) # size\n",
|
|
"ax = plt.subplot(aspect=1) # aspect ratio\n",
|
|
"ax.plot(x,y,label=\"sin\") # label\n",
|
|
"ax.set_xlabel('x') # Add an x-label to the axes.\n",
|
|
"ax.set_ylabel('y') # Add a y-label to the axes.\n",
|
|
"ax.set_title(\"Simple Plot\") # Add a title to the axes.\n",
|
|
"ax.legend() # Add a legend."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9d5ac225",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Figure : lines"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"id": "568d7656",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.legend.Legend at 0x7fdc69fe4b90>"
|
|
]
|
|
},
|
|
"execution_count": 37,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x432 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig = plt.figure(figsize=(6,6))\n",
|
|
"ax = plt.subplot(aspect=1)\n",
|
|
"ax.plot(x,y,label=\"sin\",color='blue', linewidth=3, linestyle='--') \n",
|
|
"ax.plot(x,y*y,label=\"$\\sin^2$\",color='red', linewidth=1, linestyle='dotted') \n",
|
|
"ax.set_xlabel('x') # Add an x-label to the axes.\n",
|
|
"ax.set_ylabel('y') # Add a y-label to the axes.\n",
|
|
"ax.set_title(\"Simple Plot\") # Add a title to the axes.\n",
|
|
"ax.legend() # Add a legend."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"id": "b316fd3f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"ax.plot?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "287d0813",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Figure and Axes"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "7cbcc514",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(0,0.5,'y')"
|
|
]
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x432 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"#\n",
|
|
"fig = plt.figure(figsize=(6,6))\n",
|
|
"ax = plt.subplot(aspect=1)\n",
|
|
"ax.plot(x,y,\".r\",label=\"$\\sin(x)$\") \n",
|
|
"ax.plot(x,y*y,\".g\",label=\"$\\sin(x)^2$\") \n",
|
|
"ax.legend()\n",
|
|
"ax.set_xlabel(\"x\")\n",
|
|
"ax.set_ylabel(\"y\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cf3594a3",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Figures : axes and text"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 47,
|
|
"id": "8cd16580",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(0,0.5,'y')"
|
|
]
|
|
},
|
|
"execution_count": 47,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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n24SEBM455xzmzJnDrl27GD9+PAkJCWF/F2MqY4kjTMGuoXHgwAEmTJjAgw8+yIkTJwLu85NPPmHfvn2lzzdv3sxvf/vbcusQFBcXs3LlSgYOHFhh0Zu33nqL3r17k5SUxMaNG8nIyOCqq65i//79pKSk0LVrVz788MMqfGvjS6iLQh08eJC0tDROnDhBdnY2a9euBTw9yRUrVnDJJZdw6aWX8ve//52CggIKCwsr7ENEaNSoET179uTdd9/lu+++Y8SIERXWkTamJljiqIJgShft2rXj888/Z9++fXTr1o0VK1b4bZufn19uCujdu3dz6623sm7dutLFahYuXMgdd9zBoUOHaNCgQWnbJUuW0L59e1avXk2nTp146aWXSEtLY/LkyVxzzTWsX7+edevWla5jYKpPKGXLn3/+maFDh3LggKdNfn4+kyZN4rXXXqNDhw786le/Ys2aNfz0008+Jx+MjY0lLi6OoUOHsnr1atasWUP//v19rsdtTE2xxBGEqs6W2qRJE+bPn89f//pXJk6cyJVXXskHH3xQod1VV11VrqykqiQmJjJkyBAWLVpEQUEB2dnZpKSk8OOPP5a2O3z4MEuXLqVr167ExMTQv3//0rLGzp07ad26dVhxm+CEMuvxhAkTWLduXWnvs6SXMXbsWLZt2+b3Kqm4uDji4+O57bbb2Lx5M4sXL6Zr16418n2MqYxdVVWJYK+eKi4uZuLEiXz00UcAjB07tsICN/369ePTTz9l1KhRPu/qbdasWenjvLy80gP+8OHD+dOf/oSqcvPNNwOUK3stWrSo3OJVqkqXLl0AT+Jo165duF/fBCHYK64WLlzICy+8UOGei8LCQp/lKPBMCVKvXj3Gjx/PPffcU7pIkTFOssRRiWBv/IqJieGJJ57wu5/8/HzmzZvHsWPHmDdvHs2bB74yZ/369aWTzvXv358bb7yR5ORk7r//fsBzsCmZKHjdunXlBsq///577rrrLgB27dpFr169QvrOJnSV3fS5YcMGRo8eHfBGvbISEhJITExk0qRJjBo1yqYEMa5iiaMSVb3x68SJE8yfP58dO3Zw11130aZNm8rfhOc+jXvvvReA+Ph4evXqxT33lC6OSGFhYenazfHx8axYsYL777+fDRs2MHDgQJo2bQrADz/8QExMDKtWraJXr140bNgwpPhN1WTtOMQH675j6uhBQSWNxo0bk5yczJQpUxg6dCj16tWLQJTGhEhVa91P9+7dtTpl5hzUWR99p5k5B0N63/Hjx3XGjBn61VdfBf2et956S9u0aaMtWrTQKVOmlL6em5tbrl2fPn30/PPP12effVbXr1+vrVq10t69e+ubb75Zrt3DDz+sHTt21MWLF4cUu6m6zJyD2vGhv2vDMzspMfUVCPgjIpqRkaHFxcVOh27qICBTgzzGOrYCIICIDAD+imchp7mq+ueTtot3+zXAT8AoVV1b2X7dsgJgTU4JsnfvXk4//fQa2bepHs98vJXfT7iXoxs/RIv8z1RbonHjxkybNo0777wzAtEZU15UrDkuIvWAZ4CBQGfgBhHpfFKzgUAH788YYHZEg6yimpxHypKG++1fm8HRjR8ElTQAjh07xpQpUwLObmuMG1SaOETkbhGpiYl5egBbVXW7qp4AFgHXntTmWuBlb09qDdBMRFqdvKPqUtXLbo0pa8nC+cTguRcjJqYeDRo0ID4+noSEBJo0aUJiYiJNmzaladOmJCYm0qRJE/bu3cuyZcscjtxEo0gev4I5JT4D+FJE1gLzgeVaPfWt1kBumed5QM8g2rQGdlfD55fj5vUWTHQqKZeqd1Gl/Px8CgoKSqcPKSgoqPA8Pz+f7t27Oxy5iTaRPn5VmjhU9RERmQSkAbcCs0TkTWCeqm6rwmf7utX15IQUTBtPQ5ExeMpZ5e5pCJbb1lswtYeIEBcXZ5fUmhoT6eNXUGMc3h7GHu9PEdAceFtE/lKFz84Dyl6bmgTsCqNNSYzPq2qqqqaePIdTMEK5+9eYqrKyqKlOkT5+VXpVlYj8FrgF+D9gLrBYVQtFJAb4TlXPCeuDReoD3wL9gJ3Al8CNqvp1mTaDgLvxXFXVE3hKVXtUtu9wr6pyemEmUzdYWdTUhKoev0K5qiqYMY6WwK9UdUfZF1W1WEQGhxzdf99fJCJ3A8vxXI47X1W/FpGx3u1zgKV4ksZWPJfj+p6DuprYkq8mEqwsampCJI9fwYxxpAfYtrkqH66qS/Ekh7KvzSnzWIFxVfkMY9zGLcvQmujkhsqITTliTIQ5vQytiV5uKXNa4jDGAVYWNeFwS5nT1uMwxkXsaisTiFuu/rQehzEu4ZYyhHEvt5Q5LXEY4xJuKUMYd3NDmdNKVca4hFvKEMYd3Fy2tB6HMS7hljKEcZ7by5aWOIxxETeUIYzz3F62tFKVMVHCzaULU73cXra0xOFi69evp2/fvsTFxXHKKadw7Ngxn+2ef/55RISUlBSmTZsW9P4XL17M4sWLqytcU4NKShczMrYwYu4aSx61XEnZcnxaJ9eVqcASh6ulpKSwcuVKzjjjDI4cOcL8+fMrtFFVZs2aBcCTTz7JfffdF/T+LXFED1+lC1O7dW/XnHFXtndd0gBLHDXu6aef5rPPPqvyfq6//nqefPLJCsuK/uMf/yAlJaXK+zfu5vbShQlfNJYgLXHUsP3799O/f386d+7MjBkz2L9/f1j7mTBhAtu3b+fdd98t9/qsWbMYN873PJDLly+nR48e9OzZkwsvvJAZM2aU29+yZctYtmwZffv2pW/fvuTn54cVm6l5bi9dmPBEawnSEkcNmzx5Mrt372bcuHG8+uqrtG7dmmHDhrFs2TKKi4uD3k9KSgr9+vUrd/DPysqidevWnH766RXab9q0iSFDhjBt2jS++OILMjIyeOKJJ3jhhRcAmDFjBgMGDGDAgAGsXLmSlStXEh8fX/UvbGqMm0sXJjzRWoK0xBEBTZs2Zdy4caxbt45//etftGzZkuuvv57k5GT+8Ic/BL2fCRMm8MUXX7B69WoAnnjiCcaPH++z7dSpU+nevTt9+vQBoFWrVowcOZLHH3+86l/IuEo0ljqMR7SWIC1xVJOyJZ++ffuyZ88en+1SU1OZOXMmU6dO5cCBAzz22GNBf8aAAQPo3Lkz06dPJzc3l8OHD3P++ef7bJudnU379u3Lvda+fXt27NjBkSNHgv9ixtWitdRhPKK1BOnIDYAi0gJ4A0gGcoD/VdUKv/EikgMcAX4GioJd1tAJJWWfQNauXcvcuXN5/fXXad68OQ888AC33XZb0J8hIowfP54xY8YgItxzzz1+21a2JLCpHdx+o5jxCLT4UjTe9OlUj+NB4ENV7QB86H3uz5WqmuLmpBHIoUOHmDVrFl27dqV3794cOHCAt956i+3bt5Oenk5SUlJI+7vppps49dRT2bZtG2lpaX7bXXjhhWzdurXca9u2baNdu3Y0adIEgJiY//7zFxQUUFhYGFIsxnnRWuqoS2pjr9CpKUeuBfp6Hy8AVgIPOBRLjfr1r3/N/v37uf322xk5ciSnnFK1/9gNGzZk0aJFNG7cOGC7Bx54gJSUFD799FMuv/xy9uzZwyuvvFJuTOW0005j06ZNAPzud79jyJAhDBo0qErxmciy+a3cr1b2ClU14j/ADyc9P+Sn3X+AtUAWMKaSfY4BMoHMtm3bqlv85z//Cfu927dv1z59+mjDhg21T58++sUXX1Ro895772nPnj0V0C5duujTTz9duu2f//ynXnzxxdqjRw+94IILdPr06eXeu2XLFr3ooov08ssv18GDB+vx48fDjtW4T2bOQZ310XeamXPQ6VDqtMycg9rpkaV69oNLtNMjS1377wFkapDHcNEaqoWLyAfAGT42PQwsUNVmZdoeUtUKKVhEzlTVXSJyGrACuEdVP6nss1NTUzUzM7MK0RsT3dw+u2pdE2iMwy1EJEuDHBKosVKVql7lb5uI7BWRVqq6W0RaAfv87GOX9899IvI3oAdQaeIwpq6rleWRKBaNA+CBODU4/j5wi/fxLcB7JzcQkcYi0qTkMZAGZEcsQmOimA2aO6Ou3FPj1OD4n4E3ReR24HvgOvCUpoC5qnoNcDrwNxEpifN1VV3mULzGRBUbNI+8ulQedCRxqOoBoJ+P13cB13gfbwe6RDg0Y2oNf+WRaKi3R6O6VB60FQCNqUPq0llxpJWUBwuLimt9edAShzF1SF06K65Jvnptdak8aInDmDqkLp0V15RAvbbadvWUP5Y4jKlDAp0V29hHcKzXZonDmDrH11mxjX0Ez3ptljiMMdhZtD91fSzDH0scxhg7i/bBxjL8s8RhjKn0LLoujn9YL8w/SxzGGCDwDYN1cfzDemH+WeIwxgRU28+8/fWmbCzDP0scxpiAAp15R3sJq7LeVF0fy/DHEocxJiB/Z961oYRV23tTNcUShzGmUr7OvKPtoOurd2TjGOGxxGGMCUs0lbD89Y5sHCM8ljiMMWFxawnLV9IK1DuycYzQObICoIhcJyJfi0ixiPhd41ZEBojIFhHZKiIPRjJGY0zlurdrzrgr25c78Po6SJeozhXyfO2rJGnNyNjCiLlrSrfZiojVy6keRzbwK+A5fw1EpB7wDNAfyAO+FJH3VXVTZEI0xoTDXwkrUE8kUGnL1zZ/+/LXs7CSVPVyagXAzQDeZWH96QFs9a4EiIgsAq4FLHEY42L+DtL+DuqVJZRQEkSgcRcrSVUfN49xtAZyyzzPA3r6aywiY4AxAG3btq3ZyIwxAfk6SPs7qAcafwg1QVjPIjJqLHGIyAfAGT42Payq7wWzCx+vqb/Gqvo88DxAamqq33bGGGf4O6gH6iWEkyCsZ1HzaixxqOpVVdxFHtCmzPMkYFcV92mMcZCvg3plScAShPu4uVT1JdBBRM4CdgLDgRudDckYUxMCJQFLEO7j1OW4vxSRPOAS4B8istz7+pkishRAVYuAu4HlwGbgTVX92ol4jTHG/Jeo1r7hABHZD+wI8+0tgf+rxnAiLdrjh+j/DtEeP0T/d7D4Q9dOVU8NpmGtTBxVISKZqur3pkS3i/b4Ifq/Q7THD9H/HSz+muVIqcoYY0z0ssRhjDEmJJY4Knre6QCqKNrjh+j/DtEeP0T/d7D4a5CNcRhjjAmJ9TiMMcaExBKHMcaYkFji8Ir2tT9EZL6I7BORbKdjCYeItBGRj0Vks3etlnudjilUIhInIv8WkQ3e7/Co0zGFQ0Tqicg6EVnidCzhEJEcEdkoIutFJNPpeEIlIs1E5G0R+cb7/+ESp2M6mY1xULr2x7eUWfsDuCGa1v4QkSuAo8DLqnqB0/GESkRaAa1Uda2INAGygF9E2b+BAI1V9aiIxAKrgXtVdY3DoYVERMYDqUCiqg52Op5QiUgOkKqqUXkDoIgsAD5V1bki0gBopKo/OB1XWdbj8Chd+0NVTwAla39EDVX9BDjodBzhUtXdqrrW+/gInmlmWjsbVWjU46j3aaz3J6rOzEQkCRgEzHU6lrpIRBKBK4B5AKp6wm1JAyxxlPC19kdUHbRqExFJBroCXzgbSei8ZZ71wD5ghapG23d4ErgfKHY6kCpQIENEsrzr9ESTs4H9wIvecuFcEWnsdFAns8ThEdLaH6bmiEgC8A7wO1U97HQ8oVLVn1U1Bc8yAD1EJGrKhiIyGNinqllOx1JFl6pqN2AgMM5bxo0W9YFuwGxV7QocA1w35mqJw8PW/nAB77jAO8Brqvqu0/FUhbe8sBIY4HAoobgUGOodI1gE/I+IvOpsSKFT1V3eP/cBf8NTio4WeUBemZ7q23gSiatY4vAoXfvDOxg1HHjf4ZjqFO/A8jxgs6o+4XQ84RCRU0WkmfdxPHAV8I2zUQVPVX+vqkmqmozn/8BHqnqTw2GFREQaey+uwFviSQOi5kpDVd0D5IpIJ+9L/QDXXSDi5oWcIkZVi0SkZO2PesD8aFv7Q0QWAn2Blt61Tv6gqvOcjSoklwIjgY3eMQKAh1R1qYMxhaoVsMB7lV4MnjVkovKS1ih2OvA3z3kI9YHXVXWZsyGF7B7gNe9J7HbgVofjqcAuxzXGGBMSK1UZY4wJiSUOY4wxIbHEYYwxJiSWOIwJyOBjAAAA2ElEQVQxxoTEEocxxpiQWOIwxhgTEkscxhhjQmKJw5gaJiIXi8hX3vU6GnvX6oiaOayMOZndAGhMBIjIY0AcEI9nLqI/ORySMWGzxGFMBHinj/gSKAB6q+rPDodkTNisVGVMZLQAEoAmeHoexkQt63EYEwEi8j6eqcrPwrNE7t0Oh2RM2Gx2XGNqmIjcDBSp6uvemXP/JSL/o6ofOR2bMeGwHocxxpiQ2BiHMcaYkFjiMMYYExJLHMYYY0JiicMYY0xILHEYY4wJiSUOY4wxIbHEYYwxJiT/H7PJFdLlHXCkAAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<Figure size 432x432 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# adding text\n",
|
|
"fig = plt.figure(figsize=(6,6))\n",
|
|
"ax = plt.subplot(aspect=1)\n",
|
|
"ax.plot(x,y,\".\",label=\"sin\") \n",
|
|
"ax.legend()\n",
|
|
"\n",
|
|
"\n",
|
|
"ax.text(0.3, 0.1, \"-> Mot\",family=\"cursive\",size=14)\n",
|
|
"ax.text(0.3, -0.5, \"-> Mot\",family=\"serif\",size = 14)\n",
|
|
"\n",
|
|
"ax.annotate('point (3,0)', xy=(3, 0), xytext=(4, 0.5),\n",
|
|
" arrowprops=dict(facecolor='black', shrink=0.05))\n",
|
|
"\n",
|
|
"ax.set_title('Title')\n",
|
|
"ax.set_xlabel(\"x\")\n",
|
|
"ax.set_ylabel(\"y\")\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "977fb62b",
|
|
"metadata": {},
|
|
"source": [
|
|
"### figure : scales"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 51,
|
|
"id": "5f4d1736",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.legend.Legend at 0x7fdc5860e790>"
|
|
]
|
|
},
|
|
"execution_count": 51,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x432 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig = plt.figure(figsize=(6,6)) # size\n",
|
|
"ax = plt.subplot(aspect=1) # aspect ratio\n",
|
|
"\n",
|
|
"ax.plot(x,y,label=\"sin\") # label\n",
|
|
"ax.set_xscale('log')\n",
|
|
"ax.set_yscale('log')\n",
|
|
"ax.set_xlabel('x') # Add an x-label to the axes.\n",
|
|
"ax.set_ylabel('y') # Add a y-label to the axes.\n",
|
|
"ax.set_title(\"Title\") # Add a title to the axes.\n",
|
|
"ax.legend() # Add a legend."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b2266d48",
|
|
"metadata": {},
|
|
"source": [
|
|
"### figures multiples"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 72,
|
|
"id": "45b0275c",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x7fdc69e73190>]"
|
|
]
|
|
},
|
|
"execution_count": 72,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x432 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3,\n",
|
|
" figsize=(6, 6))\n",
|
|
"fig.tight_layout()\n",
|
|
"ax0.set_title('a')\n",
|
|
"ax0.plot(x,y,label=\"sin\") # label\n",
|
|
"\n",
|
|
"ax1.plot(x,y,label=\"sin\") # label\n",
|
|
"\n",
|
|
"ax2.set_title('title')\n",
|
|
"ax2.plot(x,y,label=\"sin\") # label\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "fecf66d0",
|
|
"metadata": {},
|
|
"source": [
|
|
"### figure : save"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 71,
|
|
"id": "2f6abdd2",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#save\n",
|
|
"fig.savefig(\"output.png\")\n",
|
|
"fig.savefig(\"output.pdf\")"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|