483 lines
115 KiB
Plaintext
483 lines
115 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0a3e2a4e",
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"metadata": {},
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"source": [
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"# Feuille 1 - UE Projet CMI-L1\n",
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"Introduction to Python figures"
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]
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},
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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": 1,
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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": "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": 2,
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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": 3,
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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": 3,
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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": "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": 4,
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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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"# some data to work\n",
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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": "82c13c31",
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"metadata": {},
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"source": [
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"## Figures : 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": 5,
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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": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEWCAYAAABIVsEJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAA2xElEQVR4nO3dd3yV9fn/8deVRSBAgBACIUAYIRCmEEHEwRYQRW1LpW5rKV9FFG0dVavVaq2tq2pVQETrxlGpAoI4EBQhDFlhhLDCDCsJISHr+v1xDv5iTELGObnPybmej8d5nHPu+b4duc7nHp+PqCrGGGNMdQU5HcAYY4x/sgJijDGmRqyAGGOMqRErIMYYY2rECogxxpgasQJijDGmRqyAGHMGInKViCz00rZni8hfvbDdnSIywtPbNaY0KyDGACJynoh8KyJZInJURJaJyNkAqvqmqo5yOmNZIqIikisiJ0Rkr4g8JSLB1dzGEBHJ8FZGU7+FOB3AGKeJSFPgE+D/gPeAMOB84JSTuaqoj6qmiUg34CtgK/CSs5FMoLAWiDHQFUBV31bVYlXNU9WFqroOQESuF5Glpxd2//K/WUS2iUiOiDwiIp1F5DsRyRaR90QkzL3sEBHJEJE/ichh96mlqyoKIiLjRGStiBx3t4h6V+UAVHUz8A3Qs5xtNhCRZ0Rkn/v1jHtaBDAfiHW3Yk6ISGx1/sGZwGYFxBjXr/ZiEXlNRMaISPMqrDMa6A+cA9wFTAeuAtrh+iM+sdSyrYGWQFvgOmC6iCSW3aCI9ANmAb8HooCXgbki0uBMYUQkCVeraU05s+9z5+wL9AEGAPerai4wBtinqo3dr31nPHJj3KyAmICnqtnAeYACM4BMEZkrIjGVrPZ3Vc1W1Y3ABmChqqarahauX/VnlVn+AVU9papfA58CE8rZ5u+Al1X1e3dL6DVcp9HOqSTHahE5BvwPmAm8Ws4yVwEPq+ohVc0E/gJcU8k2jakSuwZiDKCqqcD1AO7rCW8Az/DTlkRpB0t9zivne+tS34+5f+2ftgso71RRB+A6Ebm11LSwCpY9rZ+qplUyH/f6u6qwf2OqxVogxpThvp4wm3KuJ9RQc/f1htPaA+WdKtoDPKqqzUq9Gqnq27Xc/z5cxam8/Vt33KbGrICYgCci3UTkThGJc39vh6vlsdyDu/mLiISJyPnAOGBOOcvMACaLyEBxiRCRi0WkSS33/TZwv4hEi0hL4M+4WljgajlFiUhkLfdhApCdwjIGcoCBwB0i0gw4juu23j96aPsHgGO4fvWfBCa7Wzk/oaopIvI74HkgAdepsKXAklru/69AU2Cd+/sc9zRUdbOIvA2ku58hSbIL6aaqxAaUMsZ7RGQI8IaqxjkcxRiPs1NYxhhjasQKiDHGmBqxU1jGGGNqxFogxhhjaiSg7sJq2bKlxsfHOx3DGGP8yqpVqw6ranTZ6QFVQOLj40lJSXE6hjHG+BUR2VXedDuFZYwxpkasgBhjjKkRKyDGGGNqJKCugRhjjDcUFhaSkZFBfn6+01FqJTw8nLi4OEJDQ6u0vBUQY4yppYyMDJo0aUJ8fDwi4nScGlFVjhw5QkZGBh07dqzSOo6ewhKRWSJySEQ2VDBfRORfIpImIuvcI7adnjdaRLa4591Td6mNMean8vPziYqK8tviASAiREVFVasV5fQ1kNm4hgatyBhcvZImAJOAFwHcvYa+4J6fBEx0D+lpjDGO8OficVp1j8HRU1iqukRE4itZZDzwurr6W1kuIs1EpA0QD6SpajqAiLzjXnaTlyObCmTmnGLDviw2788hSKBpw1AiS72ahofSplk4ocFO/2YxxniKr18DaYtrlLbTMtzTyps+sLwNiMgkXK0X2rdv752UAaagqIQlWzNZtzeLjXuz2LAvi4PZp864XtPwEEYmtWZMz9acl9CS8NDgOkhrTOC66aabuOOOO0hK8s4JGl8vIOW1p7SS6T+fqDodmA6QnJxsPUfWQlZeIW+v2M2ry3ZwMPsUQQKdoxtzbueW9GwbSc/YpnSPbUqwCFl5hWTnF5J1spCsvEKOnyzk+x1HWbTpAB+sziAiLJhh3WMY07M1QxKjaRTm6/8pGuN/Zs6c6dXt+/r/tRlAu1Lf43CN6hZWwXTjBRnHTvLqsp28s2I3uQXFDO4Sxd+u6MU5naIq/MMf0SCEWBr+ZNqEs9tRUNSL79KPMH/9fhZuOsj/fthHi4gw7hzVlSvPbk9wkP+fRzbGCbm5uUyYMIGMjAyKi4t54IEHePHFF/nnP/9JcnIyjRs35rbbbuOTTz6hYcOGfPzxx8TExNRqn75eQOYCU9zXOAYCWaq6X0QygQQR6QjsBa4EfuNgznpp15Fcnlq0lU/W7Qfgkt5tuOn8TvRsW/Phs8NCgriwazQXdo3mr5eV8P2Oozy7eBv3fbSBN5fv5sFLkhjYKcpTh2BMnfvL/zayaV+2R7eZFNuUBy/pUekyCxYsIDY2lk8//RSArKwsXnzxxR/n5+bmcs455/Doo49y1113MWPGDO6///5a5XK0gLjHYh4CtBSRDOBBIBRAVV8C5gFjgTRcY0nf4J5XJCJTgM+AYGCWqm6s8wOop1SVOSkZPPS/jQhww7nx3HBeR9o2a3jGdasjJDiIwV1acm7nKD5dv5/HPk3l19OXc3HvNtw7phtxzRt5dH/G1Ge9evXiD3/4A3fffTfjxo3j/PPP/8n8sLAwxo0bB0D//v1ZtGhRrffp9F1YE88wX4FbKpg3D1eBMR50NLeAez9cx2cbD3Ju5yienNCHNpGeLRxliQjjescyvFsMLy/Zzktfb+fzTQe5eUgXbhnamRC7c8v4kTO1FLyla9eurFq1innz5nHvvfcyatSon8wPDQ398Tbd4OBgioqKar1PXz+FZerQkq2Z/GHODxw/Wch9Y7vz2/M6ElSH1yQahgVz+4iu/Cq5HX+bl8rTn28lZddRnp/Yj8hGVetawZhAtW/fPlq0aMHVV19N48aNmT17ttf3aT/tDPmFxTw0dyPXzlpBs0ah/PeWwfzugk51WjxKa9usIc//ph9P/KI3y9OPcPm/l7E984QjWYzxF+vXr2fAgAH07duXRx99tNbXN6oioMZET05OVhtQ6qeO5RZw/asr+CEjixsGx3P36G4+9XzGyp1HmfyfVRQUl/DCb/pxQdefDYpmjONSU1Pp3r270zE8orxjEZFVqppcdllrgQSwQ9n5XDl9OakHcnj5mv48eEkPnyoeAGfHt+DjKYNp26wh17+6gleW7iCQfvQY48usgASoPUdP8quXv2PPsZPMvv5sLurR2ulIFYpr3ogP/u9cRnSP4ZFPNnH3B+soKCpxOpYxAc8KSADannmCCS9/x7HcAt64aSDndmnpdKQzimgQwktX9+fWYV14LyWDae+upbjEWiLGd9SHlnF1j8HuwgowG/dlce0rKxCBd38/iO5tmjodqcqCgoQ7RyXSNDyUR+el0igsmL//ordjF/uNOS08PJwjR474dZfup8cDCQ8Pr/I6VkACyKpdR7n+1ZU0aRDCGzcNpFN0Y6cj1cjvLuhEzqki/rV4GxENQnjwkiS//Z/W1A9xcXFkZGSQmZnpdJRaOT0iYVVZAQkQWw7kcN2slUQ3acAbNw30+FPldW3aiARO5Bcxa9kOmoaHcMeoRKcjmQAWGhpa5VH86hMrIAHgUE4+N85eSUSDYN763UCvP1leF0SEB8Z1J/dUEf/6Io2IBiH8/sLOTscyJqBYAann8gqKuem1FI7mFjBn8qB6UTxOExEeu6IXuQVF/G3+ZhqHh3DVwA5OxzImYFgBqcdKSpTb313D+r1ZTL8muVa96Pqq4CDh6V/3Ja+gmPv/u4HIhqGM6x3rdCxjAoLdxluPPb5gM59tPMgDFycxMql2/f77stDgIF64qh/92zfnj3PWsfmAZ7vSNsaUzwpIPfXm97uYviSdawd14IbB8U7H8brw0GD+fVU/GoeHMPk/q8jKK3Q6kjH1nhWQeujrrZn8+eONDEmM5s/jAucW11ZNw3nxqn5kHMvjzvfWUmIPGhrjVVZA6pkdh3O55c3
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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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"# implicit \n",
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"plt.plot(x,y,label=\"sin\")\n",
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"plt.xlabel('x label')\n",
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"plt.ylabel('y label')\n",
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"plt.title(\"Simple Plot\")\n",
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"plt.legend()\n",
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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",
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"execution_count": 6,
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"id": "3f2d03de",
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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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"<matplotlib.legend.Legend at 0x7fa5029586d0>"
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]
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},
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"execution_count": 6,
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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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"data": {
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"image/png": "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
|
||
|
|
"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": 7,
|
||
|
|
"id": "568d7656",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.legend.Legend at 0x7fa520ba0220>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"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": "markdown",
|
||
|
|
"id": "287d0813",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Figure and Axes"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"id": "7cbcc514",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"Text(0, 0.5, 'y')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"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": 9,
|
||
|
|
"id": "8cd16580",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"Text(0, 0.5, 'y')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"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": 10,
|
||
|
|
"id": "5f4d1736",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.legend.Legend at 0x7fa501a7a220>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"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": [
|
||
|
|
"## Figure : multiples"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"id": "45b0275c",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x7fa502cd0610>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"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": 12,
|
||
|
|
"id": "2c22bda9",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"fig.savefig(\"output.pdf\")\n",
|
||
|
|
"fig.savefig(\"output.png\")"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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
|
||
|
|
}
|