L1-python/source/Plotting I.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "0a3e2a4e",
"metadata": {},
"source": [
"# Feuille 1 - UE Projet CMI-L1\n",
"Introduction to Python figures"
]
},
{
"cell_type": "markdown",
"id": "f8579363",
"metadata": {},
"source": [
"## **Libraries**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e4d24b04",
"metadata": {},
"outputs": [],
"source": [
"# libraries\n",
"import scipy \n",
"# \"SciPy\" provides algorithms for optimization, integration, interpolation, eigenvalue problems, \n",
"# algebraic equations, differential equations, statistics and many other classes of problems.\n",
"import numpy as np\n",
"# Fast and versatile, the \"NumPy\" vectorization, indexing, and broadcasting concepts are the \n",
"# de-facto standards of array computing today.\n",
"import matplotlib.pyplot as plt\n",
"# \"Matplotlib\" is a comprehensive library for creating static, animated, and interactive \n",
"# visualizations in Python."
]
},
{
"cell_type": "markdown",
"id": "6a8dcbb5",
"metadata": {},
"source": [
"## Anatomy of a figure"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b5768c9e",
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from PIL import Image\n",
"img = Image.open('anatomy.webp')\n",
"img.save(\"anatomy.png\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8f75cee3",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"anatomy.png\" width=\"500\" height=\"500\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# import image module\n",
"from IPython.display import Image\n",
" \n",
"# get the image\n",
"Image(url=\"anatomy.png\", width=500, height=500)"
]
},
{
"cell_type": "markdown",
"id": "4c41a20d",
"metadata": {},
"source": [
"## Data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9e9a38b3",
"metadata": {},
"outputs": [],
"source": [
"# some data to work\n",
"x= np.linspace(0,2*np.pi)\n",
"y= np.sin(x)"
]
},
{
"cell_type": "markdown",
"id": "82c13c31",
"metadata": {},
"source": [
"## Figures : Implicit or explicit?\n",
"**Using figures**\n",
"- Explicitly create Figures and Axes, and call methods on them (the \"object-oriented (OO) style\").\n",
"- Rely on pyplot to implicitly create and manage the Figures and Axes, and use pyplot functions for plotting."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "0f84f0db",
"metadata": {},
"outputs": [
{
"data": {
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7mMPCaRfSOrLqI68ZE8hEZFV5j0sE1mPK9cTy9CO8vWI3vz2voxWPaggOEp78VR8Ki5W7Plhnp7KMqSUrIH4mv7CYez9cT/sWjbhjpI3CV13xLSP409huLNmayVsrdjsdxxi/ZgXEzzy7eBs7Dufytyt60TDMbtmtiavP6cD5CS159NNUdh3JPfMKxphyWQHxIxv2ZjF9SToTkuMY3KWl03H8lojw91/0JjhI+OOcddZrrzE1ZAXETxQVl3DPh+to3iiM+8ba0+a1FdusIQ+MS2LFzqO8vyrD6TjG+CUrIH7ilaU72LA3m4fH9yCyUajTceqFX/WPY0B8C/42P5VjuQVOxzHG71gB8QM7D+fy1KKtjEqKYYx1lOgxIsIjl/UkJ7+Ix+dvdjqOMX7HCoiPU1Xu/XA9YSFBPHJZT3tg0MMSWzfht+d15N2UPazadfTMKxhjfmQFxMfNScngu/Qj/Glsd2Ka2oNv3jB1eAKxkeHc99EGiopLnI5jjN+wAuLDsvIKeXzBZs6Ob86vk62vK2+JaBDCny/pweYDOcz+dqfTcYzxG1ZAfNgzn2/l+MkCHrq0B0FBdurKmy7qEcPQxGieXrSV/Vl5Tscxxi9YAfFR2w7m8Pp3u5g4oD09YiOdjlPviQh/ubQnRSXKI59scjqOMX7BCogPUlUe+t9GIsKCuXOUdVdSV9pHNWLK0C7MW3+Ar7YccjqOMT7PCogP+mzjQZalHeHOUYm0iAhzOk5AmXRhJzq1jODBuRvJL7Rx1I2pjBUQH5NfWMxfP91EYkwTrhrY3uk4AadBSDAPj+/JriMneXXZTqfjGOPTHC0gIjJaRLaISJqI3FPO/CEikiUia92vP1d1XX81Y0k6GcfyePDSJEKCrb474byElozo3ooXvkzjsA2Ba0yFHPsLJSLBwAvAGCAJmCgi5XXy9I2q9nW/Hq7mun5l3/E8XvgqjbG9WnNuZ+ss0Un3ju1OfmExTy/a6nQUY3yWkz9xBwBpqpquqgXAO8D4OljXZz02LxVV+NPY7k5HCXidoxtz9TkdeHvFbrYezHE6jjE+yckC0hbYU+p7hntaWYNE5AcRmS8iPaq5rt9Ynn6ET9btZ/KFnYlr3sjpOAa4bXgCjRuE8OinqU5HMcYnOVlAynsyruzADKuBDqraB3gO+G811nUtKDJJRFJEJCUzM7OmWb2qpER5+H+baNusIZMv7Ox0HOPWPCKMqcMT+Hprpt3Wa0w5nCwgGUDp/jnigH2lF1DVbFU94f48DwgVkZZVWbfUNqararKqJkdHR3syv8d8/MNeNu3P5q7RiTbKoI+5ZlAHOkQ14rF5qdZPljFlOFlAVgIJItJRRMKAK4G5pRcQkdbi7n5WRAbgynukKuv6i/zCYv752VZ6tm3KJb1jnY5jymgQEsy9Y7qx9eAJ3k3Zc+YVjAkgjhUQVS0CpgCfAanAe6q6UUQmi8hk92K/BDaIyA/Av4Ar1aXcdev+KGrvjeW72Hs8j3tGd7f+rnzURT1aMyC+BU8t3EpOfqHTcYzxGaIaOONBJycna0pKitMxfpR1spAL/vElfdo14/UbBzgdx1RiXcZxLn1+Gf83pDN3j+7mdBxj6pSIrFLV5LLT7Uk1B/376zSy8wu5x/4g+bzecc244qy2vLJ0B3uOnnQ6jjE+wQqIQ/Ydz+PVZTu5vG9bkmKbOh3HVMEfRyciwNOf28OFxoAVEMc85X7C+Y5RXR1OYqqqTWRDrj83no/W7GXLAXu40BgrIA7YfCCbD1ZncP258fbQoJ+ZfGFnGoeF8M+FW5yOYozjrIA44PH5m2nSIISbh9hDg/6meUQYv7+wE4s2HWTVrmNOxzHGUVZA6ti32w/z1ZZMpgzrQrNGNtaHP7phcEdaNg7jH59tJpDuYjSmLCsgdUhVeXz+Zto2a8i1g+KdjmNqKKJBCFOGdmF5+lG+2XbY6TjGOMYKSB36bONB1mVkcfuIBMJDrcsSfzZxYHvimjfkic82U1JirRATmKyA1JHiEuWpRVvoFB3B5Wf5dcfBBlcXJ9NGdGXD3mzmbzjgdBxjHGEFpI58sm4fWw+eYNqIrjbSYD1x2Vlt6RrTmCcXbrGOFk1Asr9kdaCouIRnPt9Gt9ZNuLhXG6fjGA8JDhL+MCqR9MO5vL8qw+k4xtQ5KyB14MPVe9lxOJc7RyVah4n1zMikGM5q34xnF28jv7DY6TjG1CkrIF52qqiYZxdvo09cJCO6t3I6jvEwEeGui7qxPyuf/3y3y+k4xtQpKyBe9u7KPew9nsedoxJxD21i6plBnaM4P6ElL329nZMFRU7HMabOWAHxoryCYp77Io0B8S04P6Gl03GMF90+oitHcgt43VohJoBYAfGiN5bvIjPnFHeO6mqtj3quf4fmXNg1mpe/3s6JU9YKMYHB0QIiIqNFZIuIpInIPeXMv0pE1rlf34pIn1LzdorIehFZKyK+M0qU24lTRbz49XbOT2jJwE5RTscxdWDayK4cO1nIa9/udDqKMXXCsQIiIsHAC8AYIAmYKCJJZRbbAVyoqr2BR4DpZeYPVdW+5Y2U5bRXl+7gaG4Bd45KdDqKqSN92zVjeLdWTF+STrYNfWsCgJMtkAFAmqqmq2oB8A4wvvQCqvqtqp7u8nQ5EFfHGWskK6+Q6d+kM6J7DH3bNXM6jqlD00Z2JSuvkNnLdjodxRivc7KAtAX2lPqe4Z5Wkd8C80t9V2ChiKwSkUkVrSQik0QkRURSMjMzaxW4qmYt3UFOfhHTRibUyf6M7+jZNpKRSTHM+CadrDxrhZj6zckCUt5V5XJ7pRORobgKyN2lJg9W1X64ToHdIiIXlLeuqk5X1WRVTY6Ojq5t5jPKyitk1rIdXNQjhh6xkV7fn/E9t49IICe/iFeW7nA6ijFe5WQByQDalfoeB+wru5CI9AZmAuNV9cjp6aq6z/1+CPgI1ykxx51ufUwdbq2PQNUjNpIxPVvz6tIdHD9Z4HQcY7zGyQKyEkgQkY4iEgZcCcwtvYCItAc+BK5R1a2lpkeISJPTn4FRwIY6S14Ba32Y024f0ZUTBUXM/MZaIab+cqyAqGoRMAX4DEgF3lPVjSIyWUQmuxf7MxAF/LvM7boxwFIR+QFYAXyqqgvq+BB+xlof5rTE1k0Y26sNry5z3Y1nTH0U4uTOVXUeMK/MtJdKfb4JuKmc9dKBPmWnO8laH6as24cnMG/9fqYvSeeeMd2cjmOMx9mT6B7y6jJrfZifSohpwiW9Y3n9u53WCjH1khUQD8jKK+SVpdb6MD83dXgX8gqLmflNutNRjPE4KyAeYK0PU5EurVyDiL327U67I8vUO1ZAaslaH+ZMbh2WQG5BsT0XYuodKyC1ZK0PcyauO7JaM3vZTrJO2tPppv6o8C4sEbmishVV9UPPx/Ev1vowVXXrsATmrT/ArGU7mDayq9NxjPGIym7jvaSSeYrrAb+ANnvZTmt9mCrp3qYpF/WIYdayHfz2/I40DQ91OpIxtVZhAVHVG+oyiL/JyXc99zEyyVofpmpuHZbAZxsPMnvZTvvRYeqFM14DEZEYEXlFROa7vyeJyG+9H823vf7dLrLyCpk6zP4QmKrp2TaSEd1jeGXpDnJsvBBTD1TlIvpsXN2NxLq/bwVu91Iev5B7qoiZ36QzNDGaXnHW+jBVd9vwBLLyCm3sdFMvVKWAtFTV94AS+LEPq2KvpvJxbyzfxbGThdxqpyFMNfWKi2RYt1bM+Cbdxk43fq8qBSRXRKJwj9UhIucAWV5N5cPyCoqZ8U065ye0pF/75k7HMX5o6vAEjp8s5D/WCjF+rioF5A5c3ax3FpFlwOvArV5N5cPeWrGbwycK7CKoqbG+7ZpxYddoZnyTzskCa4UY/3XGAqKqq4ELgXOB3wM9VHWdt4P5ovzCYl76ejuDOkVxdnwLp+MYPzZ1eAJHcwt4c/lup6MYU2NVuQsrHJgKPAL8BdfwseHeDuaL3l25h8ycU9b6MLXWv0NzBneJ4uUl6eQXBvQlRePHqnIK63WgB/Ac8DyQBPzHm6F80akiV+vj7PjmnNPJWh+m9qYOS+DwiVO8vcJaIcY/VaWAJKrqb1X1S/drEuCRvhhEZLSIbBGRNBG5p5z5IiL/cs9fJyL9qrqup72/KoP9WflMHZ6AiHh7dyYADOwUxYCOLXjp6+3WCjF+qSoFZI37zisARGQgsKy2OxaRYOAFYAyuVs1EEUkqs9gYIMH9mgS8WI11PaagqIR/f7mds9o347wuLb21GxOAbhuewMHsU8xZleF0FGOqrcICIiLrRWQdMBD4VkR2isgO4DvgAg/sewCQpqrpqloAvAOML7PMeOB1dVkONBORNlVc12M+WpPB3uN5TB1mrQ/jWed2jqJf+2a89NV2CopKnI5j6qFjuQVcPfN7Nuz1/NMXlbVAxuHqUHE00BHXnVhD3J8v9sC+2wJ7Sn3PcE+ryjJVWRcAEZkkIikikpKZmVmjoJk5p0ju0JwhidE1Wt+YiogIU4cnsPd4Hh+utlaI8bxXlu5gadphwkI8P3pHhVtU1V2lX0AerocJT79qq7yf8mW3W9EyVVnXNVF1uqomq2pydHTNCsCUYQm8+/tB1vowXnFh12h6x0XywldpFBZbK8R4TtbJQmZ/u5OxvVrTNaaJx7dfldt4LxWRbcAO4GtgJzDfA/vOANqV+h4H7KviMlVZ16OCg6x4GO8QEW4dlsCeo3l8vNar/xmbAPPqtzs4caqIKUO98+hBVdo0jwDnAFtVtSMwHA9cRAdWAgki0lFEwoArcT3xXtpc4Fr33VjnAFmqur+K6xrjN0Z0b0X3Nk154cs0iks80cA3gS47v5BZS11DTiTFNvXKPqpSQApV9QgQJCJBqvol0Le2O3Z3yjgFV0+/qcB7qrpRRCaLyGT3YvOAdCANmAHcXNm6tc1kjFNEhKnDurDjcC6frLNWiKm917/dSXZ+kVeHnKhsRMLTjotIY2AJ8KaIHAI80oGPqs7DVSRKT3up1GcFbqnqusb4s4t6tKZrTGOe+yKNS3rHEmSnTU0NnThVxMylOxjWrZVXh5yoSgtkPK4L6NOABcB2Kh/u1hhTA0FBwpRhCaQdOsG8DfudjmP82BvLd3H8ZCG3Duvi1f1UpTPFXFUtVtUiVX1NVf/lPqVljPGwi3u1oXN0BM8tTqPEroWYGjhZUMSMJa4hJ87y8pATlT1ImCMi2eW8ckQk26upjAlQwUGuO7K2HMxh4aYDTscxfuit73dzJLeA2+qg09fKngNpoqpNy3k1UVXvXNI3xnBJn1g6tYzgWWuFmGrKLyzm5SXpnNs5iuQ6GHLC848mGmNqJThIuGVoF1L3Z/N56kGn4xg/8s6K3XU65IQVEGN80Pi+sXSIasSzi7fhuhnRmMq5hpxIZ0B8C87pFFUn+7QCYowPCgkO4pahXdi4L5vFqYecjmP8wHspGRzIzq/TAe+q0pXJFBHx7qV8Y8zPXH5WW9q1aMi/vrBWiKncqaJiXvwyjX7tmzG4S920PqBqLZDWwEoRec89iJM93WRMHQgNDuKWIV1Yl5HFV1tq1pO0CQxzUjLYl5XPtJFd67TT16o8B3I/rgGdXgGuB7aJyGMi0tnL2YwJeFf0i6Nts4Z2LcRU6FRRMf/+Mo3+HZrX+YB3VboG4u5S5ID7VQQ0B94XkSe8mM2YgBcWEsTNQzuzds9xlmw77HQc44NOtz5uc2C47apcA5kqIquAJ3D1wttLVf8P6A/8wsv5jAl4v+wfR2xkOM9+vtVaIeYnTrc++rVvxvkJdT/cdlVaIC2BK1T1IlWdo6qFAKpagmvUQmOMFzUICeb/hnRm9e7jLEuzXoTM//f+Klfr4/YRdXvt47SqXAP5s3tEwvLmpXo+kjGmrAlnt6NNZDhPWyvEuBUUlfDCF861PsCeAzHGLzQICebmoV1YteuYXQsxAMxZtcfR1gdYATHGb0xIdt2R9fQia4UEOl9ofYBDBUREWojIIhHZ5n7/2YOKItJORL4UkVQR2Sgit5Wa95CI7BWRte7X2Lo9AmPqXoOQYKYM68LaPcftuZAAd7r1cZuDrQ9wrgVyD7BYVROAxe7vZRUBd6pqd1xjst8iIkml5j+tqn3dLxuZ0ASEX/aPo12LhnYtJIAVFJXw7y+3c1b7ZlzgYOsDnCsg44HX3J9fAy4ru4Cq7lfV1e7PObjGPm9bVwGN8UWhwUHcOjSBdRlZ1kdWgHp/VQZ7j+c5eu3jNKcKSIyq7gdXoQBaVbawiMQDZwHfl5o8RUTWicisyvrqEpFJIpIiIimZmdbsN/7v8n5t6RDViKfsWkjAyS8s5rkvtvlE6wO8WEBE5HMR2VDOa3w1t9MY+AC4XVVPj4T4ItAZ6AvsB56saH1Vna6qyaqaHB0dXbODMcaHhAYHMXVYApv2Z/PZRhsvJJC8s2I3+7Py+cOoRMdbH+DFAqKqI1S1Zzmvj4GDItIGwP1ebltcREJxFY83VfXDUts+6B6nvQSYAQzw1nEY44vG942lY8sInvl8q41aGCBOFhTx/JfbOadTC87tXHc97lbGqVNYc4Hr3J+vAz4uu4C7199XgFRVfarMvDalvl4ObPBSTmN8UkhwELcNT2DzgRwWbLSx0wPB69/t4vCJUz7T+gDnCsjjwEgR2QaMdH9HRGJF5PQdVYOBa4Bh5dyu+4SIrBeRdcBQYFod5zfGcZf0iaVzdARPL9pKsbVC6rWc/EJe+no7QxKj62Ss86oKcWKnqnoEGF7O9H3AWPfnpUC5ZVZVr/FqQGP8QHCQcNuIrkx9ew2frt/PpX1inY5kvGTW0p0cP1nInSMTnY7yE/YkujF+bFyvNiTGNOGZRVspKi5xOo7xguMnC5j5TTqje7SmV1yk03F+wgqIMX4sKEi4c1RX0g/n8v6qDKfjGC94eUk6JwqKmDayq9NRfsYKiDF+bmRSDGe1b8Yzn28jv7DY6TjGgzJzTjF72U4u7RNLYusmTsf5GSsgxvg5EeGui7pxIDuf/3xX7sgLxk/9+6s0CopLuH2E77U+wAqIMfXCoM5RXNA1mhe+SiM7v9DpOMYD9h3P483lu/llvzg6toxwOk65rIAYU0/cdVEix08WMnNJutNRjAc8/2UainLr8C5OR6mQFRBj6omebSO5uHcbZi7dQWbOKafjmFrYnnmCd1fuYeKA9sQ1b+R0nApZATGmHrlzZFdOFZXwwpdpTkcxtfCPBVsIDwli6vAEp6NUygqIMfVIp+jG/Kp/HG99v5s9R086HcfUwKpdx1iw8QCTLuhMy8YNnI5TKSsgxtQzt41IAIFnPt/mdBRTTarK4/NTadm4ATed39HpOGdkBcSYeqZNZEOuG9SBj9ZksPVgjtNxTDV8nnqIlTuPMW1kAhENHOlpqlqsgBhTD908pAsRYSH847MtTkcxVVRUXMLj81PpFB3Br5PbOR2nSqyAGFMPNY8I4/cXdmLRpoN8n37E6TimCuasymB7Zi53XdSNkGD/+NPsHymNMdV20/mdiI0M56+fptqgUz7uZEERTy/aSv8OzbmoR4zTcarMCogx9VR4aDB3je7G+r1Z/HftXqfjmEq88s0ODuWc4t4x3XxmsKiqcKSAiEgLEVkkItvc780rWG6ne+CotSKSUt31jQl0l/aJpU9cJE8s2EJegXW06IuOnDjFy0vSGZUU41ODRVWFUy2Qe4DFqpoALHZ/r8hQVe2rqsk1XN+YgBUUJNw/LokD2fnM+Ma6OPFFz32RRl5hMXeN7uZ0lGpzqoCMB15zf34NuKyO1zcmYJwd34KxvVrz4lfbOZid73QcU8r2zBO8+f0uJiS3o0urxk7HqTanCkiMqu4HcL+3qmA5BRaKyCoRmVSD9Y0xwN2ju1Fcojy50G7r9SWPfLKJ8JBg7vDBwaKqwmsFREQ+F5EN5bzGV2Mzg1W1HzAGuEVELqhBjkkikiIiKZmZmdVd3Zh6oUNUBNcPjmfOqgw27styOo4Bvth8kK+2ZHLbiASim/h2lyUV8VoBUdURqtqznNfHwEERaQPgfj9UwTb2ud8PAR8BA9yzqrS+e93pqpqsqsnR0dGeO0Bj/MwtQ7vQrGEoj36aiqrd1uukU0XFPPy/TXSOjuDaQfFOx6kxp05hzQWuc3++Dvi47AIiEiEiTU5/BkYBG6q6vjHmpyIbhjJtZFe+3X6ExakV/uYydWDW0p3sPHKSBy/pQViI/z5N4VTyx4GRIrINGOn+jojEisg89zIxwFIR+QFYAXyqqgsqW98YU7mJA9rTOTqCx+alUlBU4nScgHQwO5/nv9jGiO4xXNDVv8+KONJbl6oeAYaXM30fMNb9OR3oU531jTGVCw0O4v5xSdzw6kpmLk3n5iG+O9pdffX3+ZspLFYeGNfd6Si15r9tJ2NMjQxNbMVFPWL41+JtNmZIHVu16ygfrtnL7y7oSIco3xznvDqsgBgTgB68pAdBIvzlf5ucjhIwSkqUh+ZuonXT8HrT8rMCYkwAim3WkNuGJ/B56kEWbTrodJyAMGfVHtbvzeLesd38YqyPqrACYkyAuvG8jiTGNOGhuRs5WVDkdJx6LSuvkCcWbCG5Q3Mu7RPrdByPsQJiTIAKDQ7ir5f3ZO/xPJ77Is3pOPXa4/M3c+xkAQ9d2sOvets9EysgxgSws+Nb8Mv+ccxYks42G/7WK5anH+HtFbu56fxO9Gwb6XQcj7ICYkyAu3eM65z8/f/dYE+oe1h+YTH3fLCO9i0aMW2Ef/Z3VRkrIMYEuKjGDbh7dDe+33GUj9bYwFOe9Ozibew8cpK/XdGLhmHBTsfxOCsgxhiuPLsdfds149FPUzl+ssDpOPXChr1ZTF+SzoTkOAZ3ael0HK+wAmKMIShIePTynhzPK+ShuRudjuP3iopLuOfDdTRvFMZ9Y5OcjuM1VkCMMQD0iI1kytAu/HftPuav3+90HL82c+kONuzN5uHxPYhsFOp0HK+xAmKM+dGUYV3o1TaS+/67gcycU07H8Us7Dufy9KKtjEqKYUzP1k7H8SorIMaYH4UGB/HUhD6cOFXEfR+tt7uyqklVuffDdYSFBPHIZT3r1TMf5bECYoz5iYSYJvxxVCILNx3kw9V2V1Z1vLViN8vTj/Knsd2JaRrudByvswJijPmZG8/ryID4Fjw0dyP7juc5HccvbDuYwyOfbOL8hJZceXY7p+PUCSsgxpifCQ4S/vmrPhSrctf76+xU1hnkFxYz5a01RISF8OSEPvX+1NVpjhQQEWkhIotEZJv7vXk5yySKyNpSr2wRud097yER2Vtq3tg6Pwhj6rn2UY247+LuLE07zBvLdzkdx6f99dNNbDmYw5MT+tCqSf0/dXWaUy2Qe4DFqpoALHZ//wlV3aKqfVW1L9AfOAl8VGqRp0/PV9V5Zdc3xtTebwa058Ku0Tw2bzM7Duc6HccnLdhwgDeW7+Z353dkSGIrp+PUKacKyHjgNffn14DLzrD8cGC7qtrPIGPqkIjw91/0JiwkiJvfXE1eQbHTkXzK3uN53P3BOnq1jeSPF3VzOk6dc6qAxKjqfgD3+5nK9pXA22WmTRGRdSIyq7xTYKeJyCQRSRGRlMzMzNqlNiYAtY4M59kr+7L5QLbd2ltKUXEJt7+zhqLiEp6beBZhIYF3SdlrRywin4vIhnJe46u5nTDgUmBOqckvAp2BvsB+4MmK1lfV6aqarKrJ0dHR1T8QYwxDElsxbURXPlyzl//Y9RAA/vVFGit3HuOvl/ckvqX/j29eE14bV1FVR1Q0T0QOikgbVd0vIm2AQ5VsagywWlV/HHez9GcRmQF84onMxpiKTRnahXUZx3n4f5tIatOU5PgWTkdyzPL0Izz/xTauOKstl58V53QcxzjV5poLXOf+fB3wcSXLTqTM6St30TntcmCDR9MZY34mKEh4ckJf4po35OY3V3MoJ9/pSI7Yc/QkU95aTfsWjXj4sp5Ox3GUUwXkcWCkiGwDRrq/IyKxIvLjHVUi0sg9/8My6z8hIutFZB0wFJhWN7GNCWyRDUN56Zr+5OQXMeXNNRQWlzgdqU5l5RVy4+yVFBSVMPO6ZBo38NpJHL8ggXRBLDk5WVNSUpyOYYzfm/vDPqa+vYYbBsfz4CU9nI5TJwqLS7jh1ZUsTz/C6zcO4Nx6OsZHeURklaoml50e2OXTGFMjl/aJZe3u48xatoPecZH1/jqAqvLAfzewNO0wT/yyd0AVj8oE3n1nxhiPuHdsN87p1IK73l/Hl5sruw/G/728JJ13Vu7hlqGdmZAcGP1cVYUVEGNMjYQGBzH92mQSWzdh8hur+G77EacjecX89ft5fP5mxvVuw50jE52O41OsgBhjaqxpeCiv3ziQ9i0acdNrK1m757jTkTxq7Z7j3P7uWvq1b8Y/f9WHoKDA6CSxqqyAGGNqpUVEGG/cNJCoxg24btYKNh/IdjqSR2zYm8WNs1fSqmkDZlybTHhosNORfI4VEGNMrcU0DefNmwYSHhrE1TNX+H3Hiyk7jzJxxnIahgbz+o2u4mh+zgqIMcYj2rVoxJs3DaRElatnfs9ePx2I6pttmVzzygpaNm7Ae5MH0TFAuympCisgxhiP6dKqCa/fOIDs/EKunP4daYdynI5ULQs2HOC3s1PoENWI934/iLbNGjodyadZATHGeFTPtpH857cDySso4fIXvvWbW3w/WpPBLW+tJim2Ke9OGkR0EzttdSZWQIwxHte3XTPmThlM+6hG3PjaSqYv2e7T3cD/Z/kupr37AwM7tuCNmwYS2SjU6Uh+wQqIMcYrYps1ZM7kQYzt2YbH5m3mD3PWcarItwakyskv5A9zfuCB/25gRPdWzLr+7IDv36o67J+UMcZrGoWF8PxvzqLr4iY8/flWdhw+wUvX9PeJccNTdh5l2ntr2Xssj6nDunDr8ARCg+03dXXYPy1jjFeJCLeNSODFq/qRuj+HS59bxvz1+x07pVVYXMKTC7cw4eXvAJgzeRB3jEq04lED1gIxxtSJMb3a0D6qEXe+9wP/9+ZqBnWK4sFLk+jWummdZdhxOJfb313LD3uO88v+cTx4SRJNwu16R01Zd+7GmDpVVFzC2yv38OTCLWTnFXLVwA7cMbIrzSPCvLbPg9n5vLpsJ699u5OwkCD+dkUvxvZqc+YVDVBxd+5WQIwxjjh+soCnF23lje9307hBCHeM7MqvkuNoFOa5EyNbDuQw45t0Pl67l+ISZUyvNjxwcRKtI52/BuNPfKqAiMivgIeA7sAAVS33r7qIjAaeBYKBmap6euTCFsC7QDywE5igqsfOtF8rIMb4ni0Hcnj4k40sSztCeGgQQxNbMbpna4Z1a1Wj00uqyrfbjzB9STpfb82kYWgwvz67HTcO7kj7qEZeOIL6z9cKSHegBHgZ+EN5BUREgoGtuIa0zQBWAhNVdZOIPAEcVdXHReQeoLmq3n2m/VoBMcY3qSrf7zjKvPX7WbDhAIdyThEWHMT5CS0Z3bM13ds0pWl4KJENQ2kSHvJjr7glJcquoydZvzeLjXuz2LAviw17s8nKK6Rl4wZcf24Hrj6nA80aee/0WCDwqREJVTUVXHdnVGIAkKaq6e5l3wHGA5vc70Pcy70GfAWcsYAYY3yTiHBOpyjO6RTFQ5f0YM2eY8xbf4AFGw6wuMyT7CLQuEEIkQ1DOX6ykBOnigAICw4isXUTxvZqTXKHFlzcu431oOtlvnwXVltgT6nvGcBA9+cYVd0PoKr7RaRVRRsRkUnAJID27dt7KaoxxlOCgoT+HVrQv0ML7r+4Oxv3ZbPveB7Z+UVk5RWSlVdItvu9cYMQerWNpEfbpiS0akJYiN2KW5e8VkBE5HOgdTmz7lPVj6uyiXKmVft8m6pOB6aD6xRWddc3xjhHROjZNpKebSOdjmLK4bUCoqojarmJDKD04MNxwD7354Mi0sbd+mgD+EdvbcYYU4/4cntvJZAgIh1FJAy4EpjrnjcXuM79+TqgKi0aY4wxHuRIARGRy0UkAxgEfCoin7mnx4rIPABVLQKmAJ8BqcB7qrrRvYnHgZEisg3XXVqP1/UxGGNMoLMHCY0xxlSqott4ffkUljHGGB9mBcQYY0yNWAExxhhTI1ZAjDHG1EhAXUQXkUxgVw1Xbwkc9mAcJ/j7MVh+5/n7Mfh7fnDmGDqoanTZiQFVQGpDRFLKuwvBn/j7MVh+5/n7Mfh7fvCtY7BTWMYYY2rECogxxpgasQJSddOdDuAB/n4Mlt95/n4M/p4ffOgY7BqIMcaYGrEWiDHGmBqxAmKMMaZGrIBUgYiMFpEtIpLmHoPdr4jILBE5JCIbnM5SEyLSTkS+FJFUEdkoIrc5nak6RCRcRFaIyA/u/H9xOlNNiEiwiKwRkU+czlITIrJTRNaLyFoR8bteVUWkmYi8LyKb3f8vDHI8k10DqZyIBANbcXUbn4FrnJKJqrrJ0WDVICIXACeA11W1p9N5qss9aFgbVV0tIk2AVcBl/vLvQEQEiFDVEyISCiwFblPV5Q5HqxYRuQNIBpqq6jin81SXiOwEklXVLx8kFJHXgG9UdaZ7jKRGqnrcyUzWAjmzAUCaqqaragHwDjDe4UzVoqpLgKNO56gpVd2vqqvdn3NwjQ/T1tlUVacuJ9xfQ90vv/rlJiJxwMXATKezBCIRaQpcALwCoKoFThcPsAJSFW2BPaW+Z+BHf7zqGxGJB84Cvnc4SrW4T/+sxTX88iJV9av8wDPAXUCJwzlqQ4GFIrJKRCY5HaaaOgGZwKvu04gzRSTC6VBWQM5MypnmV78e6wsRaQx8ANyuqtlO56kOVS1W1b5AHDBARPzmVKKIjAMOqeoqp7PU0mBV7QeMAW5xn9r1FyFAP+BFVT0LyAUcvx5rBeTMMoB2pb7HAfscyhKw3NcOPgDeVNUPnc5TU+7TDl8Bo51NUi2DgUvd1xDeAYaJyBvORqo+Vd3nfj8EfITr9LS/yAAySrVc38dVUBxlBeTMVgIJItLRfeHqSmCuw5kCivsi9CtAqqo+5XSe6hKRaBFp5v7cEBgBbHY0VDWo6r2qGqeq8bj++/9CVa92OFa1iEiE+wYM3Kd+RgF+c1eiqh4A9ohIonvScMDxm0hCnA7g61S1SESmAJ8BwcAsVd3ocKxqEZG3gSFASxHJAB5U1VecTVUtg4FrgPXu6wgAf1LVec5FqpY2wGvuO/qCgPdU1S9vhfVjMcBHrt8ihABvqeoCZyNV263Am+4fsunADQ7nsdt4jTHG1IydwjLGGFMjVkCMMcbUiBUQY4wxNWIFxBhjTI1YATHGGFMjVkCMqUMicuIM8+Or22uyiMwWkV/WLpkx1WcFxBhjTI1YATHGA0TkbBFZ5x77I8I97keF/V2JSGMRWSwiq91jVJTu4TlERF5zb+99EWnkXqe/iHzt7gzwM3c398Y4xh4kNMZDROSvQDjQEFe/RX8rZ5kTqtpYREJwjeeQLSItgeVAAtAB2AGcp6rLRGQWri4rngW+BsaraqaI/Bq4SFVvFJHZwCeq+n5dHKcxp1lXJsZ4zsO4+k7LB6aeYVkBHnP3CFuCa4iAGPe8Paq6zP35Dfe2FgA9gUXu7jiCgf0eTW9MNVkBMcZzWgCNcQ0YFY6ry+2KXAVEA/1VtdDd0224e17Z0wKKq+BsVFXHhzE15jS7BmKM50wHHgDeBP5+hmUjcY2xUSgiQ3GdujqtfanxrifiGgJ3CxB9erqIhIpID4+mN6aarIAY4wEici1QpKpvAY8DZ4vIsEpWeRNIFpEUXK2R0t27pwLXicg6XK2aF93DKf8S+LuI/ACsBc71/JEYU3V2Ed0YY0yNWAvEGGNMjVgBMcYYUyNWQIwxxtSIFRBjjDE1YgXEGGNMjVgBMcYYUyNWQIwxxtTI/wMtxw8IAFTUlwAAAABJRU5ErkJggg==\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": 6,
"id": "3f2d03de",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fa5029586d0>"
]
},
"execution_count": 6,
"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": 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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\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": "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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\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": 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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\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": 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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\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": [
"## 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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\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": 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
}