388 lines
97 KiB
Plaintext
388 lines
97 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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"metadata": {},
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"source": [
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"# Matplotlib Exercices - Solutions \n",
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"\n",
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"Bienvenue sur les exercices de révision de matplotlib! Prenez votre temps surtout, Matplotlib peut être difficile à comprendre au début. Ce sont des tracés relativement simples, mais ils peuvent être difficiles si c'est la première fois que vous utilisez matplotlib, n'hésitez pas à vous référer aux solutions au fur et à mesure que vous avancez.\n",
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"\n",
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"Ne vous inquiétez pas non plus si vous trouvez la syntaxe matplotlib frustrante, nous ne l'utiliserons pas souvent tout au long du cours, nous allons passer à l'utilisation des capacités de visualisation intégrées de pandas. Mais, ceux-ci sont construits à partir de matplotlib, c'est pourquoi il est toujours important d'y être exposé !\n",
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"\n",
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"**NOTE : TOUTES LES COMMANDES POUR TRACER UNE FIGURE DOIVENT ÊTRE PRESENTES DANS LA MÊME CELLULE. LES SÉPARER EN PLUSIEURS CELLULES PEUT NE RIEN FAIRE APPARAÎTRE.**\n",
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"\n",
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"# Exercices\n",
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"\n",
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"Suivez les instructions pour recréer les tracés à l'aide de ces données:\n",
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"\n",
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"## Données"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"x = np.arange(0,100)\n",
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"y = x*2\n",
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"z = x**2"
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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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"**Importer matplotlib.pyplot en tant que plt et définir %matplotlib inline si vous utilisez le notebook jupyter. Quelle commande utilisez-vous si vous n'utilisez pas Jupyter Notebook?**"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline"
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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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"## Exercice 1\n",
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"\n",
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"**Suivre ces étapes**\n",
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"* **Créer un objet Figure qu'on nomme fig en utilisant plt.figure()**\n",
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"* **Utiliser add_axes pour ajouter un axe sur le canevas de la figure à [0,0,1,1]. Appeler ce nouvel axe ax.**\n",
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"* **Tracer (x,y) sur ces axes et définir les étiquettes et titres pour correspondre au graphique ci-dessous:**"
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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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"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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"Text(0.5, 1.0, 'title')"
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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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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"fig = plt.figure()\n",
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"ax = fig.add_axes([0,0,1,1])\n",
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"ax.plot(x,y)\n",
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"\n",
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"ax.set_xlabel('x')\n",
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"ax.set_ylabel('y')\n",
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"ax.set_title('title')"
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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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"## Exercice 2\n",
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"**Créez un objet Figure et placez-y deux axes, ax1 et ax2: situés à [0,0,1,1] et [0.2,0.5,.2,.2] respectivement.**"
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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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"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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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig = plt.figure()\n",
|
||
|
|
"\n",
|
||
|
|
"ax1 = fig.add_axes([0,0,1,1])\n",
|
||
|
|
"ax2 = fig.add_axes([.2,.5,.2,.2])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"**Tracer maintenant (x,y) sur les deux axes. Afficher le résultat.**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {
|
||
|
|
"scrolled": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"ax1.plot(x,y,color='r')\n",
|
||
|
|
"ax1.set_xlabel('x')\n",
|
||
|
|
"ax1.set_ylabel('y')\n",
|
||
|
|
"\n",
|
||
|
|
"ax2.plot(x,y,color='r')\n",
|
||
|
|
"ax2.set_xlabel('x')\n",
|
||
|
|
"ax2.set_ylabel('y')\n",
|
||
|
|
"\n",
|
||
|
|
"fig # Afficher l'objet figure"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Exercice 3\n",
|
||
|
|
"\n",
|
||
|
|
"**Créer le tracé ci-dessous en ajoutant deux axes à un objet figure à [0,0,1,1] et [0.2,0.5,.4,.4].**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAd0AAAFDCAYAAAB/UdRdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAcq0lEQVR4nO3dcXBU9b338c+3RmTs7UVUmIobC+nSSMIEaTca62il9CmQsaF/YBqcSrk6xmp6naFObZ+5j1zL1SlPnXpnFLR3LZX2Mje5lD9uMhriFCv2jiPiKs91QtQEBc1Gpya2orVFBL7PH4lpQoI5hN3fyYb3a2Zn9pzzy8lnfuzkwzl79qy5uwAAQP59Ku4AAACcLihdAAACoXQBAAiE0gUAIBBKFwCAQChdAAACGbN0zeyXZva2mbWfYLuZ2f1mts/MXjSzL+Y+JgAAhS/Kke5mSUs/YfsySXMHHvWSHjr1WAAATD5jlq67/17SHz9hyHJJv/Z+uySdY2YX5CogAACTRS7e071QUveQ5ezAOgAAMERRDvZho6wb9d6SZlav/lPQ+vSnP/2liy++OAe/HgCAcJ5//vk+d58xnp/NRelmJRUPWU5IenO0ge6elpSWpFQq5ZlMJge/HgCAcMzs9fH+bC5OL7dIWjVwFXOVpIPu/lYO9gsAwKQy5pGumTVKulrS+WaWlfTPks6UJHf/uaRWSdWS9kn6i6R/yFdYAAAK2Zil6+4rx9jukhpylggAgEmKO1IBABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQunG6IYbbtDMmTM1f/78Ube7u2677TYlk0lVVFTohRdeCJwQAJBLlG6MVq9erba2thNu3759u7q6utTV1aV0Oq1bbrklYDoAQK5RujG66qqrdO65555we3Nzs1atWiUzU1VVld5991299dZbARMCAHKJ0p3Aenp6VFxcPLicSCTU09MTYyIAwKkoijsATszdR6wzsxHr0um00um0JOnll1/WxRdfnPdsQK4cOHBAfX19cccAgqB0J7BEIqHu7u7B5Ww2q1mzZo0YV19fr/r6eklSKpVSJpMJlhE4ValUKu4IQDCcXp7Aampq9Otf/1rurl27dmnatGm64IIL4o4FABgnjnRjtHLlSu3cuVN9fX1KJBL68Y9/rI8++kiS9N3vflfV1dVqbW1VMpnU2WefrUceeSTmxACAU0HpxqixsfETt5uZNm7cGCgNACDfOL0MAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQujFqa2tTaWmpksmk1q9fP2L7G2+8oUWLFmnhwoWqqKhQa2trDCkBALlC6cbk6NGjamho0Pbt29XR0aHGxkZ1dHQMG3P33XertrZWe/bsUVNTk2699daY0gIAcoHSjcnu3buVTCZVUlKiKVOmqK6uTs3NzcPGmJnee+89SdLBgwc1a9asOKICAHKkKO4Ap6uenh4VFxcPLicSCT377LPDxtx11136+te/rgceeEAffPCBduzYETomACCHONKNibuPWGdmw5YbGxu1evVqZbNZtba26vrrr9exY8dG/Fw6nVYqlVIqlVJvb2/eMgMATg2lG5NEIqHu7u7B5Ww2O+L08aZNm1RbWytJuvzyy3Xo0CH19fWN2Fd9fb0ymYwymYxmzJiR3+AAgHGLVLpmttTMXjGzfWb2o1G2X2RmT5rZHjN70cyqcx91cqmsrFRXV5f279+vw4cPq6mpSTU1NcPGXHTRRXriiSckSS+99JIOHTpEqQJAARuzdM3sDEkbJS2TVCZppZmVHTfs/0ja6u4LJdVJejDXQSeboqIibdiwQUuWLNG8efNUW1ur8vJyrV27Vi0tLZKkn/3sZ3r44Ye1YMECrVy5Ups3bx5xChoAUDiiXEh1qaR97v6aJJlZk6TlkoZ+vsUl/f3A82mS3sxlyMmqurpa1dXDTwqsW7du8HlZWZmefvrp0LEAAHkS5fTyhZK6hyxnB9YNdZekb5tZVlKrpH8cbUdmVm9mGTPLcMEPAOB0E6V0RzufefyltyslbXb3hKRqSf9uZiP27e5pd0+5e4r3JgEAp5sopZuVVDxkOaGRp49vlLRVktz9GUlTJZ2fi4AAAEwWUUr3OUlzzWyOmU1R/4VSLceNeUPSYkkys3nqL13OHwMAMMSYpevuRyR9T9Ljkl5S/1XKe81snZl9/BmX2yXdZGb/I6lR0mof7e4PAACcxiLdBtLdW9V/gdTQdWuHPO+QdEVuowEAMLlwRyoAAAKhdAEACITSBQAgEEoXAIBAKF0AAAKhdAEACITSBQAgEEoXAIBAKN0YtbW1qbS0VMlkUuvXrx91zNatW1VWVqby8nJdd911gRMCAHIp0h2pkHtHjx5VQ0ODfvvb3yqRSKiyslI1NTUqKysbHNPV1aWf/OQnevrppzV9+nS9/fbbMSYGAJwqjnRjsnv3biWTSZWUlGjKlCmqq6tTc3PzsDEPP/ywGhoaNH36dEnSzJkz44gKAMgRSjcmPT09Ki7+2zcmJhIJ9fT0DBvT2dmpzs5OXXHFFaqqqlJbW1vomACAHOL0ckxG+xImMxu2fOTIEXV1dWnnzp3KZrO68sor1d7ernPOOWfYuHQ6rXQ6LUnq7eUbFQFgouJINyaJRELd3d2Dy9lsVrNmzRoxZvny5TrzzDM1Z84clZaWqqura8S+6uvrlclklMlkNGPGjLxnBwCMD6Ubk8rKSnV1dWn//v06fPiwmpqaVFNTM2zMN7/5TT355JOSpL6+PnV2dqqkpCSOuACAHKB0Y1JUVKQNGzZoyZIlmjdvnmpra1VeXq61a9eqpaVFkrRkyRKdd955Kisr06JFi3TvvffqvPPOizk5AGC8bLT3FkNIpVKeyWRi+d2TWSqVEvOKQsJrFoXGzJ5399R4fpYjXQAAAqF0AQAIhNIFACAQShcAgEAoXQAAAqF0AQAIhNIFACAQShcAgEAoXQAAAqF0AQAIhNIFACAQShcAgEAoXQAAAqF0AQAIhNKNUVtbm0pLS5VMJrV+/foTjtu2bZvMjK8/A4ACR+nG5OjRo2poaND27dvV0dGhxsZGdXR0jBj3/vvv6/7779dll10WQ0oAQC5RujHZvXu3ksmkSkpKNGXKFNXV1am5uXnEuDvvvFN33HGHpk6dGkNKAEAuUbox6enpUXFx8eByIpFQT0/PsDF79uxRd3e3rrnmmtDxAAB5UBR3gNOVu49YZ2aDz48dO6Y1a9Zo8+bNY+4rnU4rnU5Lknp7e3OWEQCQWxzpxiSRSKi7u3twOZvNatasWYPL77//vtrb23X11Vdr9uzZ2rVrl2pqaka9mKq+vl6ZTEaZTEYzZswIkh8AcPIo3ZhUVlaqq6tL+/fv1+HDh9XU1KSamprB7dOmTVNfX58OHDigAwcOqKqqSi0tLUqlUjGmBgCcCko3JkVFRdqwYYOWLFmiefPmqba2VuXl5Vq7dq1aWlrijgcAyAPe041RdXW1qqurh61bt27dqGN37twZIBEAIJ840gUAIBBKFwCAQChdAAACoXQBAAiE0gUAIBBKFwCAQChdAAACoXQBAAgkUuma2VIze8XM9pnZj04wptbMOsxsr5n9R25jAgBQ+Ma8I5WZnSFpo6T/JSkr6Tkza3H3jiFj5kr635KucPc/mdnMfAUGAKBQRTnSvVTSPnd/zd0PS2qStPy4MTdJ2ujuf5Ikd387tzEBACh8UUr3QkndQ5azA+uG+oKkL5jZ02a2y8yW5iogAACTRZQvPLBR1h3/DexFkuZKulpSQtJ/m9l8d3932I7M6iXVS9JFF1100mEBAChkUY50s5KKhywnJL05yphmd//I3fdLekX9JTyMu6fdPeXuKb5sHQBwuolSus9Jmmtmc8xsiqQ6Scd
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig = plt.figure()\n",
|
||
|
|
"\n",
|
||
|
|
"ax1 = fig.add_axes([0,0,1,1])\n",
|
||
|
|
"ax2 = fig.add_axes([.2,.5,.4,.4])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"**Utiliser maintenant les tableaux x, y et z pour recréer le graphique ci-dessous. Notez les limites x et y sur le tracé inséré:**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# large\n",
|
||
|
|
"ax1.plot(x,z,color='b')\n",
|
||
|
|
"ax1.set_xlabel('X')\n",
|
||
|
|
"ax1.set_ylabel('Z')\n",
|
||
|
|
"\n",
|
||
|
|
"# insert\n",
|
||
|
|
"ax2.plot(x,y,color='b')\n",
|
||
|
|
"ax2.set_xlabel('x')\n",
|
||
|
|
"ax2.set_ylabel('y')\n",
|
||
|
|
"ax2.set_title('zoom')\n",
|
||
|
|
"\n",
|
||
|
|
"ax2.set_xlim([20,22])\n",
|
||
|
|
"ax2.set_ylim([30,50])\n",
|
||
|
|
"\n",
|
||
|
|
"fig"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Exercice 4\n",
|
||
|
|
"\n",
|
||
|
|
"**Utiliser plt.subplots(nrows=1, ncols=2) pour créer le tracé ci-dessous.**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Canevas vide de sous-graphiques 1 par 2\n",
|
||
|
|
"fig, axes = plt.subplots(nrows=1, ncols=2)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"**Tracer maintenant (x,y) et (x,z) sur les axes. Jouer avec la largeur et le style de la ligne**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"axes[0].plot(x,y,color='b',lw=3,ls='-')\n",
|
||
|
|
"axes[1].plot(x,z,color='r',lw=3,ls='--')\n",
|
||
|
|
"\n",
|
||
|
|
"fig"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"**Voyez si vous pouvez redimensionner le tracé en ajoutant l'argument figsize() dans plt.subplots() et copier et coller votre code précédent.**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"Text(0, 0.5, 'z')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x144 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(12,2))\n",
|
||
|
|
"\n",
|
||
|
|
"axes[0].plot(x,y,color=\"blue\", lw=5)\n",
|
||
|
|
"axes[0].set_xlabel('x')\n",
|
||
|
|
"axes[0].set_ylabel('y')\n",
|
||
|
|
"\n",
|
||
|
|
"axes[1].plot(x,z,color=\"red\", lw=3, ls='--')\n",
|
||
|
|
"axes[1].set_xlabel('x')\n",
|
||
|
|
"axes[1].set_ylabel('z')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Bon travail!"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": "3.7.5"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 1
|
||
|
|
}
|