101 lines
23 KiB
Plaintext
101 lines
23 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": "76b94cec-6acc-4f1d-b192-8d2a34d46346",
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"metadata": {},
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"source": [
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"# Test de notebooks"
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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": "461b5a85-bf45-483d-bed8-8403c6027784",
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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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]
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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": "33c6b23c-ec36-4e60-b5e4-df72f7762fbc",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"UsageError: Line magic function `%matplotline` not found.\n"
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]
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}
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],
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"source": [
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"%matplotline inline"
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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": "19578677-ba4c-4cf6-bbb0-e8b691527286",
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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.lines.Line2D at 0x7f713c8d6000>]"
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]
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},
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"execution_count": 4,
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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": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABAD0lEQVR4nO3deVxV9b7/8dfezCDggCIK4qwoymSalqfThGlZNjnQbTp1bp6cTTuap0zrRINZznXOybrdK0aalpWZNJlD5VEG53kAFURQAUGmvdfvj355r6nFRmCxN+/n47H/cLUW+803ZL1dn73XthiGYSAiIiJiEqvZAURERKRhUxkRERERU6mMiIiIiKlURkRERMRUKiMiIiJiKpURERERMZXKiIiIiJhKZURERERM5W52gKqw2+2cOHECf39/LBaL2XFERESkCgzDoKioiFatWmG1Xvn6h1OUkRMnThAWFmZ2DBEREamGrKwsQkNDr/jfnaKM+Pv7Az9/MwEBASanERERkaooLCwkLCzswnn8SpyijPwymgkICFAZERERcTK/9xILvYBVRERETKUyIiIiIqZSGRERERFTqYyIiIiIqVRGRERExFQqIyIiImIqlRERERExlcqIiIiImEplREREREzlcBn5/vvvGTx4MK1atcJisfDxxx//7jHr1q0jLi4Ob29v2rdvz1tvvVWdrCIiIuKCHC4jxcXFREVFMX/+/Crtf/jwYQYNGkT//v1JS0vjmWeeYezYsXz00UcOhxURERHX4/Bn0wwcOJCBAwdWef+33nqLNm3a8OabbwIQERHBli1bmDVrFvfee6+jTy8iIiIuptZfM/LDDz8QHx9/0bYBAwawZcsWKioqLntMWVkZhYWFFz1ERESk5v10KJ8H3/mJkvJK0zLUehnJyckhODj4om3BwcFUVlaSl5d32WMSExMJDAy88AgLC6vtmCIiIg2KzW4w7+v9jPjnj6zfn8fCbw+alqVO3k3z648ONgzjstt/MXXqVAoKCi48srKyaj2jiIhIQ3GqqIyHF2/m9ZR92A24NzaUJ2/sYFoeh18z4qiWLVuSk5Nz0bbc3Fzc3d1p1qzZZY/x8vLCy8urtqOJiIg0OJsO5DEuOZ1TRWX4eLjxwpBI7osLNTVTrZeRvn378umnn160be3atfTq1QsPD4/afnoRERHh57HMnK/3M++b/RgGdA5uxIKEWDoF+5sdzfExzblz50hPTyc9PR34+a276enpZGZmAj+PWB566KEL+48cOZKjR48yceJEdu/ezeLFi3nnnXeYNGlSzXwHIiIi8ptOFpbywL9+ZO7XPxeR4deE8cmo6+tFEYFqXBnZsmULN95444U/T5w4EYCHH36Y9957j+zs7AvFBKBdu3asXr2aCRMmsGDBAlq1asXcuXP1tl4REZE68P2+U0xITie/uBw/TzdeuqcHd0W3NjvWRSzGL68mrccKCwsJDAykoKCAgIAAs+OIiIjUe5U2O7NT9rHwu5/fJRMREsCChBjaN29UZxmqev6u9deMiIiISN3KLjjP2KVp/PvIGQAe6NOGZ+/ohreHm8nJLk9lRERExIV8uyeXiR+mc6akgkZe7rx8bw/u6NnK7Fi/SWVERETEBVTY7Mz6ci9vf38IgMjWASxIiCW8mZ/JyX6fyoiIiIiTO3amhDFL00jLPAvAI/3aMnVQV7zc6+dY5tdURkRERJzY2p05TF6+jYLzFQR4u/PqfVHcFtnS7FgOURkRERFxQuWVdhK/2M27G48AEBXWmPkjYghr6mtusGpQGREREXEymfkljF6ayrZjBQD8uX87Jg/oiqd7nXzkXI1TGREREXEiq7dn89fl2ygqq6Sxrwez7ovilm7BZse6KiojIiIiTqC0wsbfP9/Nf/94FIC48CbMHRFD68Y+Jie7eiojIiIi9dzhvGJGJ6Wy80QhACNv6MBT8Z3xcHPOscyvqYyIiIjUY6syTjD1o20Ul9to6ufJ7KFR/LFLC7Nj1SiVERERkXqotMLGjE93sXTzzx8+27tdU+YOj6FloLfJyWqeyoiIiEg9cyD3HKOTUtmTU4TFAqNv7Mi4mzvh7iJjmV9TGREREalHVqQe428f76Ck3EZQIy/eHBbN9Z2CzI5Vq1RGRERE6oGS8kqmf7KTZVuPAdCvQzPeHB5NC3/XG8v8msqIiIiIyfadLGLUklT2557DaoFxN3dm9E0dcbNazI5WJ1RGRERETGIYBsu2HOO5VTsorbDTwt+LOcNj6NuhmdnR6pTKiIiIiAmKyyqZtnI7H6efAKB/pyDeGBZNUCMvk5PVPZURERGROrbrRCGjk1I5lFeMm9XCxFs785cbOmBtIGOZX1MZERERqSOGYZC0OZMZn+6ivNJOywBv5iXEcE3bpmZHM5XKiIiISB0oKq1g6ortfLYtG4CburZg1v1RNPXzNDmZ+VRGREREatmO4wWMSkrlaH4J7lYLT9/Whcevb99gxzK/pjIiIiJSSwzD4P0fjvL3z3dTbrPTurEP8xJiiG3TxOxo9YrKiIiISC0oOF/BX5dvY83OHABu7RbMrPuiCPT1MDlZ/aMyIiIiUsPSs84yOimVY2fO4+FmYerACB69ri0Wi8Yyl6MyIiIiUkMMw+CdDYd5Zc0eKmwGYU19mD8ilqiwxmZHq9dURkRERGrA2ZJyJi3L4KvduQAM6tGSl+/tSYC3xjK/R2VERETkKm09epoxSWmcKCjF083Ks3dE8B/XhmssU0UqIyIiItVktxv8Y/0hXvtyLza7QdtmvsxPiCWydaDZ0ZyKyoiIiEg15J8r46llGXy39xQAd0a14qV7etDIS6dWR2nFREREHLT58GnGLE3lZGEZXu5Wnr+zO8OvCdNYpppURkRERKrIbjdY+N0BZqfsw25A++Z+LEiIJSIkwOxoTk1lREREpApOFZUx8cN01u/PA+CemNa8MCQSP41lrppWUERE5HdsOpDHuOR0ThWV4ePhxsy7unN/rzCzY7kMlREREZErsNkN5n69n7nf7McwoHNwIxYkxNIp2N/saC5FZUREROQycgtLGfdBOj8cygdgWK8wnr+zOz6ebiYncz0qIyIiIr/y/b5TTEhOJ7+4HF9PN166uwdDYlqbHctlqYyIiIj8f5U2O298tY+F3x3EMKBrS38WPBBLh+aNzI7m0lRGREREgOyC84xbms7mI6cBeKBPG569oxveHhrL1DaVERERafC+3ZPLxA/TOVNSQSMvdxLv6cHgqFZmx2owVEZERKTBqrDZmfXlXt7+/hAAka0DmD8ilrZBfiYna1hURkREpEE6fvY8Y5JSSc08C8Aj/doydVBXvNw1lqlrKiMiItLgpOw6yaRlGRScr8Df253X7uvJbZEhZsdqsFRGRESkwSivtPPyF3tYvPEwAFGhgcxPiCWsqa/JyRo2lREREWkQsk6XMDoplYxjBQA8fn07nr6tK57uVpOTicqIiIi4vC+2Z/P0R9soKq0k0MeD1++P4pZuwWbHkv9PZURERFxWaYWNl1bv5v0fjgIQ26Yx8xJiad3Yx+Rk8n+pjIiIiEs6klfMqKRUdp4oBOCJG9ozKb4LHm4ay9Q3KiMiIuJyVmWc4JkV2zlXVklTP09eHxrFjV1amB1LrkBlREREXEZphY0Zn+5i6eZMAHq3bcrcETG0DPQ2OZn8FpURERFxCQdPnWPUklT25BRhscDoGzsy7uZOuGssU++pjIiIiNNbmXaMaSt3UFJuI6iRJ28Mi6Z/p+Zmx5IqUhkRERGndb7cxnOf7GDZ1mMA9G3fjDnDo2kRoLGMM1EZERERp7TvZBGjlqSyP/ccVguMu7kzo2/qiJvVYnY0cZDKiIiIOBXDMFi29RjPfbKD0go7zf29mDs8hr4dmpkdTapJZURERJxGcVklz368gxVpxwHo3ymIN4ZFE9TIy+RkcjVURkRExCnszi5kVFIqh04VY7XAU/Fd+MsNHbBqLOP0qvV+p4ULF9KuXTu8vb2Ji4tj/fr1v7n/kiVLiIqKwtfXl5CQEB599FHy8/OrFVhERBoWwzBI+imTuxZs5NCpYloGePPBf/Z
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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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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"plt.plot([0,1],[0,1])"
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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": null,
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"id": "242807dc-0203-4633-becb-9643a4b5c243",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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