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Commit 2854938f authored by 408798857b9e9b78bf182b621fb56452's avatar 408798857b9e9b78bf182b621fb56452
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Titre du document"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"hideCode": true
},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2+2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"hideCode": true,
"hideOutput": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n"
]
}
],
"source": [
"x=10\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20\n"
]
}
],
"source": [
"x=x+10\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Petit exemple de complétion"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"mu, sigma = 100, 15"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([124.96136552, 75.9640558 , 91.08229044, ..., 101.52625046,\n",
" 108.22074815, 93.11540881])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.normal(loc=mu, scale=sigma, size=10000)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
" x = np.random.normal(loc=mu, scale=sigma, size=10000)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plt.hist(x)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Utilisation d'autres langages"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"%load_ext rpy2.ipython"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R\n",
"plot(cars)"
]
}
],
"metadata": {
"celltoolbar": "Hide code",
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
module2/exo1/toy_notebook_fr.ipynb
View file @ 2854938f
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Titre du document"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"hideCode": true
},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2+2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"hideCode": true,
"hideOutput": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n"
]
}
],
"source": [
"x=10\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20\n"
]
}
],
"source": [
"x=x+10\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Petit exemple de complétion"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"mu, sigma = 100, 15"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([124.96136552, 75.9640558 , 91.08229044, ..., 101.52625046,\n",
" 108.22074815, 93.11540881])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.normal(loc=mu, scale=sigma, size=10000)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
" x = np.random.normal(loc=mu, scale=sigma, size=10000)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plt.hist(x)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Utilisation d'autres langages"
]
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%load_ext rpy2.ipython"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R\n",
"plot(cars)"
]
"source": []
}
],
"metadata": {
......
module2/exo1/tutorial.ipynb 0 → 100644
View file @ 2854938f
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Titre du document"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"hideCode": true
},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2+2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"hideCode": true,
"hideOutput": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n"
]
}
],
"source": [
"x=10\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20\n"
]
}
],
"source": [
"x=x+10\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Petit exemple de complétion"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"mu, sigma = 100, 15"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([124.96136552, 75.9640558 , 91.08229044, ..., 101.52625046,\n",
" 108.22074815, 93.11540881])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.normal(loc=mu, scale=sigma, size=10000)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
" x = np.random.normal(loc=mu, scale=sigma, size=10000)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plt.hist(x)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Utilisation d'autres langages"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"%load_ext rpy2.ipython"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R\n",
"plot(cars)"
]
}
],
"metadata": {
"celltoolbar": "Hide code",
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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