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mooc-rr
Commit be37b4ce authored by c4e0d8d31afd537cd0fb8604ea8660c1's avatar c4e0d8d31afd537cd0fb8604ea8660c1
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Final+

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module2/exo2/exercice.ipynb
View file @ be37b4ce
...@@ -16,11 +16,12 @@ ...@@ -16,11 +16,12 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 30,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"import numpy as np \n", "import numpy as np \n",
"import matplotlib.pyplot as plt \n",
"X = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]" "X = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]"
] ]
}, },
...@@ -33,7 +34,7 @@ ...@@ -33,7 +34,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 23,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -58,7 +59,7 @@ ...@@ -58,7 +59,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 24,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -83,7 +84,7 @@ ...@@ -83,7 +84,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 25,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -108,19 +109,19 @@ ...@@ -108,19 +109,19 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 26,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"2.899\n" "14.5\n"
] ]
} }
], ],
"source": [ "source": [
"Xmed = np.percentile(X,1)\n", "Xmed = np.median(X)\n",
"print(Xmed)" "print(Xmed)"
] ]
}, },
...@@ -133,7 +134,7 @@ ...@@ -133,7 +134,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 27,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -149,6 +150,107 @@ ...@@ -149,6 +150,107 @@
"print(stdX)" "print(stdX)"
] ]
}, },
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0. 1.01010101 2.02020202 3.03030303 4.04040404\n",
" 5.05050505 6.06060606 7.07070707 8.08080808 9.09090909\n",
" 10.1010101 11.11111111 12.12121212 13.13131313 14.14141414\n",
" 15.15151515 16.16161616 17.17171717 18.18181818 19.19191919\n",
" 20.2020202 21.21212121 22.22222222 23.23232323 24.24242424\n",
" 25.25252525 26.26262626 27.27272727 28.28282828 29.29292929\n",
" 30.3030303 31.31313131 32.32323232 33.33333333 34.34343434\n",
" 35.35353535 36.36363636 37.37373737 38.38383838 39.39393939\n",
" 40.4040404 41.41414141 42.42424242 43.43434343 44.44444444\n",
" 45.45454545 46.46464646 47.47474747 48.48484848 49.49494949\n",
" 50.50505051 51.51515152 52.52525253 53.53535354 54.54545455\n",
" 55.55555556 56.56565657 57.57575758 58.58585859 59.5959596\n",
" 60.60606061 61.61616162 62.62626263 63.63636364 64.64646465\n",
" 65.65656566 66.66666667 67.67676768 68.68686869 69.6969697\n",
" 70.70707071 71.71717172 72.72727273 73.73737374 74.74747475\n",
" 75.75757576 76.76767677 77.77777778 78.78787879 79.7979798\n",
" 80.80808081 81.81818182 82.82828283 83.83838384 84.84848485\n",
" 85.85858586 86.86868687 87.87878788 88.88888889 89.8989899\n",
" 90.90909091 91.91919192 92.92929293 93.93939394 94.94949495\n",
" 95.95959596 96.96969697 97.97979798 98.98989899 100. ]\n"
]
}
],
"source": [
"x = np.linspace(0,100,np.size(X))\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(1)\n",
"plt.plot(x,X,'blue')\n",
"plt.grid('true')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "<lambda>() missing 1 required positional argument: 'height'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-37-81283a82e08f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 2773\u001b[0m mplDeprecation)\n\u001b[1;32m 2774\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2775\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2776\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2777\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1865\u001b[0m \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1866\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1867\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1869\u001b[0m inner.__doc__ = _add_data_doc(inner.__doc__,\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2157\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2158\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2159\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mexps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2160\u001b[0m \u001b[0;31m# if we matched the second-case, then the user passed in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2161\u001b[0m \u001b[0;31m# left=val as a kwarg which we want to deprecate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2149\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmatcher\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmatchers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2150\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2151\u001b[0;31m \u001b[0mdp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmatcher\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2152\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2153\u001b[0m \u001b[0;31m# This can only come from a no-match as there is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: <lambda>() missing 1 required positional argument: 'height'"
]
},
{
"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": [
"plt.figure(2)\n",
"plt.bar(X)"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
......
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