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Commit 6926088f authored by 57f5b24a4a876d57f0eeecd33b98e06a's avatar 57f5b24a4a876d57f0eeecd33b98e06a
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module2/exo2/exercice.ipynb
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{ {
"cells": [], "cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"mu, sigma =100, 15"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'moyenne' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-2313fecf3cda>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m14.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m12.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m17.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4.7\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;36m6.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m17.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m21.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m21.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmoyenne\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m8.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m22.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m23.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m21.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'moyenne' is not defined"
]
}
],
"source": [
"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, moyenne (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)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-8-b05ca9f7f3e9>, line 1)",
"output_type": "error",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-8-b05ca9f7f3e9>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m def moyenne\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"def moyenne"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"mu, sigma =100, 15"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"x = np.random.normal(loc=mu, scale=sigma, size=10000)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'mean' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-14-10ccf06f9db5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m14.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m12.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m17.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m17.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m17.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m21.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m21.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m19.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m22.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m23.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m21.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'mean' is not defined"
]
}
],
"source": [
"mean(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)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"value = [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]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'mean' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-16-9966ad7eee74>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'mean' is not defined"
]
}
],
"source": [
"mean(value)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'a' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-17-cf2e21015801>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'a' is not defined"
]
}
],
"source": [
"a.mean(value)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1-D array : [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]\n",
"Mean of arr is 14.113000000000001\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"arr = [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]\n",
"print(\"1-D array :\", arr)\n",
"print(\"Mean of arr is \", np.mean(arr))\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'numpy' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-19-80bdd46abc19>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\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[0;31mNameError\u001b[0m: name 'numpy' is not defined"
]
}
],
"source": [
"numpy.median(arr, 0)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'median' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-20-f8cf31ec11c8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\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[0;31mNameError\u001b[0m: name 'median' is not defined"
]
}
],
"source": [
"median(arr,0)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'numpy' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-22-2715bd9952bc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'numpy' is not defined"
]
}
],
"source": [
"import numpy as np\n",
"numpy.std(arr, axis = None)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean of arr is 4.312369534258399\n"
]
}
],
"source": [
"print(\"Mean of arr is \", np.std(arr))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean of arr is 2.8\n"
]
}
],
"source": [
"print(\"Mean of arr is \", np.min(arr))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean of arr is 23.4\n"
]
}
],
"source": [
"print(\"Mean of arr is \", np.max(arr))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "module 'numpy' has no attribute 'med'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-26-ebb8f1e3e78a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Mean of arr is \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'med'"
]
}
],
"source": [
"print(\"Mean of arr is \", np.med(arr))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean of arr is 14.5\n"
]
}
],
"source": [
"print(\"Mean of arr is \", np.median(arr))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "module 'numpy' has no attribute 'plot'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-28-00115f1f9f67>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Mean of arr is \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'plot'"
]
}
],
"source": [
"print(\"Mean of arr is \", np.plot(arr))"
]
},
{
"cell_type": "code",
"execution_count": 29,
"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": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.hist(arr)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"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.plot(x)\n",
"plt.show()\n",
"y = np.random.randint(100, size=x.shape)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"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": [
"arr = np.arange(1, 100) \n",
"%matplotlib inline\n",
"plt.plot(arr)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "module 'matplotlib.pyplot' has no attribute 'sequenceplot'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-35-08abdfb1a13b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\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[0msequenceplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'sequenceplot'"
]
}
],
"source": [
"%matplotlib inline\n",
"plt.sequenceplot(arr)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"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": [
"x = np.array([arr])\n",
"y = np.random.randint(100, size=x.shape)\n",
"\n",
"plt.plot(arr)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ICCEoWdIO67TovokBzk0mzo7smmXIr1RbPZ8LBHPvk0QY9B3we393Dvfef37Qh9E3qk3NNjKj1kpW0w382l894enLApgG/WDFNOjcLJd6B3MeDz25dTP9/PZjUyjJWW6Qdie4jTjLcjEMiqamo2QFCN07gU3LuHkKi3J91tAj+rlcWDd3h6+9aR6droHFjWCmC+tFA5g6+lZLQ0N1Oi3ar2Ote7OpYrKQQ9anae/kBr60U4OuG8iJoOjwsVprj5xhi+LRy1v4zb89G/rzRVeqWFy9cFg4u1TDR7/xHL76lHfsYb3TxfyEKbn4syvamo5au2tq6Amm7zEeWayCEOD5C5P7I7lYRlzOZuyv210dlAJFVwofy/SptjQQ4gyaAJBo7IA3LDmq4+IlSz9//S3zAPiyy1bT8dBNyUX19EJnlJSsneXil1vcx9RLajIMipWaadCvxjDolFKz26IIig4Xmm5gra6mZhI8AHzu0SX8wVeeDdWJWUAUGD0PfdOeYuN4rWwrzppU+Q0o8+Zny7ITJEsoNxkAvr9YxXVzZUzkc6YHqfKrV+NSbWrISQSHJ/Oe4CDgeKvuG8dWU8VEPucZjZZo2qJ1DilS0EOvhXjo+VwGL7v+AADg3Eow06XaUm0PfaqQQ0PV7bUXfQa91jY9dPeOhGHHDnqc5xtN1W6pzPT9KNhzhYY+ZKRtig1gzpM0/+d7hqPsodtTbNxjyXxzJv2VmewznqsoyEkEhCSjJQOm5/bIYhW3H5sCYBqcsOrVuGy1zKrPqYITHHQGPTgpfO4slymfcbM99AQLi9zGbSIf7qFfXG/gxEwJlXwOR6cK3Fz0alOzYwBsLcxzLituycVcdy8PvdeNzB0IvbrV26Dz1pwkwqDHZKXG5kymyaBbjapCDPrlzSYmC6b3OGo3MuaZb7o8dCY1lJWsx7AxWGOu2bICQkii49iuVFtYb6i4zTLo+9EbnPVlmXCl77FgI9PQ3TeyTZcxZPRdQ1dMI8xzKi6uN+2S/hvmywHJpa3p6HQNR0O31sIMOruJma9jxih4nRaB+EFRZtAXpgu4Wu2d5aJZf09MLBoyrlmd8NJSYAM4HnqYEVncaOHYTCHxsWRJwAJ+bg+d3bhKitmoqu7LrHB76IDTeTAJ2M2DtRhg3uReAqObTRVTxRymirIdFGWSSyFEcgnP9khOcuEHRb0NxQyD4uJGEydnzZL+G+crOL/aQNclgbHP1slyMf+/bBn0kuyVXOpWHnqkht7LoFt24EUnpnGl2uKmW3rWLDz04WTF+iDT5KEzQ77d5rcvXdxs4th0EfmcZJeUjwosJc9dbMPWW85nuSXnTEM/UDINepLTe5iU4x/2sJfAaLWpYbIgY7KQtSUn9+Qe9n+k5JKkh65ba5aCQVG/h7683YbaNXB8hnnoFai6YVeOAq7WBSwPvcAkF/NaLSneLJe1egdtzQhUiQLxK2SXt9qQMgS3LUxB7RpY9w0TCayZk3ufJMKgx2QllRp6uORiGGYO+rGZYuKT4JOAeW/MUweCk+B5QdGpYs42sknuTPyNqni9ynfKVkvDdDFnp+9RSm2Pv+iRXFjpvxbqoSfhuISV/gPBdV+0UhZZ060z82UAwDmX7FL1DXv2a+glX1CUncP+Pi6Aq/Q/hoc+V1ZwzLrR9Mp04WX2JIkw6DFxJJfRMmxRMIPO0y9X6x2oXQML0wXTUx21oCgny4UZcDY42G9EWJUoI8kbmd+48XqV75SqlcI3VZChGxT1Ttfx0FlQVDYNmzmZKaihZ6UMshnSN8lFyhAUZSngVLCSf6ahX3/QNOhPLTuZLlVXHxfA8dR5Bt2dwsjz0ONKLte225ifzNsDUHrlotuZPcKgDxfMQ9cN6tHxRpntCA2dFXHYksuI3cg2LQ/d3XWw7vfQ1aCHPlt2V01KieWh+z230h499Lamo6XpmCrKtoHbammctEXz/2WrfN0vuQDWjSyBdbM1K76qSV6DrosbTeQkgiOW4SzKWRybKeDpFcdDZ60L2Hor+SwIcZwv1uqAvQbD3wsdiJ+HvrTVxuEJx6D38tA1XQRFB0Knq0cGOK5tOxWHSeYm9wvD8uAAvhFhJf92UDRBD103qO1J7hfMM2+quu1t2h66NezB7xX6PfREJ8Hbnpu/gdTu3md/10HA9NjZ++ouLAKcz5dr0HPJBIPDAoSVfDaQh35xvYFj00VPjvyZgxWv5NLySi6ZDMFkIQeDmjewjOt33d46L8vFSdeMfv+vbbVxaDKPiUIWJVnq7aGLoGj/oZTi1b/5VXzy25dCn7PimveYhsCoWcRifs0LijIPfWG6mNgFzvi/7nsML/jVL+A9H3kQn/jWBTvbZC9UW5qtB7snwQNOlovfeK7UOnZbAACJ3siCkou3cdZOsQc3uDz07ZZmr7GQc7JcAKcohskUbnYSO9hua9weKzx4kgsAlDkdFy+sOSmLjBvmK3hurWF7vdWmZg+aZrC1u1MWAW9O+m4rReudLmqdLuYn8iCE4Oh0oWdxkQiKDgBVN7C01caFtQb355puRrMPWulsadDR3RcQLyi6uNnEXEVBPichn80kWli0tNVGScni8mYL/+q+x/ET935rT39PNyi2WhqO+8aS1TtdZDNmfnlFyULVDfuCa3S6aKo6Dk64NPQEb2TMYLILnWncu5Vc3AFC5rFWWxpamo58LmN7uraHXo3w0GPGDjTdwKt+4yv4xLcuxDpGtWsgmyEerxswG3TVXU4FpRSXNpo4YQVEGTcdqkDTqV1gtNUyg7qEOH+PBXndGS7m9+a6MwSYyHMklxjBYJaDfthq7nZkqtCzuCjsJpYUwqDDnFsIIHTbzzxGlkKVhs6DboPOC4oubrRwzJrao+SkRHclatfA9QfL+PIvvwo/ec9xj7y1G7ZbGih1MiRYcVHDGnrAuu+xxwAnRsJu2kCyQVFbT7bSBDNWcHDXHrpbcnFp6O42sgDHQw+RH+Kc48tbbWw2NZwNmSbkJ6wvuF9DX2+oqHe6AQ/95TfMghDgi0+YHU+rLc3Tyx1wiotKAQ/d/H66KHukGEZOIvYxhsG0+fkJ06AfnepdXKQKDb3/sMBRmH7JLnaWqsTyaUeZWjsYLHSzuNm015u0h67qhlVqT3CgpKDe6cbexvNgxu3ULPPQnSk27ML2ByGZpOaWXMyxZMmmLbq34iVOoDYuW64iG2bQTQ1dR9HlrTLPlfW596ctAiz/vve6mSy3k66DXIOe99YEsAyXkz4Pfbas4MUnZ/C3jy8DsBpz+Y4/zEO3DTpHbgEQqzKYeeiHXB76RkONjP+ILJcB4Bh0/gfD7szMYx21IhsezCvP5zIByaVrSVALtoeebB66phu2B1PZhxJ45pGfmjVT3TxDD3xTbOp+D32iTx66FjJfc5dB0U3XOLlCToIsZbgeOls3M8ITHIOuxAwGs14/S3EHPYSMYisr3qAoy0H3e+gA8KZbD+Hscg0X1hqexlwMJiG5g6Du73k56IxehWSsSvSQy0MHEOmlC8llADDJJcw7Yt7bgu2hj75BZ4HQI5OFQFB0aasN3aA4Nm2uN8kSeMA86ZkHw/TNWkj1ahyYt3rS8tA3bYOueyomzceiJJfk1q3qpnHzZmJESy6PX92yW7f6qbbMTotFWQIhxO7n0tJ0u+zffA1z3ctbbUzkswE9G7Bu4DGcFtaNc6naitUlMkxymbCmFrG/cX61ASlDsDAdNOhvfN4hAOawmSqnFw0z8H7JpWR76MEbGKPXjWx5q43JQs5+P9kg8ajURZHlMgDYlol56n5Wah1kiGn8gHRp6EemCgFvmHletuSSS1Zy4XnoUXMme8G81cOTZlEUT3IJeuhtyFLG4/ElneXiv8hLcvjA5Fpbwzv+07fw4c8+wf05K/tnAcIpa2BymIfeNWio/BD3RsbOk4aqY7vV+/PqREgulDrX39nlGk7NlrjPPTpVwAsWJvH5x5ex1dQCHvpkD8mFl+HCkKVMdFB0u2175wDsHPkoyUnjSGtJIgw6gKbWW3KZqyj2nXnUGlXxYAbz8GQ+YESWrC0k21IqWQndBAuqNJ26DHp49724bNopfDlMh8yZ9Od9r9bM0XPujIm40gMArNc7uP/p1d5PtOh09WD6XsSQi7985Cqaqo6/f3rVNhJuzNa5jnFj5f9snihDyToZLzz9nD0nzjl+2ZWyF6uVbKjk4v3Mn7q2jZsOVUL/zhtvPYTvXaqi1ukGsnSYx+5PW5QyBNcfLOOmQxOhf7dXVtOylYPOmK8okDIk2kNn3RaFh94/Wj2Cote2zfzkuP0eho1nVmp48Py657Fa28zhnasoqLW9gxU2rIZDB6yqSSVGju5ecHurjoe+F8lFBbHS06aLciDLBXA8ONbljxl0N0xDjyMnfOybF/BTf/rt2AVSPOMWNrWIUopPPXgJSjaDeqeL71zYCDyn2tQ8gxtYT3S/QSeE2K10J0P05Lixg8WNJm6wSvKXYhp0XnDQ3XGx3ulicaOFmw+HG943WbILELwpse/LPg0dAP7ug6/C+156MvTvmh56+I3M76FnpQwOTeQjPXS7Ilh46P2jp4Ze62B+oj9zJpPgd//uHP63P/++57F6p4tKPotKPgfdoJ5A73pDRU5yCjbyVlFKUsFgdd8lF3MrzioH3Xno5UBQ1PzsV7Y7Hv0cMD02Sp2pM1Fc2WzBoPGm2ACWccsFDTovKPr9y1t4cmkbH3z9GchSJjBWD2Brdgy046F3PZN7AGft4R5677TFtqZjpdbBXadmAABXYvQGD9PQ2aDoWrtr55jfOB/uoV83V7ZvJH4NnXnsRZ/kEgczq4m/bnNiWQfzLg8dAI5M5SM/c1FYNACY5NJUda43trLdxlwlH3tM1bCxXu9gZbvjWVutzQx60CPetKa6MPkhybFkgHmxyFYecGUfgqLmmDHzQp8uyqi2VLPzoBoVFG17MlwA7GgMHcv0uBwzha/D8dDLIUHRTz14EYWchHfdfRx3n57Bl8+uBJ6z1fRKLhOFHLaaZqVoSeYX2fBGsQEsqyn6s2ZyywuPTyObIViKse6otEXAvOGeXd4GANx0ONygA46X7s9Dt7Nc5KCH3osoiW2l1gGlTlER42iP4iKWksvLfU8CYdABtCzPXDdowPtmVaLzE4prTFXyBr3R6eJ3vvBU7BzfKKpNDZ2u4dHKa20NFSXnGHRfYYe73wXzJJPy0DWO5LId4aGvbLfxH750LjRXfcs1CX6qmMOmtX7doLYxy0kZKNkMGp0u1K6BzabmyUEHXL3BYwRGmeRweQceeiAoqmTR0nTPurbbGv7q+0t4621HUMnn8OobD+KZlbqdA86otrw52VPFHGqdrpXlws/4CJNcZKm35MICoicOFDE/kY+VuhimoVdcY+ieWq6homTt+E0YP/rCBdxyeAI3+zTxo1NFvOjENO44PtXzePzIEU3J7Bz0Cb+HXsCylRXGQ+safSsqAoRBB+DNbvF7SKxKdH4in7inythqavjJjzyIf//lZ/BFq4hiLzANeb3utJLd9nno7lz0zaZq6+cAkM8mGwx2Sy55K4c6SnL55IOX8NtffBpPXN3m/tw9CHiqKGOrqXk6LTJY/vNqPZiyCMQfekAptQ1abMlFD+rJdqDWJf3d970raGk63nX3cQDAa286CAD4ylOOl97p6miquidrxZ394ffQe0ouMeoOLm84mVBHpvI9uw4C0ZWigOlUnF2q4cZDFU9wmsfJ2RL+xwde4QlSAuZkps/8o5fiBQu7M+hhHrq/SpRxsKJA06ndHM1P2K4kKYRBh6OhA8HUxWt2BaFie2xJSi4rtTZ+/N5v4bErWwD2VmADmMaGZX2wiTwAk1xy3JmOGyEeehLpmpRST5YLYFUOdsIllwesAO8TS1vcn5sBQia55KDqhn1j9g89aHS6gdFzDLsDX4/Pm+2AAKcCsxcdje+hA16n4tPfXsQthyfwgoVJAMCp2RJOHijiKy7Zxd9GFvD2aPFr6CwgzOvjApjr7tUm+vJmC3I2g7mygsOTvXuaAMy4BbXtiuscfHJ5u6fckhT+tMUvPXkN/+dfPoovn72GC1b1ql9y6SURhu1KkkIYdPg8dF9gdMXloccdJLtb2pqOH/+jB3DfdbZYAAAgAElEQVRxvYmP/tSLTU91jwa9pen2Seo16Boq1ig2wDvTcaOh4kDJ2xecHd9+wwKObuNWyWdDPfS2puN7i1UAwOMhHnq16fT4YEaLSSFlxVtk0+h0uWX/7mPqdQN3yw1xJbIOx7j5DXpb0/HE0jbe9LxDHo/1NTcdxDefXbcdEdbqwJ+2aP/dUA09PMsFiI4dLG42sTBVQCZDbNmh53zNEOPGbjDnrtVQa3dxY0RqYZL4exZ94oGL+M8PXMLPfOwh/Mbnn4KczQRugr2C+GG7kqTo+UqEkI8SQlYIIY+5HvtVQsgVQsgj1r+3JHuYyeKVXHwtVV0eelYyc3iT8tDPrzbw3FoDH37rrXjFDXMo58PzkuPCvHMAWHVJLv6gKNOsNd0wR5m5DHo+0TmTwSyAKIP+/cUq1K4ZaOJJLqoVK2DGimVBMM/Z7aFXrKZQvLJ/IH4wmOnnZ+bLO9LQg5ILS6U0X4/JGMdmvHrya248iE7XsHcq9rBkT5aL83UxRHLxBxQZ9rojdmSLGy27cvrIVB6aTrHWiG6q1gkxblkpg0JOstMxb47IQU8Sf+xgtdbBK26Yxcd/5i68++7j+F9eeTogBTEPPWwu7zBKLh8D8CbO479LKb3d+vc/9vew+ktLc4yH34Be2zarRA9Ygw/MDz3Zhk2zFatjHGcIw07ZdA2xXbMMF6XUlbbo1dCZcZjheOhJGHSNFV5IzoVSUXKhW9gHn9sAIcBbnn8YTy5tB7xCpmUyDZ0ZdqZteyUXyTbohMCzKwHir5t56HeenMFqrRNrJ8MrLPIPimbePqtQZtx9egaFnIQvsK6DvtmagNdD9xfZlHpq6L3XbQ4QN4/r8GTvniYAoHb10CZV5XwWz66assaZQRn0bNCgH50q4FVn5vBvfuT5+OAbbgz8TiwPfZgkF0rp/QCClQwpoqnq9pvuLwxZqZlVoqy6LslGVf5xVWUlt2cNvery0NctD6qpmpkUpobuLYFnRUUzHA89GcklWEkX5aE/+Nw6bpyv4KXXHUBD1XHJn+1hGbdJ20P3Sy5+DV3Haq2NAyVzB+ZGjuGpAs4k+DuOmYG4uAFChVNYBDifBfs7R3wZH0pWwj+47TA+893LuFJt9ZZcQsrgea1zzb8fvTOptTVUm5rdGoLpyr1SF6O8VZaLfnSqwO1X3g8UV3dN3aBYb6iBuIqfiR6VzcPooYfxTwghP7Akmel9O6IB0FR1e5akX3JhVaIMJaL4YK/4ixCYJLAXWIZLTiJYq5lfs5Ovks/a213mEdsGvdgfD51XSVfhTLABzPfn4YubuOf0Adxy2AwS+nV0d9k/4DLo1aDkwvpw84qKALeW3EtyaeNgRbH75ceRXXiFRWWfhn5ls4UMQSCTAwA+8LozAAV+74tPuzx0fpZLIef10N962xH8yptvCs9D933etbaGt//BN/C9S5sAnKZcrHmb3XWwR+pilLfKPN2bBxQQBbx56BsNFbpBexr0XpXNQ+ehh/CHAK4DcDuAJQC/HfZEQsj7CSEPEUIeWl2N3+siCeqdLn74P3zNziBhtFQds9YHF/TQvRd7rxabe0H1eavl/P4Z9FOzJTsoyk4+pv+5X8c26J5hycl76P6gKE+TfPRKFW3NwD2nZ3DDfBnZDAlkulRdbWQBR1dmkktZDma5rHDK/oH42T1LWy0cnszbmnKcwCivsMgfFL1SbWN+Is/NYz46VcB7XnICn/nuZXznwiayGeIJfsrZTGAwNOPYTBG/8KrrQlMD/Rr6s6sNPLJYxe988WkATjyCtVeeKuaQz2UidyZd3YBBw7sOsuKiGwcktwDOtU0pdTKfytEGvTxqQVEelNJrlFKdUmoA+GMAd0U8915K6Z2U0jvn5uZ2e5z7wqX1Jh67so1HrCwJRlPt2sOB/aXXrGqSoWSTm97j99BLSnYfNHTTMF43V7YN+rbLQ2f/s8c2mhwPPYamultYlovbaLF2qn59/IHzpvJ316kDyOckXH+wHAiMVn0pfHI2g5Is2Z6727iVlSyaqo7l7XaIhx5v3ctbbRyeLNjNmuKkLvIudH87gqvVVmSBzS++5nqU5Cy++MQ1TBXlgIFmGrlfQ++F7JNcWOzla+fW8MTVbSzaA8TNGxghBEcmC5H9XHq1kWVrj2qelTSylAGlZidKVpvQy0PP+Xa4ftydRPvBrl6JEHLY9e2PAHgs7LnDBPP6/HfTlqpjuiiDkKCHvtXSPNrkTgbo7hS/t7qXgQeMzaaKipLF/EQea3UmuZjvwwQz6K4bx4b1nOlS0ENPYt28XheVfA6UBlNIH3xuA2fmy/YN9pbDE3hiyWfQW8HjZ1KEks14dHJmRFZrnUCGCxA0bDxYUdGhyTyyUgaHJ6N7ezDMwiKv55zPZZAh3qCoXz93M1OS8f5XnrbWGJRPJkJayfbCX1DFYi9ShuCPv3YeixtNlGTJI9kc6TGOrVdPE1YPEdVlMWncaaphtQk8omI+YZk9SREnbfHTAL4F4EZCyGVCyM8C+A1CyKOEkB8AeA2Af5bwce4LLAPCfzdtajpKioSS7J0E3+nqaGm6R49MVHLxa+g9CmziUG2qmCrlMFcxR7u1Nd2loQcll82miko+6/EqHMml97oppXjtb30Vn3zwYqzj88tM5nEFt7GabuDhCxu4+9QB+7Fbjkzg2nbHk1+/2TQHPbjlBzbUwN+Bz62n+3PQAZeGHvF5b7fM8noWGFyYLvTU0Lu62YbAf6GzWadsd7K0FW3QAeBnXn4Ks2UlkKEDOEY+zynmiYLtyJz6BfMm+eMvPoa/+v5VPHRxA8dmip4dweHJfLSH3mNyz7Ql25yaLXF/3g+UBAx6v4OiPfdilNJ3ch7+SALHkjjbLb6H3lTNqS4lRfJ46OwGMFn0equJZ7lYJ0BJzqKtGejqRiADIy6bVtUkC/qu1Tu28bYlFyWH1VodgNnHxW8cnHmLvT301XoH59caePjCJt5994mez3cye1xpi5zMgceubKGh6rj79Iz92C1Wi9Unrm7jlWdMOa/aVD2DHgBHRw+OJXMMXVRQNOrzZhWSLHXv6FQR33x2LfT5QLT8wHqir9Y70HRqT8UJo6Rk8YmfvQs8OXyykENRlnbcGMq/I1uvqyjJEv7xq6/Df/3OIh67so3X3XzQ8zuHpwpYqXVCNeNOD4P+8688jTc+79Cuz/P9QHZJbKu1DspKNpZcVc7nwvPQOdlMSTJWlaI8D103KNSugWIua3roriIjXkm1nORYMp+HzgIuYX3a41BtqpgqynaMYK2u8oOilvHcbKjcSTZKROMiNywDYjFmCTwz6O785DInc+C7l8y4B2vXCpgeOgCP7OLvCw6Ez5l0e+w8ySWOhu4fHLwwXcDydjvSq48aHMwGRbPA6tGp4M7Bz82HJ7ja8/xEPnJCTxg8yWW2omBhuoh/8AJTbfWPhzs6lQelTqsMPyrnc/Yf64tPznB/1i88kkudHyjnMRHhoWsjlLY4crDAnztzhHnkRVlC0de+1PbQfWPJEguKsjJ4V9oiANT2ILtsWgbONui1DmrtLghxSsLdW0aehw6waS5xptg0rf/jV0wC3qAorwPk1WoLJVnySCNTRRlHpwqewKi7dS6DfV8OyccG+JIL2zVEGXRWVMQkl6PTBVDqGHoeUd4q64keloO+E37pdWfwsZ8OzVcIxQ6Ca07LCHZO/Lyl2V9n9SNnsB1KWNfFfvcF3w22Qdd1s2V2jwwXRiUiG00V3RaTg0ku7tasLSsVryBLKMreUnunpLo/QVG/zugvNNkNzMCx7onrDdOgl5WsLUtUlCzqqqnbbjaCBhEwA3ZxPHRmyJe327HeJ38xFeAEa91ez/J2O9DpDjC9U7+HHtojO0JD53ljcaSm5S0zV5xJNiyVLyrTJcq4sZ7oLLDaq41sFDMlGdf7DG8ceJILq5S+9cgk/uYDr8A7XrTg+Z0j1k4iLHWxl4Y+DLh3Jjvx0KMqm0cibbHfnF3e9nSX2y08DZ01OCrKkp3GxuB56FE9k/eKX092JJfdGfSubqDWNucuuiWX7bbmqcZjQ3obahcbDdWTg86IPTjYqtyktHcpOODsSnK+LBfAK7msbAcHUACm7HJ+te40quJKLnwNnXnolXzWnsrkp5fUdHWrjYOVvK39Lkz1Li5i76PCec2S5VRcrbbsiVL9xi+5rNVVOwYDmDdR//tll/+HBEZ7pS0OA+55B7yRhGEMU1B0eN9dF5984BI++GeP7PnvOGmLjqFougx6UZY8qXK8kmolKyU6WzNDYBsHu0/0LnPRq3ZfExn5nISKksWqJbkwWQNwDOi17TZU3fDkoDOUbCZWYdHiZhNZKwjnH8LAg6cn87Jc/PMcGXccm4JBgU88cAFAmORixQpCeprwAqKMnQ4OPjSZR4ZETy5ini/fQze371eq7T1553vBbdgMg2Kj0cGBUrRxKylmX6CVbX6DrlGQXFjwstbuotbu7sCg59BU9UC7YcMwW0OPQqVoX2E9N/bKFsdDZwa9IGdt78j/fLeXZHpsyeWh5zh50ruVXPxNm2YrCtbqHbt1rv91Lq6bBpgXSMv3MGyMy5st3G71NIkTGOVJLoWcBClD7BsvpRTXtjtcyeXVN87hTbcewq//zVn83RPX0OkasSUX9h7w9HOGv0e2HzO10Pl9OZvB/EQ+luQSGhTtmEHRgRl01iZa01FtaTAoPANPwohqVTEKkgs7NiZ37URDB4LXqWb0f83D++66KCumV7zXYOR2ywmKsvmabKteyJlB0abrxrHd0jCRz9qNuYDouYN7xV+EsFfJZdPXOXG2LFsGveu5SbETkjW64hn0OB66blBcrbbwopPTyEkkVmCUl7ZICPFsY6tNDWrX4Bp0Qgh+68duw3VzZfyvn/4egGCf76mQoKiSNdsh86Qc+zkR8zXtoqIJr+FdmC7EGhwcZtDrluSyl4DoXrBjB9ZgZAC2ZBdFQZY8w2Lc9EpbHAZYVtMVq+/PTiQXILiTHsSuZHjfXRcsF9RfxblTmOSiG9T2zN1ZLmUrZYwZ+2pTDXh7LA+dN0x6r5jDkoMe+m4lF9Y6lxm4AyUF63WVI7nsj4e+vN2GplOcmCnh6FRhR5KL/0J3G/RrNf74L0ZZyeKP33unfVPwa+jTIRo6IQQ3Harg+UcnQ48vKnaw3e6iqercwcFxNHR+HrpkjzTrlYOeJCx2wAx6HA+9KGdDr9FeaYvDAPs82Ge3E8kFCPZEH8SuZHjfXRd7lR4YWy3NbljEjIU/y8WgzgW31dI8QwMA88Oh1OlBsp/4I+IsrXC3694MSC5yiORi/ryXh94ra2XRnjNZwMJ00e75EYXKkVwAb+aAk+sdfoGdnC3h37/zDkwVc4HMjkMTeRybKeDmw8Fc7c/901fg515xOvTvRlUGs+M67MsVX5guYnm7HTrCLepCd990BuWhA07sgM2hjeOhmzEo/jnieKs7q1rtJ37JJSq24oaXlQUMJhA8Egbd6UK3e+1a0w00Vd1OK2PGwh0ULSleA1p1TY9nsG1ZErKLX0NnrW33KrkwD3W2rGCzqVkDooOSy0VrbmK45NJjEvwG68JXxLGZgj1IOAqt6829dx8TSy9lgbYorRsAXn3jQXzvX70e1x/09gMpyBK+9s9fa1eT7gR3j2w/S3aVqPe45icU6Aa1O1f6cbxVTpaLy6DHKSpKCnYDX2ceeowCpWKE5DJKGvrlzRYI4V8HPMI6Load20kyvO+uC7+h3Q0sZZEFmpixsA16zinzZTr6ViuY0+wMPUigURUnxWkvLXQ3mypkyWmjyrwsc7iFy0O3vl7cbCEnkUDPE4BJLtFrZhfCkak8FqaLWG+oPW9Gqq4jmyGB8nS35LLMxgBGaN2MXtPid0pUq4cle+fg9aSZUfYPHGfYWS4hpf+Mo1PFwM/7BVv3ekNFhoQPw3ATKblErHlYYHLQtVobB0py7DYEYYOiWR/9nPDQvfgb/+8GZsBZyTJ781vWCViQJTtoxlIXt5o8D51VkyUwX7NLA9JDWeHnuOoGxbef28BvfP5saO+QasPsFMmMnHvb7PbQWTqf2jUwUwq2YQXi9bBZ3Gzi0EQeSlayW6v26g2u6cE1s+Njn9G17TZmSjLXo00aJSuF5qEvbbVBSHBrzm6gYQY9juSSzZDYGm4SsHWv1VXMlBRPYkAYRVkKX/MI5aFTGk9iYoQFRXnDW5JmZ42SB8R+BEVtD92WXBwPPZsh1kAA53UoNQNTAYMec+jBbuB66EpwUPRvf+EpfOrBS1i3tvRfeWoVf/OBVwT+nj8n210cMuHy0DMZYuc/h06Cz0k9s1wub7TsKTZs3uTiRhNn5sNborKBz348QdGQfuX9ICx2QCnFFx5fxo3zlcANqdf5GpXlwpyKw1P5WEY0KVh2z1q94zlvoog06COQh+4+tp3cTMOmFkV9zkkxvO+uC3/j/92w5ZNcmIzBOi0CbmlHR0PV0TVoYJAuC+okNTBZ9hm3si+31zAo/uNXn8WxmSJ+/1134H9/4414cmkbT1+rBf5etent5e72OvyyCvs+LJshHyO7Z3GziQVrQj3bCfXKdAlrXsT6Y7AcdN4Ytn4Qlqb6tXNrOLtcw8+8/FTgZ7089OgsF/Pz8g+G7jfmMHQD6/VOrAwXACgqUZJLMD112NitQVeyEuRsJqihs95MwqB7YYZ2b5KLadD9QdGWqtsXoKOhd7ll/0C8Htm7heehl3ySy0bTnHX4I3ccxQ+/4Ah+7M5jkDIEf/m9K4G/F/DQK3zJxfzeXHuUh05puNTU6ZqTf5iHPluWUchJPTNdwmYuVvI56AZFSzP/7nyPgGhSKCE9bO69/zwOVhS87fYjgZ8V4hp0zrrZuT6ooiKGneXSUHtWiTKKOTPlUuOcIx3r3N7vGMd+kskQ+zPZqdw1kc96mskB/MZzSTMiBr132uLF9QZ+8VPfxaV1vkfIDPThyQIyxCW5aLptyG2tXtXt1rn+STBxptjsFt64qko+62lH4G+8P1dR8LLrZ3HfI1cDI9s2m5o93AEw0yDzOWd4hhsWGA3LZujVG3yp2galzg2TEIKF6d656JpucING7Pg2mxrW6h3MD8xDD+ahP3ZlC19/Zg0/8/JT/EyVPUku5u8OMgcdcLqKmo254nnoUTeyfvcF3y3s+u6VUeWHN9icBUWFh+5DyWaQzZBID/2zj1zF536whP/5P30TTy0H5QdWJTpZyHkCjS21i0KOeejshOzao8wm+umhc7zVsm+uKG+SyttvP4Ir1RYetqayA6bGy3qhMwghtrflN+jMY+f1QgeCLVX9sDJ/FgxlX/eqFo0KigLAc6sNUGqmAg4CmdMu+d77z6OsZPGuu49zfyeO5CJLfG91spDDP3r1dXjrbUHPv58o2Qy2WxrqnW7sAKGT3RO8TvvddXC3sGPcqYduxnxCCouEh+6FEBIZcAGAR69s4WBFASHAj9/7rcAg6O22BlnKIJ/LoOKaMNJ0SS7unQALovoLi5IcmBwmubh3Jrxp5G+49RDyuYxHdql3uugaNFA1yWSXgOSixPPQwwKjbLCF26AvTBd69nPphEou5vGcWzFvzgOTXKygKIsdXN5s4nOPLuGddx3zdKx0U4wwbEC0cSOE4P940024ISKQ3A+UrGSni+4kKAqEe+gjYdCZ5LKDLBeA33FxEO0Ohv8dtvAHB/08emUL95w+gP/2Cy/FRD6Hd//xA57pKVstDROFbKBPiDsoqmTNIb3Nju5Mj/dLLlJykgvPQ6/ks9B0ar8ebxp5WcniDbccwuceXbK9AruXu08TnyvL9u+4Yd+Heej5Hjeyxc0mchLxdEQ8Nl1Erd215SseYZILy8J5ZsUcjTfIoKhhTYIHgE986yIIgJ9+WTAYymA7vvAUPn2oS+ABM3agW2uOq6Hb6+YkL/S7jexuYVlsO/bQOT3RWVBUZLlwKHHS9xhr9Q6Wttp4wcIkjh8o4nd+7DY0VB0/uLxlP8dstGUaZ/f2qK05Hjob0ttQnaCoP8vFTltMIsslJG0RgC27rNY6KMlSoC/J2+84gmpTw/1PrwJwyv79Qc65ioKKr+EY4HjEYdVx/qEHfi5vms2k3H/3mJXxEuWlm/1reGmL5vt+zjLocYqKkkD2xQ4ev7qNW49ORpblSxmzuVVooypt+I2b2wjF1dB7Si6joKHvMiha5njoIigagV96cPPoFdNwP89qsnTigDk5/IrLkJgeOjPoOY+H7h4EW5Kzpofe0pDNENvYMxwPPRkN3f/h+wPCYY33X3HDHGZKMv7soUUA7rJ/7w3p515xGr/1jtsCv1+OadDDyv8XN5p2hgsjTupi2Fac3WCeXalDyhDMxvQS9xu71YP1eV+ptuzAbxTMMeAxCt6qO9gbV0O3g6IcWW5kJJdsBnI246nTiANPchlEdezwv8MWvAIbxmOWJ36rNTR4tixDyWY8Abntdtdl0PmSCwAUFQl1y0N3V1kymIceJyh63yNX8Mrf+ErsAKqm03APvYdBz0kZvOeeE/jCE9fwg8tVu9OiX3K5bq6MN956KPD77n4vPBzJJcxDbwYMHdPTowKjvMwewFn3ekPFwYqy48n1+4V7Z2IYFFc24xn0Qi66yGbYJRd5Nx66r3WGm1G4iQHmuufKyo7TKyv5HOqdri1TAaI5VyRFWQptzvWDK1s4PVeyt+mEEBydLnjKzmtWb3PAK7m4s1wA5qGbuq8/wwWINwme8dc/WMKljWbksAM3PA/dbp7PGlXV2qHbwZ97xSnMlGT828+fdUku8UaYvf2Oo/iT994ZatBtw+bz0DXdwB9+9Vms1VV7Z8SYLORQyWejhz2EZLmU5CzYNRXWNrcfuCuD1+odqLqBhRg54iUlujf4sBs39nkXrS6kcXBnifkJC34PG2UlG2i2FgdmW9wqgqb3vznXSJT+A7B7lfN47MoW7jo143lsYdqbMucu42eSC6XUykN3GXTFbAGq6cEqUaC3lswwDIrvXNgAYPYZPz0XPayXUhqa5QI4/WVWax28/PpZ7t+o5HP4J6+5Hr/210+goxkgJFgYFcZkIYfX3TIf+nPnRuas++GLm/gXf/EonrpWwxtvnce77gqm8U1w8nPdqF2de8KzdgS1dndgKYuAtzKYtVpYmO7dNKsgZyNbyQ67cWPneVzvHHAkl1aI5OJPlR1GfvWtt+5q1oG7/J9dc51u/6tjh/uschEWFGUBUf+QggWXh04pNQcjuySXrmH2aqEUHsmlZHWMq7ZUrjFkF2IvGeWpazU70+SC1ZY2CuduHiz9B8xCqLamY7vHrMN333McR6cKeOjiJibyudgd43qR9wWD1+odvPOPH8B2W8O973kR/ug9dwYyggDzBhmVbsqTmRgsiM2bJdov3HUH7HyKU/RTzEl24zc/puQyvH3BASc9N26GC+BILryd9CjITIApSfrbL8fB6bjofObsxt3P6tjhf4ctwuaK+gOijKNTBWw0VDTVLlqa6XG7PXQAuGb12S7m3Bq6+Tq8xlyA6TnmJNJTcnnw/DoAs2vexZDqVTdhept7XuEaJ2XRj5KV8MHXnwEQX26JA7vAWR76hbUG1K6B//cfPh9v4GjyjIKc5QbJGKaGzj/h7ZmfwyC5dHVbOopTll9SwiXCTlcfGcklbg46YN70CQH3RjYqGvpu4XVcHEQgeGTe4bC5oo9e3gIhTkCUwQJXVzZbdpUo8/iY3rVijTbzZrmYAyW2mlpoD2glK/X00B98bgNHpwo4M1+xB0dEoYWkODnDPbrcKlEeb7/jKG46VNnXiTf+0n9WdHK4RxOpKE8VCA+KAs5FMlAN3RUzubzZwnQxF0gZ5VGQs1zpgf2tYTdutuSyAw+dEIJijj+1aBRkpr3A64mu6v2/cQ+/qGVRlB3DJmcdQ/volS2cmi0FKh+ZQTfzo83HJgpOUBRwPHSP5GLpti1N5wZFgd7j2Cg1e5W/6sY5tFSd24rAT5iHXsxJIMQMijKD3qvPhJQh+PTP3wN9H+ee5n2l//ZYuB7GtihLWN4OLyyKMm6VIZBc3L17zAyXeEMnSrIUOV9z2OUHdiObrcT30AFrRzbClaK7heeha13a95vYyLzDYXNFH7uyxR3yyy68y9WWXeYflFyYh+7W0CXbs+IFRQFrzmREP/RnVupYb6i459QBnDhQwuJm05POxCOsCCGTISjLZic3XpVoGNMleUdN+nvhL/1f3mqjkJPsm2QYUZPggeBgbDeOhz7IQQ+Ohn55sxm7C2JBlrjpe8CIFBbldu6hA2wM3RhLLi77pOoGctn+ptuOzDvMG+u1WuMHRAGzF4MsZXB5s2n3ZXFXigLAimXQvXnojoEKyxAJ65HNeOA5M7vl7tMzOHmgCE2nuNpjck/UVHQWEF6tdXY063A/yWYIMsQruRyazPcM+PTqwRPWnAtwGfQBlf0D3oKquEVFgLVuTedmTIyGh77zLBcgfFC0KbkMdyB4L1QUjuQyAJlpuM8qF7y5oo9ZAVGeQc9kCI5M5XFlsxXobc68fTso6tPQGf7WuYxeHvqD59dxaCKP4zNFHD9g7hR6BUZZD2lukY017GGl1sFMUe5rKTGDEOKZK7q81Y7lOUfNmdQNCt0Iz3K5fq6M4zNFu3HYIGDHdnWrhbZmxG5rW5Sz0A3KDZ6PQpbLwlQRspTBjYd2lvERNig67ZJLPmd2hHVLLqac2N/PeWQ0dN5cUdaJ76bDE9zfYbnotoful1xqHMkllocuhXrolFI8cH4DL7v+AAghOGkV21zcaODl4OePA9GtNlk+tqbTAc+ZzNil/8vbbbz45EyP37Akl5DgYNRNDADe99KTeO9LTg50KAIzvOdXzcB2XA2dnVMtVbfjD4xRyHI5fqCIs//6TTuu0C3KwXqRsBqLNOE0/XM89LBpXEkyMu+wOyjKWKuryOfC+y4cnSrg8mYLW1aWC9V5E4kAABfHSURBVNvCs5vDCguKutMWXd56mIceFRQ9v9bAWr2Du08dAGAG9ORsJr6HHjL0gEkugzTozEM3DIqV7U6s7BM2xYaXFaTq0YUXhJCBlfwzmJZ8ftVsEhZXcrHL4MP6moxAxsdu3nuehx4lJ6YJ/5ALlTNSMmlG5h3mBUXXah3MRvRdWJguYK3ewUqtjZIs2Z6gZFUhrnA9dOfrsCyXKMnlwfOOfg6YF8XxmSIurEWnLkaNJWOtg1drnR33ad5PmIe+0VSh6gYOxZBcCi5P1c8ghujuFHZsz1oeelzJxW5U5Qvid3UDBh3uNe8FXsxkFAZE7wf+Bl2D2JWMzDvMDK0nKFrv4ECEgWMX39nlWsA4sz7jgNcrd3+9m6DoN55Zw8GKgtOzTl+TkweKuNRzFBsbKBu8OZXz5tSi1fpgPXRzHJvupCzGGGRsz2nVgjp6L8llGGBGaMvqBRQ21MJP2LCHQQw96Ce8QdFqytfM8EsuIigaAW+u6FpdtQc28GB659ml7YBxZvILIU5ZO+DsBAo5KTRwFeaha7qB+59exWtuPOjZNRyfKeHCeiOyR4TjxQRf09xNdKB2jQFLLuYkeMeg95ZceDdihtY1349hNuiEENsQHY2pnwMuiXDMjFuR02VyEF0HBwFXchEeOh/eXNG1eicy15p56A1VD3hWLDBayEke48s8q6imVmFB0YcubKLW6eI1Nx30PH5ytoi2ZmDFKgzi4WjoHA9dydoTcwbtobc13a4SjVPww+ITXMnFGqLLixsME0weiaufA96gqBtHTx7uLJfdUrSC4O6B5eMquZhB0f5+ziPzDtvThCyDbhgUGw010qDPVxRkrcCOvwCGeegFXwYC2wmEBUQBKyjKCXZ99akV5CSCl9/gzWZhbWWjAqORWS6uoO9ADbrloV/bbkPKkFjHYksuXA29/+1FdwMzvnGLioDwnQnb2aXVWy0qWVAKtF1JA2nflTAmXLOKAVNe62enRWCEDDpg5ojXreq7akuDbtDIwoeslMHhKdOLDGrolofum0jEPKuwgChgSS6crI0vn13BXadmAvM6T1iDHqK6LqoRerI7lfLgoDV0zcDSVhtzZSUwxo6HHRzk5KJr9lZ8sJksvdiNh16wb2Q+yUXv/xSbfsKLHaQ9bsCoWPUibHcyiAKynq9GCPkoIWSFEPKY67EZQsgXCSHnrP+nkz1ME7eHzjoP9ipvZ15VUHIxLzj/iDkm7YSV/ZvPCTbnWtxo4txKHa+58WDg+UenzVmbl2J46LwTwF1YM1cebOfBdlfHNatKNA5h0gMQfRMbJnYluYQMio7KZkoDvEHR46Khl63dCYubDGtQ9GMA3uR77EMAvkQpvQHAl6zvE8c9p3GtFs+gs8BoWFC04JvGQog5RzRKQ+d56F99agUAAvo5YBqshelCpIceWSlqGXRZyvTsnZIkihUMXt5qx26YFZbtATgdJofduMm2Qd9BUDRMcmE37txwr3m32C06NG9wEACUIf+c9wrb9W9bOvpQFhZRSu8HsOF7+G0APm59/XEAb9/n4+Liniu6Zk2PmevRDc720H0GmnnsxVwwaPGhN9+Md99zIvRvsrRFd9bKV55axYkDRU+6opvjM8V4GnpILxfA1M8HWTXJCouWt+J76JGDgyOKqYYJ1gt+Jxq6LGUgZUhoCl9ajVuBcwMfFw399Jx57T95dRsAf6Rk0uz21eYppUsAYP0fdEsTwD00IL6HziQXflDUL7kAwLvuPo7bj02F/k3FN72nren45rNrgXRFNycPRKcuRnno7FhnB6ifA+aNbKulodbp7kByMY+d14FvEDMXd4MiZVCSpchAuR/WGzy0yCalxo03KDrta2bcfmwKcjaDB59bh2FQdCP6FCVF4q9GCHk/IeQhQshDq6ure/pbJTlr56Gv1TvIZkjPQg+2TfYPq7DzzTkGvRf2GDrLCH/r2XW0NYMrtzBOHCii1u7aY+n8OO1z+WmLwGADooDpoTMjHFdyKYRoycDoXOh5WcLCdHHHu6OiEmyha0suKU5bBLzB4HHR0PM5Cbcfm8KDz20MbM27fbVrhJDDAGD9vxL2RErpvZTSOymld87Nze3y5Uw8Gnq9gwNluWe/ibtOzeBfv+1WvPKMN5WQ6V08D70Xim/Yw98/vYp8LoO7T4U3q2Itb1nnRz+qTkPnD7oll0HiDtjGnSIkZQiUbIavoY9IUPSDrz+DX3vbrTv+vSJn/N6o3MR2S6TkMuSf835wz6kZPHZlyx4oPoxBUR6fBfA+6+v3Abhvfw4nGm+WS3QOOkPKELznJScDHpEjuew8yMj0T9ag69nVOs7MVwJd9dyEVQ4yoqrKKvksMmSwk3sAr1d5eAc9yosh03t6NecaFm4/NoW7Tx/Y8e/xhj2kPW2xxKk7SPtNzM3dpw/AoOauHeh/z56e1owQ8mkArwYwSwi5DOD/BvDrAP6MEPKzAC4BeEeSB8koK07nvvUeVaK9cLJcduOhO1NsAHPM3S0hLXwZUdkeQPSw5HxOwsd++i5u3/d+4m6REFdDB1hP9AjJJaWeW1EODopmu7q0Nufi1R10xkRyAYAXHp9GTiL42jlTXu737rOnQaeUvjPkRz+0z8fSE/fA5LW6iusOlnf9t6KyXHrhHphsGBRXNlt4wy3zkb8T1dME6N334ZVn9iZX7QfMQ58s5CJ3I37Chh5oKb/QC3I2ILGlXU/mOS5OZk864wZuCrKE2xam8PVzawBGR0MfCGw7V7fma+6llexUMYecRDCzwxFbgPMhqV2zP4uqG1iYic5RtkvgO3zJxfTQh/vjYDeyncgtQPgYulHR0HdLSZYCn3fa5YeclIEsZcZWcgHM1tm2hi4MejjMQ7+23YbaNfYoueRw3y++HD/6woUd/y7zVDtdA4ubZm75sR5VhCVbQw8ZHDwCE12YVx43IMoIGxQdNhg7LRQ4NzInyyWdawbY5x0sLBr283u/YMNtgP7LiSMzgg5wZIsLVoHObI+iol7cciRa9w5DzjpB0VUrH/5YDw89qqcJYFZNDruWzIzQToOzRdkZJuJG1Vn73OEOiu6WkpwNjN9Le+k/YO5M3I6LquvIZkis3j9p4EUnpiFlCHSD9r1obqTOKpaPfckqod+Lh74XFJfksrjRAtC7irCnhj4CHjoLBu8kIArwPVXAKo0OSdVMA2ZQNCi5pHnNQHBHlvYB0X5KStZOYOh3RfBIvctMcrloTf85UBqUQXckl8ubTRysKD2DhPmsBEJGW0Nna9ypQS/mwiWXtHrngGnYOl0Duqs3eKerp1puAYKDosfNoAPOCEqhoUfAdOj9klx2izsourjZ7Cm3AOZs0UJOCtXQR2Fw8OHJPLIZgpt7pGj6iQqKpvlCL3Fa6I6DcfN/3qo+/Of2fvO6m+eRzZAdx5v2ykhq6BfXGyAEmCkOxqArLg19caOFF5+M1z04LB8bMPXkgjzcJ/3CdBGPffiNO0pZBMz0vbC0xWHflewF94BsVpk8LgZ9ra7a33fGYM1+XnxyZlfXyl4ZqXeZSS7VpoaZoozsgIwBOzkbHR1LW61YHjpg3pDCgqKj4KED2NUJWpTNkX0sTZGhdmmqDTovbtLp9n/oQb8pKkJyAXZ3reyVkXqX2fAJAJGTivpxHIA5gcigwLGYfbKLcjZQOcgw5Yd06slhVbKjEAjeC4VcsN3DOBg3f8xkVJyVNDBS7zKbKwoMLsMFcIKiz67WAcSfZBPW0wRI90nvlh7cjEKq5l7gTWtK+00MCGb3DGIU27gycu9yybpIBmnQWWbGsytm+mRcySUsOAikW0/mBQcBa80p3ZUAYZKLntrWuYyi4s2/H4ddybAwcu/yMHjohJgtYZe325AyJHYpfEnORnvoKT3peS1VAdNzS+tNDHAkl0CWS4rXDJiSC2uiB6T73B42Ru5dtg36gFIWGe6+JnGDs7zue4w0GzdbeuD0Bk+zceN56ONg3NwSG6UUi5vNPfVdEsRnpNIWAadadHZARUUMOSsB6MYOiALmBBu/UWOoKc5+CAuKarph36DTCDNsjTHLcnEPiq51NFzb7uBFJ+Kl9gr2xshdTczrGRYPPW5AFDAlF38pOCPNGjqTHoLDHgxMpXTNAH+e6jh46O4b+GNXtgAALxQGvS+M3JnFAmyD1NABx6DHDYgC5gXuLwUHgK5uwKDp7UYX6qF3aaolF9481XEosim6BkU/dGETJVnCTYd21whPsDNG7swahqAo4BjfYzPxPXTeAF3APYpt5D6OWERJLv3uRtdPpAxBPpfhFBalPMvFdZ4/fHETdxyfHptOi4Nm5K6msjU6jg1dHhRsUPRONXSA76kC6fXQw/LQOylvzgUEM5vUMWjOxT7vlVoHZ5e3hX7eR0ZOQ3/HixZwfKY4kLJaN6wt5k4kF3vIhU9H77DBwSk1bkXO4GDA9NDHwbg1O+NVWMTO828+uw6DQhj0PjJyBv30XBmn53Y/S3S/UHIZyNnMjtKxwqWHdHvoUoZAzmb4hUUplZkY7mIySulYZLmw8/xr51aRIcAdx6cGfETjQ7rPrAQpyhKOTReQ2YE2GOapjsOILl6VrKanuzkXYHXYtFJVuwYFpemeVgQ4Bv3yZgs3HpqwO00KkmfkPPRh4ZffcCPqISmIYTANvcHxVIH0BkUBpiUHb2RpXjNg3cis82QcbtyA47gAwItOCO+8nwiDvkvOzFd2/DslVzqXG/tCT7FxK8gSWppzI6OUjoWeXJSzqDbNMYXjMCAaAPK5DAgBKAXuPDEz6MMZK9J9Zg0ZPdMWU3yh+yWXrpWLn9ZAMKMoO9XBjoee7rRFQgiKVtKCCIj2F+Gh95HQvuDMc0uzh56TAj1NgHTLTIC3ley4SC6AOaWqpGR3VEkt2DvCoPcRVhQVqqGn+EL3jyVja067cSu6xu91uub/aZdcAGCuouDMfBmEpHsHNmwIg95HlGwGGRIssBkHDd2cp9q0v097dSyjKEtoqF07ZRFI/00MAP70p15sFxgJ+ocw6H2EEMIdQzcOWS4FOTiWDEj3TQwwM5sMagZE1THZlQDAoZgzAgT7S/rPrCGDN4ZuHDy3oizZ+dhA+oupGCw42FJ1bFiS0zhILoLBIDz0PlNSsp7+2IDLuKXYWy34slzGYVcCODnZ7/ijb+GZlToIgRj2IEgMYdD7TCEnBfuCj4OHnstC7Rro6gayUsaV5ZLuoNmpuRKkDMFEPosPvfkmvPHWQzg1Wxr0YQlSijDofaakBMfQOd5qeo2bnbKp6ZiQMmORew8ALz45g6f/nzeL9rGCvpDuq2kIKXIGRY+Dh+5voauNQe49QxhzQb9I/9U0ZJSUYJOqcUjh8w9MZnGDtHvoAkE/EVdTnynk+E2qgJQHRXOs06RVNWn1gE/zTUwg6DfiauozJUXiVopmM2RHrXhHjaJPclG76c/sEQj6jbia+kxRznK7LaZZPweCfWyc0v/03sQEgn6TbisyhJRkCapu2AYNGI/JPQWfQR+X5lwCQT8RV1Of8Rs2YDzmTLICG9YTfVyacwkE/WRPeeiEkAsAagB0AF1K6Z37cVBphnVcbKpdTBbM0Vxql6ZeSw6TXISHLhDsH/tRWPQaSunaPvydsYDXE30cPHR/HnpHSC4Cwb4jrqY+U+SModO6RqqrRAGnSZU/Dz3tOxOBoJ/s9WqiAL5ACHmYEPL+/TigtFOSg4Oix8FDz0oZyFKGk+WS7nULBP1kr5LLyyilVwkhBwF8kRByllJ6v/sJlqF/PwAcP358jy83+hQtDb3l6zw4DtJDwdU6WO0ayBBRFi8Q7Cd7siKU0qvW/ysA/juAuzjPuZdSeiel9M65ubm9vFwq4Hnona4xFtJDydVCd1xuYgJBP9n1FUUIKRFCKuxrAG8A8Nh+HVhasdMWO14PfRykh5KSxUbDHPIwDjKTQNBv9nJFzQP4OiHk+wC+DeBzlNLP789hpZeSHBwUrY6Jh/6y62fxtXOrWK93zJvYGKxZIOgnu9bQKaXnAdy2j8cyFhSVYNriuMgP77r7OD72zQv484cvQ+2Ox5oFgn4irqg+I0sZSBni6Yk+Dr1cAODMfAV3npjGp799aWzWLBD0E3FF9RlCCIqyd2qRptOx8VbfdfdxXFhv4pvPrqc+914g6DfjYUWGjJKc9aQtdsbIW33L8w9jspDDSq0zNjcxgaBfiCtqABR9PdHNAOF4eKv5nIR/+MKjAABlTG5iAkG/EFfUACjK3jF046Ynv+sus8BMeOgCwf6yH825BDukKGfR6Hg99HEybjfMV/CqM3OYKuYGfSgCQaoQBn0AlGQJ61aBjWFQdA06Vh46APzJ++6ERMZDZhII+sV4WZEhoag4Hro6pn3Bc1Im1TNUBYJBMF5WZEgo5hwNnRl0ESAUCAR7RViRAVBSsmK2pkAg2HeEFRkARVcbWdEXXCAQ7BfCigyAoixB0ynUriE8dIFAsG8IKzIA2Bi6lqoLD10gEOwbwooMgJLiDLlgw5LHpVJUIBAkh8hDHwDMQ//o15/DmfkKAOGhCwSCvSMM+gC48+Q07jo1g4984zlQaj4mNHSBQLBXhEEfAIcnC/izX3gJ1uodfOnJa3j86jbuOD496MMSCAQjjjDoA2S2rODHX3x80IchEAhSgtjnCwQCQUoQBl0gEAhSgjDoAoFAkBKEQRcIBIKUIAy6QCAQpARh0AUCgSAlCIMuEAgEKUEYdIFAIEgJhLLa8368GCGrAC7u8tdnAazt4+GMCuO47nFcMzCe6x7HNQM7X/cJSulcryf11aDvBULIQ5TSOwd9HP1mHNc9jmsGxnPd47hmILl1C8lFIBAIUoIw6AKBQJASRsmg3zvoAxgQ47jucVwzMJ7rHsc1Awmte2Q0dIFAIBBEM0oeukAgEAgiGAmDTgh5EyHkKULIM4SQDw36eJKAEHKMEPIVQsiThJDHCSEfsB6fIYR8kRByzvo/dZMwCCESIeR7hJC/tr4fhzVPEUL+nBBy1vrMX5L2dRNC/pl1bj9GCPk0ISSfxjUTQj5KCFkhhDzmeix0nYSQX7Fs21OEkDfu5bWH3qATQiQAfwDgzQBuAfBOQsgtgz2qROgC+GVK6c0A7gHwi9Y6PwTgS5TSGwB8yfo+bXwAwJOu78dhzf8OwOcppTcBuA3m+lO7bkLIUQD/FMCdlNLnAZAA/ATSueaPAXiT7zHuOq1r/CcA3Gr9zn+0bN6uGHqDDuAuAM9QSs9TSlUA/wXA2wZ8TPsOpXSJUvpd6+sazAv8KMy1ftx62scBvH0wR5gMhJAFAP8TgD9xPZz2NU8AeCWAjwAApVSllFaR8nXDnJBWIIRkARQBXEUK10wpvR/Ahu/hsHW+DcB/oZR2KKXPAXgGps3bFaNg0I8CWHR9f9l6LLUQQk4CuAPAgwDmKaVLgGn0ARwc3JElwu8B+OcADNdjaV/zaQCrAP7Ukpr+hBBSQorXTSm9AuC3AFwCsARgi1L6BaR4zT7C1rmv9m0UDDrhPJba1BxCSBnAZwD8EqV0e9DHkySEkB8GsEIpfXjQx9JnsgBeCOAPKaV3AGggHVJDKJZm/DYApwAcAVAihPzkYI9qKNhX+zYKBv0ygGOu7xdgbtVSByEkB9OYf5JS+hfWw9cIIYetnx8GsDKo40uAlwF4KyHkAkwp7bWEkP+MdK8ZMM/py5TSB63v/xymgU/zul8H4DlK6SqlVAPwFwBeinSv2U3YOvfVvo2CQf8OgBsIIacIITLMAMJnB3xM+w4hhMDUVJ+klP6O60efBfA+6+v3Abiv38eWFJTSX6GULlBKT8L8XL9MKf1JpHjNAEApXQawSAi50XrohwA8gXSv+xKAewghRetc/yGYcaI0r9lN2Do/C+AnCCEKIeQUgBsAfHvXr0IpHfp/AN4C4GkAzwL4l4M+noTW+HKYW60fAHjE+vcWAAdgRsXPWf/PDPpYE1r/qwH8tfV16tcM4HYAD1mf918CmE77ugF8GMBZAI8B+AQAJY1rBvBpmHECDaYH/rNR6wTwLy3b9hSAN+/ltUWlqEAgEKSEUZBcBAKBQBADYdAFAoEgJQiDLhAIBClBGHSBQCBICcKgCwQCQUoQBl0gEAhSgjDoAoFAkBKEQRcIBIKU8P8DRKN2O9aFL9kAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"\n",
"arr = [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]\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"x = 100\n",
"plt.plot(arr)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3",
...@@ -16,10 +520,9 @@ ...@@ -16,10 +520,9 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.3" "version": "3.6.4"
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 2 "nbformat_minor": 2
} }
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