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@ktzanev
Last active December 13, 2018 20:15
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Bonjour Colaboratory

Python

meijerg([[0,-2], [1]], [[1/2], [-1/2, -1]], x)

Mathematica (WolframAlpha)

MeijerG[{{0,-2}, {1}}, {{1/2}, {-1/2, -1}}, x]

Voir dans WolframAlpha

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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Bonjour Colaboratory",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ktzanev/bd204baaf61f71de2a34304864b9dd88/meijer_plot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"metadata": {
"colab_type": "text",
"id": "TbPKMXi0xZnO"
},
"cell_type": "markdown",
"source": [
"## Meijer function plot"
]
},
{
"metadata": {
"id": "8DSQIr1osa0w",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"### Importation de la bibliothèque `mpmath`"
]
},
{
"metadata": {
"id": "5mG7ozI9mUiq",
"colab_type": "code",
"outputId": "3010b4c7-5a0d-4202-f2e0-3b204fc0e6e6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"from mpmath import *\n",
"print('Meijer example for x=2:',float(meijerg([[0,-2], [1]], [[1/2], [-1/2, -1]], 2)))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Meijer example for x=2: 0.21106469777659798\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "qVjN69SksqAY",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"### Définition de la fonction de Meijer avec des paramètres spécifiques "
]
},
{
"metadata": {
"id": "ZsLoTw_TnEJ2",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"mj = lambda x : float(meijerg([[0,-2], [1]], [[1/2], [-1/2, -1]], x))"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "ucNT1xA-ncwa",
"colab_type": "code",
"outputId": "ed136fe8-1f30-427a-e6d8-d77ad09dd307",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"mj(2)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.21106469777659798"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"metadata": {
"colab_type": "text",
"id": "yv2XIwi5hQ_g"
},
"cell_type": "markdown",
"source": [
"### Visualisation"
]
},
{
"metadata": {
"colab_type": "text",
"id": "Y3dN2qDbxZnf"
},
"cell_type": "markdown",
"source": [
"### importation des bibliothèques pour la visualisation"
]
},
{
"metadata": {
"colab_type": "code",
"id": "FoYlg8NHxZng",
"colab": {}
},
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "WJQ2iCxXtkGR",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Trace la fonction entre 1 et 100 avec un pas de 1"
]
},
{
"metadata": {
"id": "M8sn0nyMn9m5",
"colab_type": "code",
"outputId": "a0e7541e-0ca3-41f7-b5f7-485f49032a0e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 347
}
},
"cell_type": "code",
"source": [
"x = np.arange(1.,100.,1)\n",
"y = [mj(n) for n in x]\n",
"_ = plt.plot(x, y, '-')\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
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tYr9bi2eXjLhNNJZQe3dU7V0DauuOpl4PPXqi6uiJ6ujpTo1181Wnw1Aw4FZp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\nM4QzAAA2QzgDAGAzhDMAADbz/wFmXN2VnrgVugAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f61565a6e10>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "T8Q7WkZmtt3x",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Trace la fonction entre 1 et 2 avec un pas de .02"
]
},
{
"metadata": {
"colab_type": "code",
"id": "FlQq0SUepQbd",
"outputId": "2a405030-5f45-4c7f-9dd3-c7369ffd41c7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 347
}
},
"cell_type": "code",
"source": [
"x = np.arange(1.2,3.,.02)\n",
"y = [mj(n) for n in x]\n",
"_ = plt.plot(x, y, '-')\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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FE0lWBgMAWEpeh/NJlu0EAFhQXodzc3vqUpHVfq7jDACwjrwOZ3rOAAArIpwl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\nAw/OZvGEBJzzAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f61557757f0>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "5FpBG5UauJsE",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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