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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"# HLMA 408: Lois gaussiennes et visualisation interactive\n", | ||
"\n", | ||
"***\n", | ||
"> __Auteur__: Joseph Salmon <joseph.salmon@umontpellier.fr>\n", | ||
"\n", | ||
"Inspiré des codes :\n", | ||
"- https://medium.com/kapernikov/ipywidgets-with-matplotlib-93646718eb84\n", | ||
"- https://medium.com/@jdchipox/how-to-interact-with-jupyter-33a98686f24e" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 1, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"# pip install download # si besoin, décommenter et exécuter cette cellule." | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 2, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"# pip install 'ipywidgets==7.5'" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 3, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"import numpy as np\n", | ||
"import matplotlib.pyplot as plt\n", | ||
"from scipy.stats import norm, laplace, t, f, expon, beta, cauchy\n", | ||
"import ipywidgets\n", | ||
"import seaborn as sns" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 4, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"%matplotlib widget" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 5, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"distributions_dict = {'Gaussienne': norm,\n", | ||
" 'Laplace': laplace,\n", | ||
" 'Exponentielle': expon,\n", | ||
" 'Cauchy': cauchy,\n", | ||
" }\n", | ||
"# plus d'exemples ici: https://docs.scipy.org/doc/scipy/reference/stats.html" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"\n", | ||
"## Visualisation : densités et tirages aléatoires" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 6, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"def make_box_layout():\n", | ||
" return ipywidgets.Layout(\n", | ||
" border='solid 1px black',\n", | ||
" margin='0px 10px 10px 0px',\n", | ||
" padding='5px 5px 5px 5px'\n", | ||
" )" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 7, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"data": { | ||
"application/vnd.jupyter.widget-view+json": { | ||
"model_id": "250e4daad58d4080931fc310a5bf5c0a", | ||
"version_major": 2, | ||
"version_minor": 0 | ||
}, | ||
"text/plain": [ | ||
"Sines(children=(VBox(children=(IntSlider(value=30, description='Nb tirages', max=500, min=1), FloatSlider(valu…" | ||
] | ||
}, | ||
"metadata": {}, | ||
"output_type": "display_data" | ||
} | ||
], | ||
"source": [ | ||
"class Sines(ipywidgets.HBox):\n", | ||
"\n", | ||
" def __init__(self):\n", | ||
" super().__init__()\n", | ||
" output = ipywidgets.Output()\n", | ||
" self.n_samples = 30\n", | ||
" self.ranges = (-5, 5) # Borne d'observation\n", | ||
" \n", | ||
" self.x = np.linspace(self.ranges[0], self.ranges[1], num=400)\n", | ||
" self.mu, self.sigma = 0, 1\n", | ||
" self.distribution = distributions_dict['Gaussienne']\n", | ||
" self.samples = self.distribution.rvs(\n", | ||
" size=self.n_samples, loc=self.mu, scale=self.sigma)\n", | ||
" self.size = 3\n", | ||
" self.initial_color = '#3646E3'\n", | ||
" self.jitter = 0.04\n", | ||
" self.params = dict(jitter=self.jitter, size=self.size,\n", | ||
" color=self.initial_color, alpha=0.70)\n", | ||
"\n", | ||
" with output:\n", | ||
" self.fig, self.ax = plt.subplots(2, 1, sharex=True,\n", | ||
" constrained_layout=True, figsize=(4, 4))\n", | ||
" self.line, = self.ax[0].plot(self.x,\n", | ||
" self.distribution.pdf(\n", | ||
" self.x, loc=self.mu, scale=self.sigma),\n", | ||
" self.initial_color)\n", | ||
" \n", | ||
" \n", | ||
" self.fig.canvas.set_window_title('My title')\n", | ||
" self.ax[0].set_xlabel('Densité')\n", | ||
" self.ax[0].set_xlim(self.ranges[0], self.ranges[1])\n", | ||
" self.ax[0].set_ylim(0, 0.6)\n", | ||
" \n", | ||
" self.ax[1].set_xlabel('Tirage')\n", | ||
" self.ax[1].set_xlim(self.ranges[0], self.ranges[1])\n", | ||
" self.ax[1].axes.get_yaxis().set_visible(False)\n", | ||
"\n", | ||
" self.points = sns.stripplot(\n", | ||
" x=self.samples, ax=self.ax[1], **self.params)\n", | ||
" self.fig.canvas.toolbar_position = 'bottom'\n", | ||
"\n", | ||
" # define widgets\n", | ||
"\n", | ||
" n_samples_slider = ipywidgets.IntSlider(\n", | ||
" value=self.n_samples, min=1, max=500, step=1, description=\"Nb tirages\")\n", | ||
" mu_slider = ipywidgets.FloatSlider(\n", | ||
" value=0, min=-2, max=2, step=0.1, description='$\\mu$')\n", | ||
" sigma_slider = ipywidgets.FloatSlider(\n", | ||
" value=1, min=1.1, max=5, step=0.1, description='$\\sigma$')\n", | ||
" color_picker = ipywidgets.ColorPicker(\n", | ||
" value=self.initial_color,\n", | ||
" description='Couleur')\n", | ||
" jitter_slider = ipywidgets.FloatSlider(\n", | ||
" value=self.jitter, min=0.005, max=0.3, step=0.005, description='jitter')\n", | ||
" text_distribution = ipywidgets.Dropdown(\n", | ||
" options=list(distributions_dict),\n", | ||
" value='Gaussienne',\n", | ||
" description='Distribution')\n", | ||
" int_range_slider = ipywidgets.FloatRangeSlider(\n", | ||
" value=(-5, 5),\n", | ||
" min=-5, max=5, step=0.1,\n", | ||
" description=\"Amplitude\")\n", | ||
"\n", | ||
" controls = ipywidgets.VBox([\n", | ||
" n_samples_slider,\n", | ||
" mu_slider,\n", | ||
" sigma_slider,\n", | ||
" text_distribution,\n", | ||
" color_picker,\n", | ||
" int_range_slider,\n", | ||
" jitter_slider\n", | ||
" ])\n", | ||
"\n", | ||
" controls.layout = make_box_layout()\n", | ||
"\n", | ||
" out_box = ipywidgets.Box([output])\n", | ||
" output.layout = make_box_layout()\n", | ||
"\n", | ||
" # Observe stuff\n", | ||
" n_samples_slider.observe(self.update_n_samples, 'value')\n", | ||
" mu_slider.observe(self.update_mu, 'value')\n", | ||
" sigma_slider.observe(self.update_sigma, 'value')\n", | ||
" color_picker.observe(self.line_color, 'value')\n", | ||
" text_distribution.observe(self.update_text_distribution, 'value')\n", | ||
" int_range_slider.observe(self.update_range_slider, 'value')\n", | ||
" jitter_slider.observe(self.update_jitter_slider, 'value')\n", | ||
"\n", | ||
" # Add to children\n", | ||
" self.children = [controls, output]\n", | ||
"\n", | ||
" def update_mu(self, change):\n", | ||
" \"\"\"Draw line in plot.\"\"\"\n", | ||
" self.mu = change.new\n", | ||
" self.samples = self.distribution.rvs(\n", | ||
" size=self.n_samples, loc=self.mu, scale=self.sigma)\n", | ||
" self.line.set_ydata(self.distribution.pdf(self.x, loc=self.mu, scale=self.sigma))\n", | ||
" self.ax[1].clear() # <-- clear axes\n", | ||
" self.points = sns.stripplot(\n", | ||
" x=self.samples, ax=self.ax[1], **self.params)\n", | ||
" self.ax[1].set_xlabel('Tirage')\n", | ||
" self.fig.canvas.draw()\n", | ||
"\n", | ||
" def update_sigma(self, change):\n", | ||
" \"\"\"Draw line in plot.\"\"\"\n", | ||
" self.sigma = change.new\n", | ||
" self.samples = self.distribution.rvs(\n", | ||
" size=self.n_samples, loc=self.mu, scale=self.sigma)\n", | ||
" self.line.set_ydata(self.distribution.pdf(\n", | ||
" self.x, loc=self.mu, scale=self.sigma))\n", | ||
" self.ax[1].clear() # <-- clear axes\n", | ||
" self.points = sns.stripplot(\n", | ||
" x=self.samples, ax=self.ax[1], size=self.size, color=self.initial_color)\n", | ||
" self.ax[1].set_xlim(self.ranges)\n", | ||
" self.ax[1].set_xlabel('Tirage')\n", | ||
"# self.fig.canvas.draw()\n", | ||
"\n", | ||
" def update_n_samples(self, change):\n", | ||
" \"\"\"Draw.\"\"\"\n", | ||
" self.n_samples = change.new\n", | ||
" self.samples = self.distribution.rvs(\n", | ||
" size=self.n_samples, loc=self.mu, scale=self.sigma)\n", | ||
" self.ax[1].clear() # <-- clear axes\n", | ||
" np.random.seed(123)\n", | ||
" self.points = sns.stripplot(\n", | ||
" x=self.samples, ax=self.ax[1], **self.params)\n", | ||
" self.ax[1].set_xlim(self.ranges)\n", | ||
" self.ax[1].set_xlabel('Tirage')\n", | ||
"# self.fig.canvas.draw()\n", | ||
"\n", | ||
" def update_jitter_slider(self, change):\n", | ||
" self.params['jitter'] = change.new\n", | ||
" self.ax[1].clear() # <-- clear axes\n", | ||
" self.points = sns.stripplot(\n", | ||
" x=self.samples, ax=self.ax[1], **self.params)\n", | ||
" self.ax[1].set_xlabel('Tirage')\n", | ||
"\n", | ||
" def line_color(self, change):\n", | ||
" self.initial_color = change.new\n", | ||
" self.line.set_color(self.initial_color)\n", | ||
" self.ax[1].set_color(self.initial_color)\n", | ||
"\n", | ||
" def update_range_slider(self, change):\n", | ||
" self.ranges = change.new\n", | ||
" self.line.set_ydata(self.distribution.pdf(\n", | ||
" self.x, loc=self.mu, scale=self.sigma))\n", | ||
" self.ax[0].set_xlim(self.ranges)\n", | ||
" self.ax[1].clear() # <-- clear axes\n", | ||
" np.random.seed(123)\n", | ||
" self.points = sns.stripplot(\n", | ||
" x=self.samples, ax=self.ax[1], size=self.size, color=self.initial_color)\n", | ||
" self.ax[1].set_xlabel('Tirage')\n", | ||
"\n", | ||
" def update_text_distribution(self, change):\n", | ||
" self.distribution = distributions_dict[change.new]\n", | ||
" self.line.set_ydata(self.distribution.pdf(\n", | ||
" self.x, loc=self.mu, scale=self.sigma))\n", | ||
"\n", | ||
" self.samples = self.distribution.rvs(\n", | ||
" size=self.n_samples, loc=self.mu, scale=self.sigma)\n", | ||
" self.ax[0].set_xlim(self.ranges)\n", | ||
" self.ax[1].clear() # <-- clear axes\n", | ||
" np.random.seed(123)\n", | ||
" self.points = sns.stripplot(\n", | ||
" x=self.samples, ax=self.ax[1], size=self.size, color=self.initial_color)\n", | ||
" self.ax[1].set_xlim(self.ranges)\n", | ||
" self.ax[1].set_xlabel('Tirage')\n", | ||
" self.fig.canvas.draw()\n", | ||
"\n", | ||
"\n", | ||
"Sines()" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
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{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
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{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
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{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
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} | ||
], | ||
"metadata": { | ||
"hide_input": false, | ||
"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": "3.8.5" | ||
}, | ||
"toc": { | ||
"base_numbering": 1, | ||
"nav_menu": {}, | ||
"number_sections": false, | ||
"sideBar": true, | ||
"skip_h1_title": false, | ||
"title_cell": "Table of Contents", | ||
"title_sidebar": "Contents", | ||
"toc_cell": false, | ||
"toc_position": {}, | ||
"toc_section_display": true, | ||
"toc_window_display": true | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 2 | ||
} |