A Jupyter notebook to illustrate the Central Limit Theorem, in Spanish

CLT.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "El teorema del limite central nos dice que, sin importar de que distribucion salga un conjunto de datos,\n",
    "si tomamos varios varias muestras de ese conjunto, cada una con N datos, y promediamos los datos de cada\n",
    "muestra, la distribucion de esos promedios se aproximara a la normal, si N es un numero grande.\n",
    "Que tan grande tiene que ser N? Veamos!\n",
    "\n",
    "### Instrucciones:\n",
    "\n",
    "1. Presiona **tres veces** \"Run\" en la parte superior de la pagina\n",
    "2. Usa los botones y la perilla para ajustar los valores como desees\n",
    "3. Vuelve a presionar \"Run\" \n",
    "4. Repite los pasos 2 y 3 las veces que quieras :-)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "\n",
    "\n",
    "w1 = widgets.IntSlider(\n",
    "    value=1,\n",
    "    min=1,\n",
    "    max=50,\n",
    "    step=1,\n",
    "    description='N:',\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='vertical',\n",
    "    readout=True,\n",
    "    readout_format='d'\n",
    ")\n",
    "\n",
    "\n",
    "w2 = widgets.ToggleButtons(\n",
    "    options=['Exponencial','Uniforme','Poisson'],\n",
    "    description='Dist.\\n inicial:',\n",
    "    disabled=False,\n",
    "    button_style='', # 'success', 'info', 'warning', 'danger' or ''\n",
    "#     icons=['check'] * 3\n",
    ")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9b5c6f653574b55a2a912c910c8cf12",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ToggleButtons(description='Dist.\\n inicial:', options=('Exponencial', 'Uniforme', 'Poisson'), value='Exponenci…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22f088ea706542e39ba7661a6c1b7883",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntSlider(value=7, continuous_update=False, description='Numeros en cada muestra:', max=50, min=1, orientation…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set completo de datos\n",
      "Media: 1.000 Desv. Estandar: 1.001\n",
      "Promedios de las muestras\n",
      "Media: 1.014 Desv. Estandar: 0.385\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "import sys\n",
    "import numpy as np\n",
    "from numpy.random import default_rng\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib import colors\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "from IPython.display import display\n",
    "\n",
    "rgen = default_rng()\n",
    "\n",
    "\n",
    "display(w2)\n",
    "display(w1)\n",
    "\n",
    "DISTRIBUCION_INICIAL = w2.value\n",
    "TAMANO_MUESTRA = w1.value\n",
    "\n",
    "NUMERO_MUESTRAS=5000\n",
    "TOTAL=1000000\n",
    "reemplazo=True\n",
    "\n",
    "data_prev=0\n",
    "\n",
    "DISTRIBUCION_INICIAL=DISTRIBUCION_INICIAL.lower()\n",
    "\n",
    "if DISTRIBUCION_INICIAL.startswith(\"unif\") or DISTRIBUCION_INICIAL.startswith(\"expo\"):\n",
    "\tdata_prev =  rgen.random(size=TOTAL) #Uniform\n",
    "\n",
    "\n",
    "if DISTRIBUCION_INICIAL.startswith(\"expo\"):\n",
    "\tlam=1 #yeah, I'll be fixing this one.\n",
    "\tdata_prev=-1*np.log(1-data_prev)/lam\n",
    "\n",
    "if DISTRIBUCION_INICIAL.startswith(\"pois\"):\n",
    "\tlam=5\n",
    "\tdata_prev=np.random.poisson(lam,size=TOTAL)\n",
    "\n",
    "\n",
    "samples=[]\n",
    "used_indexes=[]\n",
    "for i in range(NUMERO_MUESTRAS):\n",
    "\tsamples.append([])\n",
    "\tfor j in range(TAMANO_MUESTRA):\n",
    "\t\tindex=rgen.integers(0,len(data_prev))  #Aca tomamos un indice al azar dentro de todos los datos totales que tenemos.\n",
    "\t\tif not reemplazo:\n",
    "\t\t\twhile index in used_indexes:\n",
    "\t\t\t\tindex=rgen.integers(0,len(data_prev))\n",
    "\t\t\tused_indexes.append(index)\n",
    "\t\tsamples[-1].append(data_prev[index]) #sampling WITH reemplazo\n",
    "\n",
    "averages=[]\n",
    "for i in samples:\n",
    "    j=np.array(i)\n",
    "    averages.append(j.mean())\n",
    "\n",
    "\n",
    "## Esta parte esta tomada de \n",
    "# https://matplotlib.org/stable/gallery/statistics/hist.html#sphx-glr-gallery-statistics-hist-py\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, sharey=False, tight_layout=True)\n",
    "\n",
    "axs[0].hist(data_prev)\n",
    "axs[0].set_title(\"Distribucion Original\")\n",
    "\n",
    "av=np.array(averages)\n",
    "axs[1].hist(av,color=\"red\")\n",
    "axs[1].set_title(\"Promedios de las muestras\")\n",
    "\n",
    "print(\"Set completo de datos\\nMedia: %5.3f Desv. Estandar: %5.3f\"%(data_prev.mean(),data_prev.std()))\n",
    "\n",
    "print(\"Promedios de las muestras\\nMedia: %5.3f Desv. Estandar: %5.3f\"%(av.mean(),av.std()))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}

environment.yml

name: jnotebooks
channels:
  - conda-forge
dependencies:
  - python
  - numpy
  - pip
  - pip:
  #  - nbgitpuller
  #  - sphinx-gallery
  #  - pandas
    - matplotlib
    - ipywidgets