curve_fit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Plot\n",
    "\n",
    " Attempting to imitate https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.normal.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "# Attempting to imitate https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.normal.html\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mu = 0  # mean\n",
    "sigma = 0.1 # std dev\n",
    "s = np.random.normal(mu, sigma, 1000)\n",
    "count, bins, patches = plt.hist(s, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8e33a3fb00>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "count, bins, patches = plt.hist(s, bins=30, density=True)\n",
    "plt.plot(bins,\n",
    "         1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n",
    "         linewidth=2, \n",
    "         color='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}