{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import smpl\n",
    "from smpl import plot\n",
    "from smpl import stat\n",
    "from smpl import functions as f\n",
    "import numpy as np\n",
    "np.random.seed(1337)\n",
    "print(smpl.__version__)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data will be binned for histogram like treatment if bins is set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x= np.random.randn(1000000)\n",
    "# Default uncertainty of bins is poisson distributed in y direction and none for x\n",
    "plot.fit(x,f.gauss,bins=20,label=\"data\",binunc=stat.no_dist,init=True)\n",
    "plot.fit(x,f.gauss,bins=20,label=\"data\",init=True)\n",
    "plot.fit(x,f.gauss,bins=20,label=\"data\",fmt=\"hist\",init=True)\n",
    "plot.fit(x,f.gauss,bins=20,label=\"data\",sigmas=1,fmt=\"step\",init=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x= np.random.randn(100000)\n",
    "plot.fit(stat.normalize(x),stat.normalize(x**3),f.gauss,bins=50,lpos=-1,binunc=stat.no_dist,init=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.15"
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 "nbformat": 4,
 "nbformat_minor": 4
}