{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "URL: https://docs.bokeh.org/en/latest/docs/examples/topics/stats/histogram.html\n",
    "\n",
    "Most examples work across multiple plotting backends, this example is also available for:\n",
    "\n",
    "* [Bokeh - histogram_example](../bokeh/histogram_example.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy\n",
    "import scipy.special\n",
    "import holoviews as hv\n",
    "from holoviews import opts\n",
    "\n",
    "hv.extension('matplotlib')\n",
    "hv.output(fig='svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Declaring data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_overlay(hist, x, pdf, cdf, label):\n",
    "    pdf = hv.Curve((x, pdf), label='PDF')\n",
    "    cdf = hv.Curve((x, cdf), label='CDF')\n",
    "    return (hv.Histogram(hist, vdims='P(r)') * pdf * cdf).relabel(label)\n",
    "\n",
    "\n",
    "label = \"Normal Distribution (μ=0, σ=0.5)\"\n",
    "mu, sigma = 0, 0.5\n",
    "\n",
    "measured = np.random.normal(mu, sigma, 1000)\n",
    "hist = np.histogram(measured, density=True, bins=50)\n",
    "\n",
    "x = np.linspace(-2, 2, 1000)\n",
    "pdf = 1/(sigma * np.sqrt(2*np.pi)) * np.exp(-(x-mu)**2 / (2*sigma**2))\n",
    "cdf = (1+scipy.special.erf((x-mu)/np.sqrt(2*sigma**2)))/2\n",
    "norm = get_overlay(hist, x, pdf, cdf, label)\n",
    "\n",
    "np.seterr(divide='ignore', invalid='ignore')\n",
    "\n",
    "label = \"Log Normal Distribution (μ=0, σ=0.5)\"\n",
    "mu, sigma = 0, 0.5\n",
    "\n",
    "measured = np.random.lognormal(mu, sigma, 1000)\n",
    "hist = np.histogram(measured, density=True, bins=50)\n",
    "\n",
    "x = np.linspace(0, 8.0, 1000)\n",
    "pdf = 1/(x* sigma * np.sqrt(2*np.pi)) * np.exp(-(np.log(x)-mu)**2 / (2*sigma**2))\n",
    "cdf = (1+scipy.special.erf((np.log(x)-mu)/(np.sqrt(2)*sigma)))/2\n",
    "lognorm = get_overlay(hist, x, pdf, cdf, label)\n",
    "\n",
    "\n",
    "label = \"Gamma Distribution (k=1, θ=2)\"\n",
    "k, theta = 1.0, 2.0\n",
    "\n",
    "measured = np.random.gamma(k, theta, 1000)\n",
    "hist = np.histogram(measured, density=True, bins=50)\n",
    "\n",
    "x = np.linspace(0, 20.0, 1000)\n",
    "pdf = x**(k-1) * np.exp(-x/theta) / (theta**k * scipy.special.gamma(k))\n",
    "cdf = scipy.special.gammainc(k, x/theta) / scipy.special.gamma(k)\n",
    "gamma = get_overlay(hist, x, pdf, cdf, label)\n",
    "\n",
    "\n",
    "label = \"Beta Distribution (α=2, β=2)\"\n",
    "alpha, beta = 2.0, 2.0\n",
    "\n",
    "measured = np.random.beta(alpha, beta, 1000)\n",
    "hist = np.histogram(measured, density=True, bins=50)\n",
    "\n",
    "x = np.linspace(0, 1, 1000)\n",
    "pdf = x**(alpha-1) * (1-x)**(beta-1) / scipy.special.beta(alpha, beta)\n",
    "cdf = scipy.special.btdtr(alpha, beta, x)\n",
    "beta = get_overlay(hist, x, pdf, cdf, label)\n",
    "\n",
    "\n",
    "label = \"Weibull Distribution (λ=1, k=1.25)\"\n",
    "lam, k = 1, 1.25\n",
    "\n",
    "measured = lam*(-np.log(np.random.uniform(0, 1, 1000)))**(1/k)\n",
    "hist = np.histogram(measured, density=True, bins=50)\n",
    "\n",
    "x = np.linspace(0, 8, 1000)\n",
    "pdf = (k/lam)*(x/lam)**(k-1) * np.exp(-(x/lam)**k)\n",
    "cdf = 1 - np.exp(-(x/lam)**k)\n",
    "weibull = get_overlay(hist, x, pdf, cdf, label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(norm + lognorm + gamma + beta + weibull).opts(\n",
    "    opts.Curve(axiswise=True),\n",
    "    opts.Histogram(facecolor=\"#036564\", axiswise=True,  bgcolor=\"#E8DDCB\"),\n",
    "    opts.Layout(hspace=0.2)).cols(2)"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python",
   "pygments_lexer": "ipython3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
