{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Histogram, 1d and 2d\n", "\n", "Find more tutorials on my webpage: [yairmau.com](http://yairmau.com).\n", "\n", "End product:\n", "
\n", " \"histogram\"\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## import stuff" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.gridspec as gridspec\n", "import numpy as np\n", "import scipy.special\n", "from scipy.optimize import curve_fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## customize figure" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "plt.ioff()\n", "fig = plt.figure(1, figsize=(12,6)) # figsize accepts only inches.\n", "\n", "plt.rcParams.update({'font.size': 22, # more customizing https://matplotlib.org/users/customizing.html\n", " 'xtick.labelsize':20,\n", " 'ytick.labelsize':20,\n", " 'axes.labelsize':26,\n", " 'text.usetex':True,\n", " 'legend.fontsize':16,\n", " 'font.family':'serif',\n", " 'text.latex.preamble':[r'\\usepackage{amsmath}']})\n", "\n", "gs = gridspec.GridSpec(2, 2, width_ratios=[1, 0.2], height_ratios=[0.2, 1])\n", "gs.update(left=0.05, right=0.50, top=0.95, bottom=0.10,\n", " hspace=0.02, wspace=0.02)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## graphs on the left" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "sigma = 1.0 # standard deviation (spread)\n", "mu = 0.0 # mean (center) of the distribution\n", "x = np.random.normal(loc=mu, scale=sigma, size=5000)\n", "k = 2.0 # shape\n", "theta = 1.0 # scale\n", "y = np.random.gamma(shape=k, scale=theta, size=5000)\n", "\n", "# bottom left panel\n", "ax10 = plt.subplot(gs[1, 0])\n", "counts, xedges, yedges, image = ax10.hist2d(x, y, bins=40, cmap=\"YlOrRd\",\n", " normed=True)\n", "dx = xedges[1] - xedges[0]\n", "dy = yedges[1] - yedges[0]\n", "xvec = xedges[:-1] + dx / 2\n", "yvec = yedges[:-1] + dy / 2\n", "ax10.set_xlabel(r\"$x$\")\n", "ax10.set_ylabel(r\"$y$\", rotation=\"horizontal\")\n", "ax10.text(-2, 8, r\"$p(x,y)$\")\n", "ax10.set_xlim([xedges.min(), xedges.max()])\n", "ax10.set_ylim([yedges.min(), yedges.max()])\n", "\n", "# top left panel\n", "ax00 = plt.subplot(gs[0, 0])\n", "gaussian = (1.0 / np.sqrt(2.0 * np.pi * sigma ** 2)) * \\\n", " np.exp(-((xvec - mu) ** 2) / (2.0 * sigma ** 2))\n", "xdist = counts.sum(axis=1) * dy\n", "ax00.bar(xvec, xdist, width=dx, fill=False,\n", " edgecolor='black', alpha=0.8)\n", "ax00.plot(xvec, gaussian, color='black')\n", "ax00.set_xlim([xedges.min(), xedges.max()])\n", "ax00.set_xticklabels([])\n", "ax00.set_yticks([])\n", "ax00.set_xlabel(\"Normal distribution\", fontsize=16)\n", "ax00.xaxis.set_label_position(\"top\")\n", "ax00.set_ylabel(r\"$p(x)$\", rotation=\"horizontal\", labelpad=30)\n", "\n", "# bottom right panel\n", "ax11 = plt.subplot(gs[1, 1])\n", "gamma_dist = yvec ** (k - 1.0) * np.exp(-yvec / theta) / \\\n", " (theta ** k * scipy.special.gamma(k))\n", "ydist = counts.sum(axis=0) * dx\n", "ax11.barh(yvec, ydist, height=dy, fill=False,\n", " edgecolor='black', alpha=0.8)\n", "ax11.plot(gamma_dist, yvec, color='black')\n", "ax11.set_ylim([yedges.min(), yedges.max()])\n", "ax11.set_xticks([])\n", "ax11.set_yticklabels([])\n", "ax11.set_ylabel(\"Gamma distribution\")\n", "ax11.yaxis.set_label_position(\"right\")\n", "ax11.set_xlabel(r\"$p(y)$\", labelpad=10)\n", "ax11.xaxis.set_label_position(\"top\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## graphs on the right" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gs2 = gridspec.GridSpec(2, 1, width_ratios=[1], height_ratios=[1, 1])\n", "gs2.update(left=0.60, right=0.98, top=0.95, bottom=0.10,\n", " hspace=0.02, wspace=0.05)\n", "\n", "x = np.random.normal(loc=0, scale=1, size=1000)\n", "y = np.random.gamma(shape=2, size=1000)\n", "\n", "bx10 = plt.subplot(gs2[1, 0])\n", "bx00 = plt.subplot(gs2[0, 0])\n", "\n", "N = 100\n", "a = np.random.gamma(shape=5, size=N)\n", "n1, bins1, patches1 = bx00.hist(a, bins=int(np.sqrt(N)), normed=True, color=\"orange\",\n", " histtype='stepfilled', alpha=0.4, hatch='/')\n", "bx00.set_xlim([0, 15])\n", "bx00.set_ylim([0, 0.28])\n", "bx00.set_xticklabels([])\n", "bx00.set_xlabel(r\"\\texttt{plt.hist}\")\n", "bx00.xaxis.set_label_position(\"top\")\n", "\n", "# the following way is equivalent to plt.hist, but it gives\n", "# the user more flexibility when plotting and analysing the results\n", "n2, bins2 = np.histogram(a, bins=int(np.sqrt(N)), normed=True)\n", "db2 = (bins2[1] - bins2[0]) / 2.0\n", "red, = bx10.plot(bins2[:-1] + db2, n2, marker='o', color='red')\n", "wid = db2 * 2\n", "bx10.bar(bins2[:-1] + wid / 2, n2, width=wid, fill=False,\n", " edgecolor='black', linewidth=3, alpha=0.8)\n", "bx10.set_xlim([0, 15])\n", "bx10.set_ylim([0, 0.28])\n", "bx10.set_xlabel(r\"\\texttt{np.histogram};\\quad \\texttt{plt.bar}\")\n", "# best fit\n", "xdata = bins2[:-1] + db2\n", "ydata = n2\n", "def func(x, p1, p2):\n", " return x ** (p1 - 1.0) * np.exp(-x / p2) / (p2 ** p1 * scipy.special.gamma(p1))\n", "popt, pcov = curve_fit(func, xdata, ydata, p0=(1.5, 1.5)) # p0 = initial guess\n", "p1, p2 = popt\n", "SStot = ((ydata - ydata.mean()) ** 2).sum()\n", "SSres = ((ydata - func(xdata, p1, p2)) ** 1).sum()\n", "Rsquared = 1 - SSres / SStot\n", "h = np.linspace(0,15,101)\n", "bx00.plot(h, func(h, p1, p2), color='blue', linewidth=2)\n", "# dummy plot, just so we can have a legend on the bottom panel\n", "blue, = ax10.plot([100],[100], color='blue', linewidth=2, label=\"Best fit\")\n", "bx10.legend([red,blue],[r'Data',r'Best fit, $r^2=${:.2f}'.format(Rsquared)],\n", " loc='upper right', frameon=False, handlelength=4,\n", " markerfirst=False, numpoints=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save and display" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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