Analysis updated and vr2 added

Author: ocerri

.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,126 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.interpolate import PchipInterpolator\n",
+    "import matplotlib.pyplot as plt\n",
+    "from lib.histo_utilities import create_TH1D\n",
+    "import ROOT as rt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def quantile(a, p, weight=None, f=None):\n",
+    "    q = np.percentile(a, 100*p).astype(np.float64)\n",
+    "\n",
+    "    if weight == None:\n",
+    "        weight = np.full_like(a, 1.0/a.shape[0], np.float64)\n",
+    "\n",
+    "    if not f==None:\n",
+    "        f_q = f(q)\n",
+    "    else:\n",
+    "        h = create_TH1D(a, binning=[None, np.percentile(a, 2), np.percentile(a, 98)], weights=weight)\n",
+    "        f_q = h.GetBinContent(h.FindBin(q))\n",
+    "        \n",
+    "        c = rt.TCanvas('c', 'c', 800,600)\n",
+    "        h.Draw()\n",
+    "        c.Draw()\n",
+    "        \n",
+    "#         F = np.cumsum(weight, dtype=np.float64)\n",
+    "#         a_s = np.sort(a).astype(np.float64)\n",
+    "#         i = 0\n",
+    "#         while(i<a_s.shape[0]-1):\n",
+    "#             if a_s[i] == a_s[i+1]:\n",
+    "#                 a_s = np.delete(a_s, i)\n",
+    "#                 F = np.delete(F, i)\n",
+    "#             else:\n",
+    "#                 i += 1\n",
+    "#         spl = PchipInterpolator(a_s, F)\n",
+    "#         f = spl.derivative()\n",
+    "#         f_q = f(q)\n",
+    "\n",
+    "#         plt.plot(a_s, F)\n",
+    "#         a_dense = np.arange(a_s[0], a_s[-1], 0.3*(a_s[1]-a_s[0]))\n",
+    "#         plt.plot(a_dense, spl(a_dense))\n",
+    "#         plt.plot(a_dense[1:-1], f(a_dense[1:-1]))\n",
+    "\n",
+    "    sigma_q = p*(1-p)/(a.shape[0]*f_q**2)\n",
+    "    print q, sigma_q\n",
+    "    return q, sigma_q, c, h"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.525635126353564 0.0160856758844\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning in <TROOT::Append>: Replacing existing TH1: h (Potential memory leak).\n",
+      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = np.random.chisquare(df=4, size=(3000))\n",
+    "out = quantile(a, 0.75)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}