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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,244 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from typing import List\n", | ||
| "import uproot\n", | ||
| "import numpy as np\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "import mplhep as hep\n", | ||
| "import matplotlib.ticker as mticker\n", | ||
| "import os\n", | ||
| "\n", | ||
| "plt.style.use(hep.style.CMS)\n", | ||
| "hep.style.use(\"CMS\")\n", | ||
| "formatter = mticker.ScalarFormatter(useMathText=True)\n", | ||
| "formatter.set_powerlimits((-3, 3))\n", | ||
| "plt.rcParams.update({\"font.size\": 20})" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "MAIN_DIR = \"../../../../\"\n", | ||
| "\n", | ||
| "plot_dir = f\"{MAIN_DIR}/plots/BiasTest/23Jul10Res\"\n", | ||
| "_ = os.system(f\"mkdir -p {plot_dir}\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "cards_dir = \"f_tests/23May2/nTF1_2_nTF2_1\"\n", | ||
| "# biases = [0., 0.15, 0.3, 1.0]\n", | ||
| "biases = [0.0, 0.15, 0.3, 1.0]\n", | ||
| "file = uproot.open(\n", | ||
| " f\"/uscms/home/rkansal/hhcombine/cards/{cards_dir}/higgsCombinebias0.3.FitDiagnostics.mH125.*.root\"\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 8, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "r_dict = {\n", | ||
| " 0.3: {\n", | ||
| " \"r\": np.array(file[\"limit\"][\"trackedParam_r\"])[::4],\n", | ||
| " \"rerr\": np.array(file[\"limit\"][\"trackedError_r\"])[::4],\n", | ||
| " }\n", | ||
| "}" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 9, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "{'r': array([0.25547278], dtype=float32),\n", | ||
| " 'rerr': array([0.17948698], dtype=float32)}" | ||
| ] | ||
| }, | ||
| "execution_count": 9, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "r_dict[0.3]" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from scipy import stats\n", | ||
| "\n", | ||
| "\n", | ||
| "def plot_tests(\n", | ||
| " data_ts: float,\n", | ||
| " toy_ts: np.ndarray,\n", | ||
| " name: str,\n", | ||
| " title: str = None,\n", | ||
| " bins: int = 15,\n", | ||
| " fit: str = None,\n", | ||
| " fdof2: int = None,\n", | ||
| "):\n", | ||
| " plot_max = max(np.max(toy_ts), data_ts)\n", | ||
| " # plot_max = max(np.max(toy_ts), data_ts) if fit != \"chi2\" else 200\n", | ||
| " # plot_min = min(np.min(toy_ts), data_ts, 0)\n", | ||
| " plot_min = 0\n", | ||
| " pval = p_value(data_ts, toy_ts)\n", | ||
| "\n", | ||
| " plt.figure(figsize=(12, 8))\n", | ||
| " h = plt.hist(\n", | ||
| " toy_ts,\n", | ||
| " np.linspace(plot_min, plot_max, bins + 1),\n", | ||
| " color=\"#8C8C8C\",\n", | ||
| " histtype=\"step\",\n", | ||
| " label=f\"{len(toy_ts)} Toys\",\n", | ||
| " )\n", | ||
| " plt.axvline(data_ts, color=\"#FF502E\", linestyle=\":\", label=rf\"Data ($p$-value = {pval:.2f})\")\n", | ||
| "\n", | ||
| " if fit is not None:\n", | ||
| " x = np.linspace(plot_min + 0.01, plot_max, 100)\n", | ||
| "\n", | ||
| " if fit == \"chi2\":\n", | ||
| " res = stats.fit(stats.chi2, toy_ts, [(0, 200)])\n", | ||
| " pdf = stats.chi2.pdf(x, res.params.df)\n", | ||
| " label = rf\"$\\chi^2_{{DoF = {res.params.df:.2f}}}$ Fit\"\n", | ||
| " elif fit == \"f\":\n", | ||
| " pdf = stats.f.pdf(x, 1, fdof2)\n", | ||
| " label = rf\"$F-dist_{{DoF = (1, {fdof2})}}$\"\n", | ||
| " else:\n", | ||
| " raise ValueError(\"Invalid fit\")\n", | ||
| "\n", | ||
| " plt.plot(\n", | ||
| " x,\n", | ||
| " pdf * (np.max(h[0]) / np.max(pdf)),\n", | ||
| " color=\"#1f78b4\",\n", | ||
| " linestyle=\"--\",\n", | ||
| " # alpha=0.6,\n", | ||
| " label=label,\n", | ||
| " )\n", | ||
| "\n", | ||
| " hep.cms.label(\n", | ||
| " \"Work in Progress\",\n", | ||
| " data=True,\n", | ||
| " lumi=138,\n", | ||
| " year=None,\n", | ||
| " )\n", | ||
| "\n", | ||
| " _ = plt.legend()\n", | ||
| " plt.title(title)\n", | ||
| " plt.ylabel(\"Number of Toys\")\n", | ||
| " plt.xlabel(\"Test Statistics\")\n", | ||
| "\n", | ||
| " plt.savefig(f\"{plot_dir}/{name}.pdf\", bbox_inches=\"tight\")" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Nonresonant" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "o1 = 0 # order being tested\n", | ||
| "tlabel = f\"{o1}\"\n", | ||
| "\n", | ||
| "data_ts, toy_ts = test_statistics[tlabel][\"data\"][tlabel], test_statistics[tlabel][\"toys\"][tlabel]\n", | ||
| "plot_tests(data_ts, toy_ts, \"gof\" + tlabel, fit=\"chi2\", bins=20)\n", | ||
| "\n", | ||
| "ord1 = 1\n", | ||
| "tflabel = f\"{ord1}\"\n", | ||
| "data_ts, toy_ts = pval = (\n", | ||
| " test_statistics[tlabel][\"fdata\"][tflabel],\n", | ||
| " test_statistics[tlabel][\"ftoys\"][tflabel],\n", | ||
| ")\n", | ||
| "plot_tests(data_ts, toy_ts, f\"f{tlabel}_{tflabel}\", title=f\"{o1} vs. {ord1}\")" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Resonant" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "o1, o2 = 2, 0 # order being tested\n", | ||
| "tlabel = f\"{o1}{o2}\"\n", | ||
| "\n", | ||
| "data_ts, toy_ts = test_statistics[tlabel][\"data\"][tlabel], test_statistics[tlabel][\"toys\"][tlabel]\n", | ||
| "plot_tests(data_ts, toy_ts, \"gof\" + tlabel, fit=\"chi2\", bins=20)\n", | ||
| "\n", | ||
| "for ord1, ord2 in [[o1 + 1, o2], [o1, o2 + 1]]:\n", | ||
| " tflabel = f\"{ord1}{ord2}\"\n", | ||
| " data_ts, toy_ts = pval = (\n", | ||
| " test_statistics[tlabel][\"fdata\"][tflabel],\n", | ||
| " test_statistics[tlabel][\"ftoys\"][tflabel],\n", | ||
| " )\n", | ||
| " plot_tests(data_ts, toy_ts, f\"f{tlabel}_{tflabel}\", title=f\"({o1}, {o2}) vs. ({ord1}, {ord2})\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "python39", | ||
| "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" | ||
| }, | ||
| "orig_nbformat": 4 | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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