{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import flavio\n",
    "import numpy as np\n",
    "import clusterking as ck\n",
    "from clusterking.maths.metric import chi2_metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define kinematic function using the flavio package\n",
    "def dBrdq2(w, q):\n",
    "    return flavio.np_prediction(\"dBR/dq2(B+->Dtaunu)\", w, q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up and configure Scanner\n",
    "s = ck.scan.WilsonScanner(scale=5, eft=\"WET\", basis=\"flavio\")\n",
    "# Set kinematic function\n",
    "s.set_dfunction(\n",
    "    dBrdq2,\n",
    "    binning=np.linspace(3.2, 11.6, 10),\n",
    "    normalize=True\n",
    ")\n",
    "# Set sampling points in Wilson space\n",
    "s.set_spoints_equidist({\n",
    "    \"CVL_bctaunutau\": (-0.5, 0.5, 3),\n",
    "    \"CSL_bctaunutau\": (-0.5, 0.5, 3),\n",
    "    \"CT_bctaunutau\": (-0.1, 0.1, 3)\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run scanner and add errors\n",
    "d = ck.DataWithErrors()    # Create data object to write results to\n",
    "r = s.run(d)               # Run scanner\n",
    "r.write()                  # Write results back to data object\n",
    "d.add_err_poisson(1000)    # statistical uncertainties\n",
    "d.add_rel_err_uncorr(0.1)  # 0.1% relative system uncertainties, uncorrelated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clustering\n",
    "c = ck.cluster.HierarchyCluster()  # Initialize worker class\n",
    "c.set_metric(chi2_metric)\n",
    "c.set_max_d(1)   # \"Cut off\" value for hierarchy\n",
    "r = c.run(d)     # Run clustering on d\n",
    "r.write()        # Write results back to data object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Benchmarking\n",
    "b = ck.Benchmark()  # Initialize worker class\n",
    "b.set_metric(chi2_metric)\n",
    "r = b.run(d)        # Run benchmarking\n",
    "r.write()           # Write results back to data object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optional: Save data (kinematic distributions, clusters, BPs, ...)\n",
    "# d.write(\"btaunu_q2.sql\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate plots similar to figures 1, 2, 3, 4, respectively\n",
    "d.plot_clusters_scatter([\"CVL_bctaunutau\", \"CSL_bctaunutau\"])\n",
    "d.plot_dist_box()\n",
    "d.plot_clusters_scatter([\"CT_bctaunutau\", \"CVL_bctaunutau\", \"CSL_bctaunutau\"])\n",
    "d.plot_dist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find closest benchmark point to a new point in parameter space\n",
    "d.find_closest_bpoints({\"CT_bctaunutau\": 0, \"CVL_bctaunutau\": -0.2, \"CSL_bctaunutau\": 0.2}, n=1)"
   ]
  }
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