{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performing a Combination\n",
    "\n",
    "We'll demonstrate how a combination works by combining everything we've learned so far.\n",
    "\n",
    "## Loading the Workspace\n",
    "\n",
    "To do so, we'll use a simple workspace to demonstrate functionality of combinations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "import pyhf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"data/2-bin_1-channel.json\") as serialized:\n",
    "    spec = json.load(serialized)\n",
    "\n",
    "workspace = pyhf.Workspace(spec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine Workspaces\n",
    "\n",
    "Let's just try to combine naively right now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [],
   "source": [
    "pyhf.Workspace.combine(workspace, workspace)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, we can't just combine a workspace with itself if it has some channel names in common. We try very hard in `pyhf` to make sure a combination \"makes sense\".\n",
    "\n",
    "Let's go ahead and rename the channel (as well as the measurement). Then try to combine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "other_workspace = workspace.rename(\n",
    "    channels={\"singlechannel\": \"othersinglechannel\"},\n",
    "    modifiers={\"uncorr_bkguncrt\": \"otheruncorr_bkguncrt\"},\n",
    "    measurements={\"Measurement\": \"OtherMeasurement\"},\n",
    ")\n",
    "\n",
    "combined_workspace = pyhf.Workspace.combine(workspace, other_workspace)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And did we combine?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"    channels: {combined_workspace.channels}\")\n",
    "print(f\"       nbins: {combined_workspace.channel_nbins}\")\n",
    "print(f\"     samples: {combined_workspace.samples}\")\n",
    "print(f\"   modifiers: {combined_workspace.modifiers}\")\n",
    "print(f\"measurements: {combined_workspace.measurement_names}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed. And at this point, we can just use all the same functionality we expect of pyhf, such as performing a fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = workspace.model()\n",
    "data = workspace.data(model)\n",
    "test_poi = 1.0\n",
    "\n",
    "pyhf.infer.hypotest(test_poi, data, model, test_stat=\"qtilde\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "other_model = other_workspace.model()\n",
    "other_data = other_workspace.data(other_model)\n",
    "\n",
    "pyhf.infer.hypotest(test_poi, other_data, other_model, test_stat=\"qtilde\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_model = combined_workspace.model()\n",
    "combined_data = combined_workspace.data(combined_model)\n",
    "\n",
    "pyhf.infer.hypotest(test_poi, combined_data, combined_model, test_stat=\"qtilde\")"
   ]
  }
 ],
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