{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gammapy.data import DataStore\n",
    "from gammapy.spectrum import SpectrumDatasetMaker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = DataStore(...)\n",
    "obs_list = ds.get_observations(...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create datasets\n",
    "datasets = SpectrumDatasetMaker(obs_list, target, on_radius, ...) # possibly add e.g. time filtering here\n",
    "\n",
    "# internally:\n",
    "    datasets = Datasets()\n",
    "    for obs in obs_list:\n",
    "        dataset = SpectrumDatasetMakerObs(obs) # filter ON events and extract IRFs\n",
    "        datasets.append(dataset)\n",
    "    \n",
    "    return datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# could write datasets to disk here and read them again for the next step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate the background\n",
    "datasets = ReflectedRegionsBackgroundMaker(datasets, obs_list)\n",
    "\n",
    "# internally:\n",
    "    for dataset, obs in zip(datasets, obs_list):\n",
    "        dataset = ReflectedRegionsBackgroundMakerObs(dataset, obs) # create OFF regions, filter OFF events\n",
    "        # dataset is now a SpectrumDatasetOnOff!\n",
    "    \n",
    "    return datasets\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Alternatively:\n",
    "datasets = OffBackgroundMaker(datasets, off_obs_list)\n",
    "\n",
    "# internally:\n",
    "    for dataset, off_obs in zip(datasets, off_obs_list):\n",
    "        dataset = OffBackgroundMakerObs(dataset, off_obs) # extract OFF counts from off_obs\n",
    "        # dataset is now a SpectrumDatasetOnOff!\n",
    "    \n",
    "    return datasets\n",
    "\n",
    "\n",
    "\n",
    "# Alternatively:\n",
    "datasets = TemplateSpectrumBackgroundMaker(datasets)\n",
    "\n",
    "# internally:\n",
    "    for dataset in datasets:\n",
    "        dataset = TemplateSpectrumBackgroundMakerObs(dataset) # could e.g. fit norm of IRF background\n",
    "        # dataset still is a SpectrumDataset\n",
    "    \n",
    "    return datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets.stack() # if desired\n",
    "datasets.write() # if needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# modeling/fitting to follow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = MapDatasetMaker(obs_list, geom, geom_true=..., ...)\n",
    "\n",
    "# internally:\n",
    "    datasets = Datasets()\n",
    "    for obs in obs_list:\n",
    "        dataset = MapDatasetMakerObs(obs, geom) # creates MapDatasets, extracts IRFs for each obs\n",
    "        datasets.append(dataset)\n",
    "    \n",
    "    return datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# could write datasets to disk here and read them again for the next step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate the background\n",
    "datasets = RingBackgroundMaker(datasets)\n",
    "\n",
    "# internally:\n",
    "    for dataset in datasets:\n",
    "        dataset = RingBackgroundMaker(dataset) # create OFF map, OFF exposure map\n",
    "        # dataset is now a MapDatasetOnOff!\n",
    "    \n",
    "    return datasets\n",
    "\n",
    "\n",
    "\n",
    "# Alternatively:\n",
    "datasets = TemplateMapBackgroundMaker(datasets)\n",
    "\n",
    "# internally:\n",
    "    for dataset in datasets:\n",
    "        dataset = TemplateMapBackgroundMakerObs(dataset) # could e.g. fit norm, tilt of IRF background\n",
    "        # dataset still is a MapDataset\n",
    "    \n",
    "    return datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets.stack() # if desired\n",
    "datasets.write() # if needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# modeling/fitting to follow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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