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  {
   "cell_type": "markdown",
   "id": "3f31a160-8ce9-4fe6-8805-9df7a97b7adc",
   "metadata": {},
   "source": [
    "## Using `RailProject` \n",
    "\n",
    "This notebook will show you the basics using the `RailProject` class to manage an analysis project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d436cc4a-49c9-4f27-93cf-9a196d251a77",
   "metadata": {},
   "source": [
    "### Setup and teardown scripts to setup a test area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96bee599-4711-4a07-b182-1cbe257ea4d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from rail.projects import library\n",
    "\n",
    "check_dir = os.path.basename(os.path.abspath(os.curdir))\n",
    "if check_dir == 'examples':\n",
    "    os.chdir('..')\n",
    "\n",
    "setup = library.setup_project_area()\n",
    "assert setup == 0\n",
    "\n",
    "# use this to cleanup\n",
    "# library.teardown_project_area()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "454c1464-aa61-4a0c-96b9-f48f5ddb94f6",
   "metadata": {},
   "source": [
    "### Load the test project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7136e23a-43f1-4a36-ba77-03b9a649c449",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rail.projects import RailProject\n",
    "\n",
    "project = RailProject.load_config(\"tests/ci_project.yaml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57c75f4c-23f4-415b-8148-5b39754b5770",
   "metadata": {},
   "source": [
    "### Inspect the test project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f47f06c0-2718-4cdd-aaa9-156341336907",
   "metadata": {},
   "outputs": [],
   "source": [
    "catalog_files_truth = project.get_catalog_files(\"truth\")\n",
    "print(catalog_files_truth)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d37d65cf-6fbe-4fcd-818f-6eaa6a0aa88d",
   "metadata": {},
   "source": [
    "### Run a data reduction algorithm on the test project data\n",
    "\n",
    "This will use the \"roman_rubin\" reducer to apply the \"gold\" selection to the \"truth\" catalog to make a \"reduced\" catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e4a8135-8131-473b-a132-b9be2109dbe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "project.reduce_data(\n",
    "    catalog_template=\"truth\",\n",
    "    output_catalog_template=\"reduced\",\n",
    "    reducer_class_name=\"roman_rubin\",\n",
    "    input_selection=\"\",\n",
    "    selection=\"gold\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a70ff5d0-2e93-47e9-b5d4-7d791e883d9f",
   "metadata": {},
   "source": [
    "### Subsample the test project\n",
    "\n",
    "This will use the \"random_subsampler\" to apply the \"train_10\" subsample to the \"reduced\" catalog of the baseline flavor with the gold selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6b79133-b695-433c-a47f-91bfa9fffc97",
   "metadata": {},
   "outputs": [],
   "source": [
    "project.subsample_data(\n",
    "    catalog_template=\"reduced\",\n",
    "    file_template=\"train_file_10\",\n",
    "    subsampler_class_name=\"random_subsampler\",\n",
    "    subsample_name=\"train_10\",\n",
    "    flavor=\"baseline\",\n",
    "    selection=\"gold\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45aede11-ea93-4dab-b7e2-e2d108079ac3",
   "metadata": {},
   "source": [
    "### Build all the pipelines for the \"baseline\" flavor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90ca72b7-bd09-4774-996b-8f4e6ca2bc6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "project.build_pipelines(flavor=\"baseline\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37354004-48a7-48eb-af8f-b87154be1c8a",
   "metadata": {},
   "source": [
    "### Generate the commands to run the \"pz\" pipeline in the \"baseline\" flavor for the \"gold\" selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dd52439-1f2e-4ca6-91c6-ed2b9ffb859d",
   "metadata": {},
   "outputs": [],
   "source": [
    "single_ceci_command = project.make_pipeline_single_input_command(\n",
    "    pipeline_name=\"pz\",\n",
    "    flavor=\"basline\",\n",
    "    selection=\"gold\",\n",
    ")\n",
    "print(single_ceci_command)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff29b713-902e-41a5-94ce-f852eb5e461e",
   "metadata": {},
   "source": [
    "### Generate the commands to run the \"spec_selection\" pipeline in the \"baseline\" flavor for the \"gold\" selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6461441-b51e-42f0-bb86-9a5d3b0513c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ceci_catalog_commands = project.make_pipeline_catalog_commands(\n",
    "    pipeline_name=\"spec_selection\",\n",
    "    flavor=\"basline\",\n",
    "    selection=\"gold\",\n",
    "    spec_selections = list(project.get_spec_selections().keys()),\n",
    ")\n",
    "print(ceci_catalog_commands)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e8f3aee-ae84-4a99-9835-797350b94cbf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13d8b9e3-7603-45d2-bc71-4b50c326c102",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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