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  {
   "cell_type": "markdown",
   "id": "ad164cbe-c7eb-437c-a980-7940a074c79a",
   "metadata": {},
   "source": [
    "## Simple notebook to compare the Signal-to-noise between different input catalogs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4a6b5c6-c9a3-489d-9e53-38f16b83ce09",
   "metadata": {},
   "source": [
    "#### Standard imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcb14a6d-ea05-4caf-bee4-4235666689a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import hpmcm\n",
    "import tables_io\n",
    "import glob\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1caf695d-7313-46d9-b3bc-f2c4f6e0d4ba",
   "metadata": {},
   "source": [
    "#### Configuration data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc6c47f4-65f4-4754-9d94-bbe2b660e689",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATADIR = \"test_data\"\n",
    "shear_st = \"0p01\"\n",
    "\n",
    "tract = 10463\n",
    "SOURCE_TABLEFILES = sorted(glob.glob(os.path.join(DATADIR, f\"shear_*_{shear_st}_cleaned_{tract}_ns.pq\")))\n",
    "SOURCE_TABLEFILES.append(os.path.join(DATADIR, f\"object_{tract}.pq\"))\n",
    "SOURCE_TABLEFILES.reverse()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "780b62f3-1430-4f78-8eea-f525ba93cebc",
   "metadata": {},
   "source": [
    "#### Read the input files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d996a1c7-9875-4678-878d-156af617cb54",
   "metadata": {},
   "outputs": [],
   "source": [
    "dd = {i:tables_io.read(file_) for i, file_ in enumerate(SOURCE_TABLEFILES)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88830f95-9724-4ad3-bfad-8f0ea403139e",
   "metadata": {},
   "source": [
    "#### Plot the SNR for the various catalgos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daf32ac9-93ef-438a-ad2e-99f1a9b6b0ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for i, key in enumerate(['Object', 'wmom', 'pgauss', 'gauss']):\n",
    "for i, key in enumerate(['Object', 'wmom']):\n",
    "    mask = dd[i].snr > 1\n",
    "    _ = plt.hist(dd[i].snr[mask], bins=np.logspace(0, 4, 101), label=key, alpha=0.5)\n",
    "_ = plt.legend()\n",
    "_ = plt.xscale('log')\n",
    "_ = plt.xlabel(\"Signal-to-noise [r-band]\")\n",
    "_ = plt.ylabel(\"Objects [per 0.04 dex]\")\n",
    "#_ = plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cde072df-47f4-4e1f-a507-c125b4249b09",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2eb02dd6-4a76-4347-9744-694ad14d9766",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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