Producing DESI-Y1 Lya mocks with quickquasars.

bin/gen_qso_catalog

@@ -0,0 +1,9 @@
+#!/usr/bin/env python
+
+"""
+Generate the quasar catalog to input quickquasars
+"""
+
+import sys
+from desisim.scripts.gen_qso_catalog import main
+sys.exit(main())

doc/nb/generate_survey_release_catalogs.ipynb

@@ -0,0 +1,216 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Usual Imports\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from desisim.survey_release import SurveyRelease\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Observed data\n",
+    "catdir='/global/cfs/cdirs/desi/science/lya/y1-kp6/iron-tests/catalogs/'\n",
+    "cat_name='QSO_cat_iron_main_dark_healpix_zlya-altbal_zwarn_cut_20230918.fits'\n",
+    "cat_path = os.path.join(catdir,cat_name)\n",
+    "\n",
+    "# Master catalog\n",
+    "mastercatalog = '/global/cfs/cdirs/desi/mocks/lya_forest/london/v9.0/v9.0.0/master.fits'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generate mock catalog"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This function may be introduced intoo a for loop\n",
+    "\n",
+    "After making this catalog its path should be passed to quickquasars with the `--from_catalog` flag.\n",
+    "\n",
+    "Other flags like `--desi-footprint` are allowed but not needed with this approach.\n",
+    "\n",
+    "To include the 440nm dip in the throughput use `--year1-throughput` in quickquasars."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read master catalog and prepare data catalog, takes a little bit of time if data catalg is provided.\n",
+    "# Y5 mocks do not need a catalog path unless you want to use Y1 distributions for magnitudes. \n",
+    "# Pass invert = True if you want to invert the random numbers generated: 1-random_number.\n",
+    "# This allows generating independent mocks from the same seed.\n",
+    "# TODO: Should probably refactor the class to make it clearer what refers to data and what to mocks.\n",
+    "survey=SurveyRelease(mastercatalog=mastercatalog,seed=0, \n",
+    "                     qso_only=True, data_file=cat_path,invert=False)\n",
+    "# Apply redshift distribution\n",
+    "# Note: For Y1 mocks (and probably Y5 too) the target selection redshift distribution from Chaussidon et al. 2022 works better to match QSO targets Iron catalog.\n",
+    "# The option distribution='from_data' should be a better option once I finish implementing it.\n",
+    "survey.apply_redshift_dist(distribution='target_selection',zmin=1.8)\n",
+    "# Apply NPASS geometry:\n",
+    "survey.apply_data_geometry(release='iron') # Pass release = None for Y5 mocks.\n",
+    "# Assign magnitudes \n",
+    "survey.assign_rband_magnitude(from_data=True) # Pass from_data = False for Y5 mocks. Unless you want to use the Y1 magnitude distributions.\n",
+    "# Assign exposures\n",
+    "survey.assign_exposures(exptime=None) # Pass exptime = 4000 for Y5 mocks.\n",
+    "\n",
+    "# Write mock catalog uncomment if needed\n",
+    "#survey.mockcatalog.write('/path/to/output/outputcat_name.fits')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Quality check on mock catalog"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Redshift distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dndz=plt.hist(survey.mockcatalog['Z'],bins=np.arange(1.8,4,0.1),histtype='step',label='mockcat',lw=2)\n",
+    "plt.hist(survey.data['Z'],bins=np.arange(1.8,4,0.1),histtype='step',label=' data cat',lw=2)\n",
+    "plt.legend(loc='best')\n",
+    "plt.xlabel('Redshift')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Magnitude distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_magnitude = lambda f: 22.5-2.5*np.log10(f)\n",
+    "plt.hist(get_magnitude(survey.mockcatalog['FLUX_R']),bins=np.arange(18,24,0.1),histtype='step',label='mockcat',lw=2)\n",
+    "plt.hist(get_magnitude(survey.data['FLUX_R']),bins=np.arange(18,24,0.1),histtype='step',label='data cat',lw=2)\n",
+    "plt.legend(loc='best')\n",
+    "plt.xlabel('r-band Magnitude')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Footprint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(15,10))\n",
+    "plt.plot(survey.mockcatalog['RA'],survey.mockcatalog['DEC'],'.',label='mockcat',ms=0.1)\n",
+    "plt.xlabel('RA')\n",
+    "plt.ylabel('DEC')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Npasses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(15,10))\n",
+    "plt.scatter(survey.mockcatalog['RA'],survey.mockcatalog['DEC'],c=survey.mockcatalog['NPASS'],s=0.001,cmap='jet')\n",
+    "plt.colorbar(label='NPASS')\n",
+    "plt.xlabel('RA')\n",
+    "plt.ylabel('DEC')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exposure time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(15,10))\n",
+    "plt.scatter(survey.mockcatalog['RA'],survey.mockcatalog['DEC'],c=survey.mockcatalog['EXPTIME'],s=0.0001,cmap='jet')\n",
+    "plt.colorbar(label='EXPTIME')\n",
+    "plt.xlabel('RA')\n",
+    "plt.ylabel('DEC')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "DESI main",
+   "language": "python",
+   "name": "desi-main"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

py/desisim/data/redshift_dist_chaussidon2022.ecsv

@@ -0,0 +1,99 @@
+# %ECSV 0.9
+# ---
+# datatype:
+# - {name: z, datatype: float64}
+# - {name: dndz_23, datatype: float64}
+# - {name: dndz_err_23, datatype: float64}
+# - {name: dndz_227, datatype: float64}
+# - {name: dndz_err_227, datatype: float64}
+# schema: astropy-2.0
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