R02-check numbers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import SAGA\n",
    "from easyquery import Query, QueryMaker\n",
    "from SAGA.database import FitsTable\n",
    "from SAGA import ObjectCuts as C\n",
    "from SAGA import HostCuts as H\n",
    "\n",
    "from itertools import combinations\n",
    "from collections import Counter, defaultdict\n",
    "from astropy.table import Table, join, vstack\n",
    "from astropy.coordinates import SkyCoord, Distance\n",
    "from SAGA.utils import add_skycoord\n",
    "from easyquery import Query, QueryMaker\n",
    "from astropy.io import ascii\n",
    "\n",
    "import SAGA\n",
    "from SAGA.database import FitsTable, GoogleSheets\n",
    "from SAGA import ObjectCuts as C\n",
    "from SAGA.utils import add_skycoord, fill_values_by_query\n",
    "from SAGA.utils.distance import z2v, d2m, m2d, v2z\n",
    "from SAGA.utils.display import show_images\n",
    "from SAGA.objects.object_catalog import calc_fiducial_p_sat, calc_fiducial_p_sat_corrected\n",
    "from scipy.stats import spearmanr\n",
    "print(SAGA.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "saga = SAGA.QuickStart()\n",
    "saga.set_default_base_version(\"paper2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hostlist = saga.host_catalog.load(include_stats=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def annotate_base(base):\n",
    "    base[\"Mr_\"] = base[\"r_mag\"] - d2m(base[\"HOST_DIST\"])\n",
    "    base[\"Mr\"] = np.where(np.isfinite(base[\"Mr\"]) & C.has_spec.mask(base), base[\"Mr\"], base[\"Mr_\"])\n",
    "    base[\"log_sm\"] = np.where(np.isfinite(base[\"log_sm\"]), base[\"log_sm\"], 1.254 + 1.0976 * base[\"gr\"] - 0.4 * base[\"Mr\"])\n",
    "    \n",
    "    base[\"human_selected\"] = 0\n",
    "    hs = saga.database[\"human_selected\"].read()\n",
    "    for i in range(1, 4):\n",
    "        base[\"human_selected\"] += np.in1d(base[\"OBJID\"], Query(f\"score == {i}\").filter(hs, \"OBJID\")).astype(np.int) * i\n",
    "\n",
    "    return base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "base = SAGA.database.FitsTable(\"/home/yymao/Documents/Research/SAGA/PaperII/data-archive/saga_base_all.fits\").read()\n",
    "base = saga.host_catalog.construct_host_query(\"paper2_complete\").filter(base)\n",
    "base = annotate_base(base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "v2z(H.good_hosts.filter(hostlist, \"V_HELIO\")-275).min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "v2z(H.good_hosts.filter(hostlist, \"V_HELIO\")+275).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_known_telnames = {\n",
    "    \"HOST\",\n",
    "    \"2dF\",\n",
    "    \"6dF\",\n",
    "    \"SDSS\",\n",
    "    \"NSA\",\n",
    "    \"GAMA\",\n",
    "    \"OzDES\",\n",
    "    \"2dFLen\",\n",
    "    \"WIGGZ\",\n",
    "    \"UKST\",\n",
    "    \"LCRS\",\n",
    "    \"slack\",\n",
    "}\n",
    "has_our_specs_only = QueryMaker.vectorize(\n",
    "    lambda x: x and set(x.split(\"+\")).isdisjoint(_known_telnames), \"SPEC_REPEAT\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Query(C.has_spec, QueryMaker.contains(\"SPEC_REPEAT\", \"ALFALF\")).count(base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Query(C.has_spec, has_our_specs_only, QueryMaker.contains(\"SPEC_REPEAT\", \"ALFALF\")).filter(base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Query(C.is_sat, \"EW_Halpha < 0\").count(base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.modeling.functional_models import Sersic1D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.log10(Sersic1D.evaluate(0.0000001, 1, 1, 0.2)/Sersic1D.evaluate(, 1, 1, 0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "24 + np.log10(Sersic1D.evaluate(0.0000001, 1, 1, 1)/Sersic1D.evaluate(1, 1, 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "24 + np.log10(Sersic1D.evaluate(0.0000001, 1, 1, 0.2)/Sersic1D.evaluate(1, 1, 1, 0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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