R01-Surface brightness.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import sys\n",
    "sys.path.insert(0, \"/home/yymao/Dropbox/Academia/Collaborations/SAGA-local/saga\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\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\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",
    "\n",
    "from matplotlib.transforms import Bbox\n",
    "\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": [
    "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",
    "assert len(np.unique(base[\"HOSTID\"])) == 36\n",
    "\n",
    "base = annotate_base(base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nsat_missing_all = int(np.ceil(Query(~C.has_spec, C.faint_end_limit).filter(base, \"p_sat_corrected\").sum()))\n",
    "nsat_missing_limit = int(np.ceil(Query(~C.has_spec, C.r_abs_limit).filter(base, \"p_sat_corrected\").sum()))\n",
    "print(nsat_missing_all, nsat_missing_limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sats = C.is_sat.filter(base)\n",
    "sats_maybe_all = Query(~C.has_spec, \"p_sat_corrected > 0\").filter(base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 1177 #923 #444 #177 # 114 # 47\n",
    "while True:\n",
    "    #seed += 1\n",
    "    sats_maybe = Query(\n",
    "        (lambda p: p > np.random.RandomState(seed).rand(len(p)), \"p_sat_corrected\"),  # must put in 1st place\n",
    "        ~C.has_spec, \n",
    "    ).filter(base)\n",
    "\n",
    "    if (np.abs((C.faint_end_limit.count(sats_maybe) - nsat_missing_all) < 1.5 and\n",
    "        C.r_abs_limit.count(sats_maybe)) == nsat_missing_limit and\n",
    "        (~Query(C.high_priority_sb_tight, C.gr_cut_tight, \"Mr > -15.5\")).count(sats_maybe) == 0):\n",
    "        print(seed)\n",
    "        break\n",
    "    else:\n",
    "        seed += 1\n",
    "        \n",
    "print(C.r_abs_limit.count(sats_maybe), len(sats_maybe))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert len(sats) == 127\n",
    "assert C.r_abs_limit.count(sats) == 123\n",
    "assert Query(\"EW_Halpha < 2\").count(sats) == 18\n",
    "assert Query(\"EW_Halpha < 2\", C.r_abs_limit).count(sats) == 18\n",
    "assert Query(\"EW_Halpha < 2\", \"Mr < -16\").count(sats) == 2\n",
    "assert len(np.unique(sats[\"HOSTID\"])) == 34"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_p1_sats = Query(C.is_sat, saga.host_catalog.construct_host_query(\"paper1_complete\"), QueryMaker.in1d(\"OBJID\", [4443380000000000475, 3115540000000003218], invert=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lg = SAGA.database.FitsTable(\"../data/McConnachie2012.fit\").read()\n",
    "lg[\"Mr\"] = lg[\"vmag_lc\"] - 0.4\n",
    "#lg[\"SubG\"][np.argmax(lg[\"Name\"] == \"Pegasus dIrr\")] = \"M31 \"\n",
    "#lg = (Query((np.isfinite, \"vmag_lc\"), ~QueryMaker.equals(\"Name\", \"Canis Major\"))).filter(lg)\n",
    "\n",
    "mw_sat_mr = np.asarray(np.sort(QueryMaker.equals(\"SubG\", \"MW  \").filter(lg, \"Mr\")))\n",
    "m31_sat_mr = np.asarray(np.sort(QueryMaker.equals(\"SubG\", \"M31 \").filter(lg, \"Mr\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lg[\"sb_v\"] = lg[\"Vmag\"] + 2.5 * np.log10(2 * np.pi * (1-lg[\"Ell\"]) * (lg[\"R1\"] * 60)**2)\n",
    "lg[\"sb_r\"] = lg[\"Vmag\"] - 0.4 + 2.5 * np.log10(2 * np.pi * (1-lg[\"Ell\"]) * (lg[\"R1\"] * 60)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,\n",
    "                               AutoMinorLocator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(sats[\"Mr\"], sats[\"sb_r\"], c=\"C2\", s=11, alpha=0.8, label=\"SAGA satellites\")\n",
    "plt.scatter(sats_maybe[\"Mr\"], sats_maybe[\"sb_r\"], marker='o', c='None', edgecolors='k', s=11, lw=0.5)\n",
    "plt.scatter(lg[\"Mr\"], lg[\"sb_r\"], c=\"C1\", marker=\"*\", s=17, label=\"Local Group dwarfs\\n(McConnachie 2012)\")\n",
    "\n",
    "plt.axvspan(-12.3, 0, color=\"grey\", alpha=0.5)\n",
    "plt.ylim(31, 16)\n",
    "plt.xlim(-1, -20)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.set_xticks(np.arange(-2, -21, -2))\n",
    "ax.set_xticks(np.arange(-1, -20.4, -0.5), minor=True)\n",
    "\n",
    "ax.set_yticks(np.arange(30, 15, -2))\n",
    "ax.set_yticks(np.arange(31, 15.6, -0.5), minor=True)\n",
    "\n",
    "plt.text(-16.14, 30.2, \"$\\\\leftarrow$     SAGA range    $\\\\rightarrow$\", ha=\"center\", fontsize=14)\n",
    "plt.xlabel(\"$M_{r,o}$\")\n",
    "plt.ylabel(\"$\\\\mu_{{\\\\rm eff}, r_o}$  [mag arcsec$^{-2}$]\")\n",
    "plt.legend(loc=\"upper left\", fontsize=14, handletextpad=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Query(\"Mr < -12.3\", \"sb_r > 25.1\").filter(lg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Query(\"Mr < -12.3\").filter(lg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "3/37*123"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(Query(\"Mr < -15\").filter(sats_this, 'log_sm'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SAGA.database.GoogleSheets(\"1O8tGgnHXRcAT8P78J3V2pWVb3CWE1Hvcowily79tdg4\", 0).read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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