proof-g05-priors.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "5c4d1523-17fd-427b-bfa4-cb14eba503c6"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "import os\n",
    "from scipy.interpolate import interp1d as linear\n",
    "from scipy.ndimage.filters import gaussian_filter\n",
    "from scipy.misc import derivative\n",
    "from scipy.stats import norm\n",
    "\n",
    "from densitycontour import densitycontour\n",
    "\n",
    "from styles.paper import *\n",
    "from styles.colors import *\n",
    "from styles.labels import *\n",
    "\n",
    "from tools.plots import *\n",
    "from tools.stats import *\n",
    "\n",
    "greys_map = cm._generate_cmap(greys_map_name, 256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "change_name = lambda name, strip: \"spSpec\"+name.lstrip(strip).replace(\".log\", \".fit\")\n",
    "\n",
    "def gaussian(x, std):\n",
    "    return 1.0/sqrt(2*pi*std**2) * exp(-0.5*x**2/std**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "#load G05 indices from SDSS sample\n",
    "catalog = genfromtxt(\"data/sample-obs/catalog.txt\", dtype=None, names=True)\n",
    "\n",
    "g05_table = genfromtxt(os.path.expandvars(\"$phd/notebooks/data/sample-obs/G05_indexes_sample.txt\"), dtype=None, names=True)\n",
    "\n",
    "g05_mod_name = loadtxt(os.path.expandvars(\"$phd/notebooks/data/sample-obs/G05_indexes_from_mod.log\"), dtype=np.str, usecols=(0,))\n",
    "g05_mindx = loadtxt(os.path.expandvars(\"$phd/notebooks/data/sample-obs/G05_indexes_from_mod.log\"), usecols=(1,2,3,4,5))\n",
    "g05_mindx = g05_mindx[argsort(g05_mod_name)]\n",
    "\n",
    "g05_obs_name = loadtxt(os.path.expandvars(\"$phd/notebooks/data/sample-obs/G05_indexes_from_obs.log\"), dtype=np.str, usecols=(0,))\n",
    "g05_oindx = loadtxt(os.path.expandvars(\"$phd/notebooks/data/sample-obs/G05_indexes_from_obs.log\"), usecols=(1,2,3,4,5))\n",
    "g05_oindx = g05_oindx[argsort(g05_obs_name)]\n",
    "\n",
    "g05_ssag = loadtxt(os.path.expandvars(\"$phd/notebooks/data/sample-sim/G05_indexes_from_ndssag.log\"), usecols=(1,2,3,4,5,6,7,8,9,10,11,12))\n",
    "g05_ssag_dusty = loadtxt(os.path.expandvars(\"$phd/notebooks/data/sample-sim/G05_indexes_from_ssag.log\"), usecols=(1,2,3,4,5,6,7,8,9,10,11,12))\n",
    "\n",
    "m16_param = genfromtxt(os.path.expandvars(\"$phd/inputs/real_deal/outs/spec/dynbas_output.log\"), dtype=None, names=True)\n",
    "m16_param[\"input_file\"] = array([change_name(name, \"dynbasfit_processed_SPEC_\") for name in m16_param[\"input_file\"]])\n",
    "\n",
    "g05_param = genfromtxt(os.path.expandvars(\"$phd/notebooks/data/sample-obs/gal2005.txt\"), dtype=None, names=True)\n",
    "\n",
    "cat_in_g05 = array([gal in g05_param[\"input_file\"] for gal in catalog[\"input_file\"]], dtype=np.bool)\n",
    "m16_in_g05 = array([gal in g05_param[\"input_file\"] for gal in m16_param[\"input_file\"]], dtype=np.bool)\n",
    "\n",
    "print all(g05_param[\"input_file\"]==catalog[\"input_file\"][cat_in_g05])\n",
    "print all(m16_param[\"input_file\"][m16_in_g05]==catalog[\"input_file\"][cat_in_g05])\n",
    "print all(np.sort(g05_mod_name)[m16_in_g05]==catalog[\"input_file\"][cat_in_g05])\n",
    "print all(np.sort(g05_obs_name)[m16_in_g05]==g05_table[\"input_file\"][m16_in_g05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cat = catalog[cat_in_g05]\n",
    "m16 = m16_param[m16_in_g05]\n",
    "g05 = g05_param\n",
    "\n",
    "f = list(g05_table.dtype.names[:6])+[\"u-r\"]+list(g05_table.dtype.names[6:])+[\"u-r_err\"]\n",
    "dt = zip(f, [\"|S25\"]+(len(f)-1)*['<f8'])\n",
    "\n",
    "obs = np.zeros(g05_table[m16_in_g05].size, dtype=dt)\n",
    "mod = np.zeros(g05_table[m16_in_g05].size, dtype=dt)\n",
    "\n",
    "ssa = np.zeros(g05_ssag.shape[0], dtype=[('D4000', '<f8'),\n",
    "                                         ('Hbeta', '<f8'),\n",
    "                                         ('Hdg', '<f8'),\n",
    "                                         ('Mg2Fe', '<f8'),\n",
    "                                         ('MgFep', '<f8'),\n",
    "                                         ('age', '<f8'),\n",
    "                                         ('met', '<f8'),\n",
    "                                         ('u-r', '<f8')])\n",
    "ssa_dusty = np.zeros(g05_ssag.shape[0], dtype=[('D4000', '<f8'),\n",
    "                                               ('Hbeta', '<f8'),\n",
    "                                               ('Hdg', '<f8'),\n",
    "                                               ('Mg2Fe', '<f8'),\n",
    "                                               ('MgFep', '<f8'),\n",
    "                                               ('age', '<f8'),\n",
    "                                               ('met', '<f8'),\n",
    "                                               ('u-r', '<f8')])\n",
    "\n",
    "for i, kw in enumerate(\"D4000 Hbeta Hdg Mg2Fe MgFep u-r\".split()): mod[kw] = g05_mindx[m16_in_g05, i] if kw!=\"u-r\" else cat[\"u_mag\"]-cat[\"r_mag\"]\n",
    "for i, kw in enumerate(\"D4000 Hbeta Hdg Mg2Fe MgFep u-r\".split()): obs[kw] = g05_oindx[m16_in_g05, i] if kw!=\"u-r\" else cat[\"u_mag\"]-cat[\"r_mag\"]\n",
    "for kw in \"input_file D4000_err Hbeta_err Hdg_err Mg2Fe_err MgFep_err u-r_err\".split(): mod[kw] = g05_table[m16_in_g05][kw] if kw!=\"u-r_err\" else sqrt(cat[\"u_err\"]**2+cat[\"r_err\"]**2)\n",
    "for kw in \"input_file D4000_err Hbeta_err Hdg_err Mg2Fe_err MgFep_err u-r_err\".split(): obs[kw] = g05_table[m16_in_g05][kw] if kw!=\"u-r_err\" else sqrt(cat[\"u_err\"]**2+cat[\"r_err\"]**2)\n",
    "for i, kw in enumerate(\"D4000 Hbeta Hdg Mg2Fe MgFep age met u-r\".split()): ssa[kw] = g05_ssag[:, i] if kw!=\"u-r\" else g05_ssag[:, 7]-g05_ssag[:, 9]\n",
    "for i, kw in enumerate(\"D4000 Hbeta Hdg Mg2Fe MgFep age met u-r\".split()): ssa_dusty[kw] = g05_ssag_dusty[:, i] if kw!=\"u-r\" else g05_ssag_dusty[:, 7]-g05_ssag_dusty[:, 9]\n",
    "\n",
    "g_z_red = ssa_dusty[\"u-r\"] - ssa[\"u-r\"]\n",
    "ssa[\"age\"] = log10(ssa[\"age\"])\n",
    "ssa_dusty[\"age\"] = log10(ssa_dusty[\"age\"])\n",
    "\n",
    "ssa = ssa[(ssa[\"met\"]>=0.02)&(ssa[\"met\"]<=2.5)]\n",
    "ssa_dusty = ssa_dusty[(ssa_dusty[\"met\"]>=0.02)&(ssa_dusty[\"met\"]<=2.5)]\n",
    "def log_pdf(x, range):\n",
    "    a, b = range\n",
    "    \n",
    "    if hasattr(x, \"__getitem__\") and (x[0]==a and x[-1]==b):\n",
    "        x_ = x\n",
    "    else:\n",
    "        x_ = linspace(a, b, 1000)\n",
    "    \n",
    "    return (log(x)-log(a))/trapz(log(x_)-log(a), x_)\n",
    "\n",
    "u = rand(ssa.size)\n",
    "#ssa = ssa[u<log_pdf(ssa[\"met\"], range=(0.02,2.5))/log_pdf(2.5, range=(0.02,2.5))]\n",
    "#ssa_dusty = ssa_dusty[u<log_pdf(ssa_dusty[\"met\"], range=(0.02,2.5))/log_pdf(2.5, range=(0.02,2.5))]\n",
    "\n",
    "ssa[\"met\"] = log10(ssa[\"met\"])\n",
    "ssa_dusty[\"met\"] = log10(ssa_dusty[\"met\"])\n",
    "\n",
    "col = cat[\"u_mag\"]-cat[\"r_mag\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "idir = os.path.expandvars(\"$phd/notebooks/data/ssp-model-props\")\n",
    "odir = os.path.expandvars(\"$HOME/Documents/Publications/mejia+2016/\")\n",
    "\n",
    "ax_xlabel = {\n",
    "    \"age\": r\"\\bf \\log{t/\\text{yr}}\"\n",
    "}\n",
    "ax_ylabels = {\n",
    "    \"D4000\": r\"\\bf D$4000$\",\n",
    "    \"Hbeta\": r\"\\bf H$\\beta$\",\n",
    "    \"Hdg\": r\"\\bf H$\\delta_A$+H$\\gamma_A$\",\n",
    "    \"Mg2Fe\": r\"\\bf [Mg${}_2$Fe]\",\n",
    "    \"MgFep\": r\"\\bf [MgFe]'\",\n",
    "    \"CaII triplet\": r\"\\bf Ca\\textsc{ii} Triplet\",\n",
    "    \"u-g\": r\"\\bf $u-g$\",\n",
    "    \"u-r\": r\"\\bf $u-r$\",\n",
    "    \"g-r\": r\"\\bf $g-r$\",\n",
    "    \"g-i\": r\"\\bf $g-i$\",\n",
    "    \"g-z\": r\"\\bf $g-z$\",\n",
    "    \"r-i\": r\"\\bf $r-i$\",\n",
    "    \"r-z\": r\"\\bf $r-z$\",\n",
    "    \"i-z\": r\"\\bf $i-z$\",\n",
    "    \"M/L_u\": r\"\\bf $M_\\star/L_u$\",\n",
    "    \"M/L_g\": r\"\\bf $M_\\star/L_g$\",\n",
    "    \"M/L_r\": r\"\\bf $M_\\star/L_r$\",\n",
    "    \"M/L_i\": r\"\\bf $M_\\star/L_i$\",\n",
    "    \"M/L_z\": r\"\\bf $M_\\star/L_z$\",\n",
    "    \"L_u\": r\"\\bf $L_u$\",\n",
    "    \"L_g\": r\"\\bf $L_g$\",\n",
    "    \"L_r\": r\"\\bf $L_r$\",\n",
    "    \"L_i\": r\"\\bf $L_i$\",\n",
    "    \"L_z\": r\"\\bf $L_z$\"\n",
    "}\n",
    "libraries = {\n",
    "    \"stelib\": {\n",
    "        \"m32\": r\"0.02\",\n",
    "        \"m42\": r\"0.20\",\n",
    "        \"m52\": r\"0.40\",\n",
    "        \"m62\": r\"1.00\",\n",
    "        \"m72\": r\"2.50\"\n",
    "    },\n",
    "    \"miles\": {\n",
    "        \"z001\": r\"0.06\",\n",
    "        \"z004\": r\"0.23\",\n",
    "        \"z008\": r\"0.47\",\n",
    "        \"z017\": r\"1.00\",\n",
    "        \"z030\": r\"1.76\",\n",
    "        \"z040\": r\"2.35\"\n",
    "    }\n",
    "}\n",
    "metallicities = {\n",
    "    r\"0.0004\": (\"m32\", \"stelib\"),\n",
    "    r\"0.004\": (\"m42\", \"stelib\"),\n",
    "    r\"0.008\": (\"m52\", \"stelib\"),\n",
    "    r\"0.02\": (\"m62\", \"stelib\"),\n",
    "    r\"0.05\": (\"m72\", \"stelib\"),\n",
    "    r\"0.001\": (\"z001\", \"miles\"),\n",
    "    r\"0.004\": (\"z004\", \"miles\"),\n",
    "    r\"0.008\": (\"z008\", \"miles\"),\n",
    "    r\"0.017\": (\"z017\", \"miles\"),\n",
    "    r\"0.03\": (\"z030\", \"miles\"),\n",
    "    r\"0.04\": (\"z040\", \"miles\")\n",
    "}\n",
    "indx_maps = {\n",
    "    \"6lsindx_sed\": {\n",
    "        \"Mg2\": 12,\n",
    "        \"Hbeta\": 9,\n",
    "        \"Fe4531\": 7,\n",
    "        \"Fe5015\": 10,\n",
    "        \"Mgb\": 13,\n",
    "        \"Fe5270\": 14,\n",
    "        \"Fe5335\": 15,\n",
    "    },\n",
    "    \"7lsindx_sed\": {\n",
    "        \"Hdelta_A\": 2,\n",
    "        \"Hgamma_A\": 3,\n",
    "        \"D4000\": 5,\n",
    "        \"CaII8498\": 7,\n",
    "        \"CaII8542\": 8,\n",
    "        \"CaII8662\": 9\n",
    "    },\n",
    "    \"1ABmag\": {\n",
    "        \"u-g\": ((1,3), lambda mags: mags[:,0]-mags[:,1]),\n",
    "        \"u-r\": ((1,3), lambda mags: mags[:,0]-mags[:,1]),\n",
    "        \"g-r\": ((2,3), lambda mags: mags[:,0]-mags[:,1]),\n",
    "        \"g-i\": ((2,4), lambda mags: mags[:,0]-mags[:,1]),\n",
    "        \"g-z\": ((2,5), lambda mags: mags[:,0]-mags[:,1]),\n",
    "        \"r-i\": ((3,4), lambda mags: mags[:,0]-mags[:,1]),\n",
    "        \"r-z\": ((3,5), lambda mags: mags[:,0]-mags[:,1]),\n",
    "        \"i-z\": ((4,5), lambda mags: mags[:,0]-mags[:,1]),\n",
    "        \"L_u\": (1, lambda mag: 10**(-0.4*(mag-5.46))),\n",
    "        \"L_g\": (2, lambda mag: 10**(-0.4*(mag-5.21))),\n",
    "        \"L_r\": (3, lambda mag: 10**(-0.4*(mag-4.50))),\n",
    "        \"L_i\": (4, lambda mag: 10**(-0.4*(mag-4.16))),\n",
    "        \"L_z\": (5, lambda mag: 10**(-0.4*(mag-3.99)))\n",
    "    }\n",
    "}\n",
    "file_maps = {\n",
    "    \"tau0\": {\n",
    "        \"1color\": {\n",
    "            \"age\": 0\n",
    "        },\n",
    "        \"4color\": {\n",
    "            \"M\": 5\n",
    "        }\n",
    "    },\n",
    "    \"tau2\": {\n",
    "        \"w_age_rf\": {\n",
    "            \"age\": 13\n",
    "        },\n",
    "        \"4color\": {\n",
    "            \"M\": 5\n",
    "        }\n",
    "    },\n",
    "    \"tau5\": {\n",
    "        \"w_age_rf\": {\n",
    "            \"age\": 13\n",
    "        },\n",
    "        \"4color\": {\n",
    "            \"M\": 5\n",
    "        }\n",
    "    }\n",
    "}\n",
    "for modl in file_maps: file_maps[modl].update(indx_maps)\n",
    "\n",
    "Mg2Fe = lambda Mg2, Fe4531, Fe5015: 0.6*Mg2 + 0.4*log10(abs(Fe4531+Fe5015))\n",
    "MgFep = lambda Mgb, Fe5270, Fe5335: sqrt(abs(Mgb*(0.72*Fe5270+0.28*Fe5335)))\n",
    "Hdg = lambda Hdelta, Hgamma: Hdelta + Hgamma\n",
    "CaII_triplet = lambda CaII8498, CaII8542, CaII8662: CaII8498 + CaII8542 + CaII8662\n",
    "MLum = lambda M, L: M/L\n",
    "MLum_err = lambda MLR: MLR*(0.05/1.0857)\n",
    "\n",
    "def walk_map(library, model):\n",
    "    return {\n",
    "        zkw: {\n",
    "            field: loadtxt(\"{}/{}/{}_{}.{}\".format(idir,library,model,zkw,ftype), usecols=(icol,)) if not hasattr(icol,\"__getitem__\") else icol[1](loadtxt(\"{}/{}/{}_{}.{}\".format(idir,library,model,zkw,ftype), usecols=((icol[0],) if not hasattr(icol[0],\"__getitem__\") else icol[0]))) for ftype in file_maps[model] for field, icol in file_maps[model][ftype].iteritems()\n",
    "        } for zkw in libraries[library]\n",
    "    }\n",
    "\n",
    "def add_ingredient(stores, ingredients_list, directions, ingredient_name):\n",
    "    for store in stores.values():\n",
    "        store[ingredient_name] = directions(*tuple(store[ingredient] for ingredient in ingredients_list))\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "6e295b98-2105-445d-8fe1-cda2b61723b1"
    }
   },
   "outputs": [],
   "source": [
    "tau0_stelib = walk_map(\"stelib\", \"tau0\")\n",
    "tau2_stelib = walk_map(\"stelib\", \"tau2\")\n",
    "tau5_stelib = walk_map(\"stelib\", \"tau5\")\n",
    "\n",
    "add_ingredient(tau0_stelib, (\"Mg2\",\"Fe4531\",\"Fe5015\"), Mg2Fe, \"Mg2Fe\")\n",
    "add_ingredient(tau0_stelib, (\"Mgb\",\"Fe5270\",\"Fe5335\"), MgFep, \"MgFep\")\n",
    "add_ingredient(tau0_stelib, (\"Hdelta_A\",\"Hgamma_A\"), Hdg, \"Hdg\")\n",
    "add_ingredient(tau0_stelib, (\"M\", \"L_u\"), MLum, \"M/L_u\")\n",
    "add_ingredient(tau0_stelib, (\"M\", \"L_g\"), MLum, \"M/L_g\")\n",
    "add_ingredient(tau0_stelib, (\"M\", \"L_r\"), MLum, \"M/L_r\")\n",
    "add_ingredient(tau0_stelib, (\"M\", \"L_i\"), MLum, \"M/L_i\")\n",
    "add_ingredient(tau0_stelib, (\"M\", \"L_z\"), MLum, \"M/L_z\")\n",
    "add_ingredient(tau0_stelib, (\"M/L_u\",), MLum_err, \"err_M/L_u\")\n",
    "add_ingredient(tau0_stelib, (\"M/L_g\",), MLum_err, \"err_M/L_g\")\n",
    "add_ingredient(tau0_stelib, (\"M/L_r\",), MLum_err, \"err_M/L_r\")\n",
    "add_ingredient(tau0_stelib, (\"M/L_i\",), MLum_err, \"err_M/L_i\")\n",
    "add_ingredient(tau0_stelib, (\"M/L_z\",), MLum_err, \"err_M/L_z\")\n",
    "\n",
    "add_ingredient(tau2_stelib, (\"Mg2\",\"Fe4531\",\"Fe5015\"), Mg2Fe, \"Mg2Fe\")\n",
    "add_ingredient(tau2_stelib, (\"Mgb\",\"Fe5270\",\"Fe5335\"), MgFep, \"MgFep\")\n",
    "add_ingredient(tau2_stelib, (\"Hdelta_A\",\"Hgamma_A\"), Hdg, \"Hdg\")\n",
    "add_ingredient(tau2_stelib, (\"M\", \"L_u\"), MLum, \"M/L_u\")\n",
    "add_ingredient(tau2_stelib, (\"M\", \"L_g\"), MLum, \"M/L_g\")\n",
    "add_ingredient(tau2_stelib, (\"M\", \"L_r\"), MLum, \"M/L_r\")\n",
    "add_ingredient(tau2_stelib, (\"M\", \"L_i\"), MLum, \"M/L_i\")\n",
    "add_ingredient(tau2_stelib, (\"M\", \"L_z\"), MLum, \"M/L_z\")\n",
    "add_ingredient(tau2_stelib, (\"M/L_u\",), MLum_err, \"err_M/L_u\")\n",
    "add_ingredient(tau2_stelib, (\"M/L_g\",), MLum_err, \"err_M/L_g\")\n",
    "add_ingredient(tau2_stelib, (\"M/L_r\",), MLum_err, \"err_M/L_r\")\n",
    "add_ingredient(tau2_stelib, (\"M/L_i\",), MLum_err, \"err_M/L_i\")\n",
    "add_ingredient(tau2_stelib, (\"M/L_z\",), MLum_err, \"err_M/L_z\")\n",
    "\n",
    "add_ingredient(tau5_stelib, (\"Mg2\",\"Fe4531\",\"Fe5015\"), Mg2Fe, \"Mg2Fe\")\n",
    "add_ingredient(tau5_stelib, (\"Mgb\",\"Fe5270\",\"Fe5335\"), MgFep, \"MgFep\")\n",
    "add_ingredient(tau5_stelib, (\"Hdelta_A\",\"Hgamma_A\"), Hdg, \"Hdg\")\n",
    "add_ingredient(tau5_stelib, (\"M\", \"L_u\"), MLum, \"M/L_u\")\n",
    "add_ingredient(tau5_stelib, (\"M\", \"L_g\"), MLum, \"M/L_g\")\n",
    "add_ingredient(tau5_stelib, (\"M\", \"L_r\"), MLum, \"M/L_r\")\n",
    "add_ingredient(tau5_stelib, (\"M\", \"L_i\"), MLum, \"M/L_i\")\n",
    "add_ingredient(tau5_stelib, (\"M\", \"L_z\"), MLum, \"M/L_z\")\n",
    "add_ingredient(tau5_stelib, (\"M/L_u\",), MLum_err, \"err_M/L_u\")\n",
    "add_ingredient(tau5_stelib, (\"M/L_g\",), MLum_err, \"err_M/L_g\")\n",
    "add_ingredient(tau5_stelib, (\"M/L_r\",), MLum_err, \"err_M/L_r\")\n",
    "add_ingredient(tau5_stelib, (\"M/L_i\",), MLum_err, \"err_M/L_i\")\n",
    "add_ingredient(tau5_stelib, (\"M/L_z\",), MLum_err, \"err_M/L_z\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tau0_miles = walk_map(\"miles\", \"tau0\")\n",
    "tau2_miles = walk_map(\"miles\", \"tau2\")\n",
    "tau5_miles = walk_map(\"miles\", \"tau5\")\n",
    "\n",
    "add_ingredient(tau0_miles, (\"Mg2\",\"Fe4531\",\"Fe5015\"), Mg2Fe, \"Mg2Fe\")\n",
    "add_ingredient(tau0_miles, (\"Mgb\",\"Fe5270\",\"Fe5335\"), MgFep, \"MgFep\")\n",
    "add_ingredient(tau0_miles, (\"Hdelta_A\",\"Hgamma_A\"), Hdg, \"Hdg\")\n",
    "add_ingredient(tau0_miles, (\"M\", \"L_u\"), MLum, \"M/L_u\")\n",
    "add_ingredient(tau0_miles, (\"M\", \"L_g\"), MLum, \"M/L_g\")\n",
    "add_ingredient(tau0_miles, (\"M\", \"L_r\"), MLum, \"M/L_r\")\n",
    "add_ingredient(tau0_miles, (\"M\", \"L_i\"), MLum, \"M/L_i\")\n",
    "add_ingredient(tau0_miles, (\"M\", \"L_z\"), MLum, \"M/L_z\")\n",
    "add_ingredient(tau0_miles, (\"M/L_u\",), MLum_err, \"err_M/L_u\")\n",
    "add_ingredient(tau0_miles, (\"M/L_g\",), MLum_err, \"err_M/L_g\")\n",
    "add_ingredient(tau0_miles, (\"M/L_r\",), MLum_err, \"err_M/L_r\")\n",
    "add_ingredient(tau0_miles, (\"M/L_i\",), MLum_err, \"err_M/L_i\")\n",
    "add_ingredient(tau0_miles, (\"M/L_z\",), MLum_err, \"err_M/L_z\")\n",
    "\n",
    "add_ingredient(tau2_miles, (\"Mg2\",\"Fe4531\",\"Fe5015\"), Mg2Fe, \"Mg2Fe\")\n",
    "add_ingredient(tau2_miles, (\"Mgb\",\"Fe5270\",\"Fe5335\"), MgFep, \"MgFep\")\n",
    "add_ingredient(tau2_miles, (\"Hdelta_A\",\"Hgamma_A\"), Hdg, \"Hdg\")\n",
    "add_ingredient(tau2_miles, (\"M\", \"L_u\"), MLum, \"M/L_u\")\n",
    "add_ingredient(tau2_miles, (\"M\", \"L_g\"), MLum, \"M/L_g\")\n",
    "add_ingredient(tau2_miles, (\"M\", \"L_r\"), MLum, \"M/L_r\")\n",
    "add_ingredient(tau2_miles, (\"M\", \"L_i\"), MLum, \"M/L_i\")\n",
    "add_ingredient(tau2_miles, (\"M\", \"L_z\"), MLum, \"M/L_z\")\n",
    "add_ingredient(tau2_miles, (\"M/L_u\",), MLum_err, \"err_M/L_u\")\n",
    "add_ingredient(tau2_miles, (\"M/L_g\",), MLum_err, \"err_M/L_g\")\n",
    "add_ingredient(tau2_miles, (\"M/L_r\",), MLum_err, \"err_M/L_r\")\n",
    "add_ingredient(tau2_miles, (\"M/L_i\",), MLum_err, \"err_M/L_i\")\n",
    "add_ingredient(tau2_miles, (\"M/L_z\",), MLum_err, \"err_M/L_z\")\n",
    "\n",
    "add_ingredient(tau5_miles, (\"Mg2\",\"Fe4531\",\"Fe5015\"), Mg2Fe, \"Mg2Fe\")\n",
    "add_ingredient(tau5_miles, (\"Mgb\",\"Fe5270\",\"Fe5335\"), MgFep, \"MgFep\")\n",
    "add_ingredient(tau5_miles, (\"Hdelta_A\",\"Hgamma_A\"), Hdg, \"Hdg\")\n",
    "add_ingredient(tau5_miles, (\"M\", \"L_u\"), MLum, \"M/L_u\")\n",
    "add_ingredient(tau5_miles, (\"M\", \"L_g\"), MLum, \"M/L_g\")\n",
    "add_ingredient(tau5_miles, (\"M\", \"L_r\"), MLum, \"M/L_r\")\n",
    "add_ingredient(tau5_miles, (\"M\", \"L_i\"), MLum, \"M/L_i\")\n",
    "add_ingredient(tau5_miles, (\"M\", \"L_z\"), MLum, \"M/L_z\")\n",
    "add_ingredient(tau5_miles, (\"M/L_u\",), MLum_err, \"err_M/L_u\")\n",
    "add_ingredient(tau5_miles, (\"M/L_g\",), MLum_err, \"err_M/L_g\")\n",
    "add_ingredient(tau5_miles, (\"M/L_r\",), MLum_err, \"err_M/L_r\")\n",
    "add_ingredient(tau5_miles, (\"M/L_i\",), MLum_err, \"err_M/L_i\")\n",
    "add_ingredient(tau5_miles, (\"M/L_z\",), MLum_err, \"err_M/L_z\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HZyx+0N/fj/0UO4EajYbevj6dqjQzcfmKmr/8R8qPIgKiKMN/70E2Fp9kxRN/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\no/k/6hmzgF2G1H8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9fca304850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = (m16[\"LOSVD_mod\"]==40.0)&(m16[\"log_t_L_mod\"]<9.0)&(col<2.22)\n",
    "NG = mask.sum()\n",
    "prior = ssa\n",
    "\n",
    "# prior_subset = copy(m16[mask][:NG][[\"input_file\",\"Av_mod\"]])\n",
    "# prior_subset[\"input_file\"] = map(lambda name: \"processed_SPEC_\"+name.replace(\".fit\", \".txt\"), m16[mask][\"input_file\"][:NG])\n",
    "# for file, Av in zip(prior_subset[\"input_file\"], prior_subset[\"Av_mod\"]):\n",
    "#     f = open(\"../inputs/real_deal/spec/{}\".format(file), \"r\")\n",
    "#     o = open(\"../inputs/real_deal/spec/g05-prior-{}\".format(file), \"w\")\n",
    "    \n",
    "#     o.write(\"# LOSVD = 0.0\\n\")\n",
    "#     o.write(\"# Av    = {0:.5f}\\n\".format(Av))\n",
    "#     o.writelines(f.readlines()[2:])\n",
    "#     f.close()\n",
    "#     o.close()\n",
    "\n",
    "colors = cm.rainbow(linspace(0,1,len(libraries[\"stelib\"])))\n",
    "properties = [\"D4000\", \"Hbeta\", \"Hdg\", \"Mg2Fe\", \"MgFep\", \"u-r\"]\n",
    "\n",
    "fig, axs = subplots(3, 2, sharex=True, figsize=(3.3,5))\n",
    "\n",
    "axs = axs.ravel()\n",
    "xl = axs[0].set_xlim(8.0,10.2)\n",
    "axs[0].xaxis.set_major_locator(MaxNLocator(nbins=5, prune=\"both\"))\n",
    "yls = ((1.0,3.0), (1.0,8.0), (-15,20), (-0.05,0.65), (0.0,4.5), (0.0,3.5))\n",
    "\n",
    "for i in xrange(axs.size):\n",
    "    prop = properties[i]\n",
    "    \n",
    "    axs[i].set_ylabel(ax_ylabels[prop])\n",
    "    if axs[i].is_last_row(): axs[i].set_xlabel(ax_xlabel[\"age\"])\n",
    "    axs[i].set_ylim(yls[i])\n",
    "    axs[i].yaxis.set_major_locator(MaxNLocator(nbins=7, prune=\"both\"))\n",
    "    if axs[i].is_last_col():\n",
    "        axs[i].tick_params(labelleft=False, labelright=True)\n",
    "        axs[i].yaxis.set_label_position(\"right\")\n",
    "    if i==5:\n",
    "        axs[i].arrow(8.3, 1.5, 0.0, mean(g_z_red), overhang=0, head_width=0.05, color=samples[\"dark\"])\n",
    "\n",
    "    for j, zkw in enumerate(sorted(libraries[\"stelib\"])):\n",
    "        if i==1:\n",
    "            axs[i].text(0.93, 0.89-j*0.07, (r\"\\bf $Z/\\text{Z}_\\odot=%s$\" if j==0 else r\"\\bf $%s$\")%(libraries[\"stelib\"][zkw]), color=colors[j], fontsize=\"x-small\", ha=\"right\", transform=axs[i].transAxes)\n",
    "        axs[i].plot(tau0_stelib[zkw][\"age\"], tau0_stelib[zkw][prop], \"--\", lw=0.5, color=colors[j])\n",
    "        axs[i].plot(tau5_stelib[zkw][\"age\"], tau5_stelib[zkw][prop], \"-\", lw=0.5, color=colors[j])\n",
    "\n",
    "    x_, y_, xer_, yer_ = 0.0, 0.0, 0.0, 0.0\n",
    "    for index in xrange(NG):\n",
    "        x_ += g05[mask][index][\"log_t_L_mod\"]\n",
    "        y_ += mod[mask][index][prop]\n",
    "        xer_ += g05[mask][index][\"t_P84p0\"] - g05[mask][index][\"t_P16p0\"]\n",
    "        yer_ += mod[mask][index][\"{}_err\".format(prop)]\n",
    "\n",
    "        axs[i].plot(g05[mask][index][\"log_t_L_mod\"], mod[mask][index][prop], \".\", ms=3, mew=0, color=samples[\"hot\"])\n",
    "\n",
    "    x_, y_, xer_, yer_ = x_/NG, y_/NG, xer_/NG, yer_/NG\n",
    "    axs[i].errorbar(x_, y_, yer_, xer_, fmt=\"\", ecolor=samples[\"dark\"], elinewidth=0.5, mew=0.5, zorder=9)\n",
    "\n",
    "#     prior_pdf, xe, ye = histogram2d(prior[\"age\"], prior[prop], (30,30), [last(30,xl),last(30,yls[i])], normed=True)\n",
    "#     axs[i].pcolormesh(xe[:-1], ye[:-1], prior_pdf.T, cmap=greys_map, vmin=prior_pdf.min(), vmax=prior_pdf.max(), edgecolor=\"face\")\n",
    "    sample_data = densitycontour.ScatterData(prior[\"age\"], prior[prop])\n",
    "    raster = sample_data.rasterize(nbins_x=30, nbins_y=30, clipping=[last(30,xl),last(30,yls[i])])\n",
    "    raster.pdf = gaussian_filter(raster.pdf, sigma=1.5)\n",
    "    contours = densitycontour.NaiveContourVisualizer(raster, mode=\"nearest\")\n",
    "    cont = contours.plot((0.99,0.95,0.68), axes=axs[i], linewidths=1.0, colors=samples[\"medium\"])\n",
    "#     contours.cilabel(cont, fontsize=5, fmt=r\"\\bf %.0f\", inline=True)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(wspace=0.1, hspace=0.05)\n",
    "\n",
    "#fig.savefig(\"{}g05_indices\".format(odir))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9xE61GofDhxHWUfD1omsODVFSVIQgCOTk5uLptUzJPRsXQ37dpZ8FcVUDyHLC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9fc7ff7150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = (m16[\"LOSVD_mod\"]==40.0)&(m16[\"log_t_L_mod\"]<9.0)&(col<2.22)\n",
    "NG = mask.sum()\n",
    "prior = ssa_dusty\n",
    "\n",
    "# prior_subset = copy(m16[mask][:NG][[\"input_file\",\"Av_mod\"]])\n",
    "# prior_subset[\"input_file\"] = map(lambda name: \"processed_SPEC_\"+name.replace(\".fit\", \".txt\"), m16[mask][\"input_file\"][:NG])\n",
    "# for file, Av in zip(prior_subset[\"input_file\"], prior_subset[\"Av_mod\"]):\n",
    "#     f = open(\"../inputs/real_deal/spec/{}\".format(file), \"r\")\n",
    "#     o = open(\"../inputs/real_deal/spec/g05-prior-{}\".format(file), \"w\")\n",
    "    \n",
    "#     o.write(\"# LOSVD = 0.0\\n\")\n",
    "#     o.write(\"# Av    = {0:.5f}\\n\".format(Av))\n",
    "#     o.writelines(f.readlines()[2:])\n",
    "#     f.close()\n",
    "#     o.close()\n",
    "\n",
    "colors = cm.rainbow(linspace(0,1,len(libraries[\"stelib\"])))\n",
    "properties = [\"D4000\", \"Hbeta\", \"Hdg\", \"Mg2Fe\", \"MgFep\", \"u-r\"]\n",
    "properties = [\"D4000\", \"MgFep\"]\n",
    "\n",
    "fig, axs = subplots(len(properties), sharex=True, figsize=(2.5,3.3))\n",
    "\n",
    "xl = axs[0].set_xlim(8.0,10.2)\n",
    "axs[0].xaxis.set_major_locator(MaxNLocator(nbins=5, prune=\"both\"))\n",
    "yls = ((1.0,3.0), (0.0,8.0), (-15,20), (-0.05,0.65), (0.0,4.5), (0.0,4.0))\n",
    "yls = ((1.0,3.0), (0.0,4.5))\n",
    "\n",
    "for i in xrange(axs.size):\n",
    "    prop = properties[i]\n",
    "    \n",
    "    axs[i].set_ylabel(ax_ylabels[prop])\n",
    "    if axs[i].is_last_row(): axs[i].set_xlabel(ax_xlabel[\"age\"])\n",
    "    axs[i].set_ylim(yls[i])\n",
    "    axs[i].yaxis.set_major_locator(MaxNLocator(nbins=7, prune=\"both\"))\n",
    "    if i==5:\n",
    "        axs[i].arrow(8.3, 2.5, 0.0, mean(g_z_red), overhang=0, head_width=0.03, color=samples[\"dark\"])\n",
    "\n",
    "    for j, zkw in enumerate(sorted(libraries[\"stelib\"])):\n",
    "        if i==1:\n",
    "            axs[i].text(0.32, 0.89-j*0.07, (r\"\\bf $Z/\\text{Z}_\\odot=%s$\" if j==0 else r\"\\bf $%s$\")%(libraries[\"stelib\"][zkw]), color=colors[j], fontsize=\"x-small\", ha=\"right\", transform=axs[i].transAxes)\n",
    "        axs[i].plot(tau0_stelib[zkw][\"age\"], tau0_stelib[zkw][prop], \"--\", lw=0.5, color=colors[j])\n",
    "        axs[i].plot(tau5_stelib[zkw][\"age\"], tau5_stelib[zkw][prop], \"-\", lw=0.5, color=colors[j])\n",
    "\n",
    "    x_, y_, xer_, yer_ = 0.0, 0.0, 0.0, 0.0\n",
    "    for index in xrange(NG):\n",
    "        x_ += g05[mask][index][\"log_t_L_mod\"]\n",
    "        y_ += mod[mask][index][prop]\n",
    "        xer_ += g05[mask][index][\"t_P84p0\"] - g05[mask][index][\"t_P16p0\"]\n",
    "        yer_ += mod[mask][index][\"{}_err\".format(prop)]\n",
    "\n",
    "        axs[i].plot(g05[mask][index][\"log_t_L_mod\"], mod[mask][index][prop], \".\", ms=3, mew=0, color=samples[\"hot\"])\n",
    "        axs[i].plot(m16[mask][index][\"log_t_L_mod\"], mod[mask][index][prop], \".\", ms=3, mew=0, color=samples[\"cold\"])\n",
    "\n",
    "    x_, y_, xer_, yer_ = x_/NG, y_/NG, xer_/NG, yer_/NG\n",
    "    #axs[i].errorbar(10.0, y_, yer_, fmt=\"\", ecolor=samples[\"dark\"], elinewidth=0.7, mew=0.7, zorder=9)\n",
    "\n",
    "#     prior_pdf, xe, ye = histogram2d(prior[\"age\"], prior[prop], (30,30), [last(30,xl),last(30,yls[i])], normed=True)\n",
    "#     axs[i].pcolormesh(xe[:-1], ye[:-1], prior_pdf.T, cmap=greys_map, vmin=prior_pdf.min(), vmax=prior_pdf.max(), edgecolor=\"face\")\n",
    "    sample_data = densitycontour.ScatterData(prior[\"age\"], prior[prop])\n",
    "    raster = sample_data.rasterize(nbins_x=30, nbins_y=30, clipping=[last(30,xl),last(30,yls[i])])\n",
    "    raster.pdf = gaussian_filter(raster.pdf, sigma=1.5)\n",
    "    contours = densitycontour.NaiveContourVisualizer(raster, mode=\"nearest\")\n",
    "    cont = contours.plot((0.99,0.95,0.68), axes=axs[i], linewidths=1.0, colors=samples[\"medium\"])\n",
    "#     contours.cilabel(cont, fontsize=5, fmt=r\"\\bf %.0f\", inline=True)\n",
    "\n",
    "axs[0].text(0.05, 0.93, r\"\\bf G05\", color=samples[\"hot\"], fontsize=\"x-small\", ha=\"left\", va=\"top\", transform=axs[0].transAxes)\n",
    "axs[0].text(0.05, 0.85, r\"\\bf M17\", color=samples[\"cold\"], fontsize=\"x-small\", ha=\"left\", va=\"top\", transform=axs[0].transAxes)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(hspace=0.05)\n",
    "\n",
    "#fig.savefig(\"{}g05_indices\".format(\"img/\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "116\n",
      "-0.619567241379 -0.248368965517\n"
     ]
    }
   ],
   "source": [
    "properties = [\"D4000\", \"Hbeta\", \"Hdg\", \"Mg2Fe\", \"MgFep\"]\n",
    "likelihood = zeros((NG,ssa.size))\n",
    "for i in xrange(NG):\n",
    "    likelihood[i] = product([gaussian(mod[mask][prop+\"_err\"][i]*randn()+mod[mask][prop][i]-prior[prop], mod[mask][prop+\"_err\"][i]) for prop in properties], 0)\n",
    "\n",
    "nb = 1000\n",
    "xlm, ylm = (prior[\"age\"].min(), prior[\"age\"].max()), (prior[\"met\"].min(),prior[\"met\"].max())\n",
    "t_mode, Z_mode = zeros(NG), zeros(NG)\n",
    "for i in xrange(NG):\n",
    "    t_pdf, te = histogram(prior[\"age\"], nb, range=xlm, density=True, weights=likelihood[i])\n",
    "    t = (te[1:] + te[:-1])*0.5\n",
    "    t_pdf /= trapz(t_pdf, t)\n",
    "    t_mode[i] = t[argmax(t_pdf)]\n",
    "#     t_mode[i] = t[argmin(abs(median(t_pdf)-t_pdf))]\n",
    "    \n",
    "    Z_pdf, Ze = histogram(prior[\"met\"], nb, range=ylm, density=True, weights=likelihood[i])\n",
    "    Z = (Ze[1:] + Ze[:-1])*0.5\n",
    "    Z_pdf /= trapz(Z_pdf, Z)\n",
    "    Z_mode[i] = Z[argmax(Z_pdf)]\n",
    "#     Z_mode[i] = Z[argmin(abs(median(Z_pdf)-Z_pdf))]\n",
    "\n",
    "print NG\n",
    "print mean(m16[mask][\"log_t_L_mod\"][:NG]-g05[mask][\"log_t_L_mod\"][:NG]), mean(m16[mask][\"log_Z_L_mod\"][:NG]-g05[mask][\"log_Z_L_mod\"][:NG])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.6 ,  0.35,  1.  ,  0.65,  0.45,  0.55,  0.4 ,  0.45,  0.4 ,  0.45]),\n",
       " array([-1.1 , -0.95, -0.8 , -0.65, -0.5 , -0.35, -0.2 , -0.05,  0.1 ,\n",
       "         0.25,  0.4 ]),\n",
       " <a list of 1 Patch objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L1DU18+QPQwfXrZwTih2Hq1j6zEu+YHksHD52nBsfeapDzCtavCkB3jiVt5KmFVqOnqD8\nl88w5te3kjU4ctnk1tZWXn/tNZqbm8kinXMONdB26SjyzhmYMjEppdRqrXWnUh1TL7lU3/WjfyIv\nP59rvhF7Fc2/n7uACb+/r0soqVDPxB9X/8+HammllJrqN35vMmV0kimFw8nv3YsKczUyFpa/tJ6n\n/qlzAms0BEsJaDl6gj2LH6PlWOd61f6U//IZvnhlC+W/+IOle73+2mvk5eXx7ZtvJv+cgRwb348r\nrppje2uOkDq43d17AHgSs6UV8JQ5vhij/nkxRqOGhNRVefh7kbOZrZwTjhf80iJiwUoqRXtDE8fW\nljB4wcygAfBgK35e5QMwftXdIece8+tbO/yORHNzsy9bXDYHC/64XUkFbWmltb4DQCm1AljhHa+u\nrvYVyPfi5uJkySbSFppgW2Vafz0UiKx8sgYXhFVigXg3B185a1ZKbA4O/ByGo6mxkYrKSnL8Pr+p\n+LmN5pn443YlVQtMAyr8B5VSeRjW1ZNa66eTIFdKECnXKdiKX7TKxypXX3MNr7/2WoeCdYIA7ldS\nQVtaYVhWk4FlSqllWut+YHQPScW/QPHCu+TvBlJtc3A0n8NevXszcsQI8vLzU/rza/e9uVpJmd1N\nx4Q4fEeIcUEQUghZNkkh7NaSEgQ3I0oqhfAGwo+teSvZogiCY7ja3ROiww21pATBaURJpRBuCoQL\nglOIuycIgqsRJSU4ggTthXghSkpwBAnaC/FCYlKCI0jQ3l14Wts403QalZlBWkZ6ssWJCVFSQifs\n1JGSoH3stJ4+zYEDB8KeM3r0aNLTwyudHgU57PmBsec+95Lzufi1hxyTMRmIkhI64e0cI62lEktT\nUxNb33sv7DnDhw+PqKSK3lxOS9UX1Ly9m6on/+ykiElBlJTQiWg7x9glWZU/U52sIf3JGtKf05/X\nJFsURxAlJXTCSueY9vZ2/r5pE9XV1aSnpzN23LioC9SJxSZYQf5sCZ2w0jlmc3ExHo+Hyy6/nMLC\nQj6uqgrZ1cXj8bB71y62lJSwe9cuPB4PIM1ABWuIkhI64a0jNX3GjJDWUUNDA71792b4iBFMLSqi\nra0tpJIJ1R3Za7G1NzSx+7HV5GR27UJ3QnwQJSWEJFw985ycHBobG6mqrGR7aSk9evQI2ekklMXk\ntdjeefh3HF+2loJ9x+P3ZoQui8SkugCRapFHwm6AOlw98ytnzeLvmzbxj3ff9cWkQjUUDRXj8lps\n7YVjOTZmbMgcKwmwd29ESXUBItUij4SVAHUwRRCumUJGRgZzr7rK0v2D1UrvMFeEHCsJsHdvXP3n\nKExLq6DjqcrgBTMZt2yJ7WxuKwHqYHEjbz1zK33zwmElxhWr/ELq4molxdmWVguAlRbGUxKvpWHH\n1YOz7hYQMqXAzYrAivxC6uJ2d8+/pVWocV+rK2lpFZxI7hZYy42yQmvdKT747fOcnjaSvHMGcsGF\nF7J/376Y4klW5Hcbdlpa+TPS/IORSnSHllY6xLhgEirAHqw1VSBOKYIPfvs8px7/K+OWLcFzfi6b\ni4sZd955McWTrMgvpC5uV1KhWlotBdaa476Wv921pZU36F39wluceaoYrT0M+/7VUc3hlCI4PW2k\nL36W0Seb7aWlHdzIeG2xcRt2WlqlOt2xpVWo8W6HN+h9wQO3sb+ggOoLBzAs4JxY0xisknfOQDzn\n55LRJ5uqykpycnIccSOF7ovbA+eCBbxB74w+2Uy4+2YaWjtXx0xUUbrALTVXzpoVdItNe3s7G9ev\n5+V169i4fj3t7e1S3VMIiqOWlFLqHq31o07OKUTGStA7UUXpgrmNwdzIzcXFFBQUMGfePMpKS9lc\nXMz5n3piygcTUhOn3b0HAFFSCcZK0DseRem8LmTbJSN5Z/tW3/gVM2YwYuTIsNc2NDQwZ948AKYW\nFfHyunUMXmDE0bpbdc+q2kZ2Hath4uB8R+Y7unoj9WUHaK445sh8ySYmJaWUKgW2AeuBTY5IJERN\ntEFvp7aZeF3I2usnweWFLLrlFp579lneLimJqKRycnIoKy1lalER20tLycnJ6bbVPZva2lmzt9KS\nknp53bqg40OHDuWKmYZy/+i+leRMGk3PQfkULv22o7Img1hjUg8BCvgZRlqAREW7AKGqEkSLNxO+\neeowFt1yC4DvdySunDWL6upqXl63jprqaq6cNcuWDKnAgN5Zls89c+ZM8B+z/I2Xkf88nwl/WMqw\nJd9wWtyEE5OS0lqv01rfobUu0lqnAfMckkuII4HZ5bW1tUHrPUXCa/norB489+yzAL7fEa/NyGDO\nvHl8a/585sybR0ZGhgTOhaA4urqntRaXrwsQuM3kdEtLTJbVFTNmAGcVlPd1tEhbLCEYUcWkzM28\nSzGyvzcCy7TWFXGQS4gjgYH2nj17xpRwOWLkyIgxqFD4x8dyLujH2Idv73aBcyE8ES0ppVRfpdTD\nSqmDQC5wsdZ6LIaSWquUekEpNTneggrOEViVIC8/P6EbeP3dOv/4WP45A6ktGhrXZFOh6xFWSSml\n5gNlQLnWeqzW+lGtdR344lFFwCpguVLqhfiLK8QDKzXNncTfrXNz9QXBHYR197TW64Dga55nz9mE\npB90WZJR9dI/sbTm8EHZNiOERbbFdHOcSkeIBv/6WIm24oSuh61kTjMGNRczgK61/sBRqYSE4W0E\n2t7QRFrxfmqHZIDFvFAnNi1LGRYhElFbUma5lBsxSqWsA+YqpZ50WjAhMXjTEbxxop7bKixfKykD\nQiKISkkppaYAJ7XWD2itj5g/jwAblVLXx0dEIZ543a0jQzLo8+OvM/nH1rdR+NdeD5WIKQmaQqxE\n6+4txNgK43X5CrTWxVrrdaY19ZLTAgrxxeduTQSsNX/x4b/X7pM//C1oBYNYO90IQrRKKl9rXW/+\n+0GMKpljzdextRQRujShSsEkqkSMkLpEG5ParpSaBaC1XggUASilRtK5WYIQA/VNzTz5xls0NLUk\nWxRLhOpoE2unG0GIypLSWj+llFqvlCrVWtdrreuUUrnAMiDl+98lktVvbeXOFf8NwA+/FtoKaW9o\n4rM1m6m+cAANrc3k5uU50qElGhJVmljonthJQViAkWGej1GmRQP3+7mBMWMG6NeYcy/XWj9ljk8F\nXjTHV5lBex+p1NJq0cxLO/wOxbG1JRy8/ykGLL2B6XffTFVlpSMdWqJB4k6diaZ9U2tzE2eOH2Pr\n3o98Y+cOM6rUZ6QpBtlQ/C2fnqDxwMdBj6X3ySZrSP+o54yVhLW0MrfF3GHrbtbxNv/chLEt5ylz\nfDFG4L4YOAQ8EvTqFCCnV1ZYC8rL4AUzOVR+kAuWGIurw0eMSHiHFok7xUaf7GzKm07z2/c/PDv4\n4QnfP3+76Otcef6osHP07dvX9+/sEYM4cP+qkOeqzAwufes/6TX6XPtCJ5CISkop9RDG3r3fBYxP\nARZqrR+I9qamixgUUwkGbf6ptb7DvH4FsCLw2u7Y0iqjTzYFN87k6MkvGKIHsH/Vy+SMyU3oVpPu\nWlEzHNF8DsePGcXm3z3NGY/udGz6/ctJ75PL+AkTLM9XtP4R2mtPBT12pqmF96f/mNYT9QlXUvFs\nabUU0EqpuVrrm/zGC4H7MKyeaFkIXEzHhp/KvM/9hGj+qZTKw7CuntRaP23jvimJt/TKkVWvcuap\nYsY9dJtvq0lX6fjb3Tm3X/DSwRnp6VHPldEnO2RssCvmq1l193YAC5VShcBsrXVDLDc1Y0xPhTpu\nWm/BmoIeASYDy5RSy7TW/WKRw+3UNzWz+q2tLJp5KTm9QpeY9eY6tReO5diYsQxeMJPhEsAWUgSr\nSuo3GOkKLwJHlFIL4idSxKag8Y6HuQarK3xexO0SUhHL69Ja67UYeVHpGAXvFoe/QoiVRTMv5Yk7\nvhNxhS9eJHtLS7LvL7iDqJJntNZlwEiM1bW58RBIOIt3hS+cqxdPkr2BONn3F9yBFXdvLXDY+8Jc\nfZurlLoPM+NcSE2SnVqQ7PsL7iCikjK3vwQbX+68OEKsWA22WyHZMa5k319wB1YaMdyjlOob6TzB\nHXiD7c+99X5c72OnNEvL0RPsWfwYLceq4yqbkFpYcfeOAMVKqW1ICyvXY3U7TayE2goTbotM+S+f\n4YtXtgAwftXdcZVPSB2suHvrgHVKqdkYLawOAQ9JyWB3Emo7jdcNvPaSiby6dVfM7qCd0ixjfn1r\nh99WSEajCMFdWN67Z3aFKTK3wyxXSmlgqSirroHXDSzZe5Dn394GWMu9CkWoeFG4OFLW4IKoLShv\no4hEbZYW3IedDcY7gHmmsnpQKTUKQ1kVOy6d4Bhe9++bl0xixkVjY3YHE1WexdsoAhKzWVpwH7bt\nZq31DnPlbyHGlpk3nRNLcBqvGzikX56t3KvAgHiicpi8jSIgMd2VBfdhq6WVP1rrI3SjrSrdlcCA\neKQcJqcsLe/madks3X2JWUkJXRureVWBSilSDpNThfCkL59gW0kppe4BVgGFEjzvuljdxBxtYmUs\n2eLdrRyxR2s8Hk/IVcvHXyvmlfesfcW+dN4ofvWdawFoPV7LR/eu4EzTad9xfeZM7AInGLsdjKdg\nVCk4gqGoREl1UaLNq7KqQGLJFo/WCuvqaQonjh+nvr6evCDxtifu+Dbrd+yzNM+hYyd4aN3ffEqq\n5t29nNiwnaHf/1qH8/JnTCK3aFzsgieIWN095YgUQlyw4spZLVPsJRH1zKO1wlI5TeGrU8fz1anW\n4nB/2bqTN8r2dBhL69mDsf/2/XiIljCslA+eHOjOaa13mEmdo5B+e67l6Q3vcPfv19DS2sZPvznH\nkTkTsek3WitM0hRSGys2cZlSamvg/j2t9SNa6zpzdU9IENH044uHmevGPnqSppDaWHXcizAK3flQ\nSs1WSr3gvEhCOKxsIPYqshunF/HEHd/htrnTLc3dVYvMXTR+vK+me11dnaQppBhWY1LLgfuUUlu1\n1peYY3nADfEQKlTfPfNYIVCOsdm5QxMIt/fdc6KMipVAd7Rlh71EE29y0wqcG9MUoukxd6qxkV27\ndpGTk+Mbc9Pn1ins9t2zakltxbCmipRSW23dKTq8ffcWACsDjq0ANjh5s0S1NHeijIo30K3RIWW2\nW3Z48IKZjFu2xFK8SapmCokimg3GZUqpIqBUKfU3YJ3dm9rtu6eUuhdDaQUtXWy3755dyyNanCyj\nEk7maFfsvEQTsB68YCZnWlrxnG6l/VRzTNaUm6wyp4jmc9ind28mTpwYNAUhlYhn3z0f/ooKoy+e\nXWz13QNuBHKB0UCNUuohJ9q7J6oGk13lEYxEyRyKjD7ZpGdlcmDpStJ6ZsaUjiBt2oVwWKknleZv\n+ZiKah6wno5KxjJ2++5prYvM408CNU4oKHBWeSQKN8jsVDqC1DIXwmHJkjJdMP/XG02LKmj981iJ\n0HcPrfUP43FfITqcqkEutcyFcMRSqqVMa32/k8IInUlUUF8Q3ErX2eDUTYl1RdBOwwRBcBOipJKE\nVQvJajpBqPlCpQq4MYVAFKcQDMure0qpkcHGpXuMPaymPVgNkIeaz07DBC+JTA3weDyU/dd/c+rx\nv3L0008oevD7XaqSgRA/oklBOEyQ1TylFMAcrfXfnRKqO+B0CkGo+ew0TPCSyNSAvXv2MPD6Kxhy\n7lDaLx2VUpUMhNiIRkmtwljNW4WR03Q7xn6+fIxtM7HkTaUckVpIOZ1CEI+UhESmBtTV1jJhxgy4\n8DwAqWRgE49H88Z2o1xL2uEqMtraeXPFS8FPVjB51DAy0tNAKfJnTCItIz2B0lojGiU1G7hNa/0S\ngFKqHLgPY7uMtKQNwOkWUskgkakB3koGw0eM6JaVDD4oK+PKWbNimmPckEH0zsrk6l//FoDCxnYe\nb2sn+xd/DHnNXr9/j/7FLYz40bdikiEeRKOk+mF0hdkO1GDsqyvAyByvC3dhd8TpFlKpQLgYV3dv\nuPDpp5/GPMd5QwdT96f/snz+nH/530wpHMYjt97A9mt/Rnt9Y8wyxINolNT9GJt7F5ivFYabt5CA\nMi5ux4lqBJHwd7/cZkEl4v0HI1yMy42VDAR3EM0G41VKqVLOZplv0FpvMsuqHI6LdHEiURuK3Uq4\n9x9pRS9XXerbAAAd8ElEQVTaFT//82X7i2CHaGucl2OUbSkAtoFvC0uXItmbc+OFVQsp3Pv3t3YG\n3zCjk0KKdsUv8HzZ/iJESzR5UrMxNhWD4eqtVEp1ydQDN2zODSRaFyzY+U7kXvlbO8fWvNVJIUVr\nDYn1JMRKNJbUMmAHsBRDSS0zfy4Jd5FgDf/VwFV3LoqoqIIpJCcsRP8VvWAKJtoVP9k8LMRKNEpq\nKkbSZjGAUuo+zlpWQhQEs4IWzby0Q7pCJEUVTCE5bSGKghHcQDT7Dg4Di5VSI8wtMovpYgFztxC4\nabi+qZnn3nqfx269gZuumMbzb2+LuKFYBynlFWk/oFRUELoi0aYgvEjHFITFjkvUDQi0gvxdt1V3\nLrKUVxXM3Xt6wxbu/v3akH32nFrVTNSevlQsKyxETzQpCGuVUhdjlPAFeKErruy5gUC3zF9phXLZ\nAl3EYO6eitBpL9g1dhSB1RW+9vZ2NhcX09DQQE5ODlfOmkVGhvW/i1JWWABrHYyvDxjydosZpZQa\n5d0m4yShWloppUYBazHqnG8IrNDp9pZW/gQqnUiWTaAV5H+Nd64bpxeRldkjpBUW7D52FIHVFbvN\nxcUUFBQwZ948ykpL2VxczJx58yzdI5r7uJFoW1pVVFR0uMatn9tYsNvSysqftbUYysL/z7T3tQbi\nsSPR29JqE1DG2XroS4G+5n0PxeG+CSNa1yvcyl0sbpwdRWA1oN7Q0OBTSlOLinh5XXQNhiRwL4A1\nJXWx0zeNsqWVP4UYSnMlRtD+Uf+DdltaJYNo0wXCWVuxpB7EUxHk5ORQVlrK1KIitpeW+ppfejwe\n9u7ZQ11trW+fXqrVjoq2pdXIkSO7zGfXLnFraRWnuFM0La38z6k1f6dhs1ONW3AyXSDcXP5upUY7\numcvUjzrylmz2FxczMvr1vliUmDUjsrNzWXCxIlUVVb6akdJoFwIRlglpZSaj7GCd5/WemeIc/oC\nDwK5Vru42G1pBSzB2My8GFlZtIS/Kwg4umcxUjwrIyMjaAyqrrbWt5l4+IgRvtpREigXghFWSWmt\n1ymlDgM/M4PWD/nVk5qMoZw6jDtBhJZWIVtdCZ0J5go6tWcx2hLEaM2xtSXkXNAvaO2oSPOJpdU9\nseruLTSV1FKl1DLOJnEu11pviqeAqUCySqNAZ1cw0Rnp/tYRwIGlKxn78O3U9urZqXZUpPnCWVqp\noMBOnjhBz5496WPG7uJNj4x0frfxHf6ybRc/PlTJ0LKPeP93r8U8b+O5BXy/+HHSo0g3CUc0eVJH\ngDvMoHeB+VqwgNflam5tI9tMEUi0skoW/b86jdp/7KX/1y7xKY/BC2YyzIYiCWdpdXVXUWvNG6+/\nzshRo5h+xRUJuef/WfxtXivdBYCaMpH63VUxz9l85DNGv19O2+m2xCspL+bqm1TijAKve9XS2hYx\nJhSL1ZVMi81LoEVz4m/b+OKVLeRddlHMpVrCWVpdOacqWYw+ZwD//I3Zjs5Z/Ozr8H65o3M6o+qE\nsHhdroamlrDJlhBbzpP/tbdPn5YU9yfQokmU8pCcqtRFlFQCiZR2UN/UTEtrG//x/QWWgtvhtsoc\ne2FzRPcnHpZXoFKyqzxSIcYkOENKZdC1tZ/p0rv8V7+1lbt+v4aszB6WlEZgNQWvEszplcXgBTMZ\nt2xJWAsm1hbuwfAqpVgVS2CHZelu3H1JKUvqyBcneLEL1y6PNnM81Pn1Tc08vekdVEEGt6Upgq0V\nRWu1JZpAi6yrB8YF+6SUJTVqYH+euOM7rvzSWcHfEorl/NVvbeXu36/hrt+vCWklRWu12aXl6An2\nLH6MlmPBWzOGspACLbL+X53GwOum0/9rUgi2u5FSllSPjPQuaUH540ScaNHMS2lpbfP9O9Q54Y47\nRfkvn+GLV7YAMH7V3Z2OW7WQAlcJhe5DSimpVCDS6p4VJZbTKyto0bvAc6JR6C1HT1D+y2cY8+tb\nyRpc0Ol1KMb8+tYOvwOxuvonKQbdl5Ry99xCLGV6F828NKzLGqz0cCIWC7wWUfkv/hD0dSiyBhcw\nftXdIRWZb/VP67CBcacC8kLXo9tZUolIeIwl1ymShROq9HBzayvZmZlxe1+BFlEoCykwdcBqKoFV\nty+wzMt5Iwr54qUtkqqQwnQ7JRWP7sVWSvs6RajSw1ay2WPBaxGFeu0lUNlYVT5W3bnAMi8f/PZ5\nTj3+14jzC12Xbqek4qFAwpX2jQdepXjtJRN5desu33sJl82eqOTIQGUTTvl0Kn73vXkRi98Flnmp\nnDYyYj6Y0LVJuZhUpBhNpGV+OzEe/ziSUzEij8fD7l272FJSwu5du/B4PL5jXqV4zx/W+eJT3vel\n0UHv758cGUpGJxImA2NH4WJJXqto+owZ5ObmsnfPnojz5+blUVVZCUBVZSV55wyUWFWKk3KWVKzu\nnJ3r/S2nJ994yxG3K1T1SjhrBX7zkkmd2l/5y3/zzEt8bqi/RfNUiPcY74TJwO4xmT17Bi1+F46L\nxo9n7549ncq8CKlLyimpWN25wOujDbQ75U563Zr6pmb+uq+C0T3amWAe81eKgYrQ//7PvfV+B2Xk\nVTyhZIz3Mn9g95iqqqqgxe/CkZaW5lNsQvfAlUoqTEurKRhlh3OBVVrrR/yvq66upvyj/XxpcF/K\nP9oPRF/8PTCeFK1l5VQ8yuvW/HVfBXeu+G9+9o2ZWGkG5d/Z+NppkyjZe5BvXjLJkozxriQQ2D2m\noqKCurq6lLSKom5pZbqwrW1t9O7TJ+rPbevJeo7/5d2ornGCDw5/TH3z2bBBY/mnFIY4N54trZJB\nqJZWS4DngXXAeuCRoFc7SKIyswPxujWje7Tzs2/M5N5vX2fpusCa5s+/vY0ZF411RSZ+YPeYvn37\nilWE8Vwuu+wyADJ79rQ1x+HfrObYurfI7NfXSdHC0tp2hrqaWtLT0nyNafsChwoHMLNnD+dupLVO\n+A+GJRT0xzy+HrjeHDvjd91swAOcxOig3GHem2++WXd36hub9ROvb9b1jc0d/h2MusamsMedpq2t\nTW9480390tq1esObb+q2trbw59c36o9//4Zua2gK+rqrATyng3wfrvvmN2Oee+//fFzvvv3RmOeJ\nhvc/Oqy5drGubjhle45Qz8T/J1mWVDQtrfxZhtElZg1Qo5S6XWtdH39xrZGIRNFI9wjsbByOeOSM\nhSNU95hQ2M25ElKLpCgpbb+l1W8wOtQsBR52k4ICZ770kZRQuHsE5kJFkidZrqxVosm5ElIXV8ak\ndPiWVo61zgqGXWvIqfpMsSiWQEsjkhLSLu+vGhjIlxLB3RNXKqlkYtca8tZneuKO78Tk6kVSLOFW\nD3t//RI+33uAqV+/NOK5XpkT6e4Jgh1ESQVg1wVywnWKNab1/Pbd3HlwL09s32VJ6bjR3eu0VWb8\n+IhbZYTUplv/7wfbHmJ320y0VTWD4V+Gxcr2msBzgpV5CTePEzI7jZ2tMkJq062VlJ1GBPFoXuDF\nX8lYuU+4RgyJkDce1NXWMnzECMDYKlNXW5tkiYRk063dPTvuTjzdOv8YkpX7OHWOm/Bm2kezVUZI\nbVLKkjpw9POoqg/YcXecqKJgxa2zIptT57iJi8aP922VqaurS6mtMoI9UsqS2naggufeej+pK1VW\nVszCbQLu7sgGYiGQlFJS08aNTLpbY8W9itatE4TuTEq5e+OGDEq6W5PTK4ubZ17CUxve5n//eWNE\n99NpdyxRjRkSjXQw7r6klJJyAie+5EZzzrUdmnMmSnl0tdU8qwS2XRe6Dynl7jmBE33vgjXn9M5b\nc6qR3ZVHeezWGxjSz/mVq1R1H2XfXvdFlFQAkb7kVgLjwZpzeufbtPND1v2jDIA/3XObJZmiyUSP\nVxMIq81A/XGy+YPs2+u+iJIKINq+d1bwVzKzJpxHxRcn+dVN37B8vRv22EVqlx6MVCytEo+uO+kZ\nsX8NM/pk88mLm9n8t60Rz/V4NKfb2wHNu/2zefQCa390gs3Ts0cGPdLTbV1vFVFSIfBXLBrdwZIJ\npyiCWT2B1TK3H6pk0+4PGTd0kCVZ3ODCjbrvJpqOfMaopTdZviYVXbR4KN6srNgXTQofvJn86RMi\nnwhs3LmPZ/7+Hj8ZPIyZZeVcfLc1iz4Yowb1o092fBerREmFIFCxWLVkglk9wZRM4P46q4XskkXN\n27s5tfMQNSW76T36XEvXpKKL5lbFm9EnmwFXW/sj1tSjjT0HP2TCzGlU7q1izpcmx1m62BAlFYJI\niiWa6wKVTKDCCVRswZRWIqp+hsOtX85Ek4qK1+2IkgpBJMVi9TorBHZ1CWaNBRtLVFdikC+nkDxc\nq6SUUoVAudY6zW8saKsrL9XV1Z3a5kTbGihehLOEXt22s0NXl3BW3KKZl/qUk+d0K+W/+AOQOoHp\nVCGa9k1u/tw6id2WVq5M5lRKzcfoGBNY39bb6moBsDLRcsVCuCTLwDpQwbLQ/duor/nV077g7bhl\nS3wuWMvRE+xZ/Bgtx6odlT0Z2d6SYS54SYolpZTKDXVMa12ntV6nlCoDygMO5wFHzJ9OBboLCgpc\n+xcoUmdkqy7i6re2cveHu/ndLV9hxqK5Hdw8O2kCVkhGKkFXT1+I5nPo5s+tk9h9j25sabXUrwuM\nCrguVKsr1xNrZ2QvXiV3zcxLyQhwG+2kCVghGUFzCdQLXlzZ0sr/VAC/llZLgbUYra4Wx03ABGA3\n9ymc1WUnTcAKyQiaS6Be8OLawLnW+giQbv77fr9DoVpduYpk5D51BesjkSuSQmrgysB5KpDIagTe\nCgvNaYqht37V1V/+eFczkIB76uFaS6qr4/RWlnCWmRv29lkl3tZeVw+4C51JGSWllJryta99Ldli\ndFAmXoXhzQ+JZQUnnCKKRSE6IVs0RBNrsiNbolzeRD+3aIiHbMl8vymjpNxCvKyaUIoo2dtl3IYE\n3FMPUVIOE6+KBYGBdq9yamlt467frwHc7+oJgh0kcO4wiWoh5W+xBXYtFoRUQiypLoq/xZYsN0/S\nCZyhfMtOfnfTvzg2X1XNMQDK8gcHPf5FbQML6uo53tg16uArrTvtLumyKKXeBQ4nW47uwuj0vmNH\nZvS9pKK9fuuhM/UHky1PF6BQa3154GA3/9wGfSb+pJSSEgQh9ZCYlCAIrkaUlCAIrkaUlCB0cZRS\nU5RS5Uqpg0qp2wOOFSqlPEqph5yY0xwvNcfvdeo9hJVFYlKC0LVRSr0IvA9sAsoCqtl6i0eWaa0f\niHVOpdQKjDpv64D1Wuuxjr2REKRMCoJSasqECRPKJk6cmGxROlFdbVTKLCiw198snohs9rAj2+rV\nq/8RaSXLJkGLQZqWzkpgrlNzYpTv3oBRJXejTXmjImWUFMDQoUN57rnnki1GJ7rbPi+nSDXZVq9e\nHa80g1DFIG8EcoHRQI1S6iG/gpJ251yGUcttjTnn7VHMaYuUUlKC0E15CENpLAaWeItEaq2LAJRS\nTwI1USqToHMCvwEexChA+XC8FRSIkhKELo/WegdhikFqrX/o8JwvRTtfLMjqniAIrialVvcWLVqk\n3RiTEgQApdRqrfWiZMvR1RBLShAEVyNKShAEVyNKShAEVyNKShAEVyMpCC7k9dLdbD9UFdMck0YO\n5dpLJ9m+vq26nk/+35sQw8KKSk9j6A+uJiOnl+05hNAopVYCtwOLtdZPm2P3AQ9j1GmqCDh/PjBK\na/2o3TmSgSgpF/LN3zzBqEH9yett78td19jMv372F1rX/V8y0tNtzVH15KtUrHwVz+C+tq4HSD9a\nh0pLY8SPr7c9hxCWizG2rCwBnjbH5mAkblYEOf8BYFaMcyQcUVIuxKM1//mDhVxdNMHW9Zt27mfO\nL/4zJhm0x0P7sHyOL/my7TkG/tdmThw/TuuBA7bnyMvLY8DAgbavT3GmAoeAqUqpyVrrD4AiYFvg\niUqp2cC2IBniludIFnFXUkqpN4EbMOJfL2qtr4r3PQV34PF4+LiqilPvvWd7jvMvuECUVBCUUlMx\nLKBlwHKMrSvLMTYGbwhyyTKM76HVOdbHT/roSIQltYOzmxSjqmnTFWloaqHscGzxpBTKrxXixxzz\n90ZgFUZcqcwcK/M/USk1BTgUxH0LN8cOh+W1TSKU1FSgxvz3tATcL6n8cMVqVr+1NaY5+mT1pHDw\nAIckSi6qpY202mbb1+sG+9emOEVArda6wgx+34ex6VdrrYsDzl1mHrc8BzBPKbUKyAe2A4Xm3GNN\n13EZhsW1FON7PRVDwU1x2ltKhJIq01rPA4i2OmBX5GRDI/909ZU8eusNkU8OQZpSZPbo+uFCT242\nOX8/SM7f7TeSacgupuXdC8k6t7+DkqUEczDjRlrrI0qpMs7Gl3yYVpQ2Y03RzKEx3MaVwHatdbpZ\nqXMWUA08j6GcFmutr1JKlWME4Uc6/UYT8U3YZsalCjA0r+XqgF2VjPQ0sjJ7JFuMpFNz8zTSa5ps\nX5/W3Er///s2rSfqREn5oZQahVEnyj9utBJYQed41AMY1k40c2wEFEZNqTrzN36/H8BQVKXAwoCp\nHc+9TISSWoZhLm7krA8sdAN0zwzaY0hhSGs87aA0qYPW+giQHjD2FPCU/5ipiEYFs6IizWHWj+p0\nmfl7K0bKwiggz/SQtmNYZS8CXc7dq8UQfgyGX2sZ01Rdg/FwlpsP0Tv+FMZfglVa60cclVgQUoOl\nBLGirKC1vt/vZT9zzBtTLgYe7XSRQahx2yRiW8xSjGLuBRjmZDQ8ADwJLAi4dgmGTzwPo3KgIAid\nGRUkiN7liLslpbXeZAbbVti4PKpi8NXV1b7a017cWB9bSH0CP4fJIFVyEhORzHmQs0E4TXRpCK4p\nBi8IQnJIRExKaa1D1l+OQFTF4AsKCsRyElyBfA6dIxFK6rBS6gzm8qXWup/VC91UDF4QhOSQCCV1\nsflTl4B7CYKQYjiqpJRSI4PsD6rBWI3zTwQTBEGwhNOW1GGlVA1GJup6jE2KazEC5srhewmC0A1w\nWkndj5FVPo2z/ec1cBjYaKdJoSAI3RtHkzm11su11vO01gUY/eeXYOQ4jUaSLgVBsEFcAudKqckY\nCmohRqmHMqLPNhcEQXA8cH4bhnKairGatwpYaW5mFARBiBqnLalVnI1BLcOIQ1U4fA9BELoRTm8w\nfgpjRW80hnt3SCl1Uin1plLqbofvJQhCN8BRS0prvcT7b7PI+xyMEqVzzJ/HnLyfIAipT9wyzrXW\nZfgVhDcLcAmCIERFwtqsS/BcEAQ7OL26dw9GpUwpmyI4xtb33oPPD0U+MQSTp0yh/4DU6L7THXHa\n3TsCFCultgHLZGUvubz30REy0u0Zy63VtXhc0gDw5MkTtGWfsX396dNSK70r43TgfB2wzuzLtVYp\ndQh4KEQ7HSFODOtfQO+snlzxgP3S77dVnuLSdkWWg3IJgh3iEjjXWm8CisyGCcuVUhpYKsoqMYw7\ndxBfPPsobe32rY/nv/dvnNlX4ZxQgmCTuNaTMovWzTOV1YPmCt9Sq8XhQ3WLMY8VAuUYbqWUfwmg\nV89M6Gn/+oz0NNqdE0cQbJOQ1T2t9Q6t9UKMvXwLzWahVgjVLQaCN0IUBCHFSETROx9mGsIdUUwZ\ntFuMUupeDKU11/9k6RYjuAU3dItJFeJZ9G4DUBZj369Q3WJuxGgMOhqjW8xDkvbQkY8//pjamhrb\n1zc1NSGN4gU3EPeid2bQ/DCwQWt9Z5TzBe0Wo7UuMud+EqiRbjGd+biqisOH7OcWNTU2kuuODIQu\niXwOncPpFITlwHLwbYOZg9F2ajRGi/WolFSEbjFIpc/QeD6rJePT2sgnhqBXs4TNBXcgRe9SkFN7\nK2j90e8Y6LFvCg0EtozoSx/nxBIEW0jROxey70f/Rf2Og7avP9NkZFgf+5evgrLX/+LPH37M3qZm\n7rcthSA4gxS9cyHH1pYw5ObZ9B43zPYcFZ4GPL3tu2xN2T3wtLTYvl4QnMJpJfUURiPQqZjunVKq\nFrPFldba1fWk3vvoMPMfXkn7GfuZ2jWNTUweZV+5eOn/tUvpP+di29d//M47EEPgXBDcghS98+Mf\nHx5Ga80j/2N+TPPMv3yqQxIJgiBF7wLI692LW2ZdlmwxBECb8bT81aXoHum25/liZx3nPvY/nRJL\nSDBx3bvnjwTPhWjRvTKp/t6lZBxvsD1Hj6N11L36HoiS6rIkTEkJgh1aJgyJ6frsso/pfcR+5r2Q\nfBJWPlgQBMEOoqQEQXA1KeXubdq5n/Pv/IXt66tPNXFOfq6DEgmCECsppaSO1dRzx4xp5PXuZXuO\nr0w4z0GJBEGIlZRSUgA/mDOdof3zky1GjGg2FxfTemy/7Rk8Ho+D8ghC8kg5JZUqaK1F0QgCKRQ4\nV0pNodWdrYt27Njh2kqNFZWVVFRWJluMoLhZNjf/n6YaKaOkBEFITURJCYLgaiQm5TAej4e6uroO\nY/X1Rvn1mhhqjgvJo+Yfe6n67csdxg5UHwUgrcBaRnxaT6kYb5eUUlK9zniof/VdPuvTO2kytLW1\nsW3r+x3GDn9hfKBPDHzb0hz5MVTUdJK6lja2VH6RtPuf+KwagE/Itj3H8BP1TGtpZe2vnrY9R+9N\nO0mvbaRlcqFv7DPzm5Nt8RvUe+N22/fv7iit3fGFcAKl1LsYBfcEwY0Uaq0vT7YQXY2UUlKCIKQe\nEjgXBMHViJISBMHVdGklpZSaopQqVUodNFuv+4+Xm+O3u1A2j1KqWim1LUmyzfGT7foA2ZL93MLJ\nlpTnppQqVEp5zH9PDfaM3PDsUpUuHZNSSq0AyoF1GI0exprjLwLvA5swWr0nXBmHke1ejI7MAMu0\n1vaXnezLth54EdgOrNFajzHH3fDcQsmWlOemlJqP0flolNY6PdQzcsOzS1W6egrCGmAD8ACw0W88\nDzhi/iRLC4eSbaM5rjC+iAlXUhhdpVeZMvjXnnfDcwslW1Kem9Z6nVKqDOMPDnR8Rv644dmlJF1d\n2y/D+OtaCCxQSvU1x2uBaRjt3ZNFKNmKML5oiuR+mBdiKAT/XuxueG4QXDa3PDf/Z6RDjAtOorXu\nsj/A9Rg9/Q4CDwEPA28CkzH+8p0EfuAy2WaZ48mUbYrf87neZc8tlGxJe24YFt2ZIPLd5qZnl6o/\nXTomJQhC6tPV3T1BEFIcUVKCILgaUVKCILgaUVKCILgaUVKCILgaUVJdCDP7OSVIpfcixBdRUl0E\npdQUzG0hfvvYro9wWdT3UEo9HOU185VS99i43TK/BFdBCIkoqa7DA8B9fq/jkeD2IBDt5tgHMLax\nRMvD5v0EISyipLoASqlRGBtcd/oP+x2/wdyB7zErCMz2O7bYrBxwUCm1wjxnZJB7vAjMB/KUUk9a\nlGs2sE1rXW++3uCtFuD3+qRSarZ533tNOSdrY4OwuHxCRERJdQ2WYmyt6YTpBr6IsWVkjjm8XinV\nVyk1FVgBrDfnWEhoC8xrQd2ntf6hRbmWmT9eVgDazw2dDaz0O36/KYu3xPNKKWsiREKUlMtRSuUC\ns7XWL4U45UYMxbNUa12MoYzAUFgL/Y69RHi3bK55rqWOl6ZyPKS1rvCOaa3XAXXAjWZgXAfc8z6t\n9Z1eywt4yk9eQQhKVy/V0h0oIHhziUCLSPn9VuZ1XmoCzgnGNABT0RknG4oIrXUwxbWMjjEyL6vM\ncYVRV6lCKeWtGNChvInWuk4pJZtHhbCIJeVytNZHAJRSkwMOeRXOC+a/l5kxomUYCuFFztZfWmu6\nVeFcq9lArXfFzSwytwRYErh6ZyovrbX+IMg8XvduPob7Fyiv/zzz6Vhry7taWBpGTqGbIUqqa7Cc\nzithGnxWzmJgKka85wwwR2tdr7XeZF57MYZ1s5HQlGIUbltovl5l/nsBnd3EBwjhpplKtcx8uSZQ\n3iDzLAsYmwskpayy4E6kVEsXwazrPdsvnmPlmikY1pC3HO9aYJbWOt3CtaM4W673Ba/VZI6/qLWe\nFuK6qeb9SrXWN4WZfzawWGt9Y8B4KfC/tNYvB79S6G6IkuoimCtml2it74/imlyM4PQcIBfDwrlP\na/33GORYgaGkikMc92AUf5vnH1QPct56U5YPAsY9QF40ylhIbSRw3kXQWr+klJob5TV1nHXfnGJU\nKAVl3tNqCKEmiIKajRlgV0oBPKS1ftS2pEJKIJaUIAiuRgLngiC4GlFSgiC4GlFSgiC4GlFSgiC4\nGlFSgiC4GlFSgiC4GlFSgiC4GlFSgiC4GlFSgiC4GlFSgiC4mv8PxMbvcEBkarsAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9fca2e6950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlm, ylm = (8.0,10.15), (-1.1,0.4)\n",
    "\n",
    "fig = figure(figsize=(3.3,3.3))\n",
    "ax11 = subplot2grid((14,14), (0,0), rowspan=10, colspan=10, ylabel=params[\"log_Z_L\"], xticklabels=[], xlim=xlm, ylim=ylm)\n",
    "ax21 = subplot2grid((14,14), (10,0), rowspan=4, colspan=10, xlabel=params[\"log_t_L\"], ylabel=r\"\\bf $N/N_\\text{max}$\", xlim=xlm, ylim=(0.0,1.2))\n",
    "ax12 = subplot2grid((14,14), (0,10), rowspan=10, colspan=4, xlabel=r\"\\bf $N/N_\\text{max}$\", yticklabels=[], xlim=(0.0,1.2), ylim=ylm)\n",
    "\n",
    "ax21.yaxis.set_major_locator(MaxNLocator(nbins=3, prune=\"both\"))\n",
    "ax12.xaxis.set_major_locator(MaxNLocator(nbins=3, prune=\"both\"))\n",
    "\n",
    "axs = array([ax11, ax21, ax12])\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(hspace=0.5, wspace=0.5)\n",
    "\n",
    "ax11.plot(t_mode, Z_mode, \"o\", mew=0.5, ms=3, mec=samples[\"medium\"], mfc=\"none\")\n",
    "ax11.plot(m16[mask][\"log_t_L_mod\"][:NG], m16[mask][\"log_Z_L_mod\"][:NG], \".\", mew=0, ms=3, color=samples[\"cold\"])\n",
    "ax11.plot(g05[mask][\"log_t_L_mod\"][:NG], g05[mask][\"log_Z_L_mod\"][:NG], \".\", mew=0, ms=3, color=samples[\"hot\"])\n",
    "\n",
    "# sample_data = densitycontour.ScatterData(t_mode, Z_mode)\n",
    "# raster = sample_data.rasterize(nbins_x=10, nbins_y=10, clipping=[last(10,xlm),last(10,ylm)])\n",
    "# raster.pdf = gaussian_filter(raster.pdf, sigma=1.5)\n",
    "# contours = densitycontour.NaiveContourVisualizer(raster, mode=\"nearest\")\n",
    "# cont = contours.plot((0.99,0.95,0.68), axes=ax11, linewidths=1.0, colors=samples[\"medium\"])\n",
    "# contours.cilabel(cont, fontsize=5, fmt=r\"\\bf %.0f\", inline=True)\n",
    "\n",
    "ax11.text(0.02, 0.92, r\"\\bf This paper (HR$^*$)\", color=samples[\"cold\"], size=\"x-small\", ha=\"left\", va=\"top\", transform=ax11.transAxes)\n",
    "ax11.text(0.02, 0.97, r\"\\bf G05\", color=samples[\"hot\"], size=\"x-small\", ha=\"left\", va=\"top\", transform=ax11.transAxes)\n",
    "\n",
    "ax21.hist(t_mode, 10, xlm, weights=1.0/histogram(t_mode,10,xlm)[0].max().repeat(t_mode.size), histtype=\"stepfilled\", color=samples[\"medium\"], lw=0)\n",
    "ax21.hist(m16[mask][\"log_t_L_mod\"][:NG], 10, xlm, weights=1.0/histogram(m16[mask][\"log_t_L_mod\"][:NG],10,xlm)[0].max().repeat(m16[mask][\"log_t_L_mod\"][:NG].size), histtype=\"step\", color=samples[\"cold\"], lw=1)\n",
    "ax21.hist(g05[mask][\"log_t_L_mod\"][:NG], 10, xlm, weights=1.0/histogram(g05[mask][\"log_t_L_mod\"][:NG],10,xlm)[0].max().repeat(g05[mask][\"log_t_L_mod\"][:NG].size), histtype=\"step\", color=samples[\"hot\"], lw=1)\n",
    "\n",
    "ax12.hist(Z_mode, 10, ylm, weights=1.0/histogram(Z_mode,10,ylm)[0].max().repeat(Z_mode.size), histtype=\"stepfilled\", color=samples[\"medium\"], orientation=\"horizontal\", lw=0)\n",
    "ax12.hist(m16[mask][\"log_Z_L_mod\"][:NG], 10, ylm, weights=1.0/histogram(m16[mask][\"log_Z_L_mod\"][:NG],10,ylm)[0].max().repeat(m16[mask][\"log_Z_L_mod\"][:NG].size), histtype=\"step\", color=samples[\"cold\"], orientation=\"horizontal\", lw=1)\n",
    "ax12.hist(g05[mask][\"log_Z_L_mod\"][:NG], 10, ylm, weights=1.0/histogram(g05[mask][\"log_Z_L_mod\"][:NG],10,ylm)[0].max().repeat(g05[mask][\"log_Z_L_mod\"][:NG].size), histtype=\"step\", color=samples[\"hot\"], orientation=\"horizontal\", lw=1)\n",
    "\n",
    "#fig.savefig(\"{}g05_posterior\".format(odir))"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}