halotools-pipe.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primary goal is to define a function by which we give it a file name and mass threshold and the entirety of the calculation in terms of CFs, MCFs, and various cuts are output as a single data vector. We will start by assuming a value of fname and mthresh and go from there, and see what needs changing.\n",
    "\n",
    "** WARNING! NOT AS UP TO DATE AS THE PYTHON SCRIPT! BEWARE! **\n",
    "==============================================================\n",
    "** DOES GENERATE USEFUL PLOTS THOUGH!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# required imports for the function here\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "from numpy.lib.recfunctions import append_fields\n",
    "import matplotlib\n",
    "from matplotlib.colors import LogNorm\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import halotools.mock_observables as mo\n",
    "import halotools.sim_manager as sm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "...Processing ASCII data of file: \n",
      "/data/asv13/projects/haloenviron-pipeline/l0250_d200b.catalog\n",
      " \n",
      "Total number of rows in detected data = 2501871\n",
      "Number of rows in detected header = 16 \n",
      "\n",
      "... working on chunk 0 of 1\n",
      "Total runtime to read in ASCII = 20.78 seconds\n",
      "\n",
      "\u0007\n",
      "\n",
      "...Processing ASCII data of file: \n",
      "/data/asv13/projects/haloenviron-pipeline/l0250_d200b.catalog\n",
      " \n",
      "Total number of rows in detected data = 2501871\n",
      "Number of rows in detected header = 16 \n",
      "\n",
      "... working on chunk 0 of 1\n",
      "Total runtime to read in ASCII = 19.80 seconds\n",
      "\n",
      "\u0007\n",
      "Complete\n"
     ]
    }
   ],
   "source": [
    "fname = './l0250_d200b.catalog' # file name to z0.0.catalog\n",
    "mthresh = 7e11  # threshold mass in Msun/h\n",
    "gnewton = 4.302e-6\n",
    "lbox = 250.0\n",
    "vhost_min = 240.0\n",
    "vrat_frac = 0.3\n",
    "vsub_min = vhost_min * vrat_frac\n",
    "nrand = 100\n",
    "\n",
    "# define dict pointing to all marks of interest\n",
    "rs_dict = {'halo_id':(0,'i8'), 'halo_mass':(2,'f8'), 'halo_vmax':(3,'f8'), 'halo_rvir':(5,'f8'),\n",
    "           'halo_rs':(6,'f8'), 'halo_x':(8,'f8'), 'halo_y':(9,'f8'), 'halo_z':(10,'f8'),\n",
    "           'halo_spin':(17,'f8'), 'halo_ctoa':(28, 'f8'), 'halo_pid':(41,'i8')}\n",
    "\n",
    "reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_mass':mthresh},\n",
    "                               row_cut_eq_dict={'halo_pid':-1})\n",
    "hosts_data = reader.read_ascii()\n",
    "\n",
    "reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_vmax':vsub_min}, row_cut_neq_dict={'halo_pid':-1})\n",
    "subs_data = reader.read_ascii()\n",
    "\n",
    "# add calculated marks\n",
    "vratio_temp = hosts_data['halo_vmax']/(np.sqrt(gnewton * hosts_data['halo_mass']/hosts_data['halo_rvir']))\n",
    "cnfw_temp = hosts_data['halo_rvir']/hosts_data['halo_rs']\n",
    "\n",
    "# now we want to go host halo by host halo, match pid to subhalos, and\n",
    "# count the number that meet our requirements and then add this on\n",
    "# as both a number and a flag.\n",
    "nflagtemp = np.zeros(len(hosts_data))\n",
    "subcount = np.zeros(len(hosts_data))\n",
    "\n",
    "for i in range(0, len(hosts_data)):\n",
    "   if hosts_data[i]['halo_vmax'] > vhost_min:\n",
    "      nflagtemp[i] = 1\n",
    "      subs_cut = subs_data[np.where(subs_data['halo_pid'] == hosts_data[i]['halo_id'])]\n",
    "      for j in range(0, len(subs_cut)):\n",
    "         xdist = subs_cut[j]['halo_x'] - hosts_data[i]['halo_x']\n",
    "         ydist = subs_cut[j]['halo_y'] - hosts_data[i]['halo_y']\n",
    "         zdist = subs_cut[j]['halo_z'] - hosts_data[i]['halo_z']\n",
    "         totdist = (xdist**2 + ydist**2 + zdist**2)*0.001\n",
    "         if totdist < hosts_data[i]['halo_rvir']:\n",
    "            ratio = subs_cut[j]['halo_vmax']/hosts_data[i]['halo_vmax']\n",
    "            if ratio > vrat_frac:\n",
    "               subcount[i] = subcount[i] + 1\n",
    "\n",
    "hosts_data_alt =  append_fields(hosts_data, ('halo_cV', 'halo_cNFW', 'halo_satflag', 'halo_nsat'), (vratio_temp, cnfw_temp, nflagtemp, subcount))\n",
    "\n",
    "# now that we have data, we need to mass correct all of our marks\n",
    "mass_sort = np.sort(hosts_data_alt, order='halo_mass')\n",
    "\n",
    "vratio_binned = stats.binned_statistic(np.log10(mass_sort['halo_mass']),\n",
    "                                       (mass_sort['halo_cV']),\n",
    "                                       statistic='mean', bins=10)\n",
    "vratio_fix = (mass_sort['halo_cV']) / vratio_binned.statistic[vratio_binned.binnumber-1]\n",
    "mass_sort['halo_cV'] = vratio_fix\n",
    "\n",
    "cnfw_binned = stats.binned_statistic(np.log10(mass_sort['halo_mass']),\n",
    "                                     (mass_sort['halo_cNFW']),\n",
    "                                     statistic = 'mean', bins=10)\n",
    "cnfw_fix = (mass_sort['halo_cNFW']) / cnfw_binned.statistic[cnfw_binned.binnumber-1]\n",
    "mass_sort['halo_cNFW'] = cnfw_fix\n",
    "\n",
    "shape_binned = stats.binned_statistic(np.log10(mass_sort['halo_mass']),\n",
    "                                      (mass_sort['halo_ctoa']),\n",
    "                                      statistic='mean', bins=10)\n",
    "shape_fix = (mass_sort['halo_ctoa']) / shape_binned.statistic[shape_binned.binnumber-1]\n",
    "mass_sort['halo_ctoa'] = shape_fix\n",
    "\n",
    "spin_binned = stats.binned_statistic(np.log10(mass_sort['halo_mass']),\n",
    "                                     (mass_sort['halo_spin']),\n",
    "                                     statistic='mean', bins=10)\n",
    "spin_fix = (mass_sort['halo_spin']) / spin_binned.statistic[spin_binned.binnumber-1]\n",
    "mass_sort['halo_spin'] = spin_fix\n",
    "\n",
    "# now all our marks have been fixed. First let's run through the marked correlation functions\n",
    "# then we can take various correlation function comparisons\n",
    "\n",
    "pos = np.vstack((mass_sort['halo_x'], mass_sort['halo_y'], mass_sort['halo_z'])).T\n",
    "\n",
    "minlog = np.log10(2.0) #was previously set to 2.0, trying to avoid binning issue\n",
    "maxlog = np.log10(30.0)\n",
    "nstep = 10\n",
    "steplog = (maxlog - minlog) / (nstep - 1.)\n",
    "logbins = np.arange(minlog, maxlog+2*steplog, steplog) # cludgy fix to arange sometimes making binning different\n",
    "temp = logbins\n",
    "logbins = temp[0:nstep+1]\n",
    "binmids = np.zeros(nstep)\n",
    "for i in range(0,nstep):\n",
    "    binmids[i] = (logbins[i]+logbins[i+1])/2.\n",
    "\n",
    "xi = mo.tpcf(pos, 10**logbins, period=lbox)\n",
    "\n",
    "mcf_vratio = mo.marked_tpcf(pos, 10**logbins, marks1=mass_sort['halo_cV'], period=lbox,\n",
    "                            normalize_by='number_counts', weight_func_id=1)\n",
    "mcfn_vratio = (mcf_vratio - np.mean(mass_sort['halo_cV'])**2)/(np.var(mass_sort['halo_cV']))\n",
    "\n",
    "mcf_cnfw = mo.marked_tpcf(pos, 10**logbins, marks1=mass_sort['halo_cNFW'], period=lbox,\n",
    "                          normalize_by='number_counts', weight_func_id=1)\n",
    "mcfn_cnfw = (mcf_cnfw - np.mean(mass_sort['halo_cNFW'])**2)/(np.var(mass_sort['halo_cNFW']))\n",
    "\n",
    "mcf_ctoa = mo.marked_tpcf(pos, 10**logbins, marks1=mass_sort['halo_ctoa'], period=lbox,\n",
    "                          normalize_by='number_counts', weight_func_id=1)\n",
    "mcfn_ctoa = (mcf_ctoa - np.mean(mass_sort['halo_ctoa'])**2)/(np.var(mass_sort['halo_ctoa']))\n",
    "\n",
    "mcf_spin = mo.marked_tpcf(pos, 10**logbins, marks1=mass_sort['halo_spin'], period=lbox,\n",
    "                          normalize_by='number_counts', weight_func_id=1)\n",
    "mcfn_spin = (mcf_spin - np.mean(mass_sort['halo_spin'])**2)/(np.var(mass_sort['halo_spin']))\n",
    "\n",
    "mass_sort_satflag = mass_sort[np.where(mass_sort['halo_satflag']==1)]\n",
    "mcf_nsat = mo.marked_tpcf(pos[np.where(mass_sort['halo_satflag']==1)], 10**logbins, \n",
    "                          marks1=mass_sort_satflag['halo_nsat'], period=lbox, normalize_by='number_counts', weight_func_id=1)\n",
    "\n",
    "mcfn_nsat = (mcf_nsat - np.mean(mass_sort_satflag['halo_nsat'])**2)/(np.var(mass_sort_satflag['halo_nsat']))\n",
    "\n",
    "# how we'll need to shuffle the marks N times, run the calculation N times,\n",
    "# and determine the min and max range of the mark calculation. So:\n",
    "mcf_vratio_rand = np.zeros((nstep, nrand))\n",
    "mcfn_vratio_rand = np.zeros((nstep, nrand))\n",
    "mcf_cnfw_rand = np.zeros((nstep, nrand))\n",
    "mcfn_cnfw_rand = np.zeros((nstep, nrand))\n",
    "mcf_ctoa_rand = np.zeros((nstep, nrand))\n",
    "mcfn_ctoa_rand = np.zeros((nstep, nrand))\n",
    "mcf_spin_rand = np.zeros((nstep, nrand))\n",
    "mcfn_spin_rand = np.zeros((nstep, nrand))\n",
    "mcf_nsat_rand = np.zeros((nstep, nrand))\n",
    "mcfn_nsat_rand = np.zeros((nstep, nrand))\n",
    "\n",
    "for i in range(0, nrand):\n",
    "    randm_vratio = np.random.permutation(mass_sort['halo_cV'])\n",
    "    randm_cnfw = np.random.permutation(mass_sort['halo_cNFW'])\n",
    "    randm_ctoa = np.random.permutation(mass_sort['halo_ctoa'])\n",
    "    randm_spin = np.random.permutation(mass_sort['halo_spin'])\n",
    "    randm_nsat = np.random.permutation(mass_sort_satflag['halo_nsat'])\n",
    "\n",
    "    mcf_vratio_rand[:,i] = mo.marked_tpcf(pos, 10**logbins, marks1=randm_vratio, period=lbox,\n",
    "                                      normalize_by='number_counts', weight_func_id=1)\n",
    "    mcfn_vratio_rand[:,i] = (mcf_vratio_rand[:,i] - np.mean(randm_vratio)**2)/(np.var(randm_vratio))\n",
    "    mcf_cnfw_rand[:,i] = mo.marked_tpcf(pos, 10**logbins, marks1=randm_cnfw, period=lbox,\n",
    "                                        normalize_by='number_counts', weight_func_id=1)\n",
    "    mcfn_cnfw_rand[:,i] = (mcf_cnfw_rand[:,i] - np.mean(randm_cnfw)**2)/(np.var(randm_cnfw))\n",
    "    mcf_ctoa_rand[:,i] = mo.marked_tpcf(pos, 10**logbins, marks1=randm_ctoa, period=lbox,\n",
    "                                        normalize_by='number_counts', weight_func_id=1)\n",
    "    mcfn_ctoa_rand[:,i] = (mcf_ctoa_rand[:,i] - np.mean(randm_ctoa)**2)/(np.var(randm_ctoa))\n",
    "    mcf_spin_rand[:,i] = mo.marked_tpcf(pos, 10**logbins, marks1=randm_spin, period=lbox,\n",
    "                                        normalize_by='number_counts', weight_func_id=1)\n",
    "    mcfn_spin_rand[:,i] = (mcf_spin_rand[:,i] - np.mean(randm_spin)**2)/(np.var(randm_spin))\n",
    "    mcf_nsat_rand[:,i] = mo.marked_tpcf(pos[np.where(mass_sort['halo_satflag']==1)], 10**logbins, \n",
    "                                        marks1=randm_nsat, period=lbox, normalize_by='number_counts', weight_func_id=1)\n",
    "    mcfn_nsat_rand[:,i] = (mcf_nsat_rand[:,i] - np.mean(randm_nsat)**2)/(np.var(randm_nsat))\n",
    "\n",
    "mcfn_vratio_min = np.zeros(nstep)\n",
    "mcfn_vratio_max = np.zeros(nstep)\n",
    "mcfn_cnfw_min = np.zeros(nstep)\n",
    "mcfn_cnfw_max = np.zeros(nstep)\n",
    "mcfn_ctoa_min = np.zeros(nstep)\n",
    "mcfn_ctoa_max = np.zeros(nstep)\n",
    "mcfn_spin_min = np.zeros(nstep)\n",
    "mcfn_spin_max = np.zeros(nstep)\n",
    "mcfn_nsat_min = np.zeros(nstep)\n",
    "mcfn_nsat_max = np.zeros(nstep)\n",
    "\n",
    "for i in range(0, nstep):\n",
    "    mcfn_vratio_min[i] = np.percentile(mcfn_vratio_rand[i,:],2, interpolation='nearest')\n",
    "    mcfn_vratio_max[i] = np.percentile(mcfn_vratio_rand[i,:],98,interpolation='nearest')\n",
    "    mcfn_cnfw_min[i] = np.percentile(mcfn_cnfw_rand[i,:],2, interpolation='nearest')\n",
    "    mcfn_cnfw_max[i] = np.percentile(mcfn_cnfw_rand[i,:],98, interpolation='nearest')\n",
    "    mcfn_ctoa_min[i] = np.percentile(mcfn_ctoa_rand[i,:],2, interpolation='nearest')\n",
    "    mcfn_ctoa_max[i] = np.percentile(mcfn_ctoa_rand[i,:],98, interpolation='nearest')\n",
    "    mcfn_spin_min[i] = np.percentile(mcfn_spin_rand[i,:],2, interpolation='nearest')\n",
    "    mcfn_spin_max[i] = np.percentile(mcfn_spin_rand[i,:],98, interpolation='nearest')\n",
    "    mcfn_nsat_min[i] = np.percentile(mcfn_nsat_rand[i,:],2, interpolation='nearest')\n",
    "    mcfn_nsat_max[i] = np.percentile(mcfn_nsat_rand[i,:],98, interpolation='nearest')\n",
    "    \n",
    "print 'Complete'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100159\n"
     ]
    }
   ],
   "source": [
    "cnfw_sort = np.sort(mass_sort, order='halo_spin')\n",
    "print len(cnfw_sort)\n",
    "\n",
    "lowlim=int(np.floor(.2*len(cnfw_sort)))\n",
    "highlim=int(np.floor(.8*len(cnfw_sort)))\n",
    "low_cnfw_sort = cnfw_sort[0:lowlim]\n",
    "high_cnfw_sort = cnfw_sort[highlim:-1]\n",
    "lowpos = np.vstack((low_cnfw_sort['halo_x'], low_cnfw_sort['halo_y'], low_cnfw_sort['halo_z'])).T\n",
    "highpos = np.vstack((high_cnfw_sort['halo_x'], high_cnfw_sort['halo_y'], high_cnfw_sort['halo_z'])).T\n",
    "# originally set to len(lowpos)*3. Now cranking this up to see if this fixes issues.\n",
    "x_rand = lbox * np.random.random(len(lowpos)*200)\n",
    "y_rand = lbox * np.random.random(len(lowpos)*200)\n",
    "z_rand = lbox * np.random.random(len(lowpos)*200)\n",
    "randpos = np.vstack((x_rand,y_rand,z_rand)).T\n",
    "Nsub = np.array([2,2,2])\n",
    "xi_low, cov_low = mo.tpcf_jackknife(lowpos, randoms=randpos, rbins=10**logbins, Nsub=Nsub, period=lbox)\n",
    "xi_high, cov_high = mo.tpcf_jackknife(highpos, randoms=randpos, rbins=10**logbins, Nsub=Nsub, period=lbox)\n",
    "error_low = np.sqrt(1./np.diag(cov_low))\n",
    "error_high = np.sqrt(1./np.diag(cov_high))\n",
    "yerr_low_low = np.maximum(1e-2, xi_low - error_low)\n",
    "yerr_high_low = xi_low + error_low\n",
    "yerr_low_high = np.maximum(1e-2, xi_high - error_high)\n",
    "yerr_high_high = xi_high + error_high\n",
    "\n",
    "xi_comp_20p = (xi_high - xi_low) / xi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lowlim=int(np.floor(.5*len(cnfw_sort)))\n",
    "highlim=int(np.floor(.5*len(cnfw_sort)))\n",
    "low_cnfw_sort = cnfw_sort[0:lowlim]\n",
    "high_cnfw_sort = cnfw_sort[highlim:-1]\n",
    "lowpos = np.vstack((low_cnfw_sort['halo_x'], low_cnfw_sort['halo_y'], low_cnfw_sort['halo_z'])).T\n",
    "highpos = np.vstack((high_cnfw_sort['halo_x'], high_cnfw_sort['halo_y'], high_cnfw_sort['halo_z'])).T\n",
    "x_rand = lbox * np.random.random(len(lowpos)*100)\n",
    "y_rand = lbox * np.random.random(len(lowpos)*100)\n",
    "z_rand = lbox * np.random.random(len(lowpos)*100)\n",
    "randpos = np.vstack((x_rand,y_rand,z_rand)).T\n",
    "Nsub = np.array([2,2,2])\n",
    "xi_low, cov_low = mo.tpcf_jackknife(lowpos, randoms=randpos, rbins=10**logbins, Nsub=Nsub, period=lbox)\n",
    "xi_high, cov_high = mo.tpcf_jackknife(highpos, randoms=randpos, rbins=10**logbins, Nsub=Nsub, period=lbox)\n",
    "error_low = np.sqrt(1./np.diag(cov_low))\n",
    "error_high = np.sqrt(1./np.diag(cov_high))\n",
    "yerr_low_low = np.maximum(1e-2, xi_low - error_low)\n",
    "yerr_high_low = xi_low + error_low\n",
    "yerr_low_high = np.maximum(1e-2, xi_high - error_high)\n",
    "yerr_high_high = xi_high + error_high\n",
    "\n",
    "xi_comp_50p = (xi_high - xi_low) / xi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lowlim=int(np.floor(.02*len(cnfw_sort)))\n",
    "highlim=int(np.floor(.98*len(cnfw_sort)))\n",
    "low_cnfw_sort = cnfw_sort[0:lowlim]\n",
    "high_cnfw_sort = cnfw_sort[highlim:-1]\n",
    "lowpos = np.vstack((low_cnfw_sort['halo_x'], low_cnfw_sort['halo_y'], low_cnfw_sort['halo_z'])).T\n",
    "highpos = np.vstack((high_cnfw_sort['halo_x'], high_cnfw_sort['halo_y'], high_cnfw_sort['halo_z'])).T\n",
    "x_rand = lbox * np.random.random(len(lowpos)*100)\n",
    "y_rand = lbox * np.random.random(len(lowpos)*100)\n",
    "z_rand = lbox * np.random.random(len(lowpos)*100)\n",
    "randpos = np.vstack((x_rand,y_rand,z_rand)).T\n",
    "Nsub = np.array([2,2,2])\n",
    "xi_low, cov_low = mo.tpcf_jackknife(lowpos, randoms=randpos, rbins=10**logbins, Nsub=Nsub, period=lbox)\n",
    "xi_high, cov_high = mo.tpcf_jackknife(highpos, randoms=randpos, rbins=10**logbins, Nsub=Nsub, period=lbox)\n",
    "error_low = np.sqrt(1./np.diag(cov_low))\n",
    "error_high = np.sqrt(1./np.diag(cov_high))\n",
    "yerr_low_low = np.maximum(1e-2, xi_low - error_low)\n",
    "yerr_high_low = xi_low + error_low\n",
    "yerr_low_high = np.maximum(1e-2, xi_high - error_high)\n",
    "yerr_high_high = xi_high + error_high\n",
    "\n",
    "xi_comp_2p = (xi_high - xi_low) / xi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lowlim=int(np.floor(.10*len(cnfw_sort)))\n",
    "highlim=int(np.floor(.90*len(cnfw_sort)))\n",
    "low_cnfw_sort = cnfw_sort[0:lowlim]\n",
    "high_cnfw_sort = cnfw_sort[highlim:-1]\n",
    "lowpos = np.vstack((low_cnfw_sort['halo_x'], low_cnfw_sort['halo_y'], low_cnfw_sort['halo_z'])).T\n",
    "highpos = np.vstack((high_cnfw_sort['halo_x'], high_cnfw_sort['halo_y'], high_cnfw_sort['halo_z'])).T\n",
    "x_rand = lbox * np.random.random(len(lowpos)*100)\n",
    "y_rand = lbox * np.random.random(len(lowpos)*100)\n",
    "z_rand = lbox * np.random.random(len(lowpos)*100)\n",
    "randpos = np.vstack((x_rand,y_rand,z_rand)).T\n",
    "Nsub = np.array([2,2,2])\n",
    "xi_low, cov_low = mo.tpcf_jackknife(lowpos, randoms=randpos, rbins=10**logbins, Nsub=Nsub, period=lbox)\n",
    "xi_high, cov_high = mo.tpcf_jackknife(highpos, randoms=randpos, rbins=10**logbins, Nsub=Nsub, period=lbox)\n",
    "error_low = np.sqrt(1./np.diag(cov_low))\n",
    "error_high = np.sqrt(1./np.diag(cov_high))\n",
    "yerr_low_low = np.maximum(1e-2, xi_low - error_low)\n",
    "yerr_high_low = xi_low + error_low\n",
    "yerr_low_high = np.maximum(1e-2, xi_high - error_high)\n",
    "yerr_high_high = xi_high + error_high\n",
    "\n",
    "xi_comp_10p = (xi_high - xi_low) / xi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fca4b7289d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w3MyaCR7gmgZ8LtSoREQkNKkO2VAMfDi+qiEbRETGgVH36skFSvwiIiOXTq+e0JjZUWb2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\nv4hInvn/KKIekFH6JtYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcab009f090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogx(10**binmids, xi_comp_2p, 'b-', label='2p')\n",
    "plt.semilogx(10**binmids, xi_comp_10p, 'k-', label='10p')\n",
    "plt.semilogx(10**binmids, xi_comp_20p, 'r-', label='20p')\n",
    "plt.semilogx(10**binmids, xi_comp_50p, 'g-', label='50p')\n",
    "plt.xlim(4,18)\n",
    "plt.ylim(-1,5)\n",
    "plt.xlabel('r')\n",
    "plt.ylabel('concentration comparison')\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcab1988050>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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E1ASUgeqZwJFCwZtnojb+5DDP6NhiEe69Fz72sfFNRStX+vbu3fGMYRgbbEZG/L2tWz0b\nqZqDROYX9QE0uWQNoFDwjt5kAZ9s4+/qgqEhuOKK8QX7wIDnETr//Lh/4N574RWviDuFk8GmvR1u\nusnnFUw0t0BEWpsCQAs4eBDWrYMf/Qhe8xp41av8iTx6Oo8K6O7ueJjowIC3/yef3KfKE5T2fn+/\n8viIzFcKAC1g2zY491wf6gleEH/mM3DppV5Yn3dePB9g+XJ44gnfXrvWawQ9Pd5R3NnpweSee3yF\nsbTVvqqHijZjcjkRaQwFgCZXLntn79DQ2P1tbd6m/+KLY2cKt7d7IX3kCLzlLfH+bdt8BvFE6SCm\nGhKq+QMi84+GgTa5/n7Ys2f8/tFRDwrJwr+tzQvptFm+AwNjU0cMDMTpIKYa9tnZ6c0+tRT+c5Vw\nTkTmlgJABlavjkfsJLVV3e1ly+CBB+Kn+jPP9CabQgEWLICrroKPfMRrCOC/o1QRjcoTpPkDIvml\nAJCBzk5vv7/tNm+zLxS8s3fjRv/d3u7t/g8/7BlCk0/pbW3eb/DrX3sfwdNPxyOIRkZ8eUkYmydo\nxQpvPppO4a2EcyL5pT6AjESjgKLmnuScgO5u+MEPxnfoTjR/YMECbz5KduiWy9489OKL8IlP+DyB\n6Qz5VIexSGtRJ3CTK5fh9NPhmWcmPubuu+G1rx3bgZssjBcv9qf9KFncTTfBxRePn0DW3Q379nnt\nILnuQL3Xqw5jkdagANDktm0bO5onzbJlXsBXP7UfPAi33AI33ji2uSc5JPT66+Haa73QLxZhyZI4\n2CSPFZH5p5EBQLmAMvCrX43fVyjE4/6XLPHCPRrZUyp5beA1r/FZv8maQ3s7fPaz3la/YAH83u/B\nSy/F761aBddcAxde6OfbvVt5gESkNqoBZGCqGsDixR4MDh3y18WiN/UUCnHhHlm2zBPBDQ2B2dj+\ngeOPhyef9Kd9teOL5EPTNAGZ2X7gF8Ao8HIIYY2ZHQd8E+gC9gMXhRB+kfLZeRsAymVviomacKZr\n2TLPIBo93VeLJpCddVZts4W1VoBI62umiWCjQF8I4XUhhMqqt1wNbAshrAC+C2yc4Xe0pGOOqf8z\nHR3xkNEvfMEL9ze/2fsJqucQJCeQlcueW+jyy/139XDQ5Fj/tWu9hqLx/iIy0wBgKefYANxc2b4Z\nePsMv6Pl7NjhwzLr0d7uw0Dvvx9e+UqfM7BuHfz4x75u8J13en6gQsF/JyeQTTSWP5rhu2NH/H5/\nv+ch0qQvEZlpJ3AAHjSzEeBLIYSvAgtDCIcBQgiHzOyEmV5kq0nrBJ7K6Kinj3jsMS+kAfbuhdNO\n8/d6e/3JPRo5lGzGiSaFRX0AUa0gGiq6cqX/7NrlQWBkRIvGiMjMA8AbQwg/NrPjgQfMbDceFJIm\nbOjfvHnz0e2+vj76+vpmeDmta3QULrlk/P6oU3hgwDuC08b4R0tNJsfyb9vmgWRkxEcG3XuvH5tc\ndyAt/5CINJdSqUSpVMrk3A0bBWRmm4AjwPvxfoHDZrYI+F4IoSfl+HnbCXzbbfCudzXmXMUivPyy\nb/f0wA03eM6gtI7cqKO3ejhpT483A0WTyDTpS6R1NUUnsJkdY2avrGy/AjgXeAq4G3hv5bD3AHfN\n8BpbzkxH/yQlR/8MDXnBXr1QfLT4fNTRe9ppY+cS/PrX8XY9WUKrKWuoyPwykyaghcC3zCxUznNL\nCOEBM/u/wO1m9t+BZ4GLGnCdLeWEBvZ6VFeSkh29vb0+qmfXrnglsWiyWdJzz41v749qC11dta0d\nnOxT0DKTIvPDtANACGEfcHrK/p8Bvz+Ti2p1//mf2Z27UPCZxEuXxmsFgD/xL1vmhX00oay93fsW\njj3Waw/VK4ZFC8kPD3sn8Wc+48NX05qY0kYaqQNZpLVpJnAGDh70AjrtabxRVq+GT35ybF/Dli1w\n6qnwy1/6wvEdHd4kFFm6FB580FNPXH75xNeXlk+oXI5rGytXKt+QyFxRLqAmd+KJ8MEPeoGclaEh\nf9pP+vCH4ZRTfPhodzdUD6o6cCAeVlos+r6oBpDsa9i1a+In/Hkas0VySTWADDz9tC/SkrXly73p\nZ3Q0/f329vQUEuAF/5YtPnN4aMhrBE8/7e+l1QCSaxUUi7B9u5qAROaCagBN7i//Mrtzm8VP4Xv3\nTn5sWuEfPfl3dXnhf+KJXtAXCn7uri6fjVzdvJM22UxEWpuWhMzAT36S3bnrqTS1p4T3hQs9G+m+\nfXHeoB07vBYQgq9gNjQ0/nPRZLPt2zUCSGS+UA0gA1mOAqpHWg3g+efj7cFB2LnTfydNlMoimkMg\nIvOD+gAysH69J2trJmn9AcWidxo//bQ3AUUTxrSqmEjzaoqZwDKx5FP2XDPzzuIdOzxFRNLLL3tz\nT5QgrlDw/dGqYhPRjGCR+UEBIANZ9gHUKlo/oKsLHnrIn/SPP378cVEl7NRTPWdQsTh5J29ybYG0\nlBQKCiKtQwEgA0eOzPUVxEND9++Hxx/3mbyTjRr61Ke82Sfq5IX0Ar16RvDOnZ55dO3a8UFBRJqb\n+gAycMIJzVELiJxxhrfrf+c78LOfpR+zYAHcfju8/vVesF95pU8I6+72GkS0zGRUAxgcjOc6ROsM\nwPTmCNS6XKWWtRRpojWBZ/TF8zgAtLW15oxZM+8sjtJPR5Yt88Xny2UPIq9/vdcqikV43/vi2kah\n4IXz1q21JZiD2pPMKRmdiNNEsCZXLMYLubSSEMYX/uCzja+/Hj79aR8pZOZBLplLaOVKuPFG70e4\n4ILaC+pak8wpGZ1I46kPIAOtWPhPZdOmeJhoCOMTyV16qa9WFhXO0frDO3fGx6R1FEczjItFb1I6\nciS9DyF5nGYiizSGAoDUJJlvyMx/khYs8IL7yivj/oCREXj/+z07atrooahNf+vWeMnK6gVvIrM9\nE1mjmiQPFABkSh0dY19fey3cdZcX+uC/N2yAr399fBqJ/fu9QN+xY/zooSggXHCB1yqizuTBQV9W\n8+DBsYXwTFYzq8dEQ11F5hsFAJlSdZPWpz7l6SIefBAuvBA+9CEvxD/60fQ1Bg4c8EL91FO9k3nF\nCi/wk01FL74YN/GYeTrtZcu8AF67dnYL4bT+BpH5SKOAMlDdPJJHxx4Lv/iFby9Y4EGkrc2bknp7\nPePo+vXximarV8Mdd8Cb3+xP/tW2bfM+htmQHOq6apVGHElzUSoIaXpR4W/mncdRx3EIXug//DC8\n+91xsNy9G+67L73wB/jpT8fvy6qdXplPJS9UA8iAagD1S1viMqmjw1NYV09Ia+Z5AZq4JllQDUDm\nnd/9XfizP5v4/Zde8iaiSFpKikcfHdtxHNUQqjuTq2VRk1BHsrQC1QAyoBpANn7nd+CLX4xTW194\noae2WLwY/uu/4N//3fsbRkZg6VLf3rMnnt2cnKXc1RX/jiauLVoE//Iv3lk906f36S6hqVqDTEWp\nIJqcAkBzam+Hk07yUUmFghfOv/VbcPhwPHqpowOeegouumj6zUvRKmtXXul9G2kdyWkFfSs0a8nc\nUxOQyDS86lU+L2F01GsEIXjzUHLo6ksvwQc+AP/2bx4gBgbg7/7ORyGVy378l7/si+gkm42SzU1n\nn+0T2sAnuCUL8nJ54uypGn4qs001gAyoBjB/FApxgCgWPXiMjMT5kE4+Gb75Tbj4Yu+kjpqWogV2\nvvUt+M3fjJfZ/OQnfbJcdM5CwYfEnnOOB4916/zztdYaqqkJaf5TE1CTUwDIF7OJs792dEydG+qU\nU6BU8r6I/n4PItdd53Mi0pqHli6N34e4wAc1IeWBAkCTUwCQev3Jn3gqjSiPUlubB4Ybb/TawD/8\nA2zcODYn04oVXivZtcsL/Guu8Y7x4WGvWdxyiweLqDaQrB2AagqtSgGgySkAyHSk1SSipT2TBX/1\n+6OjHgiWLPHU3dXvd3fDP/5j3EzV0+PvRYEjWgFusuCgpqXmoQDQ5BQAZLZFQ2MnkuzLiBYsCsED\nx733wsc+Fjc/FQrwox95/8b69fCGN3gCwGTAqKcPQsGjsRQAmpwCgLSKnh743Ofgne9MT+RXrb3d\nA0A0p+HgQV8l7m1v88I96oNYudL7KVatGr9AECggzIQCQJNTAJBW8ba3eWG8f39tx//Gb3jt4MQT\nfSjs6tU+pLajw9d++NKX4kDS3u7NT/v3xxPi7rgDrrrK96mjenoUAJqcAoDMV21tcOed0NfnndA/\n/vHY96PFgqI+i/Z2n2F9+LAf/6tfxf0U7e2eAHC2srzOF5oIJiJzYnQU3vEOH3FUXfiD9ytccYXX\nDIpFL+RfeAEWLoyf/CPDw35sPXmSknmbZprDSau+qQaQCdUAJM8ef9x/X3UVPPRQvL9Y9OaipKny\nJFUPXU32MUBtHdMTnbdV50yoBiAiTevaaz27a7Lwh7GFf7HovwsFn6uQpjqjanJZ0V27fEb1dNNm\nJNNuDAz4EqR5rAlkFgDM7Dwz22VmT5vZx7P6nvmhNNcX0ERKc30BTaQ01xcwLbfeOvUxUTAYHvbk\nfGmShXR/fwmzeNnQlSt9BFOx6CONenvru8bVq/0z7e3+c/nl+UzbnUkAMLM24EZgPdALvMvMVmbx\nXfNDaa4voImU5voCmkhpri9gQoXCzJo6Ozq84O3tHVt4J9vlo0K6WITXvrbEmjXxSm2PPOI/0121\nLVr1bcsWDzB5TcCXVQ1gDbAnhPBsCOFl4DZgQ0bfBUCpVGr48RMdk7Y//djqfaVJ3mukes9dy/ET\nHZO2v5Z9pUnea6R6z13L8RMdk7a/ln2lSd6bmUWLvAN2euee+PiTT/Z1GGq5F21t/pS+cGG8LwS4\n6Sb47GdLYyaMve51paNNPg8/XDpa4F92mRfanZ3eX1C9Xa9Syb/3kkviIDNZTWKq8mKy92stL6r3\nJV/XW77VKqsAsBh4LvH6+cq+zDRTAHjFK47urT5qgu1Gq/fctRw/0TFp+2vZV5rkvUaq99y1HD/R\nMWn7a9lXmuS92JIlcMIJ/uS9dKlnIb3pJjj3XDjuOH+ijpbMjBxzDDzxBFx/fXzutjY4/vixxxUK\n8XZHR9QuX6JY9PdWr4bPfz5OTbFvHxw65MdEQz/T/k09PfDAA/DYY3DppSWWL48L24svhp07439v\nfz8880zp6NP4P/9z6Wghv2DBhLdlWqL/r7Wu/zxfA0Amo4DM7EJgfQjhA5XXfwysCSF8JHHM/BwC\nJCKSsUaNAmpvxElSvAAk+/ZPquw7qlH/ABERmZ6smoD+FVhuZl1m1gFcAtyd0XeJiMg0ZFIDCCGM\nmNmHgAfwIPO1EMJQFt8lIiLTM2czgUVEZG5pJrCISE4pAIiI5FRTBAAzO9nMvmpmt6e9zpOUe7HB\nzL5sZt8ws7fM9fXNlpT7sNLMbjKz283sf8z19c2mtP8PZnaMmf2rmV0wl9c221L+LtaZ2fbK38ab\n5vr6ZlPKvTAz+7SZ3WBm767lHE0RAEII+0II75/odZ6k3Iu7KvMp/hS4aO6ubHal3IddIYQ/BS4G\n1s7dlc2+Cf4/fBz45lxcz1xKuRcBKAML8AmnuZFyLzbgQ+5fosZ7kVUuoK+Z2WEz+2HV/twliGvg\nvfhz4H9lc5XZa8R9MLP/BnwH2JrltWZtpvfCzH4fGAR+ArT0fJqZ3osQwvYQwluBq4Frsr7eLDXg\n/8gK4AchhP8JXF7Ld2ZVA/g6ngjuqBoTxFX/Mbf0H3fFjO+FmX0O2BpCeDLja83SjO9DCOHblf/s\nf5zxtWZtpveiDzgT+COg1WvKjSor/gPoyOoiZ8lM78XzwM8r2zWs8JxRAAghfD9xIZEJE8SZ2avN\n7CbgdDP7ePXrLK5xtjTgXnwYOAf4QzP7wGxeeyM14D6sM7Przex/A/fM6sU32EzvRQjhz0MIVwG3\nAF+ZzWtvtAb8Xbyj8jdxM15QtqyZ3gvgDuA8M7seqFqNIV1WqSDSpCWIWwMQQvgZ3sadVP16Pqn3\nXnxxlq5rttV7H2r6o25R9d4LQgh/PzuXNuvqvRffmqXrmgv13ou6aoRN0QksIiKzbzYDwJQJ4nJE\n98LpPsR0L2K6F7FM70WWAcAY21GT5wRxuhdO9yGmexHTvYjN6r3IahjorcAjwKlmdsDMLgshjAAf\nxhPEDQDvLVZ2AAAATElEQVS35SFBnO6F032I6V7EdC9ic3EvlAxORCSn1AksIpJTCgAiIjmlACAi\nklMKACIiOaUAICKSUwoAIiI5pQAgIpJTCgAiIjn1/wHx1mimPSJnkAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcab19d95d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogx(hosts_data_alt['halo_mass'], hosts_data_alt['halo_cNFW'], 'b.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda2/lib/python2.7/site-packages/numpy/core/numeric.py:583: UserWarning: Warning: converting a masked element to nan.\n",
      "  return array(a, dtype, copy=False, order=order, subok=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.8, 1.2)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yla9gtVrx+/10dHSQy+WQJIlsNsv58+cpFAoi2vf7/YyOjjI9PU1nZyd9fX2i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\ng8FgKEP+D3LmafJNAgjwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca4b79c050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_bins = 10\n",
    "\n",
    "X = np.log10(mass_sort['halo_mass'])\n",
    "Y = mass_sort['halo_cV']\n",
    "\n",
    "bins = np.linspace(X.min(),X.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X,bins)\n",
    "running_median = [np.median(Y[idx==k]) for k in range(total_bins)]\n",
    "\n",
    "plt.scatter(X,Y,color='k',alpha=.2,s=2)\n",
    "running_std    = [Y[idx==k].std() for k in range(total_bins)]\n",
    "plt.errorbar(bins-delta/2,running_median,\n",
    "              running_std)\n",
    "plt.axis('tight')\n",
    "plt.ylim(0.8,1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8, 1.2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yla9gtVrx+/10dHSQy+WQJIlsNsv58+cpFAoi2vf7/YyOjjI9PU1nZyd9fX2i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\ng8FgKEP+D3LmafJNAgjwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcab18bdfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_bins = 10\n",
    "\n",
    "X = np.log10(mass_sort['halo_mass'])\n",
    "Y = mass_sort['halo_cV']\n",
    "\n",
    "bins = np.linspace(X.min(),X.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X,bins)\n",
    "running_median = [np.median(Y[idx==k]) for k in range(total_bins)]\n",
    "\n",
    "plt.scatter(X,Y,color='k',alpha=.2,s=2)\n",
    "running_std    = [Y[idx==k].std() for k in range(total_bins)]\n",
    "plt.errorbar(bins-delta/2,running_median,\n",
    "              running_std)\n",
    "plt.axis('tight')\n",
    "plt.ylim(0.8,1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "...Processing ASCII data of file: \n",
      "/data/asv13/projects/haloenviron-pipeline/l0500_d200b.catalog\n",
      " \n",
      "Total number of rows in detected data = 3110333\n",
      "Number of rows in detected header = 16 \n",
      "\n",
      "... working on chunk 0 of 2\n",
      "... working on chunk 1 of 2\n",
      "Total runtime to read in ASCII = 28.31 seconds\n",
      "\n",
      "\u0007\n",
      "\n",
      "...Processing ASCII data of file: \n",
      "/data/asv13/projects/haloenviron-pipeline/l0500_d200b.catalog\n",
      " \n",
      "Total number of rows in detected data = 3110333\n",
      "Number of rows in detected header = 16 \n",
      "\n",
      "... working on chunk 0 of 2\n",
      "... working on chunk 1 of 2\n",
      "Total runtime to read in ASCII = 26.41 seconds\n",
      "\n",
      "\u0007\n",
      "\n",
      "...Processing ASCII data of file: \n",
      "/data/asv13/projects/haloenviron-pipeline/l0250_d200b.catalog\n",
      " \n",
      "Total number of rows in detected data = 2501871\n",
      "Number of rows in detected header = 16 \n",
      "\n",
      "... working on chunk 0 of 1\n",
      "Total runtime to read in ASCII = 20.43 seconds\n",
      "\n",
      "\u0007\n",
      "\n",
      "...Processing ASCII data of file: \n",
      "/data/asv13/projects/haloenviron-pipeline/l0250_d200b.catalog\n",
      " \n",
      "Total number of rows in detected data = 2501871\n",
      "Number of rows in detected header = 16 \n",
      "\n",
      "... working on chunk 0 of 1\n",
      "Total runtime to read in ASCII = 19.91 seconds\n",
      "\n",
      "\u0007\n",
      "\n",
      "...Processing ASCII data of file: \n",
      "/data/asv13/projects/haloenviron-pipeline/l0125_d200b.catalog\n",
      " \n",
      "Total number of rows in detected data = 2016442\n",
      "Number of rows in detected header = 16 \n",
      "\n",
      "... working on chunk 0 of 1\n",
      "Total runtime to read in ASCII = 18.06 seconds\n",
      "\n",
      "\u0007\n",
      "\n",
      "...Processing ASCII data of file: \n",
      "/data/asv13/projects/haloenviron-pipeline/l0125_d200b.catalog\n",
      " \n",
      "Total number of rows in detected data = 2016442\n",
      "Number of rows in detected header = 16 \n",
      "\n",
      "... working on chunk 0 of 1\n",
      "Total runtime to read in ASCII = 15.46 seconds\n",
      "\n",
      "\u0007\n"
     ]
    }
   ],
   "source": [
    "fname = './l0500_d200b.catalog' # file name to z0.0.catalog\n",
    "mthresh = 1e10  # threshold mass in Msun/h\n",
    "gnewton = 4.302e-6\n",
    "lbox = 500.0\n",
    "vhost_min = 240.0\n",
    "vrat_frac = 0.3\n",
    "vsub_min = vhost_min * vrat_frac\n",
    "nrand = 10\n",
    "\n",
    "# define dict pointing to all marks of interest\n",
    "rs_dict = {'halo_id':(0,'i8'), 'halo_mass':(2,'f8'), 'halo_vmax':(3,'f8'), 'halo_rvir':(5,'f8'),\n",
    "           'halo_rs':(6,'f8'), 'halo_x':(8,'f8'), 'halo_y':(9,'f8'), 'halo_z':(10,'f8'),\n",
    "           'halo_spin':(17,'f8'), 'halo_ctoa':(28, 'f8'), 'halo_pid':(41,'i8')}\n",
    "\n",
    "reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_mass':mthresh},\n",
    "                               row_cut_eq_dict={'halo_pid':-1})\n",
    "hosts_data = reader.read_ascii()\n",
    "\n",
    "reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_vmax':vsub_min}, row_cut_neq_dict={'halo_pid':-1})\n",
    "subs_data = reader.read_ascii()\n",
    "\n",
    "# add calculated marks\n",
    "vratio_temp = hosts_data['halo_vmax']/(np.sqrt(gnewton * hosts_data['halo_mass']/hosts_data['halo_rvir']))\n",
    "cnfw_temp = hosts_data['halo_rvir']/hosts_data['halo_rs']\n",
    "\n",
    "# now we want to go host halo by host halo, match pid to subhalos, and\n",
    "# count the number that meet our requirements and then add this on\n",
    "# as both a number and a flag.\n",
    "nflagtemp = np.zeros(len(hosts_data))\n",
    "subcount = np.zeros(len(hosts_data))\n",
    "\n",
    "for i in range(0, len(hosts_data)):\n",
    "   if hosts_data[i]['halo_vmax'] > vhost_min:\n",
    "      nflagtemp[i] = 1\n",
    "      subs_cut = subs_data[np.where(subs_data['halo_pid'] == hosts_data[i]['halo_id'])]\n",
    "      for j in range(0, len(subs_cut)):\n",
    "         xdist = subs_cut[j]['halo_x'] - hosts_data[i]['halo_x']\n",
    "         ydist = subs_cut[j]['halo_y'] - hosts_data[i]['halo_y']\n",
    "         zdist = subs_cut[j]['halo_z'] - hosts_data[i]['halo_z']\n",
    "         totdist = (xdist**2 + ydist**2 + zdist**2)*0.001\n",
    "         if totdist < hosts_data[i]['halo_rvir']:\n",
    "            ratio = subs_cut[j]['halo_vmax']/hosts_data[i]['halo_vmax']\n",
    "            if ratio > vrat_frac:\n",
    "               subcount[i] = subcount[i] + 1\n",
    "\n",
    "hosts_data_l0500 =  append_fields(hosts_data, ('halo_cV', 'halo_cNFW', 'halo_satflag', 'halo_nsat'), (vratio_temp, cnfw_temp, nflagtemp, subcount))\n",
    "\n",
    "fname = './l0250_d200b.catalog' # file name to z0.0.catalog\n",
    "mthresh = 1e10  # threshold mass in Msun/h\n",
    "gnewton = 4.302e-6\n",
    "lbox = 250.0\n",
    "vhost_min = 240.0\n",
    "vrat_frac = 0.3\n",
    "vsub_min = vhost_min * vrat_frac\n",
    "nrand = 10\n",
    "\n",
    "# define dict pointing to all marks of interest\n",
    "rs_dict = {'halo_id':(0,'i8'), 'halo_mass':(2,'f8'), 'halo_vmax':(3,'f8'), 'halo_rvir':(5,'f8'),\n",
    "           'halo_rs':(6,'f8'), 'halo_x':(8,'f8'), 'halo_y':(9,'f8'), 'halo_z':(10,'f8'),\n",
    "           'halo_spin':(17,'f8'), 'halo_ctoa':(28, 'f8'), 'halo_pid':(41,'i8')}\n",
    "\n",
    "reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_mass':mthresh},\n",
    "                               row_cut_eq_dict={'halo_pid':-1})\n",
    "hosts_data = reader.read_ascii()\n",
    "\n",
    "reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_vmax':vsub_min}, row_cut_neq_dict={'halo_pid':-1})\n",
    "subs_data = reader.read_ascii()\n",
    "\n",
    "# add calculated marks\n",
    "vratio_temp = hosts_data['halo_vmax']/(np.sqrt(gnewton * hosts_data['halo_mass']/hosts_data['halo_rvir']))\n",
    "cnfw_temp = hosts_data['halo_rvir']/hosts_data['halo_rs']\n",
    "\n",
    "# now we want to go host halo by host halo, match pid to subhalos, and\n",
    "# count the number that meet our requirements and then add this on\n",
    "# as both a number and a flag.\n",
    "nflagtemp = np.zeros(len(hosts_data))\n",
    "subcount = np.zeros(len(hosts_data))\n",
    "\n",
    "for i in range(0, len(hosts_data)):\n",
    "   if hosts_data[i]['halo_vmax'] > vhost_min:\n",
    "      nflagtemp[i] = 1\n",
    "      subs_cut = subs_data[np.where(subs_data['halo_pid'] == hosts_data[i]['halo_id'])]\n",
    "      for j in range(0, len(subs_cut)):\n",
    "         xdist = subs_cut[j]['halo_x'] - hosts_data[i]['halo_x']\n",
    "         ydist = subs_cut[j]['halo_y'] - hosts_data[i]['halo_y']\n",
    "         zdist = subs_cut[j]['halo_z'] - hosts_data[i]['halo_z']\n",
    "         totdist = (xdist**2 + ydist**2 + zdist**2)*0.001\n",
    "         if totdist < hosts_data[i]['halo_rvir']:\n",
    "            ratio = subs_cut[j]['halo_vmax']/hosts_data[i]['halo_vmax']\n",
    "            if ratio > vrat_frac:\n",
    "               subcount[i] = subcount[i] + 1\n",
    "\n",
    "hosts_data_l0250 =  append_fields(hosts_data, ('halo_cV', 'halo_cNFW', 'halo_satflag', 'halo_nsat'), (vratio_temp, cnfw_temp, nflagtemp, subcount))\n",
    "\n",
    "fname = './l0125_d200b.catalog' # file name to z0.0.catalog\n",
    "mthresh = 1e10  # threshold mass in Msun/h\n",
    "gnewton = 4.302e-6\n",
    "lbox = 125.0\n",
    "vhost_min = 240.0\n",
    "vrat_frac = 0.3\n",
    "vsub_min = vhost_min * vrat_frac\n",
    "nrand = 10\n",
    "\n",
    "# define dict pointing to all marks of interest\n",
    "rs_dict = {'halo_id':(0,'i8'), 'halo_mass':(2,'f8'), 'halo_vmax':(3,'f8'), 'halo_rvir':(5,'f8'),\n",
    "           'halo_rs':(6,'f8'), 'halo_x':(8,'f8'), 'halo_y':(9,'f8'), 'halo_z':(10,'f8'),\n",
    "           'halo_spin':(17,'f8'), 'halo_ctoa':(28, 'f8'), 'halo_pid':(41,'i8')}\n",
    "\n",
    "reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_mass':mthresh},\n",
    "                               row_cut_eq_dict={'halo_pid':-1})\n",
    "hosts_data = reader.read_ascii()\n",
    "\n",
    "reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_vmax':vsub_min}, row_cut_neq_dict={'halo_pid':-1})\n",
    "subs_data = reader.read_ascii()\n",
    "\n",
    "# add calculated marks\n",
    "vratio_temp = hosts_data['halo_vmax']/(np.sqrt(gnewton * hosts_data['halo_mass']/hosts_data['halo_rvir']))\n",
    "cnfw_temp = hosts_data['halo_rvir']/hosts_data['halo_rs']\n",
    "\n",
    "# now we want to go host halo by host halo, match pid to subhalos, and\n",
    "# count the number that meet our requirements and then add this on\n",
    "# as both a number and a flag.\n",
    "nflagtemp = np.zeros(len(hosts_data))\n",
    "subcount = np.zeros(len(hosts_data))\n",
    "\n",
    "for i in range(0, len(hosts_data)):\n",
    "   if hosts_data[i]['halo_vmax'] > vhost_min:\n",
    "      nflagtemp[i] = 1\n",
    "      subs_cut = subs_data[np.where(subs_data['halo_pid'] == hosts_data[i]['halo_id'])]\n",
    "      for j in range(0, len(subs_cut)):\n",
    "         xdist = subs_cut[j]['halo_x'] - hosts_data[i]['halo_x']\n",
    "         ydist = subs_cut[j]['halo_y'] - hosts_data[i]['halo_y']\n",
    "         zdist = subs_cut[j]['halo_z'] - hosts_data[i]['halo_z']\n",
    "         totdist = (xdist**2 + ydist**2 + zdist**2)*0.001\n",
    "         if totdist < hosts_data[i]['halo_rvir']:\n",
    "            ratio = subs_cut[j]['halo_vmax']/hosts_data[i]['halo_vmax']\n",
    "            if ratio > vrat_frac:\n",
    "               subcount[i] = subcount[i] + 1\n",
    "\n",
    "hosts_data_l0125 =  append_fields(hosts_data, ('halo_cV', 'halo_cNFW', 'halo_satflag', 'halo_nsat'), (vratio_temp, cnfw_temp, nflagtemp, subcount))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:1288: UserWarning: findfont: Font family [u'normal'] not found. Falling back to Bitstream Vera Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
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jWrVqzJ49m8jISGvAg9GjR3PnnXdiMpkwGAw89NBD+Pj4sHPnTpvzSBFvAtPT0wkLC2Pm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\nQLOgM8YYE2gWdMYYYwLtfwE/xFceyRz9QQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e61ea4f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "font = {'family' : 'normal',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 16}\n",
    "\n",
    "matplotlib.rc('font', **font)\n",
    "matplotlib.rcParams['lines.linewidth'] = 3\n",
    "\n",
    "total_bins = 16\n",
    "\n",
    "X1 = np.log10(hosts_data_l0125['halo_mass'])\n",
    "Y1 = hosts_data_l0125['halo_cV']\n",
    "X2 = np.log10(hosts_data_l0250['halo_mass'])\n",
    "Y2 = hosts_data_l0250['halo_cV']\n",
    "X3 = np.log10(hosts_data_l0500['halo_mass'])\n",
    "Y3 = hosts_data_l0500['halo_cV']\n",
    "\n",
    "bins = np.linspace(X1.min(),X1.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X1,bins)\n",
    "running_median = [np.median(Y1[idx==k]) for k in range(1,total_bins)]\n",
    "running_std    = [Y1[idx==k].std() for k in range(1,total_bins)]\n",
    "running_spread = np.zeros((2,total_bins-1))\n",
    "\n",
    "for k in range(1,total_bins):\n",
    "    running_spread[0,k-1] = running_median[k-1]- np.percentile(Y1[idx==k],16)\n",
    "    running_spread[1,k-1] = np.percentile(Y1[idx==k],84) - running_median[k-1]\n",
    "\n",
    "plt.plot(bins[1:13]-delta/2,running_median[:12], color='b', label='L0125', lw=2)\n",
    "\n",
    "\n",
    "bins = np.linspace(X2.min(),X2.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X2,bins)\n",
    "running_median = [np.median(Y2[idx==k]) for k in range(1,total_bins)]\n",
    "running_std    = [Y2[idx==k].std() for k in range(1,total_bins)]\n",
    "running_spread = np.zeros((2,total_bins-1))\n",
    "for k in range(1,total_bins):\n",
    "    running_spread[0,k-1] = running_median[k-1]- np.percentile(Y2[idx==k],16)\n",
    "    running_spread[1,k-1] = np.percentile(Y2[idx==k],84) - running_median[k-1]\n",
    "\n",
    "plt.plot(bins[1:14]-delta/2,running_median[:13], color='r', label='L0250', lw=2)\n",
    "plt.errorbar(bins[1:14]-delta/2,running_median[:13], running_spread[:,:13], color='r', lw=2)\n",
    "\n",
    "bins = np.linspace(X3.min(),X3.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X3,bins)\n",
    "running_median = [np.median(Y3[idx==k]) for k in range(1,total_bins)]\n",
    "running_std    = [Y3[idx==k].std() for k in range(1,total_bins)]\n",
    "running_spread = np.zeros((2,total_bins-1))\n",
    "for k in range(1,total_bins):\n",
    "    running_spread[0,k-1] = running_median[k-1]- np.percentile(Y3[idx==k],16)\n",
    "    running_spread[1,k-1] = np.percentile(Y3[idx==k],84) - running_median[k-1]\n",
    "\n",
    "plt.plot(bins[1:15]-delta/2,running_median[:14], color='c', label='L0500', lw=2)\n",
    "\n",
    "plt.annotate('low mass cutoff', xy=(np.log10(7e10), 1.4), xytext=(np.log10(7e10), 1.5), \n",
    "             arrowprops=dict(facecolor='blue', shrink=0.05), size=13 )\n",
    "plt.annotate('mid mass cutoff', xy=(np.log10(7e11), 1.3), xytext=(np.log10(7e11), 1.4), \n",
    "             arrowprops=dict(facecolor='red', shrink=0.05), size=13 )\n",
    "plt.annotate('high mass cutoff', xy=(np.log10(4e12), 1.25), xytext=(np.log10(4e12), 1.35), \n",
    "             arrowprops=dict(facecolor='cyan', shrink=0.05), size=13 )\n",
    "\n",
    "\n",
    "plt.xlim(np.log10(1e10), 15)\n",
    "plt.ylim(0.9,1.6)\n",
    "plt.xlabel(r'$\\log M_{\\mathrm{200}} (h^{-1}\\mathrm{M}_{\\odot})$')\n",
    "plt.ylabel(r'$c_{\\mathrm{V}}$')\n",
    "plt.tight_layout(pad=0.1)\n",
    "plt.legend(numpoints=1, loc='upper right', prop={'size':13}, frameon=False)\n",
    "plt.savefig('./FIGS/masscut_cV_d200.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jY/Hw8MhS5unpSUxMDAD169fnn3/+4ejRo/z3338EBwfTsWNHzpw5k6vj4+Li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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2db2061f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_bins = 16\n",
    "\n",
    "X1 = np.log10(hosts_data_l0125['halo_mass'])\n",
    "Y1 = hosts_data_l0125['halo_cNFW']\n",
    "X2 = np.log10(hosts_data_l0250['halo_mass'])\n",
    "Y2 = hosts_data_l0250['halo_cNFW']\n",
    "X3 = np.log10(hosts_data_l0500['halo_mass'])\n",
    "Y3 = hosts_data_l0500['halo_cNFW']\n",
    "\n",
    "\n",
    "bins = np.linspace(X1.min(),X1.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X1,bins)\n",
    "running_median = [np.median(Y1[idx==k]) for k in range(1,total_bins)]\n",
    "running_std    = [Y1[idx==k].std() for k in range(1,total_bins)]\n",
    "running_spread = np.zeros((2,total_bins-1))\n",
    "\n",
    "for k in range(1,total_bins):\n",
    "    running_spread[0,k-1] = running_median[k-1]- np.percentile(Y1[idx==k],16)\n",
    "    running_spread[1,k-1] = np.percentile(Y1[idx==k],84) - running_median[k-1]\n",
    "\n",
    "plt.plot(bins[1:13]-delta/2,running_median[:12], color='b', label='L0125', lw=2)\n",
    "\n",
    "\n",
    "bins = np.linspace(X2.min(),X2.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X2,bins)\n",
    "running_median = [np.median(Y2[idx==k]) for k in range(1,total_bins)]\n",
    "running_std    = [Y2[idx==k].std() for k in range(1,total_bins)]\n",
    "running_spread = np.zeros((2,total_bins-1))\n",
    "for k in range(1,total_bins):\n",
    "    running_spread[0,k-1] = running_median[k-1]- np.percentile(Y2[idx==k],16)\n",
    "    running_spread[1,k-1] = np.percentile(Y2[idx==k],84) - running_median[k-1]\n",
    "\n",
    "plt.plot(bins[1:14]-delta/2,running_median[:13], color='r', label='L0250', lw=2)\n",
    "plt.errorbar(bins[1:14]-delta/2,running_median[:13], running_spread[:,:13], color='r', lw=2)\n",
    "\n",
    "bins = np.linspace(X3.min(),X3.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X3,bins)\n",
    "running_median = [np.median(Y3[idx==k]) for k in range(1,total_bins)]\n",
    "running_std    = [Y3[idx==k].std() for k in range(1,total_bins)]\n",
    "running_spread = np.zeros((2,total_bins-1))\n",
    "for k in range(1,total_bins):\n",
    "    running_spread[0,k-1] = running_median[k-1]- np.percentile(Y3[idx==k],16)\n",
    "    running_spread[1,k-1] = np.percentile(Y3[idx==k],84) - running_median[k-1]\n",
    "\n",
    "plt.plot(bins[1:15]-delta/2,running_median[:14], color='c', label='L0500', lw=2)\n",
    "\n",
    "plt.annotate('low mass cutoff', xy=(np.log10(7e10), 21), xytext=(np.log10(7e10), 24.7), \n",
    "             arrowprops=dict(facecolor='blue', shrink=0.05), size=13 )\n",
    "plt.annotate('mid mass cutoff', xy=(np.log10(7e11), 16), xytext=(np.log10(7e11), 19), \n",
    "             arrowprops=dict(facecolor='red', shrink=0.05), size=13 )\n",
    "plt.annotate('high mass cutoff', xy=(np.log10(4e12), 12), xytext=(np.log10(4e12), 15), \n",
    "             arrowprops=dict(facecolor='cyan', shrink=0.05), size=13 )\n",
    "\n",
    "plt.xlim(np.log10(1e10), 15)\n",
    "plt.ylim(4,27)\n",
    "\n",
    "plt.legend(numpoints=1, loc='upper right', frameon=False, prop={'size':13})\n",
    "plt.xlabel(r'$\\log M_{\\mathrm{200}} (h^{-1}\\mathrm{M}_{\\odot})$')\n",
    "plt.ylabel(r'$c_{\\mathrm{NFW}}$')\n",
    "plt.tight_layout(pad=0.1)\n",
    "plt.yscale('log')\n",
    "plt.savefig('./FIGS/masscut_cNFW_d200.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2dXBzc2PLli2cOXOG4OBg/Pz8eOGFF0hLSwPg7bffJjIykqpVqzJw4EArj08wRpAJCQlU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\nWCwWz/P/delBk6iZnfYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2db1b74290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_bins = 16\n",
    "\n",
    "X1 = np.log10(hosts_data_l0125['halo_mass'])\n",
    "Y1 = hosts_data_l0125['halo_ctoa']\n",
    "X2 = np.log10(hosts_data_l0250['halo_mass'])\n",
    "Y2 = hosts_data_l0250['halo_ctoa']\n",
    "X3 = np.log10(hosts_data_l0500['halo_mass'])\n",
    "Y3 = hosts_data_l0500['halo_ctoa']\n",
    "\n",
    "\n",
    "bins = np.linspace(X1.min(),X1.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X1,bins)\n",
    "running_median = [np.median(Y1[idx==k]) for k in range(1,total_bins)]\n",
    "running_std    = [Y1[idx==k].std() for k in range(1,total_bins)]\n",
    "running_spread = np.zeros((2,total_bins-1))\n",
    "\n",
    "for k in range(1,total_bins):\n",
    "    running_spread[0,k-1] = running_median[k-1]- np.percentile(Y1[idx==k],16)\n",
    "    running_spread[1,k-1] = np.percentile(Y1[idx==k],84) - running_median[k-1]\n",
    "\n",
    "plt.plot(bins[1:13]-delta/2,running_median[:12], color='b', label='L0125', lw=2)\n",
    "\n",
    "\n",
    "bins = np.linspace(X2.min(),X2.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X2,bins)\n",
    "running_median = [np.median(Y2[idx==k]) for k in range(1,total_bins)]\n",
    "running_std    = [Y2[idx==k].std() for k in range(1,total_bins)]\n",
    "running_spread = np.zeros((2,total_bins-1))\n",
    "for k in range(1,total_bins):\n",
    "    running_spread[0,k-1] = running_median[k-1]- np.percentile(Y2[idx==k],16)\n",
    "    running_spread[1,k-1] = np.percentile(Y2[idx==k],84) - running_median[k-1]\n",
    "\n",
    "plt.plot(bins[1:14]-delta/2,running_median[:13], color='r', label='L0250', lw=2)\n",
    "plt.errorbar(bins[1:14]-delta/2,running_median[:13], running_spread[:,:13], color='r', lw=2)\n",
    "\n",
    "bins = np.linspace(X3.min(),X3.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X3,bins)\n",
    "running_median = [np.median(Y3[idx==k]) for k in range(1,total_bins)]\n",
    "running_std    = [Y3[idx==k].std() for k in range(1,total_bins)]\n",
    "running_spread = np.zeros((2,total_bins-1))\n",
    "for k in range(1,total_bins):\n",
    "    running_spread[0,k-1] = running_median[k-1]- np.percentile(Y3[idx==k],16)\n",
    "    running_spread[1,k-1] = np.percentile(Y3[idx==k],84) - running_median[k-1]\n",
    "\n",
    "plt.plot(bins[1:15]-delta/2,running_median[:14], color='c', label='L0500', lw=2)\n",
    "\n",
    "plt.annotate('low mass cutoff', xy=(np.log10(1e11), 0.7), xytext=(np.log10(1e11), 0.8), \n",
    "             arrowprops=dict(facecolor='blue', shrink=0.05), size=13 )\n",
    "plt.annotate('mid mass cutoff', xy=(np.log10(1e12), 0.65), xytext=(np.log10(1e12), 0.75), \n",
    "             arrowprops=dict(facecolor='red', shrink=0.05), size=13 )\n",
    "plt.annotate('high mass cutoff', xy=(np.log10(1e13), 0.57), xytext=(np.log10(1e13), 0.7), \n",
    "             arrowprops=dict(facecolor='cyan', shrink=0.05), size=13 )\n",
    "plt.xlim(np.log10(1e10), 15)\n",
    "plt.ylim(0.4,0.85)\n",
    "\n",
    "plt.legend(numpoints=1, loc='upper right', frameon=False, prop={'size':13})\n",
    "plt.xlabel(r'$\\log M_{\\mathrm{200}} (h^{-1}\\mathrm{M}_{\\odot})$')\n",
    "plt.ylabel(r'$c/a$')\n",
    "plt.tight_layout(pad=0.1)\n",
    "plt.savefig('./FIGS/masscut_shape_d200.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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46KOPyMzMRCnlDCLr5eXF7t272bFjB4WFhfj4+GA2m/H2dr+SZtKkSfj6+vLII4+QmZmJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2db48b0810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_bins = 16\n",
    "\n",
    "X1 = np.log10(hosts_data_l0125['halo_mass'])\n",
    "Y1 = hosts_data_l0125['halo_spin']\n",
    "X2 = np.log10(hosts_data_l0250['halo_mass'])\n",
    "Y2 = hosts_data_l0250['halo_spin']\n",
    "X3 = np.log10(hosts_data_l0500['halo_mass'])\n",
    "Y3 = hosts_data_l0500['halo_spin']\n",
    "\n",
    "bins = np.linspace(X1.min(),X1.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X1,bins)\n",
    "running_median = [np.median(Y1[idx==k]) for k in range(1,total_bins)]\n",
    "running_std    = [Y1[idx==k].std() for k in range(1,total_bins)]\n",
    "running_spread = np.zeros((2,total_bins-1))\n",
    "\n",
    "for k in range(1,total_bins):\n",
    "    running_spread[0,k-1] = running_median[k-1]- np.percentile(Y1[idx==k],16)\n",
    "    running_spread[1,k-1] = np.percentile(Y1[idx==k],84) - running_median[k-1]\n",
    "\n",
    "plt.plot(bins[1:13]-delta/2,running_median[:12], color='b', label='L0125', lw=2)\n",
    "\n",
    "\n",
    "bins = np.linspace(X2.min(),X2.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X2,bins)\n",
    "running_median = [np.median(Y2[idx==k]) for k in range(1,total_bins)]\n",
    "running_std    = [Y2[idx==k].std() for k in range(1,total_bins)]\n",
    "running_spread = np.zeros((2,total_bins-1))\n",
    "for k in range(1,total_bins):\n",
    "    running_spread[0,k-1] = running_median[k-1]- np.percentile(Y2[idx==k],16)\n",
    "    running_spread[1,k-1] = np.percentile(Y2[idx==k],84) - running_median[k-1]\n",
    "\n",
    "plt.plot(bins[1:14]-delta/2,running_median[:13], color='r', label='L0250', lw=2)\n",
    "plt.errorbar(bins[1:14]-delta/2,running_median[:13], running_spread[:,:13], color='r', lw=2)\n",
    "\n",
    "bins = np.linspace(X3.min(),X3.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X3,bins)\n",
    "running_median = [np.median(Y3[idx==k]) for k in range(1,total_bins)]\n",
    "running_std    = [Y3[idx==k].std() for k in range(1,total_bins)]\n",
    "running_spread = np.zeros((2,total_bins-1))\n",
    "for k in range(1,total_bins):\n",
    "    running_spread[0,k-1] = running_median[k-1]- np.percentile(Y3[idx==k],16)\n",
    "    running_spread[1,k-1] = np.percentile(Y3[idx==k],84) - running_median[k-1]\n",
    "\n",
    "plt.plot(bins[1:15]-delta/2,running_median[:14], color='c', label='L0500', lw=2)\n",
    "\n",
    "plt.annotate('low mass cutoff', xy=(np.log10(7e10), 0.04), xytext=(np.log10(7e10), 0.07), \n",
    "             arrowprops=dict(facecolor='blue', shrink=0.05), size=13 )\n",
    "plt.annotate('mid mass cutoff', xy=(np.log10(7e11), 0.04), xytext=(np.log10(7e11), 0.06), \n",
    "             arrowprops=dict(facecolor='red', shrink=0.05), size=13 )\n",
    "plt.annotate('high mass cutoff', xy=(np.log10(4e12), 0.04), xytext=(np.log10(4e12), 0.05), \n",
    "             arrowprops=dict(facecolor='cyan', shrink=0.05), size=13 )\n",
    "\n",
    "plt.xlim(np.log10(1e10), 15)\n",
    "plt.ylim(0.01,0.08)\n",
    "plt.legend(numpoints=1, loc='upper right', frameon=False, prop={'size':13})\n",
    "plt.xlabel(r'$\\log M_{\\mathrm{200}} (h^{-1}\\mathrm{M}_{\\odot})$')\n",
    "plt.ylabel(r'$\\lambda$')\n",
    "plt.tight_layout(pad=0.1)\n",
    "plt.savefig('./FIGS/masscut_spin_d200.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sY9kLLju1tS5HNBdd98n2qt9vWU9w4/aq7NhjvcG7PaSl8OE9dr5vc9HaJ23aWvWr7NNY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\nSUUxuCRJRTG4JElFMbgkSUUxuCRJRfn/SFhH0LxeZpEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d813d0bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "font = {'family' : 'normal',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 16}\n",
    "\n",
    "matplotlib.rc('font', **font)\n",
    "matplotlib.rcParams['lines.linewidth'] = 3\n",
    "\n",
    "mask = np.where(hosts_data_l0250['halo_mass'] >= 7e11)\n",
    "x = hosts_data_l0250['halo_cNFW'][mask]\n",
    "y = hosts_data_l0250['halo_cV'][mask]\n",
    "plt.hist2d(x,y,bins=40, norm=LogNorm(), range=((0,50),(0,2)))\n",
    "#plt.plot( (0,25), (0,25), 'k--')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.xlabel(r'$c_{\\mathrm{NFW}}$')\n",
    "plt.ylabel(r'$c_{\\mathrm{V}}$')\n",
    "#plt.xlim(0,50)\n",
    "plt.ylim(0.8,1.8)\n",
    "plt.tight_layout(pad=0.1)\n",
    "plt.savefig('cvvscnfw_relation.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "...Processing ASCII data of file: \n",
      "/data/asv13/projects/haloenviron-pipeline/l0250_d200b.catalog\n",
      " \n",
      "Total number of rows in detected data = 2501871\n",
      "Number of rows in detected header = 16 \n",
      "\n",
      "... working on chunk 0 of 1\n",
      "Total runtime to read in ASCII = 20.72 seconds\n",
      "\n",
      "\u0007\n",
      "\n",
      "...Processing ASCII data of file: \n",
      "/data/asv13/projects/haloenviron-pipeline/l0250_d200b.catalog\n",
      " \n",
      "Total number of rows in detected data = 2501871\n",
      "Number of rows in detected header = 16 \n",
      "\n",
      "... working on chunk 0 of 1\n",
      "Total runtime to read in ASCII = 20.76 seconds\n",
      "\n",
      "\u0007\n"
     ]
    }
   ],
   "source": [
    "fname = './l0250_d200b.catalog' # file name to z0.0.catalog\n",
    "mthresh = 7e11  # threshold mass in Msun/h\n",
    "gnewton = 4.302e-6\n",
    "lbox = 250.0\n",
    "vhost_min = 240.0\n",
    "vrat_frac = 0.3\n",
    "vsub_min = vhost_min * vrat_frac\n",
    "nrand = 1\n",
    "\n",
    "# define dict pointing to all marks of interest\n",
    "rs_dict = {'halo_id':(0,'i8'), 'halo_mass':(2,'f8'), 'halo_vmax':(3,'f8'), 'halo_rvir':(5,'f8'),\n",
    "           'halo_rs':(6,'f8'), 'halo_x':(8,'f8'), 'halo_y':(9,'f8'), 'halo_z':(10,'f8'),\n",
    "           'halo_spin':(17,'f8'), 'halo_ctoa':(28, 'f8'), 'halo_pid':(41,'i8')}\n",
    "\n",
    "reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_mass':mthresh},\n",
    "                               row_cut_eq_dict={'halo_pid':-1})\n",
    "hosts_data = reader.read_ascii()\n",
    "\n",
    "reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_vmax':vsub_min}, row_cut_neq_dict={'halo_pid':-1})\n",
    "subs_data = reader.read_ascii()\n",
    "\n",
    "# add calculated marks\n",
    "vratio_temp = hosts_data['halo_vmax']/(np.sqrt(gnewton * hosts_data['halo_mass']/hosts_data['halo_rvir']))\n",
    "cnfw_temp = hosts_data['halo_rvir']/hosts_data['halo_rs']\n",
    "\n",
    "# now we want to go host halo by host halo, match pid to subhalos, and\n",
    "# count the number that meet our requirements and then add this on\n",
    "# as both a number and a flag.\n",
    "nflagtemp = np.zeros(len(hosts_data))\n",
    "subcount = np.zeros(len(hosts_data))\n",
    "\n",
    "for i in range(0, len(hosts_data)):\n",
    "   if hosts_data[i]['halo_vmax'] > vhost_min:\n",
    "      nflagtemp[i] = 1\n",
    "      subs_cut = subs_data[np.where(subs_data['halo_pid'] == hosts_data[i]['halo_id'])]\n",
    "      for j in range(0, len(subs_cut)):\n",
    "         xdist = subs_cut[j]['halo_x'] - hosts_data[i]['halo_x']\n",
    "         ydist = subs_cut[j]['halo_y'] - hosts_data[i]['halo_y']\n",
    "         zdist = subs_cut[j]['halo_z'] - hosts_data[i]['halo_z']\n",
    "         totdist = (xdist**2 + ydist**2 + zdist**2)*0.001\n",
    "         if totdist < hosts_data[i]['halo_rvir']:\n",
    "            ratio = subs_cut[j]['halo_vmax']/hosts_data[i]['halo_vmax']\n",
    "            if ratio > vrat_frac:\n",
    "               subcount[i] = subcount[i] + 1\n",
    "\n",
    "hosts_data_alt =  append_fields(hosts_data, ('halo_cV', 'halo_cNFW', 'halo_satflag', 'halo_nsat'), (vratio_temp, cnfw_temp, nflagtemp, subcount))\n",
    "\n",
    "# now that we have data, we need to mass correct all of our marks\n",
    "mass_sort_d200 = np.sort(hosts_data_alt, order='halo_mass')\n",
    "\n",
    "shape_binned = stats.binned_statistic(np.log10(mass_sort_d200['halo_mass']),\n",
    "                                      (mass_sort_d200['halo_ctoa']),\n",
    "                                      statistic='mean', bins=10)\n",
    "shape_fix = (mass_sort_d200['halo_ctoa']) / shape_binned.statistic[shape_binned.binnumber-1]\n",
    "mass_sort_d200['halo_ctoa'] = shape_fix\n",
    "mask = np.where( (mass_sort_d200['halo_x'] <= 60.0) & (mass_sort_d200['halo_y'] <= 60.0) & (mass_sort_d200['halo_z'] <= 20.0) )\n",
    "cut_d200 = mass_sort_d200[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "...Processing ASCII data of file: \n",
      "/data/asv13/projects/haloenviron-pipeline/l0250_d10b.catalog\n",
      " \n",
      "Total number of rows in detected data = 2711286\n",
      "Number of rows in detected header = 16 \n",
      "\n",
      "... working on chunk 0 of 1\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "invalid literal for long() with base 10: '0.49475'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-098ee20fdf64>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     15\u001b[0m reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_mass':mthresh},\n\u001b[0;32m     16\u001b[0m                                row_cut_eq_dict={'halo_pid':-1})\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0mhosts_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_ascii\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[0mreader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTabularAsciiReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrs_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrow_cut_min_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'halo_vmax'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mvsub_min\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrow_cut_neq_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'halo_pid'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/opt/anaconda2/lib/python2.7/site-packages/halotools/sim_manager/tabular_ascii_reader.pyc\u001b[0m in \u001b[0;36mread_ascii\u001b[1;34m(self, chunk_memory_size)\u001b[0m\n\u001b[0;32m    575\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    576\u001b[0m                 chunk_array = np.array(list(\n\u001b[1;32m--> 577\u001b[1;33m                     self.data_chunk_generator(num_rows_in_chunk, f)), dtype=self.dt)\n\u001b[0m\u001b[0;32m    578\u001b[0m                 \u001b[0mcut_chunk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_row_cut\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchunk_array\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    579\u001b[0m                 \u001b[0mchunklist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcut_chunk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: invalid literal for long() with base 10: '0.49475'"
     ]
    }
   ],
   "source": [
    "fname = './l0250_d10b.catalog' # file name to z0.0.catalog\n",
    "mthresh = 2e11  # threshold mass in Msun/h\n",
    "gnewton = 4.302e-6\n",
    "lbox = 250.0\n",
    "vhost_min = 240.0\n",
    "vrat_frac = 0.3\n",
    "vsub_min = vhost_min * vrat_frac\n",
    "nrand = 1\n",
    "\n",
    "# define dict pointing to all marks of interest\n",
    "rs_dict = {'halo_id':(0,'i8'), 'halo_mass':(2,'f8'), 'halo_vmax':(3,'f8'), 'halo_rvir':(5,'f8'),\n",
    "           'halo_rs':(6,'f8'), 'halo_x':(8,'f8'), 'halo_y':(9,'f8'), 'halo_z':(10,'f8'),\n",
    "           'halo_spin':(17,'f8'), 'halo_ctoa':(28, 'f8'), 'halo_pid':(33,'i8')}\n",
    "\n",
    "reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_mass':mthresh},\n",
    "                               row_cut_eq_dict={'halo_pid':-1})\n",
    "hosts_data = reader.read_ascii()\n",
    "\n",
    "reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_vmax':vsub_min}, row_cut_neq_dict={'halo_pid':-1})\n",
    "subs_data = reader.read_ascii()\n",
    "\n",
    "# add calculated marks\n",
    "vratio_temp = hosts_data['halo_vmax']/(np.sqrt(gnewton * hosts_data['halo_mass']/hosts_data['halo_rvir']))\n",
    "cnfw_temp = hosts_data['halo_rvir']/hosts_data['halo_rs']\n",
    "\n",
    "# now we want to go host halo by host halo, match pid to subhalos, and\n",
    "# count the number that meet our requirements and then add this on\n",
    "# as both a number and a flag.\n",
    "nflagtemp = np.zeros(len(hosts_data))\n",
    "subcount = np.zeros(len(hosts_data))\n",
    "\n",
    "for i in range(0, len(hosts_data)):\n",
    "   if hosts_data[i]['halo_vmax'] > vhost_min:\n",
    "      nflagtemp[i] = 1\n",
    "      subs_cut = subs_data[np.where(subs_data['halo_pid'] == hosts_data[i]['halo_id'])]\n",
    "      for j in range(0, len(subs_cut)):\n",
    "         xdist = subs_cut[j]['halo_x'] - hosts_data[i]['halo_x']\n",
    "         ydist = subs_cut[j]['halo_y'] - hosts_data[i]['halo_y']\n",
    "         zdist = subs_cut[j]['halo_z'] - hosts_data[i]['halo_z']\n",
    "         totdist = (xdist**2 + ydist**2 + zdist**2)*0.001\n",
    "         if totdist < hosts_data[i]['halo_rvir']:\n",
    "            ratio = subs_cut[j]['halo_vmax']/hosts_data[i]['halo_vmax']\n",
    "            if ratio > vrat_frac:\n",
    "               subcount[i] = subcount[i] + 1\n",
    "\n",
    "hosts_data_alt =  append_fields(hosts_data, ('halo_cV', 'halo_cNFW', 'halo_satflag', 'halo_nsat'), (vratio_temp, cnfw_temp, nflagtemp, subcount))\n",
    "\n",
    "# now that we have data, we need to mass correct all of our marks\n",
    "mass_sort_d50 = np.sort(hosts_data_alt, order='halo_mass')\n",
    "\n",
    "shape_binned = stats.binned_statistic(np.log10(mass_sort_d50['halo_mass']),\n",
    "                                      (mass_sort_d50['halo_ctoa']),\n",
    "                                      statistic='mean', bins=10)\n",
    "shape_fix = (mass_sort_d50['halo_ctoa']) / shape_binned.statistic[shape_binned.binnumber-1]\n",
    "mass_sort_d50['halo_ctoa'] = shape_fix\n",
    "mask = np.where( (mass_sort_d50['halo_x'] <= 60.0) & (mass_sort_d50['halo_y'] <= 60.0) & (mass_sort_d50['halo_z'] <= 20.0) )\n",
    "cut_d50 = mass_sort_d50[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "len(cut_d200), len(cut_d50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.colors as clrs\n",
    "cms = matplotlib.cm\n",
    "maps = [cms.coolwarm]\n",
    "\n",
    "circle_smin_200 = np.min(cut_d200['halo_ctoa'])\n",
    "circle_smax_200 = np.max(cut_d200['halo_ctoa'])\n",
    "circle_smin_50 = np.min(cut_d50['halo_ctoa'])\n",
    "circle_smax_50 = np.max(cut_d50['halo_ctoa'])\n",
    "\n",
    "scaling_min = np.min( (circle_smin_200, circle_smin_50) )\n",
    "scaling_max = np.max( (circle_smax_200, circle_smax_50) )\n",
    "\n",
    "step = (scaling_max - scaling_min) / 30.\n",
    "\n",
    "Z = [[0,0],[0,0]]\n",
    "levels = np.arange(scaling_min, scaling_max+step,step)\n",
    "CS3 = plt.contourf(Z, levels, cmap=cms.coolwarm)\n",
    "plt.clf()\n",
    "\n",
    "fig=plt.figure(1, figsize=(20,20))\n",
    "plt.axis([0,60,0,60])\n",
    "ax=fig.add_subplot(1,1,1)\n",
    "\n",
    "for i in range(0, len(cut_d200)):\n",
    "   circle_x = cut_d200[i]['halo_x']\n",
    "   circle_y = cut_d200[i]['halo_y']\n",
    "   circle_r = cut_d200[i]['halo_rvir']*0.001\n",
    "   circle_s = cut_d200[i]['halo_ctoa']\n",
    "   circle_s_norm = (circle_s - scaling_min) / (scaling_max - scaling_min)\n",
    "   color=plt.cm.coolwarm(circle_s_norm)\n",
    "   circ=plt.Circle((circle_x,circle_y), radius=circle_r, color=color, fill=True)\n",
    "   ax.add_patch(circ)\n",
    "for i in range(0, len(cut_d50)):\n",
    "   circle_x = cut_d50[i]['halo_x']\n",
    "   circle_y = cut_d50[i]['halo_y']\n",
    "   circle_r = cut_d50[i]['halo_rvir']*0.001\n",
    "   circle_s = cut_d50[i]['halo_ctoa']\n",
    "   circle_s_norm = (circle_s - scaling_min) / (scaling_max - scaling_min)\n",
    "   color=plt.cm.coolwarm(circle_s_norm)\n",
    "   circ=plt.Circle((circle_x,circle_y), radius=circle_r, color=color, fill=False)\n",
    "   ax.add_patch(circ)\n",
    "ax.set_aspect('equal')\n",
    "plt.xlabel(r'$x (h^{-1}\\mathrm{Mpc})$')\n",
    "plt.ylabel(r'$y (h^{-1}\\mathrm{Mpc})$')\n",
    "plt.colorbar(CS3)\n",
    "fig.tight_layout(pad=0.1)\n",
    "#plt.savefig('plotcircles.pdf',dpi=800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fname = './l0500_d200b.catalog' # file name to z0.0.catalog\n",
    "mthresh = 4e12  # threshold mass in Msun/h\n",
    "gnewton = 4.302e-6\n",
    "lbox = 500.0\n",
    "vhost_min = 240.0\n",
    "vrat_frac = 0.3\n",
    "vsub_min = vhost_min * vrat_frac\n",
    "nrand = 10\n",
    "\n",
    "# define dict pointing to all marks of interest\n",
    "rs_dict = {'halo_id':(0,'i8'), 'halo_mass':(2,'f8'), 'halo_vmax':(3,'f8'), 'halo_rvir':(5,'f8'),\n",
    "           'halo_rs':(6,'f8'), 'halo_x':(8,'f8'), 'halo_y':(9,'f8'), 'halo_z':(10,'f8'),\n",
    "           'halo_spin':(17,'f8'), 'halo_ctoa':(28, 'f8'), 'halo_pid':(41,'i8')}\n",
    "\n",
    "reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_mass':mthresh},\n",
    "                               row_cut_eq_dict={'halo_pid':-1})\n",
    "hosts_data = reader.read_ascii()\n",
    "\n",
    "reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_vmax':vsub_min}, row_cut_neq_dict={'halo_pid':-1})\n",
    "subs_data = reader.read_ascii()\n",
    "\n",
    "# add calculated marks\n",
    "vratio_temp = hosts_data['halo_vmax']/(np.sqrt(gnewton * hosts_data['halo_mass']/hosts_data['halo_rvir']))\n",
    "cnfw_temp = hosts_data['halo_rvir']/hosts_data['halo_rs']\n",
    "\n",
    "# now we want to go host halo by host halo, match pid to subhalos, and\n",
    "# count the number that meet our requirements and then add this on\n",
    "# as both a number and a flag.\n",
    "nflagtemp = np.zeros(len(hosts_data))\n",
    "subcount = np.zeros(len(hosts_data))\n",
    "\n",
    "for i in range(0, len(hosts_data)):\n",
    "   if hosts_data[i]['halo_vmax'] > vhost_min:\n",
    "      nflagtemp[i] = 1\n",
    "      subs_cut = subs_data[np.where(subs_data['halo_pid'] == hosts_data[i]['halo_id'])]\n",
    "      for j in range(0, len(subs_cut)):\n",
    "         xdist = subs_cut[j]['halo_x'] - hosts_data[i]['halo_x']\n",
    "         ydist = subs_cut[j]['halo_y'] - hosts_data[i]['halo_y']\n",
    "         zdist = subs_cut[j]['halo_z'] - hosts_data[i]['halo_z']\n",
    "         totdist = (xdist**2 + ydist**2 + zdist**2)*0.001\n",
    "         if totdist < hosts_data[i]['halo_rvir']:\n",
    "            ratio = subs_cut[j]['halo_vmax']/hosts_data[i]['halo_vmax']\n",
    "            if ratio > vrat_frac:\n",
    "               subcount[i] = subcount[i] + 1\n",
    "\n",
    "hosts_data_l0500 =  append_fields(hosts_data, ('halo_cV', 'halo_cNFW', 'halo_satflag', 'halo_nsat'), (vratio_temp, cnfw_temp, nflagtemp, subcount))\n",
    "\n",
    "fname = './l0250_d200b.catalog' # file name to z0.0.catalog\n",
    "mthresh = 7e11  # threshold mass in Msun/h\n",
    "gnewton = 4.302e-6\n",
    "lbox = 250.0\n",
    "vhost_min = 240.0\n",
    "vrat_frac = 0.3\n",
    "vsub_min = vhost_min * vrat_frac\n",
    "nrand = 10\n",
    "\n",
    "# define dict pointing to all marks of interest\n",
    "rs_dict = {'halo_id':(0,'i8'), 'halo_mass':(2,'f8'), 'halo_vmax':(3,'f8'), 'halo_rvir':(5,'f8'),\n",
    "           'halo_rs':(6,'f8'), 'halo_x':(8,'f8'), 'halo_y':(9,'f8'), 'halo_z':(10,'f8'),\n",
    "           'halo_spin':(17,'f8'), 'halo_ctoa':(28, 'f8'), 'halo_pid':(41,'i8')}\n",
    "\n",
    "reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_mass':mthresh},\n",
    "                               row_cut_eq_dict={'halo_pid':-1})\n",
    "hosts_data = reader.read_ascii()\n",
    "\n",
    "reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_vmax':vsub_min}, row_cut_neq_dict={'halo_pid':-1})\n",
    "subs_data = reader.read_ascii()\n",
    "\n",
    "# add calculated marks\n",
    "vratio_temp = hosts_data['halo_vmax']/(np.sqrt(gnewton * hosts_data['halo_mass']/hosts_data['halo_rvir']))\n",
    "cnfw_temp = hosts_data['halo_rvir']/hosts_data['halo_rs']\n",
    "\n",
    "# now we want to go host halo by host halo, match pid to subhalos, and\n",
    "# count the number that meet our requirements and then add this on\n",
    "# as both a number and a flag.\n",
    "nflagtemp = np.zeros(len(hosts_data))\n",
    "subcount = np.zeros(len(hosts_data))\n",
    "\n",
    "for i in range(0, len(hosts_data)):\n",
    "   if hosts_data[i]['halo_vmax'] > vhost_min:\n",
    "      nflagtemp[i] = 1\n",
    "      subs_cut = subs_data[np.where(subs_data['halo_pid'] == hosts_data[i]['halo_id'])]\n",
    "      for j in range(0, len(subs_cut)):\n",
    "         xdist = subs_cut[j]['halo_x'] - hosts_data[i]['halo_x']\n",
    "         ydist = subs_cut[j]['halo_y'] - hosts_data[i]['halo_y']\n",
    "         zdist = subs_cut[j]['halo_z'] - hosts_data[i]['halo_z']\n",
    "         totdist = (xdist**2 + ydist**2 + zdist**2)*0.001\n",
    "         if totdist < hosts_data[i]['halo_rvir']:\n",
    "            ratio = subs_cut[j]['halo_vmax']/hosts_data[i]['halo_vmax']\n",
    "            if ratio > vrat_frac:\n",
    "               subcount[i] = subcount[i] + 1\n",
    "\n",
    "hosts_data_l0250 =  append_fields(hosts_data, ('halo_cV', 'halo_cNFW', 'halo_satflag', 'halo_nsat'), (vratio_temp, cnfw_temp, nflagtemp, subcount))\n",
    "\n",
    "fname = './l0125_d200b.catalog' # file name to z0.0.catalog\n",
    "mthresh = 7e10  # threshold mass in Msun/h\n",
    "gnewton = 4.302e-6\n",
    "lbox = 125.0\n",
    "vhost_min = 240.0\n",
    "vrat_frac = 0.3\n",
    "vsub_min = vhost_min * vrat_frac\n",
    "nrand = 10\n",
    "\n",
    "# define dict pointing to all marks of interest\n",
    "rs_dict = {'halo_id':(0,'i8'), 'halo_mass':(2,'f8'), 'halo_vmax':(3,'f8'), 'halo_rvir':(5,'f8'),\n",
    "           'halo_rs':(6,'f8'), 'halo_x':(8,'f8'), 'halo_y':(9,'f8'), 'halo_z':(10,'f8'),\n",
    "           'halo_spin':(17,'f8'), 'halo_ctoa':(28, 'f8'), 'halo_pid':(41,'i8')}\n",
    "\n",
    "reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_mass':mthresh},\n",
    "                               row_cut_eq_dict={'halo_pid':-1})\n",
    "hosts_data = reader.read_ascii()\n",
    "\n",
    "reader = sm.TabularAsciiReader(fname, rs_dict, row_cut_min_dict={'halo_vmax':vsub_min}, row_cut_neq_dict={'halo_pid':-1})\n",
    "subs_data = reader.read_ascii()\n",
    "\n",
    "# add calculated marks\n",
    "vratio_temp = hosts_data['halo_vmax']/(np.sqrt(gnewton * hosts_data['halo_mass']/hosts_data['halo_rvir']))\n",
    "cnfw_temp = hosts_data['halo_rvir']/hosts_data['halo_rs']\n",
    "\n",
    "# now we want to go host halo by host halo, match pid to subhalos, and\n",
    "# count the number that meet our requirements and then add this on\n",
    "# as both a number and a flag.\n",
    "nflagtemp = np.zeros(len(hosts_data))\n",
    "subcount = np.zeros(len(hosts_data))\n",
    "\n",
    "for i in range(0, len(hosts_data)):\n",
    "   if hosts_data[i]['halo_vmax'] > vhost_min:\n",
    "      nflagtemp[i] = 1\n",
    "      subs_cut = subs_data[np.where(subs_data['halo_pid'] == hosts_data[i]['halo_id'])]\n",
    "      for j in range(0, len(subs_cut)):\n",
    "         xdist = subs_cut[j]['halo_x'] - hosts_data[i]['halo_x']\n",
    "         ydist = subs_cut[j]['halo_y'] - hosts_data[i]['halo_y']\n",
    "         zdist = subs_cut[j]['halo_z'] - hosts_data[i]['halo_z']\n",
    "         totdist = (xdist**2 + ydist**2 + zdist**2)*0.001\n",
    "         if totdist < hosts_data[i]['halo_rvir']:\n",
    "            ratio = subs_cut[j]['halo_vmax']/hosts_data[i]['halo_vmax']\n",
    "            if ratio > vrat_frac:\n",
    "               subcount[i] = subcount[i] + 1\n",
    "\n",
    "hosts_data_l0125 =  append_fields(hosts_data, ('halo_cV', 'halo_cNFW', 'halo_satflag', 'halo_nsat'), (vratio_temp, cnfw_temp, nflagtemp, subcount))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "total_bins = 20\n",
    "\n",
    "X1 = np.log10(hosts_data_l0125['halo_mass'])\n",
    "Y1 = hosts_data_l0125['halo_ctoa']\n",
    "X2 = np.log10(hosts_data_l0250['halo_mass'])\n",
    "Y2 = hosts_data_l0250['halo_ctoa']\n",
    "X3 = np.log10(hosts_data_l0500['halo_mass'])\n",
    "Y3 = hosts_data_l0500['halo_ctoa']\n",
    "\n",
    "\n",
    "bins = np.linspace(X1.min(),X1.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X1,bins)\n",
    "running_median = [np.median(Y1[idx==k]) for k in range(total_bins)]\n",
    "\n",
    "running_std    = [Y1[idx==k].std() for k in range(total_bins)]\n",
    "#plt.plot(bins-delta/2,running_median, 'b--', label='L0125', lw=2)\n",
    "\n",
    "bins = np.linspace(X2.min(),X2.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X2,bins)\n",
    "running_median = [np.median(Y2[idx==k]) for k in range(total_bins)]\n",
    "\n",
    "running_std    = [Y2[idx==k].std() for k in range(total_bins)]\n",
    "plt.plot(bins-delta/2,running_median, color='r', label='L0250', lw=2)\n",
    "plt.errorbar(bins-delta/2,running_median,\n",
    "              running_std, color='r', lw=2)\n",
    "\n",
    "bins = np.linspace(X3.min(),X3.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X3,bins)\n",
    "running_median = [np.median(Y3[idx==k]) for k in range(total_bins)]\n",
    "\n",
    "running_std    = [Y3[idx==k].std() for k in range(total_bins)]\n",
    "plt.plot(bins-delta/2,running_median, 'c-.', label='L0500', lw=2)\n",
    "\n",
    "plt.axvline(x=np.log10(2e12), color='b', lw=4)\n",
    "plt.axvline(x=np.log10(8e12), color='r', lw=4)\n",
    "plt.axvline(x=np.log10(4e13), color='c', lw=4)\n",
    "\n",
    "plt.xlim(np.log10(1e10), 15)\n",
    "#plt.ylim(0.4,1)\n",
    "\n",
    "plt.legend(numpoints=1, loc='lower right', prop={'size':13})\n",
    "plt.xlabel(r'$\\log M_{\\mathrm{200}} (h^{-1}M_{\\odot})$')\n",
    "plt.ylabel(r'$c/a$')\n",
    "plt.tight_layout(pad=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n1, bins1, patches1 = plt.hist(Y1[mask1], 50, facecolor='blue', normed=1, alpha=0.5)\n",
    "n2, bins2, patches2 = plt.hist(Y2[mask2], 50, facecolor='red', normed=1, alpha=0.5)\n",
    "n3, bins3, patches3 = plt.hist(Y3[mask3], 50, facecolor='cyan',normed=1, alpha=0.5)\n",
    "plt.savefig('shapedist.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "font = {'family' : 'normal',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 16}\n",
    "\n",
    "matplotlib.rc('font', **font)\n",
    "matplotlib.rcParams['lines.linewidth'] = 3\n",
    "\n",
    "total_bins = 21\n",
    "\n",
    "X2 = np.log10(hosts_data_l0250['halo_mass'])\n",
    "Y2 = hosts_data_l0250['halo_cV']\n",
    "\n",
    "bins = np.linspace(X2.min(),X2.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X2,bins)\n",
    "running_median = [np.median(Y2[idx==k]) for k in range(1,total_bins)]\n",
    "running_std    = [Y2[idx==k].std() for k in range(1,total_bins)]\n",
    "running_spread = np.zeros((2,total_bins-1))\n",
    "for k in range(1,total_bins):\n",
    "    running_spread[0,k-1] = running_median[k-1]- np.percentile(Y2[idx==k],16)\n",
    "    running_spread[1,k-1] = np.percentile(Y2[idx==k],84) - running_median[k-1]\n",
    "\n",
    "plt.plot(bins[1:]-delta/2,running_median, color='r', label='L0250', lw=2)\n",
    "plt.errorbar(bins[1:]-delta/2,running_median, running_spread, color='r', lw=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print np.percentile(Y2[idx==k],16), running_median[k-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X1 = np.log10(hosts_data_l0125['halo_mass'])\n",
    "Y1 = hosts_data_l0125['halo_cNFW']\n",
    "X2 = np.log10(hosts_data_l0250['halo_mass'])\n",
    "Y2 = hosts_data_l0250['halo_cNFW']\n",
    "X3 = np.log10(hosts_data_l0500['halo_mass'])\n",
    "Y3 = hosts_data_l0500['halo_cNFW']\n",
    "\n",
    "\n",
    "bins = np.linspace(X3.min(),X3.max(), total_bins)\n",
    "delta = bins[1]-bins[0]\n",
    "idx  = np.digitize(X3,bins)\n",
    "running_median = [np.median(Y3[idx==k]) for k in range(1,total_bins)]\n",
    "running_std    = [Y3[idx==k].std() for k in range(1,total_bins)]\n",
    "running_spread = np.zeros((2,total_bins-1))\n",
    "\n",
    "for k in range(1,total_bins):\n",
    "    running_spread[0,k-1] = running_median[k-1]- np.percentile(Y3[idx==k],16)\n",
    "    running_spread[1,k-1] = np.percentile(Y3[idx==k],84) - running_median[k-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print bins[1:14]\n",
    "print (X3[idx==14])\n",
    "bins[15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "inf"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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