DeepConsistency.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import os\n",
    "import numpy as np\n",
    "#from read_psf_cats import read_data, band_combinations\n",
    "import fitsio\n",
    "import treecorr\n",
    "import matplotlib\n",
    "#matplotlib.use('Agg')\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.io import fits\n",
    "import h5py as h\n",
    "from astropy.table import Table,join\n",
    "from astropy.table import Table\n",
    "import esutil as eu\n",
    "import fitsio\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "### Testing consistency of the 3 SN fields \n",
    "import matplotlib.colors as mcolors\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "des_deep = \"/global/cscratch1/sd/aamon/DEEP/MOFcats/run-ugriz-mof02.fits\"\n",
    "des_deep2=\"/global/cscratch1/sd/aamon/DEEP/MOFcats/run-sn-JHK-ff03.fits\"\n",
    "deep = fitsio.read(des_deep)\n",
    "deep2 = fitsio.read(des_deep2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2826988\n",
      "1906261\n"
     ]
    }
   ],
   "source": [
    "print(len(deep['bdf_mag']))\n",
    "print(len(deep2['bdf_mag']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('id', '>i8'), ('ra', '>f8'), ('dec', '>f8'), ('fof_id', '>i8'), ('fof_size', '>i4'), ('mask_flags', '>i4'), ('flags', '>i4'), ('flagstr', 'S18'), ('badpix_frac', '>f4'), ('psf_g', '>f8', (2,)), ('psf_T', '>f8'), ('psf_flux_flags', '>i4', (3,)), ('psf_flux', '>f8', (3,)), ('psf_mag', '>f8', (3,)), ('psf_flux_err', '>f8', (3,)), ('psf_flux_s2n', '>f8', (3,)), ('bdf_flags', '>i4', (3,)), ('bdf_deblend_flags', '>i4'), ('bdf_ntry', '>i2'), ('bdf_nfev', '>i4'), ('bdf_s2n', '>f8'), ('bdf_pars', '>f8', (3,)), ('bdf_pars_err', '>f8', (3,)), ('bdf_pars_cov', '>f8', (3, 3)), ('bdf_flux', '>f8', (3,)), ('bdf_mag', '>f8', (3,)), ('bdf_flux_cov', '>f8', (3, 3)), ('bdf_flux_err', '>f8', (3,)), ('gap_flux', '>f8', (3,)), ('gap_flux_err', '>f8', (3,)), ('gap_mag', '>f8', (3,)), ('tilename', 'S12'), ('EBV_SFD98', '>f8'), ('bdf_flux_dered', '>f8', (3,)), ('bdf_flux_err_dered', '>f8', (3,)), ('bdf_mag_dered', '>f8', (3,)), ('bdf_mag_err_dered', '>f8', (3,))]\n",
      "1906261\n",
      "raw length deep : 1906261\n",
      "length deep post flags: 1464986\n",
      "length deep post mask flags: 1427217\n",
      "1427217\n",
      "1427217\n",
      "[[ 37.4798511   21.22978024  20.59510937]\n",
      " [ 22.65154319  22.69168267  21.64006423]\n",
      " [ 37.4800908   24.97826824  21.31375563]\n",
      " ..., \n",
      " [ 37.48060545  24.86236759  22.691785  ]\n",
      " [ 25.26795611  37.48805331  23.88830948]\n",
      " [ 22.72117427  37.48805331  37.49165628]]\n"
     ]
    }
   ],
   "source": [
    "# set-up\n",
    "mag_lim = 26.\n",
    "lowlim=18.\n",
    "nbin = 31 # resolution of 2d histograms\n",
    "nlevel = 3 # number of contour levels\n",
    "\n",
    "\"\n",
    "des_deep = \"/global/cscratch1/sd/aamon/DEEP/MOFcats/run-ugriz-mof02_extcorr.fits\"\n",
    "des_deep=\"/global/cscratch1/sd/aamon/DEEP/MOFcats/run-sn-JHK-ff03_extcorr.fits\"\n",
    "\n",
    "deep = fitsio.read(des_deep)\n",
    "print(deep.dtype)\n",
    "print(len(deep['bdf_mag_dered']))\n",
    "\n",
    "print('raw length deep :', len(deep))\n",
    "deep=deep[deep['flags']==0]\n",
    "print('length deep post flags:', len(deep))\n",
    "deep=deep[deep['mask_flags']==0]\n",
    "print('length deep post mask flags:', len(deep))\n",
    "print(len(deep))\n",
    "\n",
    "ind = eu.numpy_util.strmatch(deep['tilename'], 'SN-C3*')\n",
    "\n",
    "print(len(ind))\n",
    "C3=deep[ind]\n",
    "ind = eu.numpy_util.strmatch(deep['tilename'], 'SN-X3*')\n",
    "X3=deep[ind]\n",
    "ind = eu.numpy_util.strmatch(deep['tilename'], 'SN-E2*')\n",
    "E2=deep[ind]\n",
    "ind = eu.numpy_util.strmatch(deep['tilename'], 'COSMOS*')\n",
    "C=deep[ind]\n",
    "\n",
    "\n",
    "Xlab = 'X3'\n",
    "Clab = 'C3'\n",
    "Coslab = 'Cosmos'\n",
    "Elab = 'E2'\n",
    "\n",
    "print(X3['bdf_mag_dered'])\n",
    "\n",
    "# Colour limits\n",
    "ug_lim = [-1., 2.8] \n",
    "gr_lim = [-1., 2.8] \n",
    "ri_lim = [-1., 2.8] \n",
    "iz_lim = [-1., 2.8] \n",
    "zy_lim = [-1., 2.8]\n",
    "yJ_lim = [-0.8, 2.0]\n",
    "JH_lim = [-1., 1.8]\n",
    "HK_lim = [-1., 1.8]\n",
    "\n",
    "\n",
    "# form colour limits into an dictionary\n",
    "col_lims = {0:ug_lim, 1:gr_lim, 2:ri_lim, 3:iz_lim } \n",
    "col_lims = { 0:JH_lim, 1:HK_lim}\n",
    "# axis label dictionary\n",
    "ax_labs = {0:r'$u-g$', 1:r'$g-r$', 2:r'$r-i$', 3:r'$i-z$'}\n",
    "ax_labs = { 0:r'$J-H$', 1:r'$H-K$'}\n",
    "# filenames\n",
    "fnames = {0:'ug', 1:'gr', 2:'ri', 3:'iz'} \n",
    "fnames= { 0:'JH', 1:'HK'}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nX3 = X3[(X3['bdf_mag'][:,3]<mag_lim)&(X3['bdf_mag'][:,3]>lowlim)]\\nC3 = C3[(C3['bdf_mag'][:,3]<mag_lim)&(C3['bdf_mag'][:,3]>lowlim)]\\nE2 = E2[(E2['bdf_mag'][:,3]<mag_lim)&(E2['bdf_mag'][:,3]>lowlim)]\\nC = C[(C['bdf_mag'][:,3]<mag_lim)&(C['bdf_mag'][:,3]>15.)]\\n\\n# cut to 'stars' or galaxies\\nif stars==True:\\n    X3 = X3[(X3['bdf_T']>-0.1)&(X3['bdf_T']<0.2)&(X3['psf_mag'][:,3]<20.)]\\n    C3 = C3[(C3['bdf_T']>-0.1)&(C3['bdf_T']<0.2)&(C3['psf_mag'][:,3]<20.)]\\n    E2 = E2[(E2['bdf_T']>-0.1)&(E2['bdf_T']<0.2)&(E2['psf_mag'][:,3]<20.)]\\n    C = C[(C['bdf_T']>-0.1)&(C['bdf_T']<0.2)&(C['psf_mag'][:,3]<20.)]\\nelse:\\n    X3 = X3[(X3['bdf_T']>0.2)]\\n    C3 = C3[(C3['bdf_T']>0.2)]\\n    E2 = E2[(E2['bdf_T']>0.2)]\\n    C = C[(C['bdf_T']>0.2)]\\n    \""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cut to mag_lim\n",
    "'''\n",
    "X3 = X3[(X3['bdf_mag'][:,3]<mag_lim)&(X3['bdf_mag'][:,3]>lowlim)]\n",
    "C3 = C3[(C3['bdf_mag'][:,3]<mag_lim)&(C3['bdf_mag'][:,3]>lowlim)]\n",
    "E2 = E2[(E2['bdf_mag'][:,3]<mag_lim)&(E2['bdf_mag'][:,3]>lowlim)]\n",
    "C = C[(C['bdf_mag'][:,3]<mag_lim)&(C['bdf_mag'][:,3]>15.)]\n",
    "\n",
    "# cut to 'stars' or galaxies\n",
    "if stars==True:\n",
    "    X3 = X3[(X3['bdf_T']>-0.1)&(X3['bdf_T']<0.2)&(X3['psf_mag'][:,3]<20.)]\n",
    "    C3 = C3[(C3['bdf_T']>-0.1)&(C3['bdf_T']<0.2)&(C3['psf_mag'][:,3]<20.)]\n",
    "    E2 = E2[(E2['bdf_T']>-0.1)&(E2['bdf_T']<0.2)&(E2['psf_mag'][:,3]<20.)]\n",
    "    C = C[(C['bdf_T']>-0.1)&(C['bdf_T']<0.2)&(C['psf_mag'][:,3]<20.)]\n",
    "else:\n",
    "    X3 = X3[(X3['bdf_T']>0.2)]\n",
    "    C3 = C3[(C3['bdf_T']>0.2)]\n",
    "    E2 = E2[(E2['bdf_T']>0.2)]\n",
    "    C = C[(C['bdf_T']>0.2)]\n",
    "    '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.8871134882 37.5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'if stars==True:\\n    X3 = X3[(X3[\\'bdf_T\\']>-0.1)&(X3[\\'bdf_T\\']<0.2)&(X3[\\'psf_mag\\'][:,3]<20.)]\\n    C3 = C3[(C3[\\'bdf_T\\']>-0.1)&(C3[\\'bdf_T\\']<0.2)&(C3[\\'psf_mag\\'][:,3]<20.)]\\n    E2 = E2[(E2[\\'bdf_T\\']>-0.1)&(E2[\\'bdf_T\\']<0.2)&(E2[\\'psf_mag\\'][:,3]<20.)]\\n    C = C[(C[\\'bdf_T\\']>-0.1)&(C[\\'bdf_T\\']<0.2)&(C[\\'psf_mag\\'][:,3]<20.)]\\nelse:\\n    print(\"here\")\\n    X3 = X3[(X3[\\'bdf_T\\']>0.2)]\\n    C3 = C3[(C3[\\'bdf_T\\']>0.2)]\\n    E2 = E2[(E2[\\'bdf_T\\']>0.2)]\\n    C = C[(C[\\'bdf_T\\']>0.2)]\\n'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cut to mag_lim\n",
    "#X3 = X3[(X3['bdf_mag'][:,3]<mag_lim)&(X3['bdf_mag'][:,3]>lowlim)]\n",
    "#C3 = C3[(C3['bdf_mag'][:,3]<mag_lim)&(C3['bdf_mag'][:,3]>lowlim)]\n",
    "#E2 = E2[(E2['bdf_mag'][:,3]<mag_lim)&(E2['bdf_mag'][:,3]>lowlim)]\n",
    "#C = C[(C['bdf_mag'][:,3]<mag_lim)&(C['bdf_mag'][:,3]>lowlim)]\n",
    "\n",
    "print(min(C3['bdf_mag'][:,0]), max(C3['bdf_mag'][:,0]))\n",
    "X3 = X3[(X3['bdf_mag'][:,0]<mag_lim)&(X3['bdf_mag'][:,0]>lowlim)]\n",
    "C3 = C3[(C3['bdf_mag'][:,0]<mag_lim)&(C3['bdf_mag'][:,0]>lowlim)]\n",
    "E2 = E2[(E2['bdf_mag'][:,0]<mag_lim)&(E2['bdf_mag'][:,0]>lowlim)]\n",
    "C = C[(C['bdf_mag'][:,0]<mag_lim)&(C['bdf_mag'][:,0]>lowlim)]\n",
    "#Will had some star selection, but I haven't applied that \n",
    "#as we don't include these cuts in the SOM\n",
    "# cut to 'stars' or galaxies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shift_cen(arr):    \n",
    "    return arr[:-1] + (arr[1]-arr[0])/2.\n",
    "\n",
    "# Colour-colour plots\n",
    "\n",
    "def col_col_contour(xcol, ycol, save_fig=False):\n",
    "\n",
    "    # 2d hist\n",
    "    x_edges = col_lims[xcol][0] + (col_lims[xcol][1] - col_lims[xcol][0]) * np.arange(nbin+1) / nbin\n",
    "    y_edges = col_lims[ycol][0] + (col_lims[ycol][1] - col_lims[ycol][0]) * np.arange(nbin+1) / nbin\n",
    "\n",
    "    if stars==True:\n",
    "        z_c3 = np.histogram2d(C3['psf_mag'][:,xcol]-C3['psf_mag'][:,xcol+1], \n",
    "                        C3['psf_mag'][:,ycol]-C3['psf_mag'][:,ycol+1],\n",
    "                       bins=[x_edges, y_edges], normed=True)\n",
    "\n",
    "        z_x3 = np.histogram2d(X3['psf_mag'][:,xcol]-X3['psf_mag'][:,xcol+1], \n",
    "                        X3['psf_mag'][:,ycol]-X3['psf_mag'][:,ycol+1],\n",
    "                       bins=[x_edges, y_edges], normed=True)\n",
    "        \n",
    "        z_e2 = np.histogram2d(E2['psf_mag'][:,xcol]-E2['psf_mag'][:,xcol+1], \n",
    "                        E2['psf_mag'][:,ycol]-E2['psf_mag'][:,ycol+1],\n",
    "                       bins=[x_edges, y_edges], normed=True)\n",
    "        \n",
    "        z_c = np.histogram2d(C['psf_mag'][:,xcol]-C['psf_mag'][:,xcol+1], \n",
    "                        C['psf_mag'][:,ycol]-C['psf_mag'][:,ycol+1],\n",
    "                       bins=[x_edges, y_edges],normed=True)\n",
    "    else:\n",
    "        z_c3 = np.histogram2d(C3['bdf_mag_dered'][:,xcol]-C3['bdf_mag_dered'][:,xcol+1], \n",
    "                        C3['bdf_mag_dered'][:,ycol]-C3['bdf_mag_dered'][:,ycol+1],\n",
    "                       bins=[x_edges, y_edges], normed=True)\n",
    "\n",
    "        z_x3 = np.histogram2d(X3['bdf_mag_dered'][:,xcol]-X3['bdf_mag_dered'][:,xcol+1], \n",
    "                        X3['bdf_mag_dered'][:,ycol]-X3['bdf_mag_dered'][:,ycol+1],\n",
    "                       bins=[x_edges, y_edges], normed=True)\n",
    "        \n",
    "        z_e2 = np.histogram2d(E2['psf_mag'][:,xcol]-E2['psf_mag'][:,xcol+1], \n",
    "                        E2['psf_mag'][:,ycol]-E2['psf_mag'][:,ycol+1],\n",
    "                       bins=[x_edges, y_edges], normed=True)\n",
    "\n",
    "        z_c = np.histogram2d(C['bdf_mag_dered'][:,xcol]-C['bdf_mag_dered'][:,xcol+1], \n",
    "                        C['bdf_mag_dered'][:,ycol]-C['bdf_mag_dered'][:,ycol+1],\n",
    "                       bins=[x_edges, y_edges],normed=True)\n",
    "        \n",
    "    # contour levels\n",
    "    levels = 10.**((np.arange(nlevel+1) * (np.log10(np.max(z_c3[0])*0.95)+1) / nlevel) -1)\n",
    "    X, Y = np.meshgrid(shift_cen(x_edges), shift_cen(y_edges))\n",
    "\n",
    "    \n",
    "    # plot\n",
    "    fig = plt.figure(figsize=[7,6],dpi=100)\n",
    "    fig.subplots_adjust(wspace=0.18, hspace=0.0,left=0.15,bottom=0.15,right=0.98,top=0.98)\n",
    "    ax = fig.add_subplot(111)\n",
    "\n",
    "    ax.set_xlabel(ax_labs[xcol], fontsize=18)\n",
    "    ax.set_ylabel(ax_labs[ycol], fontsize=18)\n",
    "    ax.set_xlim(col_lims[xcol])\n",
    "    ax.set_ylim(col_lims[ycol])\n",
    "    ax.set_title('J = %s - %s' %(lowlim, mag_lim))\n",
    "\n",
    "    ax.contourf(X, Y, z_c[0].T, levels=levels, label=Coslab, colors='orange',alpha=0.7)\n",
    "    ax.contour(X, Y, z_c3[0].T, levels=levels, label=Clab, colors='blue')\n",
    "    ax.contour(X, Y, z_x3[0].T, levels=levels, label=Xlab, colors='green')\n",
    "    ax.contour(X, Y, z_e2[0].T, levels=levels, label=Elab, colors='magenta')\n",
    "    \n",
    "    ax_ranges = [ax.get_xlim()[1]-ax.get_xlim()[0], ax.get_ylim()[1]-ax.get_ylim()[0]]\n",
    "    ax_starts = [ax.get_xlim()[1]-0.15*ax_ranges[0], ax.get_ylim()[0]+0.25*ax_ranges[1]]\n",
    "    #ax.quiver(ax_starts[0], ax_starts[1], ax_ranges[0]/10., -1.*ax_ranges[1]/10., scale=3.)\n",
    "    \n",
    "    ax.plot([ax.get_xlim()[1]+0.3*ax_ranges[0], ax.get_xlim()[1]+0.35*ax_ranges[0]], \n",
    "            [ax.get_ylim()[0]-0.3*ax_ranges[1], ax.get_ylim()[0]-0.3*ax_ranges[1]], label=Xlab, color='green')\n",
    "\n",
    "    ax.plot([ax.get_xlim()[1]+0.3*ax_ranges[0], ax.get_xlim()[1]+0.35*ax_ranges[0]], \n",
    "            [ax.get_ylim()[0]-0.3*ax_ranges[1], ax.get_ylim()[0]-0.3*ax_ranges[1]], label=Clab, color='blue')\n",
    "    ax.plot([ax.get_xlim()[1]+0.3*ax_ranges[0], ax.get_xlim()[1]+0.35*ax_ranges[0]], \n",
    "            [ax.get_ylim()[0]-0.3*ax_ranges[1], ax.get_ylim()[0]-0.3*ax_ranges[1]], label=Elab, color='magenta')\n",
    "    ax.plot([ax.get_xlim()[1]+0.3*ax_ranges[0], ax.get_xlim()[1]+0.35*ax_ranges[0]], \n",
    "            [ax.get_ylim()[0]-0.3*ax_ranges[1], ax.get_ylim()[0]-0.3*ax_ranges[1]], label=Coslab, color='orange')\n",
    "        \n",
    "\n",
    "    ax.legend(loc=1)\n",
    "    \n",
    "    \n",
    "    # save?\n",
    "    if save_fig==True:\n",
    "        if stars==True:\n",
    "            fig.savefig('plot_stars_{}_{}_{}_{}.png'.format(Xlab, Clab, fnames[xcol], fnames[ycol]))\n",
    "        else:\n",
    "            fig.savefig('plot_galaxies_{}_{}_{}_{}.png'.format(Xlab, Clab, fnames[xcol], fnames[ycol]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-36-a2d36e491fbd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mcol_col_contour\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_fig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcol_col_contour\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_fig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mcol_col_contour\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_fig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mcol_col_contour\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_fig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#col_col_contour(4, 5, save_fig=True)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-35-94cd0d105913>\u001b[0m in \u001b[0;36mcol_col_contour\u001b[0;34m(xcol, ycol, save_fig)\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;31m# 2d hist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mx_edges\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcol_lims\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcol_lims\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mcol_lims\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbin\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnbin\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0my_edges\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcol_lims\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mycol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcol_lims\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mycol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mcol_lims\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mycol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbin\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnbin\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstars\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 2"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aac135b0950>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_col_contour(0, 1, save_fig=True)\n",
    "col_col_contour(1, 2, save_fig=True)\n",
    "col_col_contour(2, 3, save_fig=True)\n",
    "col_col_contour(3, 4, save_fig=True)\n",
    "#col_col_contour(4, 5, save_fig=True)\n",
    "##col_col_contour(5, 6, save_fig=True)\n",
    "#col_col_contour(6, 7, save_fig=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "desenv",
   "language": "python",
   "name": "desenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}