check_sgng5minus2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#checking the function to interpolate (5sg-2)ng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$ \n",
      "Rescaling the galaxy number density by a factor of 104185.986576 to match the gold sample with 6330073646.61 total galaxies \n",
      "#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$#$ \n",
      "\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from __future__ import division\n",
    "import os\n",
    "import sys\n",
    "from scipy import integrate, optimize\n",
    "\n",
    "from scipy.interpolate import interp2d, interp1d\n",
    "\n",
    "import numpy as np\n",
    "from numpy import pi,sin,cos,tan,e,arctan,arcsin,arccos,sqrt\n",
    "from numpy.random import randn\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as P\n",
    "from time import clock\n",
    "\n",
    "\n",
    "\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "matplotlib.rcParams['figure.figsize'] = (9, 6)\n",
    "P.rcParams['text.usetex'] = True  # not really needed\n",
    "P.rcParams['text.latex.preamble'] = [r'\\usepackage{amsmath}'] #for \\text command\n",
    "P.rcParams[\"font.size\"] = 24\n",
    "P.rc('xtick', labelsize=20) \n",
    "P.rc('ytick', labelsize=20)\n",
    "# P.rc('xtick', labelsize=20) \n",
    "# P.rc('ytick', labelsize=20)\n",
    "np.set_printoptions(threshold=np.nan)\n",
    "\n",
    "\n",
    "from magmod import sg5minus2, dndz_fit, sgng_interp, dndz_norm, nofz\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 50) (100, 50)\n",
      "(100, 50) (100, 50)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "ztab = np.linspace(0.0005,2., 100)\n",
    "mtab = np.linspace(19,27,50)\n",
    "\n",
    "magmesh, zmesh = np.meshgrid(mtab, ztab)\n",
    "print magmesh.shape, zmesh.shape\n",
    "\n",
    "truemesh = np.zeros(zmesh.shape)\n",
    "interp_sgngmesh = np.zeros(zmesh.shape)\n",
    "\n",
    "truenorm = np.zeros(mtab.shape)\n",
    "fit_norm = np.zeros(mtab.shape)\n",
    "\n",
    "print truemesh.shape, interp_sgngmesh.shape, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 19.0 for experiment\n",
      "Using non default rmax = 19.1632653061 for experiment\n",
      "Using non default rmax = 19.3265306122 for experiment\n",
      "Using non default rmax = 19.4897959184 for experiment\n",
      "Using non default rmax = 19.6530612245 for experiment\n",
      "Using non default rmax = 19.8163265306 for experiment\n",
      "Using non default rmax = 19.9795918367 for experiment\n",
      "Using non default rmax = 20.1428571429 for experiment\n",
      "Using non default rmax = 20.306122449 for experiment\n",
      "Using non default rmax = 20.4693877551 for experiment\n",
      "Using non default rmax = 20.6326530612 for experiment\n",
      "Using non default rmax = 20.7959183673 for experiment\n",
      "Using non default rmax = 20.9591836735 for experiment\n",
      "Using non default rmax = 21.1224489796 for experiment\n",
      "Using non default rmax = 21.2857142857 for experiment\n",
      "Using non default rmax = 21.4489795918 for experiment\n",
      "Using non default rmax = 21.612244898 for experiment\n",
      "Using non default rmax = 21.7755102041 for experiment\n",
      "Using non default rmax = 21.9387755102 for experiment\n",
      "Using non default rmax = 22.1020408163 for experiment\n",
      "Using non default rmax = 22.2653061224 for experiment\n",
      "Using non default rmax = 22.4285714286 for experiment\n",
      "Using non default rmax = 22.5918367347 for experiment\n",
      "Using non default rmax = 22.7551020408 for experiment\n",
      "Using non default rmax = 22.9183673469 for experiment\n",
      "Using non default rmax = 23.0816326531 for experiment\n",
      "Using non default rmax = 23.2448979592 for experiment\n",
      "Using non default rmax = 23.4081632653 for experiment\n",
      "Using non default rmax = 23.5714285714 for experiment\n",
      "Using non default rmax = 23.7346938776 for experiment\n",
      "Using non default rmax = 23.8979591837 for experiment\n",
      "Using non default rmax = 24.0612244898 for experiment\n",
      "Using non default rmax = 24.2244897959 for experiment\n",
      "Using non default rmax = 24.387755102 for experiment\n",
      "Using non default rmax = 24.5510204082 for experiment\n",
      "Using non default rmax = 24.7142857143 for experiment\n",
      "Using non default rmax = 24.8775510204 for experiment\n",
      "Using non default rmax = 25.0408163265 for experiment\n",
      "Using non default rmax = 25.2040816327 for experiment\n",
      "Using non default rmax = 25.3673469388 for experiment\n",
      "Using non default rmax = 25.5306122449 for experiment\n",
      "Using non default rmax = 25.693877551 for experiment\n",
      "Using non default rmax = 25.8571428571 for experiment\n",
      "Using non default rmax = 26.0204081633 for experiment\n",
      "Using non default rmax = 26.1836734694 for experiment\n",
      "Using non default rmax = 26.3469387755 for experiment\n",
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      "Using non default rmax = 26.6734693878 for experiment\n",
      "Using non default rmax = 26.8367346939 for experiment\n",
      "Using non default rmax = 27.0 for experiment\n",
      "Using non default rmax = 19.0 for experiment\n",
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      "Using non default rmax = 19.3265306122 for experiment\n",
      "Using non default rmax = 19.4897959184 for experiment\n",
      "Using non default rmax = 19.6530612245 for experiment\n",
      "Using non default rmax = 19.8163265306 for experiment\n",
      "Using non default rmax = 19.9795918367 for experiment\n",
      "Using non default rmax = 20.1428571429 for experiment\n",
      "Using non default rmax = 20.306122449 for experiment\n",
      "Using non default rmax = 20.4693877551 for experiment\n",
      "Using non default rmax = 20.6326530612 for experiment\n",
      "Using non default rmax = 20.7959183673 for experiment\n",
      "Using non default rmax = 20.9591836735 for experiment\n",
      "Using non default rmax = 21.1224489796 for experiment\n",
      "Using non default rmax = 21.2857142857 for experiment\n",
      "Using non default rmax = 21.4489795918 for experiment\n",
      "Using non default rmax = 21.612244898 for experiment\n",
      "Using non default rmax = 21.7755102041 for experiment\n",
      "Using non default rmax = 21.9387755102 for experiment\n",
      "Using non default rmax = 22.1020408163 for experiment\n",
      "Using non default rmax = 22.2653061224 for experiment\n",
      "Using non default rmax = 22.4285714286 for experiment\n",
      "Using non default rmax = 22.5918367347 for experiment\n",
      "Using non default rmax = 22.7551020408 for experiment\n",
      "Using non default rmax = 22.9183673469 for experiment\n",
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      "Using non default rmax = 23.2448979592 for experiment\n",
      "Using non default rmax = 23.4081632653 for experiment\n",
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      "Using non default rmax = 23.7346938776 for experiment\n",
      "Using non default rmax = 23.8979591837 for experiment\n",
      "Using non default rmax = 24.0612244898 for experiment\n",
      "Using non default rmax = 24.2244897959 for experiment\n",
      "Using non default rmax = 24.387755102 for experiment\n",
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      "Using non default rmax = 24.7142857143 for experiment\n",
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      "Using non default rmax = 25.0408163265 for experiment\n",
      "Using non default rmax = 25.2040816327 for experiment\n",
      "Using non default rmax = 25.3673469388 for experiment\n",
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      "Using non default rmax = 26.3469387755 for experiment\n",
      "Using non default rmax = 26.5102040816 for experiment\n",
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      "Using non default rmax = 27.0 for experiment\n",
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      "Using non default rmax = 21.1224489796 for experiment\n",
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      "Using non default rmax = 22.5918367347 for experiment\n",
      "Using non default rmax = 22.7551020408 for experiment\n",
      "Using non default rmax = 22.9183673469 for experiment\n",
      "Using non default rmax = 23.0816326531 for experiment\n",
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      "Using non default rmax = 23.4081632653 for experiment\n",
      "Using non default rmax = 23.5714285714 for experiment\n",
      "Using non default rmax = 23.7346938776 for experiment\n",
      "Using non default rmax = 23.8979591837 for experiment\n",
      "Using non default rmax = 24.0612244898 for experiment\n",
      "Using non default rmax = 24.2244897959 for experiment\n",
      "Using non default rmax = 24.387755102 for experiment\n",
      "Using non default rmax = 24.5510204082 for experiment\n",
      "Using non default rmax = 24.7142857143 for experiment\n",
      "Using non default rmax = 24.8775510204 for experiment\n",
      "Using non default rmax = 25.0408163265 for experiment\n",
      "Using non default rmax = 25.2040816327 for experiment\n",
      "Using non default rmax = 25.3673469388 for experiment\n",
      "Using non default rmax = 25.5306122449 for experiment\n",
      "Using non default rmax = 25.693877551 for experiment\n",
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      "Using non default rmax = 26.1836734694 for experiment\n",
      "Using non default rmax = 26.3469387755 for experiment\n",
      "Using non default rmax = 26.5102040816 for experiment\n",
      "Using non default rmax = 26.6734693878 for experiment\n",
      "Using non default rmax = 26.8367346939 for experiment\n",
      "Using non default rmax = 27.0 for experiment\n",
      "Using non default rmax = 19.0 for experiment\n",
      "Using non default rmax = 19.1632653061 for experiment\n",
      "Using non default rmax = 19.3265306122 for experiment\n",
      "Using non default rmax = 19.4897959184 for experiment\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 19.6530612245 for experiment\n",
      "Using non default rmax = 19.8163265306 for experiment\n",
      "Using non default rmax = 19.9795918367 for experiment\n",
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      "Using non default rmax = 27.0 for experiment\n",
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      "Using non default rmax = 27.0 for experiment\n",
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      "Using non default rmax = 19.1632653061 for experiment\n",
      "Using non default rmax = 19.3265306122 for experiment\n",
      "Using non default rmax = 19.4897959184 for experiment\n",
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      "Using non default rmax = 19.9795918367 for experiment\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 20.1428571429 for experiment\n",
      "Using non default rmax = 20.306122449 for experiment\n",
      "Using non default rmax = 20.4693877551 for experiment\n",
      "Using non default rmax = 20.6326530612 for experiment\n",
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      "Using non default rmax = 22.1020408163 for experiment\n",
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      "Using non default rmax = 25.2040816327 for experiment\n",
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      "Using non default rmax = 25.5306122449 for experiment\n",
      "Using non default rmax = 25.693877551 for experiment\n",
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      "Using non default rmax = 26.0204081633 for experiment\n",
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      "Using non default rmax = 19.6530612245 for experiment\n",
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      "Using non default rmax = 21.4489795918 for experiment\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 21.612244898 for experiment\n",
      "Using non default rmax = 21.7755102041 for experiment\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 22.1020408163 for experiment\n",
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      "Using non default rmax = 23.2448979592 for experiment\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 23.4081632653 for experiment\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 23.7346938776 for experiment\n",
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      "Using non default rmax = 24.2244897959 for experiment\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 24.387755102 for experiment\n",
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      "Using non default rmax = 24.7142857143 for experiment\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 25.3673469388 for experiment\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 26.5102040816 for experiment\n",
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      "Using non default rmax = 21.4489795918 for experiment\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 21.612244898 for experiment\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 22.4285714286 for experiment\n",
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      "Using non default rmax = 19.4897959184 for experiment\n",
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      "Using non default rmax = 23.4081632653 for experiment\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 23.5714285714 for experiment\n",
      "Using non default rmax = 23.7346938776 for experiment\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 24.7142857143 for experiment\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 25.3673469388 for experiment\n",
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      "Using non default rmax = 22.4285714286 for experiment\n",
      "Using non default rmax = 22.5918367347 for experiment\n",
      "Using non default rmax = 22.7551020408 for experiment\n",
      "Using non default rmax = 22.9183673469 for experiment\n",
      "Using non default rmax = 23.0816326531 for experiment\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/integrate/quadpack.py:385: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
      "  warnings.warn(msg, IntegrationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 23.2448979592 for experiment\n",
      "Using non default rmax = 23.4081632653 for experiment\n",
      "Using non default rmax = 23.5714285714 for experiment\n",
      "Using non default rmax = 23.7346938776 for experiment\n",
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      "Using non default rmax = 27.0 for experiment\n",
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      "Using non default rmax = 26.3469387755 for experiment\n",
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      "Using non default rmax = 23.7346938776 for experiment\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 23.8979591837 for experiment\n",
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      "Using non default rmax = 24.2244897959 for experiment\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 24.387755102 for experiment\n",
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      "Using non default rmax = 26.0204081633 for experiment\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 26.1836734694 for experiment\n",
      "Using non default rmax = 26.3469387755 for experiment\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 26.5102040816 for experiment\n",
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      "Using non default rmax = 26.6734693878 for experiment\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 26.8367346939 for experiment\n",
      "Using non default rmax = 27.0 for experiment\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 22.9183673469 for experiment\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 23.5714285714 for experiment\n",
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      "Using non default rmax = 23.8979591837 for experiment\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 24.0612244898 for experiment\n",
      "Using non default rmax = 24.2244897959 for experiment\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "lf_photometric.py:272: RuntimeWarning: divide by zero encountered in divide\n",
      "  return 0.4*lumphi/lumPhi\n",
      "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/ipykernel_launcher.py:9: RuntimeWarning: invalid value encountered in multiply\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 23.7346938776 for experiment\n",
      "Using non default rmax = 23.8979591837 for experiment\n",
      "Using non default rmax = 24.0612244898 for experiment\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 26.0204081633 for experiment\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 19.9795918367 for experiment\n",
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      "Using non default rmax = 23.0816326531 for experiment\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 23.2448979592 for experiment\n",
      "Using non default rmax = 23.4081632653 for experiment\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 25.5306122449 for experiment\n",
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      "Using non default rmax = 26.0204081633 for experiment\n",
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      "Using non default rmax = 26.3469387755 for experiment\n",
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      "Using non default rmax = 19.4897959184 for experiment\n",
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      "Using non default rmax = 22.2653061224 for experiment\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 22.4285714286 for experiment\n",
      "Using non default rmax = 22.5918367347 for experiment\n",
      "Using non default rmax = 22.7551020408 for experiment\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 23.7346938776 for experiment\n",
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      "Using non default rmax = 22.2653061224 for experiment\n",
      "Using non default rmax = 22.4285714286 for experiment\n",
      "Using non default rmax = 22.5918367347 for experiment\n",
      "Using non default rmax = 22.7551020408 for experiment\n",
      "Using non default rmax = 22.9183673469 for experiment\n",
      "Using non default rmax = 23.0816326531 for experiment\n",
      "Using non default rmax = 23.2448979592 for experiment\n",
      "Using non default rmax = 23.4081632653 for experiment\n",
      "Using non default rmax = 23.5714285714 for experiment\n",
      "Using non default rmax = 23.7346938776 for experiment\n",
      "Using non default rmax = 23.8979591837 for experiment\n",
      "Using non default rmax = 24.0612244898 for experiment\n",
      "Using non default rmax = 24.2244897959 for experiment\n",
      "Using non default rmax = 24.387755102 for experiment\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using non default rmax = 24.5510204082 for experiment\n",
      "Using non default rmax = 24.7142857143 for experiment\n",
      "Using non default rmax = 24.8775510204 for experiment\n",
      "Using non default rmax = 25.0408163265 for experiment\n",
      "Using non default rmax = 25.2040816327 for experiment\n",
      "Using non default rmax = 25.3673469388 for experiment\n",
      "Using non default rmax = 25.5306122449 for experiment\n",
      "Using non default rmax = 25.693877551 for experiment\n",
      "Using non default rmax = 25.8571428571 for experiment\n",
      "Using non default rmax = 26.0204081633 for experiment\n",
      "Using non default rmax = 26.1836734694 for experiment\n",
      "Using non default rmax = 26.3469387755 for experiment\n",
      "Using non default rmax = 26.5102040816 for experiment\n",
      "Using non default rmax = 26.6734693878 for experiment\n",
      "Using non default rmax = 26.8367346939 for experiment\n",
      "Using non default rmax = 27.0 for experiment\n",
      "Using non default rmax = 19.0 for experiment\n",
      "Using non default rmax = 19.1632653061 for experiment\n",
      "Using non default rmax = 19.3265306122 for experiment\n",
      "Using non default rmax = 19.4897959184 for experiment\n",
      "Using non default rmax = 19.6530612245 for experiment\n",
      "Using non default rmax = 19.8163265306 for experiment\n",
      "Using non default rmax = 19.9795918367 for experiment\n",
      "Using non default rmax = 20.1428571429 for experiment\n",
      "Using non default rmax = 20.306122449 for experiment\n",
      "Using non default rmax = 20.4693877551 for experiment\n",
      "Using non default rmax = 20.6326530612 for experiment\n",
      "Using non default rmax = 20.7959183673 for experiment\n",
      "Using non default rmax = 20.9591836735 for experiment\n",
      "Using non default rmax = 21.1224489796 for experiment\n",
      "Using non default rmax = 21.2857142857 for experiment\n",
      "Using non default rmax = 21.4489795918 for experiment\n",
      "Using non default rmax = 21.612244898 for experiment\n",
      "Using non default rmax = 21.7755102041 for experiment\n",
      "Using non default rmax = 21.9387755102 for experiment\n",
      "Using non default rmax = 22.1020408163 for experiment\n",
      "Using non default rmax = 22.2653061224 for experiment\n",
      "Using non default rmax = 22.4285714286 for experiment\n",
      "Using non default rmax = 22.5918367347 for experiment\n",
      "Using non default rmax = 22.7551020408 for experiment\n",
      "Using non default rmax = 22.9183673469 for experiment\n",
      "Using non default rmax = 23.0816326531 for experiment\n",
      "Using non default rmax = 23.2448979592 for experiment\n",
      "Using non default rmax = 23.4081632653 for experiment\n",
      "Using non default rmax = 23.5714285714 for experiment\n",
      "Using non default rmax = 23.7346938776 for experiment\n",
      "Using non default rmax = 23.8979591837 for experiment\n",
      "Using non default rmax = 24.0612244898 for experiment\n",
      "Using non default rmax = 24.2244897959 for experiment\n",
      "Using non default rmax = 24.387755102 for experiment\n",
      "Using non default rmax = 24.5510204082 for experiment\n",
      "Using non default rmax = 24.7142857143 for experiment\n",
      "Using non default rmax = 24.8775510204 for experiment\n",
      "Using non default rmax = 25.0408163265 for experiment\n",
      "Using non default rmax = 25.2040816327 for experiment\n",
      "Using non default rmax = 25.3673469388 for experiment\n",
      "Using non default rmax = 25.5306122449 for experiment\n",
      "Using non default rmax = 25.693877551 for experiment\n",
      "Using non default rmax = 25.8571428571 for experiment\n",
      "Using non default rmax = 26.0204081633 for experiment\n",
      "Using non default rmax = 26.1836734694 for experiment\n",
      "Using non default rmax = 26.3469387755 for experiment\n",
      "Using non default rmax = 26.5102040816 for experiment\n",
      "Using non default rmax = 26.6734693878 for experiment\n",
      "Using non default rmax = 26.8367346939 for experiment\n",
      "Using non default rmax = 27.0 for experiment\n",
      "Using non default rmax = 19.0 for experiment\n",
      "Using non default rmax = 19.1632653061 for experiment\n",
      "Using non default rmax = 19.3265306122 for experiment\n",
      "Using non default rmax = 19.4897959184 for experiment\n",
      "Using non default rmax = 19.6530612245 for experiment\n",
      "Using non default rmax = 19.8163265306 for experiment\n",
      "Using non default rmax = 19.9795918367 for experiment\n",
      "Using non default rmax = 20.1428571429 for experiment\n",
      "Using non default rmax = 20.306122449 for experiment\n",
      "Using non default rmax = 20.4693877551 for experiment\n",
      "Using non default rmax = 20.6326530612 for experiment\n",
      "Using non default rmax = 20.7959183673 for experiment\n",
      "Using non default rmax = 20.9591836735 for experiment\n",
      "Using non default rmax = 21.1224489796 for experiment\n",
      "Using non default rmax = 21.2857142857 for experiment"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "lf_photometric.py:272: RuntimeWarning: invalid value encountered in divide\n",
      "  return 0.4*lumphi/lumPhi\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Using non default rmax = 21.4489795918 for experiment\n",
      "Using non default rmax = 21.612244898 for experiment\n",
      "Using non default rmax = 21.7755102041 for experiment\n",
      "Using non default rmax = 21.9387755102 for experiment\n",
      "Using non default rmax = 22.1020408163 for experiment\n",
      "Using non default rmax = 22.2653061224 for experiment\n",
      "Using non default rmax = 22.4285714286 for experiment\n",
      "Using non default rmax = 22.5918367347 for experiment\n",
      "Using non default rmax = 22.7551020408 for experiment\n",
      "Using non default rmax = 22.9183673469 for experiment\n",
      "Using non default rmax = 23.0816326531 for experiment\n",
      "Using non default rmax = 23.2448979592 for experiment\n",
      "Using non default rmax = 23.4081632653 for experiment\n",
      "Using non default rmax = 23.5714285714 for experiment\n",
      "Using non default rmax = 23.7346938776 for experiment\n",
      "Using non default rmax = 23.8979591837 for experiment\n",
      "Using non default rmax = 24.0612244898 for experiment\n",
      "Using non default rmax = 24.2244897959 for experiment\n",
      "Using non default rmax = 24.387755102 for experiment\n",
      "Using non default rmax = 24.5510204082 for experiment\n",
      "Using non default rmax = 24.7142857143 for experiment\n",
      "Using non default rmax = 24.8775510204 for experiment\n",
      "Using non default rmax = 25.0408163265 for experiment\n",
      "Using non default rmax = 25.2040816327 for experiment\n",
      "Using non default rmax = 25.3673469388 for experiment\n",
      "Using non default rmax = 25.5306122449 for experiment\n",
      "Using non default rmax = 25.693877551 for experiment\n",
      "Using non default rmax = 25.8571428571 for experiment\n",
      "Using non default rmax = 26.0204081633 for experiment\n",
      "Using non default rmax = 26.1836734694 for experiment\n",
      "Using non default rmax = 26.3469387755 for experiment\n",
      "Using non default rmax = 26.5102040816 for experiment\n",
      "Using non default rmax = 26.6734693878 for experiment\n",
      "Using non default rmax = 26.8367346939 for experiment\n",
      "Using non default rmax = 27.0 for experiment\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for i in range(len(ztab)):\n",
    "    for j in range(len(mtab)):\n",
    "        zz = ztab[i]\n",
    "        mm = mtab[j]\n",
    "#         n = dndz_fit(zz,mm)\n",
    "        n = nofz(zz,mm)\n",
    "        sg5m2 = sg5minus2(zz,mm)\n",
    "        truemesh[i,j] = sg5m2*n\n",
    "        interp_sgngmesh[i,j] = sgng_interp(zz,mm)\n",
    "        \n",
    "zmin = 0.5\n",
    "zmax = 1.5\n",
    "for j in range(len(mtab)):\n",
    "    mm = mtab[j]\n",
    "    truenorm[j] = dndz_norm(zmin, zmax, mm, FIT = False)\n",
    "    fit_norm[j] = dndz_norm(zmin, zmax, mm, FIT = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/ipykernel_launcher.py:5: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  \"\"\"\n",
      "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/mpl_toolkits/mplot3d/proj3d.py:141: RuntimeWarning: invalid value encountered in true_divide\n",
      "  txs, tys, tzs = vecw[0]/w, vecw[1]/w, vecw[2]/w\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the percent level difference in \n",
    "\n",
    "fig = P.figure(figsize = (9,9)); ax = fig.add_subplot(111, projection = '3d');\n",
    "\n",
    "ax.plot_wireframe(zmesh, magmesh, (truemesh-interp_sgngmesh)/truemesh*100, rstride=10, cstride=10, color = 'red')\n",
    "# ax.plot_wireframe(zmesh, magmesh, interp_sgngmesh, rstride=1, cstride=1, color = 'black')\n",
    "\n",
    "ax.set_xlabel(\"z\")\n",
    "ax.set_ylabel(\"m\")\n",
    "ax.set_zlabel(\"diff \\%\")\n",
    "\n",
    "ax.view_init(15, -70)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x112f1b9d0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the normalization of nofz\n",
    "\n",
    "fig = P.figure(); ax =fig.add_subplot(111);\n",
    "ax.plot(mtab, (truenorm-fit_norm)/truenorm*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}