dndz_code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# code to calculate dN/dz and dN/dX from sensitivity function g(W,z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "import numpy as np\n",
    "import sys\n",
    "import random\n",
    "import itertools\n",
    "from astropy.table import Table\n",
    "from astropy.io import ascii\n",
    "\n",
    "import matplotlib\n",
    "import scipy.integrate as integ\n",
    "import scipy.stats\n",
    "import scipy.optimize as op\n",
    "import scipy.special as sc\n",
    "import scipy.odr as odr\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rc\n",
    "import numpy.random as npr\n",
    "# from pylab import *\n",
    "\n",
    "from matplotlib.patches import Ellipse\n",
    "from matplotlib.ticker import ScalarFormatter\n",
    "\n",
    "# from stat_funcs import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#constants here:\n",
    "\n",
    "omega_m = 0.3\n",
    "omega_l = 0.7\n",
    "H0 = 70 #km/s/Mpc\n",
    "c_kms = 3e5 #km/s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define a function to find position of nearest EW to grid:\n",
    "\n",
    "def find_nearest(array,value):\n",
    "    idx = (np.abs(array-value)).argmin()\n",
    "    return idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import data:\n",
    "\n",
    "# CIV_data = np.loadtxt(\"total_CIV.table\",unpack=True,skiprows=1,usecols=(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17))\n",
    "\n",
    "CIV_gwz = np.genfromtxt(\"gwz_uniques_3sigma.grid\")\n",
    "\n",
    "CIV_data = ascii.read(\"abs_info_new.txt\",data_start=1)\n",
    "\n",
    "Qso, Zabs, Ewr, Ewsig = CIV_data['QSO'], CIV_data['z_abs'], CIV_data['EW_r'],  CIV_data['EW_r_e']\n",
    "\n",
    "logN, logN_l, logN_u = CIV_data['logN'], CIV_data['logN_l'], CIV_data['logN_u']\n",
    "\n",
    "N_abs, N_abs_l, N_abs_u = 10**logN, 10**logN_l, 10**logN_u \n",
    "\n",
    "#QSO redshifts:\n",
    "\n",
    "zem_data = ascii.read(\"qso_zems.txt\",data_start=1)\n",
    "Qso_zem, Zem = zem_data['QSO'], zem_data['zem']\n",
    "\n",
    "\n",
    "#Cooksey+(2013):\n",
    "\n",
    "CIV_Cooksey = ascii.read(\"cooksey_civabs.txt\",data_start=1)\n",
    "# QSO_Cooksey = ascii.read(\"cooksey_qsoinfo.txt\",data_start=1)\n",
    "\n",
    "Qsociv_c, Zabs_c, Ewr_c, Ewsig_c = CIV_Cooksey['z_QSO'], CIV_Cooksey['z_CIV'], CIV_Cooksey['W_1548'],  CIV_Cooksey['W_1548_e']\n",
    "\n",
    "\n",
    "# Qso_c, Zqso_c, dXmax_c, Nabs_c = QSO_Cooksey['QSO'], QSO_Cooksey['z_QSO'], QSO_Cooksey['dXmax'],  QSO_Cooksey['N_CIV']\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#EWlims:\n",
    "\n",
    "ewlims_all = np.genfromtxt(\"ewlims_all.txt\")\n",
    "\n",
    "#remove negative values:\n",
    "\n",
    "ewlim_all = ewlims_all[ewlims_all >= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cumulative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#cumulative histogram of EWlims:\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "\n",
    "\n",
    "plt.rc(\"text\", usetex=True)\n",
    "# plt.rcParams['text.latex.preamble'] = r'\\usepackage{graphicx}'\n",
    "plt.rc('font', size=40) \n",
    "# plt.rc('font', family='serif')\n",
    "\n",
    "# matplotlib.rcParams['text.latex.preamble'] = [r'\\boldmath']\n",
    "plt.rcParams[\"font.weight\"] = \"bold\"     \n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rc('axes', linewidth=3)\n",
    "\n",
    "values_cdf, base_cdf = np.histogram(ewlim_all, bins=1000, range=(0.,1.),density=True)\n",
    "#evaluate the cumulative\n",
    "cumulative = np.cumsum(values_cdf)\n",
    "# plot the cumulative function\n",
    "plt.plot(base_cdf[:-1], cumulative/1000, c='blue')\n",
    "\n",
    "\n",
    "# plt.show()\n",
    "# fig, ax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "# # plot the cumulative histogram\n",
    "# n_cdf, bins_cdf, patches_cdf = ax.hist(ewlim_all, 1000, density=True,\n",
    "#                            cumulative=True)\n",
    "\n",
    "# # Add a line showing the expected distribution.\n",
    "# # y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *\n",
    "# #      np.exp(-0.5 * (1 / sigma * (bins - mu))**2))\n",
    "# y = n_cdf.cumsum()\n",
    "# # y /= y[-1]\n",
    "\n",
    "# ax.plot(bins_cdf[:-1], y, 'k--', linewidth=1.5)\n",
    "\n",
    "# # Overlay a reversed cumulative histogram.\n",
    "# ax.hist(ewlim_all, bins=bins_cdf, density=True)\n",
    "\n",
    "# tidy up the figure\n",
    "\n",
    "plt.xlim(0,0.75)\n",
    "\n",
    "ax.set_xlabel(r'\\boldmath{$W^{\\lambda1548}_{r}$}',fontsize=44)\n",
    "ax.set_ylabel(r'\\boldmath{$CDF$}',fontsize=44)\n",
    "\n",
    "# ax.set_yscale('log')\n",
    "\n",
    "ax.tick_params(axis='both', which='major', direction='in', labelsize=40,length=20, width=4, bottom='on',top='on', left='on', right='on')\n",
    "ax.tick_params(axis='both', which='minor', direction='in', labelsize=36,length=12, width=3, bottom='on',top='on', left='on', right='on')\n",
    "ax.set_xticks(np.arange(0,0.8,0.1), minor=True)\n",
    "ax.set_yticks(np.arange(0,1.1,0.1), minor=True)\n",
    "\n",
    "# plt.savefig('EWlim_CDF.pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# len(logN), len(logN[logN!=0.0])\n",
    "# logN[43]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.432"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(logN,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.011872336272722"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10**0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#population means:\n",
    "mean_Zabs, mean_Ewr, mean_logN = np.mean(Zabs), np.mean(Ewr), np.mean(logN[logN!=0.0])\n",
    "mean_Zem = np.mean(Zem)\n",
    "\n",
    "\n",
    "#gwz grid params:\n",
    "\n",
    "min_ewl, max_ewl, ewl_spacing = 0.005, 0.405, 0.005\n",
    "min_CIV_z, max_CIV_z, z_spacing = 1.00, 5.00, 0.005\n",
    "\n",
    "w_grid = np.arange(min_ewl, max_ewl, ewl_spacing) \n",
    "\n",
    "z_grid = np.arange(min_CIV_z,max_CIV_z,z_spacing)\n",
    "\n",
    "\n",
    "\n",
    "#dN/dz grid params:\n",
    "\n",
    "dz = 0.25\n",
    "# zbins = np.arange(min_CIV_z, max_CIV_z,dz)\n",
    "\n",
    "#redshift bins for dN/dz and dN/dX:\n",
    "\n",
    "zbins_c = np.array([1.45, 1.61, 1.7, 1.78, 1.87, 1.96, 2.08, 2.24, 2.51, 2.97,4.55])\n",
    "zbins = np.array([1.0, 1.5, 1.8, 2.0, 2.2, 2.4, 2.6, 2.8, 3.1, 3.75,4.75])\n",
    "\n",
    "\n",
    "#EQW bins for EWD:\n",
    "# ewbins = np.array([0.01, 0.07, 0.15, 0.2, 0.3, 0.4, 0.6, 0.9, 1.5, 2.5, 4.0])\n",
    "ewbins=np.logspace(-2.3,0.7,num=12)\n",
    "\n",
    "#one EQW bin to rule them all:\n",
    "ewbins_all = np.array([10**-2.3,10**0.7])\n",
    "\n",
    "#N bins for CDD:\n",
    "columnbins = np.logspace(12,15.5,num=12)\n",
    "# np.array([1e12,5e12,1e13,2e13,3e13,4e13,6e13,8e13,1e14,2e14,4e14,8e14,3e15])\n",
    "\n",
    "# EW_bins = np.array([0.01, 0.04, 0.07, 0.1, 0.15, 0.2, 0.3, 0.4, 0.6, 0.9,1.5,2.5,4.0])\n",
    "\n",
    "\n",
    "#larger redshift bins for EWD and CDD:\n",
    "\n",
    "zbins_large = np.array([1,2,3,4.75])\n",
    "\n",
    "# lowz,midz,highz = 1,2,3\n",
    "\n",
    "#one redshift bin to rule them all:\n",
    "\n",
    "zbins_all = np.array([1,5])\n",
    "\n",
    "#EQW cuts:\n",
    "\n",
    "# weak, med, strong = 0.01, 0.3, 1.\n",
    "\n",
    "vweak, weak, med, strong, vstrong = 0.01, 0.3, 0.7, 1.0, 1.2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.4806915600000001, 1.9814296, 2.4180188, 2.8703275, 3.5882730599999997)"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(Zabs,5),np.percentile(Zabs,25), np.percentile(Zabs,50), np.percentile(Zabs,75), np.percentile(Zabs,95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 884,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00501187, 5.01187234])"
      ]
     },
     "execution_count": 884,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ewbins_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.00000000e+12, 2.08056754e+12, 4.32876128e+12, 9.00628020e+12,\n",
       "       1.87381742e+13, 3.89860370e+13, 8.11130831e+13, 1.68761248e+14,\n",
       "       3.51119173e+14, 7.30527154e+14, 1.51991108e+15, 3.16227766e+15])"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columnbins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot g(W,z) heat map:\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(30, 20))\n",
    "\n",
    "#TeX up labels and text:\n",
    "\n",
    "plt.rc(\"text\", usetex=True)\n",
    "plt.rc('font', size=48) \n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "# matplotlib.rcParams['text.latex.preamble'] = [r'\\boldmath']\n",
    "plt.rcParams[\"font.weight\"] = \"bold\"     \n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rc('axes', linewidth=6)\n",
    "\n",
    "\n",
    "plt1 = plt.imshow(CIV_gwz[::-1],extent=(min(z_grid),max(z_grid),min(w_grid),max(w_grid)),aspect=4,cmap='hot')\n",
    "plt.title(r'\\textbf{$g(W_r^{1548}, z)$}',fontsize=46)\n",
    "\n",
    "ax.set_xlabel(r'\\boldmath{$z$}',fontsize=44)\n",
    "ax.set_ylabel(r'\\boldmath{$W^{1548}_{r}$} \\textbf{limit (\\AA)}',fontsize=44)\n",
    "\n",
    "ax.tick_params(axis='both', which='major', direction='out', labelsize=46,length=20, width=5, bottom='on', left='on')\n",
    "ax.tick_params(axis='both', which='minor', direction='out', labelsize=46,length=12, width=3, bottom='on', left='on')\n",
    "\n",
    "\n",
    "# ax[1].set_xscale('log')\n",
    "ax.set_xticks(np.arange(1,5,0.1), minor=True)\n",
    "ax.set_yticks(np.arange(0.,0.4,0.01), minor=True)\n",
    "\n",
    "ax.set_ylim(0.005,0.4)\n",
    "\n",
    "position = fig.add_axes([0.125,0.13,0.775,0.05])  ## the parameters are the specified position you set \n",
    "\n",
    "cbar = fig.colorbar(plt1,cax=position, orientation='horizontal') ## \n",
    "\n",
    "position.tick_params(axis='both', which='major', direction='out', labelsize=46,length=20, width=3, right='on')\n",
    "\n",
    "\n",
    "# cbar = plt.colorbar(orientation='horizontal')\n",
    "cbar.ax.set_xlabel(r'\\textbf{Number of Spectra}',labelpad=10)  # horizontal colorbar\n",
    "\n",
    "# position.tick_params(axis='both', which='major', direction='out', labelsize=40,length=20, width=3, right='on')\n",
    "\n",
    "cbar.set_ticks(np.arange(0,140,10))\n",
    "\n",
    "# cbar.set_ticks([0,50,100,150,200,250,300,350,400])\n",
    "# cbar.set_ticklabels(['0','50','100','150','200','250','300','350','400+'])\n",
    "\n",
    "# plt.savefig('gwz_3sigma.pdf') #save as pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from mpl_toolkits import mplot3d\n",
    "\n",
    "# fig = plt.figure()\n",
    "# ax = plt.axes(projection='3d')\n",
    "# ax.contour3D(z_grid, w_grid, CIV_gwz, 50, cmap='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01      , 0.01003518, 0.01007049, ..., 2.97900075, 2.98948194,\n",
       "       3.        ])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logbins_ew = np.logspace(np.log10(0.01),np.log10(3),len(Ewr))\n",
    "logbins_ew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.934058198469682"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10**mean_Ewr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#make histograms:\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1,2, figsize=(20, 20), sharey=True)\n",
    "\n",
    "\n",
    "#TeX up labels and text:\n",
    "\n",
    "\n",
    "plt.rc(\"text\", usetex=True)\n",
    "\n",
    "plt.rc('font', size=48) \n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "matplotlib.rcParams['text.latex.preamble'] = [r'\\boldmath']\n",
    "plt.rcParams[\"font.weight\"] = \"bold\"     \n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rc('axes', linewidth=6)\n",
    "\n",
    "# rc('axes', linewidth=4)\n",
    "\n",
    "# weights_z = np.ones_like(Zabs)/float(len(Zabs))\n",
    "# weights_ew = np.ones_like(np.log10(Ewr))/float(len(Ewr))\n",
    "\n",
    "\n",
    "ax[0].hist(Zabs, 50, range=(1,5), facecolor='blue', edgecolor='black', linewidth=5, alpha=0.75)\n",
    "\n",
    "# logbins_ew = np.logspace(np.log10(min(Ewr)),np.log10(max(Ewr)),len(Ewr))\n",
    "\n",
    "ax[1].hist(np.log10(Ewr), bins=50, facecolor='orange',edgecolor='black', linewidth=5, alpha=0.75)\n",
    "\n",
    "\n",
    "\n",
    "mean_ew, mean_z = str(round(np.log10(mean_Ewr),4)), str(round(mean_Zabs,4))\n",
    "\n",
    "# fig.text(0.3, 0.8, r'\\boldmath{$<z_{abs}>$}'+'\\n'+ r'\\boldmath{$=~$}'+mean_z, fontsize=60, weight='bold')\n",
    "\n",
    "fig.text(0.35, 0.75, r'\\boldmath{$<z_{abs}>=$}'+mean_z, fontsize=52)\n",
    "fig.text(0.52, 0.75, r'\\boldmath{$<\\log W_{r}^{1548}>=$}'+mean_ew, fontsize=50)\n",
    "\n",
    "\n",
    "\n",
    "ax[0].set_xlabel(r'\\boldmath{$z_{abs}$}',fontsize=52)\n",
    "ax[1].set_xlabel(r'\\boldmath{$\\log W_{r}^{1548}$}',fontsize=52,labelpad=30)\n",
    "\n",
    "\n",
    "# ax[1].tick_params(axis='x', pad=30)\n",
    "\n",
    "ax[0].set_ylabel(r'\\textbf{Number of absorbers}',fontsize=52, rotation=90,labelpad=15)\n",
    "\n",
    "ax[0].axvline(mean_Zabs,ls='--',c='red',lw=8)\n",
    "ax[1].axvline(np.log10(mean_Ewr),ls='--',c='red',lw=8)\n",
    "\n",
    "\n",
    "\n",
    "ax[0].tick_params(axis='both', which='major', direction='in', labelsize=50,length=20, width=5, bottom='on',top='on', left='on', right='on')\n",
    "ax[0].tick_params(axis='both', which='minor', direction='in', labelsize=50,length=12, width=3, bottom='on',top='on', left='on', right='on')\n",
    "\n",
    "\n",
    "\n",
    "ax[1].tick_params(axis='both', which='major', direction='in', labelsize=50,length=20, width=5, bottom='on',top='on', left='on', right='on')\n",
    "ax[1].tick_params(axis='both', which='minor', direction='in', labelsize=50,length=12, width=3, bottom='on',top='on', left='on', right='on')\n",
    "\n",
    "\n",
    "# ax[1].set_xscale('log')\n",
    "\n",
    "ax[0].set_xticks(np.arange(1,5,0.1), minor=True)\n",
    "ax[1].set_xticks(np.arange(-2.4,1,0.1), minor=True)\n",
    "\n",
    "ax[0].set_yticks(np.arange(0,100,1), minor=True)\n",
    "ax[1].set_yticks(np.arange(0,100,1), minor=True)\n",
    "\n",
    "\n",
    "# ax[0].set_xlim(8.9,12.2)\n",
    "# ax[1].set_xlim(8.9,12.2)\n",
    "\n",
    "# ax[0].set_ylim(1.8,2.65)\n",
    "# ax[1].set_ylim(1.8,2.65)\n",
    "# plt.xlim(1.2,4.5)\n",
    "\n",
    "# ax.legend(loc='best',fontsize=36,framealpha=None)\n",
    "\n",
    "# fig.tight_layout()\n",
    "\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "# plt.savefig('hist_abs.pdf', dpi=50)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#make histogram of zabs:\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "\n",
    "#TeX up labels and text:\n",
    "\n",
    "\n",
    "plt.rc(\"text\", usetex=True)\n",
    "\n",
    "plt.rc('font', size=32) \n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "matplotlib.rcParams['text.latex.preamble'] = [r'\\boldmath']\n",
    "plt.rcParams[\"font.weight\"] = \"bold\"     \n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rc('axes', linewidth=6)\n",
    "\n",
    "# rc('axes', linewidth=4)\n",
    "\n",
    "# weights_z = np.ones_like(Zabs)/float(len(Zabs))\n",
    "# weights_ew = np.ones_like(np.log10(Ewr))/float(len(Ewr))\n",
    "\n",
    "\n",
    "plt.hist(Zabs, 50, range=(1,5), facecolor='blue', edgecolor='black', linewidth=5, alpha=0.75)\n",
    "\n",
    "# logbins_ew = np.logspace(np.log10(min(Ewr)),np.log10(max(Ewr)),len(Ewr))\n",
    "\n",
    "# ax[1].hist(np.log10(Ewr), bins=50, facecolor='orange',edgecolor='black', linewidth=5, alpha=0.75)\n",
    "\n",
    "\n",
    "\n",
    "mean_z = str(round(mean_Zabs,4))\n",
    "\n",
    "# fig.text(0.3, 0.8, r'\\boldmath{$<z_{abs}>$}'+'\\n'+ r'\\boldmath{$=~$}'+mean_z, fontsize=60, weight='bold')\n",
    "\n",
    "fig.text(1.02, 0.75, r'\\boldmath{$<z_{abs}>=$}'+mean_z, fontsize=52)\n",
    "# fig.text(0.52, 0.75, r'\\boldmath{$<\\log W_{r}^{1548}>=$}'+mean_ew, fontsize=50)\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlabel(r'\\boldmath{$z_{abs}$}',fontsize=52)\n",
    "# ax[1].set_xlabel(r'\\boldmath{$\\log W_{r}^{1548}$}',fontsize=52,labelpad=30)\n",
    "\n",
    "\n",
    "# ax[1].tick_params(axis='x', pad=30)\n",
    "\n",
    "ax.set_ylabel(r'\\textbf{Number of absorbers}',fontsize=52, rotation=90,labelpad=15)\n",
    "\n",
    "ax.axvline(mean_Zabs,ls='--',c='red',lw=8)\n",
    "# ax[1].axvline(np.log10(mean_Ewr),ls='--',c='red',lw=8)\n",
    "\n",
    "\n",
    "\n",
    "ax.tick_params(axis='both', which='major', direction='in', labelsize=50,length=20, width=5, bottom='on',top='on', left='on', right='on')\n",
    "ax.tick_params(axis='both', which='minor', direction='in', labelsize=50,length=12, width=3, bottom='on',top='on', left='on', right='on')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# ax[1].set_xscale('log')\n",
    "\n",
    "ax.set_xticks(np.arange(1,5,0.1), minor=True)\n",
    "# ax[1].set_xticks(np.arange(-2.4,1,0.1), minor=True)\n",
    "\n",
    "ax.set_yticks(np.arange(0,100,1), minor=True)\n",
    "# ax[1].set_yticks(np.arange(0,100,1), minor=True)\n",
    "\n",
    "\n",
    "# ax[0].set_xlim(8.9,12.2)\n",
    "# ax[1].set_xlim(8.9,12.2)\n",
    "\n",
    "# ax[0].set_ylim(1.8,2.65)\n",
    "# ax[1].set_ylim(1.8,2.65)\n",
    "# plt.xlim(1.2,4.5)\n",
    "\n",
    "# ax.legend(loc='best',fontsize=36,framealpha=None)\n",
    "\n",
    "# fig.tight_layout()\n",
    "\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "# plt.savefig('hist_zabs.pdf', dpi=50)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#make histogram of EQW:\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "\n",
    "#TeX up labels and text:\n",
    "\n",
    "\n",
    "plt.rc(\"text\", usetex=True)\n",
    "\n",
    "plt.rc('font', size=32) \n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "matplotlib.rcParams['text.latex.preamble'] = [r'\\boldmath']\n",
    "plt.rcParams[\"font.weight\"] = \"bold\"     \n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rc('axes', linewidth=6)\n",
    "\n",
    "# rc('axes', linewidth=4)\n",
    "\n",
    "# weights_z = np.ones_like(Zabs)/float(len(Zabs))\n",
    "# weights_ew = np.ones_like(np.log10(Ewr))/float(len(Ewr))\n",
    "\n",
    "\n",
    "plt.hist(np.log10(Ewr), bins=50, facecolor='orange',edgecolor='black', linewidth=5, alpha=0.75)\n",
    "\n",
    "# logbins_ew = np.logspace(np.log10(min(Ewr)),np.log10(max(Ewr)),len(Ewr))\n",
    "\n",
    "# ax[1].hist(np.log10(Ewr), bins=50, facecolor='orange',edgecolor='black', linewidth=5, alpha=0.75)\n",
    "\n",
    "\n",
    "mean_ew = str(round(np.log10(mean_Ewr),4))\n",
    "\n",
    "\n",
    "# fig.text(0.52, 0.75, r'\\boldmath{$<z_{abs}>=$}'+mean_z, fontsize=52)\n",
    "fig.text(1.02, 0.75, r'\\boldmath{$<\\log W_{r}^{1548}>=$}'+mean_ew, fontsize=50)\n",
    "\n",
    "\n",
    "# ax.set_xlabel(r'\\boldmath{$z_{abs}$}',fontsize=52)\n",
    "ax.set_xlabel(r'\\boldmath{$\\log W_{r}^{1548}$}',fontsize=52,labelpad=30)\n",
    "\n",
    "\n",
    "# ax[1].tick_params(axis='x', pad=30)\n",
    "\n",
    "ax.set_ylabel(r'\\textbf{Number of absorbers}',fontsize=52, rotation=90,labelpad=15)\n",
    "\n",
    "# ax.axvline(mean_Zabs,ls='--',c='red',lw=8)\n",
    "ax.axvline(np.log10(mean_Ewr),ls='--',c='red',lw=8)\n",
    "\n",
    "\n",
    "ax.tick_params(axis='both', which='major', direction='in', labelsize=50,length=20, width=5, bottom='on',top='on', left='on', right='on')\n",
    "ax.tick_params(axis='both', which='minor', direction='in', labelsize=50,length=12, width=3, bottom='on',top='on', left='on', right='on')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# ax[1].set_xscale('log')\n",
    "\n",
    "# ax.set_xticks(np.arange(-2.,0.1), minor=True)\n",
    "ax.set_xticks(np.arange(-2.4,1,0.1), minor=True)\n",
    "\n",
    "ax.set_yticks(np.arange(0,100,1), minor=True)\n",
    "# ax[1].set_yticks(np.arange(0,100,1), minor=True)\n",
    "\n",
    "# ax.legend(loc='best',fontsize=36,framealpha=None)\n",
    "\n",
    "# fig.tight_layout()\n",
    "\n",
    "# plt.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "# plt.savefig('hist_ewr.pdf', dpi=50)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#make histogram of logN:\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "\n",
    "#TeX up labels and text:\n",
    "\n",
    "\n",
    "plt.rc(\"text\", usetex=True)\n",
    "\n",
    "plt.rc('font', size=32) \n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "matplotlib.rcParams['text.latex.preamble'] = [r'\\boldmath']\n",
    "plt.rcParams[\"font.weight\"] = \"bold\"     \n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rc('axes', linewidth=6)\n",
    "\n",
    "# rc('axes', linewidth=4)\n",
    "\n",
    "# weights_z = np.ones_like(Zem)/float(len(Zem))\n",
    "\n",
    "\n",
    "\n",
    "ax.hist(logN, bins=50, range=(12,16), facecolor='teal', edgecolor='black', linewidth=5, alpha=0.75)\n",
    "\n",
    "# logbins_ew = np.logspace(np.log10(min(Ewr)),np.log10(max(Ewr)),len(Ewr))\n",
    "\n",
    "# ax[1].hist(np.log10(Ewr), bins=50, weights=weights_ew, facecolor='orange',edgecolor='black', linewidth=5, alpha=0.75)\n",
    "\n",
    "\n",
    "\n",
    "mean_n = str(round(mean_logN,4))\n",
    "\n",
    "fig.text(0.99, 0.8, r'\\boldmath{$<\\log N_{CIV}>=$}'+mean_n, fontsize=52, weight='bold')\n",
    "# fig.text(0.74, 0.75, r'\\boldmath{$<\\log W_{r}^{1548}>$}'+'\\n'+ r'\\boldmath{$=~$}'+mean_ew, fontsize=60)\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlabel(r'\\boldmath{$\\log N_{CIV}$}',fontsize=52)\n",
    "# ax[1].set_xlabel(r'\\boldmath{$\\log W_{r}^{1548}$}',fontsize=52,labelpad=30)\n",
    "\n",
    "\n",
    "# ax[1].tick_params(axis='x', pad=30)\n",
    "\n",
    "ax.set_ylabel(r'\\textbf{Number of absorbers}',fontsize=52, rotation=90,labelpad=15)\n",
    "\n",
    "ax.axvline(mean_logN,ls='--',c='red',lw=8)\n",
    "# ax[1].axvline(np.log10(mean_Ewr),ls='--',c='red',lw=8)\n",
    "\n",
    "\n",
    "\n",
    "ax.tick_params(axis='both', which='major', direction='in', labelsize=50,length=20, width=5, bottom='on',top='on', left='on', right='on')\n",
    "ax.tick_params(axis='both', which='minor', direction='in', labelsize=50,length=12, width=3, bottom='on',top='on', left='on', right='on')\n",
    "\n",
    "\n",
    "\n",
    "# ax[1].tick_params(axis='both', which='major', direction='in', labelsize=50,length=20, width=5, bottom='on',top='on', left='on', right='on')\n",
    "# ax[1].tick_params(axis='both', which='minor', direction='in', labelsize=50,length=12, width=3, bottom='on',top='on', left='on', right='on')\n",
    "\n",
    "\n",
    "# ax[1].set_xscale('log')\n",
    "\n",
    "ax.set_xticks(np.arange(12,16,0.1), minor=True)\n",
    "# ax[1].set_xticks(np.arange(-2.4,1,0.1), minor=True)\n",
    "\n",
    "ax.set_yticks(np.arange(0,100,1), minor=True)\n",
    "# ax[1].set_yticks(np.arange(0,0.07,0.001), minor=True)\n",
    "\n",
    "\n",
    "\n",
    "# plt.savefig('hist_logN.pdf', dpi=50)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#make histogram of zems:\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "\n",
    "\n",
    "#TeX up labels and text:\n",
    "\n",
    "\n",
    "plt.rc(\"text\", usetex=True)\n",
    "\n",
    "plt.rc('font', size=32) \n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "matplotlib.rcParams['text.latex.preamble'] = [r'\\boldmath']\n",
    "plt.rcParams[\"font.weight\"] = \"bold\"     \n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rc('axes', linewidth=6)\n",
    "\n",
    "# rc('axes', linewidth=4)\n",
    "\n",
    "# weights_z = np.ones_like(Zem)/float(len(Zem))\n",
    "\n",
    "\n",
    "\n",
    "plt.hist(Zem, 50, facecolor='navy', edgecolor='black', linewidth=5, alpha=0.75)\n",
    "\n",
    "# logbins_ew = np.logspace(np.log10(min(Ewr)),np.log10(max(Ewr)),len(Ewr))\n",
    "\n",
    "# ax[1].hist(np.log10(Ewr), bins=50, weights=weights_ew, facecolor='orange',edgecolor='black', linewidth=5, alpha=0.75)\n",
    "\n",
    "\n",
    "\n",
    "mean_zem = str(round(mean_Zem,4))\n",
    "\n",
    "fig.text(0.99, 0.8, r'\\boldmath{$<z_{em}>=$}'+mean_zem, fontsize=60, weight='bold')\n",
    "# fig.text(0.74, 0.75, r'\\boldmath{$<\\log W_{r}^{1548}>$}'+'\\n'+ r'\\boldmath{$=~$}'+mean_ew, fontsize=60)\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlabel(r'\\boldmath{$z_{em}$}',fontsize=52)\n",
    "# ax[1].set_xlabel(r'\\boldmath{$\\log W_{r}^{1548}$}',fontsize=52,labelpad=30)\n",
    "\n",
    "\n",
    "# ax[1].tick_params(axis='x', pad=30)\n",
    "\n",
    "ax.set_ylabel(r'\\textbf{Number of QSOs}',fontsize=52, rotation=90,labelpad=15)\n",
    "\n",
    "ax.axvline(mean_Zem,ls='--',c='red',lw=8)\n",
    "# ax[1].axvline(np.log10(mean_Ewr),ls='--',c='red',lw=8)\n",
    "\n",
    "\n",
    "\n",
    "ax.tick_params(axis='both', which='major', direction='in', labelsize=50,length=20, width=5, bottom='on',top='on', left='on', right='on')\n",
    "ax.tick_params(axis='both', which='minor', direction='in', labelsize=50,length=12, width=3, bottom='on',top='on', left='on', right='on')\n",
    "\n",
    "\n",
    "\n",
    "# ax[1].tick_params(axis='both', which='major', direction='in', labelsize=50,length=20, width=5, bottom='on',top='on', left='on', right='on')\n",
    "# ax[1].tick_params(axis='both', which='minor', direction='in', labelsize=50,length=12, width=3, bottom='on',top='on', left='on', right='on')\n",
    "\n",
    "\n",
    "# ax[1].set_xscale('log')\n",
    "\n",
    "ax.set_xticks(np.arange(1,5.5,0.1), minor=True)\n",
    "# ax[1].set_xticks(np.arange(-2.4,1,0.1), minor=True)\n",
    "\n",
    "ax.set_yticks(np.arange(0,25,1), minor=True)\n",
    "# ax[1].set_yticks(np.arange(0,0.0`7,0.001), minor=True)\n",
    "\n",
    "\n",
    "\n",
    "# plt.savefig('hist_zem.pdf', dpi=50)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make lists of absorber info:\n",
    "\n",
    "absorber_lists = [] #list of lists\n",
    "absorber_lists_vweak, absorber_lists_vstrong = [], []\n",
    "absorber_lists_weak, absorber_lists_med, absorber_lists_strong  = [], [], []\n",
    "\n",
    "for j in range(len(zbins)-1):\n",
    "     #lists of absorber info for this zbin:\n",
    "    abslist, abslist_w, abslist_m, abslist_s,  = [],[],[],[]\n",
    "    abslist_vw, abslist_vs = [],[]\n",
    "    \n",
    "    #find all absorbers in this bin:\n",
    "    for i in range(len(Zabs)):\n",
    "        zi = Zabs[i]\n",
    "        wi = Ewr[i]\n",
    "        wei = Ewsig[i]\n",
    "        ent = (wi,wei,zi)\n",
    "        \n",
    "        if zbins[j] <= zi <= zbins[j+1]:\n",
    "            abslist.append(ent)\n",
    "\n",
    "# greater than theresholds:\n",
    "            if wi >= vweak: #EQW >= 0.01 A\n",
    "                abslist_vw.append(ent)\n",
    "            if wi >= weak: #EQW >= 0.3 A\n",
    "                abslist_w.append(ent)\n",
    "            if wi >= med: #EQW >= 0.6 A\n",
    "                abslist_m.append(ent)\n",
    "            if wi >= strong: #EQW >= 0.9 A\n",
    "                abslist_s.append(ent)\n",
    "            if wi >= vstrong: #EQW >= 1.2 A\n",
    "                abslist_vs.append(ent)\n",
    "                \n",
    "\n",
    "##less than thresholds:\n",
    "\n",
    "#             if wi <= weak: #EQW <= 0.01 A\n",
    "#                 abslist_w.append(ent)\n",
    "#             if wi <= med: #EQW <= 0.6 A\n",
    "#                 abslist_m.append(ent)\n",
    "#             if wi <= strong: #EQW <= 0.9 A\n",
    "#                 abslist_s.append(ent)\n",
    "#             if wi <= vstrong: #EQW <= 1.2 A\n",
    "#                 abslist_vs.append(ent)\n",
    "\n",
    "##between thresholds:\n",
    "\n",
    "#             if vweak <= wi < weak: #EQW between 0.01 A and 0.4 A\n",
    "#                 abslist_vw.append(ent)\n",
    "#             if weak <= wi < strong: #EQW between 0.4 A and 1 A\n",
    "#                 abslist_m.append(ent)\n",
    "# #             if med <= wi < strong: #EQW between 0.7 A and 1 A\n",
    "# #                 abslist_m.append(ent)\n",
    "# #             if strong <= wi < vstrong: #EQW between 0.9 A and 1.2 A\n",
    "# #                 abslist_s.append(ent)               \n",
    "#             if wi >= strong: #EQW >= 1 A\n",
    "#                 abslist_s.append(ent)\n",
    "                \n",
    "    absorber_lists.append(abslist)\n",
    "    absorber_lists_vweak.append(abslist_vw)\n",
    "    absorber_lists_weak.append(abslist_w)\n",
    "    absorber_lists_med.append(abslist_m)\n",
    "    absorber_lists_strong.append(abslist_s)\n",
    "    absorber_lists_vstrong.append(abslist_vs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[14], [2], [], [], [], [], [], [], [], [], []]"
      ]
     },
     "execution_count": 761,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# xx = np.empty(shape=[len(ewbins)])\n",
    "# xx = np.zeros((len(ewbins),1))\n",
    "xy = [[] for i in range(len(ewbins)-1)]\n",
    "# xy = [[None]]*len(ewbins)\n",
    "\n",
    "# xy[1].append(1)\n",
    "\n",
    "xy[1].append(2), xy[0].append(14)\n",
    "\n",
    "xy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 820,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((0.006, 0.001, 2.248834), (0.112, 0.002, 1.3042216))"
      ]
     },
     "execution_count": 820,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "absorber_lists_ewd[0][0][0], absorber_lists_vweak[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make lists of absorber info for dN/dX between EQW bins:\n",
    "\n",
    "absorber_lists_ewd = [] #list of lists\n",
    "# absorber_lists_vweak, absorber_lists_vstrong = [], []\n",
    "# absorber_lists_weak, absorber_lists_med, absorber_lists_strong  = [], [], []\n",
    "\n",
    "for j in range(len(zbins_all)-1):\n",
    "     #lists of absorber info for this zbin:\n",
    "    abslists = [[] for i in range(len(ewbins)-1)]\n",
    "#     abslists = []\n",
    "#     print(len(abslists))\n",
    "    \n",
    "\n",
    "    #find all absorbers in this bin:\n",
    "    for i in range(len(Zabs)):\n",
    "        zi = Zabs[i]\n",
    "        wi = Ewr[i]\n",
    "        wei = Ewsig[i]\n",
    "        ent = (wi,wei,zi)\n",
    "        \n",
    "#         print(ent)\n",
    "\n",
    "#             if zbins[j] <= zi <= zbins[j+1]:\n",
    "        for k in range(len(ewbins)-1):\n",
    "        \n",
    "#             print(ewbins[k],wi)\n",
    "#             lst = []\n",
    "            if ewbins[k] <= wi <= ewbins[k+1]:\n",
    "#                 print(ewbins[k],wi)\n",
    "                abslists[k].append(ent)\n",
    "                \n",
    "#                 lst.append(ent)\n",
    "    \n",
    "\n",
    "#             print(abslists)\n",
    "\n",
    "    absorber_lists_ewd.append(abslists)\n",
    "\n",
    "#     absorber_lists.append(abslist)\n",
    "#     absorber_lists_vweak.append(abslist_vw)\n",
    "#     absorber_lists_weak.append(abslist_w)\n",
    "#     absorber_lists_med.append(abslist_m)\n",
    "#     absorber_lists_strong.append(abslist_s)\n",
    "#     absorber_lists_vstrong.append(abslist_vs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 727,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.11578075137048792, 0.21695198914988656)"
      ]
     },
     "execution_count": 727,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ewbins[5],ewbins[6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(absorber_lists_ewd[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.log10(EWD_bins_in), np.log10(EWD_array_in))\n",
    "# plt.errorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #make lists of absorber info - for Cooksey+13:\n",
    "\n",
    "# absorber_lists_c = [] #list of lists\n",
    "# absorber_lists_c_weak, absorber_lists_c_med, absorber_lists_c_strong  = [], [], []\n",
    "\n",
    "# for zbin in zbins:\n",
    "#     abslist, abslist_w, abslist_m, abslist_s = [],[],[],[] #lists of absorber info for this zbin\n",
    "    \n",
    "#     #find all absorbers in this bin:\n",
    "#     for i in range(len(Zabs_c)):\n",
    "#         zi = Zabs_c[i]\n",
    "#         wi = Ewr_c[i]\n",
    "#         wei = Ewsig_c[i]\n",
    "#         ent = (wi,wei,zi)\n",
    "        \n",
    "#         if zbin <= zi <= zbin+dz:\n",
    "#             abslist.append(ent)\n",
    "            \n",
    "#             if wi >= weak: #EQW >= 0.01 A\n",
    "#                 abslist_w.append(ent)\n",
    "#             if wi >= med: #EQW >= 0.3 A\n",
    "#                 abslist_m.append(ent)\n",
    "#             if wi >= strong: #EQW >= 1 A\n",
    "#                 abslist_s.append(ent)\n",
    "\n",
    "                \n",
    "#     absorber_lists_c.append(abslist)\n",
    "#     absorber_lists_c_weak.append(abslist_w), absorber_lists_c_med.append(abslist_m), absorber_lists_c_strong.append(abslist_s)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.  , 1.5 , 1.8 , 2.  , 2.2 , 2.4 , 2.6 , 2.8 , 3.1 , 3.75, 4.75]),\n",
       " array([1.45, 1.61, 1.7 , 1.78, 1.87, 1.96, 2.08, 2.24, 2.51, 2.97, 4.55]))"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zbins, zbins_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define comoving path length:\n",
    "\n",
    "def dXdz(z,om_m,om_l):\n",
    "    ''' computes dX/dz for a given redshift and cosmology '''\n",
    "    num = (1+z)**2\n",
    "    den = np.sqrt((om_m*(1+z)**3)+om_l)\n",
    "    return num/den\n",
    "\n",
    "#define function to compute dN/dz:\n",
    "\n",
    "def compute_dNdz(abslist,gwzgrid,wgrid,zgrid,zbin_list):\n",
    "\n",
    "    ''' computes dN/dz for given redshift and EQW of a list of absorbers and a g(W,z) grid...\n",
    "    ... Inputs: \n",
    "    -abslist = list of absorbers, with zabs and EQW\n",
    "    -gwzgrid = g(W,z) grid\n",
    "    -wgrid = array of EWlims used in g(W,z) grid\n",
    "    -zgrid = array of redshifts used in g(W,z) grid\n",
    "    -zbin_list = list of redshift bins for which dN/dz is computed\n",
    "    \n",
    "    ... Outputs:\n",
    "    -dNdzarray: dN/dz for each redshift bin\n",
    "    -dNdzerrarray: uncertainty in dN/dz for each redshift bin\n",
    "    \n",
    "    '''\n",
    "    \n",
    "    dNdzarray, dNdzerrarray = [], [] #make arrays for output\n",
    "\n",
    "    for i in range(len(zbin_list)-1): #loop over each zbin\n",
    "        \n",
    "        zbin1, zbin2 = zbin_list[i], zbin_list[i+1] #redshift limits of this bin\n",
    "        \n",
    "        dndz_arr,dndz_arr_e  = [], [] #arrays for this bin\n",
    "        \n",
    "        for ab in abslist[i]: #loop over each absorber\n",
    "            \n",
    "#             if z1 <= ab[2] <= z2: #if this absorber falls within the redshift bin\n",
    "            \n",
    "            id_i = find_nearest(wgrid,ab[0]) #find closest row in g(W,z) grid\n",
    "            \n",
    "            id_k = find_nearest(zgrid,zbin1),find_nearest(zgrid,zbin2) #relevant redshifts for this bin\n",
    "            \n",
    "#             if i < 9:\n",
    "#                 id_k = find_nearest(zgrid,zbin_list[i]),find_nearest(zgrid,zbin_list[i+1]) #relevant redshifts for this bin\n",
    "        \n",
    "#             else:\n",
    "#                 id_k = find_nearest(zgrid,zbin_list[i]),find_nearest(zgrid,4.55) #relevant redshifts for this bin\n",
    "\n",
    "            gwz_row = gwzgrid[id_i][id_k[0]:id_k[1]] #points of interest in the row\n",
    "    \n",
    "#             if sum(gwz_row) == 0:\n",
    "                \n",
    "#                 print(id_i, id_k, ab[0],sum(gwz_row))\n",
    "            \n",
    "            if gwz_row.any() > 0.0: #make sure there is any senstivity in this row\n",
    "        \n",
    "                Zsum = sum(gwz_row)*z_spacing\n",
    "\n",
    "                dndz = 1/Zsum #dN/dz for this absorber = 1/Zsum\n",
    "            \n",
    "#             if dndz is not 'inf':\n",
    "            \n",
    "                dndz_arr.append(dndz), dndz_arr_e.append(dndz**2)\n",
    "            \n",
    "            \n",
    "        dndz_tot, dndz_e_tot = sum(dndz_arr), np.sqrt(sum(dndz_arr_e)) #sum up contributions from each system\n",
    "    \n",
    "        dNdzarray.append(dndz_tot), dNdzerrarray.append(dndz_e_tot) #append \n",
    "        \n",
    "\n",
    "    return dNdzarray, dNdzerrarray #return arrays\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CIV_gwz[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.005, 2.1999999999999744, 2.39999999999997, 0.0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_grid[0], z_grid[240], z_grid[280], CIV_gwz[0][240]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([38.54592821683181,\n",
       "  12.03577066905439,\n",
       "  15.240487721142221,\n",
       "  19.47005437245443,\n",
       "  17.438996678659986,\n",
       "  37.258391370903546,\n",
       "  95.80057622653301,\n",
       "  62.03280126103664,\n",
       "  22.467303111933216,\n",
       "  14.894865605654054],\n",
       " [20.55988334732741,\n",
       "  1.475629732757355,\n",
       "  2.0565426542581164,\n",
       "  4.348048459458376,\n",
       "  4.7975680873865185,\n",
       "  14.3499692522492,\n",
       "  67.36656746775675,\n",
       "  40.24825878762301,\n",
       "  4.352058735739098,\n",
       "  5.10276811811497])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dNdz_array, dNdz_e_array = compute_dNdz(absorber_lists,CIV_gwz,w_grid,z_grid,zbins)  \n",
    "\n",
    "\n",
    "dNdz_array, dNdz_e_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#define function to compute dN/dX:\n",
    "\n",
    "def compute_dNdX(abslist,gwzgrid,wgrid,zgrid,zbin_list):\n",
    "\n",
    "    ''' computes dN/dX for given redshift and EQW of a list of absorbers and a g(W,z) grid...\n",
    "    ... Inputs: \n",
    "    -abslist = list of absorbers, with zabs and EQW\n",
    "    -gwzgrid = g(W,z) grid\n",
    "    -wgrid = array of EWlims used in g(W,z) grid\n",
    "    -zgrid = array of redshifts used in g(W,z) grid\n",
    "    -zbin_list = list of redshift bins for which dN/dX is computed\n",
    "    \n",
    "    ... Outputs:\n",
    "    -dNdXarray: dN/dX for each redshift bin\n",
    "    -dNdXerrarray: uncertainty in dN/dX for each redshift bin\n",
    "    \n",
    "    '''\n",
    "    \n",
    "    dNdXarray, dNdXerrarray = [], [] #make arrays for output\n",
    "\n",
    "    for i in range(len(zbin_list)-1): #loop over each zbin\n",
    "        \n",
    "        zbin1, zbin2 = zbin_list[i], zbin_list[i+1] #redshift limits of this bin\n",
    "        \n",
    "        dndx_arr,dndx_arr_e  = [], [] #arrays for this bin\n",
    "        \n",
    "        for ab in abslist[i]: #loop over each absorber\n",
    "            \n",
    "#             print(ab)\n",
    "            \n",
    "            id_i = find_nearest(wgrid,ab[0]) #find closest row in g(W,z) grid\n",
    "   \n",
    "            id_k = find_nearest(zgrid,zbin1),find_nearest(zgrid,zbin2) #relevant redshifts for this bin\n",
    "\n",
    "#             if i < 9:\n",
    "#                 id_k = find_nearest(zgrid,zbin_list[i]),find_nearest(zgrid,zbin_list[i+1]) #relevant redshifts for this bin\n",
    "        \n",
    "#             else:\n",
    "#                 id_k = find_nearest(zgrid,zbin_list[i]),find_nearest(zgrid,4.55) #relevant redshifts for this bin\n",
    "            \n",
    "            gwz_row = gwzgrid[id_i][id_k[0]:id_k[1]] #points of interest in the row\n",
    "            \n",
    "            if gwz_row.any() > 0.0:  #make sure there is any senstivity in this row\n",
    "                \n",
    "                dxdz = dXdz(ab[2],omega_m,omega_l)\n",
    "                Xsum = sum(gwz_row*dxdz)*z_spacing\n",
    "\n",
    "                dndx = 1/Xsum #dN/dX for this absorber = 1/Zsum\n",
    "\n",
    "                dndx_arr.append(dndx), dndx_arr_e.append(dndx**2)\n",
    "            \n",
    "            \n",
    "        dndx_tot, dndx_e_tot = sum(dndx_arr), np.sqrt(sum(dndx_arr_e)) #sum up contributions from each system\n",
    "    \n",
    "        dNdXarray.append(dndx_tot), dNdXerrarray.append(dndx_e_tot) #append \n",
    "        \n",
    "\n",
    "    return dNdXarray, dNdXerrarray #return arrays\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function compute_dNdX in module __main__:\n",
      "\n",
      "compute_dNdX(abslist, gwzgrid, wgrid, zgrid, zbin_list)\n",
      "    computes dN/dX for given redshift and EQW of a list of absorbers and a g(W,z) grid...\n",
      "    ... Inputs: \n",
      "    -abslist = list of absorbers, with zabs and EQW\n",
      "    -gwzgrid = g(W,z) grid\n",
      "    -wgrid = array of EWlims used in g(W,z) grid\n",
      "    -zgrid = array of redshifts used in g(W,z) grid\n",
      "    -zbin_list = list of redshift bins for which dN/dX is computed\n",
      "    \n",
      "    ... Outputs:\n",
      "    -dNdXarray: dN/dX for each redshift bin\n",
      "    -dNdXerrarray: uncertainty in dN/dX for each redshift bin\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(compute_dNdX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# #define function to compute dN/dX:\n",
    "\n",
    "# def compute_dNdX_2(abslist,gwzgrid,wgrid,zgrid,zbin_list):\n",
    "\n",
    "#     ''' computes dN/dX for given redshift and EQW of a list of absorbers and a g(W,z) grid...\n",
    "#     ... Inputs: \n",
    "#     -abslist = list of absorbers, with zabs and EQW\n",
    "#     -gwzgrid = g(W,z) grid\n",
    "#     -wgrid = array of EWlims used in g(W,z) grid\n",
    "#     -zgrid = array of redshifts used in g(W,z) grid\n",
    "#     -zbin_list = list of redshift bins for which dN/dX is computed\n",
    "    \n",
    "#     ... Outputs:\n",
    "#     -dNdXarray: dN/dX for each redshift bin\n",
    "#     -dNdXerrarray: uncertainty in dN/dX for each redshift bin\n",
    "    \n",
    "#     '''\n",
    "    \n",
    "#     dNdXarray, dNdXerrarray = [], [] #make arrays for output\n",
    "\n",
    "#     for i in range(len(zbin_list)-1): #loop over each zbin\n",
    "        \n",
    "#         zbin1, zbin2 = zbin_list[i], zbin_list[i+1] #redshift limits of this bin\n",
    "        \n",
    "#         dndx_arr,dndx_arr_e  = [], [] #arrays for this bin\n",
    "        \n",
    "#         for ab in abslist: #loop over each absorber\n",
    "            \n",
    "# #             print(ab)\n",
    "            \n",
    "#             id_i = find_nearest(wgrid,ab[0]) #find closest row in g(W,z) grid\n",
    "   \n",
    "#             id_k = find_nearest(zgrid,zbin1),find_nearest(zgrid,zbin2) #relevant redshifts for this bin\n",
    "\n",
    "# #             if i < 9:\n",
    "# #                 id_k = find_nearest(zgrid,zbin_list[i]),find_nearest(zgrid,zbin_list[i+1]) #relevant redshifts for this bin\n",
    "        \n",
    "# #             else:\n",
    "# #                 id_k = find_nearest(zgrid,zbin_list[i]),find_nearest(zgrid,4.55) #relevant redshifts for this bin\n",
    "            \n",
    "#             gwz_row = gwzgrid[id_i][id_k[0]:id_k[1]] #points of interest in the row\n",
    "            \n",
    "#             if gwz_row.any() > 0.0:  #make sure there is any senstivity in this row\n",
    "                \n",
    "#                 dxdz = dXdz(ab[2],omega_m,omega_l)\n",
    "#                 Zsum = sum(gwz_row*dxdz)*z_spacing\n",
    "\n",
    "#                 dndx = 1/Zsum #dN/dX for this absorber = 1/Zsum\n",
    "\n",
    "#                 dndx_arr.append(dndx), dndx_arr_e.append(dndx**2)\n",
    "            \n",
    "            \n",
    "#         dndx_tot, dndx_e_tot = sum(dndx_arr), np.sqrt(sum(dndx_arr_e)) #sum up contributions from each system\n",
    "    \n",
    "#         dNdXarray.append(dndx_tot), dNdXerrarray.append(dndx_e_tot) #append \n",
    "        \n",
    "\n",
    "#     return dNdXarray, dNdXerrarray #return arrays\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 608,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ewbins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dNdz_array, dNdz_e_array = compute_dNdz(absorber_lists,CIV_gwz,w_grid,z_grid,zbins)  \n",
    "\n",
    "dNdz_array, dNdz_e_array\n",
    "\n",
    "# for ab in absorber_lists[0]:\n",
    "#     print(ab[0])\n",
    "    \n",
    "# len(absorber_lists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "    \n",
    "# def compute_dX(abslist,gwzgrid,wgrid,zgrid,zbin_list,ewbin_list):\n",
    "    \n",
    "#     ''' computes a redshift path for a given EQW bin\n",
    "    \n",
    "#    ... Inputs: \n",
    "   \n",
    "#     -abslist = list of absorbers, with zabs and EQW\n",
    "#     -gwzgrid = g(W,z) grid\n",
    "#     -wgrid = array of EWlims used in g(W,z) grid\n",
    "#     -zgrid = array of redshifts used in g(W,z) grid\n",
    "#     -zbin_list = list of redshift bins for which dN/dX is computed\n",
    "    \n",
    "#     ... Outputs:\n",
    "#     -dXarray: dX array for each EQW bin\n",
    "\n",
    "    \n",
    "#     '''\n",
    "    \n",
    "#     dXarray = [] #make arrays for output\n",
    "\n",
    "#     for i in range(len(zbin_list)-1): #loop over each zbin\n",
    "        \n",
    "#         zbin1, zbin2 = zbin_list[i], zbin_list[i+1] #redshift limits of this bin\n",
    "        \n",
    "#         dx_arr_z  = [] #arrays for this z-bin\n",
    "        \n",
    "#         gzs = 0\n",
    "    \n",
    "#         for m in range(len(gwzgrid)): #loop thru each row in the gwz grid\n",
    "            \n",
    "#             delz_cov = gwzgrid[m][zbin1:zbin2]\n",
    "#             dxdz = dXdz(np.mean([zbin1,zbin2]),omega_m,omega_l)\n",
    "\n",
    "#             gzs += sum(delz_cov*dxdz) \n",
    "            \n",
    "#         gzsum = gzs * z_spacing\n",
    "        \n",
    "#         print(gzsum)\n",
    "        \n",
    "#         arr_all = []\n",
    "        \n",
    "#         for j in range(len(ewbin_list)-1):\n",
    "            \n",
    "#             ewbin1, ewbin2 = ewbin_list[j], ewbin_list[j+1]\n",
    "            \n",
    "#             dx_arr_w = [] #arrays for this EQW-bin\n",
    "            \n",
    "# #             id_i = find_nearest(wgrid,ewbin1) #find closest row in g(W,z) grid\n",
    "\n",
    "# #             id_k = find_nearest(zgrid,zbin1),find_nearest(zgrid,zbin2) #relevant redshifts for this bin\n",
    "\n",
    "# #             gwz_row = gwzgrid[id_i][id_k[0]:id_k[1]] #points of interest in the row\n",
    "\n",
    "# #             if gwz_row.any() > 0.0:  #make sure there is any senstivity in this row\n",
    "\n",
    "# #                 dxdz = dXdz(zabs,omega_m,omega_l)\n",
    "# #                 Zsum = sum(gwz_row*dxdz)*z_spacing\n",
    "\n",
    "#             id_k = find_nearest(zgrid,zbin1),find_nearest(zgrid,zbin2) #relevant redshifts for this bin\n",
    "\n",
    "#             w_c = np.mean([ewbin1, ewbin2])\n",
    "                       \n",
    "#             w1 = find_nearest(wgrid,w_c) #find closest row in g(W,z) grid to the EQW bin center\n",
    "\n",
    "#             delwz = gwzgrid[w1][id_k[0]:id_k[1]] #g(W,z)\n",
    "\n",
    "#             if delwz.any() > 0.0:\n",
    "        \n",
    "#                 gwzsum = sum(delwz)*z_spacing\n",
    "            \n",
    "# #                 print(gwzsum)\n",
    "            \n",
    "        \n",
    "#             for k in range(len(abslist[j])): #loop over each absorber\n",
    "    \n",
    "#                 wr, zabs = abslist[j][k][0], abslist[j][k][2]   #W_r, zabs\n",
    "\n",
    "#                 id_i = find_nearest(wgrid,wr) #find closest row in g(W,z) grid\n",
    "\n",
    "\n",
    "#                 gwz_row = gwzgrid[id_i][id_k[0]:id_k[1]] #points of interest in the row\n",
    "                               \n",
    "\n",
    "#                 if gwz_row.any() > 0.0:  #make sure there is any senstivity in this row\n",
    "\n",
    "#                     dxdz = dXdz(zabs,omega_m,omega_l)\n",
    "#     #                     Zsum = sum(gwz_row*dxdz)*z_spacing\n",
    "\n",
    "#                     Xsum = sum(gwz_row*dxdz)*z_spacing\n",
    "        \n",
    "#                     Xsum_all = gzs*dxdz*z_spacing\n",
    "\n",
    "#                     dx_arr_w.append(Xsum)\n",
    "#                     arr_all.append(Xsum_all)\n",
    "\n",
    "            \n",
    "#             dx_w = sum(dx_arr_w)  #sum up contributions from each system - for this EQW-bin\n",
    "#             dx_arr_z.append(dx_w)\n",
    "            \n",
    "#         dx_all = sum(arr_all)\n",
    "            \n",
    "#         dXarray.append(dx_arr_z/dx_all) #append \n",
    "        \n",
    "\n",
    "#     return dXarray #return arrays\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define a function to calculate redshift path completeness:\n",
    "\n",
    "\n",
    "def compute_fz(distarray,gwzgrid,wgrid,zgrid,zbin_list):\n",
    "    \n",
    "    ''' computes a redshift path completeness for a given EQW or column density bin\n",
    "    \n",
    "    ... Inputs: \n",
    "    \n",
    "    -distarray = bins for which to compute fz\n",
    "    -gwzgrid = g(W,z) grid\n",
    "    -wgrid = array of EWlims used in g(W,z) grid\n",
    "    -zgrid = array of redshifts used in g(W,z) grid\n",
    "    -zbin_list = list of redshift bins for which fz is computed\n",
    "    \n",
    "    ... Outputs:\n",
    "    \n",
    "    -fz_array = array of fz values for each distarray bin\n",
    "\n",
    "    '''\n",
    "    \n",
    "    fz_array = []\n",
    "    \n",
    "    for i in range(len(zbin_list)-1): #loop over each zbin\n",
    "        \n",
    "        zbin1, zbin2 = zbin_list[i], zbin_list[i+1] #redshift limits of this bin\n",
    "                \n",
    "        fz_arr = [] #arrays for this bin\n",
    "        \n",
    "        z1, z2 = find_nearest(zgrid,zbin1),find_nearest(zgrid,zbin2) #relevant redshifts for this bin\n",
    "        \n",
    "#         print(z1, z2)\n",
    "#         delz_cov = [m for m in gwzgrid[j][id_k[0]:id_k[1]]] #total redshift path covered in this bin\n",
    "\n",
    "        gzs = 0\n",
    "    \n",
    "        for m in range(len(gwzgrid)): #loop thru each row in the gwz grid\n",
    "            \n",
    "            delz_cov = gwzgrid[m][z1:z2]\n",
    "#             print(delz_cov)\n",
    "            gzs += sum(delz_cov)\n",
    "            \n",
    "        gzsum = gzs * z_spacing\n",
    "#         print(gzsum)        \n",
    "\n",
    "        for i in range(len(distarray)-1):\n",
    "            \n",
    "            w_c = np.mean([distarray[i],distarray[i+1]])\n",
    "            \n",
    "            w1 = find_nearest(wgrid,w_c) #find closest row in g(W,z) grid to the EQW bin center\n",
    "\n",
    "#             print(w1)\n",
    "            delwz = gwzgrid[w1][z1:z2] #g(W,z)\n",
    "#             print(delwz)\n",
    "            if delwz.any() > 0.0:\n",
    "        \n",
    "                gwzsum = sum(delwz)*z_spacing\n",
    "            \n",
    "#                 print(gwzsum)\n",
    "                fz = gwzsum/gzsum #fraction of covered redshift path\n",
    "#                 print(fz)\n",
    "                fz_arr.append(fz)\n",
    "#                 print(fz_arr)\n",
    "        fz_array.append(fz_arr)\n",
    "#                 np.append(fz_array,fz)\n",
    "        \n",
    "    \n",
    "    return fz_array\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "    \n",
    "def compute_fX(distarray,gwzgrid,wgrid,zgrid,zbin_list,abslist):\n",
    "    \n",
    "    ''' computes a redshift path completeness for a given EQW or column density bin\n",
    "    \n",
    "    ... Inputs: \n",
    "    \n",
    "    -distarray = (EQW) bins for which to compute fz\n",
    "    -gwzgrid = g(W,z) grid\n",
    "    -wgrid = array of EWlims used in g(W,z) grid\n",
    "    -zgrid = array of redshifts used in g(W,z) grid\n",
    "    -zbin_list = list of redshift bins for which fz is computed\n",
    "    -abslist = list of absorbers\n",
    "    \n",
    "    ... Outputs:\n",
    "    \n",
    "    -fX_array = array of fX values for each distarray bin\n",
    "\n",
    "    '''\n",
    "    \n",
    "    fX_arr = []\n",
    "    \n",
    "    for i in range(len(zbin_list)-1): #loop over each zbin\n",
    "        \n",
    "        zbin1, zbin2 = zbin_list[i], zbin_list[i+1] #redshift limits of this bin\n",
    "        \n",
    "        z_c = np.mean([distarray[i],distarray[i+1]]) #mean redshift of this bin\n",
    "        \n",
    "        dx_arr  = [] #arrays for this bin\n",
    "        \n",
    "        sm_w = 0\n",
    "        \n",
    "        #loop over each absorber:\n",
    "        \n",
    "        for ab in abslist: \n",
    "            \n",
    "            for l in range(len(ab)):\n",
    "                \n",
    "                wabs,zabs = ab[l][0], ab[l][2]\n",
    "\n",
    "                id_i = find_nearest(wgrid,wabs) #find closest row in g(W,z) grid\n",
    "\n",
    "                id_k = find_nearest(zgrid,zbin1),find_nearest(zgrid,zbin2) #relevant redshifts for this bin\n",
    "\n",
    "                gwz_row = gwzgrid[id_i][id_k[0]:id_k[1]] #points of interest in the row\n",
    "\n",
    "                if gwz_row.any() > 0.0:  #make sure there is any senstivity in this row\n",
    "\n",
    "                    dxdz = dXdz(zabs,omega_m,omega_l)\n",
    "                    Xsum = sum(gwz_row*dxdz)*z_spacing\n",
    "\n",
    "                    dx_arr.append(Xsum)\n",
    "            \n",
    "        sum_dx_tot = sum(dx_arr) #total redshift path from ALL absorbers\n",
    "        \n",
    "#         dX_arr.append(sum_dx)\n",
    "#         dndx_tot, dndx_e_tot = sum(dndx_arr), np.sqrt(sum(dndx_arr_e)) #sum up contributions from each system\n",
    "    \n",
    "#         dNdXarray.append(dndx_tot), dNdXerrarray.append(dndx_e_tot) #append \n",
    "\n",
    "        for j in range(len(distarray)-1): #loop over EQW bin\n",
    "            \n",
    "            dx_arr_w = []\n",
    "            \n",
    "            wbin1, wbin2 = distarray[j], distarray[j+1] #EQW limits of this bin\n",
    "\n",
    "            w_c = np.mean([distarray[j],distarray[j+1]])\n",
    "            \n",
    "            w1 = find_nearest(wgrid,w_c) #find closest row in g(W,z) grid to the EQW bin center\n",
    "\n",
    "#             for ab in abslist: \n",
    "\n",
    "#             for l in range(len(abslist[j])):\n",
    "\n",
    "#                 wabs,zabs = abslist[j][l][0], abslist[j][l][2]\n",
    "\n",
    "#                 if wbin1 <= wabs <= wbin2:\n",
    "                    \n",
    "#                     print(abslist[j])\n",
    "\n",
    "#             id_i = find_nearest(wgrid,w1) #find closest row in g(W,z) grid\n",
    "\n",
    "            z01,z02 = find_nearest(zgrid,zbin1),find_nearest(zgrid,zbin2) #relevant redshifts for this bin\n",
    "\n",
    "            gwz_row = gwzgrid[w1][z01:z02] #points of interest in the row\n",
    "\n",
    "            if gwz_row.any() > 0.0:\n",
    "\n",
    "                dxdz = dXdz(zbin1,omega_m,omega_l)\n",
    "                Xsum = sum(gwz_row*dxdz)*z_spacing\n",
    "\n",
    "                dx_arr_w.append(Xsum)\n",
    "\n",
    "            sum_dx_w = sum(dx_arr_w)      \n",
    "            \n",
    "            sm_w += sum_dx_w\n",
    "\n",
    "#             print(sum_dx_w,sum_dx_tot)\n",
    "            \n",
    "            fX_bin = (sum_dx_w/sum_dx_tot)#fX for this EQW bin\n",
    "            \n",
    "            fX_arr.append(fX_bin)\n",
    "            \n",
    "    return fX_arr, sum_dx_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([1.4667980305157778e-07,\n",
       "  2.5033353054136042e-05,\n",
       "  9.873995408755422e-05,\n",
       "  0.0002392225354602041,\n",
       "  0.00042060433525040175,\n",
       "  0.0005149683418802492,\n",
       "  0.0005551463845994606,\n",
       "  0.0005647172417485763,\n",
       "  0.0005647172417485763,\n",
       "  0.0005647172417485763,\n",
       "  0.0005647172417485763],\n",
       " 524.7967348442896)"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_fX(ewbins,CIV_gwz,w_grid,z_grid,zbins_all,absorber_lists_ewd[0])\n",
    "# compute_dX(absorber_lists_ewd[0],CIV_gwz,w_grid,z_grid,zbins_all,ewbins_all)[0]\n",
    "# compute_fz(ewbins,CIV_gwz,w_grid,z_grid,zbins_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for ls in absorber_lists_ewd[0]:\n",
    "#     for l in range(len(ls)):\n",
    "#         print(ls[l][0])\n",
    "\n",
    "for j in range(len(ewbins)-1):\n",
    "    for l in range(len(absorber_lists_ewd[0][j])):\n",
    "        wabs,zabs = absorber_lists_ewd[0][j][l][0], absorber_lists_ewd[0][j][l][2]\n",
    "        \n",
    "        print(wabs,zabs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for j in range(len(ewbins)-1):\n",
    "    \n",
    "#     for k in range(len(absorber_lists_ewd[0][j])):\n",
    "        \n",
    "#         print(absorber_lists_ewd[0][j][k][2])\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 12)"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(absorber_lists_ewd[0]), len(ewbins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([1.4667980305157778e-07,\n",
       "  2.5033353054136042e-05,\n",
       "  9.873995408755422e-05,\n",
       "  0.0002392225354602041,\n",
       "  0.00042060433525040175,\n",
       "  0.0005149683418802492,\n",
       "  0.0005551463845994606,\n",
       "  0.0005647172417485763,\n",
       "  0.0005647172417485763,\n",
       "  0.0005647172417485763,\n",
       "  0.0005647172417485763],\n",
       " [0.00025974025974025887,\n",
       "  0.044329004329004364,\n",
       "  0.17484848484848506,\n",
       "  0.4236147186147202,\n",
       "  0.7448051948051967,\n",
       "  0.9119047619047618,\n",
       "  0.9830519480519477,\n",
       "  1.0,\n",
       "  1.0,\n",
       "  1.0,\n",
       "  1.0])"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compute redshift paths:\n",
    "\n",
    "# fX_arrays_ew = compute_fX(ewbins,CIV_gwz,w_grid,z_grid,zbins_all,absorber_lists_ewd[0])\n",
    "\n",
    "#compute fX:\n",
    "\n",
    "#all redshifts:\n",
    "norm_fX = compute_fX(ewbins,CIV_gwz,w_grid,z_grid,zbins_all,absorber_lists_ewd[0])[1]\n",
    "\n",
    "\n",
    "fX_unscaled_ew = compute_fX(ewbins,CIV_gwz,w_grid,z_grid,zbins_all,absorber_lists_ewd[0])[0]\n",
    "fX_scaled_ew = []\n",
    "\n",
    "# for arr in fX_unscaled_ew:\n",
    "#     for i in range(len(arr)):\n",
    "#         scaled = arr[i]/max(arr)\n",
    "#         fX_scaled_ew.append(scaled)\n",
    "\n",
    "for i in range(len(fX_unscaled_ew)):\n",
    "    scaled = (fX_unscaled_ew[i]/max(fX_unscaled_ew))\n",
    "    fX_scaled_ew.append(scaled)\n",
    "\n",
    "#by z-bins:\n",
    "# fz_unscaled_ew_bins = compute_fz(ewbins,CIV_gwz,w_grid,z_grid,zbins_large) \n",
    "\n",
    "# fz_scaled_ew_bins = []\n",
    "\n",
    "# for arr in fz_unscaled_ew_bins:\n",
    "#     fz0 = []\n",
    "#     for i in range(len(arr)):\n",
    "#         scaled = arr[i]/max(arr)\n",
    "#         fz0.append(scaled)\n",
    "#     fz_scaled_ew_bins.append(fz0)\n",
    "# fz_scaled_ew_bins = [i/max(fz_unscaled_ew_bins) for i in fz_unscaled_ew_bins]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([1.4667980305157778e-07,\n",
       "  2.5033353054136042e-05,\n",
       "  9.873995408755422e-05,\n",
       "  0.0002392225354602041,\n",
       "  0.00042060433525040175,\n",
       "  0.0005149683418802492,\n",
       "  0.0005551463845994606,\n",
       "  0.0005647172417485763,\n",
       "  0.0005647172417485763,\n",
       "  0.0005647172417485763,\n",
       "  0.0005647172417485763],\n",
       " [0.00025974025974025887,\n",
       "  0.044329004329004364,\n",
       "  0.17484848484848506,\n",
       "  0.4236147186147202,\n",
       "  0.7448051948051967,\n",
       "  0.9119047619047618,\n",
       "  0.9830519480519477,\n",
       "  1.0,\n",
       "  1.0,\n",
       "  1.0,\n",
       "  1.0])"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dXarrays_EQW[0], dXarrays_EQW_scaled\n",
    "fX_unscaled_ew,fX_scaled_ew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make initial EQW distributions:\n",
    "\n",
    "\n",
    "absorber_lists_ew = [] #list of lists\n",
    "absorber_lists_ew_low = []\n",
    "absorber_lists_ew_mid = []\n",
    "absorber_lists_ew_high = []\n",
    "\n",
    "for j in range(len(ewbins)-1):\n",
    "    #lists of absorber info for this ewbin:\n",
    "    abslist = [] \n",
    "    abslist_l, abslist_m, abslist_h = [],[],[]\n",
    "    \n",
    "    #find all absorbers in this bin:\n",
    "    for i in range(len(Ewr)):\n",
    "        zi = Zabs[i]\n",
    "        wi = Ewr[i]\n",
    "        wei = Ewsig[i]\n",
    "        ent = (wi,wei,zi)\n",
    "        \n",
    "        if ewbins[j] <= wi < ewbins[j+1]:\n",
    "            abslist.append(ent)\n",
    "        \n",
    "            if zbins_large[0] <= zi < zbins_large[1]:\n",
    "                abslist_l.append(ent)\n",
    "                \n",
    "            if zbins_large[1] <= zi < zbins_large[2]:\n",
    "                abslist_m.append(ent)\n",
    "                \n",
    "            if zi >= zbins_large[2]:\n",
    "                abslist_h.append(ent)\n",
    "\n",
    "                \n",
    "    absorber_lists_ew.append(abslist)\n",
    "    absorber_lists_ew_low.append(abslist_l),absorber_lists_ew_mid.append(abslist_m),absorber_lists_ew_high.append(abslist_h)\n",
    "\n",
    "\n",
    "EWD_bins_in = [] #initial EQW bins\n",
    "EWD_array_in, EWD_array_e_in = [], [] #initial EWD array and error arrays\n",
    "EWD_array_in_l, EWD_array_e_in_l = [], [] #for 1 <= z < 2\n",
    "EWD_array_in_m, EWD_array_e_in_m = [], [] #for 2 <= z < 3\n",
    "EWD_array_in_h, EWD_array_e_in_h = [], [] #for z >= 3\n",
    "\n",
    "EWD_bin_width = []\n",
    "\n",
    "for i in range(len(ewbins)-1):\n",
    "    \n",
    "    del_w = ewbins[i+1]-ewbins[i]\n",
    "    bin_c = 0.5*(ewbins[i+1]+ewbins[i])\n",
    "#     print(num)\n",
    "#     print(del_w)\n",
    "    num = len(absorber_lists_ew[i])\n",
    "#     print(num)\n",
    "    ewd = num/del_w\n",
    "    ewd_e = np.sqrt(num)/del_w\n",
    "    \n",
    "    num_l = len(absorber_lists_ew_low[i])\n",
    "    ewd_l = num_l/del_w\n",
    "    ewd_e_l = np.sqrt(num_l)/del_w\n",
    "    \n",
    "    num_m = len(absorber_lists_ew_mid[i])\n",
    "    ewd_m = num_m/del_w\n",
    "    ewd_e_m = np.sqrt(num_m)/del_w\n",
    "    \n",
    "    num_h = len(absorber_lists_ew_high[i])\n",
    "    ewd_h = num_h/del_w\n",
    "    ewd_e_h = np.sqrt(num_h)/del_w\n",
    "    \n",
    "#     ewd_log = np.log10(ewd)\n",
    "#     ewd_e_log = 0.434*(ewd_e/ewd)\n",
    "    EWD_bin_width.append(del_w)\n",
    "    EWD_bins_in.append(bin_c)\n",
    "    EWD_array_in.append(ewd), EWD_array_e_in.append(ewd_e)\n",
    "    EWD_array_in_l.append(ewd_l), EWD_array_e_in_l.append(ewd_e_l)\n",
    "    EWD_array_in_m.append(ewd_m), EWD_array_e_in_m.append(ewd_e_m)\n",
    "    EWD_array_in_h.append(ewd_h), EWD_array_e_in_h.append(ewd_e_h)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.0,\n",
       "  365.57230093307453,\n",
       "  1820.8861228505377,\n",
       "  1249.3956306768048,\n",
       "  1037.1897980430663,\n",
       "  731.4331782848714,\n",
       "  443.0930620803507,\n",
       "  174.5340155154019,\n",
       "  88.63660844968287,\n",
       "  12.827848682580518,\n",
       "  0.4278648137647398],\n",
       " [0.0,\n",
       "  211.06326635198144,\n",
       "  344.11513191650926,\n",
       "  208.2326051128008,\n",
       "  138.60031676194328,\n",
       "  85.02737987450328,\n",
       "  48.345416592689865,\n",
       "  22.16584213630926,\n",
       "  11.539503527097903,\n",
       "  3.2069621706451295,\n",
       "  0.4278648137647398],\n",
       " [685.0157468035557,\n",
       "  3777.5804429751033,\n",
       "  4877.373543349655,\n",
       "  4650.5281808525515,\n",
       "  2981.9206693738156,\n",
       "  1779.1617850172547,\n",
       "  891.4610415664199,\n",
       "  247.7256994412156,\n",
       "  88.63660844968287,\n",
       "  11.224367597257954,\n",
       "  0.4278648137647398],\n",
       " [395.49402581616533,\n",
       "  678.4734763909815,\n",
       "  563.1905856382565,\n",
       "  401.74444577148404,\n",
       "  235.0082710112969,\n",
       "  132.6108898089483,\n",
       "  68.57392627434,\n",
       "  26.407648290280903,\n",
       "  11.539503527097903,\n",
       "  2.9998384237247357,\n",
       "  0.4278648137647398],\n",
       " [685.0157468035557,\n",
       "  1096.7169027992236,\n",
       "  1560.7595338718895,\n",
       "  1561.744538346006,\n",
       "  1203.881015585702,\n",
       "  553.5169997831459,\n",
       "  253.1960354744861,\n",
       "  87.26700775770095,\n",
       "  15.02315397452252,\n",
       "  0.0,\n",
       "  0.0],\n",
       " [395.49402581616533,\n",
       "  365.57230093307453,\n",
       "  318.5887057641873,\n",
       "  232.81113008204645,\n",
       "  149.32306224515926,\n",
       "  73.96682039084135,\n",
       "  36.54569980973515,\n",
       "  15.673617285294785,\n",
       "  4.7507384198902365,\n",
       "  0.0,\n",
       "  0.0])"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EWD_array_in_l, EWD_array_e_in_l, EWD_array_in_m, EWD_array_e_in_m, EWD_array_in_h, EWD_array_e_in_h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([10050.789001102317,\n",
       "  225.23785461070003,\n",
       "  90.00683245394526,\n",
       "  33.564004935540325,\n",
       "  13.362431634878053,\n",
       "  6.402712814818499,\n",
       "  3.0776169629853904,\n",
       "  0.9709029970727583,\n",
       "  0.366420669387251,\n",
       "  0.045831489951961894,\n",
       "  0.0016305925146111661],\n",
       " [4103.217427513019,\n",
       "  34.34845081291482,\n",
       "  7.986814866993386,\n",
       "  2.2890461375997537,\n",
       "  0.7957212242081285,\n",
       "  0.3636496514461503,\n",
       "  0.17739089268287883,\n",
       "  0.07216665142980108,\n",
       "  0.0323873175113299,\n",
       "  0.008367646963586965,\n",
       "  0.00115300302443358])"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compute normalized + completeness-corrected EWD:\n",
    "\n",
    "EWD_array = [(a/b)/norm_fX for a,b in zip(EWD_array_in,fX_scaled_ew)]\n",
    "\n",
    "EWD_array_e = [(a/b)/norm_fX for a,b in zip(EWD_array_e_in,fX_scaled_ew)]\n",
    "\n",
    "\n",
    "# EWD_array_f = [i/dX_tot[0] for i in EWD_array]\n",
    "\n",
    "EWD_array_in,dXarrays_EQW_scaled\n",
    "\n",
    "EWD_array, EWD_array_e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([1370.0314936071113,\n",
       "  5239.869646707401,\n",
       "  8259.019200072082,\n",
       "  7461.668349875362,\n",
       "  5222.991483002585,\n",
       "  3064.111963085272,\n",
       "  1587.7501391212568,\n",
       "  509.52672271431845,\n",
       "  192.29637087388826,\n",
       "  24.052216279838472,\n",
       "  0.8557296275294796],\n",
       " [23.18014256440293,\n",
       "  1204.683364460273,\n",
       "  17402.123827061492,\n",
       "  73155.84010747155,\n",
       "  157531.62392648778,\n",
       "  212075.24568554142,\n",
       "  220238.16868626623,\n",
       "  134589.7787879791,\n",
       "  90869.61264094512,\n",
       "  20813.850064003087,\n",
       "  1404.750452035296],\n",
       " [59.10366986745926,\n",
       "  4.34958247228307,\n",
       "  0.47459834685400537,\n",
       "  0.10199689237268819,\n",
       "  0.0331551935593573,\n",
       "  0.014448230170288906,\n",
       "  0.007209241470687305,\n",
       "  0.003785775764718226,\n",
       "  0.0021161790535380916,\n",
       "  0.0011555870829220605,\n",
       "  0.000609168430086384])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EWD_array_in, dXarrays_EQW[0], EWD_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "#compute fz:\n",
    "\n",
    "#all redshifts:\n",
    "fz_unscaled_ew = compute_fz(ewbins,CIV_gwz,w_grid,z_grid,zbins_all) \n",
    "\n",
    "fz_scaled_ew = []\n",
    "\n",
    "for arr in fz_unscaled_ew:\n",
    "    for i in range(len(arr)):\n",
    "        scaled = arr[i]/max(arr)\n",
    "        fz_scaled_ew.append(scaled)\n",
    "\n",
    "# fz_scaled_ew = [i/max(fz_unscaled_ew) for i in fz_unscaled_ew]\n",
    "\n",
    "#by z-bins:\n",
    "# fz_unscaled_ew_bins = compute_fz(ewbins,CIV_gwz,w_grid,z_grid,zbins_large) \n",
    "\n",
    "# fz_scaled_ew_bins = []\n",
    "\n",
    "# for arr in fz_unscaled_ew_bins:\n",
    "#     fz0 = []\n",
    "#     for i in range(len(arr)):\n",
    "#         scaled = arr[i]/max(arr)\n",
    "#         fz0.append(scaled)\n",
    "#     fz_scaled_ew_bins.append(fz0)\n",
    "# fz_scaled_ew_bins = [i/max(fz_unscaled_ew_bins) for i in fz_unscaled_ew_bins]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[3.952431173351655e-06,\n",
       "  0.0006745482535853491,\n",
       "  0.0026606449181945557,\n",
       "  0.006446085874472103,\n",
       "  0.011333596389585872,\n",
       "  0.013876327111108769,\n",
       "  0.014958963883342676,\n",
       "  0.01521686001740387,\n",
       "  0.01521686001740387,\n",
       "  0.01521686001740387,\n",
       "  0.01521686001740387]]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fz_unscaled_ew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Complete EWD:\n",
    "\n",
    "EWD_array = [a/b for a,b in zip(EWD_array_in,fz_scaled_ew)]\n",
    "EWD_array_e = [a/b for a,b in zip(EWD_array_e_in,fz_scaled_ew)]\n",
    "\n",
    "\n",
    "EWD_array_f = [a/b for a,b in zip(EWD_array,dXarrays_EQW[0])]\n",
    "EWD_array_e_f = [a/b for a,b in zip(EWD_array_e,dXarrays_EQW[0])]\n",
    "\n",
    "\n",
    "EWD_array_l = [a/b for a,b in zip(EWD_array_in_l,fz_scaled_ew_bins[0])]\n",
    "EWD_array_e_l = [a/b for a,b in zip(EWD_array_e_in_l,fz_scaled_ew_bins[0])]\n",
    "\n",
    "EWD_array_m = [a/b for a,b in zip(EWD_array_in_m,fz_scaled_ew_bins[1])]\n",
    "EWD_array_e_m = [a/b for a,b in zip(EWD_array_e_in_m,fz_scaled_ew_bins[1])]\n",
    "\n",
    "EWD_array_h = [a/b for a,b in zip(EWD_array_in_h,fz_scaled_ew_bins[2])]\n",
    "EWD_array_e_h = [a/b for a,b in zip(EWD_array_e_in_h,fz_scaled_ew_bins[2])]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([1370.0314936071113,\n",
       "  5239.869646707401,\n",
       "  8259.019200072082,\n",
       "  7461.668349875362,\n",
       "  5222.991483002585,\n",
       "  3064.111963085272,\n",
       "  1587.7501391212568,\n",
       "  509.52672271431845,\n",
       "  192.29637087388826,\n",
       "  24.052216279838472,\n",
       "  0.8557296275294796],\n",
       " [5274621.250387379,\n",
       "  118204.0906630283,\n",
       "  47235.29178550757,\n",
       "  17614.280198469252,\n",
       "  7012.5604915640615,\n",
       "  3360.122779362439,\n",
       "  1615.1233332761315,\n",
       "  509.52672271431845,\n",
       "  192.29637087388826,\n",
       "  24.052216279838472,\n",
       "  0.8557296275294796],\n",
       " [13652.94773938309,\n",
       "  1004.7535510973893,\n",
       "  109.63221812327525,\n",
       "  23.561282138090974,\n",
       "  7.658849712211536,\n",
       "  3.3375411693367374,\n",
       "  1.6653347797287674,\n",
       "  0.8745142016499102,\n",
       "  0.48883736136729916,\n",
       "  0.266940616154996,\n",
       "  0.1407179073499547],\n",
       " [0.00025974025974025974,\n",
       "  0.04432900432900433,\n",
       "  0.17484848484848486,\n",
       "  0.4236147186147186,\n",
       "  0.7448051948051949,\n",
       "  0.911904761904762,\n",
       "  0.9830519480519481,\n",
       "  1.0,\n",
       "  1.0,\n",
       "  1.0,\n",
       "  1.0],\n",
       " [386.3357094067155,\n",
       "  117.64485981057354,\n",
       "  430.8522858891184,\n",
       "  747.5942987836243,\n",
       "  915.6153671984177,\n",
       "  1006.7659420153876,\n",
       "  969.8490375245667,\n",
       "  582.6397350128965,\n",
       "  393.37494649759793,\n",
       "  90.1032470303164,\n",
       "  6.081170788031584])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EWD_array_in, EWD_array, EWD_array_f, fz_scaled_ew, dXarrays_EQW[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.72957829e-06])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #Normalizing to dN/dX:\n",
    "\n",
    "# # dNdX_norm, dNdX_norm_e = [], []\n",
    "\n",
    "# # for lst in absorber_lists_ewd[0]:\n",
    "# # #     lst = absorber_lists_ewd[0][i]\n",
    "# # #     print(lst[0])\n",
    "\n",
    "# # #     for i in range(len(lst)):\n",
    "# # #         print(lst[i][0])\n",
    "# #     dndx, dndx_e = compute_dNdX_2(lst,CIV_gwz,w_grid,z_grid,zbins_all)  \n",
    "# # #     print(dndx, dndx_e)\n",
    "\n",
    "# # #     print(nnn)\n",
    "# # #     dNdX_norm.append(dndx)\n",
    "# # #     dNdX_norm.append(nnn), dNdX_norm_e(nnn)\n",
    "# #     for j in range(len(dndx)):\n",
    "# # #         print(val)\n",
    "# #         dNdX_norm.append(dndx[j])\n",
    "# #         dNdX_norm_e.append(dndx_e[j])\n",
    "\n",
    "# #calculate global dN/dX:\n",
    "\n",
    "# dNdX_norm, dNdX_norm_e = compute_dNdX(absorber_lists,CIV_gwz,w_grid,z_grid,zbins_all) \n",
    "    \n",
    "# big_sigma = sum([a*b for a,b in zip(EWD_array,EWD_bin_width)]) #f_cor * bin_width summed\n",
    "\n",
    "# norm_const = dNdX_norm/big_sigma\n",
    "\n",
    "# norm_const\n",
    "\n",
    "# # dNdX_norm, dNdX_norm_e\n",
    "\n",
    "# # dNdz_array_vweak, dNdz_e_array_vweak = compute_dNdz(absorber_lists_vweak,CIV_gwz,w_grid,z_grid,zbins)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #EWD normalized to dN/dX:\n",
    "\n",
    "# # dNdX_norm, dNdX_norm_e = compute_dNdX_W(absorber_lists_ew,CIV_gwz,w_grid,z_grid,zbins_large,ewbins)  \n",
    "# EWD_norm = [a*norm_const for a in EWD_array]\n",
    "# EWD_norm_e = [a*norm_const for a in EWD_array_e]\n",
    "\n",
    "# # dNdX_norm, dNdX_norm_e\n",
    "\n",
    "\n",
    "# # EWD_norm,EWD_norm_e\n",
    "\n",
    "# EWD_norm, EWD_norm_e\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 898,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(EWD_array,EWD_norm)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([3.7017584357564544e-12,\n",
       "  1.8236862427939668e-11,\n",
       "  3.014418592003461e-11,\n",
       "  2.3325367023324137e-11,\n",
       "  1.4124947549722342e-11,\n",
       "  6.978889514987146e-12,\n",
       "  3.2630460846730417e-12,\n",
       "  9.43200023827684e-13,\n",
       "  3.0837516843858663e-13,\n",
       "  6.840777730280677e-14,\n",
       "  1.2177549322702999e-15],\n",
       " [1.8508792178782272e-12,\n",
       "  2.848119410416316e-12,\n",
       "  2.5385984081543465e-12,\n",
       "  1.5481589845005475e-12,\n",
       "  8.35225657884644e-13,\n",
       "  4.070171019756687e-13,\n",
       "  1.9294795985508112e-13,\n",
       "  7.191832758570023e-14,\n",
       "  2.850929440731202e-14,\n",
       "  9.309119379434506e-15,\n",
       "  8.61082770431694e-16])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make initial CD distribution:\n",
    "\n",
    "\n",
    "absorber_lists_cd, absorber_lists_cd_l, absorber_lists_cd_u  = [],[],[] #list of lists\n",
    "# absorber_lists_vweak_ew, absorber_lists_vstrong = [], []\n",
    "# absorber_lists_weak_ew, absorber_lists_med_ew, absorber_lists_strong_ew  = [], [], []\n",
    "\n",
    "for j in range(len(columnbins)-1):\n",
    "    #lists of absorber info for this ewbin:\n",
    "    abslist,abslist_l,abslist_u  = [],[],[]  \n",
    "#     abslist_vw, abslist_vs = [],[]\n",
    "    \n",
    "    #find all absorbers in this bin:\n",
    "    for i in range(len(N_abs)):\n",
    "        zi = Zabs[i]\n",
    "#         wi = Ewr[i]\n",
    "#         wei = Ewsig[i]\n",
    "        col = N_abs[i]\n",
    "        col_l, col_u = N_abs_l, N_abs_u\n",
    "        ent = (col,col_l, col_u, zi)\n",
    "        \n",
    "        if columnbins[j] <= col < columnbins[j+1]:\n",
    "            abslist.append(ent)\n",
    "        \n",
    "#         if columnbins[j]-N_abs_l[j] <= col < columnbins[j+1]-+N_abs_l[j+1]:\n",
    "#             abslist_l.append(ent)\n",
    "            \n",
    "#         if columnbins[j]+N_abs_u[j] <= col < columnbins[j+1]-+N_abs_u[j+1]:\n",
    "#             abslist_u.append(ent)\n",
    "                \n",
    "    absorber_lists_cd.append(abslist)\n",
    "\n",
    "\n",
    "\n",
    "CDD_bins_in = [] #initial CD bins\n",
    "CDD_array_in, CDD_array_l_in, CDD_array_u_in = [], [], [] #initial CDD array and error arrays\n",
    "CDD_array_e_in = []\n",
    "\n",
    "for i in range(len(columnbins)-1):\n",
    "    \n",
    "    del_n = columnbins[i+1]-columnbins[i]\n",
    "    bin_c = 0.5*(columnbins[i+1]+columnbins[i])\n",
    "#     print(del_w)\n",
    "    num = len(absorber_lists_cd[i])\n",
    "#     print(num)\n",
    "    cdd = (num/del_n)\n",
    "    cdd_e = np.sqrt(num)/del_n\n",
    "#     cdd_l = num\n",
    "    cdd_log = np.log10(cdd)\n",
    "    cdd_e_log = 0.434*(cdd_e/cdd)\n",
    "    \n",
    "    CDD_bins_in.append(bin_c)\n",
    "    CDD_array_in.append(cdd), CDD_array_e_in.append(cdd_e)\n",
    "#     CDD_array_l_in.append(cdd_l), CDD_array_u_in.append(cdd_u)\n",
    "\n",
    "# EWD_bins_in, EWD_array_in, EWD_array_e_in\n",
    "\n",
    "CDD_array_in, CDD_array_e_in\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ####compute dN/dz:\n",
    "\n",
    "# #for the full sample:\n",
    "# dNdz_array, dNdz_e_array = compute_dNdz(absorber_lists,CIV_gwz,w_grid,z_grid,zbins) \n",
    "\n",
    "# #for those with EQW >= 0.01 A:\n",
    "# dNdz_array_weak, dNdz_e_array_weak = compute_dNdz(absorber_lists_weak,CIV_gwz,w_grid,z_grid,zbins)\n",
    "\n",
    "# #for those with EQW >= 0.3 A:\n",
    "# dNdz_array_med, dNdz_e_array_med = compute_dNdz(absorber_lists_med,CIV_gwz,w_grid,z_grid,zbins)\n",
    "\n",
    "# #for those with EQW >= 1 A:\n",
    "# dNdz_array_strong, dNdz_e_array_strong = compute_dNdz(absorber_lists_strong,CIV_gwz,w_grid,z_grid,zbins)\n",
    "\n",
    "\n",
    "# ####compute dN/dX:\n",
    "\n",
    "# #for the full sample:\n",
    "# dNdX_array, dNdX_e_array = compute_dNdX(absorber_lists,CIV_gwz,w_grid,z_grid,zbins) \n",
    "\n",
    "# #for those with EQW >= 0.01 A:\n",
    "# dNdX_array_weak, dNdX_e_array_weak = compute_dNdX(absorber_lists_weak,CIV_gwz,w_grid,z_grid,zbins)\n",
    "\n",
    "# #for those with EQW >= 0.3 A:\n",
    "# dNdX_array_med, dNdX_e_array_med = compute_dNdX(absorber_lists_med,CIV_gwz,w_grid,z_grid,zbins)\n",
    "\n",
    "# #for those with EQW >= 1 A:\n",
    "# dNdX_array_strong, dNdX_e_array_strong = compute_dNdX(absorber_lists_strong,CIV_gwz,w_grid,z_grid,zbins)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.25948453455709436], [0.08510330214969122])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Quick computation of dN/dX for W>0.01 and W>0.3:\n",
    "\n",
    "dNdX_array_all_vw, dNdX_e_array_all_vw = compute_dNdX(absorber_lists_vweak,CIV_gwz,w_grid,z_grid,zbins_all) \n",
    "dNdX_array_all_w, dNdX_e_array_all_w = compute_dNdX(absorber_lists_weak,CIV_gwz,w_grid,z_grid,zbins_all)\n",
    "\n",
    "dNdX_array_all_vw, dNdX_array_all_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "####compute dN/dz:\n",
    "\n",
    "#for the full sample:\n",
    "dNdz_array, dNdz_e_array = compute_dNdz(absorber_lists,CIV_gwz,w_grid,z_grid,zbins) \n",
    "\n",
    "#for those with EQW >= 0.01 A:\n",
    "dNdz_array_vweak, dNdz_e_array_vweak = compute_dNdz(absorber_lists_vweak,CIV_gwz,w_grid,z_grid,zbins)\n",
    "\n",
    "#for those with EQW >= 0.3 A:\n",
    "dNdz_array_weak, dNdz_e_array_weak = compute_dNdz(absorber_lists_weak,CIV_gwz,w_grid,z_grid,zbins)\n",
    "\n",
    "#for those with EQW >= 0.6 A:\n",
    "dNdz_array_med, dNdz_e_array_med = compute_dNdz(absorber_lists_med,CIV_gwz,w_grid,z_grid,zbins)\n",
    "\n",
    "#for those with EQW >= 0.9 A:\n",
    "dNdz_array_strong, dNdz_e_array_strong = compute_dNdz(absorber_lists_strong,CIV_gwz,w_grid,z_grid,zbins)\n",
    "\n",
    "#for those with EQW >= 1.2 A:\n",
    "dNdz_array_vstrong, dNdz_e_array_vstrong = compute_dNdz(absorber_lists_vstrong,CIV_gwz,w_grid,z_grid,zbins)\n",
    "\n",
    "\n",
    "####compute dN/dX:\n",
    "\n",
    "#for the full sample:\n",
    "dNdX_array, dNdX_e_array = compute_dNdX(absorber_lists,CIV_gwz,w_grid,z_grid,zbins) \n",
    "\n",
    "#for those with EQW >= 0.01 A:\n",
    "dNdX_array_vweak, dNdX_e_array_vweak = compute_dNdX(absorber_lists_vweak,CIV_gwz,w_grid,z_grid,zbins)\n",
    "\n",
    "#for those with EQW >= 0.3 A:\n",
    "dNdX_array_weak, dNdX_e_array_weak = compute_dNdX(absorber_lists_weak,CIV_gwz,w_grid,z_grid,zbins)\n",
    "\n",
    "#for those with EQW >= 0.6 A:\n",
    "dNdX_array_med, dNdX_e_array_med = compute_dNdX(absorber_lists_med,CIV_gwz,w_grid,z_grid,zbins)\n",
    "\n",
    "#for those with EQW >= 0.9 A:\n",
    "dNdX_array_strong, dNdX_e_array_strong = compute_dNdX(absorber_lists_strong,CIV_gwz,w_grid,z_grid,zbins)\n",
    "\n",
    "#for those with EQW >= 1.2 A:\n",
    "dNdX_array_vstrong, dNdX_e_array_vstrong = compute_dNdX(absorber_lists_vstrong,CIV_gwz,w_grid,z_grid,zbins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.2, (0.041, 0.003, 2.2281858), 5.072411102953014)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zbins[4], absorber_lists[4][0], dNdX_array[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cooksey plots from above:\n",
    "\n",
    "# plt.scatter(zbins+dz/2,dNdz_array_c_weak,c='b')\n",
    "# plt.errorbar(zbins+dz/2,dNdz_array_c_weak, yerr=dNdz_e_array_c_weak, xerr=dz/2)\n",
    "# plt.scatter(zbins+dz/2,dNdz_array_c_med,c='g')\n",
    "# plt.errorbar(zbins+dz/2,dNdz_array_c_med, yerr=dNdz_e_array_c_med, xerr=dz/2)\n",
    "# plt.scatter(zbins+dz/2,dNdz_array_c_strong,c='r')\n",
    "# plt.errorbar(zbins+dz/2,dNdz_array_c_strong, yerr=dNdz_e_array_c_strong, xerr=dz/2)\n",
    "# plt.yscale('log')\n",
    "# # plt.ylim(-1,1)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# #compute dN/dz:\n",
    "\n",
    "# dNdz_array, dNdz_e_array = [], [] #for the full sample\n",
    "# dNdz_array_weak, dNdz_e_array_weak = [], [] #for those with EQW >= 0.01 A\n",
    "# dNdz_array_med, dNdz_e_array_med = [], [] #for those with EQW >= 0.3 A\n",
    "# dNdz_array_strong, dNdz_e_array_strong = [], [] #for those with EQW >= 1 A\n",
    "\n",
    "# # for i in range(len(absorber_lists)): #loop over each zbin\n",
    "    \n",
    "# #     dndz_arr,dndz_arr_e  = [], [] \n",
    "# #     dndz_arr_w,dndz_arr_e_w, dndz_arr_m,dndz_arr_e_m, dndz_arr_s,dndz_arr_e_s  = [], [], [], [], [], [] \n",
    "    \n",
    "# #     for ab in absorber_lists[i]: #loop over each absorber - full list\n",
    "\n",
    "# #         id_i = find_nearest(w_grid,ab[0]) #find closest row in g(W,z) grid\n",
    "# # #         print(id_i)\n",
    "# #         id_k = find_nearest(z_grid,zbins[i]),find_nearest(z_grid,zbins[i]+1)  #relevant redshifts for this bin\n",
    "# # #         print(id_k)\n",
    "# # #         i_row = CIV_gwz[id_i]\n",
    "# # #         k_row = CIV_gwz\n",
    "\n",
    "# #         gwz_row = CIV_gwz[id_i][id_k[0]:id_k[1]] #points of interest in the row\n",
    "# # #         print(sum(gwz_row))\n",
    "# #         Zsum = sum(gwz_row)*z_spacing\n",
    "        \n",
    "# #         dndz = 1/Zsum\n",
    "# # #         print(dndz)\n",
    "# # #         dndz_err = 1/(Zsum**2) #error\n",
    "        \n",
    "# #         dndz_arr.append(dndz), dndz_arr_e.append(dndz**2)\n",
    "        \n",
    "# #     for ab in absorber_lists_weak[i]: #loop over each absorber - EQW >= 0.01 A\n",
    "\n",
    "# #         id_i = find_nearest(w_grid,ab[0]) #find closest row in g(W,z) grid\n",
    "# #         id_k = find_nearest(z_grid,zbins[i]),find_nearest(z_grid,zbins[i]+1)  #relevant redshifts for this bin\n",
    "\n",
    "# #         gwz_row = CIV_gwz[id_i][id_k[0]:id_k[1]] #points of interest in the row\n",
    "# #         Zsum = sum(gwz_row)*z_spacing\n",
    "        \n",
    "# #         dndz = 1/Zsum\n",
    "# # #         dndz_err = 1/(Zsum**2) #error\n",
    "        \n",
    "# #         dndz_arr_w.append(dndz), dndz_arr_e_w.append((dndz)**2)\n",
    "        \n",
    "# #     for ab in absorber_lists_med[i]: #loop over each absorber - EQW >= 0.3 A\n",
    "\n",
    "# #         id_i = find_nearest(w_grid,ab[0]) #find closest row in g(W,z) grid\n",
    "# #         id_k = find_nearest(z_grid,zbins[i]),find_nearest(z_grid,zbins[i]+1)  #relevant redshifts for this bin\n",
    "\n",
    "# #         gwz_row = CIV_gwz[id_i][id_k[0]:id_k[1]] #points of interest in the row\n",
    "# #         Zsum = sum(gwz_row)*z_spacing\n",
    "        \n",
    "# #         dndz = 1/Zsum\n",
    "# # #         dndz_err = 1/(Zsum**2) #error\n",
    "        \n",
    "# #         dndz_arr_m.append(dndz), dndz_arr_e_m.append(dndz**2)\n",
    "        \n",
    "# #     for ab in absorber_lists_strong[i]: #loop over each absorber - EQW >= 1 A\n",
    "\n",
    "# #         id_i = find_nearest(w_grid,ab[0]) #find closest row in g(W,z) grid\n",
    "# #         id_k = find_nearest(z_grid,zbins[i]),find_nearest(z_grid,zbins[i]+1)  #relevant redshifts for this bin\n",
    "\n",
    "# #         gwz_row = CIV_gwz[id_i][id_k[0]:id_k[1]] #points of interest in the row\n",
    "# #         Zsum = sum(gwz_row)*z_spacing\n",
    "        \n",
    "# #         dndz = 1/Zsum\n",
    "\n",
    "# #         dndz_arr_s.append(dndz), dndz_arr_e_s.append(dndz**2)\n",
    "            \n",
    "        \n",
    "# #     dndz_tot, dndz_e_tot = sum(dndz_arr), np.sqrt(sum(dndz_arr_e)) #sum up contributions from each system\n",
    "# #     dndz_tot_w, dndz_e_tot_w = sum(dndz_arr_w), np.sqrt(sum(dndz_arr_e_w))\n",
    "# #     dndz_tot_m, dndz_e_tot_m = sum(dndz_arr_m), np.sqrt(sum(dndz_arr_e_m))\n",
    "# #     dndz_tot_s, dndz_e_tot_s = sum(dndz_arr_s), np.sqrt(sum(dndz_arr_e_s))\n",
    "\n",
    "\n",
    "# #     dNdz_array.append(dndz_tot), dNdz_e_array.append(dndz_e_tot)\n",
    "# #     dNdz_array_weak.append(dndz_tot_w), dNdz_e_array_weak.append(dndz_e_tot_w)\n",
    "# #     dNdz_array_med.append(dndz_tot_m), dNdz_e_array_med.append(dndz_e_tot_m)\n",
    "# #     dNdz_array_strong.append(dndz_tot_s), dNdz_e_array_strong.append(dndz_e_tot_s)\n",
    "    \n",
    "# # #     print(len(dNdz_array),len(dNdz_e_array))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #remove zero values:\n",
    "\n",
    "# dNdz_array_strong = [i for i in dNdz_array_strong if not i==0]\n",
    "# dNdz_e_array_strong = [i for i in dNdz_e_array_strong if not i==0]\n",
    "\n",
    "# dNdz_array_strong, dNdz_e_array_strong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #log dN/dz and errors:\n",
    "\n",
    "# #all:\n",
    "# dNdz_log = [np.log10(i) for i in dNdz_array]\n",
    "# dNdz_e_log = [0.434*b/a for a,b in zip(dNdz_array,dNdz_e_array)]\n",
    "\n",
    "# #weak:\n",
    "# dNdz_log_w = [np.log10(i) for i in dNdz_array_weak]\n",
    "# dNdz_e_log_w = [0.434*b/a for a,b in zip(dNdz_array_weak,dNdz_e_array_weak)]\n",
    "\n",
    "# #medium:\n",
    "# dNdz_log_m = [np.log10(i) for i in dNdz_array_med]\n",
    "# dNdz_e_log_m = [0.434*b/a for a,b in zip(dNdz_array_med,dNdz_e_array_med)]\n",
    "\n",
    "# #strong:\n",
    "\n",
    "# dNdz_log_s, dNdz_e_log_s = [], []\n",
    "\n",
    "# # for i in range(len(dNdz_array_strong)):\n",
    "    \n",
    "# #     if dNdz_array_strong[i] == 0: #if zero\n",
    "# #         dNdz_log_s.append(0),dNdz_e_log_s.append(0)\n",
    "# # #         print(dNdz_e_array_strong[i], dNdz_array_strong[i])\n",
    "\n",
    "# #     else:\n",
    "# # #         print(i, dNdz_e_array_strong[i], dNdz_array_strong[i])\n",
    "# #         err = 0.434*dNdz_e_array_strong[i]/dNdz_array_strong[i]\n",
    "# #         dNdz_log_s.append(np.log10(dNdz_array_strong[i]))\n",
    "# #         dNdz_e_log_s.append(err)\n",
    "        \n",
    "    \n",
    "# dNdz_log_s = [np.log10(i) for i in dNdz_array_strong]\n",
    "# # dNdz_log_s = np.ma.masked_invalid(dNdz_log_s)\n",
    "# dNdz_e_log_s = [0.434*b/a for a,b in zip(dNdz_array_strong, dNdz_e_array_strong)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.scatter((zbins_plot[i]+zbins_plot[i+1])/2, dNdz_plot_weak)\n",
    "# ax.errorbar((zbins_plot[i]+zbins_plot[i+1])/2, dNdz_plot_weak, yerr=dNdz_e_plot_weak, xerr=dz/2)\n",
    "\n",
    "# # plt.ylim(-1,1)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #new arrays for plotting:\n",
    "\n",
    "# zbins_plot = zbins[1:13]\n",
    "\n",
    "# dNdz_plot, dNdz_e_plot = dNdz_array[1:13], dNdz_e_array[1:13]\n",
    "\n",
    "# dNdz_plot_weak, dNdz_e_plot_weak = dNdz_array_weak[1:13], dNdz_e_array_weak[1:13]\n",
    "\n",
    "# dNdz_plot_med, dNdz_e_plot_med = dNdz_array_med[1:13], dNdz_e_array_med[1:13]\n",
    "\n",
    "# dNdz_plot_strong, dNdz_e_plot_strong = dNdz_array_strong[1:13], dNdz_e_array_strong[1:13]\n",
    "\n",
    "# dNdX_plot, dNdX_e_plot = dNdX_array[1:13], dNdX_e_array[1:13]\n",
    "\n",
    "# dNdX_plot_weak, dNdX_e_plot_weak = dNdX_array_weak[1:13], dNdX_e_array_weak[1:13]\n",
    "\n",
    "# dNdX_plot_med, dNdX_e_plot_med = dNdX_array_med[1:13], dNdX_e_array_med[1:13]\n",
    "\n",
    "# dNdX_plot_strong, dNdX_e_plot_strong = dNdX_array_strong[1:13], dNdX_e_array_strong[1:13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fw_exp(k,a,w):\n",
    "    \n",
    "    return k*np.exp(a*w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for plotting EWD:\n",
    "\n",
    "ewbins_plot = []\n",
    "ewerr_plot = []\n",
    "\n",
    "for i in range(len(ewbins)-1):\n",
    "    ewbin0, ewbin1 = ewbins[i], ewbins[i+1]\n",
    "    \n",
    "    mn = np.mean([ewbin0, ewbin1])    \n",
    "    ewbins_plot.append(mn)\n",
    "    ewerr_plot.append((ewbin1-ewbin0)/2)\n",
    "    \n",
    "#for plotting CDD:\n",
    "\n",
    "cdbins_plot = []\n",
    "cderr_plot = []\n",
    "\n",
    "for i in range(len(columnbins)-1):\n",
    "    cdbin0, cdbin1 = columnbins[i], columnbins[i+1]\n",
    "    \n",
    "    mn = np.mean([cdbin0, cdbin1])    \n",
    "    cdbins_plot.append(mn)\n",
    "    cderr_plot.append((cdbin1-cdbin0)/2)    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 11)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ewbins_plot), len(EWD_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.00501187, 0.00939133, 0.01759764, 0.03297477, 0.0617887 ,\n",
       "        0.11578075, 0.21695199, 0.40652842, 0.76176003, 1.42739922,\n",
       "        2.67468552, 5.01187234]),\n",
       " [array([0.35585234]),\n",
       "  array([4.46942495]),\n",
       "  array([1.92355318]),\n",
       "  array([1.00155334]),\n",
       "  array([0.57241416]),\n",
       "  array([0.30540788]),\n",
       "  array([0.16427904]),\n",
       "  array([0.08775482]),\n",
       "  array([0.04905333]),\n",
       "  array([0.02678667]),\n",
       "  array([0.01412061])],\n",
       " [array([0.35585234]),\n",
       "  array([4.46942495]),\n",
       "  array([1.92355318]),\n",
       "  array([1.00155334]),\n",
       "  array([0.57241416]),\n",
       "  array([0.30540788]),\n",
       "  array([0.16427904]),\n",
       "  array([0.08775482]),\n",
       "  array([0.04905333]),\n",
       "  array([0.02678667]),\n",
       "  array([0.01412061])])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ewbins,EWD_array, [i for i in EWD_array]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-5.        ,  0.00977125,  4.5674884 ]),\n",
       " array([[ 4.24513417e+02, -4.04296300e+00,  1.14337966e+04],\n",
       "        [-4.04296300e+00,  3.85091258e-02, -1.08905919e+02],\n",
       "        [ 1.14337966e+04, -1.08905919e+02,  3.07992017e+05]]))"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Schechter function to fit f(W):\n",
    "\n",
    "\n",
    "#3-param:\n",
    "def schechter(w, alpha, ws, phiStar):\n",
    "    \"\"\"Schechter function.\"\"\"\n",
    "    frac = (w/ws)\n",
    "    sch = phiStar * frac**(alpha) * np.exp(-(frac))\n",
    "    return sch\n",
    "\n",
    "#Fit f(W) to Schechter\n",
    "\n",
    "p0=np.array([-1.05,0.6,1.6],dtype=float)\n",
    "\n",
    "ytest = [i/1e3 for i in EWD_array]\n",
    "# sigma_bins=[10**a for a in bins_spec[3:-3]]\n",
    "# vdf_vals=[10**a for a in vdf_spec[3:-3]]\n",
    "\n",
    "\n",
    "# y = [i for i in EWD_array]\n",
    "\n",
    "fit_fw, res_fw = curve_fit(schechter,ewbins_plot,ytest, p0, bounds=((-5, 0.001, 0.01), (0, 10, 300)))\n",
    "\n",
    "fit_fw, res_fw\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot EWD:\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 20))\n",
    "\n",
    "#TeX up labels and text:\n",
    "\n",
    "plt.rc(\"text\", usetex=True)\n",
    "plt.rcParams['text.latex.preamble'] = r'\\usepackage{graphicx}'\n",
    "plt.rc('font', size=40) \n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "matplotlib.rcParams['text.latex.preamble'] = [r'\\boldmath']\n",
    "plt.rcParams[\"font.weight\"] = \"bold\"     \n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rc('axes', linewidth=6)\n",
    "\n",
    "\n",
    "plt.scatter(ewbins_plot[1:], EWD_array_in[1:], c='red', marker='.', s=100)\n",
    "ax.errorbar(ewbins_plot[1:], EWD_array_in[1:], yerr=EWD_array_e_in[1:], xerr=ewerr_plot[1:], fmt='o', capsize=10, elinewidth=3,  c='red',\n",
    "           label=r'\\textbf{incomplete}')\n",
    "\n",
    "ytest, ytest_e = [i/1e3 for i in EWD_array[1:]], [i/1e3 for i in EWD_array_e[1:]]\n",
    "\n",
    "plt.scatter(ewbins_plot[1:], EWD_array[1:], c='blue', marker='.', s=100)\n",
    "ax.errorbar(ewbins_plot[1:], EWD_array[1:], yerr=EWD_array_e[1:], xerr=ewerr_plot[1:], fmt='o', capsize=10, elinewidth=3,  c='blue',\n",
    "           label=r'\\textbf{complete}')\n",
    "\n",
    "\n",
    "# plt.scatter(ewbins_plot[1:], EWD_array_f[1:], c='blue', marker='.', s=100)\n",
    "# ax.errorbar(ewbins_plot[1:], EWD_array_f[1:], yerr=EWD_array_e_f[1:], xerr=ewerr_plot[1:], fmt='o', capsize=10, elinewidth=3,  c='blue',\n",
    "#            label=r'\\textbf{complete f(W)}')\n",
    "\n",
    "# schfuncs = [ i for i in schechter(ewbins_plot[1:],-3,0.5,0.2)]\n",
    "\n",
    "# schfuncs = []\n",
    "# for e in ewbins_plot[1:]:\n",
    "#     sc = schechter(e,-1.05,0.6,1.6)\n",
    "#     schfuncs.append(sc)\n",
    "\n",
    "# plt.plot(ewbins_plot,[schechter(i,-1.05,0.6,1.6) for i in ewbins_plot],ls='--',c='blue',lw=8)\n",
    "\n",
    "# plt.plot(ewbins_plot,[schechter(i,fit_fw[0],fit_fw[1],fit_fw[2]) for i in ewbins_plot],ls='--',c='blue',lw=8)\n",
    "\n",
    "\n",
    "#Other f(W) fits:\n",
    "\n",
    "cooksey_k, cooksey_a = 3.49, -2.62\n",
    "dodo_k, dodo_a = 3.72, -1.71\n",
    "\n",
    "plt.plot(ewbins_plot,[fw_exp(cooksey_k, cooksey_a,i) for i in ewbins_plot],c='black',lw=7,ls=':',label=r'\\textbf{Cooksey+(2013)}')\n",
    "\n",
    "# plt.plot(ewbins_plot[1:],[fw_exp(dodo_k, dodo_a,i) for i in ewbins_plot[1:]],c='brown',lw=7,ls='-.',label=r'\\textbf{Dodorico+(2010)}')\n",
    "\n",
    "\n",
    "# plt.scatter(ewbins_plot[1:], EWD_norm[1:], c='black', marker='.', s=100)\n",
    "# ax.errorbar(ewbins_plot[1:], EWD_norm[1:], yerr=EWD_norm_e[1:], xerr=ewerr_plot[1:], fmt='o', capsize=10, elinewidth=3,  c='black',\n",
    "#            label=r'\\textbf{complete f(W) + Normalized to dN/dX}')\n",
    "\n",
    "\n",
    "# plt.scatter(ewbins_plot[1:], dNdX_norm[1:], c='maroon', marker='.', s=100)\n",
    "# ax.errorbar(ewbins_plot[1:], dNdX_norm[1:], yerr=dNdX_norm_e[1:], xerr=ewerr_plot[1:], fmt='o', capsize=10, elinewidth=3,  c='maroon',\n",
    "#            label=r'\\textbf{dN/dX}')\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlabel(r'\\boldmath{$W_r[\\mathrm{\\AA{}}]$}',fontsize=54,labelpad=15)\n",
    "ax.set_ylabel(r'\\boldmath{$f(W_r)$}',fontsize=54,rotation=90)\n",
    "\n",
    "\n",
    "# ax.set_yticks(np.arange(6e-3,1e1,10))\n",
    "# ax.set_yticks(np.arange(1e-6,3e5,1), minor=True)\n",
    "\n",
    "\n",
    "ax.set_yscale('log')\n",
    "ax.set_xscale('log')\n",
    "\n",
    "\n",
    "ax.tick_params(axis='both', which='major', direction='in', pad=15, labelsize=50,length=20, width=5, bottom='on',top='on', left='on', right='on')\n",
    "ax.tick_params(axis='both', which='minor', direction='in', pad=15, labelsize=50,length=12, width=3, bottom='on',top='on', left='on', right='on')\n",
    "\n",
    "\n",
    "# ax.set_xticks(np.arange(1,5,.5))\n",
    "\n",
    "\n",
    "plt.xlim(6e-3,7)\n",
    "# plt.ylim(1e-6,3e5)\n",
    "\n",
    "# # plt.xlim(1.2,4.5)\n",
    "\n",
    "fig.text(0.7,0.9,r'\\textbf{All redshifts}',fontsize=50)\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.legend(loc='lower left',fontsize=36,framealpha=None)\n",
    "\n",
    "# plt.savefig('EWDs_new.pdf', dpi=200)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot EWD + completeness fraction:\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 20))\n",
    "\n",
    "#TeX up labels and text:\n",
    "\n",
    "plt.rc(\"text\", usetex=True)\n",
    "plt.rcParams['text.latex.preamble'] = r'\\usepackage{graphicx}'\n",
    "plt.rc('font', size=40) \n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "matplotlib.rcParams['text.latex.preamble'] = [r'\\boldmath']\n",
    "plt.rcParams[\"font.weight\"] = \"bold\"     \n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rc('axes', linewidth=6)\n",
    "\n",
    "\n",
    "ax.scatter(ewbins_plot, EWD_array_in_l, c='black', marker='.', s=100)\n",
    "ax.errorbar(ewbins_plot, EWD_array_in_l, yerr=EWD_array_e_in_l, xerr=ewerr_plot, fmt='o', capsize=10, elinewidth=3,  c='black')\n",
    "\n",
    "ax.set_yscale('log')\n",
    "ax.set_xscale('log')\n",
    "\n",
    "ax.set_xlabel(r'\\boldmath{$W_r[\\mathrm{\\AA{}}]$}',fontsize=54,labelpad=15)\n",
    "ax.set_ylabel(r'\\boldmath{$f(W_r)$}',fontsize=54,rotation=90)\n",
    "\n",
    "\n",
    "ax.tick_params(axis='both', which='major', direction='in', pad=15, labelsize=50,length=20, width=5, bottom='on',top='on', left='on', right='on')\n",
    "ax.tick_params(axis='both', which='minor', direction='in', pad=15, labelsize=50,length=12, width=3, bottom='on',top='on', left='on', right='on')\n",
    "\n",
    "\n",
    "\n",
    "ax2 = ax.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "ax2.plot(ewbins_plot, fz_scaled_ew_bins[0], c='blue',lw=7,ls='-.',label=r'\\textbf{completeness fraction}')\n",
    "# ax2.tick_params(axis='y', labelcolor=color)\n",
    "ax2.tick_params(axis='y', which='major', direction='in', pad=15, labelsize=50,length=20, width=5, right='on')\n",
    "ax2.tick_params(axis='y', which='minor', direction='in', pad=15, labelsize=50,length=12, width=3,  right='on')\n",
    "\n",
    "ax2.set_ylabel(r'\\boldmath{$f_z$}',fontsize=54,labelpad=20,rotation=0)\n",
    "\n",
    "ax2.set_yticks(np.arange(0,1,0.025),minor=True)\n",
    "\n",
    "\n",
    "# ax.set_xticks(np.arange(1,5,.1), minor=True)\n",
    "# ax.set_xticks(np.arange(1,5,.5))\n",
    "\n",
    "\n",
    "# plt.ylim(1e-2,7e1)\n",
    "# # plt.xlim(1.2,4.5)\n",
    "\n",
    "fig.text(0.48,0.2,r'\\textbf{All redshifts}',fontsize=50)\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "# plt.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "\n",
    "plt.legend(loc='lower right',fontsize=40,framealpha=None)\n",
    "\n",
    "# plt.savefig('EWD_in.pdf', dpi=200)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 914,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot EWD for different redshift bins:\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 20))\n",
    "\n",
    "#TeX up labels and text:\n",
    "\n",
    "plt.rc(\"text\", usetex=True)\n",
    "plt.rcParams['text.latex.preamble'] = r'\\usepackage{graphicx}'\n",
    "plt.rc('font', size=40) \n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "matplotlib.rcParams['text.latex.preamble'] = [r'\\boldmath']\n",
    "plt.rcParams[\"font.weight\"] = \"bold\"     \n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rc('axes', linewidth=6)\n",
    "\n",
    "# rc('axes', linewidth=4)\n",
    "\n",
    "\n",
    "# plt.scatter(ewbins_plot[1:], EWD_array_in[1:], c='red', marker='.', s=100)\n",
    "# ax.errorbar(ewbins_plot[1:], EWD_array_in[1:], yerr=EWD_array_e_in[1:], xerr=ewerr_plot[1:], fmt='o', capsize=10, elinewidth=3,  c='red',\n",
    "#            label=r'\\textbf{incomplete}')\n",
    "\n",
    "\n",
    "plt.scatter(ewbins_plot[1:], EWD_array_l[1:], c='royalblue', marker='.', s=100)\n",
    "ax.errorbar(ewbins_plot[1:], EWD_array_l[1:], yerr=EWD_array_e_l[1:], xerr=ewerr_plot[1:], fmt='o', capsize=10, \n",
    "            elinewidth=3,  c='royalblue', label=r'\\boldmath{$1 \\leq z < 2$}')\n",
    "\n",
    "\n",
    "plt.scatter(ewbins_plot[1:], EWD_array_m[1:], c='forestgreen', marker='.', s=100)\n",
    "ax.errorbar(ewbins_plot[1:], EWD_array_m[1:], yerr=EWD_array_e_m[1:], xerr=ewerr_plot[1:], fmt='o', capsize=10, \n",
    "            elinewidth=3,  c='forestgreen', label=r'\\boldmath{$2 \\leq z < 3$}')\n",
    "\n",
    "\n",
    "plt.scatter(ewbins_plot[1:], EWD_array_h[1:], c='tomato', marker='.', s=100)\n",
    "ax.errorbar(ewbins_plot[1:], EWD_array_h[1:], yerr=EWD_array_e_h[1:], xerr=ewerr_plot[1:], fmt='o', capsize=10, \n",
    "            elinewidth=3,  c='tomato', label=r'\\boldmath{$z \\geq 3$}')\n",
    "\n",
    "\n",
    "# plt.scatter(zbins_plot, dNdz_plot_weak, c='black', marker='o', s=100, label=r'\\boldmath{$W_r \\geq 0.3~\\mathrm{\\AA}$}')\n",
    "# ax.errorbar(zbins_plot, dNdz_plot_weak, yerr=dNdz_e_plot_weak, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='black')\n",
    "\n",
    "# plt.scatter(zbins_plot, dNdz_plot_med, c='red', marker='o', s=100, label=r'\\boldmath{$W_r \\geq 0.6~\\mathrm{\\AA}$}')\n",
    "# ax.errorbar(zbins_plot, dNdz_plot_med, yerr=dNdz_e_plot_med, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='red')\n",
    "\n",
    "# plt.scatter(zbins_plot, dNdz_plot_strong, c='blue', marker='o', s=100, label=r'\\boldmath{$W_r \\geq 0.9~\\mathrm{\\AA}$}')\n",
    "# ax.errorbar(zbins_plot, dNdz_plot_strong, yerr=dNdz_e_plot_strong, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='blue')\n",
    "\n",
    "\n",
    "# plt.scatter(zbins_plot, dNdz_plot_vstrong, c='green', marker='o', s=100, label=r'\\boldmath{$W_r \\geq 1.2~\\mathrm{\\AA}$}')\n",
    "# ax.errorbar(zbins_plot, dNdz_plot_vstrong, yerr=dNdz_e_plot_vstrong, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='green')\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlabel(r'\\boldmath{$W_r[\\mathrm{\\AA{}}]$}',fontsize=54,labelpad=15)\n",
    "ax.set_ylabel(r'\\boldmath{$f(W_r)$}',fontsize=54,rotation=90)\n",
    "\n",
    "ax.set_yscale('log')\n",
    "ax.set_xscale('log')\n",
    "\n",
    "\n",
    "ax.tick_params(axis='both', which='major', direction='in', pad=15, labelsize=50,length=20, width=5, bottom='on',top='on', left='on', right='on')\n",
    "ax.tick_params(axis='both', which='minor', direction='in', pad=15, labelsize=50,length=12, width=3, bottom='on',top='on', left='on', right='on')\n",
    "\n",
    "# ax.set_xticks(np.arange(1,5,.1), minor=True)\n",
    "# ax.set_xticks(np.arange(1,5,.5))\n",
    "\n",
    "\n",
    "plt.xlim(6e-3,7)\n",
    "plt.ylim(1e-2,2e5)\n",
    "\n",
    "# fig.text(0.2,0.2,r'\\textbf{All redshifts}',fontsize=50)\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.legend(loc='best',fontsize=40,framealpha=None)\n",
    "\n",
    "# plt.savefig('EWDs_zbins.pdf', dpi=200)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.674685524389081"
      ]
     },
     "execution_count": 537,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ewbins[-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot CDD:\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 20))\n",
    "\n",
    "#TeX up labels and text:\n",
    "\n",
    "plt.rc(\"text\", usetex=True)\n",
    "plt.rcParams['text.latex.preamble'] = r'\\usepackage{graphicx}'\n",
    "plt.rc('font', size=40) \n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "matplotlib.rcParams['text.latex.preamble'] = [r'\\boldmath']\n",
    "plt.rcParams[\"font.weight\"] = \"bold\"     \n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rc('axes', linewidth=6)\n",
    "\n",
    "\n",
    "\n",
    "plt.scatter(cdbins_plot, CDD_array_in, c='black', marker='.', s=100)\n",
    "ax.errorbar(cdbins_plot, CDD_array_in, yerr=CDD_array_e_in, xerr=cderr_plot, fmt='o', capsize=10, elinewidth=3,  c='black',\n",
    "           label=r'\\textbf{incomplete}')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlabel(r'\\boldmath{$\\log ~[N/\\mathrm{cm}^{-2}]$}',fontsize=54,labelpad=15)\n",
    "ax.set_ylabel(r'\\boldmath{$f(N)$}',fontsize=54,rotation=90)\n",
    "\n",
    "ax.set_yscale('log')\n",
    "ax.set_xscale('log')\n",
    "\n",
    "\n",
    "ax.tick_params(axis='both', which='major', direction='in', pad=15, labelsize=50,length=20, width=5, bottom='on',top='on', left='on', right='on')\n",
    "ax.tick_params(axis='both', which='minor', direction='in', pad=15, labelsize=50,length=12, width=3, bottom='on',top='on', left='on', right='on')\n",
    "\n",
    "# ax.set_xticks(np.arange(1,5,.1), minor=True)\n",
    "# ax.set_xticks(np.arange(1,5,.5))\n",
    "\n",
    "\n",
    "plt.ylim(1e-16,1e-10)\n",
    "# # plt.xlim(1.2,4.5)\n",
    "\n",
    "\n",
    "plt.legend(loc='best',fontsize=40,framealpha=None)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "\n",
    "# plt.savefig('CDD_in.pdf', dpi=200)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#new arrays for plotting:\n",
    "\n",
    "# zbins_plot = np.array([1.55687,1.65971,1.73999,1.82360,1.91460,2.01778,2.15320,2.35608,2.72298,3.25860])\n",
    "zbins_plot = []\n",
    "zerr_plot = []\n",
    "\n",
    "for i in range(len(zbins)-1):\n",
    "    zbin0, zbin1 = zbins[i], zbins[i+1]\n",
    "#     if i < 9:\n",
    "#         zbin1 = zbins[i+1]\n",
    "        \n",
    "#     else:\n",
    "#         zbin1 = 4.55\n",
    "    \n",
    "    mn = np.mean([zbin0,zbin1])    \n",
    "    zbins_plot.append(mn)\n",
    "    zerr_plot.append((zbin1-zbin0)/2)\n",
    "\n",
    "#dN/dz:\n",
    "\n",
    "dNdz_plot, dNdz_e_plot = dNdz_array, dNdz_e_array\n",
    "\n",
    "dNdz_plot_vweak, dNdz_e_plot_vweak = dNdz_array_vweak, dNdz_e_array_vweak\n",
    "\n",
    "dNdz_plot_weak, dNdz_e_plot_weak = dNdz_array_weak, dNdz_e_array_weak\n",
    "\n",
    "dNdz_plot_med, dNdz_e_plot_med = dNdz_array_med, dNdz_e_array_med\n",
    "\n",
    "dNdz_plot_strong, dNdz_e_plot_strong = dNdz_array_strong, dNdz_e_array_strong\n",
    "\n",
    "dNdz_plot_vstrong, dNdz_e_plot_vstrong = dNdz_array_vstrong, dNdz_e_array_vstrong\n",
    "\n",
    "\n",
    "#dN/dX:\n",
    "\n",
    "dNdX_plot, dNdX_e_plot = dNdX_array, dNdX_e_array\n",
    "\n",
    "dNdX_plot_vweak, dNdX_e_plot_vweak = dNdX_array_vweak, dNdX_e_array_vweak\n",
    "\n",
    "dNdX_plot_weak, dNdX_e_plot_weak = dNdX_array_weak, dNdX_e_array_weak\n",
    "\n",
    "dNdX_plot_med, dNdX_e_plot_med = dNdX_array_med, dNdX_e_array_med\n",
    "\n",
    "dNdX_plot_strong, dNdX_e_plot_strong = dNdX_array_strong, dNdX_e_array_strong\n",
    "\n",
    "dNdX_plot_vstrong, dNdX_e_plot_vstrong = dNdX_array_vstrong, dNdX_e_array_vstrong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 10)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(zbins_plot), len(dNdz_plot_vweak)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[38.54592821683181,\n",
       " 12.03577066905439,\n",
       " 15.240487721142221,\n",
       " 19.47005437245443,\n",
       " 17.438996678659986,\n",
       " 37.258391370903546,\n",
       " 29.133909559866254,\n",
       " 22.032801261036628,\n",
       " 16.58495017075674,\n",
       " 14.894865605654054]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dNdz_plot_vweak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#Plot dN/dz (my way):\n",
    "\n",
    "# plt.style.use('classic')\n",
    "# %matplotlib inline\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 25))\n",
    "\n",
    "#TeX up labels and text:\n",
    "\n",
    "plt.rc(\"text\", usetex=True)\n",
    "# plt.rcParams['text.latex.preamble'] = r'\\usepackage{graphicx}'\n",
    "plt.rc('font', size=40) \n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "matplotlib.rcParams['text.latex.preamble'] = [r'\\boldmath']\n",
    "plt.rcParams[\"font.weight\"] = \"bold\"     \n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rc('axes', linewidth=6)\n",
    "\n",
    "# rc('axes', linewidth=4)\n",
    "# , label=r'\\boldmath{$0.01~\\mathrm{\\AA} \\leq W_r < 0.3~\\mathrm{\\AA}$}'\n",
    "# , label=r'\\boldmath{$0.3~\\mathrm{\\AA} \\leq W_r < 0.6~\\mathrm{\\AA}$}'\n",
    "# , label=r'\\boldmath{$0.6~\\mathrm{\\AA} \\leq W_r < 0.9~\\mathrm{\\AA}$}'\n",
    "# , label=r'\\boldmath{0.9~\\mathrm{\\AA} \\leq W_r < 1.2~\\mathrm{\\AA}}$}'\n",
    "# , label=r'\\boldmath{$W_r > 1.2~\\mathrm{\\AA}$}'\n",
    "\n",
    "plt.scatter(zbins_plot, dNdz_plot_vweak, c='red', marker='o', s=100, label=r'\\boldmath{$0.01~\\mathrm{\\AA} \\leq W_r < 0.3~\\mathrm{\\AA}$}')\n",
    "ax.errorbar(zbins_plot, dNdz_plot_vweak, yerr=dNdz_e_plot_vweak, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='red')\n",
    "\n",
    "# plt.scatter(zbins_plot, dNdz_plot_weak, c='black', marker='o', s=100, label=r'\\boldmath{$0.4~\\mathrm{\\AA} \\leq W_r < 0.7~\\mathrm{\\AA}$}')\n",
    "# ax.errorbar(zbins_plot, dNdz_plot_weak, yerr=dNdz_e_plot_weak, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='black')\n",
    "\n",
    "plt.scatter(zbins_plot, dNdz_plot_med, c='green', marker='o', s=100, label=r'\\boldmath{$0.3~\\mathrm{\\AA} \\leq W_r < 1~\\mathrm{\\AA}$}')\n",
    "ax.errorbar(zbins_plot, dNdz_plot_med, yerr=dNdz_e_plot_med, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='green')\n",
    "\n",
    "plt.scatter(zbins_plot, dNdz_plot_strong, c='blue', marker='o', s=100, label=r'\\boldmath{$W_r \\geq 1~\\mathrm{\\AA}$}')\n",
    "ax.errorbar(zbins_plot, dNdz_plot_strong, yerr=dNdz_e_plot_strong, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='blue')\n",
    "\n",
    "# plt.scatter(zbins_plot, dNdz_plot_vstrong, c='green', marker='o', s=100, label=r'\\boldmath{$W_r > 1.2~\\mathrm{\\AA}$}')\n",
    "# ax.errorbar(zbins_plot, dNdz_plot_vstrong, yerr=dNdz_e_plot_vstrong, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='green')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlabel(r'\\boldmath{$z$}',fontsize=54)\n",
    "ax.set_ylabel(r'\\boldmath{$dN/dz$}',fontsize=54,rotation=90)\n",
    "\n",
    "ax.set_yscale('log')\n",
    "\n",
    "ax.tick_params(axis='both', which='major', direction='in', labelsize=50,length=20, width=5, bottom='on',top='on', left='on', right='on')\n",
    "ax.tick_params(axis='both', which='minor', direction='in', labelsize=50,length=12, width=3, bottom='on',top='on', left='on', right='on')\n",
    "\n",
    "ax.set_xticks(np.arange(1,5,.1), minor=True)\n",
    "ax.set_xticks(np.arange(1,5,.5))\n",
    "\n",
    "# ax.set_yticks(np.arange(1e-2,1e1,0.1), minor=True)\n",
    "\n",
    "\n",
    "\n",
    "# ax.get_xaxis().set_tick_params(direction='in',width=3,size=15, right=True)\n",
    "# ax.get_yaxis().set_tick_params(direction='in',width=3,size=15, top=True)\n",
    "\n",
    "# ax.tick_params(which = 'both', direction='out', length=10, width=3)\n",
    "\n",
    "plt.ylim(1e-2,1e2)\n",
    "# plt.xlim(1.2,4.5)\n",
    "\n",
    "\n",
    "plt.legend(loc='best',fontsize=38,framealpha=None)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "# plt.savefig('dNdz_btn.pdf', dpi=200)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot dN/dz (Cooksey-style):\n",
    "\n",
    "# plt.style.use('classic')\n",
    "# %matplotlib inline\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 25))\n",
    "\n",
    "#TeX up labels and text:\n",
    "\n",
    "plt.rc(\"text\", usetex=True)\n",
    "plt.rcParams['text.latex.preamble'] = r'\\usepackage{graphicx}'\n",
    "plt.rc('font', size=40) \n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "matplotlib.rcParams['text.latex.preamble'] = [r'\\boldmath']\n",
    "plt.rcParams[\"font.weight\"] = \"bold\"     \n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rc('axes', linewidth=6)\n",
    "\n",
    "# rc('axes', linewidth=4)\n",
    "\n",
    "\n",
    "plt.scatter(zbins_plot, dNdz_plot_vweak, c='purple', marker='o', s=100, label=r'\\boldmath{$W_r \\geq 0.01~\\mathrm{\\AA}$}')\n",
    "ax.errorbar(zbins_plot, dNdz_plot_vweak, yerr=dNdz_e_plot_vweak, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='purple')\n",
    "\n",
    "\n",
    "plt.scatter(zbins_plot, dNdz_plot_weak, c='black', marker='o', s=100, label=r'\\boldmath{$W_r \\geq 0.3~\\mathrm{\\AA}$}')\n",
    "ax.errorbar(zbins_plot, dNdz_plot_weak, yerr=dNdz_e_plot_weak, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='black')\n",
    "\n",
    "plt.scatter(zbins_plot, dNdz_plot_med, c='red', marker='o', s=100, label=r'\\boldmath{$W_r \\geq 0.6~\\mathrm{\\AA}$}')\n",
    "ax.errorbar(zbins_plot, dNdz_plot_med, yerr=dNdz_e_plot_med, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='red')\n",
    "\n",
    "plt.scatter(zbins_plot, dNdz_plot_strong, c='blue', marker='o', s=100, label=r'\\boldmath{$W_r \\geq 0.9~\\mathrm{\\AA}$}')\n",
    "ax.errorbar(zbins_plot, dNdz_plot_strong, yerr=dNdz_e_plot_strong, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='blue')\n",
    "\n",
    "\n",
    "plt.scatter(zbins_plot, dNdz_plot_vstrong, c='green', marker='o', s=100, label=r'\\boldmath{$W_r \\geq 1.2~\\mathrm{\\AA}$}')\n",
    "ax.errorbar(zbins_plot, dNdz_plot_vstrong, yerr=dNdz_e_plot_vstrong, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='green')\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlabel(r'\\boldmath{$z$}',fontsize=54)\n",
    "ax.set_ylabel(r'\\boldmath{$dN/dz$}',fontsize=54,rotation=90)\n",
    "\n",
    "ax.set_yscale('log')\n",
    "\n",
    "\n",
    "ax.tick_params(axis='both', which='major', direction='in', labelsize=50,length=20, width=5, bottom='on',top='on', left='on', right='on')\n",
    "ax.tick_params(axis='both', which='minor', direction='in', labelsize=50,length=12, width=3, bottom='on',top='on', left='on', right='on')\n",
    "\n",
    "ax.set_xticks(np.arange(1,5,.1), minor=True)\n",
    "ax.set_xticks(np.arange(1,5,.5))\n",
    "\n",
    "\n",
    "plt.ylim(1e-2,7e1)\n",
    "# plt.xlim(1.2,4.5)\n",
    "\n",
    "\n",
    "ax.legend(loc='best',fontsize=32,framealpha=None)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "# plt.savefig('dNdz_uniques_c.pdf', dpi=200)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot dN/dX (my way) \n",
    "\n",
    "# plt.style.use('classic')\n",
    "# %matplotlib inline\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 25))\n",
    "\n",
    "#TeX up labels and text:\n",
    "\n",
    "\n",
    "plt.rc(\"text\", usetex=True)\n",
    "\n",
    "plt.rc('font', size=40) \n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "matplotlib.rcParams['text.latex.preamble'] = [r'\\boldmath']\n",
    "plt.rcParams[\"font.weight\"] = \"bold\"     \n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rc('axes', linewidth=6)\n",
    "\n",
    "# rc('axes', linewidth=4)\n",
    "\n",
    "\n",
    "# , label=r'\\boldmath{$0.01~\\mathrm{\\AA} \\leq W_r < 0.4~\\mathrm{\\AA}$}'\n",
    "# , label=r'\\boldmath{$0.4~\\mathrm{\\AA} \\leq W_r < 0.7~\\mathrm{\\AA}$}'\n",
    "# , label=r'\\boldmath{$0.7~\\mathrm{\\AA} \\leq W_r < 1~\\mathrm{\\AA}$}'\n",
    "# , label=r'\\boldmath{0.9~\\mathrm{\\AA} \\leq W_r < 1.2~\\mathrm{\\AA}}$}'\n",
    "# , label=r'\\boldmath{$W_r > 1~\\mathrm{\\AA}$}'\n",
    "\n",
    "plt.scatter(zbins_plot, dNdX_plot_vweak, c='red', marker='o', s=100, label=r'\\boldmath{$0.01~\\mathrm{\\AA} \\leq W_r < 0.3~\\mathrm{\\AA}$}')\n",
    "ax.errorbar(zbins_plot, dNdX_plot_vweak, yerr=dNdX_e_plot_vweak, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='red')\n",
    "\n",
    "# plt.scatter(zbins_plot, dNdX_plot_weak, c='black', marker='o', s=100, label=r'\\boldmath{$0.4~\\mathrm{\\AA} \\leq W_r < 0.7~\\mathrm{\\AA}$}')\n",
    "# ax.errorbar(zbins_plot, dNdX_plot_weak, yerr=dNdX_e_plot_weak, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='black')\n",
    "\n",
    "\n",
    "plt.scatter(zbins_plot, dNdX_plot_med, c='green', marker='o', s=100, label=r'\\boldmath{$0.3~\\mathrm{\\AA} \\leq W_r < 1~\\mathrm{\\AA}$}')\n",
    "ax.errorbar(zbins_plot, dNdX_plot_med, yerr=dNdX_e_plot_med, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='green')\n",
    "\n",
    "plt.scatter(zbins_plot, dNdX_plot_strong, c='blue', marker='o', s=100, label=r'\\boldmath{$W_r \\geq 1~\\mathrm{\\AA}$}')\n",
    "ax.errorbar(zbins_plot, dNdX_plot_strong, yerr=dNdX_e_plot_strong, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='blue')\n",
    "\n",
    "\n",
    "# plt.scatter(zbins_plot, dNdX_plot_vstrong, c='green', marker='o', s=100, label=r'\\boldmath{$W_r \\geq 1.2~\\mathrm{\\AA}$}')\n",
    "# ax.errorbar(zbins_plot, dNdX_plot_vstrong, yerr=dNdX_e_plot_vstrong, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='green')\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlabel(r'\\boldmath{$z$}',fontsize=54)\n",
    "ax.set_ylabel(r'\\boldmath{$dN/dX$}',fontsize=54,rotation=90)\n",
    "\n",
    "ax.set_yscale('log')\n",
    "\n",
    "\n",
    "ax.tick_params(axis='both', which='major', direction='in', labelsize=50,length=20, width=5, bottom='on',top='on', left='on', right='on')\n",
    "ax.tick_params(axis='both', which='minor', direction='in', labelsize=50,length=12, width=3, bottom='on',top='on', left='on', right='on')\n",
    "\n",
    "ax.set_xticks(np.arange(1,5,.1), minor=True)\n",
    "ax.set_xticks(np.arange(1,5,.5))\n",
    "\n",
    "# ax.set_yticks(np.arange(0,1.1,0.1), minor=True)\n",
    "\n",
    "# ax.tick_params(axis='x', which='major', direction='in', labelsize=40,length=15, width=3)\n",
    "# ax.tick_params(axis='x', which='minor', direction='in', labelsize=36,length=10, width=2)\n",
    "\n",
    "# ax.get_xaxis().set_tick_params(direction='in',width=3,size=15, right=True)\n",
    "# ax.get_yaxis().set_tick_params(direction='in',width=3,size=15, top=True)\n",
    "\n",
    "# ax.tick_params(which = 'both', direction='out', length=10, width=3)\n",
    "\n",
    "# plt.ylim(1e-3,1e2)\n",
    "# plt.xlim(1.2,4.5)\n",
    "\n",
    "\n",
    "ax.legend(loc='best',fontsize=38,framealpha=None)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "# plt.savefig('dNdX_btn.pdf', dpi=200)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot dN/dX (Cooksey-style):\n",
    "\n",
    "# plt.style.use('classic')\n",
    "# %matplotlib inline\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 25))\n",
    "\n",
    "#TeX up labels and text:\n",
    "\n",
    "plt.rc(\"text\", usetex=True)\n",
    "plt.rcParams['text.latex.preamble'] = r'\\usepackage{graphicx}'\n",
    "plt.rc('font', size=40) \n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "matplotlib.rcParams['text.latex.preamble'] = [r'\\boldmath']\n",
    "plt.rcParams[\"font.weight\"] = \"bold\"     \n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rc('axes', linewidth=6)\n",
    "\n",
    "# rc('axes', linewidth=4)\n",
    "\n",
    "\n",
    "plt.scatter(zbins_plot, dNdX_plot_vweak, c='purple', marker='o', s=100, label=r'\\boldmath{$W_r \\geq 0.01~\\mathrm{\\AA}$}')\n",
    "ax.errorbar(zbins_plot, dNdX_plot_vweak, yerr=dNdX_e_plot_vweak, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='purple')\n",
    "\n",
    "\n",
    "plt.scatter(zbins_plot, dNdX_plot_weak, c='black', marker='o', s=100, label=r'\\boldmath{$W_r \\geq 0.3~\\mathrm{\\AA}$}')\n",
    "ax.errorbar(zbins_plot, dNdX_plot_weak, yerr=dNdX_e_plot_weak, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='black')\n",
    "\n",
    "plt.scatter(zbins_plot, dNdX_plot_med, c='red', marker='o', s=100, label=r'\\boldmath{$W_r \\geq 0.6~\\mathrm{\\AA}$}')\n",
    "ax.errorbar(zbins_plot, dNdX_plot_med, yerr=dNdX_e_plot_med, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='red')\n",
    "\n",
    "plt.scatter(zbins_plot, dNdX_plot_strong, c='blue', marker='o', s=100, label=r'\\boldmath{$W_r \\geq 0.9~\\mathrm{\\AA}$}')\n",
    "ax.errorbar(zbins_plot, dNdX_plot_strong, yerr=dNdX_e_plot_strong, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='blue')\n",
    "\n",
    "\n",
    "plt.scatter(zbins_plot, dNdX_plot_vstrong, c='green', marker='o', s=100, label=r'\\boldmath{$W_r \\geq 1.2~\\mathrm{\\AA}$}')\n",
    "ax.errorbar(zbins_plot, dNdX_plot_vstrong, yerr=dNdX_e_plot_vstrong, xerr=zerr_plot, fmt='o', capsize=10, elinewidth=3,  c='green')\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlabel(r'\\boldmath{$z$}',fontsize=54)\n",
    "ax.set_ylabel(r'\\boldmath{$dN/dX$}',fontsize=54,rotation=90)\n",
    "\n",
    "ax.set_yscale('log')\n",
    "\n",
    "\n",
    "ax.tick_params(axis='both', which='major', direction='in', labelsize=50,length=20, width=5, bottom='on',top='on', left='on', right='on')\n",
    "ax.tick_params(axis='both', which='minor', direction='in', labelsize=50,length=12, width=3, bottom='on',top='on', left='on', right='on')\n",
    "\n",
    "ax.set_xticks(np.arange(1,5,.1), minor=True)\n",
    "ax.set_xticks(np.arange(1,5,.5))\n",
    "\n",
    "\n",
    "plt.ylim(5e-3,5e1)\n",
    "# plt.xlim(1.2,4.5)\n",
    "\n",
    "\n",
    "ax.legend(loc='best',fontsize=32,framealpha=None)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "# plt.savefig('dNdX_uniques_c.pdf', dpi=200)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[13.89309888521804,\n",
       " 4.030775895380049,\n",
       " 4.812142551066472,\n",
       " 5.884623493990738,\n",
       " 5.072411102953014,\n",
       " 10.397377711548813,\n",
       " 25.99677358897909,\n",
       " 16.171830199896988,\n",
       " 5.596035891487652,\n",
       " 3.3928921483267604]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dNdX_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Characteristic sizes of absorbers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 5)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bet, alph, Lm, Ls, phis = 0.3, -1.33, 1.995e7, 5.546e9, 6.81e-3\n",
    "# a, x = 2*beta+alpha+1, Lfrac\n",
    "# gam = sc.gammainc(a, x)\n",
    "\n",
    "betas = np.arange(0.2,0.41,0.05)\n",
    "cover_fracs = np.array([0.25,0.5,0.5,0.9,0.9])\n",
    "\n",
    "len(cover_fracs), len(betas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#try to compute sizes R*:\n",
    "\n",
    "bet, alph, Lm, Ls, phis = 0.3, -1.33, 1.995e7, 5.546e9, 6.81e-3\n",
    "# a, x = 2*beta+alpha+1, Lfrac\n",
    "# gam = sc.gammainc(a, x)\n",
    "\n",
    "# betas = np.arange(0.15,0.41,0.05)\n",
    "# cover_fracs = np.arange(0.1,1,0.2)\n",
    "\n",
    "def Rstar(alpha,beta,Lmin,Lstar,dNdX,phistar,fc):\n",
    "    \n",
    "    hubble = c_kms/H0\n",
    "    Lfrac = Lmin/Lstar\n",
    "    a, x = 2*beta+alpha+1, Lfrac\n",
    "    gam = sc.gammainc(a, x)\n",
    "    \n",
    "    term1 = 1/(np.pi*hubble)\n",
    "    \n",
    "    term2 = dNdX/(phistar*fc)\n",
    "    \n",
    "    term3 = 1/(gam)\n",
    "    \n",
    "    Rs = np.sqrt(term1 * term2 * term3) #Mpc\n",
    "    \n",
    "    Rs_kpc = Rs * 1000 #kpc\n",
    "    \n",
    "    return Rs_kpc\n",
    "\n",
    "\n",
    "R_vw = np.array([Rstar(alph,b,Lm,Ls,dNdX_array_all_vw[0],phis,a) for a,b in zip(cover_fracs,betas)])\n",
    "R_w = np.array([Rstar(alph,b,Lm,Ls,dNdX_array_all_w[0],phis,a) for a,b in zip(cover_fracs,betas)])\n",
    "\n",
    "R_vw, R_w\n",
    "\n",
    "# tab = [(a,b,c,d) for a,b,c,d in zip(cover_fracs,betas,R_vw, R_w)]\n",
    "\n",
    "tab = [[round(i,2) for i in cover_fracs],[round(i,2) for i in betas],[round(i,2) for i in R_vw], [round(i,2) for i in R_w]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.25, 0.5, 0.5, 0.9, 0.9],\n",
       " [0.2, 0.25, 0.3, 0.35, 0.4],\n",
       " [127.23, 116.88, 152.84, 149.85, 198.15],\n",
       " [72.86, 66.94, 87.53, 85.82, 113.48]]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{table}\n",
      "\\begin{tabular}{cccc}\n",
      "$f_c$ & $\\beta$ & $R^{\\star} (0.01 \\leq W_r \\leq 0.3)$ [kpc] & $R^{\\star} (W_r \\geq 0.3)$ [kpc] \\\\\n",
      "0.25 & 0.2 & 127.23 & 72.86 \\\\\n",
      "0.5 & 0.25 & 116.88 & 66.94 \\\\\n",
      "0.5 & 0.3 & 152.84 & 87.53 \\\\\n",
      "0.9 & 0.35 & 149.85 & 85.82 \\\\\n",
      "0.9 & 0.4 & 198.15 & 113.48 \\\\\n",
      "\\end{tabular}\n",
      "\\end{table}\n"
     ]
    }
   ],
   "source": [
    "ascii.write(tab, format='latex', names=('$f_c$','$\\\\beta$','$R^{\\star} (0.01 \\leq W_r \\leq 0.3)$ [kpc]','$R^{\\star} (W_r \\geq 0.3)$ [kpc]'))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot EWD for different redshift bins:\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 20))\n",
    "\n",
    "#TeX up labels and text:\n",
    "\n",
    "plt.rc(\"text\", usetex=True)\n",
    "plt.rcParams['text.latex.preamble'] = r'\\usepackage{graphicx}'\n",
    "plt.rc('font', size=40) \n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "matplotlib.rcParams['text.latex.preamble'] = [r'\\boldmath']\n",
    "plt.rcParams[\"font.weight\"] = \"bold\"     \n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rc('axes', linewidth=6)\n",
    "\n",
    "\n",
    "ax.add_patch(plt.Circle((0, 0), R_vw, edgecolor='blue', facecolor=None, alpha=0.5))\n",
    "ax.add_patch(plt.Circle((0, 0), R_w, edgecolor='red', facecolor=None, alpha=0.5))\n",
    "\n",
    "\n",
    "# ax.set_xlabel(r'\\boldmath{$W_r[\\mathrm{\\AA{}}]$}',fontsize=54,labelpad=15)\n",
    "# ax.set_ylabel(r'\\boldmath{$f(W_r)$}',fontsize=54,rotation=90)\n",
    "\n",
    "# ax.set_yscale('log')\n",
    "# ax.set_xscale('log')\n",
    "\n",
    "\n",
    "ax.tick_params(axis='both', which='major', direction='in', pad=15, labelsize=50,length=20, width=5, bottom='on',top='on', left='on', right='on')\n",
    "ax.tick_params(axis='both', which='minor', direction='in', pad=15, labelsize=50,length=12, width=3, bottom='on',top='on', left='on', right='on')\n",
    "\n",
    "# ax.set_xticks(np.arange(1,5,.1), minor=True)\n",
    "# ax.set_xticks(np.arange(1,5,.5))\n",
    "\n",
    "\n",
    "plt.xlim(-200,200)\n",
    "plt.ylim(-200,200)\n",
    "\n",
    "# fig.text(0.2,0.2,r'\\textbf{All redshifts}',fontsize=50)\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "# plt.legend(loc='best',fontsize=40,framealpha=None)\n",
    "\n",
    "# plt.savefig('EWDs_zbins.pdf', dpi=200)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ODR Method to find evolution parameter, cross section, and number density:\n",
    "\n",
    "\n",
    "# Initiate some data, giving some randomness using random.random().\n",
    "# x = np.array([0, 1, 2, 3, 4, 5])\n",
    "# y = np.array([i**2 + random.random() for i in x])\n",
    "\n",
    "# Define a function to fit the data with.\n",
    "def line_density(p,z):\n",
    "    n,sigma,eps = p\n",
    "    f = n*sigma*(1+z)**eps\n",
    "    return f\n",
    "\n",
    "# Create a model for fitting.\n",
    "linear_model = odr.Model(line_density)\n",
    "\n",
    "guess = [7e-12, 1e11,0.5]\n",
    "# Create a RealData object using our initiated data from above.\n",
    "data = odr.RealData(zbins,dNdX_array)\n",
    "\n",
    "# Set up ODR with the model and data.\n",
    "reg = odr.ODR(data, linear_model, beta0=guess,maxit=2000)\n",
    "\n",
    "# Run the regression.\n",
    "out = reg.run()\n",
    "\n",
    "# Use the in-built pprint method to give us results.\n",
    "out.pprint()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nigel's architecture below (tread carefully) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #EQW limit cut:\n",
    "\n",
    "# cuts = [0.01,0.3,1.] #cuts of Wr>=0.01, 0.3, and 1 Angstrom\n",
    "\n",
    "# binspacing = 0.3\n",
    "# for cut in cuts:\n",
    "#     eqwidths = CIV_data[3][np.where((CIV_data[3] >= cut))]\n",
    "#     ewlims = CIV_data[2][np.where((CIV_data[3] >= cut))]\n",
    "#     zabs = CIV_data[0][np.where((CIV_data[3] >= cut))]\n",
    "\n",
    "#     print(\"Number of points: {}\".format(len(eqwidths)))\n",
    "    \n",
    "#     #binspacing += 0.02\n",
    "#     bins = np.arange(min(CIV_data[0]),max(CIV_data[0]),binspacing)\n",
    "#     print(bins)\n",
    "# #     bins = np.delete(bins,1)\n",
    "# #     bins = np.delete(bins,-2)\n",
    "# #     print(bins)\n",
    "\n",
    "#     # Create histograms\n",
    "#     hist,binedges = np.histogram(zabs,bins=bins)\n",
    "#     binwidth = np.zeros(len(hist))\n",
    "# #     print(hist)\n",
    "\n",
    "#     deltaZhist = np.float64(np.copy(hist))\n",
    "#     deltaXhist = np.float64(np.copy(hist))\n",
    "\n",
    "#     histerr = np.float64(np.sqrt(hist))\n",
    "#     deltaZhisterr = np.copy(histerr)\n",
    "#     deltaXhisterr = np.copy(histerr)\n",
    "    \n",
    "#     for j,element in enumerate(hist):\n",
    "#         binwidth[j] = binedges[j+1] - binedges[j]\n",
    "\n",
    "#     # Find bin centers and bin widths\n",
    "#     center = (binedges[:-1] + binedges[1:]) / 2\n",
    "# #     print(eqwidths)\n",
    "\n",
    "# # Calculate dN/dz:\n",
    "#     for j in range(0,len(bins)-1):\n",
    "#         subeqwidths = []\n",
    "#         subzabs = []\n",
    "#         subewlims = []\n",
    "#         if (hist[j] == 0):\n",
    "#             deltaZhist[j] = 0.\n",
    "#             deltaXhist[j] = 0.\n",
    "#             deltaZhisterr[j] = float('inf')\n",
    "#             deltaXhisterr[j] = float('inf')\n",
    "#             continue\n",
    "#         subeqwidths = eqwidths[np.where((eqwidths >= bins[j]) & (eqwidths < bins[j+1]))]\n",
    "#         subzabs = zabs[np.where((eqwidths >= bins[j]) & (eqwidths < bins[j+1]))]\n",
    "#         # Divide each detection by the total redshift path (delta_Z = integral(g(W,z)dz))\n",
    "#         delta_Z_inbin = 0.\n",
    "#         delta_X_inbin = 0.\n",
    "#         delta_Z_inbin_err = 0.\n",
    "#         delta_X_inbin_err = 0.\n",
    "\n",
    "#         print(subzabs)\n",
    "\n",
    "# #     for n,ew in enumerate(subeqwidths):\n",
    "# #         index = np.where(w_grid == find_nearest(w_grid,ew))[0][0]\n",
    "# #         delta_Z = np.trapz(CIV_gwz[index],CIV_z_grid)\n",
    "# #         delta_X = (1.+subzabs[n])**2 / np.sqrt(OMEGA_M * (1.+subzabs[n])**3 + OMEGA_L) * delta_Z\n",
    "# #         delta_Z_inbin += 1./delta_Z\n",
    "# #         delta_X_inbin += 1./delta_X\n",
    "# #         delta_Z_inbin_err += (1./delta_Z)**2\n",
    "# #         delta_X_inbin_err += (1./delta_X)**2\n",
    "\n",
    "# #     # Calculate completeness and add the correction factor to the hist bin\n",
    "# #     deltaZhist[j] = delta_Z_inbin\n",
    "# #     deltaXhist[j] = delta_X_inbin\n",
    "# #     deltaZhisterr[j] = np.sqrt(delta_Z_inbin_err)\n",
    "# #     deltaXhisterr[j] = np.sqrt(delta_X_inbin_err)\n",
    "\n",
    "# #     # Convert hists to 64 bit arrays\n",
    "\n",
    "# # Nhist = np.float64(Nhist)\n",
    "# # Nhisterr = np.float64(Nhisterr)\n",
    "# # binwidth = np.zeros(len(hist))\n",
    "# # # Divide by bin width for all arrays\n",
    "# # for j,element in enumerate(hist):\n",
    "# #     binwidth[j] = binedges[j+1] - binedges[j]\n",
    "\n",
    "# #     Nhisterr[j] = float(Nhisterr[j]) / float(binwidth[j])\n",
    "# #     Nhist[j] = float(Nhist[j]) / float(binwidth[j])\n",
    "\n",
    "# #     deltaZhist[j] = float(deltaZhist[j]) / float(binwidth[j])\n",
    "# #     deltaZhisterr[j] = float(deltaZhisterr[j]) / float(binwidth[j])\n",
    "\n",
    "# #     deltaXhist[j] = float(deltaXhist[j]) / float(binwidth[j])\n",
    "# #     deltaXhisterr[j] = float(deltaXhisterr[j]) / float(binwidth[j])\n",
    "\n",
    "# #     if deltaZhist[j] - deltaZhisterr[j] < 1.e-4:\n",
    "# #         deltaZhisterr[j] = deltaZhist[j] * 0.9\n",
    "# #         deltaXhisterr[j] = deltaXhist[j] * 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.where((eqwidths >= bins[0]) & (eqwidths < bins[0+1]))"
   ]
  }
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