Infusion_IA.ipynb

{
 "cells": [
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   "cell_type": "code",
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   "id": "991a9da9-18bf-4931-a62c-8d6c9c9973ed",
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   "source": [
    "%pylab inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import pyccl.ccllib as lib\n",
    "import pyccl as ccl\n",
    "import pyccl.nl_pt as pt\n",
    "from astropy.table import Table\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8d52a89-6c5e-4183-bdcc-c54b1f6f4df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "cosmo = ccl.Cosmology(\n",
    "    Omega_c=0.22, Omega_b=0.0448, \n",
    "    h=0.71, sigma8 = 0.801, n_s= 0.963,w0=-1.00,wa=0.0, Omega_k=0.0)\n",
    "\n",
    "Om_m = cosmo['Omega_m']\n",
    "rho_crit = lib.cvar.constants.RHO_CRITICAL\n",
    "Aia=1.0 #1.5,2\n",
    "C2=1.0\n",
    "sigma_epsilon = 0.27"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1af0b54a-4420-452b-a125-b08768f8a060",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Epsilon1_NLA(cosmo,z,A1,rho_crit,sxx,syy):\n",
    "    gz = ccl.growth_factor(cosmo, 1./(1+z))\n",
    "    Fact = -1*A1*5e-14*rho_crit*cosmo['Omega_m']/gz\n",
    "    e1_NLA =  Fact  * (sxx - syy) \n",
    "    return e1_NLA\n",
    "\n",
    "def Epsilon2_NLA(cosmo,z,A1,rho_crit,sxy):\n",
    "    gz = ccl.growth_factor(cosmo, 1./(1+z))\n",
    "    Fact = -1*A1*5e-14*rho_crit*cosmo['Omega_m']/gz\n",
    "    e2_NLA = 2 *Fact* sxy\n",
    "    return e2_NLA\n",
    "\n",
    "def Epsilon1_TT(cosmo,z,A2,rho_crit,sxx,syy):\n",
    "    gz = ccl.growth_factor(cosmo, 1./(1+z))\n",
    "    Fact =  -5*A2*5e-14*rho_crit*cosmo['Omega_m']/gz**2\n",
    "    e1_TT =  Fact * (sxx**2 - syy**2) \n",
    "    return e1_TT\n",
    "\n",
    "def Epsilon2_TT(cosmo,z,A2,rho_crit,sxx,syy,sxy):\n",
    "    gz = ccl.growth_factor(cosmo, 1./(1+z))\n",
    "    Fact =  -5*A2*5e-14*rho_crit*cosmo['Omega_m']/gz**2\n",
    "    e2_TT =  2 * Fact * sxy*(sxx+syy)\n",
    "    return e2_TT\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e52868b-9f29-429d-9b68-ed89c493ae5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in np.arange(1,6):\n",
    "    # descriptions=['ra_deg', 'dec_deg', 'shear1', 'shear2', 'weight', 'z_true', 'delta', 's11', 's22', 's12']\n",
    "    #catalog = Table.read('/global/cscratch1/sd/jharno/IA_Infusion/GalCat_tomo{}_All_0.06GpAM_RA_Dec_g1_g2_w_den_Sij.dat'.format(i), format='ascii')  \n",
    "    #catalog = Table.read('/global/cscratch1/sd/jharno/IA_Infusion/GalCat_tomo{}_All_0.06GpAM_RA_Dec_g1_g2_w_den_Sij_smooth.dat'.format(i), format='ascii')  \n",
    "\n",
    "    #V0\n",
    "    #catalog = Table.read('../../GalCat/StageIV_nz/V0/GalCat_tomo{}_All_0.06GpAM_RA_Dec_g1_g2_w_den_Sij.dat'.format(i), format='ascii')  \n",
    "    #catalog = Table.read('../../GalCat/StageIV_nz/V0/GalCat_tomo{}_All_0.06GpAM_RA_Dec_g1_g2_w_den_Sij_smooth.dat'.format(i), format='ascii')  \n",
    "    catalog = Table.read('../../GalCat/SRD-Y1/random_pos/GalCat_tomo{}_All_0.6GpAM_RA_Dec_g1_g2_w_z_den_Sij_smooth0.1Mpc.asc'.format(i), format='ascii')  \n",
    "    \n",
    "    #V1\n",
    "    #catalog = Table.read('../../GalCat/StageIV_nz/V1/GalCat_tomo{}_All_0.06GpAM_RA_Dec_g1_g2_w_z_den_Sij_linear_bias.asc'.format(i), format='ascii')  \n",
    "    #catalog = Table.read('../../GalCat/StageIV_nz/V1/GalCat_tomo{}_All_0.06GpAM_RA_Dec_g1_g2_w_z_den_Sij_linear_bias_smooth.asc'.format(i), format='ascii')  \n",
    "    #catalog = Table.read('../../GalCat/StageIV_nz/V1/GalCat_tomo{}_All_0.6GpAM_RA_Dec_g1_g2_w_z_den_Sij_linear_bias_smooth.asc'.format(i), format='ascii')  \n",
    "    #catalog = Table.read('../../GalCat/StageIV_nz/V1/GalCat_tomo{}_All_0.06GpAM_RA_Dec_g1_g2_w_z_den_Sij_linear_bias_smooth_1Mpc.asc'.format(i), format='ascii')  \n",
    "\n",
    "    print('Got Gatalogue')\n",
    "    \n",
    "    s11=catalog['col8']\n",
    "    s22=catalog['col9']\n",
    "    s12=catalog['col10']\n",
    "    z=catalog['col6']\n",
    "    \n",
    "    e1_NLA=Epsilon1_NLA(cosmo,z,Aia,rho_crit,s11,s22)\n",
    "    e2_NLA=Epsilon2_NLA(cosmo,z,Aia,rho_crit,s12)\n",
    "    \n",
    "    e1_TT=Epsilon1_TT(cosmo,z,C2,rho_crit,s11,s22)\n",
    "    e2_TT=Epsilon2_TT(cosmo,z,C2,rho_crit,s11,s22,s12)\n",
    "\n",
    "    random1=np.random.normal(0, sigma_epsilon, e1_NLA.shape)\n",
    "    random2=np.random.normal(0, sigma_epsilon, e2_NLA.shape)\n",
    "    \n",
    "    print('Got Shapes')\n",
    "    \n",
    "    epsilon = Table([e1_NLA, e2_NLA, e1_TT, e2_TT, random1, random2], names = ('e1_NLA', 'e2_NLA', 'e1_TT', 'e2_TT', 'e1_noise', 'e2_noise'))\n",
    "\n",
    "    #epsilon.write('/global/cscratch1/sd/jharno/epsilon_{}.dat'.format(i), format='ascii', overwrite=True)    \n",
    "    #epsilon.write('/global/cscratch1/sd/jharno/epsilon_{}_smooth.dat'.format(i), format='ascii', overwrite=True)    \n",
    "\n",
    "    #***\n",
    "    #V0:\n",
    "    #epsilon.write('../../GalCat/StageIV_nz/V0/epsilon_{}_0.06GpAM_V2.asc'.format(i), format='ascii', overwrite=True)    \n",
    "    #epsilon.write('../../GalCat/StageIV_nz/V0/epsilon_{}_0.06GpAM_smooth_V2.asc'.format(i), format='ascii', overwrite=True)    \n",
    "    epsilon.write('../../GalCat/SRD-Y1/random_pos/epsilon_{}_0.6GpAM_smooth0.1.asc'.format(i), format='ascii', overwrite=True)    \n",
    "\n",
    "    #***\n",
    "    #V1\n",
    "    #epsilon.write('../../GalCat/StageIV_nz/V1/epsilon_{}_0.06GpAM_linear_bias.asc'.format(i), format='ascii', overwrite=True)    \n",
    "    #epsilon.write('../../GalCat/StageIV_nz/V1/epsilon_{}_0.06GpAM_linear_bias_smooth.asc'.format(i), format='ascii', overwrite=True)    \n",
    "    #epsilon.write('../../GalCat/StageIV_nz/V1/epsilon_{}_0.6GpAM_linear_bias_smooth.asc'.format(i), format='ascii', overwrite=True)    \n",
    "    #epsilon.write('../../GalCat/StageIV_nz/V1/epsilon_{}_0.06GpAM_linear_bias_smooth_1Mpc.asc'.format(i), format='ascii', overwrite=True)    \n",
    "\n",
    "    print('Done tomo',i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc80065e-a899-48b1-9aa2-050f6db00f24",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in np.arange(1,6):\n",
    "    #catalog = np.loadtxt('../../GalCat/StageIV_nz/V1/GalCat_tomo'+str(i)+'_All_0.6GpAM_RA_Dec_g1_g2_w_z_den_Sij_linear_bias_smooth.asc')  \n",
    "    #catalog = np.loadtxt('../../GalCat/StageIV_nz/V1/GalCat_tomo'+str(i)+'_All_0.6GpAM_RA_Dec_g1_g2_w_z_den_Sij_linear_bias_smooth_bta2.asc')  \n",
    "    #catalog = np.loadtxt('../../GalCat/SRD-Y1/random_pos/GalCat_tomo'+str(i)+'_All_0.6GpAM_RA_Dec_g1_g2_w_z_den_Sij_smooth0.1Mpc.asc')  \n",
    "    #catalog = np.loadtxt('../../GalCat/SRD-Y1/random_pos/GalCat_tomo'+str(i)+'_All_0.6GpAM_RA_Dec_g1_g2_w_z_den_Sij_smooth0.5Mpc.asc')  \n",
    "    #catalog = np.loadtxt('../../GalCat/SRD-Y1/lin_bias_pos/GalCat_tomo'+str(i)+'_All_0.6GpAM_RA_Dec_g1_g2_w_z_den_Sij_linear_bias_bta1_smooth0.5Mpc.asc')  \n",
    "    catalog = np.loadtxt('../../GalCat/SRD-Y1/lin_bias_pos/GalCat_tomo'+str(i)+'_All_0.6GpAM_RA_Dec_g1_g2_w_z_den_Sij_linear_bias_bta2_smooth0.5Mpc.asc')  \n",
    "    print('Got Gatalogue')\n",
    "\n",
    "    s11=catalog[:,7]\n",
    "    s22=catalog[:,8]\n",
    "    s12=catalog[:,9]\n",
    "    z=catalog[:,5]\n",
    "\n",
    "    e1_NLA=Epsilon1_NLA(cosmo,z,Aia,rho_crit,s11,s22)\n",
    "    e2_NLA=Epsilon2_NLA(cosmo,z,Aia,rho_crit,s12)\n",
    "\n",
    "    e1_TT=Epsilon1_TT(cosmo,z,C2,rho_crit,s11,s22)\n",
    "    e2_TT=Epsilon2_TT(cosmo,z,C2,rho_crit,s11,s22,s12)\n",
    "\n",
    "    random1=np.random.normal(0, sigma_epsilon, e1_NLA.shape)\n",
    "    random2=np.random.normal(0, sigma_epsilon, e2_NLA.shape)\n",
    "\n",
    "    print('Got Shapes')\n",
    "\n",
    "    epsilon = Table([e1_NLA, e2_NLA, e1_TT, e2_TT, random1, random2], names = ('e1_NLA', 'e2_NLA', 'e1_TT', 'e2_TT', 'e1_noise', 'e2_noise'))\n",
    "    #epsilon.write('../../GalCat/StageIV_nz/V1/epsilon_{}_0.6GpAM_linear_bias_smooth.asc'.format(i), format='ascii', overwrite=True)    \n",
    "    #epsilon.write('../../GalCat/StageIV_nz/V1/epsilon_{}_0.6GpAM_linear_bias_smooth_bta2.asc'.format(i), format='ascii', overwrite=True)    \n",
    "    #epsilon.write('../../GalCat/StageIV_nz/V1/epsilon_{}_0.06GpAM_linear_bias_smooth_1Mpc.asc'.format(i), format='ascii', overwrite=True)    \n",
    "    #epsilon.write('../../GalCat/SRD-Y1/random_pos/epsilon_{}_0.6GpAM_smooth0.1Mpc.asc'.format(i), format='ascii', overwrite=True)    \n",
    "    #epsilon.write('../../GalCat/SRD-Y1/random_pos/epsilon_{}_0.6GpAM_smooth0.5Mpc.asc'.format(i), format='ascii', overwrite=True)    \n",
    "    #epsilon.write('../../GalCat/SRD-Y1/lin_bias_pos/epsilon_{}_0.6GpAM_smooth0.5Mpc.asc'.format(i), format='ascii', overwrite=True)    \n",
    "    epsilon.write('../../GalCat/SRD-Y1/lin_bias_pos/epsilon_{}_0.6GpAM_smooth_bta2_0.5Mpc.asc'.format(i), format='ascii', overwrite=True)    \n",
    "\n",
    "    print('Done tomo',i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b635bab1-4aff-4dcd-8428-9e38d53274a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.shape(catalog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eecf98c5-e9d1-46c5-8a03-35c80adaa51c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3603cf0-45f4-4834-900b-d7ed749ad073",
   "metadata": {},
   "outputs": [],
   "source": [
    "s11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f7a6c0a-667c-43fd-9ce6-99abb487caae",
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   "source": []
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