read_table.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9587bdbc-86f4-4fac-b511-019662d3f799",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "from astropy.io import fits\n",
    "import numpy as np\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dacdffb3-bf6c-4614-82db-cbffefbea5a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "hdul = fits.open('theo_prediction.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "63e825b3-245c-455a-ad1b-9d44c57fb7d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "header=hdul[1].header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c809d7ab-298b-49d3-8679-bcda4d2ca9f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=hdul[1].data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01e9c86f-94a1-4f2f-a167-fa657e1df902",
   "metadata": {},
   "outputs": [],
   "source": [
    "NO_IA_plus=data['NO IA+'] # Xi + (no ia)\n",
    "NO_IA_minus=data['NO IA-'] # Xi - (no ia)\n",
    "GI_plus=data['GI+']        #GI + (TATT) \n",
    "GI_minus=data['GI-']       #GI - (TATT)\n",
    "GI_NLA_plus=data['GI_NLA+']  #GI + (NLA),\n",
    "GI_NLA_minus=data['GI_NLA-']  #GI - (NLA)\n",
    "II_plus=data['II+']           #II + (TATT) \n",
    "II_minus=data['II-']         #II -   (TATT)\n",
    "II_NLA_plus=data['II_NLA+']  #II +   (NLA)\n",
    "II_NLA_minus=data['II_NLA-']     #II -  (NLA)\n",
    "theta=data['theta']            # Angular separation(s) in arcmin\n",
    "z_combination=data['bin_comb']  #redshift bin combination "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "921093e7-3dfc-4a66-87eb-a22bf023038b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8.02404187e-06, 6.14180300e-06, 4.95658525e-06, ...,\n",
       "        3.09791120e-07, 3.06581641e-07, 3.03423678e-07],\n",
       "       [1.56539415e-05, 1.17719633e-05, 9.34124707e-06, ...,\n",
       "        5.69931299e-07, 5.63525328e-07, 5.57224861e-07],\n",
       "       [4.18490321e-05, 3.09575007e-05, 2.40362959e-05, ...,\n",
       "        1.36935456e-06, 1.35143532e-06, 1.33383779e-06],\n",
       "       ...,\n",
       "       [1.15807652e-04, 8.58721509e-05, 6.62653484e-05, ...,\n",
       "        3.10667714e-06, 3.05665561e-06, 3.00768111e-06],\n",
       "       [1.68856450e-04, 1.26032382e-04, 9.81077740e-05, ...,\n",
       "        3.98527927e-06, 3.91521038e-06, 3.84672373e-06],\n",
       "       [2.57358358e-04, 1.93811314e-04, 1.53008622e-04, ...,\n",
       "        5.09783860e-06, 5.00059991e-06, 4.90581560e-06]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NO_IA_plus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea6a18ff-29d2-4c90-81f8-16b8cd367cb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [2, 1],\n",
       "       [2, 2],\n",
       "       [3, 1],\n",
       "       [3, 2],\n",
       "       [3, 3],\n",
       "       [4, 1],\n",
       "       [4, 2],\n",
       "       [4, 3],\n",
       "       [4, 4],\n",
       "       [5, 1],\n",
       "       [5, 2],\n",
       "       [5, 3],\n",
       "       [5, 4],\n",
       "       [5, 5]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_combination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b28548b9-c72b-4144-8e21-7413814b0054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 99)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(NO_IA_plus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "92e83ba9-3194-4e5c-88fa-9fb5e8fe8091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plot(theta[0,:],NO_IA_plus[0,:],'k-')\n",
    "plot(theta[0,:],NO_IA_minus[0,:],'b-')\n",
    "\n",
    "plot(theta[0,:],NO_IA_plus[2,:],'k-')\n",
    "plot(theta[0,:],NO_IA_minus[2,:],'b-')\n",
    "\n",
    "plot(theta[0,:],NO_IA_plus[5,:],'k-')\n",
    "plot(theta[0,:],NO_IA_minus[5,:],'b-')\n",
    "\n",
    "plot(theta[0,:],NO_IA_plus[9,:],'k-')\n",
    "plot(theta[0,:],NO_IA_minus[9,:],'b-')\n",
    "\n",
    "plot(theta[0,:],NO_IA_plus[14,:],'r-')\n",
    "plot(theta[0,:],NO_IA_minus[14,:],'r-')\n",
    "\n",
    "\n",
    "\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9ba303bd-c4ad-4e51-9d9d-eb4a807f4d3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 3]\n",
      "5 3\n",
      "Will use column 12\n"
     ]
    }
   ],
   "source": [
    "tomo_pair = [5, 3]\n",
    "print(tomo_pair)\n",
    "for tomo in range(0,15):\n",
    "    if(z_combination[tomo][0]==tomo_pair[0] and z_combination[tomo][1]==tomo_pair[1] ):\n",
    "        print(z_combination[tomo][0], z_combination[tomo][1])\n",
    "        print(\"Will use column\",tomo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efe1b923-bc62-4e77-9a0e-be00b28892c4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "desc-python-dev",
   "language": "python",
   "name": "desc-python-dev"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}