gwbench_dev_bias_calc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from gwbench import network\n",
    "from gwbench import snr\n",
    "from gwbench import waveform as wfc\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import math\n",
    "from scipy.optimize import minimize\n",
    "import dill\n",
    "from pycbc import conversions as conv\n",
    "\n",
    "\n",
    "from astropy.cosmology import Planck18, z_at_value\n",
    "import astropy.units as u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"serif\",\n",
    "    \"font.size\": 22\n",
    "})\n",
    "\n",
    "%config InlineBackend.figure_format='retina' # very useful command for high-res images\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_hybrid_network_response(inj_params, f_min=5., f_max=1024.,\n",
    "        network_key = None,\n",
    "        network_spec = ['CE-40_C', 'CE-20_S', 'ET_ET1', 'ET_ET2', 'ET_ET3'],\n",
    "        approximant1='IMRPhenomXAS', approximant2='IMRPhenomD',\n",
    "        deriv_symbs_string = 'Mc eta chi1z chi2z DL tc phic iota ra dec psi',\n",
    "        cond_num=1e25):\n",
    "\n",
    "    # if plain text network key is passed, override full network_spec\n",
    "    if network_key is not None:\n",
    "        network_spec = network_dict[network_key]\n",
    "\n",
    "\n",
    "    # initialize the network with the desired detectors\n",
    "    net = network.Network(network_spec)\n",
    "\n",
    "    # choose the desired waveform \n",
    "    wf_model_name =  'lal_hybrid_bbh'\n",
    "    # pass the chosen waveform to the network for initialization\n",
    "    net.set_wf_vars(wf_model_name, wf_other_var_dic = {'approximant1': approximant1,\n",
    "                                                       'approximant2': approximant2\n",
    "                                                       })\n",
    "    # pick the desired frequency range\n",
    "    d_f = 2**-4\n",
    "    f = np.arange(f_min, f_max, d_f)\n",
    "\n",
    "    # choose whether to take Earth's rotation into account\n",
    "    use_rot = 0\n",
    "\n",
    "    # pass all these variables to the network\n",
    "    net.set_net_vars(\n",
    "        f=f, inj_params=inj_params,\n",
    "        deriv_symbs_string=deriv_symbs_string,\n",
    "        use_rot=use_rot\n",
    "        )\n",
    "\n",
    "    # compute the WF polarizations\n",
    "    net.calc_wf_polarizations()\n",
    "    # compute the WF polarizations and their derivatives\n",
    "    net.calc_wf_polarizations_derivs_num()\n",
    "\n",
    "    # setup antenna patterns, location phase factors, and PSDs\n",
    "    net.setup_ant_pat_lpf_psds()\n",
    "\n",
    "    # compute the detector responses\n",
    "    net.calc_det_responses()\n",
    "    # compute the detector responses and their derivatives\n",
    "    net.calc_det_responses_derivs_num()\n",
    "\n",
    "    # calculate the network and detector SNRs\n",
    "    net.calc_snrs()\n",
    "\n",
    "    # calculate the network and detector Fisher matrices, condition numbers,\n",
    "    # covariance matrices, error estimates, and inversion errors\n",
    "    net.calc_errors(cond_sup=cond_num)\n",
    "\n",
    "    # compute the cutler-vallisneri bias with respect to the approximant1 waveform model\n",
    "    wf_true = wfc.Waveform(wf_model_name = 'lal_bbh', wf_other_var_dic = {'approximant': approximant1})\n",
    "    net.calc_cutler_vallisneri_bias(wf=wf_true)\n",
    "\n",
    "    return net"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For given 'true' and 'approximate' waveforms $h_{TR}$ and $h_{AP}$, the systematic error due to waveform inaccuracy on the parameter $\\theta^i$ is given by:\n",
    "$$ \\Delta \\theta^i = (\\Gamma_{AP}^{-1})^{ij} \\quad  \\Sigma_{D=1}^{N_{det}}  (\\partial_j (h_{AP})_D | (h_{TR})_D - (h_{AP})_D)$$\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will assume that IMRPhenomXAS is $h_{TR}$ (net1) and IMRPhenomD is $h_{AP}$ (net2).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = 'GW190521'\n",
    "\n",
    "# Detector frame\n",
    "m1 = 85 #M_sol\n",
    "m2 = 66 #M_sol\n",
    "DL = Planck18.luminosity_distance(0.82)\n",
    "q = m2/m1\n",
    "\n",
    "# chi_eff = -0.04\n",
    "# chi_p = 0.32\n",
    "a1 = 0.69\n",
    "a2 = 0.73\n",
    "\n",
    "Mc = conv.mchirp_from_mass1_mass2(m1, m2)\n",
    "eta = conv.eta_from_mass1_mass2(m1, m2)\n",
    "\n",
    "# set the injection parameters\n",
    "inj_params = {\n",
    "    'Mc':    Mc,\n",
    "    'eta':   eta,\n",
    "    'chi1x': 0,\n",
    "    'chi2x': 0,\n",
    "    'chi1y': 0,\n",
    "    'chi2y': 0,\n",
    "    'chi1z': 0.,\n",
    "    'chi2z': 0.,\n",
    "    'DL':    DL,\n",
    "    'tc':    0,\n",
    "    'phic':  0,\n",
    "    'iota':  np.pi/3,\n",
    "    'ra':    np.pi/3,\n",
    "    'dec':   np.pi/3,\n",
    "    'psi':   np.pi/3,\n",
    "    'gmst0': 0\n",
    "    }\n",
    "\n",
    "\n",
    "deriv_symbs_string = 'Mc eta DL chi1z chi2z iota ra dec psi'\n",
    "param_list = deriv_symbs_string.split()\n",
    "\n",
    "network_dict = {\n",
    "        'aLIGO':    ['aLIGO_H','aLIGO_L','V+_V'],\n",
    "        'A+':       ['A+_H', 'A+_L','A+_V'],\n",
    "        'Voyager':  ['Voyager-CBO_H', 'Voyager-CBO_L', 'Voyager-CBO_V'],\n",
    "        '3G':       ['CE-40_C', 'CE-20_S', 'ET_ET1', 'ET_ET2', 'ET_ET3']\n",
    "        }\n",
    "\n",
    "network_key = '3G'\n",
    "\n",
    "network_spec = network_dict[network_key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# name = 'GW150914'\n",
    "\n",
    "# # Detector frame\n",
    "# Mc = 30.8 #M_sol\n",
    "# m1 = 38.7 #M_sol\n",
    "# m2 = 32.5 #M_sol\n",
    "# DL = 440 #Mpc\n",
    "# q = m2/m1\n",
    "\n",
    "# chi_eff = -0.04\n",
    "# chi_p = 0.32\n",
    "# a1 = 0.26\n",
    "# a2 = 0.32\n",
    "\n",
    "# # Mc = conv.mchirp_from_mass1_mass2(m1, m2)\n",
    "# eta = conv.eta_from_mass1_mass2(m1, m2)\n",
    "\n",
    "# # set the injection parameters\n",
    "# inj_params = {\n",
    "#     'Mc':    Mc,\n",
    "#     'eta':   eta,\n",
    "#     'chi1x': 0,\n",
    "#     'chi2x': 0,\n",
    "#     'chi1y': 0,\n",
    "#     'chi2y': 0,\n",
    "#     'chi1z': a1,\n",
    "#     'chi2z': a2,\n",
    "#     'DL':    DL,\n",
    "#     'tc':    0,\n",
    "#     'phic':  0,\n",
    "#     'iota':  np.pi/3,\n",
    "#     'ra':    np.pi/3,\n",
    "#     'dec':   np.pi/3,\n",
    "#     'psi':   np.pi/3,\n",
    "#     'gmst0': 0\n",
    "#     }\n",
    "\n",
    "\n",
    "# deriv_symbs_string = 'Mc eta DL chi1z chi2z iota ra dec psi'\n",
    "# param_list = deriv_symbs_string.split()\n",
    "\n",
    "# network_dict = {\n",
    "#         'aLIGO':    ['aLIGO_H','aLIGO_L','V+_V'],\n",
    "#         'A+':       ['A+_H', 'A+_L','A+_V'],\n",
    "#         'Voyager':  ['Voyager-CBO_H', 'Voyager-CBO_L', 'Voyager-CBO_V'],\n",
    "#         '3G':       ['CE-40_C', 'CE-20_S', 'ET_ET1', 'ET_ET2', 'ET_ET3']\n",
    "#         }\n",
    "\n",
    "# network_key = '3G'\n",
    "\n",
    "# network_spec = network_dict[network_key]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute Networks from gwbench"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "in method 'SimInspiralChooseFDWaveform', argument 11 of type 'REAL8'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m net1 \u001b[39m=\u001b[39m get_network_response(inj_params\u001b[39m=\u001b[39;49minj_params, network_spec\u001b[39m=\u001b[39;49mnetwork_spec, deriv_symbs_string\u001b[39m=\u001b[39;49mderiv_symbs_string, \\\n\u001b[1;32m      2\u001b[0m                             approximant\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mIMRPhenomXAS\u001b[39;49m\u001b[39m'\u001b[39;49m, cond_num\u001b[39m=\u001b[39;49m\u001b[39m1e25\u001b[39;49m)\n",
      "Cell \u001b[0;32mIn[3], line 28\u001b[0m, in \u001b[0;36mget_network_response\u001b[0;34m(inj_params, network_spec, approximant, deriv_symbs_string, cond_num)\u001b[0m\n\u001b[1;32m     21\u001b[0m net\u001b[39m.\u001b[39mset_net_vars(\n\u001b[1;32m     22\u001b[0m     f\u001b[39m=\u001b[39mf, inj_params\u001b[39m=\u001b[39minj_params,\n\u001b[1;32m     23\u001b[0m     deriv_symbs_string\u001b[39m=\u001b[39mderiv_symbs_string,\n\u001b[1;32m     24\u001b[0m     use_rot\u001b[39m=\u001b[39muse_rot\n\u001b[1;32m     25\u001b[0m     )\n\u001b[1;32m     27\u001b[0m \u001b[39m# # compute the WF polarizations\u001b[39;00m\n\u001b[0;32m---> 28\u001b[0m net\u001b[39m.\u001b[39;49mcalc_wf_polarizations()\n\u001b[1;32m     29\u001b[0m \u001b[39m# # compute the WF polarizations and their derivatives\u001b[39;00m\n\u001b[1;32m     30\u001b[0m net\u001b[39m.\u001b[39mcalc_wf_polarizations_derivs_num()\n",
      "File \u001b[0;32m/opt/anaconda3/envs/gwbench_dev/lib/python3.9/site-packages/gwbench/network.py:268\u001b[0m, in \u001b[0;36mNetwork.calc_wf_polarizations\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    267\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcalc_wf_polarizations\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m--> 268\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhfp, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhfc \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mwf\u001b[39m.\u001b[39;49meval_np_func(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mf,utils\u001b[39m.\u001b[39;49mget_sub_dict(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49minj_params,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mwf\u001b[39m.\u001b[39;49mwf_symbs_string))\n\u001b[1;32m    269\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39minfo(\u001b[39m'\u001b[39m\u001b[39mPolarizations calculated.\u001b[39m\u001b[39m'\u001b[39m)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/gwbench_dev/lib/python3.9/site-packages/gwbench/waveform.py:118\u001b[0m, in \u001b[0;36mWaveform.eval_np_func\u001b[0;34m(self, f, inj_params)\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(inj_params, \u001b[39mdict\u001b[39m):\n\u001b[1;32m    117\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mwf_other_var_dic \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m: \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhfpc_np(f, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39minj_params)\n\u001b[0;32m--> 118\u001b[0m     \u001b[39melse\u001b[39;00m:                             \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhfpc_np(f, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49minj_params, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mwf_other_var_dic)\n\u001b[1;32m    119\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(inj_params, \u001b[39mlist\u001b[39m):\n\u001b[1;32m    120\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mwf_other_var_dic \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m: \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhfpc_np(f, \u001b[39m*\u001b[39minj_params)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/gwbench_dev/lib/python3.9/site-packages/gwbench/wf_models/lal_bbh_np.py:40\u001b[0m, in \u001b[0;36mhfpc\u001b[0;34m(f, Mc, eta, chi1x, chi1y, chi1z, chi2x, chi2y, chi2z, DL, tc, phic, iota, approximant, fRef, phiRef)\u001b[0m\n\u001b[1;32m     36\u001b[0m _DL \u001b[39m=\u001b[39m DL \u001b[39m*\u001b[39m Mpc\n\u001b[1;32m     38\u001b[0m approx \u001b[39m=\u001b[39m lalsim\u001b[39m.\u001b[39mGetApproximantFromString(approximant)\n\u001b[0;32m---> 40\u001b[0m hPlus, hCross \u001b[39m=\u001b[39m lalsim\u001b[39m.\u001b[39;49mSimInspiralChooseFDWaveform(m1\u001b[39m=\u001b[39;49m_m1, m2\u001b[39m=\u001b[39;49m_m2,\n\u001b[1;32m     41\u001b[0m                                S1x \u001b[39m=\u001b[39;49m chi1x, S1y \u001b[39m=\u001b[39;49m chi1y, S1z \u001b[39m=\u001b[39;49m chi1z,\n\u001b[1;32m     42\u001b[0m                                S2x \u001b[39m=\u001b[39;49m chi2x, S2y \u001b[39m=\u001b[39;49m chi2y, S2z \u001b[39m=\u001b[39;49m chi2z,\n\u001b[1;32m     43\u001b[0m                                distance \u001b[39m=\u001b[39;49m _DL, inclination \u001b[39m=\u001b[39;49m iota, phiRef \u001b[39m=\u001b[39;49m phiRef,\n\u001b[1;32m     44\u001b[0m                                longAscNodes\u001b[39m=\u001b[39;49m\u001b[39m0.\u001b[39;49m, eccentricity\u001b[39m=\u001b[39;49m\u001b[39m0.\u001b[39;49m, meanPerAno \u001b[39m=\u001b[39;49m \u001b[39m0.\u001b[39;49m,\n\u001b[1;32m     45\u001b[0m                                deltaF\u001b[39m=\u001b[39;49mdelta_f, f_min\u001b[39m=\u001b[39;49mf_min, f_max\u001b[39m=\u001b[39;49mf_max, f_ref\u001b[39m=\u001b[39;49mfRef,\n\u001b[1;32m     46\u001b[0m                                LALpars\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m, approximant\u001b[39m=\u001b[39;49mapprox)\n\u001b[1;32m     48\u001b[0m pf \u001b[39m=\u001b[39m exp(\u001b[39m1\u001b[39mj\u001b[39m*\u001b[39m(\u001b[39m2\u001b[39m\u001b[39m*\u001b[39mf\u001b[39m*\u001b[39mpi\u001b[39m*\u001b[39mtc \u001b[39m-\u001b[39m phic))\n\u001b[1;32m     49\u001b[0m i0 \u001b[39m=\u001b[39m \u001b[39mint\u001b[39m(\u001b[39mround\u001b[39m((f_min\u001b[39m-\u001b[39mhPlus\u001b[39m.\u001b[39mf0) \u001b[39m/\u001b[39m delta_f))\n",
      "\u001b[0;31mTypeError\u001b[0m: in method 'SimInspiralChooseFDWaveform', argument 11 of type 'REAL8'"
     ]
    }
   ],
   "source": [
    "net1 = get_network_response(inj_params=inj_params, network_spec=network_spec, deriv_symbs_string=deriv_symbs_string, \\\n",
    "                            approximant='IMRPhenomXAS', cond_num=1e25)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get approx net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "in method 'SimInspiralChooseFDWaveform', argument 11 of type 'REAL8'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m net2 \u001b[39m=\u001b[39m get_network_response(inj_params\u001b[39m=\u001b[39;49minj_params, network_spec\u001b[39m=\u001b[39;49mnetwork_spec, deriv_symbs_string\u001b[39m=\u001b[39;49mderiv_symbs_string, \\\n\u001b[1;32m      2\u001b[0m                             approximant\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mIMRPhenomD\u001b[39;49m\u001b[39m'\u001b[39;49m, cond_num\u001b[39m=\u001b[39;49m\u001b[39m1e25\u001b[39;49m)\n",
      "Cell \u001b[0;32mIn[3], line 28\u001b[0m, in \u001b[0;36mget_network_response\u001b[0;34m(inj_params, network_spec, approximant, deriv_symbs_string, cond_num)\u001b[0m\n\u001b[1;32m     21\u001b[0m net\u001b[39m.\u001b[39mset_net_vars(\n\u001b[1;32m     22\u001b[0m     f\u001b[39m=\u001b[39mf, inj_params\u001b[39m=\u001b[39minj_params,\n\u001b[1;32m     23\u001b[0m     deriv_symbs_string\u001b[39m=\u001b[39mderiv_symbs_string,\n\u001b[1;32m     24\u001b[0m     use_rot\u001b[39m=\u001b[39muse_rot\n\u001b[1;32m     25\u001b[0m     )\n\u001b[1;32m     27\u001b[0m \u001b[39m# # compute the WF polarizations\u001b[39;00m\n\u001b[0;32m---> 28\u001b[0m net\u001b[39m.\u001b[39;49mcalc_wf_polarizations()\n\u001b[1;32m     29\u001b[0m \u001b[39m# # compute the WF polarizations and their derivatives\u001b[39;00m\n\u001b[1;32m     30\u001b[0m net\u001b[39m.\u001b[39mcalc_wf_polarizations_derivs_num()\n",
      "File \u001b[0;32m/opt/anaconda3/envs/gwbench_dev/lib/python3.9/site-packages/gwbench/network.py:268\u001b[0m, in \u001b[0;36mNetwork.calc_wf_polarizations\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    267\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcalc_wf_polarizations\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m--> 268\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhfp, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhfc \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mwf\u001b[39m.\u001b[39;49meval_np_func(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mf,utils\u001b[39m.\u001b[39;49mget_sub_dict(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49minj_params,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mwf\u001b[39m.\u001b[39;49mwf_symbs_string))\n\u001b[1;32m    269\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39minfo(\u001b[39m'\u001b[39m\u001b[39mPolarizations calculated.\u001b[39m\u001b[39m'\u001b[39m)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/gwbench_dev/lib/python3.9/site-packages/gwbench/waveform.py:118\u001b[0m, in \u001b[0;36mWaveform.eval_np_func\u001b[0;34m(self, f, inj_params)\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(inj_params, \u001b[39mdict\u001b[39m):\n\u001b[1;32m    117\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mwf_other_var_dic \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m: \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhfpc_np(f, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39minj_params)\n\u001b[0;32m--> 118\u001b[0m     \u001b[39melse\u001b[39;00m:                             \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhfpc_np(f, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49minj_params, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mwf_other_var_dic)\n\u001b[1;32m    119\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(inj_params, \u001b[39mlist\u001b[39m):\n\u001b[1;32m    120\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mwf_other_var_dic \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m: \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhfpc_np(f, \u001b[39m*\u001b[39minj_params)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/gwbench_dev/lib/python3.9/site-packages/gwbench/wf_models/lal_bbh_np.py:40\u001b[0m, in \u001b[0;36mhfpc\u001b[0;34m(f, Mc, eta, chi1x, chi1y, chi1z, chi2x, chi2y, chi2z, DL, tc, phic, iota, approximant, fRef, phiRef)\u001b[0m\n\u001b[1;32m     36\u001b[0m _DL \u001b[39m=\u001b[39m DL \u001b[39m*\u001b[39m Mpc\n\u001b[1;32m     38\u001b[0m approx \u001b[39m=\u001b[39m lalsim\u001b[39m.\u001b[39mGetApproximantFromString(approximant)\n\u001b[0;32m---> 40\u001b[0m hPlus, hCross \u001b[39m=\u001b[39m lalsim\u001b[39m.\u001b[39;49mSimInspiralChooseFDWaveform(m1\u001b[39m=\u001b[39;49m_m1, m2\u001b[39m=\u001b[39;49m_m2,\n\u001b[1;32m     41\u001b[0m                                S1x \u001b[39m=\u001b[39;49m chi1x, S1y \u001b[39m=\u001b[39;49m chi1y, S1z \u001b[39m=\u001b[39;49m chi1z,\n\u001b[1;32m     42\u001b[0m                                S2x \u001b[39m=\u001b[39;49m chi2x, S2y \u001b[39m=\u001b[39;49m chi2y, S2z \u001b[39m=\u001b[39;49m chi2z,\n\u001b[1;32m     43\u001b[0m                                distance \u001b[39m=\u001b[39;49m _DL, inclination \u001b[39m=\u001b[39;49m iota, phiRef \u001b[39m=\u001b[39;49m phiRef,\n\u001b[1;32m     44\u001b[0m                                longAscNodes\u001b[39m=\u001b[39;49m\u001b[39m0.\u001b[39;49m, eccentricity\u001b[39m=\u001b[39;49m\u001b[39m0.\u001b[39;49m, meanPerAno \u001b[39m=\u001b[39;49m \u001b[39m0.\u001b[39;49m,\n\u001b[1;32m     45\u001b[0m                                deltaF\u001b[39m=\u001b[39;49mdelta_f, f_min\u001b[39m=\u001b[39;49mf_min, f_max\u001b[39m=\u001b[39;49mf_max, f_ref\u001b[39m=\u001b[39;49mfRef,\n\u001b[1;32m     46\u001b[0m                                LALpars\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m, approximant\u001b[39m=\u001b[39;49mapprox)\n\u001b[1;32m     48\u001b[0m pf \u001b[39m=\u001b[39m exp(\u001b[39m1\u001b[39mj\u001b[39m*\u001b[39m(\u001b[39m2\u001b[39m\u001b[39m*\u001b[39mf\u001b[39m*\u001b[39mpi\u001b[39m*\u001b[39mtc \u001b[39m-\u001b[39m phic))\n\u001b[1;32m     49\u001b[0m i0 \u001b[39m=\u001b[39m \u001b[39mint\u001b[39m(\u001b[39mround\u001b[39m((f_min\u001b[39m-\u001b[39mhPlus\u001b[39m.\u001b[39mf0) \u001b[39m/\u001b[39m delta_f))\n",
      "\u001b[0;31mTypeError\u001b[0m: in method 'SimInspiralChooseFDWaveform', argument 11 of type 'REAL8'"
     ]
    }
   ],
   "source": [
    "net2 = get_network_response(inj_params=inj_params, network_spec=network_spec, deriv_symbs_string=deriv_symbs_string, \\\n",
    "                            approximant='IMRPhenomD', cond_num=1e25)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perform Cutler-Vallisneri with (IMRPhenomXAS, IMRPhenomD) and 3G network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Mc': 30.8,\n",
       " 'eta': 0.24810432710516347,\n",
       " 'chi1x': 0,\n",
       " 'chi2x': 0,\n",
       " 'chi1y': 0,\n",
       " 'chi2y': 0,\n",
       " 'chi1z': 0.26,\n",
       " 'chi2z': 0.32,\n",
       " 'DL': 440,\n",
       " 'tc': 0,\n",
       " 'phic': 0,\n",
       " 'iota': 1.0471975511965976,\n",
       " 'ra': 1.0471975511965976,\n",
       " 'dec': 1.0471975511965976,\n",
       " 'psi': 1.0471975511965976,\n",
       " 'gmst0': 0}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net1.inj_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the Theoretical Bias in Parameters based on Cutler-Vallisneri formalism\n",
    "\n",
    "def compute_wf_bias(net1, net2, param_list, lams=None, inj_params=None):\n",
    "    inner_prod = np.zeros(len(param_list))\n",
    "\t\n",
    "    cov_ap = net2.cov\n",
    "\n",
    "    for d in range(len(net2.detectors)):\n",
    "        del_h_ap_all = net2.detectors[d].del_hf\n",
    "        del_params_j = list(del_h_ap_all.keys())\n",
    "\t\n",
    "        h_tr = net1.detectors[d].hf\n",
    "        h_ap = net2.detectors[d].hf\n",
    "        psd = net2.detectors[d].psd\n",
    "        freq_range = net2.detectors[d].f\n",
    "        df = freq_range[1] - freq_range[0]\n",
    "\n",
    "        for j, parameter_j in enumerate(del_params_j):\n",
    "            del_h_ap_j = del_h_ap_all[parameter_j]\n",
    "            # Inner Product\n",
    "            inner_prod[j] += snr.scalar_product_freq_array(del_h_ap_j, h_tr - h_ap, psd, freq_range, df)\n",
    "    print(inner_prod)\n",
    "    return np.matmul(cov_ap, inner_prod)\t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 6.33522412e+06 -1.65867863e+07  1.06313288e+04 -3.72086339e+06\n",
      " -3.10420088e+06  5.29131609e+06  2.28749926e+06 -7.56989497e+05\n",
      " -1.41835572e+06]\n",
      "['Mc', 'eta', 'DL', 'chi1z', 'chi2z', 'iota', 'ra', 'dec', 'psi']\n",
      "[[ 366.37551426  146.81754581 1124.59056074 -191.03649854  249.87057407\n",
      "   -12.30256012  -25.28291772  -33.96598982  -46.06042771]]\n"
     ]
    }
   ],
   "source": [
    "param_list = deriv_symbs_string.split()\n",
    "errors_th = compute_wf_bias(net1, net2, param_list)\n",
    "print(param_list)\n",
    "print(errors_th/list(net2.errs.values()))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute Cutler Vallsineri Bias the GWBENCH Way "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 6.33536962e+06 -1.65869413e+07  1.06270073e+04 -3.72092619e+06\n",
      " -3.10425039e+06  5.28923955e+06  2.28543849e+06 -7.57418503e+05\n",
      " -1.41677740e+06]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "matrix([[ 367.45238688,  146.76636973, 1124.2181151 , -190.98460659,\n",
       "          249.87431414,  -12.26614698,  -25.62014852,  -34.57121157,\n",
       "          -44.47015186]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from gwbench import Waveform\n",
    "from gwbench import waveform as wfc\n",
    "\n",
    "wf_true = wfc.Waveform(wf_model_name = 'lal_bbh', wf_other_var_dic = {'approximant': 'IMRPhenomXAS'})\n",
    "\n",
    "# wf_true = waveform(wf_model_name=wf_model_name_true, wf_other_var_dic=wf_other_var_dic_true, user_waveform=user_waveform_true)\n",
    "net2.calc_cutler_vallisneri_bias(wf=wf_true)\n",
    "print(net2.cutler_vallisneri_overlap_vec)\n",
    "net2.cutler_vallisneri_bias/list(net2.errs.values())\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check if our answers agree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[-3.51838556e-02,  1.14175666e-03,  2.51031967e+01,\n",
       "         -1.97898788e-02, -1.19851679e-03, -4.54686433e-03,\n",
       "          7.89756375e-03,  3.75978452e-02, -1.83917691e-01]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*(errors_th - net2.cutler_vallisneri_bias)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the biases agree to within 2%."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Construct Hybrid Waveform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_hybrid_network_response(inj_params, network_spec = ['aLIGO_H','aLIGO_L','aLIGO_V'], approximant1='IMRPhenomXAS', approximant2='IMRPhenomD', hybr=0.5,\n",
    "                                deriv_symbs_string = 'Mc eta DL tc phic iota ra dec psi', cond_num=1e25):\n",
    "    \n",
    "    # initialize the network with the desired detectors\n",
    "    net = network.Network(network_spec)\n",
    "\n",
    "    # choose the desired waveform \n",
    "    wf_model_name =  'lal_hybrid_bbh'\n",
    "    # pass the chosen waveform to the network for initialization\n",
    "    net.set_wf_vars(wf_model_name, wf_other_var_dic = {'approximant1': approximant1,\n",
    "                                                       'approximant2': approximant2\n",
    "                                                       })\n",
    "\n",
    "    # pick the desired frequency range\n",
    "    f_min = 5.\n",
    "    f_max = 512.\n",
    "    d_f = 2**-4\n",
    "    f = np.arange(f_min, f_max, d_f)\n",
    "\n",
    "    # choose whether to take Earth's rotation into account\n",
    "    use_rot = 0\n",
    "\n",
    "    # pass all these variables to the network\n",
    "    net.set_net_vars(\n",
    "        f=f, inj_params=inj_params,\n",
    "        deriv_symbs_string=deriv_symbs_string,\n",
    "        use_rot=use_rot\n",
    "        )\n",
    "    \n",
    "    # # compute the WF polarizations\n",
    "    net.calc_wf_polarizations()\n",
    "    # # compute the WF polarizations and their derivatives\n",
    "    net.calc_wf_polarizations_derivs_num()\n",
    "\n",
    "    # setup antenna patterns, location phase factors, and PSDs\n",
    "    net.setup_ant_pat_lpf_psds()\n",
    "\n",
    "    # compute the detector responses\n",
    "    net.calc_det_responses()\n",
    "    # compute the detector responses and their derivatives\n",
    "    net.calc_det_responses_derivs_num()\n",
    "\n",
    "    # calculate the network and detector SNRs\n",
    "    net.calc_snrs()\n",
    "\n",
    "    \n",
    "\n",
    "    # # calculate the network and detector Fisher matrices, condition numbers,\n",
    "    # # covariance matrices, error estimates, and inversion errors\n",
    "    net.calc_errors(cond_sup=cond_num)\n",
    "\n",
    "    # # calculate the 90%-credible sky area (in deg)\n",
    "    # net.calc_sky_area_90()\n",
    "\n",
    "    # compute the cutler-vallisneri bias with respect to the approximant1 waveform model\n",
    "    wf_true = wfc.Waveform(wf_model_name = 'lal_bbh', wf_other_var_dic = {'approximant': approximant1})\n",
    "    net.calc_cutler_vallisneri_bias(wf=wf_true)\n",
    "\n",
    "    return net\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = 'GW150914'\n",
    "\n",
    "# Detector frame\n",
    "Mc = 30.8 #M_sol\n",
    "m1 = 38.7 #M_sol\n",
    "m2 = 32.5 #M_sol\n",
    "DL = 440 #Mpc\n",
    "q = m2/m1\n",
    "\n",
    "chi_eff = -0.04\n",
    "chi_p = 0.32\n",
    "a1 = 0.26\n",
    "a2 = 0.32\n",
    "\n",
    "# Mc = conv.mchirp_from_mass1_mass2(m1, m2)\n",
    "eta = conv.eta_from_mass1_mass2(m1, m2)\n",
    "\n",
    "# set the injection parameters\n",
    "inj_params = {\n",
    "    'Mc':    Mc,\n",
    "    'eta':   eta,\n",
    "    'chi1x': 0,\n",
    "    'chi2x': 0,\n",
    "    'chi1y': 0,\n",
    "    'chi2y': 0,\n",
    "    'chi1z': a1,\n",
    "    'chi2z': a2,\n",
    "    'DL':    DL,\n",
    "    'tc':    0,\n",
    "    'phic':  0,\n",
    "    'iota':  np.pi/3,\n",
    "    'ra':    np.pi/3,\n",
    "    'dec':   np.pi/3,\n",
    "    'psi':   np.pi/3,\n",
    "    'gmst0': 0,\n",
    "    'hybr': 0.97\n",
    "    }\n",
    "\n",
    "deriv_symbs_string = 'Mc eta DL chi1z chi2z iota ra dec psi'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-05 22:27:21,139 - Network - INFO : Polarizations calculated.\n",
      "2023-07-05 22:27:21,140 - Network - INFO : Calculate numeric derivatives of polarizations.\n",
      "2023-07-05 22:27:21,150 - Network - INFO : Polarizations calculated.\n",
      "2023-07-05 22:27:22,546 - Network - INFO : Numeric derivatives of polarizations calculated.\n",
      "2023-07-05 22:27:22,593 - Network - INFO : PSDs loaded.\n",
      "2023-07-05 22:27:22,596 - Network - INFO : Antenna patterns and LPFs loaded.\n",
      "2023-07-05 22:27:22,637 - Network - INFO : Detector responses calculated.\n",
      "2023-07-05 22:27:22,639 - Network - INFO : Calculate numeric derivatives of detector responses.\n",
      "2023-07-05 22:27:22,639 - Network - INFO :    CE-40_C\n",
      "2023-07-05 22:27:23,658 - Network - INFO :    CE-20_S\n",
      "2023-07-05 22:27:24,679 - Network - INFO :    ET_ET1\n",
      "2023-07-05 22:27:25,724 - Network - INFO :    ET_ET2\n",
      "2023-07-05 22:27:26,756 - Network - INFO :    ET_ET3\n",
      "2023-07-05 22:27:28,001 - Network - INFO : Numeric derivatives of detector responses calculated.\n",
      "2023-07-05 22:27:28,005 - Network - INFO : SNRs calculated.\n",
      "2023-07-05 22:27:28,005 - Network - INFO : Calculate errors (Fisher & cov matrices).\n",
      "2023-07-05 22:27:28,006 - Network - INFO :    CE-40_C\n",
      "2023-07-05 22:27:28,050 - Network - INFO :    CE-20_S\n",
      "2023-07-05 22:27:28,107 - Network - INFO :    ET_ET1\n",
      "2023-07-05 22:27:28,181 - Network - INFO :    ET_ET2\n",
      "2023-07-05 22:27:28,239 - Network - INFO :    ET_ET3\n",
      "2023-07-05 22:27:28,317 - Network - INFO : Errors calculated.\n"
     ]
    }
   ],
   "source": [
    "net_hybr = get_hybrid_network_response(inj_params=inj_params, network_spec=network_spec, deriv_symbs_string=deriv_symbs_string, \\\n",
    "                            approximant1='IMRPhenomXAS', approximant2='IMRPhenomD', cond_num=1e25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.09387015e-09, -1.71878952e-05,  1.55548182e-01,  3.78929975e-04,\n",
       "       -4.31918437e-04, -3.61182758e-04, -7.07805227e-05,  4.58576963e-04,\n",
       "        1.06655919e-02])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(net_hybr.cutler_vallisneri_bias)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.783808107667912"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net_hybr.snr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x177693730>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax= plt.subplots(nrows=1, figsize=(12,5)) \n",
    "\n",
    "ax.plot(net1.f, np.real(net2.hfp), label=\"IMRPhenomD\")\n",
    "ax.plot(net1.f, np.real(net1.hfp), label=\"IMRPhenomXAS\")\n",
    "ax.plot(net1.f, np.real(net_hybr.hfp), label=f\"Hybrid, $\\lambda$={net_hybr.inj_params['hybr']}\")\n",
    "\n",
    "ax.set_xlabel(\"Frequency (Hz)\")\n",
    "ax.set_xlim(0, 40)\n",
    "ax.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.093870148915695e-09\n",
      "hybrid bias 0.021 sigma\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 6))\n",
    "\n",
    "mc_inj = net1.inj_params[\"Mc\"]\n",
    "stat_err = net2.errs[\"Mc\"]\n",
    "bias = np.array(net2.cutler_vallisneri_bias)[0][0]\n",
    "\n",
    "# print(mc_inj)\n",
    "# print(bias)\n",
    "\n",
    "mc = np.linspace(mc_inj-0.02, mc_inj+bias+0.02, 1000)\n",
    "\n",
    "plt.plot(mc, norm.pdf(mc, mc_inj, stat_err), lw=3, color='k', label=\"IMRPhenomXAS\")\n",
    "\n",
    "hybr_bias = np.array(net_hybr.cutler_vallisneri_bias)[0][0]\n",
    "print(hybr_bias)\n",
    "print(f\"hybrid bias {hybr_bias/net_hybr.errs['Mc']:.3f} sigma\")\n",
    "\n",
    "plt.plot(mc, norm.pdf(mc, mc_inj+hybr_bias, net_hybr.errs['Mc']),lw=3, color='orange', label=f\"$\\lambda \\\\approx${net_hybr.inj_params['hybr']:.3f}\")\n",
    "\n",
    "plt.plot(mc, norm.pdf(mc, mc_inj+bias, stat_err),lw=3, color='r', label=\"IMRPhenomD\")\n",
    "\n",
    "# plt.axvline(x=mc_inj, ymin=0.05, color='k', ls='--')\n",
    "# plt.axvline(x=mc_inj+0.103,  ymin=0.05, color='orange', ls='--')\n",
    "# plt.axvline(x=mc_inj+bias,  ymin=0.05, color='r', ls='--')\n",
    "\n",
    "# plt.semilogy()\n",
    "plt.xlabel(\"$\\mathcal{M}_{c}$\")\n",
    "# plt.title(net1.inj_params[\"name\"])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Bias Across Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_bias(nets, param_list, figsize=(8,20)):\n",
    "\n",
    "    f, ax= plt.subplots(nrows=len(param_list), ncols=1,  figsize=figsize) \n",
    "    ax = list(ax)\n",
    "\n",
    "\n",
    "    for i, param in enumerate((param_list)):\n",
    "        for net_ap in nets:\n",
    "            mu = net_ap.inj_params[param]\n",
    "            sigma = net_ap.errs[param]\n",
    "            bias = np.array(net_ap.cutler_vallisneri_bias)[0][i]\n",
    "\n",
    "            low = mu -100*sigma - bias\n",
    "            high = mu +100*sigma + bias\n",
    "            x = np.linspace(low, high, 10000)\n",
    "\n",
    "            stat_est = stats.norm.pdf(x, mu+bias, sigma)\n",
    "            stat_est = stat_est/ np.max(stat_est)\n",
    "\n",
    "            try:\n",
    "                lam = net_ap.inj_params['hybr']\n",
    "            except:\n",
    "                lam = 0.0\n",
    "\n",
    "            ax[i].plot(x, stat_est, label=f\"Approximate ($\\lambda$={lam:.2f})\")\n",
    "        \n",
    "        theory_est = stats.norm.pdf(x, mu, sigma)\n",
    "        theory_est = theory_est/np.max(theory_est)\n",
    "        ax[i].plot(x, theory_est, label=f\"True (IMRPhenomXAS)\")\n",
    "        \n",
    "        if i==0:\n",
    "            ax[i].legend(bbox_to_anchor=(0, 1), loc='upper left', fontsize=14)\n",
    "\n",
    "        \n",
    "        # ax[i].set_xlim(0.5*mu, mu+0.5*mu)\n",
    "        ax[i].set_xlabel(param, fontsize=20)\n",
    "\n",
    "    plt.suptitle(\"Systematic Bias b/w IMRPhenomXAS and Approximate Model\", y=1.02, fontsize=20)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/wj/l7fs8hg555n8nzz7myk9cysw0000gn/T/ipykernel_25184/3228065632.py:18: RuntimeWarning: invalid value encountered in divide\n",
      "  stat_est = stat_est/ np.max(stat_est)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x2000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_bias([net_hybr, net2], param_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-29 13:41:45,618 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:41:45,618 - Network - INFO : Calculate numeric derivatives of polarizations.\n",
      "2023-05-29 13:41:45,625 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:41:46,461 - Network - INFO : Numeric derivatives of polarizations calculated.\n",
      "2023-05-29 13:41:46,492 - Network - INFO : PSDs loaded.\n",
      "2023-05-29 13:41:46,494 - Network - INFO : Antenna patterns and LPFs loaded.\n",
      "2023-05-29 13:41:46,519 - Network - INFO : Detector responses calculated.\n",
      "2023-05-29 13:41:46,519 - Network - INFO : Calculate numeric derivatives of detector responses.\n",
      "2023-05-29 13:41:46,520 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:41:47,155 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:41:47,794 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:41:48,419 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:41:49,045 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:41:49,674 - Network - INFO : Numeric derivatives of detector responses calculated.\n",
      "2023-05-29 13:41:49,677 - Network - INFO : SNRs calculated.\n",
      "2023-05-29 13:41:49,677 - Network - INFO : Calculate errors (Fisher & cov matrices).\n",
      "2023-05-29 13:41:49,677 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:41:49,700 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:41:49,722 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:41:49,749 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:41:49,776 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:41:49,816 - Network - INFO : Errors calculated.\n",
      "2023-05-29 13:41:49,840 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:41:49,841 - Network - INFO : Calculate numeric derivatives of polarizations.\n",
      "2023-05-29 13:41:49,848 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:41:50,681 - Network - INFO : Numeric derivatives of polarizations calculated.\n",
      "2023-05-29 13:41:50,711 - Network - INFO : PSDs loaded.\n",
      "2023-05-29 13:41:50,713 - Network - INFO : Antenna patterns and LPFs loaded.\n",
      "2023-05-29 13:41:50,737 - Network - INFO : Detector responses calculated.\n",
      "2023-05-29 13:41:50,737 - Network - INFO : Calculate numeric derivatives of detector responses.\n",
      "2023-05-29 13:41:50,738 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:41:51,367 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:41:51,994 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:41:52,619 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:41:53,238 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:41:53,854 - Network - INFO : Numeric derivatives of detector responses calculated.\n",
      "2023-05-29 13:41:53,856 - Network - INFO : SNRs calculated.\n",
      "2023-05-29 13:41:53,856 - Network - INFO : Calculate errors (Fisher & cov matrices).\n",
      "2023-05-29 13:41:53,857 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:41:53,878 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:41:53,900 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:41:53,925 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:41:53,950 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:41:53,989 - Network - INFO : Errors calculated.\n",
      "2023-05-29 13:41:54,081 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:41:54,081 - Network - INFO : Calculate numeric derivatives of polarizations.\n",
      "2023-05-29 13:41:54,087 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:41:54,940 - Network - INFO : Numeric derivatives of polarizations calculated.\n",
      "2023-05-29 13:41:54,969 - Network - INFO : PSDs loaded.\n",
      "2023-05-29 13:41:54,971 - Network - INFO : Antenna patterns and LPFs loaded.\n",
      "2023-05-29 13:41:54,996 - Network - INFO : Detector responses calculated.\n",
      "2023-05-29 13:41:54,997 - Network - INFO : Calculate numeric derivatives of detector responses.\n",
      "2023-05-29 13:41:54,997 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:41:55,617 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:41:56,246 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:41:56,862 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:41:57,482 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:41:58,101 - Network - INFO : Numeric derivatives of detector responses calculated.\n",
      "2023-05-29 13:41:58,103 - Network - INFO : SNRs calculated.\n",
      "2023-05-29 13:41:58,104 - Network - INFO : Calculate errors (Fisher & cov matrices).\n",
      "2023-05-29 13:41:58,104 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:41:58,126 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:41:58,148 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:41:58,173 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:41:58,200 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:41:58,240 - Network - INFO : Errors calculated.\n",
      "2023-05-29 13:41:58,265 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:41:58,266 - Network - INFO : Calculate numeric derivatives of polarizations.\n",
      "2023-05-29 13:41:58,272 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:41:59,101 - Network - INFO : Numeric derivatives of polarizations calculated.\n",
      "2023-05-29 13:41:59,131 - Network - INFO : PSDs loaded.\n",
      "2023-05-29 13:41:59,133 - Network - INFO : Antenna patterns and LPFs loaded.\n",
      "2023-05-29 13:41:59,158 - Network - INFO : Detector responses calculated.\n",
      "2023-05-29 13:41:59,158 - Network - INFO : Calculate numeric derivatives of detector responses.\n",
      "2023-05-29 13:41:59,159 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:41:59,782 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:42:00,403 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:42:01,033 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:42:01,649 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:42:02,262 - Network - INFO : Numeric derivatives of detector responses calculated.\n",
      "2023-05-29 13:42:02,264 - Network - INFO : SNRs calculated.\n",
      "2023-05-29 13:42:02,265 - Network - INFO : Calculate errors (Fisher & cov matrices).\n",
      "2023-05-29 13:42:02,265 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:42:02,288 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:42:02,313 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:42:02,338 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:42:02,364 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:42:02,403 - Network - INFO : Errors calculated.\n",
      "2023-05-29 13:42:02,428 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:42:02,428 - Network - INFO : Calculate numeric derivatives of polarizations.\n",
      "2023-05-29 13:42:02,434 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:42:03,260 - Network - INFO : Numeric derivatives of polarizations calculated.\n",
      "2023-05-29 13:42:03,394 - Network - INFO : PSDs loaded.\n",
      "2023-05-29 13:42:03,396 - Network - INFO : Antenna patterns and LPFs loaded.\n",
      "2023-05-29 13:42:03,420 - Network - INFO : Detector responses calculated.\n",
      "2023-05-29 13:42:03,420 - Network - INFO : Calculate numeric derivatives of detector responses.\n",
      "2023-05-29 13:42:03,421 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:42:04,044 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:42:04,669 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:42:05,298 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:42:05,917 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:42:06,545 - Network - INFO : Numeric derivatives of detector responses calculated.\n",
      "2023-05-29 13:42:06,548 - Network - INFO : SNRs calculated.\n",
      "2023-05-29 13:42:06,548 - Network - INFO : Calculate errors (Fisher & cov matrices).\n",
      "2023-05-29 13:42:06,548 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:42:06,570 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:42:06,592 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:42:06,618 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:42:06,644 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:42:06,683 - Network - INFO : Errors calculated.\n",
      "2023-05-29 13:42:06,708 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:42:06,709 - Network - INFO : Calculate numeric derivatives of polarizations.\n",
      "2023-05-29 13:42:06,714 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:42:07,540 - Network - INFO : Numeric derivatives of polarizations calculated.\n",
      "2023-05-29 13:42:07,570 - Network - INFO : PSDs loaded.\n",
      "2023-05-29 13:42:07,572 - Network - INFO : Antenna patterns and LPFs loaded.\n",
      "2023-05-29 13:42:07,596 - Network - INFO : Detector responses calculated.\n",
      "2023-05-29 13:42:07,596 - Network - INFO : Calculate numeric derivatives of detector responses.\n",
      "2023-05-29 13:42:07,597 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:42:08,228 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:42:08,847 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:42:09,461 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:42:10,078 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:42:10,698 - Network - INFO : Numeric derivatives of detector responses calculated.\n",
      "2023-05-29 13:42:10,700 - Network - INFO : SNRs calculated.\n",
      "2023-05-29 13:42:10,701 - Network - INFO : Calculate errors (Fisher & cov matrices).\n",
      "2023-05-29 13:42:10,701 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:42:10,723 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:42:10,744 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:42:10,769 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:42:10,796 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:42:10,838 - Network - INFO : Errors calculated.\n",
      "2023-05-29 13:42:10,864 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:42:10,864 - Network - INFO : Calculate numeric derivatives of polarizations.\n",
      "2023-05-29 13:42:10,870 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:42:11,701 - Network - INFO : Numeric derivatives of polarizations calculated.\n",
      "2023-05-29 13:42:11,732 - Network - INFO : PSDs loaded.\n",
      "2023-05-29 13:42:11,735 - Network - INFO : Antenna patterns and LPFs loaded.\n",
      "2023-05-29 13:42:11,758 - Network - INFO : Detector responses calculated.\n",
      "2023-05-29 13:42:11,759 - Network - INFO : Calculate numeric derivatives of detector responses.\n",
      "2023-05-29 13:42:11,759 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:42:12,384 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:42:13,003 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:42:13,628 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:42:14,239 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:42:14,856 - Network - INFO : Numeric derivatives of detector responses calculated.\n",
      "2023-05-29 13:42:14,858 - Network - INFO : SNRs calculated.\n",
      "2023-05-29 13:42:14,859 - Network - INFO : Calculate errors (Fisher & cov matrices).\n",
      "2023-05-29 13:42:14,859 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:42:14,880 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:42:14,901 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:42:14,927 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:42:14,952 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:42:14,990 - Network - INFO : Errors calculated.\n",
      "2023-05-29 13:42:15,016 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:42:15,017 - Network - INFO : Calculate numeric derivatives of polarizations.\n",
      "2023-05-29 13:42:15,022 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:42:15,846 - Network - INFO : Numeric derivatives of polarizations calculated.\n",
      "2023-05-29 13:42:15,876 - Network - INFO : PSDs loaded.\n",
      "2023-05-29 13:42:15,878 - Network - INFO : Antenna patterns and LPFs loaded.\n",
      "2023-05-29 13:42:15,902 - Network - INFO : Detector responses calculated.\n",
      "2023-05-29 13:42:15,902 - Network - INFO : Calculate numeric derivatives of detector responses.\n",
      "2023-05-29 13:42:15,903 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:42:16,514 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:42:17,127 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:42:17,742 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:42:18,370 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:42:18,999 - Network - INFO : Numeric derivatives of detector responses calculated.\n",
      "2023-05-29 13:42:19,002 - Network - INFO : SNRs calculated.\n",
      "2023-05-29 13:42:19,002 - Network - INFO : Calculate errors (Fisher & cov matrices).\n",
      "2023-05-29 13:42:19,003 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:42:19,027 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:42:19,049 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:42:19,088 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:42:19,121 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:42:19,159 - Network - INFO : Errors calculated.\n",
      "2023-05-29 13:42:19,184 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:42:19,184 - Network - INFO : Calculate numeric derivatives of polarizations.\n",
      "2023-05-29 13:42:19,189 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:42:20,030 - Network - INFO : Numeric derivatives of polarizations calculated.\n",
      "2023-05-29 13:42:20,060 - Network - INFO : PSDs loaded.\n",
      "2023-05-29 13:42:20,062 - Network - INFO : Antenna patterns and LPFs loaded.\n",
      "2023-05-29 13:42:20,087 - Network - INFO : Detector responses calculated.\n",
      "2023-05-29 13:42:20,088 - Network - INFO : Calculate numeric derivatives of detector responses.\n",
      "2023-05-29 13:42:20,089 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:42:20,727 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:42:21,343 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:42:21,969 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:42:22,584 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:42:23,202 - Network - INFO : Numeric derivatives of detector responses calculated.\n",
      "2023-05-29 13:42:23,204 - Network - INFO : SNRs calculated.\n",
      "2023-05-29 13:42:23,204 - Network - INFO : Calculate errors (Fisher & cov matrices).\n",
      "2023-05-29 13:42:23,205 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:42:23,226 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:42:23,248 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:42:23,273 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:42:23,298 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:42:23,336 - Network - INFO : Errors calculated.\n",
      "2023-05-29 13:42:23,360 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:42:23,361 - Network - INFO : Calculate numeric derivatives of polarizations.\n",
      "2023-05-29 13:42:23,367 - Network - INFO : Polarizations calculated.\n",
      "2023-05-29 13:42:24,186 - Network - INFO : Numeric derivatives of polarizations calculated.\n",
      "2023-05-29 13:42:24,216 - Network - INFO : PSDs loaded.\n",
      "2023-05-29 13:42:24,218 - Network - INFO : Antenna patterns and LPFs loaded.\n",
      "2023-05-29 13:42:24,242 - Network - INFO : Detector responses calculated.\n",
      "2023-05-29 13:42:24,242 - Network - INFO : Calculate numeric derivatives of detector responses.\n",
      "2023-05-29 13:42:24,243 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:42:24,865 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:42:25,489 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:42:26,116 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:42:26,738 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:42:27,353 - Network - INFO : Numeric derivatives of detector responses calculated.\n",
      "2023-05-29 13:42:27,355 - Network - INFO : SNRs calculated.\n",
      "2023-05-29 13:42:27,356 - Network - INFO : Calculate errors (Fisher & cov matrices).\n",
      "2023-05-29 13:42:27,356 - Network - INFO :    CE-40_C\n",
      "2023-05-29 13:42:27,378 - Network - INFO :    CE-20_S\n",
      "2023-05-29 13:42:27,399 - Network - INFO :    ET_ET1\n",
      "2023-05-29 13:42:27,425 - Network - INFO :    ET_ET2\n",
      "2023-05-29 13:42:27,450 - Network - INFO :    ET_ET3\n",
      "2023-05-29 13:42:27,489 - Network - INFO : Errors calculated.\n"
     ]
    }
   ],
   "source": [
    "lams = np.linspace(0.97, 1.0, 10)\n",
    "hybrid_nets = []\n",
    "for lam in lams:\n",
    "    inj_params['hybr'] = lam\n",
    "    net = get_hybrid_network_response(inj_params=inj_params, network_spec=network_spec, deriv_symbs_string=deriv_symbs_string, \\\n",
    "                            approximant1='IMRPhenomXAS', approximant2='IMRPhenomD', cond_num=1e25)\n",
    "    hybrid_nets.append(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x176bac760>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax= plt.subplots(nrows=1, figsize=(12,5)) \n",
    "\n",
    "\n",
    "for net_ap in hybrid_nets:\n",
    "    ax.plot(net_ap.f, np.real(net_ap.hfp), label=f\"$\\lambda$={net_ap.inj_params['hybr']:.2f}\")\n",
    "\n",
    "ax.plot(net1.f, np.real(net2.hfp), lw=3, label=\"IMRPhenomD\")\n",
    "ax.plot(net1.f, np.real(net1.hfp), lw=3, label=\"IMRPhenomXAS\")\n",
    "\n",
    "ax.set_xlabel(\"Frequency (Hz)\")\n",
    "ax.set_xlim(5, 10)\n",
    "ax.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plot_bias' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[123], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m plot_bias(hybrid_nets[::\u001b[39m3\u001b[39m], param_list)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plot_bias' is not defined"
     ]
    }
   ],
   "source": [
    "plot_bias(hybrid_nets[::3], param_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_bias_sig(nets, param_list, figsize=(10,20)):\n",
    "\n",
    "    f, ax= plt.subplots(nrows=len(param_list), ncols=1,  figsize=figsize) \n",
    "    ax = list(ax)\n",
    "\n",
    "\n",
    "    for i, param in enumerate((param_list)):\n",
    "        for net_ap in nets:\n",
    "            mu = net_ap.inj_params[param]\n",
    "            sigma = net_ap.errs[param]\n",
    "            bias = np.array(net_ap.cutler_vallisneri_bias)[0][i]\n",
    "        \n",
    "            try:\n",
    "                lam = net_ap.inj_params['hybr']\n",
    "            except:\n",
    "                lam = 0.0\n",
    "\n",
    "            ax[i].scatter(lam, np.abs(bias/sigma), label=f\"Approximate ($\\lambda$={lam:.2f})\")\n",
    "    \n",
    "        \n",
    "        if i==0:\n",
    "            ax[i].legend(bbox_to_anchor=(1, 1), loc='upper left', fontsize=14)\n",
    "\n",
    "        \n",
    "        ax[i].set_xlabel(param, fontsize=20)\n",
    "\n",
    "    plt.suptitle(\"Bias Significance b/w IMRPhenomXAS and Approximate Model\", y=1.02, fontsize=20)\n",
    "    # plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x2000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_bias_sig(hybrid_nets[:], param_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.16517087e-01,  1.01941561e-02,  4.06978617e+02, -1.67806997e-01,\n",
       "        3.00929909e-01,  1.33115245e-01, -2.29341295e-02, -1.27943503e-01,\n",
       "       -1.18380838e+00])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(net2_hybrid.cutler_vallisneri_bias)[0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute PyCBC Mismatch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_waveform(inj_params, network_spec = ['aLIGO_H','aLIGO_L','aLIGO_V'], approximant='IMRPhenomXAS', deriv_symbs_string = 'Mc eta DL tc phic iota ra dec psi', df=2**-4, cond_num=1e25):\n",
    "    \n",
    "    # initialize the network with the desired detectors\n",
    "    net = network.Network(network_spec)\n",
    "\n",
    "    # choose the desired waveform \n",
    "    wf_model_name = 'lal_bbh' # lal_hybrid....\n",
    "    # pass the chosen waveform to the network for initialization\n",
    "    net.set_wf_vars(wf_model_name=wf_model_name, wf_other_var_dic = {'approximant': approximant})\n",
    "\n",
    "    # pick the desired frequency range\n",
    "    f_min = 5.\n",
    "    f_max = 512.\n",
    "    d_f = df\n",
    "    f = np.arange(f_min, f_max, d_f)\n",
    "\n",
    "    # choose whether to take Earth's rotation into account\n",
    "    use_rot = 0\n",
    "\n",
    "    # pass all these variables to the network\n",
    "    net.set_net_vars(\n",
    "        f=f, inj_params=inj_params,\n",
    "        deriv_symbs_string=deriv_symbs_string,\n",
    "        use_rot=use_rot\n",
    "        )\n",
    "    \n",
    "    # # compute the WF polarizations\n",
    "    net.calc_wf_polarizations()\n",
    "\n",
    "    return net\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "m1 = 10\n",
    "m2 = 3\n",
    "\n",
    "Mc = (m1*m2)**(3/5) / (m1+m2)**(1/5) \n",
    "eta = (m1*m2) / (m1+m2)**2\n",
    "\n",
    "inj_params = {\n",
    "    'Mc':    Mc,\n",
    "    'eta':   eta,\n",
    "    'chi1x': 0,\n",
    "    'chi2x': 0,\n",
    "    'chi1y': 0,\n",
    "    'chi2y': 0,\n",
    "    'chi1z': 0.0,\n",
    "    'chi2z': 0.0,\n",
    "    'DL':    1000,\n",
    "    'tc':    0,\n",
    "    'phic':  0,\n",
    "    'iota':  np.pi/3,\n",
    "    'ra':    np.pi/3,\n",
    "    'dec':   np.pi/3,\n",
    "    'psi':   np.pi/3,\n",
    "    'gmst0': 0\n",
    "    }\n",
    "\n",
    "deriv_symbs_string = 'Mc eta DL chi1z chi2z iota ra dec psi'\n",
    "\n",
    "network_dict = {\n",
    "        'aLIGO':    ['aLIGO_H','aLIGO_L','V+_V'],\n",
    "        'A+':       ['A+_H', 'A+_L','A+_V'],\n",
    "        'Voyager':  ['Voyager-CBO_H', 'Voyager-CBO_L', 'Voyager-CBO_V'],\n",
    "        '3G':       ['CE-40_C', 'CE-20_S', 'ET_ET1', 'ET_ET2', 'ET_ET3']\n",
    "        }\n",
    "\n",
    "network_key = '3G'\n",
    "\n",
    "network_spec = network_dict[network_key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-19 10:45:26,700 - Network - INFO : Polarizations calculated.\n",
      "2023-06-19 10:45:26,705 - Network - INFO : Polarizations calculated.\n",
      "2023-06-19 10:45:26,705 - Network - INFO : Calculate numeric derivatives of polarizations.\n",
      "2023-06-19 10:45:26,710 - Network - INFO : Polarizations calculated.\n",
      "2023-06-19 10:45:28,265 - Network - INFO : Numeric derivatives of polarizations calculated.\n",
      "2023-06-19 10:45:28,320 - Network - INFO : PSDs loaded.\n",
      "2023-06-19 10:45:28,325 - Network - INFO : Antenna patterns and LPFs loaded.\n",
      "2023-06-19 10:45:28,342 - Network - INFO : Detector responses calculated.\n",
      "2023-06-19 10:45:28,343 - Network - INFO : Calculate numeric derivatives of detector responses.\n",
      "2023-06-19 10:45:28,343 - Network - INFO :    CE-40_C\n",
      "2023-06-19 10:45:29,587 - Network - INFO :    CE-20_S\n",
      "2023-06-19 10:45:30,734 - Network - INFO :    ET_ET1\n",
      "2023-06-19 10:45:31,955 - Network - INFO :    ET_ET2\n",
      "2023-06-19 10:45:33,318 - Network - INFO :    ET_ET3\n",
      "2023-06-19 10:45:34,469 - Network - INFO : Numeric derivatives of detector responses calculated.\n",
      "2023-06-19 10:45:34,473 - Network - INFO : SNRs calculated.\n",
      "2023-06-19 10:45:34,473 - Network - INFO : Calculate errors (Fisher & cov matrices).\n",
      "2023-06-19 10:45:34,474 - Network - INFO :    CE-40_C\n",
      "2023-06-19 10:45:34,518 - Network - INFO :    CE-20_S\n",
      "2023-06-19 10:45:34,560 - Network - INFO :    ET_ET1\n",
      "2023-06-19 10:45:34,610 - Network - INFO :    ET_ET2\n",
      "2023-06-19 10:45:34,663 - Network - INFO :    ET_ET3\n",
      "2023-06-19 10:45:34,758 - Network - INFO : Errors calculated.\n"
     ]
    }
   ],
   "source": [
    "# Calculate Faithfulness using PyCBC\n",
    "from gwbench import waveform as wfc\n",
    "from pycbc.types import FrequencySeries\n",
    "from pycbc.filter import match\n",
    "\n",
    "\n",
    "net_true = get_waveform(inj_params=inj_params, network_spec=network_spec, deriv_symbs_string=deriv_symbs_string, \\\n",
    "                            approximant='IMRPhenomXAS', cond_num=1e25)\n",
    "\n",
    "net_approx = get_network_response(inj_params=inj_params, network_spec=network_spec, deriv_symbs_string=deriv_symbs_string, \\\n",
    "                            approximant='IMRPhenomD', cond_num=1e25)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Is Faithfulness robust for long binaries?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "704.7091067478275"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pycbc import pnutils\n",
    "from pycbc import conversions as conv\n",
    "f_low = net_true.f[0]\n",
    "\n",
    "mc_inj = net_true.inj_params[\"Mc\"]\n",
    "eta_inj = net_true.inj_params[\"eta\"]\n",
    "\n",
    "m1 = conv.mass1_from_mchirp_eta(mc_inj, eta_inj)\n",
    "m2 = conv.mass2_from_mchirp_eta(mc_inj, eta_inj)\n",
    "\n",
    "\n",
    "ts_5hz,fs_5hz = pnutils.get_inspiral_tf(0.,m1, m2,0.0,0.0,f_low)\n",
    "inspiral_t = -ts_5hz[0]\n",
    "inspiral_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df: 0.00625\n",
      "Faithfulness: 0.9998181\n",
      "\n",
      "df: 0.03333333333333333\n",
      "Faithfulness: 0.9998943\n",
      "\n",
      "df: 0.06041666666666667\n",
      "Faithfulness: 0.9998832\n",
      "\n",
      "df: 0.08750000000000001\n",
      "Faithfulness: 0.9999044\n",
      "\n",
      "df: 0.11458333333333334\n",
      "Faithfulness: 0.9999108\n",
      "\n",
      "df: 0.1416666666666667\n",
      "Faithfulness: 0.9999154\n",
      "\n",
      "df: 0.16875\n",
      "Faithfulness: 0.9999191\n",
      "\n",
      "df: 0.19583333333333333\n",
      "Faithfulness: 0.9999221\n",
      "\n",
      "df: 0.22291666666666668\n",
      "Faithfulness: 0.9999247\n",
      "\n",
      "df: 0.25\n",
      "Faithfulness: 0.9999268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "delta_f = net_approx.f[1] - net_approx.f[0]\n",
    "delta_fs = np.linspace(0.1, 4.0, 10) * delta_f\n",
    "\n",
    "for delta_f in delta_fs:\n",
    "    print(f'df: {delta_f}')\n",
    "    psd = FrequencySeries(net_approx.detectors[0].psd, delta_f=delta_f) # caluclate mismatch using any one detector PSD\n",
    "    \n",
    "    # make sure that the detector and waveform frequency ranges overlap\n",
    "    freq_mask = np.in1d(net_approx.f, net_approx.detectors[0].f, assume_unique=True)\n",
    "\n",
    "    hp1_pyc = FrequencySeries(net_true.hfp[freq_mask], delta_f=delta_f)\n",
    "    hp2_pyc = FrequencySeries(net_approx.hfp[freq_mask], delta_f=delta_f)\n",
    "\n",
    "\n",
    "    full_faith, index = match(hp1_pyc, hp2_pyc, psd=psd, low_frequency_cutoff=net_approx.f[0], high_frequency_cutoff=net_approx.f[-1])\n",
    "    print(f\"Faithfulness: {full_faith:.7f}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Mc': 9.990936e-08, 'eta': 0.0010539653, 'DL': 329.1166, 'chi1z': 0.34558025, 'chi2z': 0.42681187, 'iota': 0.26577675, 'ra': 0.04397556, 'dec': 0.10657012, 'psi': 0.25874454}\n",
      "0.9999999982074691\n",
      "1000\n",
      "0.15554818180895325\n"
     ]
    }
   ],
   "source": [
    "file='test.npz'\n",
    "np.savez(file, inj_params = net_hybr.inj_params, errs=net_hybr.errs, cov=net_hybr.cov, cv_bias=net_hybr.cutler_vallisneri_bias, faith=full_faith)\n",
    "data = np.load(file, allow_pickle=True)\n",
    "\n",
    "print(data['errs'].item())\n",
    "print(data['faith'])\n",
    "print(data['inj_params'].item()['DL'])\n",
    "print(list(data['cv_bias'])[0][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inj_params\n",
      "errs\n",
      "cov\n",
      "cv_bias\n",
      "faith\n"
     ]
    }
   ],
   "source": [
    "# list(data['errs'].item().keys()).index(\"DL\")\n",
    "for key in data.keys():\n",
    "    print(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.013012579571124491\n"
     ]
    }
   ],
   "source": [
    "from astropy.cosmology import Planck18, z_at_value\n",
    "import astropy.units as u\n",
    "from scipy.stats import multivariate_normal\n",
    "\n",
    "net_hybr.cov[:3, :3]\n",
    "\n",
    "dist_m = multivariate_normal(mean=[net_hybr.inj_params['Mc'], net_hybr.inj_params['eta'], net_hybr.inj_params['DL']],\n",
    "                           cov=net_hybr.cov[:3, :3], allow_singular=True)\n",
    "\n",
    "samples = dist_m.rvs(500)\n",
    "samples = samples[(samples[:,0] >= 0)]\n",
    "samples = samples[(samples[:,1] <= 0.25) * (samples[:,1] >= 0.0)]\n",
    "samples = samples[(samples[:,2] >= 0.0)]\n",
    "\n",
    "mchirp_samples = samples[:,0]\n",
    "eta_samples = samples[:,1]\n",
    "z_samples = z_at_value(Planck18.luminosity_distance, samples[:,2] * u.Mpc)\n",
    "\n",
    "m1_src_samples = conv.mass1_from_mchirp_eta(mchirp_samples/(1+z_samples), eta_samples)\n",
    "print(np.percentile(m1_src_samples, 84) - np.percentile(m1_src_samples,16))"
   ]
  }
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