power.py

#!/usr/bin/env python

# Copyright (c) 2017 The Board of Trustees of the University of Illinois
# All rights reserved.
#
# Developed by: Daniel Johnson, E. A. Huerta, Roland Haas
#               NCSA Gravity Group
#               National Center for Supercomputing Applications
#               University of Illinois at Urbana-Champaign
#               http://gravity.ncsa.illinois.edu/
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to
# deal with the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
# sell copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimers.
#
# Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimers in the documentation
# and/or other materials provided with the distribution.
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# Neither the names of the National Center for Supercomputing Applications,
# University of Illinois at Urbana-Champaign, nor the names of its
# contributors may be used to endorse or promote products derived from this
# Software without specific prior written permission.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# Based off of SimulationTools Mathematica Package
# http://www.simulationtools.org/

import numpy as np
import glob
import os
import h5py
import re
import math
import sys
import warnings
import scipy.optimize
import scipy.interpolate
import scipy.integrate
import argparse

#-----Function Definitions-----#

def joinDsets(dsets):
        """joints multiple datasets which each have a
        time like first column, eg iteration number of
        time. Removes overlapping segments, keeping the
        last segment.
    
        dsets = iterable of 2d array like objects with data"""
        # joins multiple datasets of which the first column is assumed to be "time"
        if(not dsets):
            return None
        length = 0
        for d in dsets:
            length += len(d)
        newshape = list(dsets[0].shape)
        newshape[0] = length
        dset = np.empty(shape=newshape, dtype=dsets[0].dtype)
        usedlength = 0
        for d in dsets:
            insertpointidx = np.where(dset[0:usedlength,0] >= d[0,0])
            if(insertpointidx[0].size):
                insertpoint = insertpointidx[0][0]
            else:
                insertpoint = usedlength
            newlength = insertpoint+len(d)
            dset[insertpoint:newlength] = d
            usedlength = newlength
        return dset[0:usedlength]


def loadHDF5Series(nameglob, series):
        """load HDF5 timeseries data and concatenate the content of multiple files
    
        nameglob = a shell glob that matches all files to be loaded,
        files are sorted alphabetically
        series = HDF5 dataset name of dataset to load from files"""
        dsets = list()
        for fn in sorted(glob.glob(nameglob)):
            fh = h5py.File(fn, "r")
            dsets.append(fh[series])
        return joinDsets(dsets)


# -----------------------------------------------------------------------------
# POWER Method
# -----------------------------------------------------------------------------

def POWER(argv, args):
  
    #Function used in getting psi4 from simulation

    print("argv:",argv)
    print("args:",args)
    
    #Convert radial to tortoise coordinates
    def RadialToTortoise(r, M):
        """
        Convert the radial coordinate to the tortoise coordinate
    
        r = radial coordinate
        M = ADMMass used to convert coordinate
        return = tortoise coordinate value
        """
        return r + 2. * M * math.log( r / (2. * M) - 1.)
    
    #Convert modified psi4 to strain
    def psi4ToStrain(mp_psi4, f0):
        """
        Convert the input mp_psi4 data to the strain of the gravitational wave
        
        mp_psi4 = Weyl scalar result from simulation
        f0 = cutoff frequency
        return = strain (h) of the gravitational wave
        """
        #TODO: Check for uniform spacing in time
        t0 = mp_psi4[:, 0]
        list_len = len(t0)
        complexPsi = np.zeros(list_len, dtype=np.complex_)
        complexPsi = mp_psi4[:, 1]+1.j*mp_psi4[:, 2]
    
        freq, psif = myFourierTransform(t0, complexPsi)
        dhf = ffi(freq, psif, f0)
        hf = ffi(freq, dhf, f0)
    
        time, h = myFourierTransformInverse(freq, hf, t0[0])
        hTable = np.column_stack((time, h))
        return hTable
    
    #Fixed frequency integration
    # See https://arxiv.org/abs/1508.07250 for method
    def ffi(freq, data, f0):
        """
        Integrates the data according to the input frequency and cutoff frequency
    
        freq = fourier transform frequency
        data = input on which ffi is performed
        f0 = cutoff frequency
        """
        f1 = f0/(2*math.pi)
        fs = freq
        gs = data
        mask1 = (np.sign((fs/f1) - 1) + 1)/2.
        mask2 = (np.sign((-fs/f1) - 1) + 1)/2.
        mask = 1 - (1 - mask1) * (1 - mask2)
        fs2 = mask * fs + (1-mask) * f1 * np.sign(fs - np.finfo(float).eps)
        new_gs = gs/(2*math.pi*1.j*fs2)
        return new_gs
    
    #Fourier Transform
    def myFourierTransform(t0, complexPsi):
        """
        Transforms the complexPsi data to frequency space
    
        t0 = time data points
        complexPsi = data points of Psi to be transformed
        """
        psif = np.fft.fft(complexPsi, norm="ortho")
        l = len(complexPsi)
        n = int(math.floor(l/2.))
        newpsif = psif[l-n:]
        newpsif = np.append(newpsif, psif[:l-n])
        T = np.amin(np.diff(t0))*l
        freq = range(-n, l-n)/T
        return freq, newpsif
    
    #Inverse Fourier Transform
    def myFourierTransformInverse(freq, hf, t0):
        l = len(hf)
        n = int(math.floor(l/2.))
        newhf = hf[n:]
        newhf = np.append(newhf, hf[:n])
        amp = np.fft.ifft(newhf, norm="ortho")
        df = np.amin(np.diff(freq))
        time = t0 + range(0, l)/(df*l)
        return time, amp
    
    def angular_momentum(x, q, m, chi1, chi2, LInitNR):
        eta = q/(1.+q)**2
        m1 = (1.+math.sqrt(1.-4.*eta))/2.
        m2 = m - m1
        S1 = m1**2. * chi1
        S2 = m2**2. * chi2
        Sl = S1+S2
        Sigmal = S2/m2 - S1/m1
        DeltaM = m1 - m2
        mu = eta
        nu = eta
        GammaE = 0.5772156649;
        e4 = -(123671./5760.)+(9037.* math.pi**2.)/1536.+(896.*GammaE)/15.+(-(498449./3456.)+(3157.*math.pi**2.)/576.)*nu+(301. * nu**2.)/1728.+(77.*nu**3.)/31104.+(1792. *math.log(2.))/15.
        e5 = -55.13
        j4 = -(5./7.)*e4+64./35.
        j5 = -(2./3.)*e5-4988./945.-656./135. * eta;
        a1 = -2.18522;
        a2 = 1.05185;
        a3 = -2.43395;
        a4 = 0.400665;
        a5 = -5.9991;
        CapitalDelta = (1.-4.*eta)**0.5
    
        l = (eta/x**(1./2.)*(
            1. +
            x*(3./2. + 1./6.*eta) + 
            x**2. *(27./8. - 19./8.*eta + 1./24.*eta**2.) + 
            x**3. *(135./16. + (-6889./144. + 41./24. * math.pi**2.)*eta + 31./24.*eta**2. + 7./1296.*eta**3.) + 
            x**4. *((2835./128.) + eta*j4 - (64.*eta*math.log(x)/3.))+ 
            x**5. *((15309./256.) + eta*j5 + ((9976./105.) + (1312.*eta/15.))*eta*math.log(x))+
            x**(3./2.)*(-(35./6.)*Sl - 5./2.*DeltaM* Sigmal) + 
            x**(5./2.)*((-(77./8.) + 427./72.*eta)*Sl + DeltaM* (-(21./8.) + 35./12.*eta)*Sigmal) + 
            x**(7./2.)*((-(405./16.) + 1101./16.*eta - 29./16.*eta**2.)*Sl + DeltaM*(-(81./16.) + 117./4.*eta - 15./16.*eta**2.)*Sigmal) + 
            (1./2. + (m1 - m2)/2. - eta)* chi1**2. * x**2. +
            (1./2. + (m2 - m1)/2. - eta)* chi2**2. * x**2. + 
            2.*eta*chi1*chi2*x**2. +
            ((13.*chi1**2.)/9. +
            (13.*CapitalDelta*chi1**2.)/9. -
            (55.*nu*chi1**2.)/9. - 
            29./9.*CapitalDelta*nu*chi1**2. + 
            (14.*nu**2. *chi1**2.)/9. +
            (7.*nu*chi1*chi2)/3. +
            17./18.* nu**2. * chi1 * chi2 + 
            (13.* chi2**2.)/9. -
            (13.*CapitalDelta*chi2**2.)/9. -
            (55.*nu*chi2**2.)/9. +
            29./9.*CapitalDelta*nu*chi2**2. +
            (14.*nu**2. * chi2**2.)/9.)
            * x**3.))
        return l - LInitNR
    
    #Get cutoff frequency
    def getCutoffFrequency(sim_name):
        """
        Determine cutoff frequency of simulation
    
        sim_name = string of simulation
        return = cutoff frequency
        """
        filename = main_dir+"/output-0000/%s.par" % (sim_name)
        with open(filename) as file:
            contents = file.readlines()
            for line in contents:
                line_elems = line.split(" ")
                if(line_elems[0] == "TwoPunctures::par_b"):
                    par_b = float(line_elems[-1])
                if(line_elems[0] == "TwoPunctures::center_offset[0]"):
                    center_offset = float(line_elems[-1])
                if(line_elems[0] == "TwoPunctures::par_P_plus[1]"):
                    pyp = float(line_elems[-1])
                if(line_elems[0] == "TwoPunctures::par_P_minus[1]"):
                    pym = float(line_elems[-1])
                if(line_elems[0] == "TwoPunctures::target_M_plus"):
                    m1 = float(line_elems[-1])
                if(line_elems[0] == "TwoPunctures::target_M_minus"):
                    m2 = float(line_elems[-1])
                if(line_elems[0] == "TwoPunctures::par_S_plus[2]"):
                    S1 = float(line_elems[-1])
                if(line_elems[0] == "TwoPunctures::par_S_minus[2]"):
                    S2 = float(line_elems[-1])
    
        xp = par_b + center_offset
        xm = -1*par_b + center_offset
        LInitNR = xp*pyp + xm*pym
        M = m1+m2
        q = m1/m2
        chi1 = S1/m1**2
        chi2 = S2/m2**2
        # .014 is the initial guess for cutoff frequency
        omOrbPN = scipy.optimize.fsolve(angular_momentum, .014, (q, M, chi1, chi2, LInitNR))[0]
        omOrbPN = omOrbPN**(3./2.)
        omGWPN = 2. * omOrbPN
        omCutoff = 0.75 * omGWPN
        return omCutoff
    
    #Get Energy
    def get_energy(sim):
        """
        Save the energy radiated energy
        sim = string of simulation
        """
        python_strain = np.loadtxt("./Extrapolated_Strain/"+sim+"/"+sim+"_radially_extrapolated_strain_l2_m2.dat")
        val = np.zeros(len(python_strain))
        val = val.astype(np.complex_)
        cur_max_time = python_strain[0][0]
        cur_max_amp = abs(pow(python_strain[0][1], 2))
        # TODO: rewrite as array operations (use np.argmax)
        for i in python_strain[:]:
            cur_time = i[0]
            cur_amp = abs(pow(i[1], 2))
            if(cur_amp>cur_max_amp):
                cur_max_amp = cur_amp
                cur_max_time = cur_time
    
        max_idx = 0
        for i in range(0, len(python_strain[:])):
            if(python_strain[i][1] > python_strain[max_idx][1]):
                max_idx = i
    
        paths = glob.glob("./Extrapolated_Strain/"+sim+"/"+sim+"_radially_extrapolated_strain_l[2-4]_m*.dat")
        for path in paths:
            python_strain = np.loadtxt(path)
    
            t = python_strain[:, 0]
            t = t.astype(np.complex_)
            h = python_strain[:, 1] + 1j * python_strain[:, 2]
            dh = np.zeros(len(t), dtype=np.complex_) 
            for i in range(0, len(t)-1):
                dh[i] = ((h[i+1] - h[i])/(t[i+1] - t[i]))
            dh[len(t)-1] = dh[len(t)-2]
    
            dh_conj = np.conj(dh)
            prod = np.multiply(dh, dh_conj)
            local_val = np.zeros(len(t))
            local_val = local_val.astype(np.complex_)
                    # TODO: rewrite as array notation using np.cumtrapz
            for i in range(0, len(t)):
                local_val[i] = np.trapz(prod[:i], x=(t[:i]))
            val += local_val
            
        val *= 1/(16 * math.pi)
        np.savetxt("./Extrapolated_Strain/"+sim+"/"+sim+"_radially_extrapolated_energy.dat", val)
    
    #Get angular momentum
    def get_angular_momentum(python_strain):
        """
        Save the energy radiated angular momentum
        sim = string of simulation
        """
        python_strain = np.loadtxt("./Extrapolated_Strain/"+sim+"/"+sim+"_radially_extrapolated_strain_l2_m2.dat")
        val = np.zeros(len(python_strain))
        val = val.astype(np.complex_)
        cur_max_time = python_strain[0][0]
        cur_max_amp = abs(pow(python_strain[0][1], 2))
        # TODO: rewrite as array operations (use np.argmax)
        for i in python_strain[:]:
            cur_time = i[0]
            cur_amp = abs(pow(i[1], 2))
            if(cur_amp>cur_max_amp):
                cur_max_amp = cur_amp
                cur_max_time = cur_time
    
        max_idx = 0
        for i in range(0, len(python_strain[:])):
            if(python_strain[i][1] > python_strain[max_idx][1]):
                max_idx = i
    
        paths = glob.glob("./Extrapolated_Strain/"+sim+"/"+sim+"_radially_extrapolated_strain_l[2-4]_m*.dat")
        for path in paths:
            python_strain = np.loadtxt(path)
    
            t = python_strain[:, 0]
            t = t.astype(np.complex_)
            h = python_strain[:, 1] + 1j * python_strain[:, 2]
            dh = np.zeros(len(t), dtype=np.complex_) 
                    # TODO: rewrite using array notation
            for i in range(0, len(t)-1):
                dh[i] = ((h[i+1] - h[i])/(t[i+1] - t[i]))
            dh[len(t)-1] = dh[len(t)-2]
    
            dh_conj = np.conj(dh)
            prod = np.multiply(h, dh_conj)
            local_val = np.zeros(len(t))
            local_val = local_val.astype(np.complex_)
                    # TODO: rewrite as array notation using np.cumtrapz. Move atoi call out of inner loop.
            for i in range(0, len(t)):
                local_val[i] = np.trapz(prod[:i], x=(t[:i])) * int(((path.split("_")[-1]).split("m")[-1]).split(".")[0])
            val += local_val
            
        val *= 1/(16 * math.pi)
        np.savetxt("./Extrapolated_Strain/"+sim+"/"+sim+"_radially_extrapolated_angular_momentum.dat", val)
    
    #-----Main-----#
    
    if __name__ == "__main__":
        
        
        #Initialize simulation data

              
        if(len(argv) < 2):
                print("Pass in the number n of the n innermost detector radii to be used in the extrapolation (optional, default=all) and the simulation folders (e.g., ./power.py 6 ./simulations/J0040_N40 /path/to/my_simulation_folder).")
                sys.exit()
        elif(os.path.isdir(argv[2])):    
                radiiUsedForExtrapolation = 7    #use the first n radii available i.e. no radii specified, defaults to 7
                paths = argv[2:] 
                
        # elif(len(argv) == 4):                               # if user specifies number of radii
        #         radiiUsedForExtrapolation = int(argv[2])
        #         paths = argv[4:]
                
        elif(not os.path.isdir(argv[2])):     
                radiiUsedForExtrapolation = int(argv[2])    #use the first n radii available  
                if(radiiUsedForExtrapolation < 1 or radiiUsedForExtrapolation > 7):
                        print("Invalid specified radii number")
                        sys.exit()
                paths = argv[3:]      
                
                
        print("Radii to be used:", radiiUsedForExtrapolation)
        print(argv[2])
        print(argv[2:])
        print("len(argv):",len(argv))
        

    
        for sim_path in paths:
                main_dir = sim_path
                sim = os.path.split(sim_path)[-1]
    
                simdirs = main_dir+"/output-????/%s/" % (sim)
                f0 = getCutoffFrequency(sim)
    
                #Get simulation total mass
                ADMMass = None
                two_punctures_files = sorted(glob.glob(main_dir+"/output-????/%s/TwoPunctures.bbh" % (sim)))
                out_files = sorted(glob.glob(main_dir+"/output-????/%s.out" % (sim)))
                par_files = sorted(glob.glob(main_dir+"/output-????/%s.par" % (sim)))
                if(two_punctures_files):
                    two_punctures_file = two_punctures_files[0]
                    with open(two_punctures_file) as file:
                          contents = file.readlines()
                          for line in contents:
                                  line_elems = line.split(" ")
                                  if(line_elems[0] == "initial-ADM-energy"):
                                          ADMMass = float(line_elems[-1])
                elif(out_files):
                      out_file = out_files[0]
                      with open(out_file) as file:
                            contents = file.readlines()
                            for line in contents:
                                  m = re.match("INFO \(TwoPunctures\): The total ADM mass is (.*)", line)
                                  if(m):
                                      ADMMass = float(m.group(1))
                elif(par_files):
                      par_file = par_files[0]
                      print("Not yet implemented")
                      raise ValueError
                else:
                    print("Cannot determine ADM mass")
                    raise ValueError
    
                #Create data directories
                main_directory = "Extrapolated_Strain"
                sim_dir = main_directory+"/"+sim
                if not os.path.exists(main_directory):
                        os.makedirs(main_directory)
                if not os.path.exists(sim_dir):
                        os.makedirs(sim_dir)
    
            ###CAN STEAL UP TO HERE --------------------------------------
    
    
                # TODO: fix this. It will fail if output-0000 does not contain any mp
                # output and also will open the output files multiple times
                fn = sorted(glob.glob(simdirs+"mp_psi4.h5"))[0]
                with h5py.File(fn, "r") as fh:
                        # get all radii
                        radii = set()
                        modes = set()
                        dsets = dict()
                        for dset in fh:
                                # TODO: extend Multipole to save the radii as attributes and/or
                                # use a group structure in the hdf5 file
                                m = re.match(r'l(\d*)_m(-?\d*)_r(\d*\.\d)', dset)
                                if m:
                                        radius = float(m.group(3))
                                        mode = (int(m.group(1)), int(m.group(2)))
                                        modes.add(mode)
                                        radii.add(radius)
                                        dsets[(radius, mode)] = dset
                        modes = sorted(modes)
                        radii = sorted(radii)
    
                #Get Psi4
                
                for (l,m) in modes:
                        #Get Tortoise Coordinate
                        mp_psi4_vars = []
                        tortoise = []    
                        strain = []
                        phase = []
                        amp = []
                        for i in range(len(radii)):
                                #------------------------------------------------
                                # Read in HDF5 data
                                #------------------------------------------------                 
                                radius = radii[i]
                                psi4dsetname = dsets[(radius, (l,m))]
                                mp_psi4 = loadHDF5Series(simdirs+"mp_psi4.h5", psi4dsetname)
                                mp_psi4_vars.append(mp_psi4)
                                #------------------------------------------------
                                # Coordinate conversion to Tortoise
                                #------------------------------------------------
                                tortoise.append(-RadialToTortoise(radius, ADMMass))
                                #-----------------------------------------
                                # Prepare for conversion to strain
                                #-----------------------------------------
                                #Get modified Psi4 (Multiply real and imaginary psi4 columns by radii and add the tortoise coordinate to the time column)
                                mp_psi4_vars[i][:, 0] += tortoise[i]
                                mp_psi4_vars[i][:, 1] *= radii[i]
                                mp_psi4_vars[i][:, 2] *= radii[i]
    
                                #Check for psi4 amplitude going to zero
                                cur_psi4_amp = np.sqrt(mp_psi4_vars[i][0, 1]**2 + mp_psi4_vars[i][0, 2]**2)
                                min_psi4_amp = cur_psi4_amp
                                # TODO: use array notation for this since it finds the minimum amplitude
                                for j in range(0, len(mp_psi4_vars[i][:, 0])):
                                        cur_psi4_amp = np.sqrt(mp_psi4_vars[i][j, 1]**2 + mp_psi4_vars[i][j, 2]**2)
                                        if(cur_psi4_amp < min_psi4_amp):
                                                min_psi4_amp = cur_psi4_amp
                                if(min_psi4_amp < np.finfo(float).eps and l >= 2):
                                        print("The psi4 amplitude is near zero. The phase is ill-defined.")
    
                                #Fixed-frequency integration twice to get strain
                                #-----------------------------------------------------------------
                                # Strain Conversion
                                #-----------------------------------------------------------------
                                hTable = psi4ToStrain(mp_psi4_vars[i], f0)
                                time = hTable[:, 0]
                                h = hTable[:, 1]
                                hplus = h.real
                                hcross = h.imag
                                newhTable = np.column_stack((time, hplus, hcross))
                                warnings.filterwarnings('ignore')
                                finalhTable = newhTable.astype(float)
                                np.savetxt("./Extrapolated_Strain/"+sim+"/"+sim+"_strain_at_"+str(radii[i])+"_l"+str(l)+"_m"+str(m)+".dat", finalhTable)
                                strain.append(finalhTable)
                                
                                
                                #-------------------------------------------------------------------
                                # Analysis of Strain
                                #-------------------------------------------------------------------
                                #Get phase and amplitude of strain
                                h_phase = np.unwrap(np.angle(h))
                                angleTable = np.column_stack((time, h_phase))
                                angleTable = angleTable.astype(float)
                                phase.append(angleTable)
                                h_amp = np.absolute(h)
                                ampTable = np.column_stack((time, h_amp))
                                ampTable = ampTable.astype(float)
                                amp.append(ampTable)
                                
                        #print("h_amp:" , h_amp)
                        
                        #----------------------------------------------------------------------
                        # Extrapolation
                        #----------------------------------------------------------------------
                        #Interpolate phase and amplitude
                        t = phase[0][:, 0]
                        last_t = phase[radiiUsedForExtrapolation - 1][-1, 0]
                        last_index = 0;
                        # TODO: use array notation for this (this is a boolean
                        # plus a first_of or so)
                        for i in range(0, len(phase[0][:, 0])):
                                if(t[i] > last_t):
                                        last_index = i
                                        break
                        last_index = last_index-1
                        t = phase[0][0:last_index, 0]
                        dts = t[1:] - t[:-1]
                        dt = float(np.amin(dts))
                        t = np.arange(phase[0][0, 0], phase[0][last_index, 0], dt)
                        interpolation_order = 9
                        for i in range(0, radiiUsedForExtrapolation):
                                interp_function = scipy.interpolate.interp1d(phase[i][:, 0], phase[i][:, 1], kind=interpolation_order)
                                resampled_phase_vals = interp_function(t)
                                # try and keep all initial phases within 2pi of each other
                                if(i > 0):
                                    phase_shift = round((resampled_phase_vals[0] - phase[0][0,1])/(2.*math.pi))*2.*math.pi
                                    resampled_phase_vals -= phase_shift
                                phase[i] = np.column_stack((t, resampled_phase_vals))
                                interp_function = scipy.interpolate.interp1d(amp[i][:, 0], amp[i][:, 1], kind=interpolation_order)
                                resampled_amp_vals = interp_function(t)
                                amp[i] = np.column_stack((t, resampled_amp_vals))

                        #Extrapolate
                        phase_extrapolation_order = 1
                        amp_extrapolation_order = 2
                        radii = np.asarray(radii, dtype=float)
                        radii = radii[0:radiiUsedForExtrapolation]
                        # TODO: replace by np.ones (which is all it does anyway)
                        A_phase = np.power(radii, 0)
                        A_amp = np.power(radii, 0)
                        for i in range(1, phase_extrapolation_order+1):
                                A_phase = np.column_stack((A_phase, np.power(radii, -1*i)))
    
                        for i in range(1, amp_extrapolation_order+1):
                                A_amp = np.column_stack((A_amp, np.power(radii, -1*i)))
    
                        radially_extrapolated_phase = np.empty(0)
                        radially_extrapolated_amp = np.empty(0)
                        for i in range(0, len(t)):
                                b_phase = np.empty(0)
                                for j in range(0, radiiUsedForExtrapolation):
                                        b_phase = np.append(b_phase, phase[j][i, 1])
                                x_phase = np.linalg.lstsq(A_phase, b_phase)[0]
                                radially_extrapolated_phase = np.append(radially_extrapolated_phase, x_phase[0])
    
                                b_amp = np.empty(0)
                                for j in range(0, radiiUsedForExtrapolation):
                                        b_amp = np.append(b_amp, amp[j][i, 1])
                                x_amp = np.linalg.lstsq(A_amp, b_amp)[0]
                                radially_extrapolated_amp = np.append(radially_extrapolated_amp, x_amp[0])
    
                        radially_extrapolated_h_plus = np.empty(0)
                        radially_extrapolated_h_cross = np.empty(0)
                        for i in range(0, len(radially_extrapolated_amp)):
                                radially_extrapolated_h_plus = np.append(radially_extrapolated_h_plus, radially_extrapolated_amp[i] * math.cos(radially_extrapolated_phase[i]))
                                radially_extrapolated_h_cross = np.append(radially_extrapolated_h_cross, radially_extrapolated_amp[i] * math.sin(radially_extrapolated_phase[i]))
                            
                            
                        np.savetxt("./Extrapolated_Strain/"+sim+"/"+sim+"_radially_extrapolated_strain_l"+str(l)+"_m"+str(m)+".dat", np.column_stack((t, radially_extrapolated_h_plus, radially_extrapolated_h_cross)))
                        np.savetxt("./Extrapolated_Strain/"+sim+"/"+sim+"_radially_extrapolated_amplitude_l"+str(l)+"_m"+str(m)+".dat", np.column_stack((t, radially_extrapolated_amp)))
                        np.savetxt("./Extrapolated_Strain/"+sim+"/"+sim+"_radially_extrapolated_phase_l"+str(l)+"_m"+str(m)+".dat", np.column_stack((t, radially_extrapolated_phase[:])))
                
                print('t:', len(t))
                print('radially_extrapolated_amp:',len(radially_extrapolated_amp))
                
                get_energy(sim)
                get_angular_momentum(sim)
        
        # print("simdirs:",simdirs)
        # print("out_files:",out_files)
        # print("par_files:",par_files)

# -----------------------------------------------------------------------------
# Nakano Method
# -----------------------------------------------------------------------------

        
       
        
def eq_29(argv, args):

    paths = argv[2]
    

    main_dir = paths
    sim = os.path.split(paths)[-1]
    simdirs = main_dir+"/output-????/%s/" % (sim)
  

    main_directory = "Extrapolated_Strain(Nakano_Kerr)"
    sim_dir = main_directory+"/"+sim
    if not os.path.exists(main_directory):
            os.makedirs(main_directory)
    if not os.path.exists(sim_dir):
            os.makedirs(sim_dir)
  
    
    ar = loadHDF5Series(simdirs+"mp_psi4.h5" , "l2_m2_r100.00")
    t = ar[:,0]
    psi = ar[:,1]
    impsi = ar[:,2]

        
    s_in = scipy.integrate.cumtrapz(psi,t)
    ims_in = scipy.integrate.cumtrapz(impsi,t)
    s_in = s_in - s_in[-1]
    ims_in = ims_in - ims_in[-1]

    
    d_in = scipy.integrate.cumtrapz(s_in,t[1:])
    d_in = d_in - d_in[-1]
    imd_in = scipy.integrate.cumtrapz(ims_in,t[1:])
    imd_in = imd_in - imd_in[-1]


    A_val = np.loadtxt(main_dir+"/output-0018/J0040_N40/quasilocalmeasures-qlm_scalars..asc")
    r = float(167)
    l = float(3)
    m = float(2)
    M = A_val[:,58][-1]
    a = (A_val[:,37]/A_val[:,58])[-1]
    ar_a = loadHDF5Series(simdirs+"mp_psi4.h5" , "l2_m2_r100.00")
    t_a = ar_a[:,0]
    psi_a = ar_a[:,1]
    impsi_a = ar_a[:,2]
    t_b = t_a
    psi_b = np.zeros(len(psi_a))
    impsi_b = np.zeros(len(impsi_a))
    print (a,M)
    
    
    A = 1-(2*M/r)
    a_1 = r
    a_2 = ((l-1)*(l+2))/(2*r)
    a_3 = ((l-1)*(l+2)*(l**2 + l -4))/(8*r*r)
    B = ((0+a*2j)/((l+1)**2))*((((l+3)*(l-1)*(l+m+1)*(l-m+1))/((2*l+1)*(2*l+3)))**(1/2))
    b_1 = r
    b_2 = l*(l+3)
    C = ((0+a*2j)/((l)**2))*((((l+2)*(l-2)*(l+m)*(l-m))/((2*l-1)*(2*l+1)))**(1/2))
    c_1 = r
    c_2 = (l-2)*(l+1)
    
  
    ans = A*(a_1*psi[2:] - a_2*s_in[1:] + a_3*d_in) + B*(b_1*np.gradient(psi_a, t_a)[2:] - b_2*psi_a[2:]) - C*(c_1*np.gradient(psi_b, t_b)[2:] - c_2*psi_b[2:])
    imans = A*(a_1*impsi[2:] - a_2*ims_in[1:] + a_3*imd_in) + B*(b_1*np.gradient(impsi_a, t_a)[2:] - b_2*impsi_a[2:]) - C*(c_1*np.gradient(impsi_b, t_b)[2:] - c_2*impsi_b[2:])
 
    
    f1 = scipy.integrate.cumtrapz(ans,t[2:])
    f1 = f1-f1[-1]
    imf1 = scipy.integrate.cumtrapz(imans,t[2:])
    imf1 = imf1-imf1[-1]
    
    f2 = scipy.integrate.cumtrapz(f1,t[3:])
    f2 = f2-f2[-1]
    imf2 = scipy.integrate.cumtrapz(imf1,t[3:])
    imf2 = imf2-imf2[-1]
    

    f3_cmp = f2 + imf2*1j
    imf3 = f3_cmp.imag
    f3 = f3_cmp.real



    complex_psi = f3 + 1j*imf3

    
    np.savetxt("./Extrapolated_Strain(Nakano_Kerr)/"+sim+"/"+sim+"_f2.dat" , np.column_stack((t[4:] , complex_psi.real , complex_psi.imag)))   


#### f1 and f2 is/are our gravitational wave/Strain?


# -----------------------------------------------------------------------------
        
#Attempting argparse


def dir_path(string):
    if os.path.isdir(string):
        return string
    else:
        print("Not a directory: %s" %(string))
        # raise NotADirectoryError(string)

parser = argparse.ArgumentParser(description='Choose Extrapolation method')
parser.add_argument("method" , choices=["POWER" , "Nakano"] , help="Extrapolation method to be used here")
#parser.add_argument("radii" , type=int , help="Number of radii to be used")
parser.add_argument("path" , type=dir_path , help="Simulation to be used here")
args = parser.parse_args()

# if args.method == "POWER":        
#     print("Extrapolating with POWER method...") 
#     print("sys.argv: %" % (sys.argv))
#     print("args: %" % (args))
#     #POWER(sys.argv, args)
if args.method == "POWER":
        print("Extrapolating with POWER method...")
        POWER(sys.argv, args)
elif args.method == "Nakano":
        print("Extrapolating with Nakano method...")
        eq_29(sys.argv, args)