diff --git a/code_new/Sumit/RD_Fits.ipynb b/code_new/Sumit/RD_Fits.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..407ce05aac23cd5128ffb20fbd98bd22977f5b60 --- /dev/null +++ b/code_new/Sumit/RD_Fits.ipynb @@ -0,0 +1,644 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Generate ringdown templates in the time and perform parameter estimation on them.\\n'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"Generate ringdown templates in the time and perform parameter estimation on them.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#Import relevant modules, import data and all that\n", + "import time\n", + "import numpy as np\n", + "import corner\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.ticker import MaxNLocator\n", + "from matplotlib import rc\n", + "from configparser import ConfigParser\n", + "plt.rcParams.update({'font.size': 16.5})\n", + "\n", + "from multiprocessing import Pool\n", + "import random\n", + "import dynesty\n", + "from dynesty import plotting as dyplot\n", + "from dynesty.utils import resample_equal\n", + "from dynesty import utils as dyfunc\n", + "import os\n", + "import argparse\n", + "import scipy.optimize as optimization\n", + "from scipy.optimize import minimize\n", + "import rdown as rd\n", + "import rdown_pe as rd_pe\n", + "import rdown_utilities as rd_ut\n", + "import read_data as rdata" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "## Loading and running data tested with NR data\n", + "## Loading and running data tested with Mock data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "usage: ipykernel_launcher.py [-h] [-c CONFIG_FILE]\n", + "ipykernel_launcher.py: error: unrecognized arguments: -f /work/francisco.jimenez/.local/share/jupyter/runtime/kernel-7c7f06cb-c2ec-454a-a11d-8ee0abbcb453.json\n" + ] + } + ], + "source": [ + "# Cell that calls the arguments from your 'config.ini' file. \n", + "try:\n", + " parser = argparse.ArgumentParser(description=\"Simple argument parser\")\n", + " parser.add_argument(\"-c\", action=\"store\", dest=\"config_file\")\n", + " result = parser.parse_args()\n", + " config_file=result.config_file\n", + " parser = ConfigParser()\n", + " parser.read(config_file)\n", + " parser.sections()\n", + "except SystemExit: \n", + " parser = ConfigParser()\n", + " parser.read('config_n0_to_0_mock.ini')\n", + " parser.sections()\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Load variables from config file\n", + "(simulation_path_1,simulation_path_2, metadata_file , simulation_number, output_folder,\n", + " export, overwrite, sampler,nr_code, nbcores,tshift,tend,t_align,\n", + " nmax , npoints, model, error_str, fitnoise, l_int, index_mass,index_spin,\n", + "error_type, error_val, af, mf,tau_var_str,nm_mock)=rdata.read_config_file(parser)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model: w-tau\n", + "nmax: 0\n", + "nm_mock: 0\n", + "tshift: 0.0\n", + "error: False\n", + "error value: False\n", + "export: True\n", + "nr code: Mock-data\n" + ] + } + ], + "source": [ + "# Show configuration options\n", + "dim = nmax+1\n", + "ndim = 4*dim\n", + "numbins = 32 #corner plot parameter - how many bins you want\n", + " \n", + "print('model:',model)\n", + "print('nmax:',nmax)\n", + "print('nm_mock:',nm_mock)\n", + "print('tshift:',tshift)\n", + "print('error:', error_str)\n", + "print('error value:',error_type)\n", + "print('export:',export)\n", + "print('nr code:',nr_code)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Create output directories \n", + "if not os.path.exists(output_folder):\n", + " os.mkdir(output_folder)\n", + " print(\"Directory \" , output_folder , \" Created \")\n", + "\n", + "if nr_code == 'Mock-data': \n", + " nm_mock_str = 'rec_with'+parser.get('rd-mock-parameters','nm_mock')+'_'\n", + "else:\n", + " nm_mock_str=''\n", + " \n", + "if error_str:\n", + " output_folder_1=(output_folder+'/'+model+'-nmax'+str(nmax)+'_'+nm_mock_str+str(error_str)+'_'+str(error_type)+'_fitnoise_'+str(fitnoise))\n", + "else:\n", + " output_folder_1=output_folder+'/'+model+'-nmax'+str(nmax)+'_'+nm_mock_str+str(error_str)+'_'+'fitnoise_'+str(fitnoise)\n", + "\n", + "if not os.path.exists(output_folder_1):\n", + " os.mkdir(output_folder_1)\n", + " print(\"Directory \" , output_folder_1 , \" Created \")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Define output files \n", + "pars = [simulation_number,model,nmax,tshift,npoints]\n", + "corner_plot = rdata.create_output_files(output_folder_1,pars,'corner_plot')\n", + "corner_plot_extra = rdata.create_output_files(output_folder_1,pars,'corner_plot_extra')\n", + "diagnosis_plot = rdata.create_output_files(output_folder_1,pars,'diagnosis')\n", + "fit_plot = rdata.create_output_files(output_folder_1,pars,'fit')\n", + "samples_file = rdata.create_output_files(output_folder_1,pars,'post_samples')\n", + "results_file = rdata.create_output_files(output_folder_1,pars,'sampler_results')\n", + "sumary_data = rdata.create_output_files(output_folder_1,pars,'log_z')\n", + "best_data = rdata.create_output_files(output_folder_1,pars,'best_vals')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/work/francisco.jimenez/venv/lib/python3.7/site-packages/numpy/core/_asarray.py:85: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return array(a, dtype, copy=False, order=order)\n", + "/work/francisco.jimenez/venv/lib/python3.7/site-packages/numpy/core/_asarray.py:85: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return array(a, dtype, copy=False, order=order)\n", + "/work/francisco.jimenez/venv/lib/python3.7/site-packages/numpy/core/_asarray.py:85: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return array(a, dtype, copy=False, order=order)\n", + "/work/francisco.jimenez/venv/lib/python3.7/site-packages/numpy/core/_asarray.py:85: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return array(a, dtype, copy=False, order=order)\n", + "/work/francisco.jimenez/venv/lib/python3.7/site-packages/ipykernel_launcher.py:22: RuntimeWarning: divide by zero encountered in double_scalars\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "error estimate: 0.0\n", + "mismatch: 0.0\n", + "snr: inf\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 864x576 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Load NR data, align in time and resize. Plot real part and amplitude. Finally compute the mismatch and the snr estimate\n", + "data = rdata.read_data(nr_code,simulation_path_1,RD=True,tshift=tshift,tend = tend,metadata_file=metadata_file,parser=parser)\n", + "data_l = rdata.read_data(nr_code,simulation_path_2,RD=True,tshift=tshift,tend = tend,metadata_file=metadata_file,parser=parser)\n", + "data_r, data_lr = rdata.nr_resize(data,data_l,tshift=tshift,tend=tend)\n", + "times_rd = data_r[:,0]\n", + "\n", + "plt.figure(figsize = (12, 8))\n", + "plt.plot(times_rd, data_r[:,1].real, \"r\", alpha=0.3, lw=3, label=r'$Lev6$: real')\n", + "plt.plot(times_rd, np.sqrt((data_r[:,1].real)**2+(data_r[:,1].imag)**2), \"r\", alpha=0.3, lw=3, label=r'$Lev5\\,amp$')\n", + "plt.plot(times_rd, data_lr[:,1].real, \"b\", alpha=0.3, lw=3, label=r'$Lev5: real$')\n", + "plt.plot(times_rd, np.sqrt((data_lr[:,1].real)**2+(data_lr[:,1].imag)**2), \"b\", alpha=0.3, lw=3, label=r'$Lev5\\,amp$')\n", + "if error_str and error_val==0:\n", + " error = np.sqrt(data_r[:,1]*data_r[:,1]-2*data_r[:,1]*data_lr[:,1]+data_lr[:,1]*data_lr[:,1])\n", + " error_est=np.sqrt(error.imag**2+error.real**2)\n", + " plt.plot(times_rd, error_est, \"g\", alpha=0.3, lw=2, label='error')\n", + "plt.legend()\n", + "\n", + "mismatch=1-rd_ut.EasyMatchT(times_rd,data_r[:,1],data_lr[:,1],tshift,tend)\n", + "error=np.sqrt(2*mismatch)\n", + "print('error estimate:',error)\n", + "print('mismatch:', mismatch)\n", + "print('snr:', rd_ut.EasySNRT(times_rd,data_r[:,1],data_lr[:,1],tshift,tend)/error**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "error estimate: 0.0\n", + "mismatch: 0.0\n", + "snr: inf\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/work/francisco.jimenez/venv/lib/python3.7/site-packages/ipykernel_launcher.py:16: RuntimeWarning: divide by zero encountered in double_scalars\n", + " app.launch_new_instance()\n" + ] + } + ], + "source": [ + "# Phase alignement\n", + "if parser.has_option('rd-model','phase_alignment'):\n", + " phase_alignment=eval(parser.get('rd-model','phase_alignment')) \n", + "else:\n", + " phase_alignment=False\n", + " \n", + "if phase_alignment:\n", + " datar_al = rdata.phase_align(data_r,data_lr)\n", + " gwdatanew5 = data_lr[:,1]\n", + " gwdatanew = datar_al[:,1] \n", + " timesrd_final = datar_al[:,0]\n", + " mismatch=1-rd_ut.EasyMatchT(timesrd_final,gwdatanew,gwdatanew5,tshift,tend)\n", + " error=np.sqrt(2*mismatch)\n", + " print('error estimate:',error)\n", + " print('mismatch:', mismatch)\n", + " print('snr:', rd_ut.EasySNRT(timesrd_final,gwdatanew,gwdatanew5,tshift,tend)/error)\n", + " if error_str:\n", + " error = np.sqrt(gwdatanew*gwdatanew-2*gwdatanew*gwdatanew5+gwdatanew5*gwdatanew5)\n", + " error_est=np.sqrt(error.imag**2+error.real**2)\n", + " else :\n", + " error = 1 \n", + "else:\n", + " datar_al = data_r\n", + " timesrd_final = datar_al[:,0]\n", + " \n", + "#Test the new interpolated data\n", + "if error_str and error_val==0:\n", + " plt.figure(figsize = (12, 8))\n", + " plt.plot(timesrd_final, datar_al[:,1].real, \"r\", alpha=0.3, lw=2, label='Original')\n", + " plt.plot(timesrd_final, data_lr[:,1].real, \"b\", alpha=0.3, lw=2, label='Aligned')\n", + " plt.plot(timesrd_final, error_est, \"b\", alpha=0.3, lw=2, label='error')\n", + " plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Define your noise depending on the noise configuration. Load priors and setup the likelihood with rd_pe.Ringdown_PE. \n", + "if error_str and error_val==0:\n", + " error_final = error\n", + " norm_factor = 100*len(error_final)/2*np.log(2*np.pi)\n", + "elif error_str and error_val!=0:\n", + " datar_al[:,1]+=random.uniform(0, error_val)\n", + " datar_al[:,1]+=1j*random.uniform(0, error_val)\n", + " error_tsh = error_val\n", + " error_final=(error_tsh.real**2+error_tsh.imag**2)\n", + " norm_factor = 0\n", + "else:\n", + " error_tsh=1\n", + " error_final=(error_tsh.real**2+error_tsh.imag**2)\n", + " norm_factor = 0\n", + "\n", + "priors = rd_pe.load_priors(model,parser,nmax,fitnoise=fitnoise)\n", + "rdown=rd.Ringdown_Spectrum(mf,af,2,2,n=nmax,s=-2,time=timesrd_final)\n", + "rdown_pe = rd_pe.Ringdown_PE(rdown,datar_al,dim,priors,errors2=error_final,norm_factor=norm_factor,model=model)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "best fit pars from fit: [ 0.5534 11.70339832 0.97438313 0. ]\n" + ] + } + ], + "source": [ + "# Get a first estimate by trying to fit the data.\n", + "nll = lambda *args: -rdown_pe.log_likelihood(*args)\n", + "if model == 'w-tau-fixed-m-af':\n", + " if fitnoise:\n", + " initial = np.concatenate((np.ones(2*dim),[0.8,0.9,1]))\n", + " soln = minimize(nll, initial,bounds=priors)\n", + " vars_ml=soln.x\n", + " else:\n", + " initial = np.concatenate((np.ones(2*dim),[0.8,0.9]))\n", + " soln = minimize(nll, initial,bounds=priors)\n", + " vars_ml=soln.x\n", + "elif model == 'w-tau-fixed':\n", + " if fitnoise:\n", + " initial = np.concatenate((np.ones(2*dim),[0.2]))\n", + " soln = minimize(nll, initial,bounds=priors)\n", + " vars_ml=soln.x\n", + " else:\n", + " initial = np.ones(2*dim)\n", + " soln = minimize(nll, initial,bounds=priors)\n", + " vars_ml=soln.x\n", + "else:\n", + " if fitnoise:\n", + " initial = np.concatenate((np.ones(ndim),[1]))\n", + " soln = minimize(nll, initial,bounds=priors)\n", + " vars_ml=soln.x\n", + " else: \n", + " initial = np.ones(ndim)\n", + " soln = minimize(nll, initial,bounds=priors)\n", + " vars_ml=soln.x\n", + "print(\"best fit pars from fit: \",vars_ml)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "12343it [06:23, 32.16it/s, +1000 | bound: 59 | nc: 1 | ncall: 255037 | eff(%): 5.232 | loglstar: -inf < -0.002 < inf | logz: -7.730 +/- 0.109 | dlogz: 0.000 > 0.010]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "295.620285035\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "mypool = Pool(nbcores)\n", + "mypool.size = nbcores\n", + "\n", + "start = time.process_time()\n", + "f2=dynesty.NestedSampler(rdown_pe.log_likelihood,rdown_pe.prior_transform, len(priors), nlive=npoints,sample=sampler,pool=mypool)\n", + "if parser.has_option('setup','dlogz'):\n", + " dlogz=np.float(parser.get('setup','dlogz')) \n", + " f2.run_nested(dlogz=dlogz,print_progress=False)\n", + "else:\n", + " f2.run_nested(print_progress=False)\n", + "\n", + "print(time.process_time() - start)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary\n", + "=======\n", + "nlive: 1000\n", + "niter: 12311\n", + "ncall: 254848\n", + "eff(%): 5.223\n", + "logz: -7.703 +/- 0.109\n" + ] + } + ], + "source": [ + "res = f2.results\n", + "res.samples_u.shape\n", + "res.summary()\n", + "samps=f2.results.samples\n", + "postsamps = rd_ut.posterior_samples(f2)\n", + "samps_tr=np.transpose(samps)\n", + "half_points=int(round((len(samps_tr[0])/1.25)))\n", + "evidence = res.logz[-1]\n", + "evidence_error = res.logzerr[-1]\n", + "if export:\n", + " save_object(res, results_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [], + "source": [ + "pars = nmax,model,samps_tr, half_points\n", + "npamps = rd_ut.get_best_amps(pars,parser=parser,nr_code=nr_code)" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [], + "source": [ + "if export:\n", + " pars = simulation_number, nmax, tshift, evidence, evidence_error\n", + " rd_ut.export_logz_files(sumary_data,pars)" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "labels = rd_ut.define_labels(dim,model,fitnoise)\n", + "if export:\n", + " pars = tshift, len(priors), labels \n", + " rd_ut.export_bestvals_files(best_data,postsamps,pars)" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [], + "source": [ + "w, tau = rdown.QNM_spectrum()\n", + "pars = w, tau, mf, af, npamps \n", + "truths = rd_ut.get_truths(model,pars,fitnoise)" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 698.4x698.4 with 16 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fg=corner.corner(postsamps,quantiles=[0.05,0.5,0.95],show_titles=True,max_n_ticks = 4,bins=50,truths=truths,labels=labels,truth_color='red')\n", + "plt.show()\n", + "if export:\n", + " fg.savefig(corner_plot, format = 'png', bbox_inches = 'tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [], + "source": [ + "from importlib import reload\n", + "reload(rd_ut)\n", + "if model == 'w-tau-fixed-m-af' and export == True: \n", + " truths=np.concatenate((w,tau))\n", + " labels_mf = np.concatenate((w_lab,tau_lab))\n", + " new_samples = rd_ut.convert_m_af_2_w_tau_post(res,fitnoise=False)\n", + " figure = corner.corner(new_samples,truths=truths,quantiles=[0.05,0.95],labels=labels_mf,smooth=True,color='b',truth_color='r',show_titles=True)\n", + " figure.savefig(corner_plot_extra, format = 'png', bbox_inches = 'tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 1152x1152 with 4 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#lnz_truth = ndim * -np.log(2 * 10.) # analytic evidence solution\n", + "fig, axes = dyplot.runplot(res)\n", + "fig.tight_layout()\n", + "if export:\n", + " fig.savefig(diagnosis_plot, format = 'png', dpi = 384, bbox_inches = 'tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 864x648 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "if export:\n", + " dict = {'w-tau':rdown.rd_model_wtau , 'w-q': rdown.rd_model_wq, 'w-tau-fixed':rdown.rd_model_wtau_fixed,'w-tau-fixed-m-af': rdown.rd_model_wtau_m_af}\n", + " figband = plt.figure(figsize = (12, 9))\n", + " plt.plot(datar_al[:,0].real,datar_al[:,1].real, \"green\", alpha=0.9, lw=3, label=r'$res_{240}$')\n", + " onesig_bounds = np.array([np.percentile(postsamps[:, i], [5, 95]) for i in range(len(postsamps[0]))]).T\n", + " samples_1sigma = filter(lambda sample: np.all(onesig_bounds[0] <= sample) and np.all(sample <= onesig_bounds[1]), postsamps)\n", + " samples_1sigma_down = list(samples_1sigma)[::downfactor]\n", + " for sample in samples_1sigma_down:\n", + " plt.plot(datar_al[:,0].real, dict[model](sample).real, \"r-\", alpha=0.1, lw=1)\n", + " plt.title(r'Comparison of the MC fit data and the $1-\\sigma$ error band')\n", + " plt.legend()\n", + " plt.xlabel('t')\n", + " plt.ylabel('h')\n", + " plt.show()\n", + " figband.savefig(fit_plot)" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [], + "source": [ + "if export:\n", + " with open(samples_file,'w') as file:\n", + " writer = csv.writer(file)\n", + " writer.writerow(labels)\n", + " writer.writerows(samps[::downfactor])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/code_new/Sumit/rdown_utilities.py b/code_new/Sumit/rdown_utilities.py new file mode 100644 index 0000000000000000000000000000000000000000..e8844f809127fe94e90d1de05c9f78d8e507b9f3 --- /dev/null +++ b/code_new/Sumit/rdown_utilities.py @@ -0,0 +1,306 @@ +# Copyright (C) 2021 Xisco Jimenez Forteza +# +# This program is free software; you can redistribute it and/or modify it +# under the terms of the GNU General Public License as published by the +# Free Software Foundation; either version 3 of the License, or (at your +# option) any later version. +# +# This program is distributed in the hope that it will be useful, but +# WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General +# Public License for more details. +# +# You should have received a copy of the GNU General Public License along +# with this program; if not, write to the Free Software Foundation, Inc., +# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. + + +# +# ============================================================================= +# +# Preamble +# +# ============================================================================= +# +# Module to run PE on RD data +import numpy as np +from dynesty.utils import resample_equal +from dynesty import utils as dyfunc +import os +import csv +import pandas as pd + +def posterior_samples(sampler): + """ + Returns posterior samples from nested samples and weights + given by dynsety sampler + """ + + dynesty_samples = sampler.results['samples'] + wt = np.exp(sampler.results['logwt'] - + sampler.results['logz'][-1]) + # Make sure that sum of weights equal to 1 + weights = wt/np.sum(wt) + posterior_dynesty = dyfunc.resample_equal(dynesty_samples, weights) + return posterior_dynesty + +def FFT_FreqBins(times): + Len = len(times) + DeltaT = times[-1]- times[0] + dt = DeltaT/(Len-1) + dnu = 1/(Len*dt) + maxfreq = 1/(2*dt) + add = dnu/4 + + p = np.arange(0.0,maxfreq+add,dnu) + m = np.arange(p[-1]-(2*maxfreq)+dnu,-dnu/2+add,dnu) + res=np.concatenate((p,m)) + + return res + +def hFromPsi4FFI(tpsi4,f0): + + timecheck1=tpsi4[-2,0]-tpsi4[-1,0] + timecheck2=tpsi4[1,0]-tpsi4[0,0] + + if np.abs(timecheck1-timecheck2)>=0.0001: + print("The data might not be equally sampled!!") + + times,data= tpsi4[:,0],tpsi4[:,1] + + freqs = FT_FreqBins(xaxis.real).real + position = np.argmax(freqs >= f0) + freqs[:position]=f0*np.ones(len(freqs[:position])) + freqs=2*np.pi*freqs + + fdata=fft(data) + len(myTable)*ifft(- fdata/floor**2); + np.stack((times,data)).T + + +def twopoint_autocovariance(t,n): + """ It computes the two-point autocovariance function. + """ + dt=t[1]-t[0] + res = np.zeros(len(n)) + taus = np.zeros(len(n)) + for tau in range(0,int(len(n)/2)): + ntau=np.roll(n, tau) + taus[tau] = t[tau] + res[tau]=np.sum(n*ntau).real + return (taus[:int(len(n)/2)],res[:int(len(n)/2)]) + +def save_object(obj, filename): + with open(filename, 'wb') as output: # Overwrites any existing file. + pickle.dump(obj, output, pickle.HIGHEST_PROTOCOL) + + +def EasyMatchT(t,h1,h2,tmin,tmax): + """ It computes the time-domain match for (h1|h2) complex waveforms. + """ + pos = np.argmax(t >= (tmin)); + + h1red=h1[pos:]; + h2red=h2[pos:]; + + norm1=np.sum(np.abs(h1red)**2) + norm2=np.sum(np.abs(h2red)**2) + + myTable=h1red*np.conjugate(h2red) + res=((np.sum(myTable)/np.sqrt(norm1*norm2))).real + + return res + +def EasySNRT(t,h1,h2,tmin,tmax): + """ It computes the time-domain snr for (h1|h2) complex waveforms. + """ + pos = np.argmax(t >= (tmin)); + + h1red=h1[pos:]; + h2red=h2[pos:]; + + myTable=h1red*np.conjugate(h2red) + res=2*np.sqrt((np.sum(myTable)).real) + + return res + +def FindTmaximum(y): + """ It determines the maximum absolute value of the complex waveform. + """ + absval = np.sqrt(y[:,1]*y[:,1]+y[:,2]*y[:,2]) + vmax=np.max(absval) + index = np.argmax(absval == vmax) + timemax=y[index,0] + + return timemax + +def export_logz_files(output_file,pars): + sim_num, nmax, tshift, evidence, evidence_error = pars + + """ + Generate the logz.csv files you want to export the data to. + file_type must be one of this options: [corner_plot,corner_plot_extra,diagnosis,fit,post_samples,sampler_results,log_z] + """ + + summary_titles=['n','id','t_shift','dlogz','dlogz_err'] + if os.path.exists(output_file): + outvalues = np.array([[nmax, sim_num, tshift, evidence,evidence_error]]) + else: + outvalues = np.array([summary_titles,[nmax, sim_num, tshift, evidence,evidence_error]]) + + with open(output_file, 'a') as file: + writer = csv.writer(file) + if (outvalues.shape)[0]>1 : + writer.writerows(outvalues) + else: + writer.writerow(outvalues[0]) + + return + +def export_bestvals_files(best_data_file,postsamps,pars): + + tshift, lenpriors, labels = pars + + sigma_vars_m = np.empty(lenpriors) + sigma_vars_p = np.empty(lenpriors) + sigma_vars = np.empty(lenpriors) + sigma_vars_ml = np.empty(lenpriors) + for i in range(lenpriors): + amps_aux = postsamps[:,i] + sigma_vars_m[i] = np.quantile(amps_aux, 0.05) + sigma_vars[i] = np.quantile(amps_aux, 0.5) + sigma_vars_ml[i] = postsamps[-1,i] + sigma_vars_p[i] = np.quantile(amps_aux, 0.95) + + sigma_vars_all = [sigma_vars,sigma_vars_ml,sigma_vars_m,sigma_vars_p] + sigma_vars_all=np.stack([sigma_vars,sigma_vars_ml,sigma_vars_m,sigma_vars_p], axis=0) + + key =['max val','max val ml','lower bound','higher bound'] + dfslist = [pd.DataFrame(np.concatenate(([tshift],sigma_vars_all[i])).reshape((-1,lenpriors+1)), columns=np.concatenate((['tshift'],labels)), index = [key[i]]) for i in range(4)] + df2 = pd.concat(dfslist) + if os.path.exists(best_data_file): + df2.to_csv(best_data_file, mode='a', header=False,index = True) + else: + df2.to_csv(best_data_file, index = True) + + + +def define_labels(dim,model,fitnoise): + wstr = r'$\omega_' + + if model == 'w-tau': + taustr = r'$\tau_' + elif model == 'w-q': + taustr = r'$q_' + elif model == 'w-tau-fixed': + taustr = r'$dumb_var}' + elif model == 'w-tau-fixed-m-af': + taustr = r'$\tau_' + + ampstr = r'$A_' + phasestr = r'$\phi_' + + w_lab = [None] * dim + tau_lab = [None] * dim + amp_lab = [None] * dim + pha_lab = [None] * dim + mass_lab = ['mass'] + spin_lab = ['spin'] + + for i in range(dim): + w_lab[i] = wstr+str(i)+'$' + tau_lab[i] = taustr+str(i)+'$' + amp_lab[i] = ampstr+str(i)+'$' + pha_lab[i] = phasestr+str(i)+'$' + + + labels = np.concatenate((w_lab,tau_lab,amp_lab,pha_lab)) + + if model=='w-tau-fixed': + labels = np.concatenate((amp_lab,pha_lab)) + + if model=='w-tau-fixed-m-af': + pha_lab[i] = phasestr+str(i)+'$' + + labels = np.concatenate((amp_lab,pha_lab,mass_lab,spin_lab)) + + if fitnoise: + noise_lab = ['noise'] + labels = np.concatenate((labels,noise_lab)) + + return labels + +def get_truths(model,pars,fitnoise): + w, tau, mf, af , npamps = pars + if model == 'w-q': + tau_val = np.pi*w*tau + truths = np.concatenate((w,tau_val,npamps)) + elif model == 'w-tau': + tau_val = tau + truths = np.concatenate((w,tau_val,npamps)) + elif model == 'w-tau-fixed': + truths = npamps + elif model == 'w-tau-fixed-m-af': + truths = np.concatenate((npamps,[mf],[af])) + + if fitnoise: + truths = np.concatenate((truths,[1])) + + return truths + +def get_best_amps(pars,parser=None,nr_code=None): + nmax,model,samps_tr,half_points = pars + + + if model=='w-tau-fixed': + rg = (nmax+1) + elif model=='w-tau-fixed': + rg = (nmax+1)+2 + else: + rg = (nmax+1)*2 + + + if model=='w-tau-fixed-a-mf': + npamps = np.empty((nmax+1)) + for i in range(0,(nmax+1)): + amps_aux = samps_tr[i+rg][half_points:-1] + npamps[i] = np.quantile(amps_aux, 0.5) + else : + npamps = np.empty((nmax+1)*2) + for i in range(0,(nmax+1)*2): + amps_aux = samps_tr[i][half_points:-1] + npamps[i] = np.quantile(amps_aux, 0.5) + + if nr_code == 'Mock-data': + nm_mock = parser.get('rd-mock-parameters','nm_mock') + nm_mock = np.int(nm_mock) + amp_mock=np.empty(nm_mock+1) + ph_mock=np.empty(nm_mock+1) + for i in range(nm_mock+1): + amp_mockp = parser.get('rd-mock-parameters','amp'+str(i)) + amp_mock[i] = np.float(amp_mockp) + ph_mockp=parser.get('rd-mock-parameters','phase'+str(i)) + ph_mock[i] = np.float(ph_mockp) + + npamps = np.concatenate((amp_mock,ph_mock)) + return npamps + +def convert_m_af_2_w_tau_post(res,fitnoise=False): + + samples_2=res.samples + samps=f2.results.samples + + if fitnoise: + fmass_spin=(samps.T)[-3:-1].T + else: + fmass_spin=(samps.T)[-2:].T + #fmass_spin=new_samples[-2:] + fmass_spin_dist=[None]*len(fmass_spin) + weight=np.exp(res.logwt - res.logz[-1]) + for i in range(len(fmass_spin)): + fmass_spin_dist[i]=np.concatenate(dict_omega[qnm_model](fmass_spin[i,0],fmass_spin[i,1],2,2)) + + fmass_spin_dist_v2=np.asarray(fmass_spin_dist) + new_samples = dyfunc.resample_equal(fmass_spin_dist_v2, weight) + + return new_samples \ No newline at end of file diff --git a/code_new/Sumit/read_data.py b/code_new/Sumit/read_data.py new file mode 100644 index 0000000000000000000000000000000000000000..e6e5cad67c1963bacc7c9e4dc972dedfdfd9b4f9 --- /dev/null +++ b/code_new/Sumit/read_data.py @@ -0,0 +1,344 @@ +# Copyright (C) 2021 Xisco Jimenez Forteza +# +# This program is free software; you can redistribute it and/or modify it +# under the terms of the GNU General Public License as published by the +# Free Software Foundation; either version 3 of the License, or (at your +# option) any later version. +# +# This program is distributed in the hope that it will be useful, but +# WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General +# Public License for more details. +# +# You should have received a copy of the GNU General Public License along +# with this program; if not, write to the Free Software Foundation, Inc., +# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. + + +# +# ============================================================================= +# +# Preamble +# +# ============================================================================= +# +# Module to run PE on RD data +import numpy as np +import rdown_utilities as rd_ut +import romspline +import rdown as rd +import h5py +import json +from scipy import interpolate +from scipy.interpolate import interp1d + + +def read_data(nr_code,sim_path,mf=1,af=0,parser=None,RD=True,tshift=0,tend = 100,metadata_file=None): + if nr_code=='SXS': + gw = {} + gw = h5py.File(sim_path, 'r') + gw_data = gw["Extrapolated_N3.dir"]["Y_l2_m2.dat"] + times = gw_data[:,0] + + metadata = {} + with open(metadata_file) as file: + metadata = json.load(file) + + af = metadata['remnant_dimensionless_spin'][-1] + mf = metadata['remnant_mass'] + + tmax=rd_ut.FindTmaximum(gw_data[round(len(gw_data)/2):]) + times = times - tmax + + if RD: + position = np.argmax(times >= 0) + gw_data = gw_data[:,1][position:]+1j*gw_data[:,2][position:] + times = times[times >= 0] + + elif nr_code=='Maya': + dt=0.1 + gw = {} + gw = h5py.File(sim_path, 'r') + gw_sxs_bbh_0305_amp = np.asarray(gw['amp_l2_m2/Y'])[6:] + times_1 = np.asarray(gw['amp_l2_m2/X'])[6:] + gw_sxs_bbh_0305_amp_int = romspline.ReducedOrderSpline(times_1, gw_sxs_bbh_0305_amp) + gw_sxs_bbh_0305_pha = np.asarray(gw['phase_l2_m2/Y'])[6:] + times = np.asarray(gw['phase_l2_m2/X'])[6:] + gw_sxs_bbh_0305_pha_int = romspline.ReducedOrderSpline(times, gw_sxs_bbh_0305_pha) + + tmin=max(times_1[0],times[0]) + tmax=min(times_1[-1],times[-1]) + times=np.arange(tmin,tmax,dt) + amps=gw_sxs_bbh_0305_amp_int(times) + phs=gw_sxs_bbh_0305_pha_int(times) + gw_sxs_bbh_0305 = np.asarray([times,amps*np.cos(phs),amps*np.sin(phs)]).T + gw5_sxs_bbh_0305 = gw_sxs_bbh_0305 + + times = gw_sxs_bbh_0305[:,0] + tmax=rd_ut.FindTmaximum(gw_sxs_bbh_0305[round(len(gw_sxs_bbh_0305)/2):]) + times = times - tmax + + #times 6--> x axis of your data + times5 = gw5_sxs_bbh_0305[:,0] + tmax5=rd_ut.FindTmaximum(gw5_sxs_bbh_0305[round(len(gw_sxs_bbh_0305)/2):]) + times5 = times5 - tmax5 + #Select the data from 0 onwards + position = np.argmax( times >= (t_align)) + position5 = np.argmax(times5 >= (t_align)) + gw_sxs_bbh_0305rd=gw_sxs_bbh_0305[position+1:] + gw_sxs_bbh_0305rd5=gw5_sxs_bbh_0305[position5+1:] + timesrd=gw_sxs_bbh_0305[position:-1][:,0][:] + timesrd5=gw5_sxs_bbh_0305[position5:-1][:,0][:] + + elif nr_code=='LaZeV': + dt=0.1 + gw = {} + gw = h5py.File(simulation_path_1, 'r') + gw_sxs_bbh_0305_amp = np.asarray(gw['amp_l2_m2/Y'])[6:] + times_1 = np.asarray(gw['amp_l2_m2/X'])[6:] + gw_sxs_bbh_0305_amp_int = romspline.ReducedOrderSpline(times_1, gw_sxs_bbh_0305_amp) + gw_sxs_bbh_0305_pha = np.asarray(gw['phase_l2_m2/Y'])[6:] + times = np.asarray(gw['phase_l2_m2/X'])[6:] + gw_sxs_bbh_0305_pha_int = romspline.ReducedOrderSpline(times, gw_sxs_bbh_0305_pha) + + tmin=max(times_1[0],times[0]) + tmax=min(times_1[-1],times[-1]) + times=np.arange(tmin,tmax,dt) + amps=gw_sxs_bbh_0305_amp_int(times) + phs=gw_sxs_bbh_0305_pha_int(times) + gw_sxs_bbh_0305 = np.asarray([times,amps*np.cos(phs),amps*np.sin(phs)]).T + gw5_sxs_bbh_0305 = gw_sxs_bbh_0305 + + times = gw_sxs_bbh_0305[:,0] + tmax=rd_ut.FindTmaximum(gw_sxs_bbh_0305[round(len(gw_sxs_bbh_0305)/2):]) + times = times - tmax + + #times 6--> x axis of your data + times5 = gw5_sxs_bbh_0305[:,0] + tmax5=rd_ut.FindTmaximum(gw5_sxs_bbh_0305[round(len(gw_sxs_bbh_0305)/2):]) + times5 = times5 - tmax5 + + #Select the data from 0 onwards + position = np.argmax( times >= (t_align)) + position5 = np.argmax(times5 >= (t_align)) + gw_sxs_bbh_0305rd=gw_sxs_bbh_0305[position+1:] + gw_sxs_bbh_0305rd5=gw5_sxs_bbh_0305[position5+1:] + timesrd=gw_sxs_bbh_0305[position:-1][:,0][:] + timesrd5=gw5_sxs_bbh_0305[position5:-1][:,0][:] + + elif nr_code=='Mock-data': + times = np.arange(tshift,tend+10,0.1) + + nm_mock = parser.get('rd-mock-parameters','nm_mock') + nm_mock = np.int(nm_mock) + mf = parser.get('rd-mock-parameters','mf') + mf = np.float(mf) + af = np.float(parser.get('rd-mock-parameters','af')) + af = np.float(af) + + rdown=rd.Ringdown_Spectrum(mf,af,2,2,n=nm_mock,s=-2,time=times) + + w_mock=np.empty(nm_mock+1) + tau_mock=np.empty(nm_mock+1) + amp_mock=np.empty(nm_mock+1) + ph_mock=np.empty(nm_mock+1) + + for i in range(nm_mock+1): + wp_mock = parser.get('rd-mock-parameters','w'+str(i)) + w_mock[i] = np.float(wp_mock) + tp_mock=parser.get('rd-mock-parameters','tau'+str(i)) + tau_mock[i] = np.float(tp_mock) + amp_mockp = parser.get('rd-mock-parameters','amp'+str(i)) + amp_mock[i] = np.float(amp_mockp) + ph_mockp=parser.get('rd-mock-parameters','phase'+str(i)) + ph_mock[i] = np.float(ph_mockp) + + + pars = np.concatenate((w_mock,tau_mock,amp_mock,ph_mock)) + gw_data=rdown.rd_model_wtau(pars) + + return np.stack((times,gw_data)).T + +def nr_resize(data_1,data_2,tshift=0,tend=100): + times_1 = data_1[:,0].real + times_2 = data_2[:,0].real + data_1_re = data_1[:,1].real + data_1_im = data_1[:,1].imag + data_2_re = data_2[:,1].real + data_2_im = data_2[:,1].imag + + gwnew_re = interpolate.interp1d(times_1, data_1_re, kind = 'cubic') + gwnew_im = interpolate.interp1d(times_1, data_1_im, kind = 'cubic') + gwnew_re5 = interpolate.interp1d(times_2, data_2_re, kind = 'cubic') + gwnew_im5 = interpolate.interp1d(times_2, data_2_im, kind = 'cubic') + + if times_2[-1]>= times_1[-1]: + times_rd = times_1 + else: + times_rd = times_2 + + gwdatanew_re = gwnew_re(times_rd) + gwdatanew_im = gwnew_im(times_rd) + gwdatanew_re5 = gwnew_re5(times_rd) + gwdatanew_im5 = gwnew_im5(times_rd) + gwdatanew = gwdatanew_re + 1j*gwdatanew_im + gwdatanew5 = gwdatanew_re5 + 1j*gwdatanew_im5 + + position_in = np.argmax(times_rd >= tshift) + position_end = np.argmax(times_rd >= tend) + times_rd = times_rd[position_in:position_end] + gwdatanew = gwdatanew[position_in:position_end] + gwdatanew5 = gwdatanew5[position_in:position_end] + + return(np.stack((times_rd,gwdatanew)).T,np.stack((times_rd,gwdatanew5)).T) + + +def phase_align(data_1,data_2,t_align=0): + + #timesrd_final = data_1[:,0] + #phas = np.angle(data_1[:,1]) + #phas = np.unwrap(phas) + #phas5 = np.angle(data_2[:,1]) + #phas5 = np.unwrap(phas5) + #position = np.argmax(timesrd_final >= (t_align)) + #dphase = phas5[position]-phas[position] + + #gwdatanew = data_1[:,1]*np.exp(1j*dphase) + + + phas = np.angle(data_1[:,1]) + phas = np.unwrap(phas) + phas5 = np.angle(data_2[:,1]) + phas5 = np.unwrap(phas5) + position = np.argmax(data_1[:,0] >= (0)) + dphase = phas5[position]-phas[position] + gwdatanew = data_1[:,1]*np.exp(1j*dphase) + timesrd_final = data_1[:,0] + + return np.stack((timesrd_final,gwdatanew)).T + +def create_output_files(output_folder,pars,file_type): + sim_num, model, nmax, tshift, npoints = pars + + """ + Generate the output files you want to export the data to. + file_type must be one of this options: [corner_plot,corner_plot_extra,diagnosis,fit,post_samples,sampler_results,log_z] + """ + + if file_type=='corner_plot': + outfile = output_folder+'/Dynesty_'+str(sim_num)+'_'+model+'_nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'corner_plot.png' + elif file_type=='corner_plot_extra': + outfile = output_folder+'/Dynesty_'+str(sim_num)+'_'+model+'_nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'corner_plot_extra.png' + elif file_type=='diagnosis': + outfile = output_folder+'/Dynesty_diagnosis'+str(sim_num)+'_'+model+'_nmax='+str(nmax)+'_tshift='+str(tshift)+'_'+str(npoints)+'.png' + elif file_type=='fit': + outfile = output_folder+'/Fit_results_'+str(sim_num)+'tshift_'+str(tshift)+'_'+model+'_nmax_'+str(nmax)+'.png' + elif file_type=='post_samples': + outfile = output_folder+'/posterior_samples-'+str(sim_num)+'tshift_'+str(tshift)+'_'+model+'_nmax_'+str(nmax)+'.csv' + elif file_type=='sampler_results': + outfile = output_folder+'/results_'+str(sim_num)+'tshift_'+str(tshift)+'_'+model+'_nmax_'+str(nmax)+'.pkl' + elif file_type=='log_z': + outfile = output_folder+'/summary'+str(sim_num)+'_'+model+'_nmax_'+str(nmax)+'.csv' + elif file_type=='best_vals': + outfile = output_folder+'/best_values_'+str(sim_num)+'_'+model+'_nmax_'+str(nmax)+'.csv' + else: + print ('Something went wrong') + return + + return outfile + +def read_config_file(parser): + # Setup path and output folders + rootpath=parser.get('nr-paths','rootpath') + simulation_path_1 = parser.get('nr-paths','simulation_path_1') + + if parser.get('nr-paths','simulation_path_2'): + simulation_path_2 = parser.get('nr-paths','simulation_path_2') + else: + simulation_path_2 = simulation_path_1 + + metadata_file = parser.get('nr-paths','metadata_json') + simulation_number = parser.get('nr-paths','simulation_number') + simulation_number = np.int(simulation_number) + + output_folder = parser.get('output-folder','output-folder') + if parser.has_option('setup','export'): + export=eval(parser.get('setup','export')) + else: + export=True + + # Setup sampler and output options + overwrite = eval(parser.get('setup','overwrite')) + downfactor = np.int(parser.get('setup','plot_down_factor')) + sampler = parser.get('setup','sampler') + nr_code = parser.get('setup','nr_code') + if parser.has_option('setup','nb_cores'): + nbcores = np.int(parser.get('setup','nb_cores')) + else: + nbcores = 1 + + # time shift , end and align options + tshift=parser.get('time-setup','tshift') + tshift = np.float(tshift) + + tend=parser.get('time-setup','tend') + tend = np.float(tend) + + t_align=parser.get('time-setup','t_align') + t_align = np.float(t_align) + + # n-tones & nlive points + nmax=parser.get('n-tones','nmax') + nmax = np.int(nmax) + + npoints=parser.get('n-live-points','npoints') + npoints = np.int(npoints) + + # setup the RD model + model=parser.get('rd-model','model') + error_str = eval(parser.get('rd-model','error_str')) + fitnoise=eval(parser.get('rd-model','fit_noise')) + if fitnoise: + l_int=1 + index_mass=-3 + index_spin=-2 + # prior_dim = len(priors_min) + else: + index_mass=-2 + index_spin=-1 + l_int=0 + + if error_str: + error_val=np.float(parser.get('rd-model','error_val')) + if error_val==0: + error_type='' + else: + error_type=error_val + else: + error_type='False' + error_val =0 + + if nr_code == 'SXS': + metadata = {} + with open(metadata_file) as file: + metadata = json.load(file) + af = metadata['remnant_dimensionless_spin'][-1] + mf = metadata['remnant_mass'] + else: + mf = parser.get('rd-mock-parameters','mf') + mf = np.float(mf) + af = np.float(parser.get('rd-mock-parameters','af')) + af = np.float(af) + + if model == 'w-q': + tau_var_str='q' + else: + tau_var_str='tau' + + if nr_code == 'Mock-data': + nm_mock = int(parser.get('rd-mock-parameters','nm_mock')) + else: + nm_mock = None + + res = simulation_path_1,simulation_path_2, metadata_file , simulation_number, output_folder, export, overwrite, sampler,nr_code, nbcores,tshift,tend,t_align, nmax , npoints, model, error_str, fitnoise, l_int, index_mass,index_spin, error_type, error_val, af, mf,tau_var_str,nm_mock + return res \ No newline at end of file