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plot_event.py
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plot_event.py
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"""
Plots the input, label, and prediction of a certain sample in the testing sample
"""
# -----------------------------------------------------------------------------
# IMPORTS
# -----------------------------------------------------------------------------
import argparse
import numpy as np
import matplotlib.pyplot as plt
import time
import os
from utils.configfiles import read_json_config
from utils.data_processing import get_injection_parameters, get_raw_data, get_time_info
# -----------------------------------------------------------------------------
# FUNCTION DEFINITIONS
# -----------------------------------------------------------------------------
def get_arguments() -> argparse.Namespace:
parser = argparse.ArgumentParser(description='Plots the input, label, and prediction of one sample '
'from both detectors')
parser.add_argument('--testing-config',
default='testing.json',
type=str,
help='Name of the JSON file the model used to test, Default: testing.json')
parser.add_argument('--sample-id',
help='ID of the sample to be view (an integer '
'between 0 and n_injection_samples + n_noise_samples),'
'Default: 0',
type=int,
default=0)
arguments = parser.parse_args()
return arguments
if __name__ == '__main__':
# Start stopwatch
script_start = time.time()
# Get command line arguments
args = get_arguments()
# Get JSON config file
config_path = f'config_files/{args.testing_config}'
config = read_json_config(config_path)
# Get input data & labels
hdf_file_name = config['testing_hdf_file_name']
inputs, labels = get_raw_data(hdf_file_name)
# Get injection parameters
injection_parameters = get_injection_parameters(hdf_file_name)
# Get snrs
snrs = injection_parameters['injection_snr']
# Check if sample id is in bounds
sample_id = int(args.sample_id)
if args.sample_id < 0 or args.sample_id >= len(inputs):
raise IndexError('Sample-id is not within bounds')
# Check if the sample has injection
has_injection = sample_id < len(snrs)
# Get predictions
model_name = config['model_name']
preds_name = f'{model_name[:-3]}_predictions.npy'
preds_path = f'outputs/predictions/{preds_name}'
preds = np.load(preds_path)
# Unnormalize predictions
preds = preds * 2 - 1
preds = np.array(list(map(lambda x: x/np.max(np.abs(x)), preds)))
# Get event time
time_info_dict = get_time_info(hdf_file_name, args.sample_id)
seconds_before_event = time_info_dict['seconds_before_event']
seconds_after_event = time_info_dict['seconds_after_event']
target_sampling_rate = time_info_dict['target_sampling_rate']
sample_length = time_info_dict['sample_length']
if has_injection:
time_range = np.arange(-seconds_before_event, seconds_after_event, 1 / target_sampling_rate)
else:
time_range = np.arange(0, sample_length, 1 / target_sampling_rate)
# Create figure
fig, ax1 = plt.subplots(nrows=3, ncols=2)
ax1[0][0].set_ylabel('Whitened Signal Strain', color = 'C0', fontsize= 8)
ax1[1][0].set_ylabel('Raw Signal Strain', color = 'C1', fontsize= 8)
ax1[2][0].set_ylabel('U-Net Prediction Strain', color = 'C2', fontsize= 8)
ax1[0][1].set_yticklabels([])
ax1[1][1].set_yticklabels([])
ax1[2][1].set_yticklabels([])
ax1[0][1].tick_params(left = False)
ax1[1][1].tick_params(left = False)
ax1[2][1].tick_params(left = False)
for idx, detectors in enumerate(['H1', 'L1']):
ax1[0][idx].set_title(detectors)
ax1[0][idx].plot(time_range, inputs[sample_id,:,idx])
ax1[0][idx].set_ylim(-120,120)
ax1[0][idx].tick_params('y', colors='C0', labelsize = 8)
ax1[1][idx].set_ylim(-1.2, 1.2)
if has_injection:
ax1[1][idx].plot(time_range, labels[sample_id,:,idx], color = 'C1')
else:
ax1[1][idx].plot(time_range, np.zeros(int(sample_length * target_sampling_rate)), color = 'C1')
ax1[1][idx].tick_params('y', colors='C1', labelsize=8)
ax1[2][idx].plot(time_range, preds[sample_id,:,idx], color = 'C2')
ax1[2][idx].set_ylim(-1.2, 1.2)
ax1[2][idx].tick_params('y', colors='C2', labelsize=8)
# Focuses the view around the event if sample has an injection
if has_injection:
ax1[0][idx].set_xlim(-0.15, .05)
ax1[1][idx].set_xlim(-0.15, .05)
ax1[2][idx].set_xlim(-0.15, .05)
ax1[0][idx].axvline(x=0, color='black', ls='--', lw=1)
ax1[1][idx].axvline(x=0, color='black', ls='--', lw=1)
ax1[2][idx].axvline(x=0, color='black', ls='--', lw=1)
else:
ax1[0][idx].set_xlim(0, sample_length)
ax1[1][idx].set_xlim(0, sample_length)
ax1[2][idx].set_xlim(0, sample_length)
# Set x-labels
ax1[0][idx].set_xticklabels([])
ax1[1][idx].set_xticklabels([])
ax1[2][idx].set_xlabel('Time from event (sec)')
# Adds subtitle for injection parameters
if has_injection:
keys = 'mass1', 'mass2', 'spin1z', 'spin2z', 'ra', 'dec', 'coa_phase', 'inclination', 'polarization', 'injection_snr'
string = ', '.join(['{} = {:.2f}'.format(_, injection_parameters[_][sample_id]) for _ in keys])
else:
string = '(sample does not contain an injection)'
plt.figtext(0.5, 0.9, f'Injection Parameters:\n{string}', fontsize=8, ha='center')
# Adjust the size and spacing of the subplots
plt.gcf().set_size_inches(12, 6, forward=True)
plt.tight_layout(rect=[0,0,1,0.9])
plt.subplots_adjust(wspace=.05, hspace=0)
plt.suptitle(f'Sample #{sample_id}', y = 0.975)
# Save the plot at the given location
print('Saving plot... ', end='', flush=True)
try:
plt.savefig(f'outputs/figures/injection_{sample_id}.png', bbox_inches='tight', pad_inches=0.3)
except FileNotFoundError:
os.mkdir('outputs/figures')
plt.savefig(f'outputs/figures/injection_{sample_id}.png', bbox_inches='tight', pad_inches=0.3)
print('Done!', flush=True)