def run_sim(dic):
''' This is the high level self-calibration function that actually runs
one self-calibration run (sky generation, survey and self-calibration) and
returns its performance. This function is included to allow simple Python
multiprocessing, by calling this function with different parameter
dictionaries in parallel.
Input
-----
dic : dictionary
A Python dictionary containing all the simulation parameters. See
default_parameters.py for all the required parameters.
Return
------
out : numpy array
A list containing the results of the self-calibration simulations
list[0] = the number of self-calibration iterations required to
converge to the final fitted solution (if 0 returned, then
self-calibration did not converge).
list[1] = the root-mean-squared error between the fitted source fluxes
and the true source fluxes.
list[2] = the "badness" between the fitted instrument response and
the true instrument responses.
list[3] = the "best-in-badness" between the fitted instrument
response and the true instrument responses.
list[4] = the chi2 of the final fitted solution
'''
# Create output directories
if dic['data_dir']:
if dic['data_dir'][-1] == '/':
dic['data_dir'] = dic['data_dir'][0:-1]
if os.path.isdir(dic['data_dir']):
os.system('rm -r {0}'.format(dic['data_dir']))
os.mkdir(dic['data_dir'])
os.mkdir((dic['data_dir'] + '/FF'))
# repeated runs should return consistent results
np.random.seed(dic['seed'])
# Only find best fit to the true flat-field if not already done
if 'best_fit_params' in dic:
if dic['verbose']:
print("Using pre-existing best fit to the true flat-field.")
else:
dic['best_fit_params'] = analysis.best_fit_ff(
dic['FoV'],
dic['ff_samples'],
dic['flat_field_order'],
dic['stop_condition'],
dic['max_iterations'],
verbose=dic['verbose'])
# Only create a sky catalog if one not provided in parameter dictionary
if 'sky_catalog' in dic:
if dic['verbose']:
print("Using pre-existing sky catalog.")
sky_catalog = dic['sky_catalog']
else:
if dic['verbose']:
print("Generating sky catalog...")
sky_catalog = true_functions.SourceCatalog(dic['sky_limits'],
dic['density_of_stars'],
dic['m_min'],
dic['m_max'],
dic['powerlaw_constants'],
dic['analysis_limits'])
if dic['verbose']:
print('...{0} sources generated!'.format(sky_catalog.size))
if dic['data_dir']:
save_out.source_catalog(dic['data_dir'], sky_catalog, dic['verbose'])
save_out.parameters(dic['data_dir'], dic, dic['verbose'])
# Perform sky survey
t_stamp = time.time()
measurement_catalog = survey.survey(dic['survey_file'],
sky_catalog,
dic['FoV'],
dic['eta'],
dic['delta'],
dic['epsilon_max'],
data_dir=dic['data_dir'],
verbose=dic['verbose'])
if dic['data_dir']:
save_out.measurement_catalog(dic['data_dir'], measurement_catalog,
dic['verbose'])
text = 'Measurement Catalog (s): '
save_out.save_time(dic['data_dir'] + '/timings.txt',
text, time.time() - t_stamp)
t_stamp = time.time()
performance = self_calibration.self_calibration(measurement_catalog,
sky_catalog,
dic['flat_field_order'],
dic['FoV'],
dic['ff_samples'],
dic['stop_condition'],
dic['max_iterations'],
dic['best_fit_params'],
data_dir=dic['data_dir'],
verbose=dic['verbose'])
if dic['data_dir']:
save_out.results(dic['data_dir'], performance,
header='Final Results\n=============\n\n',
verbose=dic['verbose'])
text = 'Self-Calibration (s): '
save_out.save_time(dic['data_dir'] + '/timings.txt',
text, time.time() - t_stamp)
return performance
# Multiprocessing Flag:
# False - then does not use multiprocessing
# int - uses that many separate processes
multi_proc = False
# The four survey directories
survey_files = ['A', 'B', 'C', 'D']
# Load the default parameters
dic = eval(open('parameters.py').read())
# Fit true flat-field only once
dic['best_fit_params'] = analysis.best_fit_ff(
dic['FoV'],
dic['ff_samples'],
dic['flat_field_order'],
dic['stop_condition'],
dic['max_iterations'],
verbose=dic['verbose'])
# Create parameter list
parameter_dictionaries = []
for srvy in survey_files:
dic['survey_file'] = srvy + '.txt'
dic['data_dir'] = srvy
parameter_dictionaries.append(dic.copy())
# Perform simulations
if multi_proc:
os.nice(19)
from multiprocessing import Pool
def run_sim(dic):
''' This is the high level self-calibration function that actually runs
one self-calibration run (sky generation, survey and self-calibration) and
returns its performance. This function is included to allow simple Python
multiprocessing, by calling this function with different parameter
dictionaries in parallel.
Input
-----
dic : dictionary
A Python dictionary containing all the simulation parameters. See
default_parameters.py for all the required parameters.
Return
------
out : numpy array
A list containing the results of the self-calibration simulations
list[0] = the number of self-calibration iterations required to
converge to the final fitted solution (if 0 returned, then
self-calibration did not converge).
list[1] = the root-mean-squared error between the fitted source fluxes
and the true source fluxes.
list[2] = the "badness" between the fitted instrument response and
the true instrument responses.
list[3] = the "best-in-badness" between the fitted instrument
response and the true instrument responses.
list[4] = the chi2 of the final fitted solution
'''
# Create output directories
if dic['data_dir']:
if dic['data_dir'][-1] == '/':
dic['data_dir'] = dic['data_dir'][0:-1]
if os.path.isdir(dic['data_dir']):
os.system('rm -r {0}'.format(dic['data_dir']))
os.mkdir(dic['data_dir'])
os.mkdir((dic['data_dir'] + '/FF'))
# repeated runs should return consistent results
np.random.seed(dic['seed'])
# Only find best fit to the true flat-field if not already done
if 'best_fit_params' in dic:
if dic['verbose']:
print("Using pre-existing best fit to the true flat-field.")
else:
dic['best_fit_params'] = analysis.best_fit_ff(dic['FoV'],
dic['ff_samples'],
dic['flat_field_order'],
dic['stop_condition'],
dic['max_iterations'],
verbose=dic['verbose'])
# Only create a sky catalog if one not provided in parameter dictionary
if 'sky_catalog' in dic:
if dic['verbose']:
print("Using pre-existing sky catalog.")
sky_catalog = dic['sky_catalog']
else:
if dic['verbose']:
print("Generating sky catalog...")
sky_catalog = true_functions.SourceCatalog(dic['sky_limits'],
dic['density_of_stars'],
dic['m_min'], dic['m_max'],
dic['powerlaw_constants'],
dic['analysis_limits'])
if dic['verbose']:
print('...{0} sources generated!'.format(sky_catalog.size))
if dic['data_dir']:
save_out.source_catalog(dic['data_dir'], sky_catalog, dic['verbose'])
save_out.parameters(dic['data_dir'], dic, dic['verbose'])
# Perform sky survey
t_stamp = time.time()
measurement_catalog = survey.survey(dic['survey_file'],
sky_catalog,
dic['FoV'],
dic['eta'],
dic['delta'],
dic['epsilon_max'],
data_dir=dic['data_dir'],
verbose=dic['verbose'])
if dic['data_dir']:
save_out.measurement_catalog(dic['data_dir'], measurement_catalog,
dic['verbose'])
text = 'Measurement Catalog (s): '
save_out.save_time(dic['data_dir'] + '/timings.txt', text,
time.time() - t_stamp)
t_stamp = time.time()
performance = self_calibration.self_calibration(measurement_catalog,
sky_catalog,
dic['flat_field_order'],
dic['FoV'],
dic['ff_samples'],
dic['stop_condition'],
dic['max_iterations'],
dic['best_fit_params'],
data_dir=dic['data_dir'],
verbose=dic['verbose'])
if dic['data_dir']:
save_out.results(dic['data_dir'],
performance,
header='Final Results\n=============\n\n',
verbose=dic['verbose'])
text = 'Self-Calibration (s): '
save_out.save_time(dic['data_dir'] + '/timings.txt', text,
time.time() - t_stamp)
return performance
