def load_config(config_source, select=None, verbose=True):
"""Loads config parameters from file and returns as dict
parameters
----------
config_source : str
soure to load
select : str (optional)
select and return only a single section of the config
verbose : bool
"""
config_filepath = grid_strings.config_filepath(source=config_source,
module_dir='grids')
printv(f'Loading config: {config_filepath}', verbose=verbose)
if not os.path.exists(config_filepath):
raise FileNotFoundError(
f'Config file not found: {config_filepath}.'
"\nTry making one from the template 'default.ini'")
ini = configparser.ConfigParser()
ini.read(config_filepath)
config = {}
for section in ini.sections():
config[section] = {}
for option in ini.options(section):
config[section][option] = ast.literal_eval(ini.get(
section, option))
if select is None:
return config
else:
return config[select]
def load_model_table(batch, source, filename='MODELS.txt', verbose=True):
"""Returns the model_table of a batch
"""
source = grid_strings.source_shorthand(source=source)
filepath = grid_strings.get_model_table_filepath(batch, source, filename)
printv(f'Loading: {filepath}', verbose)
model_table = pd.read_csv(filepath, delim_whitespace=True)
return model_table
def load_chain(source, version, n_steps, n_walkers, verbose=True):
"""Loads from file and returns np array of chain
"""
filename = get_mcmc_string(source=source, version=version,
n_steps=n_steps, n_walkers=n_walkers,
prefix='chain', extension='.npy')
mcmc_path = get_mcmc_path(source)
filepath = os.path.join(mcmc_path, filename)
pyprint.printv(f'Loading chain: {filepath}', verbose=verbose)
return np.load(filepath)
def save_compressed_chain(chain, source, version, verbose=True):
"""Saves a chain as a compressed zip
"""
n_walkers, n_steps, n_dim = chain.shape
filename = get_mcmc_string(source=source, version=version,
n_steps=n_steps, n_walkers=n_walkers,
prefix='chain', extension='.npy.gz')
mcmc_path = get_mcmc_path(source)
filepath = os.path.join(mcmc_path, filename)
pyprint.printv(f'Saving compressed chain: {filepath}', verbose=verbose)
with gzip.GzipFile(filepath, 'w') as f:
np.save(f, chain)
def load_dump(cycle,
run,
batch,
source,
basename='xrb',
prefix='',
verbose=False):
batch_str = grid_strings.get_batch_string(batch, source)
run_str = grid_strings.get_run_string(run, basename)
filename = get_dump_filename(cycle, run, basename, prefix=prefix)
filepath = os.path.join(MODELS_PATH, batch_str, run_str, filename)
printv(f'Loading: {filepath}', verbose=verbose)
return kepdump.load(filepath, graphical=False, silent=True)
def save_all_plots(source, version, discard, n_steps, n_walkers=1000, display=False,
save=True, cap=None, posteriors=True, contours=True,
redshift=True, mass_radius=True, verbose=True, compressed=False):
"""Saves (and/or displays) main MCMC plots
"""
chain = mcmc_tools.load_chain(source, version=version, n_steps=n_steps,
n_walkers=n_walkers, verbose=verbose,
compressed=compressed)
if posteriors:
printv('Plotting posteriors', verbose=verbose)
plot_posteriors(chain, source=source, save=save, discard=discard, cap=cap,
display=display, version=version)
if contours:
printv('Plotting contours', verbose=verbose)
plot_contours(chain, source=source, save=save, discard=discard, cap=cap,
display=display, version=version)
if mass_radius:
printv('Plotting mass-radius', verbose=verbose)
plot_mass_radius(chain, source=source, save=save, discard=discard, cap=cap,
display=display, version=version)
if redshift:
printv('Plotting redshift', verbose=verbose)
plot_redshift(chain, source=source, save=save, discard=discard, cap=cap,
display=display, version=version)
def load_dump(cycle,
run,
batch,
source,
basename='xrb',
prefix='',
verbose=False):
filename = get_dump_filename(cycle, run, basename, prefix=prefix)
model_path = grid_strings.get_model_path(run=run,
batch=batch,
source=source,
basename=basename)
filepath = os.path.join(model_path, filename)
printv(f'Loading: {filepath}', verbose=verbose)
return kepdump.load(filepath, graphical=False, silent=True)
def try_mkdir(path, skip=False, verbose=True):
printv(f'Creating directory {path}', verbose)
if os.path.exists(path):
if skip:
printv('Directory already exists - skipping', verbose)
else:
print('Directory exists')
cont = input('specified? (DESTROY) [y/n]: ')
if cont == 'y' or cont == 'Y':
subprocess.run(['rm', '-r', path])
subprocess.run(['mkdir', path])
elif cont == 'n' or cont == 'N':
sys.exit()
else:
subprocess.run(['mkdir', '-p', path], check=True)
def load_chain(source, version, n_steps, n_walkers, compressed=False, verbose=True):
"""Loads from file and returns np array of chain
"""
extension = {True: '.npy.gz', False: '.npy'}[compressed]
filename = get_mcmc_string(source=source, version=version,
n_steps=n_steps, n_walkers=n_walkers,
prefix='chain', extension=extension)
mcmc_path = get_mcmc_path(source)
filepath = os.path.join(mcmc_path, filename)
pyprint.printv(f'Loading chain: {filepath}', verbose=verbose)
if compressed:
f = gzip.GzipFile(filepath, 'r')
chain = np.load(f)
else:
chain = np.load(filepath)
return chain
def setup_config(source, select=None, specified=None, verbose=True):
"""Returns combined dict of params from default, source, and supplied
parameters
----------
source : str
select : str (optional)
select and return only a single section of the config
specified : {}
Overwrite default/source config with user-specified values
verbose : bool
"""
def overwrite_option(old_dict, new_dict):
for key, val in new_dict.items():
old_dict[key] = val
if specified is None:
specified = {}
default_config = load_config(config_source='default',
select=select,
verbose=verbose)
source_config = load_config(config_source=source,
select=select,
verbose=verbose)
combined_config = dict(default_config)
for category, contents in combined_config.items():
printv(
f'Overwriting default {category} with source-specific and '
f'user-supplied {category}',
verbose=verbose)
if source_config.get(category) is not None:
overwrite_option(old_dict=contents,
new_dict=source_config[category])
if specified.get(category) is not None:
overwrite_option(old_dict=contents, new_dict=specified[category])
return combined_config
def load_grid_table(tablename, source, verbose=True, lampe_analyser=False):
"""Returns table of grid input/output
tablename = str : table name (e.g. 'params', 'summ', 'bursts')
source = str : name of source object
lampe_analyser = bool : if the table is from Lampe's analyser (as opposed to pyburst)
"""
source = grid_strings.source_shorthand(source)
prefix_map = {'summ': 'summary'}
prefix = prefix_map.get(tablename, tablename)
if tablename in ('summ', 'bursts') and not lampe_analyser:
table_path = grid_strings.burst_analyser_path(source)
else:
table_path = grid_strings.get_source_subdir(source, tablename)
filename = f'{prefix}_{source}.txt'
filepath = os.path.join(table_path, filename)
printv(f'Loading {tablename} table: {filepath}', verbose)
table = pd.read_csv(filepath, delim_whitespace=True)
return table
def load_lum(run, batch, source, basename='xrb', reload=False, save=True,
silent=True, check_monotonic=True, verbose=True):
"""Attempts to load pre-extracted luminosity data, or load raw binary.
Returns [time (s), luminosity (erg/s)]
"""
def load_save(load_filepath, save_filepath):
lum_loaded = extract_lcdata(filepath=load_filepath, silent=silent)
if save:
grid_tools.try_mkdir(input_path, skip=True, verbose=False)
save_ascii(lum=lum_loaded, filepath=save_filepath, verbose=verbose)
return lum_loaded
batch_str = grid_strings.get_batch_string(batch, source)
analysis_path = grid_strings.burst_analyser_path(source)
input_path = os.path.join(analysis_path, batch_str, 'input')
presaved_filepath = os.path.join(input_path, f'{batch_str}_{run}.txt')
run_str = grid_strings.get_run_string(run, basename)
model_path = grid_strings.get_model_path(run, batch, source, basename)
binary_filepath = os.path.join(model_path, f'{run_str}.lc')
if reload:
pyprint.printv('Deleting preloaded file, reloading binary file', verbose=verbose)
subprocess.run(['rm', '-f', presaved_filepath])
try:
lum = load_save(binary_filepath, presaved_filepath)
except FileNotFoundError:
pyprint.printv('XXXXXXX lumfile not found. Skipping XXXXXXXX', verbose=verbose)
return
else:
try:
lum = load_ascii(presaved_filepath, verbose=verbose)
except (FileNotFoundError, OSError):
pyprint.printv('No preloaded file found. Reloading binary', verbose=verbose)
try:
lum = load_save(binary_filepath, presaved_filepath)
except FileNotFoundError:
pyprint.printv('XXXXXXX lumfile not found. Skipping XXXXXXX', verbose=verbose)
return
if check_monotonic:
dt = np.diff(lum[:, 0])
if True in (dt < 0):
pyprint.print_warning('Lightcurve timesteps are not in order. '
+ 'Something has gone horribly wrong!', n=80)
raise RuntimeError('Lightcurve timesteps are not in order')
return lum
def save_ascii(lum, filepath, verbose=True):
"""Saves extracted [time, lum]
"""
pyprint.printv(f'Saving data for faster loading in: {filepath}', verbose=verbose)
header = 'time (s), luminosity (erg/s)'
np.savetxt(filepath, lum, header=header)
def load_ascii(filepath, verbose=True):
"""Loads pre-extracted .txt file of [time, lum]
"""
pyprint.printv(f'Loading preloaded luminosity file: {filepath}', verbose=verbose)
return np.loadtxt(filepath, skiprows=1)