def get_parameter_names(self, paramname_file, params_in_header=False):
if (paramname_file is None) and params_in_header:
reader = utils.open_if_exists(self.chain_files[0], "r")
parameters = reader.readline().strip("#").split()
reader.close()
else:
paramname_reader = utils.open_if_exists(paramname_file, "r")
lines = paramname_reader.readlines()
paramname_reader.close()
parameters = []
for line in lines:
# remove trailing * used to denote derived parameters
parameters.append(line.strip().split()[0].split("*")[0])
return parameters
def get_parameter_names(self, paramname_file, params_in_header=False):
if (paramname_file is None) and params_in_header:
reader = utils.open_if_exists(self.chain_files[0], 'r')
parameters = reader.readline().strip('#').split()
reader.close()
else:
paramname_reader = utils.open_if_exists(paramname_file, 'r')
lines = paramname_reader.readlines()
paramname_reader.close()
parameters = []
for line in lines:
# remove trailing * used to denote derived parameters
parameters.append(line.strip().split()[0].split('*')[0])
return parameters
def load_contour_data(self, parameters, n_samples, grid_size,
smoothing, contour_pct):
contour_data = None
print self.settings['contour_data_files']
for f in self.settings['contour_data_files']:
reader = utils.open_if_exists(f, 'r')
test_parameters = reader.readline().split('#')[0].split()
test_n_samples = int(float(reader.readline().split('#')[0]))
test_grid_size = [int(float(x)) for x in \
reader.readline().split('#')[0].split()]
test_smoothing = float(reader.readline().split('#')[0])
test_contour_pct = [float(x) for x in \
reader.readline().split('#')[0].split()]
match = (test_parameters == parameters) and \
(test_n_samples == n_samples) and \
np.all([x == y for x, y in \
zip(test_grid_size, grid_size)]) and \
test_smoothing == smoothing and \
np.all([x == y for x, y in \
zip(test_contour_pct, contour_pct)])
if match:
print 'Plotting contours from ' + f
contour_levels = [float(x) for x in \
reader.readline().split('#')[0].split()]
X, Y, Z = np.loadtxt(reader, skiprows=1, unpack=True)
contour_data = (contour_levels, X.reshape(grid_size),
Y.reshape(grid_size), Z.reshape(grid_size))
reader.close()
return contour_data
def load_contour_data(self, parameters, n_samples, grid_size, smoothing,
contour_pct):
contour_data = None
print self.settings['contour_data_files']
for f in self.settings['contour_data_files']:
reader = utils.open_if_exists(f, 'r')
test_parameters = reader.readline().split('#')[0].split()
test_n_samples = int(float(reader.readline().split('#')[0]))
test_grid_size = [int(float(x)) for x in \
reader.readline().split('#')[0].split()]
test_smoothing = float(reader.readline().split('#')[0])
test_contour_pct = [float(x) for x in \
reader.readline().split('#')[0].split()]
match = (test_parameters == parameters) and \
(test_n_samples == n_samples) and \
np.all([x == y for x, y in \
zip(test_grid_size, grid_size)]) and \
test_smoothing == smoothing and \
np.all([x == y for x, y in \
zip(test_contour_pct, contour_pct)])
if match:
print 'Plotting contours from ' + f
contour_levels = [float(x) for x in \
reader.readline().split('#')[0].split()]
X, Y, Z = np.loadtxt(reader, skiprows=1, unpack=True)
contour_data = (contour_levels, X.reshape(grid_size),
Y.reshape(grid_size), Z.reshape(grid_size))
reader.close()
return contour_data
def __init__(
self,
name,
chain_files,
burn_in=None,
mult_column=0,
lnlike_column=1,
first_par_column=2,
paramname_file=None,
params_in_header=False,
):
self.rename(name)
self.chain_files = chain_files
first_file = True
for chain_file in chain_files:
# does this raise an error if the file doesn't exist???
reader = utils.open_if_exists(chain_file, "r")
new_samples = np.loadtxt(reader)
reader.close()
if first_file:
self.samples = np.copy(new_samples)
first_file = False
else:
# check that number of columns are the same
self.samples = np.vstack((self.samples, new_samples))
if mult_column is None:
self.mult_column = 0
self.multiplicity = np.ones(len(self.samples))
self.samples = np.vstack((self.multiplicity, self.samples.T)).T
if lnlike_column is not None:
self.lnlike_column = lnlike_column + 1
else:
self.lnlike_column = None
self.first_par_column = first_par_column + 1
else:
self.mult_column = mult_column
self.multiplicity = self.samples[:, mult_column]
self.lnlike_column = lnlike_column
self.first_par_column = first_par_column
if (paramname_file is None) and (not params_in_header):
paramname_file = "_".join(chain_files[0].split("_")[:-1]) + ".paramnames"
self.parameters = self.get_parameter_names(paramname_file, params_in_header)
self.column_names = list(self.parameters)
self.column_names.insert(mult_column, "mult")
if lnlike_column is not None:
self.column_names.insert(lnlike_column, "-ln(L)")
self.burn_in = burn_in
if burn_in is not None:
self.remove_burn_in_samples()
def __init__(self,
name,
chain_files,
burn_in=None,
mult_column=0,
lnlike_column=1,
first_par_column=2,
paramname_file=None,
params_in_header=False):
self.rename(name)
self.chain_files = chain_files
first_file = True
for chain_file in chain_files:
# does this raise an error if the file doesn't exist???
reader = utils.open_if_exists(chain_file, 'r')
new_samples = np.loadtxt(reader)
reader.close()
if first_file:
self.samples = np.copy(new_samples)
first_file = False
else:
# check that number of columns are the same
self.samples = np.vstack((self.samples, new_samples))
if mult_column is None:
self.mult_column = 0
self.multiplicity = np.ones(len(self.samples))
self.samples = np.vstack((self.multiplicity, self.samples.T)).T
if lnlike_column is not None:
self.lnlike_column = lnlike_column + 1
else:
self.lnlike_column = None
self.first_par_column = first_par_column + 1
else:
self.mult_column = mult_column
self.multiplicity = self.samples[:, mult_column]
self.lnlike_column = lnlike_column
self.first_par_column = first_par_column
if (paramname_file is None) and (not params_in_header):
paramname_file = '_'.join(chain_files[0].split('_')[:-1]) + \
'.paramnames'
self.parameters = self.get_parameter_names(paramname_file,
params_in_header)
self.column_names = list(self.parameters)
self.column_names.insert(mult_column, 'mult')
if lnlike_column is not None:
self.column_names.insert(lnlike_column, '-ln(L)')
self.burn_in = burn_in
if burn_in is not None:
self.remove_burn_in_samples()
def __init__(self, name, path=None, save=True):
self.settings = {}
self.history_file = '.session_history'
self.name = name
self.save = save
if save:
self.log_file = os.path.join('Logs', time.strftime('%Y-%m-%d'),
name)
self.plot = None
self.pdfs = []
self.settings['pdfs'] = {}
# check for file with inputs from all previous sessions
# (e.g. chains, likelihoods, joint pdfs) and load it
self.load_history()
log_reader = None
# if path is given, search it for the named log (error if not found)
if path:
old_log_file = os.path.join(path, name)
log_reader = utils.open_if_exists(old_log_file, 'rb')
# if no path given, search multiple paths for named log file
# (sort date directories in reverse order so newest
# log files are found first)
else:
log_paths = sorted(os.listdir('Logs'), reverse=True)
for x in log_paths:
p = os.path.join('Logs', x)
old_log_file = os.path.join(p, name)
if os.path.isdir(p) and os.path.isfile(old_log_file):
log_reader = open(old_log_file, 'rb')
break
# if existing log found, set up environment using old settings
if log_reader:
print 'Using settings from ' + old_log_file
self.log_file = old_log_file
self.load_log(log_reader)
# if not found, notify that new log file is assumed
elif self.save:
print 'No log file found. Creating new file:\n ' + \
self.log_file
for attr in ['name', 'log_file']:
self.settings[attr] = getattr(self, attr)