def save(self, output_file, overwrite=False):
"""Save the model to disk"""
if os.path.exists(output_file) and overwrite is False:
raise ModelFileExists("The file %s exists already. If you want to overwrite it, use the 'overwrite=True' "
"options as 'model.save(\"%s\", overwrite=True)'. " % (output_file, output_file))
else:
data = self.to_dict_with_types()
# Write it to disk
try:
# Get the YAML representation of the data
representation = my_yaml.dump(data)
with open(output_file, "w+") as f:
# Add a new line at the end of each voice (just for clarity)
f.write(representation.replace("\n", "\n\n"))
except IOError:
raise CannotWriteModel(os.path.dirname(os.path.abspath(output_file)),
"Could not write model file %s. Check your permissions to write or the "
"report on the free space which follows: " % output_file)
def save(self, output_file, overwrite=False):
"""Save the model to disk"""
if os.path.exists(output_file) and overwrite is False:
raise ModelFileExists(
"The file %s exists already. If you want to overwrite it, use the 'overwrite=True' "
"options as 'model.save(\"%s\", overwrite=True)'. " %
(output_file, output_file))
else:
data = self.to_dict_with_types()
# Write it to disk
try:
# Get the YAML representation of the data
representation = my_yaml.dump(data)
with open(output_file, "w+") as f:
# Add a new line at the end of each voice (just for clarity)
f.write(representation.replace("\n", "\n\n"))
except IOError:
raise CannotWriteModel(
os.path.dirname(os.path.abspath(output_file)),
"Could not write model file %s. Check your permissions to write or the "
"report on the free space which follows: " % output_file)
def xspec_model_factory(model_name, xspec_function, model_type, definition):
class_name = 'XS_%s' % model_name
# Get the path to the user data directory
user_data_path = get_user_data_path()
# Check if the code for this function already exists
code_file_name = os.path.join(user_data_path, '%s.py' % class_name)
if os.path.exists(code_file_name):
# Code already exists
pass
else:
print("Generating code for Xspec model %s..." % model_name)
# If this is an additive model (model_type == 'add') we need to add
# one more parameter (normalization)
if model_type == 'add':
definition['parameters']['norm'] = {'initial value': 1.0,
'desc': '(see https://heasarc.gsfc.nasa.gov/xanadu/xspec/manual/'
'XspecModels.html)',
'min': 0,
'max': None,
'delta': 0.1,
'unit': 'keV / (cm2 s)',
'free': True}
assert model_type != 'con', "Convolution models are not yet supported"
# Get a list of the parameter names
parameters_names = ", ".join(definition['parameters'].keys())
# Create the docstring
docstring = my_yaml.dump(definition)
# Create the class by substituting in the class_definition_code the
# relevant things for this model
code = class_definition_code.replace('$MODEL_NAME$', model_name)
code = code.replace('$DOCSTRING$', docstring)
code = code.replace('$PARAMETERS_NAMES$', parameters_names)
code = code.replace('$XSPEC_FUNCTION$', xspec_function)
code = code.replace('$MODEL_TYPE$', model_type)
# Write to the file
with open(code_file_name, 'w+') as f:
f.write("# This code has been automatically generated. Do not edit.\n")
f.write("\n\n%s\n" % code)
# Add the path to sys.path if it doesn't
if user_data_path not in sys.path:
sys.path.append(user_data_path)
# Import the class in the current namespace (locals)
with warnings.catch_warnings():
warnings.simplefilter("error")
exec('from %s import %s' % (class_name, class_name))
# Return the class we just created
return class_name, locals()[class_name]
def xspec_model_factory(model_name, xspec_function, model_type, definition):
class_name = 'XS_%s' % model_name
# Get the path to the user data directory
user_data_path = get_user_data_path()
# Check if the code for this function already exists
code_file_name = os.path.join(user_data_path, '%s.py' % class_name)
if os.path.exists(code_file_name):
# Code already exists
pass
else:
print("Generating code for Xspec model %s..." % model_name)
# If this is an additive model (model_type == 'add') we need to add
# one more parameter (normalization)
if model_type == 'add':
definition['parameters']['norm'] = {
'initial value': 1.0,
'desc':
'(see https://heasarc.gsfc.nasa.gov/xanadu/xspec/manual/'
'XspecModels.html)',
'min': 0,
'max': None,
'delta': 0.1,
'unit': 'keV / (cm2 s)',
'free': True
}
assert model_type != 'con', "Convolution models are not yet supported"
# Get a list of the parameter names
parameters_names = ", ".join(definition['parameters'].keys())
# Create the docstring
docstring = my_yaml.dump(definition)
# Create the class by substituting in the class_definition_code the
# relevant things for this model
code = class_definition_code.replace('$MODEL_NAME$', model_name)
code = code.replace('$DOCSTRING$', docstring)
code = code.replace('$PARAMETERS_NAMES$', parameters_names)
code = code.replace('$XSPEC_FUNCTION$', xspec_function)
code = code.replace('$MODEL_TYPE$', model_type)
# Write to the file
with open(code_file_name, 'w+') as f:
f.write(
"# This code has been automatically generated. Do not edit.\n")
f.write("\n\n%s\n" % code)
# Add the path to sys.path if it doesn't
if user_data_path not in sys.path:
sys.path.append(user_data_path)
# Import the class in the current namespace (locals)
with warnings.catch_warnings():
warnings.simplefilter("error")
exec('from %s import %s' % (class_name, class_name))
# Return the class we just created
return class_name, locals()[class_name]
