def load_expectation(filename): """ Load an expected fitting problem from a json file. :param filename: The path to the expectation file :type filename: string :return: A fitting problem to test against :rtype: fitbenchmarking.parsing.FittingProblem """ with open(filename, 'r') as f: expectation_dict = load(f) expectation = FittingProblem(OPTIONS) expectation.name = expectation_dict['name'] expectation.start_x = expectation_dict['start_x'] expectation.end_x = expectation_dict['end_x'] expectation.data_x = np.array(expectation_dict['data_x']) expectation.data_y = np.array(expectation_dict['data_y']) expectation.data_e = expectation_dict['data_e'] if expectation.data_e is not None: expectation.data_e = np.array(expectation.data_e) expectation.starting_values = expectation_dict['starting_values'] expectation.value_ranges = expectation_dict['value_ranges'] return expectation
def parse(self): """ Parse the Fitbenchmark problem file into a Fitting Problem. :return: The fully parsed fitting problem :rtype: fitbenchmarking.parsing.fitting_problem.FittingProblem """ # pylint: disable=attribute-defined-outside-init fitting_problem = FittingProblem(self.options) self._entries = self._get_data_problem_entries() software = self._entries['software'].lower() if not (software in import_success and import_success[software][0]): error = import_success[software][1] raise MissingSoftwareError('Requirements are missing for {} parser' ': {}'.format(software, error)) self._parsed_func = self._parse_function() if software == 'mantid' and self._entries['input_file'][0] == '[': fitting_problem.multifit = True # NAME fitting_problem.name = self._entries['name'] # DATA data_points = [_get_data_points(p) for p in self._get_data_file()] # FUNCTION if software == 'mantid': fitting_problem.function = self._create_mantid_function() fitting_problem.format = 'mantid' elif software == 'sasview': fitting_problem.function = self._create_sasview_function() fitting_problem.format = 'sasview' # EQUATION equation_count = len(self._parsed_func) if equation_count == 1: fitting_problem.equation = self._parsed_func[0]['name'] else: fitting_problem.equation = '{} Functions'.format(equation_count) # STARTING VALUES fitting_problem.starting_values = self._get_starting_values() # PARAMETER RANGES vr = _parse_range(self._entries.get('parameter_ranges', '')) fitting_problem.value_ranges = vr if vr != {} else None # FIT RANGES fit_ranges_str = self._entries.get('fit_ranges', '') # this creates a list of strs like '{key: val, ...}' and parses each fit_ranges = [ _parse_range('{' + r.split('}')[0] + '}') for r in fit_ranges_str.split('{')[1:] ] if fitting_problem.multifit: num_files = len(data_points) fitting_problem.data_x = [d[:, 0] for d in data_points] fitting_problem.data_y = [d[:, 1] for d in data_points] fitting_problem.data_e = [ d[:, 2] if d.shape[1] > 2 else None for d in data_points ] if not fit_ranges: fit_ranges = [{} for _ in range(num_files)] fitting_problem.start_x = [ f['x'][0] if 'x' in f else None for f in fit_ranges ] fitting_problem.end_x = [ f['x'][1] if 'x' in f else None for f in fit_ranges ] else: fitting_problem.data_x = data_points[0][:, 0] fitting_problem.data_y = data_points[0][:, 1] if data_points[0].shape[1] > 2: fitting_problem.data_e = data_points[0][:, 2] if fit_ranges and 'x' in fit_ranges[0]: fitting_problem.start_x = fit_ranges[0]['x'][0] fitting_problem.end_x = fit_ranges[0]['x'][1] if software == 'mantid': # String containing the function name(s) and the starting parameter # values for each function. fitting_problem.additional_info['mantid_equation'] \ = self._entries['function'] if fitting_problem.multifit: fitting_problem.additional_info['mantid_ties'] \ = self._parse_ties() return fitting_problem
def parse(self): """ Parse the Fitbenchmark problem file into a Fitting Problem. :return: The fully parsed fitting problem :rtype: fitbenchmarking.parsing.fitting_problem.FittingProblem """ fitting_problem = FittingProblem() self._entries = self._get_data_problem_entries() software = self._entries['software'].lower() if not (software in import_success and import_success[software][0]): e = import_success[software][1] raise MissingSoftwareError('Requirements are missing for {} parser' ': {}'.format(software, e)) self._parsed_func = self._parse_function() # NAME fitting_problem.name = self._entries['name'] # DATA data_points = self._get_data_points() fitting_problem.data_x = data_points[:, 0] fitting_problem.data_y = data_points[:, 1] if data_points.shape[1] > 2: fitting_problem.data_e = data_points[:, 2] # FUNCTION if software == 'mantid': fitting_problem.function = self._create_mantid_function() elif software == 'sasview': fitting_problem.function = self._create_sasview_function() # EQUATION equation_count = len(self._parsed_func) if equation_count == 1: fitting_problem.equation = self._parsed_func[0]['name'] else: fitting_problem.equation = '{} Functions'.format(equation_count) if software == 'mantid': # String containing the function name(s) and the starting parameter # values for each function fitting_problem._mantid_equation = self._entries['function'] # STARTING VALUES fitting_problem.starting_values = self._get_starting_values() # PARAMETER RANGES vr = self._parse_range('parameter_ranges') fitting_problem.value_ranges = vr if vr != {} else None # FIT RANGES fit_ranges = self._parse_range('fit_ranges') try: fitting_problem.start_x = fit_ranges['x'][0] fitting_problem.end_x = fit_ranges['x'][1] except KeyError: pass return fitting_problem