def parse(self): """ Parse the NIST problem file into a Fitting Problem. :return: The fully parsed fitting problem :rtype: fitbenchmarking.parsing.fitting_problem.FittingProblem """ fitting_problem = FittingProblem() equation, data, starting_values, name = self._parse_line_by_line() data = self._parse_data(data) fitting_problem.data_x = data[:, 1] fitting_problem.data_y = data[:, 0] if len(data[0, :]) > 2: fitting_problem.data_e = data[:, 2] fitting_problem.name = name # String containing a mathematical expression fitting_problem.equation = self._parse_equation(equation) fitting_problem.starting_values = starting_values fitting_problem.function = \ nist_func_definition(function=fitting_problem.equation, param_names=starting_values[0].keys()) return fitting_problem
def parse(self): """ Get data into a Fitting Problem via cutest. :return: The fully parsed fitting problem :rtype: fitbenchmarking.parsing.fitting_problem.FittingProblem """ self.mastsif_dir = TemporaryDirectory() # set the MASTSIF environment variable so that pycutest # can find the sif files os.environ["MASTSIF"] = self.mastsif_dir.name self._num_params = None # get just the short filename (minus the .SIF) fp = FittingProblem(self.options) # Collect x and create new file with blank y fname, fp.data_x, fp.data_y, fp.data_e = self._setup_data() self._p = _import_problem(fname) fp.name = self._p.name fp.function = self._function # self._p.objcons fp.jacobian = self._jacobian # self._p.lagjac fp.equation = None fp.starting_values = self._get_starting_values() fp.start_x = None fp.end_x = None fp.format = "cutest" # Create a list of x and f (function evaluation) and x and g (Jacobian # evaluation). # If a new x is given we will create and parse a new file self._cache_f = [(fp.data_x, self._p.objcons)] self._cache_g = [(fp.data_x, self._p.lagjac)] return fp
def parse(self): """ Parse the NIST problem file into a Fitting Problem. :return: The fully parsed fitting problem :rtype: fitbenchmarking.parsing.fitting_problem.FittingProblem """ fitting_problem = FittingProblem(self.options) equation, data, starting_values, name = self._parse_line_by_line() data = self._parse_data(data) fitting_problem.data_x = data[:, 1] fitting_problem.data_y = data[:, 0] if len(data[0, :]) > 2: fitting_problem.data_e = data[:, 2] fitting_problem.name = name # String containing a mathematical expression fitting_problem.equation = self._parse_equation(equation) fitting_problem.starting_values = starting_values fitting_problem.function = \ nist_func_definition(function=fitting_problem.equation, param_names=starting_values[0].keys()) fitting_problem.format = "nist" try: jacobian = self._parse_jacobian(name) fitting_problem.jacobian = \ nist_jacobian_definition(jacobian=jacobian, param_names=starting_values[0].keys()) except NoJacobianError: LOGGER.warn("WARNING: Could not find analytic Jacobian " "information for {} problem".format(name)) 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