def test_correct_data_multi_fit(self):
"""
Tests correct data on a multifit problem.
"""
fitting_problem = FittingProblem(self.options)
fitting_problem.multifit = True
x_data = [
np.array([-0.5, 0.0, 1.0, 0.5, 1.5, 2.0, 2.5, 3.0, 4.0]),
np.array([-0.5, 0.0, 1.0, 0.5, 1.4, 2.0, 2.5, 3.0, 4.0]),
np.array([-0.5, 0.0, 1.0, 0.5, 1.7, 2.0, 2.5, 3.0, 4.0])
]
y_data = [
np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]),
np.array([0.0, 1.0, 2.0, 3.0, 24.0, 5.0, 6.0, 7.0, 8.0]),
np.array([0.0, 1.0, 2.8, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0])
]
e_data = [
np.array([1.0, 20.0, 30.0, 40.0, 50.0, 60.0, 1.0, 6.0, 9.0]),
np.array([1.0, 20.0, 30.0, 40.0, 50.0, 60.0, 1.0, 6.0, 9.0]),
np.array([1.0, 20.0, 30.0, 40.0, 50.0, 60.0, 1.0, 6.0, 9.0])
]
start_x = [0.5, 1.1, 0.0]
end_x = [2.5, 2.6, 1.0]
expected_x_data = [
np.array([0.5, 1.0, 1.5, 2.0, 2.5]),
np.array([1.4, 2.0, 2.5]),
np.array([0.0, 0.5, 1.0])
]
expected_y_data = [
np.array([3.0, 2.0, 4.0, 5.0, 6.0]),
np.array([24.0, 5.0, 6.0]),
np.array([1.0, 3.0, 2.8])
]
expected_e_data = [
np.array([40.0, 30.0, 50.0, 60.0, 1.0]),
np.array([50.0, 60.0, 1.0]),
np.array([20.0, 40.0, 30.0])
]
fitting_problem.data_x = x_data
fitting_problem.data_y = y_data
fitting_problem.data_e = e_data
fitting_problem.start_x = start_x
fitting_problem.end_x = end_x
fitting_problem.correct_data()
for i in range(3):
self.assertTrue(
np.isclose(
fitting_problem.data_x[i][fitting_problem.sorted_index[i]],
expected_x_data[i]).all())
self.assertTrue(
np.isclose(
fitting_problem.data_y[i][fitting_problem.sorted_index[i]],
expected_y_data[i]).all())
self.assertTrue(
np.isclose(
fitting_problem.data_e[i][fitting_problem.sorted_index[i]],
expected_e_data[i]).all())
self.options.cost_func_type = "nlls"
fitting_problem.correct_data()
for i in range(3):
self.assertTrue(
np.isclose(
fitting_problem.data_x[i][fitting_problem.sorted_index[i]],
expected_x_data[i]).all())
self.assertTrue(
np.isclose(
fitting_problem.data_y[i][fitting_problem.sorted_index[i]],
expected_y_data[i]).all())
self.assertIs(fitting_problem.data_e[i], None)
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
