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
