def test_diff_cache(self):
poly1 = Polynom1D()
poly2 = Polynom1D()
poly3 = Polynom1D()
poly1.pars[1].thaw()
poly2.pars[1].thaw()
poly3.pars[1].thaw()
sdata = DataSimulFit('d123', (self.d1, self.d2, self.d3))
smodel = SimulFitModel('diff', (poly1, poly2, poly3))
sfit = Fit(sdata, smodel, method=NelderMead(), stat=Cash())
result = sfit.fit()
self.compare_results(self._fit_diff_poly_bench, result)
def test_data_1d_int_eval_model_to_fit_filter(data):
model = Polynom1D()
model.c0 = 0
model.c1 = MULTIPLIER
data.mask = X_ARRAY <= X_THRESHOLD
evaluated_data = data.eval_model_to_fit(model)
numpy.testing.assert_array_equal(evaluated_data, MULTIPLIER * X_ARRAY[:X_THRESHOLD + 1])
def test_same_cache(self):
poly = Polynom1D()
poly.pars[1].thaw()
sdata = DataSimulFit('d1d2d3', (self.d1, self.d2, self.d3))
smodel = SimulFitModel('same', (poly, poly, poly))
sfit = Fit(sdata, smodel, method=NelderMead(), stat=Cash())
result = sfit.fit()
self.compare_results(self._fit_same_poly_bench, result)
def test_data_simul_fit_eval_model_to_fit(data_simul_fit):
model = Polynom1D()
model.c0 = 0
model.c1 = MULTIPLIER
data_simul_fit.datasets[0].mask = X_ARRAY <= X_THRESHOLD
data_simul_fit.datasets[1].mask = X_ARRAY <= X_THRESHOLD
evaluated_data = data_simul_fit.eval_model_to_fit((model, model))
expected_data = numpy.concatenate((MULTIPLIER * X_ARRAY[:X_THRESHOLD+1],
MULTIPLIER**2 * X_ARRAY[:X_THRESHOLD+1]))
numpy.testing.assert_array_equal(evaluated_data, expected_data)
def test_data_eval_model_to_fit_no_filter(data):
model = Polynom1D()
model.c0 = 0
model.c1 = MULTIPLIER
evaluated_data = data.eval_model_to_fit(model)
numpy.testing.assert_array_equal(evaluated_data, MULTIPLIER * X_ARRAY)