def run_test():
zplus = kvector_t(0.0, 0.0, 1.0)
zmin = kvector_t(0.0, 0.0, -1.0)
# IntensityDataIOFactory.writeIntensityData(getSimulationIntensity(zplus, 1.0), 'polmagcylinders2_reference_00.int')
# IntensityDataIOFactory.writeIntensityData(getSimulationIntensity(zplus, -1.0), 'polmagcylinders2_reference_01.int')
# IntensityDataIOFactory.writeIntensityData(getSimulationIntensity(zmin, 1.0), 'polmagcylinders2_reference_10.int')
# IntensityDataIOFactory.writeIntensityData(getSimulationIntensity(zmin, -1.0), 'polmagcylinders2_reference_11.int')
diff = 0.0
diff += get_difference(
getSimulationIntensity(zplus, 1.0).array(),
get_reference_data('polmagcylinders2_reference_00.int.gz').getArray())
diff += get_difference(
getSimulationIntensity(zplus, -1.0).array(),
get_reference_data('polmagcylinders2_reference_01.int.gz').getArray())
diff += get_difference(
getSimulationIntensity(zmin, 1.0).array(),
get_reference_data('polmagcylinders2_reference_10.int.gz').getArray())
diff += get_difference(
getSimulationIntensity(zmin, -1.0).array(),
get_reference_data('polmagcylinders2_reference_11.int.gz').getArray())
diff /= 4.0
status = "OK"
if diff > 2e-10:
status = "FAILED"
return "PolarizedDWBAZeroMag", "functional test: polarized DWBA with non-zero magnetic field", diff, status
def run_test():
result = runSimulation()
# ba.IntensityDataIOFactory.writeIntensityData(result, 'polmagcylinders1_reference.int')
reference = utils.get_reference_data('polmagcylinders1_reference.int.gz')
diff = utils.get_difference(result.array(), reference.getArray())
status = "OK"
if diff > 2e-10:
status = "FAILED"
return "PolarizedDWBAZeroMag", "Polarized DWBA with zero magnetic field", diff, status
def _create_bins(num_of_bins: int, diff: pd.DataFrame) -> [float]:
labels = diff.index.values.tolist()
# get list of all differences
all_diff = []
# create buckets
for i in range(len(labels)):
for j in range(i):
all_diff.append(utils.get_difference(labels[i], labels[j], diff))
# create bins
_, bins = pd.qcut(all_diff, num_of_bins, retbins=True)
bins = list(bins)
return bins
def get_feature_from_bin(label_of_feature: int, bins: bs.EquisizedBins,
diff: pd.DataFrame):
feature = [0] * bins.get_number_of_bins()
labels = utils.get_row_labels(diff)
for other_label in labels:
if other_label == label_of_feature:
continue
category = bins.get_category(
utils.get_difference(label_of_feature, other_label, diff))
feature[category] += 1
# normalise
feature = list(map(lambda x: float(x) / len(labels), feature))
return feature