def test_dataset_with_coords_only(): d = sc.Dataset() N = 10 d.coords[Dim.Tof] = sc.Variable([Dim.Tof], values=np.arange(N).astype(np.float64), variances=0.1 * np.random.rand(N)) sc.table(d)
def test_variable(): N = 10 v = sc.Variable([Dim.Tof], values=np.arange(N).astype(np.float64), unit=sc.units.us, variances=0.1 * np.random.rand(N)) sc.table(v)
def test_dataset_with_1d_data(): d = sc.Dataset() N = 10 d.coords[Dim.Tof] = sc.Variable([Dim.Tof], values=np.arange(N).astype(np.float64)) d['Counts'] = sc.Variable([Dim.Tof], values=10.0 * np.random.rand(N)) sc.table(d)
def test_dataset_with_1d_data_with_bin_edges(): d = sc.Dataset() N = 10 d.coords['row'] = sc.Variable(['row'], values=np.arange(N + 1).astype(np.float64)) d['Counts'] = sc.Variable(['row'], values=10.0 * np.random.rand(N)) sc.table(d)
def test_dataset_with_1d_data_with_coord_variances(): d = sc.Dataset() N = 10 d.coords['tof'] = sc.Variable(['tof'], values=np.arange(N).astype(np.float64), variances=0.1 * np.random.rand(N)) d['Counts'] = sc.Variable(['tof'], values=10.0 * np.random.rand(N), variances=np.random.rand(N)) sc.table(d)
def test_dataset_with_labels(): d = sc.Dataset() N = 10 d.coords[Dim.Tof] = sc.Variable([Dim.Tof], values=np.arange(N).astype(np.float64), variances=0.1 * np.random.rand(N)) d['Counts'] = sc.Variable([Dim.Tof], values=10.0 * np.random.rand(N), variances=np.random.rand(N)) d.labels["Normalized"] = sc.Variable([Dim.Tof], values=np.arange(N)) sc.table(d)
def test_dataset_with_non_dimensional_coord(): d = sc.Dataset() N = 10 d.coords['tof'] = sc.Variable(['tof'], values=np.arange(N).astype(np.float64), variances=0.1 * np.random.rand(N)) d['Counts'] = sc.Variable(['tof'], values=10.0 * np.random.rand(N), variances=np.random.rand(N)) d.coords["Normalized"] = sc.Variable(['tof'], values=np.arange(N)) sc.table(d)
def test_dataset_with_1d_data_with_units(): d = sc.Dataset() N = 10 d.coords[Dim.Tof] = sc.Variable([Dim.Tof], values=np.arange(N).astype(np.float64), unit=sc.units.us, variances=0.1 * np.random.rand(N)) d['Sample'] = sc.Variable([Dim.Tof], values=10.0 * np.random.rand(N), unit=sc.units.m, variances=np.random.rand(N)) sc.table(d)
def test_dataset_with_everything(): d = sc.Dataset() N = 10 d.coords[Dim.Tof] = sc.Variable([Dim.Tof], values=np.arange(N).astype(np.float64), unit=sc.units.us, variances=0.1 * np.random.rand(N)) d['Counts'] = sc.Variable([Dim.Tof], values=10.0 * np.random.rand(N)) d['Sample'] = sc.Variable([Dim.Tof], values=10.0 * np.random.rand(N), unit=sc.units.m, variances=np.random.rand(N)) d['Scalar'] = sc.Variable(1.2) sc.table(d)
def test_dataset_with_masks(): d = sc.Dataset() N = 10 d.coords['tof'] = sc.Variable(['tof'], values=np.arange(N).astype(np.float64), variances=0.1 * np.random.rand(N)) d['Counts'] = sc.Variable(['tof'], values=10.0 * np.random.rand(N), variances=np.random.rand(N)) d.coords["Normalized"] = sc.Variable(['tof'], values=np.arange(N)) d.masks["Mask"] = sc.Variable(['tof'], values=np.zeros(N, dtype=np.bool)) sc.table(d)
def test_dataset_histogram_with_masks(): N = 10 d = sc.Dataset( { "Counts": sc.Variable([Dim.X], values=10.0 * np.random.rand(N), variances=np.random.rand(N)) }, {Dim.X: sc.Variable([Dim.X], values=np.arange(N + 1))}) d.labels["Normalized"] = sc.Variable([Dim.X], values=np.arange(N)) d.masks["Mask"] = sc.Variable([Dim.X], values=np.zeros(N, dtype=np.bool)) sc.table(d)
def test_dataset_with_0d_data(): d = sc.Dataset() d['Scalar'] = sc.Variable(1.2) sc.table(d)