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)
