def test_plot_2d_with_dimension_of_size_2():
a = sc.DataArray(data=sc.Variable(dims=['y', 'x'], shape=[2, 4]),
coords={
'x': sc.Variable(dims=['x'], values=[1, 2, 3, 4]),
'y': sc.Variable(dims=['y'], values=[1, 2])
})
plot(a)
def test_plot_customized_axes():
d = make_dense_dataset(ndim=3)
plot(d["Sample"],
projection="3d",
xlabel="MyXlabel",
ylabel="MyYlabel",
zlabel="MyZlabel")
def test_plot_2d_with_decreasing_edges():
a = sc.DataArray(data=sc.Variable(dims=['y', 'x'],
values=np.arange(12).reshape(3, 4)),
coords={
'x': sc.Variable(dims=['x'], values=[4, 3, 2, 1]),
'y': sc.Variable(dims=['y'], values=[1, 2, 3])
})
plot(a)
def test_plot_1d_two_entries_hide_variances():
d = make_dense_dataset(ndim=1, variances=True)
d["Background"] = sc.Variable(['tof'],
values=2.0 * np.random.rand(50),
unit=sc.units.counts)
plot(d, errorbars=False)
# When variances are not present, the plot does not fail, is silently does
# not show variances
plot(d, errorbars={"Sample": False, "Background": True})
def test_plot_string_axis_labels_1d_short():
d = sc.Dataset()
N = 5
d.coords[Dim.X] = sc.Variable(dims=[Dim.X],
values=["a", "b", "c", "d", "e"],
unit=sc.units.m)
d["Sample"] = sc.Variable([Dim.X],
values=np.random.rand(N),
unit=sc.units.counts)
plot(d)
def test_plot_4d_with_masks_projection_3d():
data = sc.DataArray(data=sc.Variable(
dims=['pack', 'tube', 'straw', 'pixel'],
values=np.random.rand(2, 8, 7, 256)),
coords={})
a = np.sin(np.linspace(0, 3.14, num=256))
data += sc.Variable(dims=['pixel'], values=a)
data.masks['tube_ends'] = sc.Variable(dims=['pixel'],
values=np.where(
a > 0.5, True, False))
plot(data, projection="3d")
def test_plot_string_axis_labels_1d():
d = sc.Dataset()
N = 10
d.coords['x'] = sc.Variable(
dims=['x'],
values=["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"],
unit=sc.units.m)
d["Sample"] = sc.Variable(['x'],
values=np.random.rand(N),
unit=sc.units.counts)
plot(d)
def test_plot_2d_with_labels_but_no_dimension_coord():
N = 50
M = 10
y = np.arange(M).astype(np.float)
z = np.random.random([M, N])
d = sc.Dataset()
d.coords['y'] = sc.Variable(['y'], values=y, unit=sc.units.m)
d['Signal'] = sc.Variable(['y', 'x'], values=z, unit=sc.units.kg)
d.coords['somelabels'] = sc.Variable(['x'],
values=np.linspace(101., 155., N),
unit=sc.units.s)
plot(d, axes=['y', 'somelabels'])
def test_plot_3d_data_ragged():
"""
This test has caught MANY bugs and should not be disabled.
"""
d = make_dense_dataset(ndim=3, ragged=True)
plot(d)
# Also check that it raises an error if we try to have ragged coord along
# slider dim
with pytest.raises(RuntimeError) as e:
plot(d, axes={'x': 'tof', 'y': 'x'})
assert str(e.value) == ("A ragged coordinate cannot lie along "
"a slider dimension, it must be one of "
"the displayed dimensions.")
def test_plot_2d_ragged_coord_x_and_y_edges():
N = 10
M = 5
x = np.arange(N).astype(np.float64)
y = np.arange(M).astype(np.float64)
xx, yy = np.meshgrid(x, y)
z = np.random.random([M, N])
for i in range(M):
xx[i] *= (i + 1.0)
d = sc.Dataset()
d.coords['x'] = sc.Variable(['y', 'x'], values=xx, unit=sc.units.m)
d.coords['y'] = sc.Variable(['y'], values=y, unit=sc.units.m)
d['a'] = sc.Variable(['y', 'x'], values=z, unit=sc.units.counts)
plot(d)
def test_plot_vector_axis_labels_1d():
d = sc.Dataset()
N = 10
vecs = []
for i in range(N):
vecs.append(np.random.random(3))
d.coords[Dim.X] = sc.Variable([Dim.X],
values=vecs,
unit=sc.units.m,
dtype=sc.dtype.vector_3_float64)
d["Sample"] = sc.Variable([Dim.X],
values=np.random.rand(N),
unit=sc.units.counts)
plot(d)
def test_plot_1d_three_entries_with_labels():
N = 50
d = make_dense_dataset(ndim=1, labels=True)
d["Background"] = sc.Variable([Dim.Tof],
values=2.0 * np.random.rand(N),
unit=sc.units.counts)
d.coords[Dim.X] = sc.Variable([Dim.X],
values=np.arange(N).astype(np.float64),
unit=sc.units.m)
d["Sample2"] = sc.Variable([Dim.X],
values=10.0 * np.random.rand(N),
unit=sc.units.counts)
d.labels["Xlabels"] = sc.Variable([Dim.X],
values=np.linspace(151., 155., N),
unit=sc.units.s)
plot(d, axes={Dim.X: "Xlabels", Dim.Tof: "somelabels"})
def test_plot_from_dict_data_array_2d():
plot({
"data": {
"dims": ["x", "y"],
"values": np.random.random([20, 10])
},
"coords": {
"x": {
"dims": ["x"],
"values": np.arange(21)
},
"y": {
"dims": ["y"],
"values": np.arange(11)
}
}
})
def test_plot_2d_ragged_coord_with_masks():
N = 10
M = 5
x = np.arange(N + 1).astype(np.float64)
y = np.arange(M).astype(np.float64)
xx, yy = np.meshgrid(x, y)
z = np.random.random([M, N])
for i in range(M):
xx[i] *= (i + 1.0)
d = sc.Dataset()
d.coords['x'] = sc.Variable(['y', 'x'], values=xx, unit=sc.units.m)
d.coords['y'] = sc.Variable(['y'], values=y, unit=sc.units.m)
d['a'] = sc.Variable(['y', 'x'], values=z, unit=sc.units.counts)
d['a'].masks['b'] = sc.Variable(['y', 'x'],
values=np.where(z < 0.5, True, False),
dtype=bool)
plot(d)
def test_sparse_data_slice_with_on_the_fly_histogram():
N = 50
M = 10
var = sc.Variable(dims=[Dim.X, Dim.Tof],
shape=[M, sc.Dimensions.Sparse],
unit=sc.units.us)
for i in range(M):
v = np.random.normal(50.0, scale=20.0, size=int(np.random.rand() * N))
var[Dim.X, i].values = v
d = sc.Dataset()
d.coords[Dim.X] = sc.Variable([Dim.X],
values=np.arange(M),
unit=sc.units.m)
d['a'] = sc.DataArray(coords={Dim.Tof: var})
d['b'] = sc.DataArray(coords={Dim.Tof: var * 1.1})
plot(d[Dim.X, 4], bins=100)
def test_plot_2d_with_dimension_of_size_1():
N = 10
M = 1
x = np.arange(N, dtype=np.float64)
y = np.arange(M, dtype=np.float64)
z = np.arange(M + 1, dtype=np.float64)
d = sc.Dataset()
d.coords[Dim.X] = sc.Variable([Dim.X], values=x, unit=sc.units.m)
d.coords[Dim.Y] = sc.Variable([Dim.Y], values=y, unit=sc.units.m)
d.coords[Dim.Z] = sc.Variable([Dim.Z], values=z, unit=sc.units.m)
d["a"] = sc.Variable([Dim.Y, Dim.X],
values=np.random.random([M, N]),
unit=sc.units.counts)
d["b"] = sc.Variable([Dim.Z, Dim.X],
values=np.random.random([M, N]),
unit=sc.units.counts)
plot(d["a"])
plot(d["b"])
def test_plot_string_and_vector_axis_labels_2d():
N = 10
M = 5
vecs = []
for i in range(N):
vecs.append(np.random.random(3))
d = sc.Dataset()
d.coords['x'] = sc.Variable(['x'],
values=vecs,
unit=sc.units.m,
dtype=sc.dtype.vector_3_float64)
d.coords['y'] = sc.Variable(['y'],
values=['a', 'b', 'c', 'd', 'e'],
unit=sc.units.m)
d['Signal'] = sc.Variable(['y', 'x'],
values=np.random.random([M, N]),
unit=sc.units.counts)
plot(d)
def test_plot_string_and_vector_axis_labels_2d():
N = 10
M = 5
vecs = []
for i in range(N):
vecs.append(np.random.random(3))
d = sc.Dataset()
d.coords[Dim.X] = sc.Variable([Dim.X],
values=vecs,
unit=sc.units.m,
dtype=sc.dtype.vector_3_float64)
d.coords[Dim.Y] = sc.Variable([Dim.Y],
values=["a", "b", "c", "d", "e"],
unit=sc.units.m)
d["Signal"] = sc.Variable([Dim.Y, Dim.X],
values=np.random.random([M, N]),
unit=sc.units.counts)
plot(d)
def test_plot_projection_3d_with_vectors():
N = 1000
M = 100
theta = np.random.random(N) * np.pi
phi = np.random.random(N) * 2.0 * np.pi
r = 10.0 + (np.random.random(N) - 0.5)
x = r * np.sin(theta) * np.sin(phi)
y = r * np.sin(theta) * np.cos(phi)
z = r * np.cos(theta)
tof = np.arange(M).astype(np.float)
a = np.arange(M * N).reshape([M, N]) * np.sin(y)
d = sc.Dataset()
d.coords['xyz'] = sc.Variable(['xyz'],
values=np.array([x, y, z]).T,
dtype=sc.dtype.vector_3_float64)
d.coords['tof'] = sc.Variable(['tof'], values=tof)
d['a'] = sc.Variable(['tof', 'xyz'], values=a)
plot(d, projection="3d", positions="xyz")
def test_plot_2d_with_dimension_of_size_1():
N = 10
M = 1
x = np.arange(N, dtype=np.float64)
y = np.arange(M, dtype=np.float64)
z = np.arange(M + 1, dtype=np.float64)
d = sc.Dataset()
d.coords['x'] = sc.Variable(['x'], values=x, unit=sc.units.m)
d.coords['y'] = sc.Variable(['y'], values=y, unit=sc.units.m)
d.coords['z'] = sc.Variable(['z'], values=z, unit=sc.units.m)
d['a'] = sc.Variable(['y', 'x'],
values=np.random.random([M, N]),
unit=sc.units.counts)
d['b'] = sc.Variable(['z', 'x'],
values=np.random.random([M, N]),
unit=sc.units.counts)
plot(d['a'])
plot(d['b'])
def test_plot_3d_data_with_ragged_bins():
N = 10
M = 8
L = 5
x = np.arange(N + 1).astype(np.float64)
y = np.arange(M).astype(np.float64)
z = np.arange(L).astype(np.float64)
zz, yy, xx = np.meshgrid(z, y, x, indexing='ij')
a = np.random.random([L, M, N])
for i in range(M):
for j in range(L):
xx[j, i, :] *= (i + j + 1.0)
d = sc.Dataset()
d.coords['x'] = sc.Variable(['z', 'y', 'x'], values=xx, unit=sc.units.m)
d.coords['y'] = sc.Variable(['y'], values=y, unit=sc.units.m)
d.coords['z'] = sc.Variable(['z'], values=z, unit=sc.units.m)
d['a'] = sc.Variable(['z', 'y', 'x'], values=a, unit=sc.units.counts)
plot(d)
def test_plot_sliceviewer_with_1d_projection_with_nans():
d = make_dense_dataset(ndim=3, binedges=True, variances=True)
d['Sample'].values = np.where(d['Sample'].values < -0.8, np.nan,
d['Sample'].values)
d['Sample'].variances = np.where(d['Sample'].values < 0., np.nan,
d['Sample'].variances)
p = plot(d, projection='1d')
# Move the sliders
p['tof.x.y.counts']['widgets']['sliders']['tof'].value = 10
p['tof.x.y.counts']['widgets']['sliders']['x'].value = 10
p['tof.x.y.counts']['widgets']['sliders']['y'].value = 10
def test_plot_variable():
N = 50
v1d = sc.Variable([Dim.Tof],
values=np.random.rand(N),
unit=sc.units.counts)
v2d = sc.Variable([Dim.Tof, Dim.X],
values=np.random.rand(N, N),
unit=sc.units.K)
v3d = sc.Variable([Dim.Tof, Dim.X, Dim.Y],
values=np.random.rand(N, N, N),
unit=sc.units.m)
plot(v1d)
plot(v2d)
plot(v3d)
def test_plot_3d_sparse_data():
d = make_sparse_dataset(ndim=3)
plot(d)
def test_plot_data_array():
d = make_dense_dataset(ndim=1)
plot(d["Sample"])
def test_plot_dataset_view():
d = make_dense_dataset(ndim=2)
plot(d[Dim.X, 0])
def test_plot_3d_sparse_data_with_Variable_bins():
d = make_sparse_dataset(ndim=3)
bins = sc.Variable([Dim.Tof],
values=np.linspace(0.0, 105.0, 50),
unit=sc.units.us)
plot(d, bins=bins)
def test_plot_3d_sparse_data_with_nparray_bins():
d = make_sparse_dataset(ndim=3)
plot(d, bins=np.linspace(0.0, 105.0, 50))
def test_plot_3d_sparse_data_with_int_bins():
d = make_sparse_dataset(ndim=3)
plot(d, bins=50)
def test_plot_3d_sparse_data_with_weights():
d = make_sparse_dataset(ndim=3, data=True)
plot(d)
