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_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_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_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_1d_three_entries_with_labels():
N = 50
d = make_dense_dataset(ndim=1, labels=True)
d["Background"] = sc.Variable(['tof'],
values=2.0 * np.random.rand(N),
unit=sc.units.counts)
d.coords['x'] = sc.Variable(['x'],
values=np.arange(N).astype(np.float64),
unit=sc.units.m)
d["Sample2"] = sc.Variable(['x'],
values=10.0 * np.random.rand(N),
unit=sc.units.counts)
d.coords["Xlabels"] = sc.Variable(['x'],
values=np.linspace(151., 155., N),
unit=sc.units.s)
plot(d, axes={'x': "Xlabels", 'tof': "somelabels"})
def test_plot_dataset_view():
d = make_dense_dataset(ndim=2)
plot(d['x', 0])
def test_plot_data_array():
d = make_dense_dataset(ndim=1)
plot(d["Sample"])
def test_plot_sliceviewer_with_1d_projection():
d = make_dense_dataset(ndim=3)
plot(d, projection="1d")
def test_plot_2d_image_with_log_scale_xy():
plot(make_dense_dataset(ndim=2), scale={'tof': 'log', 'x': 'log'})
def test_plot_1d_bin_edges_with_variances():
d = make_dense_dataset(ndim=1, variances=True, binedges=True)
plot(d)
def test_plot_2d_image_with_cmap():
plot(make_dense_dataset(ndim=2), cmap='jet')
def test_plot_2d_with_masks():
plot(make_dense_dataset(ndim=2, masks=True))
def test_plot_1d_with_labels():
d = make_dense_dataset(ndim=1, labels=True)
plot(d, axes=["somelabels"])
def test_plot_2d_image_with_filename():
plot(make_dense_dataset(ndim=2), filename='image.pdf')
def test_plot_2d_image_with_bin_edges():
plot(make_dense_dataset(ndim=2, binedges=True))
def test_plot_2d_image_with_attrss():
plot(make_dense_dataset(ndim=2, attrs=True), axes={'x': 'attr'})
def test_plot_2d_image_with_labels():
plot(make_dense_dataset(ndim=2, labels=True), axes={'x': 'somelabels'})
def test_plot_2d_image_with_yaxis_specified():
plot(make_dense_dataset(ndim=2), axes={'y': 'tof'})
def test_plot_1d():
d = make_dense_dataset(ndim=1)
plot(d)
def test_plot_2d_with_masks_and_labels():
plot(make_dense_dataset(ndim=2, masks=True, labels=True),
axes={'x': 'somelabels'})
def test_plot_1d_bin_edges():
d = make_dense_dataset(ndim=1, binedges=True)
plot(d)
def test_plot_1d_two_entries_on_same_plot():
d = make_dense_dataset(ndim=1)
d["Background"] = sc.Variable(['tof'],
values=2.0 * np.random.rand(50),
unit=sc.units.counts)
plot(d)
def test_plot_1d_log_axes():
d = make_dense_dataset(ndim=1)
plot(d, logx=True)
plot(d, logy=True)
plot(d, logxy=True)
def test_plot_2d_image_with_vmin_vmax_with_log():
plot(make_dense_dataset(ndim=2), vmin=0.1, vmax=0.9, norm='log')
def test_plot_1d_two_separate_entries():
d = make_dense_dataset(ndim=1)
d["Background"] = sc.Variable(['tof'],
values=2.0 * np.random.rand(50),
unit=sc.units.kg)
plot(d)
def test_plot_2d_image_with_with_nan_with_log():
d = make_dense_dataset(ndim=2)
d['Sample'].values[0, 0] = np.nan
plot(d, norm='log')
def test_plot_2d_image_with_non_regular_bin_edges_resolution():
d = make_dense_dataset(ndim=2, binedges=True)
d.coords['tof'].values = d.coords['tof'].values**2
plot(d, resolution=128)
def test_plot_2d_image_int32():
plot(make_dense_dataset(ndim=2, dtype=sc.dtype.int32))
def test_plot_1d_with_masks():
d = make_dense_dataset(ndim=1, masks=True)
plot(d)
def test_plot_collapse():
d = make_dense_dataset(ndim=2)
plot(sc.collapse(d["Sample"], keep='tof'))
