def quick_plot(self):
try:
plot_dataset(self.ds)
plt.show(block=True)
except Exception as e:
self.parent.show_error(
'Error',
"Couldn't plot the data (likely due to a lack of data)", e)
def _plot_ds(ds: DataSet) -> None:
try:
# `get_data_by_id` might fail
nplots = len(get_data_by_id(ds.run_id)) # TODO: might be a better way
nrows = math.ceil(nplots / 2) if nplots != 1 else 1
ncols = 2 if nplots != 1 else 1
fig, axes = plt.subplots(nrows, ncols, figsize=(6 * ncols, 4 * nrows))
# `plot_dataset` might also fail.
plot_dataset(ds, axes=axes.flatten())
fig.tight_layout()
plt.show(fig)
except Exception as e:
print(e) # TODO: print complete traceback
def plot(
data: DataSetProtocol,
save_pdf: bool = True,
save_png: bool = True
) -> Tuple[DataSetProtocol, List[matplotlib.axes.Axes],
List[Optional[matplotlib.colorbar.Colorbar]], ]:
"""
The utility function to plot results and save the figures either in pdf or
png or both formats.
Args:
data: The QCoDeS dataset to be plotted.
save_pdf: Save figure in pdf format.
save_png: Save figure in png format.
"""
dataid = data.captured_run_id
axes, cbs = plot_dataset(data)
mainfolder = config.user.mainfolder
experiment_name = data.exp_name
sample_name = data.sample_name
storage_dir = os.path.join(mainfolder, experiment_name, sample_name)
os.makedirs(storage_dir, exist_ok=True)
png_dir = os.path.join(storage_dir, 'png')
pdf_dif = os.path.join(storage_dir, 'pdf')
os.makedirs(png_dir, exist_ok=True)
os.makedirs(pdf_dif, exist_ok=True)
for i, ax in enumerate(axes):
if save_pdf:
full_path = os.path.join(pdf_dif, f'{dataid}_{i}.pdf')
ax.figure.savefig(full_path, dpi=500)
if save_png:
full_path = os.path.join(png_dir, f'{dataid}_{i}.png')
ax.figure.savefig(full_path, dpi=500)
res = data, axes, cbs
return res
def _plot_ds(ds: DataSet) -> None:
plot_dataset(ds) # might fail
plt.show()
def test_plot_dataset_2d_shaped(experiment, request, nan_setpoints, shifted):
"""
Test plotting of preshaped data on a grid that may or may not be shifted
with and without nans in the set points.
"""
inst = DummyInstrument("dummy", gates=["s1", "m1", "s2"])
request.addfinalizer(inst.close)
inst.m1.get = np.random.randn
meas = Measurement()
meas.register_parameter(inst.s1)
meas.register_parameter(inst.s2)
meas.register_parameter(inst.m1, setpoints=(inst.s1, inst.s2))
outer_shape = 10
inner_shape = 20
meas.set_shapes(
detect_shape_of_measurement((inst.m1, ), (outer_shape, inner_shape)))
shift = 0
with meas.run() as datasaver:
try:
for outer in np.linspace(0, 9, outer_shape):
for inner in np.linspace(0 + shift, 10 + shift, inner_shape):
datasaver.add_result((inst.s1, outer), (inst.s2, inner),
(inst.m1, inst.m1()))
if inner > 7 and outer > 6 and nan_setpoints:
raise TerminateLoopException
if shifted:
shift += 1
except TerminateLoopException:
pass
axes, cbs = plot_dataset(datasaver.dataset)
xlims = axes[0].get_xlim()
ylims = axes[0].get_ylim()
# check that this generates a QuadMesh which is the expected output of pcolormesh
assert any(
isinstance(mplobj, QuadMesh) for mplobj in axes[0].get_children())
if nan_setpoints and shifted:
assert xlims[0] == -0.5
assert xlims[1] == 7.5
assert ylims[0] < 0
assert ylims[0] > -1.0
assert ylims[1] > 16
assert ylims[1] < 17
elif not nan_setpoints and shifted:
assert xlims[0] == -0.5
assert xlims[1] == 9.5
assert ylims[0] < 0
assert ylims[0] > -1.0
assert ylims[1] > 19
assert ylims[1] < 20
elif nan_setpoints and not shifted:
assert xlims[0] == -0.5
assert xlims[1] == 7.5
assert ylims[0] < 0
assert ylims[0] > -1.0
assert ylims[1] > 10
assert ylims[1] < 11
else:
assert xlims[0] == -0.5
assert xlims[1] == 9.5
assert ylims[0] < 0
assert ylims[0] > -1.0
assert ylims[1] > 10
assert ylims[1] < 11