def show_measurement_2d(measurement_or_func):
try:
measurement = measurement_or_func()
return_callback = True
except TypeError:
measurement = measurement_or_func
return_callback = False
figure, image = create_image_figure()
toolbar = Toolbar(figure=figure)
figure.axes[0].label = format_label(measurement.calibrations[-2])
figure.axes[1].label = format_label(measurement.calibrations[-1])
figure.marks = (image, )
if return_callback:
update_bqplot_image(image, measurement)
def callback(*args, **kwargs):
update_bqplot_image(image, measurement_or_func())
return widgets.VBox([figure, toolbar]), callback
else:
return widgets.VBox([figure, toolbar])
def show_measurement_1d(measurement,
ax=None,
figsize=None,
legend=False,
title=None,
label=None,
**kwargs):
"""
Show line function
Function to display a line scan.
Parameters
----------
array : ndarray
Array of measurement values along a line.
calibration : calibration object
Spatial calibration for the line.
ax : axes object, optional
pyplot axes object.
title : str, optional
Title for the plot. Default is None.
legend : bool, optional
Option to display a plot legend. Default is False.
kwargs :
Remaining keyword arguments are passed to pyplot.
"""
calibration = measurement.calibrations[0]
array = measurement.array
if calibration is None:
x = np.arange(len(array))
else:
x = np.linspace(calibration.offset,
calibration.offset + len(array) * calibration.sampling,
len(array))
if ax is None:
fig, ax = plt.subplots(figsize=figsize)
if not label:
label = measurement.name
lines = ax.plot(x, array, label=label, **kwargs)
ax.set_xlabel(format_label(calibration))
ax.set_ylabel(format_label(measurement))
if legend:
ax.legend()
if title is not None:
ax.set_title(title)
return ax, lines[0]
def show_measurement_1d(measurements_or_func,
figure=None,
throttling=False,
**kwargs):
if figure is None:
figure = plt.figure(fig_margin={
'top': 0,
'bottom': 50,
'left': 50,
'right': 0
})
figure.layout.height = '250px'
figure.layout.width = '300px'
try:
measurements = measurements_or_func()
return_callback = True
except TypeError:
measurements = measurements_or_func
return_callback = False
lines = []
for measurement, color in zip(measurements, TABLEAU_COLORS.values()):
calibration = measurement.calibrations[0]
array = measurement.array
x = np.linspace(calibration.offset,
calibration.offset + len(array) * calibration.sampling,
len(array))
line = plt.plot(x, array, colors=[color], **kwargs)
figure.axes[0].label = format_label(measurement.calibrations[0])
lines.append(line)
# figure.axes[1].label = format_label(measurement)
if return_callback:
@throttle(throttling)
def callback(*args, **kwargs):
for line, measurement in zip(lines, measurements_or_func()):
x = np.linspace(
calibration.offset,
calibration.offset + len(array) * calibration.sampling,
len(array))
line.x = x
line.y = measurement.array
return figure, callback
else:
return figure
def show_measurement_2d(measurement,
ax=None,
figsize=None,
cbar=False,
cbar_label=None,
cmap='gray',
discrete_cmap=False,
vmin=None,
vmax=None,
power=1.,
log_scale=False,
title=None,
equal_ticks=False,
is_rgb=False,
x_label=None,
y_label=None,
**kwargs):
"""
Show image function
Function to display an image.
Parameters
----------
array : ndarray
Image array.
calibrations : tuple of calibration objects.
Spatial calibrations.
ax : axes object
pyplot axes object.
title : str, optional
Image title. Default is None.
colorbar : bool, optional
Option to show a colorbar. Default is False.
cmap : str, optional
Colormap name. Default is 'gray'.
figsize : float, pair of float, optional
Size of the figure in inches, either as a square for one number or a rectangle for two. Default is None.
scans : ndarray, optional
Array of scans. Default is None.
discrete : bool, optional
Option to discretize intensity values to integers. Default is False.
cbar_label : str, optional
Text label for the color bar. Default is None.
vmin : float, optional
Minimum of the intensity scale. Default is None.
vmax : float, optional
Maximum of the intensity scale. Default is None.
kwargs :
Remaining keyword arguments are passed to pyplot.
"""
if ax is None:
fig, ax = plt.subplots(figsize=figsize)
if is_rgb:
calibrations = measurement.calibrations[-3:-1]
else:
calibrations = measurement.calibrations[-2:]
if not is_rgb:
array = measurement.array[(0, ) * (measurement.dimensions - 2) +
(slice(None), ) * 2]
else:
array = measurement.array[:, :, :]
if np.iscomplexobj(array):
array = domain_coloring(array)
if power != 1:
array = array**power
if log_scale:
array = np.log(array)
extent = []
for calibration, num_elem in zip(calibrations, array.shape):
extent.append(calibration.offset)
extent.append(calibration.offset + num_elem * calibration.sampling -
calibration.sampling)
if vmin is None:
vmin = np.min(array)
if discrete_cmap:
vmin -= .5
if vmax is None:
vmax = np.max(array)
if discrete_cmap:
vmax += .5
if discrete_cmap:
cmap = plt.get_cmap(cmap, np.max(array) - np.min(array) + 1)
im = ax.imshow(np.swapaxes(array, 0, 1),
extent=extent,
cmap=cmap,
origin='lower',
vmin=vmin,
vmax=vmax,
interpolation='nearest',
**kwargs)
if cbar:
if cbar_label is None:
cbar_label = format_label(measurement)
cax = plt.colorbar(im, ax=ax, label=cbar_label)
if discrete_cmap:
cax.set_ticks(ticks=np.arange(np.min(array), np.max(array) + 1))
if x_label is None:
x_label = format_label(calibrations[-2])
if y_label is None:
y_label = format_label(calibrations[-1])
ax.set_xlabel(x_label)
ax.set_ylabel(y_label)
if title is not None:
ax.set_title(title)
elif len(measurement.array.shape) > 2:
if any([n > 1 for n in measurement.array.shape[:-2]]):
ax.set_title(
f'Slice {(0,) * (len(measurement.array.shape) - 2)} of {measurement.array.shape} measurement'
)
if equal_ticks:
d = max(np.diff(ax.get_xticks())[0], np.diff(ax.get_yticks())[0])
xticks = np.arange(*ax.get_xlim(), d)
yticks = np.arange(*ax.get_ylim(), d)
ax.set_xticks(xticks)
ax.set_yticks(yticks)
return ax, im
def show_measurement_2d(measurement,
ax=None,
figsize=None,
colorbar=False,
cmap='gray',
discrete_cmap=False,
vmin=None,
vmax=None,
power=1.,
log_scale=False,
title=None,
**kwargs):
"""
Show image function
Function to display an image.
Parameters
----------
array : ndarray
Image array.
calibrations : tuple of calibration objects.
Spatial calibrations.
ax : axes object
pyplot axes object.
title : str, optional
Image title. Default is None.
colorbar : bool, optional
Option to show a colorbar. Default is False.
cmap : str, optional
Colormap name. Default is 'gray'.
figsize : float, pair of float, optional
Size of the figure in inches, either as a square for one number or a rectangle for two. Default is None.
scans : ndarray, optional
Array of scans. Default is None.
discrete : bool, optional
Option to discretize intensity values to integers. Default is False.
cbar_label : str, optional
Text label for the color bar. Default is None.
vmin : float, optional
Minimum of the intensity scale. Default is None.
vmax : float, optional
Maximum of the intensity scale. Default is None.
kwargs :
Remaining keyword arguments are passed to pyplot.
"""
if ax is None:
fig, ax = plt.subplots(figsize=figsize)
calibrations = measurement.calibrations[-2:]
array = measurement.array[(0, ) * (measurement.dimensions - 2) +
(slice(None), ) * 2]
if power != 1:
array = array**power
if log_scale:
array = np.log(array)
extent = []
for calibration, num_elem in zip(calibrations, array.shape):
extent.append(calibration.offset)
extent.append(calibration.offset + num_elem * calibration.sampling -
calibration.sampling)
if vmin is None:
vmin = np.min(array)
if discrete_cmap:
vmin -= .5
if vmax is None:
vmax = np.max(array)
if discrete_cmap:
vmax += .5
if discrete_cmap:
cmap = plt.get_cmap(cmap, np.max(array) - np.min(array) + 1)
im = ax.imshow(array.T,
extent=extent,
cmap=cmap,
origin='lower',
vmin=vmin,
vmax=vmax,
interpolation='nearest',
**kwargs)
if colorbar:
cax = plt.colorbar(im, ax=ax, label=format_label(measurement))
if discrete_cmap:
cax.set_ticks(ticks=np.arange(np.min(array), np.max(array) + 1))
ax.set_xlabel(format_label(calibrations[-2]))
ax.set_ylabel(format_label(calibrations[-1]))
if title is not None:
ax.set_title(title)
return ax, im