def fit_component_ipy(obj, **kwargs):
wdict = {}
only_current = Checkbox()
iterpath = enum2dropdown(obj.traits()["iterpath"])
def disable_iterpath(change):
iterpath.disabled = change.new
only_current.observe(disable_iterpath, "value")
wdict["only_current"] = only_current
wdict["iterpath"] = iterpath
help_text = HTML(
"Click on the signal figure and drag to the right to select a"
"range. Press `Fit` to fit the component in that range. If only "
"current is unchecked the fit is performed in the whole dataset.",
layout=ipywidgets.Layout(width="auto"))
wdict["help_text"] = help_text
help = Accordion(children=[help_text], selected_index=None)
set_title_container(help, ["Help"])
link((obj, "only_current"), (only_current, "value"))
link((obj, "iterpath"), (iterpath, "value"))
fit = Button(description="Fit", tooltip="Fit in the selected signal range")
close = Button(
description="Close",
tooltip="Close widget and remove span selector from the signal figure."
)
wdict["close_button"] = close
wdict["fit_button"] = fit
def on_fit_clicked(b):
obj._fit_fired()
fit.on_click(on_fit_clicked)
box = VBox([
labelme("Only current", only_current),
labelme("Iterpath", wdict["iterpath"]), help,
HBox((fit, close))
])
def on_close_clicked(b):
obj.span_selector_switch(False)
box.close()
close.on_click(on_close_clicked)
return {
"widget": box,
"wdict": wdict,
}
def spikes_removal_ipy(obj, **kwargs):
wdict = {}
threshold = ipywidgets.FloatText()
add_noise = ipywidgets.Checkbox()
default_spike_width = ipywidgets.IntText()
interpolator_kind = enum2dropdown(obj.traits()["interpolator_kind"])
spline_order = ipywidgets.IntSlider(min=1, max=10)
progress_bar = ipywidgets.IntProgress(max=len(obj.coordinates) - 1)
help = ipywidgets.HTML(
value=SPIKES_REMOVAL_INSTRUCTIONS.replace('\n', '<br/>'))
help = ipywidgets.Accordion(children=[help])
help.set_title(0, "Help")
show_diff = ipywidgets.Button(
description="Show derivative histogram",
tooltip="This figure is useful to estimate the threshold.",
layout=ipywidgets.Layout(width="auto"))
close = ipywidgets.Button(
description="Close",
tooltip="Close widget and remove span selector from the signal figure.")
next = ipywidgets.Button(
description="Find next",
tooltip="Find next spike")
previous = ipywidgets.Button(
description="Find previous",
tooltip="Find previous spike")
remove = ipywidgets.Button(
description="Remove spike",
tooltip="Remove spike and find next one.")
wdict["threshold"] = threshold
wdict["add_noise"] = add_noise
wdict["default_spike_width"] = default_spike_width
wdict["interpolator_kind"] = interpolator_kind
wdict["spline_order"] = spline_order
wdict["progress_bar"] = progress_bar
wdict["show_diff_button"] = show_diff
wdict["close_button"] = close
wdict["next_button"] = next
wdict["previous_button"] = previous
wdict["remove_button"] = remove
def on_show_diff_clicked(b):
obj._show_derivative_histogram_fired()
show_diff.on_click(on_show_diff_clicked)
def on_next_clicked(b):
obj.find()
next.on_click(on_next_clicked)
def on_previous_clicked(b):
obj.find(back=True)
previous.on_click(on_previous_clicked)
def on_remove_clicked(b):
obj.apply()
remove.on_click(on_remove_clicked)
labeled_spline_order = labelme("Spline order", spline_order)
def enable_interpolator_kind(change):
if change.new == "Spline":
for child in labeled_spline_order.children:
child.layout.display = ""
else:
for child in labeled_spline_order.children:
child.layout.display = "none"
interpolator_kind.observe(enable_interpolator_kind, "value")
link((obj, "interpolator_kind"),
(interpolator_kind, "value"))
link((obj, "threshold"), (threshold, "value"))
link((obj, "add_noise"), (add_noise, "value"))
link((obj, "default_spike_width"),
(default_spike_width, "value"))
link((obj, "spline_order"), (spline_order, "value"))
link((obj, "index"), (progress_bar, "value"))
# Trigger the function that controls the visibility as
# setting the default value doesn't trigger it.
class Dummy:
new = interpolator_kind.value
enable_interpolator_kind(change=Dummy())
advanced = ipywidgets.Accordion((
ipywidgets.VBox([
labelme("Add noise", add_noise),
labelme("Interpolator kind", interpolator_kind),
labelme("Default spike width", default_spike_width),
labelme("Spline order", spline_order), ]),))
advanced.set_title(0, "Advanced settings")
box = ipywidgets.VBox([
ipywidgets.VBox([
show_diff,
labelme("Threshold", threshold),
labelme("Progress", progress_bar), ]),
advanced,
help,
ipywidgets.HBox([previous, next, remove, close])
])
def on_close_clicked(b):
obj.span_selector_switch(False)
box.close()
close.on_click(on_close_clicked)
return {
"widget": box,
"wdict": wdict,
}
def remove_background_ipy(obj, **kwargs):
wdict = {}
left = ipywidgets.FloatText(disabled=True, description="Left")
right = ipywidgets.FloatText(disabled=True, description="Right")
link((obj, "ss_left_value"), (left, "value"))
link((obj, "ss_right_value"), (right, "value"))
fast = ipywidgets.Checkbox(description="Fast")
zero_fill = ipywidgets.Checkbox(description="Zero Fill")
help = ipywidgets.HTML(
"Click on the signal figure and drag to the right to select a "
"range. Press `Apply` to remove the background in the whole dataset. "
"If \"Fast\" is checked, the background parameters are estimated "
"using a fast (analytical) method that can compromise accuracy. "
"When unchecked, non-linear least squares is employed instead. "
"If \"Zero Fill\" is checked, all the channels prior to the fitting "
"region will be set to zero. "
"Otherwise the background subtraction will be performed in the "
"pre-fitting region as well.",)
wdict["help"] = help
help = ipywidgets.Accordion(children=[help])
help.set_title(0, "Help")
close = ipywidgets.Button(
description="Close",
tooltip="Close widget and remove span selector from the signal figure.")
apply = ipywidgets.Button(
description="Apply",
tooltip="Remove the background in the whole dataset.")
polynomial_order = ipywidgets.IntText(description="Polynomial order")
background_type = enum2dropdown(obj.traits()["background_type"])
background_type.description = "Background type"
def enable_poly_order(change):
if change.new == "Polynomial":
polynomial_order.layout.display = ""
else:
polynomial_order.layout.display = "none"
background_type.observe(enable_poly_order, "value")
link((obj, "background_type"), (background_type, "value"))
# Trigger the function that controls the visibility of poly order as
# setting the default value doesn't trigger it.
class Dummy:
new = background_type.value
enable_poly_order(change=Dummy())
link((obj, "polynomial_order"), (polynomial_order, "value"))
link((obj, "fast"), (fast, "value"))
link((obj, "zero_fill"), (zero_fill, "value"))
wdict["left"] = left
wdict["right"] = right
wdict["fast"] = fast
wdict["zero_fill"] = zero_fill
wdict["polynomial_order"] = polynomial_order
wdict["background_type"] = background_type
wdict["apply_button"] = apply
box = ipywidgets.VBox([
left, right,
background_type,
polynomial_order,
fast,
zero_fill,
help,
ipywidgets.HBox((apply, close)),
])
def on_apply_clicked(b):
obj.apply()
box.close()
apply.on_click(on_apply_clicked)
def on_close_clicked(b):
obj.span_selector_switch(False)
box.close()
close.on_click(on_close_clicked)
return {
"widget": box,
"wdict": wdict,
}
def find_peaks2D_ipy(obj, **kwargs):
wdict = {}
# Define widgets
# For "local max" method
local_max_distance = ipywidgets.IntSlider(min=1, max=20, value=3)
local_max_threshold = ipywidgets.FloatSlider(min=0, max=20, value=10)
# For "max" method
max_alpha = ipywidgets.FloatSlider(min=0, max=6, value=3)
max_distance = ipywidgets.IntSlider(min=1, max=20, value=10)
# For "minmax" method
minmax_distance = ipywidgets.FloatSlider(min=0, max=6, value=3)
minmax_threshold = ipywidgets.FloatSlider(min=0, max=20, value=10)
# For "Zaefferer" method
zaefferer_grad_threshold = ipywidgets.FloatSlider(min=0,
max=0.2,
value=0.1,
step=0.2 * 1E-1)
zaefferer_window_size = ipywidgets.IntSlider(min=2, max=80, value=40)
zaefferer_distance_cutoff = ipywidgets.FloatSlider(min=0,
max=100,
value=50)
# For "stat" method
stat_alpha = ipywidgets.FloatSlider(min=0, max=2, value=1)
stat_window_radius = ipywidgets.IntSlider(min=5, max=20, value=10)
stat_convergence_ratio = ipywidgets.FloatSlider(min=0, max=0.1, value=0.05)
# For "Laplacian of Gaussians" method
log_min_sigma = ipywidgets.FloatSlider(min=0, max=2, value=1)
log_max_sigma = ipywidgets.FloatSlider(min=0, max=100, value=50)
log_num_sigma = ipywidgets.FloatSlider(min=0, max=20, value=10)
log_threshold = ipywidgets.FloatSlider(min=0, max=0.4, value=0.2)
log_overlap = ipywidgets.FloatSlider(min=0, max=1, value=0.5)
log_log_scale = ipywidgets.Checkbox()
# For "Difference of Gaussians" method
dog_min_sigma = ipywidgets.FloatSlider(min=0, max=2, value=1)
dog_max_sigma = ipywidgets.FloatSlider(min=0, max=100, value=50)
dog_sigma_ratio = ipywidgets.FloatSlider(min=0, max=3.2, value=1.6)
dog_threshold = ipywidgets.FloatSlider(min=0, max=0.4, value=0.2)
dog_overlap = ipywidgets.FloatSlider(min=0, max=1, value=0.5)
# For "Cross correlation" method
xc_distance = ipywidgets.FloatSlider(min=0, max=10., value=5.)
xc_threshold = ipywidgets.FloatSlider(min=0, max=2., value=0.5)
wdict["local_max_distance"] = local_max_distance
wdict["local_max_threshold"] = local_max_threshold
wdict["max_alpha"] = max_alpha
wdict["max_distance"] = max_distance
wdict["minmax_distance"] = minmax_distance
wdict["minmax_threshold"] = minmax_threshold
wdict["zaefferer_grad_threshold"] = zaefferer_grad_threshold
wdict["zaefferer_window_size"] = zaefferer_window_size
wdict["zaefferer_distance_cutoff"] = zaefferer_distance_cutoff
wdict["stat_alpha"] = stat_alpha
wdict["stat_window_radius"] = stat_window_radius
wdict["stat_convergence_ratio"] = stat_convergence_ratio
wdict["log_min_sigma"] = log_min_sigma
wdict["log_max_sigma"] = log_max_sigma
wdict["log_num_sigma"] = log_num_sigma
wdict["log_threshold"] = log_threshold
wdict["log_overlap"] = log_overlap
wdict["log_log_scale"] = log_log_scale
wdict["dog_min_sigma"] = dog_min_sigma
wdict["dog_max_sigma"] = dog_max_sigma
wdict["dog_sigma_ratio"] = dog_sigma_ratio
wdict["dog_threshold"] = dog_threshold
wdict["dog_overlap"] = dog_overlap
wdict["xc_distance"] = xc_distance
wdict["xc_threshold"] = xc_threshold
# Connect
link((obj, "local_max_distance"), (local_max_distance, "value"))
link((obj, "local_max_threshold"), (local_max_threshold, "value"))
link((obj, "max_alpha"), (max_alpha, "value"))
link((obj, "max_distance"), (max_distance, "value"))
link((obj, "minmax_distance"), (minmax_distance, "value"))
link((obj, "minmax_threshold"), (minmax_threshold, "value"))
link((obj, "zaefferer_grad_threshold"),
(zaefferer_grad_threshold, "value"))
link((obj, "zaefferer_window_size"), (zaefferer_window_size, "value"))
link((obj, "zaefferer_distance_cutoff"),
(zaefferer_distance_cutoff, "value"))
link((obj, "stat_alpha"), (stat_alpha, "value"))
link((obj, "stat_window_radius"), (stat_window_radius, "value"))
link((obj, "stat_convergence_ratio"), (stat_convergence_ratio, "value"))
link((obj, "log_min_sigma"), (log_min_sigma, "value"))
link((obj, "log_max_sigma"), (log_max_sigma, "value"))
link((obj, "log_num_sigma"), (log_num_sigma, "value"))
link((obj, "log_threshold"), (log_threshold, "value"))
link((obj, "log_overlap"), (log_overlap, "value"))
link((obj, "log_log_scale"), (log_log_scale, "value"))
link((obj, "dog_min_sigma"), (dog_min_sigma, "value"))
link((obj, "dog_max_sigma"), (dog_max_sigma, "value"))
link((obj, "dog_sigma_ratio"), (dog_sigma_ratio, "value"))
link((obj, "dog_threshold"), (dog_threshold, "value"))
link((obj, "dog_overlap"), (dog_overlap, "value"))
link((obj, "xc_distance"), (xc_distance, "value"))
link((obj, "xc_threshold"), (xc_threshold, "value"))
close = ipywidgets.Button(description="Close",
tooltip="Close widget and close figure.")
compute = ipywidgets.Button(
description="Compute over navigation axes.",
tooltip="Find the peaks by iterating over the navigation axes.")
box_local_max = ipywidgets.VBox([
labelme("Distance", local_max_distance),
labelme("Threshold", local_max_threshold),
])
box_max = ipywidgets.VBox([ #max_alpha, max_distance
labelme("Alpha", max_alpha),
labelme("Distance", max_distance),
])
box_minmax = ipywidgets.VBox([
labelme("Distance", minmax_distance),
labelme("Threshold", minmax_threshold),
])
box_zaefferer = ipywidgets.VBox([
labelme("Gradient threshold", zaefferer_grad_threshold),
labelme("Window size", zaefferer_window_size),
labelme("Distance cutoff", zaefferer_distance_cutoff),
])
box_stat = ipywidgets.VBox([
labelme("Alpha", stat_alpha),
labelme("Radius", stat_window_radius),
labelme("Convergence ratio", stat_convergence_ratio),
])
box_log = ipywidgets.VBox([
labelme("Min sigma", log_min_sigma),
labelme("Max sigma", log_max_sigma),
labelme("Num sigma", log_num_sigma),
labelme("Threshold", log_threshold),
labelme("Overlap", log_overlap),
labelme("Log scale", log_log_scale),
])
box_dog = ipywidgets.VBox([
labelme("Min sigma", dog_min_sigma),
labelme("Max sigma", dog_max_sigma),
labelme("Sigma ratio", dog_sigma_ratio),
labelme("Threshold", dog_threshold),
labelme("Overlap", dog_overlap),
])
box_xc = ipywidgets.VBox([
labelme("Distance", xc_distance),
labelme("Threshold", xc_threshold),
])
box_dict = {
"Local Max": box_local_max,
"Max": box_max,
"Minmax": box_minmax,
"Zaefferer": box_zaefferer,
"Stat": box_stat,
"Laplacian of Gaussians": box_log,
"Difference of Gaussians": box_dog,
"Template matching": box_xc
}
method = enum2dropdown(obj.traits()["method"])
def update_method_parameters(change):
# Remove all parameters vbox widgets
for item, value in box_dict.items():
value.layout.display = "none"
if change.new == "Local max":
box_local_max.layout.display = ""
elif change.new == "Max":
box_max.layout.display = ""
elif change.new == "Minmax":
box_minmax.layout.display = ""
elif change.new == "Zaefferer":
box_zaefferer.layout.display = ""
elif change.new == "Stat":
box_stat.layout.display = ""
elif change.new == "Laplacian of Gaussians":
box_log.layout.display = ""
elif change.new == "Difference of Gaussians":
box_dog.layout.display = ""
elif change.new == "Template matching":
box_xc.layout.display = ""
method.observe(update_method_parameters, "value")
link((obj, "method"), (method, "value"))
# Trigger the function that controls the visibility as
# setting the default value doesn't trigger it.
class Dummy:
new = method.value
update_method_parameters(change=Dummy())
widgets_list = []
if obj.show_navigation_sliders:
nav_widget = get_ipy_navigation_sliders(
obj.signal.axes_manager.navigation_axes,
in_accordion=True,
random_position_button=True)
widgets_list.append(nav_widget['widget'])
wdict.update(nav_widget['wdict'])
l = [labelme("Method", method)]
l.extend([value for item, value in box_dict.items()])
method_parameters = ipywidgets.Accordion((ipywidgets.VBox(l), ))
set_title_container(method_parameters, ["Method parameters"])
widgets_list.extend([method_parameters, ipywidgets.HBox([compute, close])])
box = ipywidgets.VBox(widgets_list)
def on_compute_clicked(b):
obj.compute_navigation()
obj.signal._plot.close()
obj.close()
box.close()
compute.on_click(on_compute_clicked)
def on_close_clicked(b):
obj.signal._plot.close()
obj.close()
box.close()
close.on_click(on_close_clicked)
return {
"widget": box,
"wdict": wdict,
}