def setup_layout(self):
#layout setup
self.flights = bkwidgets.MultiSelect()
tile_provider = get_provider(Vendors.CARTODBPOSITRON)
p = bkplot.figure(x_axis_type='mercator', y_axis_type='mercator')
p.add_tile(tile_provider)
self.p2 = bkplot.figure()
self.lines = self.p2.line(x='time', y='param', source=self.source2)
self.hist = self.p2.quad(source=self.source3,
top='top',
bottom='bottom',
left='left',
right='right')
self.params = bkwidgets.Select(
options=['altitude', 'tas', 'fpf', 'performance_hist'],
value='altitude')
p2control = bklayouts.WidgetBox(self.params)
layout = bklayouts.layout(self.controls, p)
self.tables = bkwidgets.Select(options=self.tableList,
value=self.tableList[0])
self.time = bkwidgets.RangeSlider()
self.populated = False
#plot setup
self.points = p.triangle(x='longitude',
y='latitude',
angle='heading',
angle_units='deg',
alpha=0.5,
source=self.source)
hover = bk.models.HoverTool()
hover.tooltips = [("Callsign", "@callsign"), ("Time", "@time"),
("Phase", "@status"), ("Heading", "@heading"),
("Altitude", "@altitude"), ("Speed", "@tas")]
hover2 = bk.models.HoverTool()
hover2.tooltips = [("Callsign", "@callsign")]
self.p2.add_tools(hover2)
p.add_tools(hover)
#callback setup
self.params.on_change('value', self.plot_param)
self.tables.on_change('value', self.set_data_source)
self.cmdline.on_change('value', self.runCmd)
self.controls = bklayouts.row(
bklayouts.WidgetBox(self.tables, self.cmdline),
bk.models.Div(width=20), p2control)
self.layout = bklayouts.column(self.controls,
bklayouts.row(p, self.p2))
def set_data_source(self, attr, new, old):
t = self.tables.value
try:
self.results = checkForTable(t)
except dbError:
if os.path.exists(NATS_DIR + t):
cmd = readNATS.Command(t)
elif os.path.exists(SHERLOCK_DIR + t):
cmd = readIFF.Command(t)
else:
cmd = readIFF.Command(t)
self.results = cmd.executeCommand()[1]
self.db_access.addTable(t, self.results)
self.results['time'] = self.results['time'].astype(
'datetime64[s]').astype(int)
acids = np.unique(self.results['callsign']).tolist()
times = sorted(np.unique(self.results['time']))
self.flights = bkwidgets.MultiSelect(options=acids, value=[
acids[0],
])
self.flights.on_change('value', self.update)
self.time = bkwidgets.RangeSlider(title="time",
value=(times[0], times[-1]),
start=times[0],
end=times[-1],
step=1)
self.time.on_change('value', self.update)
if self.populated:
self.controls.children[1] = self.flights
self.controls.children[2] = self.time
else:
self.controls.children.insert(1, self.flights)
self.controls.children.insert(2, self.time)
self.populated = True
self.results['longitude'], self.results['latitude'] = merc(
np.asarray(self.results['latitude'].astype(float)),
np.asarray(self.results['longitude'].astype(float)))
self.update('attr', 'new', 'old')
def tool_handler(self, doc):
from bokeh.layouts import row, column, widgetbox, Spacer
from bokeh.models.mappers import LinearColorMapper
from bokeh.models import widgets
from bokeh.models.widgets.markups import Div
from bokeh.plotting import figure
self.arr = self.arr.copy(deep=True)
if not isinstance(self.arr, xr.Dataset):
self.use_dataset = False
residual = None
if self.use_dataset:
raw_data = self.arr.data
raw_data.values[np.isnan(raw_data.values)] = 0
fit_results = self.arr.results
residual = self.arr.residual
residual.values[np.isnan(residual.values)] = 0
else:
raw_data = self.arr.attrs['original_data']
fit_results = self.arr
fit_direction = [d for d in raw_data.dims if d not in fit_results.dims]
fit_direction = fit_direction[0]
two_dimensional = False
if len(raw_data.dims) != 2:
two_dimensional = True
x_coords, y_coords = fit_results.coords[
fit_results.dims[0]], fit_results.coords[fit_results.dims[1]]
z_coords = raw_data.coords[fit_direction]
else:
x_coords, y_coords = raw_data.coords[
raw_data.dims[0]], raw_data.coords[raw_data.dims[1]]
if two_dimensional:
self.settings['palette'] = 'coolwarm'
default_palette = self.default_palette
self.app_context.update({
'data': raw_data,
'fits': fit_results,
'residual': residual,
'original': self.arr,
'data_range': {
'x': (np.min(x_coords.values), np.max(x_coords.values)),
'y': (np.min(y_coords.values), np.max(y_coords.values)),
}
})
if two_dimensional:
self.app_context['data_range']['z'] = (np.min(z_coords.values),
np.max(z_coords.values))
figures, plots, app_widgets = self.app_context['figures'], self.app_context['plots'],\
self.app_context['widgets']
self.cursor_dims = raw_data.dims
if two_dimensional:
self.cursor = [
np.mean(self.data_range['x']),
np.mean(self.data_range['y']),
np.mean(self.data_range['z'])
]
else:
self.cursor = [
np.mean(self.data_range['x']),
np.mean(self.data_range['y'])
]
app_widgets['fit_info_div'] = Div(text='')
self.app_context['color_maps']['main'] = LinearColorMapper(
default_palette,
low=np.min(raw_data.values),
high=np.max(raw_data.values),
nan_color='black')
main_tools = ["wheel_zoom", "tap", "reset", "save"]
main_title = 'Fit Inspection Tool: WARNING Unidentified'
try:
main_title = 'Fit Inspection Tool: {}'.format(
raw_data.S.label[:60])
except:
pass
figures['main'] = figure(tools=main_tools,
plot_width=self.app_main_size,
plot_height=self.app_main_size,
min_border=10,
min_border_left=50,
toolbar_location='left',
x_axis_location='below',
y_axis_location='right',
title=main_title,
x_range=self.data_range['x'],
y_range=self.app_context['data_range']['y'])
figures['main'].xaxis.axis_label = raw_data.dims[0]
figures['main'].yaxis.axis_label = raw_data.dims[1]
figures['main'].toolbar.logo = None
figures['main'].background_fill_color = "#fafafa"
data_for_main = raw_data
if two_dimensional:
data_for_main = data_for_main.sel(**dict(
[[fit_direction, self.cursor[2]]]),
method='nearest')
plots['main'] = figures['main'].image(
[data_for_main.values.T],
x=self.app_context['data_range']['x'][0],
y=self.app_context['data_range']['y'][0],
dw=self.app_context['data_range']['x'][1] -
self.app_context['data_range']['x'][0],
dh=self.app_context['data_range']['y'][1] -
self.app_context['data_range']['y'][0],
color_mapper=self.app_context['color_maps']['main'])
band_centers = [b.center for b in fit_results.F.bands.values()]
bands_xs = [b.coords[b.dims[0]].values for b in band_centers]
bands_ys = [b.values for b in band_centers]
if fit_results.dims[0] == raw_data.dims[1]:
bands_ys, bands_xs = bands_xs, bands_ys
plots['band_locations'] = figures['main'].multi_line(
xs=bands_xs,
ys=bands_ys,
line_color='white',
line_width=1,
line_dash='dashed')
# add cursor lines
cursor_lines = self.add_cursor_lines(figures['main'])
# marginals
if not two_dimensional:
figures['bottom'] = figure(plot_width=self.app_main_size,
plot_height=self.app_marginal_size,
min_border=10,
title=None,
x_range=figures['main'].x_range,
x_axis_location='above',
toolbar_location=None,
tools=[])
else:
figures['bottom'] = Spacer(width=self.app_main_size,
height=self.app_marginal_size)
right_y_range = figures['main'].y_range
if two_dimensional:
right_y_range = self.data_range['z']
figures['right'] = figure(plot_width=self.app_marginal_size,
plot_height=self.app_main_size,
min_border=10,
title=None,
y_range=right_y_range,
y_axis_location='left',
toolbar_location=None,
tools=[])
marginal_line_width = 2
if not two_dimensional:
bottom_data = raw_data.sel(**dict(
[[raw_data.dims[1], self.cursor[1]]]),
method='nearest')
right_data = raw_data.sel(**dict(
[[raw_data.dims[0], self.cursor[0]]]),
method='nearest')
plots['bottom'] = figures['bottom'].line(
x=bottom_data.coords[raw_data.dims[0]].values,
y=bottom_data.values,
line_width=marginal_line_width)
plots['bottom_residual'] = figures['bottom'].line(
x=[], y=[], line_color='red', line_width=marginal_line_width)
plots['bottom_fit'] = figures['bottom'].line(
x=[],
y=[],
line_color='blue',
line_width=marginal_line_width,
line_dash='dashed')
plots['bottom_init_fit'] = figures['bottom'].line(
x=[],
y=[],
line_color='green',
line_width=marginal_line_width,
line_dash='dotted')
plots['right'] = figures['right'].line(
y=right_data.coords[raw_data.dims[1]].values,
x=right_data.values,
line_width=marginal_line_width)
plots['right_residual'] = figures['right'].line(
x=[], y=[], line_color='red', line_width=marginal_line_width)
plots['right_fit'] = figures['right'].line(
x=[],
y=[],
line_color='blue',
line_width=marginal_line_width,
line_dash='dashed')
plots['right_init_fit'] = figures['right'].line(
x=[],
y=[],
line_color='green',
line_width=marginal_line_width,
line_dash='dotted')
else:
right_data = raw_data.sel(**{
k: v
for k, v in self.cursor_dict.items() if k != fit_direction
},
method='nearest')
plots['right'] = figures['right'].line(
y=right_data.coords[right_data.dims[0]].values,
x=right_data.values,
line_width=marginal_line_width)
plots['right_residual'] = figures['right'].line(
x=[], y=[], line_color='red', line_width=marginal_line_width)
plots['right_fit'] = figures['right'].line(
x=[],
y=[],
line_color='blue',
line_width=marginal_line_width,
line_dash='dashed')
plots['right_init_fit'] = figures['right'].line(
x=[],
y=[],
line_color='green',
line_width=marginal_line_width,
line_dash='dotted')
def on_change_main_view(attr, old, data_source):
self.selected_data = data_source
data = None
if data_source == 'data':
data = raw_data.sel(**{
k: v
for k, v in self.cursor_dict.items() if k == fit_direction
},
method='nearest')
elif data_source == 'residual':
data = residual.sel(**{
k: v
for k, v in self.cursor_dict.items() if k == fit_direction
},
method='nearest')
elif two_dimensional:
data = fit_results.F.s(data_source)
data.values[np.isnan(data.values)] = 0
if data is not None:
if self.remove_outliers:
data = data.T.clean_outliers(clip=self.outlier_clip)
plots['main'].data_source.data = {
'image': [data.values.T],
}
update_main_colormap(None, None, main_color_range_slider.value)
def update_fit_display():
target = 'right'
if fit_results.dims[0] == raw_data.dims[1]:
target = 'bottom'
if two_dimensional:
target = 'right'
current_fit = fit_results.sel(**{
k: v
for k, v in self.cursor_dict.items() if k != fit_direction
},
method='nearest').item()
coord_vals = raw_data.coords[fit_direction].values
else:
current_fit = fit_results.sel(**dict([[
fit_results.dims[0],
self.cursor[0 if target == 'right' else 1]
]]),
method='nearest').item()
coord_vals = raw_data.coords[
raw_data.dims[0 if target == 'bottom' else 1]].values
if current_fit is not None:
app_widgets['fit_info_div'].text = current_fit._repr_html_(
short=True) # pylint: disable=protected-access
else:
app_widgets['fit_info_div'].text = 'No fit here.'
plots['{}_residual'.format(target)].data_source.data = {
'x': [],
'y': [],
}
plots['{}_fit'.format(target)].data_source.data = {
'x': [],
'y': [],
}
plots['{}_init_fit'.format(target)].data_source.data = {
'x': [],
'y': [],
}
return
if target == 'bottom':
residual_x = coord_vals
residual_y = current_fit.residual
init_fit_x = coord_vals
init_fit_y = current_fit.init_fit
fit_x = coord_vals
fit_y = current_fit.best_fit
else:
residual_y = coord_vals
residual_x = current_fit.residual
init_fit_y = coord_vals
init_fit_x = current_fit.init_fit
fit_y = coord_vals
fit_x = current_fit.best_fit
plots['{}_residual'.format(target)].data_source.data = {
'x': residual_x,
'y': residual_y,
}
plots['{}_fit'.format(target)].data_source.data = {
'x': fit_x,
'y': fit_y,
}
plots['{}_init_fit'.format(target)].data_source.data = {
'x': init_fit_x,
'y': init_fit_y,
}
def click_right_marginal(event):
self.cursor = [self.cursor[0], self.cursor[1], event.y]
on_change_main_view(None, None, self.selected_data)
def click_main_image(event):
if two_dimensional:
self.cursor = [event.x, event.y, self.cursor[2]]
else:
self.cursor = [event.x, event.y]
if not two_dimensional:
right_marginal_data = raw_data.sel(**dict(
[[raw_data.dims[0], self.cursor[0]]]),
method='nearest')
bottom_marginal_data = raw_data.sel(**dict(
[[raw_data.dims[1], self.cursor[1]]]),
method='nearest')
plots['bottom'].data_source.data = {
'x': bottom_marginal_data.coords[raw_data.dims[0]].values,
'y': bottom_marginal_data.values,
}
else:
right_marginal_data = raw_data.sel(**{
k: v
for k, v in self.cursor_dict.items() if k != fit_direction
},
method='nearest')
plots['right'].data_source.data = {
'y':
right_marginal_data.coords[right_marginal_data.dims[0]].values,
'x': right_marginal_data.values,
}
update_fit_display()
def on_change_outlier_clip(attr, old, new):
self.outlier_clip = new
on_change_main_view(None, None, self.selected_data)
def set_remove_outliers(should_remove_outliers):
if self.remove_outliers != should_remove_outliers:
self.remove_outliers = should_remove_outliers
on_change_main_view(None, None, self.selected_data)
update_main_colormap = self.update_colormap_for('main')
MAIN_CONTENT_OPTIONS = [
('Residual', 'residual'),
('Data', 'data'),
]
if two_dimensional:
available_parameters = fit_results.F.parameter_names
for param_name in available_parameters:
MAIN_CONTENT_OPTIONS.append((
param_name,
param_name,
))
remove_outliers_toggle = widgets.Toggle(label='Remove Outliers',
button_type='primary',
active=self.remove_outliers)
remove_outliers_toggle.on_click(set_remove_outliers)
outlier_clip_slider = widgets.Slider(title='Clip',
start=0,
end=10,
value=self.outlier_clip,
callback_throttle=150,
step=0.2)
outlier_clip_slider.on_change('value', on_change_outlier_clip)
main_content_select = widgets.Dropdown(label='Main Content',
button_type='primary',
menu=MAIN_CONTENT_OPTIONS)
main_content_select.on_change('value', on_change_main_view)
# Widgety things
main_color_range_slider = widgets.RangeSlider(start=0,
end=100,
value=(
0,
100,
),
title='Color Range')
# Attach callbacks
main_color_range_slider.on_change('value', update_main_colormap)
figures['main'].on_event(events.Tap, click_main_image)
if two_dimensional:
figures['right'].on_event(events.Tap, click_right_marginal)
layout = row(
column(figures['main'], figures.get('bottom')),
column(figures['right'], app_widgets['fit_info_div']),
column(
widgetbox(*[
widget for widget in [
self._cursor_info,
main_color_range_slider,
main_content_select,
remove_outliers_toggle if two_dimensional else None,
outlier_clip_slider if two_dimensional else None,
] if widget is not None
]), ))
update_fit_display()
doc.add_root(layout)
doc.title = 'Band Tool'
def tool_handler_2d(self, doc):
from bokeh import events
from bokeh.layouts import row, column, widgetbox, Spacer
from bokeh.models import ColumnDataSource, widgets
from bokeh.models.mappers import LinearColorMapper
from bokeh.models.widgets.markups import Div
from bokeh.plotting import figure
arr = self.arr
# Set up the data
x_coords, y_coords = arr.coords[arr.dims[0]], arr.coords[arr.dims[1]]
# Styling
default_palette = self.default_palette
if arr.S.is_subtracted:
default_palette = cc.coolwarm
error_alpha = 0.3
error_fill = '#3288bd'
# Application Organization
self.app_context.update({
'data': arr,
'data_range': {
'x': (np.min(x_coords.values), np.max(x_coords.values)),
'y': (np.min(y_coords.values), np.max(y_coords.values)),
},
'show_stat_variation': False,
'color_mode': 'linear',
})
def stats_patch_from_data(data, subsampling_rate=None):
if subsampling_rate is None:
subsampling_rate = int(min(data.values.shape[0] / 50, 5))
if subsampling_rate == 0:
subsampling_rate = 1
x_values = data.coords[data.dims[0]].values[::subsampling_rate]
values = data.values[::subsampling_rate]
sq = np.sqrt(values)
lower, upper = values - sq, values + sq
return {
'x': np.append(x_values, x_values[::-1]),
'y': np.append(lower, upper[::-1]),
}
def update_stat_variation(plot_name, data):
patch_data = stats_patch_from_data(data)
if plot_name != 'right': # the right plot is on transposed axes
plots[plot_name +
'_marginal_err'].data_source.data = patch_data
else:
plots[plot_name + '_marginal_err'].data_source.data = {
'x': patch_data['y'],
'y': patch_data['x'],
}
figures, plots, app_widgets = self.app_context[
'figures'], self.app_context['plots'], self.app_context['widgets']
if self.cursor_default is not None and len(self.cursor_default) == 2:
self.cursor = self.cursor_default
else:
self.cursor = [
np.mean(self.app_context['data_range']['x']),
np.mean(self.app_context['data_range']['y'])
] # try a sensible default
# create the main inset plot
main_image = arr
prepped_main_image = self.prep_image(main_image)
self.app_context['color_maps']['main'] = LinearColorMapper(
default_palette,
low=np.min(prepped_main_image),
high=np.max(prepped_main_image),
nan_color='black')
main_tools = ["wheel_zoom", "tap", "reset", "save"]
main_title = 'Bokeh Tool: WARNING Unidentified'
try:
main_title = "Bokeh Tool: %s" % arr.S.label[:60]
except:
pass
figures['main'] = figure(tools=main_tools,
plot_width=self.app_main_size,
plot_height=self.app_main_size,
min_border=10,
min_border_left=50,
toolbar_location='left',
x_axis_location='below',
y_axis_location='right',
title=main_title,
x_range=self.app_context['data_range']['x'],
y_range=self.app_context['data_range']['y'])
figures['main'].xaxis.axis_label = arr.dims[0]
figures['main'].yaxis.axis_label = arr.dims[1]
figures['main'].toolbar.logo = None
figures['main'].background_fill_color = "#fafafa"
plots['main'] = figures['main'].image(
[prepped_main_image.T],
x=self.app_context['data_range']['x'][0],
y=self.app_context['data_range']['y'][0],
dw=self.app_context['data_range']['x'][1] -
self.app_context['data_range']['x'][0],
dh=self.app_context['data_range']['y'][1] -
self.app_context['data_range']['y'][0],
color_mapper=self.app_context['color_maps']['main'])
app_widgets['info_div'] = Div(text='',
width=self.app_marginal_size,
height=100)
# Create the bottom marginal plot
bottom_marginal = arr.sel(**dict([[arr.dims[1], self.cursor[1]]]),
method='nearest')
figures['bottom_marginal'] = figure(
plot_width=self.app_main_size,
plot_height=200,
title=None,
x_range=figures['main'].x_range,
y_range=(np.min(bottom_marginal.values),
np.max(bottom_marginal.values)),
x_axis_location='above',
toolbar_location=None,
tools=[])
plots['bottom_marginal'] = figures['bottom_marginal'].line(
x=bottom_marginal.coords[arr.dims[0]].values,
y=bottom_marginal.values)
plots['bottom_marginal_err'] = figures['bottom_marginal'].patch(
x=[],
y=[],
color=error_fill,
fill_alpha=error_alpha,
line_color=None)
# Create the right marginal plot
right_marginal = arr.sel(**dict([[arr.dims[0], self.cursor[0]]]),
method='nearest')
figures['right_marginal'] = figure(
plot_width=200,
plot_height=self.app_main_size,
title=None,
y_range=figures['main'].y_range,
x_range=(np.min(right_marginal.values),
np.max(right_marginal.values)),
y_axis_location='left',
toolbar_location=None,
tools=[])
plots['right_marginal'] = figures['right_marginal'].line(
y=right_marginal.coords[arr.dims[1]].values,
x=right_marginal.values)
plots['right_marginal_err'] = figures['right_marginal'].patch(
x=[],
y=[],
color=error_fill,
fill_alpha=error_alpha,
line_color=None)
cursor_lines = self.add_cursor_lines(figures['main'])
# Attach tools and callbacks
toggle = widgets.Toggle(label="Show Stat. Variation",
button_type="success",
active=False)
def set_show_stat_variation(should_show):
self.app_context['show_stat_variation'] = should_show
if should_show:
main_image_data = arr
update_stat_variation(
'bottom',
main_image_data.sel(**dict([[arr.dims[1],
self.cursor[1]]]),
method='nearest'))
update_stat_variation(
'right',
main_image_data.sel(**dict([[arr.dims[0],
self.cursor[0]]]),
method='nearest'))
plots['bottom_marginal_err'].visible = True
plots['right_marginal_err'].visible = True
else:
plots['bottom_marginal_err'].visible = False
plots['right_marginal_err'].visible = False
toggle.on_click(set_show_stat_variation)
scan_keys = [
'x', 'y', 'z', 'pass_energy', 'hv', 'location', 'id', 'probe_pol',
'pump_pol'
]
scan_info_source = ColumnDataSource({
'keys': [k for k in scan_keys if k in arr.attrs],
'values': [
str(v) if isinstance(v, float) and np.isnan(v) else v
for v in [arr.attrs[k] for k in scan_keys if k in arr.attrs]
],
})
scan_info_columns = [
widgets.TableColumn(field='keys', title='Attr.'),
widgets.TableColumn(field='values', title='Value'),
]
POINTER_MODES = [
(
'Cursor',
'cursor',
),
(
'Path',
'path',
),
]
COLOR_MODES = [
(
'Adaptive Hist. Eq. (Slow)',
'adaptive_equalization',
),
# ('Histogram Eq.', 'equalization',), # not implemented
(
'Linear',
'linear',
),
# ('Log', 'log',), # not implemented
]
def on_change_color_mode(attr, old, new_color_mode):
self.app_context['color_mode'] = new_color_mode
if old is None or old != new_color_mode:
right_image_data = arr.sel(**dict(
[[arr.dims[0], self.cursor[0]]]),
method='nearest')
bottom_image_data = arr.sel(**dict(
[[arr.dims[1], self.cursor[1]]]),
method='nearest')
main_image_data = arr
prepped_right_image = self.prep_image(right_image_data)
prepped_bottom_image = self.prep_image(bottom_image_data)
prepped_main_image = self.prep_image(main_image_data)
plots['right'].data_source.data = {
'image': [prepped_right_image]
}
plots['bottom'].data_source.data = {
'image': [prepped_bottom_image.T]
}
plots['main'].data_source.data = {
'image': [prepped_main_image.T]
}
update_main_colormap(None, None, main_color_range_slider.value)
color_mode_dropdown = widgets.Dropdown(label='Color Mode',
button_type='primary',
menu=COLOR_MODES)
color_mode_dropdown.on_change('value', on_change_color_mode)
symmetry_point_name_input = widgets.TextInput(
title='Symmetry Point Name', value="G")
snap_checkbox = widgets.CheckboxButtonGroup(labels=['Snap Axes'],
active=[])
place_symmetry_point_at_cursor_button = widgets.Button(
label="Place Point", button_type="primary")
def update_symmetry_points_for_display():
pass
def place_symmetry_point():
cursor_dict = dict(zip(arr.dims, self.cursor))
skip_dimensions = {'eV', 'delay', 'cycle'}
if 'symmetry_points' not in arr.attrs:
arr.attrs['symmetry_points'] = {}
snap_distance = {
'phi': 2,
'beta': 2,
'kx': 0.01,
'ky': 0.01,
'kz': 0.01,
'kp': 0.01,
'hv': 4,
}
cursor_dict = {
k: v
for k, v in cursor_dict.items() if k not in skip_dimensions
}
snapped = copy.copy(cursor_dict)
if 'Snap Axes' in [
snap_checkbox.labels[i] for i in snap_checkbox.active
]:
for axis, value in cursor_dict.items():
options = [
point[axis]
for point in arr.attrs['symmetry_points'].values()
if axis in point
]
options = sorted(options, key=lambda x: np.abs(x - value))
if options and np.abs(options[0] -
value) < snap_distance[axis]:
snapped[axis] = options[0]
arr.attrs['symmetry_points'][
symmetry_point_name_input.value] = snapped
place_symmetry_point_at_cursor_button.on_click(place_symmetry_point)
main_color_range_slider = widgets.RangeSlider(
start=0, end=100, value=(
0,
100,
), title='Color Range (Main)')
layout = row(
column(figures['main'], figures['bottom_marginal']),
column(figures['right_marginal'], Spacer(width=200, height=200)),
column(
widgetbox(
widgets.Dropdown(label='Pointer Mode',
button_type='primary',
menu=POINTER_MODES)),
widgets.Tabs(tabs=[
widgets.Panel(child=widgetbox(
Div(text='<h2>Colorscale:</h2>'),
color_mode_dropdown,
main_color_range_slider,
Div(text=
'<h2 style="padding-top: 30px;">General Settings:</h2>'
),
toggle,
self._cursor_info,
sizing_mode='scale_width'),
title='Settings'),
widgets.Panel(child=widgetbox(
app_widgets['info_div'],
Div(text=
'<h2 style="padding-top: 30px; padding-bottom: 10px;">Scan Info</h2>'
),
widgets.DataTable(source=scan_info_source,
columns=scan_info_columns,
width=400,
height=400),
sizing_mode='scale_width',
width=400),
title='Info'),
widgets.Panel(child=widgetbox(
Div(text='<h2>Preparation</h2>'),
symmetry_point_name_input,
snap_checkbox,
place_symmetry_point_at_cursor_button,
sizing_mode='scale_width'),
title='Preparation'),
],
width=400)))
update_main_colormap = self.update_colormap_for('main')
def on_click_save(event):
save_dataset(arr)
print(event)
def click_main_image(event):
self.cursor = [event.x, event.y]
right_marginal_data = arr.sel(**dict(
[[arr.dims[0], self.cursor[0]]]),
method='nearest')
bottom_marginal_data = arr.sel(**dict(
[[arr.dims[1], self.cursor[1]]]),
method='nearest')
plots['bottom_marginal'].data_source.data = {
'x': bottom_marginal_data.coords[arr.dims[0]].values,
'y': bottom_marginal_data.values,
}
plots['right_marginal'].data_source.data = {
'y': right_marginal_data.coords[arr.dims[1]].values,
'x': right_marginal_data.values,
}
if self.app_context['show_stat_variation']:
update_stat_variation('right', right_marginal_data)
update_stat_variation('bottom', bottom_marginal_data)
figures['bottom_marginal'].y_range.start = np.min(
bottom_marginal_data.values)
figures['bottom_marginal'].y_range.end = np.max(
bottom_marginal_data.values)
figures['right_marginal'].x_range.start = np.min(
right_marginal_data.values)
figures['right_marginal'].x_range.end = np.max(
right_marginal_data.values)
self.save_app()
figures['main'].on_event(events.Tap, click_main_image)
main_color_range_slider.on_change('value', update_main_colormap)
doc.add_root(layout)
doc.title = "Bokeh Tool"
self.load_app()
self.save_app()
def tool_handler(self, doc):
from bokeh import events
from bokeh.layouts import row, column, widgetbox
from bokeh.models.mappers import LinearColorMapper
from bokeh.models import widgets
from bokeh.plotting import figure
if len(self.arr.shape) != 2:
raise AnalysisError(
'Cannot use the band tool on non image-like spectra')
self.data_for_display = self.arr
x_coords, y_coords = self.data_for_display.coords[
self.data_for_display.dims[0]], self.data_for_display.coords[
self.data_for_display.dims[1]]
default_palette = self.default_palette
self.app_context.update({
'data': self.arr,
'data_range': {
'x': (np.min(x_coords.values), np.max(x_coords.values)),
'y': (np.min(y_coords.values), np.max(y_coords.values)),
},
})
figures, plots = self.app_context['figures'], self.app_context['plots']
self.cursor = [
np.mean(self.data_range['x']),
np.mean(self.data_range['y'])
]
self.color_maps['main'] = LinearColorMapper(
default_palette,
low=np.min(self.data_for_display.values),
high=np.max(self.data_for_display.values),
nan_color='black')
main_tools = ["wheel_zoom", "tap", "reset"]
main_title = '{} Tool: WARNING Unidentified'.format(
self.analysis_fn.__name__)
try:
main_title = '{} Tool: {}'.format(
self.analysis_fn.__name__, self.data_for_display.S.label[:60])
except:
pass
figures['main'] = figure(tools=main_tools,
plot_width=self.app_main_size,
plot_height=self.app_main_size,
min_border=10,
min_border_left=50,
toolbar_location='left',
x_axis_location='below',
y_axis_location='right',
title=main_title,
x_range=self.data_range['x'],
y_range=self.data_range['y'])
figures['main'].xaxis.axis_label = self.data_for_display.dims[0]
figures['main'].yaxis.axis_label = self.data_for_display.dims[1]
figures['main'].toolbar.logo = None
figures['main'].background_fill_color = "#fafafa"
plots['main'] = figures['main'].image(
[self.data_for_display.values.T],
x=self.app_context['data_range']['x'][0],
y=self.app_context['data_range']['y'][0],
dw=self.app_context['data_range']['x'][1] -
self.app_context['data_range']['x'][0],
dh=self.app_context['data_range']['y'][1] -
self.app_context['data_range']['y'][0],
color_mapper=self.app_context['color_maps']['main'])
# Create the bottom marginal plot
bottom_marginal = self.data_for_display.sel(**dict(
[[self.data_for_display.dims[1], self.cursor[1]]]),
method='nearest')
bottom_marginal_original = self.arr.sel(**dict(
[[self.data_for_display.dims[1], self.cursor[1]]]),
method='nearest')
figures['bottom_marginal'] = figure(
plot_width=self.app_main_size,
plot_height=200,
title=None,
x_range=figures['main'].x_range,
y_range=(np.min(bottom_marginal.values),
np.max(bottom_marginal.values)),
x_axis_location='above',
toolbar_location=None,
tools=[])
plots['bottom_marginal'] = figures['bottom_marginal'].line(
x=bottom_marginal.coords[self.data_for_display.dims[0]].values,
y=bottom_marginal.values)
plots['bottom_marginal_original'] = figures['bottom_marginal'].line(
x=bottom_marginal_original.coords[self.arr.dims[0]].values,
y=bottom_marginal_original.values,
line_color='red')
# Create the right marginal plot
right_marginal = self.data_for_display.sel(**dict(
[[self.data_for_display.dims[0], self.cursor[0]]]),
method='nearest')
right_marginal_original = self.arr.sel(**dict(
[[self.data_for_display.dims[0], self.cursor[0]]]),
method='nearest')
figures['right_marginal'] = figure(
plot_width=200,
plot_height=self.app_main_size,
title=None,
y_range=figures['main'].y_range,
x_range=(np.min(right_marginal.values),
np.max(right_marginal.values)),
y_axis_location='left',
toolbar_location=None,
tools=[])
plots['right_marginal'] = figures['right_marginal'].line(
y=right_marginal.coords[self.data_for_display.dims[1]].values,
x=right_marginal.values)
plots['right_marginal_original'] = figures['right_marginal'].line(
y=right_marginal_original.coords[
self.data_for_display.dims[1]].values,
x=right_marginal_original.values,
line_color='red')
# add lines
self.add_cursor_lines(figures['main'])
_ = figures['main'].multi_line(xs=[],
ys=[],
line_color='white',
line_width=1) # band lines
# prep the widgets for the analysis function
signature = inspect.signature(self.analysis_fn)
# drop the first which has to be the input data, we can revisit later if this is too limiting
parameter_names = list(signature.parameters)[1:]
named_widgets = dict(zip(parameter_names, self.widget_specification))
built_widgets = {}
def update_marginals():
right_marginal_data = self.data_for_display.sel(**dict(
[[self.data_for_display.dims[0], self.cursor[0]]]),
method='nearest')
bottom_marginal_data = self.data_for_display.sel(**dict(
[[self.data_for_display.dims[1], self.cursor[1]]]),
method='nearest')
plots['bottom_marginal'].data_source.data = {
'x': bottom_marginal_data.coords[
self.data_for_display.dims[0]].values,
'y': bottom_marginal_data.values,
}
plots['right_marginal'].data_source.data = {
'y': right_marginal_data.coords[
self.data_for_display.dims[1]].values,
'x': right_marginal_data.values,
}
right_marginal_data = self.arr.sel(**dict(
[[self.data_for_display.dims[0], self.cursor[0]]]),
method='nearest')
bottom_marginal_data = self.arr.sel(**dict(
[[self.data_for_display.dims[1], self.cursor[1]]]),
method='nearest')
plots['bottom_marginal_original'].data_source.data = {
'x': bottom_marginal_data.coords[
self.data_for_display.dims[0]].values,
'y': bottom_marginal_data.values,
}
plots['right_marginal_original'].data_source.data = {
'y': right_marginal_data.coords[
self.data_for_display.dims[1]].values,
'x': right_marginal_data.values,
}
figures['bottom_marginal'].y_range.start = np.min(
bottom_marginal_data.values)
figures['bottom_marginal'].y_range.end = np.max(
bottom_marginal_data.values)
figures['right_marginal'].x_range.start = np.min(
right_marginal_data.values)
figures['right_marginal'].x_range.end = np.max(
right_marginal_data.values)
def click_main_image(event):
self.cursor = [event.x, event.y]
update_marginals()
error_msg = widgets.Div(text='')
@Debounce(0.25)
def update_data_for_display():
try:
self.data_for_display = self.analysis_fn(
self.arr, *[
built_widgets[p].value for p in parameter_names
if p in built_widgets
])
error_msg.text = ''
except Exception as e:
error_msg.text = '{}'.format(e)
# flush + update
update_marginals()
plots['main'].data_source.data = {
'image': [self.data_for_display.values.T]
}
def update_data_change_wrapper(attr, old, new):
if old != new:
update_data_for_display()
for parameter_name in named_widgets.keys():
specification = named_widgets[parameter_name]
widget = None
if specification == int:
widget = widgets.Slider(start=-20,
end=20,
value=0,
title=parameter_name)
if specification == float:
widget = widgets.Slider(start=-20,
end=20,
value=0.,
step=0.1,
title=parameter_name)
if widget is not None:
built_widgets[parameter_name] = widget
widget.on_change('value', update_data_change_wrapper)
update_main_colormap = self.update_colormap_for('main')
self.app_context['run'] = lambda x: x
main_color_range_slider = widgets.RangeSlider(start=0,
end=100,
value=(
0,
100,
),
title='Color Range')
# Attach callbacks
main_color_range_slider.on_change('value', update_main_colormap)
figures['main'].on_event(events.Tap, click_main_image)
layout = row(
column(figures['main'], figures['bottom_marginal']),
column(figures['right_marginal']),
column(
widgetbox(*[
built_widgets[p] for p in parameter_names
if p in built_widgets
]),
widgetbox(
self._cursor_info,
main_color_range_slider,
error_msg,
)))
doc.add_root(layout)
doc.title = 'Band Tool'
def tool_handler(self, doc):
from bokeh.layouts import row, column, widgetbox
from bokeh.models import widgets, Spacer
from bokeh.models.mappers import LinearColorMapper
from bokeh.plotting import figure
default_palette = self.default_palette
x_coords, y_coords = self.arr.coords[
self.arr.dims[1]], self.arr.coords[self.arr.dims[0]]
self.app_context.update({
'data': self.arr,
'cached_data': {},
'gamma_cached_data': {},
'plots': {},
'data_range': self.arr.T.range(),
'figures': {},
'widgets': {},
'color_maps': {}
})
self.app_context['color_maps']['d2'] = LinearColorMapper(
default_palette,
low=np.min(self.arr.values),
high=np.max(self.arr.values),
nan_color='black')
self.app_context['color_maps']['curvature'] = LinearColorMapper(
default_palette,
low=np.min(self.arr.values),
high=np.max(self.arr.values),
nan_color='black')
self.app_context['color_maps']['raw'] = LinearColorMapper(
default_palette,
low=np.min(self.arr.values),
high=np.max(self.arr.values),
nan_color='black')
plots, figures, data_range, cached_data, gamma_cached_data = (
self.app_context['plots'],
self.app_context['figures'],
self.app_context['data_range'],
self.app_context['cached_data'],
self.app_context['gamma_cached_data'],
)
cached_data['raw'] = self.arr.values
gamma_cached_data['raw'] = self.arr.values
figure_kwargs = {
'tools': ['reset', 'wheel_zoom'],
'plot_width': self.app_main_size,
'plot_height': self.app_main_size,
'min_border': 10,
'toolbar_location': 'left',
'x_range': data_range['x'],
'y_range': data_range['y'],
'x_axis_location': 'below',
'y_axis_location': 'right',
}
figures['d2'] = figure(title='d2 Spectrum', **figure_kwargs)
figure_kwargs.update({
'y_range': self.app_context['figures']['d2'].y_range,
'x_range': self.app_context['figures']['d2'].x_range,
'toolbar_location': None,
'y_axis_location': 'left',
})
figures['curvature'] = figure(title='Curvature', **figure_kwargs)
figures['raw'] = figure(title='Raw Image', **figure_kwargs)
figures['curvature'].yaxis.major_label_text_font_size = '0pt'
# TODO add support for color mapper
plots['d2'] = figures['d2'].image(
[self.arr.values],
x=data_range['x'][0],
y=data_range['y'][0],
dw=data_range['x'][1] - data_range['x'][0],
dh=data_range['y'][1] - data_range['y'][0],
color_mapper=self.app_context['color_maps']['d2'])
plots['curvature'] = figures['curvature'].image(
[self.arr.values],
x=data_range['x'][0],
y=data_range['y'][0],
dw=data_range['x'][1] - data_range['x'][0],
dh=data_range['y'][1] - data_range['y'][0],
color_mapper=self.app_context['color_maps']['curvature'])
plots['raw'] = figures['raw'].image(
[self.arr.values],
x=data_range['x'][0],
y=data_range['y'][0],
dw=data_range['x'][1] - data_range['x'][0],
dh=data_range['y'][1] - data_range['y'][0],
color_mapper=self.app_context['color_maps']['raw'])
smoothing_sliders_by_name = {}
smoothing_sliders = [] # need one for each axis
axis_resolution = self.arr.T.stride(generic_dim_names=False)
for dim in self.arr.dims:
coords = self.arr.coords[dim]
resolution = float(axis_resolution[dim])
high_resolution = len(coords) / 3 * resolution
low_resolution = resolution
# could make this axis dependent for more reasonable defaults
default = 15 * resolution
if default > high_resolution:
default = (high_resolution + low_resolution) / 2
new_slider = widgets.Slider(title='{} Window'.format(dim),
start=low_resolution,
end=high_resolution,
step=resolution,
value=default)
smoothing_sliders.append(new_slider)
smoothing_sliders_by_name[dim] = new_slider
n_smoothing_steps_slider = widgets.Slider(title="Smoothing Steps",
start=0,
end=5,
step=1,
value=2)
beta_slider = widgets.Slider(title="β",
start=-8,
end=8,
step=1,
value=0)
direction_select = widgets.Select(
options=list(self.arr.dims),
value='eV' if 'eV' in self.arr.dims else
self.arr.dims[0], # preference to energy,
title='Derivative Direction')
interleave_smoothing_toggle = widgets.Toggle(
label='Interleave smoothing with d/dx',
active=True,
button_type='primary')
clamp_spectrum_toggle = widgets.Toggle(
label='Clamp positive values to 0',
active=True,
button_type='primary')
filter_select = widgets.Select(options=['Gaussian', 'Boxcar'],
value='Boxcar',
title='Type of Filter')
color_slider = widgets.RangeSlider(start=0,
end=100,
value=(
0,
100,
),
title='Color Clip')
gamma_slider = widgets.Slider(start=0.1,
end=4,
value=1,
step=0.1,
title='Gamma')
# don't need any cacheing here for now, might if this ends up being too slow
def smoothing_fn(n_passes):
if n_passes == 0:
return lambda x: x
filter_factory = {
'Gaussian': gaussian_filter,
'Boxcar': boxcar_filter,
}.get(filter_select.value, boxcar_filter)
filter_size = {
d: smoothing_sliders_by_name[d].value
for d in self.arr.dims
}
return filter_factory(filter_size, n_passes)
@Debounce(0.25)
def force_update():
n_smoothing_steps = n_smoothing_steps_slider.value
d2_data = self.arr
if interleave_smoothing_toggle.active:
f = smoothing_fn(n_smoothing_steps // 2)
d2_data = d1_along_axis(f(d2_data), direction_select.value)
f = smoothing_fn(n_smoothing_steps - (n_smoothing_steps // 2))
d2_data = d1_along_axis(f(d2_data), direction_select.value)
else:
f = smoothing_fn(n_smoothing_steps)
d2_data = d2_along_axis(f(self.arr), direction_select.value)
d2_data.values[
d2_data.values != d2_data.
values] = 0 # remove NaN values until Bokeh fixes NaNs over the wire
if clamp_spectrum_toggle.active:
d2_data.values = -d2_data.values
d2_data.values[d2_data.values < 0] = 0
cached_data['d2'] = d2_data.values
gamma_cached_data['d2'] = d2_data.values**gamma_slider.value
plots['d2'].data_source.data = {'image': [gamma_cached_data['d2']]}
curv_smoothing_fn = smoothing_fn(n_smoothing_steps)
smoothed_curvature_data = curv_smoothing_fn(self.arr)
curvature_data = curvature(smoothed_curvature_data,
self.arr.dims,
beta=beta_slider.value)
curvature_data.values[
curvature_data.values != curvature_data.values] = 0
if clamp_spectrum_toggle.active:
curvature_data.values = -curvature_data.values
curvature_data.values[curvature_data.values < 0] = 0
cached_data['curvature'] = curvature_data.values
gamma_cached_data[
'curvature'] = curvature_data.values**gamma_slider.value
plots['curvature'].data_source.data = {
'image': [gamma_cached_data['curvature']]
}
update_color_slider(color_slider.value)
# TODO better integrate these, they can share code with the above if we are more careful.
def take_d2(d2_data):
n_smoothing_steps = n_smoothing_steps_slider.value
if interleave_smoothing_toggle.active:
f = smoothing_fn(n_smoothing_steps // 2)
d2_data = d1_along_axis(f(d2_data), direction_select.value)
f = smoothing_fn(n_smoothing_steps - (n_smoothing_steps // 2))
d2_data = d1_along_axis(f(d2_data), direction_select.value)
else:
f = smoothing_fn(n_smoothing_steps)
d2_data = d2_along_axis(f(self.arr), direction_select.value)
d2_data.values[
d2_data.values != d2_data.
values] = 0 # remove NaN values until Bokeh fixes NaNs over the wire
if clamp_spectrum_toggle.active:
d2_data.values = -d2_data.values
d2_data.values[d2_data.values < 0] = 0
return d2_data
def take_curvature(curvature_data, curve_dims):
curv_smoothing_fn = smoothing_fn(n_smoothing_steps_slider.value)
smoothed_curvature_data = curv_smoothing_fn(curvature_data)
curvature_data = curvature(smoothed_curvature_data,
curve_dims,
beta=beta_slider.value)
curvature_data.values[
curvature_data.values != curvature_data.values] = 0
if clamp_spectrum_toggle.active:
curvature_data.values = -curvature_data.values
curvature_data.values[curvature_data.values < 0] = 0
return curvature_data
# These functions will always be linked to the current context of the curvature tool.
self.app_context['d2_fn'] = take_d2
self.app_context['curvature_fn'] = take_curvature
def force_update_change_wrapper(attr, old, new):
if old != new:
force_update()
def force_update_click_wrapper(event):
force_update()
@Debounce(0.1)
def update_color_slider(new):
def update_plot(name, data):
low, high = np.min(data), np.max(data)
dynamic_range = high - low
self.app_context['color_maps'][name].update(
low=low + new[0] / 100 * dynamic_range,
high=low + new[1] / 100 * dynamic_range)
update_plot('d2', gamma_cached_data['d2'])
update_plot('curvature', gamma_cached_data['curvature'])
update_plot('raw', gamma_cached_data['raw'])
@Debounce(0.1)
def update_gamma_slider(new):
gamma_cached_data['d2'] = cached_data['d2']**new
gamma_cached_data['curvature'] = cached_data['curvature']**new
gamma_cached_data['raw'] = cached_data['raw']**new
update_color_slider(color_slider.value)
def update_color_handler(attr, old, new):
update_color_slider(new)
def update_gamma_handler(attr, old, new):
update_gamma_slider(new)
layout = column(
row(
column(self.app_context['figures']['d2'],
interleave_smoothing_toggle, direction_select),
column(self.app_context['figures']['curvature'], beta_slider,
clamp_spectrum_toggle),
column(self.app_context['figures']['raw'], color_slider,
gamma_slider)),
widgetbox(
filter_select,
*smoothing_sliders,
n_smoothing_steps_slider,
),
Spacer(height=100),
)
# Attach event handlers
for w in (n_smoothing_steps_slider, beta_slider, direction_select,
*smoothing_sliders, filter_select):
w.on_change('value', force_update_change_wrapper)
interleave_smoothing_toggle.on_click(force_update_click_wrapper)
clamp_spectrum_toggle.on_click(force_update_click_wrapper)
color_slider.on_change('value', update_color_handler)
gamma_slider.on_change('value', update_gamma_handler)
force_update()
doc.add_root(layout)
doc.title = 'Curvature Tool'
def tool_handler(self, doc):
from bokeh.layouts import row, column, widgetbox
from bokeh.models.mappers import LinearColorMapper
from bokeh.models import widgets
from bokeh.plotting import figure
if len(self.arr.shape) != 2:
raise AnalysisError(
'Cannot use the band tool on non image-like spectra')
arr = self.arr
x_coords, y_coords = arr.coords[arr.dims[0]], arr.coords[arr.dims[1]]
default_palette = self.default_palette
self.app_context.update({
'bands': {},
'center_float': None,
'data': arr,
'data_range': {
'x': (np.min(x_coords.values), np.max(x_coords.values)),
'y': (np.min(y_coords.values), np.max(y_coords.values)),
},
'direction_normal': True,
'fit_mode': 'mdc',
})
figures, plots, app_widgets = self.app_context['figures'], self.app_context['plots'], \
self.app_context['widgets']
self.cursor = [
np.mean(self.data_range['x']),
np.mean(self.data_range['y'])
]
self.color_maps['main'] = LinearColorMapper(default_palette,
low=np.min(arr.values),
high=np.max(arr.values),
nan_color='black')
main_tools = ["wheel_zoom", "tap", "reset"]
main_title = 'Band Tool: WARNING Unidentified'
try:
main_title = 'Band Tool: {}'.format(arr.S.label[:60])
except:
pass
figures['main'] = figure(tools=main_tools,
plot_width=self.app_main_size,
plot_height=self.app_main_size,
min_border=10,
min_border_left=50,
toolbar_location='left',
x_axis_location='below',
y_axis_location='right',
title=main_title,
x_range=self.data_range['x'],
y_range=self.data_range['y'])
figures['main'].xaxis.axis_label = arr.dims[0]
figures['main'].yaxis.axis_label = arr.dims[1]
figures['main'].toolbar.logo = None
figures['main'].background_fill_color = "#fafafa"
plots['main'] = figures['main'].image(
[arr.values.T],
x=self.app_context['data_range']['x'][0],
y=self.app_context['data_range']['y'][0],
dw=self.app_context['data_range']['x'][1] -
self.app_context['data_range']['x'][0],
dh=self.app_context['data_range']['y'][1] -
self.app_context['data_range']['y'][0],
color_mapper=self.app_context['color_maps']['main'])
# add lines
self.add_cursor_lines(figures['main'])
band_lines = figures['main'].multi_line(xs=[],
ys=[],
line_color='white',
line_width=1)
def append_point_to_band():
cursor = self.cursor
if self.active_band in self.app_context['bands']:
self.app_context['bands'][self.active_band]['points'].append(
list(cursor))
update_band_display()
def click_main_image(event):
self.cursor = [event.x, event.y]
if self.pointer_mode == 'band':
append_point_to_band()
update_main_colormap = self.update_colormap_for('main')
POINTER_MODES = [
(
'Cursor',
'cursor',
),
(
'Band',
'band',
),
]
FIT_MODES = [
(
'EDC',
'edc',
),
(
'MDC',
'mdc',
),
]
DIRECTIONS = [
(
'From Bottom/Left',
'forward',
),
('From Top/Right', 'reverse'),
]
BAND_TYPES = [(
'Lorentzian',
'Lorentzian',
), (
'Voigt',
'Voigt',
), (
'Gaussian',
'Gaussian',
)]
band_classes = {
'Lorentzian': band.Band,
'Gaussian': band.BackgroundBand,
'Voigt': band.VoigtBand,
}
self.app_context['band_options'] = []
def pack_bands():
packed_bands = {}
for band_name, band_description in self.app_context['bands'].items(
):
if not band_description['points']:
raise AnalysisError('Band {} is empty.'.format(band_name))
stray = None
try:
stray = float(band_description['center_float'])
except (KeyError, ValueError, TypeError):
try:
stray = float(self.app_context['center_float'])
except Exception:
pass
packed_bands[band_name] = {
'name': band_name,
'band': band_classes.get(band_description['type'],
band.Band),
'dims': self.arr.dims,
'params': {
'amplitude': {
'min': 0
},
},
'points': band_description['points'],
}
if stray is not None:
packed_bands[band_name]['params']['stray'] = stray
return packed_bands
def fit(override_data=None):
packed_bands = pack_bands()
dims = list(self.arr.dims)
if 'eV' in dims:
dims.remove('eV')
angular_direction = dims[0]
if isinstance(override_data, xr.Dataset):
override_data = normalize_to_spectrum(override_data)
return fit_patterned_bands(
override_data if override_data is not None else self.arr,
packed_bands,
fit_direction='eV' if self.app_context['fit_mode'] == 'edc'
else angular_direction,
direction_normal=self.app_context['direction_normal'])
self.app_context['pack_bands'] = pack_bands
self.app_context['fit'] = fit
self.pointer_dropdown = widgets.Dropdown(label='Pointer Mode',
button_type='primary',
menu=POINTER_MODES)
self.direction_dropdown = widgets.Dropdown(label='Fit Direction',
button_type='primary',
menu=DIRECTIONS)
self.band_dropdown = widgets.Dropdown(
label='Active Band',
button_type='primary',
menu=self.app_context['band_options'])
self.fit_mode_dropdown = widgets.Dropdown(label='Mode',
button_type='primary',
menu=FIT_MODES)
self.band_type_dropdown = widgets.Dropdown(label='Band Type',
button_type='primary',
menu=BAND_TYPES)
self.band_name_input = widgets.TextInput(placeholder='Band name...')
self.center_float_widget = widgets.TextInput(
placeholder='Center Constraint')
self.center_float_copy = widgets.Button(label='Copy to all...')
self.add_band_button = widgets.Button(label='Add Band')
self.clear_band_button = widgets.Button(label='Clear Band')
self.remove_band_button = widgets.Button(label='Remove Band')
self.main_color_range_slider = widgets.RangeSlider(start=0,
end=100,
value=(
0,
100,
),
title='Color Range')
def add_band(band_name):
if band_name not in self.app_context['bands']:
self.app_context['band_options'].append((
band_name,
band_name,
))
self.band_dropdown.menu = self.app_context['band_options']
self.app_context['bands'][band_name] = {
'type': 'Lorentzian',
'points': [],
'name': band_name,
'center_float': None,
}
if self.active_band is None:
self.active_band = band_name
self.save_app()
def on_copy_center_float():
for band_name in self.app_context['bands'].keys():
self.app_context['bands'][band_name][
'center_float'] = self.app_context['center_float']
self.save_app()
def on_change_active_band(attr, old_band_id, band_id):
self.app_context['active_band'] = band_id
self.active_band = band_id
def on_change_pointer_mode(attr, old_pointer_mode, pointer_mode):
self.app_context['pointer_mode'] = pointer_mode
self.pointer_mode = pointer_mode
def set_center_float_value(attr, old_value, new_value):
self.app_context['center_float'] = new_value
if self.active_band in self.app_context['bands']:
self.app_context['bands'][
self.active_band]['center_float'] = new_value
self.save_app()
def set_fit_direction(attr, old_direction, new_direction):
self.app_context['direction_normal'] = new_direction == 'forward'
self.save_app()
def set_fit_mode(attr, old_mode, new_mode):
self.app_context['fit_mode'] = new_mode
self.save_app()
def set_band_type(attr, old_type, new_type):
if self.active_band in self.app_context['bands']:
self.app_context['bands'][self.active_band]['type'] = new_type
self.save_app()
def update_band_display():
band_names = self.app_context['bands'].keys()
band_lines.data_source.data = {
'xs': [[p[0] for p in self.app_context['bands'][b]['points']]
for b in band_names],
'ys': [[p[1] for p in self.app_context['bands'][b]['points']]
for b in band_names],
}
self.save_app()
self.update_band_display = update_band_display
def on_clear_band():
if self.active_band in self.app_context['bands']:
self.app_context['bands'][self.active_band]['points'] = []
update_band_display()
def on_remove_band():
if self.active_band in self.app_context['bands']:
del self.app_context['bands'][self.active_band]
new_band_options = [
b for b in self.app_context['band_options']
if b[0] != self.active_band
]
self.band_dropdown.menu = new_band_options
self.app_context['band_options'] = new_band_options
self.active_band = None
update_band_display()
# Attach callbacks
self.main_color_range_slider.on_change('value', update_main_colormap)
figures['main'].on_event(events.Tap, click_main_image)
self.band_dropdown.on_change('value', on_change_active_band)
self.pointer_dropdown.on_change('value', on_change_pointer_mode)
self.add_band_button.on_click(
lambda: add_band(self.band_name_input.value))
self.clear_band_button.on_click(on_clear_band)
self.remove_band_button.on_click(on_remove_band)
self.center_float_copy.on_click(on_copy_center_float)
self.center_float_widget.on_change('value', set_center_float_value)
self.direction_dropdown.on_change('value', set_fit_direction)
self.fit_mode_dropdown.on_change('value', set_fit_mode)
self.band_type_dropdown.on_change('value', set_band_type)
layout = row(
column(figures['main']),
column(
widgetbox(
self.pointer_dropdown,
self.band_dropdown,
self.fit_mode_dropdown,
self.band_type_dropdown,
self.direction_dropdown,
), row(
self.band_name_input,
self.add_band_button,
), row(
self.clear_band_button,
self.remove_band_button,
), row(self.center_float_widget, self.center_float_copy),
widgetbox(
self._cursor_info,
self.main_color_range_slider,
)))
doc.add_root(layout)
doc.title = 'Band Tool'
self.load_app()
self.save_app()
def tool_handler(self, doc):
from bokeh import events
from bokeh.layouts import row, column, widgetbox
from bokeh.models.mappers import LinearColorMapper
from bokeh.models import widgets
from bokeh.plotting import figure
if len(self.arr.shape) != 2:
raise AnalysisError(
'Cannot use mask tool on non image-like spectra')
arr = self.arr
x_coords, y_coords = arr.coords[arr.dims[0]], arr.coords[arr.dims[1]]
default_palette = self.default_palette
self.app_context.update({
'region_options': [],
'regions': {},
'data': arr,
'data_range': {
'x': (np.min(x_coords.values), np.max(x_coords.values)),
'y': (np.min(y_coords.values), np.max(y_coords.values)),
},
})
self.cursor = [
np.mean(self.data_range['x']),
np.mean(self.data_range['y'])
]
self.color_maps['main'] = LinearColorMapper(default_palette,
low=np.min(arr.values),
high=np.max(arr.values),
nan_color='black')
main_tools = ["wheel_zoom", "tap", "reset"]
main_title = 'Mask Tool: WARNING Unidentified'
try:
main_title = 'Mask Tool: {}'.format(arr.S.label[:60])
except:
pass
self.figures['main'] = figure(
tools=main_tools,
plot_width=self.app_main_size,
plot_height=self.app_main_size,
min_border=10,
min_border_left=20,
toolbar_location='left',
x_axis_location='below',
y_axis_location='right',
title=main_title,
x_range=self.data_range['x'],
y_range=self.data_range['y'],
)
self.figures['main'].xaxis.axis_label = arr.dims[0]
self.figures['main'].yaxis.axis_label = arr.dims[1]
self.plots['main'] = self.figures['main'].image(
[np.asarray(arr.values.T)],
x=self.data_range['x'][0],
y=self.data_range['y'][0],
dw=self.data_range['x'][1] - self.data_range['x'][0],
dh=self.data_range['y'][1] - self.data_range['y'][0],
color_mapper=self.color_maps['main'])
self.add_cursor_lines(self.figures['main'])
region_patches = self.figures['main'].patches(xs=[],
ys=[],
color='white',
alpha=0.35,
line_width=1)
def add_point_to_region():
if self.active_region in self.regions:
self.regions[self.active_region]['points'].append(
list(self.cursor))
update_region_display()
self.save_app()
def click_main_image(event):
self.cursor = [event.x, event.y]
if self.pointer_mode == 'region':
add_point_to_region()
POINTER_MODES = [
(
'Cursor',
'cursor',
),
(
'Region',
'region',
),
]
def perform_mask(data=None, **kwargs):
if data is None:
data = arr
data = normalize_to_spectrum(data)
return apply_mask(data, self.app_context['mask'], **kwargs)
self.app_context['perform_mask'] = perform_mask
self.app_context['mask'] = None
pointer_dropdown = widgets.Dropdown(label='Pointer Mode',
button_type='primary',
menu=POINTER_MODES)
self.region_dropdown = widgets.Dropdown(label='Active Region',
button_type='primary',
menu=self.region_options)
edge_mask_button = widgets.Button(label='Edge Mask')
region_name_input = widgets.TextInput(placeholder='Region name...')
add_region_button = widgets.Button(label='Add Region')
clear_region_button = widgets.Button(label='Clear Region')
remove_region_button = widgets.Button(label='Remove Region')
main_color_range_slider = widgets.RangeSlider(start=0,
end=100,
value=(
0,
100,
),
title='Color Range')
def on_click_edge_mask():
if self.active_region in self.regions:
old_points = self.regions[self.active_region]['points']
dims = [d for d in arr.dims if 'eV' != d]
energy_index = arr.dims.index('eV')
max_energy = np.max([p[energy_index] for p in old_points])
other_dim = dims[0]
other_coord = arr.coords[other_dim].values
min_other, max_other = np.min(other_coord), np.max(other_coord)
min_e = np.min(arr.coords['eV'].values)
if arr.dims.index('eV') == 0:
before = [[min_e - 3, min_other - 1], [0, min_other - 1]]
after = [[0, max_other + 1], [min_e - 3, max_other + 1]]
else:
before = [[min_other - 1, min_e - 3], [min_other - 1, 0]]
after = [[max_other + 1, 0], [max_other + 1, min_e - 3]]
self.regions[
self.active_region]['points'] = before + old_points + after
self.app_context['mask'] = self.app_context['mask'] or {}
self.app_context['mask']['fermi'] = max_energy
update_region_display()
self.save_app()
def add_region(region_name):
if region_name not in self.regions:
self.region_options.append((
region_name,
region_name,
))
self.region_dropdown.menu = self.region_options
self.regions[region_name] = {
'points': [],
'name': region_name,
}
if self.active_region is None:
self.active_region = region_name
self.save_app()
def on_change_active_region(attr, old_region_id, region_id):
self.app_context['active_region'] = region_id
self.active_region = region_id
self.save_app()
def on_change_pointer_mode(attr, old_pointer_mode, pointer_mode):
self.app_context['pointer_mode'] = pointer_mode
self.pointer_mode = pointer_mode
self.save_app()
def update_region_display():
region_names = self.regions.keys()
if self.app_context['mask'] is None:
self.app_context['mask'] = {}
self.app_context['mask'].update({
'dims':
arr.dims,
'polys': [r['points'] for r in self.regions.values()]
})
region_patches.data_source.data = {
'xs': [[p[0] for p in self.regions[r]['points']]
for r in region_names],
'ys': [[p[1] for p in self.regions[r]['points']]
for r in region_names],
}
self.save_app()
self.update_region_display = update_region_display
def on_clear_region():
if self.active_region in self.regions:
self.regions[self.active_region]['points'] = []
update_region_display()
def on_remove_region():
if self.active_region in self.regions:
del self.regions[self.active_region]
new_region_options = [
b for b in self.region_options
if b[0] != self.active_region
]
self.region_dropdown.menu = new_region_options
self.region_options = new_region_options
self.active_region = None
update_region_display()
# Attach callbacks
main_color_range_slider.on_change('value',
self.update_colormap_for('main'))
self.figures['main'].on_event(events.Tap, click_main_image)
self.region_dropdown.on_change('value', on_change_active_region)
pointer_dropdown.on_change('value', on_change_pointer_mode)
add_region_button.on_click(lambda: add_region(region_name_input.value))
edge_mask_button.on_click(on_click_edge_mask)
clear_region_button.on_click(on_clear_region)
remove_region_button.on_click(on_remove_region)
layout = row(
column(self.figures['main']),
column(*[
f for f in [
widgetbox(
pointer_dropdown,
self.region_dropdown,
),
row(
region_name_input,
add_region_button,
),
edge_mask_button if 'eV' in arr.dims else None,
row(
clear_region_button,
remove_region_button,
),
widgetbox(
self._cursor_info,
main_color_range_slider,
),
] if f is not None
]))
doc.add_root(layout)
doc.title = 'Mask Tool'
self.load_app()
self.save_app()
def tool_handler(self, doc):
from bokeh import events
from bokeh.layouts import row, column, widgetbox
from bokeh.models.mappers import LinearColorMapper
from bokeh.models import widgets, warnings
from bokeh.plotting import figure
if len(self.arr.shape) != 2:
raise AnalysisError(
'Cannot use path tool on non image-like spectra')
arr = self.arr
x_coords, y_coords = arr.coords[arr.dims[0]], arr.coords[arr.dims[1]]
default_palette = self.default_palette
self.app_context.update({
'path_options': [],
'active_path': None,
'paths': {},
'data': arr,
'data_range': {
'x': (np.min(x_coords.values), np.max(x_coords.values)),
'y': (np.min(y_coords.values), np.max(y_coords.values)),
},
})
self.cursor = [
np.mean(self.data_range['x']),
np.mean(self.data_range['y'])
]
self.color_maps['main'] = LinearColorMapper(default_palette,
low=np.min(arr.values),
high=np.max(arr.values),
nan_color='black')
main_tools = ["wheel_zoom", "tap", "reset"]
main_title = 'Path Tool: WARNING Unidentified'
try:
main_title = 'Path Tool: {}'.format(arr.S.label[:60])
except:
pass
self.figures['main'] = figure(
tools=main_tools,
plot_width=self.app_main_size,
plot_height=self.app_main_size,
min_border=10,
min_border_left=20,
toolbar_location='left',
x_axis_location='below',
y_axis_location='right',
title=main_title,
x_range=self.data_range['x'],
y_range=self.data_range['y'],
)
self.figures['main'].xaxis.axis_label = arr.dims[0]
self.figures['main'].yaxis.axis_label = arr.dims[1]
self.plots['main'] = self.figures['main'].image(
[np.asarray(arr.values.T)],
x=self.data_range['x'][0],
y=self.data_range['y'][0],
dw=self.data_range['x'][1] - self.data_range['x'][0],
dh=self.data_range['y'][1] - self.data_range['y'][0],
color_mapper=self.color_maps['main'])
self.plots['paths'] = self.figures['main'].multi_line(
xs=[], ys=[], line_color='white', line_width=2)
self.add_cursor_lines(self.figures['main'])
def add_point_to_path():
if self.active_path in self.paths:
self.paths[self.active_path]['points'].append(list(
self.cursor))
update_path_display()
self.save_app()
def click_main_image(event):
self.cursor = [event.x, event.y]
if self.pointer_mode == 'path':
add_point_to_path()
POINTER_MODES = [
(
'Cursor',
'cursor',
),
(
'Path',
'path',
),
]
def convert_to_xarray():
"""
For each of the paths, we will create a dataset which has an index dimension,
and datavariables for each of the coordinate dimensions
:return:
"""
def convert_single_path_to_xarray(points):
vars = {
d: np.array([p[i] for p in points])
for i, d in enumerate(self.arr.dims)
}
coords = {
'index': np.array(range(len(points))),
}
vars = {
k: xr.DataArray(v, coords=coords, dims=['index'])
for k, v in vars.items()
}
return xr.Dataset(data_vars=vars, coords=coords)
return {
path['name']: convert_single_path_to_xarray(path['points'])
for path in self.paths.values()
}
def select(data=None, **kwargs):
if data is None:
data = self.arr
if len(self.paths) > 1:
warnings.warn('Only using first path.')
return select_along_path(path=list(
convert_to_xarray().items())[0][1],
data=data,
**kwargs)
self.app_context['to_xarray'] = convert_to_xarray
self.app_context['select'] = select
pointer_dropdown = widgets.Dropdown(label='Pointer Mode',
button_type='primary',
menu=POINTER_MODES)
self.path_dropdown = widgets.Dropdown(label='Active Path',
button_type='primary',
menu=self.path_options)
path_name_input = widgets.TextInput(placeholder='Path name...')
add_path_button = widgets.Button(label='Add Path')
clear_path_button = widgets.Button(label='Clear Path')
remove_path_button = widgets.Button(label='Remove Path')
main_color_range_slider = widgets.RangeSlider(start=0,
end=100,
value=(
0,
100,
),
title='Color Range')
def add_path(path_name):
if path_name not in self.paths:
self.path_options.append((
path_name,
path_name,
))
self.path_dropdown.menu = self.path_options
self.paths[path_name] = {
'points': [],
'name': path_name,
}
if self.active_path is None:
self.active_path = path_name
self.save_app()
def on_change_active_path(attr, old_path_id, path_id):
self.debug_text = path_id
self.app_context['active_path'] = path_id
self.active_path = path_id
self.save_app()
def on_change_pointer_mode(attr, old_pointer_mode, pointer_mode):
self.app_context['pointer_mode'] = pointer_mode
self.pointer_mode = pointer_mode
self.save_app()
def update_path_display():
self.plots['paths'].data_source.data = {
'xs': [[point[0] for point in p['points']]
for p in self.paths.values()],
'ys': [[point[1] for point in p['points']]
for p in self.paths.values()],
}
self.save_app()
self.update_path_display = update_path_display
def on_clear_path():
if self.active_path in self.paths:
self.paths[self.active_path]['points'] = []
update_path_display()
def on_remove_path():
if self.active_path in self.paths:
del self.paths[self.active_path]
new_path_options = [
b for b in self.path_options if b[0] != self.active_path
]
self.path_dropdown.menu = new_path_options
self.path_options = new_path_options
self.active_path = None
update_path_display()
# Attach callbacks
main_color_range_slider.on_change('value',
self.update_colormap_for('main'))
self.figures['main'].on_event(events.Tap, click_main_image)
self.path_dropdown.on_change('value', on_change_active_path)
pointer_dropdown.on_change('value', on_change_pointer_mode)
add_path_button.on_click(lambda: add_path(path_name_input.value))
clear_path_button.on_click(on_clear_path)
remove_path_button.on_click(on_remove_path)
layout = row(
column(self.figures['main']),
column(
widgetbox(
pointer_dropdown,
self.path_dropdown,
),
row(
path_name_input,
add_path_button,
),
row(
clear_path_button,
remove_path_button,
),
widgetbox(
self._cursor_info,
main_color_range_slider,
),
self.debug_div,
))
doc.add_root(layout)
doc.title = 'Path Tool'
self.load_app()
self.save_app()