def modify_doc(doc):
h_input = Div(text="""<h2>Upload your mp4 file</h2> """, max_height=40)
file_input = FileInput()
source = ColumnDataSource(dict())
def value_changed(attr, old, new):
data = b64decode(new)
print(type(data))
with open("fake_video.mp4", "wb") as binary_file:
# Write bytes to file
binary_file.write(data)
file_input.on_change("value", value_changed)
doc.add_root(column(h_input, file_input))
def setup_bokeh_server(self, doc):
"""
Setup the bokeh server in the mainwindow.py. The server
must be started on the main thread.
Use the doc given to create a layout.
Also create a callback to update the plot to
view the live data.
:param doc: Doc used to display the data to the webpage
:return:
"""
#self.create_bokeh_plots()
#plot_layout_dash = layout([
# [self.plot_range],
# [self.plot_earth_east, self.plot_earth_north],
# [self.plot_mag, self.plot_dir]
#], sizing_mode='stretch_both')
#plot_layout_profile = layout([
# [self.plot_amp]
#], sizing_mode='stretch_both')
# Create tabs
#tab1 = Panel(child=plot_layout_dash, title="Dashboard")
#tab2 = Panel(child=plot_layout_profile, title="Profile")
#tabs = Tabs(tabs=[tab1, tab2])
file_input = FileInput(accept=".ens,.bin,.rtf")
file_input.on_change("value", self.file_input_handler)
ol = layout([file_input])
tab1 = Panel(child=ol, title="Playback")
tabs = Tabs(tabs=[tab1])
# Document to display
doc.add_root(tabs)
# Callback toupdate the plot
callback_rate = 2500
doc.add_periodic_callback(self.update_live_plot, callback_rate)
doc.title = "ADCP Dashboard"
def modify_doc(doc):
h_input = Div(text="""<h2>Upload your mp4 file</h2> """, max_height=40)
file_input = FileInput()
source = ColumnDataSource(dict())
def value_changed(attr, old, new):
# print(type(data))
# with open("fake_video.mp4", "wb") as binary_file:
# Write bytes to file
# binary_file.write(data)
print("init new process")
# data = b64decode(new)
print(file_input.filename)
self.spawn_new_worker(file_input.filename)
print(self.pool)
file_input.on_change("filename", value_changed)
doc.add_root(column(h_input, file_input))
def modify_doc(doc):
h_input = Div(text="""<h2>Upload your mp4 file</h2> """, max_height=40)
file_input = FileInput()
source = ColumnDataSource(dict())
def value_changed(attr, old, new):
data = b64decode(new)
print(type(data))
with open("fake_video.mp4", "wb") as binary_file:
# Write bytes to file
# binary_file.write(data)
print("trigger os popen here")
cmd = "echo 'run command for stream debugger'"
output = subprocess.Popen(
cmd,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
universal_newlines=True,
)
exit_code = output.wait()
print(exit_code)
result = ""
if exit_code != 0:
print("run failed")
for line in output.stderr:
result = result + line
else:
print("run successfully")
for line in output.stdout:
print(f"line: {line}")
result = result + line
print(f"result: {result}")
file_input.on_change("value", value_changed)
doc.add_root(column(h_input, file_input))
# plot-figure visual settings
img_path = "https://raw.githubusercontent.com/sanam407/Simulator/master/AstaZero.png"
plot.image_url(url=[img_path], x=-62, y=35, w=124, h=70)
plot.x_range.bounds = (-62, 62)
plot.y_range.bounds = (-35, 35)
plot.background_fill_color = "#252e38"
plot.border_fill_color = "#252e38"
plot.grid.grid_line_color = None
plot.axis.axis_label = None
plot.axis.visible = False
plot.outline_line_color = '#41454a'
plot.title.align = "center"
plot.title.text_color = "white"
plot.title.text_font_size = "15px"
# Adding plot-figure to root of current html document
curdoc().add_root(plot)
# Importing JSON files
file_input = FileInput(accept=".json",
name="top") # "Choose JSOn file exported from Vista"
file_input.on_change("value", show_plot)
curdoc().add_root(file_input)
# Column layout setting
left_panel = column(Div(text="No File Selected"), width=400, name="left")
curdoc().add_root(left_panel)
# Title of current document
curdoc().title = 'Vista Reachability Analysis'
def __init__(self, m):
if debug: print("Initializing new View object...")
self.curdoc = curdoc # reference to the current Bokeh document
self.m = m
self.firstrun = True
self.subject_select = Select(title="Subjects:",
value=sorted_xs[0],
options=sorted_xs,
width=200)
self.model_select = Select(title="Model:",
value=selected_model,
options=stored_models,
width=200)
self.slice_slider_frontal = Slider(start=1,
end=m.subj_bg.shape[2],
value=50,
step=1,
title="Coronal slice",
width=200)
self.slice_slider_axial = Slider(start=1,
end=m.subj_bg.shape[0],
value=50,
step=1,
title="Axial slice",
width=200)
self.slice_slider_sagittal = Slider(start=1,
end=m.subj_bg.shape[1],
value=50,
step=1,
title="Sagittal slice",
width=200)
self.threshold_slider = Slider(start=0,
end=1,
value=0.4,
step=0.05,
title="Relevance threshold",
width=200)
self.clustersize_slider = Slider(start=0,
end=250,
value=50,
step=10,
title="Minimum cluster size",
width=200)
self.transparency_slider = Slider(start=0,
end=1,
value=0.3,
step=0.05,
title="Overlay transparency",
width=200)
# Initialize the figures:
self.guide_frontal = figure(plot_width=208,
plot_height=70,
title='Relevance>threshold per slice:',
toolbar_location=None,
active_drag=None,
active_inspect=None,
active_scroll=None,
active_tap=None)
self.guide_frontal.title.text_font = 'arial'
self.guide_frontal.title.text_font_style = 'normal'
# guide_frontal.title.text_font_size = '10pt'
self.guide_frontal.axis.visible = False
self.guide_frontal.x_range.range_padding = 0
self.guide_frontal.y_range.range_padding = 0
self.guide_axial = figure(plot_width=208,
plot_height=70,
title='Relevance>threshold per slice:',
toolbar_location=None,
active_drag=None,
active_inspect=None,
active_scroll=None,
active_tap=None)
self.guide_axial.title.text_font = 'arial'
self.guide_axial.title.text_font_style = 'normal'
# guide_axial.title.text_font_size = '10pt'
self.guide_axial.axis.visible = False
self.guide_axial.x_range.range_padding = 0
self.guide_axial.y_range.range_padding = 0
self.guide_sagittal = figure(plot_width=208,
plot_height=70,
title='Relevance>threshold per slice:',
toolbar_location=None,
active_drag=None,
active_inspect=None,
active_scroll=None,
active_tap=None)
self.guide_sagittal.title.text_font = 'arial'
self.guide_sagittal.title.text_font_style = 'normal'
# guide_sagittal.title.text_font_size = '10pt'
self.guide_sagittal.axis.visible = False
self.guide_sagittal.x_range.range_padding = 0
self.guide_sagittal.y_range.range_padding = 0
self.clusthist = figure(plot_width=208,
plot_height=70,
title='Distribution of cluster sizes:',
toolbar_location=None,
active_drag=None,
active_inspect=None,
active_scroll=None,
active_tap=None)
self.clusthist.title.text_font = 'arial'
self.clusthist.title.text_font_style = 'normal'
self.clusthist.axis.visible = False
self.clusthist.x_range.range_padding = 0
self.clusthist.y_range.range_padding = 0
self.p_frontal = figure(
plot_width=int(np.floor(m.subj_bg.shape[1] * scale_factor)),
plot_height=int(np.floor(m.subj_bg.shape[0] * scale_factor)),
title='',
toolbar_location=None,
active_drag=None,
active_inspect=None,
active_scroll=None,
active_tap=None)
self.p_frontal.axis.visible = False
self.p_frontal.x_range.range_padding = 0
self.p_frontal.y_range.range_padding = 0
self.flip_frontal_view = Toggle(label='Flip L/R orientation',
button_type='default',
width=200,
active=flip_left_right_in_frontal_plot)
self.orientation_label_shown_left = Label(
text='R' if flip_left_right_in_frontal_plot else 'L',
render_mode='css',
x=3,
y=self.m.subj_bg.shape[0] - 13,
text_align='left',
text_color='white',
text_font_size='20px',
border_line_color='white',
border_line_alpha=0,
background_fill_color='black',
background_fill_alpha=0,
level='overlay',
visible=True)
self.orientation_label_shown_right = Label(
text='L' if flip_left_right_in_frontal_plot else 'R',
render_mode='css',
x=self.m.subj_bg.shape[1] - 3,
y=self.m.subj_bg.shape[0] - 13,
text_align='right',
text_color='white',
text_font_size='20px',
border_line_color='white',
border_line_alpha=0,
background_fill_color='black',
background_fill_alpha=0,
level='overlay',
visible=True)
self.p_frontal.add_layout(self.orientation_label_shown_left, 'center')
self.p_frontal.add_layout(self.orientation_label_shown_right, 'center')
# The vertical crosshair line on the frontal view that indicates the selected sagittal slice.
self.frontal_crosshair_from_sagittal = Span(
location=self.slice_slider_sagittal.value - 1,
dimension='height',
line_color='green',
line_width=1,
render_mode="css")
# The horizontal crosshair line on the frontal view that indicates the selected axial slice.
self.frontal_crosshair_from_axial = Span(
location=self.slice_slider_axial.value - 1,
dimension='width',
line_color='green',
line_width=1,
render_mode="css")
self.p_frontal.add_layout(self.frontal_crosshair_from_sagittal)
self.p_frontal.add_layout(self.frontal_crosshair_from_axial)
self.p_axial = figure(
plot_width=int(np.floor(m.subj_bg.shape[1] * scale_factor)),
plot_height=int(np.floor(m.subj_bg.shape[2] * scale_factor)),
title='',
toolbar_location=None,
active_drag=None,
active_inspect=None,
active_scroll=None,
active_tap=None)
self.p_axial.axis.visible = False
self.p_axial.x_range.range_padding = 0
self.p_axial.y_range.range_padding = 0
self.axial_crosshair_from_sagittal = Span(
location=self.slice_slider_sagittal.value - 1,
dimension='height',
line_color='green',
line_width=1,
render_mode="css")
self.axial_crosshair_from_frontal = Span(
location=self.slice_slider_frontal.end -
self.slice_slider_frontal.value + 1,
dimension='width',
line_color='green',
line_width=1,
render_mode="css")
self.p_axial.add_layout(self.axial_crosshair_from_sagittal)
self.p_axial.add_layout(self.axial_crosshair_from_frontal)
self.p_sagittal = figure(
plot_width=int(np.floor(m.subj_bg.shape[2] * scale_factor)),
plot_height=int(np.floor(m.subj_bg.shape[0] * scale_factor)),
title='',
toolbar_location=None,
active_drag=None,
active_inspect=None,
active_scroll=None,
active_tap=None)
self.p_sagittal.axis.visible = False
self.p_sagittal.x_range.range_padding = 0
self.p_sagittal.y_range.range_padding = 0
self.sagittal_crosshair_from_frontal = Span(
location=self.slice_slider_frontal.value - 1,
dimension='height',
line_color='green',
line_width=1,
render_mode="css")
self.sagittal_crosshair_from_axial = Span(
location=self.slice_slider_axial.end -
self.slice_slider_axial.value - 1,
dimension='width',
line_color='green',
line_width=1,
render_mode="css")
self.p_sagittal.add_layout(self.sagittal_crosshair_from_frontal)
self.p_sagittal.add_layout(self.sagittal_crosshair_from_axial)
self.loading_label = Label(text='Processing scan...',
render_mode='css',
x=self.m.subj_bg.shape[1] // 2,
y=self.m.subj_bg.shape[2] // 2,
text_align='center',
text_color='white',
text_font_size='25px',
text_font_style='italic',
border_line_color='white',
border_line_alpha=1.0,
background_fill_color='black',
background_fill_alpha=0.5,
level='overlay',
visible=False)
self.p_axial.add_layout(self.loading_label)
self.render_backround()
# create empty plot objects with empty ("fully transparent") ColumnDataSources):
self.frontal_zeros = np.zeros_like(
np.flipud(self.bg[:, :, self.slice_slider_frontal.value - 1]))
self.axial_zeros = np.zeros_like(
np.rot90(self.bg[self.m.subj_bg.shape[0] -
self.slice_slider_axial.value, :, :]))
self.sagittal_zeros = np.zeros_like(
np.flipud(
self.
bg[:, self.slice_slider_sagittal.end -
self.slice_slider_sagittal.value if self.flip_frontal_view.
active else self.slice_slider_sagittal.value - 1, :]))
self.frontal_zeros[True] = 255 # value for a fully transparent pixel
self.axial_zeros[True] = 255
self.sagittal_zeros[True] = 255
self.frontal_data = ColumnDataSource(data=dict(
image=[self.frontal_zeros, self.frontal_zeros, self.frontal_zeros],
x=[0, 0, 0],
y=[0, 0, 0]))
self.axial_data = ColumnDataSource(data=dict(
image=[self.axial_zeros, self.axial_zeros, self.axial_zeros],
x=[0, 0, 0],
y=[0, 0, 0]))
self.sagittal_data = ColumnDataSource(data=dict(image=[
self.sagittal_zeros, self.sagittal_zeros, self.sagittal_zeros
],
x=[0, 0, 0],
y=[0, 0, 0]))
self.p_frontal.image_rgba(image="image",
x="x",
y="y",
dw=self.frontal_zeros.shape[1],
dh=self.frontal_zeros.shape[0],
source=self.frontal_data)
self.p_axial.image_rgba(image="image",
x="x",
y="y",
dw=self.axial_zeros.shape[1],
dh=self.axial_zeros.shape[0],
source=self.axial_data)
self.p_sagittal.image_rgba(image="image",
x="x",
y="y",
dw=self.sagittal_zeros.shape[1],
dh=self.sagittal_zeros.shape[0],
source=self.sagittal_data)
self.toggle_transparency = Toggle(label='Hide relevance overlay',
button_type='default',
width=200)
self.toggle_regions = Toggle(label='Show outline of atlas region',
button_type='default',
width=200)
self.region_ID = get_region_id(
self.slice_slider_axial.value - 1, self.slice_slider_sagittal.end -
self.slice_slider_sagittal.value if self.flip_frontal_view.active
else self.slice_slider_sagittal.value - 1,
self.slice_slider_frontal.value - 1)
self.selected_region = get_region_name(
self.slice_slider_axial.value - 1, self.slice_slider_sagittal.end -
self.slice_slider_sagittal.value if self.flip_frontal_view.active
else self.slice_slider_sagittal.value - 1,
self.slice_slider_frontal.value - 1)
self.region_div = Div(text="Region: " + self.selected_region,
sizing_mode="stretch_both",
css_classes=["region_divs"])
self.cluster_size_div = Div(
text="Cluster Size: " + "0", css_classes=[
"cluster_divs"
]) # initialize with 0 because clust_labelimg does not exist yet
self.cluster_mean_div = Div(text="Mean Intensity: " + "0",
css_classes=["cluster_divs"])
self.cluster_peak_div = Div(text="Peak Intensity: " + "0",
css_classes=["cluster_divs"])
# see InteractiveVis/static/ for default formatting/style definitions
self.age_spinner = Spinner(
title="Age:",
placeholder="years",
mode="int",
low=55,
high=99,
width=int(np.floor(m.subj_bg.shape[1] * scale_factor) // 2 - 10),
disabled=True) #no subject selected at time of initialization
self.sex_select = Select(
title="Sex:",
value="N/A",
options=["male", "female", "N/A"],
width=int(np.floor(m.subj_bg.shape[1] * scale_factor) // 2 - 10),
disabled=True)
self.tiv_spinner = Spinner(
title="TIV:",
placeholder="cm³",
mode="float",
low=1000,
high=2100,
width=int(np.floor(m.subj_bg.shape[1] * scale_factor) // 2 - 10),
disabled=True)
self.field_strength_select = Select(
title="Field Strength [T]:",
value="1.5",
options=["1.5", "3.0"],
width=int(np.floor(m.subj_bg.shape[1] * scale_factor) // 2 - 10),
disabled=True)
# Empty dummy figure to add ColorBar to, because annotations (like a ColorBar) must have a
# parent figure in Bokeh:
self.p_color_bar = figure(
plot_width=100,
plot_height=int(np.floor(m.subj_bg.shape[0] * scale_factor)),
title='',
toolbar_location=None,
active_drag=None,
active_inspect=None,
active_scroll=None,
active_tap=None,
outline_line_alpha=0.0)
self.p_color_bar.axis.visible = False
self.p_color_bar.x_range.range_padding = 0
self.p_color_bar.y_range.range_padding = 0
self.color_mapper = LinearColorMapper(palette=color_palette,
low=-1,
high=1)
self.color_bar = ColorBar(color_mapper=self.color_mapper,
title="Relevance")
self.p_color_bar.add_layout(self.color_bar)
self.scan_upload = FileInput(accept='.nii.gz, .nii')
self.residualize_button = Button(
label="Start residualization and view scan", disabled=True)
def dummy():
pass
self.residualize_button.on_click(
dummy
) # TODO: remove this once on_click is working when setting callback only from the model class (bug in Bokeh 2.2.x ?)
# Initialize column layout:
self.layout = row(
column(
self.subject_select, self.model_select,
Spacer(height=40, width=200, sizing_mode='scale_width'),
self.threshold_slider, self.clusthist, self.clustersize_slider,
self.transparency_slider, self.toggle_transparency,
self.toggle_regions, self.region_div,
column(self.cluster_size_div, self.cluster_mean_div,
self.cluster_peak_div)),
column(
row(self.age_spinner,
self.sex_select,
self.tiv_spinner,
self.field_strength_select,
self.scan_upload,
css_classes=["subject_divs"]),
row(self.residualize_button),
row(
column(self.p_frontal, self.slice_slider_frontal,
self.guide_frontal, self.flip_frontal_view),
column(self.p_axial, self.slice_slider_axial,
self.guide_axial),
column(self.p_sagittal, self.slice_slider_sagittal,
self.guide_sagittal), column(self.p_color_bar))))
self.clust_hist_bins = list(range(
0, 250 + 1,
10)) # list from (0, 10, .., 250); range max is slider_max_size+1
def create(palm):
doc = curdoc()
# Calibration averaged waveforms per photon energy
waveform_plot = Plot(
title=Title(text="eTOF calibration waveforms"),
x_range=DataRange1d(),
y_range=DataRange1d(),
plot_height=760,
plot_width=PLOT_CANVAS_WIDTH,
toolbar_location="right",
)
# ---- tools
waveform_plot.toolbar.logo = None
waveform_plot_hovertool = HoverTool(
tooltips=[("energy, eV", "@en"), ("eTOF bin", "$x{0.}")])
waveform_plot.add_tools(PanTool(), BoxZoomTool(), WheelZoomTool(),
ResetTool(), waveform_plot_hovertool)
# ---- axes
waveform_plot.add_layout(LinearAxis(axis_label="eTOF time bin"),
place="below")
waveform_plot.add_layout(LinearAxis(axis_label="Intensity",
major_label_orientation="vertical"),
place="left")
# ---- grid lines
waveform_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
waveform_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
# ---- multiline glyphs
waveform_ref_source = ColumnDataSource(dict(xs=[], ys=[], en=[]))
waveform_ref_multiline = waveform_plot.add_glyph(
waveform_ref_source, MultiLine(xs="xs", ys="ys", line_color="blue"))
waveform_str_source = ColumnDataSource(dict(xs=[], ys=[], en=[]))
waveform_str_multiline = waveform_plot.add_glyph(
waveform_str_source, MultiLine(xs="xs", ys="ys", line_color="red"))
# ---- legend
waveform_plot.add_layout(
Legend(items=[(
"reference",
[waveform_ref_multiline]), ("streaked",
[waveform_str_multiline])]))
waveform_plot.legend.click_policy = "hide"
# ---- vertical spans
photon_peak_ref_span = Span(location=0,
dimension="height",
line_dash="dashed",
line_color="blue")
photon_peak_str_span = Span(location=0,
dimension="height",
line_dash="dashed",
line_color="red")
waveform_plot.add_layout(photon_peak_ref_span)
waveform_plot.add_layout(photon_peak_str_span)
# Calibration fit plot
fit_plot = Plot(
title=Title(text="eTOF calibration fit"),
x_range=DataRange1d(),
y_range=DataRange1d(),
plot_height=PLOT_CANVAS_HEIGHT,
plot_width=PLOT_CANVAS_WIDTH,
toolbar_location="right",
)
# ---- tools
fit_plot.toolbar.logo = None
fit_plot.add_tools(PanTool(), BoxZoomTool(), WheelZoomTool(), ResetTool())
# ---- axes
fit_plot.add_layout(LinearAxis(axis_label="Photoelectron peak shift"),
place="below")
fit_plot.add_layout(LinearAxis(axis_label="Photon energy, eV",
major_label_orientation="vertical"),
place="left")
# ---- grid lines
fit_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
fit_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
# ---- circle glyphs
fit_ref_circle_source = ColumnDataSource(dict(x=[], y=[]))
fit_ref_circle = fit_plot.add_glyph(
fit_ref_circle_source, Circle(x="x", y="y", line_color="blue"))
fit_str_circle_source = ColumnDataSource(dict(x=[], y=[]))
fit_str_circle = fit_plot.add_glyph(fit_str_circle_source,
Circle(x="x", y="y", line_color="red"))
# ---- line glyphs
fit_ref_line_source = ColumnDataSource(dict(x=[], y=[]))
fit_ref_line = fit_plot.add_glyph(fit_ref_line_source,
Line(x="x", y="y", line_color="blue"))
fit_str_line_source = ColumnDataSource(dict(x=[], y=[]))
fit_str_line = fit_plot.add_glyph(fit_str_line_source,
Line(x="x", y="y", line_color="red"))
# ---- legend
fit_plot.add_layout(
Legend(items=[
("reference", [fit_ref_circle, fit_ref_line]),
("streaked", [fit_str_circle, fit_str_line]),
]))
fit_plot.legend.click_policy = "hide"
# Calibration results datatables
def datatable_ref_source_callback(_attr, _old_value, new_value):
for en, ps, use in zip(new_value["energy"], new_value["peak_pos_ref"],
new_value["use_in_fit"]):
palm.etofs["0"].calib_data.loc[
en, "calib_tpeak"] = ps if ps != "NaN" else np.nan
palm.etofs["0"].calib_data.loc[en, "use_in_fit"] = use
calib_res = {}
for etof_key in palm.etofs:
calib_res[etof_key] = palm.etofs[etof_key].fit_calibration_curve()
update_calibration_plot(calib_res)
datatable_ref_source = ColumnDataSource(
dict(energy=["", "", ""],
peak_pos_ref=["", "", ""],
use_in_fit=[True, True, True]))
datatable_ref_source.on_change("data", datatable_ref_source_callback)
datatable_ref = DataTable(
source=datatable_ref_source,
columns=[
TableColumn(field="energy",
title="Photon Energy, eV",
editor=IntEditor()),
TableColumn(field="peak_pos_ref",
title="Reference Peak",
editor=IntEditor()),
TableColumn(field="use_in_fit",
title=" ",
editor=CheckboxEditor(),
width=80),
],
index_position=None,
editable=True,
height=300,
width=250,
)
def datatable_str_source_callback(_attr, _old_value, new_value):
for en, ps, use in zip(new_value["energy"], new_value["peak_pos_str"],
new_value["use_in_fit"]):
palm.etofs["1"].calib_data.loc[
en, "calib_tpeak"] = ps if ps != "NaN" else np.nan
palm.etofs["1"].calib_data.loc[en, "use_in_fit"] = use
calib_res = {}
for etof_key in palm.etofs:
calib_res[etof_key] = palm.etofs[etof_key].fit_calibration_curve()
update_calibration_plot(calib_res)
datatable_str_source = ColumnDataSource(
dict(energy=["", "", ""],
peak_pos_str=["", "", ""],
use_in_fit=[True, True, True]))
datatable_str_source.on_change("data", datatable_str_source_callback)
datatable_str = DataTable(
source=datatable_str_source,
columns=[
TableColumn(field="energy",
title="Photon Energy, eV",
editor=IntEditor()),
TableColumn(field="peak_pos_str",
title="Streaked Peak",
editor=IntEditor()),
TableColumn(field="use_in_fit",
title=" ",
editor=CheckboxEditor(),
width=80),
],
index_position=None,
editable=True,
height=350,
width=250,
)
# eTOF calibration folder path text input
def path_textinput_callback(_attr, _old_value, _new_value):
path_periodic_update()
update_load_dropdown_menu()
path_textinput = TextInput(title="eTOF calibration path:",
value=os.path.join(os.path.expanduser("~")),
width=510)
path_textinput.on_change("value", path_textinput_callback)
# eTOF calibration eco scans dropdown
def scans_dropdown_callback(event):
scans_dropdown.label = event.item
scans_dropdown = Dropdown(label="ECO scans",
button_type="default",
menu=[])
scans_dropdown.on_click(scans_dropdown_callback)
# ---- etof scans periodic update
def path_periodic_update():
new_menu = []
if os.path.isdir(path_textinput.value):
for entry in os.scandir(path_textinput.value):
if entry.is_file() and entry.name.endswith(".json"):
new_menu.append((entry.name, entry.name))
scans_dropdown.menu = sorted(new_menu, reverse=True)
doc.add_periodic_callback(path_periodic_update, 5000)
path_tab = Panel(child=column(
path_textinput,
scans_dropdown,
),
title="Path")
upload_div = Div(text="Upload ECO scan (top) and all hdf5 files (bottom):")
# ECO scan upload FileInput
def eco_fileinput_callback(_attr, _old, new):
with io.BytesIO(base64.b64decode(new)) as eco_scan:
data = json.load(eco_scan)
print(data)
eco_fileinput = FileInput(accept=".json", disabled=True)
eco_fileinput.on_change("value", eco_fileinput_callback)
# HDF5 upload FileInput
def hdf5_fileinput_callback(_attr, _old, new):
for base64_str in new:
with io.BytesIO(base64.b64decode(base64_str)) as hdf5_file:
with h5py.File(hdf5_file, "r") as h5f:
print(h5f.keys())
hdf5_fileinput = FileInput(accept=".hdf5,.h5",
multiple=True,
disabled=True)
hdf5_fileinput.on_change("value", hdf5_fileinput_callback)
upload_tab = Panel(child=column(upload_div, eco_fileinput, hdf5_fileinput),
title="Upload")
# Calibrate button
def calibrate_button_callback():
try:
palm.calibrate_etof_eco(eco_scan_filename=os.path.join(
path_textinput.value, scans_dropdown.label))
except Exception:
palm.calibrate_etof(folder_name=path_textinput.value)
datatable_ref_source.data.update(
energy=palm.etofs["0"].calib_data.index.tolist(),
peak_pos_ref=palm.etofs["0"].calib_data["calib_tpeak"].tolist(),
use_in_fit=palm.etofs["0"].calib_data["use_in_fit"].tolist(),
)
datatable_str_source.data.update(
energy=palm.etofs["0"].calib_data.index.tolist(),
peak_pos_str=palm.etofs["1"].calib_data["calib_tpeak"].tolist(),
use_in_fit=palm.etofs["1"].calib_data["use_in_fit"].tolist(),
)
def update_calibration_plot(calib_res):
etof_ref = palm.etofs["0"]
etof_str = palm.etofs["1"]
shift_val = 0
etof_ref_wf_shifted = []
etof_str_wf_shifted = []
for wf_ref, wf_str in zip(etof_ref.calib_data["waveform"],
etof_str.calib_data["waveform"]):
shift_val -= max(wf_ref.max(), wf_str.max())
etof_ref_wf_shifted.append(wf_ref + shift_val)
etof_str_wf_shifted.append(wf_str + shift_val)
waveform_ref_source.data.update(
xs=len(etof_ref.calib_data) *
[list(range(etof_ref.internal_time_bins))],
ys=etof_ref_wf_shifted,
en=etof_ref.calib_data.index.tolist(),
)
waveform_str_source.data.update(
xs=len(etof_str.calib_data) *
[list(range(etof_str.internal_time_bins))],
ys=etof_str_wf_shifted,
en=etof_str.calib_data.index.tolist(),
)
photon_peak_ref_span.location = etof_ref.calib_t0
photon_peak_str_span.location = etof_str.calib_t0
def plot_fit(time, calib_a, calib_b):
time_fit = np.linspace(np.nanmin(time), np.nanmax(time), 100)
en_fit = (calib_a / time_fit)**2 + calib_b
return time_fit, en_fit
def update_plot(calib_results, circle, line):
(a, c), x, y = calib_results
x_fit, y_fit = plot_fit(x, a, c)
circle.data.update(x=x, y=y)
line.data.update(x=x_fit, y=y_fit)
update_plot(calib_res["0"], fit_ref_circle_source, fit_ref_line_source)
update_plot(calib_res["1"], fit_str_circle_source, fit_str_line_source)
calib_const_div.text = f"""
a_str = {etof_str.calib_a:.2f}<br>
b_str = {etof_str.calib_b:.2f}<br>
<br>
a_ref = {etof_ref.calib_a:.2f}<br>
b_ref = {etof_ref.calib_b:.2f}
"""
calibrate_button = Button(label="Calibrate eTOF",
button_type="default",
width=250)
calibrate_button.on_click(calibrate_button_callback)
# Photon peak noise threshold value text input
def phot_peak_noise_thr_spinner_callback(_attr, old_value, new_value):
if new_value > 0:
for etof in palm.etofs.values():
etof.photon_peak_noise_thr = new_value
else:
phot_peak_noise_thr_spinner.value = old_value
phot_peak_noise_thr_spinner = Spinner(title="Photon peak noise threshold:",
value=1,
step=0.1)
phot_peak_noise_thr_spinner.on_change(
"value", phot_peak_noise_thr_spinner_callback)
# Electron peak noise threshold value text input
def el_peak_noise_thr_spinner_callback(_attr, old_value, new_value):
if new_value > 0:
for etof in palm.etofs.values():
etof.electron_peak_noise_thr = new_value
else:
el_peak_noise_thr_spinner.value = old_value
el_peak_noise_thr_spinner = Spinner(title="Electron peak noise threshold:",
value=10,
step=0.1)
el_peak_noise_thr_spinner.on_change("value",
el_peak_noise_thr_spinner_callback)
# Save calibration button
def save_button_callback():
palm.save_etof_calib(path=path_textinput.value)
update_load_dropdown_menu()
save_button = Button(label="Save", button_type="default", width=250)
save_button.on_click(save_button_callback)
# Load calibration button
def load_dropdown_callback(event):
new_value = event.item
if new_value:
palm.load_etof_calib(os.path.join(path_textinput.value, new_value))
datatable_ref_source.data.update(
energy=palm.etofs["0"].calib_data.index.tolist(),
peak_pos_ref=palm.etofs["0"].calib_data["calib_tpeak"].tolist(
),
use_in_fit=palm.etofs["0"].calib_data["use_in_fit"].tolist(),
)
datatable_str_source.data.update(
energy=palm.etofs["0"].calib_data.index.tolist(),
peak_pos_str=palm.etofs["1"].calib_data["calib_tpeak"].tolist(
),
use_in_fit=palm.etofs["1"].calib_data["use_in_fit"].tolist(),
)
def update_load_dropdown_menu():
new_menu = []
calib_file_ext = ".palm_etof"
if os.path.isdir(path_textinput.value):
for entry in os.scandir(path_textinput.value):
if entry.is_file() and entry.name.endswith((calib_file_ext)):
new_menu.append(
(entry.name[:-len(calib_file_ext)], entry.name))
load_dropdown.button_type = "default"
load_dropdown.menu = sorted(new_menu, reverse=True)
else:
load_dropdown.button_type = "danger"
load_dropdown.menu = new_menu
doc.add_next_tick_callback(update_load_dropdown_menu)
doc.add_periodic_callback(update_load_dropdown_menu, 5000)
load_dropdown = Dropdown(label="Load", menu=[], width=250)
load_dropdown.on_click(load_dropdown_callback)
# eTOF fitting equation
fit_eq_div = Div(
text="""Fitting equation:<br><br><img src="/palm/static/5euwuy.gif">"""
)
# Calibration constants
calib_const_div = Div(text=f"""
a_str = {0}<br>
b_str = {0}<br>
<br>
a_ref = {0}<br>
b_ref = {0}
""")
# assemble
tab_layout = column(
row(
column(waveform_plot, fit_plot),
Spacer(width=30),
column(
Tabs(tabs=[path_tab, upload_tab]),
calibrate_button,
phot_peak_noise_thr_spinner,
el_peak_noise_thr_spinner,
row(save_button, load_dropdown),
row(datatable_ref, datatable_str),
calib_const_div,
fit_eq_div,
),
))
return Panel(child=tab_layout, title="eTOF Calibration")
def slider_handler(attr, old, new):
# view.filters[0] = IndexFilter([new])
# print(view.filters[0])
img_plot.view = CDSView(source=source, filters=[IndexFilter([new])])
# make the handler itself
def file_handler(attr, old, new):
add_image(new, source, filepicker.filename)
ind = img_plot.view.filters[0].indices
img_plot.view = CDSView(source=source, filters=[IndexFilter(ind)])
# print(source.to_df().head())
# Make the file picker
filepicker = FileInput(accept="image/*", multiple=False)
filepicker.on_change('value', file_handler)
# Make the slider
slider = Slider(start=0, end=1, value=0, step=1, title="Image")
slider.on_change('value', slider_handler)
slider_handler_js = CustomJS(args=dict(source=source,
view=img_plot.view,
filter=img_plot.view.filters[0]),
code="""
console.log('range_slider: value=' + this.value, this.toString())
filter.indices = [this.value]
//console.log('filter_ind: value=' + filter.indices[0])
source.change.emit()
view.change.emit()
filter.change.emit()
def get_file_widget():
# button_input = FileInput(accept=".csv,.txt")
button_input = FileInput()
return button_input
def create():
doc = curdoc()
det_data = {}
cami_meta = {}
def proposal_textinput_callback(_attr, _old, new):
nonlocal cami_meta
proposal = new.strip()
for zebra_proposals_path in pyzebra.ZEBRA_PROPOSALS_PATHS:
proposal_path = os.path.join(zebra_proposals_path, proposal)
if os.path.isdir(proposal_path):
# found it
break
else:
raise ValueError(f"Can not find data for proposal '{proposal}'.")
file_list = []
for file in os.listdir(proposal_path):
if file.endswith(".hdf"):
file_list.append((os.path.join(proposal_path, file), file))
file_select.options = file_list
cami_meta = {}
proposal_textinput = TextInput(title="Proposal number:", width=210)
proposal_textinput.on_change("value", proposal_textinput_callback)
def upload_button_callback(_attr, _old, new):
nonlocal cami_meta
with io.StringIO(base64.b64decode(new).decode()) as file:
cami_meta = pyzebra.parse_h5meta(file)
file_list = cami_meta["filelist"]
file_select.options = [(entry, os.path.basename(entry))
for entry in file_list]
upload_div = Div(text="or upload .cami file:", margin=(5, 5, 0, 5))
upload_button = FileInput(accept=".cami", width=200)
upload_button.on_change("value", upload_button_callback)
def update_image(index=None):
if index is None:
index = index_spinner.value
current_image = det_data["data"][index]
proj_v_line_source.data.update(x=np.arange(0, IMAGE_W) + 0.5,
y=np.mean(current_image, axis=0))
proj_h_line_source.data.update(x=np.mean(current_image, axis=1),
y=np.arange(0, IMAGE_H) + 0.5)
image_source.data.update(
h=[np.zeros((1, 1))],
k=[np.zeros((1, 1))],
l=[np.zeros((1, 1))],
)
image_source.data.update(image=[current_image])
if main_auto_checkbox.active:
im_min = np.min(current_image)
im_max = np.max(current_image)
display_min_spinner.value = im_min
display_max_spinner.value = im_max
image_glyph.color_mapper.low = im_min
image_glyph.color_mapper.high = im_max
if "mf" in det_data:
metadata_table_source.data.update(mf=[det_data["mf"][index]])
else:
metadata_table_source.data.update(mf=[None])
if "temp" in det_data:
metadata_table_source.data.update(temp=[det_data["temp"][index]])
else:
metadata_table_source.data.update(temp=[None])
gamma, nu = calculate_pol(det_data, index)
omega = np.ones((IMAGE_H, IMAGE_W)) * det_data["omega"][index]
image_source.data.update(gamma=[gamma], nu=[nu], omega=[omega])
def update_overview_plot():
h5_data = det_data["data"]
n_im, n_y, n_x = h5_data.shape
overview_x = np.mean(h5_data, axis=1)
overview_y = np.mean(h5_data, axis=2)
overview_plot_x_image_source.data.update(image=[overview_x],
dw=[n_x],
dh=[n_im])
overview_plot_y_image_source.data.update(image=[overview_y],
dw=[n_y],
dh=[n_im])
if proj_auto_checkbox.active:
im_min = min(np.min(overview_x), np.min(overview_y))
im_max = max(np.max(overview_x), np.max(overview_y))
proj_display_min_spinner.value = im_min
proj_display_max_spinner.value = im_max
overview_plot_x_image_glyph.color_mapper.low = im_min
overview_plot_y_image_glyph.color_mapper.low = im_min
overview_plot_x_image_glyph.color_mapper.high = im_max
overview_plot_y_image_glyph.color_mapper.high = im_max
frame_range.start = 0
frame_range.end = n_im
frame_range.reset_start = 0
frame_range.reset_end = n_im
frame_range.bounds = (0, n_im)
scan_motor = det_data["scan_motor"]
overview_plot_y.axis[1].axis_label = f"Scanning motor, {scan_motor}"
var = det_data[scan_motor]
var_start = var[0]
var_end = var[-1] + (var[-1] - var[0]) / (n_im - 1)
scanning_motor_range.start = var_start
scanning_motor_range.end = var_end
scanning_motor_range.reset_start = var_start
scanning_motor_range.reset_end = var_end
# handle both, ascending and descending sequences
scanning_motor_range.bounds = (min(var_start,
var_end), max(var_start, var_end))
def file_select_callback(_attr, old, new):
nonlocal det_data
if not new:
# skip empty selections
return
# Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
if len(new) > 1:
# drop selection to the previous one
file_select.value = old
return
if len(old) > 1:
# skip unnecessary update caused by selection drop
return
det_data = pyzebra.read_detector_data(new[0])
if cami_meta and "crystal" in cami_meta:
det_data["ub"] = cami_meta["crystal"]["UB"]
index_spinner.value = 0
index_spinner.high = det_data["data"].shape[0] - 1
index_slider.end = det_data["data"].shape[0] - 1
zebra_mode = det_data["zebra_mode"]
if zebra_mode == "nb":
metadata_table_source.data.update(geom=["normal beam"])
else: # zebra_mode == "bi"
metadata_table_source.data.update(geom=["bisecting"])
update_image(0)
update_overview_plot()
file_select = MultiSelect(title="Available .hdf files:",
width=210,
height=250)
file_select.on_change("value", file_select_callback)
def index_callback(_attr, _old, new):
update_image(new)
index_slider = Slider(value=0, start=0, end=1, show_value=False, width=400)
index_spinner = Spinner(title="Image index:", value=0, low=0, width=100)
index_spinner.on_change("value", index_callback)
index_slider.js_link("value_throttled", index_spinner, "value")
index_spinner.js_link("value", index_slider, "value")
plot = Plot(
x_range=Range1d(0, IMAGE_W, bounds=(0, IMAGE_W)),
y_range=Range1d(0, IMAGE_H, bounds=(0, IMAGE_H)),
plot_height=IMAGE_PLOT_H,
plot_width=IMAGE_PLOT_W,
toolbar_location="left",
)
# ---- tools
plot.toolbar.logo = None
# ---- axes
plot.add_layout(LinearAxis(), place="above")
plot.add_layout(LinearAxis(major_label_orientation="vertical"),
place="right")
# ---- grid lines
plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
# ---- rgba image glyph
image_source = ColumnDataSource(
dict(
image=[np.zeros((IMAGE_H, IMAGE_W), dtype="float32")],
h=[np.zeros((1, 1))],
k=[np.zeros((1, 1))],
l=[np.zeros((1, 1))],
gamma=[np.zeros((1, 1))],
nu=[np.zeros((1, 1))],
omega=[np.zeros((1, 1))],
x=[0],
y=[0],
dw=[IMAGE_W],
dh=[IMAGE_H],
))
h_glyph = Image(image="h", x="x", y="y", dw="dw", dh="dh", global_alpha=0)
k_glyph = Image(image="k", x="x", y="y", dw="dw", dh="dh", global_alpha=0)
l_glyph = Image(image="l", x="x", y="y", dw="dw", dh="dh", global_alpha=0)
gamma_glyph = Image(image="gamma",
x="x",
y="y",
dw="dw",
dh="dh",
global_alpha=0)
nu_glyph = Image(image="nu",
x="x",
y="y",
dw="dw",
dh="dh",
global_alpha=0)
omega_glyph = Image(image="omega",
x="x",
y="y",
dw="dw",
dh="dh",
global_alpha=0)
plot.add_glyph(image_source, h_glyph)
plot.add_glyph(image_source, k_glyph)
plot.add_glyph(image_source, l_glyph)
plot.add_glyph(image_source, gamma_glyph)
plot.add_glyph(image_source, nu_glyph)
plot.add_glyph(image_source, omega_glyph)
image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh")
plot.add_glyph(image_source, image_glyph, name="image_glyph")
# ---- projections
proj_v = Plot(
x_range=plot.x_range,
y_range=DataRange1d(),
plot_height=150,
plot_width=IMAGE_PLOT_W,
toolbar_location=None,
)
proj_v.add_layout(LinearAxis(major_label_orientation="vertical"),
place="right")
proj_v.add_layout(LinearAxis(major_label_text_font_size="0pt"),
place="below")
proj_v.add_layout(Grid(dimension=0, ticker=BasicTicker()))
proj_v.add_layout(Grid(dimension=1, ticker=BasicTicker()))
proj_v_line_source = ColumnDataSource(dict(x=[], y=[]))
proj_v.add_glyph(proj_v_line_source,
Line(x="x", y="y", line_color="steelblue"))
proj_h = Plot(
x_range=DataRange1d(),
y_range=plot.y_range,
plot_height=IMAGE_PLOT_H,
plot_width=150,
toolbar_location=None,
)
proj_h.add_layout(LinearAxis(), place="above")
proj_h.add_layout(LinearAxis(major_label_text_font_size="0pt"),
place="left")
proj_h.add_layout(Grid(dimension=0, ticker=BasicTicker()))
proj_h.add_layout(Grid(dimension=1, ticker=BasicTicker()))
proj_h_line_source = ColumnDataSource(dict(x=[], y=[]))
proj_h.add_glyph(proj_h_line_source,
Line(x="x", y="y", line_color="steelblue"))
# add tools
hovertool = HoverTool(tooltips=[
("intensity", "@image"),
("gamma", "@gamma"),
("nu", "@nu"),
("omega", "@omega"),
("h", "@h"),
("k", "@k"),
("l", "@l"),
])
box_edit_source = ColumnDataSource(dict(x=[], y=[], width=[], height=[]))
box_edit_glyph = Rect(x="x",
y="y",
width="width",
height="height",
fill_alpha=0,
line_color="red")
box_edit_renderer = plot.add_glyph(box_edit_source, box_edit_glyph)
boxedittool = BoxEditTool(renderers=[box_edit_renderer], num_objects=1)
def box_edit_callback(_attr, _old, new):
if new["x"]:
h5_data = det_data["data"]
x_val = np.arange(h5_data.shape[0])
left = int(np.floor(new["x"][0]))
right = int(np.ceil(new["x"][0] + new["width"][0]))
bottom = int(np.floor(new["y"][0]))
top = int(np.ceil(new["y"][0] + new["height"][0]))
y_val = np.sum(h5_data[:, bottom:top, left:right], axis=(1, 2))
else:
x_val = []
y_val = []
roi_avg_plot_line_source.data.update(x=x_val, y=y_val)
box_edit_source.on_change("data", box_edit_callback)
wheelzoomtool = WheelZoomTool(maintain_focus=False)
plot.add_tools(
PanTool(),
BoxZoomTool(),
wheelzoomtool,
ResetTool(),
hovertool,
boxedittool,
)
plot.toolbar.active_scroll = wheelzoomtool
# shared frame ranges
frame_range = Range1d(0, 1, bounds=(0, 1))
scanning_motor_range = Range1d(0, 1, bounds=(0, 1))
det_x_range = Range1d(0, IMAGE_W, bounds=(0, IMAGE_W))
overview_plot_x = Plot(
title=Title(text="Projections on X-axis"),
x_range=det_x_range,
y_range=frame_range,
extra_y_ranges={"scanning_motor": scanning_motor_range},
plot_height=400,
plot_width=IMAGE_PLOT_W - 3,
)
# ---- tools
wheelzoomtool = WheelZoomTool(maintain_focus=False)
overview_plot_x.toolbar.logo = None
overview_plot_x.add_tools(
PanTool(),
BoxZoomTool(),
wheelzoomtool,
ResetTool(),
)
overview_plot_x.toolbar.active_scroll = wheelzoomtool
# ---- axes
overview_plot_x.add_layout(LinearAxis(axis_label="Coordinate X, pix"),
place="below")
overview_plot_x.add_layout(LinearAxis(axis_label="Frame",
major_label_orientation="vertical"),
place="left")
# ---- grid lines
overview_plot_x.add_layout(Grid(dimension=0, ticker=BasicTicker()))
overview_plot_x.add_layout(Grid(dimension=1, ticker=BasicTicker()))
# ---- rgba image glyph
overview_plot_x_image_source = ColumnDataSource(
dict(image=[np.zeros((1, 1), dtype="float32")],
x=[0],
y=[0],
dw=[IMAGE_W],
dh=[1]))
overview_plot_x_image_glyph = Image(image="image",
x="x",
y="y",
dw="dw",
dh="dh")
overview_plot_x.add_glyph(overview_plot_x_image_source,
overview_plot_x_image_glyph,
name="image_glyph")
det_y_range = Range1d(0, IMAGE_H, bounds=(0, IMAGE_H))
overview_plot_y = Plot(
title=Title(text="Projections on Y-axis"),
x_range=det_y_range,
y_range=frame_range,
extra_y_ranges={"scanning_motor": scanning_motor_range},
plot_height=400,
plot_width=IMAGE_PLOT_H + 22,
)
# ---- tools
wheelzoomtool = WheelZoomTool(maintain_focus=False)
overview_plot_y.toolbar.logo = None
overview_plot_y.add_tools(
PanTool(),
BoxZoomTool(),
wheelzoomtool,
ResetTool(),
)
overview_plot_y.toolbar.active_scroll = wheelzoomtool
# ---- axes
overview_plot_y.add_layout(LinearAxis(axis_label="Coordinate Y, pix"),
place="below")
overview_plot_y.add_layout(
LinearAxis(
y_range_name="scanning_motor",
axis_label="Scanning motor",
major_label_orientation="vertical",
),
place="right",
)
# ---- grid lines
overview_plot_y.add_layout(Grid(dimension=0, ticker=BasicTicker()))
overview_plot_y.add_layout(Grid(dimension=1, ticker=BasicTicker()))
# ---- rgba image glyph
overview_plot_y_image_source = ColumnDataSource(
dict(image=[np.zeros((1, 1), dtype="float32")],
x=[0],
y=[0],
dw=[IMAGE_H],
dh=[1]))
overview_plot_y_image_glyph = Image(image="image",
x="x",
y="y",
dw="dw",
dh="dh")
overview_plot_y.add_glyph(overview_plot_y_image_source,
overview_plot_y_image_glyph,
name="image_glyph")
roi_avg_plot = Plot(
x_range=DataRange1d(),
y_range=DataRange1d(),
plot_height=150,
plot_width=IMAGE_PLOT_W,
toolbar_location="left",
)
# ---- tools
roi_avg_plot.toolbar.logo = None
# ---- axes
roi_avg_plot.add_layout(LinearAxis(), place="below")
roi_avg_plot.add_layout(LinearAxis(major_label_orientation="vertical"),
place="left")
# ---- grid lines
roi_avg_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
roi_avg_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
roi_avg_plot_line_source = ColumnDataSource(dict(x=[], y=[]))
roi_avg_plot.add_glyph(roi_avg_plot_line_source,
Line(x="x", y="y", line_color="steelblue"))
cmap_dict = {
"gray": Greys256,
"gray_reversed": Greys256[::-1],
"plasma": Plasma256,
"cividis": Cividis256,
}
def colormap_callback(_attr, _old, new):
image_glyph.color_mapper = LinearColorMapper(palette=cmap_dict[new])
overview_plot_x_image_glyph.color_mapper = LinearColorMapper(
palette=cmap_dict[new])
overview_plot_y_image_glyph.color_mapper = LinearColorMapper(
palette=cmap_dict[new])
colormap = Select(title="Colormap:",
options=list(cmap_dict.keys()),
width=210)
colormap.on_change("value", colormap_callback)
colormap.value = "plasma"
STEP = 1
def main_auto_checkbox_callback(state):
if state:
display_min_spinner.disabled = True
display_max_spinner.disabled = True
else:
display_min_spinner.disabled = False
display_max_spinner.disabled = False
update_image()
main_auto_checkbox = CheckboxGroup(labels=["Main Auto Range"],
active=[0],
width=145,
margin=[10, 5, 0, 5])
main_auto_checkbox.on_click(main_auto_checkbox_callback)
def display_max_spinner_callback(_attr, _old_value, new_value):
display_min_spinner.high = new_value - STEP
image_glyph.color_mapper.high = new_value
display_max_spinner = Spinner(
low=0 + STEP,
value=1,
step=STEP,
disabled=bool(main_auto_checkbox.active),
width=100,
height=31,
)
display_max_spinner.on_change("value", display_max_spinner_callback)
def display_min_spinner_callback(_attr, _old_value, new_value):
display_max_spinner.low = new_value + STEP
image_glyph.color_mapper.low = new_value
display_min_spinner = Spinner(
low=0,
high=1 - STEP,
value=0,
step=STEP,
disabled=bool(main_auto_checkbox.active),
width=100,
height=31,
)
display_min_spinner.on_change("value", display_min_spinner_callback)
PROJ_STEP = 0.1
def proj_auto_checkbox_callback(state):
if state:
proj_display_min_spinner.disabled = True
proj_display_max_spinner.disabled = True
else:
proj_display_min_spinner.disabled = False
proj_display_max_spinner.disabled = False
update_overview_plot()
proj_auto_checkbox = CheckboxGroup(labels=["Projections Auto Range"],
active=[0],
width=145,
margin=[10, 5, 0, 5])
proj_auto_checkbox.on_click(proj_auto_checkbox_callback)
def proj_display_max_spinner_callback(_attr, _old_value, new_value):
proj_display_min_spinner.high = new_value - PROJ_STEP
overview_plot_x_image_glyph.color_mapper.high = new_value
overview_plot_y_image_glyph.color_mapper.high = new_value
proj_display_max_spinner = Spinner(
low=0 + PROJ_STEP,
value=1,
step=PROJ_STEP,
disabled=bool(proj_auto_checkbox.active),
width=100,
height=31,
)
proj_display_max_spinner.on_change("value",
proj_display_max_spinner_callback)
def proj_display_min_spinner_callback(_attr, _old_value, new_value):
proj_display_max_spinner.low = new_value + PROJ_STEP
overview_plot_x_image_glyph.color_mapper.low = new_value
overview_plot_y_image_glyph.color_mapper.low = new_value
proj_display_min_spinner = Spinner(
low=0,
high=1 - PROJ_STEP,
value=0,
step=PROJ_STEP,
disabled=bool(proj_auto_checkbox.active),
width=100,
height=31,
)
proj_display_min_spinner.on_change("value",
proj_display_min_spinner_callback)
def hkl_button_callback():
index = index_spinner.value
h, k, l = calculate_hkl(det_data, index)
image_source.data.update(h=[h], k=[k], l=[l])
hkl_button = Button(label="Calculate hkl (slow)", width=210)
hkl_button.on_click(hkl_button_callback)
def events_list_callback(_attr, _old, new):
doc.events_list_spind.value = new
events_list = TextAreaInput(rows=7, width=830)
events_list.on_change("value", events_list_callback)
doc.events_list_hdf_viewer = events_list
def add_event_button_callback():
diff_vec = []
p0 = [1.0, 0.0, 1.0]
maxfev = 100000
wave = det_data["wave"]
ddist = det_data["ddist"]
gamma = det_data["gamma"][0]
omega = det_data["omega"][0]
nu = det_data["nu"][0]
chi = det_data["chi"][0]
phi = det_data["phi"][0]
scan_motor = det_data["scan_motor"]
var_angle = det_data[scan_motor]
x0 = int(np.floor(det_x_range.start))
xN = int(np.ceil(det_x_range.end))
y0 = int(np.floor(det_y_range.start))
yN = int(np.ceil(det_y_range.end))
fr0 = int(np.floor(frame_range.start))
frN = int(np.ceil(frame_range.end))
data_roi = det_data["data"][fr0:frN, y0:yN, x0:xN]
cnts = np.sum(data_roi, axis=(1, 2))
coeff, _ = curve_fit(gauss,
range(len(cnts)),
cnts,
p0=p0,
maxfev=maxfev)
m = cnts.mean()
sd = cnts.std()
snr_cnts = np.where(sd == 0, 0, m / sd)
frC = fr0 + coeff[1]
var_F = var_angle[math.floor(frC)]
var_C = var_angle[math.ceil(frC)]
frStep = frC - math.floor(frC)
var_step = var_C - var_F
var_p = var_F + var_step * frStep
if scan_motor == "gamma":
gamma = var_p
elif scan_motor == "omega":
omega = var_p
elif scan_motor == "nu":
nu = var_p
elif scan_motor == "chi":
chi = var_p
elif scan_motor == "phi":
phi = var_p
intensity = coeff[1] * abs(
coeff[2] * var_step) * math.sqrt(2) * math.sqrt(np.pi)
projX = np.sum(data_roi, axis=(0, 1))
coeff, _ = curve_fit(gauss,
range(len(projX)),
projX,
p0=p0,
maxfev=maxfev)
x_pos = x0 + coeff[1]
projY = np.sum(data_roi, axis=(0, 2))
coeff, _ = curve_fit(gauss,
range(len(projY)),
projY,
p0=p0,
maxfev=maxfev)
y_pos = y0 + coeff[1]
ga, nu = pyzebra.det2pol(ddist, gamma, nu, x_pos, y_pos)
diff_vector = pyzebra.z1frmd(wave, ga, omega, chi, phi, nu)
d_spacing = float(pyzebra.dandth(wave, diff_vector)[0])
diff_vector = diff_vector.flatten() * 1e10
dv1, dv2, dv3 = diff_vector
diff_vec.append(diff_vector)
if events_list.value and not events_list.value.endswith("\n"):
events_list.value = events_list.value + "\n"
events_list.value = (
events_list.value +
f"{x_pos} {y_pos} {intensity} {snr_cnts} {dv1} {dv2} {dv3} {d_spacing}"
)
add_event_button = Button(label="Add spind event")
add_event_button.on_click(add_event_button_callback)
metadata_table_source = ColumnDataSource(
dict(geom=[""], temp=[None], mf=[None]))
num_formatter = NumberFormatter(format="0.00", nan_format="")
metadata_table = DataTable(
source=metadata_table_source,
columns=[
TableColumn(field="geom", title="Geometry", width=100),
TableColumn(field="temp",
title="Temperature",
formatter=num_formatter,
width=100),
TableColumn(field="mf",
title="Magnetic Field",
formatter=num_formatter,
width=100),
],
width=300,
height=50,
autosize_mode="none",
index_position=None,
)
# Final layout
import_layout = column(proposal_textinput, upload_div, upload_button,
file_select)
layout_image = column(
gridplot([[proj_v, None], [plot, proj_h]], merge_tools=False))
colormap_layout = column(
colormap,
main_auto_checkbox,
row(display_min_spinner, display_max_spinner),
proj_auto_checkbox,
row(proj_display_min_spinner, proj_display_max_spinner),
)
layout_controls = column(
row(metadata_table, index_spinner,
column(Spacer(height=25), index_slider)),
row(add_event_button, hkl_button),
row(events_list),
)
layout_overview = column(
gridplot(
[[overview_plot_x, overview_plot_y]],
toolbar_options=dict(logo=None),
merge_tools=True,
toolbar_location="left",
), )
tab_layout = row(
column(import_layout, colormap_layout),
column(layout_overview, layout_controls),
column(roi_avg_plot, layout_image),
)
return Panel(child=tab_layout, title="hdf viewer")
def create():
det_data = {}
roi_selection = {}
upload_div = Div(text="Open .cami file:")
def upload_button_callback(_attr, _old, new):
with io.StringIO(base64.b64decode(new).decode()) as file:
h5meta_list = pyzebra.parse_h5meta(file)
file_list = h5meta_list["filelist"]
filelist.options = file_list
filelist.value = file_list[0]
upload_button = FileInput(accept=".cami")
upload_button.on_change("value", upload_button_callback)
def update_image(index=None):
if index is None:
index = index_spinner.value
current_image = det_data["data"][index]
proj_v_line_source.data.update(x=np.arange(0, IMAGE_W) + 0.5,
y=np.mean(current_image, axis=0))
proj_h_line_source.data.update(x=np.mean(current_image, axis=1),
y=np.arange(0, IMAGE_H) + 0.5)
image_source.data.update(
h=[np.zeros((1, 1))],
k=[np.zeros((1, 1))],
l=[np.zeros((1, 1))],
)
image_source.data.update(image=[current_image])
if auto_toggle.active:
im_max = int(np.max(current_image))
im_min = int(np.min(current_image))
display_min_spinner.value = im_min
display_max_spinner.value = im_max
image_glyph.color_mapper.low = im_min
image_glyph.color_mapper.high = im_max
def update_overview_plot():
h5_data = det_data["data"]
n_im, n_y, n_x = h5_data.shape
overview_x = np.mean(h5_data, axis=1)
overview_y = np.mean(h5_data, axis=2)
overview_plot_x_image_source.data.update(image=[overview_x], dw=[n_x])
overview_plot_y_image_source.data.update(image=[overview_y], dw=[n_y])
if frame_button_group.active == 0: # Frame
overview_plot_x.axis[1].axis_label = "Frame"
overview_plot_y.axis[1].axis_label = "Frame"
overview_plot_x_image_source.data.update(y=[0], dh=[n_im])
overview_plot_y_image_source.data.update(y=[0], dh=[n_im])
elif frame_button_group.active == 1: # Omega
overview_plot_x.axis[1].axis_label = "Omega"
overview_plot_y.axis[1].axis_label = "Omega"
om = det_data["rot_angle"]
om_start = om[0]
om_end = (om[-1] - om[0]) * n_im / (n_im - 1)
overview_plot_x_image_source.data.update(y=[om_start], dh=[om_end])
overview_plot_y_image_source.data.update(y=[om_start], dh=[om_end])
def filelist_callback(_attr, _old, new):
nonlocal det_data
det_data = pyzebra.read_detector_data(new)
index_spinner.value = 0
index_spinner.high = det_data["data"].shape[0] - 1
update_image(0)
update_overview_plot()
filelist = Select()
filelist.on_change("value", filelist_callback)
def index_spinner_callback(_attr, _old, new):
update_image(new)
index_spinner = Spinner(title="Image index:", value=0, low=0)
index_spinner.on_change("value", index_spinner_callback)
plot = Plot(
x_range=Range1d(0, IMAGE_W, bounds=(0, IMAGE_W)),
y_range=Range1d(0, IMAGE_H, bounds=(0, IMAGE_H)),
plot_height=IMAGE_H * 3,
plot_width=IMAGE_W * 3,
toolbar_location="left",
)
# ---- tools
plot.toolbar.logo = None
# ---- axes
plot.add_layout(LinearAxis(), place="above")
plot.add_layout(LinearAxis(major_label_orientation="vertical"),
place="right")
# ---- grid lines
plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
# ---- rgba image glyph
image_source = ColumnDataSource(
dict(
image=[np.zeros((IMAGE_H, IMAGE_W), dtype="float32")],
h=[np.zeros((1, 1))],
k=[np.zeros((1, 1))],
l=[np.zeros((1, 1))],
x=[0],
y=[0],
dw=[IMAGE_W],
dh=[IMAGE_H],
))
h_glyph = Image(image="h", x="x", y="y", dw="dw", dh="dh", global_alpha=0)
k_glyph = Image(image="k", x="x", y="y", dw="dw", dh="dh", global_alpha=0)
l_glyph = Image(image="l", x="x", y="y", dw="dw", dh="dh", global_alpha=0)
plot.add_glyph(image_source, h_glyph)
plot.add_glyph(image_source, k_glyph)
plot.add_glyph(image_source, l_glyph)
image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh")
plot.add_glyph(image_source, image_glyph, name="image_glyph")
# ---- projections
proj_v = Plot(
x_range=plot.x_range,
y_range=DataRange1d(),
plot_height=200,
plot_width=IMAGE_W * 3,
toolbar_location=None,
)
proj_v.add_layout(LinearAxis(major_label_orientation="vertical"),
place="right")
proj_v.add_layout(LinearAxis(major_label_text_font_size="0pt"),
place="below")
proj_v.add_layout(Grid(dimension=0, ticker=BasicTicker()))
proj_v.add_layout(Grid(dimension=1, ticker=BasicTicker()))
proj_v_line_source = ColumnDataSource(dict(x=[], y=[]))
proj_v.add_glyph(proj_v_line_source,
Line(x="x", y="y", line_color="steelblue"))
proj_h = Plot(
x_range=DataRange1d(),
y_range=plot.y_range,
plot_height=IMAGE_H * 3,
plot_width=200,
toolbar_location=None,
)
proj_h.add_layout(LinearAxis(), place="above")
proj_h.add_layout(LinearAxis(major_label_text_font_size="0pt"),
place="left")
proj_h.add_layout(Grid(dimension=0, ticker=BasicTicker()))
proj_h.add_layout(Grid(dimension=1, ticker=BasicTicker()))
proj_h_line_source = ColumnDataSource(dict(x=[], y=[]))
proj_h.add_glyph(proj_h_line_source,
Line(x="x", y="y", line_color="steelblue"))
# add tools
hovertool = HoverTool(tooltips=[("intensity",
"@image"), ("h", "@h"), ("k",
"@k"), ("l",
"@l")])
box_edit_source = ColumnDataSource(dict(x=[], y=[], width=[], height=[]))
box_edit_glyph = Rect(x="x",
y="y",
width="width",
height="height",
fill_alpha=0,
line_color="red")
box_edit_renderer = plot.add_glyph(box_edit_source, box_edit_glyph)
boxedittool = BoxEditTool(renderers=[box_edit_renderer], num_objects=1)
def box_edit_callback(_attr, _old, new):
if new["x"]:
h5_data = det_data["data"]
x_val = np.arange(h5_data.shape[0])
left = int(np.floor(new["x"][0]))
right = int(np.ceil(new["x"][0] + new["width"][0]))
bottom = int(np.floor(new["y"][0]))
top = int(np.ceil(new["y"][0] + new["height"][0]))
y_val = np.sum(h5_data[:, bottom:top, left:right], axis=(1, 2))
else:
x_val = []
y_val = []
roi_avg_plot_line_source.data.update(x=x_val, y=y_val)
box_edit_source.on_change("data", box_edit_callback)
wheelzoomtool = WheelZoomTool(maintain_focus=False)
plot.add_tools(
PanTool(),
BoxZoomTool(),
wheelzoomtool,
ResetTool(),
hovertool,
boxedittool,
)
plot.toolbar.active_scroll = wheelzoomtool
# shared frame range
frame_range = DataRange1d()
det_x_range = DataRange1d()
overview_plot_x = Plot(
title=Title(text="Projections on X-axis"),
x_range=det_x_range,
y_range=frame_range,
plot_height=400,
plot_width=400,
toolbar_location="left",
)
# ---- tools
wheelzoomtool = WheelZoomTool(maintain_focus=False)
overview_plot_x.toolbar.logo = None
overview_plot_x.add_tools(
PanTool(),
BoxZoomTool(),
wheelzoomtool,
ResetTool(),
)
overview_plot_x.toolbar.active_scroll = wheelzoomtool
# ---- axes
overview_plot_x.add_layout(LinearAxis(axis_label="Coordinate X, pix"),
place="below")
overview_plot_x.add_layout(LinearAxis(axis_label="Frame",
major_label_orientation="vertical"),
place="left")
# ---- grid lines
overview_plot_x.add_layout(Grid(dimension=0, ticker=BasicTicker()))
overview_plot_x.add_layout(Grid(dimension=1, ticker=BasicTicker()))
# ---- rgba image glyph
overview_plot_x_image_source = ColumnDataSource(
dict(image=[np.zeros((1, 1), dtype="float32")],
x=[0],
y=[0],
dw=[1],
dh=[1]))
overview_plot_x_image_glyph = Image(image="image",
x="x",
y="y",
dw="dw",
dh="dh")
overview_plot_x.add_glyph(overview_plot_x_image_source,
overview_plot_x_image_glyph,
name="image_glyph")
det_y_range = DataRange1d()
overview_plot_y = Plot(
title=Title(text="Projections on Y-axis"),
x_range=det_y_range,
y_range=frame_range,
plot_height=400,
plot_width=400,
toolbar_location="left",
)
# ---- tools
wheelzoomtool = WheelZoomTool(maintain_focus=False)
overview_plot_y.toolbar.logo = None
overview_plot_y.add_tools(
PanTool(),
BoxZoomTool(),
wheelzoomtool,
ResetTool(),
)
overview_plot_y.toolbar.active_scroll = wheelzoomtool
# ---- axes
overview_plot_y.add_layout(LinearAxis(axis_label="Coordinate Y, pix"),
place="below")
overview_plot_y.add_layout(LinearAxis(axis_label="Frame",
major_label_orientation="vertical"),
place="left")
# ---- grid lines
overview_plot_y.add_layout(Grid(dimension=0, ticker=BasicTicker()))
overview_plot_y.add_layout(Grid(dimension=1, ticker=BasicTicker()))
# ---- rgba image glyph
overview_plot_y_image_source = ColumnDataSource(
dict(image=[np.zeros((1, 1), dtype="float32")],
x=[0],
y=[0],
dw=[1],
dh=[1]))
overview_plot_y_image_glyph = Image(image="image",
x="x",
y="y",
dw="dw",
dh="dh")
overview_plot_y.add_glyph(overview_plot_y_image_source,
overview_plot_y_image_glyph,
name="image_glyph")
def frame_button_group_callback(_active):
update_overview_plot()
frame_button_group = RadioButtonGroup(labels=["Frames", "Omega"], active=0)
frame_button_group.on_click(frame_button_group_callback)
roi_avg_plot = Plot(
x_range=DataRange1d(),
y_range=DataRange1d(),
plot_height=IMAGE_H * 3,
plot_width=IMAGE_W * 3,
toolbar_location="left",
)
# ---- tools
roi_avg_plot.toolbar.logo = None
# ---- axes
roi_avg_plot.add_layout(LinearAxis(), place="below")
roi_avg_plot.add_layout(LinearAxis(major_label_orientation="vertical"),
place="left")
# ---- grid lines
roi_avg_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
roi_avg_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
roi_avg_plot_line_source = ColumnDataSource(dict(x=[], y=[]))
roi_avg_plot.add_glyph(roi_avg_plot_line_source,
Line(x="x", y="y", line_color="steelblue"))
cmap_dict = {
"gray": Greys256,
"gray_reversed": Greys256[::-1],
"plasma": Plasma256,
"cividis": Cividis256,
}
def colormap_callback(_attr, _old, new):
image_glyph.color_mapper = LinearColorMapper(palette=cmap_dict[new])
overview_plot_x_image_glyph.color_mapper = LinearColorMapper(
palette=cmap_dict[new])
overview_plot_y_image_glyph.color_mapper = LinearColorMapper(
palette=cmap_dict[new])
colormap = Select(title="Colormap:", options=list(cmap_dict.keys()))
colormap.on_change("value", colormap_callback)
colormap.value = "plasma"
radio_button_group = RadioButtonGroup(labels=["nb", "nb_bi"], active=0)
STEP = 1
# ---- colormap auto toggle button
def auto_toggle_callback(state):
if state:
display_min_spinner.disabled = True
display_max_spinner.disabled = True
else:
display_min_spinner.disabled = False
display_max_spinner.disabled = False
update_image()
auto_toggle = Toggle(label="Auto Range",
active=True,
button_type="default")
auto_toggle.on_click(auto_toggle_callback)
# ---- colormap display max value
def display_max_spinner_callback(_attr, _old_value, new_value):
display_min_spinner.high = new_value - STEP
image_glyph.color_mapper.high = new_value
display_max_spinner = Spinner(
title="Maximal Display Value:",
low=0 + STEP,
value=1,
step=STEP,
disabled=auto_toggle.active,
)
display_max_spinner.on_change("value", display_max_spinner_callback)
# ---- colormap display min value
def display_min_spinner_callback(_attr, _old_value, new_value):
display_max_spinner.low = new_value + STEP
image_glyph.color_mapper.low = new_value
display_min_spinner = Spinner(
title="Minimal Display Value:",
high=1 - STEP,
value=0,
step=STEP,
disabled=auto_toggle.active,
)
display_min_spinner.on_change("value", display_min_spinner_callback)
def hkl_button_callback():
index = index_spinner.value
setup_type = "nb_bi" if radio_button_group.active else "nb"
h, k, l = calculate_hkl(det_data, index, setup_type)
image_source.data.update(h=[h], k=[k], l=[l])
hkl_button = Button(label="Calculate hkl (slow)")
hkl_button.on_click(hkl_button_callback)
selection_list = TextAreaInput(rows=7)
def selection_button_callback():
nonlocal roi_selection
selection = [
int(np.floor(det_x_range.start)),
int(np.ceil(det_x_range.end)),
int(np.floor(det_y_range.start)),
int(np.ceil(det_y_range.end)),
int(np.floor(frame_range.start)),
int(np.ceil(frame_range.end)),
]
filename_id = filelist.value[-8:-4]
if filename_id in roi_selection:
roi_selection[f"{filename_id}"].append(selection)
else:
roi_selection[f"{filename_id}"] = [selection]
selection_list.value = str(roi_selection)
selection_button = Button(label="Add selection")
selection_button.on_click(selection_button_callback)
# Final layout
layout_image = column(
gridplot([[proj_v, None], [plot, proj_h]], merge_tools=False),
row(index_spinner))
colormap_layout = column(colormap, auto_toggle, display_max_spinner,
display_min_spinner)
hkl_layout = column(radio_button_group, hkl_button)
layout_overview = column(
gridplot(
[[overview_plot_x, overview_plot_y]],
toolbar_options=dict(logo=None),
merge_tools=True,
),
frame_button_group,
)
tab_layout = row(
column(
upload_div,
upload_button,
filelist,
layout_image,
row(colormap_layout, hkl_layout),
),
column(
roi_avg_plot,
layout_overview,
row(selection_button, selection_list),
),
)
return Panel(child=tab_layout, title="Data Viewer")
from bokeh.io import output_file, show
from bokeh.models import FileInput
output_file("file_input.html")
file_input = FileInput()
show(file_input)
def create():
config = pyzebra.AnatricConfig()
def _load_config_file(file):
config.load_from_file(file)
logfile_textinput.value = config.logfile
logfile_verbosity_select.value = config.logfile_verbosity
filelist_type.value = config.filelist_type
filelist_format_textinput.value = config.filelist_format
filelist_datapath_textinput.value = config.filelist_datapath
filelist_ranges_textareainput.value = "\n".join(
map(str, config.filelist_ranges))
crystal_sample_textinput.value = config.crystal_sample
lambda_textinput.value = config.crystal_lambda
zeroOM_textinput.value = config.crystal_zeroOM
zeroSTT_textinput.value = config.crystal_zeroSTT
zeroCHI_textinput.value = config.crystal_zeroCHI
ub_textareainput.value = config.crystal_UB
dataFactory_implementation_select.value = config.dataFactory_implementation
dataFactory_dist1_textinput.value = config.dataFactory_dist1
reflectionPrinter_format_select.value = config.reflectionPrinter_format
set_active_widgets(config.algorithm)
if config.algorithm == "adaptivemaxcog":
threshold_textinput.value = config.threshold
shell_textinput.value = config.shell
steepness_textinput.value = config.steepness
duplicateDistance_textinput.value = config.duplicateDistance
maxequal_textinput.value = config.maxequal
aps_window_textinput.value = str(
tuple(map(int, config.aps_window.values())))
elif config.algorithm == "adaptivedynamic":
adm_window_textinput.value = str(
tuple(map(int, config.adm_window.values())))
border_textinput.value = str(
tuple(map(int, config.border.values())))
minWindow_textinput.value = str(
tuple(map(int, config.minWindow.values())))
reflectionFile_textinput.value = config.reflectionFile
targetMonitor_textinput.value = config.targetMonitor
smoothSize_textinput.value = config.smoothSize
loop_textinput.value = config.loop
minPeakCount_textinput.value = config.minPeakCount
displacementCurve_textinput.value = "\n".join(
map(str, config.displacementCurve))
else:
raise ValueError("Unknown processing mode.")
def set_active_widgets(implementation):
if implementation == "adaptivemaxcog":
mode_radio_button_group.active = 0
disable_adaptivemaxcog = False
disable_adaptivedynamic = True
elif implementation == "adaptivedynamic":
mode_radio_button_group.active = 1
disable_adaptivemaxcog = True
disable_adaptivedynamic = False
else:
raise ValueError(
"Implementation can be either 'adaptivemaxcog' or 'adaptivedynamic'"
)
threshold_textinput.disabled = disable_adaptivemaxcog
shell_textinput.disabled = disable_adaptivemaxcog
steepness_textinput.disabled = disable_adaptivemaxcog
duplicateDistance_textinput.disabled = disable_adaptivemaxcog
maxequal_textinput.disabled = disable_adaptivemaxcog
aps_window_textinput.disabled = disable_adaptivemaxcog
adm_window_textinput.disabled = disable_adaptivedynamic
border_textinput.disabled = disable_adaptivedynamic
minWindow_textinput.disabled = disable_adaptivedynamic
reflectionFile_textinput.disabled = disable_adaptivedynamic
targetMonitor_textinput.disabled = disable_adaptivedynamic
smoothSize_textinput.disabled = disable_adaptivedynamic
loop_textinput.disabled = disable_adaptivedynamic
minPeakCount_textinput.disabled = disable_adaptivedynamic
displacementCurve_textinput.disabled = disable_adaptivedynamic
upload_div = Div(text="Open XML configuration file:")
def upload_button_callback(_attr, _old, new):
with io.BytesIO(base64.b64decode(new)) as file:
_load_config_file(file)
upload_button = FileInput(accept=".xml")
upload_button.on_change("value", upload_button_callback)
# General parameters
# ---- logfile
def logfile_textinput_callback(_attr, _old, new):
config.logfile = new
logfile_textinput = TextInput(title="Logfile:",
value="logfile.log",
width=520)
logfile_textinput.on_change("value", logfile_textinput_callback)
def logfile_verbosity_select_callback(_attr, _old, new):
config.logfile_verbosity = new
logfile_verbosity_select = Select(title="verbosity:",
options=["0", "5", "10", "15", "30"],
width=70)
logfile_verbosity_select.on_change("value",
logfile_verbosity_select_callback)
# ---- FileList
def filelist_type_callback(_attr, _old, new):
config.filelist_type = new
filelist_type = Select(title="File List:",
options=["TRICS", "SINQ"],
width=100)
filelist_type.on_change("value", filelist_type_callback)
def filelist_format_textinput_callback(_attr, _old, new):
config.filelist_format = new
filelist_format_textinput = TextInput(title="format:", width=490)
filelist_format_textinput.on_change("value",
filelist_format_textinput_callback)
def filelist_datapath_textinput_callback(_attr, _old, new):
config.filelist_datapath = new
filelist_datapath_textinput = TextInput(title="datapath:")
filelist_datapath_textinput.on_change(
"value", filelist_datapath_textinput_callback)
def filelist_ranges_textareainput_callback(_attr, _old, new):
ranges = []
for line in new.splitlines():
ranges.append(re.findall(r"\b\d+\b", line))
config.filelist_ranges = ranges
filelist_ranges_textareainput = TextAreaInput(title="ranges:", height=100)
filelist_ranges_textareainput.on_change(
"value", filelist_ranges_textareainput_callback)
# ---- crystal
def crystal_sample_textinput_callback(_attr, _old, new):
config.crystal_sample = new
crystal_sample_textinput = TextInput(title="Sample Name:")
crystal_sample_textinput.on_change("value",
crystal_sample_textinput_callback)
def lambda_textinput_callback(_attr, _old, new):
config.crystal_lambda = new
lambda_textinput = TextInput(title="lambda:", width=140)
lambda_textinput.on_change("value", lambda_textinput_callback)
def ub_textareainput_callback(_attr, _old, new):
config.crystal_UB = new
ub_textareainput = TextAreaInput(title="UB matrix:", height=100)
ub_textareainput.on_change("value", ub_textareainput_callback)
def zeroOM_textinput_callback(_attr, _old, new):
config.crystal_zeroOM = new
zeroOM_textinput = TextInput(title="zeroOM:", width=140)
zeroOM_textinput.on_change("value", zeroOM_textinput_callback)
def zeroSTT_textinput_callback(_attr, _old, new):
config.crystal_zeroSTT = new
zeroSTT_textinput = TextInput(title="zeroSTT:", width=140)
zeroSTT_textinput.on_change("value", zeroSTT_textinput_callback)
def zeroCHI_textinput_callback(_attr, _old, new):
config.crystal_zeroCHI = new
zeroCHI_textinput = TextInput(title="zeroCHI:", width=140)
zeroCHI_textinput.on_change("value", zeroCHI_textinput_callback)
# ---- DataFactory
def dataFactory_implementation_select_callback(_attr, _old, new):
config.dataFactory_implementation = new
dataFactory_implementation_select = Select(
title="DataFactory implementation:",
options=DATA_FACTORY_IMPLEMENTATION,
width=300,
)
dataFactory_implementation_select.on_change(
"value", dataFactory_implementation_select_callback)
def dataFactory_dist1_textinput_callback(_attr, _old, new):
config.dataFactory_dist1 = new
dataFactory_dist1_textinput = TextInput(title="dist1:", width=290)
dataFactory_dist1_textinput.on_change(
"value", dataFactory_dist1_textinput_callback)
# ---- BackgroundProcessor
# ---- DetectorEfficency
# ---- ReflectionPrinter
def reflectionPrinter_format_select_callback(_attr, _old, new):
config.reflectionPrinter_format = new
reflectionPrinter_format_select = Select(
title="ReflectionPrinter format:",
options=REFLECTION_PRINTER_FORMATS,
width=300,
)
reflectionPrinter_format_select.on_change(
"value", reflectionPrinter_format_select_callback)
# Adaptive Peak Detection (adaptivemaxcog)
# ---- threshold
def threshold_textinput_callback(_attr, _old, new):
config.threshold = new
threshold_textinput = TextInput(title="Threshold:")
threshold_textinput.on_change("value", threshold_textinput_callback)
# ---- shell
def shell_textinput_callback(_attr, _old, new):
config.shell = new
shell_textinput = TextInput(title="Shell:")
shell_textinput.on_change("value", shell_textinput_callback)
# ---- steepness
def steepness_textinput_callback(_attr, _old, new):
config.steepness = new
steepness_textinput = TextInput(title="Steepness:")
steepness_textinput.on_change("value", steepness_textinput_callback)
# ---- duplicateDistance
def duplicateDistance_textinput_callback(_attr, _old, new):
config.duplicateDistance = new
duplicateDistance_textinput = TextInput(title="Duplicate Distance:")
duplicateDistance_textinput.on_change(
"value", duplicateDistance_textinput_callback)
# ---- maxequal
def maxequal_textinput_callback(_attr, _old, new):
config.maxequal = new
maxequal_textinput = TextInput(title="Max Equal:")
maxequal_textinput.on_change("value", maxequal_textinput_callback)
# ---- window
def aps_window_textinput_callback(_attr, _old, new):
config.aps_window = dict(
zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
aps_window_textinput = TextInput(title="Window (x, y, z):")
aps_window_textinput.on_change("value", aps_window_textinput_callback)
# Adaptive Dynamic Mask Integration (adaptivedynamic)
# ---- window
def adm_window_textinput_callback(_attr, _old, new):
config.adm_window = dict(
zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
adm_window_textinput = TextInput(title="Window (x, y, z):")
adm_window_textinput.on_change("value", adm_window_textinput_callback)
# ---- border
def border_textinput_callback(_attr, _old, new):
config.border = dict(zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
border_textinput = TextInput(title="Border (x, y, z):")
border_textinput.on_change("value", border_textinput_callback)
# ---- minWindow
def minWindow_textinput_callback(_attr, _old, new):
config.minWindow = dict(
zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
minWindow_textinput = TextInput(title="Min Window (x, y, z):")
minWindow_textinput.on_change("value", minWindow_textinput_callback)
# ---- reflectionFile
def reflectionFile_textinput_callback(_attr, _old, new):
config.reflectionFile = new
reflectionFile_textinput = TextInput(title="Reflection File:")
reflectionFile_textinput.on_change("value",
reflectionFile_textinput_callback)
# ---- targetMonitor
def targetMonitor_textinput_callback(_attr, _old, new):
config.targetMonitor = new
targetMonitor_textinput = TextInput(title="Target Monitor:")
targetMonitor_textinput.on_change("value",
targetMonitor_textinput_callback)
# ---- smoothSize
def smoothSize_textinput_callback(_attr, _old, new):
config.smoothSize = new
smoothSize_textinput = TextInput(title="Smooth Size:")
smoothSize_textinput.on_change("value", smoothSize_textinput_callback)
# ---- loop
def loop_textinput_callback(_attr, _old, new):
config.loop = new
loop_textinput = TextInput(title="Loop:")
loop_textinput.on_change("value", loop_textinput_callback)
# ---- minPeakCount
def minPeakCount_textinput_callback(_attr, _old, new):
config.minPeakCount = new
minPeakCount_textinput = TextInput(title="Min Peak Count:")
minPeakCount_textinput.on_change("value", minPeakCount_textinput_callback)
# ---- displacementCurve
def displacementCurve_textinput_callback(_attr, _old, new):
maps = []
for line in new.splitlines():
maps.append(re.findall(r"\d+(?:\.\d+)?", line))
config.displacementCurve = maps
displacementCurve_textinput = TextAreaInput(
title="Displacement Curve (twotheta, x, y):", height=100)
displacementCurve_textinput.on_change(
"value", displacementCurve_textinput_callback)
def mode_radio_button_group_callback(active):
if active == 0:
config.algorithm = "adaptivemaxcog"
set_active_widgets("adaptivemaxcog")
else:
config.algorithm = "adaptivedynamic"
set_active_widgets("adaptivedynamic")
mode_radio_button_group = RadioButtonGroup(
labels=["Adaptive Peak Detection", "Adaptive Dynamic Integration"],
active=0)
mode_radio_button_group.on_click(mode_radio_button_group_callback)
set_active_widgets("adaptivemaxcog")
def process_button_callback():
with tempfile.TemporaryDirectory() as temp_dir:
temp_file = temp_dir + "/temp.xml"
config.save_as(temp_file)
pyzebra.anatric(temp_file)
with open(config.logfile) as f_log:
output_log.value = f_log.read()
process_button = Button(label="Process", button_type="primary")
process_button.on_click(process_button_callback)
output_log = TextAreaInput(title="Logfile output:",
height=700,
disabled=True)
output_config = TextAreaInput(title="Current config:",
height=700,
width=400,
disabled=True)
tab_layout = row(
column(
upload_div,
upload_button,
row(logfile_textinput, logfile_verbosity_select),
row(filelist_type, filelist_format_textinput),
filelist_datapath_textinput,
filelist_ranges_textareainput,
crystal_sample_textinput,
row(lambda_textinput, zeroOM_textinput, zeroSTT_textinput,
zeroCHI_textinput),
ub_textareainput,
row(dataFactory_implementation_select,
dataFactory_dist1_textinput),
reflectionPrinter_format_select,
process_button,
),
column(
mode_radio_button_group,
row(
column(
threshold_textinput,
shell_textinput,
steepness_textinput,
duplicateDistance_textinput,
maxequal_textinput,
aps_window_textinput,
),
column(
adm_window_textinput,
border_textinput,
minWindow_textinput,
reflectionFile_textinput,
targetMonitor_textinput,
smoothSize_textinput,
loop_textinput,
minPeakCount_textinput,
displacementCurve_textinput,
),
),
),
output_config,
output_log,
)
async def update_config():
config.save_as("debug.xml")
with open("debug.xml") as f_config:
output_config.value = f_config.read()
curdoc().add_periodic_callback(update_config, 1000)
return Panel(child=tab_layout, title="Anatric")
def create():
det_data = {}
fit_params = {}
js_data = ColumnDataSource(
data=dict(content=["", ""], fname=["", ""], ext=["", ""]))
def proposal_textinput_callback(_attr, _old, new):
proposal = new.strip()
for zebra_proposals_path in pyzebra.ZEBRA_PROPOSALS_PATHS:
proposal_path = os.path.join(zebra_proposals_path, proposal)
if os.path.isdir(proposal_path):
# found it
break
else:
raise ValueError(f"Can not find data for proposal '{proposal}'.")
file_list = []
for file in os.listdir(proposal_path):
if file.endswith((".ccl", ".dat")):
file_list.append((os.path.join(proposal_path, file), file))
file_select.options = file_list
file_open_button.disabled = False
file_append_button.disabled = False
proposal_textinput = TextInput(title="Proposal number:", width=210)
proposal_textinput.on_change("value", proposal_textinput_callback)
def _init_datatable():
scan_list = [s["idx"] for s in det_data]
hkl = [f'{s["h"]} {s["k"]} {s["l"]}' for s in det_data]
export = [s.get("active", True) for s in det_data]
scan_table_source.data.update(
scan=scan_list,
hkl=hkl,
fit=[0] * len(scan_list),
export=export,
)
scan_table_source.selected.indices = []
scan_table_source.selected.indices = [0]
merge_options = [(str(i), f"{i} ({idx})")
for i, idx in enumerate(scan_list)]
merge_from_select.options = merge_options
merge_from_select.value = merge_options[0][0]
file_select = MultiSelect(title="Available .ccl/.dat files:",
width=210,
height=250)
def file_open_button_callback():
nonlocal det_data
det_data = []
for f_name in file_select.value:
with open(f_name) as file:
base, ext = os.path.splitext(f_name)
if det_data:
append_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(append_data,
monitor_spinner.value)
pyzebra.merge_datasets(det_data, append_data)
else:
det_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(det_data, monitor_spinner.value)
pyzebra.merge_duplicates(det_data)
js_data.data.update(fname=[base, base])
_init_datatable()
append_upload_button.disabled = False
file_open_button = Button(label="Open New", width=100, disabled=True)
file_open_button.on_click(file_open_button_callback)
def file_append_button_callback():
for f_name in file_select.value:
with open(f_name) as file:
_, ext = os.path.splitext(f_name)
append_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(append_data, monitor_spinner.value)
pyzebra.merge_datasets(det_data, append_data)
_init_datatable()
file_append_button = Button(label="Append", width=100, disabled=True)
file_append_button.on_click(file_append_button_callback)
def upload_button_callback(_attr, _old, new):
nonlocal det_data
det_data = []
for f_str, f_name in zip(new, upload_button.filename):
with io.StringIO(base64.b64decode(f_str).decode()) as file:
base, ext = os.path.splitext(f_name)
if det_data:
append_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(append_data,
monitor_spinner.value)
pyzebra.merge_datasets(det_data, append_data)
else:
det_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(det_data, monitor_spinner.value)
pyzebra.merge_duplicates(det_data)
js_data.data.update(fname=[base, base])
_init_datatable()
append_upload_button.disabled = False
upload_div = Div(text="or upload new .ccl/.dat files:",
margin=(5, 5, 0, 5))
upload_button = FileInput(accept=".ccl,.dat", multiple=True, width=200)
upload_button.on_change("value", upload_button_callback)
def append_upload_button_callback(_attr, _old, new):
for f_str, f_name in zip(new, append_upload_button.filename):
with io.StringIO(base64.b64decode(f_str).decode()) as file:
_, ext = os.path.splitext(f_name)
append_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(append_data, monitor_spinner.value)
pyzebra.merge_datasets(det_data, append_data)
_init_datatable()
append_upload_div = Div(text="append extra files:", margin=(5, 5, 0, 5))
append_upload_button = FileInput(accept=".ccl,.dat",
multiple=True,
width=200,
disabled=True)
append_upload_button.on_change("value", append_upload_button_callback)
def monitor_spinner_callback(_attr, old, new):
if det_data:
pyzebra.normalize_dataset(det_data, new)
_update_plot(_get_selected_scan())
monitor_spinner = Spinner(title="Monitor:",
mode="int",
value=100_000,
low=1,
width=145)
monitor_spinner.on_change("value", monitor_spinner_callback)
def _update_table():
fit_ok = [(1 if "fit" in scan else 0) for scan in det_data]
scan_table_source.data.update(fit=fit_ok)
def _update_plot(scan):
scan_motor = scan["scan_motor"]
y = scan["counts"]
x = scan[scan_motor]
plot.axis[0].axis_label = scan_motor
plot_scatter_source.data.update(x=x,
y=y,
y_upper=y + np.sqrt(y),
y_lower=y - np.sqrt(y))
fit = scan.get("fit")
if fit is not None:
x_fit = np.linspace(x[0], x[-1], 100)
plot_fit_source.data.update(x=x_fit, y=fit.eval(x=x_fit))
x_bkg = []
y_bkg = []
xs_peak = []
ys_peak = []
comps = fit.eval_components(x=x_fit)
for i, model in enumerate(fit_params):
if "linear" in model:
x_bkg = x_fit
y_bkg = comps[f"f{i}_"]
elif any(val in model
for val in ("gaussian", "voigt", "pvoigt")):
xs_peak.append(x_fit)
ys_peak.append(comps[f"f{i}_"])
plot_bkg_source.data.update(x=x_bkg, y=y_bkg)
plot_peak_source.data.update(xs=xs_peak, ys=ys_peak)
fit_output_textinput.value = fit.fit_report()
else:
plot_fit_source.data.update(x=[], y=[])
plot_bkg_source.data.update(x=[], y=[])
plot_peak_source.data.update(xs=[], ys=[])
fit_output_textinput.value = ""
# Main plot
plot = Plot(
x_range=DataRange1d(),
y_range=DataRange1d(only_visible=True),
plot_height=470,
plot_width=700,
)
plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
plot_scatter_source = ColumnDataSource(
dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
plot_scatter = plot.add_glyph(
plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue"))
plot.add_layout(
Whisker(source=plot_scatter_source,
base="x",
upper="y_upper",
lower="y_lower"))
plot_fit_source = ColumnDataSource(dict(x=[0], y=[0]))
plot_fit = plot.add_glyph(plot_fit_source, Line(x="x", y="y"))
plot_bkg_source = ColumnDataSource(dict(x=[0], y=[0]))
plot_bkg = plot.add_glyph(
plot_bkg_source,
Line(x="x", y="y", line_color="green", line_dash="dashed"))
plot_peak_source = ColumnDataSource(dict(xs=[[0]], ys=[[0]]))
plot_peak = plot.add_glyph(
plot_peak_source,
MultiLine(xs="xs", ys="ys", line_color="red", line_dash="dashed"))
fit_from_span = Span(location=None, dimension="height", line_dash="dashed")
plot.add_layout(fit_from_span)
fit_to_span = Span(location=None, dimension="height", line_dash="dashed")
plot.add_layout(fit_to_span)
plot.add_layout(
Legend(
items=[
("data", [plot_scatter]),
("best fit", [plot_fit]),
("peak", [plot_peak]),
("linear", [plot_bkg]),
],
location="top_left",
click_policy="hide",
))
plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
plot.toolbar.logo = None
# Scan select
def scan_table_select_callback(_attr, old, new):
if not new:
# skip empty selections
return
# Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
if len(new) > 1:
# drop selection to the previous one
scan_table_source.selected.indices = old
return
if len(old) > 1:
# skip unnecessary update caused by selection drop
return
_update_plot(det_data[new[0]])
def scan_table_source_callback(_attr, _old, _new):
_update_preview()
scan_table_source = ColumnDataSource(
dict(scan=[], hkl=[], fit=[], export=[]))
scan_table_source.on_change("data", scan_table_source_callback)
scan_table = DataTable(
source=scan_table_source,
columns=[
TableColumn(field="scan", title="Scan", width=50),
TableColumn(field="hkl", title="hkl", width=100),
TableColumn(field="fit", title="Fit", width=50),
TableColumn(field="export",
title="Export",
editor=CheckboxEditor(),
width=50),
],
width=310, # +60 because of the index column
height=350,
autosize_mode="none",
editable=True,
)
scan_table_source.selected.on_change("indices", scan_table_select_callback)
def _get_selected_scan():
return det_data[scan_table_source.selected.indices[0]]
merge_from_select = Select(title="scan:", width=145)
def merge_button_callback():
scan_into = _get_selected_scan()
scan_from = det_data[int(merge_from_select.value)]
if scan_into is scan_from:
print("WARNING: Selected scans for merging are identical")
return
pyzebra.merge_scans(scan_into, scan_from)
_update_plot(_get_selected_scan())
merge_button = Button(label="Merge into current", width=145)
merge_button.on_click(merge_button_callback)
def restore_button_callback():
pyzebra.restore_scan(_get_selected_scan())
_update_plot(_get_selected_scan())
restore_button = Button(label="Restore scan", width=145)
restore_button.on_click(restore_button_callback)
def fit_from_spinner_callback(_attr, _old, new):
fit_from_span.location = new
fit_from_spinner = Spinner(title="Fit from:", width=145)
fit_from_spinner.on_change("value", fit_from_spinner_callback)
def fit_to_spinner_callback(_attr, _old, new):
fit_to_span.location = new
fit_to_spinner = Spinner(title="to:", width=145)
fit_to_spinner.on_change("value", fit_to_spinner_callback)
def fitparams_add_dropdown_callback(click):
# bokeh requires (str, str) for MultiSelect options
new_tag = f"{click.item}-{fitparams_select.tags[0]}"
fitparams_select.options.append((new_tag, click.item))
fit_params[new_tag] = fitparams_factory(click.item)
fitparams_select.tags[0] += 1
fitparams_add_dropdown = Dropdown(
label="Add fit function",
menu=[
("Linear", "linear"),
("Gaussian", "gaussian"),
("Voigt", "voigt"),
("Pseudo Voigt", "pvoigt"),
# ("Pseudo Voigt1", "pseudovoigt1"),
],
width=145,
)
fitparams_add_dropdown.on_click(fitparams_add_dropdown_callback)
def fitparams_select_callback(_attr, old, new):
# Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
if len(new) > 1:
# drop selection to the previous one
fitparams_select.value = old
return
if len(old) > 1:
# skip unnecessary update caused by selection drop
return
if new:
fitparams_table_source.data.update(fit_params[new[0]])
else:
fitparams_table_source.data.update(
dict(param=[], value=[], vary=[], min=[], max=[]))
fitparams_select = MultiSelect(options=[], height=120, width=145)
fitparams_select.tags = [0]
fitparams_select.on_change("value", fitparams_select_callback)
def fitparams_remove_button_callback():
if fitparams_select.value:
sel_tag = fitparams_select.value[0]
del fit_params[sel_tag]
for elem in fitparams_select.options:
if elem[0] == sel_tag:
fitparams_select.options.remove(elem)
break
fitparams_select.value = []
fitparams_remove_button = Button(label="Remove fit function", width=145)
fitparams_remove_button.on_click(fitparams_remove_button_callback)
def fitparams_factory(function):
if function == "linear":
params = ["slope", "intercept"]
elif function == "gaussian":
params = ["amplitude", "center", "sigma"]
elif function == "voigt":
params = ["amplitude", "center", "sigma", "gamma"]
elif function == "pvoigt":
params = ["amplitude", "center", "sigma", "fraction"]
elif function == "pseudovoigt1":
params = ["amplitude", "center", "g_sigma", "l_sigma", "fraction"]
else:
raise ValueError("Unknown fit function")
n = len(params)
fitparams = dict(
param=params,
value=[None] * n,
vary=[True] * n,
min=[None] * n,
max=[None] * n,
)
if function == "linear":
fitparams["value"] = [0, 1]
fitparams["vary"] = [False, True]
fitparams["min"] = [None, 0]
elif function == "gaussian":
fitparams["min"] = [0, None, None]
return fitparams
fitparams_table_source = ColumnDataSource(
dict(param=[], value=[], vary=[], min=[], max=[]))
fitparams_table = DataTable(
source=fitparams_table_source,
columns=[
TableColumn(field="param", title="Parameter"),
TableColumn(field="value", title="Value", editor=NumberEditor()),
TableColumn(field="vary", title="Vary", editor=CheckboxEditor()),
TableColumn(field="min", title="Min", editor=NumberEditor()),
TableColumn(field="max", title="Max", editor=NumberEditor()),
],
height=200,
width=350,
index_position=None,
editable=True,
auto_edit=True,
)
# start with `background` and `gauss` fit functions added
fitparams_add_dropdown_callback(types.SimpleNamespace(item="linear"))
fitparams_add_dropdown_callback(types.SimpleNamespace(item="gaussian"))
fitparams_select.value = ["gaussian-1"] # add selection to gauss
fit_output_textinput = TextAreaInput(title="Fit results:",
width=750,
height=200)
def proc_all_button_callback():
for scan, export in zip(det_data, scan_table_source.data["export"]):
if export:
pyzebra.fit_scan(scan,
fit_params,
fit_from=fit_from_spinner.value,
fit_to=fit_to_spinner.value)
pyzebra.get_area(
scan,
area_method=AREA_METHODS[area_method_radiobutton.active],
lorentz=lorentz_checkbox.active,
)
_update_plot(_get_selected_scan())
_update_table()
proc_all_button = Button(label="Process All",
button_type="primary",
width=145)
proc_all_button.on_click(proc_all_button_callback)
def proc_button_callback():
scan = _get_selected_scan()
pyzebra.fit_scan(scan,
fit_params,
fit_from=fit_from_spinner.value,
fit_to=fit_to_spinner.value)
pyzebra.get_area(
scan,
area_method=AREA_METHODS[area_method_radiobutton.active],
lorentz=lorentz_checkbox.active,
)
_update_plot(scan)
_update_table()
proc_button = Button(label="Process Current", width=145)
proc_button.on_click(proc_button_callback)
area_method_div = Div(text="Intensity:", margin=(5, 5, 0, 5))
area_method_radiobutton = RadioGroup(labels=["Function", "Area"],
active=0,
width=145)
lorentz_checkbox = CheckboxGroup(labels=["Lorentz Correction"],
width=145,
margin=(13, 5, 5, 5))
export_preview_textinput = TextAreaInput(title="Export file preview:",
width=500,
height=400)
def _update_preview():
with tempfile.TemporaryDirectory() as temp_dir:
temp_file = temp_dir + "/temp"
export_data = []
for s, export in zip(det_data, scan_table_source.data["export"]):
if export:
export_data.append(s)
pyzebra.export_1D(
export_data,
temp_file,
export_target_select.value,
hkl_precision=int(hkl_precision_select.value),
)
exported_content = ""
file_content = []
for ext in EXPORT_TARGETS[export_target_select.value]:
fname = temp_file + ext
if os.path.isfile(fname):
with open(fname) as f:
content = f.read()
exported_content += f"{ext} file:\n" + content
else:
content = ""
file_content.append(content)
js_data.data.update(content=file_content)
export_preview_textinput.value = exported_content
def export_target_select_callback(_attr, _old, new):
js_data.data.update(ext=EXPORT_TARGETS[new])
_update_preview()
export_target_select = Select(title="Export target:",
options=list(EXPORT_TARGETS.keys()),
value="fullprof",
width=80)
export_target_select.on_change("value", export_target_select_callback)
js_data.data.update(ext=EXPORT_TARGETS[export_target_select.value])
def hkl_precision_select_callback(_attr, _old, _new):
_update_preview()
hkl_precision_select = Select(title="hkl precision:",
options=["2", "3", "4"],
value="2",
width=80)
hkl_precision_select.on_change("value", hkl_precision_select_callback)
save_button = Button(label="Download File(s)",
button_type="success",
width=200)
save_button.js_on_click(
CustomJS(args={"js_data": js_data}, code=javaScript))
fitpeak_controls = row(
column(fitparams_add_dropdown, fitparams_select,
fitparams_remove_button),
fitparams_table,
Spacer(width=20),
column(fit_from_spinner, lorentz_checkbox, area_method_div,
area_method_radiobutton),
column(fit_to_spinner, proc_button, proc_all_button),
)
scan_layout = column(
scan_table,
row(monitor_spinner, column(Spacer(height=19), restore_button)),
row(column(Spacer(height=19), merge_button), merge_from_select),
)
import_layout = column(
proposal_textinput,
file_select,
row(file_open_button, file_append_button),
upload_div,
upload_button,
append_upload_div,
append_upload_button,
)
export_layout = column(
export_preview_textinput,
row(export_target_select, hkl_precision_select,
column(Spacer(height=19), row(save_button))),
)
tab_layout = column(
row(import_layout, scan_layout, plot, Spacer(width=30), export_layout),
row(fitpeak_controls, fit_output_textinput),
)
return Panel(child=tab_layout, title="ccl integrate")
def production_tab(beam, acc):
'''Simulation
'''
pre = PreText(text='''Track beam in the accelerator.''')
track_button = Button(label='Track', button_type="success", width=315)
refresh_button = Button(label='Refresh', button_type="warning", width=315)
mode_button = RadioButtonGroup(labels=['Phase', 'Line', 'Field'], active=0)
phase_button = RadioButtonGroup(
labels=['z-x', 'z-y', 'x-y', 'x-px', 'x-py', 'y-px', 'y-py', 'px-py'],
active=0)
field_button = RadioButtonGroup(
labels=['z-Ez', 'z-Ex', 'z-Ey', 'z-Bz', 'z-Bx', 'z-By'], active=0)
line_button = RadioButtonGroup(labels=[
'rms x', 'rms y', 'rms x Emitt', 'rms y Emitt', 'avg Energy',
'x β-func', 'y β-func', 'avg x', 'avg y'
],
active=0)
file_input = FileInput(accept='.csv')
phase_source = ColumnDataSource(data={
'x': beam.df['z'],
'y': beam.df['x']
})
phase_plot = figure(x_axis_label='z [m]',
y_axis_label='x [m]',
width=650,
height=250)
phase_plot.scatter('x',
'y',
source=phase_source,
size=0.5,
color="#3A5785",
alpha=0.5)
beam.df['x2'] = beam.df['x'] * beam.df['x']
beam.df['rms x'] = beam.df['x2'].mean()**0.5
line_source = ColumnDataSource(data={
'x': beam.df['z'],
'y': beam.df['rms x']
})
line_plot = figure(x_axis_label='z [m]',
y_axis_label='rms x [m]',
width=650,
height=250)
line_plot.line('x',
'y',
source=line_source,
line_color='#3A5785',
line_alpha=0.7,
line_width=1)
field_source = ColumnDataSource(data={
'x': beam.df['z'],
'y': beam.df['rms x']
})
field_plot = figure(x_axis_label='z [m]',
y_axis_label='Ez [MV/m]',
width=650,
height=250)
field_plot.line('x',
'y',
source=field_source,
line_color='#3A5785',
line_alpha=0.7,
line_width=1)
buttons = row(track_button, refresh_button)
controls = row(pre, file_input)
phase_tab = Panel(child=column(phase_button, phase_plot), title='Phase')
line_tab = Panel(child=column(line_button, line_plot), title='Line')
field_tab = Panel(child=column(field_button, field_plot), title='Field')
tabs = Tabs(tabs=[phase_tab, line_tab, field_tab])
tab = Panel(child=column(controls, tabs, buttons), title='Production')
def calculate(df):
df = df.sort_values('z')
df['avg p'] = window_mean((df['px'] * df['px'] + df['py'] * df['py'] +
df['pz'] * df['pz'])**0.5)
df['xp'] = df['px'] / df['pz']
df['yp'] = df['py'] / df['pz']
df['x xp'] = window_mean(df['x'] * df['xp'])
df['y yp'] = window_mean(df['y'] * df['yp'])
df['rms x'] = window_rms(df['x'])
df['rms y'] = window_rms(df['y'])
df['rms xp'] = window_rms(df['xp'])
df['rms yp'] = window_rms(df['yp'])
df['rms x Emittance'] = (df['rms x']**2 * df['rms xp']**2 -
df['x xp']**2)**0.5
df['rms y Emittance'] = (df['rms y']**2 * df['rms yp']**2 -
df['y yp']**2)**0.5
df['avg Energy'] = df['avg p'] - beam.type.mass * rp.c**2 / rp.e / 1e6
df['x β-function'] = df['rms x']**2 / df['rms x Emittance']
df['y β-function'] = df['rms y']**2 / df['rms y Emittance']
df['centroid x'] = window_mean(df['x'])
df['centroid y'] = window_mean(df['y'])
df['avg Ez'] = window_mean(df['Ez'])
df['avg Ex'] = window_mean(df['Ex'])
df['avg Ey'] = window_mean(df['Ey'])
df['avg Bz'] = window_mean(df['Bz'])
df['avg Bx'] = window_mean(df['Bx'])
df['avg By'] = window_mean(df['By'])
return df
def track_handler(new, beam=beam, acc=acc):
simulation = rp.Simulation(beam, acc)
simulation.track(n_files=30, path=dirname(__file__) + '/data/')
def refresh_handler(beam=beam, acc=acc):
fname = dirname(__file__) + '/data/' + file_input.filename
df = pd.read_csv(fname, dtype='float32')
df = calculate(df)
df = df[df.z >= acc.z_start]
df = df[df.z <= acc.z_stop]
if tabs.active == 0:
if phase_button.active == 0:
phase_plot.xaxis.axis_label = 'z [m]'
phase_plot.yaxis.axis_label = 'x [m]'
phase_source.data = {'x': df['z'], 'y': df['x']}
if phase_button.active == 1:
phase_plot.xaxis.axis_label = 'z [m]'
phase_plot.yaxis.axis_label = 'y [m]'
phase_source.data = {'x': df['z'], 'y': df['y']}
if phase_button.active == 2:
phase_plot.xaxis.axis_label = 'x [m]'
phase_plot.yaxis.axis_label = 'y [m]'
phase_source.data = {'x': df['x'], 'y': df['y']}
if phase_button.active == 3:
phase_plot.xaxis.axis_label = 'x [m]'
phase_plot.yaxis.axis_label = 'px [MeV/c]'
phase_source.data = {'x': df['x'], 'y': df['px']}
if phase_button.active == 4:
phase_plot.xaxis.axis_label = 'x [m]'
phase_plot.yaxis.axis_label = 'py [MeV/c]'
phase_source.data = {'x': df['x'], 'y': df['py']}
if phase_button.active == 5:
phase_plot.xaxis.axis_label = 'y [m]'
phase_plot.yaxis.axis_label = 'px [MeV/c]'
phase_source.data = {'x': df['y'], 'y': df['px']}
if phase_button.active == 6:
phase_plot.xaxis.axis_label = 'y [m]'
phase_plot.yaxis.axis_label = 'py [MeV/c]'
phase_source.data = {'x': df['y'], 'y': df['py']}
if phase_button.active == 7:
phase_plot.xaxis.axis_label = 'px [MeV/c]'
phase_plot.yaxis.axis_label = 'py [MeV/c]'
phase_source.data = {'x': df['px'], 'y': df['py']}
if tabs.active == 1:
if line_button.active == 0:
line_plot.xaxis.axis_label = 'z [m]'
line_plot.yaxis.axis_label = 'rms x [m]'
line_source.data = {'x': df['z'], 'y': df['rms x']}
if line_button.active == 1:
line_plot.xaxis.axis_label = 'z [m]'
line_plot.yaxis.axis_label = 'rms y [m]'
line_source.data = {'x': df['z'], 'y': df['rms y']}
if line_button.active == 2:
line_plot.xaxis.axis_label = 'z [m]'
line_plot.yaxis.axis_label = 'rms x Emittance [m rad]'
line_source.data = {'x': df['z'], 'y': df['rms x Emittance']}
if line_button.active == 3:
line_plot.xaxis.axis_label = 'z [m]'
line_plot.yaxis.axis_label = 'rms y Emittance [m rad]'
line_source.data = {'x': df['z'], 'y': df['rms y Emittance']}
if line_button.active == 4:
line_plot.xaxis.axis_label = 'z [m]'
line_plot.yaxis.axis_label = 'avg Energy [MeV]'
line_source.data = {'x': df['z'], 'y': df['avg Energy']}
if line_button.active == 5:
line_plot.xaxis.axis_label = 'z [m]'
line_plot.yaxis.axis_label = 'x β-function [m]'
line_source.data = {'x': df['z'], 'y': df['x β-function']}
if line_button.active == 6:
line_plot.xaxis.axis_label = 'z [m]'
line_plot.yaxis.axis_label = 'y β-function [m]'
line_source.data = {'x': df['z'], 'y': df['y β-function']}
if line_button.active == 7:
line_plot.xaxis.axis_label = 'z [m]'
line_plot.yaxis.axis_label = 'Centroid x [m]'
line_source.data = {'x': df['z'], 'y': df['centroid x']}
if line_button.active == 8:
line_plot.xaxis.axis_label = 'z [m]'
line_plot.yaxis.axis_label = 'Centroid y [m]'
line_source.data = {'x': df['z'], 'y': df['centroid y']}
if tabs.active == 2:
if field_button.active == 0:
field_plot.xaxis.axis_label = 'z [m]'
field_plot.yaxis.axis_label = 'Ez [MV/m]'
field_source.data = {'x': df['z'], 'y': df['avg Ez']}
if field_button.active == 1:
field_plot.xaxis.axis_label = 'z [m]'
field_plot.yaxis.axis_label = 'Ex [MV/m]'
field_source.data = {'x': df['z'], 'y': df['avg Ex']}
if field_button.active == 2:
field_plot.xaxis.axis_label = 'z [m]'
field_plot.yaxis.axis_label = 'Ey [MV/m]'
field_source.data = {'x': df['z'], 'y': df['avg Ey']}
if field_button.active == 3:
field_plot.xaxis.axis_label = 'z [m]'
field_plot.yaxis.axis_label = 'Bz [T]'
field_source.data = {'x': df['z'], 'y': df['avg Bz']}
if field_button.active == 4:
field_plot.xaxis.axis_label = 'z [m]'
field_plot.yaxis.axis_label = 'Bx [T]'
field_source.data = {'x': df['z'], 'y': df['avg Bx']}
if field_button.active == 5:
field_plot.xaxis.axis_label = 'z [m]'
field_plot.yaxis.axis_label = 'By [T]'
field_source.data = {'x': df['z'], 'y': df['avg By']}
track_button.on_click(track_handler)
refresh_button.on_click(refresh_handler)
return tab
def accelerator_tab(acc):
'''Creating an accelerator with parameters
'''
length = RangeSlider(start=0,
end=25,
value=(0, 1),
step=.1,
title='Length [m]',
format='0[.]0')
pre = PreText(text='''Here you can compile an accelerator.''')
compile_button = Button(label='Compile', button_type="success", height=45)
element_button = RadioButtonGroup(
labels=['Accels', 'Solenoids', 'Quadrupoles'], active=0)
name_input = TextInput(value="Acc. ", title="Name:", width=100)
z_input = TextInput(value="0", title="z [m]:", width=100)
field_input = TextInput(value="0", title="MaxField [MV/m]:", width=100)
file_input = FileInput(accept='.csv, .dat, .txt')
parameters_input = column(file_input, row(name_input, z_input,
field_input))
source = ColumnDataSource(data={'z': acc.z, 'Fz': acc.Ez(acc.z)})
field_plot = figure(x_axis_label='z [m]',
y_axis_label='Ez [MV/m]',
width=650,
height=250)
field_plot.line('z',
'Fz',
source=source,
line_color='#3A5785',
line_alpha=0.7,
line_width=1)
controls = row(column(pre, length, compile_button),
column(element_button, parameters_input))
# Create a row layout
layout = column(controls, field_plot)
tab = Panel(child=layout, title='Accelerator')
def compile_handler(new, acc=acc):
(acc.z_start, acc.z_stop) = length.value
acc.z = acc.parameter = np.arange(acc.z_start, acc.z_stop, acc.dz)
name = name_input.value
file_name = dirname(__file__) + '/data/' + file_input.filename
position = float(z_input.value)
max_field = float(field_input.value)
if element_button.active == 0:
acc.add_accel(name, position, max_field, file_name)
acc.compile()
source.data = {'z': acc.z, 'Fz': acc.Ez(acc.z)}
field_input.title = 'MaxField [MV/m]:'
field_plot.yaxis.axis_label = 'Ez [MV/m]'
if element_button.active == 1:
acc.add_solenoid(name, position, max_field, file_name)
acc.compile()
source.data = {'z': acc.z, 'Fz': acc.Bz(acc.z)}
field_input.title = 'MaxField [T]:'
field_plot.yaxis.axis_label = 'Bz [T]'
if element_button.active == 2:
acc.add_quad(name, position, max_field, file_name)
acc.compile()
source.data = {'z': acc.z, 'Fz': acc.Gz(acc.z)}
field_input.title = 'MaxField [T/m]:'
field_plot.yaxis.axis_label = 'Gz [T/m]'
print(acc)
def element_handler(new):
if new == 0:
source.data = {'z': acc.z, 'Fz': acc.Ez(acc.z)}
field_input.title = 'MaxField [MV/m]:'
name_input.value = 'Acc. '
field_plot.yaxis.axis_label = 'Ez [MV/m]'
if new == 1:
source.data = {'z': acc.z, 'Fz': acc.Bz(acc.z)}
field_input.title = 'MaxField [T]:'
name_input.value = 'Sol. '
field_plot.yaxis.axis_label = 'Bz [T]'
if new == 2:
source.data = {'z': acc.z, 'Fz': acc.Gz(acc.z)}
field_input.title = 'MaxField [T/m]:'
name_input.value = 'Quad. '
field_plot.yaxis.axis_label = 'Gz [T/m]'
compile_button.on_click(compile_handler)
element_button.on_click(element_handler)
return tab
def widgets():
from bokeh.io import show
from bokeh.models import Select, CheckboxButtonGroup, Button, CheckboxGroup, ColorPicker, Dropdown, \
FileInput, MultiSelect, RadioButtonGroup, RadioGroup, Slider, RangeSlider, TextAreaInput, TextInput, Toggle, \
Paragraph, PreText, Div
put_text('Button')
button = Button(label="Foo", button_type="success")
show(button)
put_text('CheckboxButtonGroup')
checkbox_button_group = CheckboxButtonGroup(
labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
show(checkbox_button_group)
put_text('CheckboxGroup')
checkbox_group = CheckboxGroup(labels=["Option 1", "Option 2", "Option 3"],
active=[0, 1])
show(checkbox_group)
put_text('ColorPicker')
color_picker = ColorPicker(color="#ff4466",
title="Choose color:",
width=200)
show(color_picker)
put_text('Dropdown')
menu = [("Item 1", "item_1"), ("Item 2", "item_2"), None,
("Item 3", "item_3")]
dropdown = Dropdown(label="Dropdown button",
button_type="warning",
menu=menu)
show(dropdown)
put_text('FileInput')
file_input = FileInput()
show(file_input)
put_text('MultiSelect')
multi_select = MultiSelect(title="Option:",
value=["foo", "quux"],
options=[("foo", "Foo"), ("bar", "BAR"),
("baz", "bAz"), ("quux", "quux")])
show(multi_select)
put_text('RadioButtonGroup')
radio_button_group = RadioButtonGroup(
labels=["Option 1", "Option 2", "Option 3"], active=0)
show(radio_button_group)
put_text('RadioGroup')
radio_group = RadioGroup(labels=["Option 1", "Option 2", "Option 3"],
active=0)
show(radio_group)
put_text('Select')
select = Select(title="Option:",
value="foo",
options=["foo", "bar", "baz", "quux"])
show(select)
put_text('Slider')
slider = Slider(start=0, end=10, value=1, step=.1, title="Stuff")
show(slider)
put_text('RangeSlider')
range_slider = RangeSlider(start=0,
end=10,
value=(1, 9),
step=.1,
title="Stuff")
show(range_slider)
put_text('TextAreaInput')
text_input = TextAreaInput(value="default", rows=6, title="Label:")
show(text_input)
put_text('TextInput')
text_input = TextInput(value="default", title="Label:")
show(text_input)
put_text('Toggle')
toggle = Toggle(label="Foo", button_type="success")
show(toggle)
put_text('Div')
div = Div(
text=
"""Your <a href="https://en.wikipedia.org/wiki/HTML">HTML</a>-supported text is initialized with the <b>text</b> argument. The
remaining div arguments are <b>width</b> and <b>height</b>. For this example, those values
are <i>200</i> and <i>100</i> respectively.""",
width=200,
height=100)
show(div)
put_text('Paragraph')
p = Paragraph(
text="""Your text is initialized with the 'text' argument. The
remaining Paragraph arguments are 'width' and 'height'. For this example, those values
are 200 and 100 respectively.""",
width=200,
height=100)
show(p)
put_text('PreText')
pre = PreText(text="""Your text is initialized with the 'text' argument.
The remaining Paragraph arguments are 'width' and 'height'. For this example,
those values are 500 and 100 respectively.""",
width=500,
height=100)
show(pre)
sel_yaxis = Select(title='Select Y Axis',
options=sorted(head_columns.keys()),
value='Amplifier words')
sel_xaxis = Select(title='Select X Axis',
options=sorted(head_columns.keys()),
value='Outcome')
options = ['scatter', 'vbar', 'varea', 'line']
sel_plot = Dropdown(label='Select plot type',
button_type='success',
menu=options,
value='line')
add_row = FileInput()
inp_id = TextInput(
placeholder='Insert ID',
value='1',
)
sh_ess = TextAreaInput(
title='Essay %s: 338 words' % inp_id.value,
rows=30,
cols=50,
value=
"""Dear local newspaper, I think effects computers have on people are great learning skills/affects because they give us time to chat with friends/new people, helps us learn about the globe(astronomy) and keeps us out of troble! Thing about! Dont you think so? How would you feel if your teenager is always on the phone with friends! Do you ever time to chat with your friends or buisness partner about things. Well now - there's a new way to chat the computer, theirs plenty of sites on the internet to do so: @ORGANIZATION1, @ORGANIZATION2, @CAPS1, facebook, myspace ect. Just think now while your setting up meeting with your boss on the computer, your teenager is having fun on the phone not rushing to get off cause you want to use it. How did you learn about other countrys/states outside of yours? Well I have by computer/internet, it's a new way to learn about what going on in our time! You might think your child spends a lot of time on the computer, but ask them so question about the economy, sea floor spreading or even about the @DATE1's you'll be surprise at how much he/she knows. Believe it or not the computer is much interesting then in class all day reading out of books. If your child is home on your computer or at a local library, it's better than being out with friends being fresh, or being perpressured to doing something they know isnt right. You might not know where your child is, @CAPS2 forbidde in a hospital bed because of a drive-by. Rather than your child on the computer learning, chatting or just playing games, safe and sound in your home or community place. Now I hope you have reached a point to understand and agree with me, because computers can have great effects on you or child because it gives us time to chat with friends/new people, helps us learn about the globe and believe or not keeps us out of troble. Thank you for listening."""
)
# ------------------------------------------------------------------------------
def beam_tab(beam):
'''Creating a beam with parameters
'''
pre = PreText(text='''Here you can generate or upload a beam.''')
species_button = RadioButtonGroup(
labels=['electron', 'positron', 'proton', 'antiproton'], active=0)
select_quantity = Slider(start=1_000, end=100_000,
step=1_000, value=1_000,
title='Number of particles')
select_current = Slider(start=0, end=5_000,
step=100, value=0,
title='Curent [A]')
# Phase ellipse
x_input = TextInput(value="0", title="X [m]:", width=75)
y_input = TextInput(value="0", title="Y [m]:", width=75)
z_input = TextInput(value="0", title="Z [m]:", width=75)
px_input = TextInput(value="0", title="Px [MeV/c]:", width=75)
py_input = TextInput(value="0", title="Py [MeV/c]:", width=75)
pz_input = TextInput(value="0", title="Pz [MeV/c]:", width=75)
x_off_input = TextInput(value="0", title="X_off [m]:", width=75)
y_off_input = TextInput(value="0", title="Y_off [m]:", width=75)
px_off_input = TextInput(value="0", title="Px_off:", width=60)
py_off_input = TextInput(value="0", title="Py_off:", width=60)
phase_ell = column(row(x_input, y_input, z_input, x_off_input),
row(px_input, py_input, pz_input, y_off_input))
select_distribution = RadioButtonGroup(labels=['Uniform', 'Gauss'],
active=0)
file_input = FileInput(accept='.csv, .ini')
generate_button = Button(label='Generate', button_type="success",
width=155)
upload_button = Button(label='Upload', button_type="warning",
width=155)
source = ColumnDataSource(data={'x': beam.df['x'], 'y': beam.df['y']})
p = figure(x_axis_label='x [m]', y_axis_label='y [m]',
width=410, height=400)
p.scatter('x', 'y', source=source, size=0.5, color='#3A5785', alpha=0.5)
controls = column(pre, species_button, select_quantity,
select_distribution, select_current,
phase_ell, file_input,
row(upload_button, generate_button))
# Create a row layout
layout = row(controls, p)
tab = Panel(child=layout, title='Beam')
def generate_handler(new, beam=beam):
I = select_current.value
N = select_quantity.value
X = float(x_input.value)
Y = float(y_input.value)
Z = float(z_input.value)
Px = float(px_input.value)
Py = float(py_input.value)
Pz = float(pz_input.value)
X_off = float(x_off_input.value)
Y_off = float(y_off_input.value)
Px_off = float(px_off_input.value)
Py_off = float(py_off_input.value)
if select_distribution.active == 0:
distribution = rp.Distribution(name='KV', x=X, y=Y, z=Z,
px=Px, py=Py, pz=Pz)
if select_distribution.active == 1:
distribution = rp.Distribution(name='GA', x=X, y=Y, z=Z,
px=Px, py=Py, pz=Pz)
if species_button.active == 0:
species = rp.electron
if species_button.active == 1:
species = rp.positron
if species_button.active == 2:
species = rp.proton
if species_button.active == 3:
species = rp.antiproton
beam.type = species
Q = np.sign(species.charge) * I * Z / rp.c
beam.generate(distribution, n=N, charge=Q, path=dirname(__file__) + '/data/',
x_off=X_off, y_off=Y_off, px_off=Px_off, py_off=Py_off)
source.data = {'x': beam.df['x'], 'y': beam.df['y']}
print(beam)
def upload_handler(new, beam=beam):
I = select_current.value
if species_button.active == 0:
species = rp.electron
if species_button.active == 1:
species = rp.positron
if species_button.active == 2:
species = rp.proton
if species_button.active == 3:
species = rp.antiproton
beam.type = species
Q = np.sign(species.charge)* I * (beam.df['z'].max()-beam.df['z'].min()) / rp.c
beam.upload(dirname(__file__) + '/data/' + file_input.filename, charge=Q,
path=dirname(__file__) + '/data/')
source.data = {'x': beam.df['x'], 'y': beam.df['y']}
x_input.value = str(np.around(beam.df['x'].max(), 3))
y_input.value = str(np.around(beam.df['y'].max(), 3))
z_input.value = str(np.around(beam.df['z'].max(), 3) * 2)
px_input.value = str(np.around(beam.df['px'].max(), 3))
py_input.value = str(np.around(beam.df['py'].max(), 3))
pz_input.value = str(np.around(beam.df['pz'].max(), 3))
select_quantity.value = beam.n
print(beam)
generate_button.on_click(generate_handler)
upload_button.on_click(upload_handler)
return tab
def create():
doc = curdoc()
config = pyzebra.AnatricConfig()
def _load_config_file(file):
config.load_from_file(file)
logfile_textinput.value = config.logfile
logfile_verbosity.value = config.logfile_verbosity
filelist_type.value = config.filelist_type
filelist_format_textinput.value = config.filelist_format
filelist_datapath_textinput.value = config.filelist_datapath
filelist_ranges_textareainput.value = "\n".join(
map(str, config.filelist_ranges))
crystal_sample_textinput.value = config.crystal_sample
lambda_textinput.value = config.crystal_lambda
zeroOM_textinput.value = config.crystal_zeroOM
zeroSTT_textinput.value = config.crystal_zeroSTT
zeroCHI_textinput.value = config.crystal_zeroCHI
ub_textareainput.value = config.crystal_UB
dataFactory_implementation_select.value = config.dataFactory_implementation
if config.dataFactory_dist1 is not None:
dataFactory_dist1_textinput.value = config.dataFactory_dist1
if config.dataFactory_dist2 is not None:
dataFactory_dist2_textinput.value = config.dataFactory_dist2
if config.dataFactory_dist3 is not None:
dataFactory_dist3_textinput.value = config.dataFactory_dist3
reflectionPrinter_format_select.value = config.reflectionPrinter_format
if config.algorithm == "adaptivemaxcog":
algorithm_params.active = 0
threshold_textinput.value = config.threshold
shell_textinput.value = config.shell
steepness_textinput.value = config.steepness
duplicateDistance_textinput.value = config.duplicateDistance
maxequal_textinput.value = config.maxequal
aps_window_textinput.value = str(
tuple(map(int, config.aps_window.values())))
elif config.algorithm == "adaptivedynamic":
algorithm_params.active = 1
adm_window_textinput.value = str(
tuple(map(int, config.adm_window.values())))
border_textinput.value = str(
tuple(map(int, config.border.values())))
minWindow_textinput.value = str(
tuple(map(int, config.minWindow.values())))
reflectionFile_textinput.value = config.reflectionFile
targetMonitor_textinput.value = config.targetMonitor
smoothSize_textinput.value = config.smoothSize
loop_textinput.value = config.loop
minPeakCount_textinput.value = config.minPeakCount
displacementCurve_textinput.value = "\n".join(
map(str, config.displacementCurve))
else:
raise ValueError("Unknown processing mode.")
def upload_button_callback(_attr, _old, new):
with io.BytesIO(base64.b64decode(new)) as file:
_load_config_file(file)
upload_div = Div(text="Open .xml config:")
upload_button = FileInput(accept=".xml", width=200)
upload_button.on_change("value", upload_button_callback)
# General parameters
# ---- logfile
def logfile_textinput_callback(_attr, _old, new):
config.logfile = new
logfile_textinput = TextInput(title="Logfile:", value="logfile.log")
logfile_textinput.on_change("value", logfile_textinput_callback)
def logfile_verbosity_callback(_attr, _old, new):
config.logfile_verbosity = new
logfile_verbosity = TextInput(title="verbosity:", width=70)
logfile_verbosity.on_change("value", logfile_verbosity_callback)
# ---- FileList
def filelist_type_callback(_attr, _old, new):
config.filelist_type = new
filelist_type = Select(title="File List:",
options=["TRICS", "SINQ"],
width=100)
filelist_type.on_change("value", filelist_type_callback)
def filelist_format_textinput_callback(_attr, _old, new):
config.filelist_format = new
filelist_format_textinput = TextInput(title="format:", width=290)
filelist_format_textinput.on_change("value",
filelist_format_textinput_callback)
def filelist_datapath_textinput_callback(_attr, _old, new):
config.filelist_datapath = new
filelist_datapath_textinput = TextInput(title="datapath:")
filelist_datapath_textinput.on_change(
"value", filelist_datapath_textinput_callback)
def filelist_ranges_textareainput_callback(_attr, _old, new):
ranges = []
for line in new.splitlines():
ranges.append(re.findall(r"\b\d+\b", line))
config.filelist_ranges = ranges
filelist_ranges_textareainput = TextAreaInput(title="ranges:", rows=1)
filelist_ranges_textareainput.on_change(
"value", filelist_ranges_textareainput_callback)
# ---- crystal
def crystal_sample_textinput_callback(_attr, _old, new):
config.crystal_sample = new
crystal_sample_textinput = TextInput(title="Sample Name:", width=290)
crystal_sample_textinput.on_change("value",
crystal_sample_textinput_callback)
def lambda_textinput_callback(_attr, _old, new):
config.crystal_lambda = new
lambda_textinput = TextInput(title="lambda:", width=100)
lambda_textinput.on_change("value", lambda_textinput_callback)
def ub_textareainput_callback(_attr, _old, new):
config.crystal_UB = new
ub_textareainput = TextAreaInput(title="UB matrix:", height=100)
ub_textareainput.on_change("value", ub_textareainput_callback)
def zeroOM_textinput_callback(_attr, _old, new):
config.crystal_zeroOM = new
zeroOM_textinput = TextInput(title="zeroOM:", width=100)
zeroOM_textinput.on_change("value", zeroOM_textinput_callback)
def zeroSTT_textinput_callback(_attr, _old, new):
config.crystal_zeroSTT = new
zeroSTT_textinput = TextInput(title="zeroSTT:", width=100)
zeroSTT_textinput.on_change("value", zeroSTT_textinput_callback)
def zeroCHI_textinput_callback(_attr, _old, new):
config.crystal_zeroCHI = new
zeroCHI_textinput = TextInput(title="zeroCHI:", width=100)
zeroCHI_textinput.on_change("value", zeroCHI_textinput_callback)
# ---- DataFactory
def dataFactory_implementation_select_callback(_attr, _old, new):
config.dataFactory_implementation = new
dataFactory_implementation_select = Select(
title="DataFactory implement.:",
options=DATA_FACTORY_IMPLEMENTATION,
width=145,
)
dataFactory_implementation_select.on_change(
"value", dataFactory_implementation_select_callback)
def dataFactory_dist1_textinput_callback(_attr, _old, new):
config.dataFactory_dist1 = new
dataFactory_dist1_textinput = TextInput(title="dist1:", width=75)
dataFactory_dist1_textinput.on_change(
"value", dataFactory_dist1_textinput_callback)
def dataFactory_dist2_textinput_callback(_attr, _old, new):
config.dataFactory_dist2 = new
dataFactory_dist2_textinput = TextInput(title="dist2:", width=75)
dataFactory_dist2_textinput.on_change(
"value", dataFactory_dist2_textinput_callback)
def dataFactory_dist3_textinput_callback(_attr, _old, new):
config.dataFactory_dist3 = new
dataFactory_dist3_textinput = TextInput(title="dist3:", width=75)
dataFactory_dist3_textinput.on_change(
"value", dataFactory_dist3_textinput_callback)
# ---- BackgroundProcessor
# ---- DetectorEfficency
# ---- ReflectionPrinter
def reflectionPrinter_format_select_callback(_attr, _old, new):
config.reflectionPrinter_format = new
reflectionPrinter_format_select = Select(
title="ReflectionPrinter format:",
options=REFLECTION_PRINTER_FORMATS,
width=145,
)
reflectionPrinter_format_select.on_change(
"value", reflectionPrinter_format_select_callback)
# Adaptive Peak Detection (adaptivemaxcog)
# ---- threshold
def threshold_textinput_callback(_attr, _old, new):
config.threshold = new
threshold_textinput = TextInput(title="Threshold:", width=145)
threshold_textinput.on_change("value", threshold_textinput_callback)
# ---- shell
def shell_textinput_callback(_attr, _old, new):
config.shell = new
shell_textinput = TextInput(title="Shell:", width=145)
shell_textinput.on_change("value", shell_textinput_callback)
# ---- steepness
def steepness_textinput_callback(_attr, _old, new):
config.steepness = new
steepness_textinput = TextInput(title="Steepness:", width=145)
steepness_textinput.on_change("value", steepness_textinput_callback)
# ---- duplicateDistance
def duplicateDistance_textinput_callback(_attr, _old, new):
config.duplicateDistance = new
duplicateDistance_textinput = TextInput(title="Duplicate Distance:",
width=145)
duplicateDistance_textinput.on_change(
"value", duplicateDistance_textinput_callback)
# ---- maxequal
def maxequal_textinput_callback(_attr, _old, new):
config.maxequal = new
maxequal_textinput = TextInput(title="Max Equal:", width=145)
maxequal_textinput.on_change("value", maxequal_textinput_callback)
# ---- window
def aps_window_textinput_callback(_attr, _old, new):
config.aps_window = dict(
zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
aps_window_textinput = TextInput(title="Window (x, y, z):", width=145)
aps_window_textinput.on_change("value", aps_window_textinput_callback)
# Adaptive Dynamic Mask Integration (adaptivedynamic)
# ---- window
def adm_window_textinput_callback(_attr, _old, new):
config.adm_window = dict(
zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
adm_window_textinput = TextInput(title="Window (x, y, z):", width=145)
adm_window_textinput.on_change("value", adm_window_textinput_callback)
# ---- border
def border_textinput_callback(_attr, _old, new):
config.border = dict(zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
border_textinput = TextInput(title="Border (x, y, z):", width=145)
border_textinput.on_change("value", border_textinput_callback)
# ---- minWindow
def minWindow_textinput_callback(_attr, _old, new):
config.minWindow = dict(
zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
minWindow_textinput = TextInput(title="Min Window (x, y, z):", width=145)
minWindow_textinput.on_change("value", minWindow_textinput_callback)
# ---- reflectionFile
def reflectionFile_textinput_callback(_attr, _old, new):
config.reflectionFile = new
reflectionFile_textinput = TextInput(title="Reflection File:", width=145)
reflectionFile_textinput.on_change("value",
reflectionFile_textinput_callback)
# ---- targetMonitor
def targetMonitor_textinput_callback(_attr, _old, new):
config.targetMonitor = new
targetMonitor_textinput = TextInput(title="Target Monitor:", width=145)
targetMonitor_textinput.on_change("value",
targetMonitor_textinput_callback)
# ---- smoothSize
def smoothSize_textinput_callback(_attr, _old, new):
config.smoothSize = new
smoothSize_textinput = TextInput(title="Smooth Size:", width=145)
smoothSize_textinput.on_change("value", smoothSize_textinput_callback)
# ---- loop
def loop_textinput_callback(_attr, _old, new):
config.loop = new
loop_textinput = TextInput(title="Loop:", width=145)
loop_textinput.on_change("value", loop_textinput_callback)
# ---- minPeakCount
def minPeakCount_textinput_callback(_attr, _old, new):
config.minPeakCount = new
minPeakCount_textinput = TextInput(title="Min Peak Count:", width=145)
minPeakCount_textinput.on_change("value", minPeakCount_textinput_callback)
# ---- displacementCurve
def displacementCurve_textinput_callback(_attr, _old, new):
maps = []
for line in new.splitlines():
maps.append(re.findall(r"\d+(?:\.\d+)?", line))
config.displacementCurve = maps
displacementCurve_textinput = TextAreaInput(
title="Displ. Curve (2θ, x, y):", width=145, height=100)
displacementCurve_textinput.on_change(
"value", displacementCurve_textinput_callback)
def algorithm_tabs_callback(_attr, _old, new):
if new == 0:
config.algorithm = "adaptivemaxcog"
else:
config.algorithm = "adaptivedynamic"
algorithm_params = Tabs(tabs=[
Panel(
child=column(
row(threshold_textinput, shell_textinput, steepness_textinput),
row(duplicateDistance_textinput, maxequal_textinput,
aps_window_textinput),
),
title="Peak Search",
),
Panel(
child=column(
row(adm_window_textinput, border_textinput,
minWindow_textinput),
row(reflectionFile_textinput, targetMonitor_textinput,
smoothSize_textinput),
row(loop_textinput, minPeakCount_textinput,
displacementCurve_textinput),
),
title="Dynamic Integration",
),
])
algorithm_params.on_change("active", algorithm_tabs_callback)
def process_button_callback():
with tempfile.TemporaryDirectory() as temp_dir:
temp_file = temp_dir + "/config.xml"
config.save_as(temp_file)
pyzebra.anatric(temp_file,
anatric_path=doc.anatric_path,
cwd=temp_dir)
with open(os.path.join(temp_dir, config.logfile)) as f_log:
output_log.value = f_log.read()
with open(os.path.join(temp_dir,
config.reflectionPrinter_file)) as f_res:
output_res.value = f_res.read()
process_button = Button(label="Process", button_type="primary")
process_button.on_click(process_button_callback)
output_log = TextAreaInput(title="Logfile output:",
height=320,
width=465,
disabled=True)
output_res = TextAreaInput(title="Result output:",
height=320,
width=465,
disabled=True)
output_config = TextAreaInput(title="Current config:",
height=320,
width=465,
disabled=True)
general_params_layout = column(
row(column(Spacer(height=2), upload_div), upload_button),
row(logfile_textinput, logfile_verbosity),
row(filelist_type, filelist_format_textinput),
filelist_datapath_textinput,
filelist_ranges_textareainput,
row(crystal_sample_textinput, lambda_textinput),
ub_textareainput,
row(zeroOM_textinput, zeroSTT_textinput, zeroCHI_textinput),
row(
dataFactory_implementation_select,
dataFactory_dist1_textinput,
dataFactory_dist2_textinput,
dataFactory_dist3_textinput,
),
row(reflectionPrinter_format_select),
)
tab_layout = row(
general_params_layout,
column(output_config, algorithm_params, row(process_button)),
column(output_log, output_res),
)
async def update_config():
output_config.value = config.tostring()
doc.add_periodic_callback(update_config, 1000)
return Panel(child=tab_layout, title="hdf anatric")
p.xaxis.axis_label = 'Time'
p.yaxis.visible = False
p.add_tools(hover)
curdoc().clear()
div.text = '<h1>' + file_input.filename + '</h1>'
curdoc().add_root(column(file_input, div, radio_button,
get_current_plot()))
def get_current_plot():
if radio_button.active == 0:
return p1
elif radio_button.active == 1:
return p2
else:
return p3
def change_current_plot(attr, old, new):
curdoc().clear()
curdoc().add_root(column(file_input, div, radio_button,
get_current_plot()))
file_input = FileInput(accept=".json")
file_input.on_change('value', load_trace_data)
radio_button.on_change('active', change_current_plot)
curdoc().add_root(file_input)
def plot_trends_scatter_bokeh(self):
lon = self.lon
lat = self.lat
dw = lon[-1] - lon[0]
dh = lat[0] - lat[-1]
self.dfs = pd.DataFrame.from_dict(
data=dict(LONGITUDE=[], LATITUDE=[], NAME=[], slope=[], id2=[]))
p = figure(plot_width=int(400. * dw / dh),
plot_height=400,
match_aspect=True,
tools="pan,wheel_zoom,box_zoom,tap,reset",
output_backend="webgl")
##--- Create a modified version of seismic colormap
from bokeh.models import LinearColorMapper, ColorBar
import matplotlib.cm as mcm
import matplotlib.colors as mcol
fcmap = mcm.get_cmap('seismic')
cmap_mod = [fcmap(i) for i in np.linspace(0, 1, 15)]
cmap_mod[7] = mcm.get_cmap('RdYlGn')(
0.5) # replace white in the middle by the yellow of RdYlGn
scmap = mcol.LinearSegmentedColormap.from_list(
"", cmap_mod) # recreate a colormap
## Extract 256 colors from the new colormap and convert them to hex
cmap_mod = [scmap(i) for i in np.linspace(0, 1, 256)]
cmap_mod = [
"#%02x%02x%02x" % (int(255 * r), int(255 * g), int(255 * b))
for r, g, b, _ in cmap_mod
]
## Make a fake colormapper to start
## based on the previous 256 colors (needed because it does not make linear interpolation between colors)
self.sn_max = 0.001
color_mapper = LinearColorMapper(palette=cmap_mod,
low=-self.sn_max,
high=self.sn_max)
##--- Select CSV file to read
def upload_input_csv(attr, old, new):
## Read, decode and save input data to tmp file
print("Data upload succeeded")
print("file_input.filename=", file_input.filename)
data = base64.b64decode(file_input.value).decode('utf8')
with open(self.app_dir / 'data/tmp_input.csv', 'w') as f:
f.write(data)
## Get csv meta data and init plot
meta = {
l.split(':')[0]: l.split(':')[1]
for l in data.split('\n') if l.startswith('#')
}
self.hf = h5py.File(
self.app_dir / 'data' / meta['#input_extract_cache_file'], 'r')
if '#input_breaks_pickle_file' in meta.keys():
self.b_breaks = True
self.df_breaks = pd.read_pickle(
self.app_dir / 'data' / meta['#input_breaks_pickle_file'])
## Init line to display timeseries segment
# timeseries segment
segment_line = p2.line(x='dates',
y='var',
source=segment_source,
line_color='red')
# vertical lines for breaks
p2.segment(x0="x",
y0="y0",
x1="x",
y1="y1",
line_color="black",
line_dash='dashed',
line_width=2,
source=breaks_source)
# Add bottom horizontal line
#p2.line(x="x", y="y0", line_color="#fb8072", line_width=2, source=breaks_source)
#p2.diamond(x="x", y="y0", color="#fb8072", size=12, source=breaks_source)
else:
self.b_breaks = False
# Get date range from h5 file
self.dates = self.hf['meta/ts_dates'][:].view(
'datetime64[s]').tolist()
self.point_names = [
i.decode('utf8') for i in self.hf['meta/point_names'][:]
]
d_init = [
d for d in self.dates if d.year != 1970
] # Some date are set to 1970 (ie stored as 0 ? to be checked)
ts_source.data = dict(dates=d_init, var=np.zeros_like(d_init))
## Read tmp file and update select widget with available variables
df = pd.read_csv(self.app_dir / 'data/tmp_input.csv',
sep=';',
comment='#')
in_var = [i for i in df.columns if i.endswith('_sn')]
df = df.dropna(subset=in_var)
in_var = [i.replace('_sn', '') for i in in_var]
print(in_var)
select.disabled = False
select.options = in_var
select.value = in_var[0]
## If there is only one variable in the csv, plot it directly
if len(in_var) == 1:
read_data_for_plotting(in_var[0])
file_input = FileInput(
accept=".plot.csv") # comma separated list if any
file_input.on_change('value', upload_input_csv)
## Add variable selection
def select_variable(attr, old, new):
read_data_for_plotting(new)
select = Select(title="Variable in csv:", disabled=True)
select.on_change('value', select_variable)
##--- Add land mask
# must give a vector of image data for image parameter
mask = self.rebin(
self.mask,
(int(self.mask.shape[0] / 5), int(self.mask.shape[1] / 5)))
#p.image(image=[np.flipud(self.mask[::20,::20])],
p.image(image=[np.flipud(mask)],
x=lon[0],
y=lat[-1],
dw=dw,
dh=dh,
palette=('#FFFFFF', '#EEEEEE', '#DDDDDD', '#CCCCCC', '#BBBBBB',
'#AAAAAA', '#999999', '#888888'),
level="image")
p.grid.grid_line_width = 0.5
##--- Read selected data, filter and convert to ColumnDataSource
def read_data_for_plotting(var):
## Get the variable from the input h5 cache file
self.ts = self.hf['vars/' + var][:, 0, :].T
## Get data from input csv
var = var + '_sn'
if self.b_breaks:
df = pd.read_csv(self.app_dir / 'data/tmp_input.csv',
sep=';',
comment='#',
parse_dates=['start_date', 'end_date'])
else:
df = pd.read_csv(self.app_dir / 'data/tmp_input.csv',
sep=';',
comment='#')
id_sites_in_cache_file = {
s: i
for i, s in enumerate(self.point_names)
}
df['id2'] = df['NAME'].map(id_sites_in_cache_file)
df = df.dropna(subset=[var])
if self.b_breaks:
# better use loc[] to select part of a df that will be modified afterward to be sure to have a copy
self.dfs = df.loc[:, [
'LONGITUDE', 'LATITUDE', 'NAME', var, 'id2', 'lvl',
'start_date', 'end_date'
]]
else:
self.dfs = df.loc[:, [
'LONGITUDE', 'LATITUDE', 'NAME', var, 'id2'
]]
self.dfs['lvl'] = np.zeros_like(self.dfs[var])
self.dfs['start_date'] = np.zeros_like(self.dfs[var])
self.dfs['end_date'] = np.zeros_like(self.dfs[var])
self.dfs = self.dfs.rename(columns={var: 'slope'})
source.data = ColumnDataSource.from_df(self.dfs)
self.sn_max = np.abs(np.nanmax(self.dfs['slope']))
color_mapper.low = -self.sn_max
color_mapper.high = self.sn_max
slider.end = self.sn_max * 1000.
slider.step = self.sn_max * 1000. / 20.
slider.value = (0.0, self.sn_max * 1000.)
#slider.disabled=False
#slider.bar_color='#e6e6e6'
if self.b_breaks:
slider_date.start = self.dates[0]
slider_date.end = self.dates[-1]
slider_date.value = (self.dates[0], self.dates[-1])
slider_date.visible = True
##--- Add scatter
## Create source that will be populated according to slider
source = ColumnDataSource(data=dict(LONGITUDE=[],
LATITUDE=[],
NAME=[],
slope=[],
id2=[],
lvl=[],
start_date=[],
end_date=[]))
#source = ColumnDataSource(dfs)
scatter_renderer = p.scatter(x='LONGITUDE',
y='LATITUDE',
size=12,
color={
'field': 'slope',
'transform': color_mapper
},
source=source)
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=12)
p.add_layout(color_bar, 'right')
## Add hover tool that only act on scatter and not on the background land mask
p.add_tools(
HoverTool(
#tooltips=[("A", "@A"), ("B", "@B"), ("C", "@C")], mode = "vline"
renderers=[scatter_renderer],
mode='mouse'))
##--- Add slider
slider = RangeSlider(start=0.0,
end=self.sn_max * 1000.,
value=(0.0, self.sn_max * 1000.),
step=self.sn_max * 1000. / 20.,
title="Trend threshold [10e-3]")
slider_date = DateRangeSlider(title="Date range: ",
start=dt.date(1981, 1, 1),
end=dt.date.today(),
value=(dt.date(1981, 1,
1), dt.date.today()),
step=1,
visible=False)
## Slider Python callback
def update_scatter(attr, old, new):
# new = new slider value
#source.data = ColumnDataSource.from_df(self.dfs.loc[ (np.abs(self.dfs['slope']) >= 0.001*new[0]) &
#(np.abs(self.dfs['slope']) <= 0.001*new[1]) ])
if self.b_breaks:
slope_sel = slider.value
date_sel = [
pd.to_datetime(d, unit='ms') for d in slider_date.value
]
source.data = ColumnDataSource.from_df(self.dfs.loc[
(np.abs(self.dfs['slope']) >= 0.001 * slope_sel[0])
& (np.abs(self.dfs['slope']) <= 0.001 * slope_sel[1]) &
(self.dfs['start_date'] >= date_sel[0]) &
(self.dfs['end_date'] <= date_sel[1])])
else:
slope_sel = slider.value
source.data = ColumnDataSource.from_df(self.dfs.loc[
(np.abs(self.dfs['slope']) >= 0.001 * slope_sel[0])
& (np.abs(self.dfs['slope']) <= 0.001 * slope_sel[1])])
slider.on_change('value', update_scatter)
slider_date.on_change('value', update_scatter)
##--- Add time series of selected point
pw = int(400. * dw / dh)
ph = 200
p2 = figure(plot_width=pw,
plot_height=ph,
tools="pan,wheel_zoom,box_zoom,reset",
output_backend="webgl",
x_axis_type="datetime",
title='---')
p2.add_tools(
HoverTool(
tooltips=[
("Date",
"@dates{%Y-%m-%d}"), # must specify desired format here
("Value", "@var")
],
formatters={"@dates": "datetime"},
mode='vline'))
## Create source and plot it
#ts_source = ColumnDataSource(data=dict(dates=[], var=[]))
d_init = [dt.datetime(1981, 9, 20), dt.datetime(2020, 6, 30)]
ts_source = ColumnDataSource(
data=dict(dates=d_init, var=np.zeros_like(d_init)))
segment_source = ColumnDataSource(
data=dict(dates=d_init, var=np.zeros_like(d_init)))
breaks_source = ColumnDataSource(data=dict(x=[], y0=[], y1=[]))
# Full timeseries line
p2.line(x='dates', y='var', source=ts_source)
## Add satellite periods
# Sensor dates
sensor_dates = []
sensor_dates.append(['NOAA7', ('20-09-1981', '31-12-1984')])
sensor_dates.append(['NOAA9', ('20-03-1985', '10-11-1988')])
sensor_dates.append(['NOAA11', ('30-11-1988', '20-09-1994')])
sensor_dates.append(['NOAA14', ('10-02-1995', '10-03-2001')])
sensor_dates.append(['NOAA16', ('20-03-2001', '10-09-2002')])
sensor_dates.append(['NOAA17', ('20-09-2002', '31-12-2005')])
sensor_dates.append(['VGT1', ('10-04-1998', '31-01-2003')])
sensor_dates.append(['VGT2', ('31-01-2003', '31-05-2014')])
sensor_dates.append(['PROBAV', ('31-10-2013', '30-06-2020')])
sensor_dates = [[
v[0], [dt.datetime.strptime(i, "%d-%m-%Y") for i in v[1]]
] for v in sensor_dates]
import itertools
from bokeh.palettes import Category10 as palette
colors = itertools.cycle(palette[10])
top_ba = []
bottom_ba = []
for v, color in zip(sensor_dates, colors):
if 'VGT' not in v[0]:
top_ba.append(
BoxAnnotation(top=ph,
top_units='screen',
bottom=int(ph / 2),
bottom_units='screen',
left=v[1][0],
right=v[1][1],
fill_alpha=0.2,
fill_color=color))
else:
bottom_ba.append(
BoxAnnotation(top=int(ph / 2),
top_units='screen',
bottom=0,
bottom_units='screen',
left=v[1][0],
right=v[1][1],
fill_alpha=0.2,
fill_color=color))
for ba in top_ba:
p2.add_layout(ba)
for ba in bottom_ba:
p2.add_layout(ba)
def update_ts(attr, old, new):
"""
attr: 'indices'
old (list): the previous selected indices
new (list): the new selected indices
"""
if 0:
print(p2.width, p2.height)
print(p2.frame_width, p2.frame_height)
print(p2.inner_width, p2.inner_height)
print(p2.x_range.start, p2.x_range.end, p2.x_scale)
print(p2.y_range.start, p2.y_range.end, p2.y_scale)
if len(new) > 0:
## Update line with the last index because this is the last drawn point that is visible
site_id = int(source.data['id2'][new[-1]])
ts_source.data = dict(dates=self.dates, var=self.ts[site_id])
if self.b_breaks:
## Add segment
multi_idx = (source.data['NAME'][new[-1]],
str(source.data['lvl'][new[-1]]))
segment_slice = self.df_breaks.loc[multi_idx]['x'].astype(
'int')
segment_source.data = dict(
dates=[self.dates[i] for i in segment_slice],
var=self.ts[site_id][segment_slice])
## Add breaks
xb = [
pd.to_datetime(i) for i in self.df_breaks.loc[
source.data['NAME'][new[-1]]]['bp_date'].values
if not pd.isnull(i)
]
# Add first and last dates
xb = [pd.to_datetime(self.dates[0])
] + xb + [pd.to_datetime(self.dates[-1])]
y0b = np.nanmin(self.ts[site_id]) * np.ones(len(xb))
y1b = np.nanmax(self.ts[site_id]) * np.ones(len(xb))
breaks_source.data = dict(x=xb, y0=y0b, y1=y1b)
## Update BoxAnnotation
ph = p2.inner_height
for ba in top_ba:
ba.top = ph
ba.bottom = int(ph / 2)
for ba in bottom_ba:
ba.top = int(ph / 2)
ba.bottom = 0
## Update p2 title text with the name of the site
p2.title.text = 'SITE : {} (#{})'.format(
source.data['NAME'][new[-1]], source.data['id2'][new[-1]])
source.selected.on_change('indices', update_ts)
##--- Save html file
#save(column(slider, p, p2))
#save(p)
##--- Serve the file
curdoc().add_root(
column(file_input, select, slider, slider_date, p, p2))
curdoc().title = "Quality monitoring"
"""
from bokeh.plotting import figure
import pandas as pd
import numpy as np
from bokeh.io import curdoc
from bokeh.models import Select, ColumnDataSource, FileInput, HoverTool, DataTable, TableColumn, DateFormatter
from bokeh.layouts import column, row
import json
from base64 import b64decode
from datetime import time
from zipfile import ZipFile
import os
# These are the intital empty widgets when the server first loads
file_input = FileInput(accept='.json, .zip')
artist_plot = figure(title='Artist X Stream Time', plot_width=1500)
select_artist = Select(title='Select an Artist', value="", options=[])
track_plot = figure(title='Track X Stream Time', plot_width=1500)
select_track = Select(title='Select a Track', value="", options=[])
# Adds the empty widgets to the document
layout = column(row(file_input), row(select_artist), row(artist_plot),
row(select_track), row(track_plot))
curdoc().add_root(layout)
def read_file(attrname, old, new):
"""
Takes the file from file input and converts into a JSON string
"""
def prepare_server(doc,
input_data,
cell_stack,
cell_markers=None,
default_cell_marker=None):
@lru_cache()
def image_markers(lower=False, mapping=False):
if mapping:
return {
y: j
for j, y in sorted(
((i, x) for i, x in enumerate(
image_markers(lower=lower, mapping=False))),
key=lambda x: x[1].lower(),
)
}
if lower:
return [
x.lower() for x in image_markers(lower=False, mapping=False)
]
return (cell_markers if cell_markers is not None else
[f"Marker {i + 1}" for i in range(cell_stack.shape[1])])
# Data sources
###########################################################################
def prepare_data(input_data):
data = input_data.copy()
if "contour" in data and not all(x in data
for x in ["contour_x", "contour_y"]):
contour = parse_contour(data["contour"])
data["contour_x"] = contour[0]
data["contour_y"] = contour[1]
if "marked" not in data:
data["marked"] = np.full(data.shape[0], "")
source.data = data
source = ColumnDataSource(data={})
prepare_data(input_data)
image_source = ColumnDataSource(
data=dict(image=[], dw=[], dh=[], contour_x=[], contour_y=[]))
# Cell picture plot
###########################################################################
def add_outline():
data = source.data
if all(x in data for x in ["contour_x", "contour_y"]):
cell_outline = cell_figure.patches(
xs="contour_x",
ys="contour_y",
fill_color=None,
line_color="red",
name="cell_outline",
source=image_source,
)
cell_outline.level = "overlay"
else:
cell_outline = cell_figure.select(name="cell_outline")
for x in cell_outline:
cell_figure.renderers.remove(x)
default_cell_marker = (0 if default_cell_marker is None else image_markers(
mapping=True)[default_cell_marker])
cell_markers_select = Select(
value=str(default_cell_marker),
options=list(
(str(i), x) for x, i in image_markers(mapping=True).items()),
title="Marker cell image",
)
cell_marker_input = AutocompleteInput(
completions=list(image_markers()) + list(image_markers(lower=True)),
min_characters=1,
placeholder="Search for marker",
)
cell_slider = RangeSlider(start=0,
end=1,
value=(0, 1),
orientation="vertical",
direction="rtl")
metric_select = RadioButtonGroup(active=0, labels=CELL_IMAGE_METRICS[0])
stats = PreText(text="", width=100)
cell_mapper = bokeh.models.mappers.LinearColorMapper(viridis(20),
low=0,
high=1000,
high_color=None)
cell_color_bar = ColorBar(color_mapper=cell_mapper,
width=12,
location=(0, 0))
cell_figure = figure(
plot_width=450,
plot_height=350,
tools="pan,wheel_zoom,reset",
toolbar_location="left",
)
cell_image = cell_figure.image(
image="image",
color_mapper=cell_mapper,
x=0,
y=0,
dw="dw",
dh="dh",
source=image_source,
)
add_outline()
cell_figure.add_layout(cell_color_bar, "right")
# Edit data of selected cells
###########################################################################
marker_edit_container = column()
marker_edit_instances = []
def add_marker_edit_callback():
editor = ColumnEditor(
source,
marker_edit_container,
log_widget=edit_selecton_log,
editor_delete_callback=delete_marker_edit_callback,
external_edit_callback=edit_selection_callback,
)
marker_edit_instances.append(editor)
def delete_marker_edit_callback(editor):
idx = next(i for i, x in enumerate(marker_edit_instances)
if x is editor)
del marker_edit_instances[idx]
file_name_text = Div()
add_marker_edit_button = Button(label="+",
button_type="success",
align=("start", "end"),
width=50)
add_marker_edit_button.on_click(add_marker_edit_callback)
edit_selection_submit = Button(label="Submit change",
button_type="primary",
align=("start", "end"))
download_button = Button(label="Download edited data",
button_type="success",
align=("start", "end"))
download_button.js_on_click(
CustomJS(args=dict(source=source), code=DOWNLOAD_JS))
edit_selecton_log = TextAreaInput(value="",
disabled=True,
css_classes=["edit_log"],
cols=30,
rows=10)
upload_file_input = FileInput(accept="text/csv", align=("end", "end"))
# Cell table
###########################################################################
default_data_table_cols = [
TableColumn(field="marked", title="Seen", width=20)
]
data_table = DataTable(source=source,
columns=default_data_table_cols,
width=800)
# Callbacks for buttons and widgets
###########################################################################
def cell_slider_change(attrname, old, new):
cell_mapper.low = new[0]
cell_mapper.high = new[1]
def selection_change(attrname, old, new):
selected = source.selected.indices
data = source.data
if not selected:
return
mean_image = CELL_IMAGE_METRICS[1][metric_select.active](
cell_stack[selected,
int(cell_markers_select.value), :, :], axis=0)
image_data = {
"image": [mean_image],
"dw": [cell_stack.shape[2]],
"dh": [cell_stack.shape[3]],
}
for coord in ["contour_x", "contour_y"]:
try:
image_data[coord] = list(data[coord][selected])
except KeyError:
pass
image_source.data = image_data
image_extr = round_signif(mean_image.min()), round_signif(
mean_image.max())
cell_slider.start = image_extr[0]
cell_slider.end = image_extr[1]
cell_slider.step = round_signif((image_extr[1] - image_extr[0]) / 50)
cell_slider.value = image_extr
stats.text = "n cells: " + str(len(selected))
def autocomplete_cell_change(attrname, old, new):
try:
idx = image_markers(mapping=True)[new]
except KeyError:
try:
idx = image_markers(lower=True, mapping=True)[new]
except KeyError:
return
cell_markers_select.value = str(idx)
cell_marker_input.value = None
def data_change(attrname, old, new):
new_keys = [n for n in new.keys() if n not in set(old.keys())]
for n in new_keys:
data_table.columns.append(TableColumn(field=n, title=n))
def edit_selection_submit_click():
for x in marker_edit_instances:
x.edit_callback()
def edit_selection_callback():
idx = source.selected.indices
try:
if len(idx) == 1 and all(
source.data[x.widgets["input_col"].value][idx] != "NA"
for x in marker_edit_instances):
source.selected.indices = [idx[0] + 1]
except KeyError:
pass
def upload_file_callback(attrname, old, new):
try:
data_text = b64decode(new)
data = pd.read_csv(BytesIO(data_text))
except Exception:
file_name_text.text = f"Error loading file {upload_file_input.filename}"
return
file_name_text.text = f"Editing file {upload_file_input.filename}"
# Have to regenerate contours
try:
del data["contour_x"]
del data["contour_y"]
except KeyError:
pass
data_table.columns = default_data_table_cols
prepare_data(data)
add_outline()
source.selected.on_change("indices", selection_change)
source.on_change("data", data_change)
cell_slider.on_change("value", cell_slider_change)
metric_select.on_change("active", selection_change)
cell_markers_select.on_change("value", selection_change)
cell_marker_input.on_change("value", autocomplete_cell_change)
edit_selection_submit.on_click(edit_selection_submit_click)
upload_file_input.on_change("value", upload_file_callback)
style_div = Div(text=CUSTOM_CSS)
# set up layout
layout = column(
row(
column(data_table),
column(
cell_markers_select,
cell_marker_input,
metric_select,
row(cell_figure, cell_slider),
stats,
),
),
file_name_text,
marker_edit_container,
add_marker_edit_button,
row(edit_selection_submit, download_button, upload_file_input),
edit_selecton_log,
style_div,
)
doc.add_root(layout)
doc.title = "Cell classifier"
from bokeh.layouts import column
from bokeh.models import CustomJS, Div, FileInput
from bokeh.plotting import output_file, show
# Set up widgets
file_input = FileInput(accept=".csv,.json")
para = Div(text="<h1>FileInput Values:</h1><p>filename:<p>b64 value:")
# Create CustomJS callback to display file_input attributes on change
callback = CustomJS(args=dict(para=para, file_input=file_input),
code="""
para.text = "<h1>FileInput Values:</h1><p>filename: " + file_input.filename + "<p>b64 value: " + file_input.value
""")
# Attach callback to FileInput widget
file_input.js_on_change('change', callback)
output_file("file_input.html")
show(column(file_input, para))
class UIClass:
def __init__(self):
self.input_df = pd.DataFrame({
'x': ['2010-01-01'] * DF_NUM_PREVIEW_ROWS,
'y': [0] * DF_NUM_PREVIEW_ROWS
})
self.forecasted_df = None
self.datefmt = DateFormatter(format='%m-%d-%Y')
self.inputs = None
self.x_range = [0, 10]
self.demand_plot = figure(
x_range=self.x_range,
x_axis_type="datetime",
tools=["pan", 'wheel_zoom']) #,wheel_zoom,box_zoom,reset,resize")
self.plot_data_source = ColumnDataSource(
data=self.input_df) #dict(x=[0], y=[0])
self.line1 = self.demand_plot.line(x='x',
y='y',
source=self.plot_data_source,
line_color='blue',
name='line1')
self.demand_plot.xaxis.formatter = DatetimeTickFormatter(
days="%d %b %Y", hours="")
self.demand_plot.axis.minor_tick_line_color = None
self.demand_plot.xaxis[
0].ticker.desired_num_ticks = 10 #num_minor_ticks = 0
self.demand_plot.xaxis.major_label_orientation = radians(
30) # from math import radians
# Set up widgets
self.data_source_selector = Select(
title='Step 1/5: Select Data',
value='Not Selected',
options=['Not Selected', 'Use Example Data', 'Upload Data'])
self.file_input = FileInput(accept='.csv,.xlsx')
self.data_table = DataTable(
height=DATATABLE_PREVIEW_HEIGHT,
width=DATATABLE_PREVIEW_WIDTH,
fit_columns=False,
index_position=None,
margin=(0, 15, 0, 15), #aspect_ratio=0.5,
#default_size=50
)
self.data_preview_paragraph = Paragraph(text='Data Preview:',
margin=(0, 15, 0, 15))
self.values_col_selector = Select(
title='Step 2/5: Select column with demand values',
value='Not Selected',
options=['Not Selected'])
self.product_id_col_selector = Select(
title='Step 3/5: Select column with product ID',
value='Not Selected',
options=['Not Selected'])
self.date_col_selector = Select(title="Step 4/5: Select date column",
value='Not Selected',
options=['Not Selected'])
self.last_date_picker = DatePicker(
title='Select the date of last observation',
max_date=datetime.datetime.date(pd.to_datetime("today")),
value=datetime.datetime.date(pd.to_datetime("today")))
self.workdays_checkboxgroup = CheckboxGroup(
labels=["Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"],
active=[],
inline=True,
margin=(0, 15, 0, 0))
self.workdays_apply_button = Button(label='Select Business Days',
button_type='primary')
self.product_selector_plotting = Select(
title='Select Product to Display',
value='v1',
options=['v1', 'v2'])
self.prediction_button = Button(
label='Forecast Demand for Selected Product ID',
button_type='primary')
self.default_info_msg = 'This window will contain additional information,\nas you interact with the app.'
self.info_paragraph = PreText(
text='Details:\n{}'.format(self.default_info_msg))
# self.text = TextInput(title='title', value='my sine wave')
# self.offset = Slider(title='offset', value=0.0, start=-5.0, end=5.0, step=0.1)
self.widgets = {
'data_source_selector': self.data_source_selector,
'file_input': self.file_input,
'values_col_selector': self.values_col_selector,
'product_id_col_selector': self.product_id_col_selector,
'data_preview_paragraph': self.data_preview_paragraph,
'data_table': self.data_table,
'product_selector': self.product_selector_plotting,
'demand_plot': self.demand_plot,
'date_col_selector': self.date_col_selector,
'last_date_picker': self.last_date_picker,
'workdays_checkboxgroup': self.workdays_checkboxgroup,
'workdays_apply_button': self.workdays_apply_button,
'prediction_button': self.prediction_button,
#'': self.,
}
self.values_colname = None
self.product_id_colname = None
self.date_colname = None
self.product_ids = []
########## WIDGETS VISIBILITY CONTROLS ##########
def _change_widgets_visibility(self, names, names_show_or_hide='show'):
displaying = True if names_show_or_hide == 'show' else False
for widget_name in self.widgets:
if widget_name in names:
self.widgets[widget_name].visible = displaying
else:
self.widgets[widget_name].visible = not displaying
def display_all_widgets_except(self, widgets=[]):
self._change_widgets_visibility(widgets, 'hide')
def hide_all_widgets_except(self, widgets=[]):
self._change_widgets_visibility(widgets, 'show')
########## LOGIC ##########
def set_widget_to_default_value(self,
widget_names,
default_val='Not Selected'):
for widget_name in widget_names:
self.widgets[widget_name].value = default_val
def prepare_values_col_selection(self):
self.values_col_selector.options = ['Not Selected'
] + self.input_df.columns.tolist()
def get_additional_cols_to_show(self):
return ['file_input'
] if self.data_source_selector.value == 'Upload Data' else []
def update_details_msg(self, msg):
self.info_paragraph.text = "Details:\n{}".format(msg)
def preview_input_df(self):
# https://stackoverflow.com/questions/40942168/how-to-create-a-bokeh-datatable-datetime-formatter
columns = [
TableColumn(field=Ci, title=Ci, width=DATATABLE_PREVIEW_COL_WIDTH)
for Ci in self.input_df.columns
]
self.data_table.update(columns=columns)
self.data_table.update(
source=ColumnDataSource(self.input_df.head(DF_NUM_PREVIEW_ROWS)))
self.data_table.visible = True
self.data_preview_paragraph.visible = True
def upload_fit_data(self, attr, old, new):
print('fit data upload succeeded')
self.update_details_msg(msg='Step 1/5: Uploading data')
base64_message = self.file_input.value
base64_bytes = base64_message.encode('ascii')
message_bytes = base64.b64decode(base64_bytes)
message = message_bytes.decode('ascii')
self.input_df = pd.read_csv(StringIO(message), sep=',')
self.update_details_msg(
msg='Step 1/5: Data has been successfully uploaded!')
print('Input DF shape: {}'.format(self.input_df.shape))
self.prepare_values_col_selection()
self.hide_all_widgets_except(
['data_source_selector', 'file_input', 'values_col_selector'])
self.preview_input_df()
def replace_selector_options(self, selector, old_value, new_options):
selector.options = [old_value] + new_options
selector.value = new_options[0]
selector.options = new_options
def date_col_integrity(self, date_colname):
if not isinstance(self.input_df[date_colname][0], str):
self.input_df[date_colname] = self.input_df[date_colname].astype(
str)
if '-' in self.input_df[date_colname][0]:
sep = '-'
elif '/' in self.input_df[date_colname][0]:
sep = '/'
else:
return 'no separator found'
date_parts = self.input_df[date_colname].apply(lambda x: x.split(sep))
if (date_parts.apply(lambda x: len(x)) == 3).all():
try:
self.input_df[date_colname] = pd.to_datetime(
self.input_df[date_colname])
return 'ok'
except:
return 'error converting to datetime'
else:
return 'not all dates have exactly 3 components'
def display_preview_plot(self):
self.replace_selector_options(self.product_selector_plotting, 'v1',
self.product_ids)
self.product_selector_plotting.visible = True
self.prediction_button.visible = True
self.demand_plot.renderers.remove(self.line1)
self.plot_data_source = None
self.plot_data_source = ColumnDataSource(data=self.input_df[
self.input_df[self.product_id_colname] == self.product_ids[0]])
self.line1 = self.demand_plot.line(x=self.date_colname,
y=self.values_colname,
source=self.plot_data_source,
line_color='blue',
name='line1')
self.update_plot(None, None, self.product_ids[0])
self.demand_plot.visible = True
def generate_dates(self, end_date: datetime.datetime, work_days: list,
num_periods: int):
work_days = ' '.join(work_days) # 'Sun Mon Tue Wed Fri'
freq = pd.offsets.CustomBusinessDay(weekmask=work_days)
return pd.date_range(end=end_date, periods=num_periods, freq=freq)
def clean_df(self):
"""
Modifies self.input_df:
1) Removing duplicates based on [self.date_colname, self.product_id_colname]
2) Sorting based on self.date_colname
:return: void
"""
self.input_df = self.input_df[~self.input_df.duplicated(
subset=[self.date_colname, self.product_id_colname], keep='first')]
self.input_df.sort_values(by=self.date_colname, inplace=True)
print('===RESULTED INPUT_DF SHAPE AFTER CLEANING: ',
self.input_df.shape)
########## WIDGETS ON_CHANGE METHODS ##########
def select_data_source(self, attrname, old_val, new_val):
self.set_widget_to_default_value([
'values_col_selector', 'product_id_col_selector',
'date_col_selector'
])
if new_val == 'Upload Data':
self.update_details_msg(
msg=
'Step 1/5: Please upload data in one of the\nfollowing formats: .CSV or .XLSX'
)
self.hide_all_widgets_except(
['data_source_selector', 'file_input'])
elif new_val == 'Use Example Data':
self.update_details_msg(
msg=
'Step 1/5: Using a sample toy data. You can use it\nto test the functionality of this app.'
)
self.input_df = pd.read_csv('default_table.csv')
self.prepare_values_col_selection()
self.preview_input_df()
self.hide_all_widgets_except([
'data_source_selector', 'values_col_selector',
'data_preview_paragraph', 'data_table'
])
else: # Not Selected
self.update_details_msg(msg=self.default_info_msg)
self.hide_all_widgets_except(['data_source_selector'])
def select_values_colname(self, attrname, old_val, new_val):
self.update_details_msg(
msg=
'Step 2/5: Please select a column that contains\nthe demand values. Note, that all the values in\nthis column should be numerical.'
)
self.set_widget_to_default_value(
['product_id_col_selector', 'date_col_selector'])
self.hide_all_widgets_except([
'data_source_selector', 'values_col_selector',
'data_preview_paragraph', 'data_table'
] + self.get_additional_cols_to_show())
if new_val == 'Not Selected':
pass
else:
self.values_colname = new_val
try:
self.input_df[self.values_colname] = self.input_df[
self.values_colname].astype(float)
available_cols = set(self.input_df.columns)
available_cols.remove(self.values_colname)
if self.date_colname in available_cols:
available_cols.remove(self.date_colname)
self.product_id_col_selector.options = [
'Not Selected'
] + list(available_cols)
self.product_id_col_selector.visible = True
except:
self.update_details_msg(
msg=
'WARNING! Step 2/5: Not all the values\nin selected column are numerical!'
)
def select_product_id_colname(self, attrname, old_val, new_val):
self.update_details_msg(
msg=
"Step 3/5: Please select a column that contains products' identifiers."
)
self.set_widget_to_default_value(['date_col_selector'])
self.hide_all_widgets_except([
'data_source_selector', 'values_col_selector',
'data_preview_paragraph', 'data_table', 'product_id_col_selector'
] + self.get_additional_cols_to_show())
if new_val == 'Not Selected':
pass
else:
self.product_id_colname = new_val
self.product_ids = self.input_df[
self.product_id_colname].unique().astype(str).tolist()
available_cols = set(self.input_df.columns)
for colname in [self.values_colname, self.product_id_colname]:
available_cols.remove(colname)
if self.date_colname in available_cols:
available_cols.remove(self.date_colname)
self.date_col_selector.options = ['Not Selected'
] + list(available_cols)
self.date_col_selector.visible = True
self.last_date_picker.visible = True
self.workdays_checkboxgroup.visible = True
self.workdays_apply_button.visible = True
def select_date_column(self, attrname, old_val, new_val):
self.update_details_msg(
msg="Step 4/5: If there is a date column, please select it's name.\n"
"Note: Dates should be in one of the following formats:\n"
"yyyy-mm-dd OR mm-dd-yyyy OR yyyy/mm/dd OR mm/dd/yyyy\n"
"If there is no such column, use 'Not Selected' option.")
self.hide_all_widgets_except([
'data_source_selector', 'values_col_selector',
'data_preview_paragraph', 'data_table', 'product_id_col_selector',
'date_col_selector'
] + self.get_additional_cols_to_show())
if new_val == 'Not Selected':
self.last_date_picker.visible = True
self.workdays_checkboxgroup.visible = True
self.workdays_apply_button.visible = True
else:
self.date_colname = new_val
date_col_integrity_status = self.date_col_integrity(
self.date_colname)
if date_col_integrity_status == 'ok':
self.clean_df()
self.display_preview_plot()
else:
print('date_col_integrity_status: ', date_col_integrity_status)
self.update_details_msg(
msg=
"ERROR: selected date column doesn't satisfy specified requirements:\n"
"Dates should be in one of the following formats:\n"
"yyyy-mm-dd OR mm-dd-yyyy OR yyyy/mm/dd OR mm/dd/yyyy\n"
"If there is no such column, use 'Not Selected' option.")
def select_last_date(self, attrname, old_val, new_val):
self.update_details_msg(
msg="Alright, dates will be automatically generated for you!\n"
"Select days when your business works.")
self.workdays_checkboxgroup.visible = True
self.workdays_apply_button.visible = True
def workdays_button_pressed(self, new):
if len(self.workdays_checkboxgroup.active) == 0:
self.update_details_msg(
msg="Please select at least one business day.")
else:
self.update_details_msg(msg="Generating dates.")
if 'generated_dates' in self.input_df.columns:
self.update_details_msg(
msg="Please rename the generated_dates column in you table."
)
else:
self.date_colname = 'generated_date'
self.input_df[self.date_colname] = ''
for product_id in self.product_ids:
inds = self.input_df[self.product_id_colname] == product_id
self.input_df.loc[
inds, self.date_colname] = self.generate_dates(
end_date=self.last_date_picker.value,
work_days=np.array(
self.workdays_checkboxgroup.labels)[
self.workdays_checkboxgroup.active],
num_periods=inds.sum())
self.input_df[self.date_colname] = pd.to_datetime(
self.input_df[self.date_colname])
self.clean_df()
self.display_preview_plot()
#self.preview_input_df() # https://stackoverflow.com/questions/40942168/how-to-create-a-bokeh-datatable-datetime-formatter
def prediction_button_pressed(self, new):
train_dataset = pd.DataFrame()
print('Preparing forecast for product: ',
self.product_selector_plotting.value)
inds = self.input_df[
self.product_id_colname] == self.product_selector_plotting.value
train_dataset['ds'] = self.input_df.loc[inds, self.date_colname]
train_dataset['y'] = self.input_df.loc[inds, self.values_colname]
# train_dataset = train_dataset[train_dataset.duplicated(subset=['ds'],keep='first')]
#train_dataset.sort_values(by=self.date_colname, inplace=True)
print('Train Dataset shape: ', train_dataset.shape)
for q in self.make_predictions(train_dataset):
if q[0] == 'msg':
print('Message: ', q[1])
else:
self.forecasted_df = q[1]
self.forecasted_df.columns = ['ds', 'y']
print('Done; shape: ', self.forecasted_df.shape)
#self.demand_plot.line(x='ds', y='yhat', source=ColumnDataSource(data=self.forecasted_df, name='line2'))
#print(self.forecasted_df.tail(30))
#combined_dataset = train_dataset.append(self.forecasted_df.tail(30), ignore_index=True)
d = {
'ds':
train_dataset['ds'].append(
self.forecasted_df.tail(30)['ds']),
'y':
train_dataset['y'].append(
self.forecasted_df.tail(30)['y'])
}
combined_dataset = pd.DataFrame(d)
try:
while len(self.demand_plot.legend[0].items) > 0:
self.demand_plot.legend[0].items.pop()
except:
print(
'FAIL: popping legends in prediction_button_pressed()')
self.demand_plot.renderers.remove(self.line1)
try:
self.demand_plot.renderers.remove(self.line2)
except:
pass
self.plot_data_source = None
self.plot_data_source = ColumnDataSource(data=combined_dataset)
self.line1 = self.demand_plot.line(x=train_dataset['ds'],
y=train_dataset['y'],
line_color='blue',
name='line1',
legend_label='Historical')
self.line2 = self.demand_plot.line(
x=train_dataset['ds'].tail(1).append(
self.forecasted_df['ds'].tail(30)),
y=train_dataset['y'].tail(1).append(
self.forecasted_df['y'].tail(30)),
line_color='red',
name='line2',
legend_label='Forecast')
#print('QQQ ', self.demand_plot.select(name="line2"))
self.demand_plot.legend.location = "top_left"
self.demand_plot.x_range.start = combined_dataset['ds'].min()
self.demand_plot.x_range.end = combined_dataset['ds'].max()
self.demand_plot.y_range.start = combined_dataset['y'].min()
self.demand_plot.y_range.end = combined_dataset['y'].max()
self.demand_plot.visible = True
########## OTHER ##########
def dates_diff_count(self, df, product_name):
days_diffs = (
df[1:][self.date_colname].values -
df[:-1][self.date_colname].values) / 1000000000 / 60 / 60 / 24
unique_diffs, diffs_counts = np.unique(days_diffs, return_counts=True)
msg = 'Product: {}:\n# Days Delta ; Count\n'.format(product_name)
for value, count in zip(unique_diffs, diffs_counts):
msg += '{:10} ; {}\n'.format(value, count)
msg += 'If there is more than one unique value\nit can make forecast less accurate'
self.update_details_msg(msg=msg)
# https://facebook.github.io/prophet/docs/non-daily_data.html
def make_predictions(self, df, days_ahead=30):
yield ['msg', 'training model']
prophet = Prophet(weekly_seasonality=False, daily_seasonality=False)
prophet.fit(df)
yield ['msg', 'making predictions']
future = prophet.make_future_dataframe(periods=days_ahead)
forecast = prophet.predict(future)
yield ['results', forecast[['ds', 'yhat']]]
def update_plot(self, attrname, old, new):
try:
while len(self.demand_plot.legend[0].items) > 0:
self.demand_plot.legend[0].items.pop()
except:
print('FAIL: popping legends in update_plot()')
try:
self.demand_plot.renderers.remove(self.line2)
except:
pass
sub_df = self.input_df[self.input_df[self.product_id_colname] == new]
self.dates_diff_count(sub_df, new)
self.demand_plot.renderers.remove(self.line1)
self.plot_data_source = None
self.plot_data_source = ColumnDataSource(data=sub_df)
self.line1 = self.demand_plot.line(x=self.date_colname,
y=self.values_colname,
source=self.plot_data_source,
line_color='blue',
legend_label='Historical',
name='line1')
self.demand_plot.legend.location = "top_left"
self.demand_plot.x_range.start = sub_df[self.date_colname].min()
self.demand_plot.x_range.end = sub_df[self.date_colname].max()
self.demand_plot.y_range.start = sub_df[self.values_colname].min()
self.demand_plot.y_range.end = sub_df[self.values_colname].max()
########## MAIN ##########
def display(self):
self.file_input.on_change('value', self.upload_fit_data)
self.plot = figure(plot_height=400,
plot_width=400,
title='my sine wave',
tools='crosshair,pan,reset,save,wheel_zoom')
# Set up layouts and add to document
self.inputs = column(self.data_source_selector, self.file_input,
self.values_col_selector,
self.product_id_col_selector,
self.date_col_selector, self.last_date_picker,
self.workdays_checkboxgroup,
self.workdays_apply_button)
#self.data_source_selector.visible = True
self.hide_all_widgets_except(['data_source_selector'])
self.data_source_selector.on_change('value', self.select_data_source)
self.values_col_selector.on_change('value', self.select_values_colname)
self.product_id_col_selector.on_change('value',
self.select_product_id_colname)
self.product_selector_plotting.on_change('value', self.update_plot)
self.date_col_selector.on_change('value', self.select_date_column)
self.last_date_picker.on_change('value', self.select_last_date)
self.workdays_apply_button.on_click(self.workdays_button_pressed)
self.prediction_button.on_click(self.prediction_button_pressed)
#self.col_left = self.inputs
columns = [
TableColumn(field=Ci, title=Ci, width=DATATABLE_PREVIEW_COL_WIDTH)
for Ci in self.input_df.columns
]
self.data_table.columns = columns
self.data_table.source = ColumnDataSource(
self.input_df.head(DF_NUM_PREVIEW_ROWS))
self.col_middle = column(self.data_preview_paragraph, self.data_table)
#self.col_info = column()
#self.col_left.width = 300
#self.col_right.max_width = 500
#self.col_right.sizing_mode = 'scale_width'
#self.row_data_input = row(self.col_left, self.col_right, self.info_paragraph)
#self.row_data_input.sizing_mode = 'scale_width'
#self.row_demand_plot = row(self.product_selector_plotting)#, self.demand_plot)
#self.layout = column(self.row_data_input, self.row_demand_plot)
self.layout = column(
row(
column(
self.data_source_selector,
self.file_input,
self.values_col_selector,
self.product_id_col_selector,
), column(self.data_preview_paragraph, self.data_table),
self.info_paragraph),
row(
column(self.date_col_selector, self.last_date_picker,
self.workdays_checkboxgroup, self.workdays_apply_button,
self.product_selector_plotting, self.prediction_button),
self.demand_plot))
curdoc().add_root(self.layout)
curdoc().title = 'Demand Forecasting'
def spectrum_slice_app(doc):
def load_wav_cb(attr, old, new):
'''Handle selection of audio file to be loaded.'''
global wavname
base_url, fname = os.path.split(new)
wavname = get_cached_fname(fname, base_url, tempdir)
if not wavname.endswith('.wav'):
return
update_snd()
playvisbtn.channels = channels
playvisbtn.visible = True
playselbtn.channels = channels
playselbtn.visible = True
playvisbtn.fs = snd.sampling_frequency
playvisbtn.start = snd.start_time
playvisbtn.end = snd.end_time
playselbtn.fs = snd.sampling_frequency
playselbtn.start = 0.0
playselbtn.end = 0.0
ch0.visible = True
update_sgram()
update_spslice(t=None)
def file_input_cb(attr, old, new):
'''Handle audio file upload.'''
with NamedTemporaryFile() as tempfile:
tempfile.write(b64decode(new))
tempsnd = parselmouth.Sound(tempfile.name)
ds_snd = tempsnd.resample(params['downsample_rate'], 50)
cachefile = os.path.join(tempdir, file_input.filename)
ds_snd.save(cachefile, parselmouth.SoundFileFormat.WAV)
options = fselect.options.copy()
options += [(cachefile, file_input.filename)]
fselect.options = options
fselect.value = fselect.options[-1][0]
def update_snd():
'''Update the sound (waveform and audio button).'''
global snd
snd = parselmouth.Sound(wavname)
if snd.n_channels > 1:
snd = snd.convert_to_mono()
if filter_sel.value not in ('no filter', 'no filter (clear)'
) and spselbox.right is not None:
if filter_sel.value.startswith('stopband'):
func = 'Filter (stop Hann band)...'
if filter_sel.value.startswith('passband'):
func = 'Filter (pass Hann band)...'
snd = parselmouth.praat.call(snd, func, spselbox.left,
spselbox.right, 100.0)
source.data = dict(
seconds=snd.ts().astype(np.float32),
ch0=snd.values[0, :].astype(np.float32),
)
def update_sgram():
'''Update spectrogram based on current values.'''
if filter_sel.value == 'no filter (clear)':
sgselbox.bottom = None
sgselbox.top = None
sgselbox.visible = False
else:
sgselbox.visible = True
sgrams[0] = snd2specgram(snd, winsize=winsize_slider.value * 10**-3)
specsource.data = dict(sgram0=[sgrams[0].values.astype(np.float32)])
spec0img.glyph.dw = sgrams[0].x_grid().max()
spec0img.glyph.dh = sgrams[0].y_grid().max()
spec0cmap.low = _low_thresh()
spec0.visible = True
def update_spslice(t=None):
'''Update spslice plot with spectrum slice at time t.'''
if t is not None:
slidx = np.round(
parselmouth.praat.call(sgrams[0],
'Get frame number from time...',
t)).astype(int)
spslice = sgrams[0].values[:, slidx]
spdata = dict(freq=np.arange(sgrams[0].values.shape[0]) *
sgrams[0].dy,
power=spslice)
spselbox.visible = True
else:
spdata = dict(freq=np.array([]), power=np.array([]))
spec0_fq_marker.visible = False
spslice0_fq_marker.visible = False
spselbox.visible = False
if filter_sel.value == 'no filter (clear)':
spselbox.left = None
spselbox.right = None
spselbox.visible = False
spslice_source.data = spdata
spslice0.x_range = Range1d(0.0, sgrams[0].get_highest_y())
spslice0.y_range = Range1d(0.0, sgrams[0].get_maximum())
thresh_box.top = _low_thresh()
try:
fqidx = np.abs(spslice_source.data['freq'] -
fq_marker_source.data['freq'][0]).argmin()
fq_marker_source.data['power'] = [
spslice_source.data['power'][fqidx]
]
except ValueError:
pass # Not set yet
def cursor_cb(e):
'''Handle cursor mouse click that creates the spectrum slice.'''
cursor.location = e.x
update_spslice(t=e.x)
idx = np.abs(spslice_source.data['freq'] - e.y).argmin()
fq_marker_source.data = dict(freq=[e.y],
power=[spslice_source.data['power'][idx]],
time=[e.x])
params['spslice_lastx'] = e.y
spec0_fq_marker.visible = True
spslice0_fq_marker.visible = True
def spslice_move_cb(e):
'''Handle a MouseMove event on spectrum slice crosshair tool.'''
try:
if params[
'spslice_lastx'] != e.x and e.x >= 0 and e.x <= spslice_source.data[
'freq'][-1]:
params['spslice_lastx'] = e.x
idx = np.abs(spslice_source.data['freq'] - e.x).argmin()
fq_marker_source.data['freq'] = [
spslice_source.data['freq'][idx]
]
fq_marker_source.data['power'] = [
spslice_source.data['power'][idx]
]
except IndexError: # data not loaded yet
pass
def x_range_cb(attr, old, new):
'''Handle change of x range in waveform/spectrogram.'''
if attr == 'start':
playvisbtn.start = new
elif attr == 'end':
playvisbtn.end = new
def selection_cb(e):
'''Handle data range selection event.'''
playselbtn.start = e.geometry['x0']
playselbtn.end = e.geometry['x1']
selbox.left = e.geometry['x0']
selbox.right = e.geometry['x1']
selbox.visible = True
def low_thresh_cb(attr, old, new):
'''Handle change in threshold slider to fade out low spectrogram values.'''
params['low_thresh_power'] = new
lt = _low_thresh()
spec0cmap.low = lt
thresh_box.top = lt
def _low_thresh():
return sgrams[0].values.min() \
+ sgrams[0].values.std()**params['low_thresh_power']
def winsize_cb(attr, old, new):
'''Handle change in winsize slider to change spectrogram analysis window.'''
params['window_size'] = new
update_sgram()
if cursor.location is not None:
update_spslice(t=cursor.location)
idx = np.abs(spslice_source.data['freq'] -
params['spslice_lastx']).argmin()
fq_marker_source.data = dict(
freq=[spslice_source.data['freq'][idx]],
power=[spslice_source.data['power'][idx]],
time=[cursor.location])
def filter_sel_cb(e):
'''Handle change of filter range.'''
lowfq = e.geometry['x0']
highfq = e.geometry['x1']
sgselbox.bottom = lowfq
sgselbox.top = highfq
spselbox.left = lowfq
spselbox.right = highfq
range_text = f' ({lowfq:.0f}-{highfq:.0f} Hz)'
# Force assignment of new options so that Bokeh detects the values have changed
# and synchronizes the JS.
options = filter_sel.options.copy()
for idx, opt in enumerate(options):
if 'stopband' in opt:
options[idx] = f'stopband {range_text}'
if 'stopband' in filter_sel.value:
filter_sel.value = options[idx]
if 'passband' in opt:
options[idx] = f'passband {range_text}'
if 'passband' in filter_sel.value:
filter_sel.value = options[idx]
filter_sel.options = options
update_snd()
update_sgram()
update_spslice(t=cursor.location)
def filter_type_cb(attr, old, new):
'''Handle change in filter type.'''
if 'clear' in new:
# Force assignment of new options so that Bokeh detects the values have changed
# and synchronizes the JS.
options = filter_sel.options.copy()
for idx, opt in enumerate(options):
if 'passband' in opt:
options[idx] = 'passband'
if 'passband' in filter_sel.value:
filter_sel.value = 'passband'
if 'stopband' in opt:
options[idx] = 'stopband'
if 'stopband' in filter_sel.value:
filter_sel.value = 'stopband'
filter_sel.options = options
update_snd()
update_sgram()
update_spslice(t=cursor.location)
manifest_text = requests.get(resource_url + manifest_name).text
manifest = yaml.safe_load(manifest_text)[manifest_key]
options = [('', 'Choose an audio file to display')] + [
(resource_url + opt['fname'], opt['label']) for opt in manifest
]
fselect = Select(options=options, value='')
fselect.on_change('value', load_wav_cb)
file_input = FileInput(accept=".wav")
fselect_row = row(fselect, file_input)
file_input.on_change('value', file_input_cb)
source = ColumnDataSource(data=dict(seconds=[], ch0=[]))
channels = ['ch0']
playvisbtn = AudioButton(label='Play visible signal',
source=source,
channels=channels,
width=120,
visible=False)
playselbtn = AudioButton(label='Play selected signal',
source=source,
channels=channels,
width=120,
visible=False)
# Instantiate and share specific select/zoom tools so that
# highlighting is synchronized on all plots.
boxsel = BoxSelectTool(dimensions='width')
spboxsel = BoxSelectTool(dimensions='width')
boxzoom = BoxZoomTool(dimensions='width')
zoomin = ZoomInTool(dimensions='width')
zoomout = ZoomOutTool(dimensions='width')
crosshair = CrosshairTool(dimensions='height')
shared_tools = [
'xpan', boxzoom, boxsel, crosshair, 'undo', 'redo', zoomin, zoomout,
'reset'
]
figargs = dict(tools=shared_tools, )
cursor = Span(dimension='height',
line_color='red',
line_dash='dashed',
line_width=1)
ch0 = figure(name='ch0', tooltips=[("time", "$x{0.0000}")], **figargs)
ch0.line(x='seconds', y='ch0', source=source, nonselection_line_alpha=0.6)
# Link pan, zoom events for plots with x_range.
ch0.x_range.on_change('start', x_range_cb)
ch0.x_range.on_change('end', x_range_cb)
ch0.on_event(SelectionGeometry, selection_cb)
ch0.on_event(Tap, cursor_cb)
ch0.add_layout(cursor)
low_thresh = 0.0
sgrams = [np.ones((1, 1))]
specsource = ColumnDataSource(data=dict(sgram0=[sgrams[0]]))
fq_marker_source = ColumnDataSource(
data=dict(freq=[0.0], power=[0.0], time=[0.0]))
spec0 = figure(
name='spec0',
x_range=ch0.x_range, # Keep times synchronized
tooltips=[("time", "$x{0.0000}"), ("freq", "$y{0.0000}"),
("value", "@sgram0{0.000000}")],
**figargs)
spec0.add_layout(cursor)
spec0_fq_marker = spec0.circle(x='time',
y='freq',
source=fq_marker_source,
size=6,
line_color='red',
fill_color='red',
visible=False)
spec0.x_range.range_padding = spec0.y_range.range_padding = 0
spec0cmap = LogColorMapper(palette=r_Greys256,
low_color=params['low_thresh_color'])
low_thresh_slider = Slider(start=1.0,
end=12.0,
step=0.125,
value=params['low_thresh_power'],
title='Low threshold')
winsize_slider = Slider(start=5.0,
end=40.0,
step=5.0,
value=params['window_size'],
title='Analysis window (ms)')
filter_sel = Select(
options=['no filter (clear)', 'no filter', 'passband', 'stopband'],
value='no filter (clear)')
spec0img = spec0.image(image='sgram0',
x=0,
y=0,
color_mapper=spec0cmap,
level='image',
source=specsource)
spec0.grid.grid_line_width = 0.0
low_thresh_slider.on_change('value', low_thresh_cb)
winsize_slider.on_change('value', winsize_cb)
filter_sel.on_change('value', filter_type_cb)
selbox = BoxAnnotation(name='selbox',
left=None,
right=None,
fill_color='green',
fill_alpha=0.1,
line_color='green',
line_width=1.5,
line_dash='dashed',
visible=False)
sgselbox = BoxAnnotation(name='sgselbox',
top=None,
bottom=None,
fill_color='red',
fill_alpha=0.1,
line_color='red',
line_width=1.5,
line_dash='dashed',
visible=False)
ch0.add_layout(selbox)
spec0.add_layout(selbox)
spec0.add_layout(sgselbox)
spec0.on_event(SelectionGeometry, selection_cb)
spec0.on_event(Tap, cursor_cb)
grid = gridplot([ch0, spec0],
ncols=1,
plot_height=200,
toolbar_location='left',
toolbar_options={'logo': None},
merge_tools=True)
spslice_chtool = CrosshairTool(dimensions='height')
spslice0 = figure(name='spslice0',
plot_width=400,
plot_height=250,
y_axis_type='log',
y_range=(10**-9, 1),
tools=[spboxsel, spslice_chtool],
toolbar_location='left')
spslice0.toolbar.logo = None
spslice_source = ColumnDataSource(
data=dict(freq=np.array([]), power=np.array([])))
spslice0.line(x='freq', y='power', source=spslice_source)
spselbox = BoxAnnotation(name='spselbox',
left=None,
right=None,
fill_color='red',
fill_alpha=0.1,
line_color='red',
line_width=1.5,
line_dash='dashed',
visible=False)
spslice0.add_layout(spselbox)
spslice0.on_event(SelectionGeometry, filter_sel_cb)
thresh_box = BoxAnnotation(fill_color=params['low_thresh_color'])
spslice0.add_layout(thresh_box)
spslice0.on_event(MouseMove, spslice_move_cb)
spslice0_fq_marker = spslice0.circle(x='freq',
y='power',
source=fq_marker_source,
size=6,
line_color='red',
fill_color='red',
visible=False)
num_fmtr = NumberFormatter(format='0.0000')
det_num_fmtr = NumberFormatter(format='0.000000000')
fq_marker_table = DataTable(source=fq_marker_source,
columns=[
TableColumn(field="freq",
title="Frequency",
formatter=num_fmtr),
TableColumn(field="power",
title="Power",
formatter=det_num_fmtr),
TableColumn(field="time",
title="Time",
formatter=num_fmtr),
],
width=300)
control_col = column(row(playvisbtn, playselbtn), low_thresh_slider,
winsize_slider, filter_sel, fq_marker_table)
grid2 = gridplot([spslice0, control_col], ncols=2)
mainLayout = column(
fselect_row,
grid, #low_thresh_slider, winsize_slider,
grid2,
name='mainLayout')
doc.add_root(mainLayout)
return doc
def __init__(self):
self.input_df = pd.DataFrame({
'x': ['2010-01-01'] * DF_NUM_PREVIEW_ROWS,
'y': [0] * DF_NUM_PREVIEW_ROWS
})
self.forecasted_df = None
self.datefmt = DateFormatter(format='%m-%d-%Y')
self.inputs = None
self.x_range = [0, 10]
self.demand_plot = figure(
x_range=self.x_range,
x_axis_type="datetime",
tools=["pan", 'wheel_zoom']) #,wheel_zoom,box_zoom,reset,resize")
self.plot_data_source = ColumnDataSource(
data=self.input_df) #dict(x=[0], y=[0])
self.line1 = self.demand_plot.line(x='x',
y='y',
source=self.plot_data_source,
line_color='blue',
name='line1')
self.demand_plot.xaxis.formatter = DatetimeTickFormatter(
days="%d %b %Y", hours="")
self.demand_plot.axis.minor_tick_line_color = None
self.demand_plot.xaxis[
0].ticker.desired_num_ticks = 10 #num_minor_ticks = 0
self.demand_plot.xaxis.major_label_orientation = radians(
30) # from math import radians
# Set up widgets
self.data_source_selector = Select(
title='Step 1/5: Select Data',
value='Not Selected',
options=['Not Selected', 'Use Example Data', 'Upload Data'])
self.file_input = FileInput(accept='.csv,.xlsx')
self.data_table = DataTable(
height=DATATABLE_PREVIEW_HEIGHT,
width=DATATABLE_PREVIEW_WIDTH,
fit_columns=False,
index_position=None,
margin=(0, 15, 0, 15), #aspect_ratio=0.5,
#default_size=50
)
self.data_preview_paragraph = Paragraph(text='Data Preview:',
margin=(0, 15, 0, 15))
self.values_col_selector = Select(
title='Step 2/5: Select column with demand values',
value='Not Selected',
options=['Not Selected'])
self.product_id_col_selector = Select(
title='Step 3/5: Select column with product ID',
value='Not Selected',
options=['Not Selected'])
self.date_col_selector = Select(title="Step 4/5: Select date column",
value='Not Selected',
options=['Not Selected'])
self.last_date_picker = DatePicker(
title='Select the date of last observation',
max_date=datetime.datetime.date(pd.to_datetime("today")),
value=datetime.datetime.date(pd.to_datetime("today")))
self.workdays_checkboxgroup = CheckboxGroup(
labels=["Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"],
active=[],
inline=True,
margin=(0, 15, 0, 0))
self.workdays_apply_button = Button(label='Select Business Days',
button_type='primary')
self.product_selector_plotting = Select(
title='Select Product to Display',
value='v1',
options=['v1', 'v2'])
self.prediction_button = Button(
label='Forecast Demand for Selected Product ID',
button_type='primary')
self.default_info_msg = 'This window will contain additional information,\nas you interact with the app.'
self.info_paragraph = PreText(
text='Details:\n{}'.format(self.default_info_msg))
# self.text = TextInput(title='title', value='my sine wave')
# self.offset = Slider(title='offset', value=0.0, start=-5.0, end=5.0, step=0.1)
self.widgets = {
'data_source_selector': self.data_source_selector,
'file_input': self.file_input,
'values_col_selector': self.values_col_selector,
'product_id_col_selector': self.product_id_col_selector,
'data_preview_paragraph': self.data_preview_paragraph,
'data_table': self.data_table,
'product_selector': self.product_selector_plotting,
'demand_plot': self.demand_plot,
'date_col_selector': self.date_col_selector,
'last_date_picker': self.last_date_picker,
'workdays_checkboxgroup': self.workdays_checkboxgroup,
'workdays_apply_button': self.workdays_apply_button,
'prediction_button': self.prediction_button,
#'': self.,
}
self.values_colname = None
self.product_id_colname = None
self.date_colname = None
self.product_ids = []
from qmp.potential import Potential
from qmp.potential import preset_potentials
from qmp.integrator.dyn_tools import create_gaussian
import numpy as np
from bokeh.layouts import column, grid, row
from bokeh.models import Button, FileInput, Select, ColumnDataSource
from bokeh.models import TextInput
from bokeh.models.widgets.buttons import Toggle, Dropdown
from bokeh.models.widgets.sliders import Slider, RangeSlider
from bokeh.models.widgets.groups import RadioButtonGroup
from bokeh.plotting import figure, curdoc
import pickle
import base64
file = FileInput()
def decode_data(data):
decode = base64.b64decode(data)
return pickle.loads(decode)
def load_data():
data = decode_data(file.value)
x = data['x']
y = data['psi_t'][0, 0].real
p = create_plot(x, y)
def create():
det_data = []
fit_params = {}
js_data = ColumnDataSource(data=dict(content=["", ""], fname=["", ""]))
def proposal_textinput_callback(_attr, _old, new):
proposal = new.strip()
for zebra_proposals_path in pyzebra.ZEBRA_PROPOSALS_PATHS:
proposal_path = os.path.join(zebra_proposals_path, proposal)
if os.path.isdir(proposal_path):
# found it
break
else:
raise ValueError(f"Can not find data for proposal '{proposal}'.")
file_list = []
for file in os.listdir(proposal_path):
if file.endswith((".ccl", ".dat")):
file_list.append((os.path.join(proposal_path, file), file))
file_select.options = file_list
file_open_button.disabled = False
file_append_button.disabled = False
proposal_textinput = TextInput(title="Proposal number:", width=210)
proposal_textinput.on_change("value", proposal_textinput_callback)
def _init_datatable():
scan_list = [s["idx"] for s in det_data]
file_list = []
for scan in det_data:
file_list.append(os.path.basename(scan["original_filename"]))
scan_table_source.data.update(
file=file_list,
scan=scan_list,
param=[None] * len(scan_list),
fit=[0] * len(scan_list),
export=[True] * len(scan_list),
)
scan_table_source.selected.indices = []
scan_table_source.selected.indices = [0]
scan_motor_select.options = det_data[0]["scan_motors"]
scan_motor_select.value = det_data[0]["scan_motor"]
param_select.value = "user defined"
file_select = MultiSelect(title="Available .ccl/.dat files:",
width=210,
height=250)
def file_open_button_callback():
nonlocal det_data
det_data = []
for f_name in file_select.value:
with open(f_name) as file:
base, ext = os.path.splitext(f_name)
if det_data:
append_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(append_data,
monitor_spinner.value)
det_data.extend(append_data)
else:
det_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(det_data, monitor_spinner.value)
js_data.data.update(
fname=[base + ".comm", base + ".incomm"])
_init_datatable()
append_upload_button.disabled = False
file_open_button = Button(label="Open New", width=100, disabled=True)
file_open_button.on_click(file_open_button_callback)
def file_append_button_callback():
for f_name in file_select.value:
with open(f_name) as file:
_, ext = os.path.splitext(f_name)
append_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(append_data, monitor_spinner.value)
det_data.extend(append_data)
_init_datatable()
file_append_button = Button(label="Append", width=100, disabled=True)
file_append_button.on_click(file_append_button_callback)
def upload_button_callback(_attr, _old, new):
nonlocal det_data
det_data = []
for f_str, f_name in zip(new, upload_button.filename):
with io.StringIO(base64.b64decode(f_str).decode()) as file:
base, ext = os.path.splitext(f_name)
if det_data:
append_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(append_data,
monitor_spinner.value)
det_data.extend(append_data)
else:
det_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(det_data, monitor_spinner.value)
js_data.data.update(
fname=[base + ".comm", base + ".incomm"])
_init_datatable()
append_upload_button.disabled = False
upload_div = Div(text="or upload new .ccl/.dat files:",
margin=(5, 5, 0, 5))
upload_button = FileInput(accept=".ccl,.dat", multiple=True, width=200)
upload_button.on_change("value", upload_button_callback)
def append_upload_button_callback(_attr, _old, new):
for f_str, f_name in zip(new, append_upload_button.filename):
with io.StringIO(base64.b64decode(f_str).decode()) as file:
_, ext = os.path.splitext(f_name)
append_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(append_data, monitor_spinner.value)
det_data.extend(append_data)
_init_datatable()
append_upload_div = Div(text="append extra files:", margin=(5, 5, 0, 5))
append_upload_button = FileInput(accept=".ccl,.dat",
multiple=True,
width=200,
disabled=True)
append_upload_button.on_change("value", append_upload_button_callback)
def monitor_spinner_callback(_attr, _old, new):
if det_data:
pyzebra.normalize_dataset(det_data, new)
_update_plot()
monitor_spinner = Spinner(title="Monitor:",
mode="int",
value=100_000,
low=1,
width=145)
monitor_spinner.on_change("value", monitor_spinner_callback)
def scan_motor_select_callback(_attr, _old, new):
if det_data:
for scan in det_data:
scan["scan_motor"] = new
_update_plot()
scan_motor_select = Select(title="Scan motor:", options=[], width=145)
scan_motor_select.on_change("value", scan_motor_select_callback)
def _update_table():
fit_ok = [(1 if "fit" in scan else 0) for scan in det_data]
scan_table_source.data.update(fit=fit_ok)
def _update_plot():
_update_single_scan_plot(_get_selected_scan())
_update_overview()
def _update_single_scan_plot(scan):
scan_motor = scan["scan_motor"]
y = scan["counts"]
x = scan[scan_motor]
plot.axis[0].axis_label = scan_motor
plot_scatter_source.data.update(x=x,
y=y,
y_upper=y + np.sqrt(y),
y_lower=y - np.sqrt(y))
fit = scan.get("fit")
if fit is not None:
x_fit = np.linspace(x[0], x[-1], 100)
plot_fit_source.data.update(x=x_fit, y=fit.eval(x=x_fit))
x_bkg = []
y_bkg = []
xs_peak = []
ys_peak = []
comps = fit.eval_components(x=x_fit)
for i, model in enumerate(fit_params):
if "linear" in model:
x_bkg = x_fit
y_bkg = comps[f"f{i}_"]
elif any(val in model
for val in ("gaussian", "voigt", "pvoigt")):
xs_peak.append(x_fit)
ys_peak.append(comps[f"f{i}_"])
plot_bkg_source.data.update(x=x_bkg, y=y_bkg)
plot_peak_source.data.update(xs=xs_peak, ys=ys_peak)
fit_output_textinput.value = fit.fit_report()
else:
plot_fit_source.data.update(x=[], y=[])
plot_bkg_source.data.update(x=[], y=[])
plot_peak_source.data.update(xs=[], ys=[])
fit_output_textinput.value = ""
def _update_overview():
xs = []
ys = []
param = []
x = []
y = []
par = []
for s, p in enumerate(scan_table_source.data["param"]):
if p is not None:
scan = det_data[s]
scan_motor = scan["scan_motor"]
xs.append(scan[scan_motor])
x.extend(scan[scan_motor])
ys.append(scan["counts"])
y.extend([float(p)] * len(scan[scan_motor]))
param.append(float(p))
par.extend(scan["counts"])
if det_data:
scan_motor = det_data[0]["scan_motor"]
ov_plot.axis[0].axis_label = scan_motor
ov_param_plot.axis[0].axis_label = scan_motor
ov_plot_mline_source.data.update(xs=xs,
ys=ys,
param=param,
color=color_palette(len(xs)))
if y:
mapper["transform"].low = np.min([np.min(y) for y in ys])
mapper["transform"].high = np.max([np.max(y) for y in ys])
ov_param_plot_scatter_source.data.update(x=x, y=y, param=par)
if y:
interp_f = interpolate.interp2d(x, y, par)
x1, x2 = min(x), max(x)
y1, y2 = min(y), max(y)
image = interp_f(
np.linspace(x1, x2, ov_param_plot.inner_width // 10),
np.linspace(y1, y2, ov_param_plot.inner_height // 10),
assume_sorted=True,
)
ov_param_plot_image_source.data.update(image=[image],
x=[x1],
y=[y1],
dw=[x2 - x1],
dh=[y2 - y1])
else:
ov_param_plot_image_source.data.update(image=[],
x=[],
y=[],
dw=[],
dh=[])
def _update_param_plot():
x = []
y = []
fit_param = fit_param_select.value
for s, p in zip(det_data, scan_table_source.data["param"]):
if "fit" in s and fit_param:
x.append(p)
y.append(s["fit"].values[fit_param])
param_plot_scatter_source.data.update(x=x, y=y)
# Main plot
plot = Plot(
x_range=DataRange1d(),
y_range=DataRange1d(only_visible=True),
plot_height=450,
plot_width=700,
)
plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
plot_scatter_source = ColumnDataSource(
dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
plot_scatter = plot.add_glyph(
plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue"))
plot.add_layout(
Whisker(source=plot_scatter_source,
base="x",
upper="y_upper",
lower="y_lower"))
plot_fit_source = ColumnDataSource(dict(x=[0], y=[0]))
plot_fit = plot.add_glyph(plot_fit_source, Line(x="x", y="y"))
plot_bkg_source = ColumnDataSource(dict(x=[0], y=[0]))
plot_bkg = plot.add_glyph(
plot_bkg_source,
Line(x="x", y="y", line_color="green", line_dash="dashed"))
plot_peak_source = ColumnDataSource(dict(xs=[[0]], ys=[[0]]))
plot_peak = plot.add_glyph(
plot_peak_source,
MultiLine(xs="xs", ys="ys", line_color="red", line_dash="dashed"))
fit_from_span = Span(location=None, dimension="height", line_dash="dashed")
plot.add_layout(fit_from_span)
fit_to_span = Span(location=None, dimension="height", line_dash="dashed")
plot.add_layout(fit_to_span)
plot.add_layout(
Legend(
items=[
("data", [plot_scatter]),
("best fit", [plot_fit]),
("peak", [plot_peak]),
("linear", [plot_bkg]),
],
location="top_left",
click_policy="hide",
))
plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
plot.toolbar.logo = None
# Overview multilines plot
ov_plot = Plot(x_range=DataRange1d(),
y_range=DataRange1d(),
plot_height=450,
plot_width=700)
ov_plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
ov_plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
ov_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
ov_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
ov_plot_mline_source = ColumnDataSource(
dict(xs=[], ys=[], param=[], color=[]))
ov_plot.add_glyph(ov_plot_mline_source,
MultiLine(xs="xs", ys="ys", line_color="color"))
hover_tool = HoverTool(tooltips=[("param", "@param")])
ov_plot.add_tools(PanTool(), WheelZoomTool(), hover_tool, ResetTool())
ov_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
ov_plot.toolbar.logo = None
# Overview perams plot
ov_param_plot = Plot(x_range=DataRange1d(),
y_range=DataRange1d(),
plot_height=450,
plot_width=700)
ov_param_plot.add_layout(LinearAxis(axis_label="Param"), place="left")
ov_param_plot.add_layout(LinearAxis(axis_label="Scan motor"),
place="below")
ov_param_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
ov_param_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
ov_param_plot_image_source = ColumnDataSource(
dict(image=[], x=[], y=[], dw=[], dh=[]))
ov_param_plot.add_glyph(
ov_param_plot_image_source,
Image(image="image", x="x", y="y", dw="dw", dh="dh"))
ov_param_plot_scatter_source = ColumnDataSource(dict(x=[], y=[], param=[]))
mapper = linear_cmap(field_name="param", palette=Turbo256, low=0, high=50)
ov_param_plot.add_glyph(
ov_param_plot_scatter_source,
Scatter(x="x", y="y", line_color=mapper, fill_color=mapper, size=10),
)
ov_param_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
ov_param_plot.toolbar.logo = None
# Parameter plot
param_plot = Plot(x_range=DataRange1d(),
y_range=DataRange1d(),
plot_height=400,
plot_width=700)
param_plot.add_layout(LinearAxis(axis_label="Fit parameter"), place="left")
param_plot.add_layout(LinearAxis(axis_label="Parameter"), place="below")
param_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
param_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
param_plot_scatter_source = ColumnDataSource(dict(x=[], y=[]))
param_plot.add_glyph(param_plot_scatter_source, Scatter(x="x", y="y"))
param_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
param_plot.toolbar.logo = None
def fit_param_select_callback(_attr, _old, _new):
_update_param_plot()
fit_param_select = Select(title="Fit parameter", options=[], width=145)
fit_param_select.on_change("value", fit_param_select_callback)
# Plot tabs
plots = Tabs(tabs=[
Panel(child=plot, title="single scan"),
Panel(child=ov_plot, title="overview"),
Panel(child=ov_param_plot, title="overview map"),
Panel(child=column(param_plot, row(fit_param_select)),
title="parameter plot"),
])
# Scan select
def scan_table_select_callback(_attr, old, new):
if not new:
# skip empty selections
return
# Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
if len(new) > 1:
# drop selection to the previous one
scan_table_source.selected.indices = old
return
if len(old) > 1:
# skip unnecessary update caused by selection drop
return
_update_plot()
def scan_table_source_callback(_attr, _old, _new):
_update_preview()
scan_table_source = ColumnDataSource(
dict(file=[], scan=[], param=[], fit=[], export=[]))
scan_table_source.on_change("data", scan_table_source_callback)
scan_table = DataTable(
source=scan_table_source,
columns=[
TableColumn(field="file", title="file", width=150),
TableColumn(field="scan", title="scan", width=50),
TableColumn(field="param",
title="param",
editor=NumberEditor(),
width=50),
TableColumn(field="fit", title="Fit", width=50),
TableColumn(field="export",
title="Export",
editor=CheckboxEditor(),
width=50),
],
width=410, # +60 because of the index column
editable=True,
autosize_mode="none",
)
def scan_table_source_callback(_attr, _old, _new):
if scan_table_source.selected.indices:
_update_plot()
scan_table_source.selected.on_change("indices", scan_table_select_callback)
scan_table_source.on_change("data", scan_table_source_callback)
def _get_selected_scan():
return det_data[scan_table_source.selected.indices[0]]
def param_select_callback(_attr, _old, new):
if new == "user defined":
param = [None] * len(det_data)
else:
param = [scan[new] for scan in det_data]
scan_table_source.data["param"] = param
_update_param_plot()
param_select = Select(
title="Parameter:",
options=["user defined", "temp", "mf", "h", "k", "l"],
value="user defined",
width=145,
)
param_select.on_change("value", param_select_callback)
def fit_from_spinner_callback(_attr, _old, new):
fit_from_span.location = new
fit_from_spinner = Spinner(title="Fit from:", width=145)
fit_from_spinner.on_change("value", fit_from_spinner_callback)
def fit_to_spinner_callback(_attr, _old, new):
fit_to_span.location = new
fit_to_spinner = Spinner(title="to:", width=145)
fit_to_spinner.on_change("value", fit_to_spinner_callback)
def fitparams_add_dropdown_callback(click):
# bokeh requires (str, str) for MultiSelect options
new_tag = f"{click.item}-{fitparams_select.tags[0]}"
fitparams_select.options.append((new_tag, click.item))
fit_params[new_tag] = fitparams_factory(click.item)
fitparams_select.tags[0] += 1
fitparams_add_dropdown = Dropdown(
label="Add fit function",
menu=[
("Linear", "linear"),
("Gaussian", "gaussian"),
("Voigt", "voigt"),
("Pseudo Voigt", "pvoigt"),
# ("Pseudo Voigt1", "pseudovoigt1"),
],
width=145,
)
fitparams_add_dropdown.on_click(fitparams_add_dropdown_callback)
def fitparams_select_callback(_attr, old, new):
# Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
if len(new) > 1:
# drop selection to the previous one
fitparams_select.value = old
return
if len(old) > 1:
# skip unnecessary update caused by selection drop
return
if new:
fitparams_table_source.data.update(fit_params[new[0]])
else:
fitparams_table_source.data.update(
dict(param=[], value=[], vary=[], min=[], max=[]))
fitparams_select = MultiSelect(options=[], height=120, width=145)
fitparams_select.tags = [0]
fitparams_select.on_change("value", fitparams_select_callback)
def fitparams_remove_button_callback():
if fitparams_select.value:
sel_tag = fitparams_select.value[0]
del fit_params[sel_tag]
for elem in fitparams_select.options:
if elem[0] == sel_tag:
fitparams_select.options.remove(elem)
break
fitparams_select.value = []
fitparams_remove_button = Button(label="Remove fit function", width=145)
fitparams_remove_button.on_click(fitparams_remove_button_callback)
def fitparams_factory(function):
if function == "linear":
params = ["slope", "intercept"]
elif function == "gaussian":
params = ["amplitude", "center", "sigma"]
elif function == "voigt":
params = ["amplitude", "center", "sigma", "gamma"]
elif function == "pvoigt":
params = ["amplitude", "center", "sigma", "fraction"]
elif function == "pseudovoigt1":
params = ["amplitude", "center", "g_sigma", "l_sigma", "fraction"]
else:
raise ValueError("Unknown fit function")
n = len(params)
fitparams = dict(
param=params,
value=[None] * n,
vary=[True] * n,
min=[None] * n,
max=[None] * n,
)
if function == "linear":
fitparams["value"] = [0, 1]
fitparams["vary"] = [False, True]
fitparams["min"] = [None, 0]
elif function == "gaussian":
fitparams["min"] = [0, None, None]
return fitparams
fitparams_table_source = ColumnDataSource(
dict(param=[], value=[], vary=[], min=[], max=[]))
fitparams_table = DataTable(
source=fitparams_table_source,
columns=[
TableColumn(field="param", title="Parameter"),
TableColumn(field="value", title="Value", editor=NumberEditor()),
TableColumn(field="vary", title="Vary", editor=CheckboxEditor()),
TableColumn(field="min", title="Min", editor=NumberEditor()),
TableColumn(field="max", title="Max", editor=NumberEditor()),
],
height=200,
width=350,
index_position=None,
editable=True,
auto_edit=True,
)
# start with `background` and `gauss` fit functions added
fitparams_add_dropdown_callback(types.SimpleNamespace(item="linear"))
fitparams_add_dropdown_callback(types.SimpleNamespace(item="gaussian"))
fitparams_select.value = ["gaussian-1"] # add selection to gauss
fit_output_textinput = TextAreaInput(title="Fit results:",
width=750,
height=200)
def proc_all_button_callback():
for scan, export in zip(det_data, scan_table_source.data["export"]):
if export:
pyzebra.fit_scan(scan,
fit_params,
fit_from=fit_from_spinner.value,
fit_to=fit_to_spinner.value)
pyzebra.get_area(
scan,
area_method=AREA_METHODS[area_method_radiobutton.active],
lorentz=lorentz_checkbox.active,
)
_update_plot()
_update_table()
for scan in det_data:
if "fit" in scan:
options = list(scan["fit"].params.keys())
fit_param_select.options = options
fit_param_select.value = options[0]
break
_update_param_plot()
proc_all_button = Button(label="Process All",
button_type="primary",
width=145)
proc_all_button.on_click(proc_all_button_callback)
def proc_button_callback():
scan = _get_selected_scan()
pyzebra.fit_scan(scan,
fit_params,
fit_from=fit_from_spinner.value,
fit_to=fit_to_spinner.value)
pyzebra.get_area(
scan,
area_method=AREA_METHODS[area_method_radiobutton.active],
lorentz=lorentz_checkbox.active,
)
_update_plot()
_update_table()
for scan in det_data:
if "fit" in scan:
options = list(scan["fit"].params.keys())
fit_param_select.options = options
fit_param_select.value = options[0]
break
_update_param_plot()
proc_button = Button(label="Process Current", width=145)
proc_button.on_click(proc_button_callback)
area_method_div = Div(text="Intensity:", margin=(5, 5, 0, 5))
area_method_radiobutton = RadioGroup(labels=["Function", "Area"],
active=0,
width=145)
lorentz_checkbox = CheckboxGroup(labels=["Lorentz Correction"],
width=145,
margin=(13, 5, 5, 5))
export_preview_textinput = TextAreaInput(title="Export file preview:",
width=450,
height=400)
def _update_preview():
with tempfile.TemporaryDirectory() as temp_dir:
temp_file = temp_dir + "/temp"
export_data = []
for s, export in zip(det_data, scan_table_source.data["export"]):
if export:
export_data.append(s)
# pyzebra.export_1D(export_data, temp_file, "fullprof")
exported_content = ""
file_content = []
for ext in (".comm", ".incomm"):
fname = temp_file + ext
if os.path.isfile(fname):
with open(fname) as f:
content = f.read()
exported_content += f"{ext} file:\n" + content
else:
content = ""
file_content.append(content)
js_data.data.update(content=file_content)
export_preview_textinput.value = exported_content
save_button = Button(label="Download File(s)",
button_type="success",
width=220)
save_button.js_on_click(
CustomJS(args={"js_data": js_data}, code=javaScript))
fitpeak_controls = row(
column(fitparams_add_dropdown, fitparams_select,
fitparams_remove_button),
fitparams_table,
Spacer(width=20),
column(fit_from_spinner, lorentz_checkbox, area_method_div,
area_method_radiobutton),
column(fit_to_spinner, proc_button, proc_all_button),
)
scan_layout = column(scan_table,
row(monitor_spinner, scan_motor_select, param_select))
import_layout = column(
proposal_textinput,
file_select,
row(file_open_button, file_append_button),
upload_div,
upload_button,
append_upload_div,
append_upload_button,
)
export_layout = column(export_preview_textinput, row(save_button))
tab_layout = column(
row(import_layout, scan_layout, plots, Spacer(width=30),
export_layout),
row(fitpeak_controls, fit_output_textinput),
)
return Panel(child=tab_layout, title="param study")
