def spectroscopy_plot(source_id):
"""TODO normalization? should this be handled at data ingestion or plot-time?"""
source = Source.query.get(source_id)
spectra = Source.query.get(source_id).spectra
if len(spectra) == 0:
return None, None, None
color_map = dict(zip([s.id for s in spectra], viridis(len(spectra))))
data = pd.concat(
[pd.DataFrame({'wavelength': s.wavelengths,
'flux': s.fluxes, 'id': s.id,
'instrument': s.instrument.telescope.nickname})
for i, s in enumerate(spectra)]
)
split = data.groupby('id')
hover = HoverTool(tooltips=[('wavelength', '$x'), ('flux', '$y'),
('instrument', '@instrument')])
plot = figure(plot_width=600, plot_height=300, sizing_mode='scale_both',
tools='box_zoom,wheel_zoom,pan,reset',
active_drag='box_zoom')
plot.add_tools(hover)
model_dict = {}
for i, (key, df) in enumerate(split):
model_dict['s' + str(i)] = plot.line(x='wavelength', y='flux',
color=color_map[key],
source=ColumnDataSource(df))
plot.xaxis.axis_label = 'Wavelength (Å)'
plot.yaxis.axis_label = 'Flux'
plot.toolbar.logo = None
# TODO how to choose a good default?
plot.y_range = Range1d(0, 1.03 * data.flux.max())
toggle = CheckboxWithLegendGroup(labels=[s.instrument.telescope.nickname
for s in spectra],
active=list(range(len(spectra))),
width=100,
colors=[color_map[k] for k, df in split])
toggle.callback = CustomJS(args={'toggle': toggle, **model_dict},
code="""
for (let i = 0; i < toggle.labels.length; i++) {
eval("s" + i).visible = (toggle.active.includes(i))
}
""")
elements = CheckboxWithLegendGroup(
labels=list(SPEC_LINES.keys()),
active=[], width=80,
colors=[c for w, c in SPEC_LINES.values()]
)
z = TextInput(value=str(source.redshift), title="z:")
v_exp = TextInput(value='0', title="v_exp:")
for i, (wavelengths, color) in enumerate(SPEC_LINES.values()):
el_data = pd.DataFrame({'wavelength': wavelengths})
el_data['x'] = el_data['wavelength'] * (1 + source.redshift)
model_dict[f'el{i}'] = plot.segment(x0='x', x1='x',
# TODO change limits
y0=0, y1=1e-13, color=color,
source=ColumnDataSource(el_data))
model_dict[f'el{i}'].visible = False
# TODO callback policy: don't require submit for text changes?
elements.callback = CustomJS(args={'elements': elements, 'z': z,
'v_exp': v_exp, **model_dict},
code="""
let c = 299792.458; // speed of light in km / s
for (let i = 0; i < elements.labels.length; i++) {
let el = eval("el" + i);
el.visible = (elements.active.includes(i))
el.data_source.data.x = el.data_source.data.wavelength.map(
x_i => (x_i * (1 + parseFloat(z.value)) /
(1 + parseFloat(v_exp.value) / c))
);
el.data_source.change.emit();
}
""")
z.callback = elements.callback
v_exp.callback = elements.callback
layout = row(plot, toggle, elements, column(z, v_exp))
return _plot_to_json(layout)
in_model_rdn_btn.callback = CustomJS(
args=dict(radioButton=in_model_rdn_btn),
code=bgui_js_handlers.in_model_rdn_btn_callback_code)
in_model_rdn_btn.on_change("active", selected_model_changed)
in_min_time = ColumnDataSource(data=dict(val=[MIN_LP_TIME]))
in_time_slider = Slider(start=MIN_LP_TIME,
end=MAX_LP_TIME,
value=10,
step=1,
title="Set the time for calculation [hrs]")
in_capacity_txt = TextInput(value="100", title="Set the capacity:")
in_capacity_txt.callback = CustomJS(
args={
'min_time': in_min_time,
'time': in_time_slider,
'radioButton': in_model_rdn_btn
},
code=bgui_js_handlers.in_capacity_txt_callback_code)
calculation_button = Button(label='Calculate', button_type="success")
upl_file_souce = ColumnDataSource({'file_contents': [], 'file_name': []})
upl_file_souce.on_change('data', upl_file_callback)
upload_btn = Button(label="Upload", button_type="default")
upload_btn.callback = CustomJS(args=dict(file_source=upl_file_souce),
code=bgui_js_handlers.upload_btn_callback_code)
################### RESULTS - bokeh models #######################
BED_fig = figure(x_range=(0, 1151),
y_range=(0, 2049),
plot_width=750,
plot_height=36 * NUM_PATIENTS) #toolbar_location="above"
