def toggle_bf_distribution(data, value, open):
if data:
cutoff = data['points'][0]['x']
rank_method = Plot(app.compass_module).plot_rank_name[value]
rank = app.compass_compendium.rank_biological_features(
app.compass_module, rank_method=rank_method,
cutoff=cutoff)['ranking']['name']
return ','.join(rank)
def toggle_bf_plot_type(value):
if app.compass_module:
plot = json.loads(
Plot(app.compass_module).plot_distribution(plot_type=value,
output_format='json'))
p = dcc.Graph(id="bf-edit-json", figure=plot)
return p
return {}
def toggle_accordion_tool_bf(value):
if app.compass_module:
plot_type = [{
'label': p,
'value': p
} for p in Plot(app.compass_module).plot_types['distribution']
if p.startswith('biological_features_')]
d = dcc.Dropdown(id='bf-edit-dropdown',
options=plot_type,
value=plot_type[0]['value'])
return d
return ''
def render_heatmap(heatmap):
if app.compass_module:
w = '100%'
h = '100%'
min = -5
max = 5
if app.compass_compendium.normalization == 'tpm':
min = np.percentile(
app.compass_module.
values[~np.isnan(app.compass_module.values)], 1)
max = np.percentile(
app.compass_module.
values[~np.isnan(app.compass_module.values)], 95)
plot, sorted_bf, sorted_ss = Plot(app.compass_module).plot_heatmap(
output_format='json', min=min, max=max)
js = json.loads(plot)
js['layout'][
'plot_bgcolor'] = "rgba(100,100,100,100)" # add gray background to missing values
return js, {"height": h, "width": w}
return {}
def update_annotation_network_bf(value):
if app.compass_module:
return json.loads(
Plot(app.compass_module).plot_network(output_format='json',
threshold=0.5))
return '', None
def update_network_output(value):
if app.compass_module:
return 'Pearson correlation threshold: {}'.format(value), json.loads(
Plot(app.compass_module).plot_network(output_format='json',
threshold=value))
return '', None