def test_add_ground_truth_rt(self):
df = pd.DataFrame({
'Time Points': [1, 2, 3, 4, 5, 6],
'R_t': [3, 3, 0.5, 0.5, 0.5, 0.5]
})
my_plot = bp.ReproductionNumberPlot()
my_plot.add_ground_truth_rt(df)
npt.assert_array_equal(np.array([my_plot.figure['data'][0]['x']]),
np.array([np.array([1, 2, 3, 4, 5, 6])]))
npt.assert_array_equal(
np.array([my_plot.figure['data'][0]['y']]),
np.array([np.array([3, 3, 0.5, 0.5, 0.5, 0.5])]))
with self.assertRaises(TypeError):
bp.ReproductionNumberPlot().add_ground_truth_rt(0)
with self.assertWarns(UserWarning):
df = pd.DataFrame({
't': [1, 2, 3, 4, 5, 6],
'R_t': [3, 3, 0.5, 0.5, 0.5, 0.5]
})
my_plot.add_ground_truth_rt(df, time_key='t')
with self.assertWarns(UserWarning):
df = pd.DataFrame({
'Time Points': [1, 2, 3, 4, 5, 6],
'r': [3, 3, 0.5, 0.5, 0.5, 0.5]
})
my_plot.add_ground_truth_rt(df, r_key='r')
def update_inference_figure(self, source=None):
"""Update the inference figure based on currently stored information.
Parameters
----------
source : str
Dash callback source
Returns
-------
plotly.Figure
Figure with updated posterior distribution
"""
data = self.session_data.get('data_storage')
posterior = self.session_data.get('posterior_storage')
if data is None or posterior is None:
raise dash.exceptions.PreventUpdate()
time_label, inc_label = data.columns[:2]
plot = bp.ReproductionNumberPlot()
plot.add_interval_rt(posterior)
if 'R_t' in data.columns:
plot.add_ground_truth_rt(data[[time_label, 'R_t']],
time_key=time_label,
r_key='R_t')
# Keeps traces visibility states fixed when changing sliders
plot.figure['layout']['legend']['uirevision'] = True
return plot.figure
def test_show_figure(self):
with patch('plotly.graph_objs.Figure.show') as show_patch:
df = pd.DataFrame({
'Time Points': [1, 2, 3, 4, 5, 6],
'R_t': [3, 3, 0.5, 0.5, 0.5, 0.5]
})
my_plot = bp.ReproductionNumberPlot()
my_plot.add_ground_truth_rt(df)
my_plot.show_figure()
# Assert show_figure is called once
assert show_patch.called
def test_update_labels(self):
df = pd.DataFrame({
'Time Points': [1, 2, 3, 4, 5, 6],
'R_t': [3, 3, 0.5, 0.5, 0.5, 0.5]
})
my_plot = bp.ReproductionNumberPlot()
my_plot.add_ground_truth_rt(df)
new_time_label = 'Time'
new_r_label = 'R Value'
my_plot.update_labels(time_label=new_time_label)
self.assertEqual(my_plot.figure['layout']['xaxis']['title']['text'],
'Time')
my_plot.update_labels(r_label=new_r_label)
self.assertEqual(my_plot.figure['layout']['yaxis']['title']['text'],
'R Value')
def test_add_interval_rt(self):
df = pd.DataFrame({
'Time': [1, 2, 3, 5, 6],
'Incidence Number': [0, 0, 0, 0, 0]
})
ser_int = [1, 2]
inference = bp.BranchProPosterior(df, ser_int, 1, 0.2)
inference.run_inference(tau=2)
intervals_df = inference.get_intervals(.95)
my_plot = bp.ReproductionNumberPlot()
my_plot.add_interval_rt(intervals_df)
npt.assert_array_equal(np.array([my_plot.figure['data'][0]['x']]),
np.array([np.array([4, 5, 6])]))
npt.assert_array_equal(np.array([my_plot.figure['data'][0]['y']]),
np.array([np.array([5.0] * 3)]))
npt.assert_array_equal(np.array([my_plot.figure['data'][1]['x']]),
np.array([np.array([4, 5, 6, 6, 5, 4])]))
npt.assert_array_almost_equal(
np.array([my_plot.figure['data'][1]['y']]),
np.array([np.array([18.444397] * 3 + [0.126589] * 3)]))
with self.assertRaises(TypeError):
bp.ReproductionNumberPlot().add_interval_rt(0)
with self.assertWarns(UserWarning):
df = pd.DataFrame({
'Time Points': [1, 2, 3, 4, 5, 6],
'R_t': [3, 3, 0.5, 0.5, 0.5, 0.5]
})
my_plot = bp.ReproductionNumberPlot()
my_plot.add_ground_truth_rt(df)
dfs1 = pd.DataFrame({
't': [4, 5, 6],
'Mean': [5.0] * 3,
'Lower bound CI': [5.0] * 3,
'Upper bound CI': [5.0] * 3,
'Central Probability': [.95] * 3
})
my_plot.add_interval_rt(dfs1, time_key='t')
with self.assertWarns(UserWarning):
df = pd.DataFrame({
'Time Points': [1, 2, 3, 4, 5, 6],
'R_t': [3, 3, 0.5, 0.5, 0.5, 0.5]
})
my_plot = bp.ReproductionNumberPlot()
my_plot.add_ground_truth_rt(df)
dfs2 = pd.DataFrame({
'Time Points': [4, 5, 6],
'r': [5.0] * 3,
'Lower bound CI': [5.0] * 3,
'Upper bound CI': [5.0] * 3,
'Central Probability': [.95] * 3
})
my_plot.add_interval_rt(dfs2, r_key='r')
def test__init__(self):
bp.ReproductionNumberPlot()
def __init__(self, long_callback_manager=None):
"""
Parameters
----------
long_callback_manager
Optional callback manager for long callbacks.
See https://dash.plotly.com/long-callbacks
"""
super(BranchProInferenceApp, self).__init__()
self.app = dash.Dash(__name__,
external_stylesheets=self.css,
long_callback_manager=long_callback_manager)
self.app.title = 'BranchproInf'
self.session_data = {
'data_storage': None,
'interval_storage': None,
'posterior_storage': None
}
button_style = {
'width': '100%',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'margin': '10px'
}
self.app.layout = html.Div([
dbc.Container(
[
html.H1('Branching Processes', id='page-title'),
html.Div([]), # Empty div for top explanation texts
html.H2('Incidence Data'),
dbc.Row(
dbc.Col(
dcc.Graph(figure=bp.IncidenceNumberPlot().figure,
id='data-fig'))),
dbc.Row(
[
dbc.Col(children=[
html.H6([
'You can upload your own ',
html.Span(
'incidence data',
id='inc-tooltip',
style={
'textDecoration': 'underline',
'cursor': 'pointer'
},
), ' here. It will appear as bars.'
]),
dbc.Modal(
self._inc_modal,
id='inc_modal',
size='xl',
),
html.Div([
'Data must be in the following column '
'format: `Time`, `Incidence number`, '
'`Imported Cases` (optional), '
'`R_t` (true value of R, optional).'
]),
dcc.Upload(
id='upload-data',
children=html.Div([
'Drag and Drop or ',
html.A('Select Files',
style={
'text-decoration':
'underline'
}), ' to upload your Incidence \
Number data.'
]),
style=button_style,
# Allow multiple files to be uploaded
multiple=True),
html.Div(id='incidence-data-upload')
]),
dbc.Col(children=[
html.H6([
'You can upload your own ',
html.Span('serial interval',
id='si-tooltip',
style={
'textDecoration':
'underline',
'cursor': 'pointer'
}), ' here.'
]),
dbc.Modal(
self._si_modal,
id='si_modal',
size='lg',
),
html.Div([
'Data must contain one or more serial '
'intervals to be used for constructing'
' the posterior distributions each '
'included as a column.'
]),
dcc.Upload(
id='upload-interval',
children=html.Div([
'Drag and Drop or ',
html.A('Select Files',
style={
'text-decoration':
'\
underline'
}), ' to upload your Serial \
Interval.'
]),
style=button_style,
# Allow multiple files to be uploaded
multiple=True),
html.Div(id='ser-interval-upload')
])
],
align='center',
),
html.H2('Plot of R values'),
html.Progress(id='progress_bar'),
html.Div(
id='first_run', # see flip_first_run() in the app
children='True',
style={'display': 'none'}),
dbc.Row(
[
dbc.Col(children=dcc.Graph(
figure=bp.ReproductionNumberPlot().figure,
id='posterior-fig',
style={'display': 'block'})),
dbc.Col(self.update_sliders(), id='all-sliders')
],
align='center',
),
html.Div([]), # Empty div for bottom text
html.Div(id='data_storage', style={'display': 'none'}),
html.Div(id='interval_storage', style={'display': 'none'}),
html.Div(id='posterior_storage', style={'display': 'none'})
],
fluid=True),
self.mathjax_script
])
# Set the app index string for mathjax
self.app.index_string = self.mathjax_html
# Save the locations of texts from the layout
self.main_text = self.app.layout.children[0].children[1].children
self.collapsed_text = self.app.layout.children[0].children[-4].children