def test_add_data(self):
df = pd.DataFrame({
'Time': [1, 2, 3, 5, 6],
'Incidence Number': [10, 3, 4, 6, 9]
})
my_plot = bp.IncidenceNumberPlot()
my_plot.add_data(df)
npt.assert_array_equal(np.array([my_plot.figure['data'][0]['x']]),
np.array([np.array([1, 2, 3, 5, 6])]))
npt.assert_array_equal(np.array([my_plot.figure['data'][0]['y']]),
np.array([np.array([10, 3, 4, 6, 9])]))
with self.assertRaises(TypeError):
bp.IncidenceNumberPlot().add_data(0)
with self.assertWarns(UserWarning):
df = pd.DataFrame({
't': [1, 2, 3, 5, 6],
'Incidence Number': [10, 3, 4, 6, 9]
})
my_plot.add_data(df, time_key='t')
with self.assertWarns(UserWarning):
df = pd.DataFrame({'Time': [1, 2, 4, 5, 6], 'i': [2, 3, 8, 10, 5]})
my_plot.add_data(df, inc_key='i')
def test_show_figure(self):
with patch('plotly.graph_objs.Figure.show') as show_patch:
df = pd.DataFrame({
'Time': [1, 2, 3, 5, 6],
'Incidence Number': [10, 3, 4, 6, 9]
})
my_plot = bp.IncidenceNumberPlot()
my_plot.add_data(df)
my_plot.show_figure()
# Assert show_figure is called once
assert show_patch.called
def test_update_labels(self):
df = pd.DataFrame({
'Time': [1, 2, 3, 5, 6],
'Incidence Number': [10, 3, 4, 6, 9]
})
my_plot = bp.IncidenceNumberPlot()
my_plot.add_data(df)
new_time_label = 'Week'
new_inc_label = 'Inc'
my_plot.update_labels(time_label=new_time_label)
self.assertEqual(my_plot.figure['layout']['xaxis']['title']['text'],
'Week')
my_plot.update_labels(inc_label=new_inc_label)
self.assertEqual(my_plot.figure['layout']['yaxis']['title']['text'],
'Inc')
def update_data_figure(self):
"""Update the data figure based on currently stored information.
Returns
-------
plotly.Figure
Figure with updated data
"""
data = self.session_data.get('data_storage')
if data is None:
raise dash.exceptions.PreventUpdate()
time_label, inc_label = data.columns[:2]
plot = bp.IncidenceNumberPlot()
if 'Imported Cases' in data.columns:
# Separate data into local and imported cases
imported_data = pd.DataFrame({
time_label: data[time_label],
inc_label: data['Imported Cases']
})
# Bar plot of local cases
plot.add_data(data,
time_key=time_label,
inc_key=inc_label,
name='Local Cases')
# Bar plot of imported cases
plot.add_data(imported_data,
time_key=time_label,
inc_key=inc_label,
name='Imported Cases')
else:
# If no imported cases are present
plot.add_data(data, time_key=time_label, inc_key=inc_label)
# Keeps traces visibility states fixed when changing sliders
plot.figure['layout']['legend']['uirevision'] = True
return plot.figure
def test__init__(self):
bp.IncidenceNumberPlot()
def __init__(self):
super().__init__()
self.session_data = {'data_storage': None, 'interval_storage': None}
self.app = dash.Dash(__name__, external_stylesheets=self.css)
self.app.title = 'BranchproSim'
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'),
html.Div([]), # Empty div for top explanation texts
dbc.Row([
dbc.Col([
html.Button(
'Add new simulation',
id='sim-button',
n_clicks=0),
dcc.Graph(
figure=bp.IncidenceNumberPlot().figure,
id='myfig')
]),
dbc.Col(
self.update_sliders(), id='all-sliders')
], align='center'),
dbc.Row(
[
dbc.Col(
children=[
html.H4([
'You can upload your own ',
html.Span(
'incidence data',
id='inc-tooltip',
style={
'textDecoration':
'underline',
'cursor':
'pointer'},
),
' here.'
]),
dbc.Modal(
self._inc_modal,
id='inc_modal',
size='xl',
),
html.Div([
'It will appear as bars, while'
' the simulation will be a line.'
' You can upload both local and '
'/ or imported incidence data.'
]),
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.H4([
'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 serial '
'interval to be used for simulation'
' displayed as a column. If multiple '
'serial intervals are uploaded, the '
'first one will be used.']),
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.Div([]), # Empty div for bottom text
html.Div(id='data_storage', style={'display': 'none'}),
html.Div(id='interval_storage', style={'display': 'none'}),
dcc.ConfirmDialog(
id='confirm',
message='Simulation failed due to overflow!',
),
], 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
def update_figure(self, fig=None, simulations=None, source=None):
"""Generate a plotly figure of incidence numbers and simulated cases.
By default, this method uses the information saved in self.session_data
to populate the figure with data. If a current figure and dash callback
source are passed, it will try to just update the existing figure for
speed improvements.
Parameters
----------
fig : dict
Current copy of the figure
simulations : pd.DataFrame
Simulation trajectories to add to the figure.
source : str
Dash callback source
Returns
-------
plotly.Figure
Figure with updated data and simulations
"""
data = self.session_data.get('data_storage')
if data is None:
raise dash.exceptions.PreventUpdate()
if fig is not None and simulations is not None:
# Check if there is a faster way to update the figure
if len(fig['data']) > 0 and source in [
'epsilon', 'init_cond', 'r0', 'r1', 't1'
]:
# Clear all traces except one simulation and the data
if ('Imported Cases' in data.columns) and ('Incidence Number'
in data.columns):
fig['data'] = [
fig['data'][0], fig['data'][1], fig['data'][-1]
]
else:
fig['data'] = [fig['data'][0], fig['data'][-1]]
# Set the y values of that trace equal to an updated simulation
fig['data'][-1]['y'] = simulations.iloc[:, -1]
return fig
elif len(fig['data']) > 0 and source == 'sim-button':
# Add one extra simulation, and set its y values
fig['data'].append(copy.deepcopy(fig['data'][-1]))
fig['data'][-1]['y'] = simulations.iloc[:, -1]
if ('Imported Cases' in data.columns) and ('Incidence Number'
in data.columns):
sim_tuple = range(1, len(fig['data']) - 2)
else:
sim_tuple = range(len(fig['data']) - 2)
for i in sim_tuple:
# Change opacity of all traces in the figure but for the
# first - the barplot of incidences
# last - the latest simulation
fig['data'][i + 1]['line']['color'] = 'rgba(255,0,0,0.25)'
fig['data'][i + 1]['showlegend'] = False
return fig
time_label, inc_label = (data.columns[0], 'Incidence Number')
num_simulations = len(simulations.columns) - 1
# Make a new figure
plot = bp.IncidenceNumberPlot()
if 'Imported Cases' in data.columns:
# Separate data into local and imported cases
imported_data = pd.DataFrame({
time_label: data[time_label],
inc_label: data['Imported Cases']
})
if 'Incidence Number' in data.columns:
# Bar plot of local cases
plot.add_data(data.iloc[:, :2],
time_key=time_label,
inc_key=inc_label,
name='Local Cases')
# Bar plot of imported cases
plot.add_data(imported_data,
time_key=time_label,
inc_key=inc_label,
name='Imported Cases')
else:
# If no imported cases are present
plot.add_data(data, time_key=time_label, inc_key=inc_label)
# Keeps traces visibility states fixed when changing sliders
plot.figure['layout']['legend']['uirevision'] = True
for sim in range(num_simulations):
df = simulations.iloc[:, [0, sim + 1]]
df.columns = [time_label, inc_label]
plot.add_simulation(df, time_key=time_label, inc_key=inc_label)
# Unless it is the most recent simulation, decrease the opacity to
# 25% and remove it from the legend
if sim < num_simulations - 1:
plot.figure['data'][-1]['line'].color = 'rgba(255,0,0,0.25)'
plot.figure['data'][-1]['showlegend'] = False
return plot.figure
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