def test_get_sliders_div(self):
sliders = bp._SliderComponent()
sliders.add_slider('param1', '1', 0, 0, 1, 0.5)
sliders.add_slider('param2', '2', 0.5, 0, 1, 0.25)
div = sliders.get_sliders_div().children
self.assertEqual(div[0].children[0].children, 'param1')
self.assertEqual(div[0].children[1].id, '1')
self.assertEqual(div[0].children[1].min, 0)
self.assertEqual(div[0].children[1].max, 1)
self.assertEqual(div[0].children[1].value, 0)
self.assertEqual(div[0].children[1].marks,
{ri: '{:.2f}'.format(ri)
for ri in [0, 0.50, 1]})
self.assertEqual(div[0].children[1].step, 0.5)
self.assertEqual(div[1].children[0].children, 'param2')
self.assertEqual(div[1].children[1].id, '2')
self.assertEqual(div[1].children[1].min, 0)
self.assertEqual(div[1].children[1].max, 1)
self.assertEqual(div[1].children[1].value, 0.5)
self.assertEqual(
div[1].children[1].marks,
{ri: '{:.2f}'.format(ri)
for ri in [0, 0.25, 0.50, 0.75, 1]})
self.assertEqual(div[1].children[1].step, 0.25)
def test_add_slider(self):
sliders = bp._SliderComponent()
sliders.add_slider('param1', '1', 0, 0, 1, 0.5)
sliders.add_slider('param2', '2', 0, 0, 15, 1, as_integer=True)
sliders.add_slider('param3', '3', 0, 0, 1, 0.5, invisible=True)
self.assertEqual(sliders._sliders[0].children[0].children, 'param1')
self.assertEqual(sliders._sliders[0].children[1].id, '1')
self.assertEqual(sliders._sliders[0].children[1].min, 0)
self.assertEqual(sliders._sliders[0].children[1].max, 1)
self.assertEqual(sliders._sliders[0].children[1].value, 0)
self.assertEqual(sliders._sliders[0].children[1].marks,
{ri: '{:.2f}'.format(ri)
for ri in [0, 0.50, 1]})
self.assertEqual(sliders._sliders[0].children[1].step, 0.5)
self.assertEqual(sliders._sliders[1].children[0].children, 'param2')
self.assertEqual(sliders._sliders[1].children[1].id, '2')
self.assertEqual(sliders._sliders[1].children[1].min, 0)
self.assertEqual(sliders._sliders[1].children[1].max, 15)
self.assertEqual(sliders._sliders[1].children[1].value, 0)
self.assertEqual(
sliders._sliders[1].children[1].marks,
{
# if the slider values need be integers
ri: '{:.0f}'.format(ri)
for ri in np.round(np.linspace(0, 15, 10), 0)
})
self.assertEqual(sliders._sliders[1].children[1].step, 1)
self.assertEqual(sliders._sliders[2].children[0].children, 'param3')
self.assertEqual(sliders._sliders[2].children[1].id, '3')
self.assertEqual(sliders._sliders[2].children[1].min, 0)
self.assertEqual(sliders._sliders[2].children[1].max, 1)
self.assertEqual(sliders._sliders[2].children[1].value, 0)
self.assertEqual(
sliders._sliders[0].children[1].marks,
{ri: '{:.2f}'.format(ri)
for ri in [0.00, 0.50, 1.00]})
self.assertEqual(sliders._sliders[2].children[1].step, 0.5)
self.assertEqual(sliders._sliders[2].style['display'], 'none')
def test_slider_ids(self):
sliders = bp._SliderComponent()
sliders.add_slider('param1', '1', 0, 0, 1, 0.5)
sliders.add_slider('param2', '2', 0.5, 0, 1, 0.25)
self.assertEqual(sliders.slider_ids(), ['1', '2'])
def test__init__(self):
bp._SliderComponent()
def update_sliders(self,
init_cond=10.0,
r0=1.0,
r1=0.5,
magnitude_init_cond=None):
"""Generate sliders for the app.
This method tunes the bounds of the sliders to the time period and
magnitude of the data.
Parameters
----------
init_cond : int
start position on the slider for the number of initial cases for
the Branch Pro model in the simulator.
r0 : float
start position on the slider for the initial reproduction number
for the Branch Pro model in the simulator.
r1 : float
start position on the slider for the second reproduction number for
the Branch Pro model in the simulator.
magnitude_init_cond : int
maximal start position on the slider for the number of initial
cases for the Branch Pro model in the simulator. By default, it
will be set to the maximum value observed in the data.
Returns
-------
html.Div
A dash html component containing the sliders
"""
data = self.session_data.get('data_storage')
# Calculate slider values that depend on the data
if data is not None:
time_label, inc_label = data.columns[:2]
if magnitude_init_cond is None:
magnitude_init_cond = max(data[inc_label])
bounds = (1, max(data[time_label]))
else:
# choose values to use if there is no data
if magnitude_init_cond is None:
magnitude_init_cond = 1000
bounds = (1, 30)
mid_point = round(sum(bounds) / 2)
# Make new sliders
sliders = bp._SliderComponent()
if (data is not None) and ('Imported Cases' in data.columns):
# Add slider for epsilon only when imported cases are detected
# in the data with default assuming equal R numbers for local
# and imported cases
sliders.add_slider('Epsilon', 'epsilon', 1.0, 0.0, 3.0, 0.01)
else:
sliders.add_slider('Epsilon',
'epsilon',
1.0,
0.0,
3.0,
0.01,
invisible=True)
sliders.add_slider('Initial Cases',
'init_cond',
init_cond,
0.0,
magnitude_init_cond,
1,
as_integer=True)
sliders.add_slider('Initial R', 'r0', r0, 0.1, 10.0, 0.01)
sliders.add_slider('Second R', 'r1', r1, 0.1, 10.0, 0.01)
sliders.add_slider('Time of change',
't1',
mid_point,
bounds[0],
bounds[1],
1,
as_integer=True)
return sliders.get_sliders_div()
def update_sliders(self, mean=5.0, stdev=5.0, tau=6, central_prob=.95):
"""Generate sliders for the app.
Parameters
----------
mean
(float) start position on the slider for the mean of the
prior for the Branch Pro model in the posterior.
stdev
(float) start position on the slider for the standard deviation of
the prior for the Branch Pro model in the posterior.
tau
(int) start position on the slider for the tau window used in the
running of the inference of the reproduction numbers of the Branch
Pro model in the posterior.
central_prob
(float) start position on the slider for the level of the computed
credible interval of the estimated R number values.
Returns
-------
html.Div
A dash html component containing the sliders
"""
data = self.session_data.get('data_storage')
if data is not None:
time_label, inc_label = data.columns[:2]
times = data[time_label]
max_tau = floor((times.max() - times.min() + 1) / 3)
if tau > max_tau:
# If default value of tau exceeds maximum accepted
# choose tau to be this maximum value
tau = max_tau
else:
max_tau = 7
sliders = bp._SliderComponent()
if (data is not None) and ('Imported Cases' in data.columns):
# Add slider for epsilon only when imported cases are detected
# in the data with default assuming equal R numbers for local
# and imported cases
sliders.add_slider('Epsilon', 'epsilon', 1.0, 0.0, 3.0, 0.01)
else:
sliders.add_slider('Epsilon',
'epsilon',
1.0,
0.0,
3.0,
0.01,
invisible=True)
sliders.add_slider('Prior Mean', 'mean', mean, 0.1, 10.0, 0.01)
sliders.add_slider('Prior Standard Deviation', 'stdev', stdev, 0.1,
10.0, 0.01)
sliders.add_slider('Inference Sliding Window',
'tau',
tau,
0,
max_tau,
1,
as_integer=True)
sliders.add_slider('Central Posterior Probability', 'central_prob',
central_prob, 0.1, 0.99, 0.01)
return sliders.get_sliders_div()