def update_figure_polar(value, value_main_tab, value_analysis, gene_name):
if gene_name is None:
raise Exception()
else:
if value_main_tab == 'main-tab-2' and value_analysis == 'temporal':
array_gene_time = np.concatenate(
(gene_data[gene_name]['rep1'], gene_data[gene_name]['rep2'],
gene_data[gene_name]['rep3'][:, [0, 2]]),
axis=1)
l_time_reg = []
for x in range(8):
l_time_reg.append(
LinearRegression.make_time_regression(
array_gene_time[x, :], simple=False, predict=True))
l_time_reg_simple = []
for x in range(8):
l_time_reg_simple.append(
LinearRegression.make_time_regression(
array_gene_time[x, :], simple=True, predict=False))
#correct value
value = int(value) - 1
B, SE, adj_r2, aic, bic, pv, X_pred, Y_pred = l_time_reg[value]
[mu_1, a_1, b_1] = B.flatten()
[std_mu_1, std_a_1, std_b_1] = np.diagonal(SE)
bic_1 = bic
aic_1 = aic
r2_1 = adj_r2
B_simple, SE_simple, adj_r2_simple, aic_simple, bic_simple, pv_simple = l_time_reg_simple[
value]
[mu_2] = B_simple.flatten()
[std_mu_2] = np.diagonal(SE_simple)
bic_2 = bic_simple
aic_2 = aic_simple
r2_2 = adj_r2_simple
table_model_1 = html.Table([
html.Tr([
Styled_th('Parameter'),
Styled_th('Mean'),
Styled_th('SE')
],
style={'background-color': colorscale[5]}),
html.Tr([
Styled_th(dash_katex.DashKatex(expression='\mu'),
{'background-color': colorscale[5]}),
Styled_th('{:.2e}'.format(mu_1)),
Styled_th('{:.2e}'.format(std_mu_1))
]),
html.Tr([
Styled_th(dash_katex.DashKatex(expression='a'),
{'background-color': colorscale[5]}),
Styled_th('{:.2e}'.format(a_1)),
Styled_th('{:.2e}'.format(std_a_1))
]),
html.Tr([
Styled_th(dash_katex.DashKatex(expression='b'),
{'background-color': colorscale[5]}),
Styled_th('{:.2e}'.format(b_1)),
Styled_th('{:.2e}'.format(std_b_1))
])
])
table_model_2 = html.Table([
html.Tr([
Styled_th('Parameter'),
Styled_th('Mean'),
Styled_th('SE')
],
style={'background-color': colorscale[5]}),
html.Tr([
Styled_th(dash_katex.DashKatex(expression='\mu'),
{'background-color': colorscale[5]}),
Styled_th('{:.2e}'.format(mu_2)),
Styled_th('{:.2e}'.format(std_mu_2))
])
])
table_comparison = html.Table([
html.Tr([
Styled_th('Model'),
Styled_th('BIC'),
Styled_th('AIC'),
Styled_th(
dash_katex.DashKatex(expression='\\text{Adj. } R^2'))
],
style={'background-color': colorscale[5]}),
html.Tr([
Styled_th('Intercept-only',
{'background-color': colorscale[5]}),
Styled_th('{:.2e}'.format(bic_2)),
Styled_th('{:.2e}'.format(aic_2)),
Styled_th('{:.2e}'.format(r2_2))
]),
html.Tr([
Styled_th('Oscillatory',
{'background-color': colorscale[5]}),
Styled_th('{:.2e}'.format(bic_1)),
Styled_th('{:.2e}'.format(aic_1)),
Styled_th('{:.2e}'.format(r2_1))
])
])
time_domain = np.concatenate(
(np.linspace(0, 24, 4, endpoint=False),
np.linspace(0, 24, 4, endpoint=False),
np.linspace(0, 24, 2, endpoint=False)))
x = int(value)
B, SE, adj_r2, aic, bic, pv, X_pred, Y_pred = l_time_reg[x]
figure = Figures.compute_figure_time_tab_3(
time_domain, array_gene_time[x, :], X_pred,
Y_pred) #, yaxis_type, yaxis_scale)
return [
html.Div(children=[
html.Div(children=[
html.H6('Intercept-only model',
style={'textAlign': 'center'}), table_model_2
]),
html.Div(children=[
html.H6('Oscillatory model',
style={'textAlign': 'center'}), table_model_1
]),
html.Div(children=[
html.H6('Models comparison',
style={'textAlign':
'center'}), table_comparison,
html.
P('P-value associated with the oscillatory model (ANOVA): '
+ str(pv))
],
style={
'display': 'flex',
'flex-direction': 'column'
}),
],
style={
'display': 'flex',
'flex-direction': 'row',
'justify-content': 'space-around',
'flex-wrap': 'wrap',
'flex-align': 'baseline'
}),
dcc.Graph(id='graph-stat-time',
figure=figure,
config={
'displayModeBar': False,
'modeBarButtonsToRemove': [],
'displaylogo': False
},
style={'width': '60vw'})
]
else:
raise PreventUpdate
