html.Div("Throw on error:"),
dcc.RadioItems(
id='radio-item-throw-on-error',
options=[
{'label': "Throw Errors", 'value': "Throw"},
{'label': "Pass Error", 'value': "Pass"}
],
value = "Pass",
),
html.Div('Here is some latex:'),
dash_katex.DashKatex(
id='katex',
expression="",
displayMode = True,
throwOnError = False,
)
])
@app.callback(Output('katex', 'expression'),
[Input('input-latex-expression', 'value')])
def updateExpression(value):
return str(value)
@app.callback(Output('katex', 'displayMode'),
[Input('radio-item-display-mode', 'value')])
def updateDisplayMode(value):
return value == "Display"
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import dash_katex
import numpy as np
import plotly.express as px
from scipy import stats
from app import app
layout = html.Div([
dash_katex.DashKatex(expression=r'''
f_X(x) = \frac{\Gamma(a + b)}{\Gamma(a)\Gamma(b)}
x^{a - 1}(1 - x)^{b - 1}
''',
displayMode=True),
dcc.Graph(id='beta_graph'),
dash_katex.DashKatex(expression=r'a'),
dcc.Slider(id='beta_a',
min=0.01,
max=5,
marks={x: str(x)
for x in range(6)},
step=0.01,
value=2,
tooltip={'placement': 'top'}),
dash_katex.DashKatex(expression=r'b'),
dcc.Slider(id='beta_b',
min=0.01,
max=5,
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
import dash_katex
import numpy as np
import plotly.express as px
from scipy import stats
import math
from app import app
layout = html.Div([
dash_katex.DashKatex(expression=r'''
f_X(x) = \frac{\lambda^x e^{-\lambda}}{x!}
''',
displayMode=True),
dcc.Graph(id='poisson_graph'),
dash_katex.DashKatex(expression=r'\lambda'),
dcc.Slider(id='poisson_rate',
value=1,
min=0.01,
max=10,
marks={i: str(i)
for i in range(11)},
step=0.01,
tooltip={'placement': 'top'})
])
@app.callback(Output('poisson_graph', 'figure'), [
render_latex(expression=r"Dh"),
dbc.Input(id="Dh-value", type="number", value=1/2)
]
),
dbc.FormGroup(
[
render_latex(expression=r"\Delta t"),
dbc.Input(id="Dt-value", type="number", value=0.01),
]
),
dbc.FormGroup(
[
dbc.Label("Wave-Function Configuration"),
dash_katex.DashKatex(expression="",
displayMode=True,
throwOnError=False,
id="wf-config-vars"
),
dbc.Input(id="wf-config-vars-value", type="text"),
]
),
dbc.FormGroup(
[
dbc.Label("Potential Configuration"),
dash_katex.DashKatex(expression="",
displayMode=True,
throwOnError=False,
id="pot-config-vars"
),
dbc.Input(id="pot-config-vars-value", type="text"),
]
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import dash_katex
import plotly.express as px
from scipy import stats
from app import app
layout = html.Div([
dash_katex.DashKatex(expression=r'''
f_X(x) = \begin{cases}
1 - p, \quad &x = 0, \\
p, \quad &x = 1
\end{cases}
''',
displayMode=True),
dcc.Graph(id='bernoulli_graph'),
dash_katex.DashKatex(expression=r'p'),
dcc.Slider(id='bernoulli_p',
value=0.5,
min=0,
max=1,
marks={p: f'{p:.1f}'
for p in [i * 0.1 for i in range(1, 11)]},
step=0.01,
tooltip={'placement': 'top'})
])
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import dash_katex
import numpy as np
import plotly.express as px
from scipy import stats
from app import app
layout = html.Div([
dash_katex.DashKatex(
expression=r'f_X(x) = \lambda e^{-\lambda x}',
displayMode=True
),
dcc.Graph(id='exponential_graph'),
dash_katex.DashKatex(expression=r'\lambda'),
dcc.Slider(
id='exponential_rate',
value=1,
min=0.01,
max=10,
marks={i: str(i) for i in range(11)},
step=0.01,
tooltip={'placement': 'top'}
)
])
@app.callback(
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
def update_figure_fits(value_main_tab, value_analysis,
gene_name): #, yaxis_type, yaxis_scale, value_tab ):
if gene_name is None:
raise Exception()
else:
if value_main_tab == 'main-tab-2' and value_analysis == 'spatiotemporal':
array_gene_time = np.concatenate(
(gene_data[gene_name]['rep1'], gene_data[gene_name]['rep2'],
gene_data[gene_name]['rep3'][:, [0, 2]]),
axis=1)
fig_3D = Figures.compute_figure_3D_tab_3(
dic_reg[gene_name],
array_gene_time) #, yaxis_type, yaxis_scale)
selected = dic_reg[gene_name][0]
pv = dic_reg[gene_name][6]
set_selected = set(selected)
if len(selected) == 1:
str_param = dash_katex.DashKatex(
expression='\\text{ } \mu_0\\text{. }')
str_param2 = 'This corresponds to the flat model.'
else:
str_param = dash_katex.DashKatex(
expression='\\text{ }' + return_str_list_param(selected) +
'\\text{. }')
if set_selected == set(
['mu0', 'a0', 'b0']) or set_selected == set(
['mu0', 'a0']) or set_selected == set(['mu0', 'b0']):
str_param2 = "This corresponds to the rhythmic model."
elif 'a0' not in selected and 'a1' not in selected and 'a2' not in selected and 'b0' not in selected and 'b1' not in selected and 'b2' not in selected:
if 'mu1' in selected or 'mu2' in selected:
str_param2 = "This corresponds to the zonated model."
else:
str_param2 = "This corresponds to the rhythmic-zonated model."
l_time_reg = []
for x in range(8):
l_time_reg.append(
LinearRegression.make_time_regression(
array_gene_time[x, :], simple=False, predict=True))
fig_polar = Figures.compute_figure_polar_fit_tab_3(
dic_reg[gene_name], l_time_reg)
if fig_polar != None:
return [
html.Div(children=[
html.P("Retained parameters: "), str_param,
html.P(str_param2)
],
style={
'display': 'flex',
'justify-content': 'center'
}),
html.Div(
children=[
dcc.Graph(id='graph-polar-fit',
figure=fig_polar,
config={
'displayModeBar': False,
'modeBarButtonsToRemove': [],
'displaylogo': False
},
style={'width': '25vw'}),
dcc.Graph(id='graph-fit-3D',
figure=fig_3D,
config={
'modeBarButtonsToRemove': [
'sendDataToCloud',
'resetCameraLastSave3d',
'hoverClosest3d', 'zoom3d',
'toImage'
],
'displaylogo':
False
},
style={
'border-width': '1px',
'border-style': 'solid',
'border-color': '#e8e8e8',
'width': '45vw'
})
],
style={
'display': 'flex',
'flex-direction': 'row',
'justify-content': 'space-between',
'height': '500px'
},
),
]
else:
return [
html.Div(children=[
html.P("Retained parameters: "), str_param,
html.P(str_param2)
],
style={
'display': 'flex',
'justify-content': 'center'
}),
html.Div(children=[
dcc.Graph(id='graph-fit-3D',
figure=fig_3D,
config={
'modeBarButtonsToRemove': [
'sendDataToCloud',
'resetCameraLastSave3d',
'hoverClosest3d', 'zoom3d', 'toImage'
],
'displaylogo':
False
},
style={
'border-width': '1px',
'border-style': 'solid',
'border-color': '#e8e8e8',
'width': '60vw'
})
],
style={
'display': 'flex',
'flex-direction': 'row',
'justify-content': 'space-around'
}),
]
else:
raise PreventUpdate
def update_figure_fits(value, value_main_tab, value_analysis,
gene_name): #yaxis_type, yaxis_scale, value_tab ):
if gene_name is None:
raise Exception()
else:
if value_main_tab == 'main-tab-2' and value_analysis == 'spatial':
#correct value
value = int(value) - 1
t = int(value)
array_gene_space = np.concatenate(
(gene_data[gene_name]['rep1'], gene_data[gene_name]['rep2'],
gene_data[gene_name]['rep3']),
axis=0)
if t == 0 or t == 2:
selected, B, SE, adj_r2, aic, bic, pv, X_pred, Y_pred = LinearRegression.make_space_regression(
array_gene_space[:, t], predict=True)
else:
selected, B, SE, adj_r2, aic, bic, pv, X_pred, Y_pred = LinearRegression.make_space_regression(
array_gene_space[:16, t], predict=True)
if len(selected) == 1:
str_param = dash_katex.DashKatex(id='katex_a0',
expression=' \mu_0')
else:
str_param = dash_katex.DashKatex(
id='katex_other_parameters',
expression=return_str_list_param(selected))
space_domain = np.concatenate(
(np.linspace(0, 8, 8, endpoint=False),
np.linspace(0, 8, 8, endpoint=False),
np.linspace(0, 8, 8, endpoint=False)))
if t == 0 or t == 2:
figure = Figures.compute_figure_space_tab_3(
space_domain, array_gene_space[:, t], X_pred,
Y_pred) #, yaxis_type, yaxis_scale)
else:
figure = Figures.compute_figure_space_tab_3(
space_domain[:16], array_gene_space[:16, t], X_pred,
Y_pred) #, yaxis_type, yaxis_scale)
return [
html.Div(children=[
html.P('Retained parameters: '),
html.Div(style={'width': '5px'}), str_param
],
style={
'display': 'flex',
'justify-content': 'center'
}),
dcc.Graph(id='graph-stat-space',
figure=figure,
config={
'displayModeBar': False,
'modeBarButtonsToRemove': [],
'displaylogo': False
},
style={'width': '60vw'})
]
else:
raise PreventUpdate
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import dash_katex
import numpy as np
import plotly.express as px
from scipy import stats
from app import app
layout = html.Div([
dash_katex.DashKatex(
expression=r'f_X(x) = {n \choose x} p^x (1 - p)^{n - x}',
displayMode=True
),
dcc.Graph(id='binomial_graph'),
dash_katex.DashKatex(expression=r'n'),
dcc.Slider(
id='binomial_n',
value=10,
min=0,
max=50,
marks={i: str(i) for i in range(51) if i % 10 == 0},
tooltip={'placement': 'top'}
),
dash_katex.DashKatex(expression=r'p'),
dcc.Slider(
id='binomial_p',
value=0.5,
min=0,
html.Div([
daq.Gauge(
id='pgpress-status1',
min=0, max=20,
units="Torr",
showCurrentValue=True,
color={
"gradient": True,
"ranges": {
"green": [0, 7],
"yellow": [7, 14],
"red": [14, 20]
}
},
),
dash_katex.DashKatex(expression='\\textrm{x } 10^{-4}') # scale
])
]),
html.Div(className='indicator-box'+class_theme['dark'], id='thePressure-container', children=[
html.H4('Blue Vac Ion'), # pressure gauge for blue vac ion
html.Div([
daq.Gauge(
id='thePressure-status1',
min=0, max=100,
units="Torr",
showCurrentValue=True,
color={
"gradient": True,
"ranges": {
"green": [0, 30],
"yellow": [30, 70],
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import dash_katex
import numpy as np
import plotly.express as px
from scipy import stats
from app import app
layout = html.Div([
dash_katex.DashKatex(expression=r'''
f_X(x) = \frac{1}{\sqrt{2\pi\sigma^2}}
\exp\left(-\frac{(x - \mu)^2}{2\sigma^2}\right)
''',
displayMode=True),
dcc.Graph(id='normal_graph'),
dash_katex.DashKatex(expression=r'\mu'),
dcc.Slider(id='normal_mean',
value=0,
min=-5,
max=5,
marks={x: str(x)
for x in range(-5, 6)},
step=0.01,
tooltip={'placement': 'top'}),
dash_katex.DashKatex(expression=r'\sigma^2'),
dcc.Slider(id='normal_variance',
value=1,
min=0.01,
'value': True
}, {
'label': "False (Inline Mode)",
'value': False
}]),
html.Div("Throw on error:"),
dcc.RadioItems(id='radio-item-throw-on-error',
options=[{
'label': 'True',
'value': True
}, {
'label': "False",
'value': False
}]),
html.Div('Here is some latex:'),
dash_katex.DashKatex(id='katex', expression="")
])
@app.callback(Output('katex', 'expression'),
[Input('input-latex-expression', 'value')])
def updateExpression(value):
return value
@app.callback(Output('katex', 'displayMode'),
[Input('radio-item-display-mode', 'value')])
def updateDisplayMode(value):
return value
