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index.py
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index.py
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from dash import Dash, html, dcc
from dash.dependencies import Input, Output, State
from dash.exceptions import PreventUpdate
from app import app
from pages import (
model_description_page,
simulate_model_page
)
from utils import (
make_dash_table,
Header,
NamedInput
)
import numpy as np
import pandas as pd
import pathlib
import data.model1.simulateModelContent
import data.model2.simulateModelContent
# layout of app
app.layout = html.Div(
[dcc.Location(id = 'url', refresh = False), html.Div(id='page-content')]
)
# layout for validation
app.validation_layout = html.Div(
[
model_description_page.layout,
simulate_model_page.layout,
data.model1.simulateModelContent.set_parameter_values_content,
data.model1.simulateModelContent.simulation_results_content,
data.model2.simulateModelContent.set_parameter_values_content,
data.model2.simulateModelContent.simulation_results_content,
]
)
# update page
@app.callback(Output('page-content', 'children'), [Input('url', 'pathname')])
def display_page(pathname):
print('CallBack: display_page')
if pathname == '/bacteriophage/model-description':
return model_description_page.layout
elif pathname == '/bacteriophage/simulate-model':
return simulate_model_page.layout
else:
return model_description_page.layout
# other callbacks
@app.callback(
Output('df-variables', 'children'),
Output('df-parameters', 'children'),
Output('df-equation', 'children'),
Input('model-kind-input', 'value')
)
def model_description_content(model_kind):
print('Callback: model_description_content')
PATH=pathlib.Path(__file__).parent
print(PATH)
DATA_PATH = PATH.joinpath('./data').resolve()
df_variables = pd.read_csv(DATA_PATH.joinpath(f"./{model_kind}/df_variables.csv"))
df_parameters = pd.read_csv(DATA_PATH.joinpath(f'./{model_kind}/df_parameters.csv'))
df_equation = pd.read_csv(DATA_PATH.joinpath(f'./{model_kind}/df_equation.csv'))
return make_dash_table(df_variables), make_dash_table(df_parameters), make_dash_table(df_equation)
@app.callback(
Output(f'set-parameter-values-content', 'children'),
Output(f'simulation-results-content', 'children'),
Input('model-kind-input', 'value')
)
def model1_simulate_model_content(model_kind):
print('Callback: model1_simulate_model_content')
if model_kind == 'model1':
return data.model1.simulateModelContent.set_parameter_values_content, data.model1.simulateModelContent.simulation_results_content
elif model_kind == 'model2':
return data.model2.simulateModelContent.set_parameter_values_content, data.model2.simulateModelContent.simulation_results_content
# model1
model_name = 'model1'
parameter_names = ['B0_123', 'B0_12', 'B0_13', 'B0_23', 'B0_1', 'B0_2', 'B0_3', 'B0_sus', 'lam0', 'lam1', 'Bmax', 'k1', 'b1', 'ktr1', 'frac1', 'pr1', 'pr21', 'k2', 'b2', 'ktr2', 'frac2', 'pr2', 'pr22', 'k3', 'b3', 'ktr3', 'frac3', 'pr3', 'pr23', 'moi1', 'moi2', 'moi3']
ids = [f'{model_name}-{i}-input' for i in parameter_names]
list1 = [Input(i, 'value') for i in ids]
@app.callback(
Output(f'{model_name}-simulation-plot', 'figure'),
Input(f'{model_name}-variable-kind', 'value'),
Input('model-kind-input', 'value'),
*list1
)
def model1_simulate_and_plot(variable_kind, model_kind, *args):
print('Callback: model1_simulate_and_plot')
if model_kind == 'model1':
times = np.arange(0, 24, 0.1)
res = data.model1.simulateModelContent.simulate(times,*args)
fig = data.model1.simulateModelContent.plot(res, variable_kind)
return fig
else:
raise PreventUpdate
# model2
model_name = 'model2'
parameter_names = ['B0_123', 'B0_12', 'B0_13', 'B0_23', 'B0_1', 'B0_2', 'B0_3', 'B0_sus', 'lam0', 'lam1', 'Bmax', 'k1', 'b1', 'ktr1', 'frac1', 'pr1', 'pr21', 'k2', 'b2', 'ktr2', 'frac2', 'pr2', 'pr22', 'k3', 'b3', 'ktr3', 'frac3', 'pr3', 'pr23', 'moi1', 'moi2', 'moi3']
ids = [f'{model_name}-{i}-input' for i in parameter_names]
list1 = [Input(i, 'value') for i in ids]
@app.callback(
Output(f'{model_name}-simulation-plot', 'figure'),
Input(f'{model_name}-variable-kind', 'value'),
Input('model-kind-input', 'value'),
*list1
)
def model2_simulate_and_plot(variable_kind, model_kind, *args):
print('Callback: model2_simulate_and_plot')
if model_kind == 'model2':
times = np.arange(0, 24, 0.1)
res = data.model2.simulateModelContent.simulate(times,*args)
fig = data.model2.simulateModelContent.plot(res, variable_kind)
return fig
else:
raise PreventUpdate