I will say, that when you start your server. It runs through each page, and makes sure that everything is working. All your variables are active and held, this is why your layouts load.
If, however, you were to make your layouts inside of a function def layout():, then the layouts wouldnt be stored in memory. But would be catered when returning the function.
You could give that a try and see if it works. Bring all of your variables that are used in that page’s layout inside the function call for the layout.
Example ( bin_browser.py):
# from msilib.schema import Component
import re
import dash
from dash import dcc, html, dash_table, callback
import plotly.express as px
import dash_bootstrap_components as dbc
import requests
from dash.dependencies import Input, Output, State
import pandas as pd
from dash_table.Format import Format, Scheme, Group
import dash_bio as dashbio
from . import bin_browser_helper
import plotly.graph_objects as go
dash.register_page(__name__)
base_url_api = "http://127.0.0.1:4999/"
def layout():
########get things from helper script########
bins_dict = bin_browser_helper.generate_bin_dropdown_options()
#############################################
# layout=dbc.Container(
layout = html.Div(
children=[
dbc.Row(
children=[
dbc.Col(
children=[
html.H2("Select a bin", className='text-center'),
html.Br(),
],
width={'size': 6}
)
],
justify='center'
),
dbc.Row(
children=[
dbc.Col(width=3),
dbc.Col(
children=[
# html.H2("From Triplet", className='text-center'),
dcc.Dropdown(
id='dropdown_bin',
# options=sorted([
# {'label': temp.title(), 'value':unique_sod_combinations_dict[temp]} for temp in unique_sod_combinations_dict
# ],key=lambda x:x['label']),
options=[
{'label': x, 'value': bins_dict[x]} for x in bins_dict
],
multi=False,
# style={
# 'color': '#212121',
# 'background-color': '#3EB489',
# }
),
html.Br(),
],
width={'size': 6}
),
dbc.Col(width=3),
]
),
dbc.Row(
children=[
dbc.Col(width=4),
dbc.Col(
children=[
# html.H2("Result Datatable", className='text-center'),
html.Div(
dbc.Button(
'Query and Visualize',
id='button_bin_visualize',
),
className="d-grid gap-2 col-4 mx-auto",
),
],
width=3
),
]
),
dbc.Row(
children=[
dbc.Col(width=2),
dbc.Col(
children=[
html.Br(),
html.Br(),
html.Br(),
html.Br(),
html.Div(
dbc.Button(
'Download as .xlsx',
id='button_download_msp',
),
className="d-grid gap-2 col-4 mx-auto",
),
dcc.Download(id="download_download_msp"),
dash_table.DataTable(
id='table_bin',
columns=[
{'name': 'Attribute', 'id': 'attribute'},
{'name': 'Value', 'id': 'value'},
],
data=[],
# page_current=0,
# page_size=10,
# #page_action='custom',
# page_action='native',
# #sort_action='custom',
# sort_action='native',
# sort_mode='multi',
# #sort_by=[],
# #filter_action='custom',
# filter_action='native',
# row_deletable=False,
# filter_query='',
style_header={
'backgroundColor': 'rgb(30, 30, 30)',
'color': 'white'
},
style_data={
'backgroundColor': 'rgb(50, 50, 50)',
'color': 'white'
},
style_cell={
'font-family': 'sans-serif',
'textOverflow': 'ellipsis',
'maxWidth': 0
}
)
],
width=3
),
# dbc.Col(
# width=1
# ),
dbc.Col(
children=[
html.Br(),
html.Br(),
html.Br(),
dcc.Graph(
id='figure_bin'
)
],
width=5
),
]
)
]
)
return layout
@callback(
[
Output(component_id="table_bin", component_property="columns"),
Output(component_id='table_bin', component_property='data'),
Output(component_id='figure_bin', component_property='figure'),
],
[
Input(component_id='button_bin_visualize', component_property='n_clicks'),
# Input(component_id='radio_items_fold_type',component_property='value')
],
[
State(component_id='dropdown_bin', component_property='value')
],
prevent_initial_call=True
)
def query_figure(button_bin_visualize_n_clicks, dropdown_bin_value):
bin_output = {
'bin_id': dropdown_bin_value
}
# leaf_output={
# "metadata_datatable":table_metadata_derived_virtual_data
# }
# print(table_metadata_derived_virtual_data)
response = requests.post(base_url_api + "/binresource/", json=bin_output)
total_panda = pd.read_json(response.json(), orient="records")
# print(total_panda)
# print(total_panda.T)
# print('***********************************')
# print(total_panda.at[0,'spectrum'])
mzs = [float(x.split(':')[0]) for x in total_panda.at[0, 'spectrum'].split(' ')]
intensities = [float(x.split(':')[1]) for x in total_panda.at[0, 'spectrum'].split(' ')]
mzs = [0] + mzs
intensities = [0] + intensities
# mzs=[4,12]
# intensities=[3,4]
# print(mzs)
# print(intensities)
# print('-'*50)
spectrum_figure = go.Figure(
go.Bar(
x=mzs,
y=intensities,
marker=dict(color="rgb(220, 53, 69)")
)
)
spectrum_figure.update_yaxes(title="Relative Intensity")
spectrum_figure.update_xaxes(title="m/z")
spectrum_figure.update_traces(width=1, hovertemplate="m/z: %{x}<br>Intensity: %{y}<br>")
spectrum_figure.update_layout(showlegend=False, font=dict(size=18))
# total_panda=total_panda.loc[total_panda['bin_type_dict']==radio_items_bin_type_value]
# print(total_panda.columns)
total_panda = total_panda[
['english_name', 'compound_identifier', 'retentionIndex', 'kovats', 'spectrum', 'quantMass', 'uniqueMass',
'splash', 'purity']]
total_panda.rename(
{
'english_name': 'Name',
'compound_identifier': 'InChIKey',
'retentionIndex': 'FAME RI',
'kovats': 'Kovats RI',
'spectrum': 'Spectrum',
'quantMass': 'Quant Mass',
'uniqueMass': 'Unique Mass',
'splash': 'SPLASH',
'purity': 'Purity'
},
axis='columns',
inplace=True
)
total_panda = total_panda.T
total_panda.reset_index(inplace=True)
# print(total_panda)
total_panda.rename(
{
'index': 'Attribute',
0: 'Value'
},
axis='columns',
inplace=True
)
# print(total_panda)
# total_panda=pd.DataFrame.from_dict(
# {
# 'Attribute':[1,2],
# 'Value':[2,3]
# }
# )
data = total_panda.to_dict(orient='records')
column_list = [
{'name': temp_column, 'id': temp_column} for temp_column in total_panda.columns
]
# data='nonsense'
return [column_list, data, spectrum_figure]
# return [spectrum_figure]
@callback(
[
Output(component_id="download_download_msp", component_property="data"),
],
[
Input(component_id="button_download_msp", component_property="n_clicks"),
],
[
State(component_id='table_bin', component_property='derived_virtual_data')
],
prevent_initial_call=True
)
def download_sunburst_datatable(
button_download_msp_n_clicks,
table_bin_derived_virual_data
):
"""
"""
# print(pd.DataFrame.from_records(table_derived_virtual_data).drop(['compound','bin'],axis='columns'))
# temp_img=venn_helper.make_venn_figure_from_panda(pd.DataFrame.from_records(table_derived_virtual_data).drop(['compound','bin'],axis='columns'))
# print(pd.DataFrame.from_records(table_data).to_excel)
# print(pd.DataFrame.from_records(table_bin_derived_virual_data).to_excel)
return [dcc.send_data_frame(
pd.DataFrame.from_records(table_bin_derived_virual_data).to_excel, "binvestigate_sunburst_datatable.xlsx",
sheet_name="sheet_1"
)]
