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app.py
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app.py
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'''
author: Michael A. Reiter
(c) ETH Zurich, Michael A. Reiter, 2022
This file is part of Dashing Growth Curves.
Dashing Growth Curves is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Dashing Growth Curves is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with Dashing Growth Curves. If not, see <https://www.gnu.org/licenses/>.
'''
from dash import Dash, html, dcc, Output, Input, State
import dash
import dash_bootstrap_components as dbc
from dash import DiskcacheManager, CeleryManager, Input, Output, html
import os
import pandas as pd
import numpy as np
import base64
from base64 import b64encode
import io
from PIL import Image
import re
import plotly.graph_objects as go
from plotly.express.colors import sample_colorscale
from plotly.subplots import make_subplots
import plotting as pl
import math_functions as mf
import growth_data as gd
import default_settings as ds
import messages as ms
import auxilliary_functions as ax
################################################
# initialize app
################################################
if 'REDIS_URL' in os.environ:
# Use Redis & Celery if REDIS_URL set as an env variable
from celery import Celery
celery_app = Celery(__name__, broker=os.environ['REDIS_URL'], backend=os.environ['REDIS_URL'], include=['growth_data'], CELERY_REDIS_MAX_CONNECTIONS=18, BROKER_POOL_LIMIT=0)
background_callback_manager = CeleryManager(celery_app)
else:
# Diskcache for non-production apps when developing locally
import diskcache
cache = diskcache.Cache("./cache")
background_callback_manager = DiskcacheManager(cache)
external_stylesheets = [dbc.themes.BOOTSTRAP, dbc.icons.FONT_AWESOME, dbc.icons.BOOTSTRAP]
app = Dash(__name__,
external_stylesheets=external_stylesheets,
background_callback_manager=background_callback_manager,
)
server = app.server
################################################
# app layout
################################################
# register external callbacks
gd.register_gd_callbacks(app)
############
# stores
############
stores = html.Div([
dcc.Store(id = 'store_sample_idx', data=0), # store idx of currently selectec sample
dcc.Store(id = 'store_data_df'), # store uploaded data as dictionary (operation on it as dataframe)
dcc.Store(id = 'store_data_df_smoothed'), # store smoothed data
dcc.Store(id = 'store_smoother_value', data=0), # store used smoothing window size
dcc.Store(id = 'store_smoother_flag', data=False), # store indicator if raw or smoothed data is used for analysis
dcc.Store(id = 'store_blank_locs'), # store locations in dataframe which are blanks
dcc.Store(id = 'store_sample_names'), # store the names associated to samples
dcc.Store(id = 'store_sample_names_old'), # temporary store of old sample names to facilitate renaming
dcc.Store(id = 'store_sample_locations'),
dcc.Store(id = 'store_upload_flag', data=False), # store indicator if data has been uploaded, yet
dcc.Store(id = 'analysis_flag', data=False), # TODO: redundant, change code and remove
dcc.Store(id = 'store_growth_data'), # store growth parameters as dictionary (operation on it as dataframe)
dcc.Store(id = 'store_growth_data_auto'), # temporary store of growth parameters found by automatic growth curve analysis (as dictionary, operation on it as dataframe)
dcc.Store(id = 'store_gd_by_replicates'), # store summarizing replicate growth data
dcc.Store(id = 'store_default_blanks'), # store of set of default blanks
dcc.Store(id = 'store_default_popsizemeasure', data=ds.default_pop_size_measure), # string to format axes of plots with the correct population size measured used in data
dcc.Store(id = 'store_auto_fit_ws', data=ds.auto_fit_default_ws),
dcc.Store(id = 'store_auto_fit_slope_range', data=ds.auto_fit_default_sr),
dcc.Store(id = 'store_auto_fit_r2_var_weight', data=ds.auto_fit_default_weight)
])
############
# upload div
############
# first page that user sees
# shows tutorial and exposes data upload interface
# after data upload div collapses and remains hidden
upload_div = dbc.Collapse([
html.H1(dcc.Markdown('<b>{}</b>'.format(ds.app_name), dangerously_allow_html=True),
style={
'text-align': 'center',
'margin-top': '1em',
'margin-bottom': '1em',
'font-size': '80px',
}
),
dbc.Row(
dcc.Upload(id='upload', max_size=3e7,
children=html.Div([
ms.upload_area
],
style={
'line-height': '8em', # vertically align text
}
),
style={
'width': '100%',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'display': 'inline-block',
'height': '8em',
},
),
style = {
'margin-right': '20em',
'margin-left': '20em',
'margin-bottom': '2em',
}
),
html.Div(id='upload_alert_area'), # error messages during upload are sent to this div
dbc.Row([
ax.create_link_button('Info', 'https://github.com/Dahlai/growthdash'),
ax.create_link_button('Ask a question', 'https://github.com/Dahlai/growthdash/issues'),
ax.create_link_button('How to cite?', 'https://github.com/mretier/growthdash#how-to-cite'),
dbc.Button(html.B('Download a sample file'),
color='secondary',
style={
'height':'55px',
'width': '150px',
'display': 'flex',
'align-items': 'center',
'justify-content': 'center',
},
id = 'button_download_sample_file'),
dcc.Download(id = 'download_sample_file'),
# ax.create_link_button('Download a sample file', 'https://github.com/Dahlai/growthdash'),
ax.create_link_button('Watch the tutorial', ds.tutorial_link),
],
style = {
'margin-left': '10em',
'margin-right': '10em',
'justify-content': 'space-evenly',
'margin-bottom': '1.5em'
}
),
dbc.Row(
ms.intro_text,
style = {
'margin-bottom': '1em',
'margin-left': '5em',
'margin-right': '5em',
'text-align': 'center',
}
),
dbc.Row(
ms.data_privacy_text,
style = {
'text-align': 'center',
}
),
dbc.Collapse([
html.H1('Data structure:',
style={'margin-top': '80px', 'margin-bottom': '30px'}
),
html.Img(src='./assets/help page_small.png',
style={'width':'80%'}
)
],
id='info_section',
is_open=False
)
],
id = 'div_upload_collapsible',
is_open = True,
)
############
# main app div
############
# components to set sample name, blanks and additional functionalities (currently only automatic exponential phase determination)
sample_name_input = html.Div([
dcc.Markdown('<b>Sample name:</b>',
style={'color':'black',
'font-size': 24,
},
dangerously_allow_html=True),
dcc.Input(id = 'input_sample_name',
type='text',
debounce=True,
style={'width':'85%'}
),
html.I(id='sample_name_info',
className='bi bi-info-circle-fill',
style={'color': 'darkslategray',
'margin-left':'10px'}
),
dbc.Tooltip(dcc.Markdown(ms.tooltip_sample_name),
target='sample_name_info',
placement='top'
)
])
blanks_input = html.Div([
dcc.Markdown('<b>Sample blanks:</b>',
style={'color':'black',
'font-size': 24
},
dangerously_allow_html=True
),
dcc.Input(id = 'blanks_input',
type='text',
debounce=True,
style={'width': '85%'}
),
html.I(id='blanks_info',
className='bi bi-info-circle-fill',
style={'color': 'darkslategray',
'margin-left':'10px'}
),
dbc.Tooltip(ms.tooltip_blanks,
target='blanks_info',
placement='top'
)
])
additional_input = html.Div([
# data smoothing
dbc.Row([
html.I(id='button_smoother',
className='bi bi-circle',
style={
'color': 'darkslategray',
'font-size':'11pt',
'display': 'inline-block',
'width': '8%','padding': '0',
'margin-top': '5px',
'text-align':'center'}
),
dbc.Tooltip(ms.tooltip_data_smoothing,
target='button_smoother',
placement='top',
id='tooltip_button_smoother'
),
html.Div(dcc.Input(id='input_smoother_ws',
value=ds.default_smoothing_window_size,
debounce=False,
style={'padding': '0',
'margin-top': '1px',
'height': '30px',
'width': '100%',
'text-align': 'center'
}
),
style={'width': '13%',
'padding-right':0}
),
],
style={'margin-bottom': '10px'}
),
# data auto fitting
dbc.Row([
html.I(
id='button_auto_fit',
className='bi bi-cpu-fill',
style={
'color': 'darkslategray',
'font-size':'15pt',
'display': 'inline-block',
'width': '8%','padding': '0',
'margin-top': '5px',
'text-align':'center'}
),
dbc.Tooltip(
ms.tooltip_autofitting_button,
target='button_auto_fit',
placement='bottom'
),
html.Div(
dcc.Dropdown(ds.fittings_algorithms,
value=ds.default_fitting_algorithm,
id='dropdown_fitting_algorithms',
clearable=False,
style={
'padding': '0',
'margin-top': '1px'}
),
style={
'width': '52%',
'padding-right': 0
}
),
html.Div(
html.A(href=ds.models_links,
target='_blank',
id='button_go_to_algorithm_doc',
className='bi bi-box-arrow-up-right',
style={
'color': 'darkslategray',
'background-color': 'transparent',
'border-color':'transparent'
}
),
style={
'width': '10%',
'display': 'inline-block',
'margin-top': '8px'}
),
dbc.Tooltip(
ms.tooltip_autofitting_docs,
target='button_go_to_algorithm_doc',
placement='bottom'
)
]),
dbc.Collapse(
dbc.Row([
html.Div('window size: ',
style={'padding-top': '2pt',
'padding-left': '45pt',
'padding-right': '0',
'width': '120pt'
}),
html.Div(
dcc.Input(id='easy_linear_window_size',
value=ds.default_easy_linear_window_size,
debounce=False,
style={'padding': '0',
'margin-top': '1px',
'height': '30px',
'width': '100%',
'text-align': 'center'
}
),
style={'width': '45pt',
'padding-right':0}
)
],
style={'padding-top': '5pt'}
),
is_open = False,
id='autofit_settings'
),
# hidden div that can be turned full screen to show progress of auto-fitting algorithm
html.Div([
dbc.Row([
dbc.Row(id='progress_count'),
dbc.Row(html.Progress(id='progress_bar', value='0')),
dbc.Row(
dbc.Button(html.B("cancel"),
color='secondary',
id='button_cancel_autofit'),
style={'justify-content': 'center'}
)
],
style={'width': '35em'}
),
],
id='auto_fit_div',
style={'visibility': 'hidden'},
),
])
# settings div
settings_div = [
html.Hr(style={'margin-bottom':'20'}),
dbc.Row([
dbc.Col([
],
width=4
),
dbc.Col([
# dcc.Markdown('<b>Fitting parameters</b>',
# style={'font-size': '14pt'},
# dangerously_allow_html=True
# )
],
width=4)
]),
dbc.Row([
dbc.Col([
html.B('Default blanks:'),
dcc.Input(id='default_blanks_input',
debounce=True,
style={
'margin-left': '38px',
'width':'52%'
}),
html.I(id='default_blanks_info',
className='bi bi-info-circle-fill',
style={
'color': 'darkslategray',
'margin-left':'10px',
},
),
dbc.Tooltip(ms.tooltip_set_blanks,
target='default_blanks_info',
),
],
width=4
),
dbc.Col([
# html.B('Window size: ',
# style={
# 'display': 'inline-block',
# 'width': '25%',
# },
# ),
# dcc.Input(value=ds.auto_fit_default_ws,
# id='input_auto_default_ws',
# debounce=True,
# style={
# 'margin-left': '10px',
# 'width':'55%'
# }
# ),
# html.I(id='auto_default_sw_info',
# className='bi bi-info-circle-fill',
# style={
# 'color': 'darkslategray',
# 'margin-left':'10px'
# }
# ),
# dbc.Tooltip(ms.tooltip_auto_ws,
# target='auto_default_sw_info'
# )
],
width=4,
)
],
style={'margin-bottom': '10px'}
),
dbc.Row([
dbc.Col([
html.B('Pop. size measure:'),
dcc.Input(id='input_popsizemeasure',
debounce=True,
value = ds.default_pop_size_measure,
style={
'margin-left': '10px',
'width':'52%'
}),
html.I(id='info_popsizemeasure',
className='bi bi-info-circle-fill',
style={
'color': 'darkslategray',
'margin-left':'10px',
},
),
dbc.Tooltip(ms.tooltip_popsizemeasure,
target='info_popsizemeasure',
)
],
width=4
),
dbc.Col([
# html.B('Slope range: ',
# style={
# 'display': 'inline-block',
# 'width': '25%'
# }
# ),
# dcc.Input(value=ds.auto_fit_default_sr,
# id='input_auto_default_sr',
# debounce=True,
# style={
# 'margin-left': '10px',
# 'width': '55%'
# }
# ),
# html.I(id='auto_default_sr_info',
# className='bi bi-info-circle-fill',
# style={
# 'color': 'darkslategray',
# 'margin-left':'10px'
# }),
# dbc.Tooltip(ds.auto_fit_default_weight, target='auto_default_sr_info')
],
width=4,
)
],
style={'margin-bottom': '10px'},
),
dbc.Row([
dbc.Col([
],
width=4
),
dbc.Col([
# html.B('Weight: ',
# style={
# 'display': 'inline-block',
# 'width': '25%'
# }),
# dcc.Input(value=ds.auto_fit_default_weight,
# id='input_auto_default_weight',
# debounce=True,
# style={
# 'margin-left': '10px',
# 'width':'55%'
# }
# ),
# html.I(id='auto_default_weight_info',
# className='bi bi-info-circle-fill',
# style={
# 'color': 'darkslategray',
# 'margin-left':'10px'
# }
# ),
# dbc.Tooltip(ms.tooltip_auto_sr,
# target='auto_default_weight_info'
# )
],
width=4,
style={'margin-bottom': '10px'},
)
])
]
# div showing results for individual samples
results_growth = [
dbc.Row(id = 'growth_rate', children = dcc.Markdown('growth rate \[h<sup>-1</sup>\] =', dangerously_allow_html=True)),
dbc.Row(id = 'doubling_time', children = dcc.Markdown('doubling time \[h\] =')),
dbc.Row(id = 'lag_time', children = dcc.Markdown('lag time \[h\] =')),
dbc.Row(id = 'doublings', children = dcc.Markdown('doublings =')),
dbc.Row(id = 'doublings_log', children = dcc.Markdown('doublings in log-phase =')),
dbc.Row(id = 'yield', children = dcc.Markdown('yield \[{}\] ='.format(ds.default_pop_size_measure), dangerously_allow_html=True)),
dbc.Row(id = 'error', children= dcc.Markdown('RMSE = '))
]
# overview graphs
overview_graph = dcc.Graph(id='fig_overview', config=ax.graph_config('overview'), style={'height': '30em'})
# sample graphs
blanks_graph = dcc.Graph(id='fig_blanks', config=ax.graph_config('blanks'), style={'height': '6em'})
sample_graph = dcc.Graph(id='fig_sample', config=ax.graph_config('sample'), style={'height': '6em'})
sample_blanked_graph = dcc.Graph(id='fig_blanked', config=ax.graph_config('sample_blanked'), style={'height': '11.25em'})
sample_log_graph = dcc.Graph(id='fig_log', config=ax.graph_config('sample_blanked_log'))
# analysis graphs
dt_graph = dcc.Graph(id='fig_dt', config=ax.graph_config('doubling time'))
mu_graph = dcc.Graph(id='fig_mu', config=ax.graph_config('growth rate'))
lag_time_graph = dcc.Graph(id='fig_lt', config=ax.graph_config('lag time'))
doublings_graph = dcc.Graph(id='fig_doublings', config=ax.graph_config('doublings'))
doublings_log_graph = dcc.Graph(id='fig_doublings_log', config=ax.graph_config('doublings_log'))
yield_graph = dcc.Graph(id='fig_yield', config=ax.graph_config('yield'))
# download buttons
download_buttons = [
# html.Hr(style={'margin-top': '40px',
# 'margin-bottom': '40px'
# }
# ),
html.Div(dcc.Markdown('<b>Downloads</b>', style={'font-size': '35pt'}, dangerously_allow_html=True)),
html.H6('Interactive .html plots '),
dbc.Row([
dbc.Col([dcc.Loading(html.A(
id="overview_download",
href="",
children=[ax.create_download_button('data overview times')],
target="_blank",
download="overview.html"))],
width=1,
),
dbc.Col([dcc.Loading(html.A(
id="dt_download",
href="",
children=[ax.create_download_button('doubling times')],
target="_blank",
download="dts.html"))],
width=1,
),
dbc.Col([dcc.Loading(html.A(
id="mu_download",
href="",
children=[ax.create_download_button('growth rates')],
target="_blank",
download="mu_max.html"))],
width=1,
),
dbc.Col([dcc.Loading(html.A(
id="lt_download",
href="",
children=[ax.create_download_button('lag time')],
target="_blank",
download="lag_times.html"))],
width=1,
)
,
dbc.Col([dcc.Loading(html.A(
id="doublings_download",
href="",
children=[ax.create_download_button('doublings')],
target="_blank",
download="doublings.html"))],
width=1,
),
dbc.Col([dcc.Loading(html.A(
id="doublings_log_download",
href="",
children=[ax.create_download_button('doublings log-phase')],
target="_blank",
download="doublings_log.html"))],
width=1,
),
dbc.Col([dcc.Loading(html.A(
id="yields_download",
href="",
children=[ax.create_download_button('yields')],
target="_blank",
download="yields.html"))],
width=1,
),
],
style={'margin-bottom': '20px'},
),
html.H6('.csv file summarizing analyzed data'),
dbc.Row([
dbc.Col([
dbc.Button(html.B('analysis'), color='secondary', style={'height':'60px', 'width': '110px'}, id = 'download_sample_data_button'),
dcc.Download(id = 'download_sample_data')
],
width=1,
),
dbc.Tooltip(ms.tooltip_csv_download,
target='download_sample_data_button',
delay={'show': 500}),
])
]
###########
# overall app layout structure
###########
# data display layout
sample_data_div = dbc.Collapse([
html.Div(dcc.Markdown('<b>Data Overview</b>', style={'font-size': '35pt'}, dangerously_allow_html=True)),
# collapsible div containing overview graphs
dbc.Row([
html.I(
id='show_hide_overview_graphs_button',
className='bi bi-caret-down-fill',
style={
'color': 'darkslategray',
'margin-left':'10px',
},
),
dbc.Collapse(
[
html.Div([overview_graph]),
html.Div([
html.I(
id='overview_subtract_blanks_button'
)
])
],
id = 'overview_graphs_div',
is_open=True,
)
]),
# separator
# html.Hr(style={'margin-top': '30px', 'margin-bottom': '20px', 'border': '3.5px solid'}),
html.Div(dcc.Markdown('<b>Data Analysis</b>', style={'font-size': '35pt'}, dangerously_allow_html=True)),
dbc.Row([
# top row containing settings button and buttons to switch between samples
dbc.Row([
dbc.Col(html.I(className='bi bi-gear-fill',
style={'font-size':25,},
id='settings_button'),
width=4
),
dbc.Col(
dbc.Row([
dbc.Col(html.I(id = 'backbutton',
n_clicks=0,
className='bi bi-arrow-left-square-fill',
style={'font-size':25}
),
width=1,
style={'padding': 0}
),
dbc.Col(id='current_sample_position',
style={
'font-size':20,
'text-align':'center'
},
width=2),
dbc.Col(html.I(id = 'forwardbutton',
n_clicks=0,
className='bi bi-arrow-right-square-fill',
style={'font-size':25}
),
width=1,
style={
'padding': 0,
'text-align':'right'
}
),
],
justify='center',
align='center',
style={'margin-bottom':'20px'}
),
width=4),
dbc.Col(width=4),
],
id='top-row1'
),
# second row with drop down menu to choose sample
dbc.Row([
dbc.Col(width=4),
dbc.Col(
dbc.Row([
dbc.Col(width=1),
dbc.Col(dcc.Dropdown(id='current_sample_name', clearable=False, optionHeight=50), width=8),
dbc.Col(width=1)
],
justify='center',
align='center',
),
width=4
),
dbc.Col(width=4),
],
align='center',
id = 'top-row2'
),
# collapsible settings div
dbc.Row([
dbc.Collapse(settings_div,
id='settings_div',
is_open=False,
style={
'margin-top': '15px',
'margin-bottom':'15px'
}
)
]),
# message areas for sample analysis
dbc.Row([
html.Div(id='message_area'),
html.Div(id='message_area_sample_names'),
html.Div(id='message_area_fits'),
html.Div(id='message_area_fits_parameters'),
html.Div(id='message_area_data_smoother'),
]),
],
style = {'background-color': 'rgb(245, 245, 245)'},
id = 'top-row',
),
# separator
html.Hr(style={'margin-top': '20px', 'margin-bottom': '30px', 'border': '1.5px solid'}),
# input areas for sample name, associated blanks and additional features
dbc.Row([
dbc.Col([sample_name_input],
id='sample_name',
width=4
),
dbc.Col([blanks_input],
id='blank_names',
width=4
),
dbc.Col([additional_input],
width=4
)
]),
# separator
html.Hr(style={'margin-top': '30px', 'margin-bottom': '20px', 'border': '1.5px solid'}),
dbc.Row([
dbc.Col([
html.Div([blanks_graph]),
html.Div([sample_graph]),
html.Div([sample_blanked_graph]),
],
width=3,
style={'padding': 0, 'margin-top': '2.5em'}
),
dbc.Col([
html.Div([sample_log_graph])
],
width=6,
style={'padding': 0}
),
dbc.Col(results_growth,
width=3,
style={'margin-top': '6em'}
)
]),
# summary graphs of all analyzed samples
dbc.Row([
dbc.Collapse([
html.Hr(style={'margin-top': '40px'}),
dbc.Row([
dbc.Col(dt_graph, width=4),
dbc.Col(mu_graph, width=4),
dbc.Col(lag_time_graph, width=4)
]),
dbc.Row([
dbc.Col(doublings_graph, width=4),
dbc.Col(doublings_log_graph, width=4),
dbc.Col(yield_graph, width=4)
]),
] + download_buttons,
is_open=False,
id='analysis_div'
)
]),
],
id = 'collapse_data',
is_open = False,
style={'margin-top': '30px'},
)
# master layout
app.layout = html.Div(
[
stores, # stores need to be inside html.Div
upload_div,
sample_data_div,
],
style={'margin': '1%', 'margin-bot': '5em'},
id = 'body_layout'
)
################################################
# callbacks
################################################
@app.callback(
Output('store_data_df', 'data'),
Output('div_upload_collapsible', 'is_open'),
Output('store_upload_flag', 'data'),
Output('upload_alert_area', 'children'),
Output('upload', 'contents'), # needs to reset to None after each upload to support uploading the same file twice (happens if the initial upload contained errors)
Output('upload', 'filename'), # see line above (https://github.com/plotly/dash-core-components/issues/816#issuecomment-1032635061)
Input('upload', 'contents'),
Input('upload', 'filename'),
State('store_upload_flag', 'data'),
prevent_initial_call=True
)
def load_data_page(contents, filename, upload_flag):
# handle and format uploaded data
if contents is None:
return dash.no_update, dash.no_update, dash.no_update, dash.no_update, None, None
else:
# load data
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
if filename.split('.')[-1] == 'csv':
df = pd.read_csv(io.StringIO(decoded.decode('utf-8')), sep=';')
elif (filename.split('.')[-1] == 'xlsx') or (filename.split('.')[-1] == '.xls'):
df = pd.read_excel(decoded)
else:
upload_alert = ax.generate_alert(ms.error_upload_file)
return dash.no_update, dash.no_update, dash.no_update, upload_alert, None, None
# check if entries can be cast to float
try:
df.iloc[:, 1:].astype(float)
df.columns[1:].astype(float)
except:
upload_alert = ax.generate_alert(ms.error_upload_duplicate_t_non_float)
print('xxx')
return dash.no_update, dash.no_update, dash.no_update, upload_alert, None, None
# format uploaded data
df.set_index(df.iloc[:,0].values, inplace=True)
df = df.iloc[:, 1:]
df.columns = df.columns.astype(float)
if len(set(df[[not x for x in df.index == '-']].index)) + df[df.index == '-'].shape[0] != df.shape[0]:
# check for duplicate sample names
upload_alert = ax.generate_alert(ms.error_duplicate_samples)
return dash.no_update, dash.no_update, dash.no_update, upload_alert, None, None
return df.to_dict('tight'), False, True, '', None, None
@app.callback(
Output('input_sample_name', 'value'),
Output('store_sample_names', 'data'),
Output('store_sample_names_old', 'data'),
Output('message_area_sample_names', 'children'),
Output('store_sample_locations', 'data'),
Input('store_sample_idx', 'data'),
Input('input_sample_name', 'value'),
Input('store_upload_flag', 'data'),
State('store_sample_names', 'data'),
State('store_data_df', 'data'),
prevent_initial_call=True
)
def update_sample_input_value(sample_idx, new_sample_name, upload_flag, sample_names, df):
# update stored sample names from different inputs
# update on data upload from names stored in uploaded data
if dash.callback_context.triggered[0]['prop_id'] == 'store_upload_flag.data':
df = pd.DataFrame.from_dict(df, orient='tight')
# set default sample names as defined in uploaded data
sample_names = []
for loc in df.index:
sample_names.append(loc)
# set sample locations
sample_locations = ax.set_sample_locations(df)
return list(sample_names)[sample_idx], sample_names, sample_names, '', sample_locations
# update on page turn
elif dash.callback_context.triggered[0]['prop_id'] == 'store_sample_idx.data':
current_sample_name = sample_names[sample_idx]
return current_sample_name, dash.no_update, dash.no_update, '', dash.no_update
# update on new name input
elif dash.callback_context.triggered[0]['prop_id'] == 'input_sample_name.value':
df = pd.DataFrame.from_dict(df, orient='tight')
# check for duplicate sample names
if new_sample_name in sample_names:
alert_sn = ax.generate_alert(ms.error_duplicate_sample)
return sample_names[sample_idx], dash.no_update, dash.no_update, alert_sn, dash.no_update
else:
sample_names_old = sample_names.copy()
sample_names[sample_idx] = new_sample_name
return new_sample_name, sample_names, sample_names_old, '', dash.no_update
@app.callback(
Output('store_sample_idx', 'data'),
Output('current_sample_name', 'options'),
Output('current_sample_name', 'value'),
Input('forwardbutton', 'n_clicks'),