df, _ = utilities.create_df_from_object_list(scrnaseq_summary_data_list,
[ScRNAseqSummary],
['scrnaseq_summary'])
print(df.head(5))
# Creating an ID column name gives us more interactive capabilities
df['id'] = df['study_id']
df.set_index('id', inplace=True, drop=False)
# -------------------------------------------------------------------------------------
# App layout
graphComponentID = 'datatable-interactivity-container-line'
dataTableComponentID = 'datatable-interactivity-line'
card_graph = utilities.spinner_wrapper(
dbc.Card(
id=graphComponentID,
body=True,
color="secondary",
))
dataTableComponent = utilities.data_table(dataTableComponentID, df)
# Sorting operators (https://dash.plotly.com/datatable/filtering)
layout = html.Div([
html.Div([
dbc.Row(dbc.Col(card_graph, width=12), justify="start"),
# justify="start", "center", "end", "between", "around"
]),
html.Br(),
html.Div([
dbc.Row(dbc.Col(dataTableComponent, width=12), justify="start"),
]),
html.Br(),
# 'textAlign': 'left'
# } for c in ['sex', 'phenotype']
# ],
# style_data={ # overflow cells' content into multiple lines
# 'whiteSpace': 'normal',
# 'height': 'auto'
# }
# )]),
# color="dark", # https://bootswatch.com/default/ for more card colors
# inverse=True, # change color of text (black or white)
# outline=False, # True = remove the block colors from the background and header
# )
card_graph = utilities.spinner_wrapper(
dbc.Card(
id='datatable-interactivity-container',
body=True,
color="secondary",
))
dataTableComponent = utilities.data_table('datatable-interactivity', df)
# Sorting operators (https://dash.plotly.com/datatable/filtering)
layout = html.Div([
html.Div([
dbc.Row(dbc.Col(card_graph, width=12), justify="start"),
# justify="start", "center", "end", "between", "around"
]),
html.Br(),
html.Div([
dbc.Row(dbc.Col(dataTableComponent, width=12), justify="start"),
]),
# dash_table.DataTable(
