This is what it looked like before:
def serve_layout():
current_time = dt.now()
prev_60_days = current_time - timedelta(days=60)
current_time_str = current_time.strftime('%B %d, %Y - %I:%M%p')
current_day_str = current_time.strftime('%Y-%m-%d')
prev_60_days_str = prev_60_days.strftime('%Y-%m-%d')
logging.info('Querying Water Chemistry Data')
all_df = pod.get_water_chem_data()
columns = ['Area_0', 'Area_1', 'Area_2', 'Area_3', 'Area_4']
split = pd.DataFrame(all_df['Description'].str.split(pat=r'\|+', n=4, regex=True).to_list(), columns=columns)
all_with_areas_df = pd.concat([all_df, split], axis=1)
logging.info('Querying Name Data')
name_df = asd.get_region_engine_names()
all_with_areas_names_df = pd.merge(all_with_areas_df, name_df, how='inner', left_on='PRD_PlantAbbr',
right_on='PlantAbbr')
prev_day = current_time - timedelta(days=1)
prev_day_str = prev_day.strftime('%Y-%m-%d %H:%M:%S')
logging.info('Querying EMS Data')
osi_key_df = ems.get_osi_key(prev_day_str)
all_with_areas_names_osi_df = pd.merge(all_with_areas_names_df, osi_key_df, how='inner',
left_on='EMS_Gross_MW_Point', right_on='OSI_KEY')
cat_with_names_df = all_with_areas_names_osi_df.dropna(subset=['EquipmentType', 'PointType', 'Location']).copy()
cat_with_names_df.reset_index(drop=True, inplace=True)
cat_with_names_df['Cat_Type'] = np.where(cat_with_names_df['Can_Trip_Alarm'] == 1, 'Critical', 'All')
cat_with_names_df['OOS'] = np.where(cat_with_names_df['Op_Level'] == 1, 'OOS', '')
cat_with_names_df['Attn'] = np.where(cat_with_names_df['Op_Level'] > 1, 'Need(s) Attention', '')
conditions = [
(cat_with_names_df['Can_Trip_Alarm'] == 1) & (cat_with_names_df['Op_Level'] == 1),
(cat_with_names_df['Can_Trip_Alarm'] == 1) & (cat_with_names_df['Op_Level'] > 1),
(cat_with_names_df['Can_Trip_Alarm'] == 0) & (cat_with_names_df['Op_Level'] > 1),
(cat_with_names_df['Can_Trip_Alarm'] == 0) & (cat_with_names_df['Op_Level'] == 1),
cat_with_names_df['Op_Level'] == 0,
cat_with_names_df['Op_Level'].isna()
]
labels = [
'Critical - Out of Spec.',
'Critical - Needs Attn.',
'All - Needs Attn.',
'All - Out of Spec.',
'',
''
]
cat_with_names_df['Prefix'] = np.select(condlist=conditions, choicelist=labels)
cat_with_names_df['Full_Description'] = cat_with_names_df['Prefix'] + ' ' + cat_with_names_df['Description']
urgency_levels = {
'Critical - Needs Attn.': 0,
'Critical - Out of Spec.': 1,
'All - Needs Attn.': 2,
'All - Out of Spec.': 3,
'': 4
}
cat_with_names_df['Prefix_Level'] = cat_with_names_df['Prefix'].map(urgency_levels)
logging.info('Querying Alarm Data')
all_alarm_data = pod.get_all_alarm_data()
all_alarm_data_w_names = pd.merge(all_alarm_data, name_df, how='inner', left_on='PRD_PlantAbbr',
right_on='PlantAbbr')
all_with_areas_names_df['Short_Description'] = all_with_areas_names_df[['Area_1', 'Area_2', 'Area_3', 'Area_4']] \
.agg('||'.join, axis=1)
limit_table = all_with_areas_names_df[['Area_0', 'Short_Description', 'Unit', 'Norm_Min', 'Norm_Max', 'L1_Min',
'L1_Max', 'L2_Min', 'L2_Max']].copy()
limit_table.rename(columns={'Area_0': 'Plant', 'Short_Description': 'Point', 'Norm_Min': 'Norm Min',
'Norm_Max': 'Norm Max', 'L1_Min': 'L1 Min', 'L1_Max': 'L1 Max', 'L2_Min': 'L2 Min',
'L2_Max': 'L2 Max'}, inplace=True)
limit_table.sort_values(by='Plant', inplace=True)
limit_table.round(2)
limit_table.fillna('N/A', inplace=True)
layout = html.Div([
nav,
dcc.Store(id='store', data={'df': all_with_areas_names_df.to_dict('records')}),
dcc.Store(id='four_tables', data={'df': cat_with_names_df.to_dict('records')}),
dcc.Store(id='alarm-table', data={'df': all_alarm_data_w_names.to_dict('records')}),
dcc.Store(id='limit-table', data={'df': limit_table.to_dict('records')}),
dcc.Download(id='limit-csv'),
dbc.Container([
dbc.Row([
dbc.Col([
....
return layout
app.layout = serve_layout
