linted

scripts/plotly_streamlit.py

@@ -39,7 +39,6 @@ st.markdown("""
         """, unsafe_allow_html=True)
 
 
-
 # @st.cache(hash_funcs={Connection: id})
 @st.cache(allow_output_mutation=True)
 def get_connection(path: str):
@@ -92,12 +91,14 @@ else:
 # start_date = datetime(2022 ,5 ,17).date()
 # end_date = datetime(2022 ,5 ,17).date()
 
+
 @st.cache()
 def date_filter(df, start_date, end_date):
     filt = (df2.index >= pd.Timestamp(start_date)) & (df2.index <= pd.Timestamp(end_date + timedelta(days=1)))
     df = df[filt]
     return(df)
 
+
 df2 = date_filter(df2, start_date, end_date)
 
 st.write('<style>div.row-widget.stRadio > div{flex-direction:row;justify-content: left;} </style>',
@@ -133,6 +134,7 @@ else:
                       }
     resample_time = resample_times[resample_sel]
 
+
 @st.cache()
 def time_resample(df, resample_time):
     if resample_time == 'Raw':
@@ -142,6 +144,8 @@ def time_resample(df, resample_time):
         df_resample = df.resample(resample_time)['Com_Name'].aggregate('unique').explode()
 
     return(df_resample)
+
+
 top_bird = df2['Com_Name'].mode()[0]
 df5 = time_resample(df2, resample_time)
 
@@ -248,7 +252,6 @@ if daily == False:
             rows=1, cols=1)
     #                     specs= [[{"type":"xy","rowspan":1},{"type":"heatmap","rowspan":1}]],
 
-
     #                     subplot_titles=('<b>Daily Top '+ str(top_N) + ' Species in Date Range '+ str(start_date) +' to '+ str(end_date) +'</b>',
     #                                     '<b>Daily ' + specie+ ' Detections on 15 minute intervals </b>'),
     # #                                     'Total Detect:'+str('{:,}'.format(df_counts))+