Linting scripts/plotly.streamlit.py

scripts/plotly_streamlit.py

@@ -4,8 +4,7 @@ import pandas as pd
 import numpy as np
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
-from datetime import timedelta, datetime
-from pathlib import Path
+from datetime import timedelta
 import sqlite3
 from sqlite3 import Connection
 import plotly.express as px
@@ -43,6 +42,7 @@ def get_data(conn: Connection):
     df1 = pd.read_sql("SELECT * FROM detections", con=conn)
     return df1
 
+
 conn = get_connection(URI_SQLITE_DB)
 # Read in the cereal data
 # df = load_data()
@@ -52,7 +52,6 @@ df2['DateTime']=pd.to_datetime(df2['Date'] + " " + df2['Time'])
 df2 = df2.set_index('DateTime')
 
 
-
 # Filter on date range
 # Date as calendars
 # Start_Date = pd.to_datetime(st.sidebar.date_input('Which date do you want to start?', value = df2.index.min()))
@@ -70,14 +69,22 @@ Date_Slider = cols1.slider('Date Range',
                            )
 
 
-
 filt = (df2.index >= pd.Timestamp(Date_Slider[0])) & (df2.index <= pd.Timestamp(Date_Slider[1] + timedelta(days=1)))
 df2 = df2[filt]
 
 st.write('<style>div.row-widget.stRadio > div{flex-direction:row;justify-content: left;} </style>', unsafe_allow_html=True)
 st.write('<style>div.st-bf{flex-direction:column;} div.st-ag{font-weight:bold;padding-left:2px;}</style>', unsafe_allow_html=True)
 
-resample_sel=cols2.radio("Select Resample Resolution - To downsample and make run faster select longer period, Daily provides a view on detections at 15 min intervals through the day", ('1 minute', '5 minutes', '10 minutes', 'Hourly', 'Daily'))
+resample_sel = cols2.radio(
+    '''
+    Select Resample Resolution - To downsample and make run faster select longer period,
+    Daily provides a view on detections at 15 min intervals through the day
+    ''',
+    ('1 minute',
+     '5 minutes',
+     '10 minutes',
+     'Hourly',
+     'Daily'))
 
 resample_times = {'1 minute': '1min',
                   '5 minutes': '5min',
@@ -124,13 +131,20 @@ filt=df2['Com_Name']==specie
 df_counts = sum(df5 == specie)
 
 
-
-
 if resample_time != '1D':
     fig = make_subplots(
         rows=3, cols=2,
         specs=[[{"type": "xy", "rowspan": 3}, {"type": "polar", "rowspan": 2}], [{"rowspan": 1}, {"rowspan": 1}], [None, {"type": "xy", "rowspan": 1}]],
-                    subplot_titles=('<b>Top '+ str(top_N) + ' Species in Date Range '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+' for '+str(resample_sel)+' sampling interval.'+'</b>',
+        subplot_titles=('<b>Top ' +
+                        str(top_N) +
+                        ' Species in Date Range ' +
+                        str(Date_Slider[0]) +
+                        ' to ' +
+                        str(Date_Slider[1]) +
+                        ' for ' +
+                        str(resample_sel) +
+                        ' sampling interval.' +
+                        '</b>',
                         'Total Detect:' + str('{:,}'.format(df_counts)) +
                         '   Confidence Max:' + str('{:.2f}%'.format(max(df2[df2['Com_Name'] == specie]['Confidence']) * 100)) +
                         '   ' + '   Median:' + str('{:.2f}%'.format(np.median(df2[df2['Com_Name'] == specie]['Confidence']) * 100))
@@ -145,7 +159,6 @@ if resample_time != '1D':
         margin=dict(l=0, r=0, t=50, b=0),
         yaxis={'categoryorder': 'total ascending'})
 
-
     # Set 360 degrees, 24 hours for polar plot
     theta = np.linspace(0.0, 360, 24, endpoint=False)
 
@@ -154,9 +167,6 @@ if resample_time != '1D':
 
     detections2 = pd.crosstab(df3, df3.index.hour)
 
-
-
-
     d = pd.DataFrame(np.zeros((23, 1))).squeeze()
     detections = hourly.loc[specie]
     detections = (d + detections).fillna(0)
@@ -178,14 +188,13 @@ if resample_time != '1D':
                 direction='clockwise',
                 tickmode='array',
                 tickvals=[0, 15, 35, 45, 60, 75, 90, 105, 120, 135, 150, 165, 180, 195, 210, 225, 240, 255, 270, 285, 300, 315, 330, 345],
-                ticktext=['12am','1am','2am','3am','4am','5am', '6am','7am','8am','9am','10am','11am','12pm','1pm','2pm','3pm','4pm','5pm', '6pm','7pm','8pm','9pm','10pm','11pm'],
+                ticktext=['12am', '1am', '2am', '3am', '4am', '5am', '6am', '7am', '8am', '9am', '10am', '11am',
+                          '12pm', '1pm', '2pm', '3pm', '4pm', '5pm', '6pm', '7pm', '8pm', '9pm', '10pm', '11pm'],
                 hoverformat="#%{theta}: <br>Popularity: %{percent} </br> %{r}"
             ),
         ),
     )
 
-
-
     daily = pd.crosstab(df5, df5.index.date, dropna=False)
 
     fig.add_trace(go.Bar(x=daily.columns, y=daily.loc[specie]), row=3, col=2)