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 datetime import timedelta
-from pathlib import Path
 import sqlite3
 from sqlite3 import Connection
 import plotly.express as px
@@ -35,22 +34,22 @@ st.markdown("""
 
 
 @st.cache(hash_funcs={Connection: id})
-def get_connection(path:str):
+def get_connection(path: str):
-    return sqlite3.connect(path,check_same_thread=False)
+    return sqlite3.connect(path, check_same_thread=False)
 
 
 def get_data(conn: Connection):
-    df1=pd.read_sql("SELECT * FROM detections", con=conn)
+    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()
-df=get_data(conn)
+df = get_data(conn)
-df2=df.copy()
+df2 = df.copy()
-df2['DateTime']=pd.to_datetime(df2['Date'] + " " + df2['Time'])
+df2['DateTime'] = pd.to_datetime(df2['Date'] + " " + df2['Time'])
-df2=df2.set_index('DateTime')
+df2 = df2.set_index('DateTime')
-
 
 
 # Filter on date range
@@ -60,174 +59,184 @@ df2=df2.set_index('DateTime')
 
 # Date as slider
 Start_Date = pd.to_datetime(df2.index.min()).date()
-End_Date =   pd.to_datetime(df2.index.max()).date()
+End_Date = pd.to_datetime(df2.index.max()).date()
-cols1,cols2= st.columns((1,1))
+cols1, cols2 = st.columns((1, 1))
 Date_Slider = cols1.slider('Date Range',
-                                min_value = Start_Date-timedelta(days=1),
+                           min_value=Start_Date - timedelta(days=1),
-                                max_value = End_Date,
+                           max_value=End_Date,
-                                value=(Start_Date,
+                           value=(Start_Date,
-                                       End_Date)                                       
+                                  End_Date)
-                                )
+                           )
 
 
-
+filt = (df2.index >= pd.Timestamp(Date_Slider[0])) & (df2.index <= pd.Timestamp(Date_Slider[1] + timedelta(days=1)))
-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',
+resample_times = {'1 minute': '1min',
-                  '5 minutes':'5min',
+                  '5 minutes': '5min',
-                  '10 minutes':'10min',
+                  '10 minutes': '10min',
-                  'Hourly':'1H',
+                  'Hourly': '1H',
-                  'Daily':'1D'
+                  'Daily': '1D'
                   }
 resample_time = resample_times[resample_sel]
 
-df5=df2.resample(resample_time)['Com_Name'].aggregate('unique').explode()
+df5 = df2.resample(resample_time)['Com_Name'].aggregate('unique').explode()
 
-#Create species count for selected date range
+# Create species count for selected date range
 
-Specie_Count=df5.value_counts()
+Specie_Count = df5.value_counts()
 
-#Create species treemap
+# Create species treemap
 
 # Create Hourly Crosstab
-hourly=pd.crosstab(df5,df5.index.hour, dropna=False)
+hourly = pd.crosstab(df5, df5.index.hour, dropna=False)
 
 # Filter on species
 species = list(hourly.index)
 
-cols1,cols2= st.columns((1,1))
+cols1, cols2 = st.columns((1, 1))
 top_N = cols1.slider(
     'Select Number of Birds to Show',
-    min_value = 1,
+    min_value=1,
-    value=min(10,len(Specie_Count))
+    value=min(10, len(Specie_Count))
-    )
+)
 
 top_N_species = (df5.value_counts()[:top_N])
 
 
-specie = cols2.selectbox('Which bird would you like to explore for the dates '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+'?', species,
+specie = cols2.selectbox('Which bird would you like to explore for the dates ' + str(Date_Slider[0]) + ' to ' + str(Date_Slider[1]) + '?', species,
-                              index=species.index(list(top_N_species.index)[0]))
+                         index=species.index(list(top_N_species.index)[0]))
 
 
-font_size=15
+font_size = 15
 
 
-#specie filter
+# specie filter
-filt=df2['Com_Name']==specie
+filt = df2['Com_Name'] == specie
-
-df_counts=sum(df5==specie)
-
 
+df_counts = sum(df5 == specie)
 
 
 if resample_time != '1D':
     fig = make_subplots(
-                    rows=3, cols =2,
+        rows=3, cols=2,
-                    specs= [[{"type":"xy","rowspan":3}, {"type":"polar","rowspan":2}], [{"rowspan":1}, {"rowspan":1} ], [None, {"type":"xy","rowspan":1}]],
+        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 ' +
-                                    'Total Detect:'+str('{:,}'.format(df_counts))+
+                        str(top_N) +
-                                    '   Confidence Max:'+str('{:.2f}%'.format(max(df2[df2['Com_Name']==specie]['Confidence'])*100))+
+                        ' Species in Date Range ' +
-                                    '   '+'   Median:'+str('{:.2f}%'.format(np.median(df2[df2['Com_Name']==specie]['Confidence'])*100))
+                        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))
                         )
-    fig.layout.annotations[1].update(x=0.7,y=0.25, font_size=15)
+    )
+    fig.layout.annotations[1].update(x=0.7, y=0.25, font_size=15)
 
-    #Plot seen species for selected date range and number of species
+    # Plot seen species for selected date range and number of species
-    fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1,col=1)
+    fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1, col=1)
 
     fig.update_layout(
         margin=dict(l=0, r=0, t=50, b=0),
-        yaxis={'categoryorder':'total ascending'})
+        yaxis={'categoryorder': 'total ascending'})
-
 
     # Set 360 degrees, 24 hours for polar plot
     theta = np.linspace(0.0, 360, 24, endpoint=False)
 
-    specie_filt= df5==specie
+    specie_filt = df5 == specie
-    df3=df5[specie_filt]
+    df3 = df5[specie_filt]
 
-    detections2= pd.crosstab(df3, df3.index.hour)
+    detections2 = pd.crosstab(df3, df3.index.hour)
 
-
+    d = pd.DataFrame(np.zeros((23, 1))).squeeze()
-
-
-    d=pd.DataFrame(np.zeros((23,1))).squeeze()
     detections = hourly.loc[specie]
-    detections=(d+detections).fillna(0)
+    detections = (d + detections).fillna(0)
-    fig.add_trace(go.Barpolar(r = detections, theta=theta), row=1, col=2)
+    fig.add_trace(go.Barpolar(r=detections, theta=theta), row=1, col=2)
     fig.update_layout(
         autosize=False,
-        width = 1000,
+        width=1000,
-        height = 500,
+        height=500,
         showlegend=False,
-        polar = dict(
+        polar=dict(
-            radialaxis = dict(
+            radialaxis=dict(
-                tickfont_size = font_size,
+                tickfont_size=font_size,
-                showticklabels = False,
+                showticklabels=False,
-                hoverformat = "#%{theta}: <br>Popularity: %{percent} </br> %{r}"
+                hoverformat="#%{theta}: <br>Popularity: %{percent} </br> %{r}"
-                ),
+            ),
-            angularaxis = dict(
+            angularaxis=dict(
-                tickfont_size= font_size,
+                tickfont_size=font_size,
-                rotation = -90,
+                rotation=-90,
-                direction = 'clockwise',
+                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],
+                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',
-                hoverformat = "#%{theta}: <br>Popularity: %{percent} </br> %{r}"
+                          '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)
-
-    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)
 
 else:
     fig = make_subplots(
-                    rows=1, cols =2,
+        rows=1, cols=2,
-                    specs= [[{"type":"xy","rowspan":1},{"type":"xy","rowspan":1}]],
+        specs=[[{"type": "xy", "rowspan": 1}, {"type": "xy", "rowspan": 1}]],
-                           
 
-                    subplot_titles=('<b>Daily Top '+ str(top_N) + ' Species in Date Range '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+'</b>',
-                                    '<b>Daily ' + specie+ ' Detections on 15 minute intervals </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))
-#                                     )
-                    )
 
-    fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1,col=1)
+        subplot_titles=('<b>Daily Top ' + str(top_N) + ' Species in Date Range ' + str(Date_Slider[0]) + ' to ' + str(Date_Slider[1]) + '</b>',
-    df4=df2['Com_Name'][df2['Com_Name']==specie].resample('15min').count()
+                        '<b>Daily ' + specie + ' Detections on 15 minute intervals </b>'),
-    df4.index=[df4.index.date, df4.index.time]
+        #                                     'Total Detect:'+str('{:,}'.format(df_counts))+
-    day_hour_freq=df4.unstack().fillna(0)
+        #                                     '   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))
+        #                                     )
+    )
+
+    fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1, col=1)
+    df4 = df2['Com_Name'][df2['Com_Name'] == specie].resample('15min').count()
+    df4.index = [df4.index.date, df4.index.time]
+    day_hour_freq = df4.unstack().fillna(0)
 
     fig_x = [d.strftime('%d-%m-%Y') for d in day_hour_freq.index.tolist()]
     fig_y = [h.strftime('%H:%M') for h in day_hour_freq.columns.tolist()]
     fig_z = day_hour_freq.values.transpose()
-    fig_heatmap = go.Figure(data=go.Heatmap(x=fig_x,y=fig_y,z=fig_z))
+    fig_heatmap = go.Figure(data=go.Heatmap(x=fig_x, y=fig_y, z=fig_z))
 
     fig.update_layout(
-    margin=dict(l=0, r=0, t=50, b=0),
+        margin=dict(l=0, r=0, t=50, b=0),
-    yaxis={'categoryorder':'total ascending'})
+        yaxis={'categoryorder': 'total ascending'})
-    color_pals= px.colors.named_colorscales()
+    color_pals = px.colors.named_colorscales()
     selected_pal = cols2.selectbox('Select Color Pallet for Daily Detections', color_pals)
-    fig.add_trace(go.Heatmap(x=fig_x,y=fig_y,z=fig_z, autocolorscale = False, colorscale = selected_pal), row=1, col=2)
+    fig.add_trace(go.Heatmap(x=fig_x, y=fig_y, z=fig_z, autocolorscale=False, colorscale=selected_pal), row=1, col=2)
 # container=st.container()
 # config={'displayModelBar': False}
-st.plotly_chart(fig, use_container_width=True) #, config=config)
+st.plotly_chart(fig, use_container_width=True)  # , config=config)
 
 # cols3,cols4=st.columns((1,1))
-# 
+#
 # extract_date=Date_Slider
-# 
+#
 # audio_file = open('/home/*/BirdSongs/Extracted/By_Date/2022-03-22/Yellow-streaked_Greenbul/Yellow-streaked_Greenbul-77-2022-03-22-birdnet-15:04:28.mp3', 'rb')
 # audio_bytes = audio_file.read()
 # cols4.audio(audio_bytes, format='audio/mp3')