Updating script/plotly_streamlit.py to correct pep8 violations

Used 'autopep8 --in-place --aggressive scripts/plotly_streamlit.py' as initial style fixes.

scripts/plotly_streamlit.py

@@ -34,22 +34,22 @@ st.markdown("""
 
 
 @st.cache(hash_funcs={Connection: id})
-def get_connection(path:str):
-    return sqlite3.connect(path,check_same_thread=False)
+def get_connection(path: str):
+    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)
-df2=df.copy()
-df2['DateTime']=pd.to_datetime(df2['Date'] + " " + df2['Time'])
-df2=df2.set_index('DateTime')
-
+df = get_data(conn)
+df2 = df.copy()
+df2['DateTime'] = pd.to_datetime(df2['Date'] + " " + df2['Time'])
+df2 = df2.set_index('DateTime')
 
 
 # Filter on date range
@@ -61,108 +61,110 @@ df2=df2.set_index('DateTime')
 Start_Date = pd.to_datetime(df2.index.min()).date()
 End_Date = pd.to_datetime(df2.index.max()).date()
 Date_Slider = st.slider('Date Range',
-                                min_value = Start_Date-timedelta(days=1),
-                                max_value = End_Date,
+                        min_value=Start_Date - timedelta(days=1),
+                        max_value=End_Date,
                         value=(Start_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]
 
-#Create species count for selected date range
+# Create species count for selected date range
 
-Specie_Count=df2['Com_Name'].value_counts()
+Specie_Count = df2['Com_Name'].value_counts()
 
-#Create species treemap
+# Create species treemap
 
 # Create Hourly Crosstab
-hourly=pd.crosstab(df2['Com_Name'],df2.index.hour, dropna=False)
+hourly = pd.crosstab(df2['Com_Name'], df2.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,
-    value=min(10,len(Specie_Count))
-    )
+    min_value=1,
+    value=min(10, len(Specie_Count))
+)
 
 top_N_species = (df2['Com_Name'].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]))
 
 
-font_size=15
+font_size = 15
 
 
-#specie filter
-filt=df2['Com_Name']==specie
+# specie filter
+filt = df2['Com_Name'] == specie
 
-df_counts=df2[filt].resample('D').count()
+df_counts = df2[filt].resample('D').count()
 
 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])+'</b>',
-                                    'Total Detect:'+str('{:,}'.format(sum(df_counts.Time)))+
-                                    '   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))
+    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]) + '</b>',
+                    'Total Detect:' + str('{:,}'.format(sum(df_counts.Time))) +
+                    '   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
-fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1,col=1)
+# 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.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)
 
-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)
-fig.add_trace(go.Barpolar(r = detections, theta=theta), row=1, col=2)
+detections = (d + detections).fillna(0)
+fig.add_trace(go.Barpolar(r=detections, theta=theta), row=1, col=2)
 
 fig.update_layout(
     autosize=False,
-    width = 1000,
-    height = 500,
+    width=1000,
+    height=500,
     showlegend=False,
-    polar = dict(
-        radialaxis = dict(
-            tickfont_size = font_size,
-            showticklabels = True,
-            hoverformat = "#%{theta}: <br>Popularity: %{percent} </br> %{r}"
+    polar=dict(
+        radialaxis=dict(
+            tickfont_size=font_size,
+            showticklabels=True,
+            hoverformat="#%{theta}: <br>Popularity: %{percent} </br> %{r}"
         ),
-        angularaxis = dict(
-            tickfont_size= font_size,
-            rotation = -90,
-            direction = 'clockwise',
+        angularaxis=dict(
+            tickfont_size=font_size,
+            rotation=-90,
+            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'],
-            hoverformat = "#%{theta}: <br>Popularity: %{percent} </br> %{r}"
+            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'],
+            hoverformat="#%{theta}: <br>Popularity: %{percent} </br> %{r}"
         ),
     ),
-    )
+)
 
 
-
-daily=pd.crosstab(df2['Com_Name'],df2.index.date, dropna=False)
+daily = pd.crosstab(df2['Com_Name'], df2.index.date, dropna=False)
 
 fig.add_trace(go.Bar(x=daily.columns, y=daily.loc[specie]), row=3, 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))
 #