new plotly and better recording service

scripts/birdnet_recording.sh

@@ -10,6 +10,8 @@ fi
 
 [ -z $RECORDING_LENGTH ] && RECORDING_LENGTH=15
 
+if ! pulseaudio --check;then pulseaudio --start;fi
+
 if pgrep arecord &> /dev/null ;then
   echo "Recording"
 else

scripts/plotly_streamlit.py

@@ -5,53 +5,82 @@ 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
 import sqlite3
 from sqlite3 import Connection
-from pathlib import Path
 
 URI_SQLITE_DB = "/home/pi/BirdNET-Pi/scripts/birds.db"
 
-
 st.set_page_config(layout='wide')
-@st.cache(ttl=60,hash_funcs={Connection: id})
-def get_connection(path: str):
-    """Put the connection in cache to reuse if path does not change between Streamlit reruns.
-    NB : https://stackoverflow.com/questions/48218065/programmingerror-sqlite-objects-created-in-a-thread-can-only-be-used-in-that-sa
-    """
-    return sqlite3.connect(path, check_same_thread=False)
 
+# Remove whitespace from the top of the page and sidebar
+st.markdown("""
+        <style>
+               .css-18e3th9 {
+                    padding-top: 2.5rem;
+                    padding-bottom: 10rem;
+                    padding-left: 5rem;
+                    padding-right: 5rem;
+                }
+               .css-1d391kg {
+                    padding-top: 3.5rem;
+                    padding-right: 1rem;
+                    padding-bottom: 3.5rem;
+                    padding-left: 1rem;
+                }
+        </style>
+        """, unsafe_allow_html=True)
+
+col1,col2,col3 = st.columns([20,20,20])
+
+col1.title('BirdNET-Pi', anchor=None)
+col2.image('/home/pi/BirdNET-Pi/homepage/images/bird.png')
+col3.text('')
+
+
+@st.cache(hash_funcs={Connection: id})
+def get_connection(path:str):
+    return sqlite3.connect(path,check_same_thread=False)
+
+
+
+
+# def load_data():
+#     df1 = pd.read_csv('/home/pi/BirdNET-Pi/BirdDB.txt', sep=';')
+#     return df1
 
 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)
-
-#@st.cache()
-#def load_data():
-#    df1 = pd.read_csv('/home/pi/BirdNET-Pi/BirdDB.txt', sep=';')
-#    return df1
-
-
 # Read in the cereal data
-#df = load_data()
+# df = load_data()
-df = get_data(conn)
+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
 # Date as calendars
-#Start_Date1 = pd.to_datetime(st.sidebar.date_input('Which date do you want to start?', value = df2.index.min()))
+# Start_Date = pd.to_datetime(st.sidebar.date_input('Which date do you want to start?', value = df2.index.min()))
-#End_Date1 =   pd.to_datetime(st.sidebar.date_input('Which date do you want to end?',  value = df2.index.max()))
+# End_Date =   pd.to_datetime(st.sidebar.date_input('Which date do you want to end?',  value = df2.index.max()))
+
 # Date as slider
-Start_Date = pd.to_datetime(df2.index.min())
+Start_Date = pd.to_datetime(df2.index.min()).date()
-End_Date =   pd.to_datetime(df2.index.max())
+End_Date =   pd.to_datetime(df2.index.max()).date()
-Date_Slider = st.sidebar.slider('Date Range',
+Date_Slider = st.slider('Date Range',
-                                value=(Start_Date.to_pydatetime(),
+                                min_value = Start_Date-timedelta(days=1),
-                                       End_Date.to_pydatetime())                                       
+                                max_value = End_Date,
+                                value=(Start_Date,
+                                       End_Date)                                       
                                 )
-filt = (df2.index >= Date_Slider[0]) &  (df2.index <= 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
@@ -61,22 +90,24 @@ Specie_Count=df2['Com_Name'].value_counts()
 #Create species treemap
 
 # Create Hourly Crosstab
-
-
 hourly=pd.crosstab(df2['Com_Name'],df2.index.hour, dropna=False)
 
 # Filter on species
 species = list(hourly.index)
 
-top_N = st.sidebar.select_slider(
+cols1,cols2= st.columns((1,1))
+top_N = cols1.slider(
     'Select Number of Birds to Show',
-    list(range(1,len(Specie_Count))),
+    min_value = 1,
-    value=min(10,len(Specie_Count)-1))
+    value=min(10,len(Specie_Count))
+    )
 
 top_N_species = (df2['Com_Name'].value_counts()[:top_N])
 
 
-specie = st.sidebar.selectbox('Which bird would you like to explore?', species, index=species.index(list(top_N_species.index)[0]))
+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
 
@@ -87,60 +118,67 @@ filt=df2['Com_Name']==specie
 df_counts=df2[filt].resample('D').count()
 
 fig = make_subplots(
-                    rows=2, cols =2,
+                    rows=3, cols =2,
-                    specs= [[{"type":"xy","rowspan":2}, {"type":"polar"}], [None, {"type":"xy"}]],
+                    specs= [[{"type":"xy","rowspan":3}, {"type":"polar","rowspan":2}], [{"rowspan":1}, {"rowspan":1} ], [None, {"type":"xy","rowspan":1}]],
-                    subplot_titles=('<b style="font-size:x-large;">Species in Date Range</b>',
+                    subplot_titles=('<b>Top '+ str(top_N) + ' Species in Date Range '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+'</b>',
-                                    '<b style="font-size:large;">'+specie+'</b><br>'
+                                    'Total Detect:'+str('{:,}'.format(sum(df_counts.Time)))+
-                                    '<span style="font-size:medium;">Total Detections: '+str('{:,}'.format(sum(df_counts.Time)))+'<br>'
+                                    '   Confidence Max:'+str('{:.2f}%'.format(max(df2[df2['Com_Name']==specie]['Confidence'])*100))+
-                                    'Max Confidence: '+str('{:.2f}%'.format(max(df2[df2['Com_Name']==specie]['Confidence'])*100))+'<br>'
+                                    '   '+'   Median:'+str('{:.2f}%'.format(np.median(df2[df2['Com_Name']==specie]['Confidence'])*100))
-                                    'Median Confidence: '+str('{:.2f}%'.format(np.median(df2[df2['Com_Name']==specie]['Confidence'])*100))+'</span>'
-                                    
                                     )
                     )
-# fig.layout.height=900
+fig.layout.annotations[1].update(x=0.7,y=0.25, font_size=15)
-# fig.layout.width=1500
 
 #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=50, t=70, b=0),
+    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)
 
 d=pd.DataFrame(np.zeros((23,1))).squeeze()
-radius = hourly.loc[specie]
+detections = hourly.loc[specie]
-radius=(d+radius).fillna(0)
+detections=(d+detections).fillna(0)
-fig.add_trace(go.Barpolar(r = radius, theta=theta), row=1, col=2)
+fig.add_trace(go.Barpolar(r = detections, theta=theta), row=1, col=2)
 
 fig.update_layout(
-    autosize=True,
+    autosize=False,
     width = 1000,
-    height = 750,
+    height = 500,
     showlegend=False,
     polar = dict(
         radialaxis = dict(
             tickfont_size = font_size,
-            showticklabels = False),
+            showticklabels = True,
+            hoverformat = "#%{theta}: <br>Popularity: %{percent} </br> %{r}"
+            ),
         angularaxis = dict(
             tickfont_size= font_size,
             rotation = -90,
             direction = 'clockwise',
             tickmode='array',
-            tickvals=[0,45,90,135,180,225,270,315],
+            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','3am', '6am','9am','12pm','3pm', '6pm','9pm'],
+            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}"
+            hoverformat = "#%{theta}: <br>Popularity: %{percent} </br> %{r}"
         ),
         ),
     )
 
-fig.layout.annotations[1].update(x=0.775,y=0.4, font_size=15)
 
-x=df_counts.index
-y=df_counts['Com_Name']
 
-fig.add_trace(go.Bar(x=df_counts.index,y=df_counts['Time']), row=2, col=2)
+daily=pd.crosstab(df2['Com_Name'],df2.index.date, dropna=False)
 
-container=st.container()
+fig.add_trace(go.Bar(x=daily.columns, y=daily.loc[specie]), row=3, col=2)
-container.plotly_chart(fig, use_container_width=True)
+
+# container=st.container()
+# config={'displayModelBar': False}
+st.plotly_chart(fig, use_container_width=True) #, config=config)
+
+# cols3,cols4=st.columns((1,1))
+# 
+# extract_date=Date_Slider
+# 
+# audio_file = open('/home/pi/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')