AvianVisitors/scripts/plotly_streamlit.py

#!/home/patrick/BirdNET-Pi/birdnet/bin/python3
import os
import streamlit as st
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
import sqlite3
from sqlite3 import Connection

userDir = os.path.expanduser('~')
URI_SQLITE_DB = userDir + '/BirdNET-Pi/scripts/birds.db'

st.set_page_config(layout='wide')

# 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)


@st.cache(hash_funcs={Connection: id})
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)
    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')



# 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()))
# 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()).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,
                                value=(Start_Date,
                                       End_Date)                                       
                                )



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

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)

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))
    )

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,
                              index=species.index(list(top_N_species.index)[0]))


font_size=15


#specie filter
filt=df2['Com_Name']==specie

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))
                                    )
                    )
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)

fig.update_layout(
    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()
detections = hourly.loc[specie]
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,
    showlegend=False,
    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',
            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}"
        ),
        ),
    )



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)

# 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')