CaiusX
227 lines
8.2 KiB
Python
Executable File
227 lines
8.2 KiB
Python
Executable File
import os
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import streamlit as st
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import pandas as pd
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import numpy as np
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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from datetime import timedelta, datetime
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from pathlib import Path
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import sqlite3
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from sqlite3 import Connection
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userDir = os.path.expanduser('~')
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URI_SQLITE_DB = userDir + '/BirdNET-Pi/scripts/birds.db'
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st.set_page_config(layout='wide')
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# Remove whitespace from the top of the page and sidebar
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st.markdown("""
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<style>
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.css-18e3th9 {
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padding-top: 2.5rem;
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padding-bottom: 10rem;
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padding-left: 5rem;
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padding-right: 5rem;
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}
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.css-1d391kg {
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padding-top: 3.5rem;
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padding-right: 1rem;
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padding-bottom: 3.5rem;
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padding-left: 1rem;
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}
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</style>
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""", unsafe_allow_html=True)
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@st.cache(hash_funcs={Connection: id})
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def get_connection(path:str):
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return sqlite3.connect(path,check_same_thread=False)
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def get_data(conn: Connection):
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df1=pd.read_sql("SELECT * FROM detections", con=conn)
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return df1
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conn = get_connection(URI_SQLITE_DB)
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# Read in the cereal data
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# df = load_data()
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df=get_data(conn)
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df2=df.copy()
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df2['DateTime']=pd.to_datetime(df2['Date'] + " " + df2['Time'])
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df2=df2.set_index('DateTime')
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# Filter on date range
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# Date as calendars
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# Start_Date = pd.to_datetime(st.sidebar.date_input('Which date do you want to start?', value = df2.index.min()))
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# End_Date = pd.to_datetime(st.sidebar.date_input('Which date do you want to end?', value = df2.index.max()))
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# Date as slider
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Start_Date = pd.to_datetime(df2.index.min()).date()
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End_Date = pd.to_datetime(df2.index.max()).date()
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cols1,cols2= st.columns((1,1))
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Date_Slider = cols1.slider('Date Range',
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min_value = Start_Date-timedelta(days=1),
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max_value = End_Date,
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value=(Start_Date,
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End_Date)
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)
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filt = (df2.index >= pd.Timestamp(Date_Slider[0])) & (df2.index <= pd.Timestamp(Date_Slider[1]+timedelta(days=1)))
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df2 = df2[filt]
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st.write('<style>div.row-widget.stRadio > div{flex-direction:row;justify-content: left;} </style>', unsafe_allow_html=True)
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st.write('<style>div.st-bf{flex-direction:column;} div.st-ag{font-weight:bold;padding-left:2px;}</style>', unsafe_allow_html=True)
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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'))
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resample_times = {'1 minute':'1min',
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'5 minutes':'5min',
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'10 minutes':'10min',
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'Hourly':'1H',
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'Daily':'1D'
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}
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resample_time = resample_times[resample_sel]
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df5=df2.resample(resample_time)['Com_Name'].aggregate('unique').explode()
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#Create species count for selected date range
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Specie_Count=df5.value_counts()
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#Create species treemap
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# Create Hourly Crosstab
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hourly=pd.crosstab(df5,df5.index.hour, dropna=False)
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# Filter on species
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species = list(hourly.index)
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cols1,cols2= st.columns((1,1))
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top_N = cols1.slider(
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'Select Number of Birds to Show',
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min_value = 1,
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value=min(10,len(Specie_Count))
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)
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top_N_species = (df5.value_counts()[:top_N])
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specie = cols2.selectbox('Which bird would you like to explore for the dates '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+'?', species,
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index=species.index(list(top_N_species.index)[0]))
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font_size=15
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#specie filter
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filt=df2['Com_Name']==specie
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df_counts=sum(df5==specie)
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fig = make_subplots(
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rows=3, cols =2,
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specs= [[{"type":"xy","rowspan":3}, {"type":"polar","rowspan":2}], [{"rowspan":1}, {"rowspan":1} ], [None, {"type":"xy","rowspan":1}]],
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subplot_titles=('<b>Top '+ str(top_N) + ' Species in Date Range '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+'</b>',
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'Total Detect:'+str('{:,}'.format(df_counts))+
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' Confidence Max:'+str('{:.2f}%'.format(max(df2[df2['Com_Name']==specie]['Confidence'])*100))+
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' '+' Median:'+str('{:.2f}%'.format(np.median(df2[df2['Com_Name']==specie]['Confidence'])*100))
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)
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)
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fig.layout.annotations[1].update(x=0.7,y=0.25, font_size=15)
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#Plot seen species for selected date range and number of species
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fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1,col=1)
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fig.update_layout(
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margin=dict(l=0, r=0, t=50, b=0),
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yaxis={'categoryorder':'total ascending'})
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# Set 360 degrees, 24 hours for polar plot
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theta = np.linspace(0.0, 360, 24, endpoint=False)
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specie_filt= df5==specie
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df3=df5[specie_filt]
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detections2= pd.crosstab(df3, df3.index.hour)
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d=pd.DataFrame(np.zeros((23,1))).squeeze()
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detections = hourly.loc[specie]
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detections=(d+detections).fillna(0)
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if resample_time != '1D':
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fig.add_trace(go.Barpolar(r = detections, theta=theta), row=1, col=2)
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fig.update_layout(
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autosize=False,
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width = 1000,
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height = 500,
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showlegend=False,
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polar = dict(
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radialaxis = dict(
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tickfont_size = font_size,
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showticklabels = False,
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hoverformat = "#%{theta}: <br>Popularity: %{percent} </br> %{r}"
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),
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angularaxis = dict(
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tickfont_size= font_size,
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rotation = -90,
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direction = 'clockwise',
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tickmode='array',
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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],
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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'],
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hoverformat = "#%{theta}: <br>Popularity: %{percent} </br> %{r}"
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),
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),
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)
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daily=pd.crosstab(df5,df5.index.date, dropna=False)
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fig.add_trace(go.Bar(x=daily.columns, y=daily.loc[specie]), row=3, col=2)
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else:
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fig = make_subplots(
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rows=1, cols =2,
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# specs= [[{"type":"xy","rowspan":1}],
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# [{"rowspan":1}],
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# ],
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# subplot_titles=('<b>Top '+ str(top_N) + ' Species in Date Range '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+'</b>',
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# 'Total Detect:'+str('{:,}'.format(df_counts))+
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# ' Confidence Max:'+str('{:.2f}%'.format(max(df2[df2['Com_Name']==specie]['Confidence'])*100))+
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# ' '+' Median:'+str('{:.2f}%'.format(np.median(df2[df2['Com_Name']==specie]['Confidence'])*100))
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# )
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)
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df4=df2['Com_Name'][df2['Com_Name']==specie].resample('15min').count()
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df4.index=[df4.index.date, df4.index.time]
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day_hour_freq=df4.unstack().fillna(0)
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fig_x = [d.strftime('%d-%m-%Y') for d in day_hour_freq.index.tolist()]
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fig_y = [h.strftime('%H:%M') for h in day_hour_freq.columns.tolist()]
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fig_z = day_hour_freq.values.transpose()
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fig_heatmap = go.Figure(data=go.Heatmap(x=fig_x,y=fig_y,z=fig_z))
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fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1,col=1)
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fig.update_layout(
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margin=dict(l=0, r=0, t=50, b=0),
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yaxis={'categoryorder':'total ascending'})
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fig.add_trace(go.Heatmap(x=fig_x,y=fig_y,z=fig_z, autocolorscale = False, colorscale = 'blackbody'), row=1, col=2)
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# container=st.container()
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# config={'displayModelBar': False}
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st.plotly_chart(fig, use_container_width=True) #, config=config)
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# cols3,cols4=st.columns((1,1))
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#
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# extract_date=Date_Slider
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#
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# 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')
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# audio_bytes = audio_file.read()
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# cols4.audio(audio_bytes, format='audio/mp3')
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