mcguirepr89
122 lines
3.9 KiB
Python
Executable File
122 lines
3.9 KiB
Python
Executable File
#!/home/pi/BirdNET-Pi/birdnet/bin/python3
|
|
import streamlit as st
|
|
import pandas as pd
|
|
import plotly.express as px
|
|
import numpy as np
|
|
import plotly.graph_objects as go
|
|
from plotly.subplots import make_subplots
|
|
from datetime import timedelta, datetime
|
|
|
|
@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()
|
|
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()))
|
|
#End_Date1 = 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())
|
|
End_Date = pd.to_datetime(df2.index.max())
|
|
Date_Slider = st.sidebar.slider('Date Range',
|
|
value=(Start_Date.to_pydatetime(),
|
|
End_Date.to_pydatetime())
|
|
)
|
|
filt = (df2.index >= Date_Slider[0]) & (df2.index <= 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)
|
|
|
|
# Filter on species
|
|
species = list(hourly.index)
|
|
|
|
top_N = st.sidebar.select_slider(
|
|
'Select Number of Birds to Show',
|
|
list(range(1,len(Specie_Count))),
|
|
value=(10))
|
|
|
|
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]))
|
|
|
|
|
|
font_size=15
|
|
|
|
|
|
#specie filter
|
|
filt=df2['Com_Name']==specie
|
|
|
|
df_counts=df2[filt].resample('D').count()
|
|
|
|
fig = make_subplots(
|
|
rows=2, cols =2,
|
|
specs= [[{"type":"xy","rowspan":2}, {"type":"polar"}], [None, {"type":"xy"}]],
|
|
subplot_titles=('<b>Species in Date Range</b>',
|
|
'<b>'+specie+'</b>'
|
|
'<br>Total Detections:'+str('{:,}'.format(sum(df_counts.Time)))+
|
|
'<br>''Max Confidence:'+str('{:.2f}%'.format(max(df2[df2['Com_Name']==specie]['Confidence'])*100))+
|
|
'<br>''Median Confidence:'+str('{:.2f}%'.format(np.median(df2[df2['Com_Name']==specie]['Confidence'])*100))
|
|
|
|
)
|
|
)
|
|
# fig.layout.height=900
|
|
# 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=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)
|
|
|
|
fig.add_trace(go.Barpolar(r = hourly.loc[specie], theta=theta), row=1, col=2)
|
|
|
|
fig.update_layout(
|
|
autosize=True,
|
|
width = 1000,
|
|
height = 750,
|
|
showlegend=False,
|
|
polar = dict(
|
|
radialaxis = dict(
|
|
tickfont_size = font_size,
|
|
showticklabels = False),
|
|
angularaxis = dict(
|
|
tickfont_size= font_size,
|
|
rotation = -90,
|
|
direction = 'clockwise',
|
|
tickmode='array',
|
|
tickvals=[0,45,90,135,180,225,270,315],
|
|
ticktext=['12am','3am', '6am','9am','12pm','3pm', '6pm','9pm'],
|
|
hoverformat = ""#"%{theta}: <br>Popularity: %{percent} </br> %{r}"
|
|
),
|
|
),
|
|
)
|
|
|
|
fig.layout.annotations[1].update(x=0.8,y=0.4, font_size=25)
|
|
|
|
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)
|
|
|
|
container=st.container()
|
|
container.plotly_chart(fig, use_container_width=True)
|