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new plotly and better recording service

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This commit is contained in:
mcguirepr89
2022-03-31 08:14:18 -04:00
parent 06eab6b163
commit e5c132edf7
2 changed files with 98 additions and 58 deletions
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scripts/plotly_streamlit.py
+96 -58
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@@ -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 = get_data(conn)
# 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_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()))
# 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())
End_Date = pd.to_datetime(df2.index.max())
Date_Slider = st.sidebar.slider('Date Range',
value=(Start_Date.to_pydatetime(),
End_Date.to_pydatetime())
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 >= 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))),
value=min(10,len(Specie_Count)-1))
min_value = 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,
specs= [[{"type":"xy","rowspan":2}, {"type":"polar"}], [None, {"type":"xy"}]],
subplot_titles=('<b style="font-size:x-large;">Species in Date Range</b>',
'<b style="font-size:large;">'+specie+'</b><br>'
'<span style="font-size:medium;">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))+'</span>'
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.height=900
# fig.layout.width=1500
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=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]
radius=(d+radius).fillna(0)
fig.add_trace(go.Barpolar(r = radius, theta=theta), row=1, col=2)
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=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],
ticktext=['12am','3am', '6am','9am','12pm','3pm', '6pm','9pm'],
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}"
),
),
)
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()
container.plotly_chart(fig, use_container_width=True)
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/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')