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Further enhancements to Species Stats for user testing and feedback

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This commit is contained in:
CaiusX
2022-05-19 11:05:28 +02:00
parent 354fce1aaf
commit ed04568322
1 changed files with 33 additions and 26 deletions
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scripts/plotly_streamlit.py
+17 -10
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@@ -8,6 +8,7 @@ from datetime import timedelta, datetime
from pathlib import Path from pathlib import Path
import sqlite3 import sqlite3
from sqlite3 import Connection from sqlite3 import Connection
import plotly.express as px
userDir = os.path.expanduser('~') userDir = os.path.expanduser('~')
URI_SQLITE_DB = userDir + '/BirdNET-Pi/scripts/birds.db' URI_SQLITE_DB = userDir + '/BirdNET-Pi/scripts/birds.db'
@@ -122,10 +123,14 @@ filt=df2['Com_Name']==specie
df_counts=sum(df5==specie) df_counts=sum(df5==specie)
if resample_time != '1D':
fig = make_subplots( fig = make_subplots(
rows=3, cols =2, rows=3, cols =2,
specs= [[{"type":"xy","rowspan":3}, {"type":"polar","rowspan":2}], [{"rowspan":1}, {"rowspan":1} ], [None, {"type":"xy","rowspan":1}]], 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>', subplot_titles=('<b>Top '+ str(top_N) + ' Species in Date Range '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+' for '+str(resample_sel)+' sampling interval.'+'</b>',
'Total Detect:'+str('{:,}'.format(df_counts))+ 'Total Detect:'+str('{:,}'.format(df_counts))+
' Confidence Max:'+str('{:.2f}%'.format(max(df2[df2['Com_Name']==specie]['Confidence'])*100))+ ' 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)) ' '+' Median:'+str('{:.2f}%'.format(np.median(df2[df2['Com_Name']==specie]['Confidence'])*100))
@@ -155,9 +160,6 @@ detections2= pd.crosstab(df3, df3.index.hour)
d=pd.DataFrame(np.zeros((23,1))).squeeze() d=pd.DataFrame(np.zeros((23,1))).squeeze()
detections = hourly.loc[specie] detections = hourly.loc[specie]
detections=(d+detections).fillna(0) detections=(d+detections).fillna(0)
if resample_time != '1D':
fig.add_trace(go.Barpolar(r = detections, theta=theta), row=1, col=2) fig.add_trace(go.Barpolar(r = detections, theta=theta), row=1, col=2)
fig.update_layout( fig.update_layout(
autosize=False, autosize=False,
@@ -191,15 +193,18 @@ if resample_time != '1D':
else: else:
fig = make_subplots( fig = make_subplots(
rows=1, cols =2, rows=1, cols =2,
# specs= [[{"type":"xy","rowspan":1}], specs= [[{"type":"xy","rowspan":1},{"type":"xy","rowspan":1}]],
# [{"rowspan":1}],
# ],
# subplot_titles=('<b>Top '+ str(top_N) + ' Species in Date Range '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+'</b>', subplot_titles=('<b>Daily Top '+ str(top_N) + ' Species in Date Range '+str(Date_Slider[0])+' to '+str(Date_Slider[1])+'</b>',
'<b>Daily ' + specie+ ' Detections on 15 minute intervals </b>'),
# 'Total Detect:'+str('{:,}'.format(df_counts))+ # 'Total Detect:'+str('{:,}'.format(df_counts))+
# ' Confidence Max:'+str('{:.2f}%'.format(max(df2[df2['Com_Name']==specie]['Confidence'])*100))+ # ' 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)) # ' '+' Median:'+str('{:.2f}%'.format(np.median(df2[df2['Com_Name']==specie]['Confidence'])*100))
# ) # )
) )
fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1,col=1)
df4=df2['Com_Name'][df2['Com_Name']==specie].resample('15min').count() df4=df2['Com_Name'][df2['Com_Name']==specie].resample('15min').count()
df4.index=[df4.index.date, df4.index.time] df4.index=[df4.index.date, df4.index.time]
day_hour_freq=df4.unstack().fillna(0) day_hour_freq=df4.unstack().fillna(0)
@@ -208,11 +213,13 @@ else:
fig_y = [h.strftime('%H:%M') for h in day_hour_freq.columns.tolist()] fig_y = [h.strftime('%H:%M') for h in day_hour_freq.columns.tolist()]
fig_z = day_hour_freq.values.transpose() fig_z = day_hour_freq.values.transpose()
fig_heatmap = go.Figure(data=go.Heatmap(x=fig_x,y=fig_y,z=fig_z)) fig_heatmap = go.Figure(data=go.Heatmap(x=fig_x,y=fig_y,z=fig_z))
fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1,col=1)
fig.update_layout( fig.update_layout(
margin=dict(l=0, r=0, t=50, b=0), margin=dict(l=0, r=0, t=50, b=0),
yaxis={'categoryorder':'total ascending'}) yaxis={'categoryorder':'total ascending'})
fig.add_trace(go.Heatmap(x=fig_x,y=fig_y,z=fig_z, autocolorscale = False, colorscale = 'blackbody'), row=1, col=2) color_pals= px.colors.named_colorscales()
selected_pal = cols2.selectbox('Select Color Pallet for Daily Detections', color_pals)
fig.add_trace(go.Heatmap(x=fig_x,y=fig_y,z=fig_z, autocolorscale = False, colorscale = selected_pal), row=1, col=2)
# container=st.container() # container=st.container()
# config={'displayModelBar': False} # config={'displayModelBar': False}
st.plotly_chart(fig, use_container_width=True) #, config=config) st.plotly_chart(fig, use_container_width=True) #, config=config)