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Updated Species stats with start of Species verification

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This commit is contained in:
CaiusX
2022-05-31 17:39:25 +02:00
parent d5a2dbae58
commit 6ef05c2bc3
1 changed files with 207 additions and 101 deletions
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scripts/plotly_streamlit.py
+134 -28
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@@ -6,11 +6,13 @@ from numpy import ma
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly.io as pio
from datetime import timedelta
from datetime import timedelta, datetime
from pathlib import Path
import sqlite3
from sqlite3 import Connection
import plotly.express as px
from sklearn.preprocessing import normalize
import time
pio.templates.default = "plotly_white"
@@ -18,7 +20,7 @@ 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
st.markdown("""
<style>
@@ -38,8 +40,9 @@ st.markdown("""
""", unsafe_allow_html=True)
# @st.cache(hash_funcs={Connection: id})
@st.cache(allow_output_mutation=True)
@st.cache(hash_funcs={Connection: id})
#@st.cache(allow_output_mutation=True)
def get_connection(path: str):
return sqlite3.connect(path, check_same_thread=False)
@@ -62,7 +65,7 @@ if daily:
Start_Date = pd.to_datetime(df2.index.min()).date()
End_Date = pd.to_datetime(df2.index.max()).date()
# cols1, cols2 = st.columns((1, 1))
end_date = st.sidebar.slider('Date to View',
end_date = st.sidebar.date_input('Date to View',
min_value = Start_Date,
max_value = End_Date,
value=(End_Date),
@@ -90,14 +93,12 @@ else:
# start_date = datetime(2022 ,5 ,17).date()
# end_date = datetime(2022 ,5 ,17).date()
@st.cache()
def date_filter(df, start_date, end_date):
filt = (df2.index >= pd.Timestamp(start_date)) & (df2.index <= pd.Timestamp(end_date + timedelta(days=1)))
df = df[filt]
return(df)
df2 = date_filter(df2, start_date, end_date)
st.write('<style>div.row-widget.stRadio > div{flex-direction:row;justify-content: left;} </style>',
@@ -133,7 +134,6 @@ else:
}
resample_time = resample_times[resample_sel]
@st.cache()
def time_resample(df, resample_time):
if resample_time == 'Raw':
@@ -143,8 +143,6 @@ def time_resample(df, resample_time):
df_resample = df.resample(resample_time)['Com_Name'].aggregate('unique').explode()
return(df_resample)
top_bird = df2['Com_Name'].mode()[0]
df5 = time_resample(df2, resample_time)
@@ -153,10 +151,10 @@ df5 = time_resample(df2, resample_time)
Specie_Count = df5.value_counts()
# Create Hourly Crosstab
hourly = pd.crosstab(df5, df5.index.hour, dropna=False)
hourly = pd.crosstab(df5, df5.index.hour, dropna=True, margins= True)
# Filter on species
species = list(hourly.index)
species = list(hourly.sort_values("All", ascending= False).index)
#cols1, cols2 = st.columns((1, 1))
top_N = st.sidebar.slider(
@@ -170,35 +168,36 @@ top_N_species = (df5.value_counts()[:top_N])
font_size = 15
if daily is False:
if daily == False:
if resample_time != '1D':
specie = st.selectbox(
'Which bird would you like to explore for the dates '
+ str(start_date) + ' to ' + str(end_date) + '?',
species,
index=species.index(top_bird))
index = 0)
filt = df2['Com_Name'] == specie
df_counts = sum(df5 == specie)
if resample_time != '1D':
# filt = df2['Com_Name'] == specie
if specie == 'All':
df_counts = int(hourly[hourly.index==specie]['All'])
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(start_date) + ' to ' + str(
end_date) + '<br>for ' + str(resample_sel) + ' sampling interval.' + '</b>',
'Total Detect:' + str('{:,}'.format(df_counts)) +
' 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))
end_date) + ' for ' + str(resample_sel) + ' sampling interval.' + '</b>',
'Total Detect:' + str('{:,}'.format(df_counts))
# + ' 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', marker_color='seagreen'), row=1, col=1)
fig.update_layout(
@@ -242,21 +241,128 @@ if daily is False:
),
)
daily = pd.crosstab(df5, df5.index.date, dropna=False)
daily = pd.crosstab(df5, df5.index.date, dropna=True, margins = True)
fig.add_trace(go.Bar(x=daily.columns[:-1], y=daily.loc[specie][:-1], marker_color='seagreen'), row=3, col=2)
st.plotly_chart(fig, use_container_width=True) # , config=config)
fig.add_trace(go.Bar(x=daily.columns, y=daily.loc[specie], marker_color='seagreen'), row=3, col=2)
else:
col1, col2 = st.columns(2)
with col1:
fig = make_subplots(
rows=3, cols=1,
specs=[[{"type": "polar", "rowspan": 2}],[{"rowspan": 1}], [{"type": "xy", "rowspan": 1}]]
)
# Set 360 degrees, 24 hours for polar plot
theta = np.linspace(0.0, 360, 24, endpoint=False)
specie_filt = df5 == specie
df3 = df5[specie_filt]
detections2 = pd.crosstab(df3, df3.index.hour)
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, marker_color='seagreen'), row=1, col=1)
fig.update_layout(
autosize=False,
width=1000,
height=500,
showlegend=False,
polar=dict(
radialaxis=dict(
tickfont_size=font_size,
showticklabels=False,
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(df5, df5.index.date, dropna=True, margins = True)
fig.add_trace(go.Bar(x=daily.columns[:-1], y=daily.loc[specie][:-1], marker_color='seagreen'), row=3, col=1)
st.plotly_chart(fig, use_container_width=True) # , config=config)
df_counts = int(hourly[hourly.index==specie]['All'])
st.subheader('Total Detect:' + str('{:,}'.format(df_counts))
+ ' 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)))
recordings=df2[df2['Com_Name']==specie]['File_Name']
with col2:
try:
recording = st.selectbox('Available recordings', recordings.sort_index(ascending=False))
date_specie = df2.loc[df2['File_Name']==recording,['Date','Com_Name']]
date_dir = date_specie['Date'].values[0]
specie_dir = date_specie['Com_Name'].values[0].replace(" ","_")
st.image(userDir + '/BirdSongs/Extracted/By_Date/'+ date_dir + '/'+ specie_dir + '/' + recording + '.png')
st.audio(userDir +'/BirdSongs/Extracted/By_Date/'+ date_dir + '/'+ specie_dir + '/' + recording)
except:
st.title('RECORDING NOT AVAILABLE :(')
# try:
# con = sqlite3.connect(userDir + '/BirdNET-Pi/scripts/birds.db')
# cur = con.cursor()
cola, colb, colc, cold = st.columns((3,1,1,1))
with colb:
seen = st.checkbox('Reviewed')
if seen:
with colc:
verified = st.radio("Verification",['True Positive','False Positive'])
if verified == "False Positive":
df_names = pd.read_csv(userDir+'/BirdNET-Pi/model/labels.txt', delimiter= '_', names=['Sci_Name', 'Com_Name'])
df_unknown= pd.DataFrame({"Sci_Name":["UNKNOWN"],"Com_Name":["UNKNOWN"]})
df_names = pd.concat([df_unknown,df_names], ignore_index=True)
with cold:
corrected = st.selectbox('What specie?', df_names['Com_Name'])
# cur.execute("UPDATE detections SET Seen = seen WHERE File_Name = recording")
# con.commit()
# con.close()
# except BaseException:
# print("Database busy")
# time.sleep(2)
else:
specie = st.selectbox(
'Which bird would you like to explore for the dates '
+ str(start_date) + ' to ' + str(end_date) + '?',
species[1:],
index = 0)
# filt = df2[df2['Com_Name'] == specie]
df_counts = int(hourly[hourly.index==specie]['All'])
fig = st.container()
fig = make_subplots(
rows=1, cols =1)
# specs= [[{"type":"xy","rowspan":1},{"type":"heatmap","rowspan":1}]],
# subplot_titles=('<b>Daily Top '+ str(top_N) + ' Species in Date Range '+ str(start_date) +' to '+ str(end_date) +'</b>',
# '<b>Daily ' + specie+ ' Detections on 15 minute intervals </b>'),
# # 'Total Detect:'+str('{:,}'.format(df_counts))+
# # ' 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.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()
@@ -274,7 +380,7 @@ if daily is False:
color_pals= px.colors.named_colorscales()
selected_pal = st.sidebar.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=1)
st.plotly_chart(fig, use_container_width=True) # , config=config)
else:
fig = make_subplots(
rows=1, cols=2,