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Revert "Adding Flake8 Github Action for Python Linting "

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This commit is contained in:
Patrick McGuire
2022-05-11 09:05:59 -04:00
committed by GitHub
parent bffd6a889d
commit 528802a0e6
6 changed files with 256 additions and 473 deletions
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.flake8
-3
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@@ -1,3 +0,0 @@
[flake8]
max-line-length = 128
.github/workflows/ci.yml
-23
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@@ -1,23 +0,0 @@
name: CI Jobs
on: pull_request
jobs:
python-lint:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v3
- name: Setup Python
uses: actions/setup-python@v3
with:
python-version: '3.9.x'
cache: 'pip'
architecture: 'x64'
- name: Install flake8
run: pip install flake8
- name: Run Flake8 Lint
uses: py-actions/flake8@v2
scripts/analyze.py
+20 -76
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@@ -1,16 +1,3 @@
#!/usr/bin/env python3
"""
# Example calls
python3 analyze.py --i 'example/XC558716 - Soundscape.mp3' \
--lat 35.4244 --lon -120.7463 --week 18
python3 analyze.py --i 'example/XC563936 - Soundscape.mp3' \
--lat 47.6766 --lon -122.294 --week 11 \
--overlap 1.5 --min_conf 0.25 --sensitivity 1.25 \
--custom_list 'example/custom_species_list.txt'
"""
import argparse
import socket
@@ -24,7 +11,6 @@ ADDR = (SERVER, PORT)
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client.connect(ADDR)
def send(msg):
message = msg.encode(FORMAT)
msg_length = len(message)
@@ -34,7 +20,6 @@ def send(msg):
client.send(message)
print(client.recv(2048).decode(FORMAT))
def main():
global INCLUDE_LIST
@@ -42,62 +27,17 @@ def main():
# Parse passed arguments
parser = argparse.ArgumentParser()
parser.add_argument(
'--i',
help='Path to input file.')
parser.add_argument(
'--o',
default='result.csv',
help='Path to output file. Defaults to result.csv.')
parser.add_argument(
'--lat',
type=float,
default=-1,
help='Recording location latitude. Set -1 to ignore.')
parser.add_argument(
'--lon',
type=float,
default=-1,
help='Recording location longitude. Set -1 to ignore.')
parser.add_argument(
'--week',
type=int,
default=-1,
help='''Week of the year when the recording was made.
Values in [1, 48] (4 weeks per month). Set -1 to ignore.''')
parser.add_argument(
'--overlap',
type=float,
default=0.0,
help='''Overlap in seconds between extracted spectrograms.
Values in [0.0, 2.9]. Defaults tp 0.0.''')
parser.add_argument(
'--sensitivity',
type=float,
default=1.0,
help='''Detection sensitivity;
Higher values result in higher sensitivity.
Values in [0.5, 1.5]. Defaults to 1.0.''')
parser.add_argument(
'--min_conf',
type=float,
default=0.1,
help='''Minimum confidence threshold.
Values in [0.01, 0.99]. Defaults to 0.1.''')
parser.add_argument(
'--include_list',
default='null',
help='''Path to text file containing a list of included species.
Not used if not provided.''')
parser.add_argument(
'--exclude_list',
default='null',
help='''Path to text file containing a list of excluded species.
Not used if not provided.''')
parser.add_argument(
'--birdweather_id',
default='99999',
help='Private Station ID for BirdWeather.')
parser.add_argument('--i', help='Path to input file.')
parser.add_argument('--o', default='result.csv', help='Path to output file. Defaults to result.csv.')
parser.add_argument('--lat', type=float, default=-1, help='Recording location latitude. Set -1 to ignore.')
parser.add_argument('--lon', type=float, default=-1, help='Recording location longitude. Set -1 to ignore.')
parser.add_argument('--week', type=int, default=-1, help='Week of the year when the recording was made. Values in [1, 48] (4 weeks per month). Set -1 to ignore.')
parser.add_argument('--overlap', type=float, default=0.0, help='Overlap in seconds between extracted spectrograms. Values in [0.0, 2.9]. Defaults tp 0.0.')
parser.add_argument('--sensitivity', type=float, default=1.0, help='Detection sensitivity; Higher values result in higher sensitivity. Values in [0.5, 1.5]. Defaults to 1.0.')
parser.add_argument('--min_conf', type=float, default=0.1, help='Minimum confidence threshold. Values in [0.01, 0.99]. Defaults to 0.1.')
parser.add_argument('--include_list', default='null', help='Path to text file containing a list of included species. Not used if not provided.')
parser.add_argument('--exclude_list', default='null', help='Path to text file containing a list of excluded species. Not used if not provided.')
parser.add_argument('--birdweather_id', default='99999', help='Private Station ID for BirdWeather.')
args = parser.parse_args()
@@ -124,15 +64,19 @@ def main():
sockParams += 'lat=' + str(args.lat) + '||'
if args.lon:
sockParams += 'lon=' + str(args.lon) + '||'
send(sockParams)
send(DISCONNECT_MESSAGE)
# time.sleep(3)
###############################################################################
###############################################################################
#time.sleep(3)
###############################################################################
###############################################################################
if __name__ == '__main__':
main()
# Example calls
# python3 analyze.py --i 'example/XC558716 - Soundscape.mp3' --lat 35.4244 --lon -120.7463 --week 18
# python3 analyze.py --i 'example/XC563936 - Soundscape.mp3' --lat 47.6766 --lon -122.294 --week 11 --overlap 1.5 --min_conf 0.25 --sensitivity 1.25 --custom_list 'example/custom_species_list.txt'
scripts/daily_plot.py
+72 -138
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@@ -1,5 +1,6 @@
import sqlite3
import os
import configparser
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
@@ -11,113 +12,80 @@ userDir = os.path.expanduser('~')
conn = sqlite3.connect(userDir + '/BirdNET-Pi/scripts/birds.db')
df = pd.read_sql_query("SELECT * from detections", conn)
cursor = conn.cursor()
cursor.execute(
'SELECT * FROM detections WHERE Date = DATE(\'now\', \'localtime\')')
cursor.execute('SELECT * FROM detections WHERE Date = DATE(\'now\', \'localtime\')')
table_rows = cursor.fetchall()
# df=pd.DataFrame(table_rows)
#df=pd.DataFrame(table_rows)
# Convert Date and Time Fields to Panda's format
df['Date'] = pd.to_datetime(df['Date'])
df['Time'] = pd.to_datetime(df['Time'], unit='ns')
#Convert Date and Time Fields to Panda's format
df['Date']=pd.to_datetime(df['Date'])
df['Time']=pd.to_datetime(df['Time'], unit='ns')
# Add round hours to dataframe
#Add round hours to dataframe
df['Hour of Day'] = [r.hour for r in df.Time]
# Create separate dataframes for separate locations
df_plt = df # Default to use the whole Dbase
#Create separate dataframes for separate locations
df_plt=df #Default to use the whole Dbase
# Get todays readings
#Get todays readings
now = datetime.now()
df_plt_today = df_plt[df_plt['Date'] == now.strftime("%Y-%m-%d")]
df_plt_today = df_plt[df_plt['Date']==now.strftime("%Y-%m-%d")]
# Set number of species to report
readings = 10
#Set number of species to report
readings=10
plt_top10_today = (df_plt_today['Com_Name'].value_counts()[:readings])
df_plt_top10_today = df_plt_today[df_plt_today.Com_Name.isin(
plt_top10_today.index)]
df_plt_top10_today = df_plt_today[df_plt_today.Com_Name.isin(plt_top10_today.index)]
# Set Palette for graphics
#Set Palette for graphics
pal = "Greens"
# Set up plot axes and titles
f, axs = plt.subplots(
1, 2, figsize=(
10, 4), gridspec_kw=dict(
width_ratios=[
3, 6]), facecolor='#77C487')
plt.subplots_adjust(
left=None,
bottom=None,
right=None,
top=None,
wspace=0,
hspace=0)
#Set up plot axes and titles
f, axs = plt.subplots(1, 2, figsize = (10, 4), gridspec_kw=dict(width_ratios=[3, 6]), facecolor='#77C487')
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0, hspace=0)
# generate y-axis order for all figures based on frequency
freq_order = pd.value_counts(
df_plt_top10_today['Com_Name']).iloc[:readings].index
#generate y-axis order for all figures based on frequency
freq_order = pd.value_counts(df_plt_top10_today['Com_Name']).iloc[:readings].index
# make color for max confidence --> this groups by name and calculates max conf
#make color for max confidence --> this groups by name and calculates max conf
confmax = df_plt_top10_today.groupby('Com_Name')['Confidence'].max()
# reorder confmax to detection frequency order
#reorder confmax to detection frequency order
confmax = confmax.reindex(freq_order)
# norm values for color palette
norm = plt.Normalize(confmax.values.min(), confmax.values.max())
colors = plt.cm.Greens(norm(confmax))
# Generate frequency plot
plot = sns.countplot(
y='Com_Name',
data=df_plt_top10_today,
palette=colors,
order=freq_order,
ax=axs[0])
#Generate frequency plot
plot=sns.countplot(y='Com_Name', data = df_plt_top10_today, palette = colors, order=freq_order, ax=axs[0])
# Try plot grid lines between bars - problem at the moment plots grid
# lines on bars - want between bars
z = plot.get_ymajorticklabels()
plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(), 15))
for ticklabel in plot.get_yticklabels()], fontsize=10)
#Try plot grid lines between bars - problem at the moment plots grid lines on bars - want between bars
z=plot.get_ymajorticklabels()
plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(),15)) for ticklabel in plot.get_yticklabels()], fontsize = 10)
plot.set(ylabel=None)
plot.set(xlabel="Detections")
# Generate crosstab matrix for heatmap plot
#Generate crosstab matrix for heatmap plot
heat = pd.crosstab(
df_plt_top10_today['Com_Name'],
df_plt_top10_today['Hour of Day'])
# Order heatmap Birds by frequency of occurrance
heat.index = pd.CategoricalIndex(heat.index, categories=freq_order)
heat = pd.crosstab(df_plt_top10_today['Com_Name'],df_plt_top10_today['Hour of Day'])
#Order heatmap Birds by frequency of occurrance
heat.index = pd.CategoricalIndex(heat.index, categories = freq_order)
heat.sort_index(level=0, inplace=True)
hours_in_day = pd.Series(data=range(0, 24))
heat_frame = pd.DataFrame(data=0, index=heat.index, columns=hours_in_day)
heat = (heat + heat_frame).fillna(0)
hours_in_day = pd.Series(data = range(0,24))
heat_frame = pd.DataFrame(data=0, index=heat.index, columns = hours_in_day)
heat=(heat+heat_frame).fillna(0)
# Generatie heatmap plot
plot = sns.heatmap(
heat,
norm=LogNorm(),
annot=True,
annot_kws={
"fontsize": 7},
fmt="g",
cmap=pal,
square=False,
cbar=False,
linewidths=0.5,
linecolor="Grey",
ax=axs[1],
yticklabels=False)
plot.set_xticklabels(plot.get_xticklabels(), rotation=0, size=7)
#Generatie heatmap plot
plot = sns.heatmap(heat, norm=LogNorm(), annot=True, annot_kws={"fontsize":7}, fmt="g", cmap = pal , square = False, cbar=False, linewidths = 0.5, linecolor = "Grey", ax=axs[1], yticklabels = False)
plot.set_xticklabels(plot.get_xticklabels(), rotation = 0, size = 7)
# Set heatmap border
for _, spine in plot.spines.items():
@@ -125,14 +93,13 @@ for _, spine in plot.spines.items():
plot.set(ylabel=None)
plot.set(xlabel="Hour of Day")
# Set combined plot layout and titles
#Set combined plot layout and titles
f.subplots_adjust(top=0.9)
plt.suptitle("Top 10 Last Updated: " + str(now.strftime("%Y-%m-%d %H:%M")))
plt.suptitle("Top 10 Last Updated: "+ str(now.strftime("%Y-%m-%d %H:%M")))
# Save combined plot
#Save combined plot
userDir = os.path.expanduser('~')
savename = userDir + '/BirdSongs/Extracted/Charts/Combo-' + \
str(now.strftime("%Y-%m-%d")) + '.png'
savename=userDir + '/BirdSongs/Extracted/Charts/Combo-'+str(now.strftime("%Y-%m-%d"))+'.png'
plt.savefig(savename)
plt.show()
plt.close()
@@ -140,32 +107,20 @@ plt.close()
# Get Bottom detection frequency
plt_Bot10_today = (df_plt_today['Com_Name'].value_counts()[-readings:])
df_plt_Bot10_today = df_plt_today[df_plt_today.Com_Name.isin(
plt_Bot10_today.index)]
df_plt_Bot10_today = df_plt_today[df_plt_today.Com_Name.isin(plt_Bot10_today.index)]
# Set Palette for graphics
#Set Palette for graphics
pal = "Reds"
# Set up plot axes and titles
#Set up plot axes and titles
f, axs = plt.subplots(
1, 2, figsize=(
10, 4), gridspec_kw=dict(
width_ratios=[
3, 6]), facecolor='#77C487')
plt.subplots_adjust(
left=None,
bottom=None,
right=None,
top=None,
wspace=0,
hspace=0)
f, axs = plt.subplots(1, 2, figsize = (10, 4), gridspec_kw=dict(width_ratios=[3, 6]), facecolor='#77C487')
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0, hspace=0)
# generate y-axis order for all figures based on frequency
freq_order = pd.value_counts(
df_plt_Bot10_today['Com_Name']).iloc[-readings:].index
#generate y-axis order for all figures based on frequency
freq_order = pd.value_counts(df_plt_Bot10_today['Com_Name']).iloc[-readings:].index
# make color for max confidence --> this groups by name and calculates max conf
#make color for max confidence --> this groups by name and calculates max conf
confmax = df_plt_Bot10_today.groupby('Com_Name')['Confidence'].max()
confmax = confmax.reindex(freq_order)
# probably wrong order . . . how to sort by no. of detections ?
@@ -173,53 +128,33 @@ confmax = confmax.reindex(freq_order)
norm = plt.Normalize(confmax.values.min(), confmax.values.max())
colors = plt.cm.Reds(norm(confmax))
# Generate frequency plot
plot = sns.countplot(
y='Com_Name',
data=df_plt_Bot10_today,
palette=colors,
order=freq_order,
ax=axs[0])
#Generate frequency plot
plot=sns.countplot(y='Com_Name', data = df_plt_Bot10_today, palette = colors, order=freq_order, ax=axs[0])
# Try plot grid lines between bars - problem at the moment plots grid
# lines on bars - want between bars
z = plot.get_ymajorticklabels()
plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(), 15))
for ticklabel in plot.get_yticklabels()], fontsize=10)
#Try plot grid lines between bars - problem at the moment plots grid lines on bars - want between bars
z=plot.get_ymajorticklabels()
plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(),15)) for ticklabel in plot.get_yticklabels()], fontsize = 10)
plot.set(ylabel=None)
plot.set(xlabel="Detections")
# Generate crosstab matrix for heatmap plot
#Generate crosstab matrix for heatmap plot
heat = pd.crosstab(
df_plt_Bot10_today['Com_Name'],
df_plt_Bot10_today['Hour of Day'])
# Order heatmap Birds by frequency of occurrance
heat.index = pd.CategoricalIndex(heat.index, categories=freq_order)
heat = pd.crosstab(df_plt_Bot10_today['Com_Name'],df_plt_Bot10_today['Hour of Day'])
#Order heatmap Birds by frequency of occurrance
heat.index = pd.CategoricalIndex(heat.index, categories = freq_order)
heat.sort_index(level=0, inplace=True)
hours_in_day = pd.Series(data=range(0, 24))
heat_frame = pd.DataFrame(data=0, index=heat.index, columns=hours_in_day)
heat = (heat + heat_frame).fillna(0)
hours_in_day = pd.Series(data = range(0,24))
heat_frame = pd.DataFrame(data=0, index=heat.index, columns = hours_in_day)
heat=(heat+heat_frame).fillna(0)
# Generatie heatmap plot
plot = sns.heatmap(
heat,
norm=LogNorm(),
annot=True,
fmt="g",
annot_kws={
"fontsize": 7},
cmap=pal,
square=False,
cbar=False,
linewidths=0.5,
linecolor="Grey",
ax=axs[1],
yticklabels=False)
plot.set_xticklabels(plot.get_xticklabels(), rotation=0, size=7)
#Generatie heatmap plot
plot = sns.heatmap(heat, norm=LogNorm(), annot=True, fmt="g", annot_kws={"fontsize":7}, cmap = pal , square = False, cbar=False, linewidths = 0.5, linecolor = "Grey", ax=axs[1], yticklabels = False)
plot.set_xticklabels(plot.get_xticklabels(), rotation = 0, size = 7)
# Set heatmap border
for _, spine in plot.spines.items():
@@ -227,13 +162,12 @@ for _, spine in plot.spines.items():
plot.set(ylabel=None)
plot.set(xlabel="Hour of Day")
# Set combined plot layout and titles
#Set combined plot layout and titles
f.subplots_adjust(top=0.9)
plt.suptitle("Bottom 10 Last Updated: " + str(now.strftime("%Y-%m-%d %H:%M")))
plt.suptitle("Bottom 10 Last Updated: "+ str(now.strftime("%Y-%m-%d %H:%M")))
# Save combined plot
savename = userDir + '/BirdSongs/Extracted/Charts/Combo2-' + \
str(now.strftime("%Y-%m-%d")) + '.png'
#Save combined plot
savename=userDir + '/BirdSongs/Extracted/Charts/Combo2-'+str(now.strftime("%Y-%m-%d"))+'.png'
plt.savefig(savename)
plt.show()
plt.close()
scripts/plotly_streamlit.py
+70 -78
View File
@@ -4,7 +4,8 @@ import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from datetime import timedelta
from datetime import timedelta, datetime
from pathlib import Path
import sqlite3
from sqlite3 import Connection
@@ -33,22 +34,22 @@ st.markdown("""
@st.cache(hash_funcs={Connection: id})
def get_connection(path: str):
return sqlite3.connect(path, check_same_thread=False)
def get_connection(path:str):
return sqlite3.connect(path,check_same_thread=False)
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)
# Read in the cereal data
# df = load_data()
df = get_data(conn)
df2 = df.copy()
df2['DateTime'] = pd.to_datetime(df2['Date'] + " " + df2['Time'])
df2 = df2.set_index('DateTime')
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
@@ -58,124 +59,115 @@ df2 = df2.set_index('DateTime')
# Date as slider
Start_Date = pd.to_datetime(df2.index.min()).date()
End_Date = pd.to_datetime(df2.index.max()).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)
)
min_value = Start_Date-timedelta(days=1),
max_value = End_Date,
value=(Start_Date,
End_Date)
)
filt = (df2.index >= pd.Timestamp(Date_Slider[0])) & (df2.index <= pd.Timestamp(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
#Create species count for selected date range
Specie_Count = df2['Com_Name'].value_counts()
Specie_Count=df2['Com_Name'].value_counts()
# Create species treemap
#Create species treemap
# Create Hourly Crosstab
hourly = pd.crosstab(df2['Com_Name'], df2.index.hour, dropna=False)
hourly=pd.crosstab(df2['Com_Name'],df2.index.hour, dropna=False)
# Filter on species
species = list(hourly.index)
cols1, cols2 = st.columns((1, 1))
cols1,cols2= st.columns((1,1))
top_N = cols1.slider(
'Select Number of Birds to Show',
min_value=1,
value=min(10, len(Specie_Count))
)
min_value = 1,
value=min(10,len(Specie_Count))
)
top_N_species = (df2['Com_Name'].value_counts()[:top_N])
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]))
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
font_size=15
# specie filter
filt = df2['Com_Name'] == specie
#specie filter
filt=df2['Com_Name']==specie
df_counts = df2[filt].resample('D').count()
df_counts=df2[filt].resample('D').count()
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(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.annotations[1].update(x=0.7, y=0.25, font_size=15)
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.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)
#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'})
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()
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), row=1, col=2)
detections=(d+detections).fillna(0)
fig.add_trace(go.Barpolar(r = detections, theta=theta), row=1, col=2)
fig.update_layout(
autosize=False,
width=1000,
height=500,
width = 1000,
height = 500,
showlegend=False,
polar=dict(
radialaxis=dict(
tickfont_size=font_size,
showticklabels=True,
hoverformat="#%{theta}: <br>Popularity: %{percent} </br> %{r}"
),
angularaxis=dict(
tickfont_size=font_size,
rotation=-90,
direction='clockwise',
polar = dict(
radialaxis = dict(
tickfont_size = font_size,
showticklabels = True,
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}"
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(df2['Com_Name'], df2.index.date, dropna=False)
daily=pd.crosstab(df2['Com_Name'],df2.index.date, dropna=False)
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)
st.plotly_chart(fig, use_container_width=True) #, config=config)
# cols3,cols4=st.columns((1,1))
#
#
# extract_date=Date_Slider
#
# 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') # noqa: E501
#
# 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')
# audio_bytes = audio_file.read()
# cols4.audio(audio_bytes, format='audio/mp3')
scripts/server.py
+94 -155
View File
@@ -1,27 +1,32 @@
import os
import socket
import socket
import threading
import operator
import librosa
import numpy as np
import math
import time
import json
import requests
import sqlite3
import datetime
from tzlocal import get_localzone
from pathlib import Path
import apprise
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = ''
try:
import tflite_runtime.interpreter as tflite
except BaseException:
except:
from tensorflow import lite as tflite
import argparse
import operator
import librosa
import numpy as np
import math
import time
from decimal import Decimal
import json
import requests
import sqlite3
import datetime
from time import sleep
import pytz
from tzlocal import get_localzone
from pathlib import Path
import apprise
HEADER = 64
PORT = 5050
SERVER = socket.gethostbyname(socket.gethostname())
@@ -32,9 +37,10 @@ DISCONNECT_MESSAGE = "!DISCONNECT"
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
try:
server.bind(ADDR)
except BaseException:
except:
print("Waiting on socket")
time.sleep(5)
# Open most recent Configuration and grab DB_PWD as a python variable
@@ -42,8 +48,7 @@ userDir = os.path.expanduser('~')
with open(userDir + '/BirdNET-Pi/scripts/thisrun.txt', 'r') as f:
this_run = f.readlines()
audiofmt = "." + str(str(str([i for i in this_run if i.startswith('AUDIOFMT')]).split('=')[1]).split('\\')[0])
priv_thresh = float(
"." + str(str(str([i for i in this_run if i.startswith('PRIVACY_THRESHOLD')]).split('=')[1]).split('\\')[0])) / 10
priv_thresh = float("." + str(str(str([i for i in this_run if i.startswith('PRIVACY_THRESHOLD')]).split('=')[1]).split('\\')[0]))/10
def loadModel():
@@ -57,7 +62,7 @@ def loadModel():
# Load TFLite model and allocate tensors.
modelpath = userDir + '/BirdNET-Pi/model/BirdNET_6K_GLOBAL_MODEL.tflite'
myinterpreter = tflite.Interpreter(model_path=modelpath, num_threads=2)
myinterpreter = tflite.Interpreter(model_path=modelpath,num_threads=2)
myinterpreter.allocate_tensors()
# Get input and output tensors.
@@ -80,7 +85,6 @@ def loadModel():
return myinterpreter
def loadCustomSpeciesList(path):
slist = []
@@ -91,7 +95,6 @@ def loadCustomSpeciesList(path):
return slist
def splitSignal(sig, rate, overlap, seconds=3.0, minlen=1.5):
# Split signal with overlap
@@ -102,18 +105,17 @@ def splitSignal(sig, rate, overlap, seconds=3.0, minlen=1.5):
# End of signal?
if len(split) < int(minlen * rate):
break
# Signal chunk too short? Fill with zeros.
if len(split) < int(rate * seconds):
temp = np.zeros((int(rate * seconds)))
temp[:len(split)] = split
split = temp
sig_splits.append(split)
return sig_splits
def readAudioData(path, overlap, sample_rate=48000):
print('READING AUDIO DATA...', end=' ', flush=True)
@@ -128,12 +130,11 @@ def readAudioData(path, overlap, sample_rate=48000):
return chunks
def convertMetadata(m):
# Convert week to cosine
if m[2] >= 1 and m[2] <= 48:
m[2] = math.cos(math.radians(m[2] * 7.5)) + 1
m[2] = math.cos(math.radians(m[2] * 7.5)) + 1
else:
m[2] = -1
@@ -146,11 +147,9 @@ def convertMetadata(m):
return np.concatenate([m, mask])
def custom_sigmoid(x, sensitivity=1.0):
return 1 / (1.0 + np.exp(-sensitivity * x))
def predict(sample, sensitivity):
global INTERPRETER
# Make a prediction
@@ -167,22 +166,21 @@ def predict(sample, sensitivity):
# Sort by score
p_sorted = sorted(p_labels.items(), key=operator.itemgetter(1), reverse=True)
# #print("DATABASE SIZE:", len(p_sorted))
# #print("HUMAN-CUTOFF AT:", int(len(p_sorted)*priv_thresh)/10)
#
#
# # Remove species that are on blacklist
human_cutoff = max(10, int(len(p_sorted) * priv_thresh))
human_cutoff = max(10,int(len(p_sorted)*priv_thresh))
for i in range(min(10, len(p_sorted))):
if p_sorted[i][0] == 'Human_Human':
if p_sorted[i][0]=='Human_Human':
with open(userDir + '/BirdNET-Pi/HUMAN.txt', 'a') as rfile:
rfile.write(str(datetime.datetime.now()) + str(p_sorted[i]) + ' ' + str(human_cutoff) + '\n')
rfile.write(str(datetime.datetime.now())+str(p_sorted[i])+ ' ' + str(human_cutoff)+ '\n')
return p_sorted[:human_cutoff]
def analyzeAudioData(chunks, lat, lon, week, sensitivity, overlap,):
global INTERPRETER
@@ -204,57 +202,47 @@ def analyzeAudioData(chunks, lat, lon, week, sensitivity, overlap,):
# Make prediction
p = predict([sig, mdata], sensitivity)
# print("PPPPP",p)
HUMAN_DETECTED = False
# Catch if Human is recognized
HUMAN_DETECTED=False
#Catch if Human is recognized
for x in range(len(p)):
if "Human" in p[x][0]:
HUMAN_DETECTED = True
HUMAN_DETECTED=True
# Save result and timestamp
pred_end = pred_start + 3.0
# If human detected set all detections to human to make sure voices are not saved
if HUMAN_DETECTED is True:
p = [('Human_Human', 0.0)] * 10
#If human detected set all detections to human to make sure voices are not saved
if HUMAN_DETECTED == True:
p=[('Human_Human',0.0)]*10
detections[str(pred_start) + ';' + str(pred_end)] = p
pred_start = pred_end - overlap
print('DONE! Time', int((time.time() - start) * 10) / 10.0, 'SECONDS')
# print('DETECTIONS:::::',detections)
return detections
def sendAppriseNotifications(species, confidence):
def sendAppriseNotifications(species,confidence):
if os.path.exists(userDir + '/BirdNET-Pi/apprise.txt') and os.path.getsize(userDir + '/BirdNET-Pi/apprise.txt') > 0:
with open(userDir + '/BirdNET-Pi/scripts/thisrun.txt', 'r') as f:
this_run = f.readlines()
title = str(str(str([i for i in this_run if i.startswith('APPRISE_NOTIFICATION_TITLE')]
).split('=')[1]).split('\\')[0]).replace('"', '')
body = str(str(str([i for i in this_run if i.startswith('APPRISE_NOTIFICATION_BODY')]
).split('=')[1]).split('\\')[0]).replace('"', '')
title = str(str(str([i for i in this_run if i.startswith('APPRISE_NOTIFICATION_TITLE')]).split('=')[1]).split('\\')[0]).replace('"', '')
body = str(str(str([i for i in this_run if i.startswith('APPRISE_NOTIFICATION_BODY')]).split('=')[1]).split('\\')[0]).replace('"', '')
if str(str(str([i for i in this_run if i.startswith('APPRISE_NOTIFY_EACH_DETECTION')]).split('=')[1]).split('\\')[0]) == "1": # noqa E501
if str(str(str([i for i in this_run if i.startswith('APPRISE_NOTIFY_EACH_DETECTION')]).split('=')[1]).split('\\')[0]) == "1":
apobj = apprise.Apprise()
config = apprise.AppriseConfig()
config.add(userDir + '/BirdNET-Pi/apprise.txt')
apobj.add(config)
apobj.notify(
body=body.replace(
"$sciname",
species.split("_")[0]).replace(
"$comname",
species.split("_")[1]).replace(
"$confidence",
confidence),
body=body.replace("$sciname",species.split("_")[0]).replace("$comname",species.split("_")[1]).replace("$confidence",confidence),
title=title,
)
def writeResultsToFile(detections, min_conf, path):
print('WRITING RESULTS TO', path, '...', end=' ')
@@ -263,15 +251,13 @@ def writeResultsToFile(detections, min_conf, path):
rfile.write('Start (s);End (s);Scientific name;Common name;Confidence\n')
for d in detections:
for entry in detections[d]:
if entry[1] >= min_conf and ((entry[0] in INCLUDE_LIST or len(INCLUDE_LIST) == 0)
and (entry[0] not in EXCLUDE_LIST or len(EXCLUDE_LIST) == 0)):
sendAppriseNotifications(str(entry[0]), str(entry[1]))
if entry[1] >= min_conf and ((entry[0] in INCLUDE_LIST or len(INCLUDE_LIST) == 0) and (entry[0] not in EXCLUDE_LIST or len(EXCLUDE_LIST) == 0) ):
sendAppriseNotifications(str(entry[0]),str(entry[1]));
rfile.write(d + ';' + entry[0].replace('_', ';') + ';' + str(entry[1]) + '\n')
rcnt += 1
print('DONE! WROTE', rcnt, 'RESULTS.')
return
def handle_client(conn, addr):
global INCLUDE_LIST
global EXCLUDE_LIST
@@ -286,10 +272,10 @@ def handle_client(conn, addr):
if msg == DISCONNECT_MESSAGE:
connected = False
else:
# print(f"[{addr}] {msg}")
#print(f"[{addr}] {msg}")
args = type('', (), {})()
args.i = ''
args.o = ''
args.birdweather_id = '99999'
@@ -300,7 +286,8 @@ def handle_client(conn, addr):
args.sensitivity = 1.25
args.min_conf = 0.70
args.lat = -1
args.lon = -1
args.lon = -1
for line in msg.split('||'):
inputvars = line.split('=')
@@ -327,12 +314,14 @@ def handle_client(conn, addr):
elif inputvars[0] == 'lon':
args.lon = float(inputvars[1])
# Load custom species lists - INCLUDED and EXCLUDED
if not args.include_list == 'null':
INCLUDE_LIST = loadCustomSpeciesList(args.include_list)
else:
INCLUDE_LIST = []
if not args.exclude_list == 'null':
EXCLUDE_LIST = loadCustomSpeciesList(args.exclude_list)
else:
@@ -344,7 +333,7 @@ def handle_client(conn, addr):
audioData = readAudioData(args.i, args.overlap)
# Get Date/Time from filename in case Pi gets behind
# now = datetime.now()
#now = datetime.now()
full_file_name = args.i
print('FULL FILENAME: -' + full_file_name + '-')
file_name = Path(full_file_name).stem
@@ -352,14 +341,14 @@ def handle_client(conn, addr):
file_time = file_name.split('-birdnet-')[1]
date_time_str = file_date + ' ' + file_time
date_time_obj = datetime.datetime.strptime(date_time_str, '%Y-%m-%d %H:%M:%S')
# print('Date:', date_time_obj.date())
# print('Time:', date_time_obj.time())
#print('Date:', date_time_obj.date())
#print('Time:', date_time_obj.time())
print('Date-time:', date_time_obj)
now = date_time_obj
current_date = now.strftime("%Y-%m-%d")
current_time = now.strftime("%H:%M:%S")
current_iso8601 = now.astimezone(get_localzone()).isoformat()
week_number = int(now.strftime("%V"))
week = max(1, min(week_number, 48))
@@ -371,33 +360,32 @@ def handle_client(conn, addr):
# Write detections to output file
min_conf = max(0.01, min(args.min_conf, 0.99))
writeResultsToFile(detections, min_conf, args.o)
###############################################################################
###############################################################################
###############################################################################
###############################################################################
soundscape_uploaded = False
# Write detections to Database
myReturn = ''
for i in detections:
myReturn += str(i) + '-' + str(detections[i][0]) + '\n'
myReturn += str(i) + '-' + str(detections[i][0]) + '\n'
with open(userDir + '/BirdNET-Pi/BirdDB.txt', 'a') as rfile:
for d in detections:
for entry in detections[d]:
if entry[1] >= min_conf and ((entry[0] in INCLUDE_LIST or len(INCLUDE_LIST) == 0) and (
entry[0] not in EXCLUDE_LIST or len(EXCLUDE_LIST) == 0)):
rfile.write(str(current_date) + ';' + str(current_time) + ';' + entry[0].replace('_', ';') + ';'
+ str(entry[1]) + ";" + str(args.lat) + ';' + str(args.lon) +
';' + str(min_conf) + ';' + str(week) + ';'
+ str(args.sensitivity) + ';' + str(args.overlap) + '\n')
if entry[1] >= min_conf and ((entry[0] in INCLUDE_LIST or len(INCLUDE_LIST) == 0) and (entry[0] not in EXCLUDE_LIST or len(EXCLUDE_LIST) == 0) ):
rfile.write(str(current_date) + ';' + str(current_time) + ';' + entry[0].replace('_', ';') + ';' \
+ str(entry[1]) +";" + str(args.lat) + ';' + str(args.lon) + ';' + str(min_conf) + ';' + str(week) + ';' \
+ str(args.sensitivity) +';' + str(args.overlap) + '\n')
Date = str(current_date)
Time = str(current_time)
species = entry[0]
Sci_Name, Com_Name = species.split('_')
Sci_Name,Com_Name = species.split('_')
score = entry[1]
Confidence = str(round(score * 100))
Confidence = str(round(score*100))
Lat = str(args.lat)
Lon = str(args.lon)
Cutoff = str(args.min_conf)
@@ -406,91 +394,47 @@ def handle_client(conn, addr):
Overlap = str(args.overlap)
Com_Name = Com_Name.replace("'", "")
File_Name = Com_Name.replace(" ", "_") + '-' + Confidence + '-' + \
Date.replace("/", "-") + '-birdnet-' + Time + audiofmt
Date.replace("/", "-") + '-birdnet-' + Time + audiofmt
# Connect to SQLite Database
#Connect to SQLite Database
for attempt_number in range(3):
try:
try:
con = sqlite3.connect(userDir + '/BirdNET-Pi/scripts/birds.db')
cur = con.cursor()
cur.execute(
"INSERT INTO detections VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
(Date,
Time,
Sci_Name,
Com_Name,
str(score),
Lat,
Lon,
Cutoff,
Week,
Sens,
Overlap,
File_Name))
cur.execute("INSERT INTO detections VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", (Date, Time, Sci_Name, Com_Name, str(score), Lat, Lon, Cutoff, Week, Sens, Overlap, File_Name))
con.commit()
con.close()
break
except BaseException:
except:
print("Database busy")
time.sleep(2)
print(str(current_date) +
';' +
str(current_time) +
';' +
entry[0].replace('_', ';') +
';' +
str(entry[1]) +
';' +
str(args.lat) +
';' +
str(args.lon) +
';' +
str(min_conf) +
';' +
str(week) +
';' +
str(args.sensitivity) +
';' +
str(args.overlap) +
Com_Name.replace(" ", "_") +
'-' +
str(score) +
'-' +
str(current_date) +
'-birdnet-' +
str(current_time) +
audiofmt +
'\n')
print(str(current_date) + ';' + str(current_time) + ';' + entry[0].replace('_', ';') + ';' + str(entry[1]) + ';' + str(args.lat) + ';' + str(args.lon) + ';' + str(min_conf) + ';' + str(week) + ';' + str(args.sensitivity) +';' + str(args.overlap) + Com_Name.replace(" ", "_") + '-' + str(score) + '-' + str(current_date) + '-birdnet-' + str(current_time) + audiofmt + '\n')
if birdweather_id != "99999":
try:
if soundscape_uploaded is False:
# POST soundscape to server
soundscape_url = "https://app.birdweather.com/api/v1/stations/" + \
birdweather_id + "/soundscapes" + "?timestamp=" + current_iso8601
soundscape_url = "https://app.birdweather.com/api/v1/stations/" + birdweather_id + "/soundscapes" + "?timestamp=" + current_iso8601
with open(args.i, 'rb') as f:
wav_data = f.read()
response = requests.post(
url=soundscape_url, data=wav_data, headers={
'Content-Type': 'application/octet-stream'})
response = requests.post(url=soundscape_url, data=wav_data, headers={'Content-Type': 'application/octet-stream'})
print("Soundscape POST Response Status - ", response.status_code)
sdata = response.json()
soundscape_id = sdata['soundscape']['id']
soundscape_uploaded = True
# POST detection to server
detection_url = "https://app.birdweather.com/api/v1/stations/" + \
birdweather_id + "/detections"
detection_url = "https://app.birdweather.com/api/v1/stations/" + birdweather_id + "/detections"
start_time = d.split(';')[0]
end_time = d.split(';')[1]
post_begin = "{ "
now_p_start = now + datetime.timedelta(seconds=float(start_time))
current_iso8601 = now_p_start.astimezone(get_localzone()).isoformat()
post_timestamp = "\"timestamp\": \"" + current_iso8601 + "\","
post_timestamp = "\"timestamp\": \"" + current_iso8601 + "\","
post_lat = "\"lat\": " + str(args.lat) + ","
post_lon = "\"lon\": " + str(args.lon) + ","
post_soundscape_id = "\"soundscapeId\": " + str(soundscape_id) + ","
@@ -501,23 +445,18 @@ def handle_client(conn, addr):
post_algorithm = "\"algorithm\": " + "\"alpha\"" + ","
post_confidence = "\"confidence\": " + str(entry[1])
post_end = " }"
post_json = post_begin + \
post_timestamp + post_lat + post_lon + \
post_soundscape_id + post_soundscape_start_time + \
post_soundscape_end_time + post_commonName + post_scientificName + \
post_algorithm + post_confidence + post_end
post_json = post_begin + post_timestamp + post_lat + post_lon + post_soundscape_id + post_soundscape_start_time + post_soundscape_end_time + post_commonName + post_scientificName + post_algorithm + post_confidence + post_end
print(post_json)
response = requests.post(detection_url, json=json.loads(post_json))
print("Detection POST Response Status - ", response.status_code)
except BaseException:
except:
print("Cannot POST right now")
conn.send(myReturn.encode(FORMAT))
# time.sleep(3)
conn.close()
#time.sleep(3)
conn.close()
def start():
# Load model