Merge branch 'main' of github.com:mcguirepr89/BirdNET-Pi

.flake8

@@ -0,0 +1,2 @@
+[flake8]
+max-line-length = 160

.github/workflows/python-ci.yml

@@ -13,9 +13,6 @@ jobs:
     steps:
       - name: Checkout
         uses: actions/checkout@v3
-        with:
-          # This is a bit heavy handed, but we need `origin/main` to get a ref to diff against
-          fetch-depth: 0
 
       - name: Setup Python
         uses: actions/setup-python@v3
@@ -29,5 +26,4 @@ jobs:
 
       - name: Run Flake8 Lint
         run: |
-          DIFF="$(git --no-pager diff -u $(git merge-base HEAD origin/main) -- '**/*.py')"
+          flake8
-          echo "$DIFF" | flake8 --diff

README.md

@@ -46,7 +46,7 @@ Currently listening in these countries . . . that I know of . . .
 * 24/7 recording and BirdNET-Lite analysis
 * Automatic extraction of detected data (creating audio clips of detected bird sounds)
 * Spectrograms available for all extractions
-* Live audio stream
+* Live audio stream & spectrogram
 * [BirdWeather](https://app.birdweather.com) integration -- you can request a BirdWeather ID from BirdNET-Pi's "Tools" > "Settings" page
 * Web interface access to all data and logs provided by [Caddy](https://caddyserver.com)
 * Web Terminal
@@ -110,6 +110,7 @@ I hope that if you find BirdNET-Pi has been worth your time, you will share your
 
 - [Marie Lelouche's <i>Out of Spaces</i>](https://www.lestanneries.fr/exposition/marie-lelouche-out-of-spaces/) using BirdNET-Pi in post-sculpture VR! [Press Kit](https://github.com/mcguirepr89/BirdNET-Pi-assets/blob/main/dp_out_of_spaces_marie_lelouche_digital_05_01_22.pdf)
 - [Research on noded BirdNET-Pi networks for farming](https://github.com/mcguirepr89/BirdNET-Pi-assets/blob/main/G23_Report_ModelBasedSysEngineering_FarmMarkBirdDetector_V1__Copy_.pdf)
+- [PixCams Build Guide](https://pixcams.com/building-a-birdnet-pi-real-time-acoustic-bird-id-station/)
 - <ins>[Core-Electronics](https://core-electronics.com.au/projects/bird-calls-raspberry-pi)</ins> Build Article
 
 

homepage/style.css

@@ -551,9 +551,11 @@ button:hover {
   width:25px !important;
   height:25px !important;
 }
+
 .relative {
   position:relative;
 }
+
 .sortbutton {
   margin-top:10px;
   font-size:x-large;
@@ -561,6 +563,7 @@ button:hover {
   padding:5px;
   box-shadow: 0px 0px 5px 2px rgba(0, 0, 0, 0.10);
 }
+
 button.legacyview {
   color:gray;
   margin:5px;
@@ -573,6 +576,7 @@ button.legacyview {
   transition:box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);
   box-shadow:0px 3px 1px -2px rgb(0 0 0 / 20%), 0px 2px 2px 0px rgb(0 0 0 / 14%), 0px 1px 5px 0px rgb(0 0 0 / 12%);
 }
+
 button.legacyview:hover  { 
   box-shadow:0px 2px 4px -1px rgb(0 0 0 / 20%), 0px 4px 5px 0px rgb(0 0 0 / 14%), 0px 1px 10px 0px rgb(0 0 0 / 12%);
 }
@@ -586,12 +590,15 @@ button.loadmore {
   box-shadow:0px 3px 1px -2px rgb(0 0 0 / 20%), 0px 2px 2px 0px rgb(0 0 0 / 14%), 0px 1px 5px 0px rgb(0 0 0 / 12%);
   
 }
+
 button.loadmore:hover { 
   box-shadow:0px 2px 4px -1px rgb(0 0 0 / 20%), 0px 4px 5px 0px rgb(0 0 0 / 14%), 0px 1px 10px 0px rgb(0 0 0 / 12%);
 }
+
 #searchterm {
   transition:box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);
 }
+
 #searchterm:hover {
   box-shadow:0px 2px 4px -1px rgb(0 0 0 / 20%), 0px 4px 5px 0px rgb(0 0 0 / 14%), 0px 1px 10px 0px rgb(0 0 0 / 12%);
 }
@@ -599,29 +606,36 @@ button.loadmore:hover {
 tr {
   background-color:#9fe29b;
 }
+
 .history.centered form {
   display:flex;
   justify-content: center;
 }
+
 .history.centered input {
   margin-right:5px;
   border:0px;
 }
+
 .centered form#views button {
   box-shadow: 0px 0px 5px 2px rgba(0, 0, 0, 0.10);
   margin:2px;
   transition:box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);
   box-shadow:0px 3px 1px -2px rgb(0 0 0 / 20%), 0px 2px 2px 0px rgb(0 0 0 / 14%), 0px 1px 5px 0px rgb(0 0 0 / 12%);
 }
+
 .centered form#views button:hover {
   box-shadow:0px 2px 4px -1px rgb(0 0 0 / 20%), 0px 4px 5px 0px rgb(0 0 0 / 14%), 0px 1px 10px 0px rgb(0 0 0 / 12%);
 }
+
 input {
   box-shadow: 0px 0px 17px 1px rgba(0, 0, 0, 0.10);
 }
+
 dialog {
   border:none;
 }
+
 dialog::backdrop {
   background: repeating-linear-gradient(
     30deg,
@@ -632,12 +646,14 @@ dialog::backdrop {
   );
   backdrop-filter: blur(1px)
 }
+
 .centered_image_container {
   font-size:19px !important;
   display:inline-block;
   position:relative;
   margin-bottom:3px;
 }
+
 .centered_image_container img.img1 {
   transition:box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);
   box-shadow:0px 3px 1px -2px rgb(0 0 0 / 20%), 0px 2px 2px 0px rgb(0 0 0 / 14%), 0px 1px 5px 0px rgb(0 0 0 / 12%);
@@ -649,19 +665,27 @@ dialog::backdrop {
   border-radius: 5px;
   width:unset;
 }
+
 .centered_image_container img.img1:hover{
   opacity:0.8;
   box-shadow:0px 2px 4px -1px rgb(0 0 0 / 20%), 0px 4px 5px 0px rgb(0 0 0 / 14%), 0px 1px 10px 0px rgb(0 0 0 / 12%);
 }
+
 .centered_image_container * {
   font-size:19px !important;
 }
+
 .centered_image_container form {
   margin-bottom:0px;
 }
+
 .brbanner {
   padding:15px;
   background-color:rgb(159, 226, 155);
   text-align:center;
   font-size:large;
+}
+
+#gain.centered {
+  margin-bottom:10px;
 }

homepage/views.php

@@ -12,6 +12,9 @@ body::-webkit-scrollbar {
 <form action="" method="GET" id="views">
   <button type="submit" name="view" value="Today's Detections" form="views">Today's Detections</button>
 </form>
+<form action="" method="GET" id="views">
+  <button type="submit" name="view" value="Spectrogram" form="views">Spectrogram</button>
+</form>
 <form action="" method="GET" id="views">
   <button type="submit" name="view" value="Species Stats" form="views">Best Recordings</button>
 </form>
@@ -24,9 +27,6 @@ body::-webkit-scrollbar {
 <form action="" method="GET" id="views">
   <button type="submit" name="view" value="Recordings" form="views">Recordings</button>
 </form>
-<form action="" method="GET" id="views">
-  <button type="submit" name="view" value="Spectrogram" form="views">Spectrogram</button>
-</form>
 <form action="" method="GET" id="views">
   <button type="submit" name="view" value="View Log" form="views">View Log</button>
 </form>

scripts/analyze.py

@@ -11,6 +11,7 @@ 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)
@@ -20,6 +21,7 @@ def send(msg):
     client.send(message)
     print(client.recv(2048).decode(FORMAT))
 
+
 def main():
 
     global INCLUDE_LIST
@@ -28,16 +30,52 @@ 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(
-    parser.add_argument('--lat', type=float, default=-1, help='Recording location latitude. Set -1 to ignore.')
+        '--o',
-    parser.add_argument('--lon', type=float, default=-1, help='Recording location longitude. Set -1 to ignore.')
+        default='result.csv',
-    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.')
+        help='Path to output file. Defaults to result.csv.')
-    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(
-    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.')
+        '--lat',
-    parser.add_argument('--min_conf', type=float, default=0.1, help='Minimum confidence threshold. Values in [0.01, 0.99]. Defaults to 0.1.')   
+        type=float,
-    parser.add_argument('--include_list', default='null', help='Path to text file containing a list of included species. Not used if not provided.')
+        default=-1,
-    parser.add_argument('--exclude_list', default='null', help='Path to text file containing a list of excluded species. Not used if not provided.')
+        help='Recording location latitude. Set -1 to ignore.')
-    parser.add_argument('--birdweather_id', default='99999', help='Private Station ID for BirdWeather.')    
+    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()
 
@@ -64,14 +102,15 @@ 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__':
 
@@ -79,4 +118,6 @@ if __name__ == '__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'
+    # 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

@@ -1,6 +1,5 @@
 import sqlite3
 import os
-import configparser
 import pandas as pd
 import seaborn as sns
 import matplotlib.pyplot as plt
@@ -18,84 +17,95 @@ 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
+# Convert Date and Time Fields to Panda's format
-df['Date']=pd.to_datetime(df['Date'])
+df['Date'] = pd.to_datetime(df['Date'])
-df['Time']=pd.to_datetime(df['Time'], unit='ns')
+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
+# Create separate dataframes for separate locations
-df_plt=df #Default to use the whole Dbase
+df_plt = df  # Default to use the whole Dbase
 
 # Add every font at the specified location
-font_dir = [userDir+'/BirdNET-Pi/homepage/static']
+font_dir = [userDir + '/BirdNET-Pi/homepage/static']
 for font in font_manager.findSystemFonts(font_dir):
     font_manager.fontManager.addfont(font)
 
 # Set font family globally
 rcParams['font.family'] = 'Roboto Flex'
 
-#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
+# Set number of species to report
-readings=10
+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)]
 
-#Set Palette for graphics
+# Set Palette for graphics
 pal = "Greens"
 
-#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')
+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
+# 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
+# Generate frequency plot
-plot=sns.countplot(y='Com_Name', data = df_plt_top10_today, palette = colors,  order=freq_order, ax=axs[0])
+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()
-#Try plot grid lines between bars - problem at the moment plots grid lines on bars - want between bars
+plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(), 15)) for ticklabel in plot.get_yticklabels()], fontsize=10)
-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'])
+heat = pd.crosstab(df_plt_top10_today['Com_Name'], df_plt_top10_today['Hour of Day'])
-#Order heatmap Birds by frequency of occurrance
+# Order heatmap Birds by frequency of occurrance
-heat.index = pd.CategoricalIndex(heat.index, categories = freq_order)
+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))
+hours_in_day = pd.Series(data=range(0, 24))
-heat_frame = pd.DataFrame(data=0, index=heat.index, columns = hours_in_day)
+heat_frame = pd.DataFrame(data=0, index=heat.index, columns=hours_in_day)
-heat=(heat+heat_frame).fillna(0)
+heat = (heat + heat_frame).fillna(0)
 
-#Generatie heatmap plot
+# 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 = sns.heatmap(
-plot.set_xticklabels(plot.get_xticklabels(), rotation = 0, size = 7)
+    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():
@@ -103,13 +113,13 @@ 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()
@@ -119,18 +129,18 @@ plt.close()
 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)]
 
-#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')
+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
+# 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 ?
@@ -138,33 +148,44 @@ confmax = confmax.reindex(freq_order)
 norm = plt.Normalize(confmax.values.min(), confmax.values.max())
 colors = plt.cm.Reds(norm(confmax))
 
-#Generate frequency plot
+# Generate frequency plot
-plot=sns.countplot(y='Com_Name', data = df_plt_Bot10_today, palette = colors,  order=freq_order, ax=axs[0])
+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()
-#Try plot grid lines between bars - problem at the moment plots grid lines on bars - want between bars
+plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(), 15)) for ticklabel in plot.get_yticklabels()], fontsize=10)
-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'])
+heat = pd.crosstab(df_plt_Bot10_today['Com_Name'], df_plt_Bot10_today['Hour of Day'])
-#Order heatmap Birds by frequency of occurrance
+# Order heatmap Birds by frequency of occurrance
-heat.index = pd.CategoricalIndex(heat.index, categories = freq_order)
+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))
+hours_in_day = pd.Series(data=range(0, 24))
-heat_frame = pd.DataFrame(data=0, index=heat.index, columns = hours_in_day)
+heat_frame = pd.DataFrame(data=0, index=heat.index, columns=hours_in_day)
-heat=(heat+heat_frame).fillna(0)
+heat = (heat + heat_frame).fillna(0)
 
-#Generatie heatmap plot
+# 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 = sns.heatmap(
-plot.set_xticklabels(plot.get_xticklabels(), rotation = 0, size = 7)
+    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():
@@ -172,12 +193,12 @@ 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
+# Save combined plot
-savename=userDir + '/BirdSongs/Extracted/Charts/Combo2-'+str(now.strftime("%Y-%m-%d"))+'.png'
+savename = userDir + '/BirdSongs/Extracted/Charts/Combo2-' + str(now.strftime("%Y-%m-%d")) + '.png'
 plt.savefig(savename)
 plt.show()
 plt.close()

scripts/plotly_streamlit.py

@@ -4,8 +4,7 @@ import pandas as pd
 import numpy as np
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
-from datetime import timedelta, datetime
+from datetime import timedelta
-from pathlib import Path
 import sqlite3
 from sqlite3 import Connection
 import plotly.express as px
@@ -35,22 +34,22 @@ st.markdown("""
 
 
 @st.cache(hash_funcs={Connection: id})
-def get_connection(path:str):
+def get_connection(path: str):
-    return sqlite3.connect(path,check_same_thread=False)
+    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)
+df = get_data(conn)
-df2=df.copy()
+df2 = df.copy()
-df2['DateTime']=pd.to_datetime(df2['Date'] + " " + df2['Time'])
+df2['DateTime'] = pd.to_datetime(df2['Date'] + " " + df2['Time'])
-df2=df2.set_index('DateTime')
+df2 = df2.set_index('DateTime')
-
 
 
 # Filter on date range
@@ -60,174 +59,184 @@ 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()
-cols1,cols2= st.columns((1,1))
+cols1, cols2 = st.columns((1, 1))
 Date_Slider = cols1.slider('Date Range',
-                                min_value = Start_Date-timedelta(days=1),
+                           min_value=Start_Date - timedelta(days=1),
-                                max_value = End_Date,
+                           max_value=End_Date,
-                                value=(Start_Date,
+                           value=(Start_Date,
-                                       End_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]
 
 st.write('<style>div.row-widget.stRadio > div{flex-direction:row;justify-content: left;} </style>', unsafe_allow_html=True)
 st.write('<style>div.st-bf{flex-direction:column;} div.st-ag{font-weight:bold;padding-left:2px;}</style>', unsafe_allow_html=True)
 
-resample_sel=cols2.radio("Select Resample Resolution - To downsample and make run faster select longer period, Daily provides a view on detections at 15 min intervals through the day", ('1 minute', '5 minutes', '10 minutes', 'Hourly', 'Daily'))
+resample_sel = cols2.radio(
+    '''
+    Select Resample Resolution - To downsample and make run faster select longer period,
+    Daily provides a view on detections at 15 min intervals through the day
+    ''',
+    ('1 minute',
+     '5 minutes',
+     '10 minutes',
+     'Hourly',
+     'Daily'))
 
-resample_times = {'1 minute':'1min',
+resample_times = {'1 minute': '1min',
-                  '5 minutes':'5min',
+                  '5 minutes': '5min',
-                  '10 minutes':'10min',
+                  '10 minutes': '10min',
-                  'Hourly':'1H',
+                  'Hourly': '1H',
-                  'Daily':'1D'
+                  'Daily': '1D'
                   }
 resample_time = resample_times[resample_sel]
 
-df5=df2.resample(resample_time)['Com_Name'].aggregate('unique').explode()
+df5 = df2.resample(resample_time)['Com_Name'].aggregate('unique').explode()
 
-#Create species count for selected date range
+# Create species count for selected date range
 
-Specie_Count=df5.value_counts()
+Specie_Count = df5.value_counts()
 
-#Create species treemap
+# Create species treemap
 
 # Create Hourly Crosstab
-hourly=pd.crosstab(df5,df5.index.hour, dropna=False)
+hourly = pd.crosstab(df5, df5.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,
+    min_value=1,
-    value=min(10,len(Specie_Count))
+    value=min(10, len(Specie_Count))
-    )
+)
 
 top_N_species = (df5.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,
+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]))
+                         index=species.index(list(top_N_species.index)[0]))
 
 
-font_size=15
+font_size = 15
 
 
-#specie filter
+# specie filter
-filt=df2['Com_Name']==specie
+filt = df2['Com_Name'] == specie
-
-df_counts=sum(df5==specie)
-
 
+df_counts = sum(df5 == specie)
 
 
 if resample_time != '1D':
     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])+' for '+str(resample_sel)+' sampling interval.'+'</b>',
+        subplot_titles=('<b>Top ' +
-                                    'Total Detect:'+str('{:,}'.format(df_counts))+
+                        str(top_N) +
-                                    '   Confidence Max:'+str('{:.2f}%'.format(max(df2[df2['Com_Name']==specie]['Confidence'])*100))+
+                        ' Species in Date Range ' +
-                                    '   '+'   Median:'+str('{:.2f}%'.format(np.median(df2[df2['Com_Name']==specie]['Confidence'])*100))
+                        str(Date_Slider[0]) +
-                                    )
+                        ' to ' +
+                        str(Date_Slider[1]) +
+                        ' 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)
+    )
+    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
+    # 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.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)
 
-    specie_filt= df5==specie
+    specie_filt = df5 == specie
-    df3=df5[specie_filt]
+    df3 = df5[specie_filt]
 
-    detections2= pd.crosstab(df3, df3.index.hour)
+    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=(d+detections).fillna(0)
+    detections = (d + detections).fillna(0)
-    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(
         autosize=False,
-        width = 1000,
+        width=1000,
-        height = 500,
+        height=500,
         showlegend=False,
-        polar = dict(
+        polar=dict(
-            radialaxis = dict(
+            radialaxis=dict(
-                tickfont_size = font_size,
+                tickfont_size=font_size,
-                showticklabels = False,
+                showticklabels=False,
-                hoverformat = "#%{theta}: <br>Popularity: %{percent} </br> %{r}"
+                hoverformat="#%{theta}: <br>Popularity: %{percent} </br> %{r}"
-                ),
+            ),
-            angularaxis = dict(
+            angularaxis=dict(
-                tickfont_size= font_size,
+                tickfont_size=font_size,
-                rotation = -90,
+                rotation=-90,
-                direction = 'clockwise',
+                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],
+                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'],
+                ticktext=['12am', '1am', '2am', '3am', '4am', '5am', '6am', '7am', '8am', '9am', '10am', '11am',
-                hoverformat = "#%{theta}: <br>Popularity: %{percent} </br> %{r}"
+                          '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=False)
-
-    daily=pd.crosstab(df5,df5.index.date, dropna=False)
 
     fig.add_trace(go.Bar(x=daily.columns, y=daily.loc[specie]), row=3, col=2)
 
 else:
     fig = make_subplots(
-                    rows=1, cols =2,
+        rows=1, cols=2,
-                    specs= [[{"type":"xy","rowspan":1},{"type":"xy","rowspan":1}]],
+        specs=[[{"type": "xy", "rowspan": 1}, {"type": "xy", "rowspan": 1}]],
-                           
 
-                    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))+
-#                                     '   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)
+        subplot_titles=('<b>Daily Top ' + str(top_N) + ' Species in Date Range ' + str(Date_Slider[0]) + ' to ' + str(Date_Slider[1]) + '</b>',
-    df4=df2['Com_Name'][df2['Com_Name']==specie].resample('15min').count()
+                        '<b>Daily ' + specie + ' Detections on 15 minute intervals </b>'),
-    df4.index=[df4.index.date, df4.index.time]
+        #                                     'Total Detect:'+str('{:,}'.format(df_counts))+
-    day_hour_freq=df4.unstack().fillna(0)
+        #                                     '   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()
+    df4.index = [df4.index.date, df4.index.time]
+    day_hour_freq = df4.unstack().fillna(0)
 
     fig_x = [d.strftime('%d-%m-%Y') for d in day_hour_freq.index.tolist()]
     fig_y = [h.strftime('%H:%M') for h in day_hour_freq.columns.tolist()]
     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.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'})
-    color_pals= px.colors.named_colorscales()
+    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)
+    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()
 # 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')
 # audio_bytes = audio_file.read()
 # cols4.audio(audio_bytes, format='audio/mp3')

scripts/server.py

@@ -1,4 +1,16 @@
-import socket 
+import apprise
+from pathlib import Path
+from tzlocal import get_localzone
+import datetime
+import sqlite3
+import requests
+import json
+import time
+import math
+import numpy as np
+import librosa
+import operator
+import socket
 import threading
 import os
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
@@ -6,26 +18,9 @@ os.environ['CUDA_VISIBLE_DEVICES'] = ''
 
 try:
     import tflite_runtime.interpreter as tflite
-except:
+except BaseException:
     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
@@ -37,10 +32,9 @@ DISCONNECT_MESSAGE = "!DISCONNECT"
 server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
 try:
     server.bind(ADDR)
-except:
+except BaseException:
     print("Waiting on socket")
     time.sleep(5)
-    
 
 
 # Open most recent Configuration and grab DB_PWD as a python variable
@@ -48,7 +42,7 @@ 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():
@@ -62,7 +56,7 @@ 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.
@@ -85,6 +79,7 @@ def loadModel():
 
     return myinterpreter
 
+
 def loadCustomSpeciesList(path):
 
     slist = []
@@ -95,6 +90,7 @@ def loadCustomSpeciesList(path):
 
     return slist
 
+
 def splitSignal(sig, rate, overlap, seconds=3.0, minlen=1.5):
 
     # Split signal with overlap
@@ -105,17 +101,18 @@ 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)
@@ -130,11 +127,12 @@ 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
 
@@ -147,9 +145,11 @@ 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
@@ -166,21 +166,22 @@ 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("DATABASE SIZE:", len(p_sorted))
-#     #print("HUMAN-CUTOFF AT:", int(len(p_sorted)*priv_thresh)/10)
+#     # 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
 
@@ -202,29 +203,30 @@ def analyzeAudioData(chunks, lat, lon, week, sensitivity, overlap,):
         # Make prediction
         p = predict([sig, mdata], sensitivity)
 #        print("PPPPP",p)
-        HUMAN_DETECTED=False
+        HUMAN_DETECTED = False
-        
+
-        #Catch if Human is recognized
+        # 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 set all detections to human to make sure voices are not saved
-        if HUMAN_DETECTED == True:
+        if HUMAN_DETECTED is True:
-            p=[('Human_Human',0.0)]*10
+            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()
@@ -237,39 +239,40 @@ def sendAppriseNotifications(species,confidence):
             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,
             )
 
         if str(str(str([i for i in this_run if i.startswith('APPRISE_NOTIFY_NEW_SPECIES')]).split('=')[1]).split('\\')[0]) == "1":
-            try: 
+            try:
                 con = sqlite3.connect(userDir + '/BirdNET-Pi/scripts/birds.db')
                 con.row_factory = lambda cursor, row: row[0]
                 cur = con.cursor()
                 cur.execute("SELECT DISTINCT(Com_Name) FROM detections")
                 known_species = cur.fetchall()
-                sciName,comName = species.split("_")
+                sciName, comName = species.split("_")
-                
+
-                print("\ncomName: ",comName)
+                print("\ncomName: ", comName)
-                print("\nknown_species: ",known_species)
+                print("\nknown_species: ", known_species)
                 if comName not in known_species:
                     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,
+                        title=title,
                     )
-                
+
                 con.close()
-            except:
+            except BaseException:
                 print("Database busy")
                 time.sleep(2)
 
+
 def writeResultsToFile(detections, min_conf, path):
 
     print('WRITING RESULTS TO', path, '...', end=' ')
@@ -278,17 +281,18 @@ 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) ):
+                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]));
+                    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
-    #print(f"[NEW CONNECTION] {addr} connected.")
+    # print(f"[NEW CONNECTION] {addr} connected.")
 
     connected = True
     while connected:
@@ -299,10 +303,10 @@ 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'
@@ -313,8 +317,7 @@ 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('=')
@@ -341,14 +344,12 @@ 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:
@@ -360,22 +361,22 @@ 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 + '-')
+                # print('FULL FILENAME: -' + full_file_name + '-')
                 file_name = Path(full_file_name).stem
                 file_date = file_name.split('-birdnet-')[0]
                 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('Date:', date_time_obj.date())
-                #print('Time:', date_time_obj.time())
+                # 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))
 
@@ -387,32 +388,32 @@ 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) ):
+                            if entry[1] >= min_conf and ((entry[0] in INCLUDE_LIST or len(INCLUDE_LIST) == 0)
-                                rfile.write(str(current_date) + ';' + str(current_time) + ';' + entry[0].replace('_', ';') + ';' \
+                                                         and (entry[0] not in EXCLUDE_LIST or len(EXCLUDE_LIST) == 0)):
-                                + str(entry[1]) +";" + str(args.lat) + ';' + str(args.lon) + ';' + str(min_conf) + ';' + str(week) + ';' \
+                                rfile.write(str(current_date) + ';' + str(current_time) + ';' + entry[0].replace('_', ';') + ';'
-                                + str(args.sensitivity) +';' + str(args.overlap) + '\n')
+                                            + 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)
@@ -421,31 +422,63 @@ 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:
+                                    except BaseException:
                                         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'})
@@ -453,7 +486,7 @@ def handle_client(conn, addr):
                                             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"
                                         start_time = d.split(';')[0]
@@ -461,7 +494,7 @@ def handle_client(conn, addr):
                                         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) + ","
@@ -472,31 +505,33 @@ 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:
+                                    except BaseException:
                                         print("Cannot POST right now")
                 conn.send(myReturn.encode(FORMAT))
 
-                                #time.sleep(3)
+                # time.sleep(3)
+
+    conn.close()
 
-    conn.close() 
 
 def start():
     # Load model
     global INTERPRETER, INCLUDE_LIST, EXCLUDE_LIST
     INTERPRETER = loadModel()
     server.listen()
-    #print(f"[LISTENING] Server is listening on {SERVER}")
+    # print(f"[LISTENING] Server is listening on {SERVER}")
     while True:
         conn, addr = server.accept()
         thread = threading.Thread(target=handle_client, args=(conn, addr))
         thread.start()
-        #print(f"[ACTIVE CONNECTIONS] {threading.activeCount() - 1}")
+        # print(f"[ACTIVE CONNECTIONS] {threading.activeCount() - 1}")
 
 
-#print("[STARTING] server is starting...")
+# print("[STARTING] server is starting...")
 start()

scripts/spectrogram.php

@@ -5,6 +5,7 @@
 //  before a user gesture. This fixes it.
 var started = null;
 var player = null;
+var gain = 128;
 const ctx = null;
 window.onload = function(){
   // if user agent includes iPhone or Mac use legacy mode
@@ -36,13 +37,19 @@ window.onload = function(){
     document.getElementById('player').remove();
 
     player = audioelement;
-  } else {
+    if (started) return;
-    player = document.getElementById('player');
-  }
-  player.play();
-  if (started) return;
     started = true;
     initialize();
+  } else {
+    player = document.getElementById('player');
+    player.oncanplay = function() {
+      if (started) return;
+        started = true;
+        initialize();
+    };
+  }
+  player.play();
+  
   }
 };
 
@@ -95,7 +102,12 @@ function initialize() {
 
   ANALYSER.fftSize = 2048;  
   
-  process();
+
+  try{
+    process();
+  } catch(e) {
+    window.top.location.reload();
+  }
 
   function process() {
     const SOURCE = ACTX.createMediaElementSource(player);
@@ -118,7 +130,7 @@ function initialize() {
       CTX.putImageData(imgData, 0, 0);
       ANALYSER.getByteFrequencyData(DATA);
       for (let i = 0; i < LEN; i++) {
-        let rat = DATA[i] / 128 ;
+        let rat = DATA[i] / gain ;
         let hue = Math.round((rat * 120) + 280 % 360);
         let sat = '100%';
         let lit = 10 + (70 * rat) + '%';
@@ -155,6 +167,26 @@ h1 {
 
 <img id="spectrogramimage" style="width:100%;height:100%;display:none" src="/spectrogram.png?nocache=<?php echo $time;?>">
 
+<div id="gain" class="centered">
+<label>Gain: </label>
+<span class="slidecontainer">
+  <input name="gain_input" type="range" min="0" max="255" value="128" class="slider" id="gain_input">
+  <span id="gain_value"></span>%
+</span>
+</div>
+
 <audio style="display:none" controls="" crossorigin="anonymous" id='player' preload="none"><source src="/stream"></audio>
 <h1>Loading...</h1>
 <canvas></canvas>
+
+<script>
+var slider = document.getElementById("gain_input");
+var output = document.getElementById("gain_value");
+output.innerHTML = slider.value; // Display the default slider value
+
+// Update the current slider value (each time you drag the slider handle)
+slider.oninput = function() {
+  output.innerHTML = this.value;
+  gain=Math.abs(this.value - 255);
+}
+</script>