time to test installation

scripts/daily_plot.py.newer

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+#!/home/pi/BirdNET-Pi/birdnet/bin/python3
+
+import os
+import configparser
+import pandas as pd
+import seaborn as sns
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.colors import LogNorm
+from datetime import datetime
+import textwrap
+import sqlite3
+
+conn = sqlite3.connect('/home/pi/BirdNET-Pi/scripts/birds.db')
+df = pd.read_sql_query("SELECT * from detections", conn)
+cursor = conn.cursor()
+
+table_rows = cursor.fetchall()
+
+#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')
+
+
+#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
+
+#Get todays readings
+now = datetime.now()
+df_plt_today = df_plt[df_plt['Date']==now.strftime("%Y-%m-%d")]
+
+#Set number of species to report
+#For ALL
+readings = len(df_plt_today['Com_Name'].value_counts())
+# Uncomment for user selection
+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
+pal = "Greens"
+
+#Set up plot axes and titles
+
+# f, axs = plt.subplots(1, 3, figsize = (10, 5 * vert_scale), gridspec_kw=dict(width_ratios=[3, 2, 5]))
+
+vert_scale = readings / 10
+f, axs = plt.subplots(1, 2, figsize = (10, 5 * vert_scale), 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
+
+# this groups by name and calculates mean/max conf
+confmax = df_plt_top10_today.groupby('Com_Name')['Confidence'].max()
+confavg = df_plt_top10_today.groupby('Com_Name')['Confidence'].mean()
+#reorder confmax/avg to detection frequency order
+confmax = confmax.reindex(freq_order)
+confavg = confavg.reindex(freq_order)
+# norm avg values for color palette
+norm = plt.Normalize(confavg.values.min(), confavg.values.max())
+# bars of frequency plot based on avg color palette
+colors = plt.cm.Greens(norm(confavg))
+
+#Generate frequency plot
+plot=sns.countplot(y='Com_Name', data = df_plt_top10_today, palette = colors,  order=freq_order, ax=axs[0])
+
+# for container in axs[0].containers:
+#     axs[0].bar_label(containers)
+
+# Function to show value on bars - from https://stackoverflow.com/questions/43214978/seaborn-barplot-displaying-values
+def show_values_on_bars(ax,label):
+    i = 0
+    for p in ax.patches:
+        _x = p.get_x() + p.get_width()* 0.9
+        _y = p.get_y() + p.get_height() / 2
+        value = '{:.0%}'.format(label[i])
+        # Uncomment for Species Count Total
+        # value = '{:,}'.format(p.get_width())
+        ax.text(_x, _y, value, ha='center', va='center', size=8, fontweight='bold', color='darkgreen', bbox=dict(facecolor='lightgrey',pad = 4.0))
+        i=i+1
+
+# Prints Max Confidence on bars     
+show_values_on_bars(axs[0],confmax)
+
+#Try plot grid lines between bars - problem at the moment plots grid lines on bars - want between bars
+# plot.grid(True, axis='y')
+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")
+# Comma formatting for when your Detections are >1,000
+# current_values=plot.gca().get_xticks()
+# plt.gca().set_xticklabels(['{:,0f}'.format(x) for x in current_values])
+
+#If you want violin/box plots uncomment here and ** above
+# plot = sns.boxenplot(x=df_plt_top10_today['Confidence']*100,color='Green',  y=df_plt_top10_today['Com_Name'], ax=axs[1],order=freq_order)
+# plot.set(xlabel="Confidence", ylabel=None,yticklabels=[])
+
+
+#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.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)
+
+#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 = 8)
+
+# Set heatmap border
+for _, spine in plot.spines.items():
+    spine.set_visible(True)
+
+plot.set(ylabel=None)
+plot.set(xlabel="Hour of Day")
+#Set combined plot layout and titles
+#f.tight_layout()
+#f.subplots_adjust(top=0.95)
+#f.suptitle("DAILY OVERVIEW FOR "+ str(now.strftime("%d-%m-%Y %H:%M")),x=0.5,y=1.5,va='top')
+
+#Save combined plot
+savename='/home/pi/BirdSongs/Extracted/Charts/Combo-'+str(now.strftime("%Y-%m-%d"))+'.png'
+plt.savefig(savename)
+plt.show()
+plt.close()
+