correct daily_plot.py for Combo*.png

scripts/daily_plot.py

@@ -3,9 +3,12 @@ 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
 
+
+
 #Read database into Pandas dataframe
 df = pd.read_csv('~/BirdNET-Pi/BirdDB.txt', sep=';')
 
@@ -13,50 +16,139 @@ df = pd.read_csv('~/BirdNET-Pi/BirdDB.txt', sep=';')
 df['Date']=pd.to_datetime(df['Date'])
 df['Time']=pd.to_datetime(df['Time'])
 
+#Add round hours to dataframe
+df['Hour of day'] = [r.hour for r in df.Time]
 
 #Create separate dataframes for separate locations
-df_clt=df[df.Lat == 35.0]
+df_jhb=df[df.Lat > -32]
+df_ec = df[df.Lat < -32]
 
+#Get todays readings for Joburg
 now = datetime.now()
-df_clt_today = df_clt[df_clt['Date']==now.strftime("%d-%m-%y")]
+df_jhb_today = df_jhb[df_jhb['Date']==now.strftime("%d-%m-%y")]
 
-#Get top 10 today
-clt_top10_today=(df_clt_today['Com_Name'].value_counts()[:10])
-df_clt_top10_today = df_clt_today[df_clt_today.Com_Name.isin(clt_top10_today.index)]
+# Definition to start getting top N detections - work in process
+def filter_by_freq(df: pd.DataFrame, column: str, min_freq: int) -> pd.DataFrame:
+    """Filters the DataFrame based on the value frequency in the specified column.
 
+    :param df: DataFrame to be filtered.
+    :param column: Column name that should be frequency filtered.
+    :param min_freq: Minimal value frequency for the row to be accepted.
+    :return: Frequency filtered DataFrame.
+    """
+    # Frequencies of each value in the column.
+    freq = df[column].value_counts()
+    # Select frequent values. Value is in the index.
+    frequent_values = freq[freq >= min_freq].index
+    # Return only rows with value frequency above threshold.
+    return df[df[column].isin(frequent_values)]
+
+#Get top readings today
+min_valuecounts = 2
+
+
+jhb_gt_min = filter_by_freq (df_jhb_today,'Com_Name', min_valuecounts)
+
+jhb_gt_min_counts = jhb_gt_min['Com_Name'].value_counts()
+print(jhb_gt_min_counts)
+
+
+jhb_top10_today = (df_jhb_today['Com_Name'].value_counts()[:10])
+df_jhb_top10_today = df_jhb_today[df_jhb_today.Com_Name.isin(jhb_top10_today.index)]
+
+#Get bottom 10 today
+jhb_bot10_today=(df_jhb_today['Com_Name'].value_counts()[-10:])
+df_jhb_bot10_today = df_jhb_today[df_jhb_today.Com_Name.isin(jhb_bot10_today.index)]
+
+#Set Palette for graphics
 pal = "Greens"
 
 #Set up plot axes and titles
-plot=sns.countplot(y='Com_Name', data = df_clt_top10_today, order = pd.value_counts(df_clt_top10_today['Com_Name']).iloc[:10].index)
-plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(),15)) for ticklabel in plot.get_yticklabels()])
-plot.set_title('Top 10 Today --'+ str(now.strftime(" Last updated %B %d, %Y %I:%M%P")))
-plot.set_xlabel("Detections", fontsize = 12)
-plot.set_ylabel("", fontsize = 12)
-plt.tight_layout()
+f, axs = plt.subplots(1, 2, figsize = (10, 4), gridspec_kw=dict(width_ratios=[3, 5]))
+plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0, hspace=None)
 
-#Save plot
-savename='/home/pi/BirdSongs/Extracted/Top_10_Today-'+str(now.strftime("%d-%m-%Y"))+'.png'
+
+#Generate frequency plot
+plot=sns.countplot(y='Com_Name',  data = df_jhb_top10_today, palette = pal+"_r", order=pd.value_counts(df_jhb_top10_today['Com_Name']).iloc[:20].index, ax=axs[0])
+
+#Try plot grid lines between bars - problem at the moment plots grid lines on bars - want between bars
+# plot.grid(True, axis='y')
+
+plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(),15)) for ticklabel in plot.get_yticklabels()])
+plot.set(ylabel=None)
+plot.set(xlabel="Detections")
+
+#Generate crosstab matrix for heatmap plot
+
+heat = pd.crosstab(df_jhb_top10_today['Com_Name'],df_jhb_top10_today['Hour of day'])
+#Order heatmap Birds by frequency of occurrance
+heat.index = pd.CategoricalIndex(heat.index, categories = pd.value_counts(df_jhb_top10_today['Com_Name']).iloc[:10].index)
+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,  annot_kws={"fontsize":7}, cmap = pal , square = False, cbar=False, linewidths = 0.5, linecolor = "Grey", ax=axs[1], yticklabels = False)
+
+# 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
+plt.tight_layout()
+f.subplots_adjust(top=0.9)
+plt.suptitle("Last Updated: "+ str(now.strftime("%B %d, %Y %I:%M%P")))
+
+#Save combined plot
+savename='/home/pi/BirdSongs/Extracted/Combo-'+str(now.strftime("%d-%m-%Y"))+'.png'
 plt.savefig(savename)
-plt.clf()
+
+
+
+#Get bottom 10 today
+jhb_bot10_today=(df_jhb_today['Com_Name'].value_counts()[-10:])
+df_jhb_bot10_today = df_jhb_today[df_jhb_today.Com_Name.isin(jhb_bot10_today.index)]
+
+#Set Palette for graphics
+pal = "Reds"
+
+#Set up plot axes and titles
+f, axs = plt.subplots(1, 2, figsize = (8, 4), gridspec_kw=dict(width_ratios=[3, 5]))
+
+#Generate frequency plot
+plot=sns.countplot(y='Com_Name', data = df_jhb_bot10_today, palette = pal+"_r", order=pd.value_counts(df_jhb_bot10_today['Com_Name']).iloc[:10].index, ax=axs[0])
+plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(),17)) for ticklabel in plot.get_yticklabels()])
+plot.set(ylabel=None)
+plot.set(xlabel="Detections")
+#Generate crosstab matrix for heatmap plot
+heat = pd.crosstab(df_jhb_bot10_today['Com_Name'],df_jhb_bot10_today['Hour of day'])
+
+#Order heatmap Birds by frequency of occurrance
+heat.index = pd.CategoricalIndex(heat.index, categories = pd.value_counts(df_jhb_bot10_today['Com_Name']).iloc[:10].index)
+heat.sort_index(level=0, inplace=True)
+heat_frame = pd.DataFrame(data=0, index=heat.index, columns = hours_in_day)
+heat=(heat+heat_frame).fillna(0)
 
 #Generate heatmap plot
-df_clt_top10_today['Hour'] = [r.hour for r in df_clt_top10_today.Time]
-heat = pd.crosstab(df_clt_top10_today['Com_Name'],df_clt_top10_today['Hour'])
+plot = sns.heatmap(heat, norm=LogNorm(), annot=True, annot_kws={"fontsize":7}, cmap = pal , square = False, cbar=False, linewidths = 0.5, linecolor = "Grey", ax=axs[1], yticklabels = False)
 
+# Set heatmap border
+for _, spine in plot.spines.items():
+    spine.set_visible(True)
+plot.set(ylabel=None)
 
-
-plot = sns.heatmap(heat, annot=True, annot_kws={"fontsize":7}, cmap = "gray_r", square = False, cbar=False, linewidths = 0)
-plot.set_yticklabels(['\n'.join(textwrap.wrap(ticklabel.get_text(),15)) for ticklabel in plot.get_yticklabels()])
-plot.set_title('Phenology --'+ str(now.strftime(" Last updated %B %d, %Y %I:%M%P")))
-plot.set_xlabel("Hour (24h)", fontsize = 12)
-plot.set_ylabel("", fontsize = 12)
+#Set combined plot layout and titles
 plt.tight_layout()
-
-#Save plot
-savename='/home/pi/BirdSongs/Extracted/When_today-'+str(now.strftime("%d-%m-%Y"))+'.png'
+f.subplots_adjust(top=0.9)
+plt.suptitle("Bottom 10 Detected: "+ str(now.strftime("%d-%h-%Y %H:%M")))
+plot.set(xlabel="Hour of day")
+#Save combined plot
+savename='/home/pi/BirdSongs/Extracted/Combo2-'+str(now.strftime("%d-%m-%Y"))+'.png'
 plt.savefig(savename)
 
-plt.clf()
-
-
-
+plt.show()