BirdNET-stream/daemon/plotter/chart.py

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2022-08-16 05:21:53 +02:00
#! /usr/bin/env python3
from curses import def_prog_mode
import sqlite3
from xml.sax.handler import feature_external_ges
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import seaborn as sns
from datetime import datetime
CONFIG = {
"readings": 10,
"palette": "Greens",
}
db = None
def get_database():
global db
if db is None:
db = sqlite3.connect('/home/ortion/Desktop/db.sqlite')
return db
def get_detection_hourly(date):
db = get_database()
df = pd.read_sql_query("""SELECT common_name, date, location_id, confidence
FROM observation
INNER JOIN taxon
ON observation.taxon_id = taxon.taxon_id""", db)
df['date'] = pd.to_datetime(df['date'])
df['hour'] = df['date'].dt.hour
df['date'] = df['date'].dt.date
df['date'] = df['date'].astype(str)
df_on_date = df[df['date'] == date]
return df_on_date
def get_top_species(df, limit=10):
return df['common_name'].value_counts()[:CONFIG['readings']]
def get_top_detections(df, limit=10):
df_top_species = get_top_species(df, limit=limit)
return df[df['common_name'].isin(df_top_species.index)]
def get_frequence_order(df, limit=10):
pd.value_counts(df['common_name']).iloc[:limit]
def presence_chart(date, filename):
df_detections = get_detection_hourly(date)
df_top_detections = get_top_detections(df_detections, limit=CONFIG['readings'])
fig, axs = plt.subplots(1, 2, figsize=(15, 4), gridspec_kw=dict(
width_ratios=[3, 6]))
plt.subplots_adjust(left=None, bottom=None, right=None,
top=None, wspace=0, hspace=0)
frequencies_order = get_frequence_order(df_detections, limit=CONFIG["readings"])
# Get min max confidences
confidence_minmax = df_detections.groupby('common_name')['confidence'].max()
# Norm values for color palette
norm = plt.Normalize(confidence_minmax.values.min(),
confidence_minmax.values.max())
colors = plt.cm.Greens(norm(confidence_minmax))
plot = sns.countplot(y='common_name', data=df_top_detections, palette=colors, order=frequencies_order, ax=axs[0])
plot.set(ylabel=None)
plot.set(xlabel="Detections")
heat = pd.crosstab(df_top_detections['common_name'], df_top_detections['hour'])
# Order heatmap Birds by frequency of occurrance
heat.index = pd.CategoricalIndex(heat.index, categories=frequencies_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)
# Generate heatmap plot
plot = sns.heatmap(
heat,
norm=LogNorm(),
annot=True,
annot_kws={
"fontsize": 7
},
fmt="g",
cmap=CONFIG['palette'],
square=False,
cbar=False,
linewidth=0.5,
linecolor="Grey",
ax=axs[1],
yticklabels=False
)
plot.set_xticklabels(plot.get_xticklabels(), rotation=0, size=7)
for _, spine in plot.spines.items():
spine.set_visible(True)
plot.set(ylabel=None)
plot.set(xlabel="Hour of day")
fig.subplots_adjust(top=0.9)
plt.suptitle(f"Top {CONFIG['readings']} species (Updated on {datetime.now().strftime('%Y/%m-%d %H:%M')})")
plt.savefig(filename)
plt.close()
def main():
date = datetime.now().strftime('%Y%m%d')
presence_chart(date, f'./var/charts/chart_{date}.png')
# print(get_top_detections(get_detection_hourly(date), limit=10))
if not db is None:
db.close()
if __name__ == "__main__":
main()