121 lines
3.7 KiB
Python
Executable File
121 lines
3.7 KiB
Python
Executable File
#! /usr/bin/env python3
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from curses import def_prog_mode
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import sqlite3
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from xml.sax.handler import feature_external_ges
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import pandas as pd
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import matplotlib.pyplot as plt
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from matplotlib.colors import LogNorm
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import seaborn as sns
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from datetime import datetime
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CONFIG = {
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"readings": 10,
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"palette": "Greens",
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}
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db = None
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def get_database():
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global db
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if db is None:
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db = sqlite3.connect('/home/ortion/Desktop/db.sqlite')
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return db
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def get_detection_hourly(date):
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db = get_database()
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df = pd.read_sql_query("""SELECT common_name, date, location_id, confidence
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FROM observation
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INNER JOIN taxon
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ON observation.taxon_id = taxon.taxon_id""", db)
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df['date'] = pd.to_datetime(df['date'])
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df['hour'] = df['date'].dt.hour
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df['date'] = df['date'].dt.date
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df['date'] = df['date'].astype(str)
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df_on_date = df[df['date'] == date]
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return df_on_date
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def get_top_species(df, limit=10):
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return df['common_name'].value_counts()[:CONFIG['readings']]
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def get_top_detections(df, limit=10):
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df_top_species = get_top_species(df, limit=limit)
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return df[df['common_name'].isin(df_top_species.index)]
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def get_frequence_order(df, limit=10):
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pd.value_counts(df['common_name']).iloc[:limit]
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def presence_chart(date, filename):
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df_detections = get_detection_hourly(date)
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df_top_detections = get_top_detections(df_detections, limit=CONFIG['readings'])
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fig, axs = plt.subplots(1, 2, figsize=(15, 4), gridspec_kw=dict(
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width_ratios=[3, 6]))
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plt.subplots_adjust(left=None, bottom=None, right=None,
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top=None, wspace=0, hspace=0)
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frequencies_order = get_frequence_order(df_detections, limit=CONFIG["readings"])
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# Get min max confidences
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confidence_minmax = df_detections.groupby('common_name')['confidence'].max()
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# Norm values for color palette
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norm = plt.Normalize(confidence_minmax.values.min(),
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confidence_minmax.values.max())
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colors = plt.cm.Greens(norm(confidence_minmax))
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plot = sns.countplot(y='common_name', data=df_top_detections, palette=colors, order=frequencies_order, ax=axs[0])
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plot.set(ylabel=None)
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plot.set(xlabel="Detections")
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heat = pd.crosstab(df_top_detections['common_name'], df_top_detections['hour'])
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# Order heatmap Birds by frequency of occurrance
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heat.index = pd.CategoricalIndex(heat.index, categories=frequencies_order)
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heat.sort_index(level=0, inplace=True)
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hours_in_day = pd.Series(data=range(0, 24))
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heat_frame = pd.DataFrame(data=0, index=heat.index, columns=hours_in_day)
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heat = (heat + heat_frame).fillna(0)
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# Generate heatmap plot
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plot = sns.heatmap(
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heat,
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norm=LogNorm(),
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annot=True,
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annot_kws={
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"fontsize": 7
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},
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fmt="g",
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cmap=CONFIG['palette'],
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square=False,
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cbar=False,
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linewidth=0.5,
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linecolor="Grey",
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ax=axs[1],
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yticklabels=False
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)
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plot.set_xticklabels(plot.get_xticklabels(), rotation=0, size=7)
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for _, spine in plot.spines.items():
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spine.set_visible(True)
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plot.set(ylabel=None)
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plot.set(xlabel="Hour of day")
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fig.subplots_adjust(top=0.9)
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plt.suptitle(f"Top {CONFIG['readings']} species (Updated on {datetime.now().strftime('%Y/%m-%d %H:%M')})")
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plt.savefig(filename)
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plt.close()
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def main():
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date = datetime.now().strftime('%Y%m%d')
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presence_chart(date, f'./var/charts/chart_{date}.png')
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# print(get_top_detections(get_detection_hourly(date), limit=10))
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if not db is None:
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db.close()
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if __name__ == "__main__":
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main()
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