124 lines
4.0 KiB
Python
Executable File
124 lines
4.0 KiB
Python
Executable File
#! /usr/bin/env python3
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import sqlite3
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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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import os
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import glob
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CONFIG = {
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"readings": 10,
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"palette": "Greens",
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"db": "./var/db.sqlite",
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"date": datetime.now().strftime("%Y-%m-%d"),
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"charts_dir": "./var/charts"
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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(CONFIG["db"])
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return db
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def chart(date):
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db = get_database()
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df = pd.read_sql_query(f"""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
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WHERE STRFTIME("%Y-%m-%d", `date`) = '{date}'""", 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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top_on_date = (df_on_date['common_name'].value_counts()[:CONFIG['readings']])
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if top_on_date.empty:
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print("No observations on {}".format(date))
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return
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else:
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print(f"Found observations on {date}")
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df_top_on_date = df_on_date[df_on_date['common_name'].isin(top_on_date.index)]
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# Create a figure with 2 subplots
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fig, axs = plt.subplots(1, 2, figsize=(20, 5), gridspec_kw=dict(
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width_ratios=[2, 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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# Get species frequencies
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frequencies_order = pd.value_counts(df_top_on_date['common_name']).iloc[:CONFIG['readings']].index
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# Get min max confidences
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confidence_minmax = df_top_on_date.groupby('common_name')['confidence'].max()
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confidence_minmax = confidence_minmax.reindex(frequencies_order)
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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_on_date, 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_on_date['common_name'], df_top_on_date['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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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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plt.suptitle(f"Top {CONFIG['readings']} species on {date}", fontsize=14)
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plt.text(15, 11, f"(Updated on {datetime.now().strftime('%Y/%m-%d %H:%M')})")
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plt.savefig(f"./var/charts/chart_{date}.png", dpi=300)
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print(f"Plot for {date} saved.")
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plt.close()
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def main():
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done_charts = glob.glob(f"{CONFIG['charts_dir']}/*.png")
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last_modified = max(done_charts, key=os.path.getctime)
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last_modified_date = last_modified.split("_")[-1].split(".")[0]
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missing_dates = pd.date_range(start=last_modified_date, end=CONFIG['date'], freq='D')
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print(missing_dates)
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for missing_date in missing_dates:
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date = missing_date.strftime("%Y-%m-%d")
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chart(date)
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chart(CONFIG['date'])
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if db is not None:
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db.close()
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print("Done.")
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if __name__ == "__main__":
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main() |