92 lines
2.8 KiB
Python
Executable File
92 lines
2.8 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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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": "2022-08-14"
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}
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db = sqlite3.connect(CONFIG['db'])
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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'] == CONFIG['date']]
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top_on_date = (df_on_date['common_name'].value_counts()[:CONFIG['readings']])
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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=(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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# Get species frequencies
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frequencies_order = pd.value_counts(df['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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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(f"./var/charts/chart_{CONFIG['date']}.png", dpi=300)
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plt.close()
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db.close() |