141 lines
4.8 KiB
Python
141 lines
4.8 KiB
Python
""" recreate json file to populate last year comparison table """
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from datetime import datetime
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import numpy as np
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import pandas as pd
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from matplotlib import pyplot as plt
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from app.db_connect import db_connect, db_close
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from app.graph_pm import color_colums
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def get_rows(config):
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""" get rows from last 7 days
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and last 7 days one year ago """
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now = datetime.now()
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# last 10
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now_until = int(now.date().strftime('%s'))
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now_from = now_until - 7 * 24 * 60 * 60
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# last 10 one year ago
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year_until = now_until - 365 * 24 * 60 * 60
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year_from = now_until - 372 * 24 * 60 * 60
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# make the call
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conn, cur = db_connect(config)
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cur.execute(
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f'SELECT epoch_time, aqi_value FROM aqi \
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WHERE epoch_time > {now_from} \
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AND epoch_time < {now_until} \
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ORDER BY epoch_time DESC;'
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)
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now_rows = cur.fetchall()
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cur.execute(
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f'SELECT epoch_time, aqi_value FROM aqi \
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WHERE epoch_time > {year_from} \
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AND epoch_time < {year_until} \
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ORDER BY epoch_time DESC;'
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)
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year_rows = cur.fetchall()
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# close and return
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db_close(conn, cur)
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return now_rows, year_rows
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def initial_df(now_rows, year_rows):
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""" build mean df with year data split into columns """
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# first df with current data
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x_timeline = [datetime.fromtimestamp(i[0]) for i in now_rows]
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y_aqi_values = [int(i[1]) for i in now_rows]
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data = {'timestamp': x_timeline, 'now_aqi': y_aqi_values}
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df = pd.DataFrame(data)
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indexed = df.set_index('timestamp')
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indexed.sort_values(by=['timestamp'], inplace=True)
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mean = indexed.resample('1d').mean().round()
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# second df with last year data
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x_timeline = [datetime.fromtimestamp(i[0]) for i in year_rows]
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y_aqi_values = [int(i[1]) for i in year_rows]
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data = {'timestamp': x_timeline, 'year_aqi': y_aqi_values}
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df = pd.DataFrame(data)
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indexed = df.set_index('timestamp')
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indexed.sort_values(by=['timestamp'], inplace=True)
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year_mean = indexed.resample('1d').mean().round()
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year_mean.reset_index(level=0, inplace=True)
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# merge the two
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mean.reset_index(level=0, inplace=True)
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mean['year_aqi'] = year_mean['year_aqi']
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mean.sort_values(by='timestamp', ascending=False, inplace=True)
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mean['timestamp'] = mean['timestamp'].dt.strftime('%d %b')
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# return result
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return mean
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def write_df(mean):
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""" finalize df and compare values """
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# build temp column with diff
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mean['diff'] = (mean['now_aqi'] - mean['year_aqi']) / mean['now_aqi']
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mean['change'] = np.where(mean['diff'].abs() < 0.15, 'same', mean['diff'])
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mean['change'] = np.where(mean['diff'] <= -0.15, 'down', mean['change'])
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mean['change'] = np.where(mean['diff'] >= 0.15, 'up', mean['change'])
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del mean['diff']
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# build average row on top
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now_avg = mean['now_aqi'].mean()
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year_avg = mean['year_aqi'].mean()
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diff_avg = (now_avg - year_avg) / now_avg
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if diff_avg <= -0.15:
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avg_change = 'down'
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elif diff_avg >= 0.15:
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avg_change = 'up'
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else:
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avg_change = 'same'
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# build avg df
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avg_row = {'timestamp': 'avg 7 days', 'now_aqi': now_avg, 'year_aqi': year_avg, 'change': avg_change}
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new_row = pd.DataFrame(avg_row, index = [0]).round()
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mean = pd.concat([new_row, mean]).reset_index(drop = True)
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# convert to int
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mean['now_aqi'] = mean['now_aqi'].astype('int')
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mean['year_aqi'] = mean['year_aqi'].astype('int')
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# extract and write json from df
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mean_json = mean.to_json(orient='split')
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with open('dyn/year-table.json', 'w') as f:
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f.write(mean_json)
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def write_graph(mean):
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""" recreate barchart with yearly comparison """
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# build axis
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mean.sort_index(inplace=True)
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x = mean['timestamp'].to_list()
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y_1 = mean['now_aqi'].to_list()
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y_2 = mean['year_aqi'].to_list()
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# build color lists
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col_y_1 = color_colums(y_1)
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col_y_2 = color_colums(y_2)
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# set ticks
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y_max = int(np.ceil(max(y_1 + y_2)/50) * 50 + 50)
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x_indexes = np.arange(len(x))
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# build plot
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width = 0.25
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plt_title = 'Daily avg AQI values compared to last year'
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plt_suptitle = 'left: this year, right: last year'
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plt.style.use('seaborn')
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# write bars
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plt.bar(x_indexes - (width / 2) - 0.02, y_1, color=col_y_1, width=width)
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plt.bar(x_indexes + (width / 2) + 0.02, y_2, color=col_y_2, width=width)
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plt.title(plt_suptitle,fontsize=15)
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plt.suptitle(plt_title,fontsize=20, y=0.96)
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plt.yticks(np.arange(0, y_max, step=50))
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plt.xticks(ticks=x_indexes, labels=x)
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plt.tight_layout()
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plt.savefig('dyn/year-graph.png', dpi=300)
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plt.figure()
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def rebuild_table(config):
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""" main function to recreate year comparison table """
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now_rows, year_rows = get_rows(config)
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mean = initial_df(now_rows, year_rows)
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write_df(mean)
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write_graph(mean)
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# done
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print('recreated year comparison graph and json file')
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