216 lines
7.8 KiB
Python
216 lines
7.8 KiB
Python
""" handles monthly tasks """
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import calendar
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import json
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import numpy as np
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import pandas as pd
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from datetime import datetime, timedelta
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from matplotlib import pyplot as plt
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from os import path
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from app.db_connect import db_connect, db_close
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def get_epoch():
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""" returns epoch for last month and last month last year """
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# run within first 7 days of month
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now = datetime.now()
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# last month
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last_day = now.replace(day=1) - timedelta(days=1)
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month_start = last_day.replace(day=1,hour=0,minute=0,second=0)
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month_end = last_day.replace(hour=23,minute=59,second=59)
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# last year
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last_year = last_day.year - 1
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month_start_year = month_start.replace(year=last_year)
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m_start_year_next = month_start_year + timedelta(days=31)
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m_start_year_first = m_start_year_next.replace(day=1)
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month_end_year = (m_start_year_first - timedelta(days=1)).replace(hour=23,minute=59,second=59)
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# build tpl and return
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last_month_tpl = (month_start.strftime('%s'), month_end.strftime('%s'))
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last_year_tpl = (month_start_year.strftime('%s'), month_end_year.strftime('%s'))
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return last_month_tpl, last_year_tpl
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def get_rows(last_month_tpl, last_year_tpl, config):
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""" get rows from postgres """
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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 > {last_month_tpl[0]} \
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AND epoch_time < {last_month_tpl[1]} \
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ORDER BY epoch_time DESC;'
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)
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rows_month = 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 > {last_year_tpl[0]} \
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AND epoch_time < {last_year_tpl[1]} \
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ORDER BY epoch_time DESC;'
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)
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rows_year = cur.fetchall()
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db_close(conn, cur)
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return rows_month, rows_year
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def get_axis(rows_month, rows_year):
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""" takes rows and returns axis """
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# initial df
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x_timeline = [datetime.fromtimestamp(i[0]) for i in rows_month]
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y_aqi_values = [int(i[1]) for i in rows_month]
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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('8h').mean().round()
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# reset timestamp to day
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mean.reset_index(level=0, inplace=True)
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mean['timestamp'] = mean['timestamp'].dt.strftime('%d %H:%M')
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mean.set_index('timestamp', inplace=True)
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# second df with last year data
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x_timeline = [datetime.fromtimestamp(i[0]) for i in rows_year]
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y_aqi_values = [int(i[1]) for i in rows_year]
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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('8h').mean().round()
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# reset timestamp to day
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year_mean.reset_index(level=0, inplace=True)
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year_mean['timestamp'] = year_mean['timestamp'].dt.strftime('%d %H:%M')
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year_mean.set_index('timestamp', inplace=True)
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# merge the two
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mean['year_aqi'] = year_mean['year_aqi']
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mean.reset_index(level=0, inplace=True)
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mean.sort_values(by='timestamp', ascending=True, inplace=True)
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# return axis
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x = mean['timestamp']
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y_1 = mean['now_aqi']
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y_2 = mean['year_aqi']
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return x, y_1, y_2, mean
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def write_monthly_plot(x, y_1, y_2, timestamp):
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""" plot last-7 only """
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# parse timestamp
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date_from = datetime.fromtimestamp(timestamp)
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date_title = date_from.strftime('%b %Y')
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month_short = date_from.strftime('%b')
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file_name = 'dyn/monthly/' + date_from.strftime('%Y-%m') + '.png'
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plt_title = f'AQI values for: {date_title}'
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# build ticks
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y_max = np.ceil(max(y_1.append(y_2))/50)*50 + 50
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x_range = np.arange(0, len(x), step=9)
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last_day = int(x.max().split()[0])
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x_numbers = np.arange(1, last_day + 1, step=3)
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x_dates = [f'{str(i).zfill(2)} {month_short}' for i in x_numbers]
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x_ticks = x_range, x_dates
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# plot
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plt.style.use('seaborn')
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plt.plot(x, y_1, color='#313131', label='this year')
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plt.plot(x, y_2, color='#666666', linestyle='dashed', label='last year')
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plt.fill_between(x, y_1, y2=0, where=(y_1 > 0), color='#85a762', interpolate=True) # good
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plt.fill_between(x, y_1, y2=50, where=(y_1 > 50), color='#d4b93c', interpolate=True) # moderate
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plt.fill_between(x, y_1, y2=100, where=(y_1 > 100), color='#e96843', interpolate=True) # ufsg
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plt.fill_between(x, y_1, y2=150, where=(y_1 > 150), color='#d03f3b', interpolate=True) # unhealthy
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plt.fill_between(x, y_1, y2=200, where=(y_1 > 200), color='#be4173', interpolate=True) # vunhealthy
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plt.fill_between(x, y_1, y2=300, where=(y_1 > 300), color='#714261', interpolate=True) # hazardous
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plt.fill_between(x, y_1, y2=0, where=(y_1 > 0), color='#ffffff', alpha=0.1, interpolate=True) # soft
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plt.xticks(x_ticks[0], x_ticks[1])
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plt.yticks(np.arange(0, y_max, step=50))
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plt.title(plt_title, fontsize=20)
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plt.legend()
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plt.tight_layout()
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plt.savefig(file_name, dpi = 300)
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plt.figure()
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def get_change(curr, year):
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""" helper function to get change on thresh """
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diff_avg = (curr - year) / curr
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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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return avg_change
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def get_aqi(val):
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""" helper function to get aqi category """
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if val <= 50:
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category = 'Good'
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elif val > 50 and val <= 100:
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category = 'Moderate'
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elif val > 100 and val <= 150:
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category = 'Unhealthy for Sensitive Groups'
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elif val > 150 and val <= 200:
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category = 'Unhealthy'
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elif val > 200 and val <= 300:
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category = 'Very Unhealthy'
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else:
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category = 'Hazardous'
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return category
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def write_monthly_json(mean, timestamp):
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""" write json file with monthly details """
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date_from = datetime.fromtimestamp(timestamp)
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file_name = 'dyn/monthly/' + date_from.strftime('%Y-%m') + '.json'
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# current
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curr_min = int(mean['now_aqi'].min())
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curr_max = int(mean['now_aqi'].max())
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curr_mean = int(mean['now_aqi'].mean())
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curr_cat = get_aqi(curr_mean)
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# last
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year_min = int(mean['year_aqi'].min())
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year_max = int(mean['year_aqi'].max())
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year_mean = int(mean['year_aqi'].mean())
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year_cat = get_aqi(year_mean)
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# change
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min_change = get_change(curr_min, year_min)
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max_change = get_change(curr_max, year_max)
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mean_change = get_change(curr_mean, year_mean)
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# build rows
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data_rows = []
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data_rows.append(['min: ', curr_min, year_min, min_change])
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data_rows.append(['max: ', curr_max, year_max, max_change])
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data_rows.append(['avg: ', curr_mean, year_mean, mean_change])
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data_rows.append(['avg aqi: ', curr_cat, year_cat, mean_change])
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# build dict
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monthly_dict = {}
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monthly_dict['data'] = data_rows
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# write to disk
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json_str = json.dumps(monthly_dict)
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with open(file_name, 'w') as f:
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f.write(json_str)
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def monthly_found(timestamp):
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""" check if monthly graph already created """
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date_from = datetime.fromtimestamp(timestamp)
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file_name = 'dyn/monthly/' + date_from.strftime('%Y-%m') + '.png'
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found = path.isfile(file_name)
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return found
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def create_monthly(config):
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""" check if last month plot exists, create if needed """
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last_month_tpl, last_year_tpl = get_epoch()
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timestamp = int(last_month_tpl[0])
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found = monthly_found(timestamp)
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if found:
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print('monthly already created, skipping...')
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return
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else:
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print('creating monthly graph and json file')
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# get rows
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rows_month, rows_year = get_rows(last_month_tpl, last_year_tpl, config)
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# get axis
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x, y_1, y_2, mean = get_axis(rows_month, rows_year)
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# write plot
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write_monthly_plot(x, y_1, y_2, timestamp)
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# write data json
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write_monthly_json(mean, timestamp)
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