creating json file in backend to populate year comp table

backend/flask/app/table_export.py

@@ -0,0 +1,107 @@
+""" recreate json file to populate last year comparison table """
+
+from datetime import datetime
+import numpy as np
+import pandas as pd
+
+from app.db_connect import db_connect, db_close
+
+
+def get_rows(config):
+    """ get rows from last 10 days 
+    and last 10 days one year ago """
+    now = datetime.now()
+    # last 10
+    now_until = int(now.date().strftime('%s'))
+    now_from = now_until - 10 * 24 * 60 * 60
+    # last 10 one year ago
+    year_until = now_until - 365 * 24 * 60 * 60
+    year_from = now_until - 375 * 24 * 60 * 60
+    # make the call
+    conn, cur = db_connect(config)
+    cur.execute(
+        f'SELECT epoch_time, aqi_value FROM aqi \
+        WHERE epoch_time > {now_from} \
+        AND epoch_time < {now_until} \
+        ORDER BY epoch_time DESC;'
+    )
+    now_rows = cur.fetchall()
+    cur.execute(
+        f'SELECT epoch_time, aqi_value FROM aqi \
+        WHERE epoch_time > {year_from} \
+        AND epoch_time < {year_until} \
+        ORDER BY epoch_time DESC;'
+    )
+    year_rows = cur.fetchall()
+    # close and return
+    db_close(conn, cur)
+    return now_rows, year_rows
+
+
+def initial_df(now_rows, year_rows):
+    """ build mean df with year data split into columns """
+    # first df with current data
+    x_timeline = [datetime.fromtimestamp(i[0]) for i in now_rows]
+    y_aqi_values = [int(i[1]) for i in now_rows]
+    data = {'timestamp': x_timeline, 'now_aqi': y_aqi_values}
+    df = pd.DataFrame(data)
+    indexed = df.set_index('timestamp')
+    indexed.sort_values(by=['timestamp'], inplace=True)
+    mean = indexed.resample('1d').mean().round()
+    # second df with last year data
+    x_timeline = [datetime.fromtimestamp(i[0]) for i in year_rows]
+    y_aqi_values = [int(i[1]) for i in year_rows]
+    data = {'timestamp': x_timeline, 'year_aqi': y_aqi_values}
+    df = pd.DataFrame(data)
+    indexed = df.set_index('timestamp')
+    indexed.sort_values(by=['timestamp'], inplace=True)
+    year_mean = indexed.resample('1d').mean().round()
+    year_mean.reset_index(level=0, inplace=True)
+    # merge the two
+    mean.reset_index(level=0, inplace=True)
+    mean['year_aqi'] = year_mean['year_aqi']
+    mean.sort_values(by='timestamp', ascending=False, inplace=True)
+    mean['timestamp'] = mean['timestamp'].dt.strftime('%d %b')
+    # return result
+    return mean
+
+
+def write_df(mean):
+    """ finalize df and compare values """
+    # build temp column with diff
+    mean['diff'] = (mean['now_aqi'] - mean['year_aqi']) / mean['now_aqi']
+    mean['change'] = np.where(mean['diff'].abs() < 0.15, 'same', mean['diff'])
+    mean['change'] = np.where(mean['diff'] <= -0.15, 'down', mean['change'])
+    mean['change'] = np.where(mean['diff'] >= 0.15, 'up', mean['change'])
+    del mean['diff']
+    # build average row on top
+    now_avg = mean['now_aqi'].mean()
+    year_avg = mean['year_aqi'].mean()
+    diff_avg = (now_avg - year_avg) / now_avg
+    if diff_avg <= -0.15:
+        avg_change = 'down'
+    elif diff_avg >= 0.15:
+        avg_change = 'up'
+    else:
+        avg_change = 'same'
+    
+    # build avg df
+    avg_row = {'timestamp': 'avg', 'now_aqi': now_avg, 'year_aqi': year_avg, 'change': avg_change}
+    new_row = pd.DataFrame(avg_row, index = [0]).round()
+    mean = pd.concat([new_row, mean]).reset_index(drop = True)
+    # convert to int
+    mean['now_aqi'] = mean['now_aqi'].astype('int')
+    mean['year_aqi'] = mean['year_aqi'].astype('int')
+    # extract and write json from df
+    mean_json = mean.to_json(orient='split')
+    with open('dyn/year-table.json', 'w') as f:
+        f.write(mean_json)
+
+
+def rebuild_table(config):
+    """ main function to recreate year comparison table """
+    now_rows, year_rows = get_rows(config)
+    mean = initial_df(now_rows, year_rows)
+    write_df(mean)
+    # done
+    print('recreated year comparison json file')

backend/flask/app/views.py

@@ -11,6 +11,7 @@ from app import aqi_parser
 from app import weather
 from app import graph
 from app import graph_pm
+from app import table_export
 from app.db_connect import db_insert
 
 
@@ -43,6 +44,7 @@ graph_pm.rebuild_pm_bar(config)
 graph.rebuild_3days(config)
 graph.rebuild_7days(config)
 graph_pm.rebuild_hour_bar(config)
+table_export.rebuild_table(config)
 
 # build username / pw dict for basic auth
 USER_DATA = {}
@@ -69,6 +71,9 @@ scheduler.add_job(
 scheduler.add_job(
     graph_pm.rebuild_hour_bar, args=[config], trigger="cron", day='*', hour='1', minute='4', name='hour_bar'
 )
+scheduler.add_job(
+    table_export.rebuild_table, args=[config], trigger="cron", day='*', hour='1', minute='6', name='rebuild_table'
+)
 scheduler.start()