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aqi_monitor
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major app rebuild into a single flask application

This commit is contained in:
simon 2021-07-07 19:08:46 +07:00
parent a8641d94a6
commit 0ac8d21284
84 changed files with 2021 additions and 2161 deletions
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4
.gitignore vendored
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@ -2,7 +2,7 @@
__pycache__
# protect real config files
config
config.json
config.h
# env files
@ -12,6 +12,4 @@ postgres.env
*.ttf
# dynamic files
**/dyn/*.json
**/dyn/
update.html

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backend/flask/.dockerignore
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__pycache__
/dyn/*

16
backend/flask/Dockerfile
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# Use the Python3.7.2 image
FROM python
# Set the working directory to /app
WORKDIR /app
VOLUME /app/dyn
# Copy the current directory contents into the container at /app
ADD . /app
# Install the dependencies
RUN pip install -r requirements.txt
# run the command to start uWSGI
CMD ["uwsgi", "app.ini"]

5
backend/flask/app/__init__.py
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from flask import Flask
app = Flask(__name__)
from app import views

88
backend/flask/app/aqi_parser.py
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import json
from datetime import datetime
import numpy as np
def input_process(data):
"""
parsing aqi post data and combine it with weather data
return: dict of combined values
"""
# error check
error_found = False
# get weather data
try:
with open('dyn/weather.json', 'r') as f:
weather_data = f.read()
weather_data_json = json.loads(weather_data)
del weather_data_json['timestamp']
del weather_data_json['epoch_time']
except FileNotFoundError:
# will get recreated on next run
weather_data_json = {}
# parse aqi data
json_dict = data
pm25 = json_dict['pm25']
aqi, aqi_category = get_AQI(pm25)
json_dict['aqi_value'] = float(aqi)
json_dict['aqi_category'] = aqi_category
if pm25 == 0:
# something went wrong
error_found = True
# set timestamp
now = datetime.now()
timestamp = now.strftime("%Y-%m-%d %H:%M:%S")
epoch_time = int(now.strftime('%s'))
json_dict['timestamp'] = timestamp
json_dict['epoch_time'] = epoch_time
# combine the two and return
json_dict.update(weather_data_json)
return json_dict, error_found
def get_AQI(pm25):
""" takes the pm2.5 value and returns AQI and AQI category """
if pm25 <= 12:
aqi = (pm25 / 12) * 50
aqi_category = "Good"
elif pm25 > 12 and pm25 <= 35.4:
perc = (pm25 - 12) / (35.4 - 12)
aqi = (100 - 50) * perc + 50
aqi_category = "Moderate"
elif pm25 > 35.4 and pm25 <= 55.4:
perc = (pm25 - 35.4) / (55.4 - 35.4)
aqi = (150 - 100) * perc + 100
aqi_category = "Unhealthy for Sensitive Groups"
elif pm25 > 55.4 and pm25 <= 150.4:
perc = (pm25 - 55.4) / (150.4 - 55.4)
aqi = (200 - 150) * perc + 150
aqi_category = "Unhealthy"
elif pm25 > 150.4 and pm25 <= 199.9:
perc = (pm25 - 150.4) / (199.9 - 150.4)
aqi = (250 - 200) * perc + 200
aqi_category = "Very Unhealthy"
elif pm25 > 199.9 and pm25 <= 250.4:
perc = (pm25 - 199.9) / (250.4 - 199.9)
aqi = (300 - 250) * perc + 250
aqi_category = "Very Unhealthy"
elif pm25 > 250.4 and pm25 <= 299.9:
perc = (pm25 - 250.4) / (299.9 - 250.4)
aqi = (350 - 300) * perc + 300
aqi_category = "Hazardous"
elif pm25 > 299.9 and pm25 <= 350.4:
perc = (pm25 - 299.9) / (350.4 - 299.9)
aqi = (400 - 350) * perc + 350
aqi_category = "Hazardous"
elif pm25 > 350.4 and pm25 <= 424.6:
perc = (pm25 - 350.4) / (424.6 - 350.4)
aqi = (450 - 400) * perc + 400
aqi_category = "Hazardous"
elif pm25 > 424.6 and pm25 <= 500.4:
perc = (pm25 - 424.6) / (500.4 - 424.6)
aqi = (500 - 450) * perc + 450
aqi_category = "Hazardous"
elif pm25 > 500.4:
aqi = pm25
aqi_category = "Hazardous"
aqi = np.round_(int(aqi), decimals=0, out=None)
return aqi, aqi_category

71
backend/flask/app/db_connect.py
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""" handles insert into postgres db """
import psycopg2
def db_connect(config):
""" returns connection and curser """
# set config
db_host = config['db_host']
db_database = config['db_database']
db_user = config['db_user']
db_password = config['db_password']
# Connect to database
conn = psycopg2.connect(
host = db_host,
database = db_database,
user = db_user,
password = db_password
)
# Open a cursor to perform database operations
cur = conn.cursor()
return conn, cur
def db_close(conn, cur):
""" clean close the conn and curser """
conn.commit()
cur.close()
conn.close()
def db_insert(config, json_dict):
""" make the db insert """
# read out data dict
uptime = json_dict['uptime']
temperature = json_dict['temperature']
pressure = json_dict['pressure']
humidity = json_dict['humidity']
pm25 = json_dict['pm25']
pm10 = json_dict['pm10']
aqi_value = json_dict['aqi_value']
aqi_category = json_dict['aqi_category']
time_stamp = json_dict['timestamp']
epoch_time = json_dict['epoch_time']
weather_name = json_dict['weather_name']
weather_icon = json_dict['weather_icon']
wind_speed = json_dict['wind_speed']
wind_direction = json_dict['wind_direction']
sensor_id = json_dict['sensor_id']
# connect
conn, cur = db_connect(config)
# insert aqi
cur.execute("INSERT INTO aqi \
(epoch_time, sensor_id, time_stamp, uptime, pm25, pm10, aqi_value, aqi_category) \
VALUES (%s, %s, %s, %s, %s, %s, %s, %s)",
(epoch_time, sensor_id, time_stamp, uptime, pm25, pm10, aqi_value, aqi_category)
)
# insert weather
cur.execute("INSERT INTO weather \
(epoch_time, sensor_id, time_stamp, temperature, pressure, humidity, \
wind_speed, wind_direction, weather_name, weather_icon) \
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)",
(epoch_time, sensor_id, time_stamp, temperature, pressure, humidity,
wind_speed, wind_direction, weather_name, weather_icon)
)
# close
db_close(conn, cur)
return time_stamp

223
backend/flask/app/graph.py
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""" makes the nice plots """
from datetime import datetime, timedelta
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import psycopg2
from app.db_connect import db_connect, db_close
def create_current(config):
""" recreate current graph """
# last three hours
now = datetime.now()
now_human = now.strftime('%c')
now_epoch = int(now.strftime('%s'))
last_3h = now_epoch - 3 * 60 * 60
last_3h_limit = int(60 * 3)
# connect
conn, cur = db_connect(config)
# get data
cur.execute(
f'SELECT epoch_time, aqi_value FROM aqi \
WHERE epoch_time > {last_3h} ORDER BY epoch_time DESC \
LIMIT {last_3h_limit};')
rows = cur.fetchall()
# close db
db_close(conn, cur)
# set title
time_from = datetime.fromtimestamp(rows[-1][0]).strftime('%H:%M')
time_until = datetime.fromtimestamp(rows[0][0]).strftime('%H:%M')
plt_title = f'AQI values last 3h: {time_from} - {time_until}'
# parse rows
sample_rate = '3min'
x, y = build_plt(rows, sample_rate, '%H:%M')
# calc x_ticks
x_ticks = []
for num, i in enumerate(x):
minute = int(i.split(':')[1])
if minute % 15 == 0:
x_ticks.append(num)
# write plt
file_name = 'current'
write_plt(x, y, plt_title, x_ticks, file_name)
message = f'recreated current graph: {now_human}'
print(message)
def rebuild_3days(config):
""" wrapper to recreate all three days of graphs """
now = datetime.now()
# get axis
x_1, y_1, plt_title_1, x_ticks_1 = get_axis(1, now, config)
x_2, y_2, plt_title_2, x_ticks_2 = get_axis(2, now, config)
x_3, y_3, plt_title_3, x_ticks_3 = get_axis(3, now, config)
# set max
y_max = max(y_1.append(y_2).append(y_3)) + 50
# write plot
write_plt(x_1, y_1, plt_title_1, x_ticks_1, 'day-1', y_max)
write_plt(x_2, y_2, plt_title_2, x_ticks_2, 'day-2', y_max)
write_plt(x_3, y_3, plt_title_3, x_ticks_3, 'day-3', y_max)
print('recreaded last three days plt')
def get_axis(day, now, config):
""" recreate plot for single days """
day_delta = now.date() - timedelta(days = day)
day_from = int(day_delta.strftime('%s'))
day_until = int(day_delta.strftime('%s')) + 60 * 60 * 24
# make the SELECT
conn, cur = db_connect(config)
cur.execute(
f'SELECT epoch_time, aqi_value FROM aqi \
WHERE epoch_time > {day_from} \
AND epoch_time < {day_until} \
ORDER BY epoch_time DESC LIMIT 720;'
)
rows = cur.fetchall()
db_close(conn, cur)
# title
time_stamp = day_delta.strftime('%Y-%m-%d')
plt_title = f'AQI values from: {time_stamp}'
# build plt
x_ticks = np.arange(0, 97, step=8)
sample_rate = '15min'
x, y = build_plt(rows, sample_rate, '%H:%M')
return x, y, plt_title, x_ticks
def rebuild_7days(config):
""" recreate last-7 days from db """
# setup
now = datetime.now()
day_until = int(now.date().strftime('%s'))
day_from = day_until - 7 * 24 * 60 * 60
# get data
conn, cur = db_connect(config)
cur.execute(
f'SELECT epoch_time, aqi_value FROM aqi \
WHERE epoch_time > {day_from} \
AND epoch_time < {day_until} \
ORDER BY epoch_time DESC LIMIT 30 * 24 * 7;'
)
rows = cur.fetchall()
db_close(conn, cur)
# title
date_from = datetime.fromtimestamp(rows[-1][0]).strftime('%d %b')
date_until = datetime.fromtimestamp(rows[0][0]).strftime('%d %b')
plt_title = f'AQI values from: {date_from} until {date_until}'
# build axis of plot
x, y_1, y_2 = build_last7_plt(rows)
# make ticks
x_range = np.arange(0, 84, step=12)
x_date_time = pd.to_datetime(x).dt.date.unique()
x_dates = np.asarray([i.strftime('%d %b') for i in x_date_time])
x_ticks = x_range, x_dates
# write the plot
write_last7_plt(x, y_1, y_2, x_ticks, plt_title)
print('recreaded last-7 days graph')
def build_plt(rows, sample_rate, time_format):
""" parse rows returns axis"""
# build x y
x_timeline = [datetime.fromtimestamp(i[0]) for i in rows]
y_aqi_values = [int(i[1]) for i in rows]
# build dataframe
data = {'timestamp': x_timeline, 'aqi': y_aqi_values}
df = pd.DataFrame(data)
# reindex as timeseries
indexed = df.set_index('timestamp')
indexed.sort_values(by=['timestamp'], inplace=True)
mean = indexed.resample(sample_rate).mean()
mean.interpolate(method='linear', limit=1, inplace=True, limit_area='inside')
mean.reset_index(level=0, inplace=True)
mean['timestamp'] = mean['timestamp'].dt.strftime(time_format)
mean['aqi'] = mean['aqi'].round()
# set axis
x = mean['timestamp']
y = mean['aqi']
return x, y
def build_last7_plt(rows):
""" build axis for last7 plot """
sample_rate = '2h'
# build x y
x_timeline = [datetime.fromtimestamp(i[0]) for i in rows]
y_aqi_values = [int(i[1]) for i in rows]
# build dataframe
data = {'timestamp': x_timeline, 'aqi': y_aqi_values}
df = pd.DataFrame(data)
indexed = df.set_index('timestamp')
indexed.sort_values(by=['timestamp'], inplace=True)
mean = indexed.resample(sample_rate).mean()
mean['avg'] = mean['aqi'].resample('1d').mean()
mean['avg'] = mean.avg.shift(6)
mean['avg'][0] = (mean['avg'].iloc[6] + mean['aqi'][0]) / 2
mean['avg'][-1] = (mean['avg'].iloc[-6] + mean['aqi'][-1]) / 2
mean['avg'].interpolate(method='polynomial', order=3, inplace=True)
mean.reset_index(level=0, inplace=True)
mean['timestamp'] = mean['timestamp'].dt.strftime('%Y-%m-%d %H:%M')
mean['aqi'] = mean['aqi'].round()
mean['avg'] = mean['avg'].round()
x = mean['timestamp']
y_1 = mean['aqi']
y_2 = mean['avg']
return x, y_1, y_2
def write_plt(x, y, plt_title, x_ticks, file_name, y_max=''):
""" save plot to file """
# calc ticks
if not y_max:
y_max = np.ceil(y.max()/50)*50 + 50
# setup plot
plt.style.use('seaborn')
plt.plot(x, y, color='#313131',)
plt.fill_between(x, y, y2=0, where=(y > 0), color='#85a762', interpolate=True) # good
plt.fill_between(x, y, y2=50, where=(y > 50), color='#d4b93c', interpolate=True) # moderate
plt.fill_between(x, y, y2=100, where=(y > 100), color='#e96843', interpolate=True) # ufsg
plt.fill_between(x, y, y2=150, where=(y > 150), color='#d03f3b', interpolate=True) # unhealthy
plt.fill_between(x, y, y2=200, where=(y > 200), color='#be4173', interpolate=True) # vunhealthy
plt.fill_between(x, y, y2=300, where=(y > 300), color='#714261', interpolate=True) # hazardous
plt.fill_between(x, y, y2=0, where=(y > 0), color='#ffffff', alpha=0.1, interpolate=True) # soft
# handle passing ticks and lables separatly
if len(x_ticks) == 2:
plt.xticks(x_ticks[0], x_ticks[1])
else:
plt.xticks(x_ticks)
plt.yticks(np.arange(0, y_max, step=50))
plt.title(plt_title, fontsize=20)
plt.tight_layout()
plt.savefig(f'dyn/{file_name}.png', dpi = 300)
plt.figure()
plt.close('all')
def write_last7_plt(x, y_1, y_2, x_ticks, plt_title):
""" plot last-7 only """
y_max = np.ceil(max(y_1.append(y_2))/50)*50 + 50
# plot
plt.style.use('seaborn')
plt.plot(x, y_1, color='#313131', label='2hour avg')
plt.plot(x, y_2, color='#cc0000', label='daily avg')
plt.fill_between(x, y_1, y2=0, where=(y_1 > 0), color='#85a762', interpolate=True) # good
plt.fill_between(x, y_1, y2=50, where=(y_1 > 50), color='#d4b93c', interpolate=True) # moderate
plt.fill_between(x, y_1, y2=100, where=(y_1 > 100), color='#e96843', interpolate=True) # ufsg
plt.fill_between(x, y_1, y2=150, where=(y_1 > 150), color='#d03f3b', interpolate=True) # unhealthy
plt.fill_between(x, y_1, y2=200, where=(y_1 > 200), color='#be4173', interpolate=True) # vunhealthy
plt.fill_between(x, y_1, y2=300, where=(y_1 > 300), color='#714261', interpolate=True) # hazardous
plt.fill_between(x, y_1, y2=0, where=(y_1 > 0), color='#ffffff', alpha=0.1, interpolate=True) # soft
plt.xticks(x_ticks[0], x_ticks[1])
plt.yticks(np.arange(0, y_max, step=50))
plt.title(plt_title, fontsize=20)
plt.legend()
plt.tight_layout()
plt.savefig('dyn/last-7.png', dpi = 300)
plt.figure()

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backend/flask/app/graph_monthly.py
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""" handles monthly tasks """
import calendar
import json
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
from matplotlib import pyplot as plt
from os import path
from app.db_connect import db_connect, db_close
def get_epoch():
""" returns epoch for last month and last month last year """
# run within first 7 days of month
now = datetime.now()
# last month
last_day = now.replace(day=1) - timedelta(days=1)
month_start = last_day.replace(day=1,hour=0,minute=0,second=0)
month_end = last_day.replace(hour=23,minute=59,second=59)
# last year
last_year = last_day.year - 1
month_start_year = month_start.replace(year=last_year)
m_start_year_next = month_start_year + timedelta(days=31)
m_start_year_first = m_start_year_next.replace(day=1)
month_end_year = (m_start_year_first - timedelta(days=1)).replace(hour=23,minute=59,second=59)
# build tpl and return
last_month_tpl = (month_start.strftime('%s'), month_end.strftime('%s'))
last_year_tpl = (month_start_year.strftime('%s'), month_end_year.strftime('%s'))
return last_month_tpl, last_year_tpl
def get_rows(last_month_tpl, last_year_tpl, config):
""" get rows from postgres """
conn, cur = db_connect(config)
cur.execute(
f'SELECT epoch_time, aqi_value FROM aqi \
WHERE epoch_time > {last_month_tpl[0]} \
AND epoch_time < {last_month_tpl[1]} \
ORDER BY epoch_time DESC;'
)
rows_month = cur.fetchall()
cur.execute(
f'SELECT epoch_time, aqi_value FROM aqi \
WHERE epoch_time > {last_year_tpl[0]} \
AND epoch_time < {last_year_tpl[1]} \
ORDER BY epoch_time DESC;'
)
rows_year = cur.fetchall()
db_close(conn, cur)
return rows_month, rows_year
def get_axis(rows_month, rows_year):
""" takes rows and returns axis """
# initial df
x_timeline = [datetime.fromtimestamp(i[0]) for i in rows_month]
y_aqi_values = [int(i[1]) for i in rows_month]
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('8h').mean().round()
# reset timestamp to day
mean.reset_index(level=0, inplace=True)
mean['timestamp'] = mean['timestamp'].dt.strftime('%d %H:%M')
mean.set_index('timestamp', inplace=True)
# second df with last year data
x_timeline = [datetime.fromtimestamp(i[0]) for i in rows_year]
y_aqi_values = [int(i[1]) for i in rows_year]
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('8h').mean().round()
# reset timestamp to day
year_mean.reset_index(level=0, inplace=True)
year_mean['timestamp'] = year_mean['timestamp'].dt.strftime('%d %H:%M')
year_mean.set_index('timestamp', inplace=True)
# merge the two
mean['year_aqi'] = year_mean['year_aqi']
mean.reset_index(level=0, inplace=True)
mean.sort_values(by='timestamp', ascending=True, inplace=True)
# return axis
x = mean['timestamp']
y_1 = mean['now_aqi']
y_2 = mean['year_aqi']
return x, y_1, y_2, mean
def write_monthly_plot(x, y_1, y_2, timestamp):
""" plot last-7 only """
# parse timestamp
date_from = datetime.fromtimestamp(timestamp)
date_title = date_from.strftime('%b %Y')
month_short = date_from.strftime('%b')
file_name = 'dyn/monthly/' + date_from.strftime('%Y-%m') + '.png'
plt_title = f'AQI values for: {date_title}'
# build ticks
y_max = np.ceil(max(y_1.append(y_2))/50)*50 + 50
x_range = np.arange(0, len(x), step=9)
last_day = int(x.max().split()[0])
x_numbers = np.arange(1, last_day + 1, step=3)
x_dates = [f'{str(i).zfill(2)} {month_short}' for i in x_numbers]
x_ticks = x_range, x_dates
# plot
plt.style.use('seaborn')
plt.plot(x, y_1, color='#313131', label='this year')
plt.plot(x, y_2, color='#666666', linestyle='dashed', label='last year')
plt.fill_between(x, y_1, y2=0, where=(y_1 > 0), color='#85a762', interpolate=True) # good
plt.fill_between(x, y_1, y2=50, where=(y_1 > 50), color='#d4b93c', interpolate=True) # moderate
plt.fill_between(x, y_1, y2=100, where=(y_1 > 100), color='#e96843', interpolate=True) # ufsg
plt.fill_between(x, y_1, y2=150, where=(y_1 > 150), color='#d03f3b', interpolate=True) # unhealthy
plt.fill_between(x, y_1, y2=200, where=(y_1 > 200), color='#be4173', interpolate=True) # vunhealthy
plt.fill_between(x, y_1, y2=300, where=(y_1 > 300), color='#714261', interpolate=True) # hazardous
plt.fill_between(x, y_1, y2=0, where=(y_1 > 0), color='#ffffff', alpha=0.1, interpolate=True) # soft
plt.xticks(x_ticks[0], x_ticks[1])
plt.yticks(np.arange(0, y_max, step=50))
plt.title(plt_title, fontsize=20)
plt.legend()
plt.tight_layout()
plt.savefig(file_name, dpi = 300)
plt.figure()
def get_change(curr, year):
""" helper function to get change on thresh """
diff_avg = (curr - year) / curr
if diff_avg <= -0.15:
avg_change = 'down'
elif diff_avg >= 0.15:
avg_change = 'up'
else:
avg_change = 'same'
return avg_change
def get_aqi(val):
""" helper function to get aqi category """
if val <= 50:
category = 'Good'
elif val > 50 and val <= 100:
category = 'Moderate'
elif val > 100 and val <= 150:
category = 'Unhealthy for Sensitive Groups'
elif val > 150 and val <= 200:
category = 'Unhealthy'
elif val > 200 and val <= 300:
category = 'Very Unhealthy'
else:
category = 'Hazardous'
return category
def write_monthly_json(mean, timestamp):
""" write json file with monthly details """
date_from = datetime.fromtimestamp(timestamp)
file_name = 'dyn/monthly/' + date_from.strftime('%Y-%m') + '.json'
# current
curr_min = int(mean['now_aqi'].min())
curr_max = int(mean['now_aqi'].max())
curr_mean = int(mean['now_aqi'].mean())
curr_cat = get_aqi(curr_mean)
# last
year_min = int(mean['year_aqi'].min())
year_max = int(mean['year_aqi'].max())
year_mean = int(mean['year_aqi'].mean())
year_cat = get_aqi(year_mean)
# change
min_change = get_change(curr_min, year_min)
max_change = get_change(curr_max, year_max)
mean_change = get_change(curr_mean, year_mean)
# build rows
data_rows = []
data_rows.append(['min: ', curr_min, year_min, min_change])
data_rows.append(['max: ', curr_max, year_max, max_change])
data_rows.append(['avg: ', curr_mean, year_mean, mean_change])
data_rows.append(['avg aqi: ', curr_cat, year_cat, mean_change])
# build dict
monthly_dict = {}
monthly_dict['data'] = data_rows
# write to disk
json_str = json.dumps(monthly_dict)
with open(file_name, 'w') as f:
f.write(json_str)
def monthly_found(timestamp):
""" check if monthly graph already created """
date_from = datetime.fromtimestamp(timestamp)
file_name = 'dyn/monthly/' + date_from.strftime('%Y-%m') + '.png'
found = path.isfile(file_name)
return found
def create_monthly(config):
""" check if last month plot exists, create if needed """
last_month_tpl, last_year_tpl = get_epoch()
timestamp = int(last_month_tpl[0])
found = monthly_found(timestamp)
if found:
print('monthly already created, skipping...')
return
else:
print('creating monthly graph and json file')
# get rows
rows_month, rows_year = get_rows(last_month_tpl, last_year_tpl, config)
# get axis
x, y_1, y_2, mean = get_axis(rows_month, rows_year)
# write plot
write_monthly_plot(x, y_1, y_2, timestamp)
# write data json
write_monthly_json(mean, timestamp)

187
backend/flask/app/graph_pm.py
View File

@ -1,187 +0,0 @@
""" creates the PM 2.5 and pm 10 graphs """
from datetime import datetime
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from app.db_connect import db_connect, db_close
def color_colums(y):
""" helper function to color bar columns """
col = []
for val in y:
if val <= 50:
# good
col.append('#85a762')
elif val > 50 and val <= 100:
# moderate
col.append('#d4b93c')
elif val > 100 and val <= 150:
# ufsg
col.append('#e96843')
elif val > 150 and val <= 200:
# unhealthy
col.append('#d03f3b')
elif val > 200 and val <= 300:
# vunhealthy
col.append('#be4173')
else:
# hazardous
col.append('#714261')
return col
def get_pm_data(config):
""" gets last 10 days worth of data"""
now = datetime.now()
day_until = int(now.date().strftime('%s'))
day_from = day_until - 10 * 24 * 60 * 60
conn, cur = db_connect(config)
cur.execute(
f'SELECT epoch_time, pm25, pm10 FROM aqi \
WHERE epoch_time > {day_from} \
AND epoch_time < {day_until} \
ORDER BY epoch_time DESC;'
)
rows = cur.fetchall()
db_close(conn, cur)
return rows
def get_pm_axis(rows):
""" build axis """
# build dataframe
x_timeline = [datetime.fromtimestamp(i[0]) for i in rows]
y_pm25_values = [int(i[1]) for i in rows]
y_pm10_values = [int(i[2]) for i in rows]
data = {
'timestamp': x_timeline,
'pm25': y_pm25_values,
'pm10': y_pm10_values
}
df = pd.DataFrame(data)
indexed = df.set_index('timestamp')
indexed.sort_values(by=['timestamp'], inplace=True, ascending=True)
mean = indexed.resample('1d').mean()
mean.reset_index(level=0, inplace=True)
# axis
mean['pm25'] = mean['pm25'].round()
mean['pm10'] = mean['pm10'].round()
x = mean['timestamp']
y_1 = mean['pm25']
y_2 = mean['pm10']
return x, y_1, y_2
def build_pm_plot(x, y, y_max, thresh, title):
""" write plots to file """
file_name = title.replace('.', '')
# make ticks
x_range = np.arange(10).tolist()
x_date_time = pd.to_datetime(x).dt.date.unique()
x_dates = [i.strftime('%d %b') for i in x_date_time]
# color
col = []
for val in y:
if val < thresh:
col.append('#6ecd65')
else:
col.append('#ff4d4d')
# title
plt_title = f'Daily avg PM {title} exposure'
# plot
plt.style.use('seaborn')
plt.bar(x_dates, y, color=col, width=0.5)
plt.axhline(y=thresh, color='#6ecd65', linestyle=':')
plt.xticks(ticks=x_range, labels=x_dates)
plt.yticks(np.arange(0, y_max, step=25))
plt.title(plt_title, fontsize=20)
plt.tight_layout()
plt.savefig(f'dyn/pm{file_name}.png', dpi=300)
plt.close('all')
plt.figure()
def rebuild_pm_bar(config):
""" main function to rebuild pm2.5 and pm10 values """
# get data
rows = get_pm_data(config)
x, y_1, y_2 = get_pm_axis(rows)
# max
y_max = np.ceil(max(y_1.append(y_2))/25)*25 + 25
# pm 2.5
build_pm_plot(x, y_1, y_max, thresh=25, title='2.5')
# pm 10
build_pm_plot(x, y_2, y_max, thresh=50, title='10')
# done
print('recreated PM 2.5 and PM 10 graphs')
# hour bar chart
def get_hour_data(config):
""" get last three days worth of data from postgres """
# time
now = datetime.now()
day_until = int(now.date().strftime('%s'))
day_from = day_until - 3 * 24 * 60 * 60
# call db
conn, cur = db_connect(config)
cur.execute(
f'SELECT epoch_time, aqi_value FROM aqi \
WHERE epoch_time > {day_from} \
AND epoch_time < {day_until} \
ORDER BY epoch_time DESC;'
)
rows = cur.fetchall()
db_close(conn, cur)
return rows
def get_hour_axis(rows):
""" build x and y from the rows """
x_timeline = [datetime.fromtimestamp(i[0]) for i in rows]
y_aqi_values = [int(i[1]) for i in rows]
# build dataframe
data = {'timestamp': x_timeline, 'aqi': y_aqi_values}
df = pd.DataFrame(data)
indexed = df.set_index('timestamp')
indexed.sort_values(by=['timestamp'], inplace=True)
mean = indexed.resample('1h').mean()
# regroup by hour
mean_hour = mean.groupby([mean.index.hour]).mean()
mean_hour.reset_index(level=0, inplace=True)
# set axis
x = mean_hour['timestamp']
y = mean_hour['aqi'].round()
return x, y
def build_hour_plot(x, y):
""" takes x and y and writes plot to file """
plt_title = 'Last three days average AQI for each hour'
# ticks
x_range = np.arange(0, 24, step=3)
x_hours = [str(i).zfill(2) + ":00" for i in x_range]
y_max = np.ceil(max(y)/50) * 50 + 50
# color columns
col = color_colums(y)
# create plot
plt.style.use('seaborn')
plt.bar(x, y, color=col, width=0.5)
plt.yticks(np.arange(0, y_max, step=50))
plt.xticks(ticks=x_range, labels=x_hours)
plt.title(plt_title, fontsize=20)
plt.tight_layout()
plt.savefig('dyn/hours.png', dpi=300)
plt.close('all')
plt.figure()
def rebuild_hour_bar(config):
""" main function to rebuild houly bar avg """
rows = get_hour_data(config)
x, y = get_hour_axis(rows)
build_hour_plot(x, y)

140
backend/flask/app/table_export.py
View File

@ -1,140 +0,0 @@
""" recreate json file to populate last year comparison table """
from datetime import datetime
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from app.db_connect import db_connect, db_close
from app.graph_pm import color_colums
def get_rows(config):
""" get rows from last 7 days
and last 7 days one year ago """
now = datetime.now()
# last 10
now_until = int(now.date().strftime('%s'))
now_from = now_until - 7 * 24 * 60 * 60
# last 10 one year ago
year_until = now_until - 365 * 24 * 60 * 60
year_from = now_until - 372 * 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 7 days', '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 write_graph(mean):
""" recreate barchart with yearly comparison """
# build axis
mean.sort_index(inplace=True)
x = mean['timestamp'].to_list()
y_1 = mean['now_aqi'].to_list()
y_2 = mean['year_aqi'].to_list()
# build color lists
col_y_1 = color_colums(y_1)
col_y_2 = color_colums(y_2)
# set ticks
y_max = int(np.ceil(max(y_1 + y_2)/50) * 50 + 50)
x_indexes = np.arange(len(x))
# build plot
width = 0.25
plt_title = 'Daily avg AQI values compared to last year'
plt_suptitle = 'left: this year, right: last year'
plt.style.use('seaborn')
# write bars
plt.bar(x_indexes - (width / 2) - 0.02, y_1, color=col_y_1, width=width)
plt.bar(x_indexes + (width / 2) + 0.02, y_2, color=col_y_2, width=width)
plt.title(plt_suptitle,fontsize=15)
plt.suptitle(plt_title,fontsize=20, y=0.96)
plt.yticks(np.arange(0, y_max, step=50))
plt.xticks(ticks=x_indexes, labels=x)
plt.tight_layout()
plt.savefig('dyn/year-graph.png', dpi=300)
plt.figure()
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)
write_graph(mean)
# done
print('recreated year comparison graph and json file')

124
backend/flask/app/views.py
View File

@ -1,124 +0,0 @@
import configparser
import json
from flask import request
from flask_httpauth import HTTPBasicAuth
from apscheduler.schedulers.background import BackgroundScheduler
from app import app
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 import graph_monthly
from app.db_connect import db_insert
def get_config():
""" read out config file """
# parse
config_parser = configparser.ConfigParser()
config_parser.read('config')
# build dict
config = {}
config["authUsername"] = config_parser.get('aqi_monitor', "authUsername")
config["authPassword"] = config_parser.get('aqi_monitor', "authPassword")
config["api_key"] = config_parser.get('openweathermap', "api_key")
config["lat"] = config_parser.get('openweathermap', "lat")
config["lon"] = config_parser.get('openweathermap', "lon")
# db
config["db_host"] = config_parser.get('postgres', "db_host")
config["db_database"] = config_parser.get('postgres', "db_database")
config["db_user"] = config_parser.get('postgres', "db_user")
config["db_password"] = config_parser.get('postgres', "db_password")
return config
# start up
auth = HTTPBasicAuth()
config = get_config()
weather.handle_weather(config)
graph.create_current(config)
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)
graph_monthly.create_monthly(config)
# build username / pw dict for basic auth
USER_DATA = {}
USER_DATA[config['authUsername']] = config['authPassword']
# start scheduler
scheduler = BackgroundScheduler()
scheduler.add_job(
weather.handle_weather, args=[config], trigger="interval", name='weather_api', seconds=900
)
scheduler.add_job(
graph.create_current, args=[config], trigger="cron", minute='*/5', name='current_graph'
)
scheduler.add_job(
graph.rebuild_3days, args=[config], trigger="cron", day='*', hour='1', minute='1', name='3_days'
)
scheduler.add_job(
graph.rebuild_7days, args=[config], trigger="cron", day='*', hour='1', minute='2', name='7_days'
)
scheduler.add_job(
graph_pm.rebuild_pm_bar, args=[config], trigger="cron", day='*', hour='1', minute='3', name='pm_bar'
)
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.add_job(
graph_monthly.create_monthly, args=[config], trigger="cron", day='*', hour='1', minute='7', name='create_monthly'
)
scheduler.start()
@auth.verify_password
def verify(username, password):
if not (username and password):
return False
return USER_DATA.get(username) == password
# ingest
@app.route('/ingest', methods=['POST'])
@auth.login_required
def ingest():
data = request.json
if data:
# populate data dict
json_dict, error_found = aqi_parser.input_process(data)
if error_found:
print('pm25 read failed')
print(json_dict)
else:
# save to db
time_stamp = db_insert(config, json_dict)
print(f'db insert done at {time_stamp}')
# save to webserver
data = json.dumps(json_dict)
with open('dyn/air.json', 'w') as f:
f.write(data)
print(data)
return 'ingest'
# output
@app.route('/')
def home():
try:
with open('dyn/air.json', 'r') as f:
data = f.read()
except FileNotFoundError:
# will get regeneratod on next run
data = '{}'
return data

48
backend/flask/app/weather.py
View File

@ -1,48 +0,0 @@
""" get data from openweathermap.org """
from datetime import datetime
from time import sleep
import json
import requests
def get_weather(config):
"""
gets the missing weather data from openweathermap
return: json string
"""
api_key = config['api_key']
lat = config['lat']
lon = config['lon']
# get data
r = requests.get("https://api.openweathermap.org/data/2.5/weather?&units=metric&appid=" + api_key + "&lat=" + lat + "&lon=" + lon, timeout=20)
# format data
r_json = r.json()
weather_name = r_json['weather'][0]['main']
weather_icon = r_json['weather'][0]['icon']
wind_speed = r_json['wind']['speed']
wind_direction = r_json['wind']['deg']
# timestamp
now = datetime.now()
timestamp = now.strftime("%Y-%m-%d %H:%M:%S")
epoch_time = int(now.strftime('%s'))
# form dict
json_dict = {}
json_dict['weather_name'] = weather_name
json_dict['weather_icon'] = weather_icon
json_dict['wind_speed'] = wind_speed
json_dict['wind_direction'] = wind_direction
json_dict['timestamp'] = timestamp
json_dict['epoch_time'] = epoch_time
# return json string
weather_json = json.dumps(json_dict)
return weather_json, timestamp
def handle_weather(config):
""" sets infinite loop to collect api data """
weather_json, timestamp = get_weather(config)
with open('dyn/weather.json', 'w') as f:
f.write(weather_json)
print(f'weather data updated: {timestamp}')

14
backend/flask/config.sample
View File

@ -1,14 +0,0 @@
[aqi_monitor]
authUsername = username
authPassword = password
[openweathermap]
api_key = xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
lat = 40.71
lon = -73.99
[postgres]
db_host = postgres
db_database = aqi
db_user = aqi
db_password = aaaaaaaaaaaaaaaaa

26
backend/flask/run.py
View File

@ -1,26 +0,0 @@
"""
Documentation: https://flask.palletsprojects.com
Install on Arch
- sudo pacman -S python-flask
- sudo pacman -S python-flask-httpauth
or with pip
- pip install Flask
- pip install Flask-HTTPAuth
Run Debug env:
export FLASK_APP=run.py
export FLASK_DEBUG=1
"""
from app import app
if __name__ == "__main__":
app.run()

9
deploy.sh
View File

@ -4,12 +4,9 @@
rsync --progress -a docker-compose.yml vps2:docker/
rsync --progress -a env vps2:docker/
rsync --progress -a --delete-after helper_scripts vps2:docker/
rsync --progress -a --delete-after \
--exclude dyn --exclude config.sample --exclude __pychache__ \
backend vps2:docker/
rsync --progress -a --delete-after \
--exclude dyn \
frontend vps2:docker/
rsync --progress -a --delete-after nginx vps2:docker/
rsync --progress -a --delete-after --exclude config.json.sample --exclude **/__pychache__ --exclude static/dyn \
web vps2:docker/
##
exit 0

42
docker-compose.yml
View File

@ -34,26 +34,28 @@ services:
- "com.github.jrcs.letsencrypt_nginx_proxy_companion.nginx_proxy"
# backend flask
flask:
build: ./backend/flask
build: ./web
container_name: flask
restart: always
volumes:
- ./volume/flask/dyn:/app/dyn
- ./volume/flask/dyn:/app/static/dyn
environment:
- APP_NAME=FlaskBackend
- APP_NAME=FlaskAqi
- TZ=Asia/Bangkok
expose:
- "8080"
# backend nginx
# nginx infront of uwsgi
nginx:
build: ./backend/nginx
build: ./nginx
container_name: backend_nginx
restart: always
environment:
- VIRTUAL_HOST=data.lpb-air.com
- LETSENCRYPT_HOST=data.lpb-air.com
- VIRTUAL_HOST=www.lpb-air.com,lpb-air.com
- LETSENCRYPT_HOST=www.lpb-air.com,lpb-air.com
expose:
- "80"
depends_on:
- flask
# backend postgres
postgres:
image: postgres
@ -65,29 +67,3 @@ services:
- ./env/postgres.env
expose:
- "5432"
# php fast-cgi
php:
image: php:7-fpm
container_name: php-fpm
restart: always
volumes:
- ./frontend/nginx:/air
- type: bind
source: ./volume/flask/dyn
target: /air/dyn
# lpb-air
air:
image: nginx
container_name: lpb-air_nginx
restart: always
expose:
- "80"
volumes:
- type: bind
source: ./volume/flask/dyn
target: /air/dyn
- ./frontend/nginx:/air:rw
- ./frontend/nginx.conf:/etc/nginx/conf.d/default.conf
environment:
- VIRTUAL_HOST=www.lpb-air.com,lpb-air.com
- LETSENCRYPT_HOST=www.lpb-air.com,lpb-air.com

23
frontend/nginx.conf
View File

@ -1,23 +0,0 @@
server {
listen 80;
index index.php index.html;
server_name localhost;
error_log /var/log/nginx/error.log;
access_log /var/log/nginx/access.log;
root /air;
location ~ \.php$ {
try_files $uri =404;
fastcgi_split_path_info ^(.+\.php)(/.+)$;
fastcgi_pass php:9000;
fastcgi_index index.php;
include fastcgi_params;
fastcgi_param SCRIPT_FILENAME $document_root$fastcgi_script_name;
fastcgi_param PATH_INFO $fastcgi_path_info;
}
}
server {
server_name lpb-air.com;
return 301 https://www.lpb-air.com$request_uri;
}

92
frontend/nginx/about/index.php
View File

@ -1,92 +0,0 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<link rel="stylesheet" href="/css/style.css">
<title>About</title>
<script src="/js/aqi.js"></script>
</head>
<body>
<div class="preload">
<img src="/img/cloud_colors.gif" alt="cloud_animation">
</div>
<div class="block_wrap light_background">
<?php include($_SERVER['DOCUMENT_ROOT'] . '/incl/topnav.php'); ?>
</div>
<div class="block_wrap">
<div class="content">
<h2>About</h2>
<p>This page and its contents are still under construction. More content is coming soon.</p>
<p>The data for this page is collected from an air monitor located just outside of Luang Prabang, Laos. While we do our best, no guarantee is given for the accuracy of this data.</p>
<p>The data is updated every 3 minutes. Contrary to international websites who measure the air pollution via satellite images and rely on estimates and averages, an on-site air monitor delivers real time values that paint a much more accurate picture of the local situation.</p>
<p>Roughly, the Air Quality Index (AQI) is the internationally used air quality standard to measure the pollution of the air. It is divided into 6 levels, and according to these levels, certain health advices are given:</p>
</div>
<div class="aqidesc content">
<div class="aqirow">
<div class="leftcolumn">
<p>Aqi Values</p>
</div>
<div class="rightcolumn hide">
<p>Description</p>
</div>
</div>
<div class="aqirow">
<div class="leftcolumn category-class good">
<p>0 to 50:</p>
</div>
<div class="rightcolumn">
<p>Good: No health concerns, enjoy activities.</p>
</div>
</div>
<div class="aqirow">
<div class="leftcolumn category-class moderate">
<p>51 - 100:</p>
</div>
<div class="rightcolumn">
<p>Moderate: Active children and adults, and people with respiratory disease, such as asthma, should limit prolonged outdoor exertion.</p>
</div>
</div>
<div class="aqirow">
<div class="leftcolumn category-class ufsg">
<p>101 - 150:</p>
</div>
<div class="rightcolumn">
<p>Unhealthy for Sensitive Groups: Active children and adults, and people with respiratory disease, such as asthma, should limit prolonged outdoor exertion.</p>
</div>
</div>
<div class="aqirow">
<div class="leftcolumn category-class unhealthy">
<p>151 - 200:</p>
</div>
<div class="rightcolumn">
<p>Unhealthy: Everyone may begin to experience health effects: Active children and adults, and people with respiratory disease, such as asthma, should avoid prolonged outdoor exertion; everyone else, especially children, should limit prolonged outdoor exertion</p>
</div>
</div>
<div class="aqirow">
<div class="leftcolumn category-class vunhealthy">
<p>201 - 300:</p>
</div>
<div class="rightcolumn">
<p>Very Unhealthy: Active children and adults, and people with respiratory disease, such as asthma, should avoid all outdoor exertion; everyone else, especially children, should limit outdoor exertion.</p>
</div>
</div>
<div class="aqirow">
<div class="leftcolumn category-class hazardous">
<p>301 - 500:</p>
</div>
<div class="rightcolumn">
<p>Hazardous: Everyone should avoid all outdoor exertion.</p>
</div>
</div>
</div>
<div class="credits content">
<h2>Credits</h2>
<p>Partial Weather data, namely weather icon, weather description and windspeed are provided from <a href="https://openweathermap.org/ " target="_blank">openweather.org</a> API distributed under the <a href="https://openweathermap.org/full-price" target="_blank">Creative Commons Attribution-ShareAlike 4.0 Generic License</a>.</p>
<p><a target="_blank" href="https://github.com/lokesh/lightbox2">Lightbox</a> made by Lokesh Dhakar, released under the <a target="_blank" href="https://raw.githubusercontent.com/lokesh/lightbox2/master/LICENSE">MIT license</a>.</p>
</div>
</div>
<?php include($_SERVER['DOCUMENT_ROOT'] . '/incl/footer.html'); ?>
</body>
</html>

476
frontend/nginx/css/style.css
View File

@ -1,476 +0,0 @@
@font-face {
font-family: Rubik-Bold;
src: url(/font/Rubik-Bold.ttf);
}
@font-face {
font-family: Rubik-Light;
src: url(/font/Rubik-Light.ttf);
}
@font-face {
font-family: Rubik-Regular;
src: url(/font/Rubik-Regular.ttf);
}
body {
margin: 0;
padding: 0;
overflow: hidden;
}
h1 {
font-family: Rubik-Bold;
font-size: 3em;
}
h2 {
font-family: Rubik-Bold;
}
h3 {
font-family: Rubik-Light;
font-size: 1.3em;
}
p, li, td, th {
font-family: Rubik-Regular;
font-size: 1.1em;
}
.preload {
position: fixed;
top: 0;
width: 100%;
height: 100vh;
background: #e6e6e6;
display: flex;
justify-content: center;
align-items: center;
z-index: 1;
}
.preload img {
width: 100px;
}
.preload-finish {
opacity: 0;
pointer-events: none;
}
a {
color: inherit;
font-family: Rubik-Regular;
}
.content {
width: 70%;
max-width: 900px;
margin-left: auto;
margin-right: auto;
}
.content-date {
display: inline-block;
min-width: 90px;
}
.colorbox {
color: #fff;
min-height: 40px;
margin: 0;
padding: 0;
top: 0;
z-index: 1;
}
.colorbox p {
margin: 0;
padding: 10px 0;
}
.colorbox ul {
margin: 0;
padding-bottom: 10px;
}
.top_content {
display: flex;
flex-wrap: nowrap;
}
.nav {
display: flex;
align-items: center;
flex: 20%;
}
.nav ul {
list-style-type:none;
}
.nav li {
padding: 5px 40px;
margin: 5px;
border-style: none none solid none;
border-width: 2px;
}
.nav a {
text-decoration: none;
}
.title {
flex: 80%;
}
.divider {
padding: 30px 0;
}
.divider hr {
border: 1px solid;
}
.cloud {
position: relative;
padding-bottom: 30px;
}
.cloud img {
width: 100%;
}
.aqi_box {
position: absolute;
top: 45%;
left: 57%;
text-align: right;
transform: translate(-50%, -50%);
color: #fff;
}
.aqi_box h1 {
font-size: 15em;
margin: 0;
}
.aqi_box h2 {
font-family: Rubik-Light;
font-size: 1.8em;
margin: 0;
}
.aqi_box p {
margin: 0;
}
/* weather styling */
.block_wrap {
margin-bottom: 40px;
}
.weather_box {
border: solid 2px;
border-radius: 20px;
height: 100px;
position: relative;
}
.weather_content {
display: grid;
grid-template-columns: auto auto auto auto;
grid-column-gap: 10px;
}
.weather_icon {
width: 50px;
padding: 10px;
position: absolute;
top: 50%;
transform: translateY(-50%);
}
.weather_icon img {
width: 100%;
}
.weather_text {
width: 60%;
position: absolute;
top: 50%;
left: 40%;
transform: translateY(-50%);
}
.weather_text h3,
.weather_text p {
margin: 10px 0;
}
/* descriptions */
.desc_wrap {
margin: 40px 0;
padding: 30px 0;
}
.desc_content {
padding-top: 30px;
padding-bottom: 30px;
display: grid;
grid-template-columns: 33% 33% 33%;
grid-column-gap: 10px;
}
.desc_box {
margin: auto;
}
.desc_item_wrap {
display: flex;
align-items: center;
justify-content: center;
flex-wrap: wrap;
}
.desc_item {
padding: 8px 10px;
margin: 4px;
color: #fff;
text-align: center;
width: 200px;
transform: scale(1);
transition-timing-function: ease;
-webkit-transition: transform 2s;
-moz-transition: transform 2s;
-ms-transition: transform 2s;
-o-transition: transform 2s;
transition: transform 2s;
}
.good {
background-color: #85a762;
}
.moderate {
background-color: #d4b93c;
}
.ufsg {
background-color: #e96843;
}
.unhealthy {
background-color: #d03f3b;
}
.vunhealthy {
background-color: #be4173;
}
.hazardous {
background-color: #714261;
}
.category_icon {
padding: 30px;
}
.category_icon img {
width: 100%;
}
.tagline_content {
text-align: center;
}
.desc_item.active {
transform: scale(1.2);
font-size: 1.1em;
margin: 15px;
box-shadow: darkgray 5px 5px;
font-weight: bold;
}
/* table */
.year-table {
display: flex;
justify-content: center;
}
.year-table table{
width: 100%;
max-width: 500px;
table-layout: fixed;
margin: auto;
}
.year-table thead th {
padding: 5px 0;
background-color: #eeeeee;
}
.year-table tbody td:nth-child(1) {
padding: 3px 0 3px 10px;
background-color: #eeeeee;
}
.year-table td:nth-child(2),
.year-table td:nth-child(3),
.year-table td:nth-child(4) {
text-align: center;
color: #fff;
}
/* about */
.aqirow {
display: flex;
width: 100%;
height: 100%;
margin: 10px auto;
padding: 0;
}
.leftcolumn {
width: 150px;
margin: 0;
padding: 10px 15px;
display: flex;
}
.leftcolumn.category-class {
font-weight: bold;
color: #FFFFFF;
}
.rightcolumn {
width: 100%;
margin: 0px;
padding: 10px;
justify-content: center;
}
.credits {
padding-top: 30px;
padding-bottom: 30px;
}
.leftcolumn p,
.rightcolumn p {
margin: 0;
padding: 0;
align-items: center;
display: flex;
}
/* graphs */
.graph2 {
display: grid;
grid-template-columns: 50% 50%;
grid-column-gap: 10px;
}
.graph3 {
display: flex;
}
.graph_item img {
width: 100%;
}
/* footer */
.footer_wrap {
height: 50px;
display: flex;
justify-content: center;
align-items: center;
color: #fff;
}
/* responsiv */
@media screen and (max-width: 1100px) {
.top_content,
.tagline_content,
.weather_content {
width: 90%;
}
.desc_content {
grid-template-columns: repeat(2, 1fr);
}
.desc_box:nth-child(1) {
grid-column: span 1;
grid-row: span 2;
}
.desc_box:nth-child(2),
.desc_box:nth-child(3) {
grid-column: span 1;
grid-row: span 1;
}
}
@media screen and (max-width: 800px) {
h1 {
font-size: 2.5em;
}
.top_content {
flex-direction: column-reverse;
}
.content {
width: 95%;
}
.nav {
flex-wrap: nowrap;
}
.nav li {
display: inline-flex;
padding: 5px;
}
.nav ul {
margin: 10px auto;
padding: 0;
}
.title h1 {
margin: 0;
}
.title {
padding: 0;
}
.aqi_box {
width: 85%;
top: 40%;
text-align: center;
}
.aqi_box h1 {
font-size: 5em;
}
.aqi_box h2 {
font-size: 1.5em;
}
.weather_content {
grid-template-columns: auto auto;
grid-row-gap: 10px;
}
.weather_icon {
padding: 5px;
}
.weather_icon img {
width: 100%;
}
.weather_text {
left: 35%;
}
.weather_text h3 {
font-size: 1.2;
}
.desc_content {
display: block;
}
.desc_item {
padding: 0 10px;
width: 50%;
}
.category_icon {
padding: 0 20%;
}
.graph2 {
display: block;
}
.graph3 {
flex-direction: column-reverse;
}
}

147
frontend/nginx/graphs/index.php
View File

@ -1,147 +0,0 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<link rel="stylesheet" href="/css/style.css">
<link rel="stylesheet" href="/css/lightbox.css">
<title>Graphs</title>
<script src="/js/aqi.js"></script>
<script src="/js/lightbox.js"></script>
</head>
<body>
<div class="preload">
<img src="/img/cloud_colors.gif" alt="cloud_animation">
</div>
<div class="block_wrap light_background">
<?php include($_SERVER['DOCUMENT_ROOT'] . '/incl/topnav.php'); ?>
</div>
<div class="block_wrap">
<div class="content">
<h1>Graphs</h1>
<p>All the graphs and table on this page will get recreated every night with the newest values.</p>
</div>
<div class="content">
<h2 id="last3">Last three days</h2>
</div>
<div class="graph3 content">
<div class="graph_item">
<p>Three days ago</p>
<a href="/dyn/day-3.png" data-lightbox="graph">
<img src="/dyn/day-3.png" alt="day-3">
</a>
</div>
<div class="graph_item">
<p>Two days ago</p>
<a href="/dyn/day-2.png" data-lightbox="graph">
<img src="/dyn/day-2.png" alt="day-2">
</a>
</div>
<div class="graph_item">
<p>Yesterday</p>
<a href="/dyn/day-1.png" data-lightbox="graph">
<img src="/dyn/day-1.png" alt="day-1">
</a>
</div>
</div>
<div class="content divider">
<hr class="col_border">
</div>
<div class="content">
<h2 id="pm">Particle Matter sizes</h2>
<p><b>There is no healthy level of pollution.</b> Particle matter (PM) are defined in two different sizes: PM 2.5 which represents particle sizes smaller than 2.5 &#xB5;m or less than 1/20th of the diameter of a human hair and PM 10 which represents particle sizer smaller than 10 &#xB5;m or 1/5th of the diameter of a human hair.</p>
<p>The <a href="https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health" target="_blank">WHO</a> is providing more details on their website regarding particle matter and their health implications. On <a href="https://en.wikipedia.org/wiki/Particulates" target="blank">Wikipedia</a> there are some interesting links to studies for further reading.</p>
</div>
<div class="graph2 content">
<div class="graph_item">
<a href="/dyn/pm25.png" data-lightbox="pm-bar">
<img src="/dyn/pm25.png" alt="pm 2.5 bar chart">
</a>
</div>
<div>
<h3>PM 2.5</h3>
<p>Particle matter sizes smaller than 2.5&#xB5;m are the most problematic as these particles will find their way through the lungs into the bloodstream.</p>
<p>The WHO Air quality guideline values set a 25 &#xB5;g/m&sup3; 24-hour average as an upper level threshold. In the 10 days overview you can see:</p>
<p>Green: Daily average exposure below 25 &#xB5;g/m&sup3;<br>
Red: Daily average exposure above 25 &#xB5;g/m&sup3;</p>
</div>
</div>
<div class="graph2 content">
<div class="graph_item">
<a href="/dyn/pm10.png" data-lightbox="pm-bar">
<img src="/dyn/pm10.png" alt="pm 10 bar chart">
</a>
</div>
<div>
<h3>PM 10</h3>
<p>The threshold for the daily average PM 10 exposure is set to 50 &#xB5;g/m&sup3; by the WHO. Particles this size can penetrate and lodge deep inside the lungs but are too big to enter the blood stream. For this reason the threshold is higher.</p>
<p>In the 10 days overview you can see:</p>
<p>Green: Daily average exposure below 50 &#xB5;g/m&sup3;<br>
Red: Daily average exposure above 50 &#xB5;g/m&sup3;</p>
</div>
</div>
<div class="content divider">
<hr class="col_border">
</div>
<div class="content">
<h2 id="hour">Hour by Hour</h2>
</div>
<div class="graph2 content">
<div class="graph_item">
<a href="/dyn/hours.png" data-lightbox="hours-bar">
<img src="/dyn/hours.png" alt="hours 10 bar chart">
</a>
</div>
<div>
<h3>Hourly AQI average</h3>
<p>The AQI value can change a lot during the day. This can depend on the wind, cooking on fire or just another additional source of pollution nearby.</p>
<p>In this chart you can see the average AQI for each hour. This data is based on the last three days. This data can help to visualize which hours in the past three days have been better or worse on average.</p>
</div>
</div>
<div class="content divider">
<hr class="col_border">
</div>
<div class="content">
<h2>Compared to last year</h2>
<p>This year's daily average AQI values from last 7 days compared to corresponding values from last year.</p>
</div>
<div class="graph2 content">
<div class="year-table" id="compare">
<table>
<thead>
<tr>
<th></th>
<th>this year</th>
<th>last year</th>
<th>change</th>
</tr>
</thead>
<tbody class="aqi-table">
<!-- start trs -->
<?php
$json_file = $_SERVER['DOCUMENT_ROOT'] . '/dyn/year-table.json';
$json = json_decode(file_get_contents($json_file), true);
$rows = $json['data'];
foreach($rows as $row) {
echo '<tr>';
foreach($row as $cell) {
echo '<td>' . $cell . '</td>';
}
echo '</tr>';
}
?>
<!-- end trs -->
</tbody>
</table>
</div>
<div class="graph_item">
<a href="/dyn/year-graph.png" data-lightbox="graph">
<img src="/dyn/year-graph.png" alt="last year comparison">
</a>
</div>
</div>
</div>
<?php include($_SERVER['DOCUMENT_ROOT'] . '/incl/footer.html'); ?>
</body>
</html>

5
frontend/nginx/incl/footer.html
View File

@ -1,5 +0,0 @@
<div class="col_bg footer_wrap">
<div class="content footer">
<p>© 2021 | <a href="https://github.com/bbilly1/aqi_monitor" target="_blank">Documentation</a></p>
</div>
</div>

18
frontend/nginx/incl/topnav.php
View File

@ -1,18 +0,0 @@
<div class="col_bg colorbox" id="colorbox">
<?php include($_SERVER['DOCUMENT_ROOT'] . '/incl/update.html'); ?>
</div>
<div class="top_content content">
<div class="title">
<h1>Live Air Quality</h1>
<h2>in Luang Prabang Laos PDR</h2>
<p>Last updated: <span id="timestamp"></span></p>
</div>
<div class="nav">
<ul>
<a href="/"><li class="col_border">Home</li></a>
<a href="/about"><li class="col_border">About</li></a>
<a href="/graphs"><li class="col_border">Graphs</li></a>
<a href="/monthly"><li class="col_border">Monthly</li></a>
</ul>
</div>
</div>

129
frontend/nginx/index.php
View File

@ -1,129 +0,0 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<link rel="stylesheet" href="/css/style.css">
<link rel="stylesheet" href="/css/lightbox.css">
<title>AQI</title>
<script src="/js/aqi.js"></script>
<script src="/js/lightbox.js"></script>
<meta property="og:title" content="Live Air Quality in Luang Prabang Laos PDR" />
<meta property="og:url" content="https://www.lpb-air.com/" />
<meta property="og:image" content="https://www.lpb-air.com/img/social_preview.jpg" />
<meta property="og:type" content="website" />
<meta property="og:description" content="Real-time and on site air quality measurment and changes over time." />
</head>
<body>
<div class="preload">
<img src="/img/cloud_colors.gif" alt="cloud_animation">
</div>
<div class="block_wrap light_background">
<?php include($_SERVER['DOCUMENT_ROOT'] . '/incl/topnav.php'); ?>
<div class="top_content content">
<div class="cloud">
<img src="/img/cloud.png" alt="cloud" class="col_filter">
<div class="aqi_box">
<h1 id="aqiValue"></h1>
<p id="aqi-label">US AQI</p>
<h2 id="aqiCategory"></h2>
</div>
</div>
</div>
</div>
<div class="block_wrap">
<div class="weather_content content">
<div class="weather_box col_border">
<div class="weather_icon">
<img src="/img/icon/000.png" alt="weather_icon" class="col_filter" id="weather_icon">
</div>
<div class="weather_text">
<h3 class="col_font"><span id="temperature"></span><span> °C</span></h3>
<p id="weather_name"></p>
</div>
</div>
<div class="weather_box col_border">
<div class="weather_icon">
<img src="/img/icon/wind.png" alt="wind_icon" class="col_filter">
</div>
<div class="weather_text">
<h3 class="col_font">Wind</h3>
<p><span id="wind_speed"></span><span> km/h</span></p>
</div>
</div>
<div class="weather_box col_border">
<div class="weather_icon">
<img src="/img/icon/humidity.png" alt="humidity_icon" class="col_filter">
</div>
<div class="weather_text">
<h3 class="col_font">Humidity</h3>
<p><span id="humidity"></span><span> %</span></p>
</div>
</div>
<div class="weather_box col_border">
<div class="weather_icon">
<img src="/img/icon/pressure.png" alt="pressure_icon" class="col_filter">
</div>
<div class="weather_text">
<h3 class="col_font">Pressure</h3>
<p><span id="pressure"></span><span> mbar</span></p>
</div>
</div>
</div>
</div>
<div class="block_wrap light_background">
<div class="desc_content content">
<div class="desc_box">
<div class="desc_item_wrap">
<div class="desc_item good">
<p>GOOD</p>
</div>
<div class="desc_item moderate">
<p>MODERATE</p>
</div>
<div class="desc_item ufsg">
<p>UNHEALTHY FOR SENSITIVE GROUPS</p>
</div>
<div class="desc_item unhealthy">
<p>UNHEALTHY</p>
</div>
<div class="desc_item vunhealthy">
<p>VERY UNHEALTHY</p>
</div>
<div class="desc_item hazardous">
<p>HAZARDOUS</p>
</div>
</div>
</div>
<div class="desc_box">
<div class="category_icon">
<img src="/img/icon/category-plain.png" alt="category_icon" id="categoryIcon">
</div>
</div>
<div class="desc_box">
<h2 class="col_font" id="aqiName"></h2>
<h3 id="aqiRange"></h3>
<p id="aqiDesc">Good: No health concerns, enjoy activities.</p>
</div>
</div>
</div>
<div class="block_wrap">
<div class="graph2 content">
<div class="graph_item">
<h3 id="last3">Last three hours</h3>
<a href="/dyn/current.png" data-lightbox="graph" id="last3-a">
<img src="/dyn/current.png" alt="current" id="last3-img">
</a>
</div>
<div class="graph_item">
<h3 id=last7>Last 7 days</h3>
<a href="/dyn/last-7.png" data-lightbox="graph">
<img src="/dyn/last-7.png" alt="last-7 days">
</a>
</div>
</div>
</div>
<?php include($_SERVER['DOCUMENT_ROOT'] . '/incl/footer.html'); ?>
</body>
</html>

56
frontend/nginx/monthly/index.php
View File

@ -1,56 +0,0 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<link rel="stylesheet" href="/css/style.css">
<link rel="stylesheet" href="/css/lightbox.css">
<title>Monthly</title>
<script src="/js/aqi.js"></script>
<script src="/js/lightbox.js"></script>
</head>
<body>
<div class="preload">
<img src="/img/cloud_colors.gif" alt="cloud_animation">
</div>
<div class="block_wrap light_background">
<?php include($_SERVER['DOCUMENT_ROOT'] . '/incl/topnav.php'); ?>
</div>
<div class="block_wrap">
<div class="content">
<h1>Month by month</h1>
<p>Month compared to last year. Values are in 8h average.</p>
</div>
<!-- list start -->
<?php
foreach(array_reverse(glob($_SERVER['DOCUMENT_ROOT'] . '/dyn/monthly/*.png')) as $month) {
$file_name = basename($month,".png");
$json_file = $_SERVER['DOCUMENT_ROOT'] . '/dyn/monthly/'.$file_name.'.json';
$json = json_decode(file_get_contents($json_file), true);
$rows = $json['data'];
$date = new DateTime($file_name);
$date_str = $date->format('F Y');
echo '<div class="content"><h2>'.$date_str.'</h2></div>';
echo '<div class="graph2 content">';
echo '<div class="graph_item"><a href="/dyn/monthly/'.$file_name.'.png" data-lightbox="monthly">';
echo '<img src="/dyn/monthly/'.$file_name.'.png" alt="'.$file_name.'"></a></div>';
echo '<div class="year-table"><table>';
echo '<thead><tr><th></th><th>this year</th><th>last year</th><th>change</th></tr></thead>';
echo '<tbody class="aqi-table">';
foreach($rows as $row) {
echo '<tr>';
foreach($row as $cell) {
echo '<td>' . $cell . '</td>';
}
echo '</tr>';
}
echo '</tbody>';
echo '</table></div>';
echo '</div>';
}
?>
<!-- list end -->
</div>
</body>
</html>

2
backend/nginx/Dockerfile → nginx/Dockerfile
View File

@ -5,4 +5,4 @@ FROM nginx
RUN rm /etc/nginx/conf.d/default.conf
# Replace with our own nginx.conf
COPY nginx.conf /etc/nginx/conf.d/
COPY nginx.conf /etc/nginx/conf.d/

0
backend/nginx/nginx.conf → nginx/nginx.conf
View File

19
web/Dockerfile Normal file
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@ -0,0 +1,19 @@
FROM python
COPY . /srv/flask_app
WORKDIR /srv/flask_app
RUN mkdir -p static/dyn/monthly
RUN chmod 777 -R .
RUN apt-get clean && apt-get -y update
RUN apt-get -y install python3-dev \
&& apt-get -y install build-essential
RUN pip install -r requirements.txt --src /usr/local/src
RUN chmod +x ./start.sh
CMD ["./start.sh"]

17
web/config.json.sample Normal file
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{
"aqi_monitor": {
"authUsername": "user",
"authPassword": "password"
},
"openweathermap": {
"api_key": "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
"lat": 40.71,
"lon": -74.00
},
"postgres": {
"db_host": "postgres",
"db_database": "aqi",
"db_user": "aqi",
"db_password": "xxxxxxxxxxxxxxxxxxxx"
}
}

5
backend/flask/requirements.txt → web/requirements.txt
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@ -1,6 +1,7 @@
apscheduler
APScheduler
Flask_HTTPAuth
Flask_Table
Flask
Flask-HTTPAuth
matplotlib
numpy
pandas

0
web/src/__init__.py Normal file
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245
web/src/db.py Normal file
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""" handle db connections """
import json
from datetime import datetime
import psycopg2
import requests
from src.helper import get_config
class DatabaseConnect:
""" handle db """
CONFIG = get_config()
def __init__(self):
self.conn, self.cur = self.db_connect()
def db_connect(self):
""" returns connection and curser """
# Connect to database
conn = psycopg2.connect(
host=self.CONFIG['postgres']['db_host'],
database=self.CONFIG['postgres']['db_database'],
user=self.CONFIG['postgres']['db_user'],
password=self.CONFIG['postgres']['db_password']
)
# Open a cursor to perform database operations
cur = conn.cursor()
return conn, cur
def db_execute(self, query):
""" run a query """
if isinstance(query, str):
self.cur.execute(
query
)
rows = self.cur.fetchall()
elif isinstance(query, tuple):
self.cur.execute(
query[0], query[1]
)
rows = False
return rows
def db_close(self):
""" clean close the conn and curser """
self.conn.commit()
self.cur.close()
self.conn.close()
class IngestLine:
""" handle data input from monitor """
def __init__(self, data):
self.aqi_query = None
self.weather_query = None
self.input_json = data
self.add_aqi()
self.add_timestamp()
self.add_weather()
self.add_query()
def add_aqi(self):
""" add aqi_value and aqi_category keys from pm2.5 value """
aqi_breakpoints = [
('Good', 0, 12.0, 0, 50),
('Moderate', 12.1, 35.4, 51, 100),
('Unhealthy for Sensitive Groups', 35.5, 55.4, 101, 150),
('Unhealthy', 55.5, 150.4, 151, 200),
('Very Unhealthy', 150.5, 250.4, 201, 300),
('Hazardous', 250.5, 500.4, 301, 500),
]
pm25 = self.input_json['pm25']
for i in aqi_breakpoints:
aqi_category, p_low, p_high, a_low, a_high = i
if p_low < pm25 < p_high:
# found it
break
aqi = (a_high - a_low) / (p_high - p_low) * (pm25 - p_low) + a_low
aqi_dict = {
'aqi_value': round(aqi),
'aqi_category': aqi_category
}
self.input_json.update(aqi_dict)
def add_timestamp(self):
""" add timestamp to dict """
now = datetime.now()
timestamp = now.strftime("%Y-%m-%d %H:%M:%S")
epoch_time = int(now.strftime('%s'))
time_stamp_dict = {
'time_stamp': timestamp,
'epoch_time': epoch_time
}
self.input_json.update(time_stamp_dict)
def add_weather(self):
""" add weather data to dict """
weather = Weather().last_weather
self.input_json.update(weather)
def add_query(self):
""" add aqi and weather query to self """
aqi_keys = (
'epoch_time', 'sensor_id', 'time_stamp', 'uptime',
'pm25', 'pm10', 'aqi_value', 'aqi_category'
)
aqi_query = self.build_query(aqi_keys, 'aqi')
weather_keys = (
'epoch_time', 'sensor_id', 'time_stamp', 'temperature',
'pressure', 'humidity', 'wind_speed', 'wind_direction',
'weather_name', 'weather_icon'
)
weather_query = self.build_query(weather_keys, 'weather')
self.aqi_query = aqi_query
self.weather_query = weather_query
def build_query(self, keys, table):
""" stitch query together for psycopg2 """
keys_str = ', '.join(keys)
valid = ', '.join(['%s' for i in keys])
values = tuple(self.input_json[i] for i in keys)
query = (f'INSERT INTO {table} ({keys_str}) VALUES ({valid});', values)
return query
class Weather:
""" handle weather lookup from API """
CONFIG = get_config()
def __init__(self):
now = datetime.now()
self.epoch_time = int(now.strftime('%s'))
self.last_weather = self.get_weather()
def get_weather(self):
""" get weather from disk or api if too old """
try:
last_dict = self.get_cache()
except FileNotFoundError:
# create for first time
last_dict = self.get_openweather()
last_epoch = last_dict['epoch_time']
if self.epoch_time - last_epoch > 10 * 60:
print('get new weather data')
weather = self.get_openweather()
else:
print('reuse weather data')
weather = last_dict
del weather['epoch_time']
return weather
def get_openweather(self):
""" get missing weatherdata from openweathermap api """
api_key = self.CONFIG['openweathermap']['api_key']
lat = self.CONFIG['openweathermap']['lat']
lon = self.CONFIG['openweathermap']['lon']
url = ('https://api.openweathermap.org/data/2.5/weather' +
f'?&units=metric&appid={api_key}&lat={lat}&lon={lon}')
resp = requests.get(url, timeout=20).json()
weather = {
'weather_name': resp['weather'][0]['main'],
'weather_icon': resp['weather'][0]['icon'],
'wind_speed': resp['wind']['speed'],
'wind_direction': resp['wind']['deg'],
'epoch_time': self.epoch_time
}
self.write_cache(weather)
return weather
@staticmethod
def get_cache():
""" get last stored dict """
with open('static/dyn/weather.json', 'r') as f:
last = f.read()
last_dict = json.loads(last)
return last_dict
@staticmethod
def write_cache(weather):
""" update last stored value """
weather_str = json.dumps(weather)
with open('static/dyn/weather.json', 'w') as f:
f.write(weather_str)
def get_current():
""" get last values from db """
db_handler = DatabaseConnect()
aqi = db_handler.db_execute(
'SELECT time_stamp, aqi_value, aqi_category \
FROM aqi ORDER BY epoch_time DESC LIMIT 1;'
)
weather = db_handler.db_execute(
'SELECT temperature, pressure, humidity, \
wind_speed, weather_name, weather_icon \
FROM weather ORDER BY epoch_time DESC LIMIT 1;'
)
db_handler.db_close()
json_dict = {
"temperature": weather[0][0],
"pressure": weather[0][1],
"humidity": weather[0][2],
"weather_name": weather[0][4],
"weather_icon": weather[0][5],
"timestamp": aqi[0][0],
"aqi_value": aqi[0][1],
"aqi_category": aqi[0][2],
"wind_speed": weather[0][3]
}
json_data = json.dumps(json_dict)
return json_data
def insert_data(data):
""" called from ingest route to make the db insert """
ingest = IngestLine(data)
db_handler = DatabaseConnect()
_ = db_handler.db_execute(ingest.aqi_query)
_ = db_handler.db_execute(ingest.weather_query)
db_handler.db_close()

114
web/src/graph_current.py Normal file
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""" handle current graph export """
from datetime import datetime
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from src.db import DatabaseConnect
from src.helper import get_config, plt_fill
class CurrentPlot:
""" recreate the last 3h plot """
CONFIG = get_config()
FILENAME = 'static/dyn/current.png'
def __init__(self):
self.now = datetime.now()
self.rows = self.get_data()
self.axis = None
def get_data(self):
""" export from postgres """
now_epoch = int(self.now.strftime('%s'))
last_3h = now_epoch - 3 * 60 * 60
query = ('SELECT epoch_time, aqi_value FROM aqi '
f'WHERE epoch_time > {last_3h} ORDER BY epoch_time DESC;')
db_handler = DatabaseConnect()
rows = db_handler.db_execute(query)
db_handler.db_close()
return rows
def build_title(self):
""" build title from timestamps """
time_from = datetime.fromtimestamp(self.rows[-1][0]).strftime('%H:%M')
time_until = datetime.fromtimestamp(self.rows[0][0]).strftime('%H:%M')
plt_title = f'AQI values last 3h: {time_from} - {time_until}'
return plt_title
def build_axis(self):
""" build plot axis """
rows = self.rows
x_timeline = [datetime.fromtimestamp(i[0]) for i in rows]
y_aqi_values = [int(i[1]) for i in rows]
data = {'timestamp': x_timeline, 'aqi': y_aqi_values}
df = pd.DataFrame(data)
indexed = df.set_index('timestamp')
indexed.sort_values(by=['timestamp'], inplace=True)
mean = indexed.resample('3min').mean()
mean.interpolate(
method='linear', limit=1, inplace=True, limit_area='inside'
)
mean.reset_index(level=0, inplace=True)
mean['timestamp'] = mean['timestamp'].dt.strftime('%H:%M')
mean['aqi'] = mean['aqi'].round()
plt_title = self.build_title()
# xticks
x_ticks = []
for num, i in enumerate(mean['timestamp']):
minute = int(i.split(':')[1])
if minute % 15 == 0:
x_ticks.append(num)
axis = {
'x': mean['timestamp'],
'y': mean['aqi'],
'x_ticks': x_ticks,
'plt_title': plt_title
}
self.axis = axis
def write_plt(self):
""" save plot to file """
x = self.axis['x']
y = self.axis['y']
x_ticks = self.axis['x_ticks']
# calc ticks
y_max = np.ceil(y.max()/50)*50 + 50
# setup plot
plt.style.use('seaborn')
plt.plot(x, y, color='#313131',)
# fill colors
plt_fill(plt, x, y)
# handle passing ticks and lables separatly
if len(x_ticks) == 2:
plt.xticks(x_ticks[0], x_ticks[1])
else:
plt.xticks(x_ticks)
plt.yticks(np.arange(0, y_max, step=50))
plt.title(self.axis['plt_title'], fontsize=20)
plt.tight_layout()
plt.savefig(self.FILENAME, dpi=300)
plt.figure()
plt.close('all')
def main():
""" main function to export current plot """
print('current graph export')
current = CurrentPlot()
if current.rows:
current.build_axis()
current.write_plt()
else:
print('no rows found to export current graph')

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web/src/graph_monthly.py Normal file
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""" handle all monthly tasks """
import json
from os import path
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from src.db import DatabaseConnect
from src.helper import plt_fill
class MonthStatus:
""" check what needs to be done """
def __init__(self):
self.m_stamp, self.y_stamp = (None, None)
self.get_epoch()
self.found = self.check_needed()
def get_epoch(self):
""" create relevant timestamps """
# last month
now = datetime.now()
m_end = datetime(now.year, now.month, day=1) - timedelta(seconds=1)
m_start = datetime(m_end.year, m_end.month, day=1)
m_stamp = (int(m_start.strftime('%s')), int(m_end.strftime('%s')))
# last year
y_now = now.replace(year=now.year - 1)
y_end = datetime(y_now.year, y_now.month, day=1) - timedelta(seconds=1)
y_start = datetime(y_end.year, y_end.month, day=1)
y_stamp = (int(y_start.strftime('%s')), int(y_end.strftime('%s')))
# set
self.m_stamp = m_stamp
self.y_stamp = y_stamp
def check_needed(self):
""" check if current months already exists """
file_name = datetime.fromtimestamp(self.m_stamp[0]).strftime('%Y-%m')
file_path = path.join('static/dyn/monthly', file_name + '.png')
found = path.isfile(file_path)
return found
class MonthGenerator(MonthStatus):
""" create the monthly graph and json table """
def __init__(self):
super().__init__()
self.m_rows, self.y_rows = self.get_data()
self.axis = self.build_axis()
def get_data(self):
""" export from postgres """
m_query = ('SELECT epoch_time, aqi_value FROM aqi WHERE '
f'epoch_time > {self.m_stamp[0]} AND '
f'epoch_time < {self.m_stamp[1]} '
'ORDER BY epoch_time DESC;')
y_query = ('SELECT epoch_time, aqi_value FROM aqi WHERE '
f'epoch_time > {self.y_stamp[0]} AND '
f'epoch_time < {self.y_stamp[1]} '
'ORDER BY epoch_time DESC;')
# make the call
db_handler = DatabaseConnect()
m_rows = db_handler.db_execute(m_query)
y_rows = db_handler.db_execute(y_query)
db_handler.db_close()
return m_rows, y_rows
def build_axis(self):
""" build axis from rows """
# initial df
x_timeline = [datetime.fromtimestamp(i[0]) for i in self.m_rows]
y_aqi_values = [int(i[1]) for i in self.m_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('8h').mean().round()
# reset timestamp to day
mean.reset_index(level=0, inplace=True)
mean['timestamp'] = mean['timestamp'].dt.strftime('%d %H:%M')
mean.set_index('timestamp', inplace=True)
# second df with last year data
x_timeline = [datetime.fromtimestamp(i[0]) for i in self.y_rows]
y_aqi_values = [int(i[1]) for i in self.y_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)
y_mean = indexed.resample('8h').mean().round()
# reset timestamp to day
y_mean.reset_index(level=0, inplace=True)
y_mean['timestamp'] = y_mean['timestamp'].dt.strftime('%d %H:%M')
y_mean.set_index('timestamp', inplace=True)
# merge the two
mean['year_aqi'] = y_mean['year_aqi']
mean.reset_index(level=0, inplace=True)
mean.sort_values(by='timestamp', ascending=True, inplace=True)
# return axis
axis = {
'x': mean['timestamp'],
'y_1': mean['now_aqi'],
'y_2': mean['year_aqi']
}
return axis
def write_plt(self):
""" write monthly plot """
x = self.axis['x']
y_1 = self.axis['y_1']
y_2 = self.axis['y_2']
# parse timestamp
date_month = datetime.fromtimestamp(self.m_rows[-1][0]).date()
date_title = date_month.strftime('%b %Y')
date_file = date_month.strftime('%Y-%m')
month_short = date_month.strftime('%b')
file_name = 'static/dyn/monthly/' + date_file + '.png'
# build ticks
y_max = np.ceil(max(y_1.append(y_2)) / 50) * 50 + 50
x_range = np.arange(0, len(x), step=9)
last_day = int(x.max().split()[0])
x_numbers = np.arange(1, last_day + 1, step=3)
x_dates = [f'{str(i).zfill(2)} {month_short}' for i in x_numbers]
x_ticks = x_range, x_dates
# plot
plt.style.use('seaborn')
plt.plot(x, y_1, color='#313131', label='this year')
plt.plot(
x, y_2, color='#666666', linestyle='dashed', label='last year'
)
# fill colors
plt_fill(plt, x, y_1)
plt.xticks(x_ticks[0], x_ticks[1])
plt.yticks(np.arange(0, y_max, step=50))
plt.title(f'AQI values for: {date_title}', fontsize=20)
plt.legend()
plt.tight_layout()
plt.savefig(file_name, dpi=300)
plt.figure()
@staticmethod
def get_aqi(val):
""" helper function to get aqi category """
breakpoints = [
('Good', 0, 50),
('Moderate', 50, 100),
('Unhealthy for Sensitive Groups', 100, 150),
('Unhealthy', 150, 200),
('Very Unhealthy', 200, 300),
('Hazardous', 300, 500),
]
for break_point in breakpoints:
category, min_val, max_val = break_point
if min_val < val <= max_val:
# found it
break
return category
@staticmethod
def get_change(m_val, y_val):
""" helper function to get change on thresh """
diff_avg = (m_val - y_val) / m_val
if diff_avg <= -0.15:
avg_change = 'down'
elif diff_avg >= 0.15:
avg_change = 'up'
else:
avg_change = 'same'
return avg_change
def write_table(self):
""" write json file with monthly details """
date_month = datetime.fromtimestamp(self.m_rows[-1][0]).date()
date_file = date_month.strftime('%Y-%m')
file_name = 'static/dyn/monthly/' + date_file + '.json'
# current
m_min = int(self.axis['y_1'].min())
m_max = int(self.axis['y_1'].max())
m_avg = int(self.axis['y_1'].mean())
m_cat = self.get_aqi(m_avg)
# last
y_min = int(self.axis['y_2'].min())
y_max = int(self.axis['y_2'].max())
y_avg = int(self.axis['y_2'].mean())
y_cat = self.get_aqi(y_avg)
# build dict
monthly_dict = {
'data': [
['min: ', m_min, y_min, self.get_change(m_min, y_min)],
['max: ', m_max, y_max, self.get_change(m_max, y_max)],
['avg: ', m_avg, y_avg, self.get_change(m_avg, y_avg)],
['avg aqi: ', m_cat, y_cat, self.get_change(m_avg, y_avg)]
]
}
# write to disk
with open(file_name, 'w') as f:
f.write(json.dumps(monthly_dict))
def main():
""" main to export monthly graph an table json """
# check if needed
month_status = MonthStatus()
if month_status.found:
print('monthly already created, skipping...')
return
# create
print('creating monthly graph and json file')
month_generator = MonthGenerator()
month_generator.write_plt()
month_generator.write_table()

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web/src/graph_nightly.py Normal file
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""" handle nightly graph export """
from datetime import datetime, timedelta
import json
import numpy as np
import pandas as pd
import scipy # pylint: disable=unused-import
from matplotlib import pyplot as plt
from src.db import DatabaseConnect
from src.helper import get_config, plt_fill
class NightlyPlots:
""" get nightly data """
CONFIG = get_config()
def __init__(self):
self.now = datetime.now()
print('get data from db')
self.rows, self.y_rows = self.get_data()
@staticmethod
def color_colums(y):
""" helper function to color bar columns """
breakpoints = [
('#85a762', 0, 50), # good
('#d4b93c', 50, 100), # moderate
('#e96843', 100, 150), # ufsg
('#d03f3b', 150, 200), # unhealthy
('#be4173', 200, 300), # vunhealthy
('#714261', 300, 500), # hazardous
]
colors = []
for value in y:
for break_point in breakpoints:
color, min_val, max_val = break_point
if min_val < value <= max_val:
# found it
colors.append(color)
break
return colors
def get_data(self):
""" export from postgres """
# current
day_until = int(self.now.date().strftime('%s'))
day_from = day_until - 10 * 24 * 60 * 60
query = ('SELECT epoch_time, aqi_value, pm25, pm10 FROM aqi WHERE '
f'epoch_time > {day_from} AND epoch_time < {day_until} '
'ORDER BY epoch_time DESC;')
# last year
y_until = day_until - 365 * 24 * 60 * 60
y_from = y_until - 10 * 24 * 60 * 60
y_query = ('SELECT epoch_time, aqi_value FROM aqi WHERE '
f'epoch_time > {y_from} AND epoch_time < {y_until} '
'ORDER BY epoch_time DESC;')
db_handler = DatabaseConnect()
rows = db_handler.db_execute(query)
y_rows = db_handler.db_execute(y_query)
db_handler.db_close()
return rows, y_rows
def recreate_last_7(self):
""" last seven days """
day_until = int(self.now.date().strftime('%s'))
day_from = day_until - 7 * 24 * 60 * 60
rows = [i for i in self.rows if day_from < i[0] < day_until]
date_from = datetime.fromtimestamp(day_from).strftime('%d %b')
date_until = datetime.fromtimestamp(day_until).strftime('%d %b')
plt_title = f'AQI values from: {date_from} until {date_until}'
_ = LastSevenDays(rows, plt_title)
def recreate_last_3(self):
""" last three days """
_ = LastThreeDays(self.rows, self.now)
def recreate_pm_chart(self):
""" recreating pm2.5 and pm10 charts """
_ = PmGraphs(self.rows)
def recreate_hour_bar(self):
""" recreate hourly average through day bar chart """
day_until = int(self.now.date().strftime('%s'))
day_from = day_until - 3 * 24 * 60 * 60
rows = [i for i in self.rows if day_from < i[0] < day_until]
_ = HourBar(rows)
def recreate_year_comparison(self):
""" recreate year comparison chart and table for json """
_ = YearComparison(self.rows, self.y_rows)
class LastSevenDays:
""" recreate last 7 days """
FILENAME = 'static/dyn/last-7.png'
def __init__(self, rows, plt_title):
print('recreating last seven days')
self.plt_title = plt_title
self.rows = rows
self.axis = self.build_axis()
self.write_plt()
def build_axis(self):
""" calc x and y """
x_timeline = [datetime.fromtimestamp(i[0]) for i in self.rows]
y_aqi_values = [int(i[1]) for i in self.rows]
data = {'timestamp': x_timeline, 'aqi': y_aqi_values}
df = pd.DataFrame(data)
indexed = df.set_index('timestamp')
indexed.sort_values(by=['timestamp'], inplace=True)
mean = indexed.resample('2h').mean()
mean['avg'] = mean['aqi'].resample('1d').mean()
mean['avg'] = mean.avg.shift(6)
# set first and last
mean['avg'][0] = (mean['avg'].iloc[6] + mean['aqi'][0]) / 2
mean['avg'][-1] = (mean['avg'].iloc[-6] + mean['aqi'][-1]) / 2
# smooth
mean['avg'].interpolate(method='polynomial', order=3, inplace=True)
mean.reset_index(level=0, inplace=True)
mean['timestamp'] = mean['timestamp'].dt.strftime('%Y-%m-%d %H:%M')
mean['aqi'] = mean['aqi'].round()
mean['avg'] = mean['avg'].round()
# make ticks
x_range = np.arange(0, 84, step=12)
x_date_time = pd.to_datetime(mean['timestamp']).dt.date.unique()
x_dates = np.asarray([i.strftime('%d %b') for i in x_date_time])
x_ticks = x_range, x_dates
# set axis
axis = {
"x": mean['timestamp'],
"y_1": mean['aqi'],
"y_2": mean['avg'],
"x_ticks": x_ticks,
"plt_title": self.plt_title
}
return axis
def write_plt(self):
""" write last 7 days plot to disk """
x = self.axis['x']
y_1 = self.axis['y_1']
y_2 = self.axis['y_2']
x_ticks = self.axis['x_ticks']
y_max = np.ceil(max(y_1.append(y_2))/50)*50 + 50
# plot
plt.style.use('seaborn')
plt.plot(x, y_1, color='#313131', label='2hour avg')
plt.plot(x, y_2, color='#cc0000', label='daily avg')
# fill colors
plt_fill(plt, x, y_1)
# ticks and plot
plt.xticks(x_ticks[0], x_ticks[1])
plt.yticks(np.arange(0, y_max, step=50))
plt.title(self.axis['plt_title'], fontsize=20)
plt.legend()
plt.tight_layout()
plt.savefig(self.FILENAME, dpi=300)
plt.figure()
class LastThreeDays:
""" recreate last three days plot """
def __init__(self, rows, now):
print('recreating last three days')
self.y_max = None
self.now = now
self.rows = rows
self.rebuild_last_three()
def rebuild_last_three(self):
""" recreate all three graphs """
# get axis
all_axis = []
for day in range(1, 4):
axis = self.get_axis(day)
all_axis.append(axis)
# set y_max
self.y_max = max([max(i['y']) for i in all_axis]) + 50
# plt
for idx, axis in enumerate(all_axis):
day = idx + 1
self.write_plt(axis, day)
def get_axis(self, day):
""" get axis for day passed in """
day_delta = self.now.date() - timedelta(days=day)
day_from = int(day_delta.strftime('%s'))
day_until = int(day_delta.strftime('%s')) + 60 * 60 * 24
day_rows = [i for i in self.rows if day_from < i[0] < day_until]
# title
time_stamp = day_delta.strftime('%Y-%m-%d')
# build
x_timeline = [datetime.fromtimestamp(i[0]) for i in day_rows]
y_aqi_values = [int(i[1]) for i in day_rows]
data = {'timestamp': x_timeline, 'aqi': y_aqi_values}
df = pd.DataFrame(data)
indexed = df.set_index('timestamp')
indexed.sort_values(by=['timestamp'], inplace=True)
mean = indexed.resample('15min').mean()
mean.interpolate(
method='linear', limit=1, inplace=True, limit_area='inside'
)
mean.reset_index(level=0, inplace=True)
mean['timestamp'] = mean['timestamp'].dt.strftime('%H:%M')
mean['aqi'] = mean['aqi'].round()
# set axis
axis = {
"x": mean['timestamp'],
"y": mean['aqi'],
"x_ticks": np.arange(0, 97, step=8),
"plt_title": f'AQI values from: {time_stamp}'
}
return axis
def write_plt(self, axis, day):
""" write daily plot to disk """
x = axis['x']
y = axis['y']
x_ticks = np.arange(0, 97, step=8)
plt.style.use('seaborn')
plt.plot(x, y, color='#313131',)
# fill colors
plt_fill(plt, x, y)
# ticks and plot
plt.xticks(x_ticks)
plt.yticks(np.arange(0, self.y_max, step=50))
plt.title(axis['plt_title'], fontsize=20)
plt.tight_layout()
plt.savefig(f'static/dyn/day-{day}.png', dpi=300)
plt.figure()
plt.close('all')
class PmGraphs:
""" recreate avg pm10 and pm2.5 exposure graphs """
def __init__(self, rows):
print('recreating pm bar charts')
self.rows = rows
self.y_max = None
self.axis = self.get_axis()
self.write_plt(thresh=25, title='2.5')
self.write_plt(thresh=50, title='10')
def get_axis(self):
""" get pm2.5 and pm20 axis """
x_timeline = [datetime.fromtimestamp(i[0]) for i in self.rows]
y_pm25_values = [int(i[2]) for i in self.rows]
y_pm10_values = [int(i[3]) for i in self.rows]
data = {
'timestamp': x_timeline,
'pm25': y_pm25_values,
'pm10': y_pm10_values
}
df = pd.DataFrame(data)
indexed = df.set_index('timestamp')
indexed.sort_values(by=['timestamp'], inplace=True, ascending=True)
mean = indexed.resample('1d').mean()
mean.reset_index(level=0, inplace=True)
# axis
axis = {
'x': mean['timestamp'],
'y_pm25': mean['pm25'].round(),
'y_pm10': mean['pm10'].round()
}
# max
self.y_max = np.ceil(
max(axis['y_pm25'].append(axis['y_pm10'])) / 25
) * 25 + 25
return axis
def write_plt(self, thresh, title):
""" write plot to disk """
file_name = title.replace('.', '')
plt_title = f'Daily avg PM {title} exposure'
x = self.axis['x']
y = self.axis['y_pm' + file_name]
# make ticks
x_range = np.arange(10).tolist()
x_date_time = pd.to_datetime(x).dt.date.unique()
x_dates = [i.strftime('%d %b') for i in x_date_time]
# col
col = []
for val in y:
if val < thresh:
col.append('#6ecd65')
else:
col.append('#ff4d4d')
# plot
plt.style.use('seaborn')
plt.bar(x_dates, y, color=col, width=0.5)
plt.axhline(y=thresh, color='#6ecd65', linestyle=':')
plt.xticks(ticks=x_range, labels=x_dates)
plt.yticks(np.arange(0, self.y_max, step=25))
plt.title(plt_title, fontsize=20)
plt.tight_layout()
plt.savefig(f'static/dyn/pm{file_name}.png', dpi=300)
plt.close('all')
plt.figure()
class HourBar:
""" recreate hour by our avg bar chart """
def __init__(self, rows):
print('recreating hour avg bar chart')
self.rows = rows
self.axis = self.get_axis()
self.write_plt()
def get_axis(self):
""" get hourly bar chart axis """
x_timeline = [datetime.fromtimestamp(i[0]) for i in self.rows]
y_aqi_values = [int(i[1]) for i in self.rows]
data = {
'timestamp': x_timeline,
'aqi': y_aqi_values
}
df = pd.DataFrame(data)
indexed = df.set_index('timestamp')
indexed.sort_values(by=['timestamp'], inplace=True)
mean = indexed.resample('1h').mean()
# regroup by hour
mean_hour = mean.groupby([mean.index.hour]).mean()
mean_hour.reset_index(level=0, inplace=True)
axis = {
'x': mean_hour['timestamp'],
'y': mean_hour['aqi'].round()
}
return axis
def write_plt(self):
""" write the hour bar chart to disk """
plt_title = 'Last three days average AQI for each hour'
x = self.axis['x']
y = self.axis['y']
# ticks
x_range = np.arange(0, 24, step=3)
x_hours = [str(i).zfill(2) + ":00" for i in x_range]
y_max = np.ceil(max(y)/50) * 50 + 50
# color columns
col = NightlyPlots.color_colums(y)
# create plot
plt.style.use('seaborn')
plt.bar(x, y, color=col, width=0.5)
plt.yticks(np.arange(0, y_max, step=50))
plt.xticks(ticks=x_range, labels=x_hours)
plt.title(plt_title, fontsize=20)
plt.tight_layout()
plt.savefig('static/dyn/hours.png', dpi=300)
plt.close('all')
plt.figure()
class YearComparison:
""" export year on year graph and table """
def __init__(self, rows, y_rows):
print('recreating year comparison')
self.rows = rows
self.y_rows = y_rows
self.axis = self.get_axis()
self.write_table()
self.write_plt()
def get_axis(self):
""" build axis """
# first df with current data
x_timeline = [datetime.fromtimestamp(i[0]) for i in self.rows]
y_aqi_values = [int(i[1]) for i in self.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()
mean.reset_index(level=0, inplace=True)
# second df with last year data
x_timeline = [datetime.fromtimestamp(i[0]) for i in self.y_rows]
y_aqi_values = [int(i[1]) for i in self.y_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['year_aqi'] = year_mean['year_aqi']
mean.sort_values(by='timestamp', inplace=True)
mean['timestamp'] = mean['timestamp'].dt.strftime('%d %b')
# build 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'])
# return axis
axis = {
'x': mean['timestamp'],
'y_1': mean['now_aqi'].astype('int'),
'y_2': mean['year_aqi'].astype('int'),
'change': mean['change']
}
return axis
def write_table(self):
""" write year comparison table json """
# build average row on top
avg = int(self.axis['y_1'].mean())
y_avg = int(self.axis['y_2'].mean())
diff_avg = (avg - y_avg) / avg
if diff_avg <= -0.15:
avg_change = 'down'
elif diff_avg >= 0.15:
avg_change = 'up'
else:
avg_change = 'same'
avg_row = ('avg 7 days', avg, y_avg, avg_change)
# zip it
zipped = zip(
self.axis['x'], self.axis['y_1'],
self.axis['y_2'], self.axis['change']
)
data_rows = list(zipped)
data_rows.insert(0, avg_row)
json_dict = json.dumps({"data": data_rows})
# write to file
with open('static/dyn/year-table.json', 'w') as f:
f.write(json_dict)
def write_plt(self):
""" write year comparison bar chart """
x = self.axis['x']
y_1 = self.axis['y_1']
y_2 = self.axis['y_2']
# build colors
col_y_1 = NightlyPlots.color_colums(y_1)
col_y_2 = NightlyPlots.color_colums(y_2)
# set ticks
y_max = int(np.ceil((max(y_1.append(y_2)) / 50)) * 50 + 50)
x_indexes = np.arange(len(x))
# build plot
width = 0.25
plt_title = 'Daily avg AQI values compared to last year'
plt_suptitle = 'left: this year, right: last year'
plt.style.use('seaborn')
# write bars
plt.bar(
x_indexes - (width / 2) - 0.02, y_1, color=col_y_1, width=width
)
plt.bar(
x_indexes + (width / 2) + 0.02, y_2, color=col_y_2, width=width
)
plt.title(plt_suptitle, fontsize=15)
plt.suptitle(plt_title, fontsize=20, y=0.96)
plt.yticks(np.arange(0, y_max, step=50))
plt.xticks(ticks=x_indexes, labels=x)
plt.tight_layout()
plt.savefig('static/dyn/year-graph.png', dpi=300)
plt.figure()
def main():
""" collection of nightly exports """
nightly = NightlyPlots()
nightly.recreate_last_7()
nightly.recreate_last_3()
nightly.recreate_pm_chart()
nightly.recreate_hour_bar()
nightly.recreate_year_comparison()

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""" collection of helper function and classes """
import json
from flask_table import create_table, Col
def get_config():
""" read config file """
config_path = 'config.json'
with open(config_path, 'r') as config_file:
data = config_file.read()
config = json.loads(data)
return config
def plt_fill(plt, x, y):
""" fill colors between break points """
plt.fill_between(
x, y, y2=0, where=(y > 0), color='#85a762', interpolate=True
) # good
plt.fill_between(
x, y, y2=50, where=(y > 50), color='#d4b93c', interpolate=True
) # moderate
plt.fill_between(
x, y, y2=100, where=(y > 100), color='#e96843', interpolate=True
) # ufsg
plt.fill_between(
x, y, y2=150, where=(y > 150), color='#d03f3b', interpolate=True
) # unhealthy
plt.fill_between(
x, y, y2=200, where=(y > 200), color='#be4173', interpolate=True
) # vunhealthy
plt.fill_between(
x, y, y2=300, where=(y > 300), color='#714261', interpolate=True
) # hazardous
plt.fill_between(
x, y, y2=0, where=(y > 0), color='#ffffff', alpha=0.1, interpolate=True
) # soft
class Table:
""" create html table from filename to pass to template """
COLUMNS = [' ', 'this year', 'last year', 'change']
def __init__(self, filename):
self.filename = filename
self.rows = self.get_rows()
def get_rows(self):
""" read filename to build rows dict """
with open(self.filename, 'r') as json_file:
json_raw = json_file.read()
table_json = json.loads(json_raw)
rows = []
for i in table_json['data']:
row = dict(zip(self.COLUMNS, i))
rows.append(row)
return rows
def create_table(self):
""" create the table with rows and columns """
blank_table = create_table(options={'classes': ['comp-table']})
for i in self.COLUMNS:
blank_table.add_column(i, Col(i))
table_obj = blank_table(self.rows)
return table_obj

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#!/usr/bin/env bash
uwsgi --ini uwsgi.ini

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* {
padding: 0;
margin: 0;
}
@font-face {
font-family: Rubik-Bold;
src: url(../font/Rubik-Bold.ttf);
}
@font-face {
font-family: Rubik-Light;
src: url(../font/Rubik-Light.ttf);
}
@font-face {
font-family: Rubik-Regular;
src: url(../font/Rubik-Regular.ttf);
}
body {
margin: 0;
padding: 0;
overflow: hidden;
}
h1 {
font-family: Rubik-Bold;
font-size: 3em;
margin-bottom: 15px;
}
h2 {
font-family: Rubik-Bold;
margin-bottom: 10px;
}
h3 {
font-family: Rubik-Light;
font-size: 1.3em;
margin-bottom: 10px;
}
p, li, td, th {
font-family: Rubik-Regular;
font-size: 1.1em;
}
a {
color: inherit;
font-family: Rubik-Regular;
}
.block-text {
margin-bottom: 15px;
}
.full-width {
padding: 10px;
}
.content {
width: 70%;
max-width: 900px;
margin-left: auto;
margin-right: auto;
padding: 20px 0;
}
.content-subtitle {
max-width: 900px;
margin-left: auto;
margin-right: auto;
padding-top: 20px;
}
.col-box {
width: 70%;
min-height: 20px;
max-width: 900px;
margin-left: auto;
margin-right: auto;
color: #fff;
}
.preload {
position: fixed;
top: 0;
width: 100%;
height: 100vh;
background: #e6e6e6;
display: flex;
justify-content: center;
align-items: center;
z-index: 1;
}
.preload img {
width: 100px;
}
.preload-finish {
opacity: 0;
pointer-events: none;
}
.good {
background-color: #85a762;
}
.moderate {
background-color: #d4b93c;
}
.ufsg {
background-color: #e96843;
}
.unhealthy {
background-color: #d03f3b;
}
.vunhealthy {
background-color: #be4173;
}
.hazardous {
background-color: #714261;
}
/* title start */
.title {
display: flex;
justify-content: space-between;
align-items: center;
}
.title-nav ul {
list-style-type:none;
}
.title-nav li {
padding: 5px 40px;
margin: 5px;
border-style: none none solid none;
border-width: 2px;
}
.title-nav a {
text-decoration: none;
}
/* title end */
/* cloud start */
.cloud {
position: relative;
padding-bottom: 30px;
}
.cloud img {
width: 100%;
}
.aqi_box {
position: absolute;
top: 45%;
left: 57%;
text-align: right;
transform: translate(-50%, -50%);
color: #fff;
}
.aqi_box h1 {
font-size: 15em;
margin: 0;
}
.aqi_box h2 {
font-family: Rubik-Light;
font-size: 1.8em;
margin: 0;
}
.aqi_box p {
margin: 0;
}
/* cloud end */
/* weather start */
.weather {
display: flex;
justify-content: space-between;
align-items: center;
}
.weather_box {
border: solid 2px;
border-radius: 20px;
display: flex;
align-items: center;
justify-content: flex-start;
padding: 10px 0;
width: 24%;
}
.weather_icon img {
width: 50px;
margin: 10px;
}
/* weather end */
/* category start */
.category {
display: flex;
align-items: center;
justify-content: space-between;
}
.cat-icon img {
width: 100%;
max-width: 200px;
}
.cat-item {
color: #fff;
margin: 10px;
padding: 15px;
text-align: center;
}
.cat-item.active {
transform: scale(1.2);
font-size: 1.1em;
margin: 15px;
box-shadow: darkgray 5px 5px;
font-weight: bold;
}
/* category end */
/* last graph start */
.graph {
display: flex;
align-items: center;
justify-content: initial;
}
.graph-item {
box-sizing: border-box;
width: 100%;
}
.graph-item img {
width: 100%;
}
/* last graph end */
/* table start */
.comp-table {
width: 100%;
table-layout: fixed;
margin: auto;
}
.comp-table thead th {
padding: 5px 0;
background-color: #eeeeee;
}
.comp-table tbody td:nth-child(1) {
padding: 3px 0 3px 10px;
background-color: #eeeeee;
}
.comp-table td:nth-child(2),
.comp-table td:nth-child(3),
.comp-table td:nth-child(4) {
text-align: center;
color: #fff;
}
/* table end */
/* about start */
.category-table th {
text-align: left;
padding: 0 10px;
min-width: 150px;
}
.category-table td {
padding: 10px;
min-width: 150px;
}
.category-table td:nth-child(1) {
color: #fff;
}
.category-table tr {
display: block;
margin: 10px 0;
}
/* about end */
/* responsiv start */
@media screen and (max-width: 1000px) {
.content {
width: 90%;
}
}
@media screen and (max-width: 600px) {
h1 {
font-size: 2.5em;
}
.content {
width: 100%;
}
.title {
flex-direction: column-reverse;
width: 100%;
padding-top: 0;
}
.title-nav li {
float: left;
padding: 10px;
}
#aqiValue {
font-size: 6em;
}
.aqi_box {
top: 38%;
left: 50%;
width: 60%;
}
.weather {
flex-wrap: wrap;
}
.weather_box {
width: 47%;
}
.weather_box:nth-child(1),
.weather_box:nth-child(2) {
margin-bottom: 10px;
}
.category {
display: block;
text-align: center;
}
.cat-item {
width: 70%;
margin-left: auto;
margin-right: auto;
}
.cat-item.active {
margin: 20px auto;
}
.cat-icon img {
max-width: unset;
width: 70%;
margin: 20px 0;
}
.graph {
display: block;
padding: 0;
}
.graph-item {
margin: 20px 0;
}
.category-table td {
min-width: 80px;
}
}
/* responsiv end */

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18
frontend/nginx/js/aqi.js → web/static/js/aqi.js
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@ -64,9 +64,9 @@ function rmPreload() {
// scrollbar
document.querySelector('body').style.overflow = 'unset'
// sticky
const topBar = document.querySelector('.colorbox');
topBar.style.position = 'sticky';
topBar.style.position = '-webkit-sticky';
// const topBar = document.querySelector('.colorbox');
// topBar.style.position = 'sticky';
// topBar.style.position = '-webkit-sticky';
}
// reload current.png from remote
@ -88,7 +88,7 @@ function refreshAqiValues() {
return new Promise((resolve, reject) => {
var req = new XMLHttpRequest();
req.responseType = 'json';
req.open('GET', '/dyn/air.json', true);
req.open('GET', '/data/out', true);
req.setRequestHeader('cache-control', 'no-cache');
req.onload = function() {
var responseAqi = req.response;
@ -149,7 +149,7 @@ function setAqiColors(aqiCategory) {
};
}
// apply light background change
var lightBg = document.getElementsByClassName('light_background');
var lightBg = document.getElementsByClassName('col_lightbg');
if (lightBg) {
for (var i = 0; i < lightBg.length; i++) {
lightBg[i].style.backgroundColor = colSecond;
@ -170,7 +170,7 @@ function setAqiColors(aqiCategory) {
};
}
// apply hover color
var css = '.nav li:hover {background-color: ' + colMain + ';}';
var css = '.title-nav li:hover {background-color: ' + colMain + ';}';
var style = document.createElement('style');
style.appendChild(document.createTextNode(css));
document.getElementsByTagName('head')[0].appendChild(style);
@ -185,7 +185,7 @@ function setWeatherDetails(responseAqi) {
var humidity = Math.round(responseAqi['humidity']);
var pressure = Math.round(responseAqi['pressure']);
// weather icon
weatherIconSrc = '/img/icon/' + weatherIcon + '.png';
weatherIconSrc = '/static/img/icon/' + weatherIcon + '.png';
document.getElementById('weather_icon').src = weatherIconSrc;
// weather name
document.getElementById('weather_name').innerHTML = weatherName;
@ -203,7 +203,7 @@ function setDesc(responseAqi) {
// parse response
var aqiCategory = responseAqi['aqi_category'];
var aqiCatClean = aqiCategory.toLowerCase().replaceAll(' ', '');
var iconSrc = '/img/icon/category-' + aqiCatClean + ".png";
var iconSrc = '/static/img/icon/category-' + aqiCatClean + ".png";
// parse config
var aqiRange = colorConfig[aqiCategory][3];
var aqiDesc = colorConfig[aqiCategory][4];
@ -220,7 +220,7 @@ function setDesc(responseAqi) {
// figure out which to activate
var allCategories = Object.keys(colorConfig);
var indexMatch = allCategories.indexOf(aqiCategory);
var activeCat = document.getElementsByClassName('desc_item')[indexMatch];
var activeCat = document.getElementsByClassName('cat-item')[indexMatch];
// activate
activeCat.classList.add("active");
}

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@ -0,0 +1,55 @@
{% extends "layout.html" %}
{% block content %}
<div class="full-width">
<div class="content">
<h1>About</h1>
<p class="block-text">This page and its contents are still under construction. More content is coming soon.</p>
<p class="block-text">The data for this page is collected from an air monitor located just outside of Luang Prabang, Laos. While we do our best, no guarantee is given for the accuracy of this data.</p>
<p class="block-text">The data is updated every 3 minutes. Contrary to international websites who measure the air pollution via satellite images and rely on estimates and averages, an on-site air monitor delivers real time values that paint a much more accurate picture of the local situation.</p>
<p class="block-text">Roughly, the Air Quality Index (AQI) is the internationally used air quality standard to measure the pollution of the air. It is divided into 6 levels, and according to these levels, certain health advices are given:</p>
</div>
<div class="content">
<hr class="col_border">
</div>
<div class="content">
<table class="category-table">
<tr>
<th>AQI Values</th>
<th>Description</th>
</tr>
<tr>
<td class="good">0 to 50:</td>
<td>Good: No health concerns, enjoy activities.</td>
</tr>
<tr>
<td class="moderate">51 - 100:</td>
<td>Moderate: Active children and adults, and people with respiratory disease, such as asthma, should limit prolonged outdoor exertion.</td>
</tr>
<tr>
<td class="ufsg">101 - 150:</td>
<td>Unhealthy for Sensitive Groups: Active children and adults, and people with respiratory disease, such as asthma, should limit prolonged outdoor exertion.</td>
</tr>
<tr>
<td class="unhealthy">151 - 200:</td>
<td>Unhealthy: Everyone may begin to experience health effects: Active children and adults, and people with respiratory disease, such as asthma, should avoid prolonged outdoor exertion; everyone else, especially children, should limit prolonged outdoor exertion.</td>
</tr>
<tr>
<td class="vunhealthy">201 - 300:</td>
<td>Very Unhealthy: Active children and adults, and people with respiratory disease, such as asthma, should avoid all outdoor exertion; everyone else, especially children, should limit outdoor exertion.</td>
</tr>
<tr>
<td class="hazardous">301 - 500:</td>
<td>Hazardous: Everyone should avoid all outdoor exertion.</td>
</tr>
</table>
</div>
<div class="content">
<hr class="col_border">
</div>
<div class="content">
<h2>Credits</h2>
<p class="block-text">Partial Weather data, namely weather icon, weather description and windspeed are provided from <a href="https://openweathermap.org/ " target="_blank">openweather.org</a> API distributed under the <a href="https://openweathermap.org/full-price" target="_blank">Creative Commons Attribution-ShareAlike 4.0 Generic License</a>.</p>
<p class="block-text"><a target="_blank" href="https://github.com/lokesh/lightbox2">Lightbox</a> made by Lokesh Dhakar, released under the <a target="_blank" href="https://raw.githubusercontent.com/lokesh/lightbox2/master/LICENSE">MIT license</a>.</p>
</div>
</div>
{% endblock content %}

103
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@ -0,0 +1,103 @@
{% extends "layout.html" %}
{% block content %}
<div class="full-width">
<div class="content">
<h1>Graphs</h1>
<p>All the graphs and table on this page will get recreated every night with the newest values.</p>
</div>
<div class="content-subtitle">
<h2>Last three days</h2>
</div>
<div class="content graph">
<div class="graph-item">
<p>Three days ago</p>
<a href="/static/dyn/day-3.png" data-lightbox="graph">
<img src="/static/dyn/day-3.png" alt="Three days ago graph">
</a>
</div>
<div class="graph-item">
<p>Two days ago</p>
<a href="/static/dyn/day-2.png" data-lightbox="graph">
<img src="/static/dyn/day-2.png" alt="Two days ago graph">
</a>
</div>
<div class="graph-item">
<p>Yesterday</p>
<a href="/static/dyn/day-1.png" data-lightbox="graph">
<img src="/static/dyn/day-1.png" alt="Yesterday graph">
</a>
</div>
</div>
<div class="content">
<hr class="col_border">
</div>
<div class="content">
<h2>Particle Matter sizes</h2>
<p class="block-text"><b>There is no healthy level of pollution.</b> Particle matter (PM) are defined in two different sizes: PM 2.5 which represents particle sizes smaller than 2.5 µm or less than 1/20th of the diameter of a human hair and PM 10 which represents particle sizer smaller than 10 µm or 1/5th of the diameter of a human hair.</p>
<p class="block-text">The <a href="https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health" target="_blank">WHO</a> is providing more details on their website regarding particle matter and their health implications. On <a href="https://en.wikipedia.org/wiki/Particulates" target="blank">Wikipedia</a> there are some interesting links to studies for further reading.</p>
</div>
<div class="content graph">
<div class="graph-item">
<a href="/static/dyn/pm25.png" data-lightbox="pm">
<img src="/static/dyn/pm25.png" alt="pm 2.5 value graph">
</a>
</div>
<div class="graph-item">
<h3>PM 2.5</h3>
<p class="block-text">Particle matter sizes smaller than 2.5&#xB5;m are the most problematic as these particles will find their way through the lungs into the bloodstream.</p>
<p class="block-text">The WHO Air quality guideline values set a 25 µg/m³ 24-hour average as an upper level threshold. In the 10 days overview you can see:</p>
<p class="block-text">Green: Daily average exposure below 25 &#xB5;g/m&sup3;<br>
Red: Daily average exposure above 25 &#xB5;g/m&sup3;</p>
</div>
</div>
<div class="content graph">
<div class="graph-item">
<a href="/static/dyn/pm10.png" data-lightbox="pm">
<img src="/static/dyn/pm10.png" alt="pm 10 value graph">
</a>
</div>
<div class="graph-item">
<h3>PM 10</h3>
<p class="block-text">The threshold for the daily average PM 10 exposure is set to 50 &#xB5;g/m&sup3; by the WHO. Particles this size can penetrate and lodge deep inside the lungs but are too big to enter the blood stream. For this reason the threshold is higher.</p>
<p class="block-text">In the 10 days overview you can see:</p>
<p class="block-text">Green: Daily average exposure below 50 &#xB5;g/m&sup3;<br>
Red: Daily average exposure above 50 &#xB5;g/m&sup3;</p>
</div>
</div>
<div class="content">
<hr class="col_border">
</div>
<div class="content-subtitle">
<h2>Hour by Hour</h2>
</div>
<div class="content graph">
<div class="graph-item">
<a href="/static/dyn/hours.png" data-lightbox="hours">
<img src="/static/dyn/hours.png" alt="hour by hour graph">
</a>
</div>
<div class="graph-item">
<h3>Hourly AQI average</h3>
<p class="block-text">The AQI value can change a lot during the day. This can depend on the wind, cooking on fire or just another additional source of pollution nearby.</p>
<p class="block-text">In this chart you can see the average AQI for each hour. This data is based on the last three days. This data can help to visualize which hours in the past three days have been better or worse on average.</p>
</div>
</div>
<div class="content">
<hr class="col_border">
</div>
<div class="content">
<h2>Compared to last year</h2>
<p>This year's daily average AQI values from last 10 days compared to corresponding values from last year.</p>
</div>
<div class="content graph">
<div class="graph-item">
{{ table }}
</div>
<div class="graph-item">
<a href="/static/dyn/year-graph.png" data-lightbox="year">
<img src="/static/dyn/year-graph.png" alt="year comparison graph">
</a>
</div>
</div>
</div>
{% endblock content %}

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@ -0,0 +1,103 @@
{% extends "layout.html" %}
{% block content %}
<div class="full-width col_lightbg">
<div class="content">
<div class="cloud">
<img src="{{ url_for('static', filename='img/cloud.png') }}" alt="cloud" class="col_filter">
<div class="aqi_box">
<h1 id="aqiValue"></h1>
<p id="aqi-label">US AQI</p>
<h2 id="aqiCategory"></h2>
</div>
</div>
</div>
</div>
<div class="full-width">
<div class="content weather">
<div class="weather_box col_border">
<div class="weather_icon">
<img src="{{ url_for('static', filename='img/icon/000.png') }}" alt="weather_icon" class="col_filter" id="weather_icon">
</div>
<div class="weather_text">
<h3 class="col_font"><span id="temperature"></span><span> °C</span></h3>
<p id="weather_name"></p>
</div>
</div>
<div class="weather_box col_border">
<div class="weather_icon">
<img src="{{ url_for('static', filename='img/icon/wind.png') }}" alt="wind_icon" class="col_filter">
</div>
<div class="weather_text">
<h3 class="col_font">Wind</h3>
<p><span id="wind_speed"></span><span> km/h</span></p>
</div>
</div>
<div class="weather_box col_border">
<div class="weather_icon">
<img src="{{ url_for('static', filename='img/icon/humidity.png') }}" alt="humidity_icon" class="col_filter">
</div>
<div class="weather_text">
<h3 class="col_font">Humidity</h3>
<p><span id="humidity"></span><span> %</span></p>
</div>
</div>
<div class="weather_box col_border">
<div class="weather_icon">
<img src="{{ url_for('static', filename='img/icon/pressure.png') }}" alt="pressure_icon" class="col_filter">
</div>
<div class="weather_text">
<h3 class="col_font">Pressure</h3>
<p><span id="pressure"></span><span> mbar</span></p>
</div>
</div>
</div>
</div>
<div class="full-width col_lightbg">
<div class="content category">
<div class="cat-bar">
<div class="cat-item good">
<p>Good</p>
</div>
<div class="cat-item moderate">
<p>Moderate</p>
</div>
<div class="cat-item ufsg">
<p>Unhealthy for Sensitive Groups</p>
</div>
<div class="cat-item unhealthy">
<p>Unhealthy</p>
</div>
<div class="cat-item vunhealthy">
<p>Very Unhealthy</p>
</div>
<div class="cat-item hazardous">
<p>Hazardous</p>
</div>
</div>
<div class="cat-icon">
<img src="static/img/icon/category-plain.png" alt="category_icon" id="categoryIcon">
</div>
<div class="cat-desc">
<h2 class="col_font" id="aqiName"></h2>
<h3 id="aqiRange"></h3>
<p id="aqiDesc"></p>
</div>
</div>
</div>
<div class="full-width">
<div class="content graph">
<div class="graph-item">
<h3>Last three hours</h3>
<a href="/static/dyn/current.png" data-lightbox="graph">
<img src="/static/dyn/current.png" alt="current graph">
</a>
</div>
<div class="graph-item">
<h3>Last 7 days</h3>
<a href="/static/dyn/last-7.png" data-lightbox="graph">
<img src="/static/dyn/last-7.png" alt="last-7 graph">
</a>
</div>
</div>
</div>
{% endblock content %}

53
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@ -0,0 +1,53 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
{% if title %}
<title>AQI - {{ title }}</title>
{% else %}
<title>AQI</title>
{% endif %}
<link rel="shortcut icon" href="{{ url_for('static', filename='favicon.ico') }}">
<script src="{{ url_for('static', filename='js/lightbox.js') }}"></script>
<script src="{{ url_for('static', filename='js/aqi.js') }}"></script>
<link rel="stylesheet" type="text/css" href="{{ url_for('static', filename='css/style.css') }}">
<link rel="stylesheet" type="text/css" href="{{ url_for('static', filename='css/lightbox.css') }}">
</head>
<body>
<div class="preload">
<img src="{{ url_for('static', filename='img/cloud_colors.gif') }}" alt="cloud_animation">
</div>
<div class="full-width col_bg">
<div class="col-box">
<!-- <ul>
<p>Latest updates:</p>
<li><span class="content-date">03.04.2021:</span> Added March summary on new <a href="/monthly">monthly</a> page</li>
</ul> -->
</div>
</div>
<div class="full-width col_lightbg">
<div class="content title">
<div class="title-text">
<h1>Live Air Quality</h1>
<h2>in Luang Prabang Laos PDR</h2>
<p>Last updated: <span id="timestamp"></span></p>
</div>
<div class="title-nav">
<ul>
<a href="/"><li class="col_border">Home</li></a>
<a href="/about"><li class="col_border">About</li></a>
<a href="/graphs"><li class="col_border">Graphs</li></a>
<a href="/monthly"><li class="col_border">Monthly</li></a>
</ul>
</div>
</div>
</div>
{% block content %}{% endblock %}
<div class="full-width col_bg">
<div class="col-box">
<p>© 2021 | <a href="https://github.com/bbilly1/aqi_monitor" target="_blank">Documentation</a></p>
</div>
</div>
</body>
</html>

22
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@ -0,0 +1,22 @@
{% extends "layout.html" %}
{% block content %}
<div class="full-width">
<div class="content">
<h1>Month by month</h1>
<p>Month compared to last year. Values are in 8h average.</p>
</div>
{% for month in months %}
<div class="content-subtitle">
<h2>{{month.month_name}}</h2>
</div>
<div class="content graph">
<div class="graph-item">
<img src="{{month.month_graph}}" alt="{{month.month_name}} graph">
</div>
<div class="graph-item">
{{month.table}}
</div>
</div>
{% endfor %}
</div>
{% endblock content %}

4
backend/flask/app.ini → web/uwsgi.ini
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@ -1,5 +1,5 @@
[uwsgi]
wsgi-file = run.py
wsgi-file = views.py
callable = app
socket = :8080
processes = 4
@ -7,4 +7,4 @@ threads = 2
master = true
chmod-socket = 660
vacuum = true
die-on-term = true
die-on-term = true

114
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@ -0,0 +1,114 @@
""" main entry page to handle all the routes """
import os
from datetime import datetime
from flask import Flask, render_template, request, app
from flask import url_for # pylint: disable=unused-import
from flask_httpauth import HTTPBasicAuth
from apscheduler.schedulers.background import BackgroundScheduler
from src.helper import Table, get_config
from src.db import get_current, insert_data
from src.graph_current import main as current_graph
from src.graph_nightly import main as nightly_graph
from src.graph_monthly import main as monthly_graph
import matplotlib
matplotlib.use('Agg')
# start up
app = Flask(__name__)
CONFIG = get_config()
auth = HTTPBasicAuth()
aqi_user = CONFIG['aqi_monitor']
USER_DATA = {
aqi_user['authUsername']: aqi_user['authPassword']
}
# initial export
print('initial export')
current_graph()
nightly_graph()
monthly_graph()
# start scheduler
scheduler = BackgroundScheduler()
scheduler.add_job(
current_graph, trigger="cron", minute='*/5', name='current_graph'
)
scheduler.add_job(
nightly_graph, trigger="cron", day='*', hour='1', minute='1', name='night'
)
scheduler.add_job(
nightly_graph, trigger="cron", day='*', hour='1', minute='2', name='month'
)
scheduler.start()
@auth.verify_password
def verify(username, password):
""" get password """
if not (username and password):
return False
return USER_DATA.get(username) == password
@app.route("/")
def home():
""" home page """
return render_template('home.html')
@app.route("/about")
def about():
""" about page """
return render_template('about.html', title='About')
@app.route("/graphs")
def graphs():
""" graphs page """
table = Table('static/dyn/year-table.json').create_table()
return render_template('graphs.html', title='Graphs', table=table)
@app.route("/monthly")
def monthly():
""" monthly statistics page """
months = [i for i in os.listdir('static/dyn/monthly') if '.json' in i]
months.sort(reverse=True)
month_dicts = []
for month in months:
month_clean = os.path.splitext(month)[0]
month_graph = os.path.join('static/dyn/monthly', month_clean + '.png')
month_name = datetime.strptime(month_clean, "%Y-%m").strftime('%B %Y')
month_json = os.path.join('static/dyn/monthly', month)
table = Table(month_json).create_table()
month_dict = {
'month_graph': month_graph,
'month_name': month_name,
'table': table
}
month_dicts.append(month_dict)
return render_template('monthly.html', title='Monthly', months=month_dicts)
@app.route("/data/in", methods=['POST'])
@auth.login_required
def ingest():
""" handle post request from monitor """
post_data = request.json
insert_data(post_data)
return 'ingest'
@app.route("/data/out")
def data():
""" return data from db """
json_data = get_current()
return json_data