1 from random import uniform
6 from flask import Flask, render_template, jsonify, request
8 from sqlalchemy import create_engine
11 class JSONEncoder(flask.json.JSONEncoder):
12 def default(self, object):
13 if isinstance(object, datetime.datetime):
14 return object.isoformat()
15 return super().default(object)
18 def parse_datetime(date_str):
19 return datetime.datetime.strptime(date_str, '%Y-%m-%dT%H:%M:%S')
23 app.json_encoder = JSONEncoder
24 config = configparser.ConfigParser()
25 config.read(os.environ['SEEPARKINI'])
26 apikey = config.get('openweathermap', 'apikey')
29 def open_engine(config):
30 user = config.get('database', 'user')
31 pwd = config.get('database', 'password')
32 host = config.get('database', 'hostname')
33 db = config.get('database', 'database')
34 engine = create_engine('mysql+mysqldb://{}:{}@{}/{}'.format(user, pwd, host, db), echo=False)
38 def select_sensordata(initial_where, initial_sql_args):
39 engine = open_engine(config)
40 with engine.connect() as conn:
41 where = [initial_where]
42 sql_args = [initial_sql_args]
45 if 'begin' in request.args:
46 where.append('timestamp>=%s')
47 begin = request.args.get('begin', None, parse_datetime)
48 sql_args.append(begin)
49 if 'end' in request.args:
50 where.append('timestamp<=%s')
51 end = request.args.get('end', None, parse_datetime)
53 sql = 'select * from sensors where {} order by id'.format(' and '.join(where))
54 cursor = conn.execute(sql, *sql_args)
55 result = [dict(row) for row in cursor]
57 mode = request.args.get('mode', 'full')
58 if mode == 'consolidated':
59 if begin is None or end is None:
62 # copied from munin/master/_bin/munin-cgi-graph.in
69 duration = (end - begin).total_seconds()
72 resolution = resolutions['day']
73 elif duration < 7 * day:
74 resolution = resolutions['week']
75 elif duration < 31 * day:
76 resolution = resolutions['month']
78 resolution = resolutions['year']
79 # TODO: filter out samples from 'result'
80 # like loop over results and skip if timestamp(n+1)-timestamp(n)<resolution
82 format = request.args.get('format', 'default')
86 if not row['sensor_id'] in c3result:
87 c3result[row['sensor_id']] = list()
88 c3result[row['sensor_id']].append(row['value'])
89 if not row['sensor_id'] + '_x' in c3result:
90 c3result[row['sensor_id'] + '_x'] = list()
91 dt = row['timestamp'].strftime('%Y-%m-%d %H:%M:%S')
92 c3result[row['sensor_id'] + '_x'].append(dt)
97 @app.route('/api/<version>/sensors/')
99 """List all sensors found in the database"""
100 engine = open_engine(config)
101 with engine.connect() as conn:
102 cursor = conn.execute('select distinct sensor_id, sensor_name, value_type from sensors')
103 result = [dict(row) for row in cursor]
104 return jsonify(result)
107 @app.route('/api/<version>/sensor/id/<sensor_id>')
108 def sensorid(version, sensor_id):
109 """Return all data for a specific sensor
112 begin=<datetime>, optional, format like "2018-05-19T21:07:53"
113 end=<datetime>, optional, format like "2018-05-19T21:07:53"
114 mode=<full|consolidated>, optional. return all rows (default) or with lower resolution (for charts)
115 format=<default|c3>, optional. return result as returned by sqlalchemy (default) or formatted for c3.js
117 result = select_sensordata('sensor_id=%s', sensor_id)
118 return jsonify(result)
121 @app.route('/api/<version>/sensor/type/<sensor_type>')
122 def sensortype(version, sensor_type):
123 """Return all data for a specific sensor type
126 begin=<datetime>, optional, format like "2018-05-19T21:07:53"
127 end=<datetime>, optional, format like "2018-05-19T21:07:53"
128 mode=<full|consolidated>, optional. return all rows (default) or with lower resolution (for charts)
129 format=<default|c3>, optional. return result as returned by sqlalchemy (default) or formatted for c3.js
131 result = select_sensordata('value_type=%s', sensor_type)
132 return jsonify(result)
135 @app.route('/data/', defaults={'timespan': 1})
136 @app.route("/data/<int:timespan>", methods=['GET'])
138 granularity = 5 * timespan # (every) minute(s) per day
139 samples = 60/granularity * 24 * timespan # per hour over whole timespan
145 start = end - samples * granularity * 60
147 for i in range(int(samples)):
148 s4m.append(uniform(-10,30))
149 s5m.append(uniform(-10,30))
150 s4mt = uniform(start, end)
151 s5mt = uniform(start, end)
152 s4m_x.append(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(s4mt)))
153 s5m_x.append(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(s5mt)))
157 '0316a2193bff_x': s4m_x,
159 '0316a21383ff_x': s5m_x,
167 return render_template('seepark_web.html', apikey=apikey)