-from random import uniform
import datetime
import time
import configparser
from flask import Flask, render_template, jsonify, request, abort, Response
import flask.json
from flask_sqlalchemy import SQLAlchemy, inspect
-
-
-app_path = os.path.dirname(os.path.realpath(__file__))
-lib_path = os.path.join(app_path, '..')
-sys.path.append(lib_path)
-from seeparklib.openweathermap import openweathermap_json, OpenWeatherMapError
+from sqlalchemy import func
# https://stackoverflow.com/a/37350445
config.read(os.environ['SEEPARKINI'])
apikey = config.get('openweathermap', 'apikey')
cityid = config.get('openweathermap', 'cityid')
-mainsensor = config.get('temperature', 'mainsensor')
+mainsensor = config.get('webapp', 'mainsensor')
app = Flask(__name__)
app.json_encoder = JSONEncoder
__tablename__ = 'openweathermap'
-def select_sensordata(sensor_id, sensor_type, begin, end, mode):
+def calc_grouping_resolution(begin, end):
+ """How many data points should be between the timestamps begin and end?"""
+ # copied from munin/master/_bin/munin-cgi-graph.in
+ resolutions = dict(
+ day = 300,
+ week = 1800,
+ month = 7200,
+ year = 86400,
+ )
+ duration = (end - begin).total_seconds()
+ day = 60 * 60 * 24
+ if duration <= day:
+ resolution = resolutions['day']
+ elif duration <= 7 * day:
+ resolution = resolutions['week']
+ elif duration <= 31 * day:
+ resolution = resolutions['month']
+ else:
+ resolution = resolutions['year']
+ return resolution
+
+
+def select_sensordata(sensor_id, sensor_type, begin, end):
query = Sensors.query
if sensor_id is not None:
query = query.filter(Sensors.sensor_id == sensor_id)
query = query.filter(Sensors.timestamp >= begin)
if end is not None:
query = query.filter(Sensors.timestamp <= end)
- if mode == 'consolidated' and begin is None and end is None:
- # copied from munin/master/_bin/munin-cgi-graph.in
- # interval in seconds for data points
- resolutions = dict(
- day = 300,
- week = 1800,
- month = 7200,
- year = 86400,
- )
- duration = (end - begin).total_seconds()
- day = 60 * 60 * 24
- if duration < day:
- resolution = resolutions['day']
- elif duration < 7 * day:
- resolution = resolutions['week']
- elif duration < 31 * day:
- resolution = resolutions['month']
- else:
- resolution = resolutions['year']
- # TODO: filter out samples from 'result'
- # something like
- # select to_seconds(datetime) DIV (60*60*24) as interval_id, min(datetime), max(datetime), min(temp), avg(temp), max(temp), count(temp) from openweathermap group by interval_id order by interval_id;
return query.all()
-def select_openweatherdata(cityid, begin, end, mode):
+def select_sensordata_grouped(sensor_id, sensor_type, begin, end):
+ # determine resolution (interval in seconds for data points)
+ resolution = calc_grouping_resolution(begin, end)
+
+ # Let the database do the grouping. Example in SQL (MySQL):
+ # select to_seconds(datetime) DIV (60*60*24) as interval_id, min(datetime), max(datetime), min(temp), avg(temp), max(temp), count(temp) from openweathermap group by interval_id order by interval_id;
+ query = db.session.query(func.to_seconds(Sensors.timestamp).op('div')(resolution).label('g'),
+ func.from_unixtime(func.avg(func.unix_timestamp(Sensors.timestamp))).label('timestamp'),
+ func.avg(Sensors.value).label('value'),
+ Sensors.sensor_id, Sensors.value_type, Sensors.sensor_name)
+ if sensor_id is not None:
+ query = query.filter(Sensors.sensor_id == sensor_id)
+ if sensor_type is not None:
+ query = query.filter(Sensors.value_type == sensor_type)
+ query = query.filter(Sensors.timestamp >= begin)
+ query = query.filter(Sensors.timestamp <= end)
+ query = query.group_by('g', Sensors.sensor_id, Sensors.value_type, Sensors.sensor_name)
+ return query.all()
+
+
+def select_openweatherdata(cityid, begin, end):
query = OpenWeatherMap.query.filter(OpenWeatherMap.cityid == cityid)
if begin is not None:
query = query.filter(OpenWeatherMap.datetime >= begin)
if end is not None:
query = query.filter(OpenWeatherMap.datetime <= end)
- if mode == 'consolidated' and begin is None and end is None:
- # copied from munin/master/_bin/munin-cgi-graph.in
- # interval in seconds for data points
- resolutions = dict(
- day = 300,
- week = 1800,
- month = 7200,
- year = 86400,
- )
- duration = (end - begin).total_seconds()
- day = 60 * 60 * 24
- if duration < day:
- resolution = resolutions['day']
- elif duration < 7 * day:
- resolution = resolutions['week']
- elif duration < 31 * day:
- resolution = resolutions['month']
- else:
- resolution = resolutions['year']
- # TODO: filter out samples from 'result'
- # something like
- # select to_seconds(datetime) DIV (60*60*24) as interval_id, min(datetime), max(datetime), min(temp), avg(temp), max(temp), count(temp) from openweathermap group by interval_id order by interval_id;
return query.all()
-def convert_to_c3(result):
+def select_openweatherdata_grouped(cityid, begin, end):
+ # determine resolution (interval in seconds for data points)
+ resolution = calc_grouping_resolution(begin, end)
+
+ # Let the database do the grouping. Example in SQL (MySQL):
+ # select to_seconds(datetime) DIV (60*60*24) as interval_id, min(datetime), max(datetime), min(temp), avg(temp), max(temp), count(temp) from openweathermap group by interval_id order by interval_id;
+ query = db.session.query(func.to_seconds(OpenWeatherMap.datetime).op('div')(resolution).label('g'),
+ func.from_unixtime(func.avg(func.unix_timestamp(OpenWeatherMap.datetime))).label('datetime'),
+ func.avg(OpenWeatherMap.temp).label('temp'),
+ OpenWeatherMap.cityid)
+ OpenWeatherMap.query.filter(OpenWeatherMap.cityid == cityid)
+ query = query.filter(OpenWeatherMap.datetime >= begin)
+ query = query.filter(OpenWeatherMap.datetime <= end)
+ query = query.group_by('g', OpenWeatherMap.cityid)
+ return query.all()
+
+
+def convert_to_c3(result, id, field_x, field_y):
c3result = defaultdict(list)
for row in result:
- c3result[row.sensor_id].append(row.value)
- dt = row.timestamp.strftime('%Y-%m-%d %H:%M:%S')
- c3result[row.sensor_id + '_x'].append(dt)
+ c3result[str(getattr(row, id))].append(getattr(row, field_y))
+ dt = getattr(row, field_x).strftime('%Y-%m-%d %H:%M:%S')
+ c3result[str(getattr(row, id)) + '_x'].append(dt)
return c3result
mode = request.args.get('mode', 'full')
format = request.args.get('format', 'default')
- result = select_sensordata(sensor_id, sensor_type, begin, end, mode)
+ if mode == 'full':
+ result = select_sensordata(sensor_id, sensor_type, begin, end)
+ elif mode == 'consolidated':
+ if begin is None or end is None:
+ abort(Response('begin and end have to be set for mode==consolidated', 400))
+ result = select_sensordata_grouped(sensor_id, sensor_type, begin, end)
+ else:
+ abort(Response('unknown value for mode', 400))
if format == 'c3':
- return convert_to_c3(result)
+ return convert_to_c3(result, 'sensor_id', 'timestamp', 'value')
return result
mode = request.args.get('mode', 'full')
format = request.args.get('format', 'default')
- result = select_openweatherdata(cityid, begin, end, mode)
+ if mode == 'full':
+ result = select_openweatherdata(cityid, begin, end)
+ elif mode == 'consolidated':
+ if begin is None or end is None:
+ abort(Response('begin and end have to be set for mode==consolidated', 400))
+ result = select_openweatherdata_grouped(cityid, begin, end)
+ else:
+ abort(Response('unknown value for mode', 400))
if format == 'c3':
- return convert_to_c3(result)
+ return convert_to_c3(result, 'cityid', 'datetime', 'temp')
return result
-def currentairtemperature(apikey, cityid):
- """Retruns the tuple temperature, datetime (as float, datetime) in case of success, otherwise None, None."""
- try:
- url, weatherdata = openweathermap_json(apikey, cityid)
- return weatherdata['main']['temp'], datetime.datetime.fromtimestamp(weatherdata['dt'])
- except OpenWeatherMapError:
- return None, None
+def currentairtemperature(cityid):
+ result = OpenWeatherMap.query.filter_by(cityid=cityid).order_by(OpenWeatherMap.datetime.desc()).first()
+ return result.temp, result.datetime
def currentwatertemperature(sensorid):
return jsonify(result)
-@app.route('/data/', defaults={'timespan': 1})
-@app.route("/data/<int:timespan>", methods=['GET'])
-def data(timespan):
- granularity = 5 * timespan # (every) minute(s) per day
- samples = 60/granularity * 24 * timespan # per hour over whole timespan
- s4m = []
- s4m_x = []
- s5m = []
- s5m_x = []
- end = time.time()
- start = end - samples * granularity * 60
-
- for i in range(int(samples)):
- s4m.append(uniform(-10,30))
- s5m.append(uniform(-10,30))
- s4mt = uniform(start, end)
- s5mt = uniform(start, end)
- s4m_x.append(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(s4mt)))
- s5m_x.append(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(s5mt)))
-
- data = {
- '0316a2193bff': s4m,
- '0316a2193bff_x': s4m_x,
- '0316a21383ff': s5m,
- '0316a21383ff_x': s5m_x,
- }
-
- return jsonify(data)
-
-
@app.route("/")
def index():
- airvalue, airtime = currentairtemperature(apikey, cityid)
+ airvalue, airtime = currentairtemperature(cityid)
watervalue, watertime = currentwatertemperature(mainsensor)
return render_template(
ToastFreeware / chrisu / seepark.git / blobdiff