import os
import sys
from collections import defaultdict
-from flask import Flask, render_template, jsonify, request, abort, Response
+import io
+import numpy as np
+import matplotlib
+matplotlib.use('pdf')
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_pdf import PdfPages
+
+from flask import Flask, render_template, jsonify, request, abort, Response, make_response
import flask.json
from flask_sqlalchemy import SQLAlchemy, inspect
from sqlalchemy import func
+MONTH_DE = [
+ 'Jänner',
+ 'Februar',
+ 'März',
+ 'April',
+ 'Mai',
+ 'Juni',
+ 'Juli',
+ 'August',
+ 'September',
+ 'Oktober',
+ 'November',
+ 'Dezember']
+
+DAY_OF_WEEK_DE = [
+ 'Montag',
+ 'Dienstag',
+ 'Mittwoch',
+ 'Donnerstag',
+ 'Freitag',
+ 'Samstag',
+ 'Sonntag']
+
# https://stackoverflow.com/a/37350445
def sqlalchemy_model_to_dict(model):
__tablename__ = 'openweathermap'
-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)
- if sensor_type is not None:
- query = query.filter(Sensors.value_type == sensor_type)
- if begin is not None:
- query = query.filter(Sensors.timestamp >= begin)
- if end is not None:
- query = query.filter(Sensors.timestamp <= end)
- return query.all()
-
-
-def select_sensordata_grouped(sensor_id, sensor_type, begin, end):
- # determine resolution (interval in seconds for data points)
+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
+ # except day: 300 -> 600
resolutions = dict(
- day = 300,
+ day = 600,
week = 1800,
month = 7200,
year = 86400,
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)
+ if sensor_type is not None:
+ query = query.filter(Sensors.value_type == sensor_type)
+ if begin is not None:
+ query = query.filter(Sensors.timestamp >= begin)
+ if end is not None:
+ query = query.filter(Sensors.timestamp <= end)
+ return query.all()
+
+
+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.min(Sensors.timestamp), func.mean(Sensors.value),
+ 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)
return query.all()
-def select_openweatherdata(cityid, begin, end, mode):
+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 not None and end is not 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_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[getattr(row, id)].append(getattr(row, field_y))
+ 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_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, 'cityid', 'datetime', 'temp')
return result.value, result.timestamp
+def add_month(date):
+ return (date + datetime.timedelta(days=42)).replace(day=1)
+
+
@app.route('/api/<version>/sensors/')
def sensors(version):
"""List all sensors found in the database"""
return jsonify(result)
+@app.route('/api/<version>/currentairtemperature')
+def currentair(version):
+ value, timestamp = currentairtemperature(cityid)
+ return jsonify({"value": value, "timestamp": timestamp})
+
+
+@app.route('/api/<version>/currentwatertemperature')
+def currentwater(version):
+ value, timestamp = currentwatertemperature(mainsensor)
+ return jsonify({"value": value, "timestamp": timestamp})
+
+
+@app.route('/report/<int:year>-<int:month>')
+def report(year, month):
+
+ begin = datetime.datetime(year, month, 1)
+ end = add_month(begin)
+ data = list(select_sensordata(mainsensor, 'Wassertemperatur', begin, end))
+ x = np.array([d.timestamp for d in data])
+ y = np.array([d.value for d in data])
+
+ days_datetime = []
+ d = begin
+ while d < end:
+ days_datetime.append(d)
+ d = d + datetime.timedelta(1)
+
+ binary_pdf = io.BytesIO()
+ with PdfPages(binary_pdf) as pdf:
+ a4 = (29.7/2.54, 21./2.54)
+ title = 'Seepark Wassertemperatur {} {}'.format(MONTH_DE[begin.month-1], begin.year)
+ report_times = [datetime.time(10), datetime.time(15)]
+
+ # graphic
+ plt.figure(figsize=a4)
+ plt.plot(x, y)
+ plt.xticks(days_datetime, [''] * len(days_datetime))
+ plt.ylabel('Temperatur in °C')
+ plt.axis(xmin=begin, xmax=end)
+ plt.grid()
+ plt.title(title)
+
+ # table
+ columns = []
+ for d in days_datetime:
+ columns.append('{}.'.format(d.day))
+ rows = []
+ for t in report_times:
+ rows.append('Wasser {:02d}:{:02d} °C'.format(t.hour, t.minute))
+ cells = []
+ for t in report_times:
+ columns.append('{}.'.format(d.day))
+ row_cells = []
+ for d in days_datetime:
+ report_datetime = datetime.datetime.combine(d.date(), t)
+ closest_index = np.argmin(np.abs(x - report_datetime))
+ if abs(x[closest_index] - report_datetime) > datetime.timedelta(hours=1):
+ cell = 'N/A'
+ else:
+ value = y[closest_index]
+ cell = '{:.1f}'.format(value)
+ row_cells.append(cell)
+ cells.append(row_cells)
+ table = plt.table(cellText=cells, colLabels=columns, rowLabels=rows, loc='bottom')
+ table.scale(xscale=1, yscale=2)
+ plt.title(title)
+ plt.subplots_adjust(left=0.15, right=0.97, bottom=0.3) # do not cut row labels
+ pdf.savefig()
+
+ pdf_info = pdf.infodict()
+ pdf_info['Title'] = title
+ pdf_info['Author'] = 'Chrisu Jähnl'
+ pdf_info['Subject'] = 'Wassertemperatur'
+ pdf_info['Keywords'] = 'Seepark Wassertemperatur'
+ pdf_info['CreationDate'] = datetime.datetime.now()
+ pdf_info['ModDate'] = datetime.datetime.today()
+
+ response = make_response(binary_pdf.getvalue())
+ response.headers['Content-Type'] = 'application/pdf'
+ response.headers['Content-Disposition'] = 'attachment; filename=seepark_{:04d}-{:02d}.pdf'.format(year, month)
+ return response
+
+
@app.route("/")
def index():
airvalue, airtime = currentairtemperature(cityid)
ToastFreeware / chrisu / seepark.git / blobdiff