+import collections
import datetime
+import itertools
import time
import configparser
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
return datetime.datetime.strptime(date_str, '%Y-%m-%dT%H:%M:%S')
+def ntimes(it, n):
+ for v in it:
+ yield from itertools.repeat(v, n)
+
+
def get_sqlalchemy_database_uri(config):
user = config.get('database', 'user')
pwd = config.get('database', 'password')
__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
+ # except day: 300 -> 600
+ resolutions = dict(
+ day = 600,
+ 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 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;
- # seepark_web.db.session.query(func.to_seconds(Sensors.timestamp).op('div')(60*60*24).label('g'), func.min(Sensors.timestamp), func.min(Sensors.value)).group_by('g').all()
return query.all()
-def select_openweatherdata(cityid, begin, end, mode):
+def sensordata_to_xy(sensordata):
+ sensordata = list(sensordata)
+ x = np.array([d.timestamp for d in sensordata])
+ y = np.array([d.value for d in sensordata])
+ return x, y
+
+
+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 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 openweatherdata_to_xy(openweatherdata):
+ openweatherdata = list(openweatherdata)
+ x = np.array([d.datetime for d in openweatherdata])
+ y = np.array([d.temp for d in openweatherdata])
+ return x, y
+
+
+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 estimate_swimmer_count(date):
+ return date.day
+
+
+def select_swimmerdata(begin, end):
+ def report_times(begin, end):
+ d = begin
+ while d < end:
+ for t in [10, 15]:
+ a = datetime.datetime.combine(d.date(), datetime.time(t))
+ if a >= d:
+ yield a
+ d += datetime.timedelta(days=1)
+ SwimmerData = collections.namedtuple('SwimmerData', ['datetime', 'count'])
+ for d in report_times(begin, end):
+ count = estimate_swimmer_count(d)
+ yield SwimmerData(d, count)
+
+
+def swimmerdata_to_xy(swimmerdata):
+ swimmerdata = list(swimmerdata)
+ x = np.array([d.datetime for d in swimmerdata])
+ y = np.array([d.count for d in swimmerdata])
+ return x, y
+
+
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_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, 'sensor_id', 'timestamp', 'value')
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 first_of_month(date, month):
+ date = date.replace(day=1)
+ if month == 0:
+ return date
+ if month == 1:
+ return (date + datetime.timedelta(days=42)).replace(day=1)
+ if month == -1:
+ return (date - datetime.timedelta(days=1)).replace(day=1)
+ assert False
+
+
@app.route('/api/<version>/sensors/')
def sensors(version):
"""List all sensors found in the database"""
"""Return all data for a specific sensor
URL parameters:
- begin=<datetime>, optional, format like "2018-05-19T21:07:53"
- end=<datetime>, optional, format like "2018-05-19T21:07:53"
- mode=<full|consolidated>, optional. return all rows (default) or with lower resolution (for charts)
- format=<default|c3>, optional. return result as returned by sqlalchemy (default) or formatted for c3.js
+
+ * ``begin=<datetime>``, optional, format like ``2018-05-19T21:07:53``
+ * ``end=<datetime>``, optional, format like ``2018-05-19T21:07:53``
+ * ``mode=<full|consolidated>``, optional. return all rows (default) or with lower resolution (for charts)
+ * ``format=<default|c3>``, optional. return result as returned by sqlalchemy (default) or formatted for c3.js
"""
result = sensordata(sensor_id=sensor_id)
return jsonify(result)
"""Return all data for a specific sensor type
URL parameters:
- begin=<datetime>, optional, format like "2018-05-19T21:07:53"
- end=<datetime>, optional, format like "2018-05-19T21:07:53"
- mode=<full|consolidated>, optional. return all rows (default) or with lower resolution (for charts)
- format=<default|c3>, optional. return result as returned by sqlalchemy (default) or formatted for c3.js
+
+ * ``begin=<datetime>``, optional, format like ``2018-05-19T21:07:53``
+ * ``end=<datetime>``, optional, format like ``2018-05-19T21:07:53``
+ * ``mode=<full|consolidated>``, optional. return all rows (default) or with lower resolution (for charts)
+ * ``format=<default|c3>``, optional. return result as returned by sqlalchemy (default) or formatted for c3.js
"""
result = sensordata(sensor_type=sensor_type)
return jsonify(result)
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(fixed_digits=4):year>/<int(fixed_digits=2):month>')
+def report(year, month):
+ """Report for given year (4 digits) and month (2 digits)
+ """
+ paper_size = (29.7 / 2.54, 21. / 2.54) # A4
+
+ begin = datetime.datetime(year, month, 1)
+ end = first_of_month(begin, 1)
+
+ water_data = sensordata_to_xy(select_sensordata(mainsensor, 'Wassertemperatur', begin, end))
+ air_data = openweatherdata_to_xy(select_openweatherdata(cityid, begin, end))
+ swimmer_data = swimmerdata_to_xy(select_swimmerdata(begin, end))
+
+ report_times = [datetime.time(10), datetime.time(15)]
+ report_data = {'Wasser': water_data, 'Luft': air_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:
+ title = 'Seepark Obsteig {} {}'.format(MONTH_DE[begin.month-1], begin.year)
+
+ # graphic
+ plt.figure(figsize=paper_size)
+ report_colors = []
+ for label, data in sorted(report_data.items(), reverse=True):
+ x, y = data
+ lines = plt.plot(x, y, label=label)
+ report_colors.append(lines[0].get_color())
+ plt.xticks(days_datetime, [''] * len(days_datetime))
+ plt.ylabel('Temperatur in °C')
+ plt.axis(xmin=begin, xmax=end)
+ plt.legend()
+ plt.grid()
+ plt.title(title)
+
+ # table
+ columns = []
+ for d in days_datetime:
+ columns.append('{}.'.format(d.day))
+ rows = []
+ for label in sorted(report_data.keys(), reverse=True):
+ for t in report_times:
+ rows.append('{:02d}:{:02d} {} °C'.format(t.hour, t.minute, label))
+ for t in report_times:
+ rows.append('{:02d}:{:02d} Badende'.format(t.hour, t.minute))
+ cells = []
+ for label, data in sorted(report_data.items(), reverse=True):
+ for t in report_times:
+ row_cells = []
+ x, y = data
+ 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)
+ for t in report_times:
+ row_cells = []
+ x, y = swimmer_data
+ 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:
+ cell = y[closest_index]
+ row_cells.append(cell)
+ cells.append(row_cells)
+ row_colors = list(ntimes(report_colors + ['w'], len(report_times)))
+ table = plt.table(cellText=cells, colLabels=columns, rowLabels=rows, rowColours=row_colors, 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'] = 'Temperaturen'
+ pdf_info['Keywords'] = 'Seepark Obsteig'
+ 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)
watervalue, watertime = currentwatertemperature(mainsensor)
+ this_month = first_of_month(datetime.date.today(), 0)
+ last_month = first_of_month(this_month, -1)
return render_template(
'seepark_web.html',
watertime=watertime,
airvalue=airvalue,
airtime=airtime,
+ this_month=this_month,
+ last_month=last_month
)
ToastFreeware / chrisu / seepark.git / blobdiff