+import collections
import datetime
+import itertools
import time
import configparser
import os
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')
return query.all()
+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)
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)
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:
@app.route('/report/<int:year>-<int:month>')
def report(year, month):
+ """Report for given year and month
+ """
+ paper_size = (29.7 / 2.54, 21. / 2.54) # A4
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])
+
+ 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
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)]
+ title = 'Seepark Obsteig {} {}'.format(MONTH_DE[begin.month-1], begin.year)
# graphic
- plt.figure(figsize=a4)
- plt.plot(x, y)
+ 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)
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('Wasser {:02d}:{:02d} °C'.format(t.hour, t.minute))
+ 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:
- columns.append('{}.'.format(d.day))
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:
- value = y[closest_index]
- cell = '{:.1f}'.format(value)
+ cell = y[closest_index]
row_cells.append(cell)
cells.append(row_cells)
- table = plt.table(cellText=cells, colLabels=columns, rowLabels=rows, loc='bottom')
+ 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_info = pdf.infodict()
pdf_info['Title'] = title
pdf_info['Author'] = 'Chrisu Jähnl'
- pdf_info['Subject'] = 'Wassertemperatur'
- pdf_info['Keywords'] = 'Seepark Wassertemperatur'
+ pdf_info['Subject'] = 'Temperaturen'
+ pdf_info['Keywords'] = 'Seepark Obsteig'
pdf_info['CreationDate'] = datetime.datetime.now()
pdf_info['ModDate'] = datetime.datetime.today()
ToastFreeware / chrisu / seepark.git / blobdiff