X-Git-Url: https://git.toastfreeware.priv.at/chrisu/seepark.git/blobdiff_plain/5c7ebd0e18a0d09e517518f15a283dc0ac8cd3ef..7419f864747b4cb78abcdf86b6f6f5cd523d334c:/web/seepark_web.py diff --git a/web/seepark_web.py b/web/seepark_web.py index 131489b..4123029 100644 --- a/web/seepark_web.py +++ b/web/seepark_web.py @@ -1,4 +1,6 @@ +import collections import datetime +import itertools import time import configparser import os @@ -30,6 +32,15 @@ MONTH_DE = [ 'November', 'Dezember'] +DAY_OF_WEEK_DE = [ + 'Montag', + 'Dienstag', + 'Mittwoch', + 'Donnerstag', + 'Freitag', + 'Samstag', + 'Sonntag'] + # https://stackoverflow.com/a/37350445 def sqlalchemy_model_to_dict(model): @@ -50,6 +61,11 @@ def parse_datetime(date_str): 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') @@ -116,6 +132,13 @@ def select_sensordata(sensor_id, sensor_type, begin, end): 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) @@ -145,6 +168,13 @@ def select_openweatherdata(cityid, 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) @@ -162,6 +192,32 @@ def select_openweatherdata_grouped(cityid, 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: @@ -302,39 +358,93 @@ def currentwater(version): @app.route('/report/-') 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_grouped(mainsensor, 'Wassertemperatur', begin, end)) - x = [d.timestamp for d in data] - y = [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 while d < end: days_datetime.append(d) d = d + datetime.timedelta(1) - days_str = [d.strftime('%d') for d in days_datetime] binary_pdf = io.BytesIO() with PdfPages(binary_pdf) as pdf: - a4 = (21./2.54, 29.7/2.54) - plt.figure(figsize=a4) - plt.plot(x, y) - plt.xticks(days_datetime, days_str, rotation='vertical') - plt.xlabel('Tag') - plt.ylabel('Temparatur in °C') + 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() - title = 'Seepark Wassertemperatur {} {}'.format(MONTH_DE[begin.month-1], begin.year) 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'] = '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()