def result_plot(self,
min_duration=5.0,
max_duration=720.0,
logx=False,
fmt='png',
show=False):
duration_steps = np.arange(min_duration, max_duration + 1, 1)
plt.style.use('bmh')
return_periods = [0.5, 1, 10, 50, 100]
return_periods = [1, 2, 5, 10, 50]
table = self.result_table(durations=duration_steps,
return_periods=return_periods)
ax = table.plot(color='black', logx=logx, legend=False)
for _, return_time in enumerate(return_periods):
p = self.measured_points(return_time, max_duration=max_duration)
ax.plot(p, 'k' + 'x')
x, y = list(p.tail(1).items())[0]
ax.text(
x + 10,
y,
'{} a'.format(return_time),
verticalalignment='center',
horizontalalignment='left',
#bbox=dict(facecolor='white', alpha=1.0, lw=1)
)
ax.tick_params(axis='both', which='both', direction='out')
ax.set_xlabel('Duration D in (min)')
ax.set_ylabel('Rainfall h$_N$ in (mm)')
ax.set_title('IDF curves')
fig = ax.get_figure()
fn = self.output_filename + '_idf_plot.' + fmt
cm_to_inch = 2.54
fig.set_size_inches(h=21 / cm_to_inch,
w=29.7 / cm_to_inch) # (11.69, 8.27)
fig.tight_layout()
fig.savefig(fn, dpi=260)
plt.close(fig)
if show:
show_file(fn)
return fn
def result_plot_v2(idf,
filename,
min_duration=5.0,
max_duration=720.0,
logx=False,
show=False):
duration_steps = np.arange(min_duration, max_duration + 1, 1)
colors = ['r', 'g', 'b', 'y', 'm']
plt.style.use('bmh')
# return_periods = [0.5, 1, 10, 50, 100]
return_periods = [1, 2, 5, 10, 50]
table = idf.result_table(durations=duration_steps,
return_periods=return_periods)
table.index = pd.to_timedelta(table.index, unit='m')
ax = table.plot(color=colors, logx=logx)
ax.tick_params(axis='both', which='both', direction='out')
for i in range(len(return_periods)):
return_time = return_periods[i]
color = colors[i]
p = measured_points(idf, return_time, max_duration=max_duration)
p.index = pd.to_timedelta(p.index, unit='m')
ax.plot(p, color + 'x')
# plt.text(max_duration * ((10 - offset) / 10), depth_of_rainfall(max_duration * ((10 - offset) / 10),
# return_time, parameter_1,
# parameter_2) + offset, '$T_n=$' + str(
# return_time))
ax.set_xlabel('Dauerstufe $D$ in $[min]$')
ax.set_ylabel('Regenhöhe $h_N$ in $[mm]$')
ax.set_title('Regenhöhenlinien')
ax.legend(title='$T_n$= ... [a]')
if max_duration > 1.5 * 60:
pass
else:
pass
major_ticks = np.array(
[d * 60 * 1.0e9 for d in idf.duration_steps if d <= max_duration])
# minor_ticks = pd.date_range("00:00", "23:59", freq='15T').time
# print(major_ticks)
# exit()
ax.set_xticks(major_ticks)
# print(ax.get_xticks())
from matplotlib import ticker
def time_ticks(x, _):
x = pd.to_timedelta(x, unit='ns').total_seconds() / 60
h = int(x / 60)
m = int(x % 60)
s = ''
if h:
s += '{}h'.format(h)
if m:
s += '{}min'.format(m)
return s
formatter = ticker.FuncFormatter(time_ticks)
ax.xaxis.set_major_formatter(formatter)
# print(ax.get_xticks())
# plt.axis([0, max_duration, 0, depth_of_rainfall(max_duration,
# return_periods[len(return_periods) - 1],
# parameter_1, parameter_2) + 10])
fig = ax.get_figure()
cm_to_inch = 2.54
fig.set_size_inches(h=21 / cm_to_inch,
w=29.7 / cm_to_inch) # (11.69, 8.27)
fig.tight_layout()
fig.savefig(filename, dpi=260)
plt.close(fig)
if show:
show_file(filename)
return filename
def run_script(start=None,
end=None,
id_=None,
input_=None,
meta=False,
max10a=False,
add_gaps=True,
statistics=False,
to_csv=False,
to_parquet=False,
plot=False,
unix=False):
# __________________________________________________________________________________________________________________
if start is not None:
start = convert_time(start, 'start')
# __________________________________________________________________________________________________________________
if end is not None:
end = convert_time(end, 'end')
# __________________________________________________________________________________________________________________
series, name = get_data(id_, input_, meta, unix)
# __________________________________________________________________________________________________________________
series = series[start:end].copy()
check_period(series)
# ______________________________________________________
tags = None
availability = None
# __________________________________________________________________________________________________________________
if max10a:
tags = data_validation(series)
availability = data_availability(tags)
start, end = max_10a(availability)
series = series.loc[start:end].copy()
check_period(series)
if start or end or max10a:
print('The time-series is clipped to '
'start at "{:%Y-%m-%d}" and end at "{:%Y-%m-%d}".'.format(
*start_end_date(series)))
# __________________________________________________________________________________________________________________
if add_gaps:
if tags is None:
tags = data_validation(series)
if availability is None:
availability = data_availability(tags)
gaps = span_table(~availability)
gaps['delta'] = (gaps['end'] -
gaps['start']).dt.total_seconds() / (60 * 60 * 24)
gaps = gaps.sort_values('delta', ascending=False)
gaps.columns = ['start', 'end', 'gaps in days']
gaps_fn = '{}_gaps.csv'.format(name)
gaps.to_csv(gaps_fn, float_format='%0.3f', **csv_args(unix))
print('The gaps table is saved {}.'.format(output_filename(gaps_fn)))
# __________________________________________________________________________________________________________________
if statistics:
if tags is None:
tags = data_validation(series)
if availability is None:
availability = data_availability(tags)
availability_cut = 0.9
stats = create_statistics(series,
availability,
availability_cut=availability_cut)
with open('{}_stats.txt'.format(name), 'w+') as f:
rain_fmt = '{:0.0f} mm'
date_fmt = '{:%Y}'
avail_fmt = '{:0.0%}'
f.write(
'The annual totals of the data series serve as the data basis.\n'
'The following statistics were analyzed:\n'
'Only years with a availability of {avail} will be evaluated.\n'
'\n'
'The maximum is {rain} and was in the year {date} (with {avail} Data available).\n'
'The minimum is {rain} and was in the year {date} (with {avail} Data available).\n'
'The mean is {rain} (with {avail} Data available in average).'
''.format(rain=rain_fmt, date=date_fmt,
avail=avail_fmt).format(availability_cut,
*stats['maximum'],
*stats['minimum'],
*stats['mean']))
print('The statistics are saved {}.'.format(output_filename(
f.name)))
# __________________________________________________________________________________________________________________
save_formats = list()
if to_csv:
save_formats.append('csv')
if to_parquet:
save_formats.append('parquet')
for save_as in save_formats:
out_fn = export_series(series,
filename=name,
save_as=save_as,
unix=unix)
print('The time-series is saved {}.'.format(output_filename(out_fn)))
# __________________________________________________________________________________________________________________
if plot:
if tags is None:
tags = data_validation(series)
if availability is None:
availability = data_availability(tags)
plot_fn = '{}_plot.png'.format(name)
rain_plot(series, availability, plot_fn)
print('The plot is saved {}.'.format(output_filename(plot_fn)))
show = True
if show:
show_file(plot_fn)
def command_line_tool(cls):
user = heavy_rain_parser()
# --------------------------------------------------
# use the same directory as the input file and make as subdir with the name of the input_file + "_idf_data"
out = '{label}_idf_data'.format(
label='.'.join(user.input.split('.')[:-1]))
if not path.isdir(out):
mkdir(out)
action = 'Creating'
else:
action = 'Using'
print(
'{} the subfolder "{}" for the interim- and final-results.'.format(
action, out))
fn_pattern = path.join(out, 'idf_{}')
# --------------------------------------------------
idf = cls(series_kind=user.series_kind,
worksheet=user.worksheet,
extended_durations=True)
# --------------------------------------------------
parameters_fn = fn_pattern.format('parameters.yaml')
if path.isfile(parameters_fn):
print(
'Found existing interim-results in "{}" and using them for calculations.'
.format(parameters_fn))
else:
print('Start reading the time-series {} for the analysis.'.format(
user.input))
ts = import_series(user.input).replace(0, np.NaN).dropna()
# --------------------------------------------------
idf.set_series(ts)
print('Finished reading.')
# --------------------------------------------------
idf.auto_save_parameters(parameters_fn)
# --------------------------------------------------
h = user.height_of_rainfall
r = user.flow_rate_of_rainfall
d = user.duration
t = user.return_period
if r is not None:
if h is None and d is not None:
h = rate2height(rain_flow_rate=r, duration=d)
elif d is None and h is not None:
d = h / r * 1000 / 6
if user.r_720_1:
d = 720
t = 1
if any((h, d, t)):
if all((d, t)):
pass
elif all((d, h)):
t = idf.get_return_period(h, d)
print('The return period is {:0.1f} years.'.format(t))
elif all((h, t)):
d = idf.get_duration(h, t)
print('The duration is {:0.1f} minutes.'.format(d))
print(
'Resultierende Regenhöhe h_N(T_n={t:0.1f}a, D={d:0.1f}min) = {h:0.2f} mm'
''.format(t=t, d=d, h=idf.depth_of_rainfall(d, t)))
print(
'Resultierende Regenspende r_N(T_n={t:0.1f}a, D={d:0.1f}min) = {r:0.2f} L/(s*ha)'
''.format(t=t, d=d, r=idf.rain_flow_rate(d, t)))
# --------------------------------------------------
if user.plot:
fig, ax = idf.result_figure()
plot_fn = fn_pattern.format('curves_plot.png')
fig.savefig(plot_fn, dpi=260)
plt.close(fig)
show_file(plot_fn)
print('Created the IDF-curves-plot and saved the file as "{}".'.
format(plot_fn))
# --------------------------------------------------
if user.export_table:
table = idf.result_table(add_names=True)
print(table.round(2).to_string())
table_fn = fn_pattern.format('table.csv')
table.to_csv(table_fn, sep=';', decimal=',', float_format='%0.2f')
print('Created the IDF-curves-plot and saved the file as "{}".'.
format(table_fn))
def execute_tool():
args = ehyd_parser()
# __________________________________________________________________________________________________________________
if args.start is not None:
try:
start = to_datetime(args.start)
except ValueError():
raise UserWarning(
'Wrong time format for the start time. Use the format="YYYY-MM-DD"'
)
else:
start = None
# __________________________________________________________________________________________________________________
if args.end is not None:
try:
end = to_datetime(args.end)
except ValueError():
raise UserWarning(
'Wrong time format for the end time. Use the format="YYYY-MM-DD"'
)
else:
end = None
# __________________________________________________________________________________________________________________
if args.id is not None:
id_number = args.id
name = 'ehyd_{}'.format(id_number)
if args.meta:
with open('{}_meta.txt'.format(name), 'w') as f:
f.write(get_station_meta(id_number))
print('Meta-data written in "{}".'.format(f.name))
series = get_series(id_number)
# __________________________________________________________________________________________________________________
elif args.input is not None:
fn = args.input
series = import_series(fn, args.unix)
print('Series "{}" was imported.'.format(fn))
name = path.basename(fn).split('.')[0]
print('The filename basis of the output files is "{}".'.format(name))
# __________________________________________________________________________________________________________________
else:
raise UserWarning(
'No data selected. Use either a input file or a id. See help menu.'
)
print('The original time series has the start at "{:%Y-%m-%d}"'
' and the end at "{:%Y-%m-%d}".'.format(
*series.index[[0, -1]].tolist()))
# __________________________________________________________________________________________________________________
if start is not None:
series = series[start:].copy()
check_period(series)
if end is not None:
series = series[:end].copy()
check_period(series)
# __________________________________________________________________________________________________________________
if args.max10a:
tags = data_validation(series)
availability = data_availability(tags)
start, end = max_10a(availability)
series = series.loc[start:end].copy()
check_period(series)
else:
tags = DataFrame()
availability = Series()
print('Data was clipped to start="{:%Y-%m-%d}" and end="{:%Y-%m-%d}".'
''.format(*series.index[[0, -1]].tolist()))
# __________________________________________________________________________________________________________________
if args.add_gaps:
if tags.empty:
tags = data_validation(series)
if availability.empty:
availability = data_availability(tags)
gaps = span_table(availability.index, ~availability)
gaps['delta'] = gaps['delta'].dt.total_seconds() / (60 * 60 * 24)
gaps = gaps.sort_values('delta', ascending=False)
gaps.columns = ['start', 'end', 'gaps in days']
gaps_fn = '{}_gaps.csv'.format(name)
gaps.to_csv(gaps_fn, float_format='%0.3f', **csv_args(args.unix))
print('The gaps table was written in "{}".'.format(gaps_fn))
# __________________________________________________________________________________________________________________
if args.statistics:
with open('{}_stats.txt'.format(name), 'w+') as f:
if tags.empty:
tags = data_validation(series)
if availability.empty:
availability = data_availability(tags)
stats = statistics(series, availability)
rain_fmt = '{:0.0f} mm'
date_fmt = '{:%Y}'
avail_fmt = '{:0.0%}'
f.write(
'The annual totals of the data series serve as the data basis.\n'
'The following statistics were analyzed:\n'
'\n'
'The maximum is {rain} and was in the year {date} (with {avail} Data available).\n'
'The minimum is {rain} and was in the year {date} (with {avail} Data available).\n'
'The mean is {rain} (with {avail} Data available in average).'
''.format(rain=rain_fmt, date=date_fmt,
avail=avail_fmt).format(*stats['maximum'],
*stats['minimum'],
*stats['mean']))
print('The statistics was written in the file "{}".'.format(
f.name))
# __________________________________________________________________________________________________________________
if args.to_csv:
out_fn = export_series(series,
filename=name,
save_as='csv',
unix=args.unix)
print('The time series was saved in the file "{}".'.format(out_fn))
# __________________________________________________________________________________________________________________
if args.to_ixx:
out_fn = export_series(series,
filename=name,
save_as='ixx',
unix=args.unix)
print('The time series was saved in the file "{}".'.format(out_fn))
# __________________________________________________________________________________________________________________
if args.to_parquet:
out_fn = export_series(series, filename=name, save_as='parquet')
print('The time series was saved in the file "{}".'.format(out_fn))
# __________________________________________________________________________________________________________________
if args.plot:
if tags.empty:
tags = data_validation(series)
if availability.empty:
availability = data_availability(tags)
plot_fn = '{}_plot.png'.format(name)
rain_plot(series, availability, plot_fn)
print('The plot was saved in the file "{}".'.format(plot_fn))
show = True
if show:
show_file(plot_fn)
def run_script(identifier=None,
filename=None,
start=None,
end=None,
max10a=False,
add_gaps=True,
to_csv=False,
to_parquet=False,
plot=False,
statistics=False,
meta=False,
unix=False,
availability_cut=0.9,
agg='sum',
field=FIELDS.NIEDERSCHLAG):
"""
get eHYD.gv.at data and run simple analysis on it and finally save the data to a local file.
"""
# __________________________________________________________________________________________________________________
if start is not None:
start = _convert_time(start, 'start')
# __________________________________________________________________________________________________________________
if end is not None:
end = _convert_time(end, 'end')
# __________________________________________________________________________________________________________________
if identifier is not None:
identifier = int(identifier)
name = f'ehyd_{field}_{identifier}'
print(f'You choose the id: "{identifier}" with the meta-data: '
f'{get_basic_station_meta(identifier, field=field)}.')
if meta:
with open('{}_meta.json'.format(name), 'w') as f:
json.dump(get_station_reference_data(
identifier, field=field, data_kind=DATA_KIND.MEASUREMENT),
f,
indent=4)
print('The meta-data are saved in {}.'.format(
_output_filename(f.name)))
series = get_ehyd_data(identifier,
field=field,
data_kind=DATA_KIND.MEASUREMENT)
# __________________________________________________________________________________________________________________
elif filename is not None:
series = import_series(filename, unix=unix)
print(
f'The data in "{filename}" were imported and are used as the precipitation time-series.'
)
name = path.basename(filename).split('.')[0]
# __________________________________________________________________________________________________________________
else:
raise UserWarning(
'No data selected. Use either a input file or a id. See help menu.'
)
print(
f'The selected time-series start at "{series.index[0]:%Y-%m-%d}" and ends at "{series.index[0]:%Y-%m-%d}".'
)
# __________________________________________________________________________________________________________________
series = series[start:end].copy()
check_period(series)
# ______________________________________________________
tags = None
availability = None
# __________________________________________________________________________________________________________________
if max10a:
tags = data_validation(series)
availability = data_availability(tags)
start, end = max_10a(availability)
series = series.loc[start:end].copy()
check_period(series)
if start or end or max10a:
print(
f'The time-series is clipped to start at "{series.index[0]:%Y-%m-%d}" and end at "'
f'{series.index[0]:%Y-%m-%d}".')
# __________________________________________________________________________________________________________________
if add_gaps:
if tags is None:
tags = data_validation(series)
if availability is None:
availability = data_availability(tags)
gaps = span_table(~availability)
gaps['delta'] = (gaps['end'] -
gaps['start']).dt.total_seconds() / (60 * 60 * 24)
gaps = gaps.sort_values('delta', ascending=False)
gaps.columns = ['start', 'end', 'gaps in days']
gaps_fn = f'{name}_gaps.csv'
gaps.to_csv(gaps_fn, float_format='%0.3f', **csv_args(unix))
print(f'The gaps table is saved {_output_filename(gaps_fn)}.')
# __________________________________________________________________________________________________________________
if statistics:
if tags is None:
tags = data_validation(series)
if availability is None:
availability = data_availability(tags)
stats = create_statistics(series,
availability,
availability_cut=availability_cut)
with open('{}_stats.txt'.format(name), 'w+') as f:
rain_fmt = '{:0.0f} mm'
date_fmt = '{:%Y}'
avail_fmt = '{:0.0%}'
f.write(
f'The annual totals of the data series serve as the data basis.\n'
f'The following statistics were analyzed:\n'
f'Only years with a availability of {availability_cut:{avail_fmt}} will be evaluated.\n'
f'\n'
f'The maximum is {stats["maximum"]:{rain_fmt}} and was in the year {stats["maximum_date"]:{date_fmt}} '
f'(with {stats["maximum_avail"]:{avail_fmt}} Data available).\n'
f'The minimum is {stats["minimum"]:{rain_fmt}} and was in the year {stats["minimum_date"]:{date_fmt}} '
f'(with {stats["minimum_avail"]:{avail_fmt}} Data available).\n'
f'The mean is {stats["mean"]:{rain_fmt}} '
f'(with {stats["mean_avail"]:{avail_fmt}} Data available in average).'
)
print(f'The statistics are saved {_output_filename(f.name)}.')
# __________________________________________________________________________________________________________________
save_formats = []
if to_csv:
save_formats.append('csv')
if to_parquet:
save_formats.append('parquet')
for save_as in save_formats:
out_fn = export_series(series,
filename=name,
save_as=save_as,
unix=unix)
print(f'The time-series is saved {_output_filename(out_fn)}.')
# __________________________________________________________________________________________________________________
if plot:
if tags is None:
tags = data_validation(series)
if availability is None:
availability = data_availability(tags)
plot_fn = f'{name}_plot.png'
agg_data_plot(series, availability, plot_fn, agg=agg)
print(f'The plot is saved {_output_filename(plot_fn)}.')
show = True
if show:
show_file(plot_fn)
