def run_semsvann_eb(startDate, endDate):
# TODO: get coordinates from the ObsLocation in regObs
location_name = 'Semsvannet v/Lo 145 moh'
wsTemp = gws.getMetData(19710, 'TAM', startDate, endDate, 0, 'list')
wsSno = gws.getMetData(19710, 'SA', startDate, endDate, 0, 'list')
wsPrec = gws.getMetData(19710, 'RR', startDate, endDate, 0, 'list')
wsWind = gws.getMetData(18700, 'FFM', startDate, endDate, 0, 'list')
wsCC = gws.getMetData(18700, 'NNM', startDate, endDate, 0, 'list')
utm33_y = 6644410
utm33_x = 243940
temp, date = we.strip_metadata(wsTemp, get_date_times=True)
sno_tot = we.strip_metadata(wsSno)
prec_snow = dp.delta_snow_from_total_snow(sno_tot)
prec = we.strip_metadata(wsPrec)
wind = we.strip_metadata(wsWind)
cloud_cover = we.strip_metadata(wsCC)
rel_hum = [const.rel_hum_air] * len(date)
pressure_atm = [const.pressure_atm] * len(date)
observed_ice = gro.get_all_season_ice_on_location(location_name, startDate,
endDate)
ice_cover, energy_balance = it.calculate_ice_cover_eb(
utm33_x,
utm33_y,
date,
temp,
prec,
prec_snow,
cloud_cover=cloud_cover,
wind=wind,
rel_hum=rel_hum,
pressure_atm=pressure_atm,
inn_column=copy.deepcopy(observed_ice[0]))
# Need datetime objects from now on
from_date = dt.datetime.strptime(startDate, "%Y-%m-%d")
to_date = dt.datetime.strptime(endDate, "%Y-%m-%d")
plot_filename = '{0}Semsvann EB {1}-{2}.png'.format(
se.plot_folder, from_date.year, to_date.year)
# pts.plot_ice_cover(ice_cover, observed_ice, date, temp, sno_tot, plot_filename)
plot_filename = '{0}Semsvann MET with EB {1}-{2}.png'.format(
se.plot_folder, from_date.year, to_date.year)
pts.plot_ice_cover_eb(ice_cover,
energy_balance,
observed_ice,
date,
temp,
sno_tot,
plot_filename,
prec=prec,
wind=wind,
clouds=cloud_cover)
def run_otrovann_eb(startDate, endDate):
location_name = 'Otrøvatnet v/Nystuen 971 moh'
wsTemp = gws.getMetData(54710, 'TAM', startDate, endDate, 0, 'list')
wsSno = gws.getMetData(54710, 'SA', startDate, endDate, 0, 'list')
wsPrec = gws.getMetData(54710, 'RR', startDate, endDate, 0, 'list')
utm33_y = 6802070
utm33_x = 130513
temp, date = we.strip_metadata(wsTemp, get_date_times=True)
sno_tot = we.strip_metadata(wsSno)
prec_snow = dp.delta_snow_from_total_snow(sno_tot)
prec = we.strip_metadata(wsPrec)
cloud_cover = dp.clouds_from_precipitation(prec)
wind = [const.avg_wind_const] * len(date)
rel_hum = [const.rel_hum_air] * len(date)
pressure_atm = [const.pressure_atm] * len(date)
# available_elements = gws.getElementsFromTimeserieTypeStation(54710, 0, 'csv')
observed_ice = gro.get_all_season_ice_on_location(location_name, startDate,
endDate)
ice_cover, energy_balance = it.calculate_ice_cover_eb(
utm33_x,
utm33_y,
date,
temp,
prec,
prec_snow,
cloud_cover,
wind,
rel_hum=rel_hum,
pressure_atm=pressure_atm,
inn_column=copy.deepcopy(observed_ice[0]))
# Need datetime objects from now on
from_date = dt.datetime.strptime(startDate, "%Y-%m-%d")
to_date = dt.datetime.strptime(endDate, "%Y-%m-%d")
plot_filename = '{0}Ortovann MET EB {1}-{2}.png'.format(
se.plot_folder, from_date.year, to_date.year)
# pts.plot_ice_cover(ice_cover, observed_ice, date, temp, sno_tot, plot_filename)
plot_filename = '{0}Ortovann MET with EB {1}-{2}.png'.format(
se.plot_folder, from_date.year, to_date.year)
pts.plot_ice_cover_eb(ice_cover,
energy_balance,
observed_ice,
date,
temp,
sno_tot,
plot_filename,
prec=prec,
wind=wind,
clouds=cloud_cover)
def calculate_and_plot9d_regid(regid,
plot_folder=se.plot_folder,
observed_ice=None):
"""For an ice thickness on a given regObs RegID, a plot of will be made of the following 9 days development.
If observed_ice is not given, it is looked up on regObs api by using its RegID. If observation is older
than 11 days, no plot is made. Weather data is from GTS.
1.1 If observed ice is none, get some on this regid
1.2 Else use what is provided, but it has to be a list.
2. Get weather data.
3. Plot file if it is missing or it is newer than 11 days.
:param regid: [Int] RegID as defined in regObs.
:param plot_folder: [string] Path of folder for plots.
:param observed_ice: [ice.IceColumn] Optional. If not given, one will be looked up.
"""
# if no observed ice is given, get it. Also, observed ice in the plotting routine is a list, so make it so.
if not observed_ice:
observed_ice = [gro.get_ice_thickness_on_regid(regid)]
else:
observed_ice = [observed_ice]
x, y = observed_ice[0].metadata['UTMEast'], observed_ice[0].metadata[
'UTMNorth']
from_date = observed_ice[0].date.date()
to_date = from_date + dt.timedelta(days=9)
# Get weather and snow data
gridTemp = gts.getgts(x, y, 'tm', from_date, to_date)
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
temp, date_times = we.strip_metadata(gridTemp, get_date_times=True)
dates = [d.date() for d in date_times]
sno = we.strip_metadata(gridSno)
snotot = we.strip_metadata(gridSnoTot)
cc = dp.clouds_from_precipitation(sno)
# Define file name and tests for modelling and plotting
plot_filename = '{0}{1}.png'.format(plot_folder, regid)
try:
icecover = it.calculate_ice_cover_air_temp(observed_ice[0], date_times,
temp, sno, cc)
pts.plot_ice_cover_9dogn(icecover, observed_ice[0], dates, temp, sno,
snotot, plot_filename)
except:
# raise
error_msg = sys.exc_info()[0]
ml.log_and_print(
"[Error] calculateandplot.py -> calculate_and_plot9d_regid: {}. Could not plot {}."
.format(error_msg, regid))
def calculate_and_plot9d_regid(regid, plot_folder=se.plot_folder, observed_ice=None):
"""For an ice thickness on a given regObs RegID, a plot of will be made of the following 9 days development.
If observed_ice is not given, it is looked up on regObs api by using its RegID. If observation is older
than 11 days, no plot is made. Weather data is from GTS.
1.1 If observed ice is none, get some on this regid
1.2 Else use what is provided, but it has to be a list.
2. Get weather data.
3. Plot file if it is missing or it is newer than 11 days.
:param regid: [Int] RegID as defined in regObs.
:param plot_folder: [string] Path of folder for plots.
:param observed_ice: [ice.IceColumn] Optional. If not given, one will be looked up.
"""
# if no observed ice is given, get it. Also, observed ice in the plotting routine is a list, so make it so.
if not observed_ice:
observed_ice = [gro.get_ice_thickness_on_regid(regid)]
else:
observed_ice = [observed_ice]
x, y = observed_ice[0].metadata['UTMEast'], observed_ice[0].metadata['UTMNorth']
from_date = observed_ice[0].date.date()
to_date = from_date + dt.timedelta(days=9)
# Get weather and snow data
gridTemp = gts.getgts(x, y, 'tm', from_date, to_date)
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
temp, date_times = we.strip_metadata(gridTemp, get_date_times=True)
dates = [d.date() for d in date_times]
sno = we.strip_metadata(gridSno)
snotot = we.strip_metadata(gridSnoTot)
cc = dp.clouds_from_precipitation(sno)
# Define file name and tests for modelling and plotting
plot_filename = '{0}{1}.png'.format(plot_folder, regid)
try:
icecover = it.calculate_ice_cover_air_temp(observed_ice[0], date_times, temp, sno, cc)
pts.plot_ice_cover_9dogn(icecover, observed_ice[0], dates, temp, sno, snotot, plot_filename)
except:
# raise
error_msg = sys.exc_info()[0]
ml.log_and_print("[Error] calculateandplot.py -> calculate_and_plot9d_regid: {}. Could not plot {}.".format(error_msg, regid))
def __test_clouds_from_short_wave():
date_list = [dt.date.today() - dt.timedelta(days=x) for x in range(0, 365)]
date_list = [
dt.datetime.combine(d, dt.datetime.min.time()) for d in date_list
]
date_list.reverse()
# test Nordnesfjellet i Troms
station_id = 91500
short_wave_id = 'QSI'
long_wave_id = 'QLI'
temperature_id = 'TA'
timeseries_type = 2
utm_e = 711075
utm_n = 7727719
short_wave = gws.getMetData(station_id, short_wave_id, date_list[0],
date_list[-1], timeseries_type)
long_wave = gws.getMetData(station_id, long_wave_id, date_list[0],
date_list[-1], timeseries_type)
temperature = gws.getMetData(station_id, temperature_id, date_list[0],
date_list[-1], timeseries_type)
short_wave = we.fix_data_quick(short_wave)
long_wave = we.fix_data_quick(long_wave)
temperature_daily = we.fix_data_quick(temperature)
short_wave_daily = we.make_daily_average(short_wave)
short_wave_daily = we.multiply_constant(
short_wave_daily,
24 * 3600 / 1000) # Wh/m2 * 3600 s/h * kJ/1000J (energy) over 24hrs
long_wave_daily = we.make_daily_average(long_wave)
long_wave_daily = we.multiply_constant(long_wave_daily, 24 * 3600 / 1000)
temperature_daily = we.make_daily_average(temperature)
Short_wave_list = we.strip_metadata(short_wave_daily)
I_clear_sky_list = [
irradiance_clear_sky(utm_e, utm_n, d) for d in date_list
]
Cloud_cover = clouds_from_short_wave(utm_e, utm_n, Short_wave_list,
date_list)
import matplotlib.pyplot as plt
plt.plot(date_list, Short_wave_list)
plt.plot(date_list, I_clear_sky_list)
plt.plot(date_list, Cloud_cover)
plt.ylim(0, 50000)
plt.show()
def run_semsvann_eb(startDate, endDate):
# TODO: get coordinates from the ObsLocation in regObs
location_name = 'Semsvannet v/Lo 145 moh'
wsTemp = gws.getMetData(19710, 'TAM', startDate, endDate, 0, 'list')
wsSno = gws.getMetData(19710, 'SA', startDate, endDate, 0, 'list')
wsPrec = gws.getMetData(19710, 'RR', startDate, endDate, 0, 'list')
wsWind = gws.getMetData(18700, 'FFM', startDate, endDate, 0, 'list')
wsCC = gws.getMetData(18700, 'NNM', startDate, endDate, 0, 'list')
utm33_y = 6644410
utm33_x = 243940
temp, date = we.strip_metadata(wsTemp, get_date_times=True)
sno_tot = we.strip_metadata(wsSno)
prec_snow = dp.delta_snow_from_total_snow(sno_tot)
prec = we.strip_metadata(wsPrec)
wind = we.strip_metadata(wsWind)
cloud_cover = we.strip_metadata(wsCC)
rel_hum = [const.rel_hum_air] * len(date)
pressure_atm = [const.pressure_atm] * len(date)
observed_ice = gro.get_all_season_ice_on_location(location_name, startDate, endDate)
ice_cover, energy_balance = it.calculate_ice_cover_eb(
utm33_x, utm33_y, date,
temp, prec, prec_snow, cloud_cover=cloud_cover, wind=wind, rel_hum=rel_hum, pressure_atm=pressure_atm,
inn_column=copy.deepcopy(observed_ice[0]))
# Need datetime objects from now on
from_date = dt.datetime.strptime(startDate, "%Y-%m-%d")
to_date = dt.datetime.strptime(endDate, "%Y-%m-%d")
plot_filename = '{0}Semsvann EB {1}-{2}.png'.format(se.plot_folder, from_date.year, to_date.year)
# pts.plot_ice_cover(ice_cover, observed_ice, date, temp, sno_tot, plot_filename)
plot_filename = '{0}Semsvann MET with EB {1}-{2}.png'.format(se.plot_folder, from_date.year, to_date.year)
pts.plot_ice_cover_eb(ice_cover, energy_balance, observed_ice, date, temp, sno_tot, plot_filename, prec=prec, wind=wind, clouds=cloud_cover)
def run_otrovann_eb(startDate, endDate):
location_name = 'Otrøvatnet v/Nystuen 971 moh'
wsTemp = gws.getMetData(54710, 'TAM', startDate, endDate, 0, 'list')
wsSno = gws.getMetData(54710, 'SA', startDate, endDate, 0, 'list')
wsPrec = gws.getMetData(54710, 'RR', startDate, endDate, 0, 'list')
utm33_y = 6802070
utm33_x = 130513
temp, date = we.strip_metadata(wsTemp, get_date_times=True)
sno_tot = we.strip_metadata(wsSno)
prec_snow = dp.delta_snow_from_total_snow(sno_tot)
prec = we.strip_metadata(wsPrec)
cloud_cover = dp.clouds_from_precipitation(prec)
wind = [const.avg_wind_const] * len(date)
rel_hum = [const.rel_hum_air] * len(date)
pressure_atm = [const.pressure_atm] * len(date)
# available_elements = gws.getElementsFromTimeserieTypeStation(54710, 0, 'csv')
observed_ice = gro.get_all_season_ice_on_location(location_name, startDate, endDate)
ice_cover, energy_balance = it.calculate_ice_cover_eb(
utm33_x, utm33_y, date, temp, prec, prec_snow, cloud_cover, wind, rel_hum=rel_hum, pressure_atm=pressure_atm,
inn_column=copy.deepcopy(observed_ice[0]))
# Need datetime objects from now on
from_date = dt.datetime.strptime(startDate, "%Y-%m-%d")
to_date = dt.datetime.strptime(endDate, "%Y-%m-%d")
plot_filename = '{0}Ortovann MET EB {1}-{2}.png'.format(se.plot_folder, from_date.year, to_date.year)
# pts.plot_ice_cover(ice_cover, observed_ice, date, temp, sno_tot, plot_filename)
plot_filename = '{0}Ortovann MET with EB {1}-{2}.png'.format(se.plot_folder, from_date.year, to_date.year)
pts.plot_ice_cover_eb(ice_cover, energy_balance, observed_ice, date, temp, sno_tot, plot_filename,
prec=prec, wind=wind, clouds=cloud_cover)
def __test_clouds_from_short_wave():
date_list = [dt.date.today() - dt.timedelta(days=x) for x in range(0, 365)]
date_list = [dt.datetime.combine(d, dt.datetime.min.time()) for d in date_list]
date_list.reverse()
# test Nordnesfjellet i Troms
station_id = 91500
short_wave_id = 'QSI'
long_wave_id = 'QLI'
temperature_id = 'TA'
timeseries_type = 2
utm_e = 711075
utm_n = 7727719
short_wave = gws.getMetData(station_id, short_wave_id, date_list[0], date_list[-1], timeseries_type)
long_wave = gws.getMetData(station_id, long_wave_id, date_list[0], date_list[-1], timeseries_type)
temperature = gws.getMetData(station_id, temperature_id, date_list[0], date_list[-1], timeseries_type)
short_wave = we.fix_data_quick(short_wave)
long_wave = we.fix_data_quick(long_wave)
temperature_daily = we.fix_data_quick(temperature)
short_wave_daily = we.make_daily_average(short_wave)
short_wave_daily = we.multiply_constant(short_wave_daily, 24 * 3600 / 1000) # Wh/m2 * 3600 s/h * kJ/1000J (energy) over 24hrs
long_wave_daily = we.make_daily_average(long_wave)
long_wave_daily = we.multiply_constant(long_wave_daily, 24 * 3600 / 1000)
temperature_daily = we.make_daily_average(temperature)
Short_wave_list = we.strip_metadata(short_wave_daily)
I_clear_sky_list = [irradiance_clear_sky(utm_e, utm_n, d) for d in date_list]
Cloud_cover = clouds_from_short_wave(utm_e, utm_n, Short_wave_list, date_list)
import matplotlib.pyplot as plt
plt.plot(date_list, Short_wave_list)
plt.plot(date_list, I_clear_sky_list)
plt.plot(date_list, Cloud_cover)
plt.ylim(0, 50000)
plt.show()
def calculate_and_plot_location(location_name,
from_date,
to_date,
sub_plot_folder='',
make_plots=True,
return_values=False):
""" due to get_all_season_ice returns data grouped be location_id
For a given LocationName in regObs calculate the ice cover between two dates. Optional, make plots
and/or return the calculations and observations for this location. Different sources for weather data
may be given, chartserver grid is default.
:param location_name:
:param from_date: [String] 'yyyy-mm-dd'
:param to_date: [String] 'yyyy-mm-dd'
:param sub_plot_folder:
:param make_plots:
:param return_values: [bool] If true the calculated and observed data is returned
"""
loc = slp.get_for_location(location_name)
year = '{0}-{1}'.format(from_date[0:4], to_date[2:4])
lake_file_name = '{0} {1}'.format(
fe.make_standard_file_name(loc.file_name), year)
observed_ice = gro.get_observations_on_location_id(loc.regobs_location_id,
year)
# Change dates to datetime. Some of the getdata modules require datetime
from_date = dt.datetime.strptime(from_date, '%Y-%m-%d')
to_date = dt.datetime.strptime(to_date, '%Y-%m-%d')
# special rule for this season.
if year == '2018-19':
from_date = dt.datetime(2017, 10, 15)
# if to_date forward in time, make sure it doesnt go to far..
if to_date > dt.datetime.now():
to_date = dt.datetime.now() + dt.timedelta(days=7)
if loc.weather_data_source == 'eKlima':
wsTemp = gws.getMetData(loc.eklima_TAM, 'TAM', from_date, to_date, 0,
'list')
temp, date = we.strip_metadata(wsTemp, True)
wsSno = gws.getMetData(loc.eklima_SA, 'SA', from_date, to_date, 0,
'list')
snotot = we.strip_metadata(wsSno)
sno = dp.delta_snow_from_total_snow(snotot)
# Clouds. If not from met.no it is parametrised from precipitation.
if loc.eklima_NNM:
wsCC = gws.getMetData(loc.eklima_NNM, 'NNM', from_date, to_date, 0,
'list')
cc = we.strip_metadata(wsCC)
else:
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}{1} eklima.png'.format(
se.plot_folder + sub_plot_folder, lake_file_name)
elif loc.weather_data_source == 'grid':
x, y = loc.utm_east, loc.utm_north
gridTemp = gts.getgts(x, y, 'tm', from_date, to_date)
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
temp, date = we.strip_metadata(gridTemp, get_date_times=True)
sno = we.strip_metadata(gridSno, False)
snotot = we.strip_metadata(gridSnoTot, False)
if loc.eklima_NNM:
wsCC = gws.getMetData(loc.eklima_NNM, 'NNM', from_date, to_date, 0,
'list')
cc = we.strip_metadata(wsCC)
else:
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}{1} grid.png'.format(
se.plot_folder + sub_plot_folder, lake_file_name)
elif loc.weather_data_source == 'nve':
x, y = loc.utm_east, loc.utm_north
# Temp from NVE station or grid if not.
if loc.nve_temp:
temp_obj = gcsd.getStationdata(loc.nve_temp,
'17.1',
from_date,
to_date,
timeseries_type=0)
else:
temp_obj = gcsd.getGriddata(x, y, 'tm', from_date, to_date)
temp, date = we.strip_metadata(temp_obj, get_date_times=True)
# Snow from NVE station or grid if not.
if loc.nve_snow:
snotot_obj = gcsd.getStationdata(loc.nve_snow,
'2002.1',
from_date,
to_date,
timeseries_type=0)
snotot = we.strip_metadata(snotot_obj)
sno = dp.delta_snow_from_total_snow(snotot_obj)
else:
snotot_obj = gcsd.getGriddata(x,
y,
'sd',
from_date,
to_date,
timeseries_type=0)
sno_obj = gcsd.getGriddata(x,
y,
'fsw',
from_date,
to_date,
timeseries_type=0)
snotot = we.strip_metadata(snotot_obj)
sno = we.strip_metadata(sno_obj)
# Clouds. If not from met.no it is parametrised from precipitation.
if loc.eklima_NNM:
cc_obj = gws.getMetData(18700, 'NNM', from_date, to_date, 0,
'list')
else:
cc_obj = dp.clouds_from_precipitation(sno)
cc = we.strip_metadata(cc_obj)
plot_filename = '{0}{1} nve.png'.format(
se.plot_folder + sub_plot_folder, lake_file_name)
elif loc.weather_data_source == 'file':
date, temp, sno, snotot = gfd.read_weather(from_date, to_date,
loc.input_file)
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}{1} file.png'.format(
se.plot_folder + sub_plot_folder, lake_file_name)
else:
ml.log_and_print(
"[Error] runicethickness -> calculate_and_plot_location: Invalid scource for weather data."
)
return
try:
if len(observed_ice) == 0:
calculated_ice = it.calculate_ice_cover_air_temp(
ice.IceColumn(date[0], []), date, temp, sno, cc)
else:
calculated_ice = it.calculate_ice_cover_air_temp(
copy.deepcopy(observed_ice[0]), date, temp, sno, cc)
if make_plots:
pts.plot_ice_cover(calculated_ice, observed_ice, date, temp, sno,
snotot, plot_filename)
except:
error_msg = sys.exc_info()[0]
ml.log_and_print(
"[Error] calculateandplot.py -> calculate_and_plot_location: {}. Could not plot {}."
.format(error_msg, location_name))
calculated_ice = None
if return_values:
return calculated_ice, observed_ice
def _plot_season(location_id,
from_date,
to_date,
observed_ice,
make_plots=True,
plot_folder=se.plot_folder):
"""Given a location id, a time period and some observations on this location id and this method
calculates and optionally plots the ice evolution that season. Weather data from GTS.
It is a sub method of plot_season_for_location_id and plot_season_for_all_regobs_locations.
:param location_id:
:param from_date:
:param to_date:
:param observed_ice:
:param make_plots:
:param plot_folder: [string] Path of folder for plots.
:return calculated_ice, observed_ice: [list of Ice.IceColumn] observed_ice is returned as given inn.
TODO: should accept observerd_ice=None and then query for the observations. If still missing, set icecover on to start date.
"""
year = '{0}-{1}'.format(from_date[0:4], to_date[2:4])
# Change dates to datetime. Some of the get data modules require datetime
from_date = dt.datetime.strptime(from_date, '%Y-%m-%d')
to_date = dt.datetime.strptime(to_date, '%Y-%m-%d')
# special rule for this season.
if year == '2018-19':
from_date = dt.datetime(2018, 9, 1)
# if to_date forward in time, make sure it doesnt go to far..
if to_date > dt.datetime.now():
to_date = dt.datetime.now() + dt.timedelta(days=7)
x, y = observed_ice[0].metadata['UTMEast'], observed_ice[0].metadata[
'UTMNorth']
# get weather data
gridTemp = gts.getgts(x, y, 'tm', from_date, to_date)
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
# adjust grid temperature (at grid elevation) to lake elevation.
lake_altitude = gm.get_masl_from_utm33(x, y)
gridTempNewElevation = we.adjust_temperature_to_new_altitude(
gridTemp, lake_altitude)
# strip metadata
temp, date = we.strip_metadata(gridTempNewElevation, get_date_times=True)
sno = we.strip_metadata(gridSno, False)
snotot = we.strip_metadata(gridSnoTot, False)
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}_{1}.png'.format(location_id, year)
plot_path_and_filename = '{0}{1}'.format(plot_folder, plot_filename)
try:
if len(observed_ice) == 0:
calculated_ice = it.calculate_ice_cover_air_temp(
ice.IceColumn(date[0], []), date, temp, sno, cc)
else:
calculated_ice = it.calculate_ice_cover_air_temp(
copy.deepcopy(observed_ice[0]), date, temp, sno, cc)
if make_plots:
pts.plot_ice_cover(calculated_ice, observed_ice, date, temp, sno,
snotot, plot_path_and_filename)
except:
# raise
error_msg = sys.exc_info()[0]
ml.log_and_print(
"[Error] calculateandplot.py -> _plot_season: {}. Could not plot {}."
.format(error_msg, location_id))
calculated_ice = None
return calculated_ice, observed_ice, plot_filename
def calculate_and_plot_location(location_name, from_date, to_date, sub_plot_folder='', make_plots=True, return_values=False):
""" due to get_all_season_ice returns data grouped be location_id
For a given LocationName in regObs calculate the ice cover between two dates. Optional, make plots
and/or return the calculations and observations for this location. Different sources for weather data
may be given, chartserver grid is default.
:param location_name:
:param from_date: [String] 'yyyy-mm-dd'
:param to_date: [String] 'yyyy-mm-dd'
:param sub_plot_folder:
:param make_plots:
:param return_values: [bool] If true the calculated and observed data is returned
"""
loc = slp.get_for_location(location_name)
year = '{0}-{1}'.format(from_date[0:4], to_date[2:4])
lake_file_name = '{0} {1}'.format(fe.make_standard_file_name(loc.file_name), year)
observed_ice = gro.get_observations_on_location_id(loc.regobs_location_id, year)
# Change dates to datetime. Some of the getdata modules require datetime
from_date = dt.datetime.strptime(from_date, '%Y-%m-%d')
to_date = dt.datetime.strptime(to_date, '%Y-%m-%d')
# special rule for this season.
if year == '2018-19':
from_date = dt.datetime(2017, 10, 15)
# if to_date forward in time, make sure it doesnt go to far..
if to_date > dt.datetime.now():
to_date = dt.datetime.now() + dt.timedelta(days=7)
if loc.weather_data_source == 'eKlima':
wsTemp = gws.getMetData(loc.eklima_TAM, 'TAM', from_date, to_date, 0, 'list')
temp, date = we.strip_metadata(wsTemp, True)
wsSno = gws.getMetData(loc.eklima_SA, 'SA', from_date, to_date, 0, 'list')
snotot = we.strip_metadata(wsSno)
sno = dp.delta_snow_from_total_snow(snotot)
# Clouds. If not from met.no it is parametrised from precipitation.
if loc.eklima_NNM:
wsCC = gws.getMetData(loc.eklima_NNM, 'NNM', from_date, to_date, 0, 'list')
cc = we.strip_metadata(wsCC)
else:
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}{1} eklima.png'.format(se.plot_folder + sub_plot_folder, lake_file_name)
elif loc.weather_data_source == 'grid':
x, y = loc.utm_east, loc.utm_north
gridTemp = gts.getgts(x, y, 'tm', from_date, to_date)
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
temp, date = we.strip_metadata(gridTemp, get_date_times=True)
sno = we.strip_metadata(gridSno, False)
snotot = we.strip_metadata(gridSnoTot, False)
if loc.eklima_NNM:
wsCC = gws.getMetData(loc.eklima_NNM, 'NNM', from_date, to_date, 0, 'list')
cc = we.strip_metadata(wsCC)
else:
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}{1} grid.png'.format(se.plot_folder + sub_plot_folder, lake_file_name)
elif loc.weather_data_source == 'nve':
x, y = loc.utm_east, loc.utm_north
# Temp from NVE station or grid if not.
if loc.nve_temp:
temp_obj = gcsd.getStationdata(loc.nve_temp, '17.1', from_date, to_date, timeseries_type=0)
else:
temp_obj = gcsd.getGriddata(x, y, 'tm', from_date, to_date)
temp, date = we.strip_metadata(temp_obj, get_date_times=True)
# Snow from NVE station or grid if not.
if loc.nve_snow:
snotot_obj = gcsd.getStationdata(loc.nve_snow, '2002.1', from_date, to_date, timeseries_type=0)
snotot = we.strip_metadata(snotot_obj)
sno = dp.delta_snow_from_total_snow(snotot_obj)
else:
snotot_obj = gcsd.getGriddata(x, y, 'sd', from_date, to_date, timeseries_type=0)
sno_obj = gcsd.getGriddata(x, y, 'fsw', from_date, to_date, timeseries_type=0)
snotot = we.strip_metadata(snotot_obj)
sno = we.strip_metadata(sno_obj)
# Clouds. If not from met.no it is parametrised from precipitation.
if loc.eklima_NNM:
cc_obj = gws.getMetData(18700, 'NNM', from_date, to_date, 0, 'list')
else:
cc_obj = dp.clouds_from_precipitation(sno)
cc = we.strip_metadata(cc_obj)
plot_filename = '{0}{1} nve.png'.format(se.plot_folder + sub_plot_folder, lake_file_name)
elif loc.weather_data_source == 'file':
date, temp, sno, snotot = gfd.read_weather(from_date, to_date, loc.input_file)
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}{1} file.png'.format(se.plot_folder + sub_plot_folder, lake_file_name)
else:
ml.log_and_print("[Error] runicethickness -> calculate_and_plot_location: Invalid scource for weather data.")
return
try:
if len(observed_ice) == 0:
calculated_ice = it.calculate_ice_cover_air_temp(ice.IceColumn(date[0], []), date, temp, sno, cc)
else:
calculated_ice = it.calculate_ice_cover_air_temp(copy.deepcopy(observed_ice[0]), date, temp, sno, cc)
if make_plots:
pts.plot_ice_cover(calculated_ice, observed_ice, date, temp, sno, snotot, plot_filename)
except:
error_msg = sys.exc_info()[0]
ml.log_and_print("[Error] calculateandplot.py -> calculate_and_plot_location: {}. Could not plot {}.".format(error_msg, location_name))
calculated_ice = None
if return_values:
return calculated_ice, observed_ice
def _plot_season(location_id, from_date, to_date, observed_ice, make_plots=True, plot_folder=se.plot_folder):
"""Given a location id, a time period and some observations on this location id and this method
calculates and optionally plots the ice evolution that season. Weather data from GTS.
It is a sub method of plot_season_for_location_id and plot_season_for_all_regobs_locations.
:param location_id:
:param from_date:
:param to_date:
:param observed_ice:
:param make_plots:
:param plot_folder: [string] Path of folder for plots.
:return calculated_ice, observed_ice: [list of Ice.IceColumn] observed_ice is returned as given inn.
TODO: should accept observerd_ice=None and then query for the observations. If still missing, set icecover on to start date.
"""
year = '{0}-{1}'.format(from_date[0:4], to_date[2:4])
# Change dates to datetime. Some of the get data modules require datetime
from_date = dt.datetime.strptime(from_date, '%Y-%m-%d')
to_date = dt.datetime.strptime(to_date, '%Y-%m-%d')
# special rule for this season.
if year == '2018-19':
from_date = dt.datetime(2018, 9, 1)
# if to_date forward in time, make sure it doesnt go to far..
if to_date > dt.datetime.now():
to_date = dt.datetime.now() + dt.timedelta(days=7)
x, y = observed_ice[0].metadata['UTMEast'], observed_ice[0].metadata['UTMNorth']
# get weather data
gridTemp = gts.getgts(x, y, 'tm', from_date, to_date)
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
# adjust grid temperature (at grid elevation) to lake elevation.
lake_altitude = gm.get_masl_from_utm33(x, y)
gridTempNewElevation = we.adjust_temperature_to_new_altitude(gridTemp, lake_altitude)
# strip metadata
temp, date = we.strip_metadata(gridTempNewElevation, get_date_times=True)
sno = we.strip_metadata(gridSno, False)
snotot = we.strip_metadata(gridSnoTot, False)
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}_{1}.png'.format(location_id, year)
plot_path_and_filename = '{0}{1}'.format(plot_folder, plot_filename)
try:
if len(observed_ice) == 0:
calculated_ice = it.calculate_ice_cover_air_temp(ice.IceColumn(date[0], []), date, temp, sno, cc)
else:
calculated_ice = it.calculate_ice_cover_air_temp(copy.deepcopy(observed_ice[0]), date, temp, sno, cc)
if make_plots:
pts.plot_ice_cover(calculated_ice, observed_ice, date, temp, sno, snotot, plot_path_and_filename)
except:
# raise
error_msg = sys.exc_info()[0]
ml.log_and_print("[Error] calculateandplot.py -> _plot_season: {}. Could not plot {}.".format(error_msg, location_id))
calculated_ice = None
return calculated_ice, observed_ice, plot_filename
def run_mosselva(from_date,
to_date,
make_plots=True,
plot_folder=se.plot_folder,
forcing='grid'):
"""
:param from_date:
:param to_date:
:param make_plots:
:param plot_folder:
:return:
"""
location_name = 'Mosselva'
y = 6595744
x = 255853
altitude = 25
met_stnr = 17150 # Rygge målestasjon (met.no)
first_ice = ice.IceColumn(dt.datetime(int(from_date[0:4]), 12, 31), [])
first_ice.add_metadata('LocationName', location_name) # used when plotting
observed_ice = [first_ice]
year = '{0}-{1}'.format(from_date[0:4], to_date[2:4])
# Change dates to datetime. Some of the getdata modules require datetime
from_date = dt.datetime.strptime(from_date, '%Y-%m-%d')
to_date = dt.datetime.strptime(to_date, '%Y-%m-%d')
# if to_date forward in time, make sure it doesnt go to far..
if to_date > dt.datetime.now():
to_date = dt.datetime.now() + dt.timedelta(days=7)
if forcing == 'eKlima':
wsTemp = gws.getMetData(met_stnr, 'TAM', from_date, to_date, 0, 'list')
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
temp, date = we.strip_metadata(wsTemp, get_date_times=True)
sno = we.strip_metadata(gridSno)
sno_tot = we.strip_metadata(gridSnoTot)
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}_{1}_eklima.png'.format(location_name, year)
elif forcing == 'grid':
gridTemp = gts.getgts(x, y, 'tm', from_date, to_date)
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
gridTempNewElevation = we.adjust_temperature_to_new_altitude(
gridTemp, altitude)
temp, date = we.strip_metadata(gridTempNewElevation,
get_date_times=True)
sno = we.strip_metadata(gridSno)
sno_tot = we.strip_metadata(gridSnoTot)
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}_{1}_grid.png'.format(location_name, year)
else:
temp, date = None, None
sno = None
sno_tot = None
cc = None
plot_filename = '{0}_{1}_no_forcing.png'.format(location_name, year)
calculated_ice = it.calculate_ice_cover_air_temp(copy.deepcopy(first_ice),
date,
temp,
sno,
cloud_cover=cc)
if make_plots:
plot_path_and_filename = '{0}{1}'.format(plot_folder, plot_filename)
pts.plot_ice_cover(calculated_ice, observed_ice, date, temp, sno,
sno_tot, plot_path_and_filename)
def run_semsvann(from_date,
to_date,
make_plots=True,
plot_folder=se.plot_folder,
forcing='grid'):
"""
:param from_date:
:param to_date:
:param make_plots:
:param plot_folder:
:return:
"""
location_name = 'Semsvann'
regobs_location_id = 2227
x = 243655
y = 6644286
altitude = 145
met_stnr = 19710 # Asker (Sem)
met_stnr_NNM = 18700 # Blindern
year = '{0}-{1}'.format(from_date[0:4], to_date[2:4])
observed_ice = gro.get_observations_on_location_id(regobs_location_id,
year)
first_ice = observed_ice[0]
# Change dates to datetime. Some of the getdata modules require datetime
from_date = dt.datetime.strptime(from_date, '%Y-%m-%d')
to_date = dt.datetime.strptime(to_date, '%Y-%m-%d')
# if to_date forward in time, make sure it doesnt go to far..
if to_date > dt.datetime.now():
to_date = dt.datetime.now() + dt.timedelta(days=7)
if forcing == 'eKlima':
wsTemp = gws.getMetData(met_stnr, 'TAM', from_date, to_date, 0, 'list')
wsSnoTot = gws.getMetData(met_stnr, 'SA', from_date, to_date, 0,
'list')
wsCC = gws.getMetData(met_stnr_NNM, 'NNM', from_date, to_date, 0,
'list')
temp, date = we.strip_metadata(wsTemp, get_date_times=True)
sno_tot = we.strip_metadata(wsSnoTot)
sno = dp.delta_snow_from_total_snow(sno_tot)
cc = we.strip_metadata(wsCC)
plot_filename = '{0}_{1}_eklima.png'.format(location_name, year)
elif forcing == 'grid':
gridTemp = gts.getgts(x, y, 'tm', from_date, to_date)
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
# Grid altitude and lake at same elevations.
gridTempNewElevation = we.adjust_temperature_to_new_altitude(
gridTemp, altitude)
temp, date = we.strip_metadata(gridTempNewElevation,
get_date_times=True)
sno = we.strip_metadata(gridSno)
sno_tot = we.strip_metadata(gridSnoTot)
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}_{1}_grid.png'.format(location_name, year)
else:
temp, date = None, None
sno = None
sno_tot = None
cc = None
plot_filename = '{0}_{1}_no_forcing.png'.format(location_name, year)
calculated_ice = it.calculate_ice_cover_air_temp(copy.deepcopy(first_ice),
date,
temp,
sno,
cloud_cover=cc)
if make_plots:
plot_path_and_filename = '{0}{1}'.format(plot_folder, plot_filename)
pts.plot_ice_cover(calculated_ice, observed_ice, date, temp, sno,
sno_tot, plot_path_and_filename)
def run_semsvann(from_date, to_date, make_plots=True, plot_folder=se.plot_folder, forcing='grid'):
"""
:param from_date:
:param to_date:
:param make_plots:
:param plot_folder:
:return:
"""
location_name = 'Semsvann'
regobs_location_id = 2227
x = 243655
y = 6644286
altitude = 145
met_stnr = 19710 # Asker (Sem)
met_stnr_NNM = 18700 # Blindern
year = '{0}-{1}'.format(from_date[0:4], to_date[2:4])
observed_ice = gro.get_observations_on_location_id(regobs_location_id, year)
first_ice = observed_ice[0]
# Change dates to datetime. Some of the getdata modules require datetime
from_date = dt.datetime.strptime(from_date, '%Y-%m-%d')
to_date = dt.datetime.strptime(to_date, '%Y-%m-%d')
# if to_date forward in time, make sure it doesnt go to far..
if to_date > dt.datetime.now():
to_date = dt.datetime.now() + dt.timedelta(days=7)
if forcing == 'eKlima':
wsTemp = gws.getMetData(met_stnr, 'TAM', from_date, to_date, 0, 'list')
wsSnoTot = gws.getMetData(met_stnr, 'SA', from_date, to_date, 0, 'list')
wsCC = gws.getMetData(met_stnr_NNM, 'NNM', from_date, to_date, 0, 'list')
temp, date = we.strip_metadata(wsTemp, get_date_times=True)
sno_tot = we.strip_metadata(wsSnoTot)
sno = dp.delta_snow_from_total_snow(sno_tot)
cc = we.strip_metadata(wsCC)
plot_filename = '{0}_{1}_eklima.png'.format(location_name, year)
elif forcing == 'grid':
gridTemp = gts.getgts(x, y, 'tm', from_date, to_date)
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
# Grid altitude and lake at same elevations.
gridTempNewElevation = we.adjust_temperature_to_new_altitude(gridTemp, altitude)
temp, date = we.strip_metadata(gridTempNewElevation, get_date_times=True)
sno = we.strip_metadata(gridSno)
sno_tot = we.strip_metadata(gridSnoTot)
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}_{1}_grid.png'.format(location_name, year)
else:
temp, date = None, None
sno = None
sno_tot = None
cc = None
plot_filename = '{0}_{1}_no_forcing.png'.format(location_name, year)
calculated_ice = it.calculate_ice_cover_air_temp(copy.deepcopy(first_ice), date, temp, sno, cloud_cover=cc)
if make_plots:
plot_path_and_filename = '{0}{1}'.format(plot_folder, plot_filename)
pts.plot_ice_cover(calculated_ice, observed_ice, date, temp, sno, sno_tot, plot_path_and_filename)
def calculate_reference_lakes(calculation_date=dt.datetime.now(), make_plots=True, get_new_obs=True):
"""Plot ice thickness for this season for all reference lakes. Create a json-file with information about
changes in the ice thickness last week and expected changes next week.
# Always plot
:param make_plots: [bool] If true plots of reference lakes are made.
:param calculation_date: [datetime] Defines the day the plots are made for. Datetime since the ice model uses this.
:param get_new_obs: [bool] If true, new observations ar requested from Regobs and added to local storage.
"""
# Filename including path for input json file with reference lake information
input_json_file = se.reference_lakes_input_json_file
# Folder for output json file with ice changes
output_json_folder = se.reference_lakes_output_json_folder
# Folder to place the plots
plot_folder = se.reference_lakes_plot_folder
# Filename for adjusted position for locations outside the GTS-grid
adjusted_location_json_file = se.reference_lakes_adjusted_location
# Calculate season text. Set 1. september as start of season
fyear = calculation_date.year
month = calculation_date.month
if month < 9:
fyear = fyear - 1
season = str(fyear) + '-' + str(fyear+1)[2:]
# Calculate start of plot, use 30. september
start_season_date = dt.datetime(fyear, 9, 30)
# Use 10 days before present date for the final plot
start_plot_date = calculation_date - dt.timedelta(days=11)
# Get new observations from regobs. This updates the pickle in local storage so we may only get data once.
gro.get_all_season_ice(season, get_new=get_new_obs)
# Create json filename, Prognoserte_endringer_date. Save one with todays date, and one copy as overwrite 'latest'
output_folder = output_json_folder
if output_folder[-1:] != '/':
output_folder += '/'
output_filename = output_folder + f'Endringer_istykkelse-{calculation_date.year}-{calculation_date.month:02}-{calculation_date.day:02}.json'
output_latest_filename_core = 'Endringer_istykkelse-latest.json'
output_latest_filename = output_folder + output_latest_filename_core
# Make sure plot_folder ends with '/'
if plot_folder[-1:] != '/':
plot_folder += '/'
with open(adjusted_location_json_file, encoding='utf-8-sig') as adjusted_location_json:
adjusted_location_data = json.load(adjusted_location_json)
# Read json-file
reference_lakes_jsonfile = input_json_file
with open(reference_lakes_jsonfile, encoding='utf-8-sig') as reference_lakes_json:
reference_lakes_data = json.load(reference_lakes_json)
# Store plot file names for later ftp
lastregion = ''
ftp_files = []
for fcr in reference_lakes_data['forecastRegions']:
# Message
if fcr['name'] != lastregion:
lastregion = fcr['name']
print("--- ", lastregion, " ---")
for ls in fcr['lakeSize']:
for lh in ls['lakeHeight']:
ref_lake = lh['reference_lake']
if ref_lake['locationId'] != '':
# Create filename 'locationid_season_forecastregionindex_lakesizeindex_lakeheightindex.png'
plot_filename = fcr['index'] + '_' + ls['index'] + '_' + lh['index'] + '_' + season + '_' \
+ ref_lake['locationId'] + '.png'
ftp_files.append(plot_filename)
# Calculate ice growth for this location
# Use grid data
location_name = ref_lake['name']
regobs_location_id_text = ref_lake['locationId']
regobs_location_id = int(ref_lake['locationId'])
# Should we adjust the position for this location to match the GTS weather grid?
found_in_list = False
for station in adjusted_location_data:
if station['locationID'] == regobs_location_id_text:
found_in_list = True
adjusted33x = int(station['valid33x'])
adjusted33y = int(station['valid33y'])
break
if found_in_list:
# Use adjusted values
x = adjusted33x
y = adjusted33y
print('x,y', x, y)
else:
x = int(ref_lake['utm33x'])
y = int(ref_lake['utm33y'])
altitude = int(ref_lake['height'])
# Message
print(str(regobs_location_id) + ' - ' + location_name)
# Get start and end date for simulation
first_possible_date = dt.datetime(fyear,9,1)
if ref_lake['FreezeUpThisYear'] != '':
freezeup = ref_lake['FreezeUpThisYear']
freezup_dt = dt.datetime(int(freezeup[6:]), int(freezeup[3:5]), int(freezeup[0:2]))
if freezup_dt < first_possible_date:
# Ignore this date, probably a left over from last season
freezeup = ref_lake['FreezeUpNormal']
else:
freezeup = ref_lake['FreezeUpNormal']
# Adjust to correct start year and get from_date in datetime
if int(freezeup[3:5]) < 9:
# Passed new year
from_date = dt.datetime(fyear + 1, int(freezeup[3:5]), int(freezeup[0:2]))
else:
from_date = dt.datetime(fyear, int(freezeup[3:5]), int(freezeup[0:2]))
to_date = calculation_date + dt.timedelta(days=9)
# Get regobs-observations, if any
observed_ice = gro.get_observations_on_location_id(regobs_location_id, season)
if len(observed_ice) > 0:
first_ice = observed_ice[0]
first_ice.ignore_slush_event_variable = False
first_ice.slush_event = False
else:
first_ice = ice.IceColumn(from_date, [])
first_ice.add_metadata('LocationName', location_name) # used when plotting
observed_ice.append(first_ice)
# Set from_date equal to when the calculations should start
from_date = start_season_date
gridTemp = gts.getgts(x, y, 'tm', from_date, to_date)
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
# Grid altitude and lake at same elevations.
gridTempNewElevation = we.adjust_temperature_to_new_altitude(gridTemp, altitude)
temp, date = we.strip_metadata(gridTempNewElevation, get_date_times=True)
sno = we.strip_metadata(gridSno)
sno_tot = we.strip_metadata(gridSnoTot)
cc = dp.clouds_from_precipitation(sno)
calculated_ice = it.calculate_ice_cover_air_temp(copy.deepcopy(first_ice), date, temp, sno, cloud_cover=cc)
# Create output json
wanted_output_dates = []
# 7 days ago at 00:00
wdate = calculation_date - dt.timedelta(days=7)
wdate = dt.datetime(wdate.year, wdate.month, wdate.day)
wanted_output_dates.append(wdate)
# Today, at 00:00
wdate = dt.datetime(calculation_date.year, calculation_date.month, calculation_date.day)
wanted_output_dates.append(wdate)
# + 7 days, at 00:00
wdate = calculation_date + dt.timedelta(days=7)
wdate = dt.datetime(wdate.year, wdate.month, wdate.day)
wanted_output_dates.append(wdate)
# Get Ice columns at these dates
wanted_snow_thickness = []
wanted_slush_thickness = []
wanted_ice_thickness = []
wanted_thickest_icelayer = []
for wanted_date in wanted_output_dates:
# Find calculated ice at this date, if any
# The method allows to merge slush_ice, black_ice and unknown into one layer
# Unknown and slush_ice counts as half the ice thickness to simulate black ice strength
found = False
tot_snow = 0
tot_slush = 0
tot_ice = 0
thickest_ice_layer = 0
for ci in calculated_ice:
if ci.date == wanted_date:
# Find snow and slush
for il in ci.column:
if il.type == 'new snow' or il.type == 'snow' or il.type == 'drained_snow':
tot_snow += il.height
elif il.type == 'slush':
tot_slush += il.height
# Total thickness of pure ice layers avoid type = 5 (water intermediate)
merged_ice_thickness = 0
for il in ci.column:
if il.type == 'slush_ice' or il.type == 'black_ice' or il.type == 'unknown':
tot_ice += il.height
if il.type == 'black_ice':
merged_ice_thickness = il.height + merged_ice_thickness
else:
# Weaker ice than black ice. Use half the height
merged_ice_thickness = il.height * 0.5 + merged_ice_thickness
if merged_ice_thickness > thickest_ice_layer:
thickest_ice_layer = merged_ice_thickness
else:
# Not an ice layer
merged_ice_thickness = 0
found = True
break
elif ci.date > wanted_date:
# No ice at this date
break
# Add the ice information to the array
wanted_snow_thickness.append(tot_snow)
wanted_slush_thickness.append(tot_slush)
wanted_ice_thickness.append(tot_ice)
wanted_thickest_icelayer.append(thickest_ice_layer)
# Add todays date
today_date = f'{calculation_date.day:02}.{calculation_date.month:02}.{calculation_date.year}'
ref_lake['Updated'] = today_date
# Save present status
ref_lake['SnowThickness'] = f'{wanted_snow_thickness[1]:.2f}'
ref_lake['SlushThickness'] = f'{wanted_slush_thickness[1]:.2f}'
ref_lake['TotalPureIceThickness'] = f'{wanted_ice_thickness[1]:.2f}'
ref_lake['ThickestIceLayer'] = f'{wanted_thickest_icelayer[1]:.2f}'
# Save status 7 days ago
ref_lake['SnowThicknessLast7days'] = f'{wanted_snow_thickness[0]:.2f}'
ref_lake['SlushThicknessLast7days'] = f'{wanted_slush_thickness[0]:.2f}'
ref_lake['TotalPureIceThicknessLast7days'] = f'{wanted_ice_thickness[0]:.2f}'
ref_lake['ThickestIceLayerLast7days'] = f'{wanted_thickest_icelayer[0]:.2f}'
# Save status 7 days ahead
ref_lake['SnowThicknessNext7days'] = f'{wanted_snow_thickness[2]:.2f}'
ref_lake['SlushThicknessNext7days'] = f'{wanted_slush_thickness[2]:.2f}'
ref_lake['TotalPureIceThicknessNext7days'] = f'{wanted_ice_thickness[2]:.2f}'
ref_lake['ThickestIceLayerNext7days'] = f'{wanted_thickest_icelayer[2]:.2f}'
# Calculate differences and put in the dictionary
tdiff = wanted_snow_thickness[1] - wanted_snow_thickness[0]
ref_lake['SnowThicknessChangeLast7days'] = f'{tdiff:.2f}'
tdiff = wanted_slush_thickness[1] - wanted_slush_thickness[0]
ref_lake['SlushThicknessChangeLast7days'] = f'{tdiff:.2f}'
tdiff = wanted_ice_thickness[1] - wanted_ice_thickness[0]
ref_lake['TotalPureIceThicknessChangeLast7days'] = f'{tdiff:.2f}'
tdiff = wanted_thickest_icelayer[1] - wanted_thickest_icelayer[0]
ref_lake['ThickestIceLayerChangeLast7days'] = f'{tdiff:.2f}'
tdiff = wanted_snow_thickness[2] - wanted_snow_thickness[1]
ref_lake['SnowThicknessChangeNext7days'] = f'{tdiff:.2f}'
tdiff = wanted_slush_thickness[2] - wanted_slush_thickness[1]
ref_lake['SlushThicknessChangeNext7days'] = f'{tdiff:.2f}'
tdiff = wanted_ice_thickness[2] - wanted_ice_thickness[1]
ref_lake['TotalPureIceThicknessChangeNext7days'] = f'{tdiff:.2f}'
tdiff = wanted_thickest_icelayer[2] - wanted_thickest_icelayer[1]
ref_lake['ThickestIceLayerChangeNext7days'] = f'{tdiff:.2f}'
if make_plots:
# First remove dates before wanted plot date
newdate = []
newtemp = []
newsno = []
newsno_tot = []
for i in range(len(date)):
if date[i] >= start_plot_date:
newdate.append(date[i])
newtemp.append(temp[i])
newsno.append(sno[i])
newsno_tot.append(sno_tot[i])
plot_path_and_filename = '{0}{1}'.format(plot_folder, plot_filename)
# pts.plot_ice_cover(calculated_ice, observed_ice, newdate, temp, sno, sno_tot, plot_path_and_filename)
pts.plot_reference_lake(calculated_ice, observed_ice, newdate, newtemp, newsno, newsno_tot, plot_path_and_filename)
# Add todays date
today_date = f'{calculation_date.day:02}.{calculation_date.month:02}.{calculation_date.year}'
reference_lakes_data['icemodelRunDate'] = today_date
# Create pretty string json
reference_lakes_output_json = json.dumps(reference_lakes_data, ensure_ascii=False, indent=2)
with open(output_filename, 'w', encoding='utf-8-sig') as outfile:
outfile.write(reference_lakes_output_json)
outfile.close()
# Save revised json-files
with open(output_latest_filename, 'w', encoding='utf-8-sig') as outfile_latest:
outfile_latest.write(reference_lakes_output_json)
outfile_latest.close()
return
def run_mosselva(from_date, to_date, make_plots=True, plot_folder=se.plot_folder, forcing='grid'):
"""
:param from_date:
:param to_date:
:param make_plots:
:param plot_folder:
:return:
"""
location_name = 'Mosselva'
y = 6595744
x = 255853
altitude = 25
met_stnr = 17150 # Rygge målestasjon (met.no)
first_ice = ice.IceColumn(dt.datetime(int(from_date[0:4]), 12, 31), [])
first_ice.add_metadata('LocationName', location_name) # used when plotting
observed_ice = [first_ice]
year = '{0}-{1}'.format(from_date[0:4], to_date[2:4])
# Change dates to datetime. Some of the getdata modules require datetime
from_date = dt.datetime.strptime(from_date, '%Y-%m-%d')
to_date = dt.datetime.strptime(to_date, '%Y-%m-%d')
# if to_date forward in time, make sure it doesnt go to far..
if to_date > dt.datetime.now():
to_date = dt.datetime.now() + dt.timedelta(days=7)
if forcing == 'eKlima':
wsTemp = gws.getMetData(met_stnr, 'TAM', from_date, to_date, 0, 'list')
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
temp, date = we.strip_metadata(wsTemp, get_date_times=True)
sno = we.strip_metadata(gridSno)
sno_tot = we.strip_metadata(gridSnoTot)
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}_{1}_eklima.png'.format(location_name, year)
elif forcing == 'grid':
gridTemp = gts.getgts(x, y, 'tm', from_date, to_date)
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
gridTempNewElevation = we.adjust_temperature_to_new_altitude(gridTemp, altitude)
temp, date = we.strip_metadata(gridTempNewElevation, get_date_times=True)
sno = we.strip_metadata(gridSno)
sno_tot = we.strip_metadata(gridSnoTot)
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}_{1}_grid.png'.format(location_name, year)
else:
temp, date = None, None
sno = None
sno_tot = None
cc = None
plot_filename = '{0}_{1}_no_forcing.png'.format(location_name, year)
calculated_ice = it.calculate_ice_cover_air_temp(copy.deepcopy(first_ice), date, temp, sno, cloud_cover=cc)
if make_plots:
plot_path_and_filename = '{0}{1}'.format(plot_folder, plot_filename)
pts.plot_ice_cover(calculated_ice, observed_ice, date, temp, sno, sno_tot, plot_path_and_filename)