def runOrovannEB(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_dates=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(location_name, startDate, endDate)
ice_cover, energy_balance = 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(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(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 cc_gamma_prec_and_temp_change(*args):
if len(args) == 2:
ccPrec = pz.clouds_from_precipitation(args[0], method='Binary')
ccPrec = cc_gamma_smoothing(ccPrec)
ccTemp = cc_gamma_temp(args[1])
elif len(args) == 4:
ccPrec = pz.clouds_from_precipitation(args[0], method='Binary')
ccPrec = cc_gamma_smoothing(ccPrec, args[2])
ccTemp = cc_gamma_temp(args[1], args[3])
else:
print("Wrong input. Method takes 2 or 4 arguments.")
cc = []
for i in range(0, len(ccPrec), 1):
cc.append(ccPrec[i] + ccTemp[i])
if cc[i] > 1.:
cc[i] = 1.
return cc
def cc_gamma_prec_and_temp_change(*args):
if len(args) == 2:
ccPrec = pz.clouds_from_precipitation(args[0], method='Binary')
ccPrec = cc_gamma_smoothing(ccPrec)
ccTemp = cc_gamma_temp(args[1])
elif len(args) == 4:
ccPrec = pz.clouds_from_precipitation(args[0], method='Binary')
ccPrec = cc_gamma_smoothing(ccPrec, args[2])
ccTemp = cc_gamma_temp(args[1], args[3])
else:
print("Wrong resources. Method takes 2 or 4 arguments.")
cc = []
for i in range(0, len(ccPrec), 1):
cc.append(ccPrec[i] + ccTemp[i])
if cc[i] > 1.:
cc[i] = 1.
return cc
def runOrovannEB(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_dates=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(location_name, startDate, endDate)
ice_cover, energy_balance = 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(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(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 energy_balance_from_temp_sfc(
utm33_x, utm33_y, ice_column, temp_atm, prec, prec_snow, albedo_prim, time_span_in_sec,
temp_surface, age_factor_tau=None, cloud_cover=None, wind=None, rel_hum=None, pressure_atm=None):
"""Given surface temperature, the daily energy budget of a column of snow is expressed as:
EB = S + (L_a - L_t) + (LE + H) + G + R - CC - SC
TODO: Need to implement shortwave attenuation in the column top layers. Use q_s(z,t) in eq 10 in Yang et al (2012)
Mandatory
:param utm33_x:
:param utm33_y:
:param ice_column:
:param temp_atm:
:param prec:
:param prec_snow:
:param albedo_prim:
:param time_span_in_sec:
:param temp_surface:
Optional
:param age_factor_tau:
:param cloud_cover:
:param wind:
:param pressure_atm:
:return:
For reference: 1 kWh is 3600 kJ.
10 000 kJ can melt 30kg ice or (3cm/m2 ice).
300 W/m2 is on avarage approx 26000kJ/m2/24hrs
"""
date = ice_column.date
day_no = ice_column.date.timetuple().tm_yday
# time given as the end of the time span
time_hour = (ice_column.date + dt.timedelta(seconds=time_span_in_sec)).hour
# Variables picked out from ice_column
is_ice = True
snow_depth = 0.
snow_density = const.rho_snow
if len(ice_column.column) == 0:
is_ice = False
# albedo_prim = 0.10 # no ice, water absobs much of the short wave
else:
if ice_column.column[0].type == "snow":
snow_density = ice_column.column[0].density
snow_depth = ice_column.column[0].height
if ice_column.column[0].type == "black_ice":
albedo_prim = const.alfa_black_ice
if ice_column.column[0].type == "slush_ice":
albedo_prim = const.alfa_slush_ice
# Calculate some parameters
if cloud_cover is None:
cloud_cover = dp.clouds_from_precipitation(prec, method=defaults.default_cloud_cover_method)
# This scenario should be avoided but I keep it for now because it it the method used in senorge_eb
#if temp_surface is None:
# temp_surface = ice_column.get_surface_temperature_estimate(temp_atm)
# Define an energy balance object to put inn all input data.
energy_balance = ebe(date)
energy_balance.add_model_input(
utm33_x_inn=utm33_x, utm33_y_inn=utm33_y, snow_depth_inn=snow_depth, snow_density_inn=snow_density,
temp_surface_inn=temp_surface, is_ice_inn=is_ice, temp_atm_inn=temp_atm,
prec_inn=prec, prec_snow_inn=prec_snow, cloud_cover_inn=cloud_cover,
age_factor_tau_inn=age_factor_tau, albedo_prim_inn=albedo_prim,
day_no_inn=day_no, time_hour_inn=time_hour, time_span_in_sec_inn=time_span_in_sec)
# Calculate the energy balance terms
S, s_inn, albedo, albedo_prim, age_factor_tau = get_short_wave(
utm33_x, utm33_y, day_no, temp_atm, cloud_cover, snow_depth, snow_density, prec_snow, time_hour,
time_span_in_sec, temp_surface, albedo_prim, age_factor_tau=age_factor_tau, albedo_method=defaults.default_albedo_method)
L_a, L_t = get_long_wave(cloud_cover, temp_atm, temp_surface, snow_depth, is_ice, time_span_in_sec)
H, LE, R_i, stability_correction = get_turbulent_flux(
temp_atm, temp_surface, time_span_in_sec, ice_column, pressure_atm=pressure_atm, wind=wind, rel_hum=rel_hum)
G = get_ground_heat(time_span_in_sec)
R = get_prec_heat(temp_atm, prec)
CC = get_cold_content_change(ice_column, temp_surface)
SC, conductance = get_surface_heat_conduction(ice_column, temp_surface, time_span_in_sec)
EB = S + (L_a + L_t) + (LE + H) + G + R + (CC + SC)
energy_balance.add_short_wave(S, s_inn, albedo, albedo_prim, age_factor_tau)
energy_balance.add_long_wave(L_a, L_t)
energy_balance.add_sensible_and_latent_heat(H, LE, R_i, stability_correction)
energy_balance.add_ground_heat(G)
energy_balance.add_prec_heat(R)
energy_balance.add_cold_content(CC)
energy_balance.add_surface_heat_conduction(SC, conductance)
energy_balance.add_energy_budget(EB)
return energy_balance
def testCloudMaker(stnr, startDate, endDate, method):
"""Gets data from a eKlima station and smooths the data depending on which method we wish to test.
We find the nash-sutcliffe to observations of clouds. And we plot and save the result to Plots folder.
:param stnr: eKlima station where there is cloud cover for testing method.
:param startDate: simulations and reference data from this data
:param endDate: simulations and reference data to this data
:param method: specify which method to be tested
:return:
Available methods to test:
cc_from_prec
ccFromAveragePrec
ccFromAverageObsCc
ccGammaPrec
ccGammaTemp
ccGammaPrecAndTemp"""
wsTemp = getMetData(stnr, 'TAM', startDate, endDate, 0, 'list')
wsPrec = getMetData(stnr, 'RR', startDate, endDate, 0, 'list')
wsCC = getMetData(stnr, 'NNM', startDate, endDate, 0, 'list')
temp, date = strip_metadata(wsTemp, get_dates=True)
prec = strip_metadata(wsPrec)
clouds = strip_metadata(wsCC)
dayShift = 1
clouds = __shiftClouds(clouds, dayShift)
if method == 'ccFromRandomThomas':
estClouds = dpz.clouds_from_precipitation(prec, method='Random Thomas')
gammaFigtext = 'No gamma smoothing and dayshift = {0}'.format(dayShift)
elif method == 'ccFromPrec':
estClouds = dpz.clouds_from_precipitation(prec, method='Binary')
gammaFigtext = 'No gamma smoothing and dayshift = {0}'.format(dayShift)
elif method == 'ccFromAveragePrec':
estClouds = dpz.clouds_from_precipitation(prec, method='Average')
gammaFigtext = 'No gamma smoothing and dayshift = {0}'.format(dayShift)
elif method == 'ccFromPrecAndAveragePrec':
estClouds = dpz.clouds_from_precipitation(prec, method='Binary and average')
gammaFigtext = ''.format()
elif method == 'ccFromAverageObsCc':
estClouds = [sum(clouds)/float(len(clouds))] * len(clouds)
gammaFigtext = ''.format()
elif method == 'ccGammaPrec':
gammaPrec = [2.8, 2., 0.3, 0.4]
estClouds = dpz.clouds_from_precipitation(prec, method='Binary')
estClouds = dgc.cc_gamma_smoothing(estClouds, gammaPrec)
gammaFigtext = "gamma smoothing prec = {0} and dayshift = {1}".format(gammaPrec, dayShift)
elif method == 'ccGammaTempChange':
gammaTemp = [4.8, 1., 5., 0.03]
estClouds = dgc.cc_gamma_temp(temp, gammaTemp)
gammaFigtext = "gamma smoothing temp = {0} and dayshift = {1}".format(gammaTemp, dayShift)
elif method == 'ccGammaPrecAndTempChange':
gammaPrec = [2.8, 2., 0.3, 0.4]
gammaTemp = [4.8, 1., 5., 0.03]
estClouds = dgc.cc_gamma_prec_and_temp_change(prec, temp, gammaPrec, gammaTemp)
gammaFigtext = "prec = {0} and temp = {1} and dayshift = {2}".format(gammaPrec, gammaTemp, dayShift)
fileName = "{3} {0} {1} {2}.png".format(stnr, startDate[0:7], endDate[0:7], method)
# What is the root mean square of estimatet vs observed clouds?
# rms = np.sqrt(((np.array(estClouds) - np.array(clouds)) ** 2).mean())
# Nash–Sutcliffe model efficiency coefficient from
# https://en.wikipedia.org/wiki/Nash%E2%80%93Sutcliffe_model_efficiency_coefficient
numerator = 0
denominator = 0
mean_clouds = sum(clouds)/float(len(clouds))
for i in range(0, len(clouds), 1):
numerator += (clouds[i] - estClouds[i])**2
denominator += (clouds[i] - mean_clouds)**2
nash_sutcliffe = 1 - numerator/denominator
# Figure dimensions
fsize = (16, 10)
plt.figure(figsize=fsize)
plt.clf()
# plot total snowdepth on land
plt.bar(date, prec, width=1, color="0.4")
# plot the estimated cloud cover
for i in range(0, len(estClouds) - 1, 1):
if estClouds[i] > 0:
plt.hlines(max(prec) * 1.2, date[i], date[i + 1], lw=45, color=str(-(estClouds[i] - 1.)))
elif estClouds[i] == None:
plt.hlines(max(prec) * 1.2, date[i], date[i + 1], lw=45, color="pink")
else:
plt.hlines(max(prec) * 1.2, date[i], date[i + 1], lw=45, color=str(-(estClouds[i] - 1.)))
# plot cloud cover from met
for i in range(0, len(clouds) - 1, 1):
if clouds[i] > 0:
plt.hlines(max(prec) * 1.1, date[i], date[i + 1], lw=45, color=str(-(clouds[i] - 1.)))
elif clouds[i] == None:
plt.hlines(max(prec) * 1.1, date[i], date[i + 1], lw=45, color="pink")
else:
plt.hlines(max(prec) * 1.1, date[i], date[i + 1], lw=45, color=str(-(clouds[i] - 1.)))
# this plots temperature on separate right side axis
plt.twinx()
temp_pluss = []
temp_minus = []
for i in range(0, len(temp), 1):
if temp[i] >= 0:
temp_pluss.append(temp[i])
temp_minus.append(np.nan)
else:
temp_minus.append(temp[i])
temp_pluss.append(np.nan)
plt.plot(date, temp, "black")
plt.plot(date, temp_pluss, "red")
plt.plot(date, temp_minus, "blue")
# title and text fields
plt.title("{3} {0} {1} {2}".format(stnr, startDate[0:7], endDate[0:7], method))
plt.text(date[len(date)/2], min(temp)*1.2, 'gamma smoothing [shape, scale, days back, amplification]')
plt.text(date[len(date)/2], min(temp)*1.3, gammaFigtext)
# this is a scatter plot of modelled and estimated cloud cover
xfrac = 0.15
yfrac = (float(fsize[0])/float(fsize[1])) * xfrac
xpos = 0.95-xfrac
ypos = 0.42-yfrac
a = plt.axes([xpos, ypos, xfrac, yfrac])
a.scatter(clouds, estClouds)
plt.setp(a, xticks=[0, 0.5, 1], yticks=[0, 0.5, 1])
plt.text(0.0, 0.1, 'na_su = {0}'.format(round(nash_sutcliffe, 2)), color='yellow', bbox={'facecolor':'black'})
plt.savefig("{0}{1}".format(plot_folder, fileName))
return nash_sutcliffe
def energy_balance_from_temp_sfc(utm33_x,
utm33_y,
ice_column,
temp_atm,
prec,
prec_snow,
albedo_prim,
time_span_in_sec,
temp_surface,
age_factor_tau=None,
cloud_cover=None,
wind=None,
rel_hum=None,
pressure_atm=None):
"""Given surface temperature, the daily energy budget of a column of snow is expressed as:
EB = S + (L_a - L_t) + (LE + H) + G + R - CC - SC
TODO: Need to implement shortwave attenuation in the column top layers. Use q_s(z,t) in eq 10 in Yang et al (2012)
Mandatory
:param utm33_x:
:param utm33_y:
:param ice_column:
:param temp_atm:
:param prec:
:param prec_snow:
:param albedo_prim:
:param time_span_in_sec:
:param temp_surface:
Optional
:param age_factor_tau:
:param cloud_cover:
:param wind:
:param pressure_atm:
:return:
For reference: 1 kWh is 3600 kJ.
10 000 kJ can melt 30kg ice or (3cm/m2 ice).
300 W/m2 is on avarage approx 26000kJ/m2/24hrs
"""
date = ice_column.date
day_no = ice_column.date.timetuple().tm_yday
# time given as the end of the time span
time_hour = (ice_column.date + dt.timedelta(seconds=time_span_in_sec)).hour
# Variables picked out from ice_column
is_ice = True
snow_depth = 0.
snow_density = const.rho_snow
if len(ice_column.column) == 0:
is_ice = False
# albedo_prim = 0.10 # no ice, water absobs much of the short wave
else:
if ice_column.column[0].type == "snow":
snow_density = ice_column.column[0].density
snow_depth = ice_column.column[0].height
if ice_column.column[0].type == "black_ice":
albedo_prim = const.alfa_black_ice
if ice_column.column[0].type == "slush_ice":
albedo_prim = const.alfa_slush_ice
# Calculate some parameters
if cloud_cover is None:
cloud_cover = dp.clouds_from_precipitation(
prec, method=defaults.default_cloud_cover_method)
# This scenario should be avoided but I keep it for now because it it the method used in senorge_eb
#if temp_surface is None:
# temp_surface = ice_column.get_surface_temperature_estimate(temp_atm)
# Define an energy balance object to put inn all input data.
energy_balance = ebe(date)
energy_balance.add_model_input(utm33_x_inn=utm33_x,
utm33_y_inn=utm33_y,
snow_depth_inn=snow_depth,
snow_density_inn=snow_density,
temp_surface_inn=temp_surface,
is_ice_inn=is_ice,
temp_atm_inn=temp_atm,
prec_inn=prec,
prec_snow_inn=prec_snow,
cloud_cover_inn=cloud_cover,
age_factor_tau_inn=age_factor_tau,
albedo_prim_inn=albedo_prim,
day_no_inn=day_no,
time_hour_inn=time_hour,
time_span_in_sec_inn=time_span_in_sec)
# Calculate the energy balance terms
S, s_inn, albedo, albedo_prim, age_factor_tau = get_short_wave(
utm33_x,
utm33_y,
day_no,
temp_atm,
cloud_cover,
snow_depth,
snow_density,
prec_snow,
time_hour,
time_span_in_sec,
temp_surface,
albedo_prim,
age_factor_tau=age_factor_tau,
albedo_method=defaults.default_albedo_method)
L_a, L_t = get_long_wave(cloud_cover, temp_atm, temp_surface, snow_depth,
is_ice, time_span_in_sec)
H, LE, R_i, stability_correction = get_turbulent_flux(
temp_atm,
temp_surface,
time_span_in_sec,
ice_column,
pressure_atm=pressure_atm,
wind=wind,
rel_hum=rel_hum)
G = get_ground_heat(time_span_in_sec)
R = get_prec_heat(temp_atm, prec)
CC = get_cold_content_change(ice_column, temp_surface)
SC, conductance = get_surface_heat_conduction(ice_column, temp_surface,
time_span_in_sec)
EB = S + (L_a + L_t) + (LE + H) + G + R + (CC + SC)
energy_balance.add_short_wave(S, s_inn, albedo, albedo_prim,
age_factor_tau)
energy_balance.add_long_wave(L_a, L_t)
energy_balance.add_sensible_and_latent_heat(H, LE, R_i,
stability_correction)
energy_balance.add_ground_heat(G)
energy_balance.add_prec_heat(R)
energy_balance.add_cold_content(CC)
energy_balance.add_surface_heat_conduction(SC, conductance)
energy_balance.add_energy_budget(EB)
return energy_balance
def testCloudMaker(stnr, startDate, endDate, method):
"""Gets data from a eKlima station and smooths the data depending on which method we wish to test.
We find the nash-sutcliffe to observations of clouds. And we plot and save the result to Plots folder.
:param stnr: eKlima station where there is cloud cover for testing method.
:param startDate: simulations and reference data from this data
:param endDate: simulations and reference data to this data
:param method: specify which method to be tested
:return:
Available methods to test:
cc_from_prec
ccFromAveragePrec
ccFromAverageObsCc
ccGammaPrec
ccGammaTemp
ccGammaPrecAndTemp"""
wsTemp = getMetData(stnr, 'TAM', startDate, endDate, 0, 'list')
wsPrec = getMetData(stnr, 'RR', startDate, endDate, 0, 'list')
wsCC = getMetData(stnr, 'NNM', startDate, endDate, 0, 'list')
temp, date = strip_metadata(wsTemp, get_dates=True)
prec = strip_metadata(wsPrec)
clouds = strip_metadata(wsCC)
dayShift = 1
clouds = __shiftClouds(clouds, dayShift)
if method == 'ccFromRandomThomas':
estClouds = dpz.clouds_from_precipitation(prec, method='Random Thomas')
gammaFigtext = 'No gamma smoothing and dayshift = {0}'.format(dayShift)
elif method == 'ccFromPrec':
estClouds = dpz.clouds_from_precipitation(prec, method='Binary')
gammaFigtext = 'No gamma smoothing and dayshift = {0}'.format(dayShift)
elif method == 'ccFromAveragePrec':
estClouds = dpz.clouds_from_precipitation(prec, method='Average')
gammaFigtext = 'No gamma smoothing and dayshift = {0}'.format(dayShift)
elif method == 'ccFromPrecAndAveragePrec':
estClouds = dpz.clouds_from_precipitation(prec, method='Binary and average')
gammaFigtext = ''.format()
elif method == 'ccFromAverageObsCc':
estClouds = [sum(clouds)/float(len(clouds))] * len(clouds)
gammaFigtext = ''.format()
elif method == 'ccGammaPrec':
gammaPrec = [2.8, 2., 0.3, 0.4]
estClouds = dpz.clouds_from_precipitation(prec, method='Binary')
estClouds = dgc.cc_gamma_smoothing(estClouds, gammaPrec)
gammaFigtext = "gamma smoothing prec = {0} and dayshift = {1}".format(gammaPrec, dayShift)
elif method == 'ccGammaTempChange':
gammaTemp = [4.8, 1., 5., 0.03]
estClouds = dgc.cc_gamma_temp(temp, gammaTemp)
gammaFigtext = "gamma smoothing temp = {0} and dayshift = {1}".format(gammaTemp, dayShift)
elif method == 'ccGammaPrecAndTempChange':
gammaPrec = [2.8, 2., 0.3, 0.4]
gammaTemp = [4.8, 1., 5., 0.03]
estClouds = dgc.cc_gamma_prec_and_temp_change(prec, temp, gammaPrec, gammaTemp)
gammaFigtext = "prec = {0} and temp = {1} and dayshift = {2}".format(gammaPrec, gammaTemp, dayShift)
fileName = "{3} {0} {1} {2}.png".format(stnr, startDate[0:7], endDate[0:7], method)
# What is the root mean square of estimatet vs observed clouds?
# rms = np.sqrt(((np.array(estClouds) - np.array(clouds)) ** 2).mean())
# Nash–Sutcliffe model efficiency coefficient from
# https://en.wikipedia.org/wiki/Nash%E2%80%93Sutcliffe_model_efficiency_coefficient
numerator = 0
denominator = 0
mean_clouds = sum(clouds)/float(len(clouds))
for i in range(0, len(clouds), 1):
numerator += (clouds[i] - estClouds[i])**2
denominator += (clouds[i] - mean_clouds)**2
nash_sutcliffe = 1 - numerator/denominator
# Figure dimensions
fsize = (16, 10)
plt.figure(figsize=fsize)
plt.clf()
# plot total snowdepth on land
plt.bar(date, prec, width=1, color="0.4")
# plot the estimated cloud cover
for i in range(0, len(estClouds) - 1, 1):
if estClouds[i] > 0:
plt.hlines(max(prec) * 1.2, date[i], date[i + 1], lw=45, color=str(-(estClouds[i] - 1.)))
elif estClouds[i] == None:
plt.hlines(max(prec) * 1.2, date[i], date[i + 1], lw=45, color="pink")
else:
plt.hlines(max(prec) * 1.2, date[i], date[i + 1], lw=45, color=str(-(estClouds[i] - 1.)))
# plot cloud cover from met
for i in range(0, len(clouds) - 1, 1):
if clouds[i] > 0:
plt.hlines(max(prec) * 1.1, date[i], date[i + 1], lw=45, color=str(-(clouds[i] - 1.)))
elif clouds[i] == None:
plt.hlines(max(prec) * 1.1, date[i], date[i + 1], lw=45, color="pink")
else:
plt.hlines(max(prec) * 1.1, date[i], date[i + 1], lw=45, color=str(-(clouds[i] - 1.)))
# this plots temperature on separate right side axis
plt.twinx()
temp_pluss = []
temp_minus = []
for i in range(0, len(temp), 1):
if temp[i] >= 0:
temp_pluss.append(temp[i])
temp_minus.append(np.nan)
else:
temp_minus.append(temp[i])
temp_pluss.append(np.nan)
plt.plot(date, temp, "black")
plt.plot(date, temp_pluss, "red")
plt.plot(date, temp_minus, "blue")
# title and text fields
plt.title("{3} {0} {1} {2}".format(stnr, startDate[0:7], endDate[0:7], method))
plt.text(date[len(date)/2], min(temp)*1.2, 'gamma smoothing [shape, scale, days back, amplification]')
plt.text(date[len(date)/2], min(temp)*1.3, gammaFigtext)
# this is a scatter plot of modelled and estimated cloud cover
xfrac = 0.15
yfrac = (float(fsize[0])/float(fsize[1])) * xfrac
xpos = 0.95-xfrac
ypos = 0.42-yfrac
a = plt.axes([xpos, ypos, xfrac, yfrac])
a.scatter(clouds, estClouds)
plt.setp(a, xticks=[0, 0.5, 1], yticks=[0, 0.5, 1])
plt.text(0.0, 0.1, 'na_su = {0}'.format(round(nash_sutcliffe, 2)), color='yellow', bbox={'facecolor':'black'})
plt.savefig("{0}{1}".format(plot_folder, fileName))
return nash_sutcliffe
