def do_calculations(data_path=None, lay_widths=None, start_year=None, end_year=None):
cache = get_cache_file(**locals())
if os.path.isfile(cache):
return pickle.load(open(cache, "rb"))
alt_data_manager = CRCMDataManager(layer_widths=lay_widths)
tsoil3d_field = None
for lev in range(len(lay_widths)):
field_dict = analysis.get_annual_maxima(path_to_hdf_file=data_path, var_name="I0", level=lev,
start_year=start_year, end_year=end_year)
field = np.max([fi for fi in field_dict.values()], axis=0)
if tsoil3d_field is None:
tsoil3d_field = np.zeros(field.shape + (len(lay_widths), ))
print("lev, min, max = {}, {}, {}".format(lev, field.min(), field.max()))
plt.figure()
img = plt.contourf(field)
plt.colorbar(img)
plt.show()
tsoil3d_field[:, :, lev] = field
alt_field = alt_data_manager.get_alt(tsoil3d_field)
with open(cache, "wb") as f:
pickle.dump(alt_field, f)
return alt_field
def main():
soiltemp_var_name = "TBAR"
path1 = "/skynet1_rech3/huziy/class_offline_simulations_VG/dpth_var/CLASS_output_CLASSoff1_Arctic_0.5_ERA40_dpth_to_bdrck_var_1980-2009/TBAR.rpn"
label1 = "Variable (real) depth to bedrock"
path2 = "/skynet1_rech3/huziy/class_offline_simulations_VG/dpth_3.6m/CLASS_output_CLASSoff1_Arctic_0.5_ERA40_dpth_to_bdrck_constant_1980-2009/TBAR.rpn"
label2 = "Fixed depth to bedrock (3.6 m)"
path_to_dpth_to_bedrock = "/skynet1_rech3/huziy/CLASS_offline_VG/GEOPHYSICAL_FIELDS/test_analysis.rpn"
# read depth to bedrock
r = RPN(path_to_dpth_to_bedrock)
dpth = r.get_first_record_for_name("DPTH")
r.close()
layer_widths = [
0.1, 0.2, 0.3, 0.5, 0.9, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5
]
print(len(layer_widths))
crcm_data_manager = CRCMDataManager(layer_widths=layer_widths)
# Read in the temperature profiles
r1 = RPN(path1)
soiltemp1 = r1.get_4d_field(name=soiltemp_var_name)
lons2d, lats2d = r1.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**r1.get_proj_parameters_for_the_last_read_rec())
b = rll.get_basemap_object_for_lons_lats(lons2d=lons2d,
lats2d=lats2d,
resolution="c")
r1.close()
r2 = RPN(path2)
soiltemp2 = r2.get_4d_field(name=soiltemp_var_name)
r2.close()
dates_sorted = list(sorted(soiltemp1.keys()))
levels_sorted = list(sorted(list(soiltemp1.items())[0][1].keys()))
# group dates for each year
alt1_list = []
alt2_list = []
for year, dates_group in itertools.groupby(dates_sorted,
lambda par: par.year):
t1 = []
t2 = []
for d in dates_group:
t1.append([soiltemp1[d][lev] for lev in levels_sorted])
t2.append([soiltemp2[d][lev] for lev in levels_sorted])
# Get maximum temperature profiles
t1max = np.max(t1, axis=0).transpose(1, 2, 0)
t2max = np.max(t2, axis=0).transpose(1, 2, 0)
print(t1max.shape, t2max.shape)
#calculate and save alts
h1 = crcm_data_manager.get_alt(t1max)
h2 = crcm_data_manager.get_alt(t2max)
h1[h1 < 0] = np.nan
h2[h2 < 0] = np.nan
alt1_list.append(h1)
alt2_list.append(h2)
#take into account permafrost
alt1_list_pf = []
alt2_list_pf = []
n_years_for_pf = 3
for i in range(len(alt1_list) - n_years_for_pf):
alt1_list_pf.append(np.max(alt1_list[i:i + n_years_for_pf], axis=0))
alt2_list_pf.append(np.max(alt2_list[i:i + n_years_for_pf], axis=0))
# calculate climatological mean
alt1 = np.mean(alt1_list_pf, axis=0)
alt2 = np.mean(alt2_list_pf, axis=0)
print(np.isnan(alt1).any(), np.isnan(alt2).any())
#mask nans
alt1 = np.ma.masked_where(np.isnan(alt1), alt1)
alt2 = np.ma.masked_where(np.isnan(alt2), alt2)
#calculate change due to fixed depth to bedrock
delta = alt2 - alt1
#
for i in range(len(alt1_list)):
alt1_list[i] = np.ma.masked_where(np.isnan(alt1_list[i]), alt1_list[i])
alt2_list[i] = np.ma.masked_where(np.isnan(alt2_list[i]), alt2_list[i])
#tval, pval = ttest_ind(alt1_list, alt2_list)
#mask oceans
lons2d[lons2d > 180] -= 360
dpth = maskoceans(lons2d, lats2d, dpth)
delta = np.ma.masked_where(dpth.mask, delta)
#calculate differences in SWE
path_swe_1 = path1.replace("TBAR.rpn", "SNO.rpn")
r1 = RPN(path_swe_1)
swe1 = r1.get_all_time_records_for_name("SNO")
r1.close()
swe1_winter_clim = np.mean(
[field for key, field in swe1.items() if key.month in [1, 2, 12]],
axis=0)
swe1_winter_clim = np.ma.masked_where(
(swe1_winter_clim >= 999) | dpth.mask, swe1_winter_clim)
path_swe_2 = path2.replace("TBAR.rpn", "SNO.rpn")
r2 = RPN(path_swe_2)
swe2 = r2.get_all_time_records_for_name("SNO")
r2.close()
swe2_winter_clim = np.mean(
[field for key, field in swe2.items() if key.month in [1, 2, 12]],
axis=0)
swe2_winter_clim = np.ma.masked_where(
(swe2_winter_clim >= 999) | dpth.mask, swe2_winter_clim)
#plotting
print("Start plotting ...")
plot_differences(b,
lons2d,
lats2d,
dpth,
delta,
label="ALT(DPTH=3.6m) - ALT(DPTH~)",
pvalue=None,
swe_diff=swe2_winter_clim - swe1_winter_clim)
def main():
soiltemp_var_name = "TBAR"
path1 = "/skynet1_rech3/huziy/class_offline_simulations_VG/dpth_var/CLASS_output_CLASSoff1_Arctic_0.5_ERA40_dpth_to_bdrck_var_1980-2009/TBAR.rpn"
label1 = "Variable (real) depth to bedrock"
path2 = "/skynet1_rech3/huziy/class_offline_simulations_VG/dpth_3.6m/CLASS_output_CLASSoff1_Arctic_0.5_ERA40_dpth_to_bdrck_constant_1980-2009/TBAR.rpn"
label2 = "Fixed depth to bedrock (3.6 m)"
path_to_dpth_to_bedrock = "/skynet1_rech3/huziy/CLASS_offline_VG/GEOPHYSICAL_FIELDS/test_analysis.rpn"
# read depth to bedrock
r = RPN(path_to_dpth_to_bedrock)
dpth = r.get_first_record_for_name("DPTH")
r.close()
layer_widths = [0.1, 0.2, 0.3, 0.5, 0.9, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5,
1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5]
print(len(layer_widths))
crcm_data_manager = CRCMDataManager(layer_widths=layer_widths)
# Read in the temperature profiles
r1 = RPN(path1)
soiltemp1 = r1.get_4d_field(name=soiltemp_var_name)
lons2d, lats2d = r1.get_longitudes_and_latitudes_for_the_last_read_rec()
rll = RotatedLatLon(**r1.get_proj_parameters_for_the_last_read_rec())
b = rll.get_basemap_object_for_lons_lats(lons2d=lons2d, lats2d=lats2d, resolution="c")
r1.close()
r2 = RPN(path2)
soiltemp2 = r2.get_4d_field(name=soiltemp_var_name)
r2.close()
dates_sorted = list(sorted(soiltemp1.keys()))
levels_sorted = list(sorted(list(soiltemp1.items())[0][1].keys()))
# group dates for each year
alt1_list = []
alt2_list = []
for year, dates_group in itertools.groupby(dates_sorted, lambda par: par.year):
t1 = []
t2 = []
for d in dates_group:
t1.append([soiltemp1[d][lev] for lev in levels_sorted])
t2.append([soiltemp2[d][lev] for lev in levels_sorted])
# Get maximum temperature profiles
t1max = np.max(t1, axis=0).transpose(1, 2, 0)
t2max = np.max(t2, axis=0).transpose(1, 2, 0)
print(t1max.shape, t2max.shape)
#calculate and save alts
h1 = crcm_data_manager.get_alt(t1max)
h2 = crcm_data_manager.get_alt(t2max)
h1[h1 < 0] = np.nan
h2[h2 < 0] = np.nan
alt1_list.append(h1)
alt2_list.append(h2)
#take into account permafrost
alt1_list_pf = []
alt2_list_pf = []
n_years_for_pf = 3
for i in range(len(alt1_list) - n_years_for_pf):
alt1_list_pf.append(np.max(alt1_list[i:i+n_years_for_pf], axis=0))
alt2_list_pf.append(np.max(alt2_list[i:i+n_years_for_pf], axis=0))
# calculate climatological mean
alt1 = np.mean(alt1_list_pf, axis=0)
alt2 = np.mean(alt2_list_pf, axis=0)
print(np.isnan(alt1).any(), np.isnan(alt2).any())
#mask nans
alt1 = np.ma.masked_where(np.isnan(alt1), alt1)
alt2 = np.ma.masked_where(np.isnan(alt2), alt2)
#calculate change due to fixed depth to bedrock
delta = alt2 - alt1
#
for i in range(len(alt1_list)):
alt1_list[i] = np.ma.masked_where(np.isnan(alt1_list[i]), alt1_list[i])
alt2_list[i] = np.ma.masked_where(np.isnan(alt2_list[i]), alt2_list[i])
#tval, pval = ttest_ind(alt1_list, alt2_list)
#mask oceans
lons2d[lons2d > 180] -= 360
dpth = maskoceans(lons2d, lats2d, dpth)
delta = np.ma.masked_where(dpth.mask, delta)
#calculate differences in SWE
path_swe_1 = path1.replace("TBAR.rpn", "SNO.rpn")
r1 = RPN(path_swe_1)
swe1 = r1.get_all_time_records_for_name("SNO")
r1.close()
swe1_winter_clim = np.mean(
[field for key, field in swe1.items() if key.month in [1, 2, 12]], axis=0)
swe1_winter_clim = np.ma.masked_where((swe1_winter_clim >= 999) | dpth.mask, swe1_winter_clim)
path_swe_2 = path2.replace("TBAR.rpn", "SNO.rpn")
r2 = RPN(path_swe_2)
swe2 = r2.get_all_time_records_for_name("SNO")
r2.close()
swe2_winter_clim = np.mean(
[field for key, field in swe2.items() if key.month in [1, 2, 12]], axis=0)
swe2_winter_clim = np.ma.masked_where((swe2_winter_clim >= 999) | dpth.mask, swe2_winter_clim)
#plotting
print("Start plotting ...")
plot_differences(b, lons2d, lats2d, dpth, delta, label="ALT(DPTH=3.6m) - ALT(DPTH~)", pvalue=None,
swe_diff=swe2_winter_clim - swe1_winter_clim)