def main(dfs_var_name="t2", cru_var_name="tmp",
dfs_folder="/home/huziy/skynet3_rech1/NEMO_OFFICIAL/DFS5.2_interpolated",
cru_file = "data/cru_data/CRUTS3.1/cru_ts_3_10.1901.2009.tmp.dat.nc"):
if not os.path.isdir(NEMO_IMAGES_DIR):
os.mkdir(NEMO_IMAGES_DIR)
#year range is inclusive [start_year, end_year]
start_year = 1981
end_year = 2009
season_name_to_months = OrderedDict([
("Winter", (1, 2, 12)),
("Spring", list(range(3, 6))),
("Summer", list(range(6, 9))),
("Fall", list(range(9, 12)))])
cru_t_manager = CRUDataManager(var_name=cru_var_name, path=cru_file)
cru_lons, cru_lats = cru_t_manager.lons2d, cru_t_manager.lats2d
#get seasonal means (CRU)
season_to_mean_cru = cru_t_manager.get_seasonal_means(season_name_to_months=season_name_to_months,
start_year=start_year,
end_year=end_year)
#get seasonal means Drakkar
dfs_manager = DFSDataManager(folder_path=dfs_folder, var_name=dfs_var_name)
season_to_mean_dfs = dfs_manager.get_seasonal_means(season_name_to_months=season_name_to_months,
start_year=start_year,
end_year=end_year)
dfs_lons, dfs_lats = dfs_manager.get_lons_and_lats_2d()
xt, yt, zt = lat_lon.lon_lat_to_cartesian(dfs_lons.flatten(), dfs_lats.flatten())
xs, ys, zs = lat_lon.lon_lat_to_cartesian(cru_lons.flatten(), cru_lats.flatten())
ktree = cKDTree(data=list(zip(xs, ys, zs)))
dists, inds = ktree.query(list(zip(xt, yt, zt)))
season_to_err = OrderedDict()
for k in season_to_mean_dfs:
interpolated_cru = season_to_mean_cru[k].flatten()[inds].reshape(dfs_lons.shape)
if dfs_var_name.lower() == "t2":
#interpolated_cru += 273.15
season_to_mean_dfs[k] -= 273.15
elif dfs_var_name.lower() == "precip": # precipitation in mm/day
season_to_mean_dfs[k] *= 24 * 60 * 60
season_to_err[k] = season_to_mean_dfs[k] #- interpolated_cru
season_indicator = "-".join(sorted(season_to_err.keys()))
fig_path = os.path.join(NEMO_IMAGES_DIR, "{3}_errors_{0}-{1}_{2}_dfs.jpeg".format(start_year,
end_year,
season_indicator,
dfs_var_name))
basemap = nemo_commons.get_default_basemap_for_glk(dfs_lons, dfs_lats, resolution="l")
x, y = basemap(dfs_lons, dfs_lats)
coords_and_basemap = {
"basemap": basemap, "x": x, "y": y
}
plot_errors_in_one_figure(season_to_err, fig_path=fig_path, **coords_and_basemap)
def get_seasonal_clim_obs_data(rconfig=None,
vname="TT",
bmp_info=None,
season_to_months=None,
obs_path=None,
nx_agg=None,
ny_agg=None):
# Number of points for aggregation
"""
return aggregated BasemapInfo object corresponding to the CRU resolution
:param rconfig:
:param vname:
:param bmp_info: BasemapInfo object for the model field (will be upscaled to the CRU resolution)
:param season_to_months:
"""
if bmp_info is None:
bmp_info = analysis.get_basemap_info_from_hdf(
file_path=rconfig.data_path)
if nx_agg is not None:
bmp_info_agg = bmp_info.get_aggregated(nagg_x=nx_agg, nagg_y=ny_agg)
else:
bmp_info_agg = bmp_info
# Validate temperature and precip
model_vars = ["TT", "PR"]
obs_vars = ["tmp", "pre"]
obs_paths = [
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc",
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc"
]
model_var_to_obs_var = dict(zip(model_vars, obs_vars))
model_var_to_obs_path = dict(zip(model_vars, obs_paths))
if obs_path is None:
obs_path = model_var_to_obs_path[vname]
cru = CRUDataManager(var_name=model_var_to_obs_var[vname], path=obs_path)
seasonal_clim_fields_obs = cru.get_seasonal_means(
season_name_to_months=season_to_months,
start_year=rconfig.start_year,
end_year=rconfig.end_year)
seasonal_clim_fields_obs_interp = OrderedDict()
for season, cru_field in seasonal_clim_fields_obs.items():
seasonal_clim_fields_obs_interp[season] = cru.interpolate_data_to(
cru_field,
lons2d=bmp_info_agg.lons,
lats2d=bmp_info_agg.lats,
nneighbours=1)
# assert hasattr(seasonal_clim_fields_obs_interp[season], "mask")
return bmp_info_agg, seasonal_clim_fields_obs_interp
def compare_vars(vname_model="TT",
vname_obs="tmp",
r_config=None,
season_to_months=None,
obs_path=None,
nx_agg=5,
ny_agg=5,
bmp_info_agg=None,
axes_list=None):
seasonal_clim_fields_model = analysis.get_seasonal_climatology_for_runconfig(
run_config=r_config,
varname=vname_model,
level=0,
season_to_months=season_to_months)
season_to_clim_fields_model_agg = OrderedDict()
for season, field in seasonal_clim_fields_model.items():
print(field.shape)
season_to_clim_fields_model_agg[season] = aggregate_array(
field, nagg_x=nx_agg, nagg_y=ny_agg)
if vname_model == "PR":
season_to_clim_fields_model_agg[season] *= 1.0e3 * 24 * 3600
if obs_path is None:
cru = CRUDataManager(var_name=vname_obs)
else:
cru = CRUDataManager(var_name=vname_obs, path=obs_path)
seasonal_clim_fields_obs = cru.get_seasonal_means(
season_name_to_months=season_to_months,
start_year=r_config.start_year,
end_year=r_config.end_year)
seasonal_clim_fields_obs_interp = OrderedDict()
for season, cru_field in seasonal_clim_fields_obs.items():
seasonal_clim_fields_obs_interp[season] = cru.interpolate_data_to(
cru_field,
lons2d=bmp_info_agg.lons,
lats2d=bmp_info_agg.lats,
nneighbours=1)
assert hasattr(seasonal_clim_fields_obs_interp[season], "mask")
season_to_err = OrderedDict()
for season in seasonal_clim_fields_obs_interp:
if vname_model == "PR":
# * 10, because in the UDEL dataset, precipitation is in cm
season_to_err[season] = season_to_clim_fields_model_agg[season] - \
seasonal_clim_fields_obs_interp[season] * MM_PER_CM
else:
season_to_err[season] = season_to_clim_fields_model_agg[
season] - seasonal_clim_fields_obs_interp[season]
plot_performance_err_with_cru.plot_seasonal_mean_biases(
season_to_error_field=season_to_err,
varname=vname_model,
basemap_info=bmp_info_agg,
axes_list=axes_list)
def get_seasonal_clim_obs_data(rconfig=None, vname="TT", bmp_info=None, season_to_months=None, obs_path=None):
# Number of points for aggregation
"""
return aggregated BasemapInfo object corresponding to the CRU resolution
:param rconfig:
:param vname:
:param bmp_info: BasemapInfo object for the model field (will be upscaled to the CRU resolution)
:param season_to_months:
"""
nx_agg = 5
ny_agg = 5
if bmp_info is None:
bmp_info = analysis.get_basemap_info_from_hdf(file_path=rconfig.data_path)
bmp_info_agg = bmp_info.get_aggregated(nagg_x=nx_agg, nagg_y=ny_agg)
# Validate temperature and precip
model_vars = ["TT", "PR"]
obs_vars = ["tmp", "pre"]
obs_paths = [
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc",
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc"
]
model_var_to_obs_var = dict(zip(model_vars, obs_vars))
model_var_to_obs_path = dict(zip(model_vars, obs_paths))
if obs_path is None:
obs_path = model_var_to_obs_path[vname]
cru = CRUDataManager(var_name=model_var_to_obs_var[vname], path=obs_path)
seasonal_clim_fields_obs = cru.get_seasonal_means(season_name_to_months=season_to_months,
start_year=rconfig.start_year,
end_year=rconfig.end_year)
seasonal_clim_fields_obs_interp = OrderedDict()
for season, cru_field in seasonal_clim_fields_obs.items():
seasonal_clim_fields_obs_interp[season] = cru.interpolate_data_to(cru_field,
lons2d=bmp_info_agg.lons,
lats2d=bmp_info_agg.lats, nneighbours=1)
# assert hasattr(seasonal_clim_fields_obs_interp[season], "mask")
return bmp_info_agg, seasonal_clim_fields_obs_interp
def compare_vars(vname_model="TT", vname_obs="tmp", r_config=None,
season_to_months=None,
obs_path=None, nx_agg=5, ny_agg=5, bmp_info_agg=None, axes_list=None):
seasonal_clim_fields_model = analysis.get_seasonal_climatology_for_runconfig(run_config=r_config,
varname=vname_model, level=0,
season_to_months=season_to_months)
season_to_clim_fields_model_agg = OrderedDict()
for season, field in seasonal_clim_fields_model.items():
print(field.shape)
season_to_clim_fields_model_agg[season] = aggregate_array(field, nagg_x=nx_agg, nagg_y=ny_agg)
if vname_model == "PR":
season_to_clim_fields_model_agg[season] *= 1.0e3 * 24 * 3600
if obs_path is None:
cru = CRUDataManager(var_name=vname_obs)
else:
cru = CRUDataManager(var_name=vname_obs, path=obs_path)
seasonal_clim_fields_obs = cru.get_seasonal_means(season_name_to_months=season_to_months,
start_year=r_config.start_year,
end_year=r_config.end_year)
seasonal_clim_fields_obs_interp = OrderedDict()
for season, cru_field in seasonal_clim_fields_obs.items():
seasonal_clim_fields_obs_interp[season] = cru.interpolate_data_to(cru_field,
lons2d=bmp_info_agg.lons,
lats2d=bmp_info_agg.lats, nneighbours=1)
assert hasattr(seasonal_clim_fields_obs_interp[season], "mask")
season_to_err = OrderedDict()
for season in seasonal_clim_fields_obs_interp:
if vname_model == "PR":
# * 10, because in the UDEL dataset, precipitation is in cm
season_to_err[season] = season_to_clim_fields_model_agg[season] - \
seasonal_clim_fields_obs_interp[season] * MM_PER_CM
else:
season_to_err[season] = season_to_clim_fields_model_agg[season] - seasonal_clim_fields_obs_interp[season]
plot_performance_err_with_cru.plot_seasonal_mean_biases(season_to_error_field=season_to_err, varname=vname_model,
basemap_info=bmp_info_agg,
axes_list=axes_list)
def main():
erainterim_075_folder = "/HOME/data/Validation/ERA-Interim_0.75/Offline_driving_data/3h_Forecast"
vname = "PR"
start_year = 1980
end_year = 2010
season_key = "summer"
season_labels = {season_key: "Summer"}
season_to_months = OrderedDict([
(season_key, [6, 7, 8])
])
# Validate temperature and precip
model_vars = ["TT", "PR"]
obs_vars = ["tmp", "pre"]
obs_paths = [
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc",
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc"
]
model_var_to_obs_var = dict(zip(model_vars, obs_vars))
model_var_to_obs_path = dict(zip(model_vars, obs_paths))
obs_path = model_var_to_obs_path[vname]
cru = CRUDataManager(var_name=model_var_to_obs_var[vname], path=obs_path)
# Calculate seasonal means for CRU
seasonal_clim_fields_cru = cru.get_seasonal_means(season_name_to_months=season_to_months,
start_year=start_year,
end_year=end_year)
# Calculate seasonal mean for erai
flist = get_files_for_season(erainterim_075_folder, start_year=start_year, end_year=end_year, months=season_to_months[season_key])
rpf = MultiRPN(flist)
date_to_field_erai075 = rpf.get_all_time_records_for_name_and_level(varname=vname, level=-1)
# Convert to mm/day
era075 = np.mean([field for field in date_to_field_erai075.values()], axis=0) * 24 * 3600 * 1000
lons_era, lats_era = rpf.get_longitudes_and_latitudes_of_the_last_read_rec()
seasonal_clim_fields_cru_interp = OrderedDict()
# Calculate biases
for season, cru_field in seasonal_clim_fields_cru.items():
seasonal_clim_fields_cru_interp[season] = cru.interpolate_data_to(cru_field,
lons2d=lons_era,
lats2d=lats_era,
nneighbours=1)
# Do the plotting ------------------------------------------------------------------------------
plot_utils.apply_plot_params()
fig = plt.figure()
b = Basemap()
gs = gridspec.GridSpec(nrows=3, ncols=1)
ax = fig.add_subplot(gs[0, 0])
xx, yy = b(cru.lons2d, cru.lats2d)
cs = b.contourf(xx, yy, seasonal_clim_fields_cru[season_key], 20)
b.drawcoastlines(ax=ax)
ax.set_title("CRU")
plt.colorbar(cs, ax=ax)
ax = fig.add_subplot(gs[1, 0])
lons_era[lons_era > 180] -= 360
lons_era, era075 = b.shiftdata(lons_era, datain=era075, lon_0=0)
xx, yy = b(lons_era, lats_era)
# mask oceans in the era plot as well
era075 = maskoceans(lons_era, lats_era, era075)
cs = b.contourf(xx, yy, era075, levels=cs.levels, norm=cs.norm, cmap=cs.cmap, ax=ax)
b.drawcoastlines(ax=ax)
ax.set_title("ERA-Interim 0.75")
plt.colorbar(cs, ax=ax)
# differences
ax = fig.add_subplot(gs[2, 0])
diff = era075 - seasonal_clim_fields_cru_interp[season_key]
delta = np.percentile(np.abs(diff)[~diff.mask], 90)
clevs = np.linspace(-delta, delta, 20)
cs = b.contourf(xx, yy, diff, levels=clevs, cmap="RdBu_r", extend="both")
b.drawcoastlines(ax=ax)
ax.set_title("ERA-Interim 0.75 - CRU")
plt.colorbar(cs, ax=ax)
plt.show()
fig.savefig(os.path.join(img_folder, "erai0.75_vs_cru_precip.png"), bbox_inches="tight")
def plot_seasonal_2m_temp_cru(samples_folder, seasons=commons.default_seasons):
vname = "tmp"
file_prefix = "dm"
plot_units = "$^\circ$C"
mult_coeff = 1
add_offset = 0
out_dx = 0.5
level = 1 # in hybrid coords
img_file_name = "CRU_{}.png".format(vname)
long_name = "CRU: 2m temperature, {}".format(plot_units)
# get data from cru files
data_manager = CRUDataManager(path="/RESCUE/skynet3_rech1/huziy/CRU/cru_ts3.23.2011.2014.tmp.dat.nc",
var_name=vname,
lazy=True)
season_to_mean = data_manager.get_seasonal_means(season_name_to_months=seasons, start_year=2013, end_year=2013)
lons2d, lats2d = data_manager.lons2d, data_manager.lats2d
img_folder = samples_folder.joinpath("images/seasonal")
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
img_file = img_folder.joinpath(img_file_name)
plot_utils.apply_plot_params(width_cm=25, height_cm=15, font_size=18)
fig = plt.figure()
ncols = 2
gs = GridSpec(len(season_to_mean) // ncols + int(not (len(season_to_mean) % ncols == 0)), ncols, wspace=0, hspace=0)
xx, yy = None, None
bmp = Basemap(projection="robin", lon_0=0)
cmap = plt.get_cmap('bwr')
clevs = np.arange(-30, 32, 2)
for i, (sname, field) in enumerate(season_to_mean.items()):
row = i // ncols
col = i % ncols
ax = fig.add_subplot(gs[row, col])
lons, lats, data_out = commons.interpolate_to_uniform_global_grid(field, lons_in=lons2d, lats_in=lats2d, out_dx=out_dx)
if xx is None:
xx, yy = bmp(lons, lats)
cs = bmp.contourf(xx, yy, data_out * mult_coeff, clevs, cmap=cmap, extend="both")
# save color levels for next subplots
clevs = cs.levels
print(np.max(data_out * mult_coeff))
ax.set_title(sname)
cb = plt.colorbar(cs, ax=ax)
if not (row == 0 and col == ncols - 1):
# cb.ax.set_title(plot_units)
cb.ax.set_visible(False)
bmp.drawcoastlines(ax=ax, linewidth=LINEWIDTH)
fig.suptitle(long_name)
with img_file.open("wb") as f:
fig.savefig(f, bbox_inches="tight")
plt.close(fig)
def plot_seasonal_2m_temp_cru(samples_folder, seasons=commons.default_seasons):
vname = "tmp"
file_prefix = "dm"
plot_units = "$^\circ$C"
mult_coeff = 1
add_offset = 0
out_dx = 0.5
level = 1 # in hybrid coords
img_file_name = "CRU_{}.png".format(vname)
long_name = "CRU: 2m temperature, {}".format(plot_units)
# get data from cru files
data_manager = CRUDataManager(
path="/RESCUE/skynet3_rech1/huziy/CRU/cru_ts3.23.2011.2014.tmp.dat.nc",
var_name=vname,
lazy=True)
season_to_mean = data_manager.get_seasonal_means(
season_name_to_months=seasons, start_year=2013, end_year=2013)
lons2d, lats2d = data_manager.lons2d, data_manager.lats2d
img_folder = samples_folder.joinpath("images/seasonal")
if not img_folder.is_dir():
img_folder.mkdir(parents=True)
img_file = img_folder.joinpath(img_file_name)
plot_utils.apply_plot_params(width_cm=25, height_cm=15, font_size=18)
fig = plt.figure()
ncols = 2
gs = GridSpec(len(season_to_mean) // ncols +
int(not (len(season_to_mean) % ncols == 0)),
ncols,
wspace=0,
hspace=0)
xx, yy = None, None
bmp = Basemap(projection="robin", lon_0=0)
cmap = plt.get_cmap('bwr')
clevs = np.arange(-30, 32, 2)
for i, (sname, field) in enumerate(season_to_mean.items()):
row = i // ncols
col = i % ncols
ax = fig.add_subplot(gs[row, col])
lons, lats, data_out = commons.interpolate_to_uniform_global_grid(
field, lons_in=lons2d, lats_in=lats2d, out_dx=out_dx)
if xx is None:
xx, yy = bmp(lons, lats)
cs = bmp.contourf(xx,
yy,
data_out * mult_coeff,
clevs,
cmap=cmap,
extend="both")
# save color levels for next subplots
clevs = cs.levels
print(np.max(data_out * mult_coeff))
ax.set_title(sname)
cb = plt.colorbar(cs, ax=ax)
if not (row == 0 and col == ncols - 1):
# cb.ax.set_title(plot_units)
cb.ax.set_visible(False)
bmp.drawcoastlines(ax=ax, linewidth=LINEWIDTH)
fig.suptitle(long_name)
with img_file.open("wb") as f:
fig.savefig(f, bbox_inches="tight")
plt.close(fig)
def main():
erainterim_075_folder = "/HOME/data/Validation/ERA-Interim_0.75/Offline_driving_data/3h_Forecast"
vname = "PR"
start_year = 1980
end_year = 2010
season_key = "summer"
season_labels = {season_key: "Summer"}
season_to_months = OrderedDict([(season_key, [6, 7, 8])])
# Validate temperature and precip
model_vars = ["TT", "PR"]
obs_vars = ["tmp", "pre"]
obs_paths = [
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc",
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc",
]
model_var_to_obs_var = dict(zip(model_vars, obs_vars))
model_var_to_obs_path = dict(zip(model_vars, obs_paths))
obs_path = model_var_to_obs_path[vname]
cru = CRUDataManager(var_name=model_var_to_obs_var[vname], path=obs_path)
# Calculate seasonal means for CRU
seasonal_clim_fields_cru = cru.get_seasonal_means(
season_name_to_months=season_to_months, start_year=start_year, end_year=end_year
)
# Calculate seasonal mean for erai
flist = get_files_for_season(
erainterim_075_folder, start_year=start_year, end_year=end_year, months=season_to_months[season_key]
)
rpf = MultiRPN(flist)
date_to_field_erai075 = rpf.get_all_time_records_for_name_and_level(varname=vname, level=-1)
# Convert to mm/day
era075 = np.mean([field for field in date_to_field_erai075.values()], axis=0) * 24 * 3600 * 1000
lons_era, lats_era = rpf.get_longitudes_and_latitudes_of_the_last_read_rec()
seasonal_clim_fields_cru_interp = OrderedDict()
# Calculate biases
for season, cru_field in seasonal_clim_fields_cru.items():
seasonal_clim_fields_cru_interp[season] = cru.interpolate_data_to(
cru_field, lons2d=lons_era, lats2d=lats_era, nneighbours=1
)
# Do the plotting ------------------------------------------------------------------------------
plot_utils.apply_plot_params()
fig = plt.figure()
b = Basemap()
gs = gridspec.GridSpec(nrows=3, ncols=1)
ax = fig.add_subplot(gs[0, 0])
xx, yy = b(cru.lons2d, cru.lats2d)
cs = b.contourf(xx, yy, seasonal_clim_fields_cru[season_key], 20)
b.drawcoastlines(ax=ax)
ax.set_title("CRU")
plt.colorbar(cs, ax=ax)
ax = fig.add_subplot(gs[1, 0])
lons_era[lons_era > 180] -= 360
lons_era, era075 = b.shiftdata(lons_era, datain=era075, lon_0=0)
xx, yy = b(lons_era, lats_era)
# mask oceans in the era plot as well
era075 = maskoceans(lons_era, lats_era, era075)
cs = b.contourf(xx, yy, era075, levels=cs.levels, norm=cs.norm, cmap=cs.cmap, ax=ax)
b.drawcoastlines(ax=ax)
ax.set_title("ERA-Interim 0.75")
plt.colorbar(cs, ax=ax)
# differences
ax = fig.add_subplot(gs[2, 0])
diff = era075 - seasonal_clim_fields_cru_interp[season_key]
delta = np.percentile(np.abs(diff)[~diff.mask], 90)
clevs = np.linspace(-delta, delta, 20)
cs = b.contourf(xx, yy, diff, levels=clevs, cmap="RdBu_r", extend="both")
b.drawcoastlines(ax=ax)
ax.set_title("ERA-Interim 0.75 - CRU")
plt.colorbar(cs, ax=ax)
plt.show()
fig.savefig(os.path.join(img_folder, "erai0.75_vs_cru_precip.png"), bbox_inches="tight")
def main(
dfs_var_name="t2",
cru_var_name="tmp",
dfs_folder="/home/huziy/skynet3_rech1/NEMO_OFFICIAL/DFS5.2_interpolated",
cru_file="data/cru_data/CRUTS3.1/cru_ts_3_10.1901.2009.tmp.dat.nc"):
if not os.path.isdir(NEMO_IMAGES_DIR):
os.mkdir(NEMO_IMAGES_DIR)
#year range is inclusive [start_year, end_year]
start_year = 1981
end_year = 2009
season_name_to_months = OrderedDict([("Winter", (1, 2, 12)),
("Spring", list(range(3, 6))),
("Summer", list(range(6, 9))),
("Fall", list(range(9, 12)))])
cru_t_manager = CRUDataManager(var_name=cru_var_name, path=cru_file)
cru_lons, cru_lats = cru_t_manager.lons2d, cru_t_manager.lats2d
#get seasonal means (CRU)
season_to_mean_cru = cru_t_manager.get_seasonal_means(
season_name_to_months=season_name_to_months,
start_year=start_year,
end_year=end_year)
#get seasonal means Drakkar
dfs_manager = DFSDataManager(folder_path=dfs_folder, var_name=dfs_var_name)
season_to_mean_dfs = dfs_manager.get_seasonal_means(
season_name_to_months=season_name_to_months,
start_year=start_year,
end_year=end_year)
dfs_lons, dfs_lats = dfs_manager.get_lons_and_lats_2d()
xt, yt, zt = lat_lon.lon_lat_to_cartesian(dfs_lons.flatten(),
dfs_lats.flatten())
xs, ys, zs = lat_lon.lon_lat_to_cartesian(cru_lons.flatten(),
cru_lats.flatten())
ktree = cKDTree(data=list(zip(xs, ys, zs)))
dists, inds = ktree.query(list(zip(xt, yt, zt)))
season_to_err = OrderedDict()
for k in season_to_mean_dfs:
interpolated_cru = season_to_mean_cru[k].flatten()[inds].reshape(
dfs_lons.shape)
if dfs_var_name.lower() == "t2":
#interpolated_cru += 273.15
season_to_mean_dfs[k] -= 273.15
elif dfs_var_name.lower() == "precip": # precipitation in mm/day
season_to_mean_dfs[k] *= 24 * 60 * 60
season_to_err[k] = season_to_mean_dfs[k] #- interpolated_cru
season_indicator = "-".join(sorted(season_to_err.keys()))
fig_path = os.path.join(
NEMO_IMAGES_DIR,
"{3}_errors_{0}-{1}_{2}_dfs.jpeg".format(start_year, end_year,
season_indicator,
dfs_var_name))
basemap = nemo_commons.get_default_basemap_for_glk(dfs_lons,
dfs_lats,
resolution="l")
x, y = basemap(dfs_lons, dfs_lats)
coords_and_basemap = {"basemap": basemap, "x": x, "y": y}
plot_errors_in_one_figure(season_to_err,
fig_path=fig_path,
**coords_and_basemap)
