def main():
whole_domain_bathymetry = \
"/skynet3_rech1/huziy/NEMO_OFFICIAL/Simulations/1981-2000_Sim_per_lake_100yr_spinup_LIM3/Huron/bathy_meter.nc"
basemap, lons, lats = nemo_commons.get_basemap_and_coordinates_from_file(
path=whole_domain_bathymetry, resolution="h")
data_folder = "/home/huziy/skynet3_rech1/NEMO_OFFICIAL/Simulations/1981-2000_Sim_per_lake_100yr_spinup_LIM3/Huron"
manager_u = NemoOutputManager(file_path=os.path.join(
data_folder, "GLK_10d_19810101_20001231_grid_U.nc"),
var_name="vozocrtx")
manager_v = NemoOutputManager(file_path=os.path.join(
data_folder, "GLK_10d_19810101_20001231_grid_V.nc"),
var_name="vomecrty")
scalar_manager = NemoOutputManager(file_path=os.path.join(
data_folder, "GLK_10d_19810101_20001231_grid_T.nc"),
var_name="sosstsst")
img_dir = "animations_nemo/Huron"
if not os.path.isdir(img_dir):
os.makedirs(img_dir)
main_for_lake(bathy_path=os.path.join(data_folder, "bathy_meter.nc"),
manager_u=manager_u,
manager_v=manager_v,
basemap=basemap,
scalar_manager=scalar_manager,
img_dir=img_dir)
def main():
img_folder = "nemo/validation_1d"
if not os.path.isdir(img_folder):
os.makedirs(img_folder)
img_name_prefix = "mean_surface_temp_"
obs_data_path = (
"/skynet3_rech1/huziy/GLSEA/"
"coastwatch.glerl.noaa.gov/ftp/glsea/avgtemps/1992-1998/glsea-temps1992-1998-schwab.dat"
)
data = np.loadtxt(obs_data_path, skiprows=3)
dates = [
datetime(int(data[row, 0]), int(data[row, 2]), int(data[row, 3]))
for row in range(data.shape[0])
]
for col, lake_name in column_to_lake_name_glsea.items():
folder_path = nemo_commons.lake_to_folder_with_sim_data_1981_2000_seplakes_exp[
lake_name]
fname_tgrid = None
for fname in os.listdir(folder_path):
if fname.endswith("grid_T.nc"):
fname_tgrid = fname
break
if fname_tgrid is None:
continue
print("Processing {}".format(lake_name))
fig = plt.figure()
ax = plt.gca()
fpath = os.path.join(folder_path, fname_tgrid)
nom = NemoOutputManager(file_path=fpath, var_name="sosstsst")
ts_obs = pd.DataFrame(index=dates,
data=data[:, col],
columns=[
"GLSEA",
])
ts_mod = nom.get_area_mean_timeseries()
ts_mod = ts_mod.truncate(before=ts_obs.index[0],
after=ts_obs.index[-1])
ax.plot(ts_obs.index, ts_obs["GLSEA"], color="b", lw=2, label="GLSEA")
ax.plot(ts_mod.index, ts_mod["NEMO"], color="r", lw=2, label="NEMO")
ax.set_ylabel(r"${\rm ^{\circ}C}$")
if col == 5:
ax.legend(ncol=2)
ax.set_title(lake_name)
fig.savefig(
os.path.join(img_folder, img_name_prefix + lake_name + ".png"))
def main():
img_folder = "nemo/validation_1d"
if not os.path.isdir(img_folder):
os.makedirs(img_folder)
img_name_prefix = "mean_surface_temp_"
obs_data_path = ("/skynet3_rech1/huziy/GLSEA/"
"coastwatch.glerl.noaa.gov/ftp/glsea/avgtemps/1992-1998/glsea-temps1992-1998-schwab.dat")
data = np.loadtxt(obs_data_path, skiprows=3)
dates = [datetime(int(data[row, 0]), int(data[row, 2]), int(data[row, 3])) for row in range(data.shape[0])]
for col, lake_name in column_to_lake_name_glsea.items():
folder_path = nemo_commons.lake_to_folder_with_sim_data_1981_2000_seplakes_exp[lake_name]
fname_tgrid = None
for fname in os.listdir(folder_path):
if fname.endswith("grid_T.nc"):
fname_tgrid = fname
break
if fname_tgrid is None:
continue
print("Processing {}".format(lake_name))
fig = plt.figure()
ax = plt.gca()
fpath = os.path.join(folder_path, fname_tgrid)
nom = NemoOutputManager(
file_path=fpath, var_name="sosstsst"
)
ts_obs = pd.DataFrame(index=dates, data=data[:, col], columns=["GLSEA",])
ts_mod = nom.get_area_mean_timeseries()
ts_mod = ts_mod.truncate(before=ts_obs.index[0], after=ts_obs.index[-1])
ax.plot(ts_obs.index, ts_obs["GLSEA"], color="b", lw=2, label="GLSEA")
ax.plot(ts_mod.index, ts_mod["NEMO"], color="r", lw=2, label="NEMO")
ax.set_ylabel(r"${\rm ^{\circ}C}$")
if col == 5:
ax.legend(ncol=2)
ax.set_title(lake_name)
fig.savefig(os.path.join(img_folder, img_name_prefix + lake_name + ".png"))
def main():
img_folder = "nemo/validation_1d/"
if not os.path.isdir(img_folder):
os.makedirs(img_folder)
img_name_prefix = "max_ice_cover_"
for col, lake_name in column_to_lake_name_glsea.items():
folder_path = nemo_commons.lake_to_folder_with_sim_data_1981_2000_seplakes_exp[
lake_name]
fname_tgrid = None
for fname in os.listdir(folder_path):
if fname.endswith("grid_T.nc"):
fname_tgrid = fname
break
if fname_tgrid is None:
continue
fig = plt.figure()
ax = plt.gca()
fpath = os.path.join(folder_path, fname_tgrid)
nom = NemoOutputManager(file_path=fpath, var_name="soicecov")
ts_mod = nom.get_area_mean_timeseries()
ts_mod = ts_mod.groupby(by=lambda d: d.year).max()
ax.plot(ts_mod.index,
ts_mod["NEMO"] * 100.0,
color="r",
lw=2,
label="NEMO")
ax.set_ylabel(r"%")
ax.legend(loc=3)
ax.set_title(lake_name)
fig.savefig(
os.path.join(img_folder, img_name_prefix + lake_name + ".png"))
def main():
img_folder = "nemo/validation_1d/"
if not os.path.isdir(img_folder):
os.makedirs(img_folder)
img_name_prefix = "max_ice_cover_"
for col, lake_name in column_to_lake_name_glsea.items():
folder_path = nemo_commons.lake_to_folder_with_sim_data_1981_2000_seplakes_exp[lake_name]
fname_tgrid = None
for fname in os.listdir(folder_path):
if fname.endswith("grid_T.nc"):
fname_tgrid = fname
break
if fname_tgrid is None:
continue
fig = plt.figure()
ax = plt.gca()
fpath = os.path.join(folder_path, fname_tgrid)
nom = NemoOutputManager(
file_path=fpath, var_name="soicecov"
)
ts_mod = nom.get_area_mean_timeseries()
ts_mod = ts_mod.groupby(by=lambda d: d.year).max()
ax.plot(ts_mod.index, ts_mod["NEMO"] * 100.0, color="r", lw=2, label="NEMO")
ax.set_ylabel(r"%")
ax.legend(loc=3)
ax.set_title(lake_name)
fig.savefig(os.path.join(img_folder, img_name_prefix + lake_name + ".png"))
