def time_serie_Arctic(path, var):
yr, xr, time, depth = loading.extracting_coord(path)
TorS = loading.extracting_var(path, var)
make_plot.points_on_map(xr, yr, var, basin)
S = loading.extracting_var(path, 'sal')
TorS[np.where(S == 0.)] = np.nan
i_zmax = np.max(np.where(depth < simu.zmax))
print(np.shape(TorS))
mean_Arctic = np.mean(np.nanmean(TorS[:, 0:i_zmax, :], axis=0), axis=0)
return time, depth, mean_Arctic
def time_serie_Arctic_2D(path, var, lat_min, basin):
yr, xr, time = loading.extracting_coord_2D(path)
ice = loading.extracting_var(path, var)
S = loading.extracting_var(path, 'sal')
ice[np.where(S[:, :, 0, :] == 0.)] = np.nan
if basin == 'undefined':
mask = loading.latitudinal_band_mask(yr, lat_min, 90)
else:
mask = loading.Ocean_mask(xr, yr, basin)
make_plot.points_on_map(xr[mask], yr[mask], var, basin)
arctic = ice[mask, :]
mean_Arctic = np.nanmean(arctic, axis=0)
return time, mean_Arctic
def time_serie_Arctic(path, var, lat_min, basin):
yr, xr, time, depth = loading.extracting_coord(path)
TorS = loading.extracting_var(path, var)
print(np.shape(TorS))
S = loading.extracting_var(path, 'sal')
TorS[np.where(S == 0.)] = np.nan
if basin == 'undefined':
mask = loading.latitudinal_band_mask(yr, lat_min, 90)
else:
mask = loading.Ocean_mask(xr, yr, basin)
make_plot.points_on_map(xr[mask], yr[mask], var, basin)
arctic = TorS[mask, :, :]
print(np.nanmax(arctic))
mean_Arctic = np.nanmean(arctic, axis=0)
return time, depth, mean_Arctic
def time_serie_Arctic_2D(path, var_name, lat_min, basin):
yr, xr, time = loading.extracting_coord_2D(path)
VAR = loading.extracting_var(path, var_name)
S = loading.extracting_var(path, 'sal')
VAR[np.where(S[:, :, 0, :] == 0.)] = 0 #np.nan
print(np.max(VAR), np.min(VAR))
VAR[np.where(VAR > 1E10)] = 0 #np.nan
print(np.max(VAR), np.min(VAR))
if basin == 'undefined':
mask = loading.latitudinal_band_mask(yr, lat_min, 90)
else:
mask = loading.Ocean_mask(xr, yr, basin)
make_plot.points_on_map(xr[mask], yr[mask], var_name, basin)
VAR[np.where(VAR == 0)] = np.nan
arctic = VAR[mask, :]
mean_Arctic = np.nansum(arctic, axis=0)
return time, mean_Arctic
simu = yearly_LongPeriod(option, var, y1, y2, basin, lat_min, month)
yr, xr, time, depth = loading.extracting_coord(simu.path)
VarArray_simuRho = loading.extracting_var(simu.path,
'rho') # Practical Salinity
index_y1 = np.min(
np.where(simu.first_year + time[:] / (3600 * 24 * 364.5) > simu.y1))
index_y2 = np.min(
np.where(simu.first_year + time[:] / (3600 * 24 * 364.5) > y2))
if basin == 'undefined':
mask = loading.latitudinal_band_mask(yr, lat_min, 90)
else:
mask = loading.Ocean_mask(xr, yr, basin)
make_plot.points_on_map(xr[mask], yr[mask], var, basin)
VarArray_simuRho[np.where(VarArray_simuRho == 0)] = np.nan
region = VarArray_simuRho[mask, :, :]
mean_region = np.nanmean(region, axis=0)
#make_plot.vertical_profile(mean_region[:,index_y1],mean_region[:,index_y2],y1,y2,var,simu.vmin,simu.vmax,depth,simu.max_depth,basin,lat_min,simu.output_file)
mean_region_pro = mean_region[:, index_y1:index_y2 + 1]
MEAN = np.zeros_like((mean_region_pro[:, 0]))
MEAN2 = np.zeros_like((mean_region_pro[:, 0]))
sig = np.zeros_like(mean_region_pro[:, 0])
MEAN = np.nanmean(mean_region_pro, axis=1)
MEAN2 = np.nanmean(mean_region_pro**2, axis=1)
#for i in range(0,np.size(MEAN2[0,:])):
simu = From1950to2100(option, var, y1, y2, comparison, lat_min, basin)
yr, xr, time, depth, X = loading.extracting_coord_1D(simu.path)
VAR = loading.extracting_var(simu.path, var)
if var == 'sal':
VAR[np.where(VAR < 32.)] = np.nan
elif var == 'density':
VAR[np.where(VAR < 18)] = np.nan
else:
S = loading.extracting_var(simu.path, 'sal')
VAR[S == 0] = np.nan
print(np.nanmin(VAR))
make_plot.points_on_map(xr, yr, var, basin)
index_y1 = np.min(
np.where(simu.first_year + time[:] / (3600 * 24 * 364.5) > simu.y1))
index_y2 = np.min(
np.where(simu.first_year + time[:] / (3600 * 24 * 364.5) > simu.y2))
if var == 'Uorth':
make_plot.one_sec(
100 * np.mean(VAR[:, :, index_y1:index_y1 + 29], axis=2).transpose(),
var, X, depth, simu.max_depth, y1, simu.output_file, simu.vmin,
simu.vmax, basin)
else:
make_plot.one_sec(
np.mean(VAR[:, :, index_y1:index_y1 + 29],
axis=2).transpose(), var, X, depth, simu.max_depth, y1,
simu.output_file, simu.vmin, simu.vmax, basin)
