def _rmse(forecast, reference, dim='svd', comparison=None):
"""
Calculate the Root Mean Sqaure Error (RMSE).
.. math::
RMSE = \\sqrt{\\overline{(f - o)^{2}}}
Range:
* perfect: 0
* min: 0
* max: ∞
See also:
* xskillscore.rmse
"""
return rmse(forecast, reference, dim=dim)
def rmse(x, y, dim):
"""
Compute Root Mean Squared Error.
Parameters
----------
x : Dataset, DataArray, GroupBy, Variable, numpy/dask arrays or scalars
Mix of labeled and/or unlabeled arrays to which to apply the function.
y : Dataset, DataArray, GroupBy, Variable, numpy/dask arrays or scalars
Mix of labeled and/or unlabeled arrays to which to apply the function.
dim : str
The dimension to apply the correlation along.
Returns
-------
Root Mean Squared Error
Single value or tuple of Dataset, DataArray, Variable, dask.array.Array or
numpy.ndarray, the first type on that list to appear on an input.
"""
return xs.rmse(x, y, dim)
_model = 'FIM'
_line_color = 'red'
if _model == 'GEFS':
_line_color = 'green'
if _model == 'GEM':
_line_color = 'blue'
if _model == 'GEM':
_line_color = 'blue'
if _model == 'GEOS_V2p1':
_model = 'GEOS'
_line_color = 'purple'
if _model == 'NESM':
_line_color = 'orange'
r = xs.pearson_r(obs, fct, 'S')
_r = r.values
rmse = xs.rmse(obs, fct, 'S')
_rmse = rmse.values
_x = np.arange(1, len(da.L) + 1)
ax1.plot(_x, _r, label=_model, linewidth=2, color=_line_color)
ax2.plot(_x, _rmse, linewidth=2, color=_line_color)
ax1.legend(loc="upper right", fontsize=16)
plt.savefig(fsavename + '.png', bbox_inches='tight')
plt.savefig(fsavename + '.eps', bbox_inches='tight', format='eps')
plt.close()
hysal2 = hysal.salinity
# mean_sbe_RSS = RSS.SAL_CTD_MEAN
# mean_RSS_40km = RSS.smap_SSS_40km
# Comparing RSS SMAP data with saildrone data
if "RSS" in x:
print(x)
#hysal2 = sail.sat_anc_sss
SBE = sail.SAL_CTD_MEAN
RBR = sail.SAL_RBR_MEAN
RSS = sail.sat_sss_smap
RSS_40 = sail.sat_sss_smap_40km
# RMSE
rmse_RSS_sbe = xs.rmse(SBE, RSS, dim="time", skipna="True")
rmse_RSS_rbr = xs.rmse(RBR, RSS, dim="time", skipna="True")
rmse_RSS_40 = xs.rmse(SBE, RSS_40, dim="time", skipna="True")
# Mean Difference
RBR_RSS = (RBR - RSS).mean(dim='time').values
SBE_RSS = (SBE - RSS).mean(dim='time').values
SBE_RSS40 = (SBE - RSS_40).mean(dim='time').values
# Difference in Standard Deviation
RBR_RSS_std = (RBR - RSS).std(dim='time').values
SBE_RSS_std = (SBE - RSS).std(dim='time').values
SBE_RSS40_std = (SBE - RSS_40).std(dim='time').values
with open('RSS_8DAY_Stats' '' + str(num) + '.csv', 'w') as csvfile1:
fieldnames = ['stat', 'value']
### Deterministic metrics # Pearson's correlation coefficient r = xs.pearson_r(obs, fct, "nlocs") # 2-tailed p-value of Pearson's correlation coefficient #jkim r_p_value = xs.pearson_r_p_value(obs, fct, "nlocs") # Spearman's correlation coefficient rs = xs.spearman_r(obs, fct, "nlocs") # 2-tailed p-value associated with Spearman's correlation coefficient #jkim rs_p_value = xs.spearman_r_p_value(obs, fct, "nlocs") # Root Mean Squared Error rmse = xs.rmse(obs, fct, "nlocs") # Mean Squared Error mse = xs.mse(obs, fct, "nlocs") # Mean Absolute Error mae = xs.mae(obs, fct, "nlocs") # Median Absolute Error median_absolute_error = xs.median_absolute_error(obs, fct, "nlocs") # Mean Absolute Percentage Error mape = xs.mape(obs, fct, "nlocs") # Symmetric Mean Absolute Percentage Error #jkim smape = xs.smape(obs, fct, "nlocs")
files = [x for x in glob(data_sail+'saildrone*.nc')]
#Comparing RBR and SBE37 Saildrone Instruments
for x in files:
sail = xr.open_dataset(x).isel(trajectory=0).swap_dims({'obs': 'time'})
num=sail.trajectory.values
##Data stats
#Difference in Standard Deviation
std_rbr= sail["SAL_RBR_MEAN"].std(dim="time", skipna=None)
std_sbe37= sail["SAL_SBE37_MEAN"].std(dim="time",skipna=None)
sail_diff=std_sbe37-std_rbr
#RMSE
rmse_sal=xs.rmse(sail.SAL_SBE37_MEAN,sail.SAL_RBR_MEAN, dim="time",skipna="True")
#Mean Difference
mean_sbe=sail.SAL_SBE37_MEAN.mean(dim='time')
mean_rbr=sail.SAL_RBR_MEAN.mean(dim='time')
sail_mean=mean_sbe-mean_rbr
print("SAILDRONE " +str(num))
print("STD Difference SBE37 - RBR = " + str(sail_diff.values))
print("RMSE = " + str(rmse_sal.values))
print("Mean Difference SBE37 - RBR = " +str(sail_mean.values))
#Comparing 8 day and orbital satellite data
##Netcdf Satellite files into xarray
SMAP_8RSS=xr.open_mfdataset(data_dir+'*RSS*.nc',concat_dim="trajectory",combine="nested")
SMAPRSS=xr.open_mfdataset(data_sat+'*RSS*.nc',concat_dim="trajectory",combine="nested")