Merge branch 'feature/535_stratification_diag' of github.com:British-…

…Oceanographic-Data-Centre/COAsT into feature/535_stratification_diag

Conflicts:
	coast/data/profile.py

coast/data/profile.py

@@ -172,8 +172,9 @@ def extract_en4_profiles(self, dataset_names, region_bounds,chunks: dict = {}):
         #self.profile = Profile(config=config)
         self.read_en4(dataset_names, multiple=True, chunks = chunks)
         pr = self.subset_indices_lonlat_box(lonbounds=[x_min, x_max], latbounds=[y_min, y_max])
-        pr= pr.process_en4()
+        pr = pr.process_en4()
         return pr
+
     @staticmethod
     def make_filenames(path, dataset, yr_start, yr_stop):
         if dataset == "EN4":
@@ -186,6 +187,7 @@ def make_filenames(path, dataset, yr_start, yr_stop):
                     dataset_names.append(name)
             return dataset_names
         print("Data set not coded")
+
     """======================= Plotting ======================="""
 
     def plot_profile(self, var: str, profile_indices=None):

coast/diagnostics/gridded_monthly_hydrographic_climatology.py

@@ -29,11 +29,11 @@ def __init__(self, gridded_t, z_max=200.0):
 
     def calc_climatologies(self):
         """
-        Calculate the climatologies for SSH, sss and pea.
+                Calculate the climatologies for SSH, sss and pea.
 
-        Returns:
-#            gridded_t: Gridded dataset object.
-             dataset:  Gridded dataset object containing monthly climatologies
+                Returns:
+        #            gridded_t: Gridded dataset object.
+                     dataset:  Gridded dataset object containing monthly climatologies
         """
 
         # calculate a depth mask
@@ -49,7 +49,6 @@ def calc_climatologies(self):
         pea_monthy_clim = np.zeros((12, ny, nx))
 
         try:
-
             nyear = int(nt / 12)  # hard wired for monthly data starting in Jan
             for iy in range(nyear):
                 print("Calc pea", iy)
@@ -63,7 +62,7 @@ def calc_climatologies(self):
                     pea.calc_pea(gridded_t2, zd_mask)
                     pea_monthy_clim[im, :, :] = pea_monthy_clim[im, :, :] + pea.dataset["PEA"].values
             pea_monthy_clim = pea_monthy_clim / nyear
-        
+
         except Exception as error:
             (warn(f"Unable to perform pea calculation. Please check the error {error}"))
             debug(f"Unable to perform pea calculation. Please check the error {error}")
@@ -89,17 +88,17 @@ def calc_climatologies(self):
         attributes_sst = {"units": "o^C", "standard name": "Conservative Sea Surface Temperature"}
         attributes_sss = {"units": "", "standard name": "Absolute Sea Surface Salinity"}
         attributes_pea = {"units": "Jm^-3", "standard name": "Potential Energy Anomaly to " + str(self.z_max) + "m"}
-#jth this adds the new variables to the full data set, which makes saving difficult, easier just to keep the new variables in seperate object
-#        self.dataset = self.gridded_t.dataset["sst_monthy_clim"] = xr.DataArray(
-#            np.squeeze(sst_monthy_clim), coords=coords, dims=dims, attrs=attributes_sst
-#        )
-#        self.gridded_t.dataset["sss_monthy_clim"] = xr.DataArray(
-#            np.squeeze(sss_monthy_clim), coords=coords, dims=dims, attrs=attributes_sss
-#        )
-#        self.gridded_t.dataset["pea_monthy_clim"] = xr.DataArray(
-#            np.squeeze(pea_monthy_clim), coords=coords, dims=dims, attrs=attributes_pea
-#        )
-#        self.dataset = self.gridded_t.dataset
+        # jth this adds the new variables to the full data set, which makes saving difficult, easier just to keep the new variables in seperate object
+        #        self.dataset = self.gridded_t.dataset["sst_monthy_clim"] = xr.DataArray(
+        #            np.squeeze(sst_monthy_clim), coords=coords, dims=dims, attrs=attributes_sst
+        #        )
+        #        self.gridded_t.dataset["sss_monthy_clim"] = xr.DataArray(
+        #            np.squeeze(sss_monthy_clim), coords=coords, dims=dims, attrs=attributes_sss
+        #        )
+        #        self.gridded_t.dataset["pea_monthy_clim"] = xr.DataArray(
+        #            np.squeeze(pea_monthy_clim), coords=coords, dims=dims, attrs=attributes_pea
+        #        )
+        #        self.dataset = self.gridded_t.dataset
         self.dataset["sst_monthy_clim"] = xr.DataArray(
             np.squeeze(sst_monthy_clim), coords=coords, dims=dims, attrs=attributes_sst
         )

coast/diagnostics/profile_stratification.py

@@ -199,8 +199,12 @@ def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax):
         dims = ["id_dim"]
         attributes = {"units": "J / m^3", "standard_name": "Potential Energy Anomaly"}
         self.dataset["pea"] = xr.DataArray(pot_energy_anom, coords=coords, dims=dims, attrs=attributes)
-        self.dataset["sst"] = xr.DataArray(profile.dataset.variables["sea_surface_temperature"], coords=coords, dims=dims, attrs=attributes)
-        self.dataset["sss"] = xr.DataArray(profile.dataset.variables["sea_surface_salinity"], coords=coords, dims=dims, attrs=attributes)
+        self.dataset["sst"] = xr.DataArray(
+            profile.dataset.variables["sea_surface_temperature"], coords=coords, dims=dims, attrs=attributes
+        )
+        self.dataset["sss"] = xr.DataArray(
+            profile.dataset.variables["sea_surface_salinity"], coords=coords, dims=dims, attrs=attributes
+        )
 
     def quick_plot(self, var: xr.DataArray = None):
         """
@@ -313,4 +317,4 @@ def distance_on_grid(Y, X, jpts, ipts, Ypts, Xpts):
         DX = (Xpts - X[jpts, ipts]) * earth_radius * np.cos(Ypts * np.pi / 180.0)
         DY = (Ypts - Y[jpts, ipts]) * earth_radius
         r = np.sqrt(DX**2 + DY**2)
-        return r
+        return r