Reverted Matt's changes for regridding model output (temporarily). An…

…d created a new file that contains that work as a reference (#13)

models/algoa-bay-forecast/toolkit/post-processing/src/cli/transform/__init__.py

@@ -1,7 +1,6 @@
 import xarray as xr
 import numpy as np
 from datetime import timedelta, datetime
-import xesmf as xe #Function for regridding not sure how easy it is to add to pipenv
 from cli.transform.depth_functions import z_levels
 
 # All dates in the CROCO output are represented
@@ -120,32 +119,6 @@ def transform(options, arguments):
         attrs=dict(description="CROCO output from algoa Bay model transformed lon/lat/depth/time"),
     )
 
-    # Getting max lon and lat to define corners of new regular grid
-    lonmax = np.nanmax(lon_rho)
-    lonmin = np.nanmin(lon_rho)
-    latmax = np.nanmax(lat_rho)
-    latmin = np.nanmin(lat_rho)
-
-    #Step size of new grid ~1km
-    stepsize = 0.01
-
-    # Creating output grid in the form of a dataset
-    ds_out = xr.Dataset(
-        data_vars=dict(
-        ),
-        coords=dict(
-            time=dates,
-            depth=s_rho,
-            longitude=(["longitude"], np.arange(lonmin,lonmax,stepsize)),
-            latitude=(["latitude"],  np.arange(latmin,latmax,stepsize)),
-        ),
-        attrs=dict(description="CROCO output from algoa Bay model transformed lon/lat/depth/time"),
-    )
-
-    # Perform gridding from data_out to new dataset dr_out using the ds_out grid 
-    regridder = xe.Regridder(data_out, ds_out, "bilinear")
-    dr_out = regridder(data_out)
-
     #Print output 
-    dr_out.to_netcdf(nc_output_path)
+    data_out.to_netcdf(nc_output_path)
     print('Complete! If you don\'t see this message there was a problem')

models/algoa-bay-forecast/toolkit/post-processing/src/cli/transform/__init__.py.new-matt-work

@@ -0,0 +1,151 @@
+import xarray as xr
+import numpy as np
+from datetime import timedelta, datetime
+import xesmf as xe #Function for regridding not sure how easy it is to add to pipenv
+from cli.transform.depth_functions import z_levels
+
+# All dates in the CROCO output are represented
+# in seconds from 1 Jan 2000 (i.e. the reference date)
+REFERENCE_DATE = datetime(2000, 1, 1, 0, 0, 0)
+
+# Rounds to nearest hour by adding a timedelta hour if minute >= 30
+def hour_rounder(t):
+    return (t.replace(second=0, microsecond=0, minute=0, hour=t.hour) + timedelta(hours=t.minute//30))
+
+# Converts the v current component to the correct (rho) grid
+def v2rho_4d(var_v):
+      [T,D,M,Lp]=var_v.shape
+      var_rho=np.zeros((T,D,M+1,Lp))
+      var_rho[:,:,1:M-1,:]=0.5*np.squeeze([var_v[:,:,0:M-2,:]+var_v[:,:,1:M-1,:]])
+      var_rho[:,:,0,:]=var_rho[:,:,1,:]
+      var_rho[:,:,M,:]=var_rho[:,:,M-1,:]
+      return var_rho
+
+# Converts the u current component to the correct (rho) grid
+def u2rho_4d(var_u):
+      [T,D,Mp,L]=var_u.shape
+      var_rho=np.zeros((T,D,Mp,L+1))
+      var_rho[:,:,:,1:L-1]=0.5*np.squeeze([var_u[:,:,:,0:L-2]+var_u[:,:,:,1:L-1]])
+      var_rho[:,:,:,0]=var_rho[:,:,:,1]
+      var_rho[:,:,:,L]=var_rho[:,:,:,L-1]
+      return var_rho
+
+# Model variables use the dimensions time (time from reference date),
+# eta_rho (lat) and xi_rho (lon). We are changing eta_rho and xi_rho 
+# from grid points to real lat and lon data.
+def transform(options, arguments):
+    #Setting the paths using the bash input 
+    nc_output_path = options.nc_output_path
+    nc_input_path = options.nc_input_path
+    grid_input_path = options.grid_input_path
+
+    print('== Running Algoa Bay Forecast post-processing ==')
+    print('nc-input-path', options.nc_input_path)
+    print('grid-input-path', options.grid_input_path)
+    print('nc-output-path', options.nc_output_path)
+
+    data = xr.open_dataset(nc_input_path)
+    data_grid = xr.open_dataset(grid_input_path)
+
+    # Dimensions that need to be transformed
+    time = data.time.values
+    lon_rho = data.lon_rho.values
+    lat_rho = data.lat_rho.values
+    s_rho = data.s_rho.values
+
+    # Convert time to human readable 
+    dates = []
+    for t in time:
+        date_now = REFERENCE_DATE + timedelta(seconds=np.float64(t))
+        date_round = hour_rounder(date_now)
+        dates.append(date_round)
+
+    # Variables used in the visualisations
+    temperature = data.temp.values
+    salt = data.salt.values
+    ssh = data.zeta.values
+    u = data.u.values
+    v = data.v.values
+
+    #Variables used to calculate depth levels
+    theta_s = data.theta_s
+    theta_b = data.theta_b
+
+    #Variables used to calculate depth levels from grid (bathymetry)
+    h = data_grid.h.values
+
+    # Convert u and v current components to the rho grid
+    # use the function u2rho_4d and v2rho_4d
+    u_rho = u2rho_4d(u)
+    v_rho = v2rho_4d(v)
+
+    # Replace temperatures = 0 celsius with nan
+    # In this dataset a temperature of 0 is representative
+    # of a grid location that is not water (over land)
+    temperature[np.where(temperature == 0)] = np.nan
+    salt[np.where(salt == 0)] = np.nan
+    u[np.where(u == 0)] = np.nan
+    v[np.where(v == 0)] = np.nan
+
+    # Variables hard coded set during model configuration
+    # Relative to each model 
+    hc = 200
+    N = np.shape(data.s_rho)[0]
+    type_coordinate = 'rho'
+    vtransform = 2
+
+    # m_rho refers to the depth level in meters
+    m_rho = np.zeros(np.shape(temperature))
+
+    for x in np.arange(np.size(temperature,0)):    
+        depth_temp = z_levels(h,ssh[x,:,:],theta_s,theta_b,hc,N,type_coordinate,vtransform)
+        m_rho[x,::] = depth_temp
+
+    # Create new xarray dataset with selected variables 
+    data_out = xr.Dataset(
+        data_vars=dict(
+            temperature=(["time","depth","lat", "lon"], temperature[:,:,:,:]),
+            salt=(["time","depth","lat", "lon"], salt[:,:,:,:]),
+            u=(["time","depth","lat", "lon"], u_rho[:,:,:,:]),
+            v=(["time","depth","lat", "lon"], v_rho[:,:,:,:]),
+            m_rho= (["time","depth","lat","lon"],m_rho)
+
+        ),
+        coords=dict(
+            lon_rho=(["lat", "lon"], lon_rho),
+            lat_rho=(["lat", "lon"], lat_rho),
+            depth=s_rho,
+            time=dates,
+        ),
+        attrs=dict(description="CROCO output from algoa Bay model transformed lon/lat/depth/time"),
+    )
+
+    # Getting max lon and lat to define corners of new regular grid
+    lonmax = np.nanmax(lon_rho)
+    lonmin = np.nanmin(lon_rho)
+    latmax = np.nanmax(lat_rho)
+    latmin = np.nanmin(lat_rho)
+
+    #Step size of new grid ~1km
+    stepsize = 0.01
+
+    # Creating output grid in the form of a dataset
+    ds_out = xr.Dataset(
+        data_vars=dict(
+        ),
+        coords=dict(
+            time=dates,
+            depth=s_rho,
+            longitude=(["longitude"], np.arange(lonmin,lonmax,stepsize)),
+            latitude=(["latitude"],  np.arange(latmin,latmax,stepsize)),
+        ),
+        attrs=dict(description="CROCO output from algoa Bay model transformed lon/lat/depth/time"),
+    )
+
+    # Perform gridding from data_out to new dataset dr_out using the ds_out grid 
+    regridder = xe.Regridder(data_out, ds_out, "bilinear")
+    dr_out = regridder(data_out)
+
+    #Print output 
+    dr_out.to_netcdf(nc_output_path)
+    print('Complete! If you don\'t see this message there was a problem')