fix NORKYST800 old and new model set up

docs/index.rst

@@ -96,7 +96,7 @@ Several options for **product** are available. Please check the data catalog for
 * For coastal wave NORA3 data: product='NORAC_wave'
 
   Dataset: https://thredds.met.no/thredds/catalog/norac_wave/field/catalog.html
-* For ocean data (temperature, currents, salinity etc) Norkyst800 data: product='NORKYST800'
+* For ocean data (sea level, temperature, currents, salinity over depth ) Norkyst800 data (from 2016-09-14 to today): product='NORKYST800'
 
   Dataset: https://thredds.met.no/thredds/fou-hi/norkyst800v2.html
 

examples/example_import_NORA3.py

@@ -8,12 +8,12 @@
 #df_ts = ts.TimeSeries(lon=1.320, lat=53.324,start_time='2000-01-01', end_time='2001-03-31' , product='NORA3_stormsurge')
 #df_ts = ts.TimeSeries(lon=1.320, lat=53.324,start_time='2021-01-01', end_time='2021-03-31' , product='NORA3_atm_sub')
 #df_ts = ts.TimeSeries(lon=3.7, lat=61.8, start_time='2023-01-01', end_time='2023-02-01', product='NORA3_atm3hr_sub')
+#df_ts = ts.TimeSeries(lon=3.73, lat=64.60,start_time='2019-02-26', end_time='2019-02-27' , product='NORKYST800')
 
 
 # Import data from thredds.met.no and save it as csv
 df_ts.import_data(save_csv=True)
 
-#print(df_ts.data)
 # Load data from a local csv-file
 #df_ts.load_data(local_file=df_ts.datafile)
 

metocean_api/ts/aux_funcs.py

@@ -77,9 +77,9 @@ def get_url_info(product, date):
         x_coor_str = 'eta_rho'
         y_coor_str = 'xi_rho'
     elif product == 'NORKYST800':     
-        if date<pd.Timestamp('2017-02-20 00:00:00'):
+        if date>=pd.Timestamp('2016-09-14 00:00:00') and date<=pd.Timestamp('2019-02-26 00:00:00'):
             infile = 'https://thredds.met.no/thredds/dodsC/sea/norkyst800mv0_1h/NorKyst-800m_ZDEPTHS_his.an.'+date.strftime('%Y%m%d%H')+'.nc'
-        else:
+        elif date>pd.Timestamp('2019-02-26 00:00:00'):
             infile = 'https://thredds.met.no/thredds/dodsC/fou-hi/norkyst800m-1h/NorKyst-800m_ZDEPTHS_his.an.'+date.strftime('%Y%m%d%H')+'.nc'
         x_coor_str = 'X'
         y_coor_str = 'Y'
@@ -197,19 +197,28 @@ def create_dataframe(product,ds, lon_near, lat_near,outfile,variable, start_time
         ds = ds.drop_vars('longitude')
     elif product=='NORKYST800': 
         ds0 = ds
-        breakpoint()
+        if 'depth' in ds['zeta'].dims:
+            ds['zeta'] = ds.zeta.sel(depth=0)
+
+        var_list = []
+        for var_name in variable:
+        # Check if 'depth' is not in the dimensions of the variable
+            if 'depth' in ds[var_name].dims:
+            # Append variable name to the list
+                var_list.append(var_name)
+
         for i in range(len(height)):
-            if 'depth' in ds0[variable].dims:  # Check if 'depth' is a dimension of the variable
-                variable_height = [k + '_'+str(height[i])+'m' for k in variable]
-                ds[variable_height] = ds0[variable].sel(depth=height[i])
-        ds = ds.drop_vars(variable)
+                variable_height = [k + '_'+str(height[i])+'m' for k in var_list]
+                ds[variable_height] = ds0[var_list].sel(depth=height[i],method='nearest')
+        ds = ds.drop_vars(var_list)
         ds = ds.drop_vars('depth')
         ds = ds.drop_vars('lat')
         ds = ds.drop_vars('lon')
         ds = ds.drop_vars('X')
         ds = ds.drop_vars('Y')
         ds = ds.drop_vars('projection_stere')
         ds = ds.squeeze(drop=True)
+
     else: 
         drop_var = drop_variables(product=product) 
         ds = ds.drop_vars(drop_var)
@@ -218,7 +227,7 @@ def create_dataframe(product,ds, lon_near, lat_near,outfile,variable, start_time
     df = ds.to_dataframe()
     df = df.astype(float).round(2)
     df.index = pd.DatetimeIndex(data=ds.time.values)
-    
+
     top_header = '#'+product + ';LONGITUDE:'+str(lon_near.round(4))+';LATITUDE:' + str(lat_near.round(4)) 
     list_vars = [i for i in ds.data_vars]
     vars_info = ['#Variable_name;standard_name;long_name;units']
@@ -272,5 +281,22 @@ def read_commented_lines(datafile):
                 commented_lines = np.append(commented_lines,line)
     return commented_lines
 
-
-
+def remove_dimensions_from_netcdf(input_file, dimensions_to_remove=['X', 'Y']):
+    """
+    Remove specified dimensions from all variables in a NetCDF file and save the result to a new file.
+    
+    Parameters:
+    input_file (str): Path to the input NetCDF file.
+    output_file (str): Path to the output NetCDF file.
+    dimensions_to_remove (list): A list of dimensions to remove from each variable.
+    """
+    # Open the input NetCDF file using xarray
+    ds = xr.open_dataset(input_file)
+
+    # Squeeze out the dimensions specified if they are singleton dimensions
+    for dim in dimensions_to_remove:
+        if dim in ds.dims and ds.sizes[dim] == 1:
+            ds = ds.squeeze(dim=dim)
+
+    # Write the updated dataset to the output NetCDF file
+    ds.to_netcdf(input_file)

metocean_api/ts/read_metno.py

@@ -8,8 +8,7 @@
 from nco import Nco
 from pathlib import Path
 
-
-from .aux_funcs import get_date_list, get_url_info, get_near_coord, create_dataframe, check_datafile_exists, read_commented_lines
+from .aux_funcs import *
 
 def NORAC_ts(self, save_csv = False, save_nc = False):
     """
@@ -218,17 +217,19 @@ def NORKYST800_ts(self, save_csv = False, save_nc = False):
     # extract point and create temp files
     for i in range(len(date_list)):
         x_coor_str, y_coor_str, infile = get_url_info(product=self.product, date=date_list[i])
-             
-        if i==0:
+
+        if i==0 or date_list[i].strftime('%Y-%m-%d %H:%M:%S') == '2019-02-27 00:00:00': # '2019-02-27' change to new model set up
             x_coor, y_coor, lon_near, lat_near = get_near_coord(infile=infile, lon=self.lon, lat=self.lat, product=self.product)        
 
         opt = ['-O -v '+",".join(self.variable)+' -d '+x_coor_str+','+str(x_coor.values[0])+' -d '+y_coor_str+','+str(y_coor.values[0])]
 
         apply_nco(infile,tempfile[i],opt)
+        remove_dimensions_from_netcdf(tempfile[i], dimensions_to_remove=['X', 'Y'])
 
     check_datafile_exists(self.datafile)
     #merge temp files
-    ds = xr.open_mfdataset(paths=tempfile[:])    
+    existing_files = [f for f in tempfile if os.path.exists(f)]
+    ds = xr.open_mfdataset(paths=existing_files[:])
     #Save in csv format   
     df = create_dataframe(product=self.product,ds=ds, lon_near=lon_near, lat_near=lat_near, outfile=self.datafile, variable=self.variable[:-2], start_time = self.start_time, end_time = self.end_time, save_csv=save_csv,save_nc = save_nc, height=self.height)    
     ds.close()

tests/test_extract_data.py

@@ -62,10 +62,12 @@ def test_extract_OBS():
 
 def test_NORKYST800():
     # Define TimeSeries-object
-    df_ts = ts.TimeSeries(lon=1.320, lat=53.324,start_time='2020-01-01', end_time='2020-01-02', product='NORKYST800')
+    df_ts = ts.TimeSeries(lon=3.73, lat=64.60,start_time='2020-09-14', end_time='2020-09-14', product='NORKYST800')
     # Import data from thredds.met.no 
     df_ts.import_data(save_csv=False,save_nc=False)
-    if df_ts.data.shape == (48, 144):
+    if df_ts.data.shape == (24, 65):
         pass
     else:
         raise ValueError("Shape is not correct")   
+
+