meta_data test script

inputs/namelist_cmems.bdy

@@ -90,7 +90,7 @@
     cn_coords_file = 'coordinates.bdy.nc' !  name of bdy coordinates files 
                                           !  (if ln_coords_file=.TRUE.)
     ln_mask_file   = .false.              !  =T : read mask from file
-    cn_mask_file   = '/Users/thopri/Projects/PyNEMO/inputs/subset_mask.nc'            !  name of mask file
+    cn_mask_file   = 'mask.nc'            !  name of mask file
                                           !  (if ln_mask_file=.TRUE.)
     ln_dyn2d       = .false.              !  boundary conditions for 
                                           !  barotropic fields

pynemo/gui/nemo_bdy_mask.py

@@ -72,8 +72,8 @@ class Mask(object):
                 self.lon = np.asarray(self.bathy_nc.variables['nav_lon'])
                 self.lat = np.asarray(self.bathy_nc.variables['nav_lat'])
             except:
-                self.lon = np.asarray(self.bathy_nc.variables['lon'])
-                self.lat = np.asarray(self.bathy_nc.variables['lat'])
+                self.lon = np.asarray(self.bathy_nc.variables['longitude'])
+                self.lat = np.asarray(self.bathy_nc.variables['latitude'])
                 # expand lat and lon 1D arrays into 2D array matching nav_lat nav_lon
                 self.lon = np.tile(self.lon, (np.shape(self.lat)[0], 1))
                 self.lat = np.tile(self.lat, (np.shape(self.lon)[1], 1))

test_scripts/meta_data.py

+# -*- coding: utf-8 -*-
+"""
+Set of functions to download CMEMS files using FTP (for static mask data) and MOTU (for subsetted variable data).
+
+"""
+from netCDF4 import Dataset
+import re
+# list of datasets to check
+datasets = [ "http://opendap4gws.jasmin.ac.uk/thredds/noc_msm/dodsC/pynemo_data/ORCA025-N206_19791101d05T.nc",
+             "http://opendap4gws.jasmin.ac.uk/thredds/noc_msm/dodsC/pynemo_data/ORCA025-N206_19791101d05U.nc",
+             "http://opendap4gws.jasmin.ac.uk/thredds/noc_msm/dodsC/pynemo_data/ORCA025-N206_19791101d05V.nc",
+             "http://opendap4gws.jasmin.ac.uk/thredds/noc_msm/dodsC/pynemo_data/ORCA025-N206_19791101d05I.nc"
+            ]
+
+# list of strings to catagorise variable names... each entry can have multiple entries.
+# NOTE list in each dict entry is in priority order so most likely parameter should be first, e.g. latitude
+# DOUBLE NOTE order in dict is also important as it can result in false ID's e.g. ice data for some reason has long name
+# sea surface height so if SSH is in dict before ice variables it will assign ice variable names to SSH.
+
+chk_list = {'temperature': ['temp'],
+            'salinity': ['sal'],
+            'ice_thic': ['icethic'],
+            'snow_thic': ['snowthi'],
+            'ileadfra': ['leadfra'],
+            'SSH': ['surface', 'sea'],
+            'depth': ['depth'],
+            'time': ['time', 'counter'],
+            'latitude': ['latitude', 'y', 'nav_lat'],
+            'longitude': ['longitude', 'x', 'nav_lon'],
+            'depth': ['depth'],
+            'U': ['zonal', 'current'],
+            'V': ['meridional', 'current'],
+            }
+
+# function to use regex to find if string is in variable name or if not check long name. Case of string is ignored
+# Attribute errors are common due to long name not existing in some datasets at the moment this error is passed (maybe log?)
+def data_chk(data, str, key):
+    for i in range(len(str)):
+        try:
+            chk = re.search(str[i], data[key].name, re.IGNORECASE)
+            if chk is None:
+                chk = re.search(str[i], data[key].long_name, re.IGNORECASE)
+            return chk
+        except AttributeError:
+            pass
+
+i = 0
+meta_dataset = {}
+
+for dat in datasets:
+    # open netcdf dataset
+    F = Dataset(dat)
+    # extract variable meta data and dimension meta data
+    meta = F.variables
+    dims = F.dimensions
+    # create empty dict to save catagorised data
+    meta_dataset['dataset'+str(i)] = {}
+    meta_dataset['dataset'+str(i)]['var_names'] = {}
+    meta_dataset['dataset'+str(i)]['dim_names'] = {}
+
+    # for all variable names, compare strings on chk list and write key to meta dict on first match
+    for key in meta:
+        for chk_key,chk in chk_list.items():
+            var_match = data_chk(meta,chk,key)
+            if var_match is not None:
+                meta_dataset['dataset'+str(i)]['var_names'][chk_key] = key
+                break
+
+    # for all dimension names, compare strings on chk list and write key to meta dict on first match
+    for key in dims:
+        for chk_key,chk in chk_list.items():
+            dim_match = data_chk(dims,chk,key)
+            if dim_match is not None:
+                meta_dataset['dataset'+str(i)]['dim_names'][chk_key] = key
+                break
+    i = i + 1
+    # close netcdf file and print meta dict
+    F.close()
+
+print(meta_dataset)
+
+