options for new CAMS version

pycif/plugins/datastreams/fields/netcdf_cams/__init__.py

+"""
+Read CAMS products
+Option to define pressure coordinate names depending on CAMS version
+"""
+
+
 from .get_domain import get_domain
 from .fetch import fetch
 from .read import read
@@ -6,3 +12,13 @@ _name = "CAMS"
 _version = "netcdf"
 _fullname = "CAMS netcdf files"
 
+
+input_arguments={
+    "aibi_name": {
+       "doc": "to choose ai bi vertical coordinate names"
+              " instead of hyam and hybm",
+       "default": False,
+       "accepted": bool
+    },
+}
+

pycif/plugins/datastreams/fields/netcdf_cams/get_domain.py

@@ -61,8 +61,12 @@ def get_domain(ref_dir, ref_file, input_dates, target_dir, tracer=None):
     latc = np.linspace(lat_min - dy/2., lat_max + dy/2., nlat + 1)
 
     # Read vertical information in domain_file
-    sigma_a = nc["hyam"].values
-    sigma_b = nc["hybm"].values
+    if tracer.aibi_name:
+         sigma_a = nc["ap"].values
+         sigma_b = nc["bp"].values
+    else :
+         sigma_a = nc["hyam"].values
+         sigma_b = nc["hybm"].values
     nlevs = sigma_a.size
 
     # Initializes domain

pycif/plugins/datastreams/fields/netcdf_cams/read.py

 import datetime
+import calendar
 import os
 import pandas as pd
 import numpy as np
@@ -50,21 +51,25 @@ def read(
             opened_file = dd_file
 
         ntimes = ds.dims["time"]
-        freq = dd[0].days_in_month * 24 / ntimes
+        #freq = dd[0].days_in_month * 24 / ntimes
+        freq = (calendar.mdays[dd[0].month] + ( dd[0].month == 2 and calendar.isleap( dd[0].year))) * 24 / ntimes
+        print("to check with february")
+        print(dd[0].month)
+        print(calendar.mdays[dd[0].month] + ( dd[0].month == 2 and calendar.isleap( dd[0].year)))
         date_index = int((dd[0] - ddi) / datetime.timedelta(hours=freq))
         
         # bottom of the atmosphere = at the beginning of the table
         lat = ds['latitude']
         conc = ds[var2extract].values[date_index]
-        if lat[1] < lat[0]:
+        if lat[1] < lat[0] and conc.ndim==4:
             conc = conc[:, :, ::-1, :]
-        
+        elif  lat[1] < lat[0] and conc.ndim==3:
+            conc = conc[ :, ::-1, :]
         xout.append(conc)
-    
+
     xmod = xr.DataArray(
         np.array(xout),
         coords={"time": np.array(dates)[:, 0]},
         dims=("time", "lev", "lat", "lon"),
     )
-    
     return xmod