Merge pull request #53 from Duseong/develop

Move CESM SE reader file to monetio

monetio/models/__init__.py

@@ -1,5 +1,6 @@
 from . import (
     _cesm_fv_mm,
+    _cesm_se_mm,
     _cmaq_mm,
     _rrfs_cmaq_mm,
     _wrfchem_mm,
@@ -14,6 +15,7 @@
 )
 
 __all__ = [
+    "_cesm_se_mm",
     "_cesm_fv_mm",
     "_cmaq_mm",
     "_rrfs_cmaq_mm",

monetio/models/_cesm_se_mm.py

@@ -0,0 +1,120 @@
+""" CESM File Reader """
+import xarray as xr
+
+
+def open_mfdataset(
+    fname,
+    earth_radius=6370000,
+    convert_to_ppb=True,
+    var_list=["O3", "NO", "NO2", "lat", "lon"],
+    scrip_file="",
+    **kwargs
+):
+    """Method to open multiple (or single) CESM SE netcdf files.
+       This method extends the xarray.open_mfdataset functionality
+       It is the main method called by the driver. Other functions defined
+       in this file are internally called by open_mfdataset and are preceeded
+       by an underscore (e.g. _get_latlon).
+
+    Parameters
+    ----------
+    fname : string or list
+        fname is the path to the file or files.  It will accept wildcards in
+        strings as well.
+    earth_radius : float
+        The earth radius used for map projections
+    convert_to_ppb : boolean
+        If true the units of the gas species will be converted to ppbV
+        and units of aerosols to ug m^-3
+    var_list : string or list
+        List of variables to load from the CESM file. Default is to load ozone (O3) and PM2.5 (PM25).
+    scrip_file: string
+        Scrip file path for unstructured grid output
+
+
+    Returns
+    -------
+    xarray.DataSet
+
+
+    """
+    # check that the files are netcdf format
+    names, netcdf = _ensure_mfdataset_filenames(fname)
+
+    # open the dataset using xarray
+    try:
+        if netcdf:
+            dset_load = xr.open_mfdataset(fname, **kwargs)
+        else:
+            raise ValueError
+    except ValueError:
+        print(
+            """File format not recognized. Note that files should be in netcdf
+                format. Do not mix and match file types."""
+        )
+
+    # To keep lat & lon variables in the dataset
+    if "lat" not in var_list:
+        var_list.append("lat")
+    if "lon" not in var_list:
+        var_list.append("lon")
+
+    # ===========================
+    # Process the loaded data
+    # extract variables of choice
+    dset = dset_load.get(var_list)
+    # rename altitude variable to z for monet use
+    dset = dset.rename({"lev": "z"})
+    # re-order so surface is associated with the first vertical index
+    dset = dset.sortby("z", ascending=False)
+    # ===========================
+
+    # Make sure this dataset has unstructured grid
+    dset.attrs["mio_has_unstructured_grid"] = True
+    dset.attrs["mio_scrip_file"] = scrip_file
+
+    # convert units
+    if convert_to_ppb:
+        for i in dset.variables:
+            if "units" in dset[i].attrs:
+                # convert all gas species from mol/mol to ppbv
+                if "mol/mol" in dset[i].attrs["units"]:
+                    dset[i] *= 1e09
+                    dset[i].attrs["units"] = "ppbV"
+                # convert 'kg/m3 to \mu g/m3 '
+                elif "kg/m3" in dset[i].attrs["units"]:
+                    dset[i] *= 1e09
+                    dset[i].attrs["units"] = r"$\mu g m^{-3}$"
+
+    # dset_scrip = xr.open_dataset( scrip_file )
+    # return dset, dset_scrip
+    return dset
+
+
+# -----------------------------------------
+# Below are internal functions to this file
+# -----------------------------------------
+
+
+def _ensure_mfdataset_filenames(fname):
+    """Checks if dataset in netcdf format
+    Parameters
+    ----------
+    fname : string or list of strings
+    Returns
+    -------
+    type
+    """
+    from glob import glob
+
+    from numpy import sort
+
+    if isinstance(fname, str):
+        names = sort(glob(fname))
+    else:
+        names = sort(fname)
+    netcdfs = [True for i in names if "nc" in i]
+    netcdf = False
+    if len(netcdfs) >= 1:
+        netcdf = True
+    return names, netcdf