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cc-plugin-ugrid's Issues

conda install -c conda-forge cc-plugin-ugrid didn't work

I tried the suggested install method, and it did not work for me:

(python3) 649 [drf@V21837 tmp]$ conda install -c conda-forge cc-plugin-ugrid
Fetching package metadata ...............

PackageNotFoundError: Packages missing in current channels:
            
  - cc-plugin-ugrid

We have searched for the packages in the following channels:
            
  - https://conda.anaconda.org/conda-forge/osx-64
  - https://conda.anaconda.org/conda-forge/noarch
  - https://conda.anaconda.org/sean/osx-64
  - https://conda.anaconda.org/sean/noarch
  - https://repo.continuum.io/pkgs/main/osx-64
  - https://repo.continuum.io/pkgs/main/noarch
  - https://repo.continuum.io/pkgs/free/osx-64
  - https://repo.continuum.io/pkgs/free/noarch
  - https://repo.continuum.io/pkgs/r/osx-64
  - https://repo.continuum.io/pkgs/r/noarch
  - https://repo.continuum.io/pkgs/pro/osx-64
  - https://repo.continuum.io/pkgs/pro/noarch
            

(python3) 650 [drf@V21837 tmp]$ 

Timeline and Milestone

Hey Folks,
Do you have a timeline for this plugin? Can we set a milestone so we can keep track of it?

UGRID test showin version 2.0

when i do compliance-checker -l from the command line i get responded with below

 - UGRID:2.0
 - acdd:1.1
 - acdd:1.3
 - cf:1.6
 - cf:1.7
 - ioos:0.1
 - ioos:1.1
 - ....

where ugrid is listed as version 2.0 but from http://ugrid-conventions.github.io/ugrid-conventions/ states that they're on version 1. Am I missing where 2.0 exists?

test fail with 2D topo using a dimension other than nfaces

Hello, trying to utilze the checker to make a compliant file and thereby utilize some of the visualization packages. I ran my file and one of the tests checks the dimension name compared to number of dimensions. checker.py

         if cty == 'edge_node_connectivity':
            _dim1 = 'nedges'
            _dim2size = 2
        elif cty == 'face_node_connectivity':
            _dim1 = 'nfaces'
            _dim2size = 3
        else:
            raise NotImplementedError # haven't dealt with real 3D grids yet

In my case (2D), the logic ends up with _dim1 = 'nfaces' and _dim2size=3. if the dimension is anythin aside from nfaces for example nFaces or faces, when the comparison is made

        if (dim1.name == _dim1) and (dim2.size == _dim2size):
            # set the attr in the meshes dict
            self.meshes[mesh][_dim1] = self.ds.dimensions[_dim1]
            return True, 'regular'
        elif (dim1.size == _dim2size) and (dim2.name == _dim1):
            self.meshes[mesh][_dim1] = self.ds.dimensions[_dim1]
            return True, 'nonstd'
        else:
            return False, None

then the test fails. I don't see where in the UGRID document that the dimension name should be nfaces (exactly), infact the example uses nFaces or nMesh2_face for 2Dtopo Does the test need to be modified to not use such an explicit comparison or am I misinterpreting the conventions?

More than one cf_role:mesh_topology should be allowed.

m = ('One variable with attribute "cf_role:mesh_topology" allowed')

One could have data on multiple grids within a file, each with a separate topology. In http://ugrid-conventions.github.io/ugrid-conventions/#topology the examples could all be concatentated together since all the mesh dimensions and definitions are all distinct.

I think the checker code should drop the >1 code, or add a +1 score to this case. Maybe:

    mt = ds.get_variables_by_attributes(cf_role='mesh_topology')
    if len(mt) >= 1:
        score += 1
    elif len(mt) < 1:
        m = ('Variable with attribute "cf_role:mesh_topology" required')
        messages.append(m)

or

    mt = ds.get_variables_by_attributes(cf_role='mesh_topology')
    if len(mt) == 1:
        score += 1
    elif len(mt) > 1:
        score += 1
        m = ('One variable with attribute "cf_role:mesh_topology" allowed')
        messages.append(m)
    elif len(mt) < 1:
        m = ('Variable with attribute "cf_role:mesh_topology" required')
        messages.append(m)

The capability for multiple meshes is an important feature for nesting and perhaps for subsetting.

conda install two different versions of `netCDF4`

conda seems to install two different versions of netCDF4 when running the TravisCI builds. While this is not a critical issue, it will undoubtedly come back to bite someone.

  • for python2.7, installs v1.4.2:
    py27condanetcdf4

  • for python3.5, installs v1.4.1:
    py35condanetcdf4

  • for python3.6, installs v1.4.2:
    py36condanetcdf4

Any insight as to why this happens is much appreciated.

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