netCDF to *.csv without Loops(!)
Question: I'm having some performance and 'ugly code' problem and maybe some of you can
help. I have to export data from **netCDF-files** to ***.csv**. For this I
wrote some python code. Let's take a 3-dim netcdf-File:

    
    
    def to3dim_csv():
      var = ncf.variables['H2O'] #e.g. data for 'H2O' values
      one,two,three = var.shape #variable dimension shape e.g. (551,42,94)
      dim1,dim2,dim3 = var.dimensions #dimensions e.g. (time,lat,lon)
    
      if crit is not None:
        bool1 = foo(dim1,crit,ncf) #boolean table: ("value important?",TRUE,FALSE)
        bool2 = foo(dim2,crit,ncf)
        bool3 = foo(dim3,crit,ncf)
    
      writer.writerow([dim1,dim2,dim3,varn])
      for i in range(one):
        for k in range(two):
          for l in range(three):
            if bool1[i] and bool2[k] and bool3[l]:
              writer.writerow([
                            ncf.variables[dim1][i],
                            ncf.variables[dim2][k],
                            ncf.variables[dim3][l],
                            var[i,k,l],
                            ])
      ofile.close()
    
      # Sample csv output is like:
      # time,lat,lon,H2O
      # 1,90,10,100
      # 1,90,11,90
      # 1,91,10,101
    

I want to remove the `for val in range(d):` blocks. Perhaps using a **recursiv
function** , like:

    
    
    var = ncf.variables['H2O']
    dims = [d for d in var.dimensions]
    shapes = [var.variables[d].shape for d in dims]
    bools = [bool_table(d,crit,ncf) for d in dims]
    dims.append('H2O')
    writer.writerow(dims)
    magic_function(data)
    
    def magic_function(data):
    
       [enter code]
    
       writer.writerow(data)
       magic_function(left_data)
    

**Update:** For anyone who is interested. This works instantaneous ...

    
    
    def data_to_table(dataset, var):
        assert isinstance(dataset,xr.Dataset), 'Dataset must be xarray.Dataset'
        obj = getattr(dataset, var)
        table = np.zeros((obj.data.size, obj.data.ndim+1), dtype=np.object_)
        table[:,0] = obj.data.flat
        for i,d in enumerate(obj.dims):
            repeat = np.prod(obj.data.shape[i+1:])
            tile = np.prod(obj.data.shape[:i])
            dim = getattr(dataset, d)
            dimdata = dim.data
            dimdata = np.repeat(dimdata, repeat)
            dimdata = np.tile(dimdata, tile)
            table[:,i+1] = dimdata.flat
        return table
    
    def export_to_csv(dataset, var, filename, size=None):
        obj = getattr(dataset, var)
        header = [var] + [x for x in obj.dims]
        tabular = data_to_table(dataset, var)
        size = slice(None,size,None) if size else slice(None,None,None)
        with open(filename, 'w') as f:
            writer = csv.writer(f,dialect=csv.excel)
            writer.writerow(header)
            writer.writerows(tabular[size])
    


Answer: Something like this. Get the indexes of bol1\2\3 and combine them while
fetching the relevant values.

    
    
        with open('numpy.csv', 'wb') as f:
            out_csv = csv.writer(f)
            header = ['dim1','dim2','dim3','varn']
            out_csv.writerow(header)
            bol1_indices = np.nonzero(bol1)[0]
            bol2_indices = np.nonzero(bol2)[0]
            bol3_indices = np.nonzero(bol3)[0]
            out_csv.writerows(([a[i, k, l], dim1[i], dim2[k], dim3[l]] for i in bol1_indices for k in bol2_indices for  l in bol3_indices))
    

