Merge pull request #10 from apatlpo/netcdf

Add (parallel) netcdf file writer

shenfun/utilities/nc_writer.py

@@ -0,0 +1,96 @@
+import numpy as np
+import warnings
+from shenfun import MixedTensorProductSpace
+
+# https://github.com/Unidata/netcdf4-python/blob/master/examples/mpi_example.py
+
+try:
+    from netCDF4 import Dataset
+except:
+    warnings.warn('netcdf not installed')
+
+__all__ = ('NCWriter',)
+
+
+class NCWriter(object):
+    """Class for writing data in shenfun to netcdf format
+
+    args:
+        ncname   string               Name of netcdf file to be created
+        names    list of strings      Names of fields to be stored
+        T        TensorProductSpace   Instance of a TensorProductSpace
+                                      Must be the same as the space used
+                                      for storing with 'write_tstep'
+                                      and 'write_slice_tstep'
+        clobber  boolean
+    """
+    def __init__(self, ncname, names, T, **kwargs):
+        self.f = Dataset(ncname, "w", parallel=True, comm=T.comm, **kwargs)
+        self.T = T
+        self.N = T.shape()
+        self.names = names
+        self._dtype = 'f8'
+
+        self.f.createDimension('t', None)
+        self.dims=['t']
+        self.nc_t = self.f.createVariable('t', self._dtype, ('t'))
+        self.nc_t.set_collective(True)
+
+        x = T.mesh()
+        s = self.T.local_slice(False)
+        for i, xi in enumerate(x):
+            xyz = {0:'x', 1:'y', 2:'z'}[i]
+            self.f.createDimension(xyz, np.squeeze(x[i]).size)
+            nc_xyz = self.f.createVariable(xyz, self._dtype, (xyz))
+            self.dims.append(xyz)
+            nc_xyz[s[i]] = np.squeeze(x[i][s[i]])
+
+        self.handles = dict()
+        for i,name in enumerate(names):
+            self.handles[i] = self.f.createVariable(name, self._dtype, self.dims)
+            # switch to collective mode, rewrite the data.
+            self.handles[i].set_collective(True)
+
+        self.f.sync()
+
+    def write_tstep(self, tstep, u):
+        """Write field u to netcdf format at a given time step
+
+        args:
+            tstep        int          Time step
+            u      Function/Array     The field to be stored
+
+        """
+        assert isinstance(u, np.ndarray)
+
+        # update time
+        it = self.nc_t.size
+        print(it)
+        self.nc_t[it] = tstep
+
+        if isinstance(self.T, MixedTensorProductSpace):
+            assert self.T.ndim() == len(u.shape[1:])
+            assert len(self.names) == u.shape[0]
+            s = self.T.local_slice(False)
+            for i in range(u.shape[0]):
+                if self.T.ndim() == 3:
+                    self.handles[i][it,s[0],s[1],s[2]] = u[i]
+                elif self.T.ndim() == 2:
+                    self.handles[i][it, s[0], s[1]] = u[i]
+                else:
+                    raise(NotImplementedError)
+        else:
+            assert len(self.names) == 1
+            s = self.T.local_slice(False)
+            if self.T.ndim() == 3:
+                self.handles[0][it, s[0], s[1], s[2]] = u[:]
+            elif self.T.ndim() == 2:
+                self.handles[0][it, s[0], s[1]] = u[:]
+            else:
+                raise(NotImplementedError)
+
+        self.f.sync()
+
+    def close(self):
+        self.f.close()
+