Method test01_iterEArray

tables/tests/test_earray.py:127–222

Checking enlargeable array iterator.

(self)

        self.assertEqual(obj.atom.type, self.type)

    def test01_iterEArray(self):
        """Checking enlargeable array iterator."""

        if common.verbose:
            print("\n", "-=" * 30)
            print("Running %s.test01_iterEArray..." % self.__class__.__name__)

        # Create an instance of an HDF5 Table
        if self.reopen:
            self._reopen()
        earray = self.h5file.get_node("/earray1")

        # Choose a small value for buffer size
        earray.nrowsinbuf = 3
        if common.verbose:
            print("EArray descr:", repr(earray))
            print("shape of read array ==>", earray.shape)
            print("reopening?:", self.reopen)

        # Build the array to do comparisons
        if self.type == "string":
            object_ = np.ndarray(
                buffer=b"a" * self.objsize,
                shape=self.rowshape,
                dtype="S%s" % earray.atom.itemsize,
            )
        else:
            object_ = np.arange(self.objsize, dtype=earray.atom.dtype.base)
            object_.shape = self.rowshape
        object_ = object_.swapaxes(earray.extdim, 0)

        if self.obj is not None:
            initialrows = len(self.obj)
        else:
            initialrows = 0

        shape = self._get_shape()

        # Read all the array
        for idx, row in enumerate(earray):
            if idx < initialrows:
                self.assertTrue(
                    common.allequal(
                        row, np.asarray(self.obj[idx]), self.flavor
                    )
                )
                continue

            chunk = int((earray.nrow - initialrows) % self.chunksize)
            if chunk == 0:
                if self.type == "string":
                    object__ = object_
                else:
                    i = int(earray.nrow - initialrows)
                    object__ = object_ * (i // self.chunksize)

            object = object__[chunk]
            # The next adds much more verbosity