Method test01_readCArray_out_argument

tables/tests/test_carray.py:188–247

Checking read() of chunked layout arrays.

(self)

        self.assertTrue(common.allequal(data, obj, self.flavor))

    def test01_readCArray_out_argument(self):
        """Checking read() of chunked layout arrays."""

        # Create an instance of an HDF5 Table
        if self.reopen:
            self.h5file = tb.open_file(self.h5fname, "r")
        carray = self.h5file.get_node("/carray1")

        shape = self._get_shape()

        # Choose a small value for buffer size
        carray.nrowsinbuf = 3
        # Build the array to do comparisons
        if self.flavor == "numpy":
            if self.type == "string":
                object_ = np.ndarray(
                    buffer=b"a" * self.objsize,
                    shape=self.shape,
                    dtype=f"S{carray.atom.itemsize}",
                )
            else:
                object_ = np.arange(self.objsize, dtype=carray.atom.dtype)
                object_.shape = shape

        stop = self.stop
        # stop == None means read only the element designed by start
        # (in read() contexts)
        if self.stop is None:
            if self.start == -1:  # corner case
                stop = carray.nrows
            else:
                stop = self.start + 1
        # Protection against number of elements less than existing
        # if rowshape[self.extdim] < self.stop or self.stop == 0:
        if carray.nrows < stop:
            # self.stop == 0 means last row only in read()
            # and not in [::] slicing notation
            stop = int(carray.nrows)
        # do a copy() in order to ensure that len(object._data)
        # actually do a measure of its length
        obj = object_[self.start : stop : self.step].copy()

        # Read all the array
        try:
            data = np.empty(shape, dtype=carray.atom.dtype)
            data = data[self.start : stop : self.step].copy()
            carray.read(self.start, stop, self.step, out=data)
        except IndexError:
            if self.flavor == "numpy":
                data = np.empty(shape=shape, dtype=self.type)
            else:
                data = np.empty(shape=shape, dtype=self.type)

        if hasattr(data, "shape"):
            self.assertEqual(len(data.shape), len(shape))
        else:
            # Scalar case
            self.assertEqual(len(shape), 1)