Checking chunked layout array __setitem__ special method.
(self)
| 311 | self.assertTrue(common.allequal(data, obj, self.flavor)) |
| 312 | |
| 313 | def test03_setitemCArray(self): |
| 314 | """Checking chunked layout array __setitem__ special method.""" |
| 315 | |
| 316 | if common.verbose: |
| 317 | print("\n", "-=" * 30) |
| 318 | print( |
| 319 | "Running %s.test03_setitemCArray..." % self.__class__.__name__ |
| 320 | ) |
| 321 | |
| 322 | if not hasattr(self, "slices"): |
| 323 | # If there is not a slices attribute, create it |
| 324 | self.slices = (slice(self.start, self.stop, self.step),) |
| 325 | |
| 326 | # Create an instance of an HDF5 Table |
| 327 | if self.reopen: |
| 328 | self.h5file = tb.open_file(self.h5fname, "a") |
| 329 | carray = self.h5file.get_node("/carray1") |
| 330 | |
| 331 | if common.verbose: |
| 332 | print("CArray descr:", repr(carray)) |
| 333 | print("shape of read array ==>", carray.shape) |
| 334 | print("reopening?:", self.reopen) |
| 335 | |
| 336 | shape = self._get_shape() |
| 337 | |
| 338 | # Build the array to do comparisons |
| 339 | if self.type == "string": |
| 340 | object_ = np.ndarray( |
| 341 | buffer=b"a" * self.objsize, |
| 342 | shape=self.shape, |
| 343 | dtype=f"S{carray.atom.itemsize}", |
| 344 | ) |
| 345 | else: |
| 346 | object_ = np.arange(self.objsize, dtype=carray.atom.dtype) |
| 347 | object_.shape = shape |
| 348 | |
| 349 | # do a copy() in order to ensure that len(object._data) |
| 350 | # actually do a measure of its length |
| 351 | obj = object_.__getitem__(self.slices).copy() |
| 352 | |
| 353 | if self.type == "string": |
| 354 | if hasattr(self, "wslice"): |
| 355 | obj[self.wslize] = "xXx" |
| 356 | carray[self.wslice] = "xXx" |
| 357 | elif sum(obj[self.slices].shape) != 0: |
| 358 | obj[:] = "xXx" |
| 359 | if obj.size > 0: |
| 360 | carray[self.slices] = obj |
| 361 | else: |
| 362 | if hasattr(self, "wslice"): |
| 363 | obj[self.wslice] = obj[self.wslice] * 2 + 3 |
| 364 | carray[self.wslice] = carray[self.wslice] * 2 + 3 |
| 365 | elif sum(obj[self.slices].shape) != 0: |
| 366 | obj = obj * 2 + 3 |
| 367 | if np.prod(obj.shape) > 0: |
| 368 | carray[self.slices] = carray[self.slices] * 2 + 3 |
| 369 | # Cast again object to its original type |
| 370 | obj = np.array(obj, dtype=carray.atom.dtype) |
nothing calls this directly
no test coverage detected