(self)
| 37 | self.h5file.close() |
| 38 | |
| 39 | def populateFile(self): |
| 40 | group = self.rootgroup |
| 41 | obj = self.obj |
| 42 | if obj is None: |
| 43 | if self.type == "string": |
| 44 | atom = tb.StringAtom(itemsize=self.length) |
| 45 | else: |
| 46 | atom = tb.Atom.from_type(self.type) |
| 47 | else: |
| 48 | atom = None |
| 49 | title = self.__class__.__name__ |
| 50 | filters = tb.Filters( |
| 51 | complevel=self.compress, |
| 52 | complib=self.complib, |
| 53 | shuffle=self.shuffle, |
| 54 | fletcher32=self.fletcher32, |
| 55 | ) |
| 56 | earray = self.h5file.create_earray( |
| 57 | group, |
| 58 | "earray1", |
| 59 | atom=atom, |
| 60 | shape=self.shape, |
| 61 | title=title, |
| 62 | filters=filters, |
| 63 | expectedrows=1, |
| 64 | obj=obj, |
| 65 | ) |
| 66 | earray.flavor = self.flavor |
| 67 | |
| 68 | # Fill it with rows |
| 69 | self.rowshape = list(earray.shape) |
| 70 | if obj is not None: |
| 71 | self.rowshape[0] = 0 |
| 72 | self.objsize = self.length |
| 73 | for i in self.rowshape: |
| 74 | if i != 0: |
| 75 | self.objsize *= i |
| 76 | self.extdim = earray.extdim |
| 77 | self.objsize *= self.chunksize |
| 78 | self.rowshape[earray.extdim] = self.chunksize |
| 79 | |
| 80 | if self.type == "string": |
| 81 | object = np.ndarray( |
| 82 | buffer=b"a" * self.objsize, |
| 83 | shape=self.rowshape, |
| 84 | dtype="S%s" % earray.atom.itemsize, |
| 85 | ) |
| 86 | else: |
| 87 | object = np.arange(self.objsize, dtype=earray.atom.dtype.base) |
| 88 | object.shape = self.rowshape |
| 89 | |
| 90 | if common.verbose: |
| 91 | if self.flavor == "numpy": |
| 92 | print("Object to append -->", object) |
| 93 | else: |
| 94 | print("Object to append -->", repr(object)) |
| 95 | for i in range(self.nappends): |
| 96 | if self.type == "string": |
no test coverage detected