Method populateFile

tables/tests/test_tables.py:211–308

(self)

        )

    def populateFile(self):
        group = self.rootgroup
        if self.recarrayinit:
            # Initialize a starting buffer, if any
            self.initRecArray()
        for j in range(3):
            # Create a table
            filterprops = tb.Filters(
                complevel=self.compress,
                shuffle=self.shuffle,
                bitshuffle=self.bitshuffle,
                fletcher32=self.fletcher32,
                complib=self.complib,
            )
            if j < 2:
                byteorder = sys.byteorder
            else:
                # table2 will be byteswapped
                byteorder = {"little": "big", "big": "little"}[sys.byteorder]
            table = self.h5file.create_table(
                group,
                "table" + str(j),
                self.record,
                title=self.title,
                filters=filterprops,
                expectedrows=self.expectedrows,
                byteorder=byteorder,
            )
            if not self.recarrayinit:
                # Get the row object associated with the new table
                row = table.row
                # Fill the table
                for i in range(self.expectedrows):
                    s = "%04d" % (self.expectedrows - i)
                    row["var1"] = s.encode("ascii")
                    row["var7"] = s[-1].encode("ascii")
                    # row['var7'] = ('%04d' % (self.expectedrows - i))[-1]
                    row["var2"] = i
                    row["var3"] = i % self.maxshort
                    if isinstance(row["var4"], np.ndarray):
                        row["var4"] = [float(i), float(i * i)]
                    else:
                        row["var4"] = float(i)
                    if isinstance(row["var8"], np.ndarray):
                        row["var8"] = [0, 1]
                    else:
                        row["var8"] = 1
                    if isinstance(row["var9"], np.ndarray):
                        row["var9"] = [0.0 + float(i) * 1j, float(i) + 0.0j]
                    else:
                        row["var9"] = float(i) + 0.0j
                    if isinstance(row["var10"], np.ndarray):
                        row["var10"] = [float(i) + 0.0j, 1.0 + float(i) * 1j]
                    else:
                        row["var10"] = 1.0 + float(i) * 1j
                    if isinstance(row["var5"], np.ndarray):
                        row["var5"] = np.array((float(i),) * 4)
                    else: