1666e3ac5a46912b5e16f452e39bfdcf028e59ae,hyperspy/io_plugins/usid_hdf5.py,,_get_dim_dict,#Any#Any#Any#Any#,38

Before Change

                // often we end up here. In most cases,
                step_avg = step_size.max()
                step_size -= step_avg
                var = np.mean(np.abs(step_size))
                if var / step_avg < 1E-3:
                    step_size = [step_avg]
                else:
                    // Non-linear dimension! - see notes above

After Change

            // Empty dimension!
            continue
        else:
            try:
                step_size = usid.write_utils.get_slope(dim_vals)
            except ValueError:
                // Non-linear dimension! - see notes above
                if ignore_non_linear_dims:
                    warn("Ignoring non-linearity of dimension: "
                         "{}".format(dim_name))
                    step_size = 1
                    dim_vals[0] = 0
                else:
                    raise ValueError("Cannot load provided dataset. "
                                     "Parameter: {} was varied "
                                     "non-linearly. Supply keyword "
                                     "argument "ignore_non_linear_dims="
                                     "True" to ignore this "
                                     "error".format(dim_name))

        dim_dict[dim_name] = {"size": len(dim_vals),
                              "name": dim_name,
                              "units": units,
                              "scale": step_size,

Instances