Don't interpolate volumes at beginning/end of run #950
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tsalo
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bug
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@mattcieslak would you mind looking over the core change? |
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| To address this, XCP-D now replaces interpolated volumes at the edges of the run with the | ||
| closest non-outlier volume's data, as of 0.5.1. | ||
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I think this is an appropriate level of concern/warning
| "sub-1648798153_ses-PNC1_task-rest_acq-singleband_desc-confounds_timeseries.tsv", | ||
| ) | ||
| motion_df = pd.read_table(motion_file) | ||
| motion_df.loc[56:, "trans_x"] = np.arange(1, 5) * 20 |
| # Create an updated censoring file with outliers at first and last two volumes | ||
| censoring_df = confounds_df[["framewise_displacement"]] | ||
| censoring_df["framewise_displacement"] = False | ||
| censoring_df.iloc[:2]["framewise_displacement"] = True |
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It was just arbitrary. It could have been any number >= 1.
| if outlier_idx: | ||
| gaps = [[s, e] for s, e in zip(outlier_idx, outlier_idx[1:]) if s + 1 < e] | ||
| edges = iter(outlier_idx[:1] + sum(gaps, []) + outlier_idx[-1:]) | ||
| consecutive_outliers_idx = list(zip(edges, edges)) |
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This is hard for me to follow intuitively, but I tested it a bunch and it seems to work fine.
| ] = interpolated_unfiltered_bold[first_outliers[1] + 1, :] | ||
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| # Replace outliers at end of run | ||
| if last_outliers[1] == n_volumes - 1: |
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The problem only arises if the very last or very first points are censored?
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Yeah, it seems like cubic spline interpolation works well as long as there's some data before and after the points being interpolated, so the problem comes from interpolating with point only on one side.
Closes #949.
Changes proposed in this pull request
constantbuffer we use for the bandpass filter.Documentation that should be reviewed