Function _migrate_histogram_value

tensorboard/data_compat.py:83–123

Convert `old-style` histogram value to `new-style`. The "old-style" format can have outermost bucket limits of -DBL_MAX and DBL_MAX, which are problematic for visualization. We replace those here with the actual min and max values seen in the input data, but then in order to avoid i

(value)

def _migrate_histogram_value(value):
    """Convert `old-style` histogram value to `new-style`.

    The "old-style" format can have outermost bucket limits of -DBL_MAX and
    DBL_MAX, which are problematic for visualization. We replace those here
    with the actual min and max values seen in the input data, but then in
    order to avoid introducing "backwards" buckets (where left edge > right
    edge), we first must drop all empty buckets on the left and right ends.
    """
    histogram_value = value.histo
    bucket_counts = histogram_value.bucket
    # Find the indices of the leftmost and rightmost non-empty buckets.
    n = len(bucket_counts)
    start = next((i for i in range(n) if bucket_counts[i] > 0), n)
    end = next((i for i in reversed(range(n)) if bucket_counts[i] > 0), -1)
    if start > end:
        # If all input buckets were empty, treat it as a zero-bucket
        # new-style histogram.
        buckets = np.zeros([0, 3], dtype=np.float32)
    else:
        # Discard empty buckets on both ends, and keep only the "inner"
        # edges from the remaining buckets. Note that bucket indices range
        # from `start` to `end` inclusive, but bucket_limit indices are
        # exclusive of `end` - this is because bucket_limit[i] is the
        # right-hand edge for bucket[i].
        bucket_counts = bucket_counts[start : end + 1]
        inner_edges = histogram_value.bucket_limit[start:end]
        # Use min as the left-hand limit for the first non-empty bucket.
        bucket_lefts = [histogram_value.min] + inner_edges
        # Use max as the right-hand limit for the last non-empty bucket.
        bucket_rights = inner_edges + [histogram_value.max]
        buckets = np.array(
            [bucket_lefts, bucket_rights, bucket_counts], dtype=np.float32
        ).transpose()

    summary_metadata = histogram_metadata.create_summary_metadata(
        display_name=value.metadata.display_name or value.tag,
        description=value.metadata.summary_description,
    )

    return make_summary(value.tag, summary_metadata, buckets)


def _migrate_image_value(value):