xdem.EPC.get_stats

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xdem.EPC.get_stats#

EPC.get_stats(stats_name=None)#
Overloads:

  • self, stats_name (str | Callable[[NDArrayNum], np.floating[Any]]) → np.floating[Any]

  • self, stats_name (list[str | Callable[[NDArrayNum], np.floating[Any]]] | None) → dict[str, np.floating[Any]]

Retrieve specified statistics or all available statistics for the point cloud data. Allows passing custom callables to calculate custom stats.

Common statistics for an N-D array :

  • Mean: arithmetic mean of the data, ignoring masked values.

  • Median: middle value when the valid data points are sorted in increasing order, ignoring masked values.

  • Max: maximum value among the data, ignoring masked values.

  • Min: minimum value among the data, ignoring masked values.

  • Sum: sum of all data, ignoring masked values.

  • Sum of squares: sum of the squares of all data, ignoring masked values.

  • 90th percentile: point below which 90% of the data falls, ignoring masked values.

  • IQR (Interquartile Range): difference between the 75th and 25th percentile of a dataset,

ignoring masked values. - LE90 (Linear Error with 90% confidence): difference between the 95th and 5th percentiles of a dataset, representing the range within which 90% of the data points lie. Ignore masked values. - NMAD (Normalized Median Absolute Deviation): robust measure of variability in the data, less sensitive to outliers compared to standard deviation. Ignore masked values. - RMSE (Root Mean Square Error): commonly used to express the magnitude of errors or variability and can give insight into the spread of the data. Only relevant when the raster represents a difference of two objects. Ignore masked values. - Std (Standard deviation): measures the spread or dispersion of the data around the mean, ignoring masked values. - Valid count: number of finite data points in the array. It counts the non-masked elements. - Total count: total size of the raster. - Percentage valid points: ratio between Valid count and Total count.

For all statistics up to “Std”, functions from numpy.ma module are used (directly or in the calculation) in case of a masked array, numpy module otherwise.

“Valid count” represents all non zero and not masked pixels in the input data (final_count_nonzero). Numpy Masked functions is used is this case or if the Point Cloud was already a masked array. Percentage valid points is calculated accordingly.

If an inlier mask is passed: - Total inlier count: number of data points in the inlier mask. - Valid inlier count: number of unmasked data points in the array after applying the inlier mask. - Percentage inlier points: ratio between Valid inlier count and Valid count. Useful for classification statistics. - Percentage valid inlier points: ratio between Valid inlier count and Total inlier count.

They are all computed based on the previously stated final_count_nonzero.

Callable functions are supported as well.

Parameters:

stats_name (str | Callable[[ndarray[tuple[Any, ...], dtype[floating[Any] | integer[Any]]]], floating[Any]] | list[str | Callable[[ndarray[tuple[Any, ...], dtype[floating[Any] | integer[Any]]]], floating[Any]]] | None) – Name or list of names of the statistics to retrieve. If None, all statistics are returned. Accepted names include: mean, median, max, min, sum, sum of squares, 90th percentile, LE90, nmad, rmse, std, valid count, total count, percentage valid points and if an inlier mask is passed : valid inlier count, total inlier count, percentage inlier point, percentage valid inlier points. Custom callables can also be provided.

Returns:

The requested statistic or a dictionary of statistics if multiple or all are requested.

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