compress_shape.py
b459f0c5f6c9master

compress_shape.py
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			from types import SimpleNamespace

			import numpy as np


			def compress_shape(decompressed_shape: np.ndarray) -> np.ndarray:
			"""
			Returns a run-length encoded compressed shape.

			Parameters
			----------
			decompressed_shape : numpy.ndarray
			Decompressed shape.

			Returns
			-------
			compressed_shape : SimpleNamespace
			A `SimpleNamespace` object containing the compressed data and corresponding shape.
			"""
			quant_factor = 1e-8
			decompressed_shape_scaled = decompressed_shape / quant_factor
			datq = np.round(np.insert(np.diff(decompressed_shape_scaled), 0, decompressed_shape_scaled[0]))
			qerr = decompressed_shape_scaled - np.cumsum(datq)
			qcor = np.insert(np.diff(np.round(qerr)), 0, 0)
			datd = datq + qcor
			mask_changes = np.insert(np.asarray(np.diff(datd) != 0, dtype=np.int), 0, 1)
			vals = datd[mask_changes.nonzero()[0]] * quant_factor

			k = np.append(mask_changes, 1).nonzero()[0]
			n = np.diff(k)

			n_extra = (n - 2).astype(np.float16) # Cast as float for nan assignment to work
			vals2 = np.copy(vals)
			vals2[n_extra < 0] = np.nan
			n_extra[n_extra < 0] = np.nan
			v = np.stack((vals, vals2, n_extra))
			v = v.T[np.isfinite(v).T] # Use transposes to match Matlab's Fortran indexing order
			v[abs(v) < 1e-10] = 0
			compressed_shape = SimpleNamespace()
			compressed_shape.num_samples = len(decompressed_shape)
			compressed_shape.data = v

			return compressed_shape