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-7
	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.float32) # 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