Cython metric code accept unsigned dtypes only

src/gambit/_cython/metric.pyx

@@ -4,60 +4,12 @@ from cython.parallel import prange, parallel
 
 
 def jaccard(COORDS_T[:] coords1, COORDS_T_2[:] coords2):
-	"""Compute the Jaccard index between two k-mer sets in sparse coordinate format.
-
-	Arguments are Numpy arrays containing k-mer indices in sorted order. Data types must be 16, 32,
-	or 64-bit signed or unsigned integers, but do not need to match.
-
-	This is by far the most efficient way to calculate the metric (this is a native function) and
-	should be used wherever possible.
-
-	Parameters
-	----------
-	coords1 : numpy.ndarray
-		K-mer set in sparse coordinate format.
-	coords2 : numpy.ndarray
-		K-mer set in sparse coordinate format.
-
-	Returns
-	-------
-	numpy.float32
-		Jaccard index between the two sets, a real number between 0 and 1.
-
-	See Also
-	--------
-	.jaccarddist
-	"""
+	"""Compute the Jaccard index between two k-mer sets in sparse coordinate format."""
 	return 1 - c_jaccarddist(coords1, coords2)
 
 
 def jaccarddist(COORDS_T[:] coords1, COORDS_T_2[:] coords2):
-	"""Compute the Jaccard distance between two k-mer sets in sparse coordinate format.
-
-	The Jaccard distance is equal to one minus the Jaccard index.
-
-	Arguments are Numpy arrays containing k-mer indices in sorted order. Data types must be 16, 32,
-	or 64-bit signed or unsigned integers, but do not need to match.
-
-	This is by far the most efficient way to calculate the metric (this is a native function) and
-	should be used wherever possible.
-
-	Parameters
-	----------
-	coords1 : numpy.ndarray
-		K-mer set in sparse coordinate format.
-	coords2 : numpy.ndarray
-		K-mer set in sparse coordinate format.
-
-	Returns
-	-------
-	numpy.float32
-		Jaccard distance between the two sets, a real number between 0 and 1.
-
-	See Also
-	--------
-	.jaccard
-	"""
+	"""Compute the Jaccard distance between two k-mer sets in sparse coordinate format."""
 	return c_jaccarddist(coords1, coords2)
 
 

src/gambit/_cython/types.pxd

@@ -1,6 +1,6 @@
 """Shared typedefs."""
 
-from libc.stdint cimport int16_t, uint16_t, int32_t, uint32_t, int64_t, uint64_t, intptr_t
+from libc.stdint cimport uint16_t, uint32_t, uint64_t, intptr_t
 
 
 # Type for similarity scores
@@ -12,18 +12,12 @@ ctypedef intptr_t BOUNDS_T
 
 # Fused type for storing k-mer coordinates/indices
 ctypedef fused COORDS_T:
-	int16_t
 	uint16_t
-	int32_t
 	uint32_t
-	int64_t
 	uint64_t
 
 # Copy of COORDS_T, used when two arguments have types in this set but may be different than each other.
 ctypedef fused COORDS_T_2:
-	int16_t
 	uint16_t
-	int32_t
 	uint32_t
-	int64_t
 	uint64_t

src/gambit/metric.py

@@ -5,7 +5,7 @@
 
 import numpy as np
 
-from gambit._cython.metric import jaccard, jaccarddist, _jaccarddist_parallel
+import gambit._cython.metric as _cmetric
 from gambit.sigs.base import KmerSignature, SignatureArray, AbstractSignatureArray, SignatureList, \
 	BOUNDS_DTYPE
 from gambit.util.misc import chunk_slices
@@ -17,6 +17,91 @@
 SCORE_DTYPE = np.dtype(np.float32)
 
 
+_COORDS_UNSIGNED_DTYPES = [np.dtype(f'u{s}') for s in [2, 4, 8]]
+_COORDS_SIGNED_DTYPES = [np.dtype(f'i{s}') for s in [2, 4, 8]]
+
+
+def _cast_sigs_array(arr: np.ndarray) -> np.ndarray:
+	"""Convert signature array to proper data type for Cython metric code.
+
+	Cython code accepts k-mer coordinate arrays in 16, 32, or 64-bit unsigned data types, these are
+	returned as-is. Equivalent signed data types can safely be casted (as the values should all be
+	non-negative), for these a view into the array with unsigned data type is returned (no coyping).
+	All other data types result in a ValueError.
+	"""
+
+	dt = arr.dtype
+	if dt in _COORDS_UNSIGNED_DTYPES:
+		return arr
+	if dt in _COORDS_SIGNED_DTYPES:
+		new_dt = np.dtype(f'u{dt.itemsize}')
+		return arr.view(new_dt)
+	raise ValueError(f'Invalid dtype for k-mer coordinate array: {dt.str}')
+
+
+def jaccard(coords1: np.ndarray, coords2: np.ndarray) -> np.float32:
+	"""Compute the Jaccard index between two k-mer sets in sparse coordinate format.
+
+	Arguments are Numpy arrays containing k-mer indices in sorted order. Data types must be 16, 32,
+	or 64-bit signed or unsigned integers, but do not need to match.
+
+	This is by far the most efficient way to calculate the metric (this is a native function) and
+	should be used wherever possible.
+
+	Parameters
+	----------
+	coords1
+		K-mer set in sparse coordinate format.
+	coords2
+		K-mer set in sparse coordinate format.
+
+	Returns
+	-------
+	numpy.float32
+		Jaccard index between the two sets, a real number between 0 and 1.
+
+	See Also
+	--------
+	.jaccarddist
+	"""
+	coords1 = _cast_sigs_array(coords1)
+	coords2 = _cast_sigs_array(coords2)
+	return _cmetric.jaccard(coords1, coords2)
+
+
+def jaccarddist(coords1: np.ndarray, coords2: np.ndarray):
+	"""Compute the Jaccard distance between two k-mer sets in sparse coordinate format.
+
+	The Jaccard distance is equal to one minus the Jaccard index.
+
+	Arguments are Numpy arrays containing k-mer indices in sorted order. Data types must be 16, 32,
+	or 64-bit signed or unsigned integers, but do not need to match.
+
+	This is by far the most efficient way to calculate the metric (this is a native function) and
+	should be used wherever possible.
+
+	Parameters
+	----------
+	coords1
+		K-mer set in sparse coordinate format.
+	coords2
+		K-mer set in sparse coordinate format.
+
+	Returns
+	-------
+	numpy.float32
+		Jaccard distance between the two sets, a real number between 0 and 1.
+
+	See Also
+	--------
+	.jaccard
+	"""
+	coords1 = _cast_sigs_array(coords1)
+	coords2 = _cast_sigs_array(coords2)
+	return _cmetric.jaccarddist(coords1, coords2)
+
+
+
 def jaccard_generic(set1: Iterable, set2: Iterable) -> float:
 	"""Get the Jaccard index of of two arbitrary sets.
 
@@ -84,6 +169,8 @@ def jaccarddist_array(query: KmerSignature, refs: Sequence[KmerSignature], out:
 	.jaccarddist
 	.jaccarddist_matrix
 	"""
+	query = _cast_sigs_array(query)
+
 	if out is None:
 		out = np.empty(len(refs), SCORE_DTYPE)
 	elif out.shape != (len(refs),):
@@ -92,14 +179,15 @@ def jaccarddist_array(query: KmerSignature, refs: Sequence[KmerSignature], out:
 		raise ValueError(f'Output array dtype must be {SCORE_DTYPE}, got {out.dtype}')
 
 	if isinstance(refs, SignatureArray):
-		values = refs.values
+		values = _cast_sigs_array(refs.values)
 		bounds = refs.bounds.astype(BOUNDS_DTYPE, copy=False)
 
-		_jaccarddist_parallel(query, values, bounds, out)
+		_cmetric._jaccarddist_parallel(query, values, bounds, out)
 
 	else:
 		for i, ref in enumerate(refs):
-			out[i] = jaccarddist(query, ref)
+			ref = _cast_sigs_array(ref)
+			out[i] = _cmetric.jaccarddist(query, ref)
 
 	return out