Merge pull request #5 from nschloe/unique-rows-1dim

unique_rows for 1D

npx/_main.py

@@ -87,12 +87,13 @@ def unique_rows(a, return_inverse=False, return_counts=False):
     if not np.issubdtype(a.dtype, np.integer):
         raise ValueError(f"Input array must be integer type, got {a.dtype}.")
 
-    b = np.ascontiguousarray(a).view(np.dtype((np.void, a.dtype.itemsize * a.shape[1])))
+    p = np.prod(a.shape[1:])
+    b = np.ascontiguousarray(a).view(np.dtype((np.void, a.dtype.itemsize * p)))
     out = np.unique(b, return_inverse=return_inverse, return_counts=return_counts)
     # out[0] are the sorted, unique rows
     if return_inverse or return_counts:
-        out = (out[0].view(a.dtype).reshape(-1, a.shape[1]), *out[1:])
+        out = (out[0].view(a.dtype).reshape(-1, *a.shape[1:]), *out[1:])
     else:
-        out = out.view(a.dtype).reshape(-1, a.shape[1])
+        out = out.view(a.dtype).reshape(-1, *a.shape[1:])
 
     return out

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = npx
-version = 0.0.4
+version = 0.0.5
 author = Nico Schlömer
 author_email = nico.schloemer@gmail.com
 description = Some useful extensions for NumPy
@@ -12,7 +12,6 @@ project_urls =
 long_description = file: README.md
 long_description_content_type = text/markdown
 license = MIT
-license_file = LICENSE
 # See <https://pypi.org/classifiers/> for all classifiers.
 classifiers =
     Development Status :: 4 - Beta

test/test_npx.py

@@ -53,6 +53,10 @@ def test_subtract_at():
 
 
 def test_unique_rows():
+    a = [1, 2, 1]
+    a_unique = npx.unique_rows(a)
+    assert np.all(a_unique == [1, 2])
+
     a = [[1, 2], [1, 4], [1, 2]]
     a_unique, inv, count = npx.unique_rows(a, return_inverse=True, return_counts=True)
     assert np.all(a_unique == [[1, 2], [1, 4]])
@@ -62,6 +66,14 @@ def test_unique_rows():
     a_unique = npx.unique_rows(a)
     assert np.all(a_unique == [[1, 2], [1, 4]])
 
+    # entries are matrices
+    # fails for some reason. keep an eye on
+    # <https://stackoverflow.com/q/67128631/353337>
+    # a = [[[3, 4], [-1, 2]], [[3, 4], [-1, 2]]]
+    # a_unique = npx.unique_rows(a)
+    # print(a_unique)
+    # assert np.all(a_unique == [[[3, 4], [-1, 2]]])
+
     a = [1.1, 2.2]
     with pytest.raises(ValueError):
         a_unique = npx.unique_rows(a)