Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/scipy/sparse/tests/test_spfuncs.py

@@ -0,0 +1,98 @@
+from numpy import array, kron, diag
+from numpy.testing import assert_, assert_equal
+
+from scipy.sparse import spfuncs
+from scipy.sparse import csr_matrix, csc_matrix, bsr_matrix
+from scipy.sparse._sparsetools import (csr_scale_rows, csr_scale_columns,
+                                       bsr_scale_rows, bsr_scale_columns)
+from scipy.sparse.sputils import matrix
+
+
+class TestSparseFunctions(object):
+    def test_scale_rows_and_cols(self):
+        D = matrix([[1,0,0,2,3],
+                    [0,4,0,5,0],
+                    [0,0,6,7,0]])
+
+        #TODO expose through function
+        S = csr_matrix(D)
+        v = array([1,2,3])
+        csr_scale_rows(3,5,S.indptr,S.indices,S.data,v)
+        assert_equal(S.todense(), diag(v)*D)
+
+        S = csr_matrix(D)
+        v = array([1,2,3,4,5])
+        csr_scale_columns(3,5,S.indptr,S.indices,S.data,v)
+        assert_equal(S.todense(), D@diag(v))
+
+        # blocks
+        E = kron(D,[[1,2],[3,4]])
+        S = bsr_matrix(E,blocksize=(2,2))
+        v = array([1,2,3,4,5,6])
+        bsr_scale_rows(3,5,2,2,S.indptr,S.indices,S.data,v)
+        assert_equal(S.todense(), diag(v)@E)
+
+        S = bsr_matrix(E,blocksize=(2,2))
+        v = array([1,2,3,4,5,6,7,8,9,10])
+        bsr_scale_columns(3,5,2,2,S.indptr,S.indices,S.data,v)
+        assert_equal(S.todense(), E@diag(v))
+
+        E = kron(D,[[1,2,3],[4,5,6]])
+        S = bsr_matrix(E,blocksize=(2,3))
+        v = array([1,2,3,4,5,6])
+        bsr_scale_rows(3,5,2,3,S.indptr,S.indices,S.data,v)
+        assert_equal(S.todense(), diag(v)@E)
+
+        S = bsr_matrix(E,blocksize=(2,3))
+        v = array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15])
+        bsr_scale_columns(3,5,2,3,S.indptr,S.indices,S.data,v)
+        assert_equal(S.todense(), E@diag(v))
+
+    def test_estimate_blocksize(self):
+        mats = []
+        mats.append([[0,1],[1,0]])
+        mats.append([[1,1,0],[0,0,1],[1,0,1]])
+        mats.append([[0],[0],[1]])
+        mats = [array(x) for x in mats]
+
+        blks = []
+        blks.append([[1]])
+        blks.append([[1,1],[1,1]])
+        blks.append([[1,1],[0,1]])
+        blks.append([[1,1,0],[1,0,1],[1,1,1]])
+        blks = [array(x) for x in blks]
+
+        for A in mats:
+            for B in blks:
+                X = kron(A,B)
+                r,c = spfuncs.estimate_blocksize(X)
+                assert_(r >= B.shape[0])
+                assert_(c >= B.shape[1])
+
+    def test_count_blocks(self):
+        def gold(A,bs):
+            R,C = bs
+            I,J = A.nonzero()
+            return len(set(zip(I//R,J//C)))
+
+        mats = []
+        mats.append([[0]])
+        mats.append([[1]])
+        mats.append([[1,0]])
+        mats.append([[1,1]])
+        mats.append([[0,1],[1,0]])
+        mats.append([[1,1,0],[0,0,1],[1,0,1]])
+        mats.append([[0],[0],[1]])
+
+        for A in mats:
+            for B in mats:
+                X = kron(A,B)
+                Y = csr_matrix(X)
+                for R in range(1,6):
+                    for C in range(1,6):
+                        assert_equal(spfuncs.count_blocks(Y, (R, C)), gold(X, (R, C)))
+
+        X = kron([[1,1,0],[0,0,1],[1,0,1]],[[1,1]])
+        Y = csc_matrix(X)
+        assert_equal(spfuncs.count_blocks(X, (1, 2)), gold(X, (1, 2)))
+        assert_equal(spfuncs.count_blocks(Y, (1, 2)), gold(X, (1, 2)))