Jul 7, 2023 · Jul 5, 2023 · Jul 5, 2023 · Jul 5, 2023 · Jul 5, 2023 · Jul 5, 2023
diff --git a/numpy/core/tests/test_numeric.py b/numpy/core/tests/test_numeric.py
                       axis=1,
                       keepdims=True)

        # The returned  object should be the object specified during calling
        assert mean_out is mean

        std = np.std(A,
                     out=std_out,
                     axis=1,
                     keepdims=True,
                     mean=mean)

        # The returned  object should be the object specified during calling
        assert std_out is std

        # Shape of returned mean and std should be same
        assert std.shape == mean.shape
        assert std.shape == (10, 1, 5)
        assert std_old.shape == mean.shape
        assert_almost_equal(std, std_old)

        # The returned  objects should be the objects specified during calling
        assert mean_out is mean
        assert std_out is std


    def test_var_with_mean_keyword(self):
        # Setting the seed to make the test reproducable
                       axis=1,
                       keepdims=True)

        # The returned  object should be the object specified during calling
        assert mean_out is mean

        var = np.var(A,
                     out=var_out,
                     axis=1,
                     keepdims=True,
                     mean=mean)

        # The returned  object should be the object specified during calling
        assert var_out is var

        # Shape of returned mean and var should be same
        assert var.shape == mean.shape
        assert var.shape== (10, 1, 5)
        assert var_old.shape == mean.shape
        assert_almost_equal(var, var_old)

        # The returned  objects should be the objects specified during calling
        assert mean_out is mean
        assert var_out is var


    def test_std_with_mean_keyword_keepdims_false(self):
        rng = np.random.RandomState(1234)

        # Shape of returned mean and std should be same
        assert std.shape == ()


        # Output should be the same as from the individual algorithms
        std_old = np.std(A, axis=axis, keepdims=False)
Original file line number	Diff line number	Diff line change
Expand Up		@@ -312,12 +312,18 @@ def test_std_with_mean_keyword(self):
		axis=1,
		keepdims=True)

		# The returned object should be the object specified during calling
		assert mean_out is mean

		std = np.std(A,
		out=std_out,
		axis=1,
		keepdims=True,
		mean=mean)

		# The returned object should be the object specified during calling
		assert std_out is std

		# Shape of returned mean and std should be same
		assert std.shape == mean.shape
		assert std.shape == (10, 1, 5)
Expand All		@@ -328,9 +334,7 @@ def test_std_with_mean_keyword(self):
		assert std_old.shape == mean.shape
		assert_almost_equal(std, std_old)

		# The returned objects should be the objects specified during calling
		assert mean_out is mean
		assert std_out is std


		def test_var_with_mean_keyword(self):
		# Setting the seed to make the test reproducable
Expand All		@@ -345,12 +349,18 @@ def test_var_with_mean_keyword(self):
		axis=1,
		keepdims=True)

		# The returned object should be the object specified during calling
		assert mean_out is mean

		var = np.var(A,
		out=var_out,
		axis=1,
		keepdims=True,
		mean=mean)

		# The returned object should be the object specified during calling
		assert var_out is var

		# Shape of returned mean and var should be same
		assert var.shape == mean.shape
		assert var.shape== (10, 1, 5)
Expand All		@@ -361,9 +371,7 @@ def test_var_with_mean_keyword(self):
		assert var_old.shape == mean.shape
		assert_almost_equal(var, var_old)

		# The returned objects should be the objects specified during calling
		assert mean_out is mean
		assert var_out is var


		def test_std_with_mean_keyword_keepdims_false(self):
		rng = np.random.RandomState(1234)
Expand DownExpand Up		@@ -511,7 +519,7 @@ def test_std_with_mean_keyword_axis_None(self):

		# Shape of returned mean and std should be same
		assert std.shape == ()


		# Output should be the same as from the individual algorithms
		std_old = np.std(A, axis=axis, keepdims=False)

Expand Down