Merge pull request #3534 from charris/nan-stat-functions

Add nanmean, nanvar, and nanstd functions.

1 files changed, 417 insertions, 0 deletions

diff --git a/numpy/lib/tests/test_nanfunctions.py b/numpy/lib/tests/test_nanfunctions.py
new file mode 100644
index 000000000..93a5ef855
--- /dev/null
+++ b/numpy/lib/tests/test_nanfunctions.py
@@ -0,0 +1,417 @@
+from __future__ import division, absolute_import, print_function
+
+import warnings
+
+import numpy as np
+from numpy.testing import (
+    run_module_suite, TestCase, assert_, assert_equal, assert_almost_equal,
+    assert_raises
+    )
+from numpy.lib import (
+    nansum, nanmax, nanargmax, nanargmin, nanmin, nanmean, nanvar, nanstd,
+    NanWarning
+    )
+
+
+_ndat = np.array(
+        [[ 0.6244, np.nan, 0.2692,  0.0116, np.nan, 0.1170],
+         [ 0.5351, 0.9403, np.nan,  0.2100, 0.4759, 0.2833],
+         [ np.nan, np.nan, np.nan,  0.1042, np.nan, 0.5954],
+         [ 0.161 , np.nan, np.nan,  0.1859, 0.3146, np.nan]]
+        )
+
+# rows of _ndat with nans removed
+_rdat = [
+        np.array([ 0.6244, 0.2692, 0.0116, 0.1170]),
+        np.array([ 0.5351, 0.9403, 0.2100, 0.4759, 0.2833]),
+        np.array([ 0.1042, 0.5954]),
+        np.array([ 0.1610, 0.1859, 0.3146])
+       ]
+
+
+class TestNanFunctions_MinMax(TestCase):
+
+    nanfuncs = [nanmin, nanmax]
+    stdfuncs = [np.min, np.max]
+
+    def test_mutation(self):
+        # Check that passed array is not modified.
+        ndat = _ndat.copy()
+        for f in self.nanfuncs:
+            f(ndat)
+            assert_equal(ndat, _ndat)
+
+    def test_keepdims(self):
+        mat = np.eye(3)
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            for axis in [None, 0, 1]:
+                tgt = rf(mat, axis=axis, keepdims=True)
+                res = nf(mat, axis=axis, keepdims=True)
+                assert_(res.ndim == tgt.ndim)
+
+    def test_out(self):
+        mat = np.eye(3)
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            resout = np.zeros(3)
+            tgt = rf(mat, axis=1)
+            res = nf(mat, axis=1, out=resout)
+            assert_almost_equal(res, resout)
+            assert_almost_equal(res, tgt)
+
+    def test_dtype_from_input(self):
+        codes = 'efdgFDG'
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            for c in codes:
+                mat = np.eye(3, dtype=c)
+                tgt = rf(mat, axis=1).dtype.type
+                res = nf(mat, axis=1).dtype.type
+                assert_(res is tgt)
+                # scalar case
+                tgt = rf(mat, axis=None).dtype.type
+                res = nf(mat, axis=None).dtype.type
+                assert_(res is tgt)
+
+    def test_result_values(self):
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            tgt = [rf(d) for d in _rdat]
+            res = nf(_ndat, axis=1)
+            assert_almost_equal(res, tgt)
+
+    def test_allnans(self):
+        mat = np.array([np.nan]*9).reshape(3, 3)
+        for f in self.nanfuncs:
+            for axis in [None, 0, 1]:
+                assert_(np.isnan(f(mat, axis=axis)).all())
+
+    def test_masked(self):
+        mat = np.ma.fix_invalid(_ndat)
+        msk = mat._mask.copy()
+        for f in [nanmin]:
+            res = f(mat, axis=1)
+            tgt = f(_ndat, axis=1)
+            assert_equal(res, tgt)
+            assert_equal(mat._mask, msk)
+            assert_(not np.isinf(mat).any())
+
+
+class TestNanFunctions_ArgminArgmax(TestCase):
+
+    nanfuncs = [nanargmin, nanargmax]
+
+    def test_mutation(self):
+        # Check that passed array is not modified.
+        ndat = _ndat.copy()
+        for f in self.nanfuncs:
+            f(ndat)
+            assert_equal(ndat, _ndat)
+
+    def test_result_values(self):
+        for f, fcmp in zip(self.nanfuncs, [np.greater, np.less]):
+            for row in _ndat:
+                with warnings.catch_warnings():
+                    warnings.simplefilter('ignore')
+                    ind = f(row)
+                    val = row[ind]
+                    # comparing with NaN is tricky as the result
+                    # is always false except for NaN != NaN
+                    assert_(not np.isnan(val))
+                    assert_(not fcmp(val, row).any())
+                    assert_(not np.equal(val, row[:ind]).any())
+
+    def test_allnans(self):
+        mat = np.array([np.nan]*9).reshape(3, 3)
+        tgt = np.iinfo(np.intp).min
+        for f in self.nanfuncs:
+            for axis in [None, 0, 1]:
+                with warnings.catch_warnings(record=True) as w:
+                    warnings.simplefilter('always')
+                    res = f(mat, axis=axis)
+                    assert_((res == tgt).all())
+                    assert_(len(w) == 1)
+                    assert_(issubclass(w[0].category, NanWarning))
+
+    def test_empty(self):
+        mat = np.zeros((0,3))
+        for f in self.nanfuncs:
+            for axis in [0, None]:
+                assert_raises(ValueError, f, mat, axis=axis)
+            for axis in [1]:
+                res = f(mat, axis=axis)
+                assert_equal(res, np.zeros(0))
+
+
+class TestNanFunctions_IntTypes(TestCase):
+
+    int_types = (
+            np.int8, np.int16, np.int32, np.int64, np.uint8,
+            np.uint16, np.uint32, np.uint64)
+
+    def setUp(self, *args, **kwargs):
+        self.mat = np.array([127, 39,  93,  87, 46])
+
+    def integer_arrays(self):
+        for dtype in self.int_types:
+            yield self.mat.astype(dtype)
+
+    def test_nanmin(self):
+        min_value = min(self.mat)
+        for mat in self.integer_arrays():
+            assert_equal(nanmin(mat), min_value)
+
+    def test_nanmax(self):
+        max_value = max(self.mat)
+        for mat in self.integer_arrays():
+            assert_equal(nanmax(mat), max_value)
+
+    def test_nanargmin(self):
+        min_arg = np.argmin(self.mat)
+        for mat in self.integer_arrays():
+            assert_equal(nanargmin(mat), min_arg)
+
+    def test_nanargmax(self):
+        max_arg = np.argmax(self.mat)
+        for mat in self.integer_arrays():
+            assert_equal(nanargmax(mat), max_arg)
+
+
+class TestNanFunctions_Sum(TestCase):
+
+    def test_mutation(self):
+        # Check that passed array is not modified.
+        ndat = _ndat.copy()
+        nansum(ndat)
+        assert_equal(ndat, _ndat)
+
+    def test_keepdims(self):
+        mat = np.eye(3)
+        for axis in [None, 0, 1]:
+            tgt = np.sum(mat, axis=axis, keepdims=True)
+            res = nansum(mat, axis=axis, keepdims=True)
+            assert_(res.ndim == tgt.ndim)
+
+    def test_out(self):
+        mat = np.eye(3)
+        resout = np.zeros(3)
+        tgt = np.sum(mat, axis=1)
+        res = nansum(mat, axis=1, out=resout)
+        assert_almost_equal(res, resout)
+        assert_almost_equal(res, tgt)
+
+    def test_dtype_from_dtype(self):
+        mat = np.eye(3)
+        codes = 'efdgFDG'
+        for c in codes:
+            tgt = np.sum(mat, dtype=np.dtype(c), axis=1).dtype.type
+            res = nansum(mat, dtype=np.dtype(c), axis=1).dtype.type
+            assert_(res is tgt)
+            # scalar case
+            tgt = np.sum(mat, dtype=np.dtype(c), axis=None).dtype.type
+            res = nansum(mat, dtype=np.dtype(c), axis=None).dtype.type
+            assert_(res is tgt)
+
+    def test_dtype_from_char(self):
+        mat = np.eye(3)
+        codes = 'efdgFDG'
+        for c in codes:
+            tgt = np.sum(mat, dtype=c, axis=1).dtype.type
+            res = nansum(mat, dtype=c, axis=1).dtype.type
+            assert_(res is tgt)
+            # scalar case
+            tgt = np.sum(mat, dtype=c, axis=None).dtype.type
+            res = nansum(mat, dtype=c, axis=None).dtype.type
+            assert_(res is tgt)
+
+    def test_dtype_from_input(self):
+        codes = 'efdgFDG'
+        for c in codes:
+            mat = np.eye(3, dtype=c)
+            tgt = np.sum(mat, axis=1).dtype.type
+            res = nansum(mat, axis=1).dtype.type
+            assert_(res is tgt)
+            # scalar case
+            tgt = np.sum(mat, axis=None).dtype.type
+            res = nansum(mat, axis=None).dtype.type
+            assert_(res is tgt)
+
+    def test_result_values(self):
+            tgt = [np.sum(d) for d in _rdat]
+            res = nansum(_ndat, axis=1)
+            assert_almost_equal(res, tgt)
+
+    def test_allnans(self):
+        # Check for FutureWarning and later change of return from
+        # NaN to zero.
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always')
+            res = nansum([np.nan]*3, axis=None)
+            if np.__version__[:3] < '1.9':
+                assert_(np.isnan(res), 'result is not NaN')
+                assert_(len(w) == 1, 'no warning raised')
+                assert_(issubclass(w[0].category, FutureWarning))
+            else:
+                assert_(res == 0, 'result is not 0')
+                assert_(len(w) == 0, 'warning raised')
+
+    def test_empty(self):
+        mat = np.zeros((0,3))
+        if np.__version__[:3] < '1.9':
+            tgt = [np.nan]*3
+            res = nansum(mat, axis=0)
+            assert_equal(res, tgt)
+            tgt = []
+            res = nansum(mat, axis=1)
+            assert_equal(res, tgt)
+            tgt = np.nan
+            res = nansum(mat, axis=None)
+            assert_equal(res, tgt)
+        else:
+            tgt = [0]*3
+            res = nansum(mat, axis=0)
+            assert_equal(res, tgt)
+            tgt = []
+            res = nansum(mat, axis=1)
+            assert_equal(res, tgt)
+            tgt = 0
+            res = nansum(mat, axis=None)
+            assert_equal(res, tgt)
+
+
+class TestNanFunctions_MeanVarStd(TestCase):
+
+    nanfuncs = [nanmean, nanvar, nanstd]
+    stdfuncs = [np.mean, np.var, np.std]
+
+    def test_mutation(self):
+        # Check that passed array is not modified.
+        ndat = _ndat.copy()
+        for f in self.nanfuncs:
+            f(ndat)
+            assert_equal(ndat, _ndat)
+
+    def test_dtype_error(self):
+        for f in self.nanfuncs:
+            for dtype in [np.bool_, np.int_, np.object]:
+                assert_raises( TypeError, f, _ndat, axis=1, dtype=np.int)
+
+    def test_out_dtype_error(self):
+        for f in self.nanfuncs:
+            for dtype in [np.bool_, np.int_, np.object]:
+                out = np.empty(_ndat.shape[0], dtype=dtype)
+                assert_raises( TypeError, f, _ndat, axis=1, out=out)
+
+    def test_keepdims(self):
+        mat = np.eye(3)
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            for axis in [None, 0, 1]:
+                tgt = rf(mat, axis=axis, keepdims=True)
+                res = nf(mat, axis=axis, keepdims=True)
+                assert_(res.ndim == tgt.ndim)
+
+    def test_out(self):
+        mat = np.eye(3)
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            resout = np.zeros(3)
+            tgt = rf(mat, axis=1)
+            res = nf(mat, axis=1, out=resout)
+            assert_almost_equal(res, resout)
+            assert_almost_equal(res, tgt)
+
+    def test_dtype_from_dtype(self):
+        mat = np.eye(3)
+        codes = 'efdgFDG'
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            for c in codes:
+                tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
+                res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
+                assert_(res is tgt)
+                # scalar case
+                tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
+                res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
+                assert_(res is tgt)
+
+    def test_dtype_from_char(self):
+        mat = np.eye(3)
+        codes = 'efdgFDG'
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            for c in codes:
+                tgt = rf(mat, dtype=c, axis=1).dtype.type
+                res = nf(mat, dtype=c, axis=1).dtype.type
+                assert_(res is tgt)
+                # scalar case
+                tgt = rf(mat, dtype=c, axis=None).dtype.type
+                res = nf(mat, dtype=c, axis=None).dtype.type
+                assert_(res is tgt)
+
+    def test_dtype_from_input(self):
+        codes = 'efdgFDG'
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            for c in codes:
+                mat = np.eye(3, dtype=c)
+                tgt = rf(mat, axis=1).dtype.type
+                res = nf(mat, axis=1).dtype.type
+                assert_(res is tgt)
+                # scalar case
+                tgt = rf(mat, axis=None).dtype.type
+                res = nf(mat, axis=None).dtype.type
+                assert_(res is tgt)
+
+    def test_ddof(self):
+        nanfuncs = [nanvar, nanstd]
+        stdfuncs = [np.var, np.std]
+        for nf, rf in zip(nanfuncs, stdfuncs):
+            for ddof in [0, 1]:
+                tgt = [rf(d, ddof=ddof) for d in _rdat]
+                res = nf(_ndat, axis=1, ddof=ddof)
+                assert_almost_equal(res, tgt)
+
+    def test_ddof_too_big(self):
+        nanfuncs = [nanvar, nanstd]
+        stdfuncs = [np.var, np.std]
+        dsize = [len(d) for d in _rdat]
+        for nf, rf in zip(nanfuncs, stdfuncs):
+            for ddof in range(5):
+                with warnings.catch_warnings(record=True) as w:
+                    warnings.simplefilter('always')
+                    tgt = [ddof >= d for d in dsize]
+                    res = nf(_ndat, axis=1, ddof=ddof)
+                    assert_equal(np.isnan(res), tgt)
+                    if any(tgt):
+                        assert_(len(w) == 1)
+                        assert_(issubclass(w[0].category, NanWarning))
+                    else:
+                        assert_(len(w) == 0)
+
+    def test_result_values(self):
+        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
+            tgt = [rf(d) for d in _rdat]
+            res = nf(_ndat, axis=1)
+            assert_almost_equal(res, tgt)
+
+    def test_allnans(self):
+        mat = np.array([np.nan]*9).reshape(3, 3)
+        for f in self.nanfuncs:
+            for axis in [None, 0, 1]:
+                with warnings.catch_warnings(record=True) as w:
+                    warnings.simplefilter('always')
+                    assert_(np.isnan(f(mat, axis=axis)).all())
+                    assert_(len(w) == 1)
+                    assert_(issubclass(w[0].category, NanWarning))
+
+    def test_empty(self):
+        mat = np.zeros((0,3))
+        for f in self.nanfuncs:
+            for axis in [0, None]:
+                with warnings.catch_warnings(record=True) as w:
+                    warnings.simplefilter('always')
+                    assert_(np.isnan(f(mat, axis=axis)).all())
+                    assert_(len(w) == 1)
+                    assert_(issubclass(w[0].category, NanWarning))
+            for axis in [1]:
+                with warnings.catch_warnings(record=True) as w:
+                    warnings.simplefilter('always')
+                    assert_equal(f(mat, axis=axis), np.zeros([]))
+                    assert_(len(w) == 0)
+
+
+if __name__ == "__main__":
+    run_module_suite()