Python numpy.nansum() Examples

def _dominant_set_dense(s, k, is_thresh=False, norm=False, copy=True):
    """Compute dominant set for a dense matrix."""

    if is_thresh:
        s = s.copy() if copy else s
        s[s <= k] = 0

    else:  # keep top k
        nr, nc = s.shape
        idx = np.argpartition(s, nc - k, axis=1)
        row = np.arange(nr)[:, None]
        if copy:
            col = idx[:, -k:]  # idx largest
            data = s[row, col]
            s = np.zeros_like(s)
            s[row, col] = data
        else:
            col = idx[:, :-k]  # idx smallest
            s[row, col] = 0

    if norm:
        s /= np.nansum(s, axis=1, keepdims=True)

    return s 

def test_callable_metric():

    # Define callable metric that returns the l1 norm:
    def custom_callable(x, y, missing_values="NaN", squared=False):
        x = np.ma.array(x, mask=np.isnan(x))
        y = np.ma.array(y, mask=np.isnan(y))
        dist = np.nansum(np.abs(x-y))
        return dist

    X = np.array([
        [4, 3, 3, np.nan],
        [6, 9, 6, 9],
        [4, 8, 6, 9],
        [np.nan, 9, 11, 10.]
    ])

    X_imputed = np.array([
        [4, 3, 3, 9],
        [6, 9, 6, 9],
        [4, 8, 6, 9],
        [5, 9, 11, 10.]
    ])

    imputer = KNNImputer(n_neighbors=2, metric=custom_callable)
    assert_array_equal(imputer.fit_transform(X), X_imputed) 

def test_emg_eventrelated():

    emg = nk.emg_simulate(duration=20, sampling_rate=1000, burst_number=3)
    emg_signals, info = nk.emg_process(emg, sampling_rate=1000)
    epochs = nk.epochs_create(
        emg_signals, events=[3000, 6000, 9000], sampling_rate=1000, epochs_start=-0.1, epochs_end=1.9
    )
    emg_eventrelated = nk.emg_eventrelated(epochs)

    # Test amplitude features
    no_activation = np.where(emg_eventrelated["EMG_Activation"] == 0)[0][0]
    assert int(pd.DataFrame(emg_eventrelated.values[no_activation]).isna().sum()) == 4

    assert np.alltrue(
        np.nansum(np.array(emg_eventrelated["EMG_Amplitude_Mean"]))
        < np.nansum(np.array(emg_eventrelated["EMG_Amplitude_Max"]))
    )

    assert len(emg_eventrelated["Label"]) == 3 

def test_sum_inf(self):
        s = Series(np.random.randn(10))
        s2 = s.copy()

        s[5:8] = np.inf
        s2[5:8] = np.nan

        assert np.isinf(s.sum())

        arr = np.random.randn(100, 100).astype('f4')
        arr[:, 2] = np.inf

        with pd.option_context("mode.use_inf_as_na", True):
            assert_almost_equal(s.sum(), s2.sum())

        res = nanops.nansum(arr, axis=1)
        assert np.isinf(res).all() 

def test_transform_length():
    # GH 9697
    df = pd.DataFrame({'col1': [1, 1, 2, 2], 'col2': [1, 2, 3, np.nan]})
    expected = pd.Series([3.0] * 4)

    def nsum(x):
        return np.nansum(x)

    results = [df.groupby('col1').transform(sum)['col2'],
               df.groupby('col1')['col2'].transform(sum),
               df.groupby('col1').transform(nsum)['col2'],
               df.groupby('col1')['col2'].transform(nsum)]
    for result in results:
        assert_series_equal(result, expected, check_names=False) 

def test_nansum_with_boolean(self):
        # gh-2978
        a = np.zeros(2, dtype=bool)
        try:
            np.nansum(a)
        except Exception:
            raise AssertionError() 

def test_nansum_with_boolean(self):
        # gh-2978
        a = np.zeros(2, dtype=np.bool)
        try:
            np.nansum(a)
        except:
            raise AssertionError() 

def nanmean(a, axis=None, dtype=None, keepdims=None):  # pylint: disable=missing-docstring
  a = array_ops.array(a)
  if np.issubdtype(a.dtype, np.bool_) or np.issubdtype(a.dtype, np.integer):
    return array_ops.mean(a, axis=axis, dtype=dtype, keepdims=keepdims)
  nan_mask = logical_not(isnan(a))
  if dtype is None:
    dtype = a.dtype
  normalizer = array_ops.sum(
      nan_mask, axis=axis, dtype=dtype, keepdims=keepdims)
  return nansum(a, axis=axis, dtype=dtype, keepdims=keepdims) / normalizer 

def timespace_distance_sec(pt1, pt2, km_per_sec=KM_PER_SEC):
    # Return in seconds
    sec1, latlon1 = pt1[0], pt1[1:]
    sec2, latlon2 = pt2[0], pt2[1:]
    # Get pure gps distance and convert to seconds
    km_dist = haversine(latlon1, latlon2)
    km_dist = km_dist / km_per_sec
    # Get distance in seconds
    sec_dist = np.abs(sec1 - sec2)
    # Add distances
    # (return nan if points are not comparable, otherwise nansum)
    parts = np.array([km_dist, sec_dist])
    timespace_dist = np.nan if np.all(np.isnan(parts)) else np.nansum(parts)
    return timespace_dist 

def test_allnans(self):
        # Check for FutureWarning
        with warnings.catch_warnings(record=True) as w:
            warnings.simplefilter('always')
            res = np.nansum([np.nan]*3, axis=None)
            assert_(res == 0, 'result is not 0')
            assert_(len(w) == 0, 'warning raised')
            # Check scalar
            res = np.nansum(np.nan)
            assert_(res == 0, 'result is not 0')
            assert_(len(w) == 0, 'warning raised')
            # Check there is no warning for not all-nan
            np.nansum([0]*3, axis=None)
            assert_(len(w) == 0, 'unwanted warning raised') 

def test_nansum(self):
        tgt = np.sum(self.mat)
        for mat in self.integer_arrays():
            assert_equal(np.nansum(mat), tgt) 

def test_nanfunctions_matrices_general():
    # Check that it works and that type and
    # shape are preserved
    # 2018-04-29: moved here from core.tests.test_nanfunctions
    mat = np.matrix(np.eye(3))
    for f in (np.nanargmin, np.nanargmax, np.nansum, np.nanprod,
              np.nanmean, np.nanvar, np.nanstd):
        res = f(mat, axis=0)
        assert_(isinstance(res, np.matrix))
        assert_(res.shape == (1, 3))
        res = f(mat, axis=1)
        assert_(isinstance(res, np.matrix))
        assert_(res.shape == (3, 1))
        res = f(mat)
        assert_(np.isscalar(res))

    for f in np.nancumsum, np.nancumprod:
        res = f(mat, axis=0)
        assert_(isinstance(res, np.matrix))
        assert_(res.shape == (3, 3))
        res = f(mat, axis=1)
        assert_(isinstance(res, np.matrix))
        assert_(res.shape == (3, 3))
        res = f(mat)
        assert_(isinstance(res, np.matrix))
        assert_(res.shape == (1, 3*3)) 

def canberra(u, v):
    """
    Computes the Canberra distance between two 1-D arrays.

    The Canberra distance is defined as

    .. math::

         d(u,v) = \\sum_i \\frac{|u_i-v_i|}
                              {|u_i|+|v_i|}.

    Parameters
    ----------
    u : (N,) array_like
        Input array.
    v : (N,) array_like
        Input array.

    Returns
    -------
    canberra : double
        The Canberra distance between vectors `u` and `v`.

    Notes
    -----
    When `u[i]` and `v[i]` are 0 for given i, then the fraction 0/0 = 0 is
    used in the calculation.

    """
    u = _validate_vector(u)
    v = _validate_vector(v, dtype=np.float64)
    olderr = np.seterr(invalid='ignore')
    try:
        d = np.nansum(abs(u - v) / (abs(u) + abs(v)))
    finally:
        np.seterr(**olderr)
    return d 

def test_empty(self):
        for f, tgt_value in zip([np.nansum, np.nanprod], [0, 1]):
            mat = np.zeros((0, 3))
            tgt = [tgt_value]*3
            res = f(mat, axis=0)
            assert_equal(res, tgt)
            tgt = []
            res = f(mat, axis=1)
            assert_equal(res, tgt)
            tgt = tgt_value
            res = f(mat, axis=None)
            assert_equal(res, tgt) 

def test_nansum(self):
        tgt = np.sum(self.mat)
        for mat in self.integer_arrays():
            assert_equal(np.nansum(mat), tgt) 

def testSecurityValuesUnit(self):
        data = np.array([3, -2, np.nan, np.nan, 4, 5])
        index = [1, 2, 3, 4, 5, 6]
        test = SeriesValues(data, index)
        test = test.unit()

        expected = SeriesValues(data / np.nansum(np.abs(data)), dict(zip(index, range(len(index)))))
        for name in test.index():
            if np.isnan(test[name]):
                self.assertTrue(np.isnan(expected[name]))
            else:
                self.assertEqual(test[name], expected[name]) 

def test_agg_category_nansum(observed):
    categories = ['a', 'b', 'c']
    df = pd.DataFrame({"A": pd.Categorical(['a', 'a', 'b'],
                                           categories=categories),
                       'B': [1, 2, 3]})
    result = df.groupby("A", observed=observed).B.agg(np.nansum)
    expected = pd.Series([3, 3, 0],
                         index=pd.CategoricalIndex(['a', 'b', 'c'],
                                                   categories=categories,
                                                   name='A'),
                         name='B')
    if observed:
        expected = expected[expected != 0]
    tm.assert_series_equal(result, expected) 

def test_nansum(self):
        tgt = np.sum(self.mat)
        for mat in self.integer_arrays():
            assert_equal(np.nansum(mat), tgt) 

def test_nansum_buglet(self):
        s = Series([1.0, np.nan], index=[0, 1])
        result = np.nansum(s)
        assert_almost_equal(result, 1) 

def test_nansum(self):
        self.check_funs(nanops.nansum, np.sum, allow_str=False,
                        allow_date=False, allow_tdelta=True, check_dtype=False,
                        empty_targfunc=np.nansum) 

def test_rolling_sum(self):
        self._check_moment_func(np.nansum, name='sum',
                                zero_min_periods_equal=False) 

def test_nansum_with_boolean(self):
        # gh-2978
        a = np.zeros(2, dtype=bool)
        try:
            np.nansum(a)
        except Exception:
            raise AssertionError() 

def test_empty(self):
        for f, tgt_value in zip([np.nansum, np.nanprod], [0, 1]):
            mat = np.zeros((0, 3))
            tgt = [tgt_value]*3
            res = f(mat, axis=0)
            assert_equal(res, tgt)
            tgt = []
            res = f(mat, axis=1)
            assert_equal(res, tgt)
            tgt = tgt_value
            res = f(mat, axis=None)
            assert_equal(res, tgt) 

def test_allnans(self):
        # Check for FutureWarning
        with warnings.catch_warnings(record=True) as w:
            warnings.simplefilter('always')
            res = np.nansum([np.nan]*3, axis=None)
            assert_(res == 0, 'result is not 0')
            assert_(len(w) == 0, 'warning raised')
            # Check scalar
            res = np.nansum(np.nan)
            assert_(res == 0, 'result is not 0')
            assert_(len(w) == 0, 'warning raised')
            # Check there is no warning for not all-nan
            np.nansum([0]*3, axis=None)
            assert_(len(w) == 0, 'unwanted warning raised') 

def test_nansum(self):
        tgt = np.sum(self.mat)
        for mat in self.integer_arrays():
            assert_equal(np.nansum(mat), tgt) 

def _get_numpy_funcs(skipna):
    """
    Returns nansum and nanmean if skipna is True;
    Returns sum and mean if skipna is False.
    """
    if skipna:
        return np.nansum, np.nanmean
    else:
        return np.sum, np.mean 

def test_nansum_with_boolean(self):
        # gh-2978
        a = np.zeros(2, dtype=np.bool)
        try:
            np.nansum(a)
        except:
            raise AssertionError() 

def test_empty(self):
        for f, tgt_value in zip([np.nansum, np.nanprod], [0, 1]):
            mat = np.zeros((0, 3))
            tgt = [tgt_value]*3
            res = f(mat, axis=0)
            assert_equal(res, tgt)
            tgt = []
            res = f(mat, axis=1)
            assert_equal(res, tgt)
            tgt = tgt_value
            res = f(mat, axis=None)
            assert_equal(res, tgt) 

def test_allnans(self):
        # Check for FutureWarning
        with warnings.catch_warnings(record=True) as w:
            warnings.simplefilter('always')
            res = np.nansum([np.nan]*3, axis=None)
            assert_(res == 0, 'result is not 0')
            assert_(len(w) == 0, 'warning raised')
            # Check scalar
            res = np.nansum(np.nan)
            assert_(res == 0, 'result is not 0')
            assert_(len(w) == 0, 'warning raised')
            # Check there is no warning for not all-nan
            np.nansum([0]*3, axis=None)
            assert_(len(w) == 0, 'unwanted warning raised') 

def get_class_center(Xs,Ys,Xt,dist):
	
    source_class_center = np.array([])
    Dct = np.array([])
    for i in np.unique(Ys):
        sel_mask = Ys == i
        X_i = Xs[sel_mask.flatten()]
        mean_i = np.mean(X_i, axis=0)
        if len(source_class_center) == 0:
            source_class_center = mean_i.reshape(-1, 1)
        else:
            source_class_center = np.hstack((source_class_center, mean_i.reshape(-1, 1)))
		
        if dist == "ma":
            Dct_c = get_ma_dist(Xt, X_i)
        elif dist == "euclidean":
            Dct_c = np.sqrt(np.nansum((mean_i - Xt)**2, axis=1))
        elif dist == "sqeuc":
            Dct_c = np.nansum((mean_i - Xt)**2, axis=1)
        elif dist == "cosine":
            Dct_c = get_cosine_dist(Xt, mean_i)
        elif dist == "rbf":
            Dct_c = np.nansum((mean_i - Xt)**2, axis=1)
            Dct_c = np.exp(- Dct_c / 1);
        
        if len(Dct) == 0:
            Dct = Dct_c.reshape(-1, 1)
        else:
            Dct = np.hstack((Dct, Dct_c.reshape(-1, 1)))
    
    return source_class_center, Dct