Python numpy.bool_方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool_ [as 別名]
def nan_filter(self):
        """Populates Target List and filters out values which are nan
        
        """
        
        # filter out nan values in numerical attributes
        for att in self.catalog_atts:
            if ('close' in att) or ('bright' in att):
                continue
            if getattr(self, att).shape[0] == 0:
                pass
            elif (type(getattr(self, att)[0]) == str) or (type(getattr(self, att)[0]) == bytes):
                # FIXME: intent here unclear: 
                #   note float('nan') is an IEEE NaN, getattr(.) is a str, and != on NaNs is special
                i = np.where(getattr(self, att) != float('nan'))[0]
                self.revise_lists(i)
            # exclude non-numerical types
            elif type(getattr(self, att)[0]) not in (np.unicode_, np.string_, np.bool_, bytes):
                if att == 'coords':
                    i1 = np.where(~np.isnan(self.coords.ra.to('deg').value))[0]
                    i2 = np.where(~np.isnan(self.coords.dec.to('deg').value))[0]
                    i = np.intersect1d(i1,i2)
                else:
                    i = np.where(~np.isnan(getattr(self, att)))[0]
                self.revise_lists(i) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool_ [as 別名]
def _do(self, pop, other, is_duplicate):

        def to_float(val):
            if isinstance(val, bool) or isinstance(val, np.bool_):
                return 0.0 if val else 1.0
            else:
                return val

        if other is None:
            for i in range(len(pop)):
                for j in range(i + 1, len(pop)):
                    val = to_float(self.cmp(pop[i], pop[j]))
                    if val < self.epsilon:
                        is_duplicate[i] = True
                        break
        else:
            for i in range(len(pop)):
                for j in range(len(other)):
                    val = to_float(self.cmp(pop[i], other[j]))
                    if val < self.epsilon:
                        is_duplicate[i] = True
                        break

        return is_duplicate 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool_ [as 別名]
def test_allany_onmatrices(self):
        x = np.array([[0.13, 0.26, 0.90],
                      [0.28, 0.33, 0.63],
                      [0.31, 0.87, 0.70]])
        X = np.matrix(x)
        m = np.array([[True, False, False],
                      [False, False, False],
                      [True, True, False]], dtype=np.bool_)
        mX = masked_array(X, mask=m)
        mXbig = (mX > 0.5)
        mXsmall = (mX < 0.5)

        assert_(not mXbig.all())
        assert_(mXbig.any())
        assert_equal(mXbig.all(0), np.matrix([False, False, True]))
        assert_equal(mXbig.all(1), np.matrix([False, False, True]).T)
        assert_equal(mXbig.any(0), np.matrix([False, False, True]))
        assert_equal(mXbig.any(1), np.matrix([True, True, True]).T)

        assert_(not mXsmall.all())
        assert_(mXsmall.any())
        assert_equal(mXsmall.all(0), np.matrix([True, True, False]))
        assert_equal(mXsmall.all(1), np.matrix([False, False, False]).T)
        assert_equal(mXsmall.any(0), np.matrix([True, True, False]))
        assert_equal(mXsmall.any(1), np.matrix([True, True, False]).T) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool_ [as 別名]
def _ravel_and_check_weights(a, weights):
    """ Check a and weights have matching shapes, and ravel both """
    a = np.asarray(a)

    # Ensure that the array is a "subtractable" dtype
    if a.dtype == np.bool_:
        warnings.warn("Converting input from {} to {} for compatibility."
                      .format(a.dtype, np.uint8),
                      RuntimeWarning, stacklevel=2)
        a = a.astype(np.uint8)

    if weights is not None:
        weights = np.asarray(weights)
        if weights.shape != a.shape:
            raise ValueError(
                'weights should have the same shape as a.')
        weights = weights.ravel()
    a = a.ravel()
    return a, weights 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool_ [as 別名]
def test_allany(self):
        # Checks the any/all methods/functions.
        x = np.array([[0.13, 0.26, 0.90],
                      [0.28, 0.33, 0.63],
                      [0.31, 0.87, 0.70]])
        m = np.array([[True, False, False],
                      [False, False, False],
                      [True, True, False]], dtype=np.bool_)
        mx = masked_array(x, mask=m)
        mxbig = (mx > 0.5)
        mxsmall = (mx < 0.5)

        assert_(not mxbig.all())
        assert_(mxbig.any())
        assert_equal(mxbig.all(0), [False, False, True])
        assert_equal(mxbig.all(1), [False, False, True])
        assert_equal(mxbig.any(0), [False, False, True])
        assert_equal(mxbig.any(1), [True, True, True])

        assert_(not mxsmall.all())
        assert_(mxsmall.any())
        assert_equal(mxsmall.all(0), [True, True, False])
        assert_equal(mxsmall.all(1), [False, False, False])
        assert_equal(mxsmall.any(0), [True, True, False])
        assert_equal(mxsmall.any(1), [True, True, False]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool_ [as 別名]
def test_respect_dtype_singleton(self):
        # See gh-7203
        for dt in self.itype:
            lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
            ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1

            sample = self.rfunc(lbnd, ubnd, dtype=dt)
            assert_equal(sample.dtype, np.dtype(dt))

        for dt in (bool, int, np.long):
            lbnd = 0 if dt is bool else np.iinfo(dt).min
            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1

            # gh-7284: Ensure that we get Python data types
            sample = self.rfunc(lbnd, ubnd, dtype=dt)
            assert_(not hasattr(sample, 'dtype'))
            assert_equal(type(sample), dt) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool_ [as 別名]
def _name_get(dtype):
    # provides dtype.name.__get__

    if dtype.isbuiltin == 2:
        # user dtypes don't promise to do anything special
        return dtype.type.__name__

    # Builtin classes are documented as returning a "bit name"
    name = dtype.type.__name__

    # handle bool_, str_, etc
    if name[-1] == '_':
        name = name[:-1]

    # append bit counts to str, unicode, and void
    if np.issubdtype(dtype, np.flexible) and not _isunsized(dtype):
        name += "{}".format(dtype.itemsize * 8)

    # append metadata to datetimes
    elif dtype.type in (np.datetime64, np.timedelta64):
        name += _datetime_metadata_str(dtype)

    return name 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool_ [as 別名]
def test_ticket_1539(self):
        dtypes = [x for x in np.typeDict.values()
                  if (issubclass(x, np.number)
                      and not issubclass(x, np.timedelta64))]
        a = np.array([], np.bool_)  # not x[0] because it is unordered
        failures = []

        for x in dtypes:
            b = a.astype(x)
            for y in dtypes:
                c = a.astype(y)
                try:
                    np.dot(b, c)
                except TypeError:
                    failures.append((x, y))
        if failures:
            raise AssertionError("Failures: %r" % failures) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool_ [as 別名]
def test_to_frame_mixed(self):
        panel = self.panel.fillna(0)
        panel['str'] = 'foo'
        panel['bool'] = panel['ItemA'] > 0

        lp = panel.to_frame()
        wp = lp.to_panel()
        assert wp['bool'].values.dtype == np.bool_
        # Previously, this was mutating the underlying
        # index and changing its name
        assert_frame_equal(wp['bool'], panel['bool'], check_names=False)

        # GH 8704
        # with categorical
        df = panel.to_frame()
        df['category'] = df['str'].astype('category')

        # to_panel
        # TODO: this converts back to object
        p = df.to_panel()
        expected = panel.copy()
        expected['category'] = 'foo'
        assert_panel_equal(p, expected) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool_ [as 別名]
def test_contains_nan(self):
        # PR #14171
        s = Series([np.nan, np.nan, np.nan], dtype=np.object_)

        result = s.str.contains('foo', na=False)
        expected = Series([False, False, False], dtype=np.bool_)
        assert_series_equal(result, expected)

        result = s.str.contains('foo', na=True)
        expected = Series([True, True, True], dtype=np.bool_)
        assert_series_equal(result, expected)

        result = s.str.contains('foo', na="foo")
        expected = Series(["foo", "foo", "foo"], dtype=np.object_)
        assert_series_equal(result, expected)

        result = s.str.contains('foo')
        expected = Series([np.nan, np.nan, np.nan], dtype=np.object_)
        assert_series_equal(result, expected) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool_ [as 別名]
def test_select_dtypes_exclude_include_using_list_like(self):
        df = DataFrame({'a': list('abc'),
                        'b': list(range(1, 4)),
                        'c': np.arange(3, 6).astype('u1'),
                        'd': np.arange(4.0, 7.0, dtype='float64'),
                        'e': [True, False, True],
                        'f': pd.date_range('now', periods=3).values})
        exclude = np.datetime64,
        include = np.bool_, 'integer'
        r = df.select_dtypes(include=include, exclude=exclude)
        e = df[['b', 'c', 'e']]
        assert_frame_equal(r, e)

        exclude = 'datetime',
        include = 'bool', 'int64', 'int32'
        r = df.select_dtypes(include=include, exclude=exclude)
        e = df[['b', 'e']]
        assert_frame_equal(r, e) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool_ [as 別名]
def test_flex_comparison_nat(self):
        # GH 15697, GH 22163 df.eq(pd.NaT) should behave like df == pd.NaT,
        # and _definitely_ not be NaN
        df = pd.DataFrame([pd.NaT])

        result = df == pd.NaT
        # result.iloc[0, 0] is a np.bool_ object
        assert result.iloc[0, 0].item() is False

        result = df.eq(pd.NaT)
        assert result.iloc[0, 0].item() is False

        result = df != pd.NaT
        assert result.iloc[0, 0].item() is True

        result = df.ne(pd.NaT)
        assert result.iloc[0, 0].item() is True 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool_ [as 別名]
def test_bools(self):
        arr = np.array([True, False, True, True, True], dtype='O')
        result = lib.infer_dtype(arr, skipna=True)
        assert result == 'boolean'

        arr = np.array([np.bool_(True), np.bool_(False)], dtype='O')
        result = lib.infer_dtype(arr, skipna=True)
        assert result == 'boolean'

        arr = np.array([True, False, True, 'foo'], dtype='O')
        result = lib.infer_dtype(arr, skipna=True)
        assert result == 'mixed'

        arr = np.array([True, False, True], dtype=bool)
        result = lib.infer_dtype(arr, skipna=True)
        assert result == 'boolean'

        arr = np.array([True, np.nan, False], dtype='O')
        result = lib.infer_dtype(arr, skipna=True)
        assert result == 'boolean'

        result = lib.infer_dtype(arr, skipna=False)
        assert result == 'mixed' 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool_ [as 別名]
def test_is_number(self):

        assert is_number(True)
        assert is_number(1)
        assert is_number(1.1)
        assert is_number(1 + 3j)
        assert is_number(np.bool(False))
        assert is_number(np.int64(1))
        assert is_number(np.float64(1.1))
        assert is_number(np.complex128(1 + 3j))
        assert is_number(np.nan)

        assert not is_number(None)
        assert not is_number('x')
        assert not is_number(datetime(2011, 1, 1))
        assert not is_number(np.datetime64('2011-01-01'))
        assert not is_number(Timestamp('2011-01-01'))
        assert not is_number(Timestamp('2011-01-01', tz='US/Eastern'))
        assert not is_number(timedelta(1000))
        assert not is_number(Timedelta('1 days'))

        # questionable
        assert not is_number(np.bool_(False))
        assert is_number(np.timedelta64(1, 'D')) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool_ [as 別名]
def test_is_bool(self):
        assert is_bool(True)
        assert is_bool(np.bool(False))
        assert is_bool(np.bool_(False))

        assert not is_bool(1)
        assert not is_bool(1.1)
        assert not is_bool(1 + 3j)
        assert not is_bool(np.int64(1))
        assert not is_bool(np.float64(1.1))
        assert not is_bool(np.complex128(1 + 3j))
        assert not is_bool(np.nan)
        assert not is_bool(None)
        assert not is_bool('x')
        assert not is_bool(datetime(2011, 1, 1))
        assert not is_bool(np.datetime64('2011-01-01'))
        assert not is_bool(Timestamp('2011-01-01'))
        assert not is_bool(Timestamp('2011-01-01', tz='US/Eastern'))
        assert not is_bool(timedelta(1000))
        assert not is_bool(np.timedelta64(1, 'D'))
        assert not is_bool(Timedelta('1 days'))