python类generic()的实例源码

def set_theta(self, theta):
        """
        Function sets theta. Can be called from constructor or outside.
        """
        if isinstance(theta, (np.ndarray, np.generic)):
            self.theta = theta
        elif isinstance(theta, list):
            self.theta = np.array(theta)
        else:
            self.theta = None
        self.history = self.set_list(
            self.history, 0, (np.copy(self.theta), 0, None))
        self.step_no = 0

def filter_invalid_json_values(self, dict):
        for k, v in six.iteritems(dict):
            if isinstance(v, (np.ndarray, np.generic)):
                dict[k] = v.tolist()
            if math.isnan(v) or math.isinf(v):
                dict[k] = -1

def invalid_json_values(obj):
    if isinstance(obj, np.generic):
        return obj.item()
    if isinstance(obj, np.ndarray):
        return obj.tolist()
    if isinstance(obj, bytes):
        return obj.decode('cp437')

    if isinstance(map, type) and isinstance(obj, map):
        # python 3 map
        return list(obj)

    raise TypeError('Invalid data type passed to json encoder: ' + type(obj).__name__)

def contains(self, x):
        if isinstance(x, int):
            as_int = x
        elif isinstance(x, (np.generic, np.ndarray)) and (x.dtype.kind in np.typecodes['AllInteger'] and x.shape == ()):
            as_int = int(x)
        else:
            return False
        return as_int >= 0 and as_int < self.n

def capture_frame(self, frame):
        if not isinstance(frame, (np.ndarray, np.generic)):
            raise error.InvalidFrame('Wrong type {} for {} (must be np.ndarray or np.generic)'.format(type(frame), frame))
        if frame.shape != self.frame_shape:
            raise error.InvalidFrame("Your frame has shape {}, but the VideoRecorder is configured for shape {}.".format(frame.shape, self.frame_shape))
        if frame.dtype != np.uint8:
            raise error.InvalidFrame("Your frame has data type {}, but we require uint8 (i.e. RGB values from 0-255).".format(frame.dtype))

        if distutils.version.LooseVersion(np.__version__) >= distutils.version.LooseVersion('1.9.0'):
            self.proc.stdin.write(frame.tobytes())
        else:
            self.proc.stdin.write(frame.tostring())

def test_oddfeatures_3(self):
        # Tests some generic features
        atest = array([10], mask=True)
        btest = array([20])
        idx = atest.mask
        atest[idx] = btest[idx]
        assert_equal(atest, [20])

def test_tolist_specialcase(self):
        # Test mvoid.tolist: make sure we return a standard Python object
        a = array([(0, 1), (2, 3)], dtype=[('a', int), ('b', int)])
        # w/o mask: each entry is a np.void whose elements are standard Python
        for entry in a:
            for item in entry.tolist():
                assert_(not isinstance(item, np.generic))
        # w/ mask: each entry is a ma.void whose elements should be
        # standard Python
        a.mask[0] = (0, 1)
        for entry in a:
            for item in entry.tolist():
                assert_(not isinstance(item, np.generic))

def test_masked_where_oddities(self):
        # Tests some generic features.
        atest = ones((10, 10, 10), dtype=float)
        btest = zeros(atest.shape, MaskType)
        ctest = masked_where(btest, atest)
        assert_equal(atest, ctest)

def feature_names(self, names):
        "Stores the text labels for features"

        if len(names) != self.num_features:
            raise ValueError("Number of names do not match the number of features!")
        if not isinstance(names, (Sequence, np.ndarray, np.generic)):
            raise ValueError("Input is not a sequence. Ensure names are in the same order and length as features.")

        self.__feature_names = np.array(names)

def is_np_scalar(x):
    """
    Check np types like np.int64
    """
    return isinstance(x, np.generic)

def contains(self, x):
        if not isinstance(x, (tuple, list, np.generic, np.ndarray)):
            return False
        return np.shape(x) == self.shape and np.sum(x) == 1 and np.max(x) == 1

def contains(self, x):
    if isinstance(x, int):
      as_int = x
    elif isinstance(x, (np.generic, np.ndarray)) and (x.dtype.kind in np.typecodes['AllInteger'] and x.shape == ()):
      as_int = int(x)
    else:
      return False
    return as_int >= 0 and as_int < self.n

def __setitem__(self, in_slice, value):
        """Set ndarray value"""
        if (not isinstance(in_slice, slice) or
                in_slice.start is not None
                or in_slice.stop is not None):
            raise ValueError('Array only support set from numpy array')
        if isinstance(value, NDArray):
            if value.handle is not self.handle:
                value.copyto(self)
        elif isinstance(value, (np.ndarray, np.generic)):
            self._sync_copyfrom(value)
        else:
            raise TypeError('type %s not supported' % str(type(value)))

def _validate_date_like_dtype(dtype):
    try:
        typ = np.datetime_data(dtype)[0]
    except ValueError as e:
        raise TypeError('%s' % e)
    if typ != 'generic' and typ != 'ns':
        raise ValueError('%r is too specific of a frequency, try passing %r' %
                         (dtype.name, dtype.type.__name__))

def _get_dtype_from_object(dtype):
    """Get a numpy dtype.type-style object. This handles the datetime64[ns]
    and datetime64[ns, TZ] compat

    Notes
    -----
    If nothing can be found, returns ``object``.
    """
    # type object from a dtype
    if isinstance(dtype, type) and issubclass(dtype, np.generic):
        return dtype
    elif is_categorical(dtype):
        return CategoricalDtype().type
    elif is_datetimetz(dtype):
        return DatetimeTZDtype(dtype).type
    elif isinstance(dtype, np.dtype):  # dtype object
        try:
            _validate_date_like_dtype(dtype)
        except TypeError:
            # should still pass if we don't have a datelike
            pass
        return dtype.type
    elif isinstance(dtype, compat.string_types):
        if dtype == 'datetime' or dtype == 'timedelta':
            dtype += '64'

        try:
            return _get_dtype_from_object(getattr(np, dtype))
        except (AttributeError, TypeError):
            # handles cases like _get_dtype(int)
            # i.e., python objects that are valid dtypes (unlike user-defined
            # types, in general)
            # TypeError handles the float16 typecode of 'e'
            # further handle internal types
            pass

    return _get_dtype_from_object(np.dtype(dtype))

def test_oddfeatures_3(self):
        # Tests some generic features
        atest = array([10], mask=True)
        btest = array([20])
        idx = atest.mask
        atest[idx] = btest[idx]
        assert_equal(atest, [20])

def test_tolist_specialcase(self):
        # Test mvoid.tolist: make sure we return a standard Python object
        a = array([(0, 1), (2, 3)], dtype=[('a', int), ('b', int)])
        # w/o mask: each entry is a np.void whose elements are standard Python
        for entry in a:
            for item in entry.tolist():
                assert_(not isinstance(item, np.generic))
        # w/ mask: each entry is a ma.void whose elements should be
        # standard Python
        a.mask[0] = (0, 1)
        for entry in a:
            for item in entry.tolist():
                assert_(not isinstance(item, np.generic))

def to_numpy(var):
    #if ia.is_numpy_array(var):
    if isinstance(var, (np.ndarray, np.generic)):
        return var
    else:
        return var.data.cpu().numpy()

def test_oddfeatures_3(self):
        # Tests some generic features
        atest = array([10], mask=True)
        btest = array([20])
        idx = atest.mask
        atest[idx] = btest[idx]
        assert_equal(atest, [20])

def test_tolist_specialcase(self):
        # Test mvoid.tolist: make sure we return a standard Python object
        a = array([(0, 1), (2, 3)], dtype=[('a', int), ('b', int)])
        # w/o mask: each entry is a np.void whose elements are standard Python
        for entry in a:
            for item in entry.tolist():
                assert_(not isinstance(item, np.generic))
        # w/ mask: each entry is a ma.void whose elements should be
        # standard Python
        a.mask[0] = (0, 1)
        for entry in a:
            for item in entry.tolist():
                assert_(not isinstance(item, np.generic))