python类bool()的实例源码

def get_poly_normals(ob, type=np.float32):
    mod = False
    m_count = len(ob.modifiers)
    if m_count > 0:
        show = np.zeros(m_count, dtype=np.bool)
        ren_set = np.copy(show)
        ob.modifiers.foreach_get('show_render', show)
        ob.modifiers.foreach_set('show_render', ren_set)
        mod = True
    mesh = ob.to_mesh(bpy.context.scene, True, 'RENDER')
    p_count = len(mesh.polygons)
    normal = np.zeros(p_count * 3)#, dtype=type)
    mesh.polygons.foreach_get('normal', normal)
    normal.shape = (p_count, 3)
    bpy.data.meshes.remove(mesh)
    if mod:
        ob.modifiers.foreach_set('show_render', show)

    return normal

def get_v_normals(ob, type=np.float32):
    mod = False
    m_count = len(ob.modifiers)
    if m_count > 0:
        show = np.zeros(m_count, dtype=np.bool)
        ren_set = np.copy(show)
        ob.modifiers.foreach_get('show_render', show)
        ob.modifiers.foreach_set('show_render', ren_set)
        mod = True
    mesh = ob.to_mesh(bpy.context.scene, True, 'RENDER')
    v_count = len(mesh.vertices)
    normal = np.zeros(v_count * 3)#, dtype=type)
    mesh.vertices.foreach_get('normal', normal)
    normal.shape = (v_count, 3)
    bpy.data.meshes.remove(mesh)
    if mod:
        ob.modifiers.foreach_set('show_render', show)

    return normal

def basic_unwrap():
    ob = bpy.context.object
    mode = ob.mode
    data = ob.data
    key = ob.active_shape_key_index
    bpy.ops.object.mode_set(mode='OBJECT')        
    layers = [i.name for i in ob.data.uv_layers]
    if "UV_Shape_key" not in layers:
        bpy.ops.mesh.uv_texture_add()
        ob.data.uv_layers[len(ob.data.uv_layers) - 1].name = 'UV_Shape_key'

    ob.data.uv_layers.active_index = len(ob.data.uv_layers) - 1
    ob.active_shape_key_index = 0
    data.vertices.foreach_set('select', np.ones(len(data.vertices), dtype=np.bool))

    bpy.ops.object.mode_set(mode='EDIT')
    bpy.ops.uv.unwrap(method='ANGLE_BASED', margin=0.0635838)
    bpy.ops.object.mode_set(mode=mode)
    ob.active_shape_key_index = key

def _iter_test_masks(self, frame, y=None):
        """Generates boolean masks corresponding to the tests set.

        Parameters
        ----------

        frame : H2OFrame
            The h2o frame to split

        y : string, optional (default=None)
            The column to stratify.

        Returns
        -------

        test_mask : np.ndarray, shape=(n_samples,)
            The indices for the test split
        """
        for test_index in self._iter_test_indices(frame, y):
            test_mask = np.zeros(frame.shape[0], dtype=np.bool)
            test_mask[test_index] = True
            yield test_mask

def is_iterable(x):
    """Python 3.x adds the ``__iter__`` attribute
    to strings. Thus, our previous tests for iterable
    will fail when using ``hasattr``.

    Parameters
    ----------

    x : object
        The object or primitive to test whether
        or not is an iterable.


    Returns
    -------

    bool
        True if ``x`` is an iterable
    """
    if isinstance(x, six.string_types):
        return False
    return hasattr(x, '__iter__')

def _is_integer(x):
    """Determine whether some object ``x`` is an
    integer type (int, long, etc). This is part of the 
    ``fixes`` module, since Python 3 removes the long
    datatype, we have to check the version major.

    Parameters
    ----------

    x : object
        The item to assess whether is an integer.


    Returns
    -------

    bool
        True if ``x`` is an integer type
    """
    return (not isinstance(x, (bool, np.bool))) and \
        isinstance(x, (numbers.Integral, int, np.int, np.long, long))  # no long type in python 3

def is_entirely_numeric(X):
    """Determines whether an entire pandas frame
    is numeric in dtypes.

    Parameters
    ----------

    X : Pandas ``DataFrame`` or ``H2OFrame``, shape=(n_samples, n_features)
        The dataframe to test


    Returns
    -------

    bool
        True if the entire pd.DataFrame 
        is numeric else False
    """
    return X.shape[1] == len(get_numeric(X))

def is_float(x):
    """Determine whether some object ``x`` is a
    float type (float, np.float, etc).

    Parameters
    ----------

    x : object
        The item to assess


    Returns
    -------

    bool
        True if ``x`` is a float type
    """
    return isinstance(x, (float, np.float)) or \
        (not isinstance(x, (bool, np.bool)) and isinstance(x, numbers.Real))

def is_numeric(x):
    """Determine whether some object ``x`` is a
    numeric type (float, int, etc).

    Parameters
    ----------

    x : object
        The item to assess


    Returns
    -------

    bool
        True if ``x`` is a float or integer type
    """
    return is_float(x) or is_integer(x)

def __init__(self, config, model_dir, ob_shape_list):
    self.model_dir = model_dir

    self.cnn_format = config.cnn_format
    self.memory_size = config.memory_size
    self.actions = np.empty(self.memory_size, dtype = np.uint8)
    self.rewards = np.empty(self.memory_size, dtype = np.integer)
    # print(self.memory_size, config.screen_height, config.screen_width)
    # self.screens = np.empty((self.memory_size, config.screen_height, config.screen_width), dtype = np.float16)
    self.screens = np.empty([self.memory_size] + ob_shape_list, dtype = np.float16)
    self.terminals = np.empty(self.memory_size, dtype = np.bool)
    self.history_length = config.history_length
    # self.dims = (config.screen_height, config.screen_width)
    self.dims = tuple(ob_shape_list)
    self.batch_size = config.batch_size
    self.count = 0
    self.current = 0

    # pre-allocate prestates and poststates for minibatch
    self.prestates = np.empty((self.batch_size, self.history_length) + self.dims, dtype = np.float16)
    self.poststates = np.empty((self.batch_size, self.history_length) + self.dims, dtype = np.float16)
    # self.prestates = np.empty((self.batch_size, self.history_length, self.dims), dtype = np.float16)
    # self.poststates = np.empty((self.batch_size, self.history_length, self.dims), dtype = np.float16)

def __make_net(self, input_images, input_measure, input_actions, reuse=False):
        if reuse:
            tf.get_variable_scope().reuse_variables()
        fc_val_params = copy.deepcopy(self.__fc_joint_params)
        fc_val_params[-1]['out_dims'] = self.__target_dim

        fc_adv_params = copy.deepcopy(self.__fc_joint_params)
        fc_adv_params[-1]['out_dims'] = len(self.__net_discrete_actions) * self.__target_dim

        if self.verbose:
            print 'fc_val_params:', fc_val_params
            print 'fc_adv_params:', fc_adv_params

        p_img_conv = ly.conv_encoder(input_images, self.__conv_params, 'p_img_conv', msra_coeff=0.9)
        p_img_fc = ly.fc_net(ly.flatten(p_img_conv), self.__fc_img_params, 'p_img_fc', msra_coeff=0.9)
        p_meas_fc = ly.fc_net(input_measure, self.__fc_measure_params, 'p_meas_fc', msra_coeff=0.9)
        p_val_fc = ly.fc_net(tf.concat([p_img_fc, p_meas_fc], 1),
                             fc_val_params, 'p_val_fc', last_linear=True, msra_coeff=0.9)
        p_adv_fc = ly.fc_net(tf.concat([p_img_fc, p_meas_fc], 1),
                             fc_adv_params, 'p_adv_fc', last_linear=True, msra_coeff=0.9)
        p_adv_fc_nomean = p_adv_fc - tf.reduce_mean(p_adv_fc, reduction_indices=1, keep_dims=True)

        self.__pred_all_nomean = tf.reshape(p_adv_fc_nomean, [-1, len(self.__net_discrete_actions), self.__target_dim])
        self.__pred_all = self.__pred_all_nomean + tf.reshape(p_val_fc, [-1, 1, self.__target_dim])
        self.__pred_relevant = tf.boolean_mask(self.__pred_all, tf.cast(input_actions, tf.bool))

def setUp(self):
        self.f = np.ones(256, dtype=np.float32)
        self.ef = np.ones(self.f.size, dtype=np.bool)
        self.d = np.ones(128, dtype=np.float64)
        self.ed = np.ones(self.d.size, dtype=np.bool)
        # generate values for all permutation of 256bit simd vectors
        s = 0
        for i in range(32):
            self.f[s:s+8] = [i & 2**x for x in range(8)]
            self.ef[s:s+8] = [(i & 2**x) != 0 for x in range(8)]
            s += 8
        s = 0
        for i in range(16):
            self.d[s:s+4] = [i & 2**x for x in range(4)]
            self.ed[s:s+4] = [(i & 2**x) != 0 for x in range(4)]
            s += 4

        self.nf = self.f.copy()
        self.nd = self.d.copy()
        self.nf[self.ef] = np.nan
        self.nd[self.ed] = np.nan

def test_float(self):
        # offset for alignment test
        for i in range(4):
            assert_array_equal(self.f[i:] > 0, self.ef[i:])
            assert_array_equal(self.f[i:] - 1 >= 0, self.ef[i:])
            assert_array_equal(self.f[i:] == 0, ~self.ef[i:])
            assert_array_equal(-self.f[i:] < 0, self.ef[i:])
            assert_array_equal(-self.f[i:] + 1 <= 0, self.ef[i:])
            r = self.f[i:] != 0
            assert_array_equal(r, self.ef[i:])
            r2 = self.f[i:] != np.zeros_like(self.f[i:])
            r3 = 0 != self.f[i:]
            assert_array_equal(r, r2)
            assert_array_equal(r, r3)
            # check bool == 0x1
            assert_array_equal(r.view(np.int8), r.astype(np.int8))
            assert_array_equal(r2.view(np.int8), r2.astype(np.int8))
            assert_array_equal(r3.view(np.int8), r3.astype(np.int8))

            # isnan on amd64 takes the same code path
            assert_array_equal(np.isnan(self.nf[i:]), self.ef[i:])

def test_double(self):
        # offset for alignment test
        for i in range(2):
            assert_array_equal(self.d[i:] > 0, self.ed[i:])
            assert_array_equal(self.d[i:] - 1 >= 0, self.ed[i:])
            assert_array_equal(self.d[i:] == 0, ~self.ed[i:])
            assert_array_equal(-self.d[i:] < 0, self.ed[i:])
            assert_array_equal(-self.d[i:] + 1 <= 0, self.ed[i:])
            r = self.d[i:] != 0
            assert_array_equal(r, self.ed[i:])
            r2 = self.d[i:] != np.zeros_like(self.d[i:])
            r3 = 0 != self.d[i:]
            assert_array_equal(r, r2)
            assert_array_equal(r, r3)
            # check bool == 0x1
            assert_array_equal(r.view(np.int8), r.astype(np.int8))
            assert_array_equal(r2.view(np.int8), r2.astype(np.int8))
            assert_array_equal(r3.view(np.int8), r3.astype(np.int8))

            # isnan on amd64 takes the same code path
            assert_array_equal(np.isnan(self.nd[i:]), self.ed[i:])

def test_array_equal(self):
        res = np.array_equal(np.array([1, 2]), np.array([1, 2]))
        assert_(res)
        assert_(type(res) is bool)
        res = np.array_equal(np.array([1, 2]), np.array([1, 2, 3]))
        assert_(not res)
        assert_(type(res) is bool)
        res = np.array_equal(np.array([1, 2]), np.array([3, 4]))
        assert_(not res)
        assert_(type(res) is bool)
        res = np.array_equal(np.array([1, 2]), np.array([1, 3]))
        assert_(not res)
        assert_(type(res) is bool)
        res = np.array_equal(np.array(['a'], dtype='S1'), np.array(['a'], dtype='S1'))
        assert_(res)
        assert_(type(res) is bool)
        res = np.array_equal(np.array([('a', 1)], dtype='S1,u4'),
                             np.array([('a', 1)], dtype='S1,u4'))
        assert_(res)
        assert_(type(res) is bool)

def test_truth_table_logical(self):
        # 2, 3 and 4 serves as true values
        input1 = [0, 0, 3, 2]
        input2 = [0, 4, 0, 2]

        typecodes = (np.typecodes['AllFloat']
                     + np.typecodes['AllInteger']
                     + '?')     # boolean
        for dtype in map(np.dtype, typecodes):
            arg1 = np.asarray(input1, dtype=dtype)
            arg2 = np.asarray(input2, dtype=dtype)

            # OR
            out = [False, True, True, True]
            for func in (np.logical_or, np.maximum):
                assert_equal(func(arg1, arg2).astype(bool), out)
            # AND
            out = [False, False, False, True]
            for func in (np.logical_and, np.minimum):
                assert_equal(func(arg1, arg2).astype(bool), out)
            # XOR
            out = [False, True, True, False]
            for func in (np.logical_xor, np.not_equal):
                assert_equal(func(arg1, arg2).astype(bool), out)

def test_basic(self):
        dt_numeric = np.typecodes['AllFloat'] + np.typecodes['AllInteger']
        dt_complex = np.typecodes['Complex']

        # test real
        a = np.eye(3)
        for dt in dt_numeric + 'O':
            b = a.astype(dt)
            res = np.vdot(b, b)
            assert_(np.isscalar(res))
            assert_equal(np.vdot(b, b), 3)

        # test complex
        a = np.eye(3) * 1j
        for dt in dt_complex + 'O':
            b = a.astype(dt)
            res = np.vdot(b, b)
            assert_(np.isscalar(res))
            assert_equal(np.vdot(b, b), 3)

        # test boolean
        b = np.eye(3, dtype=np.bool)
        res = np.vdot(b, b)
        assert_(np.isscalar(res))
        assert_equal(np.vdot(b, b), True)

def test_basic(self):
        dts = [np.bool, np.int16, np.int32, np.int64, np.double, np.complex128,
               np.longdouble, np.clongdouble]
        for dt in dts:
            c = np.ones(53, dtype=np.bool)
            assert_equal(np.where( c, dt(0), dt(1)), dt(0))
            assert_equal(np.where(~c, dt(0), dt(1)), dt(1))
            assert_equal(np.where(True, dt(0), dt(1)), dt(0))
            assert_equal(np.where(False, dt(0), dt(1)), dt(1))
            d = np.ones_like(c).astype(dt)
            e = np.zeros_like(d)
            r = d.astype(dt)
            c[7] = False
            r[7] = e[7]
            assert_equal(np.where(c, e, e), e)
            assert_equal(np.where(c, d, e), r)
            assert_equal(np.where(c, d, e[0]), r)
            assert_equal(np.where(c, d[0], e), r)
            assert_equal(np.where(c[::2], d[::2], e[::2]), r[::2])
            assert_equal(np.where(c[1::2], d[1::2], e[1::2]), r[1::2])
            assert_equal(np.where(c[::3], d[::3], e[::3]), r[::3])
            assert_equal(np.where(c[1::3], d[1::3], e[1::3]), r[1::3])
            assert_equal(np.where(c[::-2], d[::-2], e[::-2]), r[::-2])
            assert_equal(np.where(c[::-3], d[::-3], e[::-3]), r[::-3])
            assert_equal(np.where(c[1::-3], d[1::-3], e[1::-3]), r[1::-3])

def test_dtype_mix(self):
        c = np.array([False, True, False, False, False, False, True, False,
                     False, False, True, False])
        a = np.uint32(1)
        b = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.],
                      dtype=np.float64)
        r = np.array([5., 1., 3., 2., -1., -4., 1., -10., 10., 1., 1., 3.],
                     dtype=np.float64)
        assert_equal(np.where(c, a, b), r)

        a = a.astype(np.float32)
        b = b.astype(np.int64)
        assert_equal(np.where(c, a, b), r)

        # non bool mask
        c = c.astype(np.int)
        c[c != 0] = 34242324
        assert_equal(np.where(c, a, b), r)
        # invert
        tmpmask = c != 0
        c[c == 0] = 41247212
        c[tmpmask] = 0
        assert_equal(np.where(c, b, a), r)

def test_allany_oddities(self):
        # Some fun with all and any
        store = empty((), dtype=bool)
        full = array([1, 2, 3], mask=True)

        self.assertTrue(full.all() is masked)
        full.all(out=store)
        self.assertTrue(store)
        self.assertTrue(store._mask, True)
        self.assertTrue(store is not masked)

        store = empty((), dtype=bool)
        self.assertTrue(full.any() is masked)
        full.any(out=store)
        self.assertTrue(not store)
        self.assertTrue(store._mask, True)
        self.assertTrue(store is not masked)