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 fetch(clobber=False):
    """
    Downloads the 3D dust map of Leike & Ensslin (2019).

    Args:
        clobber (Optional[bool]): If ``True``, any existing file will be
            overwritten, even if it appears to match. If ``False`` (the
            default), ``fetch()`` will attempt to determine if the dataset
            already exists. This determination is not 100\% robust against data
            corruption.
    """
    dest_dir = fname_pattern = os.path.join(data_dir(), 'leike_ensslin_2019')
    fname = os.path.join(dest_dir, 'simple_cube.h5')
    
    # Check if the FITS table already exists
    md5sum = 'f54e01c253453117e3770575bed35078'

    if (not clobber) and fetch_utils.check_md5sum(fname, md5sum):
        print('File appears to exist already. Call `fetch(clobber=True)` '
              'to force overwriting of existing file.')
        return

    # Download from the server
    url = 'https://zenodo.org/record/2577337/files/simple_cube.h5?download=1'
    fetch_utils.download_and_verify(url, md5sum, fname) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool [as 別名]
def dataframe_select(df, *cols, **filters):
    '''
    dataframe_select(df, k1=v1, k2=v2...) yields df after selecting all the columns in which the
      given keys (k1, k2, etc.) have been selected such that the associated columns in the dataframe
      contain only the rows whose cells match the given values.
    dataframe_select(df, col1, col2...) selects the given columns.
    dataframe_select(df, col1, col2..., k1=v1, k2=v2...) selects both.
    
    If a value is a tuple/list of 2 elements, then it is considered a range where cells must fall
    between the values. If value is a tuple/list of more than 2 elements or is a set of any length
    then it is a list of values, any one of which can match the cell.
    '''
    ii = np.ones(len(df), dtype='bool')
    for (k,v) in six.iteritems(filters):
        vals = df[k].values
        if   pimms.is_set(v):                    jj = np.isin(vals, list(v))
        elif pimms.is_vector(v) and len(v) == 2: jj = (v[0] <= vals) & (vals < v[1])
        elif pimms.is_vector(v):                 jj = np.isin(vals, list(v))
        else:                                    jj = (vals == v)
        ii = np.logical_and(ii, jj)
    if len(ii) != np.sum(ii): df = df.loc[ii]
    if len(cols) > 0: df = df[list(cols)]
    return df 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool [as 別名]
def testDecodeObjectIsCrowd(self):
    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    object_is_crowd = [0, 1]
    example = tf.train.Example(features=tf.train.Features(feature={
        'image/encoded': self._BytesFeature(encoded_jpeg),
        'image/format': self._BytesFeature('jpeg'),
        'image/object/is_crowd': self._Int64Feature(object_is_crowd),
    })).SerializeToString()

    example_decoder = tf_example_decoder.TfExampleDecoder()
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual((tensor_dict[
        fields.InputDataFields.groundtruth_is_crowd].get_shape().as_list()),
                        [None])
    with self.test_session() as sess:
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual([bool(item) for item in object_is_crowd],
                        tensor_dict[
                            fields.InputDataFields.groundtruth_is_crowd]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool [as 別名]
def testDecodeObjectDifficult(self):
    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    object_difficult = [0, 1]
    example = tf.train.Example(features=tf.train.Features(feature={
        'image/encoded': self._BytesFeature(encoded_jpeg),
        'image/format': self._BytesFeature('jpeg'),
        'image/object/difficult': self._Int64Feature(object_difficult),
    })).SerializeToString()

    example_decoder = tf_example_decoder.TfExampleDecoder()
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual((tensor_dict[
        fields.InputDataFields.groundtruth_difficult].get_shape().as_list()),
                        [None])
    with self.test_session() as sess:
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual([bool(item) for item in object_difficult],
                        tensor_dict[
                            fields.InputDataFields.groundtruth_difficult]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool [as 別名]
def store_effect(self, idx, action, reward, done):
        """Store effects of action taken after obeserving frame stored
        at index idx. The reason `store_frame` and `store_effect` is broken
        up into two functions is so that once can call `encode_recent_observation`
        in between.

        Paramters
        ---------
        idx: int
            Index in buffer of recently observed frame (returned by `store_frame`).
        action: int
            Action that was performed upon observing this frame.
        reward: float
            Reward that was received when the actions was performed.
        done: bool
            True if episode was finished after performing that action.
        """
        self.action[idx] = action
        self.reward[idx] = reward
        self.done[idx]   = done 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool [as 別名]
def test_linear_sum_assignment_input_validation():
    assert_raises(ValueError, linear_sum_assignment, [1, 2, 3])

    C = [[1, 2, 3], [4, 5, 6]]
    assert_array_equal(linear_sum_assignment(C), linear_sum_assignment(np.asarray(C)))
    # assert_array_equal(linear_sum_assignment(C),
    #                    linear_sum_assignment(matrix(C)))

    I = np.identity(3)
    assert_array_equal(linear_sum_assignment(I.astype(np.bool)), linear_sum_assignment(I))
    assert_raises(ValueError, linear_sum_assignment, I.astype(str))

    I[0][0] = np.nan
    assert_raises(ValueError, linear_sum_assignment, I)

    I = np.identity(3)
    I[1][1] = np.inf
    assert_raises(ValueError, linear_sum_assignment, I) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool [as 別名]
def put(self, enc_obs, actions, rewards, mus, dones, masks):
        # enc_obs [nenv, (nsteps + nstack), nh, nw, nc]
        # actions, rewards, dones [nenv, nsteps]
        # mus [nenv, nsteps, nact]

        if self.enc_obs is None:
            self.enc_obs = np.empty([self.size] + list(enc_obs.shape), dtype=np.uint8)
            self.actions = np.empty([self.size] + list(actions.shape), dtype=np.int32)
            self.rewards = np.empty([self.size] + list(rewards.shape), dtype=np.float32)
            self.mus = np.empty([self.size] + list(mus.shape), dtype=np.float32)
            self.dones = np.empty([self.size] + list(dones.shape), dtype=np.bool)
            self.masks = np.empty([self.size] + list(masks.shape), dtype=np.bool)

        self.enc_obs[self.next_idx] = enc_obs
        self.actions[self.next_idx] = actions
        self.rewards[self.next_idx] = rewards
        self.mus[self.next_idx] = mus
        self.dones[self.next_idx] = dones
        self.masks[self.next_idx] = masks

        self.next_idx = (self.next_idx + 1) % self.size
        self.num_in_buffer = min(self.size, self.num_in_buffer + 1) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool [as 別名]
def equirect_facetype(h, w):
    '''
    0F 1R 2B 3L 4U 5D
    '''
    tp = np.roll(np.arange(4).repeat(w // 4)[None, :].repeat(h, 0), 3 * w // 8, 1)

    # Prepare ceil mask
    mask = np.zeros((h, w // 4), np.bool)
    idx = np.linspace(-np.pi, np.pi, w // 4) / 4
    idx = h // 2 - np.round(np.arctan(np.cos(idx)) * h / np.pi).astype(int)
    for i, j in enumerate(idx):
        mask[:j, i] = 1
    mask = np.roll(np.concatenate([mask] * 4, 1), 3 * w // 8, 1)

    tp[mask] = 4
    tp[np.flip(mask, 0)] = 5

    return tp.astype(np.int32) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool [as 別名]
def get_poly_centers(ob, type=np.float32, mesh=None):
    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
    p_count = len(mesh.polygons)
    center = np.zeros(p_count * 3, dtype=type)
    mesh.polygons.foreach_get('center', center)
    center.shape = (p_count, 3)
    if mod:
        ob.modifiers.foreach_set('show_render', show)

    return center 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool [as 別名]
def get_poly_normals(ob, type=np.float32, mesh=None):
    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
    p_count = len(mesh.polygons)
    normal = np.zeros(p_count * 3, dtype=type)
    mesh.polygons.foreach_get('normal', normal)
    normal.shape = (p_count, 3)
    if mod:
        ob.modifiers.foreach_set('show_render', show)

    return normal 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool [as 別名]
def get_v_normals(ob, arr, mesh):
    """Since we're reading from a shape key we have to use
    a proxy mesh."""
    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
    #v_count = len(mesh.vertices)
    #normal = np.zeros(v_count * 3)#, dtype=type)
    mesh.vertices.foreach_get('normal', arr.ravel())
    #normal.shape = (v_count, 3)
    if mod:
        ob.modifiers.foreach_set('show_render', show) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool [as 別名]
def triangle_bounds_check(tri_co, co_min, co_max, idxer, fudge):
    """Returns a bool aray indexing the triangles that
    intersect the bounds of the object"""

    # min check cull step 1
    tri_min = np.min(tri_co, axis=1) - fudge
    check_min = co_max > tri_min
    in_min = np.all(check_min, axis=1)
    
    # max check cull step 2
    idx = idxer[in_min]
    tri_max = np.max(tri_co[in_min], axis=1) + fudge
    check_max = tri_max > co_min
    in_max = np.all(check_max, axis=1)
    in_min[idx[~in_max]] = False
    
    return in_min, tri_min[in_min], tri_max[in_max] # can reuse the min and max 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool [as 別名]
def tri_back_check(co, tri_min, tri_max, idxer, fudge):
    """Returns a bool aray indexing the vertices that
    intersect the bounds of the culled triangles"""

    # min check cull step 1
    tb_min = np.min(tri_min, axis=0) - fudge
    check_min = co > tb_min
    in_min = np.all(check_min, axis=1)
    idx = idxer[in_min]
    
    # max check cull step 2
    tb_max = np.max(tri_max, axis=0) + fudge
    check_max = co[in_min] < tb_max
    in_max = np.all(check_max, axis=1)        
    in_min[idx[~in_max]] = False    
    
    return in_min 


# -------------------------------------------------------
# ------------------------------------------------------- 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bool [as 別名]
def testDecodeObjectIsCrowd(self):
    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    object_is_crowd = [0, 1]
    example = tf.train.Example(features=tf.train.Features(feature={
        'image/encoded': self._BytesFeature(encoded_jpeg),
        'image/format': self._BytesFeature('jpeg'),
        'image/object/is_crowd': self._Int64Feature(object_is_crowd),
    })).SerializeToString()

    example_decoder = tf_example_decoder.TfExampleDecoder()
    tensor_dict = example_decoder.Decode(tf.convert_to_tensor(example))

    self.assertAllEqual((tensor_dict[
        fields.InputDataFields.groundtruth_is_crowd].get_shape().as_list()),
                        [None])
    with self.test_session() as sess:
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual([bool(item) for item in object_is_crowd],
                        tensor_dict[
                            fields.InputDataFields.groundtruth_is_crowd])