# rl_coach.filters.observation.observation_move_axis_filter — Reinforcement Learning Coach 0.12.0 documentation

# Source code for rl_coach.filters.observation.observation_move_axis_filter

    #
    # Copyright (c) 2017 Intel Corporation
    #
    # Licensed under the Apache License, Version 2.0 (the "License");
    # you may not use this file except in compliance with the License.
    # You may obtain a copy of the License at
    #
    #      http://www.apache.org/licenses/LICENSE-2.0
    #
    # Unless required by applicable law or agreed to in writing, software
    # distributed under the License is distributed on an "AS IS" BASIS,
    # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    # See the License for the specific language governing permissions and
    # limitations under the License.
    #

    import numpy as np

    from rl_coach.core_types import ObservationType
    from rl_coach.filters.observation.observation_filter import ObservationFilter
    from rl_coach.spaces import ObservationSpace, PlanarMapsObservationSpace

    class ObservationMoveAxisFilter(ObservationFilter):
        """
        Reorders the axes of the observation. This can be useful when the observation is an
        image, and we want to move the channel axis to be the last axis instead of the first axis.
        """
        def __init__(self, axis_origin: int = None, axis_target: int=None):
            """
            :param axis_origin: The axis to move
            :param axis_target: Where to move the selected axis to
            """
            super().__init__()
            self.axis_origin = axis_origin
            self.axis_target = axis_target

        def validate_input_observation_space(self, input_observation_space: ObservationSpace):
            shape = input_observation_space.shape
            if not -len(shape) <= self.axis_origin < len(shape) or not -len(shape) <= self.axis_target < len(shape):
                raise ValueError("The given axis does not exist in the context of the input observation shape. ")

        def filter(self, observation: ObservationType, update_internal_state: bool=True) -> ObservationType:
            return np.moveaxis(observation, self.axis_origin, self.axis_target)

        def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
            axis_size = input_observation_space.shape[self.axis_origin]
            input_observation_space.shape = np.delete(input_observation_space.shape, self.axis_origin)
            if self.axis_target == -1:
                input_observation_space.shape = np.append(input_observation_space.shape, axis_size)
            elif self.axis_target < -1:
                input_observation_space.shape = np.insert(input_observation_space.shape, self.axis_target+1, axis_size)
            else:
                input_observation_space.shape = np.insert(input_observation_space.shape, self.axis_target, axis_size)

            # move the channels axis according to the axis change
            if isinstance(input_observation_space, PlanarMapsObservationSpace):
                if input_observation_space.channels_axis == self.axis_origin:
                    input_observation_space.channels_axis = self.axis_target
                elif input_observation_space.channels_axis == self.axis_target:
                    input_observation_space.channels_axis = self.axis_origin
                elif self.axis_origin < input_observation_space.channels_axis < self.axis_target:
                    input_observation_space.channels_axis -= 1
                elif self.axis_target < input_observation_space.channels_axis < self.axis_origin:
                    input_observation_space.channels_axis += 1

            return input_observation_space