# Copyright 2018 The dm_control Authors.

#

# 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.

# ============================================================================

"""Math operations on variation objects."""

import abc

from dm_control.composer.variation import base

from dm_control.composer.variation.variation_values import evaluate

import numpy as np

class MathOp(base.Variation):

"""Base MathOp class for applying math operations on variation objects.

Subclasses need to implement `_callable`, which takes in a single value and

applies the desired math operation. This operation gets applied to the result

of the evaluated base variation object passed at construction. Structured

variation objects are automatically traversed.

"""

def __init__(self, *args, **kwargs):

self._args = args

self._kwargs = kwargs

def __call__(self, initial_value=None, current_value=None, random_state=None):

local_args = evaluate(

self._args,

initial_value=initial_value,

current_value=current_value,

random_state=random_state)

local_kwargs = evaluate(

self._kwargs,

initial_value=initial_value,

current_value=current_value,

random_state=random_state)

return self._callable(*local_args, **local_kwargs)

@property

@abc.abstractmethod

def _callable(self):

pass

def __eq__(self, other):

if not isinstance(other, type(self)):

return False

return (

self._args == other._args

and self._kwargs == other._kwargs

)

def __repr__(self):

return '{}(args={}, kwargs={})'.format(

type(self).__name__,

self._args,

self._kwargs,

)

class Log(MathOp):

@property

def _callable(self):

return np.log

class Max(MathOp):

@property

def _callable(self):

return np.max

class Min(MathOp):

@property

def _callable(self):

return np.min

class Norm(MathOp):

@property

def _callable(self):

return np.linalg.norm