Revert "merge (undocumented) conveyor simulation changes" (#29)

Author: Nomos11

qopt/__init__.py

@@ -82,12 +82,3 @@
 __version__ = '1.3'
 __license__ = 'GNU GPLv3+'
 __author__ = 'Julian Teske, Forschungszentrum Juelich'
-
-
-try:
-    from jax.config import config
-    config.update("jax_enable_x64", True)
-    #TODO: add new objects here/ import other stuff?
-    # __all__ += []
-except ImportError:
-    pass

qopt/amplitude_functions.py

@@ -64,11 +64,10 @@
 """
 
 from abc import ABC, abstractmethod
-from typing import Callable, Optional
+from typing import Callable
 
 import numpy as np
 
-from typing import Union
 
 class AmplitudeFunction(ABC):
     """Abstract Base class of the amplitude function. """
@@ -219,125 +218,3 @@ def derivative_by_chain_rule(self, deriv_by_ctrl_amps: np.ndarray,
         # return: shape (time, func, par)
 
         return np.einsum('imj,ikj->ikm', du_by_dx, deriv_by_ctrl_amps)
-
-
-###############################################################################
-
-try:
-    import jax.numpy as jnp
-    from jax import jit,vmap,jacfwd
-    _HAS_JAX = True
-except ImportError:
-    from unittest import mock
-    jit, vmap, jacfwd = mock.Mock(), mock.Mock(), mock.Mock()
-    jnp = mock.Mock()
-    _HAS_JAX = False
-
-
-class IdentityAmpFuncJAX(AmplitudeFunction):
-    """See docstring of class without JAX.
-    Designed to return jax-numpy-arrays.
-    """
-
-    def __init__(self):
-        if not _HAS_JAX:
-            raise ImportError("JAX not available")
-
-    def __call__(self, x: Union[np.ndarray, jnp.ndarray]) -> jnp.ndarray:
-        """See base class. """
-        return jnp.asarray(x)
-
-    def derivative_by_chain_rule(
-            self,
-            deriv_by_ctrl_amps: Union[np.ndarray,jnp.ndarray],
-            x: Union[np.ndarray, jnp.ndarray]) -> jnp.ndarray:
-        """See base class. """
-        return jnp.asarray(deriv_by_ctrl_amps)
-
-
-class UnaryAnalyticAmpFuncJAX(AmplitudeFunction):
-    """See docstring of class without JAX.
-    Designed to return jax-numpy-arrays.
-    Functions need to be compatible with jit.
-    (Includes that functions need to be pure
-    (i.e. output solely depends on input)).
-    """
-
-    def __init__(self,
-                 value_function: Callable[[float, ], float],
-                 derivative_function: [Callable[[float, ], float]]):
-        if not _HAS_JAX:
-            raise ImportError("JAX not available")
-        self.value_function = jit(jnp.vectorize(value_function))
-        self.derivative_function = jit(jnp.vectorize(derivative_function))
-
-    def __call__(self, x: Union[np.ndarray, jnp.ndarray]) -> jnp.ndarray:
-        """See base class. """
-        return jnp.asarray(self.value_function(x))
-
-    def derivative_by_chain_rule(
-            self,
-            deriv_by_ctrl_amps: Union[np.ndarray, jnp.ndarray], x):
-        """See base class. """
-        du_by_dx = self.derivative_function(x)
-        # du_by_dx shape: (n_time, n_ctrl)
-        # deriv_by_ctrl_amps shape: (n_time, n_func, n_ctrl)
-        # deriv_by_opt_par shape: (n_time, n_func, n_ctrl
-        # since the function is unary we have n_ctrl = n_amps
-        return jnp.einsum('ij,ikj->ikj', du_by_dx, deriv_by_ctrl_amps)
-
-
-class CustomAmpFuncJAX(AmplitudeFunction):
-    """See docstring of class without JAX.
-    Designed to return jax-numpy-arrays.
-    Functions need to be compatible with jit.
-    (Includes that functions need to be pure
-    (i.e. output solely depends on input)).
-    If derivative_function=None, autodiff is used.
-    t_to_vectorize: if value_function/derivative_function not yet
-    vectorized for num_t
-    """
-
-    def __init__(
-            self,
-            value_function: Callable[[Union[np.ndarray, jnp.ndarray],],
-                                      Union[np.ndarray, jnp.ndarray]],
-            derivative_function: Callable[[Union[np.ndarray, jnp.ndarray],],
-                                           Union[np.ndarray, jnp.ndarray]],
-            t_to_vectorize: bool = False
-            ):
-        if not _HAS_JAX:
-            raise ImportError("JAX not available")
-        if t_to_vectorize == True:
-            self.value_function = jit(vmap(value_function),in_axes=(0,))
-        else:
-            self.value_function = jit(value_function)
-        if derivative_function is not None:
-            if t_to_vectorize == True:
-                self.derivative_function = jit(vmap(derivative_function),in_axes=(0,))
-            else:
-                self.derivative_function = jit(derivative_function)
-        else:
-            if t_to_vectorize == True:
-                def der_wrapper(x):
-                    return jnp.swapaxes(vmap(jacfwd(lambda x: value_function(x)),in_axes=(0,))(x),1,2)
-            else:
-                def der_wrapper(x):
-                    return jnp.swapaxes(vmap(jacfwd(lambda x: value_function(jnp.expand_dims(x,axis=0))[0,:]),in_axes=(0,))(x),1,2)
-            self.derivative_function = jit(der_wrapper)
-
-    def __call__(self, x: Union[np.ndarray, jnp.ndarray]) -> jnp.ndarray:
-        """See base class. """
-        return jnp.asarray(self.value_function(x))
-
-    def derivative_by_chain_rule(
-            self,
-            deriv_by_ctrl_amps: Union[np.ndarray, jnp.ndarray],
-            x: Union[np.ndarray, jnp.ndarray]) -> jnp.ndarray:
-        """See base class. """
-        du_by_dx = self.derivative_function(x)
-        # du_by_dx: shape (time, par, ctrl)
-        # deriv_by_ctrl_amps: shape (time, func, ctrl)
-        # return: shape (time, func, par)
-
-        return jnp.einsum('imj,ikj->ikm', du_by_dx, deriv_by_ctrl_amps)