WIP: Move transforms to transformator

__init__.py

@@ -93,4 +93,4 @@ from demos import get_demo_dir
 from pickling import _pickle_method, _unpickle_method
 
 #import pyximport; pyximport.install(pyimport = True)
-
+from transforms import tf as transformator

setup.py

@@ -34,7 +34,7 @@ setup(name="ift_nifty",
       url="http://www.mpa-garching.mpg.de/ift/nifty/",
       packages=["nifty", "nifty.demos", "nifty.rg", "nifty.lm",
                 "nifty.operators", "nifty.dummys", "nifty.field_types",
-                "nifty.config", "nifty.power"],
+                "nifty.config", "nifty.power", "nifty.transforms"],
       package_dir={"nifty": ""},
       zip_safe=False,
       dependency_links=[

transforms/__init__.py

+from transform_factory import TransformFactory
+
+tf = TransformFactory()

transforms/fftw.py

+import warnings
+
+import numpy as np
+from d2o import distributed_data_object, STRATEGIES
+from nifty.config import about, dependency_injector as gdi
+import nifty.nifty_utilities as utilities
+from transform import FFT
+
+pyfftw = gdi.get('pyfftw')
+
+
+class FFTW(FFT):
+
+    """
+        The pyfftw pendant of a fft object.
+    """
+    # The plan_dict stores the FFTWTransformInfo objects which correspond
+    # to a certain set of (field_val, domain, codomain) sets.
+    info_dict = {}
+
+    # initialize the dictionary which stores the values from
+    # get_centering_mask
+    centering_mask_dict = {}
+
+    def __init__(self, domain, codomain):
+        self.domain = domain
+        self.codomain = codomain
+
+        if 'pyfftw' not in gdi:
+            raise ImportError("The module pyfftw is needed but not available.")
+
+        self.name = 'pyfftw'
+
+        # Enable caching for pyfftw.interfaces
+        pyfftw.interfaces.cache.enable()
+
+    @classmethod
+    def get_centering_mask(cls, to_center_input, dimensions_input,
+                           offset_input=False):
+        """
+            Computes the mask, used to (de-)zerocenter domain and target
+            fields.
+
+            Parameters
+            ----------
+            to_center_input : tuple, list, numpy.ndarray
+                A tuple of booleans which dimensions should be
+                zero-centered.
+
+            dimensions_input : tuple, list, numpy.ndarray
+                A tuple containing the mask's desired shape.
+
+            offset_input : int, boolean
+                Specifies whether the zero-th dimension starts with an odd
+                or and even index, i.e. if it is shifted.
+
+            Returns
+            -------
+            result : np.ndarray
+                A 1/-1-alternating mask.
+        """
+        # cast input
+        to_center = np.array(to_center_input)
+        dimensions = np.array(dimensions_input)
+
+        # if none of the dimensions are zero centered, return a 1
+        if np.all(to_center == 0):
+            return 1
+
+        if np.all(dimensions == np.array(1)) or \
+                np.all(dimensions == np.array([1])):
+            return dimensions
+        # The dimensions of size 1 must be sorted out for computing the
+        # centering_mask. The depth of the array will be restored in the
+        # end.
+        size_one_dimensions = []
+        temp_dimensions = []
+        temp_to_center = []
+        for i in range(len(dimensions)):
+            if dimensions[i] == 1:
+                size_one_dimensions += [True]
+            else:
+                size_one_dimensions += [False]
+                temp_dimensions += [dimensions[i]]
+                temp_to_center += [to_center[i]]
+        dimensions = np.array(temp_dimensions)
+        to_center = np.array(temp_to_center)
+        # cast the offset_input into the shape of to_center
+        offset = np.zeros(to_center.shape, dtype=int)
+        offset[0] = int(offset_input)
+        # check for dimension match
+        if to_center.size != dimensions.size:
+            raise TypeError(
+                'The length of the supplied lists does not match.')
+
+        # build up the value memory
+        # compute an identifier for the parameter set
+        temp_id = tuple(
+            (tuple(to_center), tuple(dimensions), tuple(offset)))
+        if temp_id not in cls.centering_mask_dict:
+            # use np.tile in order to stack the core alternation scheme
+            # until the desired format is constructed.
+            core = np.fromfunction(
+                lambda *args: (-1) **
+                (np.tensordot(to_center,
+                              args +
+                              offset.reshape(offset.shape +
+                                             (1,) *
+                                             (np.array(args).ndim - 1)),
+                              1)),
+                (2,) * to_center.size)
+            # Cast the core to the smallest integers we can get
+            core = core.astype(np.int8)
+
+            centering_mask = np.tile(core, dimensions // 2)
+            # for the dimensions of odd size corresponding slices must be
+            # added
+            for i in range(centering_mask.ndim):
+                # check if the size of the certain dimension is odd or even
+                if (dimensions % 2)[i] == 0:
+                    continue
+                # prepare the slice object
+                temp_slice = (slice(None),) * i + (slice(-2, -1, 1),) + \
+                             (slice(None),) * (centering_mask.ndim - 1 - i)
+                # append the slice to the centering_mask
+                centering_mask = np.append(centering_mask,
+                                           centering_mask[temp_slice],
+                                           axis=i)
+            # Add depth to the centering_mask where the length of a
+            # dimension was one
+            temp_slice = ()
+            for i in range(len(size_one_dimensions)):
+                if size_one_dimensions[i]:
+                    temp_slice += (None,)
+                else:
+                    temp_slice += (slice(None),)
+            centering_mask = centering_mask[temp_slice]
+            cls.centering_mask_dict[temp_id] = centering_mask
+        return cls.centering_mask_dict[temp_id]
+
+    @classmethod
+    def _get_transform_info(cls, domain, codomain, local_shape,
+                            local_offset_Q, is_local, transform_shape=None,
+                            **kwargs):
+        # generate a id-tuple which identifies the domain-codomain setting
+        temp_id = (domain.__hash__() ^
+                   (101 * codomain.__hash__()) ^
+                   (211 * transform_shape.__hash__()))
+
+        # generate the plan_and_info object if not already there
+        if temp_id not in cls.info_dict:
+            if is_local:
+                cls.info_dict[temp_id] = FFTWLocalTransformInfo(
+                    domain, codomain, local_shape,
+                    local_offset_Q, **kwargs
+                )
+            else:
+                cls.info_dict[temp_id] = FFTWMPITransfromInfo(
+                    domain, codomain, local_shape,
+                    local_offset_Q, transform_shape, **kwargs
+                )
+
+        return cls.info_dict[temp_id]
+
+    def _apply_mask(self, val, mask, axes):
+        """
+            Apply centering mask to an array.
+
+            Parameters
+            ----------
+            val: distributed_data_object or numpy.ndarray
+                The value-array on which the mask should be applied.
+
+            mask: numpy.ndarray
+                The mask to be applied.
+
+            axes: tuple
+                The axes which are to be transformed.
+
+            Returns
+            -------
+            distributed_data_object or np.nd_array
+                Mask input array by multiplying it with the mask.
+        """
+        # reshape mask if necessary
+        if axes:
+            mask = mask.reshape(
+                [y if x in axes else 1
+                    for x, y in enumerate(val.shape)]
+            )
+
+        return val * mask
+
+    def _atomic_mpi_transform(self, val, info, axes):
+        # Apply codomain centering mask
+        if reduce(lambda x, y: x+y, self.codomain.paradict['zerocenter']):
+            temp_val = np.copy(val)
+            val = self._apply_mask(temp_val, info.cmask_codomain, axes)
+
+        p = info.plan
+        # Load the value into the plan
+        if p.has_input:
+            p.input_array[:] = val
+        # Execute the plan
+        p()
+
+        if p.has_output:
+            result = p.output_array
+        else:
+            return None
+
+        # Apply domain centering mask
+        if reduce(lambda x, y: x+y, self.domain.paradict['zerocenter']):
+            result = self._apply_mask(result, info.cmask_domain, axes)
+
+        # Correct the sign if needed
+        result *= info.sign
+
+        return result
+
+    def _local_transform(self, val, axes, **kwargs):
+        ####
+        # val must be numpy array or d2o with slicing distributor
+        ###
+
+        local_offset_Q = False
+        try:
+            local_val = val.get_local_data(copy=False)
+            if axes is None or 0 in axes:
+                local_offset_Q = val.distributor.local_shape[0] % 2
+        except(AttributeError):
+            local_val = val
+        current_info = self._get_transform_info(self.domain,
+                                                self.codomain,
+                                                local_shape=local_val.shape,
+                                                local_offset_Q=local_offset_Q,
+                                                is_local=True,
+                                                **kwargs)
+
+        # Apply codomain centering mask
+        if reduce(lambda x, y: x+y, self.codomain.paradict['zerocenter']):
+            temp_val = np.copy(local_val)
+            local_val = self._apply_mask(temp_val,
+                                         current_info.cmask_codomain, axes)
+
+        local_result = current_info.fftw_interface(
+            local_val,
+            axes=axes,
+            planner_effort='FFTW_ESTIMATE'
+        )
+
+        # Apply domain centering mask
+        if reduce(lambda x, y: x+y, self.domain.paradict['zerocenter']):
+            local_result = self._apply_mask(local_result,
+                                            current_info.cmask_domain, axes)
+
+        # Correct the sign if needed
+        if current_info.sign != 1:
+            local_result *= current_info.sign
+
+        try:
+            # Create return object and insert results inplace
+            return_val = val.copy_empty(global_shape=val.shape,
+                                        dtype=self.codomain.dtype)
+            return_val.set_local_data(data=local_result, copy=False)
+        except(AttributeError):
+            return_val = local_result
+
+        return return_val
+
+    def _repack_to_fftw_and_transform(self, val, axes, **kwargs):
+        temp_val = val.copy_empty(distribution_strategy='fftw')
+        about.warnings.cprint('WARNING: Repacking d2o to fftw \
+                                distribution strategy')
+        temp_val.set_full_data(val, copy=False)
+
+        # Recursive call to transform
+        result = self.transform(temp_val, axes, **kwargs)
+
+        return_val = result.copy_empty(
+            distribution_strategy=val.distribution_strategy)
+        return_val.set_full_data(data=result, copy=False)
+
+        return return_val
+
+    def _mpi_transform(self, val, axes, **kwargs):
+
+        if axes is None or 0 in axes:
+            local_offset_list = np.cumsum(
+                np.concatenate([[0, ], val.distributor.all_local_slices[:, 2]])
+            )
+            local_offset_Q = bool(
+                local_offset_list[val.distributor.comm.rank] % 2)
+        else:
+            local_offset_Q = False
+
+        return_val = val.copy_empty(global_shape=val.shape,
+                                    dtype=self.codomain.dtype)
+
+        # Extract local data
+        local_val = val.get_local_data(copy=False)
+
+        # Create temporary storage for slices
+        temp_val = None
+
+        # If axes tuple includes all axes, set it to None
+        if axes is not None:
+            if set(axes) == set(range(len(val.shape))):
+                axes = None
+
+        current_info = None
+        for slice_list in utilities.get_slice_list(local_val.shape, axes):
+            if slice_list == [slice(None, None)]:
+                inp = local_val
+            else:
+                if temp_val is None:
+                    temp_val = np.empty_like(local_val)
+                inp = local_val[slice_list]
+
+            # This is in order to make FFTW behave properly when slicing input
+            # over MPI ranks when the input is 1-dimensional. The default
+            # behaviour is to optimize to take advantage of byte-alignment,
+            # which doesn't match the slicing strategy for multi-dimensional
+            # data.
+            original_shape = None
+            if len(inp.shape) == 1:
+                original_shape = inp.shape
+                inp = inp.reshape(inp.shape[0], 1)
+
+            if current_info is None:
+                current_info = self._get_transform_info(
+                    self.domain,
+                    self.codomain,
+                    local_shape=val.local_shape,
+                    local_offset_Q=local_offset_Q,
+                    is_local=False,
+                    transform_shape=inp.shape,
+                    **kwargs
+                )
+
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                result = self._atomic_mpi_transform(inp, current_info, axes)
+
+            if result is None:
+                temp_val = np.empty_like(local_val)
+            elif slice_list == [slice(None, None)]:
+                temp_val = result
+            else:
+                # Reverting to the original shape i.e. before the input was
+                # augmented with 1 to make FFTW behave properly.
+                if original_shape is not None:
+                    result = result.reshape(original_shape)
+                temp_val[slice_list] = result
+
+        return_val.set_local_data(data=temp_val, copy=False)
+
+        return return_val
+
+    def transform(self, val, axes=None, **kwargs):
+        """
+            The pyfftw transform function.
+
+            Parameters
+            ----------
+            val : distributed_data_object or numpy.ndarray
+                The value-array of the field which is supposed to
+                be transformed.
+
+            domain : nifty.rg.nifty_rg.rg_space
+                The domain of the space which should be transformed.
+
+            codomain : nifty.rg.nifty_rg.rg_space
+                The target into which the field should be transformed.
+
+            axes: tuple, None
+                The axes which should be transformed.
+
+            **kwargs : *optional*
+                Further kwargs are passed to the create_mpi_plan routine.
+
+            Returns
+            -------
+            result : np.ndarray or distributed_data_object
+                Fourier-transformed pendant of the input field.
+        """
+        # Check if the axes provided are valid given the shape
+        if axes is not None and \
+                not all(axis in range(len(val.shape)) for axis in axes):
+            raise ValueError("ERROR: Provided axes does not match array shape")
+
+        # If the input is a numpy array we transform it locally
+        if not isinstance(val, distributed_data_object):
+            # Cast to a np.ndarray
+            temp_val = np.asarray(val)
+
+            # local transform doesn't apply transforms inplace
+            return_val = self._local_transform(temp_val, axes)
+        else:
+            if val.distribution_strategy in STRATEGIES['slicing']:
+                if axes is None or 0 in axes:
+                    if val.distribution_strategy != 'fftw':
+                        return_val = \
+                            self._repack_to_fftw_and_transform(
+                                val, axes, **kwargs
+                            )
+                    else:
+                        return_val = self._mpi_transform(
+                            val, axes, **kwargs
+                        )
+                else:
+                    return_val = self._local_transform(
+                        val, axes, **kwargs
+                    )
+            else:
+                return_val = self._repack_to_fftw_and_transform(
+                    val, axes, **kwargs
+                )
+
+            # If domain is purely real, the result of the FFT is hermitian
+            if self.domain.paradict['complexity'] == 0:
+                return_val.hermitian = True
+
+        return return_val
+
+
+class FFTWTransformInfo(object):
+
+    def __init__(self, domain, codomain, local_shape,
+                 local_offset_Q, **kwargs):
+        if pyfftw is None:
+            raise ImportError("The module pyfftw is needed but not available.")
+
+        self.cmask_domain = FFTW.get_centering_mask(
+            domain.paradict['zerocenter'],
+            local_shape,
+            local_offset_Q)
+
+        self.cmask_codomain = FFTW.get_centering_mask(
+            codomain.paradict['zerocenter'],
+            local_shape,
+            local_offset_Q)
+
+        # If both domain and codomain are zero-centered the result,
+        # will get a global minus. Store the sign to correct it.
+        self.sign = (-1) ** np.sum(np.array(domain.paradict['zerocenter']) *
+                                   np.array(codomain.paradict['zerocenter']) *
+                                   (np.array(domain.shape) // 2 % 2))
+
+    @property
+    def cmask_domain(self):
+        return self._domain_centering_mask
+
+    @cmask_domain.setter
+    def cmask_domain(self, cmask):
+        self._domain_centering_mask = cmask
+
+    @property
+    def cmask_codomain(self):
+        return self._codomain_centering_mask
+
+    @cmask_codomain.setter
+    def cmask_codomain(self, cmask):
+        self._codomain_centering_mask = cmask
+
+    @property
+    def sign(self):
+        return self._sign
+
+    @sign.setter
+    def sign(self, sign):
+        self._sign = sign
+
+
+class FFTWLocalTransformInfo(FFTWTransformInfo):
+
+    def __init__(self, domain, codomain, local_shape,
+                 local_offset_Q, **kwargs):
+        super(FFTWLocalTransformInfo, self).__init__(domain,
+                                                     codomain,
+                                                     local_shape,
+                                                     local_offset_Q,
+                                                     **kwargs)
+        if codomain.harmonic:
+            self._fftw_interface = pyfftw.interfaces.numpy_fft.fftn
+        else:
+            self._fftw_interface = pyfftw.interfaces.numpy_fft.ifftn
+
+    @property
+    def fftw_interface(self):
+        return self._fftw_interface
+
+    @fftw_interface.setter
+    def fftw_interface(self, interface):
+        about.warnings.cprint('WARNING: FFTWLocalTransformInfo fftw_interface \
+                               cannot be modified')
+
+
+class FFTWMPITransfromInfo(FFTWTransformInfo):
+
+    def __init__(self, domain, codomain, local_shape,
+                 local_offset_Q, transform_shape, **kwargs):
+        super(FFTWMPITransfromInfo, self).__init__(domain,
+                                                   codomain,
+                                                   local_shape,
+                                                   local_offset_Q,
+                                                   **kwargs)
+        self._plan = pyfftw.create_mpi_plan(
+            input_shape=transform_shape,
+            input_dtype='complex128',
+            output_dtype='complex128',
+            direction='FFTW_FORWARD' if codomain.harmonic else 'FFTW_BACKWARD',
+            flags=["FFTW_ESTIMATE"],
+            **kwargs
+        )
+
+    @property
+    def plan(self):
+        return self._plan
+
+    @plan.setter
+    def plan(self, plan):
+        about.warnings.cprint('WARNING: FFTWMPITransfromInfo plan \
+                               cannot be modified')

transforms/gfft.py

+import numpy as np
+from transform import FFT
+from d2o import distributed_data_object
+import nifty.nifty_utilities as utilities
+
+
+class GFFT(FFT):
+
+    """
+        The gfft pendant of a fft object.
+
+        Parameters
+        ----------
+        fft_module_name : String
+            Switch between the gfft module used: 'gfft' and 'gfft_dummy'
+
+    """
+
+    def __init__(self, domain, codomain, fft_module):
+        self.domain = domain
+        self.codomain = codomain
+        self.fft_machine = fft_module
+
+    def transform(self, val, axes=None, **kwargs):
+        """
+            The gfft transform function.
+
+            Parameters
+            ----------
+            val : numpy.ndarray or distributed_data_object
+                The value-array of the field which is supposed to
+                be transformed.
+
+            axes : None or tuple
+                The axes which should be transformed.
+
+            **kwargs : *optional*
+                Further kwargs are not processed.
+
+            Returns
+            -------
+            result : np.ndarray or distributed_data_object
+                Fourier-transformed pendant of the input field.
+        """
+        # Check if the axes provided are valid given the shape
+        if axes is not None and \
+                not all(axis in range(len(val.shape)) for axis in axes):
+            raise ValueError("ERROR: Provided axes does not match array shape")
+
+        # GFFT doesn't accept d2o objects as input. Consolidate data from
+        # all nodes into numpy.ndarray before proceeding.
+        if isinstance(val, distributed_data_object):
+            temp_inp = val.get_full_data()
+        else:
+            temp_inp = val
+
+        # Cast input datatype to codomain's dtype
+        temp_inp = temp_inp.astype(np.complex128, copy=False)
+
+        # Array for storing the result
+        return_val = None
+
+        for slice_list in utilities.get_slice_list(temp_inp.shape, axes):
+
+            # don't copy the whole data array
+            if slice_list == [slice(None, None)]:
+                inp = temp_inp
+            else:
+                # initialize the return_val object if needed
+                if return_val is None:
+                    return_val = np.empty_like(temp_inp)
+                inp = temp_inp[slice_list]
+
+            inp = self.fft_machine.gfft(
+                inp,
+                in_ax=[],
+                out_ax=[],