cleanup

nifty2go/operators/fft_operator/transformations/gllmtransformation.py

@@ -17,16 +17,12 @@
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
 import numpy as np
-
-from .... import GLSpace, LMSpace
 from .slicing_transformation import SlicingTransformation
 from . import lm_transformation_helper
-
 import pyHealpix
 
 
 class GLLMTransformation(SlicingTransformation):
-
     def __init__(self, domain, codomain=None):
         super(GLLMTransformation, self).__init__(domain, codomain)
 
@@ -35,20 +31,18 @@ class GLLMTransformation(SlicingTransformation):
         return False
 
     def _transformation_of_slice(self, inp):
-        nlat = self.domain.nlat
-        nlon = self.domain.nlon
         lmax = self.codomain.lmax
         mmax = self.codomain.mmax
 
         sjob = pyHealpix.sharpjob_d()
-        sjob.set_Gauss_geometry(nlat, nlon)
+        sjob.set_Gauss_geometry(self.domain.nlat, self.domain.nlon)
         sjob.set_triangular_alm_info(lmax, mmax)
         if issubclass(inp.dtype.type, np.complexfloating):
-            return \
-                lm_transformation_helper.buildIdx(sjob.map2alm(inp.real),
-                                                  lmax=lmax)\
-                +1j*lm_transformation_helper.buildIdx(sjob.map2alm(inp.imag),
-                                                  lmax=lmax)
+            rr = sjob.map2alm(inp.real)
+            rr = lm_transformation_helper.buildIdx(rr, lmax=lmax)
+            ri = sjob.map2alm(inp.imag)
+            ri = lm_transformation_helper.buildIdx(ri, lmax=lmax)
+            return rr + 1j*ri
         else:
-            return lm_transformation_helper.buildIdx(sjob.map2alm(inp),
-                                                     lmax=lmax)
+            rr = sjob.map2alm(inp)
+            return lm_transformation_helper.buildIdx(rr, lmax=lmax)

nifty2go/operators/fft_operator/transformations/hplmtransformation.py

@@ -17,17 +17,12 @@
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
 import numpy as np
-
-from .... import HPSpace, LMSpace
 from .slicing_transformation import SlicingTransformation
-
 from . import lm_transformation_helper
-
 import pyHealpix
 
 
 class HPLMTransformation(SlicingTransformation):
-
     def __init__(self, domain, codomain=None):
         super(HPLMTransformation, self).__init__(domain, codomain)
 
@@ -40,18 +35,12 @@ class HPLMTransformation(SlicingTransformation):
         mmax = lmax
 
         if issubclass(inp.dtype.type, np.complexfloating):
-            [resultReal,
-             resultImag] = [pyHealpix.map2alm(x, lmax, mmax)
-                            for x in (inp.real, inp.imag)]
-
-            [resultReal,
-             resultImag] = [lm_transformation_helper.buildIdx(x, lmax=lmax)
-                            for x in [resultReal, resultImag]]
-
-            result = resultReal +1j*resultImag
+            rr = pyHealpix.map2alm(inp.real, lmax, mmax)
+            rr = lm_transformation_helper.buildIdx(rr, lmax=lmax)
+            ri = pyHealpix.map2alm(inp.imag, lmax, mmax)
+            ri = lm_transformation_helper.buildIdx(ri, lmax=lmax)
+            return rr + 1j*ri
 
         else:
-            result = pyHealpix.map2alm(inp, lmax, mmax)
-            result = lm_transformation_helper.buildIdx(result, lmax=lmax)
-
-        return result
+            rr = pyHealpix.map2alm(inp, lmax, mmax)
+            return lm_transformation_helper.buildIdx(rr, lmax=lmax)

nifty2go/operators/fft_operator/transformations/lmgltransformation.py

@@ -17,48 +17,31 @@
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
 import numpy as np
-from .... import GLSpace, LMSpace
-
 from .slicing_transformation import SlicingTransformation
 from . import lm_transformation_helper
-
 import pyHealpix
 
 
 class LMGLTransformation(SlicingTransformation):
-
-    # ---Overwritten properties and methods---
-
     def __init__(self, domain, codomain=None):
         super(LMGLTransformation, self).__init__(domain, codomain)
 
-    # ---Mandatory properties and methods---
-
     @property
     def unitary(self):
         return False
 
     def _transformation_of_slice(self, inp):
-        nlat = self.codomain.nlat
-        nlon = self.codomain.nlon
         lmax = self.domain.lmax
         mmax = self.domain.mmax
 
         sjob = pyHealpix.sharpjob_d()
-        sjob.set_Gauss_geometry(nlat, nlon)
+        sjob.set_Gauss_geometry(self.codomain.nlat, self.codomain.nlon)
         sjob.set_triangular_alm_info(lmax, mmax)
         if issubclass(inp.dtype.type, np.complexfloating):
-            [resultReal,
-             resultImag] = [lm_transformation_helper.buildLm(x, lmax=lmax)
-                            for x in (inp.real, inp.imag)]
-
-            [resultReal, resultImag] = [sjob.alm2map(x)
-                                        for x in [resultReal, resultImag]]
-
-            result = resultReal + 1j*resultImag
+            rr = lm_transformation_helper.buildLm(inp.real, lmax=lmax)
+            ri = lm_transformation_helper.buildLm(inp.imag, lmax=lmax)
+            return sjob.alm2map(rr) + 1j*sjob.alm2map(ri)
 
         else:
             result = lm_transformation_helper.buildLm(inp, lmax=lmax)
-            result = sjob.alm2map(result)
-
-        return result
+            return sjob.alm2map(result)

nifty2go/operators/fft_operator/transformations/lmhptransformation.py

@@ -17,10 +17,8 @@
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
 import numpy as np
-from .... import HPSpace, LMSpace
 from .slicing_transformation import SlicingTransformation
 from . import lm_transformation_helper
-
 import pyHealpix
 
 
@@ -39,17 +37,12 @@ class LMHPTransformation(SlicingTransformation):
         mmax = lmax
 
         if issubclass(inp.dtype.type, np.complexfloating):
-            [resultReal,
-             resultImag] = [lm_transformation_helper.buildLm(x, lmax=lmax)
-                            for x in (inp.real, inp.imag)]
-
-            [resultReal, resultImag] = [pyHealpix.alm2map(x, lmax, mmax, nside)
-                                        for x in [resultReal, resultImag]]
-
-            result = resultReal + 1j*resultImag
+            rr = lm_transformation_helper.buildLm(inp.real, lmax=lmax)
+            ri = lm_transformation_helper.buildLm(inp.imag, lmax=lmax)
+            rr = pyHealpix.alm2map(rr, lmax, mmax, nside)
+            ri = pyHealpix.alm2map(ri, lmax, mmax, nside)
+            return rr + 1j*ri
 
         else:
-            result = lm_transformation_helper.buildLm(inp, lmax=lmax)
-            result = pyHealpix.alm2map(result, lmax, mmax, nside)
-
-        return result
+            rr = lm_transformation_helper.buildLm(inp, lmax=lmax)
+            return pyHealpix.alm2map(rr, lmax, mmax, nside)

nifty2go/operators/fft_operator/transformations/rg_transforms.py

@@ -16,54 +16,18 @@
 # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
 # and financially supported by the Studienstiftung des deutschen Volkes.
 
-from builtins import range
-from builtins import object
-import warnings
-
-import numpy as np
-from .... import nifty_utilities as utilities
-
-from functools import reduce
-
+from builtins import object, range
 import pyfftw
 
 
-class Transform(object):
+class SerialFFT(object):
     """
-        A generic fft object without any implementation.
+        The pyfftw pendant of a fft object.
     """
-
     def __init__(self, domain, codomain):
         self.domain = domain
         self.codomain = codomain
 
-    def transform(self, val, axes):
-        """
-            A generic ff-transform function.
-
-            Parameters
-            ----------
-            field_val : distributed_data_object
-                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 taget into which the field should be transformed.
-        """
-        raise NotImplementedError
-
-
-class SerialFFT(Transform):
-    """
-        The numpy fft pendant of a fft object.
-
-    """
-    def __init__(self, domain, codomain):
-        super(SerialFFT, self).__init__(domain, codomain)
-
         pyfftw.interfaces.cache.enable()
 
     def transform(self, val, axes):
@@ -72,7 +36,7 @@ class SerialFFT(Transform):
 
             Parameters
             ----------
-            val : or numpy.ndarray
+            val : numpy.ndarray
                 The value-array of the field which is supposed to
                 be transformed.
 
@@ -90,13 +54,7 @@ class SerialFFT(Transform):
                 not all(axis in range(len(val.shape)) for axis in axes):
             raise ValueError("Provided axes does not match array shape")
 
-        return self._atomic_transform(local_val=val,
-                                      axes=axes,
-                                      local_offset_Q=False)
-
-    def _atomic_transform(self, local_val, axes, local_offset_Q):
-        # perform the transformation
         if self.codomain.harmonic:
-            return pyfftw.interfaces.numpy_fft.fftn(local_val, axes=axes)
+            return pyfftw.interfaces.numpy_fft.fftn(val, axes=axes)
         else:
-            return pyfftw.interfaces.numpy_fft.ifftn(local_val, axes=axes)
+            return pyfftw.interfaces.numpy_fft.ifftn(val, axes=axes)

nifty2go/operators/fft_operator/transformations/slicing_transformation.py

@@ -29,12 +29,9 @@ class SlicingTransformation(Transformation):
         return_shape = np.array(val.shape)
         return_shape[list(axes)] = self.codomain.shape
         return_shape = tuple(return_shape)
-        return_val = None
+        return_val = np.empty(return_shape,dtype=val.dtype)
 
         for slice_list in utilities.get_slice_list(val.shape, axes):
-            if return_val is None:
-                return_val = np.empty(return_shape,dtype=val.dtype)
-
             return_val[slice_list] = self._transformation_of_slice(
                                                                val[slice_list])
         return return_val