de-cythonize

nifty/operators/fft_operator/transformations/lm_transformation_factory.pyx → nifty/operators/fft_operator/transformations/lm_transformation_factory.py

-
 import numpy as np
-cimport numpy as np
 
 def buildLm(inp, **kwargs):
     if inp.dtype == np.dtype('float32'):
@@ -14,40 +12,36 @@ def buildIdx(inp, **kwargs):
     else:
         return _buildIdx(inp, **kwargs)
 
-cpdef np.ndarray[np.float32_t, ndim=1]  _buildIdx_f(np.ndarray[np.complex64_t,
-ndim=1] nr, np.int_t lmax):
-    cdef np.int size = (lmax+1)*(lmax+1)
+def _buildIdx_f(nr, lmax):
+    size = (lmax+1)*(lmax+1)
 
-    cdef np.ndarray final=np.zeros([size], dtype=np.float32)
+    final=np.zeros([size], dtype=np.float32)
     final[0:lmax+1] = nr[0:lmax+1].real
     final[lmax+1::2] = np.sqrt(2)*nr[lmax+1:].real
     final[lmax+2::2] = np.sqrt(2)*nr[lmax+1:].imag
     return final
 
-cpdef np.ndarray[np.float64_t, ndim=1]  _buildIdx(np.ndarray[np.complex128_t,
-ndim=1] nr, np.int_t lmax):
-    cdef np.int size = (lmax+1)*(lmax+1)
+def _buildIdx(nr, lmax):
+    size = (lmax+1)*(lmax+1)
 
-    cdef np.ndarray final=np.zeros([size], dtype=np.float64)
+    final=np.zeros([size], dtype=np.float64)
     final[0:lmax+1] = nr[0:lmax+1].real
     final[lmax+1::2] = np.sqrt(2)*nr[lmax+1:].real
     final[lmax+2::2] = np.sqrt(2)*nr[lmax+1:].imag
     return final
 
-cpdef np.ndarray[np.complex64_t, ndim=1] _buildLm_f(np.ndarray[np.float32_t,
-ndim=1] nr, np.int_t lmax):
-    cdef np.int size = (len(nr)-lmax-1)/2+lmax+1
+def _buildLm_f(nr, lmax):
+    size = (len(nr)-lmax-1)/2+lmax+1
 
-    cdef np.ndarray res=np.zeros([size], dtype=np.complex64)
+    res=np.zeros([size], dtype=np.complex64)
     res[0:lmax+1] = nr[0:lmax+1]
     res[lmax+1:] = np.sqrt(0.5)*(nr[lmax+1::2] + 1j*nr[lmax+2::2])
     return res
 
-cpdef np.ndarray[np.complex128_t, ndim=1] _buildLm(np.ndarray[np.float64_t,
-ndim=1] nr, np.int_t lmax):
-    cdef np.int size = (len(nr)-lmax-1)/2+lmax+1
+def _buildLm(nr, lmax):
+    size = (len(nr)-lmax-1)/2+lmax+1
 
-    cdef np.ndarray res=np.zeros([size], dtype=np.complex128)
+    res=np.zeros([size], dtype=np.complex128)
     res[0:lmax+1] = nr[0:lmax+1]
     res[lmax+1:] = np.sqrt(0.5)*(nr[lmax+1::2] + 1j*nr[lmax+2::2])
     return res

nifty/spaces/lm_space/lm_helper.pyx

-import numpy as np
-cimport numpy as np
-
-cpdef _distance_array_helper(np.ndarray[np.int_t] index_array, np.int_t lmax):
-    cdef np.int size = (lmax + 1) * (lmax  + 1)
-
-    cdef np.ndarray res = np.zeros([len(index_array)], dtype=np.float128)
-
-    for idx, index_full in enumerate(index_array):
-        if index_full <= lmax:
-            index_half = index_full
-        else:
-            if (index_full - lmax) % 2 == 0:
-                index_half = (index_full + lmax) / 2;
-            else:
-                index_half = (index_full + lmax + 1) / 2;
-
-        m = (np.ceil(((2 * lmax + 1) -
-                      np.sqrt((2 * lmax + 1)**2 -
-                              8 * (index_half - lmax))) / 2)).astype(int)
-        res[idx] = index_half - m * (2 * lmax + 1 - m) // 2
-
-    return res

nifty/spaces/lm_space/lm_space.py

@@ -25,13 +25,18 @@ from nifty.spaces.space import Space
 from nifty.config import nifty_configuration as gc,\
                          dependency_injector as gdi
 
-from lm_helper import _distance_array_helper
-
 from d2o import arange
 
 gl = gdi.get('libsharp_wrapper_gl')
 hp = gdi.get('healpy')
 
+def _distance_array_helper(index_array, lmax):
+    u=2*lmax+1
+    index_half=(index_array+np.minimum(lmax,index_array)+1)//2
+    m = (np.ceil((u - np.sqrt(u*u - 8 * (index_half - lmax))) / 2)).astype(int)
+    res = (index_half - m * (u - m) // 2).astype(np.float64)
+    return res
+
 
 class LMSpace(Space):
     """

setup.py

@@ -35,9 +35,6 @@ setup(name="ift_nifty",
       package_dir={"nifty": "nifty"},
       zip_safe=False,
       ext_modules=cythonize([
-          "nifty/operators/fft_operator/transformations/"
-              "lm_transformation_factory.pyx",
-          "nifty/spaces/lm_space/lm_helper.pyx",
           "nifty/operators/smoothing_operator/smooth_util.pyx"
       ]),
       include_dirs=[numpy.get_include()],