Small consistency fixes for spaces and FFTOperators.

nifty/domain_object.py

@@ -18,8 +18,6 @@
 
 import abc
 
-import numpy as np
-
 from keepers import Loggable,\
                     Versionable
 

nifty/field.py

@@ -45,8 +45,7 @@ class Field(Loggable, Versionable, object):
         self.domain_axes = self._get_axes_tuple(self.domain)
 
         self.dtype = self._infer_dtype(dtype=dtype,
-                                       val=val,
-                                       domain=self.domain)
+                                       val=val)
 
         self.distribution_strategy = self._parse_distribution_strategy(
                                 distribution_strategy=distribution_strategy,
@@ -86,16 +85,17 @@ class Field(Loggable, Versionable, object):
             axes_list += [tuple(l)]
         return tuple(axes_list)
 
-    def _infer_dtype(self, dtype, val, domain):
+    def _infer_dtype(self, dtype, val):
         if dtype is None:
-            if isinstance(val, Field) or \
-               isinstance(val, distributed_data_object):
+            try:
                 dtype = val.dtype
-            dtype_tuple = (np.dtype(gc['default_field_dtype']),)
+            except AttributeError:
+                if val is not None:
+                    dtype = np.result_type(val)
+                else:
+                    dtype = np.dtype(gc['default_field_dtype'])
         else:
-            dtype_tuple = (np.dtype(dtype),)
-
-        dtype = reduce(lambda x, y: np.result_type(x, y), dtype_tuple)
+            dtype = np.dtype(dtype)
 
         return dtype
 

nifty/operators/fft_operator/fft_operator.py

@@ -83,12 +83,14 @@ class FFTOperator(LinearOperator):
 
         # Store the dtype information
         if domain_dtype is None:
-            self.domain_dtype = None
+            self.logger.info("Setting domain_dtype to np.float.")
+            self.domain_dtype = np.float
         else:
             self.domain_dtype = np.dtype(domain_dtype)
 
         if target_dtype is None:
-            self.target_dtype = None
+            self.logger.info("Setting target_dtype to np.complex.")
+            self.target_dtype = np.complex
         else:
             self.target_dtype = np.dtype(target_dtype)
 

nifty/operators/fft_operator/transformations/gllmtransformation.py

@@ -21,7 +21,7 @@ import numpy as np
 from nifty.config import dependency_injector as gdi
 from nifty import GLSpace, LMSpace
 from slicing_transformation import SlicingTransformation
-import lm_transformation_factory
+import lm_transformation_helper
 
 pyHealpix = gdi.get('pyHealpix')
 
@@ -31,12 +31,17 @@ class GLLMTransformation(SlicingTransformation):
     # ---Overwritten properties and methods---
 
     def __init__(self, domain, codomain=None, module=None):
+        if module is None:
+            module = 'pyHealpix'
+
+        if module != 'pyHealpix':
+            raise ValueError("Unsupported SHT module.")
+
         if 'pyHealpix' not in gdi:
             raise ImportError(
                 "The module pyHealpix is needed but not available.")
 
-        super(GLLMTransformation, self).__init__(domain, codomain,
-                                                 module=module)
+        super(GLLMTransformation, self).__init__(domain, codomain, module)
 
     # ---Mandatory properties and methods---
 
@@ -63,7 +68,7 @@ class GLLMTransformation(SlicingTransformation):
         nlat = domain.nlat
         lmax = nlat - 1
 
-        result = LMSpace(lmax=lmax, dtype=domain.dtype)
+        result = LMSpace(lmax=lmax)
         return result
 
     @classmethod
@@ -91,6 +96,11 @@ class GLLMTransformation(SlicingTransformation):
         super(GLLMTransformation, cls).check_codomain(domain, codomain)
 
     def _transformation_of_slice(self, inp, **kwargs):
+        if inp.dtype not in (np.float, np.complex):
+            self.logger.warn("The input array has dtype: %s. The FFT will "
+                             "be performed at double precision." %
+                             str(inp.dtype))
+
         nlat = self.domain.nlat
         nlon = self.domain.nlon
         lmax = self.codomain.lmax
@@ -104,12 +114,12 @@ class GLLMTransformation(SlicingTransformation):
                                         for x in (inp.real, inp.imag)]
 
             [resultReal,
-             resultImag] = [lm_transformation_factory.buildIdx(x, lmax=lmax)
+             resultImag] = [lm_transformation_helper.buildIdx(x, lmax=lmax)
                             for x in [resultReal, resultImag]]
 
             result = self._combine_complex_result(resultReal, resultImag)
         else:
             result = sjob.map2alm(inp)
-            result = lm_transformation_factory.buildIdx(result, lmax=lmax)
+            result = lm_transformation_helper.buildIdx(result, lmax=lmax)
 
         return result

nifty/operators/fft_operator/transformations/hplmtransformation.py

@@ -22,7 +22,7 @@ from nifty.config import dependency_injector as gdi
 from nifty import HPSpace, LMSpace
 from slicing_transformation import SlicingTransformation
 
-import lm_transformation_factory
+import lm_transformation_helper
 
 pyHealpix = gdi.get('pyHealpix')
 
@@ -32,12 +32,17 @@ class HPLMTransformation(SlicingTransformation):
     # ---Overwritten properties and methods---
 
     def __init__(self, domain, codomain=None, module=None):
+        if module is None:
+            module = 'pyHealpix'
+
+        if module != 'pyHealpix':
+            raise ValueError("Unsupported SHT module.")
+
         if 'pyHealpix' not in gdi:
             raise ImportError(
                 "The module pyHealpix is needed but not available")
 
-        super(HPLMTransformation, self).__init__(domain, codomain,
-                                                 module=module)
+        super(HPLMTransformation, self).__init__(domain, codomain, module)
 
     # ---Mandatory properties and methods---
 
@@ -63,7 +68,7 @@ class HPLMTransformation(SlicingTransformation):
 
         lmax = 2*domain.nside
 
-        result = LMSpace(lmax=lmax, dtype=domain.dtype)
+        result = LMSpace(lmax=lmax)
         return result
 
     @classmethod
@@ -83,6 +88,11 @@ class HPLMTransformation(SlicingTransformation):
         super(HPLMTransformation, cls).check_codomain(domain, codomain)
 
     def _transformation_of_slice(self, inp, **kwargs):
+        if inp.dtype not in (np.float, np.complex):
+            self.logger.warn("The input array has dtype: %s. The FFT will "
+                             "be performed at double precision." %
+                             str(inp.dtype))
+
         lmax = self.codomain.lmax
         mmax = lmax
 
@@ -92,13 +102,13 @@ class HPLMTransformation(SlicingTransformation):
                             for x in (inp.real, inp.imag)]
 
             [resultReal,
-             resultImag] = [lm_transformation_factory.buildIdx(x, lmax=lmax)
+             resultImag] = [lm_transformation_helper.buildIdx(x, lmax=lmax)
                             for x in [resultReal, resultImag]]
 
             result = self._combine_complex_result(resultReal, resultImag)
 
         else:
             result = pyHealpix.map2alm_iter(inp, lmax, mmax, 3)
-            result = lm_transformation_factory.buildIdx(result, lmax=lmax)
+            result = lm_transformation_helper.buildIdx(result, lmax=lmax)
 
         return result

nifty/operators/fft_operator/transformations/lmgltransformation.py

@@ -21,7 +21,7 @@ from nifty.config import dependency_injector as gdi
 from nifty import GLSpace, LMSpace
 
 from slicing_transformation import SlicingTransformation
-import lm_transformation_factory
+import lm_transformation_helper
 
 pyHealpix = gdi.get('pyHealpix')
 
@@ -31,12 +31,17 @@ class LMGLTransformation(SlicingTransformation):
     # ---Overwritten properties and methods---
 
     def __init__(self, domain, codomain=None, module=None):
+        if module is None:
+            module = 'pyHealpix'
+
+        if module != 'pyHealpix':
+            raise ValueError("Unsupported SHT module.")
+
         if 'pyHealpix' not in gdi:
             raise ImportError(
                 "The module pyHealpix is needed but not available.")
 
-        super(LMGLTransformation, self).__init__(domain, codomain,
-                                                 module=module)
+        super(LMGLTransformation, self).__init__(domain, codomain, module)
 
     # ---Mandatory properties and methods---
 
@@ -97,6 +102,11 @@ class LMGLTransformation(SlicingTransformation):
         super(LMGLTransformation, cls).check_codomain(domain, codomain)
 
     def _transformation_of_slice(self, inp, **kwargs):
+        if inp.dtype not in (np.float, np.complex):
+            self.logger.warn("The input array has dtype: %s. The FFT will "
+                             "be performed at double precision." %
+                             str(inp.dtype))
+
         nlat = self.codomain.nlat
         nlon = self.codomain.nlon
         lmax = self.domain.lmax
@@ -107,7 +117,7 @@ class LMGLTransformation(SlicingTransformation):
         sjob.set_triangular_alm_info(lmax, mmax)
         if issubclass(inp.dtype.type, np.complexfloating):
             [resultReal,
-             resultImag] = [lm_transformation_factory.buildLm(x, lmax=lmax)
+             resultImag] = [lm_transformation_helper.buildLm(x, lmax=lmax)
                             for x in (inp.real, inp.imag)]
 
             [resultReal, resultImag] = [sjob.alm2map(x)
@@ -116,7 +126,7 @@ class LMGLTransformation(SlicingTransformation):
             result = self._combine_complex_result(resultReal, resultImag)
 
         else:
-            result = lm_transformation_factory.buildLm(inp, lmax=lmax)
+            result = lm_transformation_helper.buildLm(inp, lmax=lmax)
             result = sjob.alm2map(result)
 
         return result

nifty/operators/fft_operator/transformations/lmhptransformation.py

@@ -20,7 +20,7 @@ import numpy as np
 from nifty.config import dependency_injector as gdi
 from nifty import HPSpace, LMSpace
 from slicing_transformation import SlicingTransformation
-import lm_transformation_factory
+import lm_transformation_helper
 
 pyHealpix = gdi.get('pyHealpix')
 
@@ -30,12 +30,17 @@ class LMHPTransformation(SlicingTransformation):
     # ---Overwritten properties and methods---
 
     def __init__(self, domain, codomain=None, module=None):
+        if module is None:
+            module = 'pyHealpix'
+
+        if module != 'pyHealpix':
+            raise ValueError("Unsupported SHT module.")
+
         if gdi.get('pyHealpix') is None:
             raise ImportError(
                 "The module pyHealpix is needed but not available.")
 
-        super(LMHPTransformation, self).__init__(domain, codomain,
-                                                 module=module)
+        super(LMHPTransformation, self).__init__(domain, codomain, module)
 
     # ---Mandatory properties and methods---
 
@@ -85,13 +90,18 @@ class LMHPTransformation(SlicingTransformation):
         super(LMHPTransformation, cls).check_codomain(domain, codomain)
 
     def _transformation_of_slice(self, inp, **kwargs):
+        if inp.dtype not in (np.float, np.complex):
+            self.logger.warn("The input array has dtype: %s. The FFT will "
+                             "be performed at double precision." %
+                             str(inp.dtype))
+
         nside = self.codomain.nside
         lmax = self.domain.lmax
         mmax = lmax
 
         if issubclass(inp.dtype.type, np.complexfloating):
             [resultReal,
-             resultImag] = [lm_transformation_factory.buildLm(x, lmax=lmax)
+             resultImag] = [lm_transformation_helper.buildLm(x, lmax=lmax)
                             for x in (inp.real, inp.imag)]
 
             [resultReal, resultImag] = [pyHealpix.alm2map(x, lmax, mmax, nside)
@@ -100,7 +110,7 @@ class LMHPTransformation(SlicingTransformation):
             result = self._combine_complex_result(resultReal, resultImag)
 
         else:
-            result = lm_transformation_factory.buildLm(inp, lmax=lmax)
+            result = lm_transformation_helper.buildLm(inp, lmax=lmax)
             result = pyHealpix.alm2map(result, lmax, mmax, nside)
 
         return result

nifty/operators/fft_operator/transformations/rg_transforms.py

@@ -312,9 +312,8 @@ class FFTW(Transform):
 
         try:
             # Create return object and insert results inplace
-            result_dtype = np.complex
             return_val = val.copy_empty(global_shape=val.shape,
-                                        dtype=result_dtype)
+                                        dtype=np.complex)
             return_val.set_local_data(data=local_result, copy=False)
         except(AttributeError):
             return_val = local_result
@@ -341,9 +340,8 @@ class FFTW(Transform):
             np.concatenate([[0, ], val.distributor.all_local_slices[:, 2]])
         )
         local_offset_Q = bool(local_offset_list[val.distributor.comm.rank] % 2)
-        result_dtype = np.complex
         return_val = val.copy_empty(global_shape=val.shape,
-                                    dtype=result_dtype)
+                                    dtype=np.complex)
 
         # Extract local data
         local_val = val.get_local_data(copy=False)
@@ -364,7 +362,7 @@ class FFTW(Transform):
                 if temp_val is None:
                     temp_val = np.empty_like(
                         local_val,
-                        dtype=result_dtype
+                        dtype=np.complex
                     )
                 inp = local_val[slice_list]
 
@@ -439,6 +437,11 @@ class FFTW(Transform):
                 not all(axis in range(len(val.shape)) for axis in axes):
             raise ValueError("Provided axes does not match array shape")
 
+        if val.dtype not in (np.float, np.complex):
+            self.logger.warn("The input array has dtype: %s. The FFT will "
+                             "be performed at double precision." %
+                             str(val.dtype))
+
         # If the input is a numpy array we transform it locally
         if not isinstance(val, distributed_data_object):
             # Cast to a np.ndarray
@@ -583,9 +586,13 @@ class NUMPYFFT(Transform):
                 not all(axis in range(len(val.shape)) for axis in axes):
             raise ValueError("Provided axes does not match array shape")
 
-        result_dtype = np.complex
+        if val.dtype not in (np.float, np.complex):
+            self.logger.warn("The input array has dtype: %s. The FFT will "
+                             "be performed at double precision." %
+                             str(val.dtype))
+
         return_val = val.copy_empty(global_shape=val.shape,
-                                    dtype=result_dtype)
+                                    dtype=np.complex)
 
         if (axes is None) or (0 in axes) or \
            (val.distribution_strategy not in STRATEGIES['slicing']):

nifty/operators/fft_operator/transformations/rgrgtransformation.py

@@ -24,8 +24,7 @@ from nifty import RGSpace, nifty_configuration
 
 class RGRGTransformation(Transformation):
     def __init__(self, domain, codomain=None, module=None):
-        super(RGRGTransformation, self).__init__(domain, codomain,
-                                                 module=module)
+        super(RGRGTransformation, self).__init__(domain, codomain, module)
 
         if module is None:
             if nifty_configuration['fft_module'] == 'fftw':
@@ -33,7 +32,7 @@ class RGRGTransformation(Transformation):
             elif nifty_configuration['fft_module'] == 'numpy':
                 self._transform = NUMPYFFT(self.domain, self.codomain)
             else:
-                raise ValueError('ERROR: unknow default FFT module:' +
+                raise ValueError('Unsupported default FFT module:' +
                                  nifty_configuration['fft_module'])
         else:
             if module == 'fftw':
@@ -41,7 +40,7 @@ class RGRGTransformation(Transformation):
             elif module == 'numpy':
                 self._transform = NUMPYFFT(self.domain, self.codomain)
             else:
-                raise ValueError('ERROR: unknow FFT module:' + module)
+                raise ValueError('Unsupported FFT module:' + module)
 
     @classmethod
     def get_codomain(cls, domain, zerocenter=None):

nifty/operators/fft_operator/transformations/transformation.py

@@ -18,8 +18,6 @@
 
 import abc
 
-import numpy as np
-
 from keepers import Loggable
 
 
@@ -40,7 +38,7 @@ class Transformation(Loggable, object):
             self.codomain = codomain
 
     @classmethod
-    def get_codomain(cls, domain, dtype=None, zerocenter=None):
+    def get_codomain(cls, domain):
         raise NotImplementedError
 
     @classmethod

nifty/spaces/gl_space/gl_space.py

@@ -186,6 +186,3 @@ class GLSpace(Space):
             )
 
         return result
-
-    def plot(self):
-        pass

nifty/spaces/hp_space/hp_space.py

@@ -145,6 +145,3 @@ class HPSpace(Space):
             nside=hdf5_group['nside'][()],
             )
         return result
-
-    def plot(self):
-        pass

nifty/spaces/lm_space/lm_space.py

@@ -176,7 +176,3 @@ class LMSpace(Space):
             lmax=hdf5_group['lmax'][()],
             )
         return result
-
-
-    def plot(self):
-        pass

nifty/spaces/power_space/power_space.py

@@ -24,7 +24,6 @@ from power_index_factory import PowerIndexFactory
 
 from nifty.spaces.space import Space
 from nifty.spaces.rg_space import RGSpace
-from nifty.nifty_utilities import cast_axis_to_tuple
 
 
 class PowerSpace(Space):
@@ -200,14 +199,11 @@ class PowerSpace(Space):
         new_ps._pundex = hdf5_group['pundex'][:]
         new_ps._k_array = repository.get('k_array', hdf5_group)
         new_ps._ignore_for_hash += ['_pindex', '_kindex', '_rho', '_pundex',
-                                   '_k_array']
+                                    '_k_array']
 
         return new_ps
 
 
-    def plot(self):
-        pass
-
 class EmptyPowerSpace(PowerSpace):
     def __init__(self):
         pass

nifty/spaces/rg_space/rg_space.py

@@ -37,8 +37,6 @@ from d2o import distributed_data_object,\
 
 from nifty.spaces.space import Space
 
-import nifty.plotting as plt
-
 
 class RGSpace(Space):
     """
@@ -315,8 +313,3 @@ class RGSpace(Space):
             harmonic=hdf5_group['harmonic'][()],
             )
         return result
-
-    def plot(self):
-        n_dimensions = len(self._shape)
-        # if n_dimensions == 1:
-        #     fig = plt.figures.Figure(data=self.distances)

nifty/spaces/space/space.py

@@ -141,8 +141,6 @@ from __future__ import division
 
 import abc
 
-import numpy as np
-
 from nifty.domain_object import DomainObject