Removed paradict and moved the functionality into the spaces as properties.

Reordered methods in space classes.
Made Space.weight to an abc.abstractmethod.

nifty/__init__.py

@@ -39,7 +39,6 @@ from d2o import distributed_data_object, d2o_librarian
 
 from nifty_cmaps import ncmap
 from field import Field
-from paradict import Paradict
 
 # this line exists for compatibility reasons
 # TODO: Remove this once the transition to field types is done.

nifty/operators/__init__.py

@@ -23,6 +23,8 @@ from __future__ import division
 
 from linear_operator import LinearOperator
 
+from diagonal_operator import DiagonalOperator
+
 from endomorphic_operator import EndomorphicOperator
 
 from fft_operator import *

nifty/operators/endomorphic_operator/endomorphic_operator.py

@@ -6,7 +6,6 @@ from nifty.operators.linear_operator import LinearOperator
 
 
 class EndomorphicOperator(LinearOperator):
-    __metaclass__ = abc.ABCMeta
 
     # ---Overwritten properties and methods---
 

nifty/operators/fft_operator/transformations/gllmtransformation.py

@@ -44,7 +44,7 @@ class GLLMTransformation(Transformation):
         if not isinstance(domain, GLSpace):
             raise TypeError('ERROR: domain needs to be a GLSpace')
 
-        nlat = domain.paradict['nlat']
+        nlat = domain.nlat
         lmax = nlat - 1
         mmax = nlat - 1
         if domain.dtype == np.dtype('float32'):
@@ -63,10 +63,10 @@ class GLLMTransformation(Transformation):
         if not isinstance(codomain, LMSpace):
             raise TypeError('ERROR: codomain must be a LMSpace.')
 
-        nlat = domain.paradict['nlat']
-        nlon = domain.paradict['nlon']
-        lmax = codomain.paradict['lmax']
-        mmax = codomain.paradict['mmax']
+        nlat = domain.nlat
+        nlon = domain.nlon
+        lmax = codomain.lmax
+        mmax = codomain.mmax
 
         if (nlon != 2 * nlat - 1) or (lmax != nlat - 1) or (lmax != mmax):
             return False
@@ -90,10 +90,10 @@ class GLLMTransformation(Transformation):
             val = self.domain.weight(val, power=-0.5, axes=axes)
 
         # shorthands for transform parameters
-        nlat = self.domain.paradict['nlat']
-        nlon = self.domain.paradict['nlon']
-        lmax = self.codomain.paradict['lmax']
-        mmax = self.codomain.paradict['mmax']
+        nlat = self.domain.nlat
+        nlon = self.domain.nlon
+        lmax = self.codomain.lmax
+        mmax = self.codomain.mmax
 
         if isinstance(val, distributed_data_object):
             temp_val = val.get_full_data()

nifty/operators/fft_operator/transformations/hplmtransformation.py

@@ -44,7 +44,7 @@ class HPLMTransformation(Transformation):
         if not isinstance(domain, HPSpace):
             raise TypeError('ERROR: domain needs to be a HPSpace')
 
-        lmax = 3 * domain.paradict['nside'] - 1
+        lmax = 3 * domain.nside - 1
         mmax = lmax
         return LMSpace(lmax=lmax, mmax=mmax, dtype=np.dtype('complex128'))
 
@@ -59,9 +59,9 @@ class HPLMTransformation(Transformation):
         if not isinstance(codomain, LMSpace):
             raise TypeError('ERROR: codomain must be a LMSpace.')
 
-        nside = domain.paradict['nside']
-        lmax = codomain.paradict['lmax']
-        mmax = codomain.paradict['mmax']
+        nside = domain.nside
+        lmax = codomain.lmax
+        mmax = codomain.mmax
 
         if (3 * nside - 1 != lmax) or (lmax != mmax):
             return False
@@ -88,8 +88,8 @@ class HPLMTransformation(Transformation):
             val = self.domain.weight(val, power=-0.5, axes=axes)
 
         # shorthands for transform parameters
-        lmax = self.codomain.paradict['lmax']
-        mmax = self.codomain.paradict['mmax']
+        lmax = self.codomain.lmax
+        mmax = self.codomain.mmax
 
         if isinstance(val, distributed_data_object):
             temp_val = val.get_full_data()

nifty/operators/fft_operator/transformations/lmgltransformation.py

@@ -59,8 +59,8 @@ class LMGLTransformation(Transformation):
         else:
             raise ValueError('ERROR: unsupported domain dtype')
 
-        nlat = domain.paradict['lmax'] + 1
-        nlon = domain.paradict['lmax'] * 2 + 1
+        nlat = domain.lmax + 1
+        nlon = domain.lmax * 2 + 1
         return GLSpace(nlat=nlat, nlon=nlon, dtype=new_dtype)
 
     @staticmethod
@@ -74,10 +74,10 @@ class LMGLTransformation(Transformation):
         if not isinstance(codomain, GLSpace):
             raise TypeError('ERROR: codomain must be a GLSpace.')
 
-        nlat = codomain.paradict['nlat']
-        nlon = codomain.paradict['nlon']
-        lmax = domain.paradict['lmax']
-        mmax = domain.paradict['mmax']
+        nlat = codomain.nlat
+        nlon = codomain.nlon
+        lmax = domain.lmax
+        mmax = domain.mmax
 
         if (lmax != mmax) or (nlat != lmax + 1) or (nlon != 2 * lmax + 1):
             return False
@@ -112,10 +112,10 @@ class LMGLTransformation(Transformation):
                     return_val = np.empty_like(temp_val)
                 inp = temp_val[slice_list]
 
-            nlat = self.codomain.paradict['nlat']
-            nlon = self.codomain.paradict['nlon']
-            lmax = self.domain.paradict['lmax']
-            mmax = self.paradict['mmax']
+            nlat = self.codomain.nlat
+            nlon = self.codomain.nlon
+            lmax = self.domain.lmax
+            mmax = self.mmax
 
             if self.domain.dtype == np.dtype('complex64'):
                 inp = gl.alm2map_f(inp, nlat=nlat, nlon=nlon,

nifty/operators/fft_operator/transformations/lmhptransformation.py

@@ -48,7 +48,7 @@ class LMHPTransformation(Transformation):
         if not isinstance(domain, LMSpace):
             raise TypeError('ERROR: domain needs to be a LMSpace')
 
-        nside = (domain.paradict['lmax'] + 1) // 3
+        nside = (domain.lmax + 1) // 3
         return HPSpace(nside=nside)
 
     @staticmethod
@@ -61,9 +61,9 @@ class LMHPTransformation(Transformation):
 
         if not isinstance(codomain, HPSpace):
             raise TypeError('ERROR: codomain must be a HPSpace.')
-        nside = codomain.paradict['nside']
-        lmax = domain.paradict['lmax']
-        mmax = domain.paradict['mmax']
+        nside = codomain.nside
+        lmax = domain.lmax
+        mmax = domain.mmax
 
         if (lmax != mmax) or (3 * nside - 1 != lmax):
             return False
@@ -98,9 +98,9 @@ class LMHPTransformation(Transformation):
                     return_val = np.empty_like(temp_val)
                 inp = temp_val[slice_list]
 
-            nside = self.codomain.paradict['nside']
-            lmax = self.domain.paradict['lmax']
-            mmax = self.domain.paradict['mmax']
+            nside = self.codomain.nside
+            lmax = self.domain.lmax
+            mmax = self.domain.mmax
 
             inp = inp.astype(np.complex128, copy=False)
             inp = hp.alm2map(inp, nside, lmax=lmax, mmax=mmax,

nifty/operators/fft_operator/transformations/rg_transforms.py

@@ -218,7 +218,7 @@ class FFTW(Transform):
     def _atomic_mpi_transform(self, val, info, axes):
 
         # Apply codomain centering mask
-        if reduce(lambda x, y: x + y, self.codomain.paradict['zerocenter']):
+        if reduce(lambda x, y: x + y, self.codomain.zerocenter):
             temp_val = np.copy(val)
             val = self._apply_mask(temp_val, info.cmask_codomain, axes)
 
@@ -235,7 +235,7 @@ class FFTW(Transform):
             return None
 
         # Apply domain centering mask
-        if reduce(lambda x, y: x + y, self.domain.paradict['zerocenter']):
+        if reduce(lambda x, y: x + y, self.domain.zerocenter):
             result = self._apply_mask(result, info.cmask_domain, axes)
 
         # Correct the sign if needed
@@ -263,7 +263,7 @@ class FFTW(Transform):
                                                 **kwargs)
 
         # Apply codomain centering mask
-        if reduce(lambda x, y: x + y, self.codomain.paradict['zerocenter']):
+        if reduce(lambda x, y: x + y, self.codomain.zerocenter):
             temp_val = np.copy(local_val)
             local_val = self._apply_mask(temp_val,
                                          current_info.cmask_codomain, axes)
@@ -275,7 +275,7 @@ class FFTW(Transform):
         )
 
         # Apply domain centering mask
-        if reduce(lambda x, y: x + y, self.domain.paradict['zerocenter']):
+        if reduce(lambda x, y: x + y, self.domain.zerocenter):
             local_result = self._apply_mask(local_result,
                                             current_info.cmask_domain, axes)
 
@@ -446,19 +446,19 @@ class FFTWTransformInfo(object):
             raise ImportError("The module pyfftw is needed but not available.")
 
         self.cmask_domain = fftw_context.get_centering_mask(
-            domain.paradict['zerocenter'],
+            domain.zerocenter,
             local_shape,
             local_offset_Q)
 
         self.cmask_codomain = fftw_context.get_centering_mask(
-            codomain.paradict['zerocenter'],
+            codomain.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']) *
+        self.sign = (-1) ** np.sum(np.array(domain.zerocenter) *
+                                   np.array(codomain.zerocenter) *
                                    (np.array(domain.shape) // 2 % 2))
 
     @property
@@ -611,13 +611,13 @@ class GFFT(Transform):
                 out_ax=[],
                 ftmachine='fft' if self.codomain.harmonic else 'ifft',
                 in_zero_center=map(
-                    bool, self.domain.paradict['zerocenter']
+                    bool, self.domain.zerocenter
                 ),
                 out_zero_center=map(
-                    bool, self.codomain.paradict['zerocenter']
+                    bool, self.codomain.zerocenter
                 ),
                 enforce_hermitian_symmetry=bool(
-                    self.codomain.paradict['complexity']
+                    self.codomain.complexity
                 ),
                 W=-1,
                 alpha=-1,

nifty/operators/fft_operator/transformations/rgrgtransformation.py

@@ -62,20 +62,20 @@ class RGRGTransformation(Transformation):
 
         # parse the cozerocenter input
         if zerocenter is None:
-            zerocenter = domain.paradict['zerocenter']
+            zerocenter = domain.zerocenter
         # if the input is something scalar, cast it to a boolean
         else:
-            temp = np.empty_like(domain.paradict['zerocenter'])
+            temp = np.empty_like(domain.zerocenter)
             temp[:] = zerocenter
             zerocenter = temp
 
         # calculate the initialization parameters
-        distances = 1 / (np.array(domain.paradict['shape']) *
-                         np.array(domain.paradict['distances']))
+        distances = 1 / (np.array(domain.shape) *
+                         np.array(domain.distances))
         if dtype is None:
             dtype = np.complex
 
-        new_space = RGSpace(domain.paradict['shape'],
+        new_space = RGSpace(domain.shape,
                             zerocenter=zerocenter,
                             distances=distances,
                             harmonic=(not domain.harmonic),
@@ -94,8 +94,8 @@ class RGRGTransformation(Transformation):
         if not isinstance(codomain, RGSpace):
             return False
 
-        if not np.all(np.array(domain.paradict['shape']) ==
-                      np.array(codomain.paradict['shape'])):
+        if not np.all(np.array(domain.shape) ==
+                      np.array(codomain.shape)):
             return False
 
         if domain.harmonic == codomain.harmonic:
@@ -103,9 +103,9 @@ class RGRGTransformation(Transformation):
 
         # Check if the distances match, i.e. dist' = 1 / (num * dist)
         if not np.all(
-            np.absolute(np.array(domain.paradict['shape']) *
-                        np.array(domain.paradict['distances']) *
-                        np.array(codomain.paradict['distances']) - 1) <
+            np.absolute(np.array(domain.shape) *
+                        np.array(domain.distances) *
+                        np.array(codomain.distances) - 1) <
                 10**-7):
             return False
 

nifty/operators/linear_operator/linear_operator.py

@@ -16,22 +16,6 @@ class LinearOperator(object):
         self._domain = self._parse_domain(domain)
         self._field_type = self._parse_field_type(field_type)
 
-    @property
-    def domain(self):
-        return self._domain
-
-    @abc.abstractproperty
-    def target(self):
-        raise NotImplementedError
-
-    @property
-    def field_type(self):
-        return self._field_type
-
-    @abc.abstractproperty
-    def field_type_target(self):
-        raise NotImplementedError
-
     def _parse_domain(self, domain):
         if domain is None:
             domain = ()
@@ -61,6 +45,22 @@ class LinearOperator(object):
                     "ERROR: Given object is not a nifty.FieldType."))
         return field_type
 
+    @property
+    def domain(self):
+        return self._domain
+
+    @abc.abstractproperty
+    def target(self):
+        raise NotImplementedError
+
+    @property
+    def field_type(self):
+        return self._field_type
+
+    @abc.abstractproperty
+    def field_type_target(self):
+        raise NotImplementedError
+
     @abc.abstractproperty
     def implemented(self):
         raise NotImplementedError

nifty/paradict.py

-# -*- coding: utf-8 -*-
-
-class Paradict(object):
-
-    def __init__(self, **kwargs):
-        if not hasattr(self, 'parameters'):
-            self.parameters = {}
-        for key in kwargs:
-            self[key] = kwargs[key]
-
-    def __iter__(self):
-        return self.parameters.__iter__()
-
-    def __eq__(self, other):
-        return (isinstance(other, self.__class__) and
-                self.__dict__ == other.__dict__)
-
-    def __ne__(self, other):
-        return not self.__eq__(other)
-
-    def __repr__(self):
-        return self.parameters.__repr__()
-
-    def __setitem__(self, key, arg):
-        raise NotImplementedError
-
-    def __getitem__(self, key):
-        return self.parameters.__getitem__(key)
-
-    def __hash__(self):
-        result_hash = 0
-        for (key, item) in self.parameters.items():
-            try:
-                temp_hash = hash(item)
-            except TypeError:
-                temp_hash = hash(tuple(item))
-            result_hash ^= temp_hash ^ int(hash(key)/131)
-        return result_hash

nifty/spaces/__init__.py

 # -*- coding: utf-8 -*-
 
-from gl_space import GLSpace,\
-                     GLSpaceParadict
-
-from hp_space import HPSpace,\
-                     HPSpaceParadict
-
-from lm_space import LMSpace,\
-                     LMSpaceParadict
-
-from power_space import PowerSpace,\
-                        PowerSpaceParadict
-
-from rg_space import RGSpace,\
-                     RGSpaceParadict
-
-from space import Space,\
-                  SpaceParadict
+from gl_space import GLSpace
+from hp_space import HPSpace
+from lm_space import LMSpace
+from power_space import PowerSpace
+from rg_space import RGSpace
+from space import Space
\ No newline at end of file

nifty/spaces/gl_space/__init__.py

@@ -2,4 +2,3 @@
 
 
 from gl_space import GLSpace
-from gl_space_paradict import GLSpaceParadict
\ No newline at end of file

nifty/spaces/gl_space/gl_space.py

@@ -8,7 +8,6 @@ from d2o import STRATEGIES as DISTRIBUTION_STRATEGIES
 from nifty.spaces.space import Space
 from nifty.config import about, nifty_configuration as gc,\
                          dependency_injector as gdi
-from gl_space_paradict import GLSpaceParadict
 import nifty.nifty_utilities as utilities
 
 gl = gdi.get('libsharp_wrapper_gl')
@@ -69,7 +68,9 @@ class GLSpace(Space):
             An array containing the pixel sizes.
     """
 
-    def __init__(self, nlat, nlon=None, dtype=np.dtype('float')):
+    # ---Overwritten properties and methods---
+
+    def __init__(self, nlat=2, nlon=3, dtype=np.dtype('float')):
         """
             Sets the attributes for a gl_space class instance.
 
@@ -97,34 +98,41 @@ class GLSpace(Space):
         # check imports
         if not gc['use_libsharp']:
             raise ImportError(about._errors.cstring(
-                "ERROR: libsharp_wrapper_gl not loaded."))
+                "ERROR: libsharp_wrapper_gl not available or not loaded."))
+
+        super(GLSpace, self).__init__(dtype)
 
-        # setup paradict
-        self.paradict = GLSpaceParadict(nlat=nlat, nlon=nlon)
+        self._nlat = self._parse_nlat(nlat)
+        self._nlon = self._parse_nlon(nlon)
 
-        # setup dtype
-        self.dtype = np.dtype(dtype)
+    # ---Mandatory properties and methods---
 
-        # GLSpace is not harmonic
-        self._harmonic = False
+    @property
+    def harmonic(self):
+        return False
 
     @property
     def shape(self):
-        return (np.int((self.paradict['nlat'] * self.paradict['nlon'])),)
+        return (np.int((self.nlat * self.nlon)),)
 
     @property
     def dim(self):
-        return np.int((self.paradict['nlat'] * self.paradict['nlon']))
+        return np.int((self.nlat * self.nlon))
 
     @property
     def total_volume(self):
         return 4 * np.pi
 
+    def copy(self):
+        return self.__class__(nlat=self.nlat,
+                              nlon=self.nlon,
+                              dtype=self.dtype)
+
     def weight(self, x, power=1, axes=None, inplace=False):
         axes = utilities.cast_axis_to_tuple(axes, length=1)
 
-        nlon = self.paradict['nlon']
-        nlat = self.paradict['nlat']
+        nlon = self.nlon
+        nlat = self.nlat
 
         weight = np.array(list(itertools.chain.from_iterable(
                     itertools.repeat(x ** power, nlon)
@@ -145,3 +153,42 @@ class GLSpace(Space):
             result_x = x * weight
 
         return result_x
+
+    # ---Added properties and methods---
+
+    @property
+    def nlat(self):
+        return self._nlat
+
+    @property
+    def nlon(self):
+        return self._nlon
+
+    def _parse_nlat(self, nlat):
+        nlat = int(nlat)
+        if nlat < 2:
+            raise ValueError(about._errors.cstring(
+                "ERROR: nlat must be a positive number."))
+        elif nlat % 2 != 0:
+            raise ValueError(about._errors.cstring(
+                "ERROR: nlat must be a multiple of 2."))
+        return nlat
+
+    def _parse_nlon(self, nlon):
+        if nlon is None:
+            nlon = 2 * self.nlat - 1
+        else:
+            nlon = int(nlon)
+            if nlon != 2 * self.nlat - 1:
+                about.warnings.cprint(
+                    "WARNING: nlon was set to an unrecommended value: "
+                    "nlon <> 2*nlat-1.")
+        return nlon
+
+
+
+
+
+
+
+

nifty/spaces/gl_space/gl_space_paradict.py

-# -*- coding: utf-8 -*-
-
-from nifty.config import about
-from nifty.spaces.space import SpaceParadict
-
-
-class GLSpaceParadict(SpaceParadict):
-
-    def __init__(self, nlat, nlon):
-        SpaceParadict.__init__(self, nlat=nlat)
-        if nlon is None:
-            nlon = -1
-        self['nlon'] = nlon
-
-    def __setitem__(self, key, arg):
-        if key not in ['nlat', 'nlon']:
-            raise ValueError(about._errors.cstring(
-                "ERROR: Unsupported GLSpace parameter: " + key))
-
-        if key == 'nlat':
-            temp = int(arg)
-            if(temp < 1):
-                raise ValueError(about._errors.cstring(
-                    "ERROR: nlat: nonpositive number."))
-            if (temp % 2 != 0):
-                raise ValueError(about._errors.cstring(
-                    "ERROR: invalid parameter (nlat <> 2n)."))
-            try:
-                if temp < self['mmax']:
-                    about.warnings.cprint(
-                        "WARNING: mmax parameter set to lmax.")
-                    self['mmax'] = temp
-                if (temp != self['mmax']):
-                    about.warnings.cprint(
-                        "WARNING: unrecommended parameter set (mmax <> lmax).")
-            except:
-                pass
-        elif key == 'nlon':
-            temp = int(arg)
-            if (temp < 1):
-                about.warnings.cprint(
-                    "WARNING: nlon parameter set to default.")
-                temp = 2 * self['nlat'] - 1
-            if(temp != 2 * self['nlat'] - 1):
-                about.warnings.cprint(
-                    "WARNING: unrecommended parameter set (nlon <> 2*nlat-1).")
-        self.parameters.__setitem__(key, temp)

nifty/spaces/hp_space/__init__.py

@@ -2,4 +2,3 @@