cleanup

nifty/domain_object.py

@@ -20,13 +20,12 @@ from __future__ import division
 import abc
 from .nifty_meta import NiftyMeta
 
-from keepers import Loggable,\
-                    Versionable
+from keepers import Loggable
 from future.utils import with_metaclass
 
 
 class DomainObject(with_metaclass(
-        NiftyMeta, type('NewBase', (Versionable, Loggable, object), {}))):
+        NiftyMeta, type('NewBase', (Loggable, object), {}))):
     """The abstract class that can be used as a domain for a field.
 
     This holds all the information and functionality a field needs to know
@@ -224,13 +223,3 @@ class DomainObject(with_metaclass(
         """
 
         return x
-
-    # ---Serialization---
-
-    def _to_hdf5(self, hdf5_group):
-        return None
-
-    @classmethod
-    def _from_hdf5(cls, hdf5_group, repository):
-        result = cls()
-        return result

nifty/field.py

@@ -23,8 +23,7 @@ from builtins import range
 import ast
 import numpy as np
 
-from keepers import Versionable,\
-                    Loggable
+from keepers import Loggable
 
 from .domain_object import DomainObject
 
@@ -35,7 +34,7 @@ from .random import Random
 from functools import reduce
 
 
-class Field(Loggable, Versionable, object):
+class Field(Loggable, object):
     """ The discrete representation of a continuous field over multiple spaces.
 
     In NIFTY, Fields are used to store data arrays and carry all the needed
@@ -1444,47 +1443,6 @@ class Field(Loggable, Versionable, object):
                "\n  - min.,max. = " + str(minmax) + \
                "\n  - mean = " + str(mean)
 
-    # ---Serialization---
-
-    def _to_hdf5(self, hdf5_group):
-        hdf5_group.attrs['dtype'] = self.dtype.name
-        hdf5_group.attrs['domain_axes'] = str(self.domain_axes)
-        hdf5_group['num_domain'] = len(self.domain)
-
-        if self._val is None:
-            ret_dict = {}
-        else:
-            ret_dict = {'val': self.val}
-
-        for i in range(len(self.domain)):
-            ret_dict['s_' + str(i)] = self.domain[i]
-
-        return ret_dict
-
-    @classmethod
-    def _from_hdf5(cls, hdf5_group, repository):
-        # create empty field
-        new_field = EmptyField()
-        # reset class
-        new_field.__class__ = cls
-        # set values
-        temp_domain = []
-        for i in range(hdf5_group['num_domain'][()]):
-            temp_domain.append(repository.get('s_' + str(i), hdf5_group))
-        new_field.domain = tuple(temp_domain)
-
-        new_field.domain_axes = ast.literal_eval(
-                                hdf5_group.attrs['domain_axes'])
-
-        try:
-            new_field._val = repository.get('val', hdf5_group)
-        except(KeyError):
-            new_field._val = None
-
-        new_field.dtype = np.dtype(hdf5_group.attrs['dtype'])
-
-        return new_field
-
 
 class EmptyField(Field):
     def __init__(self):

nifty/field_types/field_array.py

@@ -39,14 +39,3 @@ class FieldArray(FieldType):
     @property
     def dim(self):
         return reduce(lambda x, y: x*y, self.shape)
-
-    # ---Serialization---
-
-    def _to_hdf5(self, hdf5_group):
-        hdf5_group['shape'] = self.shape
-        return None
-
-    @classmethod
-    def _from_hdf5(cls, hdf5_group, loopback_get):
-        result = cls(shape=hdf5_group['shape'][:])
-        return result

nifty/operators/linear_operator/linear_operator.py

@@ -20,15 +20,14 @@ from builtins import str
 import abc
 from ...nifty_meta import NiftyMeta
 
-from keepers import Loggable,\
-                    Versionable
+from keepers import Loggable
 from ...field import Field
 from ... import nifty_utilities as utilities
 from future.utils import with_metaclass
 
 
 class LinearOperator(with_metaclass(
-        NiftyMeta, type('NewBase', (Versionable, Loggable, object), {}))):
+        NiftyMeta, type('NewBase', (Loggable, object), {}))):
     """NIFTY base class for linear operators.
 
     The base NIFTY operator class is an abstract class from which

nifty/spaces/gl_space/gl_space.py

@@ -180,20 +180,3 @@ class GLSpace(Space):
             if nlon < 1:
                 raise ValueError("nlon must be a positive number.")
         return nlon
-
-    # ---Serialization---
-
-    def _to_hdf5(self, hdf5_group):
-        hdf5_group['nlat'] = self.nlat
-        hdf5_group['nlon'] = self.nlon
-
-        return None
-
-    @classmethod
-    def _from_hdf5(cls, hdf5_group, repository):
-        result = cls(
-            nlat=hdf5_group['nlat'][()],
-            nlon=hdf5_group['nlon'][()],
-            )
-
-        return result

nifty/spaces/hp_space/hp_space.py

@@ -139,16 +139,3 @@ class HPSpace(Space):
         if nside < 1:
             raise ValueError("nside must be >=1.")
         return nside
-
-    # ---Serialization---
-
-    def _to_hdf5(self, hdf5_group):
-        hdf5_group['nside'] = self.nside
-        return None
-
-    @classmethod
-    def _from_hdf5(cls, hdf5_group, repository):
-        result = cls(
-            nside=hdf5_group['nside'][()],
-            )
-        return result

nifty/spaces/lm_space/lm_space.py

@@ -187,16 +187,3 @@ class LMSpace(Space):
         if lmax < 0:
             raise ValueError("lmax must be >=0.")
         return lmax
-
-    # ---Serialization---
-
-    def _to_hdf5(self, hdf5_group):
-        hdf5_group['lmax'] = self.lmax
-        return None
-
-    @classmethod
-    def _from_hdf5(cls, hdf5_group, repository):
-        result = cls(
-            lmax=hdf5_group['lmax'][()],
-            )
-        return result

nifty/spaces/power_space/power_space.py

@@ -277,18 +277,3 @@ class PowerSpace(Space):
         """Degeneracy factor of the individual k-vectors.
         """
         return self._rho
-
-    # ---Serialization---
-
-    def _to_hdf5(self, hdf5_group):
-        hdf5_group.attrs['binbounds'] = str(self._binbounds)
-
-        return {
-            'harmonic_partner': self.harmonic_partner,
-        }
-
-    @classmethod
-    def _from_hdf5(cls, hdf5_group, repository):
-        hp = repository.get('harmonic_partner', hdf5_group)
-        bb = ast.literal_eval(hdf5_group.attrs['binbounds'])
-        return PowerSpace(hp, binbounds=bb)

nifty/spaces/rg_space/rg_space.py

@@ -252,21 +252,3 @@ class RGSpace(Space):
             temp = np.empty(len(self.shape), dtype=np.float64)
             temp[:] = distances
         return tuple(temp)
-
-    # ---Serialization---
-
-    def _to_hdf5(self, hdf5_group):
-        hdf5_group['shape'] = self.shape
-        hdf5_group['distances'] = self.distances
-        hdf5_group['harmonic'] = self.harmonic
-
-        return None
-
-    @classmethod
-    def _from_hdf5(cls, hdf5_group, repository):
-        result = cls(
-            shape=hdf5_group['shape'][:],
-            distances=hdf5_group['distances'][:],
-            harmonic=hdf5_group['harmonic'][()],
-            )
-        return result