Merge branch 'Branch_bug_fixes'

demos/wiener_filter.py

 
 from nifty import *
-import plotly.offline as pl
-import plotly.graph_objs as go
+#import plotly.offline as pl
+#import plotly.graph_objs as go
 
 from mpi4py import MPI
 comm = MPI.COMM_WORLD
@@ -12,7 +12,7 @@ if __name__ == "__main__":
 
     distribution_strategy = 'fftw'
 
-    s_space = RGSpace([512, 512], dtype=np.complex128)
+    s_space = RGSpace([512, 512], dtype=np.float64)
     fft = FFTOperator(s_space)
     h_space = fft.target[0]
     p_space = PowerSpace(h_space, distribution_strategy=distribution_strategy)
@@ -46,8 +46,8 @@ if __name__ == "__main__":
     m_data = m.val.get_full_data().real
     ss_data = ss.val.get_full_data().real
 
-    if rank == 0:
-        pl.plot([go.Heatmap(z=d_data)], filename='data.html')
-        pl.plot([go.Heatmap(z=m_data)], filename='map.html')
-        pl.plot([go.Heatmap(z=ss_data)], filename='map_orig.html')
-
+#    if rank == 0:
+#        pl.plot([go.Heatmap(z=d_data)], filename='data.html')
+#        pl.plot([go.Heatmap(z=m_data)], filename='map.html')
+#        pl.plot([go.Heatmap(z=ss_data)], filename='map_orig.html')
+#

nifty/field.py

@@ -17,7 +17,7 @@ from nifty.random import Random
 from keepers import Loggable
 
 
-class Field(object, Loggable):
+class Field(Loggable, object):
     # ---Initialization methods---
 
     def __init__(self, domain=None, val=None, dtype=None, field_type=None,
@@ -430,7 +430,7 @@ class Field(object, Loggable):
         if copy:
             new_val = new_val.copy()
         self._val = new_val
-        return self._val
+        return self
 
     def get_val(self, copy=False):
         if copy:

nifty/minimization/conjugate_gradient.py

@@ -7,7 +7,7 @@ import numpy as np
 from keepers import Loggable
 
 
-class ConjugateGradient(object, Loggable):
+class ConjugateGradient(Loggable, object):
     def __init__(self, convergence_tolerance=1E-4, convergence_level=3,
                  iteration_limit=None, reset_count=None,
                  preconditioner=None, callback=None):

nifty/minimization/line_searching/line_search.py

@@ -5,7 +5,7 @@ from keepers import Loggable
 from nifty import LineEnergy
 
 
-class LineSearch(object, Loggable):
+class LineSearch(Loggable, object):
     """
     Class for finding a step size.
     """

nifty/minimization/quasi_newton_minimizer.py

@@ -9,7 +9,7 @@ from keepers import Loggable
 from .line_searching import LineSearchStrongWolfe
 
 
-class QuasiNewtonMinimizer(object, Loggable):
+class QuasiNewtonMinimizer(Loggable, object):
     __metaclass__ = abc.ABCMeta
 
     def __init__(self, line_searcher=LineSearchStrongWolfe(), callback=None,

nifty/operators/endomorphic_operator/endomorphic_operator.py

@@ -60,27 +60,3 @@ class EndomorphicOperator(LinearOperator):
     @abc.abstractproperty
     def symmetric(self):
         raise NotImplementedError
-
-    def trace(self):
-        pass
-
-    def inverse_trace(self):
-        pass
-
-    def diagonal(self):
-        pass
-
-    def inverse_diagonal(self):
-        pass
-
-    def determinant(self):
-        pass
-
-    def inverse_determinant(self):
-        pass
-
-    def log_determinant(self):
-        pass
-
-    def trace_log(self):
-        pass

nifty/operators/fft_operator/fft_operator.py

@@ -95,7 +95,7 @@ class FFTOperator(LinearOperator):
             result_domain[spaces[0]] = self.target[0]
 
         result_field = x.copy_empty(domain=result_domain)
-        result_field.set_val(new_val=new_val)
+        result_field.set_val(new_val=new_val, copy=False)
 
         return result_field
 
@@ -118,7 +118,7 @@ class FFTOperator(LinearOperator):
             result_domain[spaces[0]] = self.domain[0]
 
         result_field = x.copy_empty(domain=result_domain)
-        result_field.set_val(new_val=new_val)
+        result_field.set_val(new_val=new_val, copy=False)
 
         return result_field
 

nifty/operators/fft_operator/transformations/rg_transforms.py

@@ -10,7 +10,7 @@ from keepers import Loggable
 pyfftw = gdi.get('pyfftw')
 
 
-class Transform(object, Loggable):
+class Transform(Loggable, object):
     """
         A generic fft object without any implementation.
     """

nifty/operators/fft_operator/transformations/transformation.py

@@ -4,7 +4,7 @@ import abc
 from keepers import Loggable
 
 
-class Transformation(object, Loggable):
+class Transformation(Loggable, object):
     """
         A generic transformation which defines a static check_codomain
         method for all transforms.

nifty/operators/linear_operator/linear_operator.py

@@ -9,7 +9,7 @@ from nifty.field_types import FieldType
 import nifty.nifty_utilities as utilities
 
 
-class LinearOperator(object, Loggable):
+class LinearOperator(Loggable, object):
     __metaclass__ = abc.ABCMeta
 
     def __init__(self):

nifty/operators/propagator_operator/propagator_operator.py

 # -*- coding: utf-8 -*-
-import numpy as np
 from nifty.minimization import ConjugateGradient
-from nifty.nifty_utilities import get_default_codomain
 from nifty.field import Field
 from nifty.operators import EndomorphicOperator,\
                             FFTOperator
@@ -45,10 +43,8 @@ class PropagatorOperator(EndomorphicOperator):
             self._domain = N.domain
             self._likelihood_times = lambda z: N.inverse_times(z)
 
-        fft_S = FFTOperator(S.domain, target=self._domain)
-        self._S_times = lambda z: fft_S(S(fft_S.inverse_times(z)))
-        self._S_inverse_times = lambda z: fft_S(S.inverse_times(
-                                          fft_S.inverse_times(z)))
+        self._S = S
+        self._fft_S = FFTOperator(self._domain, target=self._S.domain)
 
         if preconditioner is None:
             preconditioner = self._S_times
@@ -61,8 +57,6 @@ class PropagatorOperator(EndomorphicOperator):
             self.inverter = ConjugateGradient(
                                 preconditioner=self.preconditioner)
 
-        self.x0 = None
-
     # ---Mandatory properties and methods---
 
     @property
@@ -87,18 +81,44 @@ class PropagatorOperator(EndomorphicOperator):
 
     # ---Added properties and methods---
 
-    def _times(self, x, spaces, types):
-        if self.x0 is None:
-            x0 = Field(self.domain, val=0., dtype=np.complex128)
-        else:
-            x0 = self.x0
+    def _S_times(self, x, spaces=None, types=None):
+            transformed_x = self._fft_S(x,
+                                        spaces=spaces,
+                                        types=types)
+            y = self._S(transformed_x, spaces=spaces, types=types)
+            transformed_y = self._fft_S.inverse_times(y,
+                                                      spaces=spaces,
+                                                      types=types)
+            result = x.copy_empty()
+            result.set_val(transformed_y, copy=False)
+            return result
+
+    def _S_inverse_times(self, x, spaces=None, types=None):
+            transformed_x = self._fft_S(x,
+                                        spaces=spaces,
+                                        types=types)
+            y = self._S.inverse_times(transformed_x,
+                                      spaces=spaces,
+                                      types=types)
+            transformed_y = self._fft_S.inverse_times(y,
+                                                      spaces=spaces,
+                                                      types=types)
+            result = x.copy_empty()
+            result.set_val(transformed_y, copy=False)
+            return result
+
+    def _times(self, x, spaces, types, x0=None):
+        if x0 is None:
+            x0 = Field(self.domain, val=0., dtype=x.dtype)
+
         (result, convergence) = self.inverter(A=self.inverse_times,
                                               b=x,
                                               x0=x0)
-        self.x0 = result
         return result
 
     def _inverse_times(self, x, spaces, types):
-        result = self._S_inverse_times(x)
-        result += self._likelihood_times(x)
+        pre_result = self._S_inverse_times(x, spaces, types)
+        pre_result += self._likelihood_times(x)
+        result = x.copy_empty()
+        result.set_val(pre_result, copy=False)
         return result

nifty/operators/smoothing_operator/smoothing_operator.py

@@ -54,7 +54,7 @@ class SmoothingOperator(EndomorphicOperator):
 
     @property
     def symmetric(self):
-        return False
+        return True
 
     @property
     def unitary(self):
@@ -138,7 +138,10 @@ class SmoothingOperator(EndomorphicOperator):
 
         transformed_x.val.set_local_data(local_transformed_x, copy=False)
 
-        result = Transformator.inverse_times(transformed_x, spaces=spaces)
+        smoothed_x = Transformator.inverse_times(transformed_x, spaces=spaces)
+
+        result = x.copy_empty()
+        result.set_val(smoothed_x, copy=False)
 
         return result
 

nifty/probing/diagonal_prober.py

 # -*- coding: utf-8 -*-
 
-from nifty.operators import EndomorphicOperator
+from prober import Prober
 
-from operator_prober import OperatorProber
 
-__all__ = ['DiagonalProber', 'InverseDiagonalProber',
-           'AdjointDiagonalProber', 'AdjointInverseDiagonalProber']
-
-
-class DiagonalTypeProber(OperatorProber):
+class DiagonalProber(Prober):
 
     # ---Mandatory properties and methods---
-    # --- ->Mandatory from OperatorProber---
-
-    @property
-    def valid_operator_class(self):
-        return EndomorphicOperator
-
-    # --- ->Mandatory from Prober---
-
     def finish_probe(self, probe, pre_result):
         return probe[1].conjugate()*pre_result
-
-
-class DiagonalProber(DiagonalTypeProber):
-
-    # ---Mandatory properties and methods---
-    # --- ->Mandatory from OperatorProber---
-
-    @property
-    def is_inverse(self):
-        return False
-
-    # --- ->Mandatory from Prober---
-
-    def evaluate_probe(self, probe):
-        """ processes a probe """
-        return self.operator.times(probe[1])
-
-
-class InverseDiagonalProber(DiagonalTypeProber):
-
-    # ---Mandatory properties and methods---
-    # --- ->Mandatory from OperatorProber---
-
-    @property
-    def is_inverse(self):
-        return True
-
-    # --- ->Mandatory from Prober---
-
-    def evaluate_probe(self, probe):
-        """ processes a probe """
-        return self.operator.inverse_times(probe[1])
-
-
-class AdjointDiagonalProber(DiagonalTypeProber):
-
-    # ---Mandatory properties and methods---
-    # --- ->Mandatory from OperatorProber---
-
-    @property
-    def is_inverse(self):
-        return False
-
-    # --- ->Mandatory from Prober---
-
-    def evaluate_probe(self, probe):
-        """ processes a probe """
-        return self.operator.adjoint_times(probe[1])
-
-
-class AdjointInverseDiagonalProber(DiagonalTypeProber):
-
-    # ---Mandatory properties and methods---
-    # --- ->Mandatory from OperatorProber---
-
-    @property
-    def is_inverse(self):
-        return True
-
-    # --- ->Mandatory from Prober---
-
-    def evaluate_probe(self, probe):
-        """ processes a probe """
-        return self.operator.adjoint_inverse_times(probe[1])

nifty/probing/operator_prober.py

-# -*- coding: utf-8 -*-
-
-import abc
-
-
-from prober import Prober
-
-
-class OperatorProber(Prober):
-
-    # ---Overwritten properties and methods---
-
-    def __init__(self, operator, probe_count=8, random_type='pm1',
-                 distribution_strategy=None, compute_variance=False):
-        super(OperatorProber, self).__init__(
-                                 probe_count=probe_count,
-                                 random_type=random_type,
-                                 compute_variance=compute_variance)
-
-        if not isinstance(operator, self.valid_operator_class):
-            raise TypeError("Operator must be an instance of %s" %
-                            str(self.valid_operator_class))
-        self._operator = operator
-
-    # ---Mandatory properties and methods---
-
-    @property
-    def domain(self):
-        if self.is_inverse:
-            return self.operator.target
-        else:
-            return self.operator.domain
-
-    @property
-    def field_type(self):
-        if self.is_inverse:
-            return self.operator.field_type_target
-        else:
-            return self.operator.field_type
-
-    @property
-    def distribution_strategy(self):
-        return self.operator.distribution_strategy
-
-    # ---Added properties and methods---
-
-    @abc.abstractproperty
-    def is_inverse(self):
-        raise NotImplementedError
-
-    @abc.abstractproperty
-    def valid_operator_class(self):
-        raise NotImplementedError
-
-    @property
-    def operator(self):
-        return self._operator

nifty/probing/prober.py

@@ -4,6 +4,8 @@ import abc
 
 import numpy as np
 
+from nifty.field_types import FieldType
+from nifty.spaces import Space
 from nifty.field import Field
 
 from d2o import STRATEGIES as DISTRIBUTION_STRATEGIES
@@ -12,28 +14,67 @@ from d2o import STRATEGIES as DISTRIBUTION_STRATEGIES
 class Prober(object):
     __metaclass__ = abc.ABCMeta
 
-    def __init__(self, probe_count=8, random_type='pm1',
-                 compute_variance=False):
+    def __init__(self, domain=None, field_type=None,
+                 distribution_strategy=None, probe_count=8,
+                 random_type='pm1', compute_variance=False):
 
+        self.domain = domain
+        self.field_type = field_type
+        self.distribution_strategy = distribution_strategy
         self.probe_count = probe_count
-
         self.random_type = random_type
-
         self.compute_variance = bool(compute_variance)
 
+    def _parse_domain(self, domain):
+        if domain is None:
+            domain = ()
+        elif isinstance(domain, Space):
+            domain = (domain,)
+        elif not isinstance(domain, tuple):
+            domain = tuple(domain)
+
+        for d in domain:
+            if not isinstance(d, Space):
+                raise TypeError(
+                    "Given object contains something that is not a "
+                    "nifty.space.")
+        return domain
+
+    def _parse_field_type(self, field_type):
+        if field_type is None:
+            field_type = ()
+        elif isinstance(field_type, FieldType):
+            field_type = (field_type,)
+        elif not isinstance(field_type, tuple):
+            field_type = tuple(field_type)
+
+        for ft in field_type:
+            if not isinstance(ft, FieldType):
+                raise TypeError(
+                    "Given object is not a nifty.FieldType.")
+        return field_type
+
     # ---Properties---
 
-    @abc.abstractproperty
+    @property
     def domain(self):
-        raise NotImplementedError
+        return self._domain
+
+    @domain.setter
+    def domain(self, domain):
+        self._domain = self._parse_domain(domain)
 
-    @abc.abstractproperty
+    @property
     def field_type(self):
-        raise NotImplementedError
+        return self._field_type
+
+    @field_type.setter
+    def field_type(self, field_type):
+        self._field_type = self._parse_field_type(field_type)
 
-    @abc.abstractproperty
+    @property
     def distribution_strategy(self):
-        raise NotImplementedError
+        return self._distribution_strategy
 
     @distribution_strategy.setter
     def distribution_strategy(self, distribution_strategy):
@@ -65,14 +106,14 @@ class Prober(object):
 
     # ---Probing methods---
 
-    def probing_run(self):
+    def probing_run(self, callee):
         """ controls the generation, evaluation and finalization of probes """
         sum_of_probes = 0
         sum_of_squares = 0
 
         for index in xrange(self.probe_count):
             current_probe = self.get_probe(index)
-            pre_result = self.process_probe(current_probe, index)
+            pre_result = self.process_probe(callee, current_probe, index)
             result = self.finish_probe(current_probe, pre_result)
 
             sum_of_probes += result
@@ -95,13 +136,13 @@ class Prober(object):
         uid = np.random.randint(1e18)
         return (uid, f)
 
-    def process_probe(self, probe, index):
-        return self.evaluate_probe(probe)
+    def process_probe(self, callee, probe, index):
+        """ layer of abstraction for potential result-caching/recycling """
+        return self.evaluate_probe(callee, probe[1])
 
-    @abc.abstractmethod
-    def evaluate_probe(self, probe):
+    def evaluate_probe(self, callee, probe, **kwargs):
         """ processes a probe """
-        raise NotImplementedError
+        return callee(probe, **kwargs)
 
     @abc.abstractmethod
     def finish_probe(self, probe, pre_result):
@@ -119,5 +160,5 @@ class Prober(object):
 
         return (mean_of_probes, variance)
 
-    def __call__(self):
-        return self.probe()
+    def __call__(self, callee):
+        return self.probing_run(callee)

nifty/probing/trace_prober.py

 # -*- coding: utf-8 -*-
 
-from nifty.operators import EndomorphicOperator
+from prober import Prober
 
-from operator_prober import OperatorProber
 
-__all__ = ['TraceProber', 'InverseTraceProber',
-           'AdjointTraceProber', 'AdjointInverseTraceProber']
-
-
-class TraceTypeProber(OperatorProber):
+class TraceProber(Prober):
 
     # ---Mandatory properties and methods---
-    # --- ->Mandatory from OperatorProber---
-
-    @property
-    def valid_operator_class(self):
-        return EndomorphicOperator
-
-    # --- ->Mandatory from Prober---
-
     def finish_probe(self, probe, pre_result):
         return probe[1].conjugate().weight(power=-1).dot(pre_result)
-
-
-class TraceProber(TraceTypeProber):
-
-    # ---Mandatory properties and methods---
-    # --- ->Mandatory from OperatorProber---
-
-    @property
-    def is_inverse(self):
-        return False
-
-    # --- ->Mandatory from Prober---
-
-    def evaluate_probe(self, probe):
-        """ processes a probe """
-        return self.operator.times(probe[1])
-
-
-class InverseTraceProber(TraceTypeProber):
-
-    # ---Mandatory properties and methods---
-    # --- ->Mandatory from OperatorProber---
-
-    @property
-    def is_inverse(self):
-        return True
-
-    # --- ->Mandatory from Prober---
-
-    def evaluate_probe(self, probe):
-        """ processes a probe """
-        return self.operator.inverse_times(probe[1])
-
-
-class AdjointTraceProber(TraceTypeProber):
-
-    # ---Mandatory properties and methods---
-    # --- ->Mandatory from OperatorProber---
-
-    @property
-    def is_inverse(self):
-        return False
-
-    # --- ->Mandatory from Prober---
-
-    def evaluate_probe(self, probe):