nor more keepers

.gitlab-ci.yml

@@ -18,6 +18,7 @@ before_script:
 test_min:
   stage: test
   script:
+    - ci/install_pyHealpix.sh
     - nosetests
     - nosetests -x --with-coverage --cover-package=nifty --cover-branches
     - >

ci/requirements.txt

@@ -4,5 +4,4 @@ nose
 parameterized
 coverage
 git+https://gitlab.mpcdf.mpg.de/ift/mpi_dummy.git
-git+https://gitlab.mpcdf.mpg.de/ift/keepers.git
 pyfftw

demos/paper_demos/cartesian_wiener_filter.py

@@ -4,14 +4,9 @@ import numpy as np
 import nifty as ift
 from nifty import plotting
 
-from keepers import Repository
-
 if __name__ == "__main__":
-    ift.nifty_configuration['default_distribution_strategy'] = 'fftw'
-
     signal_to_noise = 1.5 # The signal to noise ratio
 
-
     # Setting up parameters    |\label{code:wf_parameters}|
     correlation_length_1 = 1. # Typical distance over which the field is correlated
     field_variance_1 = 2. # Variance of field in position space
@@ -28,12 +23,10 @@ if __name__ == "__main__":
 
     signal_space_1 = ift.RGSpace([N_pixels_1], distances=L_1/N_pixels_1)
     harmonic_space_1 = ift.FFTOperator.get_default_codomain(signal_space_1)
-    fft_1 = ift.FFTOperator(harmonic_space_1, target=signal_space_1,
-                            domain_dtype=np.complex, target_dtype=np.complex)
+    fft_1 = ift.FFTOperator(harmonic_space_1, target=signal_space_1)
     power_space_1 = ift.PowerSpace(harmonic_space_1)
 
-    mock_power_1 = ift.Field(power_space_1, val=power_spectrum_1,
-                             distribution_strategy='not')
+    mock_power_1 = ift.Field(power_space_1, val=power_spectrum_1)
 
 
 
@@ -53,19 +46,15 @@ if __name__ == "__main__":
 
     signal_space_2 = ift.RGSpace([N_pixels_2], distances=L_2/N_pixels_2)
     harmonic_space_2 = ift.FFTOperator.get_default_codomain(signal_space_2)
-    fft_2 = ift.FFTOperator(harmonic_space_2, target=signal_space_2,
-                            domain_dtype=np.complex, target_dtype=np.complex)
-    power_space_2 = ift.PowerSpace(harmonic_space_2, distribution_strategy='not')
+    fft_2 = ift.FFTOperator(harmonic_space_2, target=signal_space_2)
+    power_space_2 = ift.PowerSpace(harmonic_space_2)
 
-    mock_power_2 = ift.Field(power_space_2, val=power_spectrum_2,
-                         distribution_strategy='not')
+    mock_power_2 = ift.Field(power_space_2, val=power_spectrum_2)
 
     fft = ift.ComposedOperator((fft_1, fft_2))
 
     mock_power = ift.Field(domain=(power_space_1, power_space_2),
-                           val=np.outer(mock_power_1.val.get_full_data(),
-                                        mock_power_2.val.get_full_data()),
-                                        distribution_strategy='not')
+                           val=np.outer(mock_power_1.val, mock_power_2.val))
 
     diagonal = mock_power.power_synthesize(spaces=(0, 1), mean=1, std=0,
                                            real_signal=False)**2
@@ -83,7 +72,7 @@ if __name__ == "__main__":
     mask_1.val[N1_10*7:N1_10*9] = 0.
 
     N2_10 = int(N_pixels_2/10)
-    mask_2 = ift.Field(signal_space_2, val=1., distribution_strategy='not')
+    mask_2 = ift.Field(signal_space_2, val=1.)
     mask_2.val[N2_10*7:N2_10*9] = 0.
 
     R = ift.ResponseOperator((signal_space_1, signal_space_2),
@@ -102,8 +91,11 @@ if __name__ == "__main__":
 
     # Wiener filter
     j = R_harmonic.adjoint_times(N.inverse_times(data))
-    wiener_curvature = ift.library.WienerFilterCurvature(S=S, N=N, R=R_harmonic)
-    wiener_curvature._InvertibleOperatorMixin__inverter.convergence_tolerance = 1e-3
+    ctrl = ift.DefaultIterationController(verbose=True,
+                                          tol_abs_gradnorm=1.0,
+                                          tol_rel_gradnorm=1e-4)
+    inverter = ift.ConjugateGradient(controller=ctrl,preconditioner=S.times)
+    wiener_curvature = ift.library.WienerFilterCurvature(S=S, N=N, R=R_harmonic, inverter=inverter)
 
     m_k = wiener_curvature.inverse_times(j) #|\label{code:wf_wiener_filter}|
     m = fft(m_k)
@@ -116,13 +108,6 @@ if __name__ == "__main__":
 #    variance = sm(proby.diagonal.weight(-1))
     variance = proby.diagonal.weight(-1)
 
-    repo = Repository('repo_100.h5')
-    repo.add(mock_signal, 'mock_signal')
-    repo.add(data, 'data')
-    repo.add(m, 'm')
-    repo.add(variance, 'variance')
-    repo.commit()
-
     plot_space = ift.RGSpace((N_pixels_1, N_pixels_2))
     plotter = plotting.RG2DPlotter(color_map=plotting.colormaps.PlankCmap())
     plotter.figure.xaxis = ift.plotting.Axis(label='Pixel Index')
@@ -136,6 +121,6 @@ if __name__ == "__main__":
     plotter.plot.zmin = np.real(mock_signal.min());
     plotter.plot.zmax = np.real(mock_signal.max());
     plotter(ift.Field(plot_space, val=mock_signal.val.real), path='mock_signal.html')
-    plotter(ift.Field(plot_space, val=data.val.get_full_data().real), path = 'data.html')
+    plotter(ift.Field(plot_space, val=data.val.real), path = 'data.html')
     plotter(ift.Field(plot_space, val=m.val.real), path = 'map.html')
 

demos/paper_demos/wiener_filter.py

@@ -53,7 +53,7 @@ if __name__ == "__main__":
 
     # Probing the uncertainty |\label{code:wf_uncertainty_probing}|
     class Proby(ift.DiagonalProberMixin, ift.Prober): pass
-    proby = Proby(signal_space, probe_count=20)
+    proby = Proby(signal_space, probe_count=800)
     proby(lambda z: fft(wiener_curvature.inverse_times(fft.inverse_times(z))))  #|\label{code:wf_variance_fft_wrap}|
 
     sm = ift.FFTSmoothingOperator(signal_space, sigma=0.03)

nifty/__init__.py

@@ -20,14 +20,6 @@ from __future__ import division
 
 from .version import __version__
 
-# initialize the logger instance
-from keepers import MPILogger
-logger = MPILogger()
-
-from .config import nifty_configuration
-
-logger.logger.setLevel(nifty_configuration['loglevel'])
-
 from .field import Field
 
 from .random import Random

nifty/config/__init__.py

-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-#
-# Copyright(C) 2013-2017 Max-Planck-Society
-#
-# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
-# and financially supported by the Studienstiftung des deutschen Volkes.
-
-
-from .nifty_config import nifty_configuration

nifty/config/matplotlib_init.py

-# -*- coding: utf-8 -*-
-
-import os
-
-__all__ = []
-
-try:
-    import matplotlib
-except ImportError:
-    pass
-else:
-    try:
-        display = os.environ['DISPLAY']
-    except KeyError:
-        matplotlib.use('Agg')
-    else:
-        if display == '':
-            matplotlib.use('Agg')

nifty/config/nifty_config.py

-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-#
-# Copyright(C) 2013-2017 Max-Planck-Society
-#
-# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
-# and financially supported by the Studienstiftung des deutschen Volkes.
-
-from . import matplotlib_init
-
-import os
-
-import numpy as np
-import keepers
-
-__all__ = ['nifty_configuration']
-
-# Initialize the variables
-
-variable_loglevel = keepers.Variable(
-                                'loglevel',
-                                [10],
-                                lambda z: np.int(z) == z and 0 <= z <= 50,
-                                genus='int')
-
-nifty_configuration = keepers.get_Configuration(
-                 name='NIFTy',
-                 variables=[variable_loglevel],
-                 file_name='NIFTy.conf',
-                 search_paths=[os.path.expanduser('~') + "/.config/nifty/",
-                               os.path.expanduser('~') + "/.config/",
-                               './'])
-
-########
-
-try:
-    nifty_configuration.load()
-except:
-    pass

nifty/domain_object.py

@@ -20,12 +20,11 @@ from __future__ import division
 import abc
 from .nifty_meta import NiftyMeta
 
-from keepers import Loggable
 from future.utils import with_metaclass
 
 
 class DomainObject(with_metaclass(
-        NiftyMeta, type('NewBase', (Loggable, object), {}))):
+        NiftyMeta, type('NewBase', (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
@@ -42,8 +41,7 @@ class DomainObject(with_metaclass(
     """
 
     def __init__(self):
-        # _global_id is used in the Versioning module from keepers
-        self._ignore_for_hash = ['_global_id']
+        self._ignore_for_hash = []
 
     @abc.abstractmethod
     def __repr__(self):

nifty/energies/energy.py

@@ -19,11 +19,10 @@
 from ..nifty_meta import NiftyMeta
 from .memoization import memo
 
-from keepers import Loggable
 from future.utils import with_metaclass
 
 
-class Energy(with_metaclass(NiftyMeta, type('NewBase', (Loggable, object), {}))):
+class Energy(with_metaclass(NiftyMeta, type('NewBase', (object,), {}))):
     """ Provides the functional used by minimization schemes.
 
    The Energy object is an implementation of a scalar function including its

nifty/field.py

@@ -23,8 +23,6 @@ from builtins import range
 import ast
 import numpy as np
 
-from keepers import Loggable
-
 from .domain_object import DomainObject
 
 from .spaces.power_space import PowerSpace
@@ -34,7 +32,7 @@ from .random import Random
 from functools import reduce
 
 
-class Field(Loggable, object):
+class Field(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
@@ -269,7 +267,7 @@ class Field(Loggable, object):
         # power_space instances
         for sp in self.domain:
             if not sp.harmonic and not isinstance(sp, PowerSpace):
-                self.logger.info(
+                raise TypeError(
                     "Field has a space in `domain` which is neither "
                     "harmonic nor a PowerSpace.")
 

nifty/minimization/conjugate_gradient.py

@@ -83,12 +83,10 @@ class ConjugateGradient(Minimizer):
             q = energy.curvature(d)
             ddotq = d.vdot(q).real
             if ddotq==0.:
-                self.logger.error("Alpha became infinite! Stopping.")
                 return energy, controller.ERROR
             alpha = previous_gamma/ddotq
 
             if alpha < 0:
-                self.logger.warn("Positive definiteness of A violated!")
                 return energy, controller.ERROR
 
             r -= q * alpha
@@ -105,7 +103,7 @@ class ConjugateGradient(Minimizer):
 
             gamma = r.vdot(s).real
             if gamma < 0:
-                self.logger.warn(
+                raise RuntimeError(
                     "Positive definiteness of preconditioner violated!")
             if gamma == 0:
                 return energy, controller.CONVERGED

nifty/minimization/default_iteration_controller.py

@@ -56,9 +56,9 @@ class DefaultIterationController(IterationController):
                 msg += self._name+":"
             msg += " Iteration #" + str(self._itcount)
             msg += " gradnorm=" + str(energy.gradient_norm)
-            msg += " convergence level=" + str(self._ccount)
+            msg += " clvl=" + str(self._ccount)
             print (msg)
-            self.logger.info(msg)
+            #self.logger.info(msg)
 
         # Are we done?
         if self._iteration_limit is not None:

nifty/minimization/descent_minimizer.py

@@ -82,7 +82,6 @@ class DescentMinimizer(Minimizer):
         while True:
             # check if position is at a flat point
             if energy.gradient_norm == 0:
-                self.logger.info("Reached perfectly flat point. Stopping.")
                 return energy, controller.CONVERGED
 
             # current position is encoded in energy object
@@ -96,15 +95,11 @@ class DescentMinimizer(Minimizer):
                                                    pk=descent_direction,
                                                    f_k_minus_1=f_k_minus_1)
             except RuntimeError:
-                self.logger.warn(
-                        "Stopping because of RuntimeError in line-search")
                 return energy, controller.ERROR
 
             f_k_minus_1 = energy.value
             # check if new energy value is bigger than old energy value
             if (new_energy.value - energy.value) > 0:
-                self.logger.info("Line search algorithm returned a new energy "
-                                 "that was larger than the old one. Stopping.")
                 return energy, controller.ERROR
 
             energy = new_energy

nifty/minimization/iteration_controller.py

@@ -22,11 +22,10 @@ from ..nifty_meta import NiftyMeta
 
 import numpy as np
 
-from keepers import Loggable
 from future.utils import with_metaclass
 
 class IterationController(with_metaclass(NiftyMeta, type('NewBase',
-                                         (Loggable, object), {}))):
+                                         (object,), {}))):
     """The abstract base class for all iteration controllers.
     An iteration controller is an object that monitors the progress of a
     minimization iteration. At the begin of the minimization, its start()

nifty/minimization/line_searching/line_search.py

@@ -18,13 +18,11 @@
 
 import abc
 
-from keepers import Loggable
-
 from ...energies import LineEnergy
 from future.utils import with_metaclass
 
 
-class LineSearch(with_metaclass(abc.ABCMeta, with_metaclass(abc.ABCMeta, type('NewBase', (Loggable, object), {})))):
+class LineSearch(with_metaclass(abc.ABCMeta, with_metaclass(abc.ABCMeta, type('NewBase', (object,), {})))):
     """Class for determining the optimal step size along some descent direction.
 
     Initialize the line search procedure which can be used by a specific line

nifty/minimization/line_searching/line_search_strong_wolfe.py

@@ -109,8 +109,7 @@ class LineSearchStrongWolfe(LineSearch):
         phi_0 = le_0.value
         phiprime_0 = le_0.directional_derivative
         if phiprime_0 >= 0:
-            self.logger.error("Input direction must be a descent direction")
-            raise RuntimeError
+            raise RuntimeError ("search direction must be a descent direction")
 
         # set alphas
         alpha0 = 0.
@@ -131,7 +130,6 @@ class LineSearchStrongWolfe(LineSearch):
         while iteration_number < self.max_iterations:
             iteration_number += 1
             if alpha1 == 0:
-                self.logger.warn("Increment size became 0.")
                 result_energy = le_0.energy
                 break
 
@@ -161,18 +159,13 @@ class LineSearchStrongWolfe(LineSearch):
             # update alphas
             alpha0, alpha1 = alpha1, min(2*alpha1, self.max_step_size)
             if alpha1 == self.max_step_size:
-                self.logger.info("Reached max step size, bailing out")
                 return le_alpha1.energy
 
             phi_alpha0 = phi_alpha1
             phiprime_alpha0 = phiprime_alpha1
         else:
             # max_iterations was reached
-            self.logger.error("The line search algorithm did not converge.")
             return le_alpha1.energy
-        if iteration_number > 1:
-            self.logger.debug("Finished line-search after %08u steps" %
-                              iteration_number)
         return result_energy
 
     def _zoom(self, alpha_lo, alpha_hi, phi_0, phiprime_0,
@@ -269,8 +262,8 @@ class LineSearchStrongWolfe(LineSearch):
                                                    phiprime_alphaj)
 
         else:
-            self.logger.error("The line search algorithm (zoom) did not "
-                              "converge.")
+            #self.logger.error("The line search algorithm (zoom) did not "
+            #                  "converge.")
             return le_alphaj
 
     def _cubicmin(self, a, fa, fpa, b, fb, c, fc):

nifty/minimization/minimizer.py

@@ -21,10 +21,9 @@ from ..nifty_meta import NiftyMeta
 
 import numpy as np
 
-from keepers import Loggable
 from future.utils import with_metaclass
 
-class Minimizer(with_metaclass(NiftyMeta, type('NewBase', (Loggable, object), {}))):
+class Minimizer(with_metaclass(NiftyMeta, type('NewBase', (object,), {}))):
     """ A base class used by all minimizers.
     """
 

nifty/operators/fft_operator/transformations/gllmtransformation.py

@@ -77,15 +77,6 @@ class GLLMTransformation(SlicingTransformation):
         lmax = codomain.lmax
         mmax = codomain.mmax
 
-        if lmax != mmax:
-            cls.logger.warn("Unrecommended: codomain has lmax != mmax.")
-
-        if lmax != nlat - 1:
-            cls.logger.warn("Unrecommended: codomain has lmax != nlat - 1.")
-
-        if nlon != 2*nlat - 1:
-            cls.logger.warn("Unrecommended: domain has nlon != 2*nlat - 1.")
-
         super(GLLMTransformation, cls).check_codomain(domain, codomain)
 
     def _transformation_of_slice(self, inp, **kwargs):

nifty/operators/fft_operator/transformations/hplmtransformation.py

@@ -75,9 +75,6 @@ class HPLMTransformation(SlicingTransformation):
         lmax = codomain.lmax
         nside = domain.nside
 
-        if lmax != 2*nside:
-            cls.logger.warn("Unrecommended: lmax != 2*nside.")
-
         super(HPLMTransformation, cls).check_codomain(domain, codomain)
 
     def _transformation_of_slice(self, inp, **kwargs):