From d5fe215dfbabbc34604e0617bc60549d3890bca4 Mon Sep 17 00:00:00 2001
From: Martin Reinecke <martin@mpa-garching.mpg.de>
Date: Tue, 13 Mar 2018 15:43:01 +0100
Subject: [PATCH] switch to Python logging
---
demos/probing.py | 2 +-
demos/wiener_filter_easy.py | 1 -
nifty4/__init__.py | 2 ++
nifty4/data_objects/distributed_do.py | 5 -----
nifty4/data_objects/numpy_do.py | 4 ----
nifty4/dobj.py | 5 +----
nifty4/logger.py | 14 ++++++++++++++
nifty4/minimization/conjugate_gradient.py | 8 ++++----
nifty4/minimization/descent_minimizer.py | 6 +++---
nifty4/minimization/gradient_norm_controller.py | 6 +++---
nifty4/minimization/l_bfgs.py | 4 ++--
nifty4/minimization/line_energy.py | 6 +++---
nifty4/minimization/line_search_strong_wolfe.py | 12 ++++++------
nifty4/minimization/scipy_minimizer.py | 5 +++--
nifty4/operators/inversion_enabler.py | 5 +++--
nifty4/sugar.py | 3 ++-
16 files changed, 47 insertions(+), 41 deletions(-)
create mode 100644 nifty4/logger.py
diff --git a/demos/probing.py b/demos/probing.py
index ae93129e2..58810fb3a 100644
--- a/demos/probing.py
+++ b/demos/probing.py
@@ -9,4 +9,4 @@ f = ift.Field.from_random(domain=x, random_type='normal')
diagOp = ift.DiagonalOperator(f)
diag = ift.probe_diagonal(diagOp, 1000)
-ift.dobj.mprint((f - diag).norm())
+ift.logger.info((f - diag).norm())
diff --git a/demos/wiener_filter_easy.py b/demos/wiener_filter_easy.py
index 8412ac722..a2f44d270 100644
--- a/demos/wiener_filter_easy.py
+++ b/demos/wiener_filter_easy.py
@@ -1,7 +1,6 @@
import numpy as np
import nifty4 as ift
-
if __name__ == "__main__":
np.random.seed(43)
# Set up physical constants
diff --git a/nifty4/__init__.py b/nifty4/__init__.py
index 14b675522..1b9f7d62f 100644
--- a/nifty4/__init__.py
+++ b/nifty4/__init__.py
@@ -22,6 +22,8 @@ from . import extra
from .utilities import memo
+from .logger import logger
+
__all__ = ["__version__", "dobj", "DomainTuple"] + \
domains.__all__ + operators.__all__ + minimization.__all__ + \
["DomainTuple", "Field", "sqrt", "exp", "log"]
diff --git a/nifty4/data_objects/distributed_do.py b/nifty4/data_objects/distributed_do.py
index f810723d6..9f6f05520 100644
--- a/nifty4/data_objects/distributed_do.py
+++ b/nifty4/data_objects/distributed_do.py
@@ -31,11 +31,6 @@ def is_numpy():
return False
-def mprint(*args):
- if master:
- print(*args)
-
-
def _shareSize(nwork, nshares, myshare):
return (nwork//nshares) + int(myshare < nwork % nshares)
diff --git a/nifty4/data_objects/numpy_do.py b/nifty4/data_objects/numpy_do.py
index 201c106af..4a4e774e7 100644
--- a/nifty4/data_objects/numpy_do.py
+++ b/nifty4/data_objects/numpy_do.py
@@ -34,10 +34,6 @@ def is_numpy():
return True
-def mprint(*args):
- print(*args)
-
-
def from_object(object, dtype, copy, set_locked):
if dtype is None:
dtype = object.dtype
diff --git a/nifty4/dobj.py b/nifty4/dobj.py
index 1444547f6..c4f502687 100644
--- a/nifty4/dobj.py
+++ b/nifty4/dobj.py
@@ -20,18 +20,15 @@ try:
from mpi4py import MPI
if MPI.COMM_WORLD.Get_size() == 1:
from .data_objects.numpy_do import *
- # mprint("MPI found, but only with one task, using numpy_do...")
else:
from .data_objects.distributed_do import *
- # mprint("MPI with multiple tasks found, using distributed_do...")
except ImportError:
from .data_objects.numpy_do import *
- # mprint("MPI not found, using numpy_do...")
__all__ = ["ntask", "rank", "master", "local_shape", "data_object", "full",
"empty", "zeros", "ones", "empty_like", "vdot", "abs", "exp",
"log", "tanh", "sqrt", "from_object", "from_random",
"local_data", "ibegin", "ibegin_from_shape", "np_allreduce_sum",
"distaxis", "from_local_data", "from_global_data", "to_global_data",
- "redistribute", "default_distaxis", "mprint", "is_numpy",
+ "redistribute", "default_distaxis", "is_numpy",
"lock", "locked"]
diff --git a/nifty4/logger.py b/nifty4/logger.py
new file mode 100644
index 000000000..5801d07f6
--- /dev/null
+++ b/nifty4/logger.py
@@ -0,0 +1,14 @@
+def _logger_init():
+ import logging
+ from . import dobj
+ res = logging.getLogger('NIFTy4')
+ res.setLevel(logging.DEBUG)
+ if dobj.rank == 0:
+ ch = logging.StreamHandler()
+ ch.setLevel(logging.DEBUG)
+ res.addHandler(ch)
+ else:
+ res.addHandler(logging.NullHandler())
+ return res
+
+logger = _logger_init()
diff --git a/nifty4/minimization/conjugate_gradient.py b/nifty4/minimization/conjugate_gradient.py
index a752c5171..ad5d96360 100644
--- a/nifty4/minimization/conjugate_gradient.py
+++ b/nifty4/minimization/conjugate_gradient.py
@@ -18,7 +18,7 @@
from __future__ import division
from .minimizer import Minimizer
-from .. import dobj
+from ..logger import logger
class ConjugateGradient(Minimizer):
@@ -76,12 +76,12 @@ class ConjugateGradient(Minimizer):
q = energy.curvature(d)
ddotq = d.vdot(q).real
if ddotq == 0.:
- dobj.mprint("Error: ConjugateGradient: ddotq==0.")
+ logger.error("Error: ConjugateGradient: ddotq==0.")
return energy, controller.ERROR
alpha = previous_gamma/ddotq
if alpha < 0:
- dobj.mprint("Error: ConjugateGradient: alpha<0.")
+ logger.error("Error: ConjugateGradient: alpha<0.")
return energy, controller.ERROR
q *= -alpha
@@ -93,7 +93,7 @@ class ConjugateGradient(Minimizer):
gamma = r.vdot(s).real
if gamma < 0:
- dobj.mprint(
+ logger.error(
"Positive definiteness of preconditioner violated!")
return energy, controller.ERROR
if gamma == 0:
diff --git a/nifty4/minimization/descent_minimizer.py b/nifty4/minimization/descent_minimizer.py
index d3fbea686..778957675 100644
--- a/nifty4/minimization/descent_minimizer.py
+++ b/nifty4/minimization/descent_minimizer.py
@@ -20,7 +20,7 @@ from __future__ import division
import abc
from .minimizer import Minimizer
from .line_search_strong_wolfe import LineSearchStrongWolfe
-from .. import dobj
+from ..logger import logger
class DescentMinimizer(Minimizer):
@@ -92,11 +92,11 @@ class DescentMinimizer(Minimizer):
f_k_minus_1 = energy.value
if new_energy.value > energy.value:
- dobj.mprint("Error: Energy has increased")
+ logger.error("Error: Energy has increased")
return energy, controller.ERROR
if new_energy.value == energy.value:
- dobj.mprint(
+ logger.warning(
"Warning: Energy has not changed. Assuming convergence...")
return new_energy, controller.CONVERGED
diff --git a/nifty4/minimization/gradient_norm_controller.py b/nifty4/minimization/gradient_norm_controller.py
index 0da6b97ce..be94e7624 100644
--- a/nifty4/minimization/gradient_norm_controller.py
+++ b/nifty4/minimization/gradient_norm_controller.py
@@ -17,7 +17,7 @@
# and financially supported by the Studienstiftung des deutschen Volkes.
from .iteration_controller import IterationController
-from .. import dobj
+from ..logger import logger
class GradientNormController(IterationController):
@@ -116,13 +116,13 @@ class GradientNormController(IterationController):
msg += " energy={:.6E}".format(energy.value)
msg += " gradnorm={:.2E}".format(energy.gradient_norm)
msg += " clvl=" + str(self._ccount)
- dobj.mprint(msg)
+ logger.info(msg)
# self.logger.info(msg)
# Are we done?
if self._iteration_limit is not None:
if self._itcount >= self._iteration_limit:
- dobj.mprint(
+ logger.warning(
"Warning:Iteration limit reached. Assuming convergence")
return self.CONVERGED
if self._ccount >= self._convergence_level:
diff --git a/nifty4/minimization/l_bfgs.py b/nifty4/minimization/l_bfgs.py
index c7e910e34..efb7be73b 100644
--- a/nifty4/minimization/l_bfgs.py
+++ b/nifty4/minimization/l_bfgs.py
@@ -21,7 +21,7 @@ from builtins import range
import numpy as np
from .descent_minimizer import DescentMinimizer
from .line_search_strong_wolfe import LineSearchStrongWolfe
-from .. import dobj
+from ..logger import logger
class L_BFGS(DescentMinimizer):
@@ -64,7 +64,7 @@ class L_BFGS(DescentMinimizer):
idx = (k-1) % maxhist
fact = s[idx].vdot(y[idx]) / y[idx].vdot(y[idx])
if fact <= 0.:
- dobj.mprint("L-BFGS curvature not positive definite!")
+ logger.error("L-BFGS curvature not positive definite!")
p *= fact
for i in range(k-nhist, k):
idx = i % maxhist
diff --git a/nifty4/minimization/line_energy.py b/nifty4/minimization/line_energy.py
index bcf8e90cf..742f06e6c 100644
--- a/nifty4/minimization/line_energy.py
+++ b/nifty4/minimization/line_energy.py
@@ -95,7 +95,7 @@ class LineEnergy(object):
"""
res = self._energy.gradient.vdot(self._line_direction)
if abs(res.imag) / max(abs(res.real), 1.) > 1e-12:
- from ..dobj import mprint
- mprint("directional derivative has non-negligible "
- "imaginary part:", res)
+ from ..logger import logger
+ logger.warn("directional derivative has non-negligible "
+ "imaginary part:", res)
return res.real
diff --git a/nifty4/minimization/line_search_strong_wolfe.py b/nifty4/minimization/line_search_strong_wolfe.py
index 662953f81..1e0137d0e 100644
--- a/nifty4/minimization/line_search_strong_wolfe.py
+++ b/nifty4/minimization/line_search_strong_wolfe.py
@@ -21,7 +21,7 @@ from builtins import range
import numpy as np
from .line_search import LineSearch
from .line_energy import LineEnergy
-from .. import dobj
+from ..logger import logger
class LineSearchStrongWolfe(LineSearch):
@@ -100,10 +100,10 @@ class LineSearchStrongWolfe(LineSearch):
phi_0 = le_0.value
phiprime_0 = le_0.directional_derivative
if phiprime_0 == 0:
- dobj.mprint("Directional derivative is zero; assuming convergence")
+ logger.warn("Directional derivative is zero; assuming convergence")
return energy, False
if phiprime_0 > 0:
- dobj.mprint("Error: search direction is not a descent direction")
+ logger.error("Error: search direction is not a descent direction")
return energy, False
# set alphas
@@ -149,13 +149,13 @@ class LineSearchStrongWolfe(LineSearch):
# update alphas
alpha0, alpha1 = alpha1, min(2*alpha1, maxstepsize)
if alpha1 == maxstepsize:
- dobj.mprint("max step size reached")
+ logger.warn("max step size reached")
return le_alpha1.energy, False
phi_alpha0 = phi_alpha1
phiprime_alpha0 = phiprime_alpha1
- dobj.mprint("max iterations reached")
+ logger.warn("max iterations reached")
return le_alpha1.energy, False
def _zoom(self, alpha_lo, alpha_hi, phi_0, phiprime_0,
@@ -252,7 +252,7 @@ class LineSearchStrongWolfe(LineSearch):
phiprime_alphaj)
else:
- dobj.mprint("The line search algorithm (zoom) did not converge.")
+ logger.warn("The line search algorithm (zoom) did not converge.")
return le_alphaj.energy, False
def _cubicmin(self, a, fa, fpa, b, fb, c, fc):
diff --git a/nifty4/minimization/scipy_minimizer.py b/nifty4/minimization/scipy_minimizer.py
index 7948f83fc..4c94b8c41 100644
--- a/nifty4/minimization/scipy_minimizer.py
+++ b/nifty4/minimization/scipy_minimizer.py
@@ -20,6 +20,7 @@ from __future__ import division
from .minimizer import Minimizer
from ..field import Field
from .. import dobj
+from ..logger import logger
class ScipyMinimizer(Minimizer):
@@ -97,9 +98,9 @@ class ScipyMinimizer(Minimizer):
status = self._controller.check(hlp._energy)
return hlp._energy, self._controller.check(hlp._energy)
if not r.success:
- dobj.mprint("Problem in Scipy minimization:", r.message)
+ logger.error("Problem in Scipy minimization:", r.message)
else:
- dobj.mprint("Problem in Scipy minimization")
+ logger.error("Problem in Scipy minimization")
return hlp._energy, self._controller.ERROR
diff --git a/nifty4/operators/inversion_enabler.py b/nifty4/operators/inversion_enabler.py
index 2151dc90d..ea6a436c4 100644
--- a/nifty4/operators/inversion_enabler.py
+++ b/nifty4/operators/inversion_enabler.py
@@ -18,7 +18,8 @@
from ..minimization.quadratic_energy import QuadraticEnergy
from ..minimization.iteration_controller import IterationController
-from ..field import Field, dobj
+from ..field import Field
+from ..logger import logger
from .linear_operator import LinearOperator
@@ -71,5 +72,5 @@ class InversionEnabler(LinearOperator):
energy = QuadraticEnergy(A=func, b=x, position=x0)
r, stat = self._inverter(energy, preconditioner=self._preconditioner)
if stat != IterationController.CONVERGED:
- dobj.mprint("Error detected during operator inversion")
+ logger.warning("Error detected during operator inversion")
return r.position
diff --git a/nifty4/sugar.py b/nifty4/sugar.py
index 144c1952e..aa43de37f 100644
--- a/nifty4/sugar.py
+++ b/nifty4/sugar.py
@@ -23,6 +23,7 @@ from .operators.diagonal_operator import DiagonalOperator
from .operators.power_distributor import PowerDistributor
from .domain_tuple import DomainTuple
from . import dobj, utilities
+from .logger import logger
__all__ = ['PS_field',
'power_analyze',
@@ -85,7 +86,7 @@ def power_analyze(field, spaces=None, binbounds=None,
for sp in field.domain:
if not sp.harmonic and not isinstance(sp, PowerSpace):
- dobj.mprint("WARNING: Field has a space in `domain` which is "
+ logger.warn("WARNING: Field has a space in `domain` which is "
"neither harmonic nor a PowerSpace.")
spaces = utilities.parse_spaces(spaces, len(field.domain))
--
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