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Commit f0c72624 authored by Marco Selig's avatar Marco Selig
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imports cleaned up; minimizer retrun convergence level.

parent d2820f14
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Showing with 27 additions and 27 deletions
__init__.py
View file @ f0c72624
......@@ -23,6 +23,7 @@ from __future__ import division
from nifty_core import *
from nifty_cmaps import *
from nifty_power import *
#from nifty_tools impoert *
......
demos/demo_excaliwir.py
View file @ f0c72624
......@@ -33,8 +33,6 @@
"""
from __future__ import division
from nifty import *
from nifty.nifty_cmaps import *
from nifty.nifty_power import *
from scipy.sparse.linalg import LinearOperator as lo
from scipy.sparse.linalg import cg
......
demos/demo_faraday.py
View file @ f0c72624
......@@ -39,7 +39,6 @@
"""
from __future__ import division
from nifty import *
from nifty.nifty_cmaps import *
about.warnings.off()
......
nifty_power.py
View file @ f0c72624
......@@ -43,7 +43,7 @@
from __future__ import division
from scipy.interpolate import interp1d as ip ## conflicts with sphinx's autodoc
#import numpy as np
from nifty.nifty_core import *
from nifty_core import *
import smoothing as gs
......
nifty_tools.py
View file @ f0c72624
......@@ -35,7 +35,7 @@
"""
from __future__ import division
#import numpy as np
from nifty.nifty_core import *
from nifty_core import *
##-----------------------------------------------------------------------------
......@@ -191,9 +191,9 @@ class invertible_operator(operator):
A = self._inverse_multiply
else:
A = self.inverse_times
x_,converged = conjugate_gradient(A,x_,W=W,spam=spam,reset=reset,note=note)(x0=x0,tol=tol,clevel=clevel,limii=limii)
x_,convergence = conjugate_gradient(A,x_,W=W,spam=spam,reset=reset,note=note)(x0=x0,tol=tol,clevel=clevel,limii=limii)
## evaluate
if(not converged):
if(not convergence):
if(not force):
return None
about.warnings.cprint("WARNING: conjugate gradient failed.")
......@@ -257,9 +257,9 @@ class invertible_operator(operator):
A = self._multiply
else:
A = self.times
x_,converged = conjugate_gradient(A,x_,W=W,spam=spam,reset=reset,note=note)(x0=x0,tol=tol,clevel=clevel,limii=limii)
x_,convergence = conjugate_gradient(A,x_,W=W,spam=spam,reset=reset,note=note)(x0=x0,tol=tol,clevel=clevel,limii=limii)
## evaluate
if(not converged):
if(not convergence):
if(not force):
return None
about.warnings.cprint("WARNING: conjugate gradient failed.")
......@@ -517,9 +517,9 @@ class propagator_operator(operator):
A = self._inverse_multiply_1
else:
A = self._inverse_multiply_2
x_,converged = conjugate_gradient(A,x_,W=W,spam=spam,reset=reset,note=note)(x0=x0,tol=tol,clevel=clevel,limii=limii)
x_,convergence = conjugate_gradient(A,x_,W=W,spam=spam,reset=reset,note=note)(x0=x0,tol=tol,clevel=clevel,limii=limii)
## evaluate
if(not converged):
if(not convergence):
if(not force):
return None
about.warnings.cprint("WARNING: conjugate gradient failed.")
......@@ -600,8 +600,8 @@ class conjugate_gradient(object):
compared to the tolerance, and the convergence level if changed.
The minimizer will exit in two states: QUIT if the maximum number of
iterations is reached, or DONE if convergence is achieved. Returned
will be the latest `x` and a Boolean indicating convergence, which can
be ``True`` for all exit states.
will be the latest `x` and the latest convergence level, which can
evaluate ``True`` for all exit states.
References
----------
......@@ -613,14 +613,14 @@ class conjugate_gradient(object):
--------
>>> b = field(point_space(2), val=[1, 9])
>>> A = diagonal_operator(b.domain, diag=[4, 3])
>>> x,converged = conjugate_gradient(A, b, note=True)(tol=1E-4, clevel=3)
>>> x,convergence = conjugate_gradient(A, b, note=True)(tol=1E-4, clevel=3)
iteration : 00000001 alpha = 3.3E-01 beta = 1.3E-03 delta = 3.6E-02
iteration : 00000002 alpha = 2.5E-01 beta = 7.6E-04 delta = 1.0E-03
iteration : 00000003 alpha = 3.3E-01 beta = 2.5E-04 delta = 1.6E-05 convergence level : 1
iteration : 00000004 alpha = 2.5E-01 beta = 1.8E-06 delta = 2.1E-08 convergence level : 2
iteration : 00000005 alpha = 2.5E-01 beta = 2.2E-03 delta = 1.0E-09 convergence level : 3
... done.
>>> print converged
>>> bool(convergence)
True
>>> x.val # yields 1/4 and 9/3
array([ 0.25, 3. ])
......@@ -711,8 +711,9 @@ class conjugate_gradient(object):
-------
x : field
Latest `x` of the minimization.
converged : bool
Indicates whether the minimization has converged or not.
convergence : integer
Latest convergence level indicating whether the minimization
has converged or not.
"""
self.x = field(self.b.domain,val=x0,target=self.b.target)
......@@ -754,7 +755,7 @@ class conjugate_gradient(object):
self.note.cprint("\n... quit.")
break
if(gamma==0):
convergence = clevel
convergence = clevel+1
self.note.cprint(" convergence level : INF\n... done.")
break
elif(np.absolute(delta)<tol):
......@@ -774,7 +775,7 @@ class conjugate_gradient(object):
if(self.spam is not None):
self.spam(self.x,ii)
return self.x,bool(convergence)
return self.x,convergence
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
......@@ -830,7 +831,7 @@ class conjugate_gradient(object):
if(self.spam is not None):
self.spam(self.x,ii)
return self.x,bool(convergence)
return self.x,convergence
##=============================================================================
......@@ -881,8 +882,8 @@ class steepest_descent(object):
the convergence level if changed. The minimizer will exit in three
states: DEAD if no step width above 1E-13 is accepted, QUIT if the
maximum number of iterations is reached, or DONE if convergence is
achieved. Returned will be the latest `x` and a Boolean indicating
convergence, which can be ``True`` for all exit states.
achieved. Returned will be the latest `x` and the latest convergence
level, which can evaluate ``True`` for all exit states.
References
----------
......@@ -897,7 +898,7 @@ class steepest_descent(object):
... g = x # gradient
... return E,g
>>> x = field(point_space(2), val=[1, 3])
>>> x,converged = steepest_descent(egg, note=True)(x0=x, tol=1E-4, clevel=3)
>>> x,convergence = steepest_descent(egg, note=True)(x0=x, tol=1E-4, clevel=3)
iteration : 00000001 alpha = 1.0E+00 delta = 6.5E-01
iteration : 00000002 alpha = 2.0E+00 delta = 1.4E-01
iteration : 00000003 alpha = 1.6E-01 delta = 2.1E-03
......@@ -906,7 +907,7 @@ class steepest_descent(object):
iteration : 00000006 alpha = 8.2E-05 delta = 4.4E-06 convergence level : 2
iteration : 00000007 alpha = 6.6E-06 delta = 3.1E-06 convergence level : 3
... done.
>>> print converged
>>> bool(convergence)
True
>>> x.val # approximately zero
array([ -6.87299426e-07 -2.06189828e-06])
......@@ -985,8 +986,9 @@ class steepest_descent(object):
-------
x : field
Latest `x` of the minimization.
converged : bool
Indicates whether the minimization has converged or not.
convergence : integer
Latest convergence level indicating whether the minimization
has converged or not.
"""
if(not isinstance(x0,field)):
......@@ -1031,7 +1033,7 @@ class steepest_descent(object):
if(self.spam is not None):
self.spam(self.x,ii)
return self.x,bool(convergence)
return self.x,convergence
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
......
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