forked from pylhc/Python_Classes4MAD
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BCORR.py
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BCORR.py
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r"""
.. module: Python_Classes4MAD.BCORR
Created on ??
set of python routines:
1. best corrector for beta-beat (July 28, 2008)
2. Best N Corrector ~ like micado
3. changeparams contains all corrs, even if zero value
Both Numeric + Numpy should work --> Not true anymore. Changed everything to numpy (vimaier)
.. moduleauthor:: Unknown
"""
import datetime
import time
import os
import numpy as np
from numpy import dot as matrixmultiply
from numpy.linalg import pinv as generalized_inverse
default = 1
def wrtparOLD(dfam, app=0, path="./"):
if (app == 0):
mode = 'w'
if (app == 1):
mode = 'a'
a = datetime.datetime.fromtimestamp(time.time())
g = open (path+'changeparameters', mode)
f = open (path+'changeparameters.tfs', mode)
print >> f, "@", "APP", "%le", app
print >> f, "@", "PATH", "%s", path
print >> f, "@", "DATE", "%s", a.ctime()
print >> f, "*", "NAME ", " DELTA"
print >> f, "$", "%s ", " %le"
if default == 0:
items = sorted(dfam.iteritems(), key=lambda (k, v):(np.abs(v), k), reverse=True)
if default == 1:
items = [(k, v) for (k, v) in dfam.items()]
for k, v in items:
g.write("%12s = %12s + ( %e );\n" % (k, k, v))
f.write("%12s %e\n" % (k, v))
g.close()
f.close()
def wrtpar(corr, dfam, app=0, path="./"):
if (app == 0):
mode = 'w'
if (app == 1):
mode = 'a'
a = datetime.datetime.fromtimestamp(time.time())
g = open( os.path.join(path, "changeparameters"), mode )
f = open( os.path.join(path, "changeparameters.tfs"), mode )
print >> f, "@", "APP", "%le", app
print >> f, "@", "PATH", "%s", path
print >> f, "@", "DATE", "%s", a.ctime()
print >> f, "*", "NAME ", " DELTA"
print >> f, "$", "%s ", " %le"
for k in corr:
if k in dfam.keys():
g.write("%12s = %12s + ( %e );\n" % (k, k, dfam[k]))
#--- note minus sign for change.tfs to do corr
f.write("%12s %e\n" % (k, -dfam[k]))
else:
f.write("%12s %e\n" % (k, 0.0))
g.close()
f.close()
def calcRMS(R):
rms = np.sqrt( np.sum(R**2) / len(R) )
ptp = np.max(R) - np.min(R)
return rms, ptp
def calcRMSNumeric(R):
rms = np.sqrt( sum(R**2) / len(R) )
ptp = max(R)-min(R)
return rms, ptp
def sortDict(adict):
sorted_list = sorted(adict.items(), key=lambda (k, v): (v, k))
for item in sorted_list:
print item[0], ":", item[1]
def bCorr(X, Y, DX, beat_input, cut=0.001, app=0, path="./"):
R = np.transpose(beat_input.sensitivity_matrix)
b = beat_input.computevectorEXP(X, Y, DX) - beat_input.zerovector
corr = beat_input.varslist
m, n = np.shape(R)
if len(b) == m and len(corr) == n:
rms, ptop = calcRMS(b)
inva = np.linalg.pinv(R)
print "initial {RMS, Peak}: { %e , %e } mm" % (rms, ptop)
print "finding best over", n, "correctors"
for i in range(n):
dStren = np.dot(inva[i, :], b)
bvec = b - matrixmultiply(R[:, i], dStren)
rm, ptp = calcRMS(bvec)
if rm < rms:
rms = rm
rbest = i
rStren = dStren
if ptp < ptop:
ptop = ptp
pbest = i
pStren = dStren
print "final {RMS, Peak}: { %e , %e }" % (rms, ptop)
if rbest == pbest:
print "best corr:", corr[rbest], '%e'% (rStren), "1/m"
else:
print "--- warning: best corr for rms & peak are not same"
print "RMS best corr:", corr[rbest], '%e' % (rStren), "1/m"
print "Peak best corr:", corr[pbest], '%e' % (pStren), "1/m"
else:
print "dimensional mismatch in input variables"
def bCorrNumeric(X, Y, DX, beat_input, cut=0.001, app=0, path="./"):
R = np.transpose(beat_input.sensitivity_matrix)
b = beat_input.computevectorEXP(X, Y, DX) - beat_input.zerovector
corr = beat_input.varslist
m, n = np.shape(R)
if len(b) == m and len(corr) == n:
rms, ptop = calcRMSNumeric(b)
inva = generalized_inverse(R, cut)
print "initial {RMS, Peak}: { %e , %e } mm" % (rms, ptop)
print "finding best over", n, "correctors"
for i in range(n):
dStren = matrixmultiply(inva[i, :], b)
bvec = b - matrixmultiply(R[:, i], dStren)
rm, ptp = calcRMSNumeric(bvec)
if rm < rms:
rms = rm
rbest = i
rStren = dStren
if ptp < ptop:
ptop = ptp
pbest = i
pStren = dStren
print "final {RMS, Peak}: { %e , %e }" % (rms, ptop)
if rbest == pbest:
print "best corr:", corr[rbest], '%e' % (rStren), "1/m"
else:
print "--- warning: best corr for rms & peak are not same"
print "RMS best corr:", corr[rbest], '%e' % (rStren), "1/m"
print "Peak best corr:", corr[pbest], '%e' % (pStren), "1/m"
else:
print "dimensional mismatch in input variables"
def bNCorr(X, Y, DX, beat_input, cut=0.001, ncorr=3, app=0, tol=1e-9, path="./"):
R = np.transpose(beat_input.sensitivity_matrix)
n = np.shape(R)[1]
b = beat_input.computevectorEXP(X, Y, DX) - beat_input.zerovector
corr = beat_input.varslist
inva = np.linalg.pinv(R, cut)
RHO2 = {}
rmss, ptopp = calcRMS(b)
for ITER in range(ncorr):
for j in range(n):
if j not in RHO2:
RHO = [k for k in RHO2.keys()]
RHO.append(j)
RR = np.take(R, RHO, 1)
invaa = np.take(inva, RHO, 0)
dStren = matrixmultiply(invaa, b)
bvec = b - matrixmultiply(RR, dStren)
rm, ptp = calcRMS(bvec)
if rm < rmss:
rmss = rm
ptopp = ptp
rbest = j
rStren = dStren
print 'ITER:', ITER+1, ' RMS,PK2PK:', rmss, ptopp
RHO2[rbest] = (rmss, ptopp)
if (rm < tol):
print "RMS converged with", ITER, "correctors"
break
if (rm < rmss):
print "stopped after", ITER, "correctors"
break
itr = 0
RHO3 = {} #-- make dict RHO3={corr:strength,...}
for j in RHO2:
RHO3[corr[j]] = rStren[itr]
itr += 1
print '\n', sortDict(RHO3)
wrtpar(corr, RHO3, app, path)
return RHO3
def itrSVD(X, Y, DX, beat_input, cut=0.001, num_iter=1, app=0, tol=1e-9, path="./"):
R = np.transpose(beat_input.sensitivity_matrix)
b = beat_input.computevectorEXP(X, Y, DX) - beat_input.zerovector
corr = beat_input.varslist
inva = generalized_inverse(R, cut)
rmss, ptopp = calcRMSNumeric(b)
RHO2 = {}
dStren = np.zeros(len(corr))
print 'Initial Phase-Beat:', '{RMS,PK2PK}', rmss, ptopp
for ITER in range(num_iter): # @UnusedVariable
dStren = dStren + matrixmultiply(inva, b)
bvec = b - matrixmultiply(R, dStren)
rm, ptp = calcRMSNumeric(bvec)
print 'ITER', num_iter, '{RMS,PK2PK}', rm, ptp
for j in range(len(corr)):
RHO2[corr[j]] = dStren[j]
wrtpar(corr, RHO2, app, path)
def bNCorrNumeric(X, Y, DX, beat_input, cut=0.001, ncorr=3, app=0, tol=1e-9, path="./", beta_x=None, beta_y=None):
R = np.transpose(beat_input.sensitivity_matrix)
n = np.shape(R)[1]
b = beat_input.computevectorEXP(X, Y, DX, beta_x, beta_y) - beat_input.zerovector
corr = beat_input.varslist
inva = generalized_inverse(R, cut)
RHO2 = {}
rmss, ptopp = calcRMSNumeric(b)
print 'Initial Phase-beat {RMS,PK2PK}:', rmss, ptopp
for ITER in range(ncorr):
for j in range(n):
if j not in RHO2:
RHO = [k for k in RHO2.keys()]
RHO.append(j)
RR = np.take(R, RHO, 1)
invaa = np.take(inva, RHO, 0)
dStren = matrixmultiply(invaa, b)
#--- calculate residual due to 1..nth corrector
bvec = b - matrixmultiply(RR, dStren)
rm, ptp = calcRMSNumeric(bvec)
if rm < rmss:
rmss = rm
ptopp = ptp
rbest = j
rStren = dStren
print 'ITER:', ITER+1, ' RMS,PK2PK:', rmss, ptopp
RHO2[rbest] = (rmss, ptopp)
if (rm < tol):
print "RMS converged with", ITER, "correctors"
break
if (rm < rmss):
print "stopped after", ITER, "correctors"
break
itr = 0
RHO3 = {} #-- make dict RHO3={corr:strength,...}
for j in RHO2:
RHO3[corr[j]] = rStren[itr]
itr += 1
print '\n', sortDict(RHO3)
wrtpar(corr, RHO3, app, path)
return RHO3
def bNCorrNumericSim(a, beat_input, cut=0.1, ncorr=3, app=0, tol=1e-9, path="./"):
R = np.transpose(beat_input.sensitivity_matrix)
n = np.shape(R)[1]
b = beat_input.computevector(a) - beat_input.zerovector
corr = beat_input.varslist
inva = generalized_inverse(R, cut)
RHO2 = {}
rmss, ptopp = calcRMSNumeric(b)
for ITER in range(ncorr):
for j in range(n):
if j not in RHO2:
RHO = [k for k in RHO2.keys()]
RHO.append(j)
RR = np.take(R, RHO, 1)
invaa = np.take(inva, RHO, 0)
dStren = matrixmultiply(invaa, b)
bvec = b - matrixmultiply(RR, dStren)
rm, ptp = calcRMSNumeric(bvec)
if rm < rmss:
rmss = rm
ptopp = ptp
rbest = j
rStren = dStren
print 'ITER:', ITER+1, ' RMS,PK2PK:', rmss, ptopp
RHO2[rbest] = (rmss, ptopp)
if (rm < tol):
print "RMS converged with", ITER, "correctors"
break
if (rm < rmss):
print "stopped after", ITER, "correctors"
break
itr = 0
RHO3 = {} #-- make dict RHO3={corr:strength,...}
for j in RHO2:
RHO3[corr[j]] = rStren[itr]
itr += 1
print RHO3
wrtpar(corr, RHO3, app, path)
return RHO3