mcammara
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dispersiveXanes
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various changes

This commit is contained in:
Marco Cammarata 2016-11-23 17:16:39 +01:00
parent 277741ef39
commit b0914f15f8
3 changed files with 54 additions and 43 deletions
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52
alignment.py
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@ -6,7 +6,6 @@ import joblib
import collections
import numpy as np
import matplotlib.pyplot as plt
import utils
import time
# /--------\
@ -79,7 +78,7 @@ kw_2dplot = dict(
)
fit_ret = collections.namedtuple("fit_ret",["fit_result","init_pars","final_pars",\
"final_transform1","final_transform2","im1","im2","E","p1","p2","fom","ratio","tneeded"] )
"final_transform1","final_transform2","im1","im2","E","p1","p1_sum","p2","p2_sum","fom","ratio","tneeded"] )
def calcFOM(p1,p2,ratio):
idx = ( p1>p1.max()/10 ) & (p2>p2.max()/10)
@ -266,33 +265,39 @@ def saveAlignment(fname,transform,roi1,roi2):
def loadAlignment(fname):
return np.load(fname).item()
def unravel_results(res):
def unravel_results(res,nSaveImg='all'):
final_pars = dict()
# res[0].fit_result is a list if we are trying to unravel retult from getShots
if isinstance(res[0].fit_result,list):
parnames = res[0].fit_result[0].parameters
else:
parnames = res[0].fit_result.parameters
final_pars = dict()
init_pars = dict()
for n in parnames:
final_pars[n] = np.asarray( [r.final_pars[n] for r in res])
init_pars[n] = np.asarray( [r.init_pars[n] for r in res])
#out["ratio"] = np.asarray( [r.ratio for r in res])
#out["p1"] = np.asarray( [r.p1 for r in res] )
#out["p2"] = np.asarray( [r.p2 for r in res] )
#out["fom"] = np.asarray( [r.fom for r in res] )
#out["E"] = res[0].E
final_pars[n] = np.hstack( [r.final_pars[n] for r in res])
init_pars[n] = np.hstack( [r.init_pars[n] for r in res])
im1 = np.asarray( [r.im1 for r in res])
im2 = np.asarray( [r.im2 for r in res])
if nSaveImg != 'all':
im1 = im1[:nSaveImg]
im2 = im2[:nSaveImg]
return fit_ret(
fit_result = [r.fit_result for r in res],
init_pars = init_pars,
final_pars = final_pars,
final_transform1 = [r.final_transform1 for r in res],
final_transform2 = [r.final_transform2 for r in res],
im1 = np.asarray( [r.im1 for r in res]),
im2 = np.asarray( [r.im2 for r in res]),
im1 = im1,
im2 = im2,
E = defaultE,
p1 = np.hstack( [r.p1 for r in res]),
p2 = np.asarray( [r.p2 for r in res]),
ratio = np.asarray( [r.ratio for r in res]),
fom = np.asarray( [r.fom for r in res] ),
tneeded = np.asarray( [r.tneeded for r in res])
p1 = np.vstack( [r.p1 for r in res]),
p1_sum = np.hstack( [r.p1_sum for r in res]),
p2 = np.vstack( [r.p2 for r in res]),
p2_sum = np.hstack( [r.p2_sum for r in res]),
ratio = np.vstack( [r.ratio for r in res]),
fom = np.hstack( [r.fom for r in res] ),
tneeded = np.hstack( [r.tneeded for r in res])
)
@ -443,7 +448,9 @@ def transformIminuit(im1,im2,init_transform=dict(),show=False,verbose=True,zeroT
im2 = i2,
E = defaultE,
p1 = p1,
p1_sum = p1.sum(),
p2 = p2,
p2_sum = p2.sum(),
ratio = r,
fom = fom,
tneeded = time.time()-t0
@ -567,17 +574,18 @@ def doShot(i1,i2,init_pars,doFit=True,show=False):
return r
def doShots(imgs1,imgs2,initpars,nJobs=16,doFit=False,returnBestTransform=False):
def doShots(imgs1,imgs2,initpars,nJobs=16,doFit=False,returnBestTransform=False,nSaveImg='all'):
clearCache()
N = imgs1.shape[0]
pool = joblib.Parallel(backend="threading",n_jobs=nJobs) \
(joblib.delayed(doShot)(imgs1[i],imgs2[i],initpars,doFit=doFit) for i in range(N))
res = unravel_results(pool,nSaveImg=nSaveImg)
if returnBestTransform:
idx = np.argmin( np.abs([p.fom for p in pool]) ); # abs because sometime fit screws up the give negative spectra...
print("FOM for best alignment %.2f"%pool[idx].fom)
return pool,pool[idx].final_transform1
idx = np.argmin( res.fom );
print("FOM for best alignment %.2f"%res.fom[fom])
return res,res.final_transform1[idx]
else:
return pool
return res
# out = collections.OrderedDict( enumerate(pool) )
# return out

1
x3py_config
View File

@ -1 +1,2 @@
#cachepath = "/reg/d/psdm/xpp/xppl3716/scratch/mc/cache"
readlimit_GB = 6

42
xanes_analyzeRun.py
View File

@ -174,26 +174,25 @@ class AnalyzeRun(object):
gui.save(fname)
def analyzeScan(self,initpars=None,nShotsPerCalib=20,nC=None,doFit=False,fitEveryCalib=False):
def analyzeScan(self,initpars=None,nShotsPerCalib=20,nC=None,doFit=False,fitEveryCalib=False,nSaveImg=5):
""" this is a comment """
if initpars is None: initpars= self.initAlign
d = self.data
if nC is None: nC = d.opal1.nCalib
shots = slice(nShotsPerCalib)
if nC is None: nC = self.nCalib
out = []
for i in range(nC):
if nShotsPerCalib == 'all':
shots = slice(self.nShotsPerCalib[i])
else:
shots = slice(nShotsPerCalib)
s1,s2 = self.getShot(shots,calib=i)
if fitEveryCalib is not False:
res = alignment.doShots(s1[:fitEveryCalib],s2[:fitEveryCalib],doFit=True,\
initpars=initpars); #
idx = np.argmin( [p.fom for p in res] )
res = res[idx]
initpars = res.final_pars; self.initAlign=res.final_pars
print("Calib cycle %d -> %.3f aligned (best FOM: %.2f)" % (i,self.data.scan.scanmotor0_values[i],res.fom))
ret = alignment.doShots(s1,s2,initpars=initpars,doFit=doFit)
res = alignment.unravel_results(ret)
self.results[i] = res
out.append(res)
ret,bestTransf = alignment.doShots(s1[:fitEveryCalib],s2[:fitEveryCalib],doFit=True,\
initpars=initpars,nSaveImg=nSaveImg,returnBestTransform=True);
initpars = bestTransf; self.initAlign=bestTransf
ret = alignment.doShots(s1,s2,initpars=initpars,doFit=doFit,nSaveImg=nSaveImg)
self.results[i] = ret
print("Calib cycle %d -> %.3f (best FOM: %.2f)" % (i,self.scanpos[i],np.min(ret.fom)))
out.append(ret)
return out
def doShot(self,shot=0,calib=None,initpars=None,im1=None,im2=None,doFit=True,show=False,showInit=False,save=False,savePlot="auto"):
@ -212,17 +211,20 @@ class AnalyzeRun(object):
if save: self.saveTransform()
return r
def doShots(self,shots=slice(0,50),calib=None,initpars=None,doFit=False,returnBestTransform=False,unravel=True):
def doShots(self,shots=slice(0,50),calib=None,initpars=None,doFit=False,returnBestTransform=False,nSaveImg='all'):
"""
shots : slice to define shots to read, use 'all' for all shots in calibcycle
nSaveImg : save saveImg images in memory (self.results), use 'all' for all
useful for decreasing memory footprint
"""
if initpars is None: initpars= self.initAlign
d = self.data
if shots == "all": shots = slice(self.nShotsPerCalib[calib])
s1,s2 = self.getShot(shots,calib=calib)
ret,transformForBestFit = alignment.doShots(s1,s2,initpars=initpars,doFit=doFit,\
returnBestTransform=True)
returnBestTransform=True,nSaveImg=nSaveImg)
if doFit: self.initAlign = transformForBestFit
ret_unravel = alignment.unravel_results(ret)
# keep it for later !
self.results[calib] = ret_unravel
if unravel: ret = ret_unravel
self.results[calib] = ret
if returnBestTransform:
return ret,transformForBestFit
else: