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dispersiveXanes
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various cahnges, worked on data saving

This commit is contained in:
Marco Cammarata 2016-11-23 14:05:38 +01:00
parent 389d8b18a6
commit 367f48eed4
3 changed files with 41 additions and 1315 deletions
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27
alignment.py
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@ -6,6 +6,7 @@ import joblib
import collections import collections
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import utils
import time import time
# /--------\ # /--------\
@ -157,14 +158,14 @@ def plotShot(im1,im2,transf1=None,transf2=None,fig=None,ax=None,res=None,E=defau
if (save is not None) and (save is not False): plt.savefig(save,transparent=True,dpi=500) if (save is not None) and (save is not False): plt.savefig(save,transparent=True,dpi=500)
return fig return fig
def plotRatios(r,fig=None,E=defaultE,save=None): def plotRatios(r,shot='random',fig=None,E=defaultE,save=None):
if fig is None: fig = plt.subplots(2,1,sharex=True)[0] if fig is None: fig = plt.subplots(2,1,sharex=True)[0]
ax = fig.axes ax = fig.axes
n = r.shape[0] n = r.shape[0]
i = ax[0].imshow(r,extent=(E[0],E[-1],0,n),**kw_2dplot) i = ax[0].imshow(r,extent=(E[0],E[-1],0,n),**kw_2dplot)
i.set_clim(0,1.2) i.set_clim(0,1.2)
idx = np.random.random_integers(0,n-1) if shot == 'random' : shot = np.random.random_integers(0,n-1)
ax[1].plot(E,r[idx],label="Shot n %d"%idx) ax[1].plot(E,r[shot],label="Shot n %d"%shot)
ax[1].plot(E,np.nanmedian(r[:10],axis=0),label="median 10 shots") ax[1].plot(E,np.nanmedian(r[:10],axis=0),label="median 10 shots")
ax[1].plot(E,np.nanmedian(r,axis=0),label="all shots") ax[1].plot(E,np.nanmedian(r,axis=0),label="all shots")
ax[1].legend() ax[1].legend()
@ -266,11 +267,13 @@ def loadAlignment(fname):
return np.load(fname).item() return np.load(fname).item()
def unravel_results(res): def unravel_results(res):
#out = dict() final_pars = dict()
#parnames = res[0].fit_result.parameters parnames = res[0].fit_result.parameters
#out["parameters"] = dict() final_pars = dict()
#for n in parnames: init_pars = dict()
# out["parameters"][n] = np.asarray( [r.final_pars[n] for r in res]) 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["ratio"] = np.asarray( [r.ratio for r in res])
#out["p1"] = np.asarray( [r.p1 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["p2"] = np.asarray( [r.p2 for r in res] )
@ -278,14 +281,14 @@ def unravel_results(res):
#out["E"] = res[0].E #out["E"] = res[0].E
return fit_ret( return fit_ret(
fit_result = [r.fit_result for r in res], fit_result = [r.fit_result for r in res],
init_pars = [r.init_pars for r in res], init_pars = init_pars,
final_pars = [r.final_pars for r in res], final_pars = final_pars,
final_transform1 = [r.final_transform1 for r in res], final_transform1 = [r.final_transform1 for r in res],
final_transform2 = [r.final_transform2 for r in res], final_transform2 = [r.final_transform2 for r in res],
im1 = np.asarray( [r.im1 for r in res]), im1 = np.asarray( [r.im1 for r in res]),
im2 = np.asarray( [r.im2 for r in res]), im2 = np.asarray( [r.im2 for r in res]),
E = defaultE, E = defaultE,
p1 = np.asarray( [r.p1 for r in res]), p1 = np.hstack( [r.p1 for r in res]),
p2 = np.asarray( [r.p2 for r in res]), p2 = np.asarray( [r.p2 for r in res]),
ratio = np.asarray( [r.ratio for r in res]), ratio = np.asarray( [r.ratio for r in res]),
fom = np.asarray( [r.fom for r in res] ), fom = np.asarray( [r.fom for r in res] ),
@ -529,7 +532,7 @@ class GuiAlignment(object):
def getAverageTransformation(out): def getAverageTransformation(out):
res = unravel_results(out) res = append_results(out)
# get average parameters # get average parameters
tx = np.median(res["transx"]) tx = np.median(res["transx"])
ty = np.median(res["transy"]) ty = np.median(res["transy"])

1293
mcutils.py
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36
xanes_analyzeRun.py
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@ -5,6 +5,7 @@ np.warnings.simplefilter('ignore')
import time import time
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import h5py import h5py
import collections
import re import re
from x3py import x3py from x3py import x3py
@ -67,7 +68,7 @@ def readDataset(fnameOrRun=7,
else: else:
if isinstance(fnameOrRun,int): if isinstance(fnameOrRun,int):
fnameOrRun=g_folder_data+"/"+g_exp+"-r%04d.h5" % fnameOrRun fnameOrRun=g_folder_data+"/"+g_exp+"-r%04d.h5" % fnameOrRun
d = x3py.Dataset(fnameOrRun,detectors=["opal0","opal1","fee_spec","opal2"]) d = x3py.Dataset(fnameOrRun,detectors=["opal0","opal1","fee_spec","opal2","ebeam"])
if g_bml == "xpp": if g_bml == "xpp":
d.spec1 = d.opal0 d.spec1 = d.opal0
d.spec2 = d.opal1 d.spec2 = d.opal1
@ -111,15 +112,18 @@ class AnalyzeRun(object):
3. an integer (to look for xppl3716_init_pars/run????_transform.npy) 3. an integer (to look for xppl3716_init_pars/run????_transform.npy)
4. a file name (that has been previosly saved with r.saveTransform(fname) 4. a file name (that has been previosly saved with r.saveTransform(fname)
""" """
self.d = readDataset(run) self.data = readDataset(run)
self.scanpos = self.data.scan.scanmotor0_values
self.nCalib = self.data.spec1.nCalib
self.nShotsPerCalib = self.data.spec1.lens
if isinstance(run,str): if isinstance(run,str):
run = int( re.search("\d{3,4}",run).group() ) run = int( re.search("\d{3,4}",run).group() )
self.run = run self.run = run
self.results = dict() self.results = collections.OrderedDict()
self.swap = (swapx,swapy) self.swap = (swapx,swapy)
#self.clearCache() #self.clearCache()
d = self.d d = self.data
self.spec1 = d.spec1 ; # spec1 is the one that is moved self.spec1 = d.spec1 ; # spec1 is the one that is moved
self.spec2 = d.spec2 ; self.spec2 = d.spec2 ;
@ -173,9 +177,10 @@ class AnalyzeRun(object):
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):
""" this is a comment """ """ this is a comment """
if initpars is None: initpars= self.initAlign if initpars is None: initpars= self.initAlign
d = self.d d = self.data
if nC is None: nC = d.opal1.nCalib if nC is None: nC = d.opal1.nCalib
shots = slice(nShotsPerCalib) shots = slice(nShotsPerCalib)
out = []
for i in range(nC): for i in range(nC):
s1,s2 = self.getShot(shots,calib=i) s1,s2 = self.getShot(shots,calib=i)
if fitEveryCalib is not False: if fitEveryCalib is not False:
@ -184,11 +189,12 @@ class AnalyzeRun(object):
idx = np.argmin( [p.fom for p in res] ) idx = np.argmin( [p.fom for p in res] )
res = res[idx] res = res[idx]
initpars = res.final_pars; self.initAlign=res.final_pars initpars = res.final_pars; self.initAlign=res.final_pars
print("Calib cycle %d -> %.3f aligned (best FOM: %.2f)" % (i,self.d.scan.scanmotor0_values[i],res.fom)) 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) ret = alignment.doShots(s1,s2,initpars=initpars,doFit=doFit)
res = alignment.unravel_results(ret) res = alignment.unravel_results(ret)
self.results[i] = res self.results[i] = res
return list(self.results.values()) out.append(res)
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"): def doShot(self,shot=0,calib=None,initpars=None,im1=None,im2=None,doFit=True,show=False,showInit=False,save=False,savePlot="auto"):
if initpars is None: initpars= self.initAlign if initpars is None: initpars= self.initAlign
@ -208,7 +214,7 @@ class AnalyzeRun(object):
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,unravel=True):
if initpars is None: initpars= self.initAlign if initpars is None: initpars= self.initAlign
d = self.d d = self.data
s1,s2 = self.getShot(shots,calib=calib) s1,s2 = self.getShot(shots,calib=calib)
ret,transformForBestFit = alignment.doShots(s1,s2,initpars=initpars,doFit=doFit,\ ret,transformForBestFit = alignment.doShots(s1,s2,initpars=initpars,doFit=doFit,\
returnBestTransform=True) returnBestTransform=True)
@ -223,6 +229,8 @@ class AnalyzeRun(object):
return ret return ret
def save(self,fname="auto",overwrite=False): def save(self,fname="auto",overwrite=False):
if len(self.results) == 0: print("self.results are empty, returning without saving")
if not os.path.isdir(g_folder_out): os.makedirs(g_folder_out)
if fname == "auto": if fname == "auto":
fname = g_folder_out+"/run%04d_analysis.h5" % self.run fname = g_folder_out+"/run%04d_analysis.h5" % self.run
if os.path.exists(fname) and not overwrite: if os.path.exists(fname) and not overwrite:
@ -233,11 +241,19 @@ class AnalyzeRun(object):
h = h5py.File(fname) h = h5py.File(fname)
h["roi1"] = (self.roi1.start,self.roi1.stop) h["roi1"] = (self.roi1.start,self.roi1.stop)
h["roi2"] = (self.roi2.start,self.roi2.stop) h["roi2"] = (self.roi2.start,self.roi2.stop)
h["scanmot0"] = self.data.scan.scanmotor0
h["scanpos0"] = self.data.scan.scanmotor0_values
if hasattr(self.data.scan,"scanmotor1"):
h["scanmot1"] = self.data.scan.scanmotor1
h["scanpos1"] = self.data.scan.scanmotor1_values
#h["transform"] = self.initAlign #h["transform"] = self.initAlign
for (c,v) in self.results.items(): for (c,v) in self.results.items():
cname = "calib%04d/" % c if isinstance(c,int) else "calib%s/" % c cname = "calib%04d/" % c if isinstance(c,int) else "calib%s/" % c
for p,vv in v.items(): for p,vv in mc.objToDict(v).items():
if p == "parameters": # cannot save in hfd5 certain python objects
if p == "fit_result" or p.find("final_transform")==0:
continue
if isinstance(vv,dict):
for pname,parray in vv.items(): for pname,parray in vv.items():
name = cname + p + "/" + pname name = cname + p + "/" + pname
h[name] = parray h[name] = parray