various cahnges, worked on data saving
This commit is contained in:
Marco Cammarata
parent
389d8b18a6
commit
367f48eed4
27
alignment.py
27
alignment.py
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@ -6,6 +6,7 @@ import joblib
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import collections
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import numpy as np
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import matplotlib.pyplot as plt
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import utils
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import time
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# /--------\
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@ -157,14 +158,14 @@ def plotShot(im1,im2,transf1=None,transf2=None,fig=None,ax=None,res=None,E=defau
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if (save is not None) and (save is not False): plt.savefig(save,transparent=True,dpi=500)
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return fig
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def plotRatios(r,fig=None,E=defaultE,save=None):
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def plotRatios(r,shot='random',fig=None,E=defaultE,save=None):
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if fig is None: fig = plt.subplots(2,1,sharex=True)[0]
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ax = fig.axes
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n = r.shape[0]
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i = ax[0].imshow(r,extent=(E[0],E[-1],0,n),**kw_2dplot)
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i.set_clim(0,1.2)
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idx = np.random.random_integers(0,n-1)
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ax[1].plot(E,r[idx],label="Shot n %d"%idx)
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if shot == 'random' : shot = np.random.random_integers(0,n-1)
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ax[1].plot(E,r[shot],label="Shot n %d"%shot)
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ax[1].plot(E,np.nanmedian(r[:10],axis=0),label="median 10 shots")
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ax[1].plot(E,np.nanmedian(r,axis=0),label="all shots")
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ax[1].legend()
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@ -266,11 +267,13 @@ def loadAlignment(fname):
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return np.load(fname).item()
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def unravel_results(res):
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#out = dict()
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#parnames = res[0].fit_result.parameters
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#out["parameters"] = dict()
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#for n in parnames:
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# out["parameters"][n] = np.asarray( [r.final_pars[n] for r in res])
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final_pars = dict()
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parnames = res[0].fit_result.parameters
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final_pars = dict()
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init_pars = dict()
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for n in parnames:
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final_pars[n] = np.asarray( [r.final_pars[n] for r in res])
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init_pars[n] = np.asarray( [r.init_pars[n] for r in res])
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#out["ratio"] = np.asarray( [r.ratio for r in res])
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#out["p1"] = np.asarray( [r.p1 for r in res] )
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#out["p2"] = np.asarray( [r.p2 for r in res] )
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@ -278,14 +281,14 @@ def unravel_results(res):
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#out["E"] = res[0].E
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return fit_ret(
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fit_result = [r.fit_result for r in res],
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init_pars = [r.init_pars for r in res],
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final_pars = [r.final_pars for r in res],
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init_pars = init_pars,
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final_pars = final_pars,
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final_transform1 = [r.final_transform1 for r in res],
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final_transform2 = [r.final_transform2 for r in res],
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im1 = np.asarray( [r.im1 for r in res]),
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im2 = np.asarray( [r.im2 for r in res]),
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E = defaultE,
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p1 = np.asarray( [r.p1 for r in res]),
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p1 = np.hstack( [r.p1 for r in res]),
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p2 = np.asarray( [r.p2 for r in res]),
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ratio = np.asarray( [r.ratio for r in res]),
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fom = np.asarray( [r.fom for r in res] ),
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@ -529,7 +532,7 @@ class GuiAlignment(object):
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def getAverageTransformation(out):
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res = unravel_results(out)
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res = append_results(out)
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# get average parameters
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tx = np.median(res["transx"])
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ty = np.median(res["transy"])
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1293
mcutils.py
1293
mcutils.py
File diff suppressed because it is too large
Load Diff
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@ -5,6 +5,7 @@ np.warnings.simplefilter('ignore')
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import time
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import matplotlib.pyplot as plt
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import h5py
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import collections
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import re
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from x3py import x3py
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@ -67,7 +68,7 @@ def readDataset(fnameOrRun=7,
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else:
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if isinstance(fnameOrRun,int):
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fnameOrRun=g_folder_data+"/"+g_exp+"-r%04d.h5" % fnameOrRun
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d = x3py.Dataset(fnameOrRun,detectors=["opal0","opal1","fee_spec","opal2"])
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d = x3py.Dataset(fnameOrRun,detectors=["opal0","opal1","fee_spec","opal2","ebeam"])
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if g_bml == "xpp":
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d.spec1 = d.opal0
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d.spec2 = d.opal1
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@ -111,15 +112,18 @@ class AnalyzeRun(object):
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3. an integer (to look for xppl3716_init_pars/run????_transform.npy)
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4. a file name (that has been previosly saved with r.saveTransform(fname)
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"""
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self.d = readDataset(run)
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self.data = readDataset(run)
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self.scanpos = self.data.scan.scanmotor0_values
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self.nCalib = self.data.spec1.nCalib
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self.nShotsPerCalib = self.data.spec1.lens
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if isinstance(run,str):
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run = int( re.search("\d{3,4}",run).group() )
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self.run = run
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self.results = dict()
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self.results = collections.OrderedDict()
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self.swap = (swapx,swapy)
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#self.clearCache()
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d = self.d
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d = self.data
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self.spec1 = d.spec1 ; # spec1 is the one that is moved
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self.spec2 = d.spec2 ;
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@ -173,9 +177,10 @@ class AnalyzeRun(object):
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def analyzeScan(self,initpars=None,nShotsPerCalib=20,nC=None,doFit=False,fitEveryCalib=False):
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""" this is a comment """
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if initpars is None: initpars= self.initAlign
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d = self.d
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d = self.data
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if nC is None: nC = d.opal1.nCalib
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shots = slice(nShotsPerCalib)
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out = []
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for i in range(nC):
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s1,s2 = self.getShot(shots,calib=i)
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if fitEveryCalib is not False:
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@ -184,11 +189,12 @@ class AnalyzeRun(object):
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idx = np.argmin( [p.fom for p in res] )
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res = res[idx]
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initpars = res.final_pars; self.initAlign=res.final_pars
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print("Calib cycle %d -> %.3f aligned (best FOM: %.2f)" % (i,self.d.scan.scanmotor0_values[i],res.fom))
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print("Calib cycle %d -> %.3f aligned (best FOM: %.2f)" % (i,self.data.scan.scanmotor0_values[i],res.fom))
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ret = alignment.doShots(s1,s2,initpars=initpars,doFit=doFit)
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res = alignment.unravel_results(ret)
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self.results[i] = res
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return list(self.results.values())
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out.append(res)
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return out
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def doShot(self,shot=0,calib=None,initpars=None,im1=None,im2=None,doFit=True,show=False,showInit=False,save=False,savePlot="auto"):
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if initpars is None: initpars= self.initAlign
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@ -208,7 +214,7 @@ class AnalyzeRun(object):
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def doShots(self,shots=slice(0,50),calib=None,initpars=None,doFit=False,returnBestTransform=False,unravel=True):
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if initpars is None: initpars= self.initAlign
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d = self.d
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d = self.data
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s1,s2 = self.getShot(shots,calib=calib)
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ret,transformForBestFit = alignment.doShots(s1,s2,initpars=initpars,doFit=doFit,\
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returnBestTransform=True)
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@ -223,6 +229,8 @@ class AnalyzeRun(object):
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return ret
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def save(self,fname="auto",overwrite=False):
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if len(self.results) == 0: print("self.results are empty, returning without saving")
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if not os.path.isdir(g_folder_out): os.makedirs(g_folder_out)
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if fname == "auto":
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fname = g_folder_out+"/run%04d_analysis.h5" % self.run
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if os.path.exists(fname) and not overwrite:
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@ -233,11 +241,19 @@ class AnalyzeRun(object):
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h = h5py.File(fname)
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h["roi1"] = (self.roi1.start,self.roi1.stop)
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h["roi2"] = (self.roi2.start,self.roi2.stop)
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h["scanmot0"] = self.data.scan.scanmotor0
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h["scanpos0"] = self.data.scan.scanmotor0_values
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if hasattr(self.data.scan,"scanmotor1"):
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h["scanmot1"] = self.data.scan.scanmotor1
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h["scanpos1"] = self.data.scan.scanmotor1_values
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#h["transform"] = self.initAlign
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for (c,v) in self.results.items():
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cname = "calib%04d/" % c if isinstance(c,int) else "calib%s/" % c
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for p,vv in v.items():
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if p == "parameters":
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for p,vv in mc.objToDict(v).items():
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# cannot save in hfd5 certain python objects
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if p == "fit_result" or p.find("final_transform")==0:
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continue
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if isinstance(vv,dict):
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for pname,parray in vv.items():
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name = cname + p + "/" + pname
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h[name] = parray
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