changes to make the script marion's compatible + minor tweaks
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Marco Cammarata
parent
c8e1cb1a29
commit
9df711a3de
72
alignment.py
72
alignment.py
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@ -56,7 +56,12 @@ g_fit_default_kw = dict(
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iblur1 = 0,
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limit_iblur1 = (0,20),
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error_iblur1 = 0.02,
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fix_iblur1 = True
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fix_iblur1 = True,
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iblur2 = 0,
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limit_iblur2 = (0,20),
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error_iblur2 = 0.02,
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fix_iblur2 = True
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)
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@ -65,15 +70,15 @@ def rebin1D(a,shape):
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sh = shape,n0
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return a[:n0*shape].reshape(sh).mean(1)
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cmap = plt.cm.viridis if hasattr(plt.cm,"viridis") else plt.cm.gray
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kw_2dplot = dict(
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interpolation = "none",
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aspect = "auto",
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cmap = plt.cm.viridis
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cmap = cmap
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)
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fit_ret = collections.namedtuple("fit_ret",["fit_result","init_pars","final_pars",\
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"final_transform1","final_transform2","im1","im2","p1","p2","fom","ratio","tneeded"] )
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"final_transform1","final_transform2","im1","im2","E","p1","p2","fom","ratio","tneeded"] )
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def calcFOM(p1,p2,ratio):
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idx = ( p1>p1.max()/10 ) & (p2>p2.max()/10)
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@ -131,9 +136,9 @@ def plotShot(im1,im2,transf1=None,transf2=None,fig=None,ax=None,res=None,E=defau
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ax = fig.axes
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if E is None: E=np.arange(im1.shape[1])
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n = im1.shape[0]
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ax[0].imshow(im1,**kw_2dplot,extent=(E[0],E[-1],0,n))
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ax[1].imshow(im2,**kw_2dplot,extent=(E[0],E[-1],0,n))
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ax[2].imshow(im1-im2,**kw_2dplot,extent=(E[0],E[-1],0,n))
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ax[0].imshow(im1,extent=(E[0],E[-1],0,n),**kw_2dplot)
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ax[1].imshow(im2,extent=(E[0],E[-1],0,n),**kw_2dplot)
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ax[2].imshow(im1-im2,extent=(E[0],E[-1],0,n),**kw_2dplot)
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if res is None:
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p1 = np.nansum(im1,axis=0)
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p2 = np.nansum(im2,axis=0)
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@ -156,7 +161,7 @@ def plotRatios(r,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,**kw_2dplot,extent=(E[0],E[-1],0,n))
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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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@ -260,17 +265,33 @@ def saveAlignment(fname,transform,roi1,roi2):
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def loadAlignment(fname):
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return np.load(fname).item()
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def unravel_results(res,getBest=False):
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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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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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out["fom"] = np.asarray( [r.fom for r in res] )
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return out
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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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#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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#out["fom"] = np.asarray( [r.fom for r in 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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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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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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tneeded = np.asarray( [r.tneeded for r in res])
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)
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def getTransform(translation=(0,0),scale=(1,1),rotation=0,shear=0):
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t= tf.AffineTransform(scale=scale,rotation=rotation,shear=shear,\
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@ -317,33 +338,33 @@ def transformIminuit(im1,im2,init_transform=dict(),show=False,verbose=True,zeroT
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def transforms(intensity,
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igauss1cen,igauss1sig,iblur1,
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scalex,scaley,rotation,transx,transy,shear,
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igauss2cen,igauss2sig):
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igauss2cen,igauss2sig,iblur2):
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t1 = SpecrometerTransformation(translation=(transx,transy),scale=(scalex,scaley),\
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rotation=rotation,shear=shear,intensity=intensity,igauss=(igauss1cen,igauss1sig),\
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iblur=iblur1)
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t2 = SpecrometerTransformation(igauss=(igauss2cen,igauss2sig))
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t2 = SpecrometerTransformation(igauss=(igauss2cen,igauss2sig),iblur=iblur2)
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return t1,t2
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def model(intensity,
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igauss1cen,igauss1sig,iblur1,
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scalex,scaley,rotation,transx,transy,shear,
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igauss2cen,igauss2sig):
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igauss2cen,igauss2sig,iblur2):
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t1,t2 = transforms(intensity,
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igauss1cen,igauss1sig,iblur1,
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scalex,scaley,rotation,transx,transy,shear,
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igauss2cen,igauss2sig)
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igauss2cen,igauss2sig,iblur2)
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return t1.transformImage(im1_toFit),t2.transformImage(im2_toFit)
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def chi2(intensity,
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igauss1cen,igauss1sig,iblur1,
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scalex,scaley,rotation,transx,transy,shear,
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igauss2cen,igauss2sig):
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igauss2cen,igauss2sig,iblur2):
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i1,i2 = model(intensity, \
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igauss1cen,igauss1sig,iblur1, \
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scalex,scaley,rotation,transx,transy,shear, \
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igauss2cen,igauss2sig)
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igauss2cen,igauss2sig,iblur2)
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d = (i1-i2)/err
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return np.sum(d*d)
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@ -417,6 +438,7 @@ def transformIminuit(im1,im2,init_transform=dict(),show=False,verbose=True,zeroT
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final_transform2 = t2,
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im1 = i1,
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im2 = i2,
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E = defaultE,
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p1 = p1,
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p2 = p2,
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ratio = r,
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@ -11,10 +11,11 @@ from x3py import x3py
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import alignment
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import mcutils as mc
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kw_2dplot = dict(
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cmap = plt.cm.viridis if hasattr(plt.cm,"viridis") else plt.cm.gray
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kw_2dplot = dict(
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interpolation = "none",
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aspect = "auto",
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cmap = plt.cm.viridis
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cmap = cmap
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)
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@ -27,10 +28,13 @@ x3py.config.updateBeamline(g_bml)
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g_folder_init = g_exp+"_init_pars/"
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g_folder_out = g_exp+"_output/"
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g_folder_data = "/reg/d/psdm/"+g_bml+"/"+ g_exp +"/hdf5/"
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import socket
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hostname = socket.gethostname()
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if hostname == "x1":
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g_folder_data = "/home/marco/temp"
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if hostname == "apcluster0":
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g_folder_data = "/data/marcoc/singleShotXanes/"+ g_exp +"/hdf5/"
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# set defaults based on experiment
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if g_bml == "xpp":
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@ -95,12 +99,17 @@ def showShots(im1,im2):
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for a,p1,p2 in zip(ax.T,im1,im2):
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a[0].plot(p1)
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a[1].plot(p2)
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class AnalyzeRun(object):
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def __init__(self,run,initAlign="auto",swapx=g_swapx,swapy=g_swapy):
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""" swapx → swap x axis of first spectrometer
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swapy → swap y axis of first spectrometer
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initAlign: use None if you want default transformation parameters
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initAlign: could be:
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1. None if you want default transformation parameters
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2. a dict if you want to overwrite certain parameters of the default ones
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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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if isinstance(run,str):
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@ -162,7 +171,7 @@ class AnalyzeRun(object):
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def analyzeScan(self,initpars=None,nShotsPerCalib=20,nC=None,doFit=False,fitEveryCalib=False):
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""" use xhift = None; in this way the fit routine does not try to automatically find the translationx parameter """
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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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if nC is None: nC = d.opal1.nCalib
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@ -179,7 +188,7 @@ class AnalyzeRun(object):
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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 self.results.values()
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return list(self.results.values())
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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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@ -191,6 +200,7 @@ class AnalyzeRun(object):
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self.initAlign = r.final_pars
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if show:
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if savePlot == "auto":
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if not os.path.isdir(g_folder_out): os.makedirs(g_folder_out)
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savePlot = g_folder_out+"/run%04d_calib%s_shot%04d_fit.png" % (self.run,calib,shot)
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alignment.plotShot(im1,im2,res=r,save=savePlot)
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if save: self.saveTransform()
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@ -200,16 +210,15 @@ class AnalyzeRun(object):
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if initpars is None: initpars= self.initAlign
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d = self.d
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s1,s2 = self.getShot(shots,calib=calib)
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ret,t = alignment.doShots(s1,s2,initpars=initpars,doFit=doFit,\
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ret,transformForBestFit = alignment.doShots(s1,s2,initpars=initpars,doFit=doFit,\
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returnBestTransform=True)
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if doFit:
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self.initAlign = t
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if doFit: self.initAlign = transformForBestFit
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ret_unravel = alignment.unravel_results(ret)
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# keep it for later !
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self.results[calib] = ret_unravel
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if unravel: ret = ret_unravel
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if returnBestTransform:
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return ret,t
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return ret,transformForBestFit
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else:
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return ret
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@ -219,8 +228,7 @@ class AnalyzeRun(object):
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if os.path.exists(fname) and not overwrite:
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print("File %s exists, **NOT** saving, use overwrite=True is you want ..."%fname)
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return
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if os.path.exists(fname) and overwrite:
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os.unlink(fname)
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if os.path.exists(fname) and overwrite: os.unlink(fname)
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print("Saving results to %s"%fname)
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h = h5py.File(fname)
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h["roi1"] = (self.roi1.start,self.roi1.stop)
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@ -250,6 +258,7 @@ class AnalyzeRun(object):
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fname = g_folder_init+"/run%04d_transform.npy" % self.run
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elif isinstance(fname,int):
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fname = g_folder_init+"/run%04d_transform.npy" % fname
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if not os.path.exists(fname): print("Asked to read %s, but it does not exist"%fname)
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temp = np.load(fname).item()
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self.initAlign = temp["transform"]
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self.roi1 = temp["roi1"]
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