new submodules (peaks,cell) and new functions (like backgorundSubtraction (in azav.py)
This commit is contained in:
Marco Cammarata
parent
69df940711
commit
af9ecfc181
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@ -1,6 +1,7 @@
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from . import storage
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from . import utils
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from . import mask
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from . import peaks
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try:
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from . import azav
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except ImportError:
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38
xray/azav.py
38
xray/azav.py
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@ -52,7 +52,14 @@ def ai_as_dict(ai):
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def ai_as_str(ai):
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""" ai is a pyFAI azimuthal intagrator"""
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return "#" + str(ai).replace("\n","\n#")
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s=[ "# Detector : %s" % ai.detector.name,
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"# Pixel [um] : %.2fx%.2f" % (ai.pixel1*1e6,ai.pixel2*1e6),
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"# Distance [mm] : %.3f" % (ai.dist*1e3),
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"# Center [mm] : %.3f,%.3f" % (ai.poni1*1e3,ai.poni2*1e3),
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"# Center [px] : %.3f,%.3f" % (ai.poni1/ai.pixel1,ai.poni2/ai.pixel2),
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"# Wavelength [A] : %.5f" % (ai.wavelength*1e10),
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"# rot[1,2,3] [rad]: %.3f,%.3f,%.3f" % (ai.rot1,ai.rot2,ai.rot3) ]
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return "\n".join(s)
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def do1d(ai, imgs, mask = None, npt_radial = 600, method = 'csr',safe=True,dark=10., polCorr = 1):
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""" ai is a pyFAI azimuthal intagrator
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@ -218,7 +225,9 @@ def leastsq_circle(x,y):
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residu = np.sum((Ri - R)**2)
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return xc, yc, R
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def find_center(img,psize=100e-6,dist=0.1,wavelength=0.8e-10,**kwargs):
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def find_center(img,psize=100e-6,dist=0.1,wavelength=0.8e-10,center=None,reference=None,**kwargs):
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""" center is the initial centr (can be None)
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reference is a reference position to be plot in 2D plots """
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plt.ion()
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kw = dict( pixel1 = psize, pixel2 = psize, dist = dist,wavelength=wavelength )
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kw.update(kwargs)
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@ -232,23 +241,30 @@ def find_center(img,psize=100e-6,dist=0.1,wavelength=0.8e-10,**kwargs):
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ans = ""
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print("Enter 'end' when done")
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while ans != "end":
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if ans == "":
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if center is None:
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print("Click on beam center:")
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plt.sca(ax_img); # set figure to use for mouse interaction
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xc,yc = plt.ginput()[0]
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else:
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xc,yc = map(float,ans.split(","))
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print("Selected center:",xc,yc)
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ai.set_poni1(xc*psize)
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ai.set_poni2(yc*psize)
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center = plt.ginput()[0]
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print("Selected center:",center)
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ai.set_poni1(center[0]*psize)
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ai.set_poni2(center[1]*psize)
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q,az,i = do2d(ai,img)
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mesh = ax_pyfai.pcolormesh(q,az,i)
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mesh.set_clim( *np.percentile(i,(2,95) ) )
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ax_pyfai.set_title(str( (xc,yc) ))
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ax_pyfai.set_title(str(center))
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if reference is not None: ax_pyfai.axvline(reference)
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plt.pause(0.01)
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plt.draw()
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ans=input("Enter to continue with clinking or enter xc,yc values ")
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print("Final values: (in pixels) %.3f %.3f"%(xc,yc))
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if ans == '':
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center = None
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else:
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try:
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center = list(map(float,ans.split(",")))
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except Exception as e:
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center = None
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if center == []: center = None
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print("Final values: (in pixels) %.3f %.3f"%(center[0],center[1]))
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return ai
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def average(fileOrFolder,delays=slice(None),scale=1,norm=None,returnAll=False,plot=False,
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@ -0,0 +1,68 @@
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from __future__ import division,print_function
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import numpy as np
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from numpy import sin,cos
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class Triclinic(object):
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def __init__(self,a=1,b=1,c=1,alpha=90,beta=90,gamma=90):
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self.a = a
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self.b = b
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self.c = c
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alpha = alpha*np.pi/180
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beta = beta*np.pi/180
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gamma = gamma*np.pi/180
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self.alpha = alpha
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self.beta = beta
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self.gamma = gamma
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self._s11 = b**2 * c**2 * sin(alpha)**2
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self._s22 = a**2 * c**2 * sin(beta)**2
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self._s33 = a**2 * b**2 * sin(gamma)**2
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self._s12 = a*b*c**2*(cos(alpha) * cos(beta) - cos(gamma))
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self._s23 = a**2*b*c*(cos(beta) * cos(gamma) - cos(alpha))
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self._s13 = a*b**2*c*(cos(gamma) * cos(alpha) - cos(beta))
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self.V = (a*b*c)*np.sqrt(1-cos(alpha)**2 - cos(beta)**2 - cos(gamma)**2 + 2*cos(alpha)*cos(beta)*cos(gamma))
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def __call__(self,h,k,l): return self.q(h,k,l)
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def d(self,h,k,l):
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temp = self._s11*h**2 + \
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self._s22*k**2 + \
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self._s33*l**2 + \
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2*self._s12*h*k+ \
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2*self._s23*k*l+ \
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2*self._s13*h*l
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d = self.V/np.sqrt(temp)
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return d
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def q(self,h,k,l):
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return 2*np.pi/self.d(h,k,l)
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class Orthorombic(Triclinic):
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def __init__(self,a=1,b=1,c=1):
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Triclinic.__init__(self,a=a,b=b,c=c,alpha=90,beta=90,gamma=90)
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class Monoclinic(object):
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def __init__(self,a=1,b=1,c=1,beta=90.):
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self.a = a
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self.b = b
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self.c = c
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beta = beta/np.pi*180
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self.beta = beta
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self.V = (a*b*c)
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def __call__(self,h,k,l): return self.Q(h,k,l)
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def Q(self,h,k,l):
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temp = h**2/self.a**2 + (k*sin(self.beta))**2/self.b**2+l**2/self.c**2+2*h*l*cos(self.beta)/self.a/self.c
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d = 1/np.sqrt(temp)
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print(d)
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return 2*np.pi/d
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ti3o5_lambda = Triclinic(a = 9.83776, b = 3.78674, c = 9.97069, beta = 91.2567)
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ti3o5_beta = Triclinic(a = 9.7382 , b = 3.8005 , c = 9.4333 , beta = 91.496)
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#ti3o5_beta = Monoclinic(a = 9.7382 , b = 3.8005 , c = 9.4333 , beta = 91.496)
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ti3o5_alpha = Triclinic(a = 9.8372, b = 3.7921, c = 9.9717)
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#ti3o5_alpha1 = Orthorombic(a = 9.8372, b = 3.7921, c = 9.9717)
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@ -73,8 +73,10 @@ def averageScanPoints(scan,data,isRef=None,lpower=None,useRatio=False,\
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if isRef.sum()>0:
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# create a copy (subtractReferences works in place)
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diff = subtractReferences(data.copy(),np.argwhere(isRef), useRatio=useRatio)
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avNeg = funcForAveraging(data[isRef],axis=0)
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else:
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diff = data
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avNeg = np.zeros_like(avData)
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# normalize signal for laser intensity if provided
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if lpower is not None:
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@ -116,10 +118,10 @@ def averageScanPoints(scan,data,isRef=None,lpower=None,useRatio=False,\
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# store error of mean
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err[i] = noise/np.sqrt(shot_idx.sum())
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ret = dict(scan=scan_pos,data=ret,dataUnmasked=ret.copy(),err=err,errUnmasked=err.copy(),
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chi2_0=chi2_0,diffsInScanPoint=diffsInScanPoint,dataAbsAvAll=avData,dataAbsAvScanPoint=data_abs,
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dataAbs=data.copy())
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ret = dict(scan=scan_pos,data=ret,dataUnmasked=ret.copy(),err=err,
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errUnmasked=err.copy(),chi2_0=chi2_0,diffsInScanPoint=diffsInScanPoint,
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dataAbsAvNeg = avNeg, dataAsAbs=ret+avNeg,
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dataAbsAvAll=avData,dataAbsAvScanPoint=data_abs,dataAbs=data.copy())
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ret = storage.DataStorage(ret)
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return ret
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@ -0,0 +1,70 @@
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from __future__ import division,print_function
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import numpy as np
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from numpy import sin,cos
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class Triclinic(object):
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def __init__(self,a=1,b=1,c=1,alpha=90,beta=90,gamma=90):
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self.a = a
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self.b = b
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self.c = c
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alpha = alpha*np.pi/180
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beta = beta*np.pi/180
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gamma = gamma*np.pi/180
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self.alpha = alpha
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self.beta = beta
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self.gamma = gamma
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self._s11 = b**2 * c**2 * sin(alpha)**2
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self._s22 = a**2 * c**2 * sin(beta)**2
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self._s33 = a**2 * b**2 * sin(gamma)**2
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self._s12 = a*b*c**2*(cos(alpha) * cos(beta) - cos(gamma))
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self._s23 = a**2*b*c*(cos(beta) * cos(gamma) - cos(alpha))
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self._s13 = a*b**2*c*(cos(gamma) * cos(alpha) - cos(beta))
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self.V = (a*b*c)*np.sqrt(1-cos(alpha)**2 - cos(beta)**2 - cos(gamma)**2 + 2*cos(alpha)*cos(beta)*cos(gamma))
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def __call__(self,h,k,l): return self.q(h,k,l)
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def d(self,h,k,l):
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temp = self._s11*h**2 + \
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self._s22*k**2 + \
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self._s33*l**2 + \
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2*self._s12*h*k+ \
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2*self._s23*k*l+ \
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2*self._s13*h*l
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d = self.V/np.sqrt(temp)
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return d
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def q(self,h,k,l):
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return 2*np.pi/self.d(h,k,l)
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class Orthorombic(Triclinic):
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def __init__(self,a=1,b=1,c=1):
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Triclinic.__init__(self,a=a,b=b,c=c,alpha=90,beta=90,gamma=90)
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class Monoclinic(object):
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def __init__(self,a=1,b=1,c=1,beta=90.):
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self.a = a
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self.b = b
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self.c = c
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beta = beta/np.pi*180
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self.beta = beta
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self.V = (a*b*c)
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def __call__(self,h,k,l): return self.Q(h,k,l)
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def Q(self,h,k,l):
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temp = h**2/self.a**2 + (k*sin(self.beta))**2/self.b**2+l**2/self.c**2+2*h*l*cos(self.beta)/self.a/self.c
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d = 1/np.sqrt(temp)
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print(d)
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return 2*np.pi/d
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ti3o5_lambda = Triclinic(a = 9.83776, b = 3.78674, c = 9.97069, beta = 91.2567)
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ti3o5_beta = Triclinic(a = 9.7382 , b = 3.8005 , c = 9.4333 , beta = 91.496)
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#ti3o5_beta = Monoclinic(a = 9.7382 , b = 3.8005 , c = 9.4333 , beta = 91.496)
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ti3o5_alpha = Triclinic(a = 9.8372, b = 3.7921, c = 9.9717)
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#ti3o5_alpha1 = Orthorombic(a = 9.8372, b = 3.7921, c = 9.9717)
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27
xray/id9.py
27
xray/id9.py
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@ -45,18 +45,21 @@ def doFolder_azav(folder,nQ=1500,force=False,mask=None,saveChi=True,
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return azav.doFolder(folder,files="*.edf*",nQ=nQ,force=force,mask=mask,
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saveChi=saveChi,poni=poni,storageFile=storageFile,diagnostic=diag)
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def doFolder_dataRed(folder,storageFile='auto',monitor=None,
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funcForAveraging=np.nanmean,errMask=5,chi2Mask=2,qlims=None):
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""" storageFile cab the the basename of the file (to look for in folder) or
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a DataStorage instance """
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def doFolder_dataRed(azavStorage,monitor=None,funcForAveraging=np.nanmean,
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errMask=5,chi2Mask=2,qlims=None,outStorageFile='auto'):
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""" azavStorage if a DataStorage instance or the filename to read """
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if storageFile == 'auto' : storageFile = folder + "/" + "pyfai_1d" + default_extension
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if isinstance(storageFile,storage.DataStorage):
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data = storageFile
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if isinstance(azavStorage,storage.DataStorage):
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data = azavStorage
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folder = os.path.dirname(data.filename) if data.filename is not None else "./"
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elif os.path.exists(azavStorage):
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folder = os.path.dirname(azavStorage)
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data = storage.DataStorage(azavStorage)
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else:
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# read azimuthal averaged curves
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data = storage.DataStorage(storageFile)
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# assume is just a folder name
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folder = azavStorage
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azavStorage = folder + "/pyfai_1d" + default_extension
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data = storage.DataStorage(azavStorage)
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if qlims is not None:
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idx = (data.q>qlims[0]) & (data.q<qlims[1])
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@ -76,6 +79,8 @@ def doFolder_dataRed(folder,storageFile='auto',monitor=None,
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# save txt and npz file
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dataReduction.saveTxt(folder,diffs,info=data.pyfai_info)
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diffs.save(folder + "/" + "diffs" + default_extension)
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if outStorageFile == 'auto':
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outStorageFile = folder + "/diffs" + default_extension
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diffs.save(outStorageFile)
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return data,diffs
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@ -0,0 +1,20 @@
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import lmfit
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import numpy as np
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pv = lmfit.models.PseudoVoigtModel()
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def fitPeak(x,y,autorange=False):
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if autorange:
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# find fwhm
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idx = np.ravel(np.argwhere( y<y.max()/2 ))
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# find first crossing
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p1 = idx[idx<np.argmax(y)][-1]
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p2 = idx[idx>np.argmax(y)][0]
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c = int( (p1+p2)/2 )
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dp = int( np.abs(p1-p2) )
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idx = slice(c-dp,c+dp)
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x = x[idx]
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y = y[idx]
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pars = pv.guess(y,x=x)
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ret = pv.fit(y,x=x,params=pars)
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return ret
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@ -27,7 +27,7 @@ def unwrapArray(a,recursive=True,readH5pyDataset=True):
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a = dict(a); # convert to dict, otherwise can't asssign values
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for key,value in a.items(): a[key] = unwrapArray(value)
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elif isinstance(a,list):
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for index in range(len(a)): a[index] = unwrapArray(a[i])
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for index in range(len(a)): a[index] = unwrapArray(a[index])
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else:
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pass
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if isinstance(a,dict): a = DataStorage(a)
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@ -80,25 +80,29 @@ def read(fname):
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extension = os.path.splitext(fname)[1]
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log.info("Reading storage file %s"%fname)
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if extension == ".npz":
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return npzToDict(fname)
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return DataStorage(npzToDict(fname))
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elif extension == ".h5":
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return h5ToDict(fname)
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return DataStorage(h5ToDict(fname))
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else:
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raise ValueError("Extension must be h5 or npz, it was %s"%extension)
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def save(fname,d):
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extension = os.path.splitext(fname)[1]
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log.info("Saving storage file %s"%fname)
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if extension == ".npz":
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return dictToNpz(fname,d)
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elif extension == ".h5":
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return dictToH5(fname,d)
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else:
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raise ValueError("Extension must be h5 or npz")
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try:
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if extension == ".npz":
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return dictToNpz(fname,d)
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elif extension == ".h5":
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return dictToH5(fname,d)
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else:
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raise ValueError("Extension must be h5 or npz")
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except Exception as e:
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log.exception("Could not save %s"%fname)
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class DataStorage(dict):
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""" Storage for 1d integrated info """
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def __init__(self,fileOrDict,default_name='pyfai_1d',default_ext='npz'):
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def __init__(self,fileOrDict,recursive=True,
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default_name='pyfai_1d',default_ext='npz'):
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if isinstance(fileOrDict,dict):
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self.filename = None
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d = fileOrDict
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@ -109,6 +113,11 @@ class DataStorage(dict):
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self.filename = fileOrDict
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d = read(fileOrDict)
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if recursive:
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for k in d.keys():
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if not isinstance(d[k],DataStorage) and isinstance(d[k],dict):
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d[k] = DataStorage(d[k])
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# allow accessing with .data, .delays, etc.
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for k,v in d.items(): setattr(self,k,v)
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@ -128,6 +137,31 @@ class DataStorage(dict):
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delattr(self,key)
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super().__delitem__(key)
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def __str__(self):
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keys = list(self.keys())
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keys.sort()
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return "DataStorage obj containing: %s" % ",".join(keys)
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def __repr__(self):
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keys = list(self.keys())
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keys.sort()
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nchars = max(map(len,keys))
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fmt = "%%%ds %%s" % (nchars)
|
||||
s = ["DataStorage obj containing (sorted): ",]
|
||||
for k in keys:
|
||||
if isinstance(self[k],np.ndarray):
|
||||
value_str = "array %s"% "x".join(map(str,self[k].shape))
|
||||
elif isinstance(self[k],DataStorage):
|
||||
value_str = str(self[k])[:50] + "..."
|
||||
elif isinstance(self[k],(str,DataStorage)):
|
||||
value_str = self[k][:50] + "..."
|
||||
elif self[k] is None:
|
||||
value_str = "None"
|
||||
else:
|
||||
value_str = str(self[k])
|
||||
s.append( fmt % (k,value_str) )
|
||||
return "\n".join(s)
|
||||
|
||||
def save(self,fname=None):
|
||||
if fname is None: fname = self.filename
|
||||
assert fname is not None
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ import os
|
|||
import glob
|
||||
import pathlib
|
||||
import re
|
||||
import numbers
|
||||
from . import storage as storage
|
||||
|
||||
try:
|
||||
|
|
@ -72,6 +73,30 @@ def removeExt(fname):
|
|||
def getBasename(fname):
|
||||
return removeExt(os.path.basename(fname));
|
||||
|
||||
def findSlice(array,lims):
|
||||
start = np.ravel(np.argwhere(array>lims[0]))[0]
|
||||
stop = np.ravel(np.argwhere(array<lims[1]))[-1]
|
||||
return slice(int(start),int(stop))
|
||||
|
||||
def removeBackground(x,data,xlims=None,max_iter=100,background_regions=[],**kw):
|
||||
from dualtree import dualtree
|
||||
if data.ndim == 1: data = data[np.newaxis,:]
|
||||
if xlims is not None:
|
||||
idx = findSlice(x,xlims)
|
||||
x = x[idx]
|
||||
data = data[:,idx].copy()
|
||||
else:
|
||||
data = data.copy(); # create local copy
|
||||
# has to be a list of lists ..
|
||||
if background_regions != [] and isinstance(background_regions[0],numbers.Real):
|
||||
background_regions = [background_regions,]
|
||||
background_regions = [findSlice(x,brange) for brange in background_regions]
|
||||
for i in range(len(data)):
|
||||
data[i] = data[i] - dualtree.baseline(data[i],max_iter=max_iter,
|
||||
background_regions=background_regions,**kw)
|
||||
return x,np.squeeze(data)
|
||||
|
||||
|
||||
def plotdata(q,data,x=None,plot=True,showTrend=True,title=None,clim='auto'):
|
||||
if not (plot or showTrend): return
|
||||
if x is None: x = np.arange(data.shape[0])
|
||||
|
|
@ -108,7 +133,7 @@ def plotdiffs(q,diffs,t,select=None,err=None,absSignal=None,absSignalScale=10,
|
|||
indices = range(len(t))
|
||||
lines = []
|
||||
if absSignal is not None:
|
||||
line = plt.plot(q,absSignal/absSignalScale,
|
||||
line = plt.plot(q,absSignal/absSignalScale,lw=3,
|
||||
color='k',label="absSignal/%s"%str(absSignalScale))[0]
|
||||
lines.append(line)
|
||||
for idiff in indices:
|
||||
|
|
|
|||
Loading…
Reference in New Issue