source: trunk/GSASIIimage.py @ 2835

# -*- coding: utf-8 -*-
#GSASII image calculations: ellipse fitting & image integration        
########### SVN repository information ###################
# $Date: 2017-05-16 21:02:31 +0000 (Tue, 16 May 2017) $
# $Author: vondreele $
# $Revision: 2835 $
# $URL: trunk/GSASIIimage.py $
# $Id: GSASIIimage.py 2835 2017-05-16 21:02:31Z vondreele $
########### SVN repository information ###################
'''
*GSASIIimage: Image calc module*
================================

Ellipse fitting & image integration

'''

import math
import time
import numpy as np
import numpy.linalg as nl
import numpy.ma as ma
import polymask as pm
from scipy.optimize import leastsq
import scipy.signal as scsg
import scipy.cluster.vq as scvq
import copy
import GSASIIpath
GSASIIpath.SetVersionNumber("$Revision: 2835 $")
import GSASIIplot as G2plt
import GSASIIlattice as G2lat
import GSASIIpwd as G2pwd
import GSASIIspc as G2spc

# trig functions in degrees
sind = lambda x: math.sin(x*math.pi/180.)
asind = lambda x: 180.*math.asin(x)/math.pi
tand = lambda x: math.tan(x*math.pi/180.)
atand = lambda x: 180.*math.atan(x)/math.pi
atan2d = lambda y,x: 180.*math.atan2(y,x)/math.pi
cosd = lambda x: math.cos(x*math.pi/180.)
acosd = lambda x: 180.*math.acos(x)/math.pi
rdsq2d = lambda x,p: round(1.0/math.sqrt(x),p)
#numpy versions
npsind = lambda x: np.sin(x*np.pi/180.)
npasind = lambda x: 180.*np.arcsin(x)/np.pi
npcosd = lambda x: np.cos(x*np.pi/180.)
npacosd = lambda x: 180.*np.arccos(x)/np.pi
nptand = lambda x: np.tan(x*np.pi/180.)
npatand = lambda x: 180.*np.arctan(x)/np.pi
npatan2d = lambda y,x: 180.*np.arctan2(y,x)/np.pi
debug = False
    
def pointInPolygon(pXY,xy):
    'Needs a doc string'
    #pXY - assumed closed 1st & last points are duplicates
    Inside = False
    N = len(pXY)
    p1x,p1y = pXY[0]
    for i in range(N+1):
        p2x,p2y = pXY[i%N]
        if (max(p1y,p2y) >= xy[1] > min(p1y,p2y)) and (xy[0] <= max(p1x,p2x)):
            if p1y != p2y:
                xinters = (xy[1]-p1y)*(p2x-p1x)/(p2y-p1y)+p1x
            if p1x == p2x or xy[0] <= xinters:
                Inside = not Inside
        p1x,p1y = p2x,p2y
    return Inside
    
def peneCorr(tth,dep,dist,tilt=0.,azm=0.):
    'Needs a doc string'
#    return dep*(1.-npcosd(abs(tilt*npsind(azm))-tth*npcosd(azm)))  #something wrong here
    return dep*(1.-npcosd(tth))*dist**2/1000.         #best one
#    return dep*npsind(tth)             #not as good as 1-cos2Q
        
def makeMat(Angle,Axis):
    '''Make rotation matrix from Angle and Axis

    :param float Angle: in degrees
    :param int Axis: 0 for rotation about x, 1 for about y, etc.
    '''
    cs = npcosd(Angle)
    ss = npsind(Angle)
    M = np.array(([1.,0.,0.],[0.,cs,-ss],[0.,ss,cs]),dtype=np.float32)
    return np.roll(np.roll(M,Axis,axis=0),Axis,axis=1)
                    
def FitEllipse(xy):
    
    def ellipse_center(p):
        ''' gives ellipse center coordinates
        '''
        b,c,d,f,a = p[1]/2, p[2], p[3]/2, p[4]/2, p[0]
        num = b*b-a*c
        x0=(c*d-b*f)/num
        y0=(a*f-b*d)/num
        return np.array([x0,y0])
    
    def ellipse_angle_of_rotation( p ):
        ''' gives rotation of ellipse major axis from x-axis
        range will be -90 to 90 deg
        '''
        b,c,a = p[1]/2, p[2], p[0]
        return 0.5*npatand(2*b/(a-c))
    
    def ellipse_axis_length( p ):
        ''' gives ellipse radii in [minor,major] order
        '''
        b,c,d,f,g,a = p[1]/2, p[2], p[3]/2, p[4]/2, p[5], p[0]
        up = 2*(a*f*f+c*d*d+g*b*b-2*b*d*f-a*c*g)
        down1=(b*b-a*c)*( (c-a)*np.sqrt(1+4*b*b/((a-c)*(a-c)))-(c+a))
        down2=(b*b-a*c)*( (a-c)*np.sqrt(1+4*b*b/((a-c)*(a-c)))-(c+a))
        res1=np.sqrt(up/down1)
        res2=np.sqrt(up/down2)
        return np.array([ res2,res1])
    
    xy = np.array(xy)
    x = np.asarray(xy.T[0])[:,np.newaxis]
    y = np.asarray(xy.T[1])[:,np.newaxis]
    D =  np.hstack((x*x, x*y, y*y, x, y, np.ones_like(x)))
    S = np.dot(D.T,D)
    C = np.zeros([6,6])
    C[0,2] = C[2,0] = 2; C[1,1] = -1
    E, V =  nl.eig(np.dot(nl.inv(S), C))
    n = np.argmax(np.abs(E))
    a = V[:,n]
    cent = ellipse_center(a)
    phi = ellipse_angle_of_rotation(a)
    radii = ellipse_axis_length(a)
    phi += 90.
    if radii[0] > radii[1]:
        radii = [radii[1],radii[0]]
        phi -= 90.
    return cent,phi,radii

def FitDetector(rings,varyList,parmDict,Print=True):
    'Needs a doc string'
        
    def CalibPrint(ValSig,chisq,Npts):
        print 'Image Parameters: chi**2: %12.3g, Np: %d'%(chisq,Npts)
        ptlbls = 'names :'
        ptstr =  'values:'
        sigstr = 'esds  :'
        for name,value,sig in ValSig:
            ptlbls += "%s" % (name.rjust(12))
            if name == 'phi':
                ptstr += Fmt[name] % (value%360.)
            else:
                ptstr += Fmt[name] % (value)
            if sig:
                sigstr += Fmt[name] % (sig)
            else:
                sigstr += 12*' '
        print ptlbls
        print ptstr
        print sigstr        
        
    def ellipseCalcD(B,xyd,varyList,parmDict):
        
        x,y,dsp = xyd
        varyDict = dict(zip(varyList,B))
        parms = {}
        for parm in parmDict:
            if parm in varyList:
                parms[parm] = varyDict[parm]
            else:
                parms[parm] = parmDict[parm]
        phi = parms['phi']-90.               #get rotation of major axis from tilt axis
        tth = 2.0*npasind(parms['wave']/(2.*dsp))
        phi0 = npatan2d(y-parms['det-Y'],x-parms['det-X'])
        dxy = peneCorr(tth,parms['dep'],parms['dist'],parms['tilt'],phi0)
        stth = npsind(tth)
        cosb = npcosd(parms['tilt'])
        tanb = nptand(parms['tilt'])        
        tbm = nptand((tth-parms['tilt'])/2.)
        tbp = nptand((tth+parms['tilt'])/2.)
        d = parms['dist']+dxy
        fplus = d*tanb*stth/(cosb+stth)
        fminus = d*tanb*stth/(cosb-stth)
        vplus = d*(tanb+(1+tbm)/(1-tbm))*stth/(cosb+stth)
        vminus = d*(tanb+(1-tbp)/(1+tbp))*stth/(cosb-stth)
        R0 = np.sqrt((vplus+vminus)**2-(fplus+fminus)**2)/2.      #+minor axis
        R1 = (vplus+vminus)/2.                                    #major axis
        zdis = (fplus-fminus)/2.
        Robs = np.sqrt((x-parms['det-X'])**2+(y-parms['det-Y'])**2)
        rsqplus = R0**2+R1**2
        rsqminus = R0**2-R1**2
        R = rsqminus*npcosd(2.*phi0-2.*phi)+rsqplus
        Q = np.sqrt(2.)*R0*R1*np.sqrt(R-2.*zdis**2*npsind(phi0-phi)**2)
        P = 2.*R0**2*zdis*npcosd(phi0-phi)
        Rcalc = (P+Q)/R
        M = (Robs-Rcalc)*10.        #why 10? does make "chi**2" more reasonable
        return M
        
    names = ['dist','det-X','det-Y','tilt','phi','dep','wave']
    fmt = ['%12.3f','%12.3f','%12.3f','%12.3f','%12.3f','%12.4f','%12.6f']
    Fmt = dict(zip(names,fmt))
    p0 = [parmDict[key] for key in varyList]
    result = leastsq(ellipseCalcD,p0,args=(rings.T,varyList,parmDict),full_output=True,ftol=1.e-8)
    chisq = np.sum(result[2]['fvec']**2)/(rings.shape[0]-len(p0))   #reduced chi^2 = M/(Nobs-Nvar)
    parmDict.update(zip(varyList,result[0]))
    vals = list(result[0])
    sig = list(np.sqrt(chisq*np.diag(result[1])))
    sigList = np.zeros(7)
    for i,name in enumerate(varyList):
        sigList[i] = sig[varyList.index(name)]
    ValSig = zip(varyList,vals,sig)
    if Print:
        CalibPrint(ValSig,chisq,rings.shape[0])
    return [chisq,vals,sigList]

def ImageLocalMax(image,w,Xpix,Ypix):
    'Needs a doc string'
    w2 = w*2
    sizey,sizex = image.shape
    xpix = int(Xpix)            #get reference corner of pixel chosen
    ypix = int(Ypix)
    if not w:
        ZMax = np.sum(image[ypix-2:ypix+2,xpix-2:xpix+2])
        return xpix,ypix,ZMax,0.0001
    if (w2 < xpix < sizex-w2) and (w2 < ypix < sizey-w2) and image[ypix,xpix]:
        ZMax = image[ypix-w:ypix+w,xpix-w:xpix+w]
        Zmax = np.argmax(ZMax)
        ZMin = image[ypix-w2:ypix+w2,xpix-w2:xpix+w2]
        Zmin = np.argmin(ZMin)
        xpix += Zmax%w2-w
        ypix += Zmax/w2-w
        return xpix,ypix,np.ravel(ZMax)[Zmax],max(0.0001,np.ravel(ZMin)[Zmin])   #avoid neg/zero minimum
    else:
        return 0,0,0,0      
    
def makeRing(dsp,ellipse,pix,reject,scalex,scaley,image):
    'Needs a doc string'
    def ellipseC():
        'compute estimate of ellipse circumference'
        if radii[0] < 0:        #hyperbola
            theta = npacosd(1./np.sqrt(1.+(radii[0]/radii[1])**2))
            print theta
            return 0
        apb = radii[1]+radii[0]
        amb = radii[1]-radii[0]
        return np.pi*apb*(1+3*(amb/apb)**2/(10+np.sqrt(4-3*(amb/apb)**2)))
        
    cent,phi,radii = ellipse
    cphi = cosd(phi-90.)        #convert to major axis rotation
    sphi = sind(phi-90.)
    ring = []
    C = int(ellipseC())         #ring circumference
    azm = []
    for i in range(0,C,1):      #step around ring in 1mm increments
        a = 360.*i/C
        x = radii[1]*cosd(a-phi+90.)        #major axis
        y = radii[0]*sind(a-phi+90.)
        X = (cphi*x-sphi*y+cent[0])*scalex      #convert mm to pixels
        Y = (sphi*x+cphi*y+cent[1])*scaley
        X,Y,I,J = ImageLocalMax(image,pix,X,Y)
        if I and J and float(I)/J > reject:
            X += .5                             #set to center of pixel
            Y += .5
            X /= scalex                         #convert back to mm
            Y /= scaley
            if [X,Y,dsp] not in ring:           #no duplicates!
                ring.append([X,Y,dsp])
                azm.append(a)
    if len(ring) < 10:
        ring = []
        azm = []
    return ring,azm
    
def GetEllipse2(tth,dxy,dist,cent,tilt,phi):
    '''uses Dandelin spheres to find ellipse or hyperbola parameters from detector geometry
    on output
    radii[0] (b-minor axis) set < 0. for hyperbola
    
    '''
    radii = [0,0]
    stth = sind(tth)
    cosb = cosd(tilt)
    tanb = tand(tilt)
    tbm = tand((tth-tilt)/2.)
    tbp = tand((tth+tilt)/2.)
    sinb = sind(tilt)
    d = dist+dxy
    if tth+abs(tilt) < 90.:      #ellipse
        fplus = d*tanb*stth/(cosb+stth)
        fminus = d*tanb*stth/(cosb-stth)
        vplus = d*(tanb+(1+tbm)/(1-tbm))*stth/(cosb+stth)
        vminus = d*(tanb+(1-tbp)/(1+tbp))*stth/(cosb-stth)
        radii[0] = np.sqrt((vplus+vminus)**2-(fplus+fminus)**2)/2.      #+minor axis
        radii[1] = (vplus+vminus)/2.                                    #major axis
        zdis = (fplus-fminus)/2.
    else:   #hyperbola!
        f = d*abs(tanb)*stth/(cosb+stth)
        v = d*(abs(tanb)+tand(tth-abs(tilt)))
        delt = d*stth*(1.+stth*cosb)/(abs(sinb)*cosb*(stth+cosb))
        eps = (v-f)/(delt-v)
        radii[0] = -eps*(delt-f)/np.sqrt(eps**2-1.)                     #-minor axis
        radii[1] = eps*(delt-f)/(eps**2-1.)                             #major axis
        if tilt > 0:
            zdis = f+radii[1]*eps
        else:
            zdis = -f
#NB: zdis is || to major axis & phi is rotation of minor axis
#thus shift from beam to ellipse center is [Z*sin(phi),-Z*cos(phi)]
    elcent = [cent[0]+zdis*sind(phi),cent[1]-zdis*cosd(phi)]
    return elcent,phi,radii
    
def GetEllipse(dsp,data):
    '''uses Dandelin spheres to find ellipse or hyperbola parameters from detector geometry
    as given in image controls dictionary (data) and a d-spacing (dsp)
    '''
    cent = data['center']
    tilt = data['tilt']
    phi = data['rotation']
    dep = data.get('DetDepth',0.0)
    tth = 2.0*asind(data['wavelength']/(2.*dsp))
    dist = data['distance']
    dxy = peneCorr(tth,dep,dist,tilt)
    return GetEllipse2(tth,dxy,dist,cent,tilt,phi)
        
def GetDetectorXY(dsp,azm,data):
    '''Get detector x,y position from d-spacing (dsp), azimuth (azm,deg) 
    & image controls dictionary (data)
    it seems to be only used in plotting 
    '''    
    elcent,phi,radii = GetEllipse(dsp,data)
    phi = data['rotation']-90.          #to give rotation of major axis
    tilt = data['tilt']
    dist = data['distance']
    cent = data['center']
    tth = 2.0*asind(data['wavelength']/(2.*dsp))
    stth = sind(tth)
    cosb = cosd(tilt)
    if radii[0] > 0.:
        sinb = sind(tilt)
        tanb = tand(tilt)
        fplus = dist*tanb*stth/(cosb+stth)
        fminus = dist*tanb*stth/(cosb-stth)
        zdis = (fplus-fminus)/2.
        rsqplus = radii[0]**2+radii[1]**2
        rsqminus = radii[0]**2-radii[1]**2
        R = rsqminus*cosd(2.*azm-2.*phi)+rsqplus
        Q = np.sqrt(2.)*radii[0]*radii[1]*np.sqrt(R-2.*zdis**2*sind(azm-phi)**2)
        P = 2.*radii[0]**2*zdis*cosd(azm-phi)
        radius = (P+Q)/R
        xy = np.array([radius*cosd(azm),radius*sind(azm)])
        xy += cent
    else:   #hyperbola - both branches (one is way off screen!)
        sinb = abs(sind(tilt))
        tanb = abs(tand(tilt))
        f = dist*tanb*stth/(cosb+stth)
        v = dist*(tanb+tand(tth-abs(tilt)))
        delt = dist*stth*(1+stth*cosb)/(sinb*cosb*(stth+cosb))
        ecc = (v-f)/(delt-v)
        R = radii[1]*(ecc**2-1)/(1-ecc*cosd(azm))
        if tilt > 0.:
            offset = 2.*radii[1]*ecc+f      #select other branch
            xy = [-R*cosd(azm)-offset,R*sind(azm)]
        else:
            offset = -f
            xy = [-R*cosd(azm)-offset,-R*sind(azm)]
        xy = -np.array([xy[0]*cosd(phi)+xy[1]*sind(phi),xy[0]*sind(phi)-xy[1]*cosd(phi)])
        xy += cent
    return xy
    
def GetDetXYfromThAzm(Th,Azm,data):
    '''Computes a detector position from a 2theta angle and an azimultal
    angle (both in degrees) - apparently not used!
    '''
    dsp = data['wavelength']/(2.0*npsind(Th))    
    return GetDetectorXY(dsp,Azm,data)
                    
def GetTthAzmDsp(x,y,data): #expensive
    '''Computes a 2theta, etc. from a detector position and calibration constants - checked
    OK for ellipses & hyperbola.

    :returns: np.array(tth,azm,G,dsp) where tth is 2theta, azm is the azimutal angle,
       G is ? and dsp is the d-space
    '''
    wave = data['wavelength']
    cent = data['center']
    tilt = data['tilt']
    dist = data['distance']/cosd(tilt)
    x0 = dist*tand(tilt)
    phi = data['rotation']
    dep = data.get('DetDepth',0.)
    azmthoff = data['azmthOff']
    dx = np.array(x-cent[0],dtype=np.float32)
    dy = np.array(y-cent[1],dtype=np.float32)
    D = ((dx-x0)**2+dy**2+dist**2)      #sample to pixel distance
    X = np.array(([dx,dy,np.zeros_like(dx)]),dtype=np.float32).T
    X = np.dot(X,makeMat(phi,2))
    Z = np.dot(X,makeMat(tilt,0)).T[2]
    tth = npatand(np.sqrt(dx**2+dy**2-Z**2)/(dist-Z))
    dxy = peneCorr(tth,dep,dist,tilt,npatan2d(dy,dx))
    DX = dist-Z+dxy
    DY = np.sqrt(dx**2+dy**2-Z**2)
    tth = npatan2d(DY,DX) 
    dsp = wave/(2.*npsind(tth/2.))
    azm = (npatan2d(dy,dx)+azmthoff+720.)%360.
    G = D/dist**2       #for geometric correction = 1/cos(2theta)^2 if tilt=0.
    return np.array([tth,azm,G,dsp])
    
def GetTth(x,y,data):
    'Give 2-theta value for detector x,y position; calibration info in data'
    return GetTthAzmDsp(x,y,data)[0]
    
def GetTthAzm(x,y,data):
    'Give 2-theta, azimuth values for detector x,y position; calibration info in data'
    return GetTthAzmDsp(x,y,data)[0:2]
    
def GetTthAzmG(x,y,data):
    '''Give 2-theta, azimuth & geometric corr. values for detector x,y position;
     calibration info in data - only used in integration
    '''
    'Needs a doc string - checked OK for ellipses & hyperbola'
    tilt = data['tilt']
    dist = data['distance']/npcosd(tilt)
    x0 = data['distance']*nptand(tilt)
    MN = -np.inner(makeMat(data['rotation'],2),makeMat(tilt,0))
    distsq = data['distance']**2
    dx = x-data['center'][0]
    dy = y-data['center'][1]
    G = ((dx-x0)**2+dy**2+distsq)/distsq       #for geometric correction = 1/cos(2theta)^2 if tilt=0.
    X = np.dstack([dx.T,dy.T,np.zeros_like(dx.T)])
    Z = np.dot(X,MN).T[2]
    xyZ = dx**2+dy**2-Z**2    
    tth = npatand(np.sqrt(xyZ)/(dist-Z))
    dxy = peneCorr(tth,data['DetDepth'],dist,tilt,npatan2d(dy,dx))
    tth = npatan2d(np.sqrt(xyZ),dist-Z+dxy) 
    azm = (npatan2d(dy,dx)+data['azmthOff']+720.)%360.
    return tth,azm,G

def GetDsp(x,y,data):
    'Give d-spacing value for detector x,y position; calibration info in data'
    return GetTthAzmDsp(x,y,data)[3]
       
def GetAzm(x,y,data):
    'Give azimuth value for detector x,y position; calibration info in data'
    return GetTthAzmDsp(x,y,data)[1]
    
def meanAzm(a,b):
    AZM = lambda a,b: npacosd(0.5*(npsind(2.*b)-npsind(2.*a))/(np.pi*(b-a)/180.))/2.
    azm = AZM(a,b)
#    quad = int((a+b)/180.)
#    if quad == 1:
#        azm = 180.-azm
#    elif quad == 2:
#        azm += 180.
#    elif quad == 3:
#        azm = 360-azm
    return azm      
       
def ImageCompress(image,scale):
    ''' Reduces size of image by selecting every n'th point
    param: image array: original image
    param: scale int: intervsl between selected points 
    returns: array: reduced size image
    '''
    if scale == 1:
        return image
    else:
        return image[::scale,::scale]
        
def checkEllipse(Zsum,distSum,xSum,ySum,dist,x,y):
    'Needs a doc string'
    avg = np.array([distSum/Zsum,xSum/Zsum,ySum/Zsum])
    curr = np.array([dist,x,y])
    return abs(avg-curr)/avg < .02

def EdgeFinder(image,data):
    '''this makes list of all x,y where I>edgeMin suitable for an ellipse search?
    Not currently used but might be useful in future?
    '''
    import numpy.ma as ma
    Nx,Ny = data['size']
    pixelSize = data['pixelSize']
    edgemin = data['edgemin']
    scalex = pixelSize[0]/1000.
    scaley = pixelSize[1]/1000.    
    tay,tax = np.mgrid[0:Nx,0:Ny]
    tax = np.asfarray(tax*scalex,dtype=np.float32)
    tay = np.asfarray(tay*scaley,dtype=np.float32)
    tam = ma.getmask(ma.masked_less(image.flatten(),edgemin))
    tax = ma.compressed(ma.array(tax.flatten(),mask=tam))
    tay = ma.compressed(ma.array(tay.flatten(),mask=tam))
    return zip(tax,tay)
    
def MakeFrameMask(data,frame):
    pixelSize = data['pixelSize']
    scalex = pixelSize[0]/1000.
    scaley = pixelSize[1]/1000.
    blkSize = 512
    Nx,Ny = data['size']
    nXBlks = (Nx-1)/blkSize+1
    nYBlks = (Ny-1)/blkSize+1
    tam = ma.make_mask_none(data['size'])
    for iBlk in range(nXBlks):
        iBeg = iBlk*blkSize
        iFin = min(iBeg+blkSize,Nx)
        for jBlk in range(nYBlks):
            jBeg = jBlk*blkSize
            jFin = min(jBeg+blkSize,Ny)                
            nI = iFin-iBeg
            nJ = jFin-jBeg
            tax,tay = np.mgrid[iBeg+0.5:iFin+.5,jBeg+.5:jFin+.5]         #bin centers not corners
            tax = np.asfarray(tax*scalex,dtype=np.float32)
            tay = np.asfarray(tay*scaley,dtype=np.float32)
            tamp = ma.make_mask_none((1024*1024))
            tamp = ma.make_mask(pm.polymask(nI*nJ,tax.flatten(),
                tay.flatten(),len(frame),frame,tamp)[:nI*nJ])-True  #switch to exclude around frame
            if tamp.shape:
                tamp = np.reshape(tamp[:nI*nJ],(nI,nJ))
                tam[iBeg:iFin,jBeg:jFin] = ma.mask_or(tamp[0:nI,0:nJ],tam[iBeg:iFin,jBeg:jFin])
            else:
                tam[iBeg:iFin,jBeg:jFin] = True
    return tam.T
    
def ImageRecalibrate(G2frame,data,masks):
    '''Called to repeat the calibration on an image, usually called after
    calibration is done initially to improve the fit.
    '''
    import ImageCalibrants as calFile
    print 'Image recalibration:'
    time0 = time.time()
    pixelSize = data['pixelSize']
    scalex = 1000./pixelSize[0]
    scaley = 1000./pixelSize[1]
    pixLimit = data['pixLimit']
    cutoff = data['cutoff']
    data['rings'] = []
    data['ellipses'] = []
    if data['DetDepth'] > 0.5:          #patch - redefine DetDepth
        data['DetDepth'] /= data['distance']
    if not data['calibrant']:
        print 'no calibration material selected'
        return []    
    skip = data['calibskip']
    dmin = data['calibdmin']
    Bravais,SGs,Cells = calFile.Calibrants[data['calibrant']][:3]
    HKL = []
    for bravais,sg,cell in zip(Bravais,SGs,Cells):
        A = G2lat.cell2A(cell)
        if sg:
            SGData = G2spc.SpcGroup(sg)[1]
            hkl = G2pwd.getHKLpeak(dmin,SGData,A)
            HKL += hkl
        else:
            hkl = G2lat.GenHBravais(dmin,bravais,A)
            HKL += hkl
    HKL = G2lat.sortHKLd(HKL,True,False)
    varyList = [item for item in data['varyList'] if data['varyList'][item]]
    parmDict = {'dist':data['distance'],'det-X':data['center'][0],'det-Y':data['center'][1],
        'setdist':data.get('setdist',data['distance']),
        'tilt':data['tilt'],'phi':data['rotation'],'wave':data['wavelength'],'dep':data['DetDepth']}
    Found = False
    wave = data['wavelength']
    frame = masks['Frames']
    tam = ma.make_mask_none(G2frame.ImageZ.shape)
    if frame:
        tam = ma.mask_or(tam,MakeFrameMask(data,frame))
    for iH,H in enumerate(HKL):
        if debug:   print H 
        dsp = H[3]
        tth = 2.0*asind(wave/(2.*dsp))
        if tth+abs(data['tilt']) > 90.:
            print 'next line is a hyperbola - search stopped'
            break
        ellipse = GetEllipse(dsp,data)
        Ring = makeRing(dsp,ellipse,pixLimit,cutoff,scalex,scaley,ma.array(G2frame.ImageZ,mask=tam))[0]
        if Ring:
            if iH >= skip:
                data['rings'].append(np.array(Ring))
            data['ellipses'].append(copy.deepcopy(ellipse+('r',)))
            Found = True
        elif not Found:         #skipping inner rings, keep looking until ring found 
            continue
        else:                   #no more rings beyond edge of detector
            data['ellipses'].append([])
            continue
    if not data['rings']:
        print 'no rings found; try lower Min ring I/Ib'
        return []    
        
    rings = np.concatenate((data['rings']),axis=0)
    [chisq,vals,sigList] = FitDetector(rings,varyList,parmDict)
    data['wavelength'] = parmDict['wave']
    data['distance'] = parmDict['dist']
    data['center'] = [parmDict['det-X'],parmDict['det-Y']]
    data['rotation'] = np.mod(parmDict['phi'],360.0)
    data['tilt'] = parmDict['tilt']
    data['DetDepth'] = parmDict['dep']
    data['chisq'] = chisq
    N = len(data['ellipses'])
    data['ellipses'] = []           #clear away individual ellipse fits
    for H in HKL[:N]:
        ellipse = GetEllipse(H[3],data)
        data['ellipses'].append(copy.deepcopy(ellipse+('b',)))    
    print 'calibration time = %.3f'%(time.time()-time0)
    G2plt.PlotImage(G2frame,newImage=True)        
    return [vals,varyList,sigList,parmDict]
            
def ImageCalibrate(G2frame,data):
    '''Called to perform an initial image calibration after points have been
    selected for the inner ring.
    '''
    import copy
    import ImageCalibrants as calFile
    print 'Image calibration:'
    time0 = time.time()
    ring = data['ring']
    pixelSize = data['pixelSize']
    scalex = 1000./pixelSize[0]
    scaley = 1000./pixelSize[1]
    pixLimit = data['pixLimit']
    cutoff = data['cutoff']
    varyDict = data['varyList']
    if varyDict['dist'] and varyDict['wave']:
        print 'ERROR - you can not simultaneously calibrate distance and wavelength'
        return False
    if len(ring) < 5:
        print 'ERROR - not enough inner ring points for ellipse'
        return False
        
    #fit start points on inner ring
    data['ellipses'] = []
    data['rings'] = []
    outE = FitEllipse(ring)
    fmt  = '%s X: %.3f, Y: %.3f, phi: %.3f, R1: %.3f, R2: %.3f'
    fmt2 = '%s X: %.3f, Y: %.3f, phi: %.3f, R1: %.3f, R2: %.3f, chi**2: %.3f, Np: %d'
    if outE:
        print fmt%('start ellipse: ',outE[0][0],outE[0][1],outE[1],outE[2][0],outE[2][1])
        ellipse = outE
    else:
        return False
        
    #setup 360 points on that ring for "good" fit
    data['ellipses'].append(ellipse[:]+('g',))
    Ring = makeRing(1.0,ellipse,pixLimit,cutoff,scalex,scaley,G2frame.ImageZ)[0]
    if Ring:
        ellipse = FitEllipse(Ring)
        Ring = makeRing(1.0,ellipse,pixLimit,cutoff,scalex,scaley,G2frame.ImageZ)[0]    #do again
        ellipse = FitEllipse(Ring)
    else:
        print '1st ring not sufficiently complete to proceed'
        return False
    if debug:
        print fmt2%('inner ring:    ',ellipse[0][0],ellipse[0][1],ellipse[1],
            ellipse[2][0],ellipse[2][1],0.,len(Ring))     #cent,phi,radii
    data['ellipses'].append(ellipse[:]+('r',))
    data['rings'].append(np.array(Ring))
    G2plt.PlotImage(G2frame,newImage=True)
    
#setup for calibration
    data['rings'] = []
    if not data['calibrant']:
        print 'no calibration material selected'
        return True
    
    skip = data['calibskip']
    dmin = data['calibdmin']
#generate reflection set
    Bravais,SGs,Cells = calFile.Calibrants[data['calibrant']][:3]
    HKL = []
    for bravais,sg,cell in zip(Bravais,SGs,Cells):
        A = G2lat.cell2A(cell)
        if sg:
            SGData = G2spc.SpcGroup(sg)[1]
            hkl = G2pwd.getHKLpeak(dmin,SGData,A)
            HKL += hkl
        else:
            hkl = G2lat.GenHBravais(dmin,bravais,A)
            HKL += hkl
    HKL = G2lat.sortHKLd(HKL,True,False)[skip:]
#set up 1st ring
    elcent,phi,radii = ellipse              #from fit of 1st ring
    dsp = HKL[0][3]
    print '1st ring: try %.4f'%(dsp)
    if varyDict['dist']:
        wave = data['wavelength']
        tth = 2.0*asind(wave/(2.*dsp))
    else:   #varyDict['wave']!
        dist = data['distance']
        tth = npatan2d(radii[0],dist)
        data['wavelength'] = wave =  2.0*dsp*sind(tth/2.0)
    Ring0 = makeRing(dsp,ellipse,3,cutoff,scalex,scaley,G2frame.ImageZ)[0]
    ttth = nptand(tth)
    ctth = npcosd(tth)
#1st estimate of tilt; assume ellipse - don't know sign though
    if varyDict['tilt']:
        tilt = npasind(np.sqrt(max(0.,1.-(radii[0]/radii[1])**2))*ctth)
        if not tilt:
            print 'WARNING - selected ring was fitted as a circle'
            print ' - if detector was tilted we suggest you skip this ring - WARNING'
    else:
        tilt = data['tilt']
#1st estimate of dist: sample to detector normal to plane
    if varyDict['dist']:
        data['distance'] = dist = radii[0]**2/(ttth*radii[1])
    else:
        dist = data['distance']
    if varyDict['tilt']:
#ellipse to cone axis (x-ray beam); 2 choices depending on sign of tilt
        zdisp = radii[1]*ttth*tand(tilt)
        zdism = radii[1]*ttth*tand(-tilt)
#cone axis position; 2 choices. Which is right?     
#NB: zdisp is || to major axis & phi is rotation of minor axis
#thus shift from beam to ellipse center is [Z*sin(phi),-Z*cos(phi)]
        centp = [elcent[0]+zdisp*sind(phi),elcent[1]-zdisp*cosd(phi)]
        centm = [elcent[0]+zdism*sind(phi),elcent[1]-zdism*cosd(phi)]
#check get same ellipse parms either way
#now do next ring; estimate either way & do a FitDetector each way; best fit is correct one
        fail = True
        i2 = 1
        while fail:
            dsp = HKL[i2][3]
            print '2nd ring: try %.4f'%(dsp)
            tth = 2.0*asind(wave/(2.*dsp))
            ellipsep = GetEllipse2(tth,0.,dist,centp,tilt,phi)
            print fmt%('plus ellipse :',ellipsep[0][0],ellipsep[0][1],ellipsep[1],ellipsep[2][0],ellipsep[2][1])
            Ringp = makeRing(dsp,ellipsep,3,cutoff,scalex,scaley,G2frame.ImageZ)[0]
            parmDict = {'dist':dist,'det-X':centp[0],'det-Y':centp[1],
                'tilt':tilt,'phi':phi,'wave':wave,'dep':0.0}        
            varyList = [item for item in varyDict if varyDict[item]]
            if len(Ringp) > 10:
                chip = FitDetector(np.array(Ring0+Ringp),varyList,parmDict,True)[0]
                tiltp = parmDict['tilt']
                phip = parmDict['phi']
                centp = [parmDict['det-X'],parmDict['det-Y']]
                fail = False
            else:
                chip = 1e6
            ellipsem = GetEllipse2(tth,0.,dist,centm,-tilt,phi)
            print fmt%('minus ellipse:',ellipsem[0][0],ellipsem[0][1],ellipsem[1],ellipsem[2][0],ellipsem[2][1])
            Ringm = makeRing(dsp,ellipsem,3,cutoff,scalex,scaley,G2frame.ImageZ)[0]
            if len(Ringm) > 10:
                parmDict['tilt'] *= -1
                chim = FitDetector(np.array(Ring0+Ringm),varyList,parmDict,True)[0]
                tiltm = parmDict['tilt']
                phim = parmDict['phi']
                centm = [parmDict['det-X'],parmDict['det-Y']]
                fail = False
            else:
                chim = 1e6
            if fail:
                i2 += 1
        if chip < chim:
            data['tilt'] = tiltp
            data['center'] = centp
            data['rotation'] = phip
        else:
            data['tilt'] = tiltm
            data['center'] = centm
            data['rotation'] = phim
        data['ellipses'].append(ellipsep[:]+('b',))
        data['rings'].append(np.array(Ringp))
        data['ellipses'].append(ellipsem[:]+('r',))
        data['rings'].append(np.array(Ringm))
        G2plt.PlotImage(G2frame,newImage=True)
    if data['DetDepth'] > 0.5:          #patch - redefine DetDepth
        data['DetDepth'] /= data['distance']
    parmDict = {'dist':data['distance'],'det-X':data['center'][0],'det-Y':data['center'][1],
        'tilt':data['tilt'],'phi':data['rotation'],'wave':data['wavelength'],'dep':data['DetDepth']}
    varyList = [item for item in varyDict if varyDict[item]]
    data['rings'] = []
    data['ellipses'] = []
    for i,H in enumerate(HKL):
        dsp = H[3]
        tth = 2.0*asind(wave/(2.*dsp))
        if tth+abs(data['tilt']) > 90.:
            print 'next line is a hyperbola - search stopped'
            break
        if debug:   print 'HKLD:',H[:4],'2-theta: %.4f'%(tth)
        elcent,phi,radii = ellipse = GetEllipse(dsp,data)
        data['ellipses'].append(copy.deepcopy(ellipse+('g',)))
        if debug:   print fmt%('predicted ellipse:',elcent[0],elcent[1],phi,radii[0],radii[1])
        Ring = makeRing(dsp,ellipse,pixLimit,cutoff,scalex,scaley,G2frame.ImageZ)[0]
        if Ring:
            data['rings'].append(np.array(Ring))
            rings = np.concatenate((data['rings']),axis=0)
            if i:
                chisq = FitDetector(rings,varyList,parmDict,False)[0]
                data['distance'] = parmDict['dist']
                data['center'] = [parmDict['det-X'],parmDict['det-Y']]
                data['rotation'] = parmDict['phi']
                data['tilt'] = parmDict['tilt']
                data['DetDepth'] = parmDict['dep']
                data['chisq'] = chisq
                elcent,phi,radii = ellipse = GetEllipse(dsp,data)
                if debug:   print fmt2%('fitted ellipse:   ',elcent[0],elcent[1],phi,radii[0],radii[1],chisq,len(rings))
            data['ellipses'].append(copy.deepcopy(ellipse+('r',)))
#            G2plt.PlotImage(G2frame,newImage=True)
        else:
            if debug:   print 'insufficient number of points in this ellipse to fit'
#            break
    G2plt.PlotImage(G2frame,newImage=True)
    fullSize = len(G2frame.ImageZ)/scalex
    if 2*radii[1] < .9*fullSize:
        print 'Are all usable rings (>25% visible) used? Try reducing Min ring I/Ib'
    N = len(data['ellipses'])
    if N > 2:
        FitDetector(rings,varyList,parmDict)[0]
        data['wavelength'] = parmDict['wave']
        data['distance'] = parmDict['dist']
        data['center'] = [parmDict['det-X'],parmDict['det-Y']]
        data['rotation'] = parmDict['phi']
        data['tilt'] = parmDict['tilt']
        data['DetDepth'] = parmDict['dep']
    for H in HKL[:N]:
        ellipse = GetEllipse(H[3],data)
        data['ellipses'].append(copy.deepcopy(ellipse+('b',)))
    print 'calibration time = %.3f'%(time.time()-time0)
    G2plt.PlotImage(G2frame,newImage=True)        
    return True
    
def Make2ThetaAzimuthMap(data,masks,iLim,jLim,times): #most expensive part of integration!
    'Needs a doc string'
    #transforms 2D image from x,y space to 2-theta,azimuth space based on detector orientation
    pixelSize = data['pixelSize']
    scalex = pixelSize[0]/1000.
    scaley = pixelSize[1]/1000.
    
    tay,tax = np.mgrid[iLim[0]+0.5:iLim[1]+.5,jLim[0]+.5:jLim[1]+.5]         #bin centers not corners
    tax = np.asfarray(tax*scalex,dtype=np.float32)
    tay = np.asfarray(tay*scaley,dtype=np.float32)
    nI = iLim[1]-iLim[0]
    nJ = jLim[1]-jLim[0]
    t0 = time.time()
    #make position masks here
    frame = masks['Frames']
    tam = ma.make_mask_none((nI,nJ))
    if frame:
        tamp = ma.make_mask_none((1024*1024))
        tamp = ma.make_mask(pm.polymask(nI*nJ,tax.flatten(),
            tay.flatten(),len(frame),frame,tamp)[:nI*nJ])-True  #switch to exclude around frame
        tam = ma.mask_or(tam.flatten(),tamp)
    polygons = masks['Polygons']
    for polygon in polygons:
        if polygon:
            tamp = ma.make_mask_none((1024*1024))
            tamp = ma.make_mask(pm.polymask(nI*nJ,tax.flatten(),
                tay.flatten(),len(polygon),polygon,tamp)[:nI*nJ])
            tam = ma.mask_or(tam.flatten(),tamp)
    if tam.shape: tam = np.reshape(tam,(nI,nJ))
    spots = masks['Points']
    for X,Y,diam in spots:
        tamp = ma.getmask(ma.masked_less((tax-X)**2+(tay-Y)**2,(diam/2.)**2))
        tam = ma.mask_or(tam,tamp)
    times[0] += time.time()-t0
    t0 = time.time()
    TA = np.array(GetTthAzmG(tax,tay,data))     #includes geom. corr. as dist**2/d0**2 - most expensive step
    times[1] += time.time()-t0
    TA[1] = np.where(TA[1]<0,TA[1]+360,TA[1])
    return np.array(TA),tam           #2-theta, azimuth & geom. corr. arrays & position mask

def Fill2ThetaAzimuthMap(masks,TA,tam,image):
    'Needs a doc string'
    Zlim = masks['Thresholds'][1]
    rings = masks['Rings']
    arcs = masks['Arcs']
    TA = np.dstack((ma.getdata(TA[1]),ma.getdata(TA[0]),ma.getdata(TA[2])))    #azimuth, 2-theta, dist
    tax,tay,tad = np.dsplit(TA,3)    #azimuth, 2-theta, dist**2/d0**2
    for tth,thick in rings:
        tam = ma.mask_or(tam.flatten(),ma.getmask(ma.masked_inside(tay.flatten(),max(0.01,tth-thick/2.),tth+thick/2.)))
    for tth,azm,thick in arcs:
        tamt = ma.getmask(ma.masked_inside(tay.flatten(),max(0.01,tth-thick/2.),tth+thick/2.))
        tama = ma.getmask(ma.masked_inside(tax.flatten(),azm[0],azm[1]))
        tam = ma.mask_or(tam.flatten(),tamt*tama)
    taz = ma.masked_outside(image.flatten(),int(Zlim[0]),Zlim[1])
    tabs = np.ones_like(taz)
    tam = ma.mask_or(tam.flatten(),ma.getmask(taz))
    tax = ma.compressed(ma.array(tax.flatten(),mask=tam))   #azimuth
    tay = ma.compressed(ma.array(tay.flatten(),mask=tam))   #2-theta
    taz = ma.compressed(ma.array(taz.flatten(),mask=tam))   #intensity
    tad = ma.compressed(ma.array(tad.flatten(),mask=tam))   #dist**2/d0**2
    tabs = ma.compressed(ma.array(tabs.flatten(),mask=tam)) #ones - later used for absorption corr.
    return tax,tay,taz,tad,tabs
    
def ImageIntegrate(image,data,masks,blkSize=128,returnN=False):
    'Integrate an image; called from OnIntegrateAll and OnIntegrate in G2imgGUI'    #for q, log(q) bins need data['binType']
    import histogram2d as h2d
    print 'Begin image integration'
    CancelPressed = False
    LUtth = np.array(data['IOtth'])
    LRazm = np.array(data['LRazimuth'],dtype=np.float64)
    numAzms = data['outAzimuths']
    numChans = data['outChannels']
    Dazm = (LRazm[1]-LRazm[0])/numAzms
    if '2-theta' in data.get('binType','2-theta'):
        lutth = LUtth                
    elif 'log(q)' in data['binType']:
        lutth = np.log(4.*np.pi*npsind(LUtth/2.)/data['wavelength'])
    elif 'q' == data['binType'].lower():
        lutth = 4.*np.pi*npsind(LUtth/2.)/data['wavelength']
    dtth = (lutth[1]-lutth[0])/numChans
    muT = data.get('SampleAbs',[0.0,''])[0]
    if data['DetDepth'] > 0.5:          #patch - redefine DetDepth
        data['DetDepth'] /= data['distance']
    if 'SASD' in data['type']:
        muT = -np.log(muT)/2.       #Transmission to 1/2 thickness muT
    NST = np.zeros(shape=(numAzms,numChans),order='F',dtype=np.float32)
    H0 = np.zeros(shape=(numAzms,numChans),order='F',dtype=np.float32)
    Nx,Ny = data['size']
    nXBlks = (Nx-1)/blkSize+1
    nYBlks = (Ny-1)/blkSize+1
    tbeg = time.time()
    times = [0,0,0,0,0]
    for iBlk in range(nYBlks):
        iBeg = iBlk*blkSize
        iFin = min(iBeg+blkSize,Ny)
        for jBlk in range(nXBlks):
            jBeg = jBlk*blkSize
            jFin = min(jBeg+blkSize,Nx)
            # next is most expensive step!
            TA,tam = Make2ThetaAzimuthMap(data,masks,(iBeg,iFin),(jBeg,jFin),times)           #2-theta & azimuth arrays & create position mask
            Block = image[iBeg:iFin,jBeg:jFin]
            t0 = time.time()
            tax,tay,taz,tad,tabs = Fill2ThetaAzimuthMap(masks,TA,tam,Block)    #and apply masks
            del TA; del tam
            times[2] += time.time()-t0
            tax = np.where(tax > LRazm[1],tax-360.,tax)                 #put azm inside limits if possible
            tax = np.where(tax < LRazm[0],tax+360.,tax)
            if data.get('SampleAbs',[0.0,''])[1]:
                if 'Cylind' in data['SampleShape']:
                    muR = muT*(1.+npsind(tax)**2/2.)/(npcosd(tay))      #adjust for additional thickness off sample normal
                    tabs = G2pwd.Absorb(data['SampleShape'],muR,tay)
                elif 'Fixed' in data['SampleShape']:    #assumes flat plate sample normal to beam
                    tabs = G2pwd.Absorb('Fixed',muT,tay)
            if 'log(q)' in data.get('binType',''):
                tay = np.log(4.*np.pi*npsind(tay/2.)/data['wavelength'])
            elif 'q' == data.get('binType','').lower():
                tay = 4.*np.pi*npsind(tay/2.)/data['wavelength']
            t0 = time.time()
            taz = np.array((taz*tad/tabs),dtype='float32')
            if any([tax.shape[0],tay.shape[0],taz.shape[0]]):
                NST,H0 = h2d.histogram2d(len(tax),tax,tay,taz,
                    numAzms,numChans,LRazm,lutth,Dazm,dtth,NST,H0)
            times[3] += time.time()-t0
            del tax; del tay; del taz; del tad; del tabs
    t0 = time.time()
    NST = np.array(NST,dtype=np.float)
    H0 = np.divide(H0,NST)
    H0 = np.nan_to_num(H0)
    H2 = np.array([tth for tth in np.linspace(lutth[0],lutth[1],numChans+1)])
    if 'log(q)' in data.get('binType',''):
        H2 = 2.*npasind(np.exp(H2)*data['wavelength']/(4.*np.pi))
    elif 'q' == data.get('binType','').lower():
        H2 = 2.*npasind(H2*data['wavelength']/(4.*np.pi))
    if Dazm:        
        H1 = np.array([azm for azm in np.linspace(LRazm[0],LRazm[1],numAzms+1)])
    else:
        H1 = LRazm
    H0 /= npcosd(H2[:-1])           #**2? I don't think so, **1 is right for powders
    if 'SASD' in data['type']:
        H0 /= npcosd(H2[:-1])           #one more for small angle scattering data?
    if data['Oblique'][1]:
        H0 /= G2pwd.Oblique(data['Oblique'][0],H2[:-1])
    if 'SASD' in data['type'] and data['PolaVal'][1]:
        #NB: in G2pwd.Polarization azm is defined from plane of polarization, not image x axis!
        H0 /= np.array([G2pwd.Polarization(data['PolaVal'][0],H2[:-1],Azm=azm-90.)[0] for azm in (H1[:-1]+np.diff(H1)/2.)])
    times[4] += time.time()-t0
    print 'Step times: \n apply masks  %8.3fs xy->th,azm   %8.3fs fill map     %8.3fs \
        \n binning      %8.3fs cleanup      %8.3fs'%(times[0],times[1],times[2],times[3],times[4])
    print "Elapsed time:","%8.3fs"%(time.time()-tbeg)
    print 'Integration complete'
    if returnN:     #As requested by Steven Weigand
        return H0,H1,H2,NST,CancelPressed
    else:
        return H0,H1,H2,CancelPressed
    
def MakeStrStaRing(ring,Image,Controls):
    ellipse = GetEllipse(ring['Dset'],Controls)
    pixSize = Controls['pixelSize']
    scalex = 1000./pixSize[0]
    scaley = 1000./pixSize[1]
    Ring = np.array(makeRing(ring['Dset'],ellipse,ring['pixLimit'],ring['cutoff'],scalex,scaley,Image)[0]).T   #returns x,y,dsp for each point in ring
    if len(Ring):
        ring['ImxyObs'] = copy.copy(Ring[:2])
        TA = GetTthAzm(Ring[0],Ring[1],Controls)       #convert x,y to tth,azm
        TA[0] = Controls['wavelength']/(2.*npsind(TA[0]/2.))      #convert 2th to d
        ring['ImtaObs'] = TA
        ring['ImtaCalc'] = np.zeros_like(ring['ImtaObs'])
        Ring[0] = TA[0]
        Ring[1] = TA[1]
        return Ring,ring
    else:
        ring['ImxyObs'] = [[],[]]
        ring['ImtaObs'] = [[],[]]
        ring['ImtaCalc'] = [[],[]]
        return [],[]    #bad ring; no points found
    
def FitStrSta(Image,StrSta,Controls):
    'Needs a doc string'
    
    StaControls = copy.deepcopy(Controls)
    phi = StrSta['Sample phi']
    wave = Controls['wavelength']
    pixelSize = Controls['pixelSize']
    scalex = 1000./pixelSize[0]
    scaley = 1000./pixelSize[1]
    StaType = StrSta['Type']
    StaControls['distance'] += StrSta['Sample z']*cosd(phi)

    for ring in StrSta['d-zero']:       #get observed x,y,d points for the d-zeros
        dset = ring['Dset']
        Ring,R = MakeStrStaRing(ring,Image,StaControls)
        if len(Ring):
            ring.update(R)
            p0 = ring['Emat']
            val,esd,covMat = FitStrain(Ring,p0,dset,wave,phi,StaType)
            ring['Emat'] = val
            ring['Esig'] = esd
            ellipse = FitEllipse(R['ImxyObs'].T)
            ringxy,ringazm = makeRing(ring['Dcalc'],ellipse,0,0.,scalex,scaley,Image)
            ring['ImxyCalc'] = np.array(ringxy).T[:2]
            ringint = np.array([float(Image[int(x*scalex),int(y*scaley)]) for y,x in np.array(ringxy)[:,:2]])
            ringint /= np.mean(ringint)
            ring['Ivar'] = np.var(ringint)
            ring['covMat'] = covMat
            print 'Variance in normalized ring intensity: %.3f'%(ring['Ivar'])
    CalcStrSta(StrSta,Controls)
    
def IntStrSta(Image,StrSta,Controls):
    StaControls = copy.deepcopy(Controls)
    pixelSize = Controls['pixelSize']
    scalex = 1000./pixelSize[0]
    scaley = 1000./pixelSize[1]
    phi = StrSta['Sample phi']
    StaControls['distance'] += StrSta['Sample z']*cosd(phi)
    RingsAI = []
    for ring in StrSta['d-zero']:       #get observed x,y,d points for the d-zeros
        Ring,R = MakeStrStaRing(ring,Image,StaControls)
        if len(Ring):
            ellipse = FitEllipse(R['ImxyObs'].T)
            ringxy,ringazm = makeRing(ring['Dcalc'],ellipse,0,0.,scalex,scaley,Image)
            ring['ImxyCalc'] = np.array(ringxy).T[:2]
            ringint = np.array([float(Image[int(x*scalex),int(y*scaley)]) for y,x in np.array(ringxy)[:,:2]])
            ringint /= np.mean(ringint)
            print ' %s %.3f %s %.3f'%('d-spacing',ring['Dcalc'],'sig(MRD):',np.sqrt(np.var(ringint)))
            RingsAI.append(np.array(zip(ringazm,ringint)).T)
    return RingsAI
    
def CalcStrSta(StrSta,Controls):

    wave = Controls['wavelength']
    phi = StrSta['Sample phi']
    StaType = StrSta['Type']
    for ring in StrSta['d-zero']:
        Eij = ring['Emat']
        E = [[Eij[0],Eij[1],0],[Eij[1],Eij[2],0],[0,0,0]]
        th,azm = ring['ImtaObs']
        th0 = np.ones_like(azm)*npasind(wave/(2.*ring['Dset']))
        V = -np.sum(np.sum(E*calcFij(90.,phi,azm,th0).T/1.e6,axis=2),axis=1)
        if StaType == 'True':
            ring['ImtaCalc'] = np.array([np.exp(V)*ring['Dset'],azm])
        else:
            ring['ImtaCalc'] = np.array([(V+1.)*ring['Dset'],azm])
        dmin = np.min(ring['ImtaCalc'][0])
        dmax = np.max(ring['ImtaCalc'][0])
        if ring.get('fixDset',True):
            if abs(Eij[0]) < abs(Eij[2]):         #tension
                ring['Dcalc'] = dmin+(dmax-dmin)/4.
            else:                       #compression
                ring['Dcalc'] = dmin+3.*(dmax-dmin)/4.
        else:
            ring['Dcalc'] = np.mean(ring['ImtaCalc'][0])

def calcFij(omg,phi,azm,th):
    '''    Uses parameters as defined by Bob He & Kingsley Smith, Adv. in X-Ray Anal. 41, 501 (1997)

    :param omg: his omega = sample omega rotation; 0 when incident beam || sample surface, 
        90 when perp. to sample surface
    :param phi: his phi = sample phi rotation; usually = 0, axis rotates with omg.
    :param azm: his chi = azimuth around incident beam
    :param th:  his theta = theta
    '''
    a = npsind(th)*npcosd(omg)+npsind(azm)*npcosd(th)*npsind(omg)
    b = -npcosd(azm)*npcosd(th)
    c = npsind(th)*npsind(omg)-npsind(azm)*npcosd(th)*npcosd(omg)
    d = a*npsind(phi)+b*npcosd(phi)
    e = a*npcosd(phi)-b*npsind(phi)
    Fij = np.array([
        [d**2,d*e,c*d],
        [d*e,e**2,c*e],
        [c*d,c*e,c**2]])
    return -Fij

def FitStrain(rings,p0,dset,wave,phi,StaType):
    'Needs a doc string'
    def StrainPrint(ValSig,dset):
        print 'Strain tensor for Dset: %.6f'%(dset)
        ptlbls = 'names :'
        ptstr =  'values:'
        sigstr = 'esds  :'
        for name,fmt,value,sig in ValSig:
            ptlbls += "%s" % (name.rjust(12))
            ptstr += fmt % (value)
            if sig:
                sigstr += fmt % (sig)
            else:
                sigstr += 12*' '
        print ptlbls
        print ptstr
        print sigstr
        
    def strainCalc(p,xyd,dset,wave,phi,StaType):
        E = np.array([[p[0],p[1],0],[p[1],p[2],0],[0,0,0]])
        dspo,azm,dsp = xyd
        th = npasind(wave/(2.0*dspo))
        V = -np.sum(np.sum(E*calcFij(90.,phi,azm,th).T/1.e6,axis=2),axis=1)
        if StaType == 'True':
            dspc = dset*np.exp(V)
        else:
            dspc = dset*(V+1.)
        return dspo-dspc
        
    names = ['e11','e12','e22']
    fmt = ['%12.2f','%12.2f','%12.2f']
    result = leastsq(strainCalc,p0,args=(rings,dset,wave,phi,StaType),full_output=True)
    vals = list(result[0])
    chisq = np.sum(result[2]['fvec']**2)/(rings.shape[1]-3)     #reduced chi^2 = M/(Nobs-Nvar)
    covM = result[1]
    covMatrix = covM*chisq
    sig = list(np.sqrt(chisq*np.diag(result[1])))
    ValSig = zip(names,fmt,vals,sig)
    StrainPrint(ValSig,dset)
    return vals,sig,covMatrix
    
def AutoSpotMasks(Image,Masks,Controls):
    print 'auto spot search'
    maxPks = 500
    dmax = 1.5         #just beyond diamond 111 @ 2.0596
    wave = Controls['wavelength']
    tthMax = 2.0*asind(wave/(2.*dmax))
    pixelSize = Controls['pixelSize']
    lap = np.array([[0,1,0],[1,-4,1],[0,1,0]],np.float32)
    ck = scsg.cspline2d(Image.T,24.0)
    spotMask1=-scsg.convolve2d(ck,lap,mode='same',boundary='symm')
    for mul in range(10):
        spotMask = ma.array(Image,mask=(spotMask1<(mul+1)*1.e4))
        indx = np.transpose(spotMask.nonzero())
        if not len(indx):       #smooth image - no sharp points
            break
        pts,distort = scvq.kmeans(np.array(indx,np.float32),maxPks,thresh=1.0)
        peaks = pts*pixelSize/1000.
        tths = GetTth(peaks.T[0],peaks.T[1],Controls)
        masktths = ma.getmask(ma.array(tths,mask=tths>tthMax))
        pts = pts[masktths,:]
        A,B = np.mgrid[0:len(pts),0:len(pts)]   
        M = np.sqrt(np.sum((pts[A]-pts[B])**2,axis=-1))
        MX = ma.array(M,mask=(M<10.))
        Indx = ma.nonzero(ma.getmask(MX))
        print 'Spots found: ',mul,len(pts),distort,len(Indx[0])
        if distort < .3:
            break
    if not len(Indx[0]):
        print 'no auto search spots found'
        return None
    if distort > 2.:
        print 'no big spots found'
        return None
    #use clustered points to get position & spread
    
    Indx = np.sort(Indx,axis=0).T
    Nmax = np.max(Indx)
    nums = [list(set([ind[1] for ind in Indx if ind[1]>=jnd and ind[0] == jnd])) for jnd in range(Nmax+1)]
    delList = []
    for Num in nums:
        delList += list(np.sort(Num))[1:]
    delList = list(set(delList))
    Nums = [num for inum,num in enumerate(nums) if inum not in delList]
    points = []
    esds = []
    for num in Nums:
        points.append(np.mean(pts[num],axis=0))
        esds.append(np.mean(np.var(pts[num],axis=0)))
    peaks = (np.array(points)+np.array([.5,.5]))*pixelSize/1000.
    Points = np.ones((peaks.shape[0],3))*2.
    Points[:,2] += np.array(esds)*pixelSize[0]/1000.
    Points[:,:2] = peaks
    Masks['Points'] = list(Points)
    return None

#    rollImage = lambda rho,roll: np.roll(np.roll(rho,roll[0],axis=0),roll[1],axis=1)
#    print 'auto spot search'
#    pixelSize = Controls['pixelSize']
#    spotMask = ma.array(Image,mask=(Image<np.mean(Image)))
#    indices = (-1,0,1)
#    rolls = np.array([[ix,iy] for ix in indices for iy in indices])
#    for roll in rolls:
#        if np.any(roll):        #avoid [0,0]
#            spotMask = ma.array(spotMask,mask=(spotMask-rollImage(Image,roll)<0.))
#    mags = spotMask[spotMask.nonzero()]
#    indx = np.transpose(spotMask.nonzero())
#    nx,ny = Image.shape
#    jndx = []
#    for [ind,mag] in zip(indx,mags):
#        if (1 < ind[0] < nx-2) and (1 < ind[1] < ny-2):
#            cent = np.zeros((3,3))
#            cent[1,1] = mag
#            msk = np.array(Image[ind[0]-2:ind[0]+3,ind[1]-2:ind[1]+3])
#            msk = ma.array(msk,mask=(msk==mag))
#            if mag > 1.5*ma.mean(msk):
#                jndx.append([ind[1]+.5,ind[0]+.5])
#    print 'Spots found: ',len(jndx)
#    jndx = np.array(jndx)
#    peaks = jndx*pixelSize/1000.
#    tth = GetTth(peaks.T[0],peaks.T[1],Controls)
#    histth,bins = np.histogram(tth,2500)
#    for shft in [-1,1]:
#        histmsk = ma.array(histth,mask=-(histth-np.roll(histth,shft)<2.))
#    histmsk = ma.array(histmsk,mask=(histmsk>5))
#    binmsk = np.array(ma.nonzero(histmsk))
#    digts = np.digitize(tth,bins)-1
#    PeaksList = []
#    for digt,peak in zip(digts,peaks):
#        if digt in binmsk:
#            PeaksList.append(peak)
#    #should be able to filter out spotty Bragg rings here
#    PeaksList = np.array(PeaksList)
#    Points = np.ones((PeaksList.shape[0],3))
#    Points[:,:2] = PeaksList
#    Masks['Points'] = list(Points)
#    return None