source: trunk/GSASIIimage.py @ 4692

# -*- coding: utf-8 -*-
#GSASII image calculations: ellipse fitting & image integration        
########### SVN repository information ###################
# $Date: 2020-12-31 16:50:22 +0000 (Thu, 31 Dec 2020) $
# $Author: vondreele $
# $Revision: 4692 $
# $URL: trunk/GSASIIimage.py $
# $Id: GSASIIimage.py 4692 2020-12-31 16:50:22Z vondreele $
########### SVN repository information ###################
'''
*GSASIIimage: Image calc module*
================================

Ellipse fitting & image integration

'''
from __future__ import division, print_function
import math
import time
import numpy as np
import numpy.linalg as nl
import numpy.ma as ma
from scipy.optimize import leastsq
import scipy.interpolate as scint
import scipy.special as sc
import copy
import GSASIIpath
GSASIIpath.SetVersionNumber("$Revision: 4692 $")
try:
    import GSASIIplot as G2plt
except ImportError: # expected in scriptable w/o matplotlib and/or wx
    pass
import GSASIIlattice as G2lat
import GSASIIpwd as G2pwd
import GSASIIspc as G2spc
#import GSASIImath as G2mth
import GSASIIfiles as G2fil

# 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
nxs = np.newaxis
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):
    'Needs a doc string'
    return dep*(1.-npcosd(tth))*dist**2/1000.         #best one
        
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,covar=False):
    '''Fit detector calibration parameters

    :param np.array rings: vector of ring positions
    :param list varyList: calibration parameters to be refined
    :param dict parmDict: all calibration parameters
    :param bool Print: set to True (default) to print the results
    :param bool covar: set to True to return the covariance matrix (default is False)
    :returns: [chisq,vals,sigList] unless covar is True, then 
        [chisq,vals,sigList,coVarMatrix] is returned
    '''
        
    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'])
        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)*25.        #why 25? 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])
    if not len(vals):
        sig = []
        ValSig = []
        sigList = []
    else:    
        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:
        if len(sig):
            CalibPrint(ValSig,chisq,rings.shape[0])
        else:
            print(' Nothing refined')
    if covar:
        return [chisq,vals,sigList,result[1]]
    else:
        return [chisq,vals,sigList]

def FitMultiDist(rings,varyList,parmDict,Print=True,covar=False):
    '''Fit detector calibration parameters with multi-distance data

    :param np.array rings: vector of ring positions (x,y,dist,d-space)
    :param list varyList: calibration parameters to be refined
    :param dict parmDict: calibration parameters
    :param bool Print: set to True (default) to print the results
    :param bool covar: set to True to return the covariance matrix (default is False)
    :returns: [chisq,vals,sigDict] unless covar is True, then 
        [chisq,vals,sigDict,coVarMatrix] is returned
    '''
        
    def CalibPrint(parmDict,sigDict,chisq,Npts):
        ptlbls = 'names :'
        ptstr =  'values:'
        sigstr = 'esds  :'
        for d in sorted(set([i[5:] for i in parmDict.keys() if 'det-X' in i]),key=lambda x:int(x)):
            fmt = '%12.3f'
            for key in 'det-X','det-Y','delta':
                name = key+d
                if name not in parmDict: continue
                ptlbls += "%12s" % name
                ptstr += fmt % (parmDict[name])
                if name in sigDict:
                    sigstr += fmt % (sigDict[name])
                else:
                    sigstr += 12*' '
                if len(ptlbls) > 68:
                    print()
                    print (ptlbls)
                    print (ptstr)
                    print (sigstr)
                    ptlbls = 'names :'
                    ptstr =  'values:'
                    sigstr = 'esds  :'
        if len(ptlbls) > 8:
            print()
            print (ptlbls)
            print (ptstr)
            print (sigstr)
        print ('\nImage Parameters: chi**2: %12.3g, Np: %d'%(chisq,Npts))
        ptlbls = 'names :'
        ptstr =  'values:'
        sigstr = 'esds  :'
        names = ['wavelength', 'dep', 'phi', 'tilt']
        if 'deltaDist' in parmDict:
            names += ['deltaDist']
        for name in names:
            if name == 'wavelength':
                fmt = '%12.6f'
            elif name == 'dep':
                fmt = '%12.4f'
            else:
                fmt = '%12.3f'
                
            ptlbls += "%s" % (name.rjust(12))
            if name == 'phi':
                ptstr += fmt % (parmDict[name]%360.)
            else:
                ptstr += fmt % (parmDict[name])
            if name in sigDict:
                sigstr += fmt % (sigDict[name])
            else:
                sigstr += 12*' '
        print (ptlbls)
        print (ptstr)
        print (sigstr)
        print()

    def ellipseCalcD(B,xyd,varyList,parmDict):
        x,y,dist,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]
        # create arrays with detector center values
        detX = np.array([parms['det-X'+str(int(d))] for d in dist])
        detY = np.array([parms['det-Y'+str(int(d))] for d in dist])
        if 'deltaDist' in parms:
            deltaDist = parms['deltaDist']
        else:
            deltaDist = np.array([parms['delta'+str(int(d))] for d in dist])
                
        phi = parms['phi']-90.               #get rotation of major axis from tilt axis
        tth = 2.0*npasind(parms['wavelength']/(2.*dsp))
        phi0 = npatan2d(y-detY,x-detX)
        dxy = peneCorr(tth,parms['dep'],dist-deltaDist)
        stth = npsind(tth)
        cosb = npcosd(parms['tilt'])
        tanb = nptand(parms['tilt'])        
        tbm = nptand((tth-parms['tilt'])/2.)
        tbp = nptand((tth+parms['tilt'])/2.)
        d = (dist-deltaDist)+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-detX)**2+(y-detY)**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
        return (Robs-Rcalc)*25.        #why 25? does make "chi**2" more reasonable
        
    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])
    if chisq > 1:
        sig = list(np.sqrt(chisq*np.diag(result[1])))
    else:
        sig = list(np.sqrt(np.diag(result[1])))
    sigDict = {name:s for name,s in zip(varyList,sig)}
    if Print:
        CalibPrint(parmDict,sigDict,chisq,rings.shape[0])
    if covar:
        return [chisq,vals,sigDict,result[1]]
    else:
        return [chisq,vals,sigDict]
    
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,mul=1):
    '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())*mul         #ring circumference in mm
    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)
    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) - new version
    it seems to be only used in plotting 
    '''
    def LinePlaneCollision(planeNormal, planePoint, rayDirection, rayPoint, epsilon=1e-6):
     
        ndotu = planeNormal.dot(rayDirection)
        if ndotu < epsilon:    
                return None
     
        w = rayPoint - planePoint
        si = -planeNormal.dot(w) / ndotu
        Psi = w + si * rayDirection + planePoint
        return Psi
    
    
    dist = data['distance']
    cent = data['center']
    T = makeMat(data['tilt'],0)
    R = makeMat(data['rotation'],2)
    MN = np.inner(R,np.inner(R,T))
    iMN= nl.inv(MN)
    tth = 2.0*npasind(data['wavelength']/(2.*dsp))
    vect = np.array([npsind(tth)*npcosd(azm),npsind(tth)*npsind(azm),npcosd(tth)])
    dxyz0 = np.inner(np.array([0.,0.,1.0]),MN)    #tilt detector normal
    dxyz0 += np.array([0.,0.,dist])                 #translate to distance
    dxyz0 = np.inner(dxyz0,makeMat(data['det2theta'],1).T)   #rotate on 2-theta
    dxyz1 = np.inner(np.array([cent[0],cent[1],0.]),MN)    #tilt detector cent
    dxyz1 += np.array([0.,0.,dist])                 #translate to distance
    dxyz1 = np.inner(dxyz1,makeMat(data['det2theta'],1).T)   #rotate on 2-theta
    xyz = LinePlaneCollision(dxyz0,dxyz1,vect,2.*dist*vect)
    if xyz is None:
        return np.zeros(2)
#        return None
    xyz = np.inner(xyz,makeMat(-data['det2theta'],1).T)
    xyz -= np.array([0.,0.,dist])                 #translate back
    xyz = np.inner(xyz,iMN)
    return np.squeeze(xyz)[:2]+cent
        
def GetDetectorXY2(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
    if data['det2theta']:
        xy[0] += dist*nptand(data['det2theta']+data['tilt']*npsind(data['rotation']))
    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 GetTthAzmDsp2(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)
    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 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
    '''
    
    def costth(xyz):
        u = xyz/nl.norm(xyz,axis=-1)[:,:,nxs]
        return np.dot(u,np.array([0.,0.,1.]))
        
#zero detector 2-theta: tested with tilted images - perfect integrations
    wave = data['wavelength']
    dx = x-data['center'][0]
    dy = y-data['center'][1]
    tilt = data['tilt']
    dist = data['distance']/npcosd(tilt)    #sample-beam intersection point
    T = makeMat(tilt,0)
    R = makeMat(data['rotation'],2)
    MN = np.inner(R,np.inner(R,T))
    dxyz0 = np.inner(np.dstack([dx,dy,np.zeros_like(dx)]),MN)    #correct for 45 deg tilt
    dxyz0 += np.array([0.,0.,dist])
    if data['DetDepth']:
        ctth0 = costth(dxyz0)
        tth0 = npacosd(ctth0)
        dzp = peneCorr(tth0,data['DetDepth'],dist)
        dxyz0[:,:,2] += dzp
#non zero detector 2-theta:
    if data['det2theta']:        
        tthMat = makeMat(data['det2theta'],1)
        dxyz = np.inner(dxyz0,tthMat.T)
    else:
        dxyz = dxyz0
    ctth = costth(dxyz)
    tth = npacosd(ctth)
    dsp = wave/(2.*npsind(tth/2.))
    azm = (npatan2d(dxyz[:,:,1],dxyz[:,:,0])+data['azmthOff']+720.)%360.        
# G-calculation        
    x0 = data['distance']*nptand(tilt)
    x0x = x0*npcosd(data['rotation'])
    x0y = x0*npsind(data['rotation'])
    distsq = data['distance']**2
    G = ((dx-x0x)**2+(dy-x0y)**2+distsq)/distsq       #for geometric correction = 1/cos(2theta)^2 if tilt=0.
    return [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 GetTthAzmG2(x,y,data):
    '''Give 2-theta, azimuth & geometric corr. values for detector x,y position;
     calibration info in data - only used in integration - old version
    '''
    'Needs a doc string - checked OK for ellipses & hyperbola'
    tilt = data['tilt']
    dist = data['distance']/npcosd(tilt)
    MN = -np.inner(makeMat(data['rotation'],2),makeMat(tilt,0))
    dx = x-data['center'][0]
    dy = y-data['center'][1]
    dz = np.dot(np.dstack([dx.T,dy.T,np.zeros_like(dx.T)]),MN).T[2]    
    xyZ = dx**2+dy**2-dz**2    
    tth0 = npatand(np.sqrt(xyZ)/(dist-dz))
    dzp = peneCorr(tth0,data['DetDepth'],dist)
    tth = npatan2d(np.sqrt(xyZ),dist-dz+dzp) 
    azm = (npatan2d(dy,dx)+data['azmthOff']+720.)%360.
    
    distsq = data['distance']**2
    x0 = data['distance']*nptand(tilt)
    x0x = x0*npcosd(data['rotation'])
    x0y = x0*npsind(data['rotation'])
    G = ((dx-x0x)**2+(dy-x0y)**2+distsq)/distsq       #for geometric correction = 1/cos(2theta)^2 if tilt=0.
    return tth,azm,G

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
     checked OK for ellipses & hyperbola
     This is the slow step in image integration
     '''     
    def costth(xyz):
        u = xyz/nl.norm(xyz,axis=-1)[:,:,nxs]
        return np.dot(u,np.array([0.,0.,1.]))
        
#zero detector 2-theta: tested with tilted images - perfect integrations
    dx = x-data['center'][0]
    dy = y-data['center'][1]
    tilt = data['tilt']
    dist = data['distance']/npcosd(tilt)    #sample-beam intersection point
    T = makeMat(tilt,0)
    R = makeMat(data['rotation'],2)
    MN = np.inner(R,np.inner(R,T))
    dxyz0 = np.inner(np.dstack([dx,dy,np.zeros_like(dx)]),MN)    #correct for 45 deg tilt
    dxyz0 += np.array([0.,0.,dist])
    if data['DetDepth']:
        ctth0 = costth(dxyz0)
        tth0 = npacosd(ctth0)
        dzp = peneCorr(tth0,data['DetDepth'],dist)
        dxyz0[:,:,2] += dzp
#non zero detector 2-theta:
    if data['det2theta']:        
        tthMat = makeMat(data['det2theta'],1)
        dxyz = np.inner(dxyz0,tthMat.T)
    else:
        dxyz = dxyz0
    ctth = costth(dxyz)
    tth = npacosd(ctth)
    azm = (npatan2d(dxyz[:,:,1],dxyz[:,:,0])+data['azmthOff']+720.)%360.        
# G-calculation        
    x0 = data['distance']*nptand(tilt)
    x0x = x0*npcosd(data['rotation'])
    x0y = x0*npsind(data['rotation'])
    distsq = data['distance']**2
    G = ((dx-x0x)**2+(dy-x0y)**2+distsq)/distsq       #for geometric correction = 1/cos(2theta)^2 if tilt=0.
    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 GetLineScan(image,data):
    Nx,Ny = data['size']
    pixelSize = data['pixelSize']
    scalex = 1000./pixelSize[0]         #microns --> 1/mm
    scaley = 1000./pixelSize[1]
    wave = data['wavelength']
    numChans = data['outChannels']
    LUtth = np.array(data['IOtth'],dtype=np.float)
    azm = data['linescan'][1]-data['azmthOff']
    Tx = np.array([tth for tth in np.linspace(LUtth[0],LUtth[1],numChans+1)])
    Ty = np.zeros_like(Tx)
    dsp = wave/(2.0*npsind(Tx/2.0))
    xy = [GetDetectorXY(d,azm,data) for d in dsp]
    xy = np.array(xy).T
    xy[1] *= scalex
    xy[0] *= scaley
    xy = np.array(xy,dtype=int)
    Xpix = ma.masked_outside(xy[1],0,Ny-1)
    Ypix = ma.masked_outside(xy[0],0,Nx-1)
    xpix = Xpix[~(Xpix.mask+Ypix.mask)].compressed()
    ypix = Ypix[~(Xpix.mask+Ypix.mask)].compressed()
    Ty = image[xpix,ypix]
    Tx = ma.array(Tx,mask=Xpix.mask+Ypix.mask).compressed()
    return [Tx,Ty]

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):
    import polymask as pm
    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,ImageZ,data,masks,getRingsOnly=False):
    '''Called to repeat the calibration on an image, usually called after
    calibration is done initially to improve the fit.

    :param G2frame: The top-level GSAS-II frame or None, to skip plotting
    :param np.Array ImageZ: the image to calibrate
    :param dict data: the Controls dict for the image
    :param dict masks: a dict with masks
    :returns: a list containing vals,varyList,sigList,parmDict,covar or rings
      (with an array of x, y, and d-space values) if getRingsOnly is True
      or an empty list, in case of an error
    '''
    import ImageCalibrants as calFile
    if not getRingsOnly:
        G2fil.G2Print ('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']:
        G2fil.G2Print ('warning: no calibration material selected')
        return []    
    skip = data['calibskip']
    dmin = data['calibdmin']
    if data['calibrant'] not in calFile.Calibrants:
        G2fil.G2Print('Warning: %s not in local copy of image calibrants file'%data['calibrant'])
        return []
    calibrant = calFile.Calibrants[data['calibrant']]
    Bravais,SGs,Cells = 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,Inst=None,nodup=True)
            HKL += list(hkl)
        else:
            hkl = G2lat.GenHBravais(dmin,bravais,A)
            HKL += list(hkl)
    if len(calibrant) > 5:
        absent = calibrant[5]
    else:
        absent = ()
    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(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.:
            G2fil.G2Print ('next line is a hyperbola - search stopped')
            break
        ellipse = GetEllipse(dsp,data)
        if iH not in absent and iH >= skip:
            Ring = makeRing(dsp,ellipse,pixLimit,cutoff,scalex,scaley,ma.array(ImageZ,mask=tam))[0]
        else:
            Ring = makeRing(dsp,ellipse,pixLimit,1000.0,scalex,scaley,ma.array(ImageZ,mask=tam))[0]
        if Ring:
            if iH not in absent and 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']:
        G2fil.G2Print ('no rings found; try lower Min ring I/Ib',mode='warn')
        return []    
        
    rings = np.concatenate((data['rings']),axis=0)
    if getRingsOnly:
        return rings,HKL
    [chisq,vals,sigList,covar] = FitDetector(rings,varyList,parmDict,True,True)
    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',)))    
    G2fil.G2Print ('calibration time = %.3f'%(time.time()-time0))
    if G2frame:
        G2plt.PlotImage(G2frame,newImage=True)        
    return [vals,varyList,sigList,parmDict,covar]

def ImageCalibrate(G2frame,data):
    '''Called to perform an initial image calibration after points have been
    selected for the inner ring.
    '''
    import ImageCalibrants as calFile
    G2fil.G2Print ('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']:
        G2fil.G2Print ('ERROR - you can not simultaneously calibrate distance and wavelength')
        return False
    if len(ring) < 5:
        G2fil.G2Print ('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:
        G2fil.G2Print (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:
        G2fil.G2Print ('1st ring not sufficiently complete to proceed',mode='warn')
        return False
    if debug:
        G2fil.G2Print (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']:
        G2fil.G2Print ('Warning: no calibration material selected')
        return True
    
    skip = data['calibskip']
    dmin = data['calibdmin']
#generate reflection set
    calibrant = calFile.Calibrants[data['calibrant']]
    Bravais,SGs,Cells = 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,Inst=None,nodup=True)
            HKL += list(hkl)
        else:
            hkl = G2lat.GenHBravais(dmin,bravais,A)
            HKL += list(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]
    G2fil.G2Print ('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:
            G2fil.G2Print ('WARNING - selected ring was fitted as a circle')
            G2fil.G2Print (' - 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]
            G2fil.G2Print ('2nd ring: try %.4f'%(dsp))
            tth = 2.0*asind(wave/(2.*dsp))
            ellipsep = GetEllipse2(tth,0.,dist,centp,tilt,phi)
            G2fil.G2Print (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)
            G2fil.G2Print (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.:
            G2fil.G2Print ('next line is a hyperbola - search stopped')
            break
        if debug:   print ('HKLD:'+str(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:
        G2fil.G2Print ('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',)))
    G2fil.G2Print ('calibration time = %.3f'%(time.time()-time0))
    G2plt.PlotImage(G2frame,newImage=True)        
    return True
    
def Make2ThetaAzimuthMap(data,iLim,jLim): #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).flatten()
    tay = np.asfarray(tay*scaley,dtype=np.float32).flatten()
    nI = iLim[1]-iLim[0]
    nJ = jLim[1]-jLim[0]
    TA = np.empty((4,nI,nJ))
    TA[:3] = np.array(GetTthAzmG(np.reshape(tax,(nI,nJ)),np.reshape(tay,(nI,nJ)),data))     #includes geom. corr. as dist**2/d0**2 - most expensive step
    TA[1] = np.where(TA[1]<0,TA[1]+360,TA[1])
    TA[3] = G2pwd.Polarization(data['PolaVal'][0],TA[0],TA[1]-90.)[0]
    return TA           #2-theta, azimuth & geom. corr. arrays

def MakeMaskMap(data,masks,iLim,jLim,tamp):
    import polymask as pm
    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).flatten()
    tay = np.asfarray(tay*scaley,dtype=np.float32).flatten()
    nI = iLim[1]-iLim[0]
    nJ = jLim[1]-jLim[0]
    #make position masks here
    frame = masks['Frames']
    tam = ma.make_mask_none((nI*nJ))
    if frame:
        tam = ma.mask_or(tam,ma.make_mask(pm.polymask(nI*nJ,tax,
            tay,len(frame),frame,tamp)[:nI*nJ])^True)
    polygons = masks['Polygons']
    for polygon in polygons:
        if polygon:
            tam = ma.mask_or(tam,ma.make_mask(pm.polymask(nI*nJ,tax,
                tay,len(polygon),polygon,tamp)[:nI*nJ]))
    for X,Y,rsq in masks['Points'].T:
        tam = ma.mask_or(tam,ma.getmask(ma.masked_less((tax-X)**2+(tay-Y)**2,rsq)))
    if tam.shape: 
        tam = np.reshape(tam,(nI,nJ))
    else:
        tam = ma.make_mask_none((nI,nJ))
    for xline in masks.get('Xlines',[]):    #a y pixel position
        if iLim[0] <= xline <= iLim[1]:
            tam[xline-iLim[0],:] = True
    for yline in masks.get('Ylines',[]):    #a x pixel position
        if jLim[0] <= yline <= jLim[1]:
            tam[:,yline-jLim[0]] = True            
    return tam           #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]),ma.getdata(TA[3])))    #azimuth, 2-theta, dist, pol
    tax,tay,tad,pol = np.dsplit(TA,4)    #azimuth, 2-theta, dist**2/d0**2, pol
    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.
    pol = ma.compressed(ma.array(pol.flatten(),mask=tam))   #polarization
    return tax,tay,taz/pol,tad,tabs

def MakeUseTA(data,blkSize=128):
    Nx,Ny = data['size']
    nXBlks = (Nx-1)//blkSize+1
    nYBlks = (Ny-1)//blkSize+1
    useTA = []
    for iBlk in range(nYBlks):
        iBeg = iBlk*blkSize
        iFin = min(iBeg+blkSize,Ny)
        useTAj = []
        for jBlk in range(nXBlks):
            jBeg = jBlk*blkSize
            jFin = min(jBeg+blkSize,Nx)
            TA = Make2ThetaAzimuthMap(data,(iBeg,iFin),(jBeg,jFin))          #2-theta & azimuth arrays & create position mask
            useTAj.append(TA)
        useTA.append(useTAj)
    return useTA

def MakeUseMask(data,masks,blkSize=128):
    Masks = copy.deepcopy(masks)
    Masks['Points'] = np.array(Masks['Points']).T           #get spots as X,Y,R arrays
    if np.any(masks['Points']):
        Masks['Points'][2] = np.square(Masks['Points'][2]/2.)
    Nx,Ny = data['size']
    nXBlks = (Nx-1)//blkSize+1
    nYBlks = (Ny-1)//blkSize+1
    useMask = []
    tamp = ma.make_mask_none((1024*1024))       #NB: this array size used in the fortran histogram2d
    for iBlk in range(nYBlks):
        iBeg = iBlk*blkSize
        iFin = min(iBeg+blkSize,Ny)
        useMaskj = []
        for jBlk in range(nXBlks):
            jBeg = jBlk*blkSize
            jFin = min(jBeg+blkSize,Nx)
            mask = MakeMaskMap(data,Masks,(iBeg,iFin),(jBeg,jFin),tamp)          #2-theta & azimuth arrays & create position mask
            useMaskj.append(mask)
        useMask.append(useMaskj)
    return useMask

def MakeGainMap(image,Ix,Iy,data,masks,blkSize=128):
    import scipy.ndimage.filters as sdif
    Iy *= npcosd(Ix[:-1])**3       #undo parallax
    Iy *= (1000./data['distance'])**2    #undo r^2 effect
    Iy /= np.array(G2pwd.Polarization(data['PolaVal'][0],Ix[:-1],0.)[0])    #undo polarization
    if data['Oblique'][1]:
        Iy *= G2pwd.Oblique(data['Oblique'][0],Ix[:-1])     #undo penetration
    IyInt = scint.interp1d(Ix[:-1],Iy[0],bounds_error=False)
    GainMap = np.zeros_like(image,dtype=float)
    #do interpolation on all points - fills in the empty bins; leaves others the same
    Nx,Ny = data['size']
    nXBlks = (Nx-1)//blkSize+1
    nYBlks = (Ny-1)//blkSize+1
    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)
            TA = Make2ThetaAzimuthMap(data,(iBeg,iFin),(jBeg,jFin))           #2-theta & azimuth arrays & create position mask
            Ipix = IyInt(TA[0])
            GainMap[iBeg:iFin,jBeg:jFin] = image[iBeg:iFin,jBeg:jFin]/(Ipix*TA[3])
    GainMap = np.nan_to_num(GainMap)
    GainMap = sdif.gaussian_filter(GainMap,3.,order=0)
    return 1./GainMap

def ImageIntegrate(image,data,masks,blkSize=128,returnN=False,useTA=None,useMask=None):
    'Integrate an image; called from OnIntegrateAll and OnIntegrate in G2imgGUI'    #for q, log(q) bins need data['binType']
    import histogram2d as h2d
    G2fil.G2Print ('Begin image integration; image range: %d %d'%(np.min(image),np.max(image)))
    CancelPressed = False
    LUtth = np.array(data['IOtth'])
    LRazm = np.array(data['LRazimuth'],dtype=np.float64)
    numAzms = data['outAzimuths']
    numChans = (data['outChannels']//4)*4
    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
    Masks = copy.deepcopy(masks)
    Masks['Points'] = np.array(Masks['Points']).T           #get spots as X,Y,R arrays
    if np.any(masks['Points']):
        Masks['Points'][2] = np.square(Masks['Points'][2]/2.)
    NST = np.zeros(shape=(numAzms,numChans),order='F',dtype=np.float32)
    H0 = np.zeros(shape=(numAzms,numChans),order='F',dtype=np.float32)
    H2 = np.linspace(lutth[0],lutth[1],numChans+1)
    Nx,Ny = data['size']
    nXBlks = (Nx-1)//blkSize+1
    nYBlks = (Ny-1)//blkSize+1
    tbeg = time.time()
    times = [0,0,0,0,0]
    tamp = ma.make_mask_none((1024*1024))       #NB: this array size used in the fortran histogram2d
    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!

            t0 = time.time()
            if useTA:
                TA = useTA[iBlk][jBlk]
            else:
                TA = Make2ThetaAzimuthMap(data,(iBeg,iFin),(jBeg,jFin))           #2-theta & azimuth arrays & create position mask
            times[1] += time.time()-t0      #xy->th,azm

            t0 = time.time()
            if useMask:
                tam = useMask[iBlk][jBlk]
            else:
                tam = MakeMaskMap(data,Masks,(iBeg,iFin),(jBeg,jFin),tamp)
            times[0] += time.time()-t0      #apply masks

            t0 = time.time()
            Block = image[iBeg:iFin,jBeg:jFin]          #apply image spotmask here
            tax,tay,taz,tad,tabs = Fill2ThetaAzimuthMap(Masks,TA,tam,Block)    #and apply masks
            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']
            times[2] += time.time()-t0          #fill map

            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          #binning
    G2fil.G2Print('End integration loops')

    t0 = time.time()
    #prepare masked arrays of bins with pixels for interpolation setup
    H2msk = [ma.array(H2[:-1],mask=np.logical_not(nst)) for nst in NST]
#    H0msk = [ma.array(np.divide(h0,nst),mask=np.logical_not(nst)) for nst,h0 in zip(NST,H0)]
    H0msk = [ma.array(h0/nst,mask=np.logical_not(nst)) for nst,h0 in zip(NST,H0)]
    #make linear interpolators; outside limits give NaN
    H0 = []
    problemEntries = []
    for i,(h0msk,h2msk) in enumerate(zip(H0msk,H2msk)):
        try:
            h0int = scint.interp1d(h2msk.compressed(),h0msk.compressed(),bounds_error=False)
            #do interpolation on all points - fills in the empty bins; leaves others the same
            h0 = h0int(H2[:-1])
            H0.append(h0)
        except ValueError:
            problemEntries.append(i)
            H0.append(np.zeros(numChans,order='F',dtype=np.float32))
    H0 = np.nan_to_num(np.array(H0))        
    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
    if 'SASD' not in data['type']:
        H0 *= np.array(G2pwd.Polarization(data['PolaVal'][0],H2[:-1],0.)[0])
    H0 /= np.abs(npcosd(H2[:-1]-np.abs(data['det2theta'])))           #parallax correction
    H0 *= (data['distance']/1000.)**2    #remove r^2 effect
    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])
    times[4] += time.time()-t0          #cleanup

    G2fil.G2Print ('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]))
    G2fil.G2Print ("Elapsed time:","%8.3fs"%(time.time()-tbeg))
    G2fil.G2Print ('Integration complete')
    if problemEntries:
        msg = ""
        for i in problemEntries:
            if msg: msg += ', '
            msg += "{:.2f}".format((H1[i+1]+H1[i])/2.)
        print('\nWarning, integrations have no pixels at these azimuth(s)\n\t',msg,'\n')
    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
            G2fil.G2Print ('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,5)
            XY = np.array(ringxy).T
            Th,Azm = GetTthAzm(XY[0],XY[1],Controls)
            pola = G2pwd.Polarization(Controls['PolaVal'][0],Th,Azm-90.)[0]     #get pola not dpola
            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)
            ringint /= pola[0]      #just 1st column
            G2fil.G2Print (' %s %.3f %s %.3f %s %d'%('d-spacing',ring['Dcalc'],'sig(MRD):',np.sqrt(np.var(ringint)),'# points:',len(ringint)))
            RingsAI.append(np.array(list(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 FitImageSpots(Image,ImMax,ind,pixSize,nxy,spotSize=1.0):
    
    def calcMean(nxy,pixSize,img):
        gdx,gdy = np.mgrid[0:nxy,0:nxy]
        gdx = ma.array((gdx-nxy//2)*pixSize[0]/1000.,mask=~ma.getmaskarray(ImBox))
        gdy = ma.array((gdy-nxy//2)*pixSize[1]/1000.,mask=~ma.getmaskarray(ImBox))
        posx = ma.sum(gdx)/ma.count(gdx)
        posy = ma.sum(gdy)/ma.count(gdy)
        return posx,posy
    
    def calcPeak(values,nxy,pixSize,img):
        back,mag,px,py,sig = values
        peak = np.zeros([nxy,nxy])+back
        nor = 1./(2.*np.pi*sig**2)
        gdx,gdy = np.mgrid[0:nxy,0:nxy]
        gdx = (gdx-nxy//2)*pixSize[0]/1000.
        gdy = (gdy-nxy//2)*pixSize[1]/1000.
        arg = (gdx-px)**2+(gdy-py)**2        
        peak += mag*nor*np.exp(-arg/(2.*sig**2))
        return ma.compressed(img-peak)/np.sqrt(ma.compressed(img))
    
    def calc2Peak(values,nxy,pixSize,img):
        back,mag,px,py,sigx,sigy,rho = values
        peak = np.zeros([nxy,nxy])+back
        nor = 1./(2.*np.pi*sigx*sigy*np.sqrt(1.-rho**2))
        gdx,gdy = np.mgrid[0:nxy,0:nxy]
        gdx = (gdx-nxy//2)*pixSize[0]/1000.
        gdy = (gdy-nxy//2)*pixSize[1]/1000.
        argnor = -1./(2.*(1.-rho**2))
        arg = (gdx-px)**2/sigx**2+(gdy-py)**2/sigy**2-2.*rho*(gdx-px)*(gdy-py)/(sigx*sigy)        
        peak += mag*nor*np.exp(argnor*arg)
        return ma.compressed(img-peak)/np.sqrt(ma.compressed(img))        
    
    nxy2 = nxy//2
    ImBox = Image[ind[1]-nxy2:ind[1]+nxy2+1,ind[0]-nxy2:ind[0]+nxy2+1]
    back = np.min(ImBox)
    mag = np.sum(ImBox-back)
    vals = [back,mag,0.,0.,0.2,0.2,0.]
    ImBox = ma.array(ImBox,dtype=float,mask=ImBox>0.75*ImMax)
    px = (ind[0]+.5)*pixSize[0]/1000.
    py = (ind[1]+.5)*pixSize[1]/1000.
    if ma.any(ma.getmaskarray(ImBox)):
        vals = calcMean(nxy,pixSize,ImBox)
        posx,posy = [px+vals[0],py+vals[1]]
        return [posx,posy,spotSize]
    else:
        result = leastsq(calc2Peak,vals,args=(nxy,pixSize,ImBox),full_output=True)
        vals = result[0]
        ratio = vals[4]/vals[5]
        if 0.5 < ratio < 2.0 and vals[2] < 2. and vals[3] < 2.:
            posx,posy = [px+vals[2],py+vals[3]]
            return [posx,posy,min(6.*vals[4],spotSize)]
        else:
            return None
    
def AutoSpotMask(Image,Masks,Controls,numChans,dlg=None):
    
    frame = Masks['Frames']
    tam = ma.make_mask_none(Image.shape)
    if frame:
        tam = ma.mask_or(tam,MakeFrameMask(Controls,frame))
    LUtth = np.array(Controls['IOtth'])
    dtth = (LUtth[1]-LUtth[0])/numChans
    esdMul = Masks['SpotMask']['esdMul']
    prob = 100.*sc.erf(esdMul/np.sqrt(2.))
    print(' Spots greater than %.2f of band intensity are masked'%prob)
    mask = ma.make_mask_none(Image.shape)
    band = ma.array(Image,mask=ma.nomask)
    TA = Make2ThetaAzimuthMap(Controls,(0,Image.shape[0]),(0,Image.shape[1]))[0]    #2-theta array
    TThs = np.linspace(LUtth[0],LUtth[1],numChans,False)
    for it,TTh in enumerate(TThs):
        band.mask = ma.masked_outside(TA,TTh,TTh+dtth).mask+tam
        pcmax = np.percentile(band.compressed(),[prob,50.])
        mband = ma.masked_greater(band,pcmax[0])
        std = ma.std(mband)
        anom = ma.masked_greater((band-pcmax[1])/std,esdMul)
        mask ^= (anom.mask^band.mask)
        if not dlg is None:
            GoOn = dlg.Update(it,newmsg='Processed 2-theta rings = %d'%(it))
            if not GoOn[0]:
                break
    return mask

def DoPolaCalib(ImageZ,imageData,arcTth):
    ''' Determine image polarization by successive integrations with & without preset arc mask.
    After initial search, does a set of five with offset azimuth to get mean(std) result.
    '''
    from scipy.optimize import minimize_scalar
    print(' Polarization fit for mask at %.1f deg 2-theta'%arcTth)
    data = copy.deepcopy(imageData)
    data['IOtth'] = [arcTth-2.,arcTth+2.]
    data['fullIntegrate'] = True
    data['LRazimuth'] = [0.,360.]
    data['outChannels'] = 200
    data['outAzimuths'] = 1
    Arc = [arcTth,[85.,95.],2.0]
    print(' Integration 2-theta test range %.1f - %.1f in 200 steps'%(data['IOtth'][0],data['IOtth'][1]))
    print(' Mask azimuth range: %.1f - %.1f'%(Arc[1][0],Arc[1][1]))
    Masks = {'Points':[],'Rings':[],'Arcs':[],'Polygons':[],'Frames':[],
        'Thresholds':imageData['range'],'SpotMask':{'esdMul':3.,'spotMask':None}}
    AMasks = {'Points':[],'Rings':[],'Arcs':[Arc,],'Polygons':[],'Frames':[],
        'Thresholds':imageData['range'],'SpotMask':{'esdMul':3.,'spotMask':None}}
    
    def func(p):
        p = min(1.0,max(p,0.0))
        data['PolaVal'][0] = p
        H0 = ImageIntegrate(ImageZ,data,Masks)[0]
        H0A = ImageIntegrate(ImageZ,data,AMasks)[0]
        M = np.sum(H0)-np.sum(H0A)
        print(' Polarization %.4f, fxn: %.1f'%(p,M))
        return M**2
    time0 = time.time()
    res = minimize_scalar(func,bracket=[1.,.999],tol=.0001)
    print(res)
    pola = min(1.0,max(res.x,.0))
    
    Pola = []
    for arc in [75,80,85,90,95]:
        Arc = [arcTth,[arc,arc+10.],2.0]
        AMasks = {'Points':[],'Rings':[],'Arcs':[Arc,],'Polygons':[],'Frames':[],
            'Thresholds':imageData['range'],'SpotMask':{'esdMul':3.,'spotMask':None}}
        res = minimize_scalar(func,bracket=[pola-.001,pola],tol=.0001)
        print(' Mask azimuth range: %.1f - %.1f'%(Arc[1][0],Arc[1][1]))
        print(' pola: %.5f'%res.x)
        Pola.append(res.x)
    Pola = np.array(Pola)
    mean = np.mean(Pola)
    std = int(10000*np.std(Pola))
    print(' Polarization: %.4f(%d)'%(mean,std))
    print(' time: %.2fs'%(time.time()-time0))
    imageData['PolaVal'][0] = mean