def circavgfill(img, mask, x0=None, y0=None, ps=True, poisson=False):
''' Fill using circular average.
img - the image
mask - the mask
x0, y0 - the center of the image
ps - also use point symmetry
'''
if (x0 is None):
x0 = img.shape[1] / 2
if (y0 is None):
y0 = img.shape[0] / 2
SIMG = img * 0
sqx, sqy = circavg(img, mask=mask, x0=x0, y0=y0, SIMG=SIMG)
w = np.where((mask < .01))
imgcavg = np.copy(img)
if (poisson is False):
imgcavg[w] = SIMG[w]
else:
imgcavg[w] = np.random.poisson(SIMG[w])
if (ps):
mask = mask.astype(float)
img = img.astype(float)
imgr = imgcavg * 0
maskr = mask * 0
rotate(imgcavg, imgr, np.pi, x0, y0)
rotate(mask, maskr, np.pi, x0, y0)
w = np.where((mask < .1) * (maskr > .1))
imgcavg[w] = imgr[w]
return imgcavg
def sqtfromlist(filelist,imgthresh=None,x0=None,y0=None,mask=None,DDIR='.'):
''' Get sq versus item for a list.'''
for i in range(len(filelist)):
IMGS = EigerImages(DDIR + "/" + filelist[i])
IMG,ivfrm = avgimgs(IMGS,imgthresh=imgthresh)
sqx, sqy = circavg(IMG,x0=x0,y0=y0,mask=mask)
if(i == 0):
sqt = np.zeros((len(filelist),len(sqx)))
sqt[i] = sqy
return sqx, sqt
def compute_contrast_stats(IMG, mask=None, x0=None, y0=None, ptskip=None):
''' get the stats needed for the computation of an image contrast.
currently I'm skipping a lot of data to limit the number of data points plotted.
This is meant more to be qualitative.
Set ptskip = 0 to get all points
'''
if (ptskip is None):
ptskip = 1000
SIMG = IMG * 0
SIMG2 = IMG * 0
sqx, sqy = circavg(IMG, x0=x0, y0=y0, mask=mask, SIMG=SIMG)
sq2x, sq2y = circavg2(IMG, IMG, x0=x0, y0=y0, mask=mask, SIMG=SIMG2)
pxlst = np.where(mask.ravel() != 0)[0][::ptskip]
Ints2 = SIMG2.ravel()[pxlst]
Ints = SIMG.ravel()[pxlst]
return Ints, Ints2
def plotsq(self, winnum=None, clr=None, logxy=[1, 1]):
'''Plot the circularly averaged structure factor of the data.'''
if (clr is None):
clr = 'k'
#pixel average
cavgx, cavgy = circavg(self.imgFFT2)
#q perpixel
self.sq_q = cavgx
self.sq = cavgy
self.dq = 2 * np.pi / float(self.dims[0]) / self.resolution
#self.plotzfline(self.sq_q*self.dq,self.sq,clr=clr,winnum=winnum,logxy=logxy)
if (winnum is None):
winnum = self.winnum
fig = plt.figure(winnum)
plt.loglog(self.sq_q * self.dq, self.sq, clr)
fig.gca().set_xlabel(self.unit + "$^{-1}$")
fig.gca().set_ylabel("$|S(q)|^2$")
def fitAgBHring(IMG, mask=None, pars=None, plotwin=None, clims=None):
''' Fit an AgBH (silver behenate) ring
plotwin : if set, plot to the window desired.
pars : a Parameters object of the initial guess Parameters. If not set
the program will ask you for a best guess.
mask : a mask
clims : if specified, these will be the color limits of the image plot windows
default is to take min and max of *masked* image
'''
qAGBH = 2 * np.pi / CONST_AgBHpeak
if (plotwin is not None):
plt.figure(plotwin)
if (mask is None):
mask = np.ones(IMG.shape)
img = IMG * (mask + .1)
plt.imshow(img)
if (clims is None):
plt.clim(np.min(img * mask), np.max(img * mask))
plt.draw()
plt.pause(.001)
pixellist = np.where(mask.ravel() == 1)
print(
"\n\nfitAgBH: An attempt to fit the first silver behenate ring on a 2D area det"
)
print("Note if plotting, the masked region is brightened w.r.t. the rest")
print("Some tips: \n \
1. Try to set mask to only select region near where the ring is\n\
for a better fit\n\
2. Try a fit without the parasitic bg or const scattering first.\n\
")
if (pars is None):
pars = Parameters()
print(
"You did not specify parameters, so I will ask you a few questions"
)
print("Specify the variables in one of two ways, either:")
print(": value, min, max")
print("or")
print(": value")
#it is "raw_input" in python 2x
xcenvals = np.array(
input("beam center x position guess: ").split(",")).astype(float)
pars.add('x0', xcenvals[0], min=xcenvals[0] - 40, max=xcenvals[0] + 40)
if (len(xcenvals) == 3):
pars['x0'].min = xcenvals[1]
pars['x0'].max = xcenvals[2]
ycenvals = np.array(
input("beam center y position guess: ").split(",")).astype(float)
pars.add('y0', ycenvals[0], min=ycenvals[0] - 40, max=ycenvals[0] + 40)
if (len(ycenvals) == 3):
pars['y0'].min = ycenvals[1]
pars['y0'].max = ycenvals[2]
if (plotwin is not None):
print("Plotted estimate of S(q) from given xcen, ycen")
sqx, sqy = circavg(IMG, x0=xcenvals[0], y0=ycenvals[0], mask=mask)
plt.figure(plotwin + 2)
plt.cla()
w = np.where(sqy > 0)
plt.loglog(sqx[w], sqy[w])
ampvals = np.array(
input("amplitude of ring: ").split(",")).astype(float)
pars.add('amp', ampvals[0], min=0, max=1e9)
if (len(ampvals) == 3):
pars['amp'].min = ampvals[1]
pars['amp'].max = ampvals[1]
ringvals = np.array(
input("Ring location (in pixels from center of beam, approx):").
split(",")).astype(float)
pars.add('r0', ringvals[0], min=0, max=4 * ringvals[0])
if (len(ringvals) == 3):
pars['r0'].min = ringvals[1]
pars['r0'].max = ringvals[2]
sigmavals = np.array(
input("sigma of ring (FWHM for Lorentzian) :").split(",")).astype(
float)
pars.add('sigma', sigmavals[0], min=.1, max=100) #,vary=False)
if (len(sigmavals) == 3):
pars['sigma'].min = sigmavals[1]
pars['sigma'].max = sigmavals[2]
bgvals = input("1/q^4 background (enter nothing to not vary): ")
if (len(bgvals) == 0):
pars.add('bg', 0, vary=False)
pars.add('bgexp', 4., vary=False)
else:
bgvals = np.array(bgvals.split(",")).astype(float)
pars.add('bg', bgvals[0], vary=False)
pars.add('bgexp', 4., vary=True, min=1, max=4)
if (len(bgvals) == 3):
pars['bg'].min = bgvals[1]
pars['bgexp'].max = bgvals[2]
constvals = input("Constant background (enter nothing to not vary):")
if (len(constvals) == 0):
pars.add('const', 0, vary=False)
else:
constvals = np.array(constvals.split(",")).astype(float)
pars.add("const", constvals[0])
if (len(constvals) == 3):
pars['const'].min = constvals[1]
pars['const'].max = constvals[2]
data = IMG.ravel()[pixellist]
fitobj = FitObject(fn=ring2Dlorentzfitfunc, pars=pars)
x = np.arange(IMG.shape[1])
y = np.arange(IMG.shape[0])
X, Y = np.meshgrid(x, y)
xy = np.array([X.ravel()[pixellist], Y.ravel()[pixellist]])
erbs = np.ones(len(pixellist[0]))
datatest = np.zeros(IMG.shape)
fitobj.fit(xy, data, erbs=erbs)
datatest.ravel()[pixellist] = fitobj.fn(xy, fitobj.pars)
XCENFIT = fitobj.pars['x0'].value
YCENFIT = fitobj.pars['y0'].value
RFIT = fitobj.pars['r0'].value
SIGMAFIT = fitobj.pars['sigma'].value
CONSTFIT = fitobj.pars['const'].value
BGFIT = fitobj.pars['bg'].value
BGEXPFIT = fitobj.pars['bgexp'].value
print("x center fit: {:3.2f}; y center fit: {:3.2f}; radius fit: {:3.2f}".
format(XCENFIT, YCENFIT, RFIT))
print(
"ring FWHM : {:3.2f}; const background: {:3.2f}; parasitic bg: {:3.2f}/q^{:3.2f}"
.format(SIGMAFIT, CONSTFIT, BGFIT, BGEXPFIT))
sqd_x, sqd_y = circavg(IMG, x0=XCENFIT, y0=YCENFIT, mask=mask)
sqf_x, sqf_y = circavg(datatest, x0=XCENFIT, y0=YCENFIT, mask=mask)
if (plotwin is not None):
plt.figure(plotwin)
plt.cla()
plt.imshow(IMG * mask)
if (clims is None):
plt.clim(np.min(img * mask), np.max(img * mask))
plcirc([XCENFIT, YCENFIT], RFIT, 'b')
#plt.clim(np.min(IMG*mask),np.max(IMG*mask))
plt.figure(plotwin + 1)
plt.cla()
plt.imshow(datatest * mask)
if (clims is None):
plt.clim(np.min(img * mask), np.max(img * mask))
#plt.clim(np.min(IMG*mask),np.max(IMG*mask))
plcirc([XCENFIT, YCENFIT], RFIT, 'b')
plt.figure(plotwin + 2)
plt.cla()
plt.loglog(sqd_x, sqd_y)
plt.loglog(sqf_x, sqf_y, 'r')
plt.gca().autoscale_view('tight')
plt.draw()
plt.pause(0.001)
resp = input("Get det distance?(y for yes, anything else for no): ")
if (resp == 'y'):
wv = float(input("wavelength (in angs): "))
dpix = float(input("pix size (in m): "))
L, err = getdistfrompeak(qAGBH, wv, dpix, RFIT)
print(
"Your length is approximately {:4.2f} m, and error in calc (from Ewald curvature) approx {:4.2f}%"
.format(L, err))
print("done")
def compute_sq(self):
self.sqx, self.sqy = circavg(self.IMG,
x0=self.xcen,
y0=self.ycen,
mask=self.mask)