def remeshchiintegrate(data,
mask,
AIdict,
cut=None,
color=[255, 255, 255],
requestkey=None,
qvrt=None,
qpar=None):
AI = pyFAI.AzimuthalIntegrator()
AI.setPyFAI(**AIdict)
alphai = config.activeExperiment.getvalue('Incidence Angle (GIXS)')
msg.logMessage(
'Incoming angle applied to remeshed q integration: ' + str(alphai),
msg.DEBUG)
qsquared = qpar**2 + qvrt**2
remeshcenter = np.unravel_index(qsquared.argmin(), qsquared.shape)
f2d = AI.getFit2D()
f2d['centerX'] = remeshcenter[0]
f2d['centerY'] = remeshcenter[1]
AI.setFit2D(**f2d)
AIdict = AI.getPyFAI()
return chiintegratepyFAI(data,
mask,
AIdict,
cut,
color,
requestkey,
qvrt=None,
qpar=None)
def zintegrate(data,
mask,
AIdict,
cut=None,
color=[255, 255, 255],
requestkey=None,
qvrt=None,
qpar=None):
if mask is not None:
mask = mask.copy()
msg.logMessage(('image:', data.shape), msg.DEBUG)
msg.logMessage(('mask:', mask.shape), msg.DEBUG)
if not mask.shape == data.shape:
msg.logMessage("No mask match. Mask will be ignored.", msg.WARNING)
mask = np.ones_like(data)
msg.logMessage(('emptymask:', mask.shape), msg.DEBUG)
if cut is not None:
msg.logMessage(('cut:', cut.shape), msg.DEBUG)
mask &= cut.astype(bool)
# data *= cut
maskeddata = np.ma.masked_array(data, mask=1 - mask)
xprofile = np.ma.average(maskeddata, axis=0)
AI = pyFAI.AzimuthalIntegrator()
AI.setPyFAI(**AIdict)
qz = AI.qArray(data.shape[::-1])[:, AI.getFit2D()['centerX']] / 10
qz[:qz.argmin()] *= -1
return qz, xprofile, color, requestkey
def fourierAutocorrelation(dimg, calibrantkey):
yield 0
if dimg.transformdata is None:
return
yield 20
config.activeExperiment.center = center_approx.center_approx(
dimg.transformdata, dimg.mask)
yield 40
radialprofile = integration.pixel_2Dintegrate(dimg, mask=dimg.mask)
yield 60
peaks = np.array(
peakfinding.findpeaks(np.arange(len(radialprofile)), radialprofile)).T
yield 80
peaks = peaks[peaks[:, 1].argsort()[::-1]]
for peak in peaks:
if peak[0] > 15 and not np.isinf(
peak[1]
): ####This thresholds the minimum sdd which is acceptable
bestpeak = peak[0]
# print peak
break
yield 85
N = 1
# guess which order is the first
stds = [
np.std((peaks[:, 0] / (np.arange(len(peaks)) + i))[:4])
for i in range(1, 5)
]
if min(stds) < 5: # threshold accepting peak as higher N
N = np.argmin(stds) + 1 # index of the first detected peak
msg.logMessage(('Using peak #', N, ' for calibration...'))
yield 90
calibrant1stpeak = calibrant.ALL_CALIBRANTS[calibrantkey].dSpacing[N - 1]
# Calculate sample to detector distance for lowest q peak
tth = 2 * np.arcsin(0.5 * config.activeExperiment.getvalue('Wavelength') /
calibrant1stpeak / 1.e-10)
tantth = np.tan(tth)
sdd = bestpeak * config.activeExperiment.getvalue('Pixel Size X') / tantth
config.activeExperiment.setvalue('Detector Distance', sdd)
# self.refinecenter()
dimg.invalidatecache()
yield 100
def similarframe(path, N):
"""
Get the file path N ahead (or behind) the provided path frame.
"""
try:
expr = '(?<=_)[\d]+(?=[_.])'
frame = re.search(expr, os.path.basename(path)).group(0)
leadingzeroslen = len(frame)
framenum = int(frame)
prevframenum = int(framenum) + N
prevframenum = '{:0>{}}'.format(prevframenum, leadingzeroslen)
return re.sub(expr, prevframenum, path)
except ValueError:
msg.logMessage('No earlier frame found for ' + path + ' with ' + N,
msg.ERROR)
return None
def remeshqintegrate(data,
mask,
AIdict,
cut=None,
color=[255, 255, 255],
requestkey=None,
qvrt=None,
qpar=None):
AI = pyFAI.AzimuthalIntegrator()
AI.setPyFAI(**AIdict)
alphai = config.activeExperiment.getvalue('Incidence Angle (GIXS)')
msg.logMessage(
'Incoming angle applied to remeshed q integration: ' + str(alphai),
msg.DEBUG)
#qpar, qvrt = remesh.remeshqarray(data, None, AI, np.deg2rad(alphai))
qsquared = qpar**2 + qvrt**2
remeshcenter = np.unravel_index(qsquared.argmin(), qsquared.shape)
print 'center?:', remeshcenter
f2d = AI.getFit2D()
f2d['centerX'] = remeshcenter[0]
f2d['centerY'] = remeshcenter[1]
AI.setFit2D(**f2d)
AIdict = AI.getPyFAI()
msg.logMessage('remesh corrected calibration: ' + str(AIdict))
q, qprofile, color, requestkey = qintegrate(data,
mask,
AIdict,
cut,
color,
requestkey,
qvrt=None,
qpar=None)
maxq = (qsquared * mask).max()
if cut is not None: qsquared[np.logical_not(cut.astype(np.bool))] = np.inf
minq = qsquared.min()
q = np.linspace(minq, maxq, len(qprofile)) / 10.
return q, qprofile, color, requestkey
def chiintegratepyFAI(data,
mask,
AIdict,
cut=None,
color=[255, 255, 255],
requestkey=None,
qvrt=None,
qpar=None,
xres=1000,
yres=1000):
AI = pyFAI.AzimuthalIntegrator()
AI.setPyFAI(**AIdict)
# Always do mask with 1-valid, 0's excluded
if mask is not None:
mask = mask.copy()
msg.logMessage(('image:', data.shape), msg.DEBUG)
msg.logMessage(('mask:', mask.shape), msg.DEBUG)
if not mask.shape == data.shape:
msg.logMessage("No mask match. Mask will be ignored.", msg.WARNING)
mask = np.ones_like(data)
msg.logMessage(('emptymask:', mask.shape), msg.DEBUG)
if cut is not None:
msg.logMessage(('cut:', cut.shape), msg.DEBUG)
mask &= cut.astype(bool)
# data *= cut
cake, q, chi = AI.integrate2d(data.T,
xres,
yres,
mask=1 - mask.T,
method='lut_ocl')
mask, q, chi = AI.integrate2d(1 - mask.T,
xres,
yres,
mask=1 - mask.T,
method='lut_ocl')
maskedcake = np.ma.masked_array(cake, mask=mask)
chiprofile = np.ma.average(maskedcake, axis=1)
return chi, chiprofile, color, requestkey
def radialintegratepyFAI(data,
mask=None,
AIdict=None,
cut=None,
color=[255, 255, 255],
requestkey=None,
qvrt=None,
qpar=None):
if mask is None: mask = config.activeExperiment.mask
if AIdict is None:
AI = config.activeExperiment.getAI()
# p1 = AI.get_poni1()
# p2 = AI.get_poni2()
# msg.logMessage(('poni:', p1, p2),msg.DEBUG)
else:
AI = pyFAI.AzimuthalIntegrator()
AI.setPyFAI(**AIdict)
if mask is not None:
mask = mask.copy()
msg.logMessage(('image:', data.shape), msg.DEBUG)
msg.logMessage(('mask:', mask.shape), msg.DEBUG)
if not mask.shape == data.shape:
msg.logMessage("No mask match. Mask will be ignored.", msg.WARNING)
mask = np.ones_like(data)
msg.logMessage(('emptymask:', mask.shape), msg.DEBUG)
if cut is not None and type(cut) is np.ndarray:
msg.logMessage(('cut:', cut.shape), msg.DEBUG)
mask = mask.astype(bool) & cut.astype(bool)
(q, radialprofile) = AI.integrate1d(
data.T,
config.settings['Integration Bins (q)'],
mask=1 - mask.T,
method=pyFAI_method) #pyfai uses 0-valid mask
q = q / 10.
return q.tolist(), radialprofile.tolist(), color, requestkey
def chi_2Dintegrate(imgdata, cen, mu, mask=None, chires=30):
"""
Integration over r for a chi range. Output is 30*
"""
if mask is None:
msg.logMessage("No mask defined, creating temporary empty mask..",
msg.INFO)
mask = np.zeros_like(imgdata)
# mask data
data = imgdata * (1 - mask)
x, y = np.indices(data.shape).astype(np.float)
r = np.sqrt((x - cen[0])**2 + (y - cen[1])**2)
r = r.astype(np.int)
delta = 3
rinf = mu - delta / 2.
rsup = mu + delta / 2.
rmask = ((rinf < r) & (r < rsup) & (x < cen[0])).astype(np.int)
data *= rmask
chi = chires * np.arctan((y - cen[1]) / (x - cen[0]))
chi += chires * np.pi / 2.
chi = np.round(chi).astype(np.int)
chi *= (chi > 0)
tbin = np.bincount(chi.ravel(), data.ravel())
nr = np.bincount(chi.ravel(), rmask.ravel())
angleprofile = tbin / nr
# vimodel = pymodelfit.builtins.GaussianModel()
# vimodel.mu = np.pi / 2 * 100
# vimodel.A = np.nanmax(angleprofile)
# vimodel.fitData(x=np.arange(np.size(angleprofile)), y=angleprofile, weights=angleprofile)
# vimodel.plot(lower=0, upper=np.pi * 100)
# print ('len:',len(angleprofile))
return angleprofile
def remeshzintegrate(data,
mask,
AIdict,
cut=None,
color=[255, 255, 255],
requestkey=None,
qvrt=None,
qpar=None):
AI = pyFAI.AzimuthalIntegrator()
AI.setPyFAI(**AIdict)
if not mask.shape == data.shape:
msg.logMessage("No mask match. Mask will be ignored.", msg.WARNING)
mask = np.ones_like(data)
msg.logMessage(('emptymask:', mask.shape), msg.DEBUG)
if cut is not None:
msg.logMessage(('cut:', cut.shape), msg.DEBUG)
mask &= cut.astype(bool)
center = np.where(np.abs(qvrt) == np.abs(qvrt).min())
qx = -qvrt[center[1][0]] / 10.
maskeddata = np.ma.masked_array(data, mask=1 - mask)
xprofile = np.ma.average(maskeddata, axis=0)
return qx, xprofile, color, requestkey
def cakexintegrate(data,
mask,
AIdict,
cut=None,
color=[255, 255, 255],
requestkey=None,
qvrt=None,
qpar=None):
AI = pyFAI.AzimuthalIntegrator()
AI.setPyFAI(**AIdict)
if not mask.shape == data.shape:
msg.logMessage("No mask match. Mask will be ignored.", msg.WARNING)
mask = np.ones_like(data)
msg.logMessage(('emptymask:', mask.shape), msg.DEBUG)
if cut is not None:
msg.logMessage(('cut:', cut.shape), msg.DEBUG)
mask &= cut.astype(bool)
chi = np.arange(-180, 180, 360 / config.settings['Integration Bins (χ)'])
maskeddata = np.ma.masked_array(data, mask=1 - mask)
xprofile = np.ma.average(maskeddata, axis=1)
return chi.tolist(), xprofile.tolist(), color, requestkey
def cakezintegrate(data,
mask,
AIdict,
cut=None,
color=[255, 255, 255],
requestkey=None,
qvrt=None,
qpar=None):
AI = pyFAI.AzimuthalIntegrator()
AI.setPyFAI(**AIdict)
if not mask.shape == data.shape:
msg.logMessage("No mask match. Mask will be ignored.", msg.WARNING)
mask = np.ones_like(data)
msg.logMessage(('emptymask:', mask.shape), msg.DEBUG)
if cut is not None:
msg.logMessage(('cut:', cut.shape), msg.DEBUG)
mask &= cut.astype(bool)
q = np.arange(1000) * np.max(qpar) / 10000.
maskeddata = np.ma.masked_array(data, mask=1 - mask)
zprofile = np.ma.average(maskeddata, axis=0)
return q, zprofile, color, requestkey
def pixel_2Dintegrate(dimg, mask=None):
centerx = dimg.experiment.getvalue('Center X')
centery = dimg.experiment.getvalue('Center Y')
if mask is None:
msg.logMessage("No mask defined, creating temporary empty mask.",
msg.INFO)
mask = np.zeros_like(dimg.rawdata)
# mask data
data = dimg.transformdata * (mask)
# calculate data radial profile
x, y = np.indices(data.shape)
r = np.sqrt((x - centerx)**2 + (y - centery)**2)
r = r.astype(np.int)
tbin = np.bincount(r.ravel(), data.ravel())
nr = np.bincount(r.ravel(), (mask).ravel())
radialprofile = tbin / nr
return radialprofile
def read(self, f, frame=None):
with open(f, 'r') as f:
data = np.fromfile(f, dtype=np.int32)
for name, detector in detectors.ALL_DETECTORS.iteritems():
if hasattr(detector, 'MAX_SHAPE'):
# print name, detector.MAX_SHAPE, imgdata.shape[::-1]
if np.prod(detector.MAX_SHAPE) == len(data): #
detector = detector()
msg.logMessage('Detector found: ' + name, msg.INFO)
break
if hasattr(detector, 'BINNED_PIXEL_SIZE'):
# print detector.BINNED_PIXEL_SIZE.keys()
if len(data) in [
np.prod(np.array(detector.MAX_SHAPE) / b)
for b in detector.BINNED_PIXEL_SIZE.keys()
]:
detector = detector()
msg.logMessage('Detector found with binning: ' + name,
msg.INFO)
break
data.shape = detector.MAX_SHAPE
self.data = data
return self
def drawoverlay(self):
activelatticetype = self.activelatticetype()
SG = self.spacegroupparameter.value()
refbeta = self.beta.value()
refdelta = self.delta.value()
peaks = spacegrp_peaks.find_peaks(
float(activelatticetype.a.value()),
float(activelatticetype.b.value()),
float(activelatticetype.c.value()),
activelatticetype.alpha.value(),
activelatticetype.beta.value(),
activelatticetype.gamma.value(),
normal=self._getRotationVector(),
norm_type=['xyz', 'hkl', 'uvw'][self._getRotationType()],
refdelta=refdelta,
refbeta=refbeta,
order=5,
unitcell=None,
space_grp=SG)
for peak in peaks:
msg.logMessage(unicode(peak), msg.DEBUG)
# print key + " -> " + str(peaks[key])
center = config.activeExperiment.center
sdd = config.activeExperiment.getvalue('Detector Distance')
pixelsize = config.activeExperiment.getvalue('Pixel Size X')
peak.position(center, sdd, pixelsize)
# pixels = spacegrp_peaks.angles_to_pixels(np.array(peaks[key]), center, sdd)
# peaks[key] = pixels
# peaks = [peak('Transmission', p, peaks[p][0][0], peaks[p][0][1], 1, 1, 1) for p in peaks if
# not np.any(peaks[p] < -100000)] + \
# [peak('Reflection', p, peaks[p][1][0], peaks[p][1][1], 1, 1, 1) for p in peaks if
# not np.any(peaks[p] < -100000)]
self.sigDrawSGOverlay.emit(peakoverlay(peaks))
def variation(operationindex, imga, imgb=None, imgc=None, roi=None):
if imga is not None and imgb is not None and imgc is not None:
try:
with warnings.catch_warnings():
warnings.simplefilter('ignore')
# TODO: REFACTOR THIS TO USE THUMBS!!!!!
p = scipy.ndimage.zoom(imga, 0.1, order=1)
c = scipy.ndimage.zoom(imgb, 0.1, order=1)
n = scipy.ndimage.zoom(imgc, 0.1, order=1)
p = scipy.ndimage.gaussian_filter(p, 3)
c = scipy.ndimage.gaussian_filter(c, 3)
n = scipy.ndimage.gaussian_filter(n, 3)
if roi is not None:
roi = scipy.ndimage.zoom(roi, 0.1, order=1)
roi = np.flipud(roi)
else:
roi = 1
with np.errstate(divide='ignore'):
return variationoperators.operations.values()[operationindex](p, c, n, roi, None, None)
except TypeError:
msg.logMessage('Variation could not be determined for a frame.',msg.ERROR)
else:
msg.logMessage('Variation could not be determined for a frame.',msg.ERROR)
return 0
def onMove(self, pos):
pos = self.mapFromScene(pos)
peaks = self.peaksAtRelative(pos)
if len(peaks) != 0:
s = u''
#print points
for peak in peaks:
if s != u'': s += '\n\n'
s += unicode(peak)
self.display_text.setText(s)
self.display_text.setPos(pos)
self.display_text.show()
msg.logMessage(s)
else:
self.display_text.hide()
#print str(self)
if len(peaks) > 6:
self.display_text.setText('Too many points under cursor.\n'
'Zoom in or check Log info.')
self.display_text.setPos(pos)
self.display_text.show()
#! /usr/bin/env python
# from image_load import *
# import loader
import numpy as np
from pyFAI import geometry
from PySide import QtGui
import msg
try:
from cWarpImage import warp_image
except ImportError:
msg.logMessage("Remeshing C extension is NOT LOADED!",msg.ERROR)
def calc_q_range(lims, geometry, alphai, cen):
nanometer = 1.0E+09
sdd = geometry.get_dist() * nanometer
wavelen = geometry.get_wavelength() * nanometer
pixel1 = geometry.get_pixel1() * nanometer
pixel2 = geometry.get_pixel2() * nanometer
# calculate q-range for the image
y = np.array([0, lims[1] - 1], dtype=np.float) * pixel1
z = np.array([0, lims[0] - 1], dtype=np.float) * pixel2
y, z = np.meshgrid(y, z)
y -= cen[0]
z -= cen[1]
def writenexus(nexroot, path):
try:
nexroot.save(path)
except IOError:
msg.logMessage('IOError: Check that you have write permissions.',msg.ERROR)
def remeshqintegrate(data,
mask,
AIdict,
cut=None,
color=[255, 255, 255],
requestkey=None,
qvrt=None,
qpar=None):
alphai = config.activeExperiment.getvalue('Incidence Angle (GIXS)')
msg.logMessage(
'Incoming angle applied to remeshed q integration: ' + str(alphai),
msg.DEBUG)
if mask is None: mask = config.activeExperiment.mask
if AIdict is None:
AI = config.activeExperiment.getAI()
# p1 = AI.get_poni1()
# p2 = AI.get_poni2()
# msg.logMessage(('poni:', p1, p2),msg.DEBUG)
else:
AI = pyFAI.AzimuthalIntegrator()
AI.setPyFAI(**AIdict)
if mask is not None:
mask = mask.copy()
msg.logMessage(('image:', data.shape), msg.DEBUG)
msg.logMessage(('mask:', mask.shape), msg.DEBUG)
if not mask.shape == data.shape:
msg.logMessage("No mask match. Mask will be ignored.", msg.WARNING)
mask = np.ones_like(data)
msg.logMessage(('emptymask:', mask.shape), msg.DEBUG)
if cut is not None and type(cut) is np.ndarray:
msg.logMessage(('cut:', cut.shape), msg.DEBUG)
mask = mask.astype(bool) & cut.astype(bool)
#qpar, qvrt = remesh.remeshqarray(data, None, AI, np.deg2rad(alphai))
qsquared = qpar**2 + qvrt**2
remeshcenter = np.unravel_index(qsquared.argmin(), qsquared.shape)
print 'center?:', remeshcenter
f2d = AI.getFit2D()
f2d['centerX'] = remeshcenter[0]
f2d['centerY'] = remeshcenter[1]
AI.setFit2D(**f2d)
AIdict = AI.getPyFAI()
msg.logMessage('remesh corrected calibration: ' + str(AIdict))
AI._cached_array["q_center"] = np.sqrt(
qsquared
).T / 10 # This is cheating! pyFAI may give unexpected results here
(q, radialprofile) = AI.integrate1d(
data.T,
config.settings['Integration Bins (q)'],
mask=1 - mask.T,
method=pyFAI_method) #pyfai uses 0-valid mask
return q, radialprofile, color, requestkey
def jpeg(img):
buffer = StringIO.StringIO()
pilImage = Image.fromarray(img)
pilImage.save(buffer, "JPEG", quality=85)
msg.logMessage(('JPEG buffer size (bytes):', buffer.len),msg.DEBUG)
return buffer
