def test_read(self):
""" check we can read these images"""
ref = edfimage()
gzipped = edfimage()
compressed = edfimage()
refFile = "edfUncompressed_U16.edf"
gzippedFile = "edfGzip_U16.edf"
compressedFile = "edfCompressed_U16.edf"
try:
ref.read(os.path.join(self.im_dir, refFile))
except:
raise RuntimeError("Cannot read image Uncompressed image %s" %
refFile)
try:
gzipped.read(os.path.join(self.im_dir, gzippedFile))
except:
raise RuntimeError("Cannot read image gzippedFile image %s" %
gzippedFile)
try:
compressed.read(os.path.join(self.im_dir, compressedFile))
except:
raise RuntimeError("Cannot read image compressedFile image %s" %
compressedFile)
self.assertEqual((ref.data - gzipped.data).max(), 0,
"Gzipped data block is correct")
self.assertEqual((ref.data - compressed.data).max(), 0,
"Zlib compressed data block is correct")
def setUp(self):
self.multiFrameFilename = UtilsTest.getimage("MultiFrame.edf.bz2")[:-4]
self.Frame0Filename = UtilsTest.getimage(
"MultiFrame-Frame0.edf.bz2")[:-4]
self.Frame1Filename = UtilsTest.getimage(
"MultiFrame-Frame1.edf.bz2")[:-4]
self.ref = edfimage()
self.frame0 = edfimage()
self.frame1 = edfimage()
try:
self.ref.read(self.multiFrameFilename)
except:
raise RuntimeError(
"Cannot read image multiFrameFilename image %s" %
self.multiFrameFilename)
try:
self.frame0.read(self.Frame0Filename)
except:
raise RuntimeError("Cannot read image Frame0File image %s" %
self.Frame0File)
try:
self.frame1.read(self.Frame1Filename)
except:
raise RuntimeError("Cannot read image Frame1File image %s" %
self.Frame1File)
def save_as_edf(calibration: Calibration, basedir: str) -> None:
"""Save the multi calib images into edf files in order to do the first
calibration
"""
with File(calibration.filename, mode='r') as h5file:
for frame in gen_metadata_idx(h5file, calibration):
base = os.path.basename(calibration.filename)
output = os.path.join(basedir, base + "_{:02d}.edf".format(frame.idx)) # noqa
print(output)
edfimage(frame.image).write(output)
def save_as_edf(calibration: Calibration, basedir: str) -> None:
"""Save the multi calib images into edf files in order to do the first
calibration
"""
with File(calibration.filename, mode='r') as h5file:
for frame in gen_metadata_idx(h5file, calibration):
base = os.path.basename(calibration.filename)
output = os.path.join(basedir, base +
"_{:02d}.edf".format(frame.idx)) # noqa
print(output)
edfimage(frame.image).write(output)
def write(self, stemname):
"""
save the dx, dy images
"""
im = edfimage.edfimage()
im.data = self.dx
im.write("%s_dx.edf" % (stemname), force_type=numpy.float32)
im = edfimage.edfimage()
im.data = self.dy
im.write("%s_dy.edf" % (stemname), force_type=numpy.float32)
numpy.save("%s_tth.npy" % (stemname), self.tthvals)
def findgisaxsarcs2(img, experiment):
img = img.T.copy()
cake, _, _ = integration.cake(
img, experiment,
mask=experiment.mask) # TODO: refactor these parameters and check .T
maskcake, _, _ = integration.cake(experiment.mask.T, experiment)
from fabio import edfimage
fabimg = edfimage.edfimage(cake)
filename = 'cake.edf'
fabimg.write(filename)
fabimg = edfimage.edfimage(maskcake)
filename = 'cake_MASK.edf'
fabimg.write(filename)
img = inpaint(cake, maskcake)
fabimg = edfimage.edfimage(img)
filename = 'cake_LINEAR_INFILL.edf'
fabimg.write(filename)
maxchis, maxqs = findmaxs(img)
out = []
for chi, q in zip(maxchis, maxqs):
# roi=np.ones_like(img)
#roi[chi - 10:chi + 10, q - 5:q + 5]=10
#roi=np.sum(roi,axis=1)
slice = img[:, q - 5:q + 5]
if np.max(slice) / np.min(slice) < 2:
pass # continue
chiprofile = np.sum(slice, axis=1)
x = np.arange(np.size(chiprofile))
#plt.plot(chiprofile)
params = fitarcgaussian(chiprofile, chi)
if params['mu'] > chi + 5 or params['mu'] < chi - 5:
continue
params.add('q', value=q)
out.append(params)
#plt.show()
# plt.imshow(np.log(img))
#plt.show()
return out
def writeimage(image, path, mask=None, headers=None, suffix='',ext=None, dialog=False):
if dialog:
filename,ok = dialogs.savedatadialog(guesspath=path,caption="Save data to "+ext)
if filename and ok:
writeimage(image, filename, headers)
if mask is not None:
maskname = ''.join(os.path.splitext(filename)[:-1]) + "_mask" + os.path.splitext(filename)[-1]
writeimage(mask, maskname, headers)
if headers is None: headers = dict()
if ext is None: ext = os.path.splitext(path)[-1]
path = ''.join(os.path.splitext(path)[:-1]) + suffix + ext
if notexitsoroverwrite(path):
if ext.lower() == '.edf':
fabimg = edfimage.edfimage(np.rot90(image), header=headers)
fabimg.write(path)
elif ext.lower() == '.tif':
fabimg = tifimage.tifimage(np.rot90((image.astype(float)/image.max()*2**16).astype(np.int16)), header=headers)
fabimg.write(path)
elif ext.lower() == '.png':
raise NotImplementedError
elif ext.lower() == '.jpg':
scipy.misc.imsave(path,np.rot90(image))
else:
return False
return True
def writeEDF(self, basename):
"""save the distortion matrices into a couple of files called basename-x.edf and basename-y.edf
"""
try:
from fabio.edfimage import edfimage
#from EdfFile import EdfFile as EDF
except ImportError:
print "You will need the Fabio library available from the Fable sourceforge"
return
self.spline2array()
edfDispX = edfimage(data=self.xDispArray.astype("float32"), header={})
edfDispY = edfimage(data=self.yDispArray.astype("float32"), header={})
edfDispX.write(basename + "-x.edf", force_type="float32")
edfDispY.write(basename + "-y.edf", force_type="float32")
def postProcess(self, _edObject=None):
EDPluginExec.postProcess(self)
self.DEBUG("EDPluginExecShiftImagev1_0.postProcess")
# Create some output data
xsDataResult = XSDataResultShiftImage()
if self.strOutputType is None:
xsDataResult.setOutputArray(EDUtilsArray.arrayToXSData(self.npaImage))
elif self.strOutputType == "file":
image = edfimage(
data=self.npaImage,
header={"Offset_1": self.tOffset[0], "Offset_2": self.tOffset[1], "Max_Offset": self.MAX_OFFSET_VALUE},
)
image.write(self.strOutputImage, force_type=self.npaImage.dtype)
xsdimg = XSDataImageExt(path=XSDataString(self.strOutputImage))
xsDataResult.outputImage = xsdimg
elif self.strOutputType == "shared":
EDShare[self.strOutputImage] = self.npaImage
xsdimg = XSDataImageExt(shared=XSDataString(self.strOutputImage))
xsDataResult.outputImage = xsdimg
elif self.strOutputType == "array":
xsdimg = XSDataImageExt(array=EDUtilsArray.arrayToXSData(self.npaImage))
xsDataResult.outputImage = xsdimg
self.setDataOutput(xsDataResult)
self.npaImage = None
def test_getstats(self):
""" test statistics"""
obj = edfimage()
obj.read(self.filename)
self.assertEqual(obj.getmean(), 10)
self.assertEqual(obj.getmin(), 0)
self.assertEqual(obj.getmax(), 20)
def postProcess(self, _edObject=None):
EDPluginExec.postProcess(self)
self.DEBUG("EDPluginExecShiftImagev1_0.postProcess")
# Create some output data
xsDataResult = XSDataResultShiftImage()
if self.strOutputType is None:
xsDataResult.setOutputArray(
EDUtilsArray.arrayToXSData(self.npaImage))
elif self.strOutputType == "file":
image = edfimage(data=self.npaImage,
header={
"Offset_1": self.tOffset[0],
"Offset_2": self.tOffset[1],
"Max_Offset": self.MAX_OFFSET_VALUE
})
image.write(self.strOutputImage, force_type=self.npaImage.dtype)
xsdimg = XSDataImageExt(path=XSDataString(self.strOutputImage))
xsDataResult.outputImage = xsdimg
elif self.strOutputType == "shared":
EDShare[self.strOutputImage] = self.npaImage
xsdimg = XSDataImageExt(shared=XSDataString(self.strOutputImage))
xsDataResult.outputImage = xsdimg
elif self.strOutputType == "array":
xsdimg = XSDataImageExt(
array=EDUtilsArray.arrayToXSData(self.npaImage))
xsDataResult.outputImage = xsdimg
self.setDataOutput(xsDataResult)
self.npaImage = None
def test_read(self):
""" check we can read these images"""
for line in TESTIMAGES.split("\n"):
vals = line.split()
name = vals[0]
dim1, dim2 = [int(x) for x in vals[1:3]]
mini, maxi, mean, stddev = [float(x) for x in vals[3:]]
obj = edfimage()
try:
obj.read(os.path.join(self.im_dir, name))
except:
print "Cannot read image", name
raise
self.assertAlmostEqual(mini, obj.getmin(), 2,
"testedfs: %s getmin()" % name)
self.assertAlmostEqual(maxi, obj.getmax(), 2,
"testedfs: %s getmax" % name)
logger.info("%s Mean: exp=%s, obt=%s" %
(name, mean, obj.getmean()))
self.assertAlmostEqual(mean, obj.getmean(), 2,
"testedfs: %s getmean" % name)
logger.info("%s StdDev: exp=%s, obt=%s" %
(name, stddev, obj.getstddev()))
self.assertAlmostEqual(stddev, obj.getstddev(), 2,
"testedfs: %s getstddev" % name)
self.assertEqual(dim1, obj.dim1, "testedfs: %s dim1" % name)
self.assertEqual(dim2, obj.dim2, "testedfs: %s dim2" % name)
def test_getstats(self):
""" test statistics"""
obj = edfimage()
obj.read(self.filename)
self.assertEqual(obj.getmean(), 10)
self.assertEqual(obj.getmin(), 0)
self.assertEqual(obj.getmax(), 20)
def write_edf(self, framenumber, frame, usegzip=False):
e = edfimage.edfimage()
e.data = frame
edim2, edim1 = frame.shape
e.header = {}
e.header['origin'] = 'PolyXSim'
e.header['Dim_1'] = edim1
e.header['Dim_2'] = edim2
e.header['col_end'] = edim1 - 1
e.header['row_end'] = edim2 - 1
e.header['DataType'] = 'UnsignedShort'
e.header['Image'] = 1
e.header['ByteOrder'] = 'Low'
e.header['time'] = time.asctime()
e.header['Omega']= self.graindata.frameinfo[framenumber].omega +\
self.graindata.param['omega_step']/2.0
e.header['OmegaStep'] = self.graindata.param['omega_step']
e.header['grainfile']='%s/%s_%0.4dgrains.txt' \
%(self.graindata.param['direc'],self.graindata.param['stem'],self.graindata.param['no_grains'])
fname = '%s%s' % (self.graindata.frameinfo[framenumber].name, '.edf')
if usegzip:
fobj = gzip.GzipFile(fname + ".gz", "wb")
e.write(fobj)
fobj.close()
else:
e.write(fname)
def writeEDF(self, basename):
"""save the distortion matrices into a couple of files called basename-x.edf and basename-y.edf
"""
try:
from fabio.edfimage import edfimage
#from EdfFile import EdfFile as EDF
except ImportError:
print "You will need the Fabio library available from the Fable sourceforge"
return
self.spline2array()
edfDispX = edfimage(data=self.xDispArray.astype("float32"), header={})
edfDispY = edfimage(data=self.yDispArray.astype("float32"), header={})
edfDispX.write(basename + "-x.edf", force_type="float32")
edfDispY.write(basename + "-y.edf", force_type="float32")
def checkFile(self, filename):
""" check we can read EDF image with openimage"""
obj = openimage(filename)
obj2 = edfimage()
obj2.read(filename)
self.assertEqual(obj.data[10, 10], obj2.data[10, 10])
self.assertEqual(type(obj), type(obj2))
self.assertEqual(abs(obj.data.astype(int) - obj2.data.astype(int)).sum(), 0)
def test_rebin(self):
"""test the rebin of edfdata"""
f = edfimage()
f.read(os.path.join(self.im_dir, "F2K_Seb_Lyso0675.edf"))
f.rebin(1024, 1024)
self.assertEqual(
abs(numpy.array([[1547, 1439], [1536, 1494]]) - f.data).max(), 0,
"data are the same after rebin")
def testBzip2(self):
self.filename = os.path.join(self.tmpdir, "merged.azim.gz")
e = edfimage(data=self.data, header=self.header)
e.write(self.filename)
r = fabio.open(self.filename)
self.assert_(r.header["toto"] == self.header["toto"], "header are OK")
self.assert_(abs(r.data - self.data).max() == 0, "data are OK")
self.assertEqual(int(r.header["EDF_HeaderSize"]), 512, "header size is one 512 block")
def testBzip2(self):
self.filename = os.path.join(self.tmpdir, "merged.azim.gz")
e = edfimage(data=self.data, header=self.header)
e.write(self.filename)
r = fabio.open(self.filename)
self.assert_(r.header["toto"] == self.header["toto"], "header are OK")
self.assert_(abs(r.data - self.data).max() == 0, "data are OK")
self.assertEqual(int(r.header["EDF_HeaderSize"]), 512,
"header size is one 512 block")
def exportimage(self):
fabimg = edfimage.edfimage(np.rot90(self.imageitem.image))
dialog = QtGui.QFileDialog(parent=self.parentwindow.ui,
caption='blah',
directory=os.path.dirname(self.path),
filter=u'EDF (*.edf)')
dialog.selectFile(os.path.basename(self.path))
filename, _ = dialog.getSaveFileName()
fabimg.write(filename)
def setUp(self):
self.multiFrameFilename = UtilsTest.getimage("MultiFrame.edf.bz2")[:-4]
self.Frame0Filename = UtilsTest.getimage("MultiFrame-Frame0.edf.bz2")[:-4]
self.Frame1Filename = UtilsTest.getimage("MultiFrame-Frame1.edf.bz2")[:-4]
self.ref = edfimage()
self.frame0 = edfimage()
self.frame1 = edfimage()
try:
self.ref.read(self.multiFrameFilename)
except:
raise RuntimeError("Cannot read image multiFrameFilename image %s" % self.multiFrameFilename)
try:
self.frame0.read(self.Frame0Filename)
except:
raise RuntimeError("Cannot read image Frame0File image %s" % self.Frame0File)
try:
self.frame1.read(self.Frame1Filename)
except:
raise RuntimeError("Cannot read image Frame1File image %s" % self.Frame1File)
def writeEDF(self, basename):
"""
save the distortion matrices into a couple of files called
basename-x.edf and basename-y.edf
:param basename: base of the name used to save the data
:type basename: str
"""
try:
from fabio.edfimage import edfimage
except ImportError:
logger.error("You will need the Fabio library available" " from the Fable sourceforge")
return
self.spline2array()
edfDispX = edfimage(data=self.xDispArray.astype("float32"), header={})
edfDispY = edfimage(data=self.yDispArray.astype("float32"), header={})
edfDispX.write(basename + "-x.edf", force_type="float32")
edfDispY.write(basename + "-y.edf", force_type="float32")
def writespot(filename, roi, box, hdr, flt, ispt):
"""
Filename - target filename
roi - the roi in the original image
box - the pixels
hdr - original file header
flt - peaksearch output in columnfile object
spot3d_id - which peak this is
"""
# Make the header
print("writing", filename)
myheader = {"ByteOrder": hdr["ByteOrder"]}
ks = []
for k in edfimage.MINIMUM_KEYS:
ks.append(k)
if k not in mappings:
try:
myheader[k] = edfimage.DEFAULT_VALUES[k]
except:
pass
for k in flt.titles:
myheader[k] = getattr(flt, k)[ispt]
ks.append(k)
ks.append("OriginalFile")
myheader["OriginalFile"] = hdr['filename']
myheader["DataType"] = "FloatValue"
for k, v in [("ROI_slow_start", roi[0].start),
("ROI_slow_stop", roi[0].stop),
("ROI_fast_start", roi[1].start),
("ROI_fast_stop", roi[1].stop)]:
ks.append(k)
myheader[k] = int(v)
km = list(mappings.keys())
km.sort()
for k in km:
if k not in ks:
ks.append(k)
if k in km:
v = mappings[k]
if type(v) == type("String"):
myheader[k] = v
continue
if type(v) == type(["List"]): # Funky
try:
args = [getattr(flt, a)[ispt] for a in list(v[0])]
myheader[k] = v[1](*args)
except:
print(v[0])
raise
#b = box.astype(np.float32)
#print np.maximum.reduce(b), np.minimum.reduce(b)
o = edfimage.edfimage(box.astype(np.float32), myheader)
o.header_keys = ks
o.header_keys.append("filename")
o.write(filename, force_type=None)
def writeEDF(self, basename):
"""
save the distortion matrices into a couple of files called
basename-x.edf and basename-y.edf
:param basename: base of the name used to save the data
:type basename: str
"""
try:
from fabio.edfimage import edfimage
except ImportError:
logger.error("You will need the Fabio library available"
" from the Fable sourceforge")
return
self.spline2array()
edfDispX = edfimage(data=self.xDispArray.astype("float32"), header={})
edfDispY = edfimage(data=self.yDispArray.astype("float32"), header={})
edfDispX.write(basename + "-x.edf", force_type="float32")
edfDispY.write(basename + "-y.edf", force_type="float32")
def testsame(self):
"""test ADSC image match to EDF"""
im1 = edfimage()
im1.read(self.fn_edf)
im2 = adscimage()
im2.read(self.fn_adsc)
diff = (im1.data.astype("float32") - im2.data.astype("float32"))
logger.debug("type: %s %s shape %s %s " % (im1.data.dtype, im2.data.dtype, im1.data.shape, im2.data.shape))
logger.debug("im1 min %s %s max %s %s " % (im1.data.min(), im2.data.min(), im1.data.max(), im2.data.max()))
logger.debug("delta min %s max %s mean %s" % (diff.min(), diff.max(), diff.mean()))
self.assertEqual(abs(diff).max(), 0.0, "asdc data == edf data")
def test_read(self):
""" check readable"""
obj = edfimage()
obj.read(self.filename)
self.assertEqual(obj.dim1, 256, msg="dim1!=256 for file: %s" % self.filename)
self.assertEqual(obj.dim2, 256, msg="dim2!=256 for file: %s" % self.filename)
self.assertEqual(obj.bpp, 4, msg="bpp!=4 for file: %s" % self.filename)
self.assertEqual(obj.bytecode, numpy.float32, msg="bytecode!=flot32 for file: %s" % self.filename)
self.assertEqual(obj.data.shape, (256, 256), msg="shape!=(256,256) for file: %s" % self.filename)
self.assertEqual(obj.header['History-1'],
"something=something else")
def testsame(self):
"""test ADSC image match to EDF"""
im1 = edfimage()
im1.read(self.fn_edf)
im2 = adscimage()
im2.read(self.fn_adsc)
diff = (im1.data.astype("float32") - im2.data.astype("float32"))
logger.debug("type: %s %s shape %s %s " % (im1.data.dtype, im2.data.dtype, im1.data.shape, im2.data.shape))
logger.debug("im1 min %s %s max %s %s " % (im1.data.min(), im2.data.min(), im1.data.max(), im2.data.max()))
logger.debug("delta min %s max %s mean %s" % (diff.min(), diff.max(), diff.mean()))
self.assertEqual(abs(diff).max(), 0.0, "asdc data == edf data")
def write(self, fname):
e = edfimage.edfimage()
e.data = self.total_image
e.dim2, e.dim1 = self.total_image.shape
e.header = self.header
e.header['Dim_1'] = e.dim1
e.header['Dim_2'] = e.dim2
e.header['col_end'] = e.dim1 - 1
e.header['row_end'] = e.dim2 - 1
e.header['DataType'] = 'UnsignedShort'
e.header['Image'] = 1
e.write(fname)
def postProcess(self, _edObject=None):
EDPluginExec.postProcess(self)
self.DEBUG("EDPluginExecStitchOffsetedImagev1_0.postProcess")
# Create some output data
xsDataResult = XSDataResultStitchOffsetedImage()
if self._strOutFile is not None:
edf = edfimage(data=self._ndaResult, header={"Dummy":str(self._fDummy), "Blending":self._strBlending, "Autoscale":str(self._bAutoscale)})
edf.write(self._strOutFile)
xsDataResult.setOutputImage(XSDataImageExt(path=XSDataString(self._strOutFile)))
else:
xsDataResult.setOutputArray(EDUtilsArray.arrayToXSData(self._ndaResult))
self.setDataOutput(xsDataResult)
def save_as_edf(calibration: Calibration) -> None:
"""Save the multi calib images into edf files in order to do the first
calibration and print the command line in order to do the
calibration with pyFAI-calib
"""
cmds = []
with File(calibration.filename, mode='r') as h5file:
for frame in gen_metadata_idx(h5file, calibration, calibration.idxs):
base = os.path.basename(calibration.filename)
output = base + "_{:02d}.edf".format(frame.idx)
edfimage(frame.image).write(
os.path.join(calibration.basedir, output)) # noqa
# temporary until pyFAI-calib2 works
wavelength = calibration.wavelength * 1e10
cmd = "cd {directory} && pyFAI-calib2 -w {wavelength} --calibrant {calibrant} -D {detector} {filename}".format(
directory=calibration.basedir, # noqa
wavelength=wavelength, # noqa
calibrant=calibration.calibrant, # noqa
detector=calibration.detector, # noqa
filename=output) # noqa
cmds.append(cmd)
return cmds
def test_read(self):
""" check we can read these images"""
ref = edfimage()
gzipped = edfimage()
compressed = edfimage()
refFile = "edfUncompressed_U16.edf"
gzippedFile = "edfGzip_U16.edf"
compressedFile = "edfCompressed_U16.edf"
try:
ref.read(os.path.join(self.im_dir, refFile))
except:
raise RuntimeError("Cannot read image Uncompressed image %s" % refFile)
try:
gzipped.read(os.path.join(self.im_dir, gzippedFile))
except:
raise RuntimeError("Cannot read image gzippedFile image %s" % gzippedFile)
try:
compressed.read(os.path.join(self.im_dir, compressedFile))
except:
raise RuntimeError("Cannot read image compressedFile image %s" % compressedFile)
self.assertEqual((ref.data - gzipped.data).max(), 0, "Gzipped data block is correct")
self.assertEqual((ref.data - compressed.data).max(), 0, "Zlib compressed data block is correct")
def exportimage(self):
fabimg = edfimage.edfimage(
np.rot90(self.getCurrentTab().imageitem.image))
dialog = QtGui.QFileDialog(
parent=None,
caption=u"Export image as EDF",
directory=unicode(os.path.dirname(self.getCurrentTab().paths[0])),
filter=u"EDF (*.edf)")
dialog.selectFile(
unicode(os.path.dirname(self.getCurrentTab().paths[0])))
filename, ok = dialog.getSaveFileName()
if ok and filename:
fabimg.write(filename)
def postProcess(self, _edObject=None):
EDPluginExec.postProcess(self)
EDVerbose.DEBUG("EDPluginExecShiftImagev1_0.postProcess")
# Create some output data
xsDataResult = XSDataResultShiftImage()
if self.strOutputImage is None:
#ArrayOutput
xsDataResult.setOutputArray(EDUtilsArray.arrayToXSData(self.npaImage))
else:
image = edfimage(data=self.npaImage, header={"Offset_1":self.tOffset[0], "Offset_2":self.tOffset[1]})
image.write(self.strOutputImage, force_type=self.npaImage.dtype)
xsdimg = XSDataImageExt(path=XSDataString(self.strOutputImage))
xsDataResult.setOutputImage(xsdimg)
self.setDataOutput(xsDataResult)
def write(self, fname, header=None):
if self.debug: print('write median image to', fname)
e = edfimage.edfimage()
e.data = N.clip(self.median, 0, 2**16 - 1)
e.data = e.data.astype(N.uint16)
#e.header=edfimage.DEFAULT_VALUES
#e.header['DIM_1']=e.dim1
#e.header['DIM_2']=e.dim2
#e.header['col_end']=e.dim1-1
#e.header['row_end']=e.dim2-1
#e.header['DataType']='UnsignedShort'
if header != None:
for arg in header:
e.header[arg] = header[arg]
e.write(fname)
def test_read(self):
""" check readable"""
obj = edfimage()
obj.read(self.filename)
self.assertEqual(obj.dim1,
256,
msg="dim1!=256 for file: %s" % self.filename)
self.assertEqual(obj.dim2,
256,
msg="dim2!=256 for file: %s" % self.filename)
self.assertEqual(obj.bpp, 4, msg="bpp!=4 for file: %s" % self.filename)
self.assertEqual(obj.bytecode,
numpy.float32,
msg="bytecode!=flot32 for file: %s" % self.filename)
self.assertEqual(obj.data.shape, (256, 256),
msg="shape!=(256,256) for file: %s" % self.filename)
self.assertEqual(obj.header['History-1'], "something=something else")
def postProcess(self, _edObject=None):
EDPluginExec.postProcess(self)
EDVerbose.DEBUG("EDPluginExecShiftImagev1_0.postProcess")
# Create some output data
xsDataResult = XSDataResultShiftImage()
if self.strOutputImage is None:
#ArrayOutput
xsDataResult.setOutputArray(
EDUtilsArray.arrayToXSData(self.npaImage))
else:
image = edfimage(data=self.npaImage,
header={
"Offset_1": self.tOffset[0],
"Offset_2": self.tOffset[1]
})
image.write(self.strOutputImage, force_type=self.npaImage.dtype)
xsdimg = XSDataImageExt(path=XSDataString(self.strOutputImage))
xsDataResult.setOutputImage(xsdimg)
self.setDataOutput(xsDataResult)
def postProcess(self, _edObject=None):
EDPluginExec.postProcess(self)
self.DEBUG("EDPluginExecStitchOffsetedImagev1_0.postProcess")
# Create some output data
xsDataResult = XSDataResultStitchOffsetedImage()
if self._strOutFile is not None:
edf = edfimage(data=self._ndaResult,
header={
"Dummy": str(self._fDummy),
"Blending": self._strBlending,
"Autoscale": str(self._bAutoscale)
})
edf.write(self._strOutFile)
xsDataResult.setOutputImage(
XSDataImageExt(path=XSDataString(self._strOutFile)))
else:
xsDataResult.setOutputArray(
EDUtilsArray.arrayToXSData(self._ndaResult))
self.setDataOutput(xsDataResult)
def bug_27(self):
"""
import fabio
obj = fabio.open("any.edf")
obj.header["missing"]="blah"
obj.write("any.edf")
"""
# create dummy image:
shape = (32, 32)
data = numpy.random.randint(0, 6500, size=shape[0] * shape[1]).astype("uint16").reshape(shape)
fname = os.path.join(UtilsTest.tempdir, "bug27.edf")
e = edfimage(data=data, header={"key1": "value1"})
e.write(fname)
del e
obj = fabio.open(fname)
obj.header["missing"] = "blah"
obj.write(fname)
del obj
def write_edf(self, framenumber, frame):
e = edfimage.edfimage()
e.data = frame
e.dim2, e.dim1 = frame.shape
e.header = {}
e.header['origin'] = 'ModelScanning3DXRD'
e.header['Dim_1'] = e.dim1
e.header['Dim_2'] = e.dim2
e.header['col_end'] = e.dim1 - 1
e.header['row_end'] = e.dim2 - 1
e.header['DataType'] = 'UnsignedShort'
e.header['Image'] = 1
e.header['ByteOrder'] = 'Low'
e.header['time'] = time.asctime()
e.header['Omega']= self.voxeldata.frameinfo[framenumber].omega +\
self.voxeldata.param['omega_step']/2.0
e.header['OmegaStep'] = self.voxeldata.param['omega_step']
e.header['voxelfile']='%s/%s_%0.4dvoxels.txt' \
%(self.voxeldata.param['direc'],self.voxeldata.param['stem'],self.voxeldata.param['no_voxels'])
e.write('%s%s' % (self.voxeldata.frameinfo[framenumber].name, '.edf'))
def write_edf(slow,medium,omega,ang_val_s,ang_val_m,omega_val,frame,stem,format):
e=edfimage.edfimage()
e.data=frame
e.dim2,e.dim1=frame.shape
e.header = {}
e.header['origin']='dfxrm'
e.header['Dim_1']=e.dim1
e.header['Dim_2']=e.dim2
e.header['col_end']=e.dim1-1
e.header['row_end']=e.dim2-1
e.header['DataType']='UnsignedShort'
e.header['Image']=1
e.header['ByteOrder']='Low'
e.header['time']=time.asctime()
e.header['Omega']= omega_val[omega]
e.header['OmegaStep']=omega_val[1]-omega_val[0]
e.header['phi_lower']= ang_val_m[medium]
e.header['phi_lower_step']=ang_val_m[1]-ang_val_m[0]
e.header['phi_upper']= ang_val_s[slow]
e.header['phi_upper_step']=ang_val_s[1]-ang_val_s[0]
e.write('%s_%0.4d_%0.4d_%0.4d.%s' %(stem,slow,medium,omega,format))
def bug_27(self):
"""
import fabio
obj = fabio.open("any.edf")
obj.header["missing"]="blah"
obj.write("any.edf")
"""
# create dummy image:
shape = (32, 32)
data = numpy.random.randint(0, 6500, size=shape[0] *
shape[1]).astype("uint16").reshape(shape)
fname = os.path.join(UtilsTest.tempdir, "bug27.edf")
e = edfimage(data=data, header={"key1": "value1"})
e.write(fname)
del e
obj = fabio.open(fname)
obj.header["missing"] = "blah"
obj.write(fname)
del obj
def test_read(self):
""" check we can read these images"""
for line in TESTIMAGES.split("\n"):
vals = line.split()
name = vals[0]
dim1, dim2 = [int(x) for x in vals[1:3]]
mini, maxi, mean, stddev = [float(x) for x in vals[3:]]
obj = edfimage()
try:
obj.read(os.path.join(self.im_dir, name))
except:
print "Cannot read image", name
raise
self.assertAlmostEqual(mini, obj.getmin(), 2, "testedfs: %s getmin()" % name)
self.assertAlmostEqual(maxi, obj.getmax(), 2, "testedfs: %s getmax" % name)
logger.info("%s Mean: exp=%s, obt=%s" % (name, mean, obj.getmean()))
self.assertAlmostEqual(mean, obj.getmean(), 2, "testedfs: %s getmean" % name)
logger.info("%s StdDev: exp=%s, obt=%s" % (name, stddev, obj.getstddev()))
self.assertAlmostEqual(stddev, obj.getstddev(), 2, "testedfs: %s getstddev" % name)
self.assertEqual(dim1, obj.dim1, "testedfs: %s dim1" % name)
self.assertEqual(dim2, obj.dim2, "testedfs: %s dim2" % name)
def writeimage(image, path, headers=None, suffix='', ext=None):
if headers is None: headers = dict()
if ext is None: ext = os.path.splitext(path)[-1]
path = ''.join(os.path.splitext(path)[:-1]) + suffix + ext
if notexitsoroverwrite(path):
if ext.lower() == '.edf':
fabimg = edfimage.edfimage(np.rot90(image), header=headers)
fabimg.write(path)
elif ext.lower() == '.tif':
fabimg = tifimage.tifimage(np.rot90(
(image.astype(float) / image.max() * 2**16).astype(np.int16)),
header=headers)
fabimg.write(path)
elif ext.lower() == '.png':
raise NotImplementedError
elif ext.lower() == '.jpg':
scipy.misc.imsave(path, np.rot90(image))
else:
return False
return True
def test_rebin(self):
"""test the rebin of edfdata"""
f = edfimage()
f.read(os.path.join(self.im_dir, "F2K_Seb_Lyso0675.edf"))
f.rebin(1024, 1024)
self.assertEqual(abs(numpy.array([[1547, 1439], [1536, 1494]]) - f.data).max(), 0, "data are the same after rebin")
def process(self, _edObject=None):
EDPluginExec.process(self)
EDVerbose.DEBUG("EDPluginExecMatrixWritev1_0.process")
f = edfimage(self.matIn, {})
f.write(self.outputFile, force_type=self.matIn.dtype)
def process(self, _edObject=None):
EDPluginExec.process(self)
EDVerbose.DEBUG("EDPluginExecMatrixWritev1_0.process")
f = edfimage(self.matIn, {})
f.write(self.outputFile, force_type=self.matIn.dtype)
def main():
"""
A CLI user interface
"""
import sys, time, os, logging
start = time.time()
root = logging.getLogger('')
root.setLevel(logging.WARNING)
try:
from optparse import OptionParser
parser = OptionParser()
parser = get_options(parser)
options, args = parser.parse_args()
except SystemExit:
raise
except:
parser.print_help()
print("\nProblem with your options:")
raise
if options.mask is not None:
fit2dmask = (1 - openimage(options.mask).data).ravel()
else:
fit2dmask = 1.0
first_image = True
imagefiles = ImageD11_file_series.get_series_from_options(options, args)
tthvals = numpy.load(options.lookup + "_tth.npy")
try:
for fim in imagefiles:
dataim = fim.data
print(fim.filename)
if first_image: # allocate volume, compute k etc
first_image = False
dxim = openimage(options.lookup + "_dx.edf").data
dyim = openimage(options.lookup + "_dy.edf").data
outsum = numpy.ravel(numpy.zeros(dataim.shape, numpy.float32))
outnp = numpy.ravel(numpy.zeros(dataim.shape, numpy.float32))
e = edfimage()
# C code from rsv_mapper (not intended to be obfuscated)
o = blobcorrector.perfect()
idealx, idealy = o.make_pixel_lut(dataim.shape)
destx, desty = idealx + dxim, idealy + dyim
assert destx.min() >= 0
assert destx.max() < dataim.shape[1]
assert desty.min() >= 0
assert desty.max() < dataim.shape[1]
imageshape = dataim.shape
indices = numpy.ravel(destx).astype(numpy.intp)
numpy.multiply(indices, dataim.shape[1], indices)
numpy.add(indices,
numpy.ravel(desty).astype(numpy.intp), indices)
assert indices.min() >= 0
assert indices.max() < dataim.shape[0] * dataim.shape[1]
on = numpy.ones(len(outnp), numpy.float32)
if fit2dmask is not None:
on = on * fit2dmask
# Number of pixels and mask are constant
cImageD11.put_incr(outnp, indices, on)
mask = outnp < 0.1
scalar = (1.0 - mask) / (outnp + mask)
flatshape = outsum.shape
#
arsorted = mask.copy()
outmask = mask.copy()
outmask = outmask * 1e6
outmask.shape = imageshape
arsorted.shape = imageshape
arsorted.sort(axis=1)
minds = numpy.array([l.searchsorted(0.5) for l in arsorted])
# ENDIF firstimage
start = time.time()
numpy.multiply(outsum, 0, outsum)
outsum.shape = flatshape
dm = (dataim.ravel() * fit2dmask).astype(numpy.float32)
cImageD11.put_incr(outsum, indices, dm)
# outsum = outsum.reshape( dataim.shape )
# outnp = outnp.reshape( dataim.shape ).astype(numpy.int32)
# Normalise
numpy.multiply(outsum, scalar, outsum)
print(dataim.max(), dataim.min(), end=' ')
print(scalar.max(), scalar.min(), outsum.min(), outsum.max())
outsum.shape = imageshape
# saving edf
e.data = outsum
e.write("r_" + fim.filename, force_type=numpy.float32)
print(time.time() - start)
except:
raise
def process(self, _edObject=None):
EDPluginExec.process(self)
self.DEBUG("EDPluginExecStitchOffsetedImagev1_0.process")
shiftedImage = []
distanceSq = lambda t2f: (t2f[0] * t2f[0] + t2f[1] * t2f[1])
ref = self._lOffsets[0]
refOffset = distanceSq(ref)
shape = self._lImages[0].shape
if self._strMask:
mask = (1 - fabio.open(self._strMask).data).astype("bool")
else:
mask = 0
for img, offset, dummy in zip(self._lImages, self._lOffsets, self._lDummies):
shImg = scipy.ndimage.shift(img, offset, cval=dummy[0], order=0)
if dummy[1] == 0:
if self._strMask:
shImgMasked = numpy.ma.MaskedArray(shImg,
(shImg == dummy[0]) + scipy.ndimage.shift(mask, offset, cval=1, order=0))
else:
shImgMasked = numpy.ma.MaskedArray(shImg, (shImg == dummy[0]))
else:
if self._strMask:
shImgMasked = numpy.ma.MaskedArray(shImg,
(abs(shImg - dummy[0]) <= dummy[1]) + scipy.ndimage.shift(mask, offset, cval=1, order=0))
else:
shImgMasked = numpy.ma.MaskedArray(shImg, (abs(shImg - dummy[0]) <= dummy[1]))
shiftedImage.append(shImgMasked)
if (offset != ref) and (distanceSq(offset) < refOffset):
ref = offset
refOffset = distanceSq(offset)
refIdx = self._lOffsets.index(ref)
if EDVerbose.isVerboseDebug():
e = edfimage(data=shiftedImage[0].filled(self._fDummy),
header={"Dummy":str(self._fDummy), "Offset_1":str(self._lOffsets[0][0]), "Offset_2":str(self._lOffsets[0][1])})
for img, offset in zip(shiftedImage[1:], self._lOffsets[1:]):
e.appendFrame(data=img.filled(self._fDummy),
header={"Dummy":str(self._fDummy), "Offset_1":str(offset[0]), "Offset_2":str(offset[1])})
e.write("stack.edf")
ROI = False
if self.tCenter is None:
#the default center is the geometry center of the image ...
self.tCenter = [ i // 2 for i in shape ]
if self.tWidth is not None:
d0min = max(0, self.tCenter[0] - (self.tWidth[0] // 2))
d0max = min(shape[0], d0min + self.tWidth[0])
d1min = max(0, self.tCenter[1] - (self.tWidth[1] // 2))
d1max = min(shape[1], d1min + self.tWidth[1])
shape = (d0max - d0min, d1max - d1min)
else:
d0min = 0
d0max = shape[0]
d1min = 0
d1max = shape[1]
refImg = shiftedImage[refIdx]
stackMaskedImg = numpy.ma.zeros((len(self._lOffsets), refImg.shape[0], refImg.shape[1]))
self.screen("ROI[%s:%s, %s:%s]\t Autoscale: %s\tBlending method: %s" % (d0min, d0max, d1min, d1max, self._bAutoscale, self._strBlending))
if self._bAutoscale:
for idx, img in enumerate(shiftedImage):
ratio = (refImg[d0min:d0max, d1min:d1max] / img[d0min:d0max, d1min:d1max])
stackMaskedImg[idx] = ratio.mean() * img
else:
for idx, img in enumerate(shiftedImage):
stackMaskedImg[idx] = img
if self._strBlending.lower().startswith("naive"):
npaTempBool = numpy.cumsum((1 - stackMaskedImg.mask), axis=0) * (1 - stackMaskedImg.mask) == 1
result = numpy.ma.MaskedArray((npaTempBool * stackMaskedImg.data).sum(axis=0), stackMaskedImg.mask.prod(axis=0))
elif self._strBlending.lower().startswith("max"):
result = stackMaskedImg.max(axis=0)
elif self._strBlending.lower().startswith("min"):
result = stackMaskedImg.min(axis=0)
else: #self._strBlending.lower() == "mean":
result = stackMaskedImg.mean(axis=0)
self._ndaResult = result.filled(self._fDummy)