def _verifyEdf(self, subdir, multipage, outdata, outlabels):
from PyMca5.PyMcaIO import EdfFile
ext = '.edf'
if multipage:
filename = os.path.join(subdir, 'sample_dataset' + ext)
f = EdfFile.EdfFile(filename)
edfdata = {
f.GetHeader(i)['Title']: f.GetData(i)
for i in range(f.GetNumImages())
}
for group, datadict in outdata.items():
if group not in outlabels['title']:
continue
if not multipage:
edfdata = {}
for label, data in datadict.items():
suffix = outlabels['filename'][group].get(label, None)
if not suffix:
continue
filename = os.path.join(
subdir, 'sample_dataset_{}{}'.format(suffix, ext))
f = EdfFile.EdfFile(filename)
edfdata[f.GetHeader(0)['Title']] = f.GetData(0)
for label, data in datadict.items():
edflabel = outlabels['title'][group].get(label, None)
if edflabel:
numpy.testing.assert_array_equal(
data, edfdata[edflabel], '{}: {}'.format(group, label))
def getPixmap():
"""
Open an image file and return the filename and the data.
Return ``None, None`` in case of failure.
"""
fileTypeList = [
'Picture Files (*jpg *jpeg *tif *tiff *png)', 'EDF Files (*edf)',
'EDF Files (*ccd)', 'ADSC Files (*img)', 'EDF Files (*)'
]
fileList, filterUsed = PyMcaFileDialogs.getFileList(
parent=None,
filetypelist=fileTypeList,
message="Please select one object data file",
mode="OPEN",
getfilter=True)
if not fileList:
return None, None
fname = fileList[0]
if filterUsed.split()[0] == "Picture":
qimage = qt.QImage(fname)
if qimage.isNull():
msg = qt.QMessageBox()
msg.setIcon(qt.QMessageBox.Critical)
msg.setText("Cannot read file %s as an image" % fname)
msg.exec()
return None, None
return os.path.basename(fname), convertQImageToArray(qimage)
if filterUsed.split()[0] in ["EDF", "ADSC"]:
edf = EdfFile.EdfFile(fname)
data = edf.GetData(0)
return os.path.basename(fname), data
return None, None
def getMesh():
"""
Read an image data file (EDF, ADSC), return the data and image name.
This is then used to display the image as a height map.
Returns *None, None* if the file dialog is cancelled or loaing fails.
:return: legend, data
"""
fileTypeList = [
'EDF Files (*edf)', 'EDF Files (*ccd)', 'ADSC Files (*img)',
'All Files (*)'
]
old = PyMcaFileDialogs.PyMcaDirs.nativeFileDialogs * 1
PyMcaFileDialogs.PyMcaDirs.nativeFileDialogs = False
fileList, filterUsed = PyMcaFileDialogs.getFileList(
parent=None,
filetypelist=fileTypeList,
message="Please select one object data file",
mode="OPEN",
getfilter=True)
PyMcaFileDialogs.PyMcaDirs.nativeFileDialogs = old
if not fileList:
return None, None
filename = fileList[0]
edf = EdfFile.EdfFile(filename, access='rb')
data = edf.GetData(0).astype(numpy.float32)
return os.path.basename(filename), data
def save2DArrayListAsEDF(datalist, filename, labels=None, dtype=None):
if type(datalist) != type([]):
datalist = [datalist]
ndata = len(datalist)
if os.path.exists(filename):
try:
os.remove(filename)
except OSError:
pass
if labels is None:
labels = []
for i in range(ndata):
labels.append("Array_%d" % i)
if len(labels) != ndata:
raise ValueError("Incorrect number of labels")
edfout = EdfFile.EdfFile(filename, access="ab")
for i in range(ndata):
if dtype is None:
edfout.WriteImage({'Title': labels[i]},
datalist[i], Append=1)
else:
edfout.WriteImage({'Title': labels[i]},
datalist[i].astype(dtype),
Append=1)
del edfout # force file close
def processList(self):
self.__ncols = None
self.__nrows = self.filestep
counter = 0
ffile = SpecFileLayer.SpecFileLayer()
for fitfile in self._filelist:
self.onNewFile(fitfile, self._filelist)
ffile.SetSource(fitfile)
fileinfo = ffile.GetSourceInfo()
# nscans = len(fileinfo['KeyList'])
for scankey in fileinfo['KeyList']:
scan,order = scankey.split(".")
info,data = ffile.LoadSource(scankey)
scan_obj = ffile.Source.select(scankey)
if info['NbMca'] > 0:
for i in range(info['NbMca']):
point = int(i/info['NbMcaDet']) + 1
mca = (i % info['NbMcaDet']) + 1
key = "%s.%s.%05d.%d" % (scan,order,point,mca)
if i == 0:
mcainfo,mcadata = ffile.LoadSource(key)
mcadata = scan_obj.mca(i+1)
y0 = numpy.array(mcadata, numpy.float)
if counter == 0:
key0 = "%s key %s" % (os.path.basename(fitfile), key)
self.__ncols = len(y0)
image = numpy.zeros((self.__nrows,self.__ncols), \
numpy.float)
if self.__ncols != len(y0):
print("spectrum has different number of columns")
print("skipping it")
else:
image[counter,:] = y0[:]
if (counter+1) == self.filestep:
if self.filestep > 1:
key1 = "%s key %s" % (os.path.basename(fitfile), key)
title = "%s to %s" % (key0, key1)
else:
title = key0
if 1:
ddict={}
if 'Channel0' in mcainfo:
ddict['MCA start ch'] =\
int(mcainfo['Channel0'])
if 'McaCalib' in mcainfo:
ddict['MCA a'] = mcainfo['McaCalib'][0]
ddict['MCA b'] = mcainfo['McaCalib'][1]
ddict['MCA c'] = mcainfo['McaCalib'][2]
else:
ddict = mcainfo
ddict['Title'] = title
edfname = os.path.join(self.outputdir,title.replace(" ","_")+".edf")
edfout = EdfFile.EdfFile(edfname)
edfout.WriteImage (ddict , image, Append=0)
counter = 0
else:
counter += 1
self.onEnd()
def SetSource(self, source_name=None, source_obj=None):
"""
Sets a new source for data retrieving, an edf file.
If the file exists, self.Source will be the EdfFile
object associated to this file.
Parameters:
source_name: name of the edf file
"""
if source_name == self.SourceName: return 1
if (type(source_name) != type([])): source_name = [source_name]
if (source_name is not None):
if source_obj is not None:
self.Source = source_obj
else:
if (type(source_name) == type([])):
if DEBUG:
print("List of files")
self.Source = []
for name in source_name:
try:
self.Source.append(
EdfFile.EdfFile(name, fastedf=self.fastedf))
except:
#print("EdfFileLayer.SetSource: Error trying to read EDF file %s" % name)
self.Source.append(None)
else:
try:
self.Source = EdfFile.EdfFile(source_name,
fastedf=self.fastedf)
except:
#print("EdfFileLayer.SetSource: Error trying to read EDF file")
self.Source = None
else:
self.Source = None
self.SourceInfo = None
if self.Source is None:
self.SourceName = None
return 0
else:
self.SourceName = ""
for name in source_name:
if self.SourceName != "": self.SourceName += "|"
self.SourceName += name
return 1
def load_frame(self,
image_foldername,
scan_no,
frame_no,
gz_compressed=True,
normalize=False,
monitor_name=None,
monitor_names=None,
remove_rows=None,
remove_cols=None):
header = dict()
for line in self.spec_file[scan_no].header.split('\n'):
header[line.split(' ')[0]] = line[len(line.split(' ')[0]):]
frames = self.get_no_frames(scan_no)
first_frame = int(header['#UCCD'].split('#r')[-1].split('.')[0])
img_folder = header['#UCCD'].split('/')[-2] + '/'
filename_template = header['#UCCD'].split('/')[-1].split(
'#r')[0] + '#r.' + header['#UCCD'].split('/')[-1].split('.')[-1]
if (gz_compressed):
filename_template = filename_template.replace('.edf', '.edf.gz')
if (frame_no >= frames):
raise IndexError("Frame number does not exist.")
img_filename = os.path.join(
image_foldername, img_folder,
filename_template.replace('#n',
str(scan_no).zfill(3)).replace(
'#p',
str(frame_no).zfill(3)).replace(
'#r',
str(first_frame +
frame_no).zfill(3)))
edf = EdfFile.EdfFile(img_filename, 'r')
the_img = np.array(edf.GetData(0), dtype='float')
if (remove_rows != None):
the_img = np.delete(the_img, remove_rows, axis=0)
if (remove_cols != None):
the_img = np.delete(the_img, remove_cols, axis=1)
if (normalize):
if (monitor_name):
mon_count = float(
getattr(self.spec_file[scan_no], monitor_name)[frame_no])
the_img /= mon_count
if (monitor_names):
for mon_name in monitor_names:
mon_count = float(
getattr(self.spec_file[scan_no], mon_name)[frame_no])
the_img /= mon_count
return the_img
return the_img
def openEdf(filename, read=0, write=0, force=0):
if read:
checkEdfForRead(filename)
if write:
checkEdfForWrite(filename, force)
try:
edf = EdfFile.EdfFile(filename)
except:
raise XiaEdfError("Cannot open EDF file <%s>" % filename)
return edf
def __init__(self, filename):
edf = EdfFile.EdfFile(filename, 'r')
self.header = edf.GetHeader(0)
self.motors = dict()
motor_mne = self.header['motor_mne'].split()
motor_pos = self.header['motor_pos'].split()
for i in xrange(len(motor_mne)):
self.motors[motor_mne[i]] = float(motor_pos[i])
self.counters = dict()
counter_mne = self.header['counter_mne'].split()
counter_pos = self.header['counter_pos'].split()
for i in xrange(len(counter_mne)):
self.counters[counter_mne[i]] = float(counter_pos[i])
self.img = np.array(edf.GetData(0))
def _mergeEdf(self, parts, outfilename):
for i, edfname in enumerate(parts):
edf = EdfFile.EdfFile(edfname, access='rb', fastedf=0)
nImages = edf.GetNumImages()
if i == 0:
images = [edf.GetData(j).copy() for j in range(nImages)]
headers = [{
'Title': edf.GetHeader(j)['Title']
} for j in range(nImages)]
else:
headersi = [{
'Title': edf.GetHeader(j)['Title']
} for j in range(nImages)]
for header, img in zip(headers, images):
k = headersi.index(header)
self._fillPartial(img, edf.GetData(k))
del edf
if os.path.exists(outfilename):
_logger.debug("Output file already exists, trying to delete it")
os.remove(outfilename)
edfout = EdfFile.EdfFile(outfilename, access="ab")
for i, (img, header) in enumerate(zip(images, headers)):
edfout.WriteImage(header, img, Append=i > 0)
del edfout
def _parseEdfResults(self, filenames):
"""
:param list filenames:
:returns tuple: list(nparams), ndarray(nparams, nrows, ncolumns)
"""
labels = []
data = []
from PyMca5.PyMcaIO import EdfFile
for filename in filenames:
# REMARK: each file can contain multiple images (roifit)
#stack = EDFStack.EDFStack(filename)
stack = EdfFile.EdfFile(filename)
for i in range(stack.GetNumImages()):
data.append(stack.GetData(i))
labels.append(stack.GetHeader(i)['Title'])
return labels, numpy.asarray(data)
def __init__(self, filename):
edf = EdfFile.EdfFile(filename, 'r')
self.header = edf.GetHeader(0)
self.motors = dict()
motor_mne = self.header['motor_mne'].split()
motor_pos = self.header['motor_pos'].split()
for i in xrange(len(motor_mne)):
self.motors[motor_mne[i]] = float(motor_pos[i])
self.counters = dict()
counter_mne = self.header['counter_mne'].split()
counter_pos = self.header['counter_pos'].split()
for i in xrange(len(counter_mne)):
self.counters[counter_mne[i]] = float(counter_pos[i])
self.img = np.array(edf.GetData(0))
# detector was mounted rotated by 90deg to the right
self.img = np.rot90(self.img, 3)
def getObject3DInstance(config=None):
fileTypeList = [
'Picture Files (*jpg *jpeg *tif *tiff *png)', 'EDF Files (*edf)',
'EDF Files (*ccd)', 'ADSC Files (*img)', 'EDF Files (*)'
]
fileList, filterUsed = Object3DFileDialogs.getFileList(
None,
filetypelist=fileTypeList,
message="Please select one object data file",
mode="OPEN",
getfilter=True)
if not len(fileList):
return
fname = fileList[0]
if filterUsed.split()[0] == "Picture":
qimage = qt.QImage(fname)
if qimage.isNull():
msg = qt.QMessageBox(self)
msg.setIcon(qt.QMessageBox.Critical)
msg.setText("Cannot read file %s as an image" % fname)
msg.exec_()
return
object3D = Object3DPixmap(os.path.basename(fname))
object3D.setQImage(qimage)
return object3D
if filterUsed.split()[0] in ["EDF", "ADSC"]:
edf = EdfFile.EdfFile(fname)
data = edf.GetData(0)
if True:
object3D = Object3DPixmap(os.path.basename(fname))
object3D.setImageData(data)
else:
(image, size, minmax) = spslut.transform(data, (1, 0),
(spslut.LINEAR, 3.0),
"RGBX", spslut.TEMP, 1,
(0, 1), (0, 255), 1)
object3D = Object3DPixmap(os.path.basename(fname))
object3D.setPixmap(image,
size[0],
size[1],
xmirror=False,
ymirror=False)
return object3D
return None
def getObject3DInstance(config=None):
#for the time being a former configuration
#for serializing purposes is not implemented
#I do the import here for the case PyMca is not installed
#because the modules could be instanstiated without using
#this method
try:
from PyMca5.PyMcaIO import EdfFile
except ImportError:
import EdfFile
fileTypeList = ['EDF Files (*edf)',
'EDF Files (*ccd)',
'ADSC Files (*img)',
'All Files (*)']
old = PyMcaFileDialogs.PyMcaDirs.nativeFileDialogs * 1
PyMcaFileDialogs.PyMcaDirs.nativeFileDialogs = False
fileList, filterUsed = PyMcaFileDialogs.getFileList(
parent=None,
filetypelist=fileTypeList,
message="Please select one object data file",
mode="OPEN",
getfilter=True)
PyMcaFileDialogs.PyMcaDirs.nativeFileDialogs = old
if not len(fileList):
return None
if filterUsed == fileTypeList[0]:
fileindex = 2
else:
fileindex = 1
#file index is irrelevant in case of an actual 3D stack.
filename = fileList[0]
legend = os.path.basename(filename)
edf = EdfFile.EdfFile(filename, access='rb')
data = edf.GetData(0).astype(numpy.float32)
object3D = Object3DMesh(os.path.basename(filename))
object3D.setData(data, z=data[:])
return object3D
l3.setText(" " + ticks[i])
qApp = qt.QApplication.instance()
qApp.processEvents()
time.sleep(2)
msg.close()
result = sthread._result
del sthread
self.raise_()
return result
if __name__ == "__main__":
import os
from PyMca5.PyMcaIO import EdfFile
app = qt.QApplication([])
app.lastWindowClosed.connect(app.quit)
d = NNMADialog()
imageList = []
for t in ["mix1.edf", "mix2.edf", "mix3.edf"]:
fname = os.path.join(os.path.dirname(__file__), "tests", t)
if not os.path.exists(fname):
break
edf = EdfFile.EdfFile(fname)
data = edf.GetData(0)
edf = None
imageList.append(data)
if len(imageList):
d.setData(imageList)
d.show()
app.exec_()
def loadFileList(self, filelist, fileindex=0):
if type(filelist) == type(''): filelist = [filelist]
self.__keyList = []
self.sourceName = filelist
self.__indexedStack = True
self.sourceType = SOURCE_TYPE
self.info = {}
self.nbFiles = len(filelist)
#read first edf file
#get information
tempEdf = EdfFileDataSource.EdfFileDataSource(filelist[0])
keylist = tempEdf.getSourceInfo()['KeyList']
nImages = len(keylist)
dataObject = tempEdf.getDataObject(keylist[0])
self.info.update(dataObject.info)
if len(dataObject.data.shape) == 3:
#this is already a stack
self.data = dataObject.data
self.__nFiles = 1
self.__nImagesPerFile = nImages
shape = self.data.shape
for i in range(len(shape)):
key = 'Dim_%d' % (i + 1, )
self.info[key] = shape[i]
self.info["SourceType"] = SOURCE_TYPE
self.info["SourceName"] = filelist[0]
self.info["Size"] = 1
self.info["NumberOfFiles"] = 1
self.info["FileIndex"] = fileindex
return
arrRet = dataObject.data
if self.__dtype is None:
self.__dtype = arrRet.dtype
self.onBegin(self.nbFiles)
singleImageShape = arrRet.shape
actualImageStack = False
if (fileindex == 2) or (self.__imageStack):
self.__imageStack = True
if len(singleImageShape) == 1:
#single line
#be ready for specfile stack?
self.onEnd()
raise IOError("Not implemented yet")
self.data = numpy.zeros(
(arrRet.shape[0], nImages, self.nbFiles), self.__dtype)
self.incrProgressBar = 0
for tempEdfFileName in filelist:
tempEdf = EdfFile.EdfFile(tempEdfFileName, 'rb')
for i in range(nImages):
pieceOfStack = tempEdf.GetData(i)
self.data[:, i, self.incrProgressBar] = pieceOfStack[:]
self.incrProgressBar += 1
self.onProgress(self.incrProgressBar)
self.onEnd()
else:
if nImages > 1:
#this is not the common case
#should I try to convert it to a standard one
#using a 3D matrix or keep as 4D matrix?
if self.nbFiles > 1:
raise IOError(\
"Multiple files with multiple images implemented yet")
self.data = numpy.zeros((arrRet.shape[0], arrRet.shape[1],
nImages * self.nbFiles),
self.__dtype)
self.incrProgressBar = 0
for tempEdfFileName in filelist:
tempEdf = EdfFile.EdfFile(tempEdfFileName, 'rb')
for i in range(nImages):
pieceOfStack = tempEdf.GetData(i)
self.data[:,:,
nImages*self.incrProgressBar+i] = \
pieceOfStack[:,:]
self.incrProgressBar += 1
else:
#this is the common case
try:
# calculate needed megabytes
if self.__dtype == numpy.float:
bytefactor = 8
else:
bytefactor = 4
needed_ = self.nbFiles * \
arrRet.shape[0] *\
arrRet.shape[1] * bytefactor
physicalMemory = PhysicalMemory.getPhysicalMemoryOrNone(
)
if physicalMemory is not None:
# spare 5% or memory
if physicalMemory < (1.05 * needed_):
raise MemoryError(
"Not enough physical memory available")
if self.__imageStack:
self.data = numpy.zeros(
(self.nbFiles, arrRet.shape[0],
arrRet.shape[1]), self.__dtype)
self.incrProgressBar = 0
for tempEdfFileName in filelist:
tempEdf = EdfFile.EdfFile(
tempEdfFileName, 'rb')
pieceOfStack = tempEdf.GetData(0)
self.data[self.incrProgressBar] = pieceOfStack
self.incrProgressBar += 1
self.onProgress(self.incrProgressBar)
actualImageStack = True
else:
self.data = numpy.zeros(
(arrRet.shape[0], arrRet.shape[1],
self.nbFiles), self.__dtype)
self.incrProgressBar = 0
for tempEdfFileName in filelist:
tempEdf = EdfFile.EdfFile(
tempEdfFileName, 'rb')
pieceOfStack = tempEdf.GetData(0)
self.data[:, :,
self.incrProgressBar] = pieceOfStack
self.incrProgressBar += 1
self.onProgress(self.incrProgressBar)
except (MemoryError, ValueError):
hdf5done = False
if HDF5 and (('PyMcaQt' in sys.modules) or\
('PyMca.PyMcaQt' in sys.modules)):
from PyMca5 import PyMcaQt as qt
from PyMca5 import ArraySave
msg = qt.QMessageBox.information(
None, "Memory error\n",
"Do you want to convert your data to HDF5?\n",
qt.QMessageBox.Yes, qt.QMessageBox.No)
if msg != qt.QMessageBox.No:
hdf5file = qt.QFileDialog.getSaveFileName(
None, "Please select output file name",
os.path.dirname(filelist[0]),
"HDF5 files *.h5")
if not len(hdf5file):
raise IOError("Invalid output file")
hdf5file = qt.safe_str(hdf5file)
if not hdf5file.endswith(".h5"):
hdf5file += ".h5"
hdf, self.data = ArraySave.getHDF5FileInstanceAndBuffer(
hdf5file, (self.nbFiles, arrRet.shape[0],
arrRet.shape[1]))
self.incrProgressBar = 0
for tempEdfFileName in filelist:
tempEdf = EdfFile.EdfFile(
tempEdfFileName, 'rb')
pieceOfStack = tempEdf.GetData(0)
self.data[
self.
incrProgressBar, :, :] = pieceOfStack[:, :]
hdf.flush()
self.incrProgressBar += 1
self.onProgress(self.incrProgressBar)
hdf5done = True
if not hdf5done:
for i in range(3):
print("\7")
samplingStep = None
i = 2
while samplingStep is None:
print(
"**************************************************"
)
print(
" Memory error!, attempting %dx%d sampling reduction "
) % (i, i)
print(
"**************************************************"
)
s1, s2 = arrRet[::i, ::i].shape
try:
self.data = numpy.zeros(
(s1, s2, self.nbFiles), self.__dtype)
samplingStep = i
except:
i += 1
self.incrProgressBar = 0
for tempEdfFileName in filelist:
tempEdf = EdfFile.EdfFile(
tempEdfFileName, 'rb')
pieceOfStack = tempEdf.GetData(0)
self.data[:, :, self.
incrProgressBar] = pieceOfStack[::
samplingStep, ::
samplingStep]
self.incrProgressBar += 1
self.onProgress(self.incrProgressBar)
self.onEnd()
else:
self.__imageStack = False
if len(singleImageShape) == 1:
#single line
#be ready for specfile stack?
raise IOError("Not implemented yet")
self.data = numpy.zeros(
(self.nbFiles, arrRet.shape[0], nImages), self.__dtype)
self.incrProgressBar = 0
for tempEdfFileName in filelist:
tempEdf = EdfFile.EdfFile(tempEdfFileName, 'rb')
for i in range(nImages):
pieceOfStack = tempEdf.GetData(i)
self.data[self.incrProgressBar, :, i] = pieceOfStack[:]
self.incrProgressBar += 1
self.onProgress(self.incrProgressBar)
self.onEnd()
else:
if nImages > 1:
#this is not the common case
#should I try to convert it to a standard one
#using a 3D matrix or kepp as 4D matrix?
if self.nbFiles > 1:
if (arrRet.shape[0] > 1) and\
(arrRet.shape[1] > 1):
raise IOError(\
"Multiple files with multiple images not implemented yet")
elif arrRet.shape[0] == 1:
self.data = numpy.zeros(
(self.nbFiles, arrRet.shape[0] * nImages,
arrRet.shape[1]), self.__dtype)
self.incrProgressBar = 0
for tempEdfFileName in filelist:
tempEdf = EdfFile.EdfFile(
tempEdfFileName, 'rb')
for i in range(nImages):
pieceOfStack = tempEdf.GetData(i)
self.data[self.incrProgressBar, i,:] = \
pieceOfStack[:,:]
self.incrProgressBar += 1
self.onProgress(self.incrProgressBar)
elif arrRet.shape[1] == 1:
self.data = numpy.zeros(
(self.nbFiles, arrRet.shape[1] * nImages,
arrRet.shape[0]), self.__dtype)
self.incrProgressBar = 0
for tempEdfFileName in filelist:
tempEdf = EdfFile.EdfFile(
tempEdfFileName, 'rb')
for i in range(nImages):
pieceOfStack = tempEdf.GetData(i)
self.data[self.incrProgressBar, i,:] = \
pieceOfStack[:,:]
self.incrProgressBar += 1
self.onProgress(self.incrProgressBar)
else:
self.data = numpy.zeros(
(nImages * self.nbFiles, arrRet.shape[0],
arrRet.shape[1]), self.__dtype)
self.incrProgressBar = 0
for tempEdfFileName in filelist:
tempEdf = EdfFile.EdfFile(tempEdfFileName, 'rb')
for i in range(nImages):
pieceOfStack = tempEdf.GetData(i)
self.data[nImages * self.incrProgressBar +
i, :, :] = pieceOfStack[:, :]
self.incrProgressBar += 1
self.onProgress(self.incrProgressBar)
self.onEnd()
else:
if fileindex == 1:
try:
self.data = numpy.zeros(
(arrRet.shape[0], self.nbFiles,
arrRet.shape[1]), self.__dtype)
except:
try:
self.data = numpy.zeros(
(arrRet.shape[0], self.nbFiles,
arrRet.shape[1]), numpy.float32)
except:
self.data = numpy.zeros(
(arrRet.shape[0], self.nbFiles,
arrRet.shape[1]), numpy.int16)
else:
try:
# calculate needed megabytes
if self.__dtype == numpy.float:
bytefactor = 8
else:
bytefactor = 4
needed_ = self.nbFiles * \
arrRet.shape[0] *\
arrRet.shape[1] * 4
physicalMemory = PhysicalMemory.getPhysicalMemoryOrNone(
)
if physicalMemory is not None:
# spare 5% of memory
if physicalMemory < (1.05 * needed_):
raise MemoryError(
"Not enough physical memory available")
self.data = numpy.zeros(
(self.nbFiles, arrRet.shape[0],
arrRet.shape[1]), self.__dtype)
except:
try:
needed_ = self.nbFiles * \
arrRet.shape[0] *\
arrRet.shape[1] * 4
physicalMemory = PhysicalMemory.getPhysicalMemoryOrNone(
)
if physicalMemory is not None:
# spare 5 % of memory
if physicalMemory < (1.05 * needed_):
raise MemoryError(
"Not enough physical memory available"
)
self.data = numpy.zeros(
(self.nbFiles, arrRet.shape[0],
arrRet.shape[1]), numpy.float32)
except (MemoryError, ValueError):
text = "Memory Error: Attempt subsampling or convert to HDF5"
if HDF5 and (('PyMcaQt' in sys.modules) or\
('PyMca.PyMcaQt' in sys.modules)):
from PyMca5 import PyMcaQt as qt
from PyMca5 import ArraySave
msg = qt.QMessageBox.information(
None, "Memory error\n",
"Do you want to convert your data to HDF5?\n",
qt.QMessageBox.Yes, qt.QMessageBox.No)
if msg == qt.QMessageBox.No:
raise MemoryError(text)
hdf5file = qt.QFileDialog.getSaveFileName(
None, "Please select output file name",
os.path.dirname(filelist[0]),
"HDF5 files *.h5")
if not len(hdf5file):
raise IOError(\
"Invalid output file")
hdf5file = qt.safe_str(hdf5file)
if not hdf5file.endswith(".h5"):
hdf5file += ".h5"
hdf, self.data = ArraySave.getHDF5FileInstanceAndBuffer(
hdf5file,
(self.nbFiles, arrRet.shape[0],
arrRet.shape[1]))
else:
raise MemoryError("Memory Error")
self.incrProgressBar = 0
if fileindex == 1:
for tempEdfFileName in filelist:
tempEdf = EdfFile.EdfFile(tempEdfFileName, 'rb')
pieceOfStack = tempEdf.GetData(0)
self.data[:, self.
incrProgressBar, :] = pieceOfStack[:, :]
self.incrProgressBar += 1
self.onProgress(self.incrProgressBar)
else:
# test for ID24 map
ID24 = False
if "_sample_" in filelist[0]:
bckFile = filelist[0].replace(
"_sample_", "_samplebk_")
if os.path.exists(bckFile):
bckData = EdfFile.EdfFile(bckFile).GetData(0)
else:
bckData = 0
i0StartFile = filelist[0].replace(
"_sample_", "_I0start_")
if os.path.exists(i0StartFile):
ID24 = True
id24idx = 0
i0Start = EdfFile.EdfFile(
i0StartFile,
'rb').GetData(0).astype(numpy.float)
i0Start -= bckData
i0EndFile = filelist[0].replace(
"_sample_", "_I0end_")
i0Slope = 0.0
if os.path.exists(i0EndFile):
i0End = EdfFile.EdfFile(
i0EndFile, 'rb').GetData(0) - bckData
i0Slope = (i0End - i0Start) / len(filelist)
positionersFile = filelist[0].replace(
"_sample_", "_positioners_")
if os.path.exists(positionersFile):
positionersEdf = EdfFile.EdfFile(
positionersFile, 'rb')
self.info["positioners"] = {}
for i in range(positionersEdf.GetNumImages()):
motorName = positionersEdf.GetHeader(
i).get("Title", "Motor_%02d" % i)
motorValue = positionersEdf.GetData(i)
self.info["positioners"][
motorName] = motorValue
for tempEdfFileName in filelist:
tempEdf = EdfFile.EdfFile(tempEdfFileName, 'rb')
if ID24:
pieceOfStack = -numpy.log(
(tempEdf.GetData(0) - bckData) /
(i0Start[0, :] + id24idx * i0Slope))
pieceOfStack[numpy.isfinite(pieceOfStack) ==
False] = 1
id24idx += 1
else:
pieceOfStack = tempEdf.GetData(0)
try:
self.data[
self.
incrProgressBar, :, :] = pieceOfStack[:, :]
except:
if pieceOfStack.shape[1] != arrRet.shape[1]:
print(" ERROR on file %s" %
tempEdfFileName)
print(
" DIM 1 error Assuming missing data were at the end!!!"
)
if pieceOfStack.shape[0] != arrRet.shape[0]:
print(" ERROR on file %s" %
tempEdfFileName)
print(
" DIM 0 error Assuming missing data were at the end!!!"
)
self.data[self.incrProgressBar,\
:pieceOfStack.shape[0],\
:pieceOfStack.shape[1]] = pieceOfStack[:,:]
self.incrProgressBar += 1
self.onProgress(self.incrProgressBar)
self.onEnd()
self.__nFiles = self.incrProgressBar
self.__nImagesPerFile = nImages
shape = self.data.shape
for i in range(len(shape)):
key = 'Dim_%d' % (i + 1, )
self.info[key] = shape[i]
if not isinstance(self.data, numpy.ndarray):
hdf.flush()
self.info["SourceType"] = "HDF5Stack1D"
if self.__imageStack:
self.info["McaIndex"] = 0
self.info["FileIndex"] = 1
else:
self.info["McaIndex"] = 2
self.info["FileIndex"] = 0
self.info["SourceName"] = [hdf5file]
self.info["NumberOfFiles"] = 1
self.info["Size"] = 1
elif actualImageStack:
self.info["SourceType"] = SOURCE_TYPE
self.info["McaIndex"] = 0
self.info["FileIndex"] = 1
self.info["SourceName"] = self.sourceName
self.info["NumberOfFiles"] = self.__nFiles * 1
self.info["Size"] = self.__nFiles * self.__nImagesPerFile
else:
self.info["SourceType"] = SOURCE_TYPE
self.info["FileIndex"] = fileindex
self.info["SourceName"] = self.sourceName
self.info["NumberOfFiles"] = self.__nFiles * 1
self.info["Size"] = self.__nFiles * self.__nImagesPerFile
# try to use positioners to compute the scales (ID24 specific)
xPositionerName = None
yPositionerName = None
if "positioners" in self.info and len(self.info["positioners"]) == 2:
for k, v in self.info["positioners"].items():
if isinstance(v, numpy.ndarray) and v.ndim == 2:
deltaDim1 = v[:, 1:] - v[:, :-1]
deltaDim0 = v[1:, :] - v[:-1, :]
if numpy.any(deltaDim1) and not numpy.any(deltaDim0):
# positioner varying only along dim1
xPositionerName = k
# should we check that all delta values are equal?
deltaX = numpy.mean(deltaDim1)
originX = v[0, 0]
elif numpy.any(deltaDim0) and not numpy.any(deltaDim1):
# positioner varying only along dim0
yPositionerName = k
deltaY = numpy.mean(deltaDim0)
originY = v[0, 0]
if xPositionerName is not None and yPositionerName is not None:
self.info["xScale"] = (originX, deltaX)
self.info["yScale"] = (originY, deltaY)
def load_frame(self,
scan_no,
frame_no,
gz_compressed=True,
normalize=False,
monitor_name=None,
monitor_names=None,
remove_rows=None,
remove_cols=None):
header = dict()
for line in self.spec_file[scan_no].header.split('\n'):
header[line.split(' ')[0]] = line[len(line.split(' ')[0]):]
comments = []
for line in self.spec_file[scan_no].comments.split('\n'):
comments.append(line)
frames = self.get_no_frames(scan_no)
if (frame_no >= frames):
raise IndexError("Frame number does not exist.")
if ('ccoscan' in header['#S']):
for comment in comments:
if ('#C DIRECTORY' in comment):
img_folder = comment.split(':')[-1].strip().split('/')[-1]
if ('#C RADIX' in comment):
radix = comment.split(':')[-1].strip()
if ('#C ZAP SCAN NUMBER' in comment):
zap_scan_no = comment.split(':')[-1].strip()
if ('#C ZAP IMAGE NUMBER' in comment):
zap_image_no = comment.split(':')[-1].strip()
filename = img_folder + '/' + radix + '_mpx-x4_%s_0000_0000.edf' % (
str(zap_scan_no).zfill(4))
edf_frame_no = frame_no
else:
first_frame = int(header['#UCCD'].split('#r')[-1].split('.')[0])
img_folder = header['#UCCD'].split('/')[-2] + '/'
filename_template = header['#UCCD'].split('/')[-1].split('#r')[
0] + '#r.' + header['#UCCD'].split('/')[-1].split('.')[-1]
filename = img_folder + filename_template.replace(
'#n',
str(scan_no).zfill(3)).replace(
'#p',
str(frame_no).zfill(3)).replace(
'#r',
str(first_frame + frame_no).zfill(3))
edf_frame_no = 0
img_filename = os.path.join(self.image_foldername, filename)
if (not os.path.exists(img_filename)):
if (os.path.exists(img_filename.replace('.edf', '.edf.gz'))):
inF = gzip.open(img_filename.replace('.edf', '.edf.gz'), 'rb')
outF = open(img_filename, 'wb')
outF.write(inF.read())
inF.close()
outF.close()
edf = EdfFile.EdfFile(img_filename, 'r')
edf_header = edf.GetHeader(edf_frame_no)
motors = dict()
motor_mne = edf_header['motor_mne'].split()
motor_pos = edf_header['motor_pos'].split()
for i in xrange(len(motor_mne)):
motors[motor_mne[i]] = float(motor_pos[i])
counters = dict()
if (not ('ccoscan' in header['#S'])):
counter_mne = edf_header['counter_mne'].split()
counter_pos = edf_header['counter_pos'].split()
for i in xrange(len(counter_mne)):
counters[counter_mne[i]] = float(counter_pos[i])
the_img = np.array(edf.GetData(edf_frame_no), dtype='float')
if (remove_rows != None):
the_img = np.delete(the_img, remove_rows, axis=0)
if (remove_cols != None):
the_img = np.delete(the_img, remove_cols, axis=1)
if (normalize):
if (monitor_name):
mon_count = float(
getattr(self.spec_file[scan_no], monitor_name)[frame_no])
the_img /= mon_count
if (monitor_names):
for mon_name in monitor_names:
mon_count = float(
getattr(self.spec_file[scan_no], mon_name)[frame_no])
the_img /= mon_count
return DetImage(the_img, motors, counters)
def load_frame(self,
scan_no,
frame_no,
gz_compressed=True,
normalize=False,
monitor_name=None,
monitor_names=None,
remove_rows=None,
remove_cols=None):
header = dict()
print self.spec_file[scan_no].header
for line in self.spec_file[scan_no].header.split('\n'):
header[line.split(' ')[0]] = line[len(line.split(' ')[0]):]
comments = dict()
for line in self.spec_file[scan_no].comments.split('\n'):
comments[line.split(':')
[0].strip()] = line[len(line.split(':')[0]) + 1:].strip()
frames = self.get_no_frames(scan_no)
if ('ccoscan' in header['#S'] or 'zapline' in header['#S']):
img_folder = comments['#C DIRECTORY'].split('/')[-1] + '/'
if (img_folder == '/'):
img_folder = comments['#C DIRECTORY'].split('/')[-2] + '/'
zap_scan_no = int(comments['#C ZAP SCAN NUMBER'])
radix = comments['#C RADIX']
filename = radix + '_mpx-x4_%s_0000_0000.edf' % (
str(zap_scan_no).zfill(4))
multiframe_edf_frame_no = frame_no
else:
first_frame = int(header['#UCCD'].split('#r')[-1].split('.')[0])
img_folder = header['#UCCD'].replace('//', '/').split(
'/'
)[-2] + '/' #in the beginning the path of MA3886 had '//' for some reason
filename_template = header['#UCCD'].split('/')[-1].split('#r')[
0] + '#r.' + header['#UCCD'].split('/')[-1].split('.')[-1]
filename = filename_template.replace(
'#n',
str(scan_no).zfill(3)).replace('#p',
str(frame_no).zfill(3)).replace(
'#r',
str(first_frame +
frame_no).zfill(3))
multiframe_edf_frame_no = 0
# automatically detect if frame is compressed or not
if (not os.path.exists(
os.path.join(self.image_foldername, img_folder, filename))):
filename = filename.replace('.edf', '.edf.gz')
#if(gz_compressed):
# filename = filename.replace('.edf', '.edf.gz')
if (frame_no >= frames):
raise IndexError("Frame number does not exist.")
img_filename = os.path.join(self.image_foldername, img_folder,
filename)
edf = EdfFile.EdfFile(img_filename, 'r')
edf_header = edf.GetHeader(multiframe_edf_frame_no)
motors = dict()
motor_mne = edf_header['motor_mne'].split()
motor_pos = edf_header['motor_pos'].split()
for i in xrange(len(motor_mne)):
motors[motor_mne[i]] = float(motor_pos[i])
counters = dict()
if (not ('ccoscan' in header['#S'] or 'zapline' in header['#S'])):
counter_mne = edf_header['counter_mne'].split()
counter_pos = edf_header['counter_pos'].split()
for i in xrange(len(counter_mne)):
counters[counter_mne[i]] = float(counter_pos[i])
the_img = np.array(edf.GetData(multiframe_edf_frame_no), dtype='float')
if (remove_rows != None):
the_img = np.delete(the_img, remove_rows, axis=0)
if (remove_cols != None):
the_img = np.delete(the_img, remove_cols, axis=1)
if (normalize):
if (monitor_name):
mon_count = float(
getattr(self.spec_file[scan_no], monitor_name)[frame_no])
the_img /= mon_count
if (monitor_names):
for mon_name in monitor_names:
mon_count = float(
getattr(self.spec_file[scan_no], mon_name)[frame_no])
the_img /= mon_count
return DetImage(the_img, motors, counters, header)
def refresh(self):
self._sourceObjectList = []
for name in self.__sourceNameList:
self._sourceObjectList.append(
EdfFile.EdfFile(name, access='rb', fastedf=self._fastedf))
self.__lastKeyInfo = {}
def buildOutput(self, inputdir=None, outputdir=None, delete=None):
if inputdir is None: inputdir = self.inputDir
if inputdir is None: inputdir = os.getcwd()
if outputdir is None: outputdir = self.outputDir
if outputdir is None: outputdir = inputdir
if delete is None:
if outputdir == inputdir:
delete = True
if DEBUG:
print("delete option = ", delete)
allfiles = os.listdir(inputdir)
partialedflist = []
partialdatlist = []
partialconlist = []
for filename in allfiles:
if filename.endswith('000000_partial.edf'):
partialedflist.append(filename)
elif filename.endswith('000000_partial.dat'):
partialdatlist.append(filename)
elif filename.endswith('000000_partial_concentrations.txt'):
partialconlist.append(filename)
#IMAGES
edfoutlist = []
for filename in partialedflist:
if DEBUG:
print("Dealing with filename %s" % filename)
edflist = self.getIndexedFileList(os.path.join(inputdir, filename))
i = 0
for edfname in edflist:
edf = EdfFile.EdfFile(edfname, access='rb', fastedf=0)
nImages = edf.GetNumImages()
#get always the last image
data0 = edf.GetData(nImages - 1)
data0[data0 < 0] = 0
if i == 0:
header = edf.GetHeader(0)
data = data0.copy()
else:
data += data0
del edf
i += 1
edfname = filename.replace('_000000_partial.edf', ".edf")
edfoutname = os.path.join(outputdir, edfname)
if DEBUG:
print("Dealing with output filename %s" % edfoutname)
if os.path.exists(edfoutname):
if DEBUG:
print("Output file already exists, trying to delete it")
os.remove(edfoutname)
edfout = EdfFile.EdfFile(edfoutname, access="wb")
edfout.WriteImage(header, data, Append=0)
del edfout
edfoutlist.append(edfoutname)
if delete:
for filename in edflist:
try:
os.remove(filename)
except:
print("Cannot delete file %s" % filename)
#DAT IMAGES
datoutlist = []
for filename in partialdatlist:
edflist = self.getIndexedFileList(os.path.join(inputdir, filename))
first = True
for edfname in edflist:
f = open(edfname)
lines = f.readlines()
f.close()
j = 1
while (not len(lines[-j].replace("\n", ""))):
j += 1
if first:
first = False
labels = lines[0].replace("\n", "").split(" ")
nlabels = len(labels)
nrows = len(lines) - j
data = numpy.zeros((nrows, nlabels), numpy.double)
inputdata = numpy.zeros((nrows, nlabels), numpy.double)
chisqIndex = labels.index('chisq')
for i in range(nrows):
inputdata[i, :] = [float(x) for x in lines[i + 1].split()]
if inputdata[i, chisqIndex] < 0.0:
inputdata[i, chisqIndex] = 0.0
data += inputdata
outfilename = os.path.join(outputdir,
filename.replace("_000000_partial", ""))
if os.path.exists(outfilename):
os.remove(outfilename)
outfile = open(outfilename, 'w+')
outfile.write('%s' % lines[0])
line = ""
for row in range(nrows):
#line = "%d" % inputdata[row, 0]
for col in range(nlabels):
if col == 0: line += "%d" % inputdata[row, col]
elif col == 1: line += " %d" % inputdata[row, col]
else: line += " %g" % data[row, col]
line += "\n"
outfile.write("%s" % line)
line = ""
outfile.write("\n")
outfile.close()
datoutlist.append(outfilename)
if delete:
for filename in edflist:
os.remove(filename)
#CONCENTRATIONS
outconlist = []
for filename in partialconlist:
edflist = self.getIndexedFileList(os.path.join(inputdir, filename))
i = 0
for edfname in edflist:
edf = open(edfname, 'rb')
if i == 0:
outfilename = os.path.join(
outputdir, filename.replace("_000000_partial", ""))
if os.path.exists(outfilename):
os.remove(outfilename)
outfile = open(outfilename, 'wb')
lines = edf.readlines()
for line in lines:
outfile.write(line)
edf.close()
i += 1
outfile.close()
outconlist.append(outfilename)
if delete:
for filename in edflist:
os.remove(filename)
return edfoutlist, datoutlist, outconlist
def _dynamicAction(self, index):
#just support edffiles
fileName = self.imageList[index]
edf = EdfFile.EdfFile(fileName)
self.setImageData(edf.GetData(0))
self.graphWidget.graph.setGraphTitle(os.path.basename(fileName))
print "loading of " + nexfile_path + "failed!"
#get the total numbe of images in the nex container
try:
num_of_image = len(data.entry.instrument.detector.data.nxdata)
except:
num_of_image = data.entry.instrument.detector.data.shape[0]
#do only one image if you want to check first
# if CHECK:
# num_of_image=1
if CHECK:
try:
data_1D = data.entry.instrument.detector.data.nxdata[
check_image_number]
if save_edf:
f = EdfFile.EdfFile(
nexfile_path.replace('.nxs',
str(check_image_number) + '.edf'))
f.WriteImage({"potential": 0}, data_1D)
except:
data_1D = data.entry.instrument.detector.data[check_image_number]
if save_edf:
f = EdfFile.EdfFile(
nexfile_path.replace('.nxs',
str(check_image_number) + '.edf'))
f.WriteImage({"potential": 0}, data_1D)
print "fit frame {} now".format(check_image_number)
#remove dead pixes first
data_1D_filtered = (data_1D < 10000) * data_1D
try:
hor_temp, ver_temp = fit_gaussian_peak(data_1D_filtered,
plot_it=CHECK)
# -*- coding: utf-8 -*-
if __name__ == "__main__":
import glob
from PyMca5.PyMcaIO import EdfFile
files = glob.glob(
"/data/id21/inhouse/15apr/Hiram/results/fXANES5/fXANES5_data/*.edf"
)
for f in files:
h = EdfFile.EdfFile(f)
data = h.GetData(0)
print(f, data.shape)
cen_pix = [388, 338]
dx, dy = 25, 25
bg_dx = 15
roi = Roi([cen_pix[0] - dx, cen_pix[0] + dx],
[cen_pix[1] - dy, cen_pix[1] + dy])
bg_rois = []
bg_rois.append(
Roi([cen_pix[0] - dx - bg_dx, cen_pix[0] - dx],
[cen_pix[1] - dy, cen_pix[1] + dy]))
bg_rois.append(
Roi([cen_pix[0] + dx, cen_pix[0] + dx + bg_dx],
[cen_pix[1] - dy, cen_pix[1] + dy]))
integrator = ImageIntegrator(roi, bg_rois)
plt.ion()
fig = plt.figure()
for frame_no in xrange(30):
img_filename = edf_path + "ma2254_mpx01/ma2254_" + str(scan_no).zfill(
3) + '_' + str(frame_no).zfill(3) + '_' + str(
first_frame + frame_no).zfill(3) + '.edf.gz'
edf = EdfFile.EdfFile(img_filename, 'r')
img = edf.GetData(0)
integrator.plot_cuts(img, fig)
plt.draw()
time.sleep(0.5)
plt.ioff()
plt.show()
ddict['roi_min'] = ddict0['arguments'][1]
ddict['roi_max'] = ddict0['arguments'][2]
ddict['smoothing'] = ddict0['arguments'][3]
self.parametersWidget.setParameters(ddict)
else:
self.parametersWidget.setParameters(ddict0)
if __name__ == "__main__":
import numpy
app = qt.QApplication([])
if 0:
noise = numpy.random.randn(1000.)
y = numpy.arange(1000.)
w = SNIPDialog(None, y + numpy.sqrt(y) * noise)
elif len(sys.argv) > 1:
from PyMca5.PyMcaIO import EdfFile
edf = EdfFile.EdfFile(sys.argv[1])
data = edf.GetData(0)
w = SNIPDialog(None, data)
else:
x, y = numpy.ogrid[0:200:200j, 0:200:200j]
data = 50 * numpy.exp(-(x-64)*(x-64)/20.) +\
50 * numpy.exp(-(y-128)*(y-128)/20.) +\
100 * numpy.exp(-(1./20) * ((x-64)*(x-64) + (y-128)*(y-128)))
w = SNIPDialog(None, data)
w.show()
ret = w.exec_()
if ret:
print(w.getParameters())
import sys
import os
from PyMca5.Object3D import SceneGLWindow
try:
from PyMca5.PyMcaIO import EdfFile
except ImportError:
import EdfFile
app = qt.QApplication(sys.argv)
window = SceneGLWindow.SceneGLWindow()
window.show()
if len(sys.argv) > 1:
flist = []
for i in range(1, len(sys.argv)):
flist.append(sys.argv[i])
for f in flist:
edf = EdfFile.EdfFile(f, access='rb')
data = edf.GetData(0)
object3D = Object3DMesh(os.path.basename(f))
else:
data = numpy.arange(200.).astype(numpy.float32)
data.shape = [40, 5]
object3D = Object3DMesh('builtin')
#several options: regular grid, irregular grid
if len(sys.argv) > 1:
#print "IMPOSSING A 1000 OFFSET"
#offset = 1000.0
offset = 0
#irregular grid
xSize, ySize = data.shape[0:2]
zSize = 1
def saveImage(self, ffile=None):
self.savedImages = []
if ffile is None:
ffile = os.path.splitext(self._rootname)[0]
ffile = self.os_path_join(self.imgDir, ffile)
if not self.roiFit:
if (self.fileStep > 1) or (self.mcaStep > 1):
trailing = "_filestep_%02d_mcastep_%02d" % (self.fileStep,
self.mcaStep)
else:
trailing = ""
#speclabel = "#L row column"
speclabel = "row column"
if self.chunk is None:
suffix = ".edf"
else:
suffix = "_%06d_partial.edf" % self.chunk
iterationList = self.__peaks * 1
iterationList += ['chisq']
if self._concentrations:
iterationList += self.__concentrationsKeys
for peak in iterationList:
if peak in self.__peaks:
a, b = peak.split()
speclabel += " %s" % (a + "-" + b)
speclabel += " s(%s)" % (a + "-" + b)
edfname = ffile + "_" + a + "_" + b + trailing + suffix
elif peak in self.__concentrationsKeys:
speclabel += " %s" % peak.replace(" ", "-")
edfname = ffile + "_" + peak.replace(
" ", "_") + trailing + suffix
elif peak == 'chisq':
speclabel += " %s" % (peak)
edfname = ffile + "_" + peak + trailing + suffix
else:
print("Unhandled peak name: %s. Not saved." % peak)
continue
dirname = os.path.dirname(edfname)
if not os.path.exists(dirname):
try:
os.mkdir(dirname)
except:
print("I could not create directory %s" % dirname)
Append = 0
if os.path.exists(edfname):
try:
os.remove(edfname)
except:
print("I cannot delete output file")
print("trying to append image to the end")
Append = 1
edfout = EdfFile.EdfFile(edfname, access='ab')
edfout.WriteImage({'Title': peak},
self.__images[peak],
Append=Append)
edfout = None
self.savedImages.append(edfname)
#save specfile format
if self.chunk is None:
specname = ffile + trailing + ".dat"
else:
specname = ffile + trailing + "_%06d_partial.dat" % self.chunk
if os.path.exists(specname):
try:
os.remove(specname)
except:
pass
specfile = open(specname, 'w+')
#specfile.write('\n')
#specfile.write('#S 1 %s\n' % (file+trailing))
#specfile.write('#N %d\n' % (len(self.__peaks)+2))
specfile.write('%s\n' % speclabel)
specline = ""
imageRows = self.__images['chisq'].shape[0]
imageColumns = self.__images['chisq'].shape[1]
for row in range(imageRows):
for col in range(imageColumns):
specline += "%d" % row
specline += " %d" % col
for peak in self.__peaks:
#write area
specline += " %g" % self.__images[peak][row][col]
#write sigma area
specline += " %g" % self.__sigmas[peak][row][col]
#write global chisq
specline += " %g" % self.__images['chisq'][row][col]
if self._concentrations:
for peak in self.__concentrationsKeys:
specline += " %g" % self.__images[peak][row][col]
specline += "\n"
specfile.write("%s" % specline)
specline = ""
specfile.write("\n")
specfile.close()
else:
for group in self.__ROIpeaks:
i = 0
grouptext = group.replace(" ", "_")
for roi in self._ROIimages[group].keys():
#roitext = roi.replace(" ","-")
if (self.fileStep > 1) or (self.mcaStep > 1):
edfname = ffile + "_" + grouptext + (
"_%04deVROI_filestep_%02d_mcastep_%02d.edf" %
(self.roiWidth, self.fileStep, self.mcaStep))
else:
edfname = ffile + "_" + grouptext + ("_%04deVROI.edf" %
self.roiWidth)
dirname = os.path.dirname(edfname)
if not os.path.exists(dirname):
try:
os.mkdir(dirname)
except:
print("I could not create directory %s" % dirname)
edfout = EdfFile.EdfFile(edfname)
edfout.WriteImage({'Title': group + " " + roi},
self._ROIimages[group][roi],
Append=i)
if i == 0:
self.savedImages.append(edfname)
i = 1