def findPeaksAndTroughs(self, ydataset, delta, xdataset=None):
'''returns a list of peaks and troughs in tuple of (peak_position, peak_value).
If x data set is not provided, it returns as tuple of (peak_index, peak_value)'''
if xdataset is not None:
xdataset = dnp.asarray(xdataset)
return peakdet(dnp.asarray(ydataset), delta, xdataset)
def findPeaksAndTroughs(self, ydataset, delta, xdataset=None):
'''returns a list of peaks and troughs in tuple of (peak_position, peak_value).
If x data set is not provided, it returns as tuple of (peak_index, peak_value)'''
if xdataset is not None:
xdataset = dnp.asarray(xdataset)
return peakdet(dnp.asarray(ydataset), delta, xdataset)
def test_process_negative_edge(self): # outer = 148, left half (first half of data though!) xds, yds = self.getInnerAndDetDatasetForGivenOuterValue(148.0) x = dnp.asarray(xds.data[:(len(xds) / 2)]) y = dnp.asarray(yds.data[:(len(yds) / 2)]) expected = 0.000000, 0.000000, -3.896408, 0.006343, 1.699449, 0.006343 check(TwoGaussianEdges()._process(x, y), expected, TwoGaussianEdges().labelList)
def test_process_positive_edge(self): # outer = 148, right half (second half of data though!) xds, yds = self.getInnerAndDetDatasetForGivenOuterValue(148.0) x = dnp.asarray(xds.data[(len(xds) / 2):]) y = dnp.asarray(yds.data[(len(yds) / 2):]) expected = -3.944682, 0.006067, 0.000000, 0.000000, 1.683015, 0.006067 check(TwoGaussianEdges()._process(x, y), expected, TwoGaussianEdges().labelList)
def testAsArray(self):
ta = np.array([1, 2])
ata = np.asarray(ta)
self.assertEquals(ata.dtype, np.int_)
self.checkitems([1, 2], ata)
ata = np.asarray(ta, np.float)
self.assertEquals(ata.dtype, np.float_)
self.checkitems([1, 2], ata)
def testAsArray(self):
ta = np.array([1, 2])
ata = np.asarray(ta)
self.assertEquals(ata.dtype, np.int_)
self.checkitems([1, 2], ata)
ata = np.asarray(ta, np.float)
self.assertEquals(ata.dtype, np.float_)
self.checkitems([1, 2], ata)
def getDatasetFromLoadedFile(loadedFile, fieldName, scanDataPointCache=None):
'''
Gets a dataset called fieldName from an already loaded file see loadScanFile(scanOb)
returns dataset
'''
logger.debug('Getting data for %s, from %s (with cache=%s)', fieldName, loadedFile, scanDataPointCache)
# Check if the field names are the full local names if so get just the last part of
# the field names as this should be the node name. Keep original fieldname, it might
# be useful later
if '.' in fieldName:
# with scnname.fieldname strip off scnname
strippedFieldName = fieldName.split('.')[-1]
else: # fieldname doesn't require stripping
strippedFieldName = fieldName
# If we have a scanDataPointCache use it for performance
if(scanDataPointCache):
return dnp.asarray(scanDataPointCache.getPositionsFor(strippedFieldName))
# Check if its a NeXus file
if isinstance(loadedFile, NXroot):
# Note: Using first node returned, this might fail if there are multiple nodes with the same name!
# Might be possible to disambiguate this using the original fieldname?
loadedNodes = loadedFile.getnodes(strippedFieldName, group=False, data=True)
if len(loadedNodes) == 0:
raise KeyError("%s not found in data file" % strippedFieldName)
# Find nodes which have a local_name
probableNodes = [loadedNodes[_n] for _n in xrange(len(loadedNodes))
if 'local_name' in loadedNodes[_n].attrs]
# Use the first local_name which matches the fieldName or fall back on using the first node
for node in probableNodes:
if node.attrs['local_name'] == fieldName:
lazyDataset = node
break
else:
lazyDataset = loadedNodes[0]
# Use slicing to load the whole lazy dataset into a array i.e. non-lazy dataset
dataset = lazyDataset[...]
return dataset
elif isinstance(loadedFile, DataHolder):
datasetList = loadedFile[strippedFieldName]
# Convert the dataset as a list into the array
dataset = dnp.asarray(datasetList)
return dataset
# Not a supported file type
else:
print "The file format is not supported"
print loadedFile.__class__
def singlePeakProcess(self, xDataSet, yDataSet):
xarray = dnp.asarray(xDataSet)
yarray = dnp.asarray(yDataSet)
ymax=yarray.max()
ymaxindex=yarray.argmax()
#print "y max index %d" % ymaxindex
maxpos=xarray[ymaxindex]
basey=self.baseline(xarray, yarray, 1)
halfmax=ymax/2+basey/2
xcrossingvalues=dnp.crossings(yarray, halfmax, xarray)
#print xcrossingvalues, maxpos
if len(xcrossingvalues)>2:
print "multiple peaks exists in the data set!, only process the highest peak."
fwhmvalue=find_gt(xcrossingvalues, maxpos)-find_lt(xcrossingvalues,maxpos)
return [(maxpos,ymax,basey,fwhmvalue)]
def singlePeakProcess(self, xDataSet, yDataSet):
xarray = dnp.asarray(xDataSet)
yarray = dnp.asarray(yDataSet)
ymax = yarray.max()
ymaxindex = yarray.argmax()
#print "y max index %d" % ymaxindex
maxpos = xarray[ymaxindex]
basey = self.baseline(xarray, yarray, 1)
halfmax = ymax / 2 + basey / 2
xcrossingvalues = dnp.crossings(yarray, halfmax, xarray)
#print xcrossingvalues, maxpos
if len(xcrossingvalues) > 2:
print "multiple peaks exists in the data set!, only process the highest peak."
fwhmvalue = find_gt(xcrossingvalues, maxpos) - find_lt(
xcrossingvalues, maxpos)
return [(maxpos, ymax, basey, fwhmvalue)]
def baseline(self,xdataset, ydataset, smoothness):
'''find the baseline y value for a peak in y dataset'''
xdataset = dnp.asarray(xdataset)
ydataset = dnp.asarray(ydataset)
ymaxindex=ydataset.argmax()
#TODO
result=dnp.gradient(ydataset,xdataset)
#derivative(xdataset, ydataset, smoothness)
leftresult=result[:ymaxindex]
rightresult=result[ymaxindex+1:]
leftminderivativeindex=dnp.abs(leftresult).argmin()
rightminderivativeindex=dnp.abs(rightresult).argmin()
leftbasey=ydataset[leftminderivativeindex]
rightbasey=ydataset[rightminderivativeindex+1+leftresult.shape[0]]
basey=(leftbasey+rightbasey)/2
return basey
def baseline(self, xdataset, ydataset, smoothness):
'''find the baseline y value for a peak in y dataset'''
xdataset = dnp.asarray(xdataset)
ydataset = dnp.asarray(ydataset)
ymaxindex = ydataset.argmax()
#TODO
result = dnp.gradient(ydataset, xdataset)
#derivative(xdataset, ydataset, smoothness)
leftresult = result[:ymaxindex]
rightresult = result[ymaxindex + 1:]
leftminderivativeindex = dnp.abs(leftresult).argmin()
rightminderivativeindex = dnp.abs(rightresult).argmin()
leftbasey = ydataset[leftminderivativeindex]
rightbasey = ydataset[rightminderivativeindex + 1 +
leftresult.shape[0]]
basey = (leftbasey + rightbasey) / 2
return basey
def findBasePoints(self, xdataset, ydataset, delta, smoothness):
xdataset = dnp.asarray(xdataset)
ydataset = dnp.asarray(ydataset)
peaks=self.findPeaksAndTroughs(ydataset, delta)[0]
#print peaks
yslices=[]
xslices=[]
startindex=0
for index,value in peaks: #@UnusedVariable
yslices.append(ydataset[startindex:index])
xslices.append(xdataset[startindex:index])
startindex=index+1
yslices.append(ydataset[startindex:])
xslices.append(xdataset[startindex:])
bases=[]
for xset, yset in zip(xslices, yslices):
result=dnp.gradient(yset, xset)
minimumderivativeindex=dnp.abs(result).argmin()
bases.append((xset[minimumderivativeindex],yset[minimumderivativeindex]))
#print "Base Points (position, value) : ", bases
return bases
def findBasePoints(self, xdataset, ydataset, delta, smoothness):
xdataset = dnp.asarray(xdataset)
ydataset = dnp.asarray(ydataset)
peaks = self.findPeaksAndTroughs(ydataset, delta)[0]
#print peaks
yslices = []
xslices = []
startindex = 0
for index, value in peaks: #@UnusedVariable
yslices.append(ydataset[startindex:index])
xslices.append(xdataset[startindex:index])
startindex = index + 1
yslices.append(ydataset[startindex:])
xslices.append(xdataset[startindex:])
bases = []
for xset, yset in zip(xslices, yslices):
result = dnp.gradient(yset, xset)
minimumderivativeindex = dnp.abs(result).argmin()
bases.append(
(xset[minimumderivativeindex], yset[minimumderivativeindex]))
#print "Base Points (position, value) : ", bases
return bases
def getDataSet(self, fName):
nxsTree = dnp.io.load(fName)
dataSet = dnp.asarray(nxsTree['/entry/result/data'])
self.dataSet = dataSet.squeeze()
def getDatasetFromLoadedFile(loadedFile, fieldName, scanDataPointCache=None):
'''
Gets a dataset called fieldName from an already loaded file see loadScanFile(scanOb)
returns dataset
'''
logger.debug('Getting data for %s, from %s (with cache=%s)', fieldName,
loadedFile, scanDataPointCache)
# Check if the field names are the full local names if so get just the last part of
# the field names as this should be the node name. Keep original fieldname, it might
# be useful later
if '.' in fieldName:
# with scnname.fieldname strip off scnname
strippedFieldName = fieldName.split('.')[-1]
else: # fieldname doesn't require stripping
strippedFieldName = fieldName
# If we have a scanDataPointCache use it for performance
if (scanDataPointCache):
return dnp.asarray(
scanDataPointCache.getPositionsFor(strippedFieldName))
# Check if its a NeXus file
if isinstance(loadedFile, NXroot):
# Note: Using first node returned, this might fail if there are multiple nodes with the same name!
# Might be possible to disambiguate this using the original fieldname?
loadedNodes = loadedFile.getnodes(strippedFieldName,
group=False,
data=True)
if len(loadedNodes) == 0:
raise KeyError("%s not found in data file" % strippedFieldName)
# Find nodes which have a local_name
probableNodes = [
loadedNodes[_n] for _n in xrange(len(loadedNodes))
if 'local_name' in loadedNodes[_n].attrs
]
# Use the first local_name which matches the fieldName or fall back on using the first node
for node in probableNodes:
if node.attrs['local_name'] == fieldName:
lazyDataset = node
break
else:
lazyDataset = loadedNodes[0]
# Use slicing to load the whole lazy dataset into a array i.e. non-lazy dataset
dataset = lazyDataset[...]
return dataset
elif isinstance(loadedFile, DataHolder):
datasetList = loadedFile[strippedFieldName]
# Convert the dataset as a list into the array
dataset = dnp.asarray(datasetList)
return dataset
# Not a supported file type
else:
print "The file format is not supported"
print loadedFile.__class__
def getDataSet(self, fName):
nxsTree = dnp.io.load(fName)
dataSet = dnp.asarray(nxsTree['/entry/result/data'])
self.dataSet = dataSet.squeeze()
def peakdet(v, delta, x = None):
"""
Converted from MATLAB script at http://billauer.co.il/peakdet.html
Currently returns two lists of tuples, but maybe arrays would be better
function [maxtab, mintab]=peakdet(v, delta, x)
%PEAKDET Detect peaks in a vector
% [MAXTAB, MINTAB] = PEAKDET(V, DELTA) finds the local
% maxima and minima ("peaks") in the vector V.
% MAXTAB and MINTAB consists of two columns. Column 1
% contains indices in V, and column 2 the found values.
%
% With [MAXTAB, MINTAB] = PEAKDET(V, DELTA, X) the indices
% in MAXTAB and MINTAB are replaced with the corresponding
% X-values.
%
% A point is considered a maximum peak if it has the maximal
% value, and was preceded (to the left) by a value lower by
% DELTA.
% Eli Billauer, 3.4.05 (Explicitly not copyrighted).
% This function is released to the public domain; Any use is allowed.
"""
maxtab = []
mintab = []
if x is None:
x = dnp.arange(len(v))
v = dnp.asarray(v)
if len(v) != len(x):
sys.exit('Input vectors v and x must have same length')
if not isinstance(delta, (type(None),int,float,str,bool)):
sys.exit('Input argument delta must be a number')
if delta <= 0:
sys.exit('Input argument delta must be positive')
mn, mx = float('inf'), float('-inf')
mnpos, mxpos = float('NaN'), float('NaN')
lookformax = True
for i in dnp.arange(len(v)):
this = v[i]
if this > mx:
mx = this
mxpos = x[i]
if this < mn:
mn = this
mnpos = x[i]
if lookformax:
if this < mx-delta:
maxtab.append((mxpos, mx))
mn = this
mnpos = x[i]
lookformax = False
else:
if this > mn+delta:
mintab.append((mnpos, mn))
mx = this
mxpos = x[i]
lookformax = True
return maxtab, mintab