本文整理汇总了Python中PyMca5.PyMcaMath.fitting.SpecfitFuns.snip1d方法的典型用法代码示例。如果您正苦于以下问题：Python SpecfitFuns.snip1d方法的具体用法？Python SpecfitFuns.snip1d怎么用？Python SpecfitFuns.snip1d使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类PyMca5.PyMcaMath.fitting.SpecfitFuns的用法示例。
在下文中一共展示了SpecfitFuns.snip1d方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _fitBkgSubtract
# 需要导入模块: from PyMca5.PyMcaMath.fitting import SpecfitFuns [as 别名]
# 或者: from PyMca5.PyMcaMath.fitting.SpecfitFuns import snip1d [as 别名]
def _fitBkgSubtract(spectra, config=None, anchorslist=None, fitmodel=None):
"""Subtract brackground from data and add it to fit model
"""
for k in range(spectra.shape[1]):
# obtain the smoothed spectrum
background = SpecfitFuns.SavitskyGolay(spectra[:, k],
config['fit']['stripfilterwidth'])
lastAnchor = 0
for anchor in anchorslist:
if (anchor > lastAnchor) and (anchor < background.size):
background[lastAnchor:anchor] =\
SpecfitFuns.snip1d(background[lastAnchor:anchor],
config['fit']['snipwidth'],
0)
lastAnchor = anchor
if lastAnchor < background.size:
background[lastAnchor:] =\
SpecfitFuns.snip1d(background[lastAnchor:],
config['fit']['snipwidth'],
0)
spectra[:, k] -= background
if fitmodel is not None:
fitmodel[:, k] = background示例2: update
# 需要导入模块: from PyMca5.PyMcaMath.fitting import SpecfitFuns [as 别名]
# 或者: from PyMca5.PyMcaMath.fitting.SpecfitFuns import snip1d [as 别名]
def update(self):
if self._y is None:
return
pars = self.getParameters()
#smoothed data
y = numpy.ravel(numpy.array(self._y)).astype(numpy.float)
ysmooth = SpecfitFuns.SavitskyGolay(y, pars['stripfilterwidth'])
f=[0.25,0.5,0.25]
ysmooth[1:-1] = numpy.convolve(ysmooth,f,mode=0)
ysmooth[0] = 0.5 *(ysmooth[0] + ysmooth[1])
ysmooth[-1] = 0.5 * (ysmooth[-1] + ysmooth[-2])
#loop for anchors
x = self._x
niter = pars['stripiterations']
anchorslist = []
if pars['stripanchorsflag']:
if pars['stripanchorslist'] is not None:
ravelled = x
for channel in pars['stripanchorslist']:
if channel <= ravelled[0]:continue
index = numpy.nonzero(ravelled >= channel)[0]
if len(index):
index = min(index)
if index > 0:
anchorslist.append(index)
if niter > 1000:
stripBackground = SpecfitFuns.subac(ysmooth,
pars['stripconstant'],
niter,
pars['stripwidth'],
anchorslist)
#final smoothing
stripBackground = SpecfitFuns.subac(stripBackground,
pars['stripconstant'],
500,1,
anchorslist)
elif niter > 0:
stripBackground = SpecfitFuns.subac(ysmooth,
pars['stripconstant'],
niter,
pars['stripwidth'],
anchorslist)
else:
stripBackground = 0.0 * ysmooth + ysmooth.min()
if len(anchorslist) == 0:
anchorslist = [0, len(ysmooth)-1]
anchorslist.sort()
snipBackground = 0.0 * ysmooth
lastAnchor = 0
width = pars['snipwidth']
for anchor in anchorslist:
if (anchor > lastAnchor) and (anchor < len(ysmooth)):
snipBackground[lastAnchor:anchor] =\
SpecfitFuns.snip1d(ysmooth[lastAnchor:anchor], width, 0)
lastAnchor = anchor
if lastAnchor < len(ysmooth):
snipBackground[lastAnchor:] =\
SpecfitFuns.snip1d(ysmooth[lastAnchor:], width, 0)
self.graphWidget.addCurve(x, y, \
legend='Input Data',\
replace=True,
replot=False)
self.graphWidget.addCurve(x, stripBackground,\
legend='Strip Background',\
replot=False)
self.graphWidget.addCurve(x, snipBackground,\
legend='SNIP Background',
replot=True)示例3: fitMultipleSpectra
# 需要导入模块: from PyMca5.PyMcaMath.fitting import SpecfitFuns [as 别名]
# 或者: from PyMca5.PyMcaMath.fitting.SpecfitFuns import snip1d [as 别名]
#.........这里部分代码省略.........
totalSpectra = data.shape[0] * data.shape[1]
jStep = min(100, data.shape[1])
if weightPolicy == 2:
SVD = False
sigma_b = None
elif weightPolicy == 1:
# the +1 is to prevent misbehavior due to weights less than 1.0
sigma_b = 1 + numpy.sqrt(dummySpectrum)/nPixels
SVD = True
else:
SVD = True
sigma_b = None
last_svd = None
for i in range(0, data.shape[0]):
#print(i)
#chunks of nColumns spectra
if i == 0:
chunk = numpy.zeros((dummySpectrum.shape[0],
jStep),
numpy.float)
jStart = 0
while jStart < data.shape[1]:
jEnd = min(jStart + jStep, data.shape[1])
chunk[:,:(jEnd - jStart)] = data[i, jStart:jEnd, iXMin:iXMax+1].T
if config['fit']['stripflag']:
for k in range(jStep):
# obtain the smoothed spectrum
background=SpecfitFuns.SavitskyGolay(chunk[:, k],
config['fit']['stripfilterwidth'])
lastAnchor = 0
for anchor in anchorslist:
if (anchor > lastAnchor) and (anchor < background.size):
background[lastAnchor:anchor] =\
SpecfitFuns.snip1d(background[lastAnchor:anchor],
config['fit']['snipwidth'],
0)
lastAnchor = anchor
if lastAnchor < background.size:
background[lastAnchor:] =\
SpecfitFuns.snip1d(background[lastAnchor:],
config['fit']['snipwidth'],
0)
chunk[:, k] -= background
# perform the multiple fit to all the spectra in the chunk
#print("SHAPES")
#print(derivatives.shape)
#print(chunk[:,:(jEnd - jStart)].shape)
ddict=lstsq(derivatives, chunk[:,:(jEnd - jStart)],
sigma_b=sigma_b,
weight=weight,
digested_output=True,
svd=SVD,
last_svd=last_svd)
last_svd = ddict.get('svd', None)
parameters = ddict['parameters']
results[:, i, jStart:jEnd] = parameters
uncertainties[:, i, jStart:jEnd] = ddict['uncertainties']
jStart = jEnd
t = time.time() - t0
_logger.debug("First fit elapsed = %f", t)
if t > 0.:
_logger.debug("Spectra per second = %f",
data.shape[0]*data.shape[1]/float(t))
t0 = time.time()
示例4: fitMultipleSpectra
# 需要导入模块: from PyMca5.PyMcaMath.fitting import SpecfitFuns [as 别名]
# 或者: from PyMca5.PyMcaMath.fitting.SpecfitFuns import snip1d [as 别名]
#.........这里部分代码省略.........
totalSpectra = data.shape[0] * data.shape[1]
jStep = min(100, data.shape[1])
if weightPolicy == 2:
SVD = False
sigma_b = None
elif weightPolicy == 1:
# the +1 is to prevent misbehavior due to weights less than 1.0
sigma_b = 1 + numpy.sqrt(dummySpectrum)/nPixels
SVD = True
else:
SVD = True
sigma_b = None
last_svd = None
for i in range(0, data.shape[0]):
#print(i)
#chunks of nColumns spectra
if i == 0:
chunk = numpy.zeros((dummySpectrum.shape[0],
jStep),
numpy.float)
jStart = 0
while jStart < data.shape[1]:
jEnd = min(jStart + jStep, data.shape[1])
chunk[:,:(jEnd - jStart)] = data[i, jStart:jEnd, iXMin:iXMax+1].T
if config['fit']['stripflag']:
for k in range(jStep):
# obtain the smoothed spectrum
background=SpecfitFuns.SavitskyGolay(chunk[:, k],
config['fit']['stripfilterwidth'])
lastAnchor = 0
for anchor in anchorslist:
if (anchor > lastAnchor) and (anchor < background.size):
background[lastAnchor:anchor] =\
SpecfitFuns.snip1d(background[lastAnchor:anchor],
config['fit']['snipwidth'],
0)
lastAnchor = anchor
if lastAnchor < background.size:
background[lastAnchor:] =\
SpecfitFuns.snip1d(background[lastAnchor:],
config['fit']['snipwidth'],
0)
chunk[:, k] -= background
# perform the multiple fit to all the spectra in the chunk
#print("SHAPES")
#print(derivatives.shape)
#print(chunk[:,:(jEnd - jStart)].shape)
ddict=lstsq(derivatives, chunk[:,:(jEnd - jStart)],
sigma_b=sigma_b,
weight=weight,
digested_output=True,
svd=SVD,
last_svd=last_svd)
last_svd = ddict.get('svd', None)
parameters = ddict['parameters']
results[:, i, jStart:jEnd] = parameters
uncertainties[:, i, jStart:jEnd] = ddict['uncertainties']
jStart = jEnd
if DEBUG:
t = time.time() - t0
print("First fit elapsed = %f" % t)
print("Spectra per second = %f" % (data.shape[0]*data.shape[1]/float(t)))
t0 = time.time()
# cleanup zeros