def _process(self, ds, xoffset=0, yoffset=0):###dsxaxis, dsyaxis): ## assert(dsyaxis is None) # STUB ## assert(dsxaxis is None) # STUB integrator = Integrate2D() dsy, dsx = integrator.value(ds) dsyaxis = DatasetFactory.createRange(dsy.shape[0]) dsxaxis = DatasetFactory.createRange(dsx.shape[0]) gaussian = Gaussian(dsyaxis.min(), dsyaxis.max(), dsyaxis.max()-dsyaxis.min(), (dsyaxis.max()-dsyaxis.min())*(dsy.max()-dsy.min()) ) gaussian.getParameter(2).setLowerLimit(0) if self.maxwidth is not None: gaussian.getParameter(1).setUpperLimit(self.maxwidth) ansy = Fitter.fit(dsyaxis, dsy, GeneticAlg(0.001), [ gaussian, Offset( dsy.min(),dsy.max() ) ] ) # ansy = DataSetFunctionFitter().fit( dsyaxis, dsy, GeneticAlg(.001), [ gaussian, Offset( dsy.min(),dsy.max() ) ] ) # ansy = ansy.functionOutput gaussian = Gaussian(dsxaxis.min(), dsxaxis.max(), dsxaxis.max()-dsxaxis.min(), (dsxaxis.max()-dsxaxis.min())*(dsx.max()-dsx.min()) ) gaussian.getParameter(2).setLowerLimit(0) if self.maxwidth is not None: gaussian.getParameter(1).setUpperLimit(self.maxwidth) try: ansx = fitplot( dsxaxis, dsx, GeneticAlg(.001), [ gaussian, Offset( dsx.min(),dsx.max() ) ] ) except java.lang.Exception: # Probably cannot find Plot_Manager on the finder ansx = Fitter.fit(dsxaxis, dsx, GeneticAlg(0.001), [ gaussian, Offset( dsx.min(),dsx.max() ) ] ) #dsyaxis = dsyaxis.subSampleMean(dsy.dimensions[0]/2) #dsy = dsy.subSampleMean(dsy.dimensions[0]/2) #dsxaxis = dsxaxis.subSampleMean(dsx.dimensions[0]/2) #dsx = dsx.subSampleMean(dsx.dimensions[0]/2) peaky = ansy.getParameters()[0].getValue() fwhmy = ansy.getParameters()[1].getValue() areay = ansy.getParameters()[2].getValue() offsety = ansy.getParameters()[3].getValue() / dsx.shape[0] peakx = ansx.getParameters()[0].getValue() fwhmx = ansx.getParameters()[1].getValue() areax = ansx.getParameters()[2].getValue() offsetx = ansx.getParameters()[3].getValue() / dsy.shape[0] background = (offsetx+offsety)/2. fwhmarea = fwhmy*fwhmx*pi/4 topy = areay / fwhmy topx = areax / fwhmx if xoffset==None: xoffset=0 if yoffset==None: yoffset=0 return background, peakx+xoffset, peaky+yoffset, topx, topy, fwhmx, fwhmy, fwhmarea
def polyfit(x, y, deg, rcond=None, full=False):
'''Linear least squares polynomial fit
Fit a polynomial p(x) = p[0] * x**deg + ... + p[deg] of order deg to points (x, y).
Arguments:
x -- x-coordinates of sample points
y -- y-coordinates of sample points
deg -- order of fitting polynomial
Returns:
a vector of coefficients p that minimises the squared error and
a fitresult object
'''
poly = _poly(deg)
x = _asIterable(x)
if rcond is None:
rcond = 2e-16 * y.size
_fitter.polyFit(x, y, rcond, poly)
fr = fitresult(poly, x, y)
if full:
return fr.parameters, fr
else:
return fr.parameters
def polyfit(x, y, deg, rcond=None, full=False):
'''Linear least squares polynomial fit
Fit a polynomial p(x) = p[0] * x**deg + ... + p[deg] of order deg to points (x, y).
Arguments:
x -- x-coordinates of sample points
y -- y-coordinates of sample points
deg -- order of fitting polynomial
Returns:
a vector of coefficients p that minimises the squared error and
a fitresult object
'''
poly = _poly(deg)
x = _asIterable(x)
if rcond is None:
rcond = 2e-16*y.size
_fitter.polyFit(x, y, rcond, poly)
fr = fitresult(poly, x, y)
if full:
return fr.parameters, fr
else:
return fr.parameters
def fit(func,
coords,
data,
p0,
bounds=[],
args=None,
ptol=1e-4,
seed=None,
optimizer="apache_nm"):
'''
Arguments:
func -- function (or list of functions)
coords -- coordinate dataset(s)
data -- data to fit
p0 -- list of initial parameters
bounds -- list of parameter bounds, bounds are tuples of lower and upper values (any can be None)
args -- extra arguments
ptol -- parameter fit tolerance
seed -- seed value for genetic algorithm-based optimiser
optimizer -- description of the optimizer to use, e.g. ['local','global','simplex','genetic',
'gradient','apache_nm','apache_md','apache_cg']
local and global are general settings, which point the one of the specific methods
If any global methods are picked, the bounds argument must also be filled in.
Returns:
fitresult object
'''
fnlist = []
func = _toList(func)
p0 = _toList(p0)
if not isinstance(bounds, list):
bounds = [bounds]
else:
bounds = list(bounds) # make a copy
n_bounds = len(bounds)
mixed = False
for f in func:
if isinstance(f, tuple):
print "parameter count is no longer required"
f = f[0]
if function.isjclass(f):
# create bound function object
np = function.nparams(f)
pl = _createparams(np, p0, bounds)
fnlist.append(f(pl))
elif not _inspect.isfunction(f):
# instantiated Java function
# np = f.getNoOfParameters()
fnlist.append(f)
else:
np = len(_inspect.getargspec(f)[0]) - 1
if np < 1:
raise ValueError, "Function needs more than one argument (i.e. at least one parameter)"
pl = _createparams(np, p0, bounds)
fnlist.append(fitfunc(f, f.__name__, pl, args))
mixed = True
if not mixed: # no jython functions
cfunc = _compfn()
else:
cfunc = cfitfunc()
for f in fnlist:
if function.isjmethod(f): # unwrap
f = f._jfunc()
cfunc.addFunction(f)
coords = list(_asIterable(coords))
for i in range(len(coords)): # check and slice coordinates to match data
c = coords[i]
if c.shape != data.shape:
ns = [slice(d) for d in data.shape]
coords[i] = c[ns]
if seed:
_fitter.seed = int(seed)
import time
start = -time.time()
jcoords = [__cvt_jobj(c, copy=False, force=True) for c in coords]
jdata = data._jdataset()
# use the appropriate fitter for the task
if optimizer == 'local':
_fitter.simplexFit(ptol, jcoords, jdata, cfunc)
elif optimizer == 'global':
if n_bounds == 0:
print "Using a global optimizer with no bounds is unlikely to work, please use the bounds argument to narrow the search space"
_fitter.geneticFit(ptol, jcoords, jdata, cfunc)
elif optimizer == 'simplex':
_fitter.simplexFit(ptol, jcoords, jdata, cfunc)
elif optimizer == 'gradient':
_fitter.GDFit(ptol, jcoords, jdata, cfunc)
elif optimizer == 'apache_nm':
_fitter.ApacheNelderMeadFit(jcoords, jdata, cfunc)
elif optimizer == 'apache_md':
_fitter.ApacheMultiDirectionFit(jcoords, jdata, cfunc)
elif optimizer == 'apache_cg':
_fitter.ApacheConjugateGradientFit(jcoords, jdata, cfunc)
elif optimizer == 'genetic':
if n_bounds == 0:
print "Using a global optimizer with no bounds is unlikely to work, please use the bounds argument to narrow the search space"
_fitter.geneticFit(ptol, jcoords, jdata, cfunc)
start += time.time()
print "Fit took %fs" % start
return fitresult(cfunc, coords, data)
def fitplot(*args):
fitted_function = Fitter.fit(*args)
RCPPlotter.plot("Data Vector", args[0],fitted_function.display(args[0])[0]);
return fitted_function
def fit(func, coords, data, p0, bounds=[], args=None, ptol=1e-4, seed=None, optimizer="apache_nm"):
'''
Arguments:
func -- function (or list of functions)
coords -- coordinate dataset(s)
data -- data to fit
p0 -- list of initial parameters
bounds -- list of parameter bounds, bounds are tuples of lower and upper values (any can be None)
args -- extra arguments
ptol -- parameter fit tolerance
seed -- seed value for genetic algorithm-based optimiser
optimizer -- description of the optimizer to use, e.g. ['local','global','simplex','genetic',
'gradient','apache_nm','apache_md','apache_cg']
local and global are general settings, which point the one of the specific methods
If any global methods are picked, the bounds argument must also be filled in.
Returns:
fitresult object
'''
fnlist = []
func = _toList(func)
p0 = _toList(p0)
if not isinstance(bounds, list):
bounds = [bounds]
else:
bounds = list(bounds) # make a copy
n_bounds = len(bounds)
mixed = False
for f in func:
if isinstance(f, tuple):
print("parameter count is no longer required")
f = f[0]
if function.isjclass(f):
# create bound function object
np = function.nparams(f)
pl = _createparams(np, p0, bounds)
fnlist.append(f(pl))
elif not _inspect.isfunction(f):
# instantiated Java function
# np = f.getNoOfParameters()
fnlist.append(f)
else:
np = len(_inspect.getargspec(f)[0]) - 1
if np < 1:
raise ValueError("Function needs more than one argument (i.e. at least one parameter)")
pl = _createparams(np, p0, bounds)
fnlist.append(fitfunc(f, f.__name__, pl, args))
mixed = True
if not mixed: # no jython functions
cfunc = _compfn()
else:
cfunc = cfitfunc()
for f in fnlist:
if function.isjmethod(f): # unwrap
f = f._jfunc()
cfunc.addFunction(f)
coords = list(_asIterable(coords))
for i in range(len(coords)): # check and slice coordinates to match data
c = coords[i]
if c.shape != data.shape:
ns = [slice(d) for d in data.shape]
coords[i] = c[ns]
if seed:
_fitter.seed = int(seed)
jcoords = [ __cvt_jobj(c, copy=False, force=True) for c in coords ]
jdata = data._jdataset()
# use the appropriate fitter for the task
if optimizer == 'local':
_fitter.ApacheNelderMeadFit(jcoords, jdata, cfunc)
elif optimizer == 'global':
if n_bounds == 0:
print("Using a global optimizer with no bounds is unlikely to work, please use the bounds argument to narrow the search space")
_fitter.geneticFit(ptol, jcoords, jdata, cfunc)
elif optimizer == 'simplex':
_fitter.simplexFit(ptol, jcoords, jdata, cfunc)
elif optimizer == 'gradient':
_fitter.GDFit(ptol, jcoords, jdata, cfunc)
elif optimizer == 'apache_nm':
_fitter.ApacheNelderMeadFit(jcoords, jdata, cfunc)
elif optimizer == 'apache_md':
_fitter.ApacheMultiDirectionFit(jcoords, jdata, cfunc)
elif optimizer == 'apache_cg':
_fitter.ApacheConjugateGradientFit(jcoords, jdata, cfunc)
elif optimizer == 'genetic':
if n_bounds == 0:
print("Using a global optimizer with no bounds is unlikely to work, please use the bounds argument to narrow the search space")
_fitter.geneticFit(ptol, jcoords, jdata, cfunc)
return fitresult(cfunc, coords, data)