def chiSquareFit(xbins, ybins, func, nsamples, nparams, minsamples=5):
sizes = [xbins[i + 1] - xbins[i] for i in xrange(len(xbins) - 1)]
sizes.append(sizes[-1]) # NOTE: assumes bins are of equal size
# only focus on bins that are large enough
counts = [ybins[i] * sizes[i] * nsamples for i in xrange(len(xbins) - 1)]
expected = []
for i in xrange(len(xbins) - 1):
expected.append(
(func(xbins[i]) + func(xbins[i + 1])) / 2.0 * sizes[i] * nsamples)
# ensure we have enough expected samples in each bin
ind = util.find(util.gefunc(minsamples), expected)
counts = util.mget(counts, ind)
expected = util.mget(expected, ind)
if len(counts) == 0:
return [0, 1], counts, expected
else:
return chiSquare([counts], [expected], nparams), counts, expected
def chiSquareFit(xbins, ybins, func, nsamples, nparams, minsamples=5):
sizes = [xbins[i+1] - xbins[i] for i in xrange(len(xbins)-1)]
sizes.append(sizes[-1]) # NOTE: assumes bins are of equal size
# only focus on bins that are large enough
counts = [ybins[i] * sizes[i] * nsamples for i in xrange(len(xbins)-1)]
expected = []
for i in xrange(len(xbins)-1):
expected.append((func(xbins[i]) + func(xbins[i+1]))/2.0 *
sizes[i] * nsamples)
# ensure we have enough expected samples in each bin
ind = util.find(util.gefunc(minsamples), expected)
counts = util.mget(counts, ind)
expected = util.mget(expected, ind)
if len(counts) == 0:
return [0, 1], counts, expected
else:
return chiSquare([counts], [expected], nparams), counts, expected