def lsmooth(x, sigma=1, gamma=None, form='lorentzian', npad=None):
"""convolve a 1-d array with a lorentzian, gaussian, or voigt function.
fconvolve(x, sigma=1, gamma=None, form='lorentzian', npad=None)
arguments:
------------
x input 1-d array for smoothing.
sigma primary width parameter for convolving function
gamma secondary width parameter for convolving function
form name of convolving function:
'lorentzian' or 'gaussian' or 'voigt' ['lorentzian']
npad number of padding pixels to use [length of x]
returns:
--------
smoothed 1-d array with same length as input array x
"""
if npad is None:
npad = len(x)
wx = arange(2*npad)
if form.lower().startswith('gauss'):
win = gaussian(wx, cen=npad, sigma=sigma)
elif form.lower().startswith('voig'):
win = voigt(wx, cen=npad, sigma=sigma, gamma=gamma)
else:
win = lorentzian(wx, cen=npad, sigma=sigma)
xax = concatenate((x[2*npad:0:-1], x, x[-1:-2*npad-1:-1]))
out = convolve(win/win.sum(), xax, mode='valid')
nextra = int((len(out) - len(x))/2)
return (out[nextra:])[:len(x)]
def smooth(x, y, sigma=1, gamma=None, npad=None, form='lorentzian'):
"""smooth a function y(x) by convolving wih a lorentzian, gaussian,
or voigt function.
arguments:
------------
x input 1-d array for absicca
y input 1-d array for ordinate: data to be smoothed
sigma primary width parameter for convolving function
gamma secondary width parameter for convolving function
delx delta x to use for interpolation [mean of
form name of convolving function:
'lorentzian' or 'gaussian' or 'voigt' ['lorentzian']
npad number of padding pixels to use [length of x]
returns:
--------
1-d array with same length as input array y
"""
# make uniform x, y data
TINY = 1.e-12
xstep = min(diff(x))
if xstep < TINY:
raise Warning('Cannot smooth data: must be strictly increasing ')
npad = 5
xmin = xstep * int( (min(x) - npad*xstep)/xstep)
xmax = xstep * int( (max(x) + npad*xstep)/xstep)
npts = 1 + int(abs(xmax-xmin+xstep*0.1)/xstep)
x0 = linspace(xmin, xmax, npts)
y0 = interp(x0, x, y)
# put sigma in units of 1 for convolving window function
sigma *= 1.0 / xstep
if gamma is not None:
gamma *= 1.0 / xstep
wx = arange(2*npts)
if form.lower().startswith('gauss'):
win = gaussian(wx, cen=npts, sigma=sigma)
elif form.lower().startswith('voig'):
win = voigt(wx, cen=npts, sigma=sigma, gamma=gamma)
else:
win = lorentzian(wx, cen=npts, sigma=sigma)
y1 = concatenate((y0[npts:0:-1], y0, y0[-1:-npts-1:-1]))
y2 = convolve(win/win.sum(), y1, mode='valid')
if len(y2) > len(x0):
nex = int((len(y2) - len(x0))/2)
y2 = (y2[nex:])[:len(x0)]
return interp(x, x0, y2)
