def do_fit_wavelengths(pixels, lambdas, alphaguess,
sinbetaguess, gammaguess, deltaguess, band, pixel_y, error=None):
''' THIS HSOULD BE REMOVED'''
bmap = {"Y": 6, "J": 5, "H": 4, "K": 3}
order = bmap[band]
parinfo = [
{'fixed': 1, 'value': order, 'parname': 'order',
'limited': [0,0], 'limits': [0,0]},
{'fixed': 1, 'value': pixel_y, 'parname': 'Y',
'limited': [0,0], 'limits': [0,0]},
{'fixed': 0, 'value': alphaguess, 'parname': 'alpha', 'step': 1e-5,
'limited': [0,0], 'limits': [0,0]},
{'fixed': 0, 'value': sinbetaguess, 'parname': 'sinbeta',
'step': 1e-5, 'limited': [0,0], 'limits': [30,50]},
{'fixed': 0, 'value': gammaguess, 'parname': 'gamma','step': 1e-15,
'limited': [1,1], 'limits': [0,20e-13]},
{'fixed': 0, 'value': deltaguess, 'parname': 'delta', 'step': 1e-1,
'limited': [1,1], 'limits': [0,2048]},
]
fa = {"x": pixels, "y": lambdas}
if error is not None:
fa["error"] = error
lsf = mpfit.mpfit(wavelength_residuals, parinfo=parinfo, functkw=fa,
quiet=1, maxiter=20)
return lsf
def mpfitpeaks(x, y, N, error=None):
pars = [1, np.min(y), 0]
parinfo = [ {"value": N, "fixed": 1, "name": "Number of Peaks",
"limited": [0, 0], "limits": [0, 0]},
{"value": 1.6, "fixed": 0, "name": "Sigma",
"limited": [0, 0], "limits": [0, 0]},
{"value": pars[1], "fixed": 0, "name": "Offset",
"limited": [0, 0], "limits": [0, 0]},
{"value": pars[2], "fixed": 0, "name": "Slope",
"limited": [0, 0], "limits": [0, 0]}]
for i in range(N):
v = {"value": np.max(y)/2., "fixed": 0, "name": "Peak Value(%i)" % i,
"limited": [1, 0], "limits": [0, 0]}
pars.append(np.max(y))
parinfo.append(v)
v = {"value": x[np.argmax(y)], "fixed": 0, "name": "Centroid(%i)" % i}
pars.append(x[np.argmax(y)])
parinfo.append(v)
fa = {"x": x, "y": y}
if error is not None: fa["error"] = error
return mpfit.mpfit(multi_gaussian_residuals, parinfo=parinfo, functkw=fa,
quiet=1)
def mpfitpeaks(x, y, N, error=None):
pars = [1, np.min(y), 0]
parinfo = [{
"value": N,
"fixed": 1,
"name": "Number of Peaks",
"limited": [0, 0],
"limits": [0, 0]
}, {
"value": 1.6,
"fixed": 0,
"name": "Sigma",
"limited": [0, 0],
"limits": [0, 0]
}, {
"value": pars[1],
"fixed": 0,
"name": "Offset",
"limited": [0, 0],
"limits": [0, 0]
}, {
"value": pars[2],
"fixed": 0,
"name": "Slope",
"limited": [0, 0],
"limits": [0, 0]
}]
for i in range(N):
v = {
"value": np.max(y) / 2.,
"fixed": 0,
"name": "Peak Value(%i)" % i,
"limited": [1, 0],
"limits": [0, 0]
}
pars.append(np.max(y))
parinfo.append(v)
v = {"value": x[np.argmax(y)], "fixed": 0, "name": "Centroid(%i)" % i}
pars.append(x[np.argmax(y)])
parinfo.append(v)
fa = {"x": x, "y": y}
if error is not None: fa["error"] = error
return mpfit.mpfit(multi_gaussian_residuals,
parinfo=parinfo,
functkw=fa,
quiet=1)
def mpfit_do(residual_fun, # function returned from mpfit_residuals() above
x, # input x
y, # input y = f(x)
parinfo, # initial parameter guess
error=None,
maxiter=20):
#TODO : Document parinfo part
fa = {"x": x, "y": y}
if error is not None:
fa["error"] = error
lsf = mpfit.mpfit(residual_fun, parinfo=parinfo, functkw=fa,
quiet=1, maxiter=maxiter)
return lsf
def mpfitpeak(x, y, error=None):
parinfo = [{"value": np.max(y), "fixed": 0, "name": "Peak Value",
'step': 10},
{"value": x[np.argmax(y)], "fixed": 0, "name": "Centroid",
'step': .1},
{"value": 1.1, "fixed": 0, "name": "Sigma",
'step': .1},
{"value": np.min(y), "fixed": 0, "name": "Offset",
'step': 10},
{"value": 0, "fixed": 0, "name": "Slope",
'step': 1e-5}]
fa = {"x": x, "y": y}
if error is not None: fa["error"] = error
return mpfit.mpfit(gaussian_residuals, parinfo=parinfo, functkw=fa, quiet=1)
def mpfit_do(
residual_fun, # function returned from mpfit_residuals() above
x, # input x
y, # input y = f(x)
parinfo, # initial parameter guess
error=None,
maxiter=20):
#TODO : Document parinfo part
fa = {"x": x, "y": y}
if error is not None:
fa["error"] = error
lsf = mpfit.mpfit(residual_fun,
parinfo=parinfo,
functkw=fa,
quiet=1,
maxiter=maxiter)
return lsf
def mpfitpeak(x, y, error=None):
parinfo = [{
"value": np.max(y),
"fixed": 0,
"name": "Peak Value",
'step': 10
}, {
"value": x[np.argmax(y)],
"fixed": 0,
"name": "Centroid",
'step': .1
}, {
"value": 1.1,
"fixed": 0,
"name": "Sigma",
'step': .1
}, {
"value": np.min(y),
"fixed": 0,
"name": "Offset",
'step': 10
}, {
"value": 0,
"fixed": 0,
"name": "Slope",
'step': 1e-5
}]
fa = {"x": x, "y": y}
if error is not None: fa["error"] = error
return mpfit.mpfit(gaussian_residuals,
parinfo=parinfo,
functkw=fa,
quiet=1)
def do_fit_wavelengths(pixels,
lambdas,
alphaguess,
sinbetaguess,
gammaguess,
deltaguess,
band,
pixel_y,
error=None):
''' THIS HSOULD BE REMOVED'''
bmap = {"Y": 6, "J": 5, "H": 4, "K": 3}
order = bmap[band]
parinfo = [
{
'fixed': 1,
'value': order,
'parname': 'order',
'limited': [0, 0],
'limits': [0, 0]
},
{
'fixed': 1,
'value': pixel_y,
'parname': 'Y',
'limited': [0, 0],
'limits': [0, 0]
},
{
'fixed': 0,
'value': alphaguess,
'parname': 'alpha',
'step': 1e-5,
'limited': [0, 0],
'limits': [0, 0]
},
{
'fixed': 0,
'value': sinbetaguess,
'parname': 'sinbeta',
'step': 1e-5,
'limited': [0, 0],
'limits': [30, 50]
},
{
'fixed': 0,
'value': gammaguess,
'parname': 'gamma',
'step': 1e-15,
'limited': [1, 1],
'limits': [0, 20e-13]
},
{
'fixed': 0,
'value': deltaguess,
'parname': 'delta',
'step': 1e-1,
'limited': [1, 1],
'limits': [0, 2048]
},
]
fa = {"x": pixels, "y": lambdas}
if error is not None:
fa["error"] = error
lsf = mpfit.mpfit(wavelength_residuals,
parinfo=parinfo,
functkw=fa,
quiet=1,
maxiter=20)
return lsf
