def set_func(self):
if self.function in [
'poly', 'polynomial', 'spline', 'legendre', 'chebyshev'
]:
self.func = interfit(self.x_arr,
self.w_arr,
function=self.function,
order=self.order,
niter=self.niter,
thresh=self.thresh)
if self.function == 'model':
self.func = ModelSolution(self.x_arr,
self.w_arr,
sgraph=self.sgraph,
xlen=self.xlen,
yval=self.yval,
order=self.order)
def set_func(self):
if self.function in [
'poly', 'polynomial', 'spline', 'legendre', 'chebyshev']:
self.func = interfit(self.x_arr, self.w_arr,
function=self.function,
order=self.order, niter=self.niter,
thresh=self.thresh)
if self.function == 'model':
self.func = ModelSolution(self.x_arr, self.w_arr,
sgraph=self.sgraph, xlen=self.xlen,
yval=self.yval, order=self.order)
class WavelengthSolution:
"""Wavelength Solution is a task describing the functional form for
transforming pixel position to wavelength. The inputs for this task
are the given pixel position and the corresponding wavelength. The user
selects an input functional form and order for that form. The task then
calculates the coefficients for that form. Possible options for the
wavelength solution include polynomial, legendre, spline.
"""
func_options = ['poly', 'polynomial', 'spline', 'legendre',
'chebyshev', 'model']
def __init__(self, x, w, function='poly', order=3, niter=5, thresh=3,
sgraph=None, cfit='both', xlen=3162, yval=0, domain=None):
self.sgraph = sgraph
self.function = function
self.order = order
self.niter = niter
self.thresh = thresh
self.cfit = cfit
self.xlen = xlen
self.yval = yval
self.set_array(x, w)
self.set_func(domain=domain)
def set_array(self, x, w):
self.x_arr = x
self.w_arr = w
def set_thresh(self, thresh):
self.thresh = thresh
def set_niter(self, niter):
self.niter = niter
def set_func(self, domain=None):
if self.function in [
'poly', 'polynomial', 'spline', 'legendre', 'chebyshev']:
self.func = interfit(self.x_arr, self.w_arr,
function=self.function,
order=self.order, niter=self.niter,
thresh=self.thresh)
if domain:
self.func.func.domain=domain
if self.function == 'model':
self.func = ModelSolution(self.x_arr, self.w_arr,
sgraph=self.sgraph, xlen=self.xlen,
yval=self.yval, order=self.order)
def fit(self):
if self.function in [
'poly', 'polynomial', 'spline', 'legendre', 'chebyshev']:
self.func.interfit()
self.coef = self.func.func.parameters
if self.function in ['model']:
self.func.fit(cfit=self.cfit)
self.coef = np.array([c() for c in self.func.coef])
# self.set_coef(coef)
def set_coef(self, coef):
if self.function in [
'poly', 'polynomial', 'spline', 'legendre', 'chebyshev']:
self.func.func.parameters= coef
self.coef = self.func.func.parameters
if self.function in ['model']:
for i in range(len(self.func.coef)):
self.func.coef[i].set(coef[i])
self.coef = np.array([c() for c in self.func.coef])
def value(self, x):
return self.func(x)
def invvalue(self, w):
"""Given a wavelength, return the pixel position
"""
return w
def sigma(self, x, y):
"""Return the RMS of the fit """
# if there aren't many data points return the RMS
if len(x) < 4:
sig = (((y - self.value(x)) ** 2).mean()) ** 0.5
# Otherwise get the average distance between the 16th and
# 84th percentiles
# of the residuals divided by 2
# This should be less sensitive to outliers
else:
# Sort the residuals
rsdls = np.sort(y - self.value(x))
# Get the correct indices and take their difference
sig = (rsdls[int(0.84 * len(rsdls))] -
rsdls[int(0.16 * len(rsdls))]) / 2.0
return sig
def chisq(self, x, y, err):
"""Return the chi^2 of the fit"""
return (((y - self.value(x)) / err) ** 2).sum()
class WavelengthSolution:
"""Wavelength Solution is a task describing the functional form for
transforming pixel position to wavelength. The inputs for this task
are the given pixel position and the corresponding wavelength. The user
selects an input functional form and order for that form. The task then
calculates the coefficients for that form. Possible options for the
wavelength solution include polynomial, legendre, spline.
"""
func_options = [
'poly', 'polynomial', 'spline', 'legendre', 'chebyshev', 'model'
]
def __init__(self,
x,
w,
function='poly',
order=3,
niter=5,
thresh=3,
sgraph=None,
cfit='both',
xlen=3162,
yval=0):
self.sgraph = sgraph
self.function = function
self.order = order
self.niter = niter
self.thresh = thresh
self.cfit = cfit
self.xlen = xlen
self.yval = yval
self.set_array(x, w)
self.set_func()
def set_array(self, x, w):
self.x_arr = x
self.w_arr = w
def set_thresh(self, thresh):
self.thresh = thresh
def set_niter(self, niter):
self.niter = niter
def set_func(self):
if self.function in [
'poly', 'polynomial', 'spline', 'legendre', 'chebyshev'
]:
self.func = interfit(self.x_arr,
self.w_arr,
function=self.function,
order=self.order,
niter=self.niter,
thresh=self.thresh)
if self.function == 'model':
self.func = ModelSolution(self.x_arr,
self.w_arr,
sgraph=self.sgraph,
xlen=self.xlen,
yval=self.yval,
order=self.order)
def fit(self):
if self.function in [
'poly', 'polynomial', 'spline', 'legendre', 'chebyshev'
]:
self.func.interfit()
self.coef = self.func.func.parameters
if self.function in ['model']:
self.func.fit(cfit=self.cfit)
self.coef = np.array([c() for c in self.func.coef])
# self.set_coef(coef)
def set_coef(self, coef):
if self.function in [
'poly', 'polynomial', 'spline', 'legendre', 'chebyshev'
]:
self.func.func.parameters = coef
self.coef = self.func.func.parameters
if self.function in ['model']:
for i in range(len(self.func.coef)):
self.func.coef[i].set(coef[i])
self.coef = np.array([c() for c in self.func.coef])
def value(self, x):
return self.func(x)
def invvalue(self, w):
"""Given a wavelength, return the pixel position
"""
return w
def sigma(self, x, y):
"""Return the RMS of the fit """
# if there aren't many data points return the RMS
if len(x) < 4:
sig = (((y - self.value(x))**2).mean())**0.5
# Otherwise get the average distance between the 16th and
# 84th percentiles
# of the residuals divided by 2
# This should be less sensitive to outliers
else:
# Sort the residuals
rsdls = np.sort(y - self.value(x))
# Get the correct indices and take their difference
sig = (rsdls[int(0.84 * len(rsdls))] -
rsdls[int(0.16 * len(rsdls))]) / 2.0
return sig
def chisq(self, x, y, err):
"""Return the chi^2 of the fit"""
return (((y - self.value(x)) / err)**2).sum()