def fit_mn_hght_bg(xvals,zorig,invorig,sigj,mn_new,spread,powj=2):
""" Fits mean, height, and background for a gaussian of known sigma.
Height and background are fit linearly. Mean is fit through a grid
search algorithm (may be better to change to a nonlinear fitter?)
"""
# ts = time.time()
mn_old = -100
lp_ct = 0
while abs(mn_new-mn_old)>0.001:
mn_old = np.copy(mn_new)
# t1 = time.time()
hght, bg = sf.best_linear_gauss(xvals,sigj,mn_old,zorig,invorig,power=powj)
# t2 = time.time()
mn_new, mn_new_std = sf.best_mean(xvals,sigj,mn_old,hght,bg,zorig,invorig,spread,power=powj)
# t3 = time.time()
# print("Linear time = {}s".format(t2-t1))
# print("Nonlinear time = {}s".format(t3-t2))
# time.sleep(5)
lp_ct+=1
if lp_ct>1e3: break
# print "Loop count is ", lp_ct
# print("Len xvals = {}".format(len(xvals)))
# te = time.time()
# print("Total fit time is {}s".format(te-ts))
# time.sleep(5)
return mn_new, hght,bg
def linear_mn_hght_bg(xvals,yvals,invals,sigma,mn_est,power=2):
""" Find mean of gaussian with linearized procedure. Use eqn
dx = dh/h * sigma/2 * exp(1/2) where dh = max - min residual
Once mean is found, determines best fit hght and bg
"""
gauss_model = sf.gaussian(xvals,sigma,center=mn_est,height=np.max(yvals),power=power)
mn_est_err = 100
mn_est_err_old = 0
loop_ct = 0
while abs(mn_est_err-mn_est_err_old)>0.01 and loop_ct < 1:
mn_est_err_old = np.copy(mn_est_err)
mn_est_old = np.copy(mn_est)
residuals = yvals-gauss_model
dh = (np.max(residuals)-np.min(residuals))/np.max(yvals)
sign = 1
if np.argmax(residuals) < np.argmin(residuals):
sign = -1
dx = sign*sigma*dh*np.exp(1/2)/2
mn_est += dx
hght, bg = sf.best_linear_gauss(xvals,sigma,mn_est,yvals,invals,power=power)
gauss_model = sf.gaussian(xvals,sigma,center=mn_est,height=hght,bg_mean=bg,power=power)
mn_est_err = abs(mn_est_old - mn_est)
loop_ct += 1
# hght, bg = sf.best_linear_gauss(xvals,sigma,mn_est,yvals,invals,power=power)
return mn_est, hght, bg
