# fit_dark.p0_func(y, x, config=None),
# slice=fit_dark.slice_func(y, x, config=None),
# bounds=fit_dark.bounds_func(y, x, config=None),
# fixed_param=None)
if level > dark_threshold and level <= low_light_threshold: ## Low
if options.verbose: print('----> Pix', pix, 'low light')
fit_result = mpes._axis_fit(
(
level,
pix,
),
fit_low_light.fit_func,
fit_low_light.p0_func(y, x, config=prev_fit_result),
slice=fit_low_light.slice_func(y,
x,
config=prev_fit_result),
bounds=fit_low_light.bounds_func(y,
x,
config=prev_fit_result),
fixed_param=None)
if level > low_light_threshold: ## High
if options.verbose: print('----> Pix', pix, 'high light')
fit_result = mpes._axis_fit(
(
level,
pix,
def fit_consecutive_hist(hist, levels, pixels, config=None, fit_type=None):
prev_fit_result = None
for level in levels:
print("################################# Level", level)
for pix in pixels:
if len(levels) <= 1:
y = hist.data[pix]
else:
y = hist.data[level, pix]
x = hist.bin_centers
fit_result = None
if level == levels[0]:
# prev_fit_result = np.array([[0.5, np.nan], [0.08, np.nan], [5.6, np.nan], [2020, np.nan], [0.07, np.nan], [0.09,np.nan], [3500, np.nan], [0., np.nan]])
prev_fit_result = config
else:
prev_fit_result = hist.fit_result[level - 1, pix]
if np.any(np.isnan(hist.data[level, pix])):
print('----> Pix', pix, 'abort')
continue
if fit_type == 'dark': ## Dark
print('----> Pix', pix, 'as dark')
fit_result = hist._axis_fit(
(
level,
pix,
),
fit_low_light.fit_func,
fit_low_light.p0_func(y, x, config=prev_fit_result),
slice=fit_low_light.slice_func(y,
x,
config=prev_fit_result),
bounds=fit_low_light.bounds_func(y,
x,
config=prev_fit_result),
fixed_param=None)
elif fit_type == 'low_light': ## Low
print('----> Pix', pix, 'low light')
fit_result = hist._axis_fit(
(
level,
pix,
),
fit_low_light.fit_func,
fit_low_light.p0_func(y, x, config=prev_fit_result),
slice=fit_low_light.slice_func(y,
x,
config=prev_fit_result),
bounds=fit_low_light.bounds_func(y,
x,
config=prev_fit_result),
fixed_param=np.array([[7], [0.]]))
print(fit_result)
elif fit_type == 'high_light': ## High
print('----> Pix', pix, 'high light')
fit_result = hist._axis_fit(
(
level,
pix,
),
fit_low_light.fit_func,
fit_low_light.p0_func(y, x, config=prev_fit_result),
slice=fit_low_light.slice_func(y,
x,
config=prev_fit_result),
bounds=fit_low_light.bounds_func(y,
x,
config=prev_fit_result),
fixed_param=np.array(
[[1, 2, 3, 4, 5, 7],
[
prev_fit_result[1, 0], prev_fit_result[2, 0],
prev_fit_result[3, 0], prev_fit_result[4, 0],
prev_fit_result[5, 0], prev_fit_result[7, 0]
]]))
elif fit_type == 'hv_off': ## HV off
print('----> Pix', pix, 'hv off')
fit_result = hist._axis_fit(
pix,
fit_hv_off.fit_func,
fit_hv_off.p0_func(y, x, config=prev_fit_result),
slice=fit_hv_off.slice_func(y, x, config=prev_fit_result),
bounds=fit_hv_off.bounds_func(y, x,
config=prev_fit_result),
fixed_param=np.array([[0, 1, 2, 5, 7],
[
prev_fit_result[0, 0],
prev_fit_result[1, 0],
prev_fit_result[2, 0],
prev_fit_result[5, 0],
prev_fit_result[7, 0]
]]))
else:
print(' Impossible to fit', ' pixel', pix, ' in level ', level,
' !!!!!!!!!!!!!!!')
if len(levels) <= 1:
hist.fit_result[pix] = fit_result
else:
hist.fit_result[level, pix] = fit_result
## to be replaced by fit_dark
mpes_peaks.fit_result[level, pix] = [[2., 1.e3],
[prev_fit[0, pix, 1]],
[prev_fit[0, pix, 2]],
[prev_fit[0, pix, 3]],
[prev_fit[0, pix, 4]],
[0.7, 10.],
[1000, 1.e8],
[prev_fit[0, pix, 7]]]
else:
if options.verbose: print('----> Pix', pix, 'low light')
mpes_peaks.fit_result[level, pix] = \
mpes_peaks._axis_fit((level,pix,),
fit_low_light.fit_func,
fit_low_light.p0_func(mpes_peaks.data[level,pix],
mpes_peaks.bin_centers,
config=prev_fit[0,pix]),
slice=fit_low_light.slice_func(mpes_peaks.data[level,pix],
mpes_peaks.bin_centers,
config=prev_fit[0,pix]),
bounds=fit_low_light.bounds_func(mpes_peaks.data[level,pix],
mpes_peaks.bin_centers,
config=prev_fit[0,pix]),
# mu_xt,baseline,sigma_e,offset
fixed_param = np.array([[i for i in [1,3,4,7]],[prev_fit[0, pix, i , 0] for i in [1,3,4,7]]])
)
else:
## Take from previous
for pix in range(mpes_peaks.data[level].shape[0]):
if np.any(np.isnan(mpes_peaks.data[level, pix])):
if options.verbose: print('----> Pix', pix, 'abort')