def SPErough(data, n_channel_s):
fit_input, bins, check = SPE_input(data)
amplitude = get_amp(data)
gauss = GaussianModel(prefix='g_')
s, bins = np.histogram(amplitude, bins=bins)
topnoise = fit_input[0]
valley = fit_input[1]
endvalley = fit_input[2]
spebump = fit_input[3]
endbin = fit_input[4]
idx_1 = endvalley
idx_2 = spebump + (spebump-endvalley)
if idx_1 < endvalley:
idx_1 = endvalley
if idx_2 > endbin:
idx_2 = endbin
if check == 1:
gauss.set_param_hint('g_height', value=s[spebump], min=s[spebump]-30, max=s[spebump]+30)
gauss.set_param_hint('g_center', value=bins[spebump], min=bins[spebump]-30, max=bins[spebump]+30)
gauss.set_param_hint('g_sigma', value=idx_2-idx_1, max=(idx_2-idx_1)+30)
result = gauss.fit(s[idx_1:idx_2], x=bins[idx_1:idx_2], weights=1.0/np.sqrt(s[idx_1:idx_2]))
else:
gauss = 0
result = 0
return gauss, result
def gaussian_fit_2(self,
y_data,
x_data=None,
x_peak_indexes=None,
y_peak_values=None):
if x_data is None:
x_data = np.arange(np.max(y_data))
else:
pass
if x_peak_indexes is None:
x_peak_indexes = np.array(np.where(y_data == np.max(y_data)))
else:
pass
if y_peak_values is None:
y_peak_values = np.array([np.max(y_data)])
else:
pass
global multi_gauss_a
for peak_num in range(len(x_peak_indexes)):
if peak_num == 0:
multi_gauss_a = GaussianModel(prefix='g' + str(peak_num) + '_')
else:
multi_gauss_a += GaussianModel(prefix='g' + str(peak_num) +
'_')
multi_gauss_a.set_param_hint('g' + str(peak_num) + '_center',
value=x_peak_indexes[peak_num],
min=x_peak_indexes[peak_num] - 200,
max=x_peak_indexes[peak_num] + 200)
multi_gauss_a.set_param_hint('g' + str(peak_num) + '_sigma',
value=50)
multi_gauss_a.set_param_hint('g' + str(peak_num) + '_amplitude',
value=50.0 * np.sqrt(2 * np.pi) *
y_peak_values[peak_num])
# FIT ACTUALLY OUTPUTS AREA INSTEAD OF AMPLITUDE
multi_gauss_fit_a = multi_gauss_a.fit(
y_data, x=x_data) # THIS IS THE LINE ACTUALLY DOING THE FITTING
return multi_gauss_fit_a
def fit_to_gaussian(self, x, y):
gmodel = GaussianModel()
params = gmodel.guess(y, x=x)
c = params['center'].value
n = len(y)
q3 = ((np.max(x) - c) / 2) + c
min_x = np.min(x)
q1 = ((params['center'].value - min_x) / 2) + min_x
s = params['sigma'].value
h = params['height'].value
max_y = np.max(y)
if np.max([h, max_y]) < 0.5:
amp = 1 / n
diff_h = 0.6 - h
gmodel.set_param_hint('amplitude', value=amp)
gmodel.set_param_hint('amplitude', max=amp * (1 + diff_h))
gmodel.set_param_hint('amplitude', min=amp * diff_h)
gmodel.set_param_hint('center', value=c)
gmodel.set_param_hint('center', max=q3)
gmodel.set_param_hint('center', min=q1)
gmodel.set_param_hint('sigma', value=s)
gmodel.set_param_hint('sigma', min=s / 2)
gmodel.set_param_hint('sigma', max=s * 1.5)
gmodel.set_param_hint('height', min=0.6)
result = gmodel.fit(y, x=x)
# gmodel.print_param_hints()
report = result.fit_report()
chi_re = re.compile(r'chi-square\s+=\s+([0-9.]+)')
cor_re = re.compile(r'C\(sigma, amplitude\)\s+=\s+([0-9.-]+)')
chis = np.float32(chi_re.findall(report))
cors = np.float32(cor_re.findall(report))
coeffs = np.concatenate((chis, cors))
mse_model = self.assess_fit(y, result.init_fit - result.best_fit)
mse_yhat = self.assess_fit(y, result.residual)
return mse_model, mse_yhat, result, report, coeffs
