def test_my_moment(self):
x = utils.linspace_step(0, 100, 1)
y = utils.gauss1D(
x, 42.321, 13.5
) # relation between dx in the gauss1D and in the moment is np.sqrt(4*np.log(2))
x0, dx = utils.my_moment(x, y)
assert x0 == pytest.approx(42.321)
assert dx * np.sqrt(4 * np.log(2)) == pytest.approx(13.5)
def do_measurement(self, xmin, xmax, xaxis, data1D, mtype, msubtype):
try:
boundaries = utils.find_index(xaxis, [xmin, xmax])
sub_xaxis = xaxis[boundaries[0][0]:boundaries[1][0]]
sub_data = data1D[boundaries[0][0]:boundaries[1][0]]
mtypes = Measurement_type.names()
if msubtype in self.subitems:
msub_ind = self.subitems.index(msubtype)
measurement_results = dict(status=None,
value=0,
xaxis=np.array([]),
datafit=np.array([]))
if mtype == 'Cursor_Integration': # "Cursor Intensity Integration":
if msubtype == "sum":
result_measurement = np.sum(sub_data)
elif msubtype == "mean":
result_measurement = np.mean(sub_data)
elif msubtype == "std":
result_measurement = np.std(sub_data)
else:
result_measurement = 0
elif mtype == 'Max': # "Max":
result_measurement = np.max(sub_data)
elif mtype == 'Min': # "Min":
result_measurement = np.min(sub_data)
elif mtype == 'Gaussian_Fit': # "Gaussian Fit":
measurement_results['xaxis'] = sub_xaxis
offset = np.min(sub_data)
amp = np.max(sub_data) - np.min(sub_data)
m = utils.my_moment(sub_xaxis, sub_data)
p0 = [amp, m[1], m[0], offset]
popt, pcov = curve_fit(self.eval_func,
sub_xaxis,
sub_data,
p0=p0)
measurement_results['datafit'] = self.eval_func(
sub_xaxis, *popt)
result_measurement = popt[msub_ind]
elif mtype == 'Lorentzian_Fit': # "Lorentzian Fit":
measurement_results['xaxis'] = sub_xaxis
offset = np.min(sub_data)
amp = np.max(sub_data) - np.min(sub_data)
m = utils.my_moment(sub_xaxis, sub_data)
p0 = [amp, m[1], m[0], offset]
popt, pcov = curve_fit(self.eval_func,
sub_xaxis,
sub_data,
p0=p0)
measurement_results['datafit'] = self.eval_func(
sub_xaxis, *popt)
if msub_ind == 4: # amplitude
result_measurement = popt[0] * 2 / (np.pi * popt[1]
) # 2*alpha/(pi*gamma)
else:
result_measurement = popt[msub_ind]
elif mtype == 'Exponential_Decay_Fit': # "Exponential Decay Fit":
ind_x0 = utils.find_index(sub_data, np.max(sub_data))[0][0]
x0 = sub_xaxis[ind_x0]
sub_xaxis = sub_xaxis[ind_x0:]
sub_data = sub_data[ind_x0:]
offset = min([sub_data[0], sub_data[-1]])
measurement_results['xaxis'] = sub_xaxis
N0 = np.max(sub_data) - offset
t37 = sub_xaxis[utils.find_index(sub_data - offset,
0.37 * N0)[0][0]] - x0
#polynome = np.polyfit(sub_xaxis, -np.log((sub_data - 0.99 * offset) / N0), 1)
p0 = [N0, t37, x0, offset]
popt, pcov = curve_fit(self.eval_func,
sub_xaxis,
sub_data,
p0=p0)
measurement_results['datafit'] = self.eval_func(
sub_xaxis, *popt)
result_measurement = popt[msub_ind]
elif mtype == 'Sinus': #
offset = np.mean(sub_data)
A = (np.max(sub_data) - np.min(sub_data)) / 2
phi = self.fourierfilt.phase
dx = 1 / self.fourierfilt.frequency
measurement_results['xaxis'] = sub_xaxis
p0 = [A, dx, phi, offset]
popt, pcov = curve_fit(self.eval_func,
sub_xaxis,
sub_data,
p0=p0)
measurement_results['datafit'] = self.eval_func(
sub_xaxis, *popt)
result_measurement = popt[msub_ind]
# elif mtype=="Custom Formula":
# #offset=np.min(sub_data)
# #amp=np.max(sub_data)-np.min(sub_data)
# #m=utils.my_moment(sub_xaxis,sub_data)
# #p0=[amp,m[1],m[0],offset]
# popt, pcov = curve_fit(self.custom_func, sub_xaxis, sub_data,p0=[140,750,50,15])
# self.curve_fitting_sig.emit([sub_xaxis,self.gaussian_func(sub_xaxis,*popt)])
# result_measurement=popt[msub_ind]
else:
result_measurement = 0
measurement_results['value'] = result_measurement
return measurement_results
except Exception as e:
result_measurement = 0
measurement_results['status'] = str(e)
return measurement_results
def update_measurement(self, xmin, xmax, xaxis, data1D, msub_ind):
try:
mtype = self.name
names = self.names()
boundaries = find_index(xaxis, [xmin, xmax])
sub_xaxis = xaxis[boundaries[0][0]:boundaries[1][0]]
sub_data = data1D[boundaries[0][0]:boundaries[1][0]]
result_measurement = dict(Status=None, datafit=None, xaxis=None)
if mtype == names[0]: # Cursor integration:
if msub_ind == 0: # sum
result_measurement['value'] = np.sum(sub_data)
elif msub_ind == 1: # mean
result_measurement['value'] = np.mean(sub_data)
elif msub_ind == 2: # std
result_measurement['value'] = np.std(sub_data)
elif mtype == names[1]: # "Max":
result_measurement['value'] = np.max(sub_data)
elif mtype == names[2]: # "Min":
result_measurement['value'] = np.min(sub_data)
elif mtype == names[3]: # "Gaussian Fit":
offset = np.min(sub_data)
amp = np.max(sub_data) - np.min(sub_data)
m = my_moment(sub_xaxis, sub_data)
p0 = [amp, m[1], m[0], offset]
popt, pcov = curve_fit(self.gaussian_func,
sub_xaxis,
sub_data,
p0=p0)
result_measurement['xaxis'] = sub_xaxis
result_measurement['datafit'] = self.gaussian_func(
sub_xaxis, *popt)
result_measurement['value'] = popt[msub_ind]
elif mtype == names[4]: # "Lorentzian Fit":
offset = np.min(sub_data)
gamma = 1
amp = np.max(sub_data) - np.min(sub_data)
m = my_moment(sub_xaxis, sub_data)
p0 = [gamma, amp, m[1], m[0], offset]
popt, pcov = curve_fit(self.laurentzian_func,
sub_xaxis,
sub_data,
p0=p0)
result_measurement['xaxis'] = sub_xaxis
result_measurement['datafit'] = self.laurentzian_func(
sub_xaxis, *popt)
if msub_ind == 4: # amplitude
result_measurement['value'] = popt[0] * 2 / (
np.pi * popt[1]) # 2*alpha/(pi*gamma)
else:
result_measurement['value'] = popt[msub_ind]
elif mtype == names[5]: # "Exponential Decay Fit":
offset = min([sub_data[0], sub_data[-1]])
N0 = np.max(sub_data) - offset
polynome = np.polyfit(sub_xaxis, -np.log(
(sub_data - 0.99 * offset) / N0), 1)
p0 = [N0, polynome[0], offset]
popt, pcov = curve_fit(self.decaying_func,
sub_xaxis,
sub_data,
p0=p0)
self.curve_fitting_sig.emit(
[True, sub_xaxis,
self.decaying_func(sub_xaxis, *popt)])
result_measurement['xaxis'] = sub_xaxis
result_measurement['datafit'] = self.decaying_func(
sub_xaxis, *popt)
result_measurement['value'] = popt[msub_ind]
return (result_measurement)
except Exception as e:
result_measurement['Status'] = str(e)
return result_measurement