def test_preprocess():
ph = np.linspace(-3., 3., 100) # phase with linear slope
amps = np.linspace(5., 5., 100) # constant amplitude
freqs = np.arange(4, 5, 0.01)
dat = fsd.VNASweep.from_columns(freqs, amps, ph)
cplx = fsd.normalize_data(dat)
prepro = fsd.preprocess(cplx, 10)
# test phase angle is zero for all points.
np.testing.assert_array_almost_equal(np.zeros(100),
np.angle(prepro.complex_s21))
# test amplitude of all points is 1.
np.testing.assert_array_almost_equal(np.ones(100),
np.abs(prepro.complex_s21))
def test_extract_near_res():
amps = np.linspace(5., 5., 100)
freqs = np.arange(4, 5, 0.01)
f_res = 4.55
kappa = 0.3
ex_x, ex_y = fsd.extract_near_res(freqs, amps, f_res, kappa)
near_x = np.array([
4.41, 4.42, 4.43, 4.44, 4.45, 4.46, 4.47, 4.48, 4.49, 4.5, 4.51, 4.52,
4.53, 4.54, 4.55, 4.56, 4.57, 4.58, 4.59, 4.6, 4.61, 4.62, 4.63, 4.64,
4.65, 4.66, 4.67, 4.68, 4.69, 4.7
])
near_y = np.array([
5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,
5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.
])
np.testing.assert_array_almost_equal(ex_x, near_x)
np.testing.assert_array_almost_equal(ex_y, near_y)
with pytest.raises(Exception):
f_res = 3.55
fsd.extract_near_res(freqs, amps, f_res, kappa)
def test_normalize():
freqs = np.arange(4, 5, 0.01)
amps = 10 * np.ones(len(freqs))
phases = np.ones(len(freqs))
data = fsd.VNASweep.from_columns(freqs=freqs, amps=amps, phases=phases)
normed = fsd.normalize_data(data)
linear = 3.1623 * np.ones(len(freqs))
cplx_s21 = 1.7086 + 2.661j * np.ones(len(freqs))
np.testing.assert_array_almost_equal(data.linear_amps, linear, decimal=4)
np.testing.assert_array_almost_equal(normed.complex_s21,
cplx_s21,
decimal=3)
def test_normalized_with_background():
freqs = np.arange(4, 5, 0.01)
amps = 20 * np.ones(len(freqs))
phases = 2 * np.ones(len(freqs))
bg_amps = 10 * np.ones(len(freqs))
bg_phases = 1 * np.ones(len(freqs))
data = fsd.VNASweep.from_columns(freqs=freqs, amps=amps, phases=phases)
background = fsd.VNASweep.from_columns(freqs=freqs,
amps=bg_amps,
phases=bg_phases)
normed = fsd.normalize_data(data, background=background)
cplx_s21 = 1.7086 + 2.661j * np.ones(len(freqs))
np.testing.assert_array_almost_equal(normed.complex_s21,
cplx_s21,
decimal=3)
MC_rounds = 1e3
#MC_fix = ['Q','Qi','Qc','Qa','phi','w1']
MC_fix = []
manual_init = [
10000, 100000, 5.797, -0.558561
] #make your own initial guess: [Qi, Qc, freq, phi] (instead of phi used Qa for CPZM)
#manual_init = None # find initial guess by itself
#try:
Method = res.FitMethod(fit_type, MC_iteration, MC_rounds=MC_rounds,\
MC_fix=MC_fix, manual_init=manual_init, MC_step_const=0.3) #mcrounds = 100,000 unless otherwise specified
#except:
# print("Failed to initialize method, please change parameters")
# quit()
##############################################################
normalize = 10
### Fit Resonator function without background removal ###
params, conf_array, fig1, chi1, init1 = fsd.fit(filename=filename,
Method=Method,
normalize=normalize,
dir=dir,
preprocess_method="circle")
### Fit Resonator function with background removal ###
#background_file = 'example_background.csv'
#params1,fig1,chi1,init1 = Fit_Resonator(filename = filename,Method = Method,normalize = normalize,dir = dir,background = background_file)
###############################################
## create Method
fit_type = 'DCM'
MC_iteration = 10
MC_rounds = 1e3
MC_fix = ['w1']
#manual_init = [Qi,Qc,freq,phi] #make your own initial guess: [Qi, Qc, freq, phi] (instead of phi used Qa for CPZM)
manual_init = None # find initial guess by itself
try:
Method = res.FitMethod(fit_type, MC_iteration, MC_rounds=MC_rounds,\
MC_fix=MC_fix, manual_init=manual_init, MC_step_const=0.3) #mcrounds = 100,000 unless otherwise specified
except:
print("Failed to initialize method, please change parameters")
quit()
##############################################################
normalize = 10
### Fit Resonator function without background removal ###
params, conf_array, fig1, chi1, init1 = fsd.fit(filename=filename,
Method=Method,
normalize=normalize,
dir=dir)
### Fit Resonator function with background removal ###
#background_file = 'example_background.csv'
#params1,fig1,chi1,init1 = Fit_Resonator(filename = filename,Method = Method,normalize = normalize,dir = dir,background = background_file)
###############################################
manual_init = None # find initial guess by itself
try:
Method = res.FitMethod(fit_type, MC_iteration, MC_rounds=MC_rounds,\
MC_fix=MC_fix, manual_init=manual_init, MC_step_const=0.3) #mcrounds = 100,000 unless otherwise specified
except:
print("Failed to initialize method, please change parameters")
quit()
##############################################################
normalize = 10
### Fit Resonator function without background removal ###
params, conf_array, fig1, chi1, init1 = fsd.fit(
filename=filename, Method=Method, normalize=normalize,
dir=dir) #,path_to_background)
Qi_values.append((params[0]**-1 - np.real(
(params[1] / np.exp(1j * params[3]))**-1))**-1)
Qi_conf.append(conf_array[1])
Qc_values.append(1 / np.real(1 / (params[1] / np.exp(1j * params[3]))))
Qc_conf.append(conf_array[3])
### Fit Resonator function with background removal ###
#path_to_background = dir+'/'+'example_background.csv'
#params1,fig1,chi1,init1 = Fit_Resonator(filename,filepath,Method,normalize,dir,path_to_background)
###############################################
file = open(dir + "/power_sweep_params.csv", "w")