def fit_temp_pow_sweep(fname):
"""
Osmond Wen, December 10, 2019
decription: finds the resonant frequencies from the combined temperature and
power sweep performed by powsweep.sweep_temp_pow
"""
fr, Qr = {}, {}
with h5.File(fname) as fyle:
fr_list_0 = fyle["S21/fr_list"][:]
full_location = "tempsweep/2019-12-06-14-35-28"
for temperature in np.array(fyle[full_location].keys()):
print 'Temperature: ', temperature
powernams = np.array(fyle[full_location+"/"+temperature].keys())
powervals = powernams.astype(np.float)
powernams = powernams[powervals.argsort()]
fr[float(temperature)] = {}
Qr[float(temperature)] = {}
for power in powernams:
print ' Power: ', power
df = pd.read_hdf(fname, key=full_location+"/"+temperature+"/"+power)
fr[float(temperature)][float(power)] = {}
Qr[float(temperature)][float(power)] = {}
for resID, fr_nominal in enumerate(fr_list_0):
print ' Resonator: ', resID
df_thisKID = df[df.resID == resID]
f_thisKID = df_thisKID.f.values
z_thisKID = df_thisKID.z.values
fr[float(temperature)][float(power)][resID],\
Qr[float(temperature)][float(power)][resID],\
_,_,_,_,_ = \
fitres.finefit(f_thisKID, z_thisKID, fr_nominal)
return fr, Qr
def vna_file_fit(filename,pickedres,show=False,save=False):
pickedres = np.array(pickedres)
VNA_f, VNA_z = read_vna(filename, decimation=1)
VNA_f = VNA_f*1e-3 # from MHz to GHz
frs = np.zeros(len(pickedres))
Qrs = np.zeros(len(pickedres))
for MKIDnum in range(len(pickedres)):
MKID_index = np.argmin(abs(VNA_f-pickedres[MKIDnum]))
index_range = 5000
MKID_f = VNA_f[max(MKID_index-index_range,0):min(MKID_index+index_range,len(VNA_f))]
MKID_z = VNA_z[max(MKID_index-index_range,0):min(MKID_index+index_range,len(VNA_f))]
#MKID_f = VNA_f[(VNA_f>=pickedres[MKIDnum]-)*(VNA_f<=pickedres[MKIDnum]+)]
frs[MKIDnum], Qrs[MKIDnum], Qc_hat, a, phi, tau, Qc = fitres.finefit(MKID_f, MKID_z, pickedres[MKIDnum])
if show:
fit_z = fitres.resfunc3(MKID_f, frs[MKIDnum], Qrs[MKIDnum], Qc_hat, a, phi, tau)
#MKID_z_corrected = 1-((1-MKID_z/(a*np.exp(-2j*np.pi*(MKID_f-frs[MKIDnum])*tau)))*(np.cos(phi)/np.exp(1j*phi)))
#fit_z_corrected = 1-(Qrs[MKIDnum]/Qc)/(1+2j*Qrs[MKIDnum]*(MKID_f-frs[MKIDnum])/frs[MKIDnum])
plt.plot(MKID_f,20*np.log10(abs(MKID_z)))
plt.plot(MKID_f,20*np.log10(abs(fit_z)))
plt.show()
if save:
fit_z = fitres.resfunc3(MKID_f, frs[MKIDnum], Qrs[MKIDnum], Qc_hat, a, phi, tau)
#MKID_z_corrected = 1-((1-MKID_z/(a*np.exp(-2j*np.pi*(MKID_f-frs[MKIDnum])*tau)))*(np.cos(phi)/np.exp(1j*phi)))
#fit_z_corrected = 1-(Qrs[MKIDnum]/Qc)/(1+2j*Qrs[MKIDnum]*(MKID_f-frs[MKIDnum])/frs[MKIDnum])
plt.plot(MKID_f,20*np.log10(abs(MKID_z)))
plt.plot(MKID_f,20*np.log10(abs(fit_z)))
plt.savefig(filename[:-3]+'_res'+str(MKIDnum)+'.png')
plt.close()
return frs, Qrs
def fit_single_sweep(fname, sweeptype, sdate, sweep, h5_rewrite = False):
with h5.File(fname) as fyle:
fr_list_0 = fyle["S21/fr_list"][:]
df = pd.read_hdf(fname, "{}/{}/{}".format(sweeptype, sdate, sweep))
num_res = len(fr_list_0)
fr_list, Qr_list, Qc_mag_list, a_list, phi_list, tau_list, Qc_list = \
[None]*num_res, [None]*num_res, [None]*num_res, [None]*num_res, [None]*num_res, [None]*num_res, [None]*num_res
for resID, fr_nominal in enumerate(fr_list_0):
print sweep, fr_nominal
df_temp = df[df.resID == resID]
fwindow = 5e-4 # (max(df_temp.f) - min(df_temp.f)) / 2.0
f_curr = df_temp.f.values
z_curr = df_temp.z.values
# print f_curr
# print z_curr
try:
fr_list[resID], \
Qr_list[resID], \
Qc_mag_list[resID], \
a_list[resID], \
phi_list[resID], \
tau_list[resID], \
Qc_list[resID] = \
fitres.finefit(f_curr, z_curr, fr_nominal)
except:
print "couldn't find a fit"
fr_list[resID], Qr_list[resID], Qc_mag_list[resID], a_list[resID], phi_list[resID], tau_list[resID], Qc_list[resID] = \
0, 0, 0, 0, 0, 0, 0
if h5_rewrite == True:
with h5.File(fname, "r+") as fyle:
if "{}/{}/{}/fr_list".format(sweeptype,sdate,sweep) in fyle:
fyle.__delitem__("{}/{}/{}/fr_list".format(sweeptype,sdate,sweep))
if "{}/{}/{}/Qr_list".format(sweeptype,sdate,sweep) in fyle:
fyle.__delitem__("{}/{}/{}/Qr_list".format(sweeptype,sdate,sweep))
if "{}/{}/{}/Qc_list".format(sweeptype,sdate,sweep) in fyle:
fyle.__delitem__("{}/{}/{}/Qc_list".format(sweeptype,sdate,sweep))
fyle["{}/{}/{}/fr_list".format(sweeptype,sdate,sweep)] = fr_list
fyle["{}/{}/{}/Qr_list".format(sweeptype,sdate,sweep)] = Qr_list
fyle["{}/{}/{}/Qc_list".format(sweeptype,sdate,sweep)] = Qc_list
result = pd.DataFrame({"fr_list":fr_list, "Qr_list":Qr_list, "Qc_mag_list":Qc_mag_list, "a_list":a_list, "phi_list":phi_list, "tau_list":tau_list, "Qc_list": Qc_list})
result["sweep"] = float(sweep)
return result
def vna_file_fit(filename, pickedres, show=False, save=False):
pickedres = np.array(pickedres)
VNA_f, VNA_z, _ = read_vna(filename, decimation=1)
VNA_f = VNA_f * 1e-3 #VNA_f in units of GHz after this line
frs = np.zeros(len(pickedres))
Qrs = np.zeros(len(pickedres))
for MKIDnum in range(len(pickedres)):
window_f = (pickedres[MKIDnum] + 10 * pickedres[MKIDnum] / 3e5)
window_index = np.argmin(abs(VNA_f - window_f))
MKID_index = np.argmin(abs(VNA_f - pickedres[MKIDnum]))
index_range = window_index - MKID_index
MKID_f = VNA_f[max(MKID_index -
index_range, 0):min(MKID_index +
index_range, len(VNA_f))]
MKID_z = VNA_z[max(MKID_index -
index_range, 0):min(MKID_index +
index_range, len(VNA_f))]
frs[MKIDnum], Qrs[MKIDnum], Qc_hat, a, phi, tau, Qc = fitres.finefit(
MKID_f, MKID_z, pickedres[MKIDnum])
if show:
fit_z = fitres.resfunc3(MKID_f, frs[MKIDnum], Qrs[MKIDnum], Qc_hat,
a, phi, tau)
#MKID_z_corrected = 1-((1-MKID_z/(a*np.exp(-2j*np.pi*(MKID_f-frs[MKIDnum])*tau)))*(np.cos(phi)/np.exp(1j*phi)))
#fit_z_corrected = 1-(Qrs[MKIDnum]/Qc)/(1+2j*Qrs[MKIDnum]*(MKID_f-frs[MKIDnum])/frs[MKIDnum])
plt.figure('how does the fit look')
plt.plot(MKID_f, 20 * np.log10(abs(MKID_z)))
plt.plot(MKID_f, 20 * np.log10(abs(fit_z)))
plt.show()
raw_input('press enter to move on')
plt.close('all')
if save:
fit_z = fitres.resfunc3(MKID_f, frs[MKIDnum], Qrs[MKIDnum], Qc_hat,
a, phi, tau)
#MKID_z_corrected = 1-((1-MKID_z/(a*np.exp(-2j*np.pi*(MKID_f-frs[MKIDnum])*tau)))*(np.cos(phi)/np.exp(1j*phi)))
#fit_z_corrected = 1-(Qrs[MKIDnum]/Qc)/(1+2j*Qrs[MKIDnum]*(MKID_f-frs[MKIDnum])/frs[MKIDnum])
plt.plot(MKID_f, 20 * np.log10(abs(MKID_z)))
plt.plot(MKID_f, 20 * np.log10(abs(fit_z)))
plt.savefig(filename[:-3] + '_res' + str(MKIDnum) + '.png')
plt.close()
return frs, Qrs, Qc_hat, a, phi, tau, Qc
def fit_temp_res(fname, sweepnum, pickedres=None):
with h5py.File(fname, 'r') as fyle:
timestamps = fyle['tempsweep'].keys()
chosen_tempsweep = timestamps[
sweepnum] # Just the first temperature sweep saved in this file
print chosen_tempsweep
MCTemps1 = np.array(fyle['tempsweep/' + chosen_tempsweep].keys())
MCTemps1 = MCTemps1[MCTemps1 != 'MB']
MCTemps1 = MCTemps1[MCTemps1 != 'RES']
df1 = pd.read_hdf(fname,
key='tempsweep/' + chosen_tempsweep + '/' + MCTemps1[0])
if pickedres is not None:
if isinstance(pickedres, int):
MKIDnum = [pickedres]
else:
MKIDnum = pickedres
else:
MKIDnum = np.unique(df1['resID'])
ID_of_T = np.zeros((len(MCTemps1), len(MKIDnum)))
fr_of_T = np.zeros((len(MCTemps1), len(MKIDnum)))
Qr_of_T = np.zeros((len(MCTemps1), len(MKIDnum)))
Qc_of_T = np.zeros((len(MCTemps1), len(MKIDnum)))
a_of_T = (1 + 1j) * np.zeros((len(MCTemps1), len(MKIDnum)))
phi_of_T = np.zeros((len(MCTemps1), len(MKIDnum)))
tau_of_T = (1 + 1j) * np.zeros((len(MCTemps1), len(MKIDnum)))
Qc_hat_mag_of_T = np.zeros((len(MCTemps1), len(MKIDnum)))
print 'Doing resonance fits...'
for Tn in range(len(MCTemps1)):
temperature_Tn = MCTemps1[Tn]
df1 = pd.read_hdf(fname,
key='tempsweep/' + chosen_tempsweep + '/' +
temperature_Tn)
resID_index = df1['resID']
for Kn in range(len(MKIDnum)):
fr_0 = np.mean(df1['f0'][resID_index == MKIDnum[Kn]][0])
if Tn > 0:
if fr_of_T[Tn - 1, Kn] != 0:
fr_0 = fr_of_T[Tn - 1, Kn]
f1 = df1['f']
f_array = np.array(f1[resID_index == MKIDnum[Kn]])
z1 = df1['z']
z_array = np.array(z1[resID_index == MKIDnum[Kn]])
try:
fitted = fitres.finefit(f_array, z_array, fr_0)
except:
plt.plot(f_array, abs(z_array))
plt.title('MKID ' + str(MKIDnum[Kn]) + ', ' +
str(MCTemps1[Tn]) + ' K')
plt.show()
fitted = [0, 0, 0, 0, 0, 0, 0]
ID_of_T[Tn, Kn] = MKIDnum[Kn]
fr_of_T[Tn, Kn] = fitted[0]
Qr_of_T[Tn, Kn] = fitted[1]
Qc_of_T[Tn, Kn] = fitted[6]
a_of_T[Tn, Kn] = fitted[3]
phi_of_T[Tn, Kn] = fitted[4]
tau_of_T[Tn, Kn] = fitted[5]
Qc_hat_mag_of_T[Tn, Kn] = fitted[2]
df2 = pd.DataFrame()
df2['resID'] = ID_of_T.flatten(order='F')
df2['fr'] = fr_of_T.flatten(order='F')
df2['Qr'] = Qr_of_T.flatten(order='F')
df2['Qc'] = Qc_of_T.flatten(order='F')
df2['a'] = a_of_T.flatten(order='F')
df2['phi'] = phi_of_T.flatten(order='F')
df2['tau'] = tau_of_T.flatten(order='F')
df2['Qc_hat_mag'] = Qc_hat_mag_of_T.flatten(order='F')
df2.to_hdf(fname, key='/tempsweep/' + chosen_tempsweep + '/RES')
def resonator_basis(noise_timestream,readout_f,VNA_f,VNA_z,char_f,char_z):
def rotate_to_ideal(z,f,fr,a,tau,phi):
return 1-((1-z/(a*np.exp(-2j*np.pi*(f-fr)*tau)))*(np.cos(phi)/np.exp(1j*phi)))
char_res_idx = int(len(char_f)/2)
# Define region around this resonance, and fit for parameters
index_range = 1000
readout_index = PUf.find_closest(VNA_f,readout_f)
MKID_f = VNA_f[max(readout_index-index_range,0):min(readout_index+index_range,len(VNA_f))]
MKID_z = VNA_z[max(readout_index-index_range,0):min(readout_index+index_range,len(VNA_f))]
fit_fr, fit_Qr, fit_Qc_hat, fit_a, fit_phi, fit_tau, fit_Qc = fitres.finefit(MKID_f, MKID_z, readout_f)
fit_Qi = fit_Qr*fit_Qc/(fit_Qc-fit_Qr)
print(fit_Qr,fit_Qc)
# Transform VNA to ideal space
MKID_z_ideal = rotate_to_ideal(MKID_z,MKID_f,fit_fr,fit_a,fit_tau,fit_phi)
# Transform the VNA fit to ideal space
fit_f = np.linspace(MKID_f[0],MKID_f[-1],10000)
fit_z = fitres.resfunc3(fit_f, fit_fr, fit_Qr, fit_Qc_hat, fit_a, fit_phi, fit_tau)
fit_z_ideal = rotate_to_ideal(fit_z,fit_f,fit_fr,fit_a,fit_tau,fit_phi)
# find some important indices in the f vector
first_char_fit_idx = PUf.find_closest(fit_f,char_f[0])
last_char_fit_idx = PUf.find_closest(fit_f,char_f[-1])
res_f_idx = PUf.find_closest(MKID_f,readout_f)
res_fit_idx = PUf.find_closest(fit_f,readout_f)
# Find VNA-data subset that covers the characterization-data region in frequency space
char_region_f = fit_f[first_char_fit_idx:last_char_fit_idx]
char_region_z = fit_z[first_char_fit_idx:last_char_fit_idx]
char_region_z_ideal = fit_z_ideal[first_char_fit_idx:last_char_fit_idx]
# Get the angle (in complex space) of the characterization data
real_fit = np.polyfit(char_f,char_z.real,1)
imag_fit = np.polyfit(char_f,char_z.imag,1)
char_angle = np.angle((real_fit[0]*(char_region_f[-1]-char_region_f[0]))+1j*(imag_fit[0]*(char_region_f[-1]-char_region_f[0])))
# Get the angle (in complex space) of the VNA data
real_fit = np.polyfit(char_region_f,char_region_z.real,1)
imag_fit = np.polyfit(char_region_f,char_region_z.imag,1)
char_region_angle = np.angle((real_fit[0]*(char_region_f[-1]-char_region_f[0]))+1j*(imag_fit[0]*(char_region_f[-1]-char_region_f[0])))
# Get the angle (in complex ideal space) of the VNA data
real_fit = np.polyfit(char_region_f,char_region_z_ideal.real,1)
imag_fit = np.polyfit(char_region_f,char_region_z_ideal.imag,1)
char_region_ideal_angle = np.angle((real_fit[0]*(char_region_f[-1]-char_region_f[0]))+1j*(imag_fit[0]*(char_region_f[-1]-char_region_f[0])))
# Rotate characterization data to VNA data
char_z_rotated = (char_z-char_z[char_res_idx])\
*np.exp(1j*(-1*char_angle+char_region_angle))\
+fit_z[res_fit_idx]
char_z_rotated_ideal = rotate_to_ideal(char_z_rotated,char_f,fit_fr,fit_a,fit_tau,fit_phi)
timestream_rotated = (noise_timestream-np.mean(noise_timestream,dtype=complex))\
*np.exp(1j*(-1*char_angle+char_region_angle))\
+fit_z[res_fit_idx]
timestream_rotated_ideal = rotate_to_ideal(timestream_rotated,readout_f,fit_fr,fit_a,fit_tau,fit_phi)
dS21 = timestream_rotated_ideal-np.mean(timestream_rotated_ideal,dtype=complex)
# Extra rotation for imaginary direction --> frequency tangent direction
# This is necessary if data_freqs[0] != primary_fr
# Note that char_f[res_idx] == data_freqs[0] should always be true
# data_freqs[0] != primary_fr means our vna fit before data taking gave a different fr than our vna fit now
phase_adjust = 1
# phase_adjust = np.exp(1j*(np.sign(char_region_ideal_angle)*0.5*np.pi-char_region_ideal_angle))
frequency = phase_adjust*dS21.imag*fit_Qc/(2*fit_Qr**2)
dissipation = phase_adjust*dS21.real*fit_Qc/(fit_Qr**2)
# print(type(frequency[2]),type(dissipation[2]),type(fit_Qc))
ideal = {}
ideal['f'] = MKID_f
ideal['z'] = MKID_z_ideal
ideal['char z'] = char_z_rotated_ideal
ideal['timestream'] = timestream_rotated_ideal
ideal['fit f'] = fit_f
ideal['fit z'] = fit_z_ideal
resonator = {}
resonator['fr'] = fit_fr
resonator['Qr'] = fit_Qr
resonator['Qc'] = fit_Qc
if False:
# PUf.plot_noise_and_vna(noise_timestream,MKID_z,f_idx=res_f_idx,char_zs=char_z,title='S21')
PUf.plot_noise_and_vna(timestream_rotated_ideal,MKID_z_ideal,\
f_idx=res_f_idx,char_zs=char_z_rotated_ideal,title='ideal space')
plt.show(False)
# plt.pause(5)
raw_input('press enter to close all plots')
plt.close('all')
# plt.figure(2)
# fit_fr_idx = PUf.find_closest(MKID_f,fit_fr)
# # Plotting the corrected data (moved to ideal S21 space)
# plt.gca().set_aspect('equal', adjustable='box')
# plt.axvline(x=0,c='k')
# plt.axhline(y=0,c='k')
# plt.plot(MKID_z_ideal.real,MKID_z_ideal.imag,label='ideal space vna')
# plt.plot(char_z_rotated_ideal.real,char_z_rotated_ideal.imag,label='calibrated calibration data')
# plt.plot(MKID_z_ideal[res_f_idx].real,MKID_z_ideal[res_f_idx].imag,'o',label='vna closest to readout frequency')
# plt.plot(MKID_z_ideal[fit_fr_idx].real,MKID_z_ideal[fit_fr_idx].imag,'o',label='vna closest to new fr')
# plt.plot(timestream_rotated_ideal[::10].real,timestream_rotated_ideal[::10].imag,ls='',marker='.')
# # plt.legend()
# plt.show()
return frequency, dissipation, ideal, resonator
