import numpy as np
import matplotlib.pyplot as plt
import IQMixer.IQCalib as IQ
import Analyse_PSD as PSD
import FileReader as reader
IQCorrectionfile = 'IQMixer_Calib/20160803_1M_BOX/EllipseFit_0dBm_2000MHz_8000MHz.csv'
folder = '../../../MeasurementResult/'
filename = 'Noise_19dBm_4000MHz_2000K_1S_2'
freq = 4000
fs, num, I, Q = reader.ReadNoise(folder, filename)
paras = IQ.IQ_GetPara(IQCorrectionfile, freq)
Icrr, Qcrr = IQ.IQ_CorrtBarends(paras, I, Q)
IQ = (Icrr + 1j * Qcrr) / np.sqrt(np.mean(Qcrr)**2 + np.mean(Icrr)**2)
revisetheta = -np.arctan2(np.mean(IQ.imag), np.mean(IQ.real))
noisecompfinalrevised = IQ * np.exp(1j * revisetheta)
fx, Pxx, fy, Pyy, fxy, Pxy = PSD.Full_Spectrum(IQ.real, IQ.imag, fs)
fxr, Pxxr, fyr, Pyyr, fxyr, Pxyr = PSD.Full_Spectrum(
noisecompfinalrevised.real, noisecompfinalrevised.imag, fs)
plt.plot(fx, 10 * np.log10(Pxx), label='ONres Amplitude ')
plt.plot(fy, 10 * np.log10(Pyy), label='ONres Phase')
plt.plot(fxr, 10 * np.log10(Pxxr), label='Ir')
plt.plot(fyr, 10 * np.log10(Pyyr), label='Qr')
plt.xscale('log')
plt.legend()
plt.show()
noiselist = []
with open(data_folder + noiselist_file + '.txt', 'r') as f:
for line in f:
noiselist.append(line.replace("\n", ""))
num = len(sweeplist)
plt.figure(figsize=(18.5, 10.5))
for n in range(0, num):
### Get file name and resonance frequency
sweepdata_file = sweeplist[n]
noisedata_file = noiselist[n]
resfreq = float(resfreqlist[n])
# Read data
sweepfreq, sweepI, sweepQ = reader.ReadSweep(data_folder, sweepdata_file)
fsample, number, noiseI, noiseQ = reader.ReadNoise(data_folder,
noisedata_file)
meastime = len(number) / float(fsample)
# Get IQ Mixer calibration parameter
resfreqinMHz = int(round(sweepfreq[len(sweepfreq) / 2] / 1e6))
paras = IQ.IQ_GetPara(IQCalibrationfile, resfreqinMHz)
# Calibrate IQ data
I_mixercalibrated, Q_mixercalibrated = IQ.IQ_CorrtBarends(
paras, sweepI, sweepQ)
noiseI_mixercalibrated, noiseQ_mixercalibrated = IQ.IQ_CorrtBarends(
paras, noiseI, noiseQ)
# Normalize calibrated IQ Sweep data to IQ reference data
# Get complex IQ
sweepIQ_normalized = IQ.IQ_Normalize_Sweep(sweepfreq, I_mixercalibrated,
compfinal = comptiltmove * np.exp(-1j * theta) / radius
"""
rotfile = open(data_folder + data_file + '_IQDelaycalibrated_rotated.csv', 'w')
fwrite = csv.writer(rotfile)
for i in range(0, len(compfinal)):
fwrite.writerow([compfinal[i].real, compfinal[i].imag])
rotfile.close()
"""
##### Noise #####
### On resonance noise
noise_folder = "../../../MeasurementResult/20160613_NbRRR48_Noise/"
noise_file = 'Noise_19dBm_4993MHz_2000K_1S'
noisefr = fr
fs, num, noiseI, noiseQ = reader.ReadNoise(noise_folder, noise_file)
noiseIcrr, noiseQcrr = IQ.IQ_CorrtBarends(paras, noiseI, noiseQ)
noisecomp = np.asarray(
[noiseIcrr[i] + noiseQcrr[i] * 1j for i in range(0, len(noiseIcrr))])
noisecomptilt = noisecomp * np.exp(2 * np.pi * 1j * noisefr * tau)
noisecomptiltmove = noisecomptilt - (x_c + 1j * y_c)
noisecompfinal = noisecomptiltmove * np.exp(-1j * theta) / radius
#revisetheta = np.arctan2(np.max(noisecompfinal.real)-np.mean(noisecompfinal.real), np.max(noisecompfinal.imag)-np.mean(noisecompfinal.imag))
revisetheta = -np.arctan2(np.mean(noisecompfinal.imag),
np.mean(noisecompfinal.real))
noisecompfinalrevised = noisecompfinal * np.exp(1j * revisetheta)
### Off resonance noise
offnoise_file = 'Noise_19dBm_4995MHz_2000K_1S'
