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import numpy as np
import matplotlib.pyplot as plot
from scipy import signal, constants
import argparse
import os
def rp_raw_to_numpy(rp_raw):
# Convert raw buffer strings to numpy arrays
buff_string = rp_raw.split(',')
return np.array(list(map(float, buff_string)))
RP_IP_ADDRS = {
"creotech-1": "192.168.1.104",
"creotech-2": "192.168.1.105",
"mlabs-1": "rp-f05cc9",
"mlabs-2": "rp-f0612e",
}
def main():
parser = argparse.ArgumentParser(description="Data plotting tool for Sayma DAC/TTL")
parser.add_argument("dir", help="output directory", type=str)
parser.add_argument("rps", metavar="NAME:CHANNEL",
help="input <CHANNEL> of the RedPitaya at <NAME> where data is collected; "
"CHANNEL must be 1 or 2; "
"NAME must be any of: " + " ".join(list(RP_IP_ADDRS.keys())),
type=str, nargs=2)
args = parser.parse_args()
# Must only compare 2 data
name, channel = args.rps[0].split(':')
y1_filename = 'rp_{}_y{}_raw.bin'.format(name, channel)
name, channel = args.rps[1].split(':')
y2_filename = 'rp_{}_y{}_raw.bin'.format(name, channel)
if y1_filename == y2_filename:
raise ValueError("Both files are the same.")
with open(os.path.join(args.dir, y1_filename), 'rb') as f:
y1_raw = f.read().decode('utf-8')
with open(os.path.join(args.dir, y2_filename), 'rb') as f:
y2_raw = f.read().decode('utf-8')
if None in [y1_raw, y2_raw]:
raise IOError("Raw RP string files cannot be opened.")
y1 = rp_raw_to_numpy(y1_raw)
y2 = rp_raw_to_numpy(y2_raw)
# Define t as an array of timestamps (in seconds) for each sample
# (Note that RedPitaya's oscilloscope has a sampling rate @ 125MHz)
t = np.arange(y1.shape[0])/125e6
# Generate matrix Y by having arrays y1 and y2 as 2 rows
y = np.c_[y1, y2].T
# Element-wise multiply an array of cos(2pi*9e6*t) with each row in Y;
# Then, downsample the array by 10 as Z
z = signal.decimate(y*np.exp(1j*2*np.pi*9e6*t), q=10, ftype="fir", zero_phase=True)[:, 10:]
# Downsample Z by 10 again.
z = signal.decimate(z, q=10, ftype="fir", zero_phase=True)[:, 10:]
# Element-wise multiply Z[0] with the conjugate of Z[1] to get the phase difference (i.e. angle(z0) - angle(z1)), and use the mean value.
angle = np.angle(np.mean(z[0]*z[1].conj()))
print(angle)
# Normalize y1 and y2 for plotting
y1 /= abs(y1).max()
y2 /= abs(y2).max()
plot.plot(y1)
plot.plot(y2)
plot.show()
if __name__ == "__main__":
main()