ax.set_ylim(top=3, bottom=-3)
ax.legend(loc=2)
plt.show()
if __name__ == '__main__':
# logging infrastructure
logger = logging.getLogger('main')
handler = logging.StreamHandler()
colored_formatter = ColoredFormatter(
"%(log_color)s%(asctime)s%(levelname)s:%(name)s:%(message)s")
handler.setFormatter(colored_formatter)
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
correlator = Correlator(logger=logger.getChild('correlator'))
correlator.set_shift_schedule(0b00000000000)
correlator.set_accumulation_len(40000)
correlator.add_frequency_bin_calibrations('baseline.json')
time.sleep(1)
correlator.re_sync()
siggen = SCPI(host='localhost')
offsets = {}
offsets['axis'] = []
for a, b in correlator.cross_combinations:
offsets["{a}{b}".format(a=a, b=b)] = []
for f in correlator.frequency_correlations[(0, 1)].frequency_bins:
siggen.setFrequency(f)
#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
from directionFinder_backend.correlator import Correlator
import scipy.signal
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
if __name__ == '__main__':
c = Correlator()
c.fetch_time_domain_snapshot(force=True)
time_domain_padding = 10
fs = 800e6
upsample_factor = 100
a_idx = 0
b_idx = 1
a = np.concatenate(
(np.zeros(time_domain_padding), c.time_domain_signals[a_idx],
np.zeros(time_domain_padding)))
a_time = np.linspace(-(time_domain_padding / fs),
(len(a) - time_domain_padding) / fs,
len(a),
endpoint=False)
b = c.time_domain_signals[b_idx]
b_time = np.linspace(0, len(b) / fs, len(b), endpoint=False)
correlation = np.correlate(a, b, mode='valid')
correlation_time = np.linspace(a_time[0] - b_time[0],
a_time[-1] - b_time[-1],
len(correlation),
