# snap some data
pylab.figure()
n_plots = len(corr.fengs)
for fn, feng in enumerate(corr.fengs):
adc = feng.snap('snapshot_adc', man_trig=True, format='b')
pylab.subplot(n_plots, 1, fn)
pylab.plot(adc)
pylab.title('ADC values: ROACH %s, ADC %d, (ANT %d, BAND %s)' %
(feng.roachhost.host, feng.adc, feng.ant, feng.band))
# some non-general code to snap from the X-engine
print 'Snapping data...'
d = corr.snap_corr()
print 'Plotting data...'
pylab.figure()
pylab.subplot(4, 1, 1)
#pylab.plot(corr.fengs[0].gen_freq_scale(),helpers.dbs(d['corr00']))
pylab.plot(helpers.dbs(d['corr00']))
pylab.subplot(4, 1, 2)
#pylab.plot(corr.fengs[0].gen_freq_scale(),helpers.dbs(d['corr11']))
pylab.plot(helpers.dbs(d['corr11']))
pylab.subplot(4, 1, 3)
#pylab.plot(corr.fengs[0].gen_freq_scale(),helpers.dbs(np.abs(d['corr01'])))
pylab.plot(helpers.dbs(np.abs(d['corr01'])))
pylab.subplot(4, 1, 4)
#pylab.plot(corr.fengs[0].gen_freq_scale(),np.unwrap(np.angle(d['corr01'])))
pylab.plot(np.unwrap(np.angle(d['corr01'])))
pylab.show()
# turn off noise switch so we can sample powers
feng.noise_switch_enable(False)
time.sleep(1)
if load_new:
print "Computing new coefficients"
d = np.zeros(vec_width)
for n in range(opts.samples):
print '%d: Snapping data from ANT: %d, BAND: %s'%(n,feng.ant,feng.band)
d += feng.get_spectra()
# calculate target mean power, scaled for 4 bits
d /= float(opts.samples)
mean_power = np.mean(d)
lowlimit = mean_power / float(opts.cutoff)
if opts.plot:
pylab.figure(1)
pylab.plot(dbs(d),label='ANT %d, BAND %s'%(feng.ant,feng.band))
pylab.axhline(y=10*np.log10(lowlimit),label='ANT %d, BAND %s limit'%(feng.ant,feng.band), color=pylab.gca().lines[-1].get_color())
pylab.legend()
pylab.title("Autocorrelation Passbands")
pylab.ylabel("Power (db)")
pylab.xlabel("Decimated Channel Number")
eq = (np.sqrt(1./d)) * opts.targetpower
#eq[eq>np.mean(eq)*opts.cutoff] = 0
eq[d<lowlimit] = 0
eq = eq[::decimation]
if opts.plot:
pylab.figure(2)
pylab.plot(eq,label='ANT %d, BAND %s'%(feng.ant,feng.band))
pylab.title("EQ coefficients")
pylab.ylabel("Amplitude (linear)")
from optparse import OptionParser
p = OptionParser()
p.set_usage('%prog [options] [CONFIG_FILE]')
p.set_description(__doc__)
opts, args = p.parse_args(sys.argv[1:])
if args == []:
config_file = None
else:
config_file = args[0]
# initialise connection to correlator
corr = AMI.AmiDC(config_file=config_file)
time.sleep(0.1)
for feng in corr.fengs:
feng.noise_switch_enable(False)
s = corr.do_for_all('get_spectra', corr.fengs) #flush one to give time for the noise switch state to chance
s = corr.do_for_all('get_spectra', corr.fengs)
for fn, feng in enumerate(corr.fengs):
pylab.plot(dbs(s[fn]), label='ANT %d, %s band'%(feng.ant+1, feng.band))
for feng in corr.fengs:
feng.noise_switch_enable(True)
pylab.ylabel('Power (dB)')
pylab.legend()
pylab.show()
for fn,feng in enumerate(corr.fengs):
adc = feng.snap('snapshot_adc', man_trig=True, format='b')
pylab.subplot(n_plots,1,fn)
pylab.plot(adc)
pylab.title('ADC values: ROACH %s, ADC %d, (ANT %d, BAND %s)'%(feng.roachhost.host,feng.adc,feng.ant,feng.band))
# some non-general code to snap from the X-engine
print 'Snapping data...'
d = corr.snap_corr()
print 'Plotting data...'
pylab.figure()
pylab.subplot(4,1,1)
#pylab.plot(corr.fengs[0].gen_freq_scale(),helpers.dbs(d['corr00']))
pylab.plot(helpers.dbs(d['corr00']))
pylab.subplot(4,1,2)
#pylab.plot(corr.fengs[0].gen_freq_scale(),helpers.dbs(d['corr11']))
pylab.plot(helpers.dbs(d['corr11']))
pylab.subplot(4,1,3)
#pylab.plot(corr.fengs[0].gen_freq_scale(),helpers.dbs(np.abs(d['corr01'])))
pylab.plot(helpers.dbs(np.abs(d['corr01'])))
pylab.subplot(4,1,4)
#pylab.plot(corr.fengs[0].gen_freq_scale(),np.unwrap(np.angle(d['corr01'])))
pylab.plot(np.unwrap(np.angle(d['corr01'])))
pylab.show()
time.sleep(1)
for feng in corr.fengs:
print "Computing new coefficients"
d = np.zeros(vec_width)
for n in range(opts.samples):
print '%d: Snapping data from ANT: %d, BAND: %s' % (n, feng.ant,
feng.band)
d += feng.get_spectra()
# calculate target mean power, scaled for 4 bits
d /= float(opts.samples)
mean_power = np.mean(d)
lowlimit = mean_power / float(opts.cutoff)
if opts.plot and (feng.ant in ants_to_plot):
pylab.figure(1)
pylab.plot(dbs(d), label='ANT %d, BAND %s' % (feng.ant, feng.band))
pylab.axhline(y=10 * np.log10(lowlimit),
label='ANT %d, BAND %s limit' %
(feng.ant, feng.band),
color=pylab.gca().lines[-1].get_color())
pylab.legend()
pylab.title("Autocorrelation Passbands")
pylab.ylabel("Power (db)")
pylab.xlabel("Decimated Channel Number")
eq = np.sqrt((1. / d) * opts.targetpower)
#eq[eq>np.mean(eq)*opts.cutoff] = 0
eq[d < lowlimit] = 0
eq = eq[::decimation]
if feng.ant in zero_ants:
eq[:] = 0
feng.noise_switch_enable(False)
time.sleep(1)
if load_new:
print "Computing new coefficients"
d = np.zeros(vec_width)
for n in range(opts.samples):
print '%d: Snapping data from ANT: %d, BAND: %s' % (
n, feng.ant, feng.band)
d += feng.get_spectra()
# calculate target mean power, scaled for 4 bits
d /= float(opts.samples)
mean_power = np.mean(d)
lowlimit = mean_power / float(opts.cutoff)
if opts.plot:
pylab.figure(1)
pylab.plot(dbs(d),
label='ANT %d, BAND %s' % (feng.ant, feng.band))
pylab.axhline(y=10 * np.log10(lowlimit),
label='ANT %d, BAND %s limit' %
(feng.ant, feng.band),
color=pylab.gca().lines[-1].get_color())
pylab.legend()
pylab.title("Autocorrelation Passbands")
pylab.ylabel("Power (db)")
pylab.xlabel("Decimated Channel Number")
eq = (np.sqrt(1. / d)) * opts.targetpower
#eq[eq>np.mean(eq)*opts.cutoff] = 0
eq[d < lowlimit] = 0
eq = eq[::decimation]
if opts.plot:
p.set_usage('%prog [options] [CONFIG_FILE]')
p.set_description(__doc__)
opts, args = p.parse_args(sys.argv[1:])
if args == []:
config_file = None
else:
config_file = args[0]
# initialise connection to correlator
corr = AMI.AmiDC(config_file=config_file)
time.sleep(0.1)
for feng in corr.fengs:
feng.noise_switch_enable(False)
s = corr.do_for_all(
'get_spectra', corr.fengs
) #flush one to give time for the noise switch state to chance
s = corr.do_for_all('get_spectra', corr.fengs)
for fn, feng in enumerate(corr.fengs):
pylab.plot(dbs(s[fn]),
label='ANT %d, %s band' % (feng.ant + 1, feng.band))
for feng in corr.fengs:
feng.noise_switch_enable(True)
pylab.ylabel('Power (dB)')
pylab.legend()
pylab.show()