def plot_compare_ogp(self, new_ogp, old_ogp, freq, pts=50):
"""
This function plots a comparison between a snap and a fitted sine
for two different sets of ogp
Inputs:
- 'new_ogp' is an array containing measured ogp values,
- 'old_ogp' is an array containing current ogp values loaded on ADC5g
(if old_ogp = None, will use 0 ogp for all 4 cores),
- 'freq' is frequency of input CW tone (in MHz)
- 'pts' is for how many points in snap to plot (max 8192)
Outputs:
-
-
-
"""
tot_pts = 8192
del_phi = 2*math.pi*freq/self.clk #freq in MHz
x = [del_phi*i for i in range(tot_pts)]
r = np.arange(0, tot_pts*del_phi, del_phi/10.)
p0 = [128., 90., 90.]
self.syn.set_freq(freq*1e6)
self.clear_ogp()
time.sleep(0.5)
raw_u = adc5g.get_snapshot(self.roach, 'scope_raw_a0_snap')
params_u = curve_fit(syn_func, x, raw_u, p0)
self.set_ogp(new_ogp, 0, cores=[1,2])
time.sleep(0.5)
raw_c = adc5g.get_snapshot(self.roach, 'scope_raw_a0_snap')
params_c = curve_fit(syn_func, x, raw_c, p0)
f_u = syn_func(r, params_u[0][0], params_u[0][1], params_u[0][2])
f_c = syn_func(r, params_c[0][0], params_c[0][1], params_c[0][2])
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
ax1.plot(r[:(pts*10)], f_u[:(pts*10)])
ax1.plot(x[:pts], raw_u[:pts], 'o', color='r')
ax1.set_title('OGP Uncorrected Fit, freq = %d MHz' %freq)
ax2.plot(r[:(pts*10)], f_c[:(pts*10)])
ax2.plot(x[:pts], raw_c[:pts], 'o', color='r')
ax2.set_title('OGP Corrected Fit, freq = %d MHz' %freq)
plt.tight_layout()
plt.show()
def plot_compare_ogp(self, new_ogp, old_ogp, freq, pts=50):
"""
This function plots a comparison between a snap and a fitted sine
for two different sets of ogp
Inputs:
- 'new_ogp' is an array containing measured ogp values,
- 'old_ogp' is an array containing current ogp values loaded on ADC5g
(if old_ogp = None, will use 0 ogp for all 4 cores),
- 'freq' is frequency of input CW tone (in MHz)
- 'pts' is for how many points in snap to plot (max 8192)
Outputs:
-
-
-
"""
tot_pts = 8192
del_phi = 2 * math.pi * freq / self.clk #freq in MHz
x = [del_phi * i for i in range(tot_pts)]
r = np.arange(0, tot_pts * del_phi, del_phi / 10.)
p0 = [128., 90., 90.]
self.syn.set_freq(freq * 1e6)
self.clear_ogp()
time.sleep(0.5)
raw_u = adc5g.get_snapshot(self.roach, 'scope_raw_a0_snap')
params_u = curve_fit(syn_func, x, raw_u, p0)
self.set_ogp(new_ogp, 0, cores=[1, 2])
time.sleep(0.5)
raw_c = adc5g.get_snapshot(self.roach, 'scope_raw_a0_snap')
params_c = curve_fit(syn_func, x, raw_c, p0)
f_u = syn_func(r, params_u[0][0], params_u[0][1], params_u[0][2])
f_c = syn_func(r, params_c[0][0], params_c[0][1], params_c[0][2])
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
ax1.plot(r[:(pts * 10)], f_u[:(pts * 10)])
ax1.plot(x[:pts], raw_u[:pts], 'o', color='r')
ax1.set_title('OGP Uncorrected Fit, freq = %d MHz' % freq)
ax2.plot(r[:(pts * 10)], f_c[:(pts * 10)])
ax2.plot(x[:pts], raw_c[:pts], 'o', color='r')
ax2.set_title('OGP Corrected Fit, freq = %d MHz' % freq)
plt.tight_layout()
plt.show()
def dosnap(fr=0, name="t", rpt = 1, donot_clear=False):
"""
Takes a snapshot and uses fit_cores to fit a sine function to each
core separately assuming a CW signal is connected to the input. The
offset, gain and phase differences are reoprted for each core as
well as the average of all four.
The parameters are:
fr The frequency of the signal generator. It will default to the last
frequency set by set_freq()
name the name of the file into which the snapshot is written. 5 other
files are written. Name.c1 .. name.c4 contain themeasurements from
cores a, b, c and d. Note that data is taken from cores in the order
a, c, b, d. A line is appended to the file name.fit containing
signal freq, average zero, average amplitude followed by triplets
of zero, amplitude and phase differences for cores a, b, c and d
rpt The number of repeats. Defaults to 1. The c1 .. c4 files mentioned
above are overwritten with each repeat, but new rows of data are added
to the .fit file for each pass.
"""
global freq
avg_pwr_sinad = 0
if fr == 0:
fr = freq
for i in range(rpt):
snap=adc5g.get_snapshot(roach2, snap_name, man_trig=True, wait_period=2)
np.savetxt(name, snap,fmt='%d')
ogp, pwr_sinad = fit_cores.fit_snap(fr, samp_freq, name,\
clear_avgs = i == 0 and not donot_clear, prnt = i == rpt-1)
avg_pwr_sinad += pwr_sinad
return ogp, avg_pwr_sinad/rpt
def og_from_noise(fname="ogp.noise", rpt=100):
"""
Take a number of snapshots of noise. Analyze for offset and gain
for each core separately.
"""
sum_result = np.zeros((15), dtype=float)
sum_cnt = 0
for n in range(rpt):
result = np.zeros((15), dtype=float)
snap=adc5g.get_snapshot(roach2, snap_name, man_trig=True, wait_period=2)
if(rpt == 1):
np.savetxt("t.og_noise", snap,fmt='%d')
l=float(len(snap))
snap_off=np.sum(snap)/l
snap_amp=np.sum(abs(snap-snap_off))/l
result[0]=snap_off*(-500.0/256.0)
result[1]=snap_amp
for core in range(4):
# This will actually sample the cores in the order A,C,B,D
# index will fix this up when data is put in the result array
index=(3,9,6,12)[core]
c=snap[core::4]
l=float(len(c))
off=np.sum(c)/l
result[index] = off*(-500.0/256.0)
amp=np.sum(abs(c-off))/l
result[index+1]= 100.0*(snap_amp-amp)/snap_amp
sum_result += result
sum_cnt += 1
print "%.4f "*15 % tuple(result)
sum_result /= sum_cnt
print "%.4f "*15 % tuple(sum_result)
np.savetxt(fname, sum_result[3:], fmt="%8.4f")
def dosnap(fr=0, name="t", rpt=1, donot_clear=False):
"""
Takes a snapshot and uses fit_cores to fit a sine function to each
core separately assuming a CW signal is connected to the input. The
offset, gain and phase differences are reoprted for each core as
well as the average of all four.
The parameters are:
fr The frequency of the signal generator. It will default to the last
frequency set by set_freq()
name the name of the file into which the snapshot is written. 5 other
files are written. Name.c1 .. name.c4 contain themeasurements from
cores a, b, c and d. Note that data is taken from cores in the order
a, c, b, d. A line is appended to the file name.fit containing
signal freq, average zero, average amplitude followed by triplets
of zero, amplitude and phase differences for cores a, b, c and d
rpt The number of repeats. Defaults to 1. The c1 .. c4 files mentioned
above are overwritten with each repeat, but new rows of data are added
to the .fit file for each pass.
"""
global freq
avg_pwr_sinad = 0
if fr == 0:
fr = freq
for i in range(rpt):
snap = adc5g.get_snapshot(roach2,
snap_name,
man_trig=True,
wait_period=2)
np.savetxt(name, snap, fmt='%d')
ogp, pwr_sinad = fit_cores.fit_snap(fr, samp_freq, name,\
clear_avgs = i == 0 and not donot_clear, prnt = i == rpt-1)
avg_pwr_sinad += pwr_sinad
return ogp, avg_pwr_sinad / rpt
def hist_from_snapshots(rpt=10):
# hist_all = np.zeros(256,dtype=int)
hist1 = np.zeros(256, dtype=int)
hist2 = np.zeros(256, dtype=int)
hist3 = np.zeros(256, dtype=int)
hist4 = np.zeros(256, dtype=int)
for i in range(rpt):
snap = adc5g.get_snapshot(roach2,
snap_name,
man_trig=True,
wait_period=2)
snap = 128 + np.array(snap)
# hist = np.bincount(snap, minlength=256)
# hist_all += hist
hist = np.bincount(snap[0::4], minlength=256)
hist1 += hist
hist = np.bincount(snap[1::4], minlength=256)
hist2 += hist
hist = np.bincount(snap[2::4], minlength=256)
hist3 += hist
hist = np.bincount(snap[3::4], minlength=256)
hist4 += hist
data = np.column_stack((np.arange(-128., 128,
dtype=int), hist1, hist2, hist3, hist4))
np.savetxt("hist_cores", data, fmt=("%d"))
# print "all ",np.sum(hist_all[0:128]), np.sum(hist_all[128:256])
print "core a ", np.sum(hist1[0:128]), np.sum(hist1[129:256])
print "core b ", np.sum(hist3[0:128]), np.sum(hist3[129:256])
print "core c ", np.sum(hist2[0:128]), np.sum(hist2[129:256])
print "core d ", np.sum(hist4[0:128]), np.sum(hist4[129:256])
def get_rms():
global zdok
save_zdok = zdok
set_zdok(0)
snap = adc5g.get_snapshot(roach2, snap_name, man_trig=True, wait_period=2)
rmsSnap = np.std(snap)
loadingFactor = -20.0 * math.log10(128 / rmsSnap)
print "If0 Rms = %f, loading factor = %f" % (rmsSnap, loadingFactor)
set_zdok(1)
snap = adc5g.get_snapshot(roach2, snap_name, man_trig=True, wait_period=2)
rmsSnap = np.std(snap)
loadingFactor = -20.0 * math.log10(128 / rmsSnap)
print "If1 Rms = %f, loading factor = %f" % (rmsSnap, loadingFactor)
if zdok != save_zdok:
set_zdok(save_zdok)
def og_from_noise(fname="ogp.noise", rpt=100):
"""
Take a number of snapshots of noise. Analyze for offset and gain
for each core separately.
"""
sum_result = np.zeros((15), dtype=float)
sum_cnt = 0
for n in range(rpt):
result = np.zeros((15), dtype=float)
snap = adc5g.get_snapshot(roach2,
snap_name,
man_trig=True,
wait_period=2)
l = float(len(snap))
snap_off = np.sum(snap) / l
snap_amp = np.sum(abs(snap - snap_off)) / l
result[0] = snap_off
result[1] = snap_amp
for core in range(4):
# This will actually sample the cores in the order A,C,B,D
# index will fix this up when data is put in the result array
index = (3, 9, 6, 12)[core]
c = snap[core::4]
l = float(len(c))
off = np.sum(c) / l
result[index] = (-off) * 500.0 / 256.0
#result[index] = (snap_off-off)*500.0/256.0
amp = np.sum(abs(c - off)) / l
result[index + 1] = 100.0 * (snap_amp - amp) / snap_amp
sum_result += result
sum_cnt += 1
sum_result /= sum_cnt
print "%.4f " * 15 % tuple(sum_result)
np.savetxt(fname, sum_result[3:], fmt="%8.4f")
def levels_hist(self, raw):
raw = np.array(adc5g.get_snapshot(self.roach, 'scope_raw_a0_snap'),
np.int32)
bins = np.arange(-128.5, 128.5, 1)
th = 32
lim1 = -th-0.5
lim2 = -0.5
lim3 = th-0.5
ideal_gauss = mlab.normpdf(bins, 0, th)
plt.subplot(111)
plt.plot((lim1,lim1), (0,1), 'k--')
plt.plot((lim2,lim2), (0,1), 'k--')
plt.plot((lim3,lim3), (0,1), 'k--')
plt.plot(bins, ideal_gauss, 'gray', linewidth=1)
plt.hist(raw, bins, normed=1, facecolor='blue',
alpha=0.9, histtype='stepfilled')
plt.xlim(-129, 128)
plt.ylim(0, 0.06)
plt.xlabel('ADC Value')
plt.ylabel('Normalized Count')
plt.title('zdok0 levels')
plt.grid()
plt.show()
def get_rms():
global zdok
save_zdok = zdok
set_zdok(0)
snap=adc5g.get_snapshot(roach2, snap_name, man_trig=True, wait_period=2)
rmsSnap = np.std(snap)
loadingFactor = -20.0*math.log10(128/rmsSnap)
print "If0 Rms = %f, loading factor = %f" % (rmsSnap,loadingFactor)
set_zdok(1)
snap=adc5g.get_snapshot(roach2, snap_name, man_trig=True, wait_period=2)
rmsSnap = np.std(snap)
loadingFactor = -20.0*math.log10(128/rmsSnap)
print "If1 Rms = %f, loading factor = %f" % (rmsSnap,loadingFactor)
if zdok != save_zdok:
set_zdok(save_zdok)
def hist_from_snapshots(rpt = 10):
# hist_all = np.zeros(256,dtype=int)
hist1 = np.zeros(256,dtype=int)
hist2 = np.zeros(256,dtype=int)
hist3 = np.zeros(256,dtype=int)
hist4 = np.zeros(256,dtype=int)
for i in range(rpt):
snap=adc5g.get_snapshot(roach2, snap_name, man_trig=True, wait_period=2)
snap = 128 + np.array(snap)
# hist = np.bincount(snap, minlength=256)
# hist_all += hist
hist = np.bincount(snap[0:: 4], minlength=256)
hist1 += hist
hist = np.bincount(snap[1:: 4], minlength=256)
hist2 += hist
hist = np.bincount(snap[2:: 4], minlength=256)
hist3 += hist
hist = np.bincount(snap[3:: 4], minlength=256)
hist4 += hist
data=np.column_stack((np.arange(-128., 128, dtype=int), hist1, hist2,
hist3, hist4))
np.savetxt("hist_cores", data, fmt=("%d"))
# print "all ",np.sum(hist_all[0:128]), np.sum(hist_all[128:256])
print "core a ",np.sum(hist1[0:128]), np.sum(hist1[129:256])
print "core b ",np.sum(hist3[0:128]), np.sum(hist3[129:256])
print "core c ",np.sum(hist2[0:128]), np.sum(hist2[129:256])
print "core d ",np.sum(hist4[0:128]), np.sum(hist4[129:256])
def setUpClass(cls):
TestBase.setUpClass()
cls._sample_rate = cls._clk_rate * 2.
cls._tone_per = int(round(cls._sample_rate / cls._tone_freq))
cls._raw = adc5g.get_snapshot(cls._roach, 'scope_raw_%d_snap' % cls._zdok_n)
cls._raw = list(samp/128. for samp in cls._raw)
cls._bias = (cls._raw[0] + cls._raw[cls._tone_per/2])/2.
cls._amp = sqrt((cls._raw[0]-cls._bias)**2 + (cls._raw[cls._tone_per/4]-cls._bias)**2)
cls._phase = atan2((cls._raw[0]-cls._bias)/cls._amp, (cls._raw[125]-cls._bias)/cls._amp)
cls._fit = list(cls._amp*sin(t*2*pi/cls._tone_per + cls._phase) + cls._bias \
for t in range(len(cls._raw)))
def setUpClass(cls):
TestBase.setUpClass()
cls._sample_rate = cls._clk_rate * 2.0
cls._tone_per = int(round(cls._sample_rate / cls._tone_freq))
cls._raw = adc5g.get_snapshot(cls._roach, "raw_%d" % cls._zdok_n)
cls._raw = list(samp - 128 for samp in cls._raw)
cls._bias = (cls._raw[0] + cls._raw[cls._tone_per / 2]) / 2.0
cls._amp = sqrt((cls._raw[0] - cls._bias) ** 2 + (cls._raw[cls._tone_per / 4] - cls._bias) ** 2)
cls._phase = atan2((cls._raw[0] - cls._bias) / cls._amp, (cls._raw[125] - cls._bias) / cls._amp)
cls._fit = list(
cls._amp * sin(t * 2 * pi / cls._tone_per + cls._phase) + cls._bias for t in range(len(cls._raw))
)
def get_snap(self, chan, freq): #MHz
"""
Takes a snap shot on channel 'chan' of a cw tone
of frequency 'freq' (MHz)
"""
if freq != None:
# self.syn.output_on()
self.syn.set_freq(freq * 1e6)
time.sleep(1)
# reg = self.get_channel_snap_reg(chan)
raw = array(adc5g.get_snapshot(self.roach, 'snap%i' % (chan)))
return raw
def dosnap(fr=0, name=None, rpt=1, donot_clear=False, plot=True):
"""
Takes a snapshot and uses fit_cores to fit a sine function to each
core separately assuming a CW signal is connected to the input. The
offset, gain and phase differences are reoprted for each core as
well as the average of all four.
The parameters are:
fr The frequency of the signal generator. It will default to the last
frequency set by set_freq()
name the name of the file into which the snapshot is written. 5 other
files are written. Name.c1 .. name.c4 contain themeasurements from
cores a, b, c and d. Note that data is taken from cores in the order
a, c, b, d. A line is appended to the file name.fit containing
signal freq, average zero, average amplitude followed by triplets
of zero, amplitude and phase differences for cores a, b, c and d
name defaults to if0 or if1, depending on the current zdok
rpt The number of repeats. Defaults to 1. The c1 .. c4 files mentioned
above are overwritten with each repeat, but new rows of data are added
to the .fit file for each pass.
"""
global freq
if name == None:
name = "if%d" % (zdok)
avg_pwr_sinad = 0
if fr == 0:
fr = freq
for i in range(rpt):
snap = adc5g.get_snapshot(roach2,
snap_name,
man_trig=True,
wait_period=2)
if (plot):
plt.clf()
plt.plot(snap)
plt.show(block=False)
rmsSnap = np.std(snap)
loadingFactor = -20.0 * math.log10(128 / rmsSnap)
print "Rms = %f, loading factor = %f" % (rmsSnap, loadingFactor)
if i == rpt - 1:
np.savetxt(name, snap, fmt='%d')
ogp, pwr_sinad = fit_cores.fit_snap(snap, fr, samp_freq, name,\
clear_avgs = ((i == 0) and not donot_clear), prnt = (i == rpt-1))
avg_pwr_sinad += pwr_sinad
return ogp, avg_pwr_sinad / rpt
def get_snap(self, chan, freq): #MHz
"""
Takes a snap shot on channel 'chan' of a cw tone
of frequency 'freq' (MHz)
"""
# if freq != None:
# self.syn.output_on()
# self.syn.set_freq(freq*1e6)
# time.sleep(1)
reg = self.get_channel_snap_reg(chan)
raw = array(adc5g.get_snapshot(self.roach, reg))
return raw, freq
def dosnap(fr=0, name=None, rpt = 1, donot_clear=False, plot=True):
"""
Takes a snapshot and uses fit_cores to fit a sine function to each
core separately assuming a CW signal is connected to the input. The
offset, gain and phase differences are reoprted for each core as
well as the average of all four.
The parameters are:
fr The frequency of the signal generator. It will default to the last
frequency set by set_freq()
name the name of the file into which the snapshot is written. 5 other
files are written. Name.c1 .. name.c4 contain themeasurements from
cores a, b, c and d. Note that data is taken from cores in the order
a, c, b, d. A line is appended to the file name.fit containing
signal freq, average zero, average amplitude followed by triplets
of zero, amplitude and phase differences for cores a, b, c and d
name defaults to if0 or if1, depending on the current zdok
rpt The number of repeats. Defaults to 1. The c1 .. c4 files mentioned
above are overwritten with each repeat, but new rows of data are added
to the .fit file for each pass.
"""
global freq
if name == None:
name = "if%d" % (zdok)
avg_pwr_sinad = 0
if fr == 0:
fr = freq
for i in range(rpt):
snap=adc5g.get_snapshot(roach2, snap_name, man_trig=True, wait_period=2)
if(plot):
plt.clf()
plt.plot(snap)
plt.show(block = False)
rmsSnap = np.std(snap)
loadingFactor = -20.0*math.log10(128/rmsSnap)
print "Rms = %f, loading factor = %f" % (rmsSnap,loadingFactor)
if i == rpt-1:
np.savetxt(name, snap,fmt='%d')
ogp, pwr_sinad = fit_cores.fit_snap(snap, fr, samp_freq, name,\
clear_avgs = ((i == 0) and not donot_clear), prnt = (i == rpt-1))
avg_pwr_sinad += pwr_sinad
return ogp, avg_pwr_sinad/rpt
def dopsdcores(nfft=None, rpt=10):
if nfft == None:
nfft = numpoints / 4
for i in range(rpt):
snap = adc5g.get_snapshot(roach2,
snap_name,
man_trig=True,
wait_period=2)
power, freqs = mlab.psd(snap, nfft*4, Fs=samp_freq*1e6, detrend=mlab.detrend_mean, \
scale_by_freq=False)
if i == 0:
psd_all = power[:1 + nfft / 2]
else:
psd_all += power[:1 + nfft / 2]
power, freqs = mlab.psd(snap[0:: 4], nfft, Fs=samp_freq*.25e6, detrend=mlab.detrend_mean,\
scale_by_freq=True)
if i == 0:
psd1 = power
else:
psd1 += power
power, freqs = mlab.psd(snap[1:: 4], nfft, Fs=samp_freq*.25e6, detrend=mlab.detrend_mean,\
scale_by_freq=True)
if i == 0:
psd2 = power
else:
psd2 += power
power, freqs = mlab.psd(snap[2:: 4], nfft, Fs=samp_freq*.25e6, detrend=mlab.detrend_mean,\
scale_by_freq=True)
if i == 0:
psd3 = power
else:
psd3 += power
power, freqs = mlab.psd(snap[3:: 4], nfft, Fs=samp_freq*.25e6, detrend=mlab.detrend_mean,\
scale_by_freq=True)
if i == 0:
psd4 = power
else:
psd4 += power
data = np.column_stack((freqs*1e-6, 10*np.log10(psd_all/rpt), \
10*np.log10(psd1/rpt), \
10*np.log10(psd2/rpt), 10*np.log10(psd3/rpt), 10*np.log10(psd4/rpt)))
np.savetxt("psd_cores", data, fmt=('%7.2f'))
def setUpClass(cls):
TestBase.setUpClass()
cls._sample_rate = cls._clk_rate * 2.
cls._tone_per = int(round(cls._sample_rate / cls._tone_freq))
#cls._raw = adc5g.get_snapshot(cls._roach, 'delay_fifo_snap', False, 0 )
cls._raw2 = adc5g.get_snapshot(cls._roach, 'snap', False, 0)
#cls._raw = list(samp for samp in cls._raw)
cls._raw2 = list(samp for samp in cls._raw2)
#print "THE AMPLITUDE: ", cls._raw
print "THE AMPLITUDE2: ", cls._raw2
#cls._bias = (cls._raw[0] + cls._raw[cls._tone_per/2])/2.
#cls._amp = sqrt((cls._raw[0]-cls._bias)**2 + (cls._raw[cls._tone_per/4]-cls._bias)**2)
#cls._phase = atan2((cls._raw[0]-cls._bias)/cls._amp, (cls._raw[125]-cls._bias)/cls._amp)
#cls._fit = list(cls._amp*sin(t*2*pi/cls._tone_per + cls._phase) + cls._bias \
#for t in range(len(cls._raw)))
cls._roach.write_int('delay_fifo_enable', 0)
"""cls._roach.write_int('en_tri', 1)
def og_from_noise(fname=None, rpt=100, printEach=False):
"""
Take a number of snapshots of noise. Analyze for offset and gain
for each core separately.
"""
global ogp_name
if fname == None:
fname = ogp_name + ".meas"
sum_result = np.zeros((15), dtype=float)
sum_cnt = 0
for n in range(rpt):
result = np.zeros((15), dtype=float)
snap = adc5g.get_snapshot(roach2,
snap_name,
man_trig=True,
wait_period=2)
if (rpt == 1):
np.savetxt("t.og_noise", snap, fmt='%d')
l = float(len(snap))
snap_off = np.sum(snap) / l
snap_amp = np.sum(abs(snap - snap_off)) / l
result[0] = snap_off * (-500.0 / 256.0)
result[1] = snap_amp
for core in range(4):
# This will actually sample the cores in the order A,C,B,D
# index will fix this up when data is put in the result array
index = (3, 9, 6, 12)[core]
c = snap[core::4]
l = float(len(c))
off = np.sum(c) / l
result[index] = off * (-500.0 / 256.0)
amp = np.sum(abs(c - off)) / l
result[index + 1] = 100.0 * (snap_amp - amp) / snap_amp
sum_result += result
sum_cnt += 1
if printEach:
print "%.4f " * 15 % tuple(result)
sum_result /= sum_cnt
print "%.4f " * 15 % tuple(sum_result)
np.savetxt(fname, sum_result[3:], fmt="%8.4f")
def dopsdcores(nfft = None, rpt = 10):
if nfft == None:
nfft = numpoints/4
for i in range(rpt):
snap=adc5g.get_snapshot(roach2, snap_name, man_trig=True, wait_period=2)
power, freqs = mlab.psd(snap, nfft*4, Fs=samp_freq*1e6, detrend=mlab.detrend_mean, \
scale_by_freq=False)
if i == 0:
psd_all = power[:1+nfft/2]
else:
psd_all += power[:1+nfft/2]
power, freqs = mlab.psd(snap[0:: 4], nfft, Fs=samp_freq*.25e6, detrend=mlab.detrend_mean,\
scale_by_freq=True)
if i == 0:
psd1 = power
else:
psd1 += power
power, freqs = mlab.psd(snap[1:: 4], nfft, Fs=samp_freq*.25e6, detrend=mlab.detrend_mean,\
scale_by_freq=True)
if i == 0:
psd2 = power
else:
psd2 += power
power, freqs = mlab.psd(snap[2:: 4], nfft, Fs=samp_freq*.25e6, detrend=mlab.detrend_mean,\
scale_by_freq=True)
if i == 0:
psd3 = power
else:
psd3 += power
power, freqs = mlab.psd(snap[3:: 4], nfft, Fs=samp_freq*.25e6, detrend=mlab.detrend_mean,\
scale_by_freq=True)
if i == 0:
psd4 = power
else:
psd4 += power
data = np.column_stack((freqs*1e-6, 10*np.log10(psd_all/rpt), \
10*np.log10(psd1/rpt), \
10*np.log10(psd2/rpt), 10*np.log10(psd3/rpt), 10*np.log10(psd4/rpt)))
np.savetxt("psd_cores", data, fmt=('%7.2f'))
raise RuntimeError(msg)
Fs = 2*2048e6 # R2DBE sampling freq
Nif = 2 # R2DBE typically 2 IFs
Ninteg = 1 # integrate this many ADC snapshots
if len(sys.argv)==2:
Ninteg = int(sys.argv[1])
Nsamp = [0]*Nif
Lfft = [0]*Nif
data = [None]*Nif
for ii in range(Ninteg):
for ifnr in range(Nif):
data8 = adc5g.get_snapshot(roach2, 'r2dbe_snap_8bit_%d_data' % (ifnr))
Lfft[ifnr] = len(data8)
Nsamp[ifnr] = Nsamp[ifnr] + Lfft[ifnr]
if data[ifnr]==None:
data[ifnr] = data8
else:
# data[ifnr] = data[ifnr] + data8
data[ifnr] = [data[ifnr][n]+data8[n] for n in range(len(data8))]
print ' Int %d/%d : ADC %d snapshot of 8-bit data, got %d samples' % (ii+1,Ninteg,ifnr,len(data8))
for ifnr in range(Nif):
subplot(Nif,1,(ifnr+1))
plotSpectrum(data[ifnr], Fs, 'ADC %d' % (ifnr))
gcf().set_facecolor('white')
show()
def get_data():
#get the data...
hola = numpy.array(adc5g.get_snapshot(fpga, "snapshot0"))
hola1 = numpy.array(adc5g.get_snapshot(fpga, "snapshot1"))
# raw_1 = numpy.array(adc5g.get_snapshot(fpga, "raw_1"))
return hola, hola1
parser.add_argument('-v','--verbose',action='count',
help="control verbosity, use multiple times for more detailed output")
parser.add_argument('host',metavar='R2DBE',type=str,nargs='?',default='r2dbe-1',
help="hostname or ip address of r2dbe (default is 'r2dbe-1')")
args = parser.parse_args()
# connect to roach2
roach2 = corr.katcp_wrapper.FpgaClient(args.host)
if not roach2.wait_connected(timeout=args.timeout):
msg = "Could not establish connection to '{0}' within {1} seconds, aborting".format(
args.host,args.timeout)
raise RuntimeError(msg)
if args.verbose > 1:
print "connected to '{0}'".format(args.host)
for ii in ['0','1']:
# grab snapshot of 8 bit data from each input
if args.verbose > 2:
print "get snapshot form 'r2dbe_snap_8bit_{0}_data'".format(ii)
x8 = adc5g.get_snapshot(roach2,'r2dbe_snap_8bit_'+ii+'_data')
th = get_th_16_84(x8)
if args.verbose > 0:
print "threshold {0} = {1}".format(ii,th)
# write threshold to FPGA
if args.verbose > 2:
print "write threshold to 'r2dbe_quantize_{0}_thresh'".format(ii)
roach2.write_int('r2dbe_quantize_'+ii+'_thresh', th)
def cplanecmd(cmd):
af, socktype, proto, canonname, sa = socket_res
s = socket.socket(af,socktype, proto)
s.connect(sa)
s.sendall(cmd + ';') # extra ';' will not matter
ret = s.recv(8192).strip()
s.close()
return ret
recstate = cplanecmd('record?;')
if "recording" in recstate:
sys.exit('currently recording do not run!')
if hascorr:
roach2 = corr.katcp_wrapper.FpgaClient(r2_hostname)
roach2.wait_connected()
x0 = np.array(adc5g.get_snapshot(roach2, 'r2dbe_snap_8bit_0_data'))
x1 = np.array(adc5g.get_snapshot(roach2, 'r2dbe_snap_8bit_1_data'))
th0 = roach2.read_int('r2dbe_quantize_0_thresh')
th1 = roach2.read_int('r2dbe_quantize_1_thresh')
class R2EpochIsCorrect(unittest.TestCase):
def test(self):
utcnow = datetime.utcnow()
epoch = 2*(utcnow.year - 2000) + (utcnow.month > 6)
self.assertEqual(epoch, roach2.read_int('r2dbe_vdif_0_hdr_w1_ref_ep'), "epoch set in R2DBE startup script")
self.assertEqual(epoch, roach2.read_int('r2dbe_vdif_1_hdr_w1_ref_ep'), "epoch set in R2DBE startup script")
# class R2SecondsAreCorrect(unittest.TestCase):
# def test(self):
# utcnow = datetime.utcnow()
# wait = (1500000 - utcnow.microsecond) % 1e6 # get to 0.5s boundary
# OFFSET - GAIN - PHASE Calibration
print 'Doing OGP calibration...'
#ogp, sinad = rww_tools.dosnap(fr=opts.testfreq,name=FNAME,rpt=opts.n_trials,donot_clear=False)
# calibration parameters
rpt = 30 # repetitions until set of offset, ganancia y fase
snap_name = 'snapshot1' # ADC snapshot name
samp_freq = frec_samp # ADC interleave mode sample rate
FNAME = 'snapshot_adc1_raw.dat'
avg_pwr_sinad = 0
fr = opts.testfreq
donot_clear = False
for i in range(rpt):
snap = adc.get_snapshot(fpga, snap_name, man_trig=True, wait_period=2)
np.savetxt(FNAME, snap, fmt='%d')
ogp, pwr_sinad = fit_cores.fit_snap(fr, samp_freq, FNAME,\
clear_avgs = i == 0 and not donot_clear, prnt = i == rpt-1)
avg_pwr_sinad += pwr_sinad
sinad = avg_pwr_sinad / rpt
ogp = ogp[3:]
np.savetxt('ogp', ogp, fmt='%8.4f')
# print registers
print 'OGP registers: '
rww_tools.get_ogp()
help ='Interleave data from pairs of ADC cores (even the cores not used in the downstream firmware')
args = parser.parse_args()
print "Connecting to %s" % args.host
snap = casperfpga.CasperFpga(args.host)
print "Interpretting design data for %s with %s" % (args.host, args.fpgfile)
snap.get_system_information(args.fpgfile)
chani = []
chanq = []
speci = []
specq = []
for i in range(args.numsnaps):
print "Grabbing ADC data (%d of %d)" % (i+1, args.numsnaps)
all_chan_data = adc5g.get_snapshot(snap, 'ss_adc')
# Separate data into multiple channels
if args.interleave:
chani = [all_chan_data[0::2]]
chanq = [all_chan_data[1::2]]
else:
chani = [all_chan_data[0::2][0::2]]
chanq = [all_chan_data[1::2][0::2]]
speci += [np.abs(np.fft.rfft(chani[-1]))**2]
specq += [np.abs(np.fft.rfft(chanq[-1]))**2]
time.sleep(0.2)
chani = np.array(chani)
chanq = np.array(chanq)
speci = np.array(speci)
type=str,
nargs='?',
default='r2dbe-1',
help="hostname or ip address of r2dbe (default is 'r2dbe-1')")
args = parser.parse_args()
# connect to roach2
roach2 = corr.katcp_wrapper.FpgaClient(args.host)
if not roach2.wait_connected(timeout=args.timeout):
msg = "Could not establish connection to '{0}' within {1} seconds, aborting".format(
args.host, args.timeout)
raise RuntimeError(msg)
if args.verbose > 1:
print "connected to '{0}'".format(args.host)
for ii in ['0', '1']:
# grab snapshot of 8 bit data from each input
if args.verbose > 2:
print "get snapshot form 'r2dbe_snap_8bit_{0}_data'".format(ii)
x8 = adc5g.get_snapshot(roach2, 'r2dbe_snap_8bit_' + ii + '_data')
th = get_th_16_84(x8)
if args.verbose > 0:
print "threshold {0} = {1}".format(ii, th)
# write threshold to FPGA
if args.verbose > 2:
print "write threshold to 'r2dbe_quantize_{0}_thresh'".format(ii)
roach2.write_int('r2dbe_quantize_' + ii + '_thresh', th)
# reverse time order (per vdif spec)
roach2.write_int('r2dbe_vdif_0_reorder_2b_samps', 1)
roach2.write_int('r2dbe_vdif_1_reorder_2b_samps', 1)
# set to test-vector noise mode
roach2.write_int('r2dbe_quantize_0_thresh', 12)
roach2.write_int('r2dbe_quantize_1_thresh', 12)
# JanW: -7 dBm over 0-1.5 GHz band input to R2DBE
# thresh=16 produces 10%:41%:41%:8%
# thresh=12 produces 16%:35%:35%:14%
# set threshold automatically
# get snapshot of 8-bit data
print 'Setting 8-bit -> 2-bit quantization threshold levels...'
x8_0 = adc5g.get_snapshot(roach2, 'r2dbe_snap_8bit_0_data')
L = len(x8_0)
print 'Auto-thresholding input 0 using %d samples' % (L)
y = sorted(x8_0)
Lt = int(L * 0.16)
th1 = abs(y[Lt - 1])
Lt2 = int(L * 0.84)
th2 = abs(y[Lt2 - 1])
th = (th1 + th2) / 2
roach2.write_int('r2dbe_quantize_0_thresh', th)
print 'r2dbe_quantize_0_thresh : %d' % (th)
x8_1 = adc5g.get_snapshot(roach2, 'r2dbe_snap_8bit_1_data')
L = len(x8_1)
print 'Auto-thresholding input 1 using %d samples' % (L)
y = sorted(x8_1)
# OFFSET - GAIN - PHASE Calibration
print "Doing OGP calibration..."
# ogp, sinad = rww_tools.dosnap(fr=opts.testfreq,name=FNAME,rpt=opts.n_trials,donot_clear=False)
# calibration parameters
rpt = 30 # repetitions until set of offset, ganancia y fase
snap_name = "snapshot1" # ADC snapshot name
samp_freq = frec_samp # ADC interleave mode sample rate
FNAME = "snapshot_adc1_raw.dat"
avg_pwr_sinad = 0
fr = opts.testfreq
donot_clear = False
for i in range(rpt):
snap = adc.get_snapshot(fpga, snap_name, man_trig=True, wait_period=2)
np.savetxt(FNAME, snap, fmt="%d")
ogp, pwr_sinad = fit_cores.fit_snap(
fr, samp_freq, FNAME, clear_avgs=i == 0 and not donot_clear, prnt=i == rpt - 1
)
avg_pwr_sinad += pwr_sinad
sinad = avg_pwr_sinad / rpt
ogp = ogp[3:]
np.savetxt("ogp", ogp, fmt="%8.4f")
# print registers
print "OGP registers: "
rww_tools.get_ogp()
def get_data():
#get the data...
hola = numpy.array(adc5g.get_snapshot(fpga, "snapshot0"))
hola1 = numpy.array(adc5g.get_snapshot(fpga, "snapshot1"))
# raw_1 = numpy.array(adc5g.get_snapshot(fpga, "raw_1"))
return hola, hola1