def hlsvdfilt_highbw(mrs_data, nc, vals):
print(vals)
sig_cx = mrs_data.raw_cx
#create new window-----------
fit = np.zeros_like(sig_cx)
for n in vals:
observed = sig_cx[n][0:8192]
step_size = 1000 / mrs_data.bw
# Call hlsvd()
results = hlsvd.hlsvd(observed, nc, step_size)
nsv_found, singular_values, frequencies, \
damping_factors, amplitudes, phases = results
estimated = _create_hlsvd_sum_fid(frequencies,
damping_factors, amplitudes, phases,
len(observed), step_size)
# We're only concerned with the real portion of the data
sig_cx[n][0:8192] = observed - estimated
fit[n][0:8192] = estimated
# The observed values are always noisier, so plotting them first allows
# the cleaner estimation to be displayed on top.
return (sig_cx, fit)
def run (self):
# 1. read data from the socket received from the client until message 'FINISHED' is received
try:
print('waiting for data' )
order = read_string(self.client)
while order != 'FINISHED':
print(order)
if order == 'writeData':
print("real data")
realdata = read_1DArrayDouble(self.client)
print(realdata)
print("imag data")
imagdata = read_1DArrayDouble(self.client)
print(imagdata)
print("nsv_sought")
nsv_sought = read_int(self.client)
print(nsv_sought)
print("dwell_time")
dwell_time = read_float(self.client)
print(dwell_time)
print("m")
m = read_int(self.client)
print(m)
order = read_string(self.client)
if order == 'run':
data = realdata + 1j * imagdata
npts = len(data)
result = hlsvdpro.hlsvd(data, nsv_sought, dwell_time)
nsv_found, singvals, freq, damp, ampl, phas = result
print(nsv_found)
write_int(self.client, nsv_found)
write_1DArrayDouble(self.client,singvals)
write_1DArrayDouble(self.client,freq)
write_1DArrayDouble(self.client,damp)
write_1DArrayDouble(self.client,ampl)
write_1DArrayDouble(self.client,phas)
fid = hlsvdpro.create_hlsvd_fids(result, npts, dwell_time, sum_results=True, convert=False)
# chop = ((((np.arange(len(fid)) + 1) % 2) * 2) - 1)
# dat = data * chop
# fit = fid * chop
# dat[0] *= 0.5
# fit[0] *= 0.5
print(data - fid)
# plt.plot(np.fft.fft(dat).real, color='r')
# plt.plot(np.fft.fft(fit).real, color='b')
# plt.plot(np.fft.fft(dat - fit).real, color='g')
# plt.show()
except Exception as err: # except ConnectionResetError: in python 3...
print('Exception caught: %s\nClosing...' % err)
# ends connection with the client, waits for a need connection (client = sock.accept()[0] )
print ('thread closing\n')
self.client.close()
count.nClients-=1
def hlsvdquant_highbw(mrs_data, nc, vals):
print(vals)
# sig_cx=np.real(mrs_data.raw_cx)+1j*np.imag(mrs_data.raw_cx)
sig_cx = np.abs(mrs_data.raw_cx) + 0j
#create new window-----------
fit = np.zeros_like(sig_cx)
h2o_amp = np.zeros(shape=mrs_data.nsa) + 0j
for n in vals:
nc0 = nc
observed = sig_cx[n][2:mrs_data.pts_orig]
step_size = 1000 / mrs_data.bw
while nc0 > 0:
# Call hlsvd()
results = hlsvd.hlsvd(observed, nc0, step_size)
nsv_found, singular_values, frequencies, \
damping_factors, amplitudes, phases = results
if max(damping_factors) > -7.96:
nc0 = nc0 - 1
continue
else:
estimated = _create_hlsvd_sum_fid(frequencies, damping_factors,
amplitudes, phases,
len(observed), step_size)
# We're only concerned with the real portion of the data
sig_cx[n][2:mrs_data.pts_orig] = observed - estimated
fit[n][2:mrs_data.pts_orig] = estimated
# print('SPEC ['+str(n)+']: '+str(nc0)+' COMPONENTS, '+str(abs(fit[n][3])))
print(str(abs(fit[n][3])))
h2o_amp[n] = fit[n][3]
break
# The observed values are always noisier, so plotting them first allows
# the cleaner estimation to be displayed on top.
return (h2o_amp, fit)
imagdata = read_1DArrayDouble(client)
print(imagdata)
print("nsv_sought")
nsv_sought = read_int(client)
print(nsv_sought)
print("dwell_time")
dwell_time = read_float(client)
print(dwell_time)
print("m")
m = read_int(client)
print(m)
order = read_string(client)
if order == 'run':
data = realdata + 1j * imagdata
npts = len(data)
result = hlsvdpro.hlsvd(data, nsv_sought, dwell_time)
nsv_found, singvals, freq, damp, ampl, phas = result
print(nsv_found)
write_int(client, nsv_found)
write_1DArrayDouble(client, singvals)
write_1DArrayDouble(client, freq)
write_1DArrayDouble(client, damp)
write_1DArrayDouble(client, ampl)
write_1DArrayDouble(client, phas)
# print("np.allclose(freq, indat['freq0']) = ", np.allclose(freq, freq0))
# fid = hlsvdpro.create_hlsvd_fids(result, npts, dwell_time, sum_results=True, convert=False)
#
# chop = ((((np.arange(len(fid)) + 1) % 2) * 2) - 1)
# dat = data * chop