def _read(self, drain):
"Read from the file descriptor"
fd = self.fileno()
buf = os.read(fd, 4096)
if not buf and not self._buf:
return None
if "\n" not in buf:
if not drain:
self._buf += buf
return []
# prepend any data previously read, then split into lines and format
buf = self._buf + buf
if "\n" in buf:
tmp, rest = buf.rsplit("\n", 1)
else:
tmp = buf
rest = None
self._buf = rest
now = datetime.datetime.now().isoformat()
rows = tmp.split("\n")
self._rows += [(now, "%s %s:%s" % (self._name, now, r), r) for r in rows]
for idx in range(self._lastidx, len(self._rows)):
iflogger.info(self._rows[idx][1])
self._lastidx = len(self._rows)
def _process(drain=0):
try:
res = select.select(streams, [], [], timeout)
except select.error, e:
iflogger.info(str(e))
if e[0] == errno.EINTR:
return
else:
raise
def _gen_regress(self, i_onsets, i_durations, i_amplitudes, nscans):
"""Generates a regressor for a sparse/clustered-sparse acquisition
"""
bplot = False
if isdefined(self.inputs.save_plot) and self.inputs.save_plot:
bplot=True
import matplotlib
matplotlib.use(config.get("execution", "matplotlib_backend"))
import matplotlib.pyplot as plt
TR = np.round(self.inputs.time_repetition*1000) # in ms
if self.inputs.time_acquisition:
TA = np.round(self.inputs.time_acquisition*1000) # in ms
else:
TA = TR # in ms
nvol = self.inputs.volumes_in_cluster
SCANONSET = np.round(self.inputs.scan_onset*1000)
total_time = TR*(nscans-nvol)/nvol + TA*nvol + SCANONSET
SILENCE = TR-TA*nvol
dt = TA/10.;
durations = np.round(np.array(i_durations)*1000)
if len(durations) == 1:
durations = durations*np.ones((len(i_onsets)))
onsets = np.round(np.array(i_onsets)*1000)
dttemp = gcd(TA, gcd(SILENCE, TR))
if dt < dttemp:
if dttemp % dt != 0:
dt = gcd(dttemp, dt)
if dt < 1:
raise Exception("Time multiple less than 1 ms")
iflogger.info("Setting dt = %d ms\n" % dt)
npts = int(total_time/dt)
times = np.arange(0, total_time, dt)*1e-3
timeline = np.zeros((npts))
timeline2 = np.zeros((npts))
if isdefined(self.inputs.model_hrf) and self.inputs.model_hrf:
hrf = spm_hrf(dt*1e-3)
reg_scale = 1.0
if self.inputs.scale_regressors:
boxcar = np.zeros((50.*1e3/dt))
if self.inputs.stimuli_as_impulses:
boxcar[1.*1e3/dt] = 1.0
reg_scale = float(TA/dt)
else:
boxcar[1.*1e3/dt:2.*1e3/dt] = 1.0
if isdefined(self.inputs.model_hrf) and self.inputs.model_hrf:
response = np.convolve(boxcar, hrf)
reg_scale = 1./response.max()
iflogger.info('response sum: %.4f max: %.4f'%(response.sum(), response.max()))
iflogger.info('reg_scale: %.4f'%reg_scale)
for i, t in enumerate(onsets):
idx = int(t/dt)
if i_amplitudes:
if len(i_amplitudes)>1:
timeline2[idx] = i_amplitudes[i]
else:
timeline2[idx] = i_amplitudes[0]
else:
timeline2[idx] = 1
if bplot:
plt.subplot(4, 1, 1)
plt.plot(times, timeline2)
if not self.inputs.stimuli_as_impulses:
if durations[i] == 0:
durations[i] = TA*nvol
stimdur = np.ones((int(durations[i]/dt)))
timeline2 = np.convolve(timeline2, stimdur)[0:len(timeline2)]
timeline += timeline2
timeline2[:] = 0
if bplot:
plt.subplot(4, 1, 2)
plt.plot(times, timeline)
if isdefined(self.inputs.model_hrf) and self.inputs.model_hrf:
timeline = np.convolve(timeline, hrf)[0:len(timeline)]
if isdefined(self.inputs.use_temporal_deriv) and self.inputs.use_temporal_deriv:
#create temporal deriv
timederiv = np.concatenate(([0], np.diff(timeline)))
if bplot:
plt.subplot(4, 1, 3)
plt.plot(times, timeline)
if isdefined(self.inputs.use_temporal_deriv) and self.inputs.use_temporal_deriv:
plt.plot(times, timederiv)
# sample timeline
timeline2 = np.zeros((npts))
reg = []
regderiv = []
for i, trial in enumerate(np.arange(nscans)/nvol):
scanstart = int((SCANONSET + trial*TR + (i%nvol)*TA)/dt)
#print total_time/dt, SCANONSET, TR, TA, scanstart, trial, i%2, int(TA/dt)
scanidx = scanstart+np.arange(int(TA/dt))
timeline2[scanidx] = np.max(timeline)
reg.insert(i, np.mean(timeline[scanidx])*reg_scale)
if isdefined(self.inputs.use_temporal_deriv) and self.inputs.use_temporal_deriv:
regderiv.insert(i, np.mean(timederiv[scanidx])*reg_scale)
if isdefined(self.inputs.use_temporal_deriv) and self.inputs.use_temporal_deriv:
iflogger.info('orthoganlizing derivative w.r.t. main regressor')
regderiv = orth(reg, regderiv)
if bplot:
plt.subplot(4, 1, 3)
plt.plot(times, timeline2)
plt.subplot(4, 1, 4)
plt.bar(np.arange(len(reg)), reg, width=0.5)
plt.savefig('sparse.png')
plt.savefig('sparse.svg')
if regderiv:
return [reg, regderiv]
else:
return reg