def run(self):
'''Triggered at Run
'''
self.total_times = []
self.timestamp = []
self.stimmed = []
self.frametimes = []
with RunManager(self.name, self.runAcquirer, self.setup, self.q_sig,
self.q_comm) as rm:
print(rm)
self.imgs = np.array(self.saveArray)
f = h5py.File('output/sample_stream.h5', 'w', libver='latest')
f.create_dataset("default", data=self.imgs)
f.close()
np.savetxt('output/stimmed.txt', np.array(self.stimmed))
np.savetxt('output/timing/frametimes.txt', np.array(self.frametimes))
print('Acquisition complete, avg time per frame: ',
np.mean(self.total_times))
print('Acquire got through ', self.frame_num, ' frames')
np.savetxt('output/timing/acquire_frame_time.txt', self.total_times)
np.savetxt('output/timing/acquire_timestamp.txt', self.timestamp)
def run(self):
self.total_times = []
self.puttime = []
self.colortime = []
self.stimtime = []
self.timestamp = []
self.LL = []
with RunManager(self.name, self.runStep, self.setup, self.q_sig,
self.q_comm) as rm:
logger.info(rm)
print('Analysis broke, avg time per frame: ',
np.mean(self.total_times, axis=0))
print('Analysis broke, avg time per put analysis: ',
np.mean(self.puttime))
print('Analysis broke, avg time per color frame: ',
np.mean(self.colortime))
print('Analysis broke, avg time per stim avg: ',
np.mean(self.stimtime))
print('Analysis got through ', self.frame, ' frames')
N = self.p["numNeurons"]
np.savetxt('output/model_weights.txt', self.theta[:N * N].reshape(
(N, N)))
np.savetxt('output/timing/analysis_frame_time.txt',
np.array(self.total_times))
np.savetxt('output/timing/analysis_timestamp.txt',
np.array(self.timestamp))
np.savetxt('output/analysis_estsAvg.txt', np.array(self.estsAvg))
np.savetxt('output/analysis_proc_S.txt', np.array(self.S))
np.savetxt('output/analysis_LL.txt', np.array(self.LL))
np.savetxt('output_snap/stims.txt', self.currStimID)
def run(self):
''' Run indefinitely. Calls runAcquirer after checking for signals
'''
self.total_times = []
self.timestamp = []
with RunManager(self.name, self.runAcquirer, self.setup, self.q_sig,
self.q_comm) as rm:
print(rm)
print('Done running Acquire, avg time per frame: ',
np.mean(self.total_times))
print('Acquire got through ', self.frame_num, ' frames')
if not os._exists('output'):
try:
os.makedirs('output')
except:
pass
if not os._exists('output/timing'):
try:
os.makedirs('output/timing')
except:
pass
np.savetxt('output/timing/acquire_frame_time.txt',
np.array(self.total_times))
np.savetxt('output/timing/acquire_timestamp.txt',
np.array(self.timestamp))
def run(self):
self.total_times = []
self.puttime = []
self.colortime = []
self.stimtime = []
self.timestamp = []
with RunManager(self.name, self.runAvg, self.setup, self.q_sig, self.q_comm) as rm:
logger.info(rm)
print('Analysis broke, avg time per frame: ', np.mean(self.total_times, axis=0))
print('Analysis broke, avg time per put analysis: ', np.mean(self.puttime))
print('Analysis broke, avg time per color frame: ', np.mean(self.colortime))
print('Analysis broke, avg time per stim avg: ', np.mean(self.stimtime))
print('Analysis got through ', self.frame, ' frames')
if not os._exists('output'):
try:
os.makedirs('output')
except:
pass
if not os._exists('output/timing'):
try:
os.makedirs('output/timing')
except:
pass
np.savetxt('output/timing/analysis_frame_time.txt', np.array(self.total_times))
np.savetxt('output/timing/analysisput_frame_time.txt', np.array(self.puttime))
np.savetxt('output/timing/analysiscolor_frame_time.txt', np.array(self.colortime))
np.savetxt('output/timing/analysis_timestamp.txt', np.array(self.timestamp))
def run(self):
'''
continually run the watcher to check all of the
input queues for objects to save
'''
with RunManager(self.name, self.watchrun, self.setup, self.q_sig,
self.q_comm) as rm:
logger.info(rm)
print('watcher saved ' + str(self.numSaved) + ' objects')
def run(self):
''' Run the processor continually on input frames
'''
self.fitframe_time = []
self.putAnalysis_time = []
self.procFrame_time = [] #aka t_motion
self.detect_time = []
self.shape_time = []
self.flag = False
self.total_times = []
self.timestamp = []
self.counter = 0
with RunManager(self.name, self.runProcess, self.setup, self.q_sig,
self.q_comm) as rm:
logger.info(rm)
print('Processor broke, avg time per frame: ',
np.mean(self.total_times, axis=0))
print('Processor got through ', self.frame_number, ' frames')
if not os._exists('output'):
try:
os.makedirs('output')
except:
pass
if not os._exists('output/timing'):
try:
os.makedirs('output/timing')
except:
pass
np.savetxt('output/timing/process_frame_time.txt',
np.array(self.total_times))
np.savetxt('output/timing/process_timestamp.txt',
np.array(self.timestamp))
np.savetxt('output/timing/putAnalysis_time.txt',
np.array(self.putAnalysis_time))
np.savetxt('output/timing/procFrame_time.txt',
np.array(self.procFrame_time))
self.shape_time = np.array(self.onAc.t_shapes)
self.detect_time = np.array(self.onAc.t_detect)
np.savetxt('output/timing/fitframe_time.txt',
np.array(self.fitframe_time))
np.savetxt('output/timing/shape_time.txt', self.shape_time)
np.savetxt('output/timing/detect_time.txt', self.detect_time)
def run(self):
self.total_times = []
self.puttime = []
self.colortime = []
self.stimtime = []
self.timestamp = []
with RunManager(self.name, self.runAvg, self.setup, self.q_sig, self.q_comm) as rm:
logger.info(rm)
print('Analysis broke, avg time per frame: ', np.mean(self.total_times, axis=0))
print('Analysis broke, avg time per put analysis: ', np.mean(self.puttime))
print('Analysis broke, avg time per color frame: ', np.mean(self.colortime))
print('Analysis broke, avg time per stim avg: ', np.mean(self.stimtime))
print('Analysis got through ', self.frame, ' frames')
np.savetxt('output/timing/analysis_frame_time.txt', np.array(self.total_times))
np.savetxt('output/timing/analysisput_frame_time.txt', np.array(self.puttime))
np.savetxt('output/timing/analysiscolor_frame_time.txt', np.array(self.colortime))
np.savetxt('output/timing/analysis_timestamp.txt', np.array(self.timestamp))
np.savetxt('output/final/analysis_tuning_curves.txt', np.array(self.polarAvg))
def run(self):
'''Run the processor continually on input frames
'''
self.fitframe_time = []
self.putAnalysis_time = []
self.procFrame_time = [] #aka t_motion
self.detect_time = []
self.shape_time = []
self.flag = False
self.total_times = []
self.timestamp = []
self.counter = 0
with RunManager(self.name, self.runProcess, self.setup, self.q_sig,
self.q_comm) as rm:
logger.info(rm)
print('Processor broke, avg time per frame: ',
np.mean(self.total_times, axis=0))
print('Processor got through ', self.frame_number, ' frames')
np.savetxt('output/timing/process_frame_time.txt',
np.array(self.total_times))
np.savetxt('output/timing/process_timestamp.txt',
np.array(self.timestamp))
self.shape_time = np.array(self.onAc.t_shapes)
self.detect_time = np.array(self.onAc.t_detect)
np.savetxt('output/timing/fitframe_time.txt',
np.array(self.fitframe_time))
np.savetxt('output/timing/shape_time.txt', self.shape_time)
np.savetxt('output/timing/detect_time.txt', self.detect_time)
np.savetxt('output/timing/putAnalysis_time.txt',
np.array(self.putAnalysis_time))
np.savetxt('output/timing/procFrame_time.txt',
np.array(self.procFrame_time))
# before = self.params['init_batch']
# nb = self.onAc.params.get('init', 'nb')
# np.savetxt('raw_C.txt', np.array(self.onAc.estimates.C_on[nb:self.onAc.M, before:self.frame_number+before]))
print('Number of times coords updated ', self.counter)
# with open('../S.pk', 'wb') as f:
# init = self.params['init_batch']
# S = np.stack([osi.s[init:] for osi in self.onAc.estimates.OASISinstances])
# print('--------Final S shape: ', S.shape)
# pickle.dump(S, f)
# with open('../A.pk', 'wb') as f:
# nb = self.onAc.params.get('init', 'nb')
# A = self.onAc.estimates.Ab[:, nb:]
# print(type(A))
# pickle.dump(A, f)
if self.onAc.estimates.OASISinstances is not None:
try:
init = self.params['init_batch']
S = np.stack([
osi.s[init:] for osi in self.onAc.estimates.OASISinstances
])
np.savetxt('output/end_spikes.txt', S)
except Exception as e:
logger.error('Exception {}: {} during frame number {}'.format(
type(e).__name__, e, self.frame_number))
print(traceback.format_exc())
else:
print('No OASIS')
self.coords1 = [o['CoM'] for o in self.coords]
print(self.coords1[0])
print('type ', type(self.coords1[0]))
np.savetxt('output/contours.txt', np.array(self.coords1))
def run(self):
with RunManager(self.name, self.run_acquirer, self.setup, self.q_sig,
self.q_comm) as rm:
print(rm)
def run(self):
''' Run continuously, waiting for input
'''
with RunManager(self.name, self.getInput, self.setup, self.q_sig,
self.q_comm) as rm:
logger.info(rm)