def doit(fname,options):
fname = sys.argv[1]
h5 = tables.openFile(fname,mode='r')
stroke_data=h5.root.stroke_data[:]
stroke_times = stroke_data['trigger_timestamp']
time_data=h5.root.time_data[:]
gain,offset,resids = easy_decode.get_gain_offset_resids(
input=time_data['framestamp'],
output=time_data['timestamp'])
top = h5.root.time_data.attrs.top
wordstream = h5.root.ain_wordstream[:]
wordstream = wordstream['word'] # extract into normal numpy array
r=easy_decode.easy_decode(wordstream,gain,offset,top)
if r is not None:
chans = r.dtype.fields.keys()
chans.sort()
chans.remove('timestamps')
if 0:
Vcc = h5.root.ain_wordstream.attrs.Vcc
print 'Vcc read from file at',Vcc
else:
Vcc=3.3
print 'Vcc',Vcc
ADCmax = (2**10)-1
analog_gain = Vcc/ADCmax
def doit(fname, options):
fname = sys.argv[1]
h5 = tables.openFile(fname, mode='r')
time_data = h5.root.time_data[:]
gain, offset, resids = easy_decode.get_gain_offset_resids(
input=time_data['framestamp'], output=time_data['timestamp'])
if options.plot_time_data:
fig = pylab.figure()
ax = fig.add_subplot(111)
ax.plot(time_data['framestamp'],
time_data['timestamp'],
'bo',
label='data')
ax.set_xlabel('framestamp')
ax.set_ylabel('timestamp')
minx = np.min(time_data['framestamp'])
maxx = np.max(time_data['framestamp'])
ax.plot([minx, maxx], [minx * gain + offset, maxx * gain + offset],
'k-',
label='fit')
pylab.legend()
top = h5.root.time_data.attrs.top
wordstream = h5.root.ain_wordstream[:]
wordstream = wordstream['word'] # extract into normal numpy array
print 'wordstream.shape', wordstream.shape
if wordstream.shape == (0, ):
pylab.show()
return
pylab.figure() # open a new figure
r = easy_decode.easy_decode(wordstream, gain, offset, top)
chans = r.dtype.fields.keys()
chans.sort()
chans.remove('timestamps')
names = h5.root.ain_wordstream.attrs.channel_names
if hasattr(h5.root.ain_wordstream.attrs, 'Vcc'):
Vcc = h5.root.ain_wordstream.attrs.Vcc
print 'Vcc read from file at', Vcc
else:
Vcc = 3.3
print 'Vcc guessed at', Vcc
ADCmax = (2**10) - 1
analog_gain = Vcc / ADCmax
n_adc_samples = len(r['timestamps'])
dt = r['timestamps'][1] - r['timestamps'][0]
samps_per_sec = 1.0 / dt
adc_duration = n_adc_samples * dt
print '%d samples at %.1f samples/sec = %.1f seconds' % (
n_adc_samples, samps_per_sec, adc_duration)
t0 = r['timestamps'][0]
total_duration = adc_duration
if options.timestamps:
t_offset = 0
t_plot_start = t0
else:
t_offset = t0
t_plot_start = 0
ax = None
for i in range(len(chans)):
ax = pylab.subplot(len(chans), 1, i + 1, sharex=ax)
try:
label = names[int(chans[i])]
except Exception, err:
print 'ERROR: ingnoring exception %s' % (err, )
label = 'channel %s' % chans[i]
ax.plot(r['timestamps'] - t_offset,
r[chans[i]] * analog_gain,
label=label)
ax.set_ylabel('V')
ax.legend()
if options.timestamps:
ax.xaxis.set_major_formatter(mticker.FuncFormatter(format_date))
else:
ax.xaxis.set_major_formatter(mticker.FormatStrFormatter("%s"))
ax.yaxis.set_major_formatter(mticker.FormatStrFormatter("%s"))
def doit(fname,options):
fname = sys.argv[1]
h5 = tables.openFile(fname,mode='r')
time_data=h5.root.time_data[:]
gain,offset,resids = easy_decode.get_gain_offset_resids(
input=time_data['framestamp'],
output=time_data['timestamp'])
if options.plot_time_data:
fig = pylab.figure()
ax = fig.add_subplot(111)
ax.plot( time_data['framestamp'],
time_data['timestamp'], 'bo',
label='data' )
ax.set_xlabel('framestamp')
ax.set_ylabel('timestamp')
minx = np.min(time_data['framestamp'])
maxx = np.max(time_data['framestamp'])
ax.plot( [minx,maxx],
[minx*gain+offset, maxx*gain+offset],
'k-',
label='fit')
pylab.legend()
top = h5.root.time_data.attrs.top
wordstream = h5.root.ain_wordstream[:]
wordstream = wordstream['word'] # extract into normal numpy array
print 'wordstream.shape',wordstream.shape
if wordstream.shape == (0,):
pylab.show()
return
pylab.figure() # open a new figure
r=easy_decode.easy_decode(wordstream,gain,offset,top)
chans = r.dtype.fields.keys()
chans.sort()
chans.remove('timestamps')
names = h5.root.ain_wordstream.attrs.channel_names
if hasattr(h5.root.ain_wordstream.attrs,'Vcc'):
Vcc = h5.root.ain_wordstream.attrs.Vcc
print 'Vcc read from file at',Vcc
else:
Vcc=3.3
print 'Vcc guessed at',Vcc
ADCmax = (2**10)-1
analog_gain = Vcc/ADCmax
n_adc_samples = len(r['timestamps'])
dt = r['timestamps'][1]-r['timestamps'][0]
samps_per_sec = 1.0/dt
adc_duration = n_adc_samples*dt
print '%d samples at %.1f samples/sec = %.1f seconds'%(n_adc_samples,
samps_per_sec,
adc_duration)
t0 = r['timestamps'][0]
total_duration = adc_duration
if options.timestamps:
t_offset = 0
t_plot_start = t0
else:
t_offset = t0
t_plot_start = 0
ax=None
for i in range(len(chans)):
ax = pylab.subplot(len(chans),1,i+1,sharex=ax)
try:
label = names[int(chans[i])]
except Exception, err:
print 'ERROR: ingnoring exception %s'%(err,)
label = 'channel %s'%chans[i]
ax.plot(r['timestamps']-t_offset,r[chans[i]]*analog_gain,
label=label)
ax.set_ylabel('V')
ax.legend()
if options.timestamps:
ax.xaxis.set_major_formatter(
mticker.FuncFormatter(format_date))
else:
ax.xaxis.set_major_formatter(mticker.FormatStrFormatter("%s"))
ax.yaxis.set_major_formatter(mticker.FormatStrFormatter("%s"))
def main():
h5_file_dir = os.path.join(os.environ['HOME'], 'strokelitude_h5')
h5_file_list = os.listdir(h5_file_dir)
if not h5_file_list:
print '%s directory empty -- nothing to do' % (h5_file_dir, )
sys.exit(0)
# Create mat file directory if is doesn't exist
mat_file_dir = os.path.join(os.environ['HOME'], 'strokelitude_mat')
if not os.path.exists(mat_file_dir):
os.mkdir(mat_file_dir)
# Loop over input files
for fname_h5 in h5_file_list:
fname_base, fname_ext = os.path.splitext(fname_h5)
if not fname_ext == '.h5':
continue
fname_mat = '%s.mat' % (fname_base, )
fname_mat_full = os.path.join(mat_file_dir, fname_mat)
fname_h5_full = os.path.join(h5_file_dir, fname_h5)
if os.path.exists(fname_mat_full):
continue
print 'converting: %s -- > %s' % (fname_h5, fname_mat)
h5 = tables.openFile(fname_h5_full, mode='r')
stroke_data = h5.root.stroke_data[:]
stroke_times = stroke_data['trigger_timestamp']
time_data = h5.root.time_data[:]
gain, offset, resids = easy_decode.get_gain_offset_resids(
input=time_data['framestamp'], output=time_data['timestamp'])
top = h5.root.time_data.attrs.top
wordstream = h5.root.ain_wordstream[:]
wordstream = wordstream['word'] # extract into normal numpy array
r = easy_decode.easy_decode(wordstream, gain, offset, top)
if r is not None:
chans = r.dtype.fields.keys()
chans.sort()
chans.remove('timestamps')
if 1:
Vcc = h5.root.ain_wordstream.attrs.Vcc
channel_names = h5.root.ain_wordstream.attrs.channel_names
else:
Vcc = 3.3
ADCmax = (2**10) - 1
analog_gain = Vcc / ADCmax
else:
chans = []
names = h5.root.ain_wordstream.attrs.channel_names
savedict = {}
if r is not None:
t0 = r['timestamps'][0]
savedict = {'ADC_timestamp': r['timestamps']}
else:
t0 = 0
# Write data to a .mat file
savedict['data'] = {}
savedict['data']['frame'] = stroke_data['frame']
savedict['data']['triggerTimeStamp'] = stroke_times
savedict['data']['processingTimeStamp'] = stroke_data[
'processing_timestamp']
savedict['data']['leftWingAngle'] = stroke_data['left']
savedict['data']['rightWingAngle'] = stroke_data['right']
savedict['data']['leftAntennaAngle'] = stroke_data['left_antenna']
savedict['data']['rightAntennaAngle'] = stroke_data['right_antenna']
savedict['data']['headAngle'] = stroke_data['head']
savedict['data']['pulseWidth'] = stroke_data['pulse_width']
savedict['data']['pulseFrame'] = stroke_data['pulse_frame']
if chans != []:
analog_key_list = []
for i, name in enumerate(names):
ADC_data = r[chans[i]] * analog_gain
savedict["ADC" + str(name)] = ADC_data
analog_key_list.append("ADC" + str(name))
scipy.io.savemat(fname_mat_full, savedict)
h5.close()
def main():
h5_file_dir = os.path.join(os.environ['HOME'],'strokelitude_h5')
h5_file_list = os.listdir(h5_file_dir)
if not h5_file_list:
print '%s directory empty -- nothing to do'%(h5_file_dir,)
sys.exit(0)
# Create mat file directory if is doesn't exist
mat_file_dir = os.path.join(os.environ['HOME'],'strokelitude_mat')
if not os.path.exists(mat_file_dir):
os.mkdir(mat_file_dir)
# Loop over input files
for fname_h5 in h5_file_list:
fname_base, fname_ext = os.path.splitext(fname_h5)
if not fname_ext == '.h5':
continue
fname_mat = '%s.mat'%(fname_base,)
fname_mat_full = os.path.join(mat_file_dir, fname_mat)
fname_h5_full = os.path.join(h5_file_dir,fname_h5)
if os.path.exists(fname_mat_full):
continue
print 'converting: %s -- > %s'%(fname_h5,fname_mat)
h5 = tables.openFile(fname_h5_full,mode='r')
stroke_data=h5.root.stroke_data[:]
stroke_times = stroke_data['trigger_timestamp']
time_data=h5.root.time_data[:]
gain,offset,resids = easy_decode.get_gain_offset_resids(
input=time_data['framestamp'],
output=time_data['timestamp'])
top = h5.root.time_data.attrs.top
wordstream = h5.root.ain_wordstream[:]
wordstream = wordstream['word'] # extract into normal numpy array
r=easy_decode.easy_decode(wordstream,gain,offset,top)
if r is not None:
chans = r.dtype.fields.keys()
chans.sort()
chans.remove('timestamps')
if 1:
Vcc = h5.root.ain_wordstream.attrs.Vcc
channel_names = h5.root.ain_wordstream.attrs.channel_names
else:
Vcc=3.3
ADCmax = (2**10)-1
analog_gain = Vcc/ADCmax
else:
chans = []
names = h5.root.ain_wordstream.attrs.channel_names
savedict = {}
if r is not None:
t0 = r['timestamps'][0]
savedict = {'ADC_timestamp':r['timestamps']}
else:
t0 = 0
# Write data to a .mat file
savedict['data'] = {}
savedict['data']['frame'] = stroke_data['frame']
savedict['data']['triggerTimeStamp'] = stroke_times
savedict['data']['processingTimeStamp'] = stroke_data['processing_timestamp']
savedict['data']['leftWingAngle'] = stroke_data['left']
savedict['data']['rightWingAngle'] = stroke_data['right']
savedict['data']['leftAntennaAngle'] = stroke_data['left_antenna']
savedict['data']['rightAntennaAngle'] = stroke_data['right_antenna']
savedict['data']['headAngle'] = stroke_data['head']
savedict['data']['pulseWidth'] = stroke_data['pulse_width']
savedict['data']['pulseFrame'] = stroke_data['pulse_frame']
if chans != []:
analog_key_list = []
for i, name in enumerate(names):
ADC_data = r[chans[i]]*analog_gain
savedict["ADC"+str(name)] = ADC_data
analog_key_list.append("ADC"+str(name))
scipy.io.savemat(fname_mat_full,savedict)
h5.close()
h5 = tables.openFile(fname,mode='r')
stroke_data=h5.root.stroke_data[:]
stroke_times = stroke_data['trigger_timestamp']
print 'repr(stroke_times[0])',repr(stroke_times[0])
time_data=h5.root.time_data[:]
gain,offset,resids = easy_decode.get_gain_offset_resids(
input=time_data['framestamp'],
output=time_data['timestamp'])
top = h5.root.time_data.attrs.top
wordstream = h5.root.ain_wordstream[:]
wordstream = wordstream['word'] # extract into normal numpy array
r=easy_decode.easy_decode(wordstream,gain,offset,top)
if r is not None:
chans = r.dtype.fields.keys()
chans.sort()
chans.remove('timestamps')
if 1:
Vcc = h5.root.ain_wordstream.attrs.Vcc
channel_names = h5.root.ain_wordstream.attrs.channel_names
else:
Vcc=3.3
print 'Vcc',Vcc
ADCmax = (2**10)-1
analog_gain = Vcc/ADCmax
else:
chans = []
fname = args[0]
h5 = tables.openFile(fname, mode='r')
stroke_data = h5.root.stroke_data[:]
stroke_times = stroke_data['trigger_timestamp']
print 'repr(stroke_times[0])', repr(stroke_times[0])
time_data = h5.root.time_data[:]
gain, offset, resids = easy_decode.get_gain_offset_resids(
input=time_data['framestamp'], output=time_data['timestamp'])
top = h5.root.time_data.attrs.top
wordstream = h5.root.ain_wordstream[:]
wordstream = wordstream['word'] # extract into normal numpy array
r = easy_decode.easy_decode(wordstream, gain, offset, top)
if r is not None:
chans = r.dtype.fields.keys()
chans.sort()
chans.remove('timestamps')
if 1:
Vcc = h5.root.ain_wordstream.attrs.Vcc
channel_names = h5.root.ain_wordstream.attrs.channel_names
else:
Vcc = 3.3
print 'Vcc', Vcc
ADCmax = (2**10) - 1
analog_gain = Vcc / ADCmax
else:
chans = []
