def prepareData(self, data):
# prepare the data for classification
aux = preprocessing.norm9(data, R=200, sigma=1.5)
out = {
'segments': data,
'3-medianWaves-norm': misc.median_waves(aux, 3),
'3-medianWaves': misc.median_waves(data, 3),
'5-medianWaves': misc.median_waves(data, 5),
}
return out
def prepareData(self, data):
# prepare the data for classification
# normalize
data = preprocessing.norm9(data, R=200, sigma=1.5)
out = {'5-medianWaves': misc.median_waves(data, 5)}
return out
def get_data(info, data_type, label):
#sampling_rate = info['filter_paramenters']['sampling_rate'] ##
out = {}
for run in info['%s_patterns' % label]:
out[run] = {}
for name in info['%s_patterns' % label][run]:
# get data corresponding to info['label_patterns']
recid = info['%s_patterns' % label][run][name][1]
good_idxs = []
map(lambda i: good_idxs.append(int(i)),
info['%s_patterns' % label][run][name][0])
# temp
fd = gzip.open(
'D:/experiments/CVP/data/segmentation/engzee/output-%d' %
recid, 'rb')
data = scipy.array(cPickle.load(fd)[data_type]) ##
fd.close()
# a = pylab.find(data['R'] <= info['%s_time'%label][1]*sampling_rate)##
# b = pylab.find(data['R'] >= info['%s_time'%label][0]*sampling_rate)##
# idxs_time = scipy.intersect1d(a,b) ##
# data_idxs = scipy.intersect1d(idxs_time, good_idxs) ##
data_idxs = good_idxs
data = data[data_idxs] ##
#data = db2data(data_type, info['database'], refine={'_id': recid})[data_idxs]
if info['subtractmean']:
data -= scipy.mean(data)
if info['normalization'][0]:
if info['normalization'][1] == 'meanofmaxs':
data /= scipy.mean(numpy.max(data, 1))
elif info['normalization'][1] == 'medianofmaxs':
data /= scipy.median(numpy.max(data, 1))
if info['subpattern'][0]:
data = featureextraction.subpattern(
data, range(info['subpattern'][1], info['subpattern'][2]))
# determine mean waves
if info['number_mean_waves_%s' % label] == 'all':
data = misc.mean_waves(data, len(data))
elif info['number_mean_waves_%s' % label] > 1:
data = misc.mean_waves(data,
info['number_mean_waves_%s' % label])
# determine median waves
elif info['number_median_waves_%s' % label] == 'all':
data = misc.median_waves(data, len(data))
elif info['number_median_waves_%s' % label] > 1:
data = misc.median_waves(
data, info['number_median_waves_%s' % label])
# get n random indexes
# ...
# quantization
if info['quantization'] > 0:
data = misc.quantize(data, levels=info['quantization'])
if info['patterns2strings']:
res = []
for i, si in enumerate(data):
line = ''.join('%d' % i for i in si)
res.append(line)
data = res
out[run][name] = data
return out
def get_data(st, info, data_type, label):
out = {}
for run in info['%s_patterns' % label]:
out[run] = {}
for name in info['%s_patterns' % label][run]:
# get data corresponding to info['label_patterns']
recid = info['%s_patterns' % label][run][name][1]
data_idxs = []
map(lambda i: data_idxs.append(int(i)),
info['%s_patterns' % label][run][name][0])
data = st.db2data(data_type,
refine={'_id':
[recid]})[recid][0]['signal'][:, :, 4:7]
# data = st.db2data(data_type, refine={'_id': [recid]})[recid][0]['signal'].swapaxes(0, 1)
# temp
R = st.db.records.getEvent(recid, '/ECG/hand/zee5to20/R/engzee',
0)['timeStamps']
filt = st.db.records.getSignal(
recid, '/ECG/hand/zee5to20/Segments/engzee', 0)['signal']
if R[0] < 200:
# remove first segment
data = data[1:]
elif len(filt) - R[-1] < 400:
# remove last segment
data = data[:-1]
# outliers
data = data[data_idxs]
# normalization
if info['normalization'] == 'mean':
data /= scipy.mean(numpy.max(data, 0))
if info['subpattern']:
data = featureextraction.subpattern(data, range(165, 238))
# determine mean waves
if info['number_mean_waves_%s' % label] > 1:
data = misc.mean_waves(data,
info['number_mean_waves_%s' % label])
elif info['number_median_waves_%s' % label] > 1:
data = misc.median_waves(
data, info['number_median_waves_%s' % label])
# get n random indexes
# ...
# quantization
if info['quantization'] > 0:
data = misc.quantize(data, levels=info['quantization'])
if info['patterns2strings']:
res = []
for i, si in enumerate(data):
line = ''.join('%d' % i for i in si)
res.append(line)
data = res
out[run][name] = data
return out