config = {
'source':
'HDF5',
'path':
r'\\193.136.222.220\cybh\data\CVP\hdf5',
'experiments':
['T1-Sitting', 'T2-Sitting', 'T1-Recumbent', 'T2-Recumbent'],
'mapper': {
'raw': 'signals/ECG/hand/raw',
'filtered': 'signals/ECG/hand/filtered/fir5to20',
'segments': 'signals/ECG/hand/segments/engzee',
'R': 'events/ECG/hand/R',
'outlier': 'events/ECG/hand/outlier/dbscan'
}
}
st = datamanager.Store(config)
outlier_parameters = {
'method': 'dbscan',
'min_samples': 10,
'eps': 0.95,
'metric': 'euclidean'
}
outlier_results = {'info': outlier_parameters}
for i in xrange(252):
fd = gzip.open('falc_temp/outlier/dbscan/output-%d' % i, 'rb')
outlier_results[i] = cPickle.load(fd)
fd.close()
outlier_method = 'DBSCAN'
mdata = st.dbmetada()
def saveWaves(config, inputs):
# save the wavelet coefficients
# mother wavelets
waves = [
pywt.Wavelet('coif5'),
pywt.Wavelet('rbio3.3'),
pywt.Wavelet('rbio3.5'),
pywt.Wavelet('rbio3.9'),
pywt.Wavelet('rbio5.5'),
pywt.Wavelet('db3'),
pywt.Wavelet('db8')
]
# decomposition level
level = 10
# update the config
for wave in waves:
waveName = wave.name
for var in inputs:
name = var + '+' + waveName
ty = config['mapper'][var].split('/')
ty[-1] = 'Wavelets'
ty.append(waveName.replace('.', '-'))
ty = '/'.join(ty)
config['mapper'][name] = ty
# cycle the subjects
for subject in dm.Store(config):
print subject
# cycle the records
for record in subject:
print record
# cycle the inputs
for var in inputs:
ty = config['mapper'][var]
print var
try:
data = record[var]
except KeyError:
print "Skipped", var
continue
# cycle the datasets
for item in data:
print item.metadata['name']
weg = ty + '/' + item.metadata['name']
# cycle the wavelets
for wave in waves:
waveName = wave.name
print waveName
# compute the RDWT
coeffs = wavelets.RDWT(item.signal, wave, level)[0]
# save
name = var + '+' + waveName
print name
metadata = {
'units': item.metadata['units'],
'sampleRate': item.metadata['sampleRate'],
'wavelet': {
'name': waveName,
'level': level
},
'source': weg,
'labels': [waveName]
}
record[name] = dataContainer('signals',
signal=coeffs,
mdata=metadata)
return config
def segWaves(config, inputs):
# extract the wavelet segments
# update the config
for var in inputs:
# to save the R positions
name = var + '+R'
ty = config['mapper'][var].replace('signals', 'events') + '/R'
config['mapper'][name] = ty
# to save the segments
name = var + '+Segs'
ty = config['mapper'][var] + '/Segments'
config['mapper'][name] = ty
# cycle the subjects
for subject in dm.Store(config):
print subject
# cycle the records
for record in subject:
print record
# cycle the inputs
for var in inputs:
print var
try:
data = record[var]
except KeyError:
print "Skipped", var
continue
ty = config['mapper'][var]
# cycle the datasets
for item in data:
print item.metadata['name']
# convert wavelet coefficients to matrix
matrix = wavelets.coeffs2Matrix(item.signal)
# extract segments
R = wavelets.RPeaks(item.signal,
item.metadata['sampleRate'])
segs = wavelets.extractSegments(
matrix, R, item.metadata['sampleRate'])
# store
weg = ty + '/' + item.metadata['name']
# store R locations
name = var + '+R'
print name
metadata = {
'source': weg,
'eventSync': 0,
'dictionary': {}
}
record[name] = dataContainer('events',
timeStamps=R,
values=[])
# store segments
name = var + '+Segs'
print name
metadata = {
'units': item.metadata['units'],
'sampleRate': item.metadata['sampleRate'],
'source': weg,
'labels': ['Segments']
}
record[name] = dataContainer('signals',
signal=segs,
mdata=metadata)
return config
FILTER = False
SEGMENT = False
OUTLIER = False
PREPARE = False
COMPUTE_DISTANCES = False
RESULTS = True
# if RESULTS: LOAD, FILTER, SEGMENT, OUTLIER, PREPARE, COMPUTE_DISTANCES = False, False, False, False, False, False
SAVE2DB = False
# ----------------------------------------------------------------------------------------------------------
# ECG BASED BIOMETRICS SCRIPT 1 - PREPARE DATA
# Database
st = datamanager.Store(mainConfig['database'])
if LOAD:
# Get Raw Files
print "loading raw ...",
res = st.db2data('raw')
sampling_rate = float(res[0][0]['mdata']['sampleRate'])
raw_data = {}
for rid in res:
raw_data[rid] = res[rid][0]['signal']
print "done."
if FILTER:
# Filter
filter_parameters = mainConfig['filter']
################ onde vou buscar a sampleRate???????????