def resample(Data, q):
'''
Slice data with quocient q.
Parameters
----------
Data: DataObj
Data object to resample
q: int
Quocient of resample. Sample Rate should be divisible by q (sample_rat % q = 0)
'''
if Data.sample_rate % q != 0:
print 'sample_rate % q should be int'
return
else:
#reading data
data = Data.data
new_data = data[0:-1:q, :]
# calculate new sample rate
new_sample_rate = Data.sample_rate / q
# creating new time_vec
new_time_vec = Data.time_vec[0:-1:q]
# creating new DataObj
newData = DataObj(new_data, new_sample_rate, Data.amp_unit,
Data.ch_labels, new_time_vec, Data.bad_channels)
return newData
def create_avg(Data):
'''
Create averege montagem excluding bad_channels
Parameters
----------
Data: DataObj
Data object
'''
# get non-bad channels index
index = [
ch for ch in range(Data.n_channels) if ch not in Data.bad_channels
]
# empty variable
avg = np.empty(Data.data.shape)
avg[:] = np.NAN
avg_label = []
for ch in index:
avg[:, ch] = Data.data[:, ch] - np.mean(Data.data[:, index], 1)
for ch in range(Data.n_channels):
avg_label.append(Data.ch_labels[ch] + '-avg')
newData = DataObj(avg, Data.sample_rate, Data.amp_unit, avg_label,
Data.time_vec, Data.bad_channels)
return newData
def loadMAT(slice_filename,parameters_filename):
'''
Created to convert .mat files with specific configuration for ECoG data of Newcastle Hospitals and create a dict.
If you want to load other .mat file, use scipy.io. loadmat and create_DataObj
Parameters
----------
slice_filename: str
Name of the slice (.mat) file
parameters_filename: str
Name of the parameters (.mat) file
'''
mat = sio.loadmat(parameters_filename, struct_as_record=False, squeeze_me=True)
parameters = mat['parameters']
ch_l = parameters.channels
ch_labels = [str(x) for x in ch_l]
sample_rate = parameters.sr
f = sio.loadmat(slice_filename, struct_as_record=False, squeeze_me=True)
Data = f['Data']
time_vec = Data.time_vec
signal = Data.raw.T
amp_unit = '$\mu V$'
Data = DataObj(signal,sample_rate,amp_unit,ch_labels,time_vec,[])
return Data
def merge(Data1, Data2, new_time=False):
'''
Merging two DataObj
Parameters
----------
Data1: DataObj
Data object to merge
Data2: DataObj
Data object to merge
new_time: boolean, optional
False (Defautl) - merge the time_vec from the 2 DataObj
True - Create a new time_vec starting with 0
'''
# check if is the same sample rate, then get it
if Data1.sample_rate != Data2.sample_rate:
raise Exception('Data object should have same sample_rate')
sample_rate = Data1.sample_rate
# check if is the same number of channels, then get it
if Data1.n_channels != Data2.n_channels:
raise Exception('Data object should have same n_channels')
# check if is the same amplitude
if Data1.amp_unit != Data2.amp_unit:
raise Exception('Data object should have same amplitude unit')
amp_unit = Data1.amp_unit
# get the label from dict 1
ch_labels = Data1.ch_labels
# Append bad channels from both object
bad_channels = []
bad_channels = sorted(set(np.append(bad_channels, Data1.bad_channels)))
bad_channels = sorted(set(np.append(bad_channels, Data2.bad_channels)))
# get data and time_vec from dict1
data1 = Data1.data
time_vec1 = Data1.time_vec
# get data and time_vec from dict2
data2 = Data2.data
time_vec2 = Data2.time_vec
# broadcast new_data
new_data = np.concatenate((data1, data2), axis=0)
if new_time:
n_points = new_data.shape[0]
end_time = n_points / sample_rate
new_time_vec = np.linspace(0, end_time, n_points, endpoint=False)
else:
# broadcast new_time_vec
new_time_vec = np.concatenate((time_vec1, time_vec2), axis=0)
# creating new DataObj
newData = DataObj(new_data, sample_rate, amp_unit, ch_labels, new_time_vec,
bad_channels)
return newData
def pop_channel(Data, ch):
'''
Create a new DataObj just with the the choose channel
Parameters
----------
Data: DataObj
Data object to filt
ch: int
Channel number
'''
if len(Data.data.shape) == 1:
raise Exception('DataObj is single channel')
signal = Data.data[:, ch]
label = Data.ch_labels[ch]
newData = DataObj(signal, Data.sample_rate, Data.amp_unit, label,
Data.time_vec)
return newData
def decimate(Data, q):
'''
Use scipy decimate to create a new DataObj with low sample rate data
Parameters
----------
Data: DataObj
Data object to resample
q: int
Quocient of resample.
'''
#reading data
data = Data.data
# get shape
if len(data.shape) == 1:
nch = 1
npoints = data.shape[0]
else:
npoints, nch = data.shape
# creating a empty array
new_data = np.empty((npoints / q, nch))
new_data[:] = np.NAN
# decimate each channel
if nch == 1:
new_data = sig.decimate(data, q)
else:
for ch in range(nch):
new_data[:, ch] = sig.decimate(data[:, ch], q)
# calculate new sample rate
new_sample_rate = Data.sample_rate / q
# creating new time_vec
new_time_vec = Data.time_vec[0:-1:q]
# creating new Data
newData = DataObj(new_data, new_sample_rate, Data.amp_unit, Data.ch_labels,
new_time_vec, Data.bad_channels)
return newData
def loadRDH(filename):
'''
Created to load 64 channels at port A - code need change if use diferent configuration.
It use RHD.py file to read and return DataObj
Parameters
----------
file_name: str
Name of the intran (.rhd) file
'''
# load file
myData = RHD.openRhd(filename)
# get sample rate
sample_rate = myData.sample_rate
# get channels
myChannels = myData.channels
# create a empty signal
signal = np.zeros((myChannels['A-000'].getTrace().size,64))
signal[:] = np.NAN
labels = []
for ch in range(64):
if ch < 10:
label = "A-00" + str(ch)
else:
label = "A-0" + str(ch)
signal[:,ch] = myChannels[label].getTrace()
labels.append(label)
signal *= 0.195 # according the Intan, the output should be multiplied by 0.195 to be converted to micro-volts
amp_unit = '$\mu V$'
# Time vector
n_points = signal.shape[0]
end_time = n_points/sample_rate
time_vec = np.linspace(0,end_time,n_points,endpoint=False)
Data = DataObj(signal,sample_rate,amp_unit,labels,time_vec,[])
return Data
def open_dataset(file_name,dataset_name,htype = 'auto'):
'''
open a dataset in a specific file_name
Parameters
----------
file_name: str
Name of the HDF5 (.h5) file
dataset_name: str
Name of dataset to open
htype: str, optional
auto (the default) - read htype from HDF file
Data - DataObj type
Spike - SpikeObj type
hfo - hfoObj type
'''
# reading h5 file
h5 = h5py.File(file_name,'r+')
# loading dataset
dataset = h5[dataset_name]
# getting htype
if htype == 'auto':
htype = dataset.attrs['htype']
if htype == 'Data':
# Sample Rate attribute
sample_rate = dataset.attrs['SampleRate[Hz]']
n_points = dataset.shape[0]
end_time = n_points/sample_rate
# Amplitude Unit
if 'amp_unit' in dataset.attrs:
amp_unit = dataset.attrs['amp_unit']
else:
amp_unit = 'AU'
# Time vector
if 'Time_vec_edge' in dataset.attrs:
edge = dataset.attrs['Time_vec_edge']
if edge[0] == edge[1]:
time_vec = np.linspace(0,end_time,n_points,endpoint=False)
else:
time_vec = np.linspace(edge[0],edge[1],n_points,endpoint=False)
else:
time_vec = np.linspace(0,end_time,n_points,endpoint=False)
# Check if has 'Bad_channels' attribute, if not, create one empty
if len([x for x in dataset.attrs.keys() if x == 'Bad_channels']) == 0:
dataset.attrs.create("Bad_channels",[],dtype=int)
# Load bad channels
bad_channels = dataset.attrs["Bad_channels"]
# Creating dictionary
Data = DataObj(dataset[:],sample_rate,amp_unit,dataset.attrs['Channel_Labels'],time_vec,bad_channels,file_name,dataset_name)
elif htype == 'list':
# Time vector
keys = dataset.keys()
ch_labels = dataset.attrs['ch_labels']
time_edge = dataset.attrs['time_edge']
Data = EventList(ch_labels,time_edge,file_name,dataset_name)
for k in keys:
waveform = dataset[k][:]
tstamp = dataset[k].attrs['tstamp']
evhtype = dataset[k].attrs['htype']
channel = dataset[k].attrs['channel']
if evhtype == 'Spike':
clus = dataset[k].attrs['cluster']
feat = dataset[k].attrs['features']
time_edge = dataset[k].attrs['time_edge']
spk = SpikeObj(channel,waveform,tstamp,clus,feat,time_edge)
Data.__addEvent__(spk)
elif evhtype == 'HFO':
tstamp_idx = dataset[k].attrs['tstamp_idx']
start_idx = dataset[k].attrs['start_idx']
end_idx = dataset[k].attrs['end_idx']
ths_value = dataset[k].attrs['ths_value']
sample_rate = dataset[k].attrs['sample_rate']
cutoff = dataset[k].attrs['cutoff']
info = dataset[k].attrs['info']
hfo = hfoObj(channel,tstamp,tstamp_idx, waveform,start_idx,end_idx,ths_value,sample_rate,cutoff,info)
Data.__addEvent__(hfo)
h5.close()
return Data
def eegfilt(Data,
low_cut=None,
high_cut=None,
order=None,
window=('kaiser', 0.5)):
'''
Filt EEG Data object with FIR filter.
Parameters
----------
Data: DataObj
Data object to filt
low_cut: int
Low cut frequency. If None, generate a low pass filter with cut
frequency in high_cut.
high_cut: int
High cut frequency. If None, generate a high pass filter with cut
frequency in low_cut.
order: int, optional
None (default) - Order of the filter calculated as 1/10 of sample rate
window : string or tuple of string and parameter values
Desired window to use. See `scipy.signal.get_window` for a list
of windows and required parameters.
'''
if low_cut == None and high_cut == None:
raise Exception('You should determine the cutting frequencies')
signal = Data.data
sample_rate = Data.sample_rate
time_vec = Data.time_vec
labels = Data.ch_labels
if len(signal.shape) == 1:
nch = 1
npoints = signal.shape[0]
else:
npoints, nch = signal.shape
# order
if order == None:
numtaps = int(sample_rate / 10 + 1)
else:
numtaps = order
# Nyquist rate
nyq = sample_rate / 2
# cutoff frequencies
if high_cut == None: # high pass
f = [low_cut]
pass_zero = False
elif low_cut == None: # low pass
f = [high_cut]
pass_zero = True
else: # band pass
f = [low_cut, high_cut]
pass_zero = False
# Creating filter
b = sig.firwin(numtaps, f, pass_zero=pass_zero, window=window, nyq=nyq)
# Creating filtered, numpy array with the filtered signal of raw data
filtered = np.empty((npoints, nch))
filtered[:] = np.NAN
if nch == 1:
print 'Filtering channel'
filtered = sig.filtfilt(b, np.array([1]), signal)
else:
for ch in range(nch):
if ch not in Data.bad_channels:
print 'Filtering channel ' + labels[ch]
filtered[:, ch] = sig.filtfilt(b, np.array([1]), signal[:, ch])
newData = DataObj(filtered, sample_rate, Data.amp_unit, labels, time_vec,
Data.bad_channels)
return newData
def open_dataset(file_name,dataset_name,htype = 'auto'):
'''
open a dataset in a specific file_name
Parameters
----------
file_name: str
Name of the HDF5 (.h5) file
dataset_name: str
Name of dataset to open
htype: str, optional
auto (the default) - read htype from HDF file
Data - DataObj type
Spike - SpikeObj type
hfo - hfoObj type
'''
# reading h5 file
h5 = h5py.File(file_name,'r+')
# loading dataset
dataset = h5[dataset_name]
# getting htype
if htype == 'auto':
htype = dataset.attrs['htype']
if htype == 'Data':
# Sample Rate attribute
sample_rate = dataset.attrs['SampleRate[Hz]']
n_points = dataset.shape[0]
end_time = n_points/sample_rate
# Amplitude Unit
if 'amp_unit' in dataset.attrs:
amp_unit = dataset.attrs['amp_unit']
else:
amp_unit = 'AU'
# Time vector
if 'Time_vec_edge' in dataset.attrs:
edge = dataset.attrs['Time_vec_edge']
time_vec = np.linspace(edge[0],edge[1],n_points,endpoint=False)
else:
time_vec = np.linspace(0,end_time,n_points,endpoint=False)
# Check if has 'Bad_channels' attribute, if not, create one empty
if len([x for x in dataset.attrs.keys() if x == 'Bad_channels']) == 0:
dataset.attrs.create("Bad_channels",[],dtype=int)
# Load bad channels
bad_channels = dataset.attrs["Bad_channels"]
# Creating dictionary
Data = DataObj(dataset[:],sample_rate,amp_unit,dataset.attrs['Channel_Labels'],time_vec,bad_channels,file_name,dataset_name)
elif htype == 'list':
# Time vector
keys = dataset.keys()
ch_labels = dataset.attrs['ch_labels']
time_edge = dataset.attrs['time_edge']
Data = EventList(ch_labels,time_edge,file_name,dataset_name)
for k in keys:
waveform = dataset[k][:]
tstamp = dataset[k].attrs['tstamp']
evhtype = dataset[k].attrs['htype']
if evhtype == 'Spike':
clus = dataset[k].attrs['cluster']
feat = dataset[k].attrs['features']
spk = SpikeObj(waveform,tstamp,clus,feat)
Data.__addEvent__(spk)
elif evhtype == 'HFO':
channel = dataset[k].attrs['channel']
tstamp_idx = dataset[k].attrs['tstamp_idx']
start_idx = dataset[k].attrs['start_idx']
end_idx = dataset[k].attrs['end_idx']
ths_value = dataset[k].attrs['ths_value']
sample_rate = dataset[k].attrs['sample_rate']
cutoff = dataset[k].attrs['cutoff']
info = dataset[k].attrs['info']
hfo = hfoObj(channel,tstamp,tstamp_idx, waveform,start_idx,end_idx,ths_value,sample_rate,cutoff,info)
Data.__addEvent__(hfo)
h5.close()
return Data