def load_graz(filename):
# load the training
data_mat = scio.loadmat('dataset_BCIcomp1.mat')
data = data_mat['x_train'].astype('double')
#print data.shape
data = data.swapaxes(-3, -2)
data = data.swapaxes(-1, -3)
labels = data_mat['y_train'].astype('int').ravel()
#print data.shape
# convert into wyrm Data
axes = [np.arange(i) for i in data.shape]
axes[0] = labels
axes[2] = [str(i) for i in range(data.shape[2])]
names = ['Class', 'Time', 'Channel']
units = ['#', 'ms', '#']
dat_train = Data(data=data, axes=axes, names=names, units=units)
dat_train.fs = 128
dat_train.class_names = ['left', 'right']
# load the test data
#test_data_mat = loadmat(test_file)
data = data_mat['x_test'].astype('double')
data = data.swapaxes(-3, -2)
data = data.swapaxes(-1, -3)
# convert into wyrm Data
axes = [np.arange(i) for i in data.shape]
axes[2] = [str(i) for i in range(data.shape[2])]
names = ['Class','Time', 'Channel']
units = ['#','ms', '#']
dat_test = Data(data=data, axes=axes, names=names, units=units)
dat_test.fs = 128
# map labels 2 -> 0
dat_test.axes[0][dat_test.axes[0] == 2] = 0
dat_train.axes[0][dat_train.axes[0] == 2] = 0
return dat_train, dat_test
def load_bcicomp3_ds1(dirname):
"""Load the BCI Competition III Data Set 1.
This method loads the data set and converts it into Wyrm's ``Data``
format. Before you use it, you have to download the training- and
test data in Matlab format and unpack it into a directory.
.. note::
If you need the true labels of the test sets, you'll have to
download them separately from
http://bbci.de/competition/iii/results/index.html#labels
Parameters
----------
dirname : str
the directory where the ``Competition_train.mat`` and
``Competition_test.mat`` are located
Returns
-------
epo_train, epo_test : epoched ``Data`` objects
Examples
--------
>>> epo_test, epo_train = load_bcicomp3_ds1('/home/foo/bcicomp3_dataset1/')
"""
# construct the filenames from the dirname
training_file = path.sep.join([dirname, 'Competition_train.mat'])
test_file = path.sep.join([dirname, 'Competition_test.mat'])
# load the training data
training_data_mat = loadmat(training_file)
data = training_data_mat['X'].astype('double')
data = data.swapaxes(-1, -2)
labels = training_data_mat['Y'].astype('int').ravel()
# convert into wyrm Data
axes = [np.arange(i) for i in data.shape]
axes[0] = labels
axes[2] = [str(i) for i in range(data.shape[2])]
names = ['Class', 'Time', 'Channel']
units = ['#', 'ms', '#']
dat_train = Data(data=data, axes=axes, names=names, units=units)
dat_train.fs = 1000
dat_train.class_names = ['pinky', 'tongue']
# load the test data
test_data_mat = loadmat(test_file)
data = test_data_mat['X'].astype('double')
data = data.swapaxes(-1, -2)
# convert into wyrm Data
axes = [np.arange(i) for i in data.shape]
axes[2] = [str(i) for i in range(data.shape[2])]
names = ['Epoch', 'Time', 'Channel']
units = ['#', 'ms', '#']
dat_test = Data(data=data, axes=axes, names=names, units=units)
dat_test.fs = 1000
# map labels -1 -> 0
dat_test.axes[0][dat_test.axes[0] == -1] = 0
dat_train.axes[0][dat_train.axes[0] == -1] = 0
return dat_train, dat_test
def load_bcicomp3_ds1(dirname):
"""Load the BCI Competition III Data Set 1.
This method loads the data set and converts it into Wyrm's ``Data``
format. Before you use it, you have to download the training- and
test data in Matlab format and unpack it into a directory.
.. note::
If you need the true labels of the test sets, you'll have to
download them separately from
http://bbci.de/competition/iii/results/index.html#labels
Parameters
----------
dirname : str
the directory where the ``Competition_train.mat`` and
``Competition_test.mat`` are located
Returns
-------
epo_train, epo_test : epoched ``Data`` objects
Examples
--------
>>> epo_test, epo_train = load_bcicomp3_ds1('/home/foo/bcicomp3_dataset1/')
"""
# construct the filenames from the dirname
training_file = path.sep.join([dirname, 'Competition_train.mat'])
test_file = path.sep.join([dirname, 'Competition_test.mat'])
# load the training data
training_data_mat = loadmat(training_file)
data = training_data_mat['X'].astype('double')
data = data.swapaxes(-1, -2)
labels = training_data_mat['Y'].astype('int').ravel()
# convert into wyrm Data
axes = [np.arange(i) for i in data.shape]
axes[0] = labels
axes[2] = [str(i) for i in range(data.shape[2])]
names = ['Class', 'Time', 'Channel']
units = ['#', 'ms', '#']
dat_train = Data(data=data, axes=axes, names=names, units=units)
dat_train.fs = 1000
dat_train.class_names = ['pinky', 'tongue']
# load the test data
test_data_mat = loadmat(test_file)
data = test_data_mat['X'].astype('double')
data = data.swapaxes(-1, -2)
# convert into wyrm Data
axes = [np.arange(i) for i in data.shape]
axes[2] = [str(i) for i in range(data.shape[2])]
names = ['Epoch', 'Time', 'Channel']
units = ['#', 'ms', '#']
dat_test = Data(data=data, axes=axes, names=names, units=units)
dat_test.fs = 1000
# map labels -1 -> 0
dat_test.axes[0][dat_test.axes[0] == -1] = 0
dat_train.axes[0][dat_train.axes[0] == -1] = 0
return dat_train, dat_test
train_labels = np.array(train_labels, dtype=np.int8)
test_labels = np.array(test_labels, dtype=np.int8)
#sorted_by_labels = [[], [], []]
#for i, label in enumerate(train_labels):
# sorted_by_labels[int(label[0])].append(train_instances[i])
#pdb.set_trace()
#shape = (2, len(sorted_by_labels[1][0]), len(sorted_by_labels[1][0]))
#csp_training_data = np.ndarray(sorted_by_labels[1:2]) # only want positive classes in our CSP training data
train_data = Data(
np_train_instances,
[train_labels,
range(0, np_train_instances.shape[1]),
range(1, 26)], ["class", "time", "channel"], ["#", "1s/250", "#"])
train_data.fs = 250
train_data.class_names = ["none", "left", "right"]
test_data = Data(
np_test_instances,
[test_labels,
range(0, np_test_instances.shape[1]),
range(1, 26)], ["class", "time", "channel"], ["#", "1s/250", "#"])
test_data.fs = 250
#dat_train, dat_test = load_bcicomp3_ds1(DATA_DIR)
dat_train = train_data
dat_test = test_data
#pdb.set_trace()
# TODO: FILTER OUT LABELS OF NaN INSTANCES
# load true labels
