def mock_dataset(num_channels=4, num_recspans=4, ticks_per_recspan=100, hz=250):
data_format = DataFormat(hz, "uV", ["MOCK%s" % (i,) for i in xrange(num_channels)])
dataset = DataSet(data_format)
r = np.random.RandomState(0)
for i in xrange(num_recspans):
data = r.normal(size=(ticks_per_recspan, num_channels))
dataset.add_recspan(data, {})
return dataset
def mock_dataset(num_channels=4,
num_recspans=4,
ticks_per_recspan=100,
hz=250):
data_format = DataFormat(hz, "uV",
["MOCK%s" % (i, ) for i in xrange(num_channels)])
dataset = DataSet(data_format)
r = np.random.RandomState(0)
for i in xrange(num_recspans):
data = r.normal(size=(ticks_per_recspan, num_channels))
dataset.add_recspan(data, {})
return dataset
def check_transforms(dataset):
saved_datas = []
for data in dataset:
saved_datas.append(np.array(data))
tr1 = np.eye(dataset.data_format.num_channels)
tr1[0, 0] = 2
tr1[0, 1] = -0.5
dataset.transform(tr1)
for saved_data, data in zip(saved_datas, dataset):
assert np.allclose(np.dot(saved_data, tr1.T), data)
dataset_copy = DataSet(dataset.data_format)
for saved_data, copy_data in zip(saved_datas, dataset_copy):
assert np.allclose(np.dot(saved_data, tr1.T), copy_data)
tr2 = np.eye(dataset.data_format.num_channels)
tr2[-1, -1] = -3
tr2[1, 0] = 2.5
dataset.transform(tr2)
for saved_data, copy_data in zip(saved_datas, dataset_copy):
assert np.allclose(np.dot(saved_data, tr1.T), copy_data)
tr_both = np.dot(tr2, tr1)
for saved_data, data in zip(saved_datas, dataset):
assert np.allclose(np.dot(saved_data, tr_both.T), data)
def test_DataSet():
data_format = DataFormat(250, "uV", ["MOCK1", "MOCK2"])
dataset = DataSet(data_format)
assert len(dataset) == 0
assert_raises(IndexError, dataset.__getitem__, 0)
assert_raises(TypeError, dataset.__getitem__, slice(0, 0))
assert list(dataset) == []
dataset.add_recspan(np.ones((10, 2)) * 0, {"a": 0})
dataset.add_recspan(np.ones((20, 2)) * 1, {"a": 1})
dataset.add_recspan(np.ones((30, 2)) * 0, {"a": 2})
dataset.add_recspan(np.ones((40, 2)) * 1, {"a": 3})
assert len(dataset) == 4
assert_raises(IndexError, dataset.__getitem__, 4)
dataset.add_event(1, 10, 11, {"foo": "bar"})
def t(ds, recspan_id, expected_values=None):
recspan = ds[recspan_id]
assert isinstance(recspan, pandas.DataFrame)
expected_ticks = 10 * (recspan_id + 1)
assert recspan.shape == (expected_ticks, 2)
# 1/250 Hz = 4.0 ms
assert np.all(recspan.index == np.arange(expected_ticks) * 4.0)
# index is supposed to be floats. Not sure if that means float or
# np.float64, but this check should work for either:
assert isinstance(recspan.index[0], float)
assert np.all(recspan.columns == ["MOCK1", "MOCK2"])
# Values are supposed to be floating point as well.
assert type(recspan.iloc[0, 0]) is np.float64
if expected_values is None:
local_recspan_id = recspan_id % 2
expected_values = local_recspan_id
assert np.allclose(recspan, expected_values)
assert ds.recspan_infos[recspan_id]["a"] == recspan_id
assert ds.recspan_infos[recspan_id].ticks == expected_ticks
for i in xrange(4):
t(dataset, i)
# DataFormat mismatch
diff_dataset = DataSet(DataFormat(500, "uV", ["MOCK1", "MOCK2"]))
assert_raises(ValueError, diff_dataset.add_dataset, dataset)
dataset_copy = DataSet(data_format)
dataset_copy.add_dataset(dataset)
assert len(dataset_copy) == 4
for i in xrange(4):
t(dataset_copy, i)
assert len(dataset_copy.events()) == 1
assert dict(dataset_copy.events()[0]) == {"foo": "bar"}
assert dataset_copy.events()[0].recspan_id == 1
assert_raises(ValueError, dataset.transform, np.eye(2), exclude=["MOCK1"])
dataset.transform([[2, 0], [0, 3]])
# Transforming the first data set doesn't affect the second
for i in xrange(4):
t(dataset_copy, i)
# But it does affect the first!
for i in xrange(4):
t(dataset, i, expected_values=[[2 * (i % 2), 3 * (i % 2)]])
# Try a symbolic transform too -- it should stack with the previous
# transform.
dataset.transform("-MOCK1/3", exclude=["MOCK1"])
for i in xrange(4):
t(dataset, i, expected_values=[[2 * (i % 2), 3 * (i % 2) - (2.0 / 3) * (i % 2)]])
# Also check that changing one DataSet's metadata doesn't affect the copy.
dataset.recspan_infos[0]["a"] = 100
assert dataset.recspan_infos[0]["a"] == 100
assert dataset_copy.recspan_infos[0]["a"] == 0
# Set it back to avoid any confusion later in the test
dataset.recspan_infos[0]["a"] = 0
# Check __iter__
recspans = list(dataset)
assert len(recspans) == 4
from pandas.util.testing import assert_frame_equal
for i in xrange(4):
assert_frame_equal(recspans[i], dataset[i])
def load_erpss(raw, log, calibration_events="condition == 0"):
dtype = np.float64
metadata = {}
if isinstance(raw, basestring):
metadata["raw_file"] = os.path.abspath(raw)
if isinstance(log, basestring):
metadata["log_file"] = os.path.abspath(log)
metadata["calibration_events"] = str(calibration_events)
raw = maybe_open(raw)
log = maybe_open(log)
(hz, channel_names, raw_codes, data,
header_metadata) = read_raw(raw, dtype)
metadata.update(header_metadata)
data_format = DataFormat(hz, "RAW", channel_names)
raw_log_events = read_log(log)
expanded_log_codes = np.zeros(raw_codes.shape, dtype=int)
try:
expanded_log_codes[raw_log_events.index] = raw_log_events["code"]
except IndexError as e:
raise ValueError("log file claims event at position where there is "
"no data: %s" % (e, ))
if np.any(expanded_log_codes != raw_codes):
raise ValueError("raw and log files have mismatched codes")
del raw_codes
del expanded_log_codes
pause_events = (raw_log_events["code"] == PAUSE_CODE)
delete_events = (raw_log_events["code"] == DELETE_CODE)
break_events = pause_events | delete_events
break_ticks = raw_log_events.index[break_events]
# The pause/delete code appears at the last sample of the old era, so if
# used directly, adjacent pause ticks give contiguous spans of recording
# as (pause1, pause2]. (Confirmed by checking by hand in a real recording
# that the data associated with the sample that has the pause code is
# contiguous with the sample before, but not the sample after.) Adding +1
# to each of them then converts this to Python style [pause1, pause2)
# intervals. There is a pause code at the last record of the file, but not
# one at the first, so we add that in explicitly.
break_ticks += 1
span_edges = np.concatenate(([0], break_ticks))
assert span_edges[0] == 0
assert span_edges[-1] == data.shape[0]
span_slices = [
slice(span_edges[i], span_edges[i + 1])
for i in xrange(len(span_edges) - 1)
]
dataset = DataSet(data_format)
for span_slice in span_slices:
dataset.add_recspan(data[span_slice, :], metadata)
span_starts = [s.start for s in span_slices]
for tick, row in raw_log_events.iterrows():
attrs = row.to_dict()
span_id = bisect.bisect(span_starts, tick) - 1
span_slice = span_slices[span_id]
span_start = span_slice.start
span_stop = span_slice.stop
assert span_start <= tick < span_stop
dataset.add_event(span_id, tick - span_start, tick - span_start + 1,
attrs)
if attrs["code"] == DELETE_CODE:
dataset.recspan_infos[span_id]["deleted"] = True
for cal_event in dataset.events_query(calibration_events):
for key in list(cal_event):
del cal_event[key]
cal_event["calibration_pulse"] = True
return dataset
def load_erpss(raw, log, calibration_events="condition == 0"):
dtype = np.float64
metadata = {}
if isinstance(raw, basestring):
metadata["raw_file"] = os.path.abspath(raw)
if isinstance(log, basestring):
metadata["log_file"] = os.path.abspath(log)
metadata["calibration_events"] = str(calibration_events)
raw = maybe_open(raw)
log = maybe_open(log)
(hz, channel_names, raw_codes, data, header_metadata) = read_raw(raw, dtype)
metadata.update(header_metadata)
data_format = DataFormat(hz, "RAW", channel_names)
raw_log_events = read_log(log)
expanded_log_codes = np.zeros(raw_codes.shape, dtype=int)
try:
expanded_log_codes[raw_log_events.index] = raw_log_events["code"]
except IndexError as e:
raise ValueError("log file claims event at position where there is "
"no data: %s" % (e,))
if np.any(expanded_log_codes != raw_codes):
raise ValueError("raw and log files have mismatched codes")
del raw_codes
del expanded_log_codes
pause_events = (raw_log_events["code"] == PAUSE_CODE)
delete_events = (raw_log_events["code"] == DELETE_CODE)
break_events = pause_events | delete_events
break_ticks = raw_log_events.index[break_events]
# The pause/delete code appears at the last sample of the old era, so if
# used directly, adjacent pause ticks give contiguous spans of recording
# as (pause1, pause2]. (Confirmed by checking by hand in a real recording
# that the data associated with the sample that has the pause code is
# contiguous with the sample before, but not the sample after.) Adding +1
# to each of them then converts this to Python style [pause1, pause2)
# intervals. There is a pause code at the last record of the file, but not
# one at the first, so we add that in explicitly.
break_ticks += 1
span_edges = np.concatenate(([0], break_ticks))
assert span_edges[0] == 0
assert span_edges[-1] == data.shape[0]
span_slices = [slice(span_edges[i], span_edges[i + 1])
for i in xrange(len(span_edges) - 1)]
dataset = DataSet(data_format)
for span_slice in span_slices:
dataset.add_recspan(data[span_slice, :], metadata)
span_starts = [s.start for s in span_slices]
for tick, row in raw_log_events.iterrows():
attrs = row.to_dict()
span_id = bisect.bisect(span_starts, tick) - 1
span_slice = span_slices[span_id]
span_start = span_slice.start
span_stop = span_slice.stop
assert span_start <= tick < span_stop
dataset.add_event(span_id,
tick - span_start, tick - span_start + 1,
attrs)
if attrs["code"] == DELETE_CODE:
dataset.recspan_infos[span_id]["deleted"] = True
for cal_event in dataset.events_query(calibration_events):
for key in list(cal_event):
del cal_event[key]
cal_event["calibration_pulse"] = True
return dataset
def test_DataSet():
data_format = DataFormat(250, "uV", ["MOCK1", "MOCK2"])
dataset = DataSet(data_format)
assert len(dataset) == 0
assert_raises(IndexError, dataset.__getitem__, 0)
assert_raises(TypeError, dataset.__getitem__, slice(0, 0))
assert list(dataset) == []
dataset.add_recspan(np.ones((10, 2)) * 0, {"a": 0})
dataset.add_recspan(np.ones((20, 2)) * 1, {"a": 1})
dataset.add_recspan(np.ones((30, 2)) * 0, {"a": 2})
dataset.add_recspan(np.ones((40, 2)) * 1, {"a": 3})
assert len(dataset) == 4
assert_raises(IndexError, dataset.__getitem__, 4)
dataset.add_event(1, 10, 11, {"foo": "bar"})
def t(ds, recspan_id, expected_values=None):
recspan = ds[recspan_id]
assert isinstance(recspan, pandas.DataFrame)
expected_ticks = 10 * (recspan_id + 1)
assert recspan.shape == (expected_ticks, 2)
# 1/250 Hz = 4.0 ms
assert np.all(recspan.index == np.arange(expected_ticks) * 4.0)
# index is supposed to be floats. Not sure if that means float or
# np.float64, but this check should work for either:
assert isinstance(recspan.index[0], float)
assert np.all(recspan.columns == ["MOCK1", "MOCK2"])
# Values are supposed to be floating point as well.
assert type(recspan.iloc[0, 0]) is np.float64
if expected_values is None:
local_recspan_id = recspan_id % 2
expected_values = local_recspan_id
assert np.allclose(recspan, expected_values)
assert ds.recspan_infos[recspan_id]["a"] == recspan_id
assert ds.recspan_infos[recspan_id].ticks == expected_ticks
for i in xrange(4):
t(dataset, i)
# DataFormat mismatch
diff_dataset = DataSet(DataFormat(500, "uV", ["MOCK1", "MOCK2"]))
assert_raises(ValueError, diff_dataset.add_dataset, dataset)
dataset_copy = DataSet(data_format)
dataset_copy.add_dataset(dataset)
assert len(dataset_copy) == 4
for i in xrange(4):
t(dataset_copy, i)
assert len(dataset_copy.events()) == 1
assert dict(dataset_copy.events()[0]) == {"foo": "bar"}
assert dataset_copy.events()[0].recspan_id == 1
assert_raises(ValueError, dataset.transform, np.eye(2), exclude=["MOCK1"])
dataset.transform([[2, 0], [0, 3]])
# Transforming the first data set doesn't affect the second
for i in xrange(4):
t(dataset_copy, i)
# But it does affect the first!
for i in xrange(4):
t(dataset, i, expected_values=[[2 * (i % 2), 3 * (i % 2)]])
# Try a symbolic transform too -- it should stack with the previous
# transform.
dataset.transform("-MOCK1/3", exclude=["MOCK1"])
for i in xrange(4):
t(dataset,
i,
expected_values=[[2 * (i % 2), 3 * (i % 2) - (2. / 3) * (i % 2)]])
# Also check that changing one DataSet's metadata doesn't affect the copy.
dataset.recspan_infos[0]["a"] = 100
assert dataset.recspan_infos[0]["a"] == 100
assert dataset_copy.recspan_infos[0]["a"] == 0
# Set it back to avoid any confusion later in the test
dataset.recspan_infos[0]["a"] = 0
# Check __iter__
recspans = list(dataset)
assert len(recspans) == 4
from pandas.util.testing import assert_frame_equal
for i in xrange(4):
assert_frame_equal(recspans[i], dataset[i])
