def test_transforms():
dataset = mock_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)
dataset_copy.add_dataset(dataset)
assert len(saved_datas) == len(dataset_copy)
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_transforms():
dataset = mock_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)
dataset_copy.add_dataset(dataset)
assert len(saved_datas) == len(dataset_copy)
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 mock_dataset(num_channels=4, num_recspans=4, ticks_per_recspan=100,
hz=250, lazy="mixed"):
assert lazy in ["all", "mixed", "none"]
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))
if lazy == "all" or (lazy == "mixed" and i % 2 == 0):
lr = FakeLazyRecspan(data)
dataset.add_lazy_recspan(lr, ticks_per_recspan, {})
else:
dataset.add_recspan(data, {})
return dataset
def mock_dataset(num_channels=4,
num_recspans=4,
ticks_per_recspan=100,
hz=250,
lazy="mixed"):
assert lazy in ["all", "mixed", "none"]
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))
if lazy == "all" or (lazy == "mixed" and i % 2 == 0):
lr = FakeLazyRecspan(data)
dataset.add_lazy_recspan(lr, ticks_per_recspan, {})
else:
dataset.add_recspan(data, {})
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_lazy_recspan(FakeLazyRecspan(np.ones((20, 2)) * 1), 20,
{"a": 1})
dataset.add_lazy_recspan(FakeLazyRecspan(np.ones((30, 2)) * 0), 30,
{"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 np.allclose(ds.raw_slice(recspan_id, 0, recspan.shape[0]),
recspan)
assert_raises(IndexError, ds.raw_slice, recspan_id, -1, 10)
assert_raises(IndexError, ds.raw_slice, recspan_id, 10, -1)
assert ds.raw_slice(recspan_id, 2, 2).shape == (0, 2)
assert np.all(ds.raw_slice(recspan_id, 2, 5) == recspan.iloc[2:5, :])
assert_raises(IndexError, ds.raw_slice, recspan_id, 0, 200)
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])
# Smoke test
repr(dataset)
def load_erpss(raw,
log,
calibration_events="condition == 0",
lazy=True,
calibrate=False,
calibrate_half_width_ticks=5,
calibrate_low_cursor_time=None,
calibrate_high_cursor_time=None,
calibrate_pulse_size=None,
calibrate_polarity=1):
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)
(fetcher, hz, channel_names, raw_codes, data,
header_metadata) = read_raw(raw, dtype, lazy)
metadata.update(header_metadata)
if calibrate:
units = "uV"
else:
units = "RAW"
data_format = DataFormat(hz, units, channel_names)
total_ticks = raw_codes.shape[0]
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, ))
# Sometimes people "delete" events by setting the high (sign) bit of the
# code in the log file (e.g. with 'logpoke'). So we ignore this bit when
# comparing log codes to raw codes -- mismatches here do not indicate an
# error -- and then are careful to use the log codes, rather than the
# raw codes, below.
if np.any((expanded_log_codes & ~0x8000) != (raw_codes & ~0x8000)):
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] == total_ticks
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:
if lazy:
lr = LazyRecspan(fetcher, dtype, len(channel_names),
span_slice.start, span_slice.stop)
dataset.add_lazy_recspan(lr, span_slice.stop - span_slice.start,
metadata)
else:
dataset.add_recspan(data[span_slice, :], metadata)
span_starts = [s.start for s in span_slices]
recspan_ids = []
start_ticks = []
for tick in raw_log_events.index:
recspan_id = bisect.bisect(span_starts, tick) - 1
span_slice = span_slices[recspan_id]
span_start = span_slice.start
span_stop = span_slice.stop
assert span_start <= tick < span_stop
recspan_ids.append(recspan_id)
start_ticks.append(tick - span_start)
stop_ticks = [tick + 1 for tick in start_ticks]
dataset.add_events(recspan_ids, start_ticks, stop_ticks, raw_log_events)
for delete_event in dataset.events_query({"code": DELETE_CODE}):
delete_event.recspan_info["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
if calibrate:
for kwarg in [
"calibrate_low_cursor_time", "calibrate_high_cursor_time",
"calibrate_pulse_size"
]:
if locals()[kwarg] is None:
raise ValueError("when calibrating, %s= argument must be "
"specified" % (kwarg, ))
half_width = dataset.data_format.ticks_to_ms(
calibrate_half_width_ticks)
cal_vals = {}
for which, cursor_time in [("low", calibrate_low_cursor_time),
("high", calibrate_high_cursor_time)]:
# Round cursor to nearest tick
cursor_tick = dataset.data_format.ms_to_ticks(cursor_time)
cursor_time = dataset.data_format.ticks_to_ms(cursor_tick)
erp = dataset.rerp("calibration_pulse",
cursor_time - half_width,
cursor_time + half_width,
"1",
all_or_nothing=True,
overlap_correction=False,
verbose=False)
cal_vals[which] = erp.betas["Intercept"].mean()
cal_diffs = cal_vals["high"] - cal_vals["low"]
calibrate_pulse_size *= calibrate_polarity
# For each channel, we want to multiply by a factor with units uV/raw
# We have calibrate_pulse_size uV = cal_diffs raw
cal_scaler = calibrate_pulse_size / cal_diffs
dataset.transform(np.diagflat(np.asarray(cal_scaler)))
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_lazy_recspan(FakeLazyRecspan(np.ones((20, 2)) * 1),
20, {"a": 1})
dataset.add_lazy_recspan(FakeLazyRecspan(np.ones((30, 2)) * 0),
30, {"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 np.allclose(ds.raw_slice(recspan_id, 0, recspan.shape[0]),
recspan)
assert_raises(IndexError,
ds.raw_slice, recspan_id, -1, 10)
assert_raises(IndexError,
ds.raw_slice, recspan_id, 10, -1)
assert ds.raw_slice(recspan_id, 2, 2).shape == (0, 2)
assert np.all(ds.raw_slice(recspan_id, 2, 5)
== recspan.iloc[2:5, :])
assert_raises(IndexError,
ds.raw_slice, recspan_id, 0, 200)
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])
# Smoke test
repr(dataset)
def load_erpss(raw, log, calibration_events="condition == 0",
lazy=True,
calibrate=False,
calibrate_half_width_ticks=5,
calibrate_low_cursor_time=None,
calibrate_high_cursor_time=None,
calibrate_pulse_size=None,
calibrate_polarity=1):
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)
(fetcher, hz, channel_names, raw_codes, data, header_metadata
) = read_raw(raw, dtype, lazy)
metadata.update(header_metadata)
if calibrate:
units = "uV"
else:
units = "RAW"
data_format = DataFormat(hz, units, channel_names)
total_ticks = raw_codes.shape[0]
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,))
# Sometimes people "delete" events by setting the high (sign) bit of the
# code in the log file (e.g. with 'logpoke'). So we ignore this bit when
# comparing log codes to raw codes -- mismatches here do not indicate an
# error -- and then are careful to use the log codes, rather than the
# raw codes, below.
if np.any((expanded_log_codes & ~0x8000) != (raw_codes & ~0x8000)):
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] == total_ticks
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:
if lazy:
lr = LazyRecspan(fetcher, dtype, len(channel_names),
span_slice.start, span_slice.stop)
dataset.add_lazy_recspan(lr, span_slice.stop - span_slice.start,
metadata)
else:
dataset.add_recspan(data[span_slice, :], metadata)
span_starts = [s.start for s in span_slices]
recspan_ids = []
start_ticks = []
for tick in raw_log_events.index:
recspan_id = bisect.bisect(span_starts, tick) - 1
span_slice = span_slices[recspan_id]
span_start = span_slice.start
span_stop = span_slice.stop
assert span_start <= tick < span_stop
recspan_ids.append(recspan_id)
start_ticks.append(tick - span_start)
stop_ticks = [tick + 1 for tick in start_ticks]
dataset.add_events(recspan_ids, start_ticks, stop_ticks,
raw_log_events)
for delete_event in dataset.events_query({"code": DELETE_CODE}):
delete_event.recspan_info["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
if calibrate:
for kwarg in ["calibrate_low_cursor_time",
"calibrate_high_cursor_time",
"calibrate_pulse_size"]:
if locals()[kwarg] is None:
raise ValueError("when calibrating, %s= argument must be "
"specified" % (kwarg,))
half_width = dataset.data_format.ticks_to_ms(calibrate_half_width_ticks)
cal_vals = {}
for which, cursor_time in [("low", calibrate_low_cursor_time),
("high", calibrate_high_cursor_time)]:
# Round cursor to nearest tick
cursor_tick = dataset.data_format.ms_to_ticks(cursor_time)
cursor_time = dataset.data_format.ticks_to_ms(cursor_tick)
erp = dataset.rerp("calibration_pulse",
cursor_time - half_width,
cursor_time + half_width,
"1",
all_or_nothing=True,
overlap_correction=False,
verbose=False)
cal_vals[which] = erp.betas["Intercept"].mean()
cal_diffs = cal_vals["high"] - cal_vals["low"]
calibrate_pulse_size *= calibrate_polarity
# For each channel, we want to multiply by a factor with units uV/raw
# We have calibrate_pulse_size uV = cal_diffs raw
cal_scaler = calibrate_pulse_size / cal_diffs
dataset.transform(np.diagflat(np.asarray(cal_scaler)))
return dataset
