def test_write_probe_lfp_file_roundtrip(tmpdir_factory, roundtrip, lfp_data, probe_data, csd_data):
expected_csd = xr.DataArray(
name="CSD",
data=csd_data["csd"],
dims=["virtual_channel_index", "time"],
coords={
"virtual_channel_index": np.arange(csd_data["csd"].shape[0]),
"time": csd_data["relative_window"],
"vertical_position": (("virtual_channel_index",), csd_data["csd_locations"][:, 1]),
"horizontal_position": (("virtual_channel_index",), csd_data["csd_locations"][:, 0]),
}
)
expected_lfp = xr.DataArray(
name="LFP",
data=lfp_data["data"],
dims=["time", "channel"],
coords=[lfp_data["timestamps"], [2, 1]]
)
tmpdir = Path(tmpdir_factory.mktemp("probe_lfp_nwb"))
input_data_path = tmpdir / Path("lfp_data.dat")
input_timestamps_path = tmpdir / Path("lfp_timestamps.npy")
input_channels_path = tmpdir / Path("lfp_channels.npy")
input_csd_path = tmpdir / Path("csd.h5")
output_path = str(tmpdir / Path("lfp.nwb")) # pynwb.NWBHDF5IO chokes on Path
test_lfp_paths = {
"input_data_path": input_data_path,
"input_timestamps_path": input_timestamps_path,
"input_channels_path": input_channels_path,
"output_path": output_path
}
probe_data.update({"lfp": test_lfp_paths})
probe_data.update({"csd_path": input_csd_path})
write_csd_to_h5(path=input_csd_path, **csd_data)
np.save(input_timestamps_path, lfp_data["timestamps"], allow_pickle=False)
np.save(input_channels_path, lfp_data["subsample_channels"], allow_pickle=False)
with open(input_data_path, "wb") as input_data_file:
input_data_file.write(lfp_data["data"].tobytes())
write_nwb.write_probe_lfp_file(4242, None, datetime.now(), logging.INFO, probe_data)
obt = EcephysNwbSessionApi(path=None, probe_lfp_paths={12345: NWBHDF5IO(output_path, "r").read})
obtained_lfp = obt.get_lfp(12345)
obtained_csd = obt.get_current_source_density(12345)
xr.testing.assert_equal(obtained_lfp, expected_lfp)
xr.testing.assert_equal(obtained_csd, expected_csd)
def test_write_probe_lfp_file(tmpdir_factory, lfp_data, probe_data, csd_data):
tmpdir = Path(tmpdir_factory.mktemp("probe_lfp_nwb"))
input_data_path = tmpdir / Path("lfp_data.dat")
input_timestamps_path = tmpdir / Path("lfp_timestamps.npy")
input_channels_path = tmpdir / Path("lfp_channels.npy")
input_csd_path = tmpdir / Path("csd.h5")
output_path = str(tmpdir / Path("lfp.nwb")) # pynwb.NWBHDF5IO chokes on Path
test_lfp_paths = {
"input_data_path": input_data_path,
"input_timestamps_path": input_timestamps_path,
"input_channels_path": input_channels_path,
"output_path": output_path
}
test_session_metadata = {
"specimen_name": "A",
"age_in_days": 100.0,
"full_genotype": "wt",
"strain": "A strain",
"sex": "M",
"stimulus_name": "test_stim",
"species": "Mus musculus",
"donor_id": 42
}
probe_data.update({"lfp": test_lfp_paths})
probe_data.update({"csd_path": input_csd_path})
write_csd_to_h5(path=input_csd_path, **csd_data)
np.save(input_timestamps_path, lfp_data["timestamps"], allow_pickle=False)
np.save(input_channels_path, lfp_data["subsample_channels"], allow_pickle=False)
with open(input_data_path, "wb") as input_data_file:
input_data_file.write(lfp_data["data"].tobytes())
write_nwb.write_probe_lfp_file(4242, test_session_metadata, datetime.now(), logging.INFO, probe_data)
exp_electrodes = pd.DataFrame(probe_data["channels"]).set_index("id").loc[[2, 1], :]
exp_electrodes.rename(columns={"anterior_posterior_ccf_coordinate": "x",
"dorsal_ventral_ccf_coordinate": "y",
"left_right_ccf_coordinate": "z",
"manual_structure_acronym": "location"}, inplace=True)
with pynwb.NWBHDF5IO(output_path, "r") as obt_io:
obt_f = obt_io.read()
obt_ser = obt_f.get_acquisition("probe_12345_lfp").electrical_series["probe_12345_lfp_data"]
assert np.allclose(lfp_data["data"], obt_ser.data[:])
assert np.allclose(lfp_data["timestamps"], obt_ser.timestamps[:])
obt_electrodes = obt_f.electrodes.to_dataframe().loc[
:, ["local_index", "probe_horizontal_position",
"probe_id", "probe_vertical_position",
"valid_data", "x", "y", "z", "location", "impedence",
"filtering"]
]
assert obt_f.session_id == "4242"
assert obt_f.subject.subject_id == "42"
# There is a difference in how int dtypes are being saved in Windows
# that are causing tests to fail.
# Perhaps related to: https://stackoverflow.com/a/36279549
if platform.system() == "Windows":
pd.testing.assert_frame_equal(obt_electrodes, exp_electrodes, check_like=True, check_dtype=False)
else:
pd.testing.assert_frame_equal(obt_electrodes, exp_electrodes, check_like=True)
csd_series = obt_f.get_processing_module("current_source_density")["current_source_density"]
assert np.allclose(csd_data["csd"], csd_series.time_series.data[:].T)
assert np.allclose(csd_data["relative_window"], csd_series.time_series.timestamps[:])
obt_channel_locations = np.stack((csd_series.virtual_electrode_x_positions,
csd_series.virtual_electrode_y_positions),
axis=1)
assert np.allclose([[1, 2], [3, 3]], obt_channel_locations) # csd interpolated channel locations
def test_write_probe_lfp_file(tmpdir_factory, lfp_data):
tmpdir = Path(tmpdir_factory.mktemp("probe_lfp_nwb"))
input_data_path = tmpdir / Path("lfp_data.dat")
input_timestamps_path = tmpdir / Path("lfp_timestamps.npy")
input_channels_path = tmpdir / Path("lfp_channels.npy")
input_csd_path = tmpdir / Path("csd.h5")
output_path = str(tmpdir /
Path("lfp.nwb")) # pynwb.NWBHDF5IO chokes on Path
probe_data = {
"id":
12345,
"name":
"probeA",
"sampling_rate":
29.0,
"lfp_sampling_rate":
10.0,
"temporal_subsampling_factor":
2.0,
"channels": [{
'id': 0,
'probe_id': 12,
'local_index': 1,
'probe_vertical_position': 21,
'probe_horizontal_position': 33
}, {
'id': 1,
'probe_id': 12,
'local_index': 2,
'probe_vertical_position': 21,
'probe_horizontal_position': 32
}, {
'id': 2,
'probe_id': 12,
'local_index': 3,
'probe_vertical_position': 21,
'probe_horizontal_position': 31
}],
"lfp": {
"input_data_path": input_data_path,
"input_timestamps_path": input_timestamps_path,
"input_channels_path": input_channels_path,
"output_path": output_path
},
"csd_path":
input_csd_path,
"amplitude_scale_factor":
1.0
}
csd = np.arange(20).reshape([2, 10])
csd_times = np.linspace(-1, 1, 10)
csd_channels = np.array([3, 2])
csd_locations = np.array([[1, 2], [3, 3]])
write_csd_to_h5(path=input_csd_path,
csd=csd,
relative_window=csd_times,
channels=csd_channels,
csd_locations=csd_locations,
stimulus_name="foo",
stimulus_index=None,
num_trials=1000)
np.save(input_timestamps_path, lfp_data["timestamps"], allow_pickle=False)
np.save(input_channels_path,
lfp_data["subsample_channels"],
allow_pickle=False)
with open(input_data_path, "wb") as input_data_file:
input_data_file.write(lfp_data["data"].tobytes())
write_nwb.write_probe_lfp_file(datetime.now(), logging.INFO, probe_data)
exp_electrodes = pd.DataFrame(
probe_data["channels"]).set_index("id").loc[[2, 1], :]
with pynwb.NWBHDF5IO(output_path, "r") as obt_io:
obt_f = obt_io.read()
obt_ser = obt_f.get_acquisition(
"probe_12345_lfp").electrical_series["probe_12345_lfp_data"]
assert np.allclose(lfp_data["data"], obt_ser.data[:])
assert np.allclose(lfp_data["timestamps"], obt_ser.timestamps[:])
obt_electrodes = obt_f.electrodes.to_dataframe().loc[:, [
'local_index', 'probe_horizontal_position', 'probe_id',
'probe_vertical_position'
]]
pd.testing.assert_frame_equal(exp_electrodes,
obt_electrodes,
check_like=True)
csd_series = obt_f.get_processing_module(
"current_source_density")["current_source_density"]
assert np.allclose(csd, csd_series.data[:])
assert np.allclose(csd_times, csd_series.timestamps[:])
assert np.allclose(
[[1, 2], [3, 3]],
csd_series.control[:]) # csd interpolated channel locations