def test_get_running_df_one_fewer_timestamp_check_warning(
running_data, timestamps, lowpass):
with pytest.warns(
UserWarning,
match="Time array is 1 value shorter than encoder array.*"):
# Call with one fewer timestamp, check for a warning
_ = get_running_df(data=running_data,
time=timestamps[:-1],
lowpass=lowpass)
def test_get_running_df_one_fewer_timestamp_check_truncation(
running_data, timestamps, lowpass):
# Call with one fewer timestamp
output = get_running_df(data=running_data,
time=timestamps[:-1],
lowpass=lowpass)
# Check that the output is actually trimmed, and the values are the same
assert len(output) == len(timestamps) - 1
np.testing.assert_equal(
output["v_sig"],
running_data["items"]["behavior"]["encoders"][0]["vsig"][:-1])
np.testing.assert_equal(
output["v_in"],
running_data["items"]["behavior"]["encoders"][0]["vin"][:-1])
def test_get_running_df(running_data, timestamps, lowpass):
actual = get_running_df(running_data, timestamps, lowpass=lowpass)
np.testing.assert_array_equal(actual.index, timestamps)
assert sorted(list(actual)) == ["dx", "speed", "v_in", "v_sig"]
# Should bring raw data through
np.testing.assert_array_equal(
actual["v_sig"].values,
running_data["items"]["behavior"]["encoders"][0]["vsig"])
np.testing.assert_array_equal(
actual["v_in"].values,
running_data["items"]["behavior"]["encoders"][0]["vin"])
np.testing.assert_array_equal(
actual["dx"].values,
running_data["items"]["behavior"]["encoders"][0]["dx"])
if lowpass:
assert np.count_nonzero(np.isnan(actual["speed"])) == 0
def from_json(
cls,
dict_repr: dict,
) -> "RunningAcquisition":
stimulus_file = StimulusFile.from_json(dict_repr)
stimulus_timestamps = StimulusTimestamps.from_json(dict_repr)
running_acq_df = get_running_df(
data=stimulus_file.data,
time=stimulus_timestamps.value,
)
running_acq_df.drop("speed", axis=1, inplace=True)
return cls(
running_acquisition=running_acq_df,
stimulus_file=stimulus_file,
stimulus_timestamps=stimulus_timestamps,
)
def _get_running_speed_df(stimulus_file: StimulusFile,
stimulus_timestamps: StimulusTimestamps,
filtered: bool = True,
zscore_threshold: float = 1.0) -> pd.DataFrame:
running_data_df = get_running_df(data=stimulus_file.data,
time=stimulus_timestamps.value,
lowpass=filtered,
zscore_threshold=zscore_threshold)
if running_data_df.index.name != "timestamps":
raise DataFrameIndexError(
f"Expected running_data_df index to be named 'timestamps' "
f"But instead got: '{running_data_df.index.name}'")
running_speed = pd.DataFrame({
"timestamps": running_data_df.index.values,
"speed": running_data_df.speed.values
})
return running_speed
def from_lims(
cls,
db: PostgresQueryMixin,
behavior_session_id: int,
ophys_experiment_id: Optional[int] = None,
) -> "RunningAcquisition":
stimulus_file = StimulusFile.from_lims(db, behavior_session_id)
stimulus_timestamps = StimulusTimestamps.from_stimulus_file(
stimulus_file=stimulus_file)
running_acq_df = get_running_df(
data=stimulus_file.data,
time=stimulus_timestamps.value,
)
running_acq_df.drop("speed", axis=1, inplace=True)
return cls(
running_acquisition=running_acq_df,
stimulus_file=stimulus_file,
stimulus_timestamps=stimulus_timestamps,
)