nwb-linkml/nwb_linkml/tests/test_includes/test_hdmf.py

from typing import Tuple

import numpy as np
import pytest

# FIXME: Make this just be the output of the provider by patching into import machinery
from nwb_linkml.models.pydantic.core.v2_7_0.namespace import (
    Device,
    DynamicTable,
    DynamicTableRegion,
    ElectricalSeries,
    ElectrodeGroup,
    ExtracellularEphysElectrodes,
    Units,
    VectorIndex,
)


@pytest.fixture()
def electrical_series() -> Tuple["ElectricalSeries", "ExtracellularEphysElectrodes"]:
    """
    Demo electrical series with adjoining electrodes
    """
    n_electrodes = 5
    n_times = 100
    data = np.arange(0, n_electrodes * n_times).reshape(n_times, n_electrodes).astype(float)
    timestamps = np.linspace(0, 1, n_times)

    device = Device(name="my electrode")

    # electrode group is the physical description of the electrodes
    electrode_group = ElectrodeGroup(
        name="GroupA",
        device=device,
        description="an electrode group",
        location="you know where it is",
    )

    # make electrodes tables
    electrodes = ExtracellularEphysElectrodes(
        description="idk these are also electrodes",
        id=np.arange(0, n_electrodes),
        x=np.arange(0, n_electrodes).astype(float),
        y=np.arange(n_electrodes, n_electrodes * 2).astype(float),
        group=[electrode_group] * n_electrodes,
        group_name=[electrode_group.name] * n_electrodes,
        location=[str(i) for i in range(n_electrodes)],
        extra_column=["sup"] * n_electrodes,
    )

    electrical_series = ElectricalSeries(
        name="my recording!",
        electrodes=DynamicTableRegion(
            table=electrodes,
            value=np.arange(n_electrodes - 1, -1, step=-1),
            name="electrodes",
            description="hey",
        ),
        timestamps=timestamps,
        data=data,
    )
    return electrical_series, electrodes


def _ragged_array(n_units: int) -> tuple[list[np.ndarray], np.ndarray]:
    generator = np.random.default_rng()
    spike_times = [
        np.full(shape=generator.integers(10, 50), fill_value=i, dtype=float) for i in range(n_units)
    ]
    spike_idx = []
    for i in range(n_units):
        if i == 0:
            spike_idx.append(len(spike_times[0]))
        else:
            spike_idx.append(len(spike_times[i]) + spike_idx[i - 1])
    spike_idx = np.array(spike_idx)
    return spike_times, spike_idx


@pytest.fixture(params=[True, False])
def units(request) -> Tuple[Units, list[np.ndarray], np.ndarray]:
    """
    Test case for units

    Parameterized by extra_column because pandas likes to pivot dataframes
    to long when there is only one column and it's not len() == 1
    """
    spike_times, spike_idx = _ragged_array(24)

    spike_times_flat = np.concatenate(spike_times)

    kwargs = {
        "description": "units!!!!",
        "spike_times": spike_times_flat,
        "spike_times_index": spike_idx,
    }
    if request.param:
        kwargs["extra_column"] = ["hey!"] * 24
    units = Units(**kwargs)
    return units, spike_times, spike_idx


def test_dynamictable_indexing(electrical_series):
    """
    Can index values from a dynamictable
    """
    series, electrodes = electrical_series

    colnames = [
        "id",
        "x",
        "y",
        "group",
        "group_name",
        "location",
        "extra_column",
    ]
    dtypes = [
        np.dtype("int64"),
        np.dtype("float64"),
        np.dtype("float64"),
    ] + ([np.dtype("O")] * 4)

    row = electrodes[0]
    # successfully get a single row :)
    assert row.shape == (1, 7)
    assert row.dtypes.values.tolist() == dtypes
    assert row.columns.tolist() == colnames

    # slice a range of rows
    rows = electrodes[0:3]
    assert rows.shape == (3, 7)
    assert rows.dtypes.values.tolist() == dtypes
    assert rows.columns.tolist() == colnames

    # get a single column
    col = electrodes["y"]
    assert all(col == [5, 6, 7, 8, 9])

    # get a single cell
    val = electrodes[0, "y"]
    assert val == 5
    val = electrodes[0, 2]
    assert val == 5

    # get a slice of rows and columns
    subsection = electrodes[0:3, 0:3]
    assert subsection.shape == (3, 3)
    assert subsection.columns.tolist() == colnames[0:3]
    assert subsection.dtypes.values.tolist() == dtypes[0:3]


def test_dynamictable_region_basic(electrical_series):
    """
    DynamicTableRegion should be able to refer to a row or rows of another table
    itself as a column within a table
    """
    series, electrodes = electrical_series
    row = series.electrodes[0]
    # check that we correctly got the 4th row instead of the 0th row,
    # since the indexed table was constructed with inverted indexes because it's a test, ya dummy.
    # we will only vaguely check the basic functionality here bc
    # a) the indexing behavior of the indexed objects is tested above, and
    # b) every other object in the chain is strictly validated,
    # so we assume if we got a right shaped df that it is the correct one.
    # feel free to @ me when i am wrong about this
    assert row.id == 4
    assert row.shape == (1, 7)
    # and we should still be preserving the model that is the contents of the cell of this row
    # so this is a dataframe row with a column "group" that contains an array of ElectrodeGroup
    # objects and that's as far as we are going to chase the recursion in this basic indexing test
    # ElectrodeGroup is strictly validating so an instance check is all we need.
    assert isinstance(row.group.values[0], ElectrodeGroup)

    # getting a list of table rows is actually correct behavior here because
    # this list of table rows is actually the cell of another table
    rows = series.electrodes[0:3]
    assert all([row.id == idx for row, idx in zip(rows, [4, 3, 2])])


def test_dynamictable_region_ragged():
    """
    Dynamictables can also have indexes so that they are ragged arrays of column rows
    """
    spike_times, spike_idx = _ragged_array(24)
    spike_times_flat = np.concatenate(spike_times)

    # construct a secondary index that selects overlapping segments of the first table
    value = np.array([0, 1, 2, 1, 2, 3, 2, 3, 4])
    idx = np.array([3, 6, 9])

    table = DynamicTable(
        name="table",
        description="a table what else would it be",
        id=np.arange(len(spike_idx)),
        timeseries=spike_times,
        timeseries_index=spike_idx,
    )
    region = DynamicTableRegion(
        name="dynamictableregion",
        description="this field should be optional",
        table=table,
        value=value,
    )
    index = VectorIndex(name="index", description="hgggggggjjjj", target=region, value=idx)
    region._index = index
    rows = region[1]
    # i guess this is right?
    # the region should be a set of three rows of the table, with a ragged array column timeseries
    # like...
    #
    #    id                                         timeseries
    # 0   1  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...
    # 1   2  [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, ...
    # 2   3  [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, ...
    assert rows.shape == (3, 2)
    assert all(rows.id == [1, 2, 3])
    assert all([all(row[1].timeseries == i) for i, row in zip([1, 2, 3], rows.iterrows())])


def test_dynamictable_ragged(units):
    """
    Should be able to index ragged arrays using an implicit _index column

    Also tests:
    - passing arrays directly instead of wrapping in vectordata/index specifically,
      if the models in the fixture instantiate then this works
    """
    units, spike_times, spike_idx = units

    # ensure we don't pivot to long when indexing
    assert units[0].shape[0] == 1
    # check that we got the indexing boundaries corrunect
    # (and that we are forwarding attr calls to the dataframe by accessing shape
    for i in range(units.shape[0]):
        assert np.all(units.iloc[i, 0] == spike_times[i])


def test_dynamictable_append_column():
    pass


def test_dynamictable_append_row():
    pass