mirror of
https://github.com/p2p-ld/nwb-linkml.git
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244 lines
7.8 KiB
Python
244 lines
7.8 KiB
Python
from typing import Tuple
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import numpy as np
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import pytest
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# FIXME: Make this just be the output of the provider by patching into import machinery
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from nwb_linkml.models.pydantic.core.v2_7_0.namespace import (
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Device,
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DynamicTable,
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DynamicTableRegion,
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ElectricalSeries,
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ElectrodeGroup,
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ExtracellularEphysElectrodes,
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Units,
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VectorIndex,
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)
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@pytest.fixture()
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def electrical_series() -> Tuple["ElectricalSeries", "ExtracellularEphysElectrodes"]:
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"""
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Demo electrical series with adjoining electrodes
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"""
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n_electrodes = 5
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n_times = 100
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data = np.arange(0, n_electrodes * n_times).reshape(n_times, n_electrodes).astype(float)
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timestamps = np.linspace(0, 1, n_times)
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device = Device(name="my electrode")
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# electrode group is the physical description of the electrodes
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electrode_group = ElectrodeGroup(
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name="GroupA",
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device=device,
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description="an electrode group",
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location="you know where it is",
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)
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# make electrodes tables
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electrodes = ExtracellularEphysElectrodes(
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description="idk these are also electrodes",
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id=np.arange(0, n_electrodes),
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x=np.arange(0, n_electrodes).astype(float),
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y=np.arange(n_electrodes, n_electrodes * 2).astype(float),
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group=[electrode_group] * n_electrodes,
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group_name=[electrode_group.name] * n_electrodes,
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location=[str(i) for i in range(n_electrodes)],
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extra_column=["sup"] * n_electrodes,
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)
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electrical_series = ElectricalSeries(
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name="my recording!",
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electrodes=DynamicTableRegion(
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table=electrodes,
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value=np.arange(n_electrodes - 1, -1, step=-1),
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name="electrodes",
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description="hey",
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),
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timestamps=timestamps,
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data=data,
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)
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return electrical_series, electrodes
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def _ragged_array(n_units: int) -> tuple[list[np.ndarray], np.ndarray]:
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generator = np.random.default_rng()
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spike_times = [
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np.full(shape=generator.integers(10, 50), fill_value=i, dtype=float) for i in range(n_units)
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]
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spike_idx = []
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for i in range(n_units):
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if i == 0:
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spike_idx.append(len(spike_times[0]))
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else:
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spike_idx.append(len(spike_times[i]) + spike_idx[i - 1])
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spike_idx = np.array(spike_idx)
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return spike_times, spike_idx
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@pytest.fixture(params=[True, False])
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def units(request) -> Tuple[Units, list[np.ndarray], np.ndarray]:
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"""
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Test case for units
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Parameterized by extra_column because pandas likes to pivot dataframes
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to long when there is only one column and it's not len() == 1
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"""
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spike_times, spike_idx = _ragged_array(24)
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spike_times_flat = np.concatenate(spike_times)
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kwargs = {
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"description": "units!!!!",
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"spike_times": spike_times_flat,
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"spike_times_index": spike_idx,
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}
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if request.param:
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kwargs["extra_column"] = ["hey!"] * 24
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units = Units(**kwargs)
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return units, spike_times, spike_idx
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def test_dynamictable_indexing(electrical_series):
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"""
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Can index values from a dynamictable
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"""
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series, electrodes = electrical_series
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colnames = [
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"id",
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"x",
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"y",
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"group",
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"group_name",
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"location",
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"extra_column",
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]
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dtypes = [
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np.dtype("int64"),
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np.dtype("float64"),
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np.dtype("float64"),
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] + ([np.dtype("O")] * 4)
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row = electrodes[0]
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# successfully get a single row :)
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assert row.shape == (1, 7)
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assert row.dtypes.values.tolist() == dtypes
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assert row.columns.tolist() == colnames
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# slice a range of rows
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rows = electrodes[0:3]
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assert rows.shape == (3, 7)
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assert rows.dtypes.values.tolist() == dtypes
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assert rows.columns.tolist() == colnames
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# get a single column
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col = electrodes["y"]
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assert all(col == [5, 6, 7, 8, 9])
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# get a single cell
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val = electrodes[0, "y"]
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assert val == 5
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val = electrodes[0, 2]
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assert val == 5
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# get a slice of rows and columns
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subsection = electrodes[0:3, 0:3]
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assert subsection.shape == (3, 3)
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assert subsection.columns.tolist() == colnames[0:3]
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assert subsection.dtypes.values.tolist() == dtypes[0:3]
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def test_dynamictable_region_basic(electrical_series):
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"""
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DynamicTableRegion should be able to refer to a row or rows of another table
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itself as a column within a table
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"""
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series, electrodes = electrical_series
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row = series.electrodes[0]
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# check that we correctly got the 4th row instead of the 0th row,
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# since the indexed table was constructed with inverted indexes because it's a test, ya dummy.
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# we will only vaguely check the basic functionality here bc
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# a) the indexing behavior of the indexed objects is tested above, and
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# b) every other object in the chain is strictly validated,
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# so we assume if we got a right shaped df that it is the correct one.
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# feel free to @ me when i am wrong about this
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assert row.id == 4
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assert row.shape == (1, 7)
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# and we should still be preserving the model that is the contents of the cell of this row
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# so this is a dataframe row with a column "group" that contains an array of ElectrodeGroup
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# objects and that's as far as we are going to chase the recursion in this basic indexing test
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# ElectrodeGroup is strictly validating so an instance check is all we need.
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assert isinstance(row.group.values[0], ElectrodeGroup)
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# getting a list of table rows is actually correct behavior here because
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# this list of table rows is actually the cell of another table
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rows = series.electrodes[0:3]
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assert all([row.id == idx for row, idx in zip(rows, [4, 3, 2])])
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def test_dynamictable_region_ragged():
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"""
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Dynamictables can also have indexes so that they are ragged arrays of column rows
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"""
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spike_times, spike_idx = _ragged_array(24)
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spike_times_flat = np.concatenate(spike_times)
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# construct a secondary index that selects overlapping segments of the first table
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value = np.array([0, 1, 2, 1, 2, 3, 2, 3, 4])
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idx = np.array([3, 6, 9])
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table = DynamicTable(
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name="table",
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description="a table what else would it be",
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id=np.arange(len(spike_idx)),
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timeseries=spike_times,
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timeseries_index=spike_idx,
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)
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region = DynamicTableRegion(
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name="dynamictableregion",
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description="this field should be optional",
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table=table,
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value=value,
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)
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index = VectorIndex(name="index", description="hgggggggjjjj", target=region, value=idx)
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region._index = index
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rows = region[1]
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# i guess this is right?
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# the region should be a set of three rows of the table, with a ragged array column timeseries
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# like...
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#
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# id timeseries
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# 0 1 [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...
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# 1 2 [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, ...
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# 2 3 [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, ...
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assert rows.shape(3, 2)
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assert all(rows.id == [1, 2, 3])
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assert all([all(row[1].timeseries == i) for i, row in zip([1, 2, 3], rows.iterrows())])
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def test_dynamictable_ragged(units):
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"""
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Should be able to index ragged arrays using an implicit _index column
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Also tests:
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- passing arrays directly instead of wrapping in vectordata/index specifically,
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if the models in the fixture instantiate then this works
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"""
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units, spike_times, spike_idx = units
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# ensure we don't pivot to long when indexing
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assert units[0].shape[0] == 1
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# check that we got the indexing boundaries corrunect
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# (and that we are forwarding attr calls to the dataframe by accessing shape
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for i in range(units.shape[0]):
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assert np.all(units.iloc[i, 0] == spike_times[i])
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def test_dynamictable_append_column():
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pass
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def test_dynamictable_append_row():
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pass
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