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Added ability to index datasets wtih arrays with getitem
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sneakers-the-rat 2023-10-18 21:18:02 -07:00
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3
docs/Makefile
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@ -15,7 +15,8 @@ help:
.PHONY: help Makefile
serve:
sphinx-autobuild "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) --watch ../nwb_linkml/src/nwb_linkml --watch ../nwb_schema_language/src/nwb_schema_language
# env variable that makes it so we don't build all the models while in dev mode
SPHINX_MINIMAL="True" sphinx-autobuild "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) --watch ../nwb_linkml/src/nwb_linkml --watch ../nwb_schema_language/src/nwb_schema_language
serve_fast:
sphinx-autobuild -a "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) --watch ../nwb_linkml/src/nwb_linkml --watch ../nwb_schema_language/src/nwb_schema_language

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@ -0,0 +1,7 @@
.feature {
background-color: var(--color-admonition-title--hint);
padding: 2px 10px;
color: #1a1c1e;
font-style: italic;
border-radius: 4px;
}

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docs/api/nwb_schema_language/index.md
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@ -1,4 +1,4 @@
# NWB Schema Language
# nwb_schema_language
```{toctree}
:maxdepth: 1

23
docs/conf.py
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@ -2,6 +2,7 @@
#
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
import pdb
# -- Project information -----------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
@ -11,23 +12,34 @@ copyright = '2023, Jonny Saunders'
author = 'Jonny Saunders'
release = 'v0.1.0'
import os
from sphinx.util.tags import Tags
tags: Tags
# -- General configuration ---------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
extensions = [
'sphinx.ext.graphviz',
"myst_parser",
'sphinx.ext.napoleon',
'sphinx.ext.autodoc',
'sphinxcontrib.autodoc_pydantic',
'sphinx.ext.intersphinx',
'sphinx.ext.doctest',
"sphinx_design"
"sphinx_design",
'myst_parser',
'sphinx_togglebutton'
]
templates_path = ['_templates']
# exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '**/models']
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
if os.environ.get('SPHINX_MINIMAL', None) == 'True':
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '**/models']
tags.add('minimal')
else:
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
tags.add('full')
@ -36,6 +48,9 @@ exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
html_theme = 'furo'
html_static_path = ['_static']
html_css_files = [
'css/custom.css'
]
intersphinx_mapping = {
'python': ('https://docs.python.org/3', None),

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docs/guide/overview.md Normal file
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@ -0,0 +1 @@
# Overview

294
docs/index.md
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@ -1,37 +1,315 @@
# nwb-linkml
```{role} feature
```
A translation of the [Neurodata Without Borders](https://www.nwb.org/) standard
to [LinkML](https://linkml.io/).
```{admonition} Quick Links
* [Purpose](purpose) - Why this package exists
* [Overview](overview) - Overview of how it works
* [API Docs](api) - Ok *really* how it works
```
`nwb-linkml` is an independent implementation of the standard capable of:
* Translating schemas written in the {index}`NWB Schema Language` to LinkML.
* Manage multiple versions of NWB schemas with dependencies
* Generating pydantic models from nwb-flavored LinkML
* Read NWB files (including those that use custom, embedded schemas)
* {feature}`Coming Soon` Write/edit NWB files
* {feature}`Coming Soon` Export NWB to a Relational Database
* {feature}`Coming Soon` Export NWB to a Triple Store
## Samples
### Reading
```python
from pathlib import Path
from rich import print
from nwb_linkml.io import HDF5IO
# find sample data file and read
nwb_file = Path('../nwb_linkml/tests/data/aibs.nwb')
data = HDF5IO(nwb_file).read()
print(data)
```
````{admonition} Model Print Output
:class: dropdown
```{literalinclude} read_output.txt
:language: python
```
````
### TimeSeries
(Abbreviated for clarity)
`````{tab-set}
````{tab-item} NWB schema
```yaml
groups:
- neurodata_type_def: TimeSeries
neurodata_type_inc: NWBDataInterface
doc: General purpose time series.
attributes:
- name: description
dtype: text
default_value: no description
doc: Description of the time series.
required: false
- name: comments
dtype: text
default_value: no comments
doc: Human-readable comments about the TimeSeries. This second descriptive field
can be used to store additional information, or descriptive information if the
primary description field is populated with a computer-readable string.
required: false
datasets:
- name: data
dims:
- - num_times
- - num_times
- num_DIM2
- - num_times
- num_DIM2
- num_DIM3
- - num_times
- num_DIM2
- num_DIM3
- num_DIM4
shape:
- - null
- - null
- null
- - null
- null
- null
- - null
- null
- null
- null
doc: Data values. Data can be in 1-D, 2-D, 3-D, or 4-D. The first dimension
should always represent time. This can also be used to store binary data
(e.g., image frames). This can also be a link to data stored in an external file.
attributes:
- name: conversion
dtype: float32
default_value: 1.0
doc: Scalar to multiply each element in data (...)
required: false
- name: timestamps
dtype: float64
dims:
- num_times
shape:
- null
doc: Timestamps for samples stored in data, in seconds, relative to the
common experiment master-clock stored in NWBFile.timestamps_reference_time.
quantity: '?'
attributes:
- name: interval
dtype: int32
value: 1
doc: Value is '1'
- name: unit
dtype: text
value: seconds
doc: Unit of measurement for timestamps, which is fixed to 'seconds'.
```
````
````{tab-item} LinkML
```yaml
classes:
TimeSeries:
name: TimeSeries
description: General purpose time series.
is_a: NWBDataInterface
attributes:
name:
name: name
identifier: true
range: string
required: true
description:
name: description
description: Description of the time series.
range: text
comments:
name: comments
description: Human-readable comments about the TimeSeries. This second descriptive
field can be used to store additional information, or descriptive information
if the primary description field is populated with a computer-readable string.
range: text
data:
name: data
description: Data values. Data can be in 1-D, 2-D, 3-D, or 4-D. The first
dimension should always represent time. This can also be used to store binary
data (e.g., image frames). This can also be a link to data stored in an
external file.
multivalued: false
range: TimeSeries__data
required: true
timestamps:
name: timestamps
description: Timestamps for samples stored in data, in seconds, relative to
the common experiment master-clock stored in NWBFile.timestamps_reference_time.
multivalued: false
range: TimeSeries__timestamps__Array
required: false
tree_root: true
TimeSeries__data:
name: TimeSeries__data
description: Data values. Data can be in 1-D, 2-D, 3-D, or 4-D. The first dimension
should always represent time. This can also be used to store binary data (e.g.,
image frames). This can also be a link to data stored in an external file.
attributes:
name:
name: name
ifabsent: string(data)
identifier: true
range: string
required: true
equals_string: data
conversion:
name: conversion
description: Scalar to multiply each element in data to convert it to the
specified 'unit'. If the data are stored in acquisition system units or
other units that require a conversion to be interpretable, multiply the
data by 'conversion' to convert the data to the specified 'unit'. e.g. if
the data acquisition system stores values in this object as signed 16-bit
integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V
to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion'
multiplier to get from raw data acquisition values to recorded volts is
2.5/32768/8000 = 9.5367e-9.
range: float32
array:
name: array
range: TimeSeries__data__Array
TimeSeries__data__Array:
name: TimeSeries__data__Array
is_a: Arraylike
attributes:
num_times:
name: num_times
range: AnyType
required: true
num_DIM2:
name: num_DIM2
range: AnyType
required: false
num_DIM3:
name: num_DIM3
range: AnyType
required: false
num_DIM4:
name: num_DIM4
range: AnyType
required: false
TimeSeries__timestamps__Array:
name: TimeSeries__timestamps__Array
is_a: Arraylike
attributes:
num_times:
name: num_times
range: float64
required: true
```
````
````{tab-item} Pydantic
```python
class TimeSeries(NWBDataInterface):
"""
General purpose time series.
"""
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(tree_root=True), frozen=True)
name: str = Field(...)
description: Optional[str] = Field(None, description="""Description of the time series.""")
comments: Optional[str] = Field(None, description="""Human-readable comments about the TimeSeries. This second descriptive field can be used to store additional information, or descriptive information if the primary description field is populated with a computer-readable string.""")
data: TimeSeriesData = Field(..., description="""Data values. Data can be in 1-D, 2-D, 3-D, or 4-D. The first dimension should always represent time. This can also be used to store binary data (e.g., image frames). This can also be a link to data stored in an external file.""")
timestamps: Optional[NDArray[Shape["* num_times"], Float64]] = Field(None, description="""Timestamps for samples stored in data, in seconds, relative to the common experiment master-clock stored in NWBFile.timestamps_reference_time.""")
class TimeSeriesData(ConfiguredBaseModel):
"""
Data values. Data can be in 1-D, 2-D, 3-D, or 4-D. The first dimension should always represent time. This can also be used to store binary data (e.g., image frames). This can also be a link to data stored in an external file.
"""
linkml_meta: ClassVar[LinkML_Meta] = Field(LinkML_Meta(), frozen=True)
name: Literal["data"] = Field("data")
conversion: Optional[float] = Field(None, description="""Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9.""")
array: Optional[Union[
NDArray[Shape["* num_times"], Any],
NDArray[Shape["* num_times, * num_DIM2"], Any],
NDArray[Shape["* num_times, * num_DIM2, * num_DIM3"], Any],
NDArray[Shape["* num_times, * num_DIM2, * num_DIM3, * num_DIM4"], Any]
]] = Field(None)
```
````
`````
```{toctree}
:caption: Intro
:maxdepth: 3
:hidden:
intro/purpose
intro/nwb
intro/translation
```
```{toctree}
:caption: Guide
:maxdepth: 1
:hidden:
guide/overview
```
````{only} minimal
```{toctree}
:caption: API
:maxdepth: 3
:hidden:
api/nwb_linkml/index
api/nwb_schema_language/index
api/nwb_linkml/schema/index
```
````
````{only} full
```{toctree}
:caption: API
:maxdepth: 3
:hidden:
api/nwb_linkml/index
api/nwb_schema_language/index
api/models/nwb_linkml.models
api/nwb_linkml/schema/index
```
[//]: # (api/models/nwb_linkml.models)
````
```{toctree}
:caption: Meta
:hidden:
todo
changelog
meta/todo
meta/changelog
genindex
```
## Indices and tables
* {ref}`genindex`
* {ref}`modindex`
* {ref}`search`

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@ -3,8 +3,14 @@
If [pynwb](https://pynwb.readthedocs.io/en/stable/) already exists,
why `nwb_linkml`?
Two kinds of reasons:
- using NWB as a test case for a larger infrastructure project, and
- potentially improving the state of NWB itself.
## A Stepping Stone...
In the
(word on how and why we are focusing on NWB as part of a larger project)
## Interoperable Schema Language

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NWBFile(
│ hdf5_path='/',
│ name='root',
│ nwb_version='2.2.2',
│ file_create_date=array([datetime.datetime(2020, 5, 26, 0, 53, 26, 903120, tzinfo=datetime.timezone(datetime.timedelta(days=-1, seconds=61200)))],
│ dtype=object),
│ identifier='760693773',
│ session_description='Data and metadata for an Ecephys session',
│ session_start_time=datetime.datetime(2018, 10, 26, 12, 59, 23, tzinfo=TzInfo(-07:00)),
│ timestamps_reference_time=datetime.datetime(2018, 10, 26, 12, 59, 23, tzinfo=TzInfo(-07:00)),
│ acquisition={
│ │ 'raw_running_wheel_rotation': TimeSeries(
│ │ │ hdf5_path='/acquisition/raw_running_wheel_rotation',
│ │ │ name='raw_running_wheel_rotation',
│ │ │ description='no description',
│ │ │ comments='no comments',
│ │ │ data=TimeSeriesData(
│ │ │ │ hdf5_path='/acquisition/raw_running_wheel_rotation/data',
│ │ │ │ object_id=None,
│ │ │ │ name='data',
│ │ │ │ conversion=1.0,
│ │ │ │ resolution=-1.0,
│ │ │ │ unit='radians',
│ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x120933210>
│ │ │ ),
│ │ │ starting_time=None,
│ │ │ timestamps=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x123abd2d0>,
│ │ │ control=None,
│ │ │ control_description=None,
│ │ │ sync=None
│ │ ),
│ │ 'running_wheel_signal_voltage': TimeSeries(
│ │ │ hdf5_path='/acquisition/running_wheel_signal_voltage',
│ │ │ name='running_wheel_signal_voltage',
│ │ │ description='no description',
│ │ │ comments='no comments',
│ │ │ data=TimeSeriesData(
│ │ │ │ hdf5_path='/acquisition/running_wheel_signal_voltage/data',
│ │ │ │ object_id=None,
│ │ │ │ name='data',
│ │ │ │ conversion=1.0,
│ │ │ │ resolution=-1.0,
│ │ │ │ unit='V',
│ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124931e90>
│ │ │ ),
│ │ │ starting_time=None,
│ │ │ timestamps=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x123abd2d0>,
│ │ │ control=None,
│ │ │ control_description=None,
│ │ │ sync=None
│ │ ),
│ │ 'running_wheel_supply_voltage': TimeSeries(
│ │ │ hdf5_path='/acquisition/running_wheel_supply_voltage',
│ │ │ name='running_wheel_supply_voltage',
│ │ │ description='no description',
│ │ │ comments='no comments',
│ │ │ data=TimeSeriesData(
│ │ │ │ hdf5_path='/acquisition/running_wheel_supply_voltage/data',
│ │ │ │ object_id=None,
│ │ │ │ name='data',
│ │ │ │ conversion=1.0,
│ │ │ │ resolution=-1.0,
│ │ │ │ unit='V',
│ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x1249310d0>
│ │ │ ),
│ │ │ starting_time=None,
│ │ │ timestamps=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x123abd2d0>,
│ │ │ control=None,
│ │ │ control_description=None,
│ │ │ sync=None
│ │ )
│ },
│ analysis={},
│ scratch={},
│ processing={
│ │ 'eye_tracking': ProcessingModule(
│ │ │ hdf5_path='/processing/eye_tracking',
│ │ │ name='eye_tracking',
│ │ │ children={
│ │ │ │ 'cr_ellipse_fits': cr_ellipse_fits(
│ │ │ │ │ hdf5_path='/processing/eye_tracking/cr_ellipse_fits',
│ │ │ │ │ name='cr_ellipse_fits',
│ │ │ │ │ colnames=array(['center_x', 'center_y', 'height', 'phi', 'width', 'timestamps'],
│ dtype=object),
│ │ │ │ │ description='',
│ │ │ │ │ id=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ center_x=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ center_y=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ height=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ phi=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ width=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ timestamps=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ vector_data=[],
│ │ │ │ │ vector_index=[]
│ │ │ │ ),
│ │ │ │ 'eye_ellipse_fits': eye_ellipse_fits(
│ │ │ │ │ hdf5_path='/processing/eye_tracking/eye_ellipse_fits',
│ │ │ │ │ name='eye_ellipse_fits',
│ │ │ │ │ colnames=array(['center_x', 'center_y', 'height', 'phi', 'width', 'timestamps'],
│ dtype=object),
│ │ │ │ │ description='',
│ │ │ │ │ id=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ center_x=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ center_y=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ height=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ phi=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ width=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ timestamps=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ vector_data=[],
│ │ │ │ │ vector_index=[]
│ │ │ │ ),
│ │ │ │ 'pupil_ellipse_fits': pupil_ellipse_fits(
│ │ │ │ │ hdf5_path='/processing/eye_tracking/pupil_ellipse_fits',
│ │ │ │ │ name='pupil_ellipse_fits',
│ │ │ │ │ colnames=array(['center_x', 'center_y', 'height', 'phi', 'width', 'timestamps'],
│ dtype=object),
│ │ │ │ │ description='',
│ │ │ │ │ id=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ center_x=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ center_y=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ height=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ phi=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ width=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ timestamps=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ vector_data=[],
│ │ │ │ │ vector_index=[]
│ │ │ │ )
│ │ │ }
│ │ ),
│ │ 'eye_tracking_rig_metadata': ProcessingModule(
│ │ │ hdf5_path='/processing/eye_tracking_rig_metadata',
│ │ │ name='eye_tracking_rig_metadata',
│ │ │ children={
│ │ │ │ 'eye_tracking_rig_metadata': EcephysEyeTrackingRigMetadata(
│ │ │ │ │ hdf5_path='/processing/eye_tracking_rig_metadata/eye_tracking_rig_metadata',
│ │ │ │ │ name='eye_tracking_rig_metadata',
│ │ │ │ │ equipment='NP.1',
│ │ │ │ │ monitor_position=EcephysEyeTrackingRigMetadataMonitorPosition(
│ │ │ │ │ │ hdf5_path='/processing/eye_tracking_rig_metadata/eye_tracking_rig_metadata/monitor_position',
│ │ │ │ │ │ object_id=None,
│ │ │ │ │ │ name='monitor_position',
│ │ │ │ │ │ unit='mm',
│ │ │ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c4f9d0>
│ │ │ │ │ ),
│ │ │ │ │ camera_position=EcephysEyeTrackingRigMetadataCameraPosition(
│ │ │ │ │ │ hdf5_path='/processing/eye_tracking_rig_metadata/eye_tracking_rig_metadata/camera_position',
│ │ │ │ │ │ object_id=None,
│ │ │ │ │ │ name='camera_position',
│ │ │ │ │ │ unit='mm',
│ │ │ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c30c50>
│ │ │ │ │ ),
│ │ │ │ │ led_position=EcephysEyeTrackingRigMetadataLedPosition(
│ │ │ │ │ │ hdf5_path='/processing/eye_tracking_rig_metadata/eye_tracking_rig_metadata/led_position',
│ │ │ │ │ │ object_id=None,
│ │ │ │ │ │ name='led_position',
│ │ │ │ │ │ unit='mm',
│ │ │ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c4c090>
│ │ │ │ │ ),
│ │ │ │ │ monitor_rotation=EcephysEyeTrackingRigMetadataMonitorRotation(
│ │ │ │ │ │ hdf5_path='/processing/eye_tracking_rig_metadata/eye_tracking_rig_metadata/monitor_rotation',
│ │ │ │ │ │ object_id=None,
│ │ │ │ │ │ name='monitor_rotation',
│ │ │ │ │ │ unit='deg',
│ │ │ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c4fad0>
│ │ │ │ │ ),
│ │ │ │ │ camera_rotation=EcephysEyeTrackingRigMetadataCameraRotation(
│ │ │ │ │ │ hdf5_path='/processing/eye_tracking_rig_metadata/eye_tracking_rig_metadata/camera_rotation',
│ │ │ │ │ │ object_id=None,
│ │ │ │ │ │ name='camera_rotation',
│ │ │ │ │ │ unit='deg',
│ │ │ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c4cfd0>
│ │ │ │ │ )
│ │ │ │ )
│ │ │ }
│ │ ),
│ │ 'filtered_gaze_mapping': ProcessingModule(
│ │ │ hdf5_path='/processing/filtered_gaze_mapping',
│ │ │ name='filtered_gaze_mapping',
│ │ │ children={
│ │ │ │ 'eye_area': TimeSeries(
│ │ │ │ │ hdf5_path='/processing/filtered_gaze_mapping/eye_area',
│ │ │ │ │ name='eye_area',
│ │ │ │ │ description='no description',
│ │ │ │ │ comments='no comments',
│ │ │ │ │ data=TimeSeriesData(
│ │ │ │ │ │ hdf5_path='/processing/filtered_gaze_mapping/eye_area/data',
│ │ │ │ │ │ object_id=None,
│ │ │ │ │ │ name='data',
│ │ │ │ │ │ conversion=1.0,
│ │ │ │ │ │ resolution=-1.0,
│ │ │ │ │ │ unit='Pixels ^ 2',
│ │ │ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c4fd50>
│ │ │ │ │ ),
│ │ │ │ │ starting_time=None,
│ │ │ │ │ timestamps=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c606d0>,
│ │ │ │ │ control=None,
│ │ │ │ │ control_description=None,
│ │ │ │ │ sync=None
│ │ │ │ ),
│ │ │ │ 'pupil_area': TimeSeries(
│ │ │ │ │ hdf5_path='/processing/filtered_gaze_mapping/pupil_area',
│ │ │ │ │ name='pupil_area',
│ │ │ │ │ description='no description',
│ │ │ │ │ comments='no comments',
│ │ │ │ │ data=TimeSeriesData(
│ │ │ │ │ │ hdf5_path='/processing/filtered_gaze_mapping/pupil_area/data',
│ │ │ │ │ │ object_id=None,
│ │ │ │ │ │ name='data',
│ │ │ │ │ │ conversion=1.0,
│ │ │ │ │ │ resolution=-1.0,
│ │ │ │ │ │ unit='Pixels ^ 2',
│ │ │ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c60990>
│ │ │ │ │ ),
│ │ │ │ │ starting_time=None,
│ │ │ │ │ timestamps=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c60d50>,
│ │ │ │ │ control=None,
│ │ │ │ │ control_description=None,
│ │ │ │ │ sync=None
│ │ │ │ ),
│ │ │ │ 'screen_coordinates': TimeSeries(
│ │ │ │ │ hdf5_path='/processing/filtered_gaze_mapping/screen_coordinates',
│ │ │ │ │ name='screen_coordinates',
│ │ │ │ │ description='no description',
│ │ │ │ │ comments='no comments',
│ │ │ │ │ data=TimeSeriesData(
│ │ │ │ │ │ hdf5_path='/processing/filtered_gaze_mapping/screen_coordinates/data',
│ │ │ │ │ │ object_id=None,
│ │ │ │ │ │ name='data',
│ │ │ │ │ │ conversion=1.0,
│ │ │ │ │ │ resolution=-1.0,
│ │ │ │ │ │ unit='Centimeters',
│ │ │ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c612d0>
│ │ │ │ │ ),
│ │ │ │ │ starting_time=None,
│ │ │ │ │ timestamps=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c615d0>,
│ │ │ │ │ control=None,
│ │ │ │ │ control_description=None,
│ │ │ │ │ sync=None
│ │ │ │ ),
│ │ │ │ 'screen_coordinates_spherical': TimeSeries(
│ │ │ │ │ hdf5_path='/processing/filtered_gaze_mapping/screen_coordinates_spherical',
│ │ │ │ │ name='screen_coordinates_spherical',
│ │ │ │ │ description='no description',
│ │ │ │ │ comments='no comments',
│ │ │ │ │ data=TimeSeriesData(
│ │ │ │ │ │ hdf5_path='/processing/filtered_gaze_mapping/screen_coordinates_spherical/data',
│ │ │ │ │ │ object_id=None,
│ │ │ │ │ │ name='data',
│ │ │ │ │ │ conversion=1.0,
│ │ │ │ │ │ resolution=-1.0,
│ │ │ │ │ │ unit='Degrees',
│ │ │ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c61a10>
│ │ │ │ │ ),
│ │ │ │ │ starting_time=None,
│ │ │ │ │ timestamps=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c61d10>,
│ │ │ │ │ control=None,
│ │ │ │ │ control_description=None,
│ │ │ │ │ sync=None
│ │ │ │ )
│ │ │ }
│ │ ),
│ │ 'optotagging': ProcessingModule(
│ │ │ hdf5_path='/processing/optotagging',
│ │ │ name='optotagging',
│ │ │ children={
│ │ │ │ 'optogenetic_stimulation': optogenetic_stimulation(
│ │ │ │ │ hdf5_path='/processing/optotagging/optogenetic_stimulation',
│ │ │ │ │ name='optogenetic_stimulation',
│ │ │ │ │ colnames=array(['start_time', 'condition', 'level', 'stop_time', 'stimulus_name',
│ 'duration', 'tags', 'timeseries'], dtype=object),
│ │ │ │ │ description='',
│ │ │ │ │ id=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ start_time=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ stop_time=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ tags=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c72c50>,
│ │ │ │ │ tags_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ timeseries=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c72490>,
│ │ │ │ │ timeseries_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ condition=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c71990>,
│ │ │ │ │ level=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ stimulus_name=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c70a90>,
│ │ │ │ │ duration=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ │ │ vector_data=[],
│ │ │ │ │ vector_index=[]
│ │ │ │ ),
│ │ │ │ 'optotagging': TimeSeries(
│ │ │ │ │ hdf5_path='/processing/optotagging/optotagging',
│ │ │ │ │ name='optotagging',
│ │ │ │ │ description='no description',
│ │ │ │ │ comments='no comments',
│ │ │ │ │ data=TimeSeriesData(
│ │ │ │ │ │ hdf5_path='/processing/optotagging/optotagging/data',
│ │ │ │ │ │ object_id=None,
│ │ │ │ │ │ name='data',
│ │ │ │ │ │ conversion=1.0,
│ │ │ │ │ │ resolution=-1.0,
│ │ │ │ │ │ unit='seconds',
│ │ │ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c63710>
│ │ │ │ │ ),
│ │ │ │ │ starting_time=None,
│ │ │ │ │ timestamps=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c7c3d0>,
│ │ │ │ │ control=None,
│ │ │ │ │ control_description=None,
│ │ │ │ │ sync=None
│ │ │ │ )
│ │ │ }
│ │ ),
│ │ 'raw_gaze_mapping': ProcessingModule(
│ │ │ hdf5_path='/processing/raw_gaze_mapping',
│ │ │ name='raw_gaze_mapping',
│ │ │ children={
│ │ │ │ 'eye_area': TimeSeries(
│ │ │ │ │ hdf5_path='/processing/raw_gaze_mapping/eye_area',
│ │ │ │ │ name='eye_area',
│ │ │ │ │ description='no description',
│ │ │ │ │ comments='no comments',
│ │ │ │ │ data=TimeSeriesData(
│ │ │ │ │ │ hdf5_path='/processing/raw_gaze_mapping/eye_area/data',
│ │ │ │ │ │ object_id=None,
│ │ │ │ │ │ name='data',
│ │ │ │ │ │ conversion=1.0,
│ │ │ │ │ │ resolution=-1.0,
│ │ │ │ │ │ unit='Pixels ^ 2',
│ │ │ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124b6bb50>
│ │ │ │ │ ),
│ │ │ │ │ starting_time=None,
│ │ │ │ │ timestamps=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c33850>,
│ │ │ │ │ control=None,
│ │ │ │ │ control_description=None,
│ │ │ │ │ sync=None
│ │ │ │ ),
│ │ │ │ 'pupil_area': TimeSeries(
│ │ │ │ │ hdf5_path='/processing/raw_gaze_mapping/pupil_area',
│ │ │ │ │ name='pupil_area',
│ │ │ │ │ description='no description',
│ │ │ │ │ comments='no comments',
│ │ │ │ │ data=TimeSeriesData(
│ │ │ │ │ │ hdf5_path='/processing/raw_gaze_mapping/pupil_area/data',
│ │ │ │ │ │ object_id=None,
│ │ │ │ │ │ name='data',
│ │ │ │ │ │ conversion=1.0,
│ │ │ │ │ │ resolution=-1.0,
│ │ │ │ │ │ unit='Pixels ^ 2',
│ │ │ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a80150>
│ │ │ │ │ ),
│ │ │ │ │ starting_time=None,
│ │ │ │ │ timestamps=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c40190>,
│ │ │ │ │ control=None,
│ │ │ │ │ control_description=None,
│ │ │ │ │ sync=None
│ │ │ │ ),
│ │ │ │ 'screen_coordinates': TimeSeries(
│ │ │ │ │ hdf5_path='/processing/raw_gaze_mapping/screen_coordinates',
│ │ │ │ │ name='screen_coordinates',
│ │ │ │ │ description='no description',
│ │ │ │ │ comments='no comments',
│ │ │ │ │ data=TimeSeriesData(
│ │ │ │ │ │ hdf5_path='/processing/raw_gaze_mapping/screen_coordinates/data',
│ │ │ │ │ │ object_id=None,
│ │ │ │ │ │ name='data',
│ │ │ │ │ │ conversion=1.0,
│ │ │ │ │ │ resolution=-1.0,
│ │ │ │ │ │ unit='Centimeters',
│ │ │ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c41110>
│ │ │ │ │ ),
│ │ │ │ │ starting_time=None,
│ │ │ │ │ timestamps=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124b92390>,
│ │ │ │ │ control=None,
│ │ │ │ │ control_description=None,
│ │ │ │ │ sync=None
│ │ │ │ ),
│ │ │ │ 'screen_coordinates_spherical': TimeSeries(
│ │ │ │ │ hdf5_path='/processing/raw_gaze_mapping/screen_coordinates_spherical',
│ │ │ │ │ name='screen_coordinates_spherical',
│ │ │ │ │ description='no description',
│ │ │ │ │ comments='no comments',
│ │ │ │ │ data=TimeSeriesData(
│ │ │ │ │ │ hdf5_path='/processing/raw_gaze_mapping/screen_coordinates_spherical/data',
│ │ │ │ │ │ object_id=None,
│ │ │ │ │ │ name='data',
│ │ │ │ │ │ conversion=1.0,
│ │ │ │ │ │ resolution=-1.0,
│ │ │ │ │ │ unit='Degrees',
│ │ │ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124b91ed0>
│ │ │ │ │ ),
│ │ │ │ │ starting_time=None,
│ │ │ │ │ timestamps=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124b93d10>,
│ │ │ │ │ control=None,
│ │ │ │ │ control_description=None,
│ │ │ │ │ sync=None
│ │ │ │ )
│ │ │ }
│ │ ),
│ │ 'running': ProcessingModule(
│ │ │ hdf5_path='/processing/running',
│ │ │ name='running',
│ │ │ children={
│ │ │ │ 'running_speed': TimeSeries(
│ │ │ │ │ hdf5_path='/processing/running/running_speed',
│ │ │ │ │ name='running_speed',
│ │ │ │ │ description='no description',
│ │ │ │ │ comments='no comments',
│ │ │ │ │ data=TimeSeriesData(
│ │ │ │ │ │ hdf5_path='/processing/running/running_speed/data',
│ │ │ │ │ │ object_id=None,
│ │ │ │ │ │ name='data',
│ │ │ │ │ │ conversion=1.0,
│ │ │ │ │ │ resolution=-1.0,
│ │ │ │ │ │ unit='cm/s',
│ │ │ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124b91f50>
│ │ │ │ │ ),
│ │ │ │ │ starting_time=None,
│ │ │ │ │ timestamps=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c96490>,
│ │ │ │ │ control=None,
│ │ │ │ │ control_description=None,
│ │ │ │ │ sync=None
│ │ │ │ ),
│ │ │ │ 'running_speed_end_times': TimeSeries(
│ │ │ │ │ hdf5_path='/processing/running/running_speed_end_times',
│ │ │ │ │ name='running_speed_end_times',
│ │ │ │ │ description='no description',
│ │ │ │ │ comments='no comments',
│ │ │ │ │ data=TimeSeriesData(
│ │ │ │ │ │ hdf5_path='/processing/running/running_speed_end_times/data',
│ │ │ │ │ │ object_id=None,
│ │ │ │ │ │ name='data',
│ │ │ │ │ │ conversion=1.0,
│ │ │ │ │ │ resolution=-1.0,
│ │ │ │ │ │ unit='cm/s',
│ │ │ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c95a50>
│ │ │ │ │ ),
│ │ │ │ │ starting_time=None,
│ │ │ │ │ timestamps=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c95610>,
│ │ │ │ │ control=None,
│ │ │ │ │ control_description=None,
│ │ │ │ │ sync=None
│ │ │ │ ),
│ │ │ │ 'running_wheel_rotation': TimeSeries(
│ │ │ │ │ hdf5_path='/processing/running/running_wheel_rotation',
│ │ │ │ │ name='running_wheel_rotation',
│ │ │ │ │ description='no description',
│ │ │ │ │ comments='no comments',
│ │ │ │ │ data=TimeSeriesData(
│ │ │ │ │ │ hdf5_path='/processing/running/running_wheel_rotation/data',
│ │ │ │ │ │ object_id=None,
│ │ │ │ │ │ name='data',
│ │ │ │ │ │ conversion=1.0,
│ │ │ │ │ │ resolution=-1.0,
│ │ │ │ │ │ unit='radians',
│ │ │ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c94b10>
│ │ │ │ │ ),
│ │ │ │ │ starting_time=None,
│ │ │ │ │ timestamps=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c96490>,
│ │ │ │ │ control=None,
│ │ │ │ │ control_description=None,
│ │ │ │ │ sync=None
│ │ │ │ )
│ │ │ }
│ │ ),
│ │ 'stimulus': ProcessingModule(
│ │ │ hdf5_path='/processing/stimulus',
│ │ │ name='stimulus',
│ │ │ children={
│ │ │ │ 'timestamps': TimeSeries(
│ │ │ │ │ hdf5_path='/processing/stimulus/timestamps',
│ │ │ │ │ name='timestamps',
│ │ │ │ │ description='no description',
│ │ │ │ │ comments='no comments',
│ │ │ │ │ data=TimeSeriesData(
│ │ │ │ │ │ hdf5_path='/processing/stimulus/timestamps/data',
│ │ │ │ │ │ object_id=None,
│ │ │ │ │ │ name='data',
│ │ │ │ │ │ conversion=1.0,
│ │ │ │ │ │ resolution=-1.0,
│ │ │ │ │ │ unit='s',
│ │ │ │ │ │ array=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c97d10>
│ │ │ │ │ ),
│ │ │ │ │ starting_time=None,
│ │ │ │ │ timestamps=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124c96910>,
│ │ │ │ │ control=None,
│ │ │ │ │ control_description=None,
│ │ │ │ │ sync=None
│ │ │ │ )
│ │ │ }
│ │ )
│ },
│ stimulus=NWBFileStimulus(
│ │ hdf5_path=None,
│ │ object_id=None,
│ │ name='stimulus',
│ │ presentation={},
│ │ templates={}
│ ),
│ general=NWBFileGeneral(
│ │ hdf5_path=None,
│ │ object_id=None,
│ │ name='general',
│ │ data_collection=None,
│ │ experiment_description=None,
│ │ experimenter=None,
│ │ institution='Allen Institute for Brain Science',
│ │ keywords=None,
│ │ lab=None,
│ │ notes=None,
│ │ pharmacology=None,
│ │ protocol=None,
│ │ related_publications=None,
│ │ session_id='760693773',
│ │ slices=None,
│ │ source_script=None,
│ │ stimulus='brain_observatory_1.1',
│ │ surgery=None,
│ │ virus=None,
│ │ nwb_container=[],
│ │ devices={
│ │ │ 'probeA': EcephysProbe(
│ │ │ │ hdf5_path='/general/devices/probeA',
│ │ │ │ name='probeA',
│ │ │ │ description=None,
│ │ │ │ manufacturer=None,
│ │ │ │ sampling_rate=29999.9700560591,
│ │ │ │ probe_id=769322820
│ │ │ ),
│ │ │ 'probeB': EcephysProbe(
│ │ │ │ hdf5_path='/general/devices/probeB',
│ │ │ │ name='probeB',
│ │ │ │ description=None,
│ │ │ │ manufacturer=None,
│ │ │ │ sampling_rate=29999.9195957425,
│ │ │ │ probe_id=769322824
│ │ │ ),
│ │ │ 'probeC': EcephysProbe(
│ │ │ │ hdf5_path='/general/devices/probeC',
│ │ │ │ name='probeC',
│ │ │ │ description=None,
│ │ │ │ manufacturer=None,
│ │ │ │ sampling_rate=29999.9985048795,
│ │ │ │ probe_id=769322827
│ │ │ ),
│ │ │ 'probeD': EcephysProbe(
│ │ │ │ hdf5_path='/general/devices/probeD',
│ │ │ │ name='probeD',
│ │ │ │ description=None,
│ │ │ │ manufacturer=None,
│ │ │ │ sampling_rate=29999.9228144047,
│ │ │ │ probe_id=769322829
│ │ │ ),
│ │ │ 'probeE': EcephysProbe(
│ │ │ │ hdf5_path='/general/devices/probeE',
│ │ │ │ name='probeE',
│ │ │ │ description=None,
│ │ │ │ manufacturer=None,
│ │ │ │ sampling_rate=30000.0007890914,
│ │ │ │ probe_id=769322831
│ │ │ ),
│ │ │ 'probeF': EcephysProbe(
│ │ │ │ hdf5_path='/general/devices/probeF',
│ │ │ │ name='probeF',
│ │ │ │ description=None,
│ │ │ │ manufacturer=None,
│ │ │ │ sampling_rate=30000.0428393552,
│ │ │ │ probe_id=769322833
│ │ │ )
│ │ },
│ │ subject=EcephysSpecimen(
│ │ │ hdf5_path='/general/subject',
│ │ │ name='subject',
│ │ │ age='P110D',
│ │ │ date_of_birth=None,
│ │ │ description=None,
│ │ │ genotype='Sst-IRES-Cre/wt;Ai32(RCL-ChR2(H134R)_EYFP)/wt',
│ │ │ sex='F',
│ │ │ species='Mus musculus',
│ │ │ subject_id='738651046',
│ │ │ weight=None,
│ │ │ specimen_name='Sst-IRES-Cre;Ai32-406808',
│ │ │ age_in_days=110.0,
│ │ │ strain='C57BL/6J'
│ │ ),
│ │ extracellular_ephys=NWBFileGeneralExtracellularEphys(
│ │ │ hdf5_path=None,
│ │ │ object_id=None,
│ │ │ name='extracellular_ephys',
│ │ │ electrode_group=[
│ │ │ │ EcephysElectrodeGroup(
│ │ │ │ │ hdf5_path='/general/extracellular_ephys/probeA',
│ │ │ │ │ name='probeA',
│ │ │ │ │ description='Ecephys Electrode Group',
│ │ │ │ │ location='See electrode locations',
│ │ │ │ │ position=None,
│ │ │ │ │ has_lfp_data=True,
│ │ │ │ │ probe_id=769322820,
│ │ │ │ │ lfp_sampling_rate=1249.998752335795
│ │ │ │ ),
│ │ │ │ EcephysElectrodeGroup(
│ │ │ │ │ hdf5_path='/general/extracellular_ephys/probeB',
│ │ │ │ │ name='probeB',
│ │ │ │ │ description='Ecephys Electrode Group',
│ │ │ │ │ location='See electrode locations',
│ │ │ │ │ position=None,
│ │ │ │ │ has_lfp_data=True,
│ │ │ │ │ probe_id=769322824,
│ │ │ │ │ lfp_sampling_rate=1249.996649822605
│ │ │ │ ),
│ │ │ │ EcephysElectrodeGroup(
│ │ │ │ │ hdf5_path='/general/extracellular_ephys/probeC',
│ │ │ │ │ name='probeC',
│ │ │ │ │ description='Ecephys Electrode Group',
│ │ │ │ │ location='See electrode locations',
│ │ │ │ │ position=None,
│ │ │ │ │ has_lfp_data=True,
│ │ │ │ │ probe_id=769322827,
│ │ │ │ │ lfp_sampling_rate=1249.999937703315
│ │ │ │ ),
│ │ │ │ EcephysElectrodeGroup(
│ │ │ │ │ hdf5_path='/general/extracellular_ephys/probeD',
│ │ │ │ │ name='probeD',
│ │ │ │ │ description='Ecephys Electrode Group',
│ │ │ │ │ location='See electrode locations',
│ │ │ │ │ position=None,
│ │ │ │ │ has_lfp_data=True,
│ │ │ │ │ probe_id=769322829,
│ │ │ │ │ lfp_sampling_rate=1249.99678393353
│ │ │ │ ),
│ │ │ │ EcephysElectrodeGroup(
│ │ │ │ │ hdf5_path='/general/extracellular_ephys/probeE',
│ │ │ │ │ name='probeE',
│ │ │ │ │ description='Ecephys Electrode Group',
│ │ │ │ │ location='See electrode locations',
│ │ │ │ │ position=None,
│ │ │ │ │ has_lfp_data=True,
│ │ │ │ │ probe_id=769322831,
│ │ │ │ │ lfp_sampling_rate=1250.000032878805
│ │ │ │ ),
│ │ │ │ EcephysElectrodeGroup(
│ │ │ │ │ hdf5_path='/general/extracellular_ephys/probeF',
│ │ │ │ │ name='probeF',
│ │ │ │ │ description='Ecephys Electrode Group',
│ │ │ │ │ location='See electrode locations',
│ │ │ │ │ position=None,
│ │ │ │ │ has_lfp_data=True,
│ │ │ │ │ probe_id=769322833,
│ │ │ │ │ lfp_sampling_rate=1250.00178497313
│ │ │ │ )
│ │ │ ],
│ │ │ electrodes=NWBFileGeneralExtracellularEphysElectrodes(
│ │ │ │ hdf5_path=None,
│ │ │ │ name='electrodes',
│ │ │ │ colnames=None,
│ │ │ │ description=None,
│ │ │ │ vector_data=[],
│ │ │ │ vector_index=[],
│ │ │ │ x=[],
│ │ │ │ y=[],
│ │ │ │ z=[],
│ │ │ │ imp=[],
│ │ │ │ location=[],
│ │ │ │ filtering=[],
│ │ │ │ group=[],
│ │ │ │ group_name=[],
│ │ │ │ rel_x=[],
│ │ │ │ rel_y=[],
│ │ │ │ rel_z=[],
│ │ │ │ reference=[]
│ │ │ )
│ │ ),
│ │ intracellular_ephys=None,
│ │ optogenetics={},
│ │ optophysiology={}
│ ),
│ intervals={
│ │ 'drifting_gratings_presentations': drifting_gratings_presentations(
│ │ │ hdf5_path='/intervals/drifting_gratings_presentations',
│ │ │ name='drifting_gratings_presentations',
│ │ │ colnames=array(['start_time', 'stop_time', 'stimulus_name', 'stimulus_block',
│ 'temporal_frequency', 'color', 'mask', 'opacity', 'phase', 'size',
│ 'units', 'stimulus_index', 'orientation', 'spatial_frequency',
│ 'contrast', 'tags', 'timeseries'], dtype=object),
│ │ │ description="Presentation times and stimuli details for 'drifting_gratings' stimuli",
│ │ │ id=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ start_time=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ stop_time=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ tags=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a23950>,
│ │ │ tags_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ timeseries=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a23310>,
│ │ │ timeseries_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ stimulus_name=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a22d50>,
│ │ │ stimulus_block=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ temporal_frequency=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ color=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a21bd0>,
│ │ │ mask=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a21910>,
│ │ │ opacity=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ phase=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a20d10>,
│ │ │ size=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a20ad0>,
│ │ │ units=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a20cd0>,
│ │ │ stimulus_index=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ orientation=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ spatial_frequency=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a0fd90>,
│ │ │ contrast=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ vector_data=[],
│ │ │ vector_index=[]
│ │ ),
│ │ 'flashes_presentations': flashes_presentations(
│ │ │ hdf5_path='/intervals/flashes_presentations',
│ │ │ name='flashes_presentations',
│ │ │ colnames=array(['start_time', 'stop_time', 'stimulus_name', 'stimulus_block',
│ 'color', 'mask', 'opacity', 'phase', 'size', 'units',
│ 'stimulus_index', 'orientation', 'spatial_frequency', 'contrast',
│ 'tags', 'timeseries'], dtype=object),
│ │ │ description="Presentation times and stimuli details for 'flashes' stimuli",
│ │ │ id=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ start_time=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ stop_time=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ tags=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a7e490>,
│ │ │ tags_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ timeseries=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a7db50>,
│ │ │ timeseries_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ stimulus_name=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a7cf10>,
│ │ │ stimulus_block=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ color=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a7c890>,
│ │ │ mask=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a7c5d0>,
│ │ │ opacity=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ phase=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a563d0>,
│ │ │ size=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a56050>,
│ │ │ units=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a5da90>,
│ │ │ stimulus_index=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ orientation=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ spatial_frequency=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124ac0b50>,
│ │ │ contrast=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ vector_data=[],
│ │ │ vector_index=[]
│ │ ),
│ │ 'gabors_presentations': gabors_presentations(
│ │ │ hdf5_path='/intervals/gabors_presentations',
│ │ │ name='gabors_presentations',
│ │ │ colnames=array(['start_time', 'stop_time', 'stimulus_name', 'stimulus_block',
│ 'temporal_frequency', 'x_position', 'y_position', 'color', 'mask',
│ 'opacity', 'phase', 'size', 'units', 'stimulus_index',
│ 'orientation', 'spatial_frequency', 'contrast', 'tags',
│ 'timeseries'], dtype=object),
│ │ │ description="Presentation times and stimuli details for 'gabors' stimuli",
│ │ │ id=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ start_time=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ stop_time=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ tags=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124adfb10>,
│ │ │ tags_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ timeseries=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124adead0>,
│ │ │ timeseries_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ stimulus_name=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124ade890>,
│ │ │ stimulus_block=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ temporal_frequency=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ x_position=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ y_position=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ color=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124acb750>,
│ │ │ mask=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124acb650>,
│ │ │ opacity=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ phase=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124aca6d0>,
│ │ │ size=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124aca410>,
│ │ │ units=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124aca150>,
│ │ │ stimulus_index=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ orientation=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ spatial_frequency=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124ab7510>,
│ │ │ contrast=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ vector_data=[],
│ │ │ vector_index=[]
│ │ ),
│ │ 'natural_movie_one_presentations': natural_movie_one_presentations(
│ │ │ hdf5_path='/intervals/natural_movie_one_presentations',
│ │ │ name='natural_movie_one_presentations',
│ │ │ colnames=array(['start_time', 'stop_time', 'stimulus_name', 'stimulus_block',
│ 'color', 'opacity', 'size', 'units', 'stimulus_index',
│ 'orientation', 'frame', 'contrast', 'tags', 'timeseries'],
│ dtype=object),
│ │ │ description="Presentation times and stimuli details for 'natural_movie_one' stimuli",
│ │ │ id=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ start_time=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ stop_time=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ tags=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124b0cb50>,
│ │ │ tags_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ timeseries=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a23ed0>,
│ │ │ timeseries_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ stimulus_name=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124b1aad0>,
│ │ │ stimulus_block=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ color=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124b1b090>,
│ │ │ opacity=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ size=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a82d50>,
│ │ │ units=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a83210>,
│ │ │ stimulus_index=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ orientation=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ frame=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ contrast=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ vector_data=[],
│ │ │ vector_index=[]
│ │ ),
│ │ 'natural_movie_three_presentations': natural_movie_three_presentations(
│ │ │ hdf5_path='/intervals/natural_movie_three_presentations',
│ │ │ name='natural_movie_three_presentations',
│ │ │ colnames=array(['start_time', 'stop_time', 'stimulus_name', 'stimulus_block',
│ 'color', 'opacity', 'size', 'units', 'stimulus_index',
│ 'orientation', 'frame', 'contrast', 'tags', 'timeseries'],
│ dtype=object),
│ │ │ description="Presentation times and stimuli details for 'natural_movie_three' stimuli",
│ │ │ id=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ start_time=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ stop_time=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ tags=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a29e50>,
│ │ │ tags_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ timeseries=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a28510>,
│ │ │ timeseries_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ stimulus_name=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a29c50>,
│ │ │ stimulus_block=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ color=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a29450>,
│ │ │ opacity=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ size=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a2a7d0>,
│ │ │ units=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124a280d0>,
│ │ │ stimulus_index=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ orientation=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ frame=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ contrast=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ vector_data=[],
│ │ │ vector_index=[]
│ │ ),
│ │ 'natural_scenes_presentations': natural_scenes_presentations(
│ │ │ hdf5_path='/intervals/natural_scenes_presentations',
│ │ │ name='natural_scenes_presentations',
│ │ │ colnames=array(['start_time', 'stop_time', 'stimulus_name', 'stimulus_block',
│ 'stimulus_index', 'frame', 'tags', 'timeseries'], dtype=object),
│ │ │ description="Presentation times and stimuli details for 'natural_scenes' stimuli",
│ │ │ id=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ start_time=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ stop_time=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ tags=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124b75550>,
│ │ │ tags_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ timeseries=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124b75ed0>,
│ │ │ timeseries_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ stimulus_name=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124b53310>,
│ │ │ stimulus_block=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ stimulus_index=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ frame=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ vector_data=[],
│ │ │ vector_index=[]
│ │ ),
│ │ 'spontaneous_presentations': spontaneous_presentations(
│ │ │ hdf5_path='/intervals/spontaneous_presentations',
│ │ │ name='spontaneous_presentations',
│ │ │ colnames=array(['start_time', 'stop_time', 'stimulus_name', 'tags', 'timeseries'],
│ dtype=object),
│ │ │ description="Presentation times and stimuli details for 'spontaneous' stimuli",
│ │ │ id=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ start_time=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ stop_time=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ tags=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124ba7050>,
│ │ │ tags_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ timeseries=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124bbe290>,
│ │ │ timeseries_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ stimulus_name=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124bbcb10>,
│ │ │ vector_data=[],
│ │ │ vector_index=[]
│ │ ),
│ │ 'static_gratings_presentations': static_gratings_presentations(
│ │ │ hdf5_path='/intervals/static_gratings_presentations',
│ │ │ name='static_gratings_presentations',
│ │ │ colnames=array(['start_time', 'stop_time', 'stimulus_name', 'stimulus_block',
│ 'color', 'mask', 'opacity', 'phase', 'size', 'units',
│ 'stimulus_index', 'orientation', 'spatial_frequency', 'contrast',
│ 'tags', 'timeseries'], dtype=object),
│ │ │ description="Presentation times and stimuli details for 'static_gratings' stimuli",
│ │ │ id=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ start_time=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ stop_time=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ tags=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124bbef90>,
│ │ │ tags_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ timeseries=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124bbf090>,
│ │ │ timeseries_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ stimulus_name=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124bbf890>,
│ │ │ stimulus_block=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ color=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124bbf510>,
│ │ │ mask=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124bbe790>,
│ │ │ opacity=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ phase=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124bc3290>,
│ │ │ size=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124bc0e50>,
│ │ │ units=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124bc0b90>,
│ │ │ stimulus_index=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ orientation=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ spatial_frequency=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ contrast=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ │ vector_data=[],
│ │ │ vector_index=[]
│ │ )
│ },
│ units=units(
│ │ hdf5_path='/units',
│ │ name='units',
│ │ colnames=array(['quality', 'amplitude_cutoff', 'recovery_slope', 'cluster_id',
│ 'spread', 'velocity_below', 'd_prime', 'nn_miss_rate',
│ 'isolation_distance', 'silhouette_score', 'waveform_halfwidth',
│ 'PT_ratio', 'cumulative_drift', 'isi_violations', 'presence_ratio',
│ 'snr', 'l_ratio', 'amplitude', 'repolarization_slope',
│ 'local_index', 'velocity_above', 'nn_hit_rate', 'peak_channel_id',
│ 'waveform_duration', 'firing_rate', 'max_drift', 'spike_times',
│ 'spike_amplitudes', 'waveform_mean'], dtype=object),
│ │ description='',
│ │ id=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ spike_times_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ spike_times=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ waveform_mean=dask.array<array, shape=(10, 82), dtype=float64, chunksize=(10, 82), chunktype=numpy.ndarray>,
│ │ quality=<nwb_linkml.types.ndarray.NDArrayProxy object at 0x124ce0250>,
│ │ amplitude_cutoff=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ recovery_slope=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ cluster_id=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ spread=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ velocity_below=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ d_prime=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ nn_miss_rate=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ isolation_distance=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ silhouette_score=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ waveform_halfwidth=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ PT_ratio=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ cumulative_drift=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ isi_violations=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ presence_ratio=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ snr=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ l_ratio=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ amplitude=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ repolarization_slope=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ local_index=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ velocity_above=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ nn_hit_rate=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ peak_channel_id=dask.array<array, shape=(10,), dtype=int64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ waveform_duration=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ firing_rate=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ max_drift=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ spike_amplitudes=dask.array<array, shape=(10,), dtype=float64, chunksize=(10,), chunktype=numpy.ndarray>,
│ │ vector_data=[],
│ │ vector_index=[],
│ │ obs_intervals_index=None,
│ │ obs_intervals=None,
│ │ electrodes_index=None,
│ │ electrodes=None,
│ │ electrode_group=[],
│ │ waveform_sd=None
│ )
)

20
nwb_linkml/src/nwb_linkml/generators/pydantic.py
View file

@ -76,7 +76,7 @@ def default_template(pydantic_ver: str = "2", extra_classes:Optional[List[Type[B
from __future__ import annotations
from datetime import datetime, date
from enum import Enum
from typing import Dict, Optional, Any, Union, ClassVar, Annotated, TypeVar, List
from typing import Dict, Optional, Any, Union, ClassVar, Annotated, TypeVar, List, TYPE_CHECKING
from pydantic import BaseModel as BaseModel, Field"""
if pydantic_ver == '2':
template += """
@ -90,6 +90,8 @@ if sys.version_info >= (3, 8):
from typing import Literal
else:
from typing_extensions import Literal
if TYPE_CHECKING:
import numpy as np
{% for import_module, import_classes in imports.items() %}
from {{ import_module }} import (
@ -137,6 +139,22 @@ class ConfiguredBaseModel(BaseModel):
pass
{%- endif -%}
"""
### Getitem
template += """
def __getitem__(self, i: slice|int) -> 'np.ndarray':
if hasattr(self, 'array'):
return self.array[i]
else:
return super().__getitem__(i)
def __setitem__(self, i: slice|int, value: Any):
if hasattr(self, 'array'):
self.array[i] = value
else:
super().__setitem__(i, value)
"""
### Extra classes
if extra_classes is not None:
template += """{{ '\n\n' }}"""

3
nwb_linkml/src/nwb_linkml/io/__init__.py
View file

@ -1 +1,2 @@
from nwb_linkml.io import schema
from nwb_linkml.io import schema
from nwb_linkml.io.hdf5 import HDF5IO

14
nwb_linkml/tests/test_generators/test_generator_pydantic.py
View file

@ -270,3 +270,17 @@ def test_namespace(imported_schema):
assert hasattr(ns, classname)
if imported_schema['split']:
assert getattr(ns, classname).__module__ == modname
def test_get_set_item(imported_schema):
"""We can get and set without explicitly addressing array"""
cls = imported_schema['core'].MainTopLevel(
array=np.array([[1,2,3],[4,5,6]])
)
cls[0] = 50
assert (cls[0] == 50).all()
assert (cls.array[0] == 50).all()
cls[1,1] = 100
assert cls[1,1] == 100
assert cls.array[1,1] == 100

52
poetry.lock generated
View file

@ -2075,6 +2075,22 @@ files = [
{file = "ruamel.yaml.clib-0.2.8.tar.gz", hash = "sha256:beb2e0404003de9a4cab9753a8805a8fe9320ee6673136ed7f04255fe60bb512"},
]
[[package]]
name = "setuptools"
version = "68.2.2"
description = "Easily download, build, install, upgrade, and uninstall Python packages"
optional = false
python-versions = ">=3.8"
files = [
{file = "setuptools-68.2.2-py3-none-any.whl", hash = "sha256:b454a35605876da60632df1a60f736524eb73cc47bbc9f3f1ef1b644de74fd2a"},
{file = "setuptools-68.2.2.tar.gz", hash = "sha256:4ac1475276d2f1c48684874089fefcd83bd7162ddaafb81fac866ba0db282a87"},
]
[package.extras]
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.1)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
[[package]]
name = "shexjsg"
version = "0.8.2"
@ -2262,6 +2278,26 @@ theme-pydata = ["pydata-sphinx-theme (>=0.13.0,<0.14.0)"]
theme-rtd = ["sphinx-rtd-theme (>=1.0,<2.0)"]
theme-sbt = ["sphinx-book-theme (>=1.0,<2.0)"]
[[package]]
name = "sphinx-togglebutton"
version = "0.3.2"
description = "Toggle page content and collapse admonitions in Sphinx."
optional = false
python-versions = "*"
files = [
{file = "sphinx-togglebutton-0.3.2.tar.gz", hash = "sha256:ab0c8b366427b01e4c89802d5d078472c427fa6e9d12d521c34fa0442559dc7a"},
{file = "sphinx_togglebutton-0.3.2-py3-none-any.whl", hash = "sha256:9647ba7874b7d1e2d43413d8497153a85edc6ac95a3fea9a75ef9c1e08aaae2b"},
]
[package.dependencies]
docutils = "*"
setuptools = "*"
sphinx = "*"
wheel = "*"
[package.extras]
sphinx = ["matplotlib", "myst-nb", "numpy", "sphinx-book-theme", "sphinx-design", "sphinx-examples"]
[[package]]
name = "sphinxcontrib-applehelp"
version = "1.0.7"
@ -2610,6 +2646,20 @@ files = [
docs = ["furo", "sphinx", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-notfound-page", "sphinxext-opengraph"]
tests = ["pytest", "pytest-cov"]
[[package]]
name = "wheel"
version = "0.41.2"
description = "A built-package format for Python"
optional = false
python-versions = ">=3.7"
files = [
{file = "wheel-0.41.2-py3-none-any.whl", hash = "sha256:75909db2664838d015e3d9139004ee16711748a52c8f336b52882266540215d8"},
{file = "wheel-0.41.2.tar.gz", hash = "sha256:0c5ac5ff2afb79ac23ab82bab027a0be7b5dbcf2e54dc50efe4bf507de1f7985"},
]
[package.extras]
test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
[[package]]
name = "wrapt"
version = "1.15.0"
@ -2712,4 +2762,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[metadata]
lock-version = "2.0"
python-versions = ">=3.11,<3.13"
content-hash = "5dc10e6c1e6ae285bc09c270d0e8015f5705dc53a259096a6b2054e37392da34"
content-hash = "ff4389164e6c41667acfe292fb4898303c356cf07d1dfb0d3ac6d6a29da8a738"

1
pyproject.toml
View file

@ -21,6 +21,7 @@ sphinx-autobuild = "^2021.3.14"
nwb-linkml = { path = './nwb_linkml', develop = true }
nwb_schema_language = { path = './nwb_schema_language', develop = true }
sphinx-design = "^0.5.0"
sphinx-togglebutton = "^0.3.2"
[build-system]