Add core tools section and update tool categories in documentation

assets/jsconfig.json

@@ -2,7 +2,10 @@
  "compilerOptions": {
   "baseUrl": ".",
   "paths": {
-   "*": null
+   "*": [
+    "../../../Library/Caches/hugo_cache/modules/filecache/modules/pkg/mod/github.com/gohugoio/hugo-mod-jslibs-dist/popperjs/[email protected]/package/dist/cjs/*",
+    "../../../Library/Caches/hugo_cache/modules/filecache/modules/pkg/mod/github.com/twbs/[email protected]+incompatible/js/*"
+   ]
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 }

config/_default/menus.yaml

@@ -29,12 +29,16 @@ main:
     weight: 2
     url: "tools/"
     parent: "Software & Tools"
-  - name: "Acquisition Tools"
+  - name: "Core Tools"
     weight: 3
+    url: "tools/core/"
+    parent: "Software & Tools"
+  - name: "Acquisition Tools"
+    weight: 4
     url: "tools/acquisition/"
     parent: "Software & Tools"
   - name: "Analysis Tools"
-    weight: 4
+    weight: 5
     url: "tools/analysis/"
     parent: "Software & Tools"
 

content/tools/_index.md

@@ -7,6 +7,7 @@ This page is a collection of tools we are cataloging as a convenience reference
 
 ## Tool Categories
 
+- [Core NWB Tools]({{< ref "tools/core" >}}) - Key software packages of the core NWB software stack
 - [Acquisition and Control Tools]({{< ref "tools/acquisition" >}}) - Tools for data acquisition and experimental control
 - [Analysis and Visualization Tools]({{< ref "tools/analysis" >}}) - Tools for data analysis, visualization, and exploration
 

content/tools/core/_index.md

@@ -0,0 +1,26 @@
+---
+title: "Core NWB Tools"
+description: "Glossary of Core NWB Tools"
+---
+
+The glossary shown here provides a quick overview of the key software packages of the core NWB software stack. For a more general discussion of the overall organization of the core NWB software stack see the [NWB Software Ecosystem](https://www.nwb.org/nwb-software) page on the main NWB website.
+
+## Read/Write NWB File APIs
+
+{{< tool-grid category="read-write-api" >}}
+
+## Converting Data to NWB
+
+{{< tool-grid category="data-conversion" >}}
+
+## Validating NWB Files
+
+{{< tool-grid category="validation" >}}
+
+## Extending NWB
+
+{{< tool-grid category="extension" >}}
+
+## Core Development
+
+{{< tool-grid category="core-development" >}}

content/tools/core/hdmf-common-schema.md

@@ -0,0 +1,45 @@
+---
+title: "HDMF Common Schema"
+description: "Schema of common, general data structures used throughout the NWB Standard Schema"
+category: "extension"
+image: "/images/hdmf_common_schema_logo_framed.png"
+source_url: "https://github.com/hdmf-dev/hdmf-common-schema"
+docs_url: "https://hdmf-common-schema.readthedocs.io/en/stable/"
+weight: 120
+---
+
+## Description
+
+The HDMF Common Schema defines the schema of common, general data structures, which are used throughout the NWB Standard Schema but which are not specific to neurophysiology. 
+
+Example types defined in the HDMF common schema include all types related to `DynamicTable` for defining data tables, along with other common data structures that can be reused across different domains.
+
+## Installation
+
+The HDMF Common Schema is a documentation resource, not a software package to install. The documentation can be accessed at:
+
+```
+https://hdmf-common-schema.readthedocs.io/
+```
+
+## Usage
+
+The HDMF Common Schema is primarily used by the NWB schema and by extension developers. For example, when creating an extension that needs to store tabular data, you'd typically extend or use the `DynamicTable` type from HDMF Common Schema:
+
+```yaml
+groups:
+- neurodata_type_def: MyCustomTable
+  neurodata_type_inc: DynamicTable
+  doc: A custom table for storing specific experiment data.
+  datasets:
+  - name: custom_column
+    neurodata_type_inc: VectorData
+    doc: Custom column for my specific data.
+    dtype: text
+```
+
+## Additional Information
+
+The HDMF Common Schema is particularly important because it defines general-purpose data structures that enable efficient and flexible representation of complex data. For example, the `DynamicTable` type provides a way to represent tabular data with columns of different data types and potentially varying numbers of rows, making it suitable for storing experimental metadata, subject information, and other tabular data.
+
+Understanding this schema is helpful for developers creating extensions, especially when they need to represent structured data like tables, which are common in scientific datasets.

content/tools/core/hdmf-docutils.md

@@ -0,0 +1,41 @@
+---
+title: "HDMF Documentation Utilities"
+description: "Utility tools for creating documentation for extension schema"
+category: "extension"
+image: "/images/documenting_ndx_logo_framed.png"
+source_url: "https://github.com/hdmf-dev/hdmf-docutils"
+docs_url: ""
+weight: 90
+---
+
+## Description
+
+The HDMF Documentation Utilities (hdmf-docutils) provide utility tools for creating documentation for extension schema defined using the NWB Schema Language. These tools help developers create clear, comprehensive documentation for their extensions.
+
+The NDX Template automatically sets up the documentation for extensions via the hdmf-docutils, making them part of most NDX code repositories without requiring direct interaction with the tools.
+
+## Installation
+
+```bash
+pip install hdmf-docutils
+```
+
+## Usage
+
+The HDMF Documentation Utilities are typically used behind the scenes by the NDX Template, but can also be used directly for more customized documentation generation:
+
+```python
+from hdmf_docutils.doctools import rst_to_html, set_figure_dirpath
+
+# Set the directory path for figures
+set_figure_dirpath('path/to/figures')
+
+# Convert RST documentation to HTML
+html_content = rst_to_html('my_documentation.rst')
+```
+
+## Additional Information
+
+HDMF Documentation Utilities help ensure that extension documentation follows consistent formatting and includes all necessary information. This consistency improves the usability of extensions and helps users understand how to properly use them in their workflows.
+
+The tools can generate documentation from YAML schema files, create diagrams of data structures, and produce HTML and PDF documentation outputs.

content/tools/core/hdmf-specification-language.md

@@ -0,0 +1,48 @@
+---
+title: "HDMF Specification Language"
+description: "Formal structures for describing the organization of complex data"
+category: "extension"
+image: "/images/specification_language_logo_framed.png"
+source_url: "https://github.com/hdmf-dev/hdmf-schema-language"
+docs_url: "https://hdmf-schema-language.readthedocs.io/en/latest/"
+weight: 100
+---
+
+## Description
+
+The HDMF Specification Language defines formal structures for describing the organization of complex data using basic concepts, e.g., Groups, Datasets, Attributes, and Links. It provides a standardized way to define data structures and relationships.
+
+The HDMF specification language is defined by the Hierarchical Data Modeling Framework (HDMF). The NWB Specification Language is a derivative of the HDMF Specification Language with minor modifications for NWB (e.g., to use the term neurodata_type).
+
+## Installation
+
+The HDMF Specification Language is a documentation resource, not a software package to install. The documentation can be accessed at:
+
+```
+https://hdmf-schema-language.readthedocs.io/
+```
+
+## Usage
+
+When creating NWB extensions, you'll use the NWB Specification Language (which builds on HDMF) to define new data types. Here's an example of a simple extension definition in YAML format:
+
+```yaml
+groups:
+- neurodata_type_def: ElectricalSeries
+  neurodata_type_inc: TimeSeries
+  doc: A time series of electrical measurements.
+  datasets:
+  - name: data
+    dtype: numeric
+    shape:
+    - null
+    - null
+    doc: The recorded voltage data.
+  - name: electrodes
+    neurodata_type_inc: DynamicTableRegion
+    doc: The electrodes that this electrical series was recorded from.
+```
+
+## Additional Information
+
+Understanding the HDMF Specification Language is essential for developers who want to create extensions to the NWB format. It provides the foundation for defining structured, self-describing data models that can be used across different programming languages and storage backends.

content/tools/core/hdmf-zarr.md

@@ -0,0 +1,56 @@
+---
+title: "HDMF-Zarr"
+description: "Zarr backend for HDMF and PyNWB"
+category: "core-development"
+image: "/images/hdmf_zarr_logo_framed.png"
+source_url: "https://github.com/hdmf-dev/hdmf-zarr"
+docs_url: "https://hdmf-zarr.readthedocs.io/en/stable/"
+weight: 140
+---
+
+## Description
+
+The HDMF Zarr (HDMF-Z) library implements a Zarr backend for HDMF. Zarr is a format for the storage of chunked, compressed, N-dimensional arrays. HDMF-Z also provides convenience classes for integrating Zarr with the PyNWB Python API for NWB to support writing of NWB files to Zarr.
+
+Using Zarr as a storage backend for NWB can provide benefits like:
+
+- Cloud-friendly data access
+- Parallel read/write operations
+- Efficient access to data chunks
+- Flexible compression options
+
+## Installation
+
+```bash
+pip install hdmf-zarr
+```
+
+## Usage
+
+```python
+from pynwb import NWBFile, NWBHDF5IO
+from datetime import datetime
+from dateutil.tz import tzlocal
+from hdmf_zarr import NWBZarrIO
+
+# Create a new NWB file
+nwbfile = NWBFile(
+    session_description='my first recording',
+    identifier='EXAMPLE_ID',
+    session_start_time=datetime.now(tzlocal())
+)
+
+# Write the file to Zarr format
+with NWBZarrIO('example.zarr', mode='w') as io:
+    io.write(nwbfile)
+
+# Read the file from Zarr format
+with NWBZarrIO('example.zarr', mode='r') as io:
+    nwbfile = io.read()
+```
+
+## Additional Information
+
+HDMF-Zarr is particularly useful for working with very large datasets, especially in cloud environments or when parallel data access is needed. It provides an alternative storage format to the traditional HDF5 backend, offering different performance characteristics that may be beneficial for certain use cases.
+
+The library is designed to be a drop-in replacement for the HDF5 backend, making it easy to integrate into existing workflows that use PyNWB.

content/tools/core/hdmf.md

@@ -0,0 +1,72 @@
+---
+title: "HDMF"
+description: "Hierarchical Data Modeling Framework for working with hierarchical data"
+category: "core-development"
+image: "/images/hdmf_logo_framed.png"
+source_url: "https://github.com/hdmf-dev/hdmf"
+docs_url: "https://hdmf.readthedocs.io/en/stable/"
+weight: 130
+---
+
+## Description
+
+The Hierarchical Data Modeling Framework (HDMF) is a Python package for working with hierarchical data. It provides APIs for specifying data models, reading and writing data to different storage backends, and representing data with Python objects.
+
+HDMF builds the foundation for the PyNWB Python API for NWB. It offers a flexible, extensible approach to data modeling that allows for the creation of self-describing, structured data formats like NWB.
+
+## Installation
+
+```bash
+pip install hdmf
+```
+
+## Usage
+
+HDMF provides low-level functionality for working with hierarchical data, typically used by developers creating APIs like PyNWB:
+
+```python
+from hdmf.spec import GroupSpec, DatasetSpec, NamespaceBuilder
+from hdmf.common import DynamicTable
+
+# Define a new data type specification
+spec = GroupSpec(
+    doc='A custom data type',
+    data_type_def='MyType',
+    datasets=[
+        DatasetSpec(
+            doc='An example dataset',
+            name='data',
+            dtype='float'
+        )
+    ]
+)
+
+# Create a namespace for your specification
+namespace_builder = NamespaceBuilder(
+    doc='My extension',
+    name='my_extension',
+    full_name='My Custom Extension',
+    version='0.1.0'
+)
+namespace_builder.add_spec('my_extension.yaml', spec)
+
+# Working with DynamicTable
+table = DynamicTable(
+    name='example_table',
+    description='An example table'
+)
+table.add_column('column1', 'A string column', dtype='text')
+table.add_row(column1='example data')
+```
+
+## Additional Information
+
+HDMF is primarily of interest to developers who need to:
+
+1. Create or extend data APIs like PyNWB
+2. Implement new storage backends
+3. Support new serialization formats
+4. Develop data conversion tools
+5. Create validation tools
+
+Understanding HDMF is helpful for advanced NWB users who want to contribute to the core NWB ecosystem or develop sophisticated extensions and tools.

content/tools/core/matnwb.md

@@ -0,0 +1,45 @@
+---
+title: "MatNWB"
+description: "A MATLAB library for reading and writing NWB files"
+category: "read-write-api"
+image: "/images/matnwb_logo_framed.png"
+source_url: "https://github.com/NeurodataWithoutBorders/matnwb/"
+docs_url: "https://matnwb.readthedocs.io/"
+weight: 20
+---
+
+## Description
+
+MatNWB is a MATLAB library for reading and writing NWB files. It provides full support for all components of the NWB standard, including support for extensions. The API is interoperable with PyNWB, i.e., files created with MatNWB can be read in PyNWB and vice versa.
+
+MatNWB supports advanced read/write for efficient interaction with very large data files (i.e., data too large for main memory), via lazy data loading, iterative data write, and data compression among others.
+
+## Installation
+
+```bash
+# Clone the repository
+git clone https://github.com/NeurodataWithoutBorders/matnwb.git
+
+# Add to MATLAB path
+addpath(genpath('/path/to/matnwb'));
+```
+
+## Usage
+
+```matlab
+% Create a new NWB file
+nwb = NwbFile( ...
+    'session_description', 'a test NWB File', ...
+    'identifier', 'mouse001', ...
+    'session_start_time', datetime('now'));
+
+% Write the file
+nwbExport(nwb, 'test_file.nwb');
+
+% Read the file
+nwb = nwbRead('test_file.nwb');
+```
+
+## Additional Information
+
+MatNWB allows MATLAB users to work with NWB data in their preferred environment, using familiar MATLAB data structures and methods.