Dirigo is an extensible backend for scientific image acquisition
Project description
Dirigo
Dirigo is an extensible, high-performance backend for scientific image acquisition, designed with high-speed laser scanning microscopy in mind but adaptable to a wide range of medium- to high-complexity imaging systems.
Dirigo separates hardware control, acquisition logic, and user interface, making it easy to:
-
Add new hardware via plugin drivers that implement generic device interfaces (digitizers, scanners, stages, cameras, etc.)
-
Reconfigure data acquisition and processing pipelines using a Worker/publisher–subscriber model
-
Integrate adaptive acquisition strategies through feedback loops between processing and control
-
Build custom GUIs or integrate with existing tools via a clean API (a reference GUI is available as a separate package)
Performance-critical operations are accelerated with Numba JIT compilation, releasing the GIL during execution and enabling parallel/vectorized processing.
Dirigo follows a modular, package-oriented architecture: almost all components—hardware drivers, processing modules, GUIs—are separate Python packages that can be developed, installed, and updated independently.
Dirigo is in very early development. While the API and architecture are functional, documentation and ready-to-use releases are in progress.
Digitizer ↔ LSM mode compatibility
Legend: ✓ supported now · △ possible/experimental · ✗ not recommended/unsupported · — unknown
| Digitizer (vendor/model family) | Galvo–Galvo analog | Galvo–Galvo photon counting | Resonant–Galvo analog | Polygon–Galvo analog |
|---|---|---|---|---|
| NI X-Series (e.g., PCIe-63xx) | ✓* | ✓ (up to 4 chan.) | ✗† | ✗† |
| NI S-Series (e.g., PCI-6110/6115) | ✓ | ✓ (up to 2 chan.) | △§ | △§ |
| AlazarTech (e.g., ATS9440) | ✓ | ✗ | ✓ | ✓ |
| Teledyne SP Devices (e.g., ADQ32) | ✓ | ✗ | ✓ | ✓ |
| Other / custom (contact TDW) | — | — | — | — |
Notes
* Multichannel acquisition subject to aggregate AI sample rate (e.g 2 channels: 500 kS/s, 4 channels 250 kS/s).
† AI sample rate typically insufficient for resonant/polygon rates.
§ Borderline: Max sample rate may limit pixels per line, dependent on scanner frequency. Not yet validated.
Funding
Development of Dirigo has been supported in part by the National Cancer Institute of the National Institutes of Health under award number R01CA249151.
The content of this repository is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file dirigo-0.3.8.tar.gz.
File metadata
- Download URL: dirigo-0.3.8.tar.gz
- Upload date:
- Size: 81.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.13.12
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
85f06da9b1bec506539a3750a0f0a8a169aa0ba69ea17400d3ccb4bc32b1bca6
|
|
| MD5 |
1cf6c2d159729adfd77a264cffbc2914
|
|
| BLAKE2b-256 |
ccbacf2989a7e23a40c441b06c7d4718ce01d1f9a7f843a84270fbd51c422261
|
File details
Details for the file dirigo-0.3.8-py3-none-any.whl.
File metadata
- Download URL: dirigo-0.3.8-py3-none-any.whl
- Upload date:
- Size: 93.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.13.12
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
bb2de76382c93e06cdbc9584c7ef390677b9f6548057966b9a2c721128f1b021
|
|
| MD5 |
2c5716dc4494d4462f247c8ce9b510c3
|
|
| BLAKE2b-256 |
76aaf94a3e9b9f2758e1c9103caede1dd81055233537f63b44d7f2c079ed77d4
|