datajoint-python
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datajoint
Relational data pipelines for the science lab
PRs should be tested against a matrix of python versions in github actions. unless the tests are prohibitively expensive (e.g., requiring GPUs), they should be run automatically for every PR...
The codebase currently lacks type hints. Adding type hints would greatly increase the ability for static type checkers like `mypy` to enforce type-safety and catch bugs at type-checking time. Adding...
## Feature Request ### Problem Many existing DataJoint pipelines rely on the blob attribute type to store arbitrary serialized Python objects (e.g., NumPy arrays, dictionaries, custom class instances). While DataJoint...
## Feature Request ### Problem We can imagine a pipeline where ... - A table `SomeParams` was declared as `dj.Manual`. - In practice, initial users in Lab A discover `dj.Lookup`...
## Improvement Request ### Problem The `datajoint-python` project currently relies on `pkg_resources` for plugin discovery and management. The `pkg_resources` module, which is part of `setuptools`, is considered a legacy component...
## Improvement Request ### Problem The current configuration system is nonstandard and lacks common features such as validation. Secrets and regular settings are managed jointly, which is not a best...
### Discussed in https://github.com/datajoint/datajoint-python/discussions/1243 Originally posted by **ttngu207** June 12, 2025 # Problem Statement: The current dataJoint-python approach for jobs reservation, orchestration, and execution (i.e. the `autopopulate`) faces scalability limitations....
## Feature Request ### Problem Modern scientific pipelines must manage large, complex data objects (e.g., images, time-series, n-dimensional arrays) that are impractical to store directly in a relational database. The...
### Problem Currently, DataJoint's type system is closely tied to the underlying SQL backend (e.g., MySQL, PostgreSQL), exposing database-specific type names like `TINYINT UNSIGNED` or `DOUBLE`. This approach has several...
## Feature Request ### Problem Scientific research, particularly in fields like neuroscience and imaging, generates massive n-dimensional arrays that are too large to be stored efficiently in traditional blob fields....