Releases: DataHow/datahowlab-sdk-python
Releases · DataHow/datahowlab-sdk-python
v0.3.3 (2025-11-21)
v0.3.2 (2025-11-03)
Fixes
- Datetime parsing in new experiment validation.
v0.3.1 (2025-08-21)
Fixes
- Change new product code length validation (allow codes with length < 10)
- Fix
dataset.get_datamethod for modified datasets
v0.3.0 (2025-05-14)
New Features
- Added support for different Process Formats (mammalian, microbial) in the SDK.
v0.2.6 (2025-05-13)
New Features
- Added support for startingIndex in prediction configuration. This allows specifying a custom starting index for predictions, enabling more flexible use cases.
Bug Fixes
- Fixed allowed types in prediction instances to correctly support string and bool for categorical variables.
Other Changes
- Minor improvements and adjustments in type definitions for better consistency and maintainability.
v0.2.5 (2025-01-28)
New Features
- Introduced a new method to retrieve full experiment data within a dataset.
- Added a new argument to the
predictmethods in Cultivation, enabling customization of Confidence Intervals for predictions.
Bug Fixes
- Fixed an issue where timestamp values were not correctly parsed to integers before data upload.
Other Changes
- Updated the prediction functionality to use a new endpoint and data structure while maintaining existing functionality.
v0.2.4 (2024-09-19)
Bug Fixes
- Add validations and formatting to variables of variant
categoricalandlogicalwhen importing new data.
v0.2.3 (2024-08-28)
New Features
- Add a new class
FlowVariableReferenceto improve the creation of new Feeds / Flows Variables.
Bug Fixes
- Fixed data tabularization problem when uploading through the SDK.
Other Changes
- Add new
validations.mdfile, a comprehensive breakdown of the validations performed by the SDK when creating a new entity or pushing new data. - Improved the documentation on Flow Variable creation.
v0.2.2 (2024-07-24)
New Features
- Add verification options for SSL/TLS Certificates. The SDK now supports using custom CAs, self-signed certificates, and disabling SSL/TLS verification (docs).
Bug Fixes
- Fixed bug in formatting the Historical Model's prediction request body.
Other Changes
- Add Project URL to link PyPI to Github page