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Releases: CitrineInformatics/gemd-python

GEMD v0.6.2 is released!

01 Apr 16:26
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This release migrates to the official gemd name. The contents of the taurus module have been moved to gemd, but the taurus module was deprecated rather than removed. It will be removed in 0.7.0.

What's new

  • The gemd module! GEMD stands for "Generalized Expression of Materials Data" and is the official name of Citrine's data model! The "taurus" code name served us well and will be missed.

Improvements

  • The gemd.entity.bounds, gemd.entity.attribute, gemd.entity.value, and gemd.entity.template modules join gemd.entity.object in importing their members, allowing imports like from gemd.entity.template import ProcessTemplate, ParameterTemplate.

Deprecated

  • The taurus module is deprecated and will be removed in the next feature release. Migration involves replacing taurus with gemd in imports and TaurusEncoder/TaurusJson with GEMDEncoder and GEMDJson.

Taurus v0.6.0 is released!

19 Feb 02:02
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0.6.0 is released! This release focuses on significant changes to JSON support, which should provide a more intuitive experience to users and a more expressive and extensible experience to developers.

What’s New

  • taurus.json has been added as a drop-in replacement for python's builtin json module. (#86)
  • taurus.json.TaurusJson has been added to encapsulate the configuration of a taurus-compatible json serializer/deserializer. (#86)
  • TaurusJson objects have a new register_classes method to support the extension of json support to other classes in packages that develop on top of taurus. (#86)
  • taurus.util includes a public writeable_sort_order method that accepts either data objects or type strings. (#85)

Improvements

  • Output materials are now included when serializing processes. (#87)

Bugfixes

  • Measurements are now correctly included when serializing materials. (#84)

Changes

  • taurus.client.json_encoder has been moved to taurus.json. The taurus.client.json_encoder is now deprecated and will be removed in v0.7.0. (#86)

Backport v0.4.1

12 Feb 00:15
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Backport v0.4.1 Pre-release
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0.4.1 is released!

What’s New

  • The cake examples has been extended to demonstrate more of the data model
  • The Strehlow and Cook example now imports directly from the Citrination PIF and includes a substantially increased number of attributes

Taurus 0.5.0 is released!

11 Feb 01:11
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0.5.0 is released!

What’s New

  • The cake examples has been extended to demonstrate more of the data model
  • The Strehlow and Cook example now imports directly from the Citrination PIF and includes a substantially increased number of attributes

Improvements

  • The flatten method has been altered so that entire material histories can be flattened. The order of the returned objects is such that they can be created and re-linked in that order.
  • The raw_dumps and raw_loads methods were added to the json_encoder to provide access to the custom class encoder/decoder without the normalization/denormalization logic present in the full dumps and loads methods.

0.4.0 is released!

23 Jan 23:30
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0.4.0 is released! Pre-release
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What’s New

  • As our experience with the data model grows, we are working hard to both improve the tools for interacting with it and pushing its structure forward, making to more representative of the real world information Materials Scientists work with every day. This release represents both improvements in those interfaces as well as fixing some bugs.

Improvements

  • The serialization and deserialization as well as the helper functions to interact with and modify GEMD graphs have been improved. This should empower users to get more done in fewer lines of code.
  • The data example Cake has been augmented to have complete coverage of the data model. This should help users ground their understanding of Templates, Objects and Values in a practical, relatable resource.

Fixes

  • IngredientRuns and IngredientSpecs have been modified so that the logical equivalence of their name and labels fields is easier to have consistent when objects are being created.
  • And, of course, all of this is tested and documented

Coming Soon

  • More examples. We understand how much easier it is to understand a data model with concrete examples, so we're working hard to develop more and to use those examples in end-to-end demonstrations of how to use our entire data management and machine learning stack.

Taurus v0.3.0 is released!

12 Nov 00:38
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The largest change with this release is a large increase in the list of supported units. We've also improved documentation, particularly for the installation process in our README.

What's new:

  • Augmented unit sets
  • Improved documentation of installation, testing and units coverage
  • Updated keyword handling for serialization technology
  • Augmented test coverage

Coming Soon:

  • More types for supporting data provenance

Taurus v0.2.0 is released!

27 Sep 18:46
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Most of this release is test coverage, which includes some changes to the exception types to better reflect the error that is being raised. This release also includes improvements to the string representation of data objects.

What's new

  • 100% test coverage.
  • A more intelligent __repr__ method, which will include the names of soft-linked objects.

Changes

  • Some ValueError changed to TypeError when validating arguments. This better reflects the error mode.

Coming Soon

  • More units for real values, e.g. #23

Taurus v0.1.0 is released!

24 Sep 02:54
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This is the first beta release of taurus-citrine: the python binding to Citrine's next data format, also code-named taurus. It supports access to the Citrine Data Platform via citrine-python. It is a beta release that we expect to actively develop over the next several months.

What's new

  • Everything! This is the first release of taurus and the first implementation of the taurus data model.
  • Material histories with Materials, Processes, Ingredients, and Measurements
  • Differentiating between intent and reality with Specs and Runs
  • Opt-in data validation with Templates
  • Opt-out uncertainty quantification with distribution types
  • Multi-object serialization and deserialization with link normalization
  • Unit string parsing

Coming Soon

  • More units for real values, e.g. #23
  • More sources for MeasurementRun and ProcessRun