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* Bugfix - Fix assertion error when performing a union into a join (#930) PR #967
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* Update `~jobs.error_stack` from blob to mediumblob to allow error stacks >64kB in jobs (#984) PR #986
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* Bugfix - Fix error when performing a union on multiple tables (#926) PR #964
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* Add - Allow optional keyword arguments for `make()` in `populate()` PR #971
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### 0.13.2 -- May 7, 2021
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* Update `setuptools_certificate` dependency to new name `otumat`
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* Fix display of part tables in `schema.save`. (#821) PR #833
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* Add `schema.list_tables`. (#838) PR #844
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* Fix minio new version regression. PR #847
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* Add more S3 logging for debugging. (#831) PR #832
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* Add more S3 logging for debugging. (#831) PR #832
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* Convert testing framework from TravisCI to GitHub Actions (#841) PR #840
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### 0.12.7 -- Oct 27, 2020
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* Fix case sensitivity issues to adapt to MySQL 8+. PR #819
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* Fix pymysql regression bug (#814) PR #816
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* Adapted attribute types now have dtype=object in all recarray results. PR #811
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* Adapted attribute types now have dtype=object in all recarray results. PR #811
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### 0.12.6 -- May 15, 2020
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* Add `order_by` to `dj.kill` (#668, #779) PR #775, #783
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* Fix #628 - incompatibility with pyparsing 2.4.1
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### 0.11.1 -- Nov 15, 2018
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* Fix ordering of attributes in proj (#483 and#516)
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* Fix ordering of attributes in proj (#483,#516)
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* Prohibit direct insert into auto-populated tables (#511)
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### 0.11.0 -- Oct 25, 2018
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* Bugfix in restriction of the form (A & B) * B (#463)
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* Improved error messages (#466)
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### 0.10.0 -- Jan 10, 2018
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### 0.10.0 -- Jan 10, 2018
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* Deletes are more efficient (#424)
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* ERD shows table definition on tooltip hover in Jupyter (#422)
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* ERD shows table definition on tooltip hover in Jupyter (#422)
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* S3 external storage
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* Garbage collection for external sorage
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* Most operators and methods of tables can be invoked as class methods rather than instance methods (#407)
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* Implement union operator +
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* Implement file-based external storage
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### 0.8.0 -- Jul 26, 2017
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### 0.8.0 -- Jul 26, 2017
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Documentation and tutorials available at https://docs.datajoint.io and https://tutorials.datajoint.io
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* improved the ERD graphics and features using the graphviz libraries (#207, #333)
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* improved password handling logic (#322, #321)
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* Added `dj.create_virtual_module`
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### 0.4.10 (#286) -- Feb 6, 2017
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* Removed Vagrant and Readthedocs support
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* Removed Vagrant and Readthedocs support
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* Explicit saving of configuration (issue #284)
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### 0.4.9 (#285) -- Feb 2, 2017
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* Fixed setup.py for pip install
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* Fixed setup.py for pip install
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### 0.4.7 (#281) -- Jan 24, 2017
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* Fixed issues related to order of attributes in projection.
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### 0.3.8 -- Aug 2, 2016
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* added the `_update` method in `base_relation`. It allows updating values in existing tuples.
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* bugfix in reading values of type double. Previously it was cast as float32.
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* bugfix in reading values of type double. Previously it was cast as float32.
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### 0.3.7 -- Jul 31, 2016
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* added parameter `ignore_extra_fields` in `insert`
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* added parameter `ignore_extra_fields` in `insert`
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*`insert(..., skip_duplicates=True)` now relies on `SELECT IGNORE`. Previously it explicitly checked if tuple already exists.
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* table previews now include blob attributes displaying the string <BLOB>
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* ERD() no longer text the context argument.
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* ERD.draw() now takes an optional context argument. By default uses the caller's locals.
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### 0.3.2.
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### 0.3.2.
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* Fixed issue #223: `insert` can insert relations without fetching.
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* ERD() now takes the `context` argument, which specifies in which context to look for classes. The default is taken from the argument (schema or relation).
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* ERD.draw() no longer has the `prefix` argument: class names are shown as found in the context.
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# Welcome to DataJoint for Python!
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DataJoint for Python is a framework for scientific workflow management based on relational principles. DataJoint is built on the foundation of the relational data model and prescribes a consistent method for organizing, populating, computing, and querying data.
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DataJoint was initially developed in 2009 by Dimitri Yatsenko in Andreas Tolias' Lab for the distributed processing and management of large volumes of data streaming from regular experiments. Starting in 2011, DataJoint has been available as an open-source project adopted by other labs and improved through contributions from several developers.
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Vathes LLC supports DataJoint for Python as an open-source project and everyone is welcome to contribute.
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Its DataJoint Neuro (https://djneuro.io) business provides support to neuroscience labs for developing and executing custom data pipelines.
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DataJoint was initially developed in 2009 by Dimitri Yatsenko in Andreas Tolias' Lab at Baylor College of Medicine for the distributed processing and management of large volumes of data streaming from regular experiments. Starting in 2011, DataJoint has been available as an open-source project adopted by other labs and improved through contributions from several developers.
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Presently, the primary developer of DataJoint open-source software is the company DataJoint (https://datajoint.com). Related resources are listed at https://datajoint.org.
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## Installation
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```
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+ If your work uses DataJoint for Python, please cite the following Research Resource Identifier (RRID) and manuscript.
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+ DataJoint ([RRID:SCR_014543](https://scicrunch.org/resolver/SCR_014543)) - DataJoint for Python (version `<Enter version number>`)
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+ Yatsenko D, Reimer J, Ecker AS, Walker EY, Sinz F, Berens P, Hoenselaar A, Cotton RJ, Siapas AS, Tolias AS. DataJoint: managing big scientific data using MATLAB or Python. bioRxiv. 2015 Jan 1:031658. doi: https://doi.org/10.1101/031658
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## Python Native Blobs
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<details>
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<summary>Click to expand details</summary>
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DataJoint 0.12 adds full support for all native python data types in blobs: tuples, lists, sets, dicts, strings, bytes, `None`, and all their recursive combinations.
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The new blobs are a superset of the old functionality and are fully backward compatible.
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As always, be sure that your data is safely backed up before modifying any
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important DataJoint schema or records.
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## Documentation and Tutorials
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A number of labs are currently adopting DataJoint and we are quickly getting the documentation in shape in February 2017.
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