clean up repo files, README, auxiliary files (pre-BFG)
This commit is contained in:
parent
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6
.gitignore
vendored
6
.gitignore
vendored
@ -1,7 +1,7 @@
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# generic py
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# generic py
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__pycache__/
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__pycache__/
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.pytest_cache/
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.pytest_cache/
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localsys.egg-info/
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*.egg-info/
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.ipynb_checkpoints/
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.ipynb_checkpoints/
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.pytest_cache/
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.pytest_cache/
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.python-version
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.python-version
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@ -12,3 +12,7 @@ build/
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docs/_autoref/
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docs/_autoref/
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docs/_autosummary/
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docs/_autosummary/
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docs/_build/
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docs/_build/
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# local
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notebooks/
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/Makefile
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22
LICENSE
Normal file
22
LICENSE
Normal file
@ -0,0 +1,22 @@
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MIT License
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Copyright (c) 2024 Sam Griesemer
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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21
Makefile
21
Makefile
@ -1,21 +0,0 @@
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PYTHON=/home/smgr/.pyenv/versions/co4/bin/python
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BASH=/usr/bin/bash
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## ------------------ docs ------------------ ##
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docs-build:
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sphinx-apidoc --module-first --separate -o docs/_autoref/ co3
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make -C docs/ html
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docs-serve:
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cd docs/_build/html && python -m http.server 9090
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docs-clean:
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make -C docs/ clean
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rm -rf docs/_autoref
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rm -rf docs/_autosummary
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## ------------------------------------------ ##
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## ----------------- tests ------------------ ##
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test:
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pytest --pyargs tests -v
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60
README.md
60
README.md
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# Overview
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# Overview
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`co3` is a package for file conversion and associated database operations. The `CO3` base class
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`co3` is a lightweight Python ORM for hierarchical storage management. It implements a
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provides a standard interface for performing conversions, preparing inserts, and
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general type system for defining database components like relations, schemas, engines,
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interacting with database schemas that mirror the class hierarchy.
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etc. Objects inheriting from the `CO3` base class can then define data transformations
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that connect to database components, and can be automatically collected for coordinated
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database insertion.
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Simplified description of the operational model:
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`co3` attempts to provide a general interface for interacting with a storage media (e.g.,
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database, pickled objects, VSS framework, in-memory key-value stores, etc). The following
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top-level classes capture the bulk of the operational model:
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**Goal**: interact with a storage medium (database, pickled structure, VSS framework) with
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- **Database**: reference to a storage medium, with an `Accessor` for accessing data,
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a known schema.
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`Manager` for managing database state, and an `Engine` for managing connections and
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external operations.
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- **Accessor**: provides access to stored items in a `Database`, typically via a supported
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`select` operation over known `Component` types
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- **Manager**: manages database storage state (e.g., supported inserts or database sync
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operations)
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- **Mapper**: associates `CO3` types with `Schema` components, and provides automatic
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collection and composition operations for supported items
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- **Collector**: collects data from defined `CO3` type transformations and prepares for
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`Database` insert operations
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- **Component**: atomic storage groups for databases (i.e., generalized notion of a
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"relation" in relational algebra).
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- **Indexer**: automatic caching of supported access queries to a `Database`
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- **Schema**: general schema analog for grouping related `Component` sets
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- **Differ**: facilitates set operations on results from selectable resources (e.g.,
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automatic comparison between file data on disk and file rows in a SQL database)
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- **Syncer**: generalized syncing procedure for items between data resources (e.g.,
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syncing new, modified, and deleted files from disk to a SQL database that stores file
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metadata).
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- **Accessor** to provide access to stored items
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The **CO3** an abstract base class then makes it easy to integrate this model with regular
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- **Composer** to compose common access points (e.g., JOINed tables)
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Python object hierarchies that can be mapped to a storage schema.
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- **Indexer** to index/cache access queries
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- **Manager** to manage storage state (e.g., supported inserts, database syncs)
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- **Collector** to collect data for updating storage state
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- **Database** to collect data for updating storage state
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- **Mapper** to collect data for updating storage state
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- **Component** to collect data for updating storage state
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**CO3** is an abstract base class that makes it easy to integrate this model with object
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hierarchies that mirror a storage schema.
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# Detailed structural breakdown
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There are a few pillars of the CO3 model that meaningfully group up functionality:
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- Database: generic to a Component type, provides basic connection to a database at a
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specific address/location. The explicit Component type makes it easy to hook into
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appropriately typed functional objects:
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* Manager: generic to a Component and Database type, provides a supported set of
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state-modifying operations to a constituent database
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* Accessor: generic to a Component and Database type, provides a supported set of
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state inspection operations on a constituent database
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* Indexer:
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- Mapper: generic to a Component, serves as the fundamental connective component between
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types in the data representation hierarchy (CO3 subclasses) and database Components.
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@ -1,47 +0,0 @@
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Metadata-Version: 2.1
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Name: co3
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Version: 0.1.1
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Summary: Lightweight ORM
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Author-email: Sam Griesemer <samgriesemer@gmail.com>
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Classifier: Programming Language :: Python :: 3
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Classifier: License :: OSI Approved :: MIT License
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Classifier: Operating System :: OS Independent
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Requires-Python: >=3.11
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Description-Content-Type: text/markdown
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Requires-Dist: tqdm
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# Overview
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`co3` is a package for file conversion and associated database operations. The `CO3` base class
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provides a standard interface for performing conversions, preparing inserts, and
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interacting with database schemas that mirror the class hierarchy.
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Simplified description of the operational model:
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**Goal**: interact with a storage medium (database, pickled structure, VSS framework) with
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a known schema.
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- **Accessor** to provide access to stored items
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- **Composer** to compose common access points (e.g., JOINed tables)
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- **Indexer** to index/cache access queries
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- **Manager** to manage storage state (e.g., supported inserts, database syncs)
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- **Collector** to collect data for updating storage state
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- **Database** to collect data for updating storage state
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- **Mapper** to collect data for updating storage state
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- **Component** to collect data for updating storage state
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**CO3** is an abstract base class that makes it easy to integrate this model with object
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hierarchies that mirror a storage schema.
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# Detailed structural breakdown
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There are a few pillars of the CO3 model that meaningfully group up functionality:
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- Database: generic to a Component type, provides basic connection to a database at a
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specific address/location. The explicit Component type makes it easy to hook into
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appropriately typed functional objects:
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* Manager: generic to a Component and Database type, provides a supported set of
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state-modifying operations to a constituent database
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* Accessor: generic to a Component and Database type, provides a supported set of
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state inspection operations on a constituent database
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* Indexer:
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- Mapper: generic to a Component, serves as the fundamental connective component between
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types in the data representation hierarchy (CO3 subclasses) and database Components.
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@ -1,52 +0,0 @@
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MANIFEST.in
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README.md
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pyproject.toml
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co3/__init__.py
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co3/accessor.py
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co3/co3.py
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co3/collector.py
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co3/component.py
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co3/database.py
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co3/differ.py
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co3/engine.py
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co3/indexer.py
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co3/manager.py
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co3/mapper.py
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co3/resource.py
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co3/schema.py
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co3/syncer.py
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co3.egg-info/PKG-INFO
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co3.egg-info/SOURCES.txt
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co3.egg-info/dependency_links.txt
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co3.egg-info/requires.txt
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co3.egg-info/top_level.txt
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co3/accessors/__init__.py
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co3/accessors/fts.py
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co3/accessors/sql.py
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co3/accessors/vss.py
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co3/components/__init__.py
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co3/databases/__init__.py
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co3/databases/fts.py
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co3/databases/sql.py
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co3/databases/vss.py
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co3/engines/__init__.py
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co3/indexers/relational.py
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co3/managers/__init__.py
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co3/managers/fts.py
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co3/managers/sql.py
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co3/managers/vss.py
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co3/mappers/__init__.py
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co3/resources/__init__.py
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co3/resources/disk.py
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co3/schemas/__init__.py
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co3/util/__init__.py
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co3/util/db.py
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co3/util/generic.py
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co3/util/paths.py
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co3/util/regex.py
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co3/util/types.py
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tests/test_co3.py
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tests/test_database.py
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tests/test_imports.py
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tests/test_mapper.py
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tests/test_schema.py
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tqdm
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co3
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logger = logging.getLogger(__name__)
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logger = logging.getLogger(__name__)
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class VSSAccessor(Accessor):
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class VSSAccessor(Accessor):
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_model_cls = None
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def __init__(self, cache_path):
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def __init__(self, cache_path):
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super().__init__()
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super().__init__()
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@ -35,8 +37,7 @@ class VSSAccessor(Accessor):
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@property
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@property
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def model(self):
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def model(self):
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if self._model is None:
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if self._model is None:
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# model trained with 128 token seqs
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self._model = self._model_cls()
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self._model = SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')
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return self._model
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return self._model
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@property
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@property
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@ -62,10 +63,6 @@ class VSSAccessor(Accessor):
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limit : int = 10,
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limit : int = 10,
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score_threshold = 0.5,
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score_threshold = 0.5,
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):
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):
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'''
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Parameters:
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index_name: one of ['chunks','blocks','notes']
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'''
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if not query:
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if not query:
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return None
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return None
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "6f6fbc7e-4fb9-4353-b2ee-9ea819a3c896",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/smgr/.pyenv/versions/co4/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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}
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],
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"source": [
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"import vegetables"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "88fd0ea8-9c94-4569-a51b-823a04f32f55",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'age': 5}"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tomato = vegetables.Tomato('t1', 5)\n",
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"\n",
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"# test a register collation action\n",
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"tomato.collate('ripe')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "348926d9-7137-4eff-a919-508788553dd2",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['9ca6772e-6621-4511-a4a6-ad451a1da91f',\n",
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" '2a91b423-4e08-491c-b1d2-5ec25259191e',\n",
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" '4a9edb2b-4ac5-467e-82ef-b254829ac2a2']"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"vegetables.vegetable_mapper.collect(tomato, ['ripe'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "4e5e7319-11bf-4051-951b-08c84e9f3874",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Component (SQLTable)> vegetable+tomato+tomato_aging_states+tomato_cooking_states"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"vegetables.vegetable_mapper.compose(tomato, action_groups=['aging', 'cooking'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "aa290686-8074-4038-a3cc-ce6817844653",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Column('id', Integer(), table=<vegetable>, primary_key=True, nullable=False),\n",
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" Column('name', String(), table=<vegetable>),\n",
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" Column('color', String(), table=<vegetable>),\n",
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" Column('id', Integer(), table=<tomato>, primary_key=True, nullable=False),\n",
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" Column('name', String(), ForeignKey('vegetable.name'), table=<tomato>),\n",
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|
||||||
" Column('radius', Integer(), table=<tomato>),\n",
|
|
||||||
" Column('id', Integer(), table=<tomato_aging_states>, primary_key=True, nullable=False),\n",
|
|
||||||
" Column('name', String(), ForeignKey('tomato.name'), table=<tomato_aging_states>),\n",
|
|
||||||
" Column('state', String(), table=<tomato_aging_states>),\n",
|
|
||||||
" Column('age', Integer(), table=<tomato_aging_states>)]"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 5,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"list(vegetables.vegetable_mapper.compose(tomato, action_groups=['aging']).obj.columns)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 6,
|
|
||||||
"id": "f3c7e37d-ba9e-4bae-ae44-adc922bf5f4c",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"(vegetables.Tomato, vegetables.Vegetable, co3.co3.CO3, object)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 6,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"tomato.__class__.__mro__"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 7,
|
|
||||||
"id": "c21d2c54-39e2-4de3-93bc-763896ed348e",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"from co3.databases import SQLDatabase\n",
|
|
||||||
"\n",
|
|
||||||
"db = SQLDatabase('sqlite://') #, echo=True)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 8,
|
|
||||||
"id": "a785d202-99d3-4ae7-859e-ee22b481f8df",
|
|
||||||
"metadata": {
|
|
||||||
"scrolled": true
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"db.recreate(vegetables.vegetable_schema)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 9,
|
|
||||||
"id": "cda01cb0-1666-4cb1-aa64-bcdca871aff5",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"{<Component (SQLTable)> vegetable: [{'name': 't1', 'color': 'red'}],\n",
|
|
||||||
" <Component (SQLTable)> tomato: [{'name': 't1', 'radius': 5}],\n",
|
|
||||||
" <Component (SQLTable)> tomato_aging_states: [{'name': 't1',\n",
|
|
||||||
" 'state': 'ripe',\n",
|
|
||||||
" 'age': 2}]}"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 9,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"vegetables.vegetable_mapper.collector.inserts"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 10,
|
|
||||||
"id": "af7124ed-3031-4f28-89a6-553eb5b3cc7a",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"with db.engine.connect() as connection:\n",
|
|
||||||
" db.manager.insert_many(\n",
|
|
||||||
" connection,\n",
|
|
||||||
" vegetables.vegetable_mapper.collector.inserts,\n",
|
|
||||||
" )"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 11,
|
|
||||||
"id": "0149e14e-5d07-42af-847d-af5c190f8946",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"[{'id': 1, 'name': 't1', 'radius': 5}]\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"with db.engine.connect() as connection:\n",
|
|
||||||
" print(db.accessor.select(\n",
|
|
||||||
" connection, \n",
|
|
||||||
" vegetables.vegetable_schema.get_component('tomato')\n",
|
|
||||||
" ))"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 32,
|
|
||||||
"id": "668d1b8c-b47f-4a58-914d-e43402443fe6",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"[{'id': 1, 'name': 't1', 'color': 'red', 'id_1': 1, 'name_1': 't1', 'radius': 5}]\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"agg_table = vegetables.vegetable_mapper.compose(tomato)\n",
|
|
||||||
"\n",
|
|
||||||
"with db.engine.connect() as connection:\n",
|
|
||||||
" agg_res = db.accessor.select(\n",
|
|
||||||
" connection, \n",
|
|
||||||
" agg_table,\n",
|
|
||||||
" mappings=True,\n",
|
|
||||||
" )\n",
|
|
||||||
"\n",
|
|
||||||
"print(agg_res)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 33,
|
|
||||||
"id": "a051d72d-a867-46dc-bb5e-69341f39a056",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"[{'id': 1, 'name': 't1', 'color': 'red', 'id_1': 1, 'name_1': 't1', 'radius': 5, 'id_2': 1, 'name_2': 't1', 'state': 'ripe', 'age': 2}]\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"agg_table = vegetables.vegetable_mapper.compose(tomato, action_groups=['aging'])#, outer=True)\n",
|
|
||||||
"\n",
|
|
||||||
"with db.engine.connect() as connection:\n",
|
|
||||||
" agg_res = db.accessor.select(\n",
|
|
||||||
" connection, \n",
|
|
||||||
" agg_table,\n",
|
|
||||||
" mappings=True,\n",
|
|
||||||
" )\n",
|
|
||||||
"\n",
|
|
||||||
"print(agg_res)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 31,
|
|
||||||
"id": "6a80cfd7-3175-4526-96e0-374765d64a27",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"sqlalchemy.engine.row.RowMapping"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 31,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"type(agg_res[0])"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 15,
|
|
||||||
"id": "7cf05ddd-2328-4051-9cf8-4ac01352405e",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"import sqlalchemy as sa\n",
|
|
||||||
"from co3.engines import SQLEngine\n",
|
|
||||||
"\n",
|
|
||||||
"a = SQLEngine.execute(db.engine.connect(), sa.select(agg_table.obj))"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 19,
|
|
||||||
"id": "c1edf68e-1fde-4a1f-8ec3-084713a8da45",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"[]"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 19,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"a.mappings().all()\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 37,
|
|
||||||
"id": "8b8a9e47-7f5f-4828-a99e-5d9a12697f46",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"tomato2 = vegetables.Tomato('t2', 8)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 38,
|
|
||||||
"id": "062aa4de-7aea-4fd3-b5db-82af147d023e",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"ename": "AttributeError",
|
|
||||||
"evalue": "'Tomato' object has no attribute 'action_map'",
|
|
||||||
"output_type": "error",
|
|
||||||
"traceback": [
|
|
||||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
||||||
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
|
|
||||||
"Cell \u001b[0;32mIn[38], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mvegetables\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvegetable_mapper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtomato2\u001b[49m\u001b[43m)\u001b[49m\n",
|
|
||||||
"File \u001b[0;32m~/Documents/projects/ontolog/co3/build/__editable__.co3-0.1.1-py3-none-any/co3/mapper.py:198\u001b[0m, in \u001b[0;36mMapper.collect\u001b[0;34m(self, obj, action_keys, action_groups)\u001b[0m\n\u001b[1;32m 179\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[1;32m 180\u001b[0m \u001b[38;5;124;03mStages inserts up the inheritance chain, and down through components.\u001b[39;00m\n\u001b[1;32m 181\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[38;5;124;03mReturns: dict with keys and values relevant for associated SQLite tables\u001b[39;00m\n\u001b[1;32m 196\u001b[0m \u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[1;32m 197\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m action_keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 198\u001b[0m action_keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maction_map\u001b[49m\u001b[38;5;241m.\u001b[39mkeys())\n\u001b[1;32m 200\u001b[0m receipts \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 201\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _cls \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mreversed\u001b[39m(obj\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__mro__\u001b[39m[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m]):\n",
|
|
||||||
"\u001b[0;31mAttributeError\u001b[0m: 'Tomato' object has no attribute 'action_map'"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"vegetables.vegetable_mapper.collect(tomato2)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "4673ddc8-3f76-4d8c-8186-bbed4a682e0d",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"db.insert(vegetables.vegetable_schema.get_component('tomato'), "
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 36,
|
|
||||||
"id": "9314be4e-c1d5-4af8-ad23-0b208d24b3eb",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"[{'id': 1, 'name': 't1', 'radius': 5}]"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 36,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"db.select(vegetables.vegetable_schema.get_component('tomato'))"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "a2efd060-f298-4ca6-8a58-7ed5acf1dd15",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": []
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"metadata": {
|
|
||||||
"kernelspec": {
|
|
||||||
"display_name": "co3",
|
|
||||||
"language": "python",
|
|
||||||
"name": "co3"
|
|
||||||
},
|
|
||||||
"language_info": {
|
|
||||||
"codemirror_mode": {
|
|
||||||
"name": "ipython",
|
|
||||||
"version": 3
|
|
||||||
},
|
|
||||||
"file_extension": ".py",
|
|
||||||
"mimetype": "text/x-python",
|
|
||||||
"name": "python",
|
|
||||||
"nbconvert_exporter": "python",
|
|
||||||
"pygments_lexer": "ipython3",
|
|
||||||
"version": "3.12.2"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 5
|
|
||||||
}
|
|
||||||
@ -1,403 +0,0 @@
|
|||||||
{
|
|
||||||
"cells": [
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 1,
|
|
||||||
"id": "e02ccafe-e04d-4312-acba-e41cf7b1c021",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stderr",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"/home/smgr/.pyenv/versions/co4/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
|
|
||||||
" from .autonotebook import tqdm as notebook_tqdm\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"import vegetables"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "c0914069-7f3c-4213-8d34-f7566033e054",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Development notes\n",
|
|
||||||
"- No registry actually needs to take place if there's a default type2component map or one supplied on creation. Can just collect right out of the gate\n",
|
|
||||||
"- Need connective function (type to collation) and attribute map. Do we need to this with a subclass? If a func is passed in on init, I can type it appropriately I guess `Callable[[type[CO3],str,str|None],dict]`"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "ef733715-bb75-4263-b216-45e778a06b21",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Usage\n",
|
|
||||||
"The Mapper's primary job is to associate class hierarchies with database components. This can be done in a few ways:\n",
|
|
||||||
"\n",
|
|
||||||
"1. Manually attaching a type reference to a Component\n",
|
|
||||||
"2. Attaching a type reference to a Component's name as registered in a schema\n",
|
|
||||||
"3. Automatically register the CO3 heirarchy to matching schema component names (through transformation)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "d2672422-3596-4eab-ac44-5da617f74b80",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Explicit example steps"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 2,
|
|
||||||
"id": "7d80f7b9-7458-4ad4-8c1a-3ea56e796b4e",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"from co3 import Mapper\n",
|
|
||||||
"\n",
|
|
||||||
"vegetable_mapper = Mapper(\n",
|
|
||||||
" vegetables.Vegetable,\n",
|
|
||||||
" vegetables.vegetable_schema\n",
|
|
||||||
")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 3,
|
|
||||||
"id": "d24d31b4-c4a6-4a1e-8bea-c44378aadfdd",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"'\\nvegetable_mapper.attach(\\n vegetables.Vegetable,\\n vegetables.vegetable_table,\\n)\\n'"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 3,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"# not valid; tables need to be wrapped in CO3 Components\n",
|
|
||||||
"'''\n",
|
|
||||||
"vegetable_mapper.attach(\n",
|
|
||||||
" vegetables.Vegetable,\n",
|
|
||||||
" vegetables.vegetable_table,\n",
|
|
||||||
")\n",
|
|
||||||
"'''"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 4,
|
|
||||||
"id": "f9408562-bf50-4522-909c-318557f85948",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"# manually attach component\n",
|
|
||||||
"vegetable_mapper.attach(\n",
|
|
||||||
" vegetables.Tomato,\n",
|
|
||||||
" vegetables.vegetable_schema.get_component('tomato'),\n",
|
|
||||||
" coll_groups={\n",
|
|
||||||
" 'aging': vegetables.vegetable_schema.get_component('tomato_aging_states'),\n",
|
|
||||||
" 'cooking': vegetables.vegetable_schema.get_component('tomato_cooking_states'),\n",
|
|
||||||
" },\n",
|
|
||||||
")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 5,
|
|
||||||
"id": "05fdd404-87ee-4187-832f-2305272758ae",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"# attach by name in schema\n",
|
|
||||||
"vegetable_mapper.attach(\n",
|
|
||||||
" vegetables.Tomato,\n",
|
|
||||||
" 'tomato',\n",
|
|
||||||
" coll_groups={\n",
|
|
||||||
" 'aging': 'tomato_aging_states',\n",
|
|
||||||
" 'cooking': 'tomato_cooking_states',\n",
|
|
||||||
" },\n",
|
|
||||||
")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 6,
|
|
||||||
"id": "e9b6af49-a69d-41cc-beae-1b6f171cd2f5",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"# attach entire type hierarchy w/ type->name map\n",
|
|
||||||
"vegetable_mapper.attach_hierarchy(\n",
|
|
||||||
" # this might make more sense during init\n",
|
|
||||||
" vegetables.Vegetable,\n",
|
|
||||||
" lambda x:x.__name__.lower()\n",
|
|
||||||
")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 9,
|
|
||||||
"id": "0fb45a86-5c9b-41b1-a3ab-5691444f175e",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"<co3.components.SQLTable at 0x7f2012b23f80>"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 9,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"vegetable_mapper.get_collation_comp(vegetables.Tomato, 'cooking')"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 7,
|
|
||||||
"id": "2e4336ab-5b5f-484d-815d-164d4b6f40a0",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"{'co3_root': vegetables.Vegetable,\n",
|
|
||||||
" 'schema': <co3.schemas.SQLSchema at 0x732074224aa0>,\n",
|
|
||||||
" 'collector': <co3.collector.Collector at 0x7320757da120>,\n",
|
|
||||||
" 'composer': <co3.composer.Composer at 0x7320757da9c0>,\n",
|
|
||||||
" 'attribute_comps': {vegetables.Tomato: <co3.components.SQLTable at 0x732074224cb0>},\n",
|
|
||||||
" 'collation_groups': defaultdict(dict,\n",
|
|
||||||
" {vegetables.Tomato: {'aging': <co3.components.SQLTable at 0x732074224ce0>,\n",
|
|
||||||
" 'cooking': <co3.components.SQLTable at 0x732074224d10>}})}"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 7,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"vars(vegetable_mapper)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "47859e25-b803-4459-a581-f10bbcfac716",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Holistic attachment"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 2,
|
|
||||||
"id": "70c9baed-b870-4021-8949-9b713d863de6",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"def attr_name_map(cls):\n",
|
|
||||||
" return f'{cls.__name__.lower()}'\n",
|
|
||||||
"\n",
|
|
||||||
"def coll_name_map(cls, action_group):\n",
|
|
||||||
" return f'{cls.__name__.lower()}_{action_group}_states'\n",
|
|
||||||
"\n",
|
|
||||||
"vegetables.vegetable_mapper.attach_many(\n",
|
|
||||||
" vegetables.type_list,\n",
|
|
||||||
" attr_name_map,\n",
|
|
||||||
" coll_name_map,\n",
|
|
||||||
")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 3,
|
|
||||||
"id": "c16786d4-0b71-42d9-97f7-7893c542104e",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"{'age': 4}"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 3,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"# create new CO3 descendant\n",
|
|
||||||
"tomato = vegetables.Tomato('t1', 5)\n",
|
|
||||||
"\n",
|
|
||||||
"# test a register collation action\n",
|
|
||||||
"tomato.collate('ripe')"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 4,
|
|
||||||
"id": "d7fa94ca-3ecd-4ee3-b0dc-f3b2b65ee47c",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"<Component (SQLTable)> tomato"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 4,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"vegetables.vegetable_mapper.get_attribute_comp(vegetables.Tomato)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 5,
|
|
||||||
"id": "1adc3bc5-957f-4b5a-bc2c-2d172675826d",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"{'schema': <co3.schemas.SQLSchema at 0x7ab568224e60>,\n",
|
|
||||||
" 'collector': <co3.collector.Collector at 0x7ab568225190>,\n",
|
|
||||||
" 'composer': <co3.composer.Composer at 0x7ab5682251c0>,\n",
|
|
||||||
" 'attribute_comps': {vegetables.Vegetable: <Component (SQLTable)> vegetable,\n",
|
|
||||||
" vegetables.Tomato: <Component (SQLTable)> tomato},\n",
|
|
||||||
" 'collation_groups': defaultdict(dict,\n",
|
|
||||||
" {vegetables.Vegetable: {},\n",
|
|
||||||
" vegetables.Tomato: {'aging': <Component (SQLTable)> tomato_aging_states,\n",
|
|
||||||
" 'cooking': <Component (SQLTable)> tomato_cooking_states}})}"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 5,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"vars(vegetables.vegetable_mapper)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 6,
|
|
||||||
"id": "f32d1f65-9b1d-4600-b396-8551fbd1fcf7",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"['3bf42abc-8a12-452f-baf6-38a05fc5d420',\n",
|
|
||||||
" '271b7b84-846e-4d1d-87f6-bcabc90a7b55',\n",
|
|
||||||
" 'f9fc5d16-c5cb-47a7-9eca-7df8a3ba5d10']"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 6,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"vegetables.vegetable_mapper.collect(tomato, ['ripe'])"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 7,
|
|
||||||
"id": "380dfbea-90cc-49fc-aef1-ebb342872632",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"defaultdict(<function co3.collector.Collector.__init__.<locals>.<lambda>()>,\n",
|
|
||||||
" {'3bf42abc-8a12-452f-baf6-38a05fc5d420': (<Component (SQLTable)> vegetable,\n",
|
|
||||||
" {'name': 't1', 'color': 'red'}),\n",
|
|
||||||
" '271b7b84-846e-4d1d-87f6-bcabc90a7b55': (<Component (SQLTable)> tomato,\n",
|
|
||||||
" {'name': 't1', 'radius': 5}),\n",
|
|
||||||
" 'f9fc5d16-c5cb-47a7-9eca-7df8a3ba5d10': (<Component (SQLTable)> tomato_aging_states,\n",
|
|
||||||
" {'name': 't1', 'state': 'ripe', 'age': 1})})"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 7,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"vegetables.vegetable_mapper.collector._inserts"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 8,
|
|
||||||
"id": "905bb2a9-9c22-4187-be15-3dd32d206e26",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"{<Component (SQLTable)> vegetable: [{'name': 't1', 'color': 'red'}],\n",
|
|
||||||
" <Component (SQLTable)> tomato: [{'name': 't1', 'radius': 5}],\n",
|
|
||||||
" <Component (SQLTable)> tomato_aging_states: [{'name': 't1',\n",
|
|
||||||
" 'state': 'ripe',\n",
|
|
||||||
" 'age': 1}]}"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 8,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"vegetables.vegetable_mapper.collector.inserts"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "d166b9af-e3ba-4750-9dcb-d8d4e08fe4d3",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": []
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"metadata": {
|
|
||||||
"kernelspec": {
|
|
||||||
"display_name": "co3",
|
|
||||||
"language": "python",
|
|
||||||
"name": "co3"
|
|
||||||
},
|
|
||||||
"language_info": {
|
|
||||||
"codemirror_mode": {
|
|
||||||
"name": "ipython",
|
|
||||||
"version": 3
|
|
||||||
},
|
|
||||||
"file_extension": ".py",
|
|
||||||
"mimetype": "text/x-python",
|
|
||||||
"name": "python",
|
|
||||||
"nbconvert_exporter": "python",
|
|
||||||
"pygments_lexer": "ipython3",
|
|
||||||
"version": "3.12.2"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 5
|
|
||||||
}
|
|
||||||
@ -1,27 +1,49 @@
|
|||||||
[build-system]
|
[build-system]
|
||||||
requires = ["setuptools"]
|
requires = ["setuptools", "wheel", "setuptools-git-versioning>=2.0,<3"]
|
||||||
build-backend = "setuptools.build_meta"
|
build-backend = "setuptools.build_meta"
|
||||||
|
|
||||||
[project]
|
[project]
|
||||||
name = "co3"
|
name = "co3"
|
||||||
version = "0.1.1"
|
description = "Lightweight Python ORM for hierarchical storage management"
|
||||||
authors = [
|
|
||||||
{ name="Sam Griesemer", email="samgriesemer@gmail.com" },
|
|
||||||
]
|
|
||||||
description = "Lightweight ORM"
|
|
||||||
readme = "README.md"
|
readme = "README.md"
|
||||||
requires-python = ">=3.11"
|
requires-python = ">=3.12"
|
||||||
|
dynamic = ["version"]
|
||||||
|
#license = {file = "LICENSE"}
|
||||||
|
authors = [
|
||||||
|
{ name="Sam Griesemer", email="samgriesemer+git@gmail.com" },
|
||||||
|
]
|
||||||
|
keywords = ["database", "orm"]
|
||||||
classifiers = [
|
classifiers = [
|
||||||
"Programming Language :: Python :: 3",
|
"Programming Language :: Python :: 3.12",
|
||||||
"License :: OSI Approved :: MIT License",
|
"License :: OSI Approved :: MIT License",
|
||||||
"Operating System :: OS Independent",
|
"Operating System :: OS Independent",
|
||||||
|
"Development Status :: 3 - Alpha",
|
||||||
|
"Intended Audience :: Developers",
|
||||||
]
|
]
|
||||||
dependencies = [
|
dependencies = [
|
||||||
"tqdm"
|
"tqdm",
|
||||||
|
"wcmatch",
|
||||||
|
"numpy",
|
||||||
|
"sqlalchemy",
|
||||||
|
"colorama",
|
||||||
]
|
]
|
||||||
|
|
||||||
[tool.setuptools.packages.find]
|
[project.optional-dependencies]
|
||||||
#where = ["localsys"] # this is too deeply nested, need to remain at root level & use
|
tests = ["pytest"]
|
||||||
#include; by default, `where` is `.`
|
docs = [
|
||||||
include = ["co3*"] # pattern to match package names
|
"sphinx",
|
||||||
|
"sphinx-togglebutton",
|
||||||
|
"sphinx-autodoc-typehints",
|
||||||
|
"furo",
|
||||||
|
"myst-parser",
|
||||||
|
]
|
||||||
|
|
||||||
|
[project.urls]
|
||||||
|
Homepage = "https://doc.olog.io/co3"
|
||||||
|
Documentation = "https://doc.olog.io/co3"
|
||||||
|
Repository = "https://git.olog.io/olog/co3"
|
||||||
|
Issues = "https://git.olog.io/olog/co3/issues"
|
||||||
|
|
||||||
|
|
||||||
|
[tool.setuptools.packages.find]
|
||||||
|
include = ["co3*"] # pattern to match package names
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user