| Name | property-graph JSON |
| Version |
2.4.0
JSON |
| download |
| home_page | |
| Summary | python library for working with objects as graphs of property data |
| upload_time | 2024-02-25 16:13:23 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.10 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. 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| keywords |
graph
serialization
metadata
property
schema
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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# python property graph (pypg)
Object-oriented descriptor properties designed for capturing complex data-graphs and rich metadata from objects and types.
## Motivation
pypg provides a flexible and terse framework for expressing object schemas, initialization, de/serializiation, and declarative behavior.
### Example
Consider a directed acyclic graph (DAG) such as:
root
/ \
1A 1B
\ / \
2A 2B
\ /
end
note that 1B is referenced by both 2A and 2B. Naively serializing this object will result in duplication of this data and any upstream nodes, and, upon de-serialiation, the referential integrity will be lost.
To combat this issue, one can transform the data using adjacency lists or comparison of other unique identifiers to establish relationships.
Using pypg, the DAG above can be expressed like so:
```python
from pypg import Property, PropertyClass
from pypg.transcode import encode, decode
class Node(PropertyClass):
upstream = Property[list["Node"]]()
root = Node()
layer_1 = [Node(upstream=[root]), Node(upstream=[root])]
layer_2 = [Node(upstream=layer_1), Node(upstream=[layer_1[1]])]
terminal = Node(upstream=layer_2)
```
This structure can be serialized and reconstructed without duplication of object instances, and all identity relationships are preserved.
```python
nodes = [terminal, *layer_2, *layer_1, root]
serialized_nodes = encode(nodes)
copy_nodes = decode(serialized_nodes)
terminal_copy, l2a, l2b, l1a, l1b, root_copy = copy_nodes
assert terminal_copy.upstream[0] is l2a
assert terminal_copy.upstream[1] is l2b
assert l2a.upstream[0] is l1a
assert l2a.upstream[1] is l1b
assert l2b.upstream[0] is l1b
assert l1a.upstream[0] is root_copy
assert l1b.upstream[0] is root_copy
```
Note that in this case it is not necessary to collect all the objects for encoding, the terminal node is sufficient to capture the entire graph.
```python
serialized = encode(terminal)
terminal_copy = decode(serialized)
l2a, l2b = terminal_copy.upstream
l1a, l1b = l2a.upstream
(also_l1b,) = l2b.upstream
assert l1b is also_l1b
(root_copy,) = l1a.upstream
(also_root_copy,) = l1b.upstream
assert root_copy is also_root_copy
```
### Why not dataclasses?
Aside from cases when object-identity-fidelity is important, dataclasses impose limitations. For example:
```python
from dataclasses import dataclass
@dataclass
class Base:
a: int = 0
@dataclass
class Derived(Base):
b: int
```
```console
@dataclass
^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/dataclasses.py", line 1220, in dataclass
return wrap(cls)
^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/dataclasses.py", line 1210, in wrap
return _process_class(cls, init, repr, eq, order, unsafe_hash,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/dataclasses.py", line 1027, in _process_class
_init_fn(all_init_fields,
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/dataclasses.py", line 545, in _init_fn
raise TypeError(f'non-default argument {f.name!r} '
TypeError: non-default argument 'b' follows default argument
python-BaseException
```
If a base class declares a default field, any subsequent fields must also have default values. This violation of the open/closed principle makes dataclasses an unsuitable choice for extendable objects.
### Why not JSON Schema?
JSON Schema requires a separate second representation of an object just to describe it. This requires 2 edits to be made for any 1 change, increasing development effort and potential for error.
### Why descriptors?
Python descriptors provide a powerful mechanism for encoding type-information and customizing data-handling behaviors. Descriptor classes can be used to extend the semantics of working with object fields with very few limitations. A few examples of those contained in pypg are:
* the observer pattern
* value validation
* units of measure
By composing decorator-like objects, here called Traits, Property objects provide an expressive but powerful syntax for defining not just data, but also runtime-behaviors that should occur with data access semantics. For more examples, see:
https://github.com/mattefowler/pypg/blob/main/tests/test_traits.py
https://github.com/mattefowler/pypg/blob/main/tests/test_observable.py
https://github.com/mattefowler/pypg/blob/main/tests/test_overridable.py
Raw data
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"description": "# python property graph (pypg)\nObject-oriented descriptor properties designed for capturing complex data-graphs and rich metadata from objects and types.\n\n## Motivation\npypg provides a flexible and terse framework for expressing object schemas, initialization, de/serializiation, and declarative behavior. \n\n### Example\nConsider a directed acyclic graph (DAG) such as: \n\n root\n / \\\n 1A 1B\n \\ / \\\n 2A 2B\n \\ /\n end\n\nnote that 1B is referenced by both 2A and 2B. Naively serializing this object will result in duplication of this data and any upstream nodes, and, upon de-serialiation, the referential integrity will be lost. \n\nTo combat this issue, one can transform the data using adjacency lists or comparison of other unique identifiers to establish relationships.\n\nUsing pypg, the DAG above can be expressed like so:\n\n```python\n\nfrom pypg import Property, PropertyClass\nfrom pypg.transcode import encode, decode\n\n\nclass Node(PropertyClass):\n upstream = Property[list[\"Node\"]]()\n\n\nroot = Node()\n\nlayer_1 = [Node(upstream=[root]), Node(upstream=[root])]\nlayer_2 = [Node(upstream=layer_1), Node(upstream=[layer_1[1]])]\nterminal = Node(upstream=layer_2)\n```\n\nThis structure can be serialized and reconstructed without duplication of object instances, and all identity relationships are preserved.\n\n```python\n\nnodes = [terminal, *layer_2, *layer_1, root]\n\nserialized_nodes = encode(nodes)\ncopy_nodes = decode(serialized_nodes)\n\nterminal_copy, l2a, l2b, l1a, l1b, root_copy = copy_nodes\n\nassert terminal_copy.upstream[0] is l2a\nassert terminal_copy.upstream[1] is l2b\nassert l2a.upstream[0] is l1a\nassert l2a.upstream[1] is l1b\nassert l2b.upstream[0] is l1b\nassert l1a.upstream[0] is root_copy\nassert l1b.upstream[0] is root_copy\n```\n\nNote that in this case it is not necessary to collect all the objects for encoding, the terminal node is sufficient to capture the entire graph.\n\n```python\nserialized = encode(terminal)\nterminal_copy = decode(serialized)\n\nl2a, l2b = terminal_copy.upstream\nl1a, l1b = l2a.upstream\n(also_l1b,) = l2b.upstream\nassert l1b is also_l1b\n\n(root_copy,) = l1a.upstream\n(also_root_copy,) = l1b.upstream\nassert root_copy is also_root_copy\n```\n\n### Why not dataclasses? \nAside from cases when object-identity-fidelity is important, dataclasses impose limitations. For example:\n\n```python\nfrom dataclasses import dataclass\n\n@dataclass\nclass Base:\n a: int = 0\n\n@dataclass\nclass Derived(Base): \n b: int\n\n```\n```console\n @dataclass\n ^^^^^^^^^\n File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/dataclasses.py\", line 1220, in dataclass\n return wrap(cls)\n ^^^^^^^^^\n File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/dataclasses.py\", line 1210, in wrap\n return _process_class(cls, init, repr, eq, order, unsafe_hash,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/dataclasses.py\", line 1027, in _process_class\n _init_fn(all_init_fields,\n File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/dataclasses.py\", line 545, in _init_fn\n raise TypeError(f'non-default argument {f.name!r} '\nTypeError: non-default argument 'b' follows default argument\npython-BaseException\n```\n\nIf a base class declares a default field, any subsequent fields must also have default values. This violation of the open/closed principle makes dataclasses an unsuitable choice for extendable objects. \n\n### Why not JSON Schema? \nJSON Schema requires a separate second representation of an object just to describe it. This requires 2 edits to be made for any 1 change, increasing development effort and potential for error. \n\n### Why descriptors? \nPython descriptors provide a powerful mechanism for encoding type-information and customizing data-handling behaviors. Descriptor classes can be used to extend the semantics of working with object fields with very few limitations. A few examples of those contained in pypg are: \n\n* the observer pattern\n* value validation\n* units of measure\n\nBy composing decorator-like objects, here called Traits, Property objects provide an expressive but powerful syntax for defining not just data, but also runtime-behaviors that should occur with data access semantics. For more examples, see: \n\nhttps://github.com/mattefowler/pypg/blob/main/tests/test_traits.py\n\nhttps://github.com/mattefowler/pypg/blob/main/tests/test_observable.py\n\nhttps://github.com/mattefowler/pypg/blob/main/tests/test_overridable.py\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. 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Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. 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