| Name | ft3 JSON |
| Version |
0.1.17
JSON |
| download |
| home_page | None |
| Summary | Zero-dependency Python framework for object oriented development. |
| upload_time | 2024-11-13 18:38:24 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | None |
| keywords |
dataclasses
ft3
framework
openapi
rest
swagger
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# [](https://ft3.readthedocs.io)
[](https://pypi.org/project/ft3)
[](https://pypi.org/project/ft3)
[](https://ft3.readthedocs.io)
[](https://github.com/dan1hc/ft3/actions)
[](https://github.com/dan1hc/ft3/actions/workflows/codeql.yml)
[](https://github.com/dan1hc/ft3/actions)
[](https://github.com/pre-commit/pre-commit)
[](https://github.com/astral-sh/ruff)
[](http://mypy-lang.org/)
[](https://pypi.org/project/ft3)
[](https://www.gnu.org/licenses/lgpl-3.0)
# Overview
**Author:** dan@1howardcapital.com | daniel.dube@annalect.com
**Summary:** Zero-dependency python framework for object oriented development.
Implement _once_, document _once_, in _one_ place.
> With ft3, you will quickly learn established best practice...
> or face the consequences of runtime errors that will break your code
> if you deviate from it.
>
> Experienced python engineers will find a framework
> that expects and rewards intuitive magic method usage,
> consistent type annotations, and robust docstrings.
>
> Implement _pythonically_ with ft3 and you will only ever need to:
> implement _once_, document _once_, in _one_ place.
---
## Mission Statement
Ultimately, ft3 seeks to capture and abstract all recurring patterns in
application development with known, optimal implementations, so engineers
can focus more on clever implementation of application-specific logic and good
documentation than on things like how to query X database most efficiently,
whether or not everything important is being logged correctly, where to
put what documentation, and how to implement an effective change management
scheme with git in the first place.
## Getting Started
### Installation
```bash
pip install ft3
```
### Basic Usage
```python
import ft3
class Pet(ft3.Object):
"""A pet."""
id_: ft3.Field[int]
name: ft3.Field[str]
type_: ft3.Field[str] = {
'default': 'dog',
'enum': ['cat', 'dog'],
'required': True,
}
is_tail_wagging: ft3.Field[bool] = ft3.Field(
default=True,
enum=[True, False],
required=True,
)
```
## Best Practice - Guard Rails at a Bowling Alley
ft3 has been designed from the outset to teach best practice to less
experienced python engineers, without compromising their ability to
make effective and timely contributions.
> To ft3, it is more important developers are able to make
> effective contributions while learning, rather than sacrifice
> any contribution at all until the developer fully understands
> why something that could be done many ways should only ever
> be done one way.
#### Exceptions
This is achieved primarily through the raising of exceptions.
In many cases, if a developer inadvertently deviaties from a known
best practice, ft3 will raise a code-breaking error (informing
the developer of the violation) until the developer implements
the optimal solution.
#### Logging
ft3 will commandeer your application's log.
* It will automatically redact sensitive data inadvertently introduced
to your log stream that would have made your application fail audits.
* It will intercept, warn once, and subsequently silence print statements,
debug statements, and other errant attempts at logging information in ways
certain to introduce a known anti-pattern, vulnerability, or otherwise
pollute your log stream.
> In short, if ft3 raises an error or otherwise does not support
> the thing you are trying to do: it is because the way in which you
> are trying to do it contains at least one anti-pattern to a known,
> optimal solution.
## Example Usage
```python
import ft3
class Flea(ft3.Object):
"""A nuisance."""
name: ft3.Field[str] = 'FLEA'
class Pet(ft3.Object):
"""A pet."""
id_: ft3.Field[str]
_alternate_id: ft3.Field[int]
name: ft3.Field[str]
type_: ft3.Field[str] = {
'default': 'dog',
'enum': ['cat', 'dog'],
'required': True,
}
in_: ft3.Field[str]
is_tail_wagging: ft3.Field[bool] = ft3.Field(
default=True,
enum=[True, False],
required=True,
)
fleas: ft3.Field[list[Flea]] = [
Flea(name='flea1'),
Flea(name='flea2')
]
# Automatic case handling.
request_body = {
'id': 'abc123',
'alternateId': 123,
'name': 'Bob',
'type': 'dog',
'in': 'timeout',
'isTailWagging': False
}
pet = Pet(request_body)
assert pet.is_snake_case == Pet.is_snake_case is True
assert pet.is_camel_case == Pet.is_camel_case is False
assert pet['alternate_id'] == pet._alternate_id == request_body['alternateId']
assert dict(pet) == {k: v for k, v in pet.items()} == pet.to_dict()
# Automatic, mutation-safe "default factory".
dog = Pet(id='abc321', alternate_id=321, name='Fido')
assert pet.fleas[0] is not dog.fleas[0]
# Automatic memory optimization.
assert Flea().__sizeof__() == (len(Flea.__slots__) * 8) + 16 == 32
class Flet(Flea, Pet):
...
class Pea(Pet, Flea):
...
assert Flet().__sizeof__() == (len(Flet.__base__.__slots__) * 8) + 16 == 80
assert Pea().__sizeof__() == (len(Pea.__base__.__slots__) * 8) + 16 == 80
assert Flet().name == 'FLEA' != Pea().name
# Intuitive, database agnostic query generation.
assert isinstance(Pet.is_tail_wagging, ft3.Field)
assert isinstance(Pet.type_, ft3.Field)
assert dog.type_ == Pet.type_.default == 'dog'
query = (
(
(Pet.type_ == 'dog')
& (Pet.name == 'Fido')
)
| Pet.name % ('fido', 0.75)
)
query += 'name'
assert dict(query) == {
'limit': None,
'or': [
{
'and': [
{
'eq': 'dog',
'field': 'type',
'limit': None,
'sorting': []
},
{
'eq': 'Fido',
'field': 'name',
'limit': None,
'sorting': []
}
],
'limit': None,
'sorting': []
},
{
'field': 'name',
'like': 'fido',
'limit': None,
'sorting': [],
'threshold': 0.75
}
],
'sorting': [
{
'direction': 'asc',
'field': 'name'
}
]
}
```
### Local Logging
```python
import ft3
class AgentFlea(ft3.Object):
"""Still a nuisance."""
name: ft3.Field[str] = 'FLEA'
api_key: ft3.Field[str] = '9ac868264f004600bdff50b7f5b3e8ad'
aws_access_key_id: ft3.Field[str] = 'falsePositive'
sneaky: ft3.Field[str] = 'AKIARJFBAG3EGHFG2FPN'
# Automatic log configuration, cleansing, and redaction.
print(AgentFlea())
# >>>
# {
# "level": WARNING,
# "timestamp": 2024-02-26T18:50:20.317Z,
# "logger": ft3,
# "message": {
# "content": "Calls to print() will be silenced by ft3."
# }
# }
# {
# "api_key": "[ REDACTED :: API KEY ]",
# "aws_access_key_id": "falsePositive",
# "name": "FLEA",
# "sneaky": "[ REDACTED :: AWS ACCESS KEY ID ]"
# }
print(AgentFlea())
# >>>
# {
# "api_key": "[ REDACTED :: API KEY ]",
# "aws_access_key_id": "falsePositive",
# "name": "FLEA",
# "sneaky": "[ REDACTED :: AWS ACCESS KEY ID ]"
# }
```
### Deployed Logging
```python
import os
os.environ['ENV'] = 'DEV'
import ft3
assert (
ft3.core.constants.PackageConstants.ENV
in {
'dev', 'develop',
'qa', 'test', 'testing',
'uat', 'stg', 'stage', 'staging',
'prod', 'production',
}
)
class AgentFlea(ft3.Object):
"""Still a nuisance."""
name: ft3.Field[str] = 'FLEA'
api_key: ft3.Field[str] = '9ac868264f004600bdff50b7f5b3e8ad'
aws_access_key_id: ft3.Field[str] = 'falsePositive'
sneaky: ft3.Field[str] = 'AKIARJFBAG3EGHFG2FPN'
print(AgentFlea())
# >>>
# {
# "level": WARNING,
# "timestamp": 2024-02-26T19:02:29.020Z,
# "logger": ft3,
# "message": {
# "text": "Call to print() silenced by ft3.",
# "printed": "{\n \"api_key\": \"[ REDACTED :: API KEY ]\",\n \"aws_access_key_id\": \"falsePositive\",\n \"name\": \"FLEA\",\n \"sneaky\": \"[ REDACTED :: AWS ACCESS KEY ID ]\"\n}"
# }
# }
print(AgentFlea())
# >>>
ft3.log.info(AgentFlea())
# >>>
# {
# "level": INFO,
# "timestamp": 2024-02-26T19:13:21.726Z,
# "logger": ft3,
# "message": {
# "AgentFlea": {
# "api_key": "[ REDACTED :: API KEY ]",
# "aws_access_key_id": "falsePositive",
# "name": "FLEA",
# "sneaky": "[ REDACTED :: AWS ACCESS KEY ID ]"
# }
# }
# }
```
## Planned Features
* #### Database Parse & Sync
* ft3 should be able to generate a python package with fully enumerated
and optimized `Objects` (and a corresponding ft3 API package) when
supplied with access to a database for which at least one schema may be
inferred.
* CLI commands like `$ ft3-api-from-sql ${api_name} ${sql_conn_string} .`
should instantly output two ideally structured package repositories for a
RESTful python API and corresponding object management package.
* The package could use any supplied credentials to either query a database
directly or make requests to a deployed API. This means the same package
used to power the API can be distributed and pip installed across an
organization so business intelligence, data science, and other technical
team members can manipulate data for their needs, while leaning on
the package to optimize queries and stay informed around permission
boundaries and request limits.
* #### Repo Generation
* ft3 should be expanded to optionally wrap any generated packages
in a repository pre-configured with essentials and CI that should:
* implement an ideal [trunk-based branch strategy](https://trunkbaseddevelopment.com/),
inline with current best practices for change management and
developer collaboration
* enforce python code style best practices through automated
[linting and formatting](https://docs.astral.sh/ruff)
* type-check python code and generate a report with [mypy](https://mypy.readthedocs.io/en/stable/index.html)
* run tests automatically, generate reports, and prevent commits that break tests
* automatically prevent commits that do not adhere to standardized commit
message [conventions](https://www.conventionalcommits.org/en/v1.0.0/)
* using those conventions, automatically [semantically version](https://python-semantic-release.readthedocs.io/en/stable/#getting-started)
each successful PR and automatically generate and update a
CHANGELOG.md file
* automatically generate and publish secure wiki documentation
* Generated repos may contain up to all of the following:
* CHANGELOG.md
* CODEOWNERS
* CONTRIBUTING.md
* .git
* .git/hooks/
* .github/workflows/
* Support planned for gitlab and bamboo.
* .gitignore
* LICENSE
* [.pre-commit-config.yaml](https://pre-commit.com/#intro)
* pyproject.toml
* README.md
* /src
* /package
* /tests
## Acknowledgments
* #### @sol.courtney
* Teaching me the difference between chicken-scratch, duct tape, and bubble
gum versus actual engineering, and why it matters.
Raw data
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"description": "# [](https://ft3.readthedocs.io)\n\n[](https://pypi.org/project/ft3)\n[](https://pypi.org/project/ft3)\n[](https://ft3.readthedocs.io)\n[](https://github.com/dan1hc/ft3/actions)\n[](https://github.com/dan1hc/ft3/actions/workflows/codeql.yml)\n[](https://github.com/dan1hc/ft3/actions)\n[](https://github.com/pre-commit/pre-commit)\n[](https://github.com/astral-sh/ruff)\n[](http://mypy-lang.org/)\n[](https://pypi.org/project/ft3)\n[](https://www.gnu.org/licenses/lgpl-3.0)\n\n# Overview\n\n**Author:** dan@1howardcapital.com | daniel.dube@annalect.com\n\n**Summary:** Zero-dependency python framework for object oriented development.\nImplement _once_, document _once_, in _one_ place.\n\n> With ft3, you will quickly learn established best practice...\n> or face the consequences of runtime errors that will break your code\n> if you deviate from it.\n>\n> Experienced python engineers will find a framework\n> that expects and rewards intuitive magic method usage,\n> consistent type annotations, and robust docstrings.\n>\n> Implement _pythonically_ with ft3 and you will only ever need to:\n> implement _once_, document _once_, in _one_ place.\n\n---\n\n## Mission Statement\n\nUltimately, ft3 seeks to capture and abstract all recurring patterns in\napplication development with known, optimal implementations, so engineers\ncan focus more on clever implementation of application-specific logic and good\ndocumentation than on things like how to query X database most efficiently,\nwhether or not everything important is being logged correctly, where to\nput what documentation, and how to implement an effective change management\nscheme with git in the first place.\n\n## Getting Started\n\n### Installation\n\n```bash\npip install ft3\n```\n\n### Basic Usage\n\n```python\nimport ft3\n\n\nclass Pet(ft3.Object):\n \"\"\"A pet.\"\"\"\n\n id_: ft3.Field[int]\n name: ft3.Field[str]\n type_: ft3.Field[str] = {\n 'default': 'dog',\n 'enum': ['cat', 'dog'],\n 'required': True,\n }\n is_tail_wagging: ft3.Field[bool] = ft3.Field(\n default=True,\n enum=[True, False],\n required=True,\n )\n\n```\n\n## Best Practice - Guard Rails at a Bowling Alley\n\nft3 has been designed from the outset to teach best practice to less\nexperienced python engineers, without compromising their ability to\nmake effective and timely contributions.\n\n> To ft3, it is more important developers are able to make\n> effective contributions while learning, rather than sacrifice\n> any contribution at all until the developer fully understands\n> why something that could be done many ways should only ever\n> be done one way.\n\n#### Exceptions\n\nThis is achieved primarily through the raising of exceptions.\nIn many cases, if a developer inadvertently deviaties from a known\nbest practice, ft3 will raise a code-breaking error (informing\nthe developer of the violation) until the developer implements\nthe optimal solution.\n\n#### Logging\n\nft3 will commandeer your application's log.\n\n* It will automatically redact sensitive data inadvertently introduced\nto your log stream that would have made your application fail audits.\n* It will intercept, warn once, and subsequently silence print statements,\ndebug statements, and other errant attempts at logging information in ways\ncertain to introduce a known anti-pattern, vulnerability, or otherwise\npollute your log stream.\n\n> In short, if ft3 raises an error or otherwise does not support\n> the thing you are trying to do: it is because the way in which you\n> are trying to do it contains at least one anti-pattern to a known,\n> optimal solution.\n\n## Example Usage\n\n```python\nimport ft3\n\n\nclass Flea(ft3.Object):\n \"\"\"A nuisance.\"\"\"\n\n name: ft3.Field[str] = 'FLEA'\n\n\nclass Pet(ft3.Object):\n \"\"\"A pet.\"\"\"\n\n id_: ft3.Field[str]\n _alternate_id: ft3.Field[int]\n\n name: ft3.Field[str]\n type_: ft3.Field[str] = {\n 'default': 'dog',\n 'enum': ['cat', 'dog'],\n 'required': True,\n }\n\n in_: ft3.Field[str]\n is_tail_wagging: ft3.Field[bool] = ft3.Field(\n default=True,\n enum=[True, False],\n required=True,\n )\n\n fleas: ft3.Field[list[Flea]] = [\n Flea(name='flea1'),\n Flea(name='flea2')\n ]\n\n\n# Automatic case handling.\nrequest_body = {\n 'id': 'abc123',\n 'alternateId': 123,\n 'name': 'Bob',\n 'type': 'dog',\n 'in': 'timeout',\n 'isTailWagging': False\n }\npet = Pet(request_body)\n\nassert pet.is_snake_case == Pet.is_snake_case is True\nassert pet.is_camel_case == Pet.is_camel_case is False\nassert pet['alternate_id'] == pet._alternate_id == request_body['alternateId']\nassert dict(pet) == {k: v for k, v in pet.items()} == pet.to_dict()\n\n# Automatic, mutation-safe \"default factory\".\ndog = Pet(id='abc321', alternate_id=321, name='Fido')\nassert pet.fleas[0] is not dog.fleas[0]\n\n# Automatic memory optimization.\nassert Flea().__sizeof__() == (len(Flea.__slots__) * 8) + 16 == 32\n\nclass Flet(Flea, Pet):\n ...\n\nclass Pea(Pet, Flea):\n ...\n\nassert Flet().__sizeof__() == (len(Flet.__base__.__slots__) * 8) + 16 == 80\nassert Pea().__sizeof__() == (len(Pea.__base__.__slots__) * 8) + 16 == 80\nassert Flet().name == 'FLEA' != Pea().name\n\n# Intuitive, database agnostic query generation.\nassert isinstance(Pet.is_tail_wagging, ft3.Field)\nassert isinstance(Pet.type_, ft3.Field)\n\nassert dog.type_ == Pet.type_.default == 'dog'\n\nquery = (\n (\n (Pet.type_ == 'dog')\n & (Pet.name == 'Fido')\n )\n | Pet.name % ('fido', 0.75)\n )\nquery += 'name'\nassert dict(query) == {\n 'limit': None,\n 'or': [\n {\n 'and': [\n {\n 'eq': 'dog',\n 'field': 'type',\n 'limit': None,\n 'sorting': []\n },\n {\n 'eq': 'Fido',\n 'field': 'name',\n 'limit': None,\n 'sorting': []\n }\n ],\n 'limit': None,\n 'sorting': []\n },\n {\n 'field': 'name',\n 'like': 'fido',\n 'limit': None,\n 'sorting': [],\n 'threshold': 0.75\n }\n ],\n 'sorting': [\n {\n 'direction': 'asc',\n 'field': 'name'\n }\n ]\n }\n\n```\n\n### Local Logging\n```python\nimport ft3\n\n\nclass AgentFlea(ft3.Object):\n \"\"\"Still a nuisance.\"\"\"\n\n name: ft3.Field[str] = 'FLEA'\n api_key: ft3.Field[str] = '9ac868264f004600bdff50b7f5b3e8ad'\n aws_access_key_id: ft3.Field[str] = 'falsePositive'\n sneaky: ft3.Field[str] = 'AKIARJFBAG3EGHFG2FPN'\n\n\n# Automatic log configuration, cleansing, and redaction.\n\nprint(AgentFlea())\n# >>>\n# {\n# \"level\": WARNING,\n# \"timestamp\": 2024-02-26T18:50:20.317Z,\n# \"logger\": ft3,\n# \"message\": {\n# \"content\": \"Calls to print() will be silenced by ft3.\"\n# }\n# }\n# {\n# \"api_key\": \"[ REDACTED :: API KEY ]\",\n# \"aws_access_key_id\": \"falsePositive\",\n# \"name\": \"FLEA\",\n# \"sneaky\": \"[ REDACTED :: AWS ACCESS KEY ID ]\"\n# }\n\nprint(AgentFlea())\n# >>>\n# {\n# \"api_key\": \"[ REDACTED :: API KEY ]\",\n# \"aws_access_key_id\": \"falsePositive\",\n# \"name\": \"FLEA\",\n# \"sneaky\": \"[ REDACTED :: AWS ACCESS KEY ID ]\"\n# }\n\n```\n\n### Deployed Logging\n\n```python\nimport os\nos.environ['ENV'] = 'DEV'\n\nimport ft3\n\nassert (\n ft3.core.constants.PackageConstants.ENV\n in {\n 'dev', 'develop',\n 'qa', 'test', 'testing',\n 'uat', 'stg', 'stage', 'staging',\n 'prod', 'production',\n }\n )\n\n\nclass AgentFlea(ft3.Object):\n \"\"\"Still a nuisance.\"\"\"\n\n name: ft3.Field[str] = 'FLEA'\n api_key: ft3.Field[str] = '9ac868264f004600bdff50b7f5b3e8ad'\n aws_access_key_id: ft3.Field[str] = 'falsePositive'\n sneaky: ft3.Field[str] = 'AKIARJFBAG3EGHFG2FPN'\n\n\nprint(AgentFlea())\n# >>>\n# {\n# \"level\": WARNING,\n# \"timestamp\": 2024-02-26T19:02:29.020Z,\n# \"logger\": ft3,\n# \"message\": {\n# \"text\": \"Call to print() silenced by ft3.\",\n# \"printed\": \"{\\n \\\"api_key\\\": \\\"[ REDACTED :: API KEY ]\\\",\\n \\\"aws_access_key_id\\\": \\\"falsePositive\\\",\\n \\\"name\\\": \\\"FLEA\\\",\\n \\\"sneaky\\\": \\\"[ REDACTED :: AWS ACCESS KEY ID ]\\\"\\n}\"\n# }\n# }\n\nprint(AgentFlea())\n# >>>\n\nft3.log.info(AgentFlea())\n# >>>\n# {\n# \"level\": INFO,\n# \"timestamp\": 2024-02-26T19:13:21.726Z,\n# \"logger\": ft3,\n# \"message\": {\n# \"AgentFlea\": {\n# \"api_key\": \"[ REDACTED :: API KEY ]\",\n# \"aws_access_key_id\": \"falsePositive\",\n# \"name\": \"FLEA\",\n# \"sneaky\": \"[ REDACTED :: AWS ACCESS KEY ID ]\"\n# }\n# }\n# }\n\n```\n\n## Planned Features\n\n* #### Database Parse & Sync\n * ft3 should be able to generate a python package with fully enumerated\n and optimized `Objects` (and a corresponding ft3 API package) when\n supplied with access to a database for which at least one schema may be\n inferred.\n * CLI commands like `$ ft3-api-from-sql ${api_name} ${sql_conn_string} .`\n should instantly output two ideally structured package repositories for a\n RESTful python API and corresponding object management package.\n * The package could use any supplied credentials to either query a database\n directly or make requests to a deployed API. This means the same package\n used to power the API can be distributed and pip installed across an\n organization so business intelligence, data science, and other technical\n team members can manipulate data for their needs, while leaning on\n the package to optimize queries and stay informed around permission\n boundaries and request limits.\n* #### Repo Generation\n * ft3 should be expanded to optionally wrap any generated packages\n in a repository pre-configured with essentials and CI that should:\n * implement an ideal [trunk-based branch strategy](https://trunkbaseddevelopment.com/),\n inline with current best practices for change management and\n developer collaboration\n * enforce python code style best practices through automated\n [linting and formatting](https://docs.astral.sh/ruff)\n * type-check python code and generate a report with [mypy](https://mypy.readthedocs.io/en/stable/index.html)\n * run tests automatically, generate reports, and prevent commits that break tests\n * automatically prevent commits that do not adhere to standardized commit\n message [conventions](https://www.conventionalcommits.org/en/v1.0.0/)\n * using those conventions, automatically [semantically version](https://python-semantic-release.readthedocs.io/en/stable/#getting-started)\n each successful PR and automatically generate and update a\n CHANGELOG.md file\n * automatically generate and publish secure wiki documentation\n * Generated repos may contain up to all of the following:\n * CHANGELOG.md\n * CODEOWNERS\n * CONTRIBUTING.md\n * .git\n * .git/hooks/\n * .github/workflows/\n * Support planned for gitlab and bamboo.\n * .gitignore\n * LICENSE\n * [.pre-commit-config.yaml](https://pre-commit.com/#intro)\n * pyproject.toml\n * README.md\n * /src\n * /package\n * /tests\n\n## Acknowledgments\n\n* #### @sol.courtney\n * Teaching me the difference between chicken-scratch, duct tape, and bubble\n gum versus actual engineering, and why it matters.\n",
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