diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..83c8f48 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,6 @@ +[submodule "packages/tidy3d"] + path = packages/tidy3d + url = git@github.com:flexcompute/tidy3d.git +[submodule "packages/sphinx"] + path = packages/sphinx + url = git@github.com:daquintero/sphinx.git diff --git a/autoflex/__init__.py b/autoflex/__init__.py index 35093f2..fbbda09 100644 --- a/autoflex/__init__.py +++ b/autoflex/__init__.py @@ -15,6 +15,7 @@ from autoflex.directives import AutoFlex from autoflex.styles.setup import copy_autoflex_styles_to_static from autoflex.extractors import determine_pydantic_version_from_base_model, get_field_infos +from autoflex.constructors.documenter import AutoflexDocumenter __version__ = "0.0.1" __author__ = "Dario Quintero Dominguez" @@ -28,6 +29,8 @@ def setup(app) -> Dict[str, Any]: print("Started loading `autoflex` extension.") # DIRECTIVES app.add_directive("autoflex", AutoFlex) + + app.add_autodocumenter(AutoflexDocumenter) # load the icon node/role # app.add_node(icon_node, **_NODE_VISITORS) # type: ignore # app.add_role("icon", Icon()) diff --git a/autoflex/constructors/documenter.py b/autoflex/constructors/documenter.py new file mode 100644 index 0000000..8d4e4c0 --- /dev/null +++ b/autoflex/constructors/documenter.py @@ -0,0 +1,38 @@ +from autoflex.types.core import AutoflexBaseModel +from sphinx.ext.autodoc import ClassDocumenter + +from sphinx.util import inspect, logging + + +logger = logging.getLogger(__name__) + +class AutoflexDocumenter(ClassDocumenter): + objtype = 'flex' + # Optionally set a priority to override standard documenters if necessary + priority = 10 # higher priority wins if multiple documenters match + + @classmethod + def can_document_member(cls, member, membername, isattr, parent): + # Determine if this documenter should handle the given member + logger.error(f"cls: {cls}") + return isinstance(member, AutoflexBaseModel) + + def add_directive_header(self, sig): + # Customize the directive header if necessary + self.add_line(f".. autoflex:: {self.name}", self.get_sourcename()) + super().add_directive_header(sig) + + + def add_content(self, more_content, no_docstring=False): + # Handle content based on the no_docstring flag if necessary + if not no_docstring: + # Add additional content if no_docstring is False + super().add_content(more_content) + + def generate(self, *args, **kwargs): + # Customize generation logic if necessary + logger.error(f"self: {self}") + logger.error(f"*args: {args}") + logger.error(f"**kwargs: {kwargs}") + # super().generate(*args, **kwargs) + diff --git a/autoflex/directives/autoflex.py b/autoflex/directives/autoflex.py index a3b89e4..227bbbe 100644 --- a/autoflex/directives/autoflex.py +++ b/autoflex/directives/autoflex.py @@ -2,6 +2,7 @@ from docutils import nodes from docutils.parsers.rst import directives from sphinx.util.docutils import SphinxDirective +from sphinx.ext.autodoc.directive import AutodocDirective from sphinx.util.logging import getLogger from pydantic import BaseModel from pydantic.v1 import BaseModel as BaseModelV1 @@ -15,7 +16,7 @@ class AutoFlex(SphinxDirective): """ - Extension of the ``.. autoflex::`` directive. + Extension of the ``.. autodoc::`` directive in a directly compatible format. In order to maintain compatibility and extensibility with all versions of Pydantic, this tool uses the interconnected JSON definition of the data structure instead of the @@ -51,6 +52,8 @@ class AutoFlex(SphinxDirective): } def run(self) -> List[nodes.Node]: + # original_autodoc_result = super().run() + import_path = self.arguments[0] logger.debug(f"AutoFlex processing import path: {import_path}") @@ -71,6 +74,9 @@ def run(self) -> List[nodes.Node]: # return [error] pass + + # return original_autodoc_result + # Generate documentation nodes from the schema nodes_list = [] @@ -84,10 +90,10 @@ def run(self) -> List[nodes.Node]: section_node += title_node # Description - description = self.options.get('description', cls.__doc__) - if description: - description_node = nodes.paragraph(text=description) - section_node += description_node + # description = self.options.get('description', cls.__doc__) + # if description: + # description_node = nodes.paragraph(text=description) + # section_node += description_node # # # JSON Schema as a literal block # try: @@ -124,3 +130,5 @@ def run(self) -> List[nodes.Node]: nodes_list.append(section_node) return nodes_list + + diff --git a/autoflex/extractors/physical.py b/autoflex/extractors/physical.py index 927757e..67dcba5 100644 --- a/autoflex/extractors/physical.py +++ b/autoflex/extractors/physical.py @@ -75,7 +75,7 @@ def field_info_to_property(field: pd.fields.FieldInfo, field_name: str) -> Prope Returns: Property: The corresponding Property instance. """ - description = field.description or "" + description = "" default = field.default if field.default is not None else "" types = str(field.outer_type_) if hasattr(field, 'outer_type_') else "" diff --git a/autoflex/types/__init__.py b/autoflex/types/__init__.py index 73a17e9..a4579be 100644 --- a/autoflex/types/__init__.py +++ b/autoflex/types/__init__.py @@ -1,3 +1,4 @@ +from autoflex.types.input import AutoflexInputClassTypes from autoflex.types.property import PhysicalProperty, Property, PropertyTypes from autoflex.types.descriptors import Symbolic, SymbolicTypes, Unit, UnitTypes from autoflex.types.field_info import PhysicalFieldInfo, FieldTypes diff --git a/autoflex/types/input.py b/autoflex/types/input.py new file mode 100644 index 0000000..e81f2f1 --- /dev/null +++ b/autoflex/types/input.py @@ -0,0 +1,11 @@ +from typing import Union +import pydantic as pd +import pydantic.v1 as pd1 + +AutoflexInputClassTypes = Union[ + pd._internal._model_construction.ModelMetaclass, + pd.BaseModel, + pd1.BaseModel +] + + diff --git a/docs/conf.py b/docs/conf.py index 65d71d0..dc2999f 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -18,6 +18,7 @@ extensions = [ "autoflex", + "sphinx.ext.autodoc" ] templates_path = [ diff --git a/docs/examples/flow360.rst b/docs/examples/flow360.rst new file mode 100644 index 0000000..bbd9682 --- /dev/null +++ b/docs/examples/flow360.rst @@ -0,0 +1,7 @@ +``flow360`` +----------- + + +.. currentmodule:: flow360 + +.. autoflex:: flow360.RotorDisk diff --git a/docs/examples/index.rst b/docs/examples/index.rst index 1bc7925..32ad3d9 100644 --- a/docs/examples/index.rst +++ b/docs/examples/index.rst @@ -5,3 +5,4 @@ Examples demo tidy3d + flow360 diff --git a/docs/examples/tidy3d.rst b/docs/examples/tidy3d.rst index fe7c8a2..9fed468 100644 --- a/docs/examples/tidy3d.rst +++ b/docs/examples/tidy3d.rst @@ -4,3 +4,8 @@ .. currentmodule:: tidy3d + +.. autoflex:: tidy3d.Simulation + + +.. autoclass:: tidy3d.Simulation diff --git a/docs/examples/usage_performance.ipynb b/docs/examples/usage_performance.ipynb index b194759..d40dc81 100644 --- a/docs/examples/usage_performance.ipynb +++ b/docs/examples/usage_performance.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "ecd08dd3-75f6-4293-8ee4-3ece6f0e0507", "metadata": {}, "outputs": [], @@ -13,6 +13,279 @@ "import sys" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f18b598-cde1-4788-9832-003cbb808aac", + "metadata": {}, + "outputs": [], + "source": [ + "import flow360" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6511fe31-ed66-4758-b4d8-eef657444d80", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'MyBaseClass' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[2], line 13\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;124;03m Example Description\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 11\u001b[0m b: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mField(default\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m, description\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExample Description\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 13\u001b[0m our_class_v1 \u001b[38;5;241m=\u001b[39m \u001b[43mMyBaseClass\u001b[49m\n\u001b[1;32m 14\u001b[0m our_class_v1\n", + "\u001b[0;31mNameError\u001b[0m: name 'MyBaseClass' is not defined" + ] + } + ], + "source": [ + "class MyBaseClassV1(pd1.BaseModel):\n", + " \"\"\"\n", + " Test documentation\n", + " \"\"\"\n", + " \n", + " a: str = \"\"\n", + " \"\"\"\n", + " Example Description\n", + " \"\"\"\n", + " \n", + " b: str = pd.Field(default=\"\", description=\"Example Description\")\n", + "\n", + "our_class_v1 = MyBaseClass\n", + "our_class_v1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc0d77bb-452f-4780-92ab-002bcfcfb2f3", + "metadata": {}, + "outputs": [], + "source": [ + "dir(pd1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "762b994d-ef53-47f9-a651-23ba8522382a", + "metadata": {}, + "outputs": [], + "source": [ + "type(our_class)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9835cb5-4c04-4046-b86f-0459a6c97a4e", + "metadata": {}, + "outputs": [], + "source": [ + "dir(pd1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23192123-f27a-49ee-b305-eaf964e7c6fa", + "metadata": {}, + "outputs": [], + "source": [ + "type(our_class_v1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58654e5f-21bb-4e4f-9c09-d114cd08cab0", + "metadata": {}, + "outputs": [], + "source": [ + "dir(pd._internal._model_construction.ModelMetaclass)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2924d508-8643-4b45-9764-0d366545b235", + "metadata": {}, + "outputs": [], + "source": [ + "our_class_v1.__doc__" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2783eea-af92-4ebc-8e4f-e6fdf0d90f63", + "metadata": {}, + "outputs": [], + "source": [ + "import flow360" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "6bd27a7b-0d34-4129-977a-6877b60c6e8e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__abstractmethods__',\n", + " '__annotations__',\n", + " '__class__',\n", + " '__class_getitem__',\n", + " '__class_vars__',\n", + " '__copy__',\n", + " '__deepcopy__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__fields_set__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__get_pydantic_core_schema__',\n", + " '__get_pydantic_json_schema__',\n", + " '__getattr__',\n", + " '__getattribute__',\n", + " '__getstate__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__init_subclass__',\n", + " '__iter__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__pretty__',\n", + " '__private_attributes__',\n", + " '__pydantic_complete__',\n", + " '__pydantic_core_schema__',\n", + " '__pydantic_custom_init__',\n", + " '__pydantic_decorators__',\n", + " '__pydantic_extra__',\n", + " '__pydantic_fields_set__',\n", + " '__pydantic_generic_metadata__',\n", + " '__pydantic_init_subclass__',\n", + " '__pydantic_parent_namespace__',\n", + " '__pydantic_post_init__',\n", + " '__pydantic_private__',\n", + " '__pydantic_root_model__',\n", + " '__pydantic_serializer__',\n", + " '__pydantic_validator__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__repr_args__',\n", + " '__repr_name__',\n", + " '__repr_str__',\n", + " '__rich_repr__',\n", + " '__setattr__',\n", + " '__setstate__',\n", + " '__signature__',\n", + " '__sizeof__',\n", + " '__slots__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " '_abc_impl',\n", + " '_calculate_keys',\n", + " '_check_frozen',\n", + " '_copy_and_set_values',\n", + " '_get_value',\n", + " '_iter',\n", + " 'construct',\n", + " 'copy',\n", + " 'dict',\n", + " 'from_orm',\n", + " 'json',\n", + " 'model_computed_fields',\n", + " 'model_config',\n", + " 'model_construct',\n", + " 'model_copy',\n", + " 'model_dump',\n", + " 'model_dump_json',\n", + " 'model_extra',\n", + " 'model_fields',\n", + " 'model_fields_set',\n", + " 'model_json_schema',\n", + " 'model_parametrized_name',\n", + " 'model_post_init',\n", + " 'model_rebuild',\n", + " 'model_validate',\n", + " 'model_validate_json',\n", + " 'model_validate_strings',\n", + " 'parse_file',\n", + " 'parse_obj',\n", + " 'parse_raw',\n", + " 'schema',\n", + " 'schema_json',\n", + " 'update_forward_refs',\n", + " 'validate']" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(our_class_v1)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "62f42e73-760f-4ecf-bae8-61f658924c84", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'a': str, 'b': str}" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "our_class.__annotations__" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "b49390d7-b98a-40a7-aecc-f124329310b6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'a': FieldInfo(annotation=str, required=False, default=''),\n", + " 'b': FieldInfo(annotation=str, required=False, default='', description='Example Description')}" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "our_class.model_fields" + ] + }, { "cell_type": "markdown", "id": "d9a7419c-8c9e-4352-97d4-ca756fd879a9", @@ -23,17 +296,17 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 38, "id": "aec54c32-758f-48d2-8ab0-53515c21384f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "MyBaseClass(a='', b='')" + "__main__.MyBaseClass" ] }, - "execution_count": 3, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -51,10 +324,462 @@ " \n", " b: str = pd.Field(default=\"\", description=\"Example Description\")\n", "\n", - "our_class = MyBaseClass()\n", + "our_class = MyBaseClass\n", "our_class" ] }, + { + "cell_type": "code", + "execution_count": 5, + "id": "152e83b9-7d7c-4c39-bcd0-d6a308bb229a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pydantic._internal._model_construction.ModelMetaclass" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(our_class)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e392f192-48cb-4034-acab-2d9957a2818c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on class MyBaseClass in module __main__:\n", + "\n", + "class MyBaseClass(pydantic.main.BaseModel)\n", + " | MyBaseClass(*, a: str = '', b: str = '') -> None\n", + " | \n", + " | Test documentation\n", + " | \n", + " | Method resolution order:\n", + " | MyBaseClass\n", + " | pydantic.main.BaseModel\n", + " | builtins.object\n", + " | \n", + " | Data descriptors defined here:\n", + " | \n", + " | __weakref__\n", + " | list of weak references to the object\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data and other attributes defined here:\n", + " | \n", + " | __abstractmethods__ = frozenset()\n", + " | \n", + " | __annotations__ = {'a': , 'b': }\n", + " | \n", + " | __class_vars__ = set()\n", + " | \n", + " | __private_attributes__ = {}\n", + " | \n", + " | __pydantic_complete__ = True\n", + " | \n", + " | __pydantic_core_schema__ = {'cls': , 'co...\n", + " | \n", + " | __pydantic_custom_init__ = False\n", + " | \n", + " | __pydantic_decorators__ = DecoratorInfos(validators={}, field_validato...\n", + " | \n", + " | __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'paramete...\n", + " | \n", + " | __pydantic_parent_namespace__ = None\n", + " | \n", + " | __pydantic_post_init__ = None\n", + " | \n", + " | __pydantic_serializer__ = SchemaSerializer(serializer=Model(\n", + " | Model...\n", + " | \n", + " | __pydantic_validator__ = SchemaValidator(title=\"MyBaseClass\", validato...\n", + " | \n", + " | __signature__ = None>\n", + " | \n", + " | model_computed_fields = {}\n", + " | \n", + " | model_config = {}\n", + " | \n", + " | model_fields = {'a': FieldInfo(annotation=str, required=False, default...\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from pydantic.main.BaseModel:\n", + " | \n", + " | __copy__(self) -> 'Self'\n", + " | Returns a shallow copy of the model.\n", + " | \n", + " | __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'\n", + " | Returns a deep copy of the model.\n", + " | \n", + " | __delattr__(self, item: 'str') -> 'Any'\n", + " | Implement delattr(self, name).\n", + " | \n", + " | __eq__(self, other: 'Any') -> 'bool'\n", + " | Return self==value.\n", + " | \n", + " | __getattr__(self, item: 'str') -> 'Any'\n", + " | \n", + " | __getstate__(self) -> 'dict[Any, Any]'\n", + " | Helper for pickle.\n", + " | \n", + " | __init__(self, /, **data: 'Any') -> 'None'\n", + " | Create a new model by parsing and validating input data from keyword arguments.\n", + " | \n", + " | Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be\n", + " | validated to form a valid model.\n", + " | \n", + " | `self` is explicitly positional-only to allow `self` as a field name.\n", + " | \n", + " | __iter__(self) -> 'TupleGenerator'\n", + " | So `dict(model)` works.\n", + " | \n", + " | __pretty__(self, fmt: 'typing.Callable[[Any], Any]', **kwargs: 'Any') -> 'typing.Generator[Any, None, None]' from pydantic._internal._repr.Representation\n", + " | Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.\n", + " | \n", + " | __repr__(self) -> 'str'\n", + " | Return repr(self).\n", + " | \n", + " | __repr_args__(self) -> '_repr.ReprArgs'\n", + " | \n", + " | __repr_name__(self) -> 'str' from pydantic._internal._repr.Representation\n", + " | Name of the instance's class, used in __repr__.\n", + " | \n", + " | __repr_str__(self, join_str: 'str') -> 'str' from pydantic._internal._repr.Representation\n", + " | \n", + " | __rich_repr__(self) -> 'RichReprResult' from pydantic._internal._repr.Representation\n", + " | Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.\n", + " | \n", + " | __setattr__(self, name: 'str', value: 'Any') -> 'None'\n", + " | Implement setattr(self, name, value).\n", + " | \n", + " | __setstate__(self, state: 'dict[Any, Any]') -> 'None'\n", + " | \n", + " | __str__(self) -> 'str'\n", + " | Return str(self).\n", + " | \n", + " | copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'\n", + " | Returns a copy of the model.\n", + " | \n", + " | !!! warning \"Deprecated\"\n", + " | This method is now deprecated; use `model_copy` instead.\n", + " | \n", + " | If you need `include` or `exclude`, use:\n", + " | \n", + " | ```py\n", + " | data = self.model_dump(include=include, exclude=exclude, round_trip=True)\n", + " | data = {**data, **(update or {})}\n", + " | copied = self.model_validate(data)\n", + " | ```\n", + " | \n", + " | Args:\n", + " | include: Optional set or mapping specifying which fields to include in the copied model.\n", + " | exclude: Optional set or mapping specifying which fields to exclude in the copied model.\n", + " | update: Optional dictionary of field-value pairs to override field values in the copied model.\n", + " | deep: If True, the values of fields that are Pydantic models will be deep-copied.\n", + " | \n", + " | Returns:\n", + " | A copy of the model with included, excluded and updated fields as specified.\n", + " | \n", + " | dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'\n", + " | \n", + " | json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'\n", + " | \n", + " | model_copy(self, *, update: 'dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'\n", + " | Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy\n", + " | \n", + " | Returns a copy of the model.\n", + " | \n", + " | Args:\n", + " | update: Values to change/add in the new model. Note: the data is not validated\n", + " | before creating the new model. You should trust this data.\n", + " | deep: Set to `True` to make a deep copy of the model.\n", + " | \n", + " | Returns:\n", + " | New model instance.\n", + " | \n", + " | model_dump(self, *, mode: \"Literal['json', 'python'] | str\" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: \"bool | Literal['none', 'warn', 'error']\" = True, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'\n", + " | Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump\n", + " | \n", + " | Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.\n", + " | \n", + " | Args:\n", + " | mode: The mode in which `to_python` should run.\n", + " | If mode is 'json', the output will only contain JSON serializable types.\n", + " | If mode is 'python', the output may contain non-JSON-serializable Python objects.\n", + " | include: A set of fields to include in the output.\n", + " | exclude: A set of fields to exclude from the output.\n", + " | context: Additional context to pass to the serializer.\n", + " | by_alias: Whether to use the field's alias in the dictionary key if defined.\n", + " | exclude_unset: Whether to exclude fields that have not been explicitly set.\n", + " | exclude_defaults: Whether to exclude fields that are set to their default value.\n", + " | exclude_none: Whether to exclude fields that have a value of `None`.\n", + " | round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].\n", + " | warnings: How to handle serialization errors. False/\"none\" ignores them, True/\"warn\" logs errors,\n", + " | \"error\" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].\n", + " | serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.\n", + " | \n", + " | Returns:\n", + " | A dictionary representation of the model.\n", + " | \n", + " | model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: \"bool | Literal['none', 'warn', 'error']\" = True, serialize_as_any: 'bool' = False) -> 'str'\n", + " | Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json\n", + " | \n", + " | Generates a JSON representation of the model using Pydantic's `to_json` method.\n", + " | \n", + " | Args:\n", + " | indent: Indentation to use in the JSON output. If None is passed, the output will be compact.\n", + " | include: Field(s) to include in the JSON output.\n", + " | exclude: Field(s) to exclude from the JSON output.\n", + " | context: Additional context to pass to the serializer.\n", + " | by_alias: Whether to serialize using field aliases.\n", + " | exclude_unset: Whether to exclude fields that have not been explicitly set.\n", + " | exclude_defaults: Whether to exclude fields that are set to their default value.\n", + " | exclude_none: Whether to exclude fields that have a value of `None`.\n", + " | round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].\n", + " | warnings: How to handle serialization errors. False/\"none\" ignores them, True/\"warn\" logs errors,\n", + " | \"error\" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].\n", + " | serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.\n", + " | \n", + " | Returns:\n", + " | A JSON string representation of the model.\n", + " | \n", + " | model_post_init(self, _BaseModel__context: 'Any') -> 'None'\n", + " | Override this method to perform additional initialization after `__init__` and `model_construct`.\n", + " | This is useful if you want to do some validation that requires the entire model to be initialized.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Class methods inherited from pydantic.main.BaseModel:\n", + " | \n", + " | __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef'\n", + " | \n", + " | __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema'\n", + " | Hook into generating the model's CoreSchema.\n", + " | \n", + " | Args:\n", + " | source: The class we are generating a schema for.\n", + " | This will generally be the same as the `cls` argument if this is a classmethod.\n", + " | handler: A callable that calls into Pydantic's internal CoreSchema generation logic.\n", + " | \n", + " | Returns:\n", + " | A `pydantic-core` `CoreSchema`.\n", + " | \n", + " | __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue'\n", + " | Hook into generating the model's JSON schema.\n", + " | \n", + " | Args:\n", + " | core_schema: A `pydantic-core` CoreSchema.\n", + " | You can ignore this argument and call the handler with a new CoreSchema,\n", + " | wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),\n", + " | or just call the handler with the original schema.\n", + " | handler: Call into Pydantic's internal JSON schema generation.\n", + " | This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema\n", + " | generation fails.\n", + " | Since this gets called by `BaseModel.model_json_schema` you can override the\n", + " | `schema_generator` argument to that function to change JSON schema generation globally\n", + " | for a type.\n", + " | \n", + " | Returns:\n", + " | A JSON schema, as a Python object.\n", + " | \n", + " | __pydantic_init_subclass__(**kwargs: 'Any') -> 'None'\n", + " | This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`\n", + " | only after the class is actually fully initialized. In particular, attributes like `model_fields` will\n", + " | be present when this is called.\n", + " | \n", + " | This is necessary because `__init_subclass__` will always be called by `type.__new__`,\n", + " | and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that\n", + " | `type.__new__` was called in such a manner that the class would already be sufficiently initialized.\n", + " | \n", + " | This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely,\n", + " | any kwargs passed to the class definition that aren't used internally by pydantic.\n", + " | \n", + " | Args:\n", + " | **kwargs: Any keyword arguments passed to the class definition that aren't used internally\n", + " | by pydantic.\n", + " | \n", + " | construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'\n", + " | \n", + " | from_orm(obj: 'Any') -> 'Self'\n", + " | \n", + " | model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self'\n", + " | Creates a new instance of the `Model` class with validated data.\n", + " | \n", + " | Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.\n", + " | Default values are respected, but no other validation is performed.\n", + " | \n", + " | !!! note\n", + " | `model_construct()` generally respects the `model_config.extra` setting on the provided model.\n", + " | That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`\n", + " | and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.\n", + " | Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in\n", + " | an error if extra values are passed, but they will be ignored.\n", + " | \n", + " | Args:\n", + " | _fields_set: A set of field names that were originally explicitly set during instantiation. If provided,\n", + " | this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.\n", + " | Otherwise, the field names from the `values` argument will be used.\n", + " | values: Trusted or pre-validated data dictionary.\n", + " | \n", + " | Returns:\n", + " | A new instance of the `Model` class with validated data.\n", + " | \n", + " | model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = , mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]'\n", + " | Generates a JSON schema for a model class.\n", + " | \n", + " | Args:\n", + " | by_alias: Whether to use attribute aliases or not.\n", + " | ref_template: The reference template.\n", + " | schema_generator: To override the logic used to generate the JSON schema, as a subclass of\n", + " | `GenerateJsonSchema` with your desired modifications\n", + " | mode: The mode in which to generate the schema.\n", + " | \n", + " | Returns:\n", + " | The JSON schema for the given model class.\n", + " | \n", + " | model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str'\n", + " | Compute the class name for parametrizations of generic classes.\n", + " | \n", + " | This method can be overridden to achieve a custom naming scheme for generic BaseModels.\n", + " | \n", + " | Args:\n", + " | params: Tuple of types of the class. Given a generic class\n", + " | `Model` with 2 type variables and a concrete model `Model[str, int]`,\n", + " | the value `(str, int)` would be passed to `params`.\n", + " | \n", + " | Returns:\n", + " | String representing the new class where `params` are passed to `cls` as type variables.\n", + " | \n", + " | Raises:\n", + " | TypeError: Raised when trying to generate concrete names for non-generic models.\n", + " | \n", + " | model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'dict[str, Any] | None' = None) -> 'bool | None'\n", + " | Try to rebuild the pydantic-core schema for the model.\n", + " | \n", + " | This may be necessary when one of the annotations is a ForwardRef which could not be resolved during\n", + " | the initial attempt to build the schema, and automatic rebuilding fails.\n", + " | \n", + " | Args:\n", + " | force: Whether to force the rebuilding of the model schema, defaults to `False`.\n", + " | raise_errors: Whether to raise errors, defaults to `True`.\n", + " | _parent_namespace_depth: The depth level of the parent namespace, defaults to 2.\n", + " | _types_namespace: The types namespace, defaults to `None`.\n", + " | \n", + " | Returns:\n", + " | Returns `None` if the schema is already \"complete\" and rebuilding was not required.\n", + " | If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.\n", + " | \n", + " | model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None) -> 'Self'\n", + " | Validate a pydantic model instance.\n", + " | \n", + " | Args:\n", + " | obj: The object to validate.\n", + " | strict: Whether to enforce types strictly.\n", + " | from_attributes: Whether to extract data from object attributes.\n", + " | context: Additional context to pass to the validator.\n", + " | \n", + " | Raises:\n", + " | ValidationError: If the object could not be validated.\n", + " | \n", + " | Returns:\n", + " | The validated model instance.\n", + " | \n", + " | model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None) -> 'Self'\n", + " | Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing\n", + " | \n", + " | Validate the given JSON data against the Pydantic model.\n", + " | \n", + " | Args:\n", + " | json_data: The JSON data to validate.\n", + " | strict: Whether to enforce types strictly.\n", + " | context: Extra variables to pass to the validator.\n", + " | \n", + " | Returns:\n", + " | The validated Pydantic model.\n", + " | \n", + " | Raises:\n", + " | ValidationError: If `json_data` is not a JSON string or the object could not be validated.\n", + " | \n", + " | model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None) -> 'Self'\n", + " | Validate the given object with string data against the Pydantic model.\n", + " | \n", + " | Args:\n", + " | obj: The object containing string data to validate.\n", + " | strict: Whether to enforce types strictly.\n", + " | context: Extra variables to pass to the validator.\n", + " | \n", + " | Returns:\n", + " | The validated Pydantic model.\n", + " | \n", + " | parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'\n", + " | \n", + " | parse_obj(obj: 'Any') -> 'Self'\n", + " | \n", + " | parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self'\n", + " | \n", + " | schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]'\n", + " | \n", + " | schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str'\n", + " | \n", + " | update_forward_refs(**localns: 'Any') -> 'None'\n", + " | \n", + " | validate(value: 'Any') -> 'Self'\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Readonly properties inherited from pydantic.main.BaseModel:\n", + " | \n", + " | __fields_set__\n", + " | \n", + " | model_extra\n", + " | Get extra fields set during validation.\n", + " | \n", + " | Returns:\n", + " | A dictionary of extra fields, or `None` if `config.extra` is not set to `\"allow\"`.\n", + " | \n", + " | model_fields_set\n", + " | Returns the set of fields that have been explicitly set on this model instance.\n", + " | \n", + " | Returns:\n", + " | A set of strings representing the fields that have been set,\n", + " | i.e. that were not filled from defaults.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data descriptors inherited from pydantic.main.BaseModel:\n", + " | \n", + " | __dict__\n", + " | dictionary for instance variables\n", + " | \n", + " | __pydantic_extra__\n", + " | \n", + " | __pydantic_fields_set__\n", + " | \n", + " | __pydantic_private__\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data and other attributes inherited from pydantic.main.BaseModel:\n", + " | \n", + " | __hash__ = None\n", + " | \n", + " | __pydantic_root_model__ = False\n", + "\n" + ] + } + ], + "source": [ + "help(our_class)" + ] + }, { "cell_type": "markdown", "id": "a71b16ce-7fc9-408a-83b6-c52ce6983f97", @@ -70,11 +795,15 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "a_class_attribute memory: 49\n", - "b_class_attribute memory: 49\n" + "ename": "AttributeError", + "evalue": "a", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate the memory cost\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m a_class_attribute_memory_cost \u001b[38;5;241m=\u001b[39m sys\u001b[38;5;241m.\u001b[39mgetsizeof(\u001b[43mour_class\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43ma\u001b[49m)\n\u001b[1;32m 3\u001b[0m b_class_attribute_memory_cost \u001b[38;5;241m=\u001b[39m sys\u001b[38;5;241m.\u001b[39mgetsizeof(our_class\u001b[38;5;241m.\u001b[39mb)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma_class_attribute memory: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00ma_class_attribute_memory_cost\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/flexcompute/autoflex/.venv/lib/python3.11/site-packages/pydantic/_internal/_model_construction.py:262\u001b[0m, in \u001b[0;36mModelMetaclass.__getattr__\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 260\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m private_attributes \u001b[38;5;129;01mand\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m private_attributes:\n\u001b[1;32m 261\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m private_attributes[item]\n\u001b[0;32m--> 262\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(item)\n", + "\u001b[0;31mAttributeError\u001b[0m: a" ] } ], diff --git a/packages/sphinx b/packages/sphinx new file mode 160000 index 0000000..1c493a4 --- /dev/null +++ b/packages/sphinx @@ -0,0 +1 @@ +Subproject commit 1c493a4ef2fd439011c7496eeec08e64f1b898dc diff --git a/packages/tidy3d b/packages/tidy3d new file mode 160000 index 0000000..e11d7cc --- /dev/null +++ b/packages/tidy3d @@ -0,0 +1 @@ +Subproject commit e11d7cca767ccdadee324b318725ad5c26228662 diff --git a/poetry.lock b/poetry.lock index 1f69e16..640342d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,15 +1,4 @@ -# This file is 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-29,10 +29,10 @@ packages = [ [tool.poetry.dependencies] -python = ">=3.10, <3.12" -docutils = ">=0.16,<1.0" +python = ">=3.11, <3.12" +docutils = ">=0.20,<1.0" pydantic = {version="^2"} -sphinx = {version="^7"} +sphinx = {path="packages/sphinx", develop=true} # Testing, linting and docs @@ -40,7 +40,8 @@ sphinx_book_theme = {version = "*", optional = true} pytest = {version="*", optional=true} # For custom class verification with our existing classes -tidy3d = {version="*", optional=true} +tidy3d = {path="packages/tidy3d", optional=true, develop=true} +flow360 = {git="git@github.com:flexcompute/Flow360.git", optional=true} [tool.poetry.extras] diff --git a/scripts/start_autoflex_dev.sh b/scripts/start_autoflex_dev.sh new file mode 100644 index 0000000..9dc55f7 --- /dev/null +++ b/scripts/start_autoflex_dev.sh @@ -0,0 +1,4 @@ +cd /home/daquintero/flexcompute/autoflex +source .venv/bin/activate +which python +export PYTHONPATH="$PYTHONPATH:$(pwd)"