From 657f981514e6877469399dca265d797130e6e9b3 Mon Sep 17 00:00:00 2001 From: sneakers-the-rat Date: Fri, 2 Feb 2024 01:01:31 -0800 Subject: [PATCH] initial package structuring and import from nwb_linkml --- .github/workflows/tests.yml | 38 + .readthedocs.yaml | 23 + README.md | 17 + docs/Makefile | 20 + docs/conf.py | 71 + docs/hooks.md | 5 + docs/index.md | 19 + docs/make.bat | 35 + numpydantic/__init__.py | 1 + numpydantic/linkml.py | 914 +++++++++++ numpydantic/maps.py | 44 + numpydantic/ndarray.py | 248 +++ poetry.lock | 2999 +++++++++++++++++++++++++++++++++++ pyproject.toml | 106 ++ 14 files changed, 4540 insertions(+) create mode 100644 .github/workflows/tests.yml create mode 100644 .readthedocs.yaml create mode 100644 docs/Makefile create mode 100644 docs/conf.py create mode 100644 docs/hooks.md create mode 100644 docs/index.md create mode 100644 docs/make.bat create mode 100644 numpydantic/__init__.py create mode 100644 numpydantic/linkml.py create mode 100644 numpydantic/maps.py create mode 100644 numpydantic/ndarray.py create mode 100644 poetry.lock create mode 100644 pyproject.toml diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml new file mode 100644 index 0000000..e6c90ef --- /dev/null +++ b/.github/workflows/tests.yml @@ -0,0 +1,38 @@ +name: Tests + +on: + push: + +jobs: + test: + strategy: + matrix: + python-version: [3.11] + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + + - name: Set up python + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + cache: 'pip' + + - name: Install dependencies + run: pip install -e .[tests] + + - name: Run Tests + run: pytest + + - name: Report coverage + run: "coveralls --service=github" + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: psf/black@stable \ No newline at end of file diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 0000000..c369477 --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,23 @@ +# .readthedocs.yaml +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Build documentation in the "docs/" directory with Sphinx +sphinx: + configuration: docs/conf.py + +build: + os: ubuntu-22.04 + tools: + python: "3.11" + +python: + install: + - requirements: docs/requirements.txt + - method: pip + path: . + extra_requirements: + - docs \ No newline at end of file diff --git a/README.md b/README.md index 04aed98..241decd 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,19 @@ # numpydantic + +[![PyPI - Version](https://img.shields.io/pypi/v/numpydantic)](https://pypi.org/project/numpydantic) +[![Documentation Status](https://readthedocs.org/projects/numpydantic/badge/?version=latest)](https://numpydantic.readthedocs.io/en/latest/?badge=latest) +[![Coverage Status](https://coveralls.io/repos/github/p2p-ld/numpydantic/badge.svg)](https://coveralls.io/github/p2p-ld/numpydantic) +[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) + Type and shape validation and serialization for numpy arrays in pydantic models + +This package was picked out of [nwb-linkml](https://github.com/p2p-ld/nwb-linkml/), a +translation of the [NWB](https://www.nwb.org/) schema language and data format to +linkML and pydantic models. + +It does two primary things: +- **Provide types** - Annotations (based on [npytyping](https://github.com/ramonhagenaars/nptyping)) + for specifying numpy arrays in pydantic models, and +- **Generate models from LinkML** - extend the LinkML pydantic generator to create models that + that use the [linkml-arrays](https://github.com/linkml/linkml-arrays) syntax + diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d4bb2cb --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000..f3c017f --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,71 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +project = 'numpydantic' +copyright = '2024, Jonny Saunders' +author = 'Jonny Saunders' +release = 'v0.0.0' + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [ + 'sphinx.ext.napoleon', + 'sphinx.ext.autodoc', + 'sphinxcontrib.autodoc_pydantic', + 'sphinx.ext.intersphinx', + "sphinx_design", + 'myst_parser', + 'sphinx.ext.todo' +] + +templates_path = ['_templates'] +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + +intersphinx_mapping = { + 'python': ('https://docs.python.org/3', None), + 'numpy': ('https://numpy.org/doc/stable/', None), + 'pydantic': ('https://docs.pydantic.dev/latest/', None), + 'linkml': ('https://linkml.io/linkml/', None), + 'linkml_runtime': ('https://linkml.io/linkml/', None), + 'linkml-runtime': ('https://linkml.io/linkml/', None) +} + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = 'furo' +html_static_path = ['_static'] + +# autodoc +autodoc_pydantic_model_show_json_error_strategy = 'coerce' +autodoc_pydantic_model_show_json = False +autodoc_mock_imports = [] +autoclass_content = "both" +autodoc_member_order='bysource' +add_module_names = False + +# Napoleon settings +napoleon_google_docstring = True +napoleon_numpy_docstring = True +napoleon_include_init_with_doc = False +napoleon_include_private_with_doc = False +napoleon_include_special_with_doc = True +napoleon_use_admonition_for_examples = False +napoleon_use_admonition_for_notes = False +napoleon_use_admonition_for_references = False +napoleon_use_ivar = False +napoleon_use_param = True +napoleon_use_rtype = True +napoleon_preprocess_types = False +napoleon_type_aliases = None +napoleon_attr_annotations = True + +# todo +todo_include_todos = True +todo_link_only = True \ No newline at end of file diff --git a/docs/hooks.md b/docs/hooks.md new file mode 100644 index 0000000..7cd254e --- /dev/null +++ b/docs/hooks.md @@ -0,0 +1,5 @@ +# Hooks + +## TODO + +- nwb compatibility: allowable precision map in dtype check \ No newline at end of file diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..d362b2c --- /dev/null +++ b/docs/index.md @@ -0,0 +1,19 @@ +# numpydantic + +Type and shape validation and serialization for numpy arrays in pydantic models + +This package was picked out of [nwb-linkml](https://github.com/p2p-ld/nwb-linkml/), a +translation of the [NWB](https://www.nwb.org/) schema language and data format to +linkML and pydantic models. + +It does two primary things: +- **Provide types** - Annotations (based on [npytyping](https://github.com/ramonhagenaars/nptyping)) + for specifying numpy arrays in pydantic models, and +- **Generate models from LinkML** - extend the LinkML pydantic generator to create models that + that use the [linkml-arrays](https://github.com/linkml/linkml-arrays) syntax + +```{toctree} +:maxdepth: 2 +:caption: Contents: +``` + diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..954237b --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/numpydantic/__init__.py b/numpydantic/__init__.py new file mode 100644 index 0000000..8d1c8b6 --- /dev/null +++ b/numpydantic/__init__.py @@ -0,0 +1 @@ + diff --git a/numpydantic/linkml.py b/numpydantic/linkml.py new file mode 100644 index 0000000..54bc115 --- /dev/null +++ b/numpydantic/linkml.py @@ -0,0 +1,914 @@ +""" +Subclass of :class:`linkml.generators.PydanticGenerator` + +The pydantic generator is a subclass of +- :class:`linkml.utils.generator.Generator` +- :class:`linkml.generators.oocodegen.OOCodeGenerator` + +The default `__main__` method +- Instantiates the class +- Calls :meth:`~linkml.generators.PydanticGenerator.serialize` + +The `serialize` method: + +- Accepts an optional jinja-style template, otherwise it uses the default template +- Uses :class:`linkml_runtime.utils.schemaview.SchemaView` to interact with the schema +- Generates linkML Classes + - `generate_enums` runs first + +.. note:: + + This module is heinous. We will be tidying this up and trying to pull changes upstream, + but for now this is just our hacky little secret. + +""" +import inspect +import sys +import warnings +from copy import copy +from dataclasses import dataclass +from pathlib import Path +from types import ModuleType + +from jinja2 import Template +from linkml.generators import PydanticGenerator as BasePydanticGenerator +from linkml.generators.common.type_designators import ( + get_type_designator_value, +) +from linkml.utils.ifabsent_functions import ifabsent_value_declaration +from linkml_runtime.linkml_model.meta import ( + Annotation, + ClassDefinition, + ClassDefinitionName, + ElementName, + SchemaDefinition, + SlotDefinition, + SlotDefinitionName, +) +from linkml_runtime.utils.compile_python import file_text +from linkml_runtime.utils.formatutils import camelcase, underscore +from linkml_runtime.utils.schemaview import SchemaView + +# from nwb_linkml.maps import flat_to_nptyping +from pydantic import BaseModel + + +def module_case(name: str) -> str: + """ + Returns name that can be used as a python module, used for + referring to generated pydantic and linkml models. + + Replaces with underscores: + - - + - . + """ + return name.replace("-", "_").replace(".", "_").replace("/", ".").lower() + + +def version_module_case(name: str) -> str: + """ + :func:`.module_case` except ensure that it starts with "v" + """ + name = module_case(name) + if not name.startswith("v"): + name = "v" + name + return name + + +class LinkML_Meta(BaseModel): + """Extra LinkML Metadata stored as a class attribute""" + + tree_root: bool = False + + +def default_template( + pydantic_ver: str = "2", extra_classes: list[type[BaseModel]] | None = None +) -> str: + """Constructs a default template for pydantic classes based on the version of pydantic""" + ### HEADER ### + template = """ +{#- + + Jinja2 Template for a pydantic classes +-#} +from __future__ import annotations +from datetime import datetime, date +from enum import Enum +from typing import Dict, Optional, Any, Union, ClassVar, Annotated, TypeVar, List, TYPE_CHECKING +from pydantic import BaseModel as BaseModel, Field""" + if pydantic_ver == "2": + template += """ +from pydantic import ConfigDict, BeforeValidator + """ + template += """ +from nptyping import Shape, Float, Float32, Double, Float64, LongLong, Int64, Int, Int32, Int16, Short, Int8, UInt, UInt32, UInt16, UInt8, UInt64, Number, String, Unicode, Unicode, Unicode, String, Bool, Datetime64 +from nwb_linkml.types import NDArray +import sys +if sys.version_info >= (3, 8): + from typing import Literal +else: + from typing_extensions import Literal +if TYPE_CHECKING: + import numpy as np + +{% for import_module, import_classes in imports.items() %} +from {{ import_module }} import ( + {{ import_classes | join(',\n ') }} +) +{% endfor %} + +metamodel_version = "{{metamodel_version}}" +version = "{{version if version else None}}" +""" + ### BASE MODEL ### + if pydantic_ver == "1": # pragma: no cover + template += """ +List = BaseList + +class WeakRefShimBaseModel(BaseModel): + __slots__ = '__weakref__' + +class ConfiguredBaseModel(WeakRefShimBaseModel, + validate_assignment = False, + validate_all = True, + underscore_attrs_are_private = True, + extra = {% if allow_extra %}'allow'{% else %}'forbid'{% endif %}, + arbitrary_types_allowed = True, + use_enum_values = True): +""" + else: + template += """ +class ConfiguredBaseModel(BaseModel): + model_config = ConfigDict( + validate_assignment = True, + validate_default = True, + extra = {% if allow_extra %}'allow'{% else %}'forbid'{% endif %}, + arbitrary_types_allowed = True, + use_enum_values = True + ) +""" + ### Injected Fields + template += """ +{%- if injected_fields != None -%} + {% for field in injected_fields %} + {{ field }} + {% endfor %} +{%- else -%} + pass +{%- endif -%} + """ + ### Getitem + template += """ + + def __getitem__(self, i: slice|int) -> 'np.ndarray': + if hasattr(self, 'array'): + return self.array[i] + else: + return super().__getitem__(i) + + def __setitem__(self, i: slice|int, value: Any): + if hasattr(self, 'array'): + self.array[i] = value + else: + super().__setitem__(i, value) + """ + + ### Extra classes + if extra_classes is not None: + template += """{{ '\n\n' }}""" + for cls in extra_classes: + template += inspect.getsource(cls) + "\n\n" + ### ENUMS ### + template += """ +{% for e in enums.values() %} +class {{ e.name }}(str, Enum): + {% if e.description -%} + \"\"\" + {{ e.description }} + \"\"\" + {%- endif %} + {% for _, pv in e['values'].items() -%} + {% if pv.description -%} + # {{pv.description}} + {%- endif %} + {{pv.label}} = "{{pv.value}}" + {% endfor %} + {% if not e['values'] -%} + dummy = "dummy" + {% endif %} +{% endfor %} +""" + ### CLASSES ### + template += """ +{%- for c in schema.classes.values() %} +class {{ c.name }} + {%- if class_isa_plus_mixins[c.name] -%} + ({{class_isa_plus_mixins[c.name]|join(', ')}}) + {%- else -%} + (ConfiguredBaseModel) + {%- endif -%} + : + {% if c.description -%} + \"\"\" + {{ c.description }} + \"\"\" + {%- endif %} + {% for attr in c.attributes.values() if c.attributes -%} + {{attr.name}}:{{ ' ' }}{%- if attr.equals_string -%} + Literal[{{ predefined_slot_values[c.name][attr.name] }}] + {%- else -%} + {{ attr.annotations['python_range'].value }} + {%- endif -%} + {%- if attr.annotations['fixed_field'] -%} + {{ ' ' }}= {{ attr.annotations['fixed_field'].value }} + {%- else -%} + {{ ' ' }}= Field( + {%- if predefined_slot_values[c.name][attr.name] is string -%} + {{ predefined_slot_values[c.name][attr.name] }} + {%- elif attr.required -%} + ... + {%- else -%} + None + {%- endif -%} + {%- if attr.title != None %}, title="{{attr.title}}"{% endif -%} + {%- if attr.description %}, description=\"\"\"{{attr.description}}\"\"\"{% endif -%} + {%- if attr.minimum_value != None %}, ge={{attr.minimum_value}}{% endif -%} + {%- if attr.maximum_value != None %}, le={{attr.maximum_value}}{% endif -%} + ) + {%- endif %} + {% else -%} + None + {% endfor %} +{% endfor %} +""" + ### FWD REFS / REBUILD MODEL ### + if pydantic_ver == "1": # pragma: no cover + template += """ +# Update forward refs +# see https://pydantic-docs.helpmanual.io/usage/postponed_annotations/ +{% for c in schema.classes.values() -%} +{{ c.name }}.update_forward_refs() +{% endfor %} +""" + else: + template += """ +# Model rebuild +# see https://pydantic-docs.helpmanual.io/usage/models/#rebuilding-a-model +{% for c in schema.classes.values() -%} +{{ c.name }}.model_rebuild() +{% endfor %} +""" + return template + + +@dataclass +class PydanticGenerator(BasePydanticGenerator): + SKIP_ENUM: tuple[str] = ("FlatDType",) + # SKIP_SLOTS=('VectorData',) + SKIP_SLOTS: tuple[str] = ("",) + SKIP_CLASSES: tuple[str] = ("",) + INJECTED_FIELDS: tuple[str] = ( + 'hdf5_path: Optional[str] = Field(None, description="The absolute path that this object is stored in an NWB file")', + 'object_id: Optional[str] = Field(None, description="Unique UUID for each object")', + ) + # SKIP_CLASSES=('VectorData','VectorIndex') + split: bool = True + schema_map: dict[str, SchemaDefinition] | None = None + versions: dict = None + """See :meth:`.LinkMLProvider.build` for usage - a list of specific versions to import from""" + pydantic_version = "2" + + def _locate_imports( + self, needed_classes: list[str], sv: SchemaView + ) -> dict[str, list[str]]: + """ + Given a list of class names, find the python modules that need to be imported + """ + imports = {} + + # These classes are not generated by pydantic! + skips = ("AnyType",) + + for cls in needed_classes: + if cls in skips: # pragma: no cover + continue + # Find module that contains class + module_name = sv.element_by_schema_map()[ElementName(cls)] + # Don't get classes that are defined in this schema! + if module_name == self.schema.name: + continue + schema_name = module_name.split(".")[0] + if ( + self.versions + and schema_name != self.schema.name.split(".")[0] + and schema_name in self.versions + ): + version = version_module_case(self.versions[schema_name]) + if self.split: + local_mod_name = ( + "..." + + module_case(schema_name) + + "." + + version + + "." + + module_case(module_name) + ) + else: # pragma: no cover + local_mod_name = ( + "..." + + module_case(schema_name) + + "." + + version + + "." + + "namespace" + ) + + else: + local_mod_name = "." + module_case(module_name) + if local_mod_name not in imports: + imports[local_mod_name] = [camelcase(cls)] + else: + imports[local_mod_name].append(camelcase(cls)) + return imports + + def _get_namespace_imports(self, sv: SchemaView) -> dict[str, list[str]]: + """ + Get imports for namespace packages. For these we import all + the tree_root classes, ie. all the classes that are top-level classes + rather than nested classes + """ + all_classes = sv.all_classes(imports=True) + needed_classes = [] + for clsname, cls in all_classes.items(): + if cls.is_a != "Arraylike": + needed_classes.append(clsname) + + imports = self._locate_imports(needed_classes, sv) + return imports + + def _get_class_imports( + self, + cls: ClassDefinition, + sv: SchemaView, + all_classes: dict[ClassDefinitionName, ClassDefinition], + class_slots: dict[str, list[SlotDefinition]], + ) -> list[str]: + """Get the imports needed for a single class""" + needed_classes = [] + needed_classes.append(cls.is_a) + # get needed classes used as ranges in class attributes + for slot in class_slots[cls.name]: + if slot.name in self.SKIP_SLOTS: # pragma: no cover + continue + if slot.range in all_classes: + needed_classes.append(slot.range) + # handle when a range is a union of classes + if slot.any_of: + for any_slot_range in slot.any_of: + if any_slot_range.range in all_classes: + needed_classes.append(any_slot_range.range) + + return needed_classes + + def _get_imports( + self, + sv: SchemaView, + local_classes: list[ClassDefinition], + class_slots: dict[str, list[SlotDefinition]], + ) -> dict[str, list[str]]: + # import from local references, rather than serializing every class in every file + if not self.split: + # we are compiling this whole thing in one big file so we don't import anything + return {} + if "is_namespace" in sv.schema.annotations.keys() and sv.schema.annotations[ + "is_namespace" + ]["value"] in ("True", True): + return self._get_namespace_imports(sv) + + all_classes = sv.all_classes(imports=True) + # local_classes = sv.all_classes(imports=False) + needed_classes = [] + # find needed classes - is_a and slot ranges + + for cls in local_classes: + # get imports for this class + needed_classes.extend( + self._get_class_imports(cls, sv, all_classes, class_slots) + ) + + # remove duplicates and arraylikes + needed_classes = [ + cls for cls in set(needed_classes) if cls is not None and cls != "Arraylike" + ] + needed_classes = [ + cls for cls in needed_classes if sv.get_class(cls).is_a != "Arraylike" + ] + + imports = self._locate_imports(needed_classes, sv) + + return imports + + def _get_classes(self, sv: SchemaView) -> list[ClassDefinition]: + if self.split: + classes = sv.all_classes(imports=False).values() + else: + classes = sv.all_classes(imports=True).values() + + # Don't want to generate classes when class_uri is linkml:Any, will + # just swap in typing.Any instead down below + classes = [ + c + for c in list(classes) + if c.is_a != "Arraylike" and c.class_uri != "linkml:Any" + ] + + return classes + + def _get_class_slots( + self, sv: SchemaView, cls: ClassDefinition + ) -> list[SlotDefinition]: + slots = [] + for slot_name in sv.class_slots(cls.name): + if slot_name in self.SKIP_SLOTS: + continue + slots.append(sv.induced_slot(slot_name, cls.name)) + return slots + + def _build_class(self, class_original: ClassDefinition) -> ClassDefinition: + class_def: ClassDefinition + class_def = copy(class_original) + class_def.name = camelcase(class_original.name) + if class_def.is_a: + class_def.is_a = camelcase(class_def.is_a) + class_def.mixins = [camelcase(p) for p in class_def.mixins] + if class_def.description: + class_def.description = class_def.description.replace('"', '\\"') + return class_def + + def _check_anyof( + self, s: SlotDefinition, sn: SlotDefinitionName, sv: SchemaView + ): # pragma: no cover + # Confirm that the original slot range (ignoring the default that comes in from + # induced_slot) isn't in addition to setting any_of + if len(s.any_of) > 0 and sv.get_slot(sn).range is not None: + base_range_subsumes_any_of = False + base_range = sv.get_slot(sn).range + base_range_cls = sv.get_class(base_range, strict=False) + if base_range_cls is not None and base_range_cls.class_uri == "linkml:Any": + base_range_subsumes_any_of = True + if not base_range_subsumes_any_of: + raise ValueError("Slot cannot have both range and any_of defined") + + def _make_npytyping_range(self, attrs: dict[str, SlotDefinition]) -> str: + # slot always starts with... + prefix = "NDArray[" + + # and then we specify the shape: + shape_prefix = 'Shape["' + + # using the cardinality from the attributes + dim_pieces = [] + for attr in attrs.values(): + if attr.maximum_cardinality: + shape_part = str(attr.maximum_cardinality) + else: + shape_part = "*" + + # do this with the most heinous chain of string replacements rather than regex + # because i am still figuring out what needs to be subbed lol + name_part = ( + attr.name.replace(",", "_") + .replace(" ", "_") + .replace("__", "_") + .replace("|", "_") + .replace("-", "_") + .replace("+", "plus") + ) + + dim_pieces.append(" ".join([shape_part, name_part])) + + dimension = ", ".join(dim_pieces) + + shape_suffix = '"], ' + + # all dimensions should be the same dtype + try: + dtype = flat_to_nptyping[list(attrs.values())[0].range] + except KeyError as e: # pragma: no cover + warnings.warn(str(e)) + range = list(attrs.values())[0].range + return f"List[{range}] | {range}" + suffix = "]" + + slot = "".join([prefix, shape_prefix, dimension, shape_suffix, dtype, suffix]) + return slot + + def _get_numpy_slot_range(self, cls: ClassDefinition) -> str: + # if none of the dimensions are optional, we just have one possible array shape + if all([s.required for s in cls.attributes.values()]): # pragma: no cover + return self._make_npytyping_range(cls.attributes) + # otherwise we need to make permutations + # but not all permutations, because we typically just want to be able to exlude the last possible dimensions + # the array classes should always be well-defined where the optional dimensions are at the end, so + requireds = {k: v for k, v in cls.attributes.items() if v.required} + optionals = [(k, v) for k, v in cls.attributes.items() if not v.required] + + annotations = [] + if len(requireds) > 0: + # first the base case + annotations.append(self._make_npytyping_range(requireds)) + # then add back each optional dimension + for i in range(len(optionals)): + attrs = {**requireds, **{k: v for k, v in optionals[0 : i + 1]}} + annotations.append(self._make_npytyping_range(attrs)) + + # now combine with a union: + union = "Union[\n" + " " * 8 + union += (",\n" + " " * 8).join(annotations) + union += "\n" + " " * 4 + "]" + return union + + def _get_linkml_classvar(self, cls: ClassDefinition) -> SlotDefinition: + """A class variable that holds additional linkml attrs""" + slot = SlotDefinition(name="linkml_meta") + slot.annotations["python_range"] = Annotation( + "python_range", "ClassVar[LinkML_Meta]" + ) + meta_fields = { + k: getattr(cls, k, None) for k in LinkML_Meta.model_fields.keys() + } + meta_field_strings = [ + f"{k}={v}" for k, v in meta_fields.items() if v is not None + ] + meta_field_string = ", ".join(meta_field_strings) + slot.annotations["fixed_field"] = Annotation( + "fixed_field", f"Field(LinkML_Meta({meta_field_string}), frozen=True)" + ) + + return slot + + def sort_classes( + self, clist: list[ClassDefinition], imports: dict[str, list[str]] + ) -> list[ClassDefinition]: + """ + sort classes such that if C is a child of P then C appears after P in the list + + Overridden method include mixin classes + + Modified from original to allow for imported classes + """ + # unnest imports + imported_classes = [] + for i in imports.values(): + imported_classes.extend(i) + + clist = list(clist) + clist = [c for c in clist if c.name not in self.SKIP_CLASSES] + slist = [] # type: List[ClassDefinition] + while len(clist) > 0: + can_add = False + for i in range(len(clist)): + candidate = clist[i] + can_add = False + if candidate.is_a: + candidates = [candidate.is_a] + candidate.mixins + else: + candidates = candidate.mixins + if not candidates: + can_add = True + + else: + if set(candidates) <= set( + [p.name for p in slist] + imported_classes + ): + can_add = True + + if can_add: + slist = slist + [candidate] + del clist[i] + break + if not can_add: # pragma: no cover + raise ValueError( + f"could not find suitable element in {clist} that does not ref {slist}" + ) + + self.sorted_class_names = [camelcase(cname) for cname in imported_classes] + self.sorted_class_names += [camelcase(c.name) for c in slist] + return slist + + def get_class_slot_range( + self, slot_range: str, inlined: bool, inlined_as_list: bool + ) -> str: + """ + Monkeypatch to convert Array typed slots and classes into npytyped hints + """ + sv = self.schemaview + range_cls = sv.get_class(slot_range) + if range_cls.is_a == "Arraylike": + return self._get_numpy_slot_range(range_cls) + else: + return self._get_class_slot_range_origin( + slot_range, inlined, inlined_as_list + ) + + def _get_class_slot_range_origin( + self, slot_range: str, inlined: bool, inlined_as_list: bool + ) -> str: + """ + Parent class get class range + + Overriding to not use strings in the type hint when a class has an identifier value + + Not testing this method except for what we changes + """ + sv = self.schemaview + range_cls = sv.get_class(slot_range) + + # Hardcoded handling for Any + if range_cls.class_uri == "linkml:Any": # pragma: no cover + return "Any" + + # Inline the class itself only if the class is defined as inline, or if the class has no + # identifier slot and also isn't a mixin. + if ( + inlined + or inlined_as_list + or ( + # sv.get_identifier_slot(range_cls.name, use_key=True) is None and + not sv.is_mixin(range_cls.name) + ) + ): + if ( + len( + [x for x in sv.class_induced_slots(slot_range) if x.designates_type] + ) + > 0 + and len(sv.class_descendants(slot_range)) > 1 + ): # pragma: no cover + return ( + "Union[" + + ",".join([camelcase(c) for c in sv.class_descendants(slot_range)]) + + "]" + ) + else: + return f"{camelcase(slot_range)}" + + # For the more difficult cases, set string as the default and attempt to improve it + range_cls_identifier_slot_range = "str" # pragma: no cover + + # For mixins, try to use the identifier slot of descendant classes + if ( + self.gen_mixin_inheritance + and sv.is_mixin(range_cls.name) + and sv.get_identifier_slot(range_cls.name) + ): # pragma: no cover + range_cls_identifier_slot_range = self.get_mixin_identifier_range(range_cls) + + return range_cls_identifier_slot_range # pragma: no cover + + def get_class_isa_plus_mixins( + self, classes: list[ClassDefinition] | None = None + ) -> dict[str, list[str]]: + """ + Generate the inheritance list for each class from is_a plus mixins + + Patched to only get local classes + + :return: + """ + sv = self.schemaview + if classes is None: # pragma: no cover + classes = sv.all_classes(imports=False).values() + + parents = {} + for class_def in classes: + class_parents = [] + if class_def.is_a: + class_parents.append(camelcase(class_def.is_a)) + if self.gen_mixin_inheritance and class_def.mixins: # pragma: no cover + class_parents.extend([camelcase(mixin) for mixin in class_def.mixins]) + if len(class_parents) > 0: + # Use the sorted list of classes to order the parent classes, but reversed to match MRO needs + class_parents.sort(key=lambda x: self.sorted_class_names.index(x)) + class_parents.reverse() + parents[camelcase(class_def.name)] = class_parents + return parents + + def get_predefined_slot_value( + self, slot: SlotDefinition, class_def: ClassDefinition + ) -> str | None: + """ + Modified from base pydantic generator to use already grabbed induced_slot from + already-grabbed and modified classes rather than doing a fresh iteration to + save time and respect changes already made elsewhere in the serialization routine + + :return: Dictionary of dictionaries with predefined slot values for each class + """ + sv = self.schemaview + slot_value: str | None = None + # for class_def in sv.all_classes().values(): + # for slot_name in sv.class_slots(class_def.name): + # slot = sv.induced_slot(slot_name, class_def.name) + if slot.designates_type: # pragma: no cover + target_value = get_type_designator_value(sv, slot, class_def) + slot_value = f'"{target_value}"' + if slot.multivalued: + slot_value = "[" + slot_value + "]" + elif slot.ifabsent is not None: + value = ifabsent_value_declaration(slot.ifabsent, sv, class_def, slot) + slot_value = value + # Multivalued slots that are either not inlined (just an identifier) or are + # inlined as lists should get default_factory list, if they're inlined but + # not as a list, that means a dictionary + elif slot.multivalued: + # this is slow, needs to do additional induced slot calls + # has_identifier_slot = self.range_class_has_identifier_slot(slot) + + if slot.inlined and not slot.inlined_as_list: # and has_identifier_slot: + slot_value = "default_factory=dict" + else: # pragma: no cover + slot_value = "default_factory=list" + + return slot_value + + def serialize(self) -> str: + predefined_slot_values = {} + """splitting up parent class :meth:`.get_predefined_slot_values`""" + + if self.template_file is not None: # pragma: no cover + with open(self.template_file) as template_file: + template_obj = Template(template_file.read()) + else: + template_obj = Template( + default_template(self.pydantic_version, extra_classes=[LinkML_Meta]) + ) + + sv: SchemaView + sv = self.schemaview + if self.schema_map is not None: # pragma: no cover + sv.schema_map = self.schema_map + schema = sv.schema + pyschema = SchemaDefinition( + id=schema.id, + name=schema.name, + description=schema.description.replace('"', '\\"') + if schema.description + else None, + ) + # test caching if import closure + enums = self.generate_enums(sv.all_enums()) + # filter skipped enums + enums = {k: v for k, v in enums.items() if k not in self.SKIP_ENUM} + + classes = self._get_classes(sv) + # just induce slots once because that turns out to be expensive + class_slots = {} # type: Dict[str, List[SlotDefinition]] + for aclass in classes: + class_slots[aclass.name] = self._get_class_slots(sv, aclass) + + # figure out what classes we need to imports + imports = self._get_imports(sv, classes, class_slots) + + sorted_classes = self.sort_classes(classes, imports) + + for class_original in sorted_classes: + # Generate class definition + class_def = self._build_class(class_original) + + if class_def.is_a != "Arraylike": + # skip actually generating arraylike classes, just use them to generate + # the npytyping annotations + pyschema.classes[class_def.name] = class_def + else: # pragma: no cover + continue + + # Not sure why this happens + for attribute in list(class_def.attributes.keys()): + del class_def.attributes[attribute] + + # make class attr that stores extra linkml attrs + class_def.attributes["linkml_meta"] = self._get_linkml_classvar(class_def) + + class_name = class_original.name + predefined_slot_values[camelcase(class_name)] = {} + for s in class_slots[class_name]: + sn = SlotDefinitionName(s.name) + predefined_slot_value = self.get_predefined_slot_value(s, class_def) + if predefined_slot_value is not None: + predefined_slot_values[camelcase(class_name)][ + s.name + ] = predefined_slot_value + # logging.error(f'Induced slot {class_name}.{sn} == {s.name} {s.range}') + s.name = underscore(s.name) + if s.description: + s.description = s.description.replace('"', '\\"') + class_def.attributes[s.name] = s + + slot_ranges: list[str] = [] + + self._check_anyof(s, sn, sv) + + if s.any_of is not None and len(s.any_of) > 0: + # list comprehension here is pulling ranges from within AnonymousSlotExpression + slot_ranges.extend([r.range for r in s.any_of]) + else: + slot_ranges.append(s.range) + + pyranges = [ + self.generate_python_range(slot_range, s, class_def) + for slot_range in slot_ranges + ] + # -------------------------------------------------- + # Special Case - since we get abstract classes from + # potentially multiple versions (which are then different) + # model classes, we allow container classes to also + # be generic descendants of BaseModel + # -------------------------------------------------- + if "DynamicTable" in pyranges: # pragma: no cover + pyranges.append("BaseModel") + + pyranges = list(set(pyranges)) # remove duplicates + pyranges.sort() + + if len(pyranges) == 1: + pyrange = pyranges[0] + elif len(pyranges) > 1: + pyrange = f"Union[{', '.join(pyranges)}]" + else: # pragma: no cover + raise Exception( + f"Could not generate python range for {class_name}.{s.name}" + ) + + if s.multivalued: + if s.inlined or s.inlined_as_list: + collection_key = self.generate_collection_key( + slot_ranges, s, class_def + ) + else: # pragma: no cover + collection_key = None + if ( + s.inlined is False + or collection_key is None + or s.inlined_as_list is True + ): # pragma: no cover + pyrange = f"List[{pyrange}] | {pyrange}" + else: + pyrange = f"Dict[{collection_key}, {pyrange}]" + if not s.required and not s.designates_type: + pyrange = f"Optional[{pyrange}]" + ann = Annotation("python_range", pyrange) + s.annotations[ann.tag] = ann + + code = template_obj.render( + imports=imports, + schema=pyschema, + underscore=underscore, + enums=enums, + predefined_slot_values=predefined_slot_values, + allow_extra=self.allow_extra, + metamodel_version=self.schema.metamodel_version, + version=self.schema.version, + class_isa_plus_mixins=self.get_class_isa_plus_mixins(sorted_classes), + injected_fields=self.INJECTED_FIELDS, + ) + return code + + def compile_module( + self, module_path: Path = None, module_name: str = "test" + ) -> ModuleType: # pragma: no cover - replaced with provider + """ + Compiles generated python code to a module + :return: + """ + pycode = self.serialize() + if module_path is not None: + module_path = Path(module_path) + init_file = module_path / "__init__.py" + with open(init_file, "w") as ifile: + ifile.write(" ") + + try: + return compile_python(pycode, module_path, module_name) + except NameError as e: + raise e + + +def compile_python( + text_or_fn: str, package_path: Path = None, module_name: str = "test" +) -> ModuleType: + """ + Compile the text or file and return the resulting module + @param text_or_fn: Python text or file name that references python file + @param package_path: Root package path. If omitted and we've got a python file, the package is the containing + directory + @return: Compiled module + """ + python_txt = file_text(text_or_fn) + if package_path is None and python_txt != text_or_fn: + package_path = Path(text_or_fn) + spec = compile(python_txt, "", "exec") + module = ModuleType(module_name) + + exec(spec, module.__dict__) + sys.modules[module_name] = module + return module diff --git a/numpydantic/maps.py b/numpydantic/maps.py new file mode 100644 index 0000000..2707a8d --- /dev/null +++ b/numpydantic/maps.py @@ -0,0 +1,44 @@ +from datetime import datetime +from typing import Any + +import numpy as np + +np_to_python = { + Any: Any, + np.number: float, + np.object_: Any, + np.bool_: bool, + np.integer: int, + np.byte: bytes, + np.bytes_: bytes, + np.datetime64: datetime, + **{ + n: int + for n in ( + np.int8, + np.int16, + np.int32, + np.int64, + np.short, + np.uint8, + np.uint16, + np.uint32, + np.uint64, + np.uint, + ) + }, + **{ + n: float + for n in ( + np.float16, + np.float32, + np.floating, + np.float32, + np.float64, + np.single, + np.double, + np.float_, + ) + }, + **{n: str for n in (np.character, np.str_, np.string_, np.unicode_)}, +} diff --git a/numpydantic/ndarray.py b/numpydantic/ndarray.py new file mode 100644 index 0000000..78a0046 --- /dev/null +++ b/numpydantic/ndarray.py @@ -0,0 +1,248 @@ +""" +Extension of nptyping NDArray for pydantic that allows for JSON-Schema serialization + +* Order to store data in (row first) +""" +import base64 +import sys +from collections.abc import Callable +from copy import copy +from pathlib import Path +from typing import Any + +import blosc2 +import h5py +import nptyping.structure +import numpy as np + +# TODO: conditional import +from dask.array.core import Array as DaskArray +from nptyping import NDArray as _NDArray +from nptyping import Shape +from nptyping.ndarray import NDArrayMeta as _NDArrayMeta +from nptyping.nptyping_type import NPTypingType +from nptyping.shape_expression import check_shape +from pydantic_core import core_schema +from pydantic_core.core_schema import ListSchema + +from numpydantic.maps import np_to_python + +COMPRESSION_THRESHOLD = 16 * 1024 +""" +Arrays larger than this size (in bytes) will be compressed and b64 encoded when +serializing to JSON. +""" + + +def list_of_lists_schema(shape: Shape, array_type_handler) -> ListSchema: + """Make a pydantic JSON schema for an array as a list of lists.""" + shape_parts = shape.__args__[0].split(",") + split_parts = [ + p.split(" ")[1] if len(p.split(" ")) == 2 else None for p in shape_parts + ] + + # Construct a list of list schema + # go in reverse order - construct list schemas such that + # the final schema is the one that checks the first dimension + shape_labels = reversed(split_parts) + shape_args = reversed(shape.prepared_args) + list_schema = None + for arg, label in zip(shape_args, shape_labels, strict=False): + # which handler to use? for the first we use the actual type + # handler, everywhere else we use the prior list handler + inner_schema = array_type_handler if list_schema is None else list_schema + + # make a label annotation, if we have one + metadata = {"name": label} if label is not None else None + + # make the current level list schema, accounting for shape + if arg == "*": + list_schema = core_schema.list_schema(inner_schema, metadata=metadata) + else: + arg = int(arg) + list_schema = core_schema.list_schema( + inner_schema, min_length=arg, max_length=arg, metadata=metadata + ) + return list_schema + + +def jsonize_array(array: np.ndarray | DaskArray) -> list | dict: + """ + Render an array to base python types that can be serialized to JSON + + For small arrays, returns a list of lists. + + If the array is over :class:`.COMPRESSION_THRESHOLD` bytes, use :func:`.compress_array` + to return a compressed b64 encoded string. + + Args: + array (:class:`np.ndarray`, :class:`dask.DaskArray`): Array to render as a list! + """ + if isinstance(array, DaskArray): + arr = array.__array__() + elif isinstance(array, NDArrayProxy): + arr = array[:] + else: + arr = array + + # If we're larger than 16kB then compress array! + if sys.getsizeof(arr) > COMPRESSION_THRESHOLD: + packed = blosc2.pack_array2(arr) + packed = base64.b64encode(packed) + ret = { + "array": packed, + "shape": copy(arr.shape), + "dtype": copy(arr.dtype.name), + "unpack_fns": ["base64.b64decode", "blosc2.unpack_array2"], + } + return ret + else: + return arr.tolist() + + +def get_validate_shape(shape: Shape) -> Callable: + """ + Get a closure around a shape validation function that includes the shape definition + """ + + def validate_shape(value: Any) -> np.ndarray: + assert shape is Any or check_shape( + value.shape, shape + ), f"Invalid shape! expected shape {shape.prepared_args}, got shape {value.shape}" + + return value + + return validate_shape + + +def get_validate_dtype(dtype: np.dtype) -> Callable: + """ + Get a closure around a dtype validation function that includes the dtype definition + """ + + def validate_dtype(value: np.ndarray) -> np.ndarray: + if dtype is Any: + return value + + assert ( + value.dtype == dtype + ), f"Invalid dtype! expected {dtype}, got {value.dtype}" + return value + + return validate_dtype + + +def coerce_list(value: Any) -> np.ndarray: + """ + If a value is passed as a list or list of lists, try and coerce it into an array + rather than failing validation. + """ + if isinstance(value, list): + value = np.array(value) + return value + + +class NDArrayMeta(_NDArrayMeta, implementation="NDArray"): + """ + Kept here to allow for hooking into metaclass, which has + been necessary on and off as we work this class into a stable + state""" + + +class NDArray(NPTypingType, metaclass=NDArrayMeta): + """ + Following the example here: https://docs.pydantic.dev/latest/usage/types/custom/#handling-third-party-types + """ + + __args__ = (Any, Any) + + @classmethod + def __get_pydantic_core_schema__( + cls, + _source_type: "NDArray", + _handler: Callable[[Any], core_schema.CoreSchema], + ) -> core_schema.CoreSchema: + shape, dtype = _source_type.__args__ + + # get pydantic core schema for the given specified type + if isinstance(dtype, nptyping.structure.StructureMeta): + raise NotImplementedError("Jonny finish this") + # functools.reduce(operator.or_, [int, float, str]) + else: + array_type_handler = _handler.generate_schema(np_to_python[dtype]) + + # get the names of the shape constraints, if any + if shape is Any: + list_schema = core_schema.list_schema(core_schema.any_schema()) + else: + list_schema = list_of_lists_schema(shape, array_type_handler) + + return core_schema.json_or_python_schema( + json_schema=list_schema, + python_schema=core_schema.chain_schema( + [ + core_schema.no_info_plain_validator_function(coerce_list), + core_schema.union_schema( + [ + core_schema.is_instance_schema(cls=np.ndarray), + core_schema.is_instance_schema(cls=DaskArray), + core_schema.is_instance_schema(cls=NDArrayProxy), + ] + ), + core_schema.no_info_plain_validator_function( + get_validate_dtype(dtype) + ), + core_schema.no_info_plain_validator_function( + get_validate_shape(shape) + ), + ] + ), + serialization=core_schema.plain_serializer_function_ser_schema( + jsonize_array, when_used="json" + ), + ) + + +class NDArrayProxy: + """ + Thin proxy to numpy arrays stored within hdf5 files, + only read into memory when accessed, but otherwise + passthrough all attempts to access attributes. + """ + + def __init__(self, h5f_file: Path | str, path: str): + """ + Args: + h5f_file (:class:`pathlib.Path`): Path to source HDF5 file + path (str): Location within HDF5 file where this array is located + """ + self.h5f_file = Path(h5f_file) + self.path = path + + def __getattr__(self, item) -> Any: + with h5py.File(self.h5f_file, "r") as h5f: + obj = h5f.get(self.path) + return getattr(obj, item) + + def __getitem__(self, slice: slice) -> np.ndarray: + with h5py.File(self.h5f_file, "r") as h5f: + obj = h5f.get(self.path) + return obj[slice] + + def __setitem__(self, slice, value) -> None: + raise NotImplementedError("Cant write into an arrayproxy yet!") + + @classmethod + def __get_pydantic_core_schema__( + cls, + _source_type: _NDArray, + _handler: Callable[[Any], core_schema.CoreSchema], + ) -> core_schema.CoreSchema: + # return core_schema.no_info_after_validator_function( + # serialization=core_schema.plain_serializer_function_ser_schema( + # lambda array: array.tolist(), + # when_used='json' + # ) + # ) + + return NDArray_.__get_pydantic_core_schema__(cls, _source_type, _handler) diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..9e68f2b --- /dev/null +++ b/poetry.lock @@ -0,0 +1,2999 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "alabaster" +version = "0.7.16" +description = "A light, configurable Sphinx theme" +optional = true 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