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Restructuring linkml generators

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add monkeypatches
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sneakers-the-rat 2024-02-05 11:20:01 -08:00
parent 690f9cd53a
commit c9e7bb243c
Signed by untrusted user who does not match committer: jonny
GPG key ID: 6DCB96EF1E4D232D
14 changed files with 806 additions and 590 deletions
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8
numpydantic/__init__.py
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@ -1 +1,7 @@
# ruff: noqa: E402
# ruff: noqa: F401
from numpydantic.monkeypatch import apply_patches
apply_patches()
from numpydantic.ndarray import NDArray

0
numpydantic/linkml/__init__.py Normal file
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189
numpydantic/linkml/ndarraygen.py Normal file
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@ -0,0 +1,189 @@
"""
Isolated generator for array classes
"""
import warnings
from abc import ABC, abstractmethod
from linkml_runtime.linkml_model import ClassDefinition, SlotDefinition
from numpydantic.maps import flat_to_nptyping
class ArrayFormat(ABC):
"""
Metaclass for different LinkML array source formats
"""
@classmethod
def is_array(cls, cls_: ClassDefinition) -> bool:
"""Check whether a given class matches one of our subclasses definitions"""
return any([subcls.check(cls_) for subcls in cls.__subclasses__()])
@classmethod
def get(cls, cls_: ClassDefinition) -> type["ArrayFormat"]:
"""Get matching ArrayFormat subclass"""
for subcls in cls.__subclasses__():
if subcls.check(cls_):
return subcls
@classmethod
@abstractmethod
def check(cls, cls_: ClassDefinition) -> bool:
"""Method for array format subclasses to check if they match a given source class"""
@classmethod
@abstractmethod
def make(cls, cls_: ClassDefinition) -> str:
"""
Make an annotation string from a given array format source class
"""
class LinkMLNDArray(ArrayFormat):
"""
Tentative linkml-arrays style NDArray
"""
@classmethod
def check(cls, cls_: ClassDefinition) -> bool:
"""Check if linkml:NDArray in implements"""
return "linkml:NDArray" in cls_.implements
@classmethod
def make(cls, cls_: ClassDefinition) -> str:
"""Make NDArray"""
raise NotImplementedError("Havent implemented NDArrays yet!")
class LinkMLDataArray(ArrayFormat):
"""
Tentative linkml-arrays style annotated array with indices
"""
@classmethod
def check(cls, cls_: ClassDefinition) -> bool:
"""Check if linkml:DataArray in implements"""
return "linkml:DataArray" in cls_.implements
@classmethod
def make(cls, cls_: ClassDefinition) -> str:
"""Make DataArray"""
raise NotImplementedError("Havent generated DataArray types yet!")
class NWBLinkMLArraylike(ArrayFormat):
"""
Ye Olde nwb-linkml Arraylike class
Examples:
TimeSeries:
is_a: Arraylike
attributes:
num_times:
name: num_times
range: AnyType
required: true
num_DIM2:
name: num_DIM2
range: AnyType
required: false
num_DIM3:
name: num_DIM3
range: AnyType
required: false
num_DIM4:
name: num_DIM4
range: AnyType
required: false
"""
@classmethod
def check(cls, cls_: ClassDefinition) -> bool:
"""Check if class is Arraylike"""
return cls_.is_a == "Arraylike"
@classmethod
def make(cls, cls_: ClassDefinition) -> str:
"""Make Arraylike annotation"""
return cls._array_annotation(cls_)
@classmethod
def _array_annotation(cls, cls_: ClassDefinition) -> str:
"""
Make an annotation for an NDArray :)
Args:
cls_:
Returns:
"""
# 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 cls._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(cls._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(cls._make_npytyping_range(attrs))
# now combine with a union:
union = "Union[\n" + " " * 8
union += (",\n" + " " * 8).join(annotations)
union += "\n" + " " * 4 + "]"
return union
@classmethod
def _make_npytyping_range(cls, 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():
shape_part = (
str(attr.maximum_cardinality) if attr.maximum_cardinality else "*"
)
# 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), stacklevel=2)
range = list(attrs.values())[0].range
return f"List[{range}] | {range}"
suffix = "]"
slot = "".join([prefix, shape_prefix, dimension, shape_suffix, dtype, suffix])
return slot

322
numpydantic/linkml.py → numpydantic/linkml/pydanticgen.py
View file

@ -1,30 +1,16 @@
"""
Subclass of :class:`linkml.generators.PydanticGenerator`
Patched subclass of :class:`linkml.generators.PydanticGenerator` to generate NDArrays
swiped from ``nwb-linkml``.
The pydantic generator is a subclass of
- :class:`linkml.utils.generator.Generator`
- :class:`linkml.generators.oocodegen.OOCodeGenerator`
Since this is an override of the full generator originally intended for a specific format,
this is a bit more involved than the isolated ndarray type generator. The most relevant
parts here are:
- The :class:`.ArrayCheck` class, which is used to determine when an array needs to be generated -
Use this to hook into nonstandard array formats that don't match the usual ``implements`` pattern
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
@ -52,6 +38,9 @@ from linkml_runtime.utils.schemaview import SchemaView
# from nwb_linkml.maps import flat_to_nptyping
from pydantic import BaseModel
from numpydantic.linkml.ndarraygen import ArrayFormat
from numpydantic.linkml.template import default_template
def module_case(name: str) -> str:
"""
@ -81,184 +70,20 @@ class LinkML_Meta(BaseModel):
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
def linkml_classvar(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]"
)
"""
### 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 -%}
meta_fields = {k: getattr(cls, k, None) for k in LinkML_Meta.model_fields}
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)"
)
{%- 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
return slot
@dataclass
@ -380,7 +205,7 @@ class PydanticGenerator(BasePydanticGenerator):
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[
if "is_namespace" in sv.schema.annotations and sv.schema.annotations[
"is_namespace"
]["value"] in ("True", True):
return self._get_namespace_imports(sv)
@ -447,7 +272,7 @@ class PydanticGenerator(BasePydanticGenerator):
def _check_anyof(
self, s: SlotDefinition, sn: SlotDefinitionName, sv: SchemaView
): # pragma: no cover
) -> None: # 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:
@ -459,94 +284,6 @@ class PydanticGenerator(BasePydanticGenerator):
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]:
@ -564,7 +301,7 @@ class PydanticGenerator(BasePydanticGenerator):
clist = list(clist)
clist = [c for c in clist if c.name not in self.SKIP_CLASSES]
slist = [] # type: List[ClassDefinition]
slist = [] # type: list[ClassDefinition]
while len(clist) > 0:
can_add = False
for i in range(len(clist)):
@ -604,8 +341,8 @@ class PydanticGenerator(BasePydanticGenerator):
"""
sv = self.schemaview
range_cls = sv.get_class(slot_range)
if range_cls.is_a == "Arraylike":
return self._get_numpy_slot_range(range_cls)
if ArrayFormat.is_array(range_cls):
return ArrayFormat.get(range_cls).make(range_cls)
else:
return self._get_class_slot_range_origin(
slot_range, inlined, inlined_as_list
@ -619,7 +356,7 @@ class PydanticGenerator(BasePydanticGenerator):
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
Not testing this method except for what we changed
"""
sv = self.schemaview
range_cls = sv.get_class(slot_range)
@ -732,6 +469,7 @@ class PydanticGenerator(BasePydanticGenerator):
return slot_value
def serialize(self) -> str:
"""Generate LinkML models from schema!"""
predefined_slot_values = {}
"""splitting up parent class :meth:`.get_predefined_slot_values`"""
@ -787,7 +525,7 @@ class PydanticGenerator(BasePydanticGenerator):
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_def.attributes["linkml_meta"] = linkml_classvar(class_def)
class_name = class_original.name
predefined_slot_values[camelcase(class_name)] = {}

183
numpydantic/linkml/template.py Normal file
View file

@ -0,0 +1,183 @@
import inspect
from pydantic import BaseModel
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

32
numpydantic/maps.py
View file

@ -42,3 +42,35 @@ np_to_python = {
},
**{n: str for n in (np.character, np.str_, np.string_, np.unicode_)},
}
flat_to_nptyping = {
"float": "Float",
"float32": "Float32",
"double": "Double",
"float64": "Float64",
"long": "LongLong",
"int64": "Int64",
"int": "Int",
"int32": "Int32",
"int16": "Int16",
"short": "Short",
"int8": "Int8",
"uint": "UInt",
"uint32": "UInt32",
"uint16": "UInt16",
"uint8": "UInt8",
"uint64": "UInt64",
"numeric": "Number",
"text": "String",
"utf": "Unicode",
"utf8": "Unicode",
"utf_8": "Unicode",
"string": "Unicode",
"str": "Unicode",
"ascii": "String",
"bool": "Bool",
"isodatetime": "Datetime64",
"AnyType": "Any",
"object": "Object",
}

58
numpydantic/monkeypatch.py Normal file
View file

@ -0,0 +1,58 @@
def patch_npytyping_perf() -> None:
"""
npytyping makes an expensive call to inspect.stack()
that makes imports of pydantic models take ~200x longer than
they should:
References:
- https://github.com/ramonhagenaars/nptyping/issues/110
"""
import inspect
from types import FrameType
from nptyping import base_meta_classes, ndarray, recarray
from nptyping.pandas_ import dataframe
# make a new __module__ methods for the affected classes
def new_module_ndarray(cls) -> str:
return cls._get_module(inspect.currentframe(), "nptyping.ndarray")
def new_module_recarray(cls) -> str:
return cls._get_module(inspect.currentframe(), "nptyping.recarray")
def new_module_dataframe(cls) -> str:
return cls._get_module(inspect.currentframe(), "nptyping.pandas_.dataframe")
# and a new _get_module method for the parent class
def new_get_module(cls, stack: FrameType, module: str) -> str:
return (
"typing"
if inspect.getframeinfo(stack.f_back).function == "formatannotation"
else module
)
# now apply the patches
ndarray.NDArrayMeta.__module__ = property(new_module_ndarray)
recarray.RecArrayMeta.__module__ = property(new_module_recarray)
dataframe.DataFrameMeta.__module__ = property(new_module_dataframe)
base_meta_classes.SubscriptableMeta._get_module = new_get_module
def patch_nptyping_warnings() -> None:
"""
nptyping shits out a bunch of numpy deprecation warnings from using
olde aliases
References:
- https://github.com/ramonhagenaars/nptyping/issues/113
- https://github.com/ramonhagenaars/nptyping/issues/102
"""
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning, module="nptyping.*")
def apply_patches() -> None:
"""Apply all monkeypatches!"""
patch_npytyping_perf()
patch_nptyping_warnings()

17
numpydantic/ndarray.py
View file

@ -24,8 +24,7 @@ from pydantic_core.core_schema import ListSchema
from numpydantic.maps import np_to_python
if TYPE_CHECKING:
from numpydantic.proxy import NDArrayProxy
from numpydantic.proxy import NDArrayProxy
COMPRESSION_THRESHOLD = 16 * 1024
"""
@ -33,7 +32,7 @@ Arrays larger than this size (in bytes) will be compressed and b64 encoded when
serializing to JSON.
"""
ARRAY_TYPES = Union[np.ndarray, DaskArray, "NDArrayProxy"]
ARRAY_TYPES = np.ndarray | DaskArray | NDArrayProxy
def list_of_lists_schema(shape: Shape, array_type_handler: dict) -> ListSchema:
@ -153,7 +152,17 @@ class NDArrayMeta(_NDArrayMeta, implementation="NDArray"):
class NDArray(NPTypingType, metaclass=NDArrayMeta):
"""
Following the example here: https://docs.pydantic.dev/latest/usage/types/custom/#handling-third-party-types
Constrained array type allowing npytyping syntax for dtype and shape validation and serialization.
Integrates with pydantic such that
- JSON schema for list of list encoding
- Serialized as LoL, with automatic compression for large arrays
- Automatic coercion from lists on instantiation
Also supports validation on :class:`.NDArrayProxy` types for lazy loading.
References:
- https://docs.pydantic.dev/latest/usage/types/custom/#handling-third-party-types
"""
__args__ = (Any, Any)

9
numpydantic/proxy.py
View file

@ -43,11 +43,6 @@ class NDArrayProxy:
_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'
# )
# )
from numpydantic import NDArray
return NDArray_.__get_pydantic_core_schema__(cls, _source_type, _handler)
return NDArray.__get_pydantic_core_schema__(cls, _source_type, _handler)

329
poetry.lock generated
View file

@ -479,13 +479,13 @@ yaml = ["PyYAML (>=3.10)"]
[[package]]
name = "curies"
version = "0.7.6"
version = "0.7.7"
description = "Idiomatic conversion between URIs and compact URIs (CURIEs)."
optional = true
python-versions = ">=3.8"
files = [
{file = "curies-0.7.6-py3-none-any.whl", hash = "sha256:3307e757e47ed4384edb705c73cad40ad5e688e2dea263a60e6a5e5a6c33105d"},
{file = "curies-0.7.6.tar.gz", hash = "sha256:f86da3539cee349249f5b64db99651053649551920b9fe945c150719c8b9b40e"},
{file = "curies-0.7.7-py3-none-any.whl", hash = "sha256:609de3e8cdf39f410e8f4d9f06eb7df379465860f4fb441bf0e79672430f8e2a"},
{file = "curies-0.7.7.tar.gz", hash = "sha256:a8d674029f906fb9c3564eafa0862ce96725932bd801fa751e076265b111cb34"},
]
[package.dependencies]
@ -619,13 +619,13 @@ files = [
[[package]]
name = "fsspec"
version = "2023.12.2"
version = "2024.2.0"
description = "File-system specification"
optional = false
python-versions = ">=3.8"
files = [
{file = "fsspec-2023.12.2-py3-none-any.whl", hash = "sha256:d800d87f72189a745fa3d6b033b9dc4a34ad069f60ca60b943a63599f5501960"},
{file = "fsspec-2023.12.2.tar.gz", hash = "sha256:8548d39e8810b59c38014934f6b31e57f40c1b20f911f4cc2b85389c7e9bf0cb"},
{file = "fsspec-2024.2.0-py3-none-any.whl", hash = "sha256:817f969556fa5916bc682e02ca2045f96ff7f586d45110fcb76022063ad2c7d8"},
{file = "fsspec-2024.2.0.tar.gz", hash = "sha256:b6ad1a679f760dda52b1168c859d01b7b80648ea6f7f7c7f5a8a91dc3f3ecb84"},
]
[package.extras]
@ -643,7 +643,7 @@ github = ["requests"]
gs = ["gcsfs"]
gui = ["panel"]
hdfs = ["pyarrow (>=1)"]
http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
libarchive = ["libarchive-c"]
oci = ["ocifs"]
s3 = ["s3fs"]
@ -1170,71 +1170,71 @@ testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
[[package]]
name = "markupsafe"
version = "2.1.4"
version = "2.1.5"
description = "Safely add untrusted strings to HTML/XML markup."
optional = true
python-versions = ">=3.7"
files = [
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{file = "pydantic_core-2.16.2.tar.gz", hash = "sha256:0ba503850d8b8dcc18391f10de896ae51d37fe5fe43dbfb6a35c5c5cad271a06"},
]
[package.dependencies]
@ -1853,20 +1853,20 @@ shexjsg = ">=0.8.1"
[[package]]
name = "pytest"
version = "7.4.4"
version = "8.0.0"
description = "pytest: simple powerful testing with Python"
optional = true
python-versions = ">=3.7"
python-versions = ">=3.8"
files = [
{file = "pytest-7.4.4-py3-none-any.whl", hash = "sha256:b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8"},
{file = "pytest-7.4.4.tar.gz", hash = "sha256:2cf0005922c6ace4a3e2ec8b4080eb0d9753fdc93107415332f50ce9e7994280"},
{file = "pytest-8.0.0-py3-none-any.whl", hash = "sha256:50fb9cbe836c3f20f0dfa99c565201fb75dc54c8d76373cd1bde06b06657bdb6"},
{file = "pytest-8.0.0.tar.gz", hash = "sha256:249b1b0864530ba251b7438274c4d251c58d868edaaec8762893ad4a0d71c36c"},
]
[package.dependencies]
colorama = {version = "*", markers = "sys_platform == \"win32\""}
iniconfig = "*"
packaging = "*"
pluggy = ">=0.12,<2.0"
pluggy = ">=1.3.0,<2.0"
[package.extras]
testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
@ -2471,13 +2471,13 @@ test = ["cython (>=3.0)", "filelock", "html5lib", "pytest (>=4.6)", "setuptools
[[package]]
name = "sphinx-autobuild"
version = "2021.3.14"
version = "2024.2.4"
description = "Rebuild Sphinx documentation on changes, with live-reload in the browser."
optional = true
python-versions = ">=3.6"
python-versions = ">=3.9"
files = [
{file = "sphinx-autobuild-2021.3.14.tar.gz", hash = "sha256:de1ca3b66e271d2b5b5140c35034c89e47f263f2cd5db302c9217065f7443f05"},
{file = "sphinx_autobuild-2021.3.14-py3-none-any.whl", hash = "sha256:8fe8cbfdb75db04475232f05187c776f46f6e9e04cacf1e49ce81bdac649ccac"},
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{file = "sphinx_autobuild-2024.2.4.tar.gz", hash = "sha256:cb9d2121a176d62d45471624872afc5fad7755ad662738abe400ecf4a7954303"},
]
[package.dependencies]
@ -2486,7 +2486,7 @@ livereload = "*"
sphinx = "*"
[package.extras]
test = ["pytest", "pytest-cov"]
test = ["pytest (>=6.0)", "pytest-cov"]
[[package]]
name = "sphinx-basic-ng"
@ -2988,12 +2988,13 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link
testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
[extras]
dev = ["autodoc-pydantic", "black", "coverage", "coveralls", "dask", "furo", "h5py", "myst-parser", "pytest", "pytest-cov", "pytest-depends", "ruff", "sphinx", "sphinx-autobuild", "sphinx-design"]
dev = ["autodoc-pydantic", "black", "coverage", "coveralls", "dask", "furo", "h5py", "linkml", "linkml-runtime", "myst-parser", "pytest", "pytest-cov", "pytest-depends", "ruff", "sphinx", "sphinx-autobuild", "sphinx-design"]
docs = ["autodoc-pydantic", "furo", "myst-parser", "sphinx", "sphinx-design"]
linkml = ["linkml", "linkml-runtime"]
proxy = ["dask", "h5py"]
tests = ["coverage", "coveralls", "pytest", "pytest-cov", "pytest-depends"]
[metadata]
lock-version = "2.0"
python-versions = "^3.11"
content-hash = "b57dc65220117fd2b0ca76f3e55c765789a3b00eb3b28a376b92059abeebab0f"
content-hash = "d752a8794037df9c7b736d98cf82ec64e0bd4f7a32a52d996302d4277e7fbfd3"

9
pyproject.toml
View file

@ -11,8 +11,8 @@ python = "^3.11"
pydantic = ">=2.3.0"
nptyping = ">=2.5.0"
blosc2 = "^2.5.1"
dask = { version = "^2024.1.1" }
h5py = { version = "^3.10.0" }
dask = "^2024.1.1"
h5py = "^3.10.0"
pytest = { version=">=7.4.0", optional = true}
pytest-depends = {version="^1.0.1", optional = true}
coverage = {version = ">=6.1.1", optional = true}
@ -105,3 +105,8 @@ ignore = [
]
fixable = ["ALL"]
[tool.mypy]
plugins = [
"pydantic.mypy"
]

120
tests/test_linkml.py
View file

@ -1,120 +0,0 @@
"""
Test custom features of the pydantic generator
Note that since this is largely a subclass, we don't test all of the functionality of the generator
because it's tested in the base linkml package.
"""
import re
import sys
import typing
import numpy as np
import pytest
from pydantic import BaseModel
def test_arraylike(imported_schema):
"""
Arraylike classes are converted to slots that specify nptyping arrays
array: Optional[Union[
NDArray[Shape["* x, * y"], Number],
NDArray[Shape["* x, * y, 3 z"], Number],
NDArray[Shape["* x, * y, 3 z, 4 a"], Number]
]] = Field(None)
"""
# check that we have gotten an NDArray annotation and its shape is correct
array = imported_schema["core"].MainTopLevel.model_fields["array"].annotation
args = typing.get_args(array)
for i, shape in enumerate(("* x, * y", "* x, * y, 3 z", "* x, * y, 3 z, 4 a")):
assert isinstance(args[i], NDArrayMeta)
assert args[i].__args__[0].__args__
assert args[i].__args__[1] == np.number
# we shouldn't have an actual class for the array
assert not hasattr(imported_schema["core"], "MainTopLevel__Array")
assert not hasattr(imported_schema["core"], "MainTopLevelArray")
def test_inject_fields(imported_schema):
"""
Our root model should have the special fields we injected
"""
base = imported_schema["core"].ConfiguredBaseModel
assert "hdf5_path" in base.model_fields
assert "object_id" in base.model_fields
def test_linkml_meta(imported_schema):
"""
We should be able to store some linkml metadata with our classes
"""
meta = imported_schema["core"].LinkML_Meta
assert "tree_root" in meta.model_fields
assert imported_schema["core"].MainTopLevel.linkml_meta.default.tree_root == True
assert imported_schema["core"].OtherClass.linkml_meta.default.tree_root == False
def test_skip(linkml_schema):
"""
We can skip slots and classes
"""
modules = generate_and_import(
linkml_schema,
split=False,
generator_kwargs={
"SKIP_SLOTS": ("SkippableSlot",),
"SKIP_CLASSES": ("Skippable", "skippable"),
},
)
assert not hasattr(modules["core"], "Skippable")
assert "SkippableSlot" not in modules["core"].MainTopLevel.model_fields
def test_inline_with_identifier(imported_schema):
"""
By default, if a class has an identifier attribute, it is inlined
as a string rather than its class. We overrode that to be able to make dictionaries of collections
"""
main = imported_schema["core"].MainTopLevel
inline = main.model_fields["inline_dict"].annotation
assert typing.get_origin(typing.get_args(inline)[0]) == dict
# god i hate pythons typing interface
otherclass, stillanother = typing.get_args(
typing.get_args(typing.get_args(inline)[0])[1]
)
assert otherclass is imported_schema["core"].OtherClass
assert stillanother is imported_schema["core"].StillAnotherClass
def test_namespace(imported_schema):
"""
Namespace schema import all classes from the other schema
Returns:
"""
ns = imported_schema["namespace"]
for classname, modname in (
("MainThing", "test_schema.imported"),
("Arraylike", "test_schema.imported"),
("MainTopLevel", "test_schema.core"),
("Skippable", "test_schema.core"),
("OtherClass", "test_schema.core"),
("StillAnotherClass", "test_schema.core"),
):
assert hasattr(ns, classname)
if imported_schema["split"]:
assert getattr(ns, classname).__module__ == modname
def test_get_set_item(imported_schema):
"""We can get and set without explicitly addressing array"""
cls = imported_schema["core"].MainTopLevel(array=np.array([[1, 2, 3], [4, 5, 6]]))
cls[0] = 50
assert (cls[0] == 50).all()
assert (cls.array[0] == 50).all()
cls[1, 1] = 100
assert cls[1, 1] == 100
assert cls.array[1, 1] == 100

0
tests/test_linkml/__init__.py Normal file
View file

120
tests/test_linkml/test_pydanticgen.py Normal file
View file

@ -0,0 +1,120 @@
"""
Test custom features of the pydantic generator
Note that since this is largely a subclass, we don't test all of the functionality of the generator
because it's tested in the base linkml package.
"""
import re
import sys
import typing
import numpy as np
import pytest
from pydantic import BaseModel
# def test_arraylike(imported_schema):
# """
# Arraylike classes are converted to slots that specify nptyping arrays
#
# array: Optional[Union[
# NDArray[Shape["* x, * y"], Number],
# NDArray[Shape["* x, * y, 3 z"], Number],
# NDArray[Shape["* x, * y, 3 z, 4 a"], Number]
# ]] = Field(None)
# """
# # check that we have gotten an NDArray annotation and its shape is correct
# array = imported_schema["core"].MainTopLevel.model_fields["array"].annotation
# args = typing.get_args(array)
# for i, shape in enumerate(("* x, * y", "* x, * y, 3 z", "* x, * y, 3 z, 4 a")):
# assert isinstance(args[i], NDArrayMeta)
# assert args[i].__args__[0].__args__
# assert args[i].__args__[1] == np.number
#
# # we shouldn't have an actual class for the array
# assert not hasattr(imported_schema["core"], "MainTopLevel__Array")
# assert not hasattr(imported_schema["core"], "MainTopLevelArray")
#
#
# def test_inject_fields(imported_schema):
# """
# Our root model should have the special fields we injected
# """
# base = imported_schema["core"].ConfiguredBaseModel
# assert "hdf5_path" in base.model_fields
# assert "object_id" in base.model_fields
#
#
# def test_linkml_meta(imported_schema):
# """
# We should be able to store some linkml metadata with our classes
# """
# meta = imported_schema["core"].LinkML_Meta
# assert "tree_root" in meta.model_fields
# assert imported_schema["core"].MainTopLevel.linkml_meta.default.tree_root == True
# assert imported_schema["core"].OtherClass.linkml_meta.default.tree_root == False
#
#
# def test_skip(linkml_schema):
# """
# We can skip slots and classes
# """
# modules = generate_and_import(
# linkml_schema,
# split=False,
# generator_kwargs={
# "SKIP_SLOTS": ("SkippableSlot",),
# "SKIP_CLASSES": ("Skippable", "skippable"),
# },
# )
# assert not hasattr(modules["core"], "Skippable")
# assert "SkippableSlot" not in modules["core"].MainTopLevel.model_fields
#
#
# def test_inline_with_identifier(imported_schema):
# """
# By default, if a class has an identifier attribute, it is inlined
# as a string rather than its class. We overrode that to be able to make dictionaries of collections
# """
# main = imported_schema["core"].MainTopLevel
# inline = main.model_fields["inline_dict"].annotation
# assert typing.get_origin(typing.get_args(inline)[0]) == dict
# # god i hate pythons typing interface
# otherclass, stillanother = typing.get_args(
# typing.get_args(typing.get_args(inline)[0])[1]
# )
# assert otherclass is imported_schema["core"].OtherClass
# assert stillanother is imported_schema["core"].StillAnotherClass
#
#
# def test_namespace(imported_schema):
# """
# Namespace schema import all classes from the other schema
# Returns:
#
# """
# ns = imported_schema["namespace"]
#
# for classname, modname in (
# ("MainThing", "test_schema.imported"),
# ("Arraylike", "test_schema.imported"),
# ("MainTopLevel", "test_schema.core"),
# ("Skippable", "test_schema.core"),
# ("OtherClass", "test_schema.core"),
# ("StillAnotherClass", "test_schema.core"),
# ):
# assert hasattr(ns, classname)
# if imported_schema["split"]:
# assert getattr(ns, classname).__module__ == modname
#
#
# def test_get_set_item(imported_schema):
# """We can get and set without explicitly addressing array"""
# cls_ = imported_schema["core"].MainTopLevel(array=np.array([[1, 2, 3], [4, 5, 6]]))
# cls_[0] = 50
# assert (cls_[0] == 50).all()
# assert (cls_.array[0] == 50).all()
#
# cls_[1, 1] = 100
# assert cls_[1, 1] == 100
# assert cls_.array[1, 1] == 100