remove dask, nptyping

This commit is contained in:
sneakers-the-rat 2024-08-06 20:35:23 -07:00
parent a309c25c3d
commit edea802ff1
Signed by untrusted user who does not match committer: jonny
GPG key ID: 6DCB96EF1E4D232D
7 changed files with 124 additions and 382 deletions

View file

@ -5,7 +5,7 @@
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@ -53,7 +53,7 @@ files = [
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@ -61,8 +61,8 @@ dependencies = [
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@ -229,17 +229,6 @@ files = [
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{file = "rpds_py-0.20.0.tar.gz", hash = "sha256:d72a210824facfdaf8768cf2d7ca25a042c30320b3020de2fa04640920d4e121"},
]
[[package]]
@ -2080,17 +2005,6 @@ files = [
{file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
]
[[package]]
name = "toolz"
version = "0.12.1"
requires_python = ">=3.7"
summary = "List processing tools and functional utilities"
groups = ["default"]
files = [
{file = "toolz-0.12.1-py3-none-any.whl", hash = "sha256:d22731364c07d72eea0a0ad45bafb2c2937ab6fd38a3507bf55eae8744aa7d85"},
{file = "toolz-0.12.1.tar.gz", hash = "sha256:ecca342664893f177a13dac0e6b41cbd8ac25a358e5f215316d43e2100224f4d"},
]
[[package]]
name = "tqdm"
version = "4.66.5"
@ -2276,7 +2190,7 @@ name = "zipp"
version = "3.19.2"
requires_python = ">=3.8"
summary = "Backport of pathlib-compatible object wrapper for zip files"
groups = ["default", "dev", "plot", "tests"]
groups = ["dev", "plot", "tests"]
files = [
{file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
{file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},

View file

@ -15,11 +15,9 @@ dependencies = [
"rich>=13.5.2",
#"linkml>=1.7.10",
"linkml @ git+https://github.com/sneakers-the-rat/linkml@nwb-linkml",
"nptyping>=2.5.0",
"pydantic>=2.3.0",
"h5py>=3.9.0",
"pydantic-settings>=2.0.3",
"dask>=2023.9.2",
"tqdm>=4.66.1",
'typing-extensions>=4.12.2;python_version<"3.11"',
"numpydantic>=1.3.0",

View file

@ -2,7 +2,7 @@
Mapping from one domain to another
"""
from nwb_linkml.maps.dtype import flat_to_linkml, flat_to_np, flat_to_nptyping
from nwb_linkml.maps.dtype import flat_to_linkml, flat_to_np
from nwb_linkml.maps.map import Map
from nwb_linkml.maps.postload import MAP_HDMF_DATATYPE_DEF, MAP_HDMF_DATATYPE_INC
from nwb_linkml.maps.quantity import QUANTITY_MAP
@ -14,5 +14,4 @@ __all__ = [
"Map",
"flat_to_linkml",
"flat_to_np",
"flat_to_nptyping",
]

View file

@ -3,9 +3,8 @@ Dtype mappings
"""
from datetime import datetime
from typing import Any, Type
from typing import Any
import nptyping
import numpy as np
from nwb_schema_language import CompoundDtype, DTypeType, FlatDtype, ReferenceDtype
@ -40,37 +39,6 @@ flat_to_linkml = {
Map between the flat data types and the simpler linkml base types
"""
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",
}
flat_to_np = {
"float": float,
"float32": np.float32,
@ -175,20 +143,6 @@ https://github.com/hdmf-dev/hdmf/blob/ddc842b5c81d96e0b957b96e88533b16c137e206/s
"""
def struct_from_dtype(dtype: np.dtype) -> Type[nptyping.Structure]:
"""
Create a nptyping Structure from a compound numpy dtype
nptyping structures have the form::
Structure["name: Str, age: Int"]
"""
struct_pieces = [f"{k}: {flat_to_nptyping[v[0].name]}" for k, v in dtype.fields.items()]
struct_dtype = ", ".join(struct_pieces)
return nptyping.Structure[struct_dtype]
def handle_dtype(dtype: DTypeType | None) -> str:
"""
Get the string form of a dtype

View file

@ -2,69 +2,9 @@
Mapping functions for handling HDMF classes like DynamicTables
"""
from typing import Any, List, Optional, Type
from typing import Any, List, Optional
import dask.array as da
import h5py
import numpy as np
from numpydantic import NDArray
from numpydantic.interface.hdf5 import H5ArrayPath
from pydantic import BaseModel, create_model
from nwb_linkml.maps.dtype import struct_from_dtype
from nwb_linkml.types.hdf5 import HDF5_Path
def model_from_dynamictable(group: h5py.Group, base: Optional[BaseModel] = None) -> Type[BaseModel]:
"""
Create a pydantic model from a dynamic table
"""
colnames = group.attrs["colnames"]
types = {}
for col in colnames:
nptype = group[col].dtype
nptype = struct_from_dtype(nptype) if nptype.type == np.void else nptype.type
type_ = Optional[NDArray[Any, nptype]]
# FIXME: handling nested column types that appear only in some versions?
# types[col] = (List[type_ | None], ...)
types[col] = (type_, None)
model = create_model(group.name.split("/")[-1], **types, __base__=base)
return model
def dynamictable_to_model(
group: h5py.Group,
model: Optional[Type[BaseModel]] = None,
base: Optional[Type[BaseModel]] = None,
) -> BaseModel:
"""
Instantiate a dynamictable model
Calls :func:`.model_from_dynamictable` if ``model`` is not provided.
"""
if model is None:
model = model_from_dynamictable(group, base)
items = {}
for col, col_type in model.model_fields.items():
if col not in group:
if col in group.attrs:
items[col] = group.attrs[col]
continue
if col_type.annotation is HDF5_Path:
items[col] = [HDF5_Path(group[d].name) for d in group[col][:]]
else:
try:
items[col] = da.from_array(group[col])
except NotImplementedError:
items[col] = H5ArrayPath(file=group.file.filename, path=group[col].name)
return model.model_construct(hdf5_path=group.name, name=group.name.split("/")[-1], **items)
def dereference_reference_vector(dset: h5py.Dataset, data: Optional[List[Any]]) -> List:

View file

@ -5,56 +5,6 @@ Monkeypatches to external modules
# ruff: noqa: ANN001 - not well defined types for this module
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
"""
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning, module="nptyping.*")
def patch_schemaview() -> None:
"""
Patch schemaview to correctly resolve multiple layers of relative imports.
@ -211,8 +161,6 @@ def patch_pretty_print() -> None:
def apply_patches() -> None:
"""Apply all monkeypatches"""
patch_npytyping_perf()
patch_nptyping_warnings()
patch_schemaview()
patch_array_expression()
patch_pretty_print()

View file

@ -1,11 +0,0 @@
import nptyping
import numpy as np
from nwb_linkml.maps.dtype import struct_from_dtype
def test_struct_from_dtype():
# Super weak test with fixed values, will expand with parameterize if needed
np_dtype = np.dtype([("name1", "int32"), ("name2", "object"), ("name3", "str")])
struct = struct_from_dtype(np_dtype)
assert struct == nptyping.Structure["name1: Int32, name2: Object, name3: Unicode"]