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created stub for matlab compose
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‎pydra/compose/matlab/__init__.py

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from .task import Task, Outputs
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from .field import arg, out
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from .builder import define
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__all__ = ["arg", "out", "define", "Task", "Outputs"]

‎pydra/compose/matlab/builder.py

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import typing as ty
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import inspect
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import re
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from typing import dataclass_transform
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from . import field
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from .task import Task, Outputs
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from pydra.compose.base import (
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ensure_field_objects,
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build_task_class,
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check_explicit_fields_are_none,
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extract_fields_from_class,
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)
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@dataclass_transform(
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kw_only_default=True,
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field_specifiers=(field.arg,),
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)
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def define(
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wrapped: type | ty.Callable | None = None,
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/,
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inputs: list[str | field.arg] | dict[str, field.arg | type] | None = None,
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outputs: list[str | field.out] | dict[str, field.out | type] | type | None = None,
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bases: ty.Sequence[type] = (),
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outputs_bases: ty.Sequence[type] = (),
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auto_attribs: bool = True,
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name: str | None = None,
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xor: ty.Sequence[str | None] | ty.Sequence[ty.Sequence[str | None]] = (),
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) -> "Task":
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"""
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Create an interface for a function or a class.
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Parameters
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----------
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wrapped : type | callable | None
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The function or class to create an interface for.
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inputs : list[str | Arg] | dict[str, Arg | type] | None
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The inputs to the function or class.
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outputs : list[str | base.Out] | dict[str, base.Out | type] | type | None
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The outputs of the function or class.
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auto_attribs : bool
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Whether to use auto_attribs mode when creating the class.
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name: str | None
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The name of the returned class
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xor: Sequence[str | None] | Sequence[Sequence[str | None]], optional
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Names of args that are exclusive mutually exclusive, which must include
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the name of the current field. If this list includes None, then none of the
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fields need to be set.
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Returns
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-------
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Task
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The task class for the Python function
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"""
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def make(wrapped: ty.Callable | type) -> Task:
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if inspect.isclass(wrapped):
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klass = wrapped
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function = klass.function
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class_name = klass.__name__
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check_explicit_fields_are_none(klass, inputs, outputs)
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parsed_inputs, parsed_outputs = extract_fields_from_class(
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Task,
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Outputs,
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klass,
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field.arg,
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field.out,
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auto_attribs,
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skip_fields=["function"],
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)
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else:
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if not isinstance(wrapped, str):
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raise ValueError(
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f"wrapped must be a class or a string containing a MATLAB snipped, not {wrapped!r}"
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)
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klass = None
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input_helps, output_helps = {}, {}
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function_name, inferred_inputs, inferred_outputs = (
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parse_matlab_function(
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wrapped,
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inputs=inputs,
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outputs=outputs,
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)
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)
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parsed_inputs, parsed_outputs = ensure_field_objects(
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arg_type=field.arg,
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out_type=field.out,
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inputs=inferred_inputs,
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outputs=inferred_outputs,
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input_helps=input_helps,
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output_helps=output_helps,
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)
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if name:
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class_name = name
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else:
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class_name = function_name
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class_name = re.sub(r"[^\w]", "_", class_name)
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if class_name[0].isdigit():
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class_name = f"_{class_name}"
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# Add in fields from base classes
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parsed_inputs.update({n: getattr(Task, n) for n in Task.BASE_ATTRS})
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parsed_outputs.update({n: getattr(Outputs, n) for n in Outputs.BASE_ATTRS})
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function = wrapped
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parsed_inputs["function"] = field.arg(
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name="function",
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type=str,
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default=function,
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help=Task.FUNCTION_HELP,
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)
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defn = build_task_class(
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Task,
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Outputs,
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parsed_inputs,
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parsed_outputs,
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name=class_name,
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klass=klass,
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bases=bases,
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outputs_bases=outputs_bases,
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xor=xor,
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)
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return defn
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if wrapped is not None:
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if not isinstance(wrapped, (str, type)):
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raise ValueError(f"wrapped must be a class or a string, not {wrapped!r}")
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return make(wrapped)
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return make
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def parse_matlab_function(
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function: str,
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inputs: list[str | field.arg] | dict[str, field.arg | type] | None = None,
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outputs: list[str | field.out] | dict[str, field.out | type] | type | None = None,
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) -> tuple[str, dict[str, field.arg], dict[str, field.out]]:
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"""
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Parse a MATLAB function string to extract inputs and outputs.
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Parameters
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----------
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function : str
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The MATLAB function string.
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inputs : list or dict, optional
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The inputs to the function.
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outputs : list or dict, optional
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The outputs of the function.
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Returns
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-------
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tuple
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A tuple containing the function name, inferred inputs, and inferred outputs.
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"""
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raise NotImplementedError

‎pydra/compose/matlab/field.py

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import attrs
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from .. import base
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@attrs.define
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class arg(base.Arg):
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"""Argument of a matlab task
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Parameters
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----------
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help: str
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A short description of the input field.
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default : Any, optional
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the default value for the argument
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allowed_values: list, optional
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List of allowed values for the field.
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requires: list, optional
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Names of the inputs that are required together with the field.
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copy_mode: File.CopyMode, optional
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The mode of copying the file, by default it is File.CopyMode.any
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copy_collation: File.CopyCollation, optional
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The collation of the file, by default it is File.CopyCollation.any
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copy_ext_decomp: File.ExtensionDecomposition, optional
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The extension decomposition of the file, by default it is
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File.ExtensionDecomposition.single
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readonly: bool, optional
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If True the input field can’t be provided by the user but it aggregates other
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input fields (for example the fields with argstr: -o {fldA} {fldB}), by default
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it is False
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type: type, optional
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The type of the field, by default it is Any
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name: str, optional
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The name of the field, used when specifying a list of fields instead of a mapping
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from name to field, by default it is None
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"""
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pass
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@attrs.define
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class out(base.Out):
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"""Output of a Python task
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Parameters
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----------
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name: str, optional
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The name of the field, used when specifying a list of fields instead of a mapping
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from name to field, by default it is None
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type: type, optional
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The type of the field, by default it is Any
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help: str, optional
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A short description of the input field.
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requires: list, optional
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Names of the inputs that are required together with the field.
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converter: callable, optional
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The converter for the field passed through to the attrs.field, by default it is None
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validator: callable | iterable[callable], optional
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The validator(s) for the field passed through to the attrs.field, by default it is None
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position : int
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The position of the output in the output list, allows for tuple unpacking of
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outputs
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"""
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pass

‎pydra/compose/matlab/task.py

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import typing as ty
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import attrs
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# from pydra.utils.general import get_fields, asdict
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from pydra.compose import base
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if ty.TYPE_CHECKING:
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from pydra.engine.job import Job
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@attrs.define(kw_only=True, auto_attribs=False, eq=False, repr=False)
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class MatlabOutputs(base.Outputs):
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@classmethod
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def _from_job(cls, job: "Job[MatlabTask]") -> ty.Self:
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"""Collect the outputs of a job from a combination of the provided inputs,
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the objects in the output directory, and the stdout and stderr of the process.
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Parameters
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----------
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job : Job[Task]
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The job whose outputs are being collected.
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outputs_dict : dict[str, ty.Any]
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The outputs of the job, as a dictionary
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Returns
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-------
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outputs : Outputs
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The outputs of the job in dataclass
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"""
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raise NotImplementedError
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MatlabOutputsType = ty.TypeVar("MatlabOutputsType", bound=MatlabOutputs)
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@attrs.define(kw_only=True, auto_attribs=False, eq=False, repr=False)
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class MatlabTask(base.Task[MatlabOutputsType]):
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_executor_name = "function"
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FUCNTION_HELP = (
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"a string containing the definition of the MATLAB function to run in the task"
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)
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def _run(self, job: "Job[MatlabTask]", rerun: bool = True) -> None:
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raise NotImplementedError
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# Alias ShellTask to Task so we can refer to it by shell.Task
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Task = MatlabTask
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Outputs = MatlabOutputs

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