scitex-types 0.1.5

Scientific type definitions (ArrayLike, ColorLike) and validation

scitex-types

Scientific type aliases (ArrayLike, ColorLike) + runtime validation predicates.

Full Documentation · uv pip install scitex-types[all]

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Problem and Solution

#	Problem	Solution
1	numpy.typing.ArrayLike covers only NumPy — functions that also accept Torch/DataFrame/Series need a hand-rolled Union	ArrayLike, ColorLike — stable aliases spanning list/tuple/np.ndarray/pd.DataFrame/pd.Series/xr.DataArray/torch.Tensor + matplotlib color inputs
2	Runtime "is this a list of floats?" is a 3-line comprehension	is_array_like(), is_list_of_type(lst, float) — clear predicates, no isinstance chain

Architecture

scitex_types/
├── _aliases.py     # ArrayLike, ColorLike unions
└── _predicates.py  # is_array_like, is_list_of_type

flowchart LR
    A[list / tuple] --> AL[ArrayLike]
    B[np.ndarray] --> AL
    C[pd.DataFrame / Series] --> AL
    D[xr.DataArray] --> AL
    E[torch.Tensor] --> AL
    AL --> P[is_array_like]
    F[mpl color string / RGB / RGBA] --> CL[ColorLike]
    style AL fill:#4a90d9,stroke:#2c3e50,color:#fff
    style CL fill:#8e44ad,stroke:#2c3e50,color:#fff

_{Figure 1. Type surface. Two aliases unify common scientific containers and matplotlib color inputs; predicates resolve membership at runtime.}

Installation

pip install scitex-types
# Optional: enable array-library matches (numpy + pandas + torch + xarray):
pip install scitex-types[all]

Quick Start

from scitex_types import ArrayLike, is_array_like, is_list_of_type

def process(data: ArrayLike) -> None: ...

is_array_like([1, 2, 3])           # True
is_list_of_type([1, 2, 3], int)    # True

1 Interfaces

Python API

from scitex_types import ArrayLike, ColorLike, is_array_like, is_list_of_type

# Type annotations
def process(data: ArrayLike) -> None: ...
def set_color(c: ColorLike) -> None: ...

# Runtime checks
is_array_like([1, 2, 3])           # True
is_array_like("not array")         # False
is_list_of_type([1, 2, 3], int)    # True
is_list_of_type([1, "x"], int)     # False

Demo

from scitex_types import ArrayLike, is_array_like, is_list_of_type

def normalize(x: ArrayLike) -> ArrayLike:
    assert is_array_like(x)
    return x

normalize([1, 2, 3])             # OK
normalize("not array")           # AssertionError

is_list_of_type([1, 2, 3], int)  # True — uniform int list
is_list_of_type([1, "x"], int)   # False — mixed

flowchart LR
    Input[Function input] --> Pred{is_array_like?}
    Pred -- yes --> Proceed[proceed]
    Pred -- no --> Raise[raise / branch]
    style Proceed fill:#27ae60,stroke:#2c3e50,color:#fff
    style Raise fill:#e74c3c,stroke:#2c3e50,color:#fff

_{Figure 2. Demo. Use ArrayLike in annotations, is_array_like as a one-line guard.}

Part of SciTeX

scitex-types is part of SciTeX. Install via the umbrella with pip install scitex[types] to use as scitex.types (Python) or scitex types ... (CLI).

Four Freedoms for Research

0. The freedom to run your research anywhere — your machine, your terms.
1. The freedom to study how every step works — from raw data to final manuscript.
2. The freedom to redistribute your workflows, not just your papers.
3. The freedom to modify any module and share improvements with the community.

AGPL-3.0 — because we believe research infrastructure deserves the same freedoms as the software it runs on.

License

AGPL-3.0-only (see LICENSE).

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