scitex-io 0.1.1

Universal scientific data I/O with plugin registry

scitex-io

Universal scientific data I/O with plugin registry

Full Documentation · pip install scitex-io

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Problem

Operating systems already solve this: double-click a .csv and it opens in a spreadsheet, double-click a .pdf and it opens in a reader — the OS dispatches to the right application based on the file extension. Yet in Python, there is no equivalent. Loading a CSV requires pandas.read_csv(), an HDF5 file requires h5py.File(), a NumPy array requires numpy.load(), and so on — each format demands its own library, its own API, and its own boilerplate. Adding a new format means touching save and load logic scattered throughout your codebase.

Solution

scitex-io provides a single save()/load() interface for 30+ scientific formats with automatic format detection from file extensions. A two-tier plugin registry lets you register custom formats that work seamlessly with the same API — user handlers override built-ins, so you can extend or replace any format without modifying the library.

Supported Formats (30+)

Category	Extensions
Spreadsheet	.csv, .tsv, .xlsx, .xls
Scientific	.npy, .npz, .mat, .hdf5, .h5, .zarr
Serialization	.pkl, .pickle, .pkl.gz, .joblib
ML/DL	.pth, .pt, .cbm
Config	.json, .yaml, .yml
Documents	.txt, .md, .pdf, .docx, .tex
Images	.png, .jpg, .jpeg, .gif, .tiff, .tif, .svg
Media	.mp4
Web	.html
Bibliography	.bib

Installation

Requires Python >= 3.9.

pip install scitex-io

For MCP server support:

pip install scitex-io[mcp]

SciTeX users: pip install scitex already includes scitex-io.

Quickstart

from scitex_io import save, load

# Universal save/load — format auto-detected from extension
import pandas as pd
df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
save(df, "data.csv")
loaded = load("data.csv")

# 30+ formats work the same way
import numpy as np
save(np.array([1, 2, 3]), "data.npy")
save({"key": "value"}, "config.yaml")
save({"nested": [1, 2]}, "data.json")

Custom Format Registration

from scitex_io import register_saver, register_loader, save, load

@register_saver(".custom")
def save_custom(obj, path, **kwargs):
    with open(path, "w") as f:
        f.write(str(obj))

@register_loader(".custom")
def load_custom(path, **kwargs):
    with open(path) as f:
        return f.read()

save("hello", "data.custom")
assert load("data.custom") == "hello"

Three Interfaces

Python API

from scitex_io import save, load, list_formats, register_saver, register_loader

save(obj, "path.ext")        # Save any object
data = load("path.ext")      # Load any file
fmts = list_formats()        # Show all registered formats

Full API reference

CLI Commands

scitex-io --help-recursive          # Show all commands
scitex-io info                      # Show registered formats
scitex-io list-python-apis -vv      # List Python APIs with signatures
scitex-io version                   # Show version
scitex-io mcp start                 # Start MCP server
scitex-io mcp doctor                # Check MCP health
scitex-io mcp list-tools -vv        # List MCP tools with parameters

Full CLI reference

MCP Server — for AI Agents

AI agents can save, load, and discover formats autonomously.

Tool	Description
io_list_formats	List all registered save/load formats
io_load	Load data from any supported format
io_save	Save data to any supported format
io_register_info	Show how to register custom formats

scitex-io mcp start

Full MCP specification

Part of SciTeX

scitex-io is part of SciTeX. When used inside the SciTeX framework, I/O is seamless:

import scitex

@scitex.session
def main(CONFIG=scitex.INJECTED):
    data = scitex.io.load("input.csv")     # auto-tracked by clew
    result = process(data)
    scitex.io.save(result, "output.csv")   # auto-tracked by clew
    return 0

scitex.io delegates to scitex_io — they share the same API and registry.

The SciTeX ecosystem follows the Four Freedoms for researchers:

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 research infrastructure deserves the same freedoms as the software it runs on.

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