SciStack

Spend more time on your science, and less time on the code around it.

SciStack is made for scientists by scientists. It takes the analysis functions you already write in Python or MATLAB and handles everything around them: looping over every subject, session, or trial; saving and loading results; and keeping a record of how each result was produced.

pip install scistack   # one install for Python, MATLAB, the CLI, and the GUI backend

SciStack does not do any analysis itself. It helps you run code-based analyses more effectively, whether you use your own code or build on someone else's.

📖 Documentation: Code · With GUI

Who is SciStack for?

SciStack is for researchers who use code to get to their results but don't want to become software engineers. It suits you if:

• Your data is nested:
  • participants have sessions, sessions have trials
  • players have seasons, seasons have games.
• You run a series of steps on every piece of that data: load, filter, compute a metric, summarize.
• You work in Python, MATLAB, or both.
• You've ever found a file called results_v2_final_FINAL.csv and weren't sure what made it.

No database or software background is needed. If you can write a function, you can use SciStack.

The problem

Most analysis code starts out like this:

for subject in ["S01", "S02", "S03"]:
    for session in ["pre", "post"]:
        data = scipy.io.loadmat(f"data/{subject}/{session}/raw.mat")
        filtered = lowpass_filter(data["force"], cutoff_hz=10)
        peak = np.max(filtered)
        np.save(f"results/{subject}_{session}_peak_v2_final.npy", peak)

That works for a while. Then the script grows to hundreds of lines. The analysis gets tangled up with loops, file paths, and naming conventions. Change the cutoff and the old results are overwritten, or you end up with another _v3 file. Six months later, nobody can say which settings produced the figure in the paper. Sharing the code means sharing hard-coded paths that only work on your computer.

What SciStack gives you

The same analysis with SciStack:

def peak_force(force, cutoff_hz):
    return np.max(lowpass_filter(force, cutoff_hz))

scidb.for_each(peak_force,
               inputs={"force": RawForce, "cutoff_hz": 10},
               outputs=[PeakForce],
               subject=[], session=[])   # [] means "every subject and session"

Your function only contains the science. SciStack:

• Runs it once for every subject and session, handing it one piece of data at a time.
• Saves each result in a database file, at its subject and session.
• Records how each result was made: which function, which inputs, and which settings.
• Keeps both versions when you change cutoff_hz to 15, instead of overwriting the first.
• Lets you load any result later by asking for it: PeakForce.load(subject="S01", session="pre").

Three ways to use it

SciStack is a stack of tools. Start with the lightest one that does the job, and move up when you want more.

All three work from Python and MATLAB. Data saved from one language can be loaded in the other.

Quick start: the GUI

1. Install the SciStack Pipeline GUI extension in VS Code.
2. Pick a Python environment (Python: Select Interpreter in the Command Palette) and run pip install scistack in it.
3. Run SciStack: Open Pipeline from the Command Palette.
4. Create a new database: give it a name and list your schema keys, the levels your data is organized by, such as subject, session, trial.

That's it. You'll see a blank canvas, ready for your first function. The With GUI docs walk through the rest.

Quick start: code

Step 1: Replace your loops with scifor

No database and no setup beyond naming your schema keys:

import scifor

scifor.set_schema(["subject", "session"])

results = scifor.for_each(
    peak_force,
    inputs={"force": force_table, "cutoff_hz": 10},
    subject=[1, 2, 3],
    session=["pre", "post"],
)

For each of the 6 combinations, scifor takes the matching rows of force_table, passes the force column to peak_force, and collects the answers into one table with subject, session, and result columns.

scifor.set_schema(["subject", "session"]);

results = scifor.for_each(@peak_force, ...
    struct("force", force_table, "cutoff_hz", 10), ...
    subject=[1, 2, 3], session=["pre", "post"]);

Step 2: Keep your results with scidb

One hundred shoppers each rated three brands of popcorn from 1 to 10. Which brand is best?

import numpy as np
import pandas as pd
import scidb
from scidb import BaseVariable

# A variable is a named kind of data. Declaring one takes one line.
class Rating(BaseVariable):
    """One participant's rating of one brand."""

class BrandSummary(BaseVariable):
    """The mean and SD of one brand's ratings."""

def load_ratings(csv_path):
    return pd.read_csv(csv_path)

def summarize(ratings):
    r = np.ravel(ratings)
    return pd.DataFrame({"mean": [r.mean()], "sd": [r.std(ddof=1)]})

# Create (or open) the database. Each rating lives at a participant and a brand.
scidb.configure_database("popcorn.duckdb", ["participant", "brand"])

# Import the CSV: each row becomes its own Rating record
scidb.for_each(load_ratings, inputs={"csv_path": "popcorn_ratings.csv"}, outputs=[Rating])

# One summary per brand
scidb.for_each(summarize, inputs={"ratings": Rating["rating"]},
               outputs=[BrandSummary], brand=[])

Close Python, come back next week, and everything is still there:

db = scidb.configure_database("popcorn.duckdb", ["participant", "brand"])

BrandSummary.load(brand="KettleKing").data         # the saved result
db.get_provenance(BrandSummary, brand="KettleKing")  # how it was made

Running the script again doesn't create duplicates, so you can re-run after any change without cleaning up first.

The Best Popcorn and Best Player examples go through complete analyses, in both Python and MATLAB.

Big wins, small effort

These features take a line or two of code (or a click) and save the most time:

Your data isn't locked away

Everything (your data and its history) lives in one DuckDB file next to your code. Each variable gets a readable table you can open in DBeaver, the DuckDB command line, or any SQL tool:

SELECT participant, brand, rating FROM Rating;

You don't need Python or MATLAB to look at your results, and you're not tied to SciStack to get them back out. Your functions are plain functions: they take normal arrays and tables and return normal values, so they work with or without SciStack.

The GUI at a glance

The SciStack GUI runs inside VS Code, next to your code:

• See the whole pipeline. Functions and variables are drawn as a diagram, connected by what feeds into what.
• Know what's up to date. Each step is coloured by whether its results are current, so you can see what needs re-running after you edit a function.
• Run from the canvas. Run one step or the whole pipeline, and watch the progress.
• Plot without writing plotting code. Plot Studio turns any variable into a publication-ready figure: group by condition, split into panels, choose colours, and save.
• Compare versions. When a change in settings or code produces several versions of a result, the Variants view lists them side by side.
• Find missing data. The Inventory shows which combinations of your schema keys have no data.
• Share a project. Export the project as a bundle that a colleague can import and re-run.

Learn more

• Installation, including one-time MATLAB setup
• What's a schema?: the one design decision to make before you start
• Create a new project (or with the GUI)
• Example projects: complete analyses, start to finish
• API reference

Contributing

SciStack is in beta and under active development. To work on it locally:

git clone https://github.com/mtillman14/scistack
cd scistack
./dev-install.sh

See the contributing guide for details. Bug reports and feature requests are welcome on the issue tracker.