Tidypath

Tidypath automates the saving and loading of data and figures with a single line of code. It ensures deterministic naming based on call arguments and applies optimal compression out of the box.

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Overview

Keep your files tidy!

Don't spend time creating directories, deciding filenames, saving, loading, etc. Decorators savefig & savedata will do it for you.

savedata computes output and stores it in the first function call. Future calls reads it from memory. Default LZMA compression
savefig saves output figure.

Although recommended, it is not needed to start a new project using tidypath. You can continue using your previous code and apply tidypath on new code.

savedata

Example function slow_computation in module package.subpackages.module

@savedata("x+z")
def slow_computation(x, y, *args, z=1, **kwargs):
    ...
    return result

Apply to function (result of any type).
Choose the variables to record in the filenames.
Optionally, choose file extension and other specifications. Supported: lzma (default), bz2, npz, csv, JSON.
Result will be saved at data/subpackages/module/slow_computation/x-'x'_z-'z'_.lzma ('x' = value of x passed to slow_computation during call)
If you want to recompute and overwrite, you can pass overwrite=True to slow_computation. The decorator adds the arguments: save, overwrite, keys and funcname_in_filename.

savefig

@savefig("kwargs")
def plot_results(*args, **kwargs):
    ...
    return figure

Same steps as savedata. Only difference is the output type.
Supports matplotlib and plotly and all figure extensions (png, eps, ...) including html (plotly).
Decorator adds the same arguments as savedata plus return_fig (bool).

Adaptable to code modifications

Caching data depends on the specific variables set to store, since they define the filenames. Suppose we want to add a new variable method indicating a new method for computing the results, but already computed results are still useful. We can

Modify the variables to record in the savedata decorator:
```
 @savedata("x+z")     =>    @savedata("x+z+method")
```
Assign method='original' to all existing pre-computed files:
```
 add_arg(slow_computation, method='original')
```
Now access is granted for the already computed data, and data corresponding to new methods will be stored in separate files.

Use the functions add_arg, modify_arg, delete_arg to ensure cached data is loaded after modifying function arguments.

Example

tidypath and PhD-utils slides: instructions and use cases.
Defining functions
Calling functions & modifying args

Docs

Github pages

Install

pip install tidypath

Metadata

Release files for tidypath 1.5.6

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for tidypath 1.5.6
File	Size	Uploaded
tidypath-1.5.6.tar.gz	33.7 kB	May 20, 2026	Details

Release files / tidypath-1.5.6.tar.gz

Download URL	tidypath-1.5.6.tar.gz
Size	33.7 kB
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SHA-256 checksum How to use checksums	`e503e5b5f5dbcbb0f5e39be7ab498cd6f1e9275aa1624fa26bdc0a21b6c07ef6`
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Upload date	May 20, 2026
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Uploaded via	`twine/6.2.0 CPython/3.12.13`