Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/samuell/scilight

Simple task execution in Python scripts
https://github.com/samuell/scilight

Last synced: about 2 months ago
JSON representation

Simple task execution in Python scripts

Awesome Lists containing this project

README 

        
# SciLight - Simple task execution in Python scripts



[![CircleCI](https://circleci.com/gh/samuell/scilight.svg?style=shield)](https://app.circleci.com/pipelines/github/samuell/scilight)

[![PyPI](https://img.shields.io/pypi/v/scilight.svg?style=flat)](https://pypi.org/project/scilight)



A super-simple library for performing stepwise batch tasks (implemented as shell

commands, or python functions) that saves things to files, such that outputs

from already finished tasks are not needlessly re-computed. See the

[below](#example) for an example.



SciLight does not (currently) have a scheduler or central worker pool or

anything like that. Instead you simply execute your tasks manually in a

procedural way. This way task executions can easily be mixed with other

procedural python code.



SciLight can work as an alternative to full-blown workflow frameworks like Luigi

or Airflow for cases when you just have a single python script, where you want

to do a few batch steps before starting your interactive analysis, such as

downloading datasets, unpacking them, preprocessing et cetera.



SciLight is small (not much more than 100 lines of code), and has no external

dependencies, meaning that you can even copy the implementation into your own

code repos if you want to ensure maximum future reproducibility.



## What does 'SciLight' mean?



SciLight is named as such, as being a lighter version of

[SciPipe](https://scipipe.org), also written by the author, in Go.  If you need

true multicore-performance and a compiled language, you might want to have a

look at SciPipe instead.



## Prerequisites



- SciLight is so far only tested on unix-like environments (it is runnable on Windows

  via Windows Sybsystem for Linux (WSL)).



## Installation



Install from the Python Package Index using pip:



```

pip install scilight

```



## Usage



SciLight works by specifying either a shell command, or a python function to

be executed, as the first argument to `scilight.shell()` or `scilight.func()` respectively.



In shell commands, you need to replace input and output file paths with

placeholders on the form of `[i:inputname]` and `[o:outputname:outputpath]`

respectively. Additionally, you can provide parameters (as string values) using

the `[p:paramname]` syntax. You will also need to provide dicts which specify

the paths to the inputs, outputs and parameters as appropriate, by providing

them to the optional `inputs`, `outputs` and `params` parameters of

`scilight.shell()` and `scilight.func()`. See the example below for a concrete

example.



Inputs should always be provided via the `inputs`-parameter, while output paths

are easiest to provide inline in the command, in the respective placeholder.

Note that you can re-use input placeholder values to produce the output path.

So, for example, if you want to name your output the same as the input, but

with an extra `.txt` extension, you can specify it like this in the command:

`somecommand > [o:myoutput:[i:myinput].txt]`.



### Removing file extensions



If you have an existing extension in the input that you want to remove, you can

do it by adding `|%.actual-extension-here` in the input placeholder. So, if you

have an input `myinput` with the path `myfile.txt.gz`, you can reuse just the

`myfile.txt` part by writing `[i:myinput|%.gz]`, to remove the `.gz` part.

Putting that inside an output placeholder, you could for example do: `zcat

[i:archivefile] > [o:unpacked:[i:archivefile|%.gz]]`, in order to name the

unpacked file the same as the archive, but without the `.gz` extension.



### Removing parent directories from paths



Often it is the case that the input path contains a long folder path that you

don't want to re-use when re-using the input filename. To clean the path from

the parent directory structure, you can add the `|basename` modifier inside any

path placeholder.  So, if you have an input `myinput` with the path

`some/directory/structure/file.txt.gz`, you can reuse just the `myfile.txt` part

by writing `[i:myinput|basename]`, to remove the `some/directory/structure`

part. Modifiers can be compbined, so for example, given that you have an archive

file in another directory named `some/directory/structure`, you could do

the following to extract the archive, removing the `.gz` file extension and

putting the extracted file in a new directory named `other-directory`:

`zcat [i:archivefile] > other-directory/[o:unpacked:[i:archivefile|basename|%.gz]]`



### Search and replace in paths



Sometimes you want to clean up paths in more creative ways. This could be for

example replacing spaces with `_`, to avoid problems on Unix-like systems.

This can be done inside path formatters by using the format

`[i:myinput|s/SEARCH/REPLACE/]`, so, replacing spaces with underscores would be

`[i:myinput|s/ /_/]`.



See the example below for how to use some of this in practice!



## Example



Below is a small example that downloads a gzipped text file (in the so called

FASTA format), un-gzips it, and then calculates the number of A:s, T:s, G:s and

C:s and calculates the fraction of G and C:s in relation to all A, T, G, C:s

(the so-called GC-fraction measure for DNA).



The two first tasks are done by executing shell commands, and the second one

using a python function.



```python

import scilight as sl



# ------------------------------------------------------------------------

# Download a gzipped fasta file and save it as chrmt.fa.gz

# ------------------------------------------------------------------------

url = 'ftp://ftp.ensembl.org/pub/release-100/fasta/'+ \

      'homo_sapiens/dna/Homo_sapiens.GRCh38.dna.chromosome.MT.fa.gz'

download_task = sl.shell(f'wget -O [o:gz:chrmt.fa.gz] {url}')



# ------------------------------------------------------------------------

# Un-GZip the file, into a file named chrmt.fa

# ------------------------------------------------------------------------

ungzip_task = sl.shell('zcat [i:gz] > [o:fa:[i:gz|%.gz]]',

        inputs={'gz': download_task.outputs['gz']})



# ------------------------------------------------------------------------

# Count the fraction of G+C, vs G+C+A+T

# ------------------------------------------------------------------------

# A function for Count GC fraction in DNA

def count_gcfrac_func(task):

    gc_count = 0

    at_count = 0



    with open(task.inputs['fa']) as infile:

        for line in infile:

            if line[0] == '>':

                continue

            for char in line:

                if char in ['A', 'T']:

                    at_count += 1

                elif char in ['G', 'C']:

                    gc_count += 1



    gc_fraction = gc_count/(gc_count+at_count)



    with open(task.outputs['gcfrac'], 'w') as outfile:

        outfile.write(f'{gc_fraction}\n')



# Execute the function

count_task = sl.func(count_gcfrac_func,

        inputs={'fa': ungzip_task.outputs['fa']},

        outputs={'gcfrac': 'gcfrac.txt'})

```



Add this code to a file named `gcfrac.py` and run it with:



```bash

python gcfrac.py

```