semvec/pypi-packages huggingface dataset:
funes 0.0.1
Decorator to memorize function computations permanently to disk.
License: MIT
Keywords: persistent,cache,hash,stable,archive,memoize

# Funes

Funes lets you decorate functions in such a way that their computations are memorized permanently to disk.

It is named after the Borges short story [Funes the Memories](https://en.wikipedia.org/wiki/Funes_the_Memorious).

## Background

This package allows the results of expensive functions be cached to disk, by decorating these functions with the `@memorious` decorator. When that function is called again on an identical input, the cached result will be loaded from disk and returned. 

This is achieved by pickling the result and input and writing it to disk, using a unique path that identifies the particular version of the function that is being cached (based on a hash that depends on its definition, thanks to the 'dill' package), as well as a key that depends on the hashed arguments of the function.

Memorious functions should not contain (possibly mutually) recursive definitions, or the stack will overflow. Their hash values WILL change if functions that depend on are changed, but these dependencies are only followed if they remain within the current module. 

The pickled results will be cached under cache/funcname/hexhash/arg_hash.dill. These files can be deleted manually to clear the cache, or transported between computers.

Each pickled computation contains a dictionary with the following keys:
`input`: ordered dict of function arguments + their provided (or defaulting) values  
`output`: whatever the function produced
`time`: time in seconds the function took to run

A special `global_seed` keyword argument can be provided to any memorious function to set the global random seed prior to running the function. This allows controlled stochasticity to be used, while maintaining the benefits of caching. 

## Examples

The following script demonstrates the basic idea behind funes:

```
from funes import memorious, load_cached_results
from time import sleep

@memorious
def double(x):
    print('doubling', x)
    sleep(0.5)
    return x * 2

print("uncached (will be slow)")
for i in range(5):
    double(i)

print("cached (will be fast)")
for i in range(5):
    double(i)

print("uncached (will be slow, unique global seed)")
for seed in range(5):
    double(0, global_seed=seed)

print("cached (will be fast, reuse global seed)")
for seed in range(5):
    double(0, global_seed=seed)

print("all cached values")
print(list(load_cached_results(double)))
```
pst 1.5.3
A reproduction of pstree
License: MIT
Keywords: shell pstree

pst: a reproduction of pstree
=============================

[![Travis CI](https://api.travis-ci.org/mixedconnections/pst.svg?branch=master)](https://travis-ci.org/mixedconnections/pst) 
[![Python versions](https://img.shields.io/pypi/v/pst.svg)](https://pypi.org/project/pst/)
[![Codecov](https://codecov.io/github/mixedconnections/pst/coverage.svg?branch=master)](https://codecov.io/gh/mixedconnections/pst)

pst is a command-line utility that creates visual trees of your running processes on Unix-like systems. 

![pstexample](https://user-images.githubusercontent.com/833824/68803703-40952580-062e-11ea-9ea6-2a506316cafb.png)

pst is a reproduction of [pstree](https://en.wikipedia.org/wiki/Pstree), written in Python.

# Installation

pst currently supports Python 2.x-3.x.

#### PyPI

    $ sudo pip install pst

#### Manual

First clone the pst repository and go into the directory.

    $ git clone git://github.com/mixedconnections/pst.git
    $ cd pst

Then run the command below.

    $ sudo python setup.py install

If you don't have root permission (or don't want to install pst with sudo), try:

    $ python setup.py install --prefix=~/.local
    $ export PATH=~/.local/bin:$PATH

# Usage

 __pst__ shows running processes as a tree.  The tree is rooted at
 either _pid_ or __init__ if _pid_ is omitted.  If a user name is specified,
 all process trees rooted at processes owned by that user are shown
 
#### Command Line Options

##### -h, --help

Display a help message

##### -v, --version

Display the version of pst

##### -o, --output `string`
    
Directs the output to a file name of your choice

##### -w, --write

When specified, pst writes to stdout. By default, pst uses less to page the output. 

##### -u, --user `string`
    
Show only trees rooted at processes of this user

##### -p, --pid `integer`
    
Start at this pid; default is 1 (init)

# Demo
Demos speak more than a thousand words! Here's me running pst on ubuntu. As you can see, you can select a pid and see its child processes:

![pst-demo](https://user-images.githubusercontent.com/833824/68803841-7df9b300-062e-11ea-97bf-0aef1f264abd.gif)

#### More Examples

    shell> 
    shell> pst
    shell> pst --help
    shell> pst -o trees.txt 
    shell> pst --user postgres
EpigeneticPacemaker 0.0.3
Epigenetic State Modeling Utility
License: MIT
Requires Python: >=3.6

# Epigenetic Pacemaker
## A fast conditional expectation maximization algorithm for modeling epigenetic state

DNA methylation is widely used to model physiological phenotypes, such as 
aging[1](https://doi.org/10.1186/gb-2013-14-10-r115) and type II diabetes[2](https://doi.org/10.1093/hmg/ddy093). 
The epigenetic pacemaker, **EPM**, is an implementation of a fast conditional expectation maximization algorithm used to 
model epigenetic states associated with a phenotype of interest [3](https://doi.org/10.2217/epi-2017-0130) The EPM was first introduced by Snir et al. 
[4](https://doi.org/10.1371/journal.pcbi.1005183) as an extension of the Universal Pacemaker (UPM). The EPM can model non-linear 
epigenetic trait associations directly without transformation of the phenotype of interest[5](https://doi.org/10.1080/15592294.2019.1623634).

## Installation

```shell
pip3 install EpigeneticPacemaker
``` 

## Documentation

[epigeneticpacemaker.readthedocs.io](https://epigeneticpacemaker.readthedocs.io/en/latest/)

## Citations 

1. [Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).](https://doi.org/10.1186/gb-2013-14-10-r115)
2. [Orozco, L. D. et al. Epigenome-wide association in adipose tissue from the METSIM cohort. Hum. Mol. Genet. 0, 223495 (2018).](https://doi.org/10.1093/hmg/ddy093)
3. [Snir, S. & Pellegrini, M. An epigenetic pacemaker is detected via a fast conditional expectation maximization algorithm. 10, 695–706 (2018).](https://doi.org/10.1371/journal.pcbi.1005183)
4. [Snir, S., vonHoldt, B. M. & Pellegrini, M. A Statistical Framework to Identify Deviation from Time Linearity in Epigenetic Aging. PLoS Comput. Biol. 12, 1–15 (2016).](https://doi.org/10.2217/epi-2017-0130)
5. [Snir, S., Farrell, C. & Pellegrini, M. Human epigenetic ageing is logarithmic with time across the entire lifespan. Epigenetics (2019). doi:10.1080/15592294.2019.1623634](https://doi.org/10.1080/15592294.2019.1623634)