semvec/pypi-packages huggingface dataset:
hpo-similarity 0.5.3
Testing similarity of HPO terms between probands sharing         variants in genes.
License: MIT
Requires Python: >3.5.0

[![Build Status](https://travis-ci.org/jeremymcrae/hpo_similarity.svg?branch=master)](https://travis-ci.org/jeremymcrae/hpo_similarity)

#### Similarity of phenotypes in patient groups
This estimates how likely it is for groups of individuals to have similar
phenotypes.  To estimate this probability, we need three things:
- a way to quantify phenotypic similarity of two individuals. We use the maximum
  information content of the most informative common ancestor for each pair of
  HPO terms from two probands.
- a way to quantify similarity across more than two probands. We sum phenotypic
  similarity scores from all pairs of probands.
- a null distribution of similarity scores for those probands, generated by
  randomly sampled groups of probands

The P value is calculated as the proportion of simulated scores greater than the
observed probands' score.

##### Install
Install the package with:
```sh
pip install hpo_similarity
```

##### Run
```sh
hpo_similarity --genes genes.json --phenotypes phenotypes.json
```
The data directory on github includes example files, one with proband IDs
per gene ([data/example_genes.json](data/example_genes.json)), and the other
with HPO terms per proband ([data/example_phenotypes.json](data/example_phenotypes.json)).

Additional options:
- `--output PATH` to send output gene and P-values to a file.
- `--ontology PATH` to use a HPO ontology file other than the default.
- `--iterations INTEGER` to change the number of iterations (default=100000)

You can also explore the HPO graph using the hpo_similarity package within
python, for example:
```python
from hpo_similarity import open_ontology

graph, alt_ids, obsolete_ids = open_ontology()

# find all descendant terms
graph.get_descendants('HP:0001249')

# get the text for the phenotypic abnormality
graph.nodes['HP:0001249']['name']
```

##### Acknowledgments
  This code incorporates the following code and datasets:
  - a [python ontology parser](https://github.com/ntamas/gfam/blob/master/gfam/go/obo.py)
    written by Tamás Nepusz.
  - the [hp.obo file](http://purl.obolibrary.org/obo/hp.obo) from the
    [Human Phenotype Ontology Consortium](http://human-phenotype-ontology.org/).
twitter-cleaner 0.3.0
A small python script that allows you to delete your old tweets.
License: MIT
Requires Python: >=3.6.0


# Twitter Cleaner

A small python script that allows you to delete your old tweets. 

## Installation

Use pip:
```bash
pip install twitter_cleaner
```

## How to use

1. [Apply for a Twitter Developer Account](https://developer.twitter.com/en/apply).
2. While you wait for Twitter to review and approve your Developer Account, [download your Twitter archive](https://help.twitter.com/en/managing-your-account/how-to-download-your-twitter-archive). Twitter limits the access of the tweets by the API, so the only way to retrieve your old tweets is using the Twitter archive. 
3. When you receive your archive, you should look for a JS file with contains all your tweets (tweet.js).
4. After the approval of your Twitter Developer Account, [create an app](https://developer.twitter.com/en/apps). 
**Don't forget**: you should set **Read and Write** in the *Access Permissions* settings.
5. Open the *details* page of the app that you created. Go for the *Keys and Tokens* tab. You will need the consumer and access token keys to use Twitter Cleaner.
6. Run: 

``` bash
python -m twitter_cleaner --consumer_key <consumer-key> \\
       --consumer_secret <consumer-secret>   \\
       --access_token_key <access-token-key> \\
       --access_token_secret <access-token-secret> \\
       --older_than <date in YYYY-MM-DD format> \\
       --file <path/to/tweet.js>
```


### Filters


There are two filters available: filter tweets older than a date AND filter tweets newer than a date. If you need more filtering options, like filter tweets with less (or more) than a specific amount of engagement, please ask.
tarproc 0.0.6
Big data data processing for tar archives.
License: MIT
Requires Python: >=3.6
Keywords: POSIX tar,map reduce,object store,deep learning

[![Test](https://github.com/tmbdev/tarproc/workflows/Test/badge.svg)](https://github.com/tmbdev/tarproc/actions?query=workflow%3ATest)
[![TestPip](https://github.com/tmbdev/tarproc/workflows/TestPip/badge.svg)](https://github.com/tmbdev/tarproc/actions?query=workflow%3ATestPip)
[![DeepSource](https://static.deepsource.io/deepsource-badge-light-mini.svg)](https://deepsource.io/gh/tmbdev/tarproc/?ref=repository-badge)

# The Tarproc Utilities

Tarfiles are commonly used for storing large amounts of data in an efficient,
sequential access, compressed file format, in particualr for deep learning
applications. For processing and data transformation,
people usually unpack them, operate over the files, and tar up the result again.

This library and set of utilities permits operating directly on tar files. This
is faster than operating on files on file systems, and it is usually easier too.

- tarcats -- concatenate tar files sequentially
- tarsplit -- split a tar file by number of records or size
- tarpcat -- concatenate tar files in parallel
- tarproc -- map command line programs over tar files
- tarshow -- show contents of tar files
- tarsort -- sort tar files based on some key

The following are less commonly used utilities that are specifically useful
for deep learning:

- tarfirst -- extract the first file matching some criteria
- targrep -- grep through files inside tar files (this will replace tarfirst)
- tar2db, tar2lmdb, tar2tsv -- convert tar files to database files
- tarmix -- mix tar files based on statistical sampling
- tsv2tar -- build tar files based on a .tsv file plan

The utilities allow operating on stdin/stdout when necessary, allowing
command line pipes to be constructed. For example:

```Bash
    $ gsutil cat gs://bucket/file.tar | tarsort | tarsplit -o output
```

# Python Interface


```sos
from tarproclib import reader, gopen
from itertools import islice

gopen.handlers["gs"] = "gsutil cat '{}'"

for sample in islice(reader.TarIterator("gs://lpr-imagenet/imagenet_train-0000.tgz"), 0, 10):
    print(sample.keys())
```

    dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
    dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
    dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
    dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
    dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
    dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
    dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
    dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
    dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])
    dict_keys(['__key__', 'cls', 'jpg', 'json', '__source__'])