semvec/pypi-packages huggingface dataset:
gabid 0.6
Gaussian and Binomial distributions

# gabid package

This package implements the Gaussian and Binomial Distributions providing the following capabilities:
* Compute the mean and standard deviation given a file with all the samples
* Compute the pdf
* Allows to add two of the kind

For Gaussian distribution only:
* Plot histogram
* Plot histogram pdf

For Binomial distribution only:
* Plot bar
* Plot bar pdf

## Installation

To start using this package just type: `pip install gabid`

## Files

This package provides three classes: `Distribution`, `Gaussian` and `Binomial`.
The `Distribution` class is only used to read all the values from a txt file and convert them into a list.
Both `Gaussian` and `Binomial` inherit from this class.

## Quick start

See Gaussian example:

```
gaussian = Gaussian(25, 2)
gaussian.read_data_file('numbers.txt') # numbers.txt is a txt with all the samples
gaussian.calculate_mean() # to recompute mean according to the data in the txt
gaussian.calculate_stdev() # to recompute standard deviation according to the data in the txt
gaussian.pdf(25) # to compute the pdf of a value
```

## References

### Used resources

* [Gaussian Distribution](https://en.wikipedia.org/wiki/Normal_distribution)
* [Binomial Distribution](https://en.wikipedia.org/wiki/Binomial_distribution)

### Contributors

* [Sara Garci](s@saragarci.com)

## License

© Copyright 2019 by Sara Garci. All rights reserved.
VideoInfo 1.0
parse online video file metadata
Requires Python: >=3.7

简介
===

这个包是用来获取在线视频文件元数据的。

USAGE
==

```python
from VideoInfo import Mp4Url

mp4=Mp4Url('http://clips.vorwaerts-gmbh.de/big_buck_bunny.mp4')

mp4.getAll()

print(mp4)
```


TODO
===

后续没事的时候，把一些avi，rmvb，mkv什么的都弄好。

如果是本地文件
===

推荐安装一个ffmpeg，通过调用命令行获取数据
embeddingsprep 0.1.4
A word2vec preprocessing and training package
Requires Python: >=3.6

# Embeddings


This package is designed to provide easy-to-use python class and cli
interfaces to:

- clean corpuses in an efficient way in terms of computation time

- generate word2vec embeddings (based on gensim) and directly write them to a format that is compatible with [Tensorflow Projector](http://projector.tensorflow.org/)

Thus, with two classes, or two commands, anyone should be able clean a corpus and generate embeddings that can be uploaded and visualized with Tensorflow Projector.

## Getting started

### Requirements

This packages requires ```gensim```, ```nltk```, and ```docopt``` to run. If
pip doesn't install this dependencies automatically, you can install it by
running :
```bash
pip install nltk docopt gensim
```

### Installation

To install this package, simply run :
```bash
pip install embeddingsprep
```

Further versions might include conda builds, but it's currently not the case.


## Main features

### Preprocessing

For Word2Vec, we want a soft yet important preprocessing. We want to denoise the text while keeping as much variety and information as possible. A detailed version of what is done during the preprocessing is available [here](./preprocessing/index.html)


#### Usage example :

Creating and saving a loadable configuration:
```python
from embeddingsprep.preprocessing.preprocessor import PreprocessorConfig, Preprocessor
config = PreprocessorConfig('/tmp/logdir')
config.set_config(writing_dir='/tmp/outputs')
config.save_config()
```

```python
prep = Preprocessor('/tmp/logdir')  # Loads the config object in /tmp/logdir if it exists
prep.fit('~/mydata/')  # Fits the unigram & bigrams occurences
prep.filter()  # Filters with all the config parameters
prep.transform('~/mydata')  # Transforms the texts with the filtered vocab. 
```


### Word2Vec

For the Word2Vec, we just wrote a simple wrapper that takes the
preprocessed files as an input, trains a Word2Vec model with gensim and writes the vocab, embeddings .tsv files that can be visualized with tensorflow projector (http://projector.tensorflow.org/)

#### Usage example:


```python
from embeddingsprep.models.word2vec import Word2Vec
model = Word2Vec(emb_size=300, window=5, epochs=3)
model.train('./my-preprocessed-data/')
model.save('./my-output-dir')
```

## Contributing

Any github issue, contribution or suggestion is welcomed! You can open issues on the [github repository](https://github.com/sally14/embeddings).
RuNAs 0.0.1
RNAseq pipeline
Requires Python: >=3.6


# R<sub>u</sub>NA-s<sub>eq</sub> (&#5809;<sub>&#5794;</sub>&#5822;&#5800;&#5835;) */roonas/* Pipeline

![Rule Graph](images/rule-graph.png?raw=true "Rule Graph")
## Requirements
-	Python 3.6+ (using [Ana](https://anaconda.org)([mini](https://docs.conda.io/en/latest/miniconda.html))conda)

## Setup
```bash
git clone https://github.com/villegar/runas --branch v2
cd runas
conda env create -f environment.yml
python download.genome.py human-genome.json
```

## Execution
### Single node
```bash
snakemake -j CPUS \ # maximum number of CPUs available to Snakemake
	  --configfile config.json # configuration file
```

### Multi-node
```bash
snakemake -j JOBS  \ # maximum number of simultaneous jobs to spawn
	  --configfile config.json # configuration file
          --latency-wait 1000 \ # files latency in seconds
          --cluster-config cluster.json \ # cluster configuration file
          --cluster "sbatch --job-name={cluster.name} 
                            --nodes={cluster.nodes} 
                            --ntasks-per-node={cluster.ntasks} 
                            --output={cluster.log} 
                            --partition={cluster.partition} 
                            --time={cluster.time}"
```
#### Alternatively
```bash
bash run_cluster &> log &
```

#### Cluster configuration (cluster.json)
```bash
{
    "__default__" :
    {
        "time" : "1-00:00:00",
        "nodes" : 1,
        "partition" : "compute",
	"ntasks": "{threads}",
	"name": "RuNAs-{rule}",
	"log": "RuNAS-{rule}-%J.out"
    }
}
```

#### Pipeline configuration (config.json)
```bash
{
    "genome4phiX":
    {
        "PhiX": 
            "ftp://igenome:G3nom3s4u@ussd-ftp.illumina.com/PhiX/Illumina/RTA/PhiX_Illumina_RTA.tar.gz"
    },

    "genome4star":
    {
        "GRCm38.primary_assembly.genome.fa.gz": 
            "ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M22/GRCm38.primary_assembly.genome.fa.gz",
        "gencode.vM22.annotation.gtf.gz": 
            "ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M22/gencode.vM22.annotation.gtf.gz"
    },

    "krakenDB":
    {
        "minikraken_20171019_8GB.tgz": 
            "https://ccb.jhu.edu/software/kraken/dl/minikraken_20171019_8GB.tgz"
    },

    "reads":
    {
        "extension": "fastq.gz",
        "path": "/gpfs/scratch/Classes/stat736/p53reads",
        "prefix": "SRR"
    },

    "rRNAref":
    {
        "txid9606.fasta": 
            "https://raw.githubusercontent.com/villegar/RuNAs/v2/txid9606.fasta"
    }
}
```