semvec/pypi-packages huggingface dataset:
kdpeak 0.2.0
A tool to identify genomic peaks based on kernel density estimation.
Requires Python: >=3.7

# kdpeak

[![DOI](https://zenodo.org/badge/672132987.svg)](https://zenodo.org/badge/latestdoi/672132987)

kdpeak is a Python package designed to identifying genomic peaks from genomic reads in bed format using kernel density estimation (KDE).

## Installation

Install via PyPI with:

```bash
pip install kdpeak
```

Alternatively, install directly from GitHub:

```bash
pip install git+https://github.com/settylab/kdpeak.git
```

## Using kdpeak

kdpeak allows for processing of a bed file. It applies KDE, pinpoints peaks based on the fragemnt end density, and records the results in an output bed file.
The package enables customization of parameters like KDE bandwidth, sequence blacklist, minimum peak size, and others. Designed to deliver a specific fraction-in-peaks (FRiP), it defaults to 0.3.

Elementary usage:

```bash
kdpeak reads.bed --frip 0.3 --out peaks.bed
```

## Parameters

```bash
usage: kdpeak [-h] [--out OUTPUT_FILE] [-l LEVEL] [--logfile LOGFILE] [--blacklisted-seqs chrN [chrN ...]] [--kde-bw FLOAT] [--min-peak-size INT] [--fraction-in-peaks FLOAT] [--span INT] READS.BED
```

**Positional Argument:**

- `reads.bed` - Path to the bed file containing the genomic reads.

**Options:**

- `-h, --help` - Show this help message and exit.
- `--out output_file.bed` - Path to the output file where the results will be saved.
  Peaks are saved in bed format with the columns:
  start, end, peak name, AUC (area under the cut density curve
  where cut-density is in cuts per 100 base pairs). Defaults to peaks.bed.
- `--summits-out summits_file.bed` - Path to the output file where the peak summits will be saved.
  The file will have columns for start, end (start+1),
  peak name, and summit height (in cuts per 100 base pairs).
  If nothing is specified the summits will not be saved.
- `-l LEVEL, --log LEVEL` - Set the logging level. Options include: DEBUG, INFO, WARNING, ERROR, CRITICAL. Default is INFO.
- `--logfile LOGFILE` - Path to the file to write a detailed log.
- `--blacklisted-seqs chrN [chrN ...]` - List of sequences (e.g., chromosomes) to exclude from peak calling. Input as space-separated values.
- `--kde-bw FLOAT` - Bandwidth (standard deviation, sigma in base pairs) for the KDE. Increase for larger features to reduce noise. Default is 200.
- `--min-peak-size INT` - Minimal size (in base pairs) for a peak to be considered valid. Default is 100.
- `--fraction-in-peaks FLOAT, --frip FLOAT` - Expected fraction of total reads to be located in peaks. Default is 0.3.
- `--span INT` - Resolution of the analysis in base pairs, determining the granularity of the KDE and peak calling. Default is 10.

## Utilizing All Available Options:

```bash
kdpeak reads.bed --out peaks.bed --log DEBUG --logfile debug.log --blacklisted-seqs chr1 chr2 --kde-bw 500 --min-peak-size 50 --frip 0.5 --span 5
```

## Disclaimer

kdpeak, being in its Alpha stage, encourages usage with care. We warmly welcome users to report any issues experienced during utilization. Together, we can enhance kdpeak for a better genomic analysis experience.
ndsl-attn-fs-archs 0.0.7
Architecture described in paper: Transformers for high-dimensional tabular data

# Installation guide

This repository contains the developed architectures in the research of attention mechanisms as feature selectors.

Currently, not all architectures are available. This packages will be updated in the future (I hope that).

The code here is developed as a python package. Anyway, a magic makefile is included (for your comfort :wink:), and some specifications are included below.


## Installing from PyPi

By running the next line you will install it from PyPi repo:

```
pip install ndsl-attn-fs-archs 
```

## Installing from source

### Building the package

To build the package you just need to run:

```
make
```

or: 

```
make build
```

### Installing the package

To install the package just execute:

```
make install
```

## Running tests

To run the included tests just execute

```
make test
```

If you want to add more tests don't forget to add them in the makefile.

Easy, right? :sunglasses:
tabpymigrate 1.0.1
Python project to download and deploy objects to Tableau Server via REST API.
Requires Python: >=3.7

# TabPyMigrate

[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/your_username/TabPyMigrate/blob/main/LICENSE)

> Short project description

TabPyMigrate is a tool to migrate your Tableau Flow, Datasources, Workbooks from one server to another easily.
This is similar to TabMigrate and works with Tableay Rest API to download and deploy tableau objects. Easy to configure/Run from CLI.

## Table of Contents

- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)

## Installation


### Prerequisites

- Python 3.6+
- Other dependencies

```bash
# Clone the repository
git clone https://github.com/codespg/TabPyMigrate.git

# Navigate to the project directory
cd TabPyMigrate

# Install the required packages
pip install -r requirements.txt
```

## Usage
Update config.py for source and target server and Tag the objects in Source Server
```
cd tabpymigrate
python tabpymigrate.py
```
Option2: Provide all required information and run in cli.
```
cd tabpymigrate
python tabpymigrate.py -action DOWNLOAD -source_server_name yourservername -source_username username -source_password password -tag_name tag_name -filesystem_path filesystem_path
```
slurm-gpustat 0.0.15
A simple SLURM gpu summary tool
Requires Python: >=3.6

## slurm_gpustat

`slurm_gpustat` is a simple command line utility that produces a summary of GPU usage on a slurm cluster. The tool can be used in two ways:
1. To query the current usage of GPUs on the cluster.
2. To launch a daemon which will log usage over time.  This log can later be queried to provide usage statistics.

### Installation

Install via `pip install slurm_gpustat`.  If you prefer to hack around with the source code, it's a [single python file](slurm_gpustat/slurm_gpustat.py).


### Usage

To print a summary of current activity:

`slurm_gpustat`

To print a summary of current activity on particular partitions, e.g. `debug` & `normal`:

`slurm_gpustat -p debug,normal` or `slurm_gpustat --partition debug,normal`

To start the logging dameon:

`slurm_gpustat --action daemon-start`

To view a summary of logged data:

        `slurm_gpustat --action history`


### Example outputs

Running `slurm_gpustat` will produce something like this:

```
----------------------
Under SLURM management
----------------------
There are a total of 12 gpus [up]
3 rtx6k gpus
2 v100 gpus
3 m40 gpus
4 p40 gpus
----------------------
There are a total of 10 gpus [accessible]
1 rtx6k gpus
2 v100 gpus
3 m40 gpus
4 p40 gpus
----------------------
Usage by user:
user1     [total: 1 ] m40: 1
user2     [total: 1 ] p40: 1
user3     [total: 3 ] p40: 3
----------------------
There are 5 gpus available:
p40: 0
rtx6k: 1
v100: 2
m40: 2
```

Adding `--verbose` to this command will produce a more detailed breakdown for the section describing gpus that are still available.  Example output:
```
There are 18 gpus available:
m40: 5 available
 -> gnodeb3: 1 m40 [cpu: 38/40, gres/gpu: 3/4, mem: 68G/257669M] [user1,user2]
 -> gnodeb4: 2 m40 [cpu: 38/40, gres/gpu: 2/4, mem: 60G/193161M] [user1,user2]
 -> gnodec3: 2 m40 [cpu: 12/48, gres/gpu: 2/4, mem: 36G/257669M] [user1]
p40: 5 available
 -> gnodec4: 1 p40 [cpu: 20/48, gres/gpu: 3/4, mem: 216G/257669M] [user1]
 -> gnoded1: 1 p40 [cpu: 44/64, gres/gpu: 3/4, mem: 60G/385192M] [user4,user5]
 -> gnoded3: 1 p40 [cpu: 28/64, gres/gpu: 3/4, mem: 70G/385192M] [user2,user4,user5]
 -> gnoded4: 1 p40 [cpu: 28/64, gres/gpu: 3/4, mem: 112G/385192M] [user5,user4]
 -> gnodee3: 1 p40 [cpu: 36/56, gres/gpu: 3/4, mem: 60G/385192M] [user4,user2]
rtx6k: 3 available
 -> gnodef1: 1 rtx6k [cpu: 36/56, gres/gpu: 3/4, mem: 194G/257669M] [user2,user3]
 -> gnodeh2: 2 rtx6k [cpu: 16/24, gres/gpu: 2/4, mem: 96G/385345M] [user2,user1]
v100: 5 available
 -> gnodeg1: 2 v100 [cpu: 16/64, gres/gpu: 2/4, mem: 60G/191668M] [user5,user1]
 -> gnodeg2: 3 v100 [cpu: 8/64, gres/gpu: 1/4, mem: 40G/191668M] [user2]
```

Running `slurm_gpustat --action history` (after the daemon has run for a little while) will produce something like this:

```
Historical data contains 7 samples (2020-01-03 11:51:43 to 2020-01-03 11:51:45)
GPU usage for user1:
v100  > avg: 2, max: 4
m40   > avg: 1, max: 1
total > avg: 3

GPU usage for user2:
p40m  > avg: 3, max: 4
total > avg: 3
```

### Dependencies

* `Python >= 3.6`
* `numpy`
* `beartype`
* `seaborn`
* `colored`