semvec/pypi-packages huggingface dataset:
LocalFinder 0.1.12
A tool for finding significantly different genomic regions using local correlation and enrichment significance.
Requires Python: >=3.8

# LocalFinder

A tool for finding significantly different genomic regions of two tracks using local features.

## Installation Requirements

Before installing and using `LocalFinder`, please ensure that the following external tools are installed on your system:

- **bedtools**: Used for genomic interval operations.
  - Installation: [https://bedtools.readthedocs.io/en/latest/content/installation.html](https://bedtools.readthedocs.io/en/latest/content/installation.html)
  - conda install -c bioconda -c conda-forge bedtools 
  - mamba install -c bioconda -c conda-forge bedtools
- **ucsc-bigwigtobedgraph**: Used for converting BigWig files to BedGraph format.
  - Download: [http://hgdownload.soe.ucsc.edu/admin/exe/](http://hgdownload.soe.ucsc.edu/admin/exe/)
  - conda install -c bioconda -c conda-forge ucsc-bigwigtobedgraph
  - mamba install -c bioconda -c conda-forge ucsc-bigwigtobedgraph
- **samtools**: Used for processing SAM/BAM files.
  - Installation: [http://www.htslib.org/download/](http://www.htslib.org/download/)
  - conda install -c bioconda -c conda-forge samtools
  - mamba install -c bioconda -c conda-forge samtools

These tools are required for processing genomic data and must be installed separately.

## Installation

Install `LocalFinder` using `pip`:

```bash
pip install LocalFinder
```

## Usage

Go to github: [LocalFinder](https://github.com/astudentfromsustech/localfinder) for more details about usage
agi-med-utils 2.1.0
Utils for agi-med team
Requires Python: >=3.13

# agi-med-utils

Стандартизированный компонент утилит отдела

## Ответственный разработчик

@zhelvakov

## Общая информация

- Singleton 
- XMLParser
- FeatureToggler

## Тесты

- `sudo docker compose up --build`
Credito-SQLViaCode 0.2.1
A Python package to execute SQL queries and procedures and manage backups.
Requires Python: >=3.6

### Database Query and Backup Script - Summary

This script provides the `get_query_from_db` and `exec_procedure_from_db` functions, designed to interact with a SQL Server database. It supports executing queries or stored procedures, and optionally creating backups of tables.

#### Function Usage

- **`get_query_from_db(query, table_to_backup, env_file_name=None, params=None)`**:
  - Executes a SQL query on the database.
  - Parameters:
    - `query`: A SQL query string to execute on the database.
    - `table_to_backup`: The name of a table to back up. Set this to `None` if no backup is needed.
    - `env_file_name` (optional): The path to the `.env` file containing database credentials. Defaults to `.env`.
    - `params` (optional): A dictionary of parameters to bind to the query.
  - Returns:
    - A pandas DataFrame with the query results.

- **`exec_procedure_from_db(procedure_name, table_to_backup, env_file_name=None, params=None)`**:
  - Executes a stored procedure on the database.
  - Parameters:
    - `procedure_name`: The name of the stored procedure to execute.
    - `table_to_backup`: The name of a table to back up. Set this to `None` if no backup is needed.
    - `env_file_name` (optional): The path to the `.env` file containing database credentials. Defaults to `.env`.
    - `params` (optional): A dictionary of parameters to bind to the procedure.
  - Returns:
    - A pandas DataFrame with the procedure's output.

#### Setup Instructions

1. **Environment Variables**:
   - The script requires a `.env` file to load database credentials and connection details.
   - Required variables:
     ```plaintext
     USER=
     PASSWORD=
     HOST=
     NAME=
     DRIVER=ODBC+Driver+17+for+SQL+Server
     ```
     Fill in the variables with your database details. Leave `DRIVER` unchanged unless you are using a different driver.

#### Installation

Install the package directly from PyPI:

```bash
pip install Credito_SQLViaCode

#### Examples:

- **Execute a Query**:
   ```python
   from SQLViaCode import get_query_from_db

   query = "SELECT * FROM your_table"
   result_df = get_query_from_db(query, "your_table_to_backup")
   print(result_df)
ghostumap 0.0.0
GhostUMAP2 for Measuring and Analyzing (r,d)-Stability of UMAP

<p align="center">
  <h2 align="center">GhostUMAP2</h2>
	<h3 align="center">Measuring and Analyzing <i>(r,d)</i>-Stability of UMAP</h3>
</p>

### Installation

```Bash
git clone https://github.com/jjmmwon/rdumap.git
cd rdumap
hatch shell
```

### How to use GhostUMAP
```Python
from rdumap import GhostUMAP
from sklearn.datasets import fetch_openml

mnist = fetch_openml("mnist_784")
X, y = mnist["data"], mnist["target"]

mapper = GhostUMAP()
O, G, active_ghosts = mapper.fit_transform(X, n_ghosts=16) 

mapper.visualize(label=y, legend=[str(i) for i in range(10)])
```


## API
### Function 'fit_transform'
```Python
def fit_transform(X, n_ghosts, r, ghost_gen, dropping, init_dropping):
```
Fit X into an embedded space with ghosts and return the transformed outputs.

#### **Parameters**
> - `X`: array, shape (n_samples, n_features) or (n_samples, n_samples). If the metric is 'precomputed' X must be a square distance matrix. Otherwise, it contains a sample per row.

> **Ghost Configuration**
> - `n_ghosts`: The number of ghost points to embed in the embedding space. Default is 16.
> - `r`: Radius for ghost generation. Default is 0.1.
> - `ghost_gen`: Ghost generation parameter. Default is 0.2.

> **Dropping Scheme**
> - `dropping`: Whether to drop ghosts during optimization. Default is True.
> - `init_dropping`: Initial dropping parameter. Default is 0.4.

#### **Returns**
> - ```O: array, shape (n_samples, n_components)```
Embedding of the original data points, identical to the output of UMAP. It represents the transformed coordinates in the low-dimensional space.
> - ```G: array, shape (n_samples, n_ghosts, n_components)```
Embedding of ghost points which are clones of the original points. These ghost points are used to evaluate the instability of each data instance.
> - ```active_ghosts: array, shape (n_samples,)``` 
Boolean array indicating the presence of active ghost points for each data instance.


### Function 'visualize'
```Python
def visualize(title=None, label=None, legend=None):
```
Returns an interactive visualization widget.

#### **Parameters**
> - `title`: Title of the visualization.
> - `label`: Labels for the data points.
> - `legend`: Legend for the visualization.

#### **Returns**
> - `widget`: An interactive visualization widget.

### Function 'get_distances'
Get the distances between the original and ghost projections.
```Python
def get_distances(sensitivity=1):
```
**Parameters**

`sensitivity`: Sensitivity for distance calculation. Default is 1.

**Returns**

`distances`: array, shape (n_samples,). The distances between original and ghost embeddings.

### Function 'get_unstable_ghosts'
Get the boolean array indicating unstable ghost points.
```Python
def get_unstable_ghosts(distance=0.1, sensitivity=1):
```
**Parameters**

`distance`: Distance threshold for determining instability. Default is 0.1.
`sensitivity`: Sensitivity for instability calculation. Default is 1.

**Returns**

unstable_ghosts`: array, shape (n_samples,).
ghostumap2 0.0.0
GhostUMAP2 for Measuring and Analyzing (r,d)-Stability of UMAP

<p align="center">
  <h2 align="center">GhostUMAP2</h2>
	<h3 align="center">Measuring and Analyzing <i>(r,d)</i>-Stability of UMAP</h3>
</p>

### Installation

```Bash
git clone https://github.com/jjmmwon/rdumap.git
cd rdumap
hatch shell
```

### How to use GhostUMAP
```Python
from rdumap import GhostUMAP
from sklearn.datasets import fetch_openml

mnist = fetch_openml("mnist_784")
X, y = mnist["data"], mnist["target"]

mapper = GhostUMAP()
O, G, active_ghosts = mapper.fit_transform(X, n_ghosts=16) 

mapper.visualize(label=y, legend=[str(i) for i in range(10)])
```


## API
### Function 'fit_transform'
```Python
def fit_transform(X, n_ghosts, r, ghost_gen, dropping, init_dropping):
```
Fit X into an embedded space with ghosts and return the transformed outputs.

#### **Parameters**
> - `X`: array, shape (n_samples, n_features) or (n_samples, n_samples). If the metric is 'precomputed' X must be a square distance matrix. Otherwise, it contains a sample per row.

> **Ghost Configuration**
> - `n_ghosts`: The number of ghost points to embed in the embedding space. Default is 16.
> - `r`: Radius for ghost generation. Default is 0.1.
> - `ghost_gen`: Ghost generation parameter. Default is 0.2.

> **Dropping Scheme**
> - `dropping`: Whether to drop ghosts during optimization. Default is True.
> - `init_dropping`: Initial dropping parameter. Default is 0.4.

#### **Returns**
> - ```O: array, shape (n_samples, n_components)```
Embedding of the original data points, identical to the output of UMAP. It represents the transformed coordinates in the low-dimensional space.
> - ```G: array, shape (n_samples, n_ghosts, n_components)```
Embedding of ghost points which are clones of the original points. These ghost points are used to evaluate the instability of each data instance.
> - ```active_ghosts: array, shape (n_samples,)``` 
Boolean array indicating the presence of active ghost points for each data instance.


### Function 'visualize'
```Python
def visualize(title=None, label=None, legend=None):
```
Returns an interactive visualization widget.

#### **Parameters**
> - `title`: Title of the visualization.
> - `label`: Labels for the data points.
> - `legend`: Legend for the visualization.

#### **Returns**
> - `widget`: An interactive visualization widget.

### Function 'get_distances'
Get the distances between the original and ghost projections.
```Python
def get_distances(sensitivity=1):
```
**Parameters**

`sensitivity`: Sensitivity for distance calculation. Default is 1.

**Returns**

`distances`: array, shape (n_samples,). The distances between original and ghost embeddings.

### Function 'get_unstable_ghosts'
Get the boolean array indicating unstable ghost points.
```Python
def get_unstable_ghosts(distance=0.1, sensitivity=1):
```
**Parameters**

`distance`: Distance threshold for determining instability. Default is 0.1.
`sensitivity`: Sensitivity for instability calculation. Default is 1.

**Returns**

unstable_ghosts`: array, shape (n_samples,).