semvec/pypi-packages huggingface dataset:
CytoBulk 0.1.20
Integrating transcriptional data to decipher the tumor microenvironment with the graph frequency domain model
Requires Python: >=3.10

# Overview
CytoBulk aims to Integrate transcriptional and image data to depict the tumor microenvironment accurately with the graph frequency domain model


# Documentation

To install and use CytoBulk, please visit https://kristaxying.github.io/CytoBulk/

# System requirements

We have tested the package on the following systems:
- Linux: Ubuntu 20 (GPU 3080)
- Windows: Windows 11 Enterprise (CPU)

# Installation Guide

Follow the steps below to install and set up **CytoBulk**.

---

## Setting Up the Environment for Python and R
The CytoBulk package is developed based on the pytorch framework and can be implemented on both GPU and CPU. We recommend running the package on GPU. Please ensure that pytorch and CUDNN are installed correctly.
### Option 1: Set Python and R Together
```
conda config --append channels conda-forge
conda create --name cytobulk python=3.10 r-base=4.4
conda activate cytobulk
pip install cytobulk
```
This approach is suitable for users who want all dependencies managed within the same Conda environment. However, it might **not work reliably on Windows** due to potential issues with R configuration in Conda.
### Option 2: Set Only Python and Specify R Path Separately

```
conda create --name cytobulk python=3.10
conda activate cytobulk
pip install cytobulk

```
Then, before running the main program, you need to specify the path to your locally installed R. This can be done using Python by setting the R_HOME environment variable. Add the following lines at the beginning of your Python script:
```
import os
# Set the R installation path (adjust the path based on your R installation)
os.environ['R_HOME'] = r_path
```
## Install required R packages

 To run **CytoBulk**, make sure all the following **prerequisites** are installed.


**R 4.4.0 or higher and the following packages** 

- Giotto (1.1.2) <https://giottosuite.readthedocs.io/en/master/gettingstarted.html>
- scran (1.32.0) <https://bioconductor.org/packages/release/bioc/html/scran.html>
- sva (3.52.0) <https://www.bioconductor.org/packages/release/bioc/html/sva.html>


# Run demo
Please visit Examples section at https://kristaxying.github.io/CytoBulk/.



### Maintainer
WANG Xueying xywang85-c@my.cityu.edu.hk
bento-sc 0.0.8
BENchmarking Transformer-Obtained Single-Cell embeddings
Requires Python: >=3.11

<div align="center">

<img src="https://raw.githubusercontent.com/gdewael/bento-sc/refs/heads/main/assets/bento.svg" align="center" width="450" alt="bento-sc" href="https://github.com/gdewael/bento-sc">

<h1></h1>

BENchmarking Transformer-Obtained Single-Cell representations.

[![PyPi Version](https://img.shields.io/pypi/v/bento-sc.svg)](https://pypi.python.org/pypi/bento-sc/)
[![GitHub license](https://img.shields.io/github/license/gdewael/bento-sc)](https://github.com/gdewael/bento-sc/blob/main/LICENSE)
[![Documentation](https://readthedocs.org/projects/bento-sc/badge/?version=latest&style=flat-default)](https://bento-sc.readthedocs.io/en/latest/index.html)

</div>

## Single-cell language modeling

This package contains routines and definitions for pre-training single-cell (transcriptomic) language models.

Package features:
- Memory-efficient scRNA-seq dataloading from [`h5torch`-compatible HDF5 files](https://github.com/gdewael/h5torch).
- `yaml`-configurable language model training scripts.
- Modular and extendable data preprocessing pipelines.
- A diverse set of downstream tasks to evaluate scLM performance.
- Full reproducibility instructions of our study results via [bento-sc-reproducibility](https://github.com/gdewael/bento-sc-reproducibility).



## Install

`bento-sc` is distributed on PyPI.
```bash
pip install bento-sc
```
Note: The package has been tested with `torch==2.2.2` and `pytorch-lightning==2.2.5`. If you encounter errors with `bento-sc` using more recent version of these two packages, consider downgrading.

You may need to [install PyTorch](https://pytorch.org/get-started/locally/) before running this command in order to ensure the right CUDA kernels for your system are installed.

## Package usage and structure 

Please refer to our [documentation page](https://bento-sc.readthedocs.io/en/latest/index.html).

## Academic reproducibility

All config files and scripts that were used to pre-train models and fine-tune them towards downstream tasks are included in a separate GitHub repository: [bento-sc-reproducibility](https://github.com/gdewael/bento-sc-reproducibility).

In addition, all scripts to reproduce the "baselines" in our study are located in the [bento-sc-reproducibility](https://github.com/gdewael/bento-sc-reproducibility) repository.

## Citation

:eyes: :eyes: :eyes:
alphavantage-mcp-server 0.1.3
Alphavantage MCP server
Requires Python: >=3.13

# Alphavantage MCP Server

[![smithery badge](https://smithery.ai/badge/alphavantage)](https://smithery.ai/server/alphavantage)

A MCP server for the stock market data API, Alphavantage API.

## Configuration

### Getting an API Key
1. Sign up for a [Free Alphavantage API key](https://www.alphavantage.co/support/#api-key)
2. Add the API key to your environment variables as `ALPHAVANTAGE_API_KEY`


## Clone the project

```bash
git clone https://github.com/calvernaz/alphavantage.git
```

### Usage with Claude Desktop
Add this to your `claude_desktop_config.json`:

**NOTE** Make sure you replace the `<DIRECTORY-OF-CLONED-PROJECT>` with the directory of the cloned project.

```
{
  "mcpServers": {
    "alphavantage": {
      "command": "uv",
      "args": [
        "--directory",
        "<DIRECTORY-OF-CLONED-PROJECT>/alphavantage",
        "run",
        "alphavantage"
      ],
      "env": {
        "ALPHAVANTAGE_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}
```
pysqream-sqlalchemy 1.4
SQLAlchemy dialect for SQreamDB
Requires Python: >=3.9
Keywords: database sqlalchemy sqream sqreamdb

**********************************
SQLAlchemy Dialect for SQream DB
**********************************

Prerequisites
================

* SQream => 4.5.6
* Python == 3.9
* SQLAlchemy == 2.0.27
* SQream DB-API Connector == 3.2.5
* SQLite
* Cython (optional - improves performance)

Installing SQream SQLAlchemy
=============================

.. code-block:: shell

    pip3.9 install pysqream-sqlalchemy -U

Verifying Installation
------------------------

.. code-block:: python

    import sqlalchemy as sa
    import pandas as pd
    from sqlalchemy import text, orm


    conn_str = "sqream://sqream:sqream@localhost:3108/master"                                                  
    engine = sa.create_engine(conn_str, connect_args={"clustered": True})
    session = orm.sessionmaker(bind=engine)()

    session.execute(text('create or replace table test (ints int)'))
    session.execute(text('insert into test values (5), (6)'))
    df = pd.read_sql('select * from test', engine)
    print(df)

Connection String 
=====================

.. code-block:: shell

    sqream://<user_login_name>:<password>@<host>:<port>/<db_name>

Parameters
------------

.. list-table:: 
   :widths: auto
   :header-rows: 1
   
   * - Parameter
     - Description
   * - ``username``
     - Username of a role to use for connection
   * - ``password``
     - Specifies the password of the selected role
   * - ``host``
     - Specifies the hostname
   * - ``port``
     - Specifies the port number
   * - ``port_ssl``
     - An optional parameter
   * - ``database``
     - Specifies the database name 
   * - ``clustered``
     - Establishing a multi-clustered connection. Input values: ``True``, ``False``. Default is ``False``
   * - ``service``
     - Specifies service queue to use

Example
=========

Pulling a Table into Pandas
---------------------------

The following example shows how to pull a table in Pandas. This example uses the URL method to create the connection string:

.. code-block:: python

   import sqlalchemy as sa
   import pandas as pd
   from sqlalchemy.engine.url import URL


	engine_url = sa.engine.url.URL(
                 'sqream',
                 username='sqream',
                 password='12345',
                 host='127.0.0.1',
                 port=3108,
                 database='master')
	engine = sa.create_engine(engine_url,connect_args={"clustered": True, "service": "admin"})

	table_df = pd.read_sql("select * from nba", con=engine)


Limitations
=============

Arrays
-----------------------
SQream SQLAlchemy doesn't suppport ``ARRAY`` type for columns.


Parameterized Queries
-----------------------
SQream SQLAlchemy supports only the ``BULK INSERT`` statement.
date-day-estimator 0.0.4
To estimate the day of the week from date/month/year info


# DATE DAY PREDICTOR

`date_day_estimator` is a Python package that calculates the day of the week for a given date using **Zeller's Congruence**.

## Installation

```bash
pip install date-day-estimator
```

## Usage

from date_day_estimator.date_day_estimator import zellers_congruence

# Example: Find the day of the week for 5th July 1995
day_number, day_name = zellers_congruence(5, 7, 1995)

print(day_number)  # 4
print(day_name)    # 'Wednesday'


## FORMULA

**Zeller's Congruence**, which is an algorithm used to calculate the day of the week for any given date. It typically takes the following form:

\[
h = \left(q + \left\lfloor rac{13(m+1)}{5} ightfloor + K + \left\lfloor rac{K}{4} ightfloor + \left\lfloor rac{J}{4} ightfloor - 2Jight) \mod 7
\]

Where:
- \( h \) is the day of the week (0 = Saturday, 1 = Sunday, 2 = Monday, etc.),
- \( q \) is the day of the month,
- \( m \) is the month (March = 3, April = 4, ..., February = 14),
- \( K \) is the year of the century (i.e., year % 100),
- \( J \) is the zero-based century (i.e., year // 100).