semvec/pypi-packages huggingface dataset:
ordicanis 2.0.0
Advanced Collaboration Platform with Microservices Architecture
License: MIT
Requires Python: >=3.8

# Ordicanis - Advanced Collaboration Platform

![Version](https://img.shields.io/badge/version-2.0.0-blue.svg)
![License](https://img.shields.io/badge/license-MIT-green.svg)
![Python](https://img.shields.io/badge/python-3.8+-yellow.svg)

## 🚀 Latest Updates (v2.0.0)

Major enhancements to our microservices architecture with powerful new features:

### 🔒 Advanced Security System
- Multi-factor authentication
- Role-based access control (RBAC)
- Zero-trust architecture
- Real-time threat detection
- Certificate management
- Key rotation
- Security analytics
- Comprehensive audit logging

### 📊 Sophisticated Monitoring
- Health monitoring
- Performance tracking
- Distributed tracing
- Log aggregation
- Metric collection
- Alert management
- Real-time visualization
- Trend analysis

### ⚡ High-Performance Cache
- Multi-level caching (Memory, Redis, Disk)
- Cache coherence
- Smart eviction policies
- Cache warming
- Performance statistics
- Automatic invalidation
- Cache replication
- Self-optimization

### 🏗️ Core Features
- **Distributed Database System**: Multi-model support, sharding, replication
- **Machine Learning Pipeline**: Distributed training, model deployment
- **Real-time Analytics**: Stream processing, anomaly detection
- **Workflow Orchestration**: Task scheduling, dependency management
- **Resource Management**: Allocation, monitoring, optimization

## 🛠️ Installation

```bash
pip install ordicanis
```

## 📋 Requirements

- Python 3.8+
- Redis
- PostgreSQL
- Docker (optional)

## 🚦 Quick Start

```python
from ordicanis import Platform

# Initialize the platform
platform = Platform()

# Configure security
platform.security.configure(
    mfa_required=True,
    session_timeout=1800
)

# Set up monitoring
platform.monitoring.add_monitor(
    name="system_health",
    metrics=["cpu", "memory", "disk"],
    interval=60
)

# Configure caching
platform.cache.add_cache(
    name="user_data",
    levels=["l1", "l2"],
    max_size={"l1": 1000, "l2": 10000}
)

# Start the platform
platform.start()
```

## 📚 Documentation

Comprehensive documentation is available at [docs.ordicanis.io](https://docs.ordicanis.io)

## 🤝 Contributing

We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md)

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🌟 Support

- 📧 Email: support@ordicanis.io
- 💬 Discord: [Join our community](https://discord.gg/ordicanis)
- 🐦 Twitter: [@ordicanis](https://twitter.com/ordicanis)

## 🙏 Acknowledgments

Special thanks to all our contributors and the open source community!
sketchKH 0.1.2
Distribution-based sketching of single-cell samples
License: MIT
Requires Python: <3.10,>=3.6
Keywords: cytometry, single-cell bioinformatics, clinical prediction, compression, computational biology

# SketchKH
Distribution-Informed Sketching with Kernel Herding 

## Overview
We provide a set of functions for distribution-aware sketching of multiple profiled single-cell samples via Kernel Herding. Our sketches select a small, representative set of cells from each profiled sample so that all major immune cell-types and their relative frequencies are well-represented. 
* Please see our paper for more information (ACM-BCB 2022) : https://arxiv.org/abs/2207.00584
* Updated : December 27, 2024

![Sketching via KH Overview](https://github.com/CompCy-lab/SketchKH/blob/main/sketch_overview.png?raw=True)

## Installation
Dependencies
* Python >= 3.6 < 3.10, anndata >= 0.7.6, numpy >= 1.22.4, scipy >= 1.7.1, numba, joblib, tqdm_joblib

You can install the package with `pip` by,
```
pip install sketchKH
```

Alternatively, you can clone the git repository by,
```
git clone https://github.com/CompCy-lab/SketchKH.git
```

## Example usage
To perform sketching, first read in a preprocessed `.h5ad` adata object. This dataset contains multiple profiled single-cell samples. Hence, sketches will select a limited set of cells from each profiled sample. We refer to each profiled sample as a *sample-set*. 

```python
import anndata
import os
adata = anndata.read_h5ad(os.path.join('data', 'nk_cell_preprocessed.h5ad'))
```

Then simply sketch your data with 500 cells per sample-set by,
```python

# Inputs
# adata: annotated data object (dimensions = cells x features)
# sample_set_key: string referring to the key within adata.obs that contains the sample-sets to subsample
# sample_set_inds: (alternative to specifying sample_set_key) list of arrays containing the indices of the sample-sets to subsample 
# gamma: scale parameter for the normal distribution standard deviation in random Fourier frequency feature computation
# frequency_seed: random state
# num_subsamples: number of cells to subsample per sample-set
# n_jobs: number of tasks
# ----------------------------

# Returns:
# kh_indices: list of indices referencing the subsampled cells per sample-set
# adata_subsample: downsampled annotated data object (dimensions = num_subsamples*sample-sets x features)

# ----------------------------
from sketchKH import *
kh_indices, adata_subsample = sketch(adata, sample_set_key = 'FCS_File', gamma = 1, num_subsamples = 500, frequency_seed = 0, n_jobs = -1)
```