semvec/pypi-packages huggingface dataset:
cryolite 0.0.0
CRYOLITE runs your Open Lightweight Iceberg Table Engine.
Requires Python: >=3.9
Keywords: iceberg, arrow, calcite, lakehouse, query-engine

# cryolite (Python)

CRYOLITE runs your Open Lightweight Iceberg Table Engine.

This repository currently contains a minimal Python package used to reserve the
`cryolite` name on PyPI and to later host Python tooling/bindings.

- Website: https://cryolite.io
- GitHub Org: https://github.com/cryolite-io
sgn-ligo 0.2.0
LIGO Extensions for the SGN Framework
Requires Python: >=3.10

<!-- index.rst content start -->

# SGN-LIGO

SGN-LIGO is set of extensions to the core library `sgn`, that includes functionality
specific to LIGO Scientific Collaboration data analysis. This page is for documenatation
of the `sgnligo` package, but there is a family of libraries that extend the functionality of SGN
in other ways, including:

- [`sgn`](https://docs.ligo.org/greg/sgn/): Base library for SGN
- [`sgn-ts`](https://git.ligo.org/greg/sgn-ts): TimeSeries utilities for SGN
- [`sgn-try`](https://git.ligo.org/greg/sgn-try): Process monitoring and alerting utilities for SGN

## Installation

To install SGN-LIGO, simply run:

```bash
pip install sgn-ligo
```

More SGN-LIGO-specific documentation coming soon.
pywellen 0.20.2
Fast VCD, GHW and FST wavedump reading.
Requires Python: >=3.9

# Wellen Waveform Library

[![Crates.io Version](https://img.shields.io/crates/v/wellen)](https://crates.io/crates/wellen)
[![docs.rs](https://img.shields.io/docsrs/wellen)](https://docs.rs/wellen)
[![GitHub License](https://img.shields.io/github/license/ekiwi/wellen)](LICENSE)
[![DOI](https://zenodo.org/badge/707242016.svg)](https://zenodo.org/doi/10.5281/zenodo.12774824)

`wellen` provides a common interface to read both FST and VCD waveform files.
The library is optimized for use-cases where only a subset of signals need to
be accessed, like in a waveform viewer.
VCD parsing uses multiple-threads.

## Overview

![Overview of wellen components](./wellen_overview.svg)


## The Wellen Hierarchy

All meta-data about the signals in a design is accumulated in the `wellen::Hierarchy` class.
While all formats supported by `wellen` feature hierarchical scopes and variables and the associated signal
data, they vary in how much additional information they provide. We thus had to make some choices on how
to unify the data from various sources in a single data model.

### Global Metadata

We record the following information for each file:

- `date`: a string representation of when the file was created, the precise data / time format depends on the file source (VCD, FST)
- `version`: a string, generally the version of the simulator that produced the file (VCD, FST)
- `timescale`: time unit and factor that defines one tick of simulation time, GHW is always `1fs` (ALL)
- `fileformat` (ALL)


### Scope

Each scope contains the following information:

- `name`: the local name of the scope, ignoring its parent scopes (ALL)
- `full_name`: the names of all parent scopes and the local scope, connected with a `.` (ALL)
- `scope_type`: the kind of scope, e.g., module, function, wire (ALL)
- `component`: an optional type name, e.g., the name of the module that was instantiated (FST)
- `instantiation_source_loc`: optional filename and line number where the scope was instantiated;
  for VCDs, this information is encoded with non-standard attributes (FST, VCD)
- `source_loc`: optional filename and line number where the scope was declared;
  for VCDs, this information is encoded with non-standard attributes (FST, VCD)
- `items`: child variables and scopes
- `vars`: child variables
- `scopes`: child scopes


### Variable

Each variables comes with the following information:

- `name`: the local name of the scope, ignoring its parent scopes (ALL)
- `full_name`: the names of all parent scopes and the local scope, connected with a `.` (ALL)
- _to be continued_
Topsis-pritish-102303473 1.0.3
TOPSIS implementation using Python
Requires Python: >=3.7

# TOPSIS Implementation in Python

**Course:** UCS654 â€“ Predictive Analytics using Statistics  
**Assignment:** Assignment-1 (TOPSIS)  
**Author:** Pritish Dutta  
**Roll Number:** 102303473

---

##  About the Project

This project provides a Python implementation of the  
**TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)** method.

TOPSIS is a multi-criteria decision-making (MCDM) technique used to rank alternatives based on their distance from the ideal best and ideal worst solutions.


---

##  Installation (User Manual)

This package requires **Python 3.7 or higher**.

### Dependencies
- pandas  
- numpy  

Package listed on PyPI:- https://pypi.org/project/Topsis-pritish-102303473/

Install the package using pip:

```bash
pip install Topsis-pritish-102303473
```
---

## Usage

Run the following command in the Command Prompt / Terminal:

```bash
topsis <inputFile> <weights> <impacts> <outputFile>
```
 Example

 ```bash 
 topsis sample.csv "1,1,1,1" "+,+,-,+" result.csv

```
topsis-decision-analysis 1.0.1
A Python implementation of the TOPSIS method for multi-criteria decision making
Requires Python: >=3.7

# TOPSIS Implementation in Python

**Course:** UCS654 Predictive Analytics using Statistics  
**Assignment:** Assignment-1 (TOPSIS)  
**Author:** Sartaj Singh Virdi  
**Roll Number:** 102303259  

---

## About the Project

This project provides a Python implementation of the  
**TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)** method.

TOPSIS is a multi-criteria decision-making (MCDM) technique used to rank alternatives based on their distance from the ideal best and ideal worst solutions.

---

## Installation (User Manual)

This package requires **Python 3.7 or higher**.

### Dependencies

- pandas  
- numpy  

Package listed on PyPI: https://pypi.org/project/topsis-mcdm-tool/

Install the package using pip:
```bash
pip install topsis-mcdm-tool
```

---

## Usage

Run the following command in the Command Prompt / Terminal:
```bash
topsis <inputFile> <weights> <impacts> <outputFile>
```

### Example
```bash
topsis sample.csv "1,1,1,1" "+,+,-,+" result.csv
```

### Parameters

* inputFile: CSV or Excel file containing data
* weights: Comma-separated weights for each criterion
* impacts: + for benefit, - for cost criteria
* outputFile: Output CSV/Excel file with scores and ranks

---

## Sample Input Format
```csv
Model,Price,Performance,Camera,Battery
A,25000,8,7,4000
B,30000,9,8,4500
C,20000,7,6,3800
```

---

## Output Columns

* Topsis Score: Closeness coefficient
* Rank: Ranking of alternatives

---

## Project Structure
```
topsis-mcdm-tool/
  topsis/
    __init__.py
    topsis.py
  sample.csv
  output.csv
  setup.py
  README.md
```

---

## License

This project is released under the MIT License.
Topsis-Sarthak-102303497 1.0.0
A Python package for TOPSIS multi-criteria decision analysis
Requires Python: >=3.6

# Topsis-Sarthak-102303497

A Python package for TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) multi-criteria decision analysis.

## Installation

```bash
pip install Topsis-Sarthak-102303497
```

## Usage

### Command Line

```bash
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
```

### Example

```bash
topsis data.csv "1,1,1,2" "+,+,-,+" result.csv
```

### Parameters

- **InputDataFile**: Path to CSV file containing the data
- **Weights**: Comma-separated weights for each criterion (e.g., "1,1,1,2")
- **Impacts**: Comma-separated impacts for each criterion (+ for benefit, - for cost)
- **ResultFileName**: Path for the output CSV file

### Input File Format

The input CSV must have:
- First column: Object/Alternative names
- Remaining columns: Numeric criteria values

Example:
```
Fund Name,P1,P2,P3,P4,P5
M1,0.67,0.45,6.5,42.6,12.56
M2,0.6,0.36,3.6,53.3,14.47
```

### Output File Format

The output CSV will contain all input columns plus:
- **Topsis Score**: The calculated TOPSIS score
- **Rank**: Rank based on the TOPSIS score (1 = best)

## License

MIT License