semvec/pypi-packages huggingface dataset:
Topsis-Ansh-12345678 0.1
TOPSIS implementation using Python
Requires Python: >=3.6

# Topsis-Ansh-12345678

A Python package implementing the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method.

## Installation

pip install Topsis-Ansh-12345678

## Usage

topsis <InputDataFile> <Weights> <Impacts> <OutputFile>

Example:

topsis data.csv 1,1,1,2 +,+,-,+ result.csv

## Input Requirements

- Minimum 3 columns
- First column: Alternatives (non-numeric allowed)
- Remaining columns: Numeric values only
- Weights separated by commas
- Impacts must be + or -

## Output

Adds:
- Topsis Score
- Rank

Higher score = better alternative
policyengine-us-data 1.69.3
A package to create representative microdata for the US.
Requires Python: <3.14.0,>=3.12

# PolicyEngine US Data

## Installation

While it is possible to install via PyPi:
```bash
pip install policyengine-us-data
```
the recommended installation is 
```
pip install -e .[dev]
```
which installs the development dependencies in a reference-only manner (so that changes
to the package code will be reflected immediately); `policyengine-us-data` is a dev package
and not intended for direct access.

## SSA Data Sources

The following SSA data sources are used in this project:

- [Latest Trustee's Report (2025)](https://www.ssa.gov/oact/TR/2025/index.html) - Source for `social_security_aux.csv` (extracted via `extract_ssa_costs.py`)
- [Single Year Supplementary Tables (2025)](https://www.ssa.gov/oact/tr/2025/lrIndex.html) - Long-range demographic and economic projections
- [Single Year Age Demographic Projections (2024 - latest published)](https://www.ssa.gov/oact/HistEst/Population/2024/Population2024.html) - Source for `SSPopJul_TR2024.csv` population data

## Building the Paper

### Prerequisites

The paper requires a LaTeX distribution (e.g., TeXLive or MiKTeX) with the following packages:

- graphicx (for figures)
- amsmath (for mathematical notation)
- natbib (for bibliography management)
- hyperref (for PDF links)
- booktabs (for tables)
- geometry (for page layout)
- microtype (for typography)
- xcolor (for colored links)

On Ubuntu/Debian, you can install these with:

```bash
sudo apt-get install texlive-latex-base texlive-latex-recommended texlive-latex-extra texlive-fonts-recommended
```

On macOS with Homebrew:

```bash
brew install --cask mactex
```

### Building

To build the paper:

```bash
make paper
```

To clean LaTeX build files:

```bash
make clean-paper
```

The output PDF will be at `paper/main.pdf`.

## Building the Documentation

### Prerequisites

The documentation uses Jupyter Book 2 (pre-release) with MyST. To install:

```bash
# Install Jupyter Book 2 pre-release
pip install --pre "jupyter-book==2.*"

# Install MyST CLI
npm install -g mystmd
```

### Building

To build and serve the documentation locally:

```bash
cd docs
myst start
```

Or alternatively from the project root:

```bash
jupyter book start docs
```

Both commands will start a local server at http://localhost:3001 where you can view the documentation.

The legacy Makefile command:

```bash
make documentation
```

Note: The Makefile uses the older `jb` command syntax which may not work with Jupyter Book 2. Use `myst start` or `jupyter book start docs` instead.
solids 0.3.5
Solids: A tool for crystal structure prediction
Requires Python: >=3.9

# SolidASE_0.0
Crystal Structure Prediction for web-based frameworks

This code is intended to explore the energy landscape of crystalline structures using Python and its libraries such as ASE, PyXtal, Dscribe, and Aegon.

Solids relies in two separated schemes: The Stochastich Algorithm and the Evolutive Algorithm.

The Stochastich Algorithm builts and relaxes a set of Point-Group-Based structues in order to preliminarily explore the energy landscape of cystalline structures. The advantage of this process is that is based on stages where the level of theory can be refined after each stage.

The Evolutive Algorithm improves on the Stochastic one by transmiting the already-available good structural traits to new generations using Crossover and Mutation operators. Each set of crossovers and mutants are relaxed and, in turn, pass on their characteristics to new candidates, untill halting criteria is met. 

In version 1.0 Solids is interfaced with ESM optimization by ASE, GULP and VASP.

#Usage
To install in UNIX-based systems, use pip install solids.

Once installed, used ./run_solids.py input_emt to create the inputEMT file.

Then, execute the code by typping ./run_solids.py inputEMT.
o1twosum 0.1.4
Algorithmic O(1) streaming Two Sum solver using bitwise intersection.
Requires Python: >=3.7

# O1TwoSum
An experimental Python library achieving algorithmic O(1) Time and Space complexity for the Two Sum problem over data streams. 

*Note: Limited to integers 0-50. Leverages parallel bitwise intersection. While algorithmic O(1), it relies on Python's arbitrary-precision integers under the hood.*

### Usage
```python
from o1twosum import O1TwoSum

system = O1TwoSum()
system.ingest(2)
system.ingest(11)

print(system.query(13)) # Output: (2, 11)