semvec/pypi-packages huggingface dataset:
topsis-sargun-102303367 0.1.1
A Python package implementing TOPSIS for multi-criteria decision making
Requires Python: >=3.7

**Topsis-Trishti-102313056**

**For:** Project-1 (UCS654)
**Submitted by:** Trishti
**Roll No:** 102313056
**Group:** 3C33

**Topsis-Trishti-10155792** is a Python library designed to solve **Multiple Criteria Decision Making (MCDM)** problems using the **Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)** method.

The package helps users rank alternatives based on multiple quantitative criteria by calculating their closeness to the ideal best and ideal worst solutions. It is implemented as a **command-line tool**, making it easy to use for academic and real-world decision-making problems.

## Installation

The package can be installed using the Python package manager **pip**.

```bash
pip install Topsis-Trishti-10155792
```

## Usage

Provide the input CSV file name, followed by the **weights vector** and the **impacts vector**.

```bash
topsis input.csv "1,1,1,1" "+,+,-,+"
```

Alternatively, vectors can be passed without quotes:

```bash
topsis input.csv 1,1,1,1 +,+,-,+
```

**Note:**
If the vectors contain spaces, they **must** be enclosed within double quotes `" "`.

To view help instructions, use:

```bash
topsis /h
```

## Example

### Input File: `sample.csv`

A CSV file containing data for different mobile handsets with multiple attributes.

| Model | Storage (GB) | Camera (MP) | Price ($) | Looks (out of 5) |
| ----- | ------------ | ----------- | --------- | ---------------- |
| M1    | 16           | 12          | 250       | 5                |
| M2    | 16           | 8           | 200       | 3                |
| M3    | 32           | 16          | 300       | 4                |
| M4    | 32           | 8           | 275       | 4                |
| M5    | 16           | 16          | 225       | 2                |

**Weights Vector:**

```
[0.25, 0.25, 0.25, 0.25]
```

**Impacts Vector:**

```
[+, +, -, +]
```

### Command

```bash
topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+"
```


## Output

```
        TOPSIS RESULTS
-----------------------------
   Topsis Score   Rank
1     0.534277      3
2     0.308368      5
3     0.691632      1
4     0.534737      2
5     0.401046      4
```

The output file contains the **TOPSIS score** and **rank** for each alternative.

---

## Important Notes

* The first column (identifiers) and the header row are ignored during computation.
* The CSV file must contain **only numerical values** (no categorical data).
* The number of weights and impacts must match the number of criteria.
* Impacts must be either `+` (benefit) or `-` (cost).

---
dev-excuse-gen 0.1.1
Generate realistic programming excuses
Requires Python: >3.8

# dev-excuse-gen

A tiny CLI tool that generates realistic programming excuses inspired by real production incidents.

## Installation

```bash
pip install dev-excuse-gen
```

## Usage 

```bash
excuse-gen
excuse-gen --context prod
excuse-gen --context backend
```

## Example output:
```
Root Cause identified.
A race condition surfaced under production load and delayed detection.
Fix is trivial. Confidence is high.
```

## Why this exists

- Because production never fail for obvious reasons.


Author: Manish Tiwari(manish.tiwari.09@zohomail.in)
avulto 0.2.6
A Python API for working with BYOND projects.
Requires Python: >=3.12

# Avulto

[Avulto][] is a Python library for working in the BYOND environment. Its goal is
to provide a straightforward Python API which leverages the [SpacemanDMM][] and
other community libraries.

Its primary use cases are to easily

- read and modify map (`.dmm`) files 
- read, modify, and create icon (`.dmi`) files
- inspect and walk BYOND reflection data and ASTs

[Avulto]: https://github.com/warriorstar-orion/avulto
[SpacemanDMM]: https://github.com/SpaceManiac/SpacemanDMM

## Usage

Avulto is available as a [release][] on PyPI. See the **Development** section
below for directions on using the library locally.

A Quickstart and the API reference are available at the library's [documentation site][]
and in the `docs/` directory of the repository. Its API is documented in full in its [stub file][].

[documentation site]: https://warriorstar-orion.github.io/avulto/
[release]: https://pypi.org/project/avulto/
[stub file]: https://github.com/warriorstar-orion/avulto/blob/main/avulto.pyi

## Development

Avulto is written in Rust and implemented using
[PyO3](https://github.com/PyO3/pyo3), and uses
[maturin](https://www.maturin.rs/) for development. To build and install
locally:

```sh
$ python -m maturin build; python -m pip install .
$ python -m pytest
```

### Planned Development

- DMI file modification.
- Better errors and consistent API surface area.
- More improvement of AST walking and code reflection API.
- Pre-defined AST walker superclass with useful behaviors.
- Ability to create new tile definitions in DMM files.
- Passing compiler defines to parser.

## License

Avulto is licensed under the GPL. See `LICENSE` for more information.

## Acknowledgements

Portions of Avulto are originally based on
[SpacemanDMM](https://github.com/SpaceManiac/SpacemanDMM), copyright Tad
Hardesty and licensed under the GPL.

Portions of Avulto are originally based on
[StrongDMM](https://github.com/SpaiR/StrongDMM), copyright SpaiR and licensed
under the GPL.