semvec/pypi-packages huggingface dataset:
Topsis-Trishti-102313056 0.1.2
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).

---
whatenc 0.9.1
Text encoding type classifier
Requires Python: >=3.13

<div align="center">

# whatenc

<div>
   <a href="https://pypi.org/project/whatenc/"><img src="https://img.shields.io/pypi/v/whatenc.svg" alt="PyPI"></a>
   <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="License"></a>
</div>

<a href="https://heyjeremy.vercel.app/blog/classifying-encoded-text"><img src="https://img.shields.io/badge/blog-how%20can%20a%20program%20recognize%20encoded%20text%20without%20decoding%20it%3F-blue" alt="Blog"></a>

Text encoding type classifier.

</div>

`whatenc` is a command-line tool that identifies the encoding or transformation of a given string or file.

The model is trained on text samples from the English, Greek, Russian, Hebrew, and Arabic Wikipedia corpora, chosen to represent a diverse set of writing systems (Latin, Greek, Cyrillic, Hebrew, and Arabic scripts). Each line is encoded using multiple encoding schemes to generate labeled examples.

## How It Works

`whatenc` uses a character-level 1D Convolutional Neural Network trained directly on bigram token sequences. 

Each training sample is represented as:
- bigram of characters, padded to a fixed maximum length
- a true length scalar feature, allowing the network to learn relative string lengths

This neural approach achieves near-perfect classification accuracy after only a few epochs.

### Supported Encodings

`whatenc` currently recognizes the following formats and transformations:

| Category | Encodings |
| :------- | :-------- |
| Base encodings | `base32`, `base64`, `base85`, `hex`, `url` |
| Text ciphers | `morse` |
| Compression | `gzip64` |
| Hash digests | `md5`, `sha1`, `sha224`, `sha256`, `sha384`, `sha512` |

## Installation

You can install `whatenc` using [pipx](https://pypa.github.io/pipx):

```bash
pipx install whatenc
```

## Usage

### API

```python
from whatenc import Classifier

classifier = Classifier()
print(classifier.predict("hello, world!")) # returns: [('plain', 1.0), ('md5', 7.686760500681856e-26), ('base85', 2.864714171264974e-35)]
```

### CLI

```bash
whatenc hello
whatenc samples.txt
```

#### Examples

```bash
[+] input: ZW5jb2RlIHRvIGJhc2U2NCBmb3JtYXQ=
   [~] top guess   = base64
      [=] base64   = 1.000
      [=] base85   = 0.000
      [=] plain    = 0.000

[+] input: hello
   [~] top guess   = plain
      [=] plain    = 1.000
      [=] md5      = 0.000
      [=] base64   = 0.000

[*] loading model
[+] input: האקדמיה ללשון העברית
   [~] top guess   = plain
      [=] plain    = 1.000
      [=] base64   = 0.000
      [=] base85   = 0.000

[*] loading model
[+] input: bfa99df33b137bc8fb5f5407d7e58da8
   [~] top guess   = md5
      [=] md5      = 0.999
      [=] sha1     = 0.001
      [=] sha224   = 0.000
```
spyprot 1.0.4
This package provides a set of tools for accessing protein databases and manipulating PDB/CIF files.
Requires Python: >=3.6.0

# spyProt 

[![Python package](https://github.com/ilbsm/spyProt/actions/workflows/python-package.yml/badge.svg)](https://github.com/ilbsm/spyProt/actions/workflows/python-package.yml)
Rapid protein overview from multiple databases (RCSB, PDBe, UniProt etc.)

Fetch information from for EC, Uniprot, RCSB Search API etc.

Includes the  ilbsm_database_downloader used to download data from [ILBSM databases](https://jsulkowska.cent.uw.edu.pl/) such as: 
- [AlphaKnot](https://alphaknot.cent.uw.edu.pl/)
- [Knotprot](https://knotprot.cent.uw.edu.pl/)
- [LassoProt](https://lassoprot.cent.uw.edu.pl/)
- [Genus](https://genus.fuw.edu.pl/)

Have a look at tests to see how to use it.

#git tag -d v0.8.8 ; git tag v0.8.8 ; git push origin :v0.8.8 ; git push origin --tags