semvec/pypi-packages huggingface dataset:
musiclib 2.6.0
set of tools to work with scales, modes, modulations, chord progressions, voice leading, rhythm and more
Requires Python: >=3.11

![GitHub tag (latest by date)](https://img.shields.io/github/v/tag/tandav/musiclib)

# musiclib
set of tools to work with scales, modes, modulations, chord progressions, voice leading, rhythm and more

## install
### install from [pypi](https://pypi.org/project/musiclib/)
```sh
pip install musiclib
```

### or install the latest version from github
```sh
pip install git+https://github.com/tandav/musiclib
```

### development
```sh
pip install -e .[dev]
```

## examples

```py
>>> from musiclib.scale import Scale
>>> from musiclib.noteset import SpecificNoteSet
>>> from musiclib.noterange import NoteRange

>>> scale = Scale.from_name('C', 'major')

>>> scale.root
Note('C')

>>> scale.notes
frozenset({Note('C'),
           Note('D'),
           Note('E'),
           Note('F'),
           Note('G'),
           Note('A'),
           Note('B')})

>>> scale.bits
'101011010101'

>>> scale.intervals
frozenset({0, 2, 4, 5, 7, 9, 11})
```

```py
Scale.from_name('C', 'phrygian')
```
![](docs/svg_examples/C_phrygian.svg)

```py
SpecificNoteSet.from_str('C1_e1_G1_C2_b2_E3')
```
![](docs/svg_examples/C1_e1_G1_C2_b2_E3.svg)

```py
NoteRange(SpecificNote('C', 1), SpecificNote('B', 3))
```
![](docs/svg_examples/noterange_C1_B3.svg)
Topsis-UditGupta-102203835 1.0.1
A Python package to perform TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)
Requires Python: >=3.6

# Topsis_UditGupta_102203835

`Topsis_UditGupta_1022038358`is a Python package for implementing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). It is a popular multi-criteria decision-making method used to rank alternatives based on their relative closeness to the ideal solution.

## Installation

You can install the package using pip:

```bash
pip install Topsis_UditGupta_102203835
```

## Usage

### Input File Format

The input CSV file must follow this structure:

- The first column should contain the names of the alternatives (e.g., products, models, or options).
- Subsequent columns should contain the numerical values of the criteria for each alternative.
- The first row should provide headers for all columns.

### Example

Suppose you have an input CSV file named `data.csv` with the following content:

| Model | Price | Battery Life | Performance | Portability |
| ----- | ----- | ------------ | ----------- | ----------- |
| m1    | 300   | 10           | 8           | 6           |
| m2    | 250   | 8            | 7           | 9           |
| m3    | 400   | 9            | 9           | 5           |

You want to apply the TOPSIS method with the following parameters:

- Weights for the criteria: `Price` (0.5), `Battery Life` (0.3), `Performance` (0.2)
- Impacts for the criteria: `Price` (-), `Battery Life` (+), `Performance` (+)

The command would be:

````bash
topsis data.csv "0.5,0.3,0.2" "-,+,+" results.csv


### Command-line Usage

After installation, you can use the `topsis` command in the terminal:

```bash
topsis <input_file> <weights> <impacts> <output_file>
````

- `<input_file>`: Path to the input CSV file.
- `<weights>`: Comma-separated string of weights for the criteria.
- `<impacts>`: Comma-separated string of '+' or '-' indicating the desirability of the criteria.
- `<output_file>`: Path to the output CSV file to save the results.

## Example

```bash
topsis data.csv "0.5,0.3,0.2" "+,-,+" results.csv
```

This command will process the `data.csv` file using the specified weights and impacts and output the results to `results.csv`.

## NOTE

- Ensure the input file contains at least three columns: one for alternatives and at least two for criteria.
- All criteria values should be numerical.
- The number of weights and impacts should match the number of criteria columns.
- Impacts must only include + (beneficial) or - (non-beneficial).

## License

This project is licensed under the MIT License. See the LICENSE file for details.
mochi-re 0.2.5
TUI to test and develop regexes
Requires Python: >=3.8

```text
                      _     _. 
 _ __ ___   ___   ___| |__ (_)
| '_ ` _ \ / _ \ / __| '_ \| |
| | | | | | (_) | (__| | | | |
|_| |_| |_|\___/ \___|_| |_|_|
```

## Mochi - Python Regex CLI Tool!

Mochi provides a terminal UI to test and develop regexes.

The aim of this project is to make it easier to test and develop regexes without having to leave the terminal or browse the web.

---

## Note...

Mochi is an idea of a frustrated developer who got the task of developing regexes and not by a big corporation with deep pockets.

It is a project that is being developed by a single person in their free time.

If you feel Mochi makes your regex journey a bit easier, saves you time and makes you more productive, please consider [sponsoring!](https://github.com/sponsors/dor1202)
Your donations will go a long way in keeping the project alive and beers in my fridge! 🍺

**Thank you!**

---

[![Release](https://img.shields.io/github/release-pre/dor1202/mochi_re.svg)](https://github.com/dor1202/mochi_re/releases)
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/dor1202/mochi_re/blob/main/LICENSE)
<!-- [![Downloads](https://img.shields.io/github/downloads/dor1202/mochi_re/total.svg)](https://github.com/dor1202/mochi_re/releases) -->

---

## Screenshots

1. Match
   <img src="https://github.com/dor1202/mochi_re/blob/main/assets/match_example.png?raw=true"/>
2. Substitution
   <img src="https://github.com/dor1202/mochi_re/blob/main/assets/sub_example.png?raw=true"/>
3. Regex Options
   <img src="https://github.com/dor1202/mochi_re/blob/main/assets/options_example.png?raw=true"/>

## Installation

* Via [Pypi](https://pypi.org/)

   ```shell
   pip install mochi-re
   ```

---

## Starting Mochi

  ```shell
  mochi_re
  ```

---

## Building From Source

 Mochi is currently using Python v3.8.X or above.
 In order to build Mochi from source you must:

 1. Clone the repo
 2. Build and run the executable

      ```shell
      poetry run mochi_re
      ```

---

## Mochi Compatibility Matrix

|         Mochi        | python |
| ------------------ | ---------- |
|     LTS     |   "3.8", "3.9", "3.10", "3.11", "3.12"   |

---

## Contributors

Without the contributions from these fine folks, this project would be a total dud!

<a href="https://github.com/dor1202/mochi_re/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=dor1202/mochi_re" />
</a>

---

## Known Issues

This is still work in progress! If something is broken or there's a feature
that you want, please file an issue and if so inclined submit a PR!

---

## Contributions Guideline

* File an issue first prior to submitting a PR!
* Ensure all exported items are properly commented

---

## References

Big thanks to the following projects for their inspiration:

* [Textual](https://github.com/Textualize/textual)
* [k9s](https://github.com/derailed/k9s)
* [rexi](https://github.com/royreznik/rexi)

Without them, this project would not be possible.
topsis102217164Ayush 0.1.0
A basic TOPSIS package for decision analysis.
Requires Python: >=3.6

# topsis102217164Ayush

This is a basic implementation of the TOPSIS method for decision analysis, written in Python. The package provides a simple way to calculate the TOPSIS score and rank alternatives based on their performance with respect to multiple criteria.

## Installation

You can install the package using pip:

```bash
pip install topsis102217164Ayush
Topsis-Krish-102203848 1.0.2
A Python package to implement TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)
Requires Python: >=3.6

# Topsis-Krish-102203848

`Topsis-Krish-102203848` is a Python package for implementing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a multi-criteria decision-making (MCDM) method. This package simplifies the process of ranking and selecting alternatives based on multiple criteria with varying weights and impacts.

## Installation

Install the package from PyPI using `pip`:

```bash
pip install Topsis_Krish_102203848


Usage
Command-Line Interface (CLI)
Prepare a CSV file with the following format:

The first column contains the names of the alternatives (e.g., "M1", "M2").
The subsequent columns contain numeric values for each criterion.
Ensure the file has a header row.
Example:

FundName	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

E.g. Program-

import os
from topsis.topsis import main

# def run_topsis():
#     # Define input file
#     input_file = "input.csv"
    
#     # Define weights and impacts
#     weights = "1,1,1,1,1"  # Example: Equal weights for all criteria
#     impacts = "+,+,-,+,-"  # Example: Benefits (+) and costs (-) criteria
    
#     # Define output file
#     output_file = "output.csv"
    
#     # Generate the input CSV file
#     data = """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
# M3,0.82,0.67,3.8,63.1,17.1
# M4,0.6,0.36,3.5,69.2,18.42
# M5,0.76,0.58,4.8,43,12.29
# M6,0.69,0.48,6.6,48.7,14.12
# M7,0.79,0.62,4.8,59.2,16.35
# M8,0.84,0.71,6.5,34.5,10.64"""
    
#     with open(input_file, "w") as file:
#         file.write(data)
    
#     print(f"Input file '{input_file}' has been created.")
    
#     # Run TOPSIS
#     try:
#         main(input_file, weights, impacts, output_file)
#         print(f"Output file '{output_file}' has been generated.")
#     except Exception as e:
#         print(f"Error during TOPSIS execution: {e}")
    
#  
#     if os.path.exists(output_file):
#         with open(output_file, "r") as file:
#             print("\nGenerated Output File Content:")
#             print(file.read())

# if __name__ == "__main__":
#     run_topsis()


License
This package is licensed under the MIT License.

Contributing
Contributions are welcome! Feel free to fork the repository and submit pull requests.