semvec/pypi-packages huggingface dataset:
Topsis-Armaan-101903292 2.0
Topsis Calculation
License: MIT
Keywords: Topsis,TopsisPackage

## Topsis 
## A Multi-Criteria Decision Analysis Method

**Submitted By** - Armaan Bhardwaj  

**Roll No.** - 101903292 

**Group** - 3COE11


It is a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) came in the 1980s as a multi-criteria-based decision-making method. TOPSIS chooses the alternative of shortest the Euclidean distance from the ideal solution and greatest distance from the negative ideal solution. 

## Installation
This package requires pandas to be pre-installed and python to run.
Install the dependencies and devDependencies and start the server.
 **To Install and run in cmd line:**
```sh
pip install Topsis-Armaan-101903292
python
import Topsis_Armaan_101903292
Topsis_Armaan_101903292.topsis_cal(Input_File_Name.csv,"1,1,1,1 ...","+,-,+,- ...","resultfile.csv") 
```
## Project description
This package is implementation of topsis technique of multi-criteria decision analysis. This package will accept three parameters:

data.csv //file which contains the models and parameters
string of weights separated by commas(,)
string of requirements (+/-) separated by commas(,) // important install pandas,sys,operator and math libraries before installing this // You can install this package using following command pip install Topsis_Armaan_101903292

## License
Â© 2022 Armaan Bhardwaj

This repository is licensed under the MIT license. See LICENSE for details.
armaan 3.20
Topsis Calculation
License: MIT
Keywords: Topsis,TopsisPackage

## Topsis 
## A Multi-Criteria Decision Analysis Method

**Submitted By** - Armaan Bhardwaj  

**Roll No.** - 101903292 

**Group** - 3COE11


It is a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) came in the 1980s as a multi-criteria-based decision-making method. TOPSIS chooses the alternative of shortest the Euclidean distance from the ideal solution and greatest distance from the negative ideal solution. 

## Installation
This package requires pandas to be pre-installed and python to run.
Install the dependencies and devDependencies and start the server.
 **To Install and run in cmd line:**
```sh
pip install Topsis-Armaan-101903292
python
import Topsis_Armaan_101903292
Topsis_Armaan_101903292.topsis_cal(Input_File_Name.csv,"1,1,1,1 ...","+,-,+,- ...","resultfile.csv") 
```
##Project description
This package is implementation of topsis technique of multi-criteria decision analysis. This package will accept three parameters:

data.csv //file which contains the models and parameters
string of weights separated by commas(,)
string of requirements (+/-) separated by commas(,) // important install pandas,sys,operator and math libraries before installing this // You can install this package using following command pip install Topsis_Armaan_101903292

##License
Â© 2022 Armaan Bhardwaj

This repository is licensed under the MIT license. See LICENSE for details.
IReadiTunes 1.0.5
Tool to get any information about iTunes tracks and playlists quickly and easily
License: MIT

# IReadiTunes
Tool to get any information about iTunes tracks and playlists quickly and easily. 

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install IReadiTunes.

```bash
pip install IReadiTunes
```

## Example code

```python
import IReadiTunes as irit

# First of all, init the library
my_lib = irit.lib_init()

# Read iTunes XML file
my_lib.parse(r'path\to\file\iTunes Music Library.xml')

# Get name of playlists
lib_list = my_lib.get_playlist_list()

print("iTunes playlists: ")
for itunes_library in lib_list:
    print("\t" + itunes_library)  # Print name of each playlist

lib_to_analyse = ""
while lib_to_analyse not in lib_list:
    lib_to_analyse = input("Enter name of library you want to analyse: ")  # Prompt the user to enter the name of the playlist to be scanned. 


playlist_with_attributs = my_lib.get_playlist_contents(lib_to_analyse)  # Analyse choosen playlist

print("\n{} playlist content:".format(lib_to_analyse))

# Print attributs of each element stored in the given playlist
nb_of_tracks = len(playlist_with_attributs)
track_ind = 0 
for track in playlist_with_attributs:
    print("Track [{}/{}]:".format(track_ind+1, nb_of_tracks))
    print("\ttrack_id: {}".format(track.track_id))
    print("\tsize: {} Mb".format(irit.get_size(track.size)))
    print("\ttotal_time: {} seconds".format(irit.get_total_time(track.total_time)))
    print("\tdate_modified: {}".format(track.date_modified))
    print("\tdate_added: {}".format(track.date_added))
    print("\tbitrate: {} kbps".format(track.bitrate))
    print("\tsample_rate: {} kHz".format(track.sample_rate))
    print("\tplay_count: {}".format(track.play_count))
    print("\tplay_date: {}".format(track.play_date))
    print("\tplay_date_utc: {}".format(track.play_date_utc))
    print("\tskip_count: {}".format(track.skip_count))
    print("\tskip_date: {}".format(track.skip_date))
    print("\trating: {} stars".format(irit.get_rating(track.rating)))
    print("\talbum_rating: {} stars".format(irit.get_rating(track.album_rating)))
    print("\tpersistent_id: {}".format(track.persistent_id))
    print("\ttrack_type: {}".format(track.track_type))
    print("\tfile_folder_count: {}".format(track.file_folder_count))
    print("\tlibrary_folder_count: {}".format(track.library_folder_count))
    print("\tname: {}".format(track.name))
    print("\tartist: {}".format(track.artist))
    print("\tkind: {}".format(track.kind))
    print("\tlocation: {}".format(irit.get_track_path(track.location)))
    print()
    
    track_ind += 1
```

## Features

 - Fast library decoding
 - Simple, easy to understand code.


## Contributing

All contributions are welcome. Do not hesitate to contact me in case of bugs or ideas for improvement.

## License

[MIT](https://choosealicense.com/licenses/mit/)