semvec/pypi-packages huggingface dataset:
brawkapi-py 1.0
Simple python wrapper for the Brawl Stars API!
Requires Python: >=3.6

# brawlapi
Simple python wrapper for the Brawl Stars API!
This module uses: brawlapi.com (Brawlify.com), brawlstats.
## Requirements
- Python version: 3.6+
- Python Modules: requests, json, os
- OS: Windows, Linux, Android, MacOS

## Installing and Using 
You can use this API in two ways:
1. Download and place the ```brawlapi.py``` file in the directory. Then import the file to the script: ```import brawlapi```.
2. Use pip: ```pip install brawlapi-py``` and import to code: ```import brawlapi```.

## Authorization
To use this API you need access token from here: https://developer.brawlstars.com/#/account <br> Use this function to authenticate: 

```bs = brawlapi.BrawlApi()```
```bs.BrawlSession('token')```
testmodulebrawl 1.0
Simple python wrapper for the Brawl Stars API!
Requires Python: >=3.6

# brawl-api
Simple python wrapper for the Brawl Stars API!
This module uses: brawlapi.com (Brawlify.com), brawlstats.
## Requirements
- Python version: 3.6+
- Python Modules: requests, json, os
- OS: Windows, Linux, Android, MacOS

## Installing and Using 
You can use this API in two ways:
1. Download and place the ```brawlapi.py``` file in the directory. Then import the file to the script: ```import brawlapi```.
2. Use pip: ```pip install brawl-api``` and import to code: ```import brawlapi```.

## Authorization
To use this API you need access token from here: https://developer.brawlstars.com/#/account <br> Use this function to authenticate: 

```bs = brawlapi.BrawlApi()```
```bs.BrawlSession('token')```
tdci-analyis 0.0.1
TDCI data analysis package
Requires Python: >=3.6

# Data analysis package - I. S. Ulusoy, January 2021
## Features of the package

The data analysis package allows you to process tdci output data and analyse the results from your run. The main features are:
- Processing of `expect.t` data: This functionality allows you to read in `expect.t` data and plot relevant entries.
- Processing of `efield.t` data: You may read in and plot a laser pulse and generate it's Fourier transform to identify the width in frequency domain.
- Processing of `nstate_i.t` data: This will construct the autocorrelation function with respect to time `$t=0$` and plot the autocorrelation function. The Fourier transform of the autocorrelation function is generated and plotted, resulting in a spectrum. The values of the autocorrelation function will be printed to a file.
- Processing of `npop.t` data: Using the MO populations, the correlations between MO's are investigated and both printed out as well as plotted. High correlation means that two MO's exchange population.
- Procesing of `table.dat` data: If copied into a file `table.dat`, the L2 norm of the transition dipole moment in length and velocity gauge can be computed, in order to highlight the convergence of the initial wave function in Hilbert space (completeness). A complete wavefunction will result in identical transition dipole moments no matter which gauge, and the resulting L2 norm will be zero.

**Important:: Make sure you provide the correct input and output directories when using the modules!**
tdci-analyis-iulusoy 0.0.1
TDCI data analysis package
Requires Python: >=3.6

# Data analysis package - I. S. Ulusoy, January 2021
## Features of the package

The data analysis package allows you to process tdci output data and analyse the results from your run. The main features are:
- Processing of `expect.t` data: This functionality allows you to read in `expect.t` data and plot relevant entries.
- Processing of `efield.t` data: You may read in and plot a laser pulse and generate it's Fourier transform to identify the width in frequency domain.
- Processing of `nstate_i.t` data: This will construct the autocorrelation function with respect to time `$t=0$` and plot the autocorrelation function. The Fourier transform of the autocorrelation function is generated and plotted, resulting in a spectrum. The values of the autocorrelation function will be printed to a file.
- Processing of `npop.t` data: Using the MO populations, the correlations between MO's are investigated and both printed out as well as plotted. High correlation means that two MO's exchange population.
- Procesing of `table.dat` data: If copied into a file `table.dat`, the L2 norm of the transition dipole moment in length and velocity gauge can be computed, in order to highlight the convergence of the initial wave function in Hilbert space (completeness). A complete wavefunction will result in identical transition dipole moments no matter which gauge, and the resulting L2 norm will be zero.

**Important:: Make sure you provide the correct input and output directories when using the modules!**