semvec/pypi-packages huggingface dataset:
interactive-CLI-menu 0.0.5
A library for creating menus in the console
Requires Python: >=3.7

# interactive_CLI_menu

## **Under Construction!**

## Info

This is a library for creating menus in the console (CLI). There are four functions:

simple - Press **ENTER** to select an option

multi - Press **SPACE** to select an option, then press **ENTER** to confirm

mix - Same as multi, but with sliders. User **LEFT ARROW** and **RIGHT ARROW** to adjust the slider

amount - Press **SPACE** to select an option, then use **LEFT ARROW** and **RIGHT ARROW** to adjust the slider
sygnet 0.0.13
Synthetic data using Generative Adversarial Networks
Requires Python: >=3.9
Keywords: synthetic data,neural networks,GAN,torch

# SyGNet<img src="sygnet.png" alt="SyGNet Mascot" align="right" width="20%" /></a>

## **Sy**nthetic data using **G**enerative Adversarial **Net**works

*Principal Investigator: Dr Thomas Robinson (thomas.robinson@durham.ac.uk)*

*Research team: Artem Nesterov, Maksim Zubok*

![example workflow](https://github.com/tsrobinson/SyGNet/actions/workflows/python-app.yml/badge.svg)

**sygnet** ("sig·net") is a Python package for generating 
synthetic data within 
social science contexts. The **sygnet** algorithm uses cutting-edge advances in deep learning methods to learn the underlying relationships between variables in a dataset. Users can then generate brand-new, synthetic observations that mimic the real data.

### Installation
To install via pip, you can run the following command at the command line:
`pip install sygnet`

**sygnet** requires:
    
    numpy>=1.21
    torch>=1.10.0
    scikit-learn>=1.0
    pandas>=1.4
    datetime
    tqdm

### Example implementation

You can find a demonstration of **sygnet** under [examples/basic_example](examples/basic_example.ipynb).

### Current version: 0.0.13 (alpha release)

**Alpha release**: You should expect both functionality and pipelines to change (rapidly and without warning). Comments and bug reports are very welcome!

Replacing linear with sigmoid activation functions to facilitate better training, given automatic scaling of data to 0-1 space.

### Previous releases

**0.0.12**

Minor documentation updates including README.

**0.0.11**

* Bug fix in sampling method

**0.0.10**

* Minor patch to allow for conda-forge release

**0.0.9**

* Rewrite of main interface and underlying functions
* Bulding models now structured in terms of hidden "blocks"
* Added self-attention mechanism

**0.0.8**

* Update `tune()` to provide no k-fold cross validation as default
* Update numpy dependency to fix pre-processing bug

**0.0.7**
* Update internal `train_*` functions to return losses and improve logging
* Update `tune()` function

**0.0.6 and 0.0.5**
* Internal changes to improve code efficiency
* Removes `sygnet_` from all submodule names
* Lowers PyTorch requirement to 1.10 for compatability with OpenCE environments

**0.0.4**
* Adds `tune()` function to run hyperparameter tuning
* Adds model saving functionality to `SygnetModel.fit()`
* Fixes various bugs
* Improves documentation

**0.0.3**
* Fixes column ordering issue when using mixed activation layer
* Updates example

**0.0.2**
* Fixes mixed activation bug where final layer wasn't sent to `device`
* Adds `SygnetModel.transform()` alias for `SygnetModel.sample()`

**0.0.1**
Our first release! This version has been lightly tested and the core functionality has been implemented.
python-dtoc 2023.10.5
Awesome module for conver dict to class
Requires Python: ~=3.6
Keywords: json


# About this project:
- This project created to convert any dict into class in python :) check [github](https://github.com/x72x/PyDTOC/tree/main)


# Install:
- `pip install python-dtoc`


## Simple example:
- <b>Print the object :</b>
---
```python
from pydtoc import dtc
foo = dtc({'str': 'string', 'int': 1, 'list': [1, 2, 3, 4, {'hi': True}]})
print(foo)
```

- <b>Output :</b>
```json
{
    "str": "string",
    "int": 1,
    "list": [
        1,
        2,
        3,
        4,
        {
            "hi": true
        }
    ]
}
```

- <b>Print key of value :</b>
---
```python
from pydtoc import dtc
foo = dtc({'str': 'string', 'int': 1, 'list': [1, 2, 3, 4, {'hi': True}]})
print(foo.list[-1].hi)
print(foo.str)
```

- <b>Output :</b>
```
True
string
```
dsr-shelx 242
A program to model disorder in small-molecule X-ray structures
Requires Python: >=3.9

<a href="https://repology.org/project/dsr/versions">
    <img src="https://repology.org/badge/vertical-allrepos/dsr.svg" alt="Packaging status" align="right">
</a>

![Lates Release](https://img.shields.io/github/v/tag/dkratzert/DSR?label=Release)
[![Unit tests](https://github.com/dkratzert/DSR/actions/workflows/pythonapp.yml/badge.svg)](https://github.com/dkratzert/DSR/actions/workflows/pythonapp.yml)
![Contributions](https://img.shields.io/badge/contributions-welcome-blue)
[![PyPI package](https://repology.org/badge/version-for-repo/pypi/python:dsr-shelx.svg)](https://repology.org/project/python:dsr-shelx/versions)


DSR
===

The program DSR consists of a text database with fragments of molecules and the DSR program. 
It acts as a preprocessor for SHELXL .res files. The user inserts a special command in the SHELXL .res file 
and the DSR program reads this information to put a molecule or fragment with the desired atoms on the position 
of the target atoms or q-peaks in the unit cell. Bond restraints can be either applied from the database to the molecule 
or automatically generated.

* [The homepage](https://dkratzert.de/dsr.html)

I apologize for the messy code. This was my first bigger project...

You have either a command line version:
```
C:\Users\daniel>dsr
----------------------------------------------------- D S R - v238 -------------------------------------
Disordered Structure Refinement (DSR)

Example DSR .res file command line:
REM DSR PUT/REPLACE "Fragment" WITH C1 C2 C3 ON Q1 Q2 Q3 PART 1 OCC -21 =
  RESI DFIX
---------------------------------------------------------------------------------------------------------
   PUT:     Just put the fragment source atoms here.
   REPLACE: Replace atoms of PART 0 in 1.3 A distance around target atoms.
---------------------------------------------------------------------------------------------------------

optional arguments:
  -h, --help            show this help message and exit
  -r "res file" ["res file" ...]
                        res file with DSR command
  -re "res file" ["res file" ...]
                        res file with DSR command (write restraints to external file)
  -e "fragment"         export fragment as .res/.png file
  -c "fragment"         export fragment to clipboard
  -t                    inverts the current fragment
  -i "tgz file"         import a fragment from GRADE (needs .tgz file)
  -l                    list names of all database entries
  -s "string"           search the database for a name
  -g                    keep group rigid (no restraints)
  -u                    Update DSR to the most current version
  -n                    do not refine after fragment transfer
```

Or a graphical user interface in [ShelXle](https://www.shelxle.org/shelx/eingabe.php):

![DSR main window](https://github.com/dkratzert/DSR/blob/master/pictures/dsr_shelxle.png?raw=true)

![DSR editor](https://github.com/dkratzert/DSR/blob/master/pictures/dsr_editor.png?raw=true)
fyers-token-manager-v3 0.1.1
Fyers Token Manager

```
config = {
  "username": "<USERNAME>",
  "totp_key": "<TOTP_KEY>",
  "pin": "<PIN>",
  "client_id": "<CLIENT_ID>",
  "secret_key": "<SECRET_KEY>",
  "redirect_uri": <REDIRECT_URL>
}
```

## Install

```
pip install fyers-token-manager-v2
```

## Fyers Token Generator

```
from fyers_api import accessToken, fyersModel
from fyers_api.Websocket import ws

from fyers_token_manager_v2 import FyersTokenManager
```

#### Initialization

```
fyersTokenManager = FyersTokenManager(config, accessToken, fyersModel, ws)

print(fyersTokenManager.http_access_token)
print(fyersTokenManager.ws_access_token)
```

#### HTTP Client

- fyersTokenManager.http_client.get_profile()

#### WebSocket Client

- fyersTokenManager.ws_client.subscribe(payload)