semvec/pypi-packages huggingface dataset:
pygeckocircuits2 0.0.4
Control GeckoCircuits by using python.
Requires Python: ~=3.8

Welcome to PyGeckoCircuits2
==================================================
This repository was inspired by `MaurerM/pygeckocircuits <https://github.com/MauererM/pygeckocircuits>`__, added by certain functions and a pip-installable package.

Control a GeckoCircuits simulation by Python code.


.. image:: docs/source/figures/pygeckocircuits2.png


Installation
---------------------------------------
Install pygeckocircuits2 directly from pyPI:

::

    pip install pygeckocircuits2


Usage
---------------------------------------
Download the compiled gecko-version from `tinix84/gecko <https://github.com/tinix84/gecko/releases/tag/v1.1>`__.

Run the `example file <https://github.com/upb-lea/pygeckocircuits2/blob/main/examples/remote_geckocircuits_example.py>`__ to see the given functions.

Documentation
---------------------------------------

Find the documentation `here <https://upb-lea.github.io/pygeckocircuits2/intro.html>`__.


Troubleshooting
---------------------------------------
* As of now, there are issues with OpenJDK 21 (LTS). Please use OpenJDK 17 (LTS) so far. See also `this issue <https://github.com/tinix84/gecko/issues/1>`__.
predictor-gradient-descent 0.1.0
A library which Predicts the Y value(output) for given test data points using gradient descent algorithm.
Requires Python: >=3.6

# predictor_gradient_descent

A library which Predicts the Y value(output) for given test data points using gradient descent algorithm.
      Use:    import gradient_descent as gd
              gd.output_predictor() 
              #and run the code..You will be guided.
              # Predicts the Y value(output) for given test data points. The model must be trained with samples and output before.
    Just need to call the function. Accuracy of equation will depend on the no of samples entered..(Directly proportional)
    Equation should be of the form Ax(0) + Bx(1) + Cx(2) = Y, where A,B and C are coefficients of the linear equation.
    Input: No of samples, x(0),x(1) and x(2) of all the samples and their Y value
    Output: Y value for new set of x(0), x(1) and x(2)

Sample output:

ENTER THE NO OF SAMPLES AVAILABLE:3
Enter the samples:


Enter the elements in sample 1

Enter the value of X0:
1
Enter the value of X1:
2
Enter the value of X2:
3
Enter the value of Y:
6


Enter the elements in sample 2

Enter the value of X0:
2
Enter the value of X1:
3
Enter the value of X2:
4
Enter the value of Y:
9


Enter the elements in sample 3

Enter the value of X0:
1
Enter the value of X1:
1
Enter the value of X2:
1
Enter the value of Y:
3

 MODEL IS BEING TRAINED..PLEASE WAIT!!

MODEL TRAINED!!!

ENTER THE NEW SAMPLE TO GET THE Y

Enter the element X0:
10
Enter the element X1:
90
Enter the element X2:
11
PREDICTED Y:111.0

[Program finished] 


## Installation

```sh
pip install predictor-gradient-descent
fpop 0.0.8
Operators related to first-principles calculation
Requires Python: >=3.7

# fpop
`fp` stands for first-principles calculation and `op` stands for operators. The abbreviation `fpop` stands for operators related to first-principles calculation.This project is based on [dflow](https://github.com/deepmodeling/dflow) which is a Python framework for constructing scientific computing workflows (e.g. concurrent learning workflows) employing Argo Workflows as the workflow engine. 
# Installation
```
pip install fpop
```
# Develop Guide
If you want to support a new first-principles computing software in `fpop`, you can refer to the writeup of `fpop/vasp.py` and `fpop/abacus.py`.
Specifically, you need to rewrite the abstract method `prep_task` of class `PrepFp`, and rewrite `run_task` and `input_files`(if needed) methods of class `RunFp`.