semvec/pypi-packages huggingface dataset:
MDRefine 0.0.11
A package to perform refinement of MD simulation trajectories.
Requires Python: >=3.8

[![GitHub tag (latest SemVer)](https://img.shields.io/github/v/tag/bussilab/MDRefine)](https://github.com/bussilab/MDRefine/tags)
[![PyPI](https://img.shields.io/pypi/v/MDRefine)](https://pypi.org/project/MDRefine/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/MDRefine)](https://pypi.org/project/MDRefine/)
[![CI](https://github.com/bussilab/MDRefine/workflows/CI/badge.svg)](https://github.com/bussilab/MDRefine/actions?query=workflow%3ACI)

A package to perform refinement of MD simulation trajectories. Documentation can be browsed at [this page](https://bussilab.github.io/doc-MDRefine/)

To install package and dependencies with pip (version from PyPI):

```
pip install MDRefine
```

You can install the development version by cloning the repository and, from its root directory, type:

```
pip install -e .
```

In case you prefer to install MDRefine in a separate environment to avoid polluting your Python installation with unnecessary packages, we recommend using a Python virtual environment

```
python3 -m venv env
source env/bin/activate
pip install MDRefine
```

To load this environment in a new shell, type `source /path/to/env/bin/activate`.
PyEvALL 0.1.78
PyEvALL (The Python library to Evaluate ALL) is a evaluation tool for information systems that allows assessing a wide range of metrics covering various evaluation contexts, including classification, ranking, or LeWiDi (Learning with disagreement).

# PyEvALL
 PyEvALL (The Python library to Evaluate ALL) is an evaluation tool for information systems that allows assessing a wide range of metrics covering various evaluation contexts, including classification, ranking, or LeWiDi (Learning with disagreement). PyEvALL is designed based on the following concepts: (i) **persistence**, users can save evaluations and retrieve past evaluations; (ii) **replicability**, all evaluations are conducted using the same methodology, making them strictly comparable; (iii) **effectiveness**, all metrics are unified under measurement theory and have been doubly implemented and compared; (iv) **generalization**, achieved through the use of a standardized input format enabling users to evaluate all evaluation contexts.
is-prime-check 1.0.1
A simple package to check if a number is prime
Requires Python: >=3.6

# is_prime

A simple Python package to check if a number is prime.

## Installation

```bash
pip install is_prime_check
```

## Usage

```python
from is_prime import is_prime, get_primes

# Check if a single number is prime
print(is_prime(17))  # True
print(is_prime(4))   # False

# Get primes in a range
print(get_primes(10, 20))  # [11, 13, 17, 19]
```

## Features

- Check primality of integers
- Generate lists of prime numbers
- Efficient algorithm with O(sqrt(n)) time complexity

## License

MIT License
aiidalab-qe-pp 0.1.4
AiiDAlab quantum ESPRESSO app plugin for PP code
Requires Python: >=3.9

# aiidalab-qe-pp
Plugin to perform data analysis and plotting using the pp.x code for the  [AiiDAlab Quantum ESPRESSO application](https://github.com/aiidalab/aiidalab-qe)

## Installation
To install the `aiidalab-qe-pp` plugin, follow these steps:

```shell
git clone https://github.com/AndresOrtegaGuerrero/aiidalab-qe-pp.git
cd aiidalab-qe-pp
pip install -e .
```

## Python

This plugin enables the computation of reduced-size cube files generated from Python using the [AiiDA-PythonJob plugin](https://github.com/aiidateam/aiida-pythonjob).
To ensure compatibility, set up an AiiDA code that utilizes the same Python version as your AiiDAlab environment.
Ensure that your environment includes the `pymatgen` , `cloudpickle` and `scikit-image` libraries.
Once configured, you can set up your AiiDA code with the following command:


```bash
verdi code create core.code.installed --config python_code.yml
```

In the `python_code.yml` file, specify the configuration for your HPC system within AiiDAlab as follows:

```bash
---
label: 'python-py39'
description: 'python at daint alps'
default_calc_job_plugin: 'pythonjob.pythonjob'
filepath_executable: '/users/username/miniconda3/envs/py39/bin/python'
computer: 'daint-alps'
prepend_text: |
   conda activate py39

append_text: ' '
```


## Critic2 Integration

This plugin utilizes [Critic2](https://aoterodelaroza.github.io/critic2/) software to compute Scanning Tunneling Microscopy (STM) images from Quantum ESPRESSO output data.
For more information on Critic2 and its capabilities, please visit the [official Critic2 website](https://aoterodelaroza.github.io/critic2/).

## Critic2 AiiDAlab Installation

For installing Critic2 in your AiiDAlab please ensure you have gfortran and cmake

```shell
mamba install gfortran
mamba install cmake
```

Then follow the instructions for [Critic2 Installation](https://aoterodelaroza.github.io/critic2/installation/)

Onced installed critic2

```bash
verdi code create core.code.installed --config code.yml
```
where code.yml for your localhost within AiiDAlab can be

```bash
---
label: 'critic2'
description: 'Critic 2 at localhost'
default_calc_job_plugin: 'critic2'
filepath_executable: '/home/jovyan/critic2/build/src/critic2'
computer: 'localhost'
prepend_text: |

append_text: ' '
```

## License
The `aiidalab-qe-pp` plugin package is released under the MIT license.
See the `LICENSE` file for more details.

## Contact

If you have any questions or suggestions, feel free to reach out:

- **Author**: Andres Ortega-Guerrero
- **Email**: [andres.ortega-guerrero@empa.ch](andres.ortega-guerrero@empa.ch)

## Acknowledgements
We acknowledge support from:
* the [NCCR MARVEL](http://nccr-marvel.ch/) funded by the Swiss National Science Foundation;
school-management-leo 1.0.0
Read the latest Real Python tutorials
Requires Python: >=3.9
Keywords: feed, reader, tutorial


```markdown
+----------------+         +----------------+        +----------------+
|    Student     |         |    Teacher     |        |     Course     |
+----------------+         +----------------+        +----------------+
| - studentID    |         | - teacherID    |        | - courseID     |
| - name         |         | - name         |        | - courseName   |
| - dob          |         | - dob          |        | - department   |
| - address      |         | - email        |        | - creditHours  |
| - email        |         | - department   |        +----------------+
+----------------+         +----------------+        | + addStudent() |
| + enroll()     |         | + assignGrade()|        | + removeStudent()|
| + dropCourse() |         | + createCourse()|       +----------------+
| + viewGrades() |         +----------------+
+----------------+         +----------------+
        |                       | 1..*  
        |                       | 
        |1                      | 
        |                       | 
        V                       V
  +----------------+     +------------------+
  |   Department  |     |   Classroom      |
  +----------------+     +------------------+
  | - departmentID |     | - roomNumber     |
  | - departmentName|     | - capacity       |
  | - location     |     +------------------+
  +----------------+     | + assignCourse() |
  | + addCourse()  |     | + removeCourse() |
  | + removeCourse()|     +------------------+
  +----------------+      
```