semvec/pypi-packages huggingface dataset:
S_Logger 1.0.1b0
S_Logger

# S_Logger
Simple to use logger for Python code.

## Description
It is wrapper for general Python logger.
 
## Dependencies
- __python 3.6__ or more (maybe will be work on Python 2.x, but I didn't test it).
- module [__PyYAML==3.12__](https://pyyaml.org/) or more (needed for file settings.
You will install from __pip__, ex.: __pip3 install PyYAML__)

## How use
File __test.py__ have a little example.
- need install module: __pip install s_logger__
- create **'logger_settings.yaml'**. See format [below](#format-logging_settingyaml)
- in your code need use import fro importing module
```pyhton
from s_logger.S_Logger import S_Logger
```
- create the object of class S_Logger and give it settings file name

```python
log = S_Logger()
```
- get logger for current script
```python
logger = log.get_logger(__file__)
```

#### Example:
```python
from s_logger.S_Logger import S_Logger


if __name__ == '__main__':
    log = S_Logger()
    # Show current log file names
    print(str(log))

    # Get logger for this file
    logger = log.get_logger(__file__)

    # Different of level log test
    logger.critical('Test logger: CRITICAL msg')
    logger.fatal('Test logger: FATAL msg')
    logger.error('Test logger: ERROR msg')
    logger.warning('Test logger: WARNING msg')
    logger.info('Test logger: INFO msg')
    logger.debug('Test logger: DEBUG msg')
```

### format logging_setting.yaml:
```
values:
    log_folder: '/Users/same_user/logs'
    general_log_name: 'general.log'
    error_log_name: 'error.log'

    default_log_level: 'DEBUG'
    general_log_level: 'DEBUG'
    error_log_level: 'ERROR'
    console_log_level: 'DEBUG'
```

#### params
- __log_folder__ - path to folder for stored log files
- __general_log_name__ - file name of general log
- __error_log_name__ - file name of error log
- __default_log_level__ - minimal level value of default log
- __general_log_level__ - minimal level value of general log
- __error_log_level__ - minimal level value of error log
- __console_log_level__ - minimal level value of console output

## Current version: 1.0.1
pymoth 0.0.3
A package for handling MOTChallenge data sets

pymoth (Multi-Object Tracking Handler) is designed to 
make information from MOTChallenge data set sequences easy to load and access in Python.
The pymoth object is a Namespace which stores a Sequence object for each set of labels in the data set.
Each sequence object contains of a number of Frames, each containing constituent Instance objects.
Instances can return information such as the bounding box, id number, and appearance patch.
python-asip-client 1.0.9
Serial manager for Mirto robot services

# Asip serial manager for python
A Python client for the ASIP protocol.
Client should support both Python 2.7 and 3. Anyway, compatibility issues may still exist.
Package allows to communicate with MirtoRobot via serial port as well as TCP connection, using a bridge on a robot, 
and source files on remote host.
This package can be installed directly on Raspberry Pi or other computer.

## Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

### Prerequisites

```
1) python3
2) pip3
3) serial
```

### Installing

```
1) apt-get install python3
2) apt-get install pip3
3) sudo install pip3 pyserial
4) pip3 install --user python-asip-client --upgrade
4.1) pip3 install --user . --no-cache-dir (When installing local files, development mode)
```

## Running the tests

Explain how to run the automated tests for this system

### Break down into end to end tests

Explain what these tests test and why

```
Give an example
```

### And coding style tests

Explain what these tests test and why

```
Give an example
```

## Deployment

This package is deployed on pip global server. In order to make new release, please follow below:

```
In case of there is an existing buid it can be removed by follwoing command:
1) rm -r build/ dist/ python_asip_client.egg-info
2) python setup.py sdist bdist_wheel
3) twine upload --repository pypi dist/*     Note using this command make sure you have installed twine and config file.

```
## Built With
```
?
```
## Contributing

Please read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests to us.

## Versioning

We use [PEP 440](https://www.python.org/dev/peps/pep-0440/) for versioning.
PEP 440 Version Identification and Dependency Specification

## Authors

* **Gianluca Barbon** - *Computer Scientists* - [Github](https://github.com/gbarbon)
* **Adam Jarzebak** - *Computer Scientists* - [Github](https://github.com/jarzab3)
* **Franco Raimondi** - *Professor of Computer Science* - [Github](https://github.com/fraimondi)

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details

## Acknowledgments
MSM-PELE 1.1.1
Monte Carlo Protein Energy Landscape Exploration (PELE) coupled with Markov State Model (MSM) analysis with the aim to calculate absolute free energies

# MSM_Pele
--------------
Monte Carlo Protein Energy Landscape Exploration (PELE) coupled with Markov State Model (MSM) analysis  with the aim to calculate absolute free energies.

# MSM_Pele's PipeLine
-------------------------------
1) [Protein Preparation for Pele](https://github.com/Jelisa/mut-prep4pele)
2) [PlopRotTemp_SCHR2017](https://github.com/miniaoshi/PlopRotTemp_S_2017)
3) [Adaptive PELE](https://github.com/AdaptivePELE/AdaptivePELE)
4) [PELE(comercial software)](https://pele.bsc.es/pele.wt)
5) [MSM](https://github.com/miniaoshi/Pele_scripts)

# Getting Started
-------------------
0) git clone https://github.com/miniaoshi/MSM_PELE.git
1) Change all paths under **MSM_Pele/constants.py** to your local environment.

2) Change LD_LYBRARY_PATH as next in the conf.sl to make schrodinger python work:
-  `e.g. export LD_LIBRARY_PATH=**/sNow/easybuild/centos/7.4.1708/Skylake/software/schrodinger2017-4/mmshare-v4.0/lib/Linux-x86_64/**:$LD_LIBRARY_PATH`
3) Fulfill the next dependencies:
- Adaptive Pele
- pyemma
- msmtools
- Prody 1.8.2
- Pandas
4) Run the platform as:
- python MSM_PELE/main.pdb pdb_with_complex residuename chain
-  `e.g. python /home/dsoler/PelePlop/main.py PR_1A28_xray_-_minimized.pdb STR Z`
- slurm: `sbatch conf.sl` (change the complex path)

# Arguments:
---------------
- **Description:** 
    Monte Carlo Protein Energy Landscape Exploration (PELE) coupled with Markov State Model (MSM) analysis  with the aim to calculate absolute free energies
    - **Requested arguments:** 
        - **complex**: Complex with target & ligand in the binding site.
        - **resname**: Residue name of the ligand in the BS.
        - **chain**: Chain of the ligand in the BS.
    -  `e.g. python /home/dsoler/PelePlop/main.py PR_1A28_xray_-_minimized.pdb STR Z` <br />

    - **Optional arguments:**
        - **--charge_ter**       If set charge protein terminals
        - **--forc FORC**        Forcefield to use ["OPLS2005", "AMBER99sb"]
        - **--native NATIVE**    Native file to compare RMSD to
        - **--cpus CPUS**        Number of processors to use in adaptive in out
        - **--core CORE**        Give one atom of the core section for PlopRotTemp
        - **--mtor MTOR**        Gives the maximum number of torsions allowed in each
        group. Will freeze bonds to extend the core if necessary.
        - **--n N**              Maximum Number of Entries in Rotamer File
        - **--clean**            Whether to clean up all PlopRotTemp intermediate files
    - **Output:**
        - The platform will output a file results_summary.txt inside {resname}_PELE/output_pele/results_summary.txt with the           absolute free energy average over all pele trajectories and its standard deviation. If nothing is ouputted means             something is wrong and you can refear to the output.log file or mpi_{IDJOB}.err  mpi_{IDJOB}.out files to traceback           the error.