semvec/pypi-packages huggingface dataset:
crossinstaller 0.2.0
PyInstaller + Docker = CrossInstaller
License: MIT
Requires Python: >=3.9
Keywords: packaging,app,apps,bundle,convert,standalone,executable,pyinstaller,cxfreeze,freeze,py2exe,py2app,bbfreeze,docker

# CrossInstaller

[![Build](https://github.com/desty2k/crossinstaller/actions/workflows/build.yml/badge.svg)](https://github.com/desty2k/crossinstaller/actions/workflows/build.yml)
[![Version](https://img.shields.io/pypi/v/crossinstaller)](https://pypi.org/project/crossinstaller/)
[![Version](https://img.shields.io/pypi/dm/crossinstaller)](https://pypi.org/project/crossinstaller/)

Create Python executables for Linux and Windows using one command.

## Installation

From PyPI

```shell
pip install crossinstaller -U
```

From sources

```shell
git clone https://github.com/desty2k/crossinstaller.git
cd crossinstaller
pip install .
```

## Usage

### Creating executables

Target platforms are specified by the `--platform` option. Option can be specified multiple times.
At least one platform must be specified. Available default platforms are: `win32`, `win64`, `i386`, `amd64`.

```shell
crossinstaller some_script.py -p amd64 -p win64
```
The above command will create two executables: `some_script` for Linux x64 and `some_script.exe` for Windows x64.

### Adding custom platform
Use `-a`/`--add-platform` option to add custom platform. For example, if you want to create 
an executable for `platform_name` platform, which use `dockerfile` Dockerfile, you can do it like this:
```shell
crossinstaller some_script.py -a platform_name path/to/dockerfile
```
Option can be specified multiple times.

### Passing extra arguments to the Pyinstaller
You can pass extra arguments to the Pyinstaller by using `-e`/`--options` option. 
Make sure to quote the arguments. __Note the extra space in the argument.__
```shell
crossinstaller some_script.py --options " -F --icon icon.ico"
```

### Specifying the working directory
By default, the working directory is the same as the script directory. 
You can specify custom working directory by using `-w`/`--workdir` option.
```shell
crossinstaller path/to/some_script.py -w path/
```

### Using in a script
You can use crossinstaller as a Python module. Note that working directory will be changed to the script directory.

```python
from pathlib import Path
from crossinstaller import Platform, CrossInstaller, get_default_platforms

if __name__ == '__main__':
    generator = CrossInstaller()
    # add default platforms
    generator.add_platforms(get_default_platforms())
    # add custom platform
    # note: all files required by the Dockerfile must be in the same directory as the Dockerfile
    my_platform = Platform("my-platform-64", Path("/usr/bin/my-platform-64/Dockerfile"))
    generator.add_platform(my_platform)
    # start crossinstaller
    generator.start(Path("path/to/my/script.py"), keep_build=True, extra_options="-F")
    # start is non blocking, you can do other stuff here
    # or wait for the build to finish
    generator.wait()
```
python-json-config-parser 1.0.9
Project created to given the possibility of create dynamics Json config files
License: MIT

# python-json-config-parser
This project was created to give you the possibility of creating json config files dynamicaly using OOP

HOW TO INSTALL
---------------------------
Use pip to install it.

```
pip install python-json-config-parser
```


HOW TO USE
---------------------------
The Class Config takes two arguments, the first is a Schema(https://github.com/keleshev/schema) to ensure the model of config, the second is the str path of json config file.

If you don't specify the path, the default value('config.json') will be used if one exists in the same dir where the script is running.

For example:

The model file.
```
from schema import Use, And

SCHEMA_CONFIG = {
    "core":{
        "logging":{
            "format": And(Use(str), lambda string: len(string) > 0),
            "datefmt": And(Use(str), lambda string: len(string) > 0)
        },
        "allowed_clients":[
            {
                "ip":str, # <- Here you can use regex to validate the ip format
                "timeout":int
            }
        ]
    }
}

```

The config.json file
```
{
    "core":{
        "logging":{
            "format":"[%(asctime)s][%(levelname)s]: %(message)s",
            "datefmt": "%d-%b-%y %H:%M:%S"
        },
        "allowed_clients":[
            {
                "ip":"192.168.0.10",
                "timeout":60
            },
            {
                "ip":"192.168.0.11",
                "timeout":100
            }
        ]
    }
}
```

The istance of Config Class:
```
from jsonconfigparser import Config, ConfigException
import logging

try:
    config = Config(SCHEMA_CONFIG) # <- Here I'm using that SCHEMA_CONFIG we had declared, and the dir file default value is being used
except ConfigException as e:
    print(e)
    exit()

#to access your config you need just:


fmt = config.core.logging.format #look this, at this point I'm already using the config variable
dtfmt = config.core.logging.datefmt #here again

logging.getLogger(__name__)

logging.basicConfig(
    format=fmt,
    datefmt=dtfmt
    level=20
)

#the list of object example:

for client in config.core.allowed_clients:
    print(client.ip)
    print(client.timeout)

```

Instanced the first obj, you can instance Config in other files of your project
just calling the Config without args like that:

```
from jsonconfigparser import Config, ConfigException

config = Config() #At this point you already have the configuration properties in your config object
```


CONTRIBUTE
----------

Fork https://github.com/BrunoSilvaAndrade/python-json-config-parser/ , create commit and pull request to ``develop``.
connectapi-core 1.1.3
Core SDK for the ConnectAPI ecosystem
License: MIT
Keywords: connectapi,ConnectAPI,web,client,core

# python-core-client (Python + ConnectAPI ❤)
Client lib for communicating with the API backend


### My purpose
This library is responsible for secure communication with the ConnectAPI backend,
this mean refreshing the access token and validating it.

This library by it self can't communicate easily with the backend,
to communicate with a specific service you need to install the auto-generated library for that service,
you can get the information about this library and how to install it in the system dashboard under the service tab.

**This is a required library for every communication with the ConnectAPI backend**
PyoSolveWrapper 0.3.2
Wrapper for Pyomo solve method
License: MIT
Requires Python: >=3.5

# Pyomo Solver Wrapper

## Introduction

A third-party python-based solver wrapper for [Pyomo](http://www.pyomo.org/about).

## Requirements
1. [Python v3.5+](https://www.python.org/downloads/release/python-370/) 

## Installation

The package can be installed using the command:
```
pip install PyoSolveWrapper
```

Also by directly downloading an appropriate release or cloning the main development repository from GitHub:
```
git clone https://github.com/judejeh/PyomoSolverWrapper.git
```

Change into the "PyomoSolverWrapper" directory created by Git or extracted from the downloaded zip file and run:
```
python setup.py install
```

## Usage
Import the solve wrapper:
```
import PyoSolveWrapper as slw 
```
This package solely aids the optimisation model solve process. Hence, having created an appropriate pyomo model object (further referred to as 'model'), an instance of the solver wrapper is first created:
```
Solver = slw.SolverWrapper()
```
Basic solver options can be changed where appropriate:
```
Solver.solver_name = 'cbc'
Solver.threads = 2
Solver.time_limit = 1200
Solver.rel_gap = 0.1
Solver.solver_progress = True
```
The package attempts to find the appropriate solver executable from the system PATH. However, should the solver path be in a different location, this path may be included as:
``` 
Solver.solver_path = '<PATH_TO_SOLVER>'
```
Note that whatever solver supplied should be compatible with Pyomo.

Where solvers are not locally installed, optimisation models may be solved using the [NEOS server](https://neos-server.org/neos/) if doing so for non-commercial purposes.
Note that for now, results are not post-processed.

**Finally**, the model can be solved as:
```
Solver.solve_model(model)
```

Results obtained from the model solution is automatically post-processed and saved with 'Solver' and can be accessed via the dictionary:
``` 
Solver.final_results[<type>][<name>]
```
where `<type>` is one of of 'sets', 'parameters' or 'variables'; and `<name>` is the actual model component name used in 'model'.

For e.g., a parameter and variable name 'A' and 'y' in the Pyomo model object 'model' can be accessed after successful model solution via:
``` 
Solver.final_results['parameters']['A']
```
and
``` 
Solver.final_results['variables']['y']
```
respectively.

Where parameters or variables are index of set(s), they can be accessed in the order of the sets they were declared by when creating the model component.

## Disclaimer
At the time of writing, the authors of this package have no affiliation with Pyomo or NEOS server. The package was written to simplify an optimisation model solving process using Pyomo, especially relating to post-processing results and dealing with multiple solvers.