semvec/pypi-packages huggingface dataset:
env-to-class 1.1.0
Enviroment variables to class object with checking for the presence of parameters
Requires Python: >=3.1,<4.0


## Description

Simple use env settings with class type

# install
```
  pip install env-to-class
```

#### import

```python
from env_to_class import Settings
```

#### Description
```text
The library allows you to encrypt your settings stored in json format.
It is possible to convert from a simple storage option to an encrypted one. 
To work with the encrypted version of the settings, you need to pass the startup parameter - the password with which the encryption took place.
Try it, the library is very simple.
```


#### Usage
# Import lib
```python
  required_settings = 'Database.name, Database.user, Database.pwd, Database.host, Clickhouse.host, Clickhouse.user, Clickhouse.pwd'
  settings = Settings(required_settings)

  nameDB = settings.Database.name
  userDB = settings.Database.user
  pwdDb = settings.Database.pwd
  hostDB = settings.Database.host
  clickhouseHost = settings.Clickhouse.host
  clickhousePwd = settings.Clickhouse.pwd
  clickhouseUser = settings.Clickhouse.user
```
nokoskia 8.0
A fun tool like cmatrix, but for breaking bad fans.
Keywords: python,breakingbad,movie,fun,funny tools,command line tool


# nokoskia

`nokoskia` is a fun command-line tool that fills your terminal with the famous Breaking Bad dialogue.

## Installation

You can install `nokoskia` from PyPI using `pip`:

	$ pip install nokoskia

To run ``nokoskia`` :

	$ knock
monetdb_stethoscope 0.5.0
MonetDB profiler connection tool
Requires Python: >=3.7,<4.0

|PyPIBadge|_ |ActionsBadge|_ |DocsBadge|_ |CoverageBadge|_

Introduction
============

``stethoscope`` is a command line tool to filter and format the events coming
from the MonetDB profiler. The profiler is part of the MonetDB server and works
by emitting two JSON objects: one at the start and one at the end of every MAL
instruction executed. ``stethoscope`` connects to a MonetDB server process,
reads the objects emitted by the profiler and performs various transformations
specified by the user.

Installation
============

Installation is done via pip:

.. code:: shell

   pip install -U monetdb-stethoscope

This project is compatible with Python 3.6 or later and with MonetDB server
version Jun2020 or later.

We recommend the use of virtual environments (see `this
primer <https://realpython.com/python-virtual-environments-a-primer/>`__
if you are unfamiliar) for installing and using
``monetdb-stethoscope``.


Documentation
=============

For more detailed documentation please see the documentation on `readthedocs
<https://monetdb-solutions-monetdb-stethoscope.readthedocs-hosted.com/en/latest/>`__.

Developer notes
---------------

See the `documentation
<https://monetdb-solutions-monetdb-stethoscope.readthedocs-hosted.com/en/latest/>`__
for instructions.

.. |ActionsBadge| image:: https://github.com/MonetDBSolutions/monetdb-stethoscope/workflows/Test%20pystethoscope/badge.svg?branch=master
.. _ActionsBadge: https://github.com/MonetDBSolutions/monetdb-stethoscope/actions
.. |DocsBadge| image:: https://readthedocs.com/projects/monetdb-solutions-monetdb-stethoscope/badge/?version=latest&token=c659c74db0e19ebd763adc2d217404f48588e223dcc84b24583446a1f86fcc83
.. _DocsBadge: https://monetdb-solutions-monetdb-stethoscope.readthedocs-hosted.com/en/latest/?badge=latest
.. |CoverageBadge| image:: https://codecov.io/gh/MonetDBSolutions/monetdb-pystethoscope/branch/master/graph/badge.svg
.. _CoverageBadge: https://codecov.io/gh/MonetDBSolutions/monetdb-pystethoscope
.. |PyPIBadge| image:: https://img.shields.io/pypi/v/monetdb-stethoscope.svg
.. _PyPIBadge: https://pypi.org/project/monetdb-stethoscope/
convertToDecimalTime 1.0.3
A small python library to convert standard time to decimal time.
Requires Python: >=3.8

# Convert to decimal time
A small python library to convert standard time to decimal time. 

# What is decimal time 
Decimal time is the representation of the time of day using units which are decimally related. Essentially decimal times operates with powers of 10. 

- DTs = Decimal Time second
- DTm = Decimal Time minute
- DTh = Decimal Time hour
This classification is unofficial

| Unit     | Decimal Time | Equivalent in SI seconds |
|----------|--------------|--------------------------|
| Seconds  | 1 DTs        | 0.864                    |
| Minutes  | 1 DTm        | 86.4                     |
| Hours    | 1 DTh        | 8,640                    |

# Functions
The following are the descriptions of the functions and their required input. 
- `getSeconds`: Takes an int argument that represents the SI seconds and returns an int that represents the DT seconds.
- `getMinutes`: Takes an int argument that represents the SI seconds and returns an int that represents the DT minutes.
- `getHours`: Takes an int argument that represents the SI seconds and returns an int that represents the DT hours. 
- `getTimeNow`: Gets the system time and returns an array that contains the current hours, minutes and seconds in DT. 

# Example usage
After you install the library using the command `pip install /path/to/file/convertToDecimalTime-1.x.x-py3-none-any.whl` or `pip install convertToDecimalTime`, you can start using the library writing scripts like the following: 
```
from convertToDecimalTime import getMinutes

x = int(input("Minutes: ")) 
print("Minutes: ", getMinutes(x)) 
```
output: 
Let's say that my input is 3600, the output will be: `Minutes: 41`.

# Motiation for this project 
The motivation for this small project came from an article featured on Hacker news that contained a link to the wikipedia article for decimal time. Decimal time is not widely used and that's why there was no library to handle the conversions. It is likely that this library won't be used anywhere because of how little decimal time is used but it is great to always have the option.
sustech-ncs 0.0.7
A python impletation of NCS-C.

# A Python implementation of NCS-C.



参考文献：

**K. Tang, P. Yang and X. Yao, "Negatively Correlated Search," in IEEE Journal on Selected Areas in Communications, vol. 34, no. 3, pp. 542-550, March 2016, doi: 10.1109/JSAC.2016.2525458.**



算法包sustech_ncs下，`NCS`是NCS-C的多进程并行加速的实现，`NCS_noMul`是未进行多进程加速的版本，`NCS_asym`是加入了非对称优化的实现版本。



使用示例:

```python
import numpy as np

# import optproblems.cec2005 as benchmark
import opfunu.cec_based.cec2005 as benchmark
from sustech_ncs import NCS


if __name__=='__main__':

    dimension0=30
    pop_size0=10 #
    sigma0=0.2  # 注意sigma不能是整数
    r0=0.80      #
    epoch0=10   #
    T0=30000
    scope = np.array([[-np.pi, np.pi]] * dimension0)

    # 选择测试使用的fitness function
    function=benchmark.F122005(ndim=dimension0).evaluate # opfunu
    # function=benchmark.F1(num_variables=dimension0) # optproblems
    optimizer = NCS(function, dimensions=dimension0, pop_size=pop_size0, sigma=np.full(pop_size0, sigma0), r=r0, epoch=epoch0, T_max=T0 ,scope=scope)

    best_solution, best_f_solution = optimizer.NCS_run()
    print("Best solution found by NCS:", best_solution)
    print("Objective function value:", best_f_solution)
    print(f"Function: {optimizer.objective_function_individual}, \n"
        f"Dimensions: {optimizer.dimensions}, Population Size: {optimizer.pop_size}, Sigma: {sigma0}, \n"
        f"R: {optimizer.r}, Epoch: {optimizer.epoch}, T_max: {optimizer.T_max}, "
        f"Scope: {optimizer.scope[0]}")

    # # opfunu用法
    # dimension = 10
    # problem = benchmark.F12005(dimension)
    # solution = [0.1] * dimension 
    # value = problem.evaluate(solution)
    # print(value)

    # # optproblems用法
    # dimension = 10
    # problem = benchmark.F1(dimension)
    # solution = [0.1] * dimension
    # value = problem(solution)
```