semvec/pypi-packages huggingface dataset:
pgb 0.0.1
Create a progress bar
Requires Python: >=3.6

# pgb (ProgressBar)
### Discription:
Create a text based ProgressBar in python!

### pypi installation:
> $ python3 -m pip install pgb

### Install via github release:
click the latest release at the releases section, download and run the ShellScript executable file.

### How to use pgb:
visit the [wiki](https://github.com/ilia85-star/pgb/wiki) page to learn!
libff 0.0.1
a factor calculator
Requires Python: >=3.6

# libff (Factor finder library)
### Discription:
Calculate and work with int numbers and their factors

### pypi installation:
> $ python3 -m pip install libff

### Install via github release:
click the latest release at the releases section, download and run the ShellScript executable file.

### How to use libff:
visit the [wiki](https://github.com/ilia85-star/libff/wiki) page to learn!
bastion-safepost 0.0.21
Package to manage your Cyberark implementation
Requires Python: >=3.6

# Bastion

This python package allow you to use high level functions with your Cyberark implementation

## Usage
### Connection
First define a configuration file

```yaml
Connection:
  Username: Administrator #optional
  Password: "Cyberark1"   #optional
  Authtype: Cyberark      #optional, default = Cyberark
PVWA: "pvwa.acme.corp"    #mandatory parameter
CAFile: "/valid/path/to/base64_Root_CA.cer" #optional, if not specified cert is not verified
timeout: 30 #(seconds) optional, timeout for requests to PVWA, default = 30
retention: 10 #(days) optional, days of retention for objects in safe, default = 10
CPM: "PasswordManager" #optional, CPM to assign to safes, default = "" (no CPM)
```

Then in your python code, instantiate the EPV Class with your configuration file
```python
import bastion

epv = EPV("/path/to/config.yml")
```
Then you call the login function to login to PVWA
```python
epv.login()
```

If you didn't specified Connection parameters in configuration file, you can also call login function with these parameters
```python
epv.login(username="Administrator", password="Cyberark1")
```

Note that if you are already logged in, epv.login() has no effect, but if your session is timed out it create
a new token.

Once you are done, you can disconnect with logoff
```python
epv.logoff()
```

### Serialization
EPV objects can be serialized using "to_json" function, then deserialized using constructor.
However, for security reasons, login and password are not stored in serialized object so you can't relogin after a timeout
with a serialized object

```python
epv = EPV("configfile")
json_epv = epv.to_json()

epv = EPV(serialized=json_epv)
epv.do_something()
```

### Account Manipulation
We provide an object "PrivilegedAccount" that is used for account manipulation.
His parameters are the same that Cyberark account representation so you can call constructor with 
the return of a get_account function unpacked
```json
{
  "name": "string",
  "address": "string",
  "userName": "string",
  "platformId": "string",
  "safeName": "string",
  "secretType": "key",
  "secret": "string",
  "platformAccountProperties": {},
  "secretManagement": {
    "automaticManagementEnabled": true,
    "manualManagementReason": "string"
  },
  "remoteMachinesAccess": {
    "remoteMachines": "string",
    "accessRestrictedToRemoteMachines": true
  }
}
```
pylfi 0.0.1
Likelihood-free inference with Python.
Requires Python: >=3.8.0

# pyLFI

[![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)
[![Documentation Status](https://readthedocs.org/projects/pylfi/badge/?version=latest)](https://pylfi.readthedocs.io/en/latest/?badge=latest)

pyLFI is a Python toolbox for likelihood-free inference (LFI) for estimating the posterior distributions of model parameters.

This repository provides a Python library for kernel density estimation. In comparison to other Python implementations of kernel density estimation, key features of this library include:

1. Support for weighted samples
2. A variety of kernels, including a smooth, compact kernel.

**Initial development is in progress, and there has not yet been a stable, usable release suitable for the public.**