semvec/pypi-packages huggingface dataset:
robotframework-perfmon 0.1
robotframework-perfmon
Requires Python: >=3.6

# robotframework-perfmon

Robot Framework wrapper for pyperfmon, provides a simple way to collect windows performance monitor (perfmon) counter statistics from a windows machine, usually the AUT servers.

- [Installation](#installation)
- [Example Usage](#example-usage)
- [Keyword Documentation](#keyword-documentation)

## Installation

```
pip install robotframework-perfmon
```

## Example Usage
Simple example usage:
```robotframework
*** Settings ***
Library		PerfmonLibrary

*** Variables ***
${AUTServer} 	MyAUTServer

*** Test Cases ***
Check Memory
	${mempct}= 	Get Counter 	Memory\\% Committed Bytes In Use 	${AUTServer}
	Should Be True 	${mempct[1]} < 80

Check CPU
	${cputotpct}= 	Get Counter 	Processor\\_Total\\% Processor Time 	${AUTServer}
	Should Be True 	${cputotpct[1]} < 80

```

## Keyword Documentation
|Keyword|Description|
|---|---|
| Connect To | Establishes a connection to a remote windows machine. <br> The most likely reasons for  using this keyword are: <br> - You need to use different credentials to connect to the remote windows machine <br> - You want to avoid the connection time overhead on reading the first performance counter <br> All arguments are optional with the default values used if omitted <br> - `hostname` the windows machine to connect to. Default: localhost <br> - `username` the windows user to connect with, when specifying a domain you will need to escape the \\ as \\\\. Default: current logged in user. <br> - `password` the password for the specified windows user. If username no specified password is not used. Default: None <br> example usage: <br> ``` Connect To	hostname 	domnain\\username 	password ``` <br> ``` Connect To 	hostname ``` <br> It is suggested to use this keyword in [Suite setup](http://robotframework.org/robotframework/latest/RobotFrameworkUserGuide.html#toc-entry-260) |
| Get Counter | Get the performance monitor counter's value, the value is returned as a tuple of `('counterpath', value)` <br> `counterpath` argument is required, `hostname` argument is optional with the default value used if omitted <br> - `counterpath` the path to the windows performance counter, can be in either format <br> &nbsp; &nbsp; &nbsp; `<object>\\<counter>` or <br> &nbsp; &nbsp; &nbsp; `<object>\\<instance>\\<counter>` <br> - `hostname` the windows machine to connect to. Default: localhost |
| Get Objects | Get a list of available performance monitor counter objects <br> All arguments are optional with the default values used if omitted <br> - `hostname` the windows machine to connect to. Default: localhost |
| Get Counters | Get a list of available performance monitor counters for specified object <br> `object` argument is required, `hostname` argument is optional with the default value used if omitted <br> - `object` the object to get a list of windows performance counters for. <br> - `hostname` the windows machine to connect to. Default: localhost |
pynova-joshegladwell 0.0.2
An easy Python alternative to R functions relating to ANOVA
Requires Python: >=3.6

# PyNOVA
As an ANOVA professor's research assistant at Brigham Young University, I was tasked with converting sample R code designed for class instruction to Python. I was surprised with the lack of options in Python to perform tasks such as calculating power for more than two groups. This package provides easy Python function equivalents to some of the more complex R functions relating to analysis surrounding ANOVA and power.
rmlab-client 0.2.0
RMLab python client
Requires Python: >=3.9

# RMLab Python Client

This client provides python bindings for RMLab services at **[rmlab.net](https://rmlab.net)**, available upon registration.

We offer *simulation* and *revenue optimization* services to airline companies.

**Simulation as a service:**

* Unlimited number of competing carriers.

* Massively parallel simulations on arbitrarily big flight markets.

* Statistical sampling for modelling customers behavior, sensible to seasonality and events.

* Validation of pricing algorithms given demand patterns before deploying to production.


**Revenue optimization as a service:**

* Process arbitrarily large sets of historic data.

* Run state-of-the-art algorithms for forecasting and optimization.

* Fine-grain optimization per flight level.

* AI-based adaptive forecasters & optimizers operating on nightly runs.


**Server infrastructure characteristics:**

* World-wide availability hosted in **[Google Cloud Platform](https://cloud.google.com)**.

* Data scalability across high-performance in-memory storage servers.

* Per-customer fully isolated compute and data environments.

* End-to-end TLS encryption (optional IPsec access).