semvec/pypi-packages huggingface dataset:
ROA 1.8
A simple function to compute the running optimal average and effective no. of parameters.

# Running-Optimal-Average

The running optimal average is a smooth/flexible model that can describe time series data. This uses a Gaussian window function that moves through the data giving stronger weights to points close to the centre of the Gaussian. Therefore the width of the window function, delta, controls the flexibility of the model, with a small delta providing a very flexible model.

The function also calculates the effective no. of parameters as a very flexible model will correspond to large no. of parameters while 
a rigid model (low delta) has a low effective no. of parameters.

An error envelope is also calculated for the model.


## Usage:

t, model, errs, P = RunningOptimalAverage(t_data, Flux, Flux_err, delta)

Calculate running optimal average on a fine grid of 1000 equally spaced points over the range of data. Also returns errors and effective number of parameters.

Import using:

from ROA import RunningOptimalAverage

Parameters
----------
t_data  :  float array :
    Time values of the data points
    
Flux  : float array :
    Flux data values
    
Flux_err : float array :
    Errors for the flux data points
    
delta  : float :
    Window width of the Gaussian memory function - controls how flexible the model is


Returns
----------
t  : float array :
    Time values of the grid used to calculate ROA
    
model : float array :
    Running optimal average's calculated for each time t
    
errs : float array :
    Errors of the running optimal average
    
P : float :
    Effective number of parameters for the model
windows-tools.virtualization 0.3.2
Simple virtualization platform identification for Windows guest
Requires Python: >=3.5
Keywords: wmi,virtualization,file,acl,ntfs,refs,antivirus,security,firewall,office

# windows_tools
## Collection of useful python functions around Microsoft Windows

[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)
[![Percentage of issues still open](http://isitmaintained.com/badge/open/netinvent/windows_tools.svg)](http://isitmaintained.com/project/netinvent/ofunctions "Percentage of issues still open")
[![Maintainability](https://api.codeclimate.com/v1/badges/0d9732260019ec390649/maintainability)](https://codeclimate.com/github/netinvent/windows_tools/maintainability)
[![codecov](https://codecov.io/gh/netinvent/windows_tools/branch/master/graph/badge.svg?token=6Z03XTQU8G)](https://codecov.io/gh/netinvent/windows_tools)
[![windows-tests](https://github.com/netinvent/windows_tools/actions/workflows/windows.yaml/badge.svg)](https://github.com/netinvent/windows_tools/actions/workflows/windows.yaml)
[![GitHub Release](https://img.shields.io/github/release/netinvent/windows_tools.svg?label=Latest)](https://github.com/netinvent/windows_tools/releases/latest)


windows_tools is a set of various recurrent functions amongst

- antivirus: antivirus state and list of installed AV engines
- bitlocker: drive encryption status and protector key retrieval
- bitness: simple bitness identification
- file_utils: file ownership handling, NTFS & ReFS ACL handling, file listing with permission fixes
- impersonate: python Runas implementation
- installed_software: list of installed software from registry, 32 and 64 bits
- logical_disk: logical disk listing
- office: microsoft Office version identification, works for click & run, O365 and legacy
- powershell: powershell wrapper to identify interpreter and run scripts or commands
- product_key: windows product key retrieval
- registry: registry 32 and 64 bit API
- securityprivilege: enable / disable various security privileges for user
- server: windows server identification
- users: user lookup for SID/PySID/username
- virtualization: virtualization platform identification for guest
- windows_firewall: windows firewall state retrieval
- wmi_queries: windows WMI query wrapper, wmi timezone converters

It is compatible with Python 3.5+ and is tested on Windows only (obviously).

## Setup

```
pip install windows_tools.<subpackage>

```
olles-little-data-and-model-pack 0.0.3
R Passenger dataset but in list and Pandas format. Will later include time series models (will be added later).

1. # Package to Test Time Series Models

   * In the end will provide quick access to standard datasets and basic time series models for benchmarking.
   * Example code is found at the bottom of the readme file. 
   
   ## Datasets
   * All datasets are in list and pandas format.

   ______
   
   ### Univariate Seasonal Data
   #### Monthly Flight Passenger Demand
   
   * Classic dataset with flight passangers over time from the R data package.
   * flight_list = the data in a list format
   * flight_pandas = the data in pandas dataframe format
   
   ![flight](https://www.solver.com/sites/default/files/Timese1.jpg)


______

   ### Univariate Non-Linear Dataset
   #### Daily Gold Prices

   * Link: [Kaggle Gold dataset](https://www.kaggle.com/arashnic/learn-time-series-forecasting-from-gold-price)
   * gold_list = the data in a list format
   * gold_pandas = the data in a pandas df format 

   ![Gold](https://blog.minitab.com/hs-fs/hubfs/Imported_Blog_Media/time_series_gold_2.gif?width=435&height=294&name=time_series_gold_2.gif)

   ## Models (WIP)

   * Classic Time Series to benchmark more ambitious models. 
   1. Naive (give in the lag) 
   2. ARIMA
   3. Holt Winter

_______

### Example Code

import pandas as pd

flight_list

flight_pandas.head()

gold_list

gold_pandas.head()
Olles-Little-Data-and-Model-Pack 0.0.2
R Passenger dataset but in list and Pandas format. Will later include time series models (will be added later).

1. # Package to Test Time Series Models

   * In the end will provide quick access to standard datasets and basic time series models for benchmarking.
   * Example code is found at the bottom of the readme file. 
   
   ## Datasets
   * All datasets are in list and pandas format.

   ______
   
   ### Univariate Seasonal Data
   #### Monthly Flight Passenger Demand
   
   * Classic dataset with flight passangers over time from the R data package.
   * flight_list = the data in a list format
   * flight_pandas = the data in pandas dataframe format
   
   ![flight](https://www.solver.com/sites/default/files/Timese1.jpg)


______

   ### Univariate Non-Linear Dataset
   #### Daily Gold Prices

   * Link: [Kaggle Gold dataset](https://www.kaggle.com/arashnic/learn-time-series-forecasting-from-gold-price)
   * gold_list = the data in a list format
   * gold_pandas = the data in a pandas df format 

   ![Gold](https://blog.minitab.com/hs-fs/hubfs/Imported_Blog_Media/time_series_gold_2.gif?width=435&height=294&name=time_series_gold_2.gif)

   ## Models (WIP)

   * Classic Time Series to benchmark more ambitious models. 
   1. Naive (give in the lag) 
   2. ARIMA
   3. Holt Winter

_______

### Example Code

import pandas as pd

flight_list

flight_pandas.head()

gold_list

gold_pandas.head()