7.3 - A - Facet plots

In order to overcome issues with visualizing datasets containing time series of different scales, you can leverage the `subplots` argument, which will plot each column of a DataFrame on a different subplot. In addition, the layout of your subplots can be specified using the layout keyword, which accepts two integers specifying the number of rows and columns to use.
# Facet plots
```python
df.plot(subplots = True,
	linewidth = 0.5,
	layout = (2, 4),# number of rows and columns to use.  Totals must be greater or equal to the number of time series in dataframe.
	figsize = (16,10), #specify the total size of your graph (which will contain all subgraphs) using the figsize argument.
	sharex = False,  # specify if each subgraph should share the values of their x-axis and y-axis using the sharex and sharey arguments.
	sharey = False) ## specify if each subgraph sh

Créer un AE

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": "true"
   },
   "source": [
    "# Table of Contents\n",
    " <p><div class=\"lev1 toc-item\"><a href=\"#Utility-Functions\" data-toc-modified-id=\"Utility-Functions-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Utility Functions</a></div><div class=\"lev1 toc-item\"><a href=\"#MNIST\" data-toc-modified-id=\"MNIST-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>MNIST</a></div><div class=\"lev2 toc-ite

Flexera App Portal Web Extension Design Flow

# Flexera App Portal Web Extension Design Flow

### Disclaimer

The information provided in this documentation was neither created by, nor is supported by, Flexera. It is my understanding of how the product works based on objective data.

### Preface

This understanding makes the following assumptions:

  - The web browser is Microsoft Edge.
  - The 32-bit web extension supporting files are installed. (MSI)
  - The web extension is installed in the browser. (CRX)
  - The primary computer name di

1.1 - D - Visualization

# Histograms

Histograms allows to inspect the data and uncover its underlying distribution, as well as the presence of outliers and overall spread.

## Histogram using matplotlib
```python
# import matplotlib.pyplot as plt
# import pandas as pd
ax = df['col'].plot(kind = "hist", bins = 20)
ax.set_xlabel("xlabel")
ax.set_ylabel("ylabel")
ax.set_title("title")
plt.show()
```

## Histograms using seaborn
```python
import seaborn as sns
import matplotlib.pyplot as plt
sns.histplot(data = df, x = 'n

6.2 - B - Change time period analysis

Change time period analysis Assumes data contains weekly data, but you may wish to see how these values behave by month of the year. In this case the technique to use is aggregate values in data.
```python
# Aggregate value over time period
# --------------------------------

# > 0. Check index is datetime object
df = df.set_index('dte_col')
# > 1.  Extract month/year
month = df.index.month # year = df.index.year
# > 2. Compute aggregate data
agg_dta = df.groupby(month).mean()
```

6.3 - A - Rolling averages

**Moving averages** In the field of time series analysis, a moving average can be used for many different purposes: - smoothing out short-term fluctuations - removing outliers - highlighting long-term trends or cycles
```python
# Rolling Moving Average
df['mav_col'] = df['num_col'].rolling(windows = int).mean() # where int = number of periods of moving average
```

1.2.3 - A - Managing missing values

## Check which rows have missing values
`df.isnull()` & `df.notnull` returns True or False for every row with missing (or not) missing values.


```python
df.isnull()  # rows with missing values
df.notnull()  # rows with non-null values
```

07.02 - D - Visualizing seasonal averages

# Visualizing seasonal averages
```python
monthly_data = df.groupby(df.index.month).mean() #--> Calculation of monthly mean data
ax = monthly_data.plot()
ax.legend()
```

TransformOrigin trick

div {
	background: lightblue;
	width: 200px;
	height: 200px;
	border: 1px solid red;
	margin: 30px;
}

div.action {
	transition: scale 300ms;
transform-origin: right bottom;  /* zmizi animaci vpravo dole, muzu zadat i px treba 500px bottom , TO je dulezite :) */
	scale: 0;
}

[Flutter] OutlinedInputBorder

import 'dart:ui';

import 'package:flutter/material.dart';

class OutlinedInputBorder extends InputBorder {
  /// Creates a rounded rectangle outline border for an [InputDecorator].
  ///
  /// If the [borderSide] parameter is [BorderSide.none], it will not draw a
  /// border. However, it will still define a shape (which you can see if
  /// [InputDecoration.filled] is true).
  ///
  /// If an application does not specify a [borderSide] parameter of
  /// value [BorderSide.none], the input deco

Get-FlexeraAppPortalNameResolutionStats

Gather statistics from IIS logs to determine which name resolution methods are used and how frequently.
function Get-FlexeraAppPortalNameResolutionStats {
    [CmdletBinding()]
    param(
        [string]$SiteName
        ,
        [int]$Latest = 1
    )
    Import-Module -Name WebAdministration

    $Site = Get-Item -Path IIS:\Sites\$SiteName
    $LogFilesFolderPath = Join-Path -Path ([System.Environment]::ExpandEnvironmentVariables($Site.logfile.directory)) -ChildPath "W3SVC$($Site.ID)"
    $LogFiles = Get-ChildItem -Path "$($LogFilesFolderPath)\*.log" -File | Sort-Object -Property La

Reorder created points by Matricule

#  -*- coding: utf-8 -*-

"""
***************************************************************************
*                                                                         *
*   This program is free software; you can redistribute it and/or modify  *
*   it under the terms of the GNU General Public License as published by  *
*   the Free Software Foundation; either version 2 of the License, or     *
*   (at your option) any later version.                                   *
*             

Connect points by lignes depending buffer(on reference lignes) for intersection with lignes

from qgis.PyQt.QtCore import QCoreApplication
from qgis.core import (QgsProcessing, QgsProcessingException, QgsProcessingAlgorithm, 
                       QgsProcessingParameterFeatureSource, QgsProcessingParameterFeatureSink,
                       QgsFeatureSink, QgsFeature, QgsGeometry, QgsPointXY, QgsWkbTypes, QgsFeatureRequest)

class ConnectPointsAlongLineAlgorithm(QgsProcessingAlgorithm):
    INPUT_LINE = 'INPUT_LINE'
    INPUT_POINTS = 'INPUT_POINTS'
    OUTPUT = 'OUTPUT'

   

preparatoire commandes OGR2OGR with CMD

Merge all files in folder (geojson) -> gpkg
 create initial files
 ogr2ogr -f GPKG output.gpkg initial_file.geojson

mergeing the others
for %f in (*.geojson) do ogr2ogr -f GPKG -update -append output.gpkg %f -nln merged

----------------------------------------------

Merge all files in folder (geojson) -> gpkg
 create initial files
 ogr2ogr -f "GPKG" output.gpkg -nln merge -nlt PROMOTE_TO_MULTI input1.shp


mergeing the others
for %f in (*.shp) do ogr2ogr -f "GPKG" output.gpkg -

Setting Up A Windows Host To Use Ansible

# PowerShell commands (AWS specific but you can adjust to your own requirements):

# Enable PowerShell remoting
Enable-PSRemoting -Force

# Set WinRM service startup type to automatic
Set-Service WinRM -StartupType 'Automatic'

# Configure WinRM Service
Set-Item -Path WSMan:\localhost\Service\Auth\Certificate -Value $true
Set-Item -Path 'WSMan:\localhost\Service\AllowUnencrypted' -Value $true
Set-Item -Path 'WSMan:\localhost\Service\Auth\Basic' -Value $true
Set-Item -Path 'WSMan:\loc

Sage Wordpress Theme Setup

# Sage Wordpress Theme Setup

## Prerequisites
* PHP
* Composer
* Wordpress Directory
* WSL

Note: It is recommended to use WSL to properly utilize the roots/sage wordpress theme and for `Bud` (already included in Sage) and `Acorn` to work properly.

## Installation
* Go to the wordpress directory's `wp-content/themes` folder and install roots/sage package via composer:
```
composer create-project roots/sage your-theme-name
```

* Change directory into `your-theme-name` theme folder and install