html loading="lazy" polyfill 

document.addEventListener('DOMContentLoaded', function () {
  if ('loading' in HTMLImageElement.prototype) {
    const images = document.querySelectorAll('img[loading="lazy"]');
    images.forEach(img => {
      img.src = img.dataset.src;
    });
  } else {
    if (!('IntersectionObserver' in window)) {
      const images = document.querySelectorAll('img[loading="lazy"]');
      images.forEach(img => {
        img.src = img.dataset.src;
      });
    } else {
      const lazyImageObserver = new

grid masonry 表示　pinterest風 

<ul class="js_top_gallery">
      <li class="js_top_gallery_item"><a href="./images/top/gallery/gallery01.jpg"><img
            src="./images/top/gallery/gallery01.jpg" alt=""></a></li>
      <li class="js_top_gallery_item"><a href="./images/top/gallery/gallery02.jpg"><img
            src="./images/top/gallery/gallery02.jpg" alt=""></a></li>
</ul>

背景　下　三角 

.bg_square {
  position: relative;
  background: #f6f2eb;
}

.bg_square::after {
  content: "";
  position: absolute;
  height: 7rem;
  width: 100%;
  clip-path: polygon(0 0, 50% 100%, 100% 0);
  background: #f6f2eb;
  left: 0;
  bottom: -7rem;
}

シェイプノードのみをペアレント 

【MAYA】シェイプノードのみをペアレント
1：アウトライナ → ディスプレイ → □シェイプ にチェック
2：シェイプノード → トランスフォームノード　の順番で選択
3：「parent -r -s」を実行

redshift_stored_procs 

https://www.missioncloud.com/blog/get-started-with-amazon-redshift-stored-procedures

git_learn_in_general 

https://www.youtube.com/watch?v=qKxytMuUiJA

Async (mark function as async??) 

// https://stackoverflow.com/questions/44106907/when-to-mark-function-as-async

async function fnUsingAsync() {
    // Synchronously throw half of the time
    if (Math.random() < .5)
        throw new Error('synchronous error')

    // Return fulfilled/rejected promises half of the time
    return Math.random() < .5 ?
        Promise.resolve('success') :
        Promise.reject(new Error('asynchronous error');
}

function fnNotUsingAsync(x) {
    // Use the exact same body here tha

Production Data - FG Waterfall 

SELECT
    postref,
    pr_codenum,
    pr_descrip,
    fi_origpostref,
    jo_closed,
    Brand,
    ROUND(SUM(quantity), 5) AS completed,
    Stage
FROM
    (
        SELECT
            dbo.dtfifo.fi_id AS id,
            RTRIM(dbo.dtfifo.fi_postref) AS postref,
            RTRIM(dbo.dtfifo.fi_action) AS action,
            RTRIM(dbo.dmprod.pr_codenum) AS pr_codenum,
            dbo.dmprod.pr_descrip,
            dbo.dtfifo.fi_quant AS quantity,
            'JJ-' + REPLACE(RT

Production Data - Relieve 

SELECT
    postref,
    pr_codenum,
    pr_descrip,
    fi_userlot,
    fi_origpostref,
    Stage,
    ROUND(SUM(quantity), 5) AS relieved,
    MIN(Date) AS Date
FROM
    (
        SELECT
            dbo.dtfifo2.f2_id AS id,
            RTRIM(dbo.dtfifo2.f2_postref) AS postref,
            RTRIM(dbo.dtfifo2.f2_action) AS action,
            RTRIM(dbo.dmprod.pr_codenum) AS pr_codenum,
            dbo.dmprod.pr_descrip,
            dbo.dtfifo2.f2_oldquan - dbo.dtfifo2.f2_newquan A

flashcardzzz 

<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Question and Answer App</title>
<style>
  #question-container {
    text-align: center;
    margin-top: 50px;
  }
  #answer-container {
    display: none;
  }
</style>
</head>
<body>

<div id="question-container">
  <h1>Question:</h1>
  <p id="question"></p>
  <div id="answer-container">
    <h2>Answer:</h2>
    <p id="answer"></p>
  </div>
</div>

<script>

Reverse Prefix 

/**
 * @param {string} word
 * @param {character} ch
 * @return {string}
 */
var reversePrefix = function(word, ch) {
    // Find the index of the first occurrence of 'ch' in 'word'
    var index = word.indexOf(ch);

    // If 'ch' is not in 'word', return 'word' as is
    if (index === -1) {
        return word;
    }

    // If 'ch' is in 'word', reverse the segment from index 0 to 'index' (inclusive)
    // Slice the segment from 'word', reverse it, and join it back into a string
    var reve

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()
```