docs: improve README examples of ndarray/base/assert namespace

lib/node_modules/@stdlib/ndarray/base/assert/README.md

@@ -2,7 +2,7 @@
 
 @license Apache-2.0
 
-Copyright (c) 2018 The Stdlib Authors.
+Copyright (c) 2024 The Stdlib Authors.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -86,10 +86,77 @@ var o = ns;
 <!-- eslint no-undef: "error" -->
 
 ```javascript
-var objectKeys = require( '@stdlib/utils/keys' );
-var ns = require( '@stdlib/ndarray/base/assert' );
-
-console.log( objectKeys( ns ) );
+var Float64Array = require('@stdlib/array/float64');
+var ndarray = require('@stdlib/ndarray').ndarray;
+var randn = require('@stdlib/random/base/randn');
+var sqrt = require('@stdlib/math/base/special/sqrt');
+var pow = require('@stdlib/math/base/special/pow');
+var erf = require('@stdlib/math/base/special/erf');
+var cdf = require('@stdlib/stats/base/dists/normal/cdf');
+var isContiguous = require('@stdlib/ndarray/base/assert/is-contiguous');
+
+/*
+* Let's take an example of generating a 5x5 multidimensional array filled with random numbers from a normal distribution.
+* We want to calculate some statistical properties of this data, such as the mean and variance, and use some special mathematical functions to analyze the data further.
+*/
+
+var varianceValue;
+var meanValue;
+var sdValue;
+var erfValue;
+var cdfValue;
+var strides;
+var arrLen;
+var shape;
+var data;
+var array;
+var i;
+
+function calculateVariance(acc, val) {
+    return acc + pow(val - meanValue, 2);
+}
+
+function calculateMean(acc, val) {
+    return acc + val;
+}
+
+shape = [5, 5];
+data = new Float64Array(shape[0] * shape[1]);
+for (i = 0; i < data.length; i++) {
+    // Fill the array with random numbers from a normal distribution.
+    data[i] = randn();
+}
+
+strides = [shape[1], 1];
+array = ndarray('float64', data, shape, strides, 0, 'row-major');
+
+// Check if the array is contiguous and of the correct data type
+if (isContiguous(array.shape, array.strides, array.offset)) {
+    // Calculate the mean of the array data which gives us a measure of the central tendency of the data.
+    arrLen = array.data.length;
+    meanValue = array.data.reduce(calculateMean, 0) / arrLen;
+
+    // Calculate the variance of the array data which gives us a measure of how much the data varies from the mean.
+    varianceValue = array.data.reduce(calculateVariance, 0) / arrLen;
+
+    // Calculate the standard deviation of the array data which, being the square root of the variance, gives us a measure of the spread of the data around the mean.
+    sdValue = sqrt(varianceValue);
+
+    // Calculate the error function at the mean. The error function is used in statistics to find probabilities in normal distributions.
+    erfValue = erf(meanValue);
+
+    // Calculate the cumulative distribution function at the mean which gives us the probability that a random variable is less than or equal to a certain value.
+    cdfValue = cdf(meanValue, 0, sdValue);
+
+    console.log(true);
+    console.log(meanValue);
+    console.log(varianceValue);
+    console.log(sdValue);
+    console.log(erfValue);
+    console.log(cdfValue);
+} else {
+    console.log(false);
+}
 ```
 
 </section>

lib/node_modules/@stdlib/ndarray/base/assert/examples/index.js

@@ -1,7 +1,7 @@
 /**
 * @license Apache-2.0
 *
-* Copyright (c) 2018 The Stdlib Authors.
+* Copyright (c) 2024 The Stdlib Authors.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
@@ -18,7 +18,74 @@
 
 'use strict';
 
-var objectKeys = require( '@stdlib/utils/keys' );
-var ns = require( './../lib' );
+var Float64Array = require('@stdlib/array/float64');
+var ndarray = require('@stdlib/ndarray').ndarray;
+var randn = require('@stdlib/random/base/randn');
+var sqrt = require('@stdlib/math/base/special/sqrt');
+var pow = require('@stdlib/math/base/special/pow');
+var erf = require('@stdlib/math/base/special/erf');
+var cdf = require('@stdlib/stats/base/dists/normal/cdf');
+var isContiguous = require( './../is-contiguous/lib' );
 
-console.log( objectKeys( ns ) );
+/*
+* Let's take an example of generating a 5x5 multidimensional array filled with random numbers from a normal distribution.
+* We want to calculate some statistical properties of this data, such as the mean and variance, and use some special mathematical functions to analyze the data further.
+*/
+
+var varianceValue;
+var meanValue;
+var sdValue;
+var erfValue;
+var cdfValue;
+var strides;
+var arrLen;
+var shape;
+var data;
+var array;
+var i;
+
+function calculateVariance(acc, val) {
+	return acc + pow(val - meanValue, 2);
+}
+
+function calculateMean(acc, val) {
+	return acc + val;
+}
+
+shape = [5, 5];
+data = new Float64Array(shape[0] * shape[1]);
+for (i = 0; i < data.length; i++) {
+	// Fill the array with random numbers from a normal distribution.
+	data[i] = randn();
+}
+
+strides = [shape[1], 1];
+array = ndarray('float64', data, shape, strides, 0, 'row-major');
+
+// Check if the array is contiguous and of the correct data type
+if (isContiguous(array.shape, array.strides, array.offset)) {
+	// Calculate the mean of the array data which gives us a measure of the central tendency of the data.
+	arrLen = array.data.length;
+	meanValue = array.data.reduce(calculateMean, 0) / arrLen;
+
+	// Calculate the variance of the array data which gives us a measure of how much the data varies from the mean.
+	varianceValue = array.data.reduce(calculateVariance, 0) / arrLen;
+
+	// Calculate the standard deviation of the array data which, being the square root of the variance, gives us a measure of the spread of the data around the mean.
+	sdValue = sqrt(varianceValue);
+
+	// Calculate the error function at the mean. The error function is used in statistics to find probabilities in normal distributions.
+	erfValue = erf(meanValue);
+
+	// Calculate the cumulative distribution function at the mean which gives us the probability that a random variable is less than or equal to a certain value.
+	cdfValue = cdf(meanValue, 0, sdValue);
+
+	console.log(true);
+	console.log(meanValue);
+	console.log(varianceValue);
+	console.log(sdValue);
+	console.log(erfValue);
+	console.log(cdfValue);
+} else {
+	console.log(false);
+}