Smath v1.5.1 (#91)

+stdevp
+stdevs

datafit/src/test.ts

@@ -22,7 +22,7 @@ X.eq(data[8].y, 7);
 // Values should be within 20%
 // of actual, but this could
 // fail due to randomness.
-const tolerance: number = 0.10;
+const tolerance: number = 0.20;
 X.le(Math.abs((a[0] - summary.params[0]) / a[0]), tolerance);
 X.le(Math.abs((a[1] - summary.params[1]) / a[1]), tolerance);
 X.le(Math.abs((a[2] - summary.params[2]) / a[2]), tolerance);

smath/CHANGELOG.md

@@ -1,5 +1,12 @@
 # Changelog
 
+## 1.5.1
+
+- Add `stdevp()` and `stdevs()`
+- Minor fixes in tsdoc
+- Fix bug in `npx` where it would crash with 0 arguments
+- Warn for missing arguments in `npx`
+
 ## 1.5.0
 
 - Change spread syntax parameters to a single array parameter

smath/README.md

@@ -20,4 +20,4 @@ This example command returns the value 0.4.
 
 ```shell
 npx smath normalize 4 0 10
-```
+```

smath/examples/package.json

@@ -3,6 +3,6 @@
     "clean": "rm -rf node_modules package-lock.json"
   },
   "dependencies": {
-    "smath": "file:smath-1.5.0.tgz"
+    "smath": "file:smath-1.5.1.tgz"
   }
 }

smath/package.json

@@ -1,6 +1,6 @@
 {
   "name": "smath",
-  "version": "1.5.0",
+  "version": "1.5.1",
   "description": "Small math function library",
   "homepage": "https://npm.nicfv.com/smath",
   "bin": "dist/bin.js",

smath/src/bin.ts

@@ -2,7 +2,7 @@
 
 import { SMath } from '.';
 
-const func: string = process.argv[2].toLowerCase(),
+const func: string = (process.argv[2] ?? '').toLowerCase(),
     nums: Array<number> = process.argv.slice(3).map((arg, i) => {
         const num: number = Number.parseFloat(arg);
         if (Number.isFinite(num)) {
@@ -22,13 +22,14 @@ if (func.includes('help')) {
     console.log('  avg <c0> [c1] ... [cn]   : Take an average of `n` numbers');
     console.log('  varp <c0> [c1] ... [cn]  : Compute the population variance of `n` numbers');
     console.log('  vars <c0> [c1] ... [cn]  : Compute the sample variance of `n` numbers');
+    console.log('  stdevp <c0> [c1] ... [cn]: Compute the population standard deviation of `n` numbers');
+    console.log('  stdevs <c0> [c1] ... [cn]: Compute the sample standard deviation of `n` numbers');
     console.log('  approx <a> <b> [eps]     : Check if `a` and `b` are approximately equal');
     console.log('  clamp <n> <min> <max>    : Clamp `n` between `min` and `max`');
     console.log('  expand <n> <min> <max>   : Expand normalized `n` between `min` and `max`');
     console.log('  linspace <min> <max> <n> : Generate `n` linearly spaced numbers between `min` and `max`');
     console.log('  logspace <min> <max> <n> : Generate `n` logarithmically spaced numbers between `min` and `max`');
-    console.log('  normalize <n> <min> <max>');
-    console.log('                           : Normalize `n` between `min` and `max`');
+    console.log('  normalize <n> <min> <max>: Normalize `n` between `min` and `max`');
     console.log('  translate <n> <min1> <max1> <min2> <max2>');
     console.log('                           : Linearly interpolate `n` from `min1`, `max1` to `min2`, `max2`');
     console.log('  factorial <n>            : Compute `n!` (factorial)');
@@ -57,6 +58,14 @@ switch (func) {
         console.log(SMath.vars(nums));
         break;
     }
+    case ('stdevp'): {
+        console.log(SMath.stdevp(nums));
+        break;
+    }
+    case ('stdevs'): {
+        console.log(SMath.stdevs(nums));
+        break;
+    }
     case ('approx'): {
         console.log(SMath.approx(nums[0], nums[1], nums[2] ?? 1e-6));
         break;
@@ -93,6 +102,10 @@ switch (func) {
         console.log(SMath.error(nums[0], nums[1]));
         break;
     }
+    case (''): {
+        console.error('Missing argument. Use with "help" for a list of commands.');
+        process.exit(1);
+    }
     default: {
         console.error('Unknown argument "' + func + '". Use with "help" for a list of commands.');
         process.exit(1);

smath/src/index.ts

@@ -12,7 +12,7 @@ export abstract class SMath {
     /**
      * Add up all the inputs.
      * If none are present, returns 0.
-     * @param n Any amount of numeric inputs
+     * @param n An array of numeric inputs
      * @returns The sum total
      * @example
      * ```js
@@ -25,7 +25,7 @@ export abstract class SMath {
     /**
      * Multiply all the inputs.
      * If none are present, returns 1.
-     * @param n Any amount of numeric inputs
+     * @param n An array of numeric inputs
      * @returns The product
      * @example
      * ```js
@@ -37,7 +37,7 @@ export abstract class SMath {
     }
     /**
      * Compute the average, or mean, of a set of numbers.
-     * @param n Any amount of numeric inputs
+     * @param n An array of numeric inputs
      * @returns The average, or mean
      * @example
      * ```js
@@ -49,7 +49,7 @@ export abstract class SMath {
     }
     /**
      * Compute the variance of a **complete population**.
-     * @param n Any amount of numeric inputs
+     * @param n An array of numeric inputs
      * @returns The population variance
      * @example
      * ```js
@@ -63,7 +63,7 @@ export abstract class SMath {
     }
     /**
      * Compute the variance of a **sample**.
-     * @param n Any amount of numeric inputs
+     * @param n An array of numeric inputs
      * @returns The sample variance
      * @example
      * ```js
@@ -75,6 +75,22 @@ export abstract class SMath {
             squares: Array<number> = n.map(x => (x - mean) ** 2);
         return this.sum(squares) / (n.length - 1);
     }
+    /**
+     * Compute the standard deviation of a **complete population**.
+     * @param n An array of numeric inputs
+     * @returns The population standard deviation
+     */
+    public static stdevp(n: Array<number>): number {
+        return Math.sqrt(this.varp(n));
+    }
+    /**
+     * Compute the standard deviation of a **sample**.
+     * @param n An array of numeric inputs
+     * @returns The sample standard deviation
+     */
+    public static stdevs(n: Array<number>): number {
+        return Math.sqrt(this.vars(n));
+    }
     /**
      * Check if two numbers are approximately equal with a maximum abolute error.
      * @param a Any number