test: add python fixtures support for stats/incr/wmean

lib/node_modules/@stdlib/stats/incr/wmean/test/fixtures/python/data.json

@@ -0,0 +1 @@
+[{"step": 1, "x": 3.0471707975443136, "w": 3.9559294497604784, "mean": 3.0471707975443136}, {"step": 2, "x": -10.399841062404956, "w": 3.3577691973009576, "mean": -3.126444550583255}, {"step": 3, "x": 7.5045119580645725, "w": 3.555310355269042, "mean": 0.3509975744077484}, {"step": 4, "x": 9.405647163912139, "w": 3.925572252007643, "mean": 2.753544869877046}, {"step": 5, "x": -19.510351886538363, "w": 2.348687300137866, "mean": -0.2966862687887889}, {"step": 6, "x": -13.021795068623181, "w": 2.88683186016918, "mean": -2.1306885327326004}, {"step": 7, "x": 1.2784040316728538, "w": 0.7850052908255215, "mean": -2.0021205716643617}, {"step": 8, "x": -3.162425923435822, "w": 0.6611973603262699, "mean": -2.0378432392675006}, {"step": 9, "x": -0.16801157504288794, "w": 3.3751745127733113, "mean": -1.7838942273298977}, {"step": 10, "x": -8.5304392757358, "w": 2.4083714101013496, "mean": -2.379942511320534}, {"step": 11, "x": 8.793979748628285, "w": 2.8696569217578256, "mean": -1.3156926211374218}, {"step": 12, "x": 7.777919354289483, "w": 3.8484944013385256, "mean": -0.28571103145409305}, {"step": 13, "x": 0.6603069756121605, "w": 3.2101197680028952, "mean": -0.2040497079223875}, {"step": 14, "x": 11.272412069680328, "w": 2.81253906322418, "mean": 0.6028869213396181}, {"step": 15, "x": 4.675093422520456, "w": 2.8401150876525265, "mean": 0.872852516773853}, {"step": 16, "x": -8.592924628832382, "w": 1.5893554805068, "mean": 0.5342425491363226}, {"step": 17, "x": 3.6875078408249884, "w": 0.2510073893829031, "mean": 0.5519568003899903}, {"step": 18, "x": -9.588826008289988, "w": 2.239915207238576, "mean": 0.0678565402877354}, {"step": 19, "x": 8.784503013072726, "w": 1.1514648968156933, "mean": 0.2766434525404932}, {"step": 20, "x": -0.49925910986252897, "w": 2.1017903542507175, "mean": 0.2441410703285499}, {"step": 21, "x": -1.8486236354526056, "w": 4.281675059014014, "mean": 0.07959462355871812}, {"step": 22, "x": -6.809295444039414, "w": 1.2463034807401698, "mean": -0.07453994202242903}, {"step": 23, "x": 12.225413386740303, "w": 0.3856834342764235, "mean": 0.010039483490497911}, {"step": 24, "x": -1.5452948206880215, "w": 1.4787810709077833, "mean": -0.029914116527385744}, {"step": 25, "x": -4.283278221631072, "w": 1.5386094130567498, "mean": -0.14063615675192645}, {"step": 26, "x": -3.5213355048822956, "w": 3.343390922161786, "mean": -0.32163256326353584}, {"step": 27, "x": 5.323091855533487, "w": 2.829457546472264, "mean": -0.07696426621497057}, {"step": 28, "x": 3.6544406436407835, "w": 3.9411012246214265, "mean": 0.13548865366219276}, {"step": 29, "x": 4.1273261159598835, "w": 3.355136347604199, "mean": 0.3200323834598058}, {"step": 30, "x": 4.308210030078827, "w": 2.0912956210563456, "mean": 0.4317363357791127}, {"step": 31, "x": 21.416476008704613, "w": 4.08869988486357, "mean": 1.5212025421425204}, {"step": 32, "x": -4.064150163846156, "w": 0.9181673075477492, "mean": 1.4568355240616275}, {"step": 33, "x": -5.122427290715374, "w": 0.21128915835591636, "mean": 1.4394336829268486}, {"step": 34, "x": -8.137727282478778, "w": 0.5412345178006446, "mean": 1.3749825600895866}, {"step": 35, "x": 6.159794225754957, "w": 3.639560817922607, "mean": 1.582139838161509}, {"step": 36, "x": 11.289722927208917, "w": 2.3631984282317986, "mean": 1.8475743480392357}, {"step": 37, "x": -1.1394745765487508, "w": 0.8902317172646488, "mean": 1.8171205821224452}, {"step": 38, "x": -8.401564769625281, "w": 2.5551193980064815, "mean": 1.5266007308371616}, {"step": 39, "x": -8.244812156912396, "w": 0.8463293032945253, "mean": 1.435442529141028}, {"step": 40, "x": 6.505927878247011, "w": 3.511969837880907, "mean": 1.6244173800465052}, {"step": 41, "x": 7.432541712034423, "w": 2.2861657503127506, "mean": 1.761991460740914}, {"step": 42, "x": 5.4315426830519495, "w": 1.9670040078727644, "mean": 1.8352822469844088}, {"step": 43, "x": -6.655097072886943, "w": 1.577409236824595, "mean": 1.7014373651904717}, {"step": 44, "x": 2.3216132306671975, "w": 3.188384706282554, "mean": 1.7205884505077298}, {"step": 45, "x": 1.1668580914072821, "w": 1.8728817917116134, "mean": 1.7107231718926006}, {"step": 46, "x": 2.1868859672901295, "w": 0.5294846046488949, "mean": 1.7131094853391238}, {"step": 47, "x": 8.714287779481898, "w": 0.6782289203905251, "mean": 1.7577662372038756}, {"step": 48, "x": 2.2359554877468226, "w": 4.813298556292621, "mean": 1.7784750415795718}, {"step": 49, "x": 6.789135630718949, "w": 4.552045384467275, "mean": 1.9756181293054198}, {"step": 50, "x": 0.6757906948889146, "w": 3.5285649556726733, "mean": 1.9371486375865798}]

lib/node_modules/@stdlib/stats/incr/wmean/test/fixtures/python/runner.py

@@ -0,0 +1,88 @@
+#!/usr/bin/env python
+#
+# @license Apache-2.0
+#
+# Copyright (c) 2026 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.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Generate fixtures."""
+
+import os
+import json
+import numpy as np
+
+# Get the file path:
+FILE = os.path.realpath(__file__)
+
+# Extract the directory in which this file resides:
+DIR = os.path.dirname(FILE)
+
+
+def gen(n, seed, name):
+    """Generate fixture data and write to file.
+
+    # Arguments
+
+    * `n`: number of samples
+    * `seed`: random number generator seed
+    * `name::str`: output filename
+
+    # Examples
+
+    ``` python
+    python> gen(50, 42, './data.json')
+    ```
+    """
+    # Initialize random number generator for reproducibility:
+    rng = np.random.default_rng(seed)
+
+    # Generate data:
+    x = rng.normal(loc=0.0, scale=10.0, size=n)
+    w = rng.uniform(0.1, 5.0, size=n)
+
+    # Store data to be written to file as a list of records:
+    records = []
+
+    for k in range(1, n + 1):
+        x_vals = x[:k]
+        w_vals = w[:k]
+
+        weighted_mean = np.average(x_vals, weights=w_vals)
+
+        records.append({
+            "step": k,
+            "x": float(x[k - 1]),
+            "w": float(w[k - 1]),
+            "mean": float(weighted_mean)
+        })
+
+    # Based on the script directory, create an output filepath:
+    filepath = os.path.join(DIR, name)
+
+    # Write the data to the output filepath as JSON:
+    with open(filepath, "w", encoding="utf-8") as outfile:
+        json.dump(records, outfile)
+
+    # Include trailing newline:
+    with open(filepath, "a", encoding="utf-8") as outfile:
+        outfile.write("\n")
+
+
+def main():
+    """Generate fixture data."""
+    gen(50, 42, "data.json")
+
+
+if __name__ == "__main__":
+    main()

lib/node_modules/@stdlib/stats/incr/wmean/test/test.js

@@ -21,12 +21,16 @@
 // MODULES //
 
 var tape = require( 'tape' );
-var EPS = require( '@stdlib/constants/float64/eps' );
-var abs = require( '@stdlib/math/base/special/abs' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var isAlmostSameValue = require( '@stdlib/assert/is-almost-same-value' );
 var incrwmean = require( './../lib' );
 
 
+// FIXTURES //
+
+var data = require( './fixtures/python/data.json' );
+
+
 // TESTS //
 
 tape( 'main export is a function', function test( t ) {
@@ -49,53 +53,37 @@ tape( 'the initial accumulated value is `null`', function test( t ) {
 tape( 'the accumulator function incrementally computes a weighted arithmetic mean', function test( t ) {
 	var expected;
 	var actual;
-	var delta;
-	var dataX;
-	var dataW;
-	var xwSum;
-	var wSum;
 	var acc;
-	var tol;
 	var N;
 	var x;
 	var w;
 	var i;
 
-	dataX = [ 2.0, 3.0, 2.0, 4.0, 3.0, 4.0 ];
-	dataW = [ 1.0, 2.0, 0.1, 1.8, 9.9, 3.6 ];
-	N = dataX.length;
+	N = data.length;
 
 	acc = incrwmean();
 
-	xwSum = 0.0;
-	wSum = 0.0;
 	for ( i = 0; i < N; i++ ) {
-		x = dataX[ i ];
-		w = dataW[ i ];
-		xwSum += x * w;
-		wSum += w;
-		expected = xwSum / wSum;
+		x = data[ i ].x;
+		w = data[ i ].w;
+		expected = data[ i ].mean;
 		actual = acc( x, w );
-		delta = abs( actual - expected );
-		tol = EPS * abs( expected );
-		t.ok( delta <= tol, 'within tolerance. x: ' + x + '. Value: ' + actual + '. Expected: ' + expected + '. Tolerance: ' + tol + '.' );
+		t.strictEqual( isAlmostSameValue( actual, expected, 150.0 ), true, 'within tolerance. x: ' + x + '. w: ' + w + '. Value: ' + actual + '. Expected: ' + expected + '.' );
 	}
 	t.end();
 });
 
 tape( 'if not provided arguments, the accumulator function returns the current weighted mean', function test( t ) {
-	var dataX;
-	var dataW;
 	var acc;
+	var N;
 	var i;
 
-	dataX = [ 2.0, 3.0, 1.0 ];
-	dataW = [ 2.0, 2.0, 1.0 ];
+	N = data.length;
 	acc = incrwmean();
-	for ( i = 0; i < dataX.length; i++ ) {
-		acc( dataX[ i ], dataW[ i ] );
+	for ( i = 0; i < N; i++ ) {
+		acc( data[ i ].x, data[ i ].w );
 	}
-	t.strictEqual( acc(), 2.2, 'returns the current accumulated mean' );
+	t.strictEqual( isAlmostSameValue( acc(), data[ N - 1 ].mean, 150.0 ), true, 'returns the current accumulated mean' );
 	t.end();
 });