Merge remote-tracking branch 'origin/HEAD' into vitest

packages/mini/test/mini.test.mjs

@@ -50,4 +50,44 @@ describe('mini', () => {
   it('supports euclidean rhythms', () => {
     expect(minS('a(3, 8)')).toEqual(['a: 0 - 1/8', 'a: 3/8 - 1/2', 'a: 3/4 - 7/8']);
   });
+  it('supports the ? operator', () => {
+    assert.deepStrictEqual(
+      mini('a?').queryArc(0, 20).map(hap => hap.whole.begin),
+      mini('a').degradeBy(0.5).queryArc(0, 20).map(hap => hap.whole.begin));
+  });
+  // testing things that involve pseudo-randomness, so there's a probability we could fail by chance.
+  // these next few tests work with the current PRNG, and are intended to succeed with p > 0.99 even if the PRNG changes
+  //   (as long as the PRNG has a relatively-uniform distribution of values)
+  it('supports degradeBy with default of 50%', () => {
+    const haps = mini('a?').queryArc(0, 1000);
+    assert(459 <= haps.length && haps.length <= 541, 'Number of elements did not fall in 99% confidence interval for binomial with p=0.5');
+  });
+  it('supports degradeBy with an argument', () => {
+    const haps = mini('a?0.8').queryArc(0, 1000);
+    assert(haps.length > 0, 'Should have had at least one element when degradeBy was set at 0.8');
+    assert(haps.length < 230, 'Had too many cycles remaining after degradeBy 0.8');
+  });
+  it('supports the random choice operator ("|") with nesting', () => {
+    const numCycles = 900;
+    const haps = mini('a | [b | c] | [d | e | f]').queryArc(0, numCycles);
+    // Should have about 1/3 a, 1/6 each of b | c, and 1/9 each of d | e | f.
+    // Evaluating this distribution with a chi-squared test.
+    // Note: this just evaluates the overall distribution, not things like correlation/runs of values
+    const observed = haps.reduce((acc, hap) => {
+      acc[hap.value] = (acc[hap.value] || 0) + 1;
+      return acc;
+    }, {});
+    const expected = {
+      a: numCycles / 3, b: numCycles / 6, c: numCycles / 6,
+      d: numCycles / 9, e: numCycles / 9, f: numCycles / 9
+    };
+    let chisq = -numCycles;
+    for (let k in expected) {
+      chisq += observed[k] * observed[k] / expected[k];
+    }
+    // 15.086 is the chisq for 5 degrees of freedom at 99%, so for 99% of uniformly-distributed
+    //  PRNG, this test should succeed
+    assert(chisq <= 15.086,
+      chisq + ' was expected to be less than 15.086 under chi-squared test');
+  });
 });