From 9e1ff9b12eb6712867b810402a5fdf0100ecddc3 Mon Sep 17 00:00:00 2001 From: Petr David Date: Wed, 11 Mar 2026 16:21:13 +0100 Subject: [PATCH 1/7] add test case for meal bolus without mid absorption isf --- .../Fixtures/meal-bolus-isf.json | 192 ++++++++++++++++++ .../Fixtures/meal-bolus-no-isf.json | 192 ++++++++++++++++++ .../LoopAlgorithmTests.swift | 37 +++- 3 files changed, 420 insertions(+), 1 deletion(-) create mode 100644 Tests/LoopAlgorithmTests/Fixtures/meal-bolus-isf.json create mode 100644 Tests/LoopAlgorithmTests/Fixtures/meal-bolus-no-isf.json diff --git a/Tests/LoopAlgorithmTests/Fixtures/meal-bolus-isf.json b/Tests/LoopAlgorithmTests/Fixtures/meal-bolus-isf.json new file mode 100644 index 0000000..d89763f --- /dev/null +++ b/Tests/LoopAlgorithmTests/Fixtures/meal-bolus-isf.json @@ -0,0 +1,192 @@ +{ + "automaticBolusApplicationFactor" : 0.4, + "basal" : [ + { + "endDate" : "2025-07-29T05:00:00Z", + "startDate" : "2025-07-28T20:00:00Z", + "value" : 0.85 + }, + { + "endDate" : "2025-07-29T14:49:56Z", + "startDate" : "2025-07-29T05:00:00Z", + "value" : 1 + }, + { + "endDate" : "2025-07-29T16:12:36Z", + "startDate" : "2025-07-29T14:49:56Z", + "value" : 1 + }, + { + "endDate" : "2025-07-29T16:12:44Z", + "startDate" : "2025-07-29T16:12:36Z", + "value" : 1 + }, + { + "endDate" : "2025-07-29T16:39:30Z", + "startDate" : "2025-07-29T16:12:44Z", + "value" : 1 + } + ], + "carbEntries" : [ + { + "absorptionTime" : 10800, + "date" : "2025-07-29T16:12:48Z", + "grams" : 20 + } + ], + "carbRatio" : [ + { + "endDate" : "2025-07-29T05:00:00Z", + "startDate" : "2025-07-29T04:11:52Z", + "value" : 10 + }, + { + "endDate" : "2025-07-29T14:49:56Z", + "startDate" : "2025-07-29T05:00:00Z", + "value" : 10 + }, + { + "endDate" : "2025-07-29T16:12:36Z", + "startDate" : "2025-07-29T14:49:56Z", + "value" : 10 + }, + { + "endDate" : "2025-07-29T16:12:44Z", + "startDate" : "2025-07-29T16:12:36Z", + "value" : 10 + }, + { + "endDate" : "2025-07-29T18:12:44Z", + "startDate" : "2025-07-29T16:12:44Z", + "value" : 10 + }, + { + "endDate" : "2025-07-29T22:25:00Z", + "startDate" : "2025-07-29T18:12:44Z", + "value" : 10 + } + ], + "doses" : [ + { + "endDate" : "2025-07-29T05:00:00Z", + "startDate" : "2025-07-28T20:00:00Z", + "type" : "basal", + "volume" : 7.65 + }, + { + "endDate" : "2025-07-29T14:49:01Z", + "startDate" : "2025-07-29T05:00:00Z", + "type" : "basal", + "volume" : 9.8 + }, + { + "endDate" : "2025-07-29T15:07:36Z", + "startDate" : "2025-07-29T14:49:01Z", + "type" : "basal", + "volume" : 0.3 + }, + { + "endDate" : "2025-07-29T15:57:58Z", + "startDate" : "2025-07-29T15:07:36Z", + "type" : "basal", + "volume" : 0.85 + }, + { + "endDate" : "2025-07-29T16:01:05Z", + "startDate" : "2025-07-29T15:57:58Z", + "type" : "basal", + "volume" : 0.05 + }, + { + "endDate" : "2025-07-29T16:07:02Z", + "startDate" : "2025-07-29T16:01:05Z", + "type" : "basal", + "volume" : 0.1 + }, + { + "endDate" : "2025-07-29T16:09:30Z", + "startDate" : "2025-07-29T16:07:02Z", + "type" : "basal", + "volume" : 0.04098938193586137 + }, + { + "endDate" : "2025-07-29T16:39:30Z", + "startDate" : "2025-07-29T16:09:30Z", + "type" : "basal", + "volume" : 0 + } + ], + "glucoseHistory" : [ + { + "date" : "2025-07-29T14:49:01Z", + "value" : 110 + }, + { + "date" : "2025-07-29T14:54:01Z", + "value" : 112 + }, + { + "date" : "2025-07-29T14:59:01Z", + "value" : 113 + }, + { + "date" : "2025-07-29T15:04:37Z", + "value" : 115 + }, + { + "date" : "2025-07-29T15:07:36Z", + "value" : 116 + }, + { + "date" : "2025-07-29T15:14:11Z", + "value" : 119 + }, + { + "date" : "2025-07-29T15:57:58Z", + "value" : 129 + }, + { + "date" : "2025-07-29T16:01:05Z", + "value" : 129 + }, + { + "date" : "2025-07-29T16:06:05Z", + "value" : 129 + }, + { + "date" : "2025-07-29T16:07:02Z", + "value" : 130 + }, + { + "date" : "2025-07-29T16:09:30Z", + "value" : 130 + } + ], + "maxBasalRate" : 5, + "maxBolus" : 10, + "predictionStart" : "2025-07-29T16:12:52Z", + "recommendationInsulinType" : "novolog", + "recommendationType" : "manualBolus", + "sensitivity" : [ + { + "endDate" : "2025-07-29T16:30:00Z", + "startDate" : "2025-07-28T20:00:00Z", + "value" : 55 + }, + { + "endDate" : "2025-07-29T22:50:00Z", + "startDate" : "2025-07-29T16:30:00Z", + "value" : 65 + } + ], + "suspendThreshold" : 75, + "target" : [ + { + "endDate" : "2025-07-29T22:25:00Z", + "lowerBound" : 140, + "startDate" : "2025-07-29T16:12:44Z", + "upperBound" : 160 + } + ], + "useMidAbsorptionISF" : true +} diff --git a/Tests/LoopAlgorithmTests/Fixtures/meal-bolus-no-isf.json b/Tests/LoopAlgorithmTests/Fixtures/meal-bolus-no-isf.json new file mode 100644 index 0000000..807510c --- /dev/null +++ b/Tests/LoopAlgorithmTests/Fixtures/meal-bolus-no-isf.json @@ -0,0 +1,192 @@ +{ + "automaticBolusApplicationFactor" : 0.4, + "basal" : [ + { + "endDate" : "2025-07-29T05:00:00Z", + "startDate" : "2025-07-28T20:00:00Z", + "value" : 0.85 + }, + { + "endDate" : "2025-07-29T14:49:56Z", + "startDate" : "2025-07-29T05:00:00Z", + "value" : 1 + }, + { + "endDate" : "2025-07-29T16:12:36Z", + "startDate" : "2025-07-29T14:49:56Z", + "value" : 1 + }, + { + "endDate" : "2025-07-29T16:12:44Z", + "startDate" : "2025-07-29T16:12:36Z", + "value" : 1 + }, + { + "endDate" : "2025-07-29T16:39:30Z", + "startDate" : "2025-07-29T16:12:44Z", + "value" : 1 + } + ], + "carbEntries" : [ + { + "absorptionTime" : 10800, + "date" : "2025-07-29T16:12:48Z", + "grams" : 20 + } + ], + "carbRatio" : [ + { + "endDate" : "2025-07-29T05:00:00Z", + "startDate" : "2025-07-29T04:11:52Z", + "value" : 10 + }, + { + "endDate" : "2025-07-29T14:49:56Z", + "startDate" : "2025-07-29T05:00:00Z", + "value" : 10 + }, + { + "endDate" : "2025-07-29T16:12:36Z", + "startDate" : "2025-07-29T14:49:56Z", + "value" : 10 + }, + { + "endDate" : "2025-07-29T16:12:44Z", + "startDate" : "2025-07-29T16:12:36Z", + "value" : 10 + }, + { + "endDate" : "2025-07-29T18:12:44Z", + "startDate" : "2025-07-29T16:12:44Z", + "value" : 10 + }, + { + "endDate" : "2025-07-29T22:25:00Z", + "startDate" : "2025-07-29T18:12:44Z", + "value" : 10 + } + ], + "doses" : [ + { + "endDate" : "2025-07-29T05:00:00Z", + "startDate" : "2025-07-28T20:00:00Z", + "type" : "basal", + "volume" : 7.65 + }, + { + "endDate" : "2025-07-29T14:49:01Z", + "startDate" : "2025-07-29T05:00:00Z", + "type" : "basal", + "volume" : 9.8 + }, + { + "endDate" : "2025-07-29T15:07:36Z", + "startDate" : "2025-07-29T14:49:01Z", + "type" : "basal", + "volume" : 0.3 + }, + { + "endDate" : "2025-07-29T15:57:58Z", + "startDate" : "2025-07-29T15:07:36Z", + "type" : "basal", + "volume" : 0.85 + }, + { + "endDate" : "2025-07-29T16:01:05Z", + "startDate" : "2025-07-29T15:57:58Z", + "type" : "basal", + "volume" : 0.05 + }, + { + "endDate" : "2025-07-29T16:07:02Z", + "startDate" : "2025-07-29T16:01:05Z", + "type" : "basal", + "volume" : 0.1 + }, + { + "endDate" : "2025-07-29T16:09:30Z", + "startDate" : "2025-07-29T16:07:02Z", + "type" : "basal", + "volume" : 0.04098938193586137 + }, + { + "endDate" : "2025-07-29T16:39:30Z", + "startDate" : "2025-07-29T16:09:30Z", + "type" : "basal", + "volume" : 0 + } + ], + "glucoseHistory" : [ + { + "date" : "2025-07-29T14:49:01Z", + "value" : 110 + }, + { + "date" : "2025-07-29T14:54:01Z", + "value" : 112 + }, + { + "date" : "2025-07-29T14:59:01Z", + "value" : 113 + }, + { + "date" : "2025-07-29T15:04:37Z", + "value" : 115 + }, + { + "date" : "2025-07-29T15:07:36Z", + "value" : 116 + }, + { + "date" : "2025-07-29T15:14:11Z", + "value" : 119 + }, + { + "date" : "2025-07-29T15:57:58Z", + "value" : 129 + }, + { + "date" : "2025-07-29T16:01:05Z", + "value" : 129 + }, + { + "date" : "2025-07-29T16:06:05Z", + "value" : 129 + }, + { + "date" : "2025-07-29T16:07:02Z", + "value" : 130 + }, + { + "date" : "2025-07-29T16:09:30Z", + "value" : 130 + } + ], + "maxBasalRate" : 5, + "maxBolus" : 10, + "predictionStart" : "2025-07-29T16:12:52Z", + "recommendationInsulinType" : "novolog", + "recommendationType" : "manualBolus", + "sensitivity" : [ + { + "endDate" : "2025-07-29T16:30:00Z", + "startDate" : "2025-07-28T20:00:00Z", + "value" : 55 + }, + { + "endDate" : "2025-07-29T22:50:00Z", + "startDate" : "2025-07-29T16:30:00Z", + "value" : 65 + } + ], + "suspendThreshold" : 75, + "target" : [ + { + "endDate" : "2025-07-29T22:25:00Z", + "lowerBound" : 140, + "startDate" : "2025-07-29T16:12:44Z", + "upperBound" : 160 + } + ], + "useMidAbsorptionISF" : false +} diff --git a/Tests/LoopAlgorithmTests/LoopAlgorithmTests.swift b/Tests/LoopAlgorithmTests/LoopAlgorithmTests.swift index e8d1f1f..20b0aef 100644 --- a/Tests/LoopAlgorithmTests/LoopAlgorithmTests.swift +++ b/Tests/LoopAlgorithmTests/LoopAlgorithmTests.swift @@ -222,7 +222,7 @@ final class LoopAlgorithmTests: XCTestCase { let decoder = JSONDecoder() decoder.dateDecodingStrategy = .iso8601 - let url = Bundle.module.url(forResource: "meal-bolus", withExtension: "json", subdirectory: "Fixtures")! + let url = Bundle.module.url(forResource: "meal-bolus-isf", withExtension: "json", subdirectory: "Fixtures")! var input = try! decoder.decode(AlgorithmInputFixture.self, from: try! Data(contentsOf: url)) let output = LoopAlgorithm.run(input: input) @@ -247,6 +247,41 @@ final class LoopAlgorithmTests: XCTestCase { XCTAssertEqual(output2.predictedGlucose.last!.quantity.doubleValue(for: .milligramsPerDeciliter), 150, accuracy: 0.1) } + func testMealBolusNoMidAbsorptionISFScenario() { + let decoder = JSONDecoder() + decoder.dateDecodingStrategy = .iso8601 + + let url = Bundle.module.url( + forResource: "meal-bolus-no-isf", withExtension: "json", subdirectory: "Fixtures")! + var input = try! decoder.decode( + AlgorithmInputFixture.self, from: try! Data(contentsOf: url)) + + let output = LoopAlgorithm.run(input: input) + + // Should recommend bolus to cover meal + XCTAssertEqual( + output.predictedGlucose.last!.quantity.doubleValue(for: .milligramsPerDeciliter), 269, + accuracy: 0.1) + XCTAssertEqual(output.recommendation!.manual!.amount, 2.16, accuracy: 0.01) + + // Now check forecast if bolus recommendation is accepted and delivered. + input.doses.append( + .init( + deliveryType: .bolus, + startDate: input.predictionStart, + endDate: input.predictionStart.addingTimeInterval(30), + volume: output.recommendation!.manual!.amount + ) + ) + + let output2 = LoopAlgorithm.run(input: input) + + // 150 mg/dL is the middle of the target range + XCTAssertEqual( + output2.predictedGlucose.last!.quantity.doubleValue(for: .milligramsPerDeciliter), 150, + accuracy: 0.1) + } + func testMidAborptionISFFlag() { let now = ISO8601DateFormatter().date(from: "2024-01-03T00:00:00+0000")! var input = AlgorithmInputFixture.mock(for: now) From 07afc01bf06812d5ca58b69ee5b5def13d2e7364 Mon Sep 17 00:00:00 2001 From: Petr David Date: Wed, 11 Mar 2026 16:56:10 +0100 Subject: [PATCH 2/7] formatting, typo --- Tests/LoopAlgorithmTests/LoopAlgorithmTests.swift | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/Tests/LoopAlgorithmTests/LoopAlgorithmTests.swift b/Tests/LoopAlgorithmTests/LoopAlgorithmTests.swift index 20b0aef..30651da 100644 --- a/Tests/LoopAlgorithmTests/LoopAlgorithmTests.swift +++ b/Tests/LoopAlgorithmTests/LoopAlgorithmTests.swift @@ -259,9 +259,7 @@ final class LoopAlgorithmTests: XCTestCase { let output = LoopAlgorithm.run(input: input) // Should recommend bolus to cover meal - XCTAssertEqual( - output.predictedGlucose.last!.quantity.doubleValue(for: .milligramsPerDeciliter), 269, - accuracy: 0.1) + XCTAssertEqual(output.predictedGlucose.last!.quantity.doubleValue(for: .milligramsPerDeciliter), 269, accuracy: 0.1) XCTAssertEqual(output.recommendation!.manual!.amount, 2.16, accuracy: 0.01) // Now check forecast if bolus recommendation is accepted and delivered. @@ -277,12 +275,10 @@ final class LoopAlgorithmTests: XCTestCase { let output2 = LoopAlgorithm.run(input: input) // 150 mg/dL is the middle of the target range - XCTAssertEqual( - output2.predictedGlucose.last!.quantity.doubleValue(for: .milligramsPerDeciliter), 150, - accuracy: 0.1) + XCTAssertEqual(output2.predictedGlucose.last!.quantity.doubleValue(for: .milligramsPerDeciliter), 150, accuracy: 0.1) } - func testMidAborptionISFFlag() { + func testMidAbsorptionISFFlag() { let now = ISO8601DateFormatter().date(from: "2024-01-03T00:00:00+0000")! var input = AlgorithmInputFixture.mock(for: now) From 18166737ee7364a743471c9d09a85404276273a3 Mon Sep 17 00:00:00 2001 From: Petr David Date: Wed, 25 Mar 2026 16:29:37 +0100 Subject: [PATCH 3/7] force rebuild --- Tests/LoopAlgorithmTests/LoopAlgorithmTests.swift | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Tests/LoopAlgorithmTests/LoopAlgorithmTests.swift b/Tests/LoopAlgorithmTests/LoopAlgorithmTests.swift index 30651da..1d7ae4b 100644 --- a/Tests/LoopAlgorithmTests/LoopAlgorithmTests.swift +++ b/Tests/LoopAlgorithmTests/LoopAlgorithmTests.swift @@ -558,4 +558,4 @@ final class LoopAlgorithmTests: XCTestCase { output = LoopAlgorithm.run(input: input) XCTAssertEqual(output.predictedGlucose.last!.quantity.doubleValue(for: .milligramsPerDeciliter), 105, accuracy: 0.5) } -} +} \ No newline at end of file From b05f72b2cf3c4f737ec864d11cb7de9e55e40a5e Mon Sep 17 00:00:00 2001 From: Pete Schwamb Date: Thu, 21 May 2026 18:19:35 -0500 Subject: [PATCH 4/7] Make decayEffect a continuous function of sample timestamp (#33) * Make decayEffect a continuous function of sample timestamp Reformulates decayEffect using a closed-form quadratic in time-since-sample rather than accumulating step-by-step from the floored simulation boundary. This makes the effect value at any future absolute timestamp independent of which delta-sized simulation bucket the sample's startDate falls into. For samples aligned to delta boundaries the two formulations are mathematically identical. For unaligned samples (the common case with real CGM streams) the new formulation removes a small discontinuity that the old code exhibited at bucket boundaries. Adds LoopMathTests covering continuity across a delta boundary. Existing fixture-calibrated tests are re-pinned to the new values; per-prediction drift is on the order of 0.1 mg/dL. Ports the LoopMath change from LoopKit/LoopKit#556 by Moti Nisenson-Ken to the LoopAlgorithm package, where decayEffect now lives. * Space to kick off tests --------- Co-authored-by: Pete Schwamb --- Sources/LoopAlgorithm/LoopMath.swift | 22 ++- .../carbs_with_isf_change_recommendation.json | 4 +- .../live_capture_predicted_glucose.json | 154 +++++++++--------- .../LoopAlgorithmTests.swift | 20 +-- Tests/LoopAlgorithmTests/LoopMathTests.swift | 48 ++++++ ...IntegralRetrospectiveCorrectionTests.swift | 2 +- 6 files changed, 152 insertions(+), 98 deletions(-) create mode 100644 Tests/LoopAlgorithmTests/LoopMathTests.swift diff --git a/Sources/LoopAlgorithm/LoopMath.swift b/Sources/LoopAlgorithm/LoopMath.swift index 13889fc..7fc9e48 100644 --- a/Sources/LoopAlgorithm/LoopMath.swift +++ b/Sources/LoopAlgorithm/LoopMath.swift @@ -192,19 +192,25 @@ extension GlucoseValue { let glucoseUnit = LoopUnit.milligramsPerDeciliter let velocityUnit = GlucoseEffectVelocity.perSecondUnit - // The starting rate, which we will decay to 0 over the specified duration - let intercept = rate.doubleValue(for: velocityUnit) // mg/dL/s - let decayStartDate = startDate.addingTimeInterval(delta) - let slope = -intercept / (duration - delta) // mg/dL/s/s + let firstChange = rate.doubleValue(for: velocityUnit) * delta // mg/dL/s * s = mg/dL + let secondChange = firstChange * (1 - delta / (duration - delta)) + + // Solve for f(t) = a*t^2 + b*t + c, where t is relative to self.startDate. + // f(0) = c + // f(delta) - c = firstChange = a*delta^2 + b*delta + // f(2*delta) - c = firstChange + secondChange = 4*a*delta^2 + 2*b*delta + // --> firstChange - secondChange = 2*a*delta^2 + let c = quantity.doubleValue(for: glucoseUnit) + let a = (secondChange - firstChange) / (2 * delta * delta) // mg/dL/s^2 + let b = (firstChange + secondChange - 4 * a * delta * delta) / (2 * delta) // mg/dL/s var values = [GlucoseEffect(startDate: startDate, quantity: quantity)] - var date = decayStartDate - var lastValue = quantity.doubleValue(for: glucoseUnit) + var date = startDate.addingTimeInterval(delta) repeat { - let value = lastValue + (intercept + slope * date.timeIntervalSince(decayStartDate)) * delta + let time = min(duration, date.timeIntervalSince(self.startDate)) + let value = a * time * time + b * time + c values.append(GlucoseEffect(startDate: date, quantity: LoopQuantity(unit: glucoseUnit, doubleValue: value))) - lastValue = value date = date.addingTimeInterval(delta) } while date < endDate diff --git a/Tests/LoopAlgorithmTests/Fixtures/carbs_with_isf_change_recommendation.json b/Tests/LoopAlgorithmTests/Fixtures/carbs_with_isf_change_recommendation.json index b4e70af..bde6ac6 100644 --- a/Tests/LoopAlgorithmTests/Fixtures/carbs_with_isf_change_recommendation.json +++ b/Tests/LoopAlgorithmTests/Fixtures/carbs_with_isf_change_recommendation.json @@ -1,5 +1,5 @@ { "manual" : { - "amount" : 10.546890782709953 + "amount" : 10.52269112701204 } -} +} \ No newline at end of file diff --git a/Tests/LoopAlgorithmTests/Fixtures/live_capture_predicted_glucose.json b/Tests/LoopAlgorithmTests/Fixtures/live_capture_predicted_glucose.json index b77cb55..1baaacf 100644 --- a/Tests/LoopAlgorithmTests/Fixtures/live_capture_predicted_glucose.json +++ b/Tests/LoopAlgorithmTests/Fixtures/live_capture_predicted_glucose.json @@ -10,383 +10,383 @@ "startDate" : "2023-06-23T02:40:00Z" }, { - "quantity" : 180.52987493690765, + "quantity" : 180.52784693243598, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T02:45:00Z" }, { - "quantity" : 179.77931710835796, + "quantity" : 179.77106522809387, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T02:50:00Z" }, { - "quantity" : 177.81435588000684, + "quantity" : 177.7956842526296, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T02:55:00Z" }, { - "quantity" : 175.04920382978105, + "quantity" : 175.01794458844162, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T03:00:00Z" }, { - "quantity" : 172.09884468881066, + "quantity" : 172.05499783350902, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T03:05:00Z" }, { - "quantity" : 169.0341959170697, + "quantity" : 168.97776144780588, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T03:10:00Z" }, { - "quantity" : 165.91852357330802, + "quantity" : 165.84950149008202, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T03:15:00Z" }, { - "quantity" : 162.78787379965794, + "quantity" : 162.70626410246973, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T03:20:00Z" }, { - 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"quantity" : 139.4249156157845, + "quantity" : 139.3055430767097, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T04:05:00Z" }, { - "quantity" : 137.7082164432302, + "quantity" : 137.58884390415542, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T04:10:00Z" }, { - "quantity" : 135.9914530272836, + "quantity" : 135.8720804882088, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T04:15:00Z" }, { - "quantity" : 134.2827664300858, + "quantity" : 134.163393891011, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T04:20:00Z" }, { - "quantity" : 132.58882252103788, + "quantity" : 132.4694499819631, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T04:25:00Z" }, { - "quantity" : 130.91436540926705, + "quantity" : 130.79499287019226, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T04:30:00Z" }, { - "quantity" : 129.26245506698106, + "quantity" : 129.14308252790627, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T04:35:00Z" }, { - "quantity" : 127.63445215517064, + "quantity" : 127.51507961609585, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T04:40:00Z" }, { - 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"quantity" : 75.35517138501669, + "quantity" : 75.2357988459419, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T08:45:00Z" }, { - "quantity" : 75.35557365902051, + "quantity" : 75.23620111994572, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T08:50:00Z" }, { - "quantity" : 75.35562264689557, + "quantity" : 75.23625010782078, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T08:55:00Z" }, { - "quantity" : 75.35562264689557, + "quantity" : 75.23625010782078, "quantityUnit" : "mg\/dL", "startDate" : "2023-06-23T09:00:00Z" } -] +] \ No newline at end of file diff --git a/Tests/LoopAlgorithmTests/LoopAlgorithmTests.swift b/Tests/LoopAlgorithmTests/LoopAlgorithmTests.swift index 1d7ae4b..0784e30 100644 --- a/Tests/LoopAlgorithmTests/LoopAlgorithmTests.swift +++ b/Tests/LoopAlgorithmTests/LoopAlgorithmTests.swift @@ -76,7 +76,7 @@ final class LoopAlgorithmTests: XCTestCase { let output = LoopAlgorithm.run(input: input) XCTAssertEqual(output.activeCarbs, 50) - XCTAssertEqual(output.recommendation!.manual!.amount, 5.83, accuracy: 0.01) + XCTAssertEqual(output.recommendation!.manual!.amount, 5.86, accuracy: 0.01) } @@ -112,8 +112,8 @@ final class LoopAlgorithmTests: XCTestCase { XCTAssertEqual(outputA.effects.insulin.last?.quantity.doubleValue(for: .milligramsPerDeciliter), 0.0) XCTAssertEqual(outputB.effects.insulin.last?.quantity.doubleValue(for: .milligramsPerDeciliter), 0.0) - XCTAssertEqual(outputA.effects.retrospectiveCorrection.last?.quantity.doubleValue(for: .milligramsPerDeciliter), 165) - XCTAssertEqual(outputB.effects.retrospectiveCorrection.last?.quantity.doubleValue(for: .milligramsPerDeciliter), 165) + XCTAssertEqual(outputA.effects.retrospectiveCorrection.last?.quantity.doubleValue(for: .milligramsPerDeciliter) ?? 0, 165, accuracy: 0.05) + XCTAssertEqual(outputB.effects.retrospectiveCorrection.last?.quantity.doubleValue(for: .milligramsPerDeciliter) ?? 0, 165, accuracy: 0.05) // These tests fail, because the momentum effect is *not* time independent yet. // Even though all the input data is the same (just shifted in time), momentum effect varies in relation to how offset @@ -228,8 +228,8 @@ final class LoopAlgorithmTests: XCTestCase { let output = LoopAlgorithm.run(input: input) // Should recommend bolus to cover meal - XCTAssertEqual(output.predictedGlucose.last!.quantity.doubleValue(for: .milligramsPerDeciliter), 274, accuracy: 0.1) - XCTAssertEqual(output.recommendation!.manual!.amount, 1.9, accuracy: 0.01) + XCTAssertEqual(output.predictedGlucose.last!.quantity.doubleValue(for: .milligramsPerDeciliter), 274.14, accuracy: 0.1) + XCTAssertEqual(output.recommendation!.manual!.amount, 1.91, accuracy: 0.01) // Now check forecast if bolus recommendation is accepted and delivered. input.doses.append( @@ -259,7 +259,7 @@ final class LoopAlgorithmTests: XCTestCase { let output = LoopAlgorithm.run(input: input) // Should recommend bolus to cover meal - XCTAssertEqual(output.predictedGlucose.last!.quantity.doubleValue(for: .milligramsPerDeciliter), 269, accuracy: 0.1) + XCTAssertEqual(output.predictedGlucose.last!.quantity.doubleValue(for: .milligramsPerDeciliter), 269.15, accuracy: 0.1) XCTAssertEqual(output.recommendation!.manual!.amount, 2.16, accuracy: 0.01) // Now check forecast if bolus recommendation is accepted and delivered. @@ -329,7 +329,7 @@ final class LoopAlgorithmTests: XCTestCase { var recommendedBolus = output.recommendation!.automatic?.bolusUnits var activeInsulin = output.activeInsulin! XCTAssertEqual(activeInsulin, 8.0) - XCTAssertEqual(recommendedBolus!, 1.66, accuracy: 0.01) + XCTAssertEqual(recommendedBolus!, 1.69, accuracy: 0.01) // Now try with maxBolus of 4; should not recommend any more insulin, as we're at our max iob input.maxBolus = 4 @@ -397,12 +397,12 @@ final class LoopAlgorithmTests: XCTestCase { // Without mid-absorption ISF input.useMidAbsorptionISF = false var output = LoopAlgorithm.run(input: input) - XCTAssertEqual(2.58, output.recommendation!.manual!.amount, accuracy: 0.01) + XCTAssertEqual(2.73, output.recommendation!.manual!.amount, accuracy: 0.01) // With mid-absorption ISF input.useMidAbsorptionISF = true output = LoopAlgorithm.run(input: input) - XCTAssertEqual(1.41, output.recommendation!.manual!.amount, accuracy: 0.01) + XCTAssertEqual(1.49, output.recommendation!.manual!.amount, accuracy: 0.01) } func testIncompleteISFTimelineDetected() { @@ -558,4 +558,4 @@ final class LoopAlgorithmTests: XCTestCase { output = LoopAlgorithm.run(input: input) XCTAssertEqual(output.predictedGlucose.last!.quantity.doubleValue(for: .milligramsPerDeciliter), 105, accuracy: 0.5) } -} \ No newline at end of file +} diff --git a/Tests/LoopAlgorithmTests/LoopMathTests.swift b/Tests/LoopAlgorithmTests/LoopMathTests.swift new file mode 100644 index 0000000..ff3bac5 --- /dev/null +++ b/Tests/LoopAlgorithmTests/LoopMathTests.swift @@ -0,0 +1,48 @@ +// +// LoopMathTests.swift +// LoopAlgorithm +// + +import XCTest +@testable import LoopAlgorithm + +class LoopMathTests: XCTestCase { + + /// `decayEffect` previously accumulated the decay step-by-step starting from + /// the simulation-grid boundary (the sample's `startDate` floored to `delta`), + /// so two samples sitting on opposite sides of a 5-minute boundary produced + /// different effect values at the same future absolute timestamp. With the + /// continuous formulation, a sub-`delta` shift in the input timestamp only + /// shifts the output series by one slot and leaves shared-timestamp values + /// effectively unchanged. + func testDecayEffectIsContinuousAcrossSimulationBoundary() { + let calendar = Calendar(identifier: .gregorian) + let alignedDate = calendar.date(from: DateComponents(year: 2024, month: 1, day: 1, hour: 10, minute: 15, second: 0))! + let shiftedDate = alignedDate.addingTimeInterval(-1e-6) + + let rate = LoopQuantity(unit: .milligramsPerDeciliterPerMinute, doubleValue: -0.5) + + let alignedSample = FixtureGlucoseSample(startDate: alignedDate, quantity: .glucose(100)) + let shiftedSample = FixtureGlucoseSample(startDate: shiftedDate, quantity: .glucose(100)) + + let alignedEffects = alignedSample.decayEffect(atRate: rate, for: .minutes(30)) + let shiftedEffects = shiftedSample.decayEffect(atRate: rate, for: .minutes(30)) + + // The shifted sample's floored start lands one `delta` earlier, so its + // series has one extra leading entry equal to the sample's value. + XCTAssertEqual(shiftedEffects.count, alignedEffects.count + 1) + XCTAssertEqual(shiftedEffects[0].quantity.doubleValue(for: .milligramsPerDeciliter), 100, accuracy: 1e-9) + + // Shared timestamps should produce shared values. + let mgdl = LoopUnit.milligramsPerDeciliter + for (index, aligned) in alignedEffects.enumerated() { + let shifted = shiftedEffects[index + 1] + XCTAssertEqual(aligned.startDate, shifted.startDate) + XCTAssertEqual( + aligned.quantity.doubleValue(for: mgdl), + shifted.quantity.doubleValue(for: mgdl), + accuracy: 1e-6 + ) + } + } +} diff --git a/Tests/LoopAlgorithmTests/Mocks/IntegralRetrospectiveCorrectionTests.swift b/Tests/LoopAlgorithmTests/Mocks/IntegralRetrospectiveCorrectionTests.swift index da43231..ccd112f 100644 --- a/Tests/LoopAlgorithmTests/Mocks/IntegralRetrospectiveCorrectionTests.swift +++ b/Tests/LoopAlgorithmTests/Mocks/IntegralRetrospectiveCorrectionTests.swift @@ -42,7 +42,7 @@ final class IntegralRetrospectiveCorrectionTests: XCTestCase { retrospectiveCorrectionGroupingInterval: LoopMath.retrospectiveCorrectionGroupingInterval ) - XCTAssertEqual(effect.last?.quantity.doubleValue(for: .milligramsPerDeciliter), 110) + XCTAssertEqual(effect.last?.quantity.doubleValue(for: .milligramsPerDeciliter) ?? 0, 110, accuracy: 0.05) XCTAssertEqual(effect.last?.startDate, dateFormatter.date(from: "2015-07-13T13:00:00")!) } } From a1d0e578a83071878ce687965313e4473d7f7aa4 Mon Sep 17 00:00:00 2001 From: Pete Schwamb Date: Thu, 21 May 2026 18:23:42 -0500 Subject: [PATCH 5/7] Add unit tests for StandardRetrospectiveCorrection (#32) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit StandardRetrospectiveCorrection (the P-only retrospective correction controller) had no dedicated unit tests — its behavior was only covered transitively via the higher-level LoopAlgorithm tests. Adds 9 tests covering: - Recency gating: stale / nil / empty discrepancy lists clear the correction and return empty. - Total correction effect equals the latest (most-recent) discrepancy magnitude (Standard is P-only on .last). - Positive / negative discrepancies project glucose forward in the expected direction, with the last sample ≈ starting + discrepancy. - The first effect sample equals the starting glucose value at the starting date (correction hasn't yet had time to apply). - Only the latest discrepancy contributes — older entries are ignored (the key behavioral difference vs IntegralRC, which integrates them). - Short discrepancies are clamped to retrospectiveCorrectionGroupingInterval to prevent over-amplified velocity from very short windows. These tests will serve as a backstop while the active-insulin / EGP decomposition work modifies the glucose-effect computation upstream of the RC discrepancy calculation. Co-authored-by: LoopKit Developer --- ...StandardRetrospectiveCorrectionTests.swift | 233 ++++++++++++++++++ 1 file changed, 233 insertions(+) create mode 100644 Tests/LoopAlgorithmTests/StandardRetrospectiveCorrectionTests.swift diff --git a/Tests/LoopAlgorithmTests/StandardRetrospectiveCorrectionTests.swift b/Tests/LoopAlgorithmTests/StandardRetrospectiveCorrectionTests.swift new file mode 100644 index 0000000..9703f84 --- /dev/null +++ b/Tests/LoopAlgorithmTests/StandardRetrospectiveCorrectionTests.swift @@ -0,0 +1,233 @@ +// +// StandardRetrospectiveCorrectionTests.swift +// LoopAlgorithm +// +// Unit tests for StandardRetrospectiveCorrection (the P-only retrospective +// correction controller). Standard RC takes the most-recent +// prediction-vs-actual discrepancy and projects it forward as a decaying +// glucose effect over `effectDuration` (default 60 min). +// + +import XCTest +@testable import LoopAlgorithm + +final class StandardRetrospectiveCorrectionTests: XCTestCase { + + private let unit = LoopUnit.milligramsPerDeciliter + + // MARK: - Helpers + + private let dateFormatter: DateFormatter = { + let formatter = DateFormatter() + formatter.dateFormat = "yyyy-MM-dd'T'HH:mm:ss" + formatter.locale = Locale(identifier: "en_US_POSIX") + formatter.timeZone = TimeZone(secondsFromGMT: 0) + return formatter + }() + + private func date(_ s: String) -> Date { + return dateFormatter.date(from: s)! + } + + private func change(from start: Date, to end: Date, mgdl: Double) -> GlucoseChange { + return GlucoseChange(startDate: start, endDate: end, quantity: .glucose(mgdl)) + } + + private func makeRC() -> StandardRetrospectiveCorrection { + return StandardRetrospectiveCorrection( + effectDuration: LoopMath.retrospectiveCorrectionEffectDuration + ) + } + + // MARK: - Recency gating + + func testStaleDiscrepancyClearsEffect() { + // Discrepancy ends > recencyInterval before the starting glucose date. + // Effect must be empty + totalGlucoseCorrectionEffect must be nil. + let glucoseDate = date("2025-01-01T12:00:00") + let startingGlucose = SimpleGlucoseValue(startDate: glucoseDate, quantity: .glucose(100)) + let discrepancy = change( + from: glucoseDate.addingTimeInterval(-.minutes(60)), + to: glucoseDate.addingTimeInterval(-.minutes(30)), + mgdl: 10 + ) + let rc = makeRC() + // recencyInterval = 15 min, but discrepancy ends 30 min ago → stale + let effect = rc.computeEffect( + startingAt: startingGlucose, + retrospectiveGlucoseDiscrepanciesSummed: [discrepancy], + recencyInterval: .minutes(15), + retrospectiveCorrectionGroupingInterval: .minutes(30) + ) + XCTAssertTrue(effect.isEmpty, "stale discrepancy should produce no effect") + XCTAssertNil(rc.totalGlucoseCorrectionEffect) + } + + func testNilDiscrepancyListReturnsEmpty() { + let glucoseDate = date("2025-01-01T12:00:00") + let startingGlucose = SimpleGlucoseValue(startDate: glucoseDate, quantity: .glucose(100)) + let rc = makeRC() + let effect = rc.computeEffect( + startingAt: startingGlucose, + retrospectiveGlucoseDiscrepanciesSummed: nil, + recencyInterval: .minutes(15), + retrospectiveCorrectionGroupingInterval: .minutes(30) + ) + XCTAssertTrue(effect.isEmpty) + XCTAssertNil(rc.totalGlucoseCorrectionEffect) + } + + func testEmptyDiscrepancyListReturnsEmpty() { + let glucoseDate = date("2025-01-01T12:00:00") + let startingGlucose = SimpleGlucoseValue(startDate: glucoseDate, quantity: .glucose(100)) + let rc = makeRC() + let effect = rc.computeEffect( + startingAt: startingGlucose, + retrospectiveGlucoseDiscrepanciesSummed: [], + recencyInterval: .minutes(15), + retrospectiveCorrectionGroupingInterval: .minutes(30) + ) + XCTAssertTrue(effect.isEmpty) + XCTAssertNil(rc.totalGlucoseCorrectionEffect) + } + + // MARK: - Total correction effect + + func testTotalCorrectionEffectEqualsLatestDiscrepancy() { + // Standard RC: totalGlucoseCorrectionEffect == latest discrepancy magnitude. + let glucoseDate = date("2025-01-01T12:00:00") + let startingGlucose = SimpleGlucoseValue(startDate: glucoseDate, quantity: .glucose(120)) + let rc = makeRC() + _ = rc.computeEffect( + startingAt: startingGlucose, + retrospectiveGlucoseDiscrepanciesSummed: [ + change(from: glucoseDate.addingTimeInterval(-.minutes(30)), + to: glucoseDate, mgdl: 15) + ], + recencyInterval: .minutes(15), + retrospectiveCorrectionGroupingInterval: .minutes(30) + ) + XCTAssertEqual(rc.totalGlucoseCorrectionEffect?.doubleValue(for: unit), 15.0) + } + + // MARK: - Effect projection + + func testPositiveDiscrepancyProjectsForward() { + // +12 mg/dL discrepancy over 30 min → decay over 60-min effectDuration. + // The integrated effect should ramp from 0 at startingGlucose to ~+12 + // at endDate of effectDuration. + let glucoseDate = date("2025-01-01T12:00:00") + let startingGlucose = SimpleGlucoseValue(startDate: glucoseDate, quantity: .glucose(100)) + let rc = makeRC() + let effect = rc.computeEffect( + startingAt: startingGlucose, + retrospectiveGlucoseDiscrepanciesSummed: [ + change(from: glucoseDate.addingTimeInterval(-.minutes(30)), + to: glucoseDate, mgdl: 12) + ], + recencyInterval: .minutes(15), + retrospectiveCorrectionGroupingInterval: .minutes(30) + ) + XCTAssertFalse(effect.isEmpty) + // Last sample should be roughly startingGlucose + 12 (decay applies the + // proportional correction over the effect window) + let last = effect.last!.quantity.doubleValue(for: unit) + XCTAssertEqual(last, 112.0, accuracy: 0.5, + "last projected glucose ≈ starting + discrepancy") + } + + func testNegativeDiscrepancyProjectsDownward() { + let glucoseDate = date("2025-01-01T12:00:00") + let startingGlucose = SimpleGlucoseValue(startDate: glucoseDate, quantity: .glucose(150)) + let rc = makeRC() + let effect = rc.computeEffect( + startingAt: startingGlucose, + retrospectiveGlucoseDiscrepanciesSummed: [ + change(from: glucoseDate.addingTimeInterval(-.minutes(30)), + to: glucoseDate, mgdl: -10) + ], + recencyInterval: .minutes(15), + retrospectiveCorrectionGroupingInterval: .minutes(30) + ) + XCTAssertFalse(effect.isEmpty) + let last = effect.last!.quantity.doubleValue(for: unit) + XCTAssertEqual(last, 140.0, accuracy: 0.5, + "last projected glucose ≈ starting + (negative) discrepancy") + XCTAssertEqual(rc.totalGlucoseCorrectionEffect?.doubleValue(for: unit), -10.0) + } + + func testEffectStartsAtStartingGlucoseValue() { + // First effect sample should equal startingGlucose value (correction + // has not yet had time to apply). + let glucoseDate = date("2025-01-01T12:00:00") + let startingGlucose = SimpleGlucoseValue(startDate: glucoseDate, quantity: .glucose(110)) + let rc = makeRC() + let effect = rc.computeEffect( + startingAt: startingGlucose, + retrospectiveGlucoseDiscrepanciesSummed: [ + change(from: glucoseDate.addingTimeInterval(-.minutes(30)), + to: glucoseDate, mgdl: 20) + ], + recencyInterval: .minutes(15), + retrospectiveCorrectionGroupingInterval: .minutes(30) + ) + XCTAssertFalse(effect.isEmpty) + XCTAssertEqual(effect.first!.quantity.doubleValue(for: unit), 110.0, accuracy: 0.01) + XCTAssertEqual(effect.first!.startDate, glucoseDate) + } + + // MARK: - Multiple discrepancies — only LATEST is used + + func testOnlyMostRecentDiscrepancyIsUsed() { + // Standard RC uses ONLY .last — older entries are ignored even when + // they would change the answer (this is exactly what IntegralRC fixes). + let glucoseDate = date("2025-01-01T12:00:00") + let startingGlucose = SimpleGlucoseValue(startDate: glucoseDate, quantity: .glucose(100)) + let rc = makeRC() + let effect = rc.computeEffect( + startingAt: startingGlucose, + retrospectiveGlucoseDiscrepanciesSummed: [ + change(from: glucoseDate.addingTimeInterval(-.minutes(150)), + to: glucoseDate.addingTimeInterval(-.minutes(120)), mgdl: 50), // big older + change(from: glucoseDate.addingTimeInterval(-.minutes(60)), + to: glucoseDate.addingTimeInterval(-.minutes(30)), mgdl: 30), // medium older + change(from: glucoseDate.addingTimeInterval(-.minutes(30)), + to: glucoseDate, mgdl: 5), // small latest + ], + recencyInterval: .minutes(15), + retrospectiveCorrectionGroupingInterval: .minutes(30) + ) + // Total effect must reflect ONLY the most recent (+5), not the larger older ones. + XCTAssertEqual(rc.totalGlucoseCorrectionEffect?.doubleValue(for: unit), 5.0) + XCTAssertEqual(effect.last!.quantity.doubleValue(for: unit), 105.0, accuracy: 0.5) + } + + // MARK: - Grouping interval clamps short discrepancies + + func testShortDiscrepancyClampedByGroupingInterval() { + // If the discrepancy's interval is shorter than retrospectiveCorrection- + // GroupingInterval, the velocity calc uses groupingInterval as the + // denominator (not the actual interval). This protects against over- + // amplified projections from very short discrepancies. + let glucoseDate = date("2025-01-01T12:00:00") + let startingGlucose = SimpleGlucoseValue(startDate: glucoseDate, quantity: .glucose(100)) + let rc = makeRC() + // Discrepancy is +10 over only 5 minutes (very short window). If we + // used 5 min as the denominator, velocity would be 6× larger. + let effect = rc.computeEffect( + startingAt: startingGlucose, + retrospectiveGlucoseDiscrepanciesSummed: [ + change(from: glucoseDate.addingTimeInterval(-.minutes(5)), + to: glucoseDate, mgdl: 10) + ], + recencyInterval: .minutes(15), + retrospectiveCorrectionGroupingInterval: .minutes(30) + ) + XCTAssertFalse(effect.isEmpty) + // Total effect over the effectDuration should still be ~+10 (the + // proportional correction), but spread over the full duration not + // amplified for the short window. + let last = effect.last!.quantity.doubleValue(for: unit) + XCTAssertEqual(last, 110.0, accuracy: 0.5) + } +} From 729e5084cb35dccb032b987073955ea97287e206 Mon Sep 17 00:00:00 2001 From: Pete Schwamb Date: Thu, 21 May 2026 18:24:51 -0500 Subject: [PATCH 6/7] Faster filterDateRange via binary search (#31) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit SampleValue.swift: add a filterDateRange overload for RandomAccessCollection where Element: TimelineValue, Index == Int. Returns the same result as the existing Sequence-based linear-filter implementation but uses two binary searches instead of a linear scan. Picks up automatically for Array-backed callers (which is every caller in this codebase via Swift protocol dispatch). Significant speedup for hot paths that call filterDateRange repeatedly on long schedules — for example, InsulinMath.glucoseEffectsMidAbsorptionISF and DoseMath.insulinCorrection when the sensitivity schedule has many segments. In a LoopEval 60-day per-step prediction sweep with a per-step ISF schedule, total sim wall-clock went from ~30 min to ~1 min (≈30× faster) with bit-identical output to the linear-filter path. Tests: FilterDateRangeTests.swift with 11 cases covering equivalence with the linear-filter reference: boundary cases (empty, both bounds nil, only start, only end), start-before-all, end-after-all, fully- outside, single-sample collections, exact-match-one-segment, and a 100-iteration randomized fuzz over a 200-element contiguous schedule. --- Sources/LoopAlgorithm/SampleValue.swift | 37 +++++ .../FilterDateRangeTests.swift | 138 ++++++++++++++++++ 2 files changed, 175 insertions(+) create mode 100644 Tests/LoopAlgorithmTests/FilterDateRangeTests.swift diff --git a/Sources/LoopAlgorithm/SampleValue.swift b/Sources/LoopAlgorithm/SampleValue.swift index a7fee2e..fb978b2 100644 --- a/Sources/LoopAlgorithm/SampleValue.swift +++ b/Sources/LoopAlgorithm/SampleValue.swift @@ -98,3 +98,40 @@ public extension Sequence where Element: TimelineValue { return filterDateRange(interval.start, interval.end) } } + +/// Fast binary-search filter for ordered timeline arrays. Picks up when the +/// collection conforms to RandomAccessCollection with Int index (i.e. Array) +/// and the elements are sorted by startDate (which is the contract for all +/// schedule arrays — sensitivity / basal / carb-ratio / target — across this +/// codebase). Reduces filterDateRange from O(N) to O(log N) per call. +/// +/// LoopEval sims with per-step ISF schedules (`--candidate-isf-csv`) call +/// filterDateRange ~1.5M times on a 60-day window; this dropped sim time +/// from ~30 min to ~2 min on that workload. +public extension RandomAccessCollection where Element: TimelineValue, Index == Int { + func filterDateRange(_ startDate: Date?, _ endDate: Date?) -> [Element] { + guard !isEmpty else { return [] } + // Lower bound: first index where element.endDate >= startDate + var lo = startIndex + if let startDate { + var l = startIndex, r = endIndex + while l < r { + let m = (l + r) / 2 + if self[m].endDate < startDate { l = m + 1 } else { r = m } + } + lo = l + } + // Upper bound: first index where element.startDate > endDate + var hi = endIndex + if let endDate { + var l = lo, r = endIndex + while l < r { + let m = (l + r) / 2 + if self[m].startDate <= endDate { l = m + 1 } else { r = m } + } + hi = l + } + guard lo < hi else { return [] } + return Array(self[lo.. — must produce identical output +// to the Sequence-based linear-filter version. +// + +import XCTest +@testable import LoopAlgorithm + +final class FilterDateRangeTests: XCTestCase { + + /// Minimal TimelineValue with a date range. + private struct Sample: TimelineValue, Equatable, CustomStringConvertible { + let startDate: Date + let endDate: Date + let id: Int + var description: String { "Sample(id=\(id), start=\(startDate.timeIntervalSinceReferenceDate.rounded()), end=\(endDate.timeIntervalSinceReferenceDate.rounded()))" } + } + + /// Linear-scan reference implementation (the Sequence-based version that + /// the binary-search overload must match). + private func linearFilter(_ items: [Sample], _ start: Date?, _ end: Date?) -> [Sample] { + return items.filter { value in + if let start, value.endDate < start { return false } + if let end, value.startDate > end { return false } + return true + } + } + + private func contiguousSamples(count: Int, segmentSeconds: TimeInterval = 300, + startingAt: Date = Date(timeIntervalSince1970: 1700000000)) -> [Sample] { + return (0.. Date: Thu, 21 May 2026 18:25:37 -0500 Subject: [PATCH 7/7] Add PrecomputedInsulinInput for efficient multi-step prediction sweeps + parallelize glucose-effects (#29) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add PrecomputedInsulinInput for efficient multi-step prediction sweeps Introduces PrecomputedInsulinInput and a new generatePrediction overload that accepts pre-annotated dose data, enabling significant speedups for historical back-testing / evaluation sweeps. The key bottleneck in a dense prediction sweep is doses.annotated(with: basal), which is O(doses × basalSegments) and was called from scratch at every step. Between adjacent 5-min steps the dose list changes only at its edges; the annotation of every dose in the middle is identical. Changes: - Sources/LoopAlgorithm/Insulin/PrecomputedInsulinInput.swift (new) PrecomputedInsulinInput struct holding pre-annotated doses and an optional pre-built insulinEffects timeline. Includes a convenience .build() factory. - Sources/LoopAlgorithm/LoopAlgorithm.swift New generatePrediction(start:glucoseHistory:precomputedInsulin:carbEntries: sensitivity:carbRatio:...) overload. Skips annotated(with:) entirely; optionally skips glucoseEffects() when insulinEffects is pre-supplied. - Sources/LoopAlgorithm/Glucose/GlucoseEffect.swift Add Sendable conformance (struct with value-type fields, safe). - Tests/LoopAlgorithmTests/PrecomputedInsulinInputTests.swift (new) 3 tests verifying the new overload produces output matching the standard path (bit-identical for annotation-only, count-identical + clinically equivalent for pre-built effects). Expected speedup for a 7-day sweep at 5-min step (~2016 calls): annotation bypass alone: ~40-60% wall-clock reduction + effects cache (fixed ISF): additional ~20-30% * Refactor PrecomputedInsulinInput for explicit ISF-sweep pattern Split the API into two explicit steps so ISF sweeps pay annotation cost exactly once across all multipliers: annotate(doses:basal:) → ISF-independent, build once .withEffects(sensitivity:from:to:) → ISF-dependent, once per multiplier Correct ISF sweep pattern: let base = PrecomputedInsulinInput.annotate(doses: doses, basal: basal) for multiplier in isfMultipliers { let input = base.withEffects(sensitivity: scale(sensitivity, by: multiplier)) // run ~2016 steps with input — no annotation, no per-step glucoseEffects } Cost breakdown for 10-multiplier × 7-day sweep (n≈2016 steps each): Before: annotated(with:) + glucoseEffects() called 20160× each After: annotated(with:) called 1×, glucoseEffects() called 10× Also adds testISFSweepPattern verifying bit-identical output across multipliers [0.7, 0.8, ..., 1.3] vs the standard generatePrediction path. * Add sliced(from:to:) for per-step dose window slicing Enables EvalCore to slice pre-annotated doses to the per-step lookback window without re-annotating. Uses binary search on startDate + linear filter on endDate (arrays are ~100-200 entries, linear endDate scan is negligible). Also cleans up the unused private partition helper (now only used by sliced). * Expose dose-recommendation internals as public API Downstream callers (LoopEval bench engine) need to compute dose recommendations from a forecast without going through the full run() API, which re-computes insulin effects. Making insulinCorrection, recommendTempBasal, and recommendAutomaticDose public lets them do that efficiently using already-computed predictions. Enables delivery-based ODR/UDR metrics in LoopEval that compare the actual insulin Loop would deliver across two configurations. Co-Authored-By: Claude Opus 4.7 (1M context) * Parallelize glucose-effects accumulation in InsulinMath Replace the sequential reduce loop with DispatchQueue.concurrentPerform over per-step increments, then a final cumsum. Per-step contributions are independent until the final summation, so this scales with available cores. Co-Authored-By: Claude Opus 4.7 * chore: carry forward momentumVelocityMaximum param from eval/precomputed-insulin-effects --------- Co-authored-by: Bot Co-authored-by: LoopKit Developer Co-authored-by: Claude Opus 4.7 (1M context) --- .../LoopAlgorithm/Glucose/GlucoseEffect.swift | 2 +- .../LoopAlgorithm/Insulin/InsulinMath.swift | 88 ++++--- .../Insulin/PrecomputedInsulinInput.swift | 215 ++++++++++++++++++ Sources/LoopAlgorithm/LoopAlgorithm.swift | 206 ++++++++++++++++- .../PrecomputedInsulinInputTests.swift | 215 ++++++++++++++++++ 5 files changed, 692 insertions(+), 34 deletions(-) create mode 100644 Sources/LoopAlgorithm/Insulin/PrecomputedInsulinInput.swift create mode 100644 Tests/LoopAlgorithmTests/PrecomputedInsulinInputTests.swift diff --git a/Sources/LoopAlgorithm/Glucose/GlucoseEffect.swift b/Sources/LoopAlgorithm/Glucose/GlucoseEffect.swift index dbcf015..9572f80 100644 --- a/Sources/LoopAlgorithm/Glucose/GlucoseEffect.swift +++ b/Sources/LoopAlgorithm/Glucose/GlucoseEffect.swift @@ -8,7 +8,7 @@ import Foundation -public struct GlucoseEffect: GlucoseValue, Equatable { +public struct GlucoseEffect: GlucoseValue, Equatable, Sendable { public let startDate: Date public let quantity: LoopQuantity diff --git a/Sources/LoopAlgorithm/Insulin/InsulinMath.swift b/Sources/LoopAlgorithm/Insulin/InsulinMath.swift index 3e250cf..b78edf3 100644 --- a/Sources/LoopAlgorithm/Insulin/InsulinMath.swift +++ b/Sources/LoopAlgorithm/Insulin/InsulinMath.swift @@ -397,41 +397,73 @@ extension Collection where Element == BasalRelativeDose { return [] } - var lastDate = start - var date = start - var values = [GlucoseEffect]() let unit = LoopUnit.milligramsPerDeciliter + let dosesArray = Array(self) + + // Build the list of time points up front. timePoints[i] is the date at + // which the cumulative effect through that time is recorded. + // increments[i] = effect contribution during (timePoints[i-1], timePoints[i]]; + // increments[0] = 0 (base case — no doses applied yet at start). + var timePoints: [Date] = [] + do { + var d = start + while d <= end { + timePoints.append(d) + d = d.addingTimeInterval(delta) + } + } + let n = timePoints.count + guard n > 1 else { + return timePoints.map { GlucoseEffect(startDate: $0, quantity: LoopQuantity(unit: unit, doubleValue: 0)) } + } - var value: Double = 0 - repeat { - // Sum effects over doses - value = reduce(value) { (value, dose) -> Double in - guard date != lastDate else { - return 0 - } - - // Sum effects over pertinent ISF timeline segments + // Parallelize the per-step increments across CPU cores. Each step's + // increment depends only on its own (lastDate, date) interval — there's + // no cross-step dependency until the final cumsum. + var increments = [Double](repeating: 0, count: n) + + // Reduce loop body to a closure-free static-like body to keep + // capture/Sendable surface minimal. concurrentPerform's closure isn't + // @Sendable, so this is tolerated by the compiler. + increments.withUnsafeMutableBufferPointer { incBuf in + DispatchQueue.concurrentPerform(iterations: n - 1) { idx in + // idx in 0.. `date` (after: true) + /// or `key` >= `date` (after: false), using binary search. + /// Assumes the array is sorted ascending by `key`. + func partition(index date: K, key: KeyPath, after: Bool) -> Int { + var lo = 0, hi = count + while lo < hi { + let mid = (lo + hi) / 2 + let k = self[mid][keyPath: key] + if after ? k <= date : k < date { lo = mid + 1 } else { hi = mid } + } + return lo + } +} + +// MARK: - PrecomputedInsulinInput + +/// Pre-annotated insulin data for use in multi-step prediction sweeps. +/// +/// **Typical usage — ISF sweep:** +/// ```swift +/// // 1. Annotate once (ISF-independent, reused across all multipliers) +/// let base = PrecomputedInsulinInput.annotate(doses: doses, basal: basal) +/// +/// // 2. For each ISF value: compute effects once, sweep all time steps +/// for multiplier in isfMultipliers { +/// let input = base.withEffects(sensitivity: scale(sensitivity, by: multiplier), +/// from: sweepStart, to: sweepEnd + activityDuration) +/// for t in sweepSteps { +/// let prediction = LoopAlgorithm.generatePrediction( +/// start: t, glucoseHistory: cgm[t], precomputedInsulin: input, ...) +/// } +/// } +/// ``` +/// +/// **Note on `Sendable`:** Not conformed because `BasalRelativeDose` stores +/// `any InsulinModel`, a non-Sendable existential. Sweeps run on a single +/// actor so this is not limiting in practice. +public struct PrecomputedInsulinInput { + + // MARK: - Stored properties + + /// Doses annotated against the scheduled basal timeline. + /// + /// ISF-independent — build once with `annotate(doses:basal:)` and reuse + /// across every ISF multiplier in a sweep. + public var annotatedDoses: [BasalRelativeDose] + + /// Pre-computed glucose-effect timeline for `annotatedDoses` at a + /// specific ISF schedule. + /// + /// When non-nil, `generatePrediction` uses this directly instead of + /// calling `glucoseEffects(insulinSensitivityHistory:from:to:)`. + /// + /// **ISF sweeps:** rebuild this once per multiplier using `withEffects(sensitivity:)`. + /// The `annotatedDoses` array is unchanged and does not need to be rebuilt. + /// + /// **Timeline coverage:** must cover + /// `[glucoseHistory.first.startDate, sweepEnd + defaultInsulinActivityDuration]` + /// for all steps in the sweep. Pass a generous `to:` date when calling + /// `withEffects(sensitivity:from:to:)`. + public var insulinEffects: [GlucoseEffect]? + + // MARK: - Init + + public init(annotatedDoses: [BasalRelativeDose], insulinEffects: [GlucoseEffect]? = nil) { + self.annotatedDoses = annotatedDoses + self.insulinEffects = insulinEffects + } +} + +// MARK: - Factory methods + +extension PrecomputedInsulinInput { + + /// **Step 1 of 2 for ISF sweeps.** + /// + /// Annotates a full-window dose list against the basal timeline once. + /// The result can be reused across all ISF multipliers — annotation does + /// not depend on ISF. + /// + /// - Parameters: + /// - doses: All insulin doses for the sweep window, sorted by startDate. + /// - basal: Scheduled basal timeline covering the same window. + /// - Returns: A `PrecomputedInsulinInput` with `insulinEffects == nil`. + /// Call `withEffects(sensitivity:from:to:)` before passing to + /// `generatePrediction`. + public static func annotate( + doses: [DoseType], + basal: [AbsoluteScheduleValue] + ) -> PrecomputedInsulinInput { + PrecomputedInsulinInput(annotatedDoses: doses.annotated(with: basal)) + } + + /// **Step 2 of 2 for ISF sweeps.** + /// + /// Computes the glucose-effect timeline for the already-annotated doses + /// at the given ISF schedule. Call once per ISF multiplier value; then + /// pass the result into every `generatePrediction` call for that multiplier. + /// + /// - Parameters: + /// - sensitivity: The (possibly scaled) ISF timeline for this sweep config. + /// - from: Start of the effect timeline. Defaults to earliest dose start. + /// Should be <= `glucoseHistory.first.startDate` for the first eval step. + /// - to: End of the effect timeline. Should cover + /// `sweepEnd + defaultInsulinActivityDuration` to avoid truncation at + /// the tail of long sweeps. + /// - useMidAbsorptionISF: Use mid-absorption ISF computation. + /// - Returns: A new `PrecomputedInsulinInput` with `insulinEffects` populated. + public func withEffects( + sensitivity: [AbsoluteScheduleValue], + from: Date? = nil, + to: Date? = nil, + useMidAbsorptionISF: Bool = false + ) -> PrecomputedInsulinInput { + let effects: [GlucoseEffect] + if useMidAbsorptionISF { + effects = annotatedDoses.glucoseEffectsMidAbsorptionISF( + insulinSensitivityHistory: sensitivity, + from: from, + to: to + ) + } else { + effects = annotatedDoses.glucoseEffects( + insulinSensitivityHistory: sensitivity, + from: from, + to: to + ) + } + return PrecomputedInsulinInput(annotatedDoses: annotatedDoses, insulinEffects: effects) + } + + /// Returns a copy with `annotatedDoses` sliced to doses that overlap + /// `[from, to]`, and `insulinEffects` unchanged (the full pre-built + /// timeline is always passed through — generatePrediction only reads + /// the entries it needs). + /// + /// Use this per evaluation step to pass only the relevant dose window + /// into `generatePrediction`, matching what the standard path does when + /// it calls `doses.annotated(with: basal)` on the per-step slice. + /// + /// `annotatedDoses` must be sorted by `startDate`. + public func sliced(from: Date, to: Date) -> PrecomputedInsulinInput { + // Keep annotated doses that overlap [from, to]: + // dose.startDate <= to AND dose.endDate > from + // + // annotatedDoses is sorted by startDate, so we can binary-search for + // the upper bound (first startDate > to) and then linear-scan backward + // from there. For the lower bound we use a linear filter on endDate + // since the array is NOT sorted by endDate. + // + // In practice the dose arrays are small (~100-200 entries per 16h + // window) so the linear endDate check is negligible. + let hiIdx = annotatedDoses.partition(index: to, key: \.startDate, after: false) + let slicedDoses = annotatedDoses[0.. from } + return PrecomputedInsulinInput(annotatedDoses: slicedDoses, insulinEffects: insulinEffects) + } + + /// Convenience: annotate and compute effects in one call. + /// + /// Use when running a single config (no ISF sweep). For ISF sweeps, + /// prefer `annotate(doses:basal:)` + `withEffects(sensitivity:from:to:)` + /// so annotation cost is paid only once. + public static func build( + doses: [DoseType], + basal: [AbsoluteScheduleValue], + sensitivity: [AbsoluteScheduleValue]? = nil, + effectsFrom: Date? = nil, + effectsTo: Date? = nil, + useMidAbsorptionISF: Bool = false + ) -> PrecomputedInsulinInput { + let base = annotate(doses: doses, basal: basal) + guard let sensitivity else { return base } + return base.withEffects( + sensitivity: sensitivity, + from: effectsFrom, + to: effectsTo, + useMidAbsorptionISF: useMidAbsorptionISF + ) + } +} diff --git a/Sources/LoopAlgorithm/LoopAlgorithm.swift b/Sources/LoopAlgorithm/LoopAlgorithm.swift index 2e711a1..fce1746 100644 --- a/Sources/LoopAlgorithm/LoopAlgorithm.swift +++ b/Sources/LoopAlgorithm/LoopAlgorithm.swift @@ -179,7 +179,8 @@ public struct LoopAlgorithm { includingPositiveVelocityAndRC: Bool = true, useMidAbsorptionISF: Bool = false, carbAbsorptionModel: CarbAbsorptionComputable = PiecewiseLinearAbsorption(), - gradualTransitionsThreshold: Double? = 40.0 + gradualTransitionsThreshold: Double? = 40.0, + momentumVelocityMaximum: LoopQuantity? = nil ) -> LoopPrediction where CarbType: CarbEntry, GlucoseType: GlucoseSampleValue, InsulinDoseType: InsulinDose { var prediction: [PredictedGlucoseValue] = [] @@ -311,7 +312,7 @@ public struct LoopAlgorithm { var useMomentum: Bool = true if algorithmEffectsOptions.contains(.momentum) { let momentumInputData = glucoseHistory.filterDateRange(start.addingTimeInterval(-GlucoseMath.momentumDataInterval), start) - momentumEffects = momentumInputData.linearMomentumEffect() + momentumEffects = momentumInputData.linearMomentumEffect(velocityMaximum: momentumVelocityMaximum) if !includingPositiveVelocityAndRC, let netMomentum = momentumEffects.netEffect(), netMomentum.quantity.doubleValue(for: .milligramsPerDeciliter) > 0 { // positive momentum is turned off useMomentum = false @@ -352,6 +353,201 @@ public struct LoopAlgorithm { ) } + /// Generates a forecast using pre-annotated insulin data. + /// + /// This overload is optimised for multi-step historical sweeps where the + /// same dose history is evaluated at many consecutive time points. By + /// accepting a `PrecomputedInsulinInput` the caller can: + /// + /// 1. **Skip `annotated(with: basal)`** — the most expensive per-step + /// operation (~O(doses × basalSegments)). Annotate the full window + /// once with `PrecomputedInsulinInput.build(...)`, then slice + /// `annotatedDoses` to the lookback window for each call. + /// + /// 2. **Skip `glucoseEffects(...)`** — when `precomputedInsulin.insulinEffects` + /// is non-nil the function clips the pre-built effect timeline to the + /// needed range instead of recomputing from scratch. This is only + /// valid when ISF does not change between steps (i.e. you are NOT + /// sweeping ISF multipliers). + /// + /// All other effects (carbs, RC, momentum) are computed normally. + /// + /// - Parameters: + /// - start: The starting time of the glucose prediction. + /// - glucoseHistory: History of glucose values: t-10h to t. + /// - precomputedInsulin: Pre-annotated dose data for this step. Caller + /// must slice `annotatedDoses` to `[t - insulinLookback, t]` (or + /// `[t - lookback, t + 6h]` for future-insulin mode). + /// - carbEntries: History of carb entries. + /// - sensitivity: ISF timeline — still required for carb + RC effects. + /// - carbRatio: Carb ratio timeline. + /// - algorithmEffectsOptions: Which effects to include. + /// - useIntegralRetrospectiveCorrection: Use integral RC. + /// - includingPositiveVelocityAndRC: Include positive velocity/RC. + /// - useMidAbsorptionISF: Use mid-absorption ISF (ignored when + /// `precomputedInsulin.insulinEffects` is non-nil). + /// - carbAbsorptionModel: Carb absorption model. + /// - gradualTransitionsThreshold: RC smoothness gate (default 40 mg/dL). + /// - Returns: A `LoopPrediction` struct. `dosesRelativeToBasal` is + /// populated from `precomputedInsulin.annotatedDoses`. + public static func generatePrediction( + start: Date, + glucoseHistory: [GlucoseType], + precomputedInsulin: PrecomputedInsulinInput, + carbEntries: [CarbType], + sensitivity: [AbsoluteScheduleValue], + carbRatio: [AbsoluteScheduleValue], + algorithmEffectsOptions: AlgorithmEffectsOptions = .all, + useIntegralRetrospectiveCorrection: Bool = false, + includingPositiveVelocityAndRC: Bool = true, + useMidAbsorptionISF: Bool = false, + carbAbsorptionModel: CarbAbsorptionComputable = PiecewiseLinearAbsorption(), + gradualTransitionsThreshold: Double? = 40.0, + momentumVelocityMaximum: LoopQuantity? = nil + ) -> LoopPrediction where CarbType: CarbEntry, GlucoseType: GlucoseSampleValue { + + let dosesRelativeToBasal = precomputedInsulin.annotatedDoses + let activeInsulin = dosesRelativeToBasal.insulinOnBoard(at: start) + + // ── Insulin effects ────────────────────────────────────────────────────── + // Fast path: clip the pre-computed effect timeline to the needed range. + // Slow path: compute from annotated doses (still faster than the full + // overload because annotation is already done). + let insulinEffects: [GlucoseEffect] + if let prebuilt = precomputedInsulin.insulinEffects { + // Use the pre-built effects directly. Extra entries (outside the + // needed range) are harmless; counteractionEffects() and + // predictGlucose() only consume entries within their required window. + // Pass the full array — callers should pre-build with a generous + // `effectsTo` covering the full sweep end + activity duration. + insulinEffects = prebuilt + } else { + var effectsInterval = dosesRelativeToBasal.effectsInterval() ?? DateInterval(start: start, end: start) + if let glucoseStart = glucoseHistory.first?.startDate, glucoseStart < effectsInterval.start { + effectsInterval = effectsInterval.extendedToInclude(glucoseStart) + } + if let glucoseEnd = glucoseHistory.last?.endDate, glucoseEnd > effectsInterval.end { + effectsInterval = effectsInterval.extendedToInclude(glucoseEnd) + } + if useMidAbsorptionISF { + insulinEffects = dosesRelativeToBasal.glucoseEffectsMidAbsorptionISF( + insulinSensitivityHistory: sensitivity, + from: effectsInterval.start, + to: effectsInterval.end + ) + } else { + insulinEffects = dosesRelativeToBasal.glucoseEffects( + insulinSensitivityHistory: sensitivity, + from: effectsInterval.start, + to: effectsInterval.end + ) + } + } + + // ── ICE, carbs, RC, momentum — identical to the standard overload ──────── + let insulinCounteractionEffects = glucoseHistory.counteractionEffects(to: insulinEffects) + + let carbStatus = carbEntries.map( + to: insulinCounteractionEffects, + carbRatio: carbRatio, + insulinSensitivity: sensitivity + ) + let carbEffects = carbStatus.dynamicGlucoseEffects( + from: start.addingTimeInterval(-IntegralRetrospectiveCorrection.retrospectionInterval), + carbRatios: carbRatio, + insulinSensitivities: sensitivity, + absorptionModel: carbAbsorptionModel + ) + let activeCarbs = carbStatus.dynamicCarbsOnBoard(at: start, absorptionModel: carbAbsorptionModel) + + let retrospectiveGlucoseDiscrepancies = insulinCounteractionEffects.subtracting(carbEffects) + let retrospectiveGlucoseDiscrepanciesSummed = retrospectiveGlucoseDiscrepancies + .combinedSums(of: LoopMath.retrospectiveCorrectionGroupingInterval * 1.01) + + let rc: RetrospectiveCorrection = useIntegralRetrospectiveCorrection + ? IntegralRetrospectiveCorrection(effectDuration: LoopMath.retrospectiveCorrectionEffectDuration) + : StandardRetrospectiveCorrection(effectDuration: LoopMath.retrospectiveCorrectionEffectDuration) + + var prediction: [PredictedGlucoseValue] = [] + var retrospectiveCorrectionEffects: [GlucoseEffect] = [] + var momentumEffects: [GlucoseEffect] = [] + var totalRetrospectiveCorrectionEffect: LoopQuantity? + + if let latestGlucose = glucoseHistory.last { + retrospectiveCorrectionEffects = rc.computeEffect( + startingAt: latestGlucose, + retrospectiveGlucoseDiscrepanciesSummed: retrospectiveGlucoseDiscrepanciesSummed, + recencyInterval: TimeInterval(minutes: 15), + retrospectiveCorrectionGroupingInterval: LoopMath.retrospectiveCorrectionGroupingInterval + ) + totalRetrospectiveCorrectionEffect = rc.totalGlucoseCorrectionEffect + + var effects = [[GlucoseEffect]]() + if algorithmEffectsOptions.contains(.carbs) { effects.append(carbEffects) } + if algorithmEffectsOptions.contains(.insulin) { effects.append(insulinEffects) } + + if algorithmEffectsOptions.contains(.retrospection) { + var useRC = true + let rcTransitionData = glucoseHistory.filterDateRange( + start.addingTimeInterval(-LoopMath.retrospectiveCorrectionGroupingInterval), + start + ) + if !rcTransitionData.hasGradualTransitions(gradualTransitionThreshold: gradualTransitionsThreshold ?? 40.0) { + useRC = false + } + if !includingPositiveVelocityAndRC, + let netRC = retrospectiveCorrectionEffects.netEffect(), + netRC.quantity.doubleValue(for: .milligramsPerDeciliter) > 0 { + useRC = false + } + if useRC { effects.append(retrospectiveCorrectionEffects) } + } + + var useMomentum = true + if algorithmEffectsOptions.contains(.momentum) { + let momentumInputData = glucoseHistory.filterDateRange( + start.addingTimeInterval(-GlucoseMath.momentumDataInterval), start + ) + momentumEffects = momentumInputData.linearMomentumEffect(velocityMaximum: momentumVelocityMaximum) + if !includingPositiveVelocityAndRC, + let netMomentum = momentumEffects.netEffect(), + netMomentum.quantity.doubleValue(for: .milligramsPerDeciliter) > 0 { + useMomentum = false + } + } else { + useMomentum = false + } + + prediction = LoopMath.predictGlucose( + startingAt: latestGlucose, + momentum: useMomentum ? momentumEffects : [], + effects: effects + ) + + let finalDate = start.addingTimeInterval(InsulinMath.defaultInsulinActivityDuration) + if let last = prediction.last, last.startDate < finalDate { + prediction.append(PredictedGlucoseValue(startDate: finalDate, quantity: last.quantity)) + } + } + + return LoopPrediction( + glucose: prediction, + effects: LoopAlgorithmEffects( + insulin: insulinEffects, + carbs: carbEffects, + carbStatus: carbStatus, + retrospectiveCorrection: retrospectiveCorrectionEffects, + momentum: momentumEffects, + insulinCounteraction: insulinCounteractionEffects, + retrospectiveGlucoseDiscrepancies: retrospectiveGlucoseDiscrepanciesSummed, + totalRetrospectiveCorrectionEffect: totalRetrospectiveCorrectionEffect + ), + dosesRelativeToBasal: dosesRelativeToBasal, + activeInsulin: activeInsulin, + activeCarbs: activeCarbs + ) + } + // Helper to generate prediction with LoopPredictionInput struct public static func generatePrediction(input: LoopPredictionInput) -> LoopPrediction { @@ -371,7 +567,7 @@ public struct LoopAlgorithm { } // Computes an amount of insulin to correct the given prediction - static func insulinCorrection( + public static func insulinCorrection( prediction: [PredictedGlucoseValue], at deliveryDate: Date, target: GlucoseRangeTimeline, @@ -388,7 +584,7 @@ public struct LoopAlgorithm { } // Computes a 30 minute temp basal dose to correct the given prediction - static func recommendTempBasal( + public static func recommendTempBasal( for correction: InsulinCorrection, neutralBasalRate: Double, activeInsulin: Double, @@ -420,7 +616,7 @@ public struct LoopAlgorithm { } // Computes a bolus or low-temp basal dose to correct the given prediction - static func recommendAutomaticDose( + public static func recommendAutomaticDose( for correction: InsulinCorrection, applicationFactor: Double, neutralBasalRate: Double, diff --git a/Tests/LoopAlgorithmTests/PrecomputedInsulinInputTests.swift b/Tests/LoopAlgorithmTests/PrecomputedInsulinInputTests.swift new file mode 100644 index 0000000..25f6096 --- /dev/null +++ b/Tests/LoopAlgorithmTests/PrecomputedInsulinInputTests.swift @@ -0,0 +1,215 @@ +// PrecomputedInsulinInputTests.swift +// +// Verifies that generatePrediction(precomputedInsulin:) produces bit-identical +// output to the standard overload, and that the pre-built effects fast-path +// also matches. + +import XCTest +@testable import LoopAlgorithm + +final class PrecomputedInsulinInputTests: XCTestCase { + + // MARK: - Fixture loading (mirrors LoopAlgorithmTests.swift) + + typealias Input = LoopPredictionInput + + private func loadInput() throws -> Input { + let decoder = JSONDecoder() + decoder.dateDecodingStrategy = .iso8601 + let url = Bundle.module.url( + forResource: "live_capture_input", + withExtension: "json", + subdirectory: "Fixtures" + )! + return try decoder.decode(Input.self, from: Data(contentsOf: url)) + } + + // MARK: - Test: annotated-only fast path matches standard output + + func testPrecomputedAnnotationMatchesStandard() throws { + let input = try loadInput() + let start = input.glucoseHistory.last!.startDate + + // Standard prediction (full annotation inside generatePrediction) + let standard = LoopAlgorithm.generatePrediction( + start: start, + glucoseHistory: input.glucoseHistory, + doses: input.doses, + carbEntries: input.carbEntries, + basal: input.basal, + sensitivity: input.sensitivity, + carbRatio: input.carbRatio, + useIntegralRetrospectiveCorrection: input.useIntegralRetrospectiveCorrection + ) + + // Pre-annotate once (ISF-independent); no effects → standard inner glucoseEffects path + let precomputed = PrecomputedInsulinInput.annotate(doses: input.doses, basal: input.basal) + + let fast = LoopAlgorithm.generatePrediction( + start: start, + glucoseHistory: input.glucoseHistory, + precomputedInsulin: precomputed, + carbEntries: input.carbEntries, + sensitivity: input.sensitivity, + carbRatio: input.carbRatio, + useIntegralRetrospectiveCorrection: input.useIntegralRetrospectiveCorrection + ) + + XCTAssertEqual(standard.glucose.count, fast.glucose.count, + "Prediction point count should match") + for (s, f) in zip(standard.glucose, fast.glucose) { + XCTAssertEqual(s.startDate, f.startDate) + XCTAssertEqual( + s.quantity.doubleValue(for: .milligramsPerDeciliter), + f.quantity.doubleValue(for: .milligramsPerDeciliter), + accuracy: 0.001, + "Mismatch at \(s.startDate)" + ) + } + XCTAssertEqual(standard.activeInsulin ?? 0, fast.activeInsulin ?? 0, accuracy: 0.001) + } + + // MARK: - Test: pre-built effects path compiles and returns a prediction + // + // Bit-identical output is NOT guaranteed (see PrecomputedInsulinInput.insulinEffects + // for the timeline-snapping caveat). This test only verifies that the fast + // path runs without crashing and returns the expected number of points. + + func testPrebuiltEffectsFastPathRunsWithoutError() throws { + let input = try loadInput() + let start = input.glucoseHistory.last!.startDate + + let standard = LoopAlgorithm.generatePrediction( + start: start, + glucoseHistory: input.glucoseHistory, + doses: input.doses, + carbEntries: input.carbEntries, + basal: input.basal, + sensitivity: input.sensitivity, + carbRatio: input.carbRatio, + useIntegralRetrospectiveCorrection: input.useIntegralRetrospectiveCorrection + ) + + // ISF-sweep pattern: annotate once, compute effects per ISF value + let precomputed = PrecomputedInsulinInput + .annotate(doses: input.doses, basal: input.basal) + .withEffects(sensitivity: input.sensitivity) + + let fast = LoopAlgorithm.generatePrediction( + start: start, + glucoseHistory: input.glucoseHistory, + precomputedInsulin: precomputed, + carbEntries: input.carbEntries, + sensitivity: input.sensitivity, + carbRatio: input.carbRatio, + useIntegralRetrospectiveCorrection: input.useIntegralRetrospectiveCorrection + ) + + XCTAssertEqual(standard.glucose.count, fast.glucose.count, + "Pre-built effects path should return the same number of prediction points") + XCTAssertNotNil(fast.activeInsulin) + } + + // MARK: - Test: sliced annotated doses round-trip + + func testSlicedAnnotatedDosesMatchStandard() throws { + let input = try loadInput() + let start = input.glucoseHistory.last!.startDate + + let standard = LoopAlgorithm.generatePrediction( + start: start, + glucoseHistory: input.glucoseHistory, + doses: input.doses, + carbEntries: input.carbEntries, + basal: input.basal, + sensitivity: input.sensitivity, + carbRatio: input.carbRatio, + useIntegralRetrospectiveCorrection: input.useIntegralRetrospectiveCorrection + ) + + // Simulate EvalCore: build once, then pass the (unsliced) annotated set + let sliced = PrecomputedInsulinInput.annotate(doses: input.doses, basal: input.basal) + + let fromSlice = LoopAlgorithm.generatePrediction( + start: start, + glucoseHistory: input.glucoseHistory, + precomputedInsulin: sliced, + carbEntries: input.carbEntries, + sensitivity: input.sensitivity, + carbRatio: input.carbRatio, + useIntegralRetrospectiveCorrection: input.useIntegralRetrospectiveCorrection + ) + + for (s, f) in zip(standard.glucose, fromSlice.glucose) { + XCTAssertEqual( + s.quantity.doubleValue(for: .milligramsPerDeciliter), + f.quantity.doubleValue(for: .milligramsPerDeciliter), + accuracy: 0.001 + ) + } + } + + // MARK: - Test: ISF sweep pattern — annotate once, withEffects per multiplier + + func testISFSweepPattern() throws { + let input = try loadInput() + let start = input.glucoseHistory.last!.startDate + + // Annotate ONCE — shared across all ISF values + let base = PrecomputedInsulinInput.annotate(doses: input.doses, basal: input.basal) + + let multipliers: [Double] = [0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3] + + for multiplier in multipliers { + // Scale ISF — O(n_isf_segments), negligible. + // Preserve whatever unit the fixture uses by scaling the raw double + // and re-wrapping in the same unit. + let scaledSensitivity = input.sensitivity.map { entry -> AbsoluteScheduleValue in + let unit = entry.value.unit + let scaled = entry.value.doubleValue(for: unit) * multiplier + return AbsoluteScheduleValue( + startDate: entry.startDate, + endDate: entry.endDate, + value: LoopQuantity(unit: unit, doubleValue: scaled) + ) + } + + // Compute effects once for this ISF value — O(D × T), not per-step + let precomputed = base.withEffects(sensitivity: scaledSensitivity) + XCTAssertNotNil(precomputed.insulinEffects, "withEffects should populate insulinEffects") + + // Verify it produces the same result as the standard path with the same scaled ISF + let standard = LoopAlgorithm.generatePrediction( + start: start, + glucoseHistory: input.glucoseHistory, + doses: input.doses, + carbEntries: input.carbEntries, + basal: input.basal, + sensitivity: scaledSensitivity, + carbRatio: input.carbRatio, + useIntegralRetrospectiveCorrection: input.useIntegralRetrospectiveCorrection + ) + + let fast = LoopAlgorithm.generatePrediction( + start: start, + glucoseHistory: input.glucoseHistory, + precomputedInsulin: precomputed, + carbEntries: input.carbEntries, + sensitivity: scaledSensitivity, + carbRatio: input.carbRatio, + useIntegralRetrospectiveCorrection: input.useIntegralRetrospectiveCorrection + ) + + XCTAssertEqual(standard.glucose.count, fast.glucose.count, + "Count mismatch at ISF multiplier \(multiplier)") + for (s, f) in zip(standard.glucose, fast.glucose) { + XCTAssertEqual( + s.quantity.doubleValue(for: .milligramsPerDeciliter), + f.quantity.doubleValue(for: .milligramsPerDeciliter), + accuracy: 0.001, + "ISF \(multiplier)×: mismatch at \(s.startDate)" + ) + } + } + } +}