trevphil

#include <stdio.h>
#include <stdlib.h>
#include <stdint.h>
#ifdef _MSC_VER
#include <intrin.h> /* for rdtscp and clflush */
#pragma optimize("gt",on)
#else
#include <x86intrin.h> /* for rdtscp and clflush */
#endif

trevphil

func checkScenario(_ scenario: Scenario) {
    let filter = KalmanFilter()

    // Add up the distance between each location in the true path
    let trueDist = unfilteredDistance(scenario.truePath)
    
    // Add up the distance between each location in the noisy path
    let unfilteredDist = unfilteredDistance(scenario.noisyPath)
    

trevphil

import Foundation
import CoreLocation

extension CLLocation {
    // Alias for `horizontalAccuracy` (more readable)
    var uncertainty: Double {
        return horizontalAccuracy
    }
}

trevphil

enum PathType: String, CaseIterable {
    case outAndBack, loop
}

enum Distance: Double, CaseIterable {
    case short = 1000 // in meters
    case medium = 5000
    case long = 15000
}

trevphil

struct Scenario {

    /// A sequence of CLLocations representing the "true path" of the GPS track
    let truePath: [CLLocation]

    /// A sequence of CLLocations based on the true path, but with noise added to various parameters
    let noisyPath: [CLLocation]

    /// A number between 0.0-1.0 representing the minimum allowable percentage difference between

trevphil

func addNoise(to trueLocation: CLLocation) -> CLLocation {
    let distanceWiggle = sampleNormalDist(withMean: 0, std: 0.952)
    let xContribution = rng.nextUniform()
    let xoff = distanceWiggle * Double(xContribution)
    let yoff = distanceWiggle * sqrt(1 - pow(Double(xContribution), 2))
    let newCoord = trueLocation.coordinate.offsetBy(xMeters: xoff, yMeters: yoff)
    let newHacc = sampleNormalDist(withMean: 7.179, std: 1.682)
    let newVacc = sampleNormalDist(withMean: 3.643, std: 0.300)
    let timeWiggle = sampleNormalDist(withMean: 1.00, std: 0.131)

trevphil

private func sampleNormalDist(withMean mean: Double, std: Double) -> Double {
    precondition(std > 0)

    let first = Double(rng.nextUniform())
    let second = Double(rng.nextUniform())
    let zScore = sqrt(-2 * log(first)) * cos(2 * Double.pi * second)
    return zScore * std + mean
}

trevphil

"""
Helper to convert GPX files to CSV files for analysis.

Created by Trevor Phillips
"""

import os
import json

try: