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support_table_generator.py
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#
# This source file is part of the HealthKitOnFHIR open source project
#
# SPDX-FileCopyrightText: 2022 Stanford University and the project authors (see CONTRIBUTORS.md)
#
# SPDX-License-Identifier: MIT
#
import json
from operator import itemgetter
# Constants
MAPPING_FILE_PATH = '../Sources/HealthKitOnFHIR/Resources/HKSampleMapping.json'
TOC_PATH = '../Sources/HealthKitOnFHIR/HealthKitOnFHIR.docc/HKSampleSupportTables.md'
QUANTITY_TABLE_PATH = '../Sources/HealthKitOnFHIR/HealthKitOnFHIR.docc/SupportedHKQuantityTypes.md'
CATEGORY_TABLE_PATH = '../Sources/HealthKitOnFHIR/HealthKitOnFHIR.docc/SupportedHKCategoryTypes.md'
CORRELATION_TABLE_PATH = '../Sources/HealthKitOnFHIR/HealthKitOnFHIR.docc/SupportedHKCorrelationTypes.md'
CLINICAL_TABLE_PATH = '../Sources/HealthKitOnFHIR/HealthKitOnFHIR.docc/SupportedHKClinicalTypes.md'
HEALTHKIT_URL = 'https://developer.apple.com/documentation/healthkit'
LOINC_URL = 'http://loinc.org'
SUPPORTED_SYMBOL = '✅'
UNSUPPORTED_SYMBOL = '❌'
ALL_QUANTITY_TYPES = [
"HKQuantityTypeIdentifierActiveEnergyBurned",
"HKQuantityTypeIdentifierAppleExerciseTime",
"HKQuantityTypeIdentifierAppleWalkingSteadiness",
"HKQuantityTypeIdentifierBasalBodyTemperature",
"HKQuantityTypeIdentifierBasalEnergyBurned",
"HKQuantityTypeIdentifierBloodAlcoholContent",
"HKQuantityTypeIdentifierBloodGlucose",
"HKQuantityTypeIdentifierBloodPressureDiastolic",
"HKQuantityTypeIdentifierBloodPressureSystolic",
"HKQuantityTypeIdentifierBodyFatPercentage",
"HKQuantityTypeIdentifierBodyMass",
"HKQuantityTypeIdentifierBodyMassIndex",
"HKQuantityTypeIdentifierBodyTemperature",
"HKQuantityTypeIdentifierDietaryBiotin",
"HKQuantityTypeIdentifierDietaryCaffeine",
"HKQuantityTypeIdentifierDietaryCalcium",
"HKQuantityTypeIdentifierDietaryCarbohydrates",
"HKQuantityTypeIdentifierDietaryChloride",
"HKQuantityTypeIdentifierDietaryCholesterol",
"HKQuantityTypeIdentifierDietaryChromium",
"HKQuantityTypeIdentifierDietaryCopper",
"HKQuantityTypeIdentifierDietaryEnergyConsumed",
"HKQuantityTypeIdentifierDietaryFatMonounsaturated",
"HKQuantityTypeIdentifierDietaryFatPolyunsaturated",
"HKQuantityTypeIdentifierDietaryFatSaturated",
"HKQuantityTypeIdentifierDietaryFatTotal",
"HKQuantityTypeIdentifierDietaryFiber",
"HKQuantityTypeIdentifierDietaryFolate",
"HKQuantityTypeIdentifierDietaryIodine",
"HKQuantityTypeIdentifierDietaryIron",
"HKQuantityTypeIdentifierDietaryMagnesium",
"HKQuantityTypeIdentifierDietaryManganese",
"HKQuantityTypeIdentifierDietaryMolybdenum",
"HKQuantityTypeIdentifierDietaryNiacin",
"HKQuantityTypeIdentifierDietaryPantothenicAcid",
"HKQuantityTypeIdentifierDietaryPhosphorus",
"HKQuantityTypeIdentifierDietaryPotassium",
"HKQuantityTypeIdentifierDietaryProtein",
"HKQuantityTypeIdentifierDietaryRiboflavin",
"HKQuantityTypeIdentifierDietarySelenium",
"HKQuantityTypeIdentifierDietarySodium",
"HKQuantityTypeIdentifierDietarySugar",
"HKQuantityTypeIdentifierDietaryThiamin",
"HKQuantityTypeIdentifierDietaryVitaminA",
"HKQuantityTypeIdentifierDietaryVitaminB12",
"HKQuantityTypeIdentifierDietaryVitaminB6",
"HKQuantityTypeIdentifierDietaryVitaminC",
"HKQuantityTypeIdentifierDietaryVitaminD",
"HKQuantityTypeIdentifierDietaryVitaminE",
"HKQuantityTypeIdentifierDietaryVitaminK",
"HKQuantityTypeIdentifierDietaryWater",
"HKQuantityTypeIdentifierDietaryZinc",
"HKQuantityTypeIdentifierDistanceCycling",
"HKQuantityTypeIdentifierDistanceDownhillSnowSports",
"HKQuantityTypeIdentifierDistanceSwimming",
"HKQuantityTypeIdentifierDistanceWalkingRunning",
"HKQuantityTypeIdentifierDistanceWheelchair",
"HKQuantityTypeIdentifierElectrodermalActivity",
"HKQuantityTypeIdentifierFlightsClimbed",
"HKQuantityTypeIdentifierForcedExpiratoryVolume1",
"HKQuantityTypeIdentifierForcedVitalCapacity",
"HKQuantityTypeIdentifierHeartRate",
"HKQuantityTypeIdentifierHeartRateVariabilitySDNN",
"HKQuantityTypeIdentifierHeight",
"HKQuantityTypeIdentifierInhalerUsage",
"HKQuantityTypeIdentifierInsulinDelivery",
"HKQuantityTypeIdentifierLeanBodyMass",
"HKQuantityTypeIdentifierNikeFuel",
"HKQuantityTypeIdentifierNumberOfTimesFallen",
"HKQuantityTypeIdentifierOxygenSaturation",
"HKQuantityTypeIdentifierPeakExpiratoryFlowRate",
"HKQuantityTypeIdentifierPeripheralPerfusionIndex",
"HKQuantityTypeIdentifierPushCount",
"HKQuantityTypeIdentifierRespiratoryRate",
"HKQuantityTypeIdentifierRestingHeartRate",
"HKQuantityTypeIdentifierStepCount",
"HKQuantityTypeIdentifierSwimmingStrokeCount",
"HKQuantityTypeIdentifierTimeInDaylight",
"HKQuantityTypeIdentifierUVExposure",
"HKQuantityTypeIdentifierVO2Max",
"HKQuantityTypeIdentifierWaistCircumference",
"HKQuantityTypeIdentifierWalkingHeartRateAverage",
"HKQuantityTypeIdentifierWalkingAsymmetryPercentage"
]
ALL_CORRELATION_TYPES = [
"HKCorrelationTypeIdentifierBloodPressure",
"HKCorrelationTypeIdentifierFood"
]
ALL_CATEGORY_TYPES = [
"HKCategoryTypeIdentifierAppetiteChanges",
"HKCategoryTypeIdentifierAppleStandHour",
"HKCategoryTypeIdentifierAppleWalkingSteadinessEvent",
"HKCategoryTypeIdentifierCervicalMucusQuality",
"HKCategoryTypeIdentifierContraceptive",
"HKCategoryTypeIdentifierAudioExposureEvent",
"HKCategoryTypeIdentifierHeadphoneAudioExposureEvent",
"HKCategoryTypeIdentifierLowCardioFitnessEvent",
"HKCategoryTypeIdentifierMenstrualFlow",
"HKCategoryTypeIdentifierOvulationTestResult",
"HKCategoryTypeIdentifierPregnancyTestResult",
"HKCategoryTypeIdentifierProgesteroneTestResult",
"HKCategoryTypeIdentifierSleepAnalysis",
"HKCategoryTypeIdentifierAbdominalCramps",
"HKCategoryTypeIdentifierAcne",
"HKCategoryTypeIdentifierBladderIncontinence",
"HKCategoryTypeIdentifierBloating",
"HKCategoryTypeIdentifierBreastPain",
"HKCategoryTypeIdentifierChestTightnessOrPain",
"HKCategoryTypeIdentifierChills",
"HKCategoryTypeIdentifierConstipation",
"HKCategoryTypeIdentifierCoughing",
"HKCategoryTypeIdentifierDizziness",
"HKCategoryTypeIdentifierDrySkin",
"HKCategoryTypeIdentifierFainting",
"HKCategoryTypeIdentifierFatigue",
"HKCategoryTypeIdentifierFever",
"HKCategoryTypeIdentifierGeneralizedBodyAche",
"HKCategoryTypeIdentifierHairLoss",
"HKCategoryTypeIdentifierHeadache",
"HKCategoryTypeIdentifierHeartburn",
"HKCategoryTypeIdentifierHotFlashes",
"HKCategoryTypeIdentifierLossOfSmell",
"HKCategoryTypeIdentifierLossOfTaste",
"HKCategoryTypeIdentifierLowerBackPain",
"HKCategoryTypeIdentifierMemoryLapse",
"HKCategoryTypeIdentifierNausea",
"HKCategoryTypeIdentifierNightSweats",
"HKCategoryTypeIdentifierPelvicPain",
"HKCategoryTypeIdentifierRapidPoundingOrFlutteringHeartbeat",
"HKCategoryTypeIdentifierRunnyNose",
"HKCategoryTypeIdentifierShortnessOfBreath",
"HKCategoryTypeIdentifierSinusCongestion",
"HKCategoryTypeIdentifierSkippedHeartbeat",
"HKCategoryTypeIdentifierSoreThroat",
"HKCategoryTypeIdentifierVaginalDryness",
"HKCategoryTypeIdentifierVomiting",
"HKCategoryTypeIdentifierWheezing",
"HKCategoryTypeIdentifierMoodChanges",
"HKCategoryTypeIdentifierSleepChanges",
"HKCategoryTypeIdentifierIrregularHeartRhythmEvent",
"HKCategoryTypeIdentifierLowHeartRateEvent",
"HKCategoryTypeIdentifierHighHeartRateEvent",
"HKCategoryTypeIdentifierMindfulSession",
"HKCategoryTypeIdentifierToothbrushingEvent",
"HKCategoryTypeIdentifierHandwashingEvent",
"HKCategoryTypeIdentifierSexualActivity",
"HKCategoryTypeIdentifierIntermenstrualBleeding",
"HKCategoryTypeIdentifierInfrequentMenstrualCycles",
"HKCategoryTypeIdentifierIrregularMenstrualCycles",
"HKCategoryTypeIdentifierPersistentIntermenstrualBleeding",
"HKCategoryTypeIdentifierProlongedMenstrualPeriods",
"HKCategoryTypeIdentifierLactation"
]
ALL_CLINICAL_TYPES = [
"HKClinicalTypeIdentifierAllergyRecord",
"HKClinicalTypeIdentifierConditionRecord",
"HKClinicalTypeIdentifierCoverageRecord",
"HKClinicalTypeIdentifierImmunizationRecord",
"HKClinicalTypeIdentifierLabResultRecord",
"HKClinicalTypeIdentifierMedicationRecord",
"HKClinicalTypeIdentifierProcedureRecord",
"HKClinicalTypeIdentifierVitalSignRecord"
]
# Load data
mapping_file = open(MAPPING_FILE_PATH)
data = json.load(mapping_file)
toc_file = open(TOC_PATH, 'w')
quantity_file = open(QUANTITY_TABLE_PATH, 'w')
category_file = open(CATEGORY_TABLE_PATH, 'w')
correlation_file = open(CORRELATION_TABLE_PATH, 'w')
clinical_file = open(CLINICAL_TABLE_PATH, 'w')
quantity_stats_string = ""
category_stats_string = ""
correlation_stats_string = ""
clinical_stats_string = ""
def create_header():
return """
<!--
This source file is part of the HealthKitOnFHIR open source project
SPDX-FileCopyrightText: 2022 Stanford University and the project authors (see CONTRIBUTORS.md)
SPDX-License-Identifier: MIT
-->
"""
def create_code_links(type, types):
coding = types[type]['codings'][0]
code = coding['code']
code_system = types[type]['codings'][0]['system']
if code_system == LOINC_URL or code_system == HEALTHKIT_URL:
# the {url}/{code} structure works for these code systems
code_url = '{}/{}'.format(code_system, code)
else:
code_url = code_system
return '[{}]({})'.format(code, code_url)
def create_quantity_types_table():
markdown = '# Supported HKQuantityTypes'
markdown += create_header()
quantity_types = data['HKQuantitySamples']
rows = []
for type in quantity_types:
# Format code links
linked_code = create_code_links(type, quantity_types)
# Format unit links
unit_coding = quantity_types[type]['unit']
unit = unit_coding['unit']
if 'system' in unit_coding: # Some units are not from a code system
linked_unit = '[{}]({})'.format(unit, unit_coding['system'])
else:
linked_unit = unit
row = [type, SUPPORTED_SYMBOL, linked_code, linked_unit]
rows.append(row)
# Add all the unsupported types
for type in ALL_QUANTITY_TYPES:
if type not in quantity_types:
rows.append([type, UNSUPPORTED_SYMBOL, "-", "-"])
# Sort the rows alphabetically
rows = sorted(rows, key=itemgetter(0))
# Link all the HealthKit types to Apple docs
for type in rows:
url = '{}/{}'.format(HEALTHKIT_URL, type[0])
type[0] = '[{}]({})'.format(type[0].removeprefix('HKQuantityTypeIdentifier'), url)
# Add the statistics
global quantity_stats_string
quantity_stats_string = 'HealthKitOnFHIR supports {} of {} quantity types.'.format(len(quantity_types), len(rows))
markdown += quantity_stats_string + '\n\n'
# Add the table header
markdown += '|HKQuantityType|Supported|Code|Unit|' + '\n' + '|----|----|----|----|' + '\n'
# Add all rows
for row in rows:
markdown += '|' + '|'.join(row) + '|\n'
return markdown
def create_correlation_types_table():
markdown = '# Supported HKCorrelationTypes'
markdown += create_header()
correlation_types = data['HKCorrelations']
rows = []
for type in correlation_types:
# Format code links
linked_code = create_code_links(type, correlation_types)
row = [type, SUPPORTED_SYMBOL, linked_code]
rows.append(row)
# Add all the unsupported types
for type in ALL_CORRELATION_TYPES:
if type not in correlation_types:
rows.append([type, UNSUPPORTED_SYMBOL, "-"])
# Sort the rows alphabetically
rows = sorted(rows, key=itemgetter(0))
# Link all the HealthKit types to Apple docs
for type in rows:
url = '{}/{}'.format(HEALTHKIT_URL, type[0])
type[0] = '[{}]({})'.format(type[0].removeprefix('HKCorrelationTypeIdentifier'), url)
# Add the statistics
global correlation_stats_string
correlation_stats_string = 'HealthKitOnFHIR supports {} of {} correlation types.'.format(len(correlation_types), len(rows))
markdown += correlation_stats_string + '\n\n'
# Add the table header
markdown += '|HKCorrelationType|Supported|Code|' + '\n' + '|----|----|----|' + '\n'
# Add all rows
for row in rows:
markdown += '|' + '|'.join(row) + '|\n'
return markdown
def create_category_types_table():
markdown = '# Supported HKCategoryTypes'
markdown += create_header()
category_types = data['HKCategorySamples']
rows = []
for type in category_types:
row = [type, SUPPORTED_SYMBOL]
rows.append(row)
# Add all the unsupported types
for type in ALL_CATEGORY_TYPES:
if type not in category_types:
rows.append([type, UNSUPPORTED_SYMBOL, "-"])
# Sort the rows alphabetically
rows = sorted(rows, key=itemgetter(0))
# Link all the HealthKit types to Apple docs
for type in rows:
url = '{}/{}'.format(HEALTHKIT_URL, type[0])
type[0] = '[{}]({})'.format(type[0].removeprefix('HKCategoryTypeIdentifier'), url)
# Add the statistics
global category_stats_string
category_stats_string = 'HealthKitOnFHIR supports {} of {} category types.'.format(len(category_types), len(rows))
markdown += category_stats_string + '\n\n'
# Add the table header
markdown += '|HKCategoryType|Supported|' + '\n' + '|----|----|' + '\n'
# Add all rows
for row in rows:
markdown += '|' + '|'.join(row) + '|\n'
return markdown
def create_clinical_types_table():
markdown = '# Supported HKClinicalTypes'
markdown += create_header()
clinical_types = [
"HKClinicalTypeIdentifierAllergyRecord",
"HKClinicalTypeIdentifierConditionRecord",
"HKClinicalTypeIdentifierCoverageRecord",
"HKClinicalTypeIdentifierImmunizationRecord",
"HKClinicalTypeIdentifierLabResultRecord",
"HKClinicalTypeIdentifierMedicationRecord",
"HKClinicalTypeIdentifierProcedureRecord",
"HKClinicalTypeIdentifierVitalSignRecord"
]
rows = []
# Add all the supported types
for type in clinical_types:
row = [type, SUPPORTED_SYMBOL]
rows.append(row)
# Link all the HealthKit types to Apple docs
for type in rows:
url = '{}/{}'.format(HEALTHKIT_URL, type[0])
type[0] = '[{}]({})'.format(type[0].removeprefix('HKClinicalTypeIdentifier'), url)
# Add the statistics
global clinical_stats_string
clinical_stats_string = 'HealthKitOnFHIR supports {} of {} clinical types.'.format(len(clinical_types), len(rows))
markdown += clinical_stats_string + '\n\n'
# Add the table header
markdown += '|HKClinicalType|Supported|' + '\n' + '|----|----|' + '\n'
# Add all rows
for row in rows:
markdown += '|' + '|'.join(row) + '|\n'
return markdown
def create_toc():
markdown = '# HKSample Support Tables'
markdown += create_header()
markdown += """
- [HKCategoryType](<doc:SupportedHKCategoryTypes>)
- {}
- [HKCorrelation](<doc:SupportedHKCorrelationTypes>)
- {}
- [HKClinicalType](<doc:SupportedHKClinicalTypes>)
- {}
- [HKQuantityType](<doc:SupportedHKQuantityTypes>)
- {}
""".format(
category_stats_string,
correlation_stats_string,
clinical_stats_string,
quantity_stats_string
)
return markdown
def main():
category_file.write(create_category_types_table())
quantity_file.write(create_quantity_types_table())
correlation_file.write(create_correlation_types_table())
clinical_file.write(create_clinical_types_table())
toc_file.write(create_toc())
if __name__ == "__main__":
main()