-ssscoring.fs1
+ssscoringFunctions and logic for detecting, validating, analyzing, and manipulating -FlySight 1 CSV files, including detection in the file system. The functions in -this module assume that a data lake exists somewhere in the file system (whether -local or cloud-based).
+Functions and logic for analyzing and manipulating FlySight dataframes.
1# See: https://github.com/pr3d4t0r/SSScoring/blob/master/LICENSE.txt 2 3""" - 4Functions and logic for detecting, validating, analyzing, and manipulating - 5FlySight 1 CSV files, including detection in the file system. The functions in - 6this module assume that a data lake exists somewhere in the file system (whether - 7local or cloud-based). - 8""" - 9 + 4Functions and logic for analyzing and manipulating FlySight dataframes. + 5""" + 6 + 7 + 8from haversine import haversine + 9from haversine import Unit 10 - 11from haversine import haversine - 12from haversine import Unit - 13 - 14from ssscoring.constants import BREAKOFF_ALTITUDE - 15from ssscoring.constants import DEG_IN_RADIANS - 16from ssscoring.constants import EXIT_SPEED - 17from ssscoring.constants import FLYSIGHT_1_HEADER - 18from ssscoring.constants import FT_IN_M - 19from ssscoring.constants import IGNORE_LIST - 20from ssscoring.constants import LAST_TIME_TRANCHE - 21from ssscoring.constants import MAX_SPEED_ACCURACY - 22from ssscoring.constants import MIN_JUMP_FILE_SIZE - 23from ssscoring.constants import PERFORMANCE_WINDOW_LENGTH - 24from ssscoring.constants import VALIDATION_WINDOW_LENGTH - 25from ssscoring.datatypes import JumpResults - 26from ssscoring.datatypes import PerformanceWindow - 27from ssscoring.errors import SSScoringError + 11from ssscoring.constants import BREAKOFF_ALTITUDE + 12from ssscoring.constants import DEG_IN_RADIANS + 13from ssscoring.constants import EXIT_SPEED + 14from ssscoring.constants import FT_IN_M + 15from ssscoring.constants import LAST_TIME_TRANCHE + 16from ssscoring.constants import MAX_SPEED_ACCURACY + 17from ssscoring.constants import PERFORMANCE_WINDOW_LENGTH + 18from ssscoring.constants import VALIDATION_WINDOW_LENGTH + 19from ssscoring.datatypes import JumpResults + 20from ssscoring.datatypes import PerformanceWindow + 21from ssscoring.errors import SSScoringError + 22from ssscoring.flysight import FlySightVersion + 23from ssscoring.flysight import detectFlySightFileVersionOf + 24 + 25import math + 26 + 27import pandas as pd 28 - 29import csv - 30import math - 31import os - 32 - 33import pandas as pd - 34 - 35 - 36# +++ functions +++ - 37 - 38def validFlySightHeaderIn(fileCSV: str) -> bool: - 39 """ - 40 Checks if a file is a CSV in FlySight 1 format. The checks include: - 41 - 42 - Whether the file is a CSV, using a comma delimiter - 43 - Checks for the presence of all the documented FlySight headers + 29 + 30# +++ functions +++ + 31 + 32def isValidMinimumAltitude(altitude: float) -> bool: + 33 """ + 34 Reports whether an `altitude` is within the IPC and USPA valid parameters, + 35 or within `BREAKOFF_ALTITUDE` and `PERFORMACE_WINDOW_LENGTH`. In invalid + 36 altitude doesn't invalidate a FlySight data file. This function can be used + 37 for generating warnings. The stock FlySightViewer scores a speed jump even + 38 if the exit was below the minimum altitude. + 39 + 40 Arguments + 41 --------- + 42 altitude + 43 An altitude in meters, often calculated as data.hMSL - DZ altitude. 44 - 45 Arguments - 46 --------- - 47 fileCSV - 48 A file name to verify as a valid FlySight file - 49 - 50 Returns - 51 ------- - 52 `True` if `fileCSV` is a FlySight CSV file, otherwise `False`. - 53 """ - 54 delimiters = [',', ] - 55 hasAllHeaders = False - 56 with open(fileCSV, 'r') as inputFile: - 57 try: - 58 dialect = csv.Sniffer().sniff(inputFile.readline(), delimiters = delimiters) - 59 except: - 60 return False - 61 if dialect.delimiter in delimiters: - 62 inputFile.seek(0) - 63 header = next(csv.reader(inputFile)) - 64 else: - 65 return False - 66 hasAllHeaders = FLYSIGHT_1_HEADER.issubset(header) - 67 return hasAllHeaders - 68 - 69 - 70def isValidMinimumAltitude(altitude: float) -> bool: - 71 """ - 72 Reports whether an `altitude` is within the IPC and USPA valid parameters, - 73 or within `BREAKOFF_ALTITUDE` and `PERFORMACE_WINDOW_LENGTH`. In invalid - 74 altitude doesn't invalidate a FlySight data file. This function can be used - 75 for generating warnings. The stock FlySightViewer scores a speed jump even - 76 if the exit was below the minimum altitude. - 77 - 78 Arguments - 79 --------- - 80 altitude - 81 An altitude in meters, often calculated as data.hMSL - DZ altitude. - 82 - 83 Returns - 84 ------- - 85 `True` if the altitude is valid. - 86 """ - 87 minAltitude = BREAKOFF_ALTITUDE+PERFORMANCE_WINDOW_LENGTH - 88 return altitude >= minAltitude - 89 - 90 - 91def getAllSpeedJumpFilesFrom(dataLake: str) -> list: - 92 """ - 93 Get a list of all the speed jump files from a data lake, where data lake is - 94 defined as a reachable path that contains one or more FlySight CSV files. - 95 This function tests each file to ensure that it's a speed skydive FlySight - 96 file in a valid format and length. - 97 - 98 Arguments - 99 --------- -100 dataLake: str -101 A valid (absolute or relative) path name to the top level directory where -102 the data lake starts. -103 -104 Returns -105 ------- -106 A list of speed jump file names for later SSScoring processing. -107 """ -108 jumpFiles = list() -109 for root, dirs, files in os.walk(dataLake): -110 if any(name in root for name in IGNORE_LIST): -111 continue -112 for fileName in files: -113 if 'CSV' in fileName: -114 jumpFileName = os.path.join(root, fileName) -115 stat = os.stat(jumpFileName) -116 data = pd.read_csv(jumpFileName, skiprows = (1, 1)) -117 if stat.st_size >= MIN_JUMP_FILE_SIZE and validFlySightHeaderIn(jumpFileName) and isValidMinimumAltitude(data.hMSL.max()): -118 jumpFiles.append(jumpFileName) -119 return jumpFiles -120 + 45 Returns + 46 ------- + 47 `True` if the altitude is valid. + 48 """ + 49 minAltitude = BREAKOFF_ALTITUDE+PERFORMANCE_WINDOW_LENGTH + 50 return altitude >= minAltitude + 51 + 52 + 53def isValidJump(data: pd.DataFrame, + 54 window: PerformanceWindow) -> bool: + 55 """ + 56 Validates the jump according to ISC/FAI/USPA competition rules. A jump is + 57 valid when the speed accuracy parameter is less than 3 m/s for the whole + 58 validation window duration. + 59 + 60 Arguments + 61 --------- + 62 data : pd.DataFramce + 63 Jumnp data in SSScoring format + 64 window : ssscoring.PerformanceWindow + 65 Performance window start, end values in named tuple format + 66 + 67 Returns + 68 ------- + 69 `True` if the jump is valid according to ISC/FAI/USPA rules. + 70 """ + 71 accuracy = data[data.altitudeAGL < window.validationStart].speedAccuracy.max() + 72 return accuracy < MAX_SPEED_ACCURACY + 73 + 74 + 75def calculateDistance(start: tuple, end: tuple) -> float: + 76 """ + 77 Calculate the distance between two terrestrial coordinates points. + 78 + 79 Arguments + 80 --------- + 81 start + 82 A latitude, longitude tuple of floating point numbers. + 83 + 84 end + 85 A latitude, longitude tuple of floating point numbers. + 86 + 87 Returns + 88 ------- + 89 The distance, in meters, between both points. + 90 """ + 91 return haversine(start, end, unit = Unit.METERS) + 92 + 93 + 94def convertFlySight2SSScoring(rawData: pd.DataFrame, + 95 altitudeDZMeters = 0.0, + 96 altitudeDZFt = 0.0): + 97 """ + 98 Converts a raw dataframe initialized from a FlySight CSV file into the + 99 SSScoring file format. The SSScoring format uses more descriptive column +100 headers, adds the altitude in feet, and uses UNIX time instead of an ISO +101 string. +102 +103 If both `altitudeDZMeters` and `altitudeDZFt` are zero then hMSL is used. +104 Otherwise, this function adjusts the effective altitude with the value. If +105 both meters and feet values are set this throws an error. +106 +107 Arguments +108 --------- +109 rawData : pd.DataFrame +110 FlySight CSV input as a dataframe +111 +112 altitudeDZMeters : float +113 Drop zone height above MSL +114 +115 altitudeDZFt +116 Drop zone altitudde above MSL +117 +118 Returns +119 ------- +120 A dataframe in SSScoring format, featuring these columns: 121 -122def convertFlySight2SSScoring(rawData: pd.DataFrame, -123 altitudeDZMeters = 0.0, -124 altitudeDZFt = 0.0): -125 """ -126 Converts a raw dataframe initialized from a FlySight CSV file into the -127 SSScoring file format. The SSScoring format uses more descriptive column -128 headers, adds the altitude in feet, and uses UNIX time instead of an ISO -129 string. -130 -131 If both `altitudeDZMeters` and `altitudeDZFt` are zero then hMSL is used. -132 Otherwise, this function adjusts the effective altitude with the value. If -133 both meters and feet values are set this throws an error. -134 -135 Arguments -136 --------- -137 rawData : pd.DataFrame -138 FlySight CSV input as a dataframe -139 -140 altitudeDZMeters : float -141 Drop zone height above MSL -142 -143 altitudeDZFt -144 Drop zone altitudde above MSL -145 -146 Returns -147 ------- -148 A dataframe in SSScoring format, featuring these columns: -149 -150 - timeUnix -151 - altitudeMSL -152 - altitudeAGL -153 - altitudeMSLFt -154 - altitudeAGLFt -155 - hMetersPerSecond -156 - hKMh (km/h) -157 - vMetersPerSecond -158 - vKMh (km/h) -159 - angle -160 - speedAccuracy -161 -162 Errors -163 ------ -164 `SSScoringError` if the DZ altitude is set in both meters and feet. -165 """ -166 if not isinstance(rawData, pd.DataFrame): -167 raise SSScoringError('convertFlySight2SSScoring input must be a FlySight CSV dataframe') -168 -169 if altitudeDZMeters and altitudeDZFt: -170 raise SSScoringError('Cannot set altitude in meters and feet; pick one') -171 -172 if altitudeDZMeters: -173 altitudeDZFt = FT_IN_M*altitudeDZMeters -174 if altitudeDZFt: -175 altitudeDZMeters = altitudeDZFt/FT_IN_M +122 - timeUnix +123 - altitudeMSL +124 - altitudeAGL +125 - altitudeMSLFt +126 - altitudeAGLFt +127 - hMetersPerSecond +128 - hKMh (km/h) +129 - vMetersPerSecond +130 - vKMh (km/h) +131 - angle +132 - speedAccuracy +133 +134 Errors +135 ------ +136 `SSScoringError` if the DZ altitude is set in both meters and feet. +137 """ +138 if not isinstance(rawData, pd.DataFrame): +139 raise SSScoringError('convertFlySight2SSScoring input must be a FlySight CSV dataframe') +140 +141 if altitudeDZMeters and altitudeDZFt: +142 raise SSScoringError('Cannot set altitude in meters and feet; pick one') +143 +144 if altitudeDZMeters: +145 altitudeDZFt = FT_IN_M*altitudeDZMeters +146 if altitudeDZFt: +147 altitudeDZMeters = altitudeDZFt/FT_IN_M +148 +149 data = rawData.copy() +150 +151 data['altitudeMSLFt'] = data['hMSL'].apply(lambda h: FT_IN_M*h) +152 data['altitudeAGL'] = data.hMSL-altitudeDZMeters +153 data['altitudeAGLFt'] = data.altitudeMSLFt-altitudeDZFt +154 data['timeUnix'] = data['time'].apply(lambda t: pd.Timestamp(t).timestamp()) +155 data['hMetersPerSecond'] = (data.velE**2.0+data.velN**2.0)**0.5 +156 speedAngle = data['hMetersPerSecond']/data['velD'] +157 speedAngle = round(90.0-speedAngle.apply(math.atan)/DEG_IN_RADIANS, 1) +158 +159 data = pd.DataFrame(data = { +160 'timeUnix': data.timeUnix, +161 'altitudeMSL': data.hMSL, +162 'altitudeAGL': data.altitudeAGL, +163 'altitudeMSLFt': data.altitudeMSLFt, +164 'altitudeAGLFt': data.altitudeAGLFt, +165 'vMetersPerSecond': data.velD, +166 'vKMh': 3.6*data.velD, +167 'speedAngle': speedAngle, +168 'speedAccuracy': data.sAcc, +169 'hMetersPerSecond': data.hMetersPerSecond, +170 'hKMh': 3.6*data.hMetersPerSecond, +171 'latitude': data.lat, +172 'longitude': data.lon, +173 }) +174 +175 return data 176 -177 data = rawData.copy() -178 -179 data['altitudeMSLFt'] = data['hMSL'].apply(lambda h: FT_IN_M*h) -180 data['altitudeAGL'] = data.hMSL-altitudeDZMeters -181 data['altitudeAGLFt'] = data.altitudeMSLFt-altitudeDZFt -182 data['timeUnix'] = data['time'].apply(lambda t: pd.Timestamp(t).timestamp()) -183 data['hMetersPerSecond'] = (data.velE**2.0+data.velN**2.0)**0.5 -184 speedAngle = data['hMetersPerSecond']/data['velD'] -185 speedAngle = round(90.0-speedAngle.apply(math.atan)/DEG_IN_RADIANS, 1) -186 -187 data = pd.DataFrame(data = { -188 'timeUnix': data.timeUnix, -189 'altitudeMSL': data.hMSL, -190 'altitudeAGL': data.altitudeAGL, -191 'altitudeMSLFt': data.altitudeMSLFt, -192 'altitudeAGLFt': data.altitudeAGLFt, -193 'vMetersPerSecond': data.velD, -194 'vKMh': 3.6*data.velD, -195 'speedAngle': speedAngle, -196 'speedAccuracy': data.sAcc, -197 'hMetersPerSecond': data.hMetersPerSecond, -198 'hKMh': 3.6*data.hMetersPerSecond, -199 'latitude': data.lat, -200 'longitude': data.lon, -201 }) +177 +178def _dataGroups(data): +179 data_ = data.copy() +180 data_['positive'] = (data_.vMetersPerSecond > 0) +181 data_['group'] = (data_.positive != data_.positive.shift(1)).astype(int).cumsum()-1 +182 +183 return data_ +184 +185 +186def getSpeedSkydiveFrom(data: pd.DataFrame) -> tuple: +187 """ +188 Take the skydive dataframe and get the speed skydiving data: +189 +190 - Exit +191 - Speed skydiving window +192 - Drops data before exit and below breakoff altitude +193 +194 Arguments +195 --------- +196 data : pd.DataFrame +197 Jump data in SSScoring format +198 +199 Returns +200 ------- +201 A tuple of two elements: 202 -203 return data -204 -205 -206def dropNonSkydiveDataFrom(data: pd.DataFrame) -> pd.DataFrame: -207 """ -208 Discards all data rows before maximum altitude, and all "negative" altitude -209 rows because we don't skydive underground (FlySight bug?). -210 -211 Arguments -212 --------- -213 data : pd.DataFrame -214 Jump data in SSScoring format (headers differ from FlySight format) -215 -216 Returns -217 ------- -218 The jump data for the skydive -219 """ -220 timeMaxAlt = data[data.altitudeAGL == data.altitudeAGL.max()].timeUnix.iloc[0] -221 data = data[data.timeUnix > timeMaxAlt] -222 -223 data = data[data.altitudeAGL > 0] +203 - A named tuple with performance and validation window data +204 - A dataframe featuring only speed skydiving data +205 """ +206 data = _dataGroups(data) +207 groups = data.group.max()+1 +208 +209 freeFallGroup = -1 +210 MIN_DATA_POINTS = 100 # heuristic +211 MIN_MAX_SPEED = 200 # km/h, heuristic; slower ::= no free fall +212 for group in range(groups): +213 subset = data[data.group == group] +214 if len(subset) >= MIN_DATA_POINTS and subset.vKMh.max() >= MIN_MAX_SPEED: +215 freeFallGroup = group +216 +217 data = data[data.group == freeFallGroup] +218 data = data.drop('group', axis = 1).drop('positive', axis = 1) +219 +220 # Speed ~= 9.81 m/s; subtract 1 second for actual exit. +221 exitTime = data[data.vMetersPerSecond > EXIT_SPEED].head(1).timeUnix.iat[0]-2.0 +222 data = data[data.timeUnix >= exitTime] +223 data = data[data.altitudeAGL >= BREAKOFF_ALTITUDE] 224 -225 return data -226 -227 -228def _dataGroups(data): -229 data_ = data.copy() -230 data_['positive'] = (data_.vMetersPerSecond > 0) -231 data_['group'] = (data_.positive != data_.positive.shift(1)).astype(int).cumsum()-1 +225 windowStart = data.iloc[0].altitudeAGL +226 windowEnd = windowStart-PERFORMANCE_WINDOW_LENGTH +227 if windowEnd < BREAKOFF_ALTITUDE: +228 windowEnd = BREAKOFF_ALTITUDE +229 +230 validationWindowStart = windowEnd+VALIDATION_WINDOW_LENGTH +231 data = data[data.altitudeAGL >= windowEnd] 232 -233 return data_ +233 return PerformanceWindow(windowStart, windowEnd, validationWindowStart), data 234 -235 -236def getSpeedSkydiveFrom(data: pd.DataFrame) -> tuple: -237 """ -238 Take the skydive dataframe and get the speed skydiving data: +235def jumpAnalysisTable(data: pd.DataFrame) -> pd.DataFrame: +236 """ +237 Generates the HCD jump analysis table, with speed data at 5-second intervals +238 after exit. 239 -240 - Exit -241 - Speed skydiving window -242 - Drops data before exit and below breakoff altitude -243 -244 Arguments -245 --------- -246 data : pd.DataFrame -247 Jump data in SSScoring format +240 Arguments +241 --------- +242 data : pd.DataFrame +243 Jump data in SSScoring format +244 +245 Returns +246 ------- +247 A tuple with a pd.DataFrame and the max speed recorded for the jump: 248 -249 Returns -250 ------- -251 A tuple of two elements: -252 -253 - A named tuple with performance and validation window data -254 - A dataframe featuring only speed skydiving data -255 """ -256 data = _dataGroups(data) -257 groups = data.group.max()+1 -258 -259 freeFallGroup = -1 -260 MIN_DATA_POINTS = 100 # heuristic -261 MIN_MAX_SPEED = 200 # km/h, heuristic; slower ::= no free fall -262 for group in range(groups): -263 subset = data[data.group == group] -264 if len(subset) >= MIN_DATA_POINTS and subset.vKMh.max() >= MIN_MAX_SPEED: -265 freeFallGroup = group -266 -267 data = data[data.group == freeFallGroup] -268 data = data.drop('group', axis = 1).drop('positive', axis = 1) -269 -270 # Speed ~= 9.81 m/s; subtract 1 second for actual exit. -271 exitTime = data[data.vMetersPerSecond > EXIT_SPEED].head(1).timeUnix.iat[0]-2.0 -272 data = data[data.timeUnix >= exitTime] -273 data = data[data.altitudeAGL >= BREAKOFF_ALTITUDE] -274 -275 windowStart = data.iloc[0].altitudeAGL -276 windowEnd = windowStart-PERFORMANCE_WINDOW_LENGTH -277 if windowEnd < BREAKOFF_ALTITUDE: -278 windowEnd = BREAKOFF_ALTITUDE -279 -280 validationWindowStart = windowEnd+VALIDATION_WINDOW_LENGTH -281 data = data[data.altitudeAGL >= windowEnd] -282 -283 return PerformanceWindow(windowStart, windowEnd, validationWindowStart), data -284 -285 -286def isValidJump(data: pd.DataFrame, -287 window: PerformanceWindow) -> bool: -288 """ -289 Validates the jump according to ISC/FAI/USPA competition rules. A jump is -290 valid when the speed accuracy parameter is less than 3 m/s for the whole -291 validation window duration. -292 -293 Arguments -294 --------- -295 data : pd.DataFramce -296 Jumnp data in SSScoring format -297 window : ssscoring.PerformanceWindow -298 Performance window start, end values in named tuple format -299 -300 Returns -301 ------- -302 `True` if the jump is valid according to ISC/FAI/USPA rules. -303 """ -304 accuracy = data[data.altitudeAGL < window.validationStart].speedAccuracy.max() -305 return accuracy < MAX_SPEED_ACCURACY -306 -307 -308def calculateDistance(start: tuple, end: tuple) -> float: -309 """ -310 Calculate the distance between two terrestrial coordinates points. -311 -312 Arguments -313 --------- -314 start -315 A latitude, longitude tuple of floating point numbers. -316 -317 end -318 A latitude, longitude tuple of floating point numbers. +249 - A table dataframe with time and speed +250 - a floating point number +251 """ +252 table = None +253 +254 distanceStart = (data.iloc[0].latitude, data.iloc[0].longitude) +255 for column in pd.Series([ 5.0, 10.0, 15.0, 20.0, 25.0, ]): +256 for interval in range(int(column)*10, 10*(int(column)+1)): +257 # Use the next 0.1 sec interval if the current interval tranche has +258 # NaN values. +259 columnRef = interval/10.0 +260 timeOffset = data.iloc[0].timeUnix+columnRef +261 tranche = data.query('timeUnix == %f' % timeOffset).copy() +262 tranche['time'] = [ column, ] +263 currentPosition = (tranche.iloc[0].latitude, tranche.iloc[0].longitude) +264 tranche['distanceFromExit'] = [ round(calculateDistance(distanceStart, currentPosition), 1), ] +265 if not tranche.isnull().any().any(): +266 break +267 +268 if pd.isna(tranche.iloc[-1].vKMh): +269 tranche = data.tail(1).copy() +270 currentPosition = (tranche.iloc[0].latitude, tranche.iloc[0].longitude) +271 tranche['time'] = tranche.timeUnix-data.iloc[0].timeUnix +272 tranche['distanceFromExit'] = [ calculateDistance(distanceStart, currentPosition), ] +273 +274 if table is not None: +275 table = pd.concat([ table, tranche, ]) +276 else: +277 table = tranche +278 +279 table = pd.DataFrame({ +280 'time': table.time, +281 'vKMh': table.vKMh, +282 'hKMh': table.hKMh, +283 'speedAngle': table.speedAngle, +284 'distanceFromExit': table.distanceFromExit, +285 'altitude (ft)': table.altitudeAGLFt, +286 'netVectorKMh': (table.vKMh**2+table.hKMh**2)**0.5, +287 }) +288 +289 return (data.vKMh.max(), table) +290 +291 +292def dropNonSkydiveDataFrom(data: pd.DataFrame) -> pd.DataFrame: +293 """ +294 Discards all data rows before maximum altitude, and all "negative" altitude +295 rows because we don't skydive underground (FlySight bug?). +296 +297 Arguments +298 --------- +299 data : pd.DataFrame +300 Jump data in SSScoring format (headers differ from FlySight format) +301 +302 Returns +303 ------- +304 The jump data for the skydive +305 """ +306 timeMaxAlt = data[data.altitudeAGL == data.altitudeAGL.max()].timeUnix.iloc[0] +307 data = data[data.timeUnix > timeMaxAlt] +308 +309 data = data[data.altitudeAGL > 0] +310 +311 return data +312 +313 +314def processJump(data: pd.DataFrame): +315 """ +316 Take a dataframe in SSScoring format and process it for display. It +317 serializes all the steps that would be taken from the ssscoring module, but +318 includes some text/HTML data in the output. 319 -320 Returns -321 ------- -322 The distance, in meters, between both points. -323 """ -324 return haversine(start, end, unit = Unit.METERS) -325 -326 -327def jumpAnalysisTable(data: pd.DataFrame) -> pd.DataFrame: -328 """ -329 Generates the HCD jump analysis table, with speed data at 5-second intervals -330 after exit. -331 -332 Arguments -333 --------- -334 data : pd.DataFrame -335 Jump data in SSScoring format -336 -337 Returns -338 ------- -339 A tuple with a pd.DataFrame and the max speed recorded for the jump: -340 -341 - A table dataframe with time and speed -342 - a floating point number -343 """ -344 table = None -345 -346 distanceStart = (data.iloc[0].latitude, data.iloc[0].longitude) -347 for column in pd.Series([ 5.0, 10.0, 15.0, 20.0, 25.0, ]): -348 for interval in range(int(column)*10, 10*(int(column)+1)): -349 # Use the next 0.1 sec interval if the current interval tranche has -350 # NaN values. -351 columnRef = interval/10.0 -352 timeOffset = data.iloc[0].timeUnix+columnRef -353 tranche = data.query('timeUnix == %f' % timeOffset).copy() -354 tranche['time'] = [ column, ] -355 currentPosition = (tranche.iloc[0].latitude, tranche.iloc[0].longitude) -356 tranche['distanceFromExit'] = [ round(calculateDistance(distanceStart, currentPosition), 1), ] -357 if not tranche.isnull().any().any(): -358 break -359 -360 if pd.isna(tranche.iloc[-1].vKMh): -361 tranche = data.tail(1).copy() -362 currentPosition = (tranche.iloc[0].latitude, tranche.iloc[0].longitude) -363 tranche['time'] = tranche.timeUnix-data.iloc[0].timeUnix -364 tranche['distanceFromExit'] = [ calculateDistance(distanceStart, currentPosition), ] -365 -366 if table is not None: -367 table = pd.concat([ table, tranche, ]) -368 else: -369 table = tranche -370 -371 table = pd.DataFrame({ -372 'time': table.time, -373 'vKMh': table.vKMh, -374 'hKMh': table.hKMh, -375 'speedAngle': table.speedAngle, -376 'distanceFromExit': table.distanceFromExit, -377 'altitude (ft)': table.altitudeAGLFt, -378 'netVectorKMh': (table.vKMh**2+table.hKMh**2)**0.5, -379 }) +320 Arguments +321 --------- +322 data: pd.DataFrame +323 A dataframe in SSScoring format +324 +325 Returns +326 ------- +327 A `JumpResults` named tuple with these items: +328 +329 - `score` speed score +330 - `maxSpeed` maximum speed during the jump +331 - `scores` a Series of every 3-second window scores from exit to breakoff +332 - `data` an updated SSScoring dataframe `plotTime`, where 0 = exit, used +333 for plotting +334 - `window` a named tuple with the exit, breakoff, and validation window +335 altitudes +336 - `table` a dataframe featuring the speeds and altitudes at 5-sec intervals +337 - `color` a string that defines the color for the jump result; possible +338 values are _green_ for valid jump, _red_ for invalid jump, per ISC rules +339 - `result` a string with the legend of _valid_ or _invalid_ jump +340 """ +341 data = data.copy() +342 data = dropNonSkydiveDataFrom(data) +343 window, data = getSpeedSkydiveFrom(data) +344 validJump = isValidJump(data, window) +345 score = 0.0 +346 scores = dict() +347 table = None +348 +349 if validJump: +350 maxSpeed, table = jumpAnalysisTable(data) +351 color = '#0f0' +352 result = '🟢 valid' +353 baseTime = data.iloc[0].timeUnix +354 data['plotTime'] = data.timeUnix-baseTime +355 +356 for spot in data.plotTime[::1]: +357 subset = data[(data.plotTime <= spot) & (data.plotTime >= (spot-3.0))] +358 scores[subset.vKMh.mean()] = spot +359 score = max(scores) +360 +361 else: +362 color = '#f00' +363 maxSpeed = -1 +364 score = 0 +365 result = '🔴 invalid' +366 +367 return JumpResults(color, data, maxSpeed, result, score, scores, table, window) +368 +369 +370def _readVersion1CSV(jumpFile: str) -> pd.DataFrame: +371 return pd.read_csv(jumpFile, skiprows = (1, 1)) +372 +373 +374def _tagVersion1From(jumpFile: str) -> str: +375 return jumpFile.replace('CSV', '').replace('.', '').replace('/data', '').replace('/', ' ').strip()+':v1' +376 +377 +378def _tagVersion2From(jumpFile: str) -> str: +379 return jumpFile.split('/')[-2]+':v2' 380 -381 return (data.vKMh.max(), table) +381 382 -383 -384def processJump(data: pd.DataFrame): -385 """ -386 Take a dataframe in SSScoring format and process it for display. It -387 serializes all the steps that would be taken from the ssscoring module, but -388 includes some text/HTML data in the output. -389 -390 Arguments -391 --------- -392 data: pd.DataFrame -393 A dataframe in SSScoring format -394 -395 Returns -396 ------- -397 A `JumpResults` named tuple with these items: -398 -399 - `score` speed score -400 - `maxSpeed` maximum speed during the jump -401 - `scores` a Series of every 3-second window scores from exit to breakoff -402 - `data` an updated SSScoring dataframe `plotTime`, where 0 = exit, used -403 for plotting -404 - `window` a named tuple with the exit, breakoff, and validation window -405 altitudes -406 - `table` a dataframe featuring the speeds and altitudes at 5-sec intervals -407 - `color` a string that defines the color for the jump result; possible -408 values are _green_ for valid jump, _red_ for invalid jump, per ISC rules -409 - `result` a string with the legend of _valid_ or _invalid_ jump -410 """ -411 data = data.copy() -412 data = dropNonSkydiveDataFrom(data) -413 window, data = getSpeedSkydiveFrom(data) -414 validJump = isValidJump(data, window) -415 score = 0.0 -416 scores = dict() -417 table = None -418 -419 if validJump: -420 maxSpeed, table = jumpAnalysisTable(data) -421 color = '#0f0' -422 result = '🟢 valid' -423 baseTime = data.iloc[0].timeUnix -424 data['plotTime'] = data.timeUnix-baseTime -425 -426 for spot in data.plotTime[::1]: -427 subset = data[(data.plotTime <= spot) & (data.plotTime >= (spot-3.0))] -428 scores[subset.vKMh.mean()] = spot -429 score = max(scores) -430 -431 else: -432 color = '#f00' -433 maxSpeed = -1 -434 score = 0 -435 result = '🔴 invalid' -436 -437 return JumpResults(color, data, maxSpeed, result, score, scores, table, window) -438 +383def _readVersion2CSV(jumpFile: str) -> pd.DataFrame: +384 from ssscoring.flysight import FS2_COLUMNS +385 from ssscoring.flysight import skipOverFS2MetadataRowsIn +386 +387 rawData = pd.read_csv(jumpFile, names = FS2_COLUMNS, skiprows = 6) +388 rawData = skipOverFS2MetadataRowsIn(rawData) +389 rawData.drop('GNSS', inplace = True, axis = 1) +390 return rawData +391 +392 +393def processAllJumpFiles(jumpFiles: list, altitudeDZMeters = 0.0) -> dict: +394 """ +395 Process all jump files in a list of valid FlySight files. Returns a +396 dictionary of jump results with a human-readable version of the file name. +397 The `jumpFiles` list can be generated by hand or the output of the +398 `ssscoring.fs1.getAllSpeedJumpFilesFrom` called to operate on a data lake. +399 +400 Arguments +401 --------- +402 jumpFiles +403 A list of relative or absolute path names to individual FlySight CSV files. +404 +405 altitudeDZMeters : float +406 Drop zone height above MSL +407 +408 dict +409 A dictionary of jump results. The key is a human-readable version of a +410 `jumpFile` name with the extension, path, and extraneous spaces eliminated +411 or replaced by appropriate characters. File names use Unicode, so accents +412 and non-ANSI characters are allowed in file names. +413 """ +414 jumpResults = dict() +415 for jumpFile in jumpFiles.keys(): +416 version = detectFlySightFileVersionOf(jumpFile) +417 if version == FlySightVersion.V1: +418 rawData = _readVersion1CSV(jumpFile) +419 tag = _tagVersion1From(jumpFile) +420 elif version == FlySightVersion.V2: +421 rawData = _readVersion2CSV(jumpFile) +422 tag = _tagVersion2From(jumpFile) +423 jumpResult = processJump(convertFlySight2SSScoring(rawData, altitudeDZMeters = altitudeDZMeters)) +424 if 'valid' in jumpResult.result: +425 jumpResults[tag] = jumpResult +426 return jumpResults +427 +428 +429def aggregateResults(jumpResults: dict) -> pd.DataFrame: +430 """ +431 Aggregate all the results in a table fashioned after Marco Hepp's and Nklas +432 Daniel's score tracking data. +433 +434 Arguments +435 --------- +436 jumpResults: dict +437 A dictionary of jump results, in which each result corresponds to a FlySight +438 file name. See `ssscoring.processAllJumpFiles` for details. 439 -440 -441def processAllJumpFiles(jumpFiles: list, altitudeDZMeters = 0.0) -> dict: -442 """ -443 Process all jump files in a list of valid FlySight files. Returns a -444 dictionary of jump results with a human-readable version of the file name. -445 The `jumpFiles` list can be generated by hand or the output of the -446 `ssscoring.getAllSpeedJumpFilesFrom` called to operate on a data lake. -447 -448 Arguments -449 --------- -450 jumpFiles -451 A list of relative or absolute path names to individual FlySight CSV files. -452 -453 altitudeDZMeters : float -454 Drop zone height above MSL -455 -456 dict -457 A dictionary of jump results. The key is a human-readable version of a -458 `jumpFile` name with the extension, path, and extraneous spaces eliminated -459 or replaced by appropriate characters. File names use Unicode, so accents -460 and non-ANSI characters are allowed in file names. -461 """ -462 jumpResults = dict() -463 for jumpFile in jumpFiles: -464 jumpResult = processJump( -465 convertFlySight2SSScoring(pd.read_csv(jumpFile, skiprows = (1, 1)), -466 altitudeDZMeters = altitudeDZMeters)) -467 tag = jumpFile.replace('CSV', '').replace('.', '').replace('/data', '').replace('/', ' ').strip() -468 if 'valid' in jumpResult.result: -469 jumpResults[tag] = jumpResult -470 return jumpResults -471 +440 Returns +441 ------- +442 A dataframe featuring these columns: +443 +444 - Score +445 - Speeds at 5, 10, 15, 20, and 25 second tranches +446 - Final time contemplated in the analysis +447 - Max speed +448 +449 The dataframe rows are identified by the human readable jump file name. +450 """ +451 speeds = pd.DataFrame() +452 for jumpResultIndex in sorted(list(jumpResults.keys())): +453 jumpResult = jumpResults[jumpResultIndex] +454 if jumpResult.score > 0.0: +455 t = jumpResult.table +456 finalTime = t.iloc[-1].time +457 t.iloc[-1].time = LAST_TIME_TRANCHE +458 t = pd.pivot_table(t, columns = t.time) +459 t.drop(['altitude (ft)'], inplace = True) +460 d = pd.DataFrame([ jumpResult.score, ], index = [ jumpResultIndex, ], columns = [ 'score', ], dtype = object) +461 for column in t.columns: +462 # d[column] = t[column].iloc[2] +463 d[column] = t[column].vKMh +464 d['finalTime'] = [ finalTime, ] +465 d['maxSpeed'] = jumpResult.maxSpeed +466 +467 if speeds.empty: +468 speeds = d.copy() +469 else: +470 speeds = pd.concat([ speeds, d, ]) +471 return speeds.sort_index() 472 -473def aggregateResults(jumpResults: dict) -> pd.DataFrame: -474 """ -475 Aggregate all the results in a table fashioned after Marco Hepp's and Nklas -476 Daniel's score tracking data. -477 -478 Arguments -479 --------- -480 jumpResults: dict -481 A dictionary of jump results, in which each result corresponds to a FlySight -482 file name. See `ssscoring.processAllJumpFiles` for details. -483 -484 Returns -485 ------- -486 A dataframe featuring these columns: -487 -488 - Score -489 - Speeds at 5, 10, 15, 20, and 25 second tranches -490 - Final time contemplated in the analysis +473 +474def roundedAggregateResults(aggregate: pd.DataFrame) -> pd.DataFrame: +475 """ +476 Aggregate all the results in a table fashioned after Marco Hepp's and Nklas +477 Daniel's score tracking data. All speed results are rounded at `n > x.5` +478 for any value. +479 +480 Arguments +481 --------- +482 aggregate: pd.DataFrame +483 A dataframe output of `ssscoring.fs1.aggregateResults`. +484 +485 Returns +486 ------- +487 A dataframe featuring the **rounded values** for these columns: +488 +489 - Score +490 - Speeds at 5, 10, 15, 20, and 25 second tranches 491 - Max speed 492 -493 The dataframe rows are identified by the human readable jump file name. -494 """ -495 speeds = pd.DataFrame() -496 for jumpResultIndex in sorted(list(jumpResults.keys())): -497 jumpResult = jumpResults[jumpResultIndex] -498 if jumpResult.score > 0.0: -499 t = jumpResult.table -500 finalTime = t.iloc[-1].time -501 t.iloc[-1].time = LAST_TIME_TRANCHE -502 t = pd.pivot_table(t, columns = t.time) -503 t.drop(['altitude (ft)'], inplace = True) -504 d = pd.DataFrame([ jumpResult.score, ], index = [ jumpResultIndex, ], columns = [ 'score', ], dtype = object) -505 for column in t.columns: -506 # d[column] = t[column].iloc[2] -507 d[column] = t[column].vKMh -508 d['finalTime'] = [ finalTime, ] -509 d['maxSpeed'] = jumpResult.maxSpeed +493 The `finalTime` column is ignored. +494 +495 The dataframe rows are identified by the human readable jump file name. +496 +497 This is a less precise version of the `ssscoring.aggregateResults` +498 dataframe, useful during training to keep rounded results available for +499 review. +500 """ +501 for column in [col for col in aggregate.columns if 'Time' not in str(col)]: +502 aggregate[column] = aggregate[column].apply(round) +503 +504 return aggregate +505 +506 +507def totalResultsFrom(aggregate: pd.DataFrame) -> pd.DataFrame: +508 """ +509 Calculates the total and mean speeds for an aggregation of speed jumps. 510 -511 if speeds.empty: -512 speeds = d.copy() -513 else: -514 speeds = pd.concat([ speeds, d, ]) -515 return speeds.sort_index() +511 Arguments +512 --------- +513 aggregate: pd.DataFrame +514 The aggregate results dataframe resulting from calling `ssscoring.aggregateResults` +515 with valid results. 516 -517 -518def roundedAggregateResults(aggregate: pd.DataFrame) -> pd.DataFrame: -519 """ -520 Aggregate all the results in a table fashioned after Marco Hepp's and Nklas -521 Daniel's score tracking data. All speed results are rounded at `n > x.5` -522 for any value. -523 -524 Arguments -525 --------- -526 aggregate: pd.DataFrame -527 A dataframe output of `ssscoring.fs1.aggregateResults`. -528 -529 Returns -530 ------- -531 A dataframe featuring the **rounded values** for these columns: -532 -533 - Score -534 - Speeds at 5, 10, 15, 20, and 25 second tranches -535 - Max speed -536 -537 The `finalTime` column is ignored. -538 -539 The dataframe rows are identified by the human readable jump file name. -540 -541 This is a less precise version of the `ssscoring.aggregateResults` -542 dataframe, useful during training to keep rounded results available for -543 review. -544 """ -545 for column in [col for col in aggregate.columns if 'Time' not in str(col)]: -546 aggregate[column] = aggregate[column].apply(round) -547 -548 return aggregate -549 -550 -551def totalResultsFrom(aggregate: pd.DataFrame) -> pd.DataFrame: -552 """ -553 Calculates the total and mean speeds for an aggregation of speed jumps. -554 -555 Arguments -556 --------- -557 aggregate: pd.DataFrame -558 The aggregate results dataframe resulting from calling `ssscoring.aggregateResults` -559 with valid results. -560 -561 Returns -562 ------- -563 A dataframe with one row and two columns: -564 -565 - totalSpeed ::= the sum of all speeds in the aggregated results -566 - meanSpeed ::= the mean of all speeds -567 - maxScore ::= the max score among all the speed scores -568 -569 Raises -570 ------ -571 `AttributeError` if aggregate is an empty dataframe or `None`, or if the -572 `aggregate` dataframe doesn't conform to the output of `ssscoring.aggregateResults`. -573 """ -574 totals = pd.DataFrame({ 'totalSpeed': [ aggregate.score.sum(), ], 'meanSpeed': [ aggregate.score.mean(), ], 'maxScore': [ aggregate.score.max(), ], }, index = [ 'totalSpeed'],) -575 -576 return totals +517 Returns +518 ------- +519 A dataframe with one row and two columns: +520 +521 - totalSpeed ::= the sum of all speeds in the aggregated results +522 - meanSpeed ::= the mean of all speeds +523 - maxScore ::= the max score among all the speed scores +524 +525 Raises +526 ------ +527 `AttributeError` if aggregate is an empty dataframe or `None`, or if the +528 `aggregate` dataframe doesn't conform to the output of `ssscoring.aggregateResults`. +529 """ +530 totals = pd.DataFrame({ 'totalSpeed': [ aggregate.score.sum(), ], 'meanSpeed': [ aggregate.score.mean(), ], 'maxScore': [ aggregate.score.max(), ], }, index = [ 'totalSpeed'],) +531 +532 return totals
39def validFlySightHeaderIn(fileCSV: str) -> bool: -40 """ -41 Checks if a file is a CSV in FlySight 1 format. The checks include: -42 -43 - Whether the file is a CSV, using a comma delimiter -44 - Checks for the presence of all the documented FlySight headers -45 -46 Arguments -47 --------- -48 fileCSV -49 A file name to verify as a valid FlySight file -50 -51 Returns -52 ------- -53 `True` if `fileCSV` is a FlySight CSV file, otherwise `False`. -54 """ -55 delimiters = [',', ] -56 hasAllHeaders = False -57 with open(fileCSV, 'r') as inputFile: -58 try: -59 dialect = csv.Sniffer().sniff(inputFile.readline(), delimiters = delimiters) -60 except: -61 return False -62 if dialect.delimiter in delimiters: -63 inputFile.seek(0) -64 header = next(csv.reader(inputFile)) -65 else: -66 return False -67 hasAllHeaders = FLYSIGHT_1_HEADER.issubset(header) -68 return hasAllHeaders + +-33def isValidMinimumAltitude(altitude: float) -> bool: +34 """ +35 Reports whether an `altitude` is within the IPC and USPA valid parameters, +36 or within `BREAKOFF_ALTITUDE` and `PERFORMACE_WINDOW_LENGTH`. In invalid +37 altitude doesn't invalidate a FlySight data file. This function can be used +38 for generating warnings. The stock FlySightViewer scores a speed jump even +39 if the exit was below the minimum altitude. +40 +41 Arguments +42 --------- +43 altitude +44 An altitude in meters, often calculated as data.hMSL - DZ altitude. +45 +46 Returns +47 ------- +48 `True` if the altitude is valid. +49 """ +50 minAltitude = BREAKOFF_ALTITUDE+PERFORMANCE_WINDOW_LENGTH +51 return altitude >= minAltitudeChecks if a file is a CSV in FlySight 1 format. The checks include:
- --
+- Whether the file is a CSV, using a comma delimiter
-- Checks for the presence of all the documented FlySight headers
--Reports whether an
altitude
is within the IPC and USPA valid parameters, +or withinBREAKOFF_ALTITUDE
andPERFORMACE_WINDOW_LENGTH
. In invalid +altitude doesn't invalidate a FlySight data file. This function can be used +for generating warnings. The stock FlySightViewer scores a speed jump even +if the exit was below the minimum altitude.Arguments
-fileCSV +
-altitude
A file name to verify as a valid FlySight file
+An altitude in meters, often calculated as data.hMSL - DZ altitude.
Returns
-+
True
iffileCSV
is a FlySight CSV file, otherwiseFalse
.
True
if the altitude is valid.- + + def - isValidMinimumAltitude(altitude: float) -> bool: + isValidJump( data: pandas.core.frame.DataFrame, window: ssscoring.datatypes.PerformanceWindow) -> bool: - +- -71def isValidMinimumAltitude(altitude: float) -> bool: -72 """ -73 Reports whether an `altitude` is within the IPC and USPA valid parameters, -74 or within `BREAKOFF_ALTITUDE` and `PERFORMACE_WINDOW_LENGTH`. In invalid -75 altitude doesn't invalidate a FlySight data file. This function can be used -76 for generating warnings. The stock FlySightViewer scores a speed jump even -77 if the exit was below the minimum altitude. -78 -79 Arguments -80 --------- -81 altitude -82 An altitude in meters, often calculated as data.hMSL - DZ altitude. -83 -84 Returns -85 ------- -86 `True` if the altitude is valid. -87 """ -88 minAltitude = BREAKOFF_ALTITUDE+PERFORMANCE_WINDOW_LENGTH -89 return altitude >= minAltitude + +-54def isValidJump(data: pd.DataFrame, +55 window: PerformanceWindow) -> bool: +56 """ +57 Validates the jump according to ISC/FAI/USPA competition rules. A jump is +58 valid when the speed accuracy parameter is less than 3 m/s for the whole +59 validation window duration. +60 +61 Arguments +62 --------- +63 data : pd.DataFramce +64 Jumnp data in SSScoring format +65 window : ssscoring.PerformanceWindow +66 Performance window start, end values in named tuple format +67 +68 Returns +69 ------- +70 `True` if the jump is valid according to ISC/FAI/USPA rules. +71 """ +72 accuracy = data[data.altitudeAGL < window.validationStart].speedAccuracy.max() +73 return accuracy < MAX_SPEED_ACCURACYReports whether an
+altitude
is within the IPC and USPA valid parameters, -or withinBREAKOFF_ALTITUDE
andPERFORMACE_WINDOW_LENGTH
. In invalid -altitude doesn't invalidate a FlySight data file. This function can be used -for generating warnings. The stock FlySightViewer scores a speed jump even -if the exit was below the minimum altitude.-Validates the jump according to ISC/FAI/USPA competition rules. A jump is +valid when the speed accuracy parameter is less than 3 m/s for the whole +validation window duration.
Arguments
-altitude +
-data : pd.DataFramce
An altitude in meters, often calculated as data.hMSL - DZ altitude.
+Jumnp data in SSScoring format + window : ssscoring.PerformanceWindow +Performance window start, end values in named tuple format
Returns
-+
True
if the altitude is valid.
True
if the jump is valid according to ISC/FAI/USPA rules.- + + def - getAllSpeedJumpFilesFrom(dataLake: str) -> list: + calculateDistance(start: tuple, end: tuple) -> float: - +- --92def getAllSpeedJumpFilesFrom(dataLake: str) -> list: - 93 """ - 94 Get a list of all the speed jump files from a data lake, where data lake is - 95 defined as a reachable path that contains one or more FlySight CSV files. - 96 This function tests each file to ensure that it's a speed skydive FlySight - 97 file in a valid format and length. - 98 - 99 Arguments -100 --------- -101 dataLake: str -102 A valid (absolute or relative) path name to the top level directory where -103 the data lake starts. -104 -105 Returns -106 ------- -107 A list of speed jump file names for later SSScoring processing. -108 """ -109 jumpFiles = list() -110 for root, dirs, files in os.walk(dataLake): -111 if any(name in root for name in IGNORE_LIST): -112 continue -113 for fileName in files: -114 if 'CSV' in fileName: -115 jumpFileName = os.path.join(root, fileName) -116 stat = os.stat(jumpFileName) -117 data = pd.read_csv(jumpFileName, skiprows = (1, 1)) -118 if stat.st_size >= MIN_JUMP_FILE_SIZE and validFlySightHeaderIn(jumpFileName) and isValidMinimumAltitude(data.hMSL.max()): -119 jumpFiles.append(jumpFileName) -120 return jumpFiles + +-76def calculateDistance(start: tuple, end: tuple) -> float: +77 """ +78 Calculate the distance between two terrestrial coordinates points. +79 +80 Arguments +81 --------- +82 start +83 A latitude, longitude tuple of floating point numbers. +84 +85 end +86 A latitude, longitude tuple of floating point numbers. +87 +88 Returns +89 ------- +90 The distance, in meters, between both points. +91 """ +92 return haversine(start, end, unit = Unit.METERS)-Get a list of all the speed jump files from a data lake, where data lake is -defined as a reachable path that contains one or more FlySight CSV files. -This function tests each file to ensure that it's a speed skydive FlySight -file in a valid format and length.
+@@ -872,88 +797,88 @@Calculate the distance between two terrestrial coordinates points.
Arguments
-dataLake: str +
-start
A valid (absolute or relative) path name to the top level directory where -the data lake starts.
+A latitude, longitude tuple of floating point numbers.
+ ++ +end +
A latitude, longitude tuple of floating point numbers.
Returns
-A list of speed jump file names for later SSScoring processing.
+The distance, in meters, between both points.
Returns
- -123def convertFlySight2SSScoring(rawData: pd.DataFrame, -124 altitudeDZMeters = 0.0, -125 altitudeDZFt = 0.0): -126 """ -127 Converts a raw dataframe initialized from a FlySight CSV file into the -128 SSScoring file format. The SSScoring format uses more descriptive column -129 headers, adds the altitude in feet, and uses UNIX time instead of an ISO -130 string. -131 -132 If both `altitudeDZMeters` and `altitudeDZFt` are zero then hMSL is used. -133 Otherwise, this function adjusts the effective altitude with the value. If -134 both meters and feet values are set this throws an error. -135 -136 Arguments -137 --------- -138 rawData : pd.DataFrame -139 FlySight CSV input as a dataframe -140 -141 altitudeDZMeters : float -142 Drop zone height above MSL -143 -144 altitudeDZFt -145 Drop zone altitudde above MSL -146 -147 Returns -148 ------- -149 A dataframe in SSScoring format, featuring these columns: -150 -151 - timeUnix -152 - altitudeMSL -153 - altitudeAGL -154 - altitudeMSLFt -155 - altitudeAGLFt -156 - hMetersPerSecond -157 - hKMh (km/h) -158 - vMetersPerSecond -159 - vKMh (km/h) -160 - angle -161 - speedAccuracy -162 -163 Errors -164 ------ -165 `SSScoringError` if the DZ altitude is set in both meters and feet. -166 """ -167 if not isinstance(rawData, pd.DataFrame): -168 raise SSScoringError('convertFlySight2SSScoring input must be a FlySight CSV dataframe') -169 -170 if altitudeDZMeters and altitudeDZFt: -171 raise SSScoringError('Cannot set altitude in meters and feet; pick one') -172 -173 if altitudeDZMeters: -174 altitudeDZFt = FT_IN_M*altitudeDZMeters -175 if altitudeDZFt: -176 altitudeDZMeters = altitudeDZFt/FT_IN_M -177 -178 data = rawData.copy() -179 -180 data['altitudeMSLFt'] = data['hMSL'].apply(lambda h: FT_IN_M*h) -181 data['altitudeAGL'] = data.hMSL-altitudeDZMeters -182 data['altitudeAGLFt'] = data.altitudeMSLFt-altitudeDZFt -183 data['timeUnix'] = data['time'].apply(lambda t: pd.Timestamp(t).timestamp()) -184 data['hMetersPerSecond'] = (data.velE**2.0+data.velN**2.0)**0.5 -185 speedAngle = data['hMetersPerSecond']/data['velD'] -186 speedAngle = round(90.0-speedAngle.apply(math.atan)/DEG_IN_RADIANS, 1) -187 -188 data = pd.DataFrame(data = { -189 'timeUnix': data.timeUnix, -190 'altitudeMSL': data.hMSL, -191 'altitudeAGL': data.altitudeAGL, -192 'altitudeMSLFt': data.altitudeMSLFt, -193 'altitudeAGLFt': data.altitudeAGLFt, -194 'vMetersPerSecond': data.velD, -195 'vKMh': 3.6*data.velD, -196 'speedAngle': speedAngle, -197 'speedAccuracy': data.sAcc, -198 'hMetersPerSecond': data.hMetersPerSecond, -199 'hKMh': 3.6*data.hMetersPerSecond, -200 'latitude': data.lat, -201 'longitude': data.lon, -202 }) -203 -204 return data +@@ -1007,57 +932,6 @@95def convertFlySight2SSScoring(rawData: pd.DataFrame, + 96 altitudeDZMeters = 0.0, + 97 altitudeDZFt = 0.0): + 98 """ + 99 Converts a raw dataframe initialized from a FlySight CSV file into the +100 SSScoring file format. The SSScoring format uses more descriptive column +101 headers, adds the altitude in feet, and uses UNIX time instead of an ISO +102 string. +103 +104 If both `altitudeDZMeters` and `altitudeDZFt` are zero then hMSL is used. +105 Otherwise, this function adjusts the effective altitude with the value. If +106 both meters and feet values are set this throws an error. +107 +108 Arguments +109 --------- +110 rawData : pd.DataFrame +111 FlySight CSV input as a dataframe +112 +113 altitudeDZMeters : float +114 Drop zone height above MSL +115 +116 altitudeDZFt +117 Drop zone altitudde above MSL +118 +119 Returns +120 ------- +121 A dataframe in SSScoring format, featuring these columns: +122 +123 - timeUnix +124 - altitudeMSL +125 - altitudeAGL +126 - altitudeMSLFt +127 - altitudeAGLFt +128 - hMetersPerSecond +129 - hKMh (km/h) +130 - vMetersPerSecond +131 - vKMh (km/h) +132 - angle +133 - speedAccuracy +134 +135 Errors +136 ------ +137 `SSScoringError` if the DZ altitude is set in both meters and feet. +138 """ +139 if not isinstance(rawData, pd.DataFrame): +140 raise SSScoringError('convertFlySight2SSScoring input must be a FlySight CSV dataframe') +141 +142 if altitudeDZMeters and altitudeDZFt: +143 raise SSScoringError('Cannot set altitude in meters and feet; pick one') +144 +145 if altitudeDZMeters: +146 altitudeDZFt = FT_IN_M*altitudeDZMeters +147 if altitudeDZFt: +148 altitudeDZMeters = altitudeDZFt/FT_IN_M +149 +150 data = rawData.copy() +151 +152 data['altitudeMSLFt'] = data['hMSL'].apply(lambda h: FT_IN_M*h) +153 data['altitudeAGL'] = data.hMSL-altitudeDZMeters +154 data['altitudeAGLFt'] = data.altitudeMSLFt-altitudeDZFt +155 data['timeUnix'] = data['time'].apply(lambda t: pd.Timestamp(t).timestamp()) +156 data['hMetersPerSecond'] = (data.velE**2.0+data.velN**2.0)**0.5 +157 speedAngle = data['hMetersPerSecond']/data['velD'] +158 speedAngle = round(90.0-speedAngle.apply(math.atan)/DEG_IN_RADIANS, 1) +159 +160 data = pd.DataFrame(data = { +161 'timeUnix': data.timeUnix, +162 'altitudeMSL': data.hMSL, +163 'altitudeAGL': data.altitudeAGL, +164 'altitudeMSLFt': data.altitudeMSLFt, +165 'altitudeAGLFt': data.altitudeAGLFt, +166 'vMetersPerSecond': data.velD, +167 'vKMh': 3.6*data.velD, +168 'speedAngle': speedAngle, +169 'speedAccuracy': data.sAcc, +170 'hMetersPerSecond': data.hMetersPerSecond, +171 'hKMh': 3.6*data.hMetersPerSecond, +172 'latitude': data.lat, +173 'longitude': data.lon, +174 }) +175 +176 return dataErrors
- - - - def - dropNonSkydiveDataFrom(data: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame: - - - -- -- - -207def dropNonSkydiveDataFrom(data: pd.DataFrame) -> pd.DataFrame: -208 """ -209 Discards all data rows before maximum altitude, and all "negative" altitude -210 rows because we don't skydive underground (FlySight bug?). -211 -212 Arguments -213 --------- -214 data : pd.DataFrame -215 Jump data in SSScoring format (headers differ from FlySight format) -216 -217 Returns -218 ------- -219 The jump data for the skydive -220 """ -221 timeMaxAlt = data[data.altitudeAGL == data.altitudeAGL.max()].timeUnix.iloc[0] -222 data = data[data.timeUnix > timeMaxAlt] -223 -224 data = data[data.altitudeAGL > 0] -225 -226 return data -- -Discards all data rows before maximum altitude, and all "negative" altitude -rows because we don't skydive underground (FlySight bug?).
- -Arguments
- -- -data : pd.DataFrame -
Jump data in SSScoring format (headers differ from FlySight format)
- -Returns
- -The jump data for the skydive
-@@ -1070,54 +944,54 @@ Returns
237def getSpeedSkydiveFrom(data: pd.DataFrame) -> tuple: -238 """ -239 Take the skydive dataframe and get the speed skydiving data: -240 -241 - Exit -242 - Speed skydiving window -243 - Drops data before exit and below breakoff altitude -244 -245 Arguments -246 --------- -247 data : pd.DataFrame -248 Jump data in SSScoring format -249 -250 Returns -251 ------- -252 A tuple of two elements: -253 -254 - A named tuple with performance and validation window data -255 - A dataframe featuring only speed skydiving data -256 """ -257 data = _dataGroups(data) -258 groups = data.group.max()+1 -259 -260 freeFallGroup = -1 -261 MIN_DATA_POINTS = 100 # heuristic -262 MIN_MAX_SPEED = 200 # km/h, heuristic; slower ::= no free fall -263 for group in range(groups): -264 subset = data[data.group == group] -265 if len(subset) >= MIN_DATA_POINTS and subset.vKMh.max() >= MIN_MAX_SPEED: -266 freeFallGroup = group -267 -268 data = data[data.group == freeFallGroup] -269 data = data.drop('group', axis = 1).drop('positive', axis = 1) -270 -271 # Speed ~= 9.81 m/s; subtract 1 second for actual exit. -272 exitTime = data[data.vMetersPerSecond > EXIT_SPEED].head(1).timeUnix.iat[0]-2.0 -273 data = data[data.timeUnix >= exitTime] -274 data = data[data.altitudeAGL >= BREAKOFF_ALTITUDE] -275 -276 windowStart = data.iloc[0].altitudeAGL -277 windowEnd = windowStart-PERFORMANCE_WINDOW_LENGTH -278 if windowEnd < BREAKOFF_ALTITUDE: -279 windowEnd = BREAKOFF_ALTITUDE -280 -281 validationWindowStart = windowEnd+VALIDATION_WINDOW_LENGTH -282 data = data[data.altitudeAGL >= windowEnd] -283 -284 return PerformanceWindow(windowStart, windowEnd, validationWindowStart), data +@@ -1148,199 +1022,144 @@187def getSpeedSkydiveFrom(data: pd.DataFrame) -> tuple: +188 """ +189 Take the skydive dataframe and get the speed skydiving data: +190 +191 - Exit +192 - Speed skydiving window +193 - Drops data before exit and below breakoff altitude +194 +195 Arguments +196 --------- +197 data : pd.DataFrame +198 Jump data in SSScoring format +199 +200 Returns +201 ------- +202 A tuple of two elements: +203 +204 - A named tuple with performance and validation window data +205 - A dataframe featuring only speed skydiving data +206 """ +207 data = _dataGroups(data) +208 groups = data.group.max()+1 +209 +210 freeFallGroup = -1 +211 MIN_DATA_POINTS = 100 # heuristic +212 MIN_MAX_SPEED = 200 # km/h, heuristic; slower ::= no free fall +213 for group in range(groups): +214 subset = data[data.group == group] +215 if len(subset) >= MIN_DATA_POINTS and subset.vKMh.max() >= MIN_MAX_SPEED: +216 freeFallGroup = group +217 +218 data = data[data.group == freeFallGroup] +219 data = data.drop('group', axis = 1).drop('positive', axis = 1) +220 +221 # Speed ~= 9.81 m/s; subtract 1 second for actual exit. +222 exitTime = data[data.vMetersPerSecond > EXIT_SPEED].head(1).timeUnix.iat[0]-2.0 +223 data = data[data.timeUnix >= exitTime] +224 data = data[data.altitudeAGL >= BREAKOFF_ALTITUDE] +225 +226 windowStart = data.iloc[0].altitudeAGL +227 windowEnd = windowStart-PERFORMANCE_WINDOW_LENGTH +228 if windowEnd < BREAKOFF_ALTITUDE: +229 windowEnd = BREAKOFF_ALTITUDE +230 +231 validationWindowStart = windowEnd+VALIDATION_WINDOW_LENGTH +232 data = data[data.altitudeAGL >= windowEnd] +233 +234 return PerformanceWindow(windowStart, windowEnd, validationWindowStart), dataReturns
-- + + def - isValidJump( data: pandas.core.frame.DataFrame, window: ssscoring.datatypes.PerformanceWindow) -> bool: + jumpAnalysisTable(data: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame: - +- -287def isValidJump(data: pd.DataFrame, -288 window: PerformanceWindow) -> bool: -289 """ -290 Validates the jump according to ISC/FAI/USPA competition rules. A jump is -291 valid when the speed accuracy parameter is less than 3 m/s for the whole -292 validation window duration. -293 -294 Arguments -295 --------- -296 data : pd.DataFramce -297 Jumnp data in SSScoring format -298 window : ssscoring.PerformanceWindow -299 Performance window start, end values in named tuple format -300 -301 Returns -302 ------- -303 `True` if the jump is valid according to ISC/FAI/USPA rules. -304 """ -305 accuracy = data[data.altitudeAGL < window.validationStart].speedAccuracy.max() -306 return accuracy < MAX_SPEED_ACCURACY + +-236def jumpAnalysisTable(data: pd.DataFrame) -> pd.DataFrame: +237 """ +238 Generates the HCD jump analysis table, with speed data at 5-second intervals +239 after exit. +240 +241 Arguments +242 --------- +243 data : pd.DataFrame +244 Jump data in SSScoring format +245 +246 Returns +247 ------- +248 A tuple with a pd.DataFrame and the max speed recorded for the jump: +249 +250 - A table dataframe with time and speed +251 - a floating point number +252 """ +253 table = None +254 +255 distanceStart = (data.iloc[0].latitude, data.iloc[0].longitude) +256 for column in pd.Series([ 5.0, 10.0, 15.0, 20.0, 25.0, ]): +257 for interval in range(int(column)*10, 10*(int(column)+1)): +258 # Use the next 0.1 sec interval if the current interval tranche has +259 # NaN values. +260 columnRef = interval/10.0 +261 timeOffset = data.iloc[0].timeUnix+columnRef +262 tranche = data.query('timeUnix == %f' % timeOffset).copy() +263 tranche['time'] = [ column, ] +264 currentPosition = (tranche.iloc[0].latitude, tranche.iloc[0].longitude) +265 tranche['distanceFromExit'] = [ round(calculateDistance(distanceStart, currentPosition), 1), ] +266 if not tranche.isnull().any().any(): +267 break +268 +269 if pd.isna(tranche.iloc[-1].vKMh): +270 tranche = data.tail(1).copy() +271 currentPosition = (tranche.iloc[0].latitude, tranche.iloc[0].longitude) +272 tranche['time'] = tranche.timeUnix-data.iloc[0].timeUnix +273 tranche['distanceFromExit'] = [ calculateDistance(distanceStart, currentPosition), ] +274 +275 if table is not None: +276 table = pd.concat([ table, tranche, ]) +277 else: +278 table = tranche +279 +280 table = pd.DataFrame({ +281 'time': table.time, +282 'vKMh': table.vKMh, +283 'hKMh': table.hKMh, +284 'speedAngle': table.speedAngle, +285 'distanceFromExit': table.distanceFromExit, +286 'altitude (ft)': table.altitudeAGLFt, +287 'netVectorKMh': (table.vKMh**2+table.hKMh**2)**0.5, +288 }) +289 +290 return (data.vKMh.max(), table)Validates the jump according to ISC/FAI/USPA competition rules. A jump is -valid when the speed accuracy parameter is less than 3 m/s for the whole -validation window duration.
+- - - -Generates the HCD jump analysis table, with speed data at 5-second intervals +after exit.
Arguments
-data : pd.DataFramce +
-data : pd.DataFrame
Jumnp data in SSScoring format - window : ssscoring.PerformanceWindow -Performance window start, end values in named tuple format
+Jump data in SSScoring format
Returns
--
True
if the jump is valid according to ISC/FAI/USPA rules.- - -- - def - calculateDistance(start: tuple, end: tuple) -> float: - - - -- -- - -309def calculateDistance(start: tuple, end: tuple) -> float: -310 """ -311 Calculate the distance between two terrestrial coordinates points. -312 -313 Arguments -314 --------- -315 start -316 A latitude, longitude tuple of floating point numbers. -317 -318 end -319 A latitude, longitude tuple of floating point numbers. -320 -321 Returns -322 ------- -323 The distance, in meters, between both points. -324 """ -325 return haversine(start, end, unit = Unit.METERS) -Calculate the distance between two terrestrial coordinates points.
- -Arguments
- -- -start -
A latitude, longitude tuple of floating point numbers.
- -- -end -
A latitude, longitude tuple of floating point numbers.
- -Returns
+A tuple with a pd.DataFrame and the max speed recorded for the jump:
-The distance, in meters, between both points.
++
- A table dataframe with time and speed
+- a floating point number
+- + + def - jumpAnalysisTable(data: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame: + dropNonSkydiveDataFrom(data: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame: - +- -328def jumpAnalysisTable(data: pd.DataFrame) -> pd.DataFrame: -329 """ -330 Generates the HCD jump analysis table, with speed data at 5-second intervals -331 after exit. -332 -333 Arguments -334 --------- -335 data : pd.DataFrame -336 Jump data in SSScoring format -337 -338 Returns -339 ------- -340 A tuple with a pd.DataFrame and the max speed recorded for the jump: -341 -342 - A table dataframe with time and speed -343 - a floating point number -344 """ -345 table = None -346 -347 distanceStart = (data.iloc[0].latitude, data.iloc[0].longitude) -348 for column in pd.Series([ 5.0, 10.0, 15.0, 20.0, 25.0, ]): -349 for interval in range(int(column)*10, 10*(int(column)+1)): -350 # Use the next 0.1 sec interval if the current interval tranche has -351 # NaN values. -352 columnRef = interval/10.0 -353 timeOffset = data.iloc[0].timeUnix+columnRef -354 tranche = data.query('timeUnix == %f' % timeOffset).copy() -355 tranche['time'] = [ column, ] -356 currentPosition = (tranche.iloc[0].latitude, tranche.iloc[0].longitude) -357 tranche['distanceFromExit'] = [ round(calculateDistance(distanceStart, currentPosition), 1), ] -358 if not tranche.isnull().any().any(): -359 break -360 -361 if pd.isna(tranche.iloc[-1].vKMh): -362 tranche = data.tail(1).copy() -363 currentPosition = (tranche.iloc[0].latitude, tranche.iloc[0].longitude) -364 tranche['time'] = tranche.timeUnix-data.iloc[0].timeUnix -365 tranche['distanceFromExit'] = [ calculateDistance(distanceStart, currentPosition), ] -366 -367 if table is not None: -368 table = pd.concat([ table, tranche, ]) -369 else: -370 table = tranche -371 -372 table = pd.DataFrame({ -373 'time': table.time, -374 'vKMh': table.vKMh, -375 'hKMh': table.hKMh, -376 'speedAngle': table.speedAngle, -377 'distanceFromExit': table.distanceFromExit, -378 'altitude (ft)': table.altitudeAGLFt, -379 'netVectorKMh': (table.vKMh**2+table.hKMh**2)**0.5, -380 }) -381 -382 return (data.vKMh.max(), table) + +-293def dropNonSkydiveDataFrom(data: pd.DataFrame) -> pd.DataFrame: +294 """ +295 Discards all data rows before maximum altitude, and all "negative" altitude +296 rows because we don't skydive underground (FlySight bug?). +297 +298 Arguments +299 --------- +300 data : pd.DataFrame +301 Jump data in SSScoring format (headers differ from FlySight format) +302 +303 Returns +304 ------- +305 The jump data for the skydive +306 """ +307 timeMaxAlt = data[data.altitudeAGL == data.altitudeAGL.max()].timeUnix.iloc[0] +308 data = data[data.timeUnix > timeMaxAlt] +309 +310 data = data[data.altitudeAGL > 0] +311 +312 return data-Generates the HCD jump analysis table, with speed data at 5-second intervals -after exit.
+@@ -1356,60 +1175,60 @@Discards all data rows before maximum altitude, and all "negative" altitude +rows because we don't skydive underground (FlySight bug?).
Arguments
-data : pd.DataFrame
Jump data in SSScoring format
+Jump data in SSScoring format (headers differ from FlySight format)
Returns
-A tuple with a pd.DataFrame and the max speed recorded for the jump:
- --
+- A table dataframe with time and speed
-- a floating point number
-The jump data for the skydive
Returns
-385def processJump(data: pd.DataFrame): -386 """ -387 Take a dataframe in SSScoring format and process it for display. It -388 serializes all the steps that would be taken from the ssscoring module, but -389 includes some text/HTML data in the output. -390 -391 Arguments -392 --------- -393 data: pd.DataFrame -394 A dataframe in SSScoring format -395 -396 Returns -397 ------- -398 A `JumpResults` named tuple with these items: -399 -400 - `score` speed score -401 - `maxSpeed` maximum speed during the jump -402 - `scores` a Series of every 3-second window scores from exit to breakoff -403 - `data` an updated SSScoring dataframe `plotTime`, where 0 = exit, used -404 for plotting -405 - `window` a named tuple with the exit, breakoff, and validation window -406 altitudes -407 - `table` a dataframe featuring the speeds and altitudes at 5-sec intervals -408 - `color` a string that defines the color for the jump result; possible -409 values are _green_ for valid jump, _red_ for invalid jump, per ISC rules -410 - `result` a string with the legend of _valid_ or _invalid_ jump -411 """ -412 data = data.copy() -413 data = dropNonSkydiveDataFrom(data) -414 window, data = getSpeedSkydiveFrom(data) -415 validJump = isValidJump(data, window) -416 score = 0.0 -417 scores = dict() -418 table = None -419 -420 if validJump: -421 maxSpeed, table = jumpAnalysisTable(data) -422 color = '#0f0' -423 result = '🟢 valid' -424 baseTime = data.iloc[0].timeUnix -425 data['plotTime'] = data.timeUnix-baseTime -426 -427 for spot in data.plotTime[::1]: -428 subset = data[(data.plotTime <= spot) & (data.plotTime >= (spot-3.0))] -429 scores[subset.vKMh.mean()] = spot -430 score = max(scores) -431 -432 else: -433 color = '#f00' -434 maxSpeed = -1 -435 score = 0 -436 result = '🔴 invalid' -437 -438 return JumpResults(color, data, maxSpeed, result, score, scores, table, window) +@@ -1456,43 +1275,47 @@315def processJump(data: pd.DataFrame): +316 """ +317 Take a dataframe in SSScoring format and process it for display. It +318 serializes all the steps that would be taken from the ssscoring module, but +319 includes some text/HTML data in the output. +320 +321 Arguments +322 --------- +323 data: pd.DataFrame +324 A dataframe in SSScoring format +325 +326 Returns +327 ------- +328 A `JumpResults` named tuple with these items: +329 +330 - `score` speed score +331 - `maxSpeed` maximum speed during the jump +332 - `scores` a Series of every 3-second window scores from exit to breakoff +333 - `data` an updated SSScoring dataframe `plotTime`, where 0 = exit, used +334 for plotting +335 - `window` a named tuple with the exit, breakoff, and validation window +336 altitudes +337 - `table` a dataframe featuring the speeds and altitudes at 5-sec intervals +338 - `color` a string that defines the color for the jump result; possible +339 values are _green_ for valid jump, _red_ for invalid jump, per ISC rules +340 - `result` a string with the legend of _valid_ or _invalid_ jump +341 """ +342 data = data.copy() +343 data = dropNonSkydiveDataFrom(data) +344 window, data = getSpeedSkydiveFrom(data) +345 validJump = isValidJump(data, window) +346 score = 0.0 +347 scores = dict() +348 table = None +349 +350 if validJump: +351 maxSpeed, table = jumpAnalysisTable(data) +352 color = '#0f0' +353 result = '🟢 valid' +354 baseTime = data.iloc[0].timeUnix +355 data['plotTime'] = data.timeUnix-baseTime +356 +357 for spot in data.plotTime[::1]: +358 subset = data[(data.plotTime <= spot) & (data.plotTime >= (spot-3.0))] +359 scores[subset.vKMh.mean()] = spot +360 score = max(scores) +361 +362 else: +363 color = '#f00' +364 maxSpeed = -1 +365 score = 0 +366 result = '🔴 invalid' +367 +368 return JumpResults(color, data, maxSpeed, result, score, scores, table, window)Returns
442def processAllJumpFiles(jumpFiles: list, altitudeDZMeters = 0.0) -> dict: -443 """ -444 Process all jump files in a list of valid FlySight files. Returns a -445 dictionary of jump results with a human-readable version of the file name. -446 The `jumpFiles` list can be generated by hand or the output of the -447 `ssscoring.getAllSpeedJumpFilesFrom` called to operate on a data lake. -448 -449 Arguments -450 --------- -451 jumpFiles -452 A list of relative or absolute path names to individual FlySight CSV files. -453 -454 altitudeDZMeters : float -455 Drop zone height above MSL -456 -457 dict -458 A dictionary of jump results. The key is a human-readable version of a -459 `jumpFile` name with the extension, path, and extraneous spaces eliminated -460 or replaced by appropriate characters. File names use Unicode, so accents -461 and non-ANSI characters are allowed in file names. -462 """ -463 jumpResults = dict() -464 for jumpFile in jumpFiles: -465 jumpResult = processJump( -466 convertFlySight2SSScoring(pd.read_csv(jumpFile, skiprows = (1, 1)), -467 altitudeDZMeters = altitudeDZMeters)) -468 tag = jumpFile.replace('CSV', '').replace('.', '').replace('/data', '').replace('/', ' ').strip() -469 if 'valid' in jumpResult.result: -470 jumpResults[tag] = jumpResult -471 return jumpResults +394def processAllJumpFiles(jumpFiles: list, altitudeDZMeters = 0.0) -> dict: +395 """ +396 Process all jump files in a list of valid FlySight files. Returns a +397 dictionary of jump results with a human-readable version of the file name. +398 The `jumpFiles` list can be generated by hand or the output of the +399 `ssscoring.fs1.getAllSpeedJumpFilesFrom` called to operate on a data lake. +400 +401 Arguments +402 --------- +403 jumpFiles +404 A list of relative or absolute path names to individual FlySight CSV files. +405 +406 altitudeDZMeters : float +407 Drop zone height above MSL +408 +409 dict +410 A dictionary of jump results. The key is a human-readable version of a +411 `jumpFile` name with the extension, path, and extraneous spaces eliminated +412 or replaced by appropriate characters. File names use Unicode, so accents +413 and non-ANSI characters are allowed in file names. +414 """ +415 jumpResults = dict() +416 for jumpFile in jumpFiles.keys(): +417 version = detectFlySightFileVersionOf(jumpFile) +418 if version == FlySightVersion.V1: +419 rawData = _readVersion1CSV(jumpFile) +420 tag = _tagVersion1From(jumpFile) +421 elif version == FlySightVersion.V2: +422 rawData = _readVersion2CSV(jumpFile) +423 tag = _tagVersion2From(jumpFile) +424 jumpResult = processJump(convertFlySight2SSScoring(rawData, altitudeDZMeters = altitudeDZMeters)) +425 if 'valid' in jumpResult.result: +426 jumpResults[tag] = jumpResult +427 return jumpResults-Process all jump files in a list of valid FlySight files. Returns a dictionary of jump results with a human-readable version of the file name. The
+jumpFiles
list can be generated by hand or the output of the -ssscoring.getAllSpeedJumpFilesFrom
called to operate on a data lake.ssscoring.fs1.getAllSpeedJumpFilesFrom
called to operate on a data lake.Arguments
@@ -1528,49 +1351,49 @@Arguments
-474def aggregateResults(jumpResults: dict) -> pd.DataFrame: -475 """ -476 Aggregate all the results in a table fashioned after Marco Hepp's and Nklas -477 Daniel's score tracking data. -478 -479 Arguments -480 --------- -481 jumpResults: dict -482 A dictionary of jump results, in which each result corresponds to a FlySight -483 file name. See `ssscoring.processAllJumpFiles` for details. -484 -485 Returns -486 ------- -487 A dataframe featuring these columns: -488 -489 - Score -490 - Speeds at 5, 10, 15, 20, and 25 second tranches -491 - Final time contemplated in the analysis -492 - Max speed -493 -494 The dataframe rows are identified by the human readable jump file name. -495 """ -496 speeds = pd.DataFrame() -497 for jumpResultIndex in sorted(list(jumpResults.keys())): -498 jumpResult = jumpResults[jumpResultIndex] -499 if jumpResult.score > 0.0: -500 t = jumpResult.table -501 finalTime = t.iloc[-1].time -502 t.iloc[-1].time = LAST_TIME_TRANCHE -503 t = pd.pivot_table(t, columns = t.time) -504 t.drop(['altitude (ft)'], inplace = True) -505 d = pd.DataFrame([ jumpResult.score, ], index = [ jumpResultIndex, ], columns = [ 'score', ], dtype = object) -506 for column in t.columns: -507 # d[column] = t[column].iloc[2] -508 d[column] = t[column].vKMh -509 d['finalTime'] = [ finalTime, ] -510 d['maxSpeed'] = jumpResult.maxSpeed -511 -512 if speeds.empty: -513 speeds = d.copy() -514 else: -515 speeds = pd.concat([ speeds, d, ]) -516 return speeds.sort_index() +@@ -1612,37 +1435,37 @@430def aggregateResults(jumpResults: dict) -> pd.DataFrame: +431 """ +432 Aggregate all the results in a table fashioned after Marco Hepp's and Nklas +433 Daniel's score tracking data. +434 +435 Arguments +436 --------- +437 jumpResults: dict +438 A dictionary of jump results, in which each result corresponds to a FlySight +439 file name. See `ssscoring.processAllJumpFiles` for details. +440 +441 Returns +442 ------- +443 A dataframe featuring these columns: +444 +445 - Score +446 - Speeds at 5, 10, 15, 20, and 25 second tranches +447 - Final time contemplated in the analysis +448 - Max speed +449 +450 The dataframe rows are identified by the human readable jump file name. +451 """ +452 speeds = pd.DataFrame() +453 for jumpResultIndex in sorted(list(jumpResults.keys())): +454 jumpResult = jumpResults[jumpResultIndex] +455 if jumpResult.score > 0.0: +456 t = jumpResult.table +457 finalTime = t.iloc[-1].time +458 t.iloc[-1].time = LAST_TIME_TRANCHE +459 t = pd.pivot_table(t, columns = t.time) +460 t.drop(['altitude (ft)'], inplace = True) +461 d = pd.DataFrame([ jumpResult.score, ], index = [ jumpResultIndex, ], columns = [ 'score', ], dtype = object) +462 for column in t.columns: +463 # d[column] = t[column].iloc[2] +464 d[column] = t[column].vKMh +465 d['finalTime'] = [ finalTime, ] +466 d['maxSpeed'] = jumpResult.maxSpeed +467 +468 if speeds.empty: +469 speeds = d.copy() +470 else: +471 speeds = pd.concat([ speeds, d, ]) +472 return speeds.sort_index()Returns
-519def roundedAggregateResults(aggregate: pd.DataFrame) -> pd.DataFrame: -520 """ -521 Aggregate all the results in a table fashioned after Marco Hepp's and Nklas -522 Daniel's score tracking data. All speed results are rounded at `n > x.5` -523 for any value. -524 -525 Arguments -526 --------- -527 aggregate: pd.DataFrame -528 A dataframe output of `ssscoring.fs1.aggregateResults`. -529 -530 Returns -531 ------- -532 A dataframe featuring the **rounded values** for these columns: -533 -534 - Score -535 - Speeds at 5, 10, 15, 20, and 25 second tranches -536 - Max speed -537 -538 The `finalTime` column is ignored. -539 -540 The dataframe rows are identified by the human readable jump file name. -541 -542 This is a less precise version of the `ssscoring.aggregateResults` -543 dataframe, useful during training to keep rounded results available for -544 review. -545 """ -546 for column in [col for col in aggregate.columns if 'Time' not in str(col)]: -547 aggregate[column] = aggregate[column].apply(round) -548 -549 return aggregate +@@ -1689,32 +1512,32 @@475def roundedAggregateResults(aggregate: pd.DataFrame) -> pd.DataFrame: +476 """ +477 Aggregate all the results in a table fashioned after Marco Hepp's and Nklas +478 Daniel's score tracking data. All speed results are rounded at `n > x.5` +479 for any value. +480 +481 Arguments +482 --------- +483 aggregate: pd.DataFrame +484 A dataframe output of `ssscoring.fs1.aggregateResults`. +485 +486 Returns +487 ------- +488 A dataframe featuring the **rounded values** for these columns: +489 +490 - Score +491 - Speeds at 5, 10, 15, 20, and 25 second tranches +492 - Max speed +493 +494 The `finalTime` column is ignored. +495 +496 The dataframe rows are identified by the human readable jump file name. +497 +498 This is a less precise version of the `ssscoring.aggregateResults` +499 dataframe, useful during training to keep rounded results available for +500 review. +501 """ +502 for column in [col for col in aggregate.columns if 'Time' not in str(col)]: +503 aggregate[column] = aggregate[column].apply(round) +504 +505 return aggregateReturns
552def totalResultsFrom(aggregate: pd.DataFrame) -> pd.DataFrame: -553 """ -554 Calculates the total and mean speeds for an aggregation of speed jumps. -555 -556 Arguments -557 --------- -558 aggregate: pd.DataFrame -559 The aggregate results dataframe resulting from calling `ssscoring.aggregateResults` -560 with valid results. -561 -562 Returns -563 ------- -564 A dataframe with one row and two columns: -565 -566 - totalSpeed ::= the sum of all speeds in the aggregated results -567 - meanSpeed ::= the mean of all speeds -568 - maxScore ::= the max score among all the speed scores -569 -570 Raises -571 ------ -572 `AttributeError` if aggregate is an empty dataframe or `None`, or if the -573 `aggregate` dataframe doesn't conform to the output of `ssscoring.aggregateResults`. -574 """ -575 totals = pd.DataFrame({ 'totalSpeed': [ aggregate.score.sum(), ], 'meanSpeed': [ aggregate.score.mean(), ], 'maxScore': [ aggregate.score.max(), ], }, index = [ 'totalSpeed'],) -576 -577 return totals +diff --git a/docs/ssscoring/constants.html b/docs/ssscoring/constants.html index 4d1ddb8..8d928e9 100644 --- a/docs/ssscoring/constants.html +++ b/docs/ssscoring/constants.html @@ -231,7 +231,7 @@508def totalResultsFrom(aggregate: pd.DataFrame) -> pd.DataFrame: +509 """ +510 Calculates the total and mean speeds for an aggregation of speed jumps. +511 +512 Arguments +513 --------- +514 aggregate: pd.DataFrame +515 The aggregate results dataframe resulting from calling `ssscoring.aggregateResults` +516 with valid results. +517 +518 Returns +519 ------- +520 A dataframe with one row and two columns: +521 +522 - totalSpeed ::= the sum of all speeds in the aggregated results +523 - meanSpeed ::= the mean of all speeds +524 - maxScore ::= the max score among all the speed scores +525 +526 Raises +527 ------ +528 `AttributeError` if aggregate is an empty dataframe or `None`, or if the +529 `aggregate` dataframe doesn't conform to the output of `ssscoring.aggregateResults`. +530 """ +531 totals = pd.DataFrame({ 'totalSpeed': [ aggregate.score.sum(), ], 'meanSpeed': [ aggregate.score.mean(), ], 'maxScore': [ aggregate.score.max(), ], }, index = [ 'totalSpeed'],) +532 +533 return totals
FLYSIGHT_1_HEADER = - {'gpsFix', 'lon', 'velN', 'vAcc', 'velE', 'heading', 'sAcc', 'hAcc', 'cAcc', 'hMSL', 'numSV', 'lat', 'time', 'velD'} + {'gpsFix', 'numSV', 'vAcc', 'lat', 'velD', 'lon', 'cAcc', 'velN', 'sAcc', 'velE', 'hAcc', 'heading', 'hMSL', 'time'}diff --git a/docs/ssscoring/flysight.html b/docs/ssscoring/flysight.html new file mode 100644 index 0000000..dd0f021 --- /dev/null +++ b/docs/ssscoring/flysight.html @@ -0,0 +1,809 @@ + + + + + + +ssscoring.flysight API documentation + + + + + + + + + + ++ + + \ No newline at end of file diff --git a/manpages/ssscoring.1 b/manpages/ssscoring.1 index 605f545..76a32a2 100644 --- a/manpages/ssscoring.1 +++ b/manpages/ssscoring.1 @@ -14,7 +14,7 @@ . ftr VB CB . ftr VBI CBI .\} -.TH "ssscoring" "3" "" "Version 1.6.0" "Speed Skydiving Scoring API documentation" +.TH "ssscoring" "3" "" "Version 1.7.0" "Speed Skydiving Scoring API documentation" .hy .SH Name .PP diff --git a/proto-app-03-bulk-processor.ipynb b/proto-app-03-bulk-processor.ipynb index 4c0d6c9..fb867d5 100644 --- a/proto-app-03-bulk-processor.ipynb +++ b/proto-app-03-bulk-processor.ipynb @@ -13,31 +13,27 @@ "cell_type": "code", "execution_count": null, "id": "98c7a164-9492-4672-904b-5bf3abf61773", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, + "metadata": {}, "outputs": [], "source": [ "from collections import namedtuple\n", "from copy import deepcopy\n", "\n", + "from ssscoring.calc import aggregateResults\n", + "from ssscoring.calc import convertFlySight2SSScoring\n", + "from ssscoring.calc import dropNonSkydiveDataFrom\n", + "from ssscoring.calc import getSpeedSkydiveFrom\n", + "from ssscoring.calc import isValidJump\n", + "from ssscoring.calc import isValidMinimumAltitude\n", + "from ssscoring.calc import jumpAnalysisTable\n", + "from ssscoring.calc import processAllJumpFiles\n", + "from ssscoring.calc import roundedAggregateResults\n", + "from ssscoring.calc import totalResultsFrom\n", "from ssscoring.constants import BREAKOFF_ALTITUDE\n", "from ssscoring.constants import FT_IN_M\n", "from ssscoring.constants import PERFORMANCE_WINDOW_LENGTH\n", - "from ssscoring.fs1 import aggregateResults\n", - "from ssscoring.fs1 import convertFlySight2SSScoring\n", - "from ssscoring.fs1 import dropNonSkydiveDataFrom\n", - "from ssscoring.fs1 import getAllSpeedJumpFilesFrom\n", - "from ssscoring.fs1 import getSpeedSkydiveFrom\n", - "from ssscoring.fs1 import isValidJump\n", - "from ssscoring.fs1 import isValidMinimumAltitude\n", - "from ssscoring.fs1 import jumpAnalysisTable\n", - "from ssscoring.fs1 import processAllJumpFiles\n", - "from ssscoring.fs1 import roundedAggregateResults\n", - "from ssscoring.fs1 import totalResultsFrom\n", - "from ssscoring.fs1 import validFlySightHeaderIn\n", + "from ssscoring.flysight import getAllSpeedJumpFilesFrom\n", + "from ssscoring.flysight import validFlySightHeaderIn\n", "from ssscoring.notebook import SPEED_COLORS\n", "from ssscoring.notebook import graphAltitude\n", "from ssscoring.notebook import graphAngle\n", @@ -114,11 +110,7 @@ "cell_type": "code", "execution_count": null, "id": "b1273b5c-44c1-46c2-a125-d01ad9268a1a", - "metadata": { - "jupyter": { - "source_hidden": true - } - }, + "metadata": {}, "outputs": [], "source": [ "jumpFiles = getAllSpeedJumpFilesFrom(DATA_LAKE_ROOT)" @@ -343,7 +335,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/pyproject.toml b/pyproject.toml index d13a633..827f1c8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,7 +28,7 @@ license = { text = 'BSD-3-Clause' } name = "ssscoring" # $(PACKAGE) and package.txt readme = "README.md" requires-python = ">=3.9.9" -version = '1.6.0' +version = '1.7.0' [project.scripts] diff --git a/resources/test-data-FS2/01-00-00/BAD_CSV_FILE.CSV b/resources/test-data-FS2/01-00-00/BAD_CSV_FILE.CSV new file mode 100644 index 0000000..02c91f4 --- /dev/null +++ b/resources/test-data-FS2/01-00-00/BAD_CSV_FILE.CSV @@ -0,0 +1,31 @@ +26998.182,"6/15 timers used before active mode initialization" +26998.184,"----------" +29276.099,"----------" +29276.101,"640/2048 slots used in GNSS buffer" +29276.107,"0 ms average time spent in GNSS update task" +29276.109,"2 ms maximum time spent in GNSS update task" +29276.111,"68 ms maximum time between calls to GNSS update task" +29276.115,"----------" +29276.117,"1019/2048 slots used in audio buffer" +29276.120,"0 ms average time spent in audio update task" +29276.125,"4 ms maximum time spent in audio update task" +29276.128,"44 ms maximum time between calls to audio update task" +29276.130,"----------" +29276.132,"6/15 timers used after active mode de-initialization" +29276.135,"----------" +29276.137,"1/30 slots used in $BARO message buffer" +29276.144,"1/3 slots used in $HUM message buffer" +29276.146,"1/15 slots used in $MAG message buffer" +29276.148,"2/6 slots used in $GNSS message buffer" +29276.151,"1/2 slots used in $TIME message buffer" +29276.153,"2/5 slots used in $RAW message buffer" +29276.156,"1/667 slots used in $IMU message buffer" +29276.161,"1/2 slots used in $VBAT message buffer" +29276.164,"----------" +29276.166,"0 ms average time spent in log update task" +29276.169,"29 ms maximum time spent in log update task" +29276.172,"53 ms maximum time between calls to log update task" +29276.174,"----------" +29276.179,"2 ms average time spent in log sync task" +29276.182,"35 ms maximum time spent in log sync task" +29276.184,"327 ms maximum time between calls to log sync task" diff --git a/resources/test-data-FS2/01-00-00/EVENT.CSV b/resources/test-data-FS2/01-00-00/EVENT.CSV new file mode 100644 index 0000000..ae96a89 --- /dev/null +++ b/resources/test-data-FS2/01-00-00/EVENT.CSV @@ -0,0 +1,38 @@ +$FLYS,1 +$VAR,FIRMWARE_VER,v2024.08.07.release_candidate +$VAR,DEVICE_ID,004e003a484e501420353131 +$VAR,SESSION_ID,d2c4f114f570e0f76a5b1ad1 +$COL,EVNT,time,description +$UNIT,EVNT,s, +$DATA +$EVNT,26998.182,"6/15 timers used before active mode initialization" +$EVNT,26998.184,"----------" +$EVNT,29276.099,"----------" +$EVNT,29276.101,"640/2048 slots used in GNSS buffer" +$EVNT,29276.107,"0 ms average time spent in GNSS update task" +$EVNT,29276.109,"2 ms maximum time spent in GNSS update task" +$EVNT,29276.111,"68 ms maximum time between calls to GNSS update task" +$EVNT,29276.115,"----------" +$EVNT,29276.117,"1019/2048 slots used in audio buffer" +$EVNT,29276.120,"0 ms average time spent in audio update task" +$EVNT,29276.125,"4 ms maximum time spent in audio update task" +$EVNT,29276.128,"44 ms maximum time between calls to audio update task" +$EVNT,29276.130,"----------" +$EVNT,29276.132,"6/15 timers used after active mode de-initialization" +$EVNT,29276.135,"----------" +$EVNT,29276.137,"1/30 slots used in $BARO message buffer" +$EVNT,29276.144,"1/3 slots used in $HUM message buffer" +$EVNT,29276.146,"1/15 slots used 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You will need Python 3.9 or later, pandas, and NumPy as minimum requirements. -You may the latest stable source files at: +You may study or download the latest stable source files at: https://github.com/pr3d4t0r/SSScoring/tree/master/ssscoring """ diff --git a/ssscoring/fs1.py b/ssscoring/calc.py similarity index 84% rename from ssscoring/fs1.py rename to ssscoring/calc.py index f4dcb9d..48f7b10 100644 --- a/ssscoring/fs1.py +++ b/ssscoring/calc.py @@ -1,10 +1,7 @@ # See: https://github.com/pr3d4t0r/SSScoring/blob/master/LICENSE.txt """ -Functions and logic for detecting, validating, analyzing, and manipulating -FlySight 1 CSV files, including detection in the file system. The functions in -this module assume that a data lake exists somewhere in the file system (whether -local or cloud-based). +Functions and logic for analyzing and manipulating FlySight dataframes. """ @@ -14,59 +11,24 @@ from ssscoring.constants import BREAKOFF_ALTITUDE from ssscoring.constants import DEG_IN_RADIANS from ssscoring.constants import EXIT_SPEED -from ssscoring.constants import FLYSIGHT_1_HEADER from ssscoring.constants import FT_IN_M -from ssscoring.constants import IGNORE_LIST from ssscoring.constants import LAST_TIME_TRANCHE from ssscoring.constants import MAX_SPEED_ACCURACY -from ssscoring.constants import MIN_JUMP_FILE_SIZE from ssscoring.constants import PERFORMANCE_WINDOW_LENGTH from ssscoring.constants import VALIDATION_WINDOW_LENGTH from ssscoring.datatypes import JumpResults from ssscoring.datatypes import PerformanceWindow from ssscoring.errors import SSScoringError +from ssscoring.flysight import FlySightVersion +from ssscoring.flysight import detectFlySightFileVersionOf -import csv import math -import os import pandas as pd # +++ functions +++ -def validFlySightHeaderIn(fileCSV: str) -> bool: - """ - Checks if a file is a CSV in FlySight 1 format. The checks include: - - - Whether the file is a CSV, using a comma delimiter - - Checks for the presence of all the documented FlySight headers - - Arguments - --------- - fileCSV - A file name to verify as a valid FlySight file - - Returns - ------- - `True` if `fileCSV` is a FlySight CSV file, otherwise `False`. - """ - delimiters = [',', ] - hasAllHeaders = False - with open(fileCSV, 'r') as inputFile: - try: - dialect = csv.Sniffer().sniff(inputFile.readline(), delimiters = delimiters) - except: - return False - if dialect.delimiter in delimiters: - inputFile.seek(0) - header = next(csv.reader(inputFile)) - else: - return False - hasAllHeaders = FLYSIGHT_1_HEADER.issubset(header) - return hasAllHeaders - - def isValidMinimumAltitude(altitude: float) -> bool: """ Reports whether an `altitude` is within the IPC and USPA valid parameters, @@ -88,35 +50,45 @@ def isValidMinimumAltitude(altitude: float) -> bool: return altitude >= minAltitude -def getAllSpeedJumpFilesFrom(dataLake: str) -> list: +def isValidJump(data: pd.DataFrame, + window: PerformanceWindow) -> bool: """ - Get a list of all the speed jump files from a data lake, where data lake is - defined as a reachable path that contains one or more FlySight CSV files. - This function tests each file to ensure that it's a speed skydive FlySight - file in a valid format and length. + Validates the jump according to ISC/FAI/USPA competition rules. A jump is + valid when the speed accuracy parameter is less than 3 m/s for the whole + validation window duration. Arguments --------- - dataLake: str - A valid (absolute or relative) path name to the top level directory where - the data lake starts. + data : pd.DataFramce + Jumnp data in SSScoring format + window : ssscoring.PerformanceWindow + Performance window start, end values in named tuple format Returns ------- - A list of speed jump file names for later SSScoring processing. + `True` if the jump is valid according to ISC/FAI/USPA rules. """ - jumpFiles = list() - for root, dirs, files in os.walk(dataLake): - if any(name in root for name in IGNORE_LIST): - continue - for fileName in files: - if 'CSV' in fileName: - jumpFileName = os.path.join(root, fileName) - stat = os.stat(jumpFileName) - data = pd.read_csv(jumpFileName, skiprows = (1, 1)) - if stat.st_size >= MIN_JUMP_FILE_SIZE and validFlySightHeaderIn(jumpFileName) and isValidMinimumAltitude(data.hMSL.max()): - jumpFiles.append(jumpFileName) - return jumpFiles + accuracy = data[data.altitudeAGL < window.validationStart].speedAccuracy.max() + return accuracy < MAX_SPEED_ACCURACY + + +def calculateDistance(start: tuple, end: tuple) -> float: + """ + Calculate the distance between two terrestrial coordinates points. + + Arguments + --------- + start + A latitude, longitude tuple of floating point numbers. + + end + A latitude, longitude tuple of floating point numbers. + + Returns + ------- + The distance, in meters, between both points. + """ + return haversine(start, end, unit = Unit.METERS) def convertFlySight2SSScoring(rawData: pd.DataFrame, @@ -203,28 +175,6 @@ def convertFlySight2SSScoring(rawData: pd.DataFrame, return data -def dropNonSkydiveDataFrom(data: pd.DataFrame) -> pd.DataFrame: - """ - Discards all data rows before maximum altitude, and all "negative" altitude - rows because we don't skydive underground (FlySight bug?). - - Arguments - --------- - data : pd.DataFrame - Jump data in SSScoring format (headers differ from FlySight format) - - Returns - ------- - The jump data for the skydive - """ - timeMaxAlt = data[data.altitudeAGL == data.altitudeAGL.max()].timeUnix.iloc[0] - data = data[data.timeUnix > timeMaxAlt] - - data = data[data.altitudeAGL > 0] - - return data - - def _dataGroups(data): data_ = data.copy() data_['positive'] = (data_.vMetersPerSecond > 0) @@ -282,48 +232,6 @@ def getSpeedSkydiveFrom(data: pd.DataFrame) -> tuple: return PerformanceWindow(windowStart, windowEnd, validationWindowStart), data - -def isValidJump(data: pd.DataFrame, - window: PerformanceWindow) -> bool: - """ - Validates the jump according to ISC/FAI/USPA competition rules. A jump is - valid when the speed accuracy parameter is less than 3 m/s for the whole - validation window duration. - - Arguments - --------- - data : pd.DataFramce - Jumnp data in SSScoring format - window : ssscoring.PerformanceWindow - Performance window start, end values in named tuple format - - Returns - ------- - `True` if the jump is valid according to ISC/FAI/USPA rules. - """ - accuracy = data[data.altitudeAGL < window.validationStart].speedAccuracy.max() - return accuracy < MAX_SPEED_ACCURACY - - -def calculateDistance(start: tuple, end: tuple) -> float: - """ - Calculate the distance between two terrestrial coordinates points. - - Arguments - --------- - start - A latitude, longitude tuple of floating point numbers. - - end - A latitude, longitude tuple of floating point numbers. - - Returns - ------- - The distance, in meters, between both points. - """ - return haversine(start, end, unit = Unit.METERS) - - def jumpAnalysisTable(data: pd.DataFrame) -> pd.DataFrame: """ Generates the HCD jump analysis table, with speed data at 5-second intervals @@ -381,6 +289,28 @@ def jumpAnalysisTable(data: pd.DataFrame) -> pd.DataFrame: return (data.vKMh.max(), table) +def dropNonSkydiveDataFrom(data: pd.DataFrame) -> pd.DataFrame: + """ + Discards all data rows before maximum altitude, and all "negative" altitude + rows because we don't skydive underground (FlySight bug?). + + Arguments + --------- + data : pd.DataFrame + Jump data in SSScoring format (headers differ from FlySight format) + + Returns + ------- + The jump data for the skydive + """ + timeMaxAlt = data[data.altitudeAGL == data.altitudeAGL.max()].timeUnix.iloc[0] + data = data[data.timeUnix > timeMaxAlt] + + data = data[data.altitudeAGL > 0] + + return data + + def processJump(data: pd.DataFrame): """ Take a dataframe in SSScoring format and process it for display. It @@ -437,13 +367,35 @@ def processJump(data: pd.DataFrame): return JumpResults(color, data, maxSpeed, result, score, scores, table, window) +def _readVersion1CSV(jumpFile: str) -> pd.DataFrame: + return pd.read_csv(jumpFile, skiprows = (1, 1)) + + +def _tagVersion1From(jumpFile: str) -> str: + return jumpFile.replace('CSV', '').replace('.', '').replace('/data', '').replace('/', ' ').strip()+':v1' + + +def _tagVersion2From(jumpFile: str) -> str: + return jumpFile.split('/')[-2]+':v2' + + + +def _readVersion2CSV(jumpFile: str) -> pd.DataFrame: + from ssscoring.flysight import FS2_COLUMNS + from ssscoring.flysight import skipOverFS2MetadataRowsIn + + rawData = pd.read_csv(jumpFile, names = FS2_COLUMNS, skiprows = 6) + rawData = skipOverFS2MetadataRowsIn(rawData) + rawData.drop('GNSS', inplace = True, axis = 1) + return rawData + def processAllJumpFiles(jumpFiles: list, altitudeDZMeters = 0.0) -> dict: """ Process all jump files in a list of valid FlySight files. Returns a dictionary of jump results with a human-readable version of the file name. The `jumpFiles` list can be generated by hand or the output of the - `ssscoring.getAllSpeedJumpFilesFrom` called to operate on a data lake. + `ssscoring.fs1.getAllSpeedJumpFilesFrom` called to operate on a data lake. Arguments --------- @@ -460,11 +412,15 @@ def processAllJumpFiles(jumpFiles: list, altitudeDZMeters = 0.0) -> dict: and non-ANSI characters are allowed in file names. """ jumpResults = dict() - for jumpFile in jumpFiles: - jumpResult = processJump( - convertFlySight2SSScoring(pd.read_csv(jumpFile, skiprows = (1, 1)), - altitudeDZMeters = altitudeDZMeters)) - tag = jumpFile.replace('CSV', '').replace('.', '').replace('/data', '').replace('/', ' ').strip() + for jumpFile in jumpFiles.keys(): + version = detectFlySightFileVersionOf(jumpFile) + if version == FlySightVersion.V1: + rawData = _readVersion1CSV(jumpFile) + tag = _tagVersion1From(jumpFile) + elif version == FlySightVersion.V2: + rawData = _readVersion2CSV(jumpFile) + tag = _tagVersion2From(jumpFile) + jumpResult = processJump(convertFlySight2SSScoring(rawData, altitudeDZMeters = altitudeDZMeters)) if 'valid' in jumpResult.result: jumpResults[tag] = jumpResult return jumpResults diff --git a/ssscoring/flysight.py b/ssscoring/flysight.py new file mode 100644 index 0000000..6aa79e1 --- /dev/null +++ b/ssscoring/flysight.py @@ -0,0 +1,187 @@ +# See: https://github.com/pr3d4t0r/SSScoring/blob/master/LICENSE.txt + +""" +Functions and logic for detecting, validating and manipulating +FlySight CSV files, including detection in the file system. The functions in +this module assume that a data lake exists somewhere in the file system (whether +local or cloud-based). +""" + + +from enum import Enum + +from ssscoring.constants import FLYSIGHT_1_HEADER +from ssscoring.constants import IGNORE_LIST +from ssscoring.constants import MIN_JUMP_FILE_SIZE +from ssscoring.errors import SSScoringError + +import csv +import os + +import pandas as pd + + +# +++ constants +++ + +FS2_COLUMNS = ('GNSS', 'time', 'lat', 'lon', 'hMSL', 'velN', 'velE', 'velD', 'hAcc', 'vAcc', 'sAcc', 'numSV', ) + + +# --- classes and objects --- + +class FlySightVersion(Enum): + V1 = 1000 + V2 = 2000 + + + +# +++ functions +++ + + +def skipOverFS2MetadataRowsIn(data: pd.DataFrame) -> pd.DataFrame: + """ + Returns a clean dataframe on which any metadata rows within the first 100 + are skipped. This function uses the `time` column to detect valid rows. A + `time == NaN` is considered invalid and skipped. + + Arguments + --------- + data + A FlySight 2 dataframe suspected of having dirty N first rows with metadata + + Returns + ------- + A FlySight 2 clean dataframe without any leading metadata rows. + """ + for ref in range(0,100): + if pd.notnull(data.iloc[ref].time): + break + return data.iloc[ref:] + + +def validFlySightHeaderIn(fileCSV: str) -> bool: + """ + Checks if a file is a CSV in FlySight 1 or FlySight 2 formats. The checks + include: + + - Whether the file is a CSV, using a comma delimiter + - Checks for the presence of all the documented FlySight 1 headers + - Checks for the presence of the FlySight 2 line 1 identifier + + Arguments + --------- + fileCSV + A file name to verify as a valid FlySight file + + Returns + ------- + `True` if `fileCSV` is a FlySight CSV file, otherwise `False`. + """ + delimiters = [',', ] + hasAllHeaders = False + with open(fileCSV, 'r') as inputFile: + try: + dialect = csv.Sniffer().sniff(inputFile.readline(), delimiters = delimiters) + except: + return False + if dialect.delimiter in delimiters: + inputFile.seek(0) + header = next(csv.reader(inputFile)) + else: + return False + hasAllHeaders = True if header[0] == '$FLYS' else FLYSIGHT_1_HEADER.issubset(header) + return hasAllHeaders + + +def getAllSpeedJumpFilesFrom(dataLake: str) -> dict: + """ + Get a list of all the speed jump files from a data lake, where data lake is + defined as a reachable path that contains one or more FlySight CSV files. + This function tests each file to ensure that it's a speed skydive FlySight + file in a valid format and length. + + Arguments + --------- + dataLake: str + A valid (absolute or relative) path name to the top level directory where + the data lake starts. + + Returns + ------- + A dictionary of speed jump file names for later SSScoring processing: + - keys are the file names + - values are a FlySight version string tag + """ + from ssscoring.calc import isValidMinimumAltitude # skirt circular dependency + + jumpFiles = dict() + for root, dirs, files in os.walk(dataLake): + if any(name in root for name in IGNORE_LIST): + continue + for fileName in files: + data = None + if '.swp' in fileName: # Ignore Vim, other editors swap file + continue + if '.CSV' in fileName: + version = '1' + jumpFileName = os.path.join(root, fileName) + stat = os.stat(jumpFileName) + if all(x not in fileName for x in ('EVENT', 'SENSOR', 'TRACK')): + # FlySight 1 track format + data = pd.read_csv(jumpFileName, skiprows = (1, 1)) + elif 'TRACK' in fileName: + # FlySight 2 track custom format + data = pd.read_csv(jumpFileName, names = FS2_COLUMNS, skiprows = 6) + data = skipOverFS2MetadataRowsIn(data) + data.drop('GNSS', inplace = True, axis = 1) + version = '2' + if data is not None and stat.st_size >= MIN_JUMP_FILE_SIZE and validFlySightHeaderIn(jumpFileName) and isValidMinimumAltitude(data.hMSL.max()): + # explicit because `not data` is ambiguous for dataframes + jumpFiles[jumpFileName] = version + return jumpFiles + + +def detectFlySightFileVersionOf(fileName: str) -> FlySightVersion: + """ + Detects the FlySight file version based on its file name and format. + + Arguments + --------- + fileName + A string corresponding to the file name. + + Returns + ------- + An instance of `ssscoring.flysight.FlySightVersion` with a valid version + symbolic value. + + Errors + ------ + `ssscoring.errors.SSScoringError` if the file is not a CSV and it's some + other invalid format. + """ + if not '.CSV' in fileName: + raise SSScoringError('Invalid file extension type') + if any(x in fileName for x in ('EVENT.CSV', 'SENSOR.CSV')): + raise SSScoringError('Only TRACK.CSV v2 files can be processed at this time') + if not os.path.exists(fileName): + raise SSScoringError('%s - file not found in data lake' % fileName) + if not validFlySightHeaderIn(fileName): + raise SSScoringError('CSV is not a valid FlySight file') + delimiters = [',', ] + with open(fileName, 'r') as inputFile: + try: + dialect = csv.Sniffer().sniff(inputFile.readline(), delimiters = delimiters) + except: + raise SSScoringError('Error while trying to validate %s file format' % fileName) + if dialect.delimiter in delimiters: + inputFile.seek(0) + header = next(csv.reader(inputFile)) + else: + raise SSScoringError('CSV uses a different delimiter from FlySigh') + if header[0] == '$FLYS': + return FlySightVersion.V2 + elif FLYSIGHT_1_HEADER.issubset(header): + return FlySightVersion.V1 + else: + raise SSScoringError('%s file is not a FlySight v1 or v2 file') + diff --git a/tests/test_fs1.py b/tests/test_calc.py similarity index 72% rename from tests/test_fs1.py rename to tests/test_calc.py index 9e3d07a..73b290d 100644 --- a/tests/test_fs1.py +++ b/tests/test_calc.py @@ -1,23 +1,22 @@ # See: https://github.com/pr3d4t0r/SSScoring/blob/master/LICENSE.txt +from ssscoring.calc import aggregateResults +from ssscoring.calc import calculateDistance +from ssscoring.calc import convertFlySight2SSScoring +from ssscoring.calc import dropNonSkydiveDataFrom +from ssscoring.calc import getSpeedSkydiveFrom +from ssscoring.calc import isValidJump +from ssscoring.calc import isValidMinimumAltitude +from ssscoring.calc import jumpAnalysisTable +from ssscoring.calc import processAllJumpFiles +from ssscoring.calc import processJump +from ssscoring.calc import roundedAggregateResults +from ssscoring.calc import totalResultsFrom from ssscoring.constants import BREAKOFF_ALTITUDE from ssscoring.constants import FT_IN_M from ssscoring.errors import SSScoringError -from ssscoring.fs1 import aggregateResults -from ssscoring.fs1 import calculateDistance -from ssscoring.fs1 import convertFlySight2SSScoring -from ssscoring.fs1 import dropNonSkydiveDataFrom -from ssscoring.fs1 import getAllSpeedJumpFilesFrom -from ssscoring.fs1 import getSpeedSkydiveFrom -from ssscoring.fs1 import isValidJump -from ssscoring.fs1 import isValidMinimumAltitude -from ssscoring.fs1 import jumpAnalysisTable -from ssscoring.fs1 import processAllJumpFiles -from ssscoring.fs1 import processJump -from ssscoring.fs1 import roundedAggregateResults -from ssscoring.fs1 import totalResultsFrom -from ssscoring.fs1 import validFlySightHeaderIn +from ssscoring.flysight import getAllSpeedJumpFilesFrom import os import pytest @@ -43,25 +42,6 @@ # +++ tests +++ -@pytest.fixture -def _badDelimitersCSV(tmp_path_factory): - fileName = tmp_path_factory.mktemp('data')/'bogus.CSV' - data = pd.read_csv(TEST_FLYSIGHT_DATA) - data.to_csv(fileName, sep = '\t') - yield fileName - os.unlink(fileName.as_posix()) - - -@pytest.fixture -def _missingColumnInCSV(tmp_path_factory): - fileName = tmp_path_factory.mktemp('data')/'bogus.CSV' - data = pd.read_csv(TEST_FLYSIGHT_DATA) - data = data.drop('velD', axis = 1) - data.to_csv(fileName, sep = ',') - yield fileName.as_posix() - os.unlink(fileName.as_posix()) - - @pytest.fixture def _invalidMaxAltitude(tmp_path_factory): fileName = tmp_path_factory.mktemp('data')/'bogus.CSV' @@ -72,13 +52,6 @@ def _invalidMaxAltitude(tmp_path_factory): os.unlink(fileName.as_posix()) -def test_validFlySightHeaderIn(_badDelimitersCSV, _missingColumnInCSV, _invalidMaxAltitude): - assert validFlySightHeaderIn(TEST_FLYSIGHT_DATA) - assert not validFlySightHeaderIn(_badDelimitersCSV) - assert not validFlySightHeaderIn(_missingColumnInCSV) - assert validFlySightHeaderIn(_invalidMaxAltitude) - - def test_isValidMinimumAltitude(_invalidMaxAltitude): d = pd.read_csv(_invalidMaxAltitude, skiprows = (1, 1)) assert not isValidMinimumAltitude(d.hMSL.max()) @@ -86,11 +59,6 @@ def test_isValidMinimumAltitude(_invalidMaxAltitude): assert isValidMinimumAltitude(d.hMSL.max()) -def test_getAllSpeedJumpFilesFrom(): - assert len(getAllSpeedJumpFilesFrom(TEST_FLYSIGHG_DATA_LAKE)) >= 1 - assert not len(getAllSpeedJumpFilesFrom('./bogus')) - - def test_convertFlySight2SSScoring(): global _data @@ -227,14 +195,14 @@ def test_totalResultsFrom(): totalResultsFrom(bogus) -test_convertFlySight2SSScoring() -test_dropNonSkydiveDataFrom() -test_getSpeedSkydiveFrom() -test_jumpAnalysisTable() +# test_convertFlySight2SSScoring() +# test_dropNonSkydiveDataFrom() +# test_getSpeedSkydiveFrom() +# test_jumpAnalysisTable() # test_isValidMinimumAltitude(_invalidAltFileName) -test_processJump() -test_processAllJumpFiles() -test_aggregateResults() -test_roundedAggregateResults() +# test_processJump() +# test_processAllJumpFiles() +# test_aggregateResults() +# test_roundedAggregateResults() diff --git a/tests/test_flysight.py b/tests/test_flysight.py new file mode 100644 index 0000000..66904e5 --- /dev/null +++ b/tests/test_flysight.py @@ -0,0 +1,116 @@ +# See: https://github.com/pr3d4t0r/SSScoring/blob/master/LICENSE.txt + + +from ssscoring.constants import BREAKOFF_ALTITUDE +from ssscoring.errors import SSScoringError +from ssscoring.flysight import FlySightVersion +from ssscoring.flysight import FS2_COLUMNS +from ssscoring.flysight import skipOverFS2MetadataRowsIn +from ssscoring.flysight import detectFlySightFileVersionOf +from ssscoring.flysight import getAllSpeedJumpFilesFrom +from ssscoring.flysight import validFlySightHeaderIn + + + +import os +import pathlib +import pytest + +import pandas as pd + + +# +++ constants *** + +TEST_FLYSIGHT_DATA_LAKE = './resources' +TEST_FLYSIGHT_1_DATA = pathlib.Path(TEST_FLYSIGHT_DATA_LAKE, 'test-data-00.CSV') +TEST_FLYSIGHT_2_DATA = pathlib.Path(TEST_FLYSIGHT_DATA_LAKE) / 'test-data-FS2' / '01-00-00' / 'TRACK.CSV' + + +# +++ tests +++ + + +@pytest.fixture +def _badDelimitersCSV(tmp_path_factory): + fileName = tmp_path_factory.mktemp('data')/'bogus.CSV' + data = pd.read_csv(TEST_FLYSIGHT_1_DATA) + data.to_csv(fileName, sep = '\t') + yield fileName + os.unlink(fileName.as_posix()) + + +@pytest.fixture +def _missingColumnInCSV(tmp_path_factory): + fileName = tmp_path_factory.mktemp('data')/'bogus.CSV' + data = pd.read_csv(TEST_FLYSIGHT_1_DATA) + data = data.drop('velD', axis = 1) + data.to_csv(fileName, sep = ',') + yield fileName.as_posix() + os.unlink(fileName.as_posix()) + + +@pytest.fixture +def _invalidMaxAltitude(tmp_path_factory): + fileName = tmp_path_factory.mktemp('data')/'bogus.CSV' + data = pd.read_csv(TEST_FLYSIGHT_1_DATA) + data.hMSL = BREAKOFF_ALTITUDE-100.0 + data.to_csv(fileName, sep = ',') + yield fileName.as_posix() + os.unlink(fileName.as_posix()) + + +@pytest.fixture +def _tooSmallCSV(tmp_path_factory): + fileName = TEST_FLYSIGHT_2_DATA.as_posix().replace('TRACK', 'BOGUS') + data = pd.read_csv(TEST_FLYSIGHT_2_DATA, names = FS2_COLUMNS, skiprows = 6).head(50) + data.to_csv(fileName, sep = ',') + yield fileName + os.unlink(fileName) + + +def test_skipOverFS2MetadataRowsIn(): + data = pd.read_csv(TEST_FLYSIGHT_2_DATA, names = FS2_COLUMNS, skiprows = 6).head(50) + dataMod = skipOverFS2MetadataRowsIn(data) + assert len(dataMod) < len(data) + assert pd.isnull(data.iloc[0].time) + assert pd.notnull(dataMod.iloc[0].time) + + +def test_validFlySightHeaderIn(_badDelimitersCSV, _missingColumnInCSV, _invalidMaxAltitude): + assert validFlySightHeaderIn(TEST_FLYSIGHT_1_DATA) + assert validFlySightHeaderIn(TEST_FLYSIGHT_2_DATA) + assert not validFlySightHeaderIn(_badDelimitersCSV) + assert not validFlySightHeaderIn(_missingColumnInCSV) + assert validFlySightHeaderIn(_invalidMaxAltitude) + + +def test_getAllSpeedJumpFilesFrom(_tooSmallCSV): + files = getAllSpeedJumpFilesFrom(TEST_FLYSIGHT_DATA_LAKE) + # Manual ./ because path.resolve(strict = False) resolves to the full path + # vs implementation that uses os.walk() uses the relative path. + testFiles = [ './'+TEST_FLYSIGHT_1_DATA.as_posix(), './'+TEST_FLYSIGHT_2_DATA.as_posix() ] + assert len(files) >= 1 + assert set(testFiles).issubset(set(files.keys())) + assert not len(getAllSpeedJumpFilesFrom('./bogus')) + + +def test_detectFlySightFileVersionOf(_missingColumnInCSV): + invalidFile = TEST_FLYSIGHT_2_DATA.as_posix().replace('TRACK', 'BAD_CSV_FILE') + + with pytest.raises(SSScoringError): + detectFlySightFileVersionOf('bogus.dat') + + with pytest.raises(SSScoringError): + detectFlySightFileVersionOf('EVENT.CSV') + + with pytest.raises(SSScoringError): + detectFlySightFileVersionOf('bogus.CSV') + + with pytest.raises(SSScoringError): + detectFlySightFileVersionOf(invalidFile) + + with pytest.raises(SSScoringError): + detectFlySightFileVersionOf(_missingColumnInCSV) + + assert detectFlySightFileVersionOf(TEST_FLYSIGHT_1_DATA.as_posix()) == FlySightVersion.V1 + assert detectFlySightFileVersionOf(TEST_FLYSIGHT_2_DATA.as_posix()) == FlySightVersion.V2 ++ ++ssscoring
+ +.flysight + + + + + +Functions and logic for detecting, validating and manipulating +FlySight CSV files, including detection in the file system. The functions in +this module assume that a data lake exists somewhere in the file system (whether +local or cloud-based).
++ + +1# See: https://github.com/pr3d4t0r/SSScoring/blob/master/LICENSE.txt + 2 + 3""" + 4Functions and logic for detecting, validating and manipulating + 5FlySight CSV files, including detection in the file system. The functions in + 6this module assume that a data lake exists somewhere in the file system (whether + 7local or cloud-based). + 8""" + 9 + 10 + 11from enum import Enum + 12 + 13from ssscoring.constants import FLYSIGHT_1_HEADER + 14from ssscoring.constants import IGNORE_LIST + 15from ssscoring.constants import MIN_JUMP_FILE_SIZE + 16from ssscoring.errors import SSScoringError + 17 + 18import csv + 19import os + 20 + 21import pandas as pd + 22 + 23 + 24# +++ constants +++ + 25 + 26FS2_COLUMNS = ('GNSS', 'time', 'lat', 'lon', 'hMSL', 'velN', 'velE', 'velD', 'hAcc', 'vAcc', 'sAcc', 'numSV', ) + 27 + 28 + 29# --- classes and objects --- + 30 + 31class FlySightVersion(Enum): + 32 V1 = 1000 + 33 V2 = 2000 + 34 + 35 + 36 + 37# +++ functions +++ + 38 + 39 + 40def skipOverFS2MetadataRowsIn(data: pd.DataFrame) -> pd.DataFrame: + 41 """ + 42 Returns a clean dataframe on which any metadata rows within the first 100 + 43 are skipped. This function uses the `time` column to detect valid rows. A + 44 `time == NaN` is considered invalid and skipped. + 45 + 46 Arguments + 47 --------- + 48 data + 49 A FlySight 2 dataframe suspected of having dirty N first rows with metadata + 50 + 51 Returns + 52 ------- + 53 A FlySight 2 clean dataframe without any leading metadata rows. + 54 """ + 55 for ref in range(0,100): + 56 if pd.notnull(data.iloc[ref].time): + 57 break + 58 return data.iloc[ref:] + 59 + 60 + 61def validFlySightHeaderIn(fileCSV: str) -> bool: + 62 """ + 63 Checks if a file is a CSV in FlySight 1 or FlySight 2 formats. The checks + 64 include: + 65 + 66 - Whether the file is a CSV, using a comma delimiter + 67 - Checks for the presence of all the documented FlySight 1 headers + 68 - Checks for the presence of the FlySight 2 line 1 identifier + 69 + 70 Arguments + 71 --------- + 72 fileCSV + 73 A file name to verify as a valid FlySight file + 74 + 75 Returns + 76 ------- + 77 `True` if `fileCSV` is a FlySight CSV file, otherwise `False`. + 78 """ + 79 delimiters = [',', ] + 80 hasAllHeaders = False + 81 with open(fileCSV, 'r') as inputFile: + 82 try: + 83 dialect = csv.Sniffer().sniff(inputFile.readline(), delimiters = delimiters) + 84 except: + 85 return False + 86 if dialect.delimiter in delimiters: + 87 inputFile.seek(0) + 88 header = next(csv.reader(inputFile)) + 89 else: + 90 return False + 91 hasAllHeaders = True if header[0] == '$FLYS' else FLYSIGHT_1_HEADER.issubset(header) + 92 return hasAllHeaders + 93 + 94 + 95def getAllSpeedJumpFilesFrom(dataLake: str) -> dict: + 96 """ + 97 Get a list of all the speed jump files from a data lake, where data lake is + 98 defined as a reachable path that contains one or more FlySight CSV files. + 99 This function tests each file to ensure that it's a speed skydive FlySight +100 file in a valid format and length. +101 +102 Arguments +103 --------- +104 dataLake: str +105 A valid (absolute or relative) path name to the top level directory where +106 the data lake starts. +107 +108 Returns +109 ------- +110 A dictionary of speed jump file names for later SSScoring processing: +111 - keys are the file names +112 - values are a FlySight version string tag +113 """ +114 from ssscoring.calc import isValidMinimumAltitude # skirt circular dependency +115 +116 jumpFiles = dict() +117 for root, dirs, files in os.walk(dataLake): +118 if any(name in root for name in IGNORE_LIST): +119 continue +120 for fileName in files: +121 data = None +122 if '.swp' in fileName: # Ignore Vim, other editors swap file +123 continue +124 if '.CSV' in fileName: +125 version = '1' +126 jumpFileName = os.path.join(root, fileName) +127 stat = os.stat(jumpFileName) +128 if all(x not in fileName for x in ('EVENT', 'SENSOR', 'TRACK')): +129 # FlySight 1 track format +130 data = pd.read_csv(jumpFileName, skiprows = (1, 1)) +131 elif 'TRACK' in fileName: +132 # FlySight 2 track custom format +133 data = pd.read_csv(jumpFileName, names = FS2_COLUMNS, skiprows = 6) +134 data = skipOverFS2MetadataRowsIn(data) +135 data.drop('GNSS', inplace = True, axis = 1) +136 version = '2' +137 if data is not None and stat.st_size >= MIN_JUMP_FILE_SIZE and validFlySightHeaderIn(jumpFileName) and isValidMinimumAltitude(data.hMSL.max()): +138 # explicit because `not data` is ambiguous for dataframes +139 jumpFiles[jumpFileName] = version +140 return jumpFiles +141 +142 +143def detectFlySightFileVersionOf(fileName: str) -> FlySightVersion: +144 """ +145 Detects the FlySight file version based on its file name and format. +146 +147 Arguments +148 --------- +149 fileName +150 A string corresponding to the file name. +151 +152 Returns +153 ------- +154 An instance of `ssscoring.flysight.FlySightVersion` with a valid version +155 symbolic value. +156 +157 Errors +158 ------ +159 `ssscoring.errors.SSScoringError` if the file is not a CSV and it's some +160 other invalid format. +161 """ +162 if not '.CSV' in fileName: +163 raise SSScoringError('Invalid file extension type') +164 if any(x in fileName for x in ('EVENT.CSV', 'SENSOR.CSV')): +165 raise SSScoringError('Only TRACK.CSV v2 files can be processed at this time') +166 if not os.path.exists(fileName): +167 raise SSScoringError('%s - file not found in data lake' % fileName) +168 if not validFlySightHeaderIn(fileName): +169 raise SSScoringError('CSV is not a valid FlySight file') +170 delimiters = [',', ] +171 with open(fileName, 'r') as inputFile: +172 try: +173 dialect = csv.Sniffer().sniff(inputFile.readline(), delimiters = delimiters) +174 except: +175 raise SSScoringError('Error while trying to validate %s file format' % fileName) +176 if dialect.delimiter in delimiters: +177 inputFile.seek(0) +178 header = next(csv.reader(inputFile)) +179 else: +180 raise SSScoringError('CSV uses a different delimiter from FlySigh') +181 if header[0] == '$FLYS': +182 return FlySightVersion.V2 +183 elif FLYSIGHT_1_HEADER.issubset(header): +184 return FlySightVersion.V1 +185 else: +186 raise SSScoringError('%s file is not a FlySight v1 or v2 file') ++ ++ FS2_COLUMNS = +('GNSS', 'time', 'lat', 'lon', 'hMSL', 'velN', 'velE', 'velD', 'hAcc', 'vAcc', 'sAcc', 'numSV') + + ++ + + + ++ + ++ + class + FlySightVersion+ + + + + + +(enum.Enum): + + + + +++ V1 = +<FlySightVersion.V1: 1000> + + ++ + + + ++++ V2 = +<FlySightVersion.V2: 2000> + + ++ + + + +++Inherited Members
++
++- enum.Enum
+- name
+- value
+ ++ + ++ + def + skipOverFS2MetadataRowsIn(data: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame: + + + ++ ++ + +41def skipOverFS2MetadataRowsIn(data: pd.DataFrame) -> pd.DataFrame: +42 """ +43 Returns a clean dataframe on which any metadata rows within the first 100 +44 are skipped. This function uses the `time` column to detect valid rows. A +45 `time == NaN` is considered invalid and skipped. +46 +47 Arguments +48 --------- +49 data +50 A FlySight 2 dataframe suspected of having dirty N first rows with metadata +51 +52 Returns +53 ------- +54 A FlySight 2 clean dataframe without any leading metadata rows. +55 """ +56 for ref in range(0,100): +57 if pd.notnull(data.iloc[ref].time): +58 break +59 return data.iloc[ref:] ++ + +Returns a clean dataframe on which any metadata rows within the first 100 +are skipped. This function uses the
+ +time
column to detect valid rows. A +time == NaN
is considered invalid and skipped.Arguments
+ ++ +data +
A FlySight 2 dataframe suspected of having dirty N first rows with metadata
+ +Returns
+ +A FlySight 2 clean dataframe without any leading metadata rows.
++ + ++ + def + validFlySightHeaderIn(fileCSV: str) -> bool: + + + ++ ++ + +62def validFlySightHeaderIn(fileCSV: str) -> bool: +63 """ +64 Checks if a file is a CSV in FlySight 1 or FlySight 2 formats. The checks +65 include: +66 +67 - Whether the file is a CSV, using a comma delimiter +68 - Checks for the presence of all the documented FlySight 1 headers +69 - Checks for the presence of the FlySight 2 line 1 identifier +70 +71 Arguments +72 --------- +73 fileCSV +74 A file name to verify as a valid FlySight file +75 +76 Returns +77 ------- +78 `True` if `fileCSV` is a FlySight CSV file, otherwise `False`. +79 """ +80 delimiters = [',', ] +81 hasAllHeaders = False +82 with open(fileCSV, 'r') as inputFile: +83 try: +84 dialect = csv.Sniffer().sniff(inputFile.readline(), delimiters = delimiters) +85 except: +86 return False +87 if dialect.delimiter in delimiters: +88 inputFile.seek(0) +89 header = next(csv.reader(inputFile)) +90 else: +91 return False +92 hasAllHeaders = True if header[0] == '$FLYS' else FLYSIGHT_1_HEADER.issubset(header) +93 return hasAllHeaders ++ + +Checks if a file is a CSV in FlySight 1 or FlySight 2 formats. The checks +include:
+ ++
+ +- Whether the file is a CSV, using a comma delimiter
+- Checks for the presence of all the documented FlySight 1 headers
+- Checks for the presence of the FlySight 2 line 1 identifier
+Arguments
+ ++ +fileCSV +
A file name to verify as a valid FlySight file
+ +Returns
+ ++
True
iffileCSV
is a FlySight CSV file, otherwiseFalse
.+ + ++ + def + getAllSpeedJumpFilesFrom(dataLake: str) -> dict: + + + ++ ++ + +96def getAllSpeedJumpFilesFrom(dataLake: str) -> dict: + 97 """ + 98 Get a list of all the speed jump files from a data lake, where data lake is + 99 defined as a reachable path that contains one or more FlySight CSV files. +100 This function tests each file to ensure that it's a speed skydive FlySight +101 file in a valid format and length. +102 +103 Arguments +104 --------- +105 dataLake: str +106 A valid (absolute or relative) path name to the top level directory where +107 the data lake starts. +108 +109 Returns +110 ------- +111 A dictionary of speed jump file names for later SSScoring processing: +112 - keys are the file names +113 - values are a FlySight version string tag +114 """ +115 from ssscoring.calc import isValidMinimumAltitude # skirt circular dependency +116 +117 jumpFiles = dict() +118 for root, dirs, files in os.walk(dataLake): +119 if any(name in root for name in IGNORE_LIST): +120 continue +121 for fileName in files: +122 data = None +123 if '.swp' in fileName: # Ignore Vim, other editors swap file +124 continue +125 if '.CSV' in fileName: +126 version = '1' +127 jumpFileName = os.path.join(root, fileName) +128 stat = os.stat(jumpFileName) +129 if all(x not in fileName for x in ('EVENT', 'SENSOR', 'TRACK')): +130 # FlySight 1 track format +131 data = pd.read_csv(jumpFileName, skiprows = (1, 1)) +132 elif 'TRACK' in fileName: +133 # FlySight 2 track custom format +134 data = pd.read_csv(jumpFileName, names = FS2_COLUMNS, skiprows = 6) +135 data = skipOverFS2MetadataRowsIn(data) +136 data.drop('GNSS', inplace = True, axis = 1) +137 version = '2' +138 if data is not None and stat.st_size >= MIN_JUMP_FILE_SIZE and validFlySightHeaderIn(jumpFileName) and isValidMinimumAltitude(data.hMSL.max()): +139 # explicit because `not data` is ambiguous for dataframes +140 jumpFiles[jumpFileName] = version +141 return jumpFiles ++ + +Get a list of all the speed jump files from a data lake, where data lake is +defined as a reachable path that contains one or more FlySight CSV files. +This function tests each file to ensure that it's a speed skydive FlySight +file in a valid format and length.
+ +Arguments
+ ++ +dataLake: str +
A valid (absolute or relative) path name to the top level directory where +the data lake starts.
+ +Returns
+ +A dictionary of speed jump file names for later SSScoring processing: + - keys are the file names + - values are a FlySight version string tag
++ + + + ++ + +144def detectFlySightFileVersionOf(fileName: str) -> FlySightVersion: +145 """ +146 Detects the FlySight file version based on its file name and format. +147 +148 Arguments +149 --------- +150 fileName +151 A string corresponding to the file name. +152 +153 Returns +154 ------- +155 An instance of `ssscoring.flysight.FlySightVersion` with a valid version +156 symbolic value. +157 +158 Errors +159 ------ +160 `ssscoring.errors.SSScoringError` if the file is not a CSV and it's some +161 other invalid format. +162 """ +163 if not '.CSV' in fileName: +164 raise SSScoringError('Invalid file extension type') +165 if any(x in fileName for x in ('EVENT.CSV', 'SENSOR.CSV')): +166 raise SSScoringError('Only TRACK.CSV v2 files can be processed at this time') +167 if not os.path.exists(fileName): +168 raise SSScoringError('%s - file not found in data lake' % fileName) +169 if not validFlySightHeaderIn(fileName): +170 raise SSScoringError('CSV is not a valid FlySight file') +171 delimiters = [',', ] +172 with open(fileName, 'r') as inputFile: +173 try: +174 dialect = csv.Sniffer().sniff(inputFile.readline(), delimiters = delimiters) +175 except: +176 raise SSScoringError('Error while trying to validate %s file format' % fileName) +177 if dialect.delimiter in delimiters: +178 inputFile.seek(0) +179 header = next(csv.reader(inputFile)) +180 else: +181 raise SSScoringError('CSV uses a different delimiter from FlySigh') +182 if header[0] == '$FLYS': +183 return FlySightVersion.V2 +184 elif FLYSIGHT_1_HEADER.issubset(header): +185 return FlySightVersion.V1 +186 else: +187 raise SSScoringError('%s file is not a FlySight v1 or v2 file') ++ + +Detects the FlySight file version based on its file name and format.
+ +Arguments
+ ++ +fileName +
A string corresponding to the file name.
+ +Returns
+ +An instance of
+ +ssscoring.flysight.FlySightVersion
with a valid version +symbolic value.Errors
+ ++
ssscoring.errors.SSScoringError
if the file is not a CSV and it's some +other invalid format.