diff --git a/docs/search.js b/docs/search.js index 8aa548b..27a5b44 100644 --- a/docs/search.js +++ b/docs/search.js @@ -1,6 +1,6 @@ window.pdocSearch = (function(){ /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oSpeed Skydiving Scoring tools package for processing FlySight v1 and v2 CSV data\nfiles.

\n\n

This library relies on NumPy and pandas. When running in a Lucyfer notebook,\nit also requires the Bokeh plotting library.

\n\n

The documentation for each module in this package is linked from the navigation\nbar. The fs1 module contains functions that process v1 files. The v2\nimplementation is in progress in a private Git branch.

\n\n

Installation

\n\n
\n
pip install -U ssscoring\n
\n
\n\n

Source code

\n\n

You are welcome to fork SSScoring\non GitHub. You will need Python 3.9 or later, pandas, and NumPy as minimum\nrequirements.

\n\n

You may study or download the latest stable source files at:

\n\n

https://github.com/pr3d4t0r/SSScoring/tree/master/ssscoring

\n"}, "ssscoring.__VERSION__": {"fullname": "ssscoring.__VERSION__", "modulename": "ssscoring", "qualname": "__VERSION__", "kind": "variable", "doc": "

@public

\n", "default_value": "'1.8.2'"}, "ssscoring.calc": {"fullname": "ssscoring.calc", "modulename": "ssscoring.calc", "kind": "module", "doc": "

Functions and logic for analyzing and manipulating FlySight dataframes.

\n"}, "ssscoring.calc.isValidMinimumAltitude": {"fullname": "ssscoring.calc.isValidMinimumAltitude", "modulename": "ssscoring.calc", "qualname": "isValidMinimumAltitude", "kind": "function", "doc": "

Reports whether an altitude is within the IPC and USPA valid parameters,\nor within BREAKOFF_ALTITUDE and PERFORMACE_WINDOW_LENGTH. In invalid\naltitude doesn't invalidate a FlySight data file. This function can be used\nfor generating warnings. The stock FlySightViewer scores a speed jump even\nif the exit was below the minimum altitude.

\n\n

Arguments

\n\n
altitude\n
\n\n

An altitude in meters, often calculated as data.hMSL - DZ altitude.

\n\n

Returns

\n\n

True if the altitude is valid.

\n", "signature": "(altitude: float) -> bool:", "funcdef": "def"}, "ssscoring.calc.isValidJump": {"fullname": "ssscoring.calc.isValidJump", "modulename": "ssscoring.calc", "qualname": "isValidJump", "kind": "function", "doc": "

Validates the jump according to ISC/FAI/USPA competition rules. A jump is\nvalid when the speed accuracy parameter is less than 3 m/s for the whole\nvalidation window duration.

\n\n

Arguments

\n\n
data : pd.DataFramce\n
\n\n

Jumnp data in SSScoring format\n window : ssscoring.PerformanceWindow\nPerformance window start, end values in named tuple format

\n\n

Returns

\n\n

True if the jump is valid according to ISC/FAI/USPA rules.

\n", "signature": "(\tdata: pandas.core.frame.DataFrame,\twindow: ssscoring.datatypes.PerformanceWindow) -> bool:", "funcdef": "def"}, "ssscoring.calc.calculateDistance": {"fullname": "ssscoring.calc.calculateDistance", "modulename": "ssscoring.calc", "qualname": "calculateDistance", "kind": "function", "doc": "

Calculate the distance between two terrestrial coordinates points.

\n\n

Arguments

\n\n
start\n
\n\n

A latitude, longitude tuple of floating point numbers.

\n\n
end\n
\n\n

A latitude, longitude tuple of floating point numbers.

\n\n

Returns

\n\n

The distance, in meters, between both points.

\n", "signature": "(start: tuple, end: tuple) -> float:", "funcdef": "def"}, "ssscoring.calc.convertFlySight2SSScoring": {"fullname": "ssscoring.calc.convertFlySight2SSScoring", "modulename": "ssscoring.calc", "qualname": "convertFlySight2SSScoring", "kind": "function", "doc": "

Converts a raw dataframe initialized from a FlySight CSV file into the\nSSScoring file format. The SSScoring format uses more descriptive column\nheaders, adds the altitude in feet, and uses UNIX time instead of an ISO\nstring.

\n\n

If both altitudeDZMeters and altitudeDZFt are zero then hMSL is used.\nOtherwise, this function adjusts the effective altitude with the value. If\nboth meters and feet values are set this throws an error.

\n\n

Arguments

\n\n
rawData : pd.DataFrame\n
\n\n

FlySight CSV input as a dataframe

\n\n
altitudeDZMeters : float\n
\n\n

Drop zone height above MSL

\n\n
altitudeDZFt\n
\n\n

Drop zone altitudde above MSL

\n\n

Returns

\n\n

A dataframe in SSScoring format, featuring these columns:

\n\n
    \n
  • timeUnix
    • \n
  • altitudeMSL
    • \n
  • altitudeAGL
    • \n
  • altitudeMSLFt
    • \n
  • altitudeAGLFt
    • \n
  • hMetersPerSecond
    • \n
  • hKMh (km/h)
    • \n
  • vMetersPerSecond
    • \n
  • vKMh (km/h)
    • \n
  • angle
    • \n
  • speedAccuracy (ignore; see ISC documentation)
    • \n
  • hMetersPerSecond
    • \n
  • hKMh
    • \n
  • latitude
    • \n
  • longitude
    • \n
  • verticalAccuracy
    • \n
  • speedAccuracyISC
    • \n
\n\n

Errors

\n\n

SSScoringError if the DZ altitude is set in both meters and feet.

\n", "signature": "(\trawData: pandas.core.frame.DataFrame,\taltitudeDZMeters=0.0,\taltitudeDZFt=0.0):", "funcdef": "def"}, "ssscoring.calc.getSpeedSkydiveFrom": {"fullname": "ssscoring.calc.getSpeedSkydiveFrom", "modulename": "ssscoring.calc", "qualname": "getSpeedSkydiveFrom", "kind": "function", "doc": "

Take the skydive dataframe and get the speed skydiving data:

\n\n
    \n
  • Exit
    • \n
  • Speed skydiving window
    • \n
  • Drops data before exit and below breakoff altitude
    • \n
\n\n

Arguments

\n\n
data : pd.DataFrame\n
\n\n

Jump data in SSScoring format

\n\n

Returns

\n\n

A tuple of two elements:

\n\n
    \n
  • A named tuple with performance and validation window data
    • \n
  • A dataframe featuring only speed skydiving data
    • \n
\n", "signature": "(data: pandas.core.frame.DataFrame) -> tuple:", "funcdef": "def"}, "ssscoring.calc.jumpAnalysisTable": {"fullname": "ssscoring.calc.jumpAnalysisTable", "modulename": "ssscoring.calc", "qualname": "jumpAnalysisTable", "kind": "function", "doc": "

Generates the HCD jump analysis table, with speed data at 5-second intervals\nafter exit.

\n\n

Arguments

\n\n
data : pd.DataFrame\n
\n\n

Jump data in SSScoring format

\n\n

Returns

\n\n

A tuple with a pd.DataFrame and the max speed recorded for the jump:

\n\n
    \n
  • A table dataframe with time and speed
    • \n
  • a floating point number
    • \n
\n", "signature": "(data: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "ssscoring.calc.dropNonSkydiveDataFrom": {"fullname": "ssscoring.calc.dropNonSkydiveDataFrom", "modulename": "ssscoring.calc", "qualname": "dropNonSkydiveDataFrom", "kind": "function", "doc": "

Discards all data rows before maximum altitude, and all \"negative\" altitude\nrows because we don't skydive underground (FlySight bug?).

\n\n

This is a more accurate mean velocity calculation from a physics point of\nview, but it differs from the ISC definition using in scoring - which, if we\nget technical, is the one that counts.

\n\n

Arguments

\n\n
data : pd.DataFrame\n
\n\n

Jump data in SSScoring format (headers differ from FlySight format)

\n\n

Returns

\n\n

The jump data for the skydive

\n", "signature": "(data: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "ssscoring.calc.calcScoreMeanVelocity": {"fullname": "ssscoring.calc.calcScoreMeanVelocity", "modulename": "ssscoring.calc", "qualname": "calcScoreMeanVelocity", "kind": "function", "doc": "

Calculates the speeds over a 3-second interval as the mean of all the speeds\nrecorded within that 3-second window and resolves the maximum speed.

\n\n

Arguments

\n\n
data\n
\n\n

A pd.dataframe with speed run data.

\n\n

Returns

\n\n

A tuple with the best score throughout the speed run, and a dicitionary\nof the meanVSpeed:spotInTime used in determining the exact scoring speed\nat every datat point during the speed run.

\n", "signature": "(data: pandas.core.frame.DataFrame) -> tuple:", "funcdef": "def"}, "ssscoring.calc.calcScoreISC": {"fullname": "ssscoring.calc.calcScoreISC", "modulename": "ssscoring.calc", "qualname": "calcScoreISC", "kind": "function", "doc": "

Calculates the speeds over a 3-second interval as the ds/dt and dt is the\nis a 3-second sliding interval from exit. The window slider moves along the\nplotTime axis in the dataframe.

\n\n

Arguments

\n\n
data\n
\n\n

A pd.dataframe with speed run data.

\n\n

Returns

\n\n

A tuple with the best score throughout the speed run, and a dicitionary\nof the meanVSpeed:spotInTime used in determining the exact scoring speed\nat every datat point during the speed run.

\n", "signature": "(data: pandas.core.frame.DataFrame) -> tuple:", "funcdef": "def"}, "ssscoring.calc.processJump": {"fullname": "ssscoring.calc.processJump", "modulename": "ssscoring.calc", "qualname": "processJump", "kind": "function", "doc": "

Take a dataframe in SSScoring format and process it for display. It\nserializes all the steps that would be taken from the ssscoring module, but\nincludes some text/HTML data in the output.

\n\n

Arguments

\n\n
data: pd.DataFrame\n
\n\n

A dataframe in SSScoring format

\n\n

Returns

\n\n

A JumpResults named tuple with these items:

\n\n
    \n
  • score speed score
    • \n
  • maxSpeed maximum speed during the jump
    • \n
  • scores a Series of every 3-second window scores from exit to breakoff
    • \n
  • data an updated SSScoring dataframe plotTime, where 0 = exit, used\nfor plotting
    • \n
  • window a named tuple with the exit, breakoff, and validation window\naltitudes
    • \n
  • table a dataframe featuring the speeds and altitudes at 5-sec intervals
    • \n
  • color a string that defines the color for the jump result; possible\nvalues are _green_ for valid jump, _red_ for invalid jump, per ISC rules
    • \n
  • result a string with the legend of _valid_ or _invalid_ jump
    • \n
\n", "signature": "(data: pandas.core.frame.DataFrame):", "funcdef": "def"}, "ssscoring.calc.getFlySightDataFromCSV": {"fullname": "ssscoring.calc.getFlySightDataFromCSV", "modulename": "ssscoring.calc", "qualname": "getFlySightDataFromCSV", "kind": "function", "doc": "

Ingress a known FlySight or SkyTrax file into memory for SSScoring\nprocessing.

\n\n

Arguments

\n\n
jumpFile\n
\n\n

A string or pathlib.Path object; can be a relative or an asbolute path.

\n\n

Returns

\n\n

A tuple with two items:\n - rawData - a dataframe representation of the CSV with the original\n headers but without the data type header\n - tag - a string with an identifying tag derived from the path name\n and file version in the form some name:vX. It uses the current\n path as metadata to infer the name. There's no semantics enforcement.

\n\n

Raises

\n\n

SSScoringError if the CSV file is invalid in any way.

\n", "signature": "(jumpFile) -> tuple:", "funcdef": "def"}, "ssscoring.calc.processAllJumpFiles": {"fullname": "ssscoring.calc.processAllJumpFiles", "modulename": "ssscoring.calc", "qualname": "processAllJumpFiles", "kind": "function", "doc": "

Process all jump files in a list of valid FlySight files. Returns a\ndictionary of jump results with a human-readable version of the file name.\nThe jumpFiles list can be generated by hand or the output of the\nssscoring.fs1.getAllSpeedJumpFilesFrom called to operate on a data lake.

\n\n

Arguments

\n\n
jumpFiles\n
\n\n

A list of relative or absolute path names to individual FlySight CSV files.

\n\n
altitudeDZMeters : float\n
\n\n

Drop zone height above MSL

\n\n
dict\n
\n\n

A dictionary of jump results. The key is a human-readable version of a\njumpFile name with the extension, path, and extraneous spaces eliminated\nor replaced by appropriate characters. File names use Unicode, so accents\nand non-ANSI characters are allowed in file names.

\n", "signature": "(jumpFiles: list, altitudeDZMeters=0.0) -> dict:", "funcdef": "def"}, "ssscoring.calc.aggregateResults": {"fullname": "ssscoring.calc.aggregateResults", "modulename": "ssscoring.calc", "qualname": "aggregateResults", "kind": "function", "doc": "

Aggregate all the results in a table fashioned after Marco Hepp's and Nklas\nDaniel's score tracking data.

\n\n

Arguments

\n\n
jumpResults: dict\n
\n\n

A dictionary of jump results, in which each result corresponds to a FlySight\nfile name. See ssscoring.processAllJumpFiles for details.

\n\n

Returns

\n\n

A dataframe featuring these columns:

\n\n
    \n
  • Score
    • \n
  • Speeds at 5, 10, 15, 20, and 25 second tranches
    • \n
  • Final time contemplated in the analysis
    • \n
  • Max speed
    • \n
\n\n

The dataframe rows are identified by the human readable jump file name.

\n", "signature": "(jumpResults: dict) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "ssscoring.calc.roundedAggregateResults": {"fullname": "ssscoring.calc.roundedAggregateResults", "modulename": "ssscoring.calc", "qualname": "roundedAggregateResults", "kind": "function", "doc": "

Aggregate all the results in a table fashioned after Marco Hepp's and Nklas\nDaniel's score tracking data. All speed results are rounded at n > x.5\nfor any value.

\n\n

Arguments

\n\n
aggregate: pd.DataFrame\n
\n\n

A dataframe output of ssscoring.fs1.aggregateResults.

\n\n

Returns

\n\n

A dataframe featuring the rounded values for these columns:

\n\n
    \n
  • Score
    • \n
  • Speeds at 5, 10, 15, 20, and 25 second tranches
    • \n
  • Max speed
    • \n
\n\n

The finalTime column is ignored.

\n\n

The dataframe rows are identified by the human readable jump file name.

\n\n

This is a less precise version of the ssscoring.aggregateResults\ndataframe, useful during training to keep rounded results available for\nreview.

\n", "signature": "(aggregate: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "ssscoring.calc.totalResultsFrom": {"fullname": "ssscoring.calc.totalResultsFrom", "modulename": "ssscoring.calc", "qualname": "totalResultsFrom", "kind": "function", "doc": "

Calculates the total and mean speeds for an aggregation of speed jumps.

\n\n

Arguments

\n\n
aggregate: pd.DataFrame\n
\n\n

The aggregate results dataframe resulting from calling ssscoring.aggregateResults\nwith valid results.

\n\n

Returns

\n\n

A dataframe with one row and two columns:

\n\n
    \n
  • totalSpeed ::= the sum of all speeds in the aggregated results
    • \n
  • meanSpeed ::= the mean of all speeds
    • \n
  • maxScore ::= the max score among all the speed scores
    • \n
\n\n

Raises

\n\n

AttributeError if aggregate is an empty dataframe or None, or if the\naggregate dataframe doesn't conform to the output of ssscoring.aggregateResults.

\n", "signature": "(aggregate: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "ssscoring.cli": {"fullname": "ssscoring.cli", "modulename": "ssscoring.cli", "kind": "module", "doc": "

Command line tools package.

\n"}, "ssscoring.cli.die": {"fullname": "ssscoring.cli.die", "modulename": "ssscoring.cli", "qualname": "die", "kind": "function", "doc": "

Display an end-user message and exit the program with exitCode.

\n\n

Arguments

\n\n
message\n
\n\n

A string, most often with a descriptive error message.

\n\n
exitCode\n
\n\n

An exit code, where 0 is normal exit, and any other value is an error\ncondition.

\n\n
isUnitTest\n
\n\n

A Boolean; if True, the program returns the value of exitCode to the\ncaller, otherwise it terminates with sys.exit(exitCode).

\n", "signature": "(message: str, exitCode: int, isUnitTest=False) -> int:", "funcdef": "def"}, "ssscoring.cli.ssscore": {"fullname": "ssscoring.cli.ssscore", "modulename": "ssscoring.cli", "qualname": "ssscore", "kind": "function", "doc": "

Process all the speed skydiving files contained in dataLakeSpec.

\n\n

Arguments

\n\n
dataLake\n
\n\n

Command line argument with the path to the data lake.

\n\n

Returns

\n\n

The number of jump results from processing all the FlySight files in the\ndata lake.

\n", "signature": "(dataLake: str) -> int:", "funcdef": "def"}, "ssscoring.constants": {"fullname": "ssscoring.constants", "modulename": "ssscoring.constants", "kind": "module", "doc": "

Constants module and definitions.

\n\n

All measurements are expressed in meters unless noted otherwise.

\n"}, "ssscoring.constants.BREAKOFF_ALTITUDE": {"fullname": "ssscoring.constants.BREAKOFF_ALTITUDE", "modulename": "ssscoring.constants", "qualname": "BREAKOFF_ALTITUDE", "kind": "variable", "doc": "

Breakoff altitude or hard deck.

\n", "default_value": "1707.0"}, "ssscoring.constants.DEG_IN_RADIANS": {"fullname": "ssscoring.constants.DEG_IN_RADIANS", "modulename": "ssscoring.constants", "qualname": "DEG_IN_RADIANS", "kind": "variable", "doc": "

\u03c0/180\u00ba

\n", "default_value": "0.017453292519943295"}, "ssscoring.constants.EXIT_SPEED": {"fullname": "ssscoring.constants.EXIT_SPEED", "modulename": "ssscoring.constants", "qualname": "EXIT_SPEED", "kind": "variable", "doc": "

Guesstimate of the exit speed; 2*g

\n", "default_value": "19.62"}, "ssscoring.constants.FLYSIGHT_1_HEADER": {"fullname": "ssscoring.constants.FLYSIGHT_1_HEADER", "modulename": "ssscoring.constants", "qualname": "FLYSIGHT_1_HEADER", "kind": "variable", "doc": "

FlySight v1 CSV file headers.

\n", "default_value": "{'hAcc', 'sAcc', 'velD', 'velE', 'heading', 'vAcc', 'numSV', 'lon', 'gpsFix', 'hMSL', 'cAcc', 'velN', 'lat', 'time'}"}, "ssscoring.constants.FLYSIGHT_2_HEADER": {"fullname": "ssscoring.constants.FLYSIGHT_2_HEADER", "modulename": "ssscoring.constants", "qualname": "FLYSIGHT_2_HEADER", "kind": "variable", "doc": "

FlySight v1 CSV file headers. Unlike other constants, this is a tuple instead\nof a set because the code manipulates the headers/columns during file ingress.

\n", "default_value": "('GNSS', 'time', 'lat', 'lon', 'hMSL', 'velN', 'velE', 'velD', 'hAcc', 'vAcc', 'sAcc', 'numSV')"}, "ssscoring.constants.FT_IN_M": {"fullname": "ssscoring.constants.FT_IN_M", "modulename": "ssscoring.constants", "qualname": "FT_IN_M", "kind": "variable", "doc": "

Number of feet in a meter.

\n", "default_value": "3.2808"}, "ssscoring.constants.IGNORE_LIST": {"fullname": "ssscoring.constants.IGNORE_LIST", "modulename": "ssscoring.constants", "qualname": "IGNORE_LIST", "kind": "variable", "doc": "

Internal use - list of files to be ignored during bulk file processing in the\ndata lake (e.g. ./data).

\n", "default_value": "['.ipynb_checkpoints']"}, "ssscoring.constants.LAST_TIME_TRANCHE": {"fullname": "ssscoring.constants.LAST_TIME_TRANCHE", "modulename": "ssscoring.constants", "qualname": "LAST_TIME_TRANCHE", "kind": "variable", "doc": "

Times > 25 s are irrelevant because it means that the speed skydiver flew at\nvSpeed < 400 km/h.

\n", "default_value": "25.0"}, "ssscoring.constants.MAX_ALTITUDE_FT": {"fullname": "ssscoring.constants.MAX_ALTITUDE_FT", "modulename": "ssscoring.constants", "qualname": "MAX_ALTITUDE_FT", "kind": "variable", "doc": "

Maximum suggested altitude but irrelevant in general. Max altitude without\noxygen is 15,500 ft AGL at most DZs. A jumnp from a higher altitude would\nrequire oxygen and would be scored 3,000 ft higher than the hard deck.

\n", "default_value": "15500"}, "ssscoring.constants.MAX_ALTITUDE_METERS": {"fullname": "ssscoring.constants.MAX_ALTITUDE_METERS", "modulename": "ssscoring.constants", "qualname": "MAX_ALTITUDE_METERS", "kind": "variable", "doc": "

See

\n\n
ssscoring.constants.MAX_ALTITUDE_FT\n
\n", "default_value": "4725.609756097561"}, "ssscoring.constants.MAX_SPEED_ACCURACY": {"fullname": "ssscoring.constants.MAX_SPEED_ACCURACY", "modulename": "ssscoring.constants", "qualname": "MAX_SPEED_ACCURACY", "kind": "variable", "doc": "

Speed accuracy for the FlySight device.

\n", "default_value": "3.0"}, "ssscoring.constants.MIN_JUMP_FILE_SIZE": {"fullname": "ssscoring.constants.MIN_JUMP_FILE_SIZE", "modulename": "ssscoring.constants", "qualname": "MIN_JUMP_FILE_SIZE", "kind": "variable", "doc": "

FlySight v1 files smaller than MIN_JUMP_FILE_SIZE are ignored because they\nlack the minimum number of data points to contain a valid speed skydive.\nTODO: Revise for FlySight v2.

\n", "default_value": "65536"}, "ssscoring.constants.MPS_2_KMH": {"fullname": "ssscoring.constants.MPS_2_KMH", "modulename": "ssscoring.constants", "qualname": "MPS_2_KMH", "kind": "variable", "doc": "

m/s to km/h conversion factor:

\n\n
\n
s = mps * 60 * 60 / 1000\n  = mps * 3600 / 1000\n  = mps * 3.6\n
\n
\n", "default_value": "3.6"}, "ssscoring.constants.PERFORMANCE_WINDOW_LENGTH": {"fullname": "ssscoring.constants.PERFORMANCE_WINDOW_LENGTH", "modulename": "ssscoring.constants", "qualname": "PERFORMANCE_WINDOW_LENGTH", "kind": "variable", "doc": "

Performance window length as defined by ISSA/IPC/USPA.

\n", "default_value": "2256.0"}, "ssscoring.constants.SCORING_INTERVAL": {"fullname": "ssscoring.constants.SCORING_INTERVAL", "modulename": "ssscoring.constants", "qualname": "SCORING_INTERVAL", "kind": "variable", "doc": "

Scoring is based on the maximum speed the jumper attained within the VALIDATION_WINDOW_LENGTH\nas the average speed during a sliding window of SCORING_INTERVAL seconds. The\nvalue is set by governing bodies like ISC and USPA.

\n", "default_value": "3.0"}, "ssscoring.constants.SKYTRAX_1_HEADER": {"fullname": "ssscoring.constants.SKYTRAX_1_HEADER", "modulename": "ssscoring.constants", "qualname": "SKYTRAX_1_HEADER", "kind": "variable", "doc": "

SkyTraX GPS + barometric SMD v1 CSV file headers.

\n", "default_value": "{'hAcc', 'sAcc', 'velD', 'velE', 'heading', 'vAcc', 'numSV', 'lon', 'gpsFix', 'hMSL', 'cAcc', 'velN', 'lat', 'time'}"}, "ssscoring.constants.VALIDATION_WINDOW_LENGTH": {"fullname": "ssscoring.constants.VALIDATION_WINDOW_LENGTH", "modulename": "ssscoring.constants", "qualname": "VALIDATION_WINDOW_LENGTH", "kind": "variable", "doc": "

The validation window length as defined in the competition rules.

\n", "default_value": "1006.0"}, "ssscoring.datatypes": {"fullname": "ssscoring.datatypes", "modulename": "ssscoring.datatypes", "kind": "module", "doc": "

SSScoring custom type definitions for easier symbolic manipuation.

\n"}, "ssscoring.datatypes.JumpResults": {"fullname": "ssscoring.datatypes.JumpResults", "modulename": "ssscoring.datatypes", "qualname": "JumpResults", "kind": "class", "doc": "

A named tuple containing the score, maximum speed, scores throught the\nperformance window, the results table for a jump, the output color for the\nresult, and the result information string.

\n\n

Attributes

\n\n
    \n
  • color - a string representing a color, green if the result is valid, red\notherwise
    • \n
  • data - dataframe containing all the data points for plotting and\ncalculations
    • \n
  • maxSpeed - maximum absolute speed registered during a skydive
    • \n
  • result - dataframe of results
    • \n
  • score - maximum mean speed during a 3-second window during the skydive
    • \n
  • scores - a series with all the scored ruding the sliding 3-sec window for\nthe whole speed skydive
    • \n
  • table - summary table of results of the speed run
    • \n
  • window - the scoring window data, an instance of PerformanceWindow
    • \n
\n", "bases": "builtins.tuple"}, "ssscoring.datatypes.JumpResults.__init__": {"fullname": "ssscoring.datatypes.JumpResults.__init__", "modulename": "ssscoring.datatypes", "qualname": "JumpResults.__init__", "kind": "function", "doc": "

Create new instance of JumpResults(color, data, maxSpeed, result, score, scores, table, window)

\n", "signature": "(color, data, maxSpeed, result, score, scores, table, window)"}, "ssscoring.datatypes.JumpResults.color": {"fullname": "ssscoring.datatypes.JumpResults.color", "modulename": "ssscoring.datatypes", "qualname": "JumpResults.color", "kind": "variable", "doc": "

Alias for field number 0

\n"}, "ssscoring.datatypes.JumpResults.data": {"fullname": "ssscoring.datatypes.JumpResults.data", "modulename": "ssscoring.datatypes", "qualname": "JumpResults.data", "kind": "variable", "doc": "

Alias for field number 1

\n"}, "ssscoring.datatypes.JumpResults.maxSpeed": {"fullname": "ssscoring.datatypes.JumpResults.maxSpeed", "modulename": "ssscoring.datatypes", "qualname": "JumpResults.maxSpeed", "kind": "variable", "doc": "

Alias for field number 2

\n"}, "ssscoring.datatypes.JumpResults.result": {"fullname": "ssscoring.datatypes.JumpResults.result", "modulename": "ssscoring.datatypes", "qualname": "JumpResults.result", "kind": "variable", "doc": "

Alias for field number 3

\n"}, "ssscoring.datatypes.JumpResults.score": {"fullname": "ssscoring.datatypes.JumpResults.score", "modulename": "ssscoring.datatypes", "qualname": "JumpResults.score", "kind": "variable", "doc": "

Alias for field number 4

\n"}, "ssscoring.datatypes.JumpResults.scores": {"fullname": "ssscoring.datatypes.JumpResults.scores", "modulename": "ssscoring.datatypes", "qualname": "JumpResults.scores", "kind": "variable", "doc": "

Alias for field number 5

\n"}, "ssscoring.datatypes.JumpResults.table": {"fullname": "ssscoring.datatypes.JumpResults.table", "modulename": "ssscoring.datatypes", "qualname": "JumpResults.table", "kind": "variable", "doc": "

Alias for field number 6

\n"}, "ssscoring.datatypes.JumpResults.window": {"fullname": "ssscoring.datatypes.JumpResults.window", "modulename": "ssscoring.datatypes", "qualname": "JumpResults.window", "kind": "variable", "doc": "

Alias for field number 7

\n"}, "ssscoring.datatypes.PerformanceWindow": {"fullname": "ssscoring.datatypes.PerformanceWindow", "modulename": "ssscoring.datatypes", "qualname": "PerformanceWindow", "kind": "class", "doc": "

An object to handle the performance window (as defined in competition rules) as\na single object with all the properties necessary to interpret it and manipulate\nit across different function or method calls.

\n\n

Attributes

\n\n
    \n
  • start - beginning or start of the performance window, or exit from the\naircraft
    • \n
  • end - end of the performance window, or start-PERFORMANCE_WINDOW_LENGTH
    • \n
  • validationStart - end of the performance window - the VALIDATION_WINDOW_END
    • \n
\n\n

See

\n\n
ssscoring.constants.PERFORMANCE_WINDOW_LENGTH\nssscoring.constants.VALIDATION_WINDOW_END\n
\n", "bases": "builtins.tuple"}, "ssscoring.datatypes.PerformanceWindow.__init__": {"fullname": "ssscoring.datatypes.PerformanceWindow.__init__", "modulename": "ssscoring.datatypes", "qualname": "PerformanceWindow.__init__", "kind": "function", "doc": "

Create new instance of PerformanceWindow(start, end, validationStart)

\n", "signature": "(start, end, validationStart)"}, "ssscoring.datatypes.PerformanceWindow.start": {"fullname": "ssscoring.datatypes.PerformanceWindow.start", "modulename": "ssscoring.datatypes", "qualname": "PerformanceWindow.start", "kind": "variable", "doc": "

Alias for field number 0

\n"}, "ssscoring.datatypes.PerformanceWindow.end": {"fullname": "ssscoring.datatypes.PerformanceWindow.end", "modulename": "ssscoring.datatypes", "qualname": "PerformanceWindow.end", "kind": "variable", "doc": "

Alias for field number 1

\n"}, "ssscoring.datatypes.PerformanceWindow.validationStart": {"fullname": "ssscoring.datatypes.PerformanceWindow.validationStart", "modulename": "ssscoring.datatypes", "qualname": "PerformanceWindow.validationStart", "kind": "variable", "doc": "

Alias for field number 2

\n"}, "ssscoring.errors": {"fullname": "ssscoring.errors", "modulename": "ssscoring.errors", "kind": "module", "doc": "

\n"}, "ssscoring.errors.SSScoringError": {"fullname": "ssscoring.errors.SSScoringError", "modulename": "ssscoring.errors", "qualname": "SSScoringError", "kind": "class", "doc": "

Abstract class that defines all exceptions and errors and has a dictionary\nrepresentation of itself, for cleaner structured logging.

\n\n

Arguments

\n\n
message : str\n
\n\n

A meaningful error message, human-readable\n errno : int\nAn optional integer value, similar to ERRNO in C/UNIX

\n", "bases": "builtins.Exception"}, "ssscoring.errors.SSScoringError.__init__": {"fullname": "ssscoring.errors.SSScoringError.__init__", "modulename": "ssscoring.errors", "qualname": "SSScoringError.__init__", "kind": "function", "doc": "

\n", "signature": "(message: str, errno: int = -1)"}, "ssscoring.flysight": {"fullname": "ssscoring.flysight", "modulename": "ssscoring.flysight", "kind": "module", "doc": "

Functions and logic for detecting, validating and manipulating\nFlySight CSV files, including detection in the file system. The functions in\nthis module assume that a data lake exists somewhere in the file system (whether\nlocal or cloud-based).

\n"}, "ssscoring.flysight.FlySightVersion": {"fullname": "ssscoring.flysight.FlySightVersion", "modulename": "ssscoring.flysight", "qualname": "FlySightVersion", "kind": "class", "doc": "

\n", "bases": "enum.Enum"}, "ssscoring.flysight.FlySightVersion.V1": {"fullname": "ssscoring.flysight.FlySightVersion.V1", "modulename": "ssscoring.flysight", "qualname": "FlySightVersion.V1", "kind": "variable", "doc": "

\n", "default_value": "<FlySightVersion.V1: 1000>"}, "ssscoring.flysight.FlySightVersion.V2": {"fullname": "ssscoring.flysight.FlySightVersion.V2", "modulename": "ssscoring.flysight", "qualname": "FlySightVersion.V2", "kind": "variable", "doc": "

\n", "default_value": "<FlySightVersion.V2: 2000>"}, "ssscoring.flysight.skipOverFS2MetadataRowsIn": {"fullname": "ssscoring.flysight.skipOverFS2MetadataRowsIn", "modulename": "ssscoring.flysight", "qualname": "skipOverFS2MetadataRowsIn", "kind": "function", "doc": "

Returns a clean dataframe on which any metadata rows within the first 100\nare skipped. This function uses the time column to detect valid rows. A\ntime == NaN is considered invalid and skipped.

\n\n

Arguments

\n\n
data\n
\n\n

A FlySight 2 dataframe suspected of having dirty N first rows with metadata

\n\n

Returns

\n\n

A FlySight 2 clean dataframe without any leading metadata rows.

\n", "signature": "(data: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "ssscoring.flysight.validFlySightHeaderIn": {"fullname": "ssscoring.flysight.validFlySightHeaderIn", "modulename": "ssscoring.flysight", "qualname": "validFlySightHeaderIn", "kind": "function", "doc": "

Checks if a file is a CSV in FlySight 1 or FlySight 2 formats. The checks\ninclude:

\n\n
    \n
  • Whether the file is a CSV, using a comma delimiter
    • \n
  • Checks for the presence of all the documented FlySight 1 headers
    • \n
  • Checks for the presence of the FlySight 2 line 1 identifier
    • \n
\n\n

Arguments

\n\n
fileCSV\n
\n\n

A file name to verify as a valid FlySight file; can be a string or an\ninstance of libpath.Path

\n\n

Returns

\n\n

True if fileCSV is a FlySight CSV file, otherwise False.

\n", "signature": "(fileCSV) -> bool:", "funcdef": "def"}, "ssscoring.flysight.getAllSpeedJumpFilesFrom": {"fullname": "ssscoring.flysight.getAllSpeedJumpFilesFrom", "modulename": "ssscoring.flysight", "qualname": "getAllSpeedJumpFilesFrom", "kind": "function", "doc": "

Get a list of all the speed jump files from a data lake, where data lake is\ndefined as a reachable path that contains one or more FlySight CSV files.\nThis function tests each file to ensure that it's a speed skydive FlySight\nfile in a valid format and length.

\n\n

Arguments

\n\n
dataLake: str\n
\n\n

A valid (absolute or relative) path name to the top level directory where\nthe data lake starts.

\n\n

Returns

\n\n

A dictionary of speed jump file names for later SSScoring processing:\n - keys are the file names\n - values are a FlySight version string tag

\n", "signature": "(dataLake: str) -> dict:", "funcdef": "def"}, "ssscoring.flysight.detectFlySightFileVersionOf": {"fullname": "ssscoring.flysight.detectFlySightFileVersionOf", "modulename": "ssscoring.flysight", "qualname": "detectFlySightFileVersionOf", "kind": "function", "doc": "

Detects the FlySight file version based on its file name and format.

\n\n

Arguments

\n\n
fileThing\n
\n\n

A string or pathlib.Path object corresponding to the file name.

\n\n

Returns

\n\n

An instance of ssscoring.flysight.FlySightVersion with a valid version\nsymbolic value.

\n\n

Errors

\n\n

ssscoring.errors.SSScoringError if the file is not a CSV and it's some\nother invalid format.

\n", "signature": "(fileThing) -> ssscoring.flysight.FlySightVersion:", "funcdef": "def"}, "ssscoring.notebook": {"fullname": "ssscoring.notebook", "modulename": "ssscoring.notebook", "kind": "module", "doc": "

Utility reusable code for notebooks.

\n"}, "ssscoring.notebook.DATA_LAKE_ROOT": {"fullname": "ssscoring.notebook.DATA_LAKE_ROOT", "modulename": "ssscoring.notebook", "qualname": "DATA_LAKE_ROOT", "kind": "variable", "doc": "

\n", "default_value": "'./data'"}, "ssscoring.notebook.initializePlot": {"fullname": "ssscoring.notebook.initializePlot", "modulename": "ssscoring.notebook", "qualname": "initializePlot", "kind": "function", "doc": "

Initiialize a plotting area for notebook output.

\n\n

Arguments

\n\n
jumpTitle: str\n
\n\n

A title to identify the plot.

\n\n
height: int\n
\n\n

Height of the plot in pixels. Default = 500.

\n\n
width: int\n
\n\n

Width of the plot in pixels. Default = 900.

\n\n
xLabel: str\n
\n\n

X axis label. Default: 'seconds from exit'

\n\n
yLabel: str\n
\n\n

Y axis label. Default: 'km/h'

\n\n
xMax: float\n
\n\n

The maximum rnage for the X axis. Default = 40.0

\n\n
yMax: float\n
\n\n

The maximum range for the Y axis. Default = 550

\n", "signature": "(\tjumpTitle: str,\theight=500,\twidth=900,\txLabel='seconds from exit',\tyLabel='km/h',\txMax=35.0,\tyMax=550.0):", "funcdef": "def"}, "ssscoring.notebook.initializeExtraYRanges": {"fullname": "ssscoring.notebook.initializeExtraYRanges", "modulename": "ssscoring.notebook", "qualname": "initializeExtraYRanges", "kind": "function", "doc": "

Initialize an extra Y range for reporting other data trend (e.g. altitude)\nin the plot.

\n\n

Arguments

\n\n
plot\n
\n\n

A valid instance of bp.figure with an existing plot defined for it

\n\n
startY: float\n
\n\n

The Y range starting value

\n\n
endY: float\n
\n\n

The Y range ending value

\n\n

Returns

\n\n

An instance of bp.figure updated to report an additional Y axis.

\n", "signature": "(plot, startY: float = 0.0, endY: float = 15500):", "funcdef": "def"}, "ssscoring.notebook.graphJumpResult": {"fullname": "ssscoring.notebook.graphJumpResult", "modulename": "ssscoring.notebook", "qualname": "graphJumpResult", "kind": "function", "doc": "

Graph the jump results using the initialized plot.

\n\n

Arguments

\n\n
plot: bp.figure\n
\n\n

A Bokeh figure where to render the plot.

\n\n
jumpResult: ssscoring.JumpResults\n
\n\n

A jump results named tuple with score, max speed, scores, data, etc.

\n\n
lineColor: str\n
\n\n

A valid color from the Bokeh palette: https://docs.bokeh.org/en/2.1.1/docs/reference/colors.html\nThis module defines 8 colors for rendering a competition's results. See:\nssscoring.notebook.SPEED_COLORS for the list.

\n\n
leged: str\n
\n\n

A title for the plot.

\n\n
showIt: bool\n
\n\n

A boolean flag for whether the call should render the plot upon the function\ncall. This flag is used for combining two or more jumps on the same plot.\nIn that case, a call to this function is made with a different jumpResult\nand the showIt flag set to False. When the user is ready to view the\ncombined plot, issue a call to bp.show(plot). Example:

\n\n
\n
for result in jumpResults:\n    graphJumpResult(plot, result, showIt = False)\n\nbp.show(plot)\n\nReturns\n-------\n`None`.\n
\n
\n", "signature": "(plot, jumpResult, lineColor='green', legend='speed', showIt=True):", "funcdef": "def"}, "ssscoring.notebook.graphAltitude": {"fullname": "ssscoring.notebook.graphAltitude", "modulename": "ssscoring.notebook", "qualname": "graphAltitude", "kind": "function", "doc": "

Graph a vertical axis with additional data, often used for altitude in ft\nASL.

\n\n

Arguments

\n\n
plot: pb.figure\n
\n\n

A Bokeh figure where to render the plot.

\n\n
jumpResult: ssscoring.JumpResults\n
\n\n

A jump results named tuple with score, max speed, scores, data, etc.

\n\n
label: str\n
\n\n

The legend label for the new Y axis.

\n\n
lineColor: str\n
\n\n

A color name from the Bokeh palette.

\n\n
rangeName: str\n
\n\n

The range name to associate the LinearAxis layout with the data for\nplotting.

\n\n

Returns

\n\n

None.

\n", "signature": "(\tplot,\tjumpResult,\tlabel='Alt (ft)',\tlineColor='palegoldenrod',\trangeName='altitudeFt'):", "funcdef": "def"}, "ssscoring.notebook.graphAngle": {"fullname": "ssscoring.notebook.graphAngle", "modulename": "ssscoring.notebook", "qualname": "graphAngle", "kind": "function", "doc": "

Graph the flight angle

\n\n

Arguments

\n\n
plot: pb.figure\n
\n\n

A Bokeh figure where to render the plot.

\n\n
jumpResult: ssscoring.JumpResults\n
\n\n

A jump results named tuple with score, max speed, scores, data, etc.

\n\n
label: str\n
\n\n

The legend label for the new Y axis.

\n\n
lineColor: str\n
\n\n

A color name from the Bokeh palette.

\n\n
rangeName: str\n
\n\n

The range name to associate the LinearAxis layout with the data for\nplotting.

\n\n

Returns

\n\n

None.

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Speed Skydiving Scoring tools package for processing FlySight v1 and v2 CSV data\nfiles.

\n\n

This library relies on NumPy and pandas. When running in a Lucyfer notebook,\nit also requires the Bokeh plotting library.

\n\n

The documentation for each module in this package is linked from the navigation\nbar. The fs1 module contains functions that process v1 files. The v2\nimplementation is in progress in a private Git branch.

\n\n

Installation

\n\n
\n
pip install -U ssscoring\n
\n
\n\n

Source code

\n\n

You are welcome to fork SSScoring\non GitHub. You will need Python 3.9 or later, pandas, and NumPy as minimum\nrequirements.

\n\n

You may study or download the latest stable source files at:

\n\n

https://github.com/pr3d4t0r/SSScoring/tree/master/ssscoring

\n"}, "ssscoring.__VERSION__": {"fullname": "ssscoring.__VERSION__", "modulename": "ssscoring", "qualname": "__VERSION__", "kind": "variable", "doc": "

@public

\n", "default_value": "'1.8.2'"}, "ssscoring.calc": {"fullname": "ssscoring.calc", "modulename": "ssscoring.calc", "kind": "module", "doc": "

Functions and logic for analyzing and manipulating FlySight dataframes.

\n"}, "ssscoring.calc.isValidMinimumAltitude": {"fullname": "ssscoring.calc.isValidMinimumAltitude", "modulename": "ssscoring.calc", "qualname": "isValidMinimumAltitude", "kind": "function", "doc": "

Reports whether an altitude is within the IPC and USPA valid parameters,\nor within BREAKOFF_ALTITUDE and PERFORMACE_WINDOW_LENGTH. In invalid\naltitude doesn't invalidate a FlySight data file. This function can be used\nfor generating warnings. The stock FlySightViewer scores a speed jump even\nif the exit was below the minimum altitude.

\n\n

Arguments

\n\n
altitude\n
\n\n

An altitude in meters, often calculated as data.hMSL - DZ altitude.

\n\n

Returns

\n\n

True if the altitude is valid.

\n", "signature": "(altitude: float) -> bool:", "funcdef": "def"}, "ssscoring.calc.isValidJump": {"fullname": "ssscoring.calc.isValidJump", "modulename": "ssscoring.calc", "qualname": "isValidJump", "kind": "function", "doc": "

Validates the jump according to ISC/FAI/USPA competition rules. A jump is\nvalid when the speed accuracy parameter is less than 3 m/s for the whole\nvalidation window duration.

\n\n

Arguments

\n\n
data : pd.DataFramce\n
\n\n

Jumnp data in SSScoring format\n window : ssscoring.PerformanceWindow\nPerformance window start, end values in named tuple format

\n\n

Returns

\n\n

True if the jump is valid according to ISC/FAI/USPA rules.

\n", "signature": "(\tdata: pandas.core.frame.DataFrame,\twindow: ssscoring.datatypes.PerformanceWindow) -> bool:", "funcdef": "def"}, "ssscoring.calc.calculateDistance": {"fullname": "ssscoring.calc.calculateDistance", "modulename": "ssscoring.calc", "qualname": "calculateDistance", "kind": "function", "doc": "

Calculate the distance between two terrestrial coordinates points.

\n\n

Arguments

\n\n
start\n
\n\n

A latitude, longitude tuple of floating point numbers.

\n\n
end\n
\n\n

A latitude, longitude tuple of floating point numbers.

\n\n

Returns

\n\n

The distance, in meters, between both points.

\n", "signature": "(start: tuple, end: tuple) -> float:", "funcdef": "def"}, "ssscoring.calc.convertFlySight2SSScoring": {"fullname": "ssscoring.calc.convertFlySight2SSScoring", "modulename": "ssscoring.calc", "qualname": "convertFlySight2SSScoring", "kind": "function", "doc": "

Converts a raw dataframe initialized from a FlySight CSV file into the\nSSScoring file format. The SSScoring format uses more descriptive column\nheaders, adds the altitude in feet, and uses UNIX time instead of an ISO\nstring.

\n\n

If both altitudeDZMeters and altitudeDZFt are zero then hMSL is used.\nOtherwise, this function adjusts the effective altitude with the value. If\nboth meters and feet values are set this throws an error.

\n\n

Arguments

\n\n
rawData : pd.DataFrame\n
\n\n

FlySight CSV input as a dataframe

\n\n
altitudeDZMeters : float\n
\n\n

Drop zone height above MSL

\n\n
altitudeDZFt\n
\n\n

Drop zone altitudde above MSL

\n\n

Returns

\n\n

A dataframe in SSScoring format, featuring these columns:

\n\n
    \n
  • timeUnix
    • \n
  • altitudeMSL
    • \n
  • altitudeAGL
    • \n
  • altitudeMSLFt
    • \n
  • altitudeAGLFt
    • \n
  • hMetersPerSecond
    • \n
  • hKMh (km/h)
    • \n
  • vMetersPerSecond
    • \n
  • vKMh (km/h)
    • \n
  • angle
    • \n
  • speedAccuracy (ignore; see ISC documentation)
    • \n
  • hMetersPerSecond
    • \n
  • hKMh
    • \n
  • latitude
    • \n
  • longitude
    • \n
  • verticalAccuracy
    • \n
  • speedAccuracyISC
    • \n
\n\n

Errors

\n\n

SSScoringError if the DZ altitude is set in both meters and feet.

\n", "signature": "(\trawData: pandas.core.frame.DataFrame,\taltitudeDZMeters=0.0,\taltitudeDZFt=0.0):", "funcdef": "def"}, "ssscoring.calc.getSpeedSkydiveFrom": {"fullname": "ssscoring.calc.getSpeedSkydiveFrom", "modulename": "ssscoring.calc", "qualname": "getSpeedSkydiveFrom", "kind": "function", "doc": "

Take the skydive dataframe and get the speed skydiving data:

\n\n
    \n
  • Exit
    • \n
  • Speed skydiving window
    • \n
  • Drops data before exit and below breakoff altitude
    • \n
\n\n

Arguments

\n\n
data : pd.DataFrame\n
\n\n

Jump data in SSScoring format

\n\n

Returns

\n\n

A tuple of two elements:

\n\n
    \n
  • A named tuple with performance and validation window data
    • \n
  • A dataframe featuring only speed skydiving data
    • \n
\n", "signature": "(data: pandas.core.frame.DataFrame) -> tuple:", "funcdef": "def"}, "ssscoring.calc.jumpAnalysisTable": {"fullname": "ssscoring.calc.jumpAnalysisTable", "modulename": "ssscoring.calc", "qualname": "jumpAnalysisTable", "kind": "function", "doc": "

Generates the HCD jump analysis table, with speed data at 5-second intervals\nafter exit.

\n\n

Arguments

\n\n
data : pd.DataFrame\n
\n\n

Jump data in SSScoring format

\n\n

Returns

\n\n

A tuple with a pd.DataFrame and the max speed recorded for the jump:

\n\n
    \n
  • A table dataframe with time and speed
    • \n
  • a floating point number
    • \n
\n", "signature": "(data: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "ssscoring.calc.dropNonSkydiveDataFrom": {"fullname": "ssscoring.calc.dropNonSkydiveDataFrom", "modulename": "ssscoring.calc", "qualname": "dropNonSkydiveDataFrom", "kind": "function", "doc": "

Discards all data rows before maximum altitude, and all \"negative\" altitude\nrows because we don't skydive underground (FlySight bug?).

\n\n

This is a more accurate mean velocity calculation from a physics point of\nview, but it differs from the ISC definition using in scoring - which, if we\nget technical, is the one that counts.

\n\n

Arguments

\n\n
data : pd.DataFrame\n
\n\n

Jump data in SSScoring format (headers differ from FlySight format)

\n\n

Returns

\n\n

The jump data for the skydive

\n", "signature": "(data: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "ssscoring.calc.calcScoreMeanVelocity": {"fullname": "ssscoring.calc.calcScoreMeanVelocity", "modulename": "ssscoring.calc", "qualname": "calcScoreMeanVelocity", "kind": "function", "doc": "

Calculates the speeds over a 3-second interval as the mean of all the speeds\nrecorded within that 3-second window and resolves the maximum speed.

\n\n

Arguments

\n\n
data\n
\n\n

A pd.dataframe with speed run data.

\n\n

Returns

\n\n

A tuple with the best score throughout the speed run, and a dicitionary\nof the meanVSpeed:spotInTime used in determining the exact scoring speed\nat every datat point during the speed run.

\n", "signature": "(data: pandas.core.frame.DataFrame) -> tuple:", "funcdef": "def"}, "ssscoring.calc.calcScoreISC": {"fullname": "ssscoring.calc.calcScoreISC", "modulename": "ssscoring.calc", "qualname": "calcScoreISC", "kind": "function", "doc": "

Calculates the speeds over a 3-second interval as the ds/dt and dt is the\nis a 3-second sliding interval from exit. The window slider moves along the\nplotTime axis in the dataframe.

\n\n

Arguments

\n\n
data\n
\n\n

A pd.dataframe with speed run data.

\n\n

Returns

\n\n

A tuple with the best score throughout the speed run, and a dicitionary\nof the meanVSpeed:spotInTime used in determining the exact scoring speed\nat every datat point during the speed run.

\n", "signature": "(data: pandas.core.frame.DataFrame) -> tuple:", "funcdef": "def"}, "ssscoring.calc.processJump": {"fullname": "ssscoring.calc.processJump", "modulename": "ssscoring.calc", "qualname": "processJump", "kind": "function", "doc": "

Take a dataframe in SSScoring format and process it for display. It\nserializes all the steps that would be taken from the ssscoring module, but\nincludes some text/HTML data in the output.

\n\n

Arguments

\n\n
data: pd.DataFrame\n
\n\n

A dataframe in SSScoring format

\n\n

Returns

\n\n

A JumpResults named tuple with these items:

\n\n
    \n
  • score speed score
    • \n
  • maxSpeed maximum speed during the jump
    • \n
  • scores a Series of every 3-second window scores from exit to breakoff
    • \n
  • data an updated SSScoring dataframe plotTime, where 0 = exit, used\nfor plotting
    • \n
  • window a named tuple with the exit, breakoff, and validation window\naltitudes
    • \n
  • table a dataframe featuring the speeds and altitudes at 5-sec intervals
    • \n
  • color a string that defines the color for the jump result; possible\nvalues are _green_ for valid jump, _red_ for invalid jump, per ISC rules
    • \n
  • result a string with the legend of _valid_ or _invalid_ jump
    • \n
\n", "signature": "(data: pandas.core.frame.DataFrame):", "funcdef": "def"}, "ssscoring.calc.getFlySightDataFromCSV": {"fullname": "ssscoring.calc.getFlySightDataFromCSV", "modulename": "ssscoring.calc", "qualname": "getFlySightDataFromCSV", "kind": "function", "doc": "

Ingress a known FlySight or SkyTrax file into memory for SSScoring\nprocessing.

\n\n

Arguments

\n\n
jumpFile\n
\n\n

A string or pathlib.Path object; can be a relative or an asbolute path.

\n\n

Returns

\n\n

A tuple with two items:\n - rawData - a dataframe representation of the CSV with the original\n headers but without the data type header\n - tag - a string with an identifying tag derived from the path name\n and file version in the form some name:vX. It uses the current\n path as metadata to infer the name. There's no semantics enforcement.

\n\n

Raises

\n\n

SSScoringError if the CSV file is invalid in any way.

\n", "signature": "(jumpFile) -> tuple:", "funcdef": "def"}, "ssscoring.calc.processAllJumpFiles": {"fullname": "ssscoring.calc.processAllJumpFiles", "modulename": "ssscoring.calc", "qualname": "processAllJumpFiles", "kind": "function", "doc": "

Process all jump files in a list of valid FlySight files. Returns a\ndictionary of jump results with a human-readable version of the file name.\nThe jumpFiles list can be generated by hand or the output of the\nssscoring.fs1.getAllSpeedJumpFilesFrom called to operate on a data lake.

\n\n

Arguments

\n\n
jumpFiles\n
\n\n

A list of relative or absolute path names to individual FlySight CSV files.

\n\n
altitudeDZMeters : float\n
\n\n

Drop zone height above MSL

\n\n
dict\n
\n\n

A dictionary of jump results. The key is a human-readable version of a\njumpFile name with the extension, path, and extraneous spaces eliminated\nor replaced by appropriate characters. File names use Unicode, so accents\nand non-ANSI characters are allowed in file names.

\n", "signature": "(jumpFiles: list, altitudeDZMeters=0.0) -> dict:", "funcdef": "def"}, "ssscoring.calc.aggregateResults": {"fullname": "ssscoring.calc.aggregateResults", "modulename": "ssscoring.calc", "qualname": "aggregateResults", "kind": "function", "doc": "

Aggregate all the results in a table fashioned after Marco Hepp's and Nklas\nDaniel's score tracking data.

\n\n

Arguments

\n\n
jumpResults: dict\n
\n\n

A dictionary of jump results, in which each result corresponds to a FlySight\nfile name. See ssscoring.processAllJumpFiles for details.

\n\n

Returns

\n\n

A dataframe featuring these columns:

\n\n
    \n
  • Score
    • \n
  • Speeds at 5, 10, 15, 20, and 25 second tranches
    • \n
  • Final time contemplated in the analysis
    • \n
  • Max speed
    • \n
\n\n

The dataframe rows are identified by the human readable jump file name.

\n", "signature": "(jumpResults: dict) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "ssscoring.calc.roundedAggregateResults": {"fullname": "ssscoring.calc.roundedAggregateResults", "modulename": "ssscoring.calc", "qualname": "roundedAggregateResults", "kind": "function", "doc": "

Aggregate all the results in a table fashioned after Marco Hepp's and Nklas\nDaniel's score tracking data. All speed results are rounded at n > x.5\nfor any value.

\n\n

Arguments

\n\n
aggregate: pd.DataFrame\n
\n\n

A dataframe output of ssscoring.fs1.aggregateResults.

\n\n

Returns

\n\n

A dataframe featuring the rounded values for these columns:

\n\n
    \n
  • Score
    • \n
  • Speeds at 5, 10, 15, 20, and 25 second tranches
    • \n
  • Max speed
    • \n
\n\n

The finalTime column is ignored.

\n\n

The dataframe rows are identified by the human readable jump file name.

\n\n

This is a less precise version of the ssscoring.aggregateResults\ndataframe, useful during training to keep rounded results available for\nreview.

\n", "signature": "(aggregate: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "ssscoring.calc.totalResultsFrom": {"fullname": "ssscoring.calc.totalResultsFrom", "modulename": "ssscoring.calc", "qualname": "totalResultsFrom", "kind": "function", "doc": "

Calculates the total and mean speeds for an aggregation of speed jumps.

\n\n

Arguments

\n\n
aggregate: pd.DataFrame\n
\n\n

The aggregate results dataframe resulting from calling ssscoring.aggregateResults\nwith valid results.

\n\n

Returns

\n\n

A dataframe with one row and two columns:

\n\n
    \n
  • totalSpeed ::= the sum of all speeds in the aggregated results
    • \n
  • meanSpeed ::= the mean of all speeds
    • \n
  • maxScore ::= the max score among all the speed scores
    • \n
\n\n

Raises

\n\n

AttributeError if aggregate is an empty dataframe or None, or if the\naggregate dataframe doesn't conform to the output of ssscoring.aggregateResults.

\n", "signature": "(aggregate: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "ssscoring.cli": {"fullname": "ssscoring.cli", "modulename": "ssscoring.cli", "kind": "module", "doc": "

Command line tools package.

\n"}, "ssscoring.cli.die": {"fullname": "ssscoring.cli.die", "modulename": "ssscoring.cli", "qualname": "die", "kind": "function", "doc": "

Display an end-user message and exit the program with exitCode.

\n\n

Arguments

\n\n
message\n
\n\n

A string, most often with a descriptive error message.

\n\n
exitCode\n
\n\n

An exit code, where 0 is normal exit, and any other value is an error\ncondition.

\n\n
isUnitTest\n
\n\n

A Boolean; if True, the program returns the value of exitCode to the\ncaller, otherwise it terminates with sys.exit(exitCode).

\n", "signature": "(message: str, exitCode: int, isUnitTest=False) -> int:", "funcdef": "def"}, "ssscoring.cli.ssscore": {"fullname": "ssscoring.cli.ssscore", "modulename": "ssscoring.cli", "qualname": "ssscore", "kind": "function", "doc": "

Process all the speed skydiving files contained in dataLakeSpec. This\nfunction implements the business logic for the /usr/local/bin/ssscore\ncommand. See pyproject.toml for command details.

\n\n

Arguments

\n\n
dataLake\n
\n\n

Command line argument with the path to the data lake.

\n\n

Returns

\n\n

The number of jump results from processing all the FlySight files in the\ndata lake.

\n", "signature": "(dataLake: str) -> int:", "funcdef": "def"}, "ssscoring.constants": {"fullname": "ssscoring.constants", "modulename": "ssscoring.constants", "kind": "module", "doc": "

Constants module and definitions.

\n\n

All measurements are expressed in meters unless noted otherwise.

\n"}, "ssscoring.constants.BREAKOFF_ALTITUDE": {"fullname": "ssscoring.constants.BREAKOFF_ALTITUDE", "modulename": "ssscoring.constants", "qualname": "BREAKOFF_ALTITUDE", "kind": "variable", "doc": "

Breakoff altitude or hard deck.

\n", "default_value": "1707.0"}, "ssscoring.constants.DEG_IN_RADIANS": {"fullname": "ssscoring.constants.DEG_IN_RADIANS", "modulename": "ssscoring.constants", "qualname": "DEG_IN_RADIANS", "kind": "variable", "doc": "

\u03c0/180\u00ba

\n", "default_value": "0.017453292519943295"}, "ssscoring.constants.EXIT_SPEED": {"fullname": "ssscoring.constants.EXIT_SPEED", "modulename": "ssscoring.constants", "qualname": "EXIT_SPEED", "kind": "variable", "doc": "

Guesstimate of the exit speed; 2*g

\n", "default_value": "19.62"}, "ssscoring.constants.FLYSIGHT_1_HEADER": {"fullname": "ssscoring.constants.FLYSIGHT_1_HEADER", "modulename": "ssscoring.constants", "qualname": "FLYSIGHT_1_HEADER", "kind": "variable", "doc": "

FlySight v1 CSV file headers.

\n", "default_value": "{'velD', 'numSV', 'time', 'hMSL', 'velN', 'hAcc', 'heading', 'gpsFix', 'vAcc', 'sAcc', 'lon', 'cAcc', 'velE', 'lat'}"}, "ssscoring.constants.FLYSIGHT_2_HEADER": {"fullname": "ssscoring.constants.FLYSIGHT_2_HEADER", "modulename": "ssscoring.constants", "qualname": "FLYSIGHT_2_HEADER", "kind": "variable", "doc": "

FlySight v1 CSV file headers. Unlike other constants, this is a tuple instead\nof a set because the code manipulates the headers/columns during file ingress.

\n", "default_value": "('GNSS', 'time', 'lat', 'lon', 'hMSL', 'velN', 'velE', 'velD', 'hAcc', 'vAcc', 'sAcc', 'numSV')"}, "ssscoring.constants.FT_IN_M": {"fullname": "ssscoring.constants.FT_IN_M", "modulename": "ssscoring.constants", "qualname": "FT_IN_M", "kind": "variable", "doc": "

Number of feet in a meter.

\n", "default_value": "3.2808"}, "ssscoring.constants.IGNORE_LIST": {"fullname": "ssscoring.constants.IGNORE_LIST", "modulename": "ssscoring.constants", "qualname": "IGNORE_LIST", "kind": "variable", "doc": "

Internal use - list of files to be ignored during bulk file processing in the\ndata lake (e.g. ./data).

\n", "default_value": "['.ipynb_checkpoints']"}, "ssscoring.constants.LAST_TIME_TRANCHE": {"fullname": "ssscoring.constants.LAST_TIME_TRANCHE", "modulename": "ssscoring.constants", "qualname": "LAST_TIME_TRANCHE", "kind": "variable", "doc": "

Times > 25 s are irrelevant because it means that the speed skydiver flew at\nvSpeed < 400 km/h.

\n", "default_value": "25.0"}, "ssscoring.constants.MAX_ALTITUDE_FT": {"fullname": "ssscoring.constants.MAX_ALTITUDE_FT", "modulename": "ssscoring.constants", "qualname": "MAX_ALTITUDE_FT", "kind": "variable", "doc": "

Maximum suggested altitude but irrelevant in general. Max altitude without\noxygen is 15,500 ft AGL at most DZs. A jumnp from a higher altitude would\nrequire oxygen and would be scored 3,000 ft higher than the hard deck.

\n", "default_value": "15500"}, "ssscoring.constants.MAX_ALTITUDE_METERS": {"fullname": "ssscoring.constants.MAX_ALTITUDE_METERS", "modulename": "ssscoring.constants", "qualname": "MAX_ALTITUDE_METERS", "kind": "variable", "doc": "

See

\n\n
ssscoring.constants.MAX_ALTITUDE_FT\n
\n", "default_value": "4725.609756097561"}, "ssscoring.constants.MAX_SPEED_ACCURACY": {"fullname": "ssscoring.constants.MAX_SPEED_ACCURACY", "modulename": "ssscoring.constants", "qualname": "MAX_SPEED_ACCURACY", "kind": "variable", "doc": "

Speed accuracy for the FlySight device.

\n", "default_value": "3.0"}, "ssscoring.constants.MIN_JUMP_FILE_SIZE": {"fullname": "ssscoring.constants.MIN_JUMP_FILE_SIZE", "modulename": "ssscoring.constants", "qualname": "MIN_JUMP_FILE_SIZE", "kind": "variable", "doc": "

FlySight v1 files smaller than MIN_JUMP_FILE_SIZE are ignored because they\nlack the minimum number of data points to contain a valid speed skydive.\nTODO: Revise for FlySight v2.

\n", "default_value": "65536"}, "ssscoring.constants.MPS_2_KMH": {"fullname": "ssscoring.constants.MPS_2_KMH", "modulename": "ssscoring.constants", "qualname": "MPS_2_KMH", "kind": "variable", "doc": "

m/s to km/h conversion factor:

\n\n
\n
s = mps * 60 * 60 / 1000\n  = mps * 3600 / 1000\n  = mps * 3.6\n
\n
\n", "default_value": "3.6"}, "ssscoring.constants.PERFORMANCE_WINDOW_LENGTH": {"fullname": "ssscoring.constants.PERFORMANCE_WINDOW_LENGTH", "modulename": "ssscoring.constants", "qualname": "PERFORMANCE_WINDOW_LENGTH", "kind": "variable", "doc": "

Performance window length as defined by ISSA/IPC/USPA.

\n", "default_value": "2256.0"}, "ssscoring.constants.SCORING_INTERVAL": {"fullname": "ssscoring.constants.SCORING_INTERVAL", "modulename": "ssscoring.constants", "qualname": "SCORING_INTERVAL", "kind": "variable", "doc": "

Scoring is based on the maximum speed the jumper attained within the VALIDATION_WINDOW_LENGTH\nas the average speed during a sliding window of SCORING_INTERVAL seconds. The\nvalue is set by governing bodies like ISC and USPA.

\n", "default_value": "3.0"}, "ssscoring.constants.SKYTRAX_1_HEADER": {"fullname": "ssscoring.constants.SKYTRAX_1_HEADER", "modulename": "ssscoring.constants", "qualname": "SKYTRAX_1_HEADER", "kind": "variable", "doc": "

SkyTraX GPS + barometric SMD v1 CSV file headers.

\n", "default_value": "{'velD', 'numSV', 'time', 'hMSL', 'velN', 'hAcc', 'heading', 'gpsFix', 'vAcc', 'sAcc', 'lon', 'cAcc', 'velE', 'lat'}"}, "ssscoring.constants.VALIDATION_WINDOW_LENGTH": {"fullname": "ssscoring.constants.VALIDATION_WINDOW_LENGTH", "modulename": "ssscoring.constants", "qualname": "VALIDATION_WINDOW_LENGTH", "kind": "variable", "doc": "

The validation window length as defined in the competition rules.

\n", "default_value": "1006.0"}, "ssscoring.datatypes": {"fullname": "ssscoring.datatypes", "modulename": "ssscoring.datatypes", "kind": "module", "doc": "

SSScoring custom type definitions for easier symbolic manipuation.

\n"}, "ssscoring.datatypes.JumpResults": {"fullname": "ssscoring.datatypes.JumpResults", "modulename": "ssscoring.datatypes", "qualname": "JumpResults", "kind": "class", "doc": "

A named tuple containing the score, maximum speed, scores throught the\nperformance window, the results table for a jump, the output color for the\nresult, and the result information string.

\n\n

Attributes

\n\n
    \n
  • color - a string representing a color, green if the result is valid, red\notherwise
    • \n
  • data - dataframe containing all the data points for plotting and\ncalculations
    • \n
  • maxSpeed - maximum absolute speed registered during a skydive
    • \n
  • result - dataframe of results
    • \n
  • score - maximum mean speed during a 3-second window during the skydive
    • \n
  • scores - a series with all the scored ruding the sliding 3-sec window for\nthe whole speed skydive
    • \n
  • table - summary table of results of the speed run
    • \n
  • window - the scoring window data, an instance of PerformanceWindow
    • \n
\n", "bases": "builtins.tuple"}, "ssscoring.datatypes.JumpResults.__init__": {"fullname": "ssscoring.datatypes.JumpResults.__init__", "modulename": "ssscoring.datatypes", "qualname": "JumpResults.__init__", "kind": "function", "doc": "

Create new instance of JumpResults(color, data, maxSpeed, result, score, scores, table, window)

\n", "signature": "(color, data, maxSpeed, result, score, scores, table, window)"}, "ssscoring.datatypes.JumpResults.color": {"fullname": "ssscoring.datatypes.JumpResults.color", "modulename": "ssscoring.datatypes", "qualname": "JumpResults.color", "kind": "variable", "doc": "

Alias for field number 0

\n"}, "ssscoring.datatypes.JumpResults.data": {"fullname": "ssscoring.datatypes.JumpResults.data", "modulename": "ssscoring.datatypes", "qualname": "JumpResults.data", "kind": "variable", "doc": "

Alias for field number 1

\n"}, "ssscoring.datatypes.JumpResults.maxSpeed": {"fullname": "ssscoring.datatypes.JumpResults.maxSpeed", "modulename": "ssscoring.datatypes", "qualname": "JumpResults.maxSpeed", "kind": "variable", "doc": "

Alias for field number 2

\n"}, "ssscoring.datatypes.JumpResults.result": {"fullname": "ssscoring.datatypes.JumpResults.result", "modulename": "ssscoring.datatypes", "qualname": "JumpResults.result", "kind": "variable", "doc": "

Alias for field number 3

\n"}, "ssscoring.datatypes.JumpResults.score": {"fullname": "ssscoring.datatypes.JumpResults.score", "modulename": "ssscoring.datatypes", "qualname": "JumpResults.score", "kind": "variable", "doc": "

Alias for field number 4

\n"}, "ssscoring.datatypes.JumpResults.scores": {"fullname": "ssscoring.datatypes.JumpResults.scores", "modulename": "ssscoring.datatypes", "qualname": "JumpResults.scores", "kind": "variable", "doc": "

Alias for field number 5

\n"}, "ssscoring.datatypes.JumpResults.table": {"fullname": "ssscoring.datatypes.JumpResults.table", "modulename": "ssscoring.datatypes", "qualname": "JumpResults.table", "kind": "variable", "doc": "

Alias for field number 6

\n"}, "ssscoring.datatypes.JumpResults.window": {"fullname": "ssscoring.datatypes.JumpResults.window", "modulename": "ssscoring.datatypes", "qualname": "JumpResults.window", "kind": "variable", "doc": "

Alias for field number 7

\n"}, "ssscoring.datatypes.PerformanceWindow": {"fullname": "ssscoring.datatypes.PerformanceWindow", "modulename": "ssscoring.datatypes", "qualname": "PerformanceWindow", "kind": "class", "doc": "

An object to handle the performance window (as defined in competition rules) as\na single object with all the properties necessary to interpret it and manipulate\nit across different function or method calls.

\n\n

Attributes

\n\n
    \n
  • start - beginning or start of the performance window, or exit from the\naircraft
    • \n
  • end - end of the performance window, or start-PERFORMANCE_WINDOW_LENGTH
    • \n
  • validationStart - end of the performance window - the VALIDATION_WINDOW_END
    • \n
\n\n

See

\n\n
ssscoring.constants.PERFORMANCE_WINDOW_LENGTH\nssscoring.constants.VALIDATION_WINDOW_END\n
\n", "bases": "builtins.tuple"}, "ssscoring.datatypes.PerformanceWindow.__init__": {"fullname": "ssscoring.datatypes.PerformanceWindow.__init__", "modulename": "ssscoring.datatypes", "qualname": "PerformanceWindow.__init__", "kind": "function", "doc": "

Create new instance of PerformanceWindow(start, end, validationStart)

\n", "signature": "(start, end, validationStart)"}, "ssscoring.datatypes.PerformanceWindow.start": {"fullname": "ssscoring.datatypes.PerformanceWindow.start", "modulename": "ssscoring.datatypes", "qualname": "PerformanceWindow.start", "kind": "variable", "doc": "

Alias for field number 0

\n"}, "ssscoring.datatypes.PerformanceWindow.end": {"fullname": "ssscoring.datatypes.PerformanceWindow.end", "modulename": "ssscoring.datatypes", "qualname": "PerformanceWindow.end", "kind": "variable", "doc": "

Alias for field number 1

\n"}, "ssscoring.datatypes.PerformanceWindow.validationStart": {"fullname": "ssscoring.datatypes.PerformanceWindow.validationStart", "modulename": "ssscoring.datatypes", "qualname": "PerformanceWindow.validationStart", "kind": "variable", "doc": "

Alias for field number 2

\n"}, "ssscoring.errors": {"fullname": "ssscoring.errors", "modulename": "ssscoring.errors", "kind": "module", "doc": "

\n"}, "ssscoring.errors.SSScoringError": {"fullname": "ssscoring.errors.SSScoringError", "modulename": "ssscoring.errors", "qualname": "SSScoringError", "kind": "class", "doc": "

Abstract class that defines all exceptions and errors and has a dictionary\nrepresentation of itself, for cleaner structured logging.

\n\n

Arguments

\n\n
message : str\n
\n\n

A meaningful error message, human-readable\n errno : int\nAn optional integer value, similar to ERRNO in C/UNIX

\n", "bases": "builtins.Exception"}, "ssscoring.errors.SSScoringError.__init__": {"fullname": "ssscoring.errors.SSScoringError.__init__", "modulename": "ssscoring.errors", "qualname": "SSScoringError.__init__", "kind": "function", "doc": "

\n", "signature": "(message: str, errno: int = -1)"}, "ssscoring.flysight": {"fullname": "ssscoring.flysight", "modulename": "ssscoring.flysight", "kind": "module", "doc": "

Functions and logic for detecting, validating and manipulating\nFlySight CSV files, including detection in the file system. The functions in\nthis module assume that a data lake exists somewhere in the file system (whether\nlocal or cloud-based).

\n"}, "ssscoring.flysight.FlySightVersion": {"fullname": "ssscoring.flysight.FlySightVersion", "modulename": "ssscoring.flysight", "qualname": "FlySightVersion", "kind": "class", "doc": "

\n", "bases": "enum.Enum"}, "ssscoring.flysight.FlySightVersion.V1": {"fullname": "ssscoring.flysight.FlySightVersion.V1", "modulename": "ssscoring.flysight", "qualname": "FlySightVersion.V1", "kind": "variable", "doc": "

\n", "default_value": "<FlySightVersion.V1: 1000>"}, "ssscoring.flysight.FlySightVersion.V2": {"fullname": "ssscoring.flysight.FlySightVersion.V2", "modulename": "ssscoring.flysight", "qualname": "FlySightVersion.V2", "kind": "variable", "doc": "

\n", "default_value": "<FlySightVersion.V2: 2000>"}, "ssscoring.flysight.skipOverFS2MetadataRowsIn": {"fullname": "ssscoring.flysight.skipOverFS2MetadataRowsIn", "modulename": "ssscoring.flysight", "qualname": "skipOverFS2MetadataRowsIn", "kind": "function", "doc": "

Returns a clean dataframe on which any metadata rows within the first 100\nare skipped. This function uses the time column to detect valid rows. A\ntime == NaN is considered invalid and skipped.

\n\n

Arguments

\n\n
data\n
\n\n

A FlySight 2 dataframe suspected of having dirty N first rows with metadata

\n\n

Returns

\n\n

A FlySight 2 clean dataframe without any leading metadata rows.

\n", "signature": "(data: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "ssscoring.flysight.validFlySightHeaderIn": {"fullname": "ssscoring.flysight.validFlySightHeaderIn", "modulename": "ssscoring.flysight", "qualname": "validFlySightHeaderIn", "kind": "function", "doc": "

Checks if a file is a CSV in FlySight 1 or FlySight 2 formats. The checks\ninclude:

\n\n
    \n
  • Whether the file is a CSV, using a comma delimiter
    • \n
  • Checks for the presence of all the documented FlySight 1 headers
    • \n
  • Checks for the presence of the FlySight 2 line 1 identifier
    • \n
\n\n

Arguments

\n\n
fileCSV\n
\n\n

A file name to verify as a valid FlySight file; can be a string or an\ninstance of libpath.Path

\n\n

Returns

\n\n

True if fileCSV is a FlySight CSV file, otherwise False.

\n", "signature": "(fileCSV) -> bool:", "funcdef": "def"}, "ssscoring.flysight.getAllSpeedJumpFilesFrom": {"fullname": "ssscoring.flysight.getAllSpeedJumpFilesFrom", "modulename": "ssscoring.flysight", "qualname": "getAllSpeedJumpFilesFrom", "kind": "function", "doc": "

Get a list of all the speed jump files from a data lake, where data lake is\ndefined as a reachable path that contains one or more FlySight CSV files.\nThis function tests each file to ensure that it's a speed skydive FlySight\nfile in a valid format and length.

\n\n

Arguments

\n\n
dataLake: str\n
\n\n

A valid (absolute or relative) path name to the top level directory where\nthe data lake starts.

\n\n

Returns

\n\n

A dictionary of speed jump file names for later SSScoring processing:\n - keys are the file names\n - values are a FlySight version string tag

\n", "signature": "(dataLake: str) -> dict:", "funcdef": "def"}, "ssscoring.flysight.detectFlySightFileVersionOf": {"fullname": "ssscoring.flysight.detectFlySightFileVersionOf", "modulename": "ssscoring.flysight", "qualname": "detectFlySightFileVersionOf", "kind": "function", "doc": "

Detects the FlySight file version based on its file name and format.

\n\n

Arguments

\n\n
fileThing\n
\n\n

A string or pathlib.Path object corresponding to the file name.

\n\n

Returns

\n\n

An instance of ssscoring.flysight.FlySightVersion with a valid version\nsymbolic value.

\n\n

Errors

\n\n

ssscoring.errors.SSScoringError if the file is not a CSV and it's some\nother invalid format.

\n", "signature": "(fileThing) -> ssscoring.flysight.FlySightVersion:", "funcdef": "def"}, "ssscoring.notebook": {"fullname": "ssscoring.notebook", "modulename": "ssscoring.notebook", "kind": "module", "doc": "

Utility reusable code for notebooks.

\n"}, "ssscoring.notebook.DATA_LAKE_ROOT": {"fullname": "ssscoring.notebook.DATA_LAKE_ROOT", "modulename": "ssscoring.notebook", "qualname": "DATA_LAKE_ROOT", "kind": "variable", "doc": "

\n", "default_value": "'./data'"}, "ssscoring.notebook.initializePlot": {"fullname": "ssscoring.notebook.initializePlot", "modulename": "ssscoring.notebook", "qualname": "initializePlot", "kind": "function", "doc": "

Initiialize a plotting area for notebook output.

\n\n

Arguments

\n\n
jumpTitle: str\n
\n\n

A title to identify the plot.

\n\n
height: int\n
\n\n

Height of the plot in pixels. Default = 500.

\n\n
width: int\n
\n\n

Width of the plot in pixels. Default = 900.

\n\n
xLabel: str\n
\n\n

X axis label. Default: 'seconds from exit'

\n\n
yLabel: str\n
\n\n

Y axis label. Default: 'km/h'

\n\n
xMax: float\n
\n\n

The maximum rnage for the X axis. Default = 40.0

\n\n
yMax: float\n
\n\n

The maximum range for the Y axis. Default = 550

\n", "signature": "(\tjumpTitle: str,\theight=500,\twidth=900,\txLabel='seconds from exit',\tyLabel='km/h',\txMax=35.0,\tyMax=550.0):", "funcdef": "def"}, "ssscoring.notebook.initializeExtraYRanges": {"fullname": "ssscoring.notebook.initializeExtraYRanges", "modulename": "ssscoring.notebook", "qualname": "initializeExtraYRanges", "kind": "function", "doc": "

Initialize an extra Y range for reporting other data trend (e.g. altitude)\nin the plot.

\n\n

Arguments

\n\n
plot\n
\n\n

A valid instance of bp.figure with an existing plot defined for it

\n\n
startY: float\n
\n\n

The Y range starting value

\n\n
endY: float\n
\n\n

The Y range ending value

\n\n

Returns

\n\n

An instance of bp.figure updated to report an additional Y axis.

\n", "signature": "(plot, startY: float = 0.0, endY: float = 15500):", "funcdef": "def"}, "ssscoring.notebook.graphJumpResult": {"fullname": "ssscoring.notebook.graphJumpResult", "modulename": "ssscoring.notebook", "qualname": "graphJumpResult", "kind": "function", "doc": "

Graph the jump results using the initialized plot.

\n\n

Arguments

\n\n
plot: bp.figure\n
\n\n

A Bokeh figure where to render the plot.

\n\n
jumpResult: ssscoring.JumpResults\n
\n\n

A jump results named tuple with score, max speed, scores, data, etc.

\n\n
lineColor: str\n
\n\n

A valid color from the Bokeh palette: https://docs.bokeh.org/en/2.1.1/docs/reference/colors.html\nThis module defines 8 colors for rendering a competition's results. See:\nssscoring.notebook.SPEED_COLORS for the list.

\n\n
leged: str\n
\n\n

A title for the plot.

\n\n
showIt: bool\n
\n\n

A boolean flag for whether the call should render the plot upon the function\ncall. This flag is used for combining two or more jumps on the same plot.\nIn that case, a call to this function is made with a different jumpResult\nand the showIt flag set to False. When the user is ready to view the\ncombined plot, issue a call to bp.show(plot). Example:

\n\n
\n
for result in jumpResults:\n    graphJumpResult(plot, result, showIt = False)\n\nbp.show(plot)\n\nReturns\n-------\n`None`.\n
\n
\n", "signature": "(plot, jumpResult, lineColor='green', legend='speed', showIt=True):", "funcdef": "def"}, "ssscoring.notebook.graphAltitude": {"fullname": "ssscoring.notebook.graphAltitude", "modulename": "ssscoring.notebook", "qualname": "graphAltitude", "kind": "function", "doc": "

Graph a vertical axis with additional data, often used for altitude in ft\nASL.

\n\n

Arguments

\n\n
plot: pb.figure\n
\n\n

A Bokeh figure where to render the plot.

\n\n
jumpResult: ssscoring.JumpResults\n
\n\n

A jump results named tuple with score, max speed, scores, data, etc.

\n\n
label: str\n
\n\n

The legend label for the new Y axis.

\n\n
lineColor: str\n
\n\n

A color name from the Bokeh palette.

\n\n
rangeName: str\n
\n\n

The range name to associate the LinearAxis layout with the data for\nplotting.

\n\n

Returns

\n\n

None.

\n", "signature": "(\tplot,\tjumpResult,\tlabel='Alt (ft)',\tlineColor='palegoldenrod',\trangeName='altitudeFt'):", "funcdef": "def"}, "ssscoring.notebook.graphAngle": {"fullname": "ssscoring.notebook.graphAngle", "modulename": "ssscoring.notebook", "qualname": "graphAngle", "kind": "function", "doc": "

Graph the flight angle

\n\n

Arguments

\n\n
plot: pb.figure\n
\n\n

A Bokeh figure where to render the plot.

\n\n
jumpResult: ssscoring.JumpResults\n
\n\n

A jump results named tuple with score, max speed, scores, data, etc.

\n\n
label: str\n
\n\n

The legend label for the new Y axis.

\n\n
lineColor: str\n
\n\n

A color name from the Bokeh palette.

\n\n
rangeName: str\n
\n\n

The range name to associate the LinearAxis layout with the data for\nplotting.

\n\n

Returns

\n\n

None.

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@@ -146,46 +146,48 @@

83 84def ssscore(dataLake: str) -> int: 85 """ - 86 Process all the speed skydiving files contained in `dataLakeSpec`. - 87 - 88 Arguments - 89 --------- - 90 dataLake - 91 Command line argument with the path to the data lake. - 92 - 93 Returns - 94 ------- - 95 The number of jump results from processing all the FlySight files in the - 96 data lake. - 97 """ - 98 _assertDataLake(dataLake) - 99 -100 click.secho('Processing speed tracks in %s...\n' % dataLake) -101 jumpResults = processAllJumpFiles(getAllSpeedJumpFilesFrom(dataLake)) -102 if jumpResults: -103 resultsSummary = aggregateResults(jumpResults) -104 click.secho(resultsSummary, fg = 'bright_green') -105 click.secho('\nTotal score = %5.2f, mean speed = %5.2f\n' % (resultsSummary.score.sum(), resultsSummary.score.mean()), fg = 'bright_white') -106 else: -107 click.secho('There were no speed track files to score in %s', fg = 'bright_red') -108 return len(jumpResults) -109 -110 -111@click.command('ssscore') -112@click.argument('datalake', nargs = 1, type = click.STRING) -113@click.version_option(__VERSION__, prog_name = 'ssscore') -114def ssscoreCommand(datalake: str) -> int: -115 """ -116 @private -117 """ -118 return ssscore(datalake) -119 -120 -121# +++ main +++ + 86 Process all the speed skydiving files contained in `dataLakeSpec`. This + 87 function implements the business logic for the `/usr/local/bin/ssscore` + 88 command. See `pyproject.toml` for command details. + 89 + 90 Arguments + 91 --------- + 92 dataLake + 93 Command line argument with the path to the data lake. + 94 + 95 Returns + 96 ------- + 97 The number of jump results from processing all the FlySight files in the + 98 data lake. + 99 """ +100 _assertDataLake(dataLake) +101 +102 click.secho('Processing speed tracks in %s...\n' % dataLake) +103 jumpResults = processAllJumpFiles(getAllSpeedJumpFilesFrom(dataLake)) +104 if jumpResults: +105 resultsSummary = aggregateResults(jumpResults) +106 click.secho(resultsSummary, fg = 'bright_green') +107 click.secho('\nTotal score = %5.2f, mean speed = %5.2f\n' % (resultsSummary.score.sum(), resultsSummary.score.mean()), fg = 'bright_white') +108 else: +109 click.secho('There were no speed track files to score in %s', fg = 'bright_red') +110 return len(jumpResults) +111 +112 +113@click.command('ssscore') +114@click.argument('datalake', nargs = 1, type = click.STRING) +115@click.version_option(__VERSION__, prog_name = 'ssscore') +116def ssscoreCommand(datalake: str) -> int: +117 """ +118 @private +119 """ +120 return ssscore(datalake) +121 122 -123# For interactive testing and symbolic debugging: -124if '__main__' == __name__: -125 ssscore() +123# +++ main +++ +124 +125# For interactive testing and symbolic debugging: +126if '__main__' == __name__: +127 ssscore() @@ -263,33 +265,37 @@

Arguments

 85def ssscore(dataLake: str) -> int:
  86    """
- 87    Process all the speed skydiving files contained in `dataLakeSpec`.
- 88
- 89    Arguments
- 90    ---------
- 91        dataLake
- 92    Command line argument with the path to the data lake.
- 93
- 94    Returns
- 95    -------
- 96    The number of jump results from processing all the FlySight files in the
- 97    data lake.
- 98    """
- 99    _assertDataLake(dataLake)
-100
-101    click.secho('Processing speed tracks in %s...\n' % dataLake)
-102    jumpResults = processAllJumpFiles(getAllSpeedJumpFilesFrom(dataLake))
-103    if jumpResults:
-104        resultsSummary = aggregateResults(jumpResults)
-105        click.secho(resultsSummary, fg = 'bright_green')
-106        click.secho('\nTotal score = %5.2f, mean speed = %5.2f\n' % (resultsSummary.score.sum(), resultsSummary.score.mean()), fg = 'bright_white')
-107    else:
-108        click.secho('There were no speed track files to score in %s', fg = 'bright_red')
-109    return len(jumpResults)
+ 87    Process all the speed skydiving files contained in `dataLakeSpec`.  This
+ 88    function implements the business logic for the `/usr/local/bin/ssscore`
+ 89    command.  See `pyproject.toml` for command details.
+ 90
+ 91    Arguments
+ 92    ---------
+ 93        dataLake
+ 94    Command line argument with the path to the data lake.
+ 95
+ 96    Returns
+ 97    -------
+ 98    The number of jump results from processing all the FlySight files in the
+ 99    data lake.
+100    """
+101    _assertDataLake(dataLake)
+102
+103    click.secho('Processing speed tracks in %s...\n' % dataLake)
+104    jumpResults = processAllJumpFiles(getAllSpeedJumpFilesFrom(dataLake))
+105    if jumpResults:
+106        resultsSummary = aggregateResults(jumpResults)
+107        click.secho(resultsSummary, fg = 'bright_green')
+108        click.secho('\nTotal score = %5.2f, mean speed = %5.2f\n' % (resultsSummary.score.sum(), resultsSummary.score.mean()), fg = 'bright_white')
+109    else:
+110        click.secho('There were no speed track files to score in %s', fg = 'bright_red')
+111    return len(jumpResults)
-

Process all the speed skydiving files contained in dataLakeSpec.

+

Process all the speed skydiving files contained in dataLakeSpec. This +function implements the business logic for the /usr/local/bin/ssscore +command. See pyproject.toml for command details.

Arguments

diff --git a/docs/ssscoring/constants.html b/docs/ssscoring/constants.html index 019a499..c401bc4 100644 --- a/docs/ssscoring/constants.html +++ b/docs/ssscoring/constants.html @@ -273,7 +273,7 @@

FLYSIGHT_1_HEADER = - {'hAcc', 'sAcc', 'velD', 'velE', 'heading', 'vAcc', 'numSV', 'lon', 'gpsFix', 'hMSL', 'cAcc', 'velN', 'lat', 'time'} + {'velD', 'numSV', 'time', 'hMSL', 'velN', 'hAcc', 'heading', 'gpsFix', 'vAcc', 'sAcc', 'lon', 'cAcc', 'velE', 'lat'}
@@ -461,7 +461,7 @@

SKYTRAX_1_HEADER = - {'hAcc', 'sAcc', 'velD', 'velE', 'heading', 'vAcc', 'numSV', 'lon', 'gpsFix', 'hMSL', 'cAcc', 'velN', 'lat', 'time'} + {'velD', 'numSV', 'time', 'hMSL', 'velN', 'hAcc', 'heading', 'gpsFix', 'vAcc', 'sAcc', 'lon', 'cAcc', 'velE', 'lat'}
diff --git a/ssscoring/cli.py b/ssscoring/cli.py index 38b4715..173ad6e 100644 --- a/ssscoring/cli.py +++ b/ssscoring/cli.py @@ -124,5 +124,5 @@ def ssscoreCommand(datalake: str) -> int: # For interactive testing and symbolic debugging: if '__main__' == __name__: - ssscore() + ssscoreCommand()