locoDataMunger

extractDateStr

 extractDateStr (s)

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readMetaAndCount

 readMetaAndCount (dataFolder, companionEspObj, startMin, endMin,
                   initialResamplePeriod, smoothing, longForm=False)

Reads the MetaDatas, CountLogs, FeedLogs, PortLocations from the path provided

	Type	Default	Details
dataFolder	str		path to folder that contains all the relevant data files.
companionEspObj	data object		calculated from espresso package.
startMin	int,		the starting minute for the period to be included in the analysis.
endMin	int		the ending minute for the period to be included in the analysis.
initialResamplePeriod	int		period for resampling functions in milliseconds, default 50.
smoothing	boolean		parameter for whether or not to smooth the data.
longForm	bool	False	parameter to indicate whether or not the multiple data files were from one longitudinal experiment.
Returns	pandas dataframe		contains all the metaData tables.

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calculateSpeedinCountLog

 calculateSpeedinCountLog (countLogDf, companionPortLocationsDf,
                           smoothing, speedThreshold=30,
                           gaussianWindowSize=10, gaussianSTD=3)

Calcualtes speed from data in countLogs

	Type	Default	Details
countLogDf	pandas dataframe		contains all the countLog tables.
companionPortLocationsDf	pandas dataframe		contains all of the portLocation tables.
smoothing
speedThreshold	int	30	threshold to remove data points where speed is deemed too high, default value 30.
gaussianWindowSize	int	10	window size for gaussian smoothing, default value 10.
gaussianSTD	int	3
Returns	pandas dataframe		contains all updated countLogs, including newly calculated speed.

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calculatePeriFeedLoco

 calculatePeriFeedLoco (countLogDf, companionPortLocationsDf,
                        companionEspObj, exptSum, monitorWindow=120,
                        startMin=0, endMin=120)

Calculates speed around feeds

	Type	Default	Details
countLogDf	pandas dataframe		contains all the countLog tables.
companionPortLocationsDf	pandas dataframe		contains all of the portLocation tables.
companionEspObj	data object		calculated from espresso package.
exptSum
monitorWindow	int	120	window size in seconds for the period before and after feed for speed to be calculated
startMin	int	0	the starting minute for the period to be included in the analysis
endMin	int	120	the ending minute for the period to be included in the analysis
Returns	pandas dataframe		contains updated feeds with associated per feed metrics.

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fallEvents

 fallEvents (countLogDf, nstd=4, windowsize=1000, ewm1=12, ewm2=26,
             ewm3=9)

Calculates fall events

	Type	Default	Details
countLogDf	pandas dataframe		contains all the countLog tables.
nstd	int	4	number of standards deviations for calculating speed threshold for fall detection, default value 4
windowsize	int	1000	number of samples around the fall event for speed threshold to be calculated.
ewm1	int	12
ewm2	int	26	moving average analysis parameter 2, default value 26.
ewm3	int	9	moving average analysis parameter 3, default value 9.
Returns	pandas series		a list of timestamps where falls happened

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labelStretches

 labelStretches (vector)

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correctInPortData

 correctInPortData (countLogDf)

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intrapolateUnderThreshold

 intrapolateUnderThreshold (s, th)

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assignStatus

 assignStatus (metaDataDf)