API

from nbdev.showdoc import *
from esploco import esploco

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esploco.init

 esploco.__init__ (dataFolder, startMin=0, endMin=120,
                   companionEspObj=None, initialResamplePeriod=50,
                   smoothing=True, longForm=False)

Reads and stores information from countLogs produced by Critta espresso plugin

	Type	Default	Details
dataFolder	str		path to the directory containing all output files of espresso assay
startMin	int	0	starting minute for analysis
endMin	int	120	ending minute for analysis
companionEspObj	NoneType	None	from the espresso analysis package
initialResamplePeriod	int	50	period of sample in ms (initialResamplePeriod = 50 ms, sampling frequency = 1000/50 = 20 Hz), default 50
smoothing	bool	True	whether or not to smooth the trajectories, default True
longForm	bool	False	whether or not the data input is the same set of flies but over many days.
Returns	`esploco` object		`esploco.resultsDf` : contains relevant output metrics `esploco.resultsDf.ID` : from metadata `esploco.resultsDf.Status` : ‘Test’ or ‘Ctrl’ `esploco.resultsDf.Genotype` : genotype `esploco.resultsDf.Sex` : from metadata `esploco.resultsDf.MinimumAge` : from metadata `esploco.resultsDf.MaximumAge` : from metadata `esploco.resultsDf.Food1` : from metadata `esploco.resultsDf.Food2` : from metadata `esploco.resultsDf.Temperature` : from metadata `esploco.resultsDf.#Flies` : from metadata `esploco.resultsDf.Starvedhrs` : from metadata `esploco.resultsDf.Date` : date of feedlog `esploco.resultsDf.averageSpeed_mm/s` : instantaneous speed of the fly `esploco.resultsDf.xPosition_mm` : instantaneous x position of the fly `esploco.resultsDf.yPosition_mm` : instantaneous y position of the fly `esploco.resultsDf.inLeftPort` : proportion of time the fly was in left port `esploco.resultsDf.inRightPort` : proportion of time the fly was in right port `esploco.resultsDf.countLogDate` : date from countlog `esploco.resultsDf.feedLogDate` : date from feedlog

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esploco.calculatePeriFeedSpeed

 esploco.calculatePeriFeedSpeed (companionEspObj, monitorWindow=120,
                                 startSeconds=0, plotDiagonal=True,
                                 plotContrast=True)

Calculates speed of the fly around a feed

	Type	Default	Details
companionEspObj	object, default None		from the espresso analysis package
monitorWindow	int	120	size of the window in seconds before and after the feed to monitor speed in
startSeconds	int	0	lower range of data to analyse, deprecated in v.23.12.11
plotDiagonal	bool	True	whether or not to plot the diagonal speed plot
plotContrast	bool	True	whether or not to plot the contrast plots
Returns	`esploco.feedsRevisedDf`		pandas dataframe that contains individual feeds and info about them in a time series.

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esploco.calculateFallEvents

 esploco.calculateFallEvents (nstd=4, windowsize=1000, ewm1=12, ewm2=26,
                              ewm3=9)

Detects fall events

	Type	Default	Details
nstd	int	4	parameter in macd analysis
windowsize	int	1000	window size in macd analysis
ewm1	int	12	parameter in macd analysis
ewm2	int	26	parameter in macd analysis
ewm3	int	9	parameter in macd analysis
Returns	`self.resultsDf`		updated to include `falls`

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esploco.plotStacked

 esploco.plotStacked (endMin=120, metricsToStack=['Volume', 'Speed'],
                      colorBy='Genotype', plotTitle='',
                      customPalette=None, figsize=None,
                      plotNonFeeders=True, dotratio=20, dotbase=5,
                      dotalpha=0.4, bubbleYLabelSize=12, ylimPresets=None,
                      showRasterYticks=False, ribbonLegend=False,
                      bubbleLegend=True)

Plots a raster of feeds stacked with a selection of other metrics in a ribbon

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esploco.plotChamberSmallMultiples

 esploco.plotChamberSmallMultiples (ncol=15, customPalette=None,
                                    setNumber=None)

Plots trajectories and or heatmaps in the arrangement of the chambers for each dataset

	Type	Default	Details
ncol	int	15	number of columns for the plots.
customPalette	NoneType	None	user can supply a dict for use as a custom palette.
setNumber	NoneType	None	user specfied set to plot.
Returns	`chamberSmallsTrack`		figure object.

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esploco.plotMeanHeatMaps

 esploco.plotMeanHeatMaps (binSize=0.2, row=None, col=None,
                           reverseRows=False, reverseCols=False,
                           verbose=False, heatmapCMap='RdYlBu_r',
                           smooth=2, plotZScore=False, vmin=None,
                           vmax=None)

Plots heatmap of mean duration stayed at each location throughout the chamber grouped by criteria

	Type	Default	Details
binSize	float	0.2	the size of the pixel in the heatmap
row	NoneType	None	a column name or independent variable to use for grouping the rows
col	NoneType	None	a column name or independent variable to use for grouping the columns
reverseRows	bool	False	to reverse the order of the rows
reverseCols	bool	False	to reverse the order of the columns
verbose	bool	False	to produce output
heatmapCMap	str	RdYlBu_r	colormap used for the heatmap
smooth	int	2	this defines how much smoothing happenes in with the Gaussian Kernel.
plotZScore	bool	False	this toggles between plotting raw heatmap in seconds and z-score
vmin	NoneType	None	this forces the vmin on the heatmap color scale
vmax	NoneType	None	this forces the vmax on the heatmap color scale
Returns	`meanHeatmapFig`		figure object.