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A 4-dimensional array in the style of anijtiff_img (indexed byimg[y, x, channel, frame])or a 3-dimensional array which is a single channel of anijtiff_img (indexed byimg[y, x, frame]).

A character. Which definition of brightness do you want to use,"B" or"epsilon"?

The threshold or thresholding method (seeautothresholdr::mean_stack_thresh()) to use on the image prior todetrending and brightness calculations.

Detrend your data withdetrendr::img_detrend_rh(). This isthe best known detrending method for brightness analysis. For morefine-grained control over your detrending, use thedetrendr package. Ifthere are many channels, this may be specified as a vector, one element foreach channel.

IfFALSE (the default), the swap finding routine is runseveral times to get a consensus for the best parameter. IfTRUE, theswap finding routine is run only once.

Do you want to smooth (filt = 'mean') or median (filt = 'median') filter the number image usingsmooth_filter() ormedian_filter() respectively? If selected, these are invoked here with afilter radius of 1 (with corners included, so each median is the median of9 elements) and with the optionna_count = TRUE. If you want tosmooth/median filter the number image in a different way, first calculatethe numbers without filtering (filt = NULL) using this function and thenperform your desired filtering routine on the result. If there are manychannels, this may be specified as a vector, one element for each channel.

A positive number. TheS-factor of microscope acquisition.

Microscope acquisition parameters. See referenceDalal et al.

Microscope acquisition parameters. See referenceDalal et al.

Would you like to use multiple cores to speed up thisfunction? If so, set the number of cores here, or to use all availablecores, useparallel = TRUE.

The path (relative or absolute) to the folder you wish toprocess.

A character. Which definition of brightness do you want to use,"B" or"epsilon"?

The threshold or thresholding method (seeautothresholdr::mean_stack_thresh()) to use on the image prior todetrending and brightness calculations.

Detrend your data withdetrendr::img_detrend_rh(). This isthe best known detrending method for brightness analysis. For morefine-grained control over your detrending, use thedetrendr package. Ifthere are many channels, this may be specified as a vector, one element foreach channel.

IfFALSE (the default), the swap finding routine is runseveral times to get a consensus for the best parameter. IfTRUE, theswap finding routine is run only once.

Do you want to smooth (filt = 'mean') or median (filt = 'median') filter the number image usingsmooth_filter() ormedian_filter() respectively? If selected, these are invoked here with afilter radius of 1 (with corners included, so each median is the median of9 elements) and with the optionna_count = TRUE. If you want tosmooth/median filter the number image in a different way, first calculatethe numbers without filtering (filt = NULL) using this function and thenperform your desired filtering routine on the result. If there are manychannels, this may be specified as a vector, one element for each channel.

A positive number. TheS-factor of microscope acquisition.

Microscope acquisition parameters. See referenceDalal et al.

Microscope acquisition parameters. See referenceDalal et al.

Would you like to use multiple cores to speed up thisfunction? If so, set the number of cores here, or to use all availablecores, useparallel = TRUE.

A 4-dimensional array in the style of anijtiff_img (indexed byimg[y, x, channel, frame])or a 3-dimensional array which is a single channel of anijtiff_img (indexed byimg[y, x, frame]).

A character. Which definition of brightness do you want to use,"B" or"epsilon"?

The number of frames with which to calculate thesuccessive brightnesses.

A boolean. IfTRUE, the windows used to calculate number areoverlapped, ifFALSE, they are not. For example, for a 20-frame imageseries with 5 frames per set, if the windows are not overlapped, then theframe sets used are 1-5, 6-10, 11-15 and 16-20; whereas if they areoverlapped, the frame sets are 1-5, 2-6, 3-7, 4-8 and so on up to 16-20.

The threshold or thresholding method (seeautothresholdr::mean_stack_thresh()) to use on the image prior todetrending and brightness calculations.

Detrend your data withdetrendr::img_detrend_rh(). This isthe best known detrending method for brightness analysis. For morefine-grained control over your detrending, use thedetrendr package. Ifthere are many channels, this may be specified as a vector, one element foreach channel.

IfFALSE (the default), the swap finding routine is runseveral times to get a consensus for the best parameter. IfTRUE, theswap finding routine is run only once.

Do you want to smooth (filt = 'mean') or median (filt = 'median') filter the number image usingsmooth_filter() ormedian_filter() respectively? If selected, these are invoked here with afilter radius of 1 (with corners included, so each median is the median of9 elements) and with the optionna_count = TRUE. If you want tosmooth/median filter the number image in a different way, first calculatethe numbers without filtering (filt = NULL) using this function and thenperform your desired filtering routine on the result. If there are manychannels, this may be specified as a vector, one element for each channel.

A positive number. TheS-factor of microscope acquisition.

Microscope acquisition parameters. See referenceDalal et al.

Microscope acquisition parameters. See referenceDalal et al.

Would you like to use multiple cores to speed up thisfunction? If so, set the number of cores here, or to use all availablecores, useparallel = TRUE.

The path (relative or absolute) to the folder you wish toprocess.

A character. Which definition of brightness do you want to use,"B" or"epsilon"?

The number of frames with which to calculate thesuccessive brightnesses.

A boolean. IfTRUE, the windows used to calculate number areoverlapped, ifFALSE, they are not. For example, for a 20-frame imageseries with 5 frames per set, if the windows are not overlapped, then theframe sets used are 1-5, 6-10, 11-15 and 16-20; whereas if they areoverlapped, the frame sets are 1-5, 2-6, 3-7, 4-8 and so on up to 16-20.

The threshold or thresholding method (seeautothresholdr::mean_stack_thresh()) to use on the image prior todetrending and brightness calculations.

Detrend your data withdetrendr::img_detrend_rh(). This isthe best known detrending method for brightness analysis. For morefine-grained control over your detrending, use thedetrendr package. Ifthere are many channels, this may be specified as a vector, one element foreach channel.

IfFALSE (the default), the swap finding routine is runseveral times to get a consensus for the best parameter. IfTRUE, theswap finding routine is run only once.

Do you want to smooth (filt = 'mean') or median (filt = 'median') filter the number image usingsmooth_filter() ormedian_filter() respectively? If selected, these are invoked here with afilter radius of 1 (with corners included, so each median is the median of9 elements) and with the optionna_count = TRUE. If you want tosmooth/median filter the number image in a different way, first calculatethe numbers without filtering (filt = NULL) using this function and thenperform your desired filtering routine on the result. If there are manychannels, this may be specified as a vector, one element for each channel.

A positive number. TheS-factor of microscope acquisition.

Microscope acquisition parameters. See referenceDalal et al.

Microscope acquisition parameters. See referenceDalal et al.

Would you like to use multiple cores to speed up thisfunction? If so, set the number of cores here, or to use all availablecores, useparallel = TRUE.

The calculated cross-correlated number or brightness image.

A positive integer, possibly an object of classautothresholdr::th. If the different channels of the image had differentthresholds, this argument may be specified as a vector or list (of positiveintegers, possibly objects of classautothresholdr::th), one element foreach channel.

A non-negative integer with an attributeauto. If thedifferent channels of the image had differentswaps, this argument may bespecified as a list (of non-negative integers with attributesauto), oneelement for each channel. For undetrended images, setswaps = NA.

A string, the filtering method used. Must be either"mean" or"median", orNA for no filtering. If the different channels of theimage had different filters, this may be specified as a character vector,one element for each channel.

The calculated cross-correlated number or brightness time seriesimage series.

The number of frames used in the calculation of eachpoint in the cross-correlated number or brightness time series.

A boolean.TRUE indicates that the windows used tocalculate consecutive brightnesses over time were overlapped,FALSEindicates that they were not.

A positive integer, possibly an object of classautothresholdr::th. If the different channels of the image had differentthresholds, this argument may be specified as a vector or list (of positiveintegers, possibly objects of classautothresholdr::th), one element foreach channel.

A non-negative integer with an attributeauto. If thedifferent channels of the image had differentswaps, this argument may bespecified as a list (of non-negative integers with attributesauto), oneelement for each channel. For undetrended images, setswaps = NA.

A string, the filtering method used. Must be either"mean" or"median", orNA for no filtering. If the different channels of theimage had different filters, this may be specified as a character vector,one element for each channel.

A 4-dimensional array of images indexed byimg[y, x, channel, frame] (an object of classijtiff::ijtiff_img). The image to perform thecalculation on. To perform this on a file that has not yet been read in,set this argument to the path to that file (a string).

A natural number. The index of the first channel to use.

A natural number. The index of the second channel to use.

Do you want to apply an intensity threshold prior tocalculating cross-correlated brightness (viaautothresholdr::mean_stack_thresh())? If so, set your thresholding methodhere. If this is a single value, that same threshold will be applied toboth channels. If this is a length-2 vector or list, then these twothresholds will be applied to channels 1 and 2 respectively. A value ofNA for either channel gives no thresholding for that channel.

Detrend your data withdetrendr::img_detrend_rh(). This isthe best known detrending method for brightness analysis. For morefine-grained control over your detrending, use thedetrendr package. Todetrend one channel and not the other, specify this as a length 2 vector.

FALSE repeats the detrending procedure (which has some inherentrandomness) a few times to hone in on the best detrend.TRUE is quicker,performing the routine only once.FALSE is better.

Do you want to smooth (filt = 'smooth') or median (filt = 'median') filter the cross-correlated brightness image usingsmooth_filter() ormedian_filter() respectively? If selected, these areinvoked here with a filter radius of 1 and with the optionna_count = TRUE. A value ofNA for either channel gives no thresholding for thatchannel. If you want to smooth/median filter the cross-correlatedbrightness image in a different way, first calculate the cross-correlatedbrightnesses without filtering (filt = NULL) using this function and thenperform your desired filtering routine on the result.

Would you like to use multiple cores to speed up thisfunction? If so, set the number of cores here, or to use all availablecores, useparallel = TRUE.

The path (relative or absolute) to the folder you wish toprocess.

A natural number. The index of the first channel to use.

A natural number. The index of the second channel to use.

Do you want to apply an intensity threshold prior tocalculating cross-correlated brightness (viaautothresholdr::mean_stack_thresh())? If so, set your thresholding methodhere. If this is a single value, that same threshold will be applied toboth channels. If this is a length-2 vector or list, then these twothresholds will be applied to channels 1 and 2 respectively. A value ofNA for either channel gives no thresholding for that channel.

Detrend your data withdetrendr::img_detrend_rh(). This isthe best known detrending method for brightness analysis. For morefine-grained control over your detrending, use thedetrendr package. Todetrend one channel and not the other, specify this as a length 2 vector.

FALSE repeats the detrending procedure (which has some inherentrandomness) a few times to hone in on the best detrend.TRUE is quicker,performing the routine only once.FALSE is better.

Do you want to smooth (filt = 'smooth') or median (filt = 'median') filter the cross-correlated brightness image usingsmooth_filter() ormedian_filter() respectively? If selected, these areinvoked here with a filter radius of 1 and with the optionna_count = TRUE. A value ofNA for either channel gives no thresholding for thatchannel. If you want to smooth/median filter the cross-correlatedbrightness image in a different way, first calculate the cross-correlatedbrightnesses without filtering (filt = NULL) using this function and thenperform your desired filtering routine on the result.

Would you like to use multiple cores to speed up thisfunction? If so, set the number of cores here, or to use all availablecores, useparallel = TRUE.

A 4-dimensional array of images indexed byimg[y, x, channel, frame] (an object of classijtiff::ijtiff_img). The image to perform thecalculation on. To perform this on a file that has not yet been read in,set this argument to the path to that file (a string).

The number of frames with which to calculate thesuccessive cross-correlated brightnesses.

This may discard some images, for example if 175 frames are in the input andframes_per_set = 50, then the last 25 are discarded. If bleaching or/andthresholding are selected, they are performed on the whole image stack beforethe sectioning is done for calculation of cross-correlated brightnesses.

A boolean. IfTRUE, the windows used to calculate brightnessare overlapped, ifFALSE, they are not. For example, for a 20-frame imageseries with 5 frames per set, if the windows are not overlapped, then theframe sets used are 1-5, 6-10, 11-15 and 16-20; whereas if they areoverlapped, the frame sets are 1-5, 2-6, 3-7, 4-8 and so on up to 16-20.

A natural number. The index of the first channel to use.

A natural number. The index of the second channel to use.

Do you want to apply an intensity threshold prior tocalculating cross-correlated brightness (viaautothresholdr::mean_stack_thresh())? If so, set your thresholding methodhere. If this is a single value, that same threshold will be applied toboth channels. If this is a length-2 vector or list, then these twothresholds will be applied to channels 1 and 2 respectively. A value ofNA for either channel gives no thresholding for that channel.

Detrend your data withdetrendr::img_detrend_rh(). This isthe best known detrending method for brightness analysis. For morefine-grained control over your detrending, use thedetrendr package. Todetrend one channel and not the other, specify this as a length 2 vector.

FALSE repeats the detrending procedure (which has some inherentrandomness) a few times to hone in on the best detrend.TRUE is quicker,performing the routine only once.FALSE is better.

Do you want to smooth (filt = 'smooth') or median (filt = 'median') filter the cross-correlated brightness image usingsmooth_filter() ormedian_filter() respectively? If selected, these areinvoked here with a filter radius of 1 and with the optionna_count = TRUE. A value ofNA for either channel gives no thresholding for thatchannel. If you want to smooth/median filter the cross-correlatedbrightness image in a different way, first calculate the cross-correlatedbrightnesses without filtering (filt = NULL) using this function and thenperform your desired filtering routine on the result.

Would you like to use multiple cores to speed up thisfunction? If so, set the number of cores here, or to use all availablecores, useparallel = TRUE.

The path (relative or absolute) to the folder you wish toprocess.

The number of frames with which to calculate thesuccessive cross-correlated brightnesses.

This may discard some images, for example if 175 frames are in the input andframes_per_set = 50, then the last 25 are discarded. If bleaching or/andthresholding are selected, they are performed on the whole image stack beforethe sectioning is done for calculation of cross-correlated brightnesses.

A boolean. IfTRUE, the windows used to calculate brightnessare overlapped, ifFALSE, they are not. For example, for a 20-frame imageseries with 5 frames per set, if the windows are not overlapped, then theframe sets used are 1-5, 6-10, 11-15 and 16-20; whereas if they areoverlapped, the frame sets are 1-5, 2-6, 3-7, 4-8 and so on up to 16-20.

A natural number. The index of the first channel to use.

A natural number. The index of the second channel to use.

Do you want to apply an intensity threshold prior tocalculating cross-correlated brightness (viaautothresholdr::mean_stack_thresh())? If so, set your thresholding methodhere. If this is a single value, that same threshold will be applied toboth channels. If this is a length-2 vector or list, then these twothresholds will be applied to channels 1 and 2 respectively. A value ofNA for either channel gives no thresholding for that channel.

Detrend your data withdetrendr::img_detrend_rh(). This isthe best known detrending method for brightness analysis. For morefine-grained control over your detrending, use thedetrendr package. Todetrend one channel and not the other, specify this as a length 2 vector.

FALSE repeats the detrending procedure (which has some inherentrandomness) a few times to hone in on the best detrend.TRUE is quicker,performing the routine only once.FALSE is better.

Do you want to smooth (filt = 'smooth') or median (filt = 'median') filter the cross-correlated brightness image usingsmooth_filter() ormedian_filter() respectively? If selected, these areinvoked here with a filter radius of 1 and with the optionna_count = TRUE. A value ofNA for either channel gives no thresholding for thatchannel. If you want to smooth/median filter the cross-correlatedbrightness image in a different way, first calculate the cross-correlatedbrightnesses without filtering (filt = NULL) using this function and thenperform your desired filtering routine on the result.

Would you like to use multiple cores to speed up thisfunction? If so, set the number of cores here, or to use all availablecores, useparallel = TRUE.

A 4-dimensional array of images indexed byimg[y, x, channel, frame] (an object of classijtiff::ijtiff_img). The image to perform thecalculation on. To perform this on a file that has not yet been read in,set this argument to the path to that file (a string).

A natural number. The index of the first channel to use.

A natural number. The index of the second channel to use.

Do you want to apply an intensity threshold prior tocalculating cross-correlated number (viaautothresholdr::mean_stack_thresh())? If so, set your thresholding methodhere. If this is a single value, that same threshold will be applied toboth channels. If this is a length-2 vector or list, then these twothresholds will be applied to channels 1 and 2 respectively. A value ofNA for either channel gives no thresholding for that channel.

Detrend your data withdetrendr::img_detrend_rh(). This isthe best known detrending method for brightness analysis. For morefine-grained control over your detrending, use thedetrendr package. Todetrend one channel and not the other, specify this as a length 2 vector.

FALSE repeats the detrending procedure (which has some inherentrandomness) a few times to hone in on the best detrend.TRUE is quicker,performing the routine only once.FALSE is better.

Do you want to smooth (filt = 'smooth') or median (filt = 'median') filter the cross-correlated number image usingsmooth_filter()ormedian_filter() respectively? If selected, these are invoked here witha filter radius of 1 and with the optionna_count = TRUE. A value ofNAfor either channel gives no thresholding for that channel. If you want tosmooth/median filter the cross-correlated number image in a different way,first calculate the cross-correlated numbers without filtering (filt = NULL) using this function and then perform your desired filtering routineon the result.

Would you like to use multiple cores to speed up thisfunction? If so, set the number of cores here, or to use all availablecores, useparallel = TRUE.

The path (relative or absolute) to the folder you wish toprocess.

A natural number. The index of the first channel to use.

A natural number. The index of the second channel to use.

Do you want to apply an intensity threshold prior tocalculating cross-correlated number (viaautothresholdr::mean_stack_thresh())? If so, set your thresholding methodhere. If this is a single value, that same threshold will be applied toboth channels. If this is a length-2 vector or list, then these twothresholds will be applied to channels 1 and 2 respectively. A value ofNA for either channel gives no thresholding for that channel.

Detrend your data withdetrendr::img_detrend_rh(). This isthe best known detrending method for brightness analysis. For morefine-grained control over your detrending, use thedetrendr package. Todetrend one channel and not the other, specify this as a length 2 vector.

FALSE repeats the detrending procedure (which has some inherentrandomness) a few times to hone in on the best detrend.TRUE is quicker,performing the routine only once.FALSE is better.

Do you want to smooth (filt = 'smooth') or median (filt = 'median') filter the cross-correlated number image usingsmooth_filter()ormedian_filter() respectively? If selected, these are invoked here witha filter radius of 1 and with the optionna_count = TRUE. A value ofNAfor either channel gives no thresholding for that channel. If you want tosmooth/median filter the cross-correlated number image in a different way,first calculate the cross-correlated numbers without filtering (filt = NULL) using this function and then perform your desired filtering routineon the result.

Would you like to use multiple cores to speed up thisfunction? If so, set the number of cores here, or to use all availablecores, useparallel = TRUE.

A 4-dimensional array of images indexed byimg[y, x, channel, frame] (an object of classijtiff::ijtiff_img). The image to perform thecalculation on. To perform this on a file that has not yet been read in,set this argument to the path to that file (a string).

The number of frames with which to calculate thesuccessive cross-correlated numbers.

This may discard some images, for example if 175 frames are in the input andframes_per_set = 50, then the last 25 are discarded. If bleaching or/andthresholding are selected, they are performed on the whole image stack beforethe sectioning is done for calculation of cross-correlated numbers.

A boolean. IfTRUE, the windows used to calculate brightnessare overlapped, ifFALSE, they are not. For example, for a 20-frame imageseries with 5 frames per set, if the windows are not overlapped, then theframe sets used are 1-5, 6-10, 11-15 and 16-20; whereas if they areoverlapped, the frame sets are 1-5, 2-6, 3-7, 4-8 and so on up to 16-20.

A natural number. The index of the first channel to use.

A natural number. The index of the second channel to use.

Do you want to apply an intensity threshold prior tocalculating cross-correlated number (viaautothresholdr::mean_stack_thresh())? If so, set your thresholding methodhere. If this is a single value, that same threshold will be applied toboth channels. If this is a length-2 vector or list, then these twothresholds will be applied to channels 1 and 2 respectively. A value ofNA for either channel gives no thresholding for that channel.

Detrend your data withdetrendr::img_detrend_rh(). This isthe best known detrending method for brightness analysis. For morefine-grained control over your detrending, use thedetrendr package. Todetrend one channel and not the other, specify this as a length 2 vector.

FALSE repeats the detrending procedure (which has some inherentrandomness) a few times to hone in on the best detrend.TRUE is quicker,performing the routine only once.FALSE is better.

Do you want to smooth (filt = 'smooth') or median (filt = 'median') filter the cross-correlated number image usingsmooth_filter()ormedian_filter() respectively? If selected, these are invoked here witha filter radius of 1 and with the optionna_count = TRUE. A value ofNAfor either channel gives no thresholding for that channel. If you want tosmooth/median filter the cross-correlated number image in a different way,first calculate the cross-correlated numbers without filtering (filt = NULL) using this function and then perform your desired filtering routineon the result.

Would you like to use multiple cores to speed up thisfunction? If so, set the number of cores here, or to use all availablecores, useparallel = TRUE.

The path (relative or absolute) to the folder you wish toprocess.

The number of frames with which to calculate thesuccessive cross-correlated numbers.

This may discard some images, for example if 175 frames are in the input andframes_per_set = 50, then the last 25 are discarded. If bleaching or/andthresholding are selected, they are performed on the whole image stack beforethe sectioning is done for calculation of cross-correlated numbers.

A boolean. IfTRUE, the windows used to calculate brightnessare overlapped, ifFALSE, they are not. For example, for a 20-frame imageseries with 5 frames per set, if the windows are not overlapped, then theframe sets used are 1-5, 6-10, 11-15 and 16-20; whereas if they areoverlapped, the frame sets are 1-5, 2-6, 3-7, 4-8 and so on up to 16-20.

A natural number. The index of the first channel to use.

A natural number. The index of the second channel to use.

Do you want to apply an intensity threshold prior tocalculating cross-correlated number (viaautothresholdr::mean_stack_thresh())? If so, set your thresholding methodhere. If this is a single value, that same threshold will be applied toboth channels. If this is a length-2 vector or list, then these twothresholds will be applied to channels 1 and 2 respectively. A value ofNA for either channel gives no thresholding for that channel.

Detrend your data withdetrendr::img_detrend_rh(). This isthe best known detrending method for brightness analysis. For morefine-grained control over your detrending, use thedetrendr package. Todetrend one channel and not the other, specify this as a length 2 vector.

FALSE repeats the detrending procedure (which has some inherentrandomness) a few times to hone in on the best detrend.TRUE is quicker,performing the routine only once.FALSE is better.

Do you want to smooth (filt = 'smooth') or median (filt = 'median') filter the cross-correlated number image usingsmooth_filter()ormedian_filter() respectively? If selected, these are invoked here witha filter radius of 1 and with the optionna_count = TRUE. A value ofNAfor either channel gives no thresholding for that channel. If you want tosmooth/median filter the cross-correlated number image in a different way,first calculate the cross-correlated numbers without filtering (filt = NULL) using this function and then perform your desired filtering routineon the result.

Would you like to use multiple cores to speed up thisfunction? If so, set the number of cores here, or to use all availablecores, useparallel = TRUE.

A numeric vector.

A numeric vector with the same length asx.

A 3-dimensional array.

A 3-dimensional array with the same dimensions asx.

The matrix you wish to plot.

A string. The title of the color scale on the right of theplot.

This gives the user the option to set all values outside acertain range to their nearest value within that range (ifclip = TRUE) or toNA (ifclip = FALSE. For example, to set allvalues outside the range [1.5, 2.6) toNA, use⁠limits = c(1.5, 2.6), clip = FALSE⁠. The colour range will cover all values within thesespecified limits.

A numeric vector. If you want specific ranges of values to havethe same color, specify these ranges via an increasing numeric vector. Forexample, if you want the ranges 0.5-1.2 and 1.2-3, useranges = c(0.5, 1.2, 3). Ifranges is specified as a number (a numericvector of length 1)n, this is equivalent to setting ranges to ben equal-length intervals within the range of the matrix, i.e. it isequivalent to setting 'ranges = seq(min(mat), max(mat), length.out = n

• 1)⁠. At most one of ⁠rangesandlimits⁠should be set. If ranges is set, the behaviour for values which are not in any of the ranges are set by the⁠clip⁠arguments as in the⁠limits' argument.

A character vector. If your colour scale is discrete, hereyou can set the names which will label each range in the legend.

If you have setranges, here you may specify whichcolors you wish to colour each range. It must have the same length as thenumber of intervals you have specified inranges. If you have notspecifiedranges, here you may specify the colours (to be passed toggplot2::scale_fill_gradientn()) to create the continuouscolour band. It is specified as a character vector, with the colorsspecified either as the values incolors() or as in the valueof thergb() function. Note that this allows the use ofgrDevices::rainbow() and friends. The default usesviridis::viridis().

Which colour should theNA pixels be? Default isblack.

If eitherlimits orranges are set (one shouldnever set both), there may be values that fall outside the specifiedlimits/ranges. Ifclip = TRUE, values outside these limits/rangesare set to their nearest values within them, but ifclip = FALSE,these values are set to NA. Note that settingclip = TRUE isequivalent to setting bothclip_low andclip_high toTRUE.

Setting this toTRUE (and leavingclip = FALSE,clip_high = FALSE) will set all values falling below the specifiedlimits/ranges to their nearest value within them, but all values fallingabove those limits/ranges will be set toNA.

Setting this toTRUE (and leavingclip = FALSE,clip_low = FALSE) will set all values falling above thespecified limits/ranges to their nearest value within them, but all valuesfalling below those limits/ranges will be set toNA.

Do you want to log-transform the colour scaling?

Where do you want tick marks to appear on the legend colourscale?

If you don't want to specify all the breaks, but youwant some specific ones to be included on the legend colour scale, specifythose specific ones here.

A matrix (representing an image).

An integer; the median filter radius.

ShouldNAs be ignored?

If this is TRUE, in each median calculation, if the majorityof arguments areNAs,NA is returned but if theNAs arein the minority, they are ignored as inmedian(x, na.rm = TRUE).

The calculated number or brightness image.

The number or brightness definition used.

A positive integer, possibly an object of classautothresholdr::th. If the different channels of the image had differentthresholds, this argument may be specified as a vector or list (of positiveintegers, possibly objects of classautothresholdr::th), one element foreach channel.

A non-negative integer with an attributeauto. If thedifferent channels of the image had differentswaps, this argument may bespecified as a list (of non-negative integers with attributesauto), oneelement for each channel. For undetrended images, setswaps = NA.

A string, the filtering method used. Must be either"mean" or"median", orNA for no filtering. If the different channels of theimage had different filters, this may be specified as a character vector,one element for each channel.

The calculated number or brightness time series image series.

The number or brightness definition used.

The number of frames used in the calculation of eachpoint in the number or brightness time series.

A boolean.TRUE indicates that the windows used tocalculate consecutive brightnesses over time were overlapped,FALSEindicates that they were not.

A positive integer, possibly an object of classautothresholdr::th. If the different channels of the image had differentthresholds, this argument may be specified as a vector or list (of positiveintegers, possibly objects of classautothresholdr::th), one element foreach channel.

A non-negative integer with an attributeauto. If thedifferent channels of the image had differentswaps, this argument may bespecified as a list (of non-negative integers with attributesauto), oneelement for each channel. For undetrended images, setswaps = NA.

A string, the filtering method used. Must be either"mean" or"median", orNA for no filtering. If the different channels of theimage had different filters, this may be specified as a character vector,one element for each channel.

A 4-dimensional array of images indexed byimg[y, x, channel, frame] (an object of classijtiff::ijtiff_img). The image to perform thecalculation on. To perform this on a file that has not yet been read in,set this argument to the path to that file (a string).

A character. Which definition of number do you want to use,"n"or"N"?

The threshold or thresholding method (seeautothresholdr::mean_stack_thresh()) to use on the image prior todetrending and number calculations. If there are many channels, this may bespecified as a vector or list, one element for each channel.

Detrend your data withdetrendr::img_detrend_rh(). This isthe best known detrending method for brightness analysis. For morefine-grained control over your detrending, use thedetrendr package. Ifthere are many channels, this may be specified as a vector, one element foreach channel.

FALSE repeats the detrending procedure (which has some inherentrandomness) a few times to hone in on the best detrend.TRUE is quicker,performing the routine only once.FALSE is better.

Do you want to smooth (filt = 'mean') or median (filt = 'median') filter the number image usingsmooth_filter() ormedian_filter() respectively? If selected, these are invoked here with afilter radius of 1 (with corners included, so each median is the median of9 elements) and with the optionna_count = TRUE. If you want tosmooth/median filter the number image in a different way, first calculatethe numbers without filtering (filt = NULL) using this function and thenperform your desired filtering routine on the result. If there are manychannels, this may be specified as a vector, one element for each channel.

A positive number. TheS-factor of microscope acquisition.

Microscope acquisition parameters. See referenceDalal et al.

Factor for correction of numbern due to the illuminationprofile. The default (gamma = 1) has no effect. Changing gamma will havethe effect of dividing the result bygamma, so the result withgamma = 0.5 is two times the result withgamma = 1. For a Gaussian illuminationprofile, usegamma = 0.3536; for a Gaussian-Lorentzian illuminationprofile, usegamma = 0.0760.

Would you like to use multiple cores to speed up thisfunction? If so, set the number of cores here, or to use all availablecores, useparallel = TRUE.

The path (relative or absolute) to the folder you wish toprocess.

A character. Which definition of number do you want to use,"n"or"N"?

The threshold or thresholding method (seeautothresholdr::mean_stack_thresh()) to use on the image prior todetrending and number calculations. If there are many channels, this may bespecified as a vector or list, one element for each channel.

Detrend your data withdetrendr::img_detrend_rh(). This isthe best known detrending method for brightness analysis. For morefine-grained control over your detrending, use thedetrendr package. Ifthere are many channels, this may be specified as a vector, one element foreach channel.

FALSE repeats the detrending procedure (which has some inherentrandomness) a few times to hone in on the best detrend.TRUE is quicker,performing the routine only once.FALSE is better.

Do you want to smooth (filt = 'mean') or median (filt = 'median') filter the number image usingsmooth_filter() ormedian_filter() respectively? If selected, these are invoked here with afilter radius of 1 (with corners included, so each median is the median of9 elements) and with the optionna_count = TRUE. If you want tosmooth/median filter the number image in a different way, first calculatethe numbers without filtering (filt = NULL) using this function and thenperform your desired filtering routine on the result. If there are manychannels, this may be specified as a vector, one element for each channel.

A positive number. TheS-factor of microscope acquisition.

Microscope acquisition parameters. See referenceDalal et al.

Microscope acquisition parameters. See referenceDalal et al.

Factor for correction of numbern due to the illuminationprofile. The default (gamma = 1) has no effect. Changing gamma will havethe effect of dividing the result bygamma, so the result withgamma = 0.5 is two times the result withgamma = 1. For a Gaussian illuminationprofile, usegamma = 0.3536; for a Gaussian-Lorentzian illuminationprofile, usegamma = 0.0760.

Would you like to use multiple cores to speed up thisfunction? If so, set the number of cores here, or to use all availablecores, useparallel = TRUE.

A 4-dimensional array of images indexed byimg[y, x, channel, frame] (an object of classijtiff::ijtiff_img). The image to perform thecalculation on. To perform this on a file that has not yet been read in,set this argument to the path to that file (a string).

A character. Which definition of number do you want to use,"n"or"N"?

The number of frames with which to calculate thesuccessive numbers.

A boolean. IfTRUE, the windows used to calculate brightnessare overlapped, ifFALSE, they are not. For example, for a 20-frame imageseries with 5 frames per set, if the windows are not overlapped, then theframe sets used are 1-5, 6-10, 11-15 and 16-20; whereas if they areoverlapped, the frame sets are 1-5, 2-6, 3-7, 4-8 and so on up to 16-20.

The threshold or thresholding method (seeautothresholdr::mean_stack_thresh()) to use on the image prior todetrending and number calculations. If there are many channels, this may bespecified as a vector or list, one element for each channel.

Detrend your data withdetrendr::img_detrend_rh(). This isthe best known detrending method for brightness analysis. For morefine-grained control over your detrending, use thedetrendr package. Ifthere are many channels, this may be specified as a vector, one element foreach channel.

FALSE repeats the detrending procedure (which has some inherentrandomness) a few times to hone in on the best detrend.TRUE is quicker,performing the routine only once.FALSE is better.

Do you want to smooth (filt = 'mean') or median (filt = 'median') filter the number image usingsmooth_filter() ormedian_filter() respectively? If selected, these are invoked here with afilter radius of 1 (with corners included, so each median is the median of9 elements) and with the optionna_count = TRUE. If you want tosmooth/median filter the number image in a different way, first calculatethe numbers without filtering (filt = NULL) using this function and thenperform your desired filtering routine on the result. If there are manychannels, this may be specified as a vector, one element for each channel.

A positive number. TheS-factor of microscope acquisition.

Microscope acquisition parameters. See referenceDalal et al.

Microscope acquisition parameters. See referenceDalal et al.

Factor for correction of numbern due to the illuminationprofile. The default (gamma = 1) has no effect. Changing gamma will havethe effect of dividing the result bygamma, so the result withgamma = 0.5 is two times the result withgamma = 1. For a Gaussian illuminationprofile, usegamma = 0.3536; for a Gaussian-Lorentzian illuminationprofile, usegamma = 0.0760.

Would you like to use multiple cores to speed up thisfunction? If so, set the number of cores here, or to use all availablecores, useparallel = TRUE.

The path (relative or absolute) to the folder you wish toprocess.

A character. Which definition of number do you want to use,"n"or"N"?

The number of frames with which to calculate thesuccessive numbers.

A boolean. IfTRUE, the windows used to calculate brightnessare overlapped, ifFALSE, they are not. For example, for a 20-frame imageseries with 5 frames per set, if the windows are not overlapped, then theframe sets used are 1-5, 6-10, 11-15 and 16-20; whereas if they areoverlapped, the frame sets are 1-5, 2-6, 3-7, 4-8 and so on up to 16-20.

The threshold or thresholding method (seeautothresholdr::mean_stack_thresh()) to use on the image prior todetrending and number calculations. If there are many channels, this may bespecified as a vector or list, one element for each channel.

Detrend your data withdetrendr::img_detrend_rh(). This isthe best known detrending method for brightness analysis. For morefine-grained control over your detrending, use thedetrendr package. Ifthere are many channels, this may be specified as a vector, one element foreach channel.

FALSE repeats the detrending procedure (which has some inherentrandomness) a few times to hone in on the best detrend.TRUE is quicker,performing the routine only once.FALSE is better.

Do you want to smooth (filt = 'mean') or median (filt = 'median') filter the number image usingsmooth_filter() ormedian_filter() respectively? If selected, these are invoked here with afilter radius of 1 (with corners included, so each median is the median of9 elements) and with the optionna_count = TRUE. If you want tosmooth/median filter the number image in a different way, first calculatethe numbers without filtering (filt = NULL) using this function and thenperform your desired filtering routine on the result. If there are manychannels, this may be specified as a vector, one element for each channel.

A positive number. TheS-factor of microscope acquisition.

Microscope acquisition parameters. See referenceDalal et al.

Microscope acquisition parameters. See referenceDalal et al.

Factor for correction of numbern due to the illuminationprofile. The default (gamma = 1) has no effect. Changing gamma will havethe effect of dividing the result bygamma, so the result withgamma = 0.5 is two times the result withgamma = 1. For a Gaussian illuminationprofile, usegamma = 0.3536; for a Gaussian-Lorentzian illuminationprofile, usegamma = 0.0760.

Would you like to use multiple cores to speed up thisfunction? If so, set the number of cores here, or to use all availablecores, useparallel = TRUE.