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

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

The threshold or thresholding method (see autothresholdr::mean_stack_thresh()) to use on the image prior to detrending and brightness calculations.

Detrend your data with detrendr::img_detrend_rh(). This is the best known detrending method for brightness analysis. For more fine-grained control over your detrending, use the detrendr package. If there are many channels, this may be specified as a vector, one element for each channel.

If FALSE (the default), the swap finding routine is run several times to get a consensus for the best parameter. If TRUE, the swap finding routine is run only once.

Do you want to smooth (filt = 'mean') or median (filt = 'median') filter the number image using smooth_filter() or median_filter() respectively? If selected, these are invoked here with a filter radius of 1 (with corners included, so each median is the median of 9 elements) and with the option na_count = TRUE. If you want to smooth/median filter the number image in a different way, first calculate the numbers without filtering (filt = NULL) using this function and then perform your desired filtering routine on the result. If there are many channels, this may be specified as a vector, one element for each channel.

A positive number. The S-factor of microscope acquisition.

Microscope acquisition parameters. See reference Dalal et al.

Microscope acquisition parameters. See reference Dalal et al.

Would you like to use multiple cores to speed up this function? If so, set the number of cores here, or to use all available cores, use parallel = TRUE.

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

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

The threshold or thresholding method (see autothresholdr::mean_stack_thresh()) to use on the image prior to detrending and brightness calculations.

Detrend your data with detrendr::img_detrend_rh(). This is the best known detrending method for brightness analysis. For more fine-grained control over your detrending, use the detrendr package. If there are many channels, this may be specified as a vector, one element for each channel.

If FALSE (the default), the swap finding routine is run several times to get a consensus for the best parameter. If TRUE, the swap finding routine is run only once.

Do you want to smooth (filt = 'mean') or median (filt = 'median') filter the number image using smooth_filter() or median_filter() respectively? If selected, these are invoked here with a filter radius of 1 (with corners included, so each median is the median of 9 elements) and with the option na_count = TRUE. If you want to smooth/median filter the number image in a different way, first calculate the numbers without filtering (filt = NULL) using this function and then perform your desired filtering routine on the result. If there are many channels, this may be specified as a vector, one element for each channel.

A positive number. The S-factor of microscope acquisition.

Microscope acquisition parameters. See reference Dalal et al.

Microscope acquisition parameters. See reference Dalal et al.

Would you like to use multiple cores to speed up this function? If so, set the number of cores here, or to use all available cores, use parallel = TRUE.

A 4-dimensional array in the style of an ijtiff_img (indexed by img[y, x, channel, frame]) or a 3-dimensional array which is a single channel of an ijtiff_img (indexed by img[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 the successive brightnesses.

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

The threshold or thresholding method (see autothresholdr::mean_stack_thresh()) to use on the image prior to detrending and brightness calculations.

Detrend your data with detrendr::img_detrend_rh(). This is the best known detrending method for brightness analysis. For more fine-grained control over your detrending, use the detrendr package. If there are many channels, this may be specified as a vector, one element for each channel.

If FALSE (the default), the swap finding routine is run several times to get a consensus for the best parameter. If TRUE, the swap finding routine is run only once.

Do you want to smooth (filt = 'mean') or median (filt = 'median') filter the number image using smooth_filter() or median_filter() respectively? If selected, these are invoked here with a filter radius of 1 (with corners included, so each median is the median of 9 elements) and with the option na_count = TRUE. If you want to smooth/median filter the number image in a different way, first calculate the numbers without filtering (filt = NULL) using this function and then perform your desired filtering routine on the result. If there are many channels, this may be specified as a vector, one element for each channel.

A positive number. The S-factor of microscope acquisition.

Microscope acquisition parameters. See reference Dalal et al.

Microscope acquisition parameters. See reference Dalal et al.

Would you like to use multiple cores to speed up this function? If so, set the number of cores here, or to use all available cores, use parallel = TRUE.

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

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

The number of frames with which to calculate the successive brightnesses.

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

The threshold or thresholding method (see autothresholdr::mean_stack_thresh()) to use on the image prior to detrending and brightness calculations.

Detrend your data with detrendr::img_detrend_rh(). This is the best known detrending method for brightness analysis. For more fine-grained control over your detrending, use the detrendr package. If there are many channels, this may be specified as a vector, one element for each channel.

If FALSE (the default), the swap finding routine is run several times to get a consensus for the best parameter. If TRUE, the swap finding routine is run only once.

Do you want to smooth (filt = 'mean') or median (filt = 'median') filter the number image using smooth_filter() or median_filter() respectively? If selected, these are invoked here with a filter radius of 1 (with corners included, so each median is the median of 9 elements) and with the option na_count = TRUE. If you want to smooth/median filter the number image in a different way, first calculate the numbers without filtering (filt = NULL) using this function and then perform your desired filtering routine on the result. If there are many channels, this may be specified as a vector, one element for each channel.

A positive number. The S-factor of microscope acquisition.

Microscope acquisition parameters. See reference Dalal et al.

Microscope acquisition parameters. See reference Dalal et al.

Would you like to use multiple cores to speed up this function? If so, set the number of cores here, or to use all available cores, use parallel = TRUE.

The calculated cross-correlated number or brightness image.

A positive integer, possibly an object of class autothresholdr::th. If the different channels of the image had different thresholds, this argument may be specified as a vector or list (of positive integers, possibly objects of class autothresholdr::th), one element for each channel.

A non-negative integer with an attribute auto. If the different channels of the image had different swaps, this argument may be specified as a list (of non-negative integers with attributes auto), one element for each channel. For undetrended images, set swaps = NA.

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

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

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

A boolean. TRUE indicates that the windows used to calculate consecutive brightnesses over time were overlapped, FALSE indicates that they were not.

A positive integer, possibly an object of class autothresholdr::th. If the different channels of the image had different thresholds, this argument may be specified as a vector or list (of positive integers, possibly objects of class autothresholdr::th), one element for each channel.

A non-negative integer with an attribute auto. If the different channels of the image had different swaps, this argument may be specified as a list (of non-negative integers with attributes auto), one element for each channel. For undetrended images, set swaps = NA.

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

A 4-dimensional array of images indexed by img[y, x, channel, frame] (an object of class ijtiff::ijtiff_img). The image to perform the calculation 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 to calculating cross-correlated brightness (via autothresholdr::mean_stack_thresh())? If so, set your thresholding method here. If this is a single value, that same threshold will be applied to both channels. If this is a length-2 vector or list, then these two thresholds will be applied to channels 1 and 2 respectively. A value of NA for either channel gives no thresholding for that channel.

Detrend your data with detrendr::img_detrend_rh(). This is the best known detrending method for brightness analysis. For more fine-grained control over your detrending, use the detrendr package. To detrend one channel and not the other, specify this as a length 2 vector.

FALSE repeats the detrending procedure (which has some inherent randomness) 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 using smooth_filter() or median_filter() respectively? If selected, these are invoked here with a filter radius of 1 and with the option na_count = TRUE. A value of NA for either channel gives no thresholding for that channel. If you want to smooth/median filter the cross-correlated brightness image in a different way, first calculate the cross-correlated brightnesses without filtering (filt = NULL) using this function and then perform your desired filtering routine on the result.

Would you like to use multiple cores to speed up this function? If so, set the number of cores here, or to use all available cores, use parallel = TRUE.

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

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 to calculating cross-correlated brightness (via autothresholdr::mean_stack_thresh())? If so, set your thresholding method here. If this is a single value, that same threshold will be applied to both channels. If this is a length-2 vector or list, then these two thresholds will be applied to channels 1 and 2 respectively. A value of NA for either channel gives no thresholding for that channel.

Detrend your data with detrendr::img_detrend_rh(). This is the best known detrending method for brightness analysis. For more fine-grained control over your detrending, use the detrendr package. To detrend one channel and not the other, specify this as a length 2 vector.

FALSE repeats the detrending procedure (which has some inherent randomness) 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 using smooth_filter() or median_filter() respectively? If selected, these are invoked here with a filter radius of 1 and with the option na_count = TRUE. A value of NA for either channel gives no thresholding for that channel. If you want to smooth/median filter the cross-correlated brightness image in a different way, first calculate the cross-correlated brightnesses without filtering (filt = NULL) using this function and then perform your desired filtering routine on the result.

Would you like to use multiple cores to speed up this function? If so, set the number of cores here, or to use all available cores, use parallel = TRUE.

A 4-dimensional array of images indexed by img[y, x, channel, frame] (an object of class ijtiff::ijtiff_img). The image to perform the calculation 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 the successive cross-correlated brightnesses.

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

A boolean. If TRUE, the windows used to calculate brightness are overlapped, if FALSE, they are not. For example, for a 20-frame image series with 5 frames per set, if the windows are not overlapped, then the frame sets used are 1-5, 6-10, 11-15 and 16-20; whereas if they are overlapped, 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 to calculating cross-correlated brightness (via autothresholdr::mean_stack_thresh())? If so, set your thresholding method here. If this is a single value, that same threshold will be applied to both channels. If this is a length-2 vector or list, then these two thresholds will be applied to channels 1 and 2 respectively. A value of NA for either channel gives no thresholding for that channel.

Detrend your data with detrendr::img_detrend_rh(). This is the best known detrending method for brightness analysis. For more fine-grained control over your detrending, use the detrendr package. To detrend one channel and not the other, specify this as a length 2 vector.

FALSE repeats the detrending procedure (which has some inherent randomness) 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 using smooth_filter() or median_filter() respectively? If selected, these are invoked here with a filter radius of 1 and with the option na_count = TRUE. A value of NA for either channel gives no thresholding for that channel. If you want to smooth/median filter the cross-correlated brightness image in a different way, first calculate the cross-correlated brightnesses without filtering (filt = NULL) using this function and then perform your desired filtering routine on the result.

Would you like to use multiple cores to speed up this function? If so, set the number of cores here, or to use all available cores, use parallel = TRUE.

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

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

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

A boolean. If TRUE, the windows used to calculate brightness are overlapped, if FALSE, they are not. For example, for a 20-frame image series with 5 frames per set, if the windows are not overlapped, then the frame sets used are 1-5, 6-10, 11-15 and 16-20; whereas if they are overlapped, 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 to calculating cross-correlated brightness (via autothresholdr::mean_stack_thresh())? If so, set your thresholding method here. If this is a single value, that same threshold will be applied to both channels. If this is a length-2 vector or list, then these two thresholds will be applied to channels 1 and 2 respectively. A value of NA for either channel gives no thresholding for that channel.

Detrend your data with detrendr::img_detrend_rh(). This is the best known detrending method for brightness analysis. For more fine-grained control over your detrending, use the detrendr package. To detrend one channel and not the other, specify this as a length 2 vector.

FALSE repeats the detrending procedure (which has some inherent randomness) 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 using smooth_filter() or median_filter() respectively? If selected, these are invoked here with a filter radius of 1 and with the option na_count = TRUE. A value of NA for either channel gives no thresholding for that channel. If you want to smooth/median filter the cross-correlated brightness image in a different way, first calculate the cross-correlated brightnesses without filtering (filt = NULL) using this function and then perform your desired filtering routine on the result.

Would you like to use multiple cores to speed up this function? If so, set the number of cores here, or to use all available cores, use parallel = TRUE.

A 4-dimensional array of images indexed by img[y, x, channel, frame] (an object of class ijtiff::ijtiff_img). The image to perform the calculation 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 to calculating cross-correlated number (via autothresholdr::mean_stack_thresh())? If so, set your thresholding method here. If this is a single value, that same threshold will be applied to both channels. If this is a length-2 vector or list, then these two thresholds will be applied to channels 1 and 2 respectively. A value of NA for either channel gives no thresholding for that channel.

Detrend your data with detrendr::img_detrend_rh(). This is the best known detrending method for brightness analysis. For more fine-grained control over your detrending, use the detrendr package. To detrend one channel and not the other, specify this as a length 2 vector.

FALSE repeats the detrending procedure (which has some inherent randomness) 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 using smooth_filter() or median_filter() respectively? If selected, these are invoked here with a filter radius of 1 and with the option na_count = TRUE. A value of NA for either channel gives no thresholding for that channel. If you want to smooth/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 routine on the result.

Would you like to use multiple cores to speed up this function? If so, set the number of cores here, or to use all available cores, use parallel = TRUE.

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

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 to calculating cross-correlated number (via autothresholdr::mean_stack_thresh())? If so, set your thresholding method here. If this is a single value, that same threshold will be applied to both channels. If this is a length-2 vector or list, then these two thresholds will be applied to channels 1 and 2 respectively. A value of NA for either channel gives no thresholding for that channel.

Detrend your data with detrendr::img_detrend_rh(). This is the best known detrending method for brightness analysis. For more fine-grained control over your detrending, use the detrendr package. To detrend one channel and not the other, specify this as a length 2 vector.

FALSE repeats the detrending procedure (which has some inherent randomness) 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 using smooth_filter() or median_filter() respectively? If selected, these are invoked here with a filter radius of 1 and with the option na_count = TRUE. A value of NA for either channel gives no thresholding for that channel. If you want to smooth/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 routine on the result.

Would you like to use multiple cores to speed up this function? If so, set the number of cores here, or to use all available cores, use parallel = TRUE.

A 4-dimensional array of images indexed by img[y, x, channel, frame] (an object of class ijtiff::ijtiff_img). The image to perform the calculation 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 the successive cross-correlated numbers.

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

A boolean. If TRUE, the windows used to calculate brightness are overlapped, if FALSE, they are not. For example, for a 20-frame image series with 5 frames per set, if the windows are not overlapped, then the frame sets used are 1-5, 6-10, 11-15 and 16-20; whereas if they are overlapped, 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 to calculating cross-correlated number (via autothresholdr::mean_stack_thresh())? If so, set your thresholding method here. If this is a single value, that same threshold will be applied to both channels. If this is a length-2 vector or list, then these two thresholds will be applied to channels 1 and 2 respectively. A value of NA for either channel gives no thresholding for that channel.

Detrend your data with detrendr::img_detrend_rh(). This is the best known detrending method for brightness analysis. For more fine-grained control over your detrending, use the detrendr package. To detrend one channel and not the other, specify this as a length 2 vector.

FALSE repeats the detrending procedure (which has some inherent randomness) 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 using smooth_filter() or median_filter() respectively? If selected, these are invoked here with a filter radius of 1 and with the option na_count = TRUE. A value of NA for either channel gives no thresholding for that channel. If you want to smooth/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 routine on the result.

Would you like to use multiple cores to speed up this function? If so, set the number of cores here, or to use all available cores, use parallel = TRUE.

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

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

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

A boolean. If TRUE, the windows used to calculate brightness are overlapped, if FALSE, they are not. For example, for a 20-frame image series with 5 frames per set, if the windows are not overlapped, then the frame sets used are 1-5, 6-10, 11-15 and 16-20; whereas if they are overlapped, 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 to calculating cross-correlated number (via autothresholdr::mean_stack_thresh())? If so, set your thresholding method here. If this is a single value, that same threshold will be applied to both channels. If this is a length-2 vector or list, then these two thresholds will be applied to channels 1 and 2 respectively. A value of NA for either channel gives no thresholding for that channel.

Detrend your data with detrendr::img_detrend_rh(). This is the best known detrending method for brightness analysis. For more fine-grained control over your detrending, use the detrendr package. To detrend one channel and not the other, specify this as a length 2 vector.

FALSE repeats the detrending procedure (which has some inherent randomness) 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 using smooth_filter() or median_filter() respectively? If selected, these are invoked here with a filter radius of 1 and with the option na_count = TRUE. A value of NA for either channel gives no thresholding for that channel. If you want to smooth/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 routine on the result.

Would you like to use multiple cores to speed up this function? If so, set the number of cores here, or to use all available cores, use parallel = TRUE.

A numeric vector.

A numeric vector with the same length as x.

A 3-dimensional array.

A 3-dimensional array with the same dimensions as x.

The matrix you wish to plot.

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

This gives the user the option to set all values outside a certain range to their nearest value within that range (if clip = TRUE) or to NA (if clip = FALSE. For example, to set all values outside the range [1.5, 2.6) to NA, use ⁠limits = c(1.5, 2.6), clip = FALSE⁠. The colour range will cover all values within these specified limits.

A numeric vector. If you want specific ranges of values to have the same color, specify these ranges via an increasing numeric vector. For example, if you want the ranges 0.5-1.2 and 1.2-3, use ranges = c(0.5, 1.2, 3). If ranges is specified as a number (a numeric vector of length 1) n, this is equivalent to setting ranges to be n equal-length intervals within the range of the matrix, i.e. it is equivalent 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, here you can set the names which will label each range in the legend.

If you have set ranges, here you may specify which colors you wish to colour each range. It must have the same length as the number of intervals you have specified in ranges. If you have not specified ranges, here you may specify the colours (to be passed to ggplot2::scale_fill_gradientn()) to create the continuous colour band. It is specified as a character vector, with the colors specified either as the values in colors() or as in the value of the rgb() function. Note that this allows the use of grDevices::rainbow() and friends. The default uses viridis::viridis().

Which colour should the NA pixels be? Default is black.

If either limits or ranges are set (one should never set both), there may be values that fall outside the specified limits/ranges. If clip = TRUE, values outside these limits/ranges are set to their nearest values within them, but if clip = FALSE, these values are set to NA. Note that setting clip = TRUE is equivalent to setting both clip_low and clip_high to TRUE.

Setting this to TRUE (and leaving clip = FALSE, clip_high = FALSE) will set all values falling below the specified limits/ranges to their nearest value within them, but all values falling above those limits/ranges will be set to NA.

Setting this to TRUE (and leaving clip = FALSE, clip_low = FALSE) will set all values falling above the specified limits/ranges to their nearest value within them, but all values falling below those limits/ranges will be set to NA.

Do you want to log-transform the colour scaling?

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

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

A matrix (representing an image).

An integer; the median filter radius.

Should NAs be ignored?

If this is TRUE, in each median calculation, if the majority of arguments are NAs, NA is returned but if the NAs are in the minority, they are ignored as in median(x, na.rm = TRUE).

The calculated number or brightness image.

The number or brightness definition used.

A positive integer, possibly an object of class autothresholdr::th. If the different channels of the image had different thresholds, this argument may be specified as a vector or list (of positive integers, possibly objects of class autothresholdr::th), one element for each channel.

A non-negative integer with an attribute auto. If the different channels of the image had different swaps, this argument may be specified as a list (of non-negative integers with attributes auto), one element for each channel. For undetrended images, set swaps = NA.

A string, the filtering method used. Must be either "mean" or "median", or NA for no filtering. If the different channels of the image 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 each point in the number or brightness time series.

A boolean. TRUE indicates that the windows used to calculate consecutive brightnesses over time were overlapped, FALSE indicates that they were not.

A positive integer, possibly an object of class autothresholdr::th. If the different channels of the image had different thresholds, this argument may be specified as a vector or list (of positive integers, possibly objects of class autothresholdr::th), one element for each channel.

A non-negative integer with an attribute auto. If the different channels of the image had different swaps, this argument may be specified as a list (of non-negative integers with attributes auto), one element for each channel. For undetrended images, set swaps = NA.

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

A 4-dimensional array of images indexed by img[y, x, channel, frame] (an object of class ijtiff::ijtiff_img). The image to perform the calculation 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 (see autothresholdr::mean_stack_thresh()) to use on the image prior to detrending and number calculations. If there are many channels, this may be specified as a vector or list, one element for each channel.

Detrend your data with detrendr::img_detrend_rh(). This is the best known detrending method for brightness analysis. For more fine-grained control over your detrending, use the detrendr package. If there are many channels, this may be specified as a vector, one element for each channel.

FALSE repeats the detrending procedure (which has some inherent randomness) 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 using smooth_filter() or median_filter() respectively? If selected, these are invoked here with a filter radius of 1 (with corners included, so each median is the median of 9 elements) and with the option na_count = TRUE. If you want to smooth/median filter the number image in a different way, first calculate the numbers without filtering (filt = NULL) using this function and then perform your desired filtering routine on the result. If there are many channels, this may be specified as a vector, one element for each channel.

A positive number. The S-factor of microscope acquisition.

Microscope acquisition parameters. See reference Dalal et al.

Factor for correction of number n due to the illumination profile. The default (gamma = 1) has no effect. Changing gamma will have the effect of dividing the result by gamma, so the result with gamma = 0.5 is two times the result with gamma = 1. For a Gaussian illumination profile, use gamma = 0.3536; for a Gaussian-Lorentzian illumination profile, use gamma = 0.0760.

Would you like to use multiple cores to speed up this function? If so, set the number of cores here, or to use all available cores, use parallel = TRUE.

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

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

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

Detrend your data with detrendr::img_detrend_rh(). This is the best known detrending method for brightness analysis. For more fine-grained control over your detrending, use the detrendr package. If there are many channels, this may be specified as a vector, one element for each channel.

FALSE repeats the detrending procedure (which has some inherent randomness) 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 using smooth_filter() or median_filter() respectively? If selected, these are invoked here with a filter radius of 1 (with corners included, so each median is the median of 9 elements) and with the option na_count = TRUE. If you want to smooth/median filter the number image in a different way, first calculate the numbers without filtering (filt = NULL) using this function and then perform your desired filtering routine on the result. If there are many channels, this may be specified as a vector, one element for each channel.

A positive number. The S-factor of microscope acquisition.

Microscope acquisition parameters. See reference Dalal et al.

Microscope acquisition parameters. See reference Dalal et al.

Factor for correction of number n due to the illumination profile. The default (gamma = 1) has no effect. Changing gamma will have the effect of dividing the result by gamma, so the result with gamma = 0.5 is two times the result with gamma = 1. For a Gaussian illumination profile, use gamma = 0.3536; for a Gaussian-Lorentzian illumination profile, use gamma = 0.0760.

Would you like to use multiple cores to speed up this function? If so, set the number of cores here, or to use all available cores, use parallel = TRUE.

A 4-dimensional array of images indexed by img[y, x, channel, frame] (an object of class ijtiff::ijtiff_img). The image to perform the calculation 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 the successive numbers.

A boolean. If TRUE, the windows used to calculate brightness are overlapped, if FALSE, they are not. For example, for a 20-frame image series with 5 frames per set, if the windows are not overlapped, then the frame sets used are 1-5, 6-10, 11-15 and 16-20; whereas if they are overlapped, the frame sets are 1-5, 2-6, 3-7, 4-8 and so on up to 16-20.

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

Detrend your data with detrendr::img_detrend_rh(). This is the best known detrending method for brightness analysis. For more fine-grained control over your detrending, use the detrendr package. If there are many channels, this may be specified as a vector, one element for each channel.

FALSE repeats the detrending procedure (which has some inherent randomness) 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 using smooth_filter() or median_filter() respectively? If selected, these are invoked here with a filter radius of 1 (with corners included, so each median is the median of 9 elements) and with the option na_count = TRUE. If you want to smooth/median filter the number image in a different way, first calculate the numbers without filtering (filt = NULL) using this function and then perform your desired filtering routine on the result. If there are many channels, this may be specified as a vector, one element for each channel.

A positive number. The S-factor of microscope acquisition.

Microscope acquisition parameters. See reference Dalal et al.

Microscope acquisition parameters. See reference Dalal et al.

Factor for correction of number n due to the illumination profile. The default (gamma = 1) has no effect. Changing gamma will have the effect of dividing the result by gamma, so the result with gamma = 0.5 is two times the result with gamma = 1. For a Gaussian illumination profile, use gamma = 0.3536; for a Gaussian-Lorentzian illumination profile, use gamma = 0.0760.

Would you like to use multiple cores to speed up this function? If so, set the number of cores here, or to use all available cores, use parallel = TRUE.

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

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

The number of frames with which to calculate the successive numbers.

A boolean. If TRUE, the windows used to calculate brightness are overlapped, if FALSE, they are not. For example, for a 20-frame image series with 5 frames per set, if the windows are not overlapped, then the frame sets used are 1-5, 6-10, 11-15 and 16-20; whereas if they are overlapped, the frame sets are 1-5, 2-6, 3-7, 4-8 and so on up to 16-20.

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

Detrend your data with detrendr::img_detrend_rh(). This is the best known detrending method for brightness analysis. For more fine-grained control over your detrending, use the detrendr package. If there are many channels, this may be specified as a vector, one element for each channel.

FALSE repeats the detrending procedure (which has some inherent randomness) 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 using smooth_filter() or median_filter() respectively? If selected, these are invoked here with a filter radius of 1 (with corners included, so each median is the median of 9 elements) and with the option na_count = TRUE. If you want to smooth/median filter the number image in a different way, first calculate the numbers without filtering (filt = NULL) using this function and then perform your desired filtering routine on the result. If there are many channels, this may be specified as a vector, one element for each channel.

A positive number. The S-factor of microscope acquisition.

Microscope acquisition parameters. See reference Dalal et al.

Microscope acquisition parameters. See reference Dalal et al.

Factor for correction of number n due to the illumination profile. The default (gamma = 1) has no effect. Changing gamma will have the effect of dividing the result by gamma, so the result with gamma = 0.5 is two times the result with gamma = 1. For a Gaussian illumination profile, use gamma = 0.3536; for a Gaussian-Lorentzian illumination profile, use gamma = 0.0760.

Would you like to use multiple cores to speed up this function? If so, set the number of cores here, or to use all available cores, use parallel = TRUE.