pyopia.statistics

Module containing tools for handling particle image statistics after processing

Functions

add_best_guesses_to_stats(stats)	Calculates the most likely tensorflow classification and adds best guesses to stats dataframe.
add_depth_to_stats(stats, time, depth)	If you have a depth time-series, use this function to find the depth of each line in stats
bright_norm(im[, brightness])	Eye-candy function for normalising the image brightness
count_images_in_stats(stats)	count the number of raw images used to generate stats
crop_stats(stats, crop_stats)	Filters stats file based on whether the particles are within a rectangle specified by crop_stats.
d50_from_stats(stats, pixel_size)	Calculate the d50 from the stats and settings
d50_from_vd(volume_distribution, dias)	Calculate d50 from a volume distribution
explode_contrast(im)	Eye-candy function for exploding the contrast of a particle iamge (roi)
extract_latest_stats(stats, window_size)	Extracts the stats data from within the last number of seconds specified by window_size.
extract_nth_largest(stats[, n])	Return statistics of the nth largest particle
extract_nth_longest(stats[, n])	Return statistics of the nth longest particle
extract_oil(stats[, probability_threshold, ...])	Creates a new stats dataframe containing only oil, based on some thresholds on calculated statistics
gen_roifiles(stats[, auto_scaler])	Generates a list of filenames suitable for making montages with
get_j(dias, number_distribution)	Calculates the junge slope from a correctly-scale number distribution (number per micron per litre must be the units of nd)
get_sample_volume(pix_size, path_length[, ...])	calculate the sample volume of one image
get_size_bins()	Retrieve log-spaced size bins for PSD analysis by doing the same binning as LISST-100x, but with 53 size bins
make_montage(stats_file_or_df, pixel_size, ...)	Makes nice looking montage from a directory of extracted particle images
make_timeseries_vd(stats, pixel_size, ...)	Makes a dataframe of time-series volume distribution and d50 similar to Sequoia LISST-100 output, and exportable to things like Excel or csv.
nc_from_nd(number_distribution, sample_volume)	Calculate the number concentration from the count and sample volume
nc_vc_from_stats(stats, pix_size, path_length)	Calculates important summary statistics from a stats DataFrame
nd_from_stats(stats, pix_size)	Calculate number distirbution from stats units are number per bin per sample volume
nd_from_stats_scaled(stats, pix_size, ...)	Calcualte a scaled number distribution from stats.
nd_rescale(dias, number_distribution, ...)	Rescale a number distribution from number per bin per sample volume to number per micron per litre.
roi_from_export_name(exportname, path)	Returns an image from the export_name string in the -STATS.h5 file
show_h5_meta(h5file)	prints metadata from an exported hdf5 file created from silcam process
statscsv_to_statshdf(stats_file)	Convert old STATS.csv file to a STATS.h5 file
trim_stats(stats_file, start_time, end_time)	Chops a STATS.h5 file given a start and end time
vd_from_nd(number_distribution, dias[, ...])	Calculate volume concentration from particle count
vd_from_stats(stats, pix_size)	Calculate volume distribution from stats units of miro-litres per sample volume
vd_to_nc(volume_distribution, dias)	calculate number concentration from volume distribution
vd_to_nd(volume_distribution, dias)	convert volume distribution to number distribution

Classes

PerClassConcentration(output_csv[, ...])

PyOpia pipeline-compatible class for computing per-class number concentrations (in numbers/litre) for each processed image, and appending the result as a timestamp-indexed row to a CSV file.