Package 'iRfcb'

Description

This function generates a MANIFEST.txt file that lists all files in the specified paths, along with their sizes. It recursively includes files from directories and skips paths that do not exist. The manifest excludes the manifest file itself if present in the list.

Usage

create_package_manifest(paths, manifest_path = "MANIFEST.txt", temp_dir)

Arguments

Value

This function does not return any value. It creates a MANIFEST.txt file at the specified location, which contains a list of all files (including their sizes) in the provided paths. The file paths are relative to the specified temp_dir, and the manifest excludes the manifest file itself if present.

Description

This function adjusts the classifications in manual annotation files based on a class2use file. It loads a specified class2use file and applies the adjustments to all relevant files in the specified manual folder. Optionally, it can also perform compression on the output files. This is the R equivalent function of start_mc_adjust_classes_user_training from the ⁠ifcb-analysis repository⁠ (Sosik and Olson 2007).

Usage

ifcb_adjust_classes(class2use_file, manual_folder, do_compression = TRUE)

Arguments

Details

This function requires a python interpreter to be installed. The required python packages can be installed in a virtual environment using ifcb_py_install.

Value

None

References

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

See Also

ifcb_py_install ifcb_create_class2use https://github.com/hsosik/ifcb-analysis

Examples

## Not run: 
ifcb_adjust_classes("C:/training/config/class2use", "C:/training/manual/2014/")

## End(Not run)

Description

This function creates or updates manual .mat classlist files with a user specified class in batch, based on input vector of IFCB image names. These .mat can be used with the code in the ifcb-analysis repository (Sosik and Olson 2007).

Usage

ifcb_annotate_batch(
  png_images,
  class,
  manual_folder,
  adc_folder,
  class2use_file,
  manual_output = NULL,
  manual_recursive = FALSE,
  unclassified_id = 1,
  do_compression = TRUE
)

Arguments

Details

This function requires a python interpreter to be installed. The required python packages can be installed in a virtual environment using ifcb_py_install.

If an image belongs to a sample that already has a corresponding manual .mat file, the function updates the class IDs for the specified regions of interest (ROIs) in that file. If no manual file exists for the sample, the function creates a new one based on the sample's ADC data, assigning unclassified IDs to all ROIs initially, then applying the specified class to the relevant ROIs.

The class parameter can be provided as either a string (class name) or an integer (class index). If a string is provided, the function will attempt to match it to one of the available classes in class2use_file. If no match is found, an error is thrown.

The function assumes that the ADC files are organized in subfolders by year (YYYY) and date (DYYYYMMDD) within adc_folder.

Value

The function does not return a value. It creates or updates .mat files in the manual_folder to reflect the specified annotations.

References

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

See Also

ifcb_correct_annotation, ifcb_create_empty_manual_file

Examples

## Not run: 
# Initialize a python session if not already set up
ifcb_py_install()

# Annotate two png images with class "Nodularia_spumigena" and update or create manual files
ifcb_annotate_batch(
  png_images = c("D20230812T162908_IFCB134_01399.png",
                 "D20230714T102127_IFCB134_00069.png"),
  class = "Nodularia_spumigena",
  manual_folder = "path/to/manual",
  adc_folder = "path/to/adc",
  class2use_file = "path/to/class2use.mat"
)

## End(Not run)

Description

This function converts IFCB filenames to a data frame with separate columns for the sample name, full timestamp, year, month, day, time, and IFCB number. ROI numbers are included if available.

Usage

ifcb_convert_filenames(filenames)

Arguments

Value

A tibble with columns:

• sample: The extracted sample name.

• full_timestamp: The full timestamp in "YYYY-MM-DD HH:MM:SS" format.

• year: The year as an integer.

• month: The month as an integer.

• day: The day as an integer.

• time: The extracted time in "HH:MM:SS" format.

• ifcb_number: The IFCB instrument number.

Examples

filenames <- c("D20230314T001205_IFCB134", "D20230615T123045_IFCB135")
timestamps <- ifcb_convert_filenames(filenames)
print(timestamps)

Description

This function corrects annotations in MATLAB classlist files located in a specified manual folder, generated by the code in the ifcb-analysis repository (Sosik and Olson 2007). It replaces the class ID of specified regions of interest (ROIs) in the classlist files based on a correction file or a character vector.

Usage

ifcb_correct_annotation(
  manual_folder,
  out_folder,
  correction = NULL,
  correct_classid,
  do_compression = TRUE,
  correction_file = deprecated()
)

Arguments

Details

This function requires a python interpreter to be installed. The required python packages can be installed in a virtual environment using ifcb_py_install.

The correction file is expected to contain at least one column: image_filename, which includes the filenames of the images (with or without additional trailing information). The function processes each file, corrects the annotations, and saves the updated files in the output folder.

If a character vector is provided as correction, it will be used directly as a list of filenames for correction.

The correction is typically generated using a Shiny app that provides an interactive interface for browsing and managing IFCB (Imaging FlowCytobot) image galleries. This Shiny app can be initialized using the function ifcb_run_image_gallery().

Value

This function does not return any value; it updates the classlist files in the specified output directory.

References

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

See Also

ifcb_py_install https://github.com/hsosik/ifcb-analysis

Examples

## Not run: 
# Initialize a python session if not already set up
ifcb_py_install()

# Correct class ID in .mat classlist files using a correction file
ifcb_correct_annotation("input/manual",
                        "output/manual",
                        "corrections.txt",
                        99)

# Correct class ID in .mat classlist files using a character vector of filenames
ifcb_correct_annotation("input/manual",
                        "output/manual",
                        c("D20230917T153755_IFCB134_01724.png",
                          "D20230917T110059_IFCB134_00380.png"),
                        99)

## End(Not run)

Description

This function processes .mat files, generated by the code in the ifcb-analysis repository (Sosik and Olson 2007), to count and summarize the annotations for each class based on the class2use information provided in a file.

Usage

ifcb_count_mat_annotations(
  manual_files,
  class2use_file,
  skip_class = NULL,
  sum_level = "class",
  mat_recursive = FALSE,
  use_python = FALSE
)

Arguments

Details

If use_python = TRUE, the function tries to read the .mat file using ifcb_read_mat(), which relies on SciPy. This approach may be faster than the default approach using R.matlab::readMat(), especially for large .mat files. To enable this functionality, ensure Python is properly configured with the required dependencies. You can initialize the Python environment and install necessary packages using ifcb_py_install().

If use_python = FALSE or if SciPy is not available, the function falls back to using R.matlab::readMat().

Value

A data frame with the total count of images per class, roi or per sample.

References

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

Examples

## Not run: 
# Count annotations excluding specific class IDs
result <- ifcb_count_mat_annotations("path/to/manual_folder",
                                     "path/to/class2use_file",
                                     skip_class = c(99, 100))
print(result)

# Count annotations excluding a specific class name
result <- ifcb_count_mat_annotations("path/to/manual_folder",
                                     "path/to/class2use_file",
                                     skip_class = "unclassified")
print(result)

## End(Not run)

Description

This function creates a .mat file containing a character vector of class names. A class2use file can be used for manual annotation using the code in the ifcb-analysis repository (Sosik and Olson 2007).

Usage

ifcb_create_class2use(classes, filename, do_compression = TRUE)

Arguments

Details

This function requires a python interpreter to be installed. The required python packages can be installed in a virtual environment using ifcb_py_install.

Value

No return value. This function is called for its side effect of creating a .mat file.

References

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

See Also

ifcb_py_install ifcb_adjust_classes https://github.com/hsosik/ifcb-analysis

Examples

## Not run: 
# Example usage:
classes <- c("unclassified", "Dinobryon_spp", "Helicostomella_spp")

ifcb_create_class2use(classes, "class2use_output.mat", do_compression = TRUE)

## End(Not run)

Description

Generates a MAT file for IFCB data with an empty manual classification structure using a specified number of ROIs, class names, and saves it to a specified output file. This function utilizes a Python script for creating the structure.

Usage

ifcb_create_empty_manual_file(
  roi_length,
  class2use,
  output_file,
  unclassified_id = 1,
  do_compression = TRUE
)

Arguments

Details

This function requires a python interpreter to be installed. The required python packages can be installed in a virtual environment using ifcb_py_install.

Value

No return value. This function is called for its side effects. The created MAT file is saved at the specified output_file location.

Examples

## Not run: 
# Initialize a python session if not already set up
ifcb_py_install()

# Create a MAT file with 100 ROIs, using a vector of class names, and save it to "output.mat"
ifcb_create_empty_manual_file(roi_length = 100,
                              class2use = c("unclassified", "Aphanizomenon_spp"),
                              output_file = "output.mat")

# Create a MAT file with a different unclassified_id
ifcb_create_empty_manual_file(roi_length = 100,
                              class2use = c("unclassified", "Aphanizomenon_spp"),
                              output_file = "output.mat",
                              unclassified_id = 999)

## End(Not run)

Description

This function generates a MANIFEST.txt file listing all files in a specified folder and its subfolders, along with their sizes in bytes. The function can optionally exclude an existing MANIFEST.txt file from the generated list. A manifest may be useful when archiving images in data repositories.

Usage

ifcb_create_manifest(
  folder_path,
  manifest_path = file.path(folder_path, "MANIFEST.txt"),
  exclude_manifest = TRUE
)

Arguments

Value

No return value, called for side effects. Creates a MANIFEST.txt file at the specified location.

Examples

## Not run: 
# Create a MANIFEST.txt file for the current directory
ifcb_create_manifest(".")

# Create a MANIFEST.txt file for a specific directory, excluding an existing MANIFEST.txt file
ifcb_create_manifest("path/to/directory")

# Create a MANIFEST.txt file and save it to a specific path
ifcb_create_manifest("path/to/directory", manifest_path = "path/to/manifest/MANIFEST.txt")

# Create a MANIFEST.txt file without excluding an existing MANIFEST.txt file
ifcb_create_manifest("path/to/directory", exclude_manifest = FALSE)

## End(Not run)

Description

This function downloads a zip archive containing MATLAB files from the iRfcb dataset available in the SMHI IFCB Plankton Image Reference Library (Torstensson et al. 2024), unzips them into the specified folder and extracts png images. These data can be used, for instance, for testing iRfcb and for creating the tutorial vignette using vignette("tutorial", package = "iRfcb")

Usage

ifcb_download_test_data(
  dest_dir,
  figshare_article = "48158716",
  max_retries = 5,
  sleep_time = 10,
  verbose = TRUE
)

Arguments

Value

No return value. This function is called for its side effect of downloading, extracting, and organizing IFCB test data.

References

Torstensson, Anders; Skjevik, Ann-Turi; Mohlin, Malin; Karlberg, Maria; Karlson, Bengt (2024). SMHI IFCB Plankton Image Reference Library. Version 3. SciLifeLab. Dataset. doi:10.17044/scilifelab.25883455.v3

Examples

## Not run: 
# Download and unzip IFCB test data into the "data" directory
ifcb_download_test_data("data")

## End(Not run)

Description

This function extracts labeled images from IFCB (Imaging FlowCytobot) data, annotated using the MATLAB code from the ifcb-analysis repository (Sosik and Olson 2007). It reads manually classified data, maps class indices to class names, and extracts the corresponding Region of Interest (ROI) images, saving them to the specified directory.

Usage

ifcb_extract_annotated_images(
  manual_folder,
  class2use_file,
  roi_folders,
  out_folder,
  skip_class = NA,
  verbose = TRUE,
  manual_recursive = FALSE,
  roi_recursive = TRUE,
  overwrite = FALSE,
  use_python = FALSE,
  roi_folder = deprecated()
)

Arguments

Details

If use_python = TRUE, the function tries to read the .mat file using ifcb_read_mat(), which relies on SciPy. This approach may be fasterthan the default approach using R.matlab::readMat(), especially for large .mat files. To enable this functionality, ensure Python is properly configured with the required dependencies. You can initialize the Python environment and install necessary packages using ifcb_py_install().

If use_python = FALSE or if SciPy is not available, the function falls back to using R.matlab::readMat().

Value

None. The function saves the extracted PNG images to the specified output directory.

References

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

See Also

ifcb_extract_pngs ifcb_extract_classified_images https://github.com/hsosik/ifcb-analysis

Examples

## Not run: 
ifcb_extract_annotated_images(
  manual_folder = "path/to/manual_folder",
  class2use_file = "path/to/class2use_file.mat",
  roi_folders = "path/to/roi_folder",
  out_folder = "path/to/out_folder",
  skip_class = 1
)

## End(Not run)

Description

This function reads biovolume data from feature files generated by the ifcb-analysis repository (Sosik and Olson 2007) and matches them with corresponding classification results or manual annotations. It calculates biovolume in cubic micrometers and determines if each class is a diatom based on the World Register of Marine Species (WoRMS). Carbon content is computed for each region of interest (ROI) using conversion functions from Menden-Deuer and Lessard (2000), depending on whether the class is identified as a diatom.

Usage

ifcb_extract_biovolumes(
  feature_files,
  mat_folder = NULL,
  custom_images = NULL,
  custom_classes = NULL,
  class2use_file = NULL,
  micron_factor = 1/3.4,
  diatom_class = "Bacillariophyceae",
  marine_only = FALSE,
  threshold = "opt",
  multiblob = FALSE,
  feature_recursive = TRUE,
  mat_recursive = TRUE,
  use_python = FALSE,
  verbose = TRUE
)

Arguments

Details

• Classification Data Handling:

  • If mat_folder is provided, the function reads class annotations from MATLAB .mat files.

  • If custom_images and custom_classes are supplied, they override MATLAB classification data (e.g. data from a CNN model).

  • If both mat_folder and custom_images/custom_classes are given, mat_folder takes precedence.

• MAT File Processing:

  • If use_python = TRUE, the function reads .mat files using ifcb_read_mat() (requires Python + SciPy).

  • Otherwise, it falls back to R.matlab::readMat().

Value

A data frame containing:

• sample: The sample name.

• classifier: The classifier used (if applicable).

• roi_number: The region of interest (ROI) number.

• class: The identified taxonomic class.

• biovolume_um3: Computed biovolume in cubic micrometers.

• carbon_pg: Estimated carbon content in picograms.

References

Menden-Deuer Susanne, Lessard Evelyn J., (2000), Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton, Limnology and Oceanography, 3, doi: 10.4319/lo.2000.45.3.0569.

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

See Also

ifcb_read_features ifcb_is_diatom https://www.marinespecies.org/

Examples

## Not run: 
# Using MATLAB results:
feature_files <- "data/features"
mat_folder <- "data/classified"
biovolume_df <- ifcb_extract_biovolumes(feature_files,
                                        mat_folder)
print(biovolume_df)

# Using custom classification result:
class = c("Mesodinium_rubrum",
          "Mesodinium_rubrum")
image <- c("D20220522T003051_IFCB134_00002",
           "D20220522T003051_IFCB134_00003")
biovolume_df_custom <- ifcb_extract_biovolumes(feature_files,
                                        custom_images = image,
                                        custom_classes = class)
print(biovolume_df_custom)

## End(Not run)

Description

This function reads a MATLAB classified sample file (.mat) generated by the start_classify_batch_user_training function from the ifcb-analysis repository (Sosik and Olson 2007), extracts specified taxa images from the corresponding ROI files, and saves each image in a specified directory.

Usage

ifcb_extract_classified_images(
  sample,
  classified_folder,
  roi_folder,
  out_folder,
  taxa = "All",
  threshold = "opt",
  overwrite = FALSE,
  use_python = FALSE,
  verbose = TRUE
)

Arguments

Details

If use_python = TRUE, the function tries to read the .mat file using ifcb_read_mat(), which relies on SciPy. This approach may be faster than the default approach using R.matlab::readMat(), especially for large .mat files. To enable this functionality, ensure Python is properly configured with the required dependencies. You can initialize the Python environment and install necessary packages using ifcb_py_install().

If use_python = FALSE or if SciPy is not available, the function falls back to using R.matlab::readMat().

Value

No return value, called for side effects. Extracts and saves taxa images to a directory.

References

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

See Also

ifcb_extract_pngs ifcb_extract_annotated_images https://github.com/hsosik/ifcb-analysis

Examples

## Not run: 
# Define the parameters
sample <- "D20230311T092911_IFCB135"
classified_folder <- "path/to/classified_folder"
roi_folder <- "path/to/roi_folder"
out_folder <- "path/to/outputdir"
taxa <- "All"  # or specify a particular taxa
threshold <- "opt"  # or specify another threshold

# Extract taxa images from the classified sample
ifcb_extract_classified_images(sample, classified_folder, roi_folder, out_folder, taxa, threshold)

## End(Not run)

Description

This function reads an IFCB (.roi) file and its corresponding .adc file, extracts regions of interest (ROIs), and saves each ROI as a PNG image in a specified directory. Optionally, you can specify ROI numbers to extract, useful for specific ROIs from manual or automatic classification results.

Usage

ifcb_extract_pngs(
  roi_file,
  out_folder = dirname(roi_file),
  ROInumbers = NULL,
  taxaname = NULL,
  gamma = 1,
  verbose = TRUE,
  overwrite = FALSE
)

Arguments

Value

This function is called for its side effects: it writes PNG images to a directory.

See Also

ifcb_extract_classified_images for extracting ROIs from automatic classification.

ifcb_extract_annotated_images for extracting ROIs from manual annotation.

Examples

## Not run: 
# Convert ROI file to PNG images
ifcb_extract_pngs("path/to/your_roi_file.roi")

# Extract specific ROI numbers from ROI file
ifcb_extract_pngs("path/to/your_roi_file.roi", "output_directory", ROInumbers = c(1, 2, 3))

## End(Not run)

Description

This function reads an example EcoTaxa metadata file included in the iRfcb package.

Usage

ifcb_get_ecotaxa_example(example = "ifcb")

Arguments

Details

This function loads different types of EcoTaxa metadata examples based on the user's need. The examples include a minimal template for manual data entry, as well as fully featured datasets with or without classified objects. The default is an IFCB-specific example, originating from https://github.com/VirginieSonnet/IFCBdatabaseToEcotaxa. The example headers can used when submitting data from Imaging FlowCytobot (IFCB) instruments to EcoTaxa at https://ecotaxa.obs-vlfr.fr/.

Value

A data frame containing EcoTaxa example metadata.

Examples

ecotaxa_example <- ifcb_get_ecotaxa_example()

# Print the first five columns
dplyr::tibble(ecotaxa_example)

Description

This internal SMHI function reads .txt files from a specified folder containing Ferrybox data, filters them based on a specified ship name (default is "SveaFB" for R/V Svea), and extracts data (including GPS coordinates) for timestamps (rounded to the nearest minute) falling within the date ranges defined in the file names.

Usage

ifcb_get_ferrybox_data(
  timestamps,
  ferrybox_folder,
  parameters = c("8002", "8003"),
  ship = "SveaFB",
  latitude_param = "8002",
  longitude_param = "8003"
)

Arguments

Details

The function extracts data from files whose names match the specified ship and fall within the date ranges defined in the file names. The columns corresponding to latitude_param and longitude_param will be renamed to gpsLatitude and gpsLongitude, respectively, if they are present in the parameters argument.

The function also handles cases where the exact timestamp is missing by attempting to interpolate the data using floor and ceiling rounding methods. The final output will ensure that all specified parameters are numeric.

Value

A data frame containing the input timestamps and corresponding data for the specified parameters. Columns include 'timestamp', 'gpsLatitude', 'gpsLongitude' (if applicable), and the specified parameters.

Examples

## Not run: 
ferrybox_folder <- "/path/to/ferrybox/data"
timestamps <- as.POSIXct(c("2016-08-10 10:47:34 UTC",
                           "2016-08-10 11:12:21 UTC",
                           "2016-08-10 11:35:59 UTC"))

result <- ifcb_get_ferrybox_data(timestamps, ferrybox_folder)
print(result)

## End(Not run)

Description

This function reads a .mat file generated the ifcb-analysis repository (Sosik and Olson 2007) and retrieves the names of all variables stored within it.

Usage

ifcb_get_mat_names(mat_file, use_python = FALSE)

Arguments

Details

If use_python = TRUE, the function tries to read the .mat file using ifcb_read_mat(), which relies on SciPy. This approach may be faster than the default approach using R.matlab::readMat(), especially for large .mat files. To enable this functionality, ensure Python is properly configured with the required dependencies. You can initialize the Python environment and install necessary packages using ifcb_py_install().

If use_python = FALSE or if SciPy is not available, the function falls back to using R.matlab::readMat().

Value

A character vector of variable names.

References

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

See Also

ifcb_get_mat_variable https://github.com/hsosik/ifcb-analysis

Examples

## Not run: 
# Get variable names from a MAT file
variables <- ifcb_get_mat_names("path/to/file.mat")
print(variables)

## End(Not run)

Description

This function reads a specified variable from a .mat file generated by the ifcb-analysis repository (Sosik and Olson 2007). It can be used, for example, to extract lists of classes from the file.

Usage

ifcb_get_mat_variable(
  mat_file,
  variable_name = "class2use",
  use_python = FALSE
)

Arguments

Details

If use_python = TRUE, the function tries to read the .mat file using ifcb_read_mat(), which relies on SciPy. This approach may be faster than the default approach using R.matlab::readMat(), especially for large .mat files. To enable this functionality, ensure Python is properly configured with the required dependencies. You can initialize the Python environment and install necessary packages using ifcb_py_install().

If use_python = FALSE or if SciPy is not available, the function falls back to using R.matlab::readMat().

Value

A character vector of class names.

References

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

See Also

ifcb_get_mat_names https://github.com/hsosik/ifcb-analysis

Examples

## Not run: 
# Get class names from a class2use file
classes <- ifcb_get_mat_variable("path/to/class2use.mat", "class2use")
print(classes)

# Get class names from a classifier file
class2useTB <- ifcb_get_mat_variable("path/to/classified/sample.mat", "class2useTB")
print(class2useTB)

## End(Not run)

Description

This function imports an IFCB header file (either from a local path or URL), extracts specific target values such as runtime and inhibittime, and returns them in a structured format (in seconds). This is the R equivalent function of IFCBxxx_readhdr from the ifcb-analysis repository (Sosik and Olson 2007).

Usage

ifcb_get_runtime(hdr_file)

Arguments

Value

A list (hdr) containing runtime, inhibittime, and runType (if available) extracted from the header file.

References

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

See Also

https://github.com/hsosik/ifcb-analysis

Examples

## Not run: 
# Example: Read and extract information from an IFCB header file
hdr_info <- ifcb_get_runtime("path/to/IFCB_hdr_file.hdr")
print(hdr_info)

## End(Not run)

Description

This function reads SHARK column names from a specified tab-separated values (TSV) file included in the package. These columns are used for submitting IFCB data to https://shark.smhi.se/.

Usage

ifcb_get_shark_colnames(minimal = FALSE)

Arguments

Details

For a detailed example of a data submission, see ifcb_get_shark_example.

Value

An empty data frame containing the SHARK column names.

See Also

ifcb_get_shark_example

Examples

shark_colnames <- ifcb_get_shark_colnames()
print(shark_colnames)

shark_colnames_minimal <- ifcb_get_shark_colnames(minimal = TRUE)
print(shark_colnames_minimal)

Description

This function reads a SHARK submission example from a file included in the package. This format is used for submitting IFCB data to https://shark.smhi.se/.

Usage

ifcb_get_shark_example()

Value

A data frame containing example data following the SHARK submission format.

See Also

ifcb_get_shark_colnames

Examples

shark_example <- ifcb_get_shark_example()

# Print example as tibble
dplyr::tibble(shark_example)

Description

This function matches a specified list of taxa with a summarized list of trophic types for various plankton taxa from Northern Europe (data sourced from ⁠SMHI Trophic Type⁠).

Usage

ifcb_get_trophic_type(taxa_list = NULL, print_complete_list = FALSE)

Arguments

Details

If there are multiple trophic types for a scientific name (i.e. AU and HT size classes), the summarized trophic type is "NS".

Value

A character vector of trophic types corresponding to the scientific names in taxa_list, or a data frame containing all taxa and trophic types available in the ⁠SMHI Trophic Type⁠ list. The available trophic types are autotrophic (AU), heterotrophic (HT), mixotrophic (MX) or not specified (NS).

Examples

# Example usage:
taxa_list <- c("Acanthoceras zachariasii",
               "Nodularia spumigena",
               "Acanthoica quattrospina",
               "Noctiluca",
               "Gymnodiniales")

ifcb_get_trophic_type(taxa_list)

Description

This function takes a list of taxa names, cleans them, retrieves their corresponding classification records from the World Register of Marine Species (WoRMS), and checks if they belong to the specified diatom class. The function only uses the first name (genus name) of each taxa for classification.

Usage

ifcb_is_diatom(
  taxa_list,
  diatom_class = "Bacillariophyceae",
  max_retries = 3,
  sleep_time = 10,
  marine_only = FALSE,
  fuzzy = deprecated(),
  verbose = TRUE
)

Arguments

Value

A logical vector indicating whether each cleaned taxa name belongs to the specified diatom class.

See Also

https://www.marinespecies.org/

Examples

## Not run: 
taxa_list <- c("Nitzschia_sp", "Chaetoceros_sp", "Dinophysis_norvegica", "Thalassiosira_sp")
ifcb_is_diatom(taxa_list)

## End(Not run)

Description

This function checks if vectors of latitude and longitude points are within a user-supplied sea basin. The Baltic Sea basins are included as a pre-packaged shapefile in the iRfcb package.

Usage

ifcb_is_in_basin(latitudes, longitudes, plot = FALSE, shape_file = NULL)

Arguments

Details

This function reads a pre-packaged shapefile of the Baltic Sea Basin from the iRfcb package by default, or a user-supplied shapefile if provided. It sets the CRS, transforms the CRS to WGS84 (EPSG:4326) if necessary, and checks if the given points fall within the specified sea basin. Optionally, it plots the points and the sea basin polygons together.

Value

A logical vector indicating whether each point is within the specified sea basin, or a plot with the points and basins if plot = TRUE.

Examples

# Define example latitude and longitude vectors
latitudes <- c(55.337, 54.729, 56.311, 57.975)
longitudes <- c(12.674, 14.643, 12.237, 10.637)

# Check if the points are in the Baltic Sea Basin
points_in_the_baltic <- ifcb_is_in_basin(latitudes, longitudes)
print(points_in_the_baltic)

# Plot the points and the basin
ifcb_is_in_basin(latitudes, longitudes, plot = TRUE)

Description

Determines whether given positions are near land based on a coastline shape file. The Natural Earth 1:10m land vectors are included as default shapefile in iRfcb.

Usage

ifcb_is_near_land(
  latitudes,
  longitudes,
  distance = 500,
  shape = NULL,
  crs = 4326,
  utm_zone = 33,
  remove_small_islands = TRUE,
  small_island_threshold = 2e+06
)

Arguments

Details

This function calculates a buffered area around the coastline and checks if given positions (specified by longitudes and latitudes) are within this buffer or intersect with land.

Value

Logical vector indicating whether each position is near land.

Examples

# Define coordinates
latitudes <- c(62.500353, 58.964498, 57.638725, 56.575338)
longitudes <- c(17.845993, 20.394418, 18.284523, 16.227174)

# Call the function
near_land <- ifcb_is_near_land(latitudes, longitudes, distance = 300, crs = 4326)

# Print the result
print(near_land)

Description

This function attempts to retrieve WoRMS records using the provided taxa names. It retries the operation if an error occurs, up to a specified number of attempts.

Usage

ifcb_match_taxa_names(
  taxa_names,
  best_match_only = TRUE,
  max_retries = 3,
  sleep_time = 10,
  marine_only = FALSE,
  return_list = FALSE,
  verbose = TRUE,
  fuzzy = deprecated()
)

Arguments

Value

A data frame (or list if return_list is TRUE) of WoRMS records or NULL if the retrieval fails after the maximum number of attempts.

Examples

## Not run: 
# Example: Retrieve WoRMS records for a list of taxa names
taxa <- c("Calanus finmarchicus", "Thalassiosira pseudonana", "Phaeodactylum tricornutum")
records <- ifcb_match_taxa_names(taxa_names = taxa,
                                 max_retries = 3,
                                 sleep_time = 5,
                                 marine_only = TRUE,
                                 verbose = TRUE)

print(records)

## End(Not run)

Description

This function merges two sets of manual classification data by combining and aligning class labels from a base set and an additional set of classifications. The merged .mat data can be used with the code in the ifcb-analysis repository (Sosik and Olson 2007).

Usage

ifcb_merge_manual(
  class2use_file_base,
  class2use_file_additions,
  class2use_file_output = NULL,
  manual_folder_base,
  manual_folder_additions,
  manual_folder_output,
  do_compression = TRUE,
  temp_index_offset = 50000,
  skip_class = NULL,
  quiet = FALSE
)

Arguments

Details

This function requires a python interpreter to be installed. The required python packages can be installed in a virtual environment using ifcb_py_install.

The base set consists of the original classifications that are used as a reference for the merging process. The additions set contains the additional classifications that need to be merged with the base set. When merging, unique class names from the additions set that are not present in the base set are appended.

The function works by aligning the class labels from the additions set with those in the base set, handling conflicts by using a temporary index system. It copies .mat files from both the base and additions folders into the output folder, while adjusting indices and and class names for the additions.

Note that the maximum limit for uint16 is 65,535, so ensure that temp_index_offset remains below this value.

Value

No return value. Outputs the combined class2use file in the same folder as class2use_file_base is located or at a user-specified location, and merged .mat files into the output folder.

References

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

See Also

ifcb_py_install https://github.com/hsosik/ifcb-analysis

Examples

## Not run: 
ifcb_merge_manual("path/to/class2use_base.mat", "path/to/class2use_additions.mat",
                  "path/to/class2use_combined.mat", "path/to/manual/base_folder",
                  "path/to/manual/additions_folder", "path/to/manual/output_folder",
                  do_compression = TRUE, temp_index_offset = 50000, quiet = FALSE)

## End(Not run)

Description

This function generates and saves data about a dataset's Particle Size Distribution (PSD) from Imaging FlowCytobot (IFCB) feature and hdr files, which can be used for data quality assurance and quality control.

Usage

ifcb_psd(
  feature_folder,
  hdr_folder,
  save_data = FALSE,
  output_file = NULL,
  plot_folder = NULL,
  use_marker = FALSE,
  start_fit = 10,
  r_sqr = 0.5,
  beads = NULL,
  bubbles = NULL,
  incomplete = NULL,
  missing_cells = NULL,
  biomass = NULL,
  bloom = NULL,
  humidity = NULL,
  micron_factor = 1/3.4
)

Arguments

Details

The PSD function originates from the PSD python repository (Hayashi et al. in prep), which can be found at https://github.com/kudelalab/PSD. This function requires a python interpreter to be installed. The required python packages can be installed in a virtual environment using ifcb_py_install. The function requires v2 features generated by the ifcb-analysis MATLAB package (Sosik and Olson 2007) found at https://github.com/hsosik/ifcb-analysis.

Value

A list with data, fits, and flags DataFrames if save_data is FALSE; otherwise, NULL.

References

Hayashi, K., Walton, J., Lie, A., Smith, J. and Kudela M. Using particle size distribution (PSD) to automate imaging flow cytobot (IFCB) data quality in coastal California, USA. In prep. Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

See Also

ifcb_py_install https://github.com/kudelalab/PSD https://github.com/hsosik/ifcb-analysis

Examples

## Not run: 
# Initialize the python session if not already set up
ifcb_py_install()

ifcb_psd(
  feature_folder = 'path/to/features',
  hdr_folder = 'path/to/hdr_data',
  save_data = TRUE,
  output_file = 'psd/svea_2021',
  plot_folder = 'psd/plots',
  use_marker = FALSE,
  start_fit = 13,
  r_sqr = 0.5,
  beads = 10 ** 9,
  bubbles = 150,
  incomplete = c(1500, 3),
  missing_cells = 0.7,
  biomass = 1000,
  bloom = 5,
  humidity = NULL,
  micron_factor = 1/3.0
)

## End(Not run)

Description

This function generates a plot for a given sample from Particle Size Distribution (PSD) data and fits from Imaging FlowCytobot (IFCB). The PSD data and fits can be generated by ifcb_psd (Hayashi et al. in prep).

Usage

ifcb_psd_plot(sample_name, data, fits, start_fit)

Arguments

Value

A ggplot object representing the PSD plot for the sample.

References

Hayashi, K., Walton, J., Lie, A., Smith, J. and Kudela M. Using particle size distribution (PSD) to automate imaging flow cytobot (IFCB) data quality in coastal California, USA. In prep.

See Also

ifcb_psd https://github.com/kudelalab/PSD

Examples

## Not run: 
# Analyze PSD
psd <- ifcb_psd(feature_folder = 'path/to/features',
                hdr_folder = 'path/to/hdr_data',
                save_data = TRUE,
                output_file = 'psd/svea_2021',
                plot_folder = NULL,
                use_marker = FALSE,
                start_fit = 13,
                r_sqr = 0.5,
                beads = 10 ** 9,
                bubbles = 150,
                incomplete = c(1500, 3),
                missing_cells = 0.7,
                biomass = 1000,
                bloom = 5,
                humidity = NULL)

# Plot PSD of the first sample
plot <- ifcb_psd_plot(sample_name = "D20230316T101514",
                      data = psd$data,
                      fits = psd$fits,
                      start_fit = 10)
# Inspect plot
print(plot)

## End(Not run)

Description

This function sets up the Python environment for iRfcb. By default, it creates and activates a Python virtual environment (venv) named "iRfcb" and installs the required Python packages from the "requirements.txt" file. Alternatively, users can opt to use the system Python instead of creating a virtual environment by setting use_venv = FALSE.

Usage

ifcb_py_install(
  envname = ".virtualenvs/iRfcb",
  use_venv = TRUE,
  packages = NULL
)

Arguments

Value

No return value. This function is called for its side effect of configuring the Python environment.

Examples

## Not run: 
# Install the iRfcb Python environment using a virtual environment (default)
ifcb_py_install()

# Install the iRfcb Python environment with additional packages
ifcb_py_install(packages = c("numpy", "plotly"))

# Use system Python instead of a virtual environment
ifcb_py_install(use_venv = FALSE)

## End(Not run)

Description

This function reads feature files from a given folder or a specified set of file paths, optionally filtering them based on whether they are multiblob or single blob files.

Usage

ifcb_read_features(feature_files = NULL, multiblob = FALSE, verbose = TRUE)

Arguments

Value

A named list of data frames, where each element corresponds to a feature file read from feature_files. The list is named with the base names of the feature files.

Examples

## Not run: 
# Read feature files from a folder
features <- ifcb_read_features("path/to/feature_folder")

# Read only multiblob feature files
multiblob_features <- ifcb_read_features("path/to/feature_folder", multiblob = TRUE)

# Read feature files from a list of file paths
features <- ifcb_read_features(c("path/to/file1.csv", "path/to/file2.csv"))

## End(Not run)

Description

This function reads all IFCB instrument settings information files (.hdr) from a specified directory.

Usage

ifcb_read_hdr_data(
  hdr_files,
  gps_only = FALSE,
  verbose = TRUE,
  hdr_folder = deprecated()
)

Arguments

Value

A data frame with sample names, GPS latitude, GPS longitude, and optionally timestamps.

Examples

## Not run: 
# Extract all HDR data
hdr_data <- ifcb_read_hdr_data("path/to/data")
print(hdr_data)

# Extract only GPS data
gps_data <- ifcb_read_hdr_data("path/to/data", gps_only = TRUE)
print(gps_data)

## End(Not run)

Description

This function reads a MATLAB .mat file using a Python function via reticulate.

Usage

ifcb_read_mat(file_path)

Arguments

Details

This function requires a Python interpreter with SciPy installed.

This function requires a python interpreter to be installed. The required python packages can be installed in a virtual environment using ifcb_py_install.

Value

A list containing the MATLAB variables.

See Also

ifcb_py_install

Examples

## Not run: 
data <- read_mat_file_r("C:/data/sample.mat")

## End(Not run)

Description

This function reads a MATLAB .mat file containing aggregated and classified IFCB (Imaging FlowCytobot) data generated by the countcells_allTBnew_user_training function from the ifcb-analysis repository (Sosik and Olson 2007), or a list of classified data generated by ifcb_summarize_class_counts. It returns a data frame with species counts and optionally biovolume information based on specified thresholds.

Usage

ifcb_read_summary(
  summary,
  hdr_directory = NULL,
  biovolume = FALSE,
  threshold = "opt",
  use_python = FALSE
)

Arguments

Details

If use_python = TRUE, the function tries to read the .mat file using ifcb_read_mat(), which relies on SciPy. This approach may be faster than the default approach using R.matlab::readMat(), especially for large .mat files. To enable this functionality, ensure Python is properly configured with the required dependencies. You can initialize the Python environment and install necessary packages using ifcb_py_install().

If use_python = FALSE or if SciPy is not available, the function falls back to using R.matlab::readMat().

Value

A data frame containing the summary information including file list, volume analyzed, species counts, optionally biovolume, and other metadata.

References

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

See Also

https://github.com/hsosik/ifcb-analysis

Examples

## Not run: 
summary_data <- ifcb_read_summary("path/to/summary_file.mat", biovolume = TRUE, threshold = "opt")
print(summary_data)

## End(Not run)

Description

This function replaces a target class ID with a new ID in MATLAB classlist files, generated by the code in the ifcb-analysis repository (Sosik and Olson 2007).

Usage

ifcb_replace_mat_values(
  manual_folder,
  out_folder,
  target_id,
  new_id,
  column_index = 1,
  do_compression = TRUE
)

Arguments

Details

This function requires a python interpreter to be installed. The required python packages can be installed in a virtual environment using ifcb_py_install.

Value

This function does not return any value; it updates the classlist files in the specified directory.

References

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

See Also

ifcb_py_install https://github.com/hsosik/ifcb-analysis

Examples

## Not run: 
# Initialize a python session if not already set up
ifcb_py_install()

# Replace class ID 99 with 1 in .mat classlist files
ifcb_replace_mat_values("output/manual", "output/manual", 99, 1, column_index = 1)

## End(Not run)

Description

Launches a Shiny application that provides an interactive interface for browsing and managing IFCB (Imaging FlowCytobot) image galleries.

Usage

ifcb_run_image_gallery()

Details

Users can specify a folder containing .png images, navigate through the images, select and unselect images, and download a list of selected images. This feature is particularly useful for quality control of annotated images. A downloaded list of images from the app can also be uploaded to filter and view only the selected images.

Value

No return value. This function launches a Shiny application for interactive image browsing and management.

Examples

## Not run: 
# Run the IFCB image gallery Shiny app
ifcb_run_image_gallery()

## End(Not run)

Description

This function calculates aggregated biovolumes and carbon content from Imaging FlowCytobot (IFCB) samples based on biovolume information from feature files. Images are grouped into classes either based on MATLAB classification, manually annotated files, or a user-supplied list of images and their corresponding class labels (e.g. from a CNN model).

Usage

ifcb_summarize_biovolumes(
  feature_folder,
  mat_folder = NULL,
  class2use_file = NULL,
  hdr_folder = NULL,
  custom_images = NULL,
  custom_classes = NULL,
  micron_factor = 1/3.4,
  diatom_class = "Bacillariophyceae",
  marine_only = FALSE,
  threshold = "opt",
  feature_recursive = TRUE,
  mat_recursive = TRUE,
  hdr_recursive = TRUE,
  use_python = FALSE,
  verbose = TRUE
)

Arguments

Details

This function performs the following steps:

1. Extracts biovolumes and carbon content from feature and classification results using ifcb_extract_biovolumes.

2. Optionally incorporates volume data from HDR files to calculate volume analyzed per sample.

3. Computes biovolume and carbon content per liter of sample analyzed.

The MATLAB classification or manual annotation files are generated by the ifcb-analysis repository (Sosik and Olson 2007). Users can optionally provide a custom classification by supplying a vector of image filenames (custom_images) along with corresponding class labels (custom_classes). This allows summarization of biovolume and carbon content without requiring MATLAB classification or manual annotation files (e.g. results from a CNN model).

Biovolumes are converted to carbon according to Menden-Deuer and Lessard 2000 for individual regions of interest (ROI), applying different conversion factors to diatoms and non-diatom protists. If provided, the function also incorporates sample volume data from HDR files to compute biovolume and carbon content per liter of sample.

If use_python = TRUE, the function tries to read the .mat file using ifcb_read_mat(), which relies on SciPy. This approach may be faster than the default approach using R.matlab::readMat(), especially for large .mat files. To enable this functionality, ensure Python is properly configured with the required dependencies. You can initialize the Python environment and install necessary packages using ifcb_py_install().

Value

A data frame summarizing aggregated biovolume and carbon content per class per sample. Columns include 'sample', 'classifier', 'class', 'biovolume_mm3', 'carbon_ug', 'ml_analyzed', 'biovolume_mm3_per_liter', and 'carbon_ug_per_liter'.

References

Menden-Deuer Susanne, Lessard Evelyn J., (2000), Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton, Limnology and Oceanography, 3, doi: 10.4319/lo.2000.45.3.0569.

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

Examples

## Not run: 
# Example usage:
ifcb_summarize_biovolumes("path/to/features", "path/to/mat", hdr_folder = "path/to/hdr")

# Using custom classification result:
images <- c("D20220522T003051_IFCB134_00002",
            "D20220522T003051_IFCB134_00003")
classes = c("Mesodinium_rubrum",
            "Mesodinium_rubrum")

ifcb_summarize_biovolumes(feature_folder = "path/to/features",
                          hdr_folder = "path/to/hdr",
                          custom_images = images,
                          custom_classes = classes)

## End(Not run)

Description

This function summarizes class results for a series of classifier output files and returns a summary data list.

Usage

ifcb_summarize_class_counts(
  classpath_generic,
  hdr_folder,
  year_range,
  use_python = FALSE
)

Arguments

Details

If use_python = TRUE, the function tries to read the .mat file using ifcb_read_mat(), which relies on SciPy. This approach may be faster than the default approach using R.matlab::readMat(), especially for large .mat files. To enable this functionality, ensure Python is properly configured with the required dependencies. You can initialize the Python environment and install necessary packages using ifcb_py_install().

If use_python = FALSE or if SciPy is not available, the function falls back to using R.matlab::readMat().

Value

A list containing the following elements:

Examples

## Not run: 
ifcb_summarize_class_counts('C:/work/IFCB/user_training_test_data/class/classxxxx_v1/',
                            'C:/work/IFCB/user_training_test_data/data/', 2014)

## End(Not run)

Description

This function summarizes the number of images per class for each sample and timestamps, and optionally retrieves GPS positions, and IFCB information using ifcb_read_hdr_data and ifcb_convert_filenames functions.

Usage

ifcb_summarize_png_counts(
  png_folder,
  hdr_folder = NULL,
  sum_level = "sample",
  verbose = TRUE
)

Arguments

Value

If sum_level is "sample", returns a data frame with columns: sample, ifcb_number, class_name, n_images, gpsLatitude, gpsLongitude, timestamp, year, month, day, time, roi_numbers. If sum_level is "class", returns a data frame with columns: class_name, n_images.

See Also

ifcb_read_hdr_data ifcb_convert_filenames

Examples

## Not run: 
# Example usage:
# Assuming the following directory structure:
# path/to/png_folder/
# |- class1/
# |  |- sample1_00001.png
# |  |- sample1_00002.png
# |  |- sample2_00001.png
# |- class2/
# |  |- sample1_00003.png
# |  |- sample3_00001.png

png_folder <- "path/to/png_folder"
hdr_folder <- "path/to/hdr_folder" # This folder should contain corresponding .hdr files

# Summarize by sample
summary_sample <- ifcb_summarize_png_counts(png_folder,
                                          hdr_folder,
                                          sum_level = "sample",
                                          verbose = TRUE)
print(summary_sample)

# Summarize by class
summary_class <- ifcb_summarize_png_counts(png_folder,
                                         hdr_folder,
                                         sum_level = "class",
                                         verbose = TRUE)
print(summary_class)

## End(Not run)

Description

This function processes IFCB data by reading images, matching them to the corresponding header and feature files, and joining them into a single dataframe. This function may be useful when preparing metadata files for an Ecotaxa submission.

Usage

ifcb_summarize_png_metadata(
  png_folder,
  feature_folder = NULL,
  hdr_folder = NULL
)

Arguments

Value

A dataframe that joins image data, header data, and feature data based on the sample and roi number.

Examples

## Not run: 
png_folder <- "path/to/pngs"
feature_folder <- "path/to/features"
hdr_folder <- "path/to/hdr_data"
result_df <- ifcb_summarize_png_metadata(png_folder, feature_folder, hdr_folder)

## End(Not run)

Description

This function reads an IFCB header file to extract sample run time and inhibittime, and returns the associated estimate of sample volume analyzed (in milliliters). The function assumes a standard IFCB configuration with a sample syringe operating at 0.25 mL per minute. For IFCB instruments after 007 and higher (except 008). This is the R equivalent function of IFCB_volume_analyzed from the ifcb-analysis repository (Sosik and Olson 2007).

Usage

ifcb_volume_analyzed(hdr_file, hdrOnly_flag = FALSE, flowrate = 0.25)

Arguments

Value

A numeric vector containing the estimated sample volume analyzed for each header file.

References

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

See Also

https://github.com/hsosik/ifcb-analysis

Examples

## Not run: 
# Example: Estimate volume analyzed from an IFCB header file
hdr_file <- "path/to/IFCB_hdr_file.hdr"
ml_analyzed <- ifcb_volume_analyzed(hdr_file)
print(ml_analyzed)

## End(Not run)

Description

This function reads an IFCB ADC file to extract sample run time and inhibittime, and returns the associated estimate of sample volume analyzed (in milliliters). The function assumes a standard IFCB configuration with a sample syringe operating at 0.25 mL per minute. For IFCB instruments after 007 and higher (except 008). This is the R equivalent function of IFCB_volume_analyzed_fromADC from the ⁠ifcb-analysis repository⁠ (Sosik and Olson 2007).

Usage

ifcb_volume_analyzed_from_adc(adc_file)

Arguments

Value

A list containing:

• ml_analyzed: A numeric vector of estimated sample volume analyzed for each ADC file.

• inhibittime: A numeric vector of inhibittime values extracted from ADC files.

• runtime: A numeric vector of runtime values extracted from ADC files.

References

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216.

See Also

https://github.com/hsosik/ifcb-analysis

Examples

## Not run: 
# Example: Estimate volume analyzed from an IFCB ADC file
adc_file <- "path/to/IFCB_adc_file.csv"
adc_info <- ifcb_volume_analyzed_from_adc(adc_file)
print(adc_info$ml_analyzed)

## End(Not run)

Description

This function identifies which sub-basin a set of latitude and longitude points belong to, using a user-specified or default shapefile. The default shapefile includes the Baltic Sea, Kattegat, and Skagerrak basins and is included in the iRfcb package.

Usage

ifcb_which_basin(latitudes, longitudes, plot = FALSE, shape_file = NULL)

Arguments

Details

This function reads a pre-packaged shapefile of the Baltic Sea, Kattegat, and Skagerrak basins from the iRfcb package by default, or a user-supplied shapefile if provided. The shapefiles originate from SHARK (https://shark.smhi.se/). It sets the CRS, transforms the CRS to WGS84 (EPSG:4326) if necessary, and checks if the given points fall within the specified sea basin. Optionally, it plots the points and the sea basin polygons together.

Value

A vector indicating the basin each point belongs to, or a ggplot object if plot = TRUE.

Examples

# Define example latitude and longitude vectors
latitudes <- c(55.337, 54.729, 56.311, 57.975)
longitudes <- c(12.674, 14.643, 12.237, 10.637)

# Check in which Baltic sea basin the points are in
points_in_the_baltic <- ifcb_which_basin(latitudes, longitudes)
print(points_in_the_baltic)

# Plot the points and the basins
ifcb_which_basin(latitudes, longitudes, plot = TRUE)

Description

This function creates a zip archive containing specified files and directories for manually annotated IFCB images, organized into a structured format suitable for distribution or storage. The MATLAB files are generated by the ifcb-analysis repository (Sosik and Olson 2007). The zip archive can be used to submit IFCB data to repositories like in the SMHI IFCB Plankton Image Reference Library (Torstensson et al., 2024).

Usage

ifcb_zip_matlab(
  manual_folder,
  features_folder,
  class2use_file,
  zip_filename,
  data_folder = NULL,
  readme_file = NULL,
  matlab_readme_file = NULL,
  email_address = "",
  version = "",
  print_progress = TRUE,
  feature_recursive = TRUE,
  manual_recursive = FALSE,
  data_recursive = TRUE,
  quiet = FALSE
)

Arguments

Details

This function performs the following operations:

• Lists .mat files from manual_folder.

• Lists .csv files from features_folder (including subfolders).

• Lists .roi, .adc, .hdr files from data_folder if provided.

• Copies listed files to temporary directories (manual_dir, features_dir, data_dir, config_dir).

• Renames and copies class2use_file to config_dir as class2use.mat.

• Updates readme_file with metadata (if provided) and appends PNG image statistics and MATLAB README content.

• Creates a manifest file (MANIFEST.txt) listing all files in the zip archive.

• Creates a zip archive (zip_filename) containing all copied and updated files.

• Cleans up temporary directories after creating the zip archive.

Value

No return value. This function creates a zip archive containing the specified files and directories.

References

Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216. Torstensson, Anders; Skjevik, Ann-Turi; Mohlin, Malin; Karlberg, Maria; Karlson, Bengt (2024). SMHI IFCB Plankton Image Reference Library. SciLifeLab. Dataset. doi:10.17044/scilifelab.25883455

See Also

ifcb_zip_pngs https://github.com/hsosik/ifcb-analysis

Examples

## Not run: 
ifcb_zip_matlab("path/to/manual_files", "path/to/feature_files",
                 "path/to/class2use.mat", "output_zip_archive.zip",
                 data_folder = "path/to/data_files",
                 readme_file = system.file("exdata/README-template.md", package = "iRfcb"),
                 matlab_readme_file = system.file("inst/exdata/MATLAB-template.md",
                                                  package = "iRfcb"),
                 email_address = "[email protected]",
                 version = "1.0")

## End(Not run)

Description

This function zips directories containing .png files and optionally includes README and MANIFEST files. It can also split the resulting zip file into smaller parts if it exceeds a specified size. The zip archive can be used to submit IFCB data to repositories like in the SMHI IFCB Plankton Image Reference Library (Torstensson et al., 2024).

Usage

ifcb_zip_pngs(
  png_folder,
  zip_filename,
  readme_file = NULL,
  email_address = "",
  version = "",
  print_progress = TRUE,
  include_txt = FALSE,
  split_zip = FALSE,
  max_size = 500,
  quiet = FALSE
)

Arguments

Value

This function does not return any value; it creates a zip archive and optionally splits it into smaller files if specified.

References

Torstensson, Anders; Skjevik, Ann-Turi; Mohlin, Malin; Karlberg, Maria; Karlson, Bengt (2024). SMHI IFCB Plankton Image Reference Library. SciLifeLab. Dataset. doi:10.17044/scilifelab.25883455

See Also

ifcb_zip_matlab

Examples

## Not run: 
# Zip all subdirectories in the 'images' folder with a README file
ifcb_zip_pngs("path/to/images",
             "images.zip",
             readme_file = system.file("exdata/README-template.md", package = "iRfcb"),
             email_address = "[email protected]",
             version = "1.0")

# Zip all subdirectories in the 'images' folder without a README file
ifcb_zip_pngs("path/to/images", "images.zip")

## End(Not run)

Description

This function reads an HDR file and extracts relevant lines containing parameters and their values.

Usage

read_hdr_file(file)

Arguments

Value

A data frame with columns: parameter, value, and file.

Description

This helper function takes an existing zip file, extracts its contents, and splits it into smaller zip files without splitting subfolders.

Usage

split_large_zip(zip_file, max_size = 500, quiet = FALSE)

Arguments

Value

This function does not return any value; it creates multiple smaller zip files.

Examples

## Not run: 
# Split an existing zip file into parts of up to 500 MB
split_large_zip("large_file.zip", max_size = 500)

## End(Not run)

Description

This function reads a TreeBagger classifier result file (.mat format) and summarizes the number of targets in each class based on the classification scores and thresholds.

Usage

summarize_TBclass(classfile, adhocthresh = NULL)

Arguments

Value

A list containing three elements:

Description

This function converts biovolume in microns^3 to carbon in picograms for large diatoms (> 2000 micron^3) according to Menden-Deuer and Lessard 2000. The formula used is: log pgC cell^-1 = log a + b * log V (um^3), with log a = -0.933 and b = 0.881 for diatoms > 3000 um^3.

Usage

vol2C_lgdiatom(volume)

Arguments

Value

A numeric vector of carbon measurements in picograms.

Examples

# Volumes in microns^3
volume <- c(5000, 10000, 20000)

# Convert biovolume to carbon for large diatoms
vol2C_lgdiatom(volume)

Description

This function converts biovolume in microns^3 to carbon in picograms for protists besides large diatoms (> 3000 micron^3) according to Menden-Deuer and Lessard 2000. The formula used is: log pgC cell^-1 = log a + b * log V (um^3), with log a = -0.665 and b = 0.939.

Usage

vol2C_nondiatom(volume)

Arguments

Value

A numeric vector of carbon measurements in picograms.

Examples

# Volumes in microns^3
volume <- c(5000, 10000, 20000)

# Convert biovolume to carbon for non-diatom protists
vol2C_nondiatom(volume)