---
title: "Quality Control of IFCB Data"
output:
  rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Quality Control of IFCB Data}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## Introduction

This vignette provides an overview of quality control (QC) methods for Imaging FlowCytobot (IFCB) data using the `iRfcb` package. The package offers tools to analyze Particle Size Distribution (PSD) following Hayashi et al. *in prep*, verify geographical positions, and integrate contextual data from sources like ferrybox systems. These QC workflows ensure high-quality datasets for phytoplankton and microzooplankton monitoring in marine ecosystems.

You'll learn how to:

- Set up the `iRfcb` package and `Python` environment.
- Analyze particle size distributions for data quality.
- Check spatial metadata for proximity to land, basin classification, and missing positions.

Follow this tutorial to streamline the QC process and ensure reliable IFCB data.

## Getting Started

### Installation

You can install the package from CRAN using:
```{r, eval=FALSE}
install.packages("iRfcb")
```
Some functions from the `iRfcb` package used in this tutorial require `Python` to be installed. You can download `Python` from the official website: [python.org/downloads](https://www.python.org/downloads/).

The `iRfcb` package can be configured to automatically activate an installed Python virtual environment (venv) upon loading by setting an environment variable. For more details, please refer to the package [README](https://europeanifcbgroup.github.io/iRfcb/).

Load the `iRfcb` library:
```{r, eval=FALSE}
library(iRfcb)
```

```{r, include=FALSE}
library(iRfcb)
```

```{r, include=FALSE}
library(reticulate)

# Define path to virtual environment
env_path <- file.path(tempdir(), "iRfcb") # Or your preferred venv path

# Install python virtual environment
tryCatch({
  ifcb_py_install(envname = env_path)
}, error = function(e) {
  warning("Python environment could not be installed.")
})
```

```{r, echo=FALSE}
# Check if Python is available
if (!py_available(initialize = TRUE)) {
  knitr::opts_chunk$set(eval = FALSE)
  warning("Python is not available. Skipping vignette evaluation.")
} else {
  # List available packages
  available_packages <- py_list_packages(python = reticulate::py_discover_config()$python)
  
  # Check if pandas and matplotlib are available
  if (!"pandas" %in% available_packages$package ||
      !"matplotlib" %in% available_packages$package) {
    knitr::opts_chunk$set(eval = FALSE)
    warning("Required python modules are not available. Skipping vignette evaluation.")
  }
}
```

### Download Sample Data

To get started, download sample data from the [SMHI IFCB Plankton Image Reference Library](https://doi.org/10.17044/scilifelab.25883455.v3) (Torstensson et al. 2024) with the following function:
```{r, eval=FALSE}
# Define data directory
data_dir <- "data"

# Download and extract test data in the data folder
ifcb_download_test_data(
  dest_dir = data_dir,
  max_retries = 10,
  sleep_time = 30,
  verbose = FALSE
)
```

```{r, include=FALSE}
# Define data directory
data_dir <- "data"

# Download and extract test data in the data folder
if (!dir.exists(data_dir)) {
  # Download and extract test data if the folder does not exist
  ifcb_download_test_data(
    dest_dir = data_dir,
    max_retries = 10,
    sleep_time = 30,
    verbose = FALSE
  )
}
```

## Particle Size Distribution

IFCB data can be quality controlled by analyzing the particle size distribution (PSD) (Hayashi et al. in prep). `iRfcb` uses the code available at [https://github.com/kudelalab/PSD](https://github.com/kudelalab/PSD), which is efficient in detecting samples with bubbles, beads, incomplete runs etc. Before running the PSD quality check, ensure the necessary Python environment is set up and activated:

```{r, eval=FALSE}
# Define path to virtual environment
env_path <- "~/.virtualenvs/iRfcb" # Or your preferred venv path

# Install python virtual environment
ifcb_py_install(envname = env_path)

# Run PSD quality control
psd <- ifcb_psd(
  feature_folder = "data/features/2023",
  hdr_folder = "data/data/2023",
  save_data = FALSE,
  output_file = NULL,
  plot_folder = NULL,
  use_marker = FALSE,
  start_fit = 10,
  r_sqr = 0.5,
  beads = 10 ** 12,
  bubbles = 150,
  incomplete = c(1500, 3),
  missing_cells = 0.7,
  biomass = 1000,
  bloom = 5,
  humidity = 70
)
```

```{r, include=FALSE}
# Run PSD quality control
psd <- ifcb_psd(
  feature_folder = "data/features/2023",
  hdr_folder = "data/data/2023",
  save_data = FALSE,
  output_file = NULL,
  plot_folder = NULL,
  use_marker = FALSE,
  start_fit = 10,
  r_sqr = 0.5,
  beads = 10 ** 12,
  bubbles = 150,
  incomplete = c(1500, 3),
  missing_cells = 0.7,
  biomass = 1000,
  bloom = 5,
  humidity = 70
)
```

The results can be printed and visualized through plots:
```{r}
# Print output from PSD
head(psd$fits)
head(psd$flags)

# Plot PSD of the first sample
plot <- ifcb_psd_plot(
  sample_name = psd$data$sample[1],
  data = psd$data,
  fits = psd$fits,
  start_fit = 10
)

# Print the plot
print(plot)
```

## Geographical QC/QA

### Check If IFCB Is Near Land

To determine if the IFCB is near land (i.e. ship in harbor), examine the position data in the `.hdr` files (or from vectors of latitudes and longitudes):

```{r}
# Read HDR data and extract GPS position (when available)
gps_data <- ifcb_read_hdr_data(
  "data/data/", 
  gps_only = TRUE, 
  verbose = FALSE # Do not print progress bar
)

# Create new column with the results
gps_data$near_land <- ifcb_is_near_land(
  gps_data$gpsLatitude,
  gps_data$gpsLongitude,
  distance = 100, # 100 meters from shore
  shape = NULL # Using the default NE 1:10m Land Polygon
) 

# Print output
head(gps_data)

# Alternatively, you can choose to plot the points on a map
near_land_plot <- ifcb_is_near_land(
  gps_data$gpsLatitude,
  gps_data$gpsLongitude,
  distance = 2500, # 2500 meters from shore
  plot = TRUE,
)

# Print the plot
print(near_land_plot)
```

For more accurate determination, a detailed coastline `.shp` file may be required (e.g. the [EEA Coastline Polygon](https://www.eea.europa.eu/data-and-maps/data/eea-coastline-for-analysis-2/gis-data/eea-coastline-polygon)). Refer to the help pages of `ifcb_is_near_land()` for further information.

### Check Which Sub-Basin an IFCB Sample Is From

To identify the specific sub-basin of the Baltic Sea (or using a custom shape-file) from which an IFCB sample was collected, analyze the position data:
```{r}
# 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, shape_file = NULL)
# Print output
print(points_in_the_baltic)

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

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 provided in `iRfcb` originate from [SHARK](https://shark.smhi.se/hamta-data/). 

### Check If Positions Are Within the Baltic Sea or Elsewhere

This check is useful if only you want to apply a classifier specifically to phytoplankton from the Baltic Sea.

```{r}
# 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 results
print(points_in_the_baltic)

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

This function reads a land-buffered shapefile of the Baltic Sea Basin from the `iRfcb` package by default, or a user-supplied shapefile if provided.

### Find Missing Positions from RV Svea Ferrybox

This function is used by SMHI to collect and match stored ferrybox positions when they are not available in the `.hdr` files. An example ferrybox data file is provided in `iRfcb` with data matching sample **D20220522T000439_IFCB134**.

```{r}
# Print available coordinates from .hdr files
head(gps_data, 4)

# Define path where ferrybox data are located
ferrybox_folder <- "data/ferrybox_data"

# Get GPS position from ferrybox data
positions <- ifcb_get_ferrybox_data(gps_data$timestamp, 
                                    ferrybox_folder)

# Print result
head(positions)
```

### Find Contextual Ferrybox Data from RV Svea

The `ifcb_get_ferrybox_data()` function can also be used to extract additional ferrybox parameters, such as temperature (parameter number **8180**) and salinity (parameter number **8181**). 

```{r}
# Get salinity and temperature from ferrybox data
ferrybox_data <- ifcb_get_ferrybox_data(gps_data$timestamp,
                                        ferrybox_folder,
                                        parameters = c("8180", "8181"))

# Print result
head(ferrybox_data)
```

This concludes this tutorial for the `iRfcb` package. For additional guides—such as data sharing and MATLAB integration—please refer to the other tutorials available on the project's [webpage](https://europeanifcbgroup.github.io/iRfcb/). See how data pipelines can be constructed using `iRfcb` in the following [Example Project](https://github.com/nodc-sweden/ifcb-data-pipeline). Happy analyzing!

```{r, include=FALSE}
# Clean up
unlink(data_dir, recursive = TRUE)
unlink(env_path, recursive = TRUE)
```

## Citation

```{r, echo=FALSE}
# Print citation
citation("iRfcb")
```

## 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.
- Torstensson, A., Skjevik, A-T., Mohlin, M., Karlberg, M. and Karlson, B. (2024). SMHI IFCB Plankton Image Reference Library. SciLifeLab. Dataset. https://doi.org/10.17044/scilifelab.25883455.v3
