FWIN Data Entry

Python desktop application for FWIN fisheries survey data entry.

FWIN Data Entry

Python desktop application for FWIN fisheries survey data entry.

Goals

• Fast keyboard-driven data entry
• FishNet-compatible data model
• SQLite backend
• Export to FishNet-compatible formats

Current Features

• FN011 Project Information
• FN121 Effort Data Entry
• FN123 Catch Summaries
• FN125 Fish Samples
• Create, edit, and validate records
• SQLite project databases
• User-defined database locations

Installation

Clone Repository

git clone https://github.com/HoldenJe/fwin-data-entry.git
cd fwin-data-entry

Build Locally

python -m venv .venv
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
.venv\Scripts\Activate.ps1
pip install -r requirements.txt
python src/fwin_data_entry/app.py

Build Installer

pyinstaller FWIN-Data-Entry.spec

Database Structure

The application stores data in an SQLite database using the following tables:

• FN011 - Project Information
• FN121 - Effort Information
• FN123 - Catch Summaries
• FN125 - Fish Samples

Accessing Data in R

The SQLite database can be accessed directly from R.

Read FN121

library(DBI)
library(RSQLite)

con <- dbConnect(
  SQLite(),
  "MyProject.sqlite"
)

fn121 <- dbReadTable(
  con,
  "FN121"
)

dbDisconnect(con)

head(fn121)

Read All Tables

library(DBI)
library(RSQLite)

con <- dbConnect(
  SQLite(),
  "MyProject.sqlite"
)

fn011 <- dbReadTable(con, "FN011")
fn121 <- dbReadTable(con, "FN121")
fn123 <- dbReadTable(con, "FN123")
fn125 <- dbReadTable(con, "FN125")

dbDisconnect(con)

Example Join

library(DBI)
library(RSQLite)
library(dplyr)

con <- dbConnect(
  SQLite(),
  "MyProject.sqlite"
)

fn121 <- dbReadTable(con, "FN121")
fn123 <- dbReadTable(con, "FN123")

dbDisconnect(con)

catch_summary <- fn123 %>%
  left_join(
    fn121,
    by = c(
      "PRJ_CD",
      "SAM"
    )
  )

catch_summary

Development Status

Current release: v0.4.0

This application is currently being tested by FWIN users. Feedback on workflow, validation, and usability is encouraged.

Roadmap

Potential future enhancements include:

• Date and time validation
• Dynamic field configuration
• FN012-driven validation rules
• Species-specific FN125 entry requirements
• Improved testing coverage