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Merge remote-tracking branch 'origin/feat/trajectories-and-alternative-gui' into feat/trajectories-and-alternative-gui

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eneller
2026-01-21 00:09:16 +01:00
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@@ -10,7 +10,7 @@ date: "`r Sys.Date()`"
knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$set(echo = TRUE)
``` ```
```{r preamble, message=FALSE} ```{r preamble, message=FALSE, include=FALSE}
# Load Libraries # Load Libraries
library(dplyr) library(dplyr)
library(lubridate) library(lubridate)
@@ -24,17 +24,15 @@ library(trajr)
library(shiny) library(shiny)
``` ```
# Download flights ```{r opensky, include=FALSE}
```{r opensky} # Openskies API Functions
time_now <- Sys.time() time_now <- Sys.time()
creds <- getCredentials( creds <- getCredentials(
client_id = Sys.getenv('OPENSKY_CLIENT_ID'), client_id = Sys.getenv('OPENSKY_CLIENT_ID'),
client_secret = Sys.getenv('OPENSKY_CLIENT_SECRET')) client_secret = Sys.getenv('OPENSKY_CLIENT_SECRET'))
# get departures from Frankfurt airport # Get flights for a specific aircraft from OpenSky API
departures <- getAirportDepartures(airport = "EDDF", startTime = time_now - hours(1), endTime = time_now, credentials = creds )
getFlights <- function(icao, time, creds){ getFlights <- function(icao, time, creds){
flights <-getAircraftFlights(icao, startTime = time - days(1), endTime = time, credentials = creds ) flights <-getAircraftFlights(icao, startTime = time - days(1), endTime = time, credentials = creds )
return(flights) return(flights)
@@ -56,8 +54,9 @@ getAircraftTrack <- function(icao, time, creds) {
``` ```
## Trajectory Conversion Functions ```{r trajectory-functions, include=FALSE}
```{r trajectory-functions} # Trajectory Conversion Functions
# Convert route to distance in meters # Convert route to distance in meters
getRouteDistance <- function(route_df) { getRouteDistance <- function(route_df) {
lat_ref <- route_df$lat[1] lat_ref <- route_df$lat[1]
@@ -166,25 +165,11 @@ calculate_trajectory_params <- function(icao, departure_time, creds) {
return(NULL) return(NULL)
}) })
} }
getAircraftTrajectories <- function(icao, time, creds, days = 5){
tracks <- list()
for (i in 0: (days-1)) {
flights <- getFlights(icao,time - days(i),creds)
for (f in flights){
track <- calculate_trajectory_params(icao, f[["departure_time"]], creds)
if (!is.null(track)){
tracks[[length(tracks)+1]] <- track
}
Sys.sleep(0.5) # API courtesy
}
}
return(tracks)
}
``` ```
## Statistical Helper Functions ```{r stat-functions, include=FALSE}
```{r stat-functions} # Statistical Helper Functions
# Get parameter names and labels for trajectory statistics # Get parameter names and labels for trajectory statistics
getTrajectoryParams <- function() { getTrajectoryParams <- function() {
list( list(
@@ -220,8 +205,9 @@ calculateStatsSummary <- function(trajectory_stats_df) {
} }
``` ```
## Visualization Functions ```{r viz-functions, include=FALSE}
```{r viz-functions} # Visualization Functions
# Create boxplots for trajectory statistics # Create boxplots for trajectory statistics
createBoxplots <- function(trajectory_stats_df) { createBoxplots <- function(trajectory_stats_df) {
p <- getTrajectoryParams() p <- getTrajectoryParams()
@@ -323,51 +309,247 @@ generateInterpretation <- function(trajectory_stats_df) {
} }
``` ```
# Example Usage (Demo) # Example Usage (Documentation)
```{r demo, eval=FALSE}
# This section shows how to use the functions above
# Set eval=TRUE to run this demo
# Get credentials This section demonstrates how to use all functions defined above. Each step is explained and executed with real flight data from Frankfurt Airport (EDDF).
## Step 1: Load Credentials
The `getCredentials()` function loads API credentials from environment variables.
```{r}
creds <- getCredentials( creds <- getCredentials(
client_id = Sys.getenv('OPENSKY_CLIENT_ID'), client_id = Sys.getenv("OPENSKY_CLIENT_ID"),
client_secret = Sys.getenv('OPENSKY_CLIENT_SECRET') client_secret = Sys.getenv("OPENSKY_CLIENT_SECRET")
) )
# Get departures from Frankfurt airport ```
## Step 2: Fetch Airport Departures
Use `getAirportDepartures()` to get recent departures from Frankfurt Airport.
```{r demo-departures}
time_now <- Sys.time() time_now <- Sys.time()
departures <- getAirportDepartures( departures <- getAirportDepartures(
airport = "EDDF", airport = "EDDF",
startTime = time_now - hours(1), startTime = time_now - hours(2),
endTime = time_now, endTime = time_now - hours(1),
credentials = creds credentials = creds
) )
cat("Found", length(departures), "departures from Frankfurt Airport (EDDF)\n")
```
## Step 3: Retrieve Flight Track
The `getAircraftTrack()` function fetches detailed track data for a specific aircraft. We use the first available departure.
```{r demo-track}
route_df <- NULL
icao <- "N/A"
# Get first departure's track
if (length(departures) > 0) { if (length(departures) > 0) {
icao <- departures[[1]][["ICAO24"]] for (i in seq_along(departures)) {
dep_time <- departures[[1]][["departure_time"]] icao <- departures[[i]][["ICAO24"]]
dep_time <- departures[[i]][["departure_time"]]
route_df <- getAircraftTrack(icao, dep_time, creds) route_df <- getAircraftTrack(icao, dep_time, creds)
if (!is.null(route_df) && nrow(route_df) >= 3) {
if (!is.null(route_df)) { cat("Successfully retrieved track for aircraft:", icao, "\n")
# Plot route cat("Number of track points:", nrow(route_df), "\n")
plot(route_df$lon, route_df$lat, type = "o", pch = 20, col = "blue", break
main = paste("Geographic route of", icao),
xlab = "Longitude", ylab = "Latitude")
# Plot altitude
plot(route_df$time, route_df$alt, type = "l", col = "red", lwd = 2,
main = paste("Altitude profile of", icao),
xlab = "Time (Unix)", ylab = "Height (Meter)")
# Get summary
print(calculateTrajectoryStats(route_df, icao))
# Plot trajectory
trj <- getTrajFromRoute(route_df)
plot(trj, main = paste("Trajectory of", icao))
} }
Sys.sleep(1)
}
}
if (is.null(route_df)) {
cat("No valid track data found\n")
}
```
## Step 4: Visualize Geographic Route
Plot the flight path on a simple lat/lon coordinate system.
```{r demo-route-plot, fig.width=7, fig.height=5}
if (!is.null(route_df)) {
plot(route_df$lon, route_df$lat, type = "o", pch = 20, col = "blue",
main = paste("Geographic Route of Aircraft", icao),
xlab = "Longitude", ylab = "Latitude")
points(route_df$lon[1], route_df$lat[1], pch = 17, col = "green", cex = 2)
points(route_df$lon[nrow(route_df)], route_df$lat[nrow(route_df)], pch = 15, col = "red", cex = 2)
legend("topright", legend = c("Start", "End", "Path"), pch = c(17, 15, 20), col = c("green", "red", "blue"))
} else {
cat("No route data available for plotting\n")
}
```
## Step 5: Visualize Altitude Profile
Plot the altitude over time to see climb, cruise, and descent phases.
```{r demo-altitude-plot, fig.width=7, fig.height=4}
if (!is.null(route_df)) {
time_minutes <- (route_df$time - route_df$time[1]) / 60
plot(time_minutes, route_df$alt, type = "l", col = "red", lwd = 2,
main = paste("Altitude Profile of Aircraft", icao),
xlab = "Time (minutes)", ylab = "Altitude (meters)")
grid()
} else {
cat("No route data available for altitude plot\n")
}
```
## Step 6: Convert to Trajectory Object
The `getTrajFromRoute()` function converts the route into a trajr trajectory object for analysis.
```{r demo-trajectory-plot, fig.width=7, fig.height=5}
if (!is.null(route_df)) {
trj <- getTrajFromRoute(route_df)
plot(trj, main = paste("Trajectory of Aircraft", icao))
cat("Trajectory created with", nrow(trj), "points\n")
} else {
cat("No route data available for trajectory conversion\n")
}
```
## Step 7: Calculate Trajectory Statistics (Single Flight)
The `calculateTrajectoryStats()` function computes key metrics. Use `format = "table"` for a readable display.
```{r demo-stats-table}
if (!is.null(route_df)) {
stats_table <- calculateTrajectoryStats(route_df, icao = icao, format = "table")
knitr::kable(stats_table, caption = paste("Trajectory Statistics for", icao))
} else {
cat("No route data available for statistics\n")
}
```
## Step 8: Analyze Multiple Flights
Retrieve track data for up to 5 departures to enable statistical comparison.
```{r demo-multiple-tracks}
flight_data <- list()
successful_flights <- 0
if (length(departures) > 0) {
max_attempts <- min(10, length(departures)) # Try up to 10 departures to get 5 valid tracks
for (i in seq_len(max_attempts)) {
icao_temp <- departures[[i]][["ICAO24"]]
dep_time_temp <- departures[[i]][["departure_time"]]
route_df_temp <- getAircraftTrack(icao_temp, dep_time_temp, creds)
if (!is.null(route_df_temp) && nrow(route_df_temp) >= 3) {
stats <- calculateTrajectoryStats(route_df_temp, icao = icao_temp, format = "row")
if (!is.null(stats)) {
flight_data[[length(flight_data) + 1]] <- stats
successful_flights <- successful_flights + 1
cat("Flight", successful_flights, "- ICAO:", icao_temp, "- Points:", nrow(route_df_temp), "\n")
}
}
if (successful_flights >= 5) break
}
if (length(flight_data) > 0) {
all_flights_stats <- do.call(rbind, flight_data)
cat("\nTotal flights analyzed:", nrow(all_flights_stats), "\n")
} else {
all_flights_stats <- NULL
cat("No valid flight tracks found\n")
}
} else {
all_flights_stats <- NULL
cat("No departures available\n")
}
```
## Step 9: Display All Flight Statistics
Show the raw statistics for all analyzed flights.
```{r demo-all-stats-table}
if (!is.null(all_flights_stats)) {
display_stats <- all_flights_stats
display_stats$diffusion_distance_km <- round(display_stats$diffusion_distance_km, 2)
display_stats$path_length_km <- round(display_stats$path_length_km, 2)
display_stats$straightness <- round(display_stats$straightness, 4)
display_stats$duration_min <- round(display_stats$duration_min, 1)
display_stats$mean_velocity_kmh <- round(display_stats$mean_velocity_kmh, 1)
display_stats$fractal_dimension <- round(display_stats$fractal_dimension, 4)
knitr::kable(display_stats, caption = "Statistics for All Analyzed Flights",
col.names = c("ICAO24", "Distance (km)", "Path (km)",
"Straightness", "Duration (min)", "Velocity (km/h)", "Fractal Dim"))
} else {
cat("No flight data available\n")
}
```
## Step 10: Summary Statistics
The `calculateStatsSummary()` function generates descriptive statistics across multiple flights.
```{r demo-summary-stats}
if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 2) {
summary_stats <- calculateStatsSummary(all_flights_stats)
knitr::kable(summary_stats, caption = "Summary Statistics Across All Flights")
} else {
cat("Need at least 2 flights for summary statistics\n")
}
```
## Step 11: Boxplots
The `createBoxplots()` function visualizes the distribution of each trajectory parameter.
```{r demo-boxplots, fig.width=10, fig.height=8}
if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 2) {
createBoxplots(all_flights_stats)
} else {
cat("Need at least 2 flights for boxplots\n")
}
```
## Step 12: Density Plots
The `createDensityPlots()` function shows the probability distribution of each parameter.
```{r demo-density, fig.width=10, fig.height=8}
if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 3) {
createDensityPlots(all_flights_stats)
} else {
cat("Need at least 3 flights for density plots\n")
}
```
## Step 13: Histograms
The `createHistograms()` function displays histograms with density overlays.
```{r demo-histograms, fig.width=10, fig.height=8}
if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 3) {
createHistograms(all_flights_stats)
} else {
cat("Need at least 3 flights for histograms\n")
}
```
## Step 14: Interpretation
The `generateInterpretation()` function provides a text-based analysis of the trajectory statistics.
```{r demo-interpretation}
if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 2) {
interpretation <- generateInterpretation(all_flights_stats)
cat(interpretation)
} else {
cat("Need at least 2 flights for interpretation\n")
} }
``` ```