| Title: | Find Routes for Supersonic Aircraft |
|---|---|
| Description: | For supersonic aircraft, flying subsonic over land, find the best route between airports. Allow for coastal buffer and potentially closed regions. Use a minimal model of aircraft performance: the focus is on time saved versus subsonic flight, rather than on vertical flight profile. For modelling and forecasting, not for planning your flight! |
| Authors: | David Marsh [aut, cre], Enrico Spinielli [ctb], EUROCONTROL [fnd, cph] |
| Maintainer: | David Marsh <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.3.2.9000 |
| Built: | 2025-02-12 04:42:00 UTC |
| Source: | https://github.com/david6marsh/himach |
Coordinate reference system (CRS) for plotting and analysing maps. Centred on East Asia (120E).
crs_120Ecrs_120E
CRS
"+proj=robin +lon_0=120 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs"
crs_Atlantic, crs_Pacific,
crs_N, crs_S
Coordinate reference system (CRS) for plotting and analysing maps.
Atlantic-centred. Works for most analysis, but not recommended for N-region
(eg New Zealand and Fiji), instead use crs_Pacific.
crs_Atlanticcrs_Atlantic
CRS
crs_Atlantic is "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs"
crs_Pacific, crs_120E,
crs_N, crs_S
Coordinate reference system (CRS) for creating maps from longitude-latitude coordinates. Used in analysis, but not recommended for plots.
crs_longlatcrs_longlat
CRS
crs_longlat is EPSG4326
crs_Atlantic, crs_Pacific,
crs_S, crs_N
Coordinate reference system (CRS) for plotting and analysing maps. WGS 84 / Arctic Polar Stereographic. Used in analysis, but not recommended for plots.
crs_Ncrs_N
CRS
crs_N is EPSG3995
crs_Atlantic, crs_Pacific,
crs_120E, crs_longlat, crs_S
Coordinate reference system (CRS) for plotting and analysing maps. Pacific-centred.
crs_Pacificcrs_Pacific
CRS
"+proj=robin +lon_0=180 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs"
crs_Atlantic, crs_120E,
crs_N, crs_S
Coordinate reference system (CRS) for plotting and analysing maps. WGS 84 / Antarctic Polar Stereographic. Used in analysis, but not recommended for plots.
crs_Scrs_S
CRS
crs_N is EPSG 3031
crs_Atlantic, crs_Pacific,
crs_120E, crs_longlat, crs_N
find_leg finds the quickest non-stop route for ac between
two airports ap2.
find_leg( ac, ap2, route_grid, fat_map, ap_loc, avoid = NA, enforce_range = TRUE, best_by_time = TRUE, grace_km = NA, shortcuts = TRUE, ad_dist_m = 100 * 1000, ad_nearest = 12, max_leg_circuity = 1.4, ... )find_leg( ac, ap2, route_grid, fat_map, ap_loc, avoid = NA, enforce_range = TRUE, best_by_time = TRUE, grace_km = NA, shortcuts = TRUE, ad_dist_m = 100 * 1000, ad_nearest = 12, max_leg_circuity = 1.4, ... )
ac, ap2, route_grid, fat_map, ap_loc, avoid
|
See |
enforce_range |
If TRUE (default) then leg is constrained to aircraft range, otherwise routes of excess range can be found. |
best_by_time |
If TRUE (default) then the quickest route is found, else the shortest distance. |
grace_km |
Default NA. Otherwise, if great circle distance is within
3pct of aircraft range, then add |
shortcuts |
If TRUE (default) then path will be checked for great circle shortcuts. |
ad_dist_m |
The length of arrival/departure links, in m. (Default 100,000=100km) |
ad_nearest |
The number of arrival/departure links to create (Default 12) |
max_leg_circuity |
The maximum detour over great circle distance that can be flown to find a quick over-sea route. Default 1.4. |
... |
Other parameters, passed to |
This function finds the quickest non-stop route between two airports. A
'route' is made up of one or two 'legs' (airport to airport without
intermediate stop). find_route makes one or more calls to
find_leg as required.
It assumes that the routing grid, route_grid, has already been classified as
land or sea using the map fat_map. The map is further used when
converting the grid-based route to one of great-circle segments.
In fact find_leg finds up to 4 versions of the path:
A great circle, direct between the airports
A grid path, consisting of segments of the routing grid, plus departure and arrival routes from the airports
A simplification of the grid path to great circle segments
shortcuts defaults to TRUE. Without this, you see near-raw
Dijkstra results, which are _not_ shortest great circle.
Legs are automatically saved in route_cache and retrieved from here if
available rather than re-calculated. See
vignette on caching for cache
management.
Dataframe with details of the leg
# need to load some of the built-in data (not run) ## Not run: aircraft <- make_aircraft(warn = FALSE) airports <- make_airports(crs = crs_Pacific) # get test datasets NZ_buffer30 <- hm_get_test("buffer") NZ_grid <- hm_get_test("grid") options("quiet" = 4) #for heavy reporting # from Auckland to Christchurch ap2 <- make_AP2("NZAA","NZCH",airports) routes <- find_leg(aircraft[4,], ap2, fat_map = NZ_buffer30, route_grid = NZ_grid, ap_loc = airports) ## End(Not run)# need to load some of the built-in data (not run) ## Not run: aircraft <- make_aircraft(warn = FALSE) airports <- make_airports(crs = crs_Pacific) # get test datasets NZ_buffer30 <- hm_get_test("buffer") NZ_grid <- hm_get_test("grid") options("quiet" = 4) #for heavy reporting # from Auckland to Christchurch ap2 <- make_AP2("NZAA","NZCH",airports) routes <- find_leg(aircraft[4,], ap2, fat_map = NZ_buffer30, route_grid = NZ_grid, ap_loc = airports) ## End(Not run)
find_route finds the quickest route between two airports, refuelling
if necessary
find_route( ac, ap2, fat_map, avoid = NA, route_grid, cf_subsonic = NA, refuel = NA, refuel_h = 1, refuel_only_if = TRUE, refuel_topN = 1, max_circuity = 2, ap_loc, margin_km = 200, ... )find_route( ac, ap2, fat_map, avoid = NA, route_grid, cf_subsonic = NA, refuel = NA, refuel_h = 1, refuel_only_if = TRUE, refuel_topN = 1, max_circuity = 2, ap_loc, margin_km = 200, ... )
ac |
One aircraft, as from |
ap2 |
One airport pair, as from |
fat_map |
|
avoid |
|
route_grid |
|
cf_subsonic |
Further aircraft to use as comparator, default NA. (use is not recommended) |
refuel |
Airports available for refuelling, dataframe with |
refuel_h |
Duration of refuelling stop, in hours |
refuel_only_if |
If TRUE (default) only test refuel options if necessary because the great circle distance is too far for the aircraft range |
refuel_topN |
Return the best N (default 1) refuelling options |
max_circuity |
Threshold for excluding refuelling stops (default 2.0) |
ap_loc |
Airport locations as from |
margin_km |
Great circle distance between airports must be less than aircraft range minus this operating margin (default 200km), to give a margin for arrival and departure. |
... |
Other parameters, passed to |
This function finds the quickest route between two airports. A 'route' is
made up of one or two 'legs' (airport to airport without intermediate stop).
find_route makes one or more calls to find_leg as required.
It assumes that the routing grid, route_grid, has already been
classified as land or sea using the map fat_map. The map is further
used when converting the grid-based route to one of great circles segments.
Dataframe with details of the route
If either necessary, because the great circle distance is greater than the
aircraft range, or because refuel_only_if is FALSE,
find_route searches through a list of refuelling airports and
chooses the quickest one (or refuel_topN).
Circuitous refuelling is avoided, tested against total distance <
max_circuity * great circle distance. This is separate to the limits
placed on circuity of individual legs in find_leg.
If no refuel option is found, a message is displayed. The route with 'NA' for 'time_h' is returned.
Each refuelling stop costs refuel_h in addition to the time to
descend to the airport and then to climb out again.
# need to load some of the built-in data aircraft <- make_aircraft(warn = FALSE) # get test datasets NZ_buffer30 <- hm_get_test("buffer") NZ_grid <- hm_get_test("grid") airports <- make_airports(crs = sf::st_crs(NZ_buffer30)) options("quiet" = 4) #for heavy reporting # from Auckland to Christchurch ap2 <- make_AP2("NZAA","NZCH",airports) # on some CRAN machines even this takes too long, so not run ## Not run: routes <- find_route(aircraft[4,], ap2, fat_map = NZ_buffer30, route_grid = NZ_grid, ap_loc = airports) ## End(Not run)# need to load some of the built-in data aircraft <- make_aircraft(warn = FALSE) # get test datasets NZ_buffer30 <- hm_get_test("buffer") NZ_grid <- hm_get_test("grid") airports <- make_airports(crs = sf::st_crs(NZ_buffer30)) options("quiet" = 4) #for heavy reporting # from Auckland to Christchurch ap2 <- make_AP2("NZAA","NZCH",airports) # on some CRAN machines even this takes too long, so not run ## Not run: routes <- find_route(aircraft[4,], ap2, fat_map = NZ_buffer30, route_grid = NZ_grid, ap_loc = airports) ## End(Not run)
find_routes combines an aircraft and airport-pair list and finds the
best routes between them, refuelling if necessary
find_routes(ac_ids, ap2_ids, aircraft, airports, ...)find_routes(ac_ids, ap2_ids, aircraft, airports, ...)
ac_ids |
A vector of aircraft IDs, as in column 'id' from
|
ap2_ids |
A 2-column matrix or dataframe of airport pair text IDs |
aircraft |
Specification of the aircraft, see
|
airports |
Airport locations as from |
... |
Other parameters, passed to |
This function finds is a wrapper for the single-case function
find_route. It takes (text) lists of aircraft and airport codes,
combines them, then finds routes for all of these. A 'route' is made up
of one or two 'legs' (airport to airport without intermediate stop).
For more details see find_route
Dataframe with details of the routes
# need to load some of the built-in data aircraft <- make_aircraft(warn = FALSE) airports <- make_airports(crs = crs_Pacific) # get test datasets NZ_buffer30 <- hm_get_test("buffer") NZ_grid <- hm_get_test("grid") options("quiet" = 4) #for heavy reporting # from Auckland to Christchurch ap2 <- make_AP2("NZAA","NZCH",airports) ## Not run: routes <- find_route(aircraft[4,], ap2, fat_map = NZ_buffer30, route_grid = NZ_grid, ap_loc = airports) ## End(Not run)# need to load some of the built-in data aircraft <- make_aircraft(warn = FALSE) airports <- make_airports(crs = crs_Pacific) # get test datasets NZ_buffer30 <- hm_get_test("buffer") NZ_grid <- hm_get_test("grid") options("quiet" = 4) #for heavy reporting # from Auckland to Christchurch ap2 <- make_AP2("NZAA","NZCH",airports) ## Not run: routes <- find_route(aircraft[4,], ap2, fat_map = NZ_buffer30, route_grid = NZ_grid, ap_loc = airports) ## End(Not run)
A GridLat keeps together a grid of points and a
lattice of links between those points.
It has 3 components:
* A character name, which isn't used much in anger but might help you remember what's gone into it. * A dataframe containing the points of the lattice (the vertices), which each have an ID, a longitude and latitude. * A dataframe containing the edges of the lattice, joining the points.
Empties the cache.
hm_clean_cache(cache = c("route", "star"))hm_clean_cache(cache = c("route", "star"))
cache |
Which caches to clear. Default is both |
TRUE silently
For more details see the cache section in the vignette:
vignette("Supersonic_Routes_in_depth", package = "himach"). or
Vignette on caching
hm_clean_cache("route") hm_clean_cache()hm_clean_cache("route") hm_clean_cache()
Access 5 datasets that are used in vignettes and in testing.
hm_get_test(item = c("coast", "buffer", "nofly", "grid", "route"))hm_get_test(item = c("coast", "buffer", "nofly", "grid", "route"))
item |
Any one of |
"coast"A dataset containing sf::MULTIPOLYGONS for New Zealand. Simplified version of Stats NZ data, at 1km resolution.
"buffer"As "coast" but with an added 30km buffer to keep supersonic
flight away from the coast.
"nofly"As "buffer", but limited to Buller district with a 40km buffer.
To test additional no-fly zones.
"grid"Latitude-longitude-based routing grid around New Zealand at 30km target
distance, as generated by make_route_grid, so format is GridLat
"route"Some very unlikely supersonic routes around New Zealand using the test aircraft that was given a very short range and slow subsonic cruise to get the example to 'work'. Includes one refuelling stop (!) in Wellington. [Not for operational use!] Returns a dataframe.
This is not the normal way to access package test data. But the usual, direct, way fails on some machines that have some older software (a known feature of the 'sf' package). This is a least-ugly workaround.
See list above
https://datafinder.stats.govt.nz/layer/104266-territorial-authority-2020-clipped-generalised/
NZ_coast <- hm_get_test("coast")NZ_coast <- hm_get_test("coast")
This silently overwrites any existing values in the cache.
hm_load_cache(file)hm_load_cache(file)
file |
Including the path. |
Invisible true
For more details see the cache section in the vignette:
vignette("Supersonic_Routes_in_depth", package = "himach"). or
Vignette on caching
# not run # hm_load_cache(file="") #load from this file# not run # hm_load_cache(file="") #load from this file
Filename is "route_star_cache_id_XXX.rda" where "id" is the id
parameter and XXX is made up from the name of the grid (which identifies the
map used) and the 'aircraftSet' attribute of the aircraft dataset (which
identifies the source). This is because the cache should be for a unique
combination of these (and you must have these available, because they were
needed to generate the routes).
hm_save_cache(id, grid, aircraft, path = "data/")hm_save_cache(id, grid, aircraft, path = "data/")
id |
Identifying text, see above. Recommended to use a version number or date. |
grid |
Your route grid dataset. The |
aircraft |
Your aircraft dataset. The |
path |
By default |
Invisible true
For more details see the cache section in the vignette:
vignette("Supersonic_Routes_in_depth", package = "himach"). or
Vignette on caching
# not run # hm_save_cache("v2", grid, ac) #save here# not run # hm_save_cache("v2", grid, ac) #save here
1 Mach is approximately 1062kph in standard met conditions at the altitude for supersonic flight (approx 50,000 feet).
mach_kphmach_kph
double
make_aircraft ensures a minimum set of variables describing aircraft
make_aircraft(ac = NA, sound_kph = himach::mach_kph, warn = TRUE)make_aircraft(ac = NA, sound_kph = himach::mach_kph, warn = TRUE)
ac |
Dataframe containing the minimum fields, or NA (default) |
sound_kph |
Speed of sound used to convert from Mach to kph, default
|
warn |
Warn if no |
This function provides a test set of aircraft if necessary and adds variables to a minimal set of data to give all the information that will be needed.
This minimal set needs to have the following fields:
id, type: a very short, and longer text identifier for this aircraft
over_sea_M, over_land_M: the eponymous two speeds, given as a Mach number
accel_Mpm: acceleration in Mach per minute between these two
arrdep_kph: the speed on arrival and departure from airports, given in km per hour
range_km: range in km
An attribute is set to help keep track of where the aircraft data came from
(and whether a new cache is needed). If the aircraftSet attribute of
the ac parameter is not set, the set is treated as 'disposable'.
For more details see the help vignette:
vignette("SupersonicRouting", package = "himach")
Dataframe with at least 11 variables describing the performance of one or more aircraft
# do minimal version (we know it will use the default so turn off warning) ac <- make_aircraft(warn = FALSE) # on-the-fly example ac <- data.frame(id = "test", type = "test aircraft", over_sea_M = 2.0, over_land_M = 0.9, accel_Mpm = 0.2, arrdep_kph = 300, range_km = 6000, stringsAsFactors=FALSE) ac <- make_aircraft(ac, warn = FALSE) ## Not run: # example for your own data aircraft <- utils::read.csv("data/aircraft.csv", stringsAsFactors = FALSE) aircraft <- make_aircraft(aircraft) # strongly recommended to record the file name for later reference attr(aircraft, "aircraftSet") <- "aircraft.csv" ## End(Not run)# do minimal version (we know it will use the default so turn off warning) ac <- make_aircraft(warn = FALSE) # on-the-fly example ac <- data.frame(id = "test", type = "test aircraft", over_sea_M = 2.0, over_land_M = 0.9, accel_Mpm = 0.2, arrdep_kph = 300, range_km = 6000, stringsAsFactors=FALSE) ac <- make_aircraft(ac, warn = FALSE) ## Not run: # example for your own data aircraft <- utils::read.csv("data/aircraft.csv", stringsAsFactors = FALSE) aircraft <- make_aircraft(aircraft) # strongly recommended to record the file name for later reference attr(aircraft, "aircraftSet") <- "aircraft.csv" ## End(Not run)
make_airports ensures a minimum set of variables describing airports
make_airports(ap = NA, crs = crs_longlat, warn = TRUE)make_airports(ap = NA, crs = crs_longlat, warn = TRUE)
ap |
Dataframe containing the minimum fields, or NA (default) |
crs |
Coordinate reference system for the coded lat-longs. Default 4326. |
warn |
warn if default set is used (default = TRUE) |
This function provides a test set of airports if necessary from
airportr::airports and geocodes the lat-long of this or the dataset
provide as ap.
This minimal set needs to have the following fields:
APICAO: the 4-letter ICAO code for the airport (though there is no
validity check applied, so 'TEST', or 'ZZZZ' could be used, for example)
lat, long: latitude and longitude in decimal degrees
Dataframe with, in addition, a geocoded lat-long.
# do minimal version airports <- make_airports() # on-the-fly example airports <- data.frame(APICAO = "TEST", lat = 10, long = 10, stringsAsFactors = FALSE) airports <- make_airports(airports) ## Not run: # example for your own data airports <- utils::read.csv("data/airports.csv", stringsAsFactors = FALSE) airports <- make_airports(airports) ## End(Not run)# do minimal version airports <- make_airports() # on-the-fly example airports <- data.frame(APICAO = "TEST", lat = 10, long = 10, stringsAsFactors = FALSE) airports <- make_airports(airports) ## Not run: # example for your own data airports <- utils::read.csv("data/airports.csv", stringsAsFactors = FALSE) airports <- make_airports(airports) ## End(Not run)
make_AP2 creates an airport-pair set from two sets of airports
make_AP2(adep, ades, ap = make_airports())make_AP2(adep, ades, ap = make_airports())
adep, ades
|
Identical-length lists of airport codes |
ap |
List of locations of airports, defaults to the output
of |
This function takes two lists of airports (of the same length), specified as 4-letter codes and combines them, adding the fields:
from_long, from_lat, to_long, to_lat: the airport lat-longs
with adep first
AP2: a name for the route in a specific order
gcdist_km: the great circle distance in km
In AP2 European airports (crudely, from starting letter = 'E' or 'L')
are listed first, otherwise in alphabetical order. If unidirectional is TRUE,
then ">" is the separator, otherwise "<>".
(Unidirectional not currently supported)
For more details see the introductory vignette.
Dataframe with additional variables as described above.
airports <- make_airports() #get a default set of lat-longs ap2 <- make_AP2("NZAA","NZCH", airports)airports <- make_airports() #get a default set of lat-longs ap2 <- make_AP2("NZAA","NZCH", airports)
make_route_envelope finds the range envelope for a given route
make_route_envelope(ac, ap2, envelope_points = 200, fuzz = 0.005)make_route_envelope(ac, ap2, envelope_points = 200, fuzz = 0.005)
ac, ap2
|
See |
envelope_points |
How many points are used to define the ellipse? Default 200. |
fuzz |
Add a little margin to the range, to allow the longest range to be flown, rather than be cut off at the boundary. (Default 0.005) |
The 'route envelope' is the region within which a route from A to B must remain. This is an ellipse.
It differs from the pure 'range envelope' which is the points which an aircraft can reach from a given airport.
sf POLYGON with ad hoc coordinate reference system.
# Need aircraft and airport datasets ac <- make_aircraft(warn = FALSE) ap <- make_airports() z <- make_route_envelope(ac[1,], make_AP2("EGLL","KJFK",ap))# Need aircraft and airport datasets ac <- make_aircraft(warn = FALSE) ap <- make_airports() z <- make_route_envelope(ac[1,], make_AP2("EGLL","KJFK",ap))
make_route_grid creates, and optionally classifies, a lat-long route grid
make_route_grid( fat_map, name, target_km = 800, lat_min = -60, lat_max = 86, long_min = -180, long_max = 179.95, classify = FALSE )make_route_grid( fat_map, name, target_km = 800, lat_min = -60, lat_max = 86, long_min = -180, long_max = 179.95, classify = FALSE )
fat_map |
MULTIPOLYGON map defining land regions |
name |
String assigned to the name slot of the result |
target_km |
Target length. Default 800km only to avoid accidentally starting heavy compute. 30-50km would be more useful. |
lat_min, lat_max
|
Latitude extent of grid |
long_min, long_max
|
Longitude extend of grid. Two allow small grids crossing the 180 boundary, the function accepts values outside [-180,180), then rounds to within this range. |
classify |
Whether to classify each link. Defaults to FALSE only to avoid accidentally starting heavy compute. |
This function creates a GridLat object that contains a set of point on a lat long grid (ie all the points are on lines of latitude). It also joins these points into a lattice. Optionally, but required later, it classifies each link as land, sea, or transition, with reference to a given map (typically including a coastal buffer).
The definitions are
land: both ends of the link are on land
sea: both ends are on sea, and the link does not intersect the land
transition: otherwise
The length of the links will be around target_km or 50pct longer
for the diagonal links.
For more details see the help vignette:
vignette("Supersonic Routing", package = "himach")
gridLat object containing points and lattice.
NZ_buffer <- hm_get_test("buffer") system.time( p_grid <- make_route_grid(NZ_buffer,"NZ lat-long at 300km", target_km = 300, classify = TRUE, lat_min = -49, lat_max = -32, long_min = 162, long_max = 182) )NZ_buffer <- hm_get_test("buffer") system.time( p_grid <- make_route_grid(NZ_buffer,"NZ lat-long at 300km", target_km = 300, classify = TRUE, lat_min = -49, lat_max = -32, long_min = 162, long_max = 182) )
map_routes plots routes, with many options
map_routes( thin_map, routes = NA, crs = himach::crs_Atlantic, show_route = c("speed", "aircraft", "time", "circuity", "acceleration", "traffic"), fat_map = NA, avoid_map = NA, ap_loc = NA, ap_col = "darkblue", ap_size = 0.4, forecast = NA, fc_var = NA_character_, fc_text = NA_character_, crow = FALSE, crow_col = "grey70", crow_size = 0.2, route_envelope = FALSE, bound = TRUE, bound_margin_km = 200, simplify_km = 8, land_f = "grey90", buffer_f = "grey60", land_c = "grey85", land_s = 0.2, avoid_f = "grey80", avoid_c = "grey95", avoid_s = 0.3, l_alpha = 0.8, l_size = 0.5, e_alpha = 0.4, e_size = 0.6, e_col = "grey70", refuel_airports = ap_loc, rap_col = "red", rap_size = 0.4, scale_direction = -1, title = "", subtitle = "", warn = FALSE, ... )map_routes( thin_map, routes = NA, crs = himach::crs_Atlantic, show_route = c("speed", "aircraft", "time", "circuity", "acceleration", "traffic"), fat_map = NA, avoid_map = NA, ap_loc = NA, ap_col = "darkblue", ap_size = 0.4, forecast = NA, fc_var = NA_character_, fc_text = NA_character_, crow = FALSE, crow_col = "grey70", crow_size = 0.2, route_envelope = FALSE, bound = TRUE, bound_margin_km = 200, simplify_km = 8, land_f = "grey90", buffer_f = "grey60", land_c = "grey85", land_s = 0.2, avoid_f = "grey80", avoid_c = "grey95", avoid_s = 0.3, l_alpha = 0.8, l_size = 0.5, e_alpha = 0.4, e_size = 0.6, e_col = "grey70", refuel_airports = ap_loc, rap_col = "red", rap_size = 0.4, scale_direction = -1, title = "", subtitle = "", warn = FALSE, ... )
thin_map |
The minimum is a |
routes |
as generated by |
crs |
Coordinate reference system, default |
show_route |
one of "speed", "aircraft", "time", "circuity", "accel", "traffic" to indicate what goes in the legend. |
fat_map |
optional coast + buffer map, default NA. |
avoid_map |
optional map of no-fly zones, default NA. |
ap_loc |
Show used origin and destination airports if this is a set of
airports from |
ap_col, ap_size
|
Colour and size of used airport markers (dark blue, 0.4) |
forecast, fc_var, fc_text
|
Forecast set and two strings. See details, default NA. |
crow, crow_col, crow_size
|
If TRUE, show the 'crow-flies' direct great
circle, in colour |
route_envelope |
show the route envelope (default FALSE). |
bound, bound_margin_km
|
If bound=TRUE (default) crop to bounding box of
the |
simplify_km |
Simplify the two maps to this scale before plotting (default 10). |
land_f, buffer_f, avoid_f
|
fill colours for thin, fat and no-fly maps, default grey 90, 70 and 80, respectively |
land_c, land_s
|
boundary colour and size for land areas (countries), default grey 85 and 0.2, respectively (use NA to turn off) |
avoid_c, avoid_s
|
boundary colour and size for avoid areas, default grey 95 and 0.3, respectively |
l_alpha, l_size
|
line (route) settings for alpha (transparency) and width, defaults 0.6 and 0.4. |
e_col, e_alpha, e_size
|
colour, alpha and width for the range envelope. Default "grey70", 0.4, 0.6 |
refuel_airports |
Show the used refuel airports using these locations,
or nothing if NA. (Defaults to same as |
rap_col, rap_size
|
Colour and size of refuel airport markers (red, 0.4) |
scale_direction |
Passed to scale_colour_viridis, either -1 (default) or or 1. |
title, subtitle
|
Passed to ggplot. |
warn |
if TRUE show some warnings (when defaults loaded) (default FALSE) |
... |
further parameters passed to |
This function plots the routes, with options for additional layers. Multiple routes are expected, and they can be coloured by time advantage, by speed along each segment, or by aircraft type.
The option show_route "time" requires 'advantage_h' to have been added
to the routes set, from the route summary. If it hasn't then this is done in
a local version, then discarded. Running summarise_routes to do this
requires an airport dataset; if is.na(ap_loc) then this is not
available, so a default set is used. You can turn on warn to see if
this is happening, but by default it is silent.
For show_route = "speed", "aircraft", "time", "circuity" or "accel",
the information is already available in the routes dataset. For
show_route = "traffic" you need to provide a forecast dataset that
contains at least the fullRouteID and acID fields which are normal in
the routes dataset, and a field giving the volume of the forecast
fc_var. This could be flights, seats, or something else: use
fc_text for the legend title to show the units of fc_var.
Combinations of fullRouteID and acID must be unique, which probably
means you must filter by forecast year and forecast scenario before passing
to map_routes.
The time to compute the map may not be very different with simplify_km
varying between 2km and 20km, but the time to plot on the screen, or
ggsave to a file, is longer than the compute time. It is this latter
time that's reduced by simplifying the maps. For single, or short routes, you
can probably see the difference between 2km and 10km, so it's your choice to
prefer speed or beauty.
A ggplot.
#see introductory vignette#see introductory vignette
Profile a set of routes
profile_routes( routes, yvar = c("hours", "longitude"), ap_loc = make_airports(warn = FALSE), n_max = 2 )profile_routes( routes, yvar = c("hours", "longitude"), ap_loc = make_airports(warn = FALSE), n_max = 2 )
routes |
as generated by |
yvar |
horizontal axis is hours or longitude |
ap_loc |
Airports and coordinates, by (silent) default from |
n_max |
maximum number of routes to plot (default 2) |
A list of named list pairs of plots, which can be displayed using eg result[1].
# not run --- # plot_list <- profile_routes(routes, n_max = 3) # plot_list # to display them all# not run --- # plot_list <- profile_routes(routes, n_max = 3) # plot_list # to display them all
st_transform with view window to avoid datelinest_window does a st_transform but first cuts the data to an
appropriate view window and so avoids problems with objects wrapping around
the back of the globe
st_window(m, crs = himach::crs_Atlantic, longit_margin = 0.1)st_window(m, crs = himach::crs_Atlantic, longit_margin = 0.1)
m |
A map dataframe, ie of class |
crs |
Destination coordinate reference system, as in |
longit_margin |
Amount trimmed off the 'far side' of the projection in degrees. |
st_wrap_dateline _should_ handle the break in
a map projections but uses 'GDAL' for this. Given persistent issues in
installing GDAL, st_window achieves the same using s2 instead.
It works for any 'simple' projection, in the sense of one that has a dateline that is a single line of longitude: ie the proj4string contains either "longitude_of_center", so the dateline is that +180; or not, in which case it assumes the "longitude_of_center" is 0.
sf dataframe, same as the parameter m
world <- sf::st_as_sf(rnaturalearthdata::coastline110) w_pacific <- st_window(world, crs_Pacific) ggplot2::ggplot(w_pacific) + ggplot2::geom_sf() # bad - not run - dateline problem example # ggplot2::ggplot(st_transform(world, crs_Pacific)) + # ggplot2::geom_sf()world <- sf::st_as_sf(rnaturalearthdata::coastline110) w_pacific <- st_window(world, crs_Pacific) ggplot2::ggplot(w_pacific) + ggplot2::geom_sf() # bad - not run - dateline problem example # ggplot2::ggplot(st_transform(world, crs_Pacific)) + # ggplot2::geom_sf()
Reduce a set of routes to a one-line per route summary
summarise_routes(routes, ap_loc, arrdep_h = 0.5)summarise_routes(routes, ap_loc, arrdep_h = 0.5)
routes |
Each segment in each route, as produced by
|
ap_loc |
List of airport locations, output of
|
arrdep_h |
Total time for the M084 comparator aircraft to arrive & depart in hours. Default 0.5. |
This function takes the output of find_route and summarises to
one line per (full) route.
With refuelling, there can be multiple 'full routes' for each 'route'. The
best column indicates the best route for each routeID.
The results are rounded to a reasonable number of significant figures. After
all this is just an approximate model. The arrdep_h has been checked
against actual and is reasonable (observed range roughly 0.3-0.5).
Dataframe with summary of the route, sorted in ascending order of advantage_h
so that the best route are plotted on top. The fields are:
timestamp: when the leg was originally generated (it may have been cached)
fullRouteID: including the refuel stop if any
routeID: origin and destination airport, in make_AP2 order
refuel_ap: code for the refuelling airport, or NA
acID, acType: aircraft identifiers taken from the aircraft set
M084_h: flight time for a Mach 0.84 comparator aircraft (including 2*arrdep_h)
gcdist_km: great circle distance between the origin and destination airports
sea_time_frac: Fraction of time_h time spent over sea, hence at supersonic speed,
or accelerating to, or decelerating from supersonic speed
sea_dist_frac: as sea_time_frac, but fraction of dist_km
dist_km: total length of the route, in km
time_h: total time, in hours
n_phases: number of distinct phases: arr/dep, transition, land, sea, refuel.
advantage_h: M084_h - time_h
circuity: the route distance extension (1 = perfect) dist_km / gcdist_km
best: for each routeID, the fullrouteID with maximum advantage_h
# here we use a built-in set of routes # see vignette for more details of how to obtain it airports <- make_airports(crs = crs_Pacific) NZ_routes <- hm_get_test("route") sumy <- summarise_routes(NZ_routes, airports)# here we use a built-in set of routes # see vignette for more details of how to obtain it airports <- make_airports(crs = crs_Pacific) NZ_routes <- hm_get_test("route") sumy <- summarise_routes(NZ_routes, airports)