IsleRoyaleApp (Isle Royale Wolf-Moose Platform)

Interactive offline simulation platform exploring spatial predator-prey dynamics, wolf-moose movement over Isle Royale habitat features, age class distributions, and historical census benchmarks.

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The live application below is running completely client-side in your browser using serverless Shinylive (WebAssembly). It provides an interactive Isle Royale Wolf-Moose Platform (IsleRoyaleApp()) featuring 100% offline spatial substrate mapping, continuous micro-step history tracking, side-by-side cowplot age class plots with per-species legends, custom time unit labeling (Steps vs Days), tab-aware sidebar decluttering (show_habitat, show_landmarks, norm, total), and dynamic table discovery (inputApp).

#| '!! shinylive warning !!': |
#|   shinylive does not work in self-contained HTML documents.
#|   Please set `embed-resources: false` in your metadata.
#| standalone: true
#| viewerHeight: 880
#| components: [viewer]

library(shiny)
library(bslib)
library(ggplot2)
library(cowplot)
library(dplyr)
library(tidyr)
library(tibble)
library(rlang)
library(stats)
library(graphics)

# --- Auto-Included Isle Royale Datasets List ---
isle_royale_datasets <- list()
# --- Auto-Included Data Table: future.moose ---
future.moose <- structure(list(current = c("calf", "yearling", "adult", "senior", 
"adult", "death", "predated"), future = c("yearling", "adult", 
"senior", "death", "calf", "death", "death"), fid = c(2L, 3L, 
4L, 5L, 6L, 7L, 7L), time = c(365L, 365L, 2920L, 2190L, 365L, 
0L, 0L), pch = c("0", "1", "2", "3", "G", "D", "D"), color = c("lightgreen", 
"green", "darkgreen", "brown", "black", "red", "red"), ageclass = c("calf", 
"yearling", "adult", "senior", "adult", NA, NA), event = c("future", 
"future", "future", "future", "birth", "death", "death"), init = c(50L, 
50L, 400L, 100L, 50L, 0L, 0L)), class = "data.frame", row.names = c(NA, 
-7L))
isle_royale_datasets[['future.moose']] <- future.moose

# --- Auto-Included Data Table: future.wolf ---
future.wolf <- structure(list(current = c("pup", "subadult", "adult", "adult", 
"death"), future = c("subadult", "adult", "death", "pup", "death"
), fid = 2:6, time = c(365L, 365L, 2555L, 365L, 0L), pch = c("0", 
"1", "2", "P", "D"), color = c("orange", "darkorange", "black", 
"purple", "red"), ageclass = c("pup", "subadult", "adult", "adult", 
NA), event = c("future", "future", "future", "birth", "death"
), init = c(5L, 10L, 15L, 5L, 0L)), class = "data.frame", row.names = c(NA, 
-5L))
isle_royale_datasets[['future.wolf']] <- future.wolf

# --- Auto-Included Data Table: moose.wolf ---
moose.wolf <- structure(list(c("calf", "yearling", "adult", "senior", "death", 
"predated"), feed = c(8, 3, 1, 6, 0, 0), offspring = c(1.5, 1, 
0.5, 1.2, 0, 0), male = c(NA, NA, NA, NA, NA, NA)), class = "data.frame", row.names = c(NA, 
-6L))
isle_royale_datasets[['moose.wolf']] <- moose.wolf

# --- Auto-Included Data Table: organism.features ---
organism.features <- structure(list(c("moose", "wolf", "substrate"), units = c("DD", 
"hr", NA), offspring = c("1.2", "moose", ""), attack = c(NA, 
"moose", ""), birth = c("adult", NA, ""), substrate = c("substrate", 
"substrate", ""), deplete = c(100L, 48L, NA), subclass = c("moose", 
"adult", ""), parasite = c(NA, "ecto", ""), move = c("adult", 
"adult", "")), class = "data.frame", row.names = c(NA, -3L))
isle_royale_datasets[['organism.features']] <- organism.features

# --- Auto-Included Data Table: substrate.moose ---
substrate.moose <- structure(list(c("calf", "yearling", "adult", "senior"), substrate = c(1.2, 
1, 1, 0.8)), class = "data.frame", row.names = c(NA, -4L))
isle_royale_datasets[['substrate.moose']] <- substrate.moose

# --- Auto-Included Data Table: substrate.substrate ---
substrate.substrate <- structure(list("substrate", substrate = 1), class = "data.frame", row.names = c(NA, 
-1L))
isle_royale_datasets[['substrate.substrate']] <- substrate.substrate

# --- Auto-Included Data Table: substrate.wolf ---
substrate.wolf <- structure(list(c("pup", "subadult", "adult"), substrate = c(1, 
1, 1)), class = "data.frame", row.names = c(NA, -3L))
isle_royale_datasets[['substrate.wolf']] <- substrate.wolf

# --- Auto-Included Historical Census Data: wolf_moose ---
wolf_moose <- structure(list(Year = 1980:2019, Wolves = c(50L, 30L, 14L, 23L, 
24L, 22L, 20L, 16L, 12L, 11L, 15L, 12L, 12L, 13L, 15L, 16L, 22L, 
24L, 14L, 25L, 29L, 19L, 17L, 19L, 29L, 30L, 30L, 21L, 23L, 24L, 
19L, 16L, 9L, 8L, 9L, 3L, 2L, 2L, 2L, 14L), Moose = c(664L, 650L, 
700L, 900L, 811L, 1062L, 1025L, 1380L, 1653L, 1397L, 1216L, 1313L, 
1600L, 1880L, 1800L, 2400L, 1200L, 500L, 700L, 750L, 850L, 900L, 
1000L, 900L, 750L, 540L, 385L, 450L, 650L, 530L, 510L, 515L, 
750L, 975L, 1050L, 1250L, 1300L, 1600L, 1500L, 2060L)), class = "data.frame", row.names = c(NA, 
-40L))
isle_royale_datasets[['wolf_moose']] <- wolf_moose

# --- Auto-Included Spatial RDS Object: isle_royale_features ---
isle_royale_features <- structure(list(habitat_type = c("Lake/Pond", "Lake/Pond", "Lake/Pond", 
"Forest", "Bog/Wetland"), id = c("041800000101", "041800000101", 
"041800000101", "041800000101", "041800000101"), objectid = c(40556L, 
40556L, 40556L, 40556L, 40556L), tnmid = c("{DB09E061-2929-491A-86D2-74A4F730D113}", 
"{DB09E061-2929-491A-86D2-74A4F730D113}", "{DB09E061-2929-491A-86D2-74A4F730D113}", 
"{DB09E061-2929-491A-86D2-74A4F730D113}", "{DB09E061-2929-491A-86D2-74A4F730D113}"
), metasourceid = c("{511D2AC8-11BA-45FC-AB98-F69D693D4C44}", 
"{511D2AC8-11BA-45FC-AB98-F69D693D4C44}", "{511D2AC8-11BA-45FC-AB98-F69D693D4C44}", 
"{511D2AC8-11BA-45FC-AB98-F69D693D4C44}", "{511D2AC8-11BA-45FC-AB98-F69D693D4C44}"
), sourcedatadesc = c("Watershed Boundary Dataset (WBD)", "Watershed Boundary Dataset (WBD)", 
"Watershed Boundary Dataset (WBD)", "Watershed Boundary Dataset (WBD)", 
"Watershed Boundary Dataset (WBD)"), sourceoriginator = c("Natural Resources and Conservation Service and U.S. Geological Survey", 
"Natural Resources and Conservation Service and U.S. Geological Survey", 
"Natural Resources and Conservation Service and U.S. Geological Survey", 
"Natural Resources and Conservation Service and U.S. Geological Survey", 
"Natural Resources and Conservation Service and U.S. Geological Survey"
), sourcefeatureid = c(NA_character_, NA_character_, NA_character_, 
NA_character_, NA_character_), loaddate = structure(c(1723704945, 
1723704945, 1723704945, 1723704945, 1723704945), class = c("POSIXct", 
"POSIXt"), tzone = ""), referencegnis_ids = c("1618306", "1618306", 
"1618306", "1618306", "1618306"), areaacres = c(424470.78, 424470.78, 
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"041800000101", "041800000101", "041800000101", "041800000101"
), name = c("Isle Royale", "Isle Royale", "Isle Royale", "Isle Royale", 
"Isle Royale"), hutype = c("I", "I", "I", "I", "I"), humod = c("NM", 
"NM", "NM", "NM", "NM"), tohuc = c("041800000200", "041800000200", 
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NA_character_, NA_character_, NA_character_, NA_character_), 
    noncontributingareasqkm = c(NA_character_, NA_character_, 
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    "Data © OpenStreetMap contributors, ODbL 1.0. http://osm.org/copyright", 
    "Data © OpenStreetMap contributors, ODbL 1.0. http://osm.org/copyright", 
    "Data © OpenStreetMap contributors, ODbL 1.0. http://osm.org/copyright", 
    "Data © OpenStreetMap contributors, ODbL 1.0. http://osm.org/copyright"
    ), osm_type = c("relation", "relation", "relation", "relation", 
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    addresstype = c("island", "island", "island", "island", "island"
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    "Isle Royale, Keweenaw County, Michigan, United States", 
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    48.1545329735159, 48.1545999, 48.1545944694915, 48.1546087033219, 
    48.1547569700347, 48.1548285529033), dim = c(72L, 2L)))), class = c("XY", 
    "MULTIPOLYGON", "sfg")), structure(list(list(structure(c(-89.161838, 
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    47.9096377, 47.9093431, 47.9090243, 47.9086906, 47.9084753, 
    47.908329, 47.9081958, 47.9081498, 47.9081301, 47.9080693
    ), dim = c(138L, 2L)))), class = c("XY", "MULTIPOLYGON", 
    "sfg")), structure(list(list(structure(c(-88.77, -88.73, 
    -88.73, -88.77, -88.77, 48.008, 48.008, 48.032, 48.032, 48.008
    ), dim = c(5L, 2L)))), class = c("XY", "MULTIPOLYGON", "sfg"
    ))), class = c("sfc_MULTIPOLYGON", "sfc"), precision = 0, bbox = structure(c(xmin = -89.171, 
    ymin = 47.8726972028773, xmax = -88.48, ymax = 48.157), class = "bbox"), crs = structure(list(
        input = "EPSG:4326", wkt = "GEOGCRS[\"WGS 84\",\n    ENSEMBLE[\"World Geodetic System 1984 ensemble\",\n        MEMBER[\"World Geodetic System 1984 (Transit)\"],\n        MEMBER[\"World Geodetic System 1984 (G730)\"],\n        MEMBER[\"World Geodetic System 1984 (G873)\"],\n        MEMBER[\"World Geodetic System 1984 (G1150)\"],\n        MEMBER[\"World Geodetic System 1984 (G1674)\"],\n        MEMBER[\"World Geodetic System 1984 (G1762)\"],\n        MEMBER[\"World Geodetic System 1984 (G2139)\"],\n        MEMBER[\"World Geodetic System 1984 (G2296)\"],\n        ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n            LENGTHUNIT[\"metre\",1]],\n        ENSEMBLEACCURACY[2.0]],\n    PRIMEM[\"Greenwich\",0,\n        ANGLEUNIT[\"degree\",0.0174532925199433]],\n    CS[ellipsoidal,2],\n        AXIS[\"geodetic latitude (Lat)\",north,\n            ORDER[1],\n            ANGLEUNIT[\"degree\",0.0174532925199433]],\n        AXIS[\"geodetic longitude (Lon)\",east,\n            ORDER[2],\n            ANGLEUNIT[\"degree\",0.0174532925199433]],\n    USAGE[\n        SCOPE[\"Horizontal component of 3D system.\"],\n        AREA[\"World.\"],\n        BBOX[-90,-180,90,180]],\n    ID[\"EPSG\",4326]]"), class = "crs"), n_empty = 0L)), row.names = c(NA, 
5L), class = c("sf", "data.frame"), sf_column = "geometry", agr = structure(c(habitat_type = NA_integer_, 
id = NA_integer_, objectid = NA_integer_, tnmid = NA_integer_, 
metasourceid = NA_integer_, sourcedatadesc = NA_integer_, sourceoriginator = NA_integer_, 
sourcefeatureid = NA_integer_, loaddate = NA_integer_, referencegnis_ids = NA_integer_, 
areaacres = NA_integer_, areasqkm = NA_integer_, states = NA_integer_, 
huc12 = NA_integer_, name = NA_integer_, hutype = NA_integer_, 
humod = NA_integer_, tohuc = NA_integer_, noncontributingareaacres = NA_integer_, 
noncontributingareasqkm = NA_integer_, globalid = NA_integer_, 
place_id = NA_integer_, licence = NA_integer_, osm_type = NA_integer_, 
osm_id = NA_integer_, lat = NA_integer_, lon = NA_integer_, class = NA_integer_, 
type = NA_integer_, place_rank = NA_integer_, importance = NA_integer_, 
addresstype = NA_integer_, name.1 = NA_integer_, display_name = NA_integer_
), class = "factor", levels = c("constant", "aggregate", "identity"
)))
isle_royale_datasets[['isle_royale_features']] <- isle_royale_features

# --- Auto-Included Spatial RDS Object: isle_royale_landmarks ---
isle_royale_landmarks <- structure(list(name = c("Washington Creek (Windigo)", "Ojibway Lake", 
"Feldtmann Lake", "Hidden Lake (Tobin Harbor)"), location = c("Windigo", 
"Ojibway", "Feldtmann", "Tobin Harbor"), description = c("Feeding area along stream & forest cover", 
"Aquatic vegetation feeding lake", "Major southwest inland lake habitat", 
"Aquatic plant feeding area near Tobin Harbor"), geometry = structure(list(
    structure(c(-89.146, 47.923), class = c("XY", "POINT", "sfg"
    )), structure(c(-88.618, 48.113), class = c("XY", "POINT", 
    "sfg")), structure(c(-88.961, 47.876), class = c("XY", "POINT", 
    "sfg")), structure(c(-88.49, 48.151), class = c("XY", "POINT", 
    "sfg"))), n_empty = 0L, crs = structure(list(input = "EPSG:4326", 
    wkt = "GEOGCRS[\"WGS 84\",\n    ENSEMBLE[\"World Geodetic System 1984 ensemble\",\n        MEMBER[\"World Geodetic System 1984 (Transit)\"],\n        MEMBER[\"World Geodetic System 1984 (G730)\"],\n        MEMBER[\"World Geodetic System 1984 (G873)\"],\n        MEMBER[\"World Geodetic System 1984 (G1150)\"],\n        MEMBER[\"World Geodetic System 1984 (G1674)\"],\n        MEMBER[\"World Geodetic System 1984 (G1762)\"],\n        MEMBER[\"World Geodetic System 1984 (G2139)\"],\n        MEMBER[\"World Geodetic System 1984 (G2296)\"],\n        ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n            LENGTHUNIT[\"metre\",1]],\n        ENSEMBLEACCURACY[2.0]],\n    PRIMEM[\"Greenwich\",0,\n        ANGLEUNIT[\"degree\",0.0174532925199433]],\n    CS[ellipsoidal,2],\n        AXIS[\"geodetic latitude (Lat)\",north,\n            ORDER[1],\n            ANGLEUNIT[\"degree\",0.0174532925199433]],\n        AXIS[\"geodetic longitude (Lon)\",east,\n            ORDER[2],\n            ANGLEUNIT[\"degree\",0.0174532925199433]],\n    USAGE[\n        SCOPE[\"Horizontal component of 3D system.\"],\n        AREA[\"World.\"],\n        BBOX[-90,-180,90,180]],\n    ID[\"EPSG\",4326]]"), class = "crs"), class = c("sfc_POINT", 
"sfc"), precision = 0, bbox = structure(c(xmin = -89.146, ymin = 47.876, 
xmax = -88.49, ymax = 48.151), class = "bbox"))), row.names = c(NA, 
4L), sf_column = "geometry", agr = structure(c(name = NA_integer_, 
location = NA_integer_, description = NA_integer_), class = "factor", levels = c("constant", 
"aggregate", "identity")), class = c("sf", "data.frame"))
isle_royale_datasets[['isle_royale_landmarks']] <- isle_royale_landmarks

# --- Auto-Included Spatial RDS Object: isle_royale_layer ---
isle_royale_layer <- structure(list(id = "041800000101", objectid = 40556L, tnmid = "{DB09E061-2929-491A-86D2-74A4F730D113}", 
    metasourceid = "{511D2AC8-11BA-45FC-AB98-F69D693D4C44}", 
    sourcedatadesc = "Watershed Boundary Dataset (WBD)", sourceoriginator = "Natural Resources and Conservation Service and U.S. Geological Survey", 
    sourcefeatureid = NA_character_, loaddate = structure(1723704945, class = c("POSIXct", 
    "POSIXt"), tzone = ""), referencegnis_ids = "1618306", areaacres = 424470.78, 
    areasqkm = 1717.77, states = "CN,MI,MN,WI", huc12 = "041800000101", 
    name = "Isle Royale", hutype = "I", humod = "NM", tohuc = "041800000200", 
    noncontributingareaacres = NA_character_, noncontributingareasqkm = NA_character_, 
    globalid = "{DB09E061-2929-491A-86D2-74A4F730D113}", place_id = 374424026L, 
    licence = "Data © OpenStreetMap contributors, ODbL 1.0. http://osm.org/copyright", 
    osm_type = "relation", osm_id = 3671688L, lat = "48.0073825", 
    lon = "-88.8289875", class = "place", type = "island", place_rank = 17L, 
    importance = 0.377565444769677, addresstype = "island", name.1 = "Isle Royale", 
    display_name = "Isle Royale, Keweenaw County, Michigan, United States", 
    geometry = structure(list(structure(list(list(structure(c(-88.6826186579642, 
    -88.6827079489419, -88.6827407489319, -88.6827406662774, 
    -88.6825755, -88.682362, -88.682279, -88.6822827, -88.682498, 
    -88.6826186579642, 48.1174650291646, 48.1174141510419, 48.1172826843549, 
    48.1170210035476, 48.117125, 48.1172428, 48.1174054, 48.1174916, 
    48.1174975, 48.1174650291646), dim = c(10L, 2L))), list(structure(c(-89.2354705868803, 
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    -89.235001, -89.234768, -89.2343928, -89.233846, -89.233331, 
    -89.2327254, -89.2321368, -89.2320069, -89.2313914, -89.2312344, 
    -89.2308617, -89.2307244, -89.2300133, -89.2296086, -89.229204, 
    -89.2287038, -89.2282305, -89.2276641, -89.2272815, -89.2268745, 
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    -89.2233286, -89.2226813, -89.221514, -89.2211854, -89.2204596, 
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    -89.1987381, -89.1980883, -89.1977938469372, -89.1977886674548, 
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    -89.1955657, -89.1951015, -89.1945743, -89.1942114, -89.1939441, 
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    -89.1744837, -89.1738903, -89.173238, -89.1726838, -89.1721762, 
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    -89.1506731, -89.1502881, -89.149832, -89.1495942, -89.1494201, 
    -89.149143, -89.1488708, -89.1486035, -89.1479537, -89.1471199, 
    -89.1467128, -89.1465314, -89.1459282, -89.1452244, -89.1445697, 
    -89.1439689, -89.1433264, -89.1428679, -89.1425098, -89.1423921, 
    -89.1421077, -89.1419164, -89.1417963, -89.1415658, -89.1413917, 
    -89.1411857, -89.1411023, -89.1408963, -89.1406585, -89.1405457, 
    -89.140325, -89.1401214, -89.1399449, -89.1396972, -89.1396384, 
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    -89.1392926, -89.1392166, -89.1390817, -89.1389587034786, 
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    47.9030276, 47.9028501, 47.9025608, 47.9022369, 47.9020101, 
    47.9017766, 47.9015432, 47.9013903, 47.901216, 47.9011355, 
    47.90093, 47.9007557, 47.9007607, 47.9006061, 47.9005042, 
    47.9004762, 47.9003809, 47.9002954, 47.9002395, 47.9002362, 
    47.9003266, 47.9004532, 47.9004873160812, 47.900500087167, 
    47.9004949874836, 47.9004664, 47.9004335, 47.900473, 47.9004622455515, 
    47.9004283281704, 47.9003776, 47.9002891303963, 47.8999753835437, 
    47.8998861, 47.8996986, 47.8995309, 47.8993485, 47.8991462, 
    47.8988618, 47.8985199, 47.8983472, 47.8983138958077, 47.8982252197012, 
    47.8980518555498, 47.8979329, 47.897859, 47.8978031, 47.8977955597394, 
    47.8977810861952, 47.8977208194921, 47.897709886118, 47.8976878860846, 
    47.897561819355, 47.8975595747051, 47.8975515, 47.897544981667, 
    47.8974466859243, 47.8974630858855, 47.8974246858262, 47.8973808857752, 
    47.897340688442, 47.8972605, 47.8972342, 47.8972539, 47.8972936784333, 
    47.8972930856811, 47.8967586624906, 47.8965897, 47.8963349, 
    47.896223219558, 47.8958180185291, 47.8957936172163, 47.8956279, 
    47.8954619, 47.8952859, 47.8951284734619, 47.8951094373642, 
    47.8948732, 47.8947088, 47.8945214, 47.8944326, 47.8944211, 
    47.8943224, 47.8941515, 47.8939541, 47.8937075, 47.8935184, 
    47.89328, 47.8931567, 47.8928936, 47.8926371, 47.8924037, 
    47.8922261, 47.8917969, 47.8916358, 47.8915503, 47.8912773, 
    47.8910406, 47.8909057, 47.8908317, 47.8907956, 47.8908068570745, 
    47.8907892838896, 47.8907948172152, 47.8909098838893, 47.891173083924, 
    47.8914856172707, 47.8916828172311, 47.8916662838725, 47.891589417157, 
    47.8914248837694, 47.8913096837344, 47.8908380169599, 47.8907422491423, 
    47.8904815, 47.8902546, 47.8899718, 47.8898139, 47.8897235, 
    47.8897177232829, 47.8896096166873, 47.8892694944316, 47.8892187, 
    47.8891760337529, 47.8888787598872, 47.8888026, 47.8887056, 
    47.8886942874637, 47.8886770830852, 47.8886002830583, 47.8886110163257, 
    47.8885232162888, 47.8883256828838, 47.8879636827889, 47.8877020974913, 
    47.8876795294734, 47.8875276399351, 47.887469, 47.887423, 
    47.8874197, 47.8873918, 47.8873983, 47.8874312, 47.8874493, 
    47.8875578, 47.887742, 47.8878769, 47.8879065, 47.8879081, 
    47.8877716, 47.8876368, 47.8874592, 47.8872701, 47.8870399, 
    47.88678, 47.886609, 47.8864298, 47.8862061, 47.8860664, 
    47.8859562, 47.8857687, 47.8855977, 47.8855451, 47.8855188, 
    47.8855829, 47.8856059, 47.8856618, 47.8856931, 47.8857786, 
    47.8858772, 47.8859562, 47.8859726, 47.885966, 47.8859808, 
    47.8860121, 47.8861519, 47.8861502, 47.8861272, 47.8860779, 
    47.886091, 47.8861601, 47.8861963, 47.8862818, 47.8863936, 
    47.8863673, 47.8862407, 47.8860779, 47.8859693, 47.8858148, 
    47.8856306, 47.8854004, 47.8850863, 47.8847459, 47.8843989, 
    47.8840979, 47.8837657, 47.8834599, 47.8831836, 47.88295, 
    47.8828188278061, 47.882818016422, 47.8824890163731, 47.8824580084456, 
    47.8824123, 47.8821788, 47.8820357, 47.8819156, 47.8819063585682, 
    47.8808276160688, 47.8806356826937, 47.8805150826659, 47.8803012826071, 
    47.880169615898, 47.8801256825435, 47.8802188825238, 47.8802298158485, 
    47.8802242825032, 47.8802100305662, 47.880197, 47.8798829, 
    47.8796214, 47.8793911, 47.8791411945413, 47.8791276156322, 
    47.8789796156094, 47.8788534822475, 47.8784258154739, 47.8782886820997, 
    47.8780748153822, 47.8777566819846, 47.8771150818392, 47.8768462817684, 
    47.876539081672, 47.8760016815163, 47.8754202813714, 47.8752897008934, 
    47.8752464, 47.8748649, 47.8745507, 47.8743336, 47.8742974, 
    47.8742682792536, 47.8740720143034, 47.87406743067, 47.8739487, 
    47.8737299, 47.873521, 47.8732628, 47.8730292, 47.8727726, 
    47.8725588, 47.8723844, 47.8723423323796, 47.8722180138354, 
    47.8711866802439, 47.8707478801371, 47.8703914133898, 47.8701500133334, 
    47.8698922132708, 47.8697222132243, 47.8694423905185, 47.8694219, 
    47.8691275, 47.8690788876629, 47.8687622129944, 47.8682246795497, 
    47.8680822350128, 47.86805, 47.867823, 47.8676124, 47.8673805, 
    47.8671436, 47.8669659, 47.8666896, 47.8665497, 47.8662668, 
    47.8659624, 47.8656351, 47.8654948406902, 47.8654396791912, 
    47.8652202791435, 47.8650612124352, 47.864852679055, 47.8645456123263, 
    47.8637778788452, 47.8630922787162, 47.8626042786347, 47.8619242785149, 
    47.8614526784256, 47.8610852116856, 47.8607068782826, 47.8603010115362, 
    47.859955411452, 47.8595330113543, 47.8591216112716, 47.8586938111793, 
    47.8580300776891, 47.8577392109424, 47.8570370107599, 47.8566254773237, 
    47.8560876771718, 47.8558132770907, 47.8555004769874, 47.8553028102597, 
    47.8552370102428, 47.8548584768363, 47.8546008101166, 47.8542168767195, 
    47.8539044100085, 47.8537695875252, 47.853562, 47.8534601555129
    ), dim = c(11432L, 2L)))), class = c("XY", "MULTIPOLYGON", 
    "sfg"))), class = c("sfc_MULTIPOLYGON", "sfc"), precision = 0, bbox = structure(c(xmin = -89.2354767000503, 
    ymin = 47.82376, xmax = -88.4223296792445, ymax = 48.1910236
    ), class = "bbox"), crs = structure(list(input = "EPSG:4326", 
        wkt = "GEOGCRS[\"WGS 84\",\n    ENSEMBLE[\"World Geodetic System 1984 ensemble\",\n        MEMBER[\"World Geodetic System 1984 (Transit)\"],\n        MEMBER[\"World Geodetic System 1984 (G730)\"],\n        MEMBER[\"World Geodetic System 1984 (G873)\"],\n        MEMBER[\"World Geodetic System 1984 (G1150)\"],\n        MEMBER[\"World Geodetic System 1984 (G1674)\"],\n        MEMBER[\"World Geodetic System 1984 (G1762)\"],\n        MEMBER[\"World Geodetic System 1984 (G2139)\"],\n        MEMBER[\"World Geodetic System 1984 (G2296)\"],\n        ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n            LENGTHUNIT[\"metre\",1]],\n        ENSEMBLEACCURACY[2.0]],\n    PRIMEM[\"Greenwich\",0,\n        ANGLEUNIT[\"degree\",0.0174532925199433]],\n    CS[ellipsoidal,2],\n        AXIS[\"geodetic latitude (Lat)\",north,\n            ORDER[1],\n            ANGLEUNIT[\"degree\",0.0174532925199433]],\n        AXIS[\"geodetic longitude (Lon)\",east,\n            ORDER[2],\n            ANGLEUNIT[\"degree\",0.0174532925199433]],\n    USAGE[\n        SCOPE[\"Horizontal component of 3D system.\"],\n        AREA[\"World.\"],\n        BBOX[-90,-180,90,180]],\n    ID[\"EPSG\",4326]]"), class = "crs"), n_empty = 0L)), row.names = 1L, class = c("sf", 
"tbl_df", "tbl", "data.frame"), sf_column = "geometry", agr = structure(c(id = NA_integer_, 
objectid = NA_integer_, tnmid = NA_integer_, metasourceid = NA_integer_, 
sourcedatadesc = NA_integer_, sourceoriginator = NA_integer_, 
sourcefeatureid = NA_integer_, loaddate = NA_integer_, referencegnis_ids = NA_integer_, 
areaacres = NA_integer_, areasqkm = NA_integer_, states = NA_integer_, 
huc12 = NA_integer_, name = NA_integer_, hutype = NA_integer_, 
humod = NA_integer_, tohuc = NA_integer_, noncontributingareaacres = NA_integer_, 
noncontributingareasqkm = NA_integer_, globalid = NA_integer_, 
place_id = NA_integer_, licence = NA_integer_, osm_type = NA_integer_, 
osm_id = NA_integer_, lat = NA_integer_, lon = NA_integer_, class = NA_integer_, 
type = NA_integer_, place_rank = NA_integer_, importance = NA_integer_, 
addresstype = NA_integer_, name.1 = NA_integer_, display_name = NA_integer_
), class = "factor", levels = c("constant", "aggregate", "identity"
)))
isle_royale_datasets[['isle_royale_layer']] <- isle_royale_layer

# --- Source: spline.R ---
## $Id: spline.R,v 1.0 2002/12/09 yandell@stat.wisc.edu Exp $
##
## Functions for Bland Ewing's modeling.
##
##     Copyright (C) 2000,2001,2002 Brian S. Yandell.
##
## This program is free software; you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by the
## Free Software Foundation; either version 2, or (at your option) any
## later version.
##
## These functions are distributed in the hope that they will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
## GNU General Public License for more details.
##
## The text of the GNU General Public License, version 2, is available
## as http://www.gnu.org/copyleft or by writing to the Free Software
## Foundation, 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
##
###########################################################################################
## Support routines for future.meanvalue
###########################################################################################
spline.rate <- function( meanvalue, x )
{
  coeff <- stats::coef( meanvalue )
  for( i in 2:ncol( coeff ))
    coeff[,i-1] <- i * coeff[,i]
  coeff[,ncol( coeff ) ] <- 0
  meanvalue$coefficients <- coeff
  if( missing( x ))
    stats::predict( meanvalue )
  else
    stats::predict( meanvalue, x )
}
###########################################################################################
spline.deriv <- function( s )
{
  s$coefficients <- s$coefficients[,-1]
  for( i in seq( 2, ncol( s$coefficients )))
    s$coefficients[,i] <- s$coefficients[,i] * i
  s
}
###########################################################################################
splinesum <- function( xy, fit = splines::interpSpline( xy$x, xy$y ),
                       log2 = log( 2 ), tol = 1e-5 )
{
  n <- nrow( xy )
  ## mean time to future event (assume linear off end )
  maxx <- xy[n,"x"]
  mean.y <- maxx * mean( exp( - stats::predict( fit )$y )) + xy[1,"x"]
  rate <- spline.rate( fit, maxx )$y
  if( rate > tol )
    mean.y <- mean.y + exp( - xy[n,"y"] ) / rate

  ## median time to future event
  adiff <- diff( stats::coef( fit )[,1] )
  ## undefined if mean value function not monotone
  if( !( all( adiff < 0) || all( adiff > 0 )))
    median.y <- NA
  else {
    invmvalue <- splines::backSpline( fit )
    median.y <- stats::predict( invmvalue, log2 )$y
    if( is.na( median.y ))
      median.y <- spline.extrapolate( fit, invmvalue, log2 )
  }
  c( mean = mean.y, median = median.y )
}
###########################################################################################
summaryshow <- function( xy, fit, col = "black", sums = splinesum( xy, fit ))
{
  tmpar <- graphics::par( col = col )
  graphics::mtext( paste( "mean =", round( sums[1], 2 )), 3, at = graphics::par("usr")[1], adj = 0 )
  medianshow <- if( is.na( sums[2] ))
    "curve not monotone"
  else
    paste( "median =", round( sums[2], 2 ))
  graphics::mtext( medianshow, 3, at = graphics::par("usr")[2] / 1.25, adj = 1 )
  graphics::par( tmpar )
  invisible( sums )
}
###########################################################################################
curve.plot <- function( xy = seq(0,1,by=.25), y = c(0,.1,.5,.9,1),
  z = graphics::locator(1,"n"), n=5, action="add",
  fit = splines::interpSpline( xy$x, xy$y ),
  backfit = TRUE, save.ends = 3, col = c("blue","red"), lwd = 4,
  f = function( x ) x, finv = function( x ) x )
{
  if( !is.list( xy )) {
    xy <- data.frame( x = xy )
    xy$y <- y
  }
  else
    xy <- as.data.frame( xy )
  if( !match( action, c("refresh","finish"), nomatch = 0 )) {
    z <- as.data.frame(z)
    tmp <- z$x > max( xy$x )
    if( any( tmp )) {
      if( all( tmp ))
        return( xy )
      z$x <- z$x[!tmp]
      z$y <- z$y[!tmp]
    }
  }
  remove.points <- function( xy, finv, save.ends = TRUE ) {
    ## find closest point after standardizing distances
    usr <- graphics::par( "usr" )
    tmp <- (( xy$x - z$x ) / diff( usr[1:2] )) ^ 2 +
      (( xy$y - z$y ) / diff( usr[3:4] )) ^ 2
    if( save.ends )
      tmp <- tmp[ - c( 1, nrow( xy )) ]
    tmp <- save.ends + min( seq( tmp )[ tmp == min( tmp ) ] )
    tmpd <- xy[tmp,"y"]
    tmpd <- finv( tmpd )
    graphics::points( xy[tmp,"x"], tmpd, lwd = lwd, col = "white" ) 
    tmp
  }
  for( i in 1:n) {
    switch( action,
      noaction =
        return( xy )
      ,
      add = {
        graphics::points(z$x,z$y, lwd = lwd )
        z$y <- f( z$y )
        xy <- rbind(xy,z)
        xy <- xy[order(xy$x),]
        fit <- splines::interpSpline( xy$x, xy$y )
      },
      replace = {
        graphics::points(z$x,z$y, lwd = lwd )
        z$y <- f( z$y )
        tmp <- remove.points( xy, finv, save.ends == 3 )
        tmpp <- ( tmp > 1 & tmp < length( xy$x ))
        if( save.ends != 2 | tmpp )
          xy$x[tmp] <- z$x
        if( save.ends != 1 | tmpp )
          xy$y[tmp] <- z$y
        fit <- splines::interpSpline( xy$x, xy$y )
      },
      delete = {
        z$y <- f( z$y )
        tmp <- remove.points( xy, finv, save.ends > 0 )
        xy <- xy[-tmp,]
        fit <- splines::interpSpline( xy$x, xy$y )
      },
      refresh =, finish = {
        xy
      }
    )
    tmpp <- stats::predict( fit )
    graphics::lines( tmpp$x, finv( tmpp$y ), col = col[1], lwd = lwd )

    adiff <- diff( stats::coef( fit )[,1] )
    if( backfit & ( all( adiff < 0) || all( adiff > 0 ))) {
      ## backspline (not quite a spline) fit to inverse
      tmpback <- stats::predict( splines::backSpline( fit ))
      graphics::lines( tmpback$y, finv( tmpback$x ), col = col[2], lwd = lwd )
    }
  }
  list( xy = xy[order(xy$x),], fit = fit )
}
###########################################################################################
cdf.lines <- function( data, fig = "mean value", nspline = 8,
  conf = c(50,80,90,95), rescale = 1,
  col = c("green","blue","red","orange") )
{
  rate <- fig == "mean value"
  n <- length( data )
  data <- sort( data )
  prob <- seq( n ) / ( n + 1 )
  f <- - rescale * log( 1 - prob )
  ylab <- "prob"
  if( rate )
    ylab <- "cum rate"
  else
    f <- 1 - exp( -f )
  graphics::lines( data, f, lwd = 2 )
  conf <- conf / 100
  for( i in seq( length( conf ))) {
    ## lower confidence
    tmp <- stats::qbinom( conf[i], n, prob, lower.tail = TRUE ) / ( n + 1 )
    tmp <- 1 - exp( rescale * log( 1 - tmp ))
    tmpna <- is.na( tmp )
    if( any( tmpna ))
      tmp[tmpna] <- f[1]
    if( rate )
      tmp <- - log( 1 - tmp )
    graphics::lines( data, tmp, lty = 2, col = col[i] )
    ## upper confidence
    tmp <- stats::qbinom( conf[i], n, prob, lower.tail = FALSE ) / ( n + 1 )
    tmp <- 1 - exp( rescale * log( 1 - tmp ))
    tmpna <- is.na( tmp )
    if( any( tmpna ))
      tmp[tmpna] <- f[n]
    if( rate )
      tmp <- - log( 1 - tmp )
    graphics::lines( data, tmp, lty = 2, col = col[i] )
  }
}
###########################################################################################
rspline <- function( meantime = 1,
                    fivepar = c(dispersion = 1, location = 0, intensity = 1, truncation = 0,
                      rejection = Inf ),
                    fit = NULL,
                    meanvalue = fit$meanvalue, invmvalue = fit$invmvalue,
                    span = Inf )
{
  ## dispersion = a, location = b, intensity = c
  ## truncation = -log(1-d), rejection = -log(1-e)

  ## y = a M^-1( G(d)+cV ) + b  if 1 - exp( -cV ) < G(e)
  ## y = span        if 1 - exp( -cV ) >= G(e)

  ## this is not quite right for truncation, as we know event happened before b
  ## if 1-exp(-cV) < d, but I am not sure how to pass that information along yet

  default <- is.null( meanvalue )

  ##          V ~ exp(1)
  ## intensity:      V/c
  ## truncation:      (G(d)+V)/c
  rate <- ( fivepar["truncation"] + rexp( 1 )) / fivepar["intensity"]

  ## mean value inverse:    M^-1( G(d)+V/c )
  if( default )
    y <- rate
  else {
    y <- stats::predict( invmvalue, rate )$y
    ## kludge to linearly extrapolate beyond cubic spline fit
    if( is.na( y ))
      y <- spline.extrapolate( meanvalue, invmvalue, rate )
  }

  ## dispersion and location:  y = a M^-1( cV ) + b
  y <- meantime * fivepar["dispersion"] * y + fivepar["location"]

  ## rejection:      y >= e? then set y to span
  if( y > fivepar["rejection"] )
    y <- span

  y
}
###########################################################################################
spline.extrapolate <- function( meanvalue, invmvalue, x )
{
  ## linear extrapolation of inverse spline beyond upper end
  coeff <- stats::coef( invmvalue )
  nr <- nrow( coeff ) - 1
  xknot <- splines::splineKnots( invmvalue )[nr+(0:1)]
  yknot <- splines::splineKnots( meanvalue )[nr+1]
  tmpr <- ( xknot[2] - xknot[1] )
  slope <- coeff[nr,2] + tmpr * ( 2 * coeff[nr,3] + tmpr * 3 * coeff[nr,4] )
  yknot + slope * ( x - xknot[2] )
}
###########################################################################################
### spline.design() is a prototype for designing spline curves
### Ultimately, pieces of spline.design, spline.temp() and spline.meanvalue()
### will be pulled out as subroutines to reduce code overlap
###########################################################################################
spline.design <- function (y = yinit, x = xinit, nspline = 8, xy = data.frame(x = x, 
    y = y), n = 1, horizontal = FALSE) 
{
    is.data <- !missing(y)
    if (is.data) {
        data <- y
        if (missing(x)) 
            x <- as.numeric(names(y))
        datax <- x
        ndata <- length(data)
        choose <- round(seq(1, ndata, length = nspline))
        xinit <- x <- x[choose]
        yinit <- y <- y[choose]
    }
    else {
        tmp <- seq(0, nspline - 1)
        if (missing(x)) 
            xinit <- tmp
        else xinit <- x
        if (missing(y)) 
            yinit <- rep(50, nspline)
        else yinit <- y
    }

  ## plot curve and surrounding axes
  graphics::par( mfrow = c(1,1), mar = rep(4.1,4))
  plotit <- function( xy, fig = "temp", fit = splines::interpSpline( xy$x, xy$y ),
    horizontal = FALSE, strip = .25, margin = .1 )
  {
    switch( fig, {
        y <- xy$y
        ylim <- range(y)
      }
    )
    xlim <- range(xy$x)
    xlim <- xlim + c(-1,1) * margin * diff( xlim )
    if( horizontal ) {
      if( diff( ylim ) == 0 )
        ylim <- ylim * c(.75,1.25)
      separator <- ylim[2]
      ylim[2] <- ylim[2] + strip * diff( ylim )
    }
    else {
      separator <- xlim[2]
      xlim[2] <- xlim[2] + strip * diff( xlim )
    }
    axt <- c("n","s")
    tmpar <- graphics::par( xaxt = axt[1+horizontal], yaxt = axt[2-horizontal] )
    plot( xy$x, y, xlim = xlim, ylim = ylim, type="n", xlab = "", ylab = "" )
    graphics::par( xaxt = "s", yaxt = "s" )
    graphics::points( xy$x, y, lwd = 4 )
    graphics::title( fig )
    graphics::mtext( "time", 1, 2 )
    graphics::mtext( fig, 2, 2 )
    if( horizontal ) {
      p <- pretty( c(ylim[1],separator) )
      graphics::axis( 2, p[ p <= separator ] )
      graphics::abline( h = separator, lty = 2 )
    }
    else {
      p <- pretty( c(xlim[1],separator) )
      graphics::axis( 1, p[ p <= separator ] )
      graphics::abline( v = separator, lty = 2 )
    }
    curve.plot( xy, n = n, action = "refresh", fit = fit, backfit = FALSE,
      save.ends = 0 )
    separator
  }
  ## place commands along right strip of plot, highlighting current command
  plotcmd <- function( ans, fig, cmds, cmdlocs, usr, col = "green", rest = "black",
    horizontal = TRUE )
  {
    ans <- c( ans, fig )
    tmp <- is.na( match( cmds, ans ))
    if( any( tmp )) for( i in unique( cmdlocs$adj )) {
      tmpi <- tmp & i == cmdlocs$adj
      if( any( tmpi ))
        graphics::text( cmdlocs$x[tmpi], cmdlocs$y[tmpi], cmds[tmpi], col = rest, adj = i )
    }
    if( any( !tmp )) for( i in unique( cmdlocs$adj )) {
      tmpi <- !tmp & i == cmdlocs$adj
      if( any( tmpi ))
        graphics::text( cmdlocs$x[tmpi], cmdlocs$y[tmpi], cmds[tmpi], col = col, adj = i )
    }
  }
  cmds <- c("add","delete","replace","","finish","restart","refresh","rescale",
    "","data","temp")
  newlocs <- if( horizontal )
    function( cmds, data = FALSE, usr )
    {
      if( !data )
        cmds <- cmds[ cmds != "data" ]
      n <- length( cmds )
      blank <- seq( n )[ cmds == "" | cmds == " " ]
      tmp <- diff(usr[3:4]) / 20
      m <- mean( usr[1:2] )
      y <- usr[4] + 0.5 * tmp - c( tmp * seq( blank[1] - 1 ), 0,
        tmp * seq( blank[2] - blank[1] - 1 ), 0,
        tmp * seq( n - blank[2] ))
      x <- c( rep( usr[1], blank[1] - 1 ), mean( m, usr[1] ),
        rep( m, blank[2] - blank[1] - 1 ), mean( m, usr[2] ),
        rep( usr[2], n - blank[2] ))
      adj <- c( rep( 0, blank[1] ),
        rep( 0.5, blank[2] - blank[1] ),
        rep( 1, n - blank[2] ))
      tmp <- data.frame( x = x, y = y, adj = adj )
      cmds[blank[2]] <- " "
      row.names( tmp ) <- cmds
      tmp
    }
  else
    function( cmds, data = FALSE, usr )
    {
      if( !data )
        cmds <- cmds[ cmds != "data" ]
      n <- length( cmds )
      blank <- seq( n )[ cmds == "" ]
      tmp <- diff(usr[3:4]) / 20
      m <- mean( usr[3:4] )
      tmp <- c( usr[4] - tmp * seq( blank[1] - 1 ),
        mean( m, usr[4] ),
        m + tmp * ( seq( blank[1] + 1, blank[2] - 1 ) - mean( blank )),
        mean( m, usr[3] ),
        usr[3] + tmp * seq( n - blank[2] ))
      tmp <- data.frame( x = rep( usr[2], n ), y = tmp, adj = rep( 1, n ))
      cmds[blank[2]] <- " "
      row.names( tmp ) <- cmds
      tmp
    }

  fig <- "temp"
  newans <- ans <- "replace"

  graphics::par( mar = c(4.1,4.1,3.1,4.1),omi=rep(.25,4))
  fit <- splines::interpSpline( xy$x, xy$y )
  separator <- plotit( xy, fig, fit, horizontal )

  usr <- graphics::par("usr")
  cmdlocs <- newlocs( cmds, data = is.data, usr = usr)
  cmds <- row.names( cmdlocs )
  plotcmd( ans, fig, cmds, cmdlocs, usr )
  use.data <- FALSE
  rescale.data <- 1
  repeat {
    ## get command from plot using cursor
    z <- graphics::locator(1,"n")
    if(( !horizontal & z$x > separator ) | ( horizontal * z$y > separator )) {
      if( horizontal ) { # need to look at both z&y 
        x <- abs( z$x - cmdlocs$x )
        x <- x == min( x )
        newans <- cmds[x]
        z <- abs( z$y - cmdlocs$y )[x]
        newans <- newans[ z == min( z ) ][1]
      }
      else {
        z <- abs(z$y - cmdlocs$y )
        newans <- cmds[ z == min( z ) ][1]
      }
      switch( newans,
        finish =, refresh = {
          separator <- plotit( xy, fig, fit, horizontal )
          usr <- graphics::par("usr")
          cmdlocs <- newlocs( cmds, data = is.data, usr = usr)
        },
        data = {
          use.data <- is.data & !use.data
          if( is.data & !use.data )
            plotit( xy, fig, fit, horizontal )
        },
        temp = {
          fig <- newans
          separator <- plotit( xy, fig, fit, horizontal )
          usr <- graphics::par("usr")
          cmdlocs <- newlocs( cmds, data = is.data, usr = usr)
        },
        rescale = {
          cat( "enter new values followed by RETURN key\n" )
          tmpy <- readline( paste( "maximum ", fig, "(",
            round( max( xy$y ), 2 ), "):", sep = "" ))
          if( tmpy != "" ) {
            tmpy <- suppressWarnings(as.numeric( tmpy )) / max( xy$y )
            xy$y <- tmpy * xy$y
            if( is.data )
              rescale.data <- rescale.data * tmpy
          }
          tmpx <- readline( paste( "maximum time(", 
            round( max( xy$x ), 2 ), "):", sep = "" ))
          if( tmpx != "" ) {
            tmpx <- suppressWarnings(as.numeric( tmpx )) / max( xy$x )
            xy$x <- tmpx * xy$x
          }
          fit <- splines::interpSpline( xy$x, xy$y )
          separator <- plotit( xy, fig, fit, horizontal )
          usr <- graphics::par("usr")
          cmdlocs <- newlocs( cmds, data = is.data, usr = usr)
        },
        restart = {
          if( is.data )
            rescale.data <- 1
          xy <- data.frame( x = xinit, y = yinit )
          fit <- splines::interpSpline( xy$x, xy$y )
          separator <- plotit( xy, fig, fit, horizontal )
          usr <- graphics::par("usr")
          cmdlocs <- newlocs( cmds, data = is.data, usr = usr)
        },
        add =, delete =, replace = {
          ans <- newans
        }
      )
      if( use.data ) {
        rx <- range( xy$x )
        dx <- range( datax )        
        graphics::lines( rx[1] + ( datax - dx[1] ) * diff( rx ) / diff( dx ), data * rescale.data )
      }
      plotcmd( ans, fig, cmds, cmdlocs, usr )
    }
    else {
      fit <- curve.plot( xy, n = n, action = ans, z = z, fit = fit, backfit = FALSE,
        save.ends = 0 )
      xy <- fit$xy
      fit <- fit$fit
      ans <- "replace"
      plotcmd( ans, fig, cmds, cmdlocs, usr )
    }
    if( newans == "finish" )
      break
  }
  plotcmd( newans, fig, cmds, cmdlocs, "red" )
  tmp <- curve.plot( xy, n = n, action = "refresh", backfit = FALSE, save.ends = 0 )
  tmp
}

# --- Source: triangle.R ---
## $Id: triangle.R,v 0.9 2002/12/09 yandell@stat.wisc.edu Exp $
##
## Functions for Bland Ewing's modeling.
##
##     Copyright (C) 2000,2001,2002 Brian S. Yandell.
##
## This program is free software; you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by the
## Free Software Foundation; either version 2, or (at your option) any
## later version.
##
## These functions are distributed in the hope that they will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
## GNU General Public License for more details.
##
## The text of the GNU General Public License, version 2, is available
## as http://www.gnu.org/copyleft or by writing to the Free Software
## Foundation, 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
##
###########################################################################################
## rtri( n, width )
##
## plot.current( x, species )
## text.current( x, species )
##
###########################################################################################
###########################################################################################
### Tridiagonal Coordinate System S3 Classes & Algebra
###########################################################################################
tricoord <- function(a, b = NULL, c = NULL) {
  if (is.data.frame(a) && all(c("a", "b", "c") %in% names(a))) {
    res <- a
  } else if (is.matrix(a) && ncol(a) == 3) {
    res <- as.data.frame(a)
    names(res) <- c("a", "b", "c")
  } else if (!is.null(b) && !is.null(c)) {
    res <- data.frame(a = a, b = b, c = c)
  } else if (is.numeric(a) && length(a) == 3) {
    res <- data.frame(a = a[1], b = a[2], c = a[3])
  } else {
    stop("Invalid tricoord input format")
  }
  class(res) <- c("tricoord", "data.frame")
  res
}

`+.tricoord` <- function(e1, e2) {
  # Handle vector offsets cleanly
  if (is.numeric(e2) && length(e2) == 3) {
    tricoord(e1$a + e2[1], e1$b + e2[2], e1$c + e2[3])
  } else if (inherits(e2, "tricoord")) {
    tricoord(e1$a + e2$a, e1$b + e2$b, e1$c + e2$c)
  } else {
    stop("Invalid right hand operand for tricoord addition")
  }
}

`-.tricoord` <- function(e1, e2) {
  if (is.numeric(e2) && length(e2) == 3) {
    tricoord(e1$a - e2[1], e1$b - e2[2], e1$c - e2[3])
  } else if (inherits(e2, "tricoord")) {
    tricoord(e1$a - e2$a, e1$b - e2$b, e1$c - e2$c)
  } else {
    stop("Invalid right hand operand for tricoord subtraction")
  }
}

###########################################################################################
rtri <- function( n, width, tri = matrix(0,3,n), roundoff = TRUE )
{
  tri <- as.matrix( tri )
  if( n == 1 ) {
    xy <- stats::runif( 2, 0, width )
    if( roundoff )
      xy <- round( xy )

    i <- sample( 3, 1  )
    i1 <- 1 + i%%3
    i2 <- 1 + (i+1)%%3
    tri[i1,] <- tri[i1,] + xy[1]
    tri[i2,] <- tri[i2,] - xy[2]
    tri[i,] <- - ( tri[i1,] + tri[i2,] )
    return( tri )
  }
  else {
    xy <- data.frame( x = stats::runif( n, 0, width ),
      y = - stats::runif( n, 0, width ))
    if( roundoff )
      xy <- round( xy )

    out <- sample( 3, n, replace = TRUE )
    for( i in 1:3 ) {
      outi <- out == i
      if( any( outi )) {
        i1 <- 1 + i%%3
        i2 <- 1 + (i+1)%%3
        tri[i1,outi] <- tri[i1,outi] + xy$x[outi]
        tri[i2,outi] <- tri[i2,outi] + xy$y[outi]
        tri[i,outi] <- - ( tri[i1,outi] + tri[i2,outi] )
      }
    }
  }
  tri
}
###########################################################################################
car2tri.default <- function(x,y)
  car2tri( cbind( x, y ))
car2tri <- function( xy, xmult = ( 2 + sq3 ) / 4, ymult = ( 3 + 2 * sq3 ) / 12,
  sq3 = sqrt( 3 ))
{
#  if( !is.matrix( xy ))
#    xy <- t( as.matrix( xy ))
  aa <- xmult * xy[,1] - ymult * xy[,2]
  bb <- - xmult * xy[,1] - ymult * xy[,2]
  cc <- -( aa + bb )
  rbind( a = aa, b = bb, c = cc )
}
###########################################################################################
tri2car.default <- function(aa,bb,cc=-(aa+bb))
  tri2car( rbind( aa, bb, cc ))
tri2car <- function(tri, xmult = 2 / ( 2 + sq3 ), ymult = 6 / ( 3 + 2 * sq3 ),
  sq3 = sqrt( 3 ))
{
  if( inherits(tri, "tricoord") ) {
    # If the user passes our S3 tricoord dataframe, map it correctly natively.
    x <- ( tri$a - tri$b ) * xmult
    y <- - ( tri$a + tri$b ) * ymult
  } else {
    if( !is.matrix( tri ))
      tri <- as.matrix( tri )
    x <- ( tri[1,] - tri[2,] ) * xmult
    y <- - ( tri[1,] + tri[2,] ) * ymult
  }
  data.frame( x = x, y = y )
}
###########################################################################################
cardist <- function( xy )
  sqrt( xy[,1]^2 + xy[,2]^2 )
###########################################################################################
tridist <- function( tri )
  apply( tri, 1, max )
###########################################################################################
gasket <- function( aa, bb )
{
  n <- length( aa )
  pp <- c(-1,1,0,1)
  dda <- diff( aa )
  ddb <- diff( bb )
  ss <- sign( sign( dda ) - sign( ddb ))
  dda <- 2 - abs( dda )
  ddb <- 2 - abs( ddb )
  aa <- 2 * aa
  aa <- c( aa[1], rbind( aa[-n] + pp[dda+1+ss], aa[-1] + pp[dda+1-ss], aa[-1] ))
  bb <- 2 * bb
  bb <- c( bb[1], rbind( bb[-n] + pp[ddb+1-ss], bb[-1] + pp[ddb+1+ss], bb[-1] ))
  data.frame( aa = aa, bb = bb )
}

# --- Source: substrate_triangle.R ---
get_substrate_grid <- function(width, step = 1, orientation = "up") {
  pts <- expand.grid(a = seq(0, width - step, by = step), 
                     b = seq(0, width - step, by = step))
  
  if (orientation == "up") {
    pts <- subset(pts, a + b <= width - step)
    pts$c <- -(pts$a + pts$b)
  } else {
    pts <- subset(pts, a + b <= width - step)
    pts$a <- -pts$a
    pts$b <- -pts$b
    pts$c <- -(pts$a + pts$b)
  }
  return(pts)
}

substrate_topology <- function(width = 10, step = 1) {
  W <- width - step
  
  # Topology adjacency offsets
  list(
    fr2   = list(offset = c(0, 0, 0), dir = "down"),
    fr1   = list(offset = c(-W, -W, 2*W), dir = "up"),
    fr3   = list(offset = c(-W, 0, W), dir = "up"),
    fr4   = list(offset = c(0, -W, W), dir = "up"),
    tw1   = list(offset = c(-W, 0, W), dir = "down"),
    twig  = list(offset = c(-W, 0, W), dir = "down"),
    tw2   = list(offset = c(-2*W, 0, 2*W), dir = "up"),
    lftop = list(offset = c(-2*W, W, W), dir = "down"),
    lfbot = list(offset = c(-3*W, W, 2*W), dir = "up")
  )
}

create_substrate <- function(topology, width = 10, step = 1) {
  W <- width - step
  all_points <- data.frame()
  labels_df <- data.frame()
  poly_df <- data.frame()
  
  for (sub in names(topology)) {
    cfg <- topology[[sub]]
    grid <- get_substrate_grid(width, step, cfg$dir)
    
    o_a <- cfg$offset[1]
    o_b <- cfg$offset[2]
    o_c <- cfg$offset[3]

    # Needs tricoord and tri2car which are presumably exported/available from R/triangle.R
    grid_tri <- tricoord(grid$a, grid$b, grid$c)
    grid_tri <- grid_tri + cfg$offset
    
    car_pts <- tri2car(grid_tri)
    car_pts$substrate <- sub
    all_points <- rbind(all_points, car_pts)
    
    # Determine bounds and midpoints for side labels 1,2,3
    if (cfg$dir == "up") {
      v_top <- c(o_a, o_b, o_c)
      v_br  <- c(o_a + W, o_b, o_c - W)
      v_bl  <- c(o_a, o_b + W, o_c - W)
      
      m1 <- (v_top + v_bl) / 2
      m2 <- (v_top + v_br) / 2
      m3 <- (v_bl + v_br)  / 2
      centroid <- (v_top + v_br + v_bl) / 3
      p_mat <- cbind(v_top, v_br, v_bl)
    } else {
      v_bot <- c(o_a, o_b, o_c)
      v_tr  <- c(o_a, o_b - W, o_c + W)
      v_tl  <- c(o_a - W, o_b, o_c + W)
      
      m1 <- (v_bot + v_tr) / 2
      m2 <- (v_bot + v_tl) / 2
      m3 <- (v_tl + v_tr)  / 2
      centroid <- (v_bot + v_tr + v_tl) / 3
      p_mat <- cbind(v_bot, v_tr, v_tl)
    }
    
    # Interpolate slightly towards the centroid to put text "just inside" the edges
    w_in <- 0.25 # weight towards centroid
    l1 <- m1 * (1 - w_in) + centroid * w_in
    l2 <- m2 * (1 - w_in) + centroid * w_in
    l3 <- m3 * (1 - w_in) + centroid * w_in
    
    mat_l <- cbind(l1, l2, l3)
    car_l <- tri2car(mat_l)
    car_l$label <- c("1", "2", "3")
    car_l$substrate <- sub
    labels_df <- rbind(labels_df, car_l)
    
    car_p <- tri2car(p_mat)
    car_p$substrate <- sub
    poly_df <- rbind(poly_df, car_p)
  }
  
  centers <- stats::aggregate(cbind(x,y) ~ substrate, data=all_points, mean)
  
  obj <- list(
    points = all_points, 
    labels = labels_df, 
    poly = poly_df, 
    centers = centers,
    topology = topology
  )
  class(obj) <- "substrate"
  return(obj)
}

autoplot.substrate <- function(object, ...) {
  ggplot2::ggplot() +
    # Draw the black boundary lines outlining the substrates exactly over outer dots
    ggplot2::geom_polygon(data=object$poly, ggplot2::aes(x=x, y=y, group=substrate), fill=NA, color="black", linewidth=0.7) +
    # Plot grid dots
    ggplot2::geom_point(data=object$points, ggplot2::aes(x=x, y=y, color=substrate), size=1.5) +
    # Plot Substrate Labels (Centers)
    ggplot2::geom_text(data=object$centers, ggplot2::aes(x=x, y=y, label=substrate), color="black", fontface="bold", size=5) +
    # Axis side numbers
    ggplot2::geom_text(data=object$labels, ggplot2::aes(x=x, y=y, label=label), color="darkred", fontface="bold", size=4) +
    ggplot2::theme_void() +
    ggplot2::coord_fixed() +
    ggplot2::ggtitle("Ewing Tridiagonal Substrate Network Mapping")
}

create_hex_overlay <- function(object, step = 1) {
  pts <- if (inherits(object, "substrate")) object$points else object
  if (is.null(pts) || nrow(pts) == 0) return(data.frame())
  
  xmult <- 2 / (2 + sqrt(3))
  ymult <- 6 / (3 + 2 * sqrt(3))
  d <- step * sqrt(xmult^2 + ymult^2)
  r <- d / sqrt(3)
  
  angles <- (seq(0, 5) * 60 + 30) * pi / 180
  dx <- r * cos(angles)
  dy <- r * sin(angles)
  
  n_pts <- nrow(pts)
  hex_list <- vector("list", n_pts)
  
  for (i in seq_len(n_pts)) {
    px <- pts$x[i] + dx
    py <- pts$y[i] + dy
    sub <- pts$substrate[i]
    hex_list[[i]] <- data.frame(
      x = px,
      y = py,
      cell_id = i,
      substrate = sub,
      stringsAsFactors = FALSE
    )
  }
  
  do.call(rbind, hex_list)
}


# --- Source: ewing_substrate.R ---
ewing_substrate <- function( community,
                             species,
                             headstuff = c( 0, "start", sum( to.plot )),
                             units = getOrgFeature( community, species[1], "units" ),
                             right = species[1], adj = c(0,.5,1),
                             show_sub = NULL,
                             step = 0,
                             layout = c("facet", "hex"),
                             width = 10,
                             step_density = 1,
                             rescale = TRUE,
                             x_var = c("step", "time"),
                             ...)
{
  x_var <- match.arg(x_var)
  
  if (inherits(community, "isle_royale_sim")) {
    p_map <- autoplot(community$habitat_overlay, show_landmarks = TRUE)
    moose_sf <- sf::st_as_sf(community$moose_pop, coords = c("lon", "lat"), crs = sf::st_crs(community$habitat_overlay$layer))
    wolf_sf  <- sf::st_as_sf(community$wolf_pop, coords = c("lon", "lat"), crs = sf::st_crs(community$habitat_overlay$layer))
    
    hdr_str <- if (x_var == "step") paste0("Step ", community$nstep) else paste0("Time Units ", community$nstep)
    
    p_map <- p_map +
      ggplot2::geom_sf(data = moose_sf, color = "#27ae60", shape = 21, fill = NA, stroke = 1.0, size = 0.8, alpha = 0.85) +
      ggplot2::geom_sf(data = wolf_sf, color = "#e74c3c", shape = 21, fill = NA, stroke = 1.4, size = 1.5, alpha = 0.95) +
      ggplot2::ggtitle(paste0("Isle Royale Substrate Plot (", hdr_str, ")"))
    
    return(p_map)
  }

  layout <- match.arg(layout)
  
  if (length(species) > 1) {
    res_list <- lapply(species, function(sp) {
      df <- ewing_substrate(community = community, species = sp, headstuff = headstuff,
                            units = units, right = right, adj = adj, show_sub = show_sub,
                            step = step, layout = layout, width = width, step_density = step_density,
                            rescale = rescale, ...)
      if (!is.null(df) && nrow(df) > 0) {
        df$species <- sp
      }
      df
    })
    res_list <- res_list[!sapply(res_list, is.null)]
    if (length(res_list) == 0) return(NULL)
    combined <- do.call(rbind, res_list)
    attr(combined, "species") <- paste(species, collapse = " & ")
    step_val <- if (!is.null(community$step)) community$step else if (!is.null(community$count$step)) community$count$step else if (!is.null(attr(community, "nstep"))) attr(community, "nstep") else step
    attr(combined, "step") <- step_val
    attr(combined, "layout") <- layout
    attr(combined, "width") <- width
    attr(combined, "step_density") <- step_density
    class(combined) <- c("ewing_substrate", class(combined))
    return(combined)
  }
  
  ## plot current stages for species (except random parasites)
  organism <- get.species( community, species )[,-1]
  if(is.null(organism)) # species is not in community
    return(NULL)
  
  future <- getOrgFuture( community, species, c("color","pch") )
  
  # Substrate names mapping (e.g. fr1, fr2, fr3, fr4, twig, lftop, lfbot)
  substrate_feat <- getOrgFeature( community, species, "substrate")
  sub_interact <- getOrgInteract(community, substrate_feat, species)
  substrates <- rownames(sub_interact)
  if (is.null(substrates) || length(substrates) == 0) {
    substrates <- names(getOrgInteract(community, substrate_feat, substrate_feat))
  }
  if (is.null(show_sub)) show_sub <- substrates
  
  position <- paste( "pos", letters[1:3], sep = "." )
  
  if (layout == "hex") {
    topo <- substrate_topology(width = width, step = step_density)
    n_org <- ncol(organism)
    gx <- numeric(n_org)
    gy <- numeric(n_org)
    
    sub_indices <- organism["sub.stage", ]
    org_sub_names <- substrates[sub_indices]
    
    # Process organisms per substrate patch to rescale local coordinates into substrate surface triangle
    unique_subs <- unique(org_sub_names)
    
    for (sub in unique_subs) {
      idx <- which(org_sub_names == sub)
      
      target_name <- sub
      if (!target_name %in% names(topo)) {
        if (target_name == "twig") target_name <- "tw1"
        if (target_name == "tw1") target_name <- "twig"
      }
      
      cfg <- topo[[target_name]]
      
      pa <- organism["pos.a", idx]
      pb <- organism["pos.b", idx]
      pc <- organism["pos.c", idx]
      
      if (!is.null(cfg)) {
        # Determine substrate surface width (allows substrates of different sizes in future)
        W_sub <- if (!is.null(cfg$width)) (cfg$width - step_density) else (width - step_density)
        
        if (rescale) {
          amin <- min(pa); amax <- max(pa)
          bmin <- min(pb); bmax <- max(pb)
          
          u <- if (amax > amin) (pa - amin) / (amax - amin) else rep(0.5, length(idx))
          v <- if (bmax > bmin) (pb - bmin) / (bmax - bmin) else rep(0.5, length(idx))
          
          # 15% inner padding to ensure symbols sit comfortably inside substrate polygon borders
          u_m <- 0.15 + 0.70 * u
          v_m <- 0.15 + 0.70 * v
          
          a_p <- u_m * W_sub
          b_p <- v_m * (W_sub - a_p)
          c_p <- -(a_p + b_p)
        } else {
          a_p <- pa
          b_p <- pb
          c_p <- pc
        }
        
        off <- cfg$offset
        if (cfg$dir == "up") {
          ga <- a_p + off[1]
          gb <- b_p + off[2]
          gc <- c_p + off[3]
        } else {
          ga <- -a_p + off[1]
          gb <- -b_p + off[2]
          gc <- -c_p + off[3]
        }
        car <- tri2car(tricoord(ga, gb, gc))
        gx[idx] <- car$x
        gy[idx] <- car$y
      } else {
        car <- tri2car(organism[position, idx, drop = FALSE])
        gx[idx] <- car$x
        gy[idx] <- car$y
      }
    }
    
    xy <- data.frame(x = gx, y = gy)
  } else {
    xy <- tri2car( organism[position,] )
  }
  
  dat <- dplyr::filter(
    dplyr::mutate(
      tibble::tibble(xy), 
      stage = organism["stage",],
      substrate = substrates[organism["sub.stage",]],
      pchar = factor(as.character( future$pch[.data$stage + 1] ), levels = unique(as.character(future$pch))),
      color = as.character( future$color[.data$stage + 1] ),
      species = species),
    .data$substrate %in% show_sub)
  
  attr(dat, "species") <- species
  step_val <- if (!is.null(community$step)) community$step else if (!is.null(community$count$step)) community$count$step else if (!is.null(attr(community, "nstep"))) attr(community, "nstep") else step
  attr(dat, "step") <- step_val
  attr(dat, "layout") <- layout
  attr(dat, "width") <- width
  attr(dat, "step_density") <- step_density
  class(dat) <- c("ewing_substrate", class(dat))
  dat
}

ggplot_ewing_substrate <- function(object,
                                   xlab = "horizontal", ylab = "vertical",
                                   layout = attr(object, "layout"),
                                   width = attr(object, "width"),
                                   step_density = attr(object, "step_density"),
                                   layers = c("poly", "hex", "organisms", "centers", "labels"),
                                   ...)
{
  if (inherits(object, "ggplot")) return(object)
  if (is.null(layout)) layout <- "facet"
  if (is.null(width)) width <- 10
  if (is.null(step_density)) step_density <- 1
  
  species <- attr(object, "species")
  step <- attr(object, "step")
  
  # Allows same color for different pchar, but only one color per pchar.
  tmp <- dplyr::arrange(
    dplyr::distinct(
      dplyr::distinct(object, .data$pchar, .data$color),
      .data$pchar, .keep_all = TRUE),
    .data$pchar)
  col.palate <- tmp$color
  names(col.palate) <- as.character(tmp$pchar)
  
  if (layout == "hex") {
    topo <- substrate_topology(width = width, step = step_density)
    sub_obj <- create_substrate(topo, width = width, step = step_density)
    hex_overlay <- create_hex_overlay(sub_obj, step = step_density)
    
    p <- ggplot2::ggplot()
    if ("poly" %in% layers && nrow(sub_obj$poly) > 0) {
      p <- p + ggplot2::geom_polygon(data = sub_obj$poly, ggplot2::aes(x = x, y = y, group = substrate), 
                                     fill = NA, color = "black", linewidth = 0.7)
    }
    if ("hex" %in% layers && nrow(hex_overlay) > 0) {
      p <- p + ggplot2::geom_polygon(data = hex_overlay, ggplot2::aes(x = x, y = y, group = cell_id), 
                                     fill = NA, color = "gray75", linewidth = 0.3)
    }
    if ("organisms" %in% layers && nrow(object) > 0) {
      p <- p + ggplot2::geom_text(data = object, ggplot2::aes(x = x, y = y, label = pchar, color = pchar), 
                                  fontface = "bold", size = 4) +
        ggplot2::scale_color_manual(name = "Stage", values = col.palate) +
        ggplot2::guides(color = ggplot2::guide_legend(override.aes = list(label = names(col.palate))))
    }
    if ("centers" %in% layers && nrow(sub_obj$centers) > 0) {
      p <- p + ggplot2::geom_text(data = sub_obj$centers, ggplot2::aes(x = x, y = y, label = substrate), 
                                  color = "black", fontface = "bold", size = 4.5)
    }
    if ("labels" %in% layers && nrow(sub_obj$labels) > 0) {
      p <- p + ggplot2::geom_text(data = sub_obj$labels, ggplot2::aes(x = x, y = y, label = label), 
                                  color = "darkred", fontface = "bold", size = 3.5)
    }
    
    return(p + ggplot2::theme_void() + 
             ggplot2::theme(plot.margin = ggplot2::margin(2, 2, 2, 2, "pt")) +
             ggplot2::coord_fixed() + 
             ggplot2::ggtitle(paste(species, "on Hex Substrate Grid at", step, "steps")))
  }
  
  # Default Facet View by Substrate Component
  ggplot2::ggplot(object) +
    ggplot2::aes(.data$x, .data$y, label = .data$pchar, col = .data$pchar) +
    ggplot2::geom_text() +
    ggplot2::facet_wrap(~ substrate) +
    ggplot2::xlab(xlab) +
    ggplot2::ylab(ylab) +
    ggplot2::scale_color_manual(name = "Stage", values = col.palate) + 
    ggplot2::guides(color = ggplot2::guide_legend(override.aes = list(label = names(col.palate)))) +
    ggplot2::ggtitle(paste(species, "on substrate at", step, "steps"))
}
autoplot.ewing_substrate <- function(object, ...)
  ggplot_ewing_substrate(object, ...)

# --- Source: ewing_ageclass.R ---
ewing_ageclass <- function(community, substrate = TRUE, total = TRUE,
                           normalize = TRUE, ...) {
  nsim_val <- NULL
  nstep_val <- NULL
  
  if (inherits(community, "isle_royale_sim")) {
    if (is.null(community$history) || nrow(community$history) == 0) return(NULL)
    out <- community$history
    if (!substrate) {
      out <- dplyr::filter(out, .data$Type != "substrate")
    }
    if (total) {
      tot <- dplyr::mutate(
        dplyr::ungroup(
          dplyr::summarize(
            dplyr::group_by(out, .data$Species, .data$step, .data$time, .data$Type),
            Count = sum(.data$Count),
            .groups = "drop"
          )
        ),
        State = "total"
      )
      out <- dplyr::bind_rows(out, tot)
    }
    if (normalize) {
      out <- dplyr::ungroup(
        dplyr::mutate(
          dplyr::group_by(out, .data$Species, .data$State, .data$Type),
          Count = {
            m <- max(.data$Count, na.rm = TRUE)
            if (!is.na(m) && m > 0) .data$Count / m else 0
          }
        )
      )
    }
    ordered_levels <- unique(c("calf", "yearling", "adult", "senior", "pup", "subadult", "total"))
    out$State <- factor(out$State, levels = ordered_levels[ordered_levels %in% unique(out$State)])
    attr(out, "nstep") <- community$nstep
    attr(out, "units") <- "days"
    class(out) <- c("ewing_ageclass", class(out))
    return(out)
  }
  
  if (inherits(community, "ewing_discrete")) {
    nsim_val <- attr(community, "nsim")
    nstep_val <- attr(community, "nstep")
    community <- community[[1]]
  }
  count <- readCount(community)
  if(!length(count)) return(NULL)
  species <- names(count)
  if(is.null(species)) return(NULL)
  
  ageclass <- list()
  for( i in species ) {
    ageclass[[i]] <- levels( getOrgFuture( community, i, "ageclass" ))
  }
  substrates <- list()
  for( i in species ) {
    substrates[[i]] <- levels( getOrgInteract( community,, i, "substrate" ))
  }
  out <- list()
  for(i in species) {
    out[[i]] <- dplyr::mutate(
      tidyr::pivot_longer(
        tibble::tibble(
          as.data.frame(count[[i]])),
        dplyr::any_of(c(ageclass[[i]], substrates[[i]])), 
        names_to = "State",
        values_to = "Count"),
      Type = ifelse(.data$State %in% substrates[[i]], "substrate", "ageclass"))
  }
  out <- dplyr::bind_rows(out, .id = "Species")
  if(!substrate) {
    out <- dplyr::filter(out, .data$Type != "substrate")
  }
  if(total) {
    tot <- dplyr::mutate(
      dplyr::ungroup(
        dplyr::summarize(
          dplyr::group_by(
            out,
            .data$Species, .data$step, .data$time, .data$future, .data$Type),
          Count = sum(.data$Count),
          .groups = "drop")
      ),
      State = "total")
    out <- dplyr::bind_rows(out, tot)      
  }
  if(normalize) {
    out <- dplyr::ungroup(
      dplyr::mutate(
        dplyr::group_by(
          out,
          .data$Species, .data$State, .data$Type),
        Count = {
          m <- max(.data$Count, na.rm = TRUE)
          if (!is.na(m) && m > 0) .data$Count / m else 0
        }))
  }
  subs <- if (substrate) unlist(substrates) else NULL
  if (length(species) > 1) {
    ordered_levels <- unique(c(ageclass[[species[1]]], "total", unlist(ageclass[species[-1]]), subs))
  } else {
    ordered_levels <- unique(c(unlist(ageclass), "total", subs))
  }
  out$State <- factor(out$State, levels = ordered_levels)
  
  attr(out, "nstep") <- if (!is.null(nstep_val)) nstep_val else attr(community, "nstep")
  if (is.null(attr(out, "nstep")) && !is.null(out$step)) {
    attr(out, "nstep") <- max(out$step, na.rm = TRUE)
  }
  attr(out, "nsim") <- if (!is.null(nsim_val)) nsim_val else attr(community, "nsim")
  attr(out, "units") <- if (inherits(community, "isle_royale_sim")) "days" else tryCatch(getOrgFeature(community, species[1], "units"), error = function(e) "time")
  
  class(out) <- c("ewing_ageclass", class(out))
  out
}
ggplot_ewing_ageclass <- function(object, main = NULL, title = NULL, x_var = c("step", "time"), time_unit = NULL, ... )
{
  x_var <- match.arg(x_var)
  
  if (is.null(time_unit)) {
    time_unit <- attr(object, "units")
  }
  if (is.null(time_unit) || is.na(time_unit) || time_unit == "NA") {
    time_unit <- "time"
  }
  
  if (is.null(title)) title <- main
  if (is.null(title)) {
    nstep <- attr(object, "nstep")
    if (is.null(nstep) && !is.null(object$step)) {
      nstep <- max(object$step, na.rm = TRUE)
    }
    nsim <- attr(object, "nsim")
    unit_str <- if (x_var == "step") "steps" else time_unit
    time_hdr <- if (x_var == "step") "Steps" else paste0(toupper(substring(time_unit, 1, 1)), substring(time_unit, 2))
    if (!is.null(nstep)) {
      if (!is.null(nsim) && nsim > 1) {
        title <- paste0("Age Classes over ", time_hdr, " (", nstep, " ", unit_str, ", nsim = ", nsim, ")")
      } else {
        title <- paste0("Age Classes over ", time_hdr, " (", nstep, " ", unit_str, ")")
      }
    } else {
      title <- paste0("Age Classes over ", time_hdr)
    }
  }
  
  species_vec <- unique(as.character(object$Species))
  if (length(species_vec) == 0) species_vec <- "Organism"
  
  p_list <- list()
  
  for (sp in species_vec) {
    df_sp <- object[object$Species == sp, , drop = FALSE]
    if (is.factor(df_sp$State)) {
      df_sp$State <- droplevels(df_sp$State)
    }
    
    x_col <- if (x_var == "step" && "step" %in% names(df_sp)) "step" else "time"
    x_lbl <- if (x_var == "step") "steps" else time_unit
    
    sp_title <- paste(toupper(substring(sp, 1, 1)), substring(sp, 2), " Age Classes", sep = "")
    
    p_sub <- ggplot2::ggplot(df_sp, ggplot2::aes(x = .data[[x_col]], y = .data$Count, col = .data$State, group = .data$State)) +
      ggplot2::geom_step(na.rm = TRUE, linewidth = 0.8) +
      ggplot2::geom_point(size = 2, na.rm = TRUE) +
      ggplot2::theme_minimal() +
      ggplot2::labs(
        title = sp_title,
        x = x_lbl,
        y = "Count",
        color = "Age Class"
      ) +
      ggplot2::theme(
        plot.title = ggplot2::element_text(size = 11, face = "bold", hjust = 0.5),
        legend.position = "right"
      )
      
    p_list[[sp]] <- p_sub
  }
  
  if (length(p_list) == 1) {
    return(p_list[[1]] + ggplot2::ggtitle(title))
  }
  
  grid_plots <- cowplot::plot_grid(plotlist = p_list, ncol = length(p_list), align = "h")
  title_widget <- cowplot::ggdraw() + 
    cowplot::draw_label(title, fontface = 'bold', x = 0.5, hjust = 0.5, size = 13)
  
  cowplot::plot_grid(title_widget, grid_plots, ncol = 1, rel_heights = c(0.12, 1))
}
autoplot.ewing_ageclass <- function(object, x_var = c("step", "time"), ...) {
  x_var <- match.arg(x_var)
  ggplot_ewing_ageclass(object, x_var = x_var, ...)
}


# --- Source: ewing_envelope.R ---
ewing_envelope <- function(object, species, item, ordinate = "time", increment = 0.5) {
  # Pull out `ordinate` and `item` for each run 
  pulled <-  
    tidyr::fill(
      dplyr::arrange(
        tidyr::pivot_wider(
          dplyr::bind_rows(
            purrr::map(
              object,
              function(x) {
                dplyr::distinct(
                  as.data.frame(x[[species]][,c(ordinate, item)]),
                  .data[[ordinate]],
                  .keep_all = TRUE)
              }),
            .id = "run"),
          names_from = "run",
          values_from = item),
        .data[[ordinate]]),
      -dplyr::matches(ordinate))
  
  out <- GET::create_curve_set(list(r = as.matrix(pulled)[,1], 
                                    obs = as.matrix(pulled[,-1])))
  class(out) <- c("ewing_envelope", class(out))
  attr(out, "count") <- attr(object, "count")
  attr(out, "nstep") <- attr(object, "nstep")
  attr(out, "nsim") <- attr(object, "nsim")
  
  attr(out, "species") <- species
  attr(out, "item") <- item
  attr(out, "ordinate") <- ordinate
  out
}
ewing_envelopes <- function(object) {
  species <- attr(object, "species")
  items <- attr(object, "items")
  ordinate <- attr(object, "ordinate")
  nstep <- attr(object, "nstep")
  count <- attr(object, "count")
  nsim <- attr(object, "nsim")
  confidence <- (nsim > 2)
  
  envs <- as.list(species)
  names(envs) <- species
  if(confidence) {
    confs <- envs
  } else {
    confs <- NULL
  }
  for(specy in species) {
    env1 <- as.list(items[[specy]])
    if(confidence) {
      conf1 <- env1
    }
    for(item in items[[specy]]) {
      env1[[item]] <- ewing_envelope(object, specy, item, ordinate)
      if(confidence) {
        conf1[[item]] <- GET::fBoxplot(env1[[item]], type = 'area')
      }
    }
    envs[[specy]] <- env1
    if(confidence) {
      confs[[specy]] <- conf1
    }
  }
  
  object <- list(env = envs, conf = confs)
  class(object) <- c("ewing_envelopes", class(object))
  attr(object, "species") <- species
  attr(object, "items") <- items
  attr(object, "ordinate") <- ordinate
  attr(object, "nstep") <- nstep
  attr(object, "count") <- count
  attr(object, "nsim") <- nsim
  attr(object, "confidence") <- confidence
  object
}
summary.ewing_envelopes <- function(object, species = NULL, verbose = TRUE, ...) {
  # object$conf[[specy]][[item]] is time by 6-num boxplot summary
  if(verbose) {
    nstep <- attr(object, "nstep")
    count <- attr(object, "count")
    nsim <- attr(object, "nsim")
    cat(nsim, "Runs of ",
        nstep, "Steps for", 
        paste(names(count), count, sep = "=", collapse = ", "), "\n")
  }
  out <- print(object, species, ...)
  if(!is.null(out)) {
    out <- dplyr::ungroup(
      dplyr::filter(
        dplyr::group_by(
          out,
          .data$species, .data$item),
        (.data$r == 0) | (.data$r == max(.data$r))))
  }
  out
}
print.ewing_envelopes <- function(x, species = NULL, ...) {
  # x$conf[[specy]][[item]] is time by 6-num boxplot summary
  if(is.null(x$conf)) {
    return(NULL)
  }
  out <- dplyr::bind_rows(
    purrr::map(
      x$conf,
      function(x) {
        # somehow get summary across species and items using as.data.frame
        x <- x[names(x) != ""]
        dplyr::bind_rows(
          purrr::map(
            x,
            as.data.frame),
          .id = "item")
      }),
    .id = "species")
  if(!is.null(species)) {
    if(species %in% unique(out$species)) {
      sp <- species
      out <- dplyr::filter(out, species == sp)
    }
  }
  dplyr::mutate(out, dplyr::across(where(is.numeric), function(x) pmax(x,0)))
  out
}
ggplot_ewing_envelopes <- function(object, confidence = FALSE, main = "", ...) {
  if(inherits(object, "ewing_discrete")) {
    object <- ewing_envelopes(object)
  }
  species <- attr(object, "species")
  items <- attr(object, "items")
  ordinate <- attr(object, "ordinate")
  nstep <- attr(object, "nstep")
  count <- attr(object, "count")
  nsim <- attr(object, "nsim")
  confidence <- confidence & attr(object, "confidence")
  
  patch <- list()
  for(specy in species) {
    p <- list()
    for(item in items[[specy]]) {
      if(confidence) {
        p[[item]] <- plot(object$conf[[specy]][[item]], main = main) + 
          ggplot2::labs(x = "time", y = item) +
          ggplot2::ggtitle(main) +
          ggplot2::ylim(0, NA)
        
      } else {
        p[[item]] <- ggplot_ewing_envelope(object$env[[specy]][[item]])
      }
    }
    patch[[specy]] <- cowplot::plot_grid(plotlist = p, nrow = length(p))
  }
  
  # NEED TO get attribute count and nstep in here
  patch <- cowplot::plot_grid(plotlist = patch, ncol = length(patch)) 
  # Add a title. <https://wilkelab.org/cowplot/articles/plot_grid.html>
  title <- cowplot::ggdraw() + 
    cowplot::draw_label(
      paste(nsim, "Runs of ", nstep, "Steps for", 
            paste(species, count, sep = "=", collapse = ", ")),
      x = 0, hjust = 0
    ) +
    ggplot2::theme(
      # add margin on the left of the drawing canvas,
      # so title is aligned with left edge of first plot
      plot.margin = ggplot2::margin(0, 0, 0, 7)
    )
  cowplot::plot_grid(title, patch, ncol = 1, rel_heights = c(0.1, 1))
}

ggplot_ewing_envelope <- function(object, cols = c("#21908CFF", "#440154FF", "#5DC863FF"), 
                                  main = "", ...) {
  # Kludge. GET::forder needs at least 3 points; cols can be at most length(object).
  lcols <- length(cols)
  nsim <- ncol(object$funcs)
  if(nsim >= min(3, lcols)) {
    A <- GET::forder(object, measure = 'area')
    lcols <- min(lcols, length(object))
    idx <- order(A)[seq_len(lcols)]
    cols <- cols[seq_len(lcols)]
  } else {
    lcols <- nsim
    idx <- seq_len(lcols)
    cols <- cols[seq_len(lcols)]
  }
  
  item <- attr(object, "item")
  species <- attr(object, "species")
  ordinate <- attr(object, "ordinate")
  
  if(length(object) >= 50) {
    p <- plot(object, idx = idx, col_idx = cols, main = main)
  } else {
    p <- plot(object)
  }
  p + 
    ggplot2::labs(x = ordinate, y = item) +
    ggplot2::ggtitle(paste(species, item))
}
autoplot.ewing_envelope <- function(object, ...) {
  ggplot_ewing_envelope(object, ...)
}

# --- Source: ewing_snapshot.R ---
ewing_snapshot <- function(object, step = 0, ...)
{
  out <- list(step = step,
              ageclass = ewing_ageclass(object, ...))

  species <- get.species(object)
  subs <- list()
  for(j in species) {
    subs[[j]] <- ewing_substrate(object, j, step = step, ...)
  }
  out$substrate <- subs
  
  class(out) <- c("ewing_snapshot", "ewing", "list")
  out
}

# --- Source: temp.R ---
## $Id: temp.R,v 1.0 2002/12/09 yandell@stat.wisc.edu Exp $
##
## Functions for Bland Ewing's modeling.
##
##     Copyright (C) 2000,2001,2002 Brian S. Yandell.
##
## This program is free software; you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by the
## Free Software Foundation; either version 2, or (at your option) any
## later version.
##
## These functions are distributed in the hope that they will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
## GNU General Public License for more details.
##
## The text of the GNU General Public License, version 2, is available
## as http://www.gnu.org/copyleft or by writing to the Free Software
## Foundation, 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
##
###########################################################################################
## initTemp( community, lo.hour, hi.hour )  ### creates Temperature object
## activeTemp( community, lo.hour, hi.hour )    ### updates Temperature object
## checkTime( ) # check if activeTemp needs updating to cover time interval
##
## temp.design( )
## temp.plot( )
###########################################################################################
rescale.temp <- function( low, high, newry = ry, newrx = rx,
  ry = range( c( stats::predict( low, low$knots )$y, stats::predict( high, high$knots )$y )))
{
  if( max( abs( ry - newry )) > 0 ) {
    tmpy <- diff( newry ) / diff( ry )
    low$coefficients <- low$coefficients * tmpy
    high$coefficients <- high$coefficients * tmpy
    tmpy <- newry[1] - ry[1] * tmpy
    low$coefficients[,1] <- tmpy + low$coefficients[,1]
    high$coefficients[,1] <- tmpy + high$coefficients[,1]
  }
  rx <- range( low$knots, high$knots )
  if( max( abs( rx - newrx )) > 0 ) {
    newx <- diff( newrx ) / diff( rx )
    low$knots <- newrx[1] + newx * low$knots
    high$knots <- newrx[1] + newx * high$knots
    tmp <- 1
    for( i in seq( 2, ncol( low$coefficients ))) {
      tmp <- tmp * newx
      low$coefficients[,i] <- low$coefficients[,i] / tmp
      high$coefficients[,i] <- high$coefficients[,i] / tmp
    }
  }
  list( low = low, high = high )
}
###########################################################################################
### Temperature data structure
### Min minimum temperature for degree-day computations
### Time    list of hours when temperature changes through a day
### Base    list of temperature shifts through a day
###     list names indicate first day Hour and Base applies
### Low spline fit for daily low temperatures
### High    spline fit for daily high temperatures
### DegreeDay   spline fit (hour->DD) for currently active days
### Hour        ramped backspline fit (DD->hour) for currently active days
### Idea is that DegreeDay and Hour are updated whenever a future event is scheduled
### past the latest future event already scheduled. At that time, the first
### and last time of fit are both adjusted. This can be done without rebuilding
### the whole spline fit by appropriate adjustment of knots and coefficents.
### Steps:  (1) combine Hour, Low and High to get knots
###     (2) build spline coefficients
###########################################################################################
temp.spline <- function( community, hour, temp, start = 0,
  mintemp = getTemp( community, "Min" ), cumulative = TRUE,
  mult = getTemp( community, "Unit" ) )
{
  if( is.data.frame( hour ) & missing( temp )) {
    temp <- hour$temp
    hour <- hour$hour
  }
  ## drop low value if ones on either side are low
  low <- temp <= mintemp
  toolow <- low & c(FALSE,low[-length(low)]) & c(low[-1],FALSE)
  hour <- hour[!toolow]
  temp <- temp[!toolow]
  lh <- length( hour )
  ## expand single low in middle to two
  low <- temp <= mintemp
  toolow <- low & c(FALSE,!low[-length(low)]) & c(!low[-1],FALSE)
  if( any( toolow )) {
    hour <- c(hour,hour[toolow])
    temp <- c(temp,temp[toolow])
    temp <- temp[ order( hour ) ]
    hour <- sort( hour )
  }
  ## truncate on left
  low <- temp <= mintemp & c(temp[-1] > mintemp,FALSE)
  low1 <- c(FALSE,low[-lh])
  if( any( low ))
    hour[low] <- hour[low] + ( hour[low1] - hour[low] ) * ( mintemp - temp[low] ) /
      ( temp[low1] - temp[low] )
  ## truncate on right
  low <- temp <= mintemp & c(FALSE,temp[-lh] > mintemp)
  low1 <- c(low[-1],FALSE)
  if( any( low ))
    hour[low] <- hour[low] + ( hour[low1] - hour[low] ) * ( mintemp - temp[low] ) /
      ( temp[low1] - temp[low] )
  temp[ temp < mintemp ] <- mintemp
  temp <- temp[ !duplicated( hour ) ]
  hour <- unique( hour )

  ## linear interpolating spline for temp
  lh <- length( hour ) - 1
  h0 <- 1:lh
  aa <- ( temp[1+h0] - temp[h0] ) / ( hour[1+h0]-hour[h0] )
  aa <- c( aa, aa[lh] )
  bb <- temp - mintemp
  s <- list( knots = hour, coefficients = cbind( bb,aa,0,0) )
  dimnames( s$coefficients ) <- list( paste( floor( hour / mult ), round( hour %% mult ),
    sep = "." ), c("const","linear","quad","cubic") )
  attr(s,"formula") <- temp ~ hour
  class( s ) <- c("npolySpline","polySpline","spline")
  if( !cumulative )
    return( s )

  ## quadratic spline for cumulative temp
  aa <- s$coefficients[,3] <- s$coefficients[,2] / 2 / mult / mult
  lh <- length( aa ) - 1
  h0 <- 1:lh
  h1 <- 1 + h0
  hour <- s$knots
  bb <- s$coefficients[,2] <- s$coefficients[,1] / mult
  cc <- ( hour[h1] - hour[h0] ) * ( bb[h0] + aa[h0] *
    ( hour[h1] - hour[h0] ))
  s$coefficients[,1] <- start + c( 0, cumsum( cc ))
  s
}
###########################################################################################
temp.repeat <- function( community, period = range( days ))
{
  lodays <- range( getTemp( community, "Low" )$knots )
  hidays <- range( getTemp( community, "High" )$knots )
  days <- c( max( lodays[1], hidays[1] ), min( lodays[2], hidays[2] ))
  lodays <- period[1] >= days[1] & period[1] <= days[2]
  if( lodays )
    days[1] <- period[1]
  hidays <- period[2] <= days[2] & period[2] >= days[1]
  if( hidays )
    days[2] <- period[2]
  if( !( lodays & hidays )) {
    stop( paste( "\nSimulation period is outside of Temperature days:\n period =",
      paste( round( period ), collapse = "," ), "; days =",
      paste( round( days ), collapse = "," ), "\nNeed to run temp.design() and start over!" ))
  }
  period <- days

  days <- seq( days[1], days[2] )
  low <- stats::predict( getTemp( community, "Low" ), days )$y
  high <- stats::predict( getTemp( community, "High" ), days )$y
  periods <- c( as.numeric( names( getTemp( community, "Time" ))), Inf )
  temps <- hours <- numeric( )
  period[2] <- period[2] + 1
  for( i in seq( length( periods ) - 1 )) {
    day <- max( period[1], periods[i] )
    this.period <- days >= day & days < min( period[2], periods[i+1] )
    n <- sum( this.period )
    if( n ) {
      if( !is.null( getTemp( community, "Time", i ))) {
        time <- getTemp( community, "Time", i )
        base <- getTemp( community, "Base", i )
      }
      h <- length( time )
      lotemp <- rep( low[this.period], rep(h,n) )
      hitemp <- rep( high[this.period], rep(h,n) )
      temp <- ( base - min( base )) / diff( range( base ))
      temps <- c( temps, rep( temp, n ) * ( hitemp - lotemp ) + lotemp )

      hours <- c( hours, rep( time, n ) + rep( getTemp( community, "Unit" ) *
        seq( day, day + n - 1 ), rep(h,n) ))
    }
  }
  day <- days == period[2]
  if( any( day )) {
    temps <- c( temps, low[day] + temp[1] * ( high[day] -
      getTemp( community, "Low" )[day] ))
    hours <- c( hours, getTemp( community, "Unit" ) * period[2] )
  }
  data.frame( hour = hours, temp = temps )
}
##########################################################################################
showTemp <- function( community )
{
  cat( "Temperature set for days",
      paste( range( getTemp( community, "Low" )$knots ), collapse = " to " ), "\n" )
  cat( "Daily low temperature range:",
      paste( round( range( stats::predict( getTemp( community, "Low" ))$y )),
            collapse = " to " ), "\n" )
  cat( "Daily high temperature range:",
      paste( round( range( stats::predict( getTemp( community, "High" ))$y )),
            collapse = " to " ), "\n" )
  if( !is.null( getTemp( community, "DegreeDay" ) ))
    cat( "Active temperature range:",
        paste( round( range( getTemp( community, "DegreeDay", "knots" ) )),
              collapse = " to " ), "\n" )
  cat( "Run temp.design() to adjust temperature range\n" )
}
##########################################################################################
activeTemp <- function( community,
                       lo.hour = min( getTemp( community, "DegreeDay", "knots" ) ),
                       hi.hour = max( getTemp( community, "DegreeDay", "knots" ) ),
                       degreeday, messages = TRUE )
{
  unit <- getTemp( community, "Unit" )
  if( missing( hi.hour )) {
    hi.hour <- if( length( lo.hour ) > 1 )
      lo.hour[2]
    else
      lo.hour + unit
  }
  ## period is in units of hours
  period <- c( floor( lo.hour[1] / unit ), ceiling( hi.hour / unit ))
  if( missing( degreeday )) {
    degreeday <- if( is.null( getTemp( community, "DegreeDay" ) ))
      0
    else
      getDegreeDay( community, lo.hour[1] )
  }
  if( is.na( degreeday ))
    degreeday <- 0
  community <- setTemp( community, "DegreeDay",
                       temp.spline( community, temp.repeat( community, period ),
                                   start = degreeday ))
  community <- setTemp( community, "Hour",
                       break.backSpline( getTemp( community, "DegreeDay" )))

  if(messages) {
    tmp <- period * unit
    names( tmp ) <- c("lo.hour","hi.hour")
    print( tmp )
  }
  community
}
###########################################################################################
updateTemp <- function( community,
                       period = range( getTemp( community, "DegreeDay", "knots" ) ))
{
  ## period is in units of hours
  first <- period[1]
  last <- period[2]

  s <- getTemp( community, "DegreeDay" )
  knots <- s$knots
  ## drop earlier times that are now in the past
  drop <- sum( knots < first ) - 1
  nk <- length( knots )
  change <- drop > 0
  if( change ) {
    s$knots <- knots[ - seq( drop ) ]
    s$coefficients <- s$coefficients[ - seq( drop ), ]
  }
  ## add new days to include last
  period <- ceiling( c( knots[nk], last ) / getTemp( community, "Unit" ) )
  if( period[1] <= period[2] ) {
    change <- TRUE
    news <- temp.spline( community, temp.repeat( community, period ),
      start = getHour( getTemp( community, "Unit" ) * period[1] ))
    nk <- length( s$knots )
    if( s$knots[nk] == news$knots[1] ) {
      s$knots <- s$knots[-nk]
      s$coefficients <- s$coefficients[-nk,]
    }
    else
      s$coefficients[nk,] <- news$coefficients[1,]
    s$knots <- c( s$knots, news$knots )
    s$coefficients <- rbind( s$coefficients, news$coefficients )    
  }
  if( change ) {
    community <- setTemp( community, "DegreeDay", s )
    community <- setTemp( community, "Hour", break.backSpline( s ))
  }
  community
}
###########################################################################################
ramp.backSpline <- function( s )
{
  ## ramped backspline is a trick to get backSpline when curve is flat in spots
  ## if tmp <- stats::predict( ramp.backSpline( s ))
  ## then plot tmp$y versus tmp$x-tmp$y to "recover" original curve.
  ## problem is that one cannot recover particular x this way!

  s$coefficients[,1] <- s$coefficients[,1] + s$knots
  s$coefficients[,2] <- s$coefficients[,2] + 1
  splines::backSpline( s )
}
###########################################################################################
break.backSpline <- function( tmp )
{
  ## alternative to ramped backSpline that first removes flat regions (slope 0)
  ## problem remains that leftover may still have slope 0 at a point
  ## this can cause anomolous results!

  ## find flat regions and cut out
  tmpc <- diff(tmp$coefficients[,1]) == 0 
  tmpk <- cumsum( diff(tmp$knots) * ( tmpc ))
  tmpk <- c(tmpk,tmpk[length(tmpk)])
  tmp$knots <- tmp$knots - tmpk
  tmpc <- c(!tmpc,TRUE)
  tmp$knots <- tmp$knots[tmpc]
  tmp$coefficients <- tmp$coefficients[tmpc,]
  tmp$breaks
  nb <- dim( tmp$coefficients )

  ## kludge for backspline: cannot handle slope of zero
  tmpn <- tmp$coefficients[,2] == 0
  if( any( tmpn )) {
    tmpn <- seq( tmpn )[ tmpn ]
    tmp$coefficients[ tmpn, 2:nb[2] ] <-
      ( tmp$coefficients[ pmin( nb[1], tmpn + 1 ), 2:nb[2] ] +
       tmp$coefficients[ pmax(     1, tmpn - 1 ), 2:nb[2] ] ) / 2
  }

  ## back spline
  tmpb <- splines::backSpline(tmp )

  if( any( tmpc )) {
    ## shift back spline based on breaks
    tmpb$coefficients[,1] <- tmpb$coefficients[,1] + tmpk[tmpc]
  }
  tmpb
}
###########################################################################################
getDegreeDay <- function( community, hour )
{
  stats::predict( getTemp( community, "DegreeDay" ), hour )$y
}
###########################################################################################
getHour <- function( community, dd )
{
  stats::predict( getTemp( community, "Hour" ), dd )$y
}
###########################################################################################
getTime <- function( community, species, x )
{
  if( is.na(x)){
    cat("getTime missing value\n")
    browser()
  }
  switch( getOrgFeature( community, species, "units" ),
    ## organisms on hour basis assumed to be active only 6am-6pm
    hr = getDegreeDay( community,
      ( getTemp( community, "Unit" ) / 2 + x + floor( x / getTemp( community, "Unit" ) )) / 2 ),
    DD = x )
}
###########################################################################################
checkTime <- function( community, x, base, units )
{
## NOTE: Sometimes base can be negative!! (reset to 0)
  if( units == "hr" ) {
    x <- max( x )
#    print( c( base = base, x = x, knots = range( getTemp( community, "DegreeDay", "knots" ))))
    if( x > max( getTemp( community, "DegreeDay", "knots" )))
      community <- activeTemp( community, max( base, 0 ),
                              x + getTemp( community, "Unit" ), messages = FALSE )
  }
  community
}
###########################################################################################
transTime <- function( community, org1name, org2name, x,
                      unit1 = getOrgFeature( community, org1name, "units" ),
                      unit2 = getOrgFeature( community, org2name, "units" ))
{
  if( is.na(x)){
    cat("transTime missing value\n")
    browser()
  }
  if( unit1 == unit2 )
    return( x )
  switch( unit1,
    ## organisms on hour basis assumed to be active only 6am-6pm
    hr = getDegreeDay( community,
      ( getTemp( community, "Unit" ) / 2 + x + floor( x / getTemp( community, "Unit" ))) / 2 ),
    DD = getHour( x ))
}
###########################################################################################
### To do:
### 1. interactive designer for hourly temp fluctation over one day
### 2. interactive designer for daily lows and highs over season: temp.design() DONE
### 3. check future event trees hour vs. DD
### 4. schedule interaction events with hour-DD translation
### 5. make aphytis dormant at night
###########################################################################################

# --- Source: initTemp.R ---
##########################################################################################
### simulation temperature administration
##########################################################################################
initTemp <- function( community, lo.hour = 0, hi.hour = getTemp( community, "Unit" ),
                      days = TemperaturePar["Days"], 
                      messages = TRUE, datafile = "", ... )
{
  if(messages) {
    cat( "Initializing Temperature Profile ...\n" )
  }
  Temperature <- list()
  
  TemperaturePar <- getOrgData(community, "temperature", "par", messages, datafile)
  #  mydata( "TemperaturePar", getOrgInfo( community, "package" ), messages = messages)
  TemperaturePar <- array( TemperaturePar[,"value"],
                           dimnames = list( row.names( TemperaturePar )))
  Temperature$Unit <- TemperaturePar["Unit"]
  Temperature$Min <- TemperaturePar["Min"]
  
  ## set up daily temperature base
  
  TemperatureBase <- getOrgData(community, "temperature", "base", messages, datafile)
  #  mydata( "TemperatureBase", getOrgInfo( community, "package" ), messages = messages)
  Temperature$Time <- split( TemperatureBase$Time, TemperatureBase$Day )
  Temperature$Base <- split( TemperatureBase$Base, TemperatureBase$Day )
  
  community$temp <- Temperature
  
  tmp <- seq( lo.hour / Temperature$Unit,
              days + 1 + ( hi.hour / Temperature$Unit ),
              length = TemperaturePar["Length"] )
  tmp0 <- seq( 0, 1, length = TemperaturePar["Length"] )
  tmp1 <-  0.25 * ( TemperaturePar["HighBeg"] - TemperaturePar["LowBeg"] )
  Temperature$Low <- splines::interpSpline( tmp, TemperaturePar["LowBeg"] * ( 1 - tmp0 ) +
                                              TemperaturePar["LowEnd"] * tmp0 +
                                              sin( pi * 4 * tmp0 ) * tmp1 )
  Temperature$High <- splines::interpSpline( tmp, TemperaturePar["HighBeg"] * ( 1 - tmp0 ) +
                                               TemperaturePar["HighEnd"] * tmp0 +
                                               sin( pi * ( 0.125 + 4 * tmp0 )) * tmp1 )
  
  Temperature$DegreeDay <- NULL
  
  if(messages) {
    cat( "Base daily temperature fluctuation:\n" )
  }
  for( i in names( Temperature$time )) {
    cat( "From day", i, ":\n" )
    tmp <- Temperature$Base[[i]]
    names( tmp ) <- Temperature$Time[[i]]
    print( tmp )
  }
  community$temp <- Temperature
  
  if(messages) {
    showTemp( community )
    cat( "Initial active temperature:\n" )
  }
  activeTemp( community, lo.hour, hi.hour, getTemp( community, "Time", 1 )[1],
              messages = messages)
}
###########################################################################################
getTemp <- function( community, element, sub )
{
  tempelem <- community$temp[[element]]
  if( !missing( sub ))
    tempelem <- tempelem[[sub]]
  tempelem
}
###########################################################################################
setTemp <- function( community, element, value )
{
  community$temp[[element]] <- value
  community
}

# --- Source: temp.design.R ---
temp.design <- function( community, nspline = 8, n = 1, horizontal = TRUE,
                         col = c(low="blue",high="red") )
{
  low <- getTemp( community, "Low" )
  high <- getTemp( community, "High" )
  is.data <- !is.null( low )
  if( !is.data ) {
    tmp <- seq( 0, 60, length = nspline )
    low <- interpSpline( tmp, 60 + 0.125 * tmp + sin( 0.25 * tmp ))
    high <- interpSpline( tmp, 70 + 0.15 * tmp + sin(( pi / 8 ) + 0.225 * tmp ))
  }
  ## plot curve and surrounding axes
  par( mfrow = c(1,1), mar = rep(4.1,4))
  cat( "Switch to Graphic Screen to Adjust High and Low Temperatures\n" )
  
  plotit <- function( low, high, fig = fig, horizontal = FALSE, strip = .25, margin = 0 )
  {
    lowpred <- stats::predict( low, low$knots )
    highpred <- stats::predict( high, high$knots )
    ylim <- range( c( lowpred$y, highpred$y ))
    xlim <- range( c( lowpred$x, highpred$x))
    xlim <- xlim + c(-1,1) * margin * diff( xlim )
    if( horizontal ) {
      if( diff( ylim ) == 0 )
        ylim <- ylim * c(.75,1.25)
      separator <- ylim[2]
      ylim[2] <- ylim[2] + strip * diff( ylim )
    }
    else {
      separator <- xlim[2]
      xlim[2] <- xlim[2] + strip * diff( xlim )
    }
    axt <- c("n","s")
    tmpar <- par( xaxt = axt[1+horizontal], yaxt = axt[2-horizontal] )
    plot( xlim, ylim, xlim = xlim, ylim = ylim, type="n", xlab = "", ylab = "" )
    par( xaxt = "s", yaxt = "s" )
    title( fig )
    mtext( "day", 1, 2 )
    mtext( "temp", 2, 2 )
    if( horizontal ) {
      p <- pretty( c(ylim[1],separator) )
      axis( 2, p[ p <= separator ] )
      abline( h = separator, lty = 2 )
    }
    else {
      p <- pretty( c(xlim[1],separator) )
      axis( 1, p[ p <= separator ] )
      abline( v = separator, lty = 2 )
    }
    points( highpred$x, highpred$y, lwd = 4 )
    curve.plot( highpred, n = n, action = "refresh", fit = high, backfit = FALSE,
                save.ends = 3, col = "red", lwd = 2 * ( 1 + ( fig == "high" )))
    points( lowpred$x, lowpred$y, lwd = 4 )
    curve.plot( lowpred, n = n, action = "refresh", fit = low, backfit = FALSE,
                save.ends = 3, col = "blue", lwd = 2 * ( 1 + ( fig == "low" )))
    separator
  }
  ## place commands along right strip of plot, highlighting current command
  plotcmd <- function( ans, fig, cmds, cmdlocs, usr, col = "green", rest = "black",
                       horizontal = TRUE, data = FALSE )
  {
    ans <- c( ans, fig )
    if( data )
      ans <- c( ans, "data" )
    tmp <- is.na( match( cmds, ans ))
    if( any( tmp )) for( i in unique( cmdlocs$adj )) {
      tmpi <- tmp & i == cmdlocs$adj
      if( any( tmpi ))
        text( cmdlocs$x[tmpi], cmdlocs$y[tmpi], cmds[tmpi], col = rest, adj = i )
    }
    if( any( !tmp )) for( i in unique( cmdlocs$adj )) {
      tmpi <- !tmp & i == cmdlocs$adj
      if( any( tmpi ))
        text( cmdlocs$x[tmpi], cmdlocs$y[tmpi], cmds[tmpi], col = col, adj = i )
    }
  }
  cmds <- c("add","delete","replace","rescale","","finish","refresh","restart",
            "","data","high","low")
  newlocs <- if( horizontal )
    function( cmds, data = FALSE, usr )
    {
      if( !data )
        cmds <- cmds[ cmds != "data" ]
      n <- length( cmds )
      blank <- seq( n )[ cmds == "" | cmds == " " ]
      tmp <- diff(usr[3:4]) / 20
      m <- mean( usr[1:2] )
      y <- usr[4] + 0.5 * tmp - c( tmp * seq( blank[1] - 1 ), 0,
                                   tmp * seq( blank[2] - blank[1] - 1 ), 0,
                                   tmp * seq( n - blank[2] ))
      x <- c( rep( usr[1], blank[1] - 1 ), mean( m, usr[1] ),
              rep( m, blank[2] - blank[1] - 1 ), mean( m, usr[2] ),
              rep( usr[2], n - blank[2] ))
      adj <- c( rep( 0, blank[1] ),
                rep( 0.5, blank[2] - blank[1] ),
                rep( 1, n - blank[2] ))
      tmp <- data.frame( x = x, y = y, adj = adj )
      cmds[blank[2]] <- " "
      row.names( tmp ) <- cmds
      tmp
    }
  else
    function( cmds, data = FALSE, usr )
    {
      if( !data )
        cmds <- cmds[ cmds != "data" ]
      n <- length( cmds )
      blank <- seq( n )[ cmds == "" | cmds == " " ]
      tmp <- diff(usr[3:4]) / 20
      m <- mean( usr[3:4] )
      tmp <- c( usr[4] - tmp * seq( blank[1] - 1 ),
                mean( m, usr[4] ),
                m + tmp * ( seq( blank[1] + 1, blank[2] - 1 ) - mean( blank )),
                mean( m, usr[3] ),
                usr[3] + tmp * seq( n - blank[2] ))
      tmp <- data.frame( x = rep( usr[2], n ), y = tmp, adj = rep( 1, n ))
      cmds[blank[2]] <- " "
      row.names( tmp ) <- cmds
      tmp
    }
  
  newans <- ans <- "replace"
  par( mar = c(4.1,4.1,3.1,4.1),omi=rep(.25,4))
  fig <- "low"
  fit <- low
  separator <- plotit( low, high, fig, horizontal )
  
  usr <- par("usr")
  cmdlocs <- newlocs( cmds, data = is.data, usr = usr)
  cmds <- row.names( cmdlocs )
  use.data <- FALSE
  plotcmd( ans, fig, cmds, cmdlocs, usr, data = use.data )
  rescale.data <- c( range( stats::predict( low, low$knots )$y,
                            stats::predict( high, high$knots )$y ), range( low$knots, high$knots ))
  repeat {
    ## get command from plot using cursor
    z <- locator(1,"n")
    if(( !horizontal & z$x > separator ) | ( horizontal & z$y > separator )) {
      if( horizontal ) { # need to look at both z&y 
        x <- abs( z$x - cmdlocs$x )
        x <- x == min( x )
        newans <- cmds[x]
        z <- abs( z$y - cmdlocs$y )[x]
        newans <- newans[ z == min( z ) ][1]
      }
      else {
        z <- abs(z$y - cmdlocs$y )
        newans <- cmds[ z == min( z ) ][1]
      }
      switch( newans,
              finish =, refresh = {
                separator <- plotit( low, high, fig, horizontal )
                usr <- par("usr")
                cmdlocs <- newlocs( cmds, data = is.data, usr = usr)
              },
              data = {
                use.data <- is.data & !use.data
                if( is.data & !use.data )
                  plotit( low, high, fig, horizontal )
              },
              high =, low = {
                if( newans != fig )
                  fig <- newans
                separator <- plotit( low, high, fig, horizontal )
                usr <- par("usr")
                cmdlocs <- newlocs( cmds, data = is.data, usr = usr)
              },
              rescale = {
                tmpcmds <- cmds
                tmpans <- tmpcmds[ cmds == "rescale" ] <-
                  "rescale: Switch to Character Screen"
                plotcmd( tmpans, fig, tmpcmds, cmdlocs, usr, data = use.data )
                ry <- range( c( stats::predict( low, low$knots )$y, stats::predict( high, high$knots )$y ))
                cat( "\nEnter new minimum/maximum followed by RETURN key\n" )
                newry <- ry
                show <- c("minimum","maximum")
                change <- FALSE
                for( i in 1:2 ) {
                  tmpy <- readline( paste( show[i], " temp (",
                                           round( ry[i], 2 ), "):", sep = "" ))
                  tmpy <- if( tmpy == "" ) NA
                  else as.numeric( tmpy )
                  if( is.na( tmpy ))
                    tmpy <- ry[i]
                  else
                    change <- TRUE
                  rescale.data[i] <- tmpy
                }
                tmp <- range( c( low$knots, high$knots ))
                for( i in 1:2 ) {
                  tmpx <- readline( paste( show[i], " time (", 
                                           round( tmp[i], 2 ), "):", sep = "" ))
                  tmpx <- if( tmpx == "" ) NA
                  else as.numeric( tmpx )
                  if( is.na( tmpx ))
                    tmpx <- tmp[i]
                  else
                    change <- TRUE
                  rescale.data[2+i] <- tmpx
                }
                cat( "Switch to Graphic Screen to Adjust High and Low Temperatures\n" )
                if( change ) {
                  tmp <- rescale.temp( low, high, rescale.data[1:2],
                                       rescale.data[3:4], ry )
                  low <- tmp$low
                  high <- tmp$high
                }
                separator <- plotit( low, high, fig, horizontal )
                usr <- par("usr")
                cmdlocs <- newlocs( cmds, data = is.data, usr = usr)
              },
              restart = {
                low <- getTemp( community, "Low" )
                high <- getTemp( community, "High" )
                if( is.data )
                  rescale.data <- c( range( stats::predict( low, low$knots )$y,
                                            stats::predict( high, high$knots )$y ), range( low$knots, high$knots ))
                separator <- plotit( low, high, fig, horizontal )
                usr <- par("usr")
                cmdlocs <- newlocs( cmds, data = is.data, usr = usr)
              },
              add =, delete =, replace = {
                ans <- newans
              }
      )
      if( use.data ) {
        tmp <- rescale.temp( getTemp( community, "Low" ), getTemp( community, "High" ),
                             rescale.data[1:2], rescale.data[3:4] )
        for( i in c("low","high") ) {
          datax <- tmp[[i]]$knots
          lines( datax, stats::predict( tmp[[i]], datax )$y )
        }
      }
      plotcmd( ans, fig, cmds, cmdlocs, usr, data = use.data )
    }
    else { # modify the curve knots
      fit <- get( fig )
      if( z$x >= min( fit$knots ) & z$x <= max( fit$knots )) {
        fit <- curve.plot( as.data.frame( stats::predict( fit, fit$knots )), n = n, action = ans,
                           z = z, fit = fit, backfit = FALSE, save.ends = 2, col = col[fig] )
        assign( fig, fit$fit )
      }
      ans <- "replace"
      plotcmd( ans, fig, cmds, cmdlocs, usr, data = use.data )
    }
    if( newans == "finish" )
      break
  }
  plotcmd( newans, fig, cmds, cmdlocs, "red", data = use.data )
  community <- setTemp( community, "Low", low )
  community <- setTemp( community, "High", high )
  activeTemp( community )
}

# --- Source: temp.plot.R ---
temp.plot <- function( community, lo.hour = s$knots[1], hi.hour = max( s$knots ),
                       length = 201,
                       col = NULL, derivative = FALSE, ..., printit = FALSE )
{
  s <- getTemp( community, "DegreeDay" )
  x <- seq( lo.hour, hi.hour, length = length )
  ## make sure to pick up knots to plot in this region
  x <- unique( sort( c( x, s$knots[ s$knots >= lo.hour & s$knots <= hi.hour ] )))
  ylab <- "degree-days"
  if( derivative ) {
    ylab <- "degrees above min"
    s$coefficients <- s$coefficients[,-1]
    for( i in seq( 2, ncol( s$coefficients )))
      s$coefficients[,i] <- s$coefficients[,i] * i
  }
  y <- stats::predict( s, x )$y
  plot( x / getTemp( community, "Unit" ), y, type = "l", xlab = "day",
        ylab = ylab, ... )
  if( printit )
    print( cbind( hi.hour, stats::predict( s, hi.hour )$y ))
  if( !is.null( col ))
    points( s$knots, coef(s)[,1], col = col )
  if( !derivative ) {
    s <- getTemp( community, "Hour" )
    x <- seq( min( y ), max( y ), length = length )
    tmp <- stats::predict( s, x )
    lines( tmp$y / getTemp( community, "Unit" ), tmp$x, col = "blue" )
  }
}
###########################################################################################
temp.lines <- function( s, mult = 24, col = "red" )
{
  x <- seq( s$knots[1], max( s$knots ), length = 51 )
  x <- unique( sort( c( x, s$knots )))
  p <- stats::predict( s, x )
  lines( p$x, p$y, col = col )
  if( !is.null( col ))
    points( s$knots / mult, coef(s)[,1], col = col )
}

# --- Source: my.R ---
###########################################################################################
## System files
###########################################################################################
my.read <- function(dataname, stringsAsFactors = TRUE)
{
  switch(tools::file_ext(dataname),
         "txt" =, "tsv" = read.table(dataname, header = TRUE, fill = TRUE,
                                     stringsAsFactors = stringsAsFactors),
         "csv" = read.csv(dataname, fill = TRUE,
                          stringsAsFactors = stringsAsFactors),
         "xls" = readxl::read_xls(dataname),
         "xlsx" = readxl::read_xlsx(dataname))
}
###########################################################################################
my.eval <- function(species, extension, element, checkdata = FALSE )
{
  if( !missing( extension ))
    species <- paste( species, extension, sep = ".")
  if( exists( species )) 
    organism <- get( species )
  else {
    if( checkdata ) {
      organism <- utils::data( list = species )
      if( organism == species )
        organism <- NULL
    }
    else
      organism <- NULL
  }
  if(!( missing(element) | is.null( organism )))
    organism <- organism[[element]]
  organism
}
###########################################################################################
mydata <- function( dataname, package, restart = FALSE, messages = TRUE )
{
  edata <- exists( dataname )
  if( restart & edata ) {
    remove( list = dataname, pos = 1 )
    edata <- !edata
  }
  if( !edata ) {
    utils::data( list = dataname, package = eval( package ))
    if(messages) {
      cat( "Data", dataname, "loaded\n" )
    }
  }
  else
    if(messages) {
      cat( "Data", dataname, "already loaded\n" )
    }
} 

# --- Source: Org.R ---
###########################################################################################
### Organism Features
###########################################################################################
getOrgFeature <- function( community, species, feature = names( OrgFeature ))
{
  OrgFeature <- getOrgInfo( community, "Feature" )
  if (!is.null(OrgFeature)) {
    if( missing( species ))
      return( row.names( OrgFeature ))
    f <- OrgFeature[ species, feature ]
    if( length( feature ) == 1 ) {
      if( any( is.na( f )))
        return( NA )
      f <- as.character( f )
    }
    else {
      f <- apply( f, 2, as.character )
    }
    f <- c( unlist( f ))
    numf <- suppressWarnings(as.numeric( f ))
    if( all( !is.na( numf )))
      f <- numf
    return(f)
  }
  
  # Fallback for webR standalone adapter objects
  if (missing(species)) return(c("host", "parasite"))
  if (missing(feature) || is.null(feature)) return(c(substrate = "substrate", units = "units"))
  if (length(feature) == 1) {
    if (feature == "substrate") return("substrate")
    if (feature == "units") return("units")
    return(NA)
  }
  res <- rep("substrate", length(feature))
  names(res) <- feature
  res
}
##########################################################################################
getOrgHosts <- function( community, species,
                         feature = c("offspring","attack","substrate") ###HOST SPECIFIC###
)
{
  # This seems overly complicated and adds substrate; maybe function name is wrong
  f <- unique( getOrgFeature( community, species, feature ))
  # f[match(species, f, nomatch = 0)] # this would only get species
  o <- getOrgFeature( community )
  o[ match( f, o, nomatch = 0 ) ]
}
###########################################################################################
getOrgFuture <- function( community, species, feature, current,
                          future = OrgFuture[[species]] )
{
  OrgFuture <- getOrgInfo( community, "Future" )
  if (!is.null(OrgFuture) && !is.null(OrgFuture[[species]])) {
    future <- OrgFuture[[species]]
    if( missing( current )) {
      if( missing( feature ))
        return( future )
      future <- future[,feature]
    }
    else {
      if( !is.numeric( current ))
        current <- match( current, future$current, nomatch = 0 )
      if( missing( feature ))
        future <- future[ current, ]
      else
        future <- future[ current, feature ]
    }
    if( is.null( future ))
      return( NA )
    if(is.character( future ))
      future <- as.factor(future)
    return(future)
  }
  
  # Fallback for webR standalone adapter objects
  if (!is.null(community$pop[[species]])) {
    pch_vec <- community$pop[[species]]$pch
    col_vec <- community$pop[[species]]$col
    fut <- data.frame(pch = pch_vec, color = col_vec, stringsAsFactors = FALSE)
    if (missing(feature)) return(fut)
    if (length(feature) == 1) return(fut[[feature]])
    return(fut[, feature, drop = FALSE])
  }
  NULL
}
###########################################################################################
get.interact <- function( community, species, host, avail, event )
{
  id <- get.species.element( community, host, "stage", avail )
  interact <- getOrgInteract( community, host, species, event )[id]
  interact[ is.na( interact ) ] <- 0
  interact
}
###########################################################################################
getOrgInteract <- function( community,
                            org1name = getOrgFeature( community, org2name, "substrate" ),
                            org2name, event = NULL )
{
  OrgInteract <- getOrgInfo( community, "Interact" )
  if (!is.null(OrgInteract) && !is.null(OrgInteract[[org1name]][[org2name]])) {
    tmp <- OrgInteract[[org1name]][[org2name]]
    if( is.null( event ))
      return( tmp )
    event <- as.character( event )
    inter <- tmp[,event]
    if( length( event ) == 1 )
      names( inter ) <- row.names( tmp )
    if(is.character(inter))
      inter <- factor(inter)
    return(inter)
  }
  
  # Fallback for webR standalone adapter objects
  sub_names <- if (!is.null(community$sub_names)) community$sub_names else c("fr1", "fr2", "fr3", "fr4", "twig", "lftop", "lfbot")
  mat <- matrix(1, nrow = length(sub_names), ncol = 1, dimnames = list(sub_names, "substrate"))
  as.data.frame(mat)
}
###########################################################################################
getOrgMeanValue <- function( community, species )
{
  OrgMeanValue <- getOrgInfo( community, "MeanValue" )
  ## The global org$MeanValue[[species]] contains mean value information.
  OrgMeanValue[[species]]
}
###########################################################################################
copyOrgInfo <- function( fromname, toname )
{
  out <- list()
  for( i in c("sim")) {
    from <- paste( fromname, i, sep = "." )
    if( exists( from )) {
      toto <- paste( toname, i, sep = "." )
      out[[toto]] <- get( from )
      cat( "copied", from, "to", toto, "into list\n" )
    }
  }
  return(out)
}
###########################################################################################
get.alive <- function( community, species, substrate )
{
  alive <- getOrgAlive( community, species )
  alive <- seq_along( alive )[alive]
  alive[ substrate == get.species.element( community, species, "sub.stage", alive ) ]
}
###########################################################################################
getOrgAlive <- function( community, species, element )
{
  organism <- get.species( community, species )
  ## identify dead organisms (free nodes for leftist tree)
  tmp <- c( FALSE, apply( organism[c("dist","up","left","right"),-1], 2,
                          function( x ) any( x > 1 )))
  if( !any( tmp )) {
    tmpp <- organism["up",1]
    if( tmpp > 1 )
      tmp[tmpp] <- TRUE
  }
  if( !missing( element ))
    tmp <- organism[ element, tmp ]
  tmp
}
###########################################################################################
getOrgAgeClass <- function( community, species, stage = seq_len( nrow( future )),
                            future = getOrgFuture( community, species ))
{
  ageclass <- future$ageclass[stage]
  tmp <- !is.na( ageclass )
  if( any( tmp ))
    ageclass[ !is.na( ageclass ) ]
  else
    NA
}
###########################################################################################
getOrgSubstrate <- function( community, species, elements = seq_len( nrow( inter )),
                             substrate = getOrgFeature( community, species, "substrate" ),
                             inter = getOrgInteract( community, substrate, species ))
{
  sites <- inter$substrate[elements]
  tmp <- !is.na( sites )
  if( any( tmp ))
    sites[ !is.na( sites ) ]
  else
    NA
}
###########################################################################################
sampleOrgSubstrate <- function( community, species, elements = seq_len( nrow( inter )),
                                substrate.name = getOrgFeature( community, species, "substrate" ),
                                inter = getOrgInteract( community, substrate.name, species ))
{
  if( is.na( substrate.name ))
    return( elements )
  newsub <- as.matrix( cbind( elements, inter[ elements, levels( factor(inter$substrate) ) ] ))
  apply( newsub, 1, function( x, is ) {
    ns <- sample( levels( factor(is) ), 1, prob = x[-1] / sum( x[-1] ))
    sub <- seq_len( nrow( inter ))[ ns == is ]
    if( length( sub ) > 1 ) {
      newsub <- getOrgInteract( community, substrate.name, substrate.name )[x[1],sub]
      sample( sub, 1, prob = newsub / sum( newsub ))
    }
    else
      sub
  }, inter$substrate )
} 
##########################################################################################
### simulation organism administration
##########################################################################################
initOrgInfo <- function( package, messages = TRUE, datafile = "", ... )
{
  community <- list( pop = list( ))
  community$org <- list( )
  community$org$package <- package
  ## Get data
  community$org$Feature <- getOrgData(community, "organism", "features",
                                      messages, datafile)
  
  community$pop <- list()
  community
}
##########################################################################################
setOrgInfo <- function( community, species, hosts, package, messages = TRUE,
                        datafile = "", ... )
{
  Organism <- community$org
  
  if( is.null( Organism$Future ))
    Organism$Future <- list( )
  if( is.null( Organism$Interact ))
    Organism$Interact <- list( )
  
  for( j in hosts )
    if( is.null( Organism$Interact[[j]] ))
      Organism$Interact[[j]] <- list( )
  
  ## Do not reset MeanValue as it may have important spline fits!
  if( is.null( Organism$MeanValue ))
    Organism$MeanValue <- list( )
  
  for( i in species ) {
    future <- getOrgData(community, "future", i,
                         messages, datafile)
    
    # Check that future agrees with organism.feature information
    subclass <- Organism$Feature[i,"subclass"]
    if(!(subclass %in% unique(future$ageclass))) {
      stop(paste("Future table", paste("future", i, sep = "."),
                 "does not include", subclass))
    }
    
    level.ageclass <- unique( future$ageclass )
    level.ageclass <- as.character( level.ageclass[ !is.na( level.ageclass ) ] )
    future$ageclass <- ordered( future$ageclass, level.ageclass )
    Organism$Future[[i]] <- future
    for( j in hosts )
      if( i != j ) {
        Organism$Interact[[j]][[i]] <- getOrgData(community, j, i,
                                                  messages, datafile)
        
        # Check that interaction agrees with host current stage information
        # This is messy!
        if(j %in% species) {
          if(!all(row.names(Organism$Interact[[j]][[i]]) %in%
                  c(as.character(Organism$Future[[j]]$current), i))) {
            stop(paste("Interaction table", paste(j, i, sep = "."),
                       "does not match", j, "current stages"))
          }
        }
      }
    if( is.null( Organism$MeanValue[[i]] ))
      Organism$MeanValue[[i]] <- list( )
    else
      cat( "Keeping Mean Value information for", i, "if any\n" )
  }
  for( i in unique( getOrgFeature( community, species, "substrate" ))) {
    Organism$Interact[[i]][[i]] <- getOrgData(community, i, i,
                                              messages, datafile)
  }
  community$org <- Organism
  community
}
###########################################################################################
getOrgData <- function(community, left, right,
                       messages = TRUE, datafile = "")
{
  # Get Organism Data from
  #     package data
  #     global data supplied by user
  #     external data file supplied by user
  sheet <- paste( left, right, sep = "." )
  data_exists <- FALSE
  if (is.character(datafile) && length(datafile) == 1 && nzchar(datafile)) {
    if (dir.exists(datafile)) {
      extensions <- c(".txt", ".tsv", ".csv", ".xls", ".xlsx")
      datafile_paths <- file.path(datafile, paste0(sheet, extensions))
      exist_idx <- file.exists(datafile_paths)
      if (any(exist_idx)) {
        datafile <- datafile_paths[exist_idx][1]
        data_exists <- TRUE
      } else {
        data_exists <- FALSE
      }
      sheet <- ""
    } else if (file.exists(datafile)) {
      data_exists <- TRUE
    }
  }

  if (!data_exists && (is.null(datafile) || datafile == "")) {
    def_file <- get_site_cache_file(paste0(sheet, ".txt"), site = "default")
    if (def_file != "" && file.exists(def_file)) {
      datafile <- def_file
      data_exists <- TRUE
      sheet <- ""
    }
  }

  if(!data_exists) {
    # Load package data or get user-provided global data.
    mydata( sheet, getOrgInfo( community, "package" ), messages = messages)
    my.eval( sheet )
  } else {
    # Read data file from user if provided.
    if(sheet == "")
      my.read(datafile)
    else {
      out <- as.data.frame(readxl::read_excel(datafile, sheet = sheet, .name_repair = "none"))
      if(names(out)[1] == "") { # first column is actual row names
        rownames(out) <- out[[1]]
        out[[1]] <- NULL
      }
      out
    }
  }
}
###########################################################################################
getOrgNames <- function(datafile = "") {
  if(datafile == "") {
    c("organism.features", "future.host", "future.parasite",
      "substrate.host", "substrate.parasite", "substrate.substrate",
      "temperature.base", "temperature.par")
  } else {
    readxl::excel_sheets(datafile)
  }
}
###########################################################################################
getOrgDataSimple <- function(community, dataname, datafile = ""){
  out <- getOrgData(
    community,
    left = stringr::str_remove(dataname, "\\..*"),
    right = stringr::str_remove(dataname, ".*\\."),
    messages = FALSE, datafile = datafile)
  # Kludge to reinstate rownames as a column
  if(!identical(rownames(out), as.character(seq_len(nrow(out))))) {
    out <- data.frame(rownames = rownames(out), out)
  }
  out
}
###########################################################################################
getOrgInfo <- function( community, element )
{
  community$org[[element]]
}
###########################################################################################
setOrgMeanValue <- function( community, species, stage, mvalue )
{
  ## The global Organism$MeanValue[[species]] contains mean value information.
  community$org$MeanValue[[species]][[stage]] <- mvalue
  community
}

# --- Source: organism.features.R ---
NULL

# --- Source: future.R ---
## $Id: future.R,v 1.0 2002/12/11 yandell@stat.wisc.edu Exp $
##
## Functions for Bland Ewing's modeling.
##
##     Copyright (C) 2000,2001,2002 Brian S. Yandell.
##
## This program is free software; you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by the
## Free Software Foundation; either version 2, or (at your option) any
## later version.
##
## These functions are distributed in the hope that they will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
## GNU General Public License for more details.
##
## The text of the GNU General Public License, version 2, is available
## as http://www.gnu.org/copyleft or by writing to the Free Software
## Foundation, 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
##
###############################################################################
##
## future.events( community )
##
###############################################################################




###############################################################################
### Get birth and future event
###############################################################################
get.future <- function (community, species,
                        individuals = get.individual(community, species, id),
                        id = get.base(community, species))
{
  ## NOTE: This is the slow routine. For every event, it has to check if there
  ## is a mean value function and then call rspline.

  ## the structure future.species is set up to handle competing risks!
  future <- getOrgFuture(community, species, c("current", "future", "time"))
  future$fid <- match(future$future, future$current)
  individuals <- as.matrix(individuals)

  rownames <- dimnames(individuals)[[1]]
  for (i in seq(ncol(individuals))) {
    individual <- individuals[,i]
    current <- individual["stage"]
    individual["location"] <- individual["time"]

    ## competing risks based on potential future event times
    futures <- future$current == future$current[current]
    times <- rep( individual["time"], sum( futures ))
    cur <- seq(nrow(future))[futures]
    for( j in seq( sum( futures ))) {
      meantime <- future[cur[j], "time"]
      if( meantime > 0 ) {
        for.stage <- as.character(future$current[future$fid[ cur[j] ]])
        times[j] <- rspline( meantime, individual,
                              getOrgMeanValue(community, species)[[for.stage]])
      }
    }
    individual["time"] <- min( times )
    current <- cur[ times == individual["time"] ][1]
    individual["future"] <- future$fid[current]
    if (individual["time"] == individual["rejection"])
        individual["future"] <- future$fid[future$current == "death"]
    individuals[,i] <- individual
  }
  individuals
}
###############################################################################
event.death <- function( community, species,
                        id = get.base( community, species ))
{
  ## remove dead individual from leftist tree
  community <- put.species( community, species,
                           leftist.remove( get.species( community, species ), id ))
  ## free up individual for reuse
  community <- put.base( community, species, id )
  community
}
###############################################################################
update_mintime <- function( object, species, ... )
{
  base <- get.base( object, species )
  mintime <- max( getCount( object, species, "mintime" ),
                 getTime( object, species,
                         get.species.element( object, species, "time", base )))
  setCount( object, species, list( base = base, mintime = mintime ))
}
###############################################################################
set.birth <- function( community, species, neworg )
{
  ## merge immediate new births (if any)
  newbirths <- ncol( neworg )
  if( newbirths > 0 ) {
    neworg[c("dist","left","right","up"),] <- 1
    community <- checkTime( community, neworg["time",],
              getCount( community, species, "mintime" ),
              getOrgFeature( community, species, "units" ))
    oldbase <- getCount( community, species, "base" )
    tmp <- leftist.birth( get.species( community, species ), neworg,
                         getCount( community, species, "free" ))
    
    community <- put.species( community, species, tmp$tree )
    community <- put.base( community, species, free = tmp$free )
    community <- updateCounts( community, species, newbirths )
  }
  community
}



# --- Source: future.host.R ---
NULL

# --- Source: future.meanvalue.R ---
###########################################################################################
## init.meanvalue( organism, stage )
## spline.meanvalue( x, y )
## spline.meanvalue( data = data )
##
## five.show( )
## five.plot( )
##
###########################################################################################
# Curve Designing routines -- under development
###########################################################################################
future.meanvalue <- function( community, species, event = future$current[1],
                              data )
{
  future <- getOrgFuture( community, species )
  mvalue <- getOrgMeanValue( community, species )[[event]]
  if( missing( data )) {
    if( !is.null( mvalue )) {
      mvalue <- stats::predict( mvalue$meanvalue, mvalue$meanvalue$knots )
      mvalue <- spline.meanvalue( mvalue$x, mvalue$y )$fit
    }
    else
      mvalue <- spline.meanvalue( )$fit
  }
  else
    mvalue <- spline.meanvalue( data = data )$fit
  setOrgMeanValue( community, species, event, mvalue )
}
###########################################################################################
spline.meanvalue <- function( x = xinit, y = yinit, data, nspline = 8,
                              xy = data.frame( x = x, y = y ),
                              tol = 1e-5, n = 1 )
{
  is.data <- !missing( data )
  if( !is.data ) {
    tmp <- - log( 1 - seq( 0, 1 - exp( -5 ), length = nspline ))
    if( missing( x ))
      xinit <- tmp
    else
      xinit <- x
    if( missing( y ))
      yinit <- tmp
    else
      yinit <- y
    data <- NULL
  }
  else {
    xinit <- sort( data )
    ndata <- length( data )
    yinit <- seq( ndata ) / ( 1 + ndata )
    choose <- round( seq( 1, ndata, length = nspline ))
    xinit <- xinit[choose]
    yinit <- - log( 1 - yinit[choose] )
  }
  fs <- list( probability = function( x ) { - log( 1 - x ) } )
  finvs <- list( probability = function( x ) { 1 - exp( - x ) } )
  for( i in c("mean value","rate","density") )
    fs[[i]] <- finvs[[i]] <- function( x ) x
  
  ## plot curve and surrounding axes
  graphics::par( mfrow = c(1,1), mar = rep(4.1,4))
  plotit <- function( xy, fig = "mean value", fit = splines::interpSpline( xy$x, xy$y ))
  {
    switch( fig, {
      y <- xy$y
      ylim <- range(c(0,y))
    },
    probability = {
      y <- 1 - exp( - xy$y )
      ylim <- range(c(0,y))
    },
    rate = {
      y <- spline.rate( fit, xy$x )$y
      tmp <- spline.rate( fit )
      ylim <- range(c(0,tmp$y))
    },
    density = {
      y <- spline.rate( fit, xy$x )$y * exp( - stats::predict( fit, xy$x )$y )
      tmp <- spline.rate( fit )
      tmp$y <- tmp$y * exp( - stats::predict( fit )$y )
      ylim <- range(c(0,tmp$y))
    }
    )
    plot(xy$x,y,xlim=1.25*range(c(0,xy$x)), ylim = ylim,
         type="n", xlab = "", ylab = "" )
    graphics::points( xy$x, y, lwd = 4 )
    graphics::title( fig )
    graphics::mtext( "time", 1, 2 )
    graphics::mtext( fig, 2, 2 )
    graphics::abline( v = max( xy$x ), lty = 2 )
    
    switch( fig, {
      if( fig == "probability" ) {
        tmp <- c(.1,.2,.5,1:10)
        ltmp <- 1-exp(-tmp)
        graphics::mtext( "mean value", 4, 2 )
      }
      else {  
        tmp <- c(seq(0,.9,,by=.1),.95,.98,.99,.999)
        ltmp <- -log(1-tmp)
        graphics::mtext( "probability", 4, 2 )
      }
      usr <- graphics::par("usr")
      s <- ltmp <= usr[4] & ltmp >= usr[3]  
      tmpar <- graphics::par( cex = .75 )
      graphics::axis(4,ltmp[s],tmp[s])
      graphics::par( tmpar )
      summaryshow( xy, fit, "white" )
      tmp <- curve.plot( xy, n = n, action = "refresh", fit = fit,
                         f = fs[[fig]], finv = finvs[[fig]] )
      summaryshow( xy, tmp$fit )
    },
    rate =, density = {
      graphics::lines( tmp$x, tmp$y )
      summaryshow( xy, fit )
    }
    )
  }
  ## place commands along right strip of plot, highlighting current command
  plotcmd <- function( ans, fig, cmds, cmdlocs, usr, col = "green", rest = "black",
                       data = FALSE )
  {
    ans <- c( ans, fig )
    if( data )
      ans <- c( ans, "data" )
    tmp <- is.na( match( cmds, ans ))
    if( any( tmp ))
      graphics::text( rep(usr[2],sum(tmp)), cmdlocs[tmp], cmds[tmp], col = rest, adj = 1 )
    if( any( !tmp ))
      graphics::text( rep(usr[2],sum(!tmp)), cmdlocs[!tmp], cmds[!tmp], col = col, adj = 1 )
  }
  
  fig <- "mean value"
  newans <- ans <- "replace"
  
  graphics::par( mar = c(4.1,4.1,3.1,4.1),omi=rep(.25,4))
  fit <- splines::interpSpline( xy$x, xy$y )
  sums <- plotit( xy, fig, fit )
  
  cmds <- c("refresh","add","delete","replace","rescale","shrink to 1","finish","restart",
            "","data","mean value","probability","rate","density")
  newlocs <- function( cmds, data = FALSE, usr )
  {
    if( !data )
      cmds <- cmds[ cmds != "data" ]
    n <- length( cmds )
    blank <- seq( n )[ cmds == "" ]
    tmp <- diff(usr[3:4]) / 20
    tmp <- c( usr[4] - tmp * seq( blank - 1 ), mean( usr[3:4] ),
              usr[3] + tmp * seq( n - blank ))
    names( tmp ) <- cmds
    tmp
  }
  usr <- graphics::par("usr")
  cmdlocs <- newlocs( cmds, data = is.data, usr = usr)
  cmds <- names( cmdlocs )
  use.data <- FALSE
  plotcmd( ans, fig, cmds, cmdlocs, usr )
  rescale.data <- 1
  repeat {
    ## get command from plot using cursor
    z <- graphics::locator(1,"n")
    if( z$x > max( xy$x )) {
      z <- abs(z$y - cmdlocs )
      newans <- cmds[z==min(z)][1]
      switch( newans,
              finish =, refresh = {
                sums <- plotit( xy, fig, fit )
              },
              data = {
                use.data <- is.data & !use.data
                if( is.data & !use.data & match( fig, c("mean value","probability"),
                                                 nomatch = 0 ))
                  sums <- plotit( xy, fig, fit )
              },
              "mean value" =, probability =, rate =, density = {
                fig <- newans
                sums <- plotit( xy, fig, fit )
                usr <- graphics::par("usr")
                cmdlocs <- newlocs( cmds, data = is.data, usr = usr)
              },
              "shrink to 1" = {
                while( abs( sums[1] - 1 ) > tol ) {
                  xy$y <- xy$y * sums[1]
                  if( is.data )
                    rescale.data <- rescale.data * sums[1]
                  fit <- splines::interpSpline( xy$x, xy$y )
                  sums <- splinesum( xy, fit, tol )
                }
                sums <- plotit( xy, fig, fit )
                usr <- graphics::par("usr")
                cmdlocs <- newlocs( cmds, data = is.data, usr = usr)
              },
              rescale = {
                cat( "enter new values followed by RETURN key\n" )
                tmpy <- readline( paste( "maximum mean value(",
                                         round( max( xy$y ), 2 ), "):", sep = "" ))
                if( tmpy != "" ) {
                  tmpy <- as.numeric( tmpy ) / max( xy$y )
                  xy$y <- tmpy * xy$y
                  if( is.data )
                    rescale.data <- rescale.data * tmpy
                }
                tmpx <- readline( paste( "maximum time(", 
                                         round( max( xy$x ), 2 ), "):", sep = "" ))
                if( tmpx != "" ) {
                  tmpx <- as.numeric( tmpx ) / max( xy$x )
                  xy$x <- tmpx * xy$x
                  if( is.data )
                    data <- data * tmpx
                }
                fit <- splines::interpSpline( xy$x, xy$y )
                sums <- plotit( xy, fig, fit )
                usr <- graphics::par("usr")
                cmdlocs <- newlocs( cmds, data = is.data, usr = usr)
              },
              restart = {
                if( is.data )
                  rescale.data <- 1
                xy <- data.frame( x = xinit, y = yinit )
                fit <- splines::interpSpline( xy$x, xy$y )
                sums <- plotit( xy, fig, fit )
                usr <- graphics::par("usr")
                cmdlocs <- newlocs( cmds, data = is.data, usr = usr)
              },
              add =, delete =, replace = {
                ans <- newans
              }
      )
      if( use.data & match( fig, c("mean value","probability"), nomatch = 0 ))
        cdf.lines( data, fig, rescale = rescale.data )
      plotcmd( ans, fig, cmds, cmdlocs, usr, data = use.data )
    }
    else {
      if( is.na( match( fig, c("rate","density") ))) {
        summaryshow( xy, fit, "white", sums )
        fit <- curve.plot( xy, n = n, action = ans, z = z, fit = fit,
                           f = fs[[fig]], finv = finvs[[fig]] )
        xy <- fit$xy
        fit <- fit$fit
        sums <- summaryshow( xy, fit )
        ans <- "replace"
        plotcmd( ans, fig, cmds, cmdlocs, usr, data = use.data )
      }
    }
    if( newans == "finish" )
      break
  }
  plotcmd( newans, fig, cmds, cmdlocs, "red", data = use.data )
  summaryshow( xy, fit, "white", sums )
  tmp <- curve.plot( xy, n = n, action = "refresh",
                     f = fs[[fig]], finv = finvs[[fig]] )
  tmp$meanvalue <- tmp$fit
  tmp$fit <- NULL
  tmp$invmvalue <- splines::backSpline( tmp$meanvalue )
  sums <- summaryshow( xy, tmp$meanvalue )
  tmp$mean <- sums[1]
  tmp$median <- sums[2]
  tmp
}

# --- Source: substrate.host.R ---
NULL

# --- Source: host.parasite.R ---
NULL

# --- Source: simdata.R ---
NULL

# --- Source: redscale.R ---
NULL

# --- Source: init.simulation.R ---
init.simulation <- function( package = "ewing", 
                             count = 200,
                             interact = FALSE,
                             messages = TRUE,
                             ...)
{
  community <- initOrgInfo( package, messages = messages, ... )
  community <- initTemp( community, messages = messages, ... )
  
  species <- getOrgFeature( community )[1:2]
  hosts <- getOrgHosts( community, species )
  
  if(messages) {
    cat( "Creating simulation organism set using species:\n",
         paste( species, collapse = ", " ), "\n\n" )
  }
  
  community <- setOrgInfo( community, species, hosts, package,
                           messages = messages, ... )
  
  if(messages) {
    cat( "\n" )
  }
  species <- unique( species )
  num <- numeric( length( species ))
  names( num ) <- species
  
  count <- rep_len(count, length(species))
  names(count) <- species
  
  for( i in species ) {
    num[i] <- reuse <- count[i]
    
    if(messages | (interact & interactive())) {
      cat( paste( "Initialize ", i, " at size ", reuse, sep = "" ))
    }
    if(interact & interactive()) {
      cat(" :")
      r <- readline( )
      if( r != "" & is.na( pmatch( substring( r, 1, 1 ), c("y","Y") )))
        reuse <- suppressWarnings(as.numeric( r ))
      if( is.na( reuse ))
        reuse <- count[i]
    }
    if( reuse ) {
      if(messages) {
        cat( "...\n" )
      }
      community <- init.population( community, i, n = reuse, messages = messages, ... )
      num[i] <- reuse
    }
  }
  class( community ) <- c("ewing", "list")
  attr(community, "count") <- count
  community
}

# --- Source: init.population.R ---
init.population <- function( community, species, n = 200, width = 100,
                             units = getOrgFeature( community, species, "units" ),
                             timeit = FALSE,
                             reject = Inf,
                             position = rtri( n, width ),
                             colnames = c(leftistnames,paramnames,posnames,eventnames),
                             init.stage = istage,
                             init.weight = getOrgFuture( community, species, "init" ),
                             messages = TRUE,
                             ...)
{
  leftistnames <- c("dist","left","right","up")
  paramnames <- c("dispersion","location","intensity","truncation","rejection")
  posnames <- paste("pos",letters[1:3], sep = ".")
  eventnames <- c("time","stage","future","offspring","sex","sub.stage","sub.future")
  
  organism <- matrix( 0, length( colnames ), n+1,
                      dimnames = list( colnames, NULL ))
  organism["time",1] <- Inf
  
  ## 5-parameter initialization
  organism[c("dispersion","intensity"),-1] <- 1
  organism["rejection",-1] <- if( reject == Inf )
    rep( Inf, n )
  else
    reject * stats::rexp( n )
  
  ## triangular coordinates
  organism[posnames,-1] <- position
  
  ## substrate
  substrate.name <- getOrgFeature( community, species, "substrate" )
  if( !is.na( substrate.name )) {
    substrate <- getOrgInteract( community, substrate.name, species, "init" )
    organism["sub.stage",-1] <- organism["sub.future",-1] <- sample( length( substrate ),
                                                                     n, replace = TRUE, prob = substrate / sum( substrate ))
  }
  ## randomly generate events proportional to future time units
  nstage <- length( init.weight )
  istage <- sample( nstage, n, replace = TRUE, prob = init.weight / sum( init.weight ))
  init.stage <- array( init.stage, n )
  organism["stage",-1] <- init.stage
  
  ## schedule future events
  if( timeit )
    proctime <- proc.time()
  organism[,-1] <- get.future( community, species, organism[,-1] )
  if( timeit ) {
    tmp <- proc.time() - proctime
    cat( "future time: user=", tmp[1], "system=", tmp[2], "total=", tmp[3], "\n" )
  }
  ## create leftist tree
  if( timeit )
    proctime <- proc.time()
  community <- put.species( community, species, leftist.create( organism ))
  if( timeit ) {
    tmp <- proc.time() - proctime
    cat( "leftist time: user=", tmp[1], "system=", tmp[2], "total=", tmp[3], "\n" )
  }
  ## mean number of offspring
  if( timeit )
    proctime <- proc.time()
  #***This is where parasite is crashing--no offspring?**
  community <- initOffspring( community, species )
  if( timeit ) {
    tmp <- proc.time() - proctime
    cat( "offspring time: user=", tmp[1], "system=", tmp[2], "total=", tmp[3], "\n" )
  }
  
  if(messages) {
    cat( "Initializing events for", species, "with", ncol( organism ) - 1, "individuals\n" )
  }
  community
}

# --- Source: init.timing.R ---
###########################################################################################
### Timing of simulation run
###########################################################################################
init.timing <- function( community )
{
  ## initialize timing
  events <- NULL
  for( species in get.species( community ))
    events <- c( events, levels( getOrgFuture( community, species, "event" )))
  events <- sort( unique( events ))
  tmp <- c("total",events,"refresh","other")
  cpu <- matrix( 0, 3, length( tmp ),
                 dimnames = list( c("user","system","total"), tmp ))
  community$cpu <- cpu
  community <- set.timing( community, "total" )
  community
}
###########################################################################################
set.timing <- function( community, string, flag = -1 ) {
  if( !is.null( community$cpu ))
    community$cpu[,string] <- community$cpu[,string] + flag * proc.time()[1:3]
  community
}
###########################################################################################
fini.timing <- function( community )
{
  if( !is.null( community$cpu )) {
    community <- set.timing( community, "total", 1 )
    community$cpu[,"other"] <- community$cpu[,"total"] - apply( community$cpu[,-1], 1, sum )
  }
  community
}

# --- Source: initCount.R ---
###########################################################################################
### Simulation count object administration
###########################################################################################
initCount <- function( community, species, debugit = FALSE, file = NULL, append = FALSE,
                       messages = TRUE, ... )
{
  if(messages) {
    cat( "initial" )
    for( i in species)
      cat( ":", i, sum( apply( get.species( community, i ), 2,
                               function(x) !all(x[c("dist","left","right","up")]==1))) - 1 )
    cat( "\n" )
  }
  
  old_counts <- if (append) getCount( community, , "counts" ) else NULL
  old_step <- if (append) getCount( community, , "step" ) else 0

  count <- list()
  ## leftist tree counters
  count$mintime <- numeric( length( species ))
  count$base <- numeric( length( species ))
  names( count$base ) <- names( count$mintime ) <- species
  count$free <- list()
  
  ## initialize lists to keep track of events
  count$events <- count$countage <- count$countsub <- count$nameage <- count$namesub <- list()
  
  ## Set up hour to degreeday spline based on range of hours if any
  simmin <- c(hr=Inf,DD=Inf)
  for( i in species ) {
    if( is.null( get.species( community, i )))
      stop( paste( "Missing species", i ))
    count$free[[i]] <- 1
    count$base[i] <- get.base( community, i )
    units <- getOrgFeature( community, i, "units" )
    tmp <- get.individual( community, i )["time"]
    if( tmp < simmin[units] )
      simmin[units]<- tmp
  }
  if( max( simmin ) < Inf ) {
    community <- activeTemp( community, simmin["hr"], , simmin["DD"], messages = messages )
  }
  esums <- c("initial","during","final")
  tmpfn <- function( counter )
  {
    rownames <- levels( counter )
    array( 0, length( rownames ), dimnames = list( rownames ))
  }
  subclass <- getOrgFeature( community, species, "subclass" )
  names( subclass ) <- species
  for( i in species ) {
    species.time <- get.individual( community, i )["time"]
    count$mintime[i] <- getTime( community, i, species.time )
    future <- getOrgFuture( community, i )
    ## possible future events
    count$events[[i]] <- matrix( 0, nrow( future ), length( esums ),
                                 dimnames = list( as.character( future$current ), esums ))
    ## current record of future events
    count$countage[[i]] <- tmpfn( getOrgFuture( community, i, "ageclass" ))
    count$countsub[[i]] <- tmpfn( getOrgInteract( community,, i, "substrate" ))
    
    if( species.time < Inf ) {
      ## count by age groups
      stage <- getOrgAlive( community, i, "stage" )
      if( length( stage )) {
        classes <- getOrgAgeClass( community, i, stage )
        tmp <- table( classes )
        count$countage[[i]][ names( tmp ) ] <- tmp
      }
      ## count by substrate
      substage <- getOrgAlive( community, i, "sub.stage" )
      ## only for individuals of class = subclass[i]
      substage <- substage[ subclass[i] == getOrgAgeClass( community, i, stage ) ]
      if( length( substage )) {
        classes <- getOrgInteract( community,, i, "substrate" )
        tmp <- table( classes[substage] )
        count$countsub[[i]][ names( tmp ) ] <- tmp
      }
    }
  }
  count$debug <- debugit
  
  # If file is NULL, then don't write to file; keep counts internal
  count$file <- file
  if (append && !is.null(old_counts)) {
    count$counts <- old_counts
    count$step <- old_step
  }
  
  community$count <- count
  
  ## Put counts in file
  community <- putCount( community, append )
  
  ## tally events at start of simulation
  setEvents( community, "initial" )
}
###########################################################################################
getCount <- function( community, species, element )
{
  count <- community$count[[element]]
  if( !missing( species ))
    count <- count[[species]]
  count
}
###########################################################################################
set.step <- function( community, step )
  setCount( community,, list( step = step ))
###########################################################################################
setCount <- function( community, species, elements )
{
  count <- community$count
  for( i in names( elements )) {
    if( missing( species ))
      count[[i]] <- elements[[i]]
    else
      count[[i]][[species]] <- elements[[i]]
  }
  community$count <- count
  community
}

# --- Source: future.events.R ---
future.events <- function( community,
                           nstep = 4000,
                           species = get.species( community ),
                           
                           refresh = nstep / 20, cex = 0.5,
                           substrate.plot = TRUE, extinct = TRUE,
                           timeit = TRUE, debugit = FALSE,
                           messages = TRUE, append = NULL, ... )
  
{
  ## Integrity check of dataset, and initialization of tallies.
  if( missing( community ))
    stop( "Must specify a community." )
  
  if (is.null(append)) {
    append <- !is.null(community$count$counts) && nrow(community$count$counts) > 0
  }
  
  if( debugit ) cat( "initialization\n" )
  community <- initCount( community, species, debugit = debugit, file = NULL,
                           append = append, messages = messages )
  if( timeit )
    community <- init.timing( community )
  
  mintime <- getCount( community, , "mintime" )
  species.now <- species[ mintime == min( mintime ) ][1]
  future <- getOrgFuture( community, species.now )
  
  # Set up list for plot information.
  p <- list()
  pstep <- 0
  if (!is.null(community$plot)) {
    p <- community$plot
    pstep <- length(p)
  }
  
  start_step <- if (append && !is.null(community$count$step)) community$count$step else 0
  
  ## for nstep steps schedule future events and process immediate events
  for( istep in seq( nstep )) {
    ## stop if any extinct and extinct flag on, or all extinct
    omintime <- mintime
    mintime <- getCount( community, , "mintime" )
    if( debugit ) print( mintime )
    if( min( mintime ) < min( omintime )) {
      cat( "time reversal!\n" ) # should not happen
      browser()
    }
    tmp <- mintime == Inf
    if( any( tmp )) {
      if( extinct | all( tmp )) {
        for( i in names( mintime )[tmp] )
          cat( "***", i, "is extinct ***\n" )
        if( plotit )
          plot.ewing( community, substrate = substrate.plot, cex = cex, ...)
        break
      }
    }
    ## each species is always sorted so 1st element is next future event
    species.prev <- species.now
    species.now <- species[ mintime == min( mintime ) ][1]
    individual <- get.individual( community, species.now )
    if( is.na( individual["time"] ) | individual["time"] == Inf ) {
      cat( "No more finite future events. End of simulation.\n" )
      break
    }
    if( all( individual[c("dist","left","right","up")] == 1 )) {
      cat( individual["time"], ": last", species.now, "alive",
           getCount( community, species.now, "base" ), "\n" )
    }
    if( species.now != species.prev )
      future <- getOrgFuture( community, species.now )
    ## make future event the current stage
    current <- individual["stage"]
    stage <- as.character( future$current[current] )
    if(!length(stage))
      stop(paste("no stage", current))
    community <- updateCount( community, species.now, individual, stage == "death",
                              start_step + istep )
    individual["stage"] <- current
    individual["sub.stage"] <- individual["sub.future"]
    community <- put.individual( community, species.now, individual )
    
    if( debugit ) {
      cat( species.now, istep, "base",
           getCount( community, species.now, "base" ), "\n" )
      print( c( step = istep, 
                countage = sum( getCount( community, species.now, "countage" )),
                countsub = sum( getCount( community, species.now, "countsub" )),
                round( individual["time"], 2 ), current, stage ))
    }
    
    ## processing of immediate, pending and future events
    ## this is the main show--all the rest is setup
    event.type <- as.character( future[ current, "event" ] )
    if( debugit ) cat( "do", event.type, istep, stage,
                       as.character( future$current[ individual["future"] ] ),
                       individual["time"], "\n" )
    community <- set.timing( community, event.type )
    if( event.type == "death" )
      community <- event.death( community, species.now )
    else {
      if( event.type != "future" ) {
        ## this routine could be user supplied
        ## generic routines are event.birth, event.attack
        event.parsed <- get( paste( "event", event.type, sep = "." ))
        community <- event.parsed( community, species.now )
      }
      community <- event.future( community, species.now )
    }
    community <- set.timing( community, event.type, 1 )
    
    ## refresh plot
    if( refresh & ! ( istep %% refresh )) {
      community <- set.timing( community, "refresh" )
      # Save the ewing_ageclass and ewing_substrate objects.
      pstep <- pstep + 1
      p[[pstep]] <- ewing_snapshot(community, start_step + istep, ...)
      
      if(messages) {
        cat( "refresh", istep )
        for( j in get.species( community ))
          cat( ":", j, sum( getCount( community, j, "countage" ), na.rm = TRUE ))
        cat( "\n" )
      }
      community <- set.timing( community, "refresh", 1 )
    }
    ## periodic browser if in debug mode
    if( debugit ) {
      cat( "done", istep, "\n" )    
      if( refresh & !( istep %% refresh )) {
        cat( "Type \"c\" to continue or \"Q\" to quit.\n" )
        browser()
      }
    }
  }
  community <- set.timing( community, "refresh" )
  ## end of main loop on future events
  if( debugit ) cat("done\n")
  
  if( sum( getOrgAlive( community, species.now )) > 1 ) {
    ## tally events at end of simulation
    community <- setEvents( community, "final" )
  }
  community <- set.timing( community, "refresh", 1 )
  
  community <- fini.timing( community )
  community$step <- start_step + nstep
  attr(community, "nstep") <- start_step + nstep
  
  if( !refresh | (nstep%%refresh)) {
    pstep <- pstep + 1
    p[[pstep]] <- ewing_snapshot(community, start_step + istep, ...)
  }
  community$plot <- p
  
  community
}

# --- Source: event.R ---
## $Id: event.R,v 1.0 2002/12/11 yandell@stat.wisc.edu Exp $
##
## Functions for Bland Ewing's modeling.
##
##     Copyright (C) 2000,2001,2002 Brian S. Yandell.
##
## This program is free software; you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by the
## Free Software Foundation; either version 2, or (at your option) any
## later version.
##
## These functions are distributed in the hope that they will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
## GNU General Public License for more details.
##
## The text of the GNU General Public License, version 2, is available
## as http://www.gnu.org/copyleft or by writing to the Free Software
## Foundation, 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
##
###############################################################################
##
## event.birth( community, species )
## event.attack( community, species )
##
## Issues to resolve:
## 2. search strategy for predator/parasite/parasitoid
## 3. generic calls to do.x
###############################################################################
event.birth <- function( community, species )
{
  ## now only one offspring at a time,
  ## but could depend on individual
  offspring <- get.offspring( community, species)
  ## update parent based on anticipated offspring
  community <- parent.birth( community, species, offspring )
  if( offspring ) {
    ## get new births
    newbirths <- get.birth( community, species, offspring )
    ## merge births into community
    community <- set.birth( community, species, newbirths )
  }
  community
}
###############################################################################
parent.birth <- function( community, species, offspring )
{
  ## get individual record
  individual <- get.individual( community, species )
  individual["offspring"] <- individual["offspring"] - offspring
  ## female starves when egg load depleted (less than or equal to 0)
  if( individual["offspring"] <= 0 )
    individual["stage"] <- set.future( community, species, "starved" )
  community <- put.individual( community, species, individual )
}
###############################################################################
get.birth <- function( community, species, offspring )
{
  ## get individual record
  individual <- get.individual( community, species )
  ## matrix of new offspring for community
  newbirths <- matrix( individual, length( individual ), offspring )
  dimnames( newbirths ) <- list( names( individual ), NULL )
  if( offspring ) {
    ## assumes newborn is stage 1, and next stage is 2
    newbirths["stage",] <- 1
    newbirths["future",] <- 2
    ## set up as unlinked node for leftist tree
    newbirths[c("dist","left","right","up"),] <- 1
    ## disperse offspring across substrate types
    newbirths <- event.move( community, species, newbirths )
  }
  if( getCount( community,, "debug" ))
    cat( round( individual["time"] ),
        getOrgFeature( community, species, "units" ),
        ":", species, "offspring", individual["offspring"], "\n" )
  if( offspring ) {
    ## get future events for new organisms
    get.future( community, species, newbirths)
  }
  else
    community
}
###############################################################################
get.deplete <- function( community, species )
{
  ## get individual record of attacker
  individual <- get.individual( community, species )
  ## Deplete reserves based on time spent searching for host.
  individual["offspring"] <- individual["offspring"] - 
    ( individual["time"] - individual["location"] ) /
      getOrgFeature( community, species, "deplete" )
  community <- put.individual( community, species, individual )
}
###############################################################################
set.future <- function( community, species, stage )
{
  current <- getOrgFuture( community, species, "current" )
  seq( length( current ))[ current == stage ]
}
###############################################################################
### Interaction Events (only attack for now)
###############################################################################
event.attack <- function( community, species )
{
  ## dyadic event: attack of host by adult parasitoid
  
  ## deplete individual based on time spent searching for host
  community <- get.deplete( community, species )
  ## get individual record of attacker
  individual <- get.individual( community, species )

  ## find a host if parasite has offspring reserve left
  if( individual["offspring"] > 0 ) {
    ## get name of host for attacker
    host <- getOrgFeature( community, species, "attack" )
    ## get attack parasite and event types
    attack <- get.attack( community, species, individual )
    ## find host located on the same substrate
    found <- event.find( community, species, host, attack["event"] )
    if( length( found )) {
      ## host-parasite interaction
      event.parsed <- get( paste( "event", attack["event"], sep="." ))
      community <- event.parsed( community, species, host, found )
      event.parsed <- get( paste( "host", attack["parasite"], sep="." ))
      community <- event.parsed( community, species, host, found )
      individual <- get.individual( community, species )
    }
  }
  if( individual["offspring"] > 0 ) {
    ## parasite moves along substrate
    individual <- event.move( community, species, individual )
  }
  else {
    ## parasite dies if it does not feed enough
    individual["stage"] <- set.future( community, species, "starved" )
  }
  ## put updated individual back in community
  community <- put.individual( community, species, individual )
}
###############################################################################
get.attack <- function( community, species, individual )
{
  ## get parasite type ("ecto" or "endo") and current event ("feed" or "ovip")
  parasite <- getOrgFeature( community, species, "parasite" )
  event <- getOrgFuture( community, species, "current", individual["future"] )
  event <- as.character( event )
  if( parasite=="endo" )
    event <- "ovip"
  else if( individual["offspring"] < 1 ) {
    ## must feed if depleted
    event <- "feed"
  }
  c( event = event, parasite = parasite )
}
###############################################################################
host.ecto <- function( community, species, host, dead )
{
  ## ectoparasites effectively kill their host

  ## get individual doing the attack
  individual <- get.individual( community, species )
  ## get host individual that is attacked
  hostindiv <- get.individual( community, host, dead )
  ## set host time to now, which may involve hr-DD translation
  hostindiv["time"] <- transTime( community, species, host, individual["time"] )
  ## schedule immediate death of host
  hostindiv["future"] <- set.future( community, host, "death" )
  ## update host record in community
  community <- put.individual( community, host, hostindiv, dead )
  ## update leftist tree and mintime
  community <- put.species( community, host,
                           leftist.update( get.species( community, host ), dead ))
  update_mintime( community, host )
}
###############################################################################
host.endo <- function( community, species, host, dead, harm )
{
  ## endoparasites reduces capacity of host (assumed by half here)
  
  ## get host individual that is attacked
  hostindiv <- get.individual( community, host, harm )
  ## schedule harm for hosts (reduce egg capacity by half)
  hostindiv["offspring"] <- floor( hostindiv["offspring"] / 2 )
  if( hostindiv["offspring"] == 0 ) {
    ## get individual doing the attack
    individual <- get.individual( community, species )
    ## set host time to now, which may involve hr-DD translation
    hostindiv["time"] <- transTime( community, species, host, individual["time"] )
    ## schedule immediate death of host
    hostindiv["future"] <- set.future( community, host, "death" )
  }
  ## update host record in community
  community <- put.individual( community, host, hostindiv, harm )
  if( hostindiv["offspring"] == 0 ) {
    ## update leftist tree and mintime
    community <- put.species( community, host,
                             leftist.update( get.species( community, host ), dead ))
    community <- update_mintime( community, host )
  }
  community
}
###############################################################################
event.feed <- function( community, species, host, dead )
{
  ## feed: adult parasite feeds on host

  interact <- get.interact( community, species, host, dead, "feed" )
  ## host-parasite interaction: feeding
  if( interact ) {
    individual <- get.individual( community, species )
    individual["offspring"] <- individual["offspring"] + interact
    community <- put.individual( community, species, individual )
  }
  community
}
###############################################################################
event.ovip <- function( community, species, host, dead, gender=TRUE )
{
  ## ovip: adult lays egg in the host to emerge later as adult
  offspring <- get.offspring( community, species )

  interact <- get.interact( community, species, host, dead, "ovip" )
  ## host-parasite interaction: feeding
  if( interact ) {
    ## update parent individual, depleting energy after egg laying
    community <- parent.birth( community, species, offspring )

    ## get new births
    newbirths <- get.birth( community, species, offspring )
    ## gender preference for offspring
    if( get.interact( community, species, host, dead, "male" ) < stats::runif( 1 ) |
      !gender ){
      ## set offspring for female eggs based on dead host
      newbirths["offspring", ] <- set.offspring( community, species, host, dead )
    }
    else {
      ## produce male and put on queue for immediate death
      newbirths <- set.male( community, species, host, newbirths )
    }
    community <- set.birth( community, species, newbirths )
  }
  community
}
###############################################################################
set.male <- function( community, species, host, newbirths )
{
  ## produce male offspring, which is queued for immediate death
  newbirths["future",1] <- set.future( community, species, "male" )
  newbirths["time",1] <- get.individual( community, species )["time"]
  newbirths
}

# --- Source: event.future.R ---
event.future <- function( community, species )
{
  ## schedule future event based on current stage
  individual <- get.future( community, species )
  ## move if appropriate
  individual <- event.move( community, species, individual )
  ## update time translation if needed
  community <- checkTime( community, individual["time"],
                          getCount( community, species, "mintime"),
                          getOrgFeature( community, species, "units" ))
  ## put updated individual back in community
  community <- put.individual( community, species, individual )
  
  ## reprioritize the leftist tree if time has changed
  if( individual["time"] > individual["location"] ) {
    ## and time is longer than next scheduled time
    if( individual["time"] >
        min( get.species.element( community, species, "time",
                                  individual[c("left","right")] ))) {
      ## remove individual from leftist tree
      community <- put.species( community, species,
                                leftist.update( get.species( community, species )))
    }
    community <- update_mintime( community, species )
  }
  community
}

# --- Source: Events.R ---
###########################################################################################
updateEvents <- function( community, species, event, increment = 1 )
{
  community$count$events[[species]][event,"during"] <-
    community$count$events[[species]][event,"during"] + increment
  community
}
###########################################################################################
setEvents <- function( community, period )
{
  count <- community$count
  for( species in get.species( community )) {
    current <- getOrgFuture( community, species, "current" )
    events <- rep( 0, length( current ))
    names( events ) <- as.character( current )
    
    stage <- getOrgAlive( community, species, "stage" )
    if( length( stage )) {
      tmp <- tapply( stage, current[stage], length )
      tmp[ is.na( tmp ) ] <- 0
      events[ names( tmp ) ] <- tmp
    }
    
    count$events[[species]][,period] <- events
  }
  community$count <- count
  community
}

# --- Source: leftist.R ---
## $Id: leftist.R,v 1.0 2002/12/11 yandell@stat.wisc.edu Exp $
##
## Functions for Bland Ewing's modeling.
##
##     Copyright (C) 2000,2001,2002 Brian S. Yandell.
##
## This program is free software; you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by the
## Free Software Foundation; either version 2, or (at your option) any
## later version.
##
## These functions are distributed in the hope that they will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
## GNU General Public License for more details.
##
## The text of the GNU General Public License, version 2, is available
## as http://www.gnu.org/copyleft or by writing to the Free Software
## Foundation, 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
##
###########################################################################################
## This version has list structure for individuals.
## In addition it will keep species separate.
###########################################################################################
## Triply linked leftist trees
###########################################################################################
leftist.tree <- function( x,
  tree = data.frame( root = empty ),
  key = "time",
  empty = c( time = NA, dist = 1, left = 1, right = 1, up = 1 ))
{
  node <- empty
  node[key] <- Inf
  node[c("right","left")] <- 2
  node["dist"] <- 0
  if( missing( tree ))
    names( tree ) <- NULL

  node <- empty
  node[key] <- x[1]
  base <- 2
  tree[[base]] <- node

  for( i in 1 + seq( 2, length( x ))) {
    node <- empty
    node[key] <- x[i-1]
    if( node[key] <= tree[[base]][key] ) {
      ## insert at root
      node["left"] <- base
      tree[[base]]["up"] <- i
      base <- i
      tree[[base]] <- node
    }
    else {
      tree[[i]] <- node
      ## insert in tree (e.g. merge two trees)
      tree <- as.data.frame( leftist.merge( as.matrix( tree ), i, base ))
      base <- tree["up",1]
    }
  }
  tree[[1]][c("right","left","up")] <- base
  tree
}
###########################################################################################
leftist.create <- function( tree, key = "time" )
{
  ## assume 1st element of tree is for base
  ## and keys are in place already
  base <- 2
  tree[c("right","left","up"),] <- 1
  tree["dist",-1] <- 1
  ntree <- ncol( tree )

  for( i in seq( 3, ntree )) {
    if( tree[key,i] <= tree[key,base] ) {
      ## insert at root
      tree["left",i] <- base
      tree["up",base] <- i
      base <- i
    }
    else {
      ## insert in tree (e.g. merge two trees)
      tree <- leftist.merge( tree, i, base )
      base <- tree["up",1]
    }
  }
  ## set 1st element to point to base
  tree[c("right","left","up"),1] <- base
  tree
}
###########################################################################################
leftist.merge <- function( tree, P = 1, Q = 1, R = 1, key = "time" )
{
  while( TRUE ) {
    if( P == 1 ) {
      P <- Q
      Q <- 1
    }
    if( Q == 1 ) {
      D <- tree["dist",P]
      while( R > 1 ) {
        Q <- tree["right",R]
        temp <- tree["left",R]
        if( tree["dist",temp] < D ) {
          D <- tree["dist",temp] + 1
          tree["right",R] <- tree["left",R]
          tree["left",R] <- P
        }
        else {
          D <- D + 1
          tree["right",R] <- P
        }
        tree["up",P] <- R
        tree["dist",R] <- D
        P <- R
        R <- Q
      }
      tree["up",P] <- 1
      tree[c("left","right","up"),1] <- P
      return( tree )
    }
    ## merge two right lists
    if( tree[key,P] <= tree[key,Q] ) {
      temp <- tree["right",P]
      tree["right",P] <- R
      tree["up",R] <- P
      R <- P
      P <- temp
    }
    else {
      temp <- tree["right",Q]
      tree["right",Q] <- R
      tree["up",R] <- Q
      R <- Q
      Q <- temp
    }
  }
  tree["up",1] <- P
  tree[c("left","right","up"),P] <- 1
  tree
}
###########################################################################################
leftist.remove <- function( tree, P )
{
  oldbase <- tree["up",1]
  up <- tree["up",P]

  ## merge the subtrees below P
  tree <- leftist.merge( tree, tree["left",P], tree["right",P] )

  ## reset node to empty
  tree[c("up","left","right","dist"),P] <- 1

  ## return if base node removed
  if( oldbase == P )
    return( tree )

  ## make up node leftist
  if( tree["left",up] == P )
    tree["left",up] <- tree["right",up]
  tree["right",up] <- 1
  tree["dist",up] <- 1

  ## traverse back up the tree to make sure it is leftist to base
  upup <- tree["up",up]
  left <- tree["left",upup]
  right <- tree["right",upup]
  while( tree["dist",left] < tree["dist",right] ) {
    tree["dist",upup] <- tree["dist",left] + 1
    tree["left",upup] <- right
    tree["right",upup] <- left
    upup <- tree["up",upup]
    left <- tree["left",upup]
    right <- tree["right",upup]
  }
  ## merge down and up trees
  leftist.merge( tree, oldbase, tree["up",1] )
}
###########################################################################################
leftist.birth <- function( organism, neworg, free )
{
  newbase <- organism["up",1]
  norganism <- ncol( neworg )
  j <- norganism
  nfree <- free[1]
  while( j > 0 & nfree > 1 ) {
    newbabe <- free[nfree]
    organism[,newbabe] <- neworg[,j]
    organism <- leftist.merge( organism, newbase, newbabe )
    newbase <- organism["up",1]
    nfree <- nfree - 1
    free[1] <- nfree
    j <- j - 1
  }
  if( j > 0 ) {
    norganism <- ncol( organism )
    js <- 1:j
    organism <- cbind( organism, neworg[,js] )
    for( i in norganism + js ) {
      organism <- leftist.merge( organism, newbase, i )
      newbase <- organism["up",1]
    }
  }
  list( tree = organism, base = newbase, free = free )
}
###########################################################################################
leftist.free <- function( free, id )
{
  free[1] <- tmp <- free[1] + 1
  free[tmp] <- id
  free
}
###########################################################################################
leftist.update <- function( tree, P = tree["up",1] )
{
  tree <- leftist.remove( tree, P )
  leftist.merge( tree, tree["up",1], P )
}

# --- Source: community.R ---
get.organisms <- function(datafile = "") {
  org <- list(species = c("host", "parasite"), substrates = "substrate")
  if(datafile != "") {
    if(tools::file_ext(datafile) %in% c("xls","xlsx")){
      sheets <- readxl::excel_sheets(datafile)
      species <- stringr::str_remove(
        sheets[stringr::str_detect(sheets, "future\\.")],
        "future\\.")
      substrates <- unique(stringr::str_remove(
        sheets[stringr::str_detect(sheets, paste("\\.", species, sep = "", collapse = "|")) &
        !stringr::str_detect(sheets, paste(c("future", species), "\\.", sep = "", collapse = "|"))],
        "\\..*"))
      org <- list(species = species, substrates = substrates)
    }
  }
  org
}
###########################################################################################
get.species <- function( community, species ) {
  if( missing( species ))
    return( names( community$pop ))
  if( is.numeric( species ))
    species <- names( community$pop )[species]
  if( is.null( species ) || !species %in% names( community$pop ))
    return( NULL )
  
  ans <- community$pop[[species]]
  if (is.list(ans) && !is.matrix(ans) && !is.data.frame(ans) && !is.null(ans$org)) {
    ans <- ans$org
  }
  if (is.matrix(ans) || is.data.frame(ans)) {
    if (!"up" %in% rownames(ans) && (is.null(colnames(ans)) || colnames(ans)[1] != "dummy")) {
      dummy <- ans[, 1, drop = FALSE]
      colnames(dummy) <- "dummy"
      return(cbind(dummy, ans))
    }
  }
  ans
}
###########################################################################################
get.species.element <- function( community, species, rows, cols )
  community$pop[[species]][rows,cols]
###########################################################################################
put.species <- function( community, species, value )
{
  community$pop[[species]] <- value
  community
}
###############################################################################
put.individual <- function( community, species, individual,
                           id = get.base( community, species ))
{
  community$pop[[species]][,id] <- individual
  community
}  
###############################################################################
get.individual <- function( community, species,
                           id = get.base( community, species ))
  community$pop[[species]][,id]
###############################################################################
get.base <- function( community, species )
  community$pop[[species]]["up",1]

# --- Source: c.ewing.R ---
c.ewing <- function(...) {
  communities <- list(...)
  if(length(communities) < 2) {
    if(length(communities) == 1)
      return(communities[[1]])
    return(NULL)
  }

  community <- communities[[1]]
  
  # *** need to verify that all communities have save structure
  # Elements org, temp should be identical
  
  for(comi in seq(2, length(communities))) {
    # Element pop
    for(species in names(community$pop)) {
      community$pop[[species]] <-
        cbind(community$pop[[species]], communities[[comi]]$pop[[species]])
    }
    # Element cpu
    community$cpu <- community$cpu + communities[[comi]]$cpu
  }
  # Element plot is more complicated as it contains items for plots
  # Probably want some form of appendX functions
}

# --- Source: move.R ---
## $Id: move.R,v 1.0 2002/12/09 yandell@stat.wisc.edu Exp $
##
## Functions for Bland Ewing's modeling.
##
##     Copyright (C) 2000,2001,2002 Brian S. Yandell.
##
## This program is free software; you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by the
## Free Software Foundation; either version 2, or (at your option) any
## later version.
##
## These functions are distributed in the hope that they will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
## GNU General Public License for more details.
##
## The text of the GNU General Public License, version 2, is available
## as http://www.gnu.org/copyleft or by writing to the Free Software
## Foundation, 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
##
###############################################################################
event.move <- function( community, species, individual )
{
  individual <- as.matrix( individual )
  ## move only if individual is in stage that moves
  if( all( is.move( community, species, individual ))) {
    ## move among substrates?
    individual["sub.future",] <-
      sampleOrgSubstrate( community, species, individual["sub.stage",] )
    ## move to new position in substrate
    position <- paste( "pos", letters[1:3], sep = "." )
    individual[position,] <- rtri( ncol( individual ), 10, individual[position,] )
  }
  if( ncol( individual ) == 1 )
    individual <- individual[,1]
  individual
}
###############################################################################
is.move <- function( community, species, individual )
{
  !is.na( match( getOrgFeature( community, species, "move" ), 
                getOrgFuture( community, species, "current" )[ individual["stage",] ] ))
}
###############################################################################
event.find <- function( community, species, host, event )
{
  individual <- get.individual( community, species )
  substrate <- individual["sub.stage"]
  ## pending event: need to find available hosts on substrate
  avail <- get.alive( community, host, substrate )
  navail <- length( avail )
  if( !navail )
    return( avail )
  
  ## preferences based on schedule
  interact <- get.interact( community, species, host, avail, event )
  if( length( event ) > 1 )
    interact <- apply( interact, 1, sum )
  sinteract <- sum( interact )
  if( sinteract ) {
    if( navail > 1 ) {
      found <- sample( avail, 1, prob = interact / sinteract )
    }
    else
      found <- avail
    found
  }
  else
    numeric(0)
}

# --- Source: offspring.R ---
## $Id: init.R,v 1.0 2002/12/09 yandell@stat.wisc.edu Exp $
##
## Functions for Bland Ewing's modeling.
##
##     Copyright (C) 2000,2001,2002 Brian S. Yandell.
##
## This program is free software; you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by the
## Free Software Foundation; either version 2, or (at your option) any
## later version.
##
## These functions are distributed in the hope that they will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
## GNU General Public License for more details.
##
## Offspring Information
##########################################################################################
getOffspring <- function( community, species,
                         offspring = getOrgFeature( community, species, "offspring" ))
{
  if( is.na( offspring ))
    return( 0 )
  if( is.numeric( offspring ))
    return( offspring )
  getOrgInteract( community, offspring, species, "offspring" )
}
##########################################################################################
initOffspring <- function( community, species )
{
  hostname <- getOrgFeature( community, species, "offspring" )

  ## find if there is offspring load based on host
  orgoffspring <- getOffspring( community, species, hostname )

  norganism <- sum( getOrgAlive( community, species ))

  if( length( orgoffspring ) == 1 ) {
    ## mean offspring does not depend on any host
    offspring <- stats::rpois( norganism, orgoffspring )
  }
  else {
    orgoffspring <- orgoffspring[ orgoffspring > 0 ]
    
    if(!length(orgoffspring))
      return(community)
  
    ## figure out initial offspring load based on host distribution

    ## mean offspring depends on host stages and events
    host <- get.species( community, hostname )
    if( is.null( host ))
      stop( paste( "Host", hostname, "not initiated yet" ))

    ## get weights of host stages in terms of future event times
    host <- host[ , getOrgAlive( community, hostname ) ]

    ## find host stages that are preferred by parasite
    ## need to take subset of current that are actually in host
    hoststages <- match( names( orgoffspring ), getOrgFuture( community, hostname )$current,
                        nomatch = 0 )
    host <- as.matrix( host[ , !is.na( match( host["stage",], hoststages )) ] )
    if( ncol( host ) == 0 )
      return( community )

    tmp <- !is.na( match( hoststages, host["stage",] ))
    hoststages <- hoststages[tmp]
    orgoffspring <- orgoffspring[tmp]

    if(!length(orgoffspring))
      return(community)
    
    dd <- tapply( host["time",], host["stage",], sum )
    dd[ as.character( hoststages[
      is.na( match( hoststages, names( dd ))) ] ) ] <- 0
    dd[ is.na( dd ) ] <- 0
    sdd <- sum( dd )
    if( length( dd ) > 1 & sdd > 0)
      offspring <- as.vector( sample( orgoffspring, norganism, replace = TRUE,
        prob = dd / sdd ))
    else
      offspring <- rep( ( sdd > 0 ) * orgoffspring[1], norganism )
    offspring[ is.na( offspring ) ] <- 0
  }
  organism <- get.species( community, species )
  organism["offspring",-1] <- offspring
  put.species( community, species, organism )
}
###############################################################################
get.offspring <- function( community, species )
{
  individual <- get.individual( community, species )
  if( individual["offspring"] > 0 )
    1
  else
    0
}
###########################################################################################
set.offspring <- function( community, species, host, dead )
{
  stage <- get.species.element( community, host, "stage", dead )
  current <- getOrgFuture( community, host, "current", stage )
  offspring <- getOrgInteract( community, host, species, "offspring")
  offspring <- as.vector( offspring[ as.character( current ) ] )
  offspring[ is.na( offspring ) ] <- 0
  offspring
}

# --- Source: sim.R ---
## $Id: sim.R,v 0.9 2002/12/09 yandell@stat.wisc.edu Exp $
##
## Functions for Bland Ewing's modeling.
##
##     Copyright (C) 2000,2001,2002 Brian S. Yandell.
##
## This program is free software; you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by the
## Free Software Foundation; either version 2, or (at your option) any
## later version.
##
## These functions are distributed in the hope that they will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
## GNU General Public License for more details.
##
## The text of the GNU General Public License, version 2, is available
## as http://www.gnu.org/copyleft or by writing to the Free Software
## Foundation, 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
##
### migrate count$count* to writeCount and then retrieve with readCount
###########################################################################################
updateCount <- function(community, species, individual, is.death = FALSE, step) {
  community <- updateEvents(community, species, individual["future"])
  if (!missing(step)) {
    community <- setCount(community, elements = list(step = step))
  }

  ## move individual through age classes or drop if it dies
  countage <- getCount( community, species, "countage" )
  ageclass <- getOrgAgeClass( community, species, individual["stage"] )
  if( !is.na( ageclass )) {
    ageclass <- as.character( ageclass )
    countage[ageclass] <- max(countage[ageclass] - 1, 0)
  }
  ageclass <- getOrgAgeClass( community, species, individual["future"] )
  if( !is.na( ageclass )) {
    ageclass <- as.character( ageclass )
    countage[ageclass] <- countage[ageclass] + 1
  }
  ## move individual across substrate elements or drop if it dies
  countsub <- getCount( community, species, "countsub" )
  substrate <- getOrgFeature( community, species, "substrate" )
  elements <- getOrgInteract( community, substrate, species, "substrate" )
  element <- elements[ individual["sub.stage"] ]
  subclass <- getOrgFeature( community, species, "subclass" )
  include <- subclass == getOrgAgeClass( community, species, individual[c("stage","future")] )
  # need 2 values; if only one, replicate it.
  include <- rep_len(include, 2)
  ## leave old substrate
  if (!is.na(element) && include[1]) {
    element <- as.character( element )
    countsub[element] <- max(countsub[element] - 1, 0)
  }
  if (!is.death && include[2]) {
    ## move to new substrate
    newsub <- individual["sub.future"]
    if( !is.na( newsub )) {
      element <- elements[newsub]
      if( !is.na( element )) {
        element <- as.character( element )
        countsub[element] <- countsub[element] + 1
      }
    }
  }
  ## record counts
  community <- setCount( community, species,
                        list( countage = countage, countsub = countsub ))
  community <- writeCount( community, species, individual["time"], individual["future"],
               countage, countsub )
}
###########################################################################################
updateCounts <- function( community, species, newbirths )
{
  community <- updateEvents( community, species, 1, newbirths )
  
  countage <- getCount( community, species, "countage" )
  countsub <- getCount( community, species, "countsub" )
  subclass <- getOrgFeature( community, species, "subclass" )
  ageclass <- getOrgAgeClass( community, species, 1 )
  individual <- get.individual( community, species )
  if( !is.na( ageclass[1] )) {
    ageclass <- as.character( ageclass )
    countage[ageclass] <- newbirths + countage[ageclass]
    if( subclass == ageclass ) {
      substrate <- getOrgSubstrate( community, species, individual["sub.stage"] )
      if( !is.na( substrate[1] )) {
        substrate <- as.character( substrate )
        countsub[substrate] <- newbirths + countsub[substrate]
      }
    }
  }
  community <- setCount( community, species,
                        list( countage = countage, countsub = countsub ))
  community <- writeCount( community, species, individual["time"], 1, countage, countsub )
}
###########################################################################################
put.base <- function( community, species, id,
                     free = getCount( community, species, "free" ))
{
  base <- get.base( community, species )
  if( !missing( id ))
    free <- leftist.free( free, id )
  mintime <- max( getCount( community, species, "mintime" ),
                 getTime( community, species,
                         get.species.element( community, species, "time", base )))
  setCount( community, species, list( base = base, free = free, mintime = mintime ))
}

# --- Source: simple.R ---
simpleServer <- function(id) {
  shiny::moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
  })
}
simpleInput <- function(id) {
  ns <- shiny::NS(id)
  shiny::tagList(
    shiny::h4("Simulation Settings"),
    shiny::sliderInput(ns("steps"),
                       label = "Simulation steps:",
                       min = 1000,
                       max = 10000,
                       value = 1000,
                       step = 500),
    shiny::radioButtons(ns("nsim"),
                        "Number of Simulations",
                        c(1,10,20,50,100,200),
                        1, inline = TRUE),
    shiny::actionButton(ns("go"), "Start Simulation"),
    
    shiny::HTML("<hr style='height:1px;border:none;color:#333;background-color:#333;' />"),
    shiny::h4("Save Files"),
    shiny::uiOutput(ns("version"))
  )
}
simpleOutput <- function(id) {
  ns <- shiny::NS(id)
  shiny::tagList(
    shiny::radioButtons(ns("button"), "", c("Plots", "Input Data"),
                        "Plots", inline = TRUE),
  )
}
simpleApp <- function(title = "Population Ethology") {
  ui <- shiny::fluidPage(
    shiny::titlePanel(title),
    shiny::sidebarLayout(
      shiny::sidebarPanel(
        simpleInput("simple")
      ),
      shiny::mainPanel(
        simpleOutput("simple")
    )))
  
  server <- function(input, output, server) {
    simpleServer("simple")
  }
  
  shiny::shinyApp(ui = ui, server = server)
}

# --- Source: ring.R ---
## $Id: ring.R,v 1.0 2002/12/09 yandell@stat.wisc.edu Exp $
##
## Functions for Bland Ewing's modeling.
##
##     Copyright (C) 2000,2001,2002 Brian S. Yandell.
##
## This program is free software; you can redistribute it and/or modify it
## under the terms of the GNU General Public License as published by the
## Free Software Foundation; either version 2, or (at your option) any
## later version.
##
## These functions are distributed in the hope that they will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
## GNU General Public License for more details.
##
## The text of the GNU General Public License, version 2, is available
## as http://www.gnu.org/copyleft or by writing to the Free Software
## Foundation, 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
##
###########################################################################################
## Doubly linked rings
###########################################################################################
ring.add <- function( ring = data.frame( root = c( key = NA, left = 1 + nx, right = 2 )),
  x )
{
  nx <- length( x )
  n1 <- ring["left","root"]
  ring[[ as.character( n1 + 1 ) ]] <- c(
    key = x[1],
    left = suppressWarnings(as.numeric( ring["left","root"] )),
    right = 1 )
  
  if( nx > 1 ) for( i in seq( 2, nx ))
  {
    ring[[ as.character( n1 + i ) ]] <- c(
      key = x[i],
      left = n1+i-1,
      right = 1 )
    ring[ "right", as.character( n1 + i - 1 ) ] <- n1+i
  }
  ring["left","root"] <- n1 + nx
  ring
}
###########################################################################################
ring.remove <- function( ring, P )
{
  aP <- as.character( P )
  if( is.na( match( aP, names( ring ))))
  {
    cat( paste( "Warning:", aP, "not found in ring\n" ))
    return( ring )
  }
  left <- ring["left",aP]
  right <- ring["right",aP]
  ring[ "right", as.character( left ) ] <- right
  ring[ "left", as.character( right ) ] <- left
  ring[[aP]] <- NULL
  ring
}

# --- Source: sierpinski.R ---
sierpinski <- function( stage = 5, reset = TRUE )
{
  if( reset )
    tmpar <- graphics::par( pty = "s", bty = "n", xaxt = "n", yaxt = "n", omi = rep(0,4),
                            mar = rep(0,4) )
  aa <- 0:1
  bb <- - aa
  for( i in seq( stage )) {
    tmp <- gasket( aa, bb )
    aa <- tmp$aa
    bb <- tmp$bb
    tri <- tri2car.default( aa, bb )
    r <- range( unlist( tri ))
    plot( r, r, type = "n", xlab = "", ylab = "" )
    graphics::lines(tri )
    graphics::mtext( paste( "(", letters[1+i], ") Gasket of Order ", i, sep = "" ), 3, -2 )
    #    graphics::mtext( paste( "Gasket of order", i ), 3, -2 )
  }
  if( reset )
    graphics::par( tmpar )
  invisible( tri )
}

# --- Source: fileCount.R ---
putCount <- function( community, append = FALSE )
{
  species <- get.species( community )
  
  countage <- getCount( community,, "countage")
  countsub <- getCount( community,, "countsub")
  countbase <- getCount( community,, "base")
  step_now <- if (append) (getCount( community,, "step" ) %||% 0) else 0
  
  cnames <- c("step", "time", "future",
              paste( "count", 
                     seq( max( unlist( lapply( countage, length )) +
                                 unlist( lapply( countsub, length )))),
                     sep = "" ))

  cnums <- list()
  for( i in species ) {
    cnums[[i]] <- c(
      step_now,
      get.species.element( community, i, c("time","stage"), countbase[i] ),
      countage[[i]],
      countsub[[i]])
  }

  file <- getCount( community,, "file" )
  if(!is.null(file) && is.character(file) && length(file) > 0) {
    if( !( file.exists( file ) & append ))
      cat( "species", cnames, "\n", file = file )
    for( i in species ) {
      cat( i, cnums[[i]], "\n", file = file, append = TRUE )
    }
    community
  } else {
    if(append) {
      counts <- getCount( community,, "counts")
    } else {
      counts <- NULL
    }
    newcounts <- matrix(NA, length(species), length(cnames),
                       dimnames = list(species, cnames)) 
    for(i in species) {
      newcounts[i, seq_along(cnums[[i]])] <- cnums[[i]]
    }
    newcounts <- tibble::tibble(
      data.frame(
        species = species,
        newcounts))
    counts <- dplyr::bind_rows(
      counts,
      newcounts)
    
    setCount( community,, list(counts = counts))
  }
}
###########################################################################################
writeCount <- function( community, species, time, future, countage, countsub)
{
  nstep <- getCount( community,, "step" )
  
  cnums <- c(nstep, time, future, countage, countsub)
  
  file <- getCount( community,, "file" )
  if(!is.null(file) && is.character(file) && length(file) > 0) {
    cat( species, cnums, "\n", file = file, append = TRUE )
    
    community
  } else {
    counts <- getCount( community,, "counts")
    cnames <- names(counts)[-1]
    
    newcounts <- matrix(NA, 1, length(cnames),
                          dimnames = list(species, cnames)) 
    newcounts[1, seq_along(cnums)] <- cnums
    
    newcounts <- tibble::tibble(
      data.frame(
        species = species,
        newcounts))
    counts <- dplyr::bind_rows(
      counts,
      newcounts)
    
    setCount( community,, list(counts = counts))
  }
}
###########################################################################################
readCount <- function( community, species = unique(counts$species) )
{
  file <- getCount( community,, "file" )
  if(!is.null(file)) {
    counts <- utils::read.table( file, header = TRUE, fill = TRUE )
  } else {
    counts <- getCount( community,, "counts")
  }
  
  count <- list()
  for( i in species ) {
    colnames <- c( levels( getOrgFuture( community, i, "ageclass" )),
                   levels( getOrgInteract( community,, i, "substrate" )))
    count[[i]] <- as.matrix( counts[ counts$species == i, seq( 2, 4 + length( colnames )) ] )
    dimnames( count[[i]] ) <- list( count[[i]][,"step"],
                                    c( "step", "time", "future", colnames ))
  }
  count
}


# --- Source: count.join.R ---
count.join <- function( ... )
{
  x <- list( ... )
  numnum <- list()
  for( i in seq( length( x ))) {
    for( j in names( x[[i]] )) {
      if( is.null( numnum[[j]] ))
        numnum[[j]] <- x[[i]][[j]]
      else
        numnum[[j]] <- cbind( numnum[[j]], x[[i]][[j]] )
    }
  }
  numnum
}

# --- Source: summary.ewing.R ---
summary.ewing <- function(object, ...) {
  out <- list()
  out$package <- object$org$package
  out$species <- names(object$org$Future)
  out$interact <- names(object$org$Interact)
  out$meanvalue <- list()
  for(i in out$species) {
    out$meanvalue <- names(object$org$MeanValue[[i]])
  }
  if(length(object$pop)) {
    out$stage <- list()
    for(i in out$species)
      out$stage[[i]] <- 
        table(object$org$Future[[i]]$current[getOrgAlive(object, i, "stage")])
  }
  if(!is.null(object$count)) {
    out$events <- object$count$events
    
    for( i in seq( length(out$events)))
      out$events[[i]] <- apply(out$events[[i]], 2, function(x) {
        tmp <- sum(x, na.rm = TRUE)
        if(tmp > 0)
          c(round(100 * x / tmp, 1), total = tmp)
        else
          c(x, total = 0)
      })
  }
  out$cpu <- signif(object$cpu, 4)
  class(out) <- c("summary.ewing", class(out))
  out
}
print.summary.ewing <- function(x, ...) {
  cat("Data initialization package:", x$package, "\n")
  cat("Community species:", paste(x$species, collapse = ", " ), "\n")
  cat("Community hosts:", paste(x$interact, collapse = ", "), "\n")
  cat("Mean Value curves by species:")
  mv <- FALSE
  for(i in x$species) {
    meanvalue <- x$meanvalue[[i]]
    mv <- mv | !is.null(meanvalue)
    if( !is.null(meanvalue)) {
      cat("\n  ", i, ":", paste( meanvalue, collapse = ", " ), "\n")
    }
  }
  if(!mv)
    cat(" none\n")
  
  if(!is.null(x$stage)) {
    cat("\nSimulation community has following counts:\n",
        paste(x$species, lapply(x$stage, sum), sep = "=", collapse = ", "),
        "\n")
    print(x$stage)
  }
  if( !is.null( x$temp )) {
    # ** later
  }
  if(!is.null(x$events)) {
    print(x$events)
  }
  if(!is.null(x$cpu)) {
    cat( "CPU timing by event in simulation\n" )
    print(x$cpu)
  }
}

# --- Source: summary_simobj.R ---
summary_simobj <- function(object) {
  out <- sapply(object, function(x) {
    if(is.list(x)) {
      x <- names(x)
    }
    paste(x, collapse=",")
  })
  paste(paste(names(out), out, sep = ": "), collapse = "<br>")
}

# --- Source: ewing_discrete.R ---
ewing_discrete <- function(nsim, verbose = FALSE, ...) {
  sims <- seq_len(nsim)
  
  object <- as.list(sims)
  names(object) <- sims
  
  for(i in sims) {
    if(verbose) cat(".")
    object[[i]] <- ewing_discrete1(...)
  }
  make_ewing_discrete(object)
}
ewing_discrete1 <- function(siminit = init.simulation(interact = FALSE,
                                                    messages = FALSE, ...),
                            increment = 0.5, ...)
{
  # Make sure increment is 1,2,5 x power of 10
  incr <- pretty(increment)
  increment <- incr[which.min(abs(incr - increment))[1]]
  
  out <- future.events(siminit, refresh = 1000,
                       plotit = FALSE, messages = FALSE, ...)
  attrs <- attributes(out)
  
  # Get age classes used later for summaries and plots
  items <- purrr::map(out$org$Future, function(x) levels(factor(x$ageclass)))
  
  out <- readCount(out)
  out <- purrr::map(
    out,
    function(x) {
      purrr::map_df(
        dplyr::distinct(
          purrr::map_df(
            dplyr::mutate(
              as.data.frame(x),
              time = ifelse(.data$step == 0, 0,
                            increment * ceiling(.data$time / increment))),
            rev),
          .data$time, .keep_all = TRUE),
        rev)
    })
  attr(out, "count") <- attrs$count
  attr(out, "nstep") <- attrs$nstep
  attr(out, "items") <- items
  out
}
summary.ewing_discrete <- function(object, ...) {
  summary(ewing_envelopes(object), ...)
}

# --- Source: make_ewing_discrete.R ---
make_ewing_discrete <- function(object) {  
  nsim <- length(object)
  class(object) <- c("ewing_discrete", class(object))
  
  attr(object, "species") <- species <- names(object[[1]])
  attr(object, "ordinate") <- "time"
  attr(object, "count") <- attr(object[[1]], "count")
  attr(object, "nstep") <- attr(object[[1]], "nstep")
  attr(object, "items") <- attr(object[[1]], "items")
  attr(object, "nsim") <- nsim
  object
}

# --- Source: ggplot_ewing.R ---
ggplot_ewing <- function(object, step = 0, ageclass = TRUE,
                         substrate = !ageclass, ...)
{
  if(!inherits(object, "ewing_snapshot")) {
    object <- ewing_snapshot(object, step, ...)
  }
  step <- object$step
  
  p <- list()
  i <- 0
  if(ageclass) {
    i <- i + 1
    p[[i]] <- ggplot2::autoplot(object$ageclass, ...)
  }
  if(substrate) {
    species <- names(object$substrate)
    for(j in species) {
      i <- i + 1
      p[[i]] <- ggplot2::autoplot(object$substrate[[j]], ...)
    }
  }
  if(length(p) == 1) p <- p[[1]]
  p
}
autoplot.ewing <- function(object, ...) {
  ggplot_ewing(object, ...)
}
plot.ewing <- function(x, ...) {
  ggplot_ewing(x, ...)
}

# --- Source: ggplot_current.R ---
ggplot_current <- function( x,
                          species,
                          col = as.character( future$color[stage] ),
                          headstuff = c( 0, "start"),
                          units = getOrgFeature( x, species, "units" ),
                          right = species, adj = c(0,.5,1),
                          position = paste( "pos", letters[1:3], sep = "." ),
                          pch = as.character( future$pch[stage] ), cex = 0.5,
                          stage = organism["stage",],
                          xlab = "horizontal", ylab = "vertical",
                          future = getOrgFuture( x, species, c("color","pch") ),
                          facet = TRUE, ...)
{
  ## plot current stages for species (except random parasites)
  organism <- get.species( x, species )[,-1]
  if(is.null(organism))
    return(NULL)
  
  tri_coord <- tri2car( organism[position,] )
  tri_coord$col <- NA
  tri_coord$label <- pch
  values <- future$color
  names(values) <- future$pch
  values <- unique(values)

  # Facet by Substrate.
  # This needs to use generic function to get substrate names.
  tmp <- names(x$org$Interact$substrate$substrate)
  tri_coord$substrate <- tmp[organism["sub.stage",]]
  
  p <- ggplot2::ggplot(tri_coord) +
    ggplot2::aes(x, y, col = label, label = label) +
    ggplot2::geom_text(size=3) +
    ggplot2::xlab(xlab) +
    ggplot2::ylab(ylab) +
    ggplot2::ggtitle(paste(units, "future event", right),
      subtitle = paste(headstuff, collapse = " ")) +
    ggplot2::scale_color_manual(values = values)
  if(facet) {
    p <- p + ggplot2::facet_wrap(~substrate)
  }
  p
}

# --- Source: plot_null.R ---
plot_null <- function(msg = "no data") {
  ggplot2::ggplot(data.frame(x = 1, y = 1), 
                  ggplot2::aes(.data$x, .data$y, label = msg)) +
    ggplot2::geom_text(size = 10) + 
    ggplot2::theme_void()
}

# --- Source: step_controls.R ---
step_size_choices <- c(1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000)

step_size_slider <- function(inputId, label = "Steps per click:", selected = 50) {
  idx <- match(selected, step_size_choices)
  if (is.na(idx)) idx <- 6
  sl <- shiny::sliderInput(inputId, label, min = 1, max = length(step_size_choices), value = idx, step = 1, ticks = TRUE)
  sl$children[[2]]$attribs[['data-values']] <- paste(step_size_choices, collapse = ",")
  sl
}

parse_step_size <- function(val) {
  if (is.null(val)) return(50)
  num <- round(as.numeric(val))
  if (is.na(num)) return(50)
  
  # Direct large values (e.g., 20, 50, 100, 200, 500, 1000, 2000)
  if (num %in% step_size_choices && num > 10) {
    return(num)
  }
  
  # 0-based JavaScript index from ion.rangeSlider (0 to 10)
  if (num >= 0 && num < length(step_size_choices)) {
    return(step_size_choices[num + 1])
  }
  
  # Fallback for direct value
  if (num %in% step_size_choices) {
    return(num)
  }
  
  50
}

axisUnitInput <- function(id, time_label = "Time", selected = "step") {
  ns <- shiny::NS(id)
  choices <- c("Steps" = "step")
  choices[time_label] <- "time"
  shiny::radioButtons(ns("x_var"), "Display Units:",
                      choices = choices,
                      selected = selected, inline = TRUE)
}

axisUnitServer <- function(id) {
  shiny::moduleServer(id, function(input, output, session) {
    shiny::reactive({ if (!is.null(input$x_var)) input$x_var else "step" })
  })
}

ageClassControlInput <- function(id, time_label = "Time") {
  ns <- shiny::NS(id)
  shiny::tagList(
    axisUnitInput(ns("axis_unit"), time_label = time_label),
    shiny::checkboxInput(ns("norm"), "Normalize Plot", TRUE),
    shiny::checkboxInput(ns("total"), "Include Total in Plot", TRUE)
  )
}

ageClassControlServer <- function(id) {
  shiny::moduleServer(id, function(input, output, session) {
    x_var <- axisUnitServer("axis_unit")
    list(
      x_var = x_var,
      norm = shiny::reactive({ if (!is.null(input$norm)) input$norm else TRUE }),
      total = shiny::reactive({ if (!is.null(input$total)) input$total else TRUE })
    )
  })
}

# --- Source: initParApp.R ---
initParApp <- function(title = "Population Ethology") {
  ui <- bslib::page_sidebar(
    title = title,
    sidebar = bslib::sidebar(
      initParInput("init_par")),
    initParUI("init_par"),
    initParOutput("init_par")
  )
  server <- function(input, output, server) {
    init_par <- initParServer("init_par")
  }
  shiny::shinyApp(ui = ui, server = server)
}
initParServer <- function(id, simres = shiny::reactiveVal(NULL), datafile = shiny::reactiveVal("")) {
  shiny::moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    species <- shiny::reactive({
      get.organisms()$species
    })
    output$sppsize <- shiny::renderUI({
      shiny::req(species()) # "host", "parasite"
      lapply(species(), function(x) {
        shiny::sliderInput(ns(x),
                           label = paste0("Number of ", x, "s:"),
                           min = 0,
                           max = 500,
                           value = 100,
                           step = 20)
      })
    })

    datanames <- shiny::reactive({
      getOrgNames()
    })
    output$inputfiles <- shiny::renderUI({
      shiny::tagList(
        shiny::selectInput(ns("dataname"), "", datanames(), "organism.features"),
        DT::dataTableOutput(ns("org_table")))
    })
    
    output$org_table <- DT::renderDataTable({
      getOrgDataSimple(simres(), shiny::req(input$dataname), datafile())
    }, escape = FALSE, options = list(scrollX = TRUE, pageLength = 10))
    
    # Show parameters
    output$show_par <- shiny::renderUI({
      nlist <- names(input)
      # Remove any internal inputs, which have numbers.
      glist <- grep("[0-9]", names(input))
      if(length(glist))
        nlist <- nlist[-glist]
      # Construct output string.
      out <- paste0("inputs: ", paste(nlist, collapse = ", "))
      for(i in nlist) {
        out <- paste(out, "<br>",
                     paste(i, input[[i]], sep = " = "))
      }
      shiny::HTML(out)
    })
    
    # Return.
    input
  })
}
initParInput <- function(id) {
  ns <- shiny::NS(id)
  shiny::uiOutput(ns("sppsize"))
}
initParUI <- function(id) {
  ns <- shiny::NS(id)
  shiny::uiOutput(ns("show_par"))
}
initParOutput <- function(id) {
  ns <- shiny::NS(id)
  shiny::uiOutput(ns("inputfiles"))
}

# --- Source: initApp.R ---
initApp <- function(title = "Population Ethology") {
  ui <- bslib::page_sidebar(
    title = title,
    sidebar = bslib::sidebar(
      initParInput("init_par")),
    initOutput("init"),
    substrateOutput("substrate")
  )
  server <- function(input, output, server) {
    init_par <- initParServer("init_par")
    siminit <- initServer("init", init_par)
    substrateServer("substrate", siminit)
  }
  shiny::shinyApp(ui = ui, server = server)
}
initServer <- function(id, init_par) {
  shiny::moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    siminit <- shiny::reactive({
      init.simulation(count = as.numeric(c(shiny::req(init_par$host),
                                           shiny::req(init_par$parasite))))
    })
    
    output$init <- shiny::renderUI({
      out <- summary_simobj(shiny::req(siminit()))
      shiny::HTML(out)
    })

    # Return.
    siminit
  })
}
initOutput <- function(id) {
  ns <- shiny::NS(id)
  shiny::uiOutput(ns("init"))
}

# --- Source: substrateApp.R ---
substrateApp <- function(title = "Substrate Organism Movement Explorer") {
  ui <- bslib::page_sidebar(
    title = title,
    sidebar = bslib::sidebar(
      initParInput("init_par"),
      shiny::hr(),
      substrateInput("substrate")),
    substrateOutput("substrate")
  )
  server <- function(input, output, server) {
    init_par <- initParServer("init_par")
    siminit <- initServer("init", init_par)
    substrateServer("substrate", siminit)
  }
  
  shiny::shinyApp(ui = ui, server = server)
}

substrateInput <- function(id) {
  ns <- shiny::NS(id)
  shiny::tagList(
    shiny::div(
      style = "font-size: 0.85rem;",
      shiny::h4("Substrate Display & Stepping", style = "font-size: 1rem; font-weight: 600; margin-bottom: 8px;"),
      shiny::checkboxGroupInput(ns("show_species"), "Species to Display:",
                                choices = c("Host" = "host", "Parasite" = "parasite"),
                                selected = c("host", "parasite"),
                                inline = TRUE),
      shiny::radioButtons(ns("species_mode"), "Species Mode:",
                          choices = c("Overlay (1 Map)" = "overlay", "Separate (Adjacent Maps)" = "separate"),
                          selected = "overlay", inline = TRUE),
      shiny::div(style = "border-top: 1px solid rgba(0,0,0,0.1); margin: 6px 0;"),
      shiny::radioButtons(ns("layout"), "Layout View:",
                          choices = c("Hex Substrate Overlay" = "hex", "Faceted Substrates" = "facet"),
                          selected = "hex", inline = TRUE),
      shiny::div(style = "border-top: 1px solid rgba(0,0,0,0.1); margin: 6px 0;"),
      shiny::span("Simulation Stepping:", style = "font-weight: 600; color: #1a73e8; display: block; margin-bottom: 4px;"),
      shiny::div(
        style = "display: flex; gap: 4px; margin-bottom: 8px;",
        shiny::actionButton(ns("step1"), "+1 Step", class = "btn-sm btn-outline-primary flex-fill"),
        shiny::actionButton(ns("step10"), "+10 Steps", class = "btn-sm btn-outline-primary flex-fill"),
        shiny::actionButton(ns("step100"), "+100 Steps", class = "btn-sm btn-outline-primary flex-fill"),
        shiny::actionButton(ns("reset_sim"), "Reset", class = "btn-sm btn-outline-secondary")
      ),
      shiny::div(style = "border-top: 1px solid rgba(0,0,0,0.1); margin: 6px 0;"),
      shiny::checkboxGroupInput(ns("layers"), "Display Layers:",
                                choices = c("Substrate Boundaries" = "poly",
                                            "Hex Grid Overlay" = "hex",
                                            "Organisms" = "organisms",
                                            "Substrate Names" = "centers",
                                            "Side Numbers" = "labels"),
                                selected = c("poly", "hex", "organisms", "centers", "labels")),
      shiny::div(style = "border-top: 1px solid rgba(0,0,0,0.1); margin: 6px 0;"),
      shiny::div(
        style = "display: flex; gap: 8px;",
        shiny::numericInput(ns("width"), "Radius:", value = 10, min = 2, max = 30, step = 1),
        shiny::numericInput(ns("step_density"), "Step Density:", value = 1, min = 0.5, max = 5, step = 0.5)
      )
    )
  )
}

substrateServer <- function(id, simres, width = 10, step_density = 1) {
  shiny::moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    current_sim <- shiny::reactiveVal(NULL)
    
    shiny::observeEvent(simres(), {
      current_sim(simres())
    })
    
    shiny::observeEvent(input$step1, {
      sim <- current_sim()
      if (!is.null(sim)) {
        res <- future.events(sim, nstep = 1, plotit = FALSE)
        current_sim(res)
      }
    })
    
    shiny::observeEvent(input$step10, {
      sim <- current_sim()
      if (!is.null(sim)) {
        res <- future.events(sim, nstep = 10, plotit = FALSE)
        current_sim(res)
      }
    })
    
    shiny::observeEvent(input$step100, {
      sim <- current_sim()
      if (!is.null(sim)) {
        res <- future.events(sim, nstep = 100, plotit = FALSE)
        current_sim(res)
      }
    })
    
    shiny::observeEvent(input$reset_sim, {
      current_sim(simres())
    })
    
    available_species <- shiny::reactive({
      sim <- current_sim()
      if (!is.null(sim) && !is.null(sim$pop)) names(sim$pop) else NULL
    })
    
    selected_species <- shiny::reactive({
      avail <- available_species()
      if (is.null(avail)) return(NULL)
      sel <- input$show_species
      if (is.null(sel) || length(sel) == 0) avail else intersect(sel, avail)
    })
    
    sppplot <- shiny::reactive({
      spp <- selected_species()
      shiny::req(spp)
      sim <- current_sim()
      shiny::req(sim)
      
      layout_val <- if (!is.null(input$layout)) input$layout else "hex"
      mode_val <- if (!is.null(input$species_mode)) input$species_mode else "overlay"
      w_val <- if (!is.null(input$width)) input$width else width
      sd_val <- if (!is.null(input$step_density)) input$step_density else step_density
      layers_val <- if (!is.null(input$layers)) input$layers else c("poly", "hex", "organisms", "centers", "labels")
      
      sim_single <- if (inherits(sim, "ewing_discrete") && is.list(sim) && length(sim) > 0) sim[[1]] else sim
      if (inherits(sim_single, "ewing")) {
        if (mode_val == "overlay" && layout_val == "hex") {
          sub_data <- ewing_substrate(sim_single, spp, layout = layout_val, width = w_val, step_density = sd_val)
          if (!is.null(sub_data)) {
            p_obj <- ggplot_ewing_substrate(sub_data, layout = layout_val, width = w_val, step_density = sd_val, layers = layers_val)
            list(p_obj)
          } else {
            list()
          }
        } else {
          p <- lapply(spp, function(x) {
            sub_data <- ewing_substrate(sim_single, x, layout = layout_val, width = w_val, step_density = sd_val)
            if (!is.null(sub_data)) {
              p_obj <- ggplot_ewing_substrate(sub_data, layout = layout_val, width = w_val, step_density = sd_val, layers = layers_val)
              p_obj
            } else {
              NULL
            }
          })
          p[!sapply(p, is.null)]
        }
      } else {
        list()
      }
    })
    
    output$sppPlot <- shiny::renderPlot({
      plots <- sppplot()
      if (!is.null(plots) && length(plots) > 0) {
        cowplot::plot_grid(plotlist = plots, ncol = length(plots), align = "h")
      } else {
        ggplot2::ggplot() + ggplot2::theme_void() + ggplot2::ggtitle("No active species selected to plot")
      }
    })
    
    output$substrate_plot <- shiny::renderUI({
      plots <- sppplot()
      h_px <- 500
      shiny::plotOutput(ns("sppPlot"), height = paste0(h_px, "px"))
    })
    
    # Return current simulation state for downstream composition
    current_sim
  })
}

substrateOutput <- function(id) {
  ns <- shiny::NS(id)
  shiny::uiOutput(ns("substrate_plot"))
}

# --- Source: distPlotApp.R ---
distPlotApp <- function(title = "Population Ethology") {
  ui <- bslib::page_sidebar(
    title = title,
    sidebar = bslib::sidebar(
      initParInput("init_par"),
      distPlotInput("dist_plot"),
      futureInput("future")),
    distPlotOutput("dist_plot")
  )
  server <- function(input, output, server) {
    init_par <- initParServer("init_par")
    siminit <- initServer("init", init_par)
    simres <- futureServer("future", siminit)
    distPlotServer("dist_plot", simres)
  }
  
  shiny::shinyApp(ui = ui, server = server)
}
distPlotServer <- function(id, simres, x_var = NULL, total = NULL, norm = NULL) {
  shiny::moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    dist_plot <- shiny::reactive({
      sim <- if (is.reactive(simres)) simres() else simres
      shiny::req(sim)
      
      tot_val <- if (is.reactive(total)) total() else if (!is.null(total)) total else if (!is.null(input$total)) input$total else TRUE
      norm_val <- if (is.reactive(norm)) norm() else if (!is.null(norm)) norm else if (!is.null(input$norm)) input$norm else TRUE
      xv_val <- if (is.reactive(x_var)) x_var() else if (!is.null(x_var)) x_var else if (!is.null(input$x_var)) input$x_var else "step"
      
      if (is.null(tot_val)) tot_val <- TRUE
      if (is.null(norm_val)) norm_val <- TRUE
      if (is.null(xv_val) || !xv_val %in% c("step", "time")) xv_val <- "step"
      
      object <- tryCatch({
        ewing_ageclass(sim, total = tot_val, normalize = norm_val)
      }, error = function(e) NULL)
      
      if (is.null(object)) return(plot_null("no simulation"))
      ggplot2::autoplot(object, x_var = xv_val)
    })
    
    output$dist_plot <- shiny::renderPlot({
      dist_plot()
    })
    
    # Return.
    dist_plot
  })
}
distPlotInput <- function(id) {
  ns <- shiny::NS(id)
  shiny::tagList(
    shiny::checkboxInput(ns("norm"), "Normalize Plot", TRUE),
    shiny::checkboxInput(ns("total"), "Include Total", TRUE))
}
distPlotOutput <- function(id) {
  ns <- shiny::NS(id)
  shiny::plotOutput(ns("dist_plot"), height = "400px")
}

# --- Source: multApp.R ---
multApp <- function(title = "Population Ethology") {
  ui <- shiny::fluidPage(
    shiny::titlePanel(title),
    shiny::sidebarLayout(
      shiny::sidebarPanel(
        multInput("ewing")
      ),
      shiny::mainPanel(
        multOutput("ewing")
      )))
  
  server <- function(input, output, server) {
    multServer("ewing")
  }
  
  shiny::shinyApp(ui = ui, server = server)
}
multServer <- function(id) {
  shiny::moduleServer(id, function(input, output, session) {
    ns <- session$ns
    
    simres <- shiny::reactive({
      nsim <- as.integer(shiny::req(input$nsim))
      shiny::withProgress(
        message = paste('Ewing Discrete', nsim, 'Simulations ...'),
                        value = 0,
        {
          out <- as.list(seq_len(nsim))
          inc <- 1 / nsim
          for(i in seq_len(nsim)) {
            shiny::incProgress(inc)
            out[[i]] <- ewing_discrete1(
              count = as.numeric(c(input$host, input$parasite)),
              nstep = input$steps)
          }
        }
      )
    })
    
    distplot <- shiny::reactive({
      if(inherits(simres(), "ewing")) {
        ggplot2::autoplot(ewing_ageclass(simres(), total = input$total,
                                         normalize = input$norm))
      } else {
        NULL
      }
    })
    output$distPlot <- shiny::renderPlot({
      distplot()
    })
    # *** This is not right. Need to get each species name here and in `ewing_substrate`
    # species <- ewing:::getOrgFeature(simres)
    #   gives list but includes substrates.
    # can figure out what substrate goes to species with 
    # ewing:::getOrgFeature(simres, species[i], "substrate")
    # if it is NA (or "NA"), then that is a substrate.
    # So cycle through species generating plots.
    # put as much in `ewing_substrate` as possible.
    species <- shiny::reactive({
      get.organisms(datafile())$species
    })
    substrates <- shiny::reactive({
      get.organisms(datafile())$substrates
    })
    output$sppsize <- shiny::renderUI({
      shiny::req(species())
      lapply(species(), function(x) {
        shiny::sliderInput(ns(x),
                           label = paste0("Number of ", x, "s:"),
                           min = 0,
                           max = 500,
                           value = 100,
                           step = 20)
      })
    })
    sppplot <- shiny::reactive({
      shiny::req(species(), simres())
      if(inherits(simres(), "ewing")) {
        if(!is.null(simres())) {
          p <- lapply(species(), function(x) {
            p <- ggplot2::autoplot(ewing_substrate(simres(), x))
            if(inherits(p, "ggplot"))
              p <- p + ggplot2::ggtitle(paste(x, "on", substrates()[1]))
            p
          })
          if(any(unlist(purrr::map(p, is.null))))
            p <- NULL
          p
        }
      } else {
        ggplot2::ggplot()
      }
    })
    output$sppPlot <- shiny::renderPlot({
      if(!is.null(sppplot())) {
        spp <- length(species())
        cowplot::plot_grid(plotlist = sppplot(), nrow = spp)
      } else {
        ggplot2::ggplot()
      }
    })
    envdata <- shiny::reactive({
      shiny::req(simres())
      if(inherits(simres(), "ewing_discrete")) {
        ewing_envelopes(simres())
      } else {
        NULL
      }
    })
    envelopePlot <- shiny::reactive({
      shiny::req(envdata())
      nsim <- as.integer(shiny::req(input$nsim))
      conf <- (nsim >= 10) & input$conf 
      if(inherits(simres(), "ewing_discrete")) {
        ggplot_ewing_envelopes(envdata(), conf)
      } else {
        NULL
      }
    })
    output$envPlot <- shiny::renderPlot({
      envelopePlot()
    })
    output$plots <- shiny::renderUI({
      nsim <- as.integer(shiny::req(input$nsim), simres())
      if(nsim == 1) {
        shiny::req(species())
        shiny::tagList(
          shiny::plotOutput(ns("distPlot"), height = "400px"),
          shiny::plotOutput(ns("sppPlot"), height = paste0(200 * length(species()), "px")))
      } else {
        shiny::plotOutput(ns("envPlot"))
      }
    })
    
    data <- reactive({
      nsim <- as.integer(shiny::req(input$nsim))
      species <- shiny::req(input$species)
      if(nsim == 1) {
        readCount(simres())[[species]]
      } else {
        shiny::req(envdata())
        print(envdata(), species = species)
      }
    })
    params <- shiny::reactive({
      nsim <- shiny::req(input$nsim)
      paste(shiny::req(input$host), shiny::req(input$parasite),
            shiny::req(input$steps), nsim, sep = "_")
    })
    output$downloadRun <- shiny::downloadHandler(
      filename = function() {
        paste0(paste(shiny::req(input$outfile), shiny::req(input$species), params(), sep = "_"), ".csv") },
      content = function(file) {
        utils::write.csv(data(), file, row.names = FALSE)
      }
    )
    
    output$downloadPlot <- shiny::downloadHandler(
      filename = function() {
        paste0(paste(shiny::req(input$plotfile), params(), sep = "_"), ".pdf") },
      content = function(file) {
        grDevices::pdf(file, width = 9)
        nsim <- as.integer(shiny::req(input$nsim))
        if(nsim == 1) {
          print(distplot())
          for(i in species()) {
            print(sppplot()[[i]])
          }
        } else {
          print(envelopePlot())
        }
        grDevices::dev.off()
      }
    )
    
    datanames <- shiny::reactive({
      getOrgNames(datafile())
    })
    output$inputfiles <- shiny::renderUI({
      shiny::tagList(
        shiny::selectInput(ns("dataname"), "", datanames(), "organism.features"),
        DT::renderDataTable({
          getOrgDataSimple(simres(),shiny::req(input$dataname), datafile())
        }, escape = FALSE,
        options = list(scrollX = TRUE, pageLength = 10)))
    })
    
    datafile <- shiny::reactive({
      if(shiny::isTruthy(input$datafile)) {
        input$datafile$datapath
      } else {
        ""
      }
    })
    output$outs <- shiny::renderUI({
      switch(input$button,
             Plots        =  shiny::uiOutput(ns("plots")),
             "Input Data" =  shiny::uiOutput(ns("inputfiles")))
    })
    
    output$plottype <- shiny::renderUI({
      if(input$nsim == 1) {
        shiny::tagList(
          shiny::checkboxInput(ns("norm"), "Normalize Plot", TRUE),
          shiny::checkboxInput(ns("total"), "Include Total", TRUE))
      } else {
        shiny::checkboxInput(ns("conf"), "Confidence band", FALSE)
      }
    })
    output$version <- shiny::renderText({
      paste("Ewing package version ", utils::packageVersion("ewing"))
    })
  })
}
multInput <- function(id) {
  ns <- shiny::NS(id)
  shiny::tagList(
    shiny::h4("Simulation Settings"),
    shiny::uiOutput(ns("sppsize")),
    shiny::sliderInput(ns("steps"),
                       label = "Simulation steps:",
                       min = 1000,
                       max = 10000,
                       value = 1000,
                       step = 500),
    shiny::radioButtons(ns("nsim"),
                        "Number of Simulations",
                        c(1,10,20,50,100,200),
                        1, inline = TRUE),
    
    shiny::fileInput(ns("datafile"), "Optional XLSX Input Data File",
                     multiple = FALSE,
                     accept = c(".xls", ".xlsx")),
    
    shiny::actionButton(ns("go"), "Start Simulation"),
    
    shiny::HTML("<hr style='height:1px;border:none;color:#333;background-color:#333;' />"),
    shiny::uiOutput(ns("plottype")),
    shiny::h4("Save Files"),
    shiny::fluidRow(
      shiny::column(6, shiny::textInput(ns("outfile"), "Species Table", "mysim")),
      shiny::column(3, shiny::selectInput(ns("species"), "", c("host", "parasite"), "host")),
      shiny::column(3, shiny::downloadButton(ns("downloadRun"), "Table"))),
    shiny::fluidRow(
      shiny::column(9, shiny::textInput(ns("plotfile"), "Plot File", "myplot")),
      shiny::column(3, shiny::downloadButton(ns("downloadPlot"), "Plots"))),
    
    shiny::HTML("<hr  style='height:1px;border:none;color:#333;background-color:#333;' />"),
    shiny::HTML("See <a href='https://github.com/byandell/ewing'>ewing package on github</a>"),
    shiny::uiOutput(ns("version"))
  )
}
multOutput <- function(id) {
  ns <- shiny::NS(id)
  shiny::tagList(
    shiny::radioButtons(ns("button"), "", c("Plots", "Input Data"),
                        "Plots", inline = TRUE),
    shiny::uiOutput(ns("outs"))
  )
}

# --- Source: inputApp.R ---
inputApp <- function(title = "Input Data Explorer") {
  ui <- bslib::page_sidebar(
    title = title,
    sidebar = bslib::sidebar(
      inputAppInput("input_app")
    ),
    bslib::card(
      inputAppOutput("input_app")
    )
  )
  server <- function(input, output, session) {
    inputAppServer("input_app")
  }
  shiny::shinyApp(ui = ui, server = server)
}

discover_dataset_tables <- function(datafile = "", sim = NULL) {
  found <- character(0)
  
  # 1. Inspect datafile directory
  d_path <- if (is.character(datafile) && datafile != "") datafile else if (!is.null(sim) && !is.null(sim$datafile)) sim$datafile else ""
  
  if (d_path != "" && file.exists(d_path)) {
    if (dir.exists(d_path)) {
      files <- list.files(d_path, pattern = "\\.(txt|csv|tsv)$", full.names = FALSE)
      if (length(files) > 0) {
        found <- tools::file_path_sans_ext(files)
      }
    } else if (grepl("\\.xlsx$", d_path, ignore.case = TRUE)) {
      sheets <- tryCatch(readxl::excel_sheets(d_path), error = function(e) character(0))
      if (length(sheets) > 0) found <- sheets
    }
  }
  
  # 2. Inspect sim$datasets if present
  if (!is.null(sim) && !is.null(sim$datasets)) {
    found <- unique(c(found, names(sim$datasets)))
  }
  if (!is.null(sim) && !is.null(sim$community) && !is.null(sim$community$datasets)) {
    found <- unique(c(found, names(sim$community$datasets)))
  }
  if (exists("isle_royale_datasets") && is.list(isle_royale_datasets)) {
    found <- unique(c(found, names(isle_royale_datasets)))
  }
  
  # Filter out any .rds spatial layers or non-table objects
  found <- found[!grepl("\\.rds$", found, ignore.case = TRUE) & !found %in% c("isle_royale_features", "isle_royale_landmarks", "isle_royale_layer", "huc_features")]

  # 3. Default fallback choices if nothing found
  if (length(found) == 0) {
    found <- c(
      "organism.features", "future.moose", "future.wolf",
      "substrate.moose", "substrate.wolf", "substrate.substrate",
      "moose.wolf", "future.host", "future.parasite"
    )
  }
  
  unique(found)
}

inputAppInput <- function(id, choices = NULL) {
  ns <- shiny::NS(id)
  default_choices <- if (!is.null(choices)) choices else c(
    "organism.features", "future.moose", "future.wolf",
    "substrate.moose", "substrate.wolf", "substrate.substrate",
    "moose.wolf", "future.host", "future.parasite"
  )
  shiny::tagList(
    shiny::selectInput(ns("dataname"), "Select Dataset Table:",
      choices = default_choices,
      selected = default_choices[1]
    )
  )
}

inputAppOutput <- function(id) {
  ns <- shiny::NS(id)
  shiny::tableOutput(ns("org_table"))
}

inputAppServer <- function(id, simres = shiny::reactiveVal(NULL), datafile = shiny::reactiveVal("")) {
  shiny::moduleServer(id, function(input, output, session) {
    ns <- session$ns

    # Dynamically update select choices based on folder / simulation contents
    shiny::observe({
      sim <- if (is.reactive(simres)) simres() else simres
      dfile <- if (is.reactive(datafile)) datafile() else datafile
      
      discovered <- discover_dataset_tables(dfile, sim)
      if (length(discovered) > 0) {
        current_sel <- input$dataname
        sel <- if (!is.null(current_sel) && current_sel %in% discovered) current_sel else discovered[1]
        shiny::updateSelectInput(session, "dataname", choices = discovered, selected = sel)
      }
    })

    output$org_table <- shiny::renderTable(
      {
        name <- input$dataname %||% "organism.features"
        sim <- if (is.reactive(simres)) simres() else simres
        dfile <- if (is.reactive(datafile)) datafile() else datafile

        res <- NULL

        # 0. Check direct file in datafile directory if points to folder
        if (is.character(dfile) && dfile != "" && dir.exists(dfile)) {
          txt_path <- file.path(dfile, paste0(name, ".txt"))
          if (file.exists(txt_path)) res <- tryCatch(utils::read.table(txt_path, header = TRUE, sep = "\t", stringsAsFactors = FALSE, fill = TRUE), error = function(e) NULL)
          if (is.null(res)) {
            csv_path <- file.path(dfile, paste0(name, ".csv"))
            if (file.exists(csv_path)) res <- tryCatch(utils::read.csv(csv_path, fill = TRUE, stringsAsFactors = FALSE), error = function(e) NULL)
          }
        }

        # 1. Check if dataset is stored in sim$datasets or sim$community$datasets or global isle_royale_datasets
        if (is.null(res) && !is.null(sim)) {
          if (!is.null(sim$datasets) && !is.null(sim$datasets[[name]])) {
            res <- sim$datasets[[name]]
          } else if (!is.null(sim$community) && !is.null(sim$community$datasets) && !is.null(sim$community$datasets[[name]])) {
            res <- sim$community$datasets[[name]]
          }
        }
        if (is.null(res) && exists("isle_royale_datasets") && is.list(isle_royale_datasets) && !is.null(isle_royale_datasets[[name]])) {
          res <- isle_royale_datasets[[name]]
        }

        # 2. Extract dynamically via getOrgDataSimple or getOrg* package routines
        if (is.null(res) && !is.null(sim) && inherits(sim, "ewing")) {
          sim_single <- if (inherits(sim, "ewing_discrete")) sim[[1]] else sim
          res <- tryCatch({
            getOrgDataSimple(sim_single, name, datafile = dfile)
          }, error = function(e) NULL)
        }

        # 3. Dynamic fallback to extracting from sim_single$org state structures
        if ((is.null(res) || !is.data.frame(res) || nrow(res) == 0) && !is.null(sim) && inherits(sim, "ewing")) {
          sim_single <- if (inherits(sim, "ewing_discrete")) sim[[1]] else sim
          res <- tryCatch({
            left <- stringr::str_remove(name, "\\..*")
            right <- stringr::str_remove(name, ".*\\.")

            if (left == "organism" && right == "features") {
              if (!is.null(sim_single$org$Feature)) as.data.frame(sim_single$org$Feature) else NULL
            } else if (left == "future") {
              if (!is.null(sim_single$org$Future[[right]])) sim_single$org$Future[[right]] else getOrgFuture(sim_single, right)
            } else if (!is.null(sim_single$org$Interact[[left]][[right]])) {
              sim_single$org$Interact[[left]][[right]]
            } else if (!is.null(sim_single$org[[left]][[right]])) {
              sim_single$org[[left]][[right]]
            } else {
              NULL
            }
          }, error = function(e) NULL)
        }

        if (is.null(res) || !is.data.frame(res) || nrow(res) == 0) {
          res <- data.frame(Info = paste("Dataset", name, "is not available in current simulation instance."))
        }

        res
      },
      striped = TRUE,
      hover = TRUE,
      bordered = TRUE
    )
  })
}

# --- Source: habitat.R ---
if (!exists("safe_st_intersects", mode = "function")) {
  safe_st_intersects <- function(x, y) {
    tryCatch(
      sf::st_intersects(x, y),
      error = function(e) {
        old_s2 <- sf::sf_use_s2(FALSE)
        on.exit(sf::sf_use_s2(old_s2), add = TRUE)
        x_val <- tryCatch(sf::st_make_valid(x), error = function(e2) x)
        y_val <- tryCatch(sf::st_make_valid(y), error = function(e2) y)
        sf::st_intersects(x_val, y_val)
      }
    )
  }
}

get_habitat_features <- function(watershed_obj = NULL, 
                                 categories = c("lakes", "waterways", "forests", "bogs"), 
                                 use_cache = TRUE,
                                 site = "isle_royale") {
  huc_layer <- if (is.null(watershed_obj)) {
    NULL
  } else if (inherits(watershed_obj, "sf") || inherits(watershed_obj, "sfc")) {
    watershed_obj
  } else if (is.list(watershed_obj) && "layer" %in% names(watershed_obj)) {
    watershed_obj$layer
  } else {
    NULL
  }
  
  if (use_cache) {
    cached_sf <- NULL
    feat_key <- paste0(site, "_features")
    feat_file <- paste0(site, "_features.rds")
    if (exists("isle_royale_datasets") && is.list(isle_royale_datasets) && !is.null(isle_royale_datasets[[feat_key]])) {
      cached_sf <- isle_royale_datasets[[feat_key]]
    } else if (exists(feat_key) && inherits(get(feat_key), "sf")) {
      cached_sf <- get(feat_key)
    } else {
      cache_file <- get_site_cache_file(feat_file, site = site)
      if (file.exists(cache_file)) {
        cached_sf <- tryCatch(readRDS(cache_file), error = function(e) NULL)
      }
    }
    
    if (!is.null(cached_sf) && inherits(cached_sf, "sf")) {
      if (is.null(huc_layer)) return(cached_sf)
      cached_sf <- sf::st_transform(cached_sf, sf::st_crs(huc_layer))
      clipped <- suppressWarnings(sf::st_intersection(cached_sf, huc_layer))
      if (nrow(clipped) > 0) return(clipped)
      return(cached_sf)
    }
  }
  
  # Query OpenStreetMap if osmdata package is installed
  if (!requireNamespace("osmdata", quietly = TRUE)) {
    warning("The 'osmdata' package is not installed. Returning fallback habitat geometries.")
    return(get_fallback_habitat_features(huc_layer))
  }
  
  bbox <- sf::st_bbox(sf::st_transform(huc_layer, 4326))
  bbox_str <- paste(bbox["ymin"], bbox["xmin"], bbox["ymax"], bbox["xmax"], sep = ",")
  
  old_url <- osmdata::get_overpass_url()
  osmdata::set_overpass_url("https://lz4.overpass-api.de/api/interpreter")
  
  features_list <- list()
  
  old_s2 <- sf::sf_use_s2()
  sf::sf_use_s2(FALSE)
  on.exit({
    sf::sf_use_s2(old_s2)
    osmdata::set_overpass_url(old_url)
  }, add = TRUE)
  
  # Extract Inland Lakes & Waterbodies
  if ("lakes" %in% categories) {
    ql_lakes <- paste0(
      "[out:xml][timeout:30];\n(\n",
      "  natural[\"water\"](", bbox_str, ");\n",
      "  waterway[\"riverbank\"](", bbox_str, ");\n",
      ");\nout body;\n>;\nout skel qt;\n"
    )
    res_lakes <- tryCatch(osmdata::osmdata_sf(ql_lakes), error = function(e) NULL)
    if (!is.null(res_lakes$osm_polygons) && nrow(res_lakes$osm_polygons) > 0) {
      poly <- res_lakes$osm_polygons
      poly$habitat_type <- "Lake/Pond"
      features_list$lakes <- poly[, "habitat_type"]
    }
  }
  
  # Extract Waterways & Streams
  if ("waterways" %in% categories) {
    ql_water <- paste0(
      "[out:xml][timeout:30];\n(\n",
      "  waterway[\"stream\"](", bbox_str, ");\n",
      "  waterway[\"river\"](", bbox_str, ");\n",
      "  waterway[\"drain\"](", bbox_str, ");\n",
      ");\nout body;\n>;\nout skel qt;\n"
    )
    res_water <- tryCatch(osmdata::osmdata_sf(ql_water), error = function(e) NULL)
    if (!is.null(res_water$osm_lines) && nrow(res_water$osm_lines) > 0) {
      line <- res_water$osm_lines
      line$habitat_type <- "Waterway"
      features_list$waterways <- line[, "habitat_type"]
    }
  }
  
  # Combine extracted geometries
  if (length(features_list) == 0) {
    return(get_fallback_habitat_features(huc_layer))
  }
  
  combined <- do.call(rbind, features_list)
  if (is.null(combined) || nrow(combined) == 0) {
    return(get_fallback_habitat_features(huc_layer))
  }
  
  combined <- sf::st_transform(combined, sf::st_crs(huc_layer))
  clipped <- suppressWarnings(sf::st_intersection(combined, huc_layer))
  if (nrow(clipped) > 0) return(clipped)
  return(combined)
}

get_fallback_habitat_features <- function(huc_layer = NULL) {
  if (is.null(huc_layer)) {
    pts <- matrix(c(-89.17, 47.87, -88.48, 47.87, -88.48, 48.16, -89.17, 48.16, -89.17, 47.87), ncol = 2, byrow = TRUE)
    huc_layer <- sf::st_sfc(sf::st_polygon(list(pts)), crs = 4326)
  }
  
  bbox <- sf::st_bbox(huc_layer)
  xmin <- bbox["xmin"]; xmax <- bbox["xmax"]
  ymin <- bbox["ymin"]; ymax <- bbox["ymax"]
  
  l1 <- sf::st_polygon(list(matrix(c(xmin+0.1, ymin+0.1, xmin+0.15, ymin+0.1, xmin+0.15, ymin+0.15, xmin+0.1, ymin+0.15, xmin+0.1, ymin+0.1), ncol=2, byrow=TRUE)))
  l2 <- sf::st_polygon(list(matrix(c(xmax-0.2, ymax-0.1, xmax-0.1, ymax-0.1, xmax-0.1, ymax-0.05, xmax-0.2, ymax-0.05, xmax-0.2, ymax-0.1), ncol=2, byrow=TRUE)))
  
  sfc <- sf::st_sfc(l1, l2, crs = sf::st_crs(huc_layer))
  sf::st_sf(habitat_type = c("Lake/Pond", "Lake/Pond"), geometry = sfc)
}

get_moose_landmarks <- function(watershed_obj = NULL, use_cache = TRUE, site = "isle_royale") {
  huc_layer <- if (is.null(watershed_obj)) {
    NULL
  } else if (inherits(watershed_obj, "sf") || inherits(watershed_obj, "sfc")) {
    watershed_obj
  } else if (is.list(watershed_obj) && "layer" %in% names(watershed_obj)) {
    watershed_obj$layer
  } else {
    NULL
  }
  
  if (use_cache) {
    cached_sf <- NULL
    lm_key <- paste0(site, "_landmarks")
    lm_file <- paste0(site, "_landmarks.rds")
    if (exists("isle_royale_datasets") && is.list(isle_royale_datasets) && !is.null(isle_royale_datasets[[lm_key]])) {
      cached_sf <- isle_royale_datasets[[lm_key]]
    } else if (exists(lm_key) && inherits(get(lm_key), "sf")) {
      cached_sf <- get(lm_key)
    } else {
      cache_file <- get_site_cache_file(lm_file, site = site)
      if (file.exists(cache_file)) {
        cached_sf <- tryCatch(readRDS(cache_file), error = function(e) NULL)
      }
    }
    
    if (!is.null(cached_sf) && inherits(cached_sf, "sf")) {
      if (is.null(huc_layer)) return(cached_sf)
      cached_sf <- sf::st_transform(cached_sf, sf::st_crs(huc_layer))
      return(cached_sf)
    }
  }
  
  # Fallback coordinate table for notable Isle Royale moose sighting locations
  landmarks <- data.frame(
    name = c("Washington Creek", "Ojibway Lake", "Feldtmann Lake", "Hidden Lake"),
    lon = c(-89.145, -88.618, -88.948, -88.647),
    lat = c(47.922, 48.113, 47.887, 48.148),
    description = c("Major feeding stream near Windigo",
                    "Highland lake surrounded by moose browse",
                    "SW inland lake with heavy aquatic vegetation",
                    "Shaded lake near Tobin Harbor"),
    stringsAsFactors = FALSE
  )
  
  pts <- sf::st_as_sf(landmarks, coords = c("lon", "lat"), crs = 4326)
  if (!is.null(huc_layer)) {
    pts <- sf::st_transform(pts, sf::st_crs(huc_layer))
  }
  return(pts)
}

add_watershed_hex_overlay <- function(huc_info, hex_diameter = 0.01) {
  huc_layer <- huc_info$layer
  
  hex_mesh <- sf::st_make_grid(huc_layer, square = FALSE, cellsize = c(hex_diameter, hex_diameter))
  hex_overlay <- hex_mesh[lengths(safe_st_intersects(hex_mesh, huc_layer)) > 0]
  
  huc_info$hex_overlay <- hex_overlay
  huc_info$hex_diameter <- hex_diameter
  
  class(huc_info) <- "watershed_hex_overlay"
  return(huc_info)
}

autoplot.watershed_hex_overlay <- function(object, ...) {
  huc_str <- if (!is.null(object$huc_id) && length(object$huc_id) > 1) {
    paste0(length(object$huc_id), " Combined Regions")
  } else if (!is.null(object$huc_id)) {
    paste("Region:", object$huc_id)
  } else {
    "Substrate Grid"
  }
  
  title_txt <- paste("Geographic Hexagonal Grid (", huc_str, ")", 
                     "\nHexagon Extent Diameter:", object$hex_diameter)
  if (!is.null(object$feature_name) && object$feature_name != "") {
    title_txt <- paste0(title_txt, " - Restricted to: ", object$feature_name)
  }
  
  p <- ggplot2::ggplot()
  if (!is.null(object$individual_hucs) && nrow(object$individual_hucs) > 1) {
    p <- p + ggplot2::geom_sf(data = object$individual_hucs, fill = NA, color = "purple", linetype = "dashed", linewidth = 0.4)
  }
  
  p +
    ggplot2::geom_sf(data = object$layer, fill = "lightblue", alpha = 0.3, color = "blue", linewidth = 0.7) +
    ggplot2::geom_sf(data = object$hex_overlay, fill = NA, color = "darkred", linewidth = 0.7) +
    ggplot2::theme_minimal() +
    ggplot2::ggtitle(title_txt) +
    ggplot2::labs(x = "Longitude", y = "Latitude")
}

create_isle_royale_hex_overlay <- function(hex_diameter = 0.01, features = NULL, layer = NULL, site = "isle_royale") {
  boundary_layer <- layer
  layer_key <- paste0(site, "_layer")
  layer_file <- paste0(site, "_layer.rds")
  feat_key <- paste0(site, "_features")
  feat_file <- paste0(site, "_features.rds")
  
  if (is.null(boundary_layer)) {
    if (exists("isle_royale_datasets") && is.list(isle_royale_datasets) && !is.null(isle_royale_datasets[[layer_key]])) {
      boundary_layer <- isle_royale_datasets[[layer_key]]
    } else if (exists(layer_key) && inherits(get(layer_key), c("sf", "sfc"))) {
      boundary_layer <- get(layer_key)
    } else {
      cache_file <- get_site_cache_file(layer_file, site = site)
      if (file.exists(cache_file)) {
        boundary_layer <- tryCatch(readRDS(cache_file), error = function(e) NULL)
      }
    }
  }
  
  habitat_sf <- features
  if (is.character(habitat_sf) && file.exists(habitat_sf)) {
    habitat_sf <- tryCatch(readRDS(habitat_sf), error = function(e) NULL)
  }
  if (is.null(habitat_sf)) {
    if (exists("isle_royale_datasets") && is.list(isle_royale_datasets) && !is.null(isle_royale_datasets[[feat_key]])) {
      habitat_sf <- isle_royale_datasets[[feat_key]]
    } else if (exists(feat_key) && inherits(get(feat_key), "sf")) {
      habitat_sf <- get(feat_key)
    } else {
      cache_file <- get_site_cache_file(feat_file, site = site)
      if (file.exists(cache_file)) {
        habitat_sf <- tryCatch(readRDS(cache_file), error = function(e) NULL)
      }
    }
  }
  
  if (is.null(boundary_layer) && !is.null(habitat_sf) && inherits(habitat_sf, "sf")) {
    boundary_layer <- suppressWarnings(sf::st_union(sf::st_geometry(habitat_sf)))
  }
  
  if (is.null(boundary_layer)) {
    pts <- matrix(c(-89.17, 47.87, -88.48, 47.87, -88.48, 48.16, -89.17, 48.16, -89.17, 47.87), ncol = 2, byrow = TRUE)
    boundary_layer <- sf::st_sfc(sf::st_polygon(list(pts)), crs = 4326)
  } else if (inherits(boundary_layer, "sf")) {
    boundary_layer <- sf::st_geometry(boundary_layer)
  }
  
  cent <- suppressWarnings(sf::st_centroid(boundary_layer))
  coords <- sf::st_coordinates(cent)
  
  hex_mesh <- sf::st_make_grid(boundary_layer, square = FALSE, cellsize = c(hex_diameter, hex_diameter))
  hex_overlay <- hex_mesh[lengths(safe_st_intersects(hex_mesh, boundary_layer)) > 0]
  
  res <- list(
    huc_id = "Isle Royale",
    feature_name = "Isle Royale",
    lon = as.numeric(coords[1, "X"]),
    lat = as.numeric(coords[1, "Y"]),
    layer = boundary_layer,
    hex_overlay = hex_overlay,
    hex_diameter = hex_diameter
  )
  class(res) <- "watershed_hex_overlay"
  return(res)
}

add_habitat_hex_overlay <- function(hex_obj, habitat_sf = NULL, landmarks_sf = NULL, 
                                    features = NULL, landmarks = NULL, site = "isle_royale") {
  if (is.null(habitat_sf)) habitat_sf <- features
  if (is.null(landmarks_sf)) landmarks_sf <- landmarks
  
  if (is.character(habitat_sf) && file.exists(habitat_sf)) {
    habitat_sf <- tryCatch(readRDS(habitat_sf), error = function(e) NULL)
  }
  if (is.character(landmarks_sf) && file.exists(landmarks_sf)) {
    landmarks_sf <- tryCatch(readRDS(landmarks_sf), error = function(e) NULL)
  }
  
  if (is.null(habitat_sf)) {
    habitat_sf <- get_habitat_features(hex_obj, site = site)
  }
  if (is.null(landmarks_sf)) {
    landmarks_sf <- get_moose_landmarks(hex_obj, site = site)
  }
  
  hex_mesh <- hex_obj$hex_overlay
  scores <- numeric(length(hex_mesh))
  types_list <- character(length(hex_mesh))
  
  if (!is.null(habitat_sf) && nrow(habitat_sf) > 0) {
    inter <- safe_st_intersects(hex_mesh, habitat_sf)
    for (i in seq_along(inter)) {
      indices <- inter[[i]]
      if (length(indices) > 0) {
        sub_types <- habitat_sf$habitat_type[indices]
        score <- 1
        if ("Lake/Pond" %in% sub_types) score <- score + 2
        if ("Waterway" %in% sub_types) score <- score + 1.5
        if ("Bog/Wetland" %in% sub_types) score <- score + 1.8
        if ("Forest" %in% sub_types) score <- score + 1
        scores[i] <- score
        types_list[i] <- paste(unique(sub_types), collapse = ", ")
      } else {
        scores[i] <- 1
        types_list[i] <- "Upland/Open"
      }
    }
  } else {
    scores[] <- 1
    types_list[] <- "General"
  }
  
  hex_sf <- sf::st_sf(
    hex_id = seq_along(hex_mesh),
    habitat_score = scores,
    habitat_type = types_list,
    geometry = hex_mesh
  )
  
  res <- hex_obj
  res$habitat_sf <- habitat_sf
  res$landmarks_sf <- landmarks_sf
  res$hex_habitat_sf <- hex_sf
  
  class(res) <- c("habitat_hex_overlay", class(hex_obj))
  return(res)
}

autoplot.habitat_hex_overlay <- function(object, show_landmarks = TRUE, ...) {
  p <- ggplot2::ggplot() +
    ggplot2::geom_sf(data = object$layer, fill = "#eef4f8", color = "#2c3e50", linewidth = 0.8)
  
  if (!is.null(object$habitat_sf) && nrow(object$habitat_sf) > 0) {
    p <- p + ggplot2::geom_sf(data = object$habitat_sf, ggplot2::aes(fill = .data$habitat_type), alpha = 0.5, color = NA)
  }
  
  if (!is.null(object$hex_habitat_sf)) {
    p <- p + ggplot2::geom_sf(data = object$hex_habitat_sf, ggplot2::aes(color = .data$habitat_score), fill = NA, linewidth = 0.6) +
      ggplot2::scale_color_viridis_c(option = "viridis", name = "Habitat Weight")
  }
  
  if (show_landmarks && !is.null(object$landmarks_sf) && nrow(object$landmarks_sf) > 0) {
    p <- p + ggplot2::geom_sf(data = object$landmarks_sf, color = "#d35400", size = 3, shape = 18) +
      ggplot2::geom_sf_text(data = object$landmarks_sf, ggplot2::aes(label = .data$name), color = "#900c3f", size = 3, fontface = "bold", vjust = -0.7)
  }
  
  title_txt <- "Isle Royale Moose Habitat & Substrate Overlay Model"
  if (!is.null(object$feature_name) && object$feature_name != "") {
    title_txt <- paste0(title_txt, " (", object$feature_name, ")")
  }
  
  p +
    ggplot2::theme_minimal() +
    ggplot2::ggtitle(title_txt) +
    ggplot2::labs(
      x = "Longitude", y = "Latitude",
      caption = "Habitats: Inland Lakes, Beaver Ponds/Waterways, Shaded Forests & Bogs"
    )
}

# --- Source: ecosystem_sim.R ---
init_ecosystem_sim <- function(ecosystem = "isle_royale",
                               year = 1980, 
                               n_hosts = NULL, 
                               n_predators = NULL, 
                               hex_diameter = 0.01,
                               datafile = "",
                               features_rds = NULL,
                               landmarks_rds = NULL) {
  
  # Load historical benchmark time series data if available for this ecosystem
  csv_path <- system.file(file.path("doc", ecosystem, "wolf_moose.csv"), package = "ewing")
  if (csv_path == "" || !file.exists(csv_path)) {
    csv_path <- file.path("inst", "doc", ecosystem, "wolf_moose.csv")
  }
  
  hist_data <- NULL
  if (file.exists(csv_path)) {
    hist_data <- utils::read.csv(csv_path, stringsAsFactors = FALSE)
  }
  
  # Lookup baseline population counts for start year if not explicitly provided
  if (!is.null(hist_data) && year %in% hist_data$Year) {
    row_match <- hist_data[hist_data$Year == year, ]
    if (is.null(n_hosts) && "Moose" %in% names(row_match)) n_hosts <- as.numeric(row_match$Moose[1])
    if (is.null(n_predators) && "Wolves" %in% names(row_match)) n_predators <- as.numeric(row_match$Wolves[1])
  }
  
  if (is.null(n_hosts)) n_hosts <- 664
  if (is.null(n_predators)) n_predators <- 50
  
  # 1. Initialize Base Spatial Geography & Habitat Overlay for Target Ecosystem
  hex_obj <- create_isle_royale_hex_overlay(hex_diameter = hex_diameter, features = features_rds, site = ecosystem)
  habitat_overlay <- add_habitat_hex_overlay(hex_obj, features = features_rds, landmarks = landmarks_rds, site = ecosystem)
  
  # 2. Setup Configuration Data Directory
  if (datafile == "") {
    pkg_dir <- system.file(file.path("extdata", ecosystem), package = "ewing")
    if (pkg_dir != "" && dir.exists(pkg_dir)) {
      datafile <- pkg_dir
    } else if (dir.exists(file.path("inst", "extdata", ecosystem))) {
      datafile <- file.path("inst", "extdata", ecosystem)
    }
  }
  
  # 3. Initialize ewing Community Core
  community <- NULL
  tryCatch({
    community <- init.simulation(package = "ewing", count = c(n_hosts, n_predators), datafile = datafile, messages = FALSE)
  }, error = function(e) {
    community <<- list(pop = list())
  })
  
  # 4. Spatially Sample Initial Positions Weighted by Substrate Habitat Score
  hex_sf <- habitat_overlay$hex_habitat_sf
  probs <- hex_sf$habitat_score / sum(hex_sf$habitat_score)
  
  sampled_moose_hex <- sample(seq_len(nrow(hex_sf)), size = n_hosts, replace = TRUE, prob = probs)
  sampled_wolf_hex  <- sample(seq_len(nrow(hex_sf)), size = n_predators, replace = TRUE, prob = probs)
  
  centroids <- sf::st_centroid(sf::st_geometry(hex_sf))
  moose_pts <- centroids[sampled_moose_hex]
  wolf_pts  <- centroids[sampled_wolf_hex]
  
  moose_coords <- sf::st_coordinates(moose_pts)
  wolf_coords  <- sf::st_coordinates(wolf_pts)
  
  moose_df <- data.frame(
    id = paste0("M", seq_len(n_hosts)),
    species = "Moose",
    ageclass = sample(c("calf", "yearling", "adult", "senior"), size = n_hosts, replace = TRUE, prob = c(0.15, 0.15, 0.55, 0.15)),
    hex_id = sampled_moose_hex,
    lon = moose_coords[, 1],
    lat = moose_coords[, 2],
    stringsAsFactors = FALSE
  )
  
  wolf_df <- data.frame(
    id = paste0("W", seq_len(n_predators)),
    species = "Wolf",
    ageclass = sample(c("pup", "subadult", "adult"), size = n_predators, replace = TRUE, prob = c(0.20, 0.25, 0.55)),
    hex_id = sampled_wolf_hex,
    lon = wolf_coords[, 1],
    lat = wolf_coords[, 2],
    stringsAsFactors = FALSE
  )
  
  m_counts <- table(factor(moose_df$ageclass, levels = c("calf", "yearling", "adult", "senior")))
  w_counts <- table(factor(wolf_df$ageclass, levels = c("pup", "subadult", "adult")))
  
  hist_df <- data.frame(
    step = 0,
    time = 0,
    Species = c(rep("moose", 4), rep("wolf", 3)),
    State = c(names(m_counts), names(w_counts)),
    Type = "ageclass",
    Count = c(as.numeric(m_counts), as.numeric(w_counts)),
    stringsAsFactors = FALSE
  )
  
  ds_list <- list()
  if (exists("isle_royale_datasets") && is.list(isle_royale_datasets)) {
    ds_list <- isle_royale_datasets
  } else if (is.character(datafile) && datafile != "" && dir.exists(datafile)) {
    txt_files <- list.files(datafile, pattern = "\\.(txt|csv)$", full.names = TRUE)
    for (f in txt_files) {
      tbl_name <- tools::file_path_sans_ext(basename(f))
      df_f <- tryCatch({
        if (endsWith(f, ".csv")) utils::read.csv(f, stringsAsFactors = FALSE)
        else utils::read.table(f, header = TRUE, sep = "\t", fill = TRUE,
                               check.names = FALSE, stringsAsFactors = FALSE)
      }, error = function(e) NULL)
      if (!is.null(df_f)) ds_list[[tbl_name]] <- df_f
    }
  }
  
  res <- list(
    community = community,
    habitat_overlay = habitat_overlay,
    start_year = year,
    ecosystem = ecosystem,
    moose_pop = moose_df,
    wolf_pop = wolf_df,
    historical_data = hist_data,
    history = hist_df,
    datafile = datafile,
    datasets = ds_list,
    nstep = 0
  )
  
  class(res) <- c(paste0(ecosystem, "_sim"), "ecosystem_sim", "ewing")
  return(res)
}

run_ecosystem_sim <- function(sim_obj, nstep = 1000, refresh = 10, ...) {
  if (!inherits(sim_obj, "ecosystem_sim") && !inherits(sim_obj, "isle_royale_sim")) {
    stop("Input must be an object of class 'ecosystem_sim' or 'isle_royale_sim'.")
  }
  
  hex_sf <- sim_obj$habitat_overlay$hex_habitat_sf
  n_hex <- nrow(hex_sf)
  centroids <- sf::st_coordinates(sf::st_centroid(sf::st_geometry(hex_sf)))
  
  move_pop <- function(pop_df, move_prob = 0.5) {
    if (is.null(pop_df) || nrow(pop_df) == 0) return(pop_df)
    n <- nrow(pop_df)
    movers <- which(stats::runif(n) < move_prob)
    if (length(movers) > 0) {
      new_hex <- sample(seq_len(n_hex), size = length(movers), replace = TRUE, prob = hex_sf$habitat_score / sum(hex_sf$habitat_score))
      pop_df$hex_id[movers] <- new_hex
      pop_df$lon[movers] <- centroids[new_hex, 1]
      pop_df$lat[movers] <- centroids[new_hex, 2]
    }
    return(pop_df)
  }
  
  update_demographics <- function(moose_df, wolf_df) {
    if (!is.null(moose_df) && nrow(moose_df) > 0) {
      n_m <- nrow(moose_df)
      calf_idx <- which(moose_df$ageclass == "calf")
      if (length(calf_idx) > 0) {
        trans <- calf_idx[stats::runif(length(calf_idx)) < 0.002]
        if (length(trans) > 0) moose_df$ageclass[trans] <- "yearling"
      }
      yearling_idx <- which(moose_df$ageclass == "yearling")
      if (length(yearling_idx) > 0) {
        trans <- yearling_idx[stats::runif(length(yearling_idx)) < 0.002]
        if (length(trans) > 0) moose_df$ageclass[trans] <- "adult"
      }
      adult_idx <- which(moose_df$ageclass == "adult")
      if (length(adult_idx) > 0) {
        trans <- adult_idx[stats::runif(length(adult_idx)) < 0.0003]
        if (length(trans) > 0) moose_df$ageclass[trans] <- "senior"
        
        n_births <- sum(stats::runif(length(adult_idx)) < 0.0012)
        if (n_births > 0) {
          parent_hexes <- sample(moose_df$hex_id[adult_idx], n_births, replace = TRUE)
          max_id <- suppressWarnings(max(as.numeric(gsub("[^0-9]", "", moose_df$id)), na.rm = TRUE))
          if (!is.finite(max_id)) max_id <- nrow(moose_df)
          new_ids <- paste0("M", seq(max_id + 1, length.out = n_births))
          new_calves <- data.frame(
            id = new_ids,
            species = "Moose",
            ageclass = "calf",
            hex_id = parent_hexes,
            lon = centroids[parent_hexes, 1],
            lat = centroids[parent_hexes, 2],
            stringsAsFactors = FALSE
          )
          moose_df <- rbind(moose_df, new_calves)
        }
      }
      senior_idx <- which(moose_df$ageclass == "senior")
      if (length(senior_idx) > 0) {
        deaths <- senior_idx[stats::runif(length(senior_idx)) < 0.0008]
        if (length(deaths) > 0) moose_df <- moose_df[-deaths, ]
      }
    }
    
    if (!is.null(wolf_df) && nrow(wolf_df) > 0) {
      pup_idx <- which(wolf_df$ageclass == "pup")
      if (length(pup_idx) > 0) {
        trans <- pup_idx[stats::runif(length(pup_idx)) < 0.002]
        if (length(trans) > 0) wolf_df$ageclass[trans] <- "subadult"
      }
      subadult_idx <- which(wolf_df$ageclass == "subadult")
      if (length(subadult_idx) > 0) {
        trans <- subadult_idx[stats::runif(length(subadult_idx)) < 0.002]
        if (length(trans) > 0) wolf_df$ageclass[trans] <- "adult"
      }
      adult_w_idx <- which(wolf_df$ageclass == "adult")
      if (length(adult_w_idx) > 0) {
        n_w_births <- sum(stats::runif(length(adult_w_idx)) < 0.0008)
        if (n_w_births > 0) {
          p_hexes <- sample(wolf_df$hex_id[adult_w_idx], n_w_births, replace = TRUE)
          max_wid <- suppressWarnings(max(as.numeric(gsub("[^0-9]", "", wolf_df$id)), na.rm = TRUE))
          if (!is.finite(max_wid)) max_wid <- nrow(wolf_df)
          new_wids <- paste0("W", seq(max_wid + 1, length.out = n_w_births))
          new_pups <- data.frame(
            id = new_wids,
            species = "Wolf",
            ageclass = "pup",
            hex_id = p_hexes,
            lon = centroids[p_hexes, 1],
            lat = centroids[p_hexes, 2],
            stringsAsFactors = FALSE
          )
          wolf_df <- rbind(wolf_df, new_pups)
        }
        w_deaths <- adult_w_idx[stats::runif(length(adult_w_idx)) < 0.0005]
        if (length(w_deaths) > 0) wolf_df <- wolf_df[-w_deaths, ]
      }
    }
    
    list(moose = moose_df, wolf = wolf_df)
  }
  
  curr_step <- sim_obj$nstep
  for (s in seq_len(nstep)) {
    curr_step <- curr_step + 1
    sim_obj$moose_pop <- move_pop(sim_obj$moose_pop, move_prob = 0.6)
    sim_obj$wolf_pop  <- move_pop(sim_obj$wolf_pop, move_prob = 0.8)
    
    if (!is.null(sim_obj$wolf_pop) && !is.null(sim_obj$moose_pop) && nrow(sim_obj$wolf_pop) > 0 && nrow(sim_obj$moose_pop) > 0) {
      wolf_hexes <- unique(sim_obj$wolf_pop$hex_id)
      vulnerable_idx <- which(sim_obj$moose_pop$hex_id %in% wolf_hexes & sim_obj$moose_pop$ageclass %in% c("calf", "senior"))
      if (length(vulnerable_idx) > 0) {
        pred_remove <- vulnerable_idx[stats::runif(length(vulnerable_idx)) < 0.0015]
        if (length(pred_remove) > 0) {
          sim_obj$moose_pop <- sim_obj$moose_pop[-pred_remove, ]
        }
      }
    }
    
    demog_res <- update_demographics(sim_obj$moose_pop, sim_obj$wolf_pop)
    sim_obj$moose_pop <- demog_res$moose
    sim_obj$wolf_pop  <- demog_res$wolf
    
    m_curr <- table(factor(sim_obj$moose_pop$ageclass, levels = c("calf", "yearling", "adult", "senior")))
    w_curr <- table(factor(sim_obj$wolf_pop$ageclass, levels = c("pup", "subadult", "adult")))
    
    step_df <- data.frame(
      step = curr_step,
      time = curr_step,
      Species = c(rep("moose", 4), rep("wolf", 3)),
      State = c(names(m_curr), names(w_curr)),
      Type = "ageclass",
      Count = c(as.numeric(m_curr), as.numeric(w_curr)),
      stringsAsFactors = FALSE
    )
    sim_obj$history <- rbind(sim_obj$history, step_df)
  }
  
  sim_obj$nstep <- curr_step
  return(sim_obj)
}

ggplot_ecosystem_sim <- function(x, ...) {
  if (!inherits(x, "ecosystem_sim") && !inherits(x, "isle_royale_sim")) {
    stop("Input must be an object of class 'ecosystem_sim' or 'isle_royale_sim'.")
  }
  
  p_map <- autoplot(x$habitat_overlay, ...)
  
  if (!is.null(x$moose_pop) && nrow(x$moose_pop) > 0) {
    moose_sf <- sf::st_as_sf(x$moose_pop, coords = c("lon", "lat"), crs = sf::st_crs(x$habitat_overlay$layer))
    p_map <- p_map +
      ggplot2::geom_sf(data = moose_sf, color = "#27ae60", shape = 21, fill = NA, stroke = 1.0, size = 0.8, alpha = 0.85) +
      ggplot2::geom_sf(data = moose_sf, color = "#27ae60", alpha = 0.4, size = 0.5)
  }
  
  if (!is.null(x$wolf_pop) && nrow(x$wolf_pop) > 0) {
    wolf_sf <- sf::st_as_sf(x$wolf_pop, coords = c("lon", "lat"), crs = sf::st_crs(x$habitat_overlay$layer))
    p_map <- p_map +
      ggplot2::geom_sf(data = wolf_sf, color = "#e74c3c", shape = 24, fill = "#e74c3c", size = 1.2, alpha = 0.9)
  }
  
  eco_title <- if (!is.null(x$ecosystem)) paste0(toupper(substr(x$ecosystem, 1, 1)), substring(x$ecosystem, 2)) else "Ecosystem"
  p_map <- p_map + ggplot2::ggtitle(paste(eco_title, "Simulation (Step", x$nstep, ")"))
  
  if (!is.null(x$historical_data)) {
    df_hist <- x$historical_data
    p_hist <- ggplot2::ggplot(df_hist, ggplot2::aes(x = .data$Year)) +
      ggplot2::geom_line(ggplot2::aes(y = .data$Moose, color = "Historical Moose"), linewidth = 1.0) +
      ggplot2::geom_line(ggplot2::aes(y = .data$Wolves * 40, color = "Historical Wolves (x40)"), linewidth = 1.0, linetype = "dashed") +
      ggplot2::scale_color_manual(
        name = "Empirical Benchmarks",
        values = c("Historical Moose" = "#27ae60", "Historical Wolves (x40)" = "#e74c3c")
      ) +
      ggplot2::theme_minimal() +
      ggplot2::labs(
        title = paste(eco_title, "Census Trajectories"),
        x = "Year", y = "Moose Abundance (Wolves x40)"
      )
    
    if (requireNamespace("cowplot", quietly = TRUE)) {
      return(cowplot::plot_grid(p_map, p_hist, ncol = 1, rel_heights = c(1.2, 1)))
    }
  }
  
  return(p_map)
}

plot.ecosystem_sim <- function(x, ...) {
  print(ggplot_ecosystem_sim(x, ...))
}

get_site_cache_file <- function(filename, site = "isle_royale") {
  pkg_dir <- system.file(file.path("extdata", site), package = "ewing")
  if (pkg_dir != "") {
    fp <- if (filename == "") pkg_dir else file.path(pkg_dir, filename)
    if (file.exists(fp) || dir.exists(fp)) return(fp)
  }
  dev_fp <- if (filename == "") file.path("inst", "extdata", site) else file.path("inst", "extdata", site, filename)
  if (file.exists(dev_fp) || dir.exists(dev_fp)) return(dev_fp)
  if (pkg_dir != "") file.path(pkg_dir, filename) else dev_fp
}

get_isle_royale_cache_file <- function(filename) {
  get_site_cache_file(filename, site = "isle_royale")
}

# --- Source: ecosystemApp.R ---
ecosystemApp <- function(ecosystem = "isle_royale", title = NULL) {
  if (is.null(title)) {
    title <- paste(paste0(toupper(substr(ecosystem, 1, 1)), substring(ecosystem, 2)), "Predator-Prey Simulation Platform")
  }
  
  ui <- bslib::page_sidebar(
    title = title,
    sidebar = bslib::sidebar(
      width = 340,
      ecosystemInput("eco", ecosystem = ecosystem)
    ),
    ecosystemOutput("eco")
  )

  server <- function(input, output, session) {
    ecosystemServer("eco", ecosystem = ecosystem)
  }

  shiny::shinyApp(ui = ui, server = server)
}

ecosystemInput <- function(id, ecosystem = "isle_royale") {
  ns <- shiny::NS(id)

  csv_path <- system.file(file.path("doc", ecosystem, "wolf_moose.csv"), package = "ewing")
  if (csv_path == "" || !file.exists(csv_path)) {
    csv_path <- file.path("inst", "doc", ecosystem, "wolf_moose.csv")
  }

  years <- 1980:2019
  if (file.exists(csv_path)) {
    df <- tryCatch(utils::read.csv(csv_path), error = function(e) NULL)
    if (!is.null(df) && "Year" %in% names(df)) years <- df$Year
  }

  shiny::tagList(
    shiny::tags$style(shiny::HTML(sprintf("
      #%s .form-group { margin-bottom: 4px; }
      #%s .irs { margin-bottom: 0px; }
      #%s .irs-with-grid { height: 34px; }
      #%s .checkbox { margin-top: 1px; margin-bottom: 2px; }
      #%s .radio-inline, #%s .checkbox-inline { margin-right: 10px; }
      #%s label { font-size: 0.82rem; margin-bottom: 1px; }
      #%s div.shiny-options-group { margin-top: -2px; }
    ", id, id, id, id, id, id, id, id))),

    # 1. Top Section: Step size & Action Buttons (Run / Reset on same line)
    step_size_slider(ns("step_size"), "Steps per click:", selected = 200),
    shiny::div(
      style = "display: flex; gap: 8px; margin: 4px 0 6px 0;",
      shiny::actionButton(ns("step_sim"), "Run", class = "btn-primary", style = "flex: 1; font-weight: 600; padding: 3px 6px; font-size: 0.85rem;"),
      shiny::actionButton(ns("reset_sim"), "Reset", class = "btn-warning", style = "flex: 1; font-weight: 600; padding: 3px 6px; font-size: 0.85rem;")
    ),

    # 2. Population & Hexagon Extent Sliders
    shiny::sliderInput(ns("n_hosts"), "Initial Hosts:", min = 50, max = 3000, value = 664, step = 50),
    shiny::sliderInput(ns("n_predators"), "Initial Predators:", min = 0, max = 60, value = 50, step = 2),
    shiny::sliderInput(ns("hex_diameter"), "Hex Diameter:", min = 0.005, max = 0.03, value = 0.01, step = 0.001),

    # 3. Conditional Map Overlay Options (Substrate Plot & Census Benchmarks)
    shiny::conditionalPanel(
      condition = sprintf("input['%s'] == 'Substrate Plot' || input['%s'] == 'Census Benchmarks'", ns("tabset"), ns("tabset")),
      shiny::div(style = "border-top: 1px solid rgba(0,0,0,0.1); margin: 4px 0 2px 0;"),
      shiny::div(
        style = "display: flex; gap: 12px; align-items: center; margin-bottom: 2px;",
        shiny::checkboxInput(ns("show_habitat"), "Habitat Features", value = TRUE),
        shiny::checkboxInput(ns("show_landmarks"), "Landmarks", value = TRUE)
      )
    ),

    # 4. Conditional Substrate Plot Axis Units
    shiny::conditionalPanel(
      condition = sprintf("input['%s'] == 'Substrate Plot'", ns("tabset")),
      axisUnitInput(ns("substrate_axis"), time_label = "Days")
    ),

    # 5. Conditional Age Classes Options (Age Classes tab)
    shiny::conditionalPanel(
      condition = sprintf("input['%s'] == 'Age Classes'", ns("tabset")),
      shiny::div(style = "border-top: 1px solid rgba(0,0,0,0.1); margin: 4px 0 2px 0;"),
      ageClassControlInput(ns("age_ctrls"), time_label = "Days")
    ),

    shiny::div(style = "border-top: 1px solid rgba(0,0,0,0.1); margin: 4px 0 2px 0;"),

    # 6. Bottom Section: Baseline Year & Status
    shiny::selectInput(ns("start_year"), "Baseline Year:", choices = years, selected = 1980),
    shiny::uiOutput(ns("status"))
  )
}

ecosystemOutput <- function(id) {
  ns <- shiny::NS(id)
  shiny::tagList(
    shiny::tabsetPanel(
      id = ns("tabset"),
      type = "tabs",
      shiny::tabPanel(
        "Substrate Plot",
        shiny::plotOutput(ns("substrate_plot"), height = "650px")
      ),
      shiny::tabPanel(
        "Age Classes",
        distPlotOutput(ns("dist_plot"))
      ),
      shiny::tabPanel(
        "Census Benchmarks",
        shiny::plotOutput(ns("autoplot"), height = "650px")
      ),
      shiny::tabPanel(
        "Live Demographics",
        shiny::br(),
        shiny::h4("Live Organism Population Summary (Updates Live on Simulation Step)"),
        shiny::tableOutput(ns("summary_table"))
      ),
      shiny::tabPanel(
        "Input Data",
        shiny::br(),
        inputAppInput(ns("input_data")),
        inputAppOutput(ns("input_data"))
      )
    )
  )
}

ecosystemServer <- function(id, ecosystem = "isle_royale") {
  shiny::moduleServer(id, function(input, output, session) {
    ns <- session$ns

    status_msg <- shiny::reactiveVal("")
    sim_state <- shiny::reactiveVal(NULL)
    sub_x_var <- axisUnitServer("substrate_axis")
    age_ctrls <- ageClassControlServer("age_ctrls")

    shiny::observeEvent(input$start_year, {
      yr <- as.numeric(input$start_year)
      csv_path <- system.file(file.path("doc", ecosystem, "wolf_moose.csv"), package = "ewing")
      if (csv_path == "" || !file.exists(csv_path)) {
        csv_path <- file.path("inst", "doc", ecosystem, "wolf_moose.csv")
      }

      if (file.exists(csv_path)) {
        df <- tryCatch(utils::read.csv(csv_path), error = function(e) NULL)
        if (!is.null(df) && "Year" %in% names(df) && yr %in% df$Year) {
          row_match <- df[df$Year == yr, ]
          if ("Moose" %in% names(row_match)) shiny::updateSliderInput(session, "n_hosts", value = as.numeric(row_match$Moose[1]))
          if ("Wolves" %in% names(row_match)) shiny::updateSliderInput(session, "n_predators", value = as.numeric(row_match$Wolves[1]))
        }
      }
    })

    shiny::observeEvent(list(input$reset_sim, input$start_year),
      {
        yr <- as.numeric(input$start_year)
        nh <- input$n_hosts
        np <- input$n_predators
        hd <- input$hex_diameter

        shiny::req(yr, nh, np, hd)

        sim <- init_ecosystem_sim(
          ecosystem = ecosystem,
          year = yr,
          n_hosts = nh,
          n_predators = np,
          hex_diameter = hd
        )

        steps <- parse_step_size(input$step_size)
        if (is.null(steps) || steps <= 0) steps <- 200
        sim <- run_ecosystem_sim(sim, nstep = steps)

        sim_state(sim)
        status_msg(paste0("<div style='color:green;'><b>Simulation Initialized:</b> Year ", yr, " with ", nh, " Hosts and ", np, " Predators (Executed ", steps, " initial steps).</div>"))
      },
      ignoreNULL = FALSE
    )

    shiny::observeEvent(input$step_sim, {
      sim <- sim_state()
      shiny::req(sim)
      steps <- parse_step_size(input$step_size)
      if (is.null(steps) || steps <= 0) steps <- 200

      updated_sim <- run_ecosystem_sim(sim, nstep = steps)
      sim_state(updated_sim)
      status_msg(paste0("<div style='color:blue;'><b>Executed ", steps, " steps:</b> Total Steps = ", updated_sim$nstep, "</div>"))
    })

    output$substrate_plot <- shiny::renderPlot({
      sim <- sim_state()
      shiny::req(sim)
      ewing_substrate(sim, x_var = sub_x_var())
    })

    distPlotServer("dist_plot", simres = sim_state, x_var = age_ctrls$x_var, total = age_ctrls$total, norm = age_ctrls$norm)

    output$autoplot <- shiny::renderPlot({
      sim <- sim_state()
      shiny::req(sim)
      ggplot_ecosystem_sim(sim)
    })

    output$summary_table <- shiny::renderTable({
      sim <- sim_state()
      shiny::req(sim)

      moose_counts <- table(sim$moose_pop$ageclass)
      wolf_counts <- table(sim$wolf_pop$ageclass)

      df_moose <- data.frame(
        Species = "Moose",
        AgeClass = names(moose_counts),
        Count = as.numeric(moose_counts),
        stringsAsFactors = FALSE
      )

      df_wolf <- data.frame(
        Species = "Wolf",
        AgeClass = names(wolf_counts),
        Count = as.numeric(wolf_counts),
        stringsAsFactors = FALSE
      )

      rbind(df_moose, df_wolf)
    })

    pkg_dir <- get_site_cache_file("", site = ecosystem)
    inputAppServer("input_data", simres = sim_state, datafile = shiny::reactiveVal(pkg_dir))

    output$status <- shiny::renderUI({
      shiny::HTML(status_msg())
    })
  })
}

# --- Source: isle_royale_sim.R ---
init_isle_royale_sim <- function(year = 1980, 
                                n_moose = NULL, 
                                n_wolves = NULL, 
                                hex_diameter = 0.01,
                                datafile = "",
                                features_rds = NULL,
                                landmarks_rds = NULL) {
  init_ecosystem_sim(
    ecosystem = "isle_royale",
    year = year,
    n_hosts = n_moose,
    n_predators = n_wolves,
    hex_diameter = hex_diameter,
    datafile = datafile,
    features_rds = features_rds,
    landmarks_rds = landmarks_rds
  )
}

run_isle_royale_sim <- function(sim_obj, nstep = 1000, refresh = 10, ...) {
  run_ecosystem_sim(sim_obj = sim_obj, nstep = nstep, refresh = refresh, ...)
}

ggplot_isle_royale_sim <- function(x, ...) {
  ggplot_ecosystem_sim(x, ...)
}

plot.isle_royale_sim <- function(x, ...) {
  print(ggplot_isle_royale_sim(x, ...))
}

# --- Source: IsleRoyaleApp.R ---
IsleRoyaleApp <- function(title = "Isle Royale Wolf-Moose Simulation Platform") {
  ecosystemApp(ecosystem = "isle_royale", title = title)
}

IsleRoyaleInput <- function(id) {
  ecosystemInput(id, ecosystem = "isle_royale")
}

IsleRoyaleOutput <- function(id) {
  ecosystemOutput(id)
}

IsleRoyaleServer <- function(id) {
  ecosystemServer(id, ecosystem = "isle_royale")
}

# --- Launch Application ---
IsleRoyaleApp()

---

Programmatic Application Usage

Launch the interactive application natively in R using exported package functions:

library(ewing)

# Launch interactive offline Shiny app
IsleRoyaleApp()

---

Executable Workflow: Isle Royale Spatial Predator-Prey Model

Below is an executable simulation workflow displaying spatial individual organism distributions on the Isle Royale substrate map, step-by-step demographic tallies, and historical 40-year empirical census benchmarks (1980–2019).

library(ewing)
library(ggplot2)

# 1. Initialize Isle Royale spatial simulation with 1980 baseline counts (664 Moose, 50 Wolves)
sim <- init_isle_royale_sim(year = 1980)

# 2. Run simulation steps (e.g. 200 days)
sim <- run_isle_royale_sim(sim, nstep = 200)

# 3. Visualize spatial landscape & benchmark trajectories
ggplot_isle_royale_sim(sim)

---

Age Classes Demographic Dynamics

# Plot side-by-side per-species age class dynamics over time (days)
autoplot(ewing_ageclass(sim), x_var = "time")

---

Technical Features

1. 100% Offline GIS Substrate Mapping: Renders habitat features (lakes, bogs, shaded forests, waterways) and landmark POIs using pre-computed local sf layers (inst/extdata/isle_royale/) with 0 API calls.
2. Dynamic Habitat-Weighted Movement: Organisms evaluate adjacent hexagonal substrate cells, moving with probability proportional to habitat suitability scores.
3. Continuous Demographic Stepping: Tallies births, aging (Calf \(\rightarrow\) Yearling \(\rightarrow\) Adult \(\rightarrow\) Senior; Pup \(\rightarrow\) Subadult \(\rightarrow\) Adult), and wolf predation at micro-step increments.
4. Tab-Aware Sidebar Decluttering: Displays plot-specific controls (show_habitat, show_landmarks, norm, total, Steps vs Days) strictly when their target tab is active.
5. Shared Modular Architecture: Peer application wrapper composing step_controls, distPlotApp, inputApp, and substrateApp.