From 93559cde7a44b985aa29a88fba33ffadd2f5f2ca Mon Sep 17 00:00:00 2001
From: "ruben.haag" <ruben.haag@stud.uni-goettingen.de>
Date: Mon, 7 Mar 2022 12:48:08 +0100
Subject: [PATCH] Current state for synching
---
Data/earliest_pickup_testing_phase.csv | 1601 ++
Data/results.csv | 1902 ++
Load_Models.jl | 4 +-
Manifest.toml | 20 +-
plot.ipynb | 16294 ++++------------
results.csv | 1255 ++
results/driven_distance.png | Bin 0 -> 38635 bytes
results/efficiency.png | Bin 0 -> 35818 bytes
results/mean_relative_delay.png | Bin 0 -> 35263 bytes
results/mean_relative_waiting_time.png | Bin 0 -> 36066 bytes
results/requested_distance.png | Bin 0 -> 39377 bytes
results/served_percentage.png | Bin 0 -> 39716 bytes
results/served_percentage_no_waiting.png | Bin 0 -> 41677 bytes
sim.jl | 26 +-
sim_functions.jl | 65 +-
standard_settings.jl | 40 +-
.../delays_wt1reldelta1/dispatcher.jl | 0
{example_map => test/example_map}/map/map.jl | 3 +-
.../example_map}/map/route_matrix | Bin
.../example_map}/map/timescale.txt | 0
test/example_map/map0/map.jl | 8 +
test/example_map/map0/route_matrix | Bin 0 -> 8388631 bytes
test/example_map/map0/timescale.txt | 1 +
23 files changed, 8684 insertions(+), 12535 deletions(-)
create mode 100644 Data/earliest_pickup_testing_phase.csv
create mode 100644 Data/results.csv
create mode 100644 results.csv
create mode 100644 results/driven_distance.png
create mode 100644 results/efficiency.png
create mode 100644 results/mean_relative_delay.png
create mode 100644 results/mean_relative_waiting_time.png
create mode 100644 results/requested_distance.png
create mode 100644 results/served_percentage.png
create mode 100644 results/served_percentage_no_waiting.png
rename {example_map => test/example_map}/delays_wt1reldelta1/dispatcher.jl (100%)
rename {example_map => test/example_map}/map/map.jl (70%)
rename {example_map => test/example_map}/map/route_matrix (100%)
rename {example_map => test/example_map}/map/timescale.txt (100%)
create mode 100644 test/example_map/map0/map.jl
create mode 100644 test/example_map/map0/route_matrix
create mode 100644 test/example_map/map0/timescale.txt
diff --git a/Data/earliest_pickup_testing_phase.csv b/Data/earliest_pickup_testing_phase.csv
new file mode 100644
index 0000000..4df5d50
--- /dev/null
+++ b/Data/earliest_pickup_testing_phase.csv
@@ -0,0 +1,1601 @@
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diff --git a/Data/results.csv b/Data/results.csv
new file mode 100644
index 0000000..7b4321c
--- /dev/null
+++ b/Data/results.csv
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+684598.9199470754,26.5989916706653,0.7519488351402865,1.1689666179197544,1.2230096057572126,0.9930664576864409,"(2.0840787589941185, 2.0840787589941185, 1.671280937515356, 1.671280937515356)",910432.8485585777,1.0
+667231.8916742343,26.5989916706653,0.73287326213088,1.1998449814119743,1.2927147911636887,1.0499967225608162,"(2.13947949902208, 2.13947949902208, 1.7347728345828646, 1.7347728345828646)",910432.8485585777,1.0
+651009.4549904757,26.5989916706653,0.7150548840819746,1.230723344904194,1.33946788692299,1.089021346151945,"(2.1840524692492047, 2.1840524692492047, 1.7906275477420956, 1.7906275477420956)",910432.8485585777,1.0
+635247.8356737876,26.5989916706653,0.6977426579890318,1.261601708396414,1.414384615238419,1.1536718042917091,"(2.238267808050994, 2.238267808050994, 1.8495287833479648, 1.8495287833479648)",910432.8485585777,1.0
diff --git a/Load_Models.jl b/Load_Models.jl
index 2a478a9..b18ab52 100644
--- a/Load_Models.jl
+++ b/Load_Models.jl
@@ -35,7 +35,7 @@ model_filenames = readdir(model_folder, join=false)
model = RP.loadmodel(model_folder*model_filenames[1])
data = Dict()
data[:frequency] = model.ν
-data[:earliest_pickup] = model.random_gens[:earliest_pickup][2]
+data[:resubmission] = model.random_gens[:resubmission][2]
for (name, func) in data_type_functions
data[name] = func(model)
end
@@ -47,7 +47,7 @@ for name in model_filenames[2:end]
model = RP.loadmodel(model_folder*name)
data = Dict()
data[:frequency] = model.ν
- data[:earliest_pickup] = model.random_gens[:earliest_pickup][2]
+ data[:resubmission] = model.random_gens[:resubmission][2]
for (name, func) in data_type_functions
data[name] = func(model)
end
diff --git a/Manifest.toml b/Manifest.toml
index 6e17b34..eb8c629 100644
--- a/Manifest.toml
+++ b/Manifest.toml
@@ -553,7 +553,7 @@ uuid = "093fc24a-ae57-5d10-9952-331d41423f4d"
version = "1.3.5"
[[LinearAlgebra]]
-deps = ["Libdl"]
+deps = ["Libdl", "libblastrampoline_jll"]
uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
[[LogExpFunctions]]
@@ -645,6 +645,10 @@ git-tree-sha1 = "887579a3eb005446d514ab7aeac5d1d027658b8f"
uuid = "e7412a2a-1a6e-54c0-be00-318e2571c051"
version = "1.3.5+1"
+[[OpenBLAS_jll]]
+deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"]
+uuid = "4536629a-c528-5b80-bd46-f80d51c5b363"
+
[[OpenLibm_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "05823500-19ac-5b8b-9628-191a04bc5112"
@@ -814,7 +818,7 @@ deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"]
uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
[[Random]]
-deps = ["Serialization"]
+deps = ["SHA", "Serialization"]
uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
[[Ratios]]
@@ -847,11 +851,11 @@ version = "1.1.3"
[[RidePooling]]
deps = ["Agents", "BenchmarkTools", "DelimitedFiles", "Distributions", "LinearAlgebra", "Measures", "OpenStreetMapX", "OpenStreetMapXPlot", "Pkg", "PlotlyJS", "Plots", "PolygonOps", "PrettyTables", "Random", "Serialization", "SparseArrays"]
-git-tree-sha1 = "5747741d2d85bc6363e2a552d08d32cd54dfce0d"
+git-tree-sha1 = "9fdd0e2c51526f5cf161d2a008c910a123d05674"
repo-rev = "planned_pickup"
repo-url = "https://gitlab.gwdg.de/smuehle1/RidePooling/"
uuid = "2a705bf3-b16b-4235-81e9-bc2517364992"
-version = "0.9.0"
+version = "0.9.1"
[[Rmath]]
deps = ["Random", "Rmath_jll"]
@@ -1215,6 +1219,10 @@ git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.15.1+0"
+[[libblastrampoline_jll]]
+deps = ["Artifacts", "Libdl", "OpenBLAS_jll"]
+uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
+
[[libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55"
@@ -1229,9 +1237,9 @@ version = "1.6.38+0"
[[libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
-git-tree-sha1 = "c45f4e40e7aafe9d086379e5578947ec8b95a8fb"
+git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
-version = "1.3.7+0"
+version = "1.3.7+1"
[[nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
diff --git a/plot.ipynb b/plot.ipynb
index bc0a7b8..792fb28 100644
--- a/plot.ipynb
+++ b/plot.ipynb
@@ -2,21 +2,10 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 2,
- "id": "bf456a2c",
+ "execution_count": 1,
+ "id": "c63ac08a",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "┌ Info: Precompiling DataFrames [a93c6f00-e57d-5684-b7b6-d8193f3e46c0]\n",
- "â”” @ Base loading.jl:1342\n",
- "┌ Info: Precompiling Plots [91a5bcdd-55d7-5caf-9e0b-520d859cae80]\n",
- "â”” @ Base loading.jl:1342\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"using DataFrames\n",
"using CSV\n",
@@ -25,8 +14,8 @@
},
{
"cell_type": "code",
- "execution_count": 3,
- "id": "3c92c713",
+ "execution_count": 2,
+ "id": "a3e2cd83",
"metadata": {},
"outputs": [
{
@@ -38,7 +27,7 @@
"",
"Stacktrace:",
" [1] top-level scope",
- " @ ./In[3]:16",
+ " @ ./In[2]:16",
" [2] eval",
" @ ./boot.jl:360 [inlined]",
" [3] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)",
@@ -71,8 +60,8 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "id": "6e04d847",
+ "execution_count": 3,
+ "id": "8ee9509f",
"metadata": {},
"outputs": [
{
@@ -81,7 +70,7 @@
"Plots.GRBackend()"
]
},
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -90,30 +79,30 @@
"xname = \"frequency\"\n",
"yname = \"earliest_pickup\"\n",
"\n",
- "#df = DataFrame(CSV.File(\"results_latest_dropoff.csv\"))\n",
"yname = \"earliest_pickup\"\n",
- "#path = \"/project.dcf/poss/datasets/planned_pickup/logscale_earliest_pickupresults.csv\"\n",
- "path = \"./Data/earliest_pickup_testing_phase.csv\"\n",
+ "path = \"./current_data/results.csv\"\n",
"df = DataFrame(CSV.File(path))#\"earliest_pickup_try2.csv\"))\n",
"\n",
"\n",
+ "t0 = 45.339190347724944\n",
"\n",
"sort!(df, [order(xname), order(yname)])\n",
- "xs = df[!, xname]\n",
- "ys = df[!, yname]\n",
+ "\n",
+ "xs = df[!, xname] #* t0\n",
+ "ys = df[!, yname] #/t0\n",
"#xs = unique(xs)\n",
"#ys = unique(ys)\n",
"\n",
"#print(df)\n",
"ks = names(df)\n",
- "filter!(e->e∉[xname, yname],ks)\n",
+ "filter!(e->e∉[xname, yname, \"occupancy\"],ks)\n",
"gr()"
]
},
{
"cell_type": "code",
- "execution_count": 5,
- "id": "ab1288b4",
+ "execution_count": 4,
+ "id": "72d35c84",
"metadata": {},
"outputs": [
{
@@ -122,1692 +111,503 @@
"<?xml version=\"1.0\" encoding=\"utf-8\"?>\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"600\" height=\"400\" viewBox=\"0 0 2400 1600\">\n",
"<defs>\n",
- " <clipPath id=\"clip040\">\n",
+ " <clipPath id=\"clip120\">\n",
" <rect x=\"0\" y=\"0\" width=\"2400\" height=\"1600\"/>\n",
" </clipPath>\n",
"</defs>\n",
- "<path clip-path=\"url(#clip040)\" d=\"\n",
+ "<path clip-path=\"url(#clip120)\" d=\"\n",
"M0 1600 L2400 1600 L2400 0 L0 0 Z\n",
" \" fill=\"#ffffff\" fill-rule=\"evenodd\" fill-opacity=\"1\"/>\n",
"<defs>\n",
- " <clipPath id=\"clip041\">\n",
+ " <clipPath id=\"clip121\">\n",
" <rect x=\"480\" y=\"0\" width=\"1681\" height=\"1600\"/>\n",
" </clipPath>\n",
"</defs>\n",
- "<path clip-path=\"url(#clip040)\" d=\"\n",
- "M251.372 1423.18 L2112.76 1423.18 L2112.76 123.472 L251.372 123.472 Z\n",
+ "<path clip-path=\"url(#clip120)\" d=\"\n",
+ "M219.866 1423.18 L2112.76 1423.18 L2112.76 123.472 L219.866 123.472 Z\n",
" \" fill=\"#ffffff\" fill-rule=\"evenodd\" fill-opacity=\"1\"/>\n",
"<defs>\n",
- " <clipPath id=\"clip042\">\n",
- " <rect x=\"251\" y=\"123\" width=\"1862\" height=\"1301\"/>\n",
+ " <clipPath id=\"clip122\">\n",
+ " <rect x=\"219\" y=\"123\" width=\"1894\" height=\"1301\"/>\n",
" </clipPath>\n",
"</defs>\n",
- "<polyline clip-path=\"url(#clip042)\" style=\"stroke:#000000; stroke-linecap:butt; stroke-linejoin:round; stroke-width:2; stroke-opacity:0.1; fill:none\" points=\"\n",
- " 448.749,1423.18 448.749,123.472 \n",
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@@ -7201,5754 +2433,1874 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "59.999999999999986, 1.5, (1600,)\n"
- ]
- }
- ],
- "source": [
- "#To Visualize processes, that are not yet finished\n",
- "for name in ks\n",
- " data = df[!, name]\n",
- " println(\"$(maximum(df[!, xname])), $(maximum(df[!, yname])), $(size(df[!, name]))\")\n",
- " p = scatter(xs, ys,\n",
- " marker_z= data, #attr(cmin=minimum(data), cmax=maximum(data), color=data, colorscale=\"Bluered\")\n",
- " marker = (:rect, 8),\n",
- " xlabel=\"$xname [1/tâ‚€]\", ylabel=\"$yname [tâ‚€]\",\n",
- " title=\"$name\",\n",
- " xticks= (5:7:50,[\"$i\" for i in 5:7:50]))\n",
- " xaxis!(:log)\n",
- " yaxis!(:log)\n",
- " display(p)\n",
- "end\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7e0940b2",
- "metadata": {},
- "outputs": [],
- "source": [
- "name = \"Efficiency\"\n",
- "data = df.requested_distance./df.driven_distance\n",
- "p = scatter(xs, ys,\n",
- " marker_z= data, #attr(cmin=minimum(data), cmax=maximum(data), color=data, colorscale=\"Bluered\")\n",
- " marker = (:rect, 8),\n",
- " xlabel=\"$xname [1/tâ‚€]\", ylabel=\"$yname [tâ‚€]\",\n",
- " title=\"$name\",\n",
- " xticks= (5:7:50, [\"$i\" for i in 5:7:50]))\n",
- " xaxis!(:log)\n",
- " yaxis!(:log)\n",
- " display(p)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "6ecfd7c3",
- "metadata": {},
- "outputs": [
- {
- "ename": "LoadError",
- "evalue": "UndefVarError: ks not defined",
- "output_type": "error",
- "traceback": [
- "UndefVarError: ks not defined",
- "",
- "Stacktrace:",
- " [1] top-level scope",
- " @ ./In[6]:1",
- " [2] eval",
- " @ ./boot.jl:360 [inlined]",
- " [3] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)",
- " @ Base ./loading.jl:1116"
+ "20.014269333588558, 0.7649999999999999, (393,)\n"
]
- }
- ],
- "source": [
- "for name in ks\n",
- " data = df[!, name]\n",
- " #println(\"$(maximum(df[!, xname])), $(maximum(df[!, yname])), $(size(df[!, name]))\")\n",
- " # p = scatter(xs, ys,\n",
- " # marker_z= data, #attr(cmin=minimum(data), cmax=maximum(data), color=data, colorscale=\"Bluered\")\n",
- " # markershape = :rect,\n",
- " # xlabel=\"$xname [1/tâ‚€]\", ylabel=\"$yname [tâ‚€]\",\n",
- " # title=\"$name\" )\n",
- " \n",
- " #display(p)\n",
- " \n",
- " data = reshape(data, (60, 40))\n",
- " \n",
- " map = heatmap(\n",
- " unique(xs), unique(ys), data,\n",
- " c=cgrad([:black, :white]),\n",
- " xlabel=\"$xname [1/tâ‚€]\", ylabel=\"$yname [tâ‚€]\",\n",
- " title=\"$name\")\n",
- " display(map)\n",
- " #savefig(map, \"results/$name.png\")\n",
- "end\n",
- "\n",
- "#TODO Waiting Time\n",
- "#TODO "
- ]
- },
- {
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- "name = \"Efficiency\"\n",
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+ " 2256.76,305.969 2232.76,305.969 2232.76,123.472 \n",
+ " \"/>\n",
+ "<path clip-path=\"url(#clip420)\" d=\"\n",
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "20.014269333588558, 0.7649999999999999, (393,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "#To Visualize processes, that are not yet finished\n",
+ "\n",
+ "for name in ks\n",
+ " data = df[!, name]\n",
+ " println(\"$(maximum(df[!, xname])), $(maximum(df[!, yname])), $(size(df[!, name]))\")\n",
+ " p = scatter(df[!, xname], df[!, yname],\n",
+ " marker_z= data, #attr(cmin=minimum(data), cmax=maximum(data), color=data, colorscale=\"Bluered\")\n",
+ " marker = (:rect, 9),\n",
+ " xlabel=\"$xname [1/tâ‚€]\", ylabel=\"$yname [tâ‚€]\",\n",
+ " title=\"$name\",\n",
+ " \n",
+ " )\n",
+ " #xaxis!(:log)\n",
+ " #yaxis!(:log)\n",
+ " display(p)\n",
+ " savefig(p, \"results/$name.png\")\n",
+ "end"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2c51dd7a",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "22bc2c82",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "LoadError",
+ "evalue": "DimensionMismatch(\"new dimensions (60, 40) must be consistent with array size 1128\")",
+ "output_type": "error",
+ "traceback": [
+ "DimensionMismatch(\"new dimensions (60, 40) must be consistent with array size 1128\")",
+ "",
+ "Stacktrace:",
+ " [1] (::Base.var\"#throw_dmrsa#234\")(dims::Tuple{Int64, Int64}, len::Int64)",
+ " @ Base ./reshapedarray.jl:41",
+ " [2] reshape(a::Vector{Float64}, dims::Tuple{Int64, Int64})",
+ " @ Base ./reshapedarray.jl:45",
+ " [3] top-level scope",
+ " @ ./In[6]:12",
+ " [4] eval",
+ " @ ./boot.jl:360 [inlined]",
+ " [5] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)",
+ " @ Base ./loading.jl:1116"
+ ]
+ }
+ ],
+ "source": [
+ "for name in ks\n",
+ " data = df[!, name]\n",
+ " #println(\"$(maximum(df[!, xname])), $(maximum(df[!, yname])), $(size(df[!, name]))\")\n",
+ " # p = scatter(xs, ys,\n",
+ " # marker_z= data, #attr(cmin=minimum(data), cmax=maximum(data), color=data, colorscale=\"Bluered\")\n",
+ " # markershape = :rect,\n",
+ " # xlabel=\"$xname [1/tâ‚€]\", ylabel=\"$yname [tâ‚€]\",\n",
+ " # title=\"$name\" )\n",
+ " \n",
+ " #display(p)\n",
+ " \n",
+ " data = reshape(data, (60, 40))\n",
+ " \n",
+ " map = heatmap(\n",
+ " unique(xs), unique(ys), data,\n",
+ " c=cgrad([:black, :white]),\n",
+ " xlabel=\"$xname [1/tâ‚€]\", ylabel=\"$yname [tâ‚€]\",\n",
+ " title=\"$name\")\n",
+ " display(map)\n",
+ " #savefig(map, \"results/$name.png\")\n",
+ "end\n",
+ "\n",
+ "#TODO Waiting Time\n",
+ "#TODO "
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "04e370a8",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "LoadError",
+ "evalue": "DimensionMismatch(\"new dimensions (60, 40) must be consistent with array size 1128\")",
+ "output_type": "error",
+ "traceback": [
+ "DimensionMismatch(\"new dimensions (60, 40) must be consistent with array size 1128\")",
+ "",
+ "Stacktrace:",
+ " [1] (::Base.var\"#throw_dmrsa#234\")(dims::Tuple{Int64, Int64}, len::Int64)",
+ " @ Base ./reshapedarray.jl:41",
+ " [2] reshape(a::Vector{Float64}, dims::Tuple{Int64, Int64})",
+ " @ Base ./reshapedarray.jl:45",
+ " [3] top-level scope",
+ " @ In[7]:4",
+ " [4] eval",
+ " @ ./boot.jl:360 [inlined]",
+ " [5] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)",
+ " @ Base ./loading.jl:1116"
+ ]
+ }
+ ],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "de4f89cd",
+ "id": "3aa583e3",
"metadata": {},
"outputs": [],
"source": [
@@ -12958,7 +4310,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "dcafb543",
+ "id": "a6c71416",
"metadata": {},
"outputs": [],
"source": []
@@ -12966,7 +4318,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "3f813d4f",
+ "id": "bab13709",
"metadata": {},
"outputs": [],
"source": []
diff --git a/results.csv b/results.csv
new file mode 100644
index 0000000..12804aa
--- /dev/null
+++ b/results.csv
@@ -0,0 +1,1255 @@
+driven_distance,earliest_pickup,efficiency,frequency,mean_relative_delay,mean_relative_waiting_time,occupancy,requested_distance,served_percentage,served_percentage_no_waiting
+66525.63275705591,0.1,1.727709439233872,19.999999999999996,6.502380798857242,5.55987224700855,"(3.5345347244825, 3.5345347244825, 3.3467894492535684, 3.3467894492535684)",114936.96366537157,1.0,1.0
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+65963.39069695331,0.1,1.7424356518210369,21.473600395362364,6.301782619798238,5.324737788041292,"(3.5988840315593937, 3.5988840315593937, 3.4089108599387457, 3.4089108599387457)",114936.96366537157,1.0,1.0
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+61073.3245619009,0.1,1.8819503357620093,27.540189023885382,7.186007906881827,6.1626895560735395,"(3.9361013498803925, 3.9361013498803925, 3.747855967497952, 3.747855967497952)",114936.96366537157,1.0,1.0
+62099.39272055761,0.1,1.8507992382883647,28.536739608264867,7.604742088873595,6.589898326076925,"(3.832652124728576, 3.832652124728576, 3.666170185167615, 3.666170185167615)",114933.50874537804,0.9998000399920016,0.9998000399920016
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diff --git a/sim.jl b/sim.jl
index d136e26..4498a19 100644
--- a/sim.jl
+++ b/sim.jl
@@ -25,17 +25,23 @@ try
println("Taken Command line values:\n\tIndex=$index\n\tsettings_path=$settings_path")
catch e
print(e)
+ global name = "test"
global settings_path = "./standard_settings.jl"
global index=1#eval(Meta.parse(ARGS[1]))
println("Take standard values:\n\tIndex=$index\n\tsettings_path=$settings_path")
end
include(settings_path)
-#TODO Is this actually needed? include("./sim_functions.jl")#
+#include("./sim_functions.jl")#
+params = Dict()
+for (name, bound) in bounds
+ params[name] = rand_value(bound...)
+end
if !isdir(paths[:data])
error("Please create data folder at $(paths[:data])")
+
return
end
if !isdir(paths[:model])
@@ -45,27 +51,23 @@ end
map_folder = paths[:map]
include(paths[:map]*"map.jl") #
-#include(paths[:dispatcher]*"dispatcher.jl")
-# TODO Is this better in a seperate file? I think not, because I always will use the Same Dispatcher
-x, y = indexing(index)
-println("x = $x\t y = $y")
-
-random_gens = Dict(request_type => [rng_type, y*t0])
+random_gens = Dict(params[request_type] => [rng_type, params[:dt_earliest_pickup]])
specs=(;
map=mymap,
route_matrix=RM,
subspaces=:all_edges,
routing=:lookup,
speed_dict = speed_dict,
- seed = 1,
- request_type = request_type,
+ seed = 3,
random_gens = random_gens,
- t_max_wait = t_max_wait * t0,
- t0 = t0,
+ t_max_wait = typemax(Float64), #TODO Remove t_max_wait!
+ t0 = 1,
+ rejection_criterion=((:any_waiting_time,params[:any_waiting_time]), (:any_relative_detour, params[:any_waiting_time])),
+
)
specs = merge(specs, dispatcher)
-@time data = simulate_rp(paths,N, x, y, t0, specs, request_type, served=served, requested=requested)
+@time data = simulate_rp(paths,N, t0, specs, request_type, served=served, requested=requested)
diff --git a/sim_functions.jl b/sim_functions.jl
index 8c95310..0615576 100644
--- a/sim_functions.jl
+++ b/sim_functions.jl
@@ -9,7 +9,7 @@ using DelimitedFiles
# Dict of the functions used for evaluation
# QUESTION Calculate Variance for all of these?
-efficiency(model) = RP.requested_distance(model) /RP.driven_distance(model)
+efficiency(model) = RP.requested_distance(model) /RP.driven_distance(model)
data_type_functions = Dict(
:served_percentage => RP.served_percentage,
@@ -26,6 +26,7 @@ data_type_functions = Dict(
+
"""
Converts a given index to a 2D Matrix Index with the given Matrix size
"""
@@ -49,6 +50,15 @@ function getLogValue(index, xmin, xmax, xlen::Int64, ymin, ymax, ylen::Int64)
end
+
+function rand_value(lower,upper)
+ rand(Float64) *(upper-lower) + lower
+end
+
+function rand_value(list)
+ rand(list)
+end
+
"""
"""
@@ -65,30 +75,43 @@ end
"""
Function for Running a RidePooling simulation with the normalized Frequency x
"""
-function simulate_rp(paths::Dict, N::Int64, x, y, t0::Float64, specs, request_type; served = 10*N, requested=10*N)
+function simulate_rp(paths::Dict, N::Int64, specs, params; served = 10*N, requested=10*N)
println("Get Model with with \nspecs=$specs")
-
#Make Model
- @time model=RP.get_model(;N_bus=N,ν=x/t0,specs...);
-
- println("Initializing Model to fill TODO List")
- # Initialize Models to fill todo lists
- #model.request_type=:now
- #@time RP.run!(model;requested=15*N)
- #model.request_type=request_type
-
- println("Start Simulation")
- @time RP.run!(model;requested=requested, served=served)
- data = Dict()
- data[:frequency] = x
- data[request_type] = y
- for (name, func) in data_type_functions
- data[name] = func(model)
- end
- # Save the calculated Data
- data = DataFrame(data)
+ if params[:request_type] = :drive_and_wait
+ request_type = (;
+ params[:request_type],
+ cost_idle_weight = params[:weight],
+ )
+ else if params[:request_type] = :resubmission
+ request_type = (;
+ params[:request_type],
+ resubmission_time_faktor = params[:resubmission_time_factor],
+ )
+ end
+ merge(specs, request_type)
+
+
+
+
+ @time model=RP.get_model(;N_bus=N,ν=x/t0,specs...);
+ @time RP.run!(model;requested=requested, served=served)
+
+ data = Dict()
+ for (name, param) in params
+ data[name] = [param,]
+ end
+ for (name, func) in data_type_functions
+ data[name] = [func(model),]
+ end
+
+
+
+
+
+
open(paths[:data]*"$(lpad(index, 5, "0")).csv", "w") do io
writedlm(io, Iterators.flatten(([names(data)], eachrow(data))), ';')
diff --git a/standard_settings.jl b/standard_settings.jl
index 3f7f90d..29329e9 100644
--- a/standard_settings.jl
+++ b/standard_settings.jl
@@ -6,47 +6,43 @@ include("./sim_functions.jl")
-request_type=:earliest_pickup # request_type that should be simulated
+request_type=:resubmission # request_type that should be simulated
#name="test_1" # Name of the simulation
-path="/scratch01.local/rhaag/$(String(request_type))/$name/" # Path were everything will be saved
+path="./$name/" #Change"/scratch01.local/rhaag/$(String(request_type))/$name/" # Path were everything will be saved
# Simulation Settings
-N = 10 #Number of Busses
-requested = 4000 * N # Number of requests, that need to be made
-served = 4000 * N # Number of requests, that need to be served
+N = 1#10 #Number of Busses
+requested = 1000#Change6000 * N # Number of requests, that need to be made
+served = 1000#Change6000 * N # Number of requests, that need to be served
#Random Generator
-rng_type = :notRandom
+rng_type = :even
# For mapping the 1D Index to 2D
-xBounds = (20, 60) # Frequency ν = x/t0
-yBounds = (0.1, 1.5)
-im_size = (40, 40)
-indexing_method = getLogValue
-t_max_wait = 6
+bounds = Dict()
+
+
+bounds[:dt_earliest_pickup] = (0, 5)
+bounds[:weight] = (0, 4) #TODO Weight still needs to be implemented for top_level usage
+bounds[:dt_resubmission] = (0.9, 1.5)
+bounds[:request_type] = ([:resubmission, :drive_and_wait],)
+
+
+
# File Organization
paths = Dict(:data => "/results/data/",
:model => "/results/model/",
- :map => "/example_map/map/"
+ :map => "/example_map/map0/"
#:dispatcher => "/example_map/delays_wt1reldelta1/"
- # TODO Is this better in a seperate file? I think not, because I always will use the Same Dispatcher
)
-dispatcher=(;
- cost=:trajectory_length,
- rejection_criterion=(((:total_travelling_time, 2, 0.5),))
-)
-
-
-
-
# Functions
-indexing(index) = indexing_method(index-1, xBounds..., im_size[1], yBounds..., im_size[2])
+#indexing(index) = indexing_method(index-1, xBounds..., im_size[1], yBounds..., im_size[2])
for (name, subdir) in paths
paths[name] = path * subdir
diff --git a/example_map/delays_wt1reldelta1/dispatcher.jl b/test/example_map/delays_wt1reldelta1/dispatcher.jl
similarity index 100%
rename from example_map/delays_wt1reldelta1/dispatcher.jl
rename to test/example_map/delays_wt1reldelta1/dispatcher.jl
diff --git a/example_map/map/map.jl b/test/example_map/map/map.jl
similarity index 70%
rename from example_map/map/map.jl
rename to test/example_map/map/map.jl
index c65e50d..8b77c68 100644
--- a/example_map/map/map.jl
+++ b/test/example_map/map/map.jl
@@ -1,7 +1,8 @@
map_specs=(:star_grid_map,(64,16))
#map
-speed_dict=Dict(1=>3.6)
+t0=readdlm(map_folder*"timescale.txt")[1]
+speed_dict=Dict(1=>3.6*t0)
mymap=RP.get_map(map_specs)
RM=RP.get_route_matrix(map_folder)
using DelimitedFiles
diff --git a/example_map/map/route_matrix b/test/example_map/map/route_matrix
similarity index 100%
rename from example_map/map/route_matrix
rename to test/example_map/map/route_matrix
diff --git a/example_map/map/timescale.txt b/test/example_map/map/timescale.txt
similarity index 100%
rename from example_map/map/timescale.txt
rename to test/example_map/map/timescale.txt
diff --git a/test/example_map/map0/map.jl b/test/example_map/map0/map.jl
new file mode 100644
index 0000000..23622cd
--- /dev/null
+++ b/test/example_map/map0/map.jl
@@ -0,0 +1,8 @@
+map_specs=(:star_grid_map,(10,10))
+
+#map
+t0=readdlm(map_folder*"timescale.txt")[1]
+speed_dict=Dict(1=>3.6*t0)
+mymap=RP.get_map(map_specs)
+RM=RP.get_route_matrix(map_folder)
+using DelimitedFiles
diff --git a/test/example_map/map0/route_matrix b/test/example_map/map0/route_matrix
new file mode 100644
index 0000000000000000000000000000000000000000..b590c68ad786897b728e9b4d91160db7eb8d98fa
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diff --git a/test/example_map/map0/timescale.txt b/test/example_map/map0/timescale.txt
new file mode 100644
index 0000000..d0055cc
--- /dev/null
+++ b/test/example_map/map0/timescale.txt
@@ -0,0 +1 @@
+10.68363458404237
--
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