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Commit d1285b7a authored by Ruben Haag's avatar Ruben Haag
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Merge branch 'main' of gitlab.gwdg.de:rhaag/ridepooling_simulations into main

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%% Cell type:code id:3496b943-89c8-4866-ba73-44a7beaea1d7 tags: %% Cell type:code id:6325832e tags:
``` julia ``` julia
import Pkg import Pkg
Pkg.add(url="https://gitlab.gwdg.de/smuehle1/RidePooling/", rev="planned_pickup") Pkg.add(url="https://gitlab.gwdg.de/smuehle1/RidePooling/", rev="planned_pickup")
Pkg.instantiate() #there may be an error here concerning PyCall. The dependencies assume Python to be installed on your system, with package 'matplotlib' installed. Pkg.instantiate() #there may be an error here concerning PyCall. The dependencies assume Python to be installed on your system, with package 'matplotlib' installed.
#Either just run 'conda install -c conda-forge matplotlib' in your system shell, or follow instructions below (after running this cell and getting an error) to solve everything from within Julia. #Either just run 'conda install -c conda-forge matplotlib' in your system shell, or follow instructions below (after running this cell and getting an error) to solve everything from within Julia.
``` ```
%% Output %% Output
 Updating git-repo `https://gitlab.gwdg.de/smuehle1/RidePooling`  Updating git-repo `https://gitlab.gwdg.de/smuehle1/RidePooling`
Username for 'https://gitlab.gwdg.de': rhaag@ds.mpg.de Username for 'https://gitlab.gwdg.de': rhaag@ds.mpg.de
Password for 'https://rhaag@ds.mpg.de@gitlab.gwdg.de': ··········· Password for 'https://rhaag@ds.mpg.de@gitlab.gwdg.de': ···········
 Updating registry at `~/.julia/registries/General`  Updating registry at `~/.julia/registries/General`
 Resolving package versions...  Resolving package versions...
 Updating `~/ridepooling_simulations/Project.toml`  Updating `~/ridepooling_simulations/Project.toml`
 [2a705bf3] ~ RidePooling v0.9.0 `https://gitlab.gwdg.de/smuehle1/RidePooling/#planned_pickup` ⇒ v0.9.0 `https://gitlab.gwdg.de/smuehle1/RidePooling/#planned_pickup`  [2a705bf3] ~ RidePooling v0.9.0 `https://gitlab.gwdg.de/smuehle1/RidePooling/#planned_pickup` ⇒ v0.9.0 `https://gitlab.gwdg.de/smuehle1/RidePooling/#planned_pickup`
 Updating `~/ridepooling_simulations/Manifest.toml`  Updating `~/ridepooling_simulations/Manifest.toml`
 [2a705bf3] ~ RidePooling v0.9.0 `https://gitlab.gwdg.de/smuehle1/RidePooling/#planned_pickup` ⇒ v0.9.0 `https://gitlab.gwdg.de/smuehle1/RidePooling/#planned_pickup`  [2a705bf3] ~ RidePooling v0.9.0 `https://gitlab.gwdg.de/smuehle1/RidePooling/#planned_pickup` ⇒ v0.9.0 `https://gitlab.gwdg.de/smuehle1/RidePooling/#planned_pickup`
Precompiling project... Precompiling project...
 ✓ RidePooling  ✓ RidePooling
1 dependency successfully precompiled in 13 seconds (190 already precompiled) 1 dependency successfully precompiled in 13 seconds (190 already precompiled)
1 dependency precompiled but a different version is currently loaded. Restart julia to access the new version 1 dependency precompiled but a different version is currently loaded. Restart julia to access the new version
%% Cell type:code id:76c1dfa3 tags: %% Cell type:code id:1192df61 tags:
``` julia ``` julia
using RidePooling using RidePooling
RP = RidePooling RP = RidePooling
using DataFrames using DataFrames
using CSV using CSV
``` ```
%% Cell type:code id:b828b321 tags: %% Cell type:code id:c39eaef9 tags:
``` julia ``` julia
project_folder = "/project.dcf/poss/datasets/planned_pickup/earliest_pickup_try_2" project_folder = "/project.dcf/poss/datasets/planned_pickup/earliest_pickup_try_2"
model_folder = project_folder*"/models/" model_folder = project_folder*"/models/"
output_file = "results.csv" output_file = "results.csv"
``` ```
%% Output %% Output
"results.csv" "results.csv"
%% Cell type:code id:37246aab-9fb4-4214-9bac-f4b321735878 tags: %% Cell type:code id:b99cf08c tags:
``` julia ``` julia
efficiency(model) = driven_distance(model) / requested_distance(model) efficiency(model) = driven_distance(model) / requested_distance(model)
#TODO quality = #TODO quality =
data_type_functions = Dict( data_type_functions = Dict(
:served_percentage => served_percentage, :served_percentage => served_percentage,
:driven_distance => driven_distance, :driven_distance => driven_distance,
:requested_distance => requested_distance, :requested_distance => requested_distance,
:mean_relative_delay => mean_relative_delay, :mean_relative_delay => mean_relative_delay,
:efficiency => efficiency, :efficiency => efficiency,
:occupancy => mean_occupancy, :occupancy => mean_occupancy,
:mean_relative_delay => mean_relative_delay, :mean_relative_delay => mean_relative_delay,
:mean_relative_waiting_time => mean_relative_waiting_time, :mean_relative_waiting_time => mean_relative_waiting_time,
:served_percentage_no_waiting => served_percentage_no_waiting,
# :mean_occupancy => RP.mean_occupancy, #BUG In Reading TUples from a CSV File # :mean_occupancy => RP.mean_occupancy, #BUG In Reading TUples from a CSV File
) )
``` ```
%% Output %% Output
Dict{Symbol, Function} with 7 entries: Dict{Symbol, Function} with 7 entries:
:mean_relative_delay => mean_relative_delay :mean_relative_delay => mean_relative_delay
:efficiency => efficiency :efficiency => efficiency
:requested_distance => requested_distance :requested_distance => requested_distance
:mean_relative_waiting_time => mean_relative_waiting_time :mean_relative_waiting_time => mean_relative_waiting_time
:driven_distance => driven_distance :driven_distance => driven_distance
:served_percentage => served_percentage :served_percentage => served_percentage
:occupancy => mean_occupancy :occupancy => mean_occupancy
%% Cell type:code id:e0e3a9e4-af4f-423a-bdbd-17d4a8e95bba tags: %% Cell type:code id:6c9b3021 tags:
``` julia ``` julia
model_filenames = readdir(model_folder, join=false) model_filenames = readdir(model_folder, join=false)
``` ```
%% Output %% Output
1901-element Vector{String}: 1901-element Vector{String}:
"1.model" "1.model"
"10.model" "10.model"
"100.model" "100.model"
"1000.model" "1000.model"
"1001.model" "1001.model"
"1002.model" "1002.model"
"1003.model" "1003.model"
"1004.model" "1004.model"
"1005.model" "1005.model"
"1006.model" "1006.model"
"1007.model" "1007.model"
"1008.model" "1008.model"
"1009.model" "1009.model"
⋮ ⋮
"989.model" "989.model"
"99.model" "99.model"
"990.model" "990.model"
"991.model" "991.model"
"992.model" "992.model"
"993.model" "993.model"
"994.model" "994.model"
"995.model" "995.model"
"996.model" "996.model"
"997.model" "997.model"
"998.model" "998.model"
"999.model" "999.model"
%% Cell type:code id:21e549e2-8f49-468f-a642-6d4c3ece02fd tags: %% Cell type:code id:dbde6420 tags:
``` julia ``` julia
model = RP.loadmodel(model_folder*model_filenames[1]) model = RP.loadmodel(model_folder*model_filenames[1])
data = Dict() data = Dict()
data[:frequency] = model.ν data[:frequency] = model.ν
data[:dt_latest_dropoff] = model.random_gens[:pickup][2] data[:dt_latest_dropoff] = model.random_gens[:pickup][2]
for (name, func) in data_type_functions for (name, func) in data_type_functions
data[name] = func(model) data[name] = func(model)
end end
df = DataFrame(data) df = DataFrame(data)
``` ```
%% Output %% Output
\begin{tabular}{r|cccccc} \begin{tabular}{r|cccccc}
& driven\_distance & dt\_latest\_dropoff & efficiency & frequency & mean\_relative\_delay & \\ & driven\_distance & dt\_latest\_dropoff & efficiency & frequency & mean\_relative\_delay & \\
\hline \hline
& Float64 & Float64 & Float64 & Float64 & Float64 & \\ & Float64 & Float64 & Float64 & Float64 & Float64 & \\
\hline \hline
1 & 1.08475e6 & 0.0 & 1.19147 & 0.0882239 & 0.55101 & $\dots$ \\ 1 & 1.08475e6 & 0.0 & 1.19147 & 0.0882239 & 0.55101 & $\dots$ \\
\end{tabular} \end{tabular}
1×9 DataFrame 1×9 DataFrame
 Row │ driven_distance  dt_latest_dropoff  efficiency  frequency  mean_relativ ⋯  Row │ driven_distance  dt_latest_dropoff  efficiency  frequency  mean_relativ ⋯
 │ Float64  Float64  Float64  Float64  Float64  ⋯  │ Float64  Float64  Float64  Float64  Float64  ⋯
─────┼────────────────────────────────────────────────────────────────────────── ─────┼──────────────────────────────────────────────────────────────────────────
1 │ 1.08475e6 0.0 1.19147 0.0882239 ⋯ 1 │ 1.08475e6 0.0 1.19147 0.0882239 ⋯
 5 columns omitted  5 columns omitted
%% Cell type:code id:67a6f69e-21d3-4df7-a34c-33369ec89dbe tags: %% Cell type:code id:bde0a846 tags:
``` julia ``` julia
for name in model_filenames[2:end] for name in model_filenames[2:end]
model = RP.loadmodel(model_folder*name) model = RP.loadmodel(model_folder*name)
data = Dict() data = Dict()
data[:frequency] = model.ν data[:frequency] = model.ν
data[:dt_latest_dropoff] = model.random_gens[:pickup][2] data[:dt_latest_dropoff] = model.random_gens[:pickup][2]
for (name, func) in data_type_functions for (name, func) in data_type_functions
data[name] = func(model) data[name] = func(model)
end end
data = DataFrame(data) data = DataFrame(data)
append!(df, data) append!(df, data)
end end
``` ```
%% Cell type:code id:5f3b4143-97ce-44b5-ade9-eaaa812ff168 tags: %% Cell type:code id:11ab8779 tags:
``` julia ``` julia
println("Saving Combined Dataframe to $output_file") println("Saving Combined Dataframe to $output_file")
CSV.write(output_file, df) CSV.write(output_file, df)
``` ```
%% Output %% Output
Saving Combined Dataframe to results.csv Saving Combined Dataframe to results.csv
"results.csv" "results.csv"
%% Cell type:code id:eb42ea02-baad-492a-925d-d4163914135b tags: %% Cell type:code id:ce8a0f5c tags:
``` julia ``` julia
``` ```
%% Output %% Output
\begin{tabular}{r|cccccc} \begin{tabular}{r|cccccc}
& driven\_distance & dt\_latest\_dropoff & efficiency & frequency & mean\_relative\_delay & \\ & driven\_distance & dt\_latest\_dropoff & efficiency & frequency & mean\_relative\_delay & \\
\hline \hline
& Float64 & Float64 & Float64 & Float64 & Float64 & \\ & Float64 & Float64 & Float64 & Float64 & Float64 & \\
\hline \hline
1 & 1.08475e6 & 0.0 & 1.19147 & 0.0882239 & 0.55101 & $\dots$ \\ 1 & 1.08475e6 & 0.0 & 1.19147 & 0.0882239 & 0.55101 & $\dots$ \\
2 & 9.81811e5 & 0.0 & 1.0784 & 0.366129 & 0.669109 & $\dots$ \\ 2 & 9.81811e5 & 0.0 & 1.0784 & 0.366129 & 0.669109 & $\dots$ \\
3 & 8.97935e5 & 1.66244 & 0.986272 & 0.674913 & 0.658836 & $\dots$ \\ 3 & 8.97935e5 & 1.66244 & 0.986272 & 0.674913 & 0.658836 & $\dots$ \\
4 & 6.20502e5 & 26.599 & 0.681547 & 1.29248 & 1.49626 & $\dots$ \\ 4 & 6.20502e5 & 26.599 & 0.681547 & 1.29248 & 1.49626 & $\dots$ \\
5 & 1.99027e6 & 26.599 & 2.18608 & 0.0882239 & 0.314684 & $\dots$ \\ 5 & 1.99027e6 & 26.599 & 2.18608 & 0.0882239 & 0.314684 & $\dots$ \\
6 & 1.94318e6 & 26.599 & 2.13434 & 0.119102 & 0.287703 & $\dots$ \\ 6 & 1.94318e6 & 26.599 & 2.13434 & 0.119102 & 0.287703 & $\dots$ \\
7 & 1.90231e6 & 26.599 & 2.08946 & 0.149981 & 0.281236 & $\dots$ \\ 7 & 1.90231e6 & 26.599 & 2.08946 & 0.149981 & 0.281236 & $\dots$ \\
8 & 1.86081e6 & 26.599 & 2.04387 & 0.180859 & 0.266526 & $\dots$ \\ 8 & 1.86081e6 & 26.599 & 2.04387 & 0.180859 & 0.266526 & $\dots$ \\
9 & 1.8224e6 & 26.599 & 2.00169 & 0.211737 & 0.255786 & $\dots$ \\ 9 & 1.8224e6 & 26.599 & 2.00169 & 0.211737 & 0.255786 & $\dots$ \\
10 & 1.77929e6 & 26.599 & 1.95434 & 0.242616 & 0.231513 & $\dots$ \\ 10 & 1.77929e6 & 26.599 & 1.95434 & 0.242616 & 0.231513 & $\dots$ \\
11 & 1.73854e6 & 26.599 & 1.90958 & 0.273494 & 0.243714 & $\dots$ \\ 11 & 1.73854e6 & 26.599 & 1.90958 & 0.273494 & 0.243714 & $\dots$ \\
12 & 1.69903e6 & 26.599 & 1.86618 & 0.304372 & 0.243598 & $\dots$ \\ 12 & 1.69903e6 & 26.599 & 1.86618 & 0.304372 & 0.243598 & $\dots$ \\
13 & 1.65626e6 & 26.599 & 1.8192 & 0.335251 & 0.226241 & $\dots$ \\ 13 & 1.65626e6 & 26.599 & 1.8192 & 0.335251 & 0.226241 & $\dots$ \\
14 & 888659.0 & 1.66244 & 0.976084 & 0.705791 & 0.685847 & $\dots$ \\ 14 & 888659.0 & 1.66244 & 0.976084 & 0.705791 & 0.685847 & $\dots$ \\
15 & 1.60601e6 & 26.599 & 1.764 & 0.366129 & 0.246303 & $\dots$ \\ 15 & 1.60601e6 & 26.599 & 1.764 & 0.366129 & 0.246303 & $\dots$ \\
16 & 1.55996e6 & 26.599 & 1.71343 & 0.397008 & 0.248892 & $\dots$ \\ 16 & 1.55996e6 & 26.599 & 1.71343 & 0.397008 & 0.248892 & $\dots$ \\
17 & 1.51036e6 & 26.599 & 1.65895 & 0.427886 & 0.269992 & $\dots$ \\ 17 & 1.51036e6 & 26.599 & 1.65895 & 0.427886 & 0.269992 & $\dots$ \\
18 & 1.4603e6 & 26.599 & 1.60396 & 0.458764 & 0.275677 & $\dots$ \\ 18 & 1.4603e6 & 26.599 & 1.60396 & 0.458764 & 0.275677 & $\dots$ \\
19 & 1.40943e6 & 26.599 & 1.54808 & 0.489643 & 0.295027 & $\dots$ \\ 19 & 1.40943e6 & 26.599 & 1.54808 & 0.489643 & 0.295027 & $\dots$ \\
20 & 1.35862e6 & 26.599 & 1.49228 & 0.520521 & 0.334636 & $\dots$ \\ 20 & 1.35862e6 & 26.599 & 1.49228 & 0.520521 & 0.334636 & $\dots$ \\
21 & 1.31165e6 & 26.599 & 1.44069 & 0.551399 & 0.348903 & $\dots$ \\ 21 & 1.31165e6 & 26.599 & 1.44069 & 0.551399 & 0.348903 & $\dots$ \\
22 & 1.26507e6 & 26.599 & 1.38952 & 0.582278 & 0.362135 & $\dots$ \\ 22 & 1.26507e6 & 26.599 & 1.38952 & 0.582278 & 0.362135 & $\dots$ \\
23 & 1.2188e6 & 26.599 & 1.3387 & 0.613156 & 0.367302 & $\dots$ \\ 23 & 1.2188e6 & 26.599 & 1.3387 & 0.613156 & 0.367302 & $\dots$ \\
24 & 1.17416e6 & 26.599 & 1.28967 & 0.644034 & 0.412239 & $\dots$ \\ 24 & 1.17416e6 & 26.599 & 1.28967 & 0.644034 & 0.412239 & $\dots$ \\
25 & 875625.0 & 1.66244 & 0.961768 & 0.73667 & 0.693409 & $\dots$ \\ 25 & 875625.0 & 1.66244 & 0.961768 & 0.73667 & 0.693409 & $\dots$ \\
26 & 1.13104e6 & 26.599 & 1.24231 & 0.674913 & 0.46405 & $\dots$ \\ 26 & 1.13104e6 & 26.599 & 1.24231 & 0.674913 & 0.46405 & $\dots$ \\
27 & 1.09364e6 & 28.2614 & 1.20123 & 0.705791 & 0.476762 & $\dots$ \\ 27 & 1.09364e6 & 28.2614 & 1.20123 & 0.705791 & 0.476762 & $\dots$ \\
28 & 1.05296e6 & 28.2614 & 1.15655 & 0.73667 & 0.538486 & $\dots$ \\ 28 & 1.05296e6 & 28.2614 & 1.15655 & 0.73667 & 0.538486 & $\dots$ \\
29 & 1.01717e6 & 28.2614 & 1.11724 & 0.767548 & 0.558567 & $\dots$ \\ 29 & 1.01717e6 & 28.2614 & 1.11724 & 0.767548 & 0.558567 & $\dots$ \\
30 & 981447.0 & 28.2614 & 1.078 & 0.798426 & 0.596046 & $\dots$ \\ 30 & 981447.0 & 28.2614 & 1.078 & 0.798426 & 0.596046 & $\dots$ \\
$\dots$ & $\dots$ & $\dots$ & $\dots$ & $\dots$ & $\dots$ & \\ $\dots$ & $\dots$ & $\dots$ & $\dots$ & $\dots$ & $\dots$ & \\
\end{tabular} \end{tabular}
1901×9 DataFrame 1901×9 DataFrame
 Row │ driven_distance  dt_latest_dropoff  efficiency  frequency  mean_relati ⋯  Row │ driven_distance  dt_latest_dropoff  efficiency  frequency  mean_relati ⋯
 │ Float64  Float64  Float64  Float64  Float64  ⋯  │ Float64  Float64  Float64  Float64  Float64  ⋯
──────┼───────────────────────────────────────────────────────────────────────── ──────┼─────────────────────────────────────────────────────────────────────────
1 │ 1.08475e6 0.0 1.19147 0.0882239 ⋯ 1 │ 1.08475e6 0.0 1.19147 0.0882239 ⋯
2 │ 9.81811e5 0.0 1.0784 0.366129 2 │ 9.81811e5 0.0 1.0784 0.366129
3 │ 8.97935e5 1.66244 0.986272 0.674913 3 │ 8.97935e5 1.66244 0.986272 0.674913
4 │ 6.20502e5 26.599 0.681547 1.29248 4 │ 6.20502e5 26.599 0.681547 1.29248
5 │ 1.99027e6 26.599 2.18608 0.0882239 ⋯ 5 │ 1.99027e6 26.599 2.18608 0.0882239 ⋯
6 │ 1.94318e6 26.599 2.13434 0.119102 6 │ 1.94318e6 26.599 2.13434 0.119102
7 │ 1.90231e6 26.599 2.08946 0.149981 7 │ 1.90231e6 26.599 2.08946 0.149981
8 │ 1.86081e6 26.599 2.04387 0.180859 8 │ 1.86081e6 26.599 2.04387 0.180859
9 │ 1.8224e6 26.599 2.00169 0.211737 ⋯ 9 │ 1.8224e6 26.599 2.00169 0.211737 ⋯
10 │ 1.77929e6 26.599 1.95434 0.242616 10 │ 1.77929e6 26.599 1.95434 0.242616
11 │ 1.73854e6 26.599 1.90958 0.273494 11 │ 1.73854e6 26.599 1.90958 0.273494
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱ ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱
1892 │ 8.08813e5 26.599 0.888383 0.983696 1892 │ 8.08813e5 26.599 0.888383 0.983696
1893 │ 7.85319e5 26.599 0.862578 1.01457 ⋯ 1893 │ 7.85319e5 26.599 0.862578 1.01457 ⋯
1894 │ 7.63e5 26.599 0.838062 1.04545 1894 │ 7.63e5 26.599 0.838062 1.04545
1895 │ 741804.0 26.599 0.814782 1.07633 1895 │ 741804.0 26.599 0.814782 1.07633
1896 │ 7.21872e5 26.599 0.792889 1.10721 1896 │ 7.21872e5 26.599 0.792889 1.10721
1897 │ 702890.0 26.599 0.772039 1.13809 ⋯ 1897 │ 702890.0 26.599 0.772039 1.13809 ⋯
1898 │ 6.84599e5 26.599 0.751949 1.16897 1898 │ 6.84599e5 26.599 0.751949 1.16897
1899 │ 6.67232e5 26.599 0.732873 1.19984 1899 │ 6.67232e5 26.599 0.732873 1.19984
1900 │ 6.51009e5 26.599 0.715055 1.23072 1900 │ 6.51009e5 26.599 0.715055 1.23072
1901 │ 6.35248e5 26.599 0.697743 1.2616 ⋯ 1901 │ 6.35248e5 26.599 0.697743 1.2616 ⋯
 5 columns and 1880 rows omitted  5 columns and 1880 rows omitted
%% Cell type:code id:708a864a-f09f-416b-ade5-2c0055708301 tags: %% Cell type:code id:da1f71f8 tags:
``` julia ``` julia
``` ```
......
results.csv
+ 1463
− 1902
View file @ d1285b7a
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