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Dockerfile

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FROM simwrapper/site
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COPY ./data/ /data

README.md

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# SimWrapper Example Project
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These files show some examples of some of the things you can do with SimWrapper. Check out the full docs at https://simwrapper.github.io
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## Viewing these files
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SimWrapper is at https://simwrapper.github.io/site
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- If you're using Google Chrome or MS Edge, you can click "Add Folder" on the main
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SimWrapper page, browse to this folder, and give Chrome read access to this folder
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- For Firefox/Safari, run either
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- `java -far mini-file-server.jar` in this folder; or
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- `simwrapper serve` to run the python tool (use `pip install simwrapper` first)
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Note that you have to reload your browser after you change any local files on your computer;
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all browsers cache results and won't show your changes until you click reload.
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## Instructions for hosting SimWrapper on fly.io
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You can host your files on the web easily using https://fly.io
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- Install `flyctl` tool from https://fly.io
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- Run `flyctl auth signup` once to get a free account
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- Copy your data files into the data folder here
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- run `flyctl launch`
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# Topsheet Test
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title: Topsheet
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# Input files -------------------------
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files:
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customers:
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file: "*customer_stats_drt.csv"
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useLastRow: true
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vehicles:
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file: "*vehicle_stats_drt.csv"
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useLastRow: true
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drtVehicles:
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file: "*drt__vehicles.xml.gz"
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xmlElements: "vehicles.vehicle"
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# These are text entry boxes in the UI
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userEntries:
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vehCost_fix:
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title_en: Veh-Cost (Fixed)
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title_de: Veh-Cost (Fixed)
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value: 1.0
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vehCost_km:
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title_en: Veh-Cost / km
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title_de: Veh-Cost / km
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value: 0.50
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vehCost_hour:
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title_en: Veh-Cost / hour
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title_de: Veh-Cost / hour
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value: 4.00
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sampleSize:
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title_en: Sample Size
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title_de: Sample Size
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value: 1.00
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# Calculations ------------------------
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calculations:
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userCost_km: 0.25
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userCost_fix: 3.00
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minShareIdleVehicles: 1.0
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simple: 'vehCost_hour * userCost_fix'
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rides: '${customers.rides}'
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vehicles: '${vehicles.vehicles}'
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totalDistance: '${vehicles.totalDistance}'
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opHours: '(${drtVehicles.t_1} - ${drtVehicles.t_0}) / 3600'
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operatingHours: 'opHours * sampleSize ^ (-0.662)'
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directDistanceMean: '${customers.direct_distance_mean}'
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userFare: '${customers.fare}'
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demand: 'rides / sampleSize'
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fleetSize: 'vehicles * sampleSize ^ (-0.637)'
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fleetMileage: 'round(totalDistance * sampleSize ^ (-0.928))'
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revenueDistance: 'directDistanceMean * demand'
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incomePerDay: 'userFare'
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expensesPerDay: 'fleetSize * vehCost_fix + fleetMileage*vehCost_km'
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annualProfit: '365 * (incomePerDay - expensesPerDay)'
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effectiveFleetSize: 'fleetSize * minShareIdleVehicles'
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# Table rows
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outputs:
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- title_en: Demand
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title_de: Demand
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value: demand
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style: { color: green}
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- title_en: Fleet size
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title_de: Fleet size
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value: fleetSize
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- title_en: Fleet mileage
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title_de: Fleet mileage
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value: fleetMileage
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- title_en: Revenue distance
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title_de: Revenue distance
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value: revenueDistance
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- title: Operating Hours
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value: operatingHours
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- title_en: Expenses/day
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title_de: Expenses/day
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value: expensesPerDay
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- title_en: Annual profit
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title_de: Annual profit
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value: annualProfit
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style: { textDecoration: underline }
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- title_en: Effective fleet size
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title_de: Effective fleet size
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value: effectiveFleetSize
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header:
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tab: "Düsseldorf scenario"
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title: "Overview of the Düsseldorf MATSim scenario"
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description: "This dashboards show an overview and some key insights into the Düsseldorf MATSim scenario."
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layout:
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map:
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- type: "links"
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title: "Overview"
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description: "Simulated volumes in the study area. These do not represent real volumes, but down-sampled volumes of agents using their car for private transport."
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height: 12
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props:
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csvFile: "*ink_volumes.csv"
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geojsonFile: "network.geojson.gz"
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center: [6.779091, 51.23347]
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zoom: 11
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display:
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color:
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dataset: csvFile
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columnName: volume
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colorRamp:
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ramp: RdBu
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reverse: false
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steps: 7
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width:
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dataset: csvFile
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columnName: volume
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scaleFactor: 1
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