NOTE: I can only guarantee that these functions will work on WINDOWS, particularly the database functions. For those who do not use Windows, I will not effectively be able to debug any issues.

I suggest running these ONE-BY-ONE so you know exactly where you may encounter errors. This is also because the registerUsers() function is skipped over when running things in bulk.

You will need to install a few things before getting started, most importantly Ben's SPIM package. There are two ways to go about this.

This method is the easiest. There is a .zip file in the repo called SPIM_0.1.zip.

Install the package using the install.packages dialog going through Tools > Install Packages > Install From > Package Archive File. Then select the .zip file.

The package should install without issue.

This file builds the local SQL database to draw particular settings from. Run the whole script, and you will know that it has been successful if a file settings_v2.sqlite of size 0.5GB shows up in your working directory.

Once you have installed SPIM and run the SETUP.R file, particularly on your first run run each line of the preparation block one at a time. Each package will be installed if you don't have it, or loaded if you do. Make note of any errors.

Please run source(functionsSQL.R) at the top, the program should ask you to register your netID or initials - just use one username for all your computers please! If you notice a mistake in your username, please adjust it by running

registerUser(update = T)

Once you've installed the SPIM package and all the other requisite packages, you will need only to execute the contents in RUNSCRIPT.R.

To do so, open the file in R and just hit the source button up top. You can also run the document interactively, but I'd prefer you source it, so that there are no mistakes selecting lines.

You will see things output into the localOutput folder as they are completed. For those who are more curious, you may use the Shiny app taskMonitor.R to observe the work that all the computers are doing. You will need to have the shiny library to execute, but the app will launch from either R or Rstudio. It will open a browser window with multiple tabs to observe the processes at work.

Within the setup block you may customize a few things. Specifically, you can change how many cores your computer will run on.

Set cores = detectCores() for maximum performance. Set cores = 1 for minimum load (if you need the resources for something else).

By default, numTasks = cores, so you're doing one task per core that you have.

The script automatically handles uploading of files to the Box server via subroutine_autoupload.R. There will be a cmd window that opens to provide a monitor of upload status. If it is your first time running, you will need to sign into your Box account to allow this program to access the server.

IMPORTANT

For those who are not using automation to upload the files, you will need to drag and drop the outputs to the shared folder. You should see a green icon in the lower-right hand corner indicating successful uploads.

When you go to upload new files, be sure to follow the next steps! Select all of the files in the localOutput directory, and drag and drop into the shared folder. It will show you a dialog as follows:

SELECT CANCEL.

• If you select "Keep Separate", it will duplicate all of the files you have already uploaded. We definitely don't want this.
• If you select "Update Existing", it will re-upload all of the files you have already uploaded, which will become tedious with greater amounts of files, but not harmful.

Be sure to upload your outputs frequently so that we have them all in case of system failure.

This folder hosts various graphics germane to the project.

This folder will be automatically created when you execute RUNSCRIPT.R, and will host local copies of the analysis outputs.

This folder hosts Rmarkdown files used to edit the Wiki page.

This file indicates what files are to be ignored by Git.

Functions included to build clustered traps. There is an older function build.cluster() that develops square clusters only, and a newer one build.cluster.alt() that develops clusters of irregular arrangements.

Since we will most likely use build.cluster.alt() in the production run, I list the arguments for that function here.

• ntraps : The number of traps TOTAL.
• ntrapsC : The number of traps per cluster.
• spacingin : The spacing among traps within a cluster.
• spacingout: The spacing among clusters.
• plotit : Whether to plot the clusters - default is FALSE.

Functions contained to execute SQL transactions to update tasks to be completed. Included are functions:

• reserveTasks : Will reserve a set of numTasks tasks.
• updateTasksCompleted : Will update the tasks previously reserved as completed.
• printDB : Will print a record of the task database.
• registerUser : Will register a user name with a machine name to a separate database.
• executeWithRestart : Executes SQL transactions with automatic restarts.

RUNSCRIPT.R sources this file.

C++ implementation of SCR likelihood calculation

This will be the script that users will ultimately execute to participate in the analyses. It has major components that are outlined at the start of the script.

This file writes a local SQL database of the tasks to be done. Please run this on your first initialization of the analysis.

Function to simulate SCR and occupancy data. See the Wiki page on the function for a detailed, step-by-step walkthrough.

This is a Shiny app that will automatically launch a browser window. The purpose is to monitor the completion of tasks over time and provide a rough estimate of completion time. There are three tabs:

This page displays the full table of tasks as well as some brief metrics such as machine name, time started, time ended, and duration of analysis.

This page displays some charts for the distribution of tasks taken by user, and the time efficiency of the machines.

This page displays the start/end times of each task graphically, and a linear trend of the form (taskIndex ~ timeEnded) is assessed over tasks for prediction of the date of completion.

This function scans localOutput\ and uploads files that do NOT exist on the server. It opens up a cmd window to provide a report of files uploaded through subroutine_autoupload.bat.

This function interacts with the SQL server to mark tasks completed that were reserved. It also opens up a cmd window to provide a report of files marked complete through subroutine.bat

This file, when sourced, will write a record of all the proposed settings to memory. This is used in RUNSCRIPT.R

After you have run SETUP.R, this file will generate in your working directory. It contains all tasks to be completed and their respective parameter settings. The second version pares down the settings table to 4 million settings.