Pooled testing simulation

Your goal is to implement a pooled testing strategy which minimizes the average number of tests for some specific prevalence p and sample size n. Relevant slides can be found here.

Instructions

1. Someone from the group should fork this repository.
2. Copy the StudentProtocols/Baseline.py file to StudentProtocols/<YOUR_GROUP_NAME>.py.
3. Implement the isolate_positives function in your protocol file. Essentially, the isolate_positives function is provided a TestSample object. Using the query method of the sample, you can query any set of numbers 0, 1, ..., n-1, and the query method will return true of any of the queried numbers are "positive". In other words, you provide query a pooled sample. Your task is to correctly return the IDs of the positive samples in the provided TestSample using as few tests as possible (on average).
4. Run the simulation in the CovidTester.ipynb notebook. You should get a table that looks like this

+--------------+-------+----------+
|  Group Name  |  Mean | Variance |
+--------------+-------+----------+
| ProjectOwler | 7.129 |  30.643  |
|   Baseline   |  32.0 |   0.0    |
+--------------+-------+----------+

5. Once you are satisfied, create a pull request back to this repo. Only add+commit your new file!

git add `StudentProtocols/<YOUR_GROUP_NAME>.py`
git commit `StudentProtocols/<YOUR_GROUP_NAME>.py` -m "Updates"