Initial commit - Sarah's files

Scripts to run percolation simulations on Vancouver age-structured network.
Sent by email from Sarah Kramer on July 21, 2013.
urbanage_tiv_realistic.py - accounts for reduction in vaccine efficacy
urbanage_tiv_realistic_SIMPLIFIED.py - does not include reductions in vax efficacy
pretty_print.py - module for clean output to file
add explanatory comments to Sarah's code, OR calc

- made slight aesthetic changes and added explanatory comments to Sarah's code
- added function calculateOR to perform an odds ratio calculation although it is not implemented anywhere in the main program at this point
- code is still confusing to understand. will need to make bigger reorganizational changes for next commit
add gitignore file to repo
no longer pursuing Sarah's code

Will develop my own age-based simulation code based on peregrine.hladish.com instead of using Sarah's code since we want to start with simpler cases. Abandoning this approach.
added code to time speed of percolations

Sarah's code ran much faster than the lab 4 SSID lab code so I wanted to time the speed of her percolations and compare them. The use of dictionaries to identify susceptible nodes increased the speed of the percolation by over 10 times.
first commit - toy sims for OR by T and by vaxcov

age_perc_T.py adapted from lab 5 SSMID to examine ORs between children and adults as T increases. At T values greater than 0.2, the whole network becomes infected so T ranges from 0 to 0.2 currently.

age_perc_vaxcov.py adapted from lab 5 SSMID to examine ORs as random vax coverage increases from 0 to 0.2. At values greater than 0.2, the network is very fragmented.

percolations.py is a module with the age structured percolation function and other functions for organizing the data into dictionaries.

-- things to think about:
- how to incorporate a safe way to remove simulations that infect every individual of at least one age group (the denominator for the child or adult odds will be 0 if all individuals are infected, and this division cannot be performed so the calculation breaks the code). the current values do not quite produce 100% infection rates, but it would be nice to put in a catch-all line to protect the code from crashing.
- how to implement a probabilistic change in susceptibility due to vaccination instead of 100% vax efficacy with very low levels of vax coverage?
add SSMID_Labs/ to .gitignore
examine error bars for episize for T & vaxcov sims

Draw episize by T and episize by vaxcov charts based on exported data from 1000 and 4000 simulations
Use the size of the error bars to judge how many simulations are necessary to get small error bars in epidemic size.
commit before switching to vaxcov debug branch

want to explore exported data in order to debug the reason for large standard deviations in the epidemic sizes of the vaxcov simulations
Merge branch 'master' of https://github.com/eclee25/flu-SDI-simulations-age

Github repo is new and includes a README and license file that needs to be merged with the local repository prior to pushing local repo files to the remote.