ILINet fxns - non-nested with data as sole args

functions.py
- changed structure of functions for ILINet analyses
(OR, zOR, week plotting dicts, classif zOR) so that
only the OR function reads in the data files. The
other subsequent functions build off of the data
objects created in that initial function. The functions
must be run sequentially in the main script in order
to generate the data objects in the correct order (to
feed into the next function called), but they are no
longer nested in the funcitons.py script.
- Think about removing the ILINet and SDI data distinctions
in functions; instead, have an area in functions.py
where the used parameters are set (1. dummy variable
name for plotting seasons list in all scripts 2. keep
ILINet and SDI plotting seasons lists as separate names
3. add dummy variable to section where used parameters
are set and call the ILINet or SDI plotting seasons lists
as necessary)
ignore checks scripts

.gitignore
- add create_fluseverity_figs/checks/ folder
to the ignore list
- this folder will include scripts that are
checking and playing around with data to
explore different viz representations or
normalization techniques
- likely, scripts in this folder won't make
products that go into the final manuscript
un-nest all plotting fxns, call pseasons

functions.py
- previously the functions generating incid, OR, zOR,
regional, plotting, and classif index dicts were nested:
One function would call another function within the
functions.py script. This made it difficult to
make edits to the functions and made the main script
more difficult to interpret since the function dependencies
were not explicitly listed there. Now, the upper level
functions import the data objects from the lower level
functions (instead of always importing the raw data).
- added a new section for "call parameters." These are
the parameters that must be adjusted each time a plotting
script is run. For now, the only call parameter is the
list of plotted seasons. The SDI and ILINet data have
different plotting seasons, so the call parameter
"pseasons" must be adjusted depending on whether
the script is plotting ILINet or SDI data.

SDI and ILINet plotting scripts have all been updated
to reflect this new structure. They have also been
checked and appear to plot the data correctly.

Possible next steps for streamlining:
- compare ILINet and SDI specific functions in functions.py.
They may be the same now that the "call parameters" section
has been implemented
- check arguments for functions. There are unnecessary
arguments for some functions in the updated functions.py
script
rm ILINet-specific functions where possible

functions.py
- rm ILINet-specific functions: ILINet_classif_zOR_processing,
ILINet_week_plotting_dicts, ILINet_week_zOR_processing
- keep ILINet_week_OR_processing function: it returns the same
dicts as does week_OR_processing function so it plays
well with the other upper level functions in the SDI
function hierarchy.
- Added comments to SDI functions about ILINet input data files

Changed ILINet scripts to call the correct functions
- Reran ILINet plotting scripts to check that they worked
rm unneeded args from functions

functions.py
- removed arguments that were unneeded in all
functions
- affected functions include:
classifzORindex
classifzORindexstate
classifzORprocessing
classifzORregionprocessing
classifzORstateprocessing
weekplottingdictsregion
weekplottingdictsstate
weekzORprocessing
weekzORprocessingregion
weekzORprocessingstate

updated all plotting scripts to reflect new
argument dependencies in functions. all were re-run
to check for dependency errors
sev index vs. % subtyped isolates that are H3

functions.py
- season_H3perc_NREVSS function now imports the
NREVSS_Isolates_Season_improved.csv file, which is
aggregated across the FluView data collected by My.
Previously, this function imported NREVSS_Isolates_
Season.csv, which reported the lab-confirmed cases
of flu as reported by each season's Flu Season
Summary, published at the end of the flu
season by CDC.
- the files report isolates in different ways
- season numbers should range from -2 to 14
(1998-2014)

Supp_zOR_H3.py
- data from My_Bansal_Lab folder was copied into
my folder, so imported filename extensions were
changed

ILINet_Supp_zOR_H3.py
- first commit
- severity indexes (mean retro/early zOR) for ILINet
data are plotted against %H3 isolates of all positive
specimens tested
- these plots are ILINet complements to the SDI plots
from Supp_zOR_H3.py
break into shorter benchmark normalization periods

ILINet_F3_zOR_benchmark_altnorm.py
- severity index vs. CDC benchmark index calculated
in two different normalization schemes:
1) pre-pandemic and post-pandemic periods: 1997-8
through 2008-9, 2010-11 through 2013-14 seasons
2) three normalization periods: 1997-98 through
2002-3, 2003-4 through 2008-9, 2010-11 through 2013-14
seasons
- the two normalization schemes performed similarly,
but they did provide different classifications
than the initial scheme that was used, which was to
z-normalize across all 14 seasons in the ILINet data
0 ILI cases if missing data in incid csv

functions.py
- week_incidCA_processing function had an added component
where 0 ILI cases are added in any given week-age group
combination that has missing data
- the purpose of this addition is to allow the function
to work more flexibly on smaller spatial scales, where it
is more likely for missing data to occur
- enables comparative analyses of Austin to national level
as a check that the national and city level severity
patterns are comparable
ID avg cum incid perc for retro period across seas

functions.py
- cum_incid_at_classif function returns the cumulative
incidence percentage for the retrospective and early
warning period weeks (for a given season)

cum_incid_classif_periods.py
- return the cumulative incidence percentage for each
week in the retro and early warning periods, for each
season
- print the average start and end cumulative
incidence percentages for the retro period across all
seasons

These average cumulative incidence percentages will be
used scale the time-based epidemic simulations to the
timing of the retrospective periods in the real data.