add comments, copy script for MISMS presentation

F2_incid_time.py
F3_zOR_benchmark.py
draw state severity index choropleth

F4_zORregion_choropleth.R
- draw state choropleth of mean zOR in addition to
HHS region level choropleth
fc/fc2: all ms figs for ILINet data

ILINet_F2_OR_time.py
ILINet_F2_incid_time.py
- add comments and clean formatting

ILINet_F3_zOR_benchmark.py
- first commit
- scatterplot mean retro/early zOR vs. benchmark
- benchmark does not include ILINet
- 2009-10 flu season has been removed from plot and normalization

ILINet_F3_zOR_time.py
- first commit
- zOR vs. time w/ approximate periods for retro & early warning
- 2009-10 flu season has been removed
season table - mysql db, comment out exploration

admin/create_season.sql
- used script to upload season table into
SDI database on server snow
- commented out exploration part of script so
it could be run directly from the command
line
change export filename

extract_meanzOR_by_region.py
- meanzOR_by_region saved in Py_export folder
no changes

data_extraction/clean_OR_hhsreg_week_outpatient.R
fc: compare state classif to national classif

evaluate_state_early_warning.py
- show number of accurate state classifications (both
early warning and retrospective) in reference to
national level retrospective classification
- export state, number of accurate state early warning
to nat retro classif, number of accurate state retro
to nat retro classif
- calculate background accuracy rates for early to retro
 and retro to retro by calculating average percent
correctly classified across all states for each of the
8 seasons

evaluate_state_early_warning.R
- draw histograms for state early to nat retro and
state retro to nat retro classification accuracy
draw figs from ILINet data, normalize incidence

functions.py
- separate functions are needed to draw ILINet
figures because of processing and seasons that
are included
- normalize child and adult incidence by the
maximum child incidence value
child & adult incidence normalized by child peak

F6_incid_time_CA_normalized.py
- look at relative contributions of child and adult
incidence during a season to understand whether child
incidence is decreasing or adult incidence is
increasing during severe seasons
fc: %dev attack rate for C and A

F6_AR_season_CA_normalized.py
- bipanel bar chart of percent deviation from
baseline average for child and adult age groups
for attack rate
- attack rate defined over flu season, wks 40-20
- % dev is the ((raw AR/avg AR)-1)*100

functions.py
- normalize_attackCA function: calculates
C&A attack rates by season by summing across
C&A incidence rates during flu season,
calculates baseline average for each population,
normalizes using percent deviation formula
listed above

F6_incid_time_CA_normalized.py
- no changes
fc: export sev index for diff datasets

export_zOR_classif.py
- SDI nation-level mean retro and early warning
zOR by season
- ILINet nation-level mean retro and early
warning zOR by season
- SDI region-level classifications, where regions
are represented as integer numbers
- SDI state-level classifications, where states
are represented as 2-letter abbreviations
GU massive data grant figs

Figures for the Georgetown University massive
data grant that show the size and spatial scale
 of the SDI data. State-level and HHS region
level choropleths of incidence were drawn in
R, and specific case counts and record counts
were requested across the entire dataset
and for peak flu weeks.

GU_massive_data_grant/
export_ILI_by_state.py
incid_choropleth_multiscale.R
info_extraction.sql
write_stateabbr_regnum_crosswalk.py
add comments and check week_incidCA_processing fxn

F6_AR_season_CA_normalized.py
F6_incid_time_CA_normalized.py
- added descriptions in the script header about
the purpose of the script/figure
- adjusted comments about the format of d_incid
becuase week_incidCA_processing function in
functions.py was altered to incorporate the
"other age groups" incidence per 100,000 in
the d_incid dict
week_incidCA_processing function update

functions.py
- edited week_incidCA_processing function to
incorporate other age groups incidence per
100,000 in d_incid dictionary

F6_AR_season_CA_normalized.py
- added comment about the d_incid structure
fc: stacked CAO contrib to tot attack rate

F6_AR_season_CAO_stacked100.py
- draw stacked bar graph of child, adult, and other age
group contribution to total attack rate as a percentage
- shows relative contributions of age groups to attack rates

functions.py
- added global plotting parameters for stacked 100%
bar chart
ignore exploratory scripts & ref data

.gitignore
- added reference data, codebooks, and exploratory
sql and py scripts to ignore file