Add checks for if the data's index is a multiindex, then write that into the datapackage
add scrambled neural diff data as test data
change explicit keyword args for load_{tsv,csv,json) to **kwargs for full passability of arguments to pandas
use global flotilla download dir variable for embark
join the renamed feature id (.e.g. if it's a tuple)
change "sample_id" and "common_id" strings to variables for consistency between calls
Add notes about the test datasets
set splicing_columns_name = "columns" if splicing_columns_name is None
programmatically always get axis name
reset splicing_columns_name to event name if splicing_columns_name was originally none
make feature renamer series a cached property for hopeful speed improvements
get the actual index from the axis specified
rename splicing to the common id
if splicing columns are a multi-index, assume the level1 id is the common id with the expression data
add fixture for multiple studies
install pandoc before numpydoc and sphinx?
splicing_common_id[x] --> self.splicing.feature_renamer(x) in test_study.py
self --> test_study in test_study.py
Merge pull request #212 from YeoLab/fix_filter_splicing_on_expression

Fix filter splicing on expression