Add flatline data handling and comprehensive tests. #184
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For cases when the data has sporadic, missing periods, but is still sampled with the same frequency, there will be sections of 'flatlined' data. This is handled as "bad data" using the oversampled identification for IndicesToIgnore in some cases, but not if the base dataset is also oversampled. This pull request adds functionality to discover "flatline periods" during the calculation of the oversample factor. Such periods will be disregarded when finding the oversample factor, and the resulting downsampled dataset will work as normal through the IndicesToIgnore to disregard the "flatlines" themselves.
A test group for "just" datasets with flat data sprinkled around is included, as well as a quite comprehensive test set which includes both noise, downsampling, oversampling, and data loss before identification to test that all features function together.