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Data reduction
The target format is the Hierarchical Data Format (HDF) in version 5, a well established data format with good reading routines for Python, Matlab and IDL.
The first step is just a straight forward parsing of the CSV output of the Mongo database dump.
While parsing, values of 'null' are being replaced by numpy.NaN.
I made the conscious decision to NOT replace None in the marking column by NaN because that
detail is in itself useable data.
The acquisition_date column is currently being parsed to a python datetime, if that is a problem
for someone absolutely requiring it to be a string, I could make this optional.
I produce different versions of the reduced dataset, increasing in reduction, resulting in smaller and faster to read files.
For all file names the date part indicates the date of the database dump which is delivered every by Stuart.
All data is included apart from what was removed in above filtering step.
Product file name is planet_four_level_1_20xx-xx-xx.h5
This product is reduced to the data records that are finished in Planet4 terms, which is currently defined has having 30 individual analyses performed on a specific Planet4 subframe.
Product file name is planet_four_level_2_20xx-xx-xx.h5
This product is reduced further from Level 2 by only including data records with markings!='None'.
In other words, each data record of this data product has marking data in it.
Product file name is planet_four_level_3_20xx-xx-xx.h5