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The scripts I've created take advantage of a slightly modified PyAnvilEditor to parse the region files.
I've replaced the code in the 276 (and everything else under
def close(self):) of the world class of
PyAnvilEditor with true - this is because I'm not writing to
the world file, I only intend to read from it. This saves a large amount of
time spent writing that would go to waste.
To generate the graphs I've used Matplotlib and I use mpld3 to convert these graphs into interactive html files for use on my website.
We load a large section of the world into memory - ideally, you want to set
the block size to the maximum as this is quicker than loading small sections
of the world repeatedly. Then we iterate through each coordinate in the block
- this takes about 90 seconds with a block radius of 128. At each
coordinate we iterate each count for a tile in a particular layer. Once a
whole block has been processed, we add it to the total.
The 'sum' represents the total amount of a particular ore block within the whole sample (A 1024*1024 square meter area). The relative frequency represents the proportion of the total amount of a particular ore that occurs at a particular y-level.
This sample size can be improved on but it would obviously take longer given that my RAM is limited.