Feat(dashboard) : Using external data for clustering & changing json format for the result of aggregations

[NicoPL]

DONE:

• moving functions to compute aggregation from views.py to the new file stats.py
• using of external data to make clusters in which compute aggregations (depending on years and samples)
• mapping of aggregation method (MRP, etc.) to the clusters, using 'for' and 'cod' fields for now
• changing the format of the resulting JSON using {"year":{"sample":{data}, .. }, ..}
• adding comments

Still TODO:

• write other aggregation functions (SRS, SS, etc.)
• write other aggregation functions (total and proportion) and create a configuration dictionary where write for each data what function to apply
• write the right formula to compute standard errors which is much more complex that the common standard deviation that is coded for now
• get the year in meteo.csv to map with data
• deal with data series (eg. relative_abundance)
• reorganize files:
  • make a constants.py to store constants and configurations of aggregation
  • keep in stats.py only the functions to compute aggregation
  • put in models.py the objects dealing with data and calling aggregation functions