Investigate enabling date range queries for the `/timelapse` endpoint

[leothomas]

This issue builds off of PR #113

The /timelapse endpoint returns zonal stats for a single time unit (month or day), which can be cumbersome for API consumers (eg: 2 year timelapse of a monthly dataset requires 24 API queries).

While it's very straightforward to implement a date_range parameter in the query which causes the API to return an array of zonal stats. ie:

# current behaviour: 
GET /timelapse -d {"date": "YYYY_MM_DD", ... } --> {"mean": ..., "median" } 

# additional behaviour to implement: 
GET /timelapse -d {"date_range": ["YYYY_MM_DD_start", "YYYY_MM_DD_end"], ... } --> {
    "YYYY_MM_DD-1": {"mean": ..., "median": ... }
    "YYYY_MM_DD-2": {"mean": ..., "median": ... }
    ...
}

NOTE: calculating zonal stats requires loading the COG into memory. For a big enough AOI this calculation can already be quite time consuming, repeating this calculation in a loop for a large time range (eg: daily dataset for 2 years is over 700 zonal_stats calculations) might break things, or at best be very slow.

TODO:

• [ ] Implement looping zonal stats calculation --> stress test feature and review implementation if large date ranges/AOI's cause excessive response time

[leothomas]

Update:

I've implemented a threaded stats calculation that processes the requested date range in batches of 15. I ran some tests to get an idea of run time for this endpoint with different date ranges and areas. Here are the results:

Dataset: CO2 (daily COGs)		Los Angles (106 994 km^2)	California (999 714 km^2)	Continental U.S. (14 376 697 km^2)
2 days	0.6 s	0.7 s	0.8 s
1 week	0.8 s	1.1 s	1.5 s
1 month	2.4 s	2.9 s	4.9 s
6 months	11.6 s	13.9 s	26.5 s
1 year (~330 COGs to load)	21.8 s	27.0 s	breaks API 30s timeout

I've deployed this work into a test stack accessible at this endpoint: https://l47o73bjpk.execute-api.us-east-1.amazonaws.com/v1/timelapse