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add support for velocity mosaics from granule stacks without a common reference date #57
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bbox = create_buffered_bbox(geotransform.to_gdal(), frame_map_array.shape, 90) # EPSG:3857 is in meters | ||
granule_xrs = [sw_disp.load_sw_disp_granule(x, bbox) for x in granules] | ||
granule_xrs = [sw_disp.load_sw_disp_granule(x, bbox) for x in sorted_granules] | ||
align_to_common_reference_date(granule_xrs) |
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When this function is called we're not currently guaranteed to have all granules between two dates. For example, if we're trying to get velocity between date1
and date5
, there might be granules like:
date1_date2
, date1_date3
date3_date4
, date4_date5
but calling find_needed_granules
here with the minmax
strategy would only load date1_date3
, and date4_date5
.
Two possible ways to accomplish this:
- All ways download all granules between two dates, even if some granules will not be needed
- Dynamically find the set of granules needed to reconstruct the full deformation history and load them so they can be used in
align_to_common_reference_date
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The two core use cases I see brewing have been:
For a particular frame...
- ...give me a single data band representing net/cumulative displacement from Date A to Date B. This satisfies the needs of the cumulative displacement and secant velocity visualizations.
- ...give me a time series of data bands, each representing net/cumulative displacement from a common reference date, for all available time steps from Date A to Date B. This satisfies the needs of the velocity-via-linear-regression visualization.
I suspect these are also the data-preparation steps data users will need to do for most analysis using this data set. I'm somewhat eager to revisit find_needed_granules
, update_reference_date
, and align_to_common_reference_date
to provide a better interface for those two more generic use cases.
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I suppose the latter time-series approach satisfies all use cases, but when you only care about total change the former approach would be faster and require less data access.
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