652567364 by Unknown

Author:Unknown
Language: eng
Format: epub

%%time

corr_ra = sbas.open_grids(pairs, 'corr')

corr_ra

The Dask object is nicely visualized, providing detailed information such as array dimensions, coordinates and their limits, data type, and data chunks. While most of the properties are self-explanatory, data chunking is a crucial concept for lazy computations. This entails dividing the dataset into a collection of atomic data units — chunks — and Dask manages these chunks to plan and execute its tasks. The default chunk size is 512 (or rather, 512x512) for a 2D raster, which is typically sufficient. In Google Colab, there is no need to adjust this parameter, but for special cases, it can be fine-tuned during PyGMTSAR initialization and by using the chunksize argument in open_grids() and other functions. The 3D raster stack illustrated above has dimensions (1059, 915, 1152) and a chunk size of (1,512,512), resulting in a total of 6354 chunks. Therefore, to read the complete dataset, Dask performs as many as 6354 reading operations. However, Dask can’t read a single pixel from a single grid, but only a block of raster pixels (512,512). Given the float32 data type, the single block size is 512 * 512 * 4 bytes = 1 which is adequate for standard InSAR computations. It might seem conservative and slightly better performance can be achieved with larger chunk sizes, like 1024 (1024x1024) or 2048 (2048x2048), particularly for interferogram processing, unwrapping, and detrending.

Challenges arise in steps that require pixel-wise processing, such as SBAS time series calculation, seasonal trend decomposition, etc. In such cases, the processing is performed for the full stack depth (1059 rasters) on 512x512 raster blocks, handling 1059 * 512 * 512 * 4 bytes = 264 MB data chunks. The actual analysis can require significantly more memory than the source data size, especially with numerous tasks running in parallel. To utilize 8 cores, Dask executes 8 parallel processing tasks on each 264 MB sub-stack. If the analysis requires 20 times more RAM than the data block, the total memory consumption equates to 264 MB * 8 cores * 20 = 42 GB memory. Fortunately, PyGMTSAR’s core functions can estimate memory requirements and fine-tune Dask’s chunk size for optimal performance, enabling it to run smoothly on any hardware. Without a solid understanding of the underlying principles and constraints, it is advisable not to adjust the parameters manually. Generally, allowing PyGMTSAR to manage these technical aspects automatically results in the best outcomes for most InSAR scenarios. However, for highly demanding projects and powerful hardware, PyGMTSAR does permit manual parameter tuning.

And we can open the same rasters with on-the-fly geocoding applied to transform rasters into geographic coordinates in a matter of seconds (8.71s):

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.