First, I fetched all of the Argo data up through 2021 via ftp:
wget -r ftp://ftp.ifremer.fr/ifremer/argo
# wait a long time
tree ftp.ifremer.fr
└── ifremer
└── argo
First, I fetched all of the Argo data up through 2021 via ftp:
wget -r ftp://ftp.ifremer.fr/ifremer/argo
# wait a long time
tree ftp.ifremer.fr
└── ifremer
└── argo
I managed to iterate over Awkward Arrays and rasterize the NYC buildings as polygons. The spatialpandas code is pretty well integrated with the ragged data structures you've built; there's a lot of code that twiddles offset arrays. I couldn't use the build_polygon
function directly, but ported over enough of it into my own Numba-compiled function to reproduce the output.
These are Matplotlib's imshow
displays of images made by iterating over Awkward Arrays; the axes are flipped from the normal longitude, latitude because I'm just dumping the array as an image, but I verified on one complex building that I am exactly reproducing spatialpandas's output (including the short-circuit code paths, in which a polygon is smaller than a pixel). The first is low-resolution and the second is high-resolution, the minimum and maximum number of pixels in the performance studies later in this email.
# Author: Vassil Vassilev | |
project(aarray-example) | |
cmake_minimum_required(VERSION 3.10) | |
#conda install -c conda-forge/label/llvm_rc clangdev=14.0.0.rc2 | |
set(conda_path "/home/jpivarski/mambaforge/envs/vassil-clang-python/") | |
set(CMAKE_FIND_PACKAGE_SORT_ORDER NATURAL) | |
set(CMAKE_FIND_PACKAGE_SORT_DIRECTION DEC) |