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@mdsumner
Last active July 4, 2025 12:32
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{
"cells": [
{
"cell_type": "markdown",
"id": "59c56cf3-b77d-465c-9f59-d6873a4e0b0d",
"metadata": {},
"source": [
"## Reproject coordinate array values\n",
"\n",
"In this python we aim to take the values from two arrays, 'longitude' and 'latitude' and treating each corresponding value as a coordinate pair reproject to a local map projection. \n",
"\n",
"(No need to question why this is necessary, it's for a project where I need this exact result - I'm merely concerned about the style in which I do it). \n",
"\n",
"The NetCDF file with longitude,latitude arrays `shape = (2030, 1354)` is downloaded from NASA ocean colour (we can't address it directly via URL as the getfile mechanism does not support range requests, and please note also we need Earthdata creds to access the file). \n",
"\n",
"We open the tree, and isolate the '/navigation_data' group which has longitude and latitude. Then \n",
"\n",
"- reshape the values into a matrix of longitude,latitude pairs\n",
"- define a local projection from a central coordinate\n",
"- reproject with pyproj in a function `project_array()`\n",
"- set up 'x' and 'y' DataArrays with the same properties as 'longitude' and 'latitude' and fill with the projected values\n",
"\n",
"My question is, what is the most xarray-idiomatic way to do this? Is there anything especially wrong with the style here of how the reprojection is done? \n",
"\n",
"I know that copying the longitude,latitude to fill with x,y is not ideal but I'm less concerned about that. Is there a better way to do the raw coordinate reprojection, but if there's input into how the new arrays are added that will also help a lot. Thanks! Michael Sumner\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "44c6f58e-03a9-41e3-ae40-1abe5d58bfca",
"metadata": {},
"outputs": [],
"source": [
"oc = \"http://oceandata.sci.gsfc.nasa.gov/getfile/AQUA_MODIS.20250704T003501.L2.OC.NRT.nc\"\n",
"#not sure how to automate this, but 11Mb file at this URL needs earthdata Auth\n",
"#!wget --header=\"Authorization: Bearer ey...\" http://oceandata.sci.gsfc.nasa.gov/getfile/AQUA_MODIS.20250704T003501.L2.OC.NRT.nc"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6fe3ab2a-8984-40c0-acd3-971d56566e3e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2030, 1354)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import xarray\n",
"import os\n",
"# Here assume that we have downloaded 'oc' manually and is in the wd\n",
"ds = xarray.open_datatree(os.path.basename(oc)).navigation_data\n",
"ds.longitude.shape\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0c6d38a6-fda9-44b0-86ad-1df92fe555a2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-83.80194 , -68.893936],\n",
" [-83.8573 , -68.93313 ],\n",
" [-83.91242 , -68.972 ],\n",
" [-83.9673 , -69.010544],\n",
" [-84.02195 , -69.048775],\n",
" [-84.07637 , -69.0867 ],\n",
" [-84.13056 , -69.12431 ],\n",
" [-84.184525, -69.16163 ],\n",
" [-84.238266, -69.19865 ]], dtype=float32)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pyproj import Transformer\n",
"import numpy\n",
"\n",
"def project_array(coordinates, target = 'EPSG:3412', source = 'EPSG:4326'):\n",
" \"\"\"\n",
" Project a numpy (n,2) array in projection source to projection target\n",
" Returns a numpy (n,2) array.\n",
" Adpated from Uli Kohler https://stackoverflow.com/a/42459069\n",
" \"\"\"\n",
" transformer = Transformer.from_crs(source, target, always_xy = True)\n",
" fx, fy = transformer.transform(coordinates[:,0], coordinates[:,1])\n",
" # Re-create (n,2) coordinates\n",
" return numpy.dstack([fx, fy])[0]\n",
"\n",
"ll = numpy.dstack([ds.longitude.values.flatten(), ds.latitude.values.flatten()])[0]\n",
"ll[0:9, :]\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b3b00e28-1e04-4877-bcf8-cc6235e99862",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[]\n"
]
}
],
"source": [
"## generate a local crs\n",
"crs = f'+proj=laea +lon_0={int(ds.longitude.values.mean())} +lat_0={int(ds.latitude.values.mean())} +datum=WGS84 +type=crs'\n",
"coords = project_array(ll, target = crs)\n",
"ds.x = ds.longitude ## make a copy\n",
"ds.y = ds.latitude\n",
"\n",
"ds.x.values = numpy.reshape(coords[:, 0].astype(ds.x.dtype), ds.x.shape)\n",
"ds.y.values = numpy.reshape(coords[:, 1].astype(ds.y.dtype), ds.x.shape)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "69869c97-e327-421d-9161-c6f2c7fa1d68",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x773a221d41a0>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds.y.plot()\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
@mdsumner
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mdsumner commented Jul 4, 2025

Similar approach in R, as I creep towards parity in these lang, doing something wrong in this or above?

library(terra)
library(glue)
r0 <- rast("AQUA_MODIS.20250704T003501.L2.OC.NRT.nc", c("/navigation_data/longitude", "/navigation_data/latitude"))
crs <- as.character(glue("+proj=laea +lon_0={trunc(mean(r0[[1]][]))} +lat_0={trunc(mean(r0[[2]][]))} +datum=WGS84 +type=crs"))


a <- project(values(r0), to = crs, from = "EPSG:4326")
rc <- c(r0, setNames(setValues(r0, a), c("x", "y")))
plot(rc[["y"]], add = FALSE); contour(rc[["longitude"]], add = T); contour(rc[["latitude"]], add = T, col = "hotpink");

image

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