CODEWITHSUNDEEP
python-3.xgeometryshapefilepython-xarray
Ep1c1aN
Ep1c1aN

Reputation: 733

Extract data within a geometry (shape)

I have a NetCDF dataset which I normally call using xarray function. I have recently been doing some transect analysis on the dataset, which looks something like this (area between the horizontal blue lines are the transect for analysis):

enter image description here

For now, I am only able to extract the data, which either falls within the vertical or horizontal transect (as they are easy to extract.

But now I want to select a certain shape within the data-set, on which I would like to analyse. And these shapes might not be symmetrical or in a straight line. Some examples are shown below: enter image description here

Basically, these shapes can be irregular (with known co-ordinates of each point).

Is it possible to extract the dataset/values (with co-ordinates), specifically only for the region of interest?

These steps are easy to perform just using ARCMaps clip function or Google Earth Engine clip function. But I am not able to use this in python (as I want solely use python for all steps). Can someone provide some suggestions on how to this? If anyone knows any package, that has a great integration with xarray, that would be great.

Upvotes: 2

Views: 1877

Answers (1)

Ep1c1aN
Ep1c1aN

Reputation: 733

I have used a rioxarray package which seems to have great integration with xarray and is really easy to implement.

import xarray as xr
import rioxarray as rx

Treecover  = xr.open_rasterio('/home/chandra/data/Treecover_MOD44B_2000_250m_AMAZON.tif')
[Output]: 
<xarray.DataArray (band: 1, y: 32093, x: 20818)>
[668112074 values with dtype=float64]
Coordinates:
  * band     (band) int64 1
  * y        (y) float64 13.71 13.71 13.71 13.71 ... -58.35 -58.35 -58.36 -58.36
  * x        (x) float64 -81.38 -81.37 -81.37 -81.37 ... -34.63 -34.63 -34.62
Attributes:
    transform:   (0.002245788210298804, 0.0, -81.37613580017715, 0.0, -0.0022...
    crs:         +init=epsg:4326
    res:         (0.002245788210298804, 0.002245788210298804)
    is_tiled:    0
    nodatavals:  (nan,)

geometries = [
    {
        'type': 'Polygon',
        'coordinates': [[
            [-46.23140155225633, -21.53505449239459],
          [-44.91304217725633, -20.221175092759253],
          [-70.22554217725633, 1.5816072875439455],
          [-71.36812030225633, 0.5271132528460204]
        ]]
    }
]

Treecover_clipped = Treecover.rio.clip(geometries, Treecover.rio.crs)
[Output]: 
<xarray.DataArray (band: 1, y: 10293, x: 11779)>
array([[[nan, nan, ..., nan, nan],
        [nan, nan, ..., nan, nan],
        ...,
        [nan, nan, ..., nan, nan],
        [nan, nan, ..., nan, nan]]])
Coordinates:
  * band         (band) int64 1
  * y            (y) float64 1.58 1.578 1.575 1.573 ... -21.53 -21.53 -21.53
  * x            (x) float64 -71.37 -71.36 -71.36 ... -44.92 -44.92 -44.91
    spatial_ref  int64 0
Attributes:
    transform:     (0.0022457882102988043, 0.0, -71.36665774687539, 0.0, -0.0...
    _FillValue:    nan
    grid_mapping:  spatial_ref

Upvotes: 4

Related Questions