Windowed Reading with EOReader#

Let’s see how to read a window instead of the whole bands of a given product and the potential time-consumption gain.

Warning: The durations shown hereunder may not be representative of your computer’s performances. Please take it as a hint about relative performances between constellations.

Try with Landsat-8#

Let’s open a Landsat-8 OLI collection 2 tile.

# Imports
import os
import geopandas as gpd
import rasterio
from shapely.geometry import box

from import Window, from_bounds
from eoreader.reader import Reader
from eoreader.bands import NDWI, SWIR_1

import hvplot.pandas
import hvplot.xarray

# Open the product
folder = os.path.join("/home", "data", "DS3", "CI", "eoreader")
path = os.path.join(folder, "optical", "LC08_L1TP_200030_20201220_20210310_02_T1.tar")
reader = Reader()
prod =
band = NDWI

Windows in EOReader#

There are several ways to read a band with a window in EOReader:

  • Using a vector stored on disk

  • Using a geopandas.GeoDataFrame

  • Using a tuple containing the bounds (in the same CRS than the bands, i.e UTM accessible through

  • Using directly a window. Note that if you want to pass pixel bounds, you must first create a

⚠ Even if a vector is passed to the window keyword, only its extent will be used.

# Here we have a window as a path to a vector
window_path = os.path.join(folder, "others", "20201220T104856_L8_200030_OLI_TIRS_window.geojson")
# Here we have the vector directly opened as a geopanas.GeoDataFrame
window_gdf = gpd.read_file(window_path)
/opt/conda/lib/python3.11/site-packages/cartopy/io/ DownloadWarning: Downloading:
  warnings.warn(f'Downloading: {url}', DownloadWarning)
/opt/conda/lib/python3.11/site-packages/cartopy/io/ DownloadWarning: Downloading:
  warnings.warn(f'Downloading: {url}', DownloadWarning)
# Here we have an array of the bounds (tuple or list also works)
window_bounds = window_gdf.bounds.values[0]
array([ 579046.01517122, 4765387.52325064,  678660.50985682,
# Here we construct a Window containing directly the pixels 
# It is not necessary to have a window with integers values, they will be rounded when needed.
# However, it is better to control exactly the pixels you want so it is advised to round the values yourself
with[SWIR_1])[SWIR_1])) as ds:
    window_pix = from_bounds(*window_bounds, ds.transform)
Window(col_off=1932.033839040505, row_off=2154.040731206187, width=3320.4831561867395, height=2310.2084937724576)
# Create function to time it easily
def load(prod, window):
    Function that loads the wanted band over a proposed window and cleans the temporary directory.    
    ds = prod.load(
    return ds[band]

Time without window#

Pass None to load without any window

%time band_arr = load(prod, None)
CPU times: user 20.9 s, sys: 2.21 s, total: 23.2 s
Wall time: 11.9 s

Time with window#

Use directly the window path. The keyword used to load with a window is window.

%time band_arr_win_path = load(prod, window_path)
CPU times: user 2.41 s, sys: 225 ms, total: 2.64 s
Wall time: 1.15 s

Read with a window in different ways#

Here, try to load with:

  • geopandas.GeoDataFrame

  • Array of bounds


%time band_arr_win_gdf = load(prod, window_gdf)
CPU times: user 2.45 s, sys: 146 ms, total: 2.6 s
Wall time: 1.05 s
%time band_arr_win_bounds = load(prod, window_bounds)
CPU times: user 2.24 s, sys: 213 ms, total: 2.45 s
Wall time: 1.01 s
%time band_arr_win_pix = load(prod, window_pix)
CPU times: user 2.41 s, sys: 132 ms, total: 2.54 s
Wall time: 1.07 s

Some checks#

✅ All windowed bands have the same shape
✅ Windowed bands are smaller that raw band

assert band_arr_win_path.shape == band_arr_win_gdf.shape == band_arr_win_bounds.shape == band_arr_win_pix.shape
(1, 2311, 3321)
(1, 7811, 7681)


  • The image with the [blue - white - yellow] colomap is the MNDWI band.

  • The image with the [green - blue] colomap is the windowed MNDWI band.

  • The green line corresponds to the window’s bounds

  • The blue line is the given vector used as a window

%%opts QuadMesh [fig_size=500]

# Compute bounds
bounds =
window_bounds_gpd = gpd.GeoDataFrame(

# Plot
band_arr[0, ::10, ::10].hvplot.quadmesh(
    "x", "y", 
) * band_arr_win_path[0, ::10, ::10].hvplot.quadmesh(
    "x", "y", 
) * window_bounds_gpd.hvplot(
    facecolor=(0, 0, 0, 0), 
    edgecolor="g", linewidth=4
) * window_gdf.hvplot(
    facecolor=(0, 0, 0, 0), 
    edgecolor="b", linewidth=4, 
    xlim=(bounds[0], bounds[2]), 
    ylim=(bounds[1], bounds[3])