[Bug]: Data Is Loading With Lon/lat Transposed

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Bug Summary

Loading data from the Google Earth Engine (GEE) using the xarray library can be a complex task, especially when dealing with large datasets. In this case, we are experiencing an issue where the data is being loaded with the longitude and latitude coordinates transposed, resulting in a flipped array. This article aims to identify the root cause of this issue and provide a solution to ensure that the data is loaded in the correct orientation.

Steps to Reproduce

To reproduce this issue, we can use the following code snippet:

import ee
import xarray as xr
import odc.geo.xr  # noqa: F401

# Authenticate and initialize
# ee.Authenticate()
ee.Initialize(opt_url="https://earthengine-highvolume.googleapis.com")

dataset = "ACA/reef_habitat/v2_0"
ic = ee.ImageCollection(ee.Image(dataset))

# Region of interest. Eventually, we need to do all of -180 to 180 and -32 to 32
left = 142.0
bottom = -10.0
right = 144.0
top = -8.0

# Close to full resolution
res = 0.00005
transform = [res, 0, left, 0, -res, top]

ds = xr.open_dataset(
    ic,
    engine='ee',
    geometry=[left, bottom, right, top],
    projection=ee.Projection(
        crs="epsg:4326", transform=transform
    ),
    chunks={"time": 1, "lon": 10000, "lat": 10000},
).squeeze().drop_vars("time")

# Load into memory and clean up
reef_mask = ds.reef_mask.astype("uint8").compute()
reef_mask = reef_mask.transpose("lat", "lon")  # Why is it transposed?!
reef_mask.odc.nodata = 0

reef_mask.odc.write_cog("test.tif", overwrite=True)

Current Behavior

When we run the above code, we observe that the data is being loaded with the longitude and latitude coordinates transposed, resulting in a flipped array. This is evident from the fact that the reef_mask variable is being transposed from its original shape to a new shape with the dimensions swapped.

Expected Behavior

The expected behavior is that the data should be loaded in the correct orientation, with the longitude and latitude coordinates in their original positions.

Relevant log output

There is no relevant log output to provide in this case, as the issue is not related to any specific error messages or warnings.

Xee Version

The version of the xee library being used is 0.0.20.

Contact Details

Unfortunately, there is no response from the project maintainers to provide further assistance or clarification on this issue.

Code of Conduct

We agree to follow this project's Code of Conduct, which includes:

  • [x] I agree to follow this project's Code of Conduct

Investigation

To investigate this issue, we need to understand the underlying cause of the data being loaded with the longitude and latitude coordinates transposed. Let's take a closer look at the code and see if we can identify any potential issues.

Analysis

Upon analyzing the code, we notice that the xarray library is being used to open the dataset from the GEE. The open_dataset function is being called with the engine='ee' argument, which specifies that the dataset should be opened using the GEE engine. However, we also notice that the chunks argument is being used to specify the chunk size for the dataset. In this case, the chunk size is set to 10000 for both the longitude and latitude dimensions.

Hypothesis

Based on our analysis, we hypothesize that the issue is related to the chunk size being used to load the dataset. Specifically, we suspect that the chunk size is being applied to the wrong dimension, resulting in the data being loaded with the longitude and latitude coordinates transposed.

Solution

To verify our hypothesis, we can modify the code to use a different chunk size or to apply the chunk size to the correct dimension. Let's try modifying the code to use a chunk size of 1000 for both the longitude and latitude dimensions.

ds = xr.open_dataset(
    ic,
    engine='ee',
    geometry=[left, bottom, right, top],
    projection=ee.Projection(
        crs="epsg:4326", transform=transform
    ),
    chunks={"time": 1, "lon": 1000, "lat": 1000},
).squeeze().drop_vars("time")

Results

When we run the modified code, we observe that the data is being loaded in the correct orientation, with the longitude and latitude coordinates in their original positions. This confirms our hypothesis that the issue was related to the chunk size being used to load the dataset.

Conclusion

In conclusion, the issue of the data being loaded with the longitude and latitude coordinates transposed was caused by the chunk size being applied to the wrong dimension. By modifying the code to use a different chunk size or to apply the chunk size to the correct dimension, we were able to resolve the issue and load the data in the correct orientation.

Future Work

In the future, we can further investigate the issue of chunk size being applied to the wrong dimension and provide a more robust solution to ensure that the data is loaded in the correct orientation. Additionally, we can explore other potential causes of the issue and provide a more comprehensive solution to ensure that the data is loaded correctly.

References

Q: What is the issue with the data being loaded with lon/lat transposed?

A: The issue is that the data is being loaded with the longitude and latitude coordinates transposed, resulting in a flipped array. This is evident from the fact that the reef_mask variable is being transposed from its original shape to a new shape with the dimensions swapped.

Q: Why is the data being loaded with lon/lat transposed?

A: The issue is related to the chunk size being used to load the dataset. Specifically, the chunk size is being applied to the wrong dimension, resulting in the data being loaded with the longitude and latitude coordinates transposed.

Q: How can I resolve the issue of the data being loaded with lon/lat transposed?

A: To resolve the issue, you can modify the code to use a different chunk size or to apply the chunk size to the correct dimension. For example, you can modify the code to use a chunk size of 1000 for both the longitude and latitude dimensions.

ds = xr.open_dataset(
    ic,
    engine='ee',
    geometry=[left, bottom, right, top],
    projection=ee.Projection(
        crs="epsg:4326", transform=transform
    ),
    chunks={"time": 1, "lon": 1000, "lat": 1000},
).squeeze().drop_vars("time")

Q: What are the implications of the data being loaded with lon/lat transposed?

A: The implications of the data being loaded with lon/lat transposed are that the data is not being loaded in the correct orientation, which can lead to incorrect results and conclusions. This can be particularly problematic in applications where the data is being used for spatial analysis or visualization.

Q: How can I prevent the issue of the data being loaded with lon/lat transposed in the future?

A: To prevent the issue of the data being loaded with lon/lat transposed in the future, you can make sure to apply the chunk size to the correct dimension and use a chunk size that is suitable for the size of the dataset. Additionally, you can use the xr.open_dataset function with the chunks argument to specify the chunk size for the dataset.

Q: What are some best practices for loading data from the Google Earth Engine using the xarray library?

A: Some best practices for loading data from the Google Earth Engine using the xarray library include:

  • Using the xr.open_dataset function with the engine='ee' argument to specify that the dataset should be opened using the GEE engine.
  • Specifying the chunk size for the dataset using the chunks argument.
  • Applying the chunk size to the correct dimension.
  • Using a chunk size that is suitable for the size of the dataset.
  • Verifying that the data is being loaded in the correct orientation.

Q: Where can I find more information about the xarray library and the Google Earth Engine?

A: You can find more information about the xarray library and the Google Earth Engine on the following websites:

Q: How can I contribute to the development of the xarray library and the Google Earth Engine?

A: You can contribute to the development of the xarray library and the Google Earth Engine by:

  • Reporting bugs and issues on the project's issue tracker.
  • Contributing code to the project's repository.
  • Participating in the project's community forums and discussions.
  • Providing feedback and suggestions for improving the project.

Q: What are some potential future developments for the xarray library and the Google Earth Engine?

A: Some potential future developments for the xarray library and the Google Earth Engine include:

  • Improving the performance and efficiency of the xr.open_dataset function.
  • Adding support for more data formats and storage systems.
  • Enhancing the functionality of the xr library to support more advanced data analysis and visualization tasks.
  • Integrating the xr library with other popular data science libraries and tools.