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docs: fix typos and mislabeled version headings #329

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5e395b7
docs: fix typos in why-xee.md
abidanBrito Jun 2, 2026
be9e338
docs: fix mislabeled version headings in migration-guide-v0.1.0.md
abidanBrito Jun 2, 2026
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18 changes: 9 additions & 9 deletions docs/migration-guide-v0.1.0.md
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Original file line number Diff line number Diff line change
Expand Up @@ -96,7 +96,7 @@ ds = xr.open_dataset(

#### Example 1: Global dataset at fixed scale

**Before (v0.1.0):**
**Before (v0.0.x):**
```python
ds = xr.open_dataset(
'ECMWF/ERA5_LAND/MONTHLY_AGGR',
Expand Down Expand Up @@ -128,7 +128,7 @@ ds = xr.open_dataset(

#### Example 2: Regional dataset with EE geometry

**Before (v0.1.0):**
**Before (v0.0.x):**
```python
import ee

Expand Down Expand Up @@ -167,7 +167,7 @@ ds = xr.open_dataset(

#### Example 3: Using source resolution for a custom area

**Before (v0.1.0):**
**Before (v0.0.x):**
```python
# You had to manually determine the scale from the dataset
ds = xr.open_dataset(
Expand Down Expand Up @@ -215,7 +215,7 @@ Xee v0.1.0 outputs dimensions in `[time, y, x]` order (matching CF conventions a

#### Plotting

**Before (v0.1.0):**
**Before (v0.0.x):**
```python
# Required transpose for correct visualization
ds['temperature_2m'].isel(time=0).transpose().plot()
Expand All @@ -231,7 +231,7 @@ ds['temperature_2m'].isel(time=0).plot()

Many geospatial libraries expect `[time, y, x]` ordering. You may have been using `.transpose()` to accommodate this.

**Before (v0.1.0):**
**Before (v0.0.x):**
```python
# Had to transpose for libraries expecting [time, y, x]
data_array = ds['temperature_2m'].transpose('time', 'y', 'x')
Expand All @@ -249,7 +249,7 @@ export_to_geotiff(data_array)

If you have code that explicitly references dimension positions, update it:

**Before (v0.1.0):**
**Before (v0.0.x):**
```python
# Dimensions were [time, x, y]
time_dim, x_dim, y_dim = ds['temperature_2m'].dims
Expand Down Expand Up @@ -280,7 +280,7 @@ time_length = ds.sizes['time']

### Pattern 1: Simple global analysis

**Before (v0.1.0):**
**Before (v0.0.x):**
```python
import ee
import xarray as xr
Expand Down Expand Up @@ -323,7 +323,7 @@ mean_temp.plot() # No transpose needed

### Pattern 2: Regional analysis with preprocessing

**Before (v0.1.0):**
**Before (v0.0.x):**
```python
import ee
import xarray as xr
Expand Down Expand Up @@ -384,7 +384,7 @@ ds = xr.open_dataset(collection, engine='ee', **grid_params)

### Pattern 3: Export workflows

**Before (v0.1.0):**
**Before (v0.0.x):**
```python
import xarray as xr

Expand Down
6 changes: 3 additions & 3 deletions docs/why-xee.md
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Expand Up @@ -76,7 +76,7 @@ Differences:
- **Quota vs No Quota**: Since Earth Engine is API based, there are quota
restrictions that limit IO, namely a 100 QPS limit on data requests. Readers
all need to be authenticated and tied to a GCP project quota. Zarr, on the
other hand, has a lower level access pattern. Reading is delegating to basic
other hand, has a lower level access pattern. Reading is delegated to basic
permissions on cloud buckets.
- **On the fly vs up-front data shaping**: In Zarr, the representation of data
at rest fundamentally influences performance at query time. For this reason,
Expand All @@ -87,7 +87,7 @@ Differences:
server-side), users are free to request the resolution and projection of the
data during dataset open. Similarly, while Earth Engine's internal dataset
does fit an internal chunking scheme, chunking schemes are a lot more
fungibile.
fungible.

We hope that this comparison provides the user of a set of useful precedents for
We hope that this comparison provides the user with a set of useful precedents for
working with cloud-based datasets.