Repository: pangeo-data/xESMF (language: python)

Issue:
conservative regrid changes sum by factor of almost 10
My conservative regrid is producing a regridded dataset whose sum is lower by a factor of nearly 10.  I am sure I am misunderstanding the way that conservative is defined or the input requirements of xesmf.Regridder, but I sure haven't been able to figure out how.

A minimal example, using two grids in the area of Auckland, New Zealand.  

```python
import numpy as np
import xarray as xr
import xesmf

destination_grid_dict = {'coords': {'lon': {'dims': ('lon',), 'attrs': {}, 'data': [172.0, 172.01353383458647, 172.02706766917294, 172.0406015037594, 172.05413533834587, 172.06766917293234, 172.0812030075188, 172.09473684210525, 172.10827067669172, 172.12180451127819, 172.13533834586465, 172.14887218045112, 172.1624060150376, 172.17593984962406, 172.18947368421053, 172.203007518797, 172.21654135338346, 172.23007518796993, 172.2436090225564, 172.25714285714287, 172.27067669172934, 172.2842105263158, 172.29774436090224, 172.3112781954887, 172.32481203007518, 172.33834586466165, 172.35187969924812, 172.36541353383458, 172.37894736842105, 172.39248120300752, 172.406015037594, 172.41954887218046, 172.43308270676692, 172.4466165413534, 172.46015037593986, 172.4736842105263, 172.48721804511277, 172.50075187969924, 172.5142857142857, 172.52781954887217, 172.54135338345864, 172.5548872180451, 172.56842105263158, 172.58195488721805, 172.59548872180451, 172.60902255639098, 172.62255639097745, 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#==================================================

source_data_dict = {'coords': {'latitude': {'dims': ('latitude',), 'attrs': {}, 'data': [-37.35138884, -37.34687495, -37.34236106, -37.33784717, -37.333333280000005, -37.32881939000001, -37.32430550000001, -37.31979161000001, -37.31527772000001, -37.31076383000001, -37.306249940000015, -37.30173605000002, -37.29722216000002, -37.29270827000002, -37.28819438000002, -37.28368049000002, -37.279166600000025, -37.274652710000026, -37.27013882000003, -37.26562493000003, -37.26111104000003, -37.25659715000003, -37.252083260000035, -37.247569370000036, -37.24305548000004, -37.23854159000004, -37.23402770000004, -37.22951381000004, -37.224999920000045, -37.220486030000046, -37.21597214000005, -37.21145825000005, -37.20694436000005, -37.20243047000005, -37.197916580000054, -37.193402690000056, -37.18888880000006, -37.18437491000006, -37.17986102000006, -37.17534713000006, -37.170833240000064, -37.166319350000066, -37.16180546000007, -37.15729157000007, -37.15277768000007, -37.14826379000007, 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-36.03784685000048, -36.03333296000048, -36.02881907000048, -36.02430518000048, -36.019791290000484, -36.015277400000485, -36.01076351000049, -36.00624962000049, -36.00173573000049, -35.99722184000049, -35.992707950000494, -35.988194060000495, -35.9836801700005, -35.9791662800005, -35.9746523900005]}, 'longitude': {'dims': ('longitude',), 'attrs': {}, 'data': [174.05263073, 174.05714201, 174.06165329, 174.06616457, 174.07067585000001, 174.07518713000002, 174.07969841000002, 174.08420969000002, 174.08872097000003, 174.09323225000003, 174.09774353000003, 174.10225481000003, 174.10676609000004, 174.11127737000004, 174.11578865000004, 174.12029993000004, 174.12481121000005, 174.12932249000005, 174.13383377000005, 174.13834505000005, 174.14285633000006, 174.14736761000006, 174.15187889000006, 174.15639017000007, 174.16090145000007, 174.16541273000007, 174.16992401000007, 174.17443529000008, 174.17894657000008, 174.18345785000008, 174.18796913000008, 174.1924804100001, 174.1969916900001, 174.2015029700001, 174.2060142500001, 174.2105255300001, 174.2150368100001, 174.2195480900001, 174.2240593700001, 174.2285706500001, 174.2330819300001, 174.2375932100001, 174.24210449000012, 174.24661577000012, 174.25112705000012, 174.25563833000012, 174.26014961000013, 174.26466089000013, 174.26917217000013, 174.27368345000014, 174.27819473000014, 174.28270601000014, 174.28721729000014, 174.29172857000015, 174.29623985000015, 174.30075113000015, 174.30526241000015, 174.30977369000016, 174.31428497000016, 174.31879625000016, 174.32330753000016, 174.32781881000017, 174.33233009000017, 174.33684137000017, 174.34135265000018, 174.34586393000018, 174.35037521000018, 174.35488649000018, 174.3593977700002, 174.3639090500002, 174.3684203300002, 174.3729316100002, 174.3774428900002, 174.3819541700002, 174.3864654500002, 174.3909767300002, 174.3954880100002, 174.3999992900002, 174.4045105700002, 174.40902185000022, 174.41353313000022, 174.41804441000022, 174.42255569000022, 174.42706697000023, 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174.8827062500005, 174.8872175300005, 174.8917288100005, 174.8962400900005, 174.9007513700005, 174.9052626500005, 174.9097739300005, 174.91428521000051, 174.91879649000052, 174.92330777000052, 174.92781905000052, 174.93233033000053, 174.93684161000053, 174.94135289000053, 174.94586417000053, 174.95037545000054, 174.95488673000054, 174.95939801000054, 174.96390929000054, 174.96842057000055, 174.97293185000055, 174.97744313000055, 174.98195441000055, 174.98646569000056, 174.99097697000056, 174.99548825000056, 174.99999953000057, 175.00451081000057, 175.00902209000057, 175.01353337000057, 175.01804465000058, 175.02255593000058, 175.02706721000058, 175.03157849000058, 175.0360897700006, 175.0406010500006, 175.0451123300006, 175.0496236100006, 175.0541348900006, 175.0586461700006, 175.0631574500006, 175.0676687300006, 175.0721800100006, 175.0766912900006, 175.0812025700006, 175.08571385000062, 175.09022513000062, 175.09473641000062, 175.09924769000062, 175.10375897000063, 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175.33383425000076, 175.33834553000077, 175.34285681000077, 175.34736809000077, 175.35187937000077, 175.35639065000078, 175.36090193000078, 175.36541321000078, 175.36992449000078, 175.3744357700008, 175.3789470500008, 175.3834583300008, 175.3879696100008]}}, 'attrs': {}, 'dims': {'latitude': 306, 'longitude': 297}, 'data_vars': {}}

ds_destination_grid = xr.Dataset.from_dict(destination_grid_dict)
ds_source = xr.Dataset.from_dict(source_data_dict)
ds_source = ds_source.assign(
    vals=(
        ("latitude", "longitude"),
        np.ones((ds_source.latitude.size, ds_source.longitude.size)),
    )
)
regridder = xesmf.Regridder(ds_source, ds_destination_grid, method="conservative")
ds_regridded = regridder(ds_source)

print("ds_source total: {:0.4e}".format(float(ds_source.vals.sum())))
print("ds_regridded total: {:0.4e}".format(float(ds_regridded.vals.sum())))
```

For me this results in
ds_source total: 9.0882e+04
ds_regridded total: 1.0098e+04

I expected ds_regridded.sum() to be very close to 9.0882e+04.


Discussion / hints:

Fix patch:
diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
index 892a4e82..db376f2d 100644
--- a/.github/workflows/ci.yaml
+++ b/.github/workflows/ci.yaml
@@ -48,6 +48,11 @@ jobs:
           extra-specs: |
             python=${{ matrix.python-version }}
             esmpy=${{ matrix.esmf-version }}
+      - name: Fix env for esmpy 8.4
+        run: |
+          if [ "${{ matrix.esmf-version }}" == "8.4" ]; then
+              conda install "importlib-metadata<8.0.0"
+          fi
       - name: Install Xesmf (editable)
         run: |
           python -m pip install --no-deps -e .
diff --git a/CHANGES.rst b/CHANGES.rst
index e8f65fc0..34ec9ea2 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -3,6 +3,7 @@ What's new
 
 0.8.6 (unreleased)
 ------------------
+* New ``xe.util.cell_area`` utility to compute the cell area using ESMF's internal mechanism. (:pull:`372`, :issue:`369`) By `Jiawei Zhuang <https://github.com/JiaweiZhuang>`_  and `Pascal Bourgault <https://github.com/aulemahal>`_.
 * Compatibility with Numpy 2.0 (NaN vs nan) (:pull:`373`) By `Pascal Bourgault <https://github.com/aulemahal>`_.
 
 0.8.5 (2024-04-11)
diff --git a/xesmf/util.py b/xesmf/util.py
index 4790b112..354c67dd 100644
--- a/xesmf/util.py
+++ b/xesmf/util.py
@@ -4,6 +4,11 @@
 import xarray as xr
 from shapely.geometry import MultiPolygon, Polygon
 
+try:
+    import esmpy as ESMF
+except ImportError:
+    import ESMF
+
 LON_CF_ATTRS = {'standard_name': 'longitude', 'units': 'degrees_east'}
 LAT_CF_ATTRS = {'standard_name': 'latitude', 'units': 'degrees_north'}
 
@@ -159,7 +164,6 @@ def grid_global(d_lon, d_lat, cf=False, lon1=180):
             '180 cannot be divided by d_lat = {}, '
             'might not cover the globe uniformly'.format(d_lat)
         )
-
     lon0 = lon1 - 360
 
     if cf:
@@ -356,3 +360,50 @@ def _mdist(x1, x2):
 
 
 # end code from https://github.com/NOAA-GFDL/ocean_model_grid_generator
+
+
+def cell_area(ds, earth_radius=None):
+    """
+    Get cell area of a grid, assuming a sphere.
+
+    Parameters
+    ----------
+    ds : xarray Dataset
+        Input grid, longitude and latitude required.
+        Curvilinear coordinate system also require cell bounds to be present.
+    earth_radius : float, optional
+        Earth radius, assuming a sphere, in km.
+
+    Returns
+    -------
+    area : xarray DataArray
+        Cell area. If the earth radius is given, units are km^2, otherwise they are steradian (sr).
+    """
+    from .frontend import _get_lon_lat, ds_to_ESMFgrid  # noqa
+
+    grid, _, names = ds_to_ESMFgrid(ds, need_bounds=True)
+    field = ESMF.Field(grid)
+    field.get_area()  # compute area
+
+    # F-ordering to C-ordering
+    # copy the array to make sure it persists after ESMF object is freed
+    area = field.data.T.copy()
+    field.destroy()
+
+    # Wrap in xarray
+    area = xr.DataArray(
+        area,
+        dims=names,
+        attrs={
+            'units': 'sr',
+            'standard_name': 'cell_area',
+            'long_name': 'Cell area, assuming a sphere.',
+        },
+    )
+    # Fancy trick to get all related coordinates without needing to list them explicitly.
+    # We add all and let xarray choose which one to keep when selecting the variable
+    area = ds.coords.to_dataset().assign(area=area).area
+
+    if earth_radius is not None:
+        area = (area * earth_radius**2).assign_attrs(units='km2')
+    return area


Test patch:
diff --git a/xesmf/tests/test_util.py b/xesmf/tests/test_util.py
index b2d7c2d5..5e0680fa 100644
--- a/xesmf/tests/test_util.py
+++ b/xesmf/tests/test_util.py
@@ -1,4 +1,6 @@
+import numpy as np
 import pytest
+from numpy.testing import assert_almost_equal
 
 import xesmf as xe
 
@@ -34,6 +36,14 @@ def test_grid_global_bad_resolution():
         xe.util.grid_global(1.23, 1.5)
 
 
+def test_cell_area():
+    ds = xe.util.grid_global(2.5, 2)
+    area = xe.util.cell_area(ds)
+
+    # total area of a unit sphere
+    assert_almost_equal(area.sum(), np.pi * 4)
+
+
 def test_simple_tripolar_grid():
     lon, lat = xe.util.simple_tripolar_grid(360, 180, lat_cap=60, lon_cut=-300.0)