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examples for interp and interpolate_na (#4621)
* add examples to interp * add examples to interpolate_na * add examples to DataArray.interp * add examples to Dataset.interpolate_na * fix the doctest examples * use integers as example data * fix the sphinx errors
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‎xarray/core/dataarray.py

+88-5
Original file line numberDiff line numberDiff line change
@@ -1519,12 +1519,71 @@ def interp(
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Examples
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--------
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>>> da = xr.DataArray([1, 3], [("x", np.arange(2))])
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>>> da.interp(x=0.5)
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<xarray.DataArray ()>
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array(2.)
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>>> da = xr.DataArray(
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... data=[[1, 4, 2, 9], [2, 7, 6, np.nan], [6, np.nan, 5, 8]],
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... dims=("x", "y"),
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... coords={"x": [0, 1, 2], "y": [10, 12, 14, 16]},
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... )
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>>> da
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<xarray.DataArray (x: 3, y: 4)>
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array([[ 1., 4., 2., 9.],
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[ 2., 7., 6., nan],
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[ 6., nan, 5., 8.]])
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Coordinates:
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* x (x) int64 0 1 2
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* y (y) int64 10 12 14 16
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1D linear interpolation (the default):
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>>> da.interp(x=[0, 0.75, 1.25, 1.75])
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<xarray.DataArray (x: 4, y: 4)>
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array([[1. , 4. , 2. , nan],
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[1.75, 6.25, 5. , nan],
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[3. , nan, 5.75, nan],
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[5. , nan, 5.25, nan]])
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Coordinates:
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x float64 0.5
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* y (y) int64 10 12 14 16
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* x (x) float64 0.0 0.75 1.25 1.75
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1D nearest interpolation:
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>>> da.interp(x=[0, 0.75, 1.25, 1.75], method="nearest")
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<xarray.DataArray (x: 4, y: 4)>
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array([[ 1., 4., 2., 9.],
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[ 2., 7., 6., nan],
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[ 2., 7., 6., nan],
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[ 6., nan, 5., 8.]])
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Coordinates:
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* y (y) int64 10 12 14 16
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* x (x) float64 0.0 0.75 1.25 1.75
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1D linear extrapolation:
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>>> da.interp(
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... x=[1, 1.5, 2.5, 3.5],
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... method="linear",
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... kwargs={"fill_value": "extrapolate"},
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... )
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<xarray.DataArray (x: 4, y: 4)>
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array([[ 2. , 7. , 6. , nan],
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[ 4. , nan, 5.5, nan],
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[ 8. , nan, 4.5, nan],
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[12. , nan, 3.5, nan]])
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Coordinates:
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* y (y) int64 10 12 14 16
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* x (x) float64 1.0 1.5 2.5 3.5
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2D linear interpolation:
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>>> da.interp(x=[0, 0.75, 1.25, 1.75], y=[11, 13, 15], method="linear")
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<xarray.DataArray (x: 4, y: 3)>
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array([[2.5 , 3. , nan],
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[4. , 5.625, nan],
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[ nan, nan, nan],
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[ nan, nan, nan]])
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Coordinates:
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* x (x) float64 0.0 0.75 1.25 1.75
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* y (y) int64 11 13 15
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"""
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if self.dtype.kind not in "uifc":
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raise TypeError(
@@ -2311,6 +2370,29 @@ def interpolate_na(
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--------
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numpy.interp
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scipy.interpolate
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Examples
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--------
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>>> da = xr.DataArray(
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... [np.nan, 2, 3, np.nan, 0], dims="x", coords={"x": [0, 1, 2, 3, 4]}
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... )
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>>> da
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<xarray.DataArray (x: 5)>
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array([nan, 2., 3., nan, 0.])
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Coordinates:
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* x (x) int64 0 1 2 3 4
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>>> da.interpolate_na(dim="x", method="linear")
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<xarray.DataArray (x: 5)>
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array([nan, 2. , 3. , 1.5, 0. ])
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Coordinates:
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* x (x) int64 0 1 2 3 4
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>>> da.interpolate_na(dim="x", method="linear", fill_value="extrapolate")
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<xarray.DataArray (x: 5)>
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array([1. , 2. , 3. , 1.5, 0. ])
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Coordinates:
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* x (x) int64 0 1 2 3 4
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"""
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from .missing import interp_na
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@@ -3496,6 +3578,7 @@ def map_blocks(
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... gb = da.groupby(groupby_type)
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... clim = gb.mean(dim="time")
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... return gb - clim
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...
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>>> time = xr.cftime_range("1990-01", "1992-01", freq="M")
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>>> month = xr.DataArray(time.month, coords={"time": time}, dims=["time"])
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>>> np.random.seed(123)

‎xarray/core/dataset.py

+120
Original file line numberDiff line numberDiff line change
@@ -2711,6 +2711,80 @@ def interp(
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--------
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scipy.interpolate.interp1d
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scipy.interpolate.interpn
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Examples
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--------
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>>> ds = xr.Dataset(
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... data_vars={
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... "a": ("x", [5, 7, 4]),
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... "b": (
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... ("x", "y"),
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... [[1, 4, 2, 9], [2, 7, 6, np.nan], [6, np.nan, 5, 8]],
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... ),
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... },
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... coords={"x": [0, 1, 2], "y": [10, 12, 14, 16]},
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... )
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>>> ds
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<xarray.Dataset>
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Dimensions: (x: 3, y: 4)
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Coordinates:
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* x (x) int64 0 1 2
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* y (y) int64 10 12 14 16
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Data variables:
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a (x) int64 5 7 4
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b (x, y) float64 1.0 4.0 2.0 9.0 2.0 7.0 6.0 nan 6.0 nan 5.0 8.0
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1D interpolation with the default method (linear):
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>>> ds.interp(x=[0, 0.75, 1.25, 1.75])
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<xarray.Dataset>
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Dimensions: (x: 4, y: 4)
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Coordinates:
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* y (y) int64 10 12 14 16
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* x (x) float64 0.0 0.75 1.25 1.75
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Data variables:
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a (x) float64 5.0 6.5 6.25 4.75
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b (x, y) float64 1.0 4.0 2.0 nan 1.75 6.25 ... nan 5.0 nan 5.25 nan
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1D interpolation with a different method:
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>>> ds.interp(x=[0, 0.75, 1.25, 1.75], method="nearest")
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<xarray.Dataset>
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Dimensions: (x: 4, y: 4)
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Coordinates:
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* y (y) int64 10 12 14 16
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* x (x) float64 0.0 0.75 1.25 1.75
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Data variables:
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a (x) float64 5.0 7.0 7.0 4.0
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b (x, y) float64 1.0 4.0 2.0 9.0 2.0 7.0 ... 6.0 nan 6.0 nan 5.0 8.0
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1D extrapolation:
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>>> ds.interp(
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... x=[1, 1.5, 2.5, 3.5],
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... method="linear",
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... kwargs={"fill_value": "extrapolate"},
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... )
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<xarray.Dataset>
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Dimensions: (x: 4, y: 4)
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Coordinates:
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* y (y) int64 10 12 14 16
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* x (x) float64 1.0 1.5 2.5 3.5
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Data variables:
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a (x) float64 7.0 5.5 2.5 -0.5
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b (x, y) float64 2.0 7.0 6.0 nan 4.0 nan ... 4.5 nan 12.0 nan 3.5 nan
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2D interpolation:
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>>> ds.interp(x=[0, 0.75, 1.25, 1.75], y=[11, 13, 15], method="linear")
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<xarray.Dataset>
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Dimensions: (x: 4, y: 3)
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Coordinates:
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* x (x) float64 0.0 0.75 1.25 1.75
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* y (y) int64 11 13 15
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Data variables:
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a (x) float64 5.0 6.5 6.25 4.75
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b (x, y) float64 2.5 3.0 nan 4.0 5.625 nan nan nan nan nan nan nan
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"""
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from . import missing
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@@ -4244,6 +4318,50 @@ def interpolate_na(
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--------
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numpy.interp
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scipy.interpolate
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Examples
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--------
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>>> ds = xr.Dataset(
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... {
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... "A": ("x", [np.nan, 2, 3, np.nan, 0]),
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... "B": ("x", [3, 4, np.nan, 1, 7]),
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... "C": ("x", [np.nan, np.nan, np.nan, 5, 0]),
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... "D": ("x", [np.nan, 3, np.nan, -1, 4]),
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... },
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... coords={"x": [0, 1, 2, 3, 4]},
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... )
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>>> ds
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<xarray.Dataset>
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Dimensions: (x: 5)
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Coordinates:
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* x (x) int64 0 1 2 3 4
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Data variables:
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A (x) float64 nan 2.0 3.0 nan 0.0
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B (x) float64 3.0 4.0 nan 1.0 7.0
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C (x) float64 nan nan nan 5.0 0.0
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D (x) float64 nan 3.0 nan -1.0 4.0
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>>> ds.interpolate_na(dim="x", method="linear")
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<xarray.Dataset>
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Dimensions: (x: 5)
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Coordinates:
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* x (x) int64 0 1 2 3 4
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Data variables:
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A (x) float64 nan 2.0 3.0 1.5 0.0
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B (x) float64 3.0 4.0 2.5 1.0 7.0
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C (x) float64 nan nan nan 5.0 0.0
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D (x) float64 nan 3.0 1.0 -1.0 4.0
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>>> ds.interpolate_na(dim="x", method="linear", fill_value="extrapolate")
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<xarray.Dataset>
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Dimensions: (x: 5)
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Coordinates:
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* x (x) int64 0 1 2 3 4
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Data variables:
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A (x) float64 1.0 2.0 3.0 1.5 0.0
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B (x) float64 3.0 4.0 2.5 1.0 7.0
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C (x) float64 20.0 15.0 10.0 5.0 0.0
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D (x) float64 5.0 3.0 1.0 -1.0 4.0
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"""
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from .missing import _apply_over_vars_with_dim, interp_na
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@@ -5794,6 +5912,7 @@ def filter_by_attrs(self, **kwargs):
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Examples
57955913
--------
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>>> # Create an example dataset:
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...
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>>> import numpy as np
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>>> import pandas as pd
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>>> import xarray as xr
@@ -5974,6 +6093,7 @@ def map_blocks(
59746093
... gb = da.groupby(groupby_type)
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... clim = gb.mean(dim="time")
59766095
... return gb - clim
6096+
...
59776097
>>> time = xr.cftime_range("1990-01", "1992-01", freq="M")
59786098
>>> month = xr.DataArray(time.month, coords={"time": time}, dims=["time"])
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>>> np.random.seed(123)

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