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examples for interp and interpolate_na (pydata#4621)
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* 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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@keewis
keewis authored Nov 30, 2020
1 parent 255bc8e commit 8c863c9
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93 changes: 88 additions & 5 deletions xarray/core/dataarray.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1519,12 +1519,71 @@ def interp(
Examples
--------
>>> da = xr.DataArray([1, 3], [("x", np.arange(2))])
>>> da.interp(x=0.5)
<xarray.DataArray ()>
array(2.)
>>> da = xr.DataArray(
... data=[[1, 4, 2, 9], [2, 7, 6, np.nan], [6, np.nan, 5, 8]],
... dims=("x", "y"),
... coords={"x": [0, 1, 2], "y": [10, 12, 14, 16]},
... )
>>> da
<xarray.DataArray (x: 3, y: 4)>
array([[ 1., 4., 2., 9.],
[ 2., 7., 6., nan],
[ 6., nan, 5., 8.]])
Coordinates:
* x (x) int64 0 1 2
* y (y) int64 10 12 14 16
1D linear interpolation (the default):
>>> da.interp(x=[0, 0.75, 1.25, 1.75])
<xarray.DataArray (x: 4, y: 4)>
array([[1. , 4. , 2. , nan],
[1.75, 6.25, 5. , nan],
[3. , nan, 5.75, nan],
[5. , nan, 5.25, nan]])
Coordinates:
x float64 0.5
* y (y) int64 10 12 14 16
* x (x) float64 0.0 0.75 1.25 1.75
1D nearest interpolation:
>>> da.interp(x=[0, 0.75, 1.25, 1.75], method="nearest")
<xarray.DataArray (x: 4, y: 4)>
array([[ 1., 4., 2., 9.],
[ 2., 7., 6., nan],
[ 2., 7., 6., nan],
[ 6., nan, 5., 8.]])
Coordinates:
* y (y) int64 10 12 14 16
* x (x) float64 0.0 0.75 1.25 1.75
1D linear extrapolation:
>>> da.interp(
... x=[1, 1.5, 2.5, 3.5],
... method="linear",
... kwargs={"fill_value": "extrapolate"},
... )
<xarray.DataArray (x: 4, y: 4)>
array([[ 2. , 7. , 6. , nan],
[ 4. , nan, 5.5, nan],
[ 8. , nan, 4.5, nan],
[12. , nan, 3.5, nan]])
Coordinates:
* y (y) int64 10 12 14 16
* x (x) float64 1.0 1.5 2.5 3.5
2D linear interpolation:
>>> da.interp(x=[0, 0.75, 1.25, 1.75], y=[11, 13, 15], method="linear")
<xarray.DataArray (x: 4, y: 3)>
array([[2.5 , 3. , nan],
[4. , 5.625, nan],
[ nan, nan, nan],
[ nan, nan, nan]])
Coordinates:
* x (x) float64 0.0 0.75 1.25 1.75
* y (y) int64 11 13 15
"""
if self.dtype.kind not in "uifc":
raise TypeError(
Expand Down Expand Up @@ -2311,6 +2370,29 @@ def interpolate_na(
--------
numpy.interp
scipy.interpolate
Examples
--------
>>> da = xr.DataArray(
... [np.nan, 2, 3, np.nan, 0], dims="x", coords={"x": [0, 1, 2, 3, 4]}
... )
>>> da
<xarray.DataArray (x: 5)>
array([nan, 2., 3., nan, 0.])
Coordinates:
* x (x) int64 0 1 2 3 4
>>> da.interpolate_na(dim="x", method="linear")
<xarray.DataArray (x: 5)>
array([nan, 2. , 3. , 1.5, 0. ])
Coordinates:
* x (x) int64 0 1 2 3 4
>>> da.interpolate_na(dim="x", method="linear", fill_value="extrapolate")
<xarray.DataArray (x: 5)>
array([1. , 2. , 3. , 1.5, 0. ])
Coordinates:
* x (x) int64 0 1 2 3 4
"""
from .missing import interp_na

Expand Down Expand Up @@ -3496,6 +3578,7 @@ def map_blocks(
... gb = da.groupby(groupby_type)
... clim = gb.mean(dim="time")
... return gb - clim
...
>>> time = xr.cftime_range("1990-01", "1992-01", freq="M")
>>> month = xr.DataArray(time.month, coords={"time": time}, dims=["time"])
>>> np.random.seed(123)
Expand Down
120 changes: 120 additions & 0 deletions xarray/core/dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2711,6 +2711,80 @@ def interp(
--------
scipy.interpolate.interp1d
scipy.interpolate.interpn
Examples
--------
>>> ds = xr.Dataset(
... data_vars={
... "a": ("x", [5, 7, 4]),
... "b": (
... ("x", "y"),
... [[1, 4, 2, 9], [2, 7, 6, np.nan], [6, np.nan, 5, 8]],
... ),
... },
... coords={"x": [0, 1, 2], "y": [10, 12, 14, 16]},
... )
>>> ds
<xarray.Dataset>
Dimensions: (x: 3, y: 4)
Coordinates:
* x (x) int64 0 1 2
* y (y) int64 10 12 14 16
Data variables:
a (x) int64 5 7 4
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
1D interpolation with the default method (linear):
>>> ds.interp(x=[0, 0.75, 1.25, 1.75])
<xarray.Dataset>
Dimensions: (x: 4, y: 4)
Coordinates:
* y (y) int64 10 12 14 16
* x (x) float64 0.0 0.75 1.25 1.75
Data variables:
a (x) float64 5.0 6.5 6.25 4.75
b (x, y) float64 1.0 4.0 2.0 nan 1.75 6.25 ... nan 5.0 nan 5.25 nan
1D interpolation with a different method:
>>> ds.interp(x=[0, 0.75, 1.25, 1.75], method="nearest")
<xarray.Dataset>
Dimensions: (x: 4, y: 4)
Coordinates:
* y (y) int64 10 12 14 16
* x (x) float64 0.0 0.75 1.25 1.75
Data variables:
a (x) float64 5.0 7.0 7.0 4.0
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
1D extrapolation:
>>> ds.interp(
... x=[1, 1.5, 2.5, 3.5],
... method="linear",
... kwargs={"fill_value": "extrapolate"},
... )
<xarray.Dataset>
Dimensions: (x: 4, y: 4)
Coordinates:
* y (y) int64 10 12 14 16
* x (x) float64 1.0 1.5 2.5 3.5
Data variables:
a (x) float64 7.0 5.5 2.5 -0.5
b (x, y) float64 2.0 7.0 6.0 nan 4.0 nan ... 4.5 nan 12.0 nan 3.5 nan
2D interpolation:
>>> ds.interp(x=[0, 0.75, 1.25, 1.75], y=[11, 13, 15], method="linear")
<xarray.Dataset>
Dimensions: (x: 4, y: 3)
Coordinates:
* x (x) float64 0.0 0.75 1.25 1.75
* y (y) int64 11 13 15
Data variables:
a (x) float64 5.0 6.5 6.25 4.75
b (x, y) float64 2.5 3.0 nan 4.0 5.625 nan nan nan nan nan nan nan
"""
from . import missing

Expand Down Expand Up @@ -4244,6 +4318,50 @@ def interpolate_na(
--------
numpy.interp
scipy.interpolate
Examples
--------
>>> ds = xr.Dataset(
... {
... "A": ("x", [np.nan, 2, 3, np.nan, 0]),
... "B": ("x", [3, 4, np.nan, 1, 7]),
... "C": ("x", [np.nan, np.nan, np.nan, 5, 0]),
... "D": ("x", [np.nan, 3, np.nan, -1, 4]),
... },
... coords={"x": [0, 1, 2, 3, 4]},
... )
>>> ds
<xarray.Dataset>
Dimensions: (x: 5)
Coordinates:
* x (x) int64 0 1 2 3 4
Data variables:
A (x) float64 nan 2.0 3.0 nan 0.0
B (x) float64 3.0 4.0 nan 1.0 7.0
C (x) float64 nan nan nan 5.0 0.0
D (x) float64 nan 3.0 nan -1.0 4.0
>>> ds.interpolate_na(dim="x", method="linear")
<xarray.Dataset>
Dimensions: (x: 5)
Coordinates:
* x (x) int64 0 1 2 3 4
Data variables:
A (x) float64 nan 2.0 3.0 1.5 0.0
B (x) float64 3.0 4.0 2.5 1.0 7.0
C (x) float64 nan nan nan 5.0 0.0
D (x) float64 nan 3.0 1.0 -1.0 4.0
>>> ds.interpolate_na(dim="x", method="linear", fill_value="extrapolate")
<xarray.Dataset>
Dimensions: (x: 5)
Coordinates:
* x (x) int64 0 1 2 3 4
Data variables:
A (x) float64 1.0 2.0 3.0 1.5 0.0
B (x) float64 3.0 4.0 2.5 1.0 7.0
C (x) float64 20.0 15.0 10.0 5.0 0.0
D (x) float64 5.0 3.0 1.0 -1.0 4.0
"""
from .missing import _apply_over_vars_with_dim, interp_na

Expand Down Expand Up @@ -5794,6 +5912,7 @@ def filter_by_attrs(self, **kwargs):
Examples
--------
>>> # Create an example dataset:
...
>>> import numpy as np
>>> import pandas as pd
>>> import xarray as xr
Expand Down Expand Up @@ -5974,6 +6093,7 @@ def map_blocks(
... gb = da.groupby(groupby_type)
... clim = gb.mean(dim="time")
... return gb - clim
...
>>> time = xr.cftime_range("1990-01", "1992-01", freq="M")
>>> month = xr.DataArray(time.month, coords={"time": time}, dims=["time"])
>>> np.random.seed(123)
Expand Down

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