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examples for interp and interpolate_na #4621

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examples for interp and interpolate_na #4621

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7ac9ed8
add examples to interp
keewis Nov 30, 2020
fa3e69c
add examples to interpolate_na
keewis Nov 30, 2020
41d3c37
add examples to DataArray.interp
keewis Nov 30, 2020
d6e16ef
add examples to Dataset.interpolate_na
keewis Nov 30, 2020
bc5b411
fix the doctest examples
keewis Nov 30, 2020
df05e7f
use integers as example data
keewis Nov 30, 2020
3f93fb3
fix the sphinx errors
keewis Nov 30, 2020
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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