How to interpolate 2D array from a coarser resolution to finer resolution

Suppose that I have an emission data with shape (21600,43200),
which corresponds to the lat and lon,i.e,

lat = np.arange(21600)*(-0.008333333)+90

lon = np.arange(43200)*0.00833333-180

And I also have a scaling factor with shape of (720,1440,7),which corresponds to lat , lon, day of week, and

lat = np.arange(720)*0.25-90 

lon = np.arange(1440)*0.25-180

For now, I want to apply the factor to the emission data and I think I need to interpolate the factor on (720,1440) to (21600,43200). After that I can multiply the interpolated factor with the emission data to get the new emission output.

But I have a difficulty on the interpolation method.
Could anyone give me some suggestions?

edited Nov 20 '18 at 18:00

tel

7,34121431

asked Nov 20 '18 at 11:36

Allen Zhang

178

1

scipy.interpolate.interp2d should solve your problem... as long as you can fit everything in memory (if you can't, you could compute the interpolation by pieces).

– jdehesa
Nov 20 '18 at 12:14

add a comment |

Suppose that I have an emission data with shape (21600,43200),
which corresponds to the lat and lon,i.e,

lat = np.arange(21600)*(-0.008333333)+90

lon = np.arange(43200)*0.00833333-180

And I also have a scaling factor with shape of (720,1440,7),which corresponds to lat , lon, day of week, and

lat = np.arange(720)*0.25-90 

lon = np.arange(1440)*0.25-180

But I have a difficulty on the interpolation method.
Could anyone give me some suggestions?

edited Nov 20 '18 at 18:00

tel

7,34121431

asked Nov 20 '18 at 11:36

Allen Zhang

178

1

scipy.interpolate.interp2d should solve your problem... as long as you can fit everything in memory (if you can't, you could compute the interpolation by pieces).

– jdehesa
Nov 20 '18 at 12:14

add a comment |

Suppose that I have an emission data with shape (21600,43200),
which corresponds to the lat and lon,i.e,

lat = np.arange(21600)*(-0.008333333)+90

lon = np.arange(43200)*0.00833333-180

And I also have a scaling factor with shape of (720,1440,7),which corresponds to lat , lon, day of week, and

lat = np.arange(720)*0.25-90 

lon = np.arange(1440)*0.25-180

But I have a difficulty on the interpolation method.
Could anyone give me some suggestions?

edited Nov 20 '18 at 18:00

tel

7,34121431

asked Nov 20 '18 at 11:36

Allen Zhang

178

Suppose that I have an emission data with shape (21600,43200),
which corresponds to the lat and lon,i.e,

lat = np.arange(21600)*(-0.008333333)+90

lon = np.arange(43200)*0.00833333-180

And I also have a scaling factor with shape of (720,1440,7),which corresponds to lat , lon, day of week, and

lat = np.arange(720)*0.25-90 

lon = np.arange(1440)*0.25-180

But I have a difficulty on the interpolation method.
Could anyone give me some suggestions?

numpy scipy netcdf python-xarray

edited Nov 20 '18 at 18:00

tel

7,34121431

asked Nov 20 '18 at 11:36

Allen Zhang

178

edited Nov 20 '18 at 18:00

tel

7,34121431

asked Nov 20 '18 at 11:36

Allen Zhang

178

edited Nov 20 '18 at 18:00

tel

7,34121431

edited Nov 20 '18 at 18:00

tel

7,34121431

edited Nov 20 '18 at 18:00

tel

7,34121431

asked Nov 20 '18 at 11:36

Allen Zhang

178

asked Nov 20 '18 at 11:36

Allen Zhang

178

asked Nov 20 '18 at 11:36

Allen Zhang

178

1

scipy.interpolate.interp2d should solve your problem... as long as you can fit everything in memory (if you can't, you could compute the interpolation by pieces).

– jdehesa
Nov 20 '18 at 12:14

add a comment |

1

scipy.interpolate.interp2d should solve your problem... as long as you can fit everything in memory (if you can't, you could compute the interpolation by pieces).

– jdehesa
Nov 20 '18 at 12:14

scipy.interpolate.interp2d should solve your problem... as long as you can fit everything in memory (if you can't, you could compute the interpolation by pieces).

– jdehesa
Nov 20 '18 at 12:14

add a comment |

2 Answers
2

active

oldest

votes

Here's a complete example of the kind of interpolation you're trying to do. For example purposes I used emission data with shape (10, 20) and scale data with shape (5, 10). It uses scipy.interpolate.RectBivariateSpline, which is the recommended method for interpolating on regular grids:

import scipy.interpolate as sci



def latlon(res):

    return (np.arange(res)*(180/res) - 90,

            np.arange(2*res)*(360/(2*res)) - 180)



lat_fine,lon_fine = latlon(10)

emission = np.ones(10*20).reshape(10,20)



lat_coarse,lon_coarse = latlon(5)

scale = np.linspace(0, .5, num=5).reshape(-1, 1) + np.linspace(0, .5, num=10)



f = sci.RectBivariateSpline(lat_coarse, lon_coarse, scale)

scale_interp = f(lat_em, lon_em)



with np.printoptions(precision=1, suppress=True, linewidth=9999):

    print('original emission data:n%sn' % emission)

    print('original scale data:n%sn' % scale)

    print('interpolated scale data:n%sn' % scale_interp)

    print('scaled emission data:n%sn' % (emission*scale_interp))

which outputs:

original emission data:

[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]



original scale data:

[[0.  0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0.5]

 [0.1 0.2 0.2 0.3 0.3 0.4 0.5 0.5 0.6 0.6]

 [0.2 0.3 0.4 0.4 0.5 0.5 0.6 0.6 0.7 0.8]

 [0.4 0.4 0.5 0.5 0.6 0.7 0.7 0.8 0.8 0.9]

 [0.5 0.6 0.6 0.7 0.7 0.8 0.8 0.9 0.9 1. ]]



interpolated scale data:

[[0.  0.  0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5]

 [0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6]

 [0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6]

 [0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7]

 [0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8]

 [0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8 0.8 0.8]

 [0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.8 0.9 0.9]

 [0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 0.9]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]]



scaled emission data:

[[0.  0.  0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5]

 [0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6]

 [0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6]

 [0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7]

 [0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8]

 [0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8 0.8 0.8]

 [0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.8 0.9 0.9]

 [0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 0.9]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]]

Notes

The interpolation methods in scipy.interpolate expect both x and y to be strictly increasing, so you'll have to make sure that your emission data is arranged in a grid such that:
```
lat = np.arange(21600)*0.008333333 - 90
```
instead of:
```
lat = np.arange(21600)*(-0.008333333) + 90
```
like you have above. You can flip your emission data like so:
```
emission = emission[::-1, :]
```

edited Nov 20 '18 at 18:13

answered Nov 20 '18 at 17:57

tel

7,34121431

add a comment |

If you're just looking for nearest neighbor or linear interpolation, you can use xarray's native da.interp method:

scaling_interped = scaling_factor.interp(

    lon=emissions.lon,

    lat=emissions.lat,

    method='nearest')  # or 'linear'

note that this will dramatically increase the size of the array. Assuming these are 64-bit floats, the result will be approximately (21600*43200*7)*8/(1024**3) or 48.7 GB. You could cut the in-memory size by a factor of 7 by chunking the array by day of week and doing the computing out of core with dask.

If you want to use an interpolation scheme other than nearest or linear, use the method suggested by tel.

answered Nov 26 '18 at 2:18

delgadom

812718

add a comment |

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2 Answers
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oldest

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2 Answers
2

active

oldest

votes

import scipy.interpolate as sci



def latlon(res):

    return (np.arange(res)*(180/res) - 90,

            np.arange(2*res)*(360/(2*res)) - 180)



lat_fine,lon_fine = latlon(10)

emission = np.ones(10*20).reshape(10,20)



lat_coarse,lon_coarse = latlon(5)

scale = np.linspace(0, .5, num=5).reshape(-1, 1) + np.linspace(0, .5, num=10)



f = sci.RectBivariateSpline(lat_coarse, lon_coarse, scale)

scale_interp = f(lat_em, lon_em)



with np.printoptions(precision=1, suppress=True, linewidth=9999):

    print('original emission data:n%sn' % emission)

    print('original scale data:n%sn' % scale)

    print('interpolated scale data:n%sn' % scale_interp)

    print('scaled emission data:n%sn' % (emission*scale_interp))

which outputs:

original emission data:

[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]



original scale data:

[[0.  0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0.5]

 [0.1 0.2 0.2 0.3 0.3 0.4 0.5 0.5 0.6 0.6]

 [0.2 0.3 0.4 0.4 0.5 0.5 0.6 0.6 0.7 0.8]

 [0.4 0.4 0.5 0.5 0.6 0.7 0.7 0.8 0.8 0.9]

 [0.5 0.6 0.6 0.7 0.7 0.8 0.8 0.9 0.9 1. ]]



interpolated scale data:

[[0.  0.  0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5]

 [0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6]

 [0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6]

 [0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7]

 [0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8]

 [0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8 0.8 0.8]

 [0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.8 0.9 0.9]

 [0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 0.9]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]]



scaled emission data:

[[0.  0.  0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5]

 [0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6]

 [0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6]

 [0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7]

 [0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8]

 [0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8 0.8 0.8]

 [0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.8 0.9 0.9]

 [0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 0.9]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]]

Notes

The interpolation methods in scipy.interpolate expect both x and y to be strictly increasing, so you'll have to make sure that your emission data is arranged in a grid such that:
```
lat = np.arange(21600)*0.008333333 - 90
```
instead of:
```
lat = np.arange(21600)*(-0.008333333) + 90
```
like you have above. You can flip your emission data like so:
```
emission = emission[::-1, :]
```

edited Nov 20 '18 at 18:13

answered Nov 20 '18 at 17:57

tel

7,34121431

add a comment |

import scipy.interpolate as sci



def latlon(res):

    return (np.arange(res)*(180/res) - 90,

            np.arange(2*res)*(360/(2*res)) - 180)



lat_fine,lon_fine = latlon(10)

emission = np.ones(10*20).reshape(10,20)



lat_coarse,lon_coarse = latlon(5)

scale = np.linspace(0, .5, num=5).reshape(-1, 1) + np.linspace(0, .5, num=10)



f = sci.RectBivariateSpline(lat_coarse, lon_coarse, scale)

scale_interp = f(lat_em, lon_em)



with np.printoptions(precision=1, suppress=True, linewidth=9999):

    print('original emission data:n%sn' % emission)

    print('original scale data:n%sn' % scale)

    print('interpolated scale data:n%sn' % scale_interp)

    print('scaled emission data:n%sn' % (emission*scale_interp))

which outputs:

original emission data:

[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]



original scale data:

[[0.  0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0.5]

 [0.1 0.2 0.2 0.3 0.3 0.4 0.5 0.5 0.6 0.6]

 [0.2 0.3 0.4 0.4 0.5 0.5 0.6 0.6 0.7 0.8]

 [0.4 0.4 0.5 0.5 0.6 0.7 0.7 0.8 0.8 0.9]

 [0.5 0.6 0.6 0.7 0.7 0.8 0.8 0.9 0.9 1. ]]



interpolated scale data:

[[0.  0.  0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5]

 [0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6]

 [0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6]

 [0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7]

 [0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8]

 [0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8 0.8 0.8]

 [0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.8 0.9 0.9]

 [0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 0.9]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]]



scaled emission data:

[[0.  0.  0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5]

 [0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6]

 [0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6]

 [0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7]

 [0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8]

 [0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8 0.8 0.8]

 [0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.8 0.9 0.9]

 [0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 0.9]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]]

Notes

The interpolation methods in scipy.interpolate expect both x and y to be strictly increasing, so you'll have to make sure that your emission data is arranged in a grid such that:
```
lat = np.arange(21600)*0.008333333 - 90
```
instead of:
```
lat = np.arange(21600)*(-0.008333333) + 90
```
like you have above. You can flip your emission data like so:
```
emission = emission[::-1, :]
```

edited Nov 20 '18 at 18:13

answered Nov 20 '18 at 17:57

tel

7,34121431

add a comment |

import scipy.interpolate as sci



def latlon(res):

    return (np.arange(res)*(180/res) - 90,

            np.arange(2*res)*(360/(2*res)) - 180)



lat_fine,lon_fine = latlon(10)

emission = np.ones(10*20).reshape(10,20)



lat_coarse,lon_coarse = latlon(5)

scale = np.linspace(0, .5, num=5).reshape(-1, 1) + np.linspace(0, .5, num=10)



f = sci.RectBivariateSpline(lat_coarse, lon_coarse, scale)

scale_interp = f(lat_em, lon_em)



with np.printoptions(precision=1, suppress=True, linewidth=9999):

    print('original emission data:n%sn' % emission)

    print('original scale data:n%sn' % scale)

    print('interpolated scale data:n%sn' % scale_interp)

    print('scaled emission data:n%sn' % (emission*scale_interp))

which outputs:

original emission data:

[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]



original scale data:

[[0.  0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0.5]

 [0.1 0.2 0.2 0.3 0.3 0.4 0.5 0.5 0.6 0.6]

 [0.2 0.3 0.4 0.4 0.5 0.5 0.6 0.6 0.7 0.8]

 [0.4 0.4 0.5 0.5 0.6 0.7 0.7 0.8 0.8 0.9]

 [0.5 0.6 0.6 0.7 0.7 0.8 0.8 0.9 0.9 1. ]]



interpolated scale data:

[[0.  0.  0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5]

 [0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6]

 [0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6]

 [0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7]

 [0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8]

 [0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8 0.8 0.8]

 [0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.8 0.9 0.9]

 [0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 0.9]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]]



scaled emission data:

[[0.  0.  0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5]

 [0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6]

 [0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6]

 [0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7]

 [0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8]

 [0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8 0.8 0.8]

 [0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.8 0.9 0.9]

 [0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 0.9]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]]

Notes

The interpolation methods in scipy.interpolate expect both x and y to be strictly increasing, so you'll have to make sure that your emission data is arranged in a grid such that:
```
lat = np.arange(21600)*0.008333333 - 90
```
instead of:
```
lat = np.arange(21600)*(-0.008333333) + 90
```
like you have above. You can flip your emission data like so:
```
emission = emission[::-1, :]
```

edited Nov 20 '18 at 18:13

answered Nov 20 '18 at 17:57

tel

7,34121431

import scipy.interpolate as sci



def latlon(res):

    return (np.arange(res)*(180/res) - 90,

            np.arange(2*res)*(360/(2*res)) - 180)



lat_fine,lon_fine = latlon(10)

emission = np.ones(10*20).reshape(10,20)



lat_coarse,lon_coarse = latlon(5)

scale = np.linspace(0, .5, num=5).reshape(-1, 1) + np.linspace(0, .5, num=10)



f = sci.RectBivariateSpline(lat_coarse, lon_coarse, scale)

scale_interp = f(lat_em, lon_em)



with np.printoptions(precision=1, suppress=True, linewidth=9999):

    print('original emission data:n%sn' % emission)

    print('original scale data:n%sn' % scale)

    print('interpolated scale data:n%sn' % scale_interp)

    print('scaled emission data:n%sn' % (emission*scale_interp))

which outputs:

original emission data:

[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]



original scale data:

[[0.  0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0.5]

 [0.1 0.2 0.2 0.3 0.3 0.4 0.5 0.5 0.6 0.6]

 [0.2 0.3 0.4 0.4 0.5 0.5 0.6 0.6 0.7 0.8]

 [0.4 0.4 0.5 0.5 0.6 0.7 0.7 0.8 0.8 0.9]

 [0.5 0.6 0.6 0.7 0.7 0.8 0.8 0.9 0.9 1. ]]



interpolated scale data:

[[0.  0.  0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5]

 [0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6]

 [0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6]

 [0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7]

 [0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8]

 [0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8 0.8 0.8]

 [0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.8 0.9 0.9]

 [0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 0.9]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]]



scaled emission data:

[[0.  0.  0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5]

 [0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6]

 [0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6]

 [0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7]

 [0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8]

 [0.3 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8 0.8 0.8]

 [0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.8 0.9 0.9]

 [0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 0.9]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]

 [0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 0.9 1.  1.  1. ]]

Notes

The interpolation methods in scipy.interpolate expect both x and y to be strictly increasing, so you'll have to make sure that your emission data is arranged in a grid such that:
```
lat = np.arange(21600)*0.008333333 - 90
```
instead of:
```
lat = np.arange(21600)*(-0.008333333) + 90
```
like you have above. You can flip your emission data like so:
```
emission = emission[::-1, :]
```

edited Nov 20 '18 at 18:13

answered Nov 20 '18 at 17:57

tel

7,34121431

edited Nov 20 '18 at 18:13

answered Nov 20 '18 at 17:57

tel

7,34121431

answered Nov 20 '18 at 17:57

tel

7,34121431

answered Nov 20 '18 at 17:57

tel

7,34121431

add a comment |

If you're just looking for nearest neighbor or linear interpolation, you can use xarray's native da.interp method:

scaling_interped = scaling_factor.interp(

    lon=emissions.lon,

    lat=emissions.lat,

    method='nearest')  # or 'linear'

If you want to use an interpolation scheme other than nearest or linear, use the method suggested by tel.

answered Nov 26 '18 at 2:18

delgadom

812718

add a comment |

If you're just looking for nearest neighbor or linear interpolation, you can use xarray's native da.interp method:

scaling_interped = scaling_factor.interp(

    lon=emissions.lon,

    lat=emissions.lat,

    method='nearest')  # or 'linear'

If you want to use an interpolation scheme other than nearest or linear, use the method suggested by tel.

answered Nov 26 '18 at 2:18

delgadom

812718

add a comment |

If you're just looking for nearest neighbor or linear interpolation, you can use xarray's native da.interp method:

scaling_interped = scaling_factor.interp(

    lon=emissions.lon,

    lat=emissions.lat,

    method='nearest')  # or 'linear'

If you want to use an interpolation scheme other than nearest or linear, use the method suggested by tel.

answered Nov 26 '18 at 2:18

delgadom

812718

If you're just looking for nearest neighbor or linear interpolation, you can use xarray's native da.interp method:

scaling_interped = scaling_factor.interp(

    lon=emissions.lon,

    lat=emissions.lat,

    method='nearest')  # or 'linear'

If you want to use an interpolation scheme other than nearest or linear, use the method suggested by tel.

answered Nov 26 '18 at 2:18

delgadom

812718

answered Nov 26 '18 at 2:18

delgadom

812718

answered Nov 26 '18 at 2:18

delgadom

812718

answered Nov 26 '18 at 2:18

delgadom

812718

add a comment |

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