cpc.geogrids.manipulation Module

Contains methods for interpolating data between GeoGrids.

Functions

fill_outside_mask_borders(data, passes=1)

Fill the grid points outside of the mask borders of a dataset (eg. over the ocean for land-only datasets) with the value from the nearest non-missing neighbor

Parameters

• data - numpy array - data to fill missing
• passes - int - number of passes (for each pass, 1 extra layer of grid points will be filled)

Returns

• numpy array - a filled array

Examples

Create a 5x5 array of data, mask out the outer values, and fill

>>> # Import packages
>>> from cpc.geogrids.manipulation import fill_outside_mask_borders
>>> import numpy as np
>>> # Generate random data with missing values along the border
>>> A = np.random.randint(1, 9, (5, 5)).astype('float16')
>>> A[0] = A[-1] = A[:,0] = A[:,-1] = np.nan
>>> A  # doctest: +SKIP
array([[ nan,  nan,  nan,  nan,  nan],
       [ nan,   4.,   4.,   3.,  nan],
       [ nan,   6.,   7.,   3.,  nan],
       [ nan,   3.,   8.,   8.,  nan],
       [ nan,  nan,  nan,  nan,  nan]], dtype=float16)
>>> # Fill the missing outside values with the nearest neighbor values
>>> A = fill_outside_mask_borders(A)
>>> A  # doctest: +SKIP
array([[ 4.,  4.,  4.,  3.,  3.],
       [ 4.,  4.,  4.,  3.,  3.],
       [ 6.,  6.,  7.,  3.,  3.],
       [ 3.,  3.,  8.,  8.,  8.],
       [ 3.,  3.,  8.,  8.,  8.]], dtype=float16)

grid_to_stn(gridded_data, grid, stn_lats, stn_lons)

Interpolates values from a grid to stations in a station list.

Parameters
----------

- gridded_data (array_like)
    - Array of gridded data
- grid (Grid)
    - `data_utils.gridded.grid.Grid` that the gridded data is on
- stn_lats (list)
    - List of station lats
- stn_lons (list)
    - List of station lons

Returns
-------

- list
    - List of values representing the value of the gridded data interpolated to the station
      locations

Examples
--------

interpolate(orig_data, orig_grid, new_grid)

Interpolates data from one Geogrid to another.

Parameters

• orig_data - array_like - array of original data
• orig_grid - Geogrid - original Geogrid
• new_grid - Geogrid - new Geogrid

Returns

• new_data - array_like - a data array on the desired Geogrid.

Examples

Interpolate gridded temperature obs from 2 degrees (CONUS) to 1 degree global

#!/usr/bin/env python
>>> # Import packages
>>> import numpy as np
>>> from cpc.geogrids.definition import Geogrid
>>> from cpc.geogrids.manipulation import interpolate
>>> # Create original and new GeoGrids
>>> orig_grid = Geogrid('1deg-global')
>>> new_grid = Geogrid('2deg-global')
>>> # Generate random data on the original Geogrid
>>> A = np.random.rand(orig_grid.num_y, orig_grid.num_x)
>>> # Interpolate data to the new Geogrid
>>> B = interpolate(A, orig_grid, new_grid)
>>> # Print shapes of data before and after
>>> print(A.shape)
(181, 360)
>>> print(B.shape)
(91, 180)

smooth(data, grid, factor=0.5)

Smooth an array of spatial data using a gaussian filter

Parameters

• data - array_like - array of spatial data
• grid - Geogrid - Geogrid corresponding to data
• factor - float, optional - sigma value for the gaussian filter

Returns

• array_like - array of smoothed spatial data

Examples