33import numpy as np
44
55
6- def grid_smear (grid : np .ndarray , pad : int = 4 ) -> np .ndarray :
6+ def grid_smear (grid : np .ndarray , shift : int = 4 ) -> np .ndarray :
77 """Smear data around to fill in masked values (likely near coastlines).
88
99 Args:
1010 grid: 2D numpy array
11- pad : number of pixels to smear the data around by in each direction
11+ shift : number of pixels to smear the data around by in each direction
1212
1313 Returns:
1414 2D numpy array with smeared data
1515 """
1616 # Pad grid
1717 padded = np .ma .masked_all (
18- (grid .shape [0 ] + pad * 2 , grid .shape [1 ] + pad * 2 )
18+ (grid .shape [0 ] + shift * 2 , grid .shape [1 ] + shift * 2 )
1919 )
2020 # set values from inbound grid
21- padded [pad : - pad , pad : - pad ] = grid
21+ padded [shift : - shift , shift : - shift ] = grid
2222
23- # shift the grid by 4 pixels in each direction to fill in the padded region
24- for xorigin in [0 , pad * 2 ]:
25- for yorigin in [0 , pad * 2 ]:
23+ # shift the grid by shift pixels in each direction to fill in the padded
24+ for xorigin in [0 , shift * 2 ]:
25+ for yorigin in [0 , shift * 2 ]:
2626 xslice = slice (xorigin , xorigin + grid .shape [0 ])
2727 yslice = slice (yorigin , yorigin + grid .shape [1 ])
2828 padded [xslice , yslice ] = np .ma .where (
@@ -31,4 +31,4 @@ def grid_smear(grid: np.ndarray, pad: int = 4) -> np.ndarray:
3131 padded [xslice , yslice ],
3232 )
3333
34- return padded [pad : - pad , pad : - pad ]
34+ return padded [shift : - shift , shift : - shift ]
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