how to mask the specific array data based on the shapefile

Question

Here is my question:

• the 2-d numpy array data represent some property of each grid space
• the shapefile as the administrative division of the study a

Answer 1

Step 1. Rasterize shapefile

Create a function that can determine whether a point at coordinates (x, y) is or is not in the area. See here for more details on how to rasterize your shapefile into an array of the same dimensions as your target mask

def point_is_in_mask(mask, point):
    # this is just pseudocode
    return mask.contains(point) 

Step 2. Create your mask

mask = np.zeros((height, width))
value = np.zeros((height, width))
for y in range(height):
    for x in range(width):
        if not point_is_in_mask(mask, (x, y)):
            value[y][x] = np.nan

Answer 2

Best is to use matplotlib:

def outline_to_mask(line, x, y):
    """Create mask from outline contour

    Parameters
    ----------
    line: array-like (N, 2)
    x, y: 1-D grid coordinates (input for meshgrid)

    Returns
    -------
    mask : 2-D boolean array (True inside)
    """
    import matplotlib.path as mplp
    mpath = mplp.Path(line)
    X, Y = np.meshgrid(x, y)
    points = np.array((X.flatten(), Y.flatten())).T
    mask = mpath.contains_points(points).reshape(X.shape)
    return mask

alternatively, you may use shapely contains method as suggested in the above answer. You may speed-up calculations by recursively sub-dividing the space, as indicated in this gist (but matplotlib solution was 1.5 times faster in my tests):

https://gist.github.com/perrette/a78f99b76aed54b6babf3597e0b331f8