|
| 1 | +""" |
| 2 | +The function adjust_xy will center a point in a finite volume grid cell of |
| 3 | +a given resolution on a given domain. |
| 4 | +
|
| 5 | +This can be used in particular to take adjust an approximate point |
| 6 | +(x_desired, y_desired) where you want a computational gauge, to obtain |
| 7 | +a point that lies exactly in the center of a grid cell at a given resolution. |
| 8 | +This eliminates the need to interpolate between cell values in GeoClaw output, |
| 9 | +which has issues for cells near the shoreline as described at |
| 10 | +https://www.clawpack.org/nearshore_interp.html |
| 11 | +
|
| 12 | +Functions: |
| 13 | +
|
| 14 | + - adjust: utility function to adjust in one dimension (x or y) |
| 15 | + - adjust_xy: the function to call to center a point in both x and y. |
| 16 | + - test: a simple example |
| 17 | +
|
| 18 | +""" |
| 19 | + |
| 20 | +import numpy as np |
| 21 | + |
| 22 | +def adjust(z_desired, z_edge, dz, verbose=False): |
| 23 | + """ |
| 24 | + Given a desired location z (either x or y) for a gauge or other point |
| 25 | + of interest, adjust the location is it is offset (integer + 1/2)*dz |
| 26 | + from z_edge, an arbitrary edge location (e.g. an edge of the |
| 27 | + computational domain or any point offset integer*dz from the domain edge). |
| 28 | + This will put the new location in the center of a finite volume cell |
| 29 | + provided this point is in a patch with resolution dz. |
| 30 | + """ |
| 31 | + # z represents either x or y |
| 32 | + i = np.round((z_desired-z_edge - 0.5*dz)/dz) |
| 33 | + z_centered = z_edge + (i+0.5)*dz |
| 34 | + if verbose: |
| 35 | + zshift = z_centered - z_desired |
| 36 | + zfrac = zshift / dz |
| 37 | + print("adjusted from %15.9f" % z_desired) |
| 38 | + print(" to %15.9f" % z_centered) |
| 39 | + print(" shifted by %15.9f = %.3f*dz" % (zshift,zfrac)) |
| 40 | + return z_centered |
| 41 | + |
| 42 | +def adjust_xy(x_desired, y_desired, x_edge, y_edge, dx, dy, verbose=False): |
| 43 | + """ |
| 44 | + Given a desired location (or array of locations) in 2D, center |
| 45 | + so that the point(s) are at the centers of cells of size dx by dy, |
| 46 | + with cell edges that are integer multiples of dx,dy away from |
| 47 | + from x_edge,y_edge (e.g. the edges of the computational domain or |
| 48 | + any other points offset by integer multiples of dx,dy from the edges. |
| 49 | +
|
| 50 | + This will put the new location in the center of a finite volume cell |
| 51 | + provided this point is in a grid patch with resolution dx,dy. |
| 52 | +
|
| 53 | + :Input: |
| 54 | +
|
| 55 | + - x_desired, y_desired: single floats or arrays of floats, the desired |
| 56 | + locations |
| 57 | + - x_edge, y_edge (float) the edges of the computational domain |
| 58 | + (or any other points offset by integer multiples of dx,dy from the edges) |
| 59 | + - dx, dy (float): the grid resolution on which the point(s) should be |
| 60 | + centered |
| 61 | +
|
| 62 | + :Output: |
| 63 | +
|
| 64 | + - xc,yc: centered points that lie within dx/2, dy/2 of the desired |
| 65 | + location(s) and with (xc - x_edge)/dx and (yc - y_edge)/dy |
| 66 | + equal to an integer + 0.5, |
| 67 | + """ |
| 68 | + |
| 69 | + # convert single values or lists/tuples to numpy arrays |
| 70 | + x_desired = np.array(x_desired, ndmin=1) |
| 71 | + y_desired = np.array(y_desired, ndmin=1) |
| 72 | + |
| 73 | + assert len(x_desired) == len(y_desired), \ |
| 74 | + '*** lengths of x_desired, y_desired do not match' |
| 75 | + |
| 76 | + x_centered = [] |
| 77 | + y_centered = [] |
| 78 | + for i in range(len(x_desired)): |
| 79 | + x = adjust(x_desired[i], x_edge, dx, verbose=verbose) |
| 80 | + y = adjust(y_desired[i], y_edge, dy, verbose=verbose) |
| 81 | + x_centered.append(x) |
| 82 | + y_centered.append(y) |
| 83 | + |
| 84 | + if len(x_centered) == 1: |
| 85 | + return float(x_centered[0]), float(y_centered[0]) |
| 86 | + else: |
| 87 | + return np.array(x_centered), np.array(y_centered) |
| 88 | + |
| 89 | +def test(): |
| 90 | + |
| 91 | + x_desired = [-122.01, -122.02] |
| 92 | + y_desired = [47.001, 47.002] |
| 93 | + |
| 94 | + # grid resolution on which to center point: |
| 95 | + dx = 1/(3*3600.) |
| 96 | + |
| 97 | + # lower left edge of computational domain: |
| 98 | + x_edge = -123. |
| 99 | + y_edge = 45. |
| 100 | + |
| 101 | + print('Desired x = ',x_desired) |
| 102 | + print('Desired y = ',y_desired) |
| 103 | + |
| 104 | + xc,yc = adjust_xy(x_desired,y_desired,x_edge,y_edge,dx,dx,verbose=True) |
| 105 | + |
| 106 | + print('Centered x = ',xc) |
| 107 | + print('Offsets in x in units of 1/3 arcsec: ', (xc-x_edge)*3*3600) |
| 108 | + print('Centered y = ',yc) |
| 109 | + print('Offsets in y in units of 1/3 arcsec: ', (yc-y_edge)*3*3600) |
| 110 | + |
| 111 | +if __name__ == '__main__': |
| 112 | + test() |
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