def mkSDgrd(InGrd):
Slabgrid = InGrd
dx = Slabgrid.geodict[
'xdim'] * 111.19 ## Define dx as the longitude increments and convert to km
dy = Slabgrid.geodict[
'ydim'] * 111.19 ## Define dy as the latitude increments and convert to km
Xgrad, Ygrad = np.gradient(
Slabgrid.griddata, dx,
dy) ## Define grids of the gradients in the x and y directions
Maggrid = np.sqrt(
(Xgrad**2) + (Ygrad**2)
) ## Define a grid file that is the gradient magnitude at any given node
Dirgrid = -1 * (
np.degrees(np.arctan2(Ygrad, Xgrad)) - 90
) ## Define a grid file that is the direction perpendicular to the max gradient - in degrees clockwise from N (0 - 360)
Dirgrid[Dirgrid < 0] += 360
Dipgrid = gmt.GMTGrid()
Strikegrid = gmt.GMTGrid()
Dipgrid.griddata = np.degrees(np.arctan2(Maggrid, 1))
Strikegrid.griddata = Dirgrid
Dipgrid.geodict = Slabgrid.geodict.copy()
Strikegrid.geodict = Slabgrid.geodict.copy()
return Strikegrid, Dipgrid
def clipGrid(ingrid, mask):
### Input:
# ingrid: a gmt style grid such as created by gmt.GMTgrid() or by mySurface above
# mask: clipping mask grid from mkMask
### Output:
# outgrid: same as ingrid, but clipped
outgrid = gmt.GMTGrid()
outgrid.griddata = np.multiply(ingrid.griddata, np.flipud(mask))
outgrid.geodict = ingrid.geodict.copy()
return outgrid
def gaussGridFilt(ingrid, filterwidth):
### Input:
# ingrid: a gmt style grid such as created by gmt.GMTgrid() or by mySurface above which has not been clipped using a clipping mask. ASSUMES SQUARE GRIDS
# filterwidth: width (in same units as grid spacing) of gaussian filter. equivalent to 2*sigma of the gaussian
### Output:
# outgrid: same as ingrid, but filtered (smoothed)
outgrid = gmt.GMTGrid()
outgrid.geodict = ingrid.geodict.copy()
sigma = (filterwidth / 2) / outgrid.geodict['xdim']
outgrid.griddata = ndimage.filters.gaussian_filter(ingrid.griddata, sigma)
return outgrid
def mySurface(data, node, T):
### Input:
# data: (n x 3) numpy array of form x,y,z where x,y are coordinates and z is value
# node: float to indicate node spacing *note that this code uses only square grids
# T: tension factor (float from 0-1) where 0 = minimum curvature solution
### Output:
# newgrid: gmt-style grid after NEICIO format that is a surface of input data
# xi: numpy array with x,y coordinates for each node
xmin, xmax = data[:, 0].min(), data[:, 0].max()
ymin, ymax = data[:, 1].min(), data[:, 1].max()
xall = np.arange(np.floor(xmin), np.ceil(xmax) + node / 2, node)
yall = np.arange(np.floor(ymin), np.ceil(ymax) + node, node)
n = len(xall)
m = len(yall)
xpts, ypts = np.meshgrid(xall, yall)
xi = np.zeros((m * n, 2))
xi[:, 0] = xpts.flatten()
xi[:, 1] = ypts.flatten()
np.savetxt('temp.xyz', data, fmt='%0.4f', delimiter=' ')
rflag = "-R%s/%s/%s/%s" % (np.floor(xmin), np.ceil(xmax), np.floor(ymin),
np.ceil(ymax))
iflag = "-I%s/%s" % (node, node)
gflag = "-Gtemp.grd"
tflag = "-T%s" % (T)
os.system("gmt4 blockmean temp.xyz %s %s | gmt4 surface %s %s %s %s" %
(rflag, iflag, rflag, iflag, gflag, tflag))
newgrid = gmt.GMTGrid('temp.grd')
os.system("rm temp.xyz")
os.system("rm temp.grd")
return newgrid, xi
minnum = 20
maxnum = 200
# depth threhsolds
mindepthcut = 60
maxdepthcut = 500
###########################
### Section 2: Load input files
print "Loading input files..."
Tomo = np.loadtxt(tomofile) ## Load tomography file into a numpy array
Tomo[:, 1][Tomo[:, 1] > 180] -= 360
Slabgrid = gmt.GMTGrid(
slabfile) ## Load netcdf file of slabguide into a "grid"
if Slabgrid.geodict['xmin'] > 180:
Slabgrid.geodict['xmin'] -= 360
if Slabgrid.geodict['xmax'] > 180:
Slabgrid.geodict['xmax'] -= 360
Slabgrid.griddata = Slabgrid.griddata * -1 ## WILL NEED TO CHANGE THIS IF INPUT IS IN NEGATIVE DEPTH
print "Building strike and dip grids..."
Strikegrid, Dipgrid = sf.mkSDgrd(
Slabgrid) ## Strikes follow right hand rule, dips are positive in degrees
############################
### Section 3: Create grid of slab guide info