# Note: Endianness of the binary files written here is not explicitly set and will depend on the machine you are working on. Use [numpy.dtype.newbyteorder](https://docs.scipy.org/doc/numpy/reference/generated/numpy.dtype.newbyteorder.html) to explicitly set the endianness or compile mitgcm such that the machine default will be used.
# %%
# # %matplotlib notebook
# %matplotlib inline
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import os
import gsw
import xarray as xr
import ocean_tools.utils as utils
data_dir = os.path.expanduser('~/data/SamoanPassage/Bathy/')
data_file = 'samoan_passage_bathymetry_200m_merged.mat'
B = utils.loadmat(os.path.join(data_dir, data_file))['bathy2']
# It seems like float64 is the
# default data type in numpy, but let's be
# clear about this and write a little
# test/conversion function.
def CheckFloat64(x):
if x.dtype == np.float64:
print('its a float64')
else:
print('converting to float64')
x = x.astype(np.float64)
return x
# name: spamex
# ---
# %% [markdown]
# # We want to create the best possible bathymetry...
# %%
import os
import numpy as np
import matplotlib.pyplot as plt
import ocean_tools.utils as utils
import munch
from numba import jit
import scipy.io as io
B = munch.munchify(utils.loadmat('bathy_cleaned.mat'))
@jit
def weightgen(gd, bbs):
w = np.zeros_like(gd, dtype=float)
bbmax = (2*bbs)**2
nr, nc = gd.shape
for i in range(nr):
for j in range(nc):
i1 = np.maximum(0, i-bbs)
i2 = np.minimum(nr, i+bbs)
j1 = np.maximum(0, j-bbs)
j2 = np.minimum(nc, j+bbs)
w[i, j] = gd[i1:i2, j1:j2].sum()/bbmax
return w
# %%
import os
import numpy as np
import matplotlib.pyplot as plt
import ocean_tools.sandwell as ssb
import ocean_tools.utils as utils
import munch
# LLC4320 bathymetry
depth = np.fromfile('grid/Depth_144x185', '>f').reshape((185, 144))
XC = np.fromfile('grid/XC_144x185', '>f').reshape((185, 144))
YC = np.fromfile('grid/YC_144x185', '>f').reshape((185, 144))
bathy = np.fromfile('grid/BATHY_144x185_Box13', '>f').reshape((185, 144))
MB = munch.munchify(utils.loadmat('merged_bathy.mat'))
def bilinear_interpolation(xa, ya, fg, x, y):
i2 = np.searchsorted(xa, x)
i1 = i2 - 1
j2 = np.searchsorted(ya, y)
j1 = j2 - 1
dx = xa[i2] - xa[i1]
dy = ya[j2] - ya[j1]
f11, f21, f12, f22 = fg[i1, j1], fg[i2, j1], fg[i1, j2], fg[i2, j2]
x1, y1, x2, y2 = xa[i1], ya[j1], xa[i2], ya[j2]
fi = (f11 * (x2 - x) * (y2 - y) + f21 * (x - x1) * (y2 - y) + f12 *
(x2 - x) * (y - y1) + f22 * (x - x1) * (y - y1)) / (dx * dy)
return fi
# First we load all the datasets and interpolate/resize them onto a fine spatial grid.
# %%
import os
import numpy as np
import matplotlib.pyplot as plt
import ocean_tools.sandwell as ssb
import ocean_tools.utils as utils
import munch
import scipy.io as io
import xarray as xr
# DEFINE THE GRID USING GUNNARS GRID
data_dir = os.path.expanduser('~/data/SamoanPassage/Bathy/')
data_file = 'samoan_passage_bathymetry_200m_merged.mat'
B = munch.munchify(utils.loadmat(os.path.join(data_dir, data_file))['bathy2'])
lllon = B.lon.min()
lllat = B.lat.min()
urlon = B.lon.max()
urlat = B.lat.max()
# LOAD SMITH AND SANDWELL
dll = 0.25
SSX, SSY, SSD = ssb.read_grid(
[lllon - dll, urlon + dll, lllat - dll, urlat + dll])
SSX = SSX[:, 0]
SSY = SSY[0, :]
SSD = -1 * SSD.T # Transport necessary... want Y along i axis and X along j axis.
# Gunnars merged bathymetry