<jupyter_start><jupyter_text>glamt_large.shape<jupyter_code>lon_small[0,0], lat_small[0,0]
lon_small[-1,0], lat_small[-1,0]
lon_small[0,-1], lat_small[0,-1]
lon_small[-1,-1], lat_small[-1,-1]
mat_file_str='/data/ssahu/Falkor_2013/mvp/surveyA.mat'



mat = scipy.io.loadmat(mat_file_str)

depths_survey = mat['depths'][:,0]
lat_survey = mat['latitude'][:,0]
lon_survey = mat['longitude'][:,0] - 100
# den_survey = mat['density'][:]
pden_survey = mat['pden'][:]
temp_survey = mat['temp'][:]
sal_survey = mat['salinity'][:]
mtime = mat['mtime'][:,0]

pressure_survey = np.empty_like(temp_survey)
SA_survey = np.empty_like(temp_survey)
CT_survey = np.empty_like(temp_survey)
spic_survey = np.empty_like(temp_survey)
rho_survey = np.empty_like(temp_survey)


y = np.empty_like(lat_survey)
x = np.empty_like(y)


for i in np.arange(lat_survey.shape[0]):
    y[i], x[i] = geo_tools.find_closest_model_point(
               lon_survey[i],lat_survey[i],lon_small,lat_small,tols={
        'NEMO': {'tol_lon': 0.1, 'tol_lat': 0.1},'GEM2.5': {'tol_lon': 0.1, 'tol_lat': 0.1}})


for i in np.arange(y.shape[0]):
    print (y[i], x[i])<jupyter_output>97.0 103.0
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80.0 108.0[...]
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