if __name__ == "__main__":
print("Visualization of correlation between neighbors 1.0")
print("Loading data ...")
gf = GeoField()
gf.load("data/pres.mon.mean.nc", 'pres')
gf.transform_to_anomalies()
gf.slice_date_range(date(1948, 1, 1), date(2012, 1, 1))
gf.slice_spatial(None, [20, 89])
# initialize a parallel pool
pool = Pool(POOL_SIZE)
# get the correlation
num_lats, num_lons = gf.spatial_dims()
num_gpoints = num_lats * num_lons
dfC = np.zeros([1, (num_lats - 1) * 2, num_lons], dtype = np.float64)
# compute the neighbor correlations
y = np.ones(shape=(num_gpoints, num_gpoints)) * 10e6
Clats = []
for lat in range(num_lats - 1):
Clats.append(gf.lats[lat])
Clats.append(0.5 * (gf.lats[lat] + gf.lats[lat+1]))
for lon in range(num_lons):
pt_1 = lat * num_lons + lon
pt_2 = lat * num_lons + lon + 1
lon1 = (lon + 1) % num_lons
cc = np.corrcoef(gf.d[:, lat, lon], gf.d[:, lat, lon1], rowvar = 0)[0,1]
