def pattern_cor(ds1, ds2):

# Assume latxlon grid

nlat = ds1.lat.values.shape[0]

nlon = ds1.lon.values.shape[0]

weight = np.zeros((nlat, nlon))

for i_lat in range(nlat):

weight[:,0] = np.cos(ds1.lat.values[i_lat]*(np.pi/180))

ds1_ = np.multiply(ds1.values[0,:,:],weight)

ds1_sum = np.sum(ds1_.flatten())

ds1_mean = ds1_sum/(np.sum(weight.flatten()))

ds2_ = np.multiply(ds2.values[0,:,:],weight)

ds2_sum = np.sum(ds2_.flatten())

ds2_mean = ds2_sum/(np.sum(weight.flatten()))

xycov = 0.0

xanom2 = 0.0

yanom2 = 0.0

w = weight

x = ds1.values[0,:,:]

y = ds2.values[0,:,:]

xave = ds1_mean

yave = ds2_mean

print(weight.shape, x.shape, y.shape, xave, yave)

for ml in range(nlat):

for nl in range(nlon):

xycov = xycov + w[ml,nl]*(x[ml,nl]-xave)*(y[ml,nl]-yave)

xanom2 = xanom2 + w[ml,nl]*(x[ml,nl]-xave)**2

yanom2 = yanom2 + w[ml,nl]*(y[ml,nl]-yave)**2

r = xycov/(np.sqrt(xanom2)*np.sqrt(yanom2))

return r