def Regrid_PS(PS1, Corners):
dim1, dim2 = Corners.shape[1:]
dim1 -= 1
dim2 -= 1
global px, py
px, py = np.where(PS1)
global squares
squares = np.array(Make_squares(Corners))
square_num = np.arange(0, len(squares))
points = np.zeros((len(px), 2))
points[:, 0] = px
points[:, 1] = py
global pspixels
pspixels = Footprint_square(Corners, points)
global psimage
psimage = PS1.copy()
pool = MultiPool()
values = list(pool.map(Pix_sum, square_num))
pool.close()
PS_scene = np.array(values)
PS_scene = np.nansum(PS_scene, axis=0)
PS_scene = PS_scene.astype('float')
PS_scene = PS_scene.reshape(dim1, dim2)
return PS_scene
print('Running the ML analysis - this may take a while')
optres = scipy.optimize.basinhopping(f_mlnlike,
start,
niter=n_basinhopping,
T=T,
interval=3,
minimizer_kwargs=minimizer_kwargs,
take_step=my_take_step,
callback=my_print_fun)
print('[', end='')
print(', '.join(map(lambda _: '{:.5g}'.format(_), optres.x)), end='')
print('] ', end='')
print('{}'.format(optres.fun))
if pool:
pool.close()
# -- Step 5. Output results
print('-- Likelihood maximization of variable ' + ly + ' --')
print(', '.join(map(lambda x: '{:.3g}'.format(x), optres.x)))
fit_result = optres.x
m_scale, n_scale = xGASS_auxiliary.get_slope_intercept(fit_result[0],
fit_result[1])
m_shape, n_shape = xGASS_auxiliary.get_slope_intercept(fit_result[2],
fit_result[3])
m_zero, n_zero = xGASS_auxiliary.get_slope_intercept(fit_result[4],
fit_result[5])
xx = np.linspace(max(min(df['v0']), sel_lgMstar[0]),