import matplotlib.pyplot as plt
import caustics
import torch

cosmology = caustics.FlatLambdaCDM()
sie = caustics.SIE(cosmology=cosmology, name="lens")
src = caustics.Sersic(name="source")
lnslt = caustics.Sersic(name="lenslight")

x = torch.tensor([
#   z_s  z_l   x0   y0   q    phi     b    x0   y0   q     phi    n    Re
    1.5, 0.5, -0.2, 0.0, 0.4, 1.5708, 1.7, 0.0, 0.0, 0.5, -0.985, 1.3, 1.0,
#   Ie    x0   y0   q    phi  n   Re   Ie
    5.0, -0.2, 0.0, 0.8, 0.0, 1., 1.0, 10.0
])  # fmt: skip

minisim = caustics.Lens_Source(
    lens=sie, source=src, lens_light=lnslt, pixelscale=0.05, pixels_x=100
)
plt.imshow(minisim(x, quad_level=3), origin="lower")
plt.axis("off")
plt.show()

newx = x.repeat(20, 1)
newx += torch.normal(mean=0, std=0.1 * torch.ones_like(newx))

images = torch.vmap(minisim)(newx)

fig, axarr = plt.subplots(4, 5, figsize=(20, 16))
for ax, im in zip(axarr.flatten(), images):
    ax.imshow(im, origin="lower")
plt.show()

J = torch.func.jacfwd(minisim)(x)

# Plot the new images
fig, axarr = plt.subplots(3, 7, figsize=(20, 9))
for i, ax in enumerate(axarr.flatten()):
    ax.imshow(J[..., i], origin="lower")
plt.show()