MLXID/simple_eta
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

added generation

Browse Source
This commit is contained in:
Erik Fröjdh
2025-10-31 15:30:33 +01:00
parent b6fa09fd22
commit 91dc49cd51
2 changed files with 59 additions and 16 deletions
Download Patch File Download Diff File Expand all files Collapse all files

29
play.py
View File

@@ -8,18 +8,31 @@ from simple_eta import generate
sigma_um = 12 sigma_um = 12
resolution = 200 resolution = 200
grid_size = 3 grid_size = 2
pixel_size = 25 pixel_size = 25
pos = (37.5,37.5) #x,y # pos = (37.5,47.5) #x,y
t = gaussian_2d(mx=pos[0], my = pos[1], sigma = sigma_um, res = resolution, grid_size = grid_size, pixel_size = pixel_size) # t = gaussian_2d(mx=pos[0], my = pos[1], sigma = sigma_um, res = resolution, grid_size = grid_size, pixel_size = pixel_size)
fig, ax = plot_gaussian(t, pixel_size=pixel_size, grid_size = grid_size) # fig, ax = plot_gaussian(t, pixel_size=pixel_size, grid_size = grid_size)
plt.show() # plt.show()
res = generate.sum_pixels(t, grid_size) # res = generate.sum_pixels(t, grid_size)
fig,ax = plt.subplots() # fig,ax = plt.subplots()
im = ax.imshow(res, origin = 'lower') # im = ax.imshow(res, origin = 'lower')
mx,my, pixels = generate.generate_uniform_hits(sigma = sigma_um, pixel_size = pixel_size, grid_size = grid_size, resolution=resolution, N=300)
# pixels2 = generate.sum2x2(t)
fig, ax = plt.subplots(figsize = (8,8))
ax.scatter(mx,my)
ax.set_xlim(0,grid_size*pixel_size)
ax.set_ylim(0,grid_size*pixel_size)
ticks = [tick for tick in range(0,pixel_size*grid_size+1, pixel_size)]
ax.set_xticks(ticks)
ax.set_yticks(ticks)
ax.grid()

46
simple_eta/generate.py
View File

@@ -30,11 +30,41 @@ def sum_pixels(t, grid):
# pixels[1,1] = t[resolution//2:, resolution//2:].sum() # pixels[1,1] = t[resolution//2:, resolution//2:].sum()
return pixels return pixels
def sum_pixels2(t, grid): def generate_uniform_hits(sigma, pixel_size, grid_size, resolution, N=100, device = 'cpu'):
resolution = t.shape[0] """
pixels = np.zeros((grid,grid)) Generate N gaussians with sigma. Uniformly distributed over
pixels[0,0] = t[0:resolution//2, 0:resolution//2].sum() the 2x2 pixel grid.
pixels[0,1] = t[0:resolution//2, resolution//2:].sum() """
pixels[1,0] = t[resolution//2:, 0:resolution//2].sum()
pixels[1,1] = t[resolution//2:, resolution//2:].sum() x = torch.linspace(0, pixel_size*grid_size, resolution, device = device)
return pixels x,y = torch.meshgrid(x,x, indexing="ij")
xs = x.unsqueeze(0).repeat(N,1,1)
ys = y.unsqueeze(0).repeat(N,1,1)
#TODO! The genreal cases are actually odd and even
if grid_size % 2 == 0:
low = (grid_size-1)//2*pixel_size+pixel_size/2
high =pixel_size+low
else:
low = pixel_size*(grid_size//2)
high = low+pixel_size
print(low, high)
mx = torch.rand(N,1,1, device = device) * (high-low)+low
my = torch.rand(N,1,1, device = device) * (high-low) +low
ts = 1 / (2*math.pi*sigma**2) * \
torch.exp(-((xs - my)**2 / (2*sigma**2) + (ys - mx)**2 / (2*sigma**2)))
#Sum signal in pixels for all N depositions
step = resolution//grid_size
pixels = torch.zeros((N,grid_size,grid_size))
for i in range(grid_size):
for j in range(grid_size):
pixels[:,i,j] = ts[:,i*step:(i+1)*step, j*step:(j+1)*step].sum(axis = 1).sum(axis = 1)
return mx, my, pixels