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normalise only between 1/3 and times 3 #26

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Jul 31, 2024
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normalise only between 1/3 and times 3 #26

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60 changes: 33 additions & 27 deletions paper_figures/fig2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@
from representativity import core, util
from representativity.correction_fitting import microlib_statistics
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy.ndimage import zoom

# with open("data_gen2.json", "r") as fp:
# data = json.load(fp)["generated_data"]
Expand Down Expand Up @@ -46,7 +47,7 @@
many_images = util.generate_image(netG, lf=lf, threed=False, reps=10)
many_images = many_images.detach().cpu().numpy()
pf = many_images.mean()
small_imsize = 512
small_imsize = 400
img = many_images[0][:small_imsize, :small_imsize]

csets = [["black", "black"], ["gray", "gray"]]
Expand Down Expand Up @@ -88,17 +89,22 @@
axs[i, 1].tick_params(axis="y", labelcolor=colors["cls"])
axs[i, 1].set_ylim(0, 28)

center = 40
dims = len(img.shape)
# print(f'starting tpc radial')
tpc = core.radial_tpc(img, volumetric=False)
tpc_im = tpc[256-40:256+40, 256-40:256+40]
center_im = small_imsize // 2
tpc_im = tpc[center_im-center:center_im+center, center_im-center:center_im+center]
cls = core.tpc_to_cls(tpc, img)

contour = axs[i, 2].contourf(tpc_im, cmap="plasma", levels=200)
circle_real = plt.Circle((40, 40), real_cls, fill=False, color=colors["stds"])
circle_pred = plt.Circle((40, 40), cls, fill=False, color=colors["cls"])
circle_real = plt.Circle((center, center), real_cls, fill=False, color=colors["stds"])
circle_pred = plt.Circle((center, center), cls, fill=False, color=colors["cls"])
axs[i, 2].add_artist(circle_real)
axs[i, 2].add_artist(circle_pred)
x_ticks = axs[i, 2].get_xticks()[1:-1]
axs[i, 2].set_xticks(x_ticks, np.int64(np.array(x_ticks) - center))
axs[i, 2].set_yticks(x_ticks, np.int64(np.array(x_ticks) - center))
divider = make_axes_locatable(axs[i, 2])
cax = divider.append_axes("right", size="5%", pad=0.05)
fig.colorbar(contour, cax=cax, orientation="vertical")
Expand All @@ -108,30 +114,30 @@
label_plots = [plot.get_label() for plot in plots]
axs[i, 1].legend(plots, label_plots)

# axs[i,2].legend()
# axs[i,1].set_xlabel('Image length size [pixels]')
# axs[i,1].set_ylabel('Volume fraction percentage error [%]')
# axs[i,2].set_ylabel('2-point correlation function')
# ir = np.round(d[f'ir_vf'], 2)
# im = img[0]*255
# si_size, nirs = 160, 5
# sicrop = int(ir*nirs)
# print(ir, sicrop)
# subim=torch.tensor(im[-sicrop:,-sicrop:]).unsqueeze(0).unsqueeze(0).float()
# subim = interpolate(subim, size=(si_size,si_size), mode='nearest')[0,0]
# subim = np.stack([subim]*3, axis=-1)

# subim[:5,:,:] = 125
# subim[:,:5,:] = 125
# # subim[5:20, 5:50, :]
# subim[10:15, 10:10+si_size//nirs, :] = 0
# subim[10:15, 10:10+si_size//nirs, 1:-1] = 125

# im = np.stack([im]*3, axis=-1)
# im[-si_size:,-si_size:] = subim
axs[i,1].set_xlabel('Image length size [pixels]')
axs[i,1].set_ylabel('Volume fraction percentage error [%]')
axs[i,2].set_ylabel('2-point correlation function')
# img = img*255
si_size, nirs = small_imsize//2, 5
sicrop = int(real_cls*nirs)
zoom_mag = si_size/sicrop
print(real_cls, sicrop)
subim = img[-sicrop:,-sicrop:]
subim = zoom(subim, zoom=(zoom_mag,zoom_mag), order=0)
subim = np.stack([subim]*3, axis=-1)

boundary_len = 5
subim[:boundary_len,:,:] = 0.5
subim[:,:boundary_len,:] = 0.5
# subim[5:20, 5:50, :]
subim[10:10+boundary_len, 10:10+si_size//nirs, :] = 0
subim[10:10+boundary_len, 10:10+si_size//nirs, 1:-1] = 0.5

img = np.stack([img]*3, axis=-1)
img[-si_size:,-si_size:] = subim
axs[i, 0].imshow(img, cmap="gray")
# axs[i, 0].set_xticks([])
# axs[i, 0].set_yticks([])
axs[i, 0].set_xticks([])
axs[i, 0].set_yticks([])
# axs[i, 0].set_ylabel(f'M{n[1:]}')
# axs[i, 0].set_xlabel(f'Volume fraction '+ r'$\tilde{a}_2$: '+ f'{ir} Inset mag: x{np.round(si_size/sicrop, 2)}')

Expand Down
4 changes: 2 additions & 2 deletions representativity/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -418,9 +418,9 @@ def pred_cls_is_off(
if statistical_cls > 1: # could be erroneous stat. analysis prediction
# if pred cls too low or too high compared to statistical method,
# return true and the direction of the error (1 for too low, -1 for too high)
if model_cls / statistical_cls < 2 / 3:
if model_cls / statistical_cls < 1 / 3:
return True, 1
if model_cls / statistical_cls > 2:
if model_cls / statistical_cls > 3:
return True, -1
return False, 0

Expand Down