246 lines
7.0 KiB
Python
246 lines
7.0 KiB
Python
from lattices import SCC_Lattice, VO2_Lattice
|
|
from spin_image import SpinImage
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import matplotlib.patches as patches
|
|
import matplotlib
|
|
import scipy
|
|
import scipy.signal
|
|
import tqdm
|
|
from extractors import Rect_Evaluator, Voronoi_Evaluator, Image_Wrapper
|
|
|
|
|
|
class Plotter:
|
|
def __init__(self, lat):
|
|
self.lattice = lat
|
|
self.length_2 = 0.05
|
|
|
|
def reduce(self, arr):
|
|
arr = np.array(arr)
|
|
arr = arr.flatten()
|
|
return np.mean(arr)
|
|
# return np.sum(arr[np.argpartition(arr, -8)[-8:]])
|
|
|
|
def rect_at_point(self, x, y, color):
|
|
length_2 = self.length_2
|
|
rect = patches.Rectangle(
|
|
(x - length_2, y - length_2),
|
|
2 * length_2,
|
|
2 * length_2,
|
|
linewidth=1,
|
|
edgecolor=color,
|
|
facecolor="none",
|
|
)
|
|
return rect
|
|
|
|
def plot(self, freqx, freqy, intens, ax_log=None, ax_lin=None, vmax=None):
|
|
if ax_log:
|
|
t = ax_log.imshow(
|
|
intens,
|
|
extent=(np.min(freqx), np.max(freqx),
|
|
np.min(freqy), np.max(freqy)),
|
|
norm=matplotlib.colors.LogNorm(vmin=10),
|
|
cmap="viridis",
|
|
origin="lower"
|
|
)
|
|
plt.colorbar(t, ax=ax_log)
|
|
if ax_lin:
|
|
t = ax_lin.imshow(
|
|
intens,
|
|
extent=(np.min(freqx), np.max(freqx),
|
|
np.min(freqy), np.max(freqy)),
|
|
#vmax=vmax,
|
|
cmap="viridis",
|
|
origin="lower"
|
|
)
|
|
plt.colorbar(t, ax=ax_lin)
|
|
|
|
|
|
def rotate(x, y, angle):
|
|
radian = angle / 180 * 2 * np.pi
|
|
return np.cos(radian) * x - np.sin(radian) * y, np.sin(radian) * x + np.cos(radian) * y
|
|
|
|
|
|
def test_square():
|
|
LEN = 40
|
|
# lat = SCC_Lattice(LEN, LEN)
|
|
lat = VO2_Lattice(LEN, LEN)
|
|
plot = Plotter(lat)
|
|
pos_x, pos_y = lat.get_from_mask(np.zeros((40, 40)))
|
|
# pos_x, pos_y = rotate(pos_x, pos_y, 30)
|
|
si = SpinImage(pos_x, pos_y)
|
|
fig, axs = plt.subplots(2, 2)
|
|
si.pad_it_square(10)
|
|
si.plot(axs[0, 0], 2)
|
|
# si.gaussian(LEN)
|
|
# si.blur(3)
|
|
si.plot(axs[0, 1], 2)
|
|
|
|
plt.pause(0.1)
|
|
fx, fy, intens = si.fft()
|
|
plot.plot(fx, fy, intens, axs[1, 0], axs[1, 1])
|
|
plt.savefig("test.png")
|
|
plt.show()
|
|
|
|
|
|
def test_mixed():
|
|
LEN = 40
|
|
lat = VO2_Lattice(LEN, LEN)
|
|
plot = Plotter(lat)
|
|
all_rutile = np.stack(lat.reci()[0]).T
|
|
all_mono = np.stack(lat.reci()[1]).T
|
|
all_mono2 = np.stack(lat.reci()[2]).T
|
|
rect = Voronoi_Evaluator([all_rutile, all_mono, all_mono2])
|
|
|
|
pos_x, pos_y = lat.get_from_mask(np.zeros((40, 40)))
|
|
si = SpinImage(pos_x, pos_y)
|
|
si.pad_it_square(10)
|
|
fx, fy, intens_rutile = si.fft()
|
|
|
|
pos_x, pos_y = lat.get_from_mask(np.ones((40, 40)))
|
|
si = SpinImage(pos_x, pos_y)
|
|
si.pad_it_square(10)
|
|
fx, fy, intens_mono = si.fft()
|
|
|
|
mask_misk = np.ones((40, 40))
|
|
ind = np.arange(mask_misk.size)
|
|
np.random.shuffle(ind)
|
|
mask_misk[np.unravel_index(ind[:800], (40, 40))] = False
|
|
pos_x, pos_y = lat.get_from_mask(mask_misk)
|
|
si = SpinImage(pos_x, pos_y)
|
|
si.pad_it_square(10)
|
|
fx, fy, intens_mixed = si.fft()
|
|
|
|
intens_rutile = rect.extract_paint(
|
|
Image_Wrapper(intens_rutile, fx=fx, fy=fy))
|
|
intens_mono = rect.extract_paint(
|
|
Image_Wrapper(intens_mono, fx=fx, fy=fy))
|
|
intens_mixed = rect.extract_paint(
|
|
Image_Wrapper(intens_mixed, fx=fx, fy=fy))
|
|
|
|
fig, axs = plt.subplots(2, 3)
|
|
plot.plot(freqx=fx, freqy=fy, intens=intens_rutile,
|
|
ax_log=axs[0, 0], ax_lin=axs[1, 0], vmax=10e7)
|
|
plot.plot(freqx=fx, freqy=fy, intens=intens_mono,
|
|
ax_log=axs[0, 2], ax_lin=axs[1, 2], vmax=10e7)
|
|
plot.plot(freqx=fx, freqy=fy, intens=intens_mixed,
|
|
ax_log=axs[0, 1], ax_lin=axs[1, 1], vmax=10e7)
|
|
|
|
for ax in axs.flatten():
|
|
ax.set_xlim(-1, 1)
|
|
ax.set_ylim(-1, 1)
|
|
|
|
plt.show()
|
|
|
|
|
|
def random(seed):
|
|
np.random.seed(seed)
|
|
LEN = 40
|
|
lat = VO2_Lattice(LEN, LEN)
|
|
plot = Plotter(lat)
|
|
maske = np.zeros((LEN, LEN))
|
|
ind = np.arange(LEN * LEN)
|
|
np.random.shuffle(ind)
|
|
reci_lattice = lat.reci()
|
|
|
|
out = [[] for x in range(len(reci_lattice))]
|
|
percentage = []
|
|
counter = 0
|
|
for i in tqdm.tqdm(ind):
|
|
maske[np.unravel_index(i, (LEN, LEN))] = True
|
|
counter += 1
|
|
if np.mod(counter, 100) != 0:
|
|
continue
|
|
|
|
pos_x, pos_y = lat.get_from_mask(maske)
|
|
si = SpinImage(pos_x, pos_y)
|
|
si.pad_it_square(10)
|
|
fx, fy, intens = si.fft()
|
|
|
|
for tup, lis in zip(reci_lattice, out):
|
|
point_x, point_y = tup
|
|
point_x = point_x.flatten()
|
|
point_y = point_y.flatten()
|
|
peaks = []
|
|
for px, py in zip(point_x, point_y):
|
|
peaks.append(np.sum(plot.extract_rect(intens, px, py, fx, fy)))
|
|
lis.append(peaks)
|
|
|
|
percentage.append(np.sum(maske))
|
|
|
|
percentage = np.array(percentage)
|
|
percentage /= np.max(percentage)
|
|
|
|
np.savez(f"random_{seed}.npz", percentage=percentage, out=out)
|
|
|
|
|
|
def sample_index(p):
|
|
i = np.random.choice(np.arange(p.size), p=p.ravel())
|
|
return np.unravel_index(i, p.shape)
|
|
|
|
|
|
def ising(seed):
|
|
np.random.seed(seed)
|
|
LEN = 80
|
|
temp = 0.1
|
|
maske = np.zeros((LEN, LEN), dtype=bool)
|
|
|
|
lat = VO2_Lattice(LEN, LEN)
|
|
plot = Plotter(lat)
|
|
|
|
reci_lattice = lat.reci()
|
|
out = [[] for x in range(len(reci_lattice))]
|
|
percentage = []
|
|
counter = 0
|
|
for i in tqdm.tqdm(range(LEN*LEN)):
|
|
probability = np.roll(maske, 1, axis=0).astype(float)
|
|
probability += np.roll(maske, -1, axis=0).astype(float)
|
|
probability += np.roll(maske, 1, axis=1).astype(float)
|
|
probability += np.roll(maske, -1, axis=1).astype(float)
|
|
|
|
probability = np.exp(probability/temp)
|
|
probability[maske] = 0
|
|
probability /= np.sum(probability)
|
|
maske[sample_index(probability)] = True
|
|
|
|
counter += 1
|
|
if np.mod(counter, 100) != 0:
|
|
continue
|
|
pos_x, pos_y = lat.get_from_mask(maske)
|
|
si = SpinImage(pos_x, pos_y)
|
|
si.pad_it_square(10)
|
|
# si.gaussian(LEN)
|
|
fx, fy, intens = si.fft()
|
|
|
|
for tup, lis in zip(reci_lattice, out):
|
|
point_x, point_y = tup
|
|
point_x = point_x.flatten()
|
|
point_y = point_y.flatten()
|
|
peaks = []
|
|
for px, py in zip(point_x, point_y):
|
|
peaks.append(np.sum(plot.extract_rect(intens, px, py, fx, fy)))
|
|
|
|
lis.append(peaks)
|
|
percentage.append(np.mean(maske))
|
|
percentage = np.array(percentage)
|
|
percentage /= np.max(percentage)
|
|
|
|
np.savez(f"ising_{temp}_{seed}.npz", percentage=percentage, out=out)
|
|
# for o in out:
|
|
# plt.scatter(percentage, o/o[0])
|
|
# plt.plot([0, 1], [1, o[-1]/o[0]])
|
|
# plt.pause(1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# test_square()
|
|
test_mixed()
|
|
plt.show()
|
|
# random()
|
|
np.random.seed(1234)
|
|
for i in np.random.randint(0, 10000, 2):
|
|
random(i)
|
|
ising(i)
|
|
|
|
plt.show()
|