other run
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@ -173,5 +173,5 @@ class VO2_New(VO2_Lattice):
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# maske[1::2, :] = mask
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# print(maske.shape)
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# return maske
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def parse_maske(self, mask: np.ndarray):
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def parse_mask(self, mask: np.ndarray):
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return mask
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@ -5,7 +5,7 @@ from scipy import signal
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from cache import timeit
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from extractors import Rect_Evaluator
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import tqdm
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from lattices import SCC_Lattice, VO2_Lattice
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from lattices import SCC_Lattice, VO2_Lattice, VO2_New
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import sys
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from spin_image import SpinImage
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import numpy as np
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@ -21,127 +21,12 @@ ch.setFormatter(formatter)
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logger.addHandler(ch)
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def test_mixed():
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plt.style.use("one_column")
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fig, axs = plt.subplots(3, 3)
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LEN = 50
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lat = VO2_Lattice(LEN, LEN)
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plot = Plotter(lat)
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si = SpinImage(lat.get_phases())
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mask_misk = np.ones((LEN, LEN))
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ind = np.arange(mask_misk.size)
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np.random.shuffle(ind)
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mask_misk[np.unravel_index(ind[:800], (LEN, LEN))] = 0
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si.apply_mask(lat.parse_mask(np.zeros((LEN, LEN))))
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print("Clean Rutile: ", si.get_intens(
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lat.parse_mask(np.zeros((LEN, LEN)))))
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si.gaussian(20)
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print("Rutile: ", si.get_intens(lat.parse_mask(np.zeros((LEN, LEN)))))
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intens_mono = si.fft()
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intens_mono.clean()
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plot.plot_spins(si=si, ax_lin=axs[0, 0])
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si.apply_mask(lat.parse_mask(np.ones((LEN, LEN))))
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print("Clean Mono: ", si.get_intens(lat.parse_mask(np.ones((LEN, LEN)))))
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si.gaussian(20)
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print("Mono: ", si.get_intens(lat.parse_mask(np.ones((LEN, LEN)))))
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intens_rutile = si.fft()
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intens_rutile.clean()
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plot.plot_spins(si=si, ax_lin=axs[0, 2])
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si.apply_mask(lat.parse_mask(mask_misk))
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print("Clean Mixed: ", si.get_intens(lat.parse_mask(mask_misk)))
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si.gaussian(20)
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print("Mixed: ", si.get_intens(lat.parse_mask(mask_misk)))
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intens_mixed = si.fft()
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intens_mixed.clean()
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plot.plot_spins(si=si, ax_lin=axs[0, 1])
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plot.plot_fft(intens_mono,
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ax_log=axs[1, 0], ax_lin=axs[2, 0])
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plot.plot_fft(intens_rutile,
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ax_log=axs[1, 2], ax_lin=axs[2, 2])
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plot.plot_fft(intens_mixed,
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ax_log=axs[1, 1], ax_lin=axs[2, 1])
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plt.figure()
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fig, axs = plt.subplots(1,3)
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fig.set_figheight(1.7)
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plot.plot_fft(intens_mixed,
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ax_log=axs[1])
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plot.plot_fft(intens_mono,
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ax_log=axs[0])
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plot.plot_fft(intens_rutile,
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ax_log=axs[2])
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for ax, t in zip(axs,["monoclinic", "mixed", "rutile"]):
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ax.set_title(t)
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ax.set_xlim(-1,1)
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ax.set_ylim(-1,1)
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plt.tight_layout()
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fig.savefig("diff_pattern.pdf")
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fig.savefig("diff_pattern.png")
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# Plotting cuts
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def test_pdf():
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LEN = 40
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lat = VO2_Lattice(LEN, LEN)
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plot = Plotter(lat)
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si = SpinImage(lat.get_phases())
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integrate = 10
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out_intens = None
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already_inited = False
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for i in range(integrate):
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mask_misk = np.ones((LEN, LEN))
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ind = np.arange(mask_misk.size)
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np.random.shuffle(ind)
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mask_misk[np.unravel_index(ind[:800], (LEN, LEN))] = 0
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si.apply_mask(lat.parse_mask(mask_misk))
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si.gaussian(20)
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intens = si.fft()
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intens.clean()
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if not already_inited:
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print("Init")
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rect = Rect_Evaluator(lat.get_spots())
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rect.generate_mask(intens, merge=True)
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out_intens = intens
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already_inited = True
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else:
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out_intens.intens += intens.intens
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out_intens = intens
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rect.purge(intens)
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plt.figure()
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plot.plot_fft(intens, ax_log=plt.gca())
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plt.xlim(-1, 1)
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plt.ylim(-1, 1)
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plt.savefig("diff.png")
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plt.savefig("diff.pdf")
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pdf = sfft.fft2(intens.intens)
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pdf = sfft.fftshift(pdf)
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plt.figure()
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plt.imshow(np.abs(pdf), vmax=100)
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plt.xlabel("Pos")
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plt.ylabel("Pos")
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x = pdf.shape[1] / 2.
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y = pdf.shape[0] / 2.
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off = 100
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plt.xlim(x-off, x+off)
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plt.ylim(y-off, y+off)
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plt.tight_layout()
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plt.savefig("pdf.pdf")
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plt.savefig("pdf.png")
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def random(seed):
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np.random.seed(seed)
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LEN = 40
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lat = VO2_Lattice(LEN, LEN)
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maske = np.zeros((LEN, LEN))
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ind = np.arange(LEN * LEN)
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lat = VO2_New(LEN, LEN)
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maske = np.zeros((LEN*2, LEN*2))
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ind = np.arange(maske.size)
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np.random.shuffle(ind)
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rect = Rect_Evaluator(lat.get_spots())
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@ -152,7 +37,7 @@ def random(seed):
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si = SpinImage(lat.get_phases())
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already_inited = False
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for i in tqdm.tqdm(ind):
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maske[np.unravel_index(i, (LEN, LEN))] = True
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maske[np.unravel_index(i, maske.shape)] = True
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counter += 1
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if np.mod(counter, 100) != 0 and i != ind[-1] and i != ind[0]:
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continue
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@ -182,63 +67,6 @@ def random(seed):
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w_percentage=weighted_percentage, percentage=percentage, out_1=out_rect[0],
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out_2=out_rect[1], out_3=out_rect[2], out_4=out_rect[3])
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def sample_index(p):
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i = np.random.choice(np.arange(p.size), p=p.ravel())
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return np.unravel_index(i, p.shape)
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def ising(seed, temp=0.5):
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np.random.seed(seed)
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LEN = 40
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lat = VO2_Lattice(LEN, LEN)
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maske = np.zeros((LEN, LEN))
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rect = Rect_Evaluator(lat.get_spots())
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out_rect = [[] for x in range(4)]
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percentage = []
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weighted_percentage = []
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counter = 0
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si = SpinImage(lat.get_phases())
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already_inited = False
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for i in tqdm.tqdm(range(LEN*LEN)):
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probability = np.roll(maske, 1, axis=0).astype(float)
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probability += np.roll(maske, -1, axis=0).astype(float)
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probability += np.roll(maske, 1, axis=1).astype(float)
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probability += np.roll(maske, -1, axis=1).astype(float)
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probability = np.exp(probability/temp)
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probability[maske > 0] = 0
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probability /= np.sum(probability)
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maske[sample_index(probability)] = True
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counter += 1
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if np.mod(counter, 100) != 0:
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continue
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si.apply_mask(lat.parse_mask(maske))
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si.gaussian(20)
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intens = si.fft()
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if not already_inited:
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rect.generate_mask(intens, merge=True)
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already_inited = True
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ir, vr = rect.extract(intens)
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for lis, val in zip(out_rect, vr):
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lis.append(val)
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percentage.append(np.sum(maske))
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[p1, p2] = si.get_intens(lat.parse_mask(maske))
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weighted_percentage.append(p1/(p1+p2))
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percentage = np.array(percentage)
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weighted_percentage = np.array(weighted_percentage)
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percentage /= np.max(percentage)
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np.savez(f"ising_{temp}_rect_{seed}.npz",
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w_percentage=weighted_percentage, percentage=percentage, out_1=out_rect[0],
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out_2=out_rect[1], out_3=out_rect[2], out_4=out_rect[3])
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def runner():
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np.random.seed(1234)
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seeds = np.random.randint(0, 10000, 200)
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@ -253,21 +81,4 @@ def runner():
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if __name__ == "__main__":
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np.random.seed(1234)
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# runner()
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# test_me()
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# test_square()
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test_mixed()
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# test_pdf()
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plt.show()
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# random(1234)
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# ising(1234)
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# test_pdf()
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# plt.show()
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# exit()
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# for i in np.random.randint(0, 10000, 5):
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# random(i)
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# ising(i, 0.5)
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# ising(i, 1.0)
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# ising(i, 1.5)
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# plt.show()
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runner()
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