a lot of py stuff
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@ -1,6 +1,7 @@
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import numpy as np
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from cache import timeit
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def deg_2_rad(winkel):
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return winkel / 180.0 * np.pi
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@ -42,7 +43,9 @@ class VO2_Lattice(Lattice):
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base_c_m = 5.38
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base_c_r = 2.856
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base_c_r = 2.8
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base_b_r = 4.554
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base_b_r = 4.5
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base_a_r = base_b_r
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alpha_m = 122.64 # degree
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@ -64,6 +67,14 @@ class VO2_Lattice(Lattice):
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offset_a_r, offset_c_r = self._mono_2_rutile(offset_c_m, offset_a_m)
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res = 0.05
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offset_a_r = res * int(offset_a_r/res)
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offset_c_r = res * int(offset_c_r/res)
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offset_a_r = 0.5
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offset_c_r = 0.5
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print(offset_a_r, offset_c_r)
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self.atom_x_mono = offset_a_r + X * \
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self.base_c_r + np.mod(Y, 4) * 0.5 * self.base_c_r
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self.atom_x_mono[np.mod(X, 2) == 0] -= 2 * offset_a_r
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@ -8,6 +8,7 @@ import tqdm
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from extractors import Rect_Evaluator, Voronoi_Evaluator, Image_Wrapper
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from cache import timeit
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class Plotter:
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def __init__(self, lat):
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self.lattice = lat
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@ -61,7 +62,7 @@ def rotate(x, y, angle):
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def test_square():
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LEN = 10
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LEN = 40
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lat = SCC_Lattice(LEN, LEN)
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# lat = VO2_Lattice(LEN, LEN)
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plot = Plotter(lat)
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@ -69,16 +70,26 @@ def test_square():
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# pos_x, pos_y = rotate(pos_x, pos_y, 30)
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si = SpinImage(pos_x, pos_y)
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fig, axs = plt.subplots(2, 2)
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si.plot(axs[0, 0],1)
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si.plot(axs[0, 0], 1)
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# si.gaussian(LEN)
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# si.blur(3)
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si.pad_it_square(int((5./2.)/0.1))
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si.plot(axs[0, 1],1)
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si.pad_it_square(size=int((5*LEN)/si.resolution))
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si.plot(axs[0, 1], 1)
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plt.pause(0.1)
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fx, fy, intens = si.fft()
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plot.plot(fx, fy, intens, axs[1, 0], axs[1, 1])
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plt.tight_layout()
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plt.savefig("test.pdf")
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plt.figure()
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index = np.abs(fy).argmin()
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plt.plot(fx, intens[index, :], label="fy=0")
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plt.plot(fx, intens[index+1, :], label="fy=df")
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plt.plot(fx, intens[index+2, :], label="fy=2df")
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plt.legend()
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plt.yscale("log")
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plt.show()
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@ -97,87 +108,67 @@ def helper(intens, fx, fy, voro, rect):
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def test_mixed():
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shift_x = -(5 + 3*28)
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shift_y = -43
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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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all_rutile = np.stack(lat.reci()[0]).T
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all_mono = np.stack(lat.reci()[1]).T
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all_mono2 = np.stack(lat.reci()[2]).T
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voro = Voronoi_Evaluator([all_rutile, all_mono, all_mono2])
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rect = Rect_Evaluator([all_rutile, all_mono, all_mono2])
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#voro = Voronoi_Evaluator([all_mono])
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#rect = Rect_Evaluator([all_mono])
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pos_x, pos_y = lat.get_from_mask(np.zeros((40, 40)))
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si = SpinImage(pos_x, pos_y)
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si.pad_it_square(10, size=2300)
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fx, fy, intens_mono = si.fft()
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pos_x, pos_y = lat.get_from_mask(np.zeros((LEN, LEN)))
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si_mono = SpinImage(pos_x, pos_y)
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si_mono.pad_it(int((LEN * 5.712) / si_mono.resolution)+shift_x,
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int((LEN * 4.554) / si_mono.resolution)+shift_y)
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fx, fy, intens_mono = si_mono.fft()
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pos_x, pos_y = lat.get_from_mask(np.ones((40, 40)))
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si = SpinImage(pos_x, pos_y)
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si.pad_it_square(10, size=2300)
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fx, fy, intens_rutile = si.fft()
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pos_x, pos_y = lat.get_from_mask(np.ones((LEN, LEN)))
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si_rutile = SpinImage(pos_x, pos_y)
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print(int((LEN * 5.712) / si_rutile.resolution)+shift_x,
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int((LEN * 4.554) / si_rutile.resolution)+shift_y,
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si_rutile.img.shape)
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si_rutile.pad_it(int((LEN * 5.712) / si_rutile.resolution)+shift_x,
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int((LEN * 4.554) / si_rutile.resolution)+shift_y)
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fx, fy, intens_rutile = si_rutile.fft()
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mask_misk = np.ones((40, 40))
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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], (40, 40))] = False
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mask_misk[np.unravel_index(ind[:800], (LEN, LEN))] = False
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pos_x, pos_y = lat.get_from_mask(mask_misk)
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si = SpinImage(pos_x, pos_y)
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si.pad_it_square(10, size=2300)
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fx, fy, intens_mixed = si.fft()
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img = Image_Wrapper(intens_mono, fx, fy)
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voro.generate_mask(img)
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rect.generate_mask(Image_Wrapper(intens_mono, fx, fy))
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si_mixed = SpinImage(pos_x, pos_y)
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si_mixed.pad_it(int((LEN * 5.712) / si_mixed.resolution)+shift_x,
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int((LEN * 4.554) / si_mixed.resolution)+shift_y)
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fx, fy, intens_mixed = si_mixed.fft()
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fig, axs = plt.subplots(1, 2)
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axs[0].imshow(rect.get_mask(), extent=img.ext(), origin="lower")
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axs[0].plot(all_rutile[:, 0], all_rutile[:, 1], ".")
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axs[1].plot(all_rutile[:, 0], all_rutile[:, 1], ".")
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axs[0].plot(all_mono[:, 0], all_mono[:, 1], ".")
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axs[1].plot(all_mono[:, 0], all_mono[:, 1], ".")
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axs[0].plot(all_mono2[:, 0], all_mono2[:, 1], ".")
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axs[1].plot(all_mono2[:, 0], all_mono2[:, 1], ".")
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axs[1].imshow(voro.get_mask(), extent=img.ext(), origin="lower")
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fig, axs = plt.subplots(3, 3)
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si_rutile.plot(axs[0, 0], 1)
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si_mono.plot(axs[0, 2], 1)
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si_mixed.plot(axs[0, 1], 1)
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plot.plot(freqx=fx, freqy=fy, intens=intens_rutile,
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ax_log=axs[1, 0], ax_lin=axs[2, 0], vmax=10e7)
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plot.plot(freqx=fx, freqy=fy, intens=intens_mono,
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ax_log=axs[1, 2], ax_lin=axs[2, 2], vmax=10e7)
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plot.plot(freqx=fx, freqy=fy, intens=intens_mixed,
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ax_log=axs[1, 1], ax_lin=axs[2, 1], vmax=10e7)
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print("mono")
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helper(intens=intens_mono, fx=fx, fy=fy, voro=voro, rect=rect)
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print("mixed")
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helper(intens=intens_mixed, fx=fx, fy=fy, voro=voro, rect=rect)
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print("rutile")
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helper(intens=intens_rutile, fx=fx, fy=fy, voro=voro, rect=rect)
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index = np.abs(fx).argmin()
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print(index, "/", fx.shape)
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fig, axs = plt.subplots(1, 3)
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axs[0].plot(intens_rutile[index, :])
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axs[0].plot(intens_rutile[index-1, :])
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axs[0].plot(intens_rutile[index+1, :])
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axs[1].plot(intens_mixed[index, :])
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axs[2].plot(intens_mono[index, :])
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for a in axs:
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a.set_yscale("log")
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new_intens_mono = rect.debug(Image_Wrapper(intens_mono, fx, fy))
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new_intens_mixed = rect.debug(Image_Wrapper(intens_mixed, fx, fy))
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new_intens_rutile = rect.debug(Image_Wrapper(intens_rutile, fx, fy))
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fig, axs = plt.subplots(2, 3)
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plot.plot(freqx=fx, freqy=fy, intens=new_intens_rutile,
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ax_log=axs[0, 0], ax_lin=axs[1, 0], vmax=10e7)
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plot.plot(freqx=fx, freqy=fy, intens=new_intens_mono,
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ax_log=axs[0, 2], ax_lin=axs[1, 2], vmax=10e7)
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plot.plot(freqx=fx, freqy=fy, intens=new_intens_mixed,
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ax_log=axs[0, 1], ax_lin=axs[1, 1], vmax=10e7)
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for ax in axs.flatten():
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ax.set_xlim(-1, 1)
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ax.set_ylim(-1, 1)
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new_intens_mono = voro.debug(Image_Wrapper(new_intens_mono, fx, fy))
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new_intens_mixed = voro.debug(Image_Wrapper(intens_mixed, fx, fy))
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new_intens_rutile = voro.debug(Image_Wrapper(intens_rutile, fx, fy))
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fig, axs = plt.subplots(2, 3)
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plot.plot(freqx=fx, freqy=fy, intens=new_intens_rutile,
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ax_log=axs[0, 0], ax_lin=axs[1, 0], vmax=10e7)
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plot.plot(freqx=fx, freqy=fy, intens=new_intens_mono,
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ax_log=axs[0, 2], ax_lin=axs[1, 2], vmax=10e7)
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plot.plot(freqx=fx, freqy=fy, intens=new_intens_mixed,
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ax_log=axs[0, 1], ax_lin=axs[1, 1], vmax=10e7)
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for ax in axs.flatten():
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ax.set_xlim(-1, 1)
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ax.set_ylim(-1, 1)
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index = np.abs(fy).argmin()
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print(index, "/", fy.shape)
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fig, axs = plt.subplots(1, 3)
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axs[0].plot(intens_rutile[:, index])
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axs[1].plot(intens_mixed[:, index])
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axs[2].plot(intens_mono[:, index])
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for a in axs:
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a.set_yscale("log")
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def random(seed):
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@ -198,7 +189,6 @@ def random(seed):
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percentage = []
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counter = 0
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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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@ -241,6 +231,7 @@ 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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@timeit
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def ising(seed):
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np.random.seed(seed)
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@ -305,8 +296,8 @@ def ising(seed):
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if __name__ == "__main__":
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test_square()
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#test_mixed()
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# test_square()
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test_mixed()
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plt.show()
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# random()
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# np.random.seed(1234)
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@ -3,8 +3,9 @@ import scipy
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import scipy.fftpack as sfft
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import scipy.signal
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class SpinImage:
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resolution = 0.1
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resolution = 0.05
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def __init__(self, x_pos, y_pos):
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x_pos = x_pos - np.min(x_pos)
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@ -34,7 +35,7 @@ class SpinImage:
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def pad_it_square(self, additional_pad=0, size=None):
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h = self.img.shape[0]
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w = self.img.shape[1]
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xx = np.maximum(h, w) + 2 * additional_pad
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xx = np.maximum(h, w) + 2 * additional_pad + 1
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if size is not None:
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xx = np.maximum(xx, size)
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yy = xx
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@ -50,6 +51,23 @@ class SpinImage:
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self.img = np.pad(self.img, pad_width=(
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(a, aa), (b, bb)), mode="constant")
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def pad_it(self, x_size, y_size):
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h = self.img.shape[0]
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w = self.img.shape[1]
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xx = x_size
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yy = y_size
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self.length_x = xx * self.resolution
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self.length_y = yy * self.resolution
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a = (xx - h) // 2
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aa = xx - a - h
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b = (yy - w) // 2
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bb = yy - b - w
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self.img = np.pad(self.img, pad_width=(
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(a, aa), (b, bb)), mode="constant")
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def percentage_gaussian(self, mask, sigma):
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x = np.linspace(-self.length_x / 2,
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self.length_x / 2, mask.shape[0])
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