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fixed bug

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This commit is contained in:
Jacob Holder 2023-04-24 09:20:16 +02:00
parent 3bdb036ae5
commit 635b159858
3 changed files with 56 additions and 40 deletions
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22
2d_fourie/analysis.py
View File

@ -1,3 +1,4 @@
import sys
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import glob import glob
@ -54,9 +55,9 @@ def parse_lists(out):
def eval_data_print(file): def eval_data_print(file):
data = np.load(file, allow_pickle=True) data = np.load(file, allow_pickle=True)
percentage = data["percentage"] percentage = data["percentage"]
#out = parse_lists(data["out"]) # out = parse_lists(data["out"])
out = [] out = []
for o in ["out_1","out_2","out_3","out_4"]: for o in ["out_1", "out_2", "out_3", "out_4"]:
out.append(np.array(data[o])) out.append(np.array(data[o]))
fig, all_axs = plt.subplots(2, len(out)) fig, all_axs = plt.subplots(2, len(out))
axs = all_axs[0, :] axs = all_axs[0, :]
@ -66,7 +67,7 @@ def eval_data_print(file):
ax.plot(percentage, o/o[0]) ax.plot(percentage, o/o[0])
# ax.plot(percentage, o) # ax.plot(percentage, o)
o = np.mean(o, axis=1) o = np.mean(o, axis=1)
#o = o/o[0] # o = o/o[0]
ax2.plot(percentage, o) ax2.plot(percentage, o)
ax2.plot([0, 1], [o[0], o[-1]], "k:") ax2.plot([0, 1], [o[0], o[-1]], "k:")
ax.set_title(lab) ax.set_title(lab)
@ -87,7 +88,18 @@ def eval_data_print(file):
plt.tight_layout() plt.tight_layout()
def stacked_plot(file):
data = np.load(file, allow_pickle=True)
percentage = data["percentage"]
# out = parse_lists(data["out"])
out = []
for o in ["out_1", "out_2", "out_3", "out_4"]:
out.append(np.array(data[o]))
out = np.array(out)
plt.stackplot(percentage, out)
if __name__ == "__main__": if __name__ == "__main__":
for f in glob.glob("*.npz"): for f in sys.argv[1:]:
eval_data_print(f) #eval_data_print(f)
stacked_plot(f)
plt.show() plt.show()

68
2d_fourie/main.py
View File

@ -15,12 +15,6 @@ class Plotter:
self.lattice = lat self.lattice = lat
self.length_2 = 0.05 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): def rect_at_point(self, x, y, color):
length_2 = self.length_2 length_2 = self.length_2
rect = patches.Rectangle( rect = patches.Rectangle(
@ -33,7 +27,11 @@ class Plotter:
) )
return rect return rect
def plot(self, freqx, freqy, intens, ax_log=None, ax_lin=None, vmax=None):
def plot(self, freqx, freqy, intens, ax_log=None, ax_lin=None, vmax=None, evaluator=None):
if evaluator is not None:
img = Image_Wrapper(freqx,freqy, intens)
intens = evaluator.debug(img)
if ax_log: if ax_log:
t = ax_log.imshow( t = ax_log.imshow(
intens, intens,
@ -43,6 +41,7 @@ class Plotter:
cmap="viridis", cmap="viridis",
origin="lower" origin="lower"
) )
self.add_peaks(ax_log)
plt.colorbar(t, ax=ax_log) plt.colorbar(t, ax=ax_log)
if ax_lin: if ax_lin:
t = ax_lin.imshow( t = ax_lin.imshow(
@ -132,6 +131,12 @@ def test_mixed():
np.random.shuffle(ind) np.random.shuffle(ind)
mask_misk[np.unravel_index(ind[:800], (LEN, LEN))] = False mask_misk[np.unravel_index(ind[:800], (LEN, LEN))] = False
all_rutile = np.stack(lat.reci()[0]).T
all_mono = np.stack(lat.reci()[1]).T
all_mono2 = np.stack(lat.reci()[2]).T
#voro = Voronoi_Evaluator([all_rutile, all_mono, all_mono2])
si.apply_mask(np.zeros((LEN, LEN), dtype=bool)) si.apply_mask(np.zeros((LEN, LEN), dtype=bool))
si.gaussian(20) si.gaussian(20)
fx, fy, intens_mono = si.fft() fx, fy, intens_mono = si.fft()
@ -147,12 +152,15 @@ def test_mixed():
fx, fy, intens_mixed = si.fft() fx, fy, intens_mixed = si.fft()
si.plot(axs[0, 1]) si.plot(axs[0, 1])
img =
rect = Rect_Evaluator([all_rutile, all_mono, all_mono2])
rect.generate_mask(img)
plot.plot(freqx=fx, freqy=fy, intens=intens_rutile, plot.plot(freqx=fx, freqy=fy, intens=intens_rutile,
ax_log=axs[1, 0], ax_lin=axs[2, 0], vmax=10e7) ax_log=axs[1, 0], ax_lin=axs[2, 0], evaluator=rect)
plot.plot(freqx=fx, freqy=fy, intens=intens_mono, plot.plot(freqx=fx, freqy=fy, intens=intens_mono,
ax_log=axs[1, 2], ax_lin=axs[2, 2], vmax=10e7) ax_log=axs[1, 2], ax_lin=axs[2, 2], evaluator=rect)
plot.plot(freqx=fx, freqy=fy, intens=intens_mixed, plot.plot(freqx=fx, freqy=fy, intens=intens_mixed,
ax_log=axs[1, 1], ax_lin=axs[2, 1], vmax=10e7) ax_log=axs[1, 1], ax_lin=axs[2, 1], evaluator=rect)
# Plotting cuts # Plotting cuts
@ -175,33 +183,34 @@ def random(seed):
out_voro = [[] for x in range(len(lat.reci())+1)] out_voro = [[] for x in range(len(lat.reci())+1)]
percentage = [] percentage = []
counter = 0 counter = 0
plot = Plotter(lat)
[(x, y), (x1, y1)] = lat.get_both()
si = SpinImage_Two_Phase(x, y, x1, y1)
already_inited = False already_inited = False
for i in tqdm.tqdm(ind): for i in tqdm.tqdm(ind):
maske[np.unravel_index(i, (LEN, LEN))] = True maske[np.unravel_index(i, (LEN, LEN))] = True
counter += 1 counter += 1
if np.mod(counter, 100) != 0: if np.mod(counter, 100) != 0:
continue continue
si.apply_mask(maske)
si.gaussian(20)
pos_x, pos_y = lat.get_from_mask(maske)
si = SpinImage(pos_x, pos_y)
si.pad_it_square(10, size=2300)
fx, fy, intens = si.fft() fx, fy, intens = si.fft()
img = Image_Wrapper(intens, fx, fy) img = Image_Wrapper(intens, fx, fy)
if not already_inited: #if not already_inited:
print("start_init") # print("start_init")
voro.generate_mask(img, merge=True) # voro.generate_mask(img, merge=True)
print("stop_init") # rect.generate_mask(img, merge=True)
rect.generate_mask(img, merge=True) # already_inited = True
already_inited = True
iv, vv = voro.extract(img) #iv, vv = voro.extract(img)
ir, vr = rect.extract(img) #ir, vr = rect.extract(img)
for lis, val in zip(out_rect, vr): #for lis, val in zip(out_rect, vr):
lis.append(val) # lis.append(val)
for lis, val in zip(out_voro, vv): #for lis, val in zip(out_voro, vv):
lis.append(val) # lis.append(val)
percentage.append(np.sum(maske)) #percentage.append(np.sum(maske))
percentage = np.array(percentage) percentage = np.array(percentage)
percentage /= np.max(percentage) percentage /= np.max(percentage)
@ -303,17 +312,16 @@ def test_me():
maske = np.invert(maske) maske = np.invert(maske)
si.apply_mask(maske) si.apply_mask(maske)
axs[2].imshow(si.img) axs[2].imshow(si.img)
axs[3].imshow(si.img+tmp) axs[3].imshow(si.img+tmp)
if __name__ == "__main__": if __name__ == "__main__":
np.random.seed(1234)
# test_me() # test_me()
# test_square() # test_square()
test_mixed() test_mixed()
plt.show() plt.show()
# random() #random(1234)
# np.random.seed(1234)
# for i in np.random.randint(0, 10000, 1): # for i in np.random.randint(0, 10000, 1):
# random(i) # random(i)
# ising(i) # ising(i)

6
clean_python/lattices.py
View File

@ -114,10 +114,6 @@ class VO2_Lattice(Lattice):
# offset_a_r = -offset_a_r # offset_a_r = -offset_a_r
# offset_c_r = -offset_c_r # offset_c_r = -offset_c_r
res = 0.05
offset_a_r = res * int(offset_a_r/res)
offset_c_r = res * int(offset_c_r/res)
x = offset_a_r + self.X * \ x = offset_a_r + self.X * \
self.base_c_r + np.mod(self.Y, 4) * 0.5 * self.base_c_r self.base_c_r + np.mod(self.Y, 4) * 0.5 * self.base_c_r
x[np.mod(self.X, 2) == 0] -= 2 * offset_a_r x[np.mod(self.X, 2) == 0] -= 2 * offset_a_r
@ -166,7 +162,7 @@ class VO2_New(VO2_Lattice):
# maske[0::4, :] = np.roll(maske[0::4, :], axis=1, shift=1) # maske[0::4, :] = np.roll(maske[0::4, :], axis=1, shift=1)
# maske[1::4, :] = np.roll(maske[1::4, :], axis=1, shift=1) # maske[1::4, :] = np.roll(maske[1::4, :], axis=1, shift=1)
# return maske # return maske
#def parse_mask(self, mask: np.ndarray) -> np.ndarray: # def parse_mask(self, mask: np.ndarray) -> np.ndarray:
# print(mask.shape) # print(mask.shape)
# maske = np.empty((mask.shape[0]*2, mask.shape[1])) # maske = np.empty((mask.shape[0]*2, mask.shape[1]))
# maske[0::2, :] = mask # maske[0::2, :] = mask