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Jacob Holder 2023-05-03 11:24:55 +02:00
commit 784aab8312
19 changed files with 3165 additions and 428 deletions
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24
crystal_V.xyz
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202
fft_1d.py
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import numpy as np
import matplotlib.pyplot as plt
import tqdm
import multiprocessing as mp
RESOLUTION = 0.01
LENGTH = 500
def generate_image_from_mask(mask: np.array):
pos_mono = np.arange(0, mask.size * 2.89 * 2, 2.89)
pos_mono[::2][mask] -= 0.27
pos_mono[1::2][mask] += 0.27
return pos_mono
def pad_zero(img, length):
pad = np.zeros(length)
img = np.append(img, pad)
img = np.append(pad, img)
return img
def image_from_pos(pos):
length = np.max(pos) + RESOLUTION
x = np.arange(0, length, RESOLUTION) # angstrom
y = np.zeros_like(x)
ind = np.searchsorted(x, pos)
y[ind] = 1
return y
def beugung(y, resolution):
fft = np.fft.fft(y)
fft_clean = np.fft.fftshift(fft)
fft_freq = np.fft.fftfreq(y.size, resolution)
fft_freq_clean = np.fft.fftshift(fft_freq)
return fft_freq_clean, np.abs(fft_clean) ** 2
def gaussian_convol(img):
sigma = 100 / RESOLUTION
mu = img.size/2
x = np.arange(0, img.size)
gauss = 1/(sigma * np.sqrt(2 * np.pi)) * \
np.exp(- (x - mu)**2 / (2 * sigma**2))
return img*gauss
def analyisis(mask):
pos_h = generate_image_from_mask(mask)
img = image_from_pos(pos_h)
img = gaussian_convol(img)
padded = pad_zero(img, int(100 / RESOLUTION))
freq, intens = beugung(padded, RESOLUTION)
return freq, intens
def get_peaks():
orders = np.arange(1, 2, 1)
orders = orders / 5.78
return np.array(orders)
def eval_peaks(freq, fft):
orders = get_peaks()
ind = np.searchsorted(freq, orders)
return fft[ind]
def basic_test():
mask_h = np.zeros(LENGTH).astype(bool)
mask_l = np.ones(LENGTH).astype(bool)
mask_mixed = np.zeros(LENGTH).astype(bool)
ind = (np.random.rand(30) * (LENGTH - 1)).astype(int)
mask_mixed[ind] = True
mask_ner = np.zeros(LENGTH).astype(bool)
ind = (np.random.rand(1) * (LENGTH - 31)).astype(int)
ind = np.arange(ind, ind+30).astype(int)
mask_ner[ind] = True
fig, axs = plt.subplots(4, 1)
for mask, ax in zip([mask_h, mask_l, mask_mixed, mask_ner], axs):
freq, ffty = analyisis(mask)
ax.plot(freq, ffty)
for ax in axs:
ax.plot([1.0 / 5.78, 1.0 / 2.62, 1.0 / 3.16], [0, 0, 0], "kx")
ax.plot([2.0 / 5.78, 2.0 / 2.62, 2.0 / 3.16], [0, 0, 0], "rx")
ax.plot([3.0 / 5.78, 3.0 / 2.62, 3.0 / 3.16], [0, 0, 0], "bx")
ax.set_xlim(0, 3)
plt.show()
def norm(arr):
return arr/np.sum(arr)
def norm2(arr):
return arr
# return arr/np.max(arr)
def next_mask(mask):
prob = np.exp((np.roll(mask, 1)*1.0 + np.roll(mask, -1)) / .1)
prob[mask] = 0.0
prob = norm(prob)
ind = np.random.choice(LENGTH, p=prob)
mask[ind] = True
return mask
def random_loop():
mask = np.zeros(LENGTH).astype(bool)
ind = np.arange(0, LENGTH)
np.random.shuffle(ind)
percentage = []
peaks = []
masks = []
for i in ind:
mask[i] = True
freq, fft = analyisis(mask)
peak = eval_peaks(freq, fft)
percentage.append(np.mean(mask))
peaks.append(peak)
masks.append(mask.copy())
masks = np.array(masks)
plt.figure()
plt.imshow(masks)
plt.plot([0, 500], [406, 406])
print()
percentage = np.array(percentage)
peaks = np.array(peaks)
return percentage, peaks
def nearest_loop():
mask = np.zeros(LENGTH).astype(bool)
percentage = []
peaks = []
for i in range(LENGTH):
mask = next_mask(mask)
freq, fft = analyisis(mask)
peak = eval_peaks(freq, fft)
percentage.append(np.mean(mask))
peaks.append(peak)
percentage = np.array(percentage)
peaks = np.array(peaks)
return percentage, peaks
def random_helper(seed):
np.random.seed(seed)
#percentage_near, peaks_near = nearest_loop()
percentage_rand, peaks_rand = random_loop()
print("done")
return percentage_rand, peaks_rand
# for i in range(peaks_near.shape[1]):
# axs[2].plot(percentage_near, norm2(
# peaks_near[:, i]), "-", label="near")
# for i in range(peaks_rand.shape[1]):
# axs[2].plot(percentage_rand, norm2(
# peaks_rand[:, i]), ":", label="rand")
def random_increase():
fig, axs = plt.subplots(3, 1)
results = []
for i in np.arange(10):
results.append(random_helper(i))
for percentage_rand, peaks_rand in results:
for i in range(peaks_rand.shape[1]):
axs[2].plot(percentage_rand, norm2(
peaks_rand[:, i]), ":", label="rand")
for ax in [axs[0], axs[1]]:
orders = get_peaks()
ax.plot(orders, np.zeros_like(orders), "kx")
ax.set_xlim(0, 3)
mask_l = np.ones(LENGTH).astype(bool)
mask_h = np.zeros(LENGTH).astype(bool)
freq, ffty = analyisis(mask_l)
axs[0].plot(freq, ffty)
freq, ffty = analyisis(mask_h)
axs[1].plot(freq, ffty)
plt.xlabel("percentage")
plt.ylabel("peak intensity")
plt.show()
plt.legend()
if __name__ == "__main__":
random_increase()

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92
clean_python/analysis.py → software/analysis.py
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@ -5,6 +5,8 @@ import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import glob import glob
import scipy.interpolate as ip import scipy.interpolate as ip
from spin_image import SpinImage, FFT
from ditact_pic import plot
from lattices import VO2_Lattice from lattices import VO2_Lattice
plt.style.use(["style", "colors", "one_column"]) plt.style.use(["style", "colors", "one_column"])
@ -14,6 +16,72 @@ def check_percentage(p1, p2):
plt.plot(p1, p2) plt.plot(p1, p2)
def average_mean(arr, window_size=20):
arr_sum = np.cumsum(arr)
arr = (arr_sum[window_size:] - arr_sum[:-window_size]) / window_size
return arr
def new_merge(files):
wp = []
op = []
spot_1 = []
spot_2 = []
spot_3 = []
plt.figure()
for file in files:
print(file)
data = np.load(file, allow_pickle=True)
old_percentage = data["percentage"]
w_percentage = data["w_percentage"]
wp.append(w_percentage)
op.append(old_percentage)
# check_percentage(old_percentage, w_percentage)
out = []
for o in ["out_1", "out_2", "out_3", "out_4"]:
out.append(np.array(data[o]))
print(out)
out = np.array(out)[:, :, 0]
spot_1.append(out[0, :])
spot_2.append(out[3, :])
spot_3.append(out[2, :])
wp = np.concatenate(wp, axis=0)
op = np.concatenate(op, axis=0)
spot_1 = np.concatenate(spot_1, axis=0)
spot_2 = np.concatenate(spot_2, axis=0)
spot_3 = np.concatenate(spot_3, axis=0)
arg_sort = np.argsort(op)
wp = wp[arg_sort]
op = op[arg_sort]
spot_1 = spot_1[arg_sort]
spot_2 = spot_2[arg_sort]
spot_3 = spot_3[arg_sort]
win = 100
wp = average_mean(wp, win)
op = average_mean(op, win)
spot_1 = average_mean(spot_1, win)
spot_2 = average_mean(spot_2, win)
spot_3 = average_mean(spot_3, win)
x = op
plt.plot(x, spot_1, "r.")
plt.plot(x, spot_2, "g.")
plt.plot(x, spot_3, "b.")
ma = np.max(spot_1+spot_2+spot_3)
spot_1 /= ma
spot_2 /= ma
spot_3 /= ma
print("debug....")
print(wp.shape)
plt.savefig("debug.png")
return op, np.stack([spot_2, spot_1, spot_3])
def merge(files): def merge(files):
merge = [] merge = []
plt.figure() plt.figure()
@ -34,17 +102,18 @@ def merge(files):
out = out / summe out = out / summe
merge.append(out) merge.append(out)
plt.plot(w_percentage,out[0, :], "r") plt.plot(w_percentage, out[0, :], "r.")
plt.plot(w_percentage,out[3, :], "b") plt.plot(w_percentage, out[3, :], "b.")
plt.plot(w_percentage,out[2, :], "g") plt.plot(w_percentage, out[2, :], "g.")
all = sum(merge) all = sum(merge)
summe = np.max(np.sum(all, axis=0)) summe = np.max(np.sum(all, axis=0))
all = all / summe all = all / summe
plt.plot(all[0, :], "k") # plt.plot(all[0, :], "k")
plt.plot(all[3, :], "k") # plt.plot(all[3, :], "k")
plt.plot(all[2, :], "k") # plt.plot(all[2, :], "k")
plt.savefig("debug.png")
percentage = 1-percentage percentage = 1-percentage
return percentage, all return percentage, all
@ -60,7 +129,7 @@ def debug(percentage, out):
def stacked_plot(ax, percentage, out, title=""): def stacked_plot(ax, percentage, out, title=""):
stacks = ax.stackplot(percentage, out[[0, 3, 2]], colors=[ stacks = ax.stackplot(percentage, out[[0, 1, 2]], colors=[
"w"], ls=(0, (0, 1)), ec="w") "w"], ls=(0, (0, 1)), ec="w")
hatches = ["//", "|", "\\\\"] hatches = ["//", "|", "\\\\"]
for stack, hatch, color in zip(stacks, hatches, ["C1", "C0", "C2"]): for stack, hatch, color in zip(stacks, hatches, ["C1", "C0", "C2"]):
@ -76,7 +145,7 @@ def stacked_plot(ax, percentage, out, title=""):
bbox=dict(boxstyle='square,pad=0.0', ec="None", fc="w")) bbox=dict(boxstyle='square,pad=0.0', ec="None", fc="w"))
ax.text(0.35, 0.73, "diffusive", backgroundcolor="w", ax.text(0.35, 0.73, "diffusive", backgroundcolor="w",
bbox=dict(boxstyle='square,pad=0.0', ec="None", fc="w")) bbox=dict(boxstyle='square,pad=0.0', ec="None", fc="w"))
ax.stackplot(percentage, out[[0, 3, 2]], colors=["None"], ec="k") ax.stackplot(percentage, out[[0, 1, 2]], colors=["None"], ec="k")
def time_scale(ax, p, o): def time_scale(ax, p, o):
@ -119,7 +188,6 @@ def read_file(file):
o = files["o"] o = files["o"]
return p, o return p, o
def intens(ax, file, p, o): def intens(ax, file, p, o):
intens = FFT() intens = FFT()
intens.load(file) intens.load(file)
@ -156,7 +224,7 @@ def intens(ax, file, p, o):
axins = ax.inset_axes([0.0, 0.0, 0.5, 0.5]) axins = ax.inset_axes([0.0, 0.0, 0.5, 0.5])
axins.plot(p, o[0], label="rut.", color="C1") axins.plot(p, o[0], label="rut.", color="C1")
axins.plot(p, o[2], label="mono.", color="C2") axins.plot(p, o[2], label="mono.", color="C2")
axins.plot(p, o[3], label="diff.", color="C0") axins.plot(p, o[1], label="diff.", color="C0")
axins.legend(loc='center left', bbox_to_anchor=(1, 0.5)) axins.legend(loc='center left', bbox_to_anchor=(1, 0.5))
axins.set_xlim([0, 1]) axins.set_xlim([0, 1])
axins.set_ylim([0, 1]) axins.set_ylim([0, 1])
@ -169,14 +237,14 @@ def intens(ax, file, p, o):
if __name__ == "__main__": if __name__ == "__main__":
p, o = merge(sys.argv[2:]) p, o = new_merge(sys.argv[2:])
np.savez("merged.npz", p=p, o=o) np.savez("merged.npz", p=p, o=o)
# eval_data_print(f)
fig, axs = plt.subplots(1, 3) fig, axs = plt.subplots(1, 3)
fig.set_figheight(2) fig.set_figheight(2)
stacked_plot(axs[1], p, o) stacked_plot(axs[1], p, o)
time_scale(axs[2], p, o) time_scale(axs[2], p, o)
if "intens" in sys.argv[1]:
intens(axs[0], sys.argv[1], p, o) intens(axs[0], sys.argv[1], p, o)
plt.tight_layout() plt.tight_layout()
plt.savefig("analysis.pdf") plt.savefig("analysis.pdf")

0
clean_python/cache.py → software/cache.py
View File

2
clean_python/ditact_pic.py → software/ditact_pic.py
View File

@ -128,7 +128,7 @@ def load():
if __name__ == "__main__": if __name__ == "__main__":
np.random.seed(1234) np.random.seed(1234)
simulate() simulate()
# np.savez("intens.npz", r=r, mo=mo, mi=mi) np.savez("intens.npz", r=r, mo=mo, mi=mi)
r, mo, mi = load() r, mo, mi = load()
max = norm(r, mo, mi) max = norm(r, mo, mi)
r.intens = r.intens/max r.intens = r.intens/max

0
clean_python/extractors.py → software/extractors.py
View File

10
clean_python/ising.py → software/ising.py
View File

@ -21,8 +21,8 @@ ch.setFormatter(formatter)
logger.addHandler(ch) logger.addHandler(ch)
def ising(file): def ising(file, num):
LEN = 120 LEN = 60
#lat = VO2_New(LEN, LEN) #lat = VO2_New(LEN, LEN)
lat = VO2_New(LEN, LEN) lat = VO2_New(LEN, LEN)
rect = Rect_Evaluator(lat.get_spots()) rect = Rect_Evaluator(lat.get_spots())
@ -64,10 +64,10 @@ def ising(file):
w_percentage=weighted_percentage, percentage=percentage, out_1=out_rect[0], w_percentage=weighted_percentage, percentage=percentage, out_1=out_rect[0],
out_2=out_rect[1], out_3=out_rect[2], out_4=out_rect[3]) out_2=out_rect[1], out_3=out_rect[2], out_4=out_rect[3])
def runner(file): def runner(file, idx):
np.random.seed(1234) np.random.seed(1234)
print(f"runnig: {file}") print(f"runnig: {file}")
ising(file) ising(file,idx)
if __name__ == "__main__": if __name__ == "__main__":
@ -78,4 +78,4 @@ if __name__ == "__main__":
exit() exit()
if idx < 1: if idx < 1:
exit() exit()
runner(files[idx-1]) runner(files[idx-1], idx)

0
clean_python/lattices.py → software/lattices.py
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0
clean_python/main.py → software/main.py
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0
clean_python/mypy.conf → software/mypy.conf
View File

0
clean_python/plotter.py → software/plotter.py
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0
clean_python/spin_image.py → software/spin_image.py
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0
clean_python/test.py → software/test.py
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0
clean_python/tools.py → software/tools.py
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137
test_fft.py
View File

@ -1,137 +0,0 @@
import numpy as np
import matplotlib.pyplot as plt
def analysis(y, RESOLUTION):
fft = np.fft.fft(y)
fft_clean = np.fft.fftshift(fft)
fft_freq = np.fft.fftfreq(y.size, RESOLUTION)
fft_freq_clean = np.fft.fftshift(fft_freq)
return fft_freq_clean, np.abs(fft_clean) ** 2
def play_1d():
RESOLUTION = 0.001
LENGTH = 10000
x = np.arange(0, LENGTH, RESOLUTION) # angstrom
y = np.zeros_like(x)
pos_mono = np.arange(0, x.size, 2890)
pos_mono = (
pos_mono + np.random.normal(size=pos_mono.shape, loc=0, scale=10)
).astype(int)
pos_rut = np.arange(0, x.size, 5780)
pos_rut = np.append(pos_rut, pos_rut - 3160)
# pos_rut = (pos_rut + np.random.normal(size=pos_rut.shape, loc=0, scale=10)).astype(int)
y[pos_rut] = 1
y[pos_rut + 1] = 1
y[pos_rut + 2] = 1
# y = np.sin(x)
fig, axs = plt.subplots(3, 1)
ax = axs[0]
ax.plot(x, y)
ax = axs[1]
fft_x, fft_y = analysis(y, RESOLUTION)
ax.plot(fft_x, fft_y)
ax.plot([1.0 / 5.78, 1.0 / 2.62, 1.0 / 3.16], [0, 0, 0], "kx")
ax.plot([2.0 / 5.78, 2.0 / 2.62, 2.0 / 3.16], [0, 0, 0], "rx")
ax.plot([3.0 / 5.78, 3.0 / 2.62, 3.0 / 3.16], [0, 0, 0], "bx")
ax.set_xlim(0, 3)
def from_mask(mask):
pos_mono = np.arange(0, mask.size * 2.89 * 2, 2.89)
pos_mono[::2][mask] -= 0.27
pos_mono[1::2][mask] += 0.27
return pos_mono
def image_from_pos(pos):
RESOLUTION = 0.001
LENGTH = 1000000
x = np.arange(0, LENGTH, RESOLUTION) # angstrom
y = np.zeros_like(x)
ind = np.searchsorted(x, pos)
if np.any(ind > LENGTH):
print("overflow")
ind = ind[ind < LENGTH]
y[ind] = 1
sigma = 500
mu = int(LENGTH / 2)
gaussian = (
1
/ (sigma * np.sqrt(2 * np.pi))
* np.exp(-((x - mu) ** 2) / (2 * sigma * sigma))
)
# y = np.multiply(y, gaussian)
return x, y
def plot_img(x, y, ax):
ax.plot(x, y)
if __name__ == "__main__":
RESOLUTION = 0.001
print("Done")
LENGTH = 1000
mask_h = np.ones(LENGTH).astype(bool)
pos_h = from_mask(mask_h)
x, img_h = image_from_pos(pos_h)
fftx, ffty_h = analysis(img_h, RESOLUTION)
mask_l = np.zeros(LENGTH).astype(bool)
pos_l = from_mask(mask_l)
x, img_l = image_from_pos(pos_l)
fftx, ffty_l = analysis(img_l, RESOLUTION)
print("Done")
mask_mixed = np.zeros(LENGTH).astype(bool)
ind = (np.random.rand(400) * (LENGTH - 1)).astype(int)
mask_mixed[ind] = True
pos_mixed = from_mask(mask_mixed)
x, img_mixed = image_from_pos(pos_mixed)
fftx, ffty_mixed = analysis(img_mixed, RESOLUTION)
print("Done")
mask_near = np.zeros(LENGTH).astype(bool)
ind = (np.random.rand(50) * (LENGTH - 1)).astype(int)
#for i in range(1, 8):
# ind = np.append(ind, ind+i)
print("Done")
mask_near[ind] = True
pos_near = from_mask(mask_near)
x, img_near = image_from_pos(pos_near)
fftx, ffty_near = analysis(img_near, RESOLUTION)
fig, axs = plt.subplots(4, 1)
plot_img(fftx, ffty_h, axs[0])
plot_img(fftx, ffty_l, axs[1])
plot_img(fftx, ffty_mixed, axs[2])
plot_img(fftx, ffty_near, axs[3])
for ax in axs:
ax.plot([1.0 / 5.78, 1.0 / 2.62, 1.0 / 3.16], [0, 0, 0], "kx")
ax.plot([2.0 / 5.78, 2.0 / 2.62, 2.0 / 3.16], [0, 0, 0], "rx")
ax.plot([3.0 / 5.78, 3.0 / 2.62, 3.0 / 3.16], [0, 0, 0], "bx")
ax.set_xlim(0, 3)
# play_1d()
plt.show()
print("Done")
pass