minor changes

This commit is contained in:
swim committed 2026-07-24 16:11:04 +09:00
1 parent 5550bb0064
commit 5def8d7465
1 file changed
+111 -84
+111 -84
View File
@@ -17,6 +17,7 @@
"from scipy.ndimage import gaussian_filter\n",
"from scipy.optimize import curve_fit\n",
"from scipy.special import erf\n",
"from scipy.stats import norm\n",
"\n",
"import kemstem"
]
@@ -55,21 +56,30 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 4,
"id": "89b917f7",
"metadata": {},
"outputs": [],
"source": [
"results = loadmat('./Si2V1-Niter1000.mat')\n",
"# results = loadmat('./Si5V2-Niter1000.mat')"
"# results = loadmat('./Si5V2-Niter1000.mat')\n",
"# results = loadmat('./Si8V2-Niter1000.mat')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 5,
"id": "8a9f200f",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(573, 573, 20)\n"
]
}
],
"source": [
"roi = results['p']['object_ROI'][0][0][0]\n",
"roix = roi[0].T\n",
@@ -77,28 +87,19 @@
"\n",
"DZ = results['outputs']['z_distance'].item().item()\n",
"\n",
"recon_object = results['object'][roix, roiy]"
"recon_object = results['object'][roix, roiy]\n",
"print(recon_object.shape)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 6,
"id": "477adbcb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(np.float64(-0.5), np.float64(572.5), np.float64(572.5), np.float64(-0.5))"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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truncated
"image/png": "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 truncated
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -108,21 +109,22 @@
}
],
"source": [
"LAYER = 10\n",
"LAYER = 11\n",
"\n",
"plt.imshow(np.angle(recon_object[:, :, LAYER]), cmap='gray')\n",
"plt.axis('off')"
"plt.axis('off')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 7,
"id": "9db949e0",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -134,7 +136,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"1464 columns found\n"
"1465 columns found\n"
]
}
],
@@ -156,7 +158,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 8,
"id": "96b15c03",
"metadata": {},
"outputs": [],
@@ -180,7 +182,7 @@
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": 9,
"id": "6a4aca67",
"metadata": {},
"outputs": [],
@@ -190,7 +192,7 @@
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": 10,
"id": "70437a17",
"metadata": {},
"outputs": [],
@@ -204,23 +206,13 @@
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": 11,
"id": "1cd87af9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(np.float64(-0.5), np.float64(2.5), np.float64(2.5), np.float64(-0.5))"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -231,28 +223,29 @@
],
"source": [
"WINDOW = 1\n",
"cx, cy = c0[100]\n",
"cx, cy = c0[len(c0) // 2]\n",
"wx_start, wx_end = cx-WINDOW, cx+WINDOW+1\n",
"wy_start, wy_end = cy-WINDOW, cy+WINDOW+1\n",
"plt.imshow(np.angle(recon_object)[wx_start:wx_end, wy_start:wy_end, LAYER], cmap='gray')\n",
"plt.axis('off')"
"plt.axis('off')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": 12,
"id": "93d22d50",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4dc43f314b9c4d9e9c773b48d617a060",
"model_id": "8b34ea56f6534b8da8dda5cd12e56252",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/1464 [00:00<?, ?it/s]"
" 0%| | 0/1465 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -271,7 +264,7 @@
},
{
"cell_type": "code",
"execution_count": 92,
"execution_count": 13,
"id": "4925866a",
"metadata": {},
"outputs": [],
@@ -282,13 +275,26 @@
},
{
"cell_type": "code",
"execution_count": 114,
"execution_count": 14,
"id": "eb149eb1",
"metadata": {},
"outputs": [],
"source": [
"def gaussian_plus_linear(x, p0, p1, q, mu, s):\n",
" linear = p0 + p1 * x\n",
" gauss = q * norm.pdf(x, mu, s)\n",
" return linear + gauss"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "df9db0e5",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAiwAAAGdCAYAAAAxCSikAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAPYQAAD2EBqD+naQAAUiRJREFUeJzt3XtcVHX+x/HXMAqYCWkqauhEiqhZXrAEzTRL1HLTbJOyRStLbbuZ7ZauXdTf7lpbba2umuySZhei1ktt2SaWqaXdDMwtKywJUMh0E7yiwvn98RUUucgAw5kZ3s/H4zw4czhz5nMccD58L5+vw7IsCxEREREvFmB3ACIiIiJnooRFREREvJ4SFhEREfF6SlhERETE6ylhEREREa+nhEVERES8nhIWERER8XpKWERERMTrNbI7gLpSXFzMrl27aNasGQ6Hw+5wREREpBosy2L//v20a9eOgIDK21H8JmHZtWsX7du3tzsMERERqYHs7GzCw8Mr/b7fJCzNmjUDzA2HhITYHI2IiIhUR0FBAe3bty/9HK+M3yQsJd1AISEhSlhERER8zJmGc2jQrYiIiHg9JSwiIiLi9ZSwiIiIiNdTwiIiIiJeTwmLiIiIeL0aJSwLFiwgIiKC4OBgoqOj2bBhQ7We99FHH9GoUSN69uxZ5viSJUtwOBzltiNHjtQkPBEREfEzbicsKSkpTJkyhRkzZpCWlsaAAQMYPnw4WVlZVT4vPz+fcePGceWVV1b4/ZCQEHJzc8tswcHB7oYnIiIifsjthOWvf/0rEyZM4Pbbb6dr1648++yztG/fnoULF1b5vEmTJjF27FhiY2Mr/L7D4aBNmzZlNhERERFwM2E5evQomzdvJi4urszxuLg4Nm7cWOnzFi9ezPfff89jjz1W6TkHDhzA5XIRHh7OiBEjSEtLqzKWwsJCCgoKymwiIiLin9xKWPbs2UNRURFhYWFljoeFhZGXl1fhczIyMpg2bRovv/wyjRpVXFi3S5cuLFmyhDfffJPk5GSCg4Pp378/GRkZlcYyZ84cQkNDSzetIyQiIuK/ajTo9vTyuZZlVVhSt6ioiLFjxzJr1iw6d+5c6fViYmL4zW9+Q48ePRgwYACvvfYanTt3Zt68eZU+Z/r06eTn55du2dnZNbkVEZF6k5OTw9q1a8nJybE7FBGf49ZaQi1btsTpdJZrTdm9e3e5VheA/fv38/nnn5OWlsbdd98NQHFxMZZl0ahRI1avXs3gwYPLPS8gIIBLLrmkyhaWoKAggoKC3AlfRMQ2SUlJTJw4keLiYgICAkhMTGTChAl2hyXiM9xqYQkMDCQ6OprU1NQyx1NTU+nXr1+580NCQti6dSvp6eml2+TJk4mKiiI9PZ2+fftW+DqWZZGenk7btm3dCU9ExCvl5OSUJitg/nCbNGmSWlpE3OD2as1Tp04lISGBPn36EBsbS2JiIllZWUyePBkwXTU7d+5k6dKlBAQE0L179zLPb926NcHBwWWOz5o1i5iYGCIjIykoKGDu3Lmkp6czf/78Wt6eiIj9MjIySpOVEkVFRWzfvp3w8HCbohLxLW4nLPHx8ezdu5fZs2eTm5tL9+7dWbVqFS6XC4Dc3Nwz1mQ53b59+5g4cSJ5eXmEhobSq1cv1q9fz6WXXupueCIiXicyMpKAgIAySYvT6aRTp042RiXiWxyWZVl2B1EXCgoKCA0NJT8/n5CQELvDEREpIykpiUmTJlFUVITT6WTRokUawyJC9T+/lbCIiGDGmWRkZBAZGemxbpqcnBy2b99Op06d1BUkckJ1P7+1+KGINHhJSUm4XC4GDx6My+UiKSnJI68THh7OoEGDlKyI1IBaWESkQcvJycHlcpUbX5KZmanEQqQeqIVFRKQaqprBIyLeQwmLiDRoJTN4TqUZPCLeRwmLiDRo4eHhJCYm4nQ6AUpn8Kg7SMS7aAyLiAiawSNil+p+frtdOE5ExB+Fh4crURHxYkpYRMS7WRZkZUFhIRw5AseOlf1+SAhERp58nJcHjRvDWWdBcDBUsJK8P6uPejIidlDCIiL1r7AQEhPhp59MgvHTT7B3L+zbB/n5MHQoPP/8yfPPP7/yaw0ZAqtXn3zcrRv88ovZb9wYQkNPbr17wz/+cfLclSuhSRPo0AFcLpPk+DCtCC3+TAmLiNSd48dhxw749lv45hvzdft2+PFHuPLKk8lCQADce2/l18nNPbnvcFAcGkrR8eMEnHUWzsDAsq0mrVqVj6HEsWOwZ4/ZwCQtp5o4EX7++eTjli1N4hIRATEx8MAD1b93m1W2IvTQoUPV0iJ+QQmLiNTMvn3wv//BBReYxwcOwLnnwtGjFZ+fkXFyv3FjuOUWaNoUwsLM1qoVnHOOSSpaty49NSkpiYn795tWg8OHz9xqUFBgkpZDh0xrzalbs2Ynz7MsuOQSyM42CVVBwcnkZvNmc2+nJiyXXmpi7NXLbD17mnv3ki4nrQgt/k6zhETkzIqK4OuvYeNG2LTJfM3IgCuugPffP3leRITp4omKMluXLmZ8yfnnmw/3du3cetl6rUK7bx9kZprk5YcfoE0buOkm8739+81YmdOFhpqWmNGjTWuNjVSxV3yVZgmJSN0YMwbefde0QJxu376yjz/7DFq0MF0+daBeWw3OOce0mvTsWf57wcHw4YeQng5paebr1q2m1ebdd003Uonjx2HGDBg0CAYMgLPPrts4K1FST+b0FaGVrIi/UAuLiBh79sCqVbBlCzz99Mnjw4aZD+WmTU1rQr9+Zrv0UpOceJBXtxocOwb//a9pcereHS6/3Bz/5BPz7wSm6ysmxgwM/tWvoEcPj3chqZ6M+Jrqfn4rYRFpyLZvhzfeMNtHH0FJYpCTA+edZ/Y//9y0mFx8MTSq/0bZpKSkcq0GXj3z5auv4NlnITXVdC+dqkMHmDcPrr3WltBEvJESFhGp3PLlMHOm6dY4VY8eMGIE3H23GcPhJXyy1cCyzFiY994zLVerV8Phw2b8T2ysOefrr82Yn4ED4cTSACINjcawiMhJe/earoiSLpyjR02y0qiR+bAcOdJ0WVRV78RGPlmF1uGAjh3NNnGimbX0/vumK63Es8+aqd5t28KNN8LYsRAd7TUzj0S8iRY/FPFXlgXr1pkPwXbt4LnnTn7v2mvNB+VPP8GaNXDPPV6brPiNs84yrVentqScc47ZcnPhmWfMNOtevWDBAjOgV0RKqUtIxN/s3QtLlphKst99d/J4fDy8+qptYUklCgvNoOaXXzZjiQoLzfGoKNi2Ta0t4veq+/mtFhYRf2FZpnps+/bwu9+ZZOXss2HSJDNwVsmKdwoKMi1eKSmwa5fpJura1XQRlSQrRUXw1lvmq0gDpTEsIv7C4TDVWQ8fNt0Kd95pCp/VUx0QqQMtWsB995nE89SKwW++aYrTdewI999/skqwSAOiFhYRX2RZZtps//5mpkmJRx6BDz4wpeXvuEPJiq9yOEzLS4lffoHmzeH7780Mrg4d4M9/NhV4RRoIJSwivsSyzDTZAQMgLs5MkX322ZPfj4oys3407sG/3HabWfPo7383Sxz873+mmm5EBDzxhCliJ+LnlLCI+IoNG0y596uuMkXegoJgyhSYPdvuyKQ+NG0Kd91lxia9/DJ07mwGWL/+ui0F/UTqm37KRXzB+PGwdKnZDww0A2mnTXN7MUHxA06nmao+Zgy88ooZZF3SonbokClSd/31amUTv6MWFhFfEBlpyuNPnGjGMcydq2SloWvUCMaNMytml3jqKbjhBjO26ZNPPPbSOTk5rF27lpycHI+9hsjplLCIeJuiIli4ENavP3nsgQfMooSLFoGvVXyV+nPWWWbbtMksunjzzWaqdB1KSkrC5XIxePBgXC4XSUlJdXp9kcqocJyIN/nyS9OK8sknZl2fzZu1xoy4Z9cuMyD3hRfMIO2QEJgzByZPNq10teDVq2eLz1LhOBFfcuiQGZMSHW2SlWbNwJtXJBbv1a4dLF4Mn31m1i0qKDCDdR96qNaXzsjIKJOsABQVFbF9+/ZaX1vkTJSwiNht9Wro3t1MTz1+3AyY3LbNrO9Ty9YVjTVowKKjzbT3v//drLz929/W+pKRkZEEnNZK43Q66dSpU62vLXImSlhE7PTBBzB0KOzYYWZ7vPkm/OtfcN55tb50fY01UFLkxZxO07qSmWlqtpR48kn44gu3LxceHk5iYiLOE4m00+lk0aJF6g6SeqExLCJ2Ki6Gq682NTX+/Oc6q0xbX2MNkpKSmDhxIsXFxQQEBJCYmMgEdWV5tzVrYMgQM8to1ix48EG367jk5OSwfft2OnXqpGRFak1jWES80bFj8PTTcOCAeRwQYBa1mzu3Tsvo18dYg5ycnNJkBaC4uJhJkyappcXb9expuh2PHzeDcwcONFPl3RAeHs6gQYOUrEi9UsIiUl8yM019jN/9zkxTLuGBKqX1MdZAAzB9VMuWpjru0qVmBtHGjWZG2ksv2R2ZSJWUsIjUh7fegt69zcyNc86BwYM9+nL1MdZAAzB9mMMBCQlmGv3AgXDwoHk8dardkYlUSgmLiCcdPw5/+AP86ldmxd1LL4X0dIiP9/hLT5gwgczMTNauXUtmZmadjy3RAEw/4HKZxTQfe8wkMX362B2RSKU06FbEU/Ly4KabzEwgMNOUn3rKrAXkRzQA009s2wZdu558vG+faQ0U8bDqfn5r8UMRTykqgq++MoNpk5LMYnV+KDw8XImKPzg1WcnLM3Vcbr/dtL7UskKuSF2o0U/hggULiIiIIDg4mOjoaDZs2FCt53300Uc0atSInj17lvvesmXL6NatG0FBQXTr1o0VK1bUJDQR73HeebB8OXz+ud8mK+Kn3njDlPifPdu0Eh46ZHdEIu4nLCkpKUyZMoUZM2aQlpbGgAEDGD58OFlZWVU+Lz8/n3HjxnHllVeW+96mTZuIj48nISGBLVu2kJCQwJgxY/jEg6uNitQLine truncated
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -300,77 +306,55 @@
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.09843992 0.17781052 0.36135616 3.09665732 12.8743372 ]\n"
"[-0.18965366 0.31451085 0.20175432 0.10413929 18.18937594]\n"
]
}
],
"source": [
"bounds = [[-0.2, 0, 0, -5, 0], [0.3, 1, 10, 15, 25]]\n",
"# bounds = [[-0.2, 0, 0, -5, 0], [0.3, 1, 10, 15, 25]]\n",
"\n",
"for i in [700]:\n",
" popt, _, _ = fit(\n",
"for i in [800]:\n",
" popt0, _, _ = fit(\n",
" double_erf, np.arange(len(columns[0])), columns[i], plot=True,\n",
" p0=[0.1, 0.05, 1, 1, 15],\n",
" bounds=bounds)\n",
" print(popt)\n"
" # bounds=bounds,\n",
" )\n",
" print(popt0)\n"
]
},
{
"cell_type": "code",
"execution_count": 115,
"execution_count": 16,
"id": "62a285a5",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3184816243574052b4dfb29f883fe74b",
"model_id": "16a8597078294abe83dd87e9672e3ddf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/1464 [00:00<?, ?it/s]"
" 0%| | 0/1465 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"heights = []\n",
"heights_error = []\n",
"\n",
"for column in tqdm(columns):\n",
" popt, perr, _ = fit(\n",
" double_erf, np.arange(len(column)), column,\n",
" plot=False, p0=popt, bounds=bounds, maxfev=10000)\n",
" h, dh = popt[-1], perr[-1]\n",
" heights.append(h * DZ)\n",
" heights_error.append(dh * DZ)\n",
"\n",
"heights = np.array(heights)\n",
"heights_error = np.array(heights_error)\n"
]
},
{
"cell_type": "code",
"execution_count": 116,
"id": "534cc0b1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f2226feea20>]"
"(0.0, 300.0)"
]
},
"execution_count": 116,
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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truncated
"image/png": "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 truncated
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -380,18 +364,57 @@
}
],
"source": [
"plt.plot(np.sort(heights), 'k')"
"heights = []\n",
"\n",
"\n",
"for column in tqdm(columns):\n",
" popt, perr, _ = fit(\n",
" double_erf, np.arange(len(column)), column, plot=False,\n",
" p0=popt0,\n",
" # bounds=bounds,\n",
" maxfev=10000\n",
" )\n",
" h, dh = popt[-1], perr[-1]\n",
" heights.append(h * DZ)\n",
"\n",
"\n",
"heights = np.array(heights)\n",
"\n",
"plt.plot(np.sort(heights), 'k')\n",
"plt.ylim(0, 300)"
]
},
{
"cell_type": "code",
"execution_count": 117,
"execution_count": 21,
"id": "dd62602a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([186.28758807, 159.46389009, 189.36394664, ..., 158.93214468,\n",
" 148.67459386, 145.31492308], shape=(1115,))"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"heights[(heights < 190) & (heights > 140)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a1e518a5",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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truncated
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truncated
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
@@ -403,7 +426,11 @@
"source": [
"plt.imshow(np.angle(recon_object).sum(axis=-1), cmap='gray')\n",
"plt.axis('off')\n",
"plt.scatter(c0[:, 1], c0[:, 0], c=np.array(heights), s=8, cmap='magma_r', vmax=200, vmin=100)\n",
"plt.scatter(\n",
" c0[:, 1][(heights < 190) & (heights > 140)], c0[:, 0][(heights < 190) & (heights > 140)], c=np.array(heights[(heights < 190) & (heights > 140)]), s=8, cmap='magma_r',\n",
" vmax=190, \n",
" vmin=140\n",
" )\n",
"\n",
"cbar = plt.colorbar()\n",
"\n",