mirror of
https://github.com/c-sooyoung/lemon-sandbox.git
synced 2026-11-16 04:07:59 +09:00
update bo plots
This commit is contained in:
swim committed
1 parent
1134926369
commit
dc82292e0a
1 file changed
+59
-29
@@ -2,7 +2,7 @@
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"cells": [
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"cells": [
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 1,
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"id": "65e78297",
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"id": "65e78297",
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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@@ -14,7 +14,7 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": null,
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"id": "033186ec",
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"id": "033186ec",
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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@@ -22,33 +22,36 @@
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"name": "stdout",
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"name": "stdout",
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"output_type": "stream",
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"output_type": "stream",
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"text": [
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"text": [
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"(3, 564)\n"
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"(3, 576)\n"
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]
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]
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}
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}
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],
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],
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"source": [
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"source": [
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"result_dir = \"/home/swim/bo-ptycho/results/Si8V2_2/260815\"\n",
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"result_dir = \"/home/swim/bo-ptycho/results/Si2V1_2/260817\"\n",
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"train_x = np.load(os.path.join(result_dir, 'train_x.npy')).T\n",
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"train_x = np.load(os.path.join(result_dir, 'train_x.npy')).T\n",
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"train_y = np.load(os.path.join(result_dir, 'train_y.npy')).T\n",
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"train_y = np.load(os.path.join(result_dir, 'train_y.npy')).T\n",
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"print(train_x.shape)\n",
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"\n",
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"EPSILON = 1e-4\n",
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"START = 64\n",
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"END = None\n",
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"\n",
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"\n",
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"xparams = [\n",
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"xparams = [\n",
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" 'defocus [A]',\n",
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" 'defocus [A]',\n",
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" 'layers',\n",
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" 'layers',\n",
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" 'thickness [A]'\n",
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" 'thickness [A]'\n",
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"]\n",
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"]"
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"\n",
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"print(train_x.shape)"
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]
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]
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 3,
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"id": "a5d079f2",
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"id": "a5d079f2",
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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{
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"data": {
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"data": {
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"image/png": 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truncated
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"image/png": "iVBORw0KGgoAAAANSUhEUgAABdEAAAEiCAYAAAAWHJuuAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAPYQAAD2EBqD+naQAAz1FJREFUeJzs3Xl8E9X6P/BPUkpZWspSSoFAQSibgAsgLS4gKAXZFEUQxKKlFIWvXKQIXBUQlwIFAReE0oo7UPSiKAqoFOXaFgoCyl6UQiP7Ysu+NOf3B7/kZptkskwmST/v1yuve00nM09OZs4Mz5x5jkYIIUBERERERERERERERDa0agdAREREREREREREROSvmEQnIiIiIiIiIiIiIpLAJDoRERERERERERERkQQm0YmIiIiIiIiIiIiIJDCJTkREREREREREREQkgUl0IiIiIiIiIiIiIiIJTKITEREREREREREREUlgEp2IiIiIiIiIiIiISAKT6EREREREREREREREEphEJyIiIiIiIiIiIiKSwCQ6EREREREREREREZEEJtH9zIcffgiNRoPi4mKfbG/GjBlo06YNDAaDzd9+++033HPPPYiIiIBGo0G3bt18EpMrpk+fDo1Go3YYQSk7OxsNGzbExYsX1Q6FSHGbN2/GI488gsaNGyMsLAz16tVDQkICJkyYoMj2lOjr2R8SBZ+CggIMGjQI9evXR+XKlRETE4PHHnsM+fn5Hq+7SZMmGDFihOdBepGvr4OJSBm8riIKHr///juefvppNG3aFFWqVEF4eDjuvPNOzJ49G2fPnlU7PMW50hds3LgRGo3G9Nq6davd5W7cuIE5c+bg1ltvRZUqVRAdHY1evXrh/PnzkuseM2YMNBoNRo8e7db3kNKkSRNoNBosWrTI4v2+ffuiSZMmXt2Wu7Zu3WrRrhs3blQtFibRK7CjR49i9uzZmDFjBrRa213hmWeewfnz5/HFF18gPz8fCxcuVCFKx0aOHOmVf0iSraSkJFSvXh2zZ89WOxQiRa1ZswZdunRBWVkZZs+ejfXr12PBggW4++67sWLFCrXDI6IK6p133sHdd98NvV6P2bNn48cff8ScOXPw999/45577sG7776rdohERDZ4XUUUPJYsWYIOHTqgsLAQEydOxNq1a7Fq1SoMGjQIixYtQnJystoh+qX33nsP+fn5aN26td2/Jycn49///jcGDBiA77//HkuWLEGrVq1w/fp1u8tv3rwZK1euVDJkv8z3GbVu3Rr5+fl477331A4FldQOgNSzYMEC1KxZEwMHDrT79927d2PKlClITEz0cWTy6XQ66HQ6tcMISpUqVUJqaipee+01TJo0CdWqVVM7JCJFzJ49G02bNsW6detQqdL/TotDhgzhTSQiUsWvv/6Kf/3rX3jooYewatUqm77pkUcewbhx43DHHXfg7rvvtruOS5cu+ezcXV5ejhs3biAsLMwn2yMi/8XrKqLgkJ+fj2effRYPPvggvvrqK4tz/IMPPogJEyZg7dq1kp/35XWIv2nTpg3i4+Pt/m3Tpk34+OOP8c4772Ds2LGm9wcMGGB3+Rs3bmDUqFGYMWMGnn32WUXibdGiBXbt2oVNmzbh3nvvVWQbnqhevTri4+Nx5coVtUPhSPRA8d///hc9evRAREQEqlWrhi5dumDNmjU2y3399ddo3749wsLCcMstt2DBggV2Hz+5du0asrOzMXToUItR6MZH4TQaDW7cuIHXXnvN9N/W5Vy2bduGnj17mmK677778Msvv1gsI/VondQjxBqNBtOnT8fSpUvRqlUrhIWFoXHjxli9erXFcs2bN7d4nEPK4cOHMXz4cERHRyMsLAx33HEHvvnmG7vLyt22HEpsV85ycn4TV9Y3bNgwlJWVYfny5S63AVGgOHPmDKKioiz+oWdk/ZTO559/joSEBISHhyM8PBy33347srOzTX//4YcfMGDAAOh0OlSpUgXNmzdHamoqTp8+LSuWH3/8ET169ECNGjVQrVo13H333fjpp59slluzZg1uv/12hIWFoWnTppgzZ46L35qI/Fl6ejo0Gg3ef/99m76pUqVKWLhwITQaDWbOnAngf48a//bbb3jsscdQq1YtNGvWDABw/fp1vPjii4iJiUG1atVwzz33YMuWLXa3e/z4caSmpkKn06Fy5cpo2rQpXn31Vdy4ccO0THFxMTQaDWbPno3XX38dTZs2RVhYGHJzcwHcfOS2f//+qF27NqpUqYI77rgDOTk5NtsqKCjA3XffjSpVqqBBgwaYMmWK5AgsIgocvK4iCg5vvvkmNBoNMjMz7d4kr1y5Mvr37w/A8XUIIC+fZVzH9u3bMXDgQNSoUQORkZF48skncerUKbeXLSoqwtChQ025mdatW0uOaPZFX7B8+XJUr14dKSkpspafO3cuQkJCMGrUKJe2s2/fPuzbt0/Wsk2bNkViYqLT0eiu5AB//vln9OnTB+Hh4YiNjcW8efNs1udK3sxfcCR6APj555/x4IMPon379sjOzkZYWBgWLlyIfv36YdmyZRg8eDAAYO3atRg4cCDuu+8+rFixwlRn6cSJEzbr3Lx5M86cOYP777/f4v0+ffqYyqMkJCQgOTkZI0eOBADUqFHDtNzBgwfRtWtX3HLLLVi6dClCQ0Mxa9YsPPDAA8jPz0eHDh3c/r5r1qxBWVkZpkyZgkaNGmH79u24cOGCxTJffPEFrly5gqysLIsLLXMlJSXo3LkzqlatioyMDMTExODzzz/Hww8/jO+//x49e/Z0a9vOKLldR8u585s4225MTAxatWqFNWvW4JlnnnGpHYgCRUJCArKysvD8889j2LBhuPPOOxEaGmqz3NSpU/Haa69h4MCBmDBhAiIjI7Fr1y4cPnzYtMyff/6JhIQEjBw5EpGRkSguLsZbb72Fe+65B3/88Yfd9Rp9+umneOqppzBgwAB89NFHCA0NxeLFi5GYmIh169ahR48eAICffvoJAwYMQEJCApYvX47y8nLMnj3bbl9PRIGnvLwcubm56Nixo+TTdo0aNUKHDh2wYcMGlJeXm94fOHAghgwZgtGjR5vmNElJScHHH3+MtLQ0PPjgg9i1axcGDhxoU3fz+PHjuOuuu6DVajF16lQ0a9YM+fn5eP3111FcXIylS5daLP/222+jRYsWmDNnDmrUqIG4uDjk5uaiV69e6Ny5MxYtWoTIyEgsX74cgwcPxqVLl0wDKPbs2YMePXqgSZMm+PDDD1GtWjUsXLgQn3/+uRdbkojUwOsqosBXXl6ODRs2oEOHDmjUqJHsz9m7DpGbzzJ65JFH8Pjjj2P06NHYvXs3XnnlFezZswebN2+2OeadLbtnzx506dIFjRs3xty5cxETE4N169bh+eefx+nTpzFt2jTTunzVF+zYsQNt2rTB8uXLMW3aNOj1ejRr1gz//ve/kZSUZLHsoUOHMGPGDPzwww92SzA7YiwlI4SQtfxzzz2Hxx57DCdOnEC9evVs/u5qviklJQXPP/88/vWvf2HJkiV44YUXkJCQYBqh707ezC8I8itLly4VAMShQ4dM78XHx4vo6Ghx/vx503s3btwQbdu2FTqdThgMBiGEEJ06dRKNGjUSV69eNS13/vx5UadOHWH9U8+aNUsAEMePH5eMBYCYNm2a3b+NGTNGhISEiMOHD5veO3v2rKhWrZoYNGiQw+8jhBCxsbEiKSnJ7jYjIyPF6dOnJeMyN23aNJvvZvT000+LypUri6KiIov377zzTpGQkODxtqUotV1ny8n9TVzd7rBhw0S9evUcLkMUyE6fPi3uueceAUAAEKGhoaJLly4iPT3d1O/+9ddfIiQkRAwbNkz2eg0Gg7h+/bo4fPiwACC+/vpr09+s+8aLFy+K2rVri379+lmso7y8XNx2223irrvuMr3XuXNn0aBBA3H58mXTe2VlZaJ27dqS/SERBY7jx48LAGLIkCEOlxs8eLAAIE6cOGG6Hpo6darFMnv37hUAxPjx4y3e/+yzzwQAi2ux1NRUER4ebnEdIYQQc+bMEQDE7t27hRBCHDp0SAAQzZo1E9euXbNYtlWrVuKOO+4Q169ft3i/b9++on79+qK8vNwUe9WqVS2uQ2/cuCFatWpl97qRiAIHr6uIAp/caxEjqesQIeTns4zrkLpm+fTTT22252zZxMREodPpRGlpqcVyY8eOFVWqVBFnz541veeNviA3N1cAELm5uZLLtGjRQsTExIh69eqJpUuXih9++MF0Tbd27VqLZRMTE8Xw4cNN/w1ApKamyorF2Ac7ExsbKxITE0V5ebmIjY0Vr7/+uhBCiD59+ojY2FjTcq7mAN9++23Te+fPnxehoaHilVdeMb3nat5MCHntqzSWc1GQ9cy8jl47duywu46LFy9i8+bNeOyxxxAeHm56PyQkBMOHD4der8f+/ftx8eJFbN26FQ8//DAqV65sWi48PBz9+vWzWe/Ro0eh0WgQFRXl1ncrKCjArbfeisaNG5veq1WrFrp06YKCggK31mnUr18/1KlTx6N1AMD333+P+Ph4NGnSBDdu3DC9jI8xX7t2TZFtK7ldR8u585vI2W50dDROnjxp8Sg3UTCpU6cONm3ahMLCQsycORMDBgzAgQMHMGXKFLRr1w6nT5/GDz/8gPLycowZM8bhuk6ePInRo0ejUaNGqFSpEkJDQxEbGwsA2Lt3r+Tn8vLycPbsWSQlJVn0GwaDAb169UJhYSEuXryIixcvorCwEAMHDkSVKlVMn4+IiLDb1xNR8BL/f2SReVm7Rx991GIZY4mVYcOGWbz/+OOP25Ra+Pbbb3H//fejQYMGFv1Q7969AdwcSWauf//+FiPCDh48iH379pm2Zb6Ohx56CMeOHcP+/ftNcfXo0cNipFNISIjNaDQiCjy8riKquKyvQ+Tms8xJXbMYr2nkLnvlyhX89NNPeOSRR1CtWjWb65IrV66Y8iS+7AsLine truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 1500x300 with 5 Axes>"
|
"<Figure size 1500x300 with 5 Axes>"
|
||||||
]
|
]
|
||||||
@@ -58,10 +61,6 @@
|
|||||||
}
|
}
|
||||||
],
|
],
|
||||||
"source": [
|
"source": [
|
||||||
"EPSILON = 1e-4\n",
|
|
||||||
"START = 64\n",
|
|
||||||
"END = None\n",
|
|
||||||
"\n",
|
|
||||||
"sort_index = np.argsort(train_y)\n",
|
"sort_index = np.argsort(train_y)\n",
|
||||||
"x = train_x[:,sort_index]\n",
|
"x = train_x[:,sort_index]\n",
|
||||||
"y = train_y[sort_index]\n",
|
"y = train_y[sort_index]\n",
|
||||||
@@ -86,13 +85,13 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 10,
|
"execution_count": 4,
|
||||||
"id": "71fb1484",
|
"id": "71fb1484",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": "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 truncated
|
"image/png": "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 truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 900x600 with 6 Axes>"
|
"<Figure size 900x600 with 6 Axes>"
|
||||||
]
|
]
|
||||||
@@ -104,41 +103,72 @@
|
|||||||
"name": "stdout",
|
"name": "stdout",
|
||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
" Best BO trial : index 391\n",
|
" Best BO index : 388\n",
|
||||||
" defocus [A] : -200.06\n",
|
" defocus [A] : -208.15\n",
|
||||||
" layers : 23.00\n",
|
" layers : 20.00\n",
|
||||||
" thickness [A] : 337.54\n",
|
" thickness [A] : 244.70\n",
|
||||||
" log fourier : -0.47124\n"
|
" log fourier : -0.56577099\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"source": [
|
"source": [
|
||||||
|
"CMAP = 'coolwarm'\n",
|
||||||
|
"\n",
|
||||||
"fig, axs = plt.subplots(2, len(x), figsize=(3*len(x), 6), sharey='row', sharex='col')\n",
|
"fig, axs = plt.subplots(2, len(x), figsize=(3*len(x), 6), sharey='row', sharex='col')\n",
|
||||||
"for i, axi in enumerate(axs[0]):\n",
|
"for i, axi in enumerate(axs[0]):\n",
|
||||||
" axi.scatter(x[i][START:END], -y[START:END], c=y_scaled[START:END], cmap='coolwarm', s=10)\n",
|
" axi.scatter(x[i][START:END], -y[START:END], c=y_scaled[START:END], cmap=CMAP, s=5)\n",
|
||||||
" axi.set_title(f\"Best value: {x[i,-1]:.1f}\")\n",
|
" axi.set_title(f\"Best value: {x[i,-1]:.1f}\")\n",
|
||||||
"\n",
|
"\n",
|
||||||
"axs[0][0].set_ylabel('$- \\\\log (\\\\mathtt{fourier\\\\_error})$')\n",
|
"axs[0][0].set_ylabel('$- \\\\log (\\\\mathtt{fourier\\\\_error})$')\n",
|
||||||
"\n",
|
"\n",
|
||||||
"for i, axi in enumerate(axs[1]):\n",
|
"for i, axi in enumerate(axs[1]):\n",
|
||||||
" axi.scatter(x[i][START:END], y_scaled[START:END], c=y_scaled[START:END], cmap='coolwarm', s=10)\n",
|
" axi.scatter(x[i][START:END], y_scaled[START:END], c=y_scaled[START:END], cmap=CMAP, s=5)\n",
|
||||||
" axi.set_xlabel(xparams[i])\n",
|
" axi.set_xlabel(xparams[i])\n",
|
||||||
"axs[1][0].set_ylabel('Scaled')\n",
|
"axs[1][0].set_ylabel('Scaled')\n",
|
||||||
"plt.tight_layout()\n",
|
"plt.tight_layout()\n",
|
||||||
"plt.show()\n",
|
"plt.show()\n",
|
||||||
"\n",
|
"\n",
|
||||||
"print(f\" Best BO trial : index {train_y.argmax()}\")\n",
|
"print(f\" Best BO index : {train_y.argmax():>12d}\")\n",
|
||||||
"print(\"\\n\".join([f'{xparams[i]:>15} : {x[i, -1]:> 9.2f}' for i in range(len(xparams))]))\n",
|
"print(\"\\n\".join([f'{xparams[i]:>15} : {x[i, -1]:> 12.2f}' for i in range(len(xparams))]))\n",
|
||||||
"print(f\" log fourier : {-y[-1]:9.5f}\")"
|
"print(f\" log fourier : {-y[-1]: 12.8f}\")"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": null,
|
"execution_count": 5,
|
||||||
"id": "63358241",
|
"id": "b47d359e",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [
|
||||||
"source": []
|
{
|
||||||
|
"data": {
|
||||||
|
"image/png": "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 truncated
|
||||||
|
"text/plain": [
|
||||||
|
"<Figure size 900x900 with 9 Axes>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"# Cross-correlation plots\n",
|
||||||
|
"\n",
|
||||||
|
"if len(x) < 3:\n",
|
||||||
|
" plt.figure(figsize=(3.5,3.5))\n",
|
||||||
|
" plt.scatter(train_x[1][START:END], train_x[0][START:END], c=train_y_scaled[START:END], cmap=CMAP, s=5)\n",
|
||||||
|
" plt.xlabel(xparams[1])\n",
|
||||||
|
" plt.ylabel(xparams[0])\n",
|
||||||
|
"else:\n",
|
||||||
|
" fig, axs = plt.subplots(len(x), len(x), figsize=(3*len(x), 3*len(x)), sharex='col', sharey='row')\n",
|
||||||
|
" for i, axi in enumerate(axs):\n",
|
||||||
|
" for j, axij in enumerate(axi):\n",
|
||||||
|
" axij.scatter(train_x[j][START:END], train_x[i][START:END], c=train_y_scaled[START:END], cmap=CMAP, s=5, alpha=(0 if i==j else 1))\n",
|
||||||
|
" if i == len(x)-1: axij.set_xlabel(xparams[j])\n",
|
||||||
|
" if j == 0: axij.set_ylabel(xparams[i])\n",
|
||||||
|
"\n",
|
||||||
|
"plt.tight_layout()\n",
|
||||||
|
"plt.show()"
|
||||||
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"metadata": {
|
"metadata": {
|
||||||
|
|||||||
Reference in new issue
Block a user