Si_2V1_2 batched bo job

examples/260812-Si/2V.yaml

@@ -0,0 +1,60 @@
+job:
+  type: "random+sobo"
+  random_iters: 8
+  sobo_iters: 128
+
+io:
+  input_data_path: '/home/swim/shared/Si_project/data/Si2V1_2.mat'
+  result_dir: '/home/swim/bo-ptycho/results/260812-Si/Si2V1_2'
+  verbosity: 1
+
+ptycho:
+  engine: 'fold_slice'
+  path: '/home/swim/fold_slice-stable'
+  params:
+    voltage: 200
+    alpha_max: 30
+    defocus: -250
+    rot_ang: 0.3
+    Nlayers: 20
+    thickness: 250
+    rbf: 37
+    Nprobe: 1
+    N_scan_x: 64
+    N_scan_y: 64
+    tilt_x: 2
+    tilt_y: 0 
+    scan_step_size: 0.36
+    Niter: 100
+    Niter_save_results: 100
+    CBED_size: 192
+    ADU: 1
+    extra_print_info: 'FIB'
+    scan_number: 1
+    gpu_id: 1
+    roi_label: '0_Ndp64'
+    diff_pattern_blur: 1
+    probe_change_start: 1
+    object_change_start: 1
+    grouping: 512
+    probe_posiiton_search: 1
+    regularize_layers: 0.2
+    variable_probe: false
+
+bo:
+  batch: 4
+  acquisition: 'ucb'
+  beta: 0.1
+  metric: 'log_fourier'
+  params:
+    alpha_max:
+    defocus:
+      radius: 100
+    rot_ang:
+    Nlayers:
+      radius: 5
+      type: int
+    thickness:
+      radius: 100
+  train_x:
+  train_y:

examples/260812-Si/job.sub

@@ -0,0 +1,26 @@
+#!/bin/bash
+#SBATCH --job-name=Si2V
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=1
+#SBATCH --gres=gpu:rtx-6000ada:4
+#SBATCH --time=100:00:00
+#SBATCH --output=/home/swim/slurm-logs/job_%j.log
+
+echo "2V"
+pwd
+hostname
+date
+
+export PATH=/home/shared/MATLAB/R2021a/bin:$PATH
+export PATH=/usr/local/cuda-11.4/bin:$PATH
+export LD_LIBRARY_PATH=/usr/local/cuda-11.4/lib64:$LD_LIBRARY_PATH
+
+source /home/swim/bo-ptycho/venv/bin/activate
+
+YAML=examples/260812-Si/2V.yaml
+cat $YAML
+python -u main.py $YAML
+
+date
+echo "SLURM JOB FINISHED"

notebooks/convergence.ipynb

@@ -2,43 +2,44 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "65e78297",
    "metadata": {},
    "outputs": [],
    "source": [
+    "import os\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "id": "90917d29",
    "metadata": {},
    "outputs": [],
    "source": [
-    "train_x = np.load(\"../results/260805-si/Si8V1_2/train_x.npy\").T\n",
-    "train_y = np.load(\"../results/260805-si/Si8V1_2/train_y.npy\").T"
+    "result_dir = \"../results/260812-Si/Si2V1_2\"\n",
+    "train_x = np.load(os.path.join(result_dir, 'train_x.npy')).T\n",
+    "train_y = np.load(os.path.join(result_dir, 'train_y.npy')).T\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
+   "id": "05c0f656",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "id": "47b5dd69",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAESCAYAAABTgsn9AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAPYQAAD2EBqD+naQAAPIhJREFUeJzt3XlcVPX+P/DXLMyMAkPisAoqLiiJW6Bs+a20KNLKunVdcqm0GyVdTfl+v5g+UnxodrPMrLDScikfN/u6lCU3obQkt1yYxCVEUVkECZAZUAGZOb8/+M3kCAMzCByYeT0fj/O4eeYs73MezLmv+XzO+RyJIAgCiIiIiEg0UrELICIiInJ2DGREREREImMgIyIiIhIZAxkRERGRyBjIiIiIiETGQEZEREQkMgYyIiIiIpHJxS6gPRmNRly+fBnu7u6QSCRil0NErUAQBFRWVsLf3x9Saef9jcnrE5Hjsef65FSB7PLlywgMDBS7DCJqA/n5+QgICBC7jBbj9YnIcdlyfXKqQObu7g6g/sSo1WqRqyGi1qDX6xEYGGj+fndWvD4ROR57rk9OFchM3QBqtZoXPCIH09m7+Xh9InJctlyfOu8NF0REREQOgoGMiIiISGQMZEREREQiYyAjIiIiEhkDGREREZHIGMiIiIiIROZUw14QdSaCINzR/7bGNlq6D6PR2ORkMBia/Ly2thZVVVV49NFHW3r6HFZVVRWOHTsGuVyOmJgYscsholbCQNYBXLx4EV9//TWKioqg1+tx7do1GAwGCILQKhOAVttWS/dt0t7BoDNum/5SV1cHmUwmdhkdyhdffIFXXnkFDz/8MH744QexyyGiVsJAJqLc3Fx89913WLhwIaqqqsQuh6jVyGQySKXSZidry8nlcqjVaty4cQNubm5iH06HMmLECADAkSNHIAhCpx8Ql4jqMZCJZOXKlUhMTDS3ioSGhmLs2LFQq9Vwc3ODTCaDRCK54wlAq2ynNWowuX1ea/0vty3+tqntDRkyBAqFAuXl5bhw4QL69OkjdklE1AoYyERw4sQJLFiwAIIgYOTIkXjkkUcwb948vi6FiJqlUCgwdOhQHDlyBEeOHGEgI3IQDGTtbPPmzXjhhRdQW1uL++67D3v37mXrAhHZZdCgQThy5Ahyc3PFLoWIWgmHvWhHtbW1mDt3LmprazF27Fhs2bKFYYyI7Obl5QUAKCsrE7kSImotbCFrJ6WlpUhOTkZJSQn8/PywY8cOuLi4iF0WEXVC3bt3B1B/XSEix8BA1g4EQcC4ceNw+PBhAEBiYiLDGBG1mEajAcBARuRI2GXZxqqqqhAbG2sOY++88w5ee+01kasios6MgYzI8TCQtaHffvsNwcHB+PHHHwEA//znPzFv3jzeN0bkoGpqajBs2DBIJBJotdo22w8DGZHjYSBrQ6tXr0ZRUREAYN26dVixYoXIFRFRW/qf//kf+Pv7t/l+eA8ZkeNhIGsjRqMRaWlpAIBdu3ZhxowZUCgUIldFRG3lP//5D9LS0vDOO++0+b5MLWQ6nQ43b95s8/0RUdtjIGsjy5Ytw59//glXV1c8+OCDYpdDRG3oypUrePHFF/HFF1+ga9euNq1TU1MDvV5vMdmqW7du5lsf0tPTW1QzEXUsDGRtYO/evVi0aBEAYPHixWwZI3JggiDgueeeQ3x8PMLDw21eb/ny5fDw8DBPgYGBNq8rk8nwyCOPmLdDRJ0fA1krO336NKZMmQJBEDBz5kwkJiaKXRIRtcDixYubfU/r0aNH8cEHH0Cv12P+/Pl2bX/+/PnQ6XTmKT8/3671582bBwAoLi62az0i6pg4DlkrMhgMeOSRR3D58mX069cP7733ntglEVELJSQkYOLEiU0u07t3byxduhSHDh2CUqm0+Cw8PBzPPvssNm7c2Oi6SqWywTr2MN1HVllZ2eJtEFHHwUDWivbv34/8/HwoFArs2bMHbm5uYpdERC2k0WjMoacpq1evxtKlS83/vnz5Mh5++GFs2bIFERERbVafu7s7AAYyIkfBQNZK6urqsGrVKgDAxIkT7bofhIg6r549e1r82/RDrG/fvggICGiz/ZoC2fXr12EwGCCTydpsX0TU9lp0D1lKSgqCgoKgUqkQFhaGjIwMm9bbv38/5HI5hg0b1uCziooKzJo1C35+flCpVAgJCUFqaqr588bu5/D19W1J+a2uvLwcI0eOxI4dOyCVShEfHy92SUTk4EyBDGArGZEjsLuFbMuWLZgzZw5SUlIQExODTz75BHFxcTh9+nSDX4q30ul0mDZtGsaMGYMrV65YfFZbW4uHHnoI3t7e2Lp1KwICApCfn29xwQGAQYMGmUe9B9AhfhH+9ttvePzxx3HlyhV07doVq1atQlRUlNhlEZFIevfuDUEQ2nw/SqUSLi4uuHnzJiorK3HXXXe1+T6JqO3YHchWrlyJGTNmYObMmQCAVatWYffu3VizZk2Tj1+/9NJLmDx5MmQyGb755huLzz7//HOUl5fjwIED5pdu9+rVq2GxcrldrWI1NTWoqakx/9uecX5skZWVhYcffhgVFRXo3bs3duzY0WjrHxFRa5NIJHB3d0d5eTlbyIgcgF1dlrW1tTh27BhiY2Mt5sfGxuLAgQNW11u/fj3Onz9vHpvrdjt37kRUVBRmzZoFHx8fhIaG4s0334TBYLBYLicnB/7+/ggKCsLEiRORm5vbZL13Ms6PLebPn4+KigpER0cjKyuLYYyI2hVv7CdyHHYFstLSUhgMBvj4+FjM9/HxsToWTk5ODpKSkrB582bI5Y03yOXm5mLr1q0wGAxITU3FwoUL8e6772LZsmXmZSIiIrBp0ybs3r0ba9euRXFxMaKjo1FWVma13jsd56cpRqMRv/76K4D6p6z4RCURtTcGMiLH0aKnLE2v7DARBKHBPKB+XK7JkycjOTkZwcHBVrdnNBrh7e2NTz/9FDKZDGFhYbh8+TJWrFiBN954AwAQFxdnXn7w4MGIiopC3759sXHjRsydO7fR7d7pOD9N+eOPP6DT6dClSxcMGTKkTfZBRNQUUyBr7dsxiKj92RXINBoNZDJZg9awkpKSBq1mQP2vtqNHjyIzMxMJCQkA6sOXIAiQy+VIS0vD6NGj4efnBxcXF4ub9ENCQlBcXIza2tpGXz3k6uqKwYMHIycnx55DaDXbt28HAIwcOdJ83xsRUXtSq9UA2EJG5Ajs6rJUKBQICwtr8DLb9PR0REdHN1herVYjKysLWq3WPMXHx2PAgAHQarXmQRNjYmJw7tw5GI1G87pnz56Fn5+f1fdA1tTU4MyZM/Dz87PnEFpFbm4u/vWvfwEAXnzxxXbfPxER8FcL2YULF0SuhIjulN3jkM2dOxfr1q3D559/jjNnzuC1115DXl6eeeyt+fPnY9q0afUbl0oRGhpqMXl7e0OlUiE0NBSurq4AgJdffhllZWWYPXs2zp49i127duHNN9/ErFmzzPtNTEzEL7/8ggsXLuDw4cN4+umnodfrMX369NY4D3aZN28eqqqqcO+992LSpEntvn8iIgDmoS6Sk5NRUVEhai1EdGfsvodswoQJKCsrw5IlS1BUVITQ0FCkpqaah6koKipCXl6eXdsMDAxEWloaXnvtNQwZMgQ9evTA7Nmz8b//+7/mZQoKCjBp0iSUlpbCy8sLkZGROHToUKPDY7Sl69ev44cffgAAfPjhh5BK+X52IhLHK6+8gnXr1gGo71UYOXKkyBURUUtJhPYYwbCD0Ov18PDwgE6nM997Ya/U1FSMHTsWgYGBuHTpUqMPMxBR+2mN73VH0NLjCAsLw/Hjx7Fr1y48+uijbVghEdnLnu81m3fslJWVBQAYNWoUwxgRic70AvTS0lKRKyGiO8FAZifTa5969OghciVERAxkRI6CgcxOpkDW2DAfRETtjYGMyDEwkNnJNAYbAxkRdQQMZESOgYHMTqYWMnteck5E1FYYyIgcAwOZndhlSUQdCQMZkWNgILPDzZs3zRc9BjIi6ggYyIgcAwOZHU6dOgUAkMvl6N69u8jVEBExkBE5CgYyO7z33nsAgPHjx1u8CJ2ISCymQFZWVmbxPmAi6lwYyGxkNBrx3XffAQBmz54tcjVERPVMrfVGo5HvsyTqxBjIbJSVlYWrV6/Czc0NERERYpdDRAQAUCgU5leysNuSqPNiILPR/v37AQAxMTFwcXERuRoior/wPjKizo+BzEYlJSUAgKCgIJErISKyxEBG1PkxkNno2rVrAABXV1eRKyEissRARtT5MZDZ6Pr16wAYyIio42EgI+r8GMhsZGoh69q1q8iVEBFZCggIAAAcP35c5EqIqKUYyGzELksi6qieeuopAMC3336L8vJykashopZgILMRuyyJqKO65557MHToUFRXVyMpKUnscoioBRjIbMQWMiLqqCQSCVasWAEA2LZtm8jVEFFLMJDZiPeQEVFHNmLECABAeXm5uUWfiDoPBjIbsYWMiDoyDw8P8/WpoKBA5GqIyF4MZDbiPWRE1JFJJBIEBgYCYCAj6owYyGzELksi6uhMw1/k5+eLXAkR2YuBzEbssiSijs4UyNhCRtT5tCiQpaSkICgoCCqVCmFhYcjIyLBpvf3790Mul2PYsGENPquoqMCsWbPg5+cHlUqFkJAQpKamtsp+75TRaMSNGzcAMJARUcdletfuiRMnRK6EiOxldyDbsmUL5syZgwULFiAzMxOjRo1CXFwc8vLymlxPp9Nh2rRpGDNmTIPPamtr8dBDD+HixYvYunUrsrOzsXbtWvTo0eOO99saTGEMYJclEXVcDz30EABg9+7duHnzpsjVEJE9JIIgCPasEBERgXvuuQdr1qwxzwsJCcH48eOxfPlyq+tNnDgR/fv3h0wmwzfffAOtVmv+7OOPP8aKFSvwxx9/wMXFpVX3eyu9Xg8PDw/odDqo1Wqb1gGAkpIS+Pj4AAAMBgOkUvb0EnUULf1edzSLine truncated
-      "text/plain": [
-       "<Figure size 700x300 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "xparams = [\n",
     "    'defocus [A]',\n",
@@ -51,40 +52,35 @@
     "x = train_x[:,sort_index]\n",
     "y = train_y[sort_index]\n",
     "\n",
-    "EPSILON = 1e-5 # choose best according to plot\n",
+    "EPSILON = 1e-3 # choose best according to plot\n",
     "\n",
+    "train_y_scaled = np.log(-train_y + y[-1] + EPSILON)\n",
     "y_scaled = np.log(-y + y[-1] + EPSILON)\n",
     "\n",
     "fig, ax = plt.subplots(1, 2, figsize=(7,3))\n",
     "ax[0].plot(y, 'k')\n",
     "ax[1].plot(y_scaled, 'k')\n",
+    "plt.show()\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(7,3))\n",
+    "ax[0].plot(train_y, 'k')\n",
+    "ax[1].plot(train_y_scaled, 'k')\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "id": "4990770c",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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 truncated
-      "text/plain": [
-       "<Figure size 950x350 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "cut = 45 # set to -1 to get all points\n",
+    "cut = 0 # set to -1 to get all points\n",
     "\n",
     "fig, axs = plt.subplots(1, len(x), figsize=(3*len(x)+0.5, 3.5), sharey=True)\n",
     "\n",
     "for i, axi in enumerate(axs):\n",
-    "    axi.scatter(x[i][cut:], -y[cut:], c=y_scaled[cut:], cmap='coolwarm')\n",
+    "    axi.scatter(x[i][cut:], y_scaled[cut:], c=y_scaled[cut:], cmap='coolwarm')\n",
     "    axi.set_xlabel(xparams[i])\n",
     "\n",
     "axs[0].set_ylabel('$- \\\\log (\\ \\\\mathtt{fourier\\_error}\\ )$')\n",
@@ -95,21 +91,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": null,
    "id": "a2694f3c",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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 truncated
-      "text/plain": [
-       "<Figure size 950x950 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "\n",
     "fig, axs = plt.subplots(len(x), len(x), figsize=(3*len(x)+0.5, 3*len(x)+0.5))\n",
@@ -117,7 +102,7 @@
     "for i, axi in enumerate(axs):\n",
     "    for j, axij in enumerate(axi):\n",
     "        axij.scatter(\n",
-    "            x[i], x[j],\n",
+    "            x[j], x[i],\n",
     "            c = y_scaled, # color\n",
     "            cmap = 'coolwarm',\n",
     "            s = 20, # size\n",