260723 code for treating all samples, also with roi cropping

260721-atomic-column-thickness/260723-amorphous.ipynb

@@ -0,0 +1,747 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "11108e19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "from scipy.io import loadmat\n",
+    "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e3adee3c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_R(raw_data, regressed):\n",
+    "    sse = np.sum(np.square(raw_data - regressed))\n",
+    "    sst = np.sum(np.square(raw_data - raw_data.mean()))\n",
+    "    return 1 - sse/sst\n",
+    "\n",
+    "def fit(f, xdata, ydata, plot=False, **kwargs):\n",
+    "    xdata = np.array(xdata).flatten()\n",
+    "    ydata = np.array(ydata).flatten()\n",
+    "    if plot:\n",
+    "        plt.plot(xdata, ydata, 'k.')\n",
+    "        if 'sigma' in kwargs:\n",
+    "            plt.errorbar(xdata, ydata, kwargs['sigma'], fmt='none', c='k')\n",
+    "    popt, pcov = curve_fit(f, xdata, ydata, **kwargs)\n",
+    "    perr = np.sqrt(np.diag(pcov))\n",
+    "    R = calculate_R(ydata, f(xdata, *popt))\n",
+    "    if plot:\n",
+    "        xlim, ylim = plt.xlim(), plt.ylim()\n",
+    "        plt.xlim(*xlim)\n",
+    "        plt.ylim(*ylim)\n",
+    "        x = np.linspace(*xlim, 100)\n",
+    "        plt.plot(x, f(x, *popt), 'r--')\n",
+    "        plt.show()\n",
+    "    return popt, perr, R"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "89b917f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = {\n",
+    "    '2V': loadmat('./Si2V1-Niter1000.mat'),\n",
+    "    '5V': loadmat('./Si5V2-Niter1000.mat'),\n",
+    "    '8V': loadmat('./Si8V2-Niter1000.mat'),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a9f200f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DZ = {}\n",
+    "recons = {}\n",
+    "\n",
+    "for key in results.keys():\n",
+    "    result = results[key]\n",
+    "    roi = result['p']['object_ROI'][0][0][0]\n",
+    "    roix = roi[0].T\n",
+    "    roiy = roi[1]\n",
+    "\n",
+    "    DZ.update({key: result['outputs']['z_distance'].item().item()})\n",
+    "    recons.update({key: result['object'][roix, roiy]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9db949e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "column_positions = {}\n",
+    "\n",
+    "for key in results.keys():\n",
+    "    recon = recons[key]\n",
+    "    image = np.angle(recon[:,:,recon.shape[-1] // 2])\n",
+    "    blurred_image = gaussian_filter(image, 1)\n",
+    "    distance = 2\n",
+    "    threshold = 0.5\n",
+    "    c0 = kemstem.find_columns(blurred_image, distance=distance, threshold=threshold)\n",
+    "\n",
+    "    column_positions.update({key: c0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "93d22d50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b9feef9fb2b408881559cd3f4430bb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1464 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2eb5e771356d488891678143ebe69915",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1004 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e57669164d44636ac7b8cedfc5dff35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/937 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "WINDOW = 1\n",
+    "\n",
+    "column_lists = {}\n",
+    "\n",
+    "for key in results.keys():\n",
+    "\n",
+    "    c0 = column_positions[key]\n",
+    "    recon = recons[key]\n",
+    "\n",
+    "    column_list = []\n",
+    "    for i, (cx, cy) in enumerate(tqdm(c0)):\n",
+    "        wx_start, wx_end = cx-WINDOW, cx+WINDOW+1\n",
+    "        wy_start, wy_end = cy-WINDOW, cy+WINDOW+1\n",
+    "        column = np.angle(recon)[wx_start:wx_end, wy_start:wy_end, 1:].mean(axis=(0, 1))\n",
+    "        column_list.append(column)\n",
+    "\n",
+    "    column_lists.update({key: column_list})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4925866a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def double_erf(x, p0, p1, q, x0, x1):\n",
+    "    return p0 + p1 * (erf(q*(x - x0)) - erf(q*(x - x1)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35484b47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62a285a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58f7012f541d4c95a157b0e3baadb1dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1464 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "513a59d835fb42c5b28d29b9d518a7ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1004 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a1030bbee5d4f4b84b65de67d110699",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/937 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "crystalline_heights = {}\n",
+    "safe_height_indices = {}\n",
+    "\n",
+    "for key in results.keys():\n",
+    "\n",
+    "    columns = column_lists[key]\n",
+    "    dz = DZ[key]\n",
+    "\n",
+    "    crystalline = []\n",
+    "\n",
+    "    for column in tqdm(columns):\n",
+    "        popt, _, _ = fit(\n",
+    "            double_erf, np.arange(len(column)), column, plot=False,\n",
+    "            p0=[-0.03846732,  0.28181914,  0.30406586,  3.74047066, 12.6762897 ],\n",
+    "            # bounds=bounds,\n",
+    "            maxfev=10000\n",
+    "            )\n",
+    "        p0, p1, q, x0, x1 = popt\n",
+    "\n",
+    "        crystalline.append((x1 - x0) * dz)\n",
+    "\n",
+    "    crystalline_heights.update({key: np.array(crystalline)})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "6af86cf4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "245.0 55.8145049307754\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "281.0 89.54863642387284\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "336.0 99.64506469888937\n"
+     ]
+    }
+   ],
+   "source": [
+    "for key in results.keys():\n",
+    "    crystalline = crystalline_heights[key]\n",
+    "    sorted_heights = np.sort(crystalline)\n",
+    "\n",
+    "    ymin = sorted_heights[1 * len(sorted_heights) // 20]\n",
+    "    ymax = sorted_heights[19 * len(sorted_heights) // 20]\n",
+    "\n",
+    "    plt.plot(sorted_heights, 'k.')\n",
+    "    xlim = plt.xlim(0, len(crystalline))\n",
+    "    ylim = plt.ylim(ymin, ymax)\n",
+    "    plt.show()\n",
+    "    safe_height_index = np.where(\n",
+    "        (crystalline > ymin) & (crystalline < ymax)\n",
+    "        )\n",
+    "    safe_height_indices.update({key: safe_height_index})\n",
+    "\n",
+    "    total_thickness = recons[key].shape[-1] * DZ[key]\n",
+    "\n",
+    "    print(total_thickness, (total_thickness - np.median(crystalline)) / 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "8cfc6801",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2V\n",
+      "(573, 573)\n",
+      "[71, 214, 358, 501]\n",
+      "5V\n",
+      "(580, 580)\n",
+      "[72, 217, 362, 507]\n",
+      "8V\n",
+      "(581, 581)\n",
+      "[72, 217, 363, 508]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for key, recon in recons.items():\n",
+    "    print(key)\n",
+    "    print(recon.shape[:-1])\n",
+    "    print([(recon.shape[0] * i) // 8 for i in [1, 3, 5, 7]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "9f30bf4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "thickness_rois = {\n",
+    "    '2V': [[214, 358], [214, 358]],\n",
+    "    '5V': [[217, 362], [ 72, 217]],\n",
+    "    '8V': [[217, 363], [ 72, 217]]\n",
+    "}\n",
+    "for key in results.keys():\n",
+    "\n",
+    "    recon = recons[key]\n",
+    "    heights = crystalline_heights[key]\n",
+    "    c0 = column_positions[key]\n",
+    "    idx = safe_height_indices[key]\n",
+    "    dz = DZ[key]\n",
+    "\n",
+    "    plt.imshow(np.angle(recon).sum(axis=-1), cmap='gray')\n",
+    "    # plt.axis('off')\n",
+    "    plt.scatter(\n",
+    "        c0[:, 1][idx], c0[:, 0][idx], c=np.array(heights[idx]), s=8, cmap='magma_r',\n",
+    "        )\n",
+    "    cbar = plt.colorbar()\n",
+    "\n",
+    "    (x0, x1), (y0, y1) = thickness_rois[key]\n",
+    "    plt.plot([x0, x0, x1, x1, x0], [y0, y1, y1, y0, y0], lw=3)\n",
+    "\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "8f03ee83",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "roi_indices = {}\n",
+    "\n",
+    "for key in results.keys():\n",
+    "    c0 = column_positions[key]\n",
+    "    indices = []\n",
+    "    (x0, x1), (y0, y1) = thickness_rois[key]\n",
+    "    idx = np.where(\n",
+    "        (c0[:,0] >= y0) & (c0[:,0] <= y1) & (c0[:,1] >= x0) & (c0[:,1] <= x1) \n",
+    "    )\n",
+    "    indices.append(idx)\n",
+    "    roi_indices.update({key: indices})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "45bba998",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "roi_crystalline_height_means = {}\n",
+    "roi_crystalline_height_stds = {}\n",
+    "roi_amorphous_height_means = {}\n",
+    "roi_amorphous_height_stds = {}\n",
+    "\n",
+    "for key in results.keys():\n",
+    "\n",
+    "    safe_idx = safe_height_indices[key]\n",
+    "    roi_idx = roi_indices[key]\n",
+    "    total_thickness = recons[key].shape[-1] * DZ[key]\n",
+    "\n",
+    "    idx = np.intersect1d(safe_idx, roi_idx)\n",
+    "\n",
+    "    height = crystalline_heights[key]\n",
+    "\n",
+    "    crystalline_mean = height[idx].mean()\n",
+    "    crystalline_std = height[idx].std()\n",
+    "    amorphous_mean = (total_thickness - crystalline_mean) / 2\n",
+    "    amorphous_std = crystalline_std / 2\n",
+    "\n",
+    "    roi_crystalline_height_means.update({key: crystalline_mean})\n",
+    "    roi_crystalline_height_stds.update({key: crystalline_std})\n",
+    "    roi_amorphous_height_means.update({key: amorphous_mean})\n",
+    "    roi_amorphous_height_stds.update({key: amorphous_std})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "id": "ddbd2457",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([   4,   16,   61,   72,   75,   77,   84,   92,  103,  119,  123,\n",
+       "        142,  157,  173,  174,  185,  202,  209,  240,  245,  253,  259,\n",
+       "        273,  302,  314,  323,  325,  328,  330,  333,  359,  369,  385,\n",
+       "        391,  396,  418,  448,  487,  494,  515,  518,  528,  539,  554,\n",
+       "        556,  560,  594,  601,  602,  607,  616,  631,  657,  668,  693,\n",
+       "        706,  722,  741,  747,  782,  787,  793,  801,  814,  831,  841,\n",
+       "        854,  861,  865,  873,  877,  896,  910,  921,  926,  929,  942,\n",
+       "        945,  952,  977,  981,  990, 1006, 1010, 1011, 1013, 1019, 1060,\n",
+       "       1076, 1081, 1096, 1102, 1121, 1135, 1177, 1185, 1212, 1224, 1235,\n",
+       "       1244, 1275, 1291])"
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roi_indices['2V'][0][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "id": "d0926668",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2V\n",
+      "Crystalline thickness: 136.780 ± 21.382\n",
+      "Amorphous thickness:    54.110 ±  10.691\n",
+      "(# of counted columns = 85/102)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5V\n",
+      "Crystalline thickness: 103.381 ± 18.827\n",
+      "Amorphous thickness:    88.810 ±  9.414\n",
+      "(# of counted columns = 105/106)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8V\n",
+      "Crystalline thickness: 141.043 ± 19.590\n",
+      "Amorphous thickness:    97.479 ±  9.795\n",
+      "(# of counted columns = 110/118)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for key in results.keys():\n",
+    "\n",
+    "    recon = recons[key]\n",
+    "    heights = crystalline_heights[key]\n",
+    "    c0 = column_positions[key]\n",
+    "\n",
+    "    crystalline_mean = roi_crystalline_height_means[key]\n",
+    "    crystalline_std = roi_crystalline_height_stds[key]\n",
+    "    amorphous_mean = roi_amorphous_height_means[key]\n",
+    "    amorphous_std = roi_amorphous_height_stds[key]\n",
+    "\n",
+    "\n",
+    "    safe_idx = safe_height_indices[key]\n",
+    "    roi_idx = roi_indices[key]\n",
+    "    idx = np.intersect1d(safe_idx, roi_idx)\n",
+    "\n",
+    "    print(key)\n",
+    "    print(f\"Crystalline thickness: {crystalline_mean:.3f} ± {crystalline_std:.3f}\")\n",
+    "    print(f\"Amorphous thickness:    {amorphous_mean:.3f} ±  {amorphous_std:.3f}\")\n",
+    "    print(f\"(# of counted columns = {len(idx)}/{len(roi_idx[0][0])})\")\n",
+    "\n",
+    "    (x0, x1), (y0, y1) = thickness_rois[key]\n",
+    "    plt.imshow(np.angle(recon).sum(axis=-1), cmap='gray')\n",
+    "    plt.plot([x0, x0, x1, x1, x0], [y0, y1, y1, y0, y0], lw=3)\n",
+    "\n",
+    "    # plt.axis('off')\n",
+    "    plt.scatter(\n",
+    "        c0[:, 1][idx], c0[:, 0][idx], c=np.array(heights)[idx], s=8, cmap='magma_r',\n",
+    "        vmin=heights[safe_idx].min(),\n",
+    "        vmax=heights[safe_idx].max()\n",
+    "        )\n",
+    "    # cbar = plt.colorbar()\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d23f0b2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d59fd141",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAGiCAYAAABd6zmYAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs/WlsXGl6H47+Tu17cSdFkRJFiaIkamltrdbW6r097hkvCexc5CIIgnxwkE+GbQQx/MUOAvvGAZIATvw3DARwbnJjJ7m4Y/fMeHoZdbfUUmtrbZS472RxqWKxFrL27dwP1b+Hzylpptv4x5lRwrdRUJOsc8573vfZn9/zvIZpmiZ2xs7YGTtjZ+yMn8Fh+2lPYGfsjJ2xM3bGzvhxY0dJ7YydsTN2xs74mR07Smpn7IydsTN2xs/s2FFSO2Nn7IydsTN+ZseOktoZO2Nn7Iyd8TM7dpTUztgZO2Nn7Iyf2bGjpHbGztgZO2Nn/MyOHSW1M3bGztgZO+NnduwoqZ2xM3bGztgZP7NjR0ntjJ2xM3bGzviZHT9VJfXHf/zH2LdvHzweD06fPo3PP//8pzmdnbEzdsbO2Bk/Y+OnpqT+23/7b/j1X/91/M7v/A4ePnyIy5cv41vf+hYWFxd/WlPaGTtjZ+yMnfEzNoyfVoPZc+fO4dSpU/i//q//S353+PBh/NIv/RL+4A/+4KcxpZ2xM3bGztgZP2PD8dN4aKlUwv379/HP//k/t/z+nXfewRdffPHM94vFIorFovxcq9WQSCTQ2toKwzD+1ue7M3bGztgZO+N/7jBNE1tbW+ju7obN9uODej8VJRWPx1GtVtHZ2Wn5fWdnJ9bW1p75/h/8wR/g937v9/5XTW9n7IydsTN2xv+isbS0hJ6enh/795+KkuJo9IJM03yuZ/Tbv/3b+I3f+A35OZ1OY8+ePfD5fLDb7TAMAx6PB+FwGKFQCE1NTQgGgygUClhfX8fKygq2trZQrVZRqVTgdDphGAbsdjtcLhccDgcCgQDC4TCamprg9/tRqVSQTCYRi8WQSCRQLpdhmibsdrvch/NtbW1FT08P2tvb4ff7kcvlsLGxgc3NTSSTSeTzedRqNdRqNZimCZvNBpvNBsMw4Ha70d7eLnN2uVzY2tpCIpFAMpnExsYGarUabDYbTNNEpVJBtVqFw+GAx+NBc3Mzmpub4fP54Ha7USgUkEqlsLm5iWw2K9/n+jqdTjidTlSrVfh8PrS2tiIYDMLn86FSqSCVSiGVSiGdTqNWqwEAyuWyZc9qtRocDgdaW1vR2tqKcDgMt9st8+bzq9UqbDabrBnXvFKpIBgMynv7fD4Ui0WkUikkEgm5tlQqyXWVSgVutxtutxsulwsdHR3o7OyUvdrY2MDW1hZSqRQKhQIAoFqtolgsolarweVyybpz3nz3ra0tLC8vI5FIIJPJyPvWajWUy2WhEwBCX62trWhra0OxWMT6+jo2NjaQyWTkeVy3YrEIr9cLl8sFp9OJ5uZmtLa2oru7Gw6HA9FoFKurq0ilUsjlcjBNE8ViUd7bMAw4HA44HA6Ew2F0dXWht7cX7e3tSCaTmJubExorl8sol8soFAqy5w6HA263Gy0tLTLv3t5eGIaB+fl5rKysYG1tDblcTtaItE1asdvt8Pl8aG5uRl9fHwKBAGKxGGKxGFKplPAWUI+ScL2dTidsNhu8Xi+8Xi+6urpw4MABFItFTE9PY3V1Ffl8HpVKBaVSCeVyWWjE4aiLJvJ1W1sb+vv7EQqFMD8/j6WlJWxtbSGfz6NYLKJcLsu6A5Dnu1wueL1eDA4O4uDBg0ilUhgfH8fq6qrQmWEYsNlsqFarwlu1Wg0ejwdNTU3YtWsXDh06BNM0MT8/j9XVVaTTaWxubooM0OtGOgsEAmhra8OhQ4fQ3d2NSCSCyclJxONxFItFFAoF5HI5lEolAIDNZoPH44Hb7UYoFEJzczP279+P/fv3Y2trC5OTk1hZWcHGxobwRqVSsdCJ3++Hx+NBKBSSNQsEApifn8fU1BS2trYsssHlcsHj8cBut4ssdTgcCAaDOHjwIPr7+5HNZjE5OYmlpSWZc7lchtPptLyz0+mE2+2Gx+NBf38/Tp48iUAggNHRUYyPjyOdTqNQKKBYLMI0TdRqNWxubiIYDP5EPfFTUVJtbW2w2+3PeE2xWOwZ7wqACKfnDS3wbTYbfD4fdu3aBZ/Ph5WVFTDlZrfbAUCEts1mE+bn5rhcLrS1taGzsxPJZBKZTAamaVqIAYBFAPA+AOD1etHd3Y1sNotcLofNzU0AEMYjIddqNdjtdjidTmEIl8uF3t5eeL1ezM/PY2NjA6ZpwuFwyDvUajWL4HA6naL8KPgSiQSKxSK2trbkvQzDEAVHIcr1AIDm5mYcPHgQ+Xweo6OjwvQUllQyFN4ul0uEttvtxuHDh9HS0iKEyHUl8ZL5qWDcbjf8fj/8fj8GBwdx4MABrK2tYXh4GLlcTgiZz6AgcTgc8v6GYaC5uRkvvfQSAODRo0eYmpqStTZNU9ZPCyIKr6amJpw9exY9PT0YGRnB+vq6CEdeS8HH52laOXDgAF599VWkUil8/vnnyGazKJVKolBrtRqq1aoISo/HIwZRc3MzXnvtNbS3t+PTTz8VxUojhoqKtON0OkXQNzc348yZM3j55ZcxMzOD733veygWi7DZbML8AOTdHQ6HvLPD4cC+ffvwrW99Cw6HAz/84Q+xsbEhxh7fmfMmfXPP2tvb8dprr+HAgQO4c+cOrl27hmw2C4fDIfzB9aOC4p75/X4cOnQIP//zP49kMolCoYB0Oi3Ps9vtqNVqlnW22Wxwu91wOp1ob2/H66+/jr6+Pnz00UdYX19HOp0Wpaj5hIqDRmBnZyfOnz+PK1euYG5uDul0GqlUStII5A/KBS3wQ6EQ9u/fj/feew/VahV//dd/jfX1dZEp/D6fTaOM+9bW1obLly/j+PHjuH37NmKxGJLJpAhpyg59nd1uFx7p7+/HW2+9hXg8jkQigWg0CpfLJc+rVqtiPHOvPR6PzH9oaAgHDhzAvXv3EI1GhTYpB2w2m8yDH6fTCZ/Ph/379+PKlSsy57W1NaFrACKHtJLy+Xzw+XwIh8PYv38/mpubkUqlMDc3h2q1KvKqUqmIDPq6lM1PRUm5XC6cPn0aH3/8MX75l39Zfv/xxx/jF3/xF7/xfZxOJ4LBoMVK7+jowJUrV9Da2orr168jGo0Ks5BxKQxIXLTA/H4/Tpw4gVOnTuHJkyeIRqMiZMgMXGgyktvtFkt57969uHLliii4RCIhXguFV6VSAQAhLAqvtrY2XLx4EW1tbbh27RrW1tbEgqeQByDClkrK5XIhHA7j7NmzMu94PI54PA6HwyEMR+LkPWhhezwe7N+/Hz/3cz+HbDaLzc1NbG5uwu12i9DgGtGLAiCWV1NTEy5fvoz+/n44nU5EIhHk83m4XC65ll4o5+7xeOD1ehEMBjE0NIS33noLExMTWF9fRywWE+IlA2mlqr3C1tZWXLhwAS6XC4lEAnNzc8K8eq0osLRxEgwGMTg4iCNHjqBQKODRo0dIJpNCR/qZjQIQAHbt2oXTp09jfX0dY2NjImAo7LjnVOjaUg0EAjh8+DD27NmDyclJuN1u+TsVBN+VRhX3m4bQ0NAQACAYDMo6aQNIr4NeA3o09FAo2Eib2nMD6p4R50aPbPfu3WhtbRUDhPemQtN0wt87HA54vV4EAgFks1n5uxbWfCY9Kyop7hvplcYejSkKSz1/rez4DBoAvFYr9efNge9GPuN+8j58P/7M/aVscrvdcj++q+YJm80Gv98vfzNNUzwkfsrlsngrmh9LpZLFCKNy0ftIY4deNumDc6MS57354X5VKhW5lnRNL4pz4JwoA7mGpVIJqVRKjKdKpSIGCXlYG8o/afzUwn2/8Ru/gX/wD/4Bzpw5g/Pnz+NP//RPsbi4iH/yT/7JN74Hk230plwuF5qamjAwMIDdu3djenparHeGj7S1B0CIpVKpwOFwoKOjAwMDA4jH4yJcyMhUNCQKAJaNbWpqQl9fH0KhEEKhkEVwcZCB+GzNgC0tLejq6kI4HLYIAFpsZDgKARKk3W5HMBhES0sLvF6vCA1+eB3fhwxBwgPqXqAmek20ZBK9blwXzZj6mkampaVFZVGr1VAqlbC5uWnxJPg3vi+VOhUHLUeGCtbW1uDxeJDJZIQJGTbSa0bhxP3L5XKYnZ2FaZqIRqNybz4fgDAw341rCQBra2u4d++eWJilUsmyTnqfeT3nnUwmcf/+fSwuLmJhYcESutFeCAU1352J5kePHqFarWJlZQXr6+siSKigfD6fPJO8QYG3sLCAjz76CA6HAxMTE8jlciKwyUPcLy3cASCRSODzzz/HzMwM5ubmsLW1JR43sB1i04K7Wq2iUCggk8lgYmIChmEgmUwiEonIfum1Jc1wnznvlZUVfPLJJ2hubsbExASKxaKFv6hk9BzIN1tbW/jyyy+RSCSwsbGBhYUFZLNZ4UfSKo0Dzomh04WFBXz44YdwOByYm5sTQQ1sGy3cM61gCPC6desWFhYWsLi4iHQ6Lc8jD/E67bWXSiXkcjnMzc3h+vXryOfziMViss9USg6HQ+RIrVZDoVAQXt/c3MSTJ0+wsLAgNEo65177fD7hcQ4qoKWlJdy+fRvpdBpra2sW5ULDgPtEBUUDI5fLYWZmBpFIBEtLS0LLjITw+mQy+bVy/qempP7e3/t72NjYwL/4F/8Cq6urOHr0KP76r/8ae/fu/Rvdh1rdZrOhUChgY2MDIyMjWFtbQyQSseRzgG23WntXtDSz2SxGR0fhcrkwPT2NXC5n8X40E3GDAAhxLCws4NatW8jlcojH488IWG4SlSKZoFAoIBaL4d69e2hpacHMzIwwAq/TFioFLq9Pp9N4+vQp8vk8pqamkEqlRABoq5JKnYI6n88jk8lgZmYGV69eRaFQQCQLine truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for key in results.keys():\n",
+    "\n",
+    "    recon = recons[key]\n",
+    "    heights = crystalline_heights[key]\n",
+    "    c0 = column_positions[key]\n",
+    "\n",
+    "    top = tops[key]\n",
+    "    middle = middles[key]\n",
+    "    bottom = bottoms[key]\n",
+    "    top_std = top_stds[key]\n",
+    "    middle_std = middle_stds[key]\n",
+    "    bottom_std = bottom_stds[key]\n",
+    "\n",
+    "    plt.imshow(np.angle(recon).sum(axis=-1), cmap='gray')\n",
+    "\n",
+    "    for i, roi in enumerate(thickness_rois[key]):\n",
+    "\n",
+    "        idx = roi_indices[key][i]\n",
+    "\n",
+    "        (x0, x1), (y0, y1) = roi\n",
+    "        plt.plot([x0, x0, x1, x1, x0], [y0, y1, y1, y0, y0], lw=3)\n",
+    "        plt.text(x1+5, y1-10, f\"{middle[i]:.2f} ± {middle_std[i]:.2f}\", color='white')\n",
+    "\n",
+    "        sorted_heights = np.sort(heights)\n",
+    "\n",
+    "        # plt.axis('off')\n",
+    "        plt.scatter(\n",
+    "            c0[:, 1][idx], c0[:, 0][idx], c=np.array(heights)[idx], s=8, cmap='magma_r',\n",
+    "            vmax=sorted_heights[-30], \n",
+    "            vmin=sorted_heights[30]\n",
+    "            )\n",
+    "        # cbar = plt.colorbar()\n",
+    "\n",
+    "\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0726cc98",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lemon",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}