removed dates from directory names

260414-radius-test-wrong/260414.ipynb

@@ -1,280 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "405952b4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# THIS METHOD IS WRONG FOR IDENTIFYING ATOMS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "368a5090",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from matplotlib import pyplot as plt\n",
-    "import tifffile\n",
-    "import os\n",
-    "from scipy.ndimage import gaussian_filter, binary_opening, label\n",
-    "from skimage import exposure, filters, morphology, measure\n",
-    "from scipy.signal import convolve2d\n",
-    "from skimage.morphology import disk\n",
-    "from skimage.feature import peak_local_max\n",
-    "\n",
-    "from tqdm.notebook import tqdm\n",
-    "\n",
-    "plt.rcParams['font.family'] = 'sans-serif'\n",
-    "plt.rcParams['font.sans-serif'] = ['Inter Variable ss02']\n",
-    "plt.rcParams['figure.titlesize'] = 10\n",
-    "plt.rcParams['axes.titlesize'] = 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "2912e633",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['bto02_objp_zstack_crop_08bit_iter1000.tif',\n",
-       " 'bto02_objp_zsum_crop_08bit_iter1000.tif',\n",
-       " 'bto05_objp_zstack_crop_08bit_iter1000.tif',\n",
-       " 'bto05_objp_zsum_crop_08bit_iter1000.tif',\n",
-       " 'bto08_objp_zstack_crop_08bit_iter0100.tif',\n",
-       " 'bto08_objp_zsum_crop_08bit_iter0100.tif',\n",
-       " '260414.ipynb']"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.listdir()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "2d3b2fa3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ptycho_stack = tifffile.imread('bto02_objp_zstack_crop_08bit_iter1000.tif') / 255\n",
-    "# ptycho_sum = tifffile.imread('bto02_objp_zsum_crop_08bit_iter1000.tif') / 255\n",
-    "ptycho_slice = ptycho_stack[13]\n",
-    "# bf = plt.imread('bf.png').mean(axis=2)\n",
-    "# bf = 1 - (bf - bf.min()) / (bf.max() - bf.min())\n",
-    "# haadf = plt.imread('haadf.png').mean(axis=2)\n",
-    "# haadf = (haadf - haadf.min()) / (haadf.max() - haadf.min()) \n",
-    "# laadf = plt.imread('laadf.png').mean(axis=2)\n",
-    "# laadf = (laadf - laadf.min()) / (laadf.max() - laadf.min())\n",
-    "\n",
-    "images = [ptycho_slice]\n",
-    "# names = ['Ptycho Sum', 'Ptycho Slice', 'Bright Field (Inverted)', 'HAADF', 'LAADF']\n",
-    "\n",
-    "# fig, axs = plt.subplots(1, len(images), dpi=300)\n",
-    "# for i, ax in enumerate(axs):\n",
-    "#     ax.axis('off')\n",
-    "#     ax.imshow(images[i], cmap='gray')\n",
-    "#     ax.set_title(names[i], size=8)\n",
-    "\n",
-    "# plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "a52ce371",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def feature_count_vs_radius(img, radii, high_pass=5):\n",
-    "    if high_pass:\n",
-    "        img = img - gaussian_filter(img, sigma=high_pass)\n",
-    "\n",
-    "    counts = []\n",
-    "    peak_locs = []\n",
-    "\n",
-    "    for r in tqdm(radii):\n",
-    "        # ---- 1. Build a disc kernel ------------------------------------\n",
-    "        # (skimage.disk returns a binary mask; we normalize it to sum = 1)\n",
-    "        kernel = disk(r).astype(float)\n",
-    "        kernel /= kernel.sum()\n",
-    "\n",
-    "        # ---- 2. Convolve -------------------------------------------------\n",
-    "        conv = convolve2d(img, kernel, mode='same', boundary='symm')\n",
-    "\n",
-    "        # ---- 3. Find local maxima ---------------------------------------\n",
-    "        #  * min_distance ensures that peaks are at least r pixels apart\n",
-    "        #  * threshold_abs picks peaks that stand out above the background\n",
-    "        peaks = peak_local_max(conv,\n",
-    "                            min_distance=int(r),\n",
-    "                            threshold_abs=conv.mean() + 2*conv.std(),\n",
-    "                            num_peaks=np.inf)\n",
-    "\n",
-    "        counts.append(len(peaks))\n",
-    "        peak_locs.append(peaks)\n",
-    "        \n",
-    "    return radii, counts, peak_locs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "6d8b25e3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9599a4de81f440cb9c9cdbf3d1925c78",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/32 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "r, c, pl = [], [], []\n",
-    "radii = np.arange(1, 32+1, 1)\n",
-    "\n",
-    "for img in images:\n",
-    "    r_i, c_i, pl_i = feature_count_vs_radius(img, radii, high_pass=8)\n",
-    "    r.append(r_i)\n",
-    "    c.append(c_i)\n",
-    "    pl.append(pl_i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "c18ffc6d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# fig, axs = plt.subplots(1, len(images), figsize=(18, 4), sharey=True, dpi=300)\n",
-    "# for i, ax in enumerate(axs):\n",
-    "#     ax.plot(r[i], c[i], 'ko-')\n",
-    "#     ax.set_xlim(r[i][0], r[i][-1])\n",
-    "#     ax.set_title(names[i])\n",
-    "#     ax.set_xlabel('Kernel radius (pixels)')\n",
-    "# axs[0].set_ylabel('Feature count')\n",
-    "# plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "b95333bc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# idxs = [6, 14, 20]\n",
-    "\n",
-    "# for i in range(len(images)):\n",
-    "#     plt.figure(dpi=300)\n",
-    "#     plt.imshow(images[i], cmap='gray')\n",
-    "#     for idx in idxs:\n",
-    "#         plt.scatter(\n",
-    "#             pl[i][idx][:,1],\n",
-    "#             pl[i][idx][:,0],\n",
-    "#             s=3,\n",
-    "#             edgecolors='none')\n",
-    "#     plt.axis('off')\n",
-    "\n",
-    "#     plt.legend(['Ba', 'O', 'Ti'], loc='lower right')\n",
-    "\n",
-    "#     plt.show()\n",
-    "#     plt.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "bd4f0ef8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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 truncated
-      "text/plain": [
-       "<Figure size 460.8x345.6 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "idxs = [6, 14, 20]\n",
-    "i = 0\n",
-    "\n",
-    "plt.figure(dpi=72)\n",
-    "plt.imshow(images[i], cmap='gray')\n",
-    "for idx in idxs:\n",
-    "    plt.scatter(\n",
-    "        pl[i][idx][:,1][pl[i][idx][:,1] < 300],\n",
-    "        pl[i][idx][:,0][pl[i][idx][:,1] < 300],\n",
-    "        s=30,)\n",
-    "plt.axis('off')\n",
-    "\n",
-    "plt.xlim(200, 400)\n",
-    "plt.ylim(200, 400)\n",
-    "\n",
-    "# plt.legend(['Ba', 'O', 'Ti'], loc='lower right')\n",
-    "\n",
-    "x = 385\n",
-    "L = 55\n",
-    "plt.plot([x - L, x], [215] *2, 'w-', lw=10)\n",
-    "plt.text(x - L/2, 220, '1 nm', color='w', ha='center', va='bottom', fontsize=30)\n",
-    "\n",
-    "plt.show()\n",
-    "plt.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8f1b5ac6",
-   "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
-}

silicon-atomic-column-thickness/260728_fig5_tif.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 1,
    "id": "d3d0397b",
    "metadata": {},
    "outputs": [],
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "05a381bd",
    "metadata": {},
    "outputs": [],
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "38fc7594",
    "metadata": {},
    "outputs": [
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "bbd76d4a",
    "metadata": {},
    "outputs": [
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "e8c4da69",
    "metadata": {},
    "outputs": [
@@ -153,14 +153,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "81276fdb",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c365a3ef82744a8eb6ea9066cfffb95f",
+       "model_id": "0a6acad0be424dec83a5234a9362898b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -181,7 +181,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "009d0aa0cf914fefb0d1a7c7778e7417",
+       "model_id": "497da14575304610bc40120a1e2043ff",
        "version_major": 2,
        "version_minor": 0
       },
@@ -202,7 +202,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c3e047df2ab14a35b7d355e8d268365b",
+       "model_id": "2ed5546a726842ffb5ed46d0e850da3b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -223,7 +223,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a61d78a9609c4c9a8fcfa6329f76ff98",
+       "model_id": "30832de7b6c04805971d9fcaba83108b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -265,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 7,
    "id": "570e368a",
    "metadata": {},
    "outputs": [],
@@ -277,14 +277,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "1861a787",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bc03253a67c045048ad5cb62757ca88c",
+       "model_id": "ec7460a8807f4512a2016bdf2ba06e96",
        "version_major": 2,
        "version_minor": 0
       },
@@ -298,7 +298,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fadbb2fefdab415184038d7030d9c652",
+       "model_id": "c872d0c0ba644ce6bdf6fb325c5ca2a1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -312,7 +312,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a228bcd431b84b918232df89c4f08452",
+       "model_id": "251920645cdf4d1684abe653d54bab04",
        "version_major": 2,
        "version_minor": 0
       },
@@ -326,7 +326,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e052843b27434a00a859280e19b3ac3a",
+       "model_id": "d6a6853af3f148068e67d1f8be0fd1f6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -377,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "7091733a",
    "metadata": {},
    "outputs": [],
@@ -395,7 +395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "be8c09d3",
    "metadata": {},
    "outputs": [
@@ -427,7 +427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "52eb4dd3",
    "metadata": {},
    "outputs": [
@@ -457,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "38eb518a",
    "metadata": {},
    "outputs": [
@@ -484,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "e18ff3e6",
    "metadata": {},
    "outputs": [],
@@ -504,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "80740d9b",
    "metadata": {},
    "outputs": [
@@ -571,7 +571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "1c439f46",
    "metadata": {},
    "outputs": [
@@ -695,7 +695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 16,
    "id": "707c4350",
    "metadata": {},
    "outputs": [
@@ -734,7 +734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 17,
    "id": "612bee6c",
    "metadata": {},
    "outputs": [],
@@ -764,7 +764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 18,
    "id": "9543770e",
    "metadata": {},
    "outputs": [
@@ -818,15 +818,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "id": "71fe53e1",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<Figure size 1500x900 with 1 Axes>"
+       "<Figure size 500x300 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -845,7 +845,7 @@
     "\n",
     "# fig, axs = plt.subplots(2, 2, figsize=(10, 7), dpi=300)\n",
     "\n",
-    "plt.figure(figsize=(5, 3), dpi=300)\n",
+    "plt.figure(figsize=(5, 3))\n",
     "plt.xlim(0, 300)\n",
     "plt.ylim(-0.15, 0.6)\n",
     "plt.plot(x, Y, 'k-', label=\"Sum of Gaussians\")\n",
@@ -880,15 +880,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "id": "76404cd6",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<Figure size 3000x2100 with 4 Axes>"
+       "<Figure size 1000x700 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -905,7 +905,7 @@
     "ys = np.array(ys)\n",
     "Y = ys.sum(axis=0)\n",
     "\n",
-    "fig, axs = plt.subplots(2, 2, figsize=(10, 7), dpi=300)\n",
+    "fig, axs = plt.subplots(2, 2, figsize=(10, 7))\n",
     "\n",
     "keys = ['2V', '5V', '8V', '30V']\n",
     "\n",
@@ -932,23 +932,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
-   "id": "fc9027eb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
    "id": "fcd12e61",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 1500x1500 with 1 Axes>"
       ]
@@ -996,12 +986,12 @@
     "\n",
     "eb = plt.errorbar(voltages, damage, damage_err,\n",
     "    fmt=\"o\", capsize=5, capthick=1.5, elinewidth=1.5, label=\"Current study\", markersize=MARKERSIZE,\n",
-    "    zorder=ZTOP, color='red'\n",
+    "    zorder=ZTOP, color='blue'\n",
     "    )\n",
     "handles.append(eb)\n",
     "\n",
     "for i, (v, d, mk, lab) in enumerate(literature):\n",
-    "    ln, = plt.plot(v, d, mk, label=lab, zorder=ZTOP - 1 - i, color=plt.colormaps['viridis'](i / 5), markersize=MARKERSIZE)\n",
+    "    ln, = plt.plot(v, d, mk, label=lab, zorder=ZTOP - 1 - i, color=plt.colormaps['inferno'](i / 6), markersize=MARKERSIZE)\n",
     "    handles.append(ln)\n",
     "\n",
     "plt.xlabel(\"FIB milling voltage (kV)\", fontsize=14)\n",