Upload 2 files

.gitattributes

@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+caobin.db filter=lfs diff=lfs merge=lfs -text

caobin.db

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:851e8cf2c1f696302b35fcb6f253f6976bffd74398273bf292b5bb38840c148b
+size 283357184

readin.ipynb

@@ -0,0 +1,212 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "861bf4d9-4264-428a-94b2-6a72e21db5c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ase.db import connect\n",
+    "data = connect(\"./caobin.db\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3266d3ab-794a-4b5f-a45a-ac2fd6092504",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1277"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# total PXRD amount\n",
+    "data.count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a7b76fe8-49f2-40e2-a47c-22d250c8ca49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_id=1277 # index , an int value of 1-1277\n",
+    "\n",
+    "atoms =  data.get_atoms(id=_id)\n",
+    "row = data.get(id=_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a07ca8e5-e2c7-450f-9539-89d5a1486beb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "latt_dis = eval(getattr(row, 'angle')) # two theta, Cu target\n",
+    "intensity = eval(getattr(row, 'intensity')) # two intensity "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9fb02274-7a1b-4b4b-baff-7b9d00090c8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2994cbaf2d0>]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "plt.plot(latt_dis,intensity)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5be4da33-98d6-43c3-8bd7-19a2cc8d49f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Ca', 'Mg', 'As', 'P', 'O', 'O', 'O', 'O', 'O', 'H']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "atoms.get_chemical_symbols() # atoms "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3fdbbc48-204a-4df0-b294-83b7db61498b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\binja\\anaconda3\\Lib\\site-packages\\ase\\utils\\__init__.py:62: FutureWarning: Please use atoms.cell.cellpar() instead\n",
+      "  warnings.warn(warning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([ 7.58,  9.09,  6.02, 90.  , 90.  , 90.  ])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "atoms.get_cell_lengths_and_angles() # lattice constant of the conventional unit cell "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "aba9f3ae-d3a5-4a35-8f86-c246377dbe4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.63323, 0.66993, 0.47737],\n",
+       "       [0.24093, 0.50504, 0.24576],\n",
+       "       [0.61594, 0.31858, 0.51671],\n",
+       "       [0.61594, 0.31858, 0.51671],\n",
+       "       [0.43758, 0.43594, 0.4901 ],\n",
+       "       [0.78376, 0.43523, 0.5872 ],\n",
+       "       [0.35447, 0.72683, 0.2293 ],\n",
+       "       [0.39884, 0.70196, 0.7603 ],\n",
+       "       [0.10657, 0.57687, 0.5059 ],\n",
+       "       [0.009  , 0.529  , 0.513  ]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "positions = atoms.get_positions() \n",
+    "positions # fractional  coordinates of atoms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afb8b73e-e288-4b92-9d23-679a280493d6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b1266a6-b074-49fc-8ede-f522922c399d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}