{ "cells": [ { "cell_type": "markdown", "id": "37898e50-beb6-4ca2-affb-b74b3a8f1da2", "metadata": {}, "source": [ "# Updating pySNOW's CNAPs list" ] }, { "cell_type": "code", "execution_count": 1, "id": "53dbc299-0d7e-4be0-9740-ec6cc3224d35", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from collections import Counter\n", "\n", "import sys\n", "from pathlib import Path\n", "# Define the absolute path to the directory containing the 'snow' folder\n", "local_packages_path = Path(\"/Users/davidegay/pySNOW/\")\n", "#Convert to an absolute string and inject it into Python's search path\n", "sys.path.append(str(local_packages_path.resolve()))\n", "\n", "from snow.io.xyz import read_xyz, write_xyz, read_xyz_movie\n", "#from snow.descriptors.coordination import coordination_number\n", "from snow.descriptors import cna\n", "\n", "natoms= 98\n", "\n", "growth_movie = \"1_growth_98/movie.xyz\"\n", "#melt_movie = \"2_melt_98/movie.xyz\"\n", "melt_movie = \"melting_98/movie.xyz\"\n", "#freeze_movie = \"3_freeze_98/movie.xyz\"\n", "freeze_movie = \"freezing_98/movie.xyz\"\n", "quench_xyz = \"4_quench_98/out11.xyz\"\n", "\n", "growth_data = \"1_growth_98/energy.out\"\n", "#melt_data = \"2_melt_98/energy.out\"\n", "melt_data = \"melting_98/energy.out\"\n", "#freeze_data = \"3_freeze_98/energy.out\"\n", "freeze_data = \"freezing_98/energy.out\"\n", "quench_data = \"4_quench_98/energy.out\"\n", "\n", "alat = 4.087077141\n", "cutoff = 0.85 * alat" ] }, { "cell_type": "markdown", "id": "9a7f7e25-cbbc-4725-b229-aa5103db844c", "metadata": {}, "source": [ "## \"NEW\" CNAPS RECAP:\n", "(from Ferrando paper SI; 17, 18 were already implemented)\n", "- **17** is \"*StepA 111 fcc*\": 1x[2, 1, 1], 4x[3, 1, 1], 5x[4, 2, 1]]\n", "- **18** is \"*subsusrface Ih chiral type 2*\": 4x[4,2,1], 4x[4,2,2], 2x[4,3,3], 2x[5,4,4]\n", "- **19** is \"*vertex 111_111 fcc sharp-wide*\": 2x[2,0,0], 1x[2,1,1], 2x[3,1,1]\n", "- **20** is \"*terrace hcp 10m11*\": 1x[2,1,1], 2x[3,1,1], 2x[3,2,2], 2x[4,2,1], 2x[4,2,2]\n", "- **21** is \"*edge 10m11_10m11 hcp*\": 1x[2,0,0], 2x[2,1,1], 1x[3,1,1], 1x[3,2,2], 1x[4,2,1], 1x[4,2,2]\n", "- **22** is \"*terrace hcp 10m11 & edge anti-Mackay*\": 2x[2,0,0], 1x[2,1,1], 2x[3,1,1], 1x[4,2,1], 2x[4,2,2]\n", "- **23** is \"*edge 111-111 sharp fcc*\": 2x[2,1,1], 4x[3,1,1]\n", "- **24** is \"*edge anti-Mackay close to vertex*\": 2x[2,0,0], 3x[3,2,2], 2x[4,2,2], 1x[5,5,5]\n", "- **25**: 2x[2,0,0], 1x[2,1,1], 1x[3,1,1], 1x[3,2,2], 1x[4,2,2] **UNRECOGNIZED** by Ferrando" ] }, { "cell_type": "code", "execution_count": 2, "id": "5391c7d2-760a-4acf-bfad-fadac59b6486", "metadata": {}, "outputs": [], "source": [ "CNAP_mapping = {\n", " 0: \"Unrecognised combination\",\n", " 1: \"Vertex between two (111) facets and a (100) facet\",\n", " 2: \"Edge between (100) and a slightly distorted (111)\",\n", " 3: \"Atoms lying on a (555) symmetry axis\",\n", " 4: \"FCC bulk\",\n", " 5: \"Intersection of six five-fold axes\",\n", " 6: \"Edge between (100) facets\",\n", " 7: \"Vertex on twinning planes shared by (111) facets\",\n", " 8: \"Edge between (111) re-entrances and (111) facets\",\n", " 9: \"Re-entrance delimited by (111) facets\",\n", " 10: \"Edge between (100) and (111) facets\",\n", " 11: \"Vertex shared by (100) and (111) facets\",\n", " 12: \"(100) facet\",\n", " 13: \"Five-fold symmetry axis (without center)\",\n", " 14: \"Five-fold vertex\",\n", " 15: \"(111) facet\",\n", " 16: \"Twinning plane\",\n", " 17: \"StepA 111 fcc\",\n", " 18: \"Subsusrface Ih chiral type 2\",\n", " 19: \"Vertex 111_111 fcc sharp-wide\",\n", " 20: \"Terrace hcp 10m11\",\n", " 21: \"Edge 10m11_10m11 hcp\",\n", " 22: \"Terrace hcp 10m11 & edge anti-Mackay\",\n", " 23: \"Edge 111-111 sharp fcc\",\n", " 24: \"Edge anti-Mackay close to vertex\",\n", " 25: \"Unrecognized\"\n", "}\n", "#print(CNAP_mapping.values())\n", "# Convert dictionary items to a list of tuples and print as markdown\n", "#markdown_table = tabulate(CNAP_mapping.items(), headers=[\"CNAp\", \"Description\"], tablefmt=\"github\")\n", "#print(markdown_table)\n", "\n", "# Function to map CNAp number to description\n", "CNAP_mapping_func = np.vectorize(lambda x: CNAP_mapping.get(x, \"Unknown\"))" ] }, { "cell_type": "markdown", "id": "5ac7cb4c-cb1f-49cd-a9e7-402a8195d95e", "metadata": {}, "source": [ "### All CNAPs:\n", "\n", "| CNAp | Description |\n", "|--------|---------------------------------------------------|\n", "| 0 | Unrecognised combination |\n", "| 1 | Vertex between two (111) facets and a (100) facet |\n", "| 2 | Edge between (100) and a slightly distorted (111) |\n", "| 3 | Atoms lying on a (555) symmetry axis |\n", "| 4 | FCC bulk |\n", "| 5 | Intersection of six five-fold axes |\n", "| 6 | Edge between (100) facets |\n", "| 7 | Vertex on twinning planes shared by (111) facets |\n", "| 8 | Edge between (111) re-entrances and (111) facets |\n", "| 9 | Re-entrance delimited by (111) facets |\n", "| 10 | Edge between (100) and (111) facets |\n", "| 11 | Vertex shared by (100) and (111) facets |\n", "| 12 | (100) facet |\n", "| 13 | Five-fold symmetry axis (without center) |\n", "| 14 | Five-fold vertex |\n", "| 15 | (111) facet |\n", "| 16 | Twinning plane |\n", "| 17 | StepA 111 fcc |\n", "| 18 | Subsusrface Ih chiral type 2 |\n", "| 19 | Vertex 111_111 fcc sharp-wide |\n", "| 20 | Terrace hcp 10m11 |\n", "| 21 | Edge 10m11_10m11 hcp |\n", "| 22 | Terrace hcp 10m11 & edge anti-Mackay |\n", "| 23 | Edge 111-111 sharp fcc |\n", "| 24 | Edge anti-Mackay close to vertex |\n", "| 25 | Unrecognized |" ] }, { "cell_type": "markdown", "id": "12aef184-f46e-4f93-8b64-140ef9480121", "metadata": {}, "source": [ "### Updated CNAP function:" ] }, { "cell_type": "code", "execution_count": 3, "id": "66443adc-94d8-4b9d-a505-9d4172879aab", "metadata": {}, "outputs": [], "source": [ "#================= NEW VERSION of CNAP ================#\n", "def NEW_cnap_peratom(\n", " coords: np.ndarray,\n", " cut_off: float,\n", " pbc: bool = False,\n", " box: np.ndarray = None,\n", " display_progress: bool = False) -> np.ndarray:\n", " \"\"\"\n", " Computes the per-atom CNA patterns and assigns an integer structure ID.\n", "\n", " Tries to match the cna per atom patterns to known patterns in a database for atomic \n", " environment characterization (see README.md for ID-structure mapping).\n", "\n", " Parameters\n", " ----------\n", " coords : np.ndarray\n", " (N, 3) array with atomic coordinates\n", " cut_off : float\n", " Cutoff radius for neighbor determination. If None, an adaptive cutoff is used\n", " pbc : bool, default False\n", " Whether to use or not periodic boundary conditions\n", " box : np.ndarray, default None\n", " Simulation box. Only needed if you enable PBC\n", " display_progress: bool, default False\n", " Wheter to display a progress bar - needs the tqdm optional dependency library.\n", "\n", " Returns\n", " -------\n", " pattenrs_ids : np.ndarray\n", " Array of (integers) structure IDs per atom\n", " \"\"\"\n", "\n", " # Compute CNA info\n", " new_cna = cna.cna_peratom(coords, cut_off, pbc=pbc, box=box)\n", " n_atoms = len(coords)\n", " cna_atom = np.zeros(n_atoms, dtype=int)\n", "\n", " # --- Define pattern rules as a lookup table ---\n", " # Each rule is a tuple (required signatures, required counts) -> assigned ID\n", " PATTERNS = [\n", " # n_sigs == 1\n", " ((([5, 5, 5],), (12,)), 5),\n", " (([[4, 2, 1]], (12,)), 4),\n", " # n_sigs == 2\n", " (([[4, 2, 2], [5, 5, 5]], (10, 2)), 3),\n", " (([[4, 2, 1], [3, 1, 1]], (3, 6)), 15),\n", " (([[2, 1, 1], [4, 2, 1]], (4, 1)), 11),\n", " (([[2, 1, 1], [4, 2, 1]], (4, 4)), 12),\n", " (([[3, 2, 2], [5, 5, 5]], (5, 1)), 14),\n", " (([[4, 2, 1], [4, 2, 2]], (6, 6)), 16),\n", " (([[2, 1, 1], [3, 1, 1]], (2, 4)), 23),\n", " # n_sigs == 3\n", " (([[1, 0, 0], [2, 1, 1], [4, 2, 2]], (2, 2, 2)), 6),\n", " (([[2, 0, 0], [3, 1, 1], [4, 2, 1]], (2, 4, 1)), 8),\n", " (([[2, 1, 1], [3, 1, 1], [4, 2, 1]], (3, 2, 2)), 10),\n", " (([[3, 1, 1], [3, 2, 2], [4, 2, 2]], (4, 2, 2)), 13),\n", " (([[2, 1, 1], [3, 1, 1], [4, 2, 1]], (1, 4, 5)), 17),\n", " (([[2, 0, 0], [2, 1, 1], [3, 1, 1]], (2, 1, 2)), 19),\n", " # n_sigs == 4\n", " (([[1, 0, 0], [2, 1, 1], [3, 2, 2], [4, 2, 2]], (1, 2, 1, 1)), 1),\n", " (([[2, 0, 0], [2, 1, 1], [3, 1, 1], [4, 2, 1]], (1, 2, 2, 1)), 2),\n", " (([[3, 0, 0], [3, 1, 1], [4, 2, 1], [4, 2, 2]], (2, 4, 2, 2)), 9),\n", " (([[4, 2, 1], [4, 2, 2], [4, 3, 3], [5, 4, 4]], (4, 4, 2, 2)), 18),\n", " (([[2, 0, 0], [3, 2, 2], [4, 2, 2], [5, 5, 5]], (2, 3, 2, 1)), 24),\n", " # n_sigs == 5\n", " (([[2, 0, 0], [3, 0, 0], [3, 1, 1], [3, 2, 2], [4, 2, 2]],\n", " (2, 1, 2, 1, 1)), 7),\n", " (([[2, 1, 1], [3, 1, 1], [3, 2, 2], [4, 2, 1], [4, 2, 2]],\n", " (1, 2, 2, 2, 2)), 20),\n", " (([[2, 0, 0], [2, 1, 1], [3, 1, 1], [4, 2, 1], [4, 2, 2]],\n", " (2, 1, 2, 1, 2)), 22),\n", " (([[2, 0, 0], [2, 1, 1], [3, 1, 1], [3, 2, 2], [4, 2, 2]],\n", " (2, 1, 1, 1, 1)), 25),\n", " # n_sigs ==6\n", " (([[2, 0, 0], [2, 1, 1], [3, 1, 1], [3, 2, 2], [4, 2, 1], [4, 2, 2]],\n", " (1, 2, 1, 1, 1, 1)), 21),\n", " ]\n", "\n", " def match_pattern(sigs, counts):\n", " \"\"\"Try to match CNA signatures/counts to a known structure pattern.\"\"\"\n", " for (req_sigs, req_counts), struct_id in PATTERNS:\n", " if len(req_sigs) != len(sigs):\n", " continue\n", " # Convert both to sets of tuples for order-insensitive comparison\n", " sig_dict = {tuple(sig): cnt for sig, cnt in zip(sigs, counts)}\n", " if all(\n", " tuple(rs) in sig_dict and sig_dict[tuple(rs)] == rc\n", " for rs, rc in zip(req_sigs, req_counts)\n", " ):\n", " return struct_id\n", " return 0 # default (unidentified)\n", "\n", " # --- Process atoms ---\n", " iterator = tqdm(range(n_atoms), desc=\"Processing CNA patterns\") if display_progress \\\n", " else range(n_atoms)\n", " for i in iterator:\n", " sigs = np.array(new_cna[i][0])\n", " counts = np.array(new_cna[i][1]).flatten()\n", "\n", " if len(sigs) == 0:\n", " continue\n", "\n", " cna_atom[i] = match_pattern(sigs, counts)\n", "\n", " return cna_atom" ] }, { "cell_type": "code", "execution_count": 4, "id": "0dc4380f-66d2-4f2e-8a33-39806120f0b7", "metadata": {}, "outputs": [], "source": [ "def CNAP_analysis(el, coords, cut, natoms, outmovie= \"CNAP_movie.xyz\"):\n", " \"\"\"\n", " Returns a matrix with entries (nframe, number of atoms corresponding to cnap);\n", " also writes to file movie with cnap.\n", " \"\"\"\n", " #el, coords = read_xyz_movie(inmovie)\n", " tot_frames = len(coords)\n", " \n", " CNAP_matrix = np.zeros(shape = (tot_frames, 26))\n", " \n", " #outmovie= \"CNAP_movie.xyz\"\n", " with open(outmovie, \"w\") as outfile:\n", " for frame_index, frame in enumerate(coords):\n", " outfile.write(str(natoms) + \"\\n\" + str((frame_index+1)*1000) +\n", " \" \" + str(el[0][0]) + \" \" + str(natoms) + \"\\n\")\n", " cnaps = NEW_cnap_peratom(frame, cut)\n", " \n", " for atom in range(len(frame)):\n", " outfile.write(\n", " str(el[0][atom]) + \" \" +\n", " str(frame[atom][0]) + \" \" +\n", " str(frame[atom][1]) + \" \" +\n", " str(frame[atom][2]) + \" \" +\n", " str(cnaps[atom]) + \"\\n\"\n", " )\n", " CNAP_matrix[frame_index, cnaps[atom]]= CNAP_matrix[frame_index, cnaps[atom]] +1\n", "\n", " return CNAP_matrix" ] }, { "cell_type": "markdown", "id": "4fcea701-a882-45db-a3df-d66712b41149", "metadata": {}, "source": [ "## LOAD DATA & COMPUTE CNAP" ] }, { "cell_type": "code", "execution_count": 5, "id": "589dfc7e-5c8e-406d-a0a1-aafdd6fd8f45", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1750 1850\n" ] } ], "source": [ "# === MELT ===\n", "melt_el, melt_coords = read_xyz_movie(melt_movie)\n", "CNAP_melt = CNAP_analysis(melt_el, melt_coords, cutoff, natoms,\n", " outmovie= \"CNAP/latest_CNAP_melt.xyz\")\n", "#np.savetxt('test.dat', matrix, delimiter=' ', fmt='%d')\n", "# Normalize matrix\n", "CNAP_melt /= natoms\n", "\n", "# === FREEZE ===\n", "freeze_el, freeze_coords = read_xyz_movie(freeze_movie)\n", "CNAP_freeze = CNAP_analysis(freeze_el, freeze_coords, cutoff, natoms,\n", " outmovie= \"CNAP/latest_CNAP_freeze.xyz\")\n", "#np.savetxt('test.dat', matrix, delimiter=' ', fmt='%d')\n", "# Normalize matrix\n", "CNAP_freeze /= natoms\n", "\n", "\n", "# Check len of CNAP lists\n", "print(len(CNAP_melt), len(CNAP_freeze))\n", "\n", "# Load TEMPERATURES\n", "melt_temp = np.loadtxt(melt_data, usecols=6)\n", "freeze_temp = np.loadtxt(freeze_data, usecols=6)" ] }, { "cell_type": "markdown", "id": "cd13b2ef-ffd8-4387-8807-de1be01e305f", "metadata": {}, "source": [ "## PLOTTING" ] }, { "cell_type": "code", "execution_count": 6, "id": "a9aeb333-b103-4047-897e-30054b8403e6", "metadata": {}, "outputs": [], "source": [ "datasets = [\n", " (melt_temp, CNAP_melt[:,0],\n", " freeze_temp, CNAP_freeze[:,0],\n", " \"Fraction of zero-tag CNAPs\"),\n", " (melt_temp,\n", " CNAP_melt[:,19]+CNAP_melt[:,20]+CNAP_melt[:,21]+CNAP_melt[:,22]+CNAP_melt[:,23]+CNAP_melt[:,24]+CNAP_melt[:,25],\n", " #CNAP_melt[:,25],\n", " freeze_temp,\n", " CNAP_freeze[:,19]+CNAP_freeze[:,20]+CNAP_freeze[:,21]+CNAP_freeze[:,22]+CNAP_freeze[:,23]+CNAP_freeze[:,24]+CNAP_freeze[:,25],\n", " #CNAP_freeze[:,25],\n", " \"Fraction of newly added CNAPs\")\n", "]\n", "this_xlim = [\n", " (200, 800),\n", " (200, 800)\n", "]\n", "this_ylim = [\n", " (0, 1),\n", " (0, 0.5)\n", "]\n", "leg_loc = [\n", " 'upper left',\n", " 'upper right'\n", "]" ] }, { "cell_type": "code", "execution_count": 7, "id": "a017c1b3-634f-4ab9-add0-1ff0f2980dfe", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 5.5) )\n", " #, dpi=300)\n", " #gridspec_kw={'width_ratios': [1.35, 1]})\n", "i=0\n", "x1, y1, x2, y2, this_ylabel = datasets[i]\n", "this_leftlim, this_rightlim = this_xlim[i]\n", "this_bottomlim, this_toplim = this_ylim[i]\n", "\n", "plt.plot(x1, y1,\n", " #'rs', ms=2.5, label='Melting')\n", " color='red', marker='o', linewidth=0, markersize=2,\n", " label='Melting', alpha=.3)\n", "plt.plot(x2, y2,\n", " #'bo', ms=2.5, label='Freezing')\n", " color='blue', marker='o', linewidth=0, markersize=2,\n", " label='Freezing', alpha=.3)\n", "\n", "#plt.set_title(specific_title, \n", "# fontsize=15, fontweight='bold', pad=15)\n", "plt.xlabel('$T$ [K]')\n", " #, fontsize=14, labelpad=8)\n", "#if i==0:\n", "plt.ylabel(this_ylabel)\n", " #fontsize=14, labelpad=8)\n", "plt.xlim(this_leftlim, this_rightlim)\n", "plt.ylim(this_bottomlim, this_toplim)\n", "#plt.tick_params(axis='both')\n", "#plt.grid(True, color='#E0E0E0', linestyle='-', linewidth=0.8)\n", "plt.grid(True, alpha=0.5)\n", "plt.legend(loc=leg_loc[i])\n", " #frameon=True, facecolor='white', edgecolor='#D0D0D0', framealpha=1)\n", "#plt.tight_layout()\n", "plt.savefig('NEW_IMGS/0count_CNAP.pdf', dpi=300)\n", " #, bbox_inches='tight')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "id": "3e586137-7897-45dc-83d5-c13cb50a25fc", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 5.5) )\n", " #, dpi=300)\n", " #gridspec_kw={'width_ratios': [1.35, 1]})\n", "i=1\n", "x1, y1, x2, y2, this_ylabel = datasets[i]\n", "this_leftlim, this_rightlim = this_xlim[i]\n", "this_bottomlim, this_toplim = this_ylim[i]\n", "\n", "plt.plot(x1, y1,\n", " #'rs', ms=2.5, label='Melting')\n", " color='red', marker='o', linewidth=0, markersize=2,\n", " label='Melting', alpha=.3)\n", "plt.plot(x2, y2,\n", " #'bo', ms=2.5, label='Freezing')\n", " color='blue', marker='o', linewidth=0, markersize=2,\n", " label='Freezing', alpha=.3)\n", "\n", "#plt.set_title(specific_title, \n", "# fontsize=15, fontweight='bold', pad=15)\n", "plt.xlabel('$T$ [K]')\n", " #, fontsize=14, labelpad=8)\n", "#if i==0:\n", "plt.ylabel(this_ylabel)\n", " #fontsize=14, labelpad=8)\n", "\n", "plt.xlim(this_leftlim, this_rightlim)\n", "plt.ylim(this_bottomlim, this_toplim)\n", "\n", "#plt.tick_params(axis='both')\n", "\n", "#plt.grid(True, color='#E0E0E0', linestyle='-', linewidth=0.8)\n", "plt.grid(True, alpha=0.5)\n", "plt.legend(loc=leg_loc[i])\n", " #frameon=True, facecolor='white', edgecolor='#D0D0D0', framealpha=1)\n", "\n", "#plt.tight_layout()\n", "\n", "plt.savefig('NEW_IMGS/NEWcount_CNAP.pdf', dpi=300)\n", " #, bbox_inches='tight')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "e91feffe-7bfe-42ea-a9a4-c9dcd27bc2c0", "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": "3.13.9" } }, "nbformat": 4, "nbformat_minor": 5 }