{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ase.io import read, write"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_configs = read(\"SPICE_v2_train.xyz\", \":\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['numbers', 'positions', 'formal_charges', 'mbis_multipoles', 'forces'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_configs[0].arrays.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "max_forces = [np.linalg.norm(c.arrays[\"forces\"], axis=1).max(axis=0) for c in train_configs]\n",
    "\n",
    "\n",
    "plt.yscale(\"log\")\n",
    "plt.hist(max_forces, bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_configs = [c for (c, f) in zip(train_configs, max_forces) if f > 15]\n",
    "len(bad_configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/mace-scf/lib/python3.11/site-packages/ase/io/extxyz.py:1000: UserWarning: write_xyz() overwriting array \"forces\" present in atoms.arrays with stored results from calculator\n",
      "  warnings.warn('write_xyz() overwriting array \"{0}\" present '\n"
     ]
    }
   ],
   "source": [
    "write(\"bad_configs.xyz\", bad_configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1428672"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good_configs = [c for (c, f) in zip(train_configs, max_forces) if f <= 15]\n",
    "len(good_configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/mace-openmm/lib/python3.11/site-packages/ase/io/extxyz.py:1000: UserWarning: write_xyz() overwriting array \"forces\" present in atoms.arrays with stored results from calculator\n",
      "  warnings.warn('write_xyz() overwriting array \"{0}\" present '\n"
     ]
    }
   ],
   "source": [
    "write(\"spice_v2_15_eV-A_force_filter.xyz\", good_configs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "charges = [np.sum(c.arrays[\"mbis_multipoles\"][:,0]) for c in good_configs]\n",
    "\n",
    "plt.hist(charges, bins=100)\n",
    "plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mace-openmm",
   "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",
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