anisotropy/playground/database.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c4317e7e-d7b9-4bec-9cad-e26a377458b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from peewee import (\n",
    "    SqliteDatabase, JOIN, \n",
    "    Model, Field, \n",
    "    AutoField, ForeignKeyField, \n",
    "    TextField, FloatField, \n",
    "    IntegerField, BooleanField, \n",
    "    TimeField\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "db03ea1b-e950-4f0b-8c4f-c137417981ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "db = SqliteDatabase(\"test_db.db\", pragmas = { \"foreign_keys\" : 1, \"journal_mode\": \"wal\" })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e904a64b-9108-414b-974b-2dbf96018cf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ListField(Field):\n",
    "    field_type = \"list\"\n",
    "\n",
    "    def db_value(self, value):\n",
    "        return str(value)\n",
    "\n",
    "    def python_value(self, value):\n",
    "        pval = []\n",
    "\n",
    "        for entry in value[1 : -1].split(\",\"):\n",
    "            try:\n",
    "                pval.append(float(entry))\n",
    "\n",
    "            except:\n",
    "                pval.append(entry.strip().replace(\"'\", \"\"))\n",
    "\n",
    "        return pval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a6049589-dd33-4cfa-8434-1c00285c01e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Structure(Model):\n",
    "    structure_id = AutoField()\n",
    "\n",
    "    type = TextField()\n",
    "    direction = ListField()\n",
    "    theta = FloatField()\n",
    "\n",
    "    r0 = FloatField(null = True)\n",
    "    L = FloatField(null = True)\n",
    "    radius = FloatField(null = True)\n",
    "\n",
    "    filletsEnabled = BooleanField(null = True)\n",
    "    fillets = FloatField(null = True)\n",
    "    #path = TextField()\n",
    "    \n",
    "    class Meta:\n",
    "        database = db\n",
    "        db_table = \"structures\"\n",
    "\n",
    "\n",
    "class Mesh(Model):\n",
    "    mesh_id = AutoField()\n",
    "    structure_id = ForeignKeyField(Structure, backref = \"meshes\")\n",
    "\n",
    "    maxSize = FloatField(null = True) \n",
    "\n",
    "    class Meta:\n",
    "        database = db\n",
    "        db_table = \"meshes\"\n",
    "        depends_on = Structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "77c8b505-8b72-4f41-9f6d-d0b4e44884ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "db.create_tables([Structure, Mesh])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "708acdf5-a604-44c1-8631-d274d0273c1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "62c1c0a0-74ae-43cc-bc75-1fbd353540ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = Structure(type = \"simple\", direction = [1, 0, 0], theta = 0.01)\n",
    "s1.save()\n",
    "\n",
    "m1 = Mesh(structure_id = s1, maxSize = 1e-2)\n",
    "m1.save()\n",
    "\n",
    "#db.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0e03bc08-94ea-4680-8223-dbafc13dcfe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.theta = 0.12\n",
    "s1.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a2f68e62-0324-4ec2-8113-47cf6e1dc5bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "ss = numpy.array([\n",
    "    Structure(type = \"simple\", direction = [1, 0, 0], theta = 0.01),\n",
    "    Structure(type = \"simple\", direction = [1, 0, 0], theta = 0.02)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6683ea42-5fdc-4b33-b709-5ea31ae5567f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "83cc7bde-e003-4f2c-ab9b-f9e37634c374",
   "metadata": {},
   "outputs": [],
   "source": [
    "Structure.bulk_create(ss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "962010a5-68b8-4711-a067-962677aa406d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 simple [1.0, 0.0, 0.0] 0.12\n",
      "2 simple [1.0, 0.0, 0.0] 0.01\n",
      "3 simple [1.0, 0.0, 0.0] 0.01\n",
      "4 simple [1.0, 0.0, 0.0] 0.01\n",
      "5 simple [1.0, 0.0, 0.0] 0.01\n",
      "6 simple [1.0, 0.0, 0.0] 0.01\n",
      "7 simple [1.0, 0.0, 0.0] 0.01\n",
      "8 simple [1.0, 0.0, 0.0] 0.01\n",
      "9 simple [1.0, 0.0, 0.0] 0.01\n",
      "10 simple [1.0, 0.0, 0.0] 0.01\n",
      "11 simple [1.0, 0.0, 0.0] 0.01\n",
      "12 simple [1.0, 0.0, 0.0] 0.02\n"
     ]
    }
   ],
   "source": [
    "for row in Structure.select():\n",
    "    print(row.structure_id, row.type, row.direction, row.theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a5fc7787-7045-4bb1-a846-5fff8b21417c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sel = Structure.select()[-1]\n",
    "sel2 = sel.meshes.select()[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "480edddc-478e-47ce-8678-490455badd35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Mesh: 8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8286c287-f89d-40bd-8045-a854013915c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "sel3 = Structure.select().dicts().get()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "180b07cb-6c10-4390-9f9c-07bf9f74f39e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'structure_id': 1,\n",
       " 'type': 'simple',\n",
       " 'direction': [1.0, 0.0, 0.0],\n",
       " 'theta': 0.12,\n",
       " 'r0': None,\n",
       " 'L': None,\n",
       " 'radius': None,\n",
       " 'filletsEnabled': None,\n",
       " 'fillets': None}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dc2e8b67-67ea-477a-af94-dd1ceb1a2a24",
   "metadata": {},
   "outputs": [],
   "source": [
    "from anisotropy.core.main import Anisotropy\n",
    "model = Anisotropy()\n",
    "params_: list = model.loadFromScratch(\"test_anisotropy.toml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e73c9863-e8d7-4ed5-a27e-0c733c3daeb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "171"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f532c668-965a-4c7c-aaa9-23a9e308a455",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = numpy.array(params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6ad8131f-3e0f-43b9-ab41-b6e3e56a5245",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6111802f-9e5f-47de-a6ea-f54dd062ecba",
   "metadata": {},
   "outputs": [],
   "source": [
    "pparams = Series(params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "11f04e87-e2f1-4d22-be85-8b6967935c92",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = numpy.array([], dtype = object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e1a930c3-e591-41e3-8d76-8316f4b8bfac",
   "metadata": {},
   "outputs": [],
   "source": [
    "test += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "075e261f-4387-49c3-9c40-292885cffcc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "a.fill(value)\n",
       "\n",
       "Fill the array with a scalar value.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "value : scalar\n",
       "    All elements of `a` will be assigned this value.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> a = np.array([1, 2])\n",
       ">>> a.fill(0)\n",
       ">>> a\n",
       "array([0, 0])\n",
       ">>> a = np.empty(2)\n",
       ">>> a.fill(1)\n",
       ">>> a\n",
       "array([1.,  1.])\n",
       "\u001b[0;31mType:\u001b[0m      builtin_function_or_method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?test.fill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "448c99bf-2b6f-43de-92d3-dc1267e766d3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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