{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cbaf1c39-e423-47a3-a3ea-e78a528bc4c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from anisotropy.core.database import Database, Structure\n",
    "import pandas\n",
    "from pandas import DataFrame, Series\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "import numpy\n",
    "import warnings\n",
    "\n",
    "# ignore some warnings, especially from seaborn\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "09b1bf83-6b42-4144-81a0-b57c661181b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "db = Database(\"anisotropy\", \"woPrismaticLayer\")\n",
    "db.setup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f341fbd4-797b-4d5d-8393-0f7cf2e67883",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = db.search([])\n",
    "df = DataFrame(res)\n",
    "\n",
    "df_prep = df[[\n",
    "    col for col in df.columns \n",
    "        if not isinstance(df[col][0], str) \n",
    "            and not isinstance(df[col][0], numpy.bool_)\n",
    "            and not isinstance(df[col][0], dict)\n",
    "            and not isinstance(df[col][0], list)\n",
    "            and not df[col][0] is None\n",
    "            and not col[-3: ] == \"_id\"\n",
    "]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c2dbce30-d80a-4b78-b2a6-cf9c2075f5b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_prep = df_prep.assign(direction = df[\"direction\"].astype(\"str\"))\n",
    "df_prep = df_prep.assign(type = df[\"type\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d182461e-1e42-4e0b-8dd2-ba6425654ee7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f7cb8333af0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1184x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.set_style('whitegrid')\n",
    "seaborn.lmplot(\n",
    "    x = \"theta\", y = \"flowRate\", \n",
    "    hue = \"direction\", col = \"type\", \n",
    "    data = df_prep, order = 3, \n",
    "    sharex = False, sharey = False, ci = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7b7092aa-de0e-430c-9ed5-07483b278d45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>direction</th>\n",
       "      <th>theta</th>\n",
       "      <th>meshStatus</th>\n",
       "      <th>flowStatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>bodyCentered</td>\n",
       "      <td>[0.0, 0.0, 1.0]</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Done</td>\n",
       "      <td>Failed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>bodyCentered</td>\n",
       "      <td>[0.0, 0.0, 1.0]</td>\n",
       "      <td>0.13</td>\n",
       "      <td>Done</td>\n",
       "      <td>Failed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>bodyCentered</td>\n",
       "      <td>[0.0, 0.0, 1.0]</td>\n",
       "      <td>0.14</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>bodyCentered</td>\n",
       "      <td>[0.0, 0.0, 1.0]</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>bodyCentered</td>\n",
       "      <td>[0.0, 0.0, 1.0]</td>\n",
       "      <td>0.17</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>bodyCentered</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>0.13</td>\n",
       "      <td>Done</td>\n",
       "      <td>Failed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>bodyCentered</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>0.14</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>bodyCentered</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>bodyCentered</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>0.16</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>bodyCentered</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>0.17</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>bodyCentered</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.11</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>bodyCentered</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.12</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>bodyCentered</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.13</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>bodyCentered</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.14</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>bodyCentered</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>bodyCentered</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.16</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>bodyCentered</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.17</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>faceCentered</td>\n",
       "      <td>[0.0, 0.0, 1.0]</td>\n",
       "      <td>0.06</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>faceCentered</td>\n",
       "      <td>[0.0, 0.0, 1.0]</td>\n",
       "      <td>0.09</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>faceCentered</td>\n",
       "      <td>[0.0, 0.0, 1.0]</td>\n",
       "      <td>0.10</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>faceCentered</td>\n",
       "      <td>[0.0, 0.0, 1.0]</td>\n",
       "      <td>0.11</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>faceCentered</td>\n",
       "      <td>[0.0, 0.0, 1.0]</td>\n",
       "      <td>0.12</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>faceCentered</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>faceCentered</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>0.09</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>faceCentered</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>0.10</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>faceCentered</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>0.11</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>faceCentered</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>0.12</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>faceCentered</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.09</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>faceCentered</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.10</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>faceCentered</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.11</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>faceCentered</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.12</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.05</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.06</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.09</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.10</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.11</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.12</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.13</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.14</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Idle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.27</td>\n",
       "      <td>Done</td>\n",
       "      <td>Failed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.28</td>\n",
       "      <td>Done</td>\n",
       "      <td>Failed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             type        direction  theta meshStatus flowStatus\n",
       "101  bodyCentered  [0.0, 0.0, 1.0]   0.01       Done     Failed\n",
       "113  bodyCentered  [0.0, 0.0, 1.0]   0.13       Done     Failed\n",
       "114  bodyCentered  [0.0, 0.0, 1.0]   0.14     Failed       Idle\n",
       "115  bodyCentered  [0.0, 0.0, 1.0]   0.15     Failed       Idle\n",
       "117  bodyCentered  [0.0, 0.0, 1.0]   0.17     Failed       Idle\n",
       "96   bodyCentered  [1.0, 0.0, 0.0]   0.13       Done     Failed\n",
       "97   bodyCentered  [1.0, 0.0, 0.0]   0.14     Failed       Idle\n",
       "98   bodyCentered  [1.0, 0.0, 0.0]   0.15     Failed       Idle\n",
       "99   bodyCentered  [1.0, 0.0, 0.0]   0.16     Failed       Idle\n",
       "100  bodyCentered  [1.0, 0.0, 0.0]   0.17     Failed       Idle\n",
       "128  bodyCentered  [1.0, 1.0, 1.0]   0.11     Failed       Idle\n",
       "129  bodyCentered  [1.0, 1.0, 1.0]   0.12     Failed       Idle\n",
       "130  bodyCentered  [1.0, 1.0, 1.0]   0.13     Failed       Idle\n",
       "131  bodyCentered  [1.0, 1.0, 1.0]   0.14     Failed       Idle\n",
       "132  bodyCentered  [1.0, 1.0, 1.0]   0.15     Failed       Idle\n",
       "133  bodyCentered  [1.0, 1.0, 1.0]   0.16     Failed       Idle\n",
       "134  bodyCentered  [1.0, 1.0, 1.0]   0.17     Failed       Idle\n",
       "152  faceCentered  [0.0, 0.0, 1.0]   0.06     Failed       Idle\n",
       "155  faceCentered  [0.0, 0.0, 1.0]   0.09     Failed       Idle\n",
       "156  faceCentered  [0.0, 0.0, 1.0]   0.10     Failed       Idle\n",
       "157  faceCentered  [0.0, 0.0, 1.0]   0.11     Failed       Idle\n",
       "158  faceCentered  [0.0, 0.0, 1.0]   0.12     Failed       Idle\n",
       "137  faceCentered  [1.0, 0.0, 0.0]   0.03     Failed       Idle\n",
       "143  faceCentered  [1.0, 0.0, 0.0]   0.09     Failed       Idle\n",
       "144  faceCentered  [1.0, 0.0, 0.0]   0.10     Failed       Idle\n",
       "145  faceCentered  [1.0, 0.0, 0.0]   0.11     Failed       Idle\n",
       "146  faceCentered  [1.0, 0.0, 0.0]   0.12     Failed       Idle\n",
       "167  faceCentered  [1.0, 1.0, 1.0]   0.09     Failed       Idle\n",
       "168  faceCentered  [1.0, 1.0, 1.0]   0.10     Failed       Idle\n",
       "169  faceCentered  [1.0, 1.0, 1.0]   0.11     Failed       Idle\n",
       "170  faceCentered  [1.0, 1.0, 1.0]   0.12     Failed       Idle\n",
       "58         simple  [1.0, 1.0, 1.0]   0.03     Failed       Idle\n",
       "60         simple  [1.0, 1.0, 1.0]   0.05     Failed       Idle\n",
       "61         simple  [1.0, 1.0, 1.0]   0.06     Failed       Idle\n",
       "64         simple  [1.0, 1.0, 1.0]   0.09     Failed       Idle\n",
       "65         simple  [1.0, 1.0, 1.0]   0.10     Failed       Idle\n",
       "66         simple  [1.0, 1.0, 1.0]   0.11     Failed       Idle\n",
       "67         simple  [1.0, 1.0, 1.0]   0.12     Failed       Idle\n",
       "68         simple  [1.0, 1.0, 1.0]   0.13     Failed       Idle\n",
       "69         simple  [1.0, 1.0, 1.0]   0.14     Failed       Idle\n",
       "70         simple  [1.0, 1.0, 1.0]   0.15     Failed       Idle\n",
       "82         simple  [1.0, 1.0, 1.0]   0.27       Done     Failed\n",
       "83         simple  [1.0, 1.0, 1.0]   0.28       Done     Failed"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "failed = df[[\"type\", \"direction\", \"theta\", \"meshStatus\", \"flowStatus\"]][\n",
    "    (df[\"meshStatus\"] == \"Failed\") | (df[\"flowStatus\"] == \"Failed\")\n",
    "].assign(\n",
    "    direction = df[\"direction\"].astype(\"str\")\n",
    ").sort_values(\n",
    "    by = [\"type\", \"direction\", \"theta\"]\n",
    ")\n",
    "failed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1a25020c-5bcc-4e46-985f-cfe55c30d117",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='theta', ylabel='chordalError'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "simple3 = df.assign(direction = df[\"direction\"].astype(\"str\"))\n",
    "simple3 = simple3[\n",
    "    (simple3[\"type\"] == \"simple\") & (simple3[\"direction\"] == \"[1.0, 1.0, 1.0]\")\n",
    "]\n",
    "\n",
    "seaborn.scatterplot(data = simple3, x = \"theta\", y = \"chordalError\", hue = \"meshStatus\")\n",
    "seaborn.lineplot(data = simple3, x = \"theta\", y = \"minSize\", color = \"grey\", label = \"minSize\")\n",
    "seaborn.lineplot(data = simple3, x = \"theta\", y = \"maxSize\", color = \"black\", label = \"maxSize\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "78e0c05f-3161-404a-9ed7-cd9ebc981a43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='theta', ylabel='fillets'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.scatterplot(data = simple3.round({ \"fillets\": 2 }), x = \"theta\", y = \"fillets\", hue = \"meshStatus\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7d6de026-fe25-497f-a872-66b249e0e979",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='theta', ylabel='chordalError'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "simple1 = df.assign(direction = df[\"direction\"].astype(\"str\"))\n",
    "simple1 = simple1[\n",
    "    (simple1[\"type\"] == \"simple\") & (simple1[\"direction\"] == \"[1.0, 0.0, 0.0]\")\n",
    "]\n",
    "\n",
    "seaborn.scatterplot(data = simple1, x = \"theta\", y = \"chordalError\", hue = \"meshStatus\")\n",
    "seaborn.lineplot(data = simple1, x = \"theta\", y = \"minSize\", color = \"grey\", label = \"minSize\")\n",
    "seaborn.lineplot(data = simple1, x = \"theta\", y = \"maxSize\", color = \"black\", label = \"maxSize\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e994037e-e266-444a-9ee3-80cccdf8386f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='theta', ylabel='fillets'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fc = df.assign(direction = df[\"direction\"].astype(\"str\"))\n",
    "fc = fc[\n",
    "    (fc[\"type\"] == \"faceCentered\") & (fc[\"direction\"] == \"[1.0, 1.0, 1.0]\")\n",
    "]\n",
    "seaborn.set_style('whitegrid')\n",
    "seaborn.scatterplot(data = fc, x = \"theta\", y = \"fillets\", hue = \"meshStatus\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b541a36e-f3b2-4b50-bfc9-ec8958666053",
   "metadata": {},
   "outputs": [],
   "source": [
    "general: list = db.loadGeneral()\n",
    "dfe: list = [] #DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d93a051-acab-448b-a7fd-5fe281cc323f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pandasify(listOfDicts):\n",
    "    dfe: list = []\n",
    "    \n",
    "    for entry in listOfDicts:\n",
    "        structure: dict = entry[\"structure\"]\n",
    "        extended: dict = db.load(structure[\"type\"], structure[\"direction\"], structure[\"theta\"])\n",
    "        fields: list = extended.keys()\n",
    "\n",
    "        for key in fields:\n",
    "            extended[key] = pandas.json_normalize(extended[key])\n",
    "\n",
    "        dfe.append(extended)\n",
    "\n",
    "    dfe: DataFrame = DataFrame(dfe)\n",
    "    dfe: Series = Series(\n",
    "        [ pandas.concat(dfe[field].to_list(), ignore_index = True) for field in dfe.keys() ],\n",
    "        dfe.keys()\n",
    "    )\n",
    "    \n",
    "    return dfe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "437d9432-206e-4d23-a5cb-d9958b65f68a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from anisotropy.core.main import Anisotropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "756351e4-34b6-49d1-8cdb-29e3def4361d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Anisotropy()\n",
    "params = model.loadFromScratch(\"test_anisotropy.toml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a6a7b464-045a-4d81-a7ad-eca75590179f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "171"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cdbc661b-701f-475b-a72a-51b3bd2937ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfparams = pandasify(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "73e04119-9f70-4e22-928b-caef022c365d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>structure_id</th>\n",
       "      <th>type</th>\n",
       "      <th>direction</th>\n",
       "      <th>theta</th>\n",
       "      <th>r0</th>\n",
       "      <th>L</th>\n",
       "      <th>radius</th>\n",
       "      <th>filletsEnabled</th>\n",
       "      <th>fillets</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>0.183900</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.030928</td>\n",
       "      <td>True</td>\n",
       "      <td>0.175945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.041667</td>\n",
       "      <td>True</td>\n",
       "      <td>0.168056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.052632</td>\n",
       "      <td>True</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>80</td>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.315789</td>\n",
       "      <td>True</td>\n",
       "      <td>0.028070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81</td>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>True</td>\n",
       "      <td>0.022222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>82</td>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.351351</td>\n",
       "      <td>True</td>\n",
       "      <td>0.016517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>83</td>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.369863</td>\n",
       "      <td>True</td>\n",
       "      <td>0.010959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>84</td>\n",
       "      <td>simple</td>\n",
       "      <td>[1.0, 1.0, 1.0]</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.388889</td>\n",
       "      <td>True</td>\n",
       "      <td>0.005556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    structure_id    type        direction  theta   r0    L    radius  \\\n",
       "0              1  simple  [1.0, 0.0, 0.0]   0.01  1.0  2.0  1.010101   \n",
       "1              2  simple  [1.0, 0.0, 0.0]   0.02  1.0  2.0  1.020408   \n",
       "2              3  simple  [1.0, 0.0, 0.0]   0.03  1.0  2.0  1.030928   \n",
       "3              4  simple  [1.0, 0.0, 0.0]   0.04  1.0  2.0  1.041667   \n",
       "4              5  simple  [1.0, 0.0, 0.0]   0.05  1.0  2.0  1.052632   \n",
       "..           ...     ...              ...    ...  ...  ...       ...   \n",
       "79            80  simple  [1.0, 1.0, 1.0]   0.24  1.0  2.0  1.315789   \n",
       "80            81  simple  [1.0, 1.0, 1.0]   0.25  1.0  2.0  1.333333   \n",
       "81            82  simple  [1.0, 1.0, 1.0]   0.26  1.0  2.0  1.351351   \n",
       "82            83  simple  [1.0, 1.0, 1.0]   0.27  1.0  2.0  1.369863   \n",
       "83            84  simple  [1.0, 1.0, 1.0]   0.28  1.0  2.0  1.388889   \n",
       "\n",
       "    filletsEnabled   fillets  \n",
       "0             True  0.191919  \n",
       "1             True  0.183900  \n",
       "2             True  0.175945  \n",
       "3             True  0.168056  \n",
       "4             True  0.160234  \n",
       "..             ...       ...  \n",
       "79            True  0.028070  \n",
       "80            True  0.022222  \n",
       "81            True  0.016517  \n",
       "82            True  0.010959  \n",
       "83            True  0.005556  \n",
       "\n",
       "[84 rows x 9 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfparams.structure[dfparams.structure.type == \"simple\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7abc63e-960c-4157-99b3-d947acf10c10",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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