{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a11f9159-f0af-44e4-95bd-f72857a4a145",
   "metadata": {},
   "source": [
    "(demo2022-07-06)=\n",
    "# Demo Notebook 2022-07-06\n",
    "\n",
    "Demos from the live sessions on 2022-07-06.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ed6dab2-15e9-4f60-8901-e74596d63418",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "Ye olde setup chunk below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e24123e-9838-4124-a46b-caad15fe6ab0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import grama as gr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "DF = gr.Intention()\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4fff89bb-93b9-4034-8d84-3405db151106",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>thick</th>\n",
       "      <th>E_00</th>\n",
       "      <th>mu_00</th>\n",
       "      <th>E_45</th>\n",
       "      <th>mu_45</th>\n",
       "      <th>E_90</th>\n",
       "      <th>mu_90</th>\n",
       "      <th>alloy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.022</td>\n",
       "      <td>10600</td>\n",
       "      <td>0.321</td>\n",
       "      <td>10700</td>\n",
       "      <td>0.329</td>\n",
       "      <td>10500</td>\n",
       "      <td>0.310</td>\n",
       "      <td>al_24st</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.022</td>\n",
       "      <td>10600</td>\n",
       "      <td>0.323</td>\n",
       "      <td>10500</td>\n",
       "      <td>0.331</td>\n",
       "      <td>10700</td>\n",
       "      <td>0.323</td>\n",
       "      <td>al_24st</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.032</td>\n",
       "      <td>10400</td>\n",
       "      <td>0.329</td>\n",
       "      <td>10400</td>\n",
       "      <td>0.318</td>\n",
       "      <td>10300</td>\n",
       "      <td>0.322</td>\n",
       "      <td>al_24st</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.032</td>\n",
       "      <td>10300</td>\n",
       "      <td>0.319</td>\n",
       "      <td>10500</td>\n",
       "      <td>0.326</td>\n",
       "      <td>10400</td>\n",
       "      <td>0.330</td>\n",
       "      <td>al_24st</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.064</td>\n",
       "      <td>10500</td>\n",
       "      <td>0.323</td>\n",
       "      <td>10400</td>\n",
       "      <td>0.331</td>\n",
       "      <td>10400</td>\n",
       "      <td>0.327</td>\n",
       "      <td>al_24st</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.064</td>\n",
       "      <td>10700</td>\n",
       "      <td>0.328</td>\n",
       "      <td>10500</td>\n",
       "      <td>0.328</td>\n",
       "      <td>10500</td>\n",
       "      <td>0.320</td>\n",
       "      <td>al_24st</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.081</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.315</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.320</td>\n",
       "      <td>9900</td>\n",
       "      <td>0.314</td>\n",
       "      <td>al_24st</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.081</td>\n",
       "      <td>10100</td>\n",
       "      <td>0.312</td>\n",
       "      <td>9900</td>\n",
       "      <td>0.312</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.316</td>\n",
       "      <td>al_24st</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.081</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.311</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>9900</td>\n",
       "      <td>0.314</td>\n",
       "      <td>al_24st</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   thick   E_00  mu_00   E_45  mu_45   E_90  mu_90     alloy\n",
       "0  0.022  10600  0.321  10700  0.329  10500  0.310   al_24st\n",
       "1  0.022  10600  0.323  10500  0.331  10700  0.323   al_24st\n",
       "2  0.032  10400  0.329  10400  0.318  10300  0.322   al_24st\n",
       "3  0.032  10300  0.319  10500  0.326  10400  0.330   al_24st\n",
       "4  0.064  10500  0.323  10400  0.331  10400  0.327   al_24st\n",
       "5  0.064  10700  0.328  10500  0.328  10500  0.320   al_24st\n",
       "6  0.081  10000  0.315  10000  0.320   9900  0.314   al_24st\n",
       "7  0.081  10100  0.312   9900  0.312  10000  0.316   al_24st\n",
       "8  0.081  10000  0.311     -1 -1.000   9900  0.314   al_24st"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from grama.data import df_stang_wide\n",
    "df_stang_wide"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4214613-f8c5-49fe-b9cb-5fc9f31b5c48",
   "metadata": {},
   "source": [
    "## Wrangling Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ambient-investigation",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zach/Git/py_grama/grama/tran_pivot.py:570: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>thick</th>\n",
       "      <th>alloy</th>\n",
       "      <th>angle</th>\n",
       "      <th>E</th>\n",
       "      <th>mu</th>\n",
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       "      <td>10400.0</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.064</td>\n",
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       "      <td>10700.0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.064</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>0.328</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.064</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10500.0</td>\n",
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       "      <th>19</th>\n",
       "      <td>0.081</td>\n",
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       "      <td>0.081</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.081</td>\n",
       "      <td>al_24st</td>\n",
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       "      <td>10100.0</td>\n",
       "      <td>0.312</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.081</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>0.312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.081</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.081</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.081</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>90.0</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>0.314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    thick     alloy  angle        E     mu\n",
       "0   0.022   al_24st    0.0  10600.0  0.321\n",
       "1   0.022   al_24st   45.0  10700.0  0.329\n",
       "2   0.022   al_24st   90.0  10500.0  0.310\n",
       "3   0.022   al_24st    0.0  10600.0  0.323\n",
       "4   0.022   al_24st   45.0  10500.0  0.331\n",
       "5   0.022   al_24st   90.0  10700.0  0.323\n",
       "6   0.032   al_24st    0.0  10400.0  0.329\n",
       "7   0.032   al_24st   45.0  10400.0  0.318\n",
       "8   0.032   al_24st   90.0  10300.0  0.322\n",
       "9   0.032   al_24st    0.0  10300.0  0.319\n",
       "10  0.032   al_24st   45.0  10500.0  0.326\n",
       "11  0.032   al_24st   90.0  10400.0  0.330\n",
       "12  0.064   al_24st    0.0  10500.0  0.323\n",
       "13  0.064   al_24st   45.0  10400.0  0.331\n",
       "14  0.064   al_24st   90.0  10400.0  0.327\n",
       "15  0.064   al_24st    0.0  10700.0  0.328\n",
       "16  0.064   al_24st   45.0  10500.0  0.328\n",
       "17  0.064   al_24st   90.0  10500.0  0.320\n",
       "18  0.081   al_24st    0.0  10000.0  0.315\n",
       "19  0.081   al_24st   45.0  10000.0  0.320\n",
       "20  0.081   al_24st   90.0   9900.0  0.314\n",
       "21  0.081   al_24st    0.0  10100.0  0.312\n",
       "22  0.081   al_24st   45.0   9900.0  0.312\n",
       "23  0.081   al_24st   90.0  10000.0  0.316\n",
       "24  0.081   al_24st    0.0  10000.0  0.311\n",
       "25  0.081   al_24st   90.0   9900.0  0.314"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stang = (\n",
    "    df_stang_wide\n",
    "    ## Single-call pivoting of the data\n",
    "    >> gr.tf_pivot_longer(\n",
    "        columns=[\"E_00\", \"mu_00\", \"E_45\", \"mu_45\", \"E_90\", \"mu_90\"],\n",
    "        names_to=[\".value\", \"angle\"],\n",
    "        names_sep=\"_\"\n",
    "    )\n",
    "    ## Filter out the invalid values\n",
    "    >> gr.tf_filter(DF.E > 0)\n",
    "    ## Convert angle values to numeric\n",
    "    >> gr.tf_mutate(\n",
    "        angle=gr.as_float(DF.angle)\n",
    "    )\n",
    ")\n",
    "\n",
    "df_stang"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77864ce1-8527-47ef-96e7-6d4aab2247b3",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "45fd04ca-699e-41d5-a507-a931c07018fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zach/Git/py_grama/grama/tran_pivot.py:570: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>thick</th>\n",
       "      <th>alloy</th>\n",
       "      <th>angle</th>\n",
       "      <th>E</th>\n",
       "      <th>mu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.022</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10600.0</td>\n",
       "      <td>0.321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.022</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10700.0</td>\n",
       "      <td>0.329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.022</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>0.310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.022</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10600.0</td>\n",
       "      <td>0.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.022</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>0.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.022</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10700.0</td>\n",
       "      <td>0.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.032</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>0.329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.032</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>0.318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.032</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10300.0</td>\n",
       "      <td>0.322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.032</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10300.0</td>\n",
       "      <td>0.319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.032</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>0.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.032</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>0.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.064</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>0.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.064</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>0.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.064</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>0.327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.064</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10700.0</td>\n",
       "      <td>0.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.064</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>0.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.064</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>0.320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.081</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.081</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.081</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>90.0</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>0.314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.081</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10100.0</td>\n",
       "      <td>0.312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.081</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>0.312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.081</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.081</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.081</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>45.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.081</td>\n",
       "      <td>al_24st</td>\n",
       "      <td>90.0</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>0.314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    thick     alloy  angle        E     mu\n",
       "0   0.022   al_24st    0.0  10600.0  0.321\n",
       "1   0.022   al_24st   45.0  10700.0  0.329\n",
       "2   0.022   al_24st   90.0  10500.0  0.310\n",
       "3   0.022   al_24st    0.0  10600.0  0.323\n",
       "4   0.022   al_24st   45.0  10500.0  0.331\n",
       "5   0.022   al_24st   90.0  10700.0  0.323\n",
       "6   0.032   al_24st    0.0  10400.0  0.329\n",
       "7   0.032   al_24st   45.0  10400.0  0.318\n",
       "8   0.032   al_24st   90.0  10300.0  0.322\n",
       "9   0.032   al_24st    0.0  10300.0  0.319\n",
       "10  0.032   al_24st   45.0  10500.0  0.326\n",
       "11  0.032   al_24st   90.0  10400.0  0.330\n",
       "12  0.064   al_24st    0.0  10500.0  0.323\n",
       "13  0.064   al_24st   45.0  10400.0  0.331\n",
       "14  0.064   al_24st   90.0  10400.0  0.327\n",
       "15  0.064   al_24st    0.0  10700.0  0.328\n",
       "16  0.064   al_24st   45.0  10500.0  0.328\n",
       "17  0.064   al_24st   90.0  10500.0  0.320\n",
       "18  0.081   al_24st    0.0  10000.0  0.315\n",
       "19  0.081   al_24st   45.0  10000.0  0.320\n",
       "20  0.081   al_24st   90.0   9900.0  0.314\n",
       "21  0.081   al_24st    0.0  10100.0  0.312\n",
       "22  0.081   al_24st   45.0   9900.0  0.312\n",
       "23  0.081   al_24st   90.0  10000.0  0.316\n",
       "24  0.081   al_24st    0.0  10000.0  0.311\n",
       "25  0.081   al_24st   45.0     -1.0 -1.000\n",
       "26  0.081   al_24st   90.0   9900.0  0.314"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stang_invalid = (\n",
    "    df_stang_wide\n",
    "    ## Single-call pivoting of the data\n",
    "    >> gr.tf_pivot_longer(\n",
    "        columns=[\"E_00\", \"mu_00\", \"E_45\", \"mu_45\", \"E_90\", \"mu_90\"],\n",
    "        names_to=[\".value\", \"angle\"],\n",
    "        names_sep=\"_\"\n",
    "    )\n",
    "    ## Leave in the invalid values\n",
    "    # >> gr.tf_filter(DF.E > 0)\n",
    "    ## Convert angle values to numeric\n",
    "    >> gr.tf_mutate(\n",
    "        angle=gr.as_float(DF.angle)\n",
    "    )\n",
    ")\n",
    "\n",
    "df_stang_invalid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2777501f-7048-4512-b1f6-d9b71cf830f6",
   "metadata": {},
   "source": [
    "Note that observation `df_stang_invalid.index == 25` has `E == -1`, which is not a valid number for this material property.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c98c63ed-c9fe-40f7-bb55-70d42e765a11",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zach/opt/anaconda3/envs/evc/lib/python3.9/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Saving 6.4 x 4.8 in image.\n",
      "/Users/zach/opt/anaconda3/envs/evc/lib/python3.9/site-packages/plotnine/ggplot.py:722: PlotnineWarning: Filename: stang-E-mu-incorrect.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x644.156 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8766095560424)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NOTE: No need to edit; run and inspect\n",
    "p = (\n",
    "    df_stang_invalid\n",
    "    >> gr.ggplot(gr.aes(\"E\", \"mu\", color=\"factor(thick)\"))\n",
    "    + gr.geom_point(size=4)\n",
    "    \n",
    "    + gr.scale_color_discrete(name=\"Thickness (in)\")\n",
    "    + gr.theme_minimal()\n",
    "    + gr.theme(aspect_ratio=1)\n",
    "    + gr.labs(\n",
    "        x=\"Elasticity (ksi)\",\n",
    "        y=\"Poisson's Ratio (-)\",\n",
    "    )\n",
    ")\n",
    "p.save(\"stang-E-mu-incorrect.png\")\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "081fb488-9442-4380-af24-e7fe8a15a760",
   "metadata": {},
   "source": [
    "Note that the invalid value heavily skews the plot, making it difficult to see trends in the dataset. This does help us detect that one of the values is incorrect, though!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "884d346d-219c-40b7-8378-861fe7d844e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zach/opt/anaconda3/envs/evc/lib/python3.9/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Saving 6.4 x 4.8 in image.\n",
      "/Users/zach/opt/anaconda3/envs/evc/lib/python3.9/site-packages/plotnine/ggplot.py:722: PlotnineWarning: Filename: stang-E-mu.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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DVzpkaGhoD2CPJsUPHvs6NDQ0SxHtGJYtW7Z4w4YNLFmy5IChoaFNUz+ia9hmmrDNNGWbacI205RtBujr67u47BjUnUKMzo/ohKGhoZOAE8uOQ5Ikta6vry+UHYO6k0l5h7TQU/4l4Bjc1vluhoeHF2/YsOGCJUuWHLpgwQJ7sLayzTRhm2nKNtOEbaYp2wz2lKs8Dl/pkL6+vpuAmyYqa/hY8Cr/+O9u3bp1uwLcdtttly1fvvyOsuOZK2wzzdlmJmabac42MzHbjFQuJ3pKkiRJJbOnXJJEdaDeAzwZeBywELgRqNX6K26uI0mzwKRckrpcdaD+b8BHSfNghoAAROAj1YH654DX1ford5YYoiTNew5fkaQuVh2ovxj4KrAn6TVhEamnfBHQB7wY+F51oL6otCAlqQuYlEtSl6oO1PcGTmPy14KFwGOA/5yVoCSpS5mUS1L3ejkw3EK9hcCrqwP1vg7HI0ldy6RckrrXi0jDVFqxO3BIB2ORpK5mUi5J3Wv3adQdAu7VqUAkqduZlEtS9/rbNOouADZ2KhBJ6nYm5ZLUvc4GtrRYdwvwk86FIkndzaRckrrX/yNN4pzKFmBNrb+yocPxSFLXMimXpC5V669cAnyEyVdgGQJuBk6ahZAkqWuZlEtSd3sDcAowAmxuOD5E2tXzCuCxtf7KzSXEJkldY0HZAUiSylPrr4wCb6sO1D8GvBQ4grRM4rXAZ4Gf1/orscQQJakrmJRLkqj1V/4MnFzcJEmzzOErkiRJUslMyiVJkqSSmZRLkiRJJTMplyRJkkpmUi5JkiSVzKRckiRJKplJuSRJklQyk3JJUtdYXa/2XbLl68tG43DZoUjS3bh5kCRp3ltdrx4GvAbIrhv+We91wz8j0PPNWB/9EPCtVZWau5ZKKpU95ZKkeWt1vRpW16unAOcDTwd6x8oio4cA3wC+vLpetZNKUqlMyiVJ89nri1tg20+He4vbvwEfmd2wJOnuTMolSfPS6np1Z+AkGnrHm+gDXrm6Xl3e8aAkqQmTcknSfPVcYFGLdYeAl3YwFkmalEm5JGm+eiRp2EorFgEHdzAWSZqUSbkkab6a7mvcVMNcJKljTMolSfPV1cBoi3W3AAMdjEWSJmVSLkmar740jboLgc90KhBJmopJuSRpXlpVqa0H/h9pEudktgBnr6rUru14UJLUhEm5JGk++0/g2zRPzLcAFwMvnLWIJGkCJuWSpHlrVaU2BDwbOA648u6l4Q+kpP2IVZXaxlkPTpIauK2wJGleW1WpjQCfW12vng7su2u43wP+adGLfthL38P326v/9rLjkyQwKZckdYlVlVoErhscHFxXHIplxiNJjRy+IkmSJJXMpFySJEkqmUm5JEmSVDKTckmSJKlkJuWSJElSyUzKJUmSpJKZlEuSJEklMymXJEmSSmZSLkmSJJVsTuzomWXZUuDTwFHABuCUPM8/MkG9fYEzgP1IbyiuBN6U5/lPi/IXAa8C9gc2Ad8E3pjn+cai/CTgbcDmhtMelef5TzrxvCRJkqRWzImkHPg4sAjYE9gb+EGWZVfneX7uuHrrgGOA60nbIz8LOCfLsvvmeb4FuAdwAvB/wBKgBnwAeGXDOb6e53m1k09GkiRJmo7Sk/IsyxYDzwEOyvP8DuDyLMtOA14C3C0pz/N8A6knnSzLeoARYClwL2Awz/NPNFTfnGXZp4G3d/xJSJIkSduh9KScNNSkJ8/zgYZjlwJHN3tAlmU3AstJ8Z+e5/lgk6qHAQPjjh2VZdlfgZuBNcAH8jwfnVnokiRJ0vabC0n5LsDt446tJw0/mVCe53tnWbYTUCUNY9lGlmXPAJ4PPKbh8NeA04CbgEcCXwGGgQ/OMPamBgcHF5GG5Gxj6dKli3t7exkZGVm8bt26Xdt97R3c2O99yeBgs/da3cc2MynbzARsM5OyzUzANpMsX778jrJjUHeaC0n5RmD8H/9uFMNUmsnz/C5gTZZl12ZZdmme55eNlWVZ9kTgs8Az8jyvNzzmioZT/DrLspOBV9CBpBx4C3DiRAXr168f+/aCDlx3vvhj2QHMJbaZlthmGthmWmKbaWCb+btQdgDqTnMhKb8GiFmWPawhaT6QbYedNLMQ2Be4DCDLsiNJEzyf08KqKqN07o/vvcCHJipYunTpAb29vReMjIwcun79+ssmqtPFlpBeKPdiijdm3cQ2MynbzARsM5OyzUzANiOVq/SkPM/zTVmWnQmcnGXZC0mrrxwLvHh83SLh3gBcQkrGTwB2B35ZlB8OnAk8P8/z8yd4/DOBC/I8vzXLskeQlkf8XAeeFsuXL9/M3Zde/LuhoaFNAD09PZv8mOzuGj5K3uDPZivbTHO2mYnZZpqzzUzMNiOVq/SkvHA8W8d6bwDeN7YcYpZlG9m6lvgS4GOkxH0zqXf8qIaJnieShsJ8PcuysXPfmOf5w4rvnwt8JsuynYtrnU5aMlGSJEkqTYhxwnmS6qChoaFHAb8GDurr67u47HjmksHBwV1JE393s6dmK9tMc7aZidlmmrPNTMw2I5Wrp+wAJEmSpG5nUi5JkiSVzKRckiRJKplJuSRJklQyk3JJkiSpZCblkiRJUslMyiVJkqSSmZRLkiRJJTMplyRJkkpmUi5JkiSVzKRckiRJKplJuSRJklQyk3JJkiSpZCblkiRJUslMyiVJkqSSmZRLkiRJJTMplyRJkkpmUi5JkiSVzKRckiRJKplJuSRJklQyk3JJkiSpZCblkiRJUslMyiVJkqSSLZjuA0IIPcCBwD8CewA7A38FrgZ+GmNc184AJUmSpPmu5aQ8hPAg4HjgGODewAiwHtgMLAXuAcQQwk+A04AzYoyjbY5XkiRJmndaGr4SQvg0cAVwAHAiqad8pxjjvWOMe8UYdwHuAzwNuAw4BbgyhPC4jkQtSZIkzSPTGb7ysBjjdc0KY4y3AOcC54YQXg/8O7AP8PPtilCSJEma51pKymOML5/OSWOMI8AXZhSRJEmS1GW2e/WVEMKhIYTF7QhGkiRJ6kbblZSHEHqB84F/aE84kiRJUvdpxzrloQ3nkCRJkrrWtNcplyRpR1MdqAfgUOC5vbDHwQt7uWp49Mnrb62fVeuvjJQdXzvUV1bvD7wYeDBp2eKLgc9X1tRuLTWwNqmvrO4EPBs4krQM85+AtZU1tctKDUxqk+3tKY/Aj4ENbYhFkqS2qw7UDwCuIg23fPkIPOuXW0ZYPxprwO+rA/Ujyo1w+9RXVneur6x+AbgReCvwfOAFwPuAP9dXVv+7vrK6Q+/gXV9ZfQHwZ+B0YCXwPOBVwKX1ldWf1VdW9ywxvBkLIcQWbitDCIcX3z96ivOtCSEMTDOGG0IIH9++Z1K+EMLDQwgbQgj3Lu7vU/zMnj3N8/xzCOGWEMKunYm0ue36I40xjsYYj4gxXtuugCRJapfqQP3hpKV5H0QabrkAUo8S6TVwD+C86kD9CeVEuH3qK6t9pOWIn0t6fosaihcCfcAbgU/VV1Z3yOGm9ZXVlwJrgd1Iv7+x57Gw+HowcGF9ZfV+JYS3vR477gbwsXHHvj2N872btCR1N3oPsKZhZ/mbSD+/H07nJDHGn5H25nlDe8ObWqubB91zJicPIew+k8dJktQmXyAlqr1NygPptfCM6kC9b9aiap9XAI9ja4I6kQXAS4AnzkpEbVRfWb0v8Ekmn7/WR9pp/EOzElQbxRj/r/FWHP79uOPrJj3J3c93XYzxNx0Kd84KIewLPB343NixGOPm4uc3k+FbnwVeGUKY1f8JrfaU/y6E8JEQwiOmqhhCWBxCeEEI4SLgldsXniRJM1MdqB9M2om6WUI+JgC7k17UdxhFz/frSUnpVCKwqrMRdcRLSePjp9IHPKe+snrvDsdTtmUhhC8XwzRuDCG8qbFwouErIYQ9QwhrQwh/CSHcGUK4KoTwmmYXCCHcM4RwUQjh1yGEexXHYgjhTSGEk4rz3BJCOH38ktghhL1CCF8syu8MIVwQQjhoXJ0shPCrEMLGEML64vt/bbW8iRXA9THGSxrOs83wlbGhOiGE44uf3+0hhLPHhrw0OBtYCkx13bZqdaLnP5M+ErkkhHAd6aPA3wDrgM2kwB8IHFTUXQ+8n/TuVpKkMjyT9Bq1aIp6kBLzZwNndTKgNvsH0mtvK3qBf62vrPZV1tSGOhhTuz2f1n5/kJL3o0hDXearT5I+/XkWqX2/P4Twmxjj/05UuRjp8Ivi7tuA64H9SMO5Jqp/P+A84HbgqTHG2xuKXwX8BHgRsD/wAeAvwJuLxy4DfgpsBF5dnOPVwA9DCPvFGG8OITwIOBM4A3gLqXP4AGBZcY5JyyfxRFrfQT4rfgbHA/cCPkwaMlQdqxBjvCOEcAXwJOCbLZ53u7W6o+flwDOLjwdWAE8gTbJo/EP5PfAz0uSSc2KMw22OVZKk6VhK651PPcCMhmqWaOk06/cAuwC3tT+UjtltGnVHmf7PZEfz9RjjSQAhhB8ATyW9mZwwKSd9knIf4MExxhuKYxOOsQ4hPAD4AXAD8MwY46ZxVW6KMR5TfP+/IYRHFdd+c3HstaSf/2NijDc3xHgNcALwJuCRpE81XhVjHFsk5LsN15iqfKK4A/BoUu92KwKQxRg3F4/fB3hrCKEnxjjaUO8y4B9bPGdbTGuiZ4zx+hjjSTHGQ2KMO5P+ge0J7Bxj3CfGeEyM8Rsm5JKkOeA2oNXXo1HSp787kukm16PseKulrZ9G3V52rDccM/G9sW9ijBH4LbDXJPWfAPywISFv5kGkXvArgadNkJBD6kFvdOW4a/8LaYWjW0MIC0IIC0ifXvyYNBkX0iiLEeDLIYSnhxDGv+maqnwiy0idxK3+/f54LCFveB59pDcvjW4hTQSfNdu7+sptMcabxj05SZLmgrNofehDBL7WwVg64Rqg3mLdYeCcyprajtZp9iXSEKRWBOA7HYxlLlg/7v4WYKdJ6t8TGGzhvI8BHgB8bpKcbqJrN/593Ys0pGZo3O2FwP0BYozXAE8jfQLyDWBdCCEveumnLG9i7Pm32k4meh6N5xmzGdi5xXO2xQ69bqkkSc3U+isXkzbQmSoRHSX1ik1n6bnSVdbUImnFkVbGiPcAqzsbUUd8jtZylSHgK5U1tb92OJ4dzV+B5S3UOwP4KFALIcx0edBbScNoDp7g9qyxSjHG/40xHkqaXP0C0nzE01stb3JdaP/QpaWkn9+sMSmXJM1nLwDupPkKHqNF2XNr/ZUdrRcZ4DTgR2zt7ZvIMPAJ0tCCHUplTW0dcCzp99TMEGlN6tfPSlA7lu8DR07R0wxAjPG1wOeBb4YQ/nmG13oo8NsY46/G3S6f4Hp3xBi/CtSAh0y3vKHeXaR5ja1Oem7VPsDVbT7npEzKJUnzVq2/8lvSBiK/LQ4Nwd9f/EaBPwBH1vorF5QQ3nYrhqM8nbQixwjpI/dY3LYU998DvLroWd/hVNbU1pJWYbmV9AZjLEEfG67wU+AfiwRed/dh4GbgghDCS0MIRxRf39+k/iuBrwPfCSEc3KROMx+i2Ok9hPDCEMJhIYRnhxA+EEJ4HUAI4RXFso3Vonwl6Y3zD1opn8TPSD3q7fRo0jj7WdPqrHRJknZItf7KFdWB+iNIKyk8dwHc758WLXj+lUMjR986Gs+p9Vcm64Wd8yprapuBY+srq//F1uXqRoFLgC9W1tTuKDO+dqisqX21vrL6DdIwiCNIY30HgS9U1tR+O+mDu1iM8a9Fr/d7gVOAe5BWV/mfJvVjCOElpLHi3w0hHN7qZkTFtf6J9Cbw/aTx7DcD/0caHw5pIufTSQn8PYE/k4bOvL3F8mbOBL4UQljSsGrLjBUry9yb9AZl1oQ0eVezaWho6FHAr4GD+vr6Li47nrlkcHBwV9LaprstX758h38haRfbTHO2mYnZZpqzzUzMNqMdVbHz5u+B/4wxbvc69SGEDwAHxRiP3O7gpmFGw1eK3aE+EEL4RQjh6uLrKSGEPdsdoCRJktRMjHEIeB/QdKfSVoUQdiXNYzhpe881XdNOykMI/cDlwHGkiRU/LL4eB/wmhPCwtkYoSZIkTe6TwNkhhHtt53keALw9xjjr80xmMqb8VOA64F9ijH9fpL/YXvV7RflR7QlvxzU0NLQHzRedf/DY16GhHWm3485btmzZ4g0bNrBkyZIDhoaGJtq8oFvZZpqwzTRlm2nCNtOUbQZw6M6OqVhf/d1tOM8AMLD9EU3ftMeUhxA2AsfEGL85QdkzgS/EGJe0J7wd19DQ0EnAiWXHIUmSWtfX1xfKjkHdaSY95cM03yFtEc3Xgu02nwLyJmUPJu1Sdgxw1axFtAMYHh5evGHDhguWLFly6IIFC+zB2so204RtpinbTBO2maZsM1KJZpKUfx84OYRwabEdKgAhhP1IHxuc167gdmR9fX03kcbab6PhY8Gr/Jjs7tatW7crwG233XaZqyJsZZtpzjYzMdtMc7aZidlmpHLNZPWV15OS+StDCJeGEL4bQriEtDHDAtxRS5IkSZqWafeUxxh/H0J4OPAS4PHAMuAa4HPA6THGje0NUZLmhtX16jJgL9KukNevqtQm29pckqSWzWhHzyLxXl3cJGleW12vPhZ4I5ABvcXh9avr1U8Aq1dVan8uLThJ81Z9ZTWQdjA9GtgduBU4Czi/sqbm7o/zzIw2D5KkbrG6Xn0Z8FPS1s+9DUVLgTcAv1ldrz6khNAkzWP1ldWDgTpprt4rgOcXX88D6kW55pGWkvIQwh0hhIOK7zcU95vdbu9syJI0O1bXq08kbUjRw8SfLC4kDeH7wep6dfFsxiZp/ioS7guAfbj7/58Fxf19gAtmkpiHEJaGEL5a5HODIYTXTlL3sBDCQAjhbyGEi0IIBzSUvag4dntxnk+EEHZpKP9ACOGa4jr1EMLrphtrt2l1+MoH2bqSyAcBPzKR1A1a2WtgAXBP0jJyn+5sOJLmu2LISo30pr9Z52lPUV6rr6xWpjmU5eOkJaz3BPYGfhBCuDrGeG5jpRDCPYFvAquArwDHA3kIYf9io557ACcA/wcsKWL+APDK4hR3Ac8iLQTyEOC7IYTBGONXphFrV2kpKY8xvrPh+5M6Fo0kzRGr69UKaTJ7KxaSXrhMyiVtryPY2kM+mbEe88OB81s5cQhhMfAc4KAY4x3A5SGE00iLd5w7rvrRQD3GuLZ47IeB1wFPBL4dY/xEQ93NIYRPA28fOxBjfHtD+RUhhJz0P9WkvIlpjykPIfwwhPDgJmX7hxB+uP1hSVLpHkzaLK1VlU4FIqmrHA2Mtlh3tKjfqv2BnmIr+TGXAv0T1O0vygCIaQv43zSpC3AYTbanDyH0AIc0K1cyk9VXDgd2bVK2K3DojKORpLmj1RfFMQ7rk9QOu9N6fragqN+qXYDxc//Wk4afTFT3tlbqhhCeQZqI+pgm1/0AaSnZNS1H2oVmtCQizV98HgfcPMNzStJccgV3X21lMhG4soOxSOoet5I+pWslRxsu6rdqI9t2rO4GbGhSd7ep6oYQngh8FnhGjLE+/iQhhP8CngYcVoxFVxOtrr7ylrHVVUgvPudPsOrKZuDDwNc7GbAkzYZVldqNpKXHRlqoPgx8pKMBSeoWZ9H68OKeon6rrgFiCOFhDccOZOJhJQNFGQAhhAA8orFuCOFI0gTP58QYfzL+BCGENwMrgSNjjO7nMIVWe8p/Tlp1JQDvAM4A/jiuzhbSDNtz2hadJJXrJOAJU9QZAm4EvtbxaCR1g/OBG5h6suco8DvgR62eOMa4KYRwJnByCOGFpNVXjgVePEH1s4APhBBeAHwV+I/i+PcBQgiHA2cCz48xbjPRNITwJuA4Ug/5n1qNsZu1uvrKj4EfA4QQInBajHGwk4FJUtlWVWq/WF2vPo/UEzRKWmWl0RbgT8ATVlVqd812fJLmn8qaWqyvrFZJ65Q3WxZxlPT/pzqDnT2PB04jLXW9AXjf2HKIIYSNwFExxp/EGP8aQngmaQnF00g95FnDEJQTSUNhvp460QG4McY41gv//iLGKxrKvxhjPG6a8XaNaa++EmN8pwm5pG6xqlL7OvBw4DPAnQ1Fvwf+EzhwVaX2+zJikzQ/VdbULiItnHEDKQEfWwlquLh/A3BIZU3tV9M9d4xxfYzxOTHGXWKMe8QYP9JQtkvjMJQY449ijP0xxp1jjAfHGC9tKDsixrigeMzY7WEN5SHGuGhcuQn5JGY00TOEUCGNEdof2Gl8eYwx276wJGnuWFWpXQUcv7pefS1po6AtwG2rKtPuoZKkllTW1C6qr6xWSKveHU1aZeVW0rCSH82gh1xz3LST8hDCwaShLDeSkvLfkGbj7kMaZ77NzFtJmg9WVWpDgJOVJM2KIvE+nxY3B9KObdrDV4BTSAP++0kTP18aY9yXtEtTJI0hkiRJktSimSTlB5BWXxnbWGMngBjjz0krFbyvLZFJkiRJXWImSXkEthTbrd5MWk5nzB9JQ1okSZIktWgmSfmVwIOK738BvCGE0B9C+AfgzcB17QpOkiRJ6gYzWX3l02ztHX8r8D3gsuL+JuDZbYhLkiRJ6hrTTspjjF9o+P63IYSHAI8Fdgb+L8Z4cxvjkyRJkua9mQxfuZsY48YY43kxxhwYCiG8ow1xSZIkSV1jWj3lIYT7Ag8gbaN6c8PxPYETgGOBRcC72hmkJEmaXH1l9SGk1+F+YAS4EPhMZU3tj6UGJqklLSXlIYRlwBeBpxSHRkMInwReA7wbeH1xrlpxX5IkzYL6yuquwJeAp5F2m11YFB0JvL2+svop4DWVNbWhkkKU1IJWe8pPAp4EfAa4hLR753HAI4BDgHOAN8YYr2l/iJIkaSL1ldWdgO+T9hCBrQk5pE+uIfWe715fWX2+W7NLc1erSfm/Au+JMf59WEoI4adADpwWY3xFJ4KTJEmTOp6UkC+cpE4faWW0LwLfmo2gJE1fqxM9HwD8aNyxHxZfv9i2aCRJUkvqK6s9wGuZPCFvtKpz0UjaXq32lPcBd407trn4uql94UiSpBY9GNirxbq9wBPrK6t9ji3fcayuVwNwBHA0sDtwK3AWcP6qikOR5pvprL7y/BDC4xvu9wAROCaEcHjD8Rhj/HAbYpMkSc3tOs36AVgMrG9/KGq31fXqwaQFNPYBRkk52zDwSuCG1fVqdVWldlF5EardprNO+WuAUxtup5D+wF837vipbY5RkiRt65Zp1h8BNnYiELVXkZBfQErIe9jaibqguL8PcEFRb1pCCEtDCF8NIWwIIQyGEF47Sd3DQggDIYS/hRAuCiEc0FD2jBDClSGE9SGEW0IIZxVLZI+VfyCEcE1xnXoI4XXTjbXbtJSUxxh7pnHr7XTQkiSJ64ArW6w7BJxVWVMb7mA8aoNiyEqNNFegWZ7WU5TXivrT8XHSyjx7Ak8G3hpCOGp8pRDCPYFvkjphlwFnAHkIYWxVn18BR8QYl5KGUV0HnNZwiruAZwG7Ac8A3hBCeN40Y+0q272jpyRJmn3F8oYfJA1pmMoCYHVnI1KbHMHWHvLJjPWYH97qiUMIi4HnAG+LMd4RY7yclEi/ZILqRwP1GOPaGONm4MPFNZ8IEGP8U4zxLw31R4HK2J0Y49tjjFfEGEdjjFeQVuxrHAatcUzKJUnaca0h7RUy2eTNEeB9lTW1n85KRNpeR5MS3FaMFvVbtT/QE2McaDh2KWkX2PH6izIgTRgEftNYN4Tw8BDCeuBO0kaS75/ooiGEHtK+NgMTlSsxKZckaQdVWVMbBZ5L6gXfTNrRc6jhdjvwBuBtZcWoadud1hfiWFDUb9UupDbRaD2wpEnd9ZPVjTFeXgxfuTfwLuCKJtf9AKk9rplGrF1nOquvSJKkOaYYJ35CfWX13cC/k3pDR0i9nGdW1tTGL2msue1W0pCkVnK04aJ+qzay7ao9uwEbmtTdrZW6McZbQghrgF+FEPaMMf59SFUI4b+ApwGHFcNg1IRJuSRJ80BlTe124BNlx6HtdhZp2cNW9BT1W3UNEEMIDyvGeQMcyMTDSgaAl4/dCSEE4BE0b2MLgPuQkv5bi8e8GVhJSsj/PI04u5LDVyRJkuaO84EbmHpc+SjwO7bdcb2pGOMm4Ezg5BDCkhBCP3As8LkJqp8F7BdCeEEIYSFpaWyA7wOEEJ4fQnhgSO5Hmgh6cYxxLCF/E3Ac8IQY459ajbGbTTspDyEcFEJ4QsP9ZSGE00IIPw0hnFQM5pckSdI0FTt1VknzA5ol5qNFeXUGO3seTxrffRNwHvC+GOO5ACGEjSGEQwBijH8Fngm8mTQO/RggaxiCsj/wY9Iwl0uAv5GWQBzzfmAP4IrivBtDCJ+cZqxdZSbDVz4M/KC4AXyE9Es7DziBNI7t3W2ITZIkqeusqtQuWl2vHsrEO3r2kHrSn7eqUvvVdM8dY1xPWhZxorJdxt3/EROvzEKM8Z3AOye5znTXT+96M+nVfihwIUAIYWfg2cBrY4zPBv4TeGH7wpMkSeo+qyq1i0jrfj8R+CTw5eLrE4HKTBJyzW0z6Sm/B+kjCoB/Ju0K9c3i/m9IuzpJkiRpOxRDU84vbprnZtJTfj0wth3rMcCvxwb1k2bd3tGOwCRJkqRuMZOe8g8BnwkhvJS0YH3jcJXDSb3lkiRJklo07aQ8xvi5EEIdOJi09E3jRyp/BT7aruDUXdZW6w+8x73Dfv0vWMgdf4w7Lf83P3WR1D7VgXoAHnLvnnD/FbssIkDP8rKDkqTCjDYPijFeAFwwwfGTtjcgdZ+11fqzSJOE//Fv6yIXfngzwHVXfa3+SeDUFbXKX0oNUNIOrTpQXwC8jLTd/IPWjUY+eMddAL9loP4R4GO1/srfJjmFJHVcS0l5COEBjfdjjL9vZxBZli0FPk0aq74BOCXP849MUG9f4AxgP9J4+CuBN+V5/tOi/EXAq0hrZ24iTUB9Y57nG4vyhcBq0vqfw8BpwFvzPJ/uGp9qg7XVegDeC7xxguJdgFXAC9ZW649fUatcN6vBSZoXqgP1hcA3gCcBfeOK7we8C3hudaB+RK2/4qdzkkrT6kTPG0i7Ro19bbePk1Zx2RN4MvDWLMuOmqDeOtLk0nsBy4BTgXOKZBvSyjAnkCacPoKUvH+g4fHvAB5FStofBRxN2m1K5Xgx6ffVw8RtcSHpd33e2mp9/IupJLXiVCZOyMcsJK3D/OVZi0iSJtDq8JUHdiqALMsWkxaxPyjP8zuAy7MsOw14CXBuY908zzeQetLJsqyHtFHRUlLiNpjn+Scaqm/OsuzTwNsbjr0YeEWe5zcX5zgVeDnQ+DjNgrXVeg/pTVLvFFUXAPcnbVD1tQ6HJWkeqQ7Udyd1vEz1pn4h8NTqQP3Btf7KVZ2PTJK21VJPeYzxxsZbm2PYH+jJ83yg4dilNNlBCiDLshuBzcDZwOl5ng82qXoYMFA8ZhmwvDh3S9dRRz2elGy3ooe0LbAkTccxNN+mfLwtwEs7GIskTWpGEz3HhBDuA+w0/vg0x5zvAtw+7th6YEmzB+R5vneWZTuRxoZPOB48y7JnAM8HHtNwnbFzN15npyzLFuR5PjyNmKc0ODi4iDQkZxtLly5d3Nvby8jIyOJ169bt2s7r7ij6FvPwoU1sYYL2M4Ee4MGDg4Nd+bMC28wUxv5XLBkcbPb+vPvYZqAPHjY09adxYxb2wgH+n+nuNgOwfPly5xaoFNNOykMI9wQ+RhqPPf4jwUBKklv9JwiwERj/x78bxTCVZvI8vwtYk2XZtVmWXZrn+WVjZVmWPRH4LPCMPM/rDdcZO3fj93e1OyEvvAU4caKC9evXj327zQo23WK/py/kqjO3MNriT37RbtyXbd+8dQ3bTEv+WHYAc4ltBh67aAE/3Tzcclf5g/t6noT/Z6CL20whlB2AutNMeso/QxoW8l7S6idbtjOGa4CYZdnD8jy/ojh2IMWwkxYsBPYFLgPIsuxIoAY8J8/zn4xVyvP8tizLBotz/2kG15mu95I2WtrG0qVLD+jt7b1gZGTk0PXr1182UZ357g8/HXrY6DA/b7H68JaNnAu8oJMxzWW2mUktISXkezHFm/luYpuBi7cMP3c0zRlq5bVuS3149P2kiaFdyTYjlWsmSfkRwKoY49p2BJDn+aYsy84ETs6y7IXA3sCxpEmZd1Mk3BuAS0jJ+AmkXUV/WZQfDpwJPD/P8/PHPx5YA7w9y7ILScMm3kBaIrHtli9fvpk07n0bQ0NDmwB6eno2devHZM86lV+srdZ/BRzE1L0SvXGED3XrzwpsM5NpGLKywZ/NVrYZ2Hhr/YvAB9n209iJLNgc+Z9u/VmBbUYqW6tLIjZaD9zS5jiOB4aAm4DzgPfleX4uQJZlG7MsO6Sot4SUWK8H/gAcDhzVMNHzRNI/368Xj9uYZdlY7zvAO0k96teSJnl+E/hkm5+LWvdmmswJaLAF+HFxk6SW1ford5FW4BqZouoQsLrWX/lz56OSpImFGKe3b04I4T+ApwNPjzF2Yiz2vDc0NPQo4NfAQX19fReXHU+Z1lbrzwe+QNrMqXFi7GhxuxD41xW1SteO8wTbzGSKiXm3A7vZu7eVbSapDtQDqUPmv0j/ZxrnQg2TOqe+CLyk1l+ZKnmf12wzc0/Rfo8gzePbHbgVOAs4v9ZfcePDeWYmPeUPAR4KXBdCWBtCWD3u9tE2x6h5bEWtcgapPX2SrRNwoYdLgBcCh3d7Qi5p5mr9lVjrr7wDeCxpr4MtkMbM9cL3gacAK7s9IdfcUx2oHwzUSSMIXkFaUe4Vxf16UT5tIYSlIYSvhhA2hBAGQwivnaTuYSGEgRDC30IIF4UQDhhXvncI4ewQwh0hhFtDCJ+f4ByLQghXhRD8JGoKM+kpn2pHzxhj3HfmIc1/9kZMbG21Hv7hWX332fNxC/7c0xvs9Wxgm2nOnvKJ2WYmVh2oh6fv3Hffw3ZacFNP8P9MI9vM3FEk3BeQ5s9N1IE6SnqDeWitv3LRdM4dQvgiaTjw2Dy+HwAvijGeO67ePYHrgFXAV0hDjV8D7B9j3BxC6AOuIK129/+KePpjjBePO8+JwBOKx91vOrF2m2n3lMcYHzjFzYRcM7KiVon3P6Tvzp5eV6OS1Bm1/ko8Yue+v/UE/89obiqGrNRonpBTHF8I1Ir6LQkhjO2i/rYY4x0xxsuBsV3UxzsaqMcY18YYNwMfLq77xKL8RcC6GOP7Y4wbY4xbJkjI9weeR1qRTlOYyfAVSZIkdcYRwD5MnaP1FPUOn8a59wd6Yoyt7KLeT8Mu6DENrfhNQ93HAteHEL4VQvhrCOHnIYTHjjvHJ4A3AndOI8auNaOkPISwZwjhAyGEX4QQri6+nhJC2LPdAUqSJHWRo6HlPa9Gi/qtms4u6rtw913Qx9e9P2mc+6eA+5GGsXwrhLAMIISwArgjxvjtacTX1aadlIcQ+oHLgeNISxj+sPh6HPCbEMLD2hqhJElS99id1veRWVDUb9V0dlHfWJQ1q/s34BcxxnNijEMxxs+SVod5XJGYv5M0Bl0tmklP+amkgf8PiDEeHWN8ZYzxaNJkgevp4t3QJEmSttOtpOU6WzFc1G/VNUAc14F6IBPvbj5QlAEQQgjAIxrq/obme40cACwHLixWXTkLuHcI4c923jY3k6T88cB7Yoy3NR4s7p9clEuSJGn6zqL1/KynqN+SGOMm0s7nJ4cQlhSjH44FPtckjv1CCC8IISxka6/394uva4FHhxCeEkLoDSG8CFgK/Ly47U1K6g8srvHX4vurW42328wkKR+/yUujRUy9c5okSZImdj5wA1OPKx8Ffgf8aJrn32YX9bHlEEMIG0MIhwDEGP8KPJO0+/btwDFAVqzEQoyxDlSBj5LGmv8HaWPJ24qVWP48diP15o8W9914solWxyw1+j7pHdalMcZrxg6GEPYD3k36BUuSJGmaav2VWB2oV2ltnfLqdHf2jDGuJy2LOFHZLuPu/4iJV2YZK8+BvIVr/og0GVSTmElS/nrgx8CVIYQB4C/AfYCHA78vyiVp3lhdrwbgUODlwH6kF8MLgE+tqtRuLDM2SfNPrb9yUXWgfihpvfJ9SEn4AtJohR5ST/rzav2VX5UVo9pv2kl5jPH3IYSHkxaafzywjDRx4HPA6THGjZM9XpJ2JKvr1b2Ab5EmOI2w9f/mo4E3r65XPw68blWl5tA9SW1TJOYV0jrkR5NWWbmVNNb7R9PtIdfcN5OecorEe3Vxk6R5aXW9em/ShKX7AYG7/88cm1tzHLB4db167KpKzRdJSW1TJN7nFzfNczNZp3xJCOFe444dE0J4TwjhyPaFJkmleztwX6Bvkjp9wItJu9tJkjQjM1l95YukCZ0AhBDeAXyB1Fv0vRDCc9sUmySVZnW9uhh4KWmi1VSGgVd3NiJJ0nw2k6T8YOB78PeF5I8H/jvGeC/ScJY3ti88SSrNPwE7tVi3D3hqB2ORJM1zM0nKdwduKb4/CLgXWxedz4F/aENcklS2XZjevgs7dyoQSdL8N5Ok/C/AQ4vvnwrcEGO8vri/mNa3hpWkuexmpjcZ/rapq0iSNLGZrL7yVeCUEMITgX8F3t9Q9kjg2nYEJkklu5C0493yFupuAU7vbDiSpPlsJj3lbwE+SBpreSrw3oayg0hJuyTt0Ip1xz9E2o56KguAT3U2IknSfDaTzYOGgXc1KXvWdkckSXPHR4EnFLdmq7BE4GWrKrXrm5RLkjSldq9TfkT7QpOkcq2q1IaBZ5BWlrqTNExlM3AXadvr3wPPXlWpfa7pSSRJasFMxpR/ERgEXgl/X6f8JNLWr28OIfx7jNEhLJLmhVWV2hDwxtX16ruAZwMPJCXnvwR+sKpSGy0zPknS/DCTpPxg0trk49cp/68QwodI65SblEuaV1ZVahtwMqckqUNcp1ySJEkq2Ux6ysfWKf8JrlMuSZLUEWur9QAcARxN6hS9FTgLOH9FrRLLjE3tN5Oe8rF1yr8GvAn4fEOZ65RLkiRtp7XV+sFAHTgPeAXw/OLreUC9KJ+2EMLSEMJXQwgbQgiDIYTXTlL3sBDCQAjhbyGEi0IIBzSUhRDCu0MIfwwh3BFC+GUI4XEN5UeEEM4PIdweQvjzTGLtNq5TLkmSNIcUCfcFwD6kXG1sZMOC4v4+wAUzTMw/DiwC9gSeDLw1hHDU+EohhHsC3wROAZYBZwB5CGFRUeX5wLGknvylwBeK8rFYN5GGN79+BjF2pWkn5THG4Rjju2KMT48xnhhjHGooe1aM8YPtDVGSJKk7FENWaqS9EZrlaT1Fea2o35IQwmLgOcDbYox3xBgvB04DXjJB9aOBeoxxbYxxM/Dh4rpPLMofCPwkxnhtjHGUNBH+nsAeADHGC2OMXwCuazW+bjeTnnJJkiR1xhFs7SGfzFiP+eHTOPf+QE+McaDh2KVA/wR1+4syAGKMEfhNQ90zgP1CCA8pesdfBlwO/Gka8ahBSxM9Qwh3AEfEGH8dQthA2sGuqRjjru0ITpIkqcscTdqcrJWO09Gi/vktnnsX4PZxx9YDS5rUvW2Sun8iDbG5oojjNuCootdcM9Dq6isfBG5q+N4Zv5I0z6yuV3cHHkX6WPzGVZXaFSWHJHWj3Wk9P1tQ1G/VRmB8x+luwIYmdXebpO6JwONIw1j+SFqR7zshhANjjIPTiEmFln7pMcZ3Nnx/UseikSTNutX1agV4B1AFekm9XgtW16uXAf+9qlJzAr80e24lLS/dSo42XNRv1TVADCE8LMY49qb7QGBggroDwMvH7hQbRj4C+ERx6BHAV2KMNxb38xDCOlKifuY0YlJhxmPKi6Vw/iGE8Njia8sTDSRJc8PqevXRwMWkhLyPu6/08AjgjNX16vtKCk/qRmfRen7WU9RvSYxxEylhPjmEsCSE0E9aQeVzE1Q/izRm/AUhhIXAa4rj3y++/hJ4TghhzyIn/FdgX4oEP4TQE0LYifTJGyGEnRpWbtEEZpSUhxD+gzSc5UrgZ8XXwRDCK9sYmySpg1bXq7sC3yNt/NY3QZVAep144+p69ZjZjE3qYucDN5A+sZrMKPA74EfTPP/xwBApjzsPeF+M8VyAEMLGEMIhADHGvwLPBN5MGod+DJAVK7FAWirxl8CFRfkpwEtjjFcV5YcCdwLfBe5bfH/1NGPtKtNOykMILyetcfkD4FnAY4uvPwQ+HkI4tq0RSpI65YWkhLyVVR7etrpe9RNRqcOKnTqrwBaaJ+ajRXl1ujt7xhjXxxifE2PcJca4R4zxIw1lu8QYf9Jw/0cxxv4Y484xxoNjjJc2lG2OMa6KMe4ZY9y1qPflcY8N4277TCfWbjOTnvLXAatjjMfEGPMY4y+Lr8cAHwNOaG+IkqQO+Q+Kj5Zb8BDSJFBJHbaiVrmI1NN8AykBHy6Khov7NwCHrKhVflVGfOqMmSTlDwS+1aTs26Q1MyVJc98+06i7hTReVNIsKBLzCmmznk8CXy6+PhGomJDPP60uudPoJtKQle9PUPZPbF06UZI0tw1PXeXvAmkcqqRZUgxNOZ/W1yHXDmwmSflngXcUM2jPBP4C3Ie0besbgXe1LzxJUgf9H/AE0jKIU1kA2DMnSR0yk6T8ZGAZKQF/S8PxYeBjMcaT2xGYJKnjVpOS8qmMAOeuqtT+2OF4JKlrTTspjzFG4A0hhP8G/pGUoN8KXFgsnyNJ2jH8L6m3/GCaT/iMpE6Xd8xWUJLUjVqe6BlCeGgIYXUI4ZwQwieBR8YYvxNj/FKM8VwTcknasayq1EaAp5GGpYyw7fJrm0lrCz91VaV2ySyHJ0ldpaWkPITweNKOb8eTelReCnw3hHBcB2OTJHXYqkptPXAY8GzgJ6REfAT4I2mO0L6rKrUflBagJHWJVoevvBO4Cnh6jPEPIYRdgdOB95CW55Ek7aBWVWrDwNnFTZJUglaHrzwceFeM8Q8AMcY7gDcAu4cQ7t+p4CRJkqRu0GpSfi/SR5mN/tBQJkmSJGmGprOjZ+xYFJIkSVIXm86SiOeHEMbPzAf4ybjjMca423bGtcMbGhraA9ijSfGDx74ODblBXqNly5Yt3rBhA0uWLDlgaGhoU9nxzCG2mSZsM03ZZpqwzTRlmwH6+vouLjsGdaeQlh2folIIJ07npDHGd844onliaGjoJGBaPzdJklSuvr6+UHYM6k4tJeWavhZ6yr8EHENa1UaF4eHhxRs2bLhgyZIlhy5YsMAerK1sM03YZpqyzTRhm2nKNoM95SrPtHf0VGv6+vpuAm6aqKzhY8Gr/OO/u3Xr1u0KcNttt122fPnyO8qOZ66wzTRnm5mYbaY528zEbDNSuaYz0VOSJElSB5iUS5IkSSUzKZckSZJKZlIuSZIklcykXJIkSSqZSbkkSZJUMpNySZIkqWQm5ZIkSVLJTMolSZKkkpmUS5IkSSUzKZckSZJKZlIuSZIklcykXJIkSSqZSbkkSZJUMpNySZIkqWQm5ZIkSVLJTMolSZKkkpmUS5IkSSUzKZckSZJKZlIuSZIklcykXJIkSSqZSbkkSZJUsgVlByABrK3We4B/XriE/R78bwv5y2Uj91n+n9xRdlySJEmzwaRcpSqS8VcBbwSWb9nA0OVf2EIc4eq11fpZwNtW1CrXlBulJElSZzl8RaUpEvIvAh8E9iK1x0VxBIrvnwn8am21/qiSQpQkSZoVJuUq0+uAZ9P8E5sFwD2A766t1neetagkSZJmmUm5SrG2Wl8AvAnom6JqL7AbUO14UJIkSSUxKVdZjgTu2WLdBcBxHYxFkiSpVCblKssDgC0t1g3A3h2MRZIkqVQm5SrLFqbX/lpN4CVJknY4JuUqyy+ARS3WHQLO72AskiRJpTIpVylW1CrXkhLtkRaq9wEf62xEkiRJ5TEpV5neRErK4yR1tgBfWVGr/Gp2QpIkSZp9JuUqTZFoHwX8jW3HjA+TkvUceNEshyZJkjSrTMpVqhW1yg9JK7G8BbgG2Nh3Dwi9nAMcAjx3Ra2yucwYJUmSOq3ZTorSrFlRq9wKfAj40ODg4K7A7cDK5cuX31FuZJIkSbPDnnJJkiSpZCblkiRJUslMyiVJkqSSmZRLkiRJJTMplyRJkkpmUi5JkiSVzKRckiRJKplJuSRJklQyNw9S6dZW67sA/w68DHjATssCWzbE1aPD9Q+vqFUuKzk8SZKkjrOnXKVaW60/FvgD8DHg0cB97rotMjrM84FL11brn11brfeVGqQkSVKHzYme8izLlgKfBo4CNgCn5Hn+kQnq7QucAexHekNxJfCmPM9/WpT3Ax8EDgLuCeyc5/ldDY8/CXgbsLnhtEflef6Ttj8pTWlttf5w4IfAQrZ9g7iw+PrC4utLZysuSZKk2TZXeso/DiwC9gSeDLw1y7KjJqi3DjgGuBewDDgVOCfLsrEEbgj4KrBykmt9Pc/zXRpuJuTlORXoY/J22Ae8ZG21fuCsRCRJklSC0nvKsyxbDDwHOCjP8zuAy7MsOw14CXBuY908zzeQetLJsqwHGAGWkpL0wTzPrwauzrJsn1l7ApqRtdX6PsCTgNBC9S3A8aQx55IkSfNO6Uk5sD/Qk+f5QMOxS4Gjmz0gy7IbgeWk+E/P83xwGtc7KsuyvwI3A2uAD+R5PjrdoKcyODi4iNT7v42lS5cu7u3tZWRkZPG6det2bfe1dwR9u/CkoY1socnPaJyFwFMGBwe78mcFtpkpLBn7Ojg4nX8F85ttZlK2mQnYZpLly5ffUXYM6k5zISnfBbh93LH1bP2nuY08z/fOsmwnoArEaVzra8BpwE3AI4GvAMOkcejt9hbgxIkK1q9fP/btBR247g6hctRCrv7GFkaHW6u/cBf2Ytt20jVsMy35Y9kBzCW2mZbYZhrYZv6ulU9wpbabC0n5RmD8O/LdKIapNFNM4FyTZdm1WZZdmuf5lEvn5Xl+RcPdX2dZdjLwCjqTlL8X+NBEBUuXLj2gt7f3gpGRkUPXr1/flUv+/e68oSeNDvMVoLeV+ls2cSlwWEeDmsNsM5NaQkqu9mKK/xvdxDYzKdvMBGwzUrnmQlJ+DRCzLHtYQ9J8IDDQ/CF3sxDYF5jJP5BROvSOePny5Zu5+yovfzc0NLQJoKenZ1O3fkx21/p6Tur53r2F6luIfKJbf1Zgm5lMw/CDDf5strLNNGebmZhtRipX6Ul5nuebsiw7Ezg5y7IXAnsDxwIvHl83y7IjSb0al5CS8RNISd0vi/JAGqM8Nk55UZZlY73qZFn2TOCCPM9vzbLsEaTlET/XwaenJlbUKkNrq/UPkYb4TLYO+ShwJ/DlWQlMkiSpBHNlScTjScsZ3gScB7wvz/NzAbIs25hl2SFFvSWkyZnrSRvOHE5aZ3ys22NvUgJ3VXF/fXF/zHOBa7Is2wScBXwe+EAnnpBa8n7gf0m/+4mMkFZe+dcVtcrGWYtKkiRploUYpzNPUu0wNDT0KODXwEF9fX0Xlx1PmdZW6wuA/wReT5pLMBQWsFMcJpLeoL1xRa3ymzJjnAtsM80Vq/LcDuzmR+5b2Waas81MzDYjlav04SvqbitqlWHg5LXV+inAk/ruQWW/Zyz86J9/PXzA09617+VlxydJkjQbTMo1J6yoVYaA7xQ9WB9dfvCCG8uOSZIkabbMlTHlkiRJUtcyKZckSZJKZlIuSZIklcykXJIkSSqZSbkkSZJUMpNySZIkqWQm5ZIkSVLJXKd8B7G2Wj8AeCWwBBgE3reiVvlruVFJkiSpHUzK57i11frjgRqw57iiE9ZW6xcD/2JyLkmStGNz+MoctrZafwpwAdsm5GMeBfx+bbV+79mLSpIkSe1mUj5Hra3We4FzgDBF1XsAP+18RJIkSeoUk/K56620Prxo/7XVeqWTwUiSJKlzTMrnrpdPs/67OhKFJEmSOs6kfO5aNs369+9IFJIkSeo4k/K5a3ia9e/sSBSSJEnqOJPyueuX06z/jY5EIUmSpI4zKZ+7Xj+NuluAT3YqEEmSJHWWSfkctaJWuQI4r8Xqb1tRq8ROxiNJkqTOMSmf254M/HiKOu9eUaucOhvBSJIkqTNMyuewFbVKXFGrHA4cCVxImvw5CtwFnA08cEWt8o7SApQkSVJbtLo5jUq0olY5H/jHsuOQJElSZ9hTLkmSJJXMpFySJEkqmUm5JEmSVDKTckmSJKlkJuWSJElSyUzKJUmSpJKZlEuSJEklMymXJEmSSmZSLkmSJJXMpFySJEkqmUm5JEmSVDKTckmSJKlkJuWSJElSyUzKJUmSpJKZlEuSJEklMymXJEmSSmZSLkmSJJXMpFySJEkqmUm5JEmSVDKTckmSJKlkJuWSJElSyUzKJUmSpJKZlEuSJEklMymXJEmSSrag7ADmq6GhoT2APZoUP3js69DQ0CxFtGNYtmzZ4g0bNrBkyZIDhoaGNpUdzxxim2nCNtOUbaYJ20xTthmgr6/v4rJjUHcKMcayY5iXhoaGTgJOLDsOSZLUur6+vlB2DOpOJuUd0kJP+ZeAY4CrZi2oHcDw8PDiDRs2XLBkyZJDFyxYYA/WVraZJmwzTdlmmrDNNGWbwZ5ylcfhKx3S19d3E3DTRGUNHwte5R//3a1bt25XgNtuu+2y5cuX31F2PHOFbaY528zEbDPN2WYmZpuRyuVET0mSJKlk9pTvINZW6w8CngLsAtwMnL2iVrmt3KjUqrXVegAOAQ4i/d1dC3x7Ra3SvbOpJEnS35mUz3Frq/WHAB8HjgTuAiLQC3xqbbX+ReB1K2qV20sMUVNYW60/C/gAsC/pdwjQB9y+tlp/H/DBFbWKkzskSepiDl+Zw9ZW6wcAFwKHFYd2AnYGFpKSumOAX6yt1peWEqCmtLZafxnwdeBBQCD9/nYmvSG+J/Be4NNFT7okSepSJuVz1NpqvRf4FimB621SbSEp2ft/sxWXWre2Wn8o8ElSMt7MAmAl6Q2WJEnqUiblc9fTSEsqNkvIxywEnre2Wr9v50PSNL0KGG6hXi9wQodjkSRJc5hJ+dy1ksl7WBsNA8/pXCiarmI4ygrSm6apBOCAtdX6/p2NSpIkzVUm5XPXXkzv93O/TgWiGVkELJ7mY/wdSpLUpUzK566N06gbgb91KhDNyBbS72U67uxEIJIkae4zKZ+7vk1K7FqxE/D9DsaiaVpRq4wCPwZGW3zIeuA3HQtIkiTNaSblc9fptDamfBS4fEWtcmGH49H0fZTWesu3AP+zolbZ3OF4JEnSHGVSPketqFX+CryJyXtaIzACHDcrQWm6zgHOY/JPPIaAm4APzkpEkiRpTjIpn8NW1CofAd5ISrzH96IOARuAp6yoVX4+y6GpBStqlRHgaNJQJLh7cj5CesN1NfD4FbXKrbMcniRJmkNMyue4FbXKh0grsbwHuAS4FriA1Du+x4pa5YclhqcprKhV7lxRqxwNPBL4PHAFKRHPgacAB6yoVf5YYoiSJGkOWFB2AJrailrlz6Sk/D1lx6KZWVGrXAq8vOw4JEnS3GRPuSRJklQyk3JJkiSpZCblkiRJUslMyiVJkqSSmZRLkiRJJTMplyRJkkpmUi5JkiSVzKRcc8rQ32LZIUiSJM06Nw9S6dZW63sBrwBeBtwnLABGOTuO1j8EnFtsVy9JkjRv2VOuUq2t1p8BXAe8EbgvEOIwxFEOBc4Gzl1brS8uMURJkqSOMylXadZW648Hvg4sBBaNK+4tbocBX11brYdZDk+SJGnWmJSrTO8Hpkq2FwJHAY/tfDiSJEnlMClXKdZW6w8FHkdrbXAYeHVnI5IkSSqPSbnKcjBwV4t1+4DHdzAWSZKkUpmUqyx9Ha4vSZK0wzApV1muJ40Xb0UE6h2MRZIkqVQm5SrLj4CbWqw7AvxP50KRJEkql0m5SrGiVhkF/ps0iXMyI8DNpKUTJUmS5iWTcpXpE8BpNE/Mh4D1wJNW1CqbZysoSZKk2WZSrtKsqFUicDzwUuC3xeGRYuXyzcDpwCNX1CpXlhKgJEnSLFlQdgDqbkVivnZttf4FoH+nZWH/h1YXnnlbfeRBh7x0nz+VHZ8kSdJsMCnXnFAk55cPDg7eCLD7fr0bSg5JkiRp1jh8RZIkSSqZSbkkSZJUMpNySZIkqWQm5ZIkSVLJTMolSZKkkpmUS5IkSSUzKZckSZJK5jrlkqSuUF9Z3Qc4moUL9+g74kmMXHH5g3nPKReWHZckgUm5JGmeq6+s7gmcBjwF2MyWLWHoB9+F4eFf1ldWfw68rLKmdmW5UUrqdnMiKc+ybCnwaeAoYANwSp7nH5mg3r7AGcB+pKE3VwJvyvP8p0V5P/BB4CDgnsDOeZ7f1fD4hcBqoAoMk/5JvzXP89ip5yZJKk99ZXUv4CLSa0IAdgJgeHisymOAX9ZXVh9XWVO7vIwYJQnmzpjyjwOLgD2BJwNvzbLsqAnqrQOOAe4FLANOBc4pkm2AIeCrwMom13kH8Chg/+Lr0cBx7XkKkqQ56IukhLyvSfkCYGfg7PrK6lx5TZTUhUrvKc+ybDHwHOCgPM/vAC7Psuw04CXAuY118zzfQOpJJ8uyHmAEWEpK0gfzPL8auDrLsn2aXO7FwCvyPL+5OMepwMuBT7T5aUmSSlZfWX0wcFgLVXuBfYAjge93MiZJamYu9ArsD/TkeT7QcOxSoL/ZA7IsuxHYDJwNnJ7n+eBUF8mybBmwvDh3S9eRJO3Qnkd6rWhFBP69g7FI0qRK7ykHdgFuH3dsPbCk2QPyPN87y7KdSGPDWx0PvkvDuRuvs1OWZQvyPB/e5hHbYXBwcBFpSM42li5duri3t5eRkZHF69at27Wd150Hxn7vSwYHp3yv1TVsM5OyzUzANgP09e3J0FCrr3O99PQ8YHBwsDt/VthmxixfvvyOsmNQd5oLSflGYPwf/24Uw1SaKSZwrsmy7Nosyy7N8/yyFq4zdu7G7+9qd0JeeAtw4kQF69evH/v2gg5cd774Y9kBzCW2mZbYZhrYZmDBYx/P8M8ugJGRlur3PrT/CWzbSdQ1bDN/F8oOQN1pLiTl1wAxy7KH5Xl+RXHsQGCg+UPuZiGwLzBpUp7n+W1Zlg0W5/7TDK4zXe8FPjRRwdKlSw/o7e29YGRk5ND169dP9Wai2ywhJVd7McUbs25im5mUbWYCthkYGbj88YyMfIvWkqzhkd9dfzxQ63BYc5ZtRipX6Ul5nuebsiw7Ezg5y7IXAnsDx5ImZd5NlmVHkl50LyEl4ycAuwO/LMoDacjI2LCRRVmW0bAs4hrg7VmWXUhaFusNpCUS22758uWbaTKWcWhoaBNAT0/PJj8mu7uG4Qcb/NlsZZtpzjYzMdsM1G+95VzgeuCBTD2H6k42bVy7fPnyu6aoN2/ZZqRyzYWJngDHk5YzvAk4D3hfnufnAmRZtjHLskOKektIifV64A/A4cBRDRM99wbuBK4q7q8v7o95J6lH/VrSJM9vAp9s/9ORJJWtsqYWSR08o0w+/2gUOLaypta1Cbmk8oUY3Tdntg0NDT0K+DVwUF9f38VlxzOXFJOsbgd2s6dmK9tMc7aZidlmtqqvrB4BfJ3UsdPL1uEsQ8XtpZU1ta4dtjLGNiOVa670lEuS1BGVNbXzgT2AFwHnEsIlPQ/aD+5xjzcC9zUhlzQXlD6mXJKkTqusqW0Gvgx8ueHTldOXL1++cfJHStLssKdckiRJKplJuSRJklQyk3JJkiSpZCblkiRJUslMyiVJkqSSmZRLkiRJJTMplyRJkkpmUi5JkiSVzKRckiRJKplJuSRJklQyk3JJkiSpZCblkiRJUslMyiVJkqSSmZRLkiRJJTMplyRJkkoWYoxlxyBJkiR1NXvKJUmSpJKZlEuSJEklMymXJEmSSmZSLkmSJJXMpFySJEkqmUm5JEmSVDKTckmSJKlkJuWSJElSyUzKJUmSpJKZlEuSJEklMymXJEmSSmZSLkmSJJXMpFySJEkqmUm5JEmSVDKTckmSJKlkJuWSJElSyUzKJUmSpJKZlEuSJEklMymXJEmSSmZSLkmSJJXMpFySJEkq2YKyA9D8kWXZq4CVwMOBb+R5Xm0o6wc+AzwCuAF4VZ7nP2wofzbwfmAP4BfAS/I8v7Gh/N3AccBC4GvA8Xmeby7KlgKfBo4CNgCn5Hn+kQ49TbXRTNtMlmVPBd5cPG4L8APgtXme/6UoXwl8Friz4XKvyPP8S0X5QmA1UAWGgdOAt+Z5Hjv0VNUG29Fe9gF+B2xqON0X8zw/bty53wLsBnwXODbP89uKMtuLpI6zp1ztNAi8h/SC9XdZlvUB5wA5sAx4J/CNLMvuU5Q/BFgDvBK4J/Ab4KsNjz8WOAb4R2Bf4MHAuxou8XFgEbAn8GTgrVmWHdX2Z6dOmFGbISVO7yP9zvcFRkhtqNFFeZ7v0nD7UkPZO4BHAfsXX48mvenT3DbT9jLmXg3toTEhf1LxmKeTOgZGgE82PM72IqnjTMrVNnmen5Xn+dnALeOKDgfuAbwvz/PNeZ5/BRgAnlOUvwD43zzPv5fn+Z2kF8ADsix7WFH+YuBDeZ5fn+f5X0kvni8GyLJscXGet+V5fkee55eTXrBf0qnnqfaZaZvJ8/zLeZ5/O8/zTXmebyT1Yj5+Gpd+MfCuPM9vzvP898Cp2GbmvO34HzOVlcDpeZ5fnOf5BuBtwNFZlu1WlNteJHWcSblmQz9weZ7now3HLi2Oj5VfOlZQvChe16y8+P7eWZbdl9Rz1ZPn+UCTc2vHNFWbGe8wUhLW6BFZlq3LsqyeZdn7syzbGSDLsmXAcrZtU7aZHVer7aWeZdlglmW1LMvuP+7xl47dyfP8WtKwqAfbXiTNFpNyzYZdgPXjjq0HlsywfOz7JUXZ7ZM8VjumqdrE32VZ9hjgv4ATGg5fQEqa7gs8ldSTekrDucfO13junbIsc57Njmmq9nILcDCwD2nM+SbgnCzLelt4vO1F0qzwH4pmw0bSGOBGu5EmZc6kfOz7DaQXzV0neax2TFO1CQCyLHsEaSzxsXme/2zseJ7n1zdUuzrLsjcDZwCvLs49dr7G7+/K83y4bc9As2nS9lIMcfpVcfyWLMv+oyjbD7hqisfbXiTNCnvKNRsGgIdnWdbY3g5k63CDgeI+AFmW7QI8qFl58f26YqWNa4DYMP58/Lm1Y5qqzZBl2cOB7wGvz/P8a1OcbxQIAMWKGoNs26ZsMzuuKdvLOLG4hYbHHzhWmGVZhTR5/Crbi6TZYk+52qb4KHfs1pNl2U6kVQx+RFqa7k1Zln0YyEhLmh1dPPSLwEVZlj0R+ClpIudv8jy/oihfA/xnlmXfIQ1VeQdwOkCe55uyLDsTODnLshcCewPHUkwE1dw20zZTvAk7D3jLuFVVxs57FHBpnuc3ZVm2L2mllm80VFkDvD3LsguBnYA3kCaLag7bjvbyj8AdwNWkT9ZOAeqkN/WQ2sMZWZZ9GbiWtMLLWXme395QbnuR1FH2lKud/ov0wvg20qoHdwKn5Xk+RHqRfBZpLOa7gKPzPL8ZIM/z35KS6E8DtwKPBJ7bcN7PADXgItJaw9eSEvMxxwNDwE2kRO19eZ6f25FnqHabUZshjR+/D/CxLMs2jt0aznskcEmWZZuA84GfkxKpMe8ELiO1pUuBb3L3JfA0N820vewLfJs0HOUqYHfgaXmejwDkeX4ecFJR58+k/RAalzy0vUjquBCjex9IkiRJZbKnXJIkSSqZSbkkSZJUMpNySZIkqWQm5ZIkSVLJTMolSZKkkpmUS5IkSSUzKZckSZJKZlIuSZIklcykXNJ2CSGcFEKITW5vbqgXQwgntPnaBxbXv8e44yuL691rGudaE0IYmOrcbYj5ayGEDzS77gzP+aMQwrca7r8thHDe9pxTkjS7FpQdgKR54U7S1vbj/b7D1z0QOBH4OPC3huPfBh5L2nK9Ve8GFrdw7hkLITwKeDpp2/d2+g9gpOH+/wPeFEI4IsZ4fpuvJUnqAJNySe0wGmP8v7KDGBNjXAesm+ZjrutQOI1eA3w3xjjYzpPGGK8cd399COHrxfVMyiVpB+DwFUmlCCE8NYRwXgjh5hDCHSGEX4YQnjKuztIQwmkhhD+FEO4KIfwhhFArylYCpxdV1xXDVW4YKxs/fCWEsCiE8J4QwvUhhM0hhD+GENY0lP99GEmzc4cQ7lU89mUTPJ9fhhC+OsnzXQz8G3DmFD+XnhDCZ0IIt4QQHl0c++cQwgUhhNtDCBtCCJeHEF7U8Ji7DV8pfA146nSG8EiSymNPuaS2CCFs8/8kxjg8yUMeCJwDnAqMAkcB3wkhHBlj/FFR50PF8TcDNwB7FPchDVF5D/BfwFOA24HNk1zv66QhNv8N/B9wb+DoJnUnPHeM8ZYQwjeAlwCnjVUOITwMeAzwjkmu/1jS8JifNatQ/Ay/ABwOHB5jHAgh7FrE81Pg+cVzfCiwdJJrAfwC6C3ONekbAUlS+UzKJbXDYmBo/MEQwiExxp9O9IAY48cb6vWQhlk8DHg58KOi6DHAl2OMn294aK14/LoQwtiQk1/HGG9pFlwI4UnAU4F/jzGe0VB0xkT1pzj3acD3QwgPiTH+tjj2EuAPwGSTKw8GNsYYr28S4yLgq6Sx7IfGGK8tivYHdgPeEmO8vDj2g0muM/Yc1ocQfg/8IyblkjTnmZRLaoc7gUMnOH5VsweEEPYCTgaeSOoBD0XRrxuqXQysDCHcBPxvjHGmq5Q8gTRZszbDxzf6IXA9KRF/Y9G7/QLgUzHG0UketwfQ7I3DzsC3gL2BQ2KMjRNkrwPuAD4RQlgNnF+MmW/FLcV1JUlznGPKJbXDaIzxVxPcNk5UuegZz4HHk4Z8HEHqST4X2Kmh6qtJwzneAFweQvh9COGVM4jvnsBNMcY4g8feTXGOzwAvLBLyp5GGwpw+6QPT82o2vObewGHAt8cl5MQYbwOeBGwg/Sz+XIwhf3gL4W4mJfySpDnOpFxSGSrAI4HXxxg/G2P8cYzxV4xLIGOMt8cYXxtj3AN4BPA94H9CCIdM83p/BfYIIYQpa7bmdFKi/zRSj/n5McbfTfGYW2k+Dvz3wL8Drw4hvG18YYzxwhjjUcXjnw7cBzi7hTiXkp67JGmOMymXVIax5HvL2IEQwt7APzd7QDGe+nXF3YeMe/xO2z7ibr4P3AN47jRibHruGOOfScNN3kSaePq5Fs53NXDvYhWWbcQYzwReBLwrhPDaJnXujDF+B/gE8MAQQtPnXXwa8YDiupKkOc4x5ZLaoSeE8E8THL+5ycTGq4A/Au8LIfQCuwDvBP7UWCmE8DPgG8AAaXOcFaRk+SdFlbGJlseHEM4G/tYwGfLvYozfDyF8B/hcCOFBwC+B3YFnxxif1+Q5TXXu00iroqwnrewylZ+ROkIeSVpJZRsxxi+FEHYGPhVCuDPG+KkQwlOBl5J+Dr8H7kca1vOzGONdk1zvH0g/159MUkeSNEeYlEtqh51JS/CN91ng2PEHY4ybQwhHk3ae/Bpp5ZL3kJYsfHRD1Z+REvEHkpZNvBx4+tiqJzHGS0IIJxXXeFNxnn2axPhvpB06XwGcBPyFNBxmQi2c+7ukyaNnTJEcj53vmhDC5aSe9QmT8qLeZ4oe8P8JIdxJegMxSpoUex/ScJTvAW+Z4pJHATcCF00VmySpfKEN854kqeuEEI4kLU346Bjjr6eqXzzm1aRdNvdrx6TTKa51EXBOjPFdnbyOJKk9TMolaRpCCMtJE1U/DNwZY3z8NB67M1AHXhljzDsUIiGEQ0kTQfeNMa7v1HUkSe3jRE9Jmp6XkzY6ggmG5kwmxngnsBJY2OaYxtsVWGFCLkk7DnvKJUmSpJLZUy5JkiSVzKRckiRJKplJuSRJklQyk3JJkiSpZCblkiRJUslMyiVJkqSSmZRLkiRJJTMplyRJkkr2/wEDBXns7gKOsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x644.156 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8766079044100)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NOTE: No need to edit; run and inspect\n",
    "p = (\n",
    "    df_stang\n",
    "    >> gr.ggplot(gr.aes(\"E\", \"mu\", color=\"factor(thick)\"))\n",
    "    + gr.geom_point(size=4)\n",
    "    \n",
    "    + gr.scale_color_discrete(name=\"Thickness (in)\")\n",
    "    + gr.theme_minimal()\n",
    "    + gr.theme(aspect_ratio=1)\n",
    "    + gr.labs(\n",
    "        x=\"Elasticity (ksi)\",\n",
    "        y=\"Poisson's Ratio (-)\",\n",
    "    )\n",
    ")\n",
    "p.save(\"stang-E-mu.png\")\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5db571b2-bcf2-4d2d-b3af-5b8a05cbd168",
   "metadata": {},
   "source": [
    "## Explanatory Patterns\n",
    "\n",
    "Plotting `E` against `t` directly helps us assess the supposed pattern.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ca056fb6-b06c-4a00-91fd-a4b857d0b8cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zach/opt/anaconda3/envs/evc/lib/python3.9/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Saving 6.4 x 4.8 in image.\n",
      "/Users/zach/opt/anaconda3/envs/evc/lib/python3.9/site-packages/plotnine/ggplot.py:722: PlotnineWarning: Filename: stang-cf.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x644.156 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8766133993735)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = (\n",
    "    df_stang\n",
    "    >> gr.ggplot(gr.aes(\"thick\", \"E\"))\n",
    "    + gr.geom_point()\n",
    "    + gr.theme_minimal()\n",
    "    + gr.theme(\n",
    "        aspect_ratio=1,\n",
    "    )\n",
    "    + gr.labs(\n",
    "        x=\"Thickness (in)\",\n",
    "        y=\"Elasticity (ksi)\",\n",
    "    )\n",
    ")\n",
    "p.save(\"stang-cf.png\")\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55bafdc8-cd0b-452f-bb96-bc3fac924e4e",
   "metadata": {},
   "source": [
    "Already, this pattern doesn't look very convincing; the intermediate thickness plates (`t ~= 0.06`) do not follow the downward trend in `E`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5035039d-2229-487e-aeec-f9fd78535a94",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zach/opt/anaconda3/envs/evc/lib/python3.9/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Saving 6.4 x 4.8 in image.\n",
      "/Users/zach/opt/anaconda3/envs/evc/lib/python3.9/site-packages/plotnine/ggplot.py:722: PlotnineWarning: Filename: stang-cf-lm.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x644.156 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8766079043079)>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = (\n",
    "    df_stang\n",
    "    >> gr.ggplot(gr.aes(\"thick\", \"E\"))\n",
    "    + gr.geom_point()\n",
    "    + gr.geom_smooth(\n",
    "        data=df_stang\n",
    "        >> gr.tf_mutate(source=\"All\"),\n",
    "        mapping=gr.aes(color=\"source\"),\n",
    "        method=\"lm\"\n",
    "    )\n",
    "    + gr.geom_smooth(\n",
    "        data=df_stang\n",
    "        >> gr.tf_filter(DF.thick <= 0.08)\n",
    "        >> gr.tf_mutate(source=\"t<0.08\"),\n",
    "        mapping=gr.aes(color=\"source\"),\n",
    "        method=\"lm\"\n",
    "    )\n",
    "    + gr.theme_minimal()\n",
    "    + gr.theme(\n",
    "        aspect_ratio=1,\n",
    "    )\n",
    "    + gr.labs(\n",
    "        x=\"Thickness (in)\",\n",
    "        y=\"Elasticity (ksi)\",\n",
    "    )\n",
    ")\n",
    "p.save(\"stang-cf-lm.png\")\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff92fad3-d525-4efb-9920-8bc84092c8a8",
   "metadata": {},
   "source": [
    "A more formal way to test the supposed trend is to fit a pair of lines, one with and one without the thickest plates. Here we can see that the line fitted without the thickest plates does not have a significantly downward slope.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "068ec598-c178-43a8-8657-2e936d26ed1c",
   "metadata": {},
   "source": [
    "## Considering Variability\n",
    "\n",
    "The following code shows how to make the \"targeted\" figures I showed in the presentation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b1bfcdc0-ecf5-4b84-a5a9-fd3fec516f04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model: Plate critical buckling stress\n",
       "\n",
       "  inputs:\n",
       "    var_det:\n",
       "      b: (unbounded)\n",
       "      a: (unbounded)\n",
       "      t: (unbounded)\n",
       "      mu: (unbounded)\n",
       "      m: (unbounded)\n",
       "      E: (unbounded)\n",
       "\n",
       "    var_rand:\n",
       "\n",
       "    copula:\n",
       "      None\n",
       "\n",
       "  functions:\n",
       "      f0: ['a', 'b', 'm'] -> ['k_cr']\n",
       "      f1: ['k_cr', 'E', 'mu', 't', 'b'] -> ['sigma_cr']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md_plate = (\n",
    "    gr.Model(\"Plate critical buckling stress\")\n",
    "    >> gr.cp_vec_function(\n",
    "        fun=lambda df: gr.df_make(\n",
    "            k_cr=(df.m * df.b / df.a + df.a / df.m / df.b)**2\n",
    "        ),\n",
    "        var=[\"a\", \"b\", \"m\"],\n",
    "        out=[\"k_cr\"],\n",
    "    )\n",
    "    >> gr.cp_vec_function(\n",
    "        fun=lambda df: gr.df_make(\n",
    "            sigma_cr=df.k_cr * (np.pi**2/12) * df.E*1e3 / (1 - df.mu**2)\n",
    "                    *(df.t / df.b)**2\n",
    "        ),\n",
    "        var=[\"k_cr\", \"E\", \"mu\", \"t\", \"b\"],\n",
    "        out=[\"sigma_cr\"]\n",
    "    )\n",
    ")\n",
    "\n",
    "md_plate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2dce7e7d-57ba-4c00-b378-075baac488fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zach/opt/anaconda3/envs/evc/lib/python3.9/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Saving 6.4 x 4.8 in image.\n",
      "/Users/zach/opt/anaconda3/envs/evc/lib/python3.9/site-packages/plotnine/ggplot.py:722: PlotnineWarning: Filename: stang-q7.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8766079085629)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = (\n",
    "    df_stang\n",
    "    >> gr.tf_filter(DF.thick == 0.032)\n",
    "    >> gr.tf_rename(t=\"thick\")\n",
    "    \n",
    "    # Sweep over additional variables\n",
    "    >> gr.tf_outer(\n",
    "        df_outer=gr.df_grid(\n",
    "            m=[1, 2, 3],\n",
    "            a=gr.linspace(6, 48, 25),\n",
    "            b=12,\n",
    "        )\n",
    "    )\n",
    "    # Use the model as a transformation (evaluation synonym)\n",
    "    >> gr.tf_md(md_plate)\n",
    "    \n",
    "    # Compute summaries\n",
    "    >> gr.tf_group_by(DF.a, DF.m, DF.b)\n",
    "    >> gr.tf_summarize(\n",
    "        sigma_cr_min=gr.min(DF.sigma_cr),\n",
    "        sigma_cr_mean=gr.mean(DF.sigma_cr),\n",
    "        sigma_cr_max=gr.max(DF.sigma_cr),\n",
    "    )\n",
    "    \n",
    "    # Visualize the results\n",
    "    >> gr.ggplot(gr.aes(\"a / b\", color=\"factor(m)\"))\n",
    "    + gr.geom_ribbon(\n",
    "        gr.aes(ymin=\"sigma_cr_min\", ymax=\"sigma_cr_max\"),\n",
    "        linetype=\"dashed\",\n",
    "        fill=None,\n",
    "    )\n",
    "    + gr.geom_line(gr.aes(y=\"sigma_cr_mean\"))\n",
    "    \n",
    "    + gr.scale_x_continuous(breaks=[1, 1.5, 2, 2.5, 3])\n",
    "    + gr.scale_y_continuous(limits=(200, 1200))\n",
    "    + gr.scale_color_discrete(name=\"Wavenumber\")\n",
    "    + gr.theme_minimal()\n",
    "    + gr.labs(\n",
    "        x=\"Aspect Ratio (-)\",\n",
    "        y=\"Critical Buckling Stress (psi)\"\n",
    "    )\n",
    ")\n",
    "p.save(\"stang-q7.png\")\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceae63ca-3c54-412d-b7fe-510eefac01bd",
   "metadata": {},
   "source": [
    "We can make a more targeted figure to focus on the relevant comparison (`AR == 1.0` vs `AR == 1.5`).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6b74e595-60e8-4f99-b538-2198cf4b76d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zach/opt/anaconda3/envs/evc/lib/python3.9/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Saving 6.4 x 4.8 in image.\n",
      "/Users/zach/opt/anaconda3/envs/evc/lib/python3.9/site-packages/plotnine/ggplot.py:722: PlotnineWarning: Filename: stang-q7-zoom.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8766095694734)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = (\n",
    "    df_stang\n",
    "    >> gr.tf_filter(DF.thick == 0.032)\n",
    "    >> gr.tf_rename(t=\"thick\")\n",
    "    \n",
    "    # Sweep over additional variables\n",
    "    >> gr.tf_outer(\n",
    "        df_outer=gr.df_grid(\n",
    "            m=[1, 2, 3],\n",
    "            a=[12 * 1, 12 * 1.5],\n",
    "            b=12,\n",
    "        )\n",
    "    )\n",
    "    # Use the model as a transformation (evaluation synonym)\n",
    "    >> gr.tf_md(md_plate)\n",
    "    \n",
    "    # Compute summaries\n",
    "    >> gr.tf_group_by(DF.a, DF.m, DF.b)\n",
    "    >> gr.tf_summarize(\n",
    "        sigma_cr_min=gr.min(DF.sigma_cr),\n",
    "        sigma_cr_mean=gr.mean(DF.sigma_cr),\n",
    "        sigma_cr_max=gr.max(DF.sigma_cr),\n",
    "    )\n",
    "    \n",
    "    # Visualize the results\n",
    "    >> gr.ggplot(gr.aes(\"a / b\", color=\"factor(m)\"))\n",
    "    + gr.geom_errorbar(\n",
    "        gr.aes(ymin=\"sigma_cr_min\", ymax=\"sigma_cr_max\"),\n",
    "        linetype=\"dashed\",\n",
    "        # fill=None,\n",
    "    )\n",
    "    + gr.geom_point(gr.aes(y=\"sigma_cr_mean\"))\n",
    "    \n",
    "    + gr.scale_x_continuous(breaks=[1, 1.5, 2, 2.5, 3])\n",
    "    + gr.scale_y_continuous(limits=(200, 1200))\n",
    "    + gr.coord_cartesian(\n",
    "        xlim=(1.0, 1.5),\n",
    "        ylim=(250, 500),\n",
    "    )\n",
    "    + gr.scale_color_discrete(name=\"Wavenumber\")\n",
    "    + gr.theme_minimal()\n",
    "    + gr.labs(\n",
    "        x=\"Aspect Ratio (-)\",\n",
    "        y=\"Critical Buckling Stress (psi)\"\n",
    "    )\n",
    ")\n",
    "p.save(\"stang-q7-zoom.png\")\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "338ce6b5-057e-482c-8e28-1bf78f93f7e7",
   "metadata": {},
   "source": [
    "The case `AR == 1` clearly has a lower buckling stress than the lowest `AR == 1.5` case; independent of the material variability, we can determine that `AR == 1.5` is a better choice for our application.\n",
    "\n",
    "*In this sense*, the material variability does not matter *for the purposes of choosing between `AR == 1.0` and `AR == 1.5`*.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a5d51494-7ae5-4ae6-8c6a-eab8da4edca4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zach/opt/anaconda3/envs/evc/lib/python3.9/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Saving 6.4 x 4.8 in image.\n",
      "/Users/zach/opt/anaconda3/envs/evc/lib/python3.9/site-packages/plotnine/ggplot.py:722: PlotnineWarning: Filename: stang-q8.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8766113690871)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = (\n",
    "    df_stang\n",
    "    >> gr.tf_filter(DF.thick == 0.032)\n",
    "    >> gr.tf_drop(\"thick\")\n",
    "    \n",
    "    # Sweep over thicknesses\n",
    "    >> gr.tf_outer(\n",
    "        df_outer=gr.df_grid(\n",
    "            t=gr.linspace(0.030, 0.04, 25),\n",
    "            a=12 * 1.5,\n",
    "            b=12,\n",
    "            m=2,\n",
    "        )\n",
    "    )\n",
    "    \n",
    "    # Use model as a transform\n",
    "    >> gr.tf_md(md_plate)\n",
    "    \n",
    "    # Compute summaries\n",
    "    >> gr.tf_group_by(DF.t)\n",
    "    >> gr.tf_summarize(\n",
    "        sigma_cr_min=gr.min(DF.sigma_cr),\n",
    "        sigma_cr_mean=gr.mean(DF.sigma_cr),\n",
    "        sigma_cr_max=gr.max(DF.sigma_cr),\n",
    "    )\n",
    "    \n",
    "    # Visualize\n",
    "    >> gr.ggplot(gr.aes(\"t\"))\n",
    "    + gr.geom_hline(yintercept=300, color=\"grey\", size=1.5)\n",
    "    + gr.geom_segment(\n",
    "        data=gr.df_make(\n",
    "            t=0.0323,\n",
    "            y=300-50,\n",
    "            yend=300,\n",
    "        ),\n",
    "        mapping=gr.aes(\"t\", \"y\", xend=\"t\", yend=\"yend\"),\n",
    "        color=\"salmon\",\n",
    "    )\n",
    "    + gr.geom_segment(\n",
    "        data=gr.df_make(\n",
    "            y=300 * 1.25,\n",
    "            t=0.03,\n",
    "            tend=0.0361,\n",
    "        ),\n",
    "        mapping=gr.aes(\"t\", \"y\", xend=\"tend\", yend=\"y\"),\n",
    "        color=\"blue\",\n",
    "    )\n",
    "    + gr.geom_line(gr.aes(y=\"sigma_cr_min\"), linetype=\"dashed\")\n",
    "    + gr.geom_line(gr.aes(y=\"sigma_cr_mean\"))\n",
    "    + gr.geom_line(gr.aes(y=\"sigma_cr_max\"), linetype=\"dashed\")\n",
    "    \n",
    "    + gr.theme_minimal()\n",
    "    + gr.labs(\n",
    "        x=\"Plate Thickness (in)\",\n",
    "        y=\"Critical Buckling Stress (psi)\"\n",
    "    )\n",
    ")\n",
    "p.save(\"stang-q8.png\")\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18b823f7-a459-4b99-8ffd-94ee2c0294f9",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c4068042-cc92-4f07-ac7e-f2fc5cb62c12",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zach/opt/anaconda3/envs/evc/lib/python3.9/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Saving 6.4 x 4.8 in image.\n",
      "/Users/zach/opt/anaconda3/envs/evc/lib/python3.9/site-packages/plotnine/ggplot.py:722: PlotnineWarning: Filename: stang-q8-zoom.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8766079311192)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = (\n",
    "    df_stang\n",
    "    >> gr.tf_filter(DF.thick == 0.032)\n",
    "    >> gr.tf_drop(\"thick\")\n",
    "    \n",
    "    # Sweep over thicknesses\n",
    "    >> gr.tf_outer(\n",
    "        df_outer=gr.df_grid(\n",
    "            t=gr.linspace(0.030, 0.04, 25),\n",
    "            a=12 * 1.5,\n",
    "            b=12,\n",
    "            m=2,\n",
    "        )\n",
    "    )\n",
    "    \n",
    "    # Use model as a transform\n",
    "    >> gr.tf_md(md_plate)\n",
    "    \n",
    "    # Compute summaries\n",
    "    >> gr.tf_group_by(DF.t)\n",
    "    >> gr.tf_summarize(\n",
    "        sigma_cr_min=gr.min(DF.sigma_cr),\n",
    "        sigma_cr_mean=gr.mean(DF.sigma_cr),\n",
    "        sigma_cr_max=gr.max(DF.sigma_cr),\n",
    "    )\n",
    "    \n",
    "    # Visualize\n",
    "    >> gr.ggplot(gr.aes(\"t\"))\n",
    "    + gr.geom_hline(yintercept=300, color=\"grey\", size=1.5)\n",
    "    + gr.geom_segment(\n",
    "        data=gr.df_make(\n",
    "            t=0.0323,\n",
    "            y=300-50,\n",
    "            yend=300,\n",
    "        ),\n",
    "        mapping=gr.aes(\"t\", \"y\", xend=\"t\", yend=\"yend\"),\n",
    "        color=\"salmon\",\n",
    "    )\n",
    "    + gr.geom_segment(\n",
    "        data=gr.df_make(\n",
    "            y=300 * 1.25,\n",
    "            t=0.03,\n",
    "            tend=0.0361,\n",
    "        ),\n",
    "        mapping=gr.aes(\"t\", \"y\", xend=\"tend\", yend=\"y\"),\n",
    "        color=\"blue\",\n",
    "    )\n",
    "    + gr.geom_line(gr.aes(y=\"sigma_cr_min\"), linetype=\"dashed\")\n",
    "    + gr.geom_line(gr.aes(y=\"sigma_cr_mean\"))\n",
    "    + gr.geom_line(gr.aes(y=\"sigma_cr_max\"), linetype=\"dashed\")\n",
    "    \n",
    "    + gr.coord_cartesian(\n",
    "        xlim=(0.032, 0.033),\n",
    "        ylim=(275, 325),\n",
    "    )\n",
    "    + gr.theme_minimal()\n",
    "    + gr.labs(\n",
    "        x=\"Plate Thickness (in)\",\n",
    "        y=\"Critical Buckling Stress (psi)\"\n",
    "    )\n",
    ")\n",
    "p.save(\"stang-q8-zoom.png\")\n",
    "p"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}